Conservation Biogeography

Conservation Biogeography

Conservation Biogeography Edited by Richard J. Ladle and Robert J. Whittaker © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33503-3

C OMPAN I ON W EB S I T E This book has a companion website: www.wiley.com/go/ladle/biogeography with Figures and Tables from the book for downloading

Conservation Biogeography

Edited by Richard J. Ladle and Robert J. Whittaker

A John Wiley & Sons, Ltd., Publication

This edition first published 2011, © 2011 by Blackwell Publishing Ltd Blackwell Publishing was acquired by John Wiley & Sons in February 2007. Blackwell’s publishing program has been merged with Wiley’s global Scientific, Technical and Medical business to form Wiley-Blackwell. Registered office: John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK Editorial offices: 9600 Garsington Road, Oxford, OX4 2DQ, UK The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK 111 River Street, Hoboken, NJ 07030-5774, USA For details of our global editorial offices, for customer services and for information about how to apply for permission to reuse the copyright material in this book please see our website at www.wiley.com/wiley-blackwell The right of the author to be identified as the author of this work has been asserted in accordance with the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books. Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. The publisher is not associated with any product or vendor mentioned in this book. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold on the understanding that the publisher is not engaged in rendering professional services. If professional advice or other expert assistance is required, the services of a competent professional should be sought. Library of Congress Cataloguing-in-Publication Data Conservation biogeography / edited by R. J. Ladle and R. J. Whittaker. p. cm. Includes index. ISBN 978-1-4443-3503-3 (cloth) – ISBN 978-1-4443-3504-0 (pbk.) 1. Conservation biology. 2. Biodiversity conservation. 3. Protected areas. II. Whittaker, Robert J. QH75.C657 2011 333.95′16–dc22

4. Biogeography.

I. Ladle, Richard J.

2010037700 A catalogue record for this book is available from the British Library. This book is published in the following electronic formats: eBook ISBN: 9781444390018; Wiley Online Library ISBN: 9781444390001; ePub ISBN: 9781444390025 Set in 9/11pt Photina by Toppan Best-set Premedia Limited

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Contents

Preface, ix Acknowledgements, xi Contributing authors, xii PART 1: ROOTS, RELEVANCE, AIMS AND VALUES, 1 1 THE ROOTS OF CONSERVATION BIOGEOGRAPHY, 3 1.1 What is conservation biogeography?, 3 1.2 The emergence of conservation biology and conservation biogeography, 4 1.3 The scope of conservation biogeography, 7 1.3.1 To what ends?, 7 1.4 Outline of the following chapters, 11 Suggested reading, 12 2 SOCIAL VALUES AND CONSERVATION BIOGEOGRAPHY, 13 2.1 Many values, many goals, 13 2.2 The origins and values of different protected area types, 14 2.2.1 Sacred sites, 16 2.2.2 Resource and game reserves, 17 2.2.3 State and country parks, 18 2.2.4 Nature monuments and nature reserves, 19 2.2.5 Wildlife sanctuaries and refuges, 19 2.2.6 Wilderness areas, 20 2.2.7 National parks, 21 2.2.8 Community conservation areas, 22 2.3 Reserve designations from international conventions, 22 2.4 An international system for categorizing protected areas, 23

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Social values and conservation practice, 26 2.5.1 Attitudes to non-native species, 26 2.5.2 Restoration and rewilding, 28 2.6 Concluding remarks, 29 For discussion, 30 Suggested reading, 30 3 BASELINES, PATTERNS AND PROCESS, 31 3.1 Introduction, 31 3.2 Ecosystem composition and function, 31 3.3 Balance versus flux, 32 3.4 Understanding ecosystems in flux, 34 3.5 Defining and using baselines, 38 3.5.1 Baselines derived from relict pristine systems, 38 3.5.2 Baselines derived from long-term ecology, 39 3.5.3 Rewilding, 41 3.5.4 The challenge of rapid environmental change, 42 3.6 Adaptive ecosystem management, 42 For discussion, 44 Suggested reading, 44 PART 2: THE DISTRIBUTION OF DIVERSITY: CHALLENGES AND APPLICATIONS, 45 4 BASIC BIOGEOGRAPHY: ESTIMATING BIODIVERSITY AND MAPPING NATURE, 47 4.1 Introduction, 47 4.1.1 Our incomplete knowledge of biodiversity, 47 4.1.2 Why do we map?, 48 4.2 Three knowledge shortfalls, 49 4.2.1 The Linnean shortfall, 49 4.2.2 The Wallacean shortfall, 54 4.2.3 The extinction estimate shortfall, 58

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The fundamental taxonomic units of conservation biogeography, 62 4.3.1 Species versus other genetically-based conservation units, 62 4.3.2 Evolutionarily Significant Units (ESUs), 64 4.3.3 Other conservation units, 65 4.4 Spatial distributions: from genes to biogeographical regions, 65 4.4.1 Mapping species individually and collectively, 65 4.4.2 Phylogeography, 72 4.4.3 Endemism, 74 4.4.4 Biogeographical regions, 75 4.5 Mapping function, 76 4.5.1 Biomes, ecosystems and communities, 76 4.5.2 Ecoregions, 82 4.6 Natural units in the marine realm, 83 For discussion, 91 Suggested reading, 92 5 THE SHAPING OF THE GLOBAL PROTECTED AREA ESTATE, 93 5.1 Origins, 93 5.2 Typology of frameworks, 95 5.2.1 Spatial classification of approaches – contiguous areas, landscape units and habitat islands, 97 5.2.2 Biogeographical (compositional) versus Ecological (functional) approaches, 100 5.2.3 Strategic goals – composition, function, numbers and attributes, 102 5.3 Terrestrial protected area schemes, 104 5.3.1 IUCN Biogeographical Regions (Dasmann–Udvardy) scheme, 104 5.3.2 Endemic Bird Areas, 106 5.3.3 Conservation International’s hotspots, 109 5.3.4 The WWF Ecoregions scheme, 113 5.3.5 Important Bird Areas and Key Biodiversity Areas, 117 5.4 Marine protected areas, 121 5.4.1 Status of the marine realm, 121 5.4.2 Origins and expansion of the marine protected area estate, 122 5.4.3 A global representative system of marine protected areas, 123

5.4.4

Reefs at risk – hotspots/ threatspots, 126 5.4.5 Large Marine Ecosystems, 130 5.4.6 WWF Global 200 – the marine perspective, 131 5.4.7 Coastal Zone Management and critical seascapes, 132 5.4.8 High seas protected areas, 132 5.5 Current trends and future directions, 134 For discussion, 135 Suggested reading, 135 6 SYSTEMATIC CONSERVATION PLANNING: PAST, PRESENT AND FUTURE, 136 6.1 Introduction, 136 6.2 What is systematic conservation planning and why use it?, 138 6.3 Concepts and principles, 138 6.3.1 Representativeness, 138 6.3.2 Persistence (adequacy), 139 6.3.3 Efficiency, 139 6.3.4 Flexibility, 140 6.4 Developing a systematic conservation plan, 140 6.4.1 Achieving representation, 140 6.4.2 Achieving persistence, 146 6.4.3 Achieving efficiency, 151 6.4.4 Achieving flexibility, 152 6.5 Decision support tools to identify and prioritize new protected areas, 152 6.6 Consultation and implementation of systematic conservation plans, 155 6.7 What does the future of systematic conservation planning hold?, 156 6.7.1 Conservation planning is a dynamic problem, 158 6.7.2 Conservation assets change through time, 158 6.7.3 A mix of conservation actions could occur at any site, 158 6.7.4 Better economics and socio-economics, 158 6.7.5 Dealing with uncertainty, 158 6.7.6 Properly accounting for threats, 159 6.7.7 Persistence – attainable goal or impractical utopia?, 159 6.7.8 How much should we invest in improving a conservation plan?, 159 For discussion, 159 Suggested reading, 160

Contents

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PART 3: CONSERVATION PLANNING IN A CHANGING WORLD, 161

For discussion, 222 Suggested reading, 223

7 PLANNING FOR PERSISTENCE IN A CHANGING WORLD, 163 7.1 Introduction, 163 7.2 Using the past to understand the present and predict the future, 164 7.2.1 Predicting future ecosystem responses to changing conditions, 168 7.2.2 Interpreting recent trends in their historical context, 169 7.2.3 Geographical range collapse, 170 7.3 Predicting biodiversity change, 176 7.3.1 Modelling the current distributions of species, habitats and biomes, 177 7.3.2 Modelling range shifts, 180 7.4 What do we do about it? Dynamic conservation planning, 183 7.4.1 Incorporating dynamic biotic and abiotic processes into conservation plans, 183 7.4.2 Changes in socio-economic factors, 185 7.4.3 Climate change, conservation planning and assisted migration, 185 7.5 Closing remarks, 188 For discussion, 188 Suggested reading, 189

9 BIOLOGICAL INVASIONS AND THE HOMOGENIZATION OF FAUNAS AND FLORAS, 224 9.1 The biogeography of species invasions, 224 9.1.1 The invasion process, 224 9.1.2 Human-assisted versus prehistoric invasions, 226 9.1.3 Economic and ecological impacts of invasion, 227 9.2 Biotic homogenization, 229 9.2.1 The process of biotic homogenization, 230 9.2.2 Different manifestations of biotic homogenization, 230 9.3 Patterns of biotic homogenization, 232 9.3.1 Fishes, 232 9.3.2 Birds, 235 9.3.3 Plants, 237 9.3.4 Mammals, 237 9.4 Environmental and human drivers of biotic homogenization, 238 9.5 Biotic homogenization and conservation, 240 9.6 Novel assemblages, 241 For discussion, 242 Suggested reading, 243 PART 4: FUTURE DIRECTIONS, 245

8 APPLIED ISLAND BIOGEOGRAPHY, 190 8.1 Introduction, 190 8.2 Implications of habitat loss and fragmentation: from theory to evidence, 194 8.2.1 The use of species–area relationships in conservation, 194 8.2.2 Relaxation and the extinction debt, 199 8.2.3 Ecosystem collapse and threshold responses in habitat islands, 203 8.3 Species incidence, 208 8.3.1 Minimum viable populations, minimum areas and incidence functions, 208 8.3.2 Metapopulation dynamics, 211 8.4 Nestedness, 213 8.4.1 Edge effects, 216 8.4.2 Habitat corridors, 217 8.4.3 Landscape context – matrix effects, 218 8.5 Emergent guidelines for conservation, 219

10 PROSPECTS AND CHALLENGES, 247 10.1 Why we need conservation biogeography, 247 10.2 The challenges, 248 10.2.1 Filling the Wallacean and Linnean shortfalls, 248 10.2.2 Improving models, simulations and forecasts, 250 10.2.3 Turning theory into practice, 251 10.2.4 Education, communication and public engagement, 252 10.2.5 Reconciliation ecology and a biogeography of the countryside, 257 10.3 Looking to the future, 257 Glossary of terms, 259 References, 264 Index, 297 Colour plates follow the Index

This book has a companion website: www.wiley.com/go/ladle/biogeography

Preface

Most scientists would agree that life on Earth is currently experiencing a rapid and dramatic transformation and re-sorting, reminiscent of some of the most dramatic events in the planet’s history, such as the switches in and out of ice ages, biotic interchanges driven by the collision of continents, or the handful of massive and geologically abrupt past biodiversity collapses termed mass extinctions. All over the globe natural habitats are being transformed to suit the needs of the local human population or those of distant markets. Sometimes these changes are dramatic, such as the clearing of lowland rain forest to make way for pasture or crops. Other changes are more subtle but nonetheless have severe ramifications for the native ecology, such as the introduction of non-native species from widely distant land masses or water bodies. Moreover, these changes are taking place against a backdrop of global climate change, which has the potential to re-draw the geographic boundaries of many ecosystems. The full consequences of the contemporary humaninduced revolution in the Earth’s biota remain to be seen, although many aspects of anthropogenic impacts are already on record and many further responses to these drivers seem inevitable. Numerous species have already become extinct and, in the absence of concerted efforts to prevent this, many more seem destined to follow them into oblivion. Another seemingly inexorable process is the convergence of communities (especially where habitats have been disturbed) caused by the assisted transport of generalist species. Known as ‘biotic homogenization’, this process overwrites the local and particular species with, very often, the same sets of successful commensal species, many of which present very significant negative impacts on ecosystems and economies. Other consequences are harder to predict, such as the impact

of climate change on the make-up of communities, or even its influence on the geographic distribution of a particular species. Ultimately, the degree to which human transformation of landscapes and ecosystems impacts on the diversity and distribution of life on Earth will be determined by the response of societies, organizations and individuals. However, to make rational informed decisions about where to invest conservation’s limited resources (both taxonomically and geographically) requires an understanding of the principles, concepts and techniques of biogeography. Although the application of biogeographical theory to conservation problems has a long history, most notably in the design of island nature reserves, we have for some time felt that there was insufficient attention being paid to exploiting biogeographical information and knowledge in the practice of conservation and in the education and training of those intent on contributing to conservation policy and practice. The goal to develop teaching material with this focus was, therefore, an integral part of our plans for a new Master’s programme in Biodiversity, Conservation and Management, taught in the University of Oxford, and in which several of our colleagues – contributing authors to this book – have also been involved over the last six years or so. Encouraged by the interest in conservation biogeography evident within the mission statement (below) and membership of the recently formed International Biogeography Society, we set out to outline the aspirations, applications and limitations of the field in a prospective review paper published in 2005 in the journal Diversity and Distributions – A Journal of Conservation Biogeography. This paper, however, only gave the bare bones of a cohesive set of concepts and criticisms that define an important new perspective on global conservation.

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Preface

Our aim in this book is therefore to expand the scope and agenda of conservation biogeography, to identify critical gaps and weaknesses, and to provide an introduction to the toolbox of concepts and methods – and thereby to produce a broad-based text for university courses and programmes. To this end, we have tried to provide a strong pedagogic structure, starting with the values and imperatives underpinning conservation that determine where and how biogeography can be used. Subsequently we develop the key concepts and frameworks before concluding with the application of biogeography to real world conservation problems. To ensure adequate coverage of all of the most important areas of contemporary research and practice, we invited a number of colleagues, based at other universities around the world, to join with us in developing the first comprehensive textbook on conservation biogeography. Edited textbooks can often appear bitty and may lack cohesion, but it has been our great good fortune as editors that our colleagues in this venture have accepted with good grace – and, indeed, enthusiasm – our efforts to avoid such an outcome. Working within a pre-set structure, the author teams have developed, collaboratively, a text which we hope you, the reader, will find to be readable, coherent, insightful and comprehensive. Biogeography is entering a period of innovation, growth and expansion, and it has been reinvigorated by its fusion with the younger, crisis-driven agenda of conservation science. We hope this book will contribute in a small way to attracting more students and established scientists to work in this newly emerging field, and we look forward with great interest and expectation to tracking the practical and conceptual development of the discipline in the coming years. Finally, in furtherance of this goal, and together with our fellow contributing authors, we have pledged to

donate the royalties from the present work to the International Biogeography Society, which was founded as a non-profit organization in 2000 with the following mission: • Foster communication and collaboration between biogeographers in disparate academic fields – scientists who would otherwise have little opportunity for substantive interaction and collaboration. • Increase both the awareness and interests of the scientific community and the lay public in the contributions of biogeographers. • Promote the training and education of biogeographers so that they may develop sound strategies for studying and conserving the world’s biota. For further information on the IBS visit http://www. biogeography.org. Richard J. Ladle Viçosa, Brazil

Robert J. Whittaker Oxford, UK

July 2010 Richard Ladle was the founding course director of the MSc Biodiversity Conservation and Management, School of Geography and the Environment, University of Oxford. He is currently a Senior Research Associate of the School and a Visiting Professor in the Department of Agricultural Engineering, Federal University of Viçosa, Brazil. Robert Whittaker is the current Academic Director of the MSc Biodiversity Conservation and Management, and holds the title of Professor of Biogeography in the School of Geography and the Environment, University of Oxford. He was a founding member of the International Biogeography Society and is currently editor-in-chief of the Journal of Biogeography.

Acknowledgements

First and foremost we wish to thank our students (especially from the MSc Biodiversity, Conservation and Management course, University of Oxford) for discussion in class of many of the topics covered in this book. As in any such project, many individuals have contributed to shaping the content of this book by engaging with members of the author team in discussion, by supplying artwork and permission to use it, and by providing feedback on draft chapters. We particularly wish to thank in these regards: Natalie Ban, Peter Baxter, Josie Carwardine, Megan C. Evans, Mat Gilfedder, Carissa J. Klein, Vincent Devictor, Lincoln Fishpool, Helen Fox, Richard Grenyer, Steve Jennings, Liana N. Joseph, Mike Hopkins, Andrew T. Knight, Mark V. Lomolino, Jeffrey D. Lozier, Mikko Piirainen, Thomas K. Pool, Timoth Rayden, Carsten Rahbek, David M. Richardson, Robert J. Smith, Christopher Stewart, Jens-Christian Svenning, Sebastian Troeng, and Katherine J. Willis. Carissa J. Klein and Robert J. Smith kindly contributed to two boxes within Chapter 6. We thank Ailsa Allen for redrafting several of the figures. We thank the commissioning editor Ward Cooper, copy-editor Brian Asbury, and the production team at Wiley-Blackwell, in particular Kelvin Matthews and Camille Poire. We thank the authors, publishers, and other institutions who have kindly given us their permission to

reproduce or re-draw artwork originally published elsewhere. We apologize if any permissions requests have been overlooked in error. Contributing authors to the book wish to recognize support received, as follows: Miguel Araújo thanks Delta Cafés for supporting the Rui Nabeiro Biodiversity Chair at the University of Évora. His research is also funded through the EC FP6 ECOCHANGE project (Challenges in Assessing and Forecasting Biodiversity and Ecosystem Changes in Europe, contract no. 036866-GOCE). James Watson, Richard Fuller and Hedley Grantham are supported by the Applied Environmental Decision Analysis research hub, funded through the Commonwealth Environment Research Facilities programme, Australia. Lindsey Gillson is funded by the National Research Foundation (South Africa) and the African Climate and Development Initiative (UCT). Catherine Parr is supported by the Trapnell Fund and the Higgins-Trapnell Family Foundation. Hugh Possingham is an ARC Federation Fellow and his work is supported by The Australian Research Council and an Australian Commonwealth Environmental Research Facility grant. Kostas Triantis is supported by a FCT Fellowship (SFRH/BPD/44306/2008). Kerrie Wilson is an ARC Research Fellow and her work is supported by The Australian Research Council.

Contributing Authors

Miguel B. Araújo National Museum of Natural Sciences, CSIC, Madrid, Spain and University of Évora, CIBIO, Évora, Portugal Shonil A. Bhagwat School of Geography and the Environment, University of Oxford, Oxford, UK Richard A. Fuller School of Biological Sciences, The University of Queensland, Brisbane, Australia and CSIRO Climate Adaptation Flagship and CSIRO Sustainable Ecosystems, Brisbane, Australia Lindsey Gillson Plant Conservation Unit, Botany Department, University of Cape Town, South Africa Hedley S. Grantham School of Biological Sciences, The University of Queensland, Brisbane, Australia Paul Jepson School of Geography and the Environment, University of Oxford, Oxford, UK Richard J. Ladle School of Geography and the Environment, University of Oxford, Oxford, UK and Department of Agricultural Engineering, Federal University of Viçosa, Brazil Julie L. Lockwood Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ, USA

Sara A. Lourie Redpath Museum, McGill University, Montreal, Canada Julian D. Olden School of Aquatic and Fishery Sciences, University of Washington, Seattle, USA Catherine L. Parr Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK Hugh P. Possingham School of Biological Sciences, The University of Queensland, Brisbane, Australia Brett R. Riddle School of Life Sciences, University of Nevada, Las Vegas, USA Kostas A. Triantis Azorean Biodiversity Group, University of Azores, Terceira, Portugal and School of Geography and the Environment, University of Oxford, Oxford, UK James E.M. Watson School of Biological Sciences, The University of Queensland, Brisbane, Australia Robert J. Whittaker School of Geography and the Environment, University of Oxford, Oxford, UK Kerrie A. Wilson School of Biological Sciences, The University of Queensland, Brisbane, Australia

PART 1 ROOTS, RELEVANCE, AIMS AND VALUES

Conservation Biogeography Edited by Richard J. Ladle and Robert J. Whittaker © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33503-3

CHAPTER 1 The roots of conservation biogeography Robert J. Whittaker1 and Richard J. Ladle1,2 1

School of Geography and the Environment, University of Oxford, Oxford, UK Department of Agricultural Engineering, Federal University of Viçosa, Brazil

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1. 1 WHAT I S C ONSE R V AT I ON BI O G EO GR AP HY? For those poor souls trapped in narrow scientific disciplines there may be an excuse for introspection, but that is not the nature of biogeography. Biogeographers surely have a special responsibility to broaden the perceptions and awareness of policy makers and those coming new to the profession. (Koy Thomson, 1991, p. 477) As many others have argued before us, we live in an era, sometimes dubbed the Anthropocene, in which our species has increasingly shaped the world around us, influencing the physical and biological components of ecosystems at every scale from that of our own immediate surroundings up to the whole Earth system. We have developed, sometimes purposefully, but often haphazardly and accidentally, the habit of extinguishing species of plants and animals, domesticating them, assisting their spread to new territories, messing around to varying ends with their genetics and biology, and resorting them into novel communities embedded in socalled ‘cultural’ landscapes. In short, we have become the dominant force in altering the distribution, composition and diversity of life on Earth, with outcomes that are sometimes beneficial to the human condition and sometimes not, depending on which changes we are considering and the perspective of the observer.

Biologists have documented and modelled these changes in ecology and biogeography while articulating ever-increasing concern over the many perceived threats to biodiversity. The modern conservation movement has grown and evolved in response to these threats, with the most prominent national and international conservation organizations setting their sights on using the best possible scientific guidance to target resources on conserving whatever aspect(s) of biodiversity they value most highly. This book is written with the aim of providing a resource for those wanting to contribute to this endeavour. There are many books on conservation biology, so it is valid to ask why we need one on conservation biogeography and what is the operational remit of the field? We have previously defined ‘conservation biogeography’ in the following terms: ‘the application of biogeographical principles, theories, and analyses, being those concerned with the distributional dynamics of taxa individually and collectively, to problems concerning the conservation of biodiversity’ (Whittaker et al., 2005, p. 3). As shown schematically in Figure 1.1, this identifies conservation biogeography as a sub-set or sub-field of conservation biology. If it is a sub-field, then it is one with deep roots in the natural sciences. In broad terms, conservation biogeography is concerned with pattern and process over large extents of space (and time), so we have therefore mostly excluded from this book

Conservation Biogeography Edited by Richard J. Ladle and Robert J. Whittaker © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33503-3

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The roots of conservation biogeography 1. 2 T H E EMER GEN CE OF CON S ER V AT I ON B I OLOGY AN D CON S ER V AT I ON B I OGEOGR APH Y Real-world biogeographers must balance their roles as citizens wanting to make the world a better place against their roles as scientists who are honest and skilful about their scientific limitations. Getting the science right is undoubtedly important for making policy decisions, but a wise approach to uncertainty is all the more so. (Thomson, 1991, p. 475)

Figure 1.1 Approximate chronology of the emergence of conservation biogeography, positioning the subject as a subset of conservation biology focused on pattern and process at coarser scales of analysis. The early conservation movement, labelled here wildlife conservation, in fact comprised a broader set of values, including the recognition/prioritization of wilderness and of natural monuments. In this period, much of the emphasis was on moral/aesthetic values. The emphasis on conservation of nature, embracing ecological/scientific grounds for conservation, was around from a comparatively early point but gained ground mostly during the latter half of the 20th century, giving rise to the distinct academic field of conservation biology in the final quarter of the century. The subject areas identified at each scale are illustrative not exhaustive (MVP, minimum viable population; ETIB, equilibrium theory of island biogeography; SLOSS, single large or several small reserves). Missing from this chronology is the emergence of the concept of biodiversity at the end of the 1980s (see Box 1.1).

material concerned with wildlife behaviour, population level processes, tracking and monitoring of wild animal and plant populations, and field ecology. These subject areas are important, but they are well covered in many conservation biology texts. When it comes to the coarser biogeographical scales of analysis, on the other hand, we feel that there has been something of a lag in translating updates of theory and practice into curricula. This is particularly important because many strategic conservation decisions of great significance to the ultimate effectiveness of conservation, such as how protected area networks are designed, require a deep understanding of this coarser-scale biogeographical science – the subject matter of this book.

As an applied and interdisciplinary science concerned with the conservation of nature, conservation biogeography can be seen as a product both of biogeography and of conservation biology. We briefly consider the origins of these related endeavours by the order of their emergence (Figure 1.1). Biogeography is the study at all scales of analysis of the distribution of life across space and how it has changed through time. In the broadest sense, biogeography could even be thought of as the ‘first science’, because the ability to understand and track the distribution of food and predators though time and space was arguably of even greater interest to our huntergatherer ancestors. Although the term biogeography appears to have been a 20th century innovation, the discipline has deep scientific roots. Many of the core principles and broadly known patterns of biogeography were established and debated before the end of the 19th century under the twin headings of zoogeography and phytogeography, by such towering figures as Alfred Russel Wallace (sometimes called the father of zoogeography), Charles Darwin, Philip Sclater, Georges-Louis Leclerc (Compte de Buffon), and Alexander von Humboldt (Lomolino et al., 2004, 2006). Indeed, some of the major themes were already established as areas of enquiry by the early 1800s: indicative of the foundational nature of the subject within the natural sciences (Lomolino et al., 2004, 2006). The study of biogeography thus developed in advance of the coalescence of theory and thinking that came to constitute the disciplines of ecology and evolution, with which the subject of biogeography is naturally intertwined. Biogeography has many facets, traditions, and schools of thought. They encompass deep time (historical biogeography), the recent past (palaeoecology) and contemporary pattern and process (ecological

Roots, relevance, aims and values biogeography). At the core of the discipline is an interest in describing, explaining and predicting patterns of distribution and diversity, whether at higher taxa level, species level, or most recently also sub-species levels of analysis. The modern conservation movement emerged in the late 19th century in response to fundamental changes in world views concerning the nature of the relationship between humans and the natural world, and it emanated largely from the elite society of the American East Coast and Western Europe (Jepson & Whittaker, 2002a). The movement was motivated both by a desire to preserve sites with special meaning for the intellectual and aesthetic contemplation of nature, and by acceptance that the human conquest of nature carries with it a moral responsibility to ensure the survival of threatened life forms. These early principles were later combined with a range of more utilitarian perspectives but, over the first half of the 20th century, the primary motivating forces were the conservation of wildlife, especially large game animals and birds, and the desire to preserve places of natural beauty and wonder (Figure 1.1, Box 1.1). Over time, the conservation movement diversified with increasing recognition of other (often deeply rooted) motivating ideas alongside the immediate imperative of saving particular types of species from extinction. Thus, nature conservation can be thought of as a social movement working to develop or reassert certain values in society concerning the human/ nature relationship (Jepson & Whittaker, 2002a). The movement gained new momentum in the second half of the 20th century, when science and environmentalism further expanded understandings of our relationship with nature (Frank et al., 1999; Adams 2004). Motivated by, but distinct from, the nature conservation movement, ‘conservation biology’ is the name given to applied research designed to inform management decisions concerning the conservation of biodiversity. As such, its roots lie largely within the mid 20th century. Conservation biology gained huge momentum during the 1970s and early 1980s, when it was formally identified as a sub-discipline, with dedicated journals and textbooks (e.g. Soulé 1986; Primack, 2002) and learned societies such as the Society for Conservation Biology, founded in 1986 and presently with over 10,000 members. Conservation biology can be defined narrowly as being concerned with the application of population biology, taxonomy and genetics to problems

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concerning the conservation of biodiversity. In a now classic paper, Graham Caughley (1994) pointed out that conservation biology research mainly operates within two overriding paradigms – studies that seek an understanding of the proximate causes of population decline (the declining population paradigm) and those that are concerned with the consequences of small population size (the small population paradigm). More recently there has been an acknowledgement that a biological understanding of rarity and endangerment is necessary, but not sufficient for policymaking designed to prevent biodiversity loss, and that conservation biology needs to incorporate a far broader range of disciplines. Indeed, conservation biology textbooks typically include a wider array of basic scientific and other academic disciplines, including such fields as anthropology, biogeography, environmental economics, environmental ethics, sociology and environmental law (e.g. see Primack, 2002). The incorporation of the social sciences under the umbrella of conservation (i.e. biological) science represents a recognition of the need to apply multiple forms of scholarship to address complex real-world problems. It may also reflect a general desire to bestow scholarly discourse and guidance with the additional gravitas associated with a ‘proper science’ given the status of scientific guidance within late 20th century society and politics (see, e.g. Knight & Cowling, 2007). Last to emerge within the framework shown in Figure 1.1 is conservation biogeography, a term that began to gain currency via a conference of the International Biogeography Society in Washington in January 2005 (Lomolino & Heaney, 2004), and that we ourselves promoted through a paper published the same month in the journal Diversity and Distributions, which simultaneously gained the sub-title ‘A Journal of Conservation Biogeography’. We subsequently realized that the term had been first coined at least twelve years earlier by John Grehan (1993) in the title of a paper in the first issue of the same journal (then called Biodiversity Letters), although he did not define in any precise way what he meant by the term. Whereas the formal use of the term may be recent, the use of biogeography within conservation biology has been going on for as long as scientific guidance to conservation has been offered, and biogeography indeed formed a central part of early theory within conservation biology (see, e.g. Primack, 2002). Classic foundational works combining biogeographical analysis with conservation guidance include the early papers

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The roots of conservation biogeography

Box 1.1 General characteristics of three broad themes and phases of the conservation movement The modern conservation movement can trace its roots back to various societies, clubs and social movements that formed in the late 19th century in response to a variety of changes in society and the natural world (reviewed in Chapter 2; and in Jepson & Ladle, 2010). Two themes were particularly prominent: 1 Wildlife conservation, in the sense of ‘[t]he controlled use and systematic protection of indigenous fauna’. (Matthews et al., 2001) This part of the early conservation movement was rooted in the natural history and hunting pastimes of elite society of the 19th century and is exemplified by the creation of the Boone and Crockett club (B&CC) in 1887 by future US President Theodore (Teddy) Roosevelt. The B&CC had two main goals: first, to create sanctuaries and refuges where wildlife could survive the onslaught of human expansion into frontier lands; and second, to change the culture of hunting to exalt the noble qualities of the chase above the number of animals killed (Jepson & Ladle, 2010). 2 Nature conservation, which stemmed from ideas of nature as being delicate and intricate system(s) sensitive to human interference. This idea manifested itself in the preservation of the status quo and of nature monuments – places for the contemplation of nature, antidotes to urban life. The concept of nature monuments was promoted particularly by the German forester Hugo Conwentz (discussed further in Chapter 2). Both nature conservation and wildlife conservation sometimes resulted in a ‘fortress conservation’ approach, where areas were fenced off from the local communities in order to save them – or at least save them for exclusive exploitation by Western interests. 3 Biodiversity conservation. The contemporary approach to conservation embraces both of the above sets of values and more besides, but can be crudely characterized as distinct from the above by its emphasis on attempts to conserve biodiversity. The term ‘biodiversity’ is simply a contraction of ‘biological diversity’ and may have first been used in a scientific study by Elliot Norse in a US government report in 1980. However, it is more commonly attributed to Walter Rosen around 1985 while planning a symposium; it was used in the title of the resulting 1988 symposium volume (Wilson, 1988a) and subsequently gained rapid uptake. Biodiversity has many definitions, one prominent one being ‘[t]he variability of life from all sources, including within species, between species, and of ecosystems’ (Matthews et al., 2001). It is, in its character, a scientific/technical term, although it is important to bear in mind that the study of biodiversity is not solely a branch of biology as it has an ethical/social dimension (Jeffries, 1997). Moreover, some commentators have noted that biodiversity definitions are often closer to subjective ‘value judgement’ concepts such as quality of life than an objective measure of an environmental property (Lambshead & Boucher, 2003). Although most definitions of biodiversity stress the complexity of life at multiple levels (e.g. genes, species, ecosystems), ‘biodiversity conservation activities are typically directed toward species’ (Matthews et al., 2001, p. 50).

applying island theory to the problem of habitat fragmentation (e.g. Diamond, 1975a) and Dasmann’s (1972) biogeographical regionalization approach to designing networks of protected areas (see Chapters 8 and 5, respectively). Although biogeographical science has played its part alongside other sub-fields of biology in the emergence

of current scientific guidance for biodiversity conservation, in our view it has done so as something of a poor relation – a Cinderella within conservation biology. We argue that biogeography can now cast aside its metaphorical rags as it emerges as a subject area of central importance to conservation planning. In part, this repositioning of biogeography is the result

Roots, relevance, aims and values of the recent and huge technological advances in biogeographical data collection, storage and analysis, which have enabled rapid progress in many areas of the field, both pure and applied; and, in part, it reflects theoretical and conceptual advances (e.g. Williams et al., 2000; Lomolino & Heaney, 2004). Yet we must also recognize that the underlying species distributional and other data often remain highly problematic, protocols for analysis are still in the early stages of development, and we have only recently begun the task of systematically analysing the sensitivity of our analyses to the starting assumptions and scale effects. There is an enormous degree of uncertainty in our science when it comes to predicting future distributions of taxa, diversity and biogeography (see Chapter 7). Accordingly, we argue that there is a need for more biogeographers to engage with the problems of conservation science, and for the injection of more biogeography into training for conservation scientists and practitioners.

1. 3 T H E S C OP E OF C ONS E R V A T I ON BI O G EO GR AP HY As we have indicated above, conservation biology is a large and all-embracing field. However, if it is subdivided by scale of application, we might recognize the following subdivisions of relevant theory (Figure 1.1): 1 Population scale: the development and evaluation of biological theory spanning population biological and genetic process. This is concerned with deterministic processes of population decline, population viability, genetic erosion from small populations, competitive influence of invasive species, behavioural ecology and so forth, i.e. concerned with processes in which biogeography is generally not prominent (e.g. see Caughley, 1994; Primack, 2002). 2 Landscape scale: theory concerning processes at the local–landscape scale, including the foundational influence of R.H. MacArthur and E.O. Wilson’s equilibrium theory of island biogeography, the derivative Single Large or Several Small reserves (SLOSS) debate, habitat corridors and matrix effects, metapopulation theory and nestedness (reviewed by Whittaker and Fernandéz-Palacios, 2007), i.e. issues clearly bridging ecology and biogeography. 3 Geographical scale: applications on a yet coarser scale in part are concerned with mapping and modelling biogeographical patterns, and they in part invoke

7

historical–biogeographical theory concerned with the distribution and explanation of geographical patterns in diversity. We see such coarser scale work on the geography of nature as being unambiguously within the heartland of biogeography (cf. Lomolino et al., 2004). Despite its undoubted importance within conservation science, we argue that it is here, in particular, that something of a ‘Cinderella’ tag applies to conservation biogeography; likewise, it is here where there is greatest need for critical attention to our science and for greater interaction between those involved in theory and application (see e.g. Lourie & Vincent, 2004). Conservation biogeography, the application of biogeography in conservation, is thus separable from the application of other areas of biology (i.e. community, population and behavioural ecology, macroecology, and genetics), most clearly at coarser scales of analysis. While the use of zoogeographic regions, areas of endemism, geographic patterns in species richness, or phylogeographic structure for conservation prioritization purposes are readily identifiable as conservation biogeography, applications at increasingly fine spatial scales, for example focused on habitat corridors or metapopulation dynamics, can be seen as simultaneously drawing from traditions in both ecology and biogeography. Similarly, macroecological analyses (referring to the analysis of the emergent statistical properties of ecological data sets (Brown, 1995)) may also be based on both ‘ecological’ traits (e.g. growth rates, propagule size, breeding system, body size) and ‘biogeographical’ traits (e.g. range size, region of origin). In illustration, efforts to develop explanatory and predictive models of invasiveness of non-native species have been made that use both sets of traits, frequently finding a biogeographical signal in the resulting models (DehnenSchmutz, 2004; Pyšek et al., 2004), which indicates that such analyses draw from both ecological and biogeographical traditions within conservation science to varying degrees. For further exploration of key scale and diversity concepts relevant to conservation biogeography, see Box 1.2.

1.3.1 To what ends? While our goal in this book is to provide students with a guide to the scientific underpinnings of conservation decision-making, it is important to recognize that such

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The roots of conservation biogeography

Box 1.2 Key diversity and scale concepts The first of the tables below is adapted from Whittaker et al. (2001) and highlights the varied meanings of the term species diversity, which itself is just one meaning of the term ‘biodiversity’ (see Box 1.1). Although the table refers to species as the unit of analysis, the terms can of course be applied at other taxonomic levels. The terms given here have been used in varied and inconsistent ways in the literature, leading to much confusion within diversity theory. Within species diversity terminology, perhaps the key distinction is between metrics and concepts recognizing differences in the number of species (which are forms of inventory diversity) and those that highlight the degree to which species are shared between or unique to (endemic to) the areas being compared (which are forms of differentiation diversity). This distinction is set out in Table B1.2b, which provides the foundational framework for the scale of application, designated by letters of the Greek alphabet, developed by the American ecologist Robert Harding Whittaker (e.g. 1960, 1977). R.H. Whittaker’s framework provided a nested sequence from inventory to differentiation forms reiteratively (Figure B1.2a). As his first inventory tier was termed alpha diversity and his first differentiation tier was termed beta diversity, he sometimes used the two terms interchangeably, a habit followed by many other authors, sometimes with confusing outcomes. In practice, diversity and other biogeographical patterns described at different focal scales of analysis may well be the outcome of different dominant processes, so it is of critical importance that the scale parameters of a study are explicitly taken into account when synthesizing information within biogeography, not least within conservation biogeography (see discussion in Whittaker et al., 2001, 2005). Table B1.2a Key diversity and scale concepts. Modified from Whittaker et al. (2001, Table 1). Diversity concepts

Meaning

species diversity

varied meaning: e.g. number of species, or indices weighted by abundance distributions of species (equitability); implying of itself no standardization of sampling

species richness

number of species, implying of itself no standardization of sampling

species density

number of species in a standardized sample, e.g. per unit area; more precise than the above but less widely adopted

species turnover, i.e. differentiation diversity

in the present context meaning compositional turnover in space between two inventory (typically alpha-scale) samples, expressed by a variety of indices or multivariate analyses, and thus qualitatively different from species richness or density

endemism

an endemic is simply a species (or other taxonomic entity) confined to a particular geographical area; a focus on areas of high numbers of endemics implies an interest in biogeographical distinctiveness (whether at species or other taxonomic level)

Scale concepts

Meaning

spatial scale

should refer to the size of the base unit used in sampling and analysis, but in practice usage of this term varies such that it may mean either or both of ‘extent’ and ‘focus’; moreover, size of sample unit is very often not held a constant (as it should be) but is allowed to vary within a study

(geographical) extent

the geographical space (distance) over which comparisons are made, whether they be using e.g. 1 m2 or 10,000 km2 sample units; i.e. of itself implying nothing about spatial scale in the strict sense

focal scale

the spatial scale at which data are analysed, being either the size of the sampling unit (also called the ‘grain’) or the unit to which these data are aggregated for analysis (e.g. local or field scale to regional scale); this concept, unlike ‘extent’, can be synonymous with spatial scale

Roots, relevance, aims and values Table B1.2b Terminology used in describing diversity patterns at different scales of analysis. This table has been compiled and modified from various sources, notably R.H. Whittaker (1975, 1977); Stoms & Estes (1993); R.J. Whittaker et al. (2001). R.H.W. tiers

Spatial scale

Description

Nature of diversity metric

point

species found at a precise point within a local community, e.g. contact of grassland plant species with a pin

inventory

Alpha

local

species richness within local communities/ patches

inventory

Beta

landscape

turnover of species between local communities within a landscape

differentiation

Gamma

landscape

species richness of whole landscape

inventory

Delta

regional

turnover of species between landscapes along major gradients of climate and/or physiography

differentiation

Epsilon

regional

the species diversity of a broad region of differing landscapes

inventory

inter-regional/ inter-provincial

replacement of higher taxa, e.g. placental mammals by marsupials

differentiation

Figure B1.2a An illustration of Robert Harding Whittaker’s diversity scale framework, showing how each scale of analysis nests within the next and highlighting the distinction between inventory diversity and differentiation diversity concepts. Re-drawn from Stoms and Estes (1993).

9

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The roots of conservation biogeography

scientific guidance, and the language in which it is couched, is value-laden (see, for example, the passionate polemic by Stott, 1998) and that there is still a debate to be had concerning which properties of nature we wish as a society to foster (Trudgill, 2001). Much of the scientific guidance and of current conservation practice assumes that this debate will have a particular and almost preordained outcome, without paying much attention to the possible validity of alternative value systems (but see: Redford et al., 2003). For instance, we might wish to emphasize saving species from extinction as the prime goal, without paying great attention to the assemblages and landscapes they occur in. Or, we may wish to emphasize the importance of intact megafaunal assemblages, aesthetic and cultural significance of landscapes, ecosystem health, or biotic integrity (cf. Callicott et al., 1999; Redford et al., 2003; Adams, 2004). These ideas are linked to a similar diversity of social values motivating conservation action in many nations, especially at local scales, but also globally (Chapter 2; Jepson & Canney, 2001, 2003; Trudgill, 2001). The decision to adopt a particular set of values is not within the bounds of science and, although conservation scientists are well placed to contribute to this debate, there is an important distinction between the processes leading to the adoption of a set of values and the process of deriving the scientific guidelines to implement these values. In our view, conservation biogeographers should be in the business of providing alternative scenarios that address differing end goals (cf. Williams et al., 2000; Dimitrakopoulos et al., 2004) if they are to place their science at the service of society so as to best inform decision-making processes. To expand on this a little, any system of conservation prioritization, even if based on the application of numerical algorithms to comprehensive data sets, ultimately reflects value judgements about which features are important and how to weigh them up (Knight & Cowling, 2007). Applying funding or protection to areas ranked highly by the chosen protocols may, in the end, diminish the opportunity for conservation elsewhere, perhaps including other areas of pressing conservation concern. On the scale of landscapes, regions and states, biogeography is well placed to inform such choices. Some, reading this introduction, may wish to contest the notion that values are separable from science at all, a view with which we have some sympathy. Indeed, as pointed out by Trudgill (2001), many of the terms in

use in biogeography and conservation biology (e.g. ‘equilibrium’, ‘alien species’, ‘native species’, ‘climax community’ and ‘natural’) are deeply value-laden and defy easy objective definition. Although not included in the chronology of ideas in Figure 1.1, crucial to the recent progression of the conservation movement has been the emergence during the late 1980s of the concept of ‘biodiversity’, a term of technical and scientific resonance but one that defies precise scientific definition (Takacs, 1996). Indeed, as noted in Box 1.1, it has been argued that biodiversity definitions are closer to being subjective ‘value judgement’ concepts (such as quality of life) than they are to being an objective measure of an environmental property. Most commonly used definitions imply in some way that biodiversity is a ‘good’ thing per se and that, conversely, biodiversity loss through human action is ‘bad’ and should be prevented or minimized. Another difficulty implicit in many of the definitions of biodiversity, including that adopted by the 1992 Convention on Biological Diversity (CBD), is that biodiversity can, and should, be both conserved and used. The extent to which we regard these goals of conservation and development as compatible or in conflict describes, to a large degree, where we position ourselves as members of our society. How might this influence our work as scientists? Similarly, others have observed that natural scientists working on conservation science problems have traditionally worked within rather static equilibrial frameworks that portray nature as unchanging in the face of abundant evidence of inherent variability and flux in many natural systems (Pickett et al., 1992; Wu & Loucks, 1995). The language used in the ecological and conservation literature, according to Stott (1998), frequently reveals a desire for ‘stability’ and ‘safety’ (the so-called ‘precautionary principle’), whereas in reality we live in a world in which change takes place all the time, in all sorts of directions and at all sorts of scales; everything is in flux (summarized from Stott, 1998, p. 1). Stott calls for biogeographers and ecologists to wake up to the non-equilibrium nature of the world around us and to re-examine the assumptions and the language we use in discussing environmental problems/ opportunities and conservation. Although the socalled ‘balance of nature’ paradigm is rapidly being superseded in scientific circles by more dynamic conceptions of nature, as a handy metaphor it still has

Roots, relevance, aims and values considerable traction in society and is commonly used in popular discourse on conservation issues (Ladle & Gillson, 2009). How much do such frames of reference continue to influence the science we conduct and how we interpret our data? While our focus in this book is very much on the way that biogeographical science can contribute to conservation, we recognize that these and other critiques of the objectivity of our science require that we pause to consider the interaction between social values, the conservation movement and biogeographical science. Hence we devote chapters in this opening part of the book both to a consideration of values motivating conservation action (Chapter 2), and a consideration of the concept of alternative ecological (scientific) baselines and how they may inform conservation (Chapter 3). From this brief outline we wish to highlight three points. First, the conceptual origins of biogeography as an academic discipline substantially predate the emergence of conservation biology. Second, conservation biogeography forms an important and distinctive (but not entirely distinct) subset of conservation biology. Third, the motivating force for these scientific endeavours is a diverse and dynamic social movement, representing varied values and world views.

1. 4 O U T L I NE OF T HE F OL L OW IN G CH A PT E R S Edited books can sometimes be a bit fragmented. In Conservation Biogeography we have tried to avoid this by imposing a strong structure, prescriptive content and strong editorial guidelines throughout the process of developing the text. It is our hope, therefore, that the book can be read either as a single cohesive narrative or as ‘stand-alone’ chapters. The book is divided into four major parts. The first part, Roots, relevance, aims, and values (Chapters 1–3) has the aim of providing the historical and philosophical context of conservation biogeography, as well as introducing key terminology and frames of reference. Chapter 2, Social Values and Conservation Biogeography, focuses on the frequently neglected subject of the values underlying decisions to prioritize or protect certain geographic areas for conservation, and how to manage those areas once they have been designated. The key point is that decisions about where, what and how to conserve may be based on hard data

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and scientific principles, but are ultimately a reflection of different values within society or the global conservation community. Chapter 3, Baselines, patterns and process, examines the two main conceptual approaches underpinning modern conservation practice – compositionalism and functionalism – and how they have profound influences on conservation objectives. It also introduces the concept of ecological baselines and how these have become important targets for conservation and restoration (although in fact there may be multiple alternative baselines, states or dynamic frames of reference for the same region or landscape). The second part of the book, The distribution of diversity: challenges and applications (Chapters 4–7), provides an overview of the current state of global biogeographical knowledge and how this knowledge has been used to better focus conservation efforts. Chapter 4, Basic biogeography: estimating biodiversity and mapping nature, focuses on what we know and what we don’t know about the distribution of biodiversity and the varying phenomena we may want to map (e.g. biogeographical regions, biomes, ecoregions, areas of endemism, evolutionary significant units, etc.). It also covers the varying approaches to species mapping and how to deal with challenges such as scale issues, representations of species ranges, and bioclimate envelope modelling and mapping. Chapter 5, The shaping of the global protected area estate, gives an overview of the history and development of protected area planning frameworks at global to regional geographical scales. The chapter splits these frameworks into two main approaches: zonal, involving the mapping of attributes of nature into a suite of broadly climatically or historically determined non-overlapping areas (e.g. ecoregions); and azonal, involving the application of biogeographical principles to identify a particular set of disconnected places across the world (e.g. hotspots, important areas). Schemes based on these contrasting approaches have become key determinants of global funding and conservation action. The final chapter of this part is Chapter 6, Systematic conservation planning: past, present and future. Here, the principles and applications of computer-based and data-intensive approaches to protected area network design are reviewed and discussed, covering important network design concepts such as complementarity, irreplaceability and redundancy and the development and application of reserve selection algorithms. The

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The roots of conservation biogeography

chapter also contains extensive examples of how to conduct systematic conservation planning in marine and terrestrial ecosystems. The third part of the book, Conservation planning in a changing world (Chapters 7–9), addresses some of the key challenges to undertaking effective conservation during a time of unprecedented ecological change. Chapter 7, Planning for persistence in a changing world, starts by comparing current ecological trends to past changes in the biota. It follows this by an extensive analysis of methods used to predict biodiversity change (e.g. species distribution models, range shift models). Using the insights from these studies, the chapter concludes with a discussion of the need for dynamic conservation planning approaches that are flexible and responsive to changing predictions about the potential fate of the natural environment. One of the key biogeographical questions in conservation is how biodiversity will respond to the continuing and widespread loss and fragmentation of natural and semi-natural habitats. Chapter 8, Applied island biogeography, explores the implications of this insularization of formally contiguous ecosystems and reviews the pervasive influence of the equilibrium theory of island biogeography on the conceptual development and practice of conservation. The chapter covers important applied issues such as the use of the species– area relationship, efforts to model metapopulation dynamics, analyses of nestedness, edge effects and the efficacy of habitat corridors and the influence of varying types of matrix habitats on the abilities of threatened species to disperse between high quality habitat. It ends with a discussion of how conceptual advances can be converted into practical solutions and guidelines for conservation. Part 3 concludes with Chapter 9, Biological invasions and the homogenization of faunas and floras, which tackles one of the greatest challenges in modern conservation: how to understand, control and manage introduced species. The chapter starts by reviewing the biogeography of invasion and the process by which regionally distinct, native communities are gradually replaced by locally expanding, cosmopolitan, nonnative communities (homogenization). After brief review of patterns of homogenization by taxon, it

addresses the human causes of this process and concludes with a discussion of what these novel assemblages might mean for the future of conservation. The final part of the book, Future directions, contains a single chapter entitled Prospects and challenges, which discusses the future of conservation biogeography and focuses on the global challenge of filling the Wallacean and Linnean shortfalls, the rapid evolution of predictive models and the necessity to develop tools and applications that fulfil the needs of society to develop sustainably. The chapter casts an eye over new technological developments, such as the latest generation of biodiversity information systems that have the potential to radically alter the amount and quality of data available to biogeographers. The book concludes with some reflections on the role of conservation biogeographers in shaping the future of conservation and how to engage more fully with society and real-world conservation issues. Each main chapter contains a selection of suggested key readings in addition to the extensive literature cited within the text. The purpose of these readings is to guide students to core texts, seminal papers or stimulating contributions that expand upon topics within the chapter. Each main chapter also concludes by raising a number of questions that could form the basis of a class discussion or may be used to test understanding of the material.

S U GGES T ED R EADI N G Grehan, J.R. (1993) Conservation biogeography and the biodiversity crisis: a global problem in space/time. Biodiversity Letters, 1, 134–140. [Of historic interest as the first use of the term ‘conservation biogeography’ of which we are aware] Lomolino, M.V. & Heaney, L.R. (eds.) (2004) Frontiers of biogeography: new directions in the geography of nature, Sinauer Associates, Sunderland, MA. Primack, R.B. (2002) Essentials of conservation biology, 3rd edn, Sinauer Press, Inc., Sunderland, MA. Whittaker, R.J., Araújo, M.B., Jepson, P., Ladle, R.J., Watson, J.E.M. & Willis, K.J. (2005) Conservation biogeography: assessment and prospect. Diversity and Distributions, 11, 3–23.

CHAPTER 2 Social values and conservation biogeography Richard J. Ladle1,2, Paul Jepson1 and Lindsey Gillson3 1

School of Geography and the Environment, University of Oxford, Oxford, UK Department of Agricultural Engineering, Federal University of Viçosa, Brazil 3 Plant Conservation Unit, Botany Department, University of Cape Town, South Africa 2

2. 1 MANY V AL UE S, M ANY GOALS The problems involved in saving species from extinction and maintaining biodiversity are typically presented to the public in technical or scientific terms, often alongside proposed technical scientific solutions, such as captive breeding and re-release or the creation of a new protected area. However, while conservation clearly draws upon science and technology to a large degree, the desire to conserve nature and the behaviours associated with this desire are expressions of underlying human values. Values are here taken to mean beliefs and ideas that inform assessments of worth and which are, by definition, socially constructed. The practice of conservation of the natural world in this context is clearly a social phenomenon, and one that often becomes strongly political when conservation ideals conflict with other societal aspirations such as poverty alleviation or economic development. In the broadest sense, conservation is about asserting (or reasserting) certain values in society concerning the human/nature relationship (Jepson & Canney, 2001). Common conservation practices such as the establishment and management of protected areas, ecosystem restoration, reintroduction of large predators or the eradication of invasive species are simply the outward expression of these values. It has been argued that all humans have an innate emotional affiliation to other forms of life and are

therefore predisposed to value life and living systems (Wilson, 1984). In the broadest possible sense this may be true, but it is also clear that the specific characteristics of the natural world that are valued by people, cultures and organizations can also vary considerably. Furthermore, these values can clash dramatically, especially where there are conflicts between local people and an animal that is immensely valued by the global conservation movement. Good examples include the problems caused by elephants in Asia and Africa, the numerous conflicts around the world caused by depredation of cattle by large felids and canids, and the social barriers to reintroducing large predators such as bears and wolves into human-dominated landscapes in Europe and North America. Effective conservation requires the support of key stakeholders, scientific evidence to support a particular conservation strategy and, equally importantly, awareness on the part of the conservation organization of differing social values. Put another way, the world can be seen as a geographic patchwork of different values that reflect cultural/societal differences, the varying frames of reference of different conservation organizations, and governmental concerns and imperatives. Moreover, contrasts in values and strategies also typically exist not just between different regions but within communities, organizations and government departments for any given area. These layers of values and the relative power of different conservation

Conservation Biogeography Edited by Richard J. Ladle and Robert J. Whittaker © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33503-3

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Social values and conservation biogeography

stakeholders to enforce change arguably determine the chances of success of any given science-based conservation intervention far more than the quality of the science involved (Ladle & Jepson, 2008). Therefore, examining the social values that underlie different types of conservation interventions and the organizations that endorse/promote them is vital for understanding the modern landscape of conservation and for effective conservation practice. In the context of conservation biogeography, social values have been of fundamental importance in shaping the geographic foci of action (e.g. western attitudes towards African savannas or tropical rain forests) and determining the abundance, distribution and relative success of different forms of protected areas. In a more general sense, conservation will only succeed if people, willingly and voluntarily, give the natural world the space and resources it needs to thrive. Social values are what promote the self-regulation and individual restraint required for this endeavour. Such values also legitimize the conservation laws, policies and actions and engender the willing support (or at least compliance) of citizens (Cotgrove & Duff, 1991). This chapter will examine how conservation values have been expressed in the creation of different types of protected areas and in the development of overarching international conservation treaties and conventions. We will then explore how protected areas have been formalized into an international categorization system. We will also briefly consider the values inherent in certain common forms of conservation management. Finally, we will discuss the dynamic and ever-changing nature of the conservation values ‘landscape’ and consider the ways in which conservation is adjusting to a global polity that is increasingly focused on other agendas such as climate change and poverty alleviation.

2. 2 T HE OR I GI NS AND V AL UE S OF DI F F ER E NT P R OT E C T E D AR E A T Y PES Conservation values have often been split into ‘intrinsic’ and ‘instrumental’, reflecting a perceived dichotomy between the desire to protect nature for its own sake and protecting nature because of its value to humanity. ‘Intrinsic’ value arguments typically concern the aesthetic and intellectual appreciation of nature and human compassion or reverence towards other life forms and have roots in deep-seated psychological tendencies (biophilia), cultural world views and

an eclectic variety of religious perspectives. In contrast, instrumental arguments are those concerned with ensuring human survival, well-being and the potential to develop materially (Ehrlich & Ehrlich, 1992). One of the latest and most powerful expressions of instrumental values has been the rapid rise to prominence of the concept of ecosystem services – natural processes and products such as pollination and watershed protection – which play an important role in the economy. The monetary value of such services can be crudely calculated (e.g. Costanza et al., 1997), thereby facilitating the entry of conservation into the realms of economic policy and political debate. The physical expression of conservation values, whether instrumental or intrinsic, has frequently been through the gazetting of land primarily or in part for conservation – commonly referred to as ‘protected areas’. The number and area of protected areas increased dramatically from the 1970s onwards, but in the terrestrial realm this growth has now levelled off (see Figure 2.1). In 1962 there were about 1,000 terrestrial protected areas worldwide. In 2004 the count was around 104,000, covering an area of just over 20 million km2, mostly of terrestrial habitats and amounting to 12.2 per cent of the Earth’s land surface (Chape et al., 2003, 2005). Growth in marine protected areas (MPAs) is a relatively recent phenomenon and the global MPA estate of around 2.59 million km2 (0.7 per cent of the ocean surface) remains comparatively small (Figure 2.1). It is important to note that the term ‘protected area’ is something of a catch-all category that may be applied to areas that have been allocated by states or held by private interests with the primary function of conserving attributes of nature that are valued. Existing protected areas have been designated on the basis of both instrumental and intrinsic values and often through a combination of both. Indeed, it is probably better to view the terms ‘intrinsic’ and ‘instrumental’ as umbrella concepts that incorporate a wide variety of closely related values, often with strong cultural and historical roots. It should also be noted that while social values play an important role in promoting, shaping and defining the characteristics of protected areas, there are many other legal, socio-political and scientific factors that play a role (Ladle & Malhado, 2007; Figure 2.2). Given the complexity of the drivers of protected area formation, it is perhaps unsurprising that they are difficult to divide up neatly into clear thematic categories. Indeed, there is a vast array of names and terms which are applied to specific sites, e.g. ‘national park’, ‘wildlife

Roots, relevance, aims and values

15

Figure 2.1 Growth in nationally designated protected areas (1872–2008). NB. This excludes 52,932 sites with no year of establishment, hence the total area shown is rather less than the figure of around 20 million km2 cited in the text. Re-drawn from World Database on Protected Areas (www.wdpa.org).

Figure 2.2 Factors influencing the establishment of new protected areas. The creation of a major new protected area is primarily achieved through national legislation but is heavily influenced by socio-political factors and is informed by science. Re-drawn from Ladle & Malhado (2007).

refuge’ and ‘wilderness area’, to name but a few. To add further confusion, many places carry multiple names or designations. This is has arisen because the purpose, legal definition and degree of protection of a given protected area can change over time and can vary considerably between countries. The complexity and overlapping nature of designations and terminology associated with contemporary protected areas is built upon a smaller and simpler set of foundational values of the modern-day conservation movement with roots in the late 18th and early 19th centuries. These core values (Table 2.1) arose largely out of a seismic shift in western society’s view of the relationship between humans and the natural world. A series of discoveries, events and circumstances, culminating in the formulation of Darwin’s famous theory of evolution, prompted vigorous debate that transformed understandings of the place of humans within nature. Some of the most influential of these events were the sudden and well-publicized rash of extinctions (e.g. the passenger pigeon, the great auk and Steller’s sea cow), the rapid demise of the vast forests in the American Great Lakes region and the discovery of the great apes (the first scientific description of the gorilla was published in 1847), whose anatomy was remarkably similar to our own.

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Social values and conservation biogeography

Table 2.1 Foundational values of the modern conservation movement and approximate location and date of emergence and social groups involved. Social values

Movement & origins

Location & date

Access to nature and countryside is necessary for the health and well-being of urban-dwellers

Social reformers/openspaces movement

London, mid 19th century

Free enjoyment of nature is one of humanity’s most precious privileges and access to nature should not to be abridged by private right for greed or gain

Social reformers, urban planners

New York, early 20th century

Healthy ecosystems are necessary to safeguard economic growth, high quality livelihoods and social stability

Colonial scientists

Colonies, late 18th century

Natural resources should be managed for the greatest good for the greatest number in the long term

US Foresters

US, early 20th century

Landscapes evoking wilderness should be preserved as a benchmark from which to assess urban/industrial modernity and as places for spiritual, aesthetic and physical exploration and rejuvenation

Writers & artists, Wilderness movement

US, late 19th century

Human conquest of nature carries with it a moral responsibility to ensure the survival of threatened life-forms

Big game hunters, Wildlife movement

New York & London, turn of 20th century

Wanton and unnecessary slaughter of wildlife is cruel and barbaric

Big game hunters, politicians and entrepreneurs

London, early 20th century

Aesthetic and intellectual contemplation of nature are integral to the biological and cultural inheritance of many peoples and monuments of nature should be guarded from ruin

Prominent citizens

Western European cities, early 20th century

In the early days of the global conservation movement, the purpose of protected areas was far more explicitly reflected in the terminology used to describe them. For example, proponents of the late 19th century wildlife movement advocated the establishment of wildlife sanctuaries and refuges; natural historians argued for the establishment of nature monuments and nature reserves; activists of the open spaces movement pushed for country parks (UK) and state parks (US); adherents of the wilderness movement for the creation of wilderness areas; and the ‘wise-use’ resource mangers established game reserves, forest reserves and watershed protection forests (reviewed in Jepson & Ladle, 2010). These terms, along with national parks, can be viewed as forming the basis of modern protected area types and designations.

In the following examples, we describe a variety of types of protected areas and the values that have influenced their formation and management, and which, by extension, underpin the contemporary protected area system. The list of protected area types is by no means exhaustive, but rather has been chosen to reflect types of protected area that are both numerically important for modern conservation and which reflect a distinctive suite of values.

2.2.1 Sacred sites Despite the deeply rooted and universal emotional human attachment to life and living processes (Wilson, 1984, 1993), the history of human societies has been

Roots, relevance, aims and values littered with well-documented examples of environmental destruction and unsustainable exploitation (Penn, 2003; Diamond, 2004). Indeed, the (relatively) lower ecological impact of people in many traditional societies has been argued to be primarily a function of their low population density and the limited availability of technology that would allow greater levels of exploitation (Ruttan & Borgerhoff-Mulder, 1999; Penn, 2003). Interestingly, there is also no obvious association between a society holding strong beliefs about the sacredness of nature and lower levels of environmental destruction (Low, 1996). Even so, the practice of restricting how resources are exploited or accessed on religious or spiritual grounds is widespread across human societies, and there are many cultures that refrain from exploiting particular areas or species (Colding & Folke, 2001). These beliefs are being increasingly considered as potential instruments for conservation practice, either on their own or nested in more formal arrangements (Barrett et al., 2001). The motivations for prohibitions or taboos (a word derived from Polynesian languages and culture to mean something that is forbidden) are as varied as the belief systems themselves. For example, in Northern Madagascar there is a complex system of taboos (known locally as ‘fady’) to which many Malagasy people adhere and which suffuses every element of their life (Ruud, 1960). Fady covers both habitats and particular resources and plays an important role in regulating a number of natural resource uses, including the exploitation (and protection) of a particular species or habitat (Mannle & Ladle, in press). In southern Madagascar, Lingard et al. (2003) reported that local people considered radiated tortoises as fady, a cultural association which may have saved them from extinction. Perhaps the most important type of taboo for conservation, and the basis of many informal, culturally prescribed protected areas, are habitat taboos. These form the basis of protection (from local communities) for a large number of natural sites around the globe dedicated to ancestors or deities. Bhagwat & Rutte (2006) refer to these informal protected areas as ‘natural sacred sites’ and argue that they are an important and often overlooked addition to the global network of protected areas. Indeed, natural sacred sites may be especially important because they are typically found in remote areas that are high in biodiversity but which have low levels of formal protection.

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Although the origins of many of these sites are impossible to uncover, there is some evidence to suggest that sacred forest groves in India existed before the advent of agriculture, and that this was the primary reason for these patches to be spared even though the surrounding land was cleared (Hughes & Chandran, 1998). As outlined above, sacred groves and other natural sites protected for spiritual and historical reasons probably have origins in pre-agricultural societies and animistic belief systems. Many sacred sites around the world are currently threatened as mainstream religions and secular values replace traditional beliefs. This process is by no means a recent phenomenon. In ancient Europe (4th–5th century AD), the sacred groves of the druids were destroyed with the arrival of Christianity (Matthews & Matthews, 2002). More recently, in India, local folk deities have been (and continue to be) replaced with Hindu deities – a process referred to as ‘Sanskritization’ (Kalam, 1996). The last fifty years have seen a new threat to the ancient belief systems that support sacred sites as local customs are being increasingly challenged by globalized western consumerist culture. The globalization of culture, although not a direct threat, is partly responsible for eroding the cultural importance of sacred sites for younger generations of local people (Bhagwat & Rutte, 2006).

2.2.2 Resource and game reserves Managing natural resources in a planned and rational manner has a long history. For instance, Schama (1995) describes how the strategic and political importance of timber for ship-building for the navy and for domestic use and iron smelting created powerful conservation voices within the courts of Tudor England (1550–1650), which prompted the creation of systems of forest reserves, regulations, penalties and guards. Protecting sites in order to maintain the quality of the hunting also has an ancient history. In medieval Europe, many kings and aristocrats maintained private hunting reserves with strict control of poaching. Grove (1996) traces the roots of western environmentalism to scientific societies formed in the 18th century by individuals employed by European companies to investigate the economic potential of unfamiliar flora, fauna and geologies in new territories. Through observing and debating the environmental changes

18

Social values and conservation biogeography

wrought by imperialism, these scientists significantly influenced the attitudes and policies wrought by the administrative elite of the European colonial powers. In particular they provided the first evidence of the association between deforestation and famine, and hence also of agrarian economic failure and social unrest. Between 1764 and 1791, colonial powers established forest reserves and supporting legislation in Mauritius, Tobago and St Vincent, which in turn can be regarded as the antecedents of the forest planning and protection systems that developed in the Indian sub-continent during the 19th century. This powerful movement of rational resource planning developed further in America under the leadership of Gifford Pinchot, who was from 1905–1910 the head of the US Forest Service (established in 1905 under President Theodore Roosevelt’s patronage). Pinchot understood the importance of public discussion in shaping public attitudes and social values, and he conducted massive publicity campaigns (including debates with opponents such as John Muir, who were deeply opposed to the commercialization of nature) to promote and direct a national discussion on natural resource management issues (Balogh, 2002). These debates helped embed in American society the value that natural resources should be managed for the greatest good for the greatest number in the long run; an idea also expressed as the development of natural resources for the many rather than the few (Pinchot, 1910). Pinchot was a key promoter of these ideas but, as outlined above, he was not the first to express them, and the uptake of such values by governments led to the designation of watershed protection forests and forest reserves in Europe, America and many colonial territories from the mid-19th century onwards. Additionally, colonial administrations in Africa established game reserves for the utilitarian purposes of managing ivory (elephant) resources and protecting source populations of game species that could spill out into the wider landscape and provide a source of meat for new settlers (MacKenzie, 1988). The most recent incarnation of this idea can be seen in sub-Saharan Africa, where there is a growing number of private game reserves, some in excess of 10,000 hectares. Most, if not all, are run as commercial operations. These private reserves typically offer an exclusive African experience involving luxury lodges and camps, private guides and trackers to help the guest get the perfect photograph or memorable kill. In some areas,

managing game reserves for hunting represents a viable alternative to cattle ranching that can benefit wildlife, although this option is not one that chimes well with the values of many in the conservation movement. The formation of the United Nations in the aftermath of World War II, and the rise of science-based rational resource management in international development, promoted a re-formulation of these earlier social values, which can be expressed as the notion that healthy ecosystems are necessary to safeguard economic growth, high quality livelihoods and social stability (Ehrlich & Ehrlich, 1992). Subsequently, the concept of biodiversity served to propel this value further up the political agenda, culminating in the 1992 Earth Summit and the associated Convention on Biological Diversity. The function of many older reserves, originally established in response to other conservation values, were often re-stated to align with new conservation agendas relating to the maintenance of genetic reservoirs, protection of ecosystem services and sustainable utilization linked to livelihood development.

2.2.3 State and country parks State and Country Parks were first implemented in response to the open spaces movement (also termed the amenity movement) which arose amongst social reformers concerned with urban poverty and health in industrial cities during the mid-19th century. The catalyst for action in the UK was plans to sell off Hampstead Heath in London for housing in the 1860s. The Heath, a ridge with fine views, was popular with day-trippers from London’s increasingly crowded suburbs and provided them with a vital respite from the dust, smog, fumes and waste that created the notoriously unhealthy living conditions of 19th century industrial cities. Activists in this movement recognized the health and social benefits of countryside recreation and the need to preserve natural areas both inside and on the fringes of cities and make them accessible to all classes of people. These activists were adept at influencing the parliamentary processes and, through organizations such as the Commons Preservation Society, they secured legislation empowering Metropolitan authorities to acquire open spaces to act as an informal ‘countryside’. The social value they articulated was that access to nature and countryside is necessary for

Roots, relevance, aims and values the health and well-being of urban dwellers. This value soon took root in urban planning in west European and east coast American cities, leading to the creation of urban parks (notably Central Park in New York) and prompting the acquisition of land in or close to cities for designation as country parks (UK) or state parks (US), managed primarily for informal outdoor recreation.

2.2.4 Nature monuments and nature reserves A further site-based agenda for protecting natural sites emerged from the older natural history and philosophical societies. By the mid-19th century, natural history had reached craze proportions as a popular pastime and scientific endeavour among certain sections of European society (Allen, 1994). It was considered the ideal form of self-improvement, providing exercise, education and rational amusement. In combination with romanticist sentiments, it made the field excursion and field club an important focal point of European middle class social life. Perhaps not surprisingly, these field clubs mobilized to protect favourite field sites and other natural sites with special cultural, aesthetic and scientific appeal from urban and agricultural threats (and in Germany from clear-felling policies). A call for action was made between 1900 and 1908 by a prominent German forester, Hugo Conwentz, who conducted a series of high-profile lectures in European cities, where he articulated the threats to natural sites and promoted the concept and vision of Naturdenkmal. This consisted of three inter-connected ideas: 1 that the idea of memorial – Denkmal in German and usually applied to anything in commemoration (e.g. eminent persons, works of literature and art, and ancient buildings) – could also be applied to nature; 2 that Naturdenkmal, like great works of art, should be guarded against ruin; 3 that such action had patriotic value, because, ‘by these undertakings, parts of the country at home become better known and more fully appreciated’ (Conwentz, 1909). The concept of Naturdenkmal captured the value that aesthetic and intellectual contemplation of nature is integral to the biological and cultural inheritance of many peoples, and thus that monuments of nature should be guarded from ruin. The idea of protecting places where people could develop a greater

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appreciation of their natural surroundings was simple and powerful, and found a ready support base amongst the middle class citizenry and some governments. Preservationist groups devoted to identifying and acquiring nature monuments were established in France (1901), Holland (1904), Switzerland (1909) and England (1912), while the Prussian and Swedish states appointed government commissioners for this task in 1906 and 1912 respectively. In the US, the 1906 Antiquities Act provided for the creation of ‘national monuments’, including natural sites (Sellars, 1997). The idea also spread to the Netherlands’ Indies (now Indonesia), where the government designated 24 Natuurmonumenten between 1914 and 1924 for reasons as varied as protection of botanical, faunal and geological features, beautiful panoramas, the Javan rhino, a memorial for an 18th century naturalist and a sacred fig tree (Dammerman, 1929; Jepson & Whittaker, 2002a). Unfortunately, from a conservation perspective, the nationalistic values promoted by Naturdenkmal were appropriated to a degree by right-wing and nationalist politicians in the first half of the 20th century, leading to a distinct ‘down-playing’ of this value in modern conservation propaganda. This trend of politicization was less evident in the UK, where the designation ‘nature reserve’ was preferred to the grander term ‘nature monument’.

2.2.5 Wildlife sanctuaries and refuges Hunting and natural history were two of the great passions of the Victorian age which brought the metropolitan elite into contact with nature at home, and with the frontier landscapes of Africa and the American West. These passions cut across society and led to politically influential citizens witnessing at first hand the disastrous impacts of cultivation, market hunting and resource extraction on species and landscapes (Jepson & Whittaker, 2002a). Arguably the most influential conservationist of all time was the 26th President of the United States of America, Theodore (Teddy) Roosevelt – an avid hunter whose vision for conservation was largely moulded by the excesses he perceived in many of his fellow big game and market hunters. Roosevelt is personally responsible for articulating two of the core values of the modern conservation movement (Table 2.1): first, that humanity has a moral responsibility to save

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Social values and conservation biogeography

threatened life forms; and second, that needlessly slaughtering wildlife is cruel and barbaric. Roosevelt was also a founder member in 1887 of the elite Boone and Crockett Club (B&CC), the goals of which were to create sanctuaries and refuges where wildlife could survive the onslaught of human expansion into frontier lands and to change the culture of hunting to value the noble qualities of the chase rather than the number of animals killed. The activities of Roosevelt and his colleagues in the B&CC were mirrored by European big game hunters, most notably in the formation of the Society for Preservation of the Wild Fauna of the Empire (SPWFE) founded in London in 1903 – still in existence as Fauna & Flora International (FFI). Like the B&CC, the members of the SPWFE were men of great eminence, including the Secretary of State for the Colonies, colonial administrators, hunters and other experts on game in Africa (Prendergast & Adams, 2003). The SPWFE shared the same concerns as the B&CC, but its focus was on declining game populations in the colonies, and in particular Africa. In the early decades of the 20th century, the two organizations joined forces to create the first international conservation treaty – the 1933 London Convention on African Wildlife – which created the legal basis and political clout to establish a network of parks to protect Africa’s game species. These networks established wildlife sanctuaries through a combination of informal lobbying and highlevel representations, and members of each organization held the executive power to create wildlife refuges. For example, as president, Roosevelt designated 59 wildlife refuges between 1909 and 1914, and European colonial governors designated sanctuaries in West Africa, India, Malaya and Indonesia (Jepson & Whittaker, 2002a).

2.2.6 Wilderness areas In the aftermath of the American War of Independence (1775–1783) US politicians sought to create a distinct culture as a mark of ‘true’ nationhood that would both complete and justify the American Revolution. Intellectuals of the time searched for something uniquely ‘American’ to ‘transform embarrassed provincials into proud and confident citizens’ (Nash, 1982, p. 69). A key source and inspiration in this quest for cultural autonomy was the American

landscape. Painters and poets of the Hudson River School (ca. 1825–1890) and the transcendentalist writers (ca. 1830–1870) were inspired by European romanticism to create an intellectual movement that identified wilderness as a basic ingredient of American culture. The philosophical roots of wilderness reserves can be traced back to Henry David Thoreau, who, in 1862, declared that ‘In wildness is the preservation of the world’ (cited in Cronon, 1996, p. 7). Thoreau was a leading light of the transcendentalist movement and rejected the doctrines of established religions in the belief that life was about the search for an ideal spiritual state that ‘transcends’ the physical. Thoreau believed that such a state could be more easily realized in the majestic and awe-inspiring monuments of nature that abounded in the New World. These natural places, in essence, reflected a higher transcendent truth. The motivation to preserve wilderness in parks and reserves probably emerged among the urban political and business elites of New York and other East coast cities and was motivated as much by commercial as spiritual or cultural interests. Since the 1820s, an American ‘grand tour’, visiting picturesque landscapes and supported by imagery and writings exploring their sublime qualities, had been popular among educated urban people. The first officially designated wilderness area was probably Yosemite, deeded by the US government to the state of California in 1864 as a ‘wildland park’ (Cronon, 1996). This was shortly followed by the designation of Yellowstone National Park in 1872, a move enthusiastically supported by railroad interests that saw the area’s potential as a profitable vacation resort and were keen for a nationally sanctioned reservation that would keep speculators and squatters out of the area (Nash, 1982). It took John Muir, the Scottish-born naturalist, to translate the attitudes of the transcendentalists towards the American wilderness into a dynamic social movement with the power to wield real political influence. Muir founded the Sierra Club in 1892, with the goal of establishing Glacier and Mount Rainier national parks, and saving California’s last fragments of coastal redwoods. He gained national prominence through the campaign to stop the damming of the Tuolumne River in the Hetch Hetchy valley of the Yosemite National Park (designated in 1890). The campaign, which ran

Roots, relevance, aims and values from 1908 to 1913, pitted Muir and his followers against Pinchot and his wise-use philosophy. Muir and the ‘preservationists’ adopted a deeply religious rhetoric and imagery in support of their cause. This reinforced the ethos of the 19th century cultural movement and gave shape and popular meaning to a core social value, which can be summarized by the notion that landscapes evoking wilderness should be preserved as a benchmark from which to assess urban/ industrial modernity and as places for spiritual, aesthetic and physical exploration and rejuvenation. The Sierra Club, together with Aldo Leopold’s Wilderness Society (founded in 1935), helped define wilderness areas as a significant part of the American heritage. Their efforts culminated with the signing of the US Wilderness Act in 1964 by President Lyndon Johnson. At a stroke this led to the designation of 37,000 km2 of national forest as wilderness areas, with a wilderness defined in the Act as ‘… an area where the earth and its community of life are untrammelled by man, where man himself is a visitor who does not remain’. As America’s overseas influence increased during the latter half of the 20th century, notions of wilderness strongly influenced the identification and designation of protected areas around the world. Strict nature reserves have some similarities with wilderness areas, although their origins are somewhat different. This category of reserve was invented by colonial powers negotiating the 1933 convention on African wildlife to accommodate Belgian and French views that tourism (as promoted by the British and US) was incompatible with preserving nature in a natural state. As such, this type of reserve is clearly closely related to the underlying philosophy of wilderness reserves as they were originally conceived.

2.2.7 National parks The term ‘national park’ is probably the most widely recognized category of protected area, yet the underlying purpose is not clearly evident from the term itself. This may be because national parks often appear to represent the merger of conservation goals with efforts to create or reinvigorate a sense of national identity (Jepson & Ladle, 2010). Many national parks started off as wildlife sanctuaries or nature monuments and were part of a conscious strategy of ‘nation building’

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adopted by many governments, especially in newly democratic nations. This helps explain why national parks vary so much between countries in size, scope, the nature(s) they conserve and their value for biodiversity conservation. For example, in the USA the goal was to use national parks to capture the grandeur and sense of a pristine nature present in landscapes such as the Rocky Mountains. The creation of Yosemite, Glacier and other national parks, after intense lobbying by the Sierra Club and similar organizations, was instrumental in building national pride and cultural identity in a country still trying to throw off the yoke of British colonialism. In contrast, the ‘political’ purpose of national parks in the UK might be regarded as part of a project to help redefine and renew national identity after the collapse of empire. Accordingly, these parks were initially created in cultural landscapes close to urban centres, such as the English Lake District and Yorkshire Dales. The national parks model has also been exported to the developing world. However, once again the manifestation of the parks has been altered to fit with the political ideals of countries struggling to cement their position in an increasingly globalized world. For example, in countries such as Indonesia and Madagascar, national parks have been used as important symbols of governmental commitment to the international agenda of reducing biodiversity loss, promoting sustainable development and empowering local communities (Jepson & Ladle, 2010). Conservation NGOs have strongly endorsed and promoted the national parks concept in such developing countries because these typically cover larger areas than other reserve types, and also because governments may feel a greater duty to protect and manage them. Furthermore, the governments of the developing countries gain a tangible benefit from designating new national parks, because such acts project a favourable international image, attract international funding and may have the added bonus of strengthening central state control over remote areas. National parks certainly appear to have an enduring appeal to ambitious politicians and they are still growing in number, especially across the developing world. As recently as 2002, President Omar Bongo Ondimba of Gabon pledged to create a network of 13 national parks comprising ten per cent of the country’s land cover.

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Social values and conservation biogeography

2.2.8 Community conservation areas It is now widely recognized that many animal- and plant-rich landscapes are the result of traditional human interactions with their environments. Often, however, traditional practices and forms of landscape management have been eroded either by authoritarian states, markets, environmental degradation or a combination of all three. Since the late 1980s, there has been a concerted effort from the international community towards the rebuilding or reinvigorating of community-level institutions with the authority and capacity to manage their natural resources in a sustainable manner, under the name of Community-Based Natural Resource Management (CBNRM). Tsing et al. (1999) identify three premises of CBNRM programs: first, that local populations have a greater interest in the sustainable use of resources than the state or other potentially interested parties; second, that local communities are more knowledgeable about local ecological processes and practices than are other interest groups; and finally, that such communities are better placed to effectively manage their own resources. One consequence of CBNRM and similar initiatives has been the creation of Community Conserved Areas (CCAs) where local people ‘own’ and manage wildlife rich sites and landscapes. One of the best examples of this is the ‘Communal Conservancy’ model developed in Namibia and now being adopted in other African countries. CCAs are founded on the belief that if communities have exclusive use rights of wildlife resources they will manage the ‘resource’ sustainably. In 1996, Namibia introduced the Nature Conservation Amendment Act (GRN, 1996), giving conditional use rights over wildlife to communities in communal areas. Under the terms of the Act, communities are required to form a local management institution (the conservancy) comprising a committee drawn from the community, draw up a constitution and management goals, develop approaches and monitoring protocols and create a local mandate for conservancy management staff to operate (Massyn, 2007). To help in the process, external conservation groups such as the World Wildlife Fund (WWF) and the independent policy research Institute (IIED) provide technical input, training and general support. The revenue in these areas is almost exclusively generated through safari-tourism and trophy hunting, so the community has a strong vested interest in

maintaining natural resources and key species at healthy levels. This processes of devolving authority over wildlife and tourism to local communities seems to be working well, at least in Namibia. Populations of cheetah, leopard, wild dog, springbok and zebra have been increasing on private land since the 1960s (Barnes & de Jager, 1996), and the communal conservancy model has created hundreds of jobs as field officers, community game guards, community resource monitors and as office staff. In 2006 it was estimated that approximately US$2.4 million ‘new money’ had flowed into conservancy areas in Namibia since their creation (Jones & Barnes, 2006).

2. 3 R ES ER V E DES I GN AT I ON S FR OM I N T ER N AT I ON AL CON V EN T I ON S Perhaps the main driver of the global increase in protected area numbers (and the associated proliferation of terminology) has been the creation of legal frameworks for conservation at the level of individual states or through supra-national bodies such as the United Nations or the European Commission. A good example of the former is the National Parks and Access to the Countryside Act passed by the UK government in 1949, which resulted in the creation of a protected area category known as Sites of Special Scientific Interest (SSSIs) and the formation of a government agency (The Nature Conservancy Council) responsible for their designation. Land could be designated as an SSSI irrespective of whether it was in an existing protected area or not, thereby creating a legislative building block for a more extensive and ‘joined-up’ site-based conservation strategy. N.B.: The SSSI designation is not solely concerned with ‘biodiversity’ attributes and, for example, also covers sites of importance for geological features of interest. National legislation has been vitally important in the creation of new protected areas, but in the last three decades has often been created in response to countries and multinational bodies (e.g. The European Commission) signing up to big global agreements and conservation conventions. It is these international agreements that have done the most to promote the recent growth of protected areas (Figure 2.1) and to provide a framework for their designation. Perhaps the most important such agreement to date has been the Convention on Biological Diversity,

Roots, relevance, aims and values or CBD, which was signed at the Earth Summit in Rio de Janeiro in 1992. One of the key treaty commitments of states that signed up to the CBD was to select, establish and manage a network of protected areas as outlined in Articles 8a and 8b of the convention: • CBD Article 8a. Establish a system of protected areas or areas where special measures need to be taken to conserve biological diversity. • CBD Article 8b. Develop, where necessary, guidelines for the selection, establishment and management of protected areas or areas where special measures need to be taken to conserve biological diversity. Like most international conventions, the CBD is not legally binding in a strict sense, and each individual state needed to draft or redraft legislation in order for the convention to take effect. As might be expected, the translation, transposition, and integration of international law into national law is complex and timeconsuming and has allowed for considerable flexibility in how protected areas networks have been developed and designated in different countries. The CBD is just one of a number of international conventions that require states to identify, designate and create protected areas. For example, the Convention Concerning the Protection of World Cultural and Natural Heritage adopted by UNESCO in 1972 allows member countries to propose sites for addition to the World Heritage list. Many countries proposed their most spectacular national parks, e.g. the Serengeti (Kenya), Ayers Rock or Uluru (Australia) and Yosemite (US). In such cases, the designation merely overlays the national park status. In other cases, world heritage status proposals have been developed as a tool to conserve a heavily used landscape by mobilizing the local pride and sense of international responsibility attached to the designation to strengthen existing planning laws and prompt heritage-friendly development. An example of this approach is the Jurassic Coast World Heritage site in Dorset, England, which covers a scenic coastal landscape and which has been designated largely for its landforms and geology but which has many small nature reserves ‘nested’ within it. Similarly, the European Union’s Birds Directive (1979) and Habitats Directive (1992) require that member states identify and designate Special Protection Areas (SPAs) and Special Areas of Conservation that together form a European network of protected sites called Natura 2000. These may be sites managed (or at least legally designated) as reserves, those holding other

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designations (e.g. SSSI), or sites that have not previously been designated under any legal framework.

2. 4 AN I N T ER N AT I ON AL S Y S T EM FOR CAT EGOR I Z I N G PR OT ECT ED AR EAS Protected areas are generally no longer thought of as embodying specific social values. This is possibly because they have become an integral part of the global system of environmental book-keeping that is an essential component of international conservation conventions such as the CBD. However, in order to fit comfortably into a global accounting system, a standardized classification system based on scientific principles is required. The first attempt at such a classification scheme was developed in 1978 in a joint project between the International Union for the Conservation of Nature and Natural Resources (IUCN) Commission on National Parks and the World Commission on Protected Areas) (IUCN, 2003). By 1994, and after several iterations, this framework had stabilized into six categories of protected areas distinguished by their primary management objectives (Table 2.2; IUCN, 1994). Under the IUCN system, a protected area is defined as: ‘An area of land or sea especially dedicated to the protection and maintenance of biological diversity, and of natural and associated cultural resources, and managed through legal or other effective means’ (IUCN, 1994). Significantly, under this scheme, a protected area is designated to the IUCN classification which best reflects its management aim without reference to its legal title (Fitzsimmons & Wescott, 2004). Of course, grouping the entire world’s protected areas into six management categories (whatever their origins and motivating objectives) inevitably brings with it some limitations, but this has proved useful not only for monitoring purposes but also in providing a framework for reserve planners in developing or adding to protected area systems. For example, the system was adopted and applied in the development of a complete protected area system for the Canary Islands, leading to the designation of approximately 40 per cent of the land surface area of the archipelago and providing a framework not only for strict protection but also for integrated conservation and development projects (see Martín-Esquivel et al., 1995; Whittaker & Fernández-Palacios, 2007, Chapter 12).

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Social values and conservation biogeography

Table 2.2 IUCN protected area categories as defined in the revised Guidelines for Applying Protected Area Management Categories (Dudley, 2008), which are essentially the same as, but reworded from those previously in use (see IUCN, 1994). The revised guidelines also offer a redefinition for protected areas in general, as follows: ‘A clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values’ (Dudley, 2008; and compare with IUCN 1994 definition given in text). Designation

Name

Definition

Category 1a

Strict nature reserve

Strictly protected areas set aside to protect biodiversity and also possibly geological/geomorphological features, where human visitation, use and impacts are strictly controlled and limited to ensure protection of the conservation values. Such protected areas can serve as indispensable reference areas for scientific research and monitoring.

Category 1b

Wilderness area

Usually large unmodified or slightly modified areas, retaining their natural character and influence without permanent or significant human habitation, which are protected and managed so as to preserve their natural condition.

Category 2

National park

Large natural or near-natural areas set aside to protect large-scale ecological processes, along with the complement of species and ecosystems characteristic of the area, which also provide a foundation for environmentally and culturally compatible spiritual, scientific, educational, recreational and visitor opportunities.

Category 3

Natural monument

Areas set aside to protect a specific natural monument, which can be a landform, sea mount, submarine cavern, a geological feature such as a cave or even a living feature such as an ancient grove. They are generally quite small protected areas and often have high visitor value.

Category 4

Habitat/species management area

Areas that aim to protect particular species or habitats and management reflects this priority. Many Category 4 protected areas will need regular active interventions to address the requirements of particular species or to maintain habitats, but this is not a requirement of the category.

Category 5

Protected landscape/ seascape

A protected area where the interaction of people and nature over time has produced an area of distinct character with significant ecological, biological, cultural and scenic value; and where safeguarding the integrity of this interaction is vital to protecting and sustaining the area and its associated nature conservation and other values.

Category 6

Protected area with sustainable use of natural resources (previously termed ‘Managed resource protected area’)

Areas set aside to conserve ecosystems and habitats, together with associated cultural values and traditional natural resource management systems. They are generally large, with most of the area in a natural condition, where a proportion is under sustainable natural resource management and where low-level non-industrial use of natural resources compatible with nature conservation is seen as one of the main aims of the area.

Roots, relevance, aims and values As mentioned above, one of the strengths of the IUCN system is that it appears to provide a clear and unambiguous measure of conservation progress that can be easily accounted at regional, country and global levels. However, the system has had several critics. Some commentators consider using management goals to be a rather ‘unidimensional’ indicator of progress towards biodiversity targets (Chape et al., 2005). In response to such accusations, Boitani et al. (2008) have recently suggested revising the IUCN system to make the conservation outcomes explicit. Under this new system, biodiversity outcomes would be measured through a range of biophysical metrics of species and

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ecosystems such as phylogenetic uniqueness, vulnerability, irreplaceability, richness, and ecological integrity. This new proposal may have strengths, but it still downplays the fact (as we have demonstrated above) that protected areas are not simply conservation tools – they are also value-laden institutions that simultaneously reflect, emphasize and embed specific human values about nature. Interestingly, because the IUCN classification system reclassified many protected area types that were originally created in the cause of specific values, most of the IUCN categories broadly equate to different and discrete sets of foundational conservation values (see Table 2.3).

Table 2.3 A social purpose classification of protected areas based on original protected area values. Original protected area type

Equivalent IUCN (1994) category

Places evoking wilderness should be preserved as benchmarks to assess urban/industrial modernity and for spiritual, aesthetic and physical exploration and rejuvenation

Wilderness area

Ib

Humanity has a moral responsibility to ensure that its actions do not knowingly cause the extinction of species

Wildlife sanctuary/ refuge

IV

Aesthetic and intellectual contemplation of nature is integral to the cultural and scientific inheritance of many peoples and monuments of nature should be protected

Naturdenkmal/ nature monument

III

Benchmark/representative sites are required for the study of natural systems

Nature reserve

Ia

Access to nature and countryside is necessary for the health and well-being of urban-dwellers

Urban/country/ state park

Not included, but see National Park

Natural resources should be managed to support livelihoods of settlers/local people; natural resources should be managed for the greatest good for the greatest number in the long run

Forest reserves & game reserves

VI

Healthy ecosystems are necessary to safeguard economic growth, high-quality livelihoods and social stability

Watershed protection forest

Not included, but see National park

Places that symbolize the above conservation values and their associated social practices can help create or reassert national identities

National park

III

Sensitive management of cultural landscapes evoking beauty and heritage will bring cultural, economic, and conservation benefits

Landscape protection area (various national terms)

V

Conservation values

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2. 5 S O C I AL V AL UE S AND CO N S E R V AT I ON P R AC T I C E In addition to underpinning protected area designation and classification, social values also strongly influence management within reserves and the wider landscape matrix. Perhaps the most fundamental level at which values can be seen to influence management is the decision of what is a justifiable target for conservation. Callicott et al. (1999) suggest that underlying the diverse goals and management objectives of conservation are two divergent philosophies: first, to manage for function/process (the functionalist perspective); or, second, to manage for a particular composition of species and communities (the compositionalist perspective). This theme is developed in more detail in Chapter 3, but it should be noted that the two rationales are not always separable in practice. For example, small protected areas may be incapable of functioning as fully independent ecological systems and may require intensive management to maintain particular species or assemblages and to suppress natural fluctuations in population size or community composition. Another fundamental issue in conservation management is the notion of ‘naturalness’ and to what extent it dictates conservation goals and management. Conservation, as the name implies, has always had a strong emphasis on preserving nature in its ‘original’ form. However, the word ‘original’ can mean radically different things, depending on the temporal perspective that is adopted. Clearly, the ecological baseline that is chosen will strongly influence how a reserve is perceived and managed. In Europe, and especially in the UK, a preindustrial baseline with its associated intensive management is the norm, in North America and the developing world a pre-human ecology is more frequently sought (Sutherland, 2002). These issues are fundamental to conservation and are explored in detail in the next chapter. Within this chapter, we now move on to a brief consideration of the issue of non-indigenous species. Attitudes to non-natives are likely to vary depending on whether a functionalist or compositionalist perspective and value set is adopted. From a compositionalist perspective, non-natives may be viewed as ‘unnatural’ elements that should be eradicated. From a (more pragmatic) functionalist perspective, non-native species may have become an essential component of ecosystem function by taking on the role of a species that is

no longer present. The biogeography of invasive species is explored in detail in Chapter 9, so here we restrict ourselves to a discussion of the divergent attitudes that non-native and invasive species provoke in the conservation community.

2.5.1 Attitudes to non-native species It is well known that some exotic species can have a negative impact on biodiversity and ecosystem function, and that they are a major cause of extinction as well as the driver of huge economic losses (Perrings et al., 2005). It is generally accepted that the magnitude and rate of today’s biological invasions is unprecedented and that present-day anthropogenic introductions differ from ‘natural’ invasions in the increased spatial scale over which organisms are being moved and the greatly increased frequency with which such events occur (Ricciardi, 2007). Many biogeographers are concerned that area-specific distinctiveness will be lost in a process of biotic homogenization (Olden et al., 2004; Rooney et al., 2007; Chapter 9). Nevertheless, the debate over alien species and their management has been critiqued on the grounds that not all exotic species are harmful; many are useful to humans and some, at least, have ‘positive’ biodiversity impacts (Kendle & Rose, 2000). Furthermore, several authors object to the ideology suggested by the emotive and value-laden terms that are commonly used in the literature of non-native species (Theodoropoulos, 2003; Brown & Sax, 2004). Finally, some authors have questioned whether invasions are a cause of extinction and ecological ‘harm’ (Sagoff, 2005) or merely a consequence of biodiversity loss generated principally by coincidental processes (Gurevitch & Padilla, 2004). Some authors have noted that there are similarities between terms used in invasion biology and those used in relation to human immigration, and this has raised concerns about the underlying ideology of invasion biology. Theodoropoulos (2003, p. 120), for example, states that ‘the foundational concepts and logical structure of invasion biology are identical to those of the discredited ideologies of xenophobia, racism, nationalism, and fascism.’ Similarly, but perhaps less polemically, Brown and Sax (2004) suggest that ‘[t]here seems to be something deep in our biological nature, related to xenophobia toward other humans, which colours our view of alien plants and animals. There is a tendency to treat

Roots, relevance, aims and values foreigners differently from natives: with distrust, dislike even with loathing’ (Brown & Sax, 2004, p. 530). While this direct transposition of biological terms with social critique/ideology may surprise many invasion biologists, the confluence of language is evident. In order to circumvent issues of confusing, ambiguous or emotive terminology, Colautti and MacIssac (2004) suggested that the terminology surrounding ‘invasive’ species could be made more neutral. They proposed a five-stage model in which each stage, labelled numerically, describes an operational filter that allows some species to persist, reproduce and spread while others do not. However, this scheme has not been widely adopted, perhaps because the reader needs anyway to return to a description of each filter and therefore the parsimony and neutrality of a series of numbered stages is lost. Richardson et al. (2000) prefer to distinguish the introduction (transport by humans) and naturalization (survival and reproduction) from invasion, which requires that the introduced species has spread to and reproduced in areas distant from the sites of introduction (Richardson et al., 2000). This distinction is helpful because the process of naturalization is not necessarily seen as ‘bad’. Many favoured exotic species in gardens and farms are naturalized, but do not spread independently from the locations in which they are desired (Kendle & Rose, 2000; Sagoff, 2005). In short, not all exotic species are invasive. Conversely, not all invasive species are exotic, as exemplified by bracken, Pteridium aquilinum which, although native in the UK, is an often unwelcome invader of heathlands and moorlands. Furthermore, many non-native species hold particular utilitarian, cultural or aesthetic roles. For example, the bird of paradise flower (Strelitzia) is native to South Africa, but is the official flower of the city of Los Angeles, California, USA. Nonetheless, the negative connotation of the word ‘invasion’ still remains and is problematic when one considers that ongoing climatic change, ecosystem interactions and anthropogenic management all cause variations in the distribution and abundance of species over time, and in this sense all species’ distributions may be considered the result of a past invasion (Keitt et al., 2004). For example, ice sheets covered large areas of the northern hemisphere land mass during the last glacial maximum, c. 21,000 years ago, and these areas were recolonized or invaded by species that persisted in more southerly refugia. Similarly, volcanic

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islands and other areas that experience large infrequent disturbances recover when species recolonize from surrounding areas or the mainland. A long-term perspective raises the issue that definitions of ‘native’ and ‘non-native’ are ‘based on value judgements associated with a selective time frame, and a selective categorization of which types of humans can legitimately act as modes of dispersal’ (Kendle & Rose, 2000, p. 22). Moreover, as Gurevitch & Padilla (2004) point out, the correlation between extinctions and invasions does not necessarily imply causation, and care needs to be taken to distinguish whether invasive species directly displace native ones, causing local extinction, or whether both invasion and biodiversity loss are a consequence of habitat degradation. Clearly then, it is not the process of invasion itself that is problematic, but the invasion by a species that would not ‘naturally’ be in the area and, furthermore, whose arrival to an area reduces biodiversity and threatens ecological processes, and/or economic resources or livelihoods. Returning to the concept of naturalness, Brown & Sax (2005) caution against what they term the ‘naturalist fallacy’, meaning the assumption that ecosystems prior to invasion must be natural and pristine, and that this is the ecosystem state that should be preserved. As Kendle and Rose (2000, p. 20) point out, this is problematic because ‘it commits us to supporting a flora that reflected a particular environmental and climatic state that cannot continue forever and has probably already changed’. Stability of species composition is especially unlikely if the projections of rapid 21st century climate change made by the global climate science community are borne out, as these changes will generate reshuffling and redistribution of species as they track (or in some cases fail to track) suitable climate space (Chapter 7; Araújo et al., 2004a; Williams & Jackson, 2007). As mentioned above, although the narratives of invasion scientists are not meant to apply to people, they may nevertheless have negative overtones for immigrant communities (Kendle & Rose, 2000). The potential for confusion is exacerbated by the use of the same words in both a societal and a biological context. While biologists may have an unambiguous understanding of the term ‘alien’ as referring to an animal, plant or pathogen, the same terminology is also used with regard to human immigration – specifically the use of the term ‘illegal aliens’ in the USA and other countries. To many people, this issue may seem like

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another manifestation of contemporary society being overanxious about ‘political correctness’. However, particularly at the interface of science and society, scientists need to reflect on how and with what consequences their technical use of language translates into the public domain. One option is to develop distinct terminologies for animals, plants and pathogens and a separate vocabulary for issues relating to immigration, but even so there would probably be little room in such a scheme to consider the notion of exotic plant and animal species having positive effects. An alternative approach, suggested by Kendle and Rose (2000), is to acknowledge the aesthetic, recreational and cultural role of non-indigenous species in cultural landscapes. This attitude would be congruent with those espoused regarding modern cosmopolitan cities which celebrate the diversity of human inhabitants, and the enrichment of society that comes from engaging with multiple cultures. Application of this approach would require the use of neutral terms for non-native species, while only those capable of invading and causing actual economic or ecological losses would be labelled pejoratively. As this line of argument implies, alongside or alternative to changing the terms used there is perhaps a need to re-think individual and institutional attitudes to non-native species. Consider the response among the birdwatching sub-community of ‘twitchers’ in the UK to the arrival of a straggler vagrant individual only very rarely seen in the UK; typically, many enthusiasts travel to the spot as rapidly as possible to gain a sight of the stray before the bird expires or disappears again. Such behaviour suggests that our attitudes to the exotic can, in the right circumstances, be remarkably positive. Also, given sufficient time following their introduction, many exotic species are effectively culturally assimilated to the point that they are generally regarded as native, notwithstanding that they have been introduced by people from perhaps a very distant and different biogeographical region. This again suggests that notions of ‘naturalness’ are in fact somewhat more fluid and less reactionary than some conservation commentators suggest may be the case. In summary, non-indigenous species that are invasive, such as rats, zebra mussels, and water hyacinth, to name a few, have had adverse effects on ecosystem structure and function, and are a legitimate target for conservation intervention in areas set aside for wild

nature. In protected areas, indigenous species will be favoured over exotics, while elsewhere our cultural and agricultural landscapes are often enriched by, and may even depend upon, exotic species. Focusing management interventions more specifically on invasive species in protected areas would allow a more effective and efficient targeting of conservation funds.

2.5.2 Restoration and rewilding The goal of restoration ecology can be broadly described as an attempt to return an ecological system to some historical state. Most conservationists realize the impossibility of ever reaching such a singular goal, so a better, if fuzzier, definition might take the form, ‘an attempt to move a damaged system to an ecological state that is within some acceptable limits relative to a less disturbed system’ (Falk et al., 2006). Once again there is a clear values component to these goals, since conservation managers seeking to ‘restore’ a site may need to make a choice between a range of potential historical states (the last ecological survey, the first ecological survey, pre-industrial, pre-human) and/or the key ecological processes that they seek to influence. This latter issue may be beyond the scope of many restoration projects due to the highly scale-dependent nature of many ecosystem processes and the associated ‘services’ that they provide for humanity. Issues of restoring communities and ecological processes have recently come together under proposals to recreate long-absent assemblages of large mammals that existed before humans first spread into Europe and North America. This conservation strategy, known as ‘rewilding’, is based on research that suggests that these ‘lost’ large mammals may have played essential roles in determining a range of ecosystem processes. The best example of applying the values of rewilding to a real protected area, albeit a small one, is arguably the 5,600 hectare Oostvaardersplassen reserve in the Netherlands. Conservationists have successfully introduced red, fallow and roe deer, Heck cattle and konic ponies, the latter two species as replicates of the extinct auroch and tarpan (Sutherland, 2002). Konics are considered an ancient breed, thought to be very close to Europe’s extinct horses, whilst the Heck cattle were developed in Germany in the 1920s through crossbreeding old breeds of cattle. Wild boar, lynx and wolf

Roots, relevance, aims and values have, so far, not been introduced and probably never will be due to societal resistance. The intriguing idea of reconstituting past large mammal assemblages is not confined to Holland. The Pleistocene Park project underway in Russia is transforming taiga ‘back’ into mammoth tundra steppe through creation of grasslands and the introduction of bison, musk ox, Yakutian horse, hares and marmots (Zimov, 2005). The introduction of predators is planned once populations have established. A number of private land-holders are also engaging with similar ideas; for example, Paul Lister, heir to the MFI retail empire, is pursuing a dream to rewild a Scottish estate with wild boar, elk, bears and wolves (Sidway, 2006). A third variant is the ‘Pleistocene rewilding of North America’ proposal, which involves the introduction of non-native analogues (elephant and camel) for species that became extinct coincidental with human colonization of the Americas 13,000 years ago (Donlan et al., 2005; Donlan, 2007). This suggestion has aroused intense debate, with critics arguing that introducing elephants, camels, cheetahs and lions would be both ecologically and socially unsound (e.g. Smith 2005; Rubenstein et al., 2006). In a less high profile, but no less ambitious project, the World Wildlife Fund (WWF) and the American Prairie Foundation are buying up properties in north central Montana that they eventually hope to combine with adjacent public lands to provide a habitat for nearly the entire suite of Pleistocene North American grassland species (Dinerstein & Irvin, 2005). Rewilding also raises a number of scientific, practical and social concerns that are yet to be fully resolved. First, scientific knowledge of past assemblages is often incomplete, thus making it difficult to establish the baseline conditions for restoration with accuracy (see Chapter 3, Section 3.5.3). Second, without the social consent required to reintroduce large predators back into rewilded systems, it is unlikely that the objective of recreating evolutionary process can be fully realized (Schlaepfer et al., 2005). Third, the poor record of reintroductions, especially of carnivores, suggests that the success of more ambitious rewilding projects is by no means assured. Fourth, many of the rewilded megaherbivores are closely related to domestic forms, significantly increasing risk of disease transmission between wild and domestic stock. Fifth, restoring ecosystem process and viable populations may require reserves that are too large to be realistically accommodated in many parts of the world because of competing land

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uses. Sixth, rewilding may run up against strong social barriers, especially with respect to the reintroduction of predators such as wolves and bears. Notwithstanding these serious issues, the idea is one that engages a great deal of popular interest, capturing the hostility of some but also the imagination of many. Rewilding solutions lying between the relatively enclosed nature of Oostvaardersplassen and the revolutionary ideas of Pleistocene rewilding would seem to offer considerable potential for successful ecosystem restoration. One such example is the recent introduction, as an ecological analogue for extinct Mascarene Island giant tortoise, of captive-bred populations of Aldabran tortoise to Round Island, a small island (255 ha) with noxious weed problems (Griffiths et al., 2009). The idea is that the introduced species may act as an ecosystem engineer, restoring the functionality lost due to the extinction of the indigenous species and thus contributing to efficient biodiversity conservation in the Mascarene Islands. If based on careful case-bycase planning, and experimental testing and trialling, rewilding has some role to play in 21st century conservation.

2. 6 CON CLU DI N G R EMAR K S In this chapter, we have argued that human values are both the motivation for conservation and have always influenced the science and practice of conservation. Values are always changing and it is possible that one day the foundational values of the modern conservation movement will seem outdated and out of tune with global opinion. Moreover, there is also a danger that values will be increasingly overlooked as conservation develops increasingly sophisticated ways to measure and protect nature. For example, the introduction and promotion of the term and concept of ‘biodiversity’ has undoubtedly been hugely successful as a means of raising funds and focusing resources, but is poorly understood by the public (Christie et al., 2005) and is not closely associated with any of the historically important conservation values (but see Section 2.2.2). If the underlying values of nature reserves and conservation initiatives are not clearly stated, there is a danger that the public may lose its appetite for conservation – a process that would see politicians quickly follow suit. As you read through the rest of this book, we would like you to bear in mind that although

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conservation biogeography draws on a base of hard evidence and scientific principles, its overall remit is intrinsically linked to the fundamental values of the conservation movement. Moreover, as human impacts on natural systems continue to broaden and intensify, patterns of species distribution and abundance are increasingly ‘biocultural’ – which is to say that they find explanation in the geography of social values towards nature. Decisions need to be made on what to conserve, where to spend money, and who will ultimately benefit (or lose out) from conservation. In other words, conservation is social and political as much as it is scientific and rational.

F O R DI SC USS I ON 1 How do modern conservation values differ from those apparent at the beginnings of the global conservation movement? 2 Are conservation values universal and does this matter?

3 Is rewilding a useful conservation tool or an unnecessary distraction? 4 How can an understanding of social values be used to make protected areas more relevant to the public? 5 Is ‘wilderness’ a meaningful concept in the 21st century?

S U GGES T ED R EADI N G Bhagwat, S.A. & Rutte, C. (2006) Sacred groves: potential for biodiversity management. Frontiers in Ecology and the Environment, 4, 519–524. Ehrlich, P.R. & Ehrlich, A.H. (1992) The value of biodiversity. Ambio, 21, 219–226. Jepson, P. & Whittaker, R.J. (2002) Histories of protected areas: internationalisation of conservationist values and their adoption in the Netherlands Indies (Indonesia). Environment and History, 8, 129–172. Jepson, P. & Ladle, R. (2010) Conservation: a beginner’s guide. Oneworld, Oxford. Vera, F.W.M. (2000) Grazing ecology and forest history. CABI Publishing, Oxford. Wilson, E.O. (1984) Biophilia. Harvard University Press, Cambridge, MA.

CHAPTER 3 Baselines, patterns and process Lindsey Gillson1, Richard J. Ladle2,3 and Miguel B. Araújo3,4 1

Plant Conservation Unit, Botany Department, University of Cape Town, South Africa School of Geography and the Environment, University of Oxford, Oxford, UK 3 Department of Agricultural Engineering, Federal University of Viçosa, Brazil 4 National Museum of Natural Sciences, CSIC, Madrid, Spain and University of Évora, CIBIO, Évora, Portugal 2

3. 1 I N T R OD UC T I ON If modern conservation practice is characterized by anything in particular, it is the diversity of goals that conservationists have for protected areas (see Chapter 2). Typically, site management may be directed towards one or more of the following: • maximizing biological diversity (biodiversity) or biological integrity; • protecting a particular species or subset of species; • restoring a past ecological community; • maintaining or enhancing ecological (ecosystem) services; • providing a platform for sustainable development (e.g. enhanced tourism resources, sustainable extraction, etc.); • increasing ecosystem health. On-the-ground conservation decisions depend, to a large extent, on the underlying objectives and values of the organizations or individuals involved. These values and objectives are, in their turn, shaped by perceptions of ecosystems on the one hand, as stable balanced entities or, alternatively, as dynamic systems in flux. The former perception is linked to the so-called balance of nature paradigm in ecology, and within conservation it is aligned with a compositionalist perspective, while the latter, flux of nature, paradigm is strongly associated with a greater emphasis on functionalism. In this chapter we examine the debate between these two contrasting viewpoints, with particular focus on

the baselines or benchmarks against which conservation goals are measured.

3. 2 ECOS Y S T EM COMPOS I T I ON AN D FU N CT I ON In groundbreaking papers, Noss (1990) and Callicott et al. (1999) argue that normative concepts within conservation can be grouped into two philosophies/ approaches that they term ‘functionalism’ and ‘compositionalism’. According to this classification, functionalists can be distinguished by the tendency to focus on ecological processes such as nutrient cycling and thermodynamics. Compositionalists, by contrast, derive their world view from ecological biogeography and community ecology, and they view ecosystems as interacting hierarchies of individuals, populations and communities. The importance of the functionalist/compositionalist dichotomy to conservation practice is that these differing perspectives strongly influence the desired end point of a conservation intervention. Seen through the eyes of a functionalist, the goal of conservation is to restore and maintain ecosystem processes. Conversely, a compositionalist approach emphasizes re-creating or maintaining species assemblages that closely resemble past communities, usually those that existed in preindustrial or prehistoric times. A good example is the conservation of heathlands in southern England,

Conservation Biogeography Edited by Richard J. Ladle and Robert J. Whittaker © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33503-3

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which are maintained by mimicking medieval farming practices (Ladle & Jepson, 2010). In reality, most conservationists have both compositionalist and functionalist tendencies and it may be better to think of individuals and organizations as occupying part of a functionalist/compositionalist continuum (Callicott et al., 1999). Neither compositionalist nor functionalist approaches are without drawbacks. A pure functionalist approach may lead to many rare or endangered species being replaced by ‘weedy’ species that occupy the same ‘role’ within the ecosystem with equal or superior facility. In addition, assessments of ecosystem processes are highly scale dependent, and a functionalist approach may simply be ineffective for smaller reserves such as many of those found in abundance in some industrialized European nations. Conversely, a compositionalist approach, by largely neglecting process, may necessitate huge and continuing investment of resources to maintain the desired assemblages. Furthermore, a compositionalist approach is likely to have far less traction with the polity in countries where populations are heavily reliant on natural resource extraction or ecosystem services or both. Williams and Araújo (2002) have argued that the two approaches must be linked for conservation to be successful. This makes sense, because the persistence of species and the sustainability of ecological processes are largely dependent upon one another, even if the two are not wholly interchangeable. Logically, any such linkage should begin from the compositionalist perspective, because the maintenance of ecological processes (including the politically and economically important ‘ecosystem services’) depends upon the conservation of the ecosystem components. A linkage between compositionalist and functionalist approaches will necessarily have an explicit spatial component, with smaller, more intensively managed areas of compositionalist interest being embedded within larger areas or landscapes that are managed and monitored from a functionalist perspective. In Western European countries, this typically takes the form of a semi-agricultural landscape containing a variety of small nature reserves, or semi-natural areas that are managed for a variety of conservation goals, including conservation of rare species and recreation. A further challenge for 21st century conservationists will be maintaining ecological pattern and process in the face of anthropogenic climate change. The predictions of most climate envelope models (e.g. Thomas

et al., 2004), even if highly uncertain (Chapters 4, 7; Araújo et al., 2005b), suggest that it will be difficult to retain the existing composition of species in reserves (especially small ones) and the wider landscape (e.g. Araújo et al., 2004a; Hannah et al., 2007) and that conservationists will have no choice other than to focus on restoring and strengthening ecological process so that natural systems can respond effectively to climate change. Responses may include facilitating movement of species between reserves or, more passively, relying on the evolutionary responses of species in situ. In some respects, this is merely an extreme example of a more general problem for compositionalist approaches to conservation – how best to conserve systems that are intrinsically dynamic?

3. 3 B ALAN CE V ER S U S FLU X In recent decades, there has been something of a paradigm shift in ecology, from an equilibrium or ‘balance of nature’ world view, to one of nature in flux, or not at equilibrium. This shift in perspective has profound implications for the way ecosystems are understood and managed, although whether it truly constitutes a paradigm shift is moot. Such dualities have typically existed in parallel as binary opposites (sensu Eagleton, 1983; cited in Crisci & Katinas, 2009), such that for a while one perspective is central to the discipline, and is privileged, while the opposite member of the pair is marginalized. This representation perhaps better captures the character of such debates than the notion that one view was universally held and then universally discarded in a once-and-for-all paradigm shift. Along these lines, it is almost certainly a mistake to conceptualize ecological systems as either wholly equilibrial or entirely non-equilibrial in nature (e.g. see discussion in Whittaker & Fernández-Palacios, 2007). The equilibrium paradigm dominated ecology for most of the 20th century, but its origins can be traced far back in time to ancient Greek and Judaeo-Christian traditions (Egerton, 1973; Wu & Loucks, 1995). Nature was conceptualized as a stable and unchanging entity, a view that underpinned influential ecological ideas such as the climatic climax, the logistic growth equation and ideas of carrying capacity (Bartels & Norton, 1993). These apparently diverse and unrelated ideas describe vegetation assemblages and populations as

Roots, relevance, aims and values homeostatic systems that respond to disturbance by returning to a pre-determined state, through a predictable series of changes. In the climatic climax, for example, vegetation types can be predicted according to climatic factors such as rainfall and temperature – a view later modified to incorporate finer scale patterns as a function of soil type and geology (Clements, 1916; Tansley, 1939; Whittaker, 1953). Following disturbance, ecosystems would progress through several stages to a defined end point, the climax community. Similarly, the logistic curve describes a population increasing exponentially in response to a constant supply of resources, until a point of inflection where organisms compete for resources (Pearl & Reed, 1920). Competition increases as population size grows, until resources are consumed at the same rate as they are supplied; at this point, birth rate and death rate become equal and the population stabilizes at ecological carrying capacity (Bartels & Norton 1993). The rate of population growth increases to a maximum when population is at half carrying capacity, at which point it begins to decline. At ecological carrying capacity, the rate of change is zero (see Figure 3.1). Carrying capacity and the logistic curve dominated stock management and resource harvesting for much of the 20th century. The way stocking rates were determined was based on carrying capacities of different range types, and wild populations were harvested with the aim of maintaining maximum population growth. Maximum sustainable yield was predicted at half of the ecological carrying capacity, known as the economic carrying capacity (Figure 3.1). The strength of the climax theory is that it captures the idea that climate is indeed a major determinant of vegetation type. At least at the biome scale, climate determines the distribution of deserts, rain forests, savannas and other vegetation types. Similarly, carrying capacities for stocking rates and harvest levels can be partially effective, because resources and reproductive rates are finite, and an upper limit for livestock density or the harvesting of wild populations has sometimes proved valuable in preventing overexploitation and degradation of rangelands. At finer spatial scales, however, the influence of climate is modified at landscape and local scales by topography, hydrology, fire, herbivory, anthropogenic management, other forms of disturbance and interactions and feedbacks between these factors. Furthermore, climate varies on timescales from seasonal and interannual to geological, altering primary productivity,

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Figure 3.1 (a) A graphical representation of the logistic population growth curve and its relationship with maximum sustainable yield (MSY). (b) The inflection point, shown as BMSY (the biomass at which MSY occurs) is the point at which the population replaces itself at the maximum rate. This is the MSY for exploited populations such as fish stocks, or the population density of those pests that are hardest to control. B0 = the average unexploited biomass of the stock (the average ‘carrying capacity’).

and vegetation composition. This, in turn, alters the supply and quality of resources available to animals. Populations respond to this variability through increases and decreases in their rate of population growth as well as changes in distribution. Population size also changes in response to demographic stochasticity, biotic interactions, disease and disturbance events. Feedbacks occur between these many interacting biological and environmental variables; for example, the frequency and intensity of fire will depend on seasonal rainfall, which influences standing biomass and likelihood of ignition (Sousa, 1984). For these reasons, ecological equilibria are thought to be transient (temporally unstable) and scale-specific (spatially constrained) (cf. Whittaker et al., 2001;

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Whittaker & Fernández-Palacios, 2007). Equilibriumbased models in ecology are therefore useful in describing the feedbacks that could theoretically lead to stability, but in reality this stability is elusive, or at least scale-specific; a patch dynamic landscape, for example, may retain the characteristics of stability over large spatial extents or short temporal scales, but will be highly dynamic at fine spatial scales or over long time periods. In parallel to the dominant ideas of balance and equilibrium, some ecologists pursued ideas of ecosystem change and landscape dynamics. As early as 1930, Charles Elton asserted that ‘the balance of nature does not exist and perhaps never has existed’ (Elton, 1930). Elton’s belief arose from his understanding of the complexity and dynamism of biological and environmental variables, leading him to believe that change, rather than stability, was the norm for natural systems. In the following decade, Alexander Watt’s prescient address to the British Ecological Society described dynamic landscapes in southern England in which patches of vegetation underwent cyclical changes of ‘pioneer’, ‘building’, ‘mature’ and ‘degenerate’ phases (Watt, 1947). From his own observations of landscapes, he described seven ecosystems, including dwarf heather (Calluna), grasslands and beech (Fagus) woodlands in which he had observed this pattern, leading him to believe it was of general significance to many ecosystems. In later years, Watt’s ideas proved central to the development of our understanding of many ecosystems. The concepts of minimum dynamic area (Pickett & Thompson, 1978), mosaic cycles (Remmert, 1991), forest gap dynamics (Shugart, 1984) and Holling’s adaptive cycles (Holling et al., 2001) rested on Watt’s thesis of patch dynamics, and his work informed many of the influential papers that heralded the paradigm shift to the ‘new ecology’ of nature in flux (Levin, 1992; Pickett et al., 1992; Wu & Loucks, 1995).

3. 4 U N D E R ST AND I NG E C OS YS TEMS IN FLUX Under the ‘flux of nature’ paradigm, ecosystems are understood to be heterogeneous (patchy) and dynamic (variable over time). They are often influenced by multiple variables, feedbacks and non-linear responses. Furthermore, they are prone to stochastic variations,

or ‘ecological surprises’, that may be driven by environmental or biological processes (Pickett & Ostfeld, 1995). The need to understanding complex, dynamic ecosystems has stimulated a wealth of new ecological theory, and two of the most influential themes discussed here are the Hierarchical Patch Dynamics Paradigm (HPDP) and the literature centred on ideas of resilience and ecological thresholds. The HPDP combines hierarchy theory (which proposes that scale-dependent levels of organization exist in nature) and the patch dynamics perspective, thereby providing a framework for structuring knowledge about complex systems (Wu & Loucks, 1995). Three main concepts are central to the HPDP (Wu, 1999; Wu & David, 2002): First, ecosystems may be considered to be complex systems, because they are spatially and temporally heterogeneous and are composed of many interacting components. Moreover, the interactions between ecosystem components are characterized by various feedbacks, non-linearity and threshold responses. The complex properties and dynamics that scientists observe in ecosystems systems emerge from these interactions and from the exchange of energy and materials from outside the system. Second, complex systems can be considered as a nested arrangement of interacting sub-systems, i.e. the system is composed of discrete but interacting subsystems that are themselves composed of sub-systems. Hierarchy theory provides scientists with a means of organizing information about these complex systems by identifying the systems and sub-systems and their corresponding hierarchical levels. Sub-systems at different levels in the hierarchy are dominated by different processes. Higher levels are larger and are characterized by slower processes; these higher level processes impose constraints on lower levels, whereas lower level processes provide the mechanism by which higher levels emerge. Third, ecosystems vary considerably in time and space. HPDP provides a framework for identifying and describing the constituent systems and sub-systems which generate heterogeneity over time and space. Thus, heterogeneous landscapes can be described by identifying patches – spatially discrete entities whose internal structure or function is significantly different from those of their surroundings. For example, in his seminal paper, Watt described vegetation assemblages in terms of dynamic mosaics of patches at different successional stages (Watt, 1947). Crucially, Watt’s

Roots, relevance, aims and values ideas about patch dynamics were some of the first to capture the link between pattern and process in ecology, because they made explicit the functional link between the pattern of vegetation in a landscape and the ongoing process of plant succession. To summarize, the HPDP is a powerful framework for describing and understanding ecosystems because it links pattern, process and scale in a spatial and temporal hierarchy (O’Neill et al., 1986; Pickett et al., 1987, 1989; Urban et al., 1987). A hierarchical (multi-scale) structure has been described for riverine systems (e.g. Frissell et al. 1986), and savannas (du Toit et al., 2003). Further, spatial hierarchies are being used extensively as frameworks for modelling ecological complexity (e.g. Wu & David, 2002), landscape analysis (e.g. Burnett & Blaschke, 2003) and the effects of climate change and land-cover change on species distribution (e.g. Pearson & Dawson, 2003). In savannas, Coughenour and Ellis (1993) proposed a hierarchical structure for ecological processes that nested small-scale patterns of disturbance (e.g. by fire and herbivory) within a broader spatial framework of climate, topography, geology and hydrology. Palaeoecological evidence from the savannas of Kenya provides strong support for this perspective (Gillson, 2004a; see Figure 3.2). Over the past few decades, rapid progress has been made in the development of theories that describe ecosystems in terms of their resilience, defined as their capacity to absorb disturbance, and their ability to reorganize when a critical threshold is exceeded (Holling, 1973). Ideas of resilience and thresholds provide a framework around which the complex, non-linear behaviour of ecosystems can be explained. Furthermore, resilience theory integrates human influences on ecosystems with the feedbacks between linked environmental and social systems (Berkes et al., 2003). Many ecosystems exhibit threshold behaviour, in which a critical environmental or biological threshold is crossed, causing reorganization and transition to a new quasi-stable state or phase. Such phase transitions (reviewed in Folke et al., 2004) have been observed in coral reefs, where overfishing, eutrophication and bleaching can cause a switch to an algal dominated reef; in freshwater lakes, which can switch from a clear oligotrophic to a turbid eutrophic state because of agricultural run-off; in savannas, which switch between woodland and grassland phases, depending on changes in disturbance by fire and herbivores; and in forests that can switch from evergreen needle-leaved

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to deciduous broad-leaved, depending on climatic variation linked to timing of disturbances. The principle of self-organization is one of the key features of systems that exhibit phase and transition. Transition to a new phase may be precipitated by extreme or external environmental factors, but maintenance of the new state is sustained by processes internal to this phase. Thresholds are crossed when ecological resilience is exceeded, either because environmental or biological change reaches a critical point or because of an unusually intense disturbance such as a hurricane, flood or severe fire. Alternatively, or in combination with environmental or biological change, ecological resilience may be modified because of anthropogenic activities that can affect the ability of systems to absorb disturbance. Sometimes, humans have been shown to increase resilience of favoured landscape elements, while in other cases over-exploitation or mismanagement has led to loss of resilience (Berkes & Folke, 1998; Adger, 2000; Dearing, 2008). Even before the widespread recognition of ecological thresholds, many ecosystems had been described in terms of transitions between two or more quasi-stable ‘phases’. Phase and transition describes the dynamic process by which ecosystems transform between alternative states of organization. Rather than a linear process of successional change, ecosystem behaviour clusters around regions of higher probability space, or domains of attraction. These domains are not necessarily equilibrium points, which is why ‘phase’ is a preferable term to ‘stable state’ or ‘equilibrium’. In rangelands, for example, Westoby et al. (1989) described alternate states (phases), with transitions between these states being driven by combinations of climatic factors, and management actions such as fire or changes in grazing pressure. Similarly, in savanna ecology, two apparently stable phases – woodland and grassland – are known. Relatively rapid transitions occur between these phases, and have been observed at a range of spatial scales. In east Africa, Dublin (Dublin et al., 1990) hypothesized a regional scale transition from open grassland to woodland in response to the dramatic reduction in herbivory that occurred due to the rinderpest pandemic at the end of the 19th century, and a later transition back to a more open savanna in response to growing elephant populations and fire, caused by increasing biomass build-up (for comparable landscape-scale dynamics in Australia, see, e.g. Sharp & Whittaker,

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Macro–scale

Desert Savanna Forest

The limits of the savanna biome are defined by broad-scale climatic patterns Climatic change, fire and arboriculture can all modify the extent of the savanna biome

Regional–Landscape Scale Within the savanna biome, the type of savanna depends on: Topography, hydrology, geology and rainfall Regional changes in herbivore abundance (e.g. because of Rinderpest, hunting for ivory)

Local–scale Within savanna landscapes, patchiness is determined by: Local variations in soil and hydrology Disturbance e.g. fire, herbivores

Micro–scale Within patches tree density depends on micro-climate, selective herbivory and micro-disturbance Plant–plant interactions such as competition and facilitation (represented by arrows) also occur at this scale

Figure 3.2 A hierarchy of processes determine tree density in savannas through their effects on tree dispersal, germination, recruitment and mortality. Lower tiers in the hierarchy emerge from the processes which dominate at each spatial scale. Higher-level systems constrain lower-level processes, while lower-level sub-systems provide the mechanisms and constituent parts from which higher-level systems emerge. Modified from Gillson (2004b).

Roots, relevance, aims and values 2003; Sharp & Bowman, 2004). At smaller spatial scales, grassland/woodland transitions have been observed in east and southern Africa at the sites of abandoned livestock enclosures, where confinement of animals led to enrichment of tree seeds and nutrients (Blackmore et al., 1990). A major conceptual advance on the phase and transition concept, as well as the movement to incorporate the effects of anthropogenic influence on ecosystem dynamics, was developed by a group of ecologists and economists working together, led by Crawford (Buzz) Holling and Lance Gunderson (Holling et al., 2001). Their ‘adaptive cycles’ described ecosystem change as developing through a cycle of four different phases: conservation, release, reorganization/renewal and growth/exploitation. According to this conceptual framework, an ecosystem or socio-ecological system in a relatively stable phase, maintained by internal feedbacks, becomes brittle or over-connected. As a result, internal mechanisms like senescence, or external disturbances like environmental change or a variation in anthropogenic management, will lead to a release phase, where previously stabilizing interactions break down. New interactions form in the reorganization phase, and a new organizational state emerges during the growth phase (Figure 3.3). The importance of the adaptive cycle framework is that it considers not only what happens before and after a transition, but the whole process of building, collapse and reorganization. It can also be used as a framework for integrating anthropogenic and environmental effects, and it is equally relevant to social and socio-ecological systems. Furthermore, like the HPDP, adaptive cycles can be nested hierarchically, thereby providing a framework for understanding how processes interact at different spatial and temporal scales. Adaptive cycles often take decades, centuries or millennia, and it is only by considering the long-term pattern and process of change that they can be identified. A recent paper by Dearing (2008) mapped the millennial-scale patterns of land use, erosion and monsoonal intensity (reflected in speleothem, pollen, magnetic susceptibility and sand content data from lake and alluvial fan sediments) in Yunnan, south-west China, onto the adaptive cycle of conservation, collapse, reorganization and rapid growth (Holling et al., 2001) (Figure 3.4).

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Figure 3.3 Adaptive Cycles. Four distinct phases have been identified: 1 growth or exploitation (r); 2 conservation (K); 3 collapse or release (omega); 4 reorganization (alpha). The adaptive cycle exhibits two major phases (or transitions). The first, often referred to as the foreloop, from r to K, is the slow incremental phase of growth and accumulation. The second, referred to as the backloop, from omega to alpha, is the rapid phase of reorganization leading to renewal. From the Resilience Alliance (www.resalliance.org/570.php).

There were two distinct phases of surface erosion. The first was largely resilient to monsoon intensity and corresponded to landscapes undisturbed by people between 2960 and 1430 cal yr BP. The second period of erosion, from 800 cal yr BP, was strikingly different. In this case, erosional intensity had a positive correlation with monsoonal intensity, indicating a loss of resilience of more open human-dominated landscapes. Interestingly, the loss of resilience was not associated with the initiation of intensive agriculture, but occurred during periods of social upheaval when agricultural lands were abandoned. This allowed rapid erosion from the sides of hills that were now neither covered in vegetation nor buffered by a well-maintained terrace system. Another significant aspect of this process was that the loss of ecosystem resilience appeared to be hysteretic (irreversible), even with reforestation, because the hills became criss-crossed with steep erosional gullies. The study thereby elegantly demonstrated how societal changes and corresponding changes in land use can interact with environmental variables to drive an ecosystem across a threshold of reorganization and into a new phase, itself maintained by emergent properties (Dearing, 2008).

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Baselines, patterns and process

Figure 3.4 Landscape stability in alternative steady states. Superimposed lines show possible non-linear change from a non-degraded ‘steady state’ before 1430 cal yr BP, through a 600-year transition period leading to the modern degraded ‘steady state’ after 800 cal yr BP. T1 and T2 represent likely positions of major thresholds in the system. The dashed arrows from T2 show possible future trajectories of landscape recovery. From Dearing (2008). (See Plate 3.4 for a colour version of this image.)

3. 5 DEF I NI NG AND US I NG B AS ELI N ES From a conservation perspective, ecological baselines are static points in time and space from which ecological information, typically on species composition and abundance (relative or absolute), can be compared with contemporary sites for the purposes of assessing anthropogenic impact/environmental change and informing management decisions, or both. Baselines are frequently derived from historical literature (e.g. published studies or reports) or from palaeoecological investigations, but may also be taken from ecologically similar sites that are not subject to significant levels of human impact (Arcese & Sinclair, 1997). The choice of baseline, and the way in which it is used to inform management, can have a profound influence on the ecology of a protected area. For example, choice of a baseline from the 1970s, preindustrial or pre-human colonization could profoundly change biodiversity targets, plans for reintroduction and the level of resources required to achieve the conservation objectives. Generally speaking, European conservation has tended to favour the use of preindustrial baselines that reflect a deeply rooted yearning for a lost pastoral idyll (see Chapter 2). Conservationists in North America and most of the

rest of the world, perhaps because of the continued existence of large wilderness areas, tend to use prehistoric baselines. Behind these generalizations lies enormous variation in how baselines are chosen and used, as well as an increasingly sophisticated understanding of their power and limitations. Most importantly, conservationists have begun to appreciate the ramifications of the shifting world view of the dynamism of natural ecosystems. 3.5.1 Baselines derived from relict pristine systems Clements (1934, p. 42) defined an ecological relict as a ‘community or fragment of one that has survived some important change, often to become in appearance an integral part of the existing vegetation’. Relict communities can often be found in places that are very isolated or difficult to access, such as cliffs, mountaintops and steep-sided valleys – typically places where livestock grazing is difficult or impossible – and sometimes in refugia that were buffered from past climate changes due to micro-climatic stability. These sites have been extensively used both to assess the impacts of direct or indirect human disturbance and, in some

Roots, relevance, aims and values situations, to offer inspiration for ecological restoration when historical data are missing. Relict sites are also frequently the focus of conservation themselves, as they often contain a high proportion of endemic or endangered fauna and flora. More generally, the analysis of relict or natural sites through phytosociology has played a major role in identifying sites for regional conservation, especially in Europe. Phytosociology is a sub-discipline of plant community ecology that seeks to describe and understand plant species co-occurrences – or, in the words of Ewald (2003, p. 291), it deals with the ‘compositional patterns and gradients at the “grain” of the plant community’. The tools of modern phytosociology are gradient analysis, classification and other multivariate methods used to identify characteristic plant assemblages that can be used as baselines for conservation. In the UK, phytosociological data have been used extensively to identify and assess natural and seminatural sites that qualify as Sites of Special Scientific Interest (SSSIs), one of the key national protected area designations in the UK (NCC, 1989) and a constituent part of the larger Natura 2000 network of protected areas in Europe. The desire to preserve ‘semi-natural’ habitats is interesting and could be interpreted as a desire to protect and restore pre-industrial baselines (as opposed to ‘natural’ habitats that are presumably prehuman), although the UK’s national agency (now agencies) was by no means explicit about this (e.g. NCC, 1989). A good example is the identification and prioritization of woodlands in the UK. Only ancient ‘seminatural woodland’ is considered for conservation (NCC, 1989, p. 73) and this is identified using the National Vegetation Classification (NVC) scheme, a standardized classification protocol for all the vegetation types in the UK. Under the NVC, each broad classification (e.g. woodlands) is divided into communities, sub-communities and sometimes variants based solely on the presence or absence of species. UK woodlands are divided into 25 communities which are further divided into 73 sub-communities (Hall et al., 2001). These act as references against which new sites can be assessed and existing protected sites can be monitored. For instance, Upland Oak Woodland, defined as woodland within the ‘upland region’ of England generally with at least 80 per cent oak or birch in the potential canopy, is classified as W11 or W17, depending on the nature of the field layer (see further discussion of these approaches in Chapter 4, Section 4.5.1).

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Relict communities have also been used as potential reference sites for ecosystem and habitat restoration projects. One example can be seen in attempts to restore some of the ‘lost’ forests of Tenerife (Canary Islands), where scientists are drawing upon data from both palaeoecology and contemporary studies of relict communities to provide a basis for restoration efforts. Before human occupation, the island of Tenerife contained all of the main vegetation types of the Canarian archipelago, from the hot, dry semi-desert scrub of the coastal lowlands to the lush, green laurel forest that is swathed in cloud for long periods every day (FernándezPalacios et al., 2004). These vegetation types occurred in distinct climatic zones or bands stretching from sea level to the top of Mount Teide, the 3,700 m dormant volcano that dominates the Tenerife landscape. As happened on many oceanic islands after occupation, agriculture began to expand from the coast to higher elevations and, in the process, devastated or completely removed several ‘bands’ of typical vegetation. In particular, the thermophilous forest was almost completely destroyed (less than 1 per cent remains), with only a few pockets of juniper (Juniperus spp.) forest clinging on to existence on exposed cliff sites. The European Community has recently funded a pilot conservation project to restore a 53 ha patch of thermophilous woodland on the Teno peninsula (www.tenerife.es/life/). Scientists have based the replanting scheme on phytosociological analysis of tiny remnant fragments of juniper forest in steep cliffs and remote gullies on Tenerife and nearby La Gomera. Amazingly, a recent analysis of pollen from sediments in a dried-up lake in the city of La Laguna suggests that there may also have been a native broad-leaved forest of hornbeam (Carpinus) and oak (Quercus), species now considered as non-native on the archipelago, which was most likely sandwiched somewhere between the laurel forest and the pine zone on Tenerife. These findings challenge prior conceptions of indigenous ecological baselines on the Canary Islands (de Nascimento et al., 2009).

3.5.2 Baselines derived from long-term ecology Current ecological understanding recognizes that most ecosystems are dynamic, being subject to ongoing processes of changing climate and other environmental disturbances, and that many landscapes have also

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been shaped by humans for millennia (Botkin, 1990; Pickett & Ostfeld, 1995; Wu & Loucks, 1995; Knapp, 2003). According to these contemporary ecological ideas, change is inevitable and an understanding of the past can help in predicting future community change. Environmental proxies (including fossil pollen, stable isotopes, macro-fossils and charcoal), as well as historical records, photographs and archaeological data, can reveal how communities have responded to environmental change over thousands of years (e.g. Hunter et al., 1988; Birks, 1996; Landres et al., 1999; Swetnam et al., 1999). Such information can be used to interpret current trends and predict how ecosystems might respond to changing climate and land use in the future (e.g. Delcourt & Delcourt, 1991; Hannah et al., 2002a). Although palaeoecology is traditionally seen as a mainly descriptive science that seeks to understand Quaternary change, the discipline is currently undergoing a renaissance and is becoming increasingly relevant to our understanding of ecosystem dynamics, environmental change and ecosystem management. The synergy between the ecology of flux and the ecology of temporal change is yielding many exciting opportunities for the interpretation – and reinterpretation – of palaeoecological data in terms of thresholds, resilience, phase and transition and adaptive cycles (Carrión et al., 2001; Gillson, 2004b; Dearing, 2008). The palaeoenvironmental record shows that some species and even communities can persist, despite climate change, until an ecological or climatic threshold is crossed (e.g. Theurillat & Guisan, 2001; Von Holle et al., 2003; Nogués-Bravo et al., 2008). Palaeoecological studies can help in evaluating the resilience and inertia of ecosystems and in determining critical thresholds at which dramatic ecological changes occur. Furthermore, palaeoecological evidence also indicates that extinctions are common at times of high climate variability, but that rates of loss vary geographically (Botkin et al., 2007). These findings are of critical interest to policy makers, conservation planners and subsistence users, because rapid change at an ecological threshold provides little time and opportunity for adaptation (Chapin et al., 2004). Comparison of past changes in climate and biome extent can help in predicting the ecological consequences of future climate change. Specifically, the outputs of coupled climate and biome models can be compared against the known distribution of biomes from the palaeo-record, enabling the accuracy of model outputs to be evaluated (Harrison et al., 2002).

Figure 3.5 A diagram illustrating how non-analogue communities can develop over time with changing climate. The light grey oval indicates climatic conditions in a particular area. Dark grey shapes indicate the occurrence of a species in that area. Dotted lines indicate the fundamental niche of three species in terms of climate variables 1 and 2. (a) Species 1 and 2 co-exist in the present climate space but species 3 is absent. (b) Species 1 and 3 co-exist in the future climate space – a new species association. Species 2 would need to evolve new climate tolerance to persist under the future climate of the area. After Williams & Jackson (2007).

However, the palaeoecological record provides multiple illustrations from the past, indicating that species, to a large degree, respond individualistically to climate change. This applies particularly to periods of rapid and dramatic climate change, which are notable in the records for the emergence of communities with no modern analogue (e.g. Bush et al., 2004; Williams & Jackson, 2007). Non-analogue communities arise for three main reasons (Parmesan et al., 2005; and see Figure 3.5). The first is that species differ in their ability to keep pace with climate change, so different communities may arise due to some species rapidly moving into available climate space while some persist for a time in areas that are no longer climatically suitable. Second, species may be able to extend their range individualistically when different combinations of climatic factors arise, corresponding to different dimensions of the fundamental niche (Williams & Jackson, 2007). Third, if competition, dispersal and other biotic factors restrict ranges at the outer latitudinal and elevational limits of a species range, then the fundamental niches may be wider than present or past distribution suggest (Parmesan et al., 2005). Climatic change will therefore affect different members of the same community in differing ways – a property that is consistent with the impermanence of plant and animal communities.

Roots, relevance, aims and values Changes in land use and ‘natural’ disturbance events such as hurricanes, floods, volcanic eruptions, severe fires or other Large Infrequent Disturbances (LIDs), can interact with or even override the ecological consequences of prevailing climatic trends (Turner & Dale, 1998). A good example of how palaeoecology and archaeology have elucidated feedbacks between disturbance, landscape patterns and human behaviour is from Garua and Numundo Islands, Papua New Guinea, where the wide-scale effects of catastrophic volcanic events were overlain by a patchwork of different vegetation responses and human activity at finer spatial scales. Boyd et al. (2005) used a combination of archaeology and phytolith (silica bodies that occur in plants) analysis to compare vegetation responses to tephra deposition at local, sub-regional and regional scales. They discovered that the effects of major eruptions (c. 5900, 3600, 1700 and 1400 cal yr BP) varied spatially, partly because of topographic control on deposition patterns of tephra, and this pattern was overlain in turn by human partitioning of the landscape. When tephra accumulation continued after the initial volcanic eruption, landscapes were abandoned by people and the impacts on the vegetation were severe. In contrast, after a low-impact eruption with little tephra deposition, the forest persisted and human occupation continued. The story was somewhat different on the mainland, where the spatially patchy recovery of the coastal lowland reflected the modification of the landscapes by humans prior to eruptions (Boyd et al., 2005). For example, habitats dominated by grasses and other pioneer species (e.g. gardens and forest clearings) recovered more slowly and took much longer to be recolonized by the local population. This example illustrates how a combination of data derived from long-term ecology and strong conceptual structures can generate real insights into the feedbacks and interactions between human activity and landscape pattern, process and scale.

3.5.3 Rewilding Rewilding has been defined as ‘action at the landscape level with a goal of reducing human control and allowing ecological and evolutionary processes to reassert themselves’ (Klyza, 2001, p. 285). In this context, rewilding projects can be grouped in with

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other functionalist approaches to conservation, wherein priority is given to managing and restoring ecological processes. However, it is functionalism with strong compositionalist tendencies; inspiration for rewilding projects is frequently taken from the palaeoecological literature, and evolutionary and ecological processes are typically restored through the reestablishment of ancient assemblages (or their nearest functional equivalent if the original inhabitants are extinct). For example, one of the architects of Dutch rewilding (described in Chapter 2, Section 2.5.2), Frans Vera, drew heavily on the palaeoecological record to argue that the primeval landscape of lowland Europe was much more open than previously thought, primarily due to the actions of large herbivores that are now extinct or greatly reduced in abundance (Vera, 2000). The Dutch government allowed Vera and his colleagues to produce a public demonstration of this hypothesis at the Oostvaardersplassen reserve, a 5,600 ha polder 50 km from Amsterdam, which was created in 1974– 78. This was land that had been reclaimed, but not cultivated, and where elder and willow scrub had rapidly colonized (Sutherland, 2002). They introduced a dynamic grazing management that involved the introduction of red, fallow and roe deer and Heck cattle and Konic ponies as substitutes for the extinct Auroch and Tarpan (Sutherland, 2002; Jepson & Ladle, 2010). The experiment has been a remarkable success, and by 2005 the cattle and horses were exhibiting ‘natural’ herd social structures and had mostly synchronized breeding. The habitat mosaic of pasture, woodland and wetland created by high herbivore densities has produced some novel and often unexpected conservation outcomes. The most high profile of these are the 60,000 greylag geese (Anser anser) that appear on the site each autumn (Sutherland, 2002) and the first ever breeding in Holland of whitetailed eagle (Haliaeetus albicilla) in 2006 (Jepson & Ladle, 2010). Rewilding with large herbivores may also be an effective strategy for mitigating the negative ecological effects of rapid environmental change. Zimov (2005) has suggested that large herbivores may act as ecological buffers between the changing climate and the constituent ecosystems that they help create. Introducing wild-acting herbivores, therefore, has the potential to create a more flexible form of management – one that is more responsive to climate change than the typically crude human management interventions. However,

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the success of such reintroductions might depend on the successful establishment of wild populations of predators or, alternatively, on maintaining at least some element of management of herbivore populations by humans (van Wieren, 1991). Rewilding clearly offers a potential alternative to the current target-driven, intensively managed approach to conservation that is common in many parts of the world and especially Europe. Its combination of functionalist objectives achieved through recreating past assemblages also offers a flexible approach that has potential to be scaled up to encompass entire landscapes (see also Chapter 2, Section 2.5.2).

3.5.4 The challenge of rapid environmental change One of the potential problems of using baselines derived from palaeoecology, historical records or relict populations as guidelines for restoration and conservation initiatives is that the environment may have changed to such a degree that maintaining historical species assemblages is no longer a viable option. This issue is gaining increasing attention due to the widespread concern with anthropogenic climate change. The most recent prediction of the Intergovernmental Panel on Climate Change (IPCC) is that global temperatures could rise by between 1.1 and 6.4°C (2.0 and 11.5°F) during the 21st century, accompanied by changes in precipitation regimes, extreme conditions and seasonality (IPCC, 2007). Such changes, when combined with ongoing destruction, fragmentation and modification of natural habitats and ecosystems, and the anthropogenic transportation of non-native species, will generate unique suites of environmental conditions, novel ecosystems and novel communities (see e.g. Willis & Bhagwat, 2009). In such circumstances, identifying current or past community compositions as target baselines will become increasingly problematic and impractical (Hannah et al., 2002b). Bush (2002) suggests that the solution to this problem is to concentrate on conserving animal and plant niches rather than identifiable communities, but the scientific challenges involved in determining niche requirements at the species level on the scale required are, of course, not trivial. Further discussion of these challenges follows in Chapter 6 and especially in Chapter 7.

3. 6 ADAPT I V E ECOS Y S T EM MAN AGEMEN T The complexity of ecological systems and the uncertainty with which ecosystem changes can be predicted raises dilemmas for ecosystems managers, who must make decisions when knowledge is imperfect and stakes are high (Funtowicz & Ravetz, 1994; Ravetz & Funtowicz, 1999). The management of ecosystems requires recognition that, for any given ecosystem, there may be a range of possible ecosystem states and an equally wide variety of societal responses to these states (Ravetz & Funtowicz, 1999). This uncertainty, complexity and plurality requires an adaptive approach to ecosystem management, i.e. one that continually monitors and evaluates the outcomes of management interventions and adjusts conservation and management goals in the light of new scientific understanding, inputs from stakeholders or ecological surprises (Grumbine, 1994, 1997; Sabine et al., 2004). Critically, ecosystem managers need to know the position of ecological thresholds so that they can maintain desired states or facilitate beneficial changes through management interventions. In Australian rangelands, Westoby et al. (1989) described an opportunistic management system for rangelands, based on the idea of state and transition (see Section 3.4.1). This management approach required understanding of the processes that drive transitions between phases, along with a classification of known vegetation phases according to whether they are favourable or unfavourable – primarily for livestock owners, but the principle could equally be extended to biodiversity conservation. Favourable transitions, such as a change from saltbush to grass-dominated vegetation with scattered woody plants, could be facilitated by de-stocking, whereas maintaining grazing pressure would be more likely to cause a transition to shrub cover, a phase less favourable for livestock (Westoby et al., 1989). In the Kruger National Park (KNP), South Africa, strategic adaptive ecosystem management (Figure 3.6) is used, with the aim of maintaining natural ecological dynamics (Biggs & Rogers, 2003). KNP ecologists and international collaborators have developed processorientated management goals, based on ecosystem properties, known as Thresholds of Potential Concern (TPC). These thresholds are points along a continuum of ecological or environmental change, at which managers either intervene to guide ecosystem change, or at

Roots, relevance, aims and values

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Figure 3.6 The process of Strategic Adaptive Management, as applied within Kruger National Park by South African National Parks (SANParks – the government department responsible for managing national parks in South Africa), illustrating the inter-linkages between describing the desired state, developing a management plan, implementation, monitoring and review. Source: www.sanparks.org/parks/kruger/conservation/scientific/key_issues/plans/adaptive/pdfs/chapter_03.pdf)

which they decide whether to alter their management objectives or TPCs (Biggs & Rogers, 2003). For example, concern for the impact of elephants on trees and shrubs led to the development of a TPC for elephant management. In essence, if woody vegetation cover drops below 20 per cent of its ‘highest ever value’, managers will control elephant numbers by culling or translocation, or will adjust the TPC. Using

such a TPC raises the question of how dense woody cover was in the past, and how far present-day tree cover deviates from the ‘highest ever’ value as judged over varying reference time frames. Using fossil pollen data and landscape modelling software, Gillson and Duffin (2007) estimated that, at two study sites, woody vegetation cover had fluctuated between 25 per cent and 55 per cent over a 1,400 year

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period, compared to 20–40 per cent in the present day, and between 13 and 19 per cent over a 4,900 year period, compared to 4–36 per cent in the present day. They also found that recent increases in elephant abundance did not appear to have exceeded the resilience of the woody vegetation, since no dramatic decreases in woody vegetation had occurred. Their data demonstrate that the ‘highest ever’ woody vegetation cover is indeed dependent on the length of the temporal record used. A more useful TPC might therefore be developed, based on changes in mean tree abundance over time or changes in the patchiness of tree cover. The study also revealed that the variability and abundance of tree cover was different at the two study sites, highlighting the importance of developing TPCs that are site-specific and sensitive to local conditions (Gillson & Duffin, 2007).

F O R DI SC USS I ON 1 What is the significance of a ‘natural’ habitat to the modern conservation movement? 2 In the light of present-day levels and rates of environmental change, how useful is the information derived from long term ecology for contemporary conservation practice?

3 Are baselines important for conservation approaches based on ecosystem services? 4 Does the widespread occurrence of non-native species mean that compositionalist approaches to conservation that focus on restoring past baselines are no longer viable? 5 How does the size of a protected area influence the type of conservation management (e.g. for function or for composition) that is adopted? S U GGES T ED R EADI N G Agnoletti, M. (2007) The degradation of traditional landscape in a mountain area of Tuscany during the 19th and 20th centuries: implications for biodiversity and sustainable management. Forest Ecology and Management, 249, 5–17. Bayliss-Smith, T., Hviding, E. & Whitmore, T. (2003) Rainforest composition and histories of human disturbance in Solomon Islands. Ambio, 32, 346–352. Bush, M.B. (2002) Distributional change and conservation on the Andean flank: a palaeoecological perspective. Global Ecology and Biogeography, 11, 463–473. Callicott, J.B., Crowder, L.B. & Mumford, K. (1999) Normative concepts in conservation. Conservation Biology, 13, 22–35. Sabine, E., Schreiber, G., Bearlin, A.R., Nicol, S.J. & Todd, C.R. (2004) Adaptive management: a synthesis of current understanding and effective application. Ecological Management and Restoration, 5, 177–182.

PART 2 THE DISTRIBUTION OF DIVERSITY: CHALLENGES AND APPLICATIONS

Conservation Biogeography Edited by Richard J. Ladle and Robert J. Whittaker © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33503-3

CHAPTER 4 Basic Biogeography: Estimating Biodiversity and Mapping Nature Brett R. Riddle1, Richard J. Ladle2,3, Sara A. Lourie4 and Robert J. Whittaker2 1

School of Life Sciences, University of Nevada, Las Vegas, USA School of Geography and the Environment, University of Oxford, Oxford, UK 3 Department of Agricultural Engineering, Federal University of Viçosa, Brazil 4 Redpath Museum, McGill University, Montreal, Canada 2

4. 1 I N T R OD UC T I ON Are very closely allied species ever separated by a wide interval of country? What physical features determine the boundaries of species and of genera? Do the isothermal lines ever accurately bound the range of species, or are they altogether independent of them? What are the circumstances which render certain rivers and certain mountain ranges the limits of numerous species, while others are not? None of these questions can be satisfactorily answered till we have the range of numerous species accurately determined. (Alfred Russel Wallace, 1852, p. 110)

4.1.1 Our incomplete knowledge of biodiversity We may look at the natural world through many different lenses. In Chapter 3 we divided these perspectives into two broad classes: compositionalist and functionalist. These might also be respectively labelled biogeographical versus ecological or ecosystem approaches. When they are applied for conservation

purposes, both require the identification of nature’s units, which, as will already be clear from Chapter 3 represent human impositions. In the present chapter we tackle both these themes, beginning with the compositionalist/biogeographical approaches – those concerned with describing biodiversity variation geographically. Here our prime focus is typically with the species unit, but we can also be concerned with higher or lower levels in the taxonomic hierarchy. We give most attention to the terrestrial realm, but go on to illustrate how compositionalist and functionalist approaches are applied also in the marine realm. The chapter embraces both foundational approaches within biogeography, such as the endeavour of identifying the world’s major biogeographical regions (a project that was well under way in the 19th century), as well as some of the latest approaches to analysing species interrelationships and present and future distributions. When we are contemplating conservation action, we are often concerned with saving locally or globally unique taxonomic elements, typically species which we perceive to be threatened by anthropogenic extinction. We may also be motivated by the notion of saving valued ecosystem types, some of which may be

Conservation Biogeography Edited by Richard J. Ladle and Robert J. Whittaker © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33503-3

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Basic biogeography: estimating biodiversity and mapping nature

essentially cultural in origin or maintenance (in the UK, chalk grassland, lowland heaths and hay meadows come to mind) or of conserving valuable ecosystem function (e.g. estuarine habitat for wintering wildfowl or habitat connectivity for migratory terrestrial species) (see Chapters 2 and 3). Many of the most fundamental questions in conservation biogeography require knowledge of the geographical distributions (and ecological niche requirements) of individual species. Such knowledge is, of course, essential before we can assess the threats to the viability of their populations in a rapidly changing world (see Chapter 7). Unfortunately, we all too often lack this knowledge – a deficit that has, for reasons outlined below, been called the Wallacean shortfall (Lomolino, 2004). Also, before we can even begin to understand the distributions of organisms, we need to know that they actually exist, and unfortunately our knowledge gap between formally described and yet-to-be-discovered species, referred to as the Linnean shortfall (Raven & Wilson, 1992), is vast. These two knowledge deficits contribute significantly to a third key knowledge shortfall, which is that our grasp of the magnitude of anthropogenic extinctions (past, present and especially future) is also marked by a high degree of uncertainty. There is a strong consensus that the rate of loss is already significantly above background levels of extinction derived from the fossil record, but no one can be quite sure by how much. We therefore begin the present chapter with consideration of these three themes, before moving on to describe other forms and levels of biological organization used in conservation biogeography. In terms of functionalist approaches to mapping nature, we focus particularly on two types: biomes and ecoregions. In essence, these are closely related concepts concerned with delineating and mapping Major Ecosystem Types (METs). These METs, in turn, represent a key way in which humans perceive nature in the terrestrial realm based largely on physiognomic features of the vegetation (e.g. temperate grasslands or temperate deciduous woodland). Species are also constrained to varying degrees in their distribution to particular METs, so there is a broad, albeit imperfect, correspondence between functionalist and compositionalist approaches. Both are foundational to the efforts of those involved in conservation planning, as will be discussed further in Chapters 5, 6 and 7, and in practice many fully developed multi-scalar approaches to the problem of mapping nature involve a mixture of the two.

4.1.2 Why do we map? I soon found that the Amazon, the Rio Negro and the Madeira formed the limits beyond which certain species never passed. Thus there are four districts, the Guiana, the Ecuador, the Peru and the Brazil districts, whose boundaries on one side are determined by the rivers I have mentioned. (Alfred Russel Wallace, 1852, p. 110) As a young naturalist and scientific collector in the tropical forests of the Amazon, Alfred Russel Wallace had already developed a remarkable understanding of the importance of accurate distributional mapping as a basis for whatever could subsequently be learned about biogeographical patterns and processes. For example, carefully recording the geographical distributions of species of monkeys, birds and insects gave him the insight which led him to speculate on the relationship between geographical features and attributes of species affinities and distributions to a degree not realized amongst his contemporaries. Indeed, as the above quote indicates, 24 years before reinforcing and expanding upon Sclater’s (1858) scheme of six great terrestrial zoogeographical regions of the Earth (Wallace, 1876), he was already dividing Amazonia into geographic units according to species’ distributions and features of the Earth associated with limits to their distributions. Of course, Wallace and Sclater also had predecessors who had begun to understand and summarize the non-random nature of species’ distributions on grand scales (Ebach & Goujet, 2006; Lomolino et al., 2010). Commencing largely in the 18th century, and more fully developed by Wallace and his contemporaries, these studies form the foundations for efforts to map the distribution of biodiversity across the Earth. Several persistent themes in biogeography developed in conjunction with distributional mapping at taxonomic scales, ranging from intraspecific to higher taxa, using aggregations from single taxa to entire biotas, and at geographical scales ranging from local to global (Lomolino et al., 2010, their Chapter 2). In illustration, we have picked out three deep-rooted themes foundational to modern conservation biogeography which provide the framework for predicting effects of climate change, invasive species, habitat fragmentation and loss, and other anthropogenicallymediated influences on populations, species and biotas.

The distribution of diversity: challenges and applications 1 Classifying geographical regions based on their biotas. Sclater (1858) and Wallace (1876) would have been unable to construct the six great terrestrial biogeographical regions of the world that are still used today with modifications (Cox, 2001; Kreft & Jetz, 2010) without recognition and analysis of non-random global and regional distributions of birds, mammals, and other groups. Within conservation biogeography, these early representations of global biodiversity became constituent building blocks of the Dasmann (1973) and Udvardy (1975) IUCN Biogeographical Regions framework discussed in Chapter 5. 2 Reconstructing the historical development of lineages and biotas, including their origin, spread, and diversification. Both Darwin (1859) and Wallace (below) argued strongly in favour of a ‘natural’ system of taxonomy that formed the informational basis required to study the geographical distribution of animals and plants: A little consideration will convince us, that no inquiry into the causes and laws which determine the geographical distribution of animals or plants can lead to satisfactory results, unless we have a tolerably accurate knowledge of the affinities of the several species, genera, and families to each other; in other words, we require a natural classification to work upon. (Wallace, 1876, p. 83) Most modern methods of reconstructing biogeographical history rely on either phylogenetic methods of reconstructing ‘natural classifications’ of taxa based on ‘descent with modification’ (Hennig, 1966) or some form of genetically-based population similarity analysis, with the mapping of these relationships onto geography (Avise, 2000; Riddle et al., 2008). The use of genetic data to investigate the geographical patterns of historical and ongoing connections between populations within a single species or several closely-related species is called phylogeography (Avise, 2000, 2009; Riddle & Hafner, 2006), and this emerging sub-field is generally recognized as one of the most important recent advances in biogeography. Phylogeography has also spun off several newer approaches, including landscape genetics and phylochronology (the idea of focusing on change in genetic diversity of populations in given localities over time; Hadly et al., 2004). Applications of phylogenetic and population genetic

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methods in conservation biogeography include those that incorporate a phylogenetic diversity metric (Faith et al., 2004b), and a variety of genetic approaches to evaluate the distinctiveness of populations and species (Avise & Hamrick, 1996; Frankham et al., 2002), patterns of endemism and biodiversity hotspots (Verboom et al., 2009). These metrics have considerable potential for incorporation into protected area planning processes (Chapters 5 and 6). 3 Explaining the differences in numbers as well as types of species among geographical areas and along geographical gradients, including patterns related to area, isolation, latitude, elevation and depth. One of the most pervasive patterns in nature, the species–area relationship, was recognized rather soon after geographically representative natural history specimens began to accumulate from global explorations (Forster, 1778) and regional collections (De Candolle, 1855; Watson, 1859). Formal mathematical analyses were initiated early in the 20th century (Arrhenius, 1921; Gleason, 1922). A long history of theory and modelling in conservation biogeography has developed, based on the predicted ‘meltdown’ in species diversity following habitat fragmentation and loss and the consequent impact on species–area relationships (Chapter 8). Several other themes in conservation biogeography have relied heavily on knowledge of non-random distributions of species richness and species types across geography; examples include the grand clines of diversity from poles to the equator and the concentration of endemic species in biodiversity hotspots (Chapter 5; Myers et al., 2000). While the value of mapping biodiversity for strategic conservation planning purposes is clear, the reality is that there are still many gaps in our knowledge of what is out there, how it is distributed and the extent to which it is threatened with extinction. These are the topics covered in the following section.

4. 2 T H R EE K N OW LEDGE S H OR T FALLS 4.2.1 The Linnean shortfall The Linnean shortfall is named after the Swedish naturalist Karl von Linne (1707–1778), better known through his ‘Latinized’ sobriquet of Carolus Linnaeus. He was the creator of the system of Latin binomials and is widely regarded as the father of modern

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taxonomy. The shortfall refers to the enormous discrepancy between the number of species that have been formally described by taxonomists (around 1.7 million at the last count), and the number of species that are thought to exist – somewhere between 3 and 100 million (excluding bacteria and viruses). In other words, it is the disparity between the number of described species and the total number of species in existence (Raven & Wilson, 1992; Lomolino, 2004; this definition from Lomolino et al., 2010). The shortfall is a problematic issue for conservation biogeography, because it can add considerable ‘noise’ to any attempt to map and compare biodiversity. The depth and breadth of the Linnean shortfall is clearly illustrated by the number of new species that are still being discovered – almost at a weekly rate in some parts of the world. These discoveries are not just restricted to insects and other small and inconspicuous inhabitants of tropical rain forests, although these fauna do represent the bulk of the Linnean shortfall. For example, 11 of 80 extant species of cetaceans (whales and porpoises) were discovered only in the 20th century, one as recently as 1991 (Raven & Wilson, 1992). Higher taxonomic groups are also still being discovered. For example, Raven and Wilson, in their 1992 paper, note that three new families of flowering plants were discovered in Central America and southern Mexico within the previous decade, while two new phyla were described in the last two decades of the 20th century (Lomolino et al., 2010). However, it is fair to state that most big and well-studied groups, such as birds and mammals, have been reasonably well described, while the shortfall is greatest in smaller, less charismatic taxa such as fungi and, especially, many types of arthropods. Additions to the global list of species come about both by new collections of voucher material and by re-inspection of material previously gathered and residing in museums around the world. In addition to the traditional systematic taxonomy (whereby specimens are classified based on morphology into different species), increasingly, as we will discuss later, genetic data are allowing the identification of morphologically cryptic species. A nice example comes from studies of Nesotes beetles in the Canary Islands, where genetic analyses showed that what had been thought to be a single species Nesotes fusculus, occurring on the islands of Tenerife, La Gomera and Gran Canaria, really represented a paraphyletic group (see Glossary), in which the fusculus phenotype previously recognized

as a monophyletic species (ditto) had evolved independently in the xeric coastal zones of the three islands (Rees et al., 2001). Such analyses have contributed to the pattern of species discovery in the Canaries, whereby, despite dense human populations and some three hundred years of scientific attention, new species have been described at the rate of about one species every six days in recent decades. The discoveries include two large species of lizards (Gallotia intermedia and G. gomerana) and at least two species of trees (Myrica rivas-martinezii and Dracaena tamaranae), and have resulted both from new field and laboratory work (Izquierdo et al., 2004; Whittaker & Fernández-Palacios, 2007). Many new finds on the Canaries, such as the two species of Gallotia, had escaped scientific detection because they persist only in small and endangered populations. Their discovery, as with many recent discoveries around the world, adds both to the global diversity total and to the total of threatened species. These examples are indicative of how difficult a business it is to estimate global diversity. Global estimates of the total species diversity of terrestrial animal and plant species are largely dependent on the estimated number of arthropods. A classic and influential analysis was undertaken by Terry Erwin (e.g. Erwin, 1983), based on a field study of neotropical beetles. Erwin took his samples near the city of Manaus in the heart of the Brazilian Amazon. He used insecticide fumigators to sample beetles from the canopies of three forest types, collecting the specimens from a network of collecting trays spaced out along ten transects, each of 50 m length. He recorded a remarkable 1,080 species (many unknown) from these samples and, significantly for estimates of species richness, 83 per cent of the species he sampled were restricted to one type of forest and 14 per cent to two types. To get from here to an estimate of global arthropod diversity requires some pretty big suppositions. Erwin’s method was to use an estimate of beetle host specificity of 20 per cent (derived from a study of insects on one tree species, c. 163 beetle species per tree species) and multiply this by tropical tree species richness (≈50,000 species). Assuming that beetles comprise about 40 per cent of canopy arthropod species, and that there are twice as many canopy species as ground-dwelling species, it is possible to estimate that there could be as many as 30 million species. Using similar approaches, other researchers have arrived at estimates for global biodiversity of eukaryotes as high as 100 million species (Groombridge, 1992).

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Figure 4.1 Cumulative curves of species description and fitted models of four of the five size categories of mammals analysed on four land masses. The curves start in the year 1890 (year 0), when the number of species was treated as zero. From Medellín & Soberón (1999).

More recently, Ødegaard et al. (2000) have questioned the estimates of host specificity used in such projections. Using data from 2,561 host observations of 697 beetle species on 50 canopy species in a tropical dry forest in Panama, they observed rates of host specificity of 7–10 per cent. Taking into consideration a number of studies, estimates of host specificity of tropical rain forest beetles now range from c. 2 per cent to 20 per cent, which indicates that beetles may only contribute 20 per cent (still a large number!) rather than 40 per cent of canopy arthropods in tropical forests. Based on these new figures, they conclude that 5–15 million species is a far more reasonable range for estimates of global biodiversity than 30 million. This estimate tallies well with Groombridge’s (1992) suggestion that there are probably about 12.5 million species currently in existence. However, as regards prokaryotic species, biologists hesitate even to approach an estimate and, indeed, are unable yet to agree on how to delineate prokaryotic species (Curtis et al., 2006; Doolittle & Zhaxybayeva, 2009).

Global eukaryote species richness can also be estimated based on extrapolations of rates of discovery of new species. Medellín & Soberón (1999) used this method to predict the actual number of mammals in different taxa and size classes in each of Asia, Africa, Eurasia and Oceania. Mammals are typically regarded as a well-known group and it is therefore unsurprising that the rate of new discoveries has slowed over time (Figure 4.1). Even so, extrapolating up to the year 2032 gives an overall forecast of 4,875 species. This represents 247 more than the data set (1992) used in the analysis, with the majority of the expected new discoveries being small-bodied (<100 g) insectivores, rodents and bats. Traits such as body size often show strong correlations with rates of new species discovery, but are also often correlated with other factors. For example, Gaston & Blackburn (1994) point out that, of 63 new bird species discovered between 1966 and 1990, most are small, but most were also discovered on single islands and many displayed cryptic coloration (e.g. dull plumages, nocturnal habits, etc.). Indeed, this is a very

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common pattern, with larger, more conspicuous species typically being scientifically discovered relatively early. Moreover, new species discoveries are often now restricted to a few geographically localized areas of the globe where taxonomic capacity has been low, or where local conditions have made collecting difficult (e.g. remote rain forest, mountains, swamplands, etc.). A large source of uncertainty in global species richness estimates derives from taxonomic error in species already described. One common problem is synonymy – multiple naming of the same species – which is known to be rife in many groups. For example, due to taxonomic problems, estimates of the number of known mollusc species vary from 45,000 to 150,000, with 70,000 a compromise figure (Jeffries, 1997). As new forms of taxonomic analysis become available and more molecular phylogenies are produced, and as specialist monographers revise their groups, improved precision in diversity estimates is possible. As hinted in the Canary Islands’ beetle example above, taxonomic adjustments can, of course, go in both directions. In other words, revisions can result in multiple-named species being lumped together, but may also sometimes result in what was thought to be one species being split into separate species. It can even sometimes happen that species go in and then out of synonymy again. In his analysis of neotropical mammals, Patterson (2000) noted that between 1982 and 1992, three times as many species in this group came out of synonymy as became synonyms, and these revalidated names also outnumbered species being described for the first time by the same ratio. While some of the newly described species were essentially newly trapped, for every such species, Patterson notes that three more were ‘found’ in drawers in museums or identified via molecular biological analysis. The numbers involved are surprisingly large for a group of vertebrates generally considered to be well described. Patterson notes that in the seven years prior to his study, a total of 57 species of neotropical mammals were described, ten more than the number of bird species described globally in the decade from 1981 to 1990. This indicates that our knowledge of mammal diversity (and distribution) is probably less satisfactory than for birds (Patterson 2000, 2001). The resources and expertise needed to resolve all of these uncertain taxonomies are considerable and there appears to be limited political will to provide the funding needed. Nonetheless, there are a number of initiatives under way that may speed up the rate of new

discoveries and standardize nomenclature (Table 4.1). One example is the Partnership for Enhancing Expertise in Taxonomy (PEET) (www.nsf.gov/funding/pgm_ summ.jsp?pims_id=5451&org=BIO), which aims to target poorly known organisms through supporting research projects, thereby providing opportunities for a new generation of taxonomists to be trained in ‘problematic’ taxa. A further aim of PEET is to translate current taxonomic expertise into electronic databases and other formats that allow broader accessibility. This final point is exceedingly important. Taxonomic information is only useful for conservation if it is in a form that can be easily retrieved and processed. Technological advances and efforts from numerous institutions around the world are making such ‘user-friendly’ open access databases a reality and are providing conservationists with powerful new tools for mapping and understanding nature. Probably the most ambitious bioinformatics project is the Encyclopedia of Life (www.eol.org), a project inspired by E.O. Wilson, the aim of which is to ‘make available via the Internet virtually all information about life present on Earth’ (Wilson, 2003). The encyclopaedia works through a series of linked websites, one of which is planned for every species that has been formally described. Each species’ website will be flexible and constantly evolving so that it can easily incorporate new information on ecology, genetics and conservation as it is generated. By 2014, the project hopes to have generated a million species pages – a rich resource for conservation biogeography if it can improve access to knowledge and improve the quality, accuracy and speed of data collection. However, some scientists have questioned the feasibility of such goals in the light of declines in the number of trained taxonomists and in resources for both taxonomy and the curation of collections (Gropp, 2003). Serious attempts are also under way to develop computer programs that can identify species from digital images through the use of a new generation of evolutionary algorithms (Gaston & O’Neill, 2004). In principle it may be possible to ‘train’ identification software to recognize species from images and, by extension, identify possible new species for which no records exist. As we continue to add molecular approaches to more traditional morphological ones (and behavioural, physiological, etc.) of recognizing significant breaks and relationships between ‘natural groups’, we remain uncertain about how to apply either molecular, morphological, or a combination of both kinds of information in deciding how to diagnose different species of

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Table 4.1 An illustrative selection of ongoing initiatives seeking to reduce Linnean and Wallacean shortfalls. Initiative name

Goals

Lead organization(s)

Website(s)

Biodiversity Information Standards (TDWG)

Develop standards and protocols for sharing biodiversity data

TDWG Secretariat

www.tdwg.org

Catalogue of Life

Develop a catalogue of all known organisms on Earth

Species 2000; Integrated Taxonomic Information System (ITIS)

www.catalogueoflife.org/

Barcode of Life Initiative

Develop DNA barcoding as a global standard for the identification of species

Consortium for the Barcode of Life (CBOL)

www.barcoding.si.edu

Encyclopedia of Life (EOL)

Internet access to information about life present on Earth

Six cornerstone institutions: Harvard, the Smithsonian, Woods Hole, Missouri Botanical Garden, Field Museum, Biodiversity Heritage Library

www.eol.org

European Distributed Institute of Taxonomy (EDIT)

Integrate European taxonomic effort

28 institutions with major natural history collections

www.e-taxonomy.eu

Global Biodiversity Information Facility (GBIF)

Build biodiversity information infrastructure

GBIF Secretariat

www.gbif.org

International Institute for Species Exploration

Build integrative structure for advancing taxonomy and exploration of Earth’s species

Arizona State University

www.species.asu.edu/

Legacy Infrastructure Network for Natural Environments (LINNE)

Accelerate taxonomic research and improve biological collection infrastructure

Florida Museum of Natural History

www.flmnh.ufl.edu/linne

Partnership for Enhancing Expertise in Taxonomy (PEET)

Monographic research on poorly known taxa; taxonomic training; web-based bioinformatics

USA National Science Foundation

www.nsf.gov

Planetary Biodiversity Inventories (PBI)

Accelerate discovery and study of world’s biodiversity

USA National Science Foundation

www.nsf.gov

animals (Angulo & Reichle, 2008), fungi (Leslie & Bowden, 2008), plants (Knapp, 2008) and other eukaryotes (Williams & Reid, 2009). Some reconciliation has been suggested by separating conceptual from operational objectives in diagnosing species (Mayden, 1997; Sites & Marshall, 2004; de Queiroz, 2007), or

by attempting to develop a universal standard for species recognition (Isaac et al., 2004). However, we should expect the debates over species concepts and their application to the task of naming new species and revising existing ones to continue into the distant future.

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Several biologists have questioned the overall utility of trying to name all species without, at the same time as we delimit them, making greater efforts to describe their biology, systematics, ecology and distribution (Raven, 2004). In particular, it would be difficult to imagine the utility of long lists of taxa for conservation biogeography without information on geographical distributions, on scales spanning from the local to the global.

4.2.2 The Wallacean shortfall The Wallacean shortfall refers primarily to the inadequacy of our knowledge of the geographical distributions of species, although Lomolino et al. (2010, p. 740) have defined it slightly more broadly as ‘the paucity of information on species distributions and on the geographic dynamics of extinction forces, especially the geographic dynamics of human civilizations.’ Like the Linnean shortfall, the term ‘Wallacean shortfall’ pays tribute to a great naturalist, in this case Alfred Russel Wallace (1823 to 1913), who, in addition to being the co-discoverer of the theory of evolution by natural selection, is also recognized as one of the founding fathers of zoogeography. As we have already established, there are millions of species in the world and we continue to discover and describe new ones. The scale of the task in determining their distributions is comparable to the taxonomic labour required, particularly so in many developing countries which lack the resources for the necessary field inventory and related identification work. Some habitats, such as the Amazon rain forest, are so vast so as to render systematic sampling an impossibility using the technology currently available. Indeed, it has been

commented that we do not have accurate information on the geographical distribution of any plant species in the Amazon (Bush & Lovejoy, 2007). It has recently been calculated that 43 per cent of the total area of Amazonia has never been surveyed by botanists, while another 28 per cent is poorly collected and only two per cent can be considered ‘relatively well’ collected (Schulman et al., 2007). Interestingly, the well-collected part is close to Manaus, where Erwin did his seminal work on beetle diversity. These observations highlight an important problem in conservation biogeography, whereby those seeking to provide scientific guidance as to where to locate protected areas can be tempted to base their analyses on ‘the best available data’, and in the process may pay insufficient attention either to the magnitude or the geographical structure of the Wallacean shortfall. A paper by Hopkins (2007) shows how it is possible to use data from herbarium (or museum) collections to tease apart what component of diversity variation may be an artefact of collecting intensity. Taking a lead from a seminal paper by Nelson et al. (1990), which showed a strong correspondence between regions that were considered to be ‘centres of richness and endemism’ and collecting intensity, Hopkins (2007) used the known occurrences of 1,584 species of Magnoliophyta to build models of collecting deficit. Noting that as many as 40 per cent of Amazonian plants in herbaria may bear incorrect identifications, he placed his reliance instead on the analysis of monographed taxa, being those for which specialists have examined the specimens, revised the identifications and mapped known distributions using grids of 1 degree latitude/ longitude. The steps in his analysis are demonstrated in Figure 4.2, in which he illustrates, for two species, how the

Figure 4.2 Steps in producing hypothetical distribution maps, illustrated (panels a–d) for a species with a restricted distribution (Inga plumifera), and (panels e–h) for a species with a widespread distribution (Inga capitata). (a) and (e) the degree squares with confirmed occurrences. (b) and (f) the contours of the predicted probability of occurrence, using a probability of occurrence in adjacent degree squares of 0.5 and allowing this effect to accumulate for 5 degree squares. (c) and (g) the hypothetical distribution deduced by accepting a probability of occurrence of greater than 0.5 in any degree square. (d) and (h) the degree squares for each species. Summing these values across all species modelled in the exercise allows the estimation of the total number of species hypothetically occurring in any one degree square. From Hopkins (2007). (See Plate 4.2 for a colour version of these images.)

The distribution of diversity: challenges and applications

(a)

(e)

(b)

(f)

(c)

(g)

(d)

(h)

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Basic biogeography: estimating biodiversity and mapping nature

(a)

(c)

(b)

(d)

Figure 4.3 Known and unknown plant diversity of the Amazon Basin, based on species occurrence data for 1,584 monographed species. Major rivers of the Amazon Basin are shown in grey. Deeper shades of blue indicate higher numbers of species per 0.5 degree grid cell; yellow the lowest values; brown areas represent land > 1000 m. (a) The distribution of information of species occurrences. (b) The distribution of the expected diversity as predicted by a bootstrap model that compares the contents of the checklists within a circle with a radius of five degree squares of the focal square. (c) The distribution of the diversity that can be explained by modelling the distributions of the 1,584 species as predicted by assuming that each has a likelihood of occurrence of 50% in degree squares adjacent to those where they are already known to occur, and this additive effect extends within a radius of five degree squares. (d) The modelled distribution of incompleteness of knowledge, derived as the difference between layers b and c. Major rivers of the Amazon Basin are shown in grey. From Hopkins (2007). (See Plate 4.3 for a colour version of these images.)

hypothetical full distribution might be modelled from the known geo-referenced specimens. In Figure 4.3, we see the emergent outcome of repeating this analysis across his full data set of 1,584 plant species. Comparison of the panels shows that the hypothesized real diversity map of Amazonian plant richness might be very different from the ‘known’ pattern of diversity. We stress ‘might’ because, of course, the key point here is that the distributions are incompletely known; the models used to interpolate and extrapolate the real distributions of each species could be unreliable guides (below; Chapter 7).

Information on the geographical distribution of species is not only limited by accessibility of sampling sites, but by the particular history of plant and animal collecting, analysis and compilation for particular countries. Hence, much of our knowledge pertains to political geographical units rather than ‘natural’ ones. Even within comparatively wealthy countries with a shared vision for conservation, such as those in the European Union, there is a damaging paucity of information for many taxa. For example, the Atlas Florae Europaeae (AFE), a project launched in 1965 by a group of botanists and

The distribution of diversity: challenges and applications

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1673 5 20 37 11 48°

Figure 4.4 Distribution map of Filipendula vulgaris (Rosaceae). The symbols in the key (lower right of figure) indicate distributional status and, from top to bottom, are as follows: native (including archaeophytes); status uncertain; introduction; probably extinct native; and extinct introduction. Reproduced from Kurtto, A., Lampinen, R. & Junikka, L. (eds), Atlas Florae Europaeae (AFE) 13, 2004, by permission of the Committee for Mapping the Flora of Europe and Societas Biologica Fennica Vanamo.

biogeographers at the Museum of Natural History in Helsinki, with the objective of mapping the geographical distribution of all the vascular plants in Europe, has to date published some 13 volumes, including 3,912 maps (example shown in Figure 4.4), yet is reported to include only a little over a fifth of the vascular plants of Europe (www.fmnh.helsinki.fi/english/botany/ afe/). The maps for the first 12 volumes were manually

produced and only recently has the project shifted to direct database entry for mapping purposes. The slow progress of the project reflects the large geographical extent, covering many different countries, and the need to collate the taxonomic information on the species and subspecies during the process. The significance of the Linnean and Wallacean shortfalls for conservation planning is potentially

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immense, not least for convincing politicians and the public of the need to take action. The crux of the problem is that if we don’t know what is out there or how widely species are distributed, how can we convince people about the reality and form of the biodiversity crisis? An equally problematic issue is how to go about filling the shortfalls when funding for conservation in general – and taxonomy in particular – is extremely limited.

4.2.3 The extinction estimate shortfall Whereas the births and deaths of most individuals are discrete, easily recognizable events, it is often difficult to determine when a new species has come into existence, and when the last individual of a species has died. (Lomolino et al., 2010, p. 207) Extinction is a natural process and it is often remarked that of all the species that have lived, only a small fraction are alive today. Processes generating species extinctions over geological time periods include volcanic eruptions, meteorite impacts, climatic changes, marine transgressions, ocean closures and the disappearance of lakes, in combination with biotic forcing as new forms or newly arrived forms have displaced others (Raup, 1991; Lomolino et al., 2010). Rates of extinction have varied throughout the history of life (a period of some 3,500 million years), so it is difficult to distinguish between so-called background rates of extinction and short episodes of unusually rapid or extensive losses. However, analysis of the fossil record suggests that there have been five mass extinctions, each of which may be defined as a major episode of extinction involving many different taxa and occurring fairly suddenly in the fossil record. These five events are each recognizable in the marine record, with the most recent three, the end-Permian, endTriassic and end-Cretaceous events also notable in the terrestrial tetrapod (four-limbed vertebrates) record. Other, lesser, pulses of extinction have also been recognized in the fossil record for terrestrial animals, although mass extinctions are not clearly distinguishable for plants (Willis & Bennett, 1995; Willis & McElwain, 2002). The most recent generally recognized mass extinction event, the so-called K–T event (Cretaceous–Tertiary), occurred at around 65 million years ago and saw the disappearance of the land

dinosaurs, flying reptiles, large sea reptiles and ichthyosaurs. The imperfect nature of the fossil record, the challenge of applying modern species concepts to fossilized remains and the difficulty of distinguishing mass extinction events and pulses means that it is in turn extremely difficult to estimate what might be thought of as the ‘business as usual’ or background rate of extinction. Those who have attempted such calculations estimate that most species persist for perhaps around 4 million years, with a broad range of 1–10 million years average duration, allowing the background rate to be estimated based on average duration and total richness in a group (Raup, 1991). By such back-of-the-envelope calculations of the background rate, anthropogenic extinction rates for birds and mammals have been estimated to be 100 to 1,000 times faster than background (e.g. Primack, 1993). Leaving apart the problems of calculating the background rate, where do the estimates of extinction rates for the period of human-dominance of extinction (sometimes termed the Anthropocene) come from and how good are they? The first step in dealing with the question of estimating anthropogenic extinction rates, past and present, is to recognize that the term ‘extinction’ is used in very different ways, linked to differing sets of assumptions. The typology of extinctions provided by Ladle & Jepson (2008) provides a novel framework for exploring the meaning of extinction (Table 4.2), in which the first two categories refer explicitly to the previous two sections of this chapter, viz. the Linnean and Wallacean shortfalls. Hypothetical population trajectories that might accompany these forms of extinction are provided in Figure 4.5. 1 The term Linnean extinctions refers to attempts to estimate extinctions for areas or regions that are poorly known scientifically, but where the available data leads scientists to believe that large numbers of species exist, many of which are likely to be endemic to the region. When this is applied, for example, to large swathes of tropical forest in the equatorial regions, the use of area-based extrapolations of how much diversity may be present in the pristine state of these systems allows us, in turn, to use species–area relationships to predict how many species will be lost as the habitat is destroyed. In essence, the approach taken in such estimates is closely akin to the Erwin method for extrapolating from local sampling, based on rates of host specificity and

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Table 4.2 Typology and definitions of extinction (modified from Ladle & Jepson, 2008). Type

Definition

Linnean extinction

Extinctions of undiscovered species inferred from the species–area relationship and estimates of species diversity for a given ecosystem or region. The assumed losses of these inferred species are termed Centinelan extinctions by Wilson (1992).

Wallacean extinction

Species that have not been documented for many years but in which extinction is uncertain because populations might survive in areas that have not been surveyed within the potential distributional range.

Phoenix extinction

Extinct in wild but genetic material available in the form of stored material or closely related conspecific or congeneric variety/breed/hybrid, allowing the possibility of a future reintroduction of the same or a functionally equivalent form.

Ecological extinction

Extinct in the wild but with extant captive bred population, or present in the wild but at such low densities that it no longer interacts to a meaningful degree with other species in the community (i.e. it is functionally extinct).

Local extinction

Extinct in the wild within a clearly defined geographic area but with extant free-living populations outside that area.

True extinction 1: Contemporary extinction

Extinction since the birth of the international conservation movement (mid-19th century). Last known population has been monitored and surveyed and is now considered globally extinct in the wild. No captive-bred population or genetic material available.

True extinction 2: Historical extinction

Extinction prior to the birth of the international conservation movement. No authenticated record of an extant population. No captive-bred population or viable genetic material available.

knowledge of tropical tree diversity to arrive at a global species number figure in the region of 30 million. Indeed, a figure of this order of magnitude is implicit in many estimates used by scientists in popular discourse about extinction rates. By starting with a global estimate of this magnitude, and then eating away at it by using estimates of habitat loss (e.g. rates of tropical deforestation) according to a given species–area relationship, it is possible to derive startling figures. Such estimates are characteristically provided in terms of numbers of species that are being lost per annum, often expressed in relation to the size of a country or, if using a shorter time frame, the size of a football pitch. When projected into the future, round number dates such as 2020 or 2050 are used, which allow figures in the order of a million or several million to be deployed within a time frame of relevance to the human life span; such estimates grab the attention of the media (Ladle et al., 2004), signifying the risk of serious losses and the need for action, but are also easy to criticize scientifically.

The typical reliance of such estimates on the dominant island biogeographical paradigm of the last half century, MacArthur & Wilson’s (1967) equilibrium theory, is discussed further in Chapter 8, but for present purposes it is worth noting that estimates following this rationale involve a variety of ecological methods and assumptions (e.g. Wilson, 1992; Thomas et al., 2004), yet tend to make use of the same generalization regarding the relationship between the loss of habitat (area) and inferred loss of species, that the slope (z) of the species–area relationship expressed by the power model (S = cAz, where S = species number, A = Area, and c and z are constants) can be approximated as 0.25. As Gershwin put it in the song from Porgy and Bess, ‘It ain’t necessarily so …’ (Chapter 8; Thuiller et al., 2004; Whittaker & Fernández-Palacios, 2007). 2 Wallacean extinctions is a term given by Ladle & Jepson (2008) to the apparent extinction of a species, whereby the species can no longer be detected in areas in which it previously occurred, but where there is a chance of it persisting in areas within the range that have either

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Figure 4.5 Typology and biocultural context of extinction: (a) Linnean extinction; (b) Wallacean extinction; (c) Phoenix extinction; (d) ecological extinction; (e) local extinction; (f) true extinction (contemporary and historic). From Ladle and Jepson (2008).

The distribution of diversity: challenges and applications never been surveyed or which have not recently been surveyed. In an island biogeographical context, where biologists are interested in the turnover of species through time on a single island, the problem is termed pseudo-turnover, meaning that a species has appeared to go extinct from that island and then re-immigrate from elsewhere when it was actually present throughout, but this was not detected due to inadequacies of survey efforts. This problem has, for example, influenced estimates of extinction and turnover rates of plants and of birds on the Krakatau Islands, Indonesia (Bush & Whittaker, 1991; Thornton et al., 1993). 3 Phoenix extinctions are of species that have the potential to be resurrected through human ingenuity, and they take at least two forms. The first is where species have been transformed by human action for the purposes of domestication. Thus, although European wild cattle (Bos primigenius) became extinct in the 17th century, through back-breeding and artificial selection of domesticated cattle, a wild-acting replicate of the auroch is currently part of the ecology of the Oostvaardersplassen nature reserve in Holland (Ladle & Jepson, 2008). The term ‘phoenix extinction’ might also be applied to the plains bison (Bison bison), which hybridized extensively with domesticated cattle during the late 1800s and early 1900s (Freese et al., 2007), or more recent attempts to create a quagga-like animal by selectively breeding zebra. The second form of phoenix extinction identified by Ladle and Jepson (2008) is where technology is able to recreate extinct species using genetic material stored in gene banks or extracted from preserved remains. This idea has been around for decades and is already used in agriculture for resurrecting different varieties of domesticated species. However, recent advances in genetic technologies have dramatically increased the potential for scientists to recreate extinct species and have stimulated public interest in this possibility. There are currently no credible examples of such resurrections, although the whole mammoth genome has recently been sequenced (Miller et al., 2008), leading to renewed speculation that such a technological feat might one day be possible. 4 Ecological extinction refers to a species that persists in captivity, but which is no longer found in the wild, or occurs in densities so low that it ‘no longer interacts significantly with other species’ (Estes et al., 1989, p. 253). Alternatively, ecological extinction could be described as the avoidance of complete extinction through the intervention of captive breeding. One of

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the classic examples of this is Spix’s macaw (Cyanopsitta spixii), a magnificent neotropical parrot that was collected into extinction, but which clings on to existence in the aviaries of private individuals and institutions (Juniper, 2003). 5 Local extinction or ‘extirpation’ occurs when a species has disappeared from a clearly delimited geographical area (often relating to a geographical or physical boundary), but where extant, free-living populations exist outside that area. A classic example is the red kite (Milvus milvus), which disappeared from England in the late 19th century, but was successfully reintroduced in the late 1980s and early 1990s using populations sourced from Spain, Sweden and Wales (Evans et al., 1999). Another dramatic example that combines properties of both ecological and local extinction is the still-experimental reintroduction of the Californian condor (Gymnogyps californianus) into the vicinity of the Grand Canyon in western North America, a region that had been without condors since the late Pleistocene. While the drivers of local extinction are the same as the drivers of global extinction, the response of the conservation movement may be radically different and, for collectable species, global rarity may even enhance the pressure on the remaining populations. 6 True extinction can be defined as occurring when there is no reasonable doubt that the last population is extinct and where no captive population or genetic material exists. One of the most high profile recent examples of this is the Yangtze River dolphin, or baiji (Lipotes vexillifer), whose disappearance was reported after extensive surveys in November and December 2006 (Turvey et al., 2007). Ladle and Jepson (2008) distinguish between extinctions that occurred before (e.g. the dodo, Steller’s sea cow) and after the advent of the global conservation movement, because of the differing degrees of associated knowledge, certainty and conservation action surrounding such events (Table 4.2). So how many species have humans already driven to extinction in the recent past, and how many may go extinct in the future? First, to deal with the recent past, figures given by different authorities vary depending on the criteria adopted, so the values given in Table 4.3 should be taken as of largely indicative value. They are certain to significantly underestimate the true magnitude of extinction in the last four centuries. For example, based on extrapolations from historical records and sub-fossil remains, it has been suggested

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Table 4.3 Summaries of known extinctions on islands, continents and oceans between c. AD 1600 and c. AD 2000, thus corresponding with categories 6 and 7 (combined) of Table 4.2. These numbers undoubtedly underestimate the real magnitude of species extinctions in the historic period (reproduced from Whittaker & Fernández-Palacios, 2007, their Table 11.2). Group

Islands

Continents

Oceans

Total

% insular

Mammals Birds Reptiles Molluscs Insects Plants Total

51 92 20 151 51 139 504

30 21 1 40 10 245 347

4 0 0 0 0 0 4

85 113 21 191 61 384 855

60 81 95 79 84 36 59

that as many as 2,000 bird species may have become extinct following human colonization of the Pacific island region during the course of the late Holocene (Steadman 1997), including moas, herons, swans, geese, doves, parrots, owls, many passerines and perhaps hundreds of species of flightless rails. This estimate illustrates the gulf that can exist between recorded extinctions and what we have termed Linnean extinction estimates. Going back a little further in time, the role of humans in the undisputed loss of what is commonly termed the Pleistocene mega-fauna, especially in North America and Australia, remains a hot topic of debate (Barnosky et al., 2004; Lomolino et al., 2010). It seems most likely that a combination of environmental (principally climatic) change, plus human activity, hunting and altered fire regimes, etc., were frequently involved rather than humans being the sole cause. As regards the present-day and future extinctions, there is again a strong scientific consensus that we are entering another phase of accelerated extinction, and some use the phrase a ‘sixth mass extinction spasm’ to describe it. The main message here, however, is that estimating the extent of the building biodiversity crisis presents a significant challenge. An integral and crucial part of this challenge is to identify where the threats are concentrated, which species are most at risk, and where in the world preventative action can have greatest benefits for biodiversity in the various ways in which we define and value it. It is this challenge that motivates much of the remainder of this chapter and of this book as a whole.

4. 3 T H E FU N DAMEN T AL T AXON OMI C U N I T S OF CON S ER V AT I ON B I OGEOGR APH Y As systematists and biogeographers continue to turn to molecular approaches (Riddle et al., 2008), there are signs that reductions in Linnean and Wallacean shortfalls will not progress in parallel, for reasons that include a lack of consensus on criteria used to diagnose species and the utilization of units other than named species in biogeographical and conservation analyses. Here, we explore the conceptual and empirical consequences of using a variety of different metrics of biodiversity for the practice of doing conservation biogeography as outlined in detail in subsequent chapters.

4.3.1 Species versus other geneticallybased conservation units No one definition has as yet satisfied all naturalists; yet every naturalist knows vaguely what he means when he speaks of a species. (Darwin, 1859, p. 101) What are species? An answer to this seemingly straightforward question has eluded evolutionary biologists ever since Darwin and Wallace proposed that species are products of divergence with modification from common ancestors. The species is arguably the fundamental unit for conservation, and preventing species

The distribution of diversity: challenges and applications

Table 4.4 Six major species concepts (modified from Van Dyke, 2003). Name

Basis

Morphological

Morphology

Biological

Reproductive (and geographical) isolation

Genetic

Genetic data, e.g. nucleotide sequence mutations or restriction length polymorphisms

Paleontological

Character state gaps among fossils comparable with those between present-day species

Evolutionary

Ancestral descendant sequence of populations

Cladistic or Phylogenetic

Branch within a cladogram; formal analysis of character states

from becoming extinct the primary goal of the conservation movement. These generalizations mask a high degree of uncertainty and debate regarding both the definition of a species and what taxonomic unit is the most appropriate focus for conservation interventions. More than twenty different species concepts have been recognized (Mayden, 1997), but they can be gathered under six broad headings (Table 4.4), with the starting point being the use of morphological criteria within a traditional taxonomic framework. The most widely accepted modern definition was formulated by Ernst Mayr (1942) and is referred to as the Biological Species Concept (BSC). Mayr defined species as ‘groups of actually or potentially interbreeding natural populations which are reproductively isolated from other such populations’ (Mayr 1942, p. 120). In other words, members of the same species can breed and produce viable offspring, while unrelated species cannot. In this context, speciation is the evolution of reproductive isolation, typically through behavioural or physiological mechanisms, or both. Once a population is reproductively isolated from similar populations, it sets out on a unique evolutionary trajectory. There are several practical and conceptual problems with the BSC. First, it is exceedingly difficult to demonstrate under natural conditions because spatially

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disjunct (non-overlapping, i.e. allopatric) populations rarely have the opportunity to interbreed. Second, as would be predicted under gradualist models of evolution, reproductive isolation is frequently incomplete. For example, the oak species Quercus robur and Quercus petraea sometimes interbreed and produce viable progeny, yet seem to have maintained their biological integrity over millions of years. In contrast, red deer (Cervus elephas) and introduced sika deer (Cervus nippon) in the UK are now so interbred that it is difficult to distinguish them in many localities. Other taxa, such as the bdelloid rotifers, do not even reproduce sexually (Welch & Meselson, 2000), rendering the BSC meaningless. The difficulties encountered with trying to apply the BSC have led to a number of new species concepts being proposed (Table 4.4), based on the use of genetic data. In contrast to the BSC, with its practical focus on reproductive isolation, the phylogenetic (PSC; Baum & Donoghue, 1995; Wheeler & Meier, 2000) and evolutionary species concepts place emphasis on the historical pattern of relationships resulting in distinct entities through a history of descent from a common ancestor. The application of the PSC, in common with the BSC, is beset by an array of practical problems. It has also been argued that any general attempt to replace traditional species units (morphological or biological) with the PSC would be prohibitively costly and would serve to delay progress in pure and applied biology. Nonetheless, the use of phylogenetic approaches to delimiting ‘species’ is rapidly gaining ground, so we need to know whether choice of adherence to traditional or phylogenetic species concepts really matters. As judged by an analysis of the relative numbers and boundaries of entities recognized empirically under each concept, it does (Agapow et al., 2004). In a literature survey of a broad variety of vertebrate and invertebrate groups, the re-analysis of non-PSC-based species using PSC-based criteria increased the number of species by 49 per cent and the average number of species within a group by 121 per cent. Discrepancies of this nature could significantly influence decisions about conservation prioritization if the geographical boundaries of biological species differ from those of phylogenetic species (Peterson & NavarroSiguenza, 1999; Agapow et al., 2004), although some provisional evidence suggests that these discrepancies need not lead to drastic alterations in considerations of protected areas and patterns of endemism (Fjeldså, 2000; Dillon & Fjeldså, 2005). While these issues need

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further analysis across a broader range of taxa and geographical regions, the practical offshoots of a PSC perspective have taken root in several tangible forms, which we will discuss next.

4.3.2 Evolutionarily Significant Units (ESUs) The most frequently cited molecular-based units that sidestep the task of formally naming new species are called Evolutionarily Significant Units, or ESUs. The concept of an ESU originated with Ryder (1986), but has since been developed variously by Waples (1991), Moritz (1994), and Crandall et al. (2000). The standard way to identify ESUs is through analysis of mitochondrial DNA (mtDNA) in animals or chloroplast DNA in plants (cpDNA). This is because these two organelle molecules, being maternally inherited, evolve very rapidly and therefore are more likely than nuclear genes to reflect the history of long-term population divergence through isolation. Moritz (1994) offered the definition that an ESU should be reciprocally monophyletic for mtDNA alleles and show significant divergence of allele frequencies at nuclear loci. This is an attempt to delineate the ‘major historical units’ that arise from long-term geographical isolation and evolutionary divergence (Avise, 2005). Such ‘natural groups’ may be morphologically cryptic but hold within them not only the imprint of their past history, but the possibility of differing responses to future environmental change. Whether or not they satisfy the criteria for the application of the biological species concept, or merely represent sub-specific variation, these ESUs may warrant individual conservation attention. Alternative definitions have attempted to align ESUs with the ‘Distinct Population Segments’ (DPS) amendment to the 1973 Endangered Species Act in the USA (Waples, 1991), with incorporation of a broader recognition of evolutionary processes (Crandall et al., 2000) or with the desirable goal of achieving genealogical concordance across multiple genes (Avise, 2005). Regardless of definition, the underlying power of the ESU concept – with its reliance on molecular data and the ability to ‘do’ biogeography and conservation without the necessity of formally naming new species – appears to remain intact. 420 hits were returned from a query using ‘Evolutionar* Significant Unit*’ in a recent (23 October 2010) topic search of the Web of

Science from 1990 to 2010, with 47 per cent of those records falling between 2006 and 2010, suggesting an ongoing acceleration in the use of ESUs. ESUs represent something of a conundrum for those projects that seek to fully enumerate more traditional and intuitive conservation units (i.e. species) as a baseline unit of biodiversity (e.g. the Catalogue of Life, Species 2000, Encyclopedia of Life; Table 4.1), in the sense that they do not coincide perfectly with either a species or subspecies as a formal taxonomic category (e.g. Zink, 2004) and therefore are not fully accounted for in taxonomic lists. Furthermore, discrepancies between species and ESUs could lead to very different assessments of local and regional biodiversity, which might lead to different inferences in biogeographical and ecological analyses (see Riddle & Hafner, 1999; Kelt & Brown, 2000). Under the Moritz (1994) definition, the discrepancy between ESU diversity and species diversity can be estimated and appears to be considerable, but not overwhelming. In a survey of vertebrate taxonomic species (Avise & Walker, 1999), 56 per cent contained at least two, but less than seven, lineages that could be considered as separate ESUs; Riddle and Hafner (1999) estimated an average of 2.7 ESUs per species in desert rodents from western North America; and Zink (2004) estimated 1.9 ESUs per avian species. Some have argued that ESUs delineated using the Moritz definition should qualify for recognition as distinct species under the PSC (Vogler & DeSalle, 1994). Most often, however, practitioners are properly hesitant to name new species formally based on a singlegene or -genome (most commonly mtDNA in animals and cpDNA in plants) assay of geographic, phylogenetic, and population genetic variation because a single gene tree will often not be entirely congruent with the species tree (Avise, 2005; Edwards, 2009). In other words, a single mtDNA gene tree, even if resulting in several reciprocally monophyletic lineages (i.e. ESUs), is not always going to reflect the same phylogeny for different genes drawn from the nuclear genome. At the same time, biogeographers and conservation biologists do recognize the great operational utility of mtDNA or cpDNA assays as a powerful ‘first approximation’ of evolutionary and biogeographical pattern and processes (Zink & Barrowclough, 2008). They have indeed been used extensively to assay patterns of biodiversity, to postulate associated historical processes and to develop conservation prescriptions.

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Moritz (1994) also recognized that his definition of ESU may well be too stringent to allow the recognition of some important variation and, for this reason, also proposed a lower level for conservation application, known as the Management Unit (MU). Management units are populations which may not show reciprocal monophyly for mtDNA alleles (and by extension, presumably cpDNA in plants), yet which have diverged in allele frequencies at nuclear or organelle DNA loci and are therefore worthy of some level of monitoring or protection. MUs are significant for conservation in that they represent populations connected by such low levels of gene flow that they are functionally independent.

The most promising aspect of DNA barcoding is that it represents a true union between the goals of systematists, biogeographers, ecologists and an array of other constituencies, including public health organizations. For purposes of conservation biogeography, DNA barcoding will likely have its greatest impact by filling in some of the details of species diversity in lesserknown and morphologically cryptic taxa, and of the distributions of taxa, thereby reducing the magnitude of both Linnean and Wallacean shortfalls.

4.3.3 Other conservation units

4.4.1 Mapping species individually and collectively

One of the latest additions to the units of conservation debate is the suggestion that for large, complex animals such as mammals and birds, conservationists should try to identify culturally distinct population segments, especially if the populations are small or endangered. Such Culturally Significant Units (CSUs) could be vital to future survival if the behaviour confers a distinct adaptive advantage or confers greater adaptability on the population (Ryan, 2006). A good example is the different cultures of tool use seen in wild populations of chimpanzees (Pan troglodytes) in west Africa (Whiten & Boesch, 2001). CSUs are the cultural equivalent of ESUs, in that they also require some form of population isolation although, in contrast to ESUs, significant levels of genetic divergence are not implied. Finally, yet another tool conceptually affiliated to the PSC and phylogeography, and which may speed up identification of new ‘species’, is DNA barcoding: the use of short, unique sections of the genetic profile of a species sample for the purposes of identification, rather like the barcode on your groceries (Hebert et al., 2003). DNA barcoding relies on sequencing a standardized segment of DNA – originally and still most often a portion of the mitochondrial cytochrome c oxidase subunit 1 (COI) in animals and plants, or the nuclear ribosomal internal transcribed spacer region (ITS) in fungi – and comparing that sequence against a database of many thousands of homologous (see Glossary) sequences to determine if it represents a previously unknown species or one that is already registered in the database (http://barcoding.si.edu/; http://www. boldsystems.org).

4. 4 S PAT I AL DI S T R I B U T I ON S : FR OM GEN ES T O B I OGEOGR APH I CAL R EGI ON S

The distributions of species are often represented by what are sometimes termed range-filling maps, i.e. coarsely drawn envelopes encompassing the known outer limits of the species’ distribution, within which ranges are represented as solid entities. In poorly surveyed areas of the world, such maps may involve extrapolations based on general knowledge of the habitat in which the species is known to be found. These maps do not provide a sound basis for conservation planning purposes. The generally recognized protocol for mapping species, however, is to use some form of grid-cell system, preferably using equal areas and typically using grid cells of 10 × 10 or 50 × 50 km or 0.5 degrees (latitude/longitude). The species is recognized as present in the cell only when established to be present by direct observation, ideally backed by voucher specimens collected and stored in herbaria/museums. When species ranges are mapped in this way, it becomes evident that species’ distributions are discontinuous (as exemplified in Figures 4.2 and 4.4). This may be more apparent for some taxa than for others, but it actually applies to all species if distributions are recorded and mapped on a fine enough scale of analysis, although some highly endangered species have such small ranges that they may be considered to consist of a single population and provide a single dot on the map. Given that environments are patchy, it is, of course, unremarkable that species’ distributions should also be patchy. So, for example, in the UK a plant adapted to

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Figure 4.6 The distributions of two plant species within England and Wales. (a) Vulpia unilateralis and (b) Pulmonaria longifolia, demonstrating that assessment of the range size of the two species for the same geographical extent is dependent on the size of the grid used in analysis (i.e. the focal scale). Contrast the number of dots (representing the underlying grain of the data) with the number of 100 km2 cells occupied. (c) The contrasting aggregation patterns of the two species are reflected in the slopes of the relationship between focal scale of analysis (sensu Box 1.2) and estimated range. Triangles and dashed lines for V. unilateralis, circles and solid lines for P. longifolia. From Kunin, W.E. (1998) Extrapolating species abundance across spatial scales. Science, 281, 1513–1515. Reprinted with permission from AAAS.

alkaline environments (a calcicole) would typically be found on scattered limestone outcrops rather than on the acidic soils surrounding them, while birds dependent on reed beds are again likely to have a patchy distribution, restricted to these relatively rare habitats. With further reflection, it is also unsurprising that different species are likely to exhibit different scales of patchiness, reflecting their own individual niche requirements and how they are distributed across landscapes and also reflecting the scale at which the species concerned interacts with the environment. So for example, individual snails explore less geographical space in their lifetime than large raptors or ungulates, but even within a single taxon (e.g. butterflies, or birds) there can be striking differences in the mobility or home range of different species. The upshot of these two properties – the discontinuous nature of ranges and the varying scales of pattern

of individual species – is that assessments of the range occupancy of the species found in any particular region of the world are scale dependent. This is neatly illustrated in Fig. 4.6, which shows that while two plant species, Vulpia unilateralis (panel a) and Pulmonaria longifolia (panel b), have the same range size in England and Wales when judged using 100 km2 grids, but when judged at a finer scale of analysis, Pulmonaria is shown to have a significantly larger range, while at the coarsest scale plotted the reverse occurs (panel c). The figure thus illustrates two things: first, the smaller the grid size used, the smaller is the estimate of the range size; and second, that the relative order of this measure of rarity can change as a function of the scale of analysis. The reason for this is that V. unilateralis has a generally scattered distribution while P. longifolia has a tightly constrained distribution within the south of

The distribution of diversity: challenges and applications England (Kunin, 1998). Extrapolating the scale/area curves suggests that P. longifolia may be very much more common than V. unilateralis at still finer scales, although it is dangerous to attempt to infer the size and viability of the populations of either species from such range map data. As range size is a key criterion in determining whether a species should be considered endangered (Box 4.1), especially where other data are lacking, the

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scale-dependency of range size estimates is a potentially important issue in making these assessments and in comparing data from different regions of the world (for further discussion of scale dependency see Box 1.2; Rahbek & Graves, 2000; Lennon et al., 2001; Whittaker et al., 2001; 2005). As we have repeatedly emphasized in this chapter, knowledge of the geographical distribution is fundamental in making sense of the ecological requirements,

Box 4.1 Rarity, range restriction and the Red List Rarity is often a precursor to extinction. However, not all rare species are rare for the same reasons. Rarity can mean that a species occurs at low densities, is adapted to a narrow range of environmental conditions, or occupies a small geographical range. These three categories (abundance, habitat breadth and geographical range) form the basis of Deborah Rabinowitz’s widely used categorization of rarity (Rabinowitz, 1981). Under this simple classification scheme, seven types of rarity can be recognized (Figure B4.1a), with the most vulnerable category being species that have low density, low population size and which utilize a narrow range of habitats. It should be noted that this is not the only scheme for defining rarity (e.g. Manne & Pimm, 2001), but it is one of the most frequently applied. Species are by no means evenly spread across the eight categories shown in Figure B4.1a. For example, it has long been known that while a high proportion of species have relatively small geographical ranges, there are few that are widespread and abundant. However, Brown (1995) has shown graphically that within taxonomically or ecologically similar species there tends to be a positive correlation between range size and density. One consequence of this is that species occupying large ranges tend to be more abundant throughout those ranges than are range-restricted species. When examining whole faunas, such as the Breeding Bird Survey data for North American land

Figure B4.1a Rabinowitz’s seven forms of rarity. Species in the (eighth) upper left cube at the front exhibit no component of rarity, being common across a large geographic range and possessing a wide habitat breadth. Those at the lower back right have all three components of rarity: small geographic range, narrow habitat breadth and low local density. Taken from Ricklefs (2000).

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birds, one finds that a lot more scatter is evident than within similar groups of species, but still the condition of ‘abundant and localized’ is extremely rare, at least within continental avifaunas (Gaston, 1994; Brown, 1995). Perhaps the more important question from the perspective of conservation biogeography is what constitutes a small range and how might the range size/abundance pattern vary in relation to biogeographical context (e.g. continental versus island archipelago contexts) or for taxa of differing body sizes? As previously noted by Whittaker et al. (2005), within the North American breeding bird data set analysed by Brown (1995), range size varied from c. 10,000 km2 to over 10 million km2 and fewer than 10 species had ranges <100,000 km2. In contrast, the land area of the Canary Islands is only approximately 7,500 km2 and that of the Hawaiian archipelago is 16,640 km2. Assuming that the typical pattern of many species with small ranges exists within these essentially self-contained biogeographical areas or provinces, then many native species have tiny ranges, and even the most widespread and/or abundant species on these islands will have smaller ranges than the ‘localized, low density’ rarities of the North American data set. The pragmatic solution used by conservationists has been to define range-restriction – sometimes called ‘local endemic’ species – using an arbitrary threshold of <50,000 km2 (from Terborgh & Winter, 1983). However, if this were to be universally applied, the entire endemic biota of island archipelagos such as Hawaii, the Galapagos and the Canaries would be classed as of conservation concern – which we hope would be unduly pessimistic (e.g. Martín, 2009; for fuller discussion of island conservation issues, see Whittaker & Fernández-Palacios, 2007). The 50,000 km2 threshold has been widely adopted in conservation prioritization analyses (e.g. Long et al., 1996; Rodrigues et al., 2004b), and for particular taxa and contexts it is a reasonable approximation (e.g. for freshwater fish species in North America: Rosenfield, 2002). Range restriction is also an integral part of the criteria used by the IUCN to identify and classify species in danger of global extinction – known as the IUCN Red List (version 2.3; www.redlist.org). The Red List has nine categories, ranging from ‘least concern’ to ‘extinct’ (Figure B4.1b). From a conservation perspective the priority species for actions and interventions are those classified as ‘vulnerable’, ‘endangered’ or ‘critically endangered’. Interestingly, attribution to these latter categories can default to range size if other data are lacking, in which case, the thresholds used are 100 km2 (extent of occurrence) for critically endangered, 5,000 km2 for endangered and 20,000 km2 for vulnerable. This three-point scale recognizes that, on average, threat increases incrementally as range size reduces. For conservation prioritization schemes that apply a single range size (whether 50,000 km2 or another), it would appear valuable to undertake analyses for different taxa and biogeographical contexts of the sensitivity of prioritization analyses to the adoption of that threshold (Williams et al., 2000). Moreover, to refine the meaning of ‘range-restricted’ for conservation prioritization, we need more empirical data both on range sizes and on how to translate range size distributions into

Figure B4.1b Structure of the IUCN Red List. Re-drawn from www.redlist.org/ version 2.3.

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population estimates across a large sample of species for different contexts, e.g. mainland versus island systems (Martín, 2009). The consequences of insufficient data on geographical distributions (the Wallacean shortfall) for conservation prioritization is illustrated by the case of the Tanimbar corella (Cacatua goffini), a parrot species endemic to the Tanimbar islands (Indonesia), a group of 66 islands of a total land area of about 5,400 km2. This species was initially listed as ‘threatened’ in 1989 on the basis of small global range and concern that it was being traded at a possibly unsustainable rate. However, a field survey on the largest island, Yamdena (3,250 km2), subsequently produced a population estimate of 231,500 (±33,000) for that island alone, suggesting that the initial categorization had been unwarranted (Jepson et al., 2001a). There may also be some room for debate over whether the Tanimbar corella represents a good species or a sub-species. This aside, that an island species with a global range one-tenth the size of 50,000 km2 can have a healthy population size seems good news for conservation. Of course, this may be a misleading example; it must be balanced against the knowledge that, for example, about half of Hawaii’s indigenous bird species have gone extinct since human colonization, and that population data indicate many island species to be seriously threatened (Whittaker & Fernández-Palacios, 2007).

past history, and potential future of plant and animal species. The geographical distribution of any given species is constrained by the fundamental niche requirements of the species. These often are evident in relationships with climatic variables such as (for plants) mean annual temperature, growing degree days, annual rainfall, or the seasonality of water and energy regimes, but they may also be reflected in particular edaphic or other habitat variables. Distributions are constrained within this fundamental or potential niche space by biotic interactions with competitor species, pests, pathogens, predators and mutualists, such that the realized niche is always a sub-space of the fundamental niche. In addition, distributions are constrained by dispersal limitations and past history of the geographical connectivity of their niche space. We know this to be so from fossil records that show the presence of species at some past time in an area in which present climate is suitable for their growth and persistence, but from which the species has been lost at some point in the past due to past climate change and from the numerous cases whereby humans have introduced species into areas outside their natural range, only for them to become troublesome invasives (Chapter 9). Hence, the natural geographical ranges of species are constrained within their potential ranges by: 1 biotic interactions; 2 the vagaries of history; 3 linked with 2, the difficulties of dispersing viable propagules to all areas possessing appropriate conditions for their establishment.

For conservation purposes, this somewhat theoretical discussion has considerable practical significance. First, scientists in areas of the world that have been poorly surveyed for particular taxa may be interested in modelling what the current distribution of species may be, in order to guide further survey work and to contribute to analyses of the possible failures of systematic conservation planning based upon the known ranges of species. An example of this approach was given earlier in this chapter (Figures 4.2, 4.3). In that example, Hopkins (2007) used the known distributions to project the possible unknown ranges of species, and by overlaying maps for 1,584 species was able to generate maps of possible species diversity and of sampling deficit. In general, the more solid data we have on the presence and absence of a species across the range, the easier it should be to identify those environmental variables that best represent the factors controlling the distribution, although for species of particularly small ranges this becomes particularly difficult to be sure of. This approach, because of its reliance on climate data, is termed (bio)climatic envelope modelling (BEMs or CEMs; Pearson & Dawson, 2003), or sometimes ecological niche modelling. It is a growing field with an increasing array of computer programs developed for the purpose (see further discussion in Chapter 7). BEMs seek to establish statistical relationships between the environmental variables and distribution of a species. Testing of models is generally undertaken by reserving a portion (typically 30 per cent) of the

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cells in the data set when generating the model, and then testing the ability of the model to predict the species distribution for the test subset. Scientists use this same modelling approach to generate projections not only of current distributions, but of future distributions as a function of environmental (and especially climate change). As Whitfield (2009) recently commented, ‘Such models are among the main tools in efforts to predict and plan for the biological effects of climate change. And because their predictions can be displayed as intuitive and dramatic maps, they have a psychological power beyond most scientific graphics.’ Modelling the dispersal capacity of the species and its ability to keep up with climate change or to ‘jump disperse’ between widely separated cells of suitable climate is a particularly difficult challenge. It is one that has typically been either ignored or addressed by contrasting the effect on the modelled outcome of no dispersal, on the one hand, versus free dispersal on the other. The use of BEMs in the context of climate change is of particular interest and will be discussed further in Chapter 9. Here we give just two examples that illustrate two key pitfalls of these approaches. The first pitfall is that data of dubious validity can still lead to good models, and the second is that even good model fits are no guarantee of time-transferability of models. Lozier et al. (2009) provide an analysis for a cryptozoogeographical taxon, the North American bigfoot or sasquatch. This species is so cryptic that its status belongs more to the realm of myth and legend rather than to the Linnean and Wallacean shortfalls. However, there are numerous claims of sightings or footprints (Figure 4.7), from which Lozier and colleagues generated a well-fitted BEM using a commonly used package (known as MAXENT) to represent the present-day distribution. They then ran the model with a simulated climate model (based on a doubled CO2 scenario) to project how bigfoot distribution might change in the future (Figure 4.8). Interestingly, when they ran a model for black bear (Ursus americanus) calibrated from the same region from which the bigfoot sightings were recorded, the two models for the contemporary distribution of the two species were remarkably similar. This would support the supposition that many sightings attributed to bigfoot were actually black bears. The serious point made by the authors is that the model outputs, however well they may fit the data, can only be as good as the input data – or, to put it another way, there is a danger of a ‘garbage in, garbage out’ relationship.

Figure 4.7 Map of claimed distributions of the cryptozoogeographical entity known as bigfoot (sasquatch), based on ‘encounters’ from Washington, Oregon and California. Points represent visual/auditory detection and foot symbols represent coordinates where footprint data were available. Shading indicates topography, with lighter values representing lower elevations. From Lozier et al. (2009, their Figure 1).

Our second example made use of excellent survey data for a well-known group: British birds. In this study, Araújo et al. (2005b) used four different modelling methods combined with a number of different parameterizations, providing 16 alternative models for each species, to simulate the distributions of 116 breeding bird species for two periods. Despite the fact that they had generated mostly adequate statistical models for the period 1967–1972, when these ‘time 1’ models were used to generate predictions for the distributions of the same species using the climate data for

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Figure 4.8 Predicted distributions of the cryptozoogeographical entity known as bigfoot (sasquatch) constructed from all available encounter data using the modelling method MAXENT (a) for the present climate and (b) under a possible climatechange scenario involving a doubling of atmospheric CO2 levels. Results are presented for logistic probabilities of occurrence ranging continuously from low (white) to high (black). Differences between (a) and (b) are shown in (c), with whiter values reflecting a decline in probability of occurrence under climate change, darker values reflecting a gain, and grey reflecting no change. A predicted distribution of the bear Ursus americanus in western North America under a present-day climate is also shown (d). White points indicate sampling localities in California, Oregon and Washington (n = 113 for training, 28 for testing; compare with Figure 4.7) used for the MAXENT model with shading as in (a) and (b); black points indicate additional known records not included in the model. From Lozier et al. (2009, their Figure 2).

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‘time 2’, the period 1987–1991, the models performed poorly. If judged by the crude criterion of whether they correctly predicted the expansion or contraction of the species range for the second time period, the average performance of the models was about 50 per cent, i.e. they were as successful as simply tossing a coin. The authors were able to offer some more positive findings in pointing to means of rejecting less likely models in favour of ‘consensus’ findings, increasing the predictive success of the exercise to 75 per cent or higher, but the general point to take from this study is that even with good data and seemingly well-fitting models, the ability to predict distributions for a later time period was patchy. Making such predictions using simulated future climates (as in the bigfoot example, or as carried out by Thomas et al., 2004), involves even more uncertainty (see Chapter 7, Box 7.3). Individual species range maps are, of course, the basis for generating maps of overall species richness, or of concentrations of subgroups such as rangerestricted, endemic, or endangered species. And once again, therefore, the quality of such maps depends on the underlying species range maps being systematic and complete – which is simply not the case for many taxa and regions. While the two studies we have just examined show the danger of over-reliance on BEMs, the earlier study by Hopkins (2007) also demonstrates the potential of using distributional modelling approaches to generate predictive richness maps, which at least serve to form hypotheses of sampling artefacts that can be tested by future field research. The problem of generating predictive maps of species diversity for poorly known regions can also be addressed by developing more mechanistic or theoretically based models of how species richness responds to climate, as opposed to modelling each species individually. An example of such a model is provided for woody plants globally by Field et al. (2005), although this form of species richness modelling is as yet too crude to have practical application in conservation biogeography. A final alternative approach is exemplified by a global map of plant richness developed in a series of reiterative approximations by Kier et al. (2005). In their study, they estimated richness for 867 terrestrial ecoregions (WWF-ecoregions: see Chapter 5) using published data for some 1,800 geographical units, in essence using regional inventories and curve-fitting exercises based on species–area relationships to develop their map. A welcome part of their study was an

attempt to quantify data quality and thus to identify regions for which the map was of dubious reliability. Two key conclusions of their analysis were: first, that flooded grasslands and flooded savannas were generally poorly known and should become a global priority in collecting and compiling richness data for vascular plants; and second, that future studies that rely upon species–area calculations should not rely upon a single parameter value for the slope (i.e. z = 0.25; see discussion in Chapter 8), but should instead use values estimated empirically for different regions and system types. Their analysis demonstrates how independently derived diversity data sets and maps can be generated for comparison with existing strategic conservation planning frameworks and can be used to identify poorly represented areas for future conservation action.

4.4.2 Phylogeography John Avise, the father of phylogeography, has recently expressed the opinion that, ‘… the prospects for significant phylogenetic input into decisions of conservation priority are somewhat greater at the intraspecific level than they are for species clades and higher taxa’ (Avise, 2005: p. 93). His contention is based on three observations: 1 that phylogeographical studies can target already recognized high priority species; 2 that molecular phylogeographical data alone can provide novel insights into the historical sources of diversity within species; 3 that because often the majority of intraspecific diversity is derived through historical biogeographical processes, an informed depiction of conservation units can only be built on a phylogeographical platform. The primary utility of phylogeography for conservation biogeography rests on the ‘principles of genealogical concordance’ (Avise, 2000), including: 1 congruence across independent gene trees in delineating the geographical position of a major phylogeographical break between sets of populations; 2 congruence between phylogeographical breaks and postulated historical barriers to gene flow; 3 congruence in locations and depths of phylogeographical breaks across multiple co-distributed species. Each of these recovered patterns – particularly the last two – will strengthen the link between ESUs and historical biogeographical processes. For example, phylogeographical approaches frequently recover evidence

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Figure 4.9 A summary of proposed regional boundaries, barriers, and areas of occurrence based on phylogeographical analyses across a cadre of 22 arid-adapted taxa in the warm deserts of western North America (Riddle & Hafner, 2006; Hafner & Riddle, in press). Light shading incorporates desert boundaries based on Hafner & Riddle (in press); dark shading represent the desert boundaries based on earlier work by F. Shreve (see Hafner & Riddle (in press) for source references).

for the past geographical restriction of species into two or more separate Pleistocene glacial-age refugia – a history commonly postulated to have generated distinct ESUs within a species or group of closely-related species. The third principle can also provide valuable insight into the locations of biodiversity hotspots and thus a basis for establishing protected areas, as will be discussed in Chapter 5. In order to illustrate the power of a phylogeographical framework for delineating biodiversity and potential generative processes, we can summarize what we have learned from the phylogeographical structure of 22 co-distributed clades of animals and plants in the warm deserts of western North America (Figure 4.9; Riddle & Hafner, 1999, 2006; Hafner & Riddle, in press). One of the most revealing results of interest to biodiversity conservation is that congruent phylogeographical breaks across multiple ESUs provide strong evidence for an evolutionary history of a Baja California

Peninsula Desert biota (historically and still frequently considered a subset of the Sonoran Desert based on gross floristic similarities) that is separate from the continental warm deserts (Sonoran, Mojave, and Chihuahuan). More precisely, the southern portion of the peninsula appears to have served as an important centre of diversification over the past several million years, perhaps as a response to one or more past seaways spanning the peninsula, connecting the Pacific Ocean with the Gulf of California and thus forming a dispersal barrier for terrestrial taxa (Riddle et al., 2000). Given the relatively intact nature of desert ecosystems on the peninsula today, these results have important implications for locating protected areas designed to maximize representation of the rich biodiversity that is unique to the region, and which evolved in concert with the evolution of North American warm deserts.

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Generally, phylogeographical analyses are producing novel and important insights into the distribution of biodiversity hotspots across the Earth and the role of biogeographical histories in producing them, in terrestrial systems such as Australian rain forests (Moritz et al., 2005), Brazilian Atlantic forest (Carnaval et al., 2009), Amazonian rain forest (Moritz et al., 2000), the South African Cape region (Tolley et al., 2009) and marine realms (Rocha et al., 2007). This supports Avise’s contention that the elucidation of intraspecific patterns of biodiversity will continue to become increasingly important in supplying units for conservation decision-makers.

4.4.3 Endemism The term ‘endemic’ implies that for a given area, a species (or other taxonomic entity) is naturally confined only to that area. Although some argue for its application to a particular scale of analysis, most accept that the term can be applied on any scale to any size of region. Hence, a species could be endemic to northern parts of America and Eurasia (thus straddling two continents), or could be restricted to an island region such as Macaronesia, or to the Canaries (a constituent archipelago), a single island such as Tenerife, or to a particular massif within Tenerife. It follows that endemism can be summed for regions of varying extent, often in a nested fashion (Table 4.5). Indeed, the division of the world into biogeographical

regions (below) is, in essence, an application of the same concept. Within conservation biogeography, scientists are commonly interested in identifying areas that possess exceptional concentrations of species richness and endemism (also known as centres of richness and endemism – CORE areas) for conservation prioritization, with most emphasis being given to endemics. In this sense, endemism has agency. In short, the possession of one or more endemic species, signifying something unique, can make a big political difference to resource allocation and prioritization efforts. This can work at a coarse scale with many endemics, or at a very local scale with one or two. For example, the Conservation International (CI) hotspots scheme (Myers et al., 2000) is based on two criteria: identifying areas of the world possessing more than 0.5 per cent of the world’s plant species as endemics (assuming a global total of 300,000 species); and areas that have lost more than 70 per cent of their natural vegetation. Their approach of focusing resources within hotspots (a so-called ‘silver bullet’ strategy) sold well in the biodiversity funding marketplace and attracted huge levels of donations to their organization (Chapter 5; Myers, 2003). At a finer scale, Mace (2004) suggests that some decisions regarding whether a taxonomic group warrants full species or merely sub-species/variety status can be influenced by the perceived need to maintain or enhance the level of protection it receives. For example, Karl & Bowen (1999) describe how the black

Table 4.5 Degree of species endemism among tropical Pacific and Indian Ocean island faunas (from Whittaker & Fernández-Palacios, 2007– their Table 3.4, based on studies by G.H. Adler and colleagues). Group

Total number

Continental*

Regional endemics

Local endemics

Pacific Ocean butterflies Pacific Ocean skinks Pacific Ocean birds Pacific Ocean mammals Indian Ocean birds

285 100 592 106 139

157 21 145 42 60

28 13 59 7 10

100 66 388 57 69

(55%) (21%) (25%) (40%) (43%)

(10%) (13%) (10%) (6%) (7%)

(35%) (66%) (65%) (54%) (50%)

* Continental: species also occurring on continental land masses. Regional endemics: species occurring on more than one archipelago within the region but not on continents or elsewhere. Local endemics: species restricted to a single archipelago or island. How high a proportion of an island fauna can be considered endemic depends on the level at which endemism is defined – a single island, an archipelago, an island region, etc.

The distribution of diversity: challenges and applications turtle (Chelonia agassizii) maintained its status and level of protection as a full species despite being indistinguishable from the green turtle (C. mydas) at a genetic level. While the CI hotspots scheme applies an extremely course scale and rather crude approach to analysing endemism, most other schemes focus on identifying target areas involving species of restricted range – sometimes termed local endemics. If working on oceanic islands, it may be fairly straightforward to apply the criterion of endemism to individual islands, archipelagos or groups of archipelagos (as Table 4.5), but for comparative analyses across large land masses, the choice of areas is not so straightforward. The range size used to delimit range-restricted species is an essentially subjective choice but, following Terborgh & Winter (1983), many studies, especially for birds, have adopted a range of 50,000 km2 (Box 4.1; and see brief discussion in Whittaker et al., 2005). The identification of areas with notable levels of endemism then becomes a question of identifying areas in which a given number of range-restricted species are wholly confined. BirdLife International’s Endemic Bird Areas scheme is an example of such an approach, in which EBAs are delimited based on the possession of as few as two range-restricted species (Chapter 5). The concept of endemism as employed by conservation groups such as CI needs to be differentiated from the formal identification of areas of endemism in historical biogeography, which are units of analysis used in a range of phylogenetically-based approaches to reconstructing the historical association of Earth history and biotic diversification. Like any other area of biogeography, there are various approaches to the identification and delineation of areas of endemism (see Box 12.1 in Lomolino et al., 2010). An example is provided by da Silva et al.’s (2004) analysis of endemism in passerine birds in the Atlantic forests of Brazil, a well-defined biogeographical unit of considerable conservation concern due to its high endemism and extreme levels of habitat loss. Their analysis was based on subdividing the region into one-degree latitude/longitude grid cells, to form the operational geographical units (OGUs) of the study. They then used parsimony analysis of endemism (a much-debated method in historical biogeography, but less contentious when its use is restricted to delineating potential areas of endemism; see Riddle and Hafner (2006) for another example) to produce a ‘consensus tree’ classification, from which areas of endemism

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were delimited as OGUs or groups of OGUs possessing at least two endemic species. These cells or groups of cells were then mapped to delineate the boundaries of each area of endemism. Their analysis identified four areas of endemism (two subsequently combined into a single area) of respectively nine, two, one and one cells. Their analysis has the benefit of using a pre-selected method to determine the areas objectively, but for reasons of data limitations it was based on only 24 grid cells. Also, although they report that their results are congruent with distributional patterns in other groups of organisms, it is notable for present purposes that their three areas of endemism differ from those previously identified in three earlier studies of areas of endemism for birds for this region (including one generated by BirdLife International), both in terms of the number of areas of endemism identified and in details of the boundaries of those in common. These results suggest that the data and methods selected for identifying and mapping areas of endemism can have important effects on the resulting outputs, and thus on the biogeographical science being fed into strategic conservation planning for the region. Nevertheless, their three final areas are remarkably congruent to those identified for tree frogs using a combined BEM and phylogeographical approach (Carnaval et al., 2009).

4.4.4 Biogeographical regions Patterns of endemism have long been used in one of the most fundamental approaches to summarizing biogeographical patterns across the Earth. The observation that different regions of the globe were characterized by distinct assemblages of animal and plant species was recognized long before Darwin’s famous theory of evolution. The foundations for a theory of biogeographical regions were laid by Comte de Buffon’s observation in 1761 that Old World and New World tropical regions contained different kinds of large mammals. By 1838, Augustin de Candolle had delineated 40 different biogeographical regions based on plant distributions. By 1858, Philip L. Sclater had delineated six terrestrial zoogeographical regions based on the global bird fauna, and in 1876 Wallace had expanded Sclater’s canonical scheme to incorporate mammals and other animals. Interestingly, at the same time, Wallace also

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outlined 21 continental subregions that were similar to those constructed by de Candolle based solely on plants some 56 years earlier. Biogeographical regions have traditionally been drawn up separately for animals and plants (Figure 4.10). The zoogeographical regions of Sclater and Wallace have largely survived into modern times, and differ somewhat from those for plants – the phytogeographical kingdoms/regions (Good, 1947–1974; Takhtajan, 1986) – for reasons summarized by Cox (2001). One of the first phytogeographical schemes was Ronald Good’s Floristic Kingdoms, based on coincidence of distribution of unrelated taxa of flowering plants. Good identified six separate ‘kingdoms’ and various sub-kingdoms, provinces and, finally, floristic regions. The remarkable fynbos flora in South Africa merited a kingdom of its own. While much of the terminology and the basic notions have survived from the early attempts at geographically sub-dividing the globe, recent approaches to revising and updating depictions of regions have incorporated more than a century of additional data on distributions and taxonomy but, more importantly, have emphasized different criteria for revising regional boundaries (Figure 4.10). Cox (2001) argued for more consistent apportioning of numbers of endemic taxa to delineate a more equitable set of kingdoms and regions, maintained separate zoogeographical and phytogeographical regions, and also argued for removal of the ‘Wallacean’ zone of islands lying between Southeast Asia and Australasia from any of the regions, owing to its extreme biogeographical complexity. Alternatively, Morrone (2002) used a panbiogeographical approach: first, to argue for a unified animal and plant set of regions; and second, to base those regions on the historical evolutionary affinities associated with tectonic fragmentation of the Laurasian and Gondwanan land masses into modern continents and major islands. The most significant innovation from Morrone’s approach was to divide the South American and Australian continents into two separate regions – one with historical affinities to an eastern Gondwana biota, and the other with affinities to a western Gondwana biota. However, the most recent global analysis of zoogeographical regions, by Kreft and Jetz (2010), using modern multivariate classification techniques largely rejects Morrone’s suggestions and in fact essentially reaffirms the classic Wallacean framework, while also providing a quantitative basis for identification of the six main regions and sub-divisions of them.

A number of prominent schemes for strategic conservation planning (e.g. IUCN’s biogeographical regions approach, WWF ecoregions; see Chapter 5) make use of biogeographical regionalization schemes in constructing their analyses, and it is interesting to speculate how adopting the more recently proposed amendments might affect these conservation planning exercises.

4. 5 MAPPI N G FU N CT I ON In Chapter 3 it was stated that, from a functionalist perspective, a central goal of conservation is to restore and maintain ecosystem processes. These processes include: nutrient and hydrological cycles; ecological processes such as pollination; genetic exchange; and, ultimately, evolutionary processes such as adaptation and speciation. Many of these processes, or aspects of them, can be quantified and represented in maps, as can the functional ecological units within which they operate: biomes, ecosystems and communities. The concepts involved here are foundational to modern ecology, and an investigation of the history of these ideas quickly reveals the dualism between functionalism and compositionalism. Many of the key papers discussed below are reproduced with commentary in the excellent volume edited by Real & Brown (1991), to which we refer readers who want to dig a little deeper.

4.5.1 Biomes, ecosystems and communities The ecosystem is one of the most widely used and poorly defined concepts in modern ecology. It has been applied at almost every spatial scale, from the global to the local, and is frequently used as a catch-all term that can mean almost anything from habitat patches to landscapes and geographical regions. The concept itself dates back to a paper by Tansley (1935), who described what he meant by an ecosystem in these terms: ‘… the whole system (in the sense of physics), including not only the organism-complex, but also the whole complex of physical factors forming what we call the environment of the biome – the habitat factors in the widest sense … the basic units of nature on the face of the earth … and there is

The distribution of diversity: challenges and applications

Figure 4.10 A brief history of biogeographical regions. (a) the floral kingdoms (top) based on Good (1947–1974) and Takhtajan (1986), and the zoogeographical regions (bottom) based on Sclater (1858) and Wallace (1876). (b) the floral kingdoms (top) and mammal zoogeographical regions (bottom) as modified by Cox (2001), who emphasized consistency in revising regions. (c) the combined plant and animal biogeographical kingdoms (1–2, Holarctic = Laurasia; 3–6 = Holotropical = eastern Gondwana; 7–12, Austral = western Gondwana) and regions (1, Nearctic; 2, Palaearctic; 3, Neotropical; 4, Afrotropical; 5, Oriental; 6, Australotropical; 7, Andean; 8, Cape or Afrotemperate; 9, Antarctic; 10, Neoguinean; 11, Australotemperate; 12, Neozelandic) as proposed by Morrone (2002), who emphasized evolutionary history and biogeographical affinities in revising regions (but see Kreft & Jetz, 2010). Parts (a) and (b) from Cox (2001); (c) from Morrone (2002).

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constant interchange of the most various kinds within each system, not only between the organisms but between the organic and the inorganic.’ (Tansley, 1935, p. 299) In essence, then, Tansley’s concept is an expression of the interrelationships between organisms and their environment, fundamental to which is the continual transfer of energy and chemicals between the organic and inorganic component parts. This functionalist approach to ecosystems was developed and championed in particular by the American ecologists Eugene and Howard Odum, who focused much of their research in the 1960s on the flow of energy and nutrients through ecosystems. However, Tansley’s article was written as a response to a body of theory concerned with the composition of communities, especially of vegetation communities, in which the ecologist F.E. Clements (e.g. 1916) was the prime mover. Along with many early 20th century ecologists in America and Europe, Clements promoted the idea of ecological communities as natural units, fairly tightly organized assemblages that reoccur in time and space as a function predominantly of climatic controls. This is encapsulated in Clements’s idea of the monoclimax, a single climatically determined solution in the form of a mature vegetation type for each climate type. This dominant phytosociological paradigm was challenged by Gleason (e.g. 1926), who promoted the view of communities being impermanent outcomes of inherently individualistic responses of the constituent species. Tansley was similarly unconvinced by the notion of monoclimax, preferring the notion that there are alternative mature vegetation communities (the idea of the polyclimax) to be found naturally within a single regional climate zone. Today, as outlined in Chapter 3, the Gleasonian view of communities has largely triumphed (Matthews, 1996). Communities are recognized as essentially temporary phenomena, without the tight organizational structure implied by the Clementsian view of communities as quasi-organisms (having properties like organisms) or super-organisms (a stronger form of the same idea, implying tighter integration still). On the other hand, modern statistical analyses also demonstrate plenty of non-randomness to the structure of assemblages, meaning that classification of communities has considerable utility (see below). For practical purposes, therefore, we may recognize the community

as ‘an assemblage of species populations which occur together in a particular habitat (or ecosystem) and which interact with each other’. Returning to Tansley’s description of the ecosystem, it can be seen from the above quote that he framed his remarks in relation to Clements’s earlier term ‘biome’, commenting in a somewhat reserved endorsement that Clements’s usage of this term for ‘the whole complex of organisms inhabiting a given region is unobjectionable…’ (Tansley, 1935, p. 299). He went on to comment (p. 301) that ‘the biome is determined by climate and soil and in its turn reacts, sometimes and to some extent on climate, always on soil.’ Tansley was at pains to emphasize that his concept of the ecosystem was to be applied at any and all scales of analysis, and that while for systems analytical purposes we have to ‘… isolate systems mentally for the purposes of study…’, such a separation is largely artificial, as systems ‘… overlap, interlock and interact with one another…’ (p. 300). Hence, within the conceptualization of ecosystems, it is the case that interlinkages and flows are fundamental, and the separation into distinct units in space is seen as artificial. So, at what scale does the biome emerge as a natural unit? The clues here are given in the emphasis on the regional scale, and on the significance of climate and edaphic controls on the form of the biotic community. We may, therefore, define the biome as a major type of natural vegetation that occurs wherever a particular mix of climatic and edaphic conditions is encountered, or we may equate it with the notion of a major ecosystem type. The latter usage translates rather better into the marine realm than ‘natural vegetation type’. Hence, the fundamental factors influencing the distribution of these major vegetation or major ecosystem types are energy and water regimes (Figure 4.11). Edaphic conditions also play a major role and, especially in sub-tropical regions, interact with fire regimes to generate shifting mosaics of woodland and savanna ecosystems. Biomes are not recognized by the particular species that are dominant or characteristic, but by their physiognomic features. So, we may delimit temperate deciduous woodland, thorn scrub, tundra, etc. (note that different versions of biome schemes use slightly varying subdivisions (e.g. Figures 4.11 and 4.12)). We can therefore recognize the same biome in different parts of the world, even though the biotas involved have little or no directly shared biotic history. Plants and animals have evolved in parallel (convergent evolution) in these different regions to present

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Figure 4.11 The distribution of biomes (major ecosystem types) as a function of water and energy regimes globally. Edaphic factors and fire regimes may regulate shifting patterns between closed canopy and open habitats, especially in relatively dry regions. This version is from Lomolino et al. (2006).

remarkably similar adaptive features in response to the controlling climatic regime. Biome maps thus provide a basic template of major functional types of ecosystem that have been widely used in strategic conservation planning approaches, especially those concerned with ensuring representation of ecosystem types or communities (Chapter 5). However, just as the ecosystem concept has found application at widely different scales of analysis, we should recognize that biomes, although inherently a coarsescale unit, can also be mapped at different scales. In illustration, the Canarian island of Tenerife is a little over 2000 km2 in area and possesses a highest point of some 3,718 m, generating a remarkable degree of climatic variation and giving rise to a suite of major ecosystem types ranging from semi-desert to sub-tropical evergreen woodland, open coniferous woodland and high alpine desert, each of which could reasonably be assigned to different biomes. Yet, text book maps of biomes are often so coarsely drawn that not only do such fine-scale inter-digitations of biome

types typically not appear, neither do the Canary Islands themselves. While biome maps are typically coarsely drawn, the same concept of recognizing major ecosystem types by physiognomic properties has been applied at finer scales, but often with a different label (e.g. vegetation formations, or vegetation formation types). In practice, most fully developed schemes of mapping natural units begin with similar coarse-scale physiognomic classes, but nested within these coarse-scale classes and maps are floristic (i.e. compositionalist) units, subdividing systems into communities recognized by their characteristic or dominant species at finer scales of analysis (Figure 4.13). Two such examples are the UK National Vegetation Classification (NVC) and the USGS – NPS National Vegetation Mapping Program (Rodwell, 1991–2000; Grossman et al., 1998). Although in this chapter we have separated compositionalist and functionalist approaches to identifying and mapping ‘natural’ units, when it comes to developing schemes for practical

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Figure 4.12 Distribution of land biomes. From Lomolino et al. (2006).

Figure 4.13 An example of the US National Vegetation Classification, a hierarchical, physiognomic–floristic classification of vegetation types. From http:// biology.usgs.gov/npsveg/nvcs.html (retrieved January 2004).

conservation and management purposes, the two approaches are typically seen to be complementary and, indeed, both are necessary for the development of a multi-scalar approach. In the UK, the NVC uses communities as the fundamental unit of vegetation classification, recognizing 286 of them in the UK. The data used to derive the classification were obtained from a staggering 35,000 samples, with samples taken from 80 per cent of the 10 × 10 km grid cells on the mainland. Schemes such as this have found widespread application, not only for informing practical conservation decisions, underpinning protected area legislation, and as a tool for ecological impact assessment, but also as baseline data for a wide range of scientific studies. In conclusion, the identification and mapping of ecosystems, biomes and communities continues to be fundamental to conservation planning and is the basis of many national and international conservation

The distribution of diversity: challenges and applications classification schemes. However, the link between community composition and ecosystem function is complex and poorly understood. Furthermore, vegetation classification schemes are often crude and typically dependent to a greater or lesser degree on expert judgement. It is even debatable whether biomes, communities and ecosystems can even be thought of as ‘natural’ units (Chapter 3; Matthews, 1996). To add further complication, different countries, and certainly different regions of the world, typically have pursued their own schemes, differing considerably in method and approach, such that the resulting classifications are not directly comparable. This is a problem

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if, for example, you are keen to develop a standard international approach to designating important sites for conservation (e.g. within the European Union). Finally, we should recognize that, although delimiting and mapping natural physiognomic units is important, these maps no longer represent the ecological reality across much of the Earth’s surface. A coarsescale attempt has recently been made to map the anthropogenic biomes of the world, highlighting the degree to which humans have imposed widespread and fundamental changes in the form and function of ecosystems across vast landscapes (Figure 4.14; Ellis & Ramankutty, 2008).

Figure 4.14 A map of anthropogenic biomes, attempting to show the present day ecological reality rather than the hypothetical or potential ‘natural’ biomes of F.E. Clements and others. Source: Alessa & Chapin (2008), after Ellis & Ramankutty (2008). (See Plate 4.14 for a colour version of this image.)

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4.5.2 Ecoregions If biome schemes are important foundations for several strategic conservation schemes discussed in Chapter 5, they are rather coarse units for landscape level application, while floristic (compositionalist) approaches for identifying natural communities at finer scales of analysis (above) are subject to criticism for artificially imposing a rigid structure on what are typically rather variable assemblages in space and through time (Chapter 3). Moreover, these approaches are rather dependent on prevailing views in phytosociology (vegetation science) (Carrión & Fernández, 2009). The problem, then, is how to classify ecosystems and habitats independently of composition, at fine scales of analysis and for purposes such as land management. It was to this end that ecoregional schemes were developed by R.G. Bailey (e.g. 1996, 1998) and J.M. Omernik (1987), following the coining of the term by Crowley (1967). Omernik (1987, p. 123) defines ecoregions as ‘regions of relative homogeneity in ecological systems or in relationships between organisms and their environments’. The ecoregion approach, as exemplified by Bailey’s cited works, essentially follows a controlling factors methodology, starting at the coarsest (global and continental) scales with climatic parameters (much like Figure 4.11), by which the major Ecoregional Divisions (units of the order of 105 km2) are recognized. It then recognizes major azonal topographical features (mountain chains), before proceeding to successively finer landscape mosaics (103 down to 10 km2) in which edaphics and human transformation influence ecosystem form and function. The approach of focusing on controlling factors is underpinned by the concept that ecosystems do not function independently from one another, but that smaller systems (habitat patches) are connected by ecological flows and linkages, movements of water, nutrients and animals into successively large units. Hence, the approach stresses not just floristic similarity or consistency between sites, but also the importance of internal linkages and exchanges of ‘information’ within the territory of a single ecoregional unit. Each unit at a particular point in the hierarchy is linked by cross-boundary exchanges to other units at the same level, rather as small streams join together to form higher order drainage basins. Hence, the approach provides a multi-layered scheme for classifying regions, for

purposes such as land management and planning, which stresses functional properties of ecosystems in preference to composition. The use of ecoregional schemes in conservation has been growing and it follows that tests of ecoregional schemes that explore the extent to which the boundaries between ecoregional units mark meaningful ecological boundaries are thus of increasing interest (Magnusson, 2004). Magnusson (2004) argued that if distance affects similarity in ecological attributes, any arbitrary division of the landscape will result in objects within a unit being significantly different from objects in other divisions. It follows that rigorous tests of ecoregions have to be undertaken with due attention to artefacts arising from spatial autocorrelation. He suggested testing for natural breaks in species’ distributions across ecoregional boundaries to test how well they reflect divisions in ecological composition. This form of analysis is likely to be informative from a conservation perspective even if it is, in essence, using a compositionalist criterion to judge a scheme focused on functional properties. Just such an analysis was undertaken by Karanth et al. (2006) for birds in North American ecoregions. Supporting the validity of ecoregions, they reported lower species richness, higher local turnover and higher extinction probabilities at the edges of ecoregions, but not for all regions. A less encouraging test was provided by Riitters et al. (2006) who compared ecoregional boundaries (derived both from Omernik’s and Bailey’s work) with a detailed land-cover map of the USA. They report that whereas ecoregions accounted for 65 per cent to 75 per cent of the total variance of per cent agriculture and per cent forest, this is unsurprising because dominant landcover is included in practice in ecoregional definitions within these schemes. By contrast, ecoregions explained only 13 to 34 per cent of the variance of the other seven landscape-level pattern indices they examined. They concluded that the ecoregional stratifications tested were not effective mapping units for land-cover pattern, because within-unit variance of land-cover pattern was typically two to four times larger than between-unit variance. Notwithstanding the equivocal nature of these recent tests, ecoregional schemes have found increasing application in conservation science. The most important such scheme, the WWF-ecoregions approach, is discussed in Chapter 5.

The distribution of diversity: challenges and applications 4. 6 N A T UR AL UNI T S I N T H E MA R I NE R E AL M As on land, both compositionalist and functionalist approaches are used to map marine environments. However, there are important factors of the marine realm that differ from the terrestrial, affecting the distribution of organisms and habitats, limiting our understanding (the Linnean and Wallacean shortfalls are even more pronounced in the sea than on land) and determining the tools that can be used to create biogeographical classifications. Some of these physical, biological and socio-political factors are highlighted in Table 4.6, as they have important implications for marine biogeography and the development of conservation initiatives (see Chapter 5, Section 5.4). The marine realm is more heterogeneous, fluid and obviously interconnected than the terrestrial, and the high density of water enables a fully pelagic lifestyle that is absent on land. Indeed, over 90 per cent of the marine realm’s living space is pelagic and pelagic systems support the majority of marine biomass. Pelagic ecosystems represent a three-dimensional continuum from the surface, epipelagic layer through to the deepest trenches or bathypelagic zones. Water chemistry, salinity, depth/pressure, currents and primary productivity gradients create different regions within these systems. Demersal or benthic ecosystems form a two-dimensional layer from the shoreline to deep sea, where bathymetry (depth) corresponds to elevation within the terrestrial realm and in which different physiographical features such as seamounts, submarine canyons, trenches, hydrothermal vents, volcanoes and hypersaline lakes are embedded. Overlain on the physical underwater landscape are different biogenic habitats, e.g. sea grass, coral reefs, oyster beds, and maerl (maerl beds are formed by certain species of calcified red seaweed). Efforts to undertake biogeographical classification of marine systems have a lengthy history, dating back to work on coral reefs by James D. Dana published in 1848 and on molluscs by S.P. Woodward published in 1856 (both cited in Hedgepeth, 1957). Perhaps the most widely accepted functional classification divides the sea into neritic (continental shelf) and oceanic (beyond the 200 m isobath) zones (Hedgepeth, 1957). Within the neritic zone are four primary biomes: estuarine, coastal marine, demersal shelf and pelagic shelf. Within the oceanic zone are four primary biomes:

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continental shelf, abyssal, epipelagic and meso/ bathypelagic. Regions of open ocean were first demarcated by direction, velocity and persistence of currents (Dietrich, 1963), which provided the basis for the development of a system of domains and divisions of the sea (Bailey, 1998). Temperature was also highlighted early on as an important controlling factor of species’ distributions and, hence, as a basis for spatial divisions at the highest level of biogeographical classification (Ekman, 1953). Faunal records and per cent endemism have been used to define provinces (Ekman, 1953; Briggs, 1974) and a combination of geomorphology and biotic associations have been used to define coastal zones (Ray, 1975) (Table 4.7). These older qualitative efforts can be distinguished from more recent applications of multivariate clustering algorithms as applied to faunal composition (Ray & Hayden, 1993), interpretations of remotely sensed oceanographical data (Longhurst, 1998), biophysical data (Harris & Whiteway, 2009), hierarchical geophysical approaches (Roff et al., 2003) and systematically collected community data. Such analyses can be repeated and tested for sensitivity to assumptions (Shears et al., 2008). The urgent need for marine biogeographical classifications in order to proceed with immediate conservation goals has stimulated the development of numerous regional marine systems, for example in Australia, using both a compositionalist and controlling factors approach (Environment Australia, 1998), and in Canada, primarily using controlling factors (Roff et al., 2003). A functional approach has also been taken in developing the non-hierarchical set of Large Marine Ecosystems (Sherman, 1993). Recently, Spalding et al. (2007) reviewed the global and regional marine classifications in order to develop a consensus map (the Marine Ecoregions of the World) of nested realms, provinces and ecoregions, at a scale appropriate for global/regional conservation planning in continental shelf regions (Spalding et al., 2007). Since different classifications resulted in different boundaries being drawn, comparisons were made and expert regional advice sought in order to determine the best ecoregional definitions (Figure 4.15). A classification of the high seas (areas beyond national jurisdiction), for both pelagic and benthic biomes – Global Open Oceans and Deep Seabed classification – has also recently been published, using both

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Table 4.6 Implications of the nature of the marine environment for biogeography and conservation.

Marine situation LEVEL 1 – PHYSICAL a) Size and Sea covers >70% of Earth’s physical surface and provides >99% barriers of its habitable volume; with few physical barriers, the majority of the ocean is physically interconnected; ocean currents, gyres, upwellings, can operate on a huge scale; physical structure is limited to benthic topography e.g. sea mounts, plateaus, trenches and coastal morphology

Implications for biogeography

Implications for conservation

Potential for species to have vast ranges and for individuals to move over huge distances; depth and size of sea precludes use of many of the remote sensing devices that are used on land

Need to plan on large scale; wide species distributions do not necessarily reduce their vulnerability to extinction; only ‘one ocean’ means that action taken in one area affects others (this can be positive, e.g. allowing replenishment of depleted areas; or negative, e.g. movement of pollutants, invasive species)

b) Buoyancy and density

Increased density of water compared to air enables organisms to be buoyant with little energetic expenditure and thus enables life-history strategies that would be untenable on land (e.g. pelagic jellyfish, giant kelp)

Three-dimensional nature hard to map

Challenge for planning and implementing conservation actions in three dimensions, e.g. restricting fishing to certain depth above seamounts

c) Currents, waves and tides

Sea is very dynamic over timescales from hours to years (waves, tides, currents, El Niño); these physical movements affect many marine organisms and can act as corridors or as barriers

Biogeographical boundaries can be fluid on a variety of timescales

Humans activities, e.g. dredging, sea-filling (land reclamation), coastal development, global warming, etc., can alter hydrology and therefore affect recruitment and distributions of marine organisms; planning of protected areas on the high seas may have to be fluid

Many organisms make use of multiple habitats and widely separated regions during their lives (e.g. many reef fish have pelagic larvae which grow up as juveniles in seagrass habitats before settling as adults on reefs); some species make vertical migrations

Marine protected areas cannot necessarily be treated as islands; need protection of different habitats for different parts of the life cycle to ensure population viability

LEVEL 2 – BIOLOGICAL a) Ecological High levels of connection connectivity between systems, e.g. between benthic/pelagic zones, between different habitats, between widely separated areas, between land/sea

The distribution of diversity: challenges and applications

Table 4.6 Continued

Marine situation

Implications for biogeography

Implications for conservation

b) Genetic connectivity

Reproduction and recruitment can be widely separated spatially and temporally; gene flow can occur over large spatial scales; metapopulation structure important

Some areas are net sources, others are net sinks of propagules; recruitment highly dependent upon physical processes

Potential for areas to receive recruits from far away; systems or networks of marine protected areas may be far more effective than single large ones

c) Primary productivity

Mainly in the form of highly mobile phytoplankton with high rates of turnover; results in a very responsive system

Pelagic bioregions very hard to map (compared to on land, where rooted plants are used to define bioregions and transitions are generally swift and stable over time)

Marine systems can adjust quickly to physical or biological changes (e.g. changes in community composition and phase shifts due to over-fishing) because they are not buffered by long-lived primary producers

d) ‘Rooted’ ecosystems

Some systems are ‘rooted’ (e.g. coral reefs, seagrass, mangroves, kelp beds, hydrothermal vent communities) and are stable over years

‘Rooted’ habitats can be relatively easily mapped

MPAs encompassing ‘rooted’ habitats need not be mobile

e) Land–sea connection

Sea is always ‘downstream’ from land; coastal systems in particular are highly affected by land-based activities (e.g. silt from rivers smothering coral reefs; outflow of rivers heavily polluted by agricultural, or mining chemicals causing dead zones; rivers providing input of iron to the sea); sea affects land, too, but it is not an equal exchange

Very strong ecological/ diversity gradients moving from land to coast to sea; challenge for GIS methods and area-selection algorithms which use species richness as criteria for selection (these will inevitably choose coastal regions, because their transitional nature results in high species richness)

Land–sea links need to be included in conservation plans for organisms that directly (e.g. sea snakes, marine mammals, salmon) and indirectly (e.g. bears eating salmon, riparian systems that derive nitrogen from salmon carcasses) utilize both marine and freshwater ecosystems; need whole catchment conservation

Humans live on, not in the sea, and hence we are less familiar with patterns of diversity beneath the waves

Coastal and continental shelf regions are highly threatened and also highly valuable, so should be conservation priorities

LEVEL 3 – SOCIO-POLITICAL a) Occupation Approximately 60% of the and use global population live within 50 miles of the sea; coastlines and shelf areas are heavily impacted from exploitation, pollution and development; much of the open ocean is less affected

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Table 4.6 Continued

Marine situation

Implications for biogeography

Implications for conservation

b) Political boundaries

Political boundaries have been designated in the sea only recently, e.g. United Nations Convention on the Law of the Sea (UNCLOS), 200 nm Exclusive Economic Zones (EEZ) (1982) or designation of state or municipal waters

Political designations in the sea generally have no ecological meaning (on land they often coincide with major ecological divisions, e.g. seas, mountain ranges)

Political boundaries frequently fail to coincide with biogeographical ones, especially in the sea; successful conservation, particularly on the high seas, requires international co-operation; innovative funding that crosses political boundaries is needed

c) Institutional infrastructure

Responsibility for the high seas lies with international organizations, e.g. International Maritime Organization (IMO), UNCLOS; national responsibility often under the jurisdiction of agriculture, forestry or fisheries; new marine and coastal ministries are being created, e.g. Indonesia

Institutional classification schemes, e.g. Food and Agriculture Organizations (FAO) statistical areas, generally have no basis in ecology – for example, Area 57 stretches from the tropical Indian Ocean to the Antarctic

Co-operation among nations at international level is a challenge; ministries of agriculture or forestry may have little understanding of the differences between terrestrial and marine systems as they relate to biogeography and conservation

d) Exploitation

We use the sea extensively and damage it through destructive exploitational activities, e.g. trawling, dredging, mining and through dumping of wastes; the sea is where the last wild vertebrates are hunted on a large scale; fishing as occupation of last resort

Biogeographical classifications will be most useful if they are at a scale at which exploitation is managed

High exploitation requires that any conservation is management-linked and aware of stakeholders, particularly subsistence fishers; conservation criteria commonly economic and utilitarian in the sea (e.g. MPAs for fisheries management)

e) Social attitudes

Historical perceptions of marine organisms as resources, not wildlife; sea as being limitless; open access

Lack of interest/ engagement, hence hard to get funding for taxonomic/ biogeographical research

Need education and public participation even to put forward the concept that the sea is in trouble

f) Knowledge level

Scientific and public knowledge of marine organisms, distributions and processes that maintain them is extremely limited; diversity on seamounts recently discovered; little is known about the scale of dispersal/ connectedness in most systems

Limited knowledge is hampering accurate determination of marine biogeographical regions, even where they could be easily mapped; potential to use modelling to help determine spatial distributions in unstudied areas

Clearly fewer data, but this should not result in inactivity; would be wise to follow the Precautionary Principle

Regional classification of coastal zones: based on coastal geomorphology and biotic associations

Habitat classification: specific biotic characteristics

Divisions: 14 subdivisions of the domains plus shelf regions which are interpreted as shallow variations of the domains involved

4. Domains: 3 defined by temperature

5. Zoogeographical regions: 3 based on temperature, plus 1 oceanic region

Provinces: oceanic regions based on flow characteristics

Ecoregions: biomes subdivided by ocean basin

3. Biomes: 3 main oceanic biomes based on prevailing winds, plus 1 coastal biome

(Ray, 1975)

(Bailey, 1998)

(Longhurst, 1998)

(Ekman, 1953)

Provinces: based on faunal changes, but biased towards certain taxa

2. Regions: 7 based on temperature and depth, e.g. tropical warm shelf, Mediterranean, temperate

Micro (<1 : 10,000) (Dietrich, 1963)

Meso (≥1 : 1,000,000)

Reference

1. Hydrographical regions: 7 based on characteristic motions of the surface layer (direction, velocity and persistence of surface currents) and ice conditions for higher latitudes

Macro (≥1 : 10,000,000)

SPATIAL SCALE

Table 4.7 Examples of marine classification schemes (17 in total) at a variety of spatial scales.

The distribution of diversity: challenges and applications 87

Zones: neritic (continental shelf) plus oceanic

8.

Biomes: 4 neritic plus 5 oceanic, based on depth, and position in water column (i.e. benthic or pelagic)

Realms: 7 oceanic plus 13 coastal (marginal seas/ archipelagos), based on physical characteristics of ocean/ atmospheric circulation

7.

Divisions: epipelagic plus mesopelagic plus bathypelagic plus continental slopes plus abyssal plains plus deep ocean trenches, based on faunal changes

Realms + regions: continental shelf plus pelagic plus deep benthic, based on faunal changes

6.

Macro (≥1 : 10,000,000)

SPATIAL SCALE

Table 4.7 Continued

Bioregions: pelagic (4) plus benthic (17) within the shelf area only

Provinces: 40 superimposed onto realms, based on zoogeographical data

Provinces: 53 defined based on >10% faunal endemism

Provinces and biotones: defined by endemic fish (2 pelagic plus 9 benthic); Biotones: defined by overlapping fish distributions (2 pelagic plus 8 benthic) plus 9 watermass characteristics classes and 4 seafloor topology classes for off-shelf areas

Meso-scale regions: 60 defined using biological and physical information and geographical distance along the coast

Meso (≥1 : 1,000,000)

Ecosystems/ communities: at scale of 1 : 100 000 or finer; 8 habitat categories for shallow water, from satellite images and ground truthing

(Environment Australia, 1998) [meso-scale regionalization] – N.B. different states used different methods of analysis

(Hayden et al., 1984)

(Briggs, 1974)

Micro (<1 : 10,000)

Reference

88 Basic biogeography: estimating biodiversity and mapping nature

12. Biogeographical realms: e.g. N. Indian Ocean; Indo-Malayan; Australasian

Provinces: 62 large areas with some level of endemism, principally at species level

Ecosections: based on mixing and stratification

Ecoregions: Major habitat type (MHT) nested within biogeographical realm; total of 61 priority marine ecoregions globally

Ecoregions: 232 areas of relatively homogeneous species composition resulting from a range of forcing agents

Domains/ subsystems: in some enclosed LMEs only

Ecoregions: marginal seas and marginal shelf

11. Realms: 12 very large regions with unique evolutionary history

Ecoprovinces: ocean surface circulation and continental margins

Large Marine Ecosystems: 64 worldwide covering most continental shelf areas plus some major currents, based on distinct hydrography, bathymetry, trophic dependency

Ecozones: based on ocean basins, archipelagos, ice regimes and global climate

10.

9.

Major habitat types: e.g. large deltas, mangroves and estuaries; coral reef and associated marine ecosystems; coastal marine ecosystems, biologically based

Ecounits: empirically derived based on combinations of 5 physical factors including water depth, shoreline and seabed habitat

(Olson & Dinerstein, 1998)

(Spalding et al., 2007)

(Sherman, 1993)

(Zacharias et al., 1998)

The distribution of diversity: challenges and applications 89

(IUCN, 1994)

(Connor et al., 2004)

Habitat categories: relevant to marine/ coastline: forest (1) plus coastline (8) plus sea (4) Habitat classification: 5 levels of habitat classification, using first abiotic factors and then biotic factors

17.

(Fernandes et al., 2005)

Bioregions (for Representative Areas Program): areas within which habitats, communities and physical features are more similar to each other than to those in other bioregions

15.

16.

(Ray & Hayden, 1993)

Biogeographical provinces: three dimensional units derived from multivariate analysis of distributions of fish, mammals and birds

14.

(Harris & Whiteway, 2009)

Micro (<1 : 10,000)

Seascapes: benthic classification of 53,713 polygons based on multivariate analysis of 6 biophysical variables (some are at province-scale, others at scale of geomorphological units)

Meso (≥1 : 1,000,000)

Reference

13.

Macro (≥1 : 10,000,000)

SPATIAL SCALE

Table 4.7 Continued

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The distribution of diversity: challenges and applications

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Figure 4.15 Partial illustration of eco-biogeographical classification schemes in the central Indo-Pacific, assessed during the compilation of the MEOW – Marine Ecoregions of the World (Spalding et al., 2007). (a) shows two schemes: expert-derived map of biogeographical zones and subzones (pastel shades; Kelleher et al., 1995), and bathymetry, hydrography and productivity-based Large Marine Ecosystems (blue lines and cross-hatching; Sherman & Alexander, 1989). (b) shows three schemes: prevailing wind and chlorophyll-based (pastel blocks; Longhurst, 1998), expert-derived map of the Coral Triangle and its ecoregions, based on biological and physical characteristics (green lines; Green & Sheppard, 2005), and coral distribution-based (blue lines; Veron, 2000). Some biogeographical regionalization schemes consider the entire area as part of a single Indo-Pacific province, lacking internal divisions (e.g. Briggs, 1974; Hayden et al., 1984), but this may reflect the limited data available for what is an extremely biotically diverse and complex biogeographical region. (c) shows the final MEOW classification: provinces (colour-coded) with ecoregion subdivisions. The scheme is largely based on Green & Sheppard (2005) in the east, and on Longhurst (1998) and Kelleher et al. (1995) in the west, with minor adjustments based on expert regional advice. (See Plate 4.15 for a colour version of these images.)

environmental and (where available) biological data (UNESCO, 2009). At smaller spatial scales, various habitat classifications are also now available, based on abiotic factors such as geomorphology, exposure, tidal amplitude and ice regimes, as well as biotic factors such as species composition (Allee et al., 2000; Connor et al., 2004). In summary, marine classifications are less well established than terrestrial ones, and much research is underway to develop, test, and improve them. While there is concern that the correlation between abiotic surrogates and biodiversity distribution can be low (e.g. abiotic surrogates explained less than 30 per cent of the variation in biotic data in one study in Australia; Stevens & Connolly, 2004), the lack of available biological data in the sea makes the use of geophysical data imperative (Roff et al., 2003; Harris & Whiteway, 2009).

FOR DI S CU S S I ON 1 In the light of the Linnean and Wallacean shortfalls, should top priority be given to collection of more data, to development of better models from existing data, or to putting present analyses to the service of conservation without delay? 2 Should composition or function have priority in our efforts to map nature? 3 Should molecular phylogenies be preferred to traditional systematics for identifying ‘species’ for conservation decision making? 4 How can molecular biogeographical approaches such as phylogeography, and bioclimatic envelope modelling be employed synergistically to inform conservation decisions? 5 How does map scale affect interpretations of biogeographical data available for conservation planning?

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Basic biogeography: estimating biodiversity and mapping nature

6 Should the taxonomic level of species continue to be the focus of global conservation prioritization and action? 7 How will the Encyclopedia of Life and other similar initiatives influence global conservation practice?

S U G G E S T E D R E AD I NG Avise, J. (2000) Phylogeography: the history and formation of species. Harvard University Press, Cambridge, MA. Bailey, R.G. (1998) Ecoregions: the ecosystem geography of the oceans and continents. Springer, New York. Bush, M.B. & Lovejoy, T.E. (2007) Amazonian conservation: pushing the limits of biogeographical knowledge. Journal of Biogeography, 34, 1291–1293.

Ladle, R.J. & Jepson, P. (2008) Toward a biocultural theory of avoided extinction. Conservation Letters, 1, 111–118. Lomolino, M.V., Riddle, B.R., Whittaker, R.J. & Brown, J.H. (2010) Biogeography, 4th edn. Sinauer, Sunderland, MA. Chapters 2, 4, 5 and 11. Mace, G. (2004) The role of taxonomy in species conservation. Philosophical Transactions of the Royal Society of London B, 359, 711–719. Spalding, M.D., Fox, H.E., Allen, G.R., Davidson, N., Ferdaña, Z.A., Finlayson, M., Halpern, B.S., Jorge, M.A., Lombana, A., Lourie, S.A., Martin, K.D., McManus, E., Molnar, J., Recchia, C.A. & Robertson, J. (2007) Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. BioScience, 57, 573–583.

CHAPTER 5 The Shaping of the Global Protected Area Estate Paul Jepson1, Robert J. Whittaker1 and Sara A. Lourie2 1

School of Geography and the Environment, University of Oxford, Oxford, UK Redpath Museum, McGill University, Montreal, Canada

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5. 1 O RI GI NS Protected areas are areas of land or sea designated in order to conserve or protect attributes of nature valued by society, groups or individuals. Although they have diverse origins, many protected areas are now managed to maintain or enhance their biodiversity value, ahead, alongside or secondary to other conservation goals focusing on attributes such as geological or cultural heritage. There are thus many forms of protected area, of varied purpose, character and governance, which have been motivated by diverse social values (Chapter 2). In the prehistoric and historic period, areas were typically conserved because of values concerned with spiritual importance (e.g. so-called sacred forests), hunting areas for privileged members of society (e.g. royal hunting or game reserves), common resource needs (e.g. communal grazing, fishing grounds, fuel wood) or strategic timber resources (e.g. for shipbuilding). Designation of such sites included both formal legal protection and diverse other forms of undocumented agreements and understandings. However, it is only recently (post-1970) that protected areas have been clearly formalized into a set of internationally recognized categories (i.e. IUCN protected area system; Table 2.2) that can be put together, analysed and planned as Protected Area Networks. The degree of protection afforded to sites can vary a great deal, and meaningful protection does not necessarily follow from legal designation (Jepson et al.,

2001b). Moreover, some areas that perform a biodiversity conservation function, and that may be managed with conservation in mind, in practice lack formal or legally binding protection. Many lands whose primary function is agriculture also have biodiversity or aesthetic value, and to protect these secondary land use values, a variety of landscape designations may be applied (e.g. Area of Outstanding Natural Beauty (UK)). Some of these designations meet the IUCN categories 5 or 6 (Table 2.2) and therefore are accounted for in IUCN statistics. To embrace all such areas within evaluations of conservation systems, the broader term Conservation Area Network has been suggested (e.g. Margules & Sarkar, 2007). However, our focus is largely on how sites have been selected for and incorporated into networks afforded protected area status, hence our preference for the term ‘protected area’ in the following discussion. As outlined in Chapter 2, the origins of modern protected areas can be traced back to the late 19th century (roughly the period from 1860 to about 1910), a period which saw the emergence of new world views on the human/nature relationship. These views were rooted in anglophile natural history and hunting traditions, but were also inspired by fundamental changes in human perceptions of self and the interaction of metropolitan people with frontier landscapes (Jepson & Whittaker, 2002a). Led initially by prominent citizens drawn from the political and social elite of east coast North America and Western Europe, these new world views were

Conservation Biogeography Edited by Richard J. Ladle and Robert J. Whittaker © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33503-3

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translated into social values and public policy goals embracing the designation of protected areas and enforcement of wildlife legislation. These measures were designed to halt the needless slaughter of wildlife and to save the natural beauty and tranquillity of the countryside in the face of rapid industrialization and development (Kalamandeen & Gillson, 2007). The study of natural history was a passion of the era and, prompted by threats to favoured excursion sites, natural history societies in the UK began to include site and species preservation among their objectives as early as 1860 (Lowe, 1983). Reflecting these distinct but often interlinked concerns, the motivations for designating sites within the first protected area networks varied considerably (Chapter 2; Jepson & Whittaker, 2002a). For example, we might identify: • game reserves, managed for the maintenance of game and their habitat for hunting (a concept predating the modern conservation movement); • forest reserves, established to manage timber reserves and other forest resources, motivated by utilitarian rational resource planning; • wildlife sanctuaries, motivated by the belief that humanity has a moral obligation to ensure survival of other life forms; • naturdenkmal (roughly translated as nature monuments): places for the study and contemplation of nature, motivated by the belief that monuments of nature have value to human civilization, culture and identity; • national parks, in part motivated by the ideas of promoting nation-building and patriotism through romanticized pride in natural heritage; • scientific benchmark sites: places representing typical vegetation associations and thus providing ‘type specimens’ of natural ecosystems (lest this be thought a recent invention, it is worth noting that several such sites were established in Java in the first quarter of the 20th century); • parks, state parks and country parks, motivated by the desire to enhance the well-being and health of urban residents by providing opportunities for informal outdoor recreation (Open Spaces movement, Chapter 2). As the conservation movement developed over the 20th century, these earlier disparate strands and motivations were increasingly combined into schemes and classifications that recognized and embraced different types of protected area. Moreover, as protected areas

became to be seen as a public good, government departments were established and associated legislation put in place to govern and expand the number of reserves. This approach involved the adoption of a rational and planned approach from which the idea of functional reserve networks emerged. The endeavour gained momentum during the 1970s with the rise within international development agencies of science-based recreational resource management. Such planning was informed by multi-purpose principles, whereby a dominant use co-exists with a number of compatible, but subsidiary, uses so as to maximize the use made of available land. These multipurpose principles, and the notions of efficiency they represent, influenced reserve selection in that it made sense to identify sites that delivered more than one of the conservation functions (above), as well as sites that could support conservation-compatible uses such as tourism, informal recreation, trophy hunting and, more recently, sustainable utilization linked to local livelihoods. Under the rational resource planning approach (cf. Colby, 1991), biogeographical and ecological science was employed to assess the conservation value of lands and the size and configuration of sites required to protect ecosystems services, to ensure the survival of threatened or migratory species, to protect a set of benchmark sites and so forth. The suitability of sites for subsidiary uses was assessed subsequently, although, for practical purposes, the starting point of network planning was pre-existing reserve collections, some of which may have been designated for reasons not derived from scientific ecological analyses, e.g. for reasons such as landscape beauty. The challenge thus became to develop the principles and methods needed to design coherent and effective networks of protected areas, building on whatever preexisting network there might be for a given region. This process has continued to the present day and will, no doubt, continue into the future. To address this challenge requires the various forms of biogeographical data discussed in detail in Chapters 3 and 4. It also requires a focus on identifying and refining the goals of reserve networks as a whole. Approaches to meeting such goals have varied over time, and as implemented by different organizations in differing geo-political contexts (below; Jepson & Whittaker, 2002a,b). While early approaches were constrained in their sophistication by data limitations and computer processing power, recent decades have

The distribution of diversity: challenges and applications seen the development of increasingly powerful systematic analyses, and the sub-field termed Systematic Conservation Planning (Chapter 6), with its focus on the concepts of redundancy and complementarity. These terms relate to the way in which hypothetical protected area networks are constructed from a very large number of potential solutions. The key concept here is complementarity, which is the principle that designing reserve networks to maximize the total number of species ‘saved’ with least effort (expenditure) requires seeking out sites that complement one another rather than simply designating the individually most diverse sites. As it is generally considered unwise to have species reserved in just a single site, algorithms have been developed that target, for example, the goal of having each species in a minimum of six different areas, i.e. building in a degree of redundancy, whilst adhering to the principles of complementarity and maximum return for investment. In short, the general goal is to design networks that are as efficient as possible in meeting their targets, yet which also build in a degree of redundancy, in the event that sites in the network are damaged or lost despite designation (see Chapters 6 and 7). There are both historical and pragmatic reasons for beginning our consideration of protected area planning frameworks with those developed by international governmental and non-governmental organizations (IGOs and INGOs). These schemes have had great reach and influence. The period since the landmark conference in Rio in 1992 that led to the CBD (section 2.3) has seen an enormous increase in the global protected area estate. By 2003, the UN list of protected areas contained around 104,000 sites covering about 20 million km2, equivalent to about 12.2 per cent of the terrestrial land surface area (Chape et al., 2003, 2005). While many conservationists concerned with the multiple threats to biodiversity may view the global protected area estate as grossly inadequate (Soutullo et al., 2008), the designation of such a large amount of land for conservation, much of it in just a couple of decades (the 1962 UN list detailed only 1,000 sites), should also be recognized as a remarkable outcome, indicative of widespread international valuation of nature and of biodiversity. So far, these efforts have not been matched by marine conservation measures, however, with only 0.7 per cent (2.59 million km2) of the world’s oceans within protected areas according to the recent review by Spalding et al. (2008).

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Much of the expansion that has taken place (and that is continuing today), has been guided by the schemes reviewed in this chapter and by the influential environmental organizations that have developed, promoted and implemented them. Over time, as more biogeographical data have become available and computing power has increased, conservation planning exercises have increasingly adopted systematic conservation planning methodologies, especially, as illustrated in Chapter 6, at regional scales of analysis. 5. 2 T Y POLOGY OF FR AMEW OR K S We may broadly divide approaches to the task of designing protected area planning frameworks into: 1 zonal approaches, involving the mapping of attributes of nature into a suite of broadly climatically or historically determined, non-overlapping areas, and thus dividing the world up like a giant jigsaw puzzle; versus 2 azonal approaches, involving identification of a particular set of disconnected places across the world. In turn, we find it useful to split each of these two categories to recognize four broad headings, as follows (Figure 5.1, Table 5.1).

Figure 5.1 A simple typology of protected area planning approaches, suggesting that the core character of most major frameworks can be regarded as zonal or azonal (a spatial planning distinction). Within these two classes, four main properties are emphasized: compositional representation, ecosystem functionality, numerical attributes of biodiversity (e.g. species richness or endemism) and, finally, other key attributes. The focus on each of these priorities provides a trade-off that many schemes in practice address by employing different criteria at different scales within a hierarchy of decision layers.

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Table 5.1 A typology of protected area schemes emphasizing different strategic goals: representation, ecoregions, hotspots and key areas. The data and methods used vary from scheme to scheme, so the lists given for these rows are merely indicative. Strategies shown in grey can be considered primarily ‘zonal’, while those in white can be considered primarily ‘azonal’ (see Figure 5.1). Modified from an original in Whittaker et al., 2005. Approach

Representation

Ecoregions

Hotspots

Key Areas

Focus

Composition

Function

Numbers

Attributes

Basic idea / conservation goal

An example of each

Conserve or restore ecological (dynamic) processes, flows and linkages

Pick out richest and most threatened places

Identify sites critical for the persistence of a valued natural attribute

Questions arising during prioritisation process

What are the units of nature? What do we add next?

What ecological process, flows and linkages are important? Where are the boundaries of these interlinked units? How are they nested in scale?

Where are the places exhibiting exceptional species richness, diversity and/or levels of endemism and/or levels of threat?

What are the key attributes valued by different conservation constituencies and how can they be assessed?

Input data

Vegetation formations, oceanographic and faunal regions, ecoregions

Migration routes, watershed boundaries, topography, climate envelopes, food webs, ocean currents

Species richness, endemism (or surrogates), degree of threat, rate of landscape transformation

Threatened species, endemic species, congregating species, cultural and/ or aesthetic features

Methods

Percent dissimilarity, controlling factors

Controlling factor mapping, expert review

Species counts, diversity & threat indices

Application of criteria sets (often thresholdbased)

Schemes

IUCN Biogeographical Provinces, Global Representative System of Marine Protected Areas

USFW Ecoregions, Watershed Reserves, Large Marine Ecosystems

Biodiversity Hotspots, Endemic Bird Areas, Coral Reef Hotspots

Important Bird Areas, Key Biodiversity Areas, High Conservation Value Forest, Particularly Sensitive Sea Areas

Composite schemes

WWF Ecoregions

AZE Sites

WWF Global 200 Ecoregions CI Critical Seascapes

Zonal: • Representation-compositionalist (biogeographical pattern): conserve an outstanding example of each major ecosystem type, biogeographical region, etc., whereby the key criteria for defining the units focus on natural units of differing composition and the main goal is to

conserve places that provide a representative set of high quality examples. The seminal example of this compositionalist approach was IUCN’s biogeographical regions scheme (see 5.3.1). • Functional (ecological pattern and process): develop a system for dividing land areas (or seas) into

The distribution of diversity: challenges and applications functional ecological areas, or ecoregions, and designate reserves within outstanding examples in such a way as to conserve intact interlinked systems of high biodiversity value. Here, the focus is more on ecological functionality and on population flows and linkages within seascapes or landscapes than on identifying compositional patterns or breaks (although this distinction can be a relatively subtle one). The most prominent example of this approach is the WWF Ecoregions scheme (see 5.3.2). Azonal: • Hotspots concept: designate reserves that together save the maximum number of species, given available resources. The most prominent example of this approach is Conservation International’s hotspots scheme (section 5.3.3). • Important areas (key attributes): select a suite of sites that together conserve key attributes of interest (e.g. important migratory stop-over points for birds). This approach is exemplified by BirdLife International’s Important Bird Areas scheme (section 5.3.5). This way of summarizing the goals of protected area planning frameworks, which we expand on below, is of course an over-simplification which, by the nature of such things, fits some schemes well and others poorly. Moreover, for any given region there are typically several overlapping and complementary systems of protected areas, reflecting the diverse goals, origins and strategies discussed above. Since conservation planning and prioritization operate at multiple scales of

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geography and governance (Mace et al., 2000), a combination of approaches is likely to be most effective for conservation in the long run. It is thus of benefit to conservation that differing protected area networks exist for particular areas and frequently overlap one another in designating the same places within multiple protected area frameworks.

5.2.1 Spatial classification of approaches – contiguous areas, landscape units and habitat islands Several high-profile macro-scale planning schemes aim to generate basic spatial divisions of seascapes or landscapes as a first step of analysis. The degree to which the landscape is mapped out prior to the selection of individual sites allows a spatial classification of approaches, which might be termed contiguous areas, landscape units and habitat island rationales (Figure 5.2). Contiguous area approaches are essentially zonal in nature and first require criteria for delimiting zones and drawing up and mapping boundaries between them, so that a region is divided up in a manner analogous to the way a ‘cheese-cutter’ works. The second step is to apply another set of criteria to determine priority sites within zones. In zonal approaches, the ‘cut’ may be based on functionalist criteria, designed to capture ecosystem variation as a function of environmental controls

Three means of spatial division

Contiguous areas ‘cheese cutter’

Landscape units

Habitat islands

‘cookie cutter’

‘cherry-picking’

Figure 5.2 A simple spatial (geographical) classification of protected area planning approaches. There are three steps in operationalizing all such schemes: (i) identify the units and delineate the boundaries comprising the maps; (ii) identify the particular sites for protection within the areas or landscapes identified (synonymous with step i for ‘cherry-picking’ schemes); (iii) devise a prioritization system to target resources.

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(e.g. biomes or ecoregions) or on biogeographical criteria, using compositional variation in the membership of biological assemblages (Chapter 4). Finer subdivisions of the zones are typically determined using similar criteria to those used in the first level cut. For example, within ecoregional schemes, a controlling factors approach is involved at each level of the hierarchy while, in compositionalist biogeographical schemes, the percentage (dis-)similarity may be used at each successive level (below). In these schemes, map-makers often recognize features such as mountaintops and seamounts that do not conform to the zone within which they occur, supporting distinctive climates and biota. At a coarse scale, these may be considered azonal features, meaning that they are not predictable from the general climate zone in which they occur, and are thus mapped as separate spatially transgressive units. However, the overall approach in contiguous mapping schemes remains zonal, i.e. the entire ‘cheese’ is cut up into a series of discrete pieces, with nothing left behind. In the second panel of Figure 5.2, denoting the landscape approach, the ‘cookie cutter’ analogy invokes the image of a piece of pastry with cookies cut from it. The cookies are the distinct landscapes – a mountain range, a plain, a delta, etc. – that are considered of particular conservation interest, while the discarded ‘pastry’ represents the intervening transitional landscapes where human population, industry and infrastructure is often located. This form of spatial division is not so evident in highprofile global schemes, but it is particularly suited to macro-scale protected area planning in regions dominated by cultural landscapes (such as Europe), where the goal is to conserve particular cultural/ecological interactions that shape a landscape. The network of 45 Parcs naturels régionaux de France (see www.parcsnaturels-regionaux.tm.fr) is an excellent example. In the marine realm, Large Marine Ecosystems are an example of a global ‘cookie-cutter’ classification that focuses on identifying large regions with distinct bathymetry, hydrography, productivity and which contain trophically interconnected populations (Sherman, 1993). Habitat-island or ‘cherry-picking’ approaches are azonal in character in that they are designed to pick out sites that meet a particular highly valued criterion or set of criteria. Such schemes do not rely upon producing continuous maps of ecological or biogeographical zones. In addition they are not normally hierarchical,

i.e. a single map is generated for a chosen (often only vaguely delimited) scale of analysis. The chosen criteria could be cultural, ecological, or could depend on some biodiversity accounting exercise, and the resulting maps of priority areas for conservation could be used for identifying extremely small areas for reservation or very large areas for conservation investment. Examples include hotspots of richness, endemism and threat, as exemplified at the macro-scale by Conservation International’s (CI) hotspots scheme (e.g. Myers et al., 2000) and, at the site scale, by Important Bird Areas, sacred forests, nature monuments and, most recently High Conservation Value schemes (Box 5.1). Sites of Special Scientific Interest or importance may also be considered examples of ‘cherry-picking’ unless the criteria used amount to selecting a representative set of vegetation types or habitats. Some sites identified in this way may be strongly isolated from other patches of similar habitat and thus may be functionally acting as habitat islands in the sense discussed in Chapter 8 while, in the case of CI hotspots, the hotspot areas are each so large that they are more akin to vast archipelagos of fragments. On land, the first generation of modern protected area planning schemes were largely zonal in character, with azonal approaches rising to greater prominence more recently. This might reflect the transition from an earlier focus on assisting the governments of lessdeveloped nations to create reserve networks within a state-owned forest estate, towards the creation of multi-agency frameworks that can operate in regions of complex land use and ownership and engage with modern norms of participation in land use and policy planning. In the sea, where biogeographical data for systematic zonal planning have largely been lacking, azonal approaches have dominated. Recent azonal approaches have been supported by map-based analyses of species richness and threats (Burke et al., 2002; Roberts et al., 2003). Fisheries management considerations have also ensured that marine planning has tended to focus on single species and ecosystems. Concepts of representation and connectivity have only been introduced more recently. Careful examination of this division of spatial approaches will doubtless reveal shortcomings, as with any such heuristic scheme. Its value, we believe, lies in that it encourages us to start thinking in geographical/ spatial terms about conservation planning and the extent to which planning approaches for protected

The distribution of diversity: challenges and applications

Box 5.1 The High Conservation Value approach The interplay of market forces, NGO pressure, new policies and regulation is creating a commercial imperative for international companies to identify and protect important natural habitats within their land-holdings. This is particularly the case within the forestry and plantation sectors, where regulation for a minimum renewable content in road transport fuel (10 per cent by 2020 in the case of the EU) is driving an expansion of oil palm and soya cultivation. This is happening at a time when most tropical forest land has been logged-over and the residual timber value and potential for generating tax revenues are at an all-time low. The High Conservation Value Forest (HCVF) approach emerged in 2001 as a supplementary standard within the Forest Stewardship Scheme. The standard was developed by ProForest, a specialist forestry consultancy based in Oxford, UK. It identifies six values, namely biodiversity, landscapes, ecosystems, ecosystem services, basic human needs and cultural identity (see Table B5.1a), and requires that critical values under these headings be identified and managed within a company’s risk management framework (WWF 2007). HCVF was initially intended as an additional standard that forestry companies could voluntarily elect to adopt. However, it quickly transferred to the plantation sector as progressive companies with Table B5.1a The six high conservation values and their sub-divisions (source: www.hcvnetwork.org). HCV 1 HCV HCV HCV HCV

1.1 1.2 1.3 1.4

HCV 2

HCV 2.1 HCV 2.2 HCV 2.3

Forest areas containing globally, regionally or nationally significant concentrations of biodiversity values (e.g. endemism, endangered species, refugia) Protected areas Critically endangered species Concentrations of threatened, endangered or endemic species Critical temporal concentrations Forest areas containing globally, regionally or nationally significant large landscape-level forests contained within, or containing, the management unit, where viable populations of most if not all naturally occurring species exist in natural patterns of distribution and abundance FMU (Forest Management Unit) is a large landscape-level forest FMU is an integral part of a large landscape-level forest FMU maintains viable populations of most naturally occurring species

HCV 3

Forest areas that are in or contain rare, threatened or endangered ecosystems [No subunits]

HCV 4

Forest areas that provide basic services of nature in critical situations (e.g. watershed protection, erosion control) Unique sources of water for daily use Forests critical to water catchments and erosion control Forests providing critical barriers to destructive fire Forest areas with critical impact on agriculture, aquaculture and fisheries

HCV HCV HCV HCV

4.1 4.2 4.3 4.4

HCV 5

Forest areas fundamental to meeting basic needs of local communities (e.g. subsistence, health) [No subunits]

HCV 6

Forest areas critical to local communities’ traditional cultural identity (areas of cultural, ecological, economic or religious significance identified in cooperation with such local communities) [No subunits]

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oil palm (and subsequently soya) estates sought a standard that would enable them to claim environmental responsibility and to apply the term ‘sustainable’ to their products. As a result, the standard is now also applied to non-forest habitats, and the approach is being referred to as ‘the HCV approach’ and the outputs of identification as ‘HCV areas’. The adoption of the HCV standard (or equivalent if it existed) is increasingly required in order to meet regulatory stipulations. Notably, the sustainability criteria of the 2009 European Commission’s Renewable Energy Directive (Directive 2009/28/EC) forbids biofuels to be made from raw material obtained from land with high biodiversity value. Each of the six High Conservation Values is written as a standard, and guidelines on how to apply each are contained in HCVF hand/sourcebooks (see summary in Table B5.1a). Some of these draw on the frameworks reviewed in this chapter (notably HCV1), but others, particularly HCV2, relating to landscape-level forests, have proved difficult to put into operation. Crucially, the detailed guidelines on the interpretation and application of each value/standard are developed nationally through consultation with a range of stakeholders. This ensures that standards are appropriate to local conditions, but it also means that HCV areas may not be entirely comparable from region to region. Application of these standards to forest/land management units (e.g. concessions) are conducted by specialist third party consultancies, often with an established track record in Forest Stewardship Council certification. The HCV standard is being adopted by industry associations creating and promoting sustainability standards, notably the Roundtable on Sustainable Palm Oil (RSPO), the Roundtable on Sustainable Biofuels (RSB) and the Renewable Transport Fuel Obligation (RTFO). It is being incorporated in various sourcing and purchasing policies, declarations and commitments, and is starting to inform national/regional land use planning. Several million hectares of HCV land are currently in the process of identification and protection. However, there are crucial issues that still need to be worked through. One of the most fundamental, at least from a reserve network planning perspective, is the fact that any one site’s value is relational to what is protected elsewhere. Put another way, in the case of HCV1 and HCV3, it is not the presence of endemic or threatened species or rare or endangered habits per se that matters, it is the extent to which these attributes are represented in established protected areas and the likelihood of them persisting therein. Another challenge is the political nature of HCV assessment. Companies might quite rightly argue that they have acquired land that has already been zoned for productive use and, while they are willing to protect part of their land area, they must still cultivate a significant proportion of it. Environmental and social activists, on the other hand, may wish to frame HCV as a tool that legitimizes planned deforestation, and can deploy the HCV standards to argue that all of the remaining forest in an area should be designated HCV forest and should therefore be protected. This is aided by the ‘scale-less’ HCV1 and, in particular, HCV 4 standards, and by the fact that site conservation designations (e.g. Important Bird Areas, Key Biodiversity Areas, hotspots) are being produced by private conservation groups that are under no particular pressure to identify precise or optimal boundaries.

areas are geographically contiguous or inclusive, or both. 5.2.2 Biogeographical (compositional) versus Ecological (functional) approaches A second way of subdividing protected area planning approaches is between those schemes based upon and emphasizing composition (vertical line in Figure 5.1)

and those emphasizing function (horizontal line in Figure 5.1; see also Table 5.2). We term schemes based upon mapping composition of communities, habitat types or regions ‘biogeographical approaches’, because they are based on pattern detection using data on membership of biotas at whatever scale the analysis is conducted. For the UK’s National Vegetation Classification, which has been used as a framework for both national and

The distribution of diversity: challenges and applications

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Table 5.2 Some contrasting properties of biogeographical and ecological (eco-geographical) approaches to strategic conservation planning. Properties/approach

Biogeographical

Ecological

Basic character

Compositionalist

Functional

Principle

Ensure representation of communities or biotas

Maintain ecosystem flows and linkages

Type of data

Analyses of patterns in membership

Analyses of controlling factors

Typical coarse-scale delimiters or explanatory variables

Physiographical barriers to exchange, plate-tectonic history, climate

Climate (zonal), topography (azonal)

Fine-scale delimiters

Geology, soils, rivers, etc.

Rivers, land use, geology, soils, etc.

Original scientific applications

Biogeographical regions, vegetation classification

Analyses of ecosystem energetics, land management

Examples of protected area planning application

IUCN Biogeographical Provinces (Dasmann–Udvardy); European Special Areas of Conservation

WWF Ecoregions scheme*; Large Marine Ecosystems**; Dutch Ecological Network***

* WWF Ecoregions is in practice a composite functional/compositionalist scheme when viewed globally, although the delimitation of ecoregions and their boundaries within a region largely follow ecological criteria. ** The Large Marine Ecosystems approach is really concerned with resource management rather than protected area planning (see text). *** The Dutch Ecological Network is perhaps the most complete example so far. It involves the restoration of trophic levels, ecological dynamics and connectivity through reconstitution of large herbivore assemblages, the creation of dynamic riverine areas through removal of summer dykes, and the construction of ecological corridors and more than 400 ecoducts (Van den Belt, 2004).

European-scale conservation schemes (e.g. Special Areas of Conservation – see www.jncc.gov.uk/page23), the scale of analysis is the local vegetation community or assemblage. By contrast, for the IUCN Dasmann–Udvardy scheme of the 1970s (see below), the units were coarse-scale biogeographical regions of roughly 104–108 km2. The biogeographical approach to mapping out units is generally a step in developing zonal schemes emphasizing the representation principle. A widely promoted alternative to emphasizing pattern as a basis for conservation prioritization is to focus on function. This is because ecological systems are dynamic, and interconnected, so that, for example: 1 animals migrate between different habitats, ecosystems and biogeographical regions on an annual basis; 2 animals move between different types of habitat on a daily basis, feeding in one habitat and returning to another to rest or care for young; 3 ecosystems in different parts of landscapes are connected not only biotically but also through abiotic flows (e.g. of water, nutrients).

Hence, the argument goes, a focus solely on compositional patterns may result in the selection of sites for conservation that are incapable of standing alone as secure places for the long-term persistence of the biodiversity for which they may initially have been targeted (cf. Orians, 1993). At fine scales of analysis, such flows and linkages may be influenced by habitat connectivity via corridors (see Chapter 8) or by the separation of otherwise favourable sites by hostile agricultural practices or urban and industrial areas. Beginning from a much coarser resolution, the American forester R.G. Bailey developed the ecoregional approach, intended as a tool for land managers concerned with the optimal management of resources (Bailey, 1983). The approach recognizes the importance of how different ecosystems connect with and exchange information with adjacent ecosystems. It focuses on ecosystem structure, using controlling factors rather than biotic composition as the means of identifying the units. At its coarsest it is a climatic classification, but it increasingly employs other data layers at finer levels of resolution. The most prominent application of this approach in

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Figure 5.3 Mapping and planning exercises cast at different spatial scales (extents and grains) typically employ different data layers to different purposes. This diagram suggests the relationship between information source, mapping scale, purpose and associated conservation planning framework for the marine realm. MPA, marine protected area; IMCRA, Integrated Marine and Coastal Regionalisation for Australia.

conservation planning is the WWF Ecoregions scheme, which aims to identify ecological areas that function together effectively as a biological unit at a global or regional scale. As will be shown when we examine these schemes in more detail, each of these approaches has a number of operational steps. In working down from global frameworks to decisions about site selection within regions (Figure 5.3), they each tend to involve both compositional and functional criteria in their methodologies. The classification offered here is thus intended to capture the primary emphasis of the various schemes rather than to imply that they are solely one thing or the other.

5.2.3 Strategic goals – composition, function, numbers and attributes A third way of looking at protected area schemes is in terms of their strategic goals and the practices of putting these into operation. In Figure 5.1 and Table 5.1, we identify four broad key strategic goals which align with the zonal/azonal and composition/function distinctions above. The first strategic goal, of representation (to protect an example of each), requires compositional classes to be determined (Chapter 4). Having done so, the means to determine how to locate protected areas within each class differs. For instance, some schemes focus on

The distribution of diversity: challenges and applications typical or ‘benchmark’ sites (e.g. Special Areas of Conservation across Europe), while others look for sites that have greatest species richness. The Great Barrier Reef Marine Park provides the most extensive application of the representation principle in the marine realm (Fernandes et al., 2005). The complex task of selecting sites that satisfy goals of representation across multiple habitat types is well suited to numerical analysis using computer algorithms (Chapter 6). The second approach focuses on the goal of retaining or restoring dynamic ecological processes. The underlying maps reflect ecological linkages across landscapes rather than focusing on, for example, identification of distinct assemblages of species. The WWF Ecoregions approach (below) follows this functional or controlling factors rationale. The third strategic goal identifies areas where it is appropriate to invest conservation funding to maximize the number of species conserved, given finite available resources. This so-called ‘silver bullet’ strategy gives precedence to areas containing high numbers of species, or areas that contain high numbers of local or regional endemics, and that are under risk from habitat conversion or other forces. BirdLife International’s Endemic Bird Areas scheme (ICBP, 1992) can be considered an example of a pure hotspots approach, while CI’s hotspots scheme is an example of a composite ‘hotspot/threatspot’ approach. The fourth strategic approach involves developing a set of site-based conservation attributes and then applying these criteria in a systematic and standardized manner to identify target sites for protection. These attributes may include the presence of threatened or endemic species, but they might also include sites of importance for congregating or migrating species. For example, within the island of Tenerife there are very few areas of fresh or brackish water, and several visiting and resident bird species would be lost from the island altogether if these sites were allowed to disappear. Although such sites may not hold great importance in a scheme based on vegetation types or using hotspot criteria, they do meet the criteria for inclusion within Important Areas schemes. The schemes depend on the creation of a robust set of criteria against which candidate sites can be assessed, preferably using numerical data. The most complete example is BirdLife International’s Important Bird Areas approach, which has been expanded by CI and other organizations into the Key Biodiversity Areas framework.

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Some schemes do not fit easily into a single category. For example, WWF’s Global 200 ecoregions scheme is a composite representation/hotspot scheme, as it seeks to prioritize the most valuable examples worthy of special conservation support from within the much larger global set of ecoregions (Table 5.1). Each scheme represents a blend of political, organizational and scientific imperatives operating at the time of their development. Over the last fifty years, four major trends have created the context for the development of new protected area planning schemes: 1 the adoption of rational resource planning as a central tenet of international development in the 1970s (e.g. Colby, 1991); 2 the formation of the EU and the enactment of supranational environmental directives in the 1980s; 3 the rise of biodiversity on the global policy agenda and the signing of the Convention on Biological Diversity in the early 1990s; 4 the emergence of corporate social responsibility linked to the rise of non-state market-based policy approaches (e.g. certification) at the beginning of the present millennium. The frameworks we discuss in this chapter should be seen not only as scientific constructs, but also as ‘governance devices’ put into play to alter the designation of land and to reconfigure relationships to the benefit of the goals of the organizations producing them. We focus in this chapter mostly on global frameworks. However, much conservation planning needs to occur at the regional, landscape and site level (Figure 5.3). For conservation action to be successful at all levels, both top-down and bottom-up approaches are needed in order to fund, garner public support and provide institutional frameworks for conservation planning. In practice, the choice of scheme devised and promoted is thus also influenced by organizational as well as conservation biogeography imperatives. For example, representation schemes are suited to teams or units integrated within national and regional government land use planning agencies. Hotspot schemes appeal to organizations that seek to lobby or advise the managers of major funds for biodiversity conservation on how to disperse and target these funds in ways that will deliver the ‘biggest bang for the buck’. Important Areas and other criteria-based approaches have been developed by organizations such as BirdLife International, with extensive networks of local expertise able to identity and then assess candidate sites.

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The growing popularity of these criteria-based approaches can be attributed in part to their alignment with new policy norms relating to decentralization, in part to the ease with which they can be aligned with the audit culture (policy guided by measurable targets and objectives) and in part to the relative ease with which they can be pursued without the need of partnerships with government agencies or the major biodiversity funders.

5. 3 T ER R E S T R I AL P R OT E C T E D A REA S C HE M E S ‘… a critical issue is the relationship between biogeography and conservation … The biodiversity question is really a biogeographical one, as it is a question of where the limited financial and human resources should be applied … Historical biogeographical analyses, however, are not playing the significant role in biodiversity conservation that they should.’ (Crisci, 2001, pp. 164–165.) As the foregoing discussion has outlined, the protected area planning approaches we are about to discuss present varied combinations of properties: zonal or azonal; targeting regions, landscapes, or specific sites; emphasizing ideas of representation or ecological functionality; and targeting richness, endemism, threat or particular intrinsic attributes. The schemes provide a complementary suite of approaches that have shaped the current protected area estate at every level from that of regions within a single state up to the global.

5.3.1 IUCN Biogeographical Regions (Dasmann–Udvardy) scheme The International Union for the Conservation of Nature and Natural Resources was created as a quasigovernmental body in 1948 within the newly formed United Nations. IUCN assumed two related roles: to promote the conservation of nature and natural resources; and to collect and classify information to support effective decision-making (Holdgate, 1999). To support the former, the IUCN formulated the goal of establishing a worldwide network of reserves encompassing representative areas of the world’s ecosystems (Dasmann, 1972). This ‘representation principle’ embraced the international aspirations of the

wise-use, wildlife wilderness and nature monument movements (Chapter 2) and enabled the alignment of conservation with a key development policy agenda of the time, namely the introduction of rational resource and land use planning in developing countries. This positioned protected areas firmly within the domain of newly independent administrations. As well as facilitating development funds to build state resource planning capacity, a planned expansion of protected areas also provided an expedient means for, among other things: 1 states to assert control over remote territories; 2 resource management ministries to expand the territory under their jurisdiction; and/or 3 political regimes to reign in cronyism in the allocation of forest concessions (see Jepson, 2001; Peluso & Vandergeest, 2001). The IUCN’s chief ecologist, Raymond F. Dasmann, designed a framework to apply the representation principle at the global scale. This involved a hierarchical system that defined and classified natural regions for the purpose of conserving natural ecosystems and vegetation types and the conservation of species. His framework established a classification of communities based at the macro-scale on ecoclimatic features, but at lower levels on taxonomic differences, to create a nested framework of biogeographical regions, provinces and (subsequently) units (Dasmann, 1972, 1973) (Table 5.3; Jepson & Whittaker, 2002b). It transpired that the Russian biogeographer M.D.F. Udvardy (1969) had independently been working along similar lines to Dasmann to distinguish biogeographical provinces. As a result, IUCN commissioned Udvardy to review and refine Dasmann’s system. In substance, Udvardy’s (1975) report reaffirmed Dasmann’s approach but introduced a number of terminology modifications, hence our reference to the final version as the Dasmann–Udvardy framework (after Jepson & Whittaker, 2002b). This approach fits into our earlier typologies as a cheese-cutter, zonal biogeographical approach emphasizing the representation principle, and it was a crucial first step towards modern systematic conservation planning (Chapter 6). At the top level in the hierarchy, Dasmann (1972, 1973) chose the biome system (tundra, taiga, deciduous broad-leaved forest, etc.) of Clements & Shelford (1939) on the basis that it is readily applicable globally, takes into account both plants and animals, and broadly conforms to observable reality in areas not greatly modified by humans. Because the biome approach

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Table 5.3 Properties of the Dasmann–Udvardy IUCN Biogeographical Province scheme (key source: Udvardy, 1975), a zonal biogeographical representation approach. Recognizing a need to modify the scheme to operationalize the approach within the biogeographically complex territories of Indonesia, MacKinnon & Wind (1981) added the third tier. Organization

IUCN: World Conservation Union (developed 1970–1975)

Lead authors

R.F. Dasmann, M.D.F. Udvardy

General goal

Conserve global habitat and species diversity

Purpose

Guide establishment of global network of natural reserves

Key design consideration

Give equal stress to structural and taxonomic differences of ecosystems

Scale

Level 1, biogeographical realms (regions): used Clements & Shelford’s (1939) biomes; subdivided regionally, based on a faunal regions scheme (after A.R. Wallace). Level 2, 198 biotic (biogeographical) provinces: subdivided vegetation formations (Weaver & Clements 1938) where <65% shared fauna was found either side of recognized biobarriers (also split off azonal units, e.g. high mountains). The Level 2 provinces had a median size of 306,000 km2 and a mean of 740,000 km2 (Olson et al., 2001). Level 3: biounits (biogeographical units): as instigated by MacKinnon & Wind (1981), 40 finer scale units within Indonesia were recognized by the same protocol as level 2.

Explicitness and reproducibility

Excellent; defined purpose, clear methods, based on well-established schemes; while it is based only on mammal and bird data and the 65% criterion is arbitrary, the scheme is updateable and sensitivity analyses could potentially be run.

emphasizes ecological similarities rather than taxonomic difference, Dasmann’s next step was to divide the biomes of the world into regional subdivisions based on Wallace’s (1876) faunal regions (Palearctic, Ethiopian, Nearctic, etc.) and additional long-recognized transitional areas and biotic subdivisions. The resulting classes were initially termed biotic realms (e.g. the IndoMalayan realm), but were subsequently relabelled as biogeographical realms by Udvardy (1975). The realms were further separated into biogeographical provinces by subdividing physiognomically-defined vegetation formations (based on Weaver & Clements, 1938) on the basis of faunal distinctiveness. This was assessed by comparing the number of species in common between areas divided by barriers that could have some conceivable distributional significance. Areas with less than 65 per cent of their species in common were considered to be separate provinces. Azonal features of strongly contrasting character, such as mountain ranges and island groups (e.g. Lesser Sundas), were classed as separate biotic provinces embedded within the essentially zonal system of province boundaries. In total, 198 biogeographical provinces were identified within the framework (Table 5.3).

The first implementation of the Dasmann–Udvardy scheme on a national scale was in Indonesia, in conjunction with FAO and WWF projects and the drafting of a National Conservation Plan (NCP). In practice, the biogeographical provinces were found to be too coarse a scale to capture the biogeographical variation within Indonesia, particularly in the transitional island region of Wallacea (bridging the Oriental and Australasian faunal realms). Therefore, MacKinnon and Wind (1981) applied the Dasmann–Udvardy 65 per cent similarity threshold once again, using data for birds, but based on smaller geographical units, to distinguish between provinces within the larger islands and island groups. This created a third tier in the hierarchy, termed the biogeographical unit or biounit (Table 5.3, Figure 5.4). The Indonesian NCP plan introduced a set of principles to select areas for the establishment of new protected areas. These were: 1 create a representative system of reserves within each biogeographical division (biounit) identified; 2 within each biogeographical division, give priority to establishment of a major ecosystem reserve, to include continuous habitat types and, if possible, the richest example of those habitats;

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N 0

25f

21b

24f

24e

21e

800 Kilometres

13a

24b

25h 25e 25g

21d 21a

P3c P3b

24g

21g

P3d

24a

25i

13b

P3e

21c

P3g

25b

21f

24d

22a

13c

24c 22b

P3h

13d

23c

23d

P3f 13e P3a

23a P3l

22c

Legend

23b

Alpine (Pasture)

Ironwood Forest

Forest on Ultrabasic

Limestone

Freshwater Lake

Monsoon Forest

Savanna

Tropical Montane Evergreen

Freshwater Swamp

Mangrove

Semi-Evergreen Rainforest

Tropical Montane on Limestone

Tropical Wet Evergreen

Heath Forest Vegetation

Peat Swamp

Tropical Moist Deciduous

Tropical Pine Forest

Upper Montane Forest

Source: The Asian Bureau for Conservation and the World Conservation Monitoring Centre (1995)

Figure 5.4 Dasmann–Udvardy biogeographical provinces for Indonesia, with the third-level ‘biounits’ added to the system by MacKinnon & Wind (1981) overlain on the natural habitat-type boundaries. Biogeographical provinces are indicated by the numbers and biounits within them by letters, e.g. 21, Sumatra (21a, south Sumatra; 21b, north Sumatra; 21c, Mentawi Islands; 21d, Nias and Batau islands; 21e, Simeuleu Islands; 21f, Enggano Island; 21g, Lingga Archipelago). From Jepson & Whittaker (2002b), after MacKinnon (1997).

3 augment these large reserves with smaller reserves to protect special or unique additional habitat types or covering regional variations; 4 include small reserves to protect specific sites of special beauty or interest (MacKinnon & Artha, 1982). The IUCN subsequently commissioned similar planning exercises for the Indo-Malayan and Afrotropical realms (MacKinnon & MacKinnon 1986a, 1986b; MacKinnon, 1997). The Dasmann–Udvardy framework, as implemented in MacKinnon’s regional planning exercises, emphasizes mammal and bird taxonomic differences for the initial map-making. This emphasis on zoogeographical data is balanced at the implementation level by the above reserve selection principles, which emphasize vegetation formations and conservation of large ecosystems. The application of taxonomic distinctiveness is effective at broad spatial scales and in regions with clear and meaningful biogeographical barriers, for example across the islands of Wallacea (Indonesia). However, decisions become rather subjective in areas

with limited topographical variation and weak biogeographical barriers (e.g. within Borneo). This framework was influential in countries such as Indonesia, China, Bhutan and a number of other countries in the Indo-Malayan realm, but was deployed less extensively elsewhere.

5.3.2 Endemic Bird Areas Although the International Council for Bird Preservation was established as a network of national committees in 1922, it was in the 1980s that it initiated steps to become an influential actor within international conservation. The first step was a global assessment of the extinction risk of all bird species (Collar & Andrew, 1988). The project simultaneously flagged the vulnerability of bird species with restricted ranges and equipped the ICBP secretariat with advanced databases of species locality information. This inspired a new project to map centres of bird

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Table 5.4 Properties of BirdLife International’s Endemic Bird Areas scheme: an example of a hotspot approach (modified from Jepson & Whittaker, 2002b). Organization

BirdLife International (developed 1989–1996)

Lead authors

C.J. Bibby, A.J. Stattersfield, M.J. Crosby

General goal

Identify areas richest in range-restricted endemics for priority assignment of conservation action

Purpose

Designate and/or strengthen management of protected areas within areas possessing two or more endemic bird species

Key design consideration

Select a meaningful threshold of range size and number of endemics to generate a manageable set of priority areas

Scale

Endemic Bird Areas delimited using taxonomic and range data. EBAs are areas encompassing two or more bird species, each of range <50,000 km2 (follows Terborgh & Winter, 1983)

Explicitness and reproducibility

Good: defined purpose, pretty clear on methods, but based on one taxon and arbitrary range size threshold. Updateable.

endemism and to launch this as an accessible policy brief under the title ‘Putting Biodiversity on the Map’ at the 1992 Earth Summit. The Endemic Bird Areas (EBA) scheme is a form of global hotspots analysis because it advocates greater targeting of resources on discrete areas that are rich in unique kinds of organisms that are, or may become, endangered (Table 5.4). The method of identifying and mapping EBAs was informed by earlier analyses of bird distributions in Africa by Hall & Moreau (1962), and in South America (Colombia and Ecuador) by Terborgh & Winter (1983). In particular, the latter study mapped bird species with ranges less than 50,000 km2 so as to locate areas of concentrated endemism for designation as reserves. The ICBP team adopted the same 50,000 km2 criterion to delimit 2,649 ‘restrictedrange’ bird species globally (Long et al., 1996). Locality data for these species were collated from published sources, museum catalogues and ICBPs observer networks and their distributions were then mapped (involving some interpolation and extrapolation) and overlaid. Areas where two or more of these rangerestricted species co-occurred, and where their global ranges were confined, were termed endemic bird areas (ICBP, 1992; Long et al., 1996; Stattersfield et al., 1998). Many EBAs were found to be geographically discrete and thus easy to determine. However, in some areas, with complex distributional patterns, there could be multiple possible configurations. To deal with this complexity, multivariate numerical classification analyses

were used as an aid, together with other locality and habitat data. Where there were particularly complex patterns of shared species, it was decided to take the commonest ‘sharing pattern’ to identify the EBAs. For example, the north Venezuelan mountains were made a separate EBA, despite sharing 12 species with five other EBAs, because there are six species confined to them. In total, 218 EBAs were identified. They vary in size from a few square kilometres to more than 100,000 km2, and the numbers of restricted-range bird species they support varies from two to 80. The development of the EBA scheme was linked to the re-naming and re-branding of the ICBP as BirdLife International, providing in the process both a more strategic approach to international conservation and a platform for the opening of new regional offices and co-operative agreements with governments. The first national-scale application of the EBA approach was again in Indonesia, where 24 EBAs were delimited (Figure 5.5). As biogeographical entities they nested within the biounits identified by MacKinnon and Wind (1981) and were used as an advocacy tool to give fresh impetus to implementation of the Indonesian NCP and in particular to draw attention to the conservation importance of the region of Wallacea. This approach complemented work by other international conservation groups that had focused their attention on reserve development in western Indonesia, where it was easier to attract funding due to the presence of charismatic megafauna, including the orang-utan.

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N 0

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Figure 5.5 The original (1992) version of the Endemic Bird Areas of Indonesia. Each EBA is designated by a number and shaded to distinguish it from adjacent EBAs. After International Council for Bird Preservation (1992), re-drawn from Jepson and Whittaker 2002b.

As with the Dasmann–Udvardy framework, national level application required the development of subdivisions to guide the selection of reserves or to provide a new and compelling justification for earlier proposals. In conjunction with the Indonesian governmental organization, PHPA, the Indonesian office of BirdLife International produced a policy report that subdivided EBAs by area and by habitat type. The report included maps and off-the-shelf cases for designation that could be reproduced by district and provincial forestry departments responsible for proposing key areas for designation. This strategy met with some success in that, between 1995 and 1999, two new national parks were designated on Sumba, four new key reserves were designated on Timor, and a major GEF-financed project was prepared to establish five new reserves in Maluku. The latter project was later cancelled because of political and social instability. The EBA approach has the merit of an explicit purpose and a transparent and repeatable methodology. The method could potentially be developed to identify centres of endemism at different taxonomic levels or (with greater effort) using a different range size threshold. This allows conservation recommendations derived from EBAs to be independently assessed, revised or refined.

However, while this is a repeatable methodology, decisions taken regarding several key criteria (e.g. size of range, number of endemics, and degree of range overlap), and the user decisions required in dealing with complex areas, as with all such schemes inevitably has a bearing on precisely which areas are selected. Moreover, possession of a minimum of two endemic species, each of range of <50,000 km2, is a fairly crude indicator of the degree to which the target species, and indeed other aspects of biodiversity, may actually be threatened and in need of protection. For instance, EBAs identified in western Indonesia are all in mountainous regions that are relatively intact and secure, whereas EBAs were not located within the critically threatened lowland ever-wet forests, despite their being home to some 164 bird species that are endemic to the region. The reliance upon, and focus of, EBAs on a single popular group of organisms provided both the strength (public support, quality of data), and the limitations of the EBA approach, the latter including the generally untested assumption that EBAs identified areas requiring conservation action. Despite such shortcomings, the EBA approach provided valuable new impetus to efforts to establish protected area networks and it increased the prominence of endemic species in conservation discourse.

The distribution of diversity: challenges and applications 5.3.3 Conservation International’s hotspots ‘The traditional scattergun approach of much conservation activity, seeking to be many things to many threatened species, needs to be complemented by a “silver bullet” strategy in the form of hotspots with their emphasis on cost-effective measures.’ (Myers et al., 2000, p. 858) Conservation International (CI) was formed in the USA in 1987 by former staff from The Nature Conservancy (TNC). The new organization attracted some of America’s new breed of entrepreneurs onto their board (notably the Intel founder and multimillionaire Gordon Moore) – people who really understood the power of brand and branding. By the 1990s, there were numerous conservation groups jostling for the attention of decision makers and donors and, this competition intensified with the promise of massive new inter-governmental funds for biodiversity conservation following the 1992 Earth Summit. CI stepped into this arena with a businesslike approach, seemingly viewing such potential donors and agencies rather like customers in need of a service, and the service they aimed to provide was an efficient and scientifically credible global conservation scheme to target these new funds. At the centre of their strategy, CI re-launched an approach to targeting resources at ‘biodiversity hotspots’ developed some years earlier by Norman Myers (1988, 1990). Myers’s argument was that, given the inadequacy of funding to save biodiversity, global conservation efforts should be focused into those regions of the world that supported the greatest amount of unique and threatened species diversity. CI argued that a ‘silver bullet’ approach of targeting scarce financial resources into a few such regions would provide greatest conservation return per dollar spent and should be preferred over all other ‘scattergun’ approaches. Moreover, CI not only re-ran Myers’ earlier analysis, expanding the number of hotspots, but crucially developed a strategy to invest heavily in and promote their new ‘hotspots’ brand. This involved engaging leading PR companies, the publication of a cover story article (Myers et al., 2000) and supplement in the prestigious scientific journal Nature, a substantial supporting book (Mittermeier et al., 1999), highlevel conferences and seminars, and presentations in ‘hotspot countries’.

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CI hotspots had all the key qualities of a great brand: it made people aware of the biodiversity conservation solution (strategic awareness); it had, through its publication in Nature, a claim to scientific approval and credibility (perceived quality); and, at the time, it was the only global prioritization scheme on offer (singular distinction). In the three years following the launch of the hotspots initiative, $750 million of biodiversity funds were raised, enabling the organization to extend its organizational reach geographically and across sectors. In short, it helped CI become the preferred partner of choice for many of the world’s major companies, donors and celebrities. The CI hotspots scheme swiftly attained the status of – and remains – the best known and most widely referenced global conservation prioritization framework. There have so far been four different generations of the hotspots framework. Myer’s original (1988) version of the scheme focused entirely on tropical forest areas and identified ten priority areas. He subsequently expanded this set by the addition of four more tropical forest areas and four Mediterranean areas (Myers, 1990). The approach taken in both of these original papers was to distil quantitative estimates of diversity and of habitat loss from available literature sources, comprising diverse forms of data and estimates of highly variable precision and substance. The version we term CI-2000 hotspots (Myers et al., 2000) consisted of 25 areas and was based on a much more comprehensive assessment by a large team of contributors using rather more stringent criteria. The revised CI-2004 hotspots scheme was expanded once again, to include 34 areas, largely in order to expand geographical reach in the light of fundraising success (Figure 5.6; Mittermeier et al., 2004). The criteria for designating an area as a biodiversity hotspot within the CI hotspots framework are extremely simple at first sight (Table 5.5) and amount to a combined hotspots/threatspots analysis. Areas are designated providing they possess >0.5 per cent global plant diversity (1,500 species) as endemics, and providing they have lost >70 per cent of their primary vegetation. The first criterion follows the rationale that plant diversity can be used as a surrogate for the likely diversity of other taxa and that, if there is investment in such highly diverse areas that are under such pressure, general biodiversity benefits will follow. Having designated their 25 areas in the CI-2000 scheme, the authors of the scheme reported that the areas also

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Mountains of Central Asia

Caucasus California Floristic Province

Madrean Pine-Oak Woodlands

Mountains of Southwest China

Mediterranean Basin

IranoAnatolian

Caribbean Islands

an

e

Philippines

Tropical Andes Chilean Winter RainfallValdivian Forests

New Zealand

Af r om

Guinean Forests Cerrado of West Africa

East er n

Polynesia Micronesia

on t

Mesoamerica TumbesChocóMagdalena

Japan

Himalaya

Atlantic Forest Succulent Karoo Cape Floristic Region

IndoBurma Western Ghats and Horn of Sri Lanka Africa

PolynesiaMicronesia Sundaland Wallacea East Melanesian Islands

Madagascar and the Indian Ocean Islands Coastal Forests of Eastern Africa

Southwest Australia

New Caledonia

MaputalandPondoland-Albany

Conservation International

HOTSPOTS Wilderness Areas

New Zealand

February 2005

Figure 5.6 Conservation International’s hotspots. This revised version is termed here the CI-2004 scheme. For further information see their website (www.conservation.org).

Table 5.5 Properties of Conservation International’s hotspots scheme, the most influential such scheme globally. (key sources: Myers et al., 2000; http://multimedia.conservation.org/cabs/online_pubs/hotspots2/introduction.html, visited August 2008) Organization

Conservation International (developed 2000–2004).

Lead authors

N. Myers (1988, 1990), with R.A. Mittermeier and others (2000–2004).

General goal

To allow large numbers of species to be protected for least cost outlay.

Purpose

Provide a framework for fundraising, with resources then targeted within regions combining high diversity and threat.

Key design consideration

Select a meaningful measure of diversity and threat and identify the geographical entities to be used, in order to generate a shortlist of priority regions for immediate mobilization of resources.

Scale

Regions chosen that possess >0.5% (1,500) of the world’s plant species and which have experienced the loss of >70% of their primary vegetation cover based on compilation of diverse data sources. In the 2000 version, the areas identified within the framework varied in size from 18,600 to 2,362,000 km2, judged by the data for the original extent of primary vegetation cover (median size 324,000 km2; mean size 787,760km2 (Olson et al., 2001).

Explicitness and reproducibility

Mixed: clearly defined purpose and key criteria, but the criteria for defining the regions enclosed within the hotspots were rather opaque. Most recent major update (2004) is more explicit on this point, demonstrating some sensitivity changes due to alteration of underlying choice of base map units.

The distribution of diversity: challenges and applications contained 35 per cent of the terrestrial vertebrate species of the world as endemics, while the 2004 version is claimed to embrace over 50 per cent of the world’s plant species and 42 per cent of terrestrial vertebrate species as endemics (Mittermeier et al., 2004). The second criterion presupposes that we are able to assess general drivers of species loss as a function of habitat loss, and that we can determine accurate baseline data for pristine habitats. In addition to the two stated criteria, a third key determinant of the outcome of the hotspots analysis is how the underlying areas are determined in the first place. As the CI hotspots scheme is so important and influential, it is especially relevant to evaluate the scientific merits of the scheme critically. The diversity of sources used in the analyses makes it hard to assess independently the quality and comparability of the diversity and habitat loss data used in the hotspots analyses. As discussed in Chapter 4, for plants we still lack systematic species range maps for most of the globe, with knowledge of plant species diversity regarded as poor or very poor across large parts of South America, Africa, Asia and Australia (Kier et al., 2005). Similarly, the assessment of how much of a region’s primary vegetation cover has been lost can be highly dependent on assumptions made about the nature of the ‘original’ vegetation cover (see Chapter 3). In practice, some commonly assumed baselines for pre-Anthropocene states are based on false assumptions about the degree to which a region originally supported forest habitats (Virah-Sawmy et al., 2010), while on the other hand some highly biodiverse areas of tropical forest have been found to be underlain by evidence of once dense human populations and former episodes of forest clearance (e.g. Brncic et al., 2007). In addition, estimates of habitat conversion, particularly with regard to the tropical forest regions, have been shown to produce highly inconsistent outcomes. Surveys conducted at different points in time, or by different teams, have produced conflicting results (Grainger, 2008). However, it would be fair to counter that any alternative metric of threat would encounter some operational difficulties, and that the use of the 70 per cent threshold provides a reasonable first approximation. A further concern, as with all such analyses (Chapter 4), is the problem of deciding what constitutes the most appropriate underlying map of nature, i.e. the appropriate geographical units to be used in the analysis. Examination of the output maps from the four

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generations of the hotspots scheme reveals that the original ten areas were each long, thin areas, comprising a stretch of the Atlantic coastal forest of Brazil, a thin band in the uplands of Western Amazonia, the eastern mountains and coastal plain of Madagascar and so on. The rule base for deciding where to draw the borders of these units was not specified (see Myers, 1988, 1990). As the number of hotspots has expanded, so too have the geographical extents of most of these original small areas, at least as represented on the maps. The most dramatic expansion in the size of the envelopes comes with the CI-2004 scheme, in which the problem of the underlying map base has now been partially addressed by the pragmatic step of aligning hotspot boundaries with WWF Ecoregions (themselves an amalgam of pre-existing and novel compositionalist and functionalist classifications). ‘Finally, delineating hotspots is by no means an exact science. It requires that a line – that might be easily discernible or rather vague on the ground – must be drawn to represent a transition between two habitats. The map of Ecoregions developed by the World Wildlife Fund-US is now the most widely used system for such bioregional classification. In order to facilitate analysis, interoperability, and collaboration, we have therefore gone to considerable lengths to ensure that … the boundaries of the hotspots (and those of the high biodiversity wilderness areas) correspond directly to those of the World Wildlife Fund-US Ecoregions.’ (www.biodiversityhotspots.org/xp/hotspots/ hotspotsscience/Pages/hotspots_revisited.aspx) In addition to expanding the size of envelopes in the CI-2004 framework, the number of hotspots has expanded to 34. One important goal was to greatly expand the coverage of islands of high biodiversity value (especially in the Pacific) that might otherwise have ‘slipped through the net.’ This is a pragmatic expansion, described as such by Mittermeier et al. (2004), who acknowledge that the floristic affiliations and boundaries of the resulting hotspot units are sometimes tenuous. Taking the Mediterranean as an example, neither the 1988 nor the 1990 scheme featured a Mediterranean hotspot, but it is included in the CI versions. This hotspot, as of the CI-2004 version, comprises the major part of the Iberian Peninsula (but not

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its northernmost strip), the whole of the Italian Peninsula and rather varying extents of other countries surrounding the basin, plus a large envelope extended into the Atlantic to embrace the Macaronesian islands. Macaronesia is a recognized biogeographical unit, comprising the Azores, Madeira, Cape Verde, the Salvage Islands and the Canaries, with known affinities with the south-western tip of the Iberian Peninsula and the extreme north-western fringe of North Africa. The construction of this hotspot makes some sense in biogeographical terms – all of these areas share some similarities – but does not appear to be based on a particular antecedent biogeographical map. If it is based on a specific antecedent map, it is clear that other lines and envelopes could be drawn, depending on the criteria chosen. Thus, decisions made as to the appropriate biogeographical units to use may determine whether particular areas become or remain hotspots. An example of this is the Juan Fernández Islands, which do not qualify on their own as a hotspot and were not mapped in the CI-2000 version, but which are embraced in an expanded version of the Chilean Winter Rainfall/ Valdivian Forests hotspot in the CI-2004 version. The above examples illustrate that it is entirely reasonable to undertake critical evaluation of the hotspots framework and that the scientific clarity and internal consistency of the framework can be criticized. Moreover, it can be of enormous importance whether an area is embraced or excluded, as this determines the ‘winners’ and ‘losers’ in terms of conservation investment and attention. For instance, Bates & Demos (2001) bemoan the failure to include Amazonia within the hotspots scheme, noting that while much of the forest cover of the Amazon Basin remained intact, particular areas of high biodiversity value, such as the Belém/Pará region, had experienced very significant degrees of habitat loss. Consideration of a $40 billion government road and development programme targeted on the southern Brazilian Amazon coincided with the failure to recognize this region as a global priority within CI-2000 hotspots, and Bates & Demos (2001) argued that this sent the wrong signal to the government of Brazil at a key moment in regional planning. Similarly, Jepson & Canney (2001) point out that the scheme omits systems arguably central to addressing the foundational values of conservation, such as the last great megafaunal assemblages of the African savannas.

To continue with our focus from sections 5.3.1 and 5.3.2 on Indonesia, most of the archipelago is located within the Sundaland and Wallacea CI-hotspots. However, Indonesian territories on the island of New Guinea fail to qualify as a hotspot despite exceptionally high plant because forest conversion has (fortunately) not yet reached the level of 70 per cent. This is in contrast to the East Melanesian islands, which were defined as a hotspot in the CI-2004 scheme due to recent habitat loss. Such examples are indicative of the reactive character of the hotspots criteria, responding to excessive habitat loss rather than focusing resources on areas before they reach the 70 per cent threshold. In practice, the high endemism and growing threats make the island of New Guinea an important international conservation priority, notwithstanding that they are not included in the hotspots scheme, and this is recognized explicitly by CI, which has included New Guinea in a set of five High-Biodiversity Wilderness Areas (see www.conservation.org/explore/priority_ areas/wilderness/pages/default.aspx), formerly termed Major Wilderness Areas (e.g. Myers et al., 2000). The other currently recognized areas under this ancillary framework are Amazonia, the Congo basin, North American deserts, and the Miombo-Mopane woodlands and savannas of southern Africa. Completing a three-pronged suite of Priority Areas schemes alongside the hotspots and wilderness Areas, CI have also identified three areas under an Oceans and Seascapes framework, namely the Bird’s Head Seascape in eastern Indonesia, The Eastern Tropical Pacific Seascape and the Sulu-Sulawesi Seascape. It should therefore be understood that CI is not limiting its attention only to the biodiversity hotspots areas, but rather that the hotspots scheme is embedded within a broader Priority Areas framework. Nonetheless, it is the hotspots scheme that has attracted greatest popular and scientific attention. As Myers (2003) somewhat waspishly notes in response to criticism of the approach, the science involved in the CI hotspots scheme has been deemed rigorous enough to capture funding of over $750 million. In this regard, the CI-hotspots scheme has been the centrepiece of an enormously successful strategic conservation fundraising effort. The approach has also generated an extraordinary resonance among the scientific community, with academic paper after paper making claim to their study being intrinsically important on the grounds that the work is focused on the ecology, biogeography or conservation of one of the world’s biodiversity hotspots.

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Table 5.6 Properties of the WWF Ecoregions scheme, an influential approach intersecting a functional ecosystems approach with the notion of biogeographical representation (key reference: Olson et al., 2001). Organization

WWF-US (developed 1991–present).

Lead authors

D.M. Olson, E. Dinerstein and colleagues.

General goal

Promote conservation of terrestrial, freshwater and marine ecosystems harbouring globally important biodiversity and ecological processes.

Purpose

Support two-pronged strategy of establishing protected areas and achieving sustainable management in non-reserve matrix.

Key design consideration

Moving away from geo-political boundaries to planning within ecologically and biogeographically derived areas.

Scale

Intersection of biogeographical regions with maps of major ecosystem types derived from varied pre-existing data sources and schemes, following a controlling factors rationale, to produce a zonal classification of major ecosystem types, resulting in a set of 867 terrestrial ecoregions, with a median size of 56,300 km2 and a mean size of 150,000 km2 (Olson et al., 2001).

Explicitness and reproducibility

Mixed: purpose all-embracing; where no pre-existing scheme, appears to conduct gestalt synthesis of various schemes and criteria, relying both on formal analysis of data and widespread expert consultation to refine the scheme and map.

Notwithstanding, in purely scientific terms, it is clear that the scheme is just one map of threatened nature (or so far, four versions of one sort of map of threatened nature) and one that should be subject to the closest scrutiny for the very reason that so much money is being raised and channelled through the scheme. Those who find fault with it on scientific or other grounds need not, therefore, be considered ‘dissidents’ or dismissed as adherents to ‘scattershot’ conservation (cf. Myers, 2003, p. 917, p. 916). In any event, without such ‘scattershot’ conservation many highly valued areas around the world would lack due protection. Moreover, beyond cross-examining the science underpinning the approach, there is also the key question of how well CI will manage to deliver on their goals in terms of on-the-ground conservation action within their Priority Areas. As will be evident from the above, the hotspots scheme is primarily a framework for capturing and then directing international investments in biodiversity conservation into discrete regions. It identifies very large geographical areas and lacks a framework for developing or investing in reserve networks. The development of the Key Biodiversity Areas approach (see below) can be understood as an attempt to link hotspots with a reserve selection framework (see section 3.5.5).

5.3.4 The WWF Ecoregions scheme Ecoregions, representing distinct biotas … , are nested within the biomes and realms and, together, these provide a framework for comparisons among units and the identification of representative habitats and assemblages. (Olson et al., 2001, p. 933) The WWF Ecoregions scheme (Table 5.6) represents an attempt to provide a more refined global map for conservation action than provided by the earlier IUCN biogeographical regions scheme. The scheme can also be understood as means to attract the massive increase in funds for international biodiversity conservation available from the GEF, bilateral donors and US foundations. As a member of the established WWF organizational family, WWF-US (the originator of the scheme) sought to develop a scheme that would provide a global programme framework for the entire WWF network. Just as CI had done, WWF adopted and extended an existing scheme, namely the ecoregional approach to land classification developed in North America, which subdivides regions into successively finer functional units following a controlling-factors methodology (Chapter 4). This approach, being zonal in nature,

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could accommodate all WWF international and national programmes, and it was already being used (e.g. by TNC) to guide US strategy. It thus had both established scientific credentials and the potential for multi-agency support internationally, particularly given its resonance with the human livelihood and development criteria that were increasingly being attached to biodiversity funding following the 1992 Rio conference. As a zonal approach, it also had the virtue of being distinct from, and complementary to, the CI hotspots scheme. Having first established the units, i.e. the WWF Ecoregions, the overall aims of ecoregion-based conservation were stated to be to pursue a ‘two-pronged strategy of establishing protected areas and achieving sustainable management of the lands and waters outside protected areas’ (Ricketts et al., 1999). As discussed in Chapter 4, the approach of classifying terrestrial habitats into functional ecological units was developed by R.G. Bailey (e.g. 1996) and J.M. Omernik (e.g. 1987) with the purposes of optimizing land-management goals within North America. Ecoregions sensu Bailey (1996) are delineated at three main levels. In the top tier are the domains, four ecoclimatic zones (humid tropical, humid temperate, polar, and dry) obtained by simple overlay of global thermal and moisture patterns. These domains were subdivided again on the basis of climatic data into 31 second tier ecoregions or divisions. Bailey’s divisional system also distinguishes between zonal and azonal ecoregions. Azonal ecoregions are, for example, wetlands or alpine ecosystems that can occur in any zone where the appropriate geomorphology occurs (e.g. the zonal ‘Icecap Division’ is matched by the azonal ‘Icecap regime Mountains’). The third principal tier recognized landform as a key determinant, together with maps of potential vegetation, to reflect the limits of recognizable mesoecosystems (Bailey, 1996). These landscape mosaics may be further subdivided into smaller microecosystems based on edaphic factors. Omernik’s system is broadly similar, with the same delineators used at the macroscale to produce level II ecoregions and level III ecoregional divisions informed by land-use pattern, edaphic data and vegetation maps (Omernik, 1987). While each scheme is built upon pre-existing data layers, the decision criteria for combining these data are subjective and the extent to which the resulting units accurately reflect ecological boundaries and transition zones varies (Chapter 4).

While providing a general model and methodology for determining ecoregions, the particular data layers are not available for all parts of the world and, in any event, the goals of WWF varied somewhat from the original land-management oriented schemes. It was therefore necessary to modify the approach to generate regional ecoregional schemes that, when combined, would provide a global WWF Ecoregional framework (Olson et al., 2001). The Bailey/Omernik ecoregions were delimited based on a controlling factors methodology (i.e. by using the factors believed to control ecosystem boundaries as the proxies for their delimitation). However, an ecoregion as defined in the WWF Ecoregions framework is ‘a large area of land or water that contains a geographically distinct assemblage of natural communities that: a share a large majority of their species and ecological dynamics; b share similar environmental conditions, and; c interact ecologically in ways that are critical for their long-term persistence.’ (see www.worldwildlife.org/ science/ecoregions/item1847.html) In practice, the most expedient approach to determining these areas was to amalgamate available regional and national habitat and distribution maps, using expert review to refine the outcomes. Hence, Omernik’s ecoregions were adopted for North America, a number of pre-existing schemes were used in the Latin American review, and in the Asian-Pacific region (including Indonesia), MacKinnon’s biounits (above) were combined with existing forest cover maps as the start point in the identification of ecoregional units (Figure 5.7). The upshot is that while the various regional reviews strive for consistency of approach and end result – a hierarchy based on habitat types in which an ecoregion is a recognizable ecosystem of regional extent (Dinerstein et al., 1995) – they necessarily have adopted slightly different routes, data sources and criteria to determine their units (Olson et al., 2001; Jepson & Whittaker, 2002b). The emergent outcome globally is that the ecoregions in the WWF scheme represent the intersection of various pre-existing and novel biogeographical regional frameworks and major ecosystem type analyses to generate a new synthesis that recognizes the biogeographical distinctiveness of the same major ecosystem type in different areas of the world (Olson & Dinserstein, 1998; Wikramanayake et al., 2001). Thus, while being predominantly a zonal

The distribution of diversity: challenges and applications

Bioregions

Biomes

Ecoregions

• Indian Subcontinent • Indochina • Sunda Shelf & Philippines • Wallacea • New Guinea and Melanesia

• Tropical & Subtropical Dry Forest • Tropical & Subtropical Moist Broadleaf Forests • Tropical & Subtropical Grasslands, Savannas & Shrublands • Tropical & Subtropical Conifer Forests • Deserts and Xeric Shrublands • Temperate Conifer Forests • Temperate Broadleaf & Mixed Forests • Montane Grasslands & Shrublands • Flooded Grasslands & Savannas • Mangroves

• East Deccan dry-evergreen forests • Sri Lanka dry-zone dry evergreen forests • Khathiar-Gir dry deciduous forests • Chhota-Nagpur dry deciduous forests • Northern dry deciduous forests • Narmada valley dry deciduous forests • Central Deccan plateau dry deciduous forests • South Deccan plateau dry deciduous forests

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Figure 5.7 Exemplification of the hierarchy of spatial units used in the conservation assessment of the Indo-Pacific within the WWF Ecoregions framework. Re-drawn from Wikramanayake et al., 2002 – their Figure 1.

approach to identifying regional-scale ecosystems, the WWF Ecoregions scheme also aimed to allow conservation attention to be focused on a representative array of the world’s major distinct ecosystems (Olson et al., 2001). An important goal of the WWF Ecoregions framework was that the resulting units should ‘approximate the dynamic arena within which ecological processes most strongly interact’, thereby allowing conservation planning that considered not only distributions but also the ecological phenomena involved, such as migrations, predator/prey interactions among megafauna, or the ecosystem functional properties arising from vegetation/climate interactions across large forested regions (Olson et al., 2001, p. 937). These key goals, of (i) identifying regional-scale ecosystems that are (ii) biogeographically representative and which (iii) maximize internal flows and linkages, are of course extremely difficult to optimize in a single scheme, particularly given the inconsistencies of data available and the differing spatial scales of pattern and process evident in different regions of the world. Hence, it is relatively easy to find fault with particular parts of the WWF Ecoregional framework.

We may illustrate this once again with reference to Indonesia, where the regional ecoregions map (Figure 5.8) demonstrated varying degrees of congruence and conflict with pre-established conservation planning frameworks, and in which ecoregional boundaries crossed several key major ecosystem reserves within the Indonesian protected area system (Jepson & Whittaker, 2002b). In particular, the goal of distinguishing mesoecosystems, representing ‘dynamic arenas’, appeared not to have been met in some parts of this complex island region. So, for example, the large (1.8 million ha) Middle Mahakam wetland system in East Kalimantan appears to be a clear example of a functionally (hydrologically) interconnected system, but was subdivided by Wikramanayake et al. (2001) on the basis of dominant vegetation formations into a complex of three WWF Ecoregions, when arguably they might be considered the next tier down in the hierarchy. On the other hand, two small islands (SangiheTalaud and Bangai-Sula) were combined with lowland areas of Sulawesi into a single ecoregion, despite a lack of the ecological flows and linkages that are invoked within the ecoregion rationale (Jepson & Whittaker,

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Sunda Shelf and Philippine Bioregion Sanglir-Talaud Islands

107 Danau Toba

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107 90 Middle Mahakam tectonic basin

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Wallacea Bioregion 91 93

94

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92

800

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Kilometres

Figure 5.8 World Wildlife Fund Ecoregions of the Indo-Pacific (Indonesian excerpt). The numbers refer to specific ecoregions (e.g. 82: Sumatran lowland rain forests; 83: Sumatran montane rain forests; 84: Mentawi Islands rain forest). The dashed lines distinguish the boundaries of the three bioregions, which are equivalent to the traditional zoogeographical Oriental and Australian regions, with the transitional area of Wallacea between them. Wallace’s Line itself corresponds to the dashed line that runs from top to bottom of the map. From Jepson & Whittaker (2002b), after Wikramanayake et al. (2001).

2002b; and see reply by Wikramanayake et al., 2002). Such problems arguably feature in any such exercise of biogeographical delimitation, and especially so in complex regions such as Indonesia, including as it does the transitional biogeographical area of Wallacea (see discussion of Wallacea in Cox, 2001). So far, we have discussed only one aspect of the WWF Ecoregions framework, which is the determination of the mapping units. Once the units are determined, they may then be used in priority-setting analyses, which may be undertaken reiteratively and at varying scales of resolution. Prior to the completion of the global map of 867 WWF Ecoregions, the same team had undertaken a global analysis of priority ‘ecoregions’ at a slightly coarser resolution (Olson & Dinerstein, 1998). This priority-setting exercise was termed the Global 200 (see Figure 5.9), although in fact a total of 233 Global 200 ecoregions were identified (Olson & Dinsertein, 1998), and Olson et al. (2001) refer to there being 237 units in this framework three years later. These 237 Global 200 Ecoregions contain 402 of the WWF Ecoregions that emerged from the process of compiling the eventual set of ecoregions as described above,

illustrating the difference in scale and resolution between the two sets of WWF Ecoregional analyses. As indicated, the key purpose of the Global 200 exercise was to identify high-priority examples of 21 major habitat types (MHT) from each of seven biogeographical realms globally (in their framework, the Afrotropical, Australasia, Indo-Malayan, Nearctic, Neotropical, Oceania, Palearctic realms). Consideration was given to the diversity of each MHT (species richness and endemism, higher taxonomic uniqueness), the possession of extraordinary ecological or evolutionary phenomena (e.g. large intact vertebrate assemblages or extraordinary adaptive radiations) and to the global rarity of the MHT. An example of the Global 200 is the Sumatran Islands Lowland and Montane Forests ecoregion (No. 26), which is an Indo-Malayan realm ecoregion located within Indonesia. Its MHT is tropical and subtropical broadleaf forest and it comprises areas of northern Sumatra and smaller islands to the north. Key biodiversity features warranting the inclusion of ecoregion 26 in the Global 200 priority-setting exercise include the opportunity to conserve a number of endangered and charismatic species, including Sumatran tigers,

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Figure 5.9 The ‘Global 200’ ecoregions, being those deemed ‘most important’ by WWF. Source: www.panda.org/who_we_ are/history/wwf_conservation_1961_2006/ (See Plate 5.9 for a colour version of this image.)

Sumatran rhinoceros and orang-utans, while simultaneously saving forests that are rich in lesser-known endemic birds, other animals and plants. A key pragmatic reason for focusing on the area is the huge pressure on remaining forest areas arising from rampant deforestation in Sumatra (see www.panda.org/about_ our_earth/ecoregions/). Hence, it can be seen that the Global 200 Ecoregions scheme emerged simultaneously and organically from the main WWF Ecoregions framework but served a different purpose (WWF 2000). While the latter was principally aimed at generating a (zonal) globally inclusive map of functionally interconnected regions for organizing conservation actions, the former was essentially akin to a hotspots analysis. This is reflected in our classification of the Global-200 as a composite framework in Table 5.1. 5.3.5 Important Bird Areas and Key Biodiversity Areas Important Bird Areas The Important Bird Area (IBA) programme, initiated in 1981, is based on the identification of key attributes

of conservation concern and is a distinct azonal approach to reserve selection (Figure 5.1) of growing influence (e.g. see Box 5.1). The impetus for its foundation lay in the desire for the European Commission (the executive arm of the European Union) to exert its influence over member states in the area of the environment. In the late 1970s, the chair of the International Council for Bird Preservation’s (ICBP) Continental Section also happened to be a lawyer within the EC. He secured the support of a commissioner for a directive on wild birds, developed in consultation with the ICBP. The Directive on the Conservation of Wild Birds, better known as the Birds Directive, came into being in 1979. Among other things, it required EU member states to designate protected areas (known as Special Protection Areas or SPAs) for the protection of rare and endangered species. Subsequently, the EC wanted to monitor whether or not member countries were fulfilling their conservation obligations. To enable them to do this, they contracted the ICBP secretariat in Cambridge to prepare an EU wide inventory of sites – subsequently termed Important Bird Areas. ICBP cleverly converted the Directive’s list of four types of species needing protection (Article 4) into a set

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Table 5.7 Properties of the Important Bird Areas (IBA) and Key Biodiversity Areas (KBA) schemes (key reference: Eken et al., 2004; and see www.birdlife.org/action/science/sites/index.html). Organization

Birdlife International (formerly, International Council for Bird Preservation), now in alliance with Conservation International and others.

Lead authors

e.g. Grimmett & Jones (1989), Heath et al. (2000).

General goal

Identify sites critical for the persistence of a valued natural attribute or attributes.

Purpose

Identify, protect and manage a suite of sites that are important for the long-term viability of natural occurring populations of species across the range of those species for which a site-based approach is appropriate.

Key design consideration

Construct a set of criteria that capture key dimensions of the spatial distribution of species and that are relevant to conservation action (endangerment, restricted-range, characteristic, assemblage and concentrations) and that can be applied using quantitative measures of species presence. Through the development, application and adoption of such criteria, create a common international standard for identifying important sites for the conservation of biodiversity.

Scale

As of 2009, nearly 11,000 sites in some 200 countries and territories had been identified as Important Bird Areas. Site sizes can vary from the occasional very large area (>1 million ha) to tiny (<10 ha) sites, each of which qualifies by one of the four level A criteria (see text).

Explicitness and reproducibility

Good: the IBA and KBA schemes each provide four main criteria, with Fishpool & Evans (2001) and Eken et al. (2004) providing helpful commentary on choice of thresholds in the KBA version, and data sheets published in support of each designated site. In the case of IBAs, the basis of site selection and details of criteria application have been made publicly available at www.birdlife.org/datazone. The selection of sites from an array of potential ones is conducted by self-appointed groups of experts with limited external review or critique.

of quantifiable site-selection criteria which were published, together with an accompanying site inventory, as Important Bird Areas of Europe (Grimmett & Jones, 1989; Heath et al., 2000). Projects were subsequently launched to identify IBAs in other regions and to develop a network of sites for the protection of the world’s avifauna (Table 5.7). The criteria developed to identify European IBAs were adapted, developed and standardized into criteria for application worldwide (Bennun & Fishpool, 2000; Heath et al., 2000; Fishpool & Evans, 2001). This approach complements the Endemic Bird Areas approach by focusing on sites rather than larger areas, and by its focus on three additional criteria. The ICBP was renamed BirdLife International in 1993, and details of both schemes can be found on their website (www.birdlife.org/). The four principal criteria (level ‘A’ criteria) for a site to qualify in the IBA programme may be crudely summarized as follows:

1 possesses a population of one or more species Red Listed (See Box 4.1) as vulnerable, endangered or critically endangered; 2 provides an important site contributing significantly to the conservation of restricted-range species (i.e. those of ranges <50,000 km2); 3 assists in meeting the target of having adequate representation in a network of all species restricted to a particular biome; 4 represents a key site for congregating species, including waterbirds, some seabirds and migratory species. (See cited publications for more detail.) As pointed out by Eken et al. (2004), the first of these criteria addresses the issue of vulnerability, while the other three fall under the other key consideration of planning protected area networks, i.e. the irreplaceability of the attribute (see Chapter 6 for further discussion of these concepts). The IBA criteria allow the nesting of additional criteria to enable the importance of sites to be identified

The distribution of diversity: challenges and applications and categorized at the regional (level ‘B’ criteria) and/ or sub-regional level (‘C’ criteria). For example, the second European IBA inventory used up to 20 criteria for this purpose (Heath et al., 2000). This is to enable comparisons to be made between countries and across regions. Application of the IBA criteria requires an associated list of eligible species for assessment against each criterion (A1 to A4, above), along with associated numerical population thresholds that must be matched or exceeded in order for the site to qualify under the particular category (Table 5.7). For categories A1 and A2, the data are derived from BirdLife International’s assessments of threatened birds and the lists of restricted-range species compiled to identify Endemic Bird Areas (above). Lists of biome-restricted species are generated for each region and thresholds for congregatory species derived from the best available sources (see Fishpool & Evans, 2001 for explanation). The criteria are applied by national IBA coordinators, usually working under the guidance of an advisory committee supported by experts from the BirdLife International secretariat. This involves comparing data on the population of species and assemblages of species at a site against the defined threshold. Where data are lacking, field surveys are conducted. Clearly this process is dependent on the ability of those implementing the approach to identify a discrete site. To create this possibility, IBA boundaries/ assessment units are defined on the basis of the following criteria: 1 being different in character, or habitat, or ornithological importance from the surrounding area; 2 being an actual or potential protected area, with or without buffer zones, or being an area which can be managed in some way for nature conservation; or 3 being, alone or with other sites, a self-sufficient area that provides all the requirements of the birds, when present, for which it is important (Fishpool & Evans, 2001). These all-encompassing definitions have the practical effect of prompting IBA site identification processes to first look at sites that are already known and defined (conservation areas, research sites) and then to identify and add additional sites. In contrast to the preceding frameworks, and in order to enhance the grounded legitimacy of the scheme and create local site-support networks, the IBA approach specifies a process through which sites should be identified and assessed. A preparation phase

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identifies partners, reviews institutional frameworks and sensitizes stakeholders to the process. The identification phase involves establishing a National Liaison Committee, literature compilation and field surveys, national workshops to apply criteria and define site networks and training and capacity building as required. The third and fourth action phases focus on prioritizing and planning action to protect the IBAs, the formulation of site action plans, site support networks and associated advocacy campaigns. Key Biodiversity Areas As a result of these attributes, the IBA approach is growing in influence. When linked to legislation, as in the European Union, IBA inventories empower citizen groups to hold their government to account for its conservation action. For example, in 1998 the EC took action against the Netherlands in the European court for failing to meet its obligations under the Birds Directive, using the IBA inventory in evidence (see Heath et al., 2000). Similarly, East European countries wishing to join the EU were required to identify Special Protection Areas as one condition of their accession. The BirdLife European network assisted birdconservation groups in accession countries to prepare IBA inventories for their governments as part of this process. This helped to build new relationships between conservationists and government officials in postcommunist countries as well as ensuring that key sites received the recognition and protection they needed. Criteria-based schemes, such as IBAs, align well both with the new managerialism (also called audit culture) in government and its focus on targets and indicators, and with the principle of grass-roots participation in the formulation and delivery of policy. Such schemes also provide a mechanism for identifying the sites required to make up networks of protected areas at a finer (landscape/region) scale, within socalled hotspot regions and within nations. As previously discussed, the CI hotspots scheme is typically too coarse-grained to aid in reserve selection. Hence, following from the fundraising success of the CI-2000 hotspots launch at the turn of the 21st century, there was an urgent need to adopt or generate frameworks that would allow reserve networks to be designed within hotspots and within nations. A natural starting point was to turn to pre-existing planning frameworks, of which the IBAs was the oldest and best-developed (national IBA directories had been

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published for nearly 50 countries by 2004). Others included an Important Plant Areas framework, Prime Butterfly Areas, Important Mammal Areas (in the US), and Important Sites for Freshwater Diversity. Recognizing the benefits of collaborating, a number of leading conservation organizations and networks (including Conservation International, Birdlife, Plantlife International, IUCN, and the Alliance for Zero Extinction) pooled resources and proposed the Key Biodiversity Areas scheme, first formally outlined by Eken et al. (2004), who wrote: ‘In this article, we build on these developments and propose a general framework and associated criteria for identifying key biodiversity areas (KBAs). The overall goal of the KBA methodology is to suggest universal standards for selecting sites of global significance for conservation through the application of quantitative criteria.’ (Eken et al., 2004, p. 1111). The KBA scheme adopts the same four criteria as the IBA scheme, refining them for application to other taxa in addition to birds. As well as providing a criteria-based scheme for selecting sites to add to protected area networks, the movers of the scheme expressed the hope that the KBA framework can be used to monitor compliance with the Convention on Biological Diversity as well as progress towards biodiversity-related targets in other major international environmental initiatives, such as the Millennium Ecosystem Assessment. This form of use of the KBA site lists would provide a new form of regulatory ‘bite’ to international protected area policy. Despite the growing influence of the IBA scheme and the taxonomically-expanded KBA framework, these schemes have received relatively little academic attention thus far. This may be largely a function of the methodology involved, which, as described above, involves many actors taking multiple forms of information into account, in ways that are not easily reduced to the operation of computer algorithms (Table 5.7). Unlike computer-based systematic conservation planning analyses discussed in Chapter 6, they are thus not analytically tractable within the normal framework of typical life science papers. The scheme’s originators recognize that the KBA approach does not set out to design a site network of maximized efficiency, but rather that: ‘… it provides the universe of sites significant for conservation, to which complementarity-based methods for reserve selection … can then be applied…’ (Eken et al., 2004, p. 1111).

Within the KBA framework, the leading position of the IBA scheme is such that the IBAs largely appear to provide the starting point around which other data sets are then added. The scientific and practical logic of extending the IBA approach to wider biodiversity has been critically assessed by Knight et al. (2007). Among other things, they note the following: • First, the approach is preliminary and many key methodological elements have not been validated. The strong data sets and extensive networks of local experts that underpinned the IBA foundations in most parts of the world are not readily replicated for other taxa. • Second, the approach is based on applying numerical thresholds using species as the unit of analysis, which is problematic enough in birds but far more so for many taxa (reviewed in Chapter 4). • Third, the approach fails to give any consideration to questions of connectivity between sites across landscapes. Other concerns relate to the absence of scale limits in the application of the criteria. This is not a serious issue in regions such as Europe, where landscapes are predominantly anthropogenic in character and patches of conservation-important habitat are relatively distinct, discrete and small. However, in many regions, any large remaining area of habitat could conceivable qualify as an IBA/KBA. Another concern arises from the difficultly of identifying clear universal thresholds. For example, while defining species as range-restricted on the basis of a global distribution of <50,000 km2 classifies about 25 per cent of birds and of mammals as range-restricted, the figure for amphibians is 60 per cent (Eken et al., 2004). This means that either a different threshold needs to be used or different taxa are being weighted differentially by any automated application of the threshold. Similar problems arise in determining thresholds for how many species have to be globally confined to a particular biome for them to be given priority under the ‘biome-restricted assemblage’ criterion. In addition, there is the question of the maximum permissible size of site. In some regions there is the problem that very many large areas of habitat could qualify as an IBA or KBA. In practice, in some areas of the world, such as Indonesia, the selected IBA list is largely the same as designated under older, pre-existing schemes. This is of course, an entirely pragmatic approach, building around the existing (designated or planned)

The distribution of diversity: challenges and applications protected area system, but it does mean that the extent to which KBA sites have been newly drawn in a bottomup process is quite variable. As pointed out by Eken et al. (2004), once an IBA or KBA network has been designated, it becomes possible to examine the ‘efficiency’ of the reserve network using the systematic conservation planning tools which will be discussed in Chapter 6. In illustration, O’Dea et al. (2006) have undertaken such an analysis to evaluate how well tropical Andean IBA sites represent threatened bird species across five Andean countries. They used data on the location of the 432 IBAs designated in the region and a bird data set derived from the distribution maps of 773 tropical Andean birds classified as at-risk, mapped using quarter-degree grid cells (approximately 769 km2). The bird data, being based on range-filling maps, provide an (over)estimate of the numbers of birds that occur in each cell. Having assigned each IBA to one of 381 of these grid cells, the analysis asked the question ‘do IBAs contain more at-risk bird species than would be expected by chance?’ For the purposes of analysis, this assumes that having even a relatively small IBA within a grid cell in effect ‘reserves’ it. Having determined that the IBA-containing cells did better than a random selection (mean of 34 species versus 23 species), the authors then asked if a more efficient solution could be determined by the use of a common reserve-selection algorithm (the near maximum coverage set). The answer, perhaps unsurprisingly, was that the IBA sites were not optimally distributed and, while 93 per cent of the at-risk birds were represented in the 381 cells containing IBA sites, it would be possible to represent all of these species (at least once) with slightly fewer than 100 grid cells. This test thus shows that the IBAs are certainly better than a random selection of sites, but that this is not the most efficient set of sites that could be selected if the sole goal of the IBA network were to represent range-restricted species. Of course, it is fair to reflect that, first, this is not the sole goal of the network, and second, the analysis itself is weakened by the coarseness of the bird data used, which required the authors to scale-up their analysis to crude grid cells. O’Dea et al. (2006) recognize this limitation, noting that, for example, the Maquipucuna Reserve in north-west Ecuador is a mere 6000 ha yet has records of some 347 bird species, including 27 endemic and 11 red-listed as vulnerable or near-

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threatened. So, while the rationale for their analysis was to explore whether the expert-driven IBA approach might generate a suboptimal conservation solution, its power to determine this was somewhat limited. In practice, placing both approaches in harness may produce benefits, illustrated in this case by the fact that some grid cells considered irreplaceable components of the computer-generated network of sites for at-risk birds were absent from the IBA network. These particular areas would thus be worth further consideration in any expansion of the network, or in alternative conservation planning exercises under way in the region. For further discussion of systematic conservation planning approaches at the landscape scale, see Chapter 6.

5. 4 MAR I N E PR OT ECT ED AR EAS 5.4.1 Status of the marine realm It is becoming increasingly clear that the world’s oceans are in danger of ecological collapse and that conservation action is urgently needed (Jackson, 2008). Overfishing, mechanical damage to habitats, dredging, development, invasive species, climate change, and pollution from terrestrial run-off are individually and collectively causing massive changes to marine ecosystems. Declining indices of mean trophic level (Pauly et al., 1998), loss of top predators (Baum et al., 2003; Worm et al., 2005), phase shifts between ecological communities (Hughes et al., 2005), the development of ‘dead zones’ (Diaz, 2008), and massive coral bleaching worldwide (Wilkinson, 2000) suggest that some of these changes have crossed threshold levels (Jackson et al., 2001; Hughes et al., 2007). Even when the pressures have been removed (for example following the closure of the cod fishery off the Atlantic coast of Canada), populations and ecosystems have not rebounded (Hutchings, 2000; Frank et al., 2005; Jackson, 2008). Recent efforts to quantify these impacts suggest that every square kilometre of the world’s oceans has been subject to some anthropogenic driver of ecological change (Halpern et al., 2008). Using a six-point scale of impact, they calculate that over 40 per cent of the world’s oceans are subject to medium high to very high impact (points 4–6 on the scale). Such scoring systems are, of course, hard to interpret in isolation, but they do have value in demonstrating the spatial variation in the nature and intensity of human impacts on marine

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systems. In this regard, their analyses suggest that the worst impacted marine ecosystems are areas of continental shelf and slope which are subject to both landbased and ocean-based anthropogenic drivers. The marine environment differs from the terrestrial in a number of important ways, and this has impacts both on biogeography (see Chapter 4) and conservation planning (Carr et al., 2003; Lourie & Vincent, 2004): • First, the continuity of habitats in three dimensions, linked by ubiquitous currents, across vast volumes of water, allows (at least potentially) for a greater degree of biological connectivity, especially for organisms with a planktonic life stage or lifestyle. Thus, while robust, repeatable biogeographical zonation is possible for marine regions, the boundaries between regions may be more dynamic and blurred than for terrestrial biogeography. • Second, the Wallacean shortfall (our limited understanding of species’ distributions) is particularly acute for many marine taxa. • Third, historical attitudes towards the marine realm have typically been to view them as a global resource, leading to a classic ‘tragedy of the commons’ effect. Efforts to remedy the scientific and societal/political shortfall are under way and, in common with conservation approaches in terrestrial systems, the last two decades have seen increasing efforts to apply systematic protocols to such analyses (see Chapter 6). It can be argued that marine conservation planning lags behind terrestrial. However, marine conservation practitioners can take advantage of methods and tools developed in the terrestrial arena at an earlier stage, and conversely the differing conservation needs of the marine realm can stimulate changes in governance and conservation practise potentially also applicable to the terrestrial realm (Costanza et al., 1998).

5.4.2 Origins and expansion of the marine protected area estate Marine protected areas (MPAs), place-based conservation in the sea, constitute an important conservation tool. As on land, there has been extensive debate regarding the appropriate location, size, spacing and connectivity of marine reserves (see Lubchenco et al., 2003, and other papers in the same volume). The actual level of protection within MPAs varies considerably, with most allowing some extractive activities, such as fishing, while prohibiting others, such as

drilling for oil or gas. Fully protected marine reserves – areas of the ocean completely protected from all extractive and destructive activities (also known as ‘no take’ zones) – are a special class of marine protected areas equivalent to the IUCN protected area categories I and II (Table 2.2; see Dudley, 2008). The first marine protected areas were traditional sacred places or fisheries protection zones or both (e.g. Johannes, 1978). The oldest ‘modern’ protected area with an explicitly designated marine component is the Royal National Park, New South Wales, Australia, designated in 1879. Elsewhere in the world, the oldest reserves are Breton National Wildlife Refuge, Louisiana, USA (1904), Matang Forest Reserve, Perak, Malaysia (1906), Tsitsikamma National Park, South Africa (1964), Archipiélago Los Roques National Park, Venezuela (1972) and the Underwater Reserve of Monaco, Monaco (1976) (Anon, 2001/2002). As on land, the marine protected area network has grown steadily since the mid-1970s, coincident with various international conservation conventions coming into force (UNESCO in 1970; Ramsar Convention in 1971; UNESCO World Heritage Convention in 1972). Marine protected area planning first featured on the global agenda at the Third World National Parks Congress, held in Bali in 1982, from which stemmed an objective ‘to incorporate marine, coastal and freshwater protected areas into the worldwide network’ (Hayden et al., 1984). Since then, marine planning has gained momentum, and the last 15 years have seen a proliferation of schemes and conservation plans for marine areas, some within a systematic framework, others outside of such frameworks (see Lourie & Vincent, 2004, for review). Calls for protection of 10–30 per cent of marine habitats by 2010–2012 have been made (e.g. the 2002 Plan of Implementation of the World Summit on Sustainable Development, 2003 5th World Parks Congress and 2006 8th Ordinary Conference of the Parties to the Convention on Biological Diversity). However, recent assessments of the current rate of designation suggest that we will fall far short of these targets (Wood et al., 2008). Very little of the area covered by the world’s oceans is currently protected. The most recent review (Spalding et al., 2008) identifies 5,045 marine protected areas that have been statutorily, or non-statutorily designated at national or local levels, covering 2,588,392 km2 or 4.09 per cent of the total continental shelf area. Even though some MPAs extend into deep water

The distribution of diversity: challenges and applications (beyond 200 m water depth), all currently lie within the 200 nautical mile Exclusive Economic Zones (EEZs). Overall, this represents only 1.91 per cent of the waters within EEZ areas, or 0.717 per cent of the entire world ocean surface – meagre in comparison with the 11.5 per cent global terrestrial protected area coverage (Chape et al., 2003). The culture of marine exploitation means that of those ‘protected’ areas, even fewer are ‘no-take’ zones. Overall, only 0.08 per cent of the world’s oceans and 0.2 per cent of the total marine area under national jurisdiction is designated as no-take (Wood et al., 2008), while assessments of the management of many MPAs suggest that most are effectively no more than paper parks (Jameson et al., 2002). Indeed, it has recently been estimated that less than 0.01 per cent of the world’s coral reefs are within MPAs defined as ‘no take, with no poaching, and at low risk’ (Mora et al., 2006). On the plus side, rezoning of the Great Barrier Reef Marine Park in 2004 increased the global no-take area by more than 50 per cent and 100,000 km2 (Fernandes et al., 2005). Further major achievements have come from the designation of the 341,362 km2 Northwestern Hawaiian Islands Coral Reef Ecosystem Reserve (originally designated in 2000 and redesignated in 2006 as a Marine National Monument) and the 410,500 km2 Phoenix Islands Marine Protected Area (formally recognized in 2008). Despite these advances, however, over 87 per cent of 226 coastal political entities (countries, overseas territories and the non-contiguous US states of Hawaii and Alaska) have less than the global average of 1.6 per cent of their EEZs protected. Only nine have more than ten per cent of their EEZ protected and, of those, four have relatively small maritime territories rather than a high absolute area under protection. Of the remaining five, four are overseas territories and one is the non-contiguous US state of Hawaii, and these encompass four of the ten largest marine protected areas (Wood et al., 2008). Since the completion of the various reviews we have just cited, there have, however, been exciting developments such as the Micronesia Challenge (see www.micronesiachallenge.org), whose ambitious goal is to protect more than 30 per cent of marine resources across Micronesia by 2020, as well as a declaration, in January 2009, of three new National Monuments in the Pacific covering a total of 505,775 km2. Moreover, as fish stocks collapse worldwide, there is increasing recognition by many stakeholders

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(including fishery managers) of the need for more effective approaches to sustainable use and ecosystem management (Roberts et al., 2005). Evidence that marine protected areas benefit fisheries exists and is gaining increasing recognition; for example, the single largest predictor of fish abundance and diversity on East African reefs was found to be the duration of protection from fishing (McClanahan & Arthur, 2001). Whereas the predictions of increased fish yields for areas adjacent to those protected are not always met (Hilborn et al., 2004), it is clear that other fisheries management tools, such as quotas, have frequently failed to maintain viable populations in the context of multi-species fisheries. While protected areas should not be seen as a panacea to the ocean’s ills, they are clearly an important and valuable tool. With rapidly increasing knowledge of marine systems, habitats and species distributions, increased accessibility to large-scale data sets and geographical information systems, marine protected area planning will benefit, and more creative, fluid, complementary approaches can be rendered viable. In the following sections we critically examine a selection of global NGO-led conservation schemes. The methods espoused by a particular organization are not proprietary, and many organizations now incorporate multiple approaches in their conservation portfolios. However, as in the terrestrial realm, we can once again recognize that particular schemes focus on particular properties (composition, function, numbers and attributes) and adopt matching strategies emphasizing representation, ecosystem function, biodiversity hotspots and key areas. Gap analyses (Box 5.2) and improved data availability enable assessment of the degree to which these schemes are fulfilling their goals. We once again provide some illustrations of applications of global schemes within an Indonesian/ Southeast Asian context.

5.4.3 A global representative system of marine protected areas Representation was a key early goal for marine conservation, as it was for terrestrial planning, and calls for strengthened biogeographical and ecological representation within protected area systems continue to be made (Roberts et al., 2003). Hayden et al. (1984) attempted to provide ‘an appropriate marine

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Box 5.2 Gap analysis In building networks of protected areas, a common question is ‘what is the most important area to add to the network next?’ Gap analysis is the term given for approaches to answering this question that follow the principle of assessing the properties of sites within the existing protected area network and of potential candidate sites, and of selecting those sites that best fill the gaps identified in the network. In its simplest form, such an analysis can be undertaken by drawing up a matrix of sites and the key properties of interest, and ticking the boxes where those properties exist, thereby identifying which potential sites provide the best gain to the network. Such analysis relies upon availability of reliable and comparable data across sites, but is otherwise extremely simple in execution. Gap analysis can in fact be as simple or as complex a process as desired. The availability of modern computer systems and geographically referenced electronic data has enabled increasingly technically sophisticated forms of analysis of this ilk to be undertaken (as reviewed at length in Chapter 6). Moreover, the scope of gap analysis has been extended in other ways. For example, in a study of cowpea (Vigna) species in sub-Saharan Africa, Maxted et al. (2008) demonstrate that both in situ and ex situ strategies aimed at conserving genetic diversity can be assessed within a gap analysis framework. The steps in their analysis involved: • determining the taxa for analysis and their geographical distributions; • assessing the genetic diversity and the level of threat for each taxon; • assessing the in situ conservation programme (in this case including on-farm conservation of traditional crop varieties); • assessing the ex situ conservation programmes (e.g. in seed banks and botanic gardens); • reformulation of the conservation strategy to focus on the element of the original diversity that is threatened and lacks adequate protection. A more conventional Geographical Information System (GIS) based GAP analysis was provided by Soutullo et al. (2008). They set out to find an answer to the questions of how much land area needs to be protected to meet the CBD target of protecting at least ten per cent of all terrestrial ecoregions, and where the additional protected areas required should be located. They used the World Database on Protected Areas and extracted those reserves that have an IUCN protected area designation and can be located on a map; there were 27,951 of these, covering just under three million km2. Protected Areas were then assigned to one of the 825 WWF Ecoregions from the Olson et al. (2001) data set and those ecoregions with less than ten per cent of their area protected were identified. This analysis indicated that 63 per cent of the terrestrial ecoregions fail to meet the ten per cent protected area threshold, and that to meet it a further six million km2 of protected areas would be required (see Table B5.2a). As an additional step of their analysis, Soutullo et al. (2008) overlaid these poorly protected ecoregions with the CI hotspots maps, the Global 200 Ecoregions and the boundaries of the ‘Last of the Wild’ places (Sanderson et al., 2002). They found that 427 of the 549 underprotected ecoregions were already identified as priority regions within one or more of these CI- or WWF-led strategic conservation planning exercises (62 in all three). Some of the remaining inadequately protected areas were also found to be priority areas within other conservation planning frameworks. Soutullo et al. (2008) argued that their analysis showed that the major NGO schemes can potentially make a substantial and key contribution to CBD targets by directing resources within the existing strategic planning exercises reviewed in this chapter. They also argued that the gap analysis provided a basis for the CBD secretariat to focus pressure on donors to direct conservation efforts towards those ecoregions not currently highlighted (or sufficiently highlighted) in these schemes. This analysis also serves to demonstrate the extent to which the WWF Ecoregions scheme has already become a key data layer in conservation biogeographical analyses (see also e.g. Kier et al., 2005; Loucks et al., 2008).

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Table B5.2a Coverage of the world’s network of terrestrial protected areas for each of the main biogeographical regions. For each region, the number and percentage of ecoregions for which the Convention on Biological Diversity (CBD) 10% target is still not met, and the additional land surface to be protected to meet that target, are shown. Source: Table 1 from Soutullo et al. (2008).

Region

Number of ecoregions

Number of underrepresented ecoregions

Australasian Afrotropical Indo-Malayan Nearctic Neotropical Oceanic Palearctic

83 114 106 119 219 24 196

46 77 71 70 135 17 127

Percentage underrepresented ecoregions

Region surface (km2)

Additional surface to CBD 10% target (km2)

% of region to CBD 10% target

55% 68% 67% 59% 62% 71% 65%

9,247,689 21,642,644 8,524,365 20,421,474 19,338,623 47,054 52,680,534

362,516 738,529 443,411 1,014,164 857,620 2,816 2,409,077

4% 3% 5% 5% 4% 6% 5%

biogeographical classification scheme on global, regional and national levels as a basis for ensuring adequate representation of different marine ecosystems in a wide range of protected areas’ (Hayden et al., 1984, p. 199). However, the complexities of the marine and coastal environment (Tables 4.6, 4.7) are such that, even ten years later, when a review of the global spread of MPAs was published, a full assessment of the representativeness of its coverage could not be completed because of a lack of an agreed biogeographical regionalization (Kelleher et al., 1995). Frustration over the lack of a global biogeographical classification similar to the terrestrial Dasmann– Udvardy system led to regional attempts being made to develop appropriate marine classifications that would support national conservation planning initiatives in Australia, Canada and elsewhere (e.g. Roff et al., 2003). These regional classifications have now been compiled, in combination with global schemes such as Hayden et al. (1984), Briggs’ faunal provinces (Briggs, 1974) and Large Marine Ecosystems (Sherman, 1993), to produce the Marine Ecoregions of the World (MEOW) (Figure 5.10; Spalding et al., 2007). This composite scheme, which is designed to be compatible with regional biogeographical classifications, is now being applied in global conservation planning and assessment by WWF, TNC, and other international NGOs, and has been adopted as a support tool for implementation of the Convention on Biological Diversity’s programmes of work. The MEOW covers shelf regions only. A similar consensus framework has recently been

published for the high seas: the Global Open Oceans and Deep Seabed classification (UNESCO, 2009). Using the MEOW classification, the current global distribution of MPAs can be seen to be highly biased. Approximately half (115) of the marine ecoregions have less than 1 per cent MPA coverage, with 21 (9 per cent) of them lacking any MPA. At the other end of the spectrum, 42 (18 per cent) have more than 10 per cent MPA coverage and a subset of these, 26 (11 per cent), have more than 20 per cent coverage. These statistics are heavily influenced by a few large MPAs. For example, the Great Barrier Reef Marine Park in Australia accounts for over 20 per cent of the global total shelf MPA coverage. Coastal zones are better represented, with over 12 per cent of the global total area under some form of MPA protection (Spalding et al., 2008). Despite the absence, until now, of a suitable framework to enable development of a representative system of MPAs on a global scale, the approach has been used successfully at regional levels. For example the representation principle underpinned priority-setting and zoning in the Great Barrier Reef, Australia (Fernandes et al., 2005), the Gulf of California, Mexico (Sala et al., 2002) and MPA network planning in the Channel Islands National Marine Sanctuary, California (Airame et al., 2003). Representative and distinctive marine habitats are commonly defined through multidimensional habitat classification, using depth, exposure, substrate type, dominant plant assemblages, faunal distributions and

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Figure 5.10 Marine ecoregions of the world. Figure reproduced with permission from Spalding et al. (2007), © 2007 by the American Institute of Biological Sciences, published by the University of California Press.

a variety of other features (Day & Roff, 2000; Airame et al., 2003). With such baseline information, conservation goals of, for example, including at least 30 per cent of each Major Habitat Type in the final network, and all examples of distinctive habitats, can be determined using site-selection algorithms (discussed in Chapter 6). It should be noted that changing the scale of the planning region and the size of planning units will affect the relative importance of areas within that region as well as what is considered ‘representative’ versus ‘distinctive’. For example, if the California Channel Islands are placed within the context of the Southern California Bight (≈275,000 km2), the entire Channel Islands National Marine Sanctuary (4,294 km2) could potentially be set aside as a reserve to meet the conservation goals of the larger region (Airame et al., 2003). A zonal representation approach is data-intensive. Such data often do not exist, especially over large and extremely diverse regions that lack a strong history of scientific research, such as Southeast Asia. They are commonly most effective when administered by a central government. The nature of the marine environment, its fluidity and geographical position in the ‘voids’ between national boundaries, means that governance of ecologically significant units, and planning within them, demands considerable international

co-operation – which in itself is extremely challenging (Tables 4.6, 4.7). For all these reasons, representation has driven the marine conservation agenda much less in Southeast Asia than have other approaches (see below).

5.4.4 Reefs at risk – hotspots/threatspots The promotional and fundraising success of the terrestrial hotspot approach (combining species endemism and threat criteria) spurred the development of similar analyses for the marine realm. Bryant et al. (1998) produced an influential map-based indicator of threats to the world’s coral reefs. Based on 1-degree (latitude/longitude) cells, threats were evaluated under the following categories: coastal development; marinebased pollution; sedimentation from inland sources; overfishing; and destructive fishing. These data were combined with data on species distributions of 3,235 species of fish, corals, snails and lobsters to assess hotspots of endemism and threat for coral reefs worldwide (Roberts et al., 2002). Conservation International used these data to help direct their marine conservation agenda, thus garnering much public support and funding (Table 5.8).

existing marine protected areas (MPAs)

shelf regions and upwellings

seas outside jurisdiction of coastal states

coral reefs

Global Representative System of Marine Protected Areas (World Conservation Union [IUCN])

Global 200 (World Wildlife Fund)

High Seas Marine Protected Areas (World Wildlife Fund)

Reefs at Risk (World Resources Institute)

Initiative

Areas of coverage

used by Conservation International to define hotspots and by WWF to help define priority ecoregions

10-year action plan in development

43 priority marine ecoregions defined; guiding principle for WWF’s current conservation focus

initial attempt to review global system of MPAs within a biogeographical framework, limited due to lack of agreedupon biogeographical classification

Current status (as of 2009)

ii

i, (ii)

iiia, iiib

i, ii, (iiia, iiib)

distribution of coral reefs mapped onto grid-based database of threats

distribution of biodiversity-related resources outside the jurisdiction of coastal states

five major habitat types nested within four marine biogeographical realms

descriptive summary of main physical and biological characteristics of marine environment (also based on political boundaries); distribution of major habitats and taxa

Biogeographical information used (code and selected examples)a

Bryant et al. (1998)

http://wwf.panda.org/what_ we_do/how_we_work/ conservation/marine/ protected_areas/increasing_ protection/high_seas/

ad hoc or opinionbased?

analytical: threat index, geographical information system (GIS), planning units are 50,000 km2 grid cells

Olson & Dinerstein (2002)

Kelleher et al. (1995)

Reference

relative scoring- and opinion-based: representation of habitat types on a global scale, biodiversity value and threat index, planning units are defined ecoregions

opinion-based: literature survey, regional working groups, discussions, existing MPAs assessed for their coverage of biogeographical zones and political (country) units

Decision-making methods, tools, planning unitsb

Table 5.8 Marine conservation initiatives at the global scale. Several of the initiatives listed below are discussed in more detail in the text. (Extracted and modified from Table 2 of Lourie & Vincent, 2004)

The distribution of diversity: challenges and applications 127

areas of high biodiversity, ecological and economic connectivity, cultural and aesthetic value high productivity coastal waters

coral reefs

individual threatened species

areas with shipping activity

Seascapes Program (Conservation International)

Large Marine Ecosystems

coral reef hotspots (Conservation International)

IUCN Red List of Threatened Species

Particularly Sensitive Sea Areas (PSSA) (International Maritime Organization)

Initiative

Areas of coverage

Table 5.8 Continued

12 PSSAs established

basis for species action plans; current status depends on the species

10 hotspots identified in 2002, but current status unknown

64 LMEs defined globally; currently 16 international GEF supported LME projects

3 priority seascapes

Current status (as of 2009)

o, i

ii

ii

iiib, iv

(iiia, b)

distribution of ecologically important areas within areas heavily frequented by shipping

species-by-species distributions; habitat modelling

distributions of restricted-range fish, coral, lobsters and molluscs mapped onto grid-based database of threats

bathymetry, hydrography, productivity, trophic relationships

patterns of species richness, socioeconomics and politics

Biogeographical information used (code and selected examples)a

ad hoc: proposals by individual countries, primary criterion is threat from shipping; no standardized planning units

expert opinion? tools and planning units depend on the species and data available

analytical: selection based on highest species richness, endemism and threat index; GIS; planning units are 50,000 km2 grid cells

not a priority-setting initiative

expert opinion?

Decision-making methods, tools, planning unitsb

www.imo.org/environment/ mainframe.asp?topic_id=1357

www.iucnredlist.org

Roberts et al. (2002)

Sherman et al. (2007)

http://marine.conservation.org

Reference

128 The shaping of the global protected area estate

wetlands (including coastal and shallow marine)

natural and cultural heritage

Initiative

Ramsar Convention on Wetlands (United Nations)

World Heritage Convention (United Nations)

890 sites designated (July 2009), including >37 marine and coastal; IUCN currently reviewing biogeographical representation of these sites

1847 sites already designated (May 2008), including marine and coastal

Current status (as of 2009)

i

i, iiia, iv

site-by-site information

classification of wetland type, watershed processes, migration routes

Biogeographical information used (code and selected examples)a

ad hoc: proposals by individual countries; criteria are historical, cultural, aesthetic, or ecological, but no overall framework or planning units

ad hoc: proposals by individual countries (although strategic planning within biogeographical regions is encouraged), criteria include representation of wetland type within biogeographical region, globally significant population numbers, presence of endemic species

Decision-making methods, tools, planning unitsb

http://whc.unesco.org/en/list

www.ramsar.org

Reference

a Biogeographical information used in each project is assessed as follows: o, no biogeographical information; i, using biological/biogeographical information on a site-by-site basis; ii, mapping individual targets; iii, creating/utilizing biogeographical classifications based on (a) taxon ranges and (b) abiotic surrogates; iv, incorporating processes determining/maintaining biodiversity. b For priority-setting initiatives, the way biogeographical data are used is assessed as ad hoc, opinion-based, relative scoring or analytical. Tools, methods, and planning units employed are highlighted where known. Question mark indicates approaches that are probably used but unconfirmed as such.

Areas of coverage

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As is the case for terrestrial taxa, centres of high marine endemism and species richness are not necessarily concordant (T.P. Hughes et al., 2002; Roberts et al., 2002; Allen, 2008), leading to a potential conflict in prioritization. Analyses have shown that restricted-range taxa are concentrated into centres of endemism like those described for terrestrial taxa. The ten richest centres of endemism cover 15.8 per cent of the world’s coral reefs (0.012 per cent of the oceans) and include between 44.8 per cent and 54.2 per cent of the restricted-range species (Roberts et al., 2002). These centres of endemism, however, tend to be found in peripheral locations in areas with low species diversity, while the high species richness of the so-called ‘coral triangle’ between Indonesia, the Philippines and Papua New Guinea is primarily due to a concentration of overlapping distributions (‘stacked pancakes’) of wide-ranging species (T.P. Hughes et al., 2002; Bellwood & Meyer, 2009). With nearly 100,000 km2 of coral reefs (34 per cent of the world’s total) (Spalding et al., 2001) and over 2,000 species of reef fish (Allen, 2008), Southeast Asia is unquestionably a hotspot of diversity. It is also under extremely high threat from a burgeoning human population, over-fishing, destructive techniques of fishing and development (Cheung et al., 2002). Data on marine species’ distributions were included in a regional version of the Reefs at Risk analysis (Table 5.8) for Southeast Asia (Burke et al., 2002), an analysis that has been influential in many marine conservation initiatives in the area. While not necessarily protecting the maximum number of restricted-range endemics, a focus on Southeast Asia does provide high concentrations of widespread species that can be conserved together by protecting a relatively small area (eight per cent) of the Indo-Pacific domain (T.P. Hughes et al., 2002). According to T.P. Hughes et al. (2002), 83 per cent of corals and 58 per cent of reef fishes occur within this small area, providing a cost-effective conservation target. For many species, however, this represents only a small proportion of their range, and many species with smaller ranges are not included at all. It has been suggested that biodiversity ‘coldspots’ (species-poor regions) may in cases be functionally more vulnerable than more diverse areas (Hughes et al., 2005). Such ecosystems may have less resilience

because they possess less functional redundancy (Bellwood et al., 2004). Reefs furthest from the Central Indo-Pacific may therefore be doubly vulnerable. First, they contain disproportionately large numbers of endemics (corals and, especially, fishes), which if lost due to adverse regional environmental impacts (e.g. global warming or invasive species) cannot recolonize from elsewhere. Second, the low diversity of these locations implies limited potential for functional redundancy, so that extinctions of one or a few species at peripheral locations are more likely to be associated with losses of critical ecosystem function (Bellwood & Hughes, 2001). Furthermore, the hotspots approach to prioritization does not take into account temporal changes or the complexities of evolution (Reaka et al., 2008). For example, over geological time, hotspots have shifted significantly (Renema et al., 2008), while in the North Sea, nearly two-thirds of species (exploited and nonexploited) have experienced distributional and/or depth changes within just the last 25 years (Perry et al., 2005). The hotspots approach also ‘ignores the fact that there is literally no place on Earth, land or sea, that is not critical for some form of life for some purpose (e.g. breeding or feeding), or that is not the habitat of some endemic species’ (Ray, 1999, p. 611). Indeed, there is a risk that, while raising conservation awareness of small areas, the hotspots approach may lead us to ignore and potentially lose many of our most valuable ecosystems. Many conservation threats (e.g. climate change, invasive species, human pressure, pollution) are now global in their origin and effects, and over-reliance on a hotspots approach, despite being politically appealing and analytically transparent (assuming high quality and availability of data), can disenfranchize communities who need to be engaged in the conservation effort (Kareiva & Marvier, 2003).

5.4.5 Large Marine Ecosystems The open-access nature and high connectedness of habitats in the marine realm, as well as extensive fishing activity (commercial and artisanal), means that conservation success is closely connected to resource use and management (Table 4.6). Key factors include that fisheries management institutions wield considerable political power, that fishing is commonly

The distribution of diversity: challenges and applications considered an occupation of last resort, and that issues of ownership differ widely between marine and terrestrial systems. Acknowledgement of the productivity and human use aspect of marine systems, as well as the need to conserve ecosystems as functioning wholes, led to the development of the Large Marine Ecosystems framework (Sherman, 1993). Large marine ecosystems (LMEs) are regions of the order of 200,000 km2, characterized by distinct bathymetry, hydrography, productivity and trophically interdependent populations (Table 5.8). As a biogeographical classification scheme, they represent a ‘cookie-cutter’ approach (Figure 5.2) in that they are not globally comprehensive. However, they do have a significant role to play in ecosystem-based marine management and conservation, particularly through governmental channels, in partnership with NGOs. There are currently 16 international Global Environment Facility (GEF)-supported LME projects (Sherman et al., 2007). As an example of a regional implementation, the South China Sea Project was initiated in 2005 and is instituting a five-module approach typical of LME projects. These modules focus on the application of a suite of indicators assessing changes in productivity and oceanography, fish and fisheries, pollution and ecosystem health, socio-economics, and governance (see www.unepscs.org). Note that biodiversity conservation, or nature value, does not figure explicitly in this strategic approach. However, remedial actions taken in the context of LME projects to restore damaged habitats, recover depleted fish stocks and curb coastal pollution and nutrient enrichment clearly have conservation implications. From a classification point of view, LMEs have also been incorporated in the delineation of WWF Ecoregions and the MEOW.

5.4.6 WWF Global 200 – the marine perspective Combining aspects of the hotspots and representation approaches, the World Wildlife Fund developed a system of terrestrial, freshwater and marine priority ecoregions (see section 5.3.4; Olson & Dinerstein, 2002). This analysis was a first attempt at representation within major habitat types (MHTs) on a global scale. Still lacking a comprehensive global marine

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classification, ecoregions were mapped out by combining the following MHTs with ocean basins: 1 polar; 2 temperate shelf and seas; 3 temperate upwelling; 4 tropical upwelling; 5 tropical coral. The information and classifications supporting the ecoregion delineations were derived from Kelleher et al. (1995), Sherman et al. (1990), Longhurst (1998) and Bailey (1998), and from consultation with regional experts. Although acknowledged to be incomplete (certain areas, e.g. high seas, hydrothermal vents, are not included) and less finely resolved than its terrestrial counterpart, the lack of a suitable alternative at the time put the WWF marine ecoregions in a strong position with regard to marine planning. In the priority-setting step, ecoregions were assessed based on the following biodiversity criteria: species richness, levels of endemism, higher taxonomic uniqueness, unusual ecological or evolutionary phenomena, and global rarity of habitat type. They were then categorized as globally outstanding, regionally outstanding, bioregionally outstanding or locally important. In the marine realm, a total of 43 priority marine ecoregions were included in the final Global 200 list (Olson & Dinerstein, 2002). The conservation status of each priority ecoregion was assessed using independent information on levels of threat. In the marine realm, this was carried out for the tropical coral ecoregions based on the Reefs at Risk analysis (Table 5.8; Bryant et al., 1998). Again, inadequacies in baseline data and maps limited the detail and objectivity of the analyses. The Global 200 is arguably the most ambitious attempt at global conservation priority-setting. It explicitly applies the ecosystem approach at a variety of scales: regional, ecoregional, site and community. The ecoregion approach has the advantage of being ecologically appropriate; it acknowledges, but does not give supremacy to, the complexities of cross-border multi-stakeholder interests. However, some of the criticisms that apply to the representation and hotspot approaches (above) apply here, too. For example, it might be argued that many taxa interact with their environments at a scale which differs greatly from the ecoregion, that the methods are not entirely transparent, and that in places priorities may reflect political and social realities more than

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biological ones (cf. Section 5.3.4). That said, the WWF Global 200 ecoregions have created sufficient positive momentum to bring about real change in marine conservation. With this large-scale framework now in place, more detailed planning to determine priorities within ecoregions has begun in earnest, drawing on regional-scale biogeographical information. For example, within Southeast Asia there are two priority marine ecoregions situated between the Philippines, Indonesia and Papua New Guinea (and these overlap with two of Conservation International’s marine hotspots). The Sulu-Sulawesi Sea Marine Ecoregion (Dumaup et al., 2004) is one of these priority ecoregions. To determine priorities within it, attention has been focused on information regarding species’ distributions, controlling factors (such as ocean currents) and the location of important areas such as corridors for migratory species, spawning sites for groupers and turtle nesting sites. The implicit assumption of the approach is that local site-based conservation within high global-priority ecoregions is an appropriate allocation of resources. Funding and resources for local site-based conservation are thus filtered by the global framework of priorities and may not necessarily take into account the social realities that govern the success of gazetted MPAs.

5.4.7 Coastal Zone Management and critical seascapes Although many conservation planning strategies (e.g. representative areas, hotspots) focus exclusively, or almost exclusively, on biological criteria, it is becoming clear that successful conservation initiatives need to balance socio-economic factors (costs, opportunities, and conflict resolution) as well. It is only relatively recently that such factors have been incorporated explicitly in terrestrial (Naidoo & Ricketts, 2006) and marine conservation planning (Klein et al., 2008b), and determining appropriate methods to incorporate such factors is still an active area of research (Chapter 6). One approach to conservation that involves integrated resource management and human development is the primarily ‘bottom-up’ Coastal Zone Management (CZM) approach (Salm et al., 2000). CZM (also known as community-based coastal resources management) came into force in the early 1980s and has been taken up with particular alacrity by coastal

communities in the Philippines and Indonesia, where the model fits well with recently decentralized governance (Patlis et al., 2005). Unfortunately, many of these initiatives, after initial successes, have struggled due to social conflict, lack of long-term institutional and other support, and negative external influences (Christie, 2004; Crawford et al., 2006). It may also be that, in some cases, the initial planning was insufficiently inclusive and goals were too long-term for interim success to be duly recognized. Biogeographical tools such as habitat and resource mapping at more or less sophisticated levels can be used to help visualize and aid conservation planning involving local communities (Fernandes et al., 2005). Such an inclusive approach generally leads to greater success as more people have invested in the conservation agenda. Frameworks for assessing progress in CZM have now been developed (Olsen, 2003), and these are being taken up by major NGOs (e.g. Conservation International) as a means to direct their planning as they increase their collaborations with local organizations and communities. Conservation International explicitly incorporates socio-politics into its Seascapes Program, which appears to have taken over from the earlier hotspots focus (see critique above). Seascapes, including two in Southeast Asia, are defined as ‘large, multiple-use marine areas, defined scientifically and strategically, in which government authorities, private organizations, and other stakeholders cooperate to conserve the diversity and abundance of marine life and to promote human well-being’ (http://marine.conservation.org). WWF and TNC are also working in these areas using similar collaborative approaches. The present convergence between global NGOs, who have undertaken large-scale priority-setting exercises, and community-based CZM, may help inject additional enthusiasm, enable the regional exchange of lessons learned and offer an appropriate framework for longerterm institutional support, provided that indigenous social dynamics are respected (Christie, 2004).

5.4.8 High seas protected areas Most marine conservation planning to date has focused on nearshore systems, and all currently designated MPAs are within territorial waters (i.e. within the 200 mile EEZ of individual nations) (Wood et al., 2008).

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Box 5.3 Connectivity in the marine realm – networks of protected areas In contrast to terrestrial systems, where the dominant paradigm has been to view protected areas as isolated islands in a sea of inhospitable habitat (Chapter 8), the notion of connectivity among protected areas has received more consideration in the marine context. Many taxa have pelagic life stages, and species ranges are commonly very large. Networks of protected areas have therefore been proposed with connectivity in mind (e.g. Sala et al., 2002). The critical questions are how big, and how far apart, do reserves need to be in order to maintain the connectivity of their constituent biodiversity? Early dispersal models treated propagules as passive drifting particles, while currents were modelled by simple flow patterns. More recently, complex individual-based and dynamic metapopulation models have been developed that incorporate realistic currents, flow regimes, larval behaviours and adult spawning strategies (Fogarty & Botsford, 2007, and other papers in the same issue). One such study, in the Caribbean, revealed relatively high levels of self-recruitment (≈12%) and lack of important additional recruitment from beyond 200–300 km (Cowen et al., 2006). Given that relevant magnitudes of dispersal for most reef fish are of the order 10–100 km, Cowen et al. (2006) suggest that passive dispersal is insufficient to maintain viable populations. The biogeographical corollary of the model is that distinct regions of population isolation exist. This inference accords well with phylogeographical data and has important implications for replenishment of individuals within reserves. Drawing from the world MPA database (Wood, 2007), Wood et al. (2008) assess current marine protected areas in terms of their network characteristics. They report that roughly 35–60 per cent are large enough to be self-seeding for short-dispersing species, while 2,496 MPAs (56.3 per cent of the world’s MPAs) are within 10–20 km of another MPA. Considering both the minimum size and maximum spacing recommendations, and depending on the particular recommendations that are followed, between 18 and 49 per cent of MPAs can be deemed to be part of a connected network (See Table B5.3a). Of course, such analyses depend on the accuracy of the models and it is unclear the extent to which patterns translate across biogeographical systems. For example, dispersal distances typically increase with increasing latitude; therefore, models calibrated in the tropics may not be transferable to other regions (Laurel & Bradbury, 2006).

Table B5.3a Percentage of the world’s marine protected areas by number and area that meet both minimum size and inter-marine protected area distance recommendations made by (a) Halpern & Warner (2003), (b) Shanks et al. (2003) and (c) Palumbi (2003). (Source: Wood et al., 2008 – their Table 3) Within 10–20 kmb

Within 20–150 kmc

Minimum size (km2)

%

% by area

%

% by area

>3.14b >10a >12.5b

34.1 19.9 18.4

54.6 54.4 54.4

49.1 29.9 27.6

80.3 80.1 80.0

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Coastal and continental shelf areas are the most heavily exploited areas; they provide the majority of the global fisheries catch, contain the highest levels of biodiversity, generally operate on smaller spatial scales, are more predictable than pelagic systems and are the most tractable politically (de Fontaubert, 2001). However, it is becoming clear that deep water and pelagic systems are also in trouble. The most serious impacts are from fishing, including the removal of 25–35 per cent of the primary production from upwelling and temperate continental shelves, the loss of top predators from coastal and pelagic food webs, gross depletion of target stocks and massive wastage from by-catch (Hyrenbach et al., 2000). The scale of these activities is shocking, as is the general lack of public awareness of the extent of over-harvesting of the seas, notwithstanding the publication of research findings in prominent international journals (e.g. Jackson, 2008). Pelagic species, processes and ecosystems occur in the water column and are not attached to the substrate. They present major challenges to place-based conservation planning. Such systems are highly dynamic in space and time. Upwellings and fronts can shift tens to hundreds of kilometres between seasons and years, while gyres and eddies may be ephemeral, lasting weeks or months. On the other hand, many pelagic species use highly predictable habitats in which to breed, forage, or travel. Static bathymetric features (e.g. reefs, shelf-breaks, seamounts, hydrothermal vents) create discontinuities in the ocean that can lead to aggregations of biodiversity; persistent hydrographical features (e.g. fronts, currents) act as oceanic highways and signposts; and ephemeral hydrographical features (e.g. eddies, winddriven upwellings) provide resources for high levels of productivity. These habitat types reflect increasing unpredictability and therefore increasing challenges to MPA planning, given the desirability of MPAs having explicit geographical boundaries (Hyrenbach et al., 2000). One of the limiting factors to setting conservation priorities beyond EEZs is a lack of available information on geomorphology, oceanography and marine species’ distributions. To date, only 5–10 per cent of the sea floor has been mapped with a resolution comparable to that on land (Wright & Heyman, 2008), yet even midresolution bathymetric data suggests that there are over 14,000 seamounts, the majority of which are beyond national jurisdiction (Harris, 2007). In the last

few years, temporally and spatially resolved ocean provinces have been generated from satellite data (Oliver & Irwin, 2008) and a global open oceans and deep seabed classification (GOODS) has been published (UNESCO, 2009). Recent research has also mapped global distribution patterns for seabirds, turtles, and marine mammals, with a view to using these data to set conservation priorities (Cheung et al., 2005; Worm et al., 2005). Deep benthic and pelagic systems occur outside, as well as inside, political boundaries. Where they fall in international waters, additional challenges of governance, ownership, and enforcement arise (De Fontaubert, 2001; Hislop, 2007). Despite all the aforementioned challenges, major NGOs are developing initiatives for pelagic and high seas areas. Greenpeace has proposed a set of three high seas marine reserves (the Pacific Commons), delineated primarily by political boundaries (Roberts et al., 2006). The World Wildlife Fund is also preparing a high seas priority list. These initiatives suggest that we may be preparing to take the next steps in marine conservation planning, building on technical, institutional and societal advances to make the case for large MPAs in the open ocean, with dynamic boundaries and extensive buffers. On the balance of evidence, they appear sorely needed, along with other monitoring, conservation, and management measures.

5. 5 CU R R EN T T R EN DS AN D FU T U R E DI R ECT I ON S The last 50 years have seen a remarkable period of expansion of the protected area estate globally, albeit in response to a dramatic increase of human demands for land conversion and natural resource extraction. Over this period, we may trace several phases in which different approaches and organizations have taken the lead. The initial lead given by the IUCN has been followed more recently by a phase in which a few major conservation NGOs, such as Conservation International, WWF and BirdLife International, have been dominant forces in shaping strategic conservation on the global stage, generating a raft of (to varying degrees) complementary and competing planning frameworks and initiatives. These schemes stem from a quite small number of core concepts, rationales and objectives but, as we have seen, they also sum to provide a rather

The distribution of diversity: challenges and applications bewildering array of frameworks and designations. The most rapid developments in this field have occurred in the last two decades, as very substantial resources have been mobilized towards protected area planning and conservation prioritization on land and, increasingly, in the oceans. One notable recent trend is the extent to which many of the major players are pooling resources and schemes together once again (cf. Mace et al., 2000), after a ‘breakaway’ period in which a few newly founded or re-founded NGOs changed the organizational frame of global conservation science and planning. The Key Biodiversity Areas scheme is one manifestation of this process, as is the re-drawing of hotspots boundaries on to the WWF Ecoregions maps in the latest CI-2004 hotspots iteration (above). A second notable trend is the extension of protected area planning schemes into the productive landscape and the engagement of major corporate actors in the identification, protection and management of areas with high conservation value. The number and extent of such ‘commercial’ protected areas seems set to expand, and this will require their future integration in global protected area planning and accounting frameworks. There is no particular reason to think that the phase of planning protected area systems is at an end. Even when the broad outlines of a protected area system have been decided and legally recognized, there is a need for reiterative phases of data refinement, monitoring, examination of shifting priorities, changes of designation and resourcing. This remains the case globally, as well as for particular regions (e.g. Loucks et al., 2008; Soutullo et al., 2008). It is evident, especially at finer, landscape scales of analysis, that, even in countries with strong legal codes and frameworks, designation decisions can sometimes be revised. Further discussion of the tools available for conservation planning at these finer scales of analysis, and in the light of ongoing global environmental change, is provided in the following two chapters.

F O R DI S C USS I ON 1 How well do the modern NGO-driven protected area planning approaches discussed above capture and reflect the foundational social values of the conservation movement discussed in Chapter 2?

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2 What are the respective strengths and weaknesses of hotspot approaches and biogeographical representation approaches to global conservation planning? 3 How important is it to continue to refine the science underpinning protected area planning? 4 How appropriate is it to transfer the hotspots rationale to the marine realm? 5 Ecoregion-based conservation planning enables nested scales of analysis. How important and unusual is this feature? 6 Are contemporary protected area planning approaches truly science-based, or might it be better to consider them the products of an interplay between science, politics and organizational interests?

S U GGES T ED R EADI N G Belt, H. van den (2004) Networking nature, or Serengeti behind the dikes. History and Technology, 20, 311–333. Dasmann, R.F. (1972) Towards a system for classifying natural regions of the world and their representation by national parks and reserves. Biological Conservation, 4, 247–255. Eken, G., Bennun, L., Brooks, T.M., Darwall, W., Fishpool, L.D.C., Foster, M., Knox, D., Langhammer, P. Matiku, P. Radford, E., Salaman, P., Sechrest, W., Smith, M.L., Spector, S. & Tordoff, A. (2004) Key Biodiversity Areas as site conservation targets. BioScience, 54, 1110–1118. Hughes, T.P., Bellwood, D.R., Folke, C., Steneck, R.S. & Wilson, J. (2005) New paradigms for supporting the resilience of marine ecosystems. Trends in Ecology & Evolution, 20, 380–386. Jackson, J.B.C. (2008) Ecological extinction and evolution in the brave new ocean. Proceedings of the National Academy of Sciences USA, 105 (Supp. 1), 11458–11465. Jepson, P. & Whittaker, R.J. (2002) Ecoregions in context: a critique with special reference to Indonesia. Conservation Biology, 16, 42–57. Lourie, S.A. & Vincent, A.C.J. (2004) Using biogeography to help set priorities in marine conservation. Conservation Biology, 18, 1004–1020. Olson, D.M., Dinerstein, E., Wikramanayake, E.D., Burgess, N.D., Powell, G.V.N., Underwood, E.C., D’Amico, J.A., Itoua, I., Strand, H.E., Morrison, J.C., Loucks, C.J., Allnutt, T.F., Ricketts, T.H., Kura, Y., Lamoreux, J.F., Wettengel, W.W., Hedao, P. & Kassem, K.R. (2001) Terrestrial ecoregions of the world: a new map of life on Earth. BioScience, 51, 933–938. Soutullo, A., De Castro, M. & Urios, V. (2008) Linking political and scientifically derived targets for global biodiversity conservation: implications for the expansion of the global network of protected areas. Diversity & Distributions, 14, 604–613.

CHAPTER 6 Systematic Conservation Planning: Past, Present and Future James E.M. Watson1, Hedley S. Grantham1, Kerrie A. Wilson1 and Hugh P. Possingham1 1

School of Biological Sciences, The University of Queensland, Brisbane, Australia

6 . 1 I N T R OD UC T I ON In general, the best farming land is the first to be cleared. In long-settled regions of the world, this has meant that by the time biodiversity conservation became a social priority, a very much non-random subset of the ‘original’ habitat types has been available for conservation management. Historical decisions on where protected areas were located were rarely based solely (if at all) on scientific assessment of biodiversity value or biogeographical representativeness. Rather, these decisions were based on other factors, such as the suitability of alternative land uses, availability of an area for conservation management, scenic beauty, and recreational values (Chapters 2 and 5; Pressey et al., 1993; Margules et al., 2002; Gaston et al., 2008). This has resulted in a legacy of protected areas that are biased towards habitats that are generally not threatened, such as dry, infertile or steep habitats (Pressey et al., 1993, 2002; Soulé & Terborgh, 1999). For example, five per cent of the Earth’s entire terrestrial protected area (972,000 km2) is the Greenland National Park, which contributes little to biodiversity conservation as it contains mostly ice (Chape et al., 2003; WDPA Consortium, 2006). This form of bias can be demonstrated quantitatively, as shown in a regional-scale analysis by Pressey

et al. (2002). In their paper, a part of their analysis was an assessment of protected area coverage as a function of slope and fertility in the northern eastern region of New South Wales, Australia (Figure 6.1). This analysis highlights the bias often found in reserve systems, with the steepest slopes and the soils of lowest fertility being far more represented in the reserve system than the converse. There are examples like this found on all inhabited continents on Earth (Brooks et al., 2004; Rodrigues et al., 2004a; Joppa & Pfaff, 2009). The threat to biodiversity as a result of habitat loss and change over the second half of the 20th century led to an increased interest in enhancing the coverage and representativeness of the protected areas network (McNeely, 1994). The efforts taken towards these goals at a global and regional scale gained impetus from the development of the IUCN biogeographical regions (Dasmann–Udvardy) framework discussed in Chapter 5. This coarse-scale analysis did not, however, offer guidance on designing networks within regions at the scale of landscapes. The first efforts to take a more scientific approach to designing protected area networks were based on the theory of island biogeography (e.g. Chapter 8; MacArthur & Wilson, 1967; Diamond, 1975a). The rationale followed was that nature reserves and other protected areas can be considered forms of

Conservation Biogeography Edited by Richard J. Ladle and Robert J. Whittaker © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33503-3

The distribution of diversity: challenges and applications

Figure 6.1 Assessment of reserve coverage as a function of slope and fertility in the northern eastern region of New South Wales, Australia. The vertical axis represents the percentage of the total area of each broad environmental unit captured in reserves in the region. The other two axes are measures of slope and soil fertility, with the lower numbers (i.e. 1) indicating flatter slope and lower fertility and the higher numbers (i.e. 3) indicating steep slopes and high fertility, respectively. From Pressey et al. (2002).

habitat islands, isolated from other reserves by anthropogenically transformed habitats (sometimes named the ‘matrix’) that are generally unsuitable for the species of conservation concern. These early efforts were guided by basic ecological principles, such as that bigger protected areas are better than smaller ones because they are likely to contain more species (Diamond, 1975a). Initial approaches to systematic conservation planning were developed based on simple scoring systems, using criteria such as species richness or number of endemic species, to provide an indication of how new areas might contribute to protected area networks if they were chosen (Margules & Usher, 1981; Smith & Theberge, 1986). The integration of these basic principles into conservation planning was a useful first step, but both conservation scientists and practitioners have since criticized their simplicity (e.g. Simberloff & Abele,

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1982). For example, it has been argued that the number of species contained in any single area alone should not determine its priority. More important is how any one area complements the existing protected area network, along with a suite of wider landscape conservation issues (Moilanen, 2008). Similarly, simply relying on the idea that ‘a big reserve is better’ is not useful for making planning decisions in landscapes also required for other human uses (e.g. agriculture, mining). Systematic conservation planning has evolved as a discipline to enhance the efficiency of protected area network design and, through creating alternative proposed networks, to allow scientists and stakeholders to better engage with the complexity of multi-sectoral spatial planning across landscapes and within regions. Therefore, in general, the tools discussed in this chapter are typically employed at finer scales of analysis than the global/regional approaches discussed in Chapter 5 (but see Venter et al., 2009; K.A. Wilson et al., 2009). The 1980s saw the first attempts to use detailed biogeographical information and selection algorithms in the design of protected area networks (Kirkpatrick, 1983). The field of systematic conservation planning has grown significantly since. It has influenced conservation planning by some of the major environmental organizations such as The Nature Conservancy (Groves et al., 2002) and Conservation International (Myers et al., 2000), and it has shaped policy legislation and conservation in both terrestrial (Knight et al., 2006; Kremen et al., 2008) and marine (Davis, 2005; Fernandes et al., 2005) environments. It has featured in hundreds of peer-reviewed papers (Pressey et al., 2007) and in recent books (e.g. Margules & Sarkar, 2007; Moilanen et al., 2009). In this chapter we review the key principles of systematic conservation planning and some of the current decision support tools available to assist conservation planners in making decisions. Decision support tools are information systems intended to help decisionmakers compile and analyse data to help solve conservation problems. The increasing power and ease of use of such computer-based systems in the last two decades has opened up exciting possibilities for applications to conservation planning. We illustrate some of these applications from contemporary case studies, providing examples of the use of different techniques and tools. The field of conservation planning is rapidly changing, and we discuss advances (and future challenges) in systematic conservation planning at the end of the chapter.

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6. 2 W HAT I S SY ST E M AT I C CO N S E R V AT I ON P L ANNI NG A N D WHY USE I T ? The science of systematic conservation planning is concerned with the optimal application of spatiallyexplicit conservation management actions to promote the persistence of biodiversity and other natural features in situ (Margules & Pressey, 2000; Margules & Sarkar, 2007). It involves a transparent process of setting clear goals and objectives, and of planning conservation actions that meet them (Bottrill & Pressey, 2009). A fundamental characteristic of systematic conservation planning is the principle of complementarity (Kirkpatrick, 1983). Since the first publications in this research field, systematic methods have identified systems of conservation areas that are complementary to one another in terms of collectively achieving objectives. Areas identified in this way will each contain, for example, different species or complementary portions of the required areas of different habitat types. As will be discussed further in this chapter, this represents a major improvement on the additive scoring procedures that were used extensively before the application of complementarity methods. Additive scoring approaches are incapable of dealing with the fundamental notion of building a system of protected area where the value of the whole system is not the same as summing the values of the separate protected areas. Systematic conservation planning has traditionally been applied to design strict protected area networks (those areas that are managed for conservation values only, e.g. IUCN management categories I–IV; see Table 2.2). More recently, however, it has been expanded to include planning other types of conservation actions, such as stewardship payments or other land management, in space (Carwardine et al., 2008) and time (Wilson et al., 2007). Here we use the term ‘protected area’ loosely, in reference to any place where an action is applied for conservation purposes. We acknowledge that much of what is written in this chapter is focused on the literature behind planning protected area networks, but at the end of the chapter we provide examples of other forms of systematic conservation planning. It should be noted that systematic conservation planning involves designing protected area networks based on clear objectives, as well as an understanding of constraints on where and how implementation can

occur (Smith et al., 2006). Constraints include factors such as the cost of acting in a particular area or the willingness of landholders to participate in a conservation initiative (Knight et al., 2009). Good systematic planning processes, as we will see, include input, information and values from a wide variety of stakeholders, incorporated within a transparent and inclusive process (Knight et al., 2006; Bottrill & Pressey, 2009) in order to reduce conflicts between opposed interests. However, as in any such field, the approaches discussed herein have also spawned many analyses that are of largely heuristic rather than immediate practical value. This allows analysts to explore ‘what-if ’ scenarios concerning future landscapes and climate surfaces or to undertake ‘tests’ of the effectiveness of existing protected area networks or schemes (e.g. Araújo et al., 2004a,b, 2008; see Chapter 7).

6. 3 CON CEPT S AN D PR I N CI PLES 6.3.1 Representativeness An overarching goal of conservation is to ensure that there is no loss of biodiversity. As discussed in earlier chapters, representativeness is a fundamental principle in systematic conservation planning and refers to how well protected area networks contain representative samples of every feature of biodiversity that we aim to protect. Biodiversity features normally reflect some combination of genetic, species and community diversity. However, it is also important to consider the structure of habitats, e.g. the availability of coarse woody debris in temperate woodland, and ecological processes, such as fire dynamics in Mediterranean ecosystems. It is often difficult for protected areas to achieve complete representation for two reasons: 1 in regions with high species compositional turnover over small distances, such as Mediterranean ecosystems (Judd et al., 2008), a large proportion of the region will be required to represent all of the unique biodiversity features; and 2 even for the best studied regions, systematic data are lacking for some aspects of biodiversity. This second problem has two elements, termed ‘Linnean’ and ‘Wallacean’ shortfalls (Chapter 4; Whittaker et al., 2005). The Linnean shortfall refers to our lack of knowledge of how many, and what kind,

The distribution of diversity: challenges and applications of species there are. Almost two million species have had formal scientific names given to them, but this is still only a fraction of the total of all species (Groombridge & Jenkins, 2002). Estimates have been made that if the collection and description of new species were to continue at the current rate, it would take several thousand years to catalogue the world’s biodiversity (Soulé, 1990). The Wallacean shortfall refers to our inadequate knowledge of the global, regional, and local distributions of the species that we know. Even for the best known taxa such as birds and mammals, and in the best studied regions, there are still huge gaps in our knowledge of distributions (Chapter 4).

6.3.2 Persistence (adequacy) Having a representative protected area network does not ensure that biodiversity within the network will persist into the future. This is because although protected areas might contain a particular species or habitat type, the area might not alone be sufficient to ensure their persistence. Therefore, protected areas should ideally also be designed to maximize persistence. This can involve an analysis of viability requirements (Lande et al., 2003); the configuration of protected areas, including dealing with issues such as connectivity and the permeability of the matrix (McIntyre & Hobbs, 1999; Lindenmayer & Franklin, 2002); and predicting what ecological processes are needed to sustain biodiversity (Soulé et al., 2004). While persistence is considered one of the most fundamental concepts of systematic conservation planning, exactly what constitutes adequate conservation is not well defined (Woinarski et al., 2007; Watson et al., 2008; Carwardine et al., 2009). For example, is a conservation plan that gives every species a 75 per cent chance of persisting for 1,000 years adequate?

The principle of efficiency is based on the idea that conservation planners should try to achieve biodiversity objectives for the least possible cost. ‘Cost’ here may reflect the financial cost of implementing and managing protected areas or the costs of lost opportunities for economic development (Naidoo et al., 2006). It can also include other socio-economic considerations, such as the willingness of people to assist with conservation management, with the expectation that it is more cost-effective to do conservation where people are willing to act. For example, take the matrix on the distribution of four species at five sites shown in Table 6.1. If you were to select the minimum number of sites to represent each species, the optimal combination would be sites 1 and 2 (at a cost of $25). However, when we take cost into account, the combination of sites that represents all species with the least cost is the set comprising sites 1, 4 and 5 ($11). By such consideration of cost, conservation planners are able to maximize the conservation ‘return on investment’ and hence make an efficient plan. There is an increasing number of studies that provide evidence that incorporating financial constraints into conservation planning increases the likely biodiversity benefits for a given amount of money (Ando et al., 1998; Naidoo et al., 2006; Carwardine et al., 2008). Other benefits from biodiversity conservation can be factored into such analyses, including ecosystem services – the benefits that humans derive from natural systems, such as clean air and water. By dealing with multiple measures of benefit, conservation planners may provide a more comprehensive evaluation of the returns from conservation investments.

Table 6.1 Matrix showing the distribution of four species at five sites.

6.3.3 Efficiency A simple way to ensure representativeness and persistence is to conserve everything. This is obviously impossible, and so some degree of compromise is necessary. If the impact of conservation actions on the rest of society is minimized, there is a better chance that the plan will succeed politically and socially and thus provide a platform from which to expand further actions.

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Species Species Species Species Cost

1 2 3 4

Site 1

Site 2

Site 3

Site 4

Site 5

1 0 0 1

1 1 1 0

1 0 1 0

0 0 1 0

0 1 0 0

$5

$20

$5

$4

$2

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It is also possible to modify the cost of conservation by incorporating into the analysis the benefits obtained from the delivery of ecosystem services, such as the amount of carbon sequestrated or the reduced cost to filter water (Venter et al., 2009). Such payment for ecosystem services has the potential to increase the support and resources available for conservation (Costanza et al., 1997; Daily et al., 2009).

6.3.4 Flexibility Objectives can often be achieved in a number of alternative places, particularly when the distribution of biodiversity features is widespread. Moreover, proposed new conservation areas or networks must be accepted and implemented by planning bodies, which brings economic, political and social considerations to bear upon decisions. Therefore, a key principle of systematic conservation planning is flexibility. A flexible conservation plan provides alternative solutions and assists planners to take account of opportunities (Knight & Cowling, 2007). This is because socio-economic constraints may not be fully understood and, in any event, may constantly be changing. For example, a piece of land with high conservation value might not initially be available for conservation management, but may later become available for sale, lease or other management intervention (McDonald-Madden et al., 2008). Adopting a flexible plan also gives scope for sensible resolutions of resource/use conflicts.

6. 4 DEV E L OP I NG A S YS T E M AT I C CO N S E R V AT I ON P L AN In this section we provide examples of how objectives can be set against the key principles outlined in section 6.3. The process of defining measurable objectives is one of the principal components of systematic conservation planning (Nicholson & Possingham, 2006). Defining objectives gives the planning approach transparency and a benchmark by which to evaluate progress towards goals. We discuss how all stakeholders (and not just planners sitting in academic or government institutions) need to be involved in the process of developing these objectives to ensure the plan is successfully implemented. We also provide two real-world case studies to help describe how each of these

principles has been successfully integrated into an applied systematic conservation plan through the careful choice of conservation objectives.

6.4.1 Achieving representation As discussed earlier, ‘Linnean’ and ‘Wallacean’ shortfalls in biogeographical data are highly problematic for any plan trying to achieve representation. Given such a deficit of knowledge and data on biodiversity, a partial measure of biodiversity is almost always used as a surrogate for the rest of biodiversity. To develop biodiversity surrogates, conservation planners must gather all existing data sets and determine which are fit for purpose. Decisions on which data sets to use will often be based on the likely effectiveness of the particular data set and biodiversity metric as a surrogate for other components of biodiversity for which we have no data or poor data. However, the mere existence of a data set does not necessarily guarantee fitness for purpose (see examples in Chapter 4). For instance, where the underlying survey regime is too geographically biased, it could skew the selection of protected areas towards places that have been well-surveyed but which are not particularly biodiverse. In data-poor areas, one alternative is to use environmental surrogates (e.g. vegetation types) as a ‘coarse filter’, with the aim of capturing biodiversity attributes that are likely to correlate with the chosen data layers (e.g. Faith & Walker, 1996). A limitation of such approaches is that unless very finescale environmental data are available, ‘fine filter’ features indicative of resource hotspots, such as saltlicks, are likely to be missed, as may be the factors controlling the distributions of the subset of threatened and rare species (see: Araújo et al., 2001, 2003, 2004a; Faith et al., 2004a; Noss, 2004). Below are some examples of different theoretical approaches in developing surrogates for conservation planning purposes. Species-based surrogates A variety of criteria have traditionally been used to select species-based surrogates in systematic conservation plans. These surrogates have often been called ‘indicator’ species and there are a number of different types that have been used in past planning techniques (see Box 6.1).

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Box 6.1 Some examples of species-based surrogates that have been used in systematic conservation planning approaches Keystone species have a disproportionate effect on the ecosystem relative to their abundance (Mills et al., 1993; Paine, 1995). As such, they affect the types and abundance of many other species in a community. The identification and management of these species can be important in conservation planning (Fleishman et al., 2000). The keystone concept, although intuitive, has received criticism because it is not always clear whether ecological communities have keystone species – and even if they do, this may be hard to demonstrate quantitatively because of the complexity of community structure and environmental dynamics of many ecosystems (e.g. Power et al., 1996; Andelman & Fagan, 2000).

Figure B6.1a The African elephant (Loxodonta africana) plays a significant role in altering the vegetation structure and type throughout its range, and as such is considered a keystone species. Photograph courtesy of Peter Baxter.

Focal species are, in the present context, species that are most endangered by the threatening processes within a system (Lambeck, 1997; Watson et al., 2001). The logic of using a focal species is that if a conservation plan meets their minimum needs, they should capture the needs of all the other species in that system in relation to that particular threat. This approach has, however, been criticized by Lindenmayer et al. (2002), who have argued: (i) that it may be too difficult to identify species most affected by each threatening process because of a lack of data on all taxa, and (ii) that the approach is over-reliant on the untested assumption that protecting the most threatened species will inevitably protect those that that are less threatened. Umbrella species are those species that are used as surrogates to represent the ‘health’ of an ecosystem or the distribution patterns of other species; or they are species that require such extensive resources for their conservation that many other species will be protected by default. Top predators are often used as umbrella species. There has been mixed support for the umbrella species concept in conservation planning. Andelman and Fagan (2000), in a study of umbrella species of the southern Californian sage-shrub community, found that selecting areas using umbrella surrogates performed barely better than a randomly selected set of species. However, Fleishman et al. (2001) have reported more positive results.

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Figure B6.1b The hooded robin (Melanodryas cucullata) has been identified as a focal species in the woodland ecosystems of south-eastern Australia as it is highly threatened by habitat fragmentation and requires large woodlands remnants that are close together to persist (Watson et al., 2001). Photograph courtesy of Mat Gilfedder.

Figure B6.1c The tiger (Panthera tigris) is often used as an umbrella species for conservation planning in countries such as India. Photograph courtesy of Liana Joseph.

Threatened taxa. It has been argued that conservation planning should concentrate on the needs of species currently endangered or threatened with extinction (Sarakinos et al., 2001; Conservation International, 2004). It is often less controversial to use these species as they should be of special concern for biodiversity conservation and, in some cases (and in particular regions), they may be relatively well known (and their locations mapped) (Gaston et al., 2002; Bottrill et al., 2009).

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Figure B6.1d The marine iguana (Amblyrhynchus cristatus) is found only on the Galapagos Islands. Uniquely among modern lizards, this animal lives and forages in the sea. It is threatened by predation by exotic species. Photograph: James Watson.

Phylogenetic difference. Some ecologists argue that species that are more phylogenetically distinct contribute more to the total genetic and morphological diversity and so should be given priority for protection (Weitzman, 1993; Faith et al., 2004b; Faith, 2009; and see Box 7.1). It has been suggested that a good way to generate a plan using this criterion is to use higher taxa (i.e. genus, family) instead of species in the planning process (Mooers, 2007).

Figure B6.1e The little known Guianan cock-of-the-rock (Rupicola rupicola) is a spectacular, phylogenetically distinct member of a two-species family inhabiting northern South America. Photograph: James Watson.

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Associated with the use of species surrogates (in particular using umbrella species, keystone species and focal species) to achieve representation is the concept of achieving functional or ecological redundancy, which refers to the situation where there are multiple species within an ecosystem that play similar ecological roles. Achieving functional redundancy is seen as important objective, because the consequences of losing all of the species that perform a particular ecological function within an ecosystem (e.g. losing all of the algae feeders from a coral reef) could result in a dramatic shift to a lower biodiversity system. Thus, the amount of functional redundancy in a system is of considerable importance in terms of retaining ecosystem integrity (Walker, 1992). This approach requires that species with similar ecological roles (termed functional groups) are identified, along with the key processes that maintain ecological integrity. Conservation efforts can then be aimed at maintaining a full suite of functional groups, and functional groups with little or no redundancy should be prioritized for conservation action (Walker, 1992). The functional redundancy concept has been enthusiastically applied to the problem of conserving coral reef ecosystems (Steneck & Dethier, 1994; Bellwood et al., 2004). Coral reefs are particularly prone to collapsing from a high diversity system to a low diversity system dominated by algae and a few species of fish (Scheffer et al., 2001) although, until recently, the causes of such phase shifts were poorly understood. Recent comparative analysis of functional groups in coral reefs from around the world strongly suggests that high species diversity provides the potential for functional redundancy (Bellwood et al., 2004). Hence, Caribbean reefs that are lacking several critical functional groups, or have groups represented by a small number of species, have been particularly prone to phase shifts to low diversity systems (Scheffer et al., 2001). However, it should be noted that even in high diversity coral reef systems, such as the Great Barrier Reef in Australia, there are still some functional groups with low redundancy (e.g. that are represented by a small number of species) (Bellwood et al., 2003). Despite the support of concepts such as functional redundancy by some systematic conservation planners, the overall level of support for species-based surrogates has been variable (Beger et al., 2003, 2007; Faith et al., 2004a). Since it is unlikely that it will ever be possible to measure the true variation of

biodiversity within or between regions, or the overall functional role played by all species in a region, the true effectiveness of a species-based surrogate is indeterminable. Moreover, the underlying assumption that the needs of a particular surrogate group of indicator species will ensure the long-term persistence of all of biodiversity may never be true as all individual species, have, by definition, evolved to have their own specialized needs (see discussion on individualism in Chapter 3) and these needs will never be captured by a surrogate. Because of this, many recent conservation planning exercises have used sets of species covering entire taxa (i.e. all birds, all mammals, etc.), or assemblages of species in a given area (e.g. combining plant, vertebrate and invertebrate data), as a surrogate for biodiversity in developing a conservation plan (Chapter 5, and see, for example, Williams et al., 1996; Sarakinos et al., 2001). In the case study outlined in Box 6.2, 53 species were identified that, when taken together, were considered representative of the system in Maputaland. These data were then combined with other data layers in a systematic conservation planning exercise. Environmental surrogates In the last decade, systematic conservation planning studies have predominantly used environmental surrogates as general surrogates for biodiversity representation (e.g. Carwardine et al., 2008; Klein et al., 2008). ‘Environmental surrogate’ is a generic term covering land or ecological classifications based primarily on physical and climatic variables, which can incorporate some biotic variables, such as vegetation type (Margules & Sarkar, 2007). These variables are assumed to correlate with the patterns of species distribution, and have been argued by some to be more useful than species-based surrogates (compare: Araújo et al., 2001, 2003, 2004a; Ferrier, 2002; Lombard et al., 2003; Faith et al., 2004a). Environmental surrogates are often used because these data are usually more readily available compared to more detailed biological data. In the Californian marine case study outlined in Box 6.3, a number of key habitats and a range of different depth classes were considered good environmental surrogates. In the Maputuland case study outlined in Box 6.2, it was argued that capturing the 44 land-cover types, as well as the 53 species, was the most effective way to get a representative system.

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Box 6.2 Conducting systematic conservation planning in the terrestrial environment: a Maputaland case study Written by Robert J. Smith, Durrell Institute of Conservation and Ecology, University of Kent, drawing on Smith et al. (2008). The Maputaland Centre of Endemism is a region of high conservation value that falls within the countries of Mozambique, South Africa and Swaziland (Figure B6.2a). Its climate and soils have played a large role in maintaining high levels of species richness and endemism (Steenkamp et al., 2004), but they have also influenced the conservation of this biodiversity, because much of the land has little agricultural value and so has not been cleared by commercial farmers. Instead, most people rely on small-scale farming and harvesting natural resources for their livelihoods. This, together with an increasing human population and a history of political marginalization, has led to widespread poverty. The governments of the region are keen to reduce these poverty levels and have recognized that ecotourism and game ranching are the most profitable forms of land use. Consequently, they have developed the Lubombo Transfrontier Conservation Area (TFCA) initiative, which seeks to conserve the region’s biodiversity and reconnect important large mammal populations while creating jobs by developing new conservation areas, both privately and communally managed. The TFCA initiative is guided by the Maputaland conservation planning system, which is based on systematic conservation planning principles (Margules & Pressey, 2000). This approach involves producing a list of important conservation features, setting targets for each feature and then identifying priority areas for meeting these targets.

Figure B6.2 (a) Protected areas (PA) and TFCA (Transfrontier Conservation Area) zones in the Maputaland Centre of Endemism. (b) Priority areas for conservation outside the existing protected areas. (c) Proposed conservation landscape. From Smith et al., 2008, with permission from Elsevier.

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The Maputaland system involved identifying 44 land-cover types, 53 species and 14 ecological processes as important conservation features, and mapping their distributions using satellite imagery and expert opinion (Smith et al., 2008). It also involved using data on the predicted spread of subsistence agriculture as a measure of both threat and opportunity cost, together with data on potential revenue from game ranching, which has key relevance for implementing the results (Knight et al., 2006). The first conservation assessment used the Marxan conservation planning software, which uses a simulated annealing approach (Ball & Possingham, 2000). This involved: 1 dividing the region into a number of planning units; 2 assigning a cost to each planning unit based on its modelled risk of being cleared for agriculture; 3 using Marxan to identify near-optimal portfolios of these units for meeting the targets, maintaining connectivity and minimizing impacts on subsistence agriculture (Figure B6.2b). These initial outputs were then used to develop a conservation landscape plan that could boost economic development through nature-based tourism and game ranching. The analysis identified 4,291 km2 of new core protected areas and 480 km2 of land that would function as ecological linkages (Figure B6.2c). The game ranching data were then used to estimate potential revenue from this proposed expansion of the protected area system. The results showed that these new areas could provide US$18.8 million per annum, thereby helping to create jobs and reduce poverty. These results have already been used to guide South Africa’s National Protected Area Expansion Strategy and the Critical Ecosystem Partnership Fund initiative in the Maputaland–Pondoland–Albany hotspot, although more work is needed to ensure that the system becomes part of day-to-day land use planning in all three countries.

6.4.2 Achieving persistence Identifying how to secure the long-term persistence of species, ecosystems and the ecological and evolutionary processes that maintain them is difficult. For most systematic conservation plans, persistence objectives are formed as targets. These targets should be informed by ecological theory and empirical knowledge of species autoecology and biogeography (Carwardine et al., 2009). The research that went into designing a conservation plan for the critically endangered Leadbeater’s possum (Gymnobelidius leadbeateri) is a good example of how an objective for persistence can be calculated using a species minimum viable area. This possum, considered an umbrella species (see Box 6.1), inhabits the tall forests of southern Victoria, Australia, but its habitat has receded due to industrial logging and changed fire regimes. Lindenmayer and Possingham (1995) showed that the species needed several patches, each of at least 100 ha in size, in each forest catchment in which they were present, to ensure their persistence in the long term. It was argued that all remaining patches of

habitat containing this species must be protected and, if possible, enlarged by restoration activities to hit this minimum viable patch size, which has been the basis of conservation plans in the region. In a similar example, Carroll et al. (2003) developed a conservation plan based on the needs of mammalian carnivores in the Rocky Mountains region of North America, using a spatially explicit population model that informed the design of the protected area network. Persistence targets can also be set for environmental surrogates, especially when planning at coarser spatial scales. These are often based on achieving representational targets for biodiversity features while implicitly accounting for consequences for other stakeholders (e.g. agriculturalists or the forestry sector). For example, in a series of Regional Forestry Agreements developed in Australia, it was agreed by all stakeholders, including conservation biologists, that each distinct forest type was adequately protected if at least 15 per cent of its area was within a protected area (Pressey, 1998). In the Californian marine case study outlined in Box 6.3, different persistence targets based on

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Box 6.3 Conducting systematic conservation planning in the marine environment: a case study from the central coast of California Written by Carissa J. Klein (The University of Queensland, Australia). California’s Marine Life Protection Act mandates the design and management of a network of marine protected areas to protect marine life, habitats, ecosystems and natural heritage, and to improve recreational, educational and study opportunities provided by marine ecosystems (State of California 1999). As part of the initiative to implement the Marine Life Protection Act, California’s central coast (from Pigeon Point to Point Conception, covering an area of 2,978 km2) was the first of five regions to undergo a stakeholder-driven process to design a network of marine protected areas. To help inform the design of marine protected areas consistent with the Act’s goals, a representative group of stakeholders from California’s central coast developed a very broad set of Regional Goals and Objectives with the help of administrators, managers, and scientists in the period 2005–2006. A scientific advisory team was then tasked to provide guidelines that quantified the science-related Regional Goals and Objectives. These guidelines were as follows: 1 The diversity of species, habitats, and human uses prevents a single optimum network design. 2 Every ‘key’ marine habitat should be represented in the network. 3 Protected areas should extend from the intertidal zone to deep waters. 4 Protected areas should have an alongshore span of 5–20 km. 5 Protected areas should be placed within 50–100 km of each other. 6 Each ‘key’ habitat should be replicated at least 3–5 times. 7 Placement should take into account local resource use and stakeholder activities. 8 Placement should take into account the adjacent terrestrial environment and associated human activities. 9 Network design should account for the need to evaluate and monitor biological changes within the protected areas. Systematic conservation planners were asked to produce a network of marine protected areas consistent with the scientific guidelines. These planners decided that it could be accomplished using the systematic conservation planning decision support tool Marxan (see Klein et al., 2008a,b and www.uq.edu.au/marxan/). Marxan identifies possible locations for protection that achieve a set of conservation targets (e.g. protect 20 per cent of each habitat type, 50 per cent of threatened species’ distributions) for a minimal ‘cost’ (e.g. cost of closing conservation areas to fishermen; see Box 6.4 for more information on what a minimal-set problem is). The nine guidelines outlined above, with the exception of 8 and 9, were able to be factored into the Marxan analysis as follows: • Guideline 1, which is related to the systematic conservation planning principle of flexibility, was accounted for by using Marxan to produce a number of different reserve networks which achieved a similar objective. Marxan produces multiple different solutions for the location of protected areas, all of which achieve the same set of conservation goals. • Guideline 2, which is related to the principle of representation, was addressed by representing each key habitat identified in the different reserve networks. • Guideline 3, which is also related to the principle of representation, was addressed by targeting each feature in five different depth zones. • Guidelines 4, 5, and 6, which are related to the principle of persistence, were addressed by employing user-defined parameters to ensure reserves were of an adequate size, spacing, and replication. • Guideline 7, which is related to the principle of efficiency, was addressed by minimizing the impact on commercial and recreational fisheries.

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Figure B6.3a Comparison of the impact on commercial and recreational fisheries of marine reserve networks designed by stakeholders, based on expert judgment, with that designed using Marxan. The fishing impact (defined as loss of overall fishing yield) of both solutions is displayed, and the Marxan analysis is defined as an average ± standard deviation of 100 different solutions that achieved the planning objectives. Network 1 was developed by commercial and recreational fishers, network 2 by conservationists, network 3 by a mixed interest group, and network 4 was the solution considered for implementation by The California Fish and Game Commission. Adapted from Klein et al. (2008b).

Biodiversity data used in the analysis were environmental surrogates which included rocky reef, soft bottom, kelp forests, submarine canyons, eelgrass, surfgrass, and estuaries. Each of these habitats was targeted for inclusion in a network of marine protected areas under a number of different scenarios (e.g. 10 per cent representation, 20 per cent representation and 50 per cent representation in the reserve system). The socio-economic data included information on the number of recreational fishing trips and an expert-derived assessment of the relative importance of an area for commercial fishing. The expertdriven assessment involved 109 commercial fishermen being interviewed to determine accurate spatial data on fishing effort and to map their fishing grounds. From this, an index of relative fishing effort was used to calculate the impact of fisheries in the reserve design (i.e. those marine waters that would be closed to fishing). The two types of fishing data were combined to deliver a relative index of fishing effort, which was used as a ‘cost’ to minimize in the Marxan software. Marine reserves were chosen that would meet the different biodiversity targets and minimize the impact on fishermen in terms of lost fishing effort due to reservation. Explicitly considering commercial and recreational fisheries in the analysis allowed the impact to the fisheries to be reduced by up to 21 per cent, depending on the scenario selected (Klein et al., 2008a). In a separate analysis, Klein et al. (2008b) were able to compare the marine reserve network designed without using a systematic planning tool by the three stakeholder groups (commercial and recreational fishermen, conservationists and a mixed interest group) against those designed using Marxan. They found that the Marxan analysis represented an equal or greater amount of habitat, yet for a lower cost in terms of the impact on commercial and recreational fisheries (Figure B6.3a). Interestingly, of all stakeholder groups, the proposal developed by stakeholders from the fishing industry was the most proficient at representing biodiversity and minimizing the impact to the fishing industry. These results indicate the important role stakeholders have in systematic conservation planning and that conservation planning decision support tools should be used to support stakeholder-driven planning processes, not replace them.

The distribution of diversity: challenges and applications environmental surrogates were used. The research team formed a scientific advisory team that gave them advice on what would be a good target for reservation for each habitat and water depth class. They were advised that the key habitats and different depth classes had to be captured and replicated at least three to five times to achieve an adequate outcome. See Box 6.3 for the results of this exercise. Despite their continued use, there has been a large amount of criticism over the use of simple percentage targets in systematic conservation plans (Soulé & Terborgh, 1999; Recher, 2004; Watson et al., 2008). The main criticism is that fixed percentages do not account for landscape context. The habitat fragmentation literature (Chapter 8; and see Lindenmayer & Fischer, 2006) reveals that the size and isolation of the protected area, its ‘shape’ in terms of edge to core ratio, and also the similarity (or ‘hostility’) of the matrix habitat surrounding the protected area, can each affect the chances of persistence for many species. Fixed percentage targets do not take these patch- and landscape-scale effects into account. There have been a number of recent analyses in the systematic conservation literature to address this problem. Specific design criteria based on the characteristics of environmental surrogates (e.g. a specific habitat type) have been incorporated into the persistence objective in some systematic conservation plans. For example, Leroux et al. (2007) introduced a framework for determining a minimum reserve size required to incorporate natural disturbance and maintain ecological processes by identifying criteria for estimating the size, location, and efficacy of a minimum dynamic reserve. The size and location of such a reserve is determined by the estimated maximum extent of the largest disturbance event, and by the extent and distribution of communities of species that are differentially affected by disturbance. They illustrated their approach using a study of the Mackenzie Valley region of Canada, where forest fire is the major natural disturbance that influences vegetation community dynamics and dependent fauna. In this research, Leroux et al. (2007) designed and evaluated a candidate minimum dynamic reserve using a spatially explicit dynamic simulation model that incorporates locally calibrated fire and the vegetation dynamics (i.e. the minimum area they need for persistence and recolonization following a fire event) of five broad vegetation types (closed spruce, open spruce, mixed-wood, tall shrub, small shrub).

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Using simulations, they showed that minimum extent of vegetation types ranged from 10 km2 of tall shrub to 1,948 km2 of open spruce, while the mean extent of the five communities available to burn in the study area varied from 118 km2 of mixed-wood to 3,407 km2 of open spruce. Using these thresholds, they showed that their minimum dynamic reserve maintained its recolonization sources through time, suggesting that minimum dynamic reserves may provide an operational framework for determining reserve size in dynamic landscapes under the influence of large natural disturbances such as fire. Of course, it is very difficult to validate such an estimation – hence the use of simulations. Another way in which systematic conservation planners have attempted to achieve persistence is to develop a form of redundancy within the plan, i.e. to set multiple representation targets. Here, the idea is that reserve planning algorithms are set with the goal of selecting a network of areas that ensures, for example, that each species occurs in a minimum of five separate sites. Building in this degree of redundancy may be desirable to provide the protected area networks with a degree of resilience to ensure that a species (or other desired biodiversity attribute) survives in the face of natural catastrophes, disease epidemics, the chronic ecological and genetic effects of small population size, or the loss of a reserve to legal or illegal human intervention. It should be noted that this use of the term ‘redundancy’ has somewhat negative connotations in conservation planning, as it was used as a key theme in criticisms of formerly widely used scoring procedures that disregarded complementarity and yielded systems of protected areas that had high redundancy and were inefficient (i.e. they were expensive and achieved few targets – see Pressey & Nichols, 1989; Pressey, 1994). Thus, the term redundancy is rarely used and ‘multiple representation’ is the favoured expression. This is considered more appropriate because multiple representations are not a by-product of the selection process but, rather, they are actively pursued. Rodrigues et al. (2000) provide a useful demonstration of the potential advantages of multiple representations. They used presence/absence data from the Common Birds Census (CBC) in the UK to test the effectiveness of three families of selection models: i Single and multiple representations. Single representations calculated the minimum area such that each species was represented in at least one site. The

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multiple representations method selected the minimum area needed to ensure that each species was represented in at least n sites (or the maximum number of sites, if this was less than n). ii Percentage of range. This method was used to select the minimum area of sites so that each species was represented in at least p per cent of its range within the study area. iii Permanence rate. A permanence rate was calculated for each species in each site, being the frequency with which a species was recorded in relation to the number of visits to a site within a specified time period. The minimum area was selected so that each species was represented in the site, or one of the sites, where it has the highest permanence rate. The results of the Rodrigues et al. (2000) study clearly demonstrated that a single representation strategy (a minimum of one site containing each species) leads to very high efficiency but low longterm effectiveness. A multiple representation strategy appeared to be safer than a strategy based on percentage of area. This is explained by the prioritization of rare species that is an inevitable by-product of the multiple representation approach. For example, if a rare species only occurs in three sites and the multiple representation criterion (n) is set to three sites or more, then all the sites containing the species necessarily will be included in the selection. The drawback of a simple multiple representation approach is that it assumes that all sites where the species occur have a similar potential for sustaining a population over a period of time. Strategies that target sites where species are most likely to persist give the greatest probability of long-term effectiveness (Williams, 1998). Unsurprisingly then, the Rodrigues et al. (2000) study found that choosing the best site based on permanence rate was a better strategy than investing in multiple, but blind, redundancy. Unfortunately, estimating persistence rate requires a lengthy and accurate time series, and other methods of choosing the ‘best’ site such as using abundance data are also expensive and time-consuming. Ultimately, the decision about whether built-in redundancy is a good way to select a reserve network depends on data and resource availability (e.g. what area/pattern of reserves can be maintained). An additional approach beyond planning for multiple representation is to plan ways to maximize the biophysical connections among protected areas. This is considered important for a number of reasons:

• First, as natural landscapes become more fragmented, an increasing number of species will need to disperse through an increasingly ‘hostile’ landscape matrix if they are to maintain their genetic variability in viable metapopulations. It is probable that connected landscapes improve the chances of this happening (Mackey et al., 2008). • Second, it is increasingly recognized that a large number of species need a very large area to survive – far larger than a protected area network will provide. For example, the European goshawk (Accipter principalis) has a home range of 30–50 km2, and male mountain lions (Felis concolor) in the western United States have home ranges in excess of 400 km2 (Wilcove et al., 1986). Moreover, many species have evolved to be highly dispersive and regularly migrate vast distances to find suitable conditions. These species clearly require more space than could reasonably occur in a small number of isolated protected areas, as the resources they require for existence vary both spatially and temporally (Gilmore et al. 2007). The survival of these species will depend on their ability to move between protected areas, and also the hostility of the matrix habitat between protected areas. • Third, habitat connectivity is likely to play an even larger role with the onset of anthropogenic climate change. Studies have estimated that by the middle of the 21st century, range shifts due to climate change will commonly span tens of kilometres (Kappelle et al., 1999). There will be a clear need to have some form of connectivity to find suitable locations to which species can migrate or take refuge (Peters & Darling, 1985; Mackey et al., 2008). Planning for ‘connectivity’ has recently moved beyond simply creating corridors or stepping-stones between protected habitat patches. The concept of connectivity conservation is now encompassed within the concept of maintaining the ecological and evolutionary processes that generate and sustain biodiversity at various spatial and temporal scales (Soulé et al., 2004, Pressey et al., 2007; Watson et al., 2009). Incorporating information on connectivity within a systematic conservation planning framework enables networks of priority areas to be designed with the goal of maintaining genetic and demographical flows, which may thus ensure the resilience of populations to the effects of landscape conversion and climate change. To date, few studies have incorporated ecological and evolutionary processes into conservation planning

The distribution of diversity: challenges and applications (Rouget et al., 2003; Possingham et al., 2005; Pressey et al., 2007). However, in a national scale analysis in Australia, Klein et al. (2009) accommodated ecological and evolutionary processes in four ways: 1 using sub-catchments as planning units rather than arbitrarily delineated grids; 2 targeting refugia from drought; 3 targeting evolutionary refugia; 4 preferentially selecting planning units along connected waterways. The researchers identified drought refugia as areas with relatively high and regular herbage production, while evolutionary refugia were identified as areas thought to be important for maintaining and generating biota during long-term climatic changes. They identified priority areas for conservation in Australia that met biodiversity and ecological process targets while minimizing acquisition cost. Other examples of incorporating ecological processes in conservation planning include the comprehensive analyses undertaken in South Africa, where spatial surrogates for processes, such as edaphic interfaces, animal movement corridors, and macroclimatic and environmental gradients were targeted (Cowling et al., 1999, 2003; Rouget et al., 2003, 2006). Clearly, the dynamic nature of ecological processes makes them difficult to quantify (Possingham et al., 2005), but they are now recognized as an important consideration when persistence objectives are being defined. See further discussion in Chapter 7.

6.4.3 Achieving efficiency As discussed in section 6.2, a key concept in identifying areas to achieve representation efficiently is complementarity. The basic idea behind complementarity is that conservation areas should complement one another in terms of the ‘features’ they contain, the species, communities, habitats, ecological processes, etc. Each conservation area should be as different from the others as possible until all the ‘differences’ (e.g. different species, communities, etc.) are adequately represented. Complementarity can be defined in a number of ways. The most commonly used implementation is that a proposed new conservation area is assigned a higher complementarity value than another if it has more surrogates that have not already met their assigned target of representation in a conservation area

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network. For example, two proposed new areas, both with high species richness, may have different numbers of surrogates that can be captured in the reserve network. The efficient choice would be selecting the area that adds the most complementarity. Complementarity is, therefore, related to the concept of beta diversity (Whittaker, 1972), but whereas beta diversity is the difference between two areas, complementarity is a measure of the dissimilarity between the species complements of sets of selected areas. It is important to note that the principle of efficiency is not simply about achieving complementarity. As we discussed earlier, achieving an efficient network is also a matter of achieving objectives for the least possible cost, where cost may reflect the financial cost of implementing and managing protected areas or the costs of lost opportunities for economic development (Naidoo et al., 2006). There is an increasing number of examples of where cost data have been implemented into systematic conservation analyses. For example, in the Californian marine case study outline in Box 6.3, the authors conducted and interviewed 109 commercial fishermen to find spatial data on fishing effort and to map their fishing grounds. From this, an index of relative fishing effort was used to calculate the impact of fisheries in the reserve design (i.e. those marine waters that would be closed to fishing). Using these stakeholder data, Klein et al. (2008a,b) were able to produce a systematic conservation plan that was efficient in that it maximized biodiversity conservation and minimized cost to livelihoods for fisherman. As outlined in Section 6.3.3, it is also possible to factor in the returns from ecosystem service protection into conservation planning analyses. There may, however, be trade-offs between the achievement of objectives (Mertz et al., 2007; K.A. Wilson et al., 2009), depending on the spatial congruence between ecosystem services and between ecosystem services and biodiversity features. Some analyses have found high levels of congruence (Turner et al., 2007; Venter et al., 2009), but in other areas overlap has been more limited (Chan et al., 2006; Naidoo et al., 2008). There are several ways to integrate ecosystem services into conservation planning analyses (Egoh et al., 2007). Ecosystem services can be included as a feature for which a target can be set (Chan et al., 2006) and the set of planning units that meet these and other targets for the lowest cost can be identified. Alternatively, it is possible to modify the relative weighting for

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conserving ecosystem services versus the conservation of other biodiversity features (K.A. Wilson et al., 2009) and then seek to maximize the overall benefit that is derived.

6.4.4 Achieving flexibility As we have discussed throughout this chapter, the selection and creation of new protected areas in a network is not a simplistic, one-off process. Protected area networks have to be accepted socially and politically, and it is therefore of critical importance that there should be several alternatives available when a systematic conservation plan is developed. These alternatives mean that the plan is flexible (Pressey et al., 1993). It must be clear, however, why areas are selected and why some areas are not, and hence transparency is a clear part of flexibility (Nicholls & Margules, 1993). Measuring the ‘irreplaceability’ of sites is arguably the commonest way to show flexibility in a systematic conservation plan. The irreplaceability of a site reflects the importance of including that site in the protected area network if all conservation objectives are to be achieved (Pressey et al., 1994; Ferrier et al., 2000). Irreplaceability can be viewed in two contexts: the likelihood that an area is necessary to achieve conservation objectives for the features it contains; or the extent to which the options for achieving conservation objectives are reduced if the area is unavailable for conservation. In systematic conservation planning, a completely irreplaceable area is essential for a plan to meet its conservation objectives, whereas an area with a very low irreplaceability can be substituted by other sites. For example, when planning a reserve system in a landscape, you may find that some areas are completely unique or have been altered to such as extent that the last remaining sites are highly irreplaceable. If there is a risk of these areas being lost to threatening processes, then it might be a large loss for biodiversity conservation in that region. Consequently, irreplaceability can be used as a measurement of conservation value. It is important to note that although irreplaceability can help determine which areas are priorities for conservation, other constraints and considerations may mean that areas with lower irreplaceability are more suitable for conservation. For example, some

combination of vulnerability, ecological condition, and financial cost of an area might influence its priority for protection. When this occurs, it is important to acknowledge the conservation cost of not including these sites within the overall plan. Moreover, the irreplaceability rank of an area will change as individual areas are designated as part of the conservation area network. Therefore, the process of identifying irreplaceable sites must be reiterated after each stage, when new areas are included in a network and others are removed. Such a process was involved in the Maputaland study highlighted in Box 6.2.

6. 5 DECI S I ON S U PPOR T T OOLS T O I DEN T I FY AN D PR I OR I T I Z E N EW PR OT ECT ED AR EAS As discussed in the introduction to this chapter, the development and use of systematic planning tools for designing protected areas is only a recent phenomenon. The early approaches to designing systematic conservation plans using simple scoring systems (e.g. Margules & Usher, 1981; Smith & Theberge, 1986) were perceived to be a great improvement on previous approaches due to their transparency and repeatability. However, due in large part to technical limitations of data processing up until the end of the 1980s, these early systematic conservation planning approaches did not take into consideration complementarity, nor did they have the ability to set spatial objectives like connectivity and spatial compactness (Margules et al., 1991; Pressey, 1997). Over the past decade, decision support tools have been increasingly used to help inform conservation planning decisions. Decision support tools are often computer-based information systems intended to help decision-makers compile and analyse data to help solve conservation problems. A range of mathematical techniques have been developed that are incorporated into these tools (see Box 6.4 and Moilanen et al., 2009). It is important to note that the use of any decision support tool, simple or complex, requires properly defined conservation problems. A common framework for defining conservation priorities is through the use of decision theory. This framework centres on achieving explicitly stated objectives while acknowledging constraints on conservation actions and the levels of uncertainty involved within the decision process. In Table 6.2, we outline a

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Box 6.4 Three broad classes of mathematical problems used in systematic conservation planning: the minimum set problem, the maximal coverage conservation prioritization problem and the conservation resource allocation problem Conservation planning began without a well-posed mathematical problem, which is not uncommon in conservation science (Possingham et al., 2001). Cocks and Baird’s (1989) seminal paper provided the first formal statement of a conservation planning problem – the minimum set problem. In the minimum set problem the goal is to conserve a variety of conservation features to an adequate level for minimum total cost where cost can be the cost of acquisition and management or the estimated foregone opportunity cost (Naidoo & Ricketts, 2006). The simplest variant of this problem is: NS

min ∑ ci x i i =1

given that Ns

∑x r

i ij

≥ T j,

for all features j,

i =1

where rij is the occurrence level of feature j in site i, ci is cost of site i, Ns is the total number of sites and Tj is the target level for feature j. The control variable xi has value 1 for selected sites and value 0 for sites not selected (Moilanen et al., 2009). This became the foundational problem of systematic conservation planning. Since then, various authors have produced alternatives, but arguably the maximal coverage conservation prioritization problem is the most dominant. This problem is used when resources are insufficient for satisfying all targets and the objective is to find the solution that satisfies the largest number of conservation targets, given a budget constraint. The maximal coverage problem is related to the minimum set coverage problem, in that minimum set coverage can be achieved by solving the maximal coverage problem at different budget levels and finding the minimum budget level that satisfies all targets. A simple version of the maximal coverage problem can be written as: max ∑ I j ( ∑ x i rij ≥ T j ), j

i

given that

∑xc i

i

≤ B,

i

where B is the conservation budget (money, trained personnel, time, etc.), and I(z) is an indicator function, with Ij(z) = 1 when condition z is true, i.e. the target for feature j is met when ⎛ ⎞ x i rij ≥ Tj ⎟ , ⎝⎜ ∑ ⎠ i

and Ij(z) = 0 otherwise (Moilanen et al., 2009). Both the minimum set and maximum coverage problems are limited to specific problems. However, it is possible to define a fairly general conservation resource allocation problem that includes most, if not all, previous problem definitions. In general, all of conservation involves taking actions in a place and at a time in an attempt to achieve a variety of outcomes. Our general task is therefore to decide how much to spend on each kind of action (e.g. invasive species control, changed logging practices, or reduced grazing) in each place, at a particular time

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(which we will refer to henceforth as the year). These actions will alter the dynamics of a variety of state variables, yijt., such as the size of the population of a species in a site, or the amount of an ecosystem service in a site. Mathematically this means that our control, or decision, variable is ajkt, the amount of money we spend on action k in place j in year t. A fairly general formulation of the conservation resource allocation problem is to: T

max ∑ f ( y ijt ,1 ) t =1

subject to a budgetary constraint each year N

P

∑∑ a

jkt

≤ bt for all t,

j =1 k =1

and contingent on the dynamics of the state variables, that is, how key system states move from year to year in response to actions or forces we do not control: y ijt . 1 ⋅ g( a..t y..t, x..t,) for all i, j and t, where f is a function that turns our state variables into a reward function that we are trying to maximize (this could be highly non-linear), g is a function that determines how the state variables, yijt, evolve in space and time as a consequence of actions and forces we do not control, xijt. In this formulation, N is the number of different places and P is the number of different sorts of actions. This mathematical formulation of a problem that considers expenditure of money on different conservation actions in space and time is a fairly general formulation of all resource allocation problems. It is called a resource allocation problem because there is a fixed annual budget. Evaluating actions based on their cost-effectiveness (Joseph et al., 2009) provides one algorithm that can often provide rough solutions to this very complex optimization problem.

seven-step decision theory framework which has been articulated by a number of authors for systematic conservation planning (Table 6.2; Possingham et al., 2001; K.A. Wilson et al., 2009). There is now a large amount of literature on optimal protected area design based on this decision theory framework (summarized in Moilanen et al., 2009). The problems generated using this framework can be expressed mathematically and then solved by one of a number of methods. There are two classic problem definitions commonly used in conservation planning, the minimum set and maximal coverage conservation prioritization problem (Box 6.4). The minimum set problem minimizes the resources expended while meeting the conservation objectives. For this problem, the objective is to minimize cost and the constraint is the conservation objectives. The maximal coverage conservation prioritization problem maximizes the objectives (e.g. target level achievement) given a fixed amount of resources. Here,

the problem is reversed: the constraint is the budget and the objective is to maximize conservation objectives. Methods for solving systematic conservation planning problems fall into several classes: local heuristic algorithms, which select sites in a stepwise manner (Pressey et al., 1993; 1994); global heuristic algorithms, which select sites in sets (e.g. simulated annealing, Ball & Possingham 2000); and optimization algorithms (Cocks & Baird, 1989). These methods are dealing with increasingly large and more complex problems (see section 6.7), which includes having multiple and conflicting objectives and multiple types of management actions. It must be noted that decision problems can be quite complex, and there are now several software packages that can support systematic conservation planning (e.g. Marxan, C-Plan, Zonation, ConsNet; see Moilanen et al. (2009) for a thorough review of each platform). However, as Bottrill & Pressey (2009) point out, these

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Table 6.2 The application of a seven-step systematic conservation planning decision theory framework (Possingham et al., 2001; K.A. Wilson et al., 2009) to a hypothetical example based on the problem of acquiring new land to add to a protected area network to protect threatened species.

Step

Details

Example: Acquiring new land to add to a protected area network with the aim of protecting threatened species.

1 Statement of objective(s)

This is a statement of what is hoped to be achieved and is measurable.

To maximize the representation of threatened species in protected areas.

2 List of management actions

This can range from one action to a number of actions.

Purchasing new areas to add to the protected area network. The available option is either to acquire each parcel of land or not.

3 State variables

This is the knowledge about the system, including both biodiversity and human variables.

Where the threatened species are located and how much each parcel of land costs.

4 State dynamics

This step requires knowledge about how the state variables may change (which may be dependent or independent of the management action).

Fluctuation of property prices for parcels of land. These may vary independently or may increase with the implementation of the extended reserve network (Armsworth et al., 2006).

5 Constraints

The constraints are what limit the application of any management action.

Size of budget, willingness of landholders to sell their properties, etc.

6 Uncertainty

Most data will contain a degree of uncertainty.

Inaccuracies in species data regarding presence and absence due to surveying methods and species detectability variation.

7 Solution methods

A range of mathematical approaches are used to solve problems (Box 6.4).

Algorithm to maximize representation and minimize cost.

software systems are not designed to replace people by making decisions for them; they operate interactively to facilitate decisions by people. 6. 6 CO N SUL T AT I ON AND I MPL EM E NT AT I ON OF S YS T E M A T I C CO N S ER V AT I ON P L ANS Much of the systematic conservation planning literature to date has focused on advancing the ‘tools’ of the systematic conservation planning trade. Far less attention has been dedicated to implementing conservation plans in the ‘real world’ (Salafsky et al., 2002; Knight & Cowling, 2003). Indeed, some experts have argued that the discipline of systematic conservation planning is mired in an ‘implementation crisis’ (Knight &

Cowling, 2003), because ‘… few academic conservation planners regularly climb down from their ivory towers to get their shoes muddy in the messy, political trenches, where conservation actually takes place’ (Knight et al., 2006, p. 410). There has been some critical discussion around this quite stark assertion (see, for example, Pressey & Bottrill, 2009), and a number of operational case studies show that development of a systematic conservation plan for a particular area by academics can integrate the diverse disciplines and activities needed for successful conservation action into a single, comprehensive process (Boxes 6.2 and 6.3 are good examples). Nonetheless, this debate highlights the point that while the tools of systematic conservation planning are important, they do not in themselves deliver conservation action.

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To be successfully implemented, all systematic conservation plans must be complemented with social, political, and institutional tools and processes (Knight et al., 2009). There are several operational models established that outline key considerations that can help to guide a transparent planning and implementation process (Pressey & Cowling, 2001; Knight et al., 2006). Those who have participated in such processes stress the importance of providing participants with clear and transparent explanations of the stages of a conservation planning process and what things need to be done to achieve them. This includes, for example, an assessment of conservation issues; identifying opportunities for and constraints on conservation actions; developing an implementation strategy; and products such as maps that help guide implementation. The flexibility of an overriding conservation model is also important, as it has been increasingly shown that implementing successful conservation actions is an ongoing process with feedbacks between planning and implementation. Bottrill & Pressey (2009), for example, have produced a detailed 11-step process for the successful implementation of a conservation plan. The steps are outlined in Table 6.3. They argue that all 11 stages must be completed for a conservation plan to be conducted successfully. An alternative operational framework is provided by Knight et al. (2006), who identified the key components for ‘doing’ pragmatic conservation planning (Figure 6.2). In their schematic, the thematic components are grouped into three interlinked foundations: 1 empower individuals and institutions; 2 undertake the systematic conservation assessment; 3 secure effective action. Each foundation is essential for an effective conservation planning process. The reality is that the implementation of any conservation plan is difficult. Wherever systematic plans are actually implemented, it quickly becomes apparent that human society is not an entity with a single value system (see Chapter 2). Whereas a conservationist or amateur naturalist may value a particular site because it contains habitat for an endangered species, a timber company may value that site because of the potential revenue that might be generated from harvesting trees, or a group of mountain bike enthusiasts may value the site for its recreational values.

Table 6.3 Steps in the process of developing and implementing a conservation plan, as outlined by Bottrill & Pressey (2009). Steps

Processes

Stage 1

Scoping and costing the planning process

Stage 2

Identifying and involving stakeholders

Stage 3

Identifying the context for conservation areas

Stage 4

Identifying conservation goals

Stage 5

Collecting socio-economic and threat data

Stage 6

Collecting data on biodiversity and other natural features

Stage 7

Setting conservation objectives

Stage 8

Reviewing objective achievement in existing conservation areas

Stage 9

Selecting additional conservation areas

Stage 10

Applying conservation actions to selected areas

Stage 11

Maintaining and monitoring established conservation areas

However, when a plan is integrated with expert knowledge (Pressey & Cowling, 2001) and coupled with an implementation strategy that takes into context the needs for stakeholder collaboration (Driver et al., 2003), the planning process itself can provide a foundation for effective conservation action. This is a major, often forgotten, value of systematic conservation planning – it not only identifies the priority conservation areas, but also provides a mechanism for stakeholder collaboration. 6. 7 W H AT DOES T H E FU T U R E OF S Y S T EMAT I C CON S ER V AT I ON PLAN N I N G H OLD? In this chapter, we have delved into the fundamental principles of systematic conservation planning, while also providing some contemporary case studies demonstrating the use of different techniques and tools. In

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Figure 6.2 An operational model for pragmatic conservation planning. From Knight et al., 2006.

this final section, we discuss future challenges in the field of systematic conservation planning and some recent advances. The first two decades of systematic conservation planning primarily focused on a restricted suite of problems. These problems have generally assumed that systematic conservation planning: 1 is a static problem that determines a once-off reserve system; 2 can ignore the dynamic nature (including evolution) of biodiversity assets (e.g. species, habitats); 3 assumes a binary world where sites are either protected or not;

4 can use the area or number of sites as a surrogate for cost; 5 can ignore uncertainty; 6 can ignore risk and threat, and 7 can rely on simple targets for biodiversity assets so that once achieved, we are content that persistence is achieved. All of these issues are challenges that need to be overcome if the discipline is to be taken seriously by those responsible for implementing conservation action. Here we briefly discuss some recent work that has contributed to this furthering this research agenda.

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6.7.1 Conservation planning is a dynamic problem

6.7.3 A mix of conservation actions could occur at any site

Possingham et al. (1993) provided one of the first analyses that formulated the dynamic site selection problem. In each year they assumed one site could be bought (due to a constrained budget), sites had a fixed probability of becoming available and sites that were unreserved had a fixed probability of being destroyed. At the time, these authors found that taking a static approach was suboptimal compared to solving the dynamic problem using stochastic dynamic programming. Various authors have subsequently considered and solved larger and more realistic versions of this original problem (e.g. Costello & Polasky, 2004; Meir et al., 2004; Drechsler, 2005; Strange et al., 2006). Such advances enable systematic conservation planners to include complexities like dynamic budgets and feedback between acquisition actions and the cost of reservation. In principle, any sort of dynamic complexity can be included in the site selection problem; however, the optimal solution of stochastic dynamic problems can only be found exactly using stochastic dynamic programming, which is computationally intractable for any but the smallest problem. There is therefore a need to derive simple heuristics that sequentially choose actions through time, such as choosing the actions that maximize the short term gain in biodiversity or minimize the short term loss of biodiversity.

As discussed briefly in the introduction, formal protection of habitat is just one of many conservation actions. In many cases, especially where there are multiple players in land ownership issues plus complex social and cultural constraints, reservation is an unlikely option for conservation. What we need is tools to help us determine which package of actions to activate at any site. This sort of idea is effectively zoning – a common practice in fisheries, forestry and conservation where there are multiple interests (Watts et al., 2009). These zoning tools are useful to guide broad policy decisions, and other methods have been developed to systematically select among specific conservation actions. For example, the Project Prioritization Protocol is a costeffectiveness analysis that has been demonstrated to be useful for selecting among specific management projects for threatened species in New Zealand (e.g. Joseph et al., 2009).

6.7.2 Conservation assets change through time The biodiversity assets that we would like to conserve are continually changing: local populations become extinct; species’ distributions change; species evolve; and vegetation types change through succession (as discussed in Chapter 3). This adds further complexity and uncertainty to the dynamic conservation planning problem described above, and in principle it can be dealt with within the same approach. However, there are some short cuts possible. Sites with evolutionary potential can be preferred in planning (Cowling et al., 2003), present and future predicted distributions can be accommodated in the plans (Hannah & Hansen, 2005) and successional changes can be predicted and allowed for in target setting (Drechsler et al., 2009).

6.7.4 Better economics and socio-economics Ando et al. (1998) were arguably the first to highlight in the peer-reviewed literature the naivety of building conservation plans that ignored realism in respect of financial costs. While the inclusion of the estimated cost of conservation is now more common in conservation planning (see section 6.3.3) it is still a challenge for most conservation researchers who are more familiar with the nuances of biological data (Bode et al., 2008). To this end, there is a need for more real collaboration between economists and conservation biologists. However, it is also being recognized that using simple cost-layer data (i.e. the price of land), without considering socio-economic factors such as a landholder’s willingness to conduct a conservation action, regardless of cost, may lead to some erroneous results.

6.7.5 Dealing with uncertainty There is some level of uncertainty in every aspect of conservation planning (Regan et al., 2009). For

The distribution of diversity: challenges and applications example, semantic uncertainty underpins the actual definition of the conservation problem, while parametric uncertainty is rife in all the data that are used to develop conservation plans (Whittaker et al., 2005). While Regan et al. (2005, 2009) argue that we can generally deal with parameter uncertainty quite well using sensitivity analysis, uncertainty about problem formulation or issues like species viability represent serious challenges at the interface of social science, philosophy, economics, mathematics and ecology. So far there are too few papers that deal credibly with uncertainty in conservation planning (but see Moilanen & Wintle, 2006).

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Probably the best way forward for conservation planners is to explicitly acknowledge and derive tradeoffs, recognizing that no single answer is best but offering a range of good options that reflect different societal aspirations (Whittaker et al., 2005; Polasky et al., 2008). An alternative might be to represent different levels of risk (e.g. 75 per cent, 80 per cent or 95 per cent probability of persistence for 100 years) or varying levels of persistence (80 per cent probability of persistence for 10, 100 or 1000 years) based upon available knowledge. Further discussion of the challenges of planning for persistence in a changing world is provided in the following chapter.

6.7.6 Properly accounting for threats There are several ways of dealing with threats in conservation planning. One is to rate sites in terms of the likelihood that they will be destroyed relative to their irreplaceability, with preference given to sites that are under most threat (Araújo et al., 2002a; Pressey et al., 2007). In practice, some planners use the likelihood of a site being converted to other uses as a surrogate of conservation cost and hence, by reference to the principle of efficiency, they avoid sites with a high probability of conversion. Ironically, this will give us the reverse outcome to the first approach. Indeed, some of the confusion about how we deal with threats arises because some threats are mitigated by conservation action, while others are not. Ideally, threats are dealt with properly in a full dynamic framework (Wilson et al., 2006; Game et al., 2008) within which the consequences of taking action at a site, or not, are explicitly modelled.

6.7.7 Persistence – attainable goal or impractical utopia? Persistence (also known as adequacy) is the bugbear of systematic conservation planning science because the question it asks – how much is enough? – is probably unanswerable. Governments and non-government organizations would often like to know that a suite of conservation actions in time and space is sufficient. However, in reality, more is always better, although that ‘more’ comes at an additional cost.

6.7.8 How much should we invest in improving a conservation plan? As we have discussed throughout this chapter, there are usually many assumptions about what the most appropriate conservation actions in any given area may be and whether the data are truly fit for purpose. Recent research has shown that if learning processes and data collection strategies are intentionally included into the conservation planning process, it is likely that future conservation decisions will become more effective (Grantham et al., 2009). There is a complex and not very well understood trade-off between acting and learning when developing and implementing a systematic conservation plan. It is important to recognize that any given planned conservation action has been traded off with all other actions and also against the cost of delaying a conservation action.

FOR DI S CU S S I ON 1 Describe and give examples for each of the key principles of systematic conservation planning. Describe some ways of achieving each of these principles when developing a hypothetical systematic conservation plan in both the marine and terrestrial environments. 2 How should scientists assess the fitness for purpose of data for use in systematic conservation planning?

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3 What are the limitations and strengths of using targets for achieving persistence in a conservation plan? 4 What are the respective strengths and weaknesses of using species-based surrogates versus environmentalbased surrogates when developing a systematic conservation plan? 5 What is the difference between conducting a minimum set problem as opposed to a maximum coverage problem when undertaking a systematic planning process? Give some examples of when each type of problem should be applied. 6 Are the key principles of systematic conservation relevant to both marine and terrestrial environments? What differences are there between how they are interpreted and the data used to achieve them in each of these environments? 7 Why is it important to ensure that all stakeholders participate in the planning process? How can planners ensure that stakeholders participate in the conduct of the systematic conservation plan?

S U GGES T ED R EADI N G Margules, C.R. & Pressey, R.L. (2000) Systematic conservation planning. Nature, 405, 243–253. Margules, C. & Sarkar, S. (2007) Systematic conservation planning. Cambridge University Press, Cambridge, UK. Moilanen, A., Wilson, K.A. & Possingham, H. (eds) (2009) Spatial conservation prioritization: quantitative methods and computational tools. Oxford University Press, Oxford. Possingham, H.P., Wilson, K., Andelman, S. & Vynne, C. (2006) Protected areas: goals, limitations, and design. Principles of conservation biology (ed. by M.J. Groom & G.K. Meffe & C.R. Carroll), pp 509–533. Sinauer Associates Inc, Sunderland. Williams, P.H. & Araújo, M.B. (2002) Apples, oranges and probabilities: integrating multiple factors into biodiversity conservation with consistency. Environmental Modelling and Assessment, 7, 139–151. Wilson, K.A., Carwardine, J. & Possingham, H.P. (2009) Setting conservation priorities. Annals of the New York Academy of Sciences, 1162, 237–264.

PART 3 CONSERVATION PLANNING IN A CHANGING WORLD

Conservation Biogeography Edited by Richard J. Ladle and Robert J. Whittaker © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33503-3

CHAPTER 7 Planning for Persistence in a Changing World Richard A. Fuller1,2, Richard J. Ladle3,4, Robert J. Whittaker3 and Hugh P. Possingham1 1

School of Biological Sciences, The University of Queensland, Brisbane, Australia CSIRO Climate Adaptation Flagship and CSIRO Sustainable Ecosystems, Brisbane, Australia 3 School of Geography and the Environment, University of Oxford, Oxford, UK 4 Department of Agricultural Engineering, Federal University of Viçosa, Brazil 2

7. 1 I N T R OD UC T I ON There are millions of different species on Earth, each responding uniquely to the environment and to other species, and each with a distinct geographical distribution. This results in enormously complex spatial patterns in nature, from latitudinal gradients in global species richness to the patchy distribution of plants across a meadow. Documenting and understanding these patterns has occupied biogeographers and macroecologists for decades (Chapter 4). There has been considerable progress toward a general understanding (Gaston & Blackburn, 2000; Lomolino et al., 2004), but how should we go about trying to conserve biodiversity in the face of such complex spatial patterns? So far, the conservation community has focused overwhelmingly on elements of the pattern of biodiversity, features that can be mapped spatially and thus ‘captured’ by conservation management activity (Chapters 4–6). For example, a conservation plan might attempt to represent a certain proportion of each vegetation type in a region, or to ensure that places where threatened species occur are designated as protected areas.

This approach would be sufficient if simply capturing elements of the natural world ensured their long term persistence. Unfortunately, there are at least three reasons why this is not the case: 1 Processes generate and maintain biodiversity, so these processes must themselves be conserved. 2 Conservation efforts must track continuously changing patterns of biodiversity. 3 Threats are dynamic, so mitigation efforts must be similarly dynamic for conservation to be efficient. We now expand on each of these points in turn. First, biodiversity is generated and maintained by processes. It is these processes that need conservation, along with the patterns that emerge from them, to ensure that biodiversity persists in the long term. Ecosystems are shaped by myriad physical and biological processes, including climate regimes, oceanic currents, hydrological flows, plate tectonics, demography, migration, dispersal, extinctions, colonizations, predation, competition and distributional shifts, to name but a few. Given the apparent primacy of climate in determining the distributions of species, there has been an intense effort to predict the impacts of climate change on biodiversity and how we might go about responding to the challenges this poses (Section 4.4; Peters &

Conservation Biogeography Edited by Richard J. Ladle and Robert J. Whittaker © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33503-3

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Darling, 1985; Opdam et al., 1999; Parmesan & Yohe, 2003; Felton et al., 2009). Despite the importance of dynamic processes such as climate in continually shaping biological systems, a recent review of the conservation planning literature showed that about 80 per cent of studies assume that neither biodiversity nor the processes threatening the persistence of biodiversity change over time (Pressey et al., 2007). This is despite the fact that a decade has now elapsed since the first demonstrations of dynamic approaches to planning protected area networks to promote the long-term persistence of biodiversity (Cowling et al., 1999). Second, even if we successfully conserve processes that generate and maintain biodiversity, the resulting pattern that we see today is only one brief snapshot of a continuously changing system. As if huge spatial complexity isn’t challenge enough for conservation, ecosystems are continuously changing over time (Chapter 3), species’ distributions and abundances expand and contract (Figure 7.1), land bridges between continents come and go with changes in sea level and communities are thrown together and then separate. Deep history leaves a signature in the structure of present-day communities (Box 7.1). For example, modern marine bivalve assemblages show a clear break in species’ ages corresponding with recovery from the end-Cretaceous mass extinction event (Krug et al., 2009). Pleistocene climatic change has promoted massive movements of species populations, often forming novel assemblages, and alternatively isolating and rejoining populations, generating speciation in some lineages and strong phylogenetic structure in many others (e.g. Riddle & Hafner, 1999, 2006; Hewitt, 2000; Bush & de Oliviera, 2006; Avise, 2009). To provide a specific example, Neotoma woodrats appear to have tracked oscillating climates through time by changes in their body size (Smith et al., 1995). The biology of many species thus plays out across time as much as across space, and conservation efforts must therefore track an ever-moving target. Third, threats to biodiversity change in type, distribution and severity over time (for an analysis focused on changing threats on islands, see Whittaker & Fernández-Palacios, 2007). If conservation is about buffering samples of biodiversity from threats, then both the nature of the buffers employed, and where they are instigated, must depend on the type, location and intensity of threats and how these change through time. The cost (e.g. social, economic, political) of

particular conservation action relative to its likely benefit is also highly dynamic, so conservation plans must be continually updated to reflect changing social and economic circumstances.

7. 2 U S I N G T H E PAS T T O U N DER S T AN D T H E PR ES EN T AN D PR EDI CT T H E FU T U R E To ensure long-term persistence of biodiversity, conservation is a dynamic problem in which we must plan for future changes in biodiversity pattern and process, as well as our changing ability to instigate conservation action. Much of conservation is about trying to build scenarios about the future and acting accordingly. The past is the most obvious place to look for guidance about what will happen in the future (Chapter 3). Consider a few simple questions and the critical importance to conservation of long-term ecology quickly becomes clear: • How quickly have species’ distributions responded to past changes in environmental conditions such as fire regimes, habitat availability and climate? • How quickly have species evolved in response to changing environmental conditions? • Where are the places with high rates of extinction and speciation? • Which places across the planet have acted as refugia during past periods of environmental change, and might these prove good long-term investments for conservation? • Do changes in species’ distributions occur predictably in relation to environmental conditions? • How does variability in a species’ current abundance or distribution compare with past fluctuations? • As species head towards extinction, do they show stereotypical patterns of geographical range collapse? Clearly, a long-term perspective is essential if we are to make progress in answering these kinds of questions and using them to guide present and future conservation activity. Human transformation of the planet began long before the science of ecology. While dramatic environmental changes such as Amazonian deforestation are accelerating, and can be observed more or less in real time (Shimabukuro et al., 2006; Hansen et al., 2008), much of the damage and change that humans have wrought on ecosystems occurred hundreds to thousands of years ago, and recent human impacts are

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Figure 7.1 Plots of range size as a function of the age of species within six different groups of species: (a) Old World Acrocephalus and Hippolais reed warblers; (b) New World Dendroica wood warblers; (c) New World Icterus orioles; (d) storks; (e) gannets and boobies; (f) albatrosses. Crosses represent species excluded from the regressions used to estimate the lines of best fit. The inference the authors drew from their study is that species ranges typically show a phase of relatively rapid range expansion post-speciation, followed by a longer, slower phase of range contraction, a scenario broadly consistent with taxon cycle models. Reproduced from Webb & Gaston (2000).

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Box 7.1 Integrating evolutionary considerations into conservation planning Species have traditionally been used as the primary taxonomic level for conservation and, as we have seen, many conservation prioritization analyses essentially rely on weighing up tallies of rare, threatened, or range-restricted species. This strategy has the advantage of needing little or no explanation for policymakers or the public. However, even within a single taxonomic group such as plants or mammals, such a focus may result in some highly valued species or assemblages being omitted from conservation prioritization because they happen not to fall in the most diverse areas. One aspect of the way in which we may value species is in terms of their evolutionary distinctiveness, and here the question arises as to whether a strategy focused on maximizing species richness of a taxon will also succeed in capturing a broad representation of the evolutionary tree, thus preserving the phylogenetic diversity and future evolutionary potential of that group. Phylogenetic diversity (PD) can be defined as a biodiversity index that measures the length of evolutionary pathways that connect a given set of taxa, and it is regarded as a surrogate for the ‘feature diversity’ that arises along the branches of the evolutionary tree (Faith, 2006; Forest et al., 2007). Quantifying the diversity of such ‘features’ is important because it is difficult to know which features of an organism will be advantageous as the environment changes. Thus, maximizing PD may be the best bet-hedging strategy to ensure that evolution has the necessary raw materials on which to work in a rapidly changing world. The potential application of phylogenetic diversity within conservation planning is illustrated by a recent study by Forest et al. (2007) on the remarkable flora of the Cape region of South Africa – an undisputed biodiversity hotspot containing more than 9,000 plant species, a staggering 70 per cent of which are endemic. They collected and compiled distribution data for the entire Cape and created an inventory of species and genera per quarter-degree square. They also reconstructed the phylogeny (phylogenetic tree) of the Cape flora based on analysis of plastids from 735 genera, each indigenous to the Cape. The results were fascinating, if somewhat complex. PD and species (or genus) richness were broadly correlated (areas of high PD also had high richness), suggesting that traditional conservation planning strategies of maximizing species richness may be equally effective at maintaining PD. However, as is often the case, the devil is in the detail. PD was found to scale with richness in a complex manner, so that some regions had more or less PD than would be expected from their species richness. Forest et al. (2007) uncovered a distinctive east/west division in the distribution of PD that broadly corresponded to climatic zones in the Cape region. Specifically, PD for a given number of taxa was higher in the eastern region than in the west (Figure B7.1a). The consequences of such a geographical decoupling of PD and taxon richness is that, in the event of extinctions of non-prioritized species, a traditional taxon (species or genus) richness approach to conservation planning might lose disproportionate numbers of species possessing unique evolutionary characteristics. This, in turn, would reduce the evolutionary potential to respond, adapt and diversify in the light of changes in climate, land use, species composition, etc. Whether such PD approaches will become widespread in conservation planning is debatable. As the authors themselves note, taxon diversity will remain an important conservation target, and it is by no means straightforward to balance prioritizations based on these two indices. Moreover, there is a serious scale issue that may be largely intractable: any conservation plan that operates at less than a global scale runs the risk of finding solutions that are optimal only within the study region. Using distinct phylogeographical regions such as the Cape region reduces, but does not totally resolve, this issue.

Conservation planning in a changing world

a)

b)

c)

d)

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Figure B7.1a Genus richness and phylogenetic diversity in the Cape flora: (a) genus richness (ten quantile intervals from yellow to deep red); (b) phylogenetic diversity (PD) per cell calculated using absolute age estimates in million years (colour code as for a); (c) residuals from a loess regression of PD on genus richness. Cells with negative residuals are indicated in blue, and those with positive residuals are shown in red (shading increments of half a standard deviation); (d) the distribution of unusual PD values, as assessed by comparing the observed PD in each cell with 10,000 PD values calculated by random selection of the same number of genera from the Cape flora. Cells with significantly lower PD (P <.0.05, two-tailed) than expected are shaded in blue. Figure from Forest et al. (2007). (See Plate B7.1a for a colour version of these images.)

drawn onto this already perturbed historical canvas (Jackson et al., 2001). Despite this reality, many ecologically relevant processes go well beyond the temporal span of many data sets brought to bear on conservation problems. For example, bristlecone pines (Pinus longaeva) can live for 5,000 years and their tree ring chronologies have been used to reconstruct palaeoclimates (LaMarche, 1974), human settlement patterns (Ababneh, 2008), and volcanic history (Salzer & Hughes, 2007). The

whole history of the science of ecology has played out over a few per cent of the lifespan of one of these trees. There are many ways in which long-term ecology can contribute to present-day conservation efforts (Chapter 3). Here we discuss three of them, using past dynamics to predict future responses, interpreting recent changes in abundance in their historical context and understanding patterns of geographical range collapse.

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7.2.1 Predicting future ecosystem responses to changing conditions Despite the long history of human intervention, conservation theory and practice generally focus on how present conditions, both natural and anthropogenic, determine species’ distributions and abundances. This is consistent with the notion that conservation is a crisis discipline in which we must act before knowing all the facts (Soulé, 1985). For example, according to the IUCN’s Red List, some 22 per cent of vertebrates are threatened with imminent extinction (IUCN, 2008). Action to enhance their chances of persistence is urgent, although investment thus far has been limited (Bottrill et al., 2008). Processes have generated and shaped the patterns that we see as a fleeting snapshot today, and processes are the rules that govern how ecosystems will respond to future changes. A first key contribution of long-term ecology to conservation, then, is that palaeoecological and historical data can help unravel these rules; the way that ecosystems and species responded to past changes might tell us something about how they will react in the future to management or threat. In some cases, the story of a decline and, thus, the conservation intervention required to reverse that decline, is clear. For example, the ban on trade in ivory of African elephants, introduced in 1989, successfully led to recovery of the population, although there are still hotspots of continuing trade and elephants are still declining rapidly in some places (Stiles, 2004; Lemieux & Clarke, 2009). Sometimes, the cause of population declines is a combination of natural variability and changing levels of harvesting, such that the appropriate policy response is much less certain. For example, more than ten million tonnes of Peruvian anchovetta (Engraulis ringens) were harvested in some years before the stock collapsed in the early 1970s. The collapse was apparently brought about by a combination of the level of exploitation and natural population variability in the Peruvian upwelling system. The decline persisted into the mid-1980s, when the population began a period of increase towards its earlier levels before collapsing again during the late 1990s. Because the balance between supply and demand effects remains uncertain, policy decisions relating to this fishery are difficult (Pontecorvo, 2001). However, long-term data are critical in explaining fluctuations in such fisheries, and history shows us that common species, such as the

passenger pigeon, can sometimes decline rapidly to extinction (Gaston & Fuller, 2008). Sometimes the story is much less clear and detailed historical analysis is necessary to piece together and learn from past events. For example, the Allegheny woodrat (Neotoma magister) was formerly widespread across the Appalachian mountains of the north-east USA. In the 1970s it began disappearing from the northern part of its range, and only a decade later it was extinct in New York, Connecticut and much of New Jersey, and threatened in Maryland, Ohio, Pennsylvania and Indiana (Hicks, 1989). There is no obvious single cause of this decline; the species is not exploited, its habitat remains more or less intact, and there are no (known) introduced predators or competitors that appear to be responsible for the decline. A careful analysis of historical information by LoGiudice (2006), beginning with observations in the 19th century literature, uncovered a story of ‘death by a thousand cuts’ that had begun long before the decline was apparent to wildlife biologists. A series of small anthropogenic threats, from habitat fragmentation to introduced tree pathogens, each in themselves insufficient to cause the decline, had occurred sequentially, resulting in eventual wholesale disappearance of the species from a large part of its historical range. LoGiudice (2006) ends with a plea to conservation scientists: ‘We must train ourselves to look broadly (in the ecological sense) and deeply (in the historical sense) when investigating the causes of species declines.’ Where long-term information is not available, this does not mean that we are unable to act. While it is useful to have the knowledge to make sensible predictions about which conservation interventions will be successful, it is critical not to let action become delayed by focusing exclusively on accumulating knowledge about a particular problem or system. Recent developments in decision theory allow estimates about how a system works to be updated as new information becomes available, thus, over time, improving the likelihood of success of conservation action without unnecessarily delaying action just because knowledge is incomplete (for a review, see Grantham et al., 2010). This is particularly important where there are many possible reasons for a particular observed decline or, as in the case of the Allegheny woodrat, where a decline is driven by many small effects acting in concert. A detailed historical analysis of past declines can take place alongside, and eventually guide, contemporary conservation efforts.

Conservation planning in a changing world Inference about process based on correlational pattern-based study alone can be misleading, however. For example, reef corals in the Caribbean that appear to have been stable for at least 125,000 years suddenly collapsed in a mass mortality event in the 1980s (Jackson, 1992). One of the important proximate events leading to this collapse was the widespread disease-induced mortality of the grazing sea urchin Diadema antillarum, allowing macroalgae to overgrow and choke the corals. Most contemporary ecological investigation would have concluded there. However, careful analysis of palaeoecological, archaeological and historical data showed that fishing activity between the 17th and 19th centuries had decimated populations of large consumers such as marine turtles, large fish, sharks and manatees (Jackson et al., 2001). The loss of these large species greatly simplified the community, leading to the dominance of niche space by one species of grazing sea urchin. The superabundance of the urchin created good conditions for disease spread and reduced the redundancy in the system, leading to a rapid collapse (Jackson, 2001). More generally, historical overfishing, the impacts of which are now not readily observable directly, pre-date modern anthropogenic impacts such as climate change, introduced species and the spread of diseases, which are the factors often implicated in biodiversity loss.

7.2.2 Interpreting recent trends in their historical context A second key contribution of long-term ecology to conservation is founded on the recognition that the patterns we see today can only be interpreted in their long-term context. Declines in species’ abundances or geographical ranges, insofar as they predict extinction probability, are routinely used to prioritize species for conservation action. However, an understanding of natural variability in population size is crucial in interpreting any particular observed decline, and historical records have been used on many occasions to place current declines in context. Many early human impacts (e.g. forest clearance, changes in fire regimes) have been higher in severity and commenced much earlier than previously realized and, sometimes, anthropogenic impacts have compounded natural reasons for declines. For example,

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Nogués-Bravo et al. (2008) used a combination of climate envelope models and population models to demonstrate that the woolly mammoth (Mammuthus primigenius) probably declined rapidly owing to a dwindling of the area of suitable climate space, finally reaching a point at which the reduced populations became vulnerable to increasing human hunting pressure. Along with the arrival of mechanized agriculture, stories abound of rapid extinctions of formerly common or widespread species, many of which have resulted from human intervention (Gaston & Fuller, 2007). Rocky Mountain grasshoppers (Melanoplus spretus) were once distributed across much of the western USA, numbering perhaps 15 trillion individuals in outbreak years. They destroyed crops over vast areas and devastated plains farming communities in the mid 19th century. During non-outbreak years, they were restricted to valley bottoms in the Rocky Mountains – favoured areas for a rapidly expanding agriculture. In the closing decades of the 19th century, localized habitat destruction in these areas triggered a precipitous decline to extinction (Lockwood & DeBrey, 1990). Despite the focus in much of the literature about declines, lessons from history are not always about reductions and losses. There are also examples of dramatic gains in species’ distributions. For example, the slow transition from the ice sheets of the Last Glacial Maximum (about 16,000 years ago) to forests and farmland was accompanied by dramatic responses in faunal densities and community structure and composition. Hence, for example, the total number of wild mammals in Britain has been estimated to have been as high as around 535 million about 7,000 years ago (Maroo & Yalden, 2000). Some species have declined precipitously since this period as forests have been converted for agriculture, yet populations of some other species have increased as they benefited from the opening up of habitats (Table 7.1). The five species with highest estimated abundance during the Mesolithic accounted for 80 per cent of the loss of individuals in comparison with the present-day fauna. Combined with eight extinctions over the time period, these changes have resulted in markedly different mammalian communities in the country within a few thousand years. Many farmland bird species in the UK have followed similar trajectories of expansion and decline as forest has been converted to agricultural lands and agricultural use has then progressively intensified. For example, grey partridge (Perdix perdix) numbers

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Table 7.1 Examples of dramatic changes in wild mammal populations in Great Britain since the Mesolithic period (c. 7,000 years ago), when clearance of woodland began to accelerate strongly. The table shows the three species with largest proportional increases and declines, excluding the eight species that were extirpated during the period (root vole (Microtus oeconomus), wild boar (Sus scrofa), aurochs (Bos primigenius), European beaver (Castor fiber), elk (Alces alces), brown bear (Ursus arctos), wolf (Canis lupus), Eurasian lynx (Lynx lynx)). Data from Maroo & Yalden (2000). Species

Mesolithic population

Recent population

Change

Red squirrel Sciurus vulgaris

11,774,912

160,000

−99%

Hazel dormouse Muscardinus avellanarius

25,841,031

500,000

−98%

147,474

3,650

−98%

European badger Meles meles

13,752

250,000

+1,718%

Stoat Mustela erminea

66,033

462,000

+600%

Red fox Vulpes vulpes

72,367

240,000

+232%

European pine marten Martes martes

increased significantly in the early 1800s due to agricultural changes (Holloway, 1996) but have declined sharply in the last few decades, largely due to increased use of pesticides, such that the species is now Red Listed in the UK (Potts, 1986; Gregory et al., 2002). An example of major changes in community composition over more recent history involves the colonization of urban environments by species (Blair, 1996; Johnston, 2001). Some species, termed synanthropic, or urban exploiters, are able to thrive in the novel conditions created as a landscape is urbanized. For example, although once confined to forest habitats, the Eurasian blackbird (Turdus merula) has now colonized towns and cities across most of the European part of its geographical range, with the phenomenon being first noted in Germany in the 1820s (Luniak et al., 1990). Genetic analysis of blackbirds from 12 cities supports the hypothesis that birds colonized each of the cities in separate colonization events, rather than ‘leapfrogging’ from one city to another (Evans et al., 2009). This is an elegant example of how contemporary genetic analysis can be used to piece together the historical pattern of spread of a species. The foregoing examples show that understanding how the dynamic processes of extinction and colonization have led to present patterns in species’

distributions is key to predicting how those distributions will change in the future. However, interpreting the magnitude of recent changes requires comparison to some baselines, and there are many that could be selected (Gillson & Willis, 2004; Birks, 2005; Smol et al., 2005; Willis & Birks 2006; Willis et al., 2007). Willis et al. (2007) suggest using changes in environmental regimes as a guide to identifying baselines (e.g. the Holocene shift to warm climatic conditions that persist today). However, while the use of long-term ecological data may improve the rigour and objectivity of the process, the choice of which baseline to adopt is, ultimately, a subjective one (see Chapter 3 for a fuller discussion of ecological baselines). Whatever the choice of baseline, many of the issues of using the past to understand the present and predict the future remain the same.

7.2.3 Geographical range collapse The distributions of species do not remain constant over time; in reality, they are highly dynamic over all but the narrowest of timescales (Gaston, 2003; Lomolino et al., 2010). Geographical range shifts over time have been documented empirically using the fossil

Conservation planning in a changing world record, although biases in fossil data can make interpretation difficult. Some of the most well-documented range shifts are those derived from palynology, the study of the pollen record. The outer layer of the pollen grain, the exine, is constructed of sporopollelin, which is highly resistant to decay in anaerobic conditions. The form of the grain is diagnostic to varying taxonomic resolution, sometimes species level, more often genus or family, using light microscopy. By extracting pollen from dated sediments, palaeoecologists can trace the geographical history of species. For example, analyses of the pollen record from a large number of sites suggested a rapid recolonization by trees of northern Europe and North America as the ice retreated following the glacial maximum about 16,000 years ago (Davis, 1981; Huntley & Birks, 1983). Subsequent molecular evidence has suggested that low-density populations of some species persisted close to the ice sheet in the late glacial period, and that rates of range migration were lower than those predicted by traditional pollen analysis (McLachlan et al., 2005). When a species declines towards extinction, losses of populations across its geographical range form a pattern. If we could predict which populations are most likely to persist over the long term, we could generate conservation plans that capture those places. One suggestion has been to place reserves in places where populations are most likely to persist by modelling the environmental suitability of a location for each species of interest and combining this with information on predicted future anthropogenic threats (Araújo & Williams, 2000). Environmental suitability and population abundances often decline towards the margins of a species’ geographical range, while fragmentation and isolation of constituent populations increase, suggesting that populations in the core part of the range will be more likely to persist over the long term (Carter & Prince, 1981; Brown et al., 1995; Curnutt et al., 1996). Some observational data bear out these general predictions. For example, population extinctions between 1968 and 1991 in several declining British farmland birds were concentrated around the periphery of their geographical range (Donald & Greenwood, 2001), although some species showed different patterns, including those where extinction events seemed to occur more or less randomly across the range. Araújo et al. (2002b), in their study of 78 breeding passerine bird species in Britain, found that extinction

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probabilities from 10 km × 10 km grid cells over a 20year period were negatively correlated with predicted environmental suitability and were frequently concentrated in the range margin (Figure 7.2). This is not surprising, as abundances are generally expected to decline towards the range margin (Maurer & Brown, 1989; Lawton, 1993), although spatial patterns of abundance across geographical ranges are often highly idiosyncratic (Brown et al., 1996). In another study of range contractions, using herbarium and observational data on plants in New England (USA), Farnsworth & Ogurcak (2006) studied the location of recent records within the historical range. They found that extant records were distinctly clumped, being concentrated more towards the margin of the historical range than expected under a null model. Their explanation for this result was that anthropogenic pressures drive population extinctions and overwhelm any effects of range-wide patterns in abundance or habitat suitability. Favouring range core over range periphery in conservation prioritization can correct the tendency of complementarity-based approaches to select areas where many species’ geographical ranges overlap, and hence preferentially select populations close to the geographical range periphery (Araújo & Williams 2001; Gaston et al., 2001). Selecting range core areas, is of course, desirable only if marginal populations generally have lower long-term viability than core populations, but other work has cast doubt on the assumption that viability peaks near the range core, i.e. that the range core is the most likely location of populations that will persist over the long term. Channell and Lomolino (2000) studied the pattern of range contraction in 309 declining species, mostly birds and mammals, by comparing historical and recent range maps. They found that the extant ranges of the vast majority of species (98 per cent) occurred within the peripheral half of their historical range, with populations of only five species (2 per cent) persisting solely within the core of their historical range (Box 7.2). However, because Channell and Lomolino only divided the ranges into two halves, the precise position of the area to which species are most likely to collapse remains uncertain, and may well be sensitive to the scale of resolution of the analysis. Indeed, a recent study on chukar partridges, Alectoris chukar, showed that genetic variability increased moving out from the core of the range, and then declined again at the extreme periphery (Kark et al., 2008). This raises the possibility that some region

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Figure 7.2 Geographic range change in the grasshopper warbler (Locustella naevia) in Britain. (a) Probability of occurrence in 1968–1972, estimated using the pattern of contagion among records, with blue being minimum (non-zero) probability of occurrence and warm colours high probabilities; (b) The probability of extinction by 1988–1991, where blue represents low probabilities of extinction and warm colours high probabilities. Actual occurrences in 1988–1981 are shown by black dots. Occurrence records without probability values represent range expansions. Cells of high probabilities of extinction and lacking occurrence records are concentrated towards the margins of the 1968–1972 geographic range, although other species showed markedly different patterns (and contrast with Box 7.2). Reproduced from Araújo et al. (2002b). (See Plate 7.2 for a colour version of these images.)

Box 7.2 The dynamic geography of range collapse The extinction of a species is often associated with an extended period of population decline, and this decline is normally also associated with a marked contraction of geographical range. Therefore, a clear understanding of how and why species ranges contract is essential for good conservation planning (Simberloff, 1986). Even so, until recently there has been relatively little work on range contraction, and ranges tended to be viewed as static rather than dynamic. This view was strongly challenged by Channell and Lomolino (2000) who assessed the spatial dynamics of range collapse by comparing the ranges of relict populations with their historical range. They were specifically interested in distinguishing between two hypotheses about range collapse: The demographic hypothesis (Figure B7.2a, panel a) is based on two assumptions: i that extinction probability of a population should decline with increased population size and should rise with increased variation in population size; and ii that populations tend to be larger and less variable near the centre of the species’ geographical range because the environment is more benign.

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Figure B7.2a A graphical representation of the spatial sequence of range contraction following (a) the demographic hypothesis, and (b) the contagion hypothesis. In these simple models, the historical range was divided into two equal area bands, peripheral and central. These bands were constructed by collapsing the range boundaries towards the range centre until the area occupied in the inner polygon equalled half of the area of the historical range. The dark grey represents the peripheral half of the historical range, while the light grey represents the central half of the historical range. The black represents the region in which the species is extinct. The index of centrality (C) is the proportion of the remnant range that falls within the central region of the historical range. From Channell & Lomolino (2000).

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Figure B7.2b Two examples of sequential range contractions provided by Channell and Lomolino (2000): the American chestnut (Castanea dentata) and the numbat (Myrmecobius fasciatus). Within the ranges of the two species, darker colours represent more recent occurrences. Both cases demonstrate a pattern of historical range collapse to an initially peripheral part of the species range.

Based on these assumptions, it is predicted that ranges should implode, with the final population or sub-populations of a species persisting near the centre of the historical range (Brown, 1995; Lawton, 1995; Mehlman, 1997). The contagion hypothesis (Figure B7.2a, panel b) emphasizes the geographical dynamics of the extinction process(es) in determining where populations should persist. It posits that those populations which are last impacted by the implicated extinction force will persist longer. In this hypothetical scenario, Lomolino & Channell (1995) envisaged extinction factors spreading across the landscape like a contagion – hence the name. They argued that, regardless of where the contagion begins, the final region to be impacted will be the most isolated from the initial position of the contagion. Hence, although the process may well often begin at a range margin, it will spread most readily through the centre of the range, so that the final populations persist in isolated pockets somewhere along an edge of the historical range. The two hypotheses were tested using contemporary and historical range maps for 309 species of plants and animals, with two representative cases shown in Figure B7.2b. The hypotheses were compared by examining the sequence of changes in the proportion (C) of the remnant range that fell within the central region of the historical range. Monte Carlo simulations and polynomial regressions were used to examine changes in C during the process of range contraction. The results of the analysis provided general support for the contagion hypotheses and not for the demographical hypothesis, i.e. remnant populations tended towards positions on the edge of the historical range. Unsurprisingly, the most likely cause of contagion was identified as anthropogenic factors such as over-exploitation, pollution, habitat destruction, etc.

between the core and periphery might be the area to which ranges most frequently collapse – that being the region where selection favours genotypes with the greatest ability to adapt to changing conditions. The general scenario can be visualized by the following hypothetical example. Imagine a temperate northern hemisphere species which has an environmental

envelope that is related to water and energy regimes (a pretty general scenario), such that it has a northern (mostly cold-related) and southern (mostly heatrelated and/or drought-related) margin. To the west, the distribution is limited by the hard barrier of the sea, and to the east the species is constrained by a combination of thermal and moisture conditions and

Conservation planning in a changing world seasonality. How will such a species respond to global climate change in the next 50 years? If the general trend, as anticipated by climate scientists, is a significantly warmer world, the species may have opportunity to extend its range, especially along the northern margin. It will retreat from its southern margin, remain bounded to the west by the sea, and may either expand or contract along the eastern boundary depending on the geographical pattern of climate change and the particular climatic requirements of the species. Some marginal populations – those at the trailing edge – are likely to become unsustainable, while other marginal populations – those at the leading edge – could well become crucial for maintaining the viability of threatened species (Figure 7.3). Depending on the ecology of the species concerned, and especially the mobility of the species,

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population processes at the leading and trailing edges may be very different. Resources for conservation are generally severely constrained, and the position of an area with respect to geographical range margin is rarely used in most conservation planning exercises; to achieve full representation of both core and marginal parts of species’ ranges would be hard to achieve in practice for all but a few species (Araújo et al., 2002b). The best strategy might also depend on the type of threat. First, marginal populations might be expected to persist longer than core populations in circumstances where contagious threats dominate (see Box 7.2). Second, core populations may persist longest where a threat is diffuse across the species’ entire geographical range. Third, our consideration of climate change allows us to identify a scenario where current

Figure 7.3 Hypothetical outcome of global climate change for a single species. The left hand panel shows the structure of the range in the present day, with a dense occupancy of potential habitat around the core of the range and more fragmented distributions near the northern and southern edges. A protected area in the south of the range provides a safe haven for the species in the event of widespread land cover change threatening the species. With climate change, as shown in the right hand panel, populations at the trailing edge of the range might be expected to fail, while the core range shifts northwards and new populations form beyond the historical range margin. This assumes that the species concerned is initially at equilibrium with the contemporary climate and is able to migrate as the climate changes. The populations shown in grey outline having gone extinct, the species is no longer offered protection by the protected area.

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trailing-edge populations expand in their population size and supply colonists for range expansion so that they are no longer absolutely the periphery of the species range. In such a scenario, the pattern will follow a similar geographical pattern to the contagion hypothesis, but with two key modifications: 1 the pattern of range collapse will follow a climatically predictable (e.g. low to high latitude) progression, shared across many species; 2 while generally losing territory, the species may in fact expand into new territory outside the historical range margin at the favoured range margin (Figure 7.3). Latitudinal changes in the distributions of many species have now been demonstrated, with some groups exhibiting dramatic poleward range shifts within historical time periods (Parmesan, 1996; Hickling et al., 2006). Studying the winter distributions of 254 bird species in North America, La Sorte & Thompson (2007) found that the position of the northern geographical range boundary, the centre of occurrence and the centre of abundance all increased on average during the time course of the study (1975– 2004). In a fascinating review of palaeoecological data, Arndt & Rémy (2005), highlight the importance of considering the conservation needs of relictual populations left at the trailing edge as geographical ranges shift with climate change. As well as latitudinal shifts, there is also accumulating evidence of upward elevational shifts in species’ distributions over recent history. Bryophytes respond strongly to local climatic conditions and are well adapted to colonize new locations if conditions become suitable. This makes them an ideal group within which to study responses to climate change. Recent work in Switzerland has shown that the mean elevational range and the position of the upper elevational limit of cryophilous (cold-tolerant) bryophytes both increased significantly between 1880 and 1920 and between 1980 and 2005 (Bergamini et al., 2009). The position of the lower elevational limit of cryophilous species and the elevational range of less cold-tolerant species was unchanged, consistent with the shift being a result of climate change. Another example comes from a study of the elevational distribution of breeding birds in the Italian Alps by Popy et al. (2010). They used data from two recent atlas surveys (1 km grid squares) at an 11-year interval (1992–1994 and 2003–2005). As anticipated, they

detected small increases in the mean elevation for the majority of species but, because some species showed downward shifts, the average change across all species was not significantly different from zero. They also recorded a small change in species composition, which corresponded on average to a 29 m upward elevational shift in the distribution of the avifauna. The relatively small shift in elevation is surprising, given the considerable increase in temperature in the region (≈1°C), which suggest that elevational shifts in regions of complex topography may not always have a simple relationship with increasing regional temperatures. Understanding the temporal patterns of geographical range collapses is also critical for designing conservation interventions. Species with small and declining distributions are at especially high risk of extinction, and this observation has been used by the conservation community to prioritize species based on their shortterm probability of extinction (Mace & Collar, 1995; IUCN, 2001). The distributions of species change naturally over evolutionary time, with range sizes broadly increasing following speciation and then, subsequently, declining towards extinction (Figure 7.1; Gaston, 2003; Vrba & DeGusta, 2004), but human intervention dramatically steepens the trajectory of decline towards extinction, and conservation activity is often focused on those species in which the rate of change is particularly great. However, sometimes the final trajectory to extinction may be so steep that it is too late for effective conservation action. Furthermore, applying conservation effort only in the final stages of a species’ decline may lead to a continuous cycle of ‘crisis management’ (Linklater, 2003) and may be an inefficient use of resources (Bottrill et al., 2008).

7. 3 PR EDI CT I N G B I ODI V ER S I T Y CH AN GE The foregoing discussion has revealed many examples of long-term changes in species’ distributions, and these are likely to accelerate in the future as humans increasingly dominate the ecology of the planet. As environmental conditions change over time, species could: 1 move to locations where their preferred conditions are still found; 2 adapt in some way to the changing conditions; or

Conservation planning in a changing world 3 show some combination of range shift and adaptation. Thus, there are two components to predicting biodiversity change. First, one must model the current distributions of species, ascertaining which variables best explain why a species occurs in some places and not others. Second, one can input future environmental conditions and predict the future distribution of suitable places for a species. The question of adaptation to future environmental conditions has received less attention. We briefly review this issue after first considering how to describe the current distributions of species, and then model future range shifts.

7.3.1 Modelling the current distributions of species, habitats and biomes No species is found everywhere; most are restricted to rather few locations, and there is enormous variation among species in the extent and pattern of their distributions. For example, the Taita thrush (Turdus helleri) currently occurs over only 3.5 km2 of the Earth’s surface, being restricted to four tiny forest patches in the Taita hills in southern Kenya. In contrast, its very close relative, the olive thrush (Turdus olivaceus) is found over an area of two million square kilometres of the African continent. The overall frequency distribution of geographical range sizes is strongly rightskewed, with most species showing rather narrow distributions (Gaston, 1996). Within their distributional limits, most organisms occur patchily; worse, our knowledge of species distribution is itself patchy, contributing to high levels of uncertainty about the real distribution of conservation features (e.g. threatened species or habitats) (Chapter 4). In this section we will discuss some of the tools available to reduce this uncertainty by modelling species’ distributions and hence ‘filling in the gaps’ – but first, an example of the power of these techniques. Just over a decade of intensive fieldwork in South Africa culminated in publication of The Protea Atlas (Forshaw, 1998), containing 220,000 records of 381 taxa from 40,000 locations. The Proteaceae are a large group of spectacular flowering plants, with high levels of endemism and diversity in South Africa. By progressively adding groups of 3,000 plots (representing the number of records added each year to the Atlas), Grantham et al. (2009) simulated the increase in knowledge about the distributions of proteas over a

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hypothetical 20-year period. At each time step, they constructed a conservation plan aimed at protecting the most important areas of protea habitat. Results showed that one could produce a robust conservation plan using only two years’ worth of survey data to generate models of the distributions of proteas across South Africa. This illustrates the power of species distribution models to inform conservation planning efforts in the face of incomplete knowledge. There are three broad routes to modelling the distributions of species (Graham & Hijmans, 2006; Gaston & Fuller, 2009): those based simply on documenting the pattern of a spatial distribution from known information; those that correlate a distribution with other (external) variables; and those that build explicit models of why a species occurs where it does. We will refer to these kinds of methods as pattern-based, correlative, and process-based, respectively. One can only use correlative or process-based models to predict future changes in distributions (Figure 7.4). Pattern-based techniques for modelling species’ distributions involve plotting locality records directly, perhaps also interpolating among them or applying mathematical adjustments to the spatial pattern of records to yield a distribution map. For example, one can plot the marginal occurrences of a species to

Figure 7.4 Using models of species’ distributions to predict future change. First, a species’ current distribution is related to current conditions, either by correlation or by building a process-based model of cause and effect. Some future scenario of environmental change is translated into a predicted future species’ distribution using the relationship between current environmental conditions and current distribution. For a more detailed representation of the complexities involved in such models, see Figure B7.3a.

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identify the outer limits to its distribution. Interpolation along the boundary can be used to trace the boundary on a map, thus forming a representation of the species’ distribution. This model usually takes the form of an irregular, more or less contiguous, surface such as the range maps printed in many modern field guides. Isolated populations might be given their own polygon, but typically there is no formal method to decide whether the range map should ‘bud off ’ at a particular location. The use of such maps in ecological analysis has been criticized on the basis that they ignore or grossly simplify the pattern of occupancy within species’ ranges (Hurlbert & Jetz, 2007), and it is true that maps generated from marginal occurrences tend to exhibit greater errors of commission (false occurrences) than errors of omission (false absences; Gaston, 1991; Graham & Hijmans, 2006). From a conservation viewpoint, such representations of species’ distributions are not particularly useful, because they do not effectively discriminate occupied and unoccupied areas within a species’ range; they are often at a scale too coarse to be practically useful; and, because they are purely patternbased, they cannot be used to make predictions about the future (Rondinini et al., 2006). Where the density of records is sufficiently high, simply mapping all available records might suffice. However, frequently records can be generalized by mapping them as occupied/unoccupied cells of an equal-area grid. Omission errors often outweigh commission errors in distribution maps built in this way, because sampling is usually poor in some parts of a species’ range. This phenomenon can render such distribution models inadequate at any resolution that is useful for conservation purposes (Graham & Hijmans, 2006; Rondinini et al., 2006), although some methods of interpolating such maps to improve their resolution, e.g. those based on patterns of sampling effort (Högmander & Møller, 1995) have been developed. Despite the higher resolution of this kind of distribution models, they are still inherently pattern-based and cannot be used to make predictions about future change. While such methods of plotting species’ distributions are useful in representing the distributions of species, opening the door to spatial prioritization of conservation activity, they provide little in the way of predictive power because there is no basis on which to model response to future environmental change. In other words, no model underlays these maps – they are

simply collations. They are also heavily dependent on data availability, which is often most problematic in areas with highest species richness, tropical and ecotonal areas. For quantitative predictions about future change, we need formal models of species’ distributions that can be projected to future changes in environmental conditions. The simplest and most popular approach to building distribution models with predictive ability is the correlative species distribution model. Here, one correlates a species’ distribution with external variables such as physical environmental parameters or the distribution of other species or habitat types. Perhaps the simplest way to correlate a species’ current distribution with environmental variables is to use information about habitat preferences to map the distribution of suitable habitat, making the assumption that the species of interest will occur wherever there is suitable habitat. If the occurrence of suitable habitat is imperfectly known, its distribution may be modelled statistically using other environmental variables (Early et al., 2008). Habitat models have been widely used in conservation science to predict the response of species to habitat loss, essentially by using habitat extent as a surrogate for population size (IUCN, 2001; such a process is actually a modelling exercise, even though it is often not cast in such terms). To predict future change, one still needs to model how habitats will change over time, but this approach has been used successfully to build predictions of future changes based on change in land use patterns. For example, Lee and Jetz (2008) assessed the influence of past and future land-cover transformations on the global reserve network across biogeographical and geo-political regions and 5° latitudinal bands worldwide. They concluded that past changes in land use poorly predicted future change and were therefore a poor basis for making decisions about future conservation prioritization. Clearly, models of this nature are still crude, but they do have the advantage of ‘capturing’ a wide range of ecologically important variables. If, for example, we wanted to model the future range of forest birds in a region, a map of future forest cover (albeit estimated) will provide a sensible start point. Such approaches work less well in situations where species’ distributions are limited by non-habitat related factors, such as competition, predation, food availability or dispersal limitation. Even without such

Conservation planning in a changing world complications, many species have highly specific microhabitat requirements that are difficult to capture using broad-scale or remotely sensed habitat data. Because many species’ distributions are limited by features other than broad habitat type, estimates of suitable habitat are often masked by intersecting some broad representation of the known geographical range of the species of interest, so as not to generate predictions beyond the bounds of known or likely occurrence (Rondinini et al., 2005; Harris & Pimm, 2008). This makes prediction based on habitat mapping an inexact science. Rooted in Hutchinson’s (1957) niche theory, a powerful suite of modelling techniques collectively known as species distribution models, niche modelling or bioclimate envelope models have been developed and refined over the past 25 years (Chapter 4, Section 4.4.1; Pearson & Dawson, 2003). These techniques are based on the idea that, for each environmental variable, there is an optimum value at which conditions are most suitable for the species of interest – and that suitability declines as the value of the environmental variable increase beyond, or decreases below, the optimum. Hutchinson (1957) conceptualized two kinds of niches: the fundamental niche, reflecting the underlying physiological tolerances of a species to environmental conditions; and the realized niche, in which the possible range of environmental conditions in which a species can exist is limited by biotic interactions such as predation and competition. These concepts also apply to distributions: a species’ realized distribution often does not fully occupy the geographical space of its fundamental (i.e. potential) distribution. This distinction is important when modelling the distributions of species, and different methods suit the modelling of realized, as opposed to fundamental (potential), distributions (Jiménez-Valverde et al., 2008), as we next discuss. Most efforts to model species’ realized distributions work by spatially sampling across an environmental gradient and determining whether the species is present or absent at a range of sites with different environmental conditions. If one assembles several relevant environmental variables, each geographical location at which a species has been found has an equivalent position in environmental space. These inductive models predict species’ distributions using a combination of presence/absence or presence-only locality records (in their raw form or generalized on a grid), data on spatial variation in environmental variables, and one or more modelling techniques (e.g.

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logistic regression, GLM (generalized linear models), GARP (genetic algorithm for rule-set prediction), or Maxent: Guisan & Zimmermann, 2000; Stockwell, 2007; Phillips & Dudík, 2008). Many such models have employed small numbers of original locality records (Hernandez et al., 2006; Pearson et al., 2007) and some have attempted to use coarse-grained occurrence data to predict distributions at finer resolutions (Collingham et al., 2000; Araújo et al., 2005a; McPherson et al., 2006). Methods based on habitat suitabilities and statistical modelling have been developed to predict the potential distribution of a species within the limits of its occurrence. They tend to work well when historical factors, dispersal limitation and sampling biases are not dominant processes, although some of these effects can be reduced by careful evaluation of alternative models and by consideration of how the results will be applied (Loiselle et al., 2003). Species distribution models can also be based on the fundamental, rather than realized, niche. Here the focus is on predicting the locations of physiologically suitable conditions in which a species is expected to persist (Porter et al., 2002; Kearney & Porter, 2004; Morin et al., 2007). Such models use physical principles and physiological data on tolerances to microclimatic conditions to predict the occurrence pattern of a species, and can provide surprisingly good predictions of distributions when constrained to regions in which the species are known to occur and when sufficient physiological detail is known (Gaston & Fuller, 2009). For example, Kearney and Porter (2004) modelled the fundamental niche of the nocturnal lizard Heteronotia binoei across the whole of Australia through combining physiological measurements (thermal requirements for egg development, thermal preferences and tolerances, metabolic and evaporative water loss rates) and high-resolution climatic data (air temperature, cloud cover, wind speed, humidity and radiation) with biophysical models. This methodology allowed them to calculate the climatic component of the fundamental niche of the lizard and map it onto the Australian landscape at high resolution. In general, species distribution models are particularly useful for conservation in situations where data on a species’ distribution are patchy and sparse, exemplified by the analysis of the South African protea data (Grantham et al., 2009). Most information on the distribution of species under-represents certain areas, often in places where species richness is highest, such

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as the tropics (Section 4.2.2; Collen et al., 2008). Models can help bridge this gap in the data and thereby improve the quality of planning for conservation (Loiselle et al., 2003; also see Chapter 6 this volume). However, as with all complex ecological models, there are pitfalls to avoid and numerous assumptions that need to be taken into account when interpreting their results.

7.3.2 Modelling range shifts Assuming one can model the current distributions of species effectively, an important next question is whether the relationships between environmental conditions and species’ distributions will continue to hold into the future. If they do, then extinctions can be predicted to occur in places rendered unsuitable by environmental change, and colonizations where previously unsuitable conditions become suitable (Figure 7.3). There is no doubt that species’ geographical ranges, and the resulting composition of communities, shift continuously through time. These changes occur naturally over evolutionary time (Gaston, 2003; Vrba & DeGusta, 2004), and climate is arguably the dominant driver of species’ natural distributions. There is ample evidence for this both from the fossil record (Huntley, 1999; Davis & Shaw, 2001) and from empirical studies correlating species’ distributions and changes in those distributions with climate variables (e.g. Root, 1988). This has led to a great deal of interest in predicting how human-forced climate change will alter species’ distributions. Once a good model of a species’ current distribution has been built, change in environmental parameters (such as those reflecting a future climate or land-use scenario) can be simulated, and the model used to generate a prediction of the future distribution of species in response to any given environmental change. This general approach assumes that the relationship between species occurrence and environmental conditions does not markedly change over time. There is some empirical support for this assumption. For example, population trends in 42 species of rare birds breeding in the UK were found to be positively correlated with climate suitability trend, suggesting that climate envelope models can successfully predict responses to climate change (Green et al., 2008). However, such approaches do not always work well (Davis et al., 1998; Araújo et al., 2005b; Beale et al.,

2008). For example, recent range expansion of the map butterfly (Araschnia levana), a species whose dispersal ability depends closely on late summer temperature, was poorly predicted using generalized additive models (Mitikka et al., 2008). Similarly, Araújo et al. (2005b) used data on the distribution of 116 British bird species in the 1970s to build climate envelope model-based predictions of ‘future’ distributions in the 1990s, which they were then able to test against the observed distribution at that time using the 1990s climate data and the 1970s-based models. In contrast to the result obtained by Green et al. (2008), they found that around 90 per cent of the observed distributions differed markedly from those predicted. Even in terms of predicting the directionality of range size change (expansion versus contraction), the models performed poorly. This apparent discrepancy of outcomes in relation to two studies of British birds might be explained by the differing forms of data used (population trends versus distributional range data). Predictions of geographical range shifts owing to climate change depend on a number of assumptions. Some of the most important are about dispersal, i.e. how quickly species’ distributions can track suitable climate space as it moves geographically. Generally, the slower dispersal ability is assumed to be, the more dramatic the reduction in occupied area will be, because geographical ranges will lag further and further behind climatically suitable areas as they shift across space (Peterson et al., 2002). For some taxa, such as butterflies, there is good evidence that species’ distributions are closely tracking recent changes in climate (Wilson et al., 2005; Hickling et al., 2006). However, this is unlikely to be the case in the future for all taxa, and especially for species needing to move across heavily cultivated or urbanized landscapes. Successful efforts have recently been made to couple population models directly with predictions about changes in distribution under land use and climate change (Akçakaya et al., 2004; Keith et al., 2008). Such exercises generally reveal that the extent and severity of climate change impacts on species’ ranges will vary with life history characteristics. When spatial and demographical processes are coupled directly in this way, oversimplifying assumptions about the relationship between habitat change and extinction (e.g. inferring species loss from species–area relationships) can be avoided (Buckley & Roughgarden, 2004; Thuiller et al., 2004). This appears to be a promising and potentially important area for further research efforts.

Conservation planning in a changing world A second important group of assumptions are those surrounding important biotic interactions, such as predation and site-specific competition. These further limit species’ distributions in ways that are not simply predictable from measures of environmental suitability (Davis et al., 1998). Araújo & Luoto (2007) incorporated the interaction between the European butterfly the clouded Apollo (Parnassius mnemosyne) and the presence/absence of four of its larval food plants, Corydalis spp. The results supported the proposition that both the explanatory and predictive power of climate-based species distribution models could be significantly improved when the current and modelled future distribution of food plants was incorporated. Many predictions also assume that evolutionary responses to changing climates will be slow relative to the flux in environmental conditions. While a fair amount of evidence suggests that local evolutionary change is unlikely to mitigate negative threats to species (Parmesan, 2006), a recent review of data on evolutionary response in the face of directional environmental change has challenged the validity of this assumption (Skelly et al., 2007). Once again, this appears an important area for future research. Finally, the persistence of species at low densities in small areas within regions of more generally unsuitable climate is difficult to model using traditional

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bioclimatic envelope analysis (e.g. McLachlan et al., 2005). As well as occurring at a fine spatial scale, below the resolution of most bioclimatic modelling exercises (Pearson, 2006), such effects appear to be highly idiosyncratic and would be hard to predict. While there are clearly many assumptions underlying predictive distribution models, meta-analyses of large numbers of studies have revealed changes in species’ distributions that are broadly concordant with recent climate change (Fitter & Fitter, 2002; Parmesan & Yohe, 2003; Root et al., 2003). Such ecological changes are yet to translate into widespread extinctions, although numerous studies predict that this will be an inevitable consequence of continued warming. For example, a global study by Sekercioglu et al. (2008) projected that 400–550 species of land birds will be extinct by the year 2100 due to climate change. In another global study, Jetz et al. (2007) estimated that 900 bird species will show range contractions greater than 50 per cent by 2100. Such quantitative projections are effective at garnering newspaper headlines but, given the large number of assumptions involved, they should be treated with extreme caution (Ladle, 2009; Box 7.3). Although it seems likely that impending climatic changes will lead to an accelerating wave of future extinctions (Pimm, 2008), at the present time there are too many

Box 7.3 Predicting global extinctions with species distribution models The most commonly used method of forecasting climate induced range changes and extinction is a family of models known as species distribution models. A basic species distribution model has three components: First, the climate and habitat within the observed geographical distribution of a species are analysed statistically. This produces a unique bioclimatic envelope (also known as ‘climate space’) which represents the physical conditions that allow that species to flourish. Second, the ability of the species to reach new habitats (dispersal) is quantified. Third, one or more climate change scenarios are chosen as the basis for forecasting the geographical distribution of the species’ future ‘climate space’. Typically, a set of high, medium and low impact (change) scenarios are chosen and applied to one or two significant points in the future. These points are typically ‘round number’ years such as 2050 or 2100. This type of model was used to forecast global extinctions under climate change in a paper by Thomas et al. (2004) that garnered global media coverage and, at the time of writing, had been cited over 800 times in ISI Web of Science. Thomas and his colleagues used projections of species’ distributions (a selection of vertebrates, invertebrates and plants) for future climate scenarios to assess extinction risks for sample regions that cover some 20 per cent of the Earth’s terrestrial surface. They then used three methods to estimate extinction, based on the species–area relationship to assess probability of extinction:

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i assessing changes in the summed distribution areas of all species; ii calculating the average proportional loss of the distribution area of each species to estimate the fraction of species predicted to become extinct; iii estimating the extinction risk of each species separately by substituting its area loss in the species–area relationship before averaging across species. They concluded that, under mid-range climate warming scenarios for 2050, somewhere between 15–37 per cent of the 1,103 species in their sample of regions and taxa would be ‘committed to extinction’. When the average of the three extinction probability calculation methods and two dispersal scenarios (universal dispersal or no dispersal) was used, minimal climate warming scenarios produced lower projections of species committed to extinction (18 per cent) than mid-range (24 per cent) and maximum change (35 per cent) scenarios. The worldwide media interest stemmed from the implicit assumption that the taxa are representative of all terrestrial animals, and that therefore 15–37 per cent of all terrestrial species would be committed to extinction by 2050 (reviewed in Ladle et al., 2004, 2005). In fact, the 1,103 species selected for the study were all endemics whose ranges are restricted to relatively small regions. Such range-restricted taxa are likely to experience a relatively limited range of climates, and, by extrapolation, their climate envelopes are more likely to disappear as conditions change. Moreover, the doubts about the predictive ability of such models reviewed in this chapter and in Section 4.4.1 mean that all such projections have to be treated with a healthy degree of scientific caution. Figure B7.3a highlights some of the key steps and assumptions involved in such modelling exercises, which include:

Figure B7.3a Some of the steps, choices and assumptions involved in modelling species losses resulting from future climate change using the bioclimatic envelope modelling approach. Not all studies involve all elements (e.g. land-use data, or dispersal models), but these components are important for increased realism. SPAR, Species–area relationship; z-value, a parameter of the SPAR indicative of the slope. From Whittaker et al. (2005, their Figure 1).

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1 whether the distributional models capture the mechanisms controlling distribution; 2 assumptions made about the dispersal powers of the species; 3 a lack of consideration of the role of ecological interactions with other species; 4 huge uncertainty about the climate change models used in projecting the future distributions; 5 assumptions about how area losses might translate into overall numbers of species losses (Thuiller et al., 2004; Whittaker et al., 2005; Ladle, 2009). In respect of the last of these, see the further discussion of the use of the canonical species–area value of z = 0.25 in projections of regional species losses in Section 8.2.1. Taking account of all of these sources of uncertainty, it quickly becomes apparent that the quantitative projections of losses provided by this study, although originally presented as a range of values and subsequently widely cited in scientific, policy and popular discourse, are misleadingly precise values that should be regarded as a conjecture rather than a forecast or prediction (Ladle et al., 2005).

uncertainties involved to predict the rate and magnitude of extinctions in the 21st century accurately. What we can say is that, despite the variation in performance of efforts to model climate-driven shifts in species’ geographical ranges, enormous changes in species’ distributions seem inevitable given substantial climate change. While many biologists would probably subscribe to this view, it is much less clear what actions we can take to avert, or at least minimize, these future extinctions as a result of climate change.

action in space and time that can deliver conservation outcomes. In this section we focus most of our attention on reserve system design in a dynamic world. However, these thoughts are relevant to other sorts of spatially-explicit conservation action. We close with a discussion on applying dynamic conservation planning in response to future climate change.

7. 4 W HAT D O W E D O AB OUT I T? DY N A MI C C ONSE R V AT I ON P L ANN I N G

The distribution of the conservation features that we are trying to conserve, usually species or habitat types, will change over time (Box 7.4). Dedicating a site as a reserve may affect the likelihood of losing a conservation feature from an area, or affect the time period over which the loss takes place. Species’ distributions, in particular, can change rapidly, often in response to predictable threats. Some of this change is associated with human-induced threats, such as an invasive species, habitat modification, or anthropogenic climate change, while other changes are ‘natural’. The first question that we therefore need to consider is the extent to which reserves can stop change, ameliorate it, or do next to nothing to influence a changing distribution. Where a reserve does not ameliorate a threat, such as a flood or a hurricane, then conservation planning needs to consider two things: first, how to set priorities that preferentially conserve sites with low levels of threat; and second, how much risk-spreading is necessary to provide adequate persistence in the face of that threat (e.g. how many separate sites might be required

Long-term ecology tells us that species’ distributions are highly dynamic, and our conservation response to continuing human threats must be similarly dynamic if it is to succeed. Biogeographers and ecologists have already begun to use insights derived from past changes, together with new statistical techniques, to build predictions about the future changing patterns of biodiversity. We have already argued that almost every aspect of the conservation planning problem is dynamic and that static conservation planning has its limitations. The question remains, what should we do about this? The theory, and practice, of conservation planning in a dynamic world is in its infancy compared to static conservation planning. It would nonetheless be a hard task to cover every dynamic aspect of conservation planning, so here we introduce a few problems and the ways they can be tackled. In its broadest sense, dynamic conservation planning is about taking any

7.4.1 Incorporating dynamic biotic and abiotic processes into conservation plans

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Box 7.4 Long-term ecological insights into dynamic conservation planning Studying the changes in plant and animal communities during past periods of climate upheaval may provide important clues to how contemporary communities will react to the rapid pace and high magnitude of anthropogenic climate change forecast for the 21st century. The onset of the present interglacial, the Holocene, provides one such analogue period, of rapid and dramatic climate forcing. Bush (2002) has reviewed the responses of the vegetation of the Andean flank of Peru during this period, when temperatures rose by an estimated 5–9°C (Colinvaux & De Oliveira, 2000). At first sight, this might suggest that the flora should be capable of withstanding another rapid warming event. However, Bush (2002) cautions against this simplistic interpretation for two reasons: i the current flora have spent the last two million years in cooler-than-modern conditions and may therefore be close to their upper thermal limits; ii humans have dramatically altered the landscape since the Holocene, which may both exacerbate climate change and also prevent or restrict species from migrating to new areas. In the case of the Andean flank, the frost line and the cloud base are predicted to rise higher (perhaps 600 m this century) up mountainsides. Based on the patterns at the transition into the Holocene, when non-analogue pollen assemblages occurred, we may expect species to respond individualistically to this forcing. Upslope migration of species, especially those with narrow elevational ranges, is anticipated in response to climate change, although the migration of species from the lowest elevations will be severely compromised by the abundant agricultural land that now exists between about 800 m and 1500 m above sea level in many areas. The habitat islands of forest that remain may no longer provide habitats suitable for lower montane species, and considerable species turnover could thus lead to new and often lower-diversity assemblages. Moreover, species that currently inhabit the lowest limit of the cloud forest may not be able to migrate as fast as local agriculturalists who are keen to exploit the newly available agricultural land. Bush (2002) concludes by arguing that the lesson of palaeoecology is that conservation must aim to maintain plant and animal niches and the possibility for species to respond individualistically, rather than focusing on conserving current communities. The greatest challenge will be maintaining enough habitat for critical ecosystems, such as cloud forest, and ensuring that remaining fragments are linked up to allow migrations. Conservation efforts should also be focused on the protection of lower cloud forest regions of tropical mountains as a means to mitigate anticipated habitat loss, and as a means of giving species a chance to migrate. By integrating an assessment of a past episode of extreme climate change with a consideration of current patterns of human activity in the region, Bush’s paper points to the importance of taking account of both forms of insight in modelling future climate change impacts. For an illustration of how future changes in land use might be incorporated at least for heuristic purposes in systematic conservation assessments, see Box 7.5.

that possess a given biodiversity attribute to ensure resilience of the conservation network?). It is thus common practice in conservation planning to avoid highly threatened sites, except where they contain unique features. Game et al. (2008) have shown how to include the risk of catastrophic events in coral reef reserve selection. Here, instead of aiming to conserve a fixed number of representatives of a

habitat type or species, they chose a suite of sites that give a sufficiently high probability that a conservation target is met. For example, if we know the chance that each reef of a particular type will be severely impacted by a hurricane, and how fast it can recover, we can determine how many reefs of that type need to be conserved to be 95 per cent sure that at least three are in a healthy state at any point in time.

Conservation planning in a changing world Another approach to dealing with threats that cannot be stopped by reserves and reserve management is to build a robust reserve system that buffers species against those threats. One way of buffering a single reserve against catastrophe is to ensure that any chance it is completely affected by a single catastrophe is very low. However, given that many catastrophes, such as fire and oil spills, have size distributions with long tails (there is still a small chance of huge catastrophes), the chance of having a single reserve big enough to withstand every catastrophe is negligible. We therefore need to choose enough reserves to spread the risk and deliver adequate persistence (Quinn & Hastings, 1987; Gilpin, 1988; Pressey et al., 1993; McCarthy et al., 2005). While there are many approaches to deal with threats that cannot be mitigated, their implementation in actual conservation decision-making has so far been limited. Species change their distribution in both predictable and unpredictable ways. Where the changes are part of predictable seasonal migrations, then, in principle, we can have moving reserve systems (Botsford et al., 2003; Grantham et al., 2008, 2009; Klaassen et al., 2008; Game et al., 2009). In principle, moving reserves can deliver better conservation outcomes, though frequently this might not be feasible from a socioeconomic and practical political perspective. For a discussion of dynamic conservation planning in marine systems, see Section 5.4.8.

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as a Markov Decision Process and solve that problem using stochastic dynamic programming (Possingham et al., 1993). That is to say, the system is modelling using inputs and decisions partly under the control of a decision-maker and partly randomly determined, in a step-by-step iterative process designed to simulate a dynamic real-world scenario. This has been done on a few occasions, but so far the approach is so unwieldy that it is largely of only theoretical interest (Costello & Polasky, 2004; Meir et al., 2004; Drechsler, 2005; Strange et al., 2006). Despite this, it has been possible to develop rules of thumb to help us decide where to buy conservation reserves. Such approaches can be applied to issues beyond reserve acquisition to include any sort of land use management (Hof et al., 1994; Hof & Raphael, 1997; Önal & Briers, 2003; Westphal et al., 2003). Fully incorporating socio-economic dynamics with conservation decision-making will require more interaction with economists and social scientists (cf. Box 7.5). While we regard the prediction of species persistence to be a major challenge, predicting changes in the economy and human preferences is probably even harder. Reducing this uncertainty and having better predictive models is important, but ultimately we may have to live with a high level of risk and uncertainty and factor it into our decision-making. Fortunately, there is a variety of tools we can employ to assist in making robust decisions under risk and uncertainty (for a full recent review of this topic see Regan et al., 2009).

7.4.2 Changes in socio-economic factors Proper conservation prioritization requires a consideration of the costs of different actions from a human perspective. Conservation budgets are limited, and every decision we make about nature conservation is necessarily a trade-off with other human aspirations such as providing shelter, food and water security for people (Ando et al., 1998; Wilson et al., 2006; Polasky et al., 2008). However, human aspirations are themselves dynamic. For example, what was considered high-quality (and hence high-cost) agricultural land in the past may or may not be suitable in the future as a consequence of technological change, changing markets or both (Naidoo et al., 2006). Bearing this in mind, optimal spatial conservation planning should factor in likely changes in costs and opportunities. One way to incorporate these dynamic costs is to formulate the conservation planning problem

7.4.3 Climate change, conservation planning and assisted migration Climate change is one all-pervasive threat that protected areas cannot stop. Ever since the recognition by Peters & Darling (1985; and see Figure 7.3) that some protected areas could be rendered obsolete by climate change, there has been interest in developing spatial conservation plans that deliver species persistence in the face of climate change. One approach to conservation planning under climate change is to protect every species in all its ranges – present and future (Hannah et al., 2007). If we have predictive models of future species distribution models for each species, then these future distributions can be added as features in any conservation plan. This simplest of approaches ignores the question of how a species might shift its range. However, that can be

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Box 7.5 Integrating socio-economic scenarios into reserve network design It is an interesting aspect of much contemporary conservation modelling that the dynamic nature of social change is rarely incorporated (Ladle & Jepson, 2008). For example, the greatest threat to Amazonian lowland forests has, historically, been conversion to agriculture. This was initially due to the actions of small-scale farming but, more recently, has been predominantly attributable to the actions of well-capitalized organizations producing agricultural and forestry products for distant markets (Margulis, 2004; Rudel et al., 2009). This shift in the direct drivers of deforestation is associated with decreasing deforestation rates and may even be responsible for reforestation in some tropical uplands (Rudel et al., 2009). The key point is that human development trajectories matter to conservation planning, and there is an increasing awareness that conservation planning models need to find novel ways to capture the ‘biocultural’ aspect of conservation. An example is provided by a recent study by Araújo et al. (2008), who set out to investigate whether 21st century environmental change will influence the commonly observed tendency for areas with high biodiversity also to have a high concentration of human activities. To assess this proposition, they used the most extensive available data set on species’ distributions in Europe, providing data for 3,143 species. They then generated four complementarity sets to prioritize ten per cent of the grid cells (173 cells) using a maximum coverage algorithm for four taxa in turn: birds, plants, mammals and herptiles. As indicators of contemporary anthropogenic pressure, they used observed land use in terms of urbanization, cropland and grassland use intensities. Adopting an approach employed in assessing future economic development trajectories by the Intergovernmental Panel on Climate Change, the authors used four ‘storylines’ or narratives describing alternative development pathways developed by the IPCC for Europe for 2021–2050. They measured exposure of biodiversity to human activities as changes in the three land-use pressure indices, and they analysed how these patterns of change correlated with the hypothetical reserve networks for each taxon (see Chapter 6 for details of designing reserve networks to maximize representation). The outcome of this modelling exercise was that, under each of the four alternative scenarios, there was a tendency for the areas selected in the complementarity sets to experience increasing urbanization (Figure B7.5a) and decreasing cropland intensities. Hence, irrespective of whether a future scenario based on fossil fuel-intensive industrial growth was explored, or one based on local solutions and environmental sustainability was considered, some trends appeared to be a consistent feature of the models. However, other details varied between the scenarios in quite complex ways. None of the four scenarios emerged as clearly ‘better’ for biodiversity in the analysis. Once again, it should be borne in mind that the uncertainties involved in such sophisticated modelling approaches are enormous. At the present time, such efforts should be regarded as of solely heuristic value, allowing the exploration of hypothetical ‘what-if’ scenarios for the future, rather than as a source for firm guidance for policy makers.

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Figure B7.5a Four sets of complementarity areas, for plants, breeding birds, mammals and amphibians and reptiles combined, using a 50 km grid resolution, based on a maximum coverage set algorithm and with a target of identifying the best performing 10% of grid cells. The complementarity areas, shown by the open cells, are overlapped on a map of urban land use change intensity for 2020–2050 based on one of four IPCC land use change scenarios examined: the A1FI scenario, a fossil fuel-intensive world of rapid economic growth, low population growth and rapid introduction of new and more efficient technologies. The complementarity area solutions depicted on the maps are illustrative and hypothetical networks of high priority areas for each taxon. Based on Figure 2 from Araújo et al. (2008). (See Plate B7.5a for a colour version of these images.)

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included by ensuring sequential connectivity between present and future ranges. The limitation of the approach is that it assumes we are confident with our distribution predictions, and it invariably suggests that we need to conserve unreasonably large fractions of a region. It is thus not particularly realistic. A more holistic approach that is gaining popularity in policy is to concentrate conservation efforts in continental-scale corridors. Many recent initiatives around the world have called for increases in connectivity with various corridor and linkage projects based on arguments concerning climate change impacts (Soulé et al., 2004; Mackey et al., 2008; Pearce et al., 2008). Such large-scale projects beg two questions. First, why is concentrating efforts to provide connectivity in one broad corridor better than scattering those investments more widely across the landscape? What would happen if we took the investment that is going into restoration and land management and disperse it thinly across the same region? The case for reconstructing (or maintaining and improving) one broad corridor at a large spatial scale relies on a belief that the whole is more than the sum of the parts – that several spatially connected investments deliver a greater outcome than those investments in isolation. Is this belief true? This is a big question – a critical landscape scale question – that is worth testing (see further discussion of fragmentation effects and corridors in Chapter 8). Second, if we assume that large-scale connectivity is critical to species and habitat persistence in the long term under climate change, where is the optimal location for connectivity corridors? So far there have been no global-scale or continental-scale analyses of the best options of where to restore continental-scale connectivity. Such an undertaking would need to include information addressing questions such as the following: 1 Along which gradients (temperature, rainfall, latitude, elevation) are shifts in range due to climate change most likely to occur? 2 How will other threats change with climate change? (e.g. see Box 7.5) 3 Where is a continental-scale corridor most likely to succeed for social, economic and political reasons? 4 Where is a continental-scale corridor likely to deliver most leverage from other financial sources? 5 How much will it cost and what are the expected benefits in the long-term and short-term? 6 What monitoring is required to determine whether it is all worthwhile?

7 What is likely to be the fate of these areas if we do not invest in them? If continental-scale connectivity is impossible or fails, what other actions can we take to maintain biodiversity in a world that is being impacted by rapid climate change? One of the most controversial proposals is ‘assisted colonization’ – moving species beyond their historical ranges to places where we think the climate will suit them in the future (Hoegh-Guldberg et al., 2008). This means introducing a species into a place where it has not been recently, or ever, which carries risks similar to any introduction of a species beyond its current native range (Simberloff, 2001). Assisted colonization is ecologically risky and normally expensive, and we have yet to determine its expected costs and benefits. This is an urgent scientific challenge in a situation where it may remain our only option, but where the legislative, political and social system is not in place to ensure successful implementation.

7. 5 CLOS I N G R EMAR K S Biodiversity then, is changing continuously over time. Conservation must therefore hit a moving target, so planning only for present-day patterns could lead to inefficient use of resources. Long-term ecology has revealed that biodiversity is highly dynamic, even over relatively short timescales. Species’ geographical ranges expand and contract, and multiple threats can act in concert to bring about sharp declines in once common species. Modelling techniques are available to attempt to build predictions about future change, on which conservation plans can be based. However, these models are beset with uncertainty, producing future scenarios that can differ enormously depending on the assumptions made and the methods used. The practical challenge now is to establish spatial conservation plans that are robust to this uncertainty.

FOR DI S CU S S I ON 1 What are the key challenges involved in modelling future species’ distributions for the latter part of the 21st century?

Conservation planning in a changing world 2 What are the key principles for designing robust protected area systems in the light of anticipated 21st century climate change? 3 What can palaeoecology contribute to designing dynamic reserve networks? 4 How should conservation scientists go about modelling changing land use patterns in planning conservation area networks today? 5 Should we expect species ranges to collapse towards the core or the periphery of the range as a result of 21st century anthropogenic change? 6 What are the key problems in predicting extinctions as a result of climate change and how may we solve them? 7 What kind of future socio-economic changes might we expect, and how will they impact on conservation strategies?

S U G G ES T E D R E AD I NG Cabeza, M. & Moilanen, A. (2001) Design of reserve networks and the persistence of biodiversity. Trends in Ecology & Evolution, 16, 242–248.

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Davies, T.J., Purvis, A. & Gittleman, J.L. (2009) Quaternary climate change and the geographic ranges of mammals. American Naturalist, 174, 297–307. Early, R., Anderson, B. & Thomas, C.D. (2008) Using habitat distribution models to evaluate large-scale landscape priorities for spatially dynamic species. Journal of Applied Ecology, 45, 228–238. Gaston, K.J. (2003) The structure and dynamics of geographic ranges. Oxford University Press, Oxford. Jackson, J.B.C., Kirby, M.X., Berger, W.H., Bjorndal, K.A., Botsford, L.W., Bourque, B.J., Bradbury, R.H., Cooke, R., Erlandson, J., Estes, J.A., Hughes, T.P., Kidwell, S., Lange, C.B., Lenihan, H.S., Pandolfi, J.M., Peterson, C.H., Steneck, R.S., Tegner, M.J. & Warner, R.R. (2001) Historical overfishing and the recent collapse of coastal ecosystems. Science, 293, 629–637. Meir, E., Andelman, S. & Possingham, H.P. (2004) Does conservation planning matter in a dynamic and uncertain world? Ecology Letters, 7, 615–622. Pressey, R.L., Cabeza, M., Watts, M.E., Cowling, R.M. & Wilson, K.A. (2007) Conservation planning in a changing world. Trends in Ecology & Evolution, 22, 583–592. Willis, K.J. & Birks, H.J.B. (2006) What is natural? The need for a long-term perspective in biodiversity conservation. Science, 314, 1261–1265.

CHAPTER 8 Applied Island Biogeography Kostas A. Triantis1,2 and Shonil A. Bhagwat2 1 2

Azorean Biodiversity Group, University of Azores, Terceira, Portugal School of Geography and the Environment, University of Oxford, Oxford, UK

8 . 1 I N T R OD UC T I ON When a nature preserve is set aside, it is destined to become an island in a sea of habitats modified by man. (Wilson & Willis, 1975, p. 525) Islands have played a central part in the development of conservation theory. In particular, the Equilibrium Theory of Island Biogeography (ETIB) (MacArthur & Wilson, 1963, 1967; Wilson, 1969) has played a pivotal role in diverse areas such as protected area network design theory and predicting extinction rates. The ETIB is a dynamic equilibrium model which postulates that the number of species of a given taxon found on an island will be the product of opposing forces leading respectively to the gain and loss of species, and resulting in a continual turnover of the species present on each island through time. This is captured in MacArthur and Wilson’s famous graphical model, in which immigration rate declines exponentially and extinction rate rises exponentially as an initially empty island fills up towards its equilibrium richness value (shown by the intersection; Figure 8.1). The immigration rate curve flattens with increasing island isolation and the extinction rate curve flattens with increasing area, thereby generating a family of curves providing unique combinations of richness and turnover for each combination of area and isolation. The influence of the ETIB is marked not just by the research it has inspired, but also by the theories and

applications it has spawned and influenced (e.g. species–energy theory, metapopulation theory, island assembly theory, neutral theory and stochastic niche theory; reviewed in Whittaker & Fernández-Palacios, 2007). Within a few years of publication, the application of the ETIB to the field of conservation was being vigorously debated by academics. One of the key insights was the realization that terrestrial reserves and national parks could be viewed as simply another type of island (‘habitat islands’) surrounded by a ‘sea’ of human-altered landscapes. It logically followed that these reserves would behave like islands cut off from the mainland by rising sea levels, i.e. they would lose species as they ‘relaxed towards equilibrium’ (Figure 8.1; Diamond, 1975a; Wilson & Willis, 1975). In the context of the ‘crisis’ discourse of conservation science from the 1970s onwards, several prominent conservation scientists turned to island theory in the search for an ‘off the shelf ’ general scientific guide on protected area system design to assist in both advocacy and implementation. If each protected area might become, in time, an island surrounded by habitats modified by man (Wilson & Willis, 1975, p. 18), and given a finite total area that can be set aside for conservation as a natural landscape is being converted to other uses, one of the basic questions is, ‘What configuration of reserves should conservationists advocate?’ According to Margules & Pressey (2000), reserves have two main roles: they should sample or represent the biodiversity of each region and they should

Conservation Biogeography Edited by Richard J. Ladle and Robert J. Whittaker © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33503-3

Conservation planning in a changing world

I near

191

Rate

E small

E large

I far Tnl Tfs

Sfl

Sfs

Sns

Snl

P

Number of species present

Figure 8.1 The equilibrium model of island biogeography and the implications of habitat fragmentation (bold arrows). An equilibrium number of species (S) is set by two opposing processes, immigration (I) and extinction (E). The rate of immigration decreases and the rate of extinction increases with increasing richness; the rate of immigration reaches zero when the entire pool (P) of potentially immigrating species have arrived. Immigration rates on islands far (If far) from the source pool are expected to be lower than those on near (In near) islands. Extinction rates are expected to be higher on small (Es small) islands than on large (El large) islands. Hence, different equilibrium numbers of species are established based on the area and the isolation of the islands (Sfs; Sfl; Sns; Snl); and both equilibrium species richness and rates of species turnover (T) are expected to vary with the combination of immigration and extinction rates that characterize any given island (e.g. Tfl; Tfs). Bold arrows show the direction of the changes predicted by the model upon fragmenting a more or less contiguous tract of habitat into small, isolated patches. Figure modified from MacArthur and Wilson, 1967, Figure 8, p. 22.

separate this biodiversity from processes that threaten its persistence. The extent to which protected areas fulfil this role depends on how well they meet two objectives of reserve design. The first is representation, a long-established goal referring to the need for reserves to incorporate the full variety of biodiversity in the region, ideally at all levels of organization (Chapters 2, 5, 6). The second is persistence. Reserves, once established, should promote the long-term survival of the species and other elements of biodiversity that they contain by maintaining natural processes and viable populations, and by excluding threats (Chapter 7). To meet these objectives, conservation planners must not only design systems of reserves that take into consideration natural physical and biological patterns,

they must also make decisions about reserve size, connectivity, replication and the alignment of boundaries (Margules & Pressey, 2000; Whittaker et al., 2005). All of this needs to be done while conforming to budgetary constraints and, typically, strong political and socioeconomic constraints on the size and location of reserves. Island biogeography theory provides the theoretical framework for much fragmentation research and has been invoked as the source of general principles of reserve network design (Diamond, 1975a; Wilson & Willis, 1975; Haila, 2002; see Figure 8.2). Examples include: • a large reserve is superior to a small one; • a single large reserve is better than several small reserves with the same total area;

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BETTER

WORSE

a) Large reserve

Small reserve

b) Fragmented

Unfragmented reserve

reserve

c)

500 ha reserve

Higher edge effects

200 ha core

Lower edge effects

500 ha reserve

d) Isolated reserves

e) f) g) h)

i)

Increased connectivity (corridors)

Isolated reserves

Increased connectivity (stepping stones)

Partial protection

Complete protection

Uniform habitat

Increased habitat diversity

Local perspective

Regional perspective

Humans excluded

Human integration (buffer zones)

Figure 8.2 Design guidelines for reserves, as derived initially from the theory of island biogeography and extended by subsequent contributions to theory (e.g. see Harris, 1984; Shafer, 1997). Re-drawn from Huggett (2004, Figure 18.3, p. 362).

• when two or more reserves are inevitable for some specific habitat or species, the inter-reserve distance should be as short as possible; • corridors between reserves are recommended to increase inter-reserve migration/dispersal; • a circular reserve is superior to a linear one because of the potential problems created by biotic and abiotic edge effects, i.e. the changes in biological and physical conditions that occur when for example a woodland

reserve is surrounded by a non-woodland matrix (see Diamond, 1975a; Wilson & Willis, 1975; Diamond & May, 1981). Many of these ETIB-derived ‘principles’ have been the subject of intense debate. For example, while corridors between reserves (increased connectivity) certainly may increase immigration, facilitate gene flow and reduce local extinctions through the rescue effect, they may also facilitate the spread of disease, fires and exotic

Conservation planning in a changing world species. Hence, decisions about corridors should be casespecific (see Whittaker & Fernández-Palacios, 2007)1. One of the most hotly discussed conservation topics of the 1970s and 1980s was the so-called SLOSS debate, which posed the question: ‘Given the opportunity to put a fixed percentage of land into conservation use, is it better to opt for a Single Large Or Several Small reserves?’ At one extreme is the creation of a single large reserve; the alternative is to opt for several smaller reserves that amount to the same area but which are scattered across the landscape. The answer to the SLOSS question is by no means simple. Crucially, it depends on the slope of the species– area curve, the proportion of common species in the small reserves and the gradient of colonizing abilities among species in the available pool of species. Indeed, both theoretical analyses and empirical evidence suggest that, in some circumstances, several small reserves may contain more species than a single large one. This is due to compensating advantages such as: greater overall representation of rare habitats; more effective representation of differing biogeographical elements across a region; competitive effects involving there being different ‘winners’ in different patches; less effective spread of disease and exotic species; and more habitat for edge species (e.g. Soulé & Simberloff, 1986; Zimmerman & Bierregaard, 1986; Godefroid & Koedam, 2003). Moreover, the initial debate over SLOSS generally overlooked the complexity of species diversity dynamics. Factors such as the minimum viable population (MVP) for rare/ecologically important species, the minimum area needed in order to sustain an MVP and the minimum dynamic area to maintain the ecosystem integrity must also be considered in questions concerning nature conservation (Soulé & Simberloff, 1986; Shafer, 1990; Wu & Vankat, 1995). It is now generally accepted that the species–area relationship and the equilibrium theory of island biogeography – in part an attempt to explain systematic variations in the form of the species–area relationship – are unable to provide final resolution to the SLOSS question. There are several broad explanations for the difficulty of extracting generalities from the study of habitat islands, and perhaps the most important is that 1

While in traditional biogeography the term corridor is given to a connection that allows essentially free passage of a particular biota (cf. Chapter 7; and see also: Lomolino et al., 2006), research within the present frame of reference on ecological habitat corridors can refer to very narrow connecting features, which may be highly selective in terms of the species that can move along them.

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answers to questions like SLOSS are likely to exhibit scale dependency. For example, large mammals, with their large home ranges or territories and their vulnerability to human hunting, are likely to require vastly larger reserves than are needed to capture viable populations of butterflies. The former are also likely to have less specific habitat requirements than the latter, so reserve systems designed for one are likely to be suboptimal for the other. Another aspect of scale is the range of sizes and inter-reserve distances. Imagine that you were distributing reserves across East Lincolnshire (a low-lying and rather flat part of the UK). The range in reserve sizes and distances involved in these generally agricultural landscapes would be small, and one would not expect to encounter different species pools. Imagine, by way of contrast, that you were distributing reserves across a large-scale biogeographical gradient such as the Mexican transition zone, i.e. the transition zone between the Nearctic and Neotropical regions. Here we might be contemplating rather larger reserves and potentially very large distances between them, and different reserves might contain contrasting proportions of species ultimately derived from different biogeographical regions/provinces. The falsification of the assumption of a single species pool that is inherent in the ETIB undermines the application of this theory to resolve SLOSS at such a scale. Instead, we may turn to the direct analyses of species’ distributional data discussed in the previous two chapters for reserve network planning in such a context. Hence, although some authors have attempted to apply the island analogy on these very coarse scales (e.g. Brooks et al., 1997, 2002), as we demonstrate below, applied island biogeography is essentially a framework for application at local to landscape scales (Whittaker & Fernández-Palacios, 2007). The SLOSS debate illustrates that although ETIB provides a basic conceptual model for understanding habitat fragmentation, apart from a number of broad generalizations that are largely ecological ‘good sense’ anyway (e.g. many large refuges hold important species that small ones do not, ecologically heterogeneous refuges tend to hold more than homogeneous ones, habitat connectivity can often be beneficial in terms of species richness), generating policy-relevant guidance from a broad-brush macroecological theory is not straightforward. In this chapter, we review some of the more interesting themes within applied island biogeography, starting with the most basic general question: is it realistic

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to expect habitat islands to behave according to the same principles as real islands? In answering this question, we critically review the application of ideas derived from island ecological biogeography to conservation problems and suggest a number of future directions where island theory can potentially inform applied conservation questions.

3.3 Great Britain Southern England Thames South Thames Surrey Piece Surrey Part Piece Surrey

3.1 2.9 2.7

8. 2 I MP L I C AT I ONS OF HAB I T AT LOS S A N D F R AGM E NT AT I ON: F R OM T H EO RY T O E V I D E NC E A theory is more impressive the greater the simplicity of its premises, the more different the kinds of things it relates and the more extended its range of applicability. (Albert Einstein, 1949, from Schlipp (1973, p. 33).

8.2.1 The use of species–area relationships in conservation A basic rule of thumb derived from island theory is that if a habitat is reduced by 90 per cent, then some 50 per cent of species are expected to go extinct. In this and the following section, we explore this rule of thumb and consider the extent to which we can rely upon such simple generalizations. The species–area relationship (SAR) is not simply one of ecology’s most general patterns but was also one of the first to be discovered. It also has a profound importance for conservation biogeography. Descriptions of the SAR are known from as early as 1778 (Johann Reinhold Forster) and 1820 (Augustin de Candolle) (see Lomolino, 2001, for further details). The first known plot relating species with area was made by Hewett Cottrell Watson in 1859 (see Rosenzweig, 1995), the same year that Darwin published his magnum opus On the Origin of Species. Watson presented the relationship between plant species and area, beginning with the richest county, Surrey, and then built up to the whole island (see Figure 8.3). According to Rosenzweig (1995, p. 9), ‘it is the world’s oldest known empirical example of an ecological pattern’. It was not until the 1920s that two botanists, Olof Arrhenius (1921) and Henry Allan Gleason (1922), expressed this relationship in mathematical terms. Arrhenius introduced the relationship as a power

Bit Surrey

2.5 0

1

2

3

4

5

6

Figure 8.3 The first known species–area curve, based on the number of plant species of England (Watson, 1859). Re-drawn from Rosenzweig (1995, pp. 9).

model: S = cAz, while Gleason (1922) suggested a semi-logarithmic model S = k + d log A, where S is the number of species, A is (island) area, and c, z, k and d are constants. Quantification was a critical, longawaited advance, primarily because it enabled scientifically rigorous investigations of species–area curves, therefore allowing biogeographers and ecologists to use comparative methods to search for and evaluate causal explanations. Ultimately, this breakthrough allowed modern-day conservation biogeographers to apply species–area models to make predictions and develop strategies for conserving biological diversity (see Lomolino, 2001). Today, more than 20 mathematical models have been proposed for the description of the SAR, with the power model of Arrhenius being still the most commonly applied and frequently the most effective (see Connor & McCoy, 1979; Tjørve, 2009; and see Williams et al., 2009, for a comprehensive review of all the available models and their appropriate usage). The next key breakthrough was by Frank Preston, an engineer and naturalist, who proposed a mechanistic explanation of the species–area pattern and for the values that the slope of the relationship in a logarithmic space should take. His starting point was a theoretical consideration of the species abundance distribution, which he argued typically followed a log– normal distribution (Preston, 1948, 1962). The theory states that the most numerous species are those of middling abundance, while species with very few individuals are as rare as species with a very large number of individuals, giving rise to a log–normal

Conservation planning in a changing world curve. Preston showed that a log–normal series of abundance should give rise to a SAR with a slope (z) value of approximately 0.263 – towards the low end of the range of values known at that time from islands and above those of continental patches (for details see Rosenzweig, 1995, pp. 268–276). These differences pointed to a role for isolation via population migration. Preston was also one of the first to notice that the slope of the species–area relationship (z) changes with geographical scale. He published a figure tracing bird diversity increase from a house lot to the entire world, showing how the relationship changes in form from fine to coarse spatial scales (Figure 8.4; Preston, 1960). This idea was further elaborated by Rosenzweig (1995, 2001, 2004), who suggested that the ‘species–area pattern’ is actually comprised of three different species– area relationships, whereby processes operating at different spatial and temporal scales (Schmida & Wilson, 1985; Crawley & Harral, 2001) lead to different zvalues (Figure B8.1a; Table 8.1).

World

4

Nearctic

3 Western Pennsylvania

Eastern USA

2 Preston Laboratory grounds

1 House lot, Butler county

0 0

2

4

6

8

10

Figure 8.4 Species–area curve for birds commencing within north-eastern USA, across three different spatial scales. Modified after Preston (1960) and Rosenzweig (1995).

12

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At local scales, within and among habitats within regions (‘sample areas’), species accumulation is a function of relative abundance distributions and beta diversity (encompassing aspects of habitat heterogeneity and species turnover among sites). The z-values typically observed for sample areas fall between 0.1 and 0.2. Across islands or disparate habitats within a region (the ‘archipelagic category’), species richness per island/habitat is affected by increased dispersal limitation, due either to spatial distances between similar habitat patches (e.g. islands in an archipelago) or to ecological differences between habitats (e.g. where frequency distributions of species differ greatly across habitats within the region). The z-values typically observed for intra-provincial/archipelagic areas fall between 0.25 and 0.55. At the largest scales, the accumulation of species with increasing area is due to the addition of species from separate biotic provinces (the ‘inter-provincial species–area relationship’). The z-values typically observed for inter-provincial areas have a lower margin of z = 0.6 and range upwards, with most lying around 0.9–1.0 or even higher (Table 8.1; Rosenzweig, 1995, 2001, 2004; Figure B8.1a). Rosenzweig’s work (1995; see also 2001, 2004) offered a more nuanced dynamic perception of the SARs and the biological meaning of their slopes. Fundamentally, the z is not just an indication of the isolation of the system under study – a perception that dominated the field of island biogeography for more than 40 years (Preston, 1962; MacArthur & Wilson, 1967) – but reflects the dominant processes establishing species richness patterns. As Rosenzweig (1995, p. 278) succinctly states: ‘the slope of the species–area curve reflects the timescale that determines it’. These timescales range from hours/days for curves from small sample areas, to the millennia of evolutionary time for the curves among different biotic provinces (Box 8.1).

Table 8.1 Three biological scales of species–area curves, the dominant process of species addition at each scale and the respective range of the slope (z) values, as proposed by Rosenzweig (e.g. 2004). For further discussion, see Box 8.1. Scales of SAR

Dominant process(es) of species addition

z-values range

Intra-provincial

Habitat heterogeneity, species abundance

0.1–0.2

Archipelagic

Dispersal

0.25–0.45

Inter-provincial

Speciation

Higher than 0.6 (0.8–0.10)

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Box 8.1 Scale and the species–area relationship (SAR) Box prepared by R.J. Whittaker and K.A. Triantis – excerpted and modified slightly from Triantis, Mylonas and Whittaker (2008).

Scales and types of SAR The species–area relationship (SAR) – the increase in species number with increasing area – is one of the bestdocumented general patterns in biogeography and ecology, yet controversy persists about the best means of describing the relationship and about the precise form of different types and/or scales of SAR. SARs are typically described using power functions where the exponent (i.e. the slope of the log-area/logrichness relationship) is commonly referred to as the z-parameter. The z-parameter has a simple mathematical interpretation: it is the rate at which species accumulate with increasing area. However, there is no consensus on the biological interpretation and scale dependency (e.g. at which spatial scales particular evolutionary and ecological processes predominate) of SARs. As discussed in the text, Rosenzweig (1995, 2003, 2004), has argued that the ‘species–area pattern’ is comprised of three different species–area relationships (or four if the point scale, which depends on sampling effort, is included), whereby processes operating at different spatial and temporal scales lead to different z-values (Figure B8.1a). In practice, Rosenzweig’s scheme encompasses data sets of two different structures. The first form employs a nested sampling structure and results in species accumulation curves (SACs) at two scales: the point (i.e. local) scale and the intra-provincial (i.e. regional) scale. The second form is where the independent variable is the area of (to varying degrees) geographically discrete and isolated land masses, and the dependent variable is the number of native species found within each area. Again there are two scales in his scheme: the archipelagic (i.e. a set of geographically clustered islands) and the inter-provincial (i.e. between regions). Whittaker and Fernández-Palacios (2007, Box 4.4, p. 94) term these archipelagic and inter-provincial relationships ‘true’ island species–area relationships (ISARs) to distinguish them from the phenomenologically distinct SACs arising from the nested sampling designs. It is on these z-values of true ISARs that we now focus. For real islands, MacArthur and Wilson (1967) reported archipelagic ISAR z-values as typically falling within a range of 0.2 to 0.35, while Williamson (1988) reported a much wider range in z, from 0.05 to 1.132. In his review,

a)

b)

Inter-provincial ISAR

l

cia

ce A’s Provin s island

vin

o

Province B

r r-p

te

In

B’s ince Prov nds isla

Typical z-value ranges Intra-provincial: 0.1-0.2 Archipelagic: 0.25-0.45

Log species

Log species

Province A

z2 SIE ISAR

z1

Inter-provincial: 0.8-1

Log area

Log area

Figure B8.1a Three biological scales of species–area curves, as proposed by Rosenzweig (e.g. 2004). (a) Rosenzweig’s species–area pattern includes four scales, but the point scale is not illustrated in the graphic. The point and intra-provincial scales comprise species accumulation curves from a nested sampling system, whereas the inter-provincial and archipelagic scales (termed ISARs herein, for island species–area relationships) are plots of the number of species found in discrete units of space. (b) The inter-provincial ISAR and the SIE (single-island endemics)– area relationship exhibit similar z-values (slopes) according to results reported herein. Despite the differences in the spatial scale and the number of species involved, the two systems exhibit analogous trends of increasing species number with area, as in both speciation is the major process of species addition.

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Rosenzweig (2004) essentially supports MacArthur and Wilson, reporting that values typically fall between 0.25 and 0.45. By contrast, z-values typically observed for inter-provincial ISARs have a lower margin of z = 0.6 and range upwards, with most lying around 0.8–1.0 (Rosenzweig, 2004) and some exceeding 1.0 (Rosenzweig, 1995, 2004). These observations have very considerable significance for conservation science as humans continue to alter the extent and connectivity of habitats both locally and globally. It is thus important to test the robustness and explore the properties of Rosenzweig’s species–area pattern model.

A test of the form of inter-provincial ISARs from strongly isolated islands Archipelagic ISARs have been studied for real and habitat islands for many taxa in numerous studies over several decades, and their properties are thus reasonably well known. By contrast, generalizations on the form of interprovincial ISARs derive from relatively few data sets. Biotic provinces(/regions) can be defined as self-contained areas that, for the target taxon, are relatively independent from an evolutionary perspective (i.e. wherein most species are endemic to these provinces). In this analysis, we focus on the form of ISARs for single-island endemics (SIEs), exploring the idea that for those species restricted to single islands, the islands in question can be regarded as provinces. Using this approach, and by focusing on these systems, we can study the evolutionary contribution to ISARs and gain significant insights regarding the patterns that arise when speciation is a significant or the major source of diversity, as happens in the case of continental biotic provinces/regions. Our first aim is therefore to explore the consistency in form of SIE–area relationships across taxa and archipelagos of varying proportion of SIEs. Our second aim is to use SIE data to test the robustness of the generalization that inter-provincial ISARs are significantly steeper than archipelagic ISARs, producing z-values typically falling between 0.8–1.0, and 0.25–0.45, respectively. We do so by comparing z-values for SIE with those for ‘all native species’ for the same taxa, using 13 different data sets from the Caribbean, Fiji, Hawaii, Canary Islands and Great African Lakes, and using the power (log–log) model. For further details of data set properties and methods of analysis, see the source paper. The key findings of the analyses were as follows: Eleven of the SIE–area relationships were statistically significant, explaining high proportions of the variance in SIE numbers (R2 0.57–0.95), the two exceptions being for the Canary Islands, whereby inclusion of the two oldest islands greatly reduces the fit of traditional species–area models (see Whittaker et al., 2008). The z-values of the statistically significant SIE–area relationships ranged from 0.47 to 1.13, with a mean value of 0.80 (SD ± 0.24). All the island systems in which SIE represent >50 per cent of species exhibited z-values for the SARs of native species higher than those deemed typical of archipelagic SARs. Moreover, for the three cases in which the percentage of SIE equals or exceeds 90 per cent, the mean z-value is unity (1.00 ± 0.21). The findings thus approximate the schematic shown in Figure B8.1b. Hence, the results of the present work provide significant support for Rosenzweig’s proposition that z-values from inter-provincial ISARs should be very high, approaching unity. This should hold not only for the scale of recognized global biogeographical regions, but also for any system in which speciation is the major process. Of course, the timescales in which species evolve and go extinct may differ between island systems and global biogeographical provinces. Nevertheless, it seems that the two system types exhibit analogous patterns of species accumulation with area. Therefore, studies of evolutionary dynamics in relation to area, employing data for single island endemics, would be well worth pursuing in other taxa and regions, as they could be used as model systems to test: 1 variation in critical island sizes below which within-island diversification does not occur; 2 how these thresholds vary with taxa and island groups; 3 how consistent the form of inter-provincial ISARs are; 4 their capability for predicting future diversity; 5 and they may also be used to recognize and examine the influence of additional factors such as climate, productivity, etc. Such studies can offer great insights into basic questions of conservation biogeography, such as the potential impact of habitat fragmentation and loss and homogenization on biological diversity.

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These macroecological approaches to island data, generated by the stimulus of the MacArthur–Wilson theory, have promoted the wide use of species–area curves for conservation purposes. These include predicting species endangerment globally, regionally and locally (McDonald & Brown, 1992; Tilman et al., 1994; Pimm & Askins, 1995; Brooks & Balmford, 1996) as functions of habitat loss and fragmentation; devising general reserve-design principles (Diamond, 1975a; Wilson & Willis, 1975); and identifying conservation targets for specific habitat types (Desmet & Cowling, 2004). Among the most controversial uses of the species–area relationship based implicitly on ETIB is its application in the forecasting of future species extinctions as a function of habitat loss due to factors such as deforestation (e.g. Brooks et al., 1997, 2002) or future climate change (e.g. Thomas et al., 2004 – see Box 7.3). Projected extinctions based on species–area models involve several uncertainties (Heywood et al., 1994; Whittaker & Fernández-Palacios, 2007) and can never completely replace species-level assessments for the identification of extinction threat (e.g. Kotiaho et al., 2005). However, for many species of conservation concern, the collection of appropriately detailed information is an unrealistic target. It is vital, therefore, that conservation biogeographers develop more realistic indirect measures and theoretical projections of extinctions, based on as pragmatic a set of assumptions as possible (May et al., 1995; Laurance, 2007). The wide variations in outcomes can be seen from efforts to estimate likely extinctions arising from tropical deforestation. Results of current and future rates of deforestation have varied dramatically, ranging from the alarming (e.g. Ehrlich & Wilson, 1991) to more modest (but still significant) losses (Wright & MullerLandau, 2006), thus strongly affecting projections of future species losses. Recently, Wright & Muller-Landau (2006) noted that the estimates of net tropical deforestation rates during the 1990s differ by 250 per cent (see their Table 2). Using a number of criteria, they considered 45 humid tropical countries that support 89.6 per cent of all extant closed tropical forest and 89.9 per cent of all potential tropical forest cover. They concluded that deforestation rates will decrease as population growth slows, and that a much larger area will continue to be forested than previous studies suggest. Such uncertainties, along with differences arising from choice of assumptions about species persistence

in degraded habitats, from the high sensitivity of predictions to uncertainty or errors in species–area slopes and from large uncertainties about both the global species totals and the geographical distribution of biodiversity, mean that all currently available predictions of future losses inherently posses great uncertainty (see Table 8.2, and Chapter 7 and see Laurance, 2007, 2008; Willis & Bhagwat, 2009, for general discussion). Although the most recent of the estimations presented in Table 8.2 was made in 1992, we consider the information to be useful in pointing out the problems in predicting global extinctions that can arise through different assumptions on a number of critical issues. In short, extinction rate estimates based on species–area projections involve many uncertainties (Heywood et al., 1994). The precise form of the relationship describing the loss of species from an original habitat as a function of the remaining habitat area is still an open question. There are two main associated issues. First, many species are not restricted to their ‘native’ habitat and can persist in certain anthropogenic habitats. Second, the slope of the species–area relationship used for the loss of total area of a habitat is still uncertain; there is no strong theoretical or empirical justification for the use of a ‘global’ slope value of z = 0.25 (or any other single value). Whittaker & Fernández-Palacios (2007) have criticized the use of SAR as a means of forecasting species threatened by, or committed to, extinction, noting ‘the way in which the species–area models are used … is conceptually decoupled from the island theory from which it seemingly derives’. They argue first that, a z of 0.25 is a subjective ‘middle’ value to take (see discussion above about the z-values of the different SAR categories). Second, and more crucially, this z-value has been derived from analyses of true isolates. It describes approximately how many species are held in each of a series of isolates/islands of different size. Yet, in several recent studies the z-value is applied not to separate fragments but to an entire region (e.g. Brooks & Balmford, 1996; and see also Box 10.2, pp. 272–273 in Whittaker & Fernández-Palacios, 2007). As will be discussed in the section on nestedness below, depending on the degree of shared species between different habitat islands, it is possible for relatively low or very high proportions of the original species found in a region to be represented in a

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Table 8.2 Some estimates of global species loss due to tropical deforestation and the key assumptions made (adapted from Krishnamurthy, 2003; see therein for references).

Extinction estimate

Total species (millions)/ per cent tropical

Tropical forest loss 2

Extinction/ area lost

Source

1 species/hour by 2000

5–10/40–70%

245,000 km /year

50% species extinct when 10% area left

Myers, 1979

33–50% of all species between 1970 and 2000

3–10/25%

50% deforestation by 2000

Species–area, concave curve

Lovejoy, 1980

1 million species by 2000

4/40%

33% of remaining forest destroyed

50% species in area will go extinct

Myers, 1985

10% of all species by 2000; 25% by 2015

4–5/ 50%

2% deforestation/ year

50% species in area will go extinct

Raven, 1988

17,500 species/year

10/50%

0.7% deforestation/ year

50% species in area will go extinct

Wilson, 1988b

8.8% of all species by 2000

3–10/25%

12.3% deforestation between 1980 and 2000

Species–area, concave curve

Lugo, 1988a, b

5–38% of all species between 1990 and 2000

10/>50%

0.8–1.6 % deforestation/year

Species–area; z = 0.15, 0.35

Reid & Miller, 1989

27,000 species/year

10 in tropical rain forests

1.8% deforestation/ year

Species–area; z = 0.15

Wilson, 1992

series of habitat islands. Therefore, treating what are actually archipelagos of habitat islands as though they were a single island in analyses of extinction threat is a potentially crucial oversimplification – and it is one reason why we cannot rely upon the ‘90 per cent area loss = 50 per cent species loss’ generalization with which we began this section. As a further note of caution, it is important to emphasize that while the species–area relationship is indeed a very general pattern, area rarely explains all interpretable variation in species richness, with some residual variation being attributable not only to system isolation but to other variables such as habitat diversity, elevational range, disturbance regime, etc. (Whittaker & Fernández-Palacios, 2007; Triantis et al., 2008). It follows that SARs can only provide a crude approximation for use in conservation planning. Hence, as noted by Whittaker et al. (2005), the application of the species–area relationship for informing conservation sciences is one area within conservation biogeography where the theory appears to require further work.

8.2.2 Relaxation and the extinction debt Newly emerged islands present new habitat and accumulate species through time via immigration. In contrast, habitat islands created through isolation by rising water levels or by habitat destruction (e.g. deforestation) are typically assumed to support something approximating a full complement of local species at their formation. That is, they are expected to contain both source populations (having positive population growth within the area itself) and sink or casual populations that happened to be present at the time of isolation, but which do not exhibit positive population growth within the area itself. Upon isolation these islands are thus ‘supersaturated’ for a patch of their newly reduced area and increased isolation. With time, these islands lose species, a phenomenon called species relaxation (Diamond, 1972; Wilcox, 1980). Immigration (at a lower rate than before) and extinction (at a higher rate than before isolation) should both continue during the relaxation period and subsequently they come back into balance; at this

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point, the island has found its new, lower equilibrium richness level (Figure 8.1). The time taken for relaxation to occur is referred to as the ‘lag time’ and the anticipated eventual species loss is termed the ‘extinction debt’ (see Ewers & Didham, 2006). Two classic examples of relaxation are presented in Figure 8.5. The first example takes the form of a data set for the mammals living in isolated high mountains forests of the south-western USA (Brown, 1971). The radical shift in climate following the end of the latest glacial period resulted in these mammalian populations becoming isolated from each other by the increasing aridity of the valleys surrounding them. The second example is for the mammals of the Sunda Islands of Indonesia (Wilcox, 1980). These islands, interconnected during the last glacial period, were isolated by the ocean as the glaciers melted and raised the sea level. Thus, both these systems became isolated around the start of the Holocene (c. 10 ka) and since then are thought to have gradually been losing species. Biogeographers and conservationists have been interested in three general questions related to species relaxation. First, how does relaxation proceed? In other words, what is the shape of the curve of species loss over time? Second, how much time is needed between fragmentation and extinction (the lag time)? Third, and critically, how many species will be left after relaxation is complete? Conversely, how many and which species will eventually go extinct? Relaxation after habitat loss and fragmentation is typically expected to proceed in a sequence of stages (after Wilcove, 1987): • Stage 1. Initial exclusion. Some species will be lost from the landscape simply because their original ranges did not include any of the remnant patches. • Stage 2. Extirpation due to lack of essential resources. Species vary greatly in their resource requirements and many require very large areas and/ or very rare resources. Thus, the likelihood that all of a species’ resource requirements can be met decreases as the remaining area decreases. • Stage 3. Perils associated with small populations. Small populations are much more susceptible to a host of genetic, demographical, and stochastic problems. As the total area of the remnant patches decreases, and the ability to sustain large populations decreases, these problems become increasingly severe (e.g. Frankham et al., 2002).

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1

1

2

3

4

5

Figure 8.5 Mammal diversity on Sunda Islands (circles) and south-western US mountaintops (diamonds). These isolates started forming about 10,000 years ago at the end of the Pleistocene. In both archipelagos, larger islands have experienced proportionately fewer extinctions. The above estimations are based on two general assumptions. First, it is assumed that the extinction rates are comparable. Rosenzweig (1995) considered that, as the same taxon is studied and given that the two systems have been formed due to the same event (the switch into the current interglacial) and thus began losing species at approximately the same time, we can hypothesize a similar rate of extinctions. Second, it is assumed that the original number of species for each island can be estimated from a mainland area (Malaysian mainland for Sunda Islands and Sierra Nevada for US mountaintops) of the same size as the island. Re-drawn after Rosenzweig (1995, his Fig. 6.5).

• Stage 4. Deleterious effects of isolation. Some populations may be rescued from extinction by migration and recruitment of individuals from other populations. The likelihood of such rescue effects decreases as isolation increases. • Stage 5. Ecological imbalance. Most species are strongly influenced by interactions with other species. Loss of one species during any of the aforementioned stages of relaxation may result in the subsequent loss of its predators, parasites, mutualists, or commensals (e.g. Koh et al., 2004). In addition, habitat disturbance and reductions in community diversity during the earlier stages of relaxation may facilitate the establishment of introduced species, triggering a cascade of subsequent extirpations.

Conservation planning in a changing world It has been argued that it may take several generations for the processes causing relaxation to play out following habitat destruction and fragmentation, meaning that there is a substantial lag time between the initial stimulus and the end of the process of species losses (Tilman et al., 1994; Ewers & Didham, 2006; Vellend et al., 2006). This creates an ‘extinction debt’ – a future ecological cost of habitat destruction that may not be initially apparent in studies made shortly after habitat fragmentation has occurred. Indeed, Brown’s (1971) mammal assemblages were hypothesized to still be in the process of relaxation from their relatively large mountain top habitat islands thousands of years after isolation (Figure 8.5; and see further discussion in Lomolino et al., 2006). Whether such protracted response times are typical is unknown, but it does seem highly likely that the true ecological costs of the historically recent spate of anthropogenic habitat disturbance, destruction and fragmentation across the globe are yet to be realized (see, for example, Figure 8.6). It is also noteworthy that, although the majority of recorded species extinctions since AD 1600 have occurred on oceanic islands, predictions of increasing numbers of future extinctions suggest a significant shift to continental areas (Millennium Ecosystem Assessment, 2005). Developing methods to quantify the magnitude and taxonomic distribution of the extinction debt is clearly vitally important for effective conservation planning and prioritization. However, this objective is by no means simple to attain. Accurate assessment of extinction rates and their extrapolation into the future requires good quality long-term data on species occurrences – data which are generally lacking, especially for less conspicuous and/or numerically much more species rich taxa. This lack of appropriate knowledge (Chapter 4) has led to an inevitable reliance on indirect measures and theoretical projections of extinction debt. These include: species–area models; rates at which wellknown species are shifting to increasingly more threatened categories of conservation concern; extinction probabilities associated with the IUCN categories of threat; impacts of projected habitat loss on species currently threatened with habitat loss; and the extrapolation of correlations of species loss with climate change (e.g. McDonald & Brown, 1992; Mace & Kunin, 1994; Pimm & Askins, 1995; Thomas et al., 2004 – for further discussion see Ladle, 2009).

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One of the best-known empirical examples of relaxation on an ecological timescale is the loss of bird species from Barro Colorado Island in Panama. The island was formerly a hilltop in an area of continuous neotropical lowland rain forest, but abruptly became a 15.7 km2 island when the central section of the Panama Canal Zone was flooded to make Lake Gatun in 1914. Of about 208 bird species estimated to have been breeding on Barro Colorado island immediately following isolation in the 1920s and 1930s, 45 were no longer present by 1970 (Wilson & Willis, 1975). However, only a minority of these losses were directly attributable to stochastic processes of relaxation. The others could be attributed to ecological changes such as forest regeneration following abandonment of farming activity, which reduced the availability of open habitats, or predation by terrestrial mammals (see review in Whittaker & Fernández-Palacios, 2007). For example, many of the birds lost were typical of second growth or forest edge, suggesting that the regeneration of the forest following abandonment of farming activity must have reduced the availability of these more open habitats. Additionally, some groundnesting species were probably eliminated by their terrestrial mammalian predators, which became abundant after the disappearance of top carnivores with large area requirements. This effect, of increasing numbers of smaller omnivores and predators due to the absence of large ones, has been termed mesopredator release (Soulé et al., 1988) and has been documented to occur in several other similar contexts (e.g. Laurance, 2002). A later avifaunal survey of Barro Colorado Island reported sightings of 218 species from the island or the waters immediately around it between 1994 and 1996, including five new records, none of which were thought to be of breeding species (Robinson, 1999). As anticipated from the island theory (Figure 8.1), the rate of species loss appears to have declined over time, especially for forest-interior birds. However, overall, species extinctions do appear to have continued to exceed colonizations. So, in summary, the isolation of the hilltops to form this lake-bound island has been followed by around a century in which the process of relaxation has been the dominant trend. Future changes in avifaunal species richness and composition on the island are likely to be dependent on the extent to which the

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Extinctions per thousand species per millenium 100,000

Distant past (fossil record)

Recent past (known extinctions)

Future (modelled)

10,000

Projected future extinction rate is more than ten times higher than current rate

1,000

Current extinction rate is up to one thousand times higher than the fossil record

100

10

For every thousand mammal species, less than one went extinct every millennium

1 Long-term average extinction rate

0.1

Marine Mammals species

Mammals Birds Amphibians

All species

Figure 8.6 Past and future extinctions. ‘Distant past’ refers to average extinction rates as calculated from the fossil record. ‘Recent past’ refers to extinction rates calculated from known extinctions of species (lower estimate) during the last 100 years or known extinctions plus ‘possibly extinct’ species (upper bound). ‘Future’ extinctions are model-derived estimates using a variety of techniques and, in general, refer either to future loss of species based on the level of threat that exists today or to current and future loss of species as a result of habitat changes. The techniques involved for modelling future extinctions are: species–area models; rates at which species are shifting to increasingly more threatened categories; extinction probabilities associated with the IUCN categories of threat; impacts of projected habitat loss on species currently threatened with habitat loss; and correlation of species loss with energy consumption. According to the authors of the assessment, the lower bound estimates for future modelled extinctions are low certainty estimates, and the upper bound estimates are speculative (i.e. even lower certainty). Adapted from Millennium Ecosystem Assessment (2005).

nearby mainland forest cover is retained, as these forests provide the source of the transient birds and occasional new colonists (those that stay to breed) observed on Barro Colorado Island. The most significant problem with predicting future extinctions in such systems is that we have an inadequate theoretical and empirical basis by which to estimate the rate at which species will be lost over time or the total time period required for a new (dynamic) equilibrium to be achieved. Precise estimates of the ‘time to extinction’ of each species under threat remains an unrealistic aim for both true and habitat islands, as it will largely be species- and system-specific.

A classic illustration of this problem is provided by the tropical moist forests of the Atlantic seaboard of Brazil, known as the Mata Atlantica, which have been reduced over the past few centuries to only about 7 per cent of their estimated former cover (Ribon et al., 2003). This is a large region and the remnants are numerous and widely distributed, so the ‘90 per cent habitat loss = 50 per cent species loss’ rule of thumb (above) should not really be expected to apply. Nonetheless, such habitat loss and insularization should have driven significant losses. To date, however, no extinctions have been documented with any degree of certainty, although many species appear on IUCN ‘Red Lists’ as ‘vulnerable’,

Conservation planning in a changing world ‘endangered’ or ‘critically endangered’, largely based on reductions in range or population estimates (Ribon et al., 2003). Despite the criticism, especially of methodology and taxonomic bias (e.g. Régnier et al., 2009), the IUCN Red List has become an essential source of information for conservation action and is widely recognized as the most comprehensive compilation of extinct and threatened species (Mace & Lande, 1991; Rodrigues et al., 2006). Brooks and Balmford (1996) compared losses of birds in the region, projected using a species–area model, with those listed by the IUCN as ‘threatened’, and they found congruence. They concluded that the forecasts of looming extinction are basically correct, but that there is a substantial lag between the habitat loss/fragmentation process and global extinction of the species. There is, moreover, good evidence of local extirpation within the existing range of many bird species that live in devastated habitats such as the Mata Atlantica. Thus, within the Viçosa region (a 120 km2 area in south-eastern Brazil) over the last 70 years, it appears that at least 28 bird species have become locally extinct, with 43 being classified as ‘critically endangered’ and 25 ‘vulnerable’. In total, 61 per cent of the original avifauna has been significantly reduced in incidence (Ribon et al., 2003). Nectarivorous species appear to have been affected least, followed by omnivores and carnivores, with frugivores and insectivores hit the hardest. Assuming relaxation to be well under way in these fragmented systems, the questions of estimating the time lag between habitat loss and eventual species losses, and of predicting the identities and numbers of these losses, remain unanswered. In this context, as noted by Raheem et al. (2009), it is surprising that fragment age (i.e. the time of isolation/creation of a fragment) has received little attention from ecologists and conservationists. In most landscapes, it is the productive and/or most accessible areas that are deforested first, thus producing a nonrandom spatial and temporal distribution of habitat fragments (Laurance et al., 2002; Ewers et al., 2006). A topographically diverse landscape, such as on most oceanic islands, will therefore typically contain an assortment of older, smaller and more degraded fragments at lower elevations and younger, larger and less degraded fragments at higher elevations (see for example Box 8.2). The above example illustrates the difficulties of untangling causal processes underlying species

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relaxation within real landscapes. Part of this complexity is generated by the operation of two overlapping temporal scales that are critical in determining net rates of species loss across fragments: the rate at which habitat is being lost from a region (considering also the possible recovery of habitat; see Wright & Muller-Landau, 2006); and the age of the habitat fragments created within that region. Untangling the contribution of these two interlinked age-dependent factors may be critical to a better understanding of the relaxation process and thus for more accurate predictions of species losses and relaxation lag time. Recently, Raheem et al. (2009), studying the land snails assemblages in fragments of natural rain forest in Sri Lanka’s wet zone, concluded that fragment age, along with fragment shape complexity, were the only two significant determinants of fragmentation-related changes in community composition. Attributes of fragments such as area, distance-to-edge and matrix quality, which have been traditionally linked to species losses, exhibited no obvious effect (see also below). In practice, review of the literature on such effects reveals many such idiosyncrasies between studies. At least some part of the differences in findings from one case study to the next reflects differences in the ‘experimental design’ of the fragmented systems analysed and, in particular, the range in values of properties such as area, age, distance from source, habitat complexity, etc. that each study encompasses.

8.2.3 Ecosystem collapse and threshold responses in habitat islands Reduction of habitat area can causes super-saturation as immigration rate declines and extinction rate rises (above). In the most extreme scenario, where the loss of habitat is so extreme that immigration into a patch virtually ceases, species richness may, in theory, collapse catastrophically (see Whittaker & FernándezPalacios, 2007, their figure 10.5; and also Vandermeer & Lin, 2008). Although the process of species richness collapse and associated loss of ecosystem function is not presently well-defined or understood, it is thought to be linked to extreme impoverishment of the available resources that are required for a system to sustain its functionality (e.g. Dobson et al., 2006). One of the most emblematic examples of an ecosystem collapsing comes from the island literature. Easter Island (Rapa Nui) was once one of the most isolated

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Box 8.2 Extinction debt in the Azores Box prepared by K.A. Triantis and R.J. Whittaker – excerpted and modified slightly from Triantis et al. (2010). See the source paper for a full analytical presentation of the materials and methods used. Habitat destruction is considered to be the leading cause of terrestrial species extinctions. However, there is typically a time lag between the reduction in habitat area and the eventual disappearance of the remnant populations. These ‘surviving but ultimately doomed’ species represent an extinction debt. Calculating the magnitude of such future extinction events has been hampered by potentially inaccurate assumptions about the slope of species–area relationships, which are habitat- and taxon-specific [see text]. We have overcome this challenge by introducing a novel method that uses the historical sequence of deforestation in the Azorean Islands to calculate realistic and ecologically-adjusted species–area relationships. The Azores constitute an ideal model system for assessing extinction debt because: 1 they have lost more than 90 per cent of their original native forest during the five centuries of human occupation; 2 being one of the most isolated archipelagos on Earth they support a significant number of single island endemics (SIE); 3 the history of human settlement and deforestation is well known; 4 extensive biogeographical data exist for a range of taxa. The Azorean Islands were discovered in AD 1432 by Portuguese explorers, and more than 500 years of human settlement have taken their toll on the local fauna and flora, 420 species of which are endemic to the archipelago. Today, approximately 70 per cent of the vascular plants and 58 per cent of the arthropods found in the Azores are exotic, many of them invasive. The destruction of the native ‘laurisilva’, a humid evergreen broadleaf laurel forest, in the Azores has followed a clear temporal sequence. At the time of human colonization (c. AD 1440), the archipelago was almost entirely covered by forest. By 300 years ago (c. AD 1700) human activities had restricted the native forest in most islands to areas above 300 m a.s.l. and, by c. AD 1850, areas with native forest were present only above 500 m a.s.l. The development of an economy dependent on milk production during the last decades of the 20th century drove a further reduction of native forest area, to 2.5 per cent of the total area of the archipelago (Figure B8.2a). The Azorean arthropod fauna has been intensively sampled during the last ten years. The Borges et al. (2005) checklist includes virtually all arthropod species native to the Azores, as well as an accurate description of their presence or absence in all the islands of the archipelago. The endemic arthropods belonging to three groups – Araneae, Hemiptera and Coleoptera – were classified as native forest dependent and non-forest dependent species, and only the forest dependent species endemic to the archipelago were considered for further analyses. We used four different ‘habitat areas’ to calculate our species–area relationships: these were chosen to correspond to the extent of native forest at four known points in time before and since human colonization (≈AD 1440, AD 1700, AD 1850, AD 2000; Figure B8.2a). Although the historical estimates of forest cover are crude approximations, we consider that they are accurate enough to provide a baseline for estimating the present extinction debt. Our analyses follow the rationale that if species ‘relaxation’ has not yet taken place or is incomplete (i.e. the extinction debt has not yet been paid), then the best fitting species–area model will correspond not to present forest area but to a past baseline – a hypothetical dynamic equilibrium from which the system has since departed. However, there is a complication in dealing with a system of endemic species on oceanic islands of varying age, namely that the dynamics of colonization, speciation and extinction may be at different points, depending on the age of the island. Accordingly, we fitted and compared both species–area and species–area–time models.

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a) T4 T3 T2

b) 1440 AD 1700 AD 1850 Present

AD

Log Species

T1

Time T1

T2

T3

T4

Log Area

Figure B8.2a The sequential reduction of the native forest and the respective species–area relationships. (a) The elevational occupancy of native forest in historical times for the island of Terceira (Azores). T1: Before human occupation (almost complete coverage of island’s area); T2: approximately 300 years ago (300–500 m); T3: approximately 160 years ago (above 500 m); T4: current distribution. (b) A schematic representation of the effects of the sequential reduction of the native forest on the species–area relationships of endemic forest arthropods. The dashed lines in T4 represents the future species–area relationships, extrapolated from T1 and T2 (see text). The magnitude of the extinction debt is represented by the difference between current species richness (solid line) and the future predictions (dashed lines). From Triantis et al. (2010).

For the total island area and the habitat area above 300 m, the species–area–time model applied was significant (P < 0.05) for the arthropod taxa considered, with most of the explained variance attributable to area. However, for the area above 500 m and the present area covered by native forest, neither the species–area–time relationships nor the respective species–area relationships were statistically significant. We thus used the first two benchmark relationships, for total area (≈ AD 1440) and area above 300 m (≈ AD 1700), to represent the baseline conditions for estimation of current extinction debt. Hence, we used the parameters estimated for the total area of the islands (Prediction 1 in Table B8.2a) and that of the area above 300 m (Prediction 2 in Table B8.2a) to estimate the number of endemic forest arthropods that ‘should’ be present and, by direct comparison with the number of extant species, to derive the number of future extinctions (i.e. the extinction debt) (Table B8.2a). For the arthropod taxa considered, our results clearly indicate that the majority of the endemic forest-inhabiting species (>50 per cent) are expected to go extinct in time, especially on those islands on which the native forest has been restricted to small areas or has been totally removed. Terceira, the island with the largest remnants of native forest, has the smallest number of predicted future extinctions. At face value, these figures constitute a powerful warning to island conservationists that the worst of the extinction crisis is by no means over. Furthermore, in spite of the fact that some archipelagic endemic species may benefit from a degree of population reinforcement between habitat fragments or islands, the parallel reduction of the native forest across all islands in the last 600 years has greatly diminished the probability of such source-sink dynamics rescuing species from global extinction. Hence, we would also anticipate a correspondingly large number of archipelagic-scale species extinctions for Azorean endemic arthropods in the future as the extinction debt is settled. In point of fact, at least five SIE species of beetles recorded early in the 20th century have not been recorded since 1965 and might therefore be considered extinct.

In the paper, we argued that the figures reported above are likely to be more accurate than previous predictions because we have focused our attention on endemic forest species that have evolved in,

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Table B8.2a Number of forest-dependent archipelagic endemic arthropods for the nine Azorean Islands and the respective predicted number of species that should be found based on the species–area–time models from the total area (Prediction 1) and the area above 300 metres (i.e. area occupied by native forest c. 300 years ago; Prediction 2). Currently there is no native forest on Graciosa and Corvo islands. Note that the results remain similar when the different groups, i.e. Coleoptera, Araneae and Hemiptera, which have been lumped together in the table, are analysed separately.

Island

Arthropods

Prediction 1 (total area)

Prediction 2 (area >300 m)

Species loss

Graciosa Corvo Flores Faial Pico São Jorge Terceira São Miguel Santa Maria

8 3 24 17 28 21 29 34 24

1.14 1.00 4.01 1.11 2.55 1.28 5.79 1.84 0.29

2.42 1.34 8.04 2.59 4.66 2.83 12.10 5.28 1.93

86–70% 66–55% 83–67% 93–85% 91–83% 94–87% 80–58% 95–84% 99–92%

and are only found in association with, the native forest. At the same time, we avoided additional ‘noise’ caused by generalist species that may well be able to survive in other (i.e. anthropogenic) habitats. If this logic is correct, then the implication is that large-scale conservation efforts need to be implemented if the high extinction debt we have identified is to be deferred or avoided. Humaninduced fragmentation, land-use changes and invasive species have already been identified as important threats to Azorean biodiversity. This paper argues that the conservation of the Azorean natural heritage, and that of many other oceanic islands, will largely depend on establishing an integrated large-scale strategy to manage both indigenous and non-indigenous species, while simultaneously protecting the remnants of native habitat and, ideally, increasing their extent. However, as appreciated by the authors (and pointed out by the journal’s reviewers), there are a number of key assumptions embedded in this study that may undermine the power of the analyses and which may serve as points for class discussion. These include: 1 the reliance on an assumption of a dynamic equilibrium, or something approximating to it, prior to human interference; 2 that the endemic species identified as forest-dependent can persist only in the native forests; and 3 that the remaining forest area, although in most cases fragmented, can be treated as if occurring in a single block, with the degree of fragmentation being insignificant.

fragments of inhabited land in the world. When the first Europeans arrived there on Easter Day AD 1722, they found a fascinating enigma: how could this impoverished, nearly treeless island, with its sparse and impoverished population, have supported the construction of the remarkable giant statues (moai) that could be found all over the island. How, and why, had it all gone so terribly wrong?

The flora of Easter Island currently consists of over 200 vascular plant species, of which only 46 are native. However, the native flora was once rather richer, containing several native tree and shrub species (Diamond, 2007). The forests are now known to have contained a giant palm tree and a number of other trees, some reaching over 30 m in height. These forests persisted for at least 33,000 years (as far back as the

Conservation planning in a changing world palaeoecological record goes) and survived the major climatic shifts of the late Pleistocene and early Holocene. We can thus be certain that deforestation caused directly or indirectly by humans was responsible for the treeless state of the island observed by the first Europeans (Diamond, 2007). Recent studies have recorded that more than 20 tree and woody plant species were exterminated as an eventual outcome of Polynesian settlement. The palm was almost completely gone by AD 1450 and the other large trees by AD 1650. What is not known with any certainty is exactly how long this almost suicidal environmental destruction took. The date of the first settlers arriving on the island is still debated. Estimates range from AD 300 to 1200, with the most recent date considered as the most reliable earliest date of occupation (Hunt, 2006; Hunt & Lipo, 2006). Their effect on the forest was soon detectable in the pollen record and it had been entirely eliminated for some time by the end of the 17th century (Figure 8.7; Hunt & Lipo, 2006; Diamond, 2007). The human population reached its peak around AD 1600, but subsequently was intensely reduced, along with the megalithic culture that had sustained the quarry-

AD

900–1200

Small colonization groups arrive; population grows and soon begins building ahu and moai

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1150–1250

Population continues to grow causing widespread forest clearance; additive effect of rats on regeneration of palm tree forests

AD

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ing, sculpting, transport and erection of the remarkable giant statues. The loss of trees and other plant species is matched by a more complete loss of native birds than on any comparable island in Oceania (Steadman, 1997, 2006). Bird bones associated with Polynesian artefacts 600–900 years old showed that Easter Island once sustained at least 22 species of seabird, of which only seven now occur on one or two offshore islets, and just one of which still nests on Easter Island. Bones also provide evidence for the existence of six endemic land bird species, a heron, two rails, two parrots, and an owl, none of which survive. Embodied in ecosystem collapse is the concept of trophic cascade, i.e. the chain of knock-on extinctions following the loss of one or a few species that play a critical role (e.g. as a pollinator) in ecosystem functioning. A perturbation at one trophic level propagates through lower levels with alternating positive and negative effects, as highlighted in the phenomenon of mesopredator release, outlined earlier. Thus, the removal or absence of large predators would be expected to lead to increased densities of consumers, which, in turn, would be predicted to have negative

1300–1400

Maximum human population; rate of deforestation peaks

AD

1680

Island largely deforested; plants and grass used for fuel instead of wood; population reduction

AD

1772

First Europeans arrive and find remnant population of around 3,000; some trees still remain

Figure 8.7 Major events in the sequential collapse of the Easter Island ecosystem. Adapted from Hunt (2006) and modified according to the account provided by Diamond (2004, 2007).

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consequences for producers (Oksanen & Oksanen, 2000). Terborgh et al. (2001) studied a set of large predatorfree islands created by a hydroelectric impoundment in Venezuela. The small area of the islands restricted the predator community to species predating invertebrates (e.g. birds, lizards, anurans and spiders) and seed predators (rodents), alongside herbivores (howler monkeys, iguanas, and leaf-cutter ants). Predators of vertebrates were absent, and densities of rodents, howler monkeys, iguanas and leaf-cutter ants were found to be 10 to 100 times greater than on the nearby mainland, suggesting that larger predators normally limit their populations. Moreover, the densities of seedlings and saplings of canopy trees are severely reduced on herbivore-affected islands. Terborgh et al. (2001) found support for the idea that hyper-abundant folivores could reduce species-rich forests to an odd collection of herbivoreresistant plants. The endpoint of such a process is likely to be a biologically impoverished system. All of the above examples suggest the existence of taxon- and system-dependent thresholds, beyond which species losses accelerate (Ewers & Didham, 2006; Whittaker & Fernández-Palacios, 2007). Such thresholds are highly pertinent to understanding relaxation as a result of habitat loss and fragmentation. The most dramatic changes seem to be those following the loss of a trophic tier, typically the loss of top predators. However, similarly dramatic changes can follow the addition of a tropic tier, as seen when terrestrial vertebrate predators are introduced to remote islands previously lacking them (Terborgh, 2010). Given the central importance of the topics of habitat fragmentation and species relaxation in predicting current and future extinction rates, it is surprising that more attention has not been given to experimental analyses of threshold effects and to studies of the timescales over which the ‘extinction debt’ persists (see Simberloff & Martin, 1991; Brooks et al., 1999; Laurance, 2002). Although restricted to metapopulation model simulations (of which, more follows below), Keymer et al. (2000) have shown that persistence in dynamic landscapes depends on the interaction between three factors: the amount of habitat in the landscape; the rate of change of the amount of habitat; and the life history of the species living in the landscape. More generally, they suggest that including temporal considerations into models of landscape

structure changes the extinction threshold – the amount of habitat destruction a population can tolerate – by making the threshold sensitive to the rates of destruction.

8. 3 S PECI ES I N CI DEN CE 8.3.1 Minimum viable populations, minimum areas and incidence functions In his seminal paper, Caughley (1994) identified two prevailing paradigms in conservation biology: the ‘declining population paradigm’ and the ‘small population paradigm’. The declining population paradigm is the identification and management of the processes that depress the demographical rate of a species and cause its populations to decline deterministically, whereas the small population paradigm is the study of the dynamics of small populations that have declined owing to some (deterministic) perturbation, and which are more susceptible to extinction via chance (stochastic) events. These concepts underpin the formulation of extinction-risk criteria. Theoretical and empirical work has repeatedly shown that, once reduced in size and geographical range, populations face a considerably elevated risk of extinction (MacArthur & Wilson, 1967). Or, as Darwin (1872, p. 133) put it: ‘Rarity … is the precursor to extinction’. There are actually several different forms of rarity (Box 4.1), the most extreme form of which is when a species is reduced to a small population entirely isolated from supplementary immigration, or indeed to the very last such population of the species. In the late 1970s, researchers identified the need to characterize quantitatively the long-term viability of such small and entirely isolated populations (Soulé & Wilcox, 1980). This led to the concept of the minimum viable population (MVP), the smallest number of individuals required to provide a specified probability of persistence over a given period of time (Shaffer, 1981). For instance, the MVP could be operationalized as ‘the population size required to ensure a 99 per cent probability of the species’ population persisting for 40 generations or for 1,000 years’ (see, e.g. Reed et al., 2003). Theoretical estimates of MVPs typically vary from as few as 50 to as many as 10,000 individuals, based on the postulated effects of demographical, genetic and

Conservation planning in a changing world environmental variation (Reed et al., 2003; Brook et al., 2006), with the available empirical evidence pointing to the upper end of this range (e.g. Reed et al., 2003). It has been estimated that the maximum tolerable rate of inbreeding is 1 per cent per generation, which has in turn been translated to approximately 50 individuals to ensure short-term fitness (see Shafer, 1990). However, typically only a proportion of the adult population participates in breeding and it is these animals that form the effective population size, which is often substantially smaller than the total population size (Shafer, 1990; see Crandall et al., 1999, for discussion on the concept). A study of grizzly bears in the Yellowstone National Park showed that to prevent inbreeding rates exceeding 1 per cent required an overall population size of at least 220 rather than 50 animals (Shafer, 1990). Further rule of thumb estimates have been collated by Frankham et al. (2002; their Table 14.1, p. 339) as follows: the population numbers required to avoid inbreeding depression and to retain fitness in the short term, >50; to retain evolutionary potential, 500– 5,000; and to avoid the accumulation of deleterious mutations, 12 to 1,000 individuals. Attempts to calculate the viability of single populations (i.e. whether the population is likely to persist for a given period of time) are referred to as population viability analyses (PVA) (see Reed et al., 2003). PVA can take into account the combined impacts of stochastic factors (demographical, environmental and genetic stochasticity) and deterministic factors (e.g. habitat loss, over-exploitation). According to Brook et al. (2006), PVA and the threat categories of IUCN (Box 4.1) each offer an assessment of a species’ probability of extinction based on its current population size and structure and the characteristics of the threatening processes it faces. On the other hand, the main feature of MVP analysis is that the risk of extinction is fixed and the critical question asked is how large a population must be to avoid this risk. Demographical stochasticity of initially small populations can lead to losses from a series of isolates without a need to invoke any specific mechanism such as predation or loss of fitness. However, where small populations persist for a reasonable length of time (e.g. several generations), they may also lose genetic variability as they pass through bottlenecks. They may then lose fitness by lacking the genetic flexibility to cope with either the normal fluctuations of environment or an altered environment, and they may also

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accumulate so-called deleterious genes, i.e. genes that reduce survival or fertility (see Caughley, 1994). A further complication in assessing genetic effects of fragmentation is that where a species is split into numerous separate populations in fragmented habitats, there may be multiple bottlenecks involved. This may result in reduced variation within each population, but increased genetic differentiation between populations (see Leberg, 1991). The viability of an isolated population may also be influenced by the occurrence of environmental change or disturbance, and indeed it has been argued that it is critical to take such environmental catastrophes and fluxes into account when estimating the MVP and designing conservation measures based around protecting such small, endangered populations (Mangel & Tier, 1994). An example of synergetic effects of a catastrophic event and inbreeding is provided by song sparrows (Melospiza melodia) living on Mandarte Island in western Canada. The inbred birds died at a much higher rate during a severe storm than did outbred birds (Keller et al., 1994). Although the severe weather was what caused this mortality, it appeared that inbreeding determined, in part, which individuals survived the storm. Recently, Reed et al. (2003) considered the effects of age structure, catastrophes, demographical and environmental stochasticity, and inbreeding depression, to derive MVP estimates for 102 vertebrate species. They defined an MVP as ‘one with a 99 per cent probability of persistence for 40 generations’. Across this data set, mean and median estimates of MVP were 7,316 and 5,816 adults, respectively. The estimated values did not differ systematically between major taxa, or with trophic level or latitude, but were negatively correlated with population growth rate. Reed et al. (2003) stress that although MVPs provide a useful rule of thumb for species conservation (which is that the size of vertebrate populations needed for successful long-term conservation is about 7,000 adults), MVPs should not be used as precise conservation targets. For further discussion see also Brook et al.’s (2006) study on the MVP of 1,198 species. Closely related to the concept of MVP is the idea of the minimum viable area (MVA). For some species, e.g. snail populations, a fairly small area may suffice to maintain the requisite number of individuals. Species of higher trophic levels generally require more area or space to ensure good survival prospects. It has been

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calculated that a single pair of ivory-billed woodpeckers (Campephilus principalis), a species generally considered extinct, may have required 6.5–7.6 km2 of appropriate forest habitat; that the European goshawk (Accipiter gentilis) has a home range of about 30– 50 km2 (Wilcove et al., 1986); and that populations of North Island brown kiwi (Apteryx mantelli) in New Zealand are unlikely to be viable in protected areas of less than 100 km2 (Basse & McLennan, 2003). Even some plant and insect species may need surprisingly large areas if they typically occur at very low population densities (Mawdsley et al., 1998). Thus, for many species, reserves must be really rather large if their purpose is to maintain a MVP entirely within their bounds. For instance, it has been estimated that the minimum viable area for some large mammals during a time span of 1,000 years exceeds 100 times the area of Yellowstone National Park (Shafer, 1995). The MVA approach, if focused on large-bodied flagship species and converted into policy recommendations, may have benefits for the preservation of entire ecosystems, since many other species with lesser area requirements may benefit from protection within the MVA of the flagship species. However, one limitation of the MVA approach is that it is predicated on the idea that each area is discrete and has no biotic (genetic) exchange with other surrounding areas. If there is such exchange taking place, such that the population is actually part of a network (or metapopulation), then the estimated MVA may be larger than is strictly necessary (see discussion in Whittaker & Fernández-Palacios, 2007). Another way of examining area requirements of particular species is by means of incidence functions estimating the probability of a species occurring as a function of a key controlling variable, such as island species richness, area, isolation, or sometimes a combination of two key variables (Diamond, 1975b; Wilcove et al., 1986; Watson et al., 2005; and see Figure 8.8). In 1994, Hanski introduced the incidence function model (see also Hanski, 1999; Moilanen & Hanski, 2006). The simplest form uses a snapshot of species presences and absences and predicts extinctions based on patch size and colonizations based on isolation (see MacKenzie et al., 2006, for discussion on the concept). Moilanen (2002) has drawn attention to three main types of errors likely to occur in data used for incidence functions analyses:

1 inaccurate measurements of the patch areas; 2 the existence of patches that are unknown within or around the study area (‘missing patches’); 3 patch occupancy is incorrectly observed, with patches considered to be empty actually containing a population of the focal species (the ‘false zero’ problem). Perhaps more fundamental a problem is that species incidence functions tell us, of course, the properties of ‘islands’ on which a target species currently occurs, but not those on which it may persist in the long term or in an altered ecological conditions. Thus, they are not equivalent to estimating MVAs. Biedermann (2003) provides an interesting analysis of area–incidence relationships of 50 species of vertebrates and invertebrates from 15 different fragmented landscapes, ranging from Central European grassland to Asian tropical forest, in which he demonstrates that area requirements increase essentially linearly with increasing body size on a log–log scale. Biedermann

Figure 8.8 Examples of species incidence functions based on logistic regression models across different landscapes and ecosystems, ranging from Central European grassland to Asian tropical forest: (a) Kelisia haupti (planthopper); (b) Arytaina genistae (psyllid); (c) Neophilaenus albipennis (spittlebug); (d) Chazara briseis (butterfly); (e) Dendrocopos minor (lesser spotted woodpecker); (f) Accipiter gentilis (goshawk). Re-drawn from Biedermann (2003).

Conservation planning in a changing world cautions that the relationship was based on analyses for species more or less restricted to the habitat patches considered, and that we should not consider as granted a similar relationship for generalist species. If incidence functions really reflect key controlling variables, then they might be of great value in designing reserve networks but, if they are found to be inconsistent across the range or through time, they will need more careful interpretation. Empirical work suggests that, in practice, they do vary in both time and space. In illustration, Hinsley et al.’s (1996) study of 31 woodland bird species in 151 woods in a lowland arable landscape in eastern England over three consecutive years has shown that the incidence functions vary through time in relation to density-independent mortality (extremes of weather conditions). Additionally, Hinsley et al. (1996) showed that specialist species were more likely to disappear from small woods after severe winter weather than were generalists, and that they could take more than a year to recolonize. We may interpret these patterns as reflecting underlying metapopulation dynamic processes, discussed in the following section. An illustration that incidence functions might vary across the range of a species comes from another study of woodland birds, this time undertaken in Australia and based on data from three different landscapes located quite near one another (and thus within the same biogeographical context and climate regime). The study, by Watson et al. (2005), demonstrated that area- and isolation-based incidence functions differed significantly, seemingly as a function of differences in properties of the landscape matrix within which the woodlands were embedded. The three landscapes were an urban area, a peri-urban area and a rural (agricultural) landscape. Interestingly, it was evident that while some species were able to occupy smaller woodlands within the rural landscape, others actually showed a higher incidence in small woods in the urban area (within the city of Canberra itself). This provides some indication of the difficulty of designing a reserve network system optimized for all members of the community of interest (see also Magle et al., 2009). Recently, Prugh et al. (2008) compiled occupancy data for 1,015 bird, mammal, reptile, amphibian and invertebrate populations from 89 case studies, including in total 12,370 habitat patches that were embedded within unsuitable matrix of land cover on six continents. Using incidence functions, they evaluated

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the predictive ability of patch area and isolation for species occupancy. Surprisingly, both area and isolation performed poorly as predictors. Prugh et al. (2008) concluded that is the type of land cover separating patches that most strongly affects the sensitivity of species to patch area and isolation. Thus, although patch size and isolation are indeed important for the occupancy of many species, they find, as do Watson et al. (2005) in their study, that it is crucial to take account of the properties of the intervening matrix. Hence, a key conclusion of this work is that improving the quality of matrix may result in ‘higher conservation returns than manipulating the size and configuration of remnant patches for many of the species that persist in the aftermath of habitat destruction’ (Prugh et al., 2008, p. 20,770).

8.3.2 Metapopulation dynamics Plant and animal species are typically patchily distributed; indeed all species’ ranges involve discontinuities, and especially so at finer scales of analysis. It is frequently possible to discern that within a landscape, a particular species occupies geographically separated patches that are interconnected by occasional movements of individuals and gametes. The name for this network of local populations is a metapopulation. The first metapopulation models were constructed by Richard Levins in papers published in 1969 and 1970 (Gotelli, 1991). The basic idea can be understood as follows: imagine that you have a collection of populations, each existing on patches of suitable habitat. Each patch is separated from other nearby habitat patches by unsuitable terrain. Although these separate populations each have their own essentially independent dynamics, as soon as one crashes to a low level, or indeed disappears, that patch will provide relatively uncontested space for ‘surplus’ individuals from one of the nearby patches, which will soon colonize the nowunpopulated patch. For example, Lei and Hanski (1997) studied metapopulation structure in a threatened species of butterfly, Melitaea cinxia, and its specialist parasitoid, Cotesia melitaearum, in a large network of small habitat patches. They observed that the incidence of the parasitoid in host populations was positively correlated with the size of the host population and the area of the habitat patch. C. melitaearum is thus expected to have a substantial risk of extinction from patches in which

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the number of host populations is small, meaning that the parasitoid may well go entirely extinct from certain patches. However, the network of patches provides the possibility of recolonization. Metapopulation theory, therefore, examines the dynamics of sets of semi-independent populations connected by dispersal (Hanski & Gilpin, 1991). In Levins’s (1970) model, a metapopulation is a network of extinction-prone subpopulations of a species occupying a variety of habitat patches. These subpopulations inhabit identical patches and are subject to equal but independent probabilities of extinction and recolonization. In practice, habitat patches and the landscapes in which they are embedded are very much more complex and heterogeneous than this, so a key challenge for metapopulation modellers is to develop models that are balanced between the attractive simplicity of the general model and fine-tuning to such a degree that models are restricted in application to a single system (see case studies in Whittaker & Fernández-Palacios, 2007). Sometimes, conservation scientists have suggested managing endangered species via policies that encourage the populations to function as metapopulations, thus allowing for the idea that a mixed-use landscape could be worth conserving, as opposed, for example, to insisting that a large area should remain as, or be restored entirely to, a forest cover. However, a spatial model created by Lamberson et al. (1992), in order to predict how the populations of the northern spotted owl (Strix occidentalis caurina) will survive in patches surrounded by logged forest, eventually failed to predict realistic minimum viable populations of the bird (Harrison et al., 1993). The populations of the bird declined in a pattern not predicted by the metapopulation models. On the other hand, some butterfly species have been found to behave according to the predictions of metapopulation models (Thomas & Hanski, 1997). Therefore, the more basic question is: how broadly and to which species does the metapopulation theory apply in habitat fragments? According to Harrison & Taylor (1997) and Hoopes & Harrison (1998) four scenarios of landscape structure are common in fragmented landscapes (Figure 8.9): 1 where patches are roughly of equal size and dispersal distances are comparable to the distances between patches (classic metapopulation models may apply);

2 where patches are so unequal in size and/or habitat quality that most immigrations are in one direction (from large to small patches); extinctions and recolonizations that occur in very small populations are inconsequential (mainland/island metapopulation models may apply); 3 where patches are so close together relative to dispersal distances that they support a single population and not a metapopulation (patchy populations); 4 where patches are so far away relative to dispersal distances that the populations are not interconnected and the assembly ceases to be a metapopulation (nonequilibrium metapopulation models may apply). Thus, whereas patches of tall forest in a savanna landscape may be treated within the framework of metapopulation theory, the concept may not be suitable for forest patches in a highly tree-covered landscape. This is because ecological boundaries between forest and savanna are clear-cut, whereas those between forest and highly tree-covered landscape are fuzzy. Furthermore, an insurmountable barrier for one group of organisms may be easily navigated by another – for sunbirds, forest patches spread over a 100 km2

Figure 8.9 Structures of metapopulations that can arise from fragmentation. Adapted from Hoopes & Harrison, 1998; after Harrison (1991). The four cases are those described in the text.

Conservation planning in a changing world landscape may sustain a metapopulation but, for dispersal-limited snails, they can at best sustain isolated populations. Metapopulation models, therefore, are not generally applicable to all organisms in fragmented systems (Fahrig & Paloheimo, 1988). Hoopes & Harrison (1998) caution against the general use of such models in conservation decision-making because of the prevalence of situations where functional metapopulation dynamics either do not occur, or where they fail to match the assumptions of the models. The following are additional important shortcomings of the metapopulation approach: • Several authors have noted that metapopulation models are extremely data-demanding and usually require data that are very difficult to obtain (Kindvall & Ahlén, 1992; Doak & Mills, 1994). Moreover, model results tend to be very sensitive to poorly estimated parameters, and the predictions of such models have therefore frequently been found to be inaccurate (e.g. Harrison et al., 1993; Wilson et al., 1994). • Most empirical examples of metapopulations pertain to single species or a group of interacting species (Hanski & Gilpin, 1991), but not to multi-species ecological communities. • Most metapopulation models assume no distance effects (Fahrig & Merriam, 1994) although, in practice, dispersal abilities vary from species to species. For instance, metapopulations of frogs may be influenced by the availability of suitable habitat in the surrounding ≈500 m, whereas for birds this distance may be ≈3 km, because of large differences in mobility resulting in the different abilities of frogs and birds to disperse. The issue of ‘scale’ has therefore been considered important in studying fragmented landscapes (e.g. Doak et al., 1992) – an issue which classic metapopulation models do not address. • It is important for conservationists to recognize that many local populations may not be at equilibrium and regional processes may be critical in sustaining metapopulations (Hanski 1999). In conclusion, metapopulation theory offers a useful framework for thinking about isolation and fragmentation (Hanski, 1999) but, if the concept is to be useful as a theoretical framework for conservation decisionmaking, it must be extended from the original simplistic models to allow for the differing degrees of population connectivity in fragmented landscapes and differing forms of inter-patch relationships, as in realworld systems.

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8. 4 N ES T EDN ES S The concept of nestedness was first introduced some 70 years ago (see Ulrich et al., 2009) to describe patterns of species composition within continental biotas and among isolated habitats such as islands and landscape fragments. In a perfect nested pattern, when a set of habitat patches is ordered by increasing species richness, it will be found that the smallest assemblages make up a subset of the species found in the next larger assemblage, and so on, throughout the series (see Figure 8.10). Nestedness is thus a particular form of nonrandomness of assemblage composition across a set of isolates. Any such non-random pattern is potentially of interest to conservation biogeographers, as it may inform judgements about the design of protected area systems within landscapes and regions. Nestedness may, in theory, arise from differential dispersal and colonization abilities (especially for young islands); or differential rates of extinction (e.g. for land bridge islands or newly fragmented habitat islands); or from a strong nestedness of habitat types with increasing ‘island’ size (Whittaker & Fernández-Palacios, 2007); or possibly from other processes (Table 8.3). Nestedness analyses became popular among ecologists and biogeographers only after Patterson and Atmar (1986) developed a statistically rigorous approach for analysing nested subsets. They were interested in nestedness patterns derived by extinction of species from land bridge islands, and their metric reflects this emphasis. They proposed that nestedness patterns for such islands most likely reflect orderly sequences of extinctions on such islands and in fragmented landscapes (see their Fig. 4). They introduced an intuitive ‘matrix temperature’ metric to quantify the pattern of nestedness. Hot matrices are those with more random presences of species and cool matrices are those where species presences are more nested. The matrix temperature could be calculated with a software package, The Nestedness Temperature Calculator (Atmar & Patterson, 1993, 1995). The nestedness concept, as applied by Patterson and Atmar, is based on ordering the data matrix by the size of fauna or flora, i.e. it is richness-ordered nestedness. Some authors, however, have ordered the data matrix not by species richness but by island area, which has been termed area-ordered nestedness, or even by island isolation, i.e. distance-ordered nestedness (e.g. Lomolino & Davis, 1997; Whittaker &

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Figure 8.10 Nested subset relationships. Circles represent islands of different size and letters represent species. Circle size is positively correlated with species richness. The left biota is perfectly nested; all the species present in relatively species-poor assemblages are present in relatively species-rich assemblages. The right biota is perfectly non-nested because none of the species in the species-poor assemblages is present in the richer ones. Note that although the species richness of the three islands is the same in the two cases, the overall species richness of the non-nested set is greater than of the nested system.

Table 8.3 Causes of nested subset patterns (adapted from Ulrich et al., 2009). Assumption/precondition

Hypothesis

Site and species properties

Gradient of:

Explanation/Example

Passive sampling

carrying capacities of sites

regional abundance

Species are drawn randomly from the pool with the constraint that the availability of propagules is itself strongly non-random (e.g. Higgins et al., 2006).

Neutrality

carrying capacities of sites

dispersal ability

The availability of propagules is random and species’ dispersal ability is driving the pattern (e.g. Ulrich & Zalewski, 2007).

Selective colonization

isolation

dispersal ability

There are predictable limits to species’ dispersal abilities. The system consists of islands ‘sampling’ a series of species’ isolation/incidence functions (e.g. Darlington, 1957; Patterson, 1990).

Selective extinction

carrying capacities of sites

extinction susceptibility

Selective occupancy of sites according to the area of sites, which sets their carrying capacity. Relaxation in the case of mainland/habitat islands (e.g. Patterson & Atmar, 1986).

Nested habitats

habitat heterogeneity

degrees of specialization

Absence of certain habitat types in smaller and/or resource-poor patches. Higher proportion of generalist species in smaller and/or resource-poor patches (Wright & Reeves, 1992).

Selective environmental tolerances

environmental harshness, environmental tolerances

Selective occupancy of sites according to species tolerance of environmental stress (e.g. Blake, 1991).

Habitat quality

environmental harshness

Species are distributed according to the harshness exhibited by patches of the same habitat (e.g. Bloch et al., 2007).

Conservation planning in a changing world Fernández-Palacios, 2007). Intuitively, however, ordering by species richness would appear the most appropriate approach. Apart from sequential extinctions, a variety of different mechanisms can also produce nestedness patterns (see Table 8.3), some of which are deterministic and some of which are stochastic, requiring different metrics for quantifying nestedness (Wright et al., 1998; Ulrich et al., 2009). All of the explanations for nested subsets can be seen as variations of ordered colonizations or extinctions along environmental or biological gradients (area, isolation, habitat) of the target areas. Frequently, these mechanisms cannot be distinguished by just establishing the statistical pattern of nestedness. Inferences of causation ideally require independent lines of verification beyond manipulations and analyses of the original presence/absence matrix (Ulrich et al., 2009). Although nestedness can be driven by a number of processes, it appears that differential extinction plays a major role in producing nested structure in many habitat island data sets (Wright et al., 1998). Knowledge of nested subset structure might therefore provide a basis for predicting the ultimate community composition of a fragmented landscape, particularly if it is possible to attribute patterns to particular causes (Fischer & Lindenmayer, 2005; Fleishman et al., 2007). Feeley’s (2003) study of bird communities inhabiting recently isolated land bridge islands in Lago Guri, Venezuela, showed how nestedness calculations can provide useful insights. Lago Guri is a large hydroelectric reservoir created in 1986 in east-central Venezuela. The inundation of an area of hilly terrain expanding over 4,000 km2 resulted in the fragmentation of oncecontinuous forest into hundreds of land bridge islands (e.g. Terborgh et al., 2001). Feeley found that the resident forest-interior bird communities displayed a significantly nested distributional pattern that was hypothesized to be the result of species’ differential extinction rates. In an earlier study of forest birds, Blake (1991) also found a significant degree of nestedness, particularly among birds breeding in the forest interior and among species wintering in the tropics. By contrast, species breeding in forest-edge habitat showed more variable distribution patterns. These findings concur with those of Patterson (1990) from São Paulo, Brazil (original data from Willis, 1979). Patterson reported significant nestedness amongst sedentary bird species but, when

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transient species were also included, the system as a whole was found to be non-nested. These results are indicative of a large number of studies of nestedness, which show the outcome of nestedness analyses to be variable across different systems and for different ecological groups of species, but which show that significant nestedness is a common pattern. Such analyses often indicate that species that are restrictive habitat specialists, including many of high conservation value, do require larger, more species-rich patches (Fleishman et al., 2007; Whittaker & Fernández-Palacios, 2007). In theory, a nestedness analysis can contribute a simple answer to the SLOSS question, as a strong degree of nestedness implies that most species could be represented by conserving the richest (largest) patch. According to Atmar & Patterson (1993), the widespread occurrence of nested subsets speaks for the value of larger protected areas. However, Boecklen (1997) and Fischer & Lindenmayer (2005) convincingly showed that this argument is only valid for perfectly nested subsets, which are very rare in nature. Even for highly significantly (but not perfectly) nested subsets, the total species numbers from subsets of many smaller sites are often higher than the respective number of species from a single larger site of the equivalent total area (Ulrich et al., 2009). On the other hand, a low degree of nestedness may be considered as indicative that specific habitat patches are sampling distinct species sets, and thus an array of reserves of differing size and internal richness may be required to maximize regional diversity in such circumstances (e.g. Kellman, 1996). Broadly speaking, a nestedness index can provide one compositional descriptor and can perhaps aid identification of risk-prone species. However, it should not be given primacy in conservation planning. Identifying a community as nested at a certain point in time, has limited predictive ability as to the probability of the community maintaining the same sets of species (or even a single species) over time (Simberloff & Martin, 1991). The isolates may be subject to turnover and/or species attrition in new ways dictated by the changing biogeographical circumstances of the landscape in which the fragments occur. As Worthen (1996, p. 419) put it, nestedness is not a ‘magic bullet’, ‘ … no single index should be expected to distil the informational content of an entire community, let alone predict how it will react to habitat reduction or fragmentation’.

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According to Ulrich et al. (2009), there are three key steps in a nestedness analysis: 1 calculation of a metric to quantify the pattern of nestedness in a matrix; 2 comparison with an appropriate null model or randomization test to assess the statistical significance of the metric; 3 inference of the mechanism that generated the pattern of nestedness. Unfortunately, on all three points, no consensus has yet been reached among scientists, which has hindered a general understanding of the frequency, causes, and consequences of nestedness (Whittaker & FernándezPalacios, 2007; Ulrich & Gotelli, 2007; Almeida-Neto et al., 2008). This lack of consensus, along with the growing number of applied and theoretical studies of nestedness, therefore calls for a critical review of the state of the art and for perspectives for future research. This review should take into account other patterns related to but distinct from nestedness patterns, such as island assembly theory (Diamond, 1975b) and species incidence (see also Section 8.3).

Figure 8.11 Bird species richness–area relationships in the littoral forests of southeastern Madagascar, including regression lines and r2 values. Two classifications of species richness were considered: total species richness (closed circles) and forest-dependent species richness (open circles). Linear regressions: unbroken lines; break-point regression: dashed line. The break-point regression procedure followed Lomolino & Weiser (2001). All regressions are significant (P < 0.01). From Watson et al. (2004).

8.4.1 Edge effects Where two habitats abut, they often intermingle, forming a zone of species overlap (and of locally higher diversity) – a pattern termed an ecotone. In the case of many protected areas, habitat alteration often produces quite sharp ecotones or edges, but it is often the case that species numbers are elevated in these edge habitats (Kellman, 1996). However, many of these species are dependent on the matrix habitat rather than on the habitat within reserves. Such species are unlikely to be those that are most in need of protection. Watson et al. (2004) studied birds in littoral forest habitat islands and surrounding habitats in southeastern Madagascar. Core forest locations were found to be richer than edge or matrix habitats, with some 68 per cent of the forest dependent species found to be edge-sensitive. Frugivorous species and canopy insectivores were generally edge-sensitive, while sallying insectivores preferred edges. The vegetation structure at remnant edges contributed to edge-sensitivity. The relationship between fragment area and overall species richness conceals the fact that forest-dependent species were generally lacking from fragments of less than 10 ha (Figure 8.11).

Wilcove et al. (1986) suggest that reserves of less than 100 ha cannot support viable populations of forest songbirds due to high densities of nest predators such as blue jay (Cyanocitta cristata), weasel (Mustela erminea) and racoon (Procyon lotor) around forest edges. Laurance (2000) suggests that edge effects can occur on even large spatial scales. For example, Curran et al. (1999) found that recruitment of canopy trees in the 90,000 ha Gunung Palung National Park in western Borneo collapsed because vertebrate seed predators flooded into the park from surrounding degraded areas. A core-area model proposed by Laurance (2000) illustrates the impacts of edge effects on nature reserves ranging from 1,000 to 100,000 ha (Figure 8.12). These examples illustrate that the relationship between a reserve and its surrounding matrix is not subject to easy generalization. There are species that share both zones and, just as there are matrix species that may impact negatively upon core reserve species, there may also be reserve species which exploit resources in the matrix. Therefore, the heterogeneous

Conservation planning in a changing world

Figure 8.12 A core-area model illustrating the impacts of edge effects on nature reserves ranging from 1000 to 100,000 ha. The curves show the percentage of the reserve’s total area that is influenced by edge effects that penetrate to distances of 100 m (dotted line), 500 m (dashed line) or 2 km (solid line) inside the reserve. For an edge effect that penetrates to 5 km (not shown), the reserve would need to be approximately 650,000 ha in size to ensure that half of its area is free from edge effects. Source: Laurance (2000).

nature of habitats within reserves needs to be taken into account when understanding patterns of species distribution and habitat suitability.

8.4.2 Habitat corridors Habitat connectivity can be achieved by ‘stepping stones’ or ‘corridors’ of suitable habitat linking larger reserves together. In addition to forest peninsulas or hedgerows, other linear landscape features such as rivers, roads, and railways may act as conduits for the movement of particular species. However, for others they may represent barriers or hazards (Reijnen et al., 1996). Therefore, habitat corridors act as differential filters, enabling the movement of some species but being of little value, or presenting an impediment, to others (Table 8.4). A useful illustration of how corridors can be beneficial comes from the study by Saunders and Hobbs (1989; from Whittaker & Fernández-Palacios, 2007)

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of Carnaby’s cockatoo (Calyptorhyncus funereus latirostrus) from the Western Australian wheat belt – an area of 140,000 km2 in the south-west of the state, 90 per cent of which has been cleared for agriculture. The Carnaby’s cockatoo is one of Australia’s largest and most striking parrots and was once the most widely distributed cockatoo in the region. The widespread clearance of the native forest has removed extensive areas of their foraging and breeding habitat, replacing it with annual crops of no value to the species. In more recently cleared areas, however, wide verges of native vegetation have been left uncleared along the roads. These act to channel the cockatoos to other areas where food is available. Cockatoos have not persisted in areas of earlier clearances that were carried out without these connecting strips because, once they run out of a patch of acceptable habitat, it takes a long time for the flock to find another patch of native vegetation. The big reduction in suitable habitat across the region is fairly recent, and the cockatoo is not yet in equilibrium with the new regime (and indeed is considered to be an endangered species). So, it is not clear yet if the degree of connectivity and remaining area of woodland habitat are sufficient for the long-term persistence of this cockatoo. Some scientists argue that the requirement of corridors for faunal movement may have been overstated and that corridors may not be required for many taxa (see discussion in Simberloff et al., 1992). While movement along corridors is frequently assumed to occur, there have been relatively few studies which have shown that corridors are actually required for movement (Hobbs, 1992). Some studies of marked or radiotagged animals, however, have provided clear indication that certain species use corridors for movement (e.g. Dmowski & Kozakiewicz, 1990; Merriam & Lanoue, 1990), as do observations such as those above for the Carnaby’s cockatoo. In historical biogeography, the term ‘corridor’ is used for very broad connecting areas between regions, which are assumed to provide relatively unfettered movement between then. However, in considering habitat corridors at finer scales, within landscapes, and given that each species has its own requirements for habitat, its own ability to move and its own behaviour, few corridors can be considered all-purpose (Dawson, 1994). Rather, like other elements of the landscape matrix, habitat corridors act as filters. Many rare and threatened species are unlikely to benefit from

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Table 8.4 Advantages and disadvantages of habitat corridors (adapted from Noss, 1987). Potential advantages of corridors

Potential disadvantages of corridors

1 Increase immigration rate to a reserve, which could: a increase or maintain species richness and diversity, as predicted by the equilibrium theory of island biogeography; b increase population sizes of particular species and decrease probability of extinction (rescue effect) and/or permit establishment of extinct local populations; c prevent inbreeding depression and maintain genetic variation within populations. 2 Provide increased foraging area for wide-ranging species. 3 Provide predator-escape cover for movement between patches. 4 Provide a mix of habitats for different activities or stages of their life-cycles. 5 Provide alternative refugia from large disturbances (a ‘fire escape’). 6 Provide ‘green belts’ to limit urban sprawl, abate pollution, provide recreational opportunities and enhance scenery and land values.

1 Increase immigration rate to a reserve, which could: a facilitate the spread of epidemic diseases, insect pests, exotic species, weeds, and other undesirable species into reserves and across landscapes; b decrease the level of genetic variation among populations or subpopulations, or disrupt local adaptation and coadapted gene complexes (‘outbreeding depression’). 2 Facilitate spread of fire and other abiotic disturbances (‘contagious catastrophes’). 3 Increase exposure to wildlife hunters, poachers and other predators. 4 Riparian strips, often recommended as corridor sites, might not enhance dispersal or survival of upland species. 5 Cost and conflicts with conventional land preservation strategy to preserve endangered species’ habitat (when inherent quality of corridor habitat is low).

corridors, because their specialist habitats are unlikely to be found throughout the length of most corridors. For some populations, corridors may even act as ‘sinks’, drawing out individuals from the main habitat area, perhaps into dangerous places with higher risks of predation, but not returning individuals to supplement the main source area. Alternatively, they may be fairly neutral in their ecological cost-benefit, but perhaps be quite expensive to purchase and set up if not already existing in a landscape. On the other hand, some corridors are essential in providing links between preferred habitats for animals that undertake regular seasonal migrations. In the longer term, it has been argued that climate change is likely to drive substantial shifts in the distribution of species, and that the resulting species migrations will be impeded by the human sequestration of land to agriculture and other purposes. Therefore, on these grounds, it would seem prudent to plan more or less continuous habitat corridors that straddle major climatic/elevational gradients where this is feasible (e.g. Bush, 1996, 2002 – Box 7.4).

8.4.3 Landscape context – matrix effects The presence of a species within a reserve will depend not only on the suitability of habitat within the reserve, but also on the species’ ability to use the intervening landscape matrix. If this matrix is hospitable, species can also move between reserves (Gustafson & Gardner, 1996). Therefore, although reserves are important, an increasing emphasis is now being placed on the role of the quality of the landscape matrix between reserves. For example, Baum et al. (2004) have demonstrated that corridors and stepping stones are more effective if surrounded by a hospitable matrix. In tall-grass prairie ecosystem in the central USA, they showed that the effectiveness of corridors and stepping stones for promoting dispersal of the planthopper Prokelisia crocea among patches containing prairie cordgrass Spartina pectinata (the sole host plant for the planthopper) depended strongly on the intervening matrix habitat. In a low-resistance matrix (one that facilitates high rates of inter-patch dispersal), where both stepping stones and corridors promoted high connectivity, the

Conservation planning in a changing world number of planthopper colonists increased by threefold relative to patches separated by matrix habitat only. The effectiveness of stepping stones and corridors was significantly lower in a high-resistance matrix (one that provides only low rates of interpatch dispersal), with stepping stones failing to improve connectivity for the planthoppers relative to controls (Baum et al., 2004). To test whether the type of interpatch matrix can contribute significantly to patch isolation, Ricketts (2001) conducted a mark–recapture study on a butterfly community inhabiting meadows in a naturally patchy landscape. The relative resistances of the two major matrix types (willow thicket and conifer forest) to butterfly movement between meadow patches were estimated. For four of the six butterfly taxa (subfamilies or tribes) studied, conifer forest was 3–12 times more resistant than willow thicket. For the two remaining taxa (the most vagile and least vagile in the community), resistance estimates for the two differing matrix types were not significantly different, indicating that responses to matrix differ even among closely related species. These results suggest that the surrounding matrix can significantly influence the ‘effective isolation’ of habitat patches, rendering them more or less isolated than patch size and/or isolation would indicate (as Figure 8.13). In a study conducted in grasslands in western Victoria, Australia, Williams et al. (2006) assessed how both the spatial attributes of remnant patches (area and isolation) and the landscape factors (extent of urbanization and maximum inter-fire interval) influence the persistence of native plant species. They found that, on average, 26 per cent of populations of native species distributed across 30 remnants became locally extinct between the 1980s and 2001. While area and isolation had little effect on the probability of local extinction, urbanization and longer maximum interfire intervals corresponded with increased extinction risk (Williams et al., 2006). Most nature reserves are now surrounded by human-dominated landscape matrix, which is often inhospitable to many species. For example, intensively cultivated agriculture landscape is unable to support forest-dwelling species. Modification of this agricultural matrix, therefore, may provide opportunities for reducing patch isolation and thus the extinction risk of populations in fragmented landscapes. Tropical agro-forestry systems, where crops are grown under the shade of native tree species, often

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Figure 8.13 A modified species incidence function for a hypothetical species in a series of habitat islands. The occupancy of the species depends primarily on the area and isolation of the habitat island but also varies between Landscape A and Landscape B as a function of the quality of the matrix habitat. Black circles indicate occupied habitat islands and white cells unoccupied habitat islands. The grey remnants and solid line indicate that a species would inhabit these remnants when in a landscape with matrix composition ‘B’ (favourable) but would not in matrix composition ‘A’ (less favourable; dashed line). From Whittaker et al. (2005) and based on original ideas developed by Mark V. Lomolino and James E. Watson.

provide matrix habitats suitable for a substantial proportion of native species, although typically not for certain habitat-specialist forest dwellers (Bhagwat et al., 2008). Therefore, it can be argued that for successful conservation within reserves, a wholelandscape approach is needed that accounts for maintaining suitable matrix habitat.

8. 5 EMER GEN T GU I DELI N ES FOR CON S ER V AT I ON Theories are nets cast to catch what we call ‘the world’: to rationalize, to explain, and to master it. We endeavour to make the mesh ever finer and finer. (Karl R. Popper, 1959, p. 59) MacArthur & Wilson (1967) and MacArthur (1972) have described ‘habitat patches’ such as farmer’s woodlots surrounded by fields and recent fire burns, as

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‘islands’, but they carefully differentiate them from true islands. MacArthur (1972, p. 105) pointed out that true islands are ‘separated by a vacuum insofar as land birds and insects are concerned’, whereas habitat islands are ‘separated by other habitats filled with birds and insects’, thus the spill-over of organisms from adjacent habitats is a primary factor for habitat islands. Island biogeography theory and the subsequent theories and applications it has inspired and influenced have made an important contribution to conservation biogeography. The theory has inspired much thinking about the importance of the size and connectivity of protected areas in the maintenance of species diversity, and it has stimulated an avalanche of research on fragmented ecosystems. However, generalizations derived from this theory have given rise to models that are too simplistic (e.g. Laurance, 2008). Recent advances in island theory demonstrate that we are moving towards a new synthesis, identifying and incorporating aspects of the island systems that were not considered in the past. For example: i Within oceanic island biogeography, efforts have recently been made to adjust the MacArthur–Wilson (1967) model to accommodate the dramatic changes in the carrying capacity and environmental characteristics of islands that occur through the life history (ontogeny) of an oceanic island itself (see e.g. Whittaker et al., 2008, 2010). ii Application of genetic analyses are producing a more nuanced grasp of species and gene flow between insular and mainland habitats. iii Scale-dependency of isolation and fragmentation effects are beginning to be quantified. iv Efforts have been made to incorporate matrix effects and to consider the implications of longer term changes within habitat islands post-isolation. v Assumptions of initial equilibrium in prefragmentation landscapes have been challenged (for discussion and exemplification of the foregoing, see Whittaker & Fernández-Palacios, 2007). These considerations suggest that the dynamic process at the heart of the island equilibrium theory needs to be embedded in a much more dynamic model of the physical environment (a point argued more generally in Chapter 7). We have selected four key areas that we consider important for a more successful application of island theory to conservation biogeography. These include examination of (1) the life history of habitat islands,

(2) threshold effects, (3) assembly rules, and (4) the role of the matrix for conservation in habitat islands. 1 Life history of habitat islands: As Whittaker et al. (2005) comment: ‘It is disappointing that we still know so little about the power and timescale of “species relaxation”.’ Here we suggest that the consideration of the life history (ontogeny) of habitat islands could be particularly insightful in revealing the patterns and processes shaping species richness, species assembly and disassembly. Although a number of theoretical frameworks have been put forward to describe the sequential process of species relaxation after habitat loss and fragmentation (Section 8.2.2), the temporal scale of habitat loss and fragmentation has received the least attention. This has restricted our knowledge on how, for example, the abiotic characteristics of a fragment (e.g. net primary productivity) and rates of nutrient cycling change through time after its isolation, and how this affects the fragment’s capacity in maintaining biodiversity. Habitat conversion is almost always a non-random process (e.g. Raheem et al., 2009). In forest landscapes, for example, the most accessible and productive areas tend to be deforested first. Thus, the remaining fragments show a non-random spatial distribution with respect to age, because the geographical distribution of older fragments (i.e. isolated earlier) is different from that of those isolated later. Moreover, other environmental factors, ranging from anthropogenic disturbance (e.g. hunting) to physical gradients (e.g. topography and climate) may be correlated with fragmentation and forest loss (Laurance et al., 2002). We believe that the integration of research on the ontogeny of habitat islands will help us towards estimating more accurately the rates at which species extinctions are likely to occur. The time-lags and ‘extinction debt’ involved in such extinction processes are still poorly explored and in need of much attention (Box 8.2; Tilman et al., 1994). By focusing further work on the above questions, we will be able to approach more analytically questions related to the time-lag for relaxation and extinction debt. 2 Thresholds: Taxonand system-dependent thresholds, beyond which species losses accelerate (see Ewers & Didham 2006; Whittaker & FernándezPalacios, 2007; Suding & Hobbs, 2009) have received very limited attention. Analyses of critical value ranges, where even small changes in environmental variable(s) will lead to large changes in the system, will help us towards understanding relaxation as a result

Conservation planning in a changing world of habitat loss and fragmentation (Simberloff & Martin, 1991; Laurance 2002). A highly relevant island phenomenon is the socalled ‘small island effect’ (see Lomolino & Weiser, 2001; Triantis et al., 2006). The main feature of the phenomenon is the absence of the commonly found relationship of island area and species richness below a certain island size (dashed line in Figure 8.11). The particular threshold of this effect appears to vary depending on the taxon and archipelago selected, but it generally appears to occur only with islands of a very small size and diversity. In practice, within the limits of the small island effect, species richness is independent from the direct effects of area and is mainly driven by the effects of habitat diversity. Hence, it would be interesting to assess the existence of such thresholds in habitat island data sets for which the usual explanatory variables – such as area and isolation – are not important (see Prugh et al., 2008) and other variables – such as island age, productivity, energy and environmental heterogeneity – are important. The consideration of such variables, although challenging, is necessary if we are to build up a more predictive science of species richness variation across true and habitat island systems. In a fragmented landscape, species can either become extinct or go through changes in life history traits that will adapt them to the changed living conditions. Another issue related to spatial thresholds of fragmented landscapes that has received limited attention is how the evolutionary dynamics of species change in response to landscape transformation. Adaptation in a fragmented landscape may influence measurable features of the phenotype of a species, e.g. body size. In island studies, it is well established that islands favour the change of species body size, compared to their mainland counterparts; usually small species become larger (gigantism) and large species smaller (nanism) – a phenomenon termed the ‘island rule’ (Lomolino, 1985). These changes lead to a more effective exploitation of the available resources in the context of the limited available space on islands. The absence of the full collection of competitors and predators found on the mainland contributes towards these size changes (see Lomolino, 2005; Lomolino et al., 2006; but see Meiri et al., 2006). In illustration of these effects within a habitat island context, Schmidt & Jensen (2003) studied the body size changes within the entire Danish mammalian community during the last 175 years. They found that the

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rate of body length change was increased for both smaller and larger mammals, while it was lower for the medium-sized species. Following the general trend of the island rule, small mammals have generally increased, whereas large mammals have decreased in length. Schmidt & Jensen suggested that the major, but not the only, driver of these changes was habitat fragmentation. Based on island studies, Losos & Schluter (2000) have identified that for Anolis lizards in the Great Antilles, below a certain island size threshold there is little or no cladogenesis. The identification of such size thresholds not only in the short term, but over evolutionary timescales, could be quite insightful for conservation biogeography (e.g. Triantis et al., 2008). This corresponds to the plea of Gunderson & Folke (2003), who called upon conservation biologists to work towards the ‘science of the long view’ and to integrate insights from other disciplines in the search for new predictive and transcalar models in time and space (see also Lomolino, 2006). 3 Assembly rules (phylogeny): Island biotas are not simply random draws from regional species pools. Instead, they typically exhibit compositional structure: some species, species combinations, or species types, are found more frequently, and some less frequently, than might be expected by chance. This idea was presented in Jared Diamond’s island assembly theory (Diamond, 1975b; reviewed in Whittaker & FernándezPalacios, 2007). Related to island assembly theory is an increasing number of studies appearing to show deterministic patterns of evolution on islands, i.e. independent evolutionary diversification events, producing on different islands the same set of habitat specialists adapted to use different parts of the environment (see Losos et al., 1998; Chiba, 2004; Gillespie, 2004; Losos & Ricklefs, 2010). The incorporation of phylogenetics into community ecology will offer key insights into the assembly and structure of communities (see Webb et al., 2002; Emerson & Gillespie, 2008), with insular systems having a pivotal contribution to make within this research programme. Extinction and extinction risk are often phylogenetically non-random (Purvis, 2008). Nonrandomness when species are faced with a similar threat intensity indicates that some species are more extinction resistant than others (e.g. Purvis et al., 2000). Hence the use of phylogenies for identification of those traits that are associated with a high extinction risk in declining species, e.g. high trophic level,

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low population density, slow life history, small geographical range, ‘ecological naivety’, is currently one of the great challenges of conservation biology. Studies on islands, nature’s test tubes and the location of a high proportion of globally threatened species, will certainly offer significant insights in this research program. 4 Matrix: The number of species held in a reserve (or reserve system) is actually less important than the conservation of those species which cannot survive outside the remnants (e.g. Newmark, 1991). Some recent efforts have been made to move beyond an exclusive focus on (forest) fragments and towards understanding the role of such habitat islands within mixed-use landscapes. This switch in emphasis comes under varying headers. For example, Watson et al. (2005) show that the incidence functions of woodland bird species in three different landscapes in the Canberra area of Australia differ significantly, seemingly as a function of differences in properties of the landscape matrix within which the woodlands are embedded. Hence, Watson et al. (2005) join others (e.g. Ewers & Didham, 2006) in calling for greater attention to ‘matrix effects’. J.B. Hughes et al. (2002) adopted a slightly different approach within their study in southern Costa Rica, focusing on the extent to which native forest species make use of the surrounding countryside. They found that some 46 per cent of bird species foraged often kilometres away from extensive areas of native forest. Although they stress that not all species can be so readily accommodated outside large tracts of native forests, their work supports the importance of developing ‘countryside’ landscapes that are biodiversityfriendly and penetrable by native fauna (as Harris, 1984). Daily and colleagues (e.g. Daily et al., 2001, 2003) coin the term ‘countryside biogeography’ for this switch in attention from remnants per se to the way in which remnants function within whole landscapes. This switch in emphasis is similar to that promoted by Rosenzweig (2003) under the heading ‘reconciliation ecology’. But whether we label it ‘matrix effects’, ‘countryside biogeography’ or ‘reconciliation ecology’, the common element is a realization that effective conservation must include consideration of what happens outside reserves. The way we shape the countryside, whether we farm intensively or extensively, whether we retain hedgerows and trees within mixed landscapes, can all have profound implications for regional diver-

sity and for abundances of wildlife (e.g. see Gascon et al., 1999; Gates & Donald, 2000). Conservation requires pragmatic decision-making. As we continue to fragment landscapes, island effects may inform such decision-making, but should not be oversimplified. There is no single message, and no single island effect; indeed, insularity may sometimes bring positive as well as negative effects (Lockwood & Moulton, 1994). Island effects may be weak or strong. The implications of insularity vary, depending on such factors as the type(s) of organism involved, the type(s) of landscapes involved, the nature of the environmental dynamics, the biogeographical setting and the nature of human use and involvement in the system being fragmented. In closing this chapter, we return to the basic question posed in the introduction: is it realistic to expect habitat islands to behave according to the same principles as real islands? Our answer is yes, but caution is needed in the island theories and models we are using. Island systems of generally restricted spatial extent and most importantly similar age and intrinsic rates of change in time to habitat islands (e.g. Terborgh et al., 2001, 2006; Cody, 2006) will probably continue to offer more relevant principles for the understanding of processes such as relaxation in habitat islands and their more effective preservation. As island biogeography moves towards new syntheses and theories, we anticipate that this body of work will become increasingly helpful for understanding and conserving our natural world.

FOR DI S CU S S I ON 1 In what circumstances are scattered protected areas of modest size better than a few large ones? 2 What is the relationship between the SLOSS debate and nestedness? 3 How important is it to take account of underlying biogeographical structure within a region when applying island models to projecting species extinctions? 4 Is there any optimal slope (z) value for models projecting species losses based on species–area relationships, and what is the relevance of such models if no account is taken of efforts made to mitigate these losses? 5 How important is connectivity between patches for maintaining species diversity in a landscape?

Conservation planning in a changing world 6 In what ways can the landscape outside protected areas be managed to support biological diversity? 7 How far do you agree that the problem for oceanic islands is the loss of their isolation, while within continents the reverse is the case? S U G G ES T E D R E AD I NG Bhagwat, S.A., Willis, K.J., Birks, H.J.B. & Whittaker, R.J. (2008) Agroforestry: a refuge for tropical biodiversity? Trends in Ecology & Evolution, 23, 261–267. Brook, B.W., Sodhi, N.S. & Ng, P.K.L. (2003) Catastrophic extinctions follow deforestation in Singapore. Nature, 424, 420–423. Brook, B.W., Traill, L.W. & Bradshaw, C.J.A. (2006) Minimum viable population sizes and global extinction risk are unrelated. Ecology Letters, 9, 375–382. Emerson, B.C. & Gillespie, R.G. (2008) Phylogenetic analysis of community assembly and structure over space and time. Trends in Ecology & Evolution, 23, 619–630.

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Ewers, R.M. & Didham, R.K. (2006) Confounding factors in the detection of species responses to habitat fragmentation. Biological Reviews, 81, 117–142. Ladle, R.J. (2009) Forecasting extinctions: uncertainties and limitations. Diversity, 1, 133–150. Laurance, W.F. (2008) Theory meets reality: how habitat fragmentation research has transcended island biogeographic theory. Biological Conservation, 141, 1731–1744. Prugh, L.R., Hodges, K.E., Sinclair, A.R.E. & Brashares, J.S. (2008) Effect of habitat area and isolation on fragmented animal populations. Proceedings of the National Academy of Sciences USA, 105, 20770–20775. Rosenzweig, M.L. (2003) Win-win ecology: how the earth’s species can survive in the midst of human enterprise. Oxford University Press, New York. Williams, M.R., Lamont, B.B. & Henstridge, J.D. (2009) Species–area functions revisited. Journal of Biogeography, 36, 1994–2004. Whittaker, R.J. & Fernández-Palacios, J.M. (2007) Island biogeography: ecology, evolution, and conservation, 2nd edn. Oxford University Press, Oxford.

CHAPTER 9 Biological Invasions and the Homogenization of Faunas and Floras Julian D. Olden1, Julie L. Lockwood2, and Catherine L. Parr3 1

School of Aquatic and Fishery Sciences, University of Washington, Seattle, USA Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ, USA 3 Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK 2

9 . 1 T HE B I OGE OGR AP HY OF S PECI E S I NV AS I ONS In considering the distribution of organic beings over the face of the globe, the first great fact which strikes us is, that neither the similarity nor the dissimilarity of the inhabitants of various regions can be accounted for by their climatal and other physical conditions … A second great fact which strikes us in our general review is, that barriers of any kind, or obstacles to free migration, are related in a close and important manner to differences between the productions of various regions. (Charles Darwin, 1859, pp. 395–396)

9.1.1 The invasion process One of the fundamental elements of life on Earth is change. Species appear through time via evolution and disappear by the natural actions of environmental change (e.g. volcanic eruptions, changing sea levels, glaciation). Species have also regularly shifted their geographical ranges in response to biological and physical forces, sometimes becoming less common and other times becoming more widespread. In general,

however, the large majority of species are not distributed broadly, because individuals of most species have limited dispersal capabilities. These limitations on dispersal ability have produced the interesting phenomenon that many, perhaps even most, species do not occupy all of the areas of the world in which they could quite happily thrive. Instead, they are restricted to certain regions, where they are able to interact with only those species with which they cooccur. The limited geography of species is responsible, in part, for the fantastic array of diversity that presently carpets the Earth, as it provides opportunity for convergent evolution in disparate unconnected regions. With the range expansion of modern humans, initially out of Africa, then across the globe, came the possibility of human-mediated dispersal of a large variety of other species. By this, we mean that humans provided the conduit for individuals of some species to disperse much farther abroad than they could naturally. Species were moved within, or on, humans as parasites or disease organisms, in their household goods as hitchhikers, as their livestock or working animals, as their crop plants, as their pets, and as commodities themselves. There is written evidence that intentional movements of species by humans traces back to ancient

Conservation Biogeography Edited by Richard J. Ladle and Robert J. Whittaker © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33503-3

Conservation planning in a changing world times, such as the introduction of the tamarind tree (Tamarindus indica) into China by way of commerce along the Shu-Yan trade route that linked China to India 8,000 years ago (Yan et al., 2001). Some species apparently have nearly circumglobal distributions because of ancient trade activities, with many of these examples only recently coming to light thanks to the power of molecular analyses to locate the evolutionary origins of now very widespread species (e.g. Wares et al., 2002). There is ample historical evidence that the number of species that were moved out of their native ranges and introduced to somewhere novel via human actions increased as the world began to become ever more interconnected (Elton, 1958). As this number grew, the need to understand how this process occurs, and to differentiate natural species’ range expansions from those mediated by humans, became critical. Without making this distinction, it becomes difficult to untangle the mechanisms that are driving historical biodiversity changes, to understand the role of new arrivals in driving evolutionary dynamics and, more practically, to stem the flow of species that cause ecological or economic harm (see below). Before continuing, however, it is very important to recognize that a multitude of names have been given to species that are introduced to a novel location via human actions – such as ‘exotic’, ‘invasive’ or ‘alien’ species (Lockwood et al., 2007). We use the term ‘invasion’ to refer to the process whereby species expand their geographical distribution outside of their natural dispersal range via the actions of humans, while we refer to populations that have become otherwise established outside the bounds of their native ranges as ‘non-native’. A more lucid understanding of the invasion process may be achieved if it is considered as a stepwise progression of events, whereby individuals of some species are moved out of their native ranges, released into a novel location, establish self-sustaining populations there and then spread to new locations (Figure 9.1; Sakai et al., 2001). Fundamental to this process is that not all individuals successfully pass through all these stages. The tens rule of Williamson (1996) states that only ≈10 per cent of transported individuals are released into a foreign location, ≈10 per cent of these introduced species will go on to survive and successfully breed (i.e. establish a new population) and ≈10 per cent of these established species will expand their geographical

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Figure 9.1 Generalized stages common to all species invasions. A species must successfully transition through each sequential stage, and the proportion of species that proceed from one stage to the next is less than the previous one (depicted by arrow width).

ranges and become pests. These estimates were based, in large part, on non-native animals and plants of Britain. More recently, Jeschke and Strayer (2005) investigated all freshwater fish, mammal and bird species native to Europe or North America that have been introduced outside their native range. They found that the frequencies of transitions across all three of the above stages averaged 6.1 per cent, 56.0 per cent and 59.7 per cent, respectively. Regardless of the specific percentages for each stage, it is apparent that only a fraction of the species that are moved by people, either on purpose or by accident, will complete all stages of the invasion process. A considerable amount of research within invasion biology has

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therefore focused on attempts to understand which factors differentiate between those species that successfully progress through all invasion stages and those that do not (Lockwood et al., 2007).

9.1.2 Human-assisted versus prehistoric invasions A valid and persistent question is the extent to which modern trends in species invasions differ from those that occur naturally. This question is especially relevant to students of biogeography because range expansions are a very clear component of palaeoecological and historical biodiversity patterns (Vermeij, 2005). Do modern invasions warrant the attention currently given to them by scientists? How different are the mechanisms, spatial patterns and rates of modern versus prehistoric invasions? Can we use prehistoric trends to help predict the consequences of modern biological invasions? Human-assisted dispersal of non-native species differs from natural dispersal events in several important aspects (J.R.U. Wilson et al., 2009). Ricciardi (2007) detailed the differences between prehistoric and human-assisted invasions, which we summarize below and in Table 9.1. The most obvious differences are in the number and frequency of ‘dispersal’ events. Natural dispersal events are characteristically rare, both in the number of species being transported and in the temporal frequency with which species disperse. By contrast, modern human-assisted dispersal events happen constantly and involve a wide variety of species, which

show a much wider array of biological traits than those species that are likely to experience natural longdistance dispersal. The rate at which non-native populations are establishing around the world is consistently several orders of magnitude larger than fossil-derived estimates for natural dispersal events at the same locations. For example, the invasion rate of terrestrial species for the Hawaiian Islands was approximately 30 species per million years (0.00003 per year) prior to human settlement, but increased to 20,000 species per million years (0.02 per year) after the arrival of the Polynesians and to approximately 20 per year during the past two centuries (Ricciardi, 2007). In other words, contemporary rates of biological invasions are nearly one million times higher than the prehistoric rate for Hawaii before human influence. The number of individuals of each species being transported is also vastly different between natural and human-assisted invasion events. Natural dispersal events typically involve a few individuals of a species finding their way out of the native range and attempting to establish a self-sustaining population in the novel locale. Occasionally the number of individuals in these natural events can be quite high – as for instance, during biotic interchanges involving episodic events of mass dispersal. For example, the opening of the transpolar corridor between the Pacific and Atlantic oceans and the formation of the Panamanian land bridge between North and South America during the Great American Interchange permitted a massive flux of species between formerly isolated regions (Vermeij, 2005; Lomolino et al., 2006). By contrast, humanassisted dispersal events are commonly characterized

Table 9.1 A comparison of key characteristics of prehistoric versus human-assisted invasions. Modified from Table 1 of Ricciardi (2007). Characteristics

Prehistoric invasions

Human-assisted invasions

Frequency of long-distance dispersal event Number of species transported per event Propagule size per event Number of mechanisms and routes of dispersal Temporal and spatial scales of mass transport events Degree of homogenizing effect Potential for interactions with other stressors

Very low Low* Small* Low Episodic (short-distance) Regional Low

Very high High Potentially large High Continuous (long-distance) Global Very high

* Except during biotic interchange events.

Conservation planning in a changing world by the release of hundreds to thousands of individuals of a species into one novel locale, although there is much variation around this number. Finally, human-assisted invasions serve to connect two or more locations that are geographically very distant from one another, whereas natural dispersal events tend to link sites that are comparatively close together or otherwise linked naturally. Quite simply, patterns of modern dispersal unite parts of the world solely by social and economic ties, as opposed to biophysical pathways such as prevailing wind directions, jet streams or ocean currents, as would happen for natural dispersal events (Box 9.1).

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9.1.3 Economic and ecological impacts of invasion The interest in human-assisted invasions has grown rapidly over the past two decades, which is attributable to three factors (Lockwood et al., 2007): • First, as the world economy globalizes, there are increased trade and social connections between geographical localities, and along with these connections come the introduction of non-native species (Perrings et al., 2005; Hulme, 2009). Thus, the sheer number of non-native populations establishing worldwide has increased substantially in recent times.

Box 9.1 The human imprint on modern day species dispersal patterns The Earth is now better connected via human transport than ever before. In recent decades, human activities have greatly increased the frequency and spatial extent of species introductions across the globe through both intentional and unintentional actions. These include ballast-water discharge from international shipping; bait-bucket releases associated with recreational fishing; the global pet trade; intentional translocations of wildlife for recreation purposes; biological control; and inadvertent releases from aquaculture and horticulture activities. The following two case studies illustrate how modern biotas are connected via social and economic networks and by sea and air.

Ship traffic In marine and estuarine systems, the dominant invasion pathway worldwide is the ballast water of commercial ships (Carlton & Geller, 1993; Drake & Lodge, 2004). Ocean-going vessels must achieve proper stability to minimize drag (and thus maximize speed) and to reduce the likelihood of capsizing in rough seas. To achieve this, early ships strategically filled ballast compartments within the hull with soil, rocks or scrap metal – essentially, anything with some weight that could be easily loaded into a ship at dock. Today, ships pump water into ballast tanks, and a typical commercial bulk vessel might carry over 30,000 metric tonnes of ballast water during an inter-oceanic voyage. Ballast water is usually taken from the harbour in one port and subsequently may be discharged in a recipient port through openings in the ship’s hull. The number of non-native species that are transported via ship ballast has increased with the rise in global commerce and the consequent upsurge in the number of ships travelling the world’s oceans and major waterways (Figure B9.1a). Current estimates suggest that a global fleet of approximately 35,000 commercial vessels transports an annual volume of about 3.5 × 109 metric tonnes of ballast water, containing some 7,000–10,000 species (mostly marine) at any one time (Wonham et al., 2005). Even if only a small fraction of these species establish non-native populations, it is easy to see that ballast water is a primary mechanism by which aquatic invasions are occurring. By tracking the number of ships that visit ports worldwide, Drake and Lodge (2004) were able to map ‘hotspots’ of marine invasions and, via network modelling, to determine which ports are likely to have increased rates of invasions in the coming years (Figure B9.1a). These hotspots are clearly the product of economic and social influences on global trade and are in marked contrast to what we might expect given natural dispersal patterns of marine species via oceanic currents.

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Figure B9.1a (a) The frequency of commercial shipping traffic along shipping routes around the world, ranging from low (blue) to high (red). From Halpern et al. (2008). (b) Global hotspots for biological invasion from ballast water, ranging from low (blue) to high (red). From Drake and Lodge (2004). (See Plate B9.1a for a colour version of these images.)

Airline traffic International air travel has been recently pinpointed as a significant factor in the movement of economically damaging pest species and infectious diseases (Tatem, 2009). Among others, the Mediterranean fruit fly Ceratitis capitata has been consistently imported in airline baggage (Liebhold et al., 2006), plant pathogens are often found in air cargo (McCullough et al., 2006) and diseasecarrying mosquitoes have survived long haul flights in aircraft cabins (Lounibos, 2002). Far-removed regions with similar climates have now been suddenly linked by a busy flight schedule, which has resulted in an elevated risk of foreign invasions. This risk, however, depends greatly on the time of year. Tatem and Hay (2007) identified an ‘invasion window’ across the global air network from June to August, when climatic conditions in regions linked by long-haul routes are most similar to one another and the higher number of flights increases the chances of exotic species hitching a ride to somewhere new. With expected increases in global trade and travel (Perrings et al., 2005; Hulme, 2009), opportunities for such extreme hitchhiking through the world airline transportation and shipping network look set to increase further (see trend in Figure B9.1b).

Figure B9.1b Trends in global shipping cargo volumes and air freight, 1970–2005. From Hulme (2009).

Conservation planning in a changing world • Second, as the number of non-native populations increases, scientists find it increasingly hard to ignore them. It is important to recognize that many of these species present unique opportunities to test various ecological, evolutionary and biogeographical concepts and theories. Certainly the basic insights gained from the study of modern invasion events are substantial (Sax et al., 2007). • Third, some of the non-native populations that have established have gone on to impart substantial economic and ecological cost (Simberloff et al., 2005; Pimentel et al., 2006). As detailed above and shown in Figure 9.1, not all species that are dispersed via human actions have negative impacts within their new environment. The definition of what constituents ‘impact’ is somewhat problematic for at least two reasons: 1 There are scientific and societal influences on the perception of impact (not to mention that the effects of invasive species are often subtle and difficult to observe). 2 After impact is perceived, there is a variety of ecological factors that determine the level of impact produced (Lockwood et al., 2007). Let us move past this issue by simply conceding that human perception and valuation are an integral part of the integration stage of the invasion process (Figure 9.1). It is important to recognize that the proportion of species that do cause harm as compared to those that are simply moved out of their native range is quite low. Nevertheless, these few species will eat, parasitize and compete with native species, often driving the latter extinct or into very low population numbers (Elton, 1958; Clavero & García-Berthou, 2005; Strayer et al., 2006). Some non-native populations invade natural areas such as parks or wildlife reserves and disrupt native species communities (Simberloff et al., 2005). In these instances, the value of the natural area in terms of its ability to conserve biodiversity may be reduced if the non-native is not controlled or eradicated. Many species threaten human economic interests, notable examples including the zebra mussels (Dreissena polymorpha) that clog utility companies’ water intake valves (MacIsaac, 1996); emerald ash borers (Agrilus planipennis) that devastate urban and commercial forests (Poland & McCollough, 2006); and monk parakeets (Myiopsitta monachus), whose bulky nests can cause electric power line failures (Avery et al., 2002). A substantial number of non-native species have adverse impacts on human health by transmitting diseases (Lounibos, 2002; Tatem, 2009), the most obvious

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of which is the widespread distribution of Norway rats (Rattus norvegicus). This rodent species was regularly, and inadvertently, transported with human colonists as they expanded across the globe. They serve as the reservoir and vector for a variety of particularly troublesome human diseases, the most well know being bubonic plague. In general, scientists reserve the term ‘invasive’ for these few non-native species that cause ecological or economic harm. It is an open question as to whether these few invasive species have characteristics that make them unique amongst the world’s species, but there is a clear need to be able to identify them as potentially harmful long before they have the chance to become invasive.

9. 2 B I OT I C H OMOGEN I Z AT I ON The regional connectivity of the world is stronger and more varied than ever before and, consequently, there are very few places where non-native species have not become established. Looking back over human history, it is apparent that changes in species diversity are frequently the result of the widespread invasion of ubiquitous non-native species into areas containing rare, and often unique, native species (Elton, 1958, Ricciardi, 2007). If the same non-native species are being introduced to multiple locations, then there is potential for disparate regions to become more similar in their species composition through time, a process known as biotic homogenization. There are certainly well-known invaders that can be found nearly everywhere. These days, for example, you can land at nearly any airport in the world and, while waiting for your next flight, watch house sparrows (Passer domesticus) cavorting on the tarmac. This species is native to Eurasia, but it has realized a very broad geographical distribution via human-mediated introductions. For many years, the biodiversity crisis has been focused on the loss of species through global extinction. Although this is clearly of prime importance, at sub-global scales the loss of populations through local extirpation, combined with the invasion of already common non-native species, may be the more dramatic reconfiguration of modern biodiversity. In fact, changes in diversity patterns at fine and coarse scales of analysis can be either concordant or, alternatively, can be decoupled and even conflicting.

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For example, Pautasso (2007) conducted a metaanalysis of the relationship between human population size and change in the plant and animal species richness of study areas. The study reported negative changes in richness at small spatial scales of analysis (or small extent) but positive changes at larger spatial scales. The introduction of non-native species by humans is typically integral to such changes. In essence, anthropogenic changes driving habitat loss, fragmentation, species invasions and ecosystem transformation may result in declining local richness but, across larger landscapes and regions, relatively few native species may become entirely extinct, while nonnatives boost the richness above natural baseline levels. Changes such as these, in the inventory richness of smaller areas nested within larger regions, may also be accompanied by changing patterns in differentiation diversity, i.e. in the degree of compositional turnover between localities – also known as ‘beta diversity’. A change in beta diversity can, in fact, occur either through a reduction in the total number of species in the region (regional species richness or sometimes ‘epsilon diversity’) or through a change in the species similarity between areas. Basically, if a similar suite of species is shared across the areas in a region, beta diversity will be quite low. If very different species occur in different areas, beta diversity will be high. Biotic homogenization is thus a term describing the process of reducing differentiation diversity between regions, but it may be accompanied by varying patterns of change in inventory richness at different scales of analysis. See Box 1.2 for an explanation of terminology. Put another way, biotic homogenization is described as the process by which regionally distinct native communities are gradually replaced by locally expanding, cosmopolitan, non-native communities (McKinney & Lockwood, 1999). Some have likened the process of biotic homogenization to the now global distribution of fast-food restaurants, coffee houses and big-box retailers (Olden et al., 2005). The more connected we are as a society, the more likely we are to see the trans-global distribution of both species and businesses. In circumstances where invasive species impact negatively on locally co-occurring native species, rare and endemic native species may be lost, resulting in rapid loss of differentiation diversity. However, it is also important to recognize that the reverse can also occur and that, in cases, the combined effects of invasions and extirpations can be to increase the mean differentiation

diversity across a study region, a phenomenon termed ‘biotic differentiation’ by Olden and Poff (2003).

9.2.1 The process of biotic homogenization In the simplest sense, human activities that increase rates of species invasions and extirpations are the ultimate cause of biotic homogenization. However, biotic homogenization can arise when only invasions occur without the concurrent loss of species, or conversely where only species extirpations occur. In other words, species additions or replacements need not occur for regions to become homogenized or even differentiated over time (Olden & Poff, 2003). To illustrate this point, we provide a simple graphical example showing how the number and manner in which non-native species establishment and native species extirpations occur may lead to very different levels of homogenization or differentiation (Figure 9.2). In the absence of any extirpation, the establishment of the same non-native species at two separate localities will lead to increases in the similarity of the invaded communities. Conversely, the establishment of a different non-native species at each locality will decrease community similarity. Although this example is useful to illustrate the simplest way biotic homogenization can occur, both empirical data and theoretical modelling suggests that the process is both complex and sensitive to the spatial and temporal scale of investigation (Olden, 2006).

9.2.2 Different manifestations of biotic homogenization Biotic homogenization is considered an overarching process that encompasses either the loss of taxonomic, genetic or functional distinctiveness over time (Olden et al., 2004). Taxonomic homogenization, which we used to introduce the concept of homogenization above, has been the primary focus of previous research and is commonly referred to as biotic homogenization. However, imposing a narrow definition of biotic homogenization does not truly reflect the multidimensional nature of this process. Consequently, it is useful to think of biotic homogenization as a broader ecological process by which formerly disparate biotas lose biological distinctiveness at any level of organization, including in their genetic and functional characteristics.

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Figure 9.2 Illustration of how species invasions and extinctions can cause either biotic (taxonomic) homogenization in scenario 1 or differentiation in scenario 2, depending on the identity of the species involved. A pair of communities (shaded ovals) for each scenario is illustrated, where extirpation events are represented by the disappearance of a species icon over a time step, whereas introduction events are represented by the arrow and appearance of a species icon. Importantly, both scenarios share the same species pool (6 native butterflies, 2 introduced butterflies) and species richness through time is identical for both scenarios. From Olden and Rooney (2006).

Let us spend a moment exploring these two additional ways in which biotic homogenization can be manifested. Genetic homogenization refers to a reduction in genetic variability within a species or among populations of a species. It can occur through at least three mechanisms:

• First, the intentional translocation of populations from one part of the range to another enhances the potential for intraspecific hybridization (i.e. hybridization between different sub-species within a species), with the end result being the assimilation of gene pools that were previously differentiated in space (Stockwell et al., 1996).

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• Second, introductions of species outside of their original range(s) increases the likelihood of a founder effect and reduced levels of genetic variability, as well as setting the stage for interspecific hybridization (i.e. hybridization between different species within the same genus) (Rhymer & Simberloff, 1996). • Third, if extirpations were a cause for faunal homogenization, then one consequence might be bottleneck(s) in local populations of the impacted species, along with lowered effective population size(s) (Lee, 2002). Functional homogenization refers to an increase in the functional similarity of biotas over time resulting from the replacement of ecological specialists by the same widespread generalists. It occurs primarily because patterns of species invasions and extirpations are not random, but instead are related to particular biological traits that commonly predispose native species to extirpation and non-native species to successful establishment. The end result is an increase in the functional convergence of biotas over time associated with the establishment of species with similar ‘roles’ in the ecosystem (e.g. high redundancy of functional forms or traits) and the loss of species possessing unique functional ‘roles’ (McKinney & Lockwood, 1999; Olden et al., 2004). For example, Winter et al. (2008) examined how the presence of non-native plant species in Germany affected the distribution of a genetic trait, namely ploidy level (referring to the number of homologous sets of chromosomes in a biological cell), at two spatial scales. It is commonly accepted that polyploidy species should have a greater ability to colonize or invade new habitats due to greater genetic variability. Interestingly, this study found evidence for functional differentiation at fine spatial scales (<130 km2) due to more heterogeneous ploidy levels of non-native plants compared to native plants, whereas, at a coarser spatial scale, more homogeneous ploidy levels of non-native species lead to functional homogenization.

9. 3 PAT T E R NS OF B I OT I C H O MO GE NI Z AT I ON Many scientists, including ourselves, have argued that we are entering a period characterized by widespread faunal and floral homogenization, fittingly dubbed the ‘Homogecene’, in a place appropriately called the ‘New Pangaea’ (the original Pangaea being the global supercontinent of approximately 250 million years ago).

Although the jury is still out on this, it is clear that the study of biotic homogenization represents a unique challenge because it is a multifaceted process, encompassing both species invasions and extirpations, which requires the explicit consideration of how the identities of species (not just species richness) change over both space and time. A simple perusal of the literature shows that the majority of research to date has focused on quantifying patterns of taxonomic homogenization, whereas the processes of genetic and functional homogenization have received considerably less attention. Moreover, even estimates of taxonomic homogenization are sparse and highly variable within and between taxonomic groups. Despite this trend, tremendous progress has been made in recent years to better understand and quantify patterns of taxonomic homogenization (Table 9.2). We review the taxonomic groups (fishes, birds, plants and mammals) that have received the most attention next.

9.3.1 Fishes The homogenization of freshwater fish faunas has received the greatest attention thus far. In a landmark study, Rahel (2000) compared the species similarity of US states between present-day and pre-European settlement time frames and found that pairs of states averaged 15.4 more species in common now than they did in the past. On average, fish faunas became more similar by 7.2 per cent, with the highest increases in similarity observed in western and north-eastern states (Figure 9.3a). The high degree of biotic homogenization is best illustrated by the fact that the 89 pairs of states that historically had zero similarity (no species in common) now share an average of 25.2 species, resulting in an average present-day similarity of 12.2 per cent. Patterns of fish homogenization were primarily the result of non-native species establishment associated with fish stocking for recreational purposes (e.g. brown trout (Salmo trutta), rainbow trout (Oncorhynchus mykiss) and smallmouth bass (Micropterus dolomieu) or aquaculture (e.g. common carp, Cyprinus carpio), and to a smaller degree the extirpation of endemic species (harelip sucker, Lagochila lacera). Taylor (2004) found a similar pattern among Canadian provinces and territories, where average faunal similarity increased from 27.8 per cent to 29.1 per cent – a trend driven in large part by the differential

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Table 9.2 Review of the published studies that report estimates of community similarity change between two time periods in the context of biotic (taxonomic) homogenization. Change in similarity refers to mean pair-wise difference between historical and extant community similarity across all sites, unless otherwise noted. Positive values indicate homogenization and negative values indicate differentiation. Note that this table only includes studies for which estimates of per cent change in community composition were reported. Taxonomic group Location

Change in similarity

Spatial extent

Unit

Country-wide

Basin divisions

North-eastern coastal

Coastal watersheds

−1.4%

Country-wide

Provinces/territories

1.3%

British Columbia

Aquatic ecoregions

−3.5%

Country-wide

Major basins

2.2%

Iberian Peninsula and France

Major basins

17.1%

Country-wide

States

California

Zoogeographic provinces

Reference

Freshwater fishes Australia

Canada

Europe

USA

Watersheds South Dakota

3.0%

7.2% 20.3%

Olden et al. (2008)3

Taylor (2004)1

Leprieur et al. (2008)1 Clavero & García-Berthou (2006)1 Rahel (2000)1 Marchetti et al. (2001)1

−10.7% Hoagstrom et al. (2007)1

Geomorphic provinces

8.0%

Watersheds

2.4%

Minnesota

Lakes

9.0%

Kansas

Streams

−0.2%

Eberle & Channell (2006)1

Florida

Select counties

−0.8%

Smith (2006)1

Canada & USA

Country-wide

States and provinces

1.2%

Canada & USA

Select regions

States and provinces

−0.6%

Chile

Country-wide

Administrative regions

0.3%

Castro & Jaksic (2008)1,7

South-eastern Pacific

Islands

2.0%

Castro et al. (2007)1

Europe

Germany

Grid cells (130 km2)

3.9%

Kühn & Klotz (2006)5

Europe & USA

Select regions

Forest stands

3.9%

Vellend et al. (2006)6

Great Britain

Country-wide

Grid cells (1 km2)

−1.0%

Smart et al. (2006)1

United States

Select regions

Parks and local areas

0.8%

McKinney (2004)1

Countries

0.5%

Schwartz et al. (2006)3

Forest stands

2.6%

Rooney et al. (2004)2

Radomski & Goeman (1995)1

Amphibians and reptiles USA Terrestrial plants

Wisconsin

Qian & Ricklefs (2006)1 Rejmánek (2000)1

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Biological invasions and the homogenization of faunas and floras

Table 9.2 Continued Taxonomic group Location

Change in similarity

Spatial extent

Unit

Reference

Canada & USA

Country-wide

Transects

Netherlands

Country-wide

Grid cells (5 km)

2.8%

Van Turnhout et al. (2007)2

Global

Atlantic Ocean

Oceanic Islands

0.9%

Cassey et al. (2007)3

Terrestrial birds −2.0%

La Sorte & Boecklen (2005)4

−0.9%

Caribbean Ocean Indian Oceans

1.8%

Pacific Ocean

−0.2%

Terrestrial mammals Global

Select countries

Country

2.1%

South Africa

Country-wide

Grid cells (0.25 degree)

Spear & Chown (2008)1

−1.3%

Grid cells (1 degree)

4.2%

Grid cells (2 degrees)

8.1%

Taxonomic similarity based on: 1 Jaccard’s Similarity Index, 2 Bray-Curtis Similarity Index, 3 Sörensen’s Similarity Index, 4 Beta-sim Index, 5 Simpson’s Index, 6 Raup and Crick Index of beta diversity, 7 Mean values based on a published range.

invasion of 48 non-native fishes over the past century (Figure 9.3a). Similar broad-scale efforts have been conducted in other parts of the world. Recent evidence points to the homogenization of Australian fish faunas in response to human-mediated species introductions (Olden et al., 2008). Fish compositional similarity among major drainages increased 3.0 per cent, from a historical similarity of 17.1 per cent to a present-day similarity of 20.1 per cent. Sometimes, the degree of faunal similarity between drainages doubled or even tripled with time. This trend was particularly obvious in the southern corners of the continent – areas which are highly populated relative to other regions of Australia (Figure 9.3b). Similar to the United States and Canada, fish faunal homogenization in Australia was the result of the widespread introduction and subsequent escape/spread of non-native fishes for recreation (rainbow trout), aquaculture (common carp) and mosquito control (western mosquito fish, Gambusia affinis), and from the ornamental/aquarium trade (goldfish, Carassius auratus; guppy, Poecilia reticulata). Recent efforts in Europe have shown that exotic and translocated native species generate distinct geographical patterns of biotic homogenization because of their

contrasting effects on the changes in community similarity (Leprieur et al., 2008). Although biological invasions have resulted in an overall increase in faunal similarity on the order of 2.2 per cent (Figure 9.4a), this research found that translocated native species (i.e. species introduced by humans into regions where they were not historically found) promoted homogenization among basins (+5.0 per cent: Figure 9.4b), whereas exotic species (i.e. species originating from outside Europe) tended to decrease their compositional similarity (−1.6 per cent: Figure 9.4c). This finding is highly consistent with patterns in floral homogenization (discussed in Section 9.3.3), suggesting that differences in the geographical distribution of exotic and translocated species may play an important role in shaping patterns of homogenization. Clavero and García-Berthou (2006) used distributional data for freshwater fish in four time periods to assess the temporal dynamics of biotic homogenization among river basins in the Iberian Peninsula. They found strong evidence for biotic homogenization, with faunal similarity among rivers basins increasing by 17.1 per cent from historical times to the present day. Changes in faunal similarity were highly dynamic in time. The establishment of non-native species in 1995

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Figure 9.3 Fish faunal homogenization of: (a) states and provinces in the United States and Canada (data from Rahel (2000) and Taylor (2004), respectively); (b) major drainage divisions of Australia (from Figure 2 of Olden et al., 2008).

resulted in slight differentiation, but by 2001 the range expansion of previously established non-native species caused biotic homogenization in some regions and the continuing addition of new non-native species led to biotic differentiation in others.

9.3.2 Birds Avifaunal homogenization has been another area of recent focus, although the number of studies are limited compared to fishes. In the Netherlands, Van Turnhout et al. (2007) evaluated changes in breeding bird composition over a 25-year period and found that

regions exhibited a 2.8 per cent increase in community similarity. Significant spatial variation in patterns of homogenization existed. Low-lying western regions exhibiting low historical species richness showed the greatest increase in resemblance by converging towards those avifaunas more characteristic of eastern regions. Based on breeding bird surveys for North America (exclusive of Mexico), La Sorte and Boecklen (2005) showed substantial change in the diversity structure of avian assemblages at the local scale in non-urban areas from 1968 to 2003. However, there was little evidence that overall similarity in species composition was increasing – in fact, the general trend was towards a two per cent level of biotic differentiation. Despite

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Biological invasions and the homogenization of faunas and floras

Figure 9.4 Fish faunal homogenization of major river drainages in Europe based on: (a) all non-native species; (b) only translocated native species; (c) only non-native species originating from outside Europe. Adapted from Figure 1 of Leprieur et al. (2008).

this, their study did find that more highly populated regions located closer to the Atlantic and the Pacific coasts of the United States experienced the strongest patterns of homogenization. At the global scale, Cassey et al. (2007) explored patterns of invasion and extirpation and their influence on the similarity of oceanic island bird assemblages from the Atlantic, the Caribbean, and the Indian and Pacific Oceans. The authors found that patterns of homogenization differed significantly between and

among archipelagos but, in general, avian assemblages tended to show increased similarity over time to other islands within their archipelago, compared with islands outside their archipelago. Islands in the Indian Ocean exhibited the greatest homogenization, whereas biotic differentiation occurred for most islands in the Atlantic Ocean. However, although avifaunal homogenization was apparently the rule rather than the exception for islands in the Indian Ocean, the authors found that the relationship of this change to initial similarity was

Conservation planning in a changing world scale-dependent. At smaller spatial scales (islands within archipelagos), the expected pattern of low initial similarity leading to greater homogenization was observed, whereas this relationship reversed at the larger spatial scale of islands between archipelagos. This study illustrates that the spatial extent of investigation and the evolutionary history of the region under consideration can influence patterns of taxonomic homogenization and differentiation within and across what appear to be equivalent spatial units (i.e. ocean basins).

9.3.3 Plants Evidence for floral homogenization comes from studies conducted in many countries at a variety of spatial scales. However, evidence to date suggests that levels of floral homogenization are considerably smaller than those observed for freshwater fishes (Table 9.2). Within the United States, McKinney (2004) found that non-native plant species contributed significantly to floral homogenization of 20 parks and local conservation areas, although the magnitude was relatively low and sometimes negative (indicating differentiation). Cosmopolitan plant species most responsible for the observed homogenization included curly dock (Rumex crispus), dandelion (Taraxacum officinale) and bluegrass (Poa annua). Similarly, Schwartz et al. (2006) found that the county floras of California, USA, have shown slight homogenization. The establishment of noxious weeds played a central role in shaping patterns of homogenization, but the authors suggest that the greatest potential for future homogenization may come from extirpations of extant native populations within counties. At a finer spatial scale, Rooney et al. (2004) re-surveyed 62 upland forest stands in northern Wisconsin, USA, to assess the degree of floral homogenization of under-storey communities between 1950 and 2000. By incorporating changes in both species occurrence and abundance, the authors found that two-thirds of the sites had become more similar in their composition as a result of declines in rare species and increases in already regionally abundant native and non-native species. Interestingly, levels of homogenization were greatest in areas without deer hunting, suggesting that selective grazing by overabundant deer populations was acting as a key driver of floral homogenization.

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Distributional patterns of native and non-native species may vary in such a way that they will have opposing effects on patterns of homogenization. For example, Qian and Ricklefs (2006) evaluated changes in differentiation diversity of vascular plants across North America (excluding Mexico) and found that nonnative species tended to homogenize floras in distant areas whose native plant species differ greatly, but differentiate neighbouring areas that exhibited more closely related native floras (Figure 9.5). Because few native species have yet been extirpated from state and provincial floras, these authors reason that the pattern of homogenization and differentiation probably reflects the haphazard introduction and establishment of nonnative species with respect to suitable habitats. At play is also the natural and human-assisted spread of nonnative species with no regard to the ecological constraints acting on native species. Smart et al. (2006) used botanical data for flowering plants in Great Britain to test the hypothesis that plant communities have become taxonomically and functionally more similar over the past 20 years in humandominated landscapes. Although little evidence was found for the taxonomic homogenization of plant communities, this study revealed that plant traits related to dispersal ability and canopy height increased in their occurrence across the communities over time. The authors suggest that environmental change has caused different plant communities to converge on a narrower range of winning trait syndromes (i.e. functional homogenization), while species’ identities remained relatively constant. Similarly, Castro and Jaksic (2008) reported that the compositional similarity of the continental flora of Chile has not shown significant modifications over time. Interestingly, this result is not shared for oceanic island floral assemblages off the coast of Chile, in which present-day islands share a greater number of species compared to the pre-European condition (Castro et al., 2007).

9.3.4 Mammals Interest in the process of biotic homogenization has thankfully expanded beyond fishes, plants and birds in recent years. In a compelling study, Spear and Chown (2008) examined the effects of ungulate introductions on biotic similarity across four spatial scales, at three spatial resolutions within South Africa and among

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Biological invasions and the homogenization of faunas and floras

Figure 9.5 Distribution of homogenization indices (H) among pairs of state- and province-level floras of the United States and Canada. The pairs of floras are grouped by degree of native plant similarity (Jnative). Native floras portrayed in the left hand side panel are more distant (Jnative = 0.00–0.20) than those in the middle (Jnative = 0.20–0.40 and 0.40–0.60) and right-hand side (Jnative > 0.60) panels. Jtotal refers to floral similarity based on native and non-native species composition. Floral similarity is based on Jaccard’s coefficient of similarity (J), which ranges from 0 (no species in common) to 1 (all species in common). From Figure 1 of Qian and Ricklefs (2006).

41 nations located worldwide. They found that between 1965 and 2005, ungulate assemblages had become two per cent more similar for countries globally and eight per cent more similar at the coarsest resolution within South Africa. Interestingly, species introduced from other continents, as opposed to those introduced from within Africa, were found to have different effects on patterns of homogenization. Homogenization was most affected by translocations of species from neighbouring localities (extra-limital species) (4.6 per cent increase in similarity), whereas introductions of ungulates from more distant areas (extra-regional species) tended to differentiate assemblages (3.8 per cent decreased in similarity). Quite simply, non-native species introduced from distant regions are more likely to establish in only a few localities, resulting in differentiation. Similar findings have also been reported for plants and freshwater fishes in the United States (McKinney, 2005; LaSorte & McKinney, 2006). Levels of homogenization were found to increase with increasing resolution (see Table 9.2) and with time. In the South African study, from 1971 to 2005, homogenization by extra-limital introductions increased rapidly after

initially having a smaller homogenizing effect than the differentiating effect of extra-regional introductions (Figure 9.6).

9. 4 EN V I R ON MEN T AL AN D H U MAN DR I V ER S OF B I OT I C H OMOGEN I Z AT I ON Environmental change ultimately promotes the geographical expansion of some species and the geographical reduction of others, leading to biotic homogenization (McKinney & Lockwood, 1999). Habitat loss, pollution, climate change or other sources of disturbance often precede, and in a sense prepare, the environment for changes in beta diversity over time. The research highlighted above, in addition to a number of other studies in the literature, has provided compelling evidence linking human-induced environmental change to biotic homogenization across taxonomic groups. Collectively, this research has shown that human activities on the landscape are often characterized by greater increases in taxonomic similarity, suggesting that

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Figure 9.6 Temporal trends in ungulate homogenization as a result of extra-regional and extra-limital introductions in South Africa, at the quarter-degree grid cell resolution, between 1971 and 2005. Redrawn from Figure 4 of Spear and Chown (2008).

humans are playing a central role in promoting the homogenization process by introducing new species and favouring the persistence of non-native species over native species. For freshwater ecosystems, Scott and Helfman (2001) reported that cosmopolitan species’ richness increased and endemic species’ richness decreased in response to increased watershed deforestation and density of buildings and roads in Tennessee, USA. At a larger spatial scale, Marchetti et al. (2001) observed that measures of human occupancy and aquatic habitat alteration, including the density of dams and aqueducts in the watershed, were associated with increased similarity of zoogeographical provinces in fish communities in California, USA. However, at a finer spatial scale, Marchetti et al. (2006) found a negative relationship between change in community similarity and the proportion of the watershed in development (including commercial, industrial, urban and suburban) – or, in other words, more developed watersheds showed greater biotic differentiation. Olden et al. (2008) found that geographical patterns of homogenization in Australia were highly concordant with levels of disturbance associated with human settlement, infrastructure and land use. These results

suggest that human settlement may directly increase the likelihood of intentional or accidental non-native species introductions, and disturbance associated with physical infrastructure and land-use change may promote the establishment of these species by disrupting environmental conditions. Wetland degradation has also led to the homogenization of aquatic and invertebrate communities in Michigan, USA (Lougheed et al., 2008). Specifically, habitat homogenization at both the local and landscape scales were found to shift community structure from a species-rich and spatially heterogeneous community dominated by floating-leaved plants in undeveloped wetlands, to nutrient-rich wetlands dominated by ubiquitous duckweed (Lemnaceae). Urban/rural gradient studies have provided important insights into associations between urbanization and bird and plant homogenization. Blair (2004) found that temporal changes in bird community composition varied in a similar fashion along an urban/rural gradient in the oak woodlands of northern California and the eastern broadleaf forests of Ohio, USA. The degree of taxonomic overlap in the bird communities increased from approximately 5 per cent in the least developed sites to approximately 20 per cent in the

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Biological invasions and the homogenization of faunas and floras

most urbanized sites – an outcome of the replacement of local endemic species (often urban-sensitive species) by ubiquitous non-native species (urban-adapted species). By contrast, Clergeau et al. (2006) found that avifaunal similarity of town centres in Europe was actually lower than in less urbanized habitats – a result that may have been connected to the larger size of towns and, thus, greater types of potential habitat in this study system. The results from this study also suggested that urbanization might cause homogenization by decreasing the abundance of ground-nesting bird species and bird species that preferred bush/shrub habitats. Schwartz et al. (2006) reported floristic homogenization of urbanized counties in southern California, whereas they found no change in more rural areas of northern California. The study of Kühn and Klotz (2006), on the other hand, found no overall relationship between patterns of homogenization and urbanization across Germany. In summary, although urbanization undoubtedly plays a role in shaping patterns of biotic homogenization, the exact nature and generality of this relationship is still unclear (McKinney, 2006). Environmentally mediated interactions between species may also be an important driver of biotic homogenization. Holway and Suarez (2006) examined native ant communities in scrub and riparian habitats of mediterranean California to test the hypothesis that the invasion of Argentine ant (Linepithema humile) has caused biotic homogenization. By comparing invaded and un-invaded sites across similar habitats, the authors showed that sites invaded by Argentine ants have lower beta diversity compared to un-invaded sites. Specifically, functional homogenization of ant communities occurred via shifting community dominance to smaller-bodied workers with lower thermal tolerance and a reduced diversity of behaviours (i.e. nesting habits, dispersal strategies and foraging behaviours). Because Argentine ant abundance in seasonally-dry mediterranean environments is positively correlated with soil moisture, the authors hypothesized that the homogenizing effects of the Argentine ant are facilitated by inputs of urban and agricultural water run-off that acts to create mesic soil conditions. This observation supports the notion that anthropogenic modifications to the environment indirectly cause biotic homogenization by creating opportunities for the invasion of the Argentine ant, as opposed to threatening the persistence of native ants directly.

9. 5 B I OT I C H OMOGEN I Z AT I ON AN D CON S ER V AT I ON Biotic homogenization is an important dimension of the modern biodiversity crisis, with significant ecological, evolutionary and social implications (McKinney & Lockwood, 1999; Olden et al., 2004; 2005). It extends beyond the narrow focus on elevated extinction rates to incorporate the other side of the equation: the establishment of non-native species. Biotic homogenization conjures the prospect of Kunstler’s (1993) The Geography of Nowhere, in which biotic distinctiveness is gradually dissolving over time. Consequently, a major challenge within conservation biogeography is to identify and understand present-day patterns of biotic homogenization to guide policy aimed at mitigating its future effects (Rooney et al., 2007). Clearly, the most effective conservation of biodiversity involves reducing and, where possible, preventing the two processes generating biotic homogenization – species invasions and extinctions. The conundrum is determining the best way to achieve this goal. Because the key factors facilitating homogenization include people and habitat transformation (through extinctions or the establishment of non-native species), a first step towards achieving biodiversity conservation goals is to focus efforts in areas subject to human activities and to reduce human-related impacts. Unfortunately, there is a strong correlation between human population density and species richness, and the areas of high biotic diversity that are under the greatest threat are often in the most populated areas (Chown et al., 2003; McDonald et al., 2008). Indeed, at a finer scale of analysis, designated conservation areas may often attract people to them through perceived benefits of employment, market access and foreign aid (Wittmeyer et al. 2008). The increased external threat from accelerated human population growth does not bode well for the native biota in these areas, which consequently face the risk of increased homogenization. In the past, purposeful homogenization was undertaken within countries such as Australia and some Pacific island territories by acclimatization societies within colonist human societies who, for a variety of reasons, wanted to surround themselves with familiar, colourful or (regarding birds) tuneful species. Even today, some conservation organizations encourage the intentional movement or translocation of species, which may also have the unintended consequence of promoting homogenization.

Conservation planning in a changing world This act is a problem when species are introduced and become established outside of their historical distribution, or where the genetic consequences (e.g. interspecific hybridization) are not considered. For example, in parks across southern Africa there has been a trend to introduce the same suite of species across nature reserves. Fuelled by tourism and the public’s desire to see large mammals (especially predators), spotted hyena (Crocuta crocuta), wild dog (Lycaon pictus) and antelope such as roan (Hippotragus equinus) have been introduced and have established within areas where they did not historically occur, or to areas that are now unsuitable due to small park sizes. In fact, Spear and Chown (2008) demonstrated that it is extra-limital introductions that are driving the homogenization of ungulate assemblages in South Africa (Figure 9.6). They warn that the potential for changes in local diversity and ecosystem functioning as a consequence of translocations should not be underestimated. These concerns contrast with other conservation actors arguing for various forms of rewilding, or for assisted migrations of species as a climate-change mitigation strategy (see, e.g. Chapter 3; Donlan, 2007). The concept of biotic homogenization and differentiation may provide a useful tool in conservation planning (Rooney et al., 2007). Much attention in conservation has focused on reserve selection and choosing the best network of reserves to maximize biodiversity coverage. Such efforts have largely focused on species number, endemism and complementarity as the metrics that should be optimized (Chapters 6 and 7; Pressey et al., 1993). Complementarity exists when an area has some biodiversity components that are unrepresented in other areas. It may thus be possible to use biotic homogenization to monitor whether complementarity goals are being met. For example, if a network of reserves becomes more similar over time due to the loss of unique species, this reduces complementarity (Rooney et al., 2007). Importantly, any assessment of complementarity related to conservation planning should be restricted to indigenous species only. The inclusion of non-native species could show increased biotic homogenization when, in reality, the full set of native species that the reserve network was designed to conserve still occur. This idea has much potential, but there are a few caveats. For example, when dealing with a minimum set complementarity (each area contains distinctive species) goal, all areas may lose the same number of

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unique species over time, and neither complementarity nor the level of biotic homogenization would change, yet the true state of biodiversity loss will not be reflected.

9. 6 N OV EL AS S EMB LAGES Novel assemblages, sometimes referred to as novel or emerging ecosystems, are communities that consist of extant species which have not occurred previously in the same combinations found today (Hobbs et al., 2006). Increased homogenization of biotas associated with the massive and accelerating movement of species within and between regions/provinces is likely to contribute substantially to the creation of novel or noanalogue assemblages. Although, technically, any area that has lost native species or gained non-native species is novel in some respect, some current assemblages have been transformed to such an extent that they are verging on becoming entirely new assemblages (Williams & Jackson, 2007). Certainly, in terms of system functioning, many ecosystems have already become ‘novel’. One of the best examples comes from the San Francisco Bay, California, which has the dubious distinction of being the most invaded aquatic region on Earth, with more than half its fish and most of its bottom-dwelling organisms representing non-native species (Cohen & Carlton, 1998). The total dominance (number of species and biomass) of non-native species has transformed the bay from a pelagic (mid-water) system to a benthic (bottom) one and productivity has declined. Invasive species such as Corbula amurensis (Asian clam), Sphaeroma quoyanum (a burrowing isopod from Australia and New Zealand) and Spartina alterniflora (smooth cordgrass) have become among the most important species in the bay in terms of both biomass and their role in controlling biological processes in the bay (Cohen & Carlton, 1998). Although the process of homogenization can create novel assemblages, global climate change is increasingly likely to magnify this effect. Thus, any prediction of where novel assemblages will form needs to take into account not only non-native species introductions, but also global climate change and the individualistic responses of species (native and non-native) to environmental change (Chapters 4, 7). Recent models suggest there will be substantial regions of the world with novel climates by 2100 (particularly in tropical and sub-tropical regions) and also that some extant

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Biological invasions and the homogenization of faunas and floras

Figure 9.7 A conceptual diagram showing how nonanalogue combinations of species arise in response to novel climates. The set of climates in existence at two periods are represented as open ellipses. Novel climates are the portions of the 21st century envelope that do not overlap 20th century climates, and disappearing climates are the portions of the 20th century envelope that do not overlap 21st century climates. Species co-occur only if their fundamental niches simultaneously intersect with each other and the current climatic space. Future climate change may cause a variety of ecological responses, including shifts in species’ distributions (species 1–3), community disaggregation (species 1 and 3), new communities forming (species 2 and 3), and extinction (species 4). From Figure 1 of Williams et al. (2007); copyright (2007) National Academy of Sciences, USA.

create novel environmental conditions or, as Saxon et al. (2005) refers to them, ‘environmental domains’. The disappearance or contraction of present environmental domains and the appearance of new domains will have profound consequences for most species and the identity of communities today. Climate change is expected to alter the effectiveness of environmental filters; to alter the likelihood of species establishing; to change pathways of species introductions; and to affect the impact of non-native species (Rahel & Olden, 2008). The combination of novel assemblages and altered biophysical conditions will result in new systems that have unknown functional characteristics, and whose processes and interactions are hard to predict (Hobbs et al., 2006). Given the dynamic nature of species’ distributions, current homogenization patterns and trends are likely to change too. It is very difficult to predict the make-up of novel assemblages, given that it is almost impossible to know which species will co-occur, whether they will interact and how altered climatic regimes will influence any interaction. Importantly, many of these communities may be more, or less, similar across locations than the native assemblages they replaced. In other words, homogenization is not the only outcome of the massive movement of species across the globe. Perhaps the only certainty is that conservation efforts will have to intensify to tackle the threat of anthropogenically-assisted novel assemblages, and society will be faced with some tough decisions as to what biodiversity it values.

FOR DI S CU S S I ON climate types will have disappeared (Williams et al., 2007). Because climate is a primary control on species’ distributions and ecosystem processes, novel 21st century climates may promote the formation of novel species associations and other ecological surprises. On the other hand, the disappearance of some extant climates increases the risk of extinction for species with narrow geographical or climatic distributions, as well as the risk of disruption of existing communities (Figure 9.7). Of greater concern, perhaps, is the combined effect of altered climate and other abiotic environmental characteristics (such as topography or soil type) which

1 How do natural patterns of species invasion differ from anthropogenically assisted species invasions, and with what consequences? 2 In the light of social demands and economic development, what are the most likely timescales and scenarios of introduction, establishment and spread of non-native species in the future? 3 What are the ecological consequences of faunal and floral homogenization? 4 What are the temporal dynamics of taxonomic and functional homogenization? 5 What are the primary environmental and biological drivers of biotic homogenization at different spatial and temporal scales?

Conservation planning in a changing world 6 How will rates and patterns of biotic homogenization respond to shifting pathways of species introductions and future environmental change? 7 What novel species assemblages are likely to emerge in response to climate change? 8 What might be the consequences of novel ecosystems for biodiversity, ecosystem functioning, and human societies?

S U G G ES T E D R E AD I NG Elton, C.S. (1958) The ecology of invasions by animals and plants. Methuen, London. Hobbs, R.J., Arico, S., Aronson, J., Baron, J.S., Bridgewater, P., Cramer, V.A., Epstein, P.R., Ewel, J.J., Klink, C.A., Lugo, A.E., Norton, D., Ojima, D., Richardson, D.M., Sanderson, E.W., Valladares, F., Vilà, M., Zamora, R., & Zobel, M. (2006) Novel ecosystems: theoretical and management aspects

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of the new ecological world order. Global Ecology and Biogeography, 15, 1–7. McKinney, M.L. & Lockwood, J.L. (1999) Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends in Ecology & Evolution, 14, 450–453. Olden, J.D. (2006) Biotic homogenization: a new research agenda for conservation biogeography. Journal of Biogeography, 33, 2027–2039. Rahel, F.J. (2002) Homogenization of freshwater faunas. Annual Review of Ecology and Systematics, 33, 291–315. Riccardi, A. (2007) Are modern biological invasions an unprecedented form of global change? Conservation Biology, 21, 329–336. Sax, D.F., Stachowicz, J.J., Brown, J.H., Bruno, J.F., Dawson, M.N., Gaines, S.D., Grosberg, R.K., Hastings, A., Holt, R.D., Mayfield, M.M., O’Connor, M.I., & Rice, W.R. (2007) Ecological and evolutionary insights from species invasions. Trends in Ecology & Evolution, 22, 465–471. Strayer, D.L., Eviner, V.T., Jeschke, J.M., & Pace, M.L. (2006) Understanding the long-term effects of species invasions. Trends in Ecology & Evolution, 21, 645–651.

PART 4 FUTURE DIRECTIONS

Conservation Biogeography Edited by Richard J. Ladle and Robert J. Whittaker © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33503-3

CHAPTER 10 Prospects and Challenges Richard J. Ladle1,2 and Robert J. Whittaker1 1

School of Geography and the Environment, University of Oxford, Oxford, UK Department of Agricultural Engineering, Federal University of Viçosa, Brazil

2

10. 1 WHY W E NE E D C ONS E R V A T I ON BI O G EO GR AP HY The death of a species is a more remarkable event than the end of an imperial dynasty. (Orton 1869, p. 540, commenting on the demise of the Great Auk.) The world is in the midst of an unparalleled period of biotic change driven by human alterations of the natural environment. Even with the considerable gaps in our basic knowledge of global biodiversity, there is still good evidence for an increase in species extinctions relative to natural background rates (Lawton & May, 1995). This human-induced crisis began towards the end of the Pleistocene as modern humans began to spread out from Africa, bringing their tool-using and ecosystem-transforming habits to other land masses (Wilson, 1992; Lomolino et al., 2010). Notwithstanding that there remains huge uncertainty surrounding the magnitude and significance of current patterns of species extinction (Ladle, 2009), much of this uncertainty relates to poor knowledge of baseline diversity levels, rather than to whether species extinction rates have been elevated by humans. For example, in 2000, at a discussion hosted by the National Academy of Sciences of the United States of America on the ‘Future of Evolution’ the expert panel unanimously agreed that current extinction rates are 50–500 times background and are still increasing (Woodruff, 2001).

Additional to the recent historical patterns of elevated rates of extinction driven by habitat conversion, hunting and biotic homogenization is the spectre of rapid anthropogenic climate change, which has the potential to cause dramatic shifts in the distributions of species and ecosystems before the end of the current century (Chapter 7). Each of these processes is difficult enough to model on its own, but there are, of course, interactions and synergies (multiplying effects) between them. For example, as habitats shift and transform (and sometimes disappear completely), exotic species will shift in their distributions alongside native species, with both new arrivals and old members of the regional biota invading new territories, forming up assemblages and communities with no modern or past analogues (Chapters 3, 7 and 9). Conservation biogeography will not be a source of cure-alls, but it can provide the tools and concepts that are needed to make scientifically informed choices about what and where to protect, the consequences of different policies, or of not acting at all (Table 10.1). This is essential, because effective biodiversity conservation requires that governments and other policy-making bodies make rational decisions about land use (or ocean use) and management that are based on the most accurate and up-to-date information. Biogeographers also have an important role to play by contributing to the debate about the ultimate goals of conservation through education and public engagement (Ladle, 2008; Devictor et al., 2010). As

Conservation Biogeography Edited by Richard J. Ladle and Robert J. Whittaker © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33503-3

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Table 10.1 Prominent areas of current research in conservation biogeography as identified by Richardson & Whittaker (2010; their Table 1). The biogeography of degradation (habitat fragmentation, homogenization, urbanization and other humaninduced impacts) Processes (colonization, climate as a fundamental determinant of distribution, dispersal, disturbance, extinction, persistence, range expansion, resilience, speciation) Inventory, mapping and data issues (atlas data, breeding bird surveys, citizen science, detectability/ discovery probabilities, herbaria and other collections, sampling intensity and biases) Species distribution modelling (bioclimatic modelling, habitat suitability analysis, model performance, niche-based models, presence-only data vs. presence–absence data, dispersal kernel analysis) Characterizing biotas (conservation status, diversity indices and patterns, ecoregions, endemism, rarity, range size, species–area relationships, threatened species, identification of alternative baselines from longterm ecological data) Conservation planning (complementarity, congruence, conservation units, ecosystem services, gap analysis, global conservation assessments, irreplaceability, reserve networks, surrogates) Methods (molecular methods, palaeoecology, remote sensing, scenario development) Related fields (global change biology, invasion ecology, bioinformatics, molecular phylogenetics, network analysis, reintroduction ecology, risk analysis, behavioural ecology, population viability analysis) Overarching themes: niche (fundamental vs. realized), novel climates/ecosystems, scale issues, uncertainty, Linnean shortfall, Wallacean shortfall)

new biogeographical knowledge enters the public domain on issues such as extinction rates, range shifts, species invasions and habitat transformation, public attitudes may shift, putting pressure on policymakers and presenting both challenges and opportunities to conservation organizations. However, it is important to remember that the relationship between knowledge, attitudes and behaviours is complex and multi-faceted, and that the act of providing new information may not, in itself, be sufficient to influence attitudes and actions. Explaining, contextualizing and framing biogeographical information for diverse constituencies is therefore arguably as important as generating the data.

1 Barriers to scientific development: • Filling biogeographical shortfalls • Improving the accuracy and specificity of forecasts 2 Barriers to application: • Turning theory into practice • Education and communication • Social values and lifestyles In the following sub-sections, we will address each of these challenges, the prospects for overcoming them and how doing so may impact on the discipline and contribute to the long-term conservation of biodiversity.

10. 2 T HE C HAL L E NGE S

10.2.1 Filling the Wallacean and Linnean shortfalls

Conservation biogeographers face a range of different challenges over the next few decades if they are to make a significant and lasting impact on the conservation of global diversity (Richardson & Whittaker, 2010). The five challenges identified below reflect two different categories of barriers to the development of the discipline:

As we have repeatedly stressed within this book, data on species identities and distributions are central to research and practice in conservation biogeography. It is therefore essential that these data are of appropriate quality, i.e. that they are fit for purpose. For example, global conservation prioritization frameworks (Chapter 5) such as Conservation

Future directions International’s hotspots scheme require data on both richness and geographical distribution (to assess endemism) but, being a coarse global analysis, the data required do not need to be of the same resolution as needed for within-country protected area planning frameworks. Even so, the CI hotspots analysis is premised on the assumptions that the plant species richness of large geographical areas can be deemed reasonably complete and that the distributions of species is generally known well enough to judge the endemism of each area. In addition, the scheme requires baseline assessments of ‘original’ and present natural vegetation cover – data that are typically crudely specified at regional scales, and which often become increasingly poorly specified at finer scales of analysis (Chapter 3). These problems are general to strategic conservation planning; the application of selection algorithms and other methods for the optimal design of representative protected area networks also require detailed information on species numbers, identities and geographical distributions (Chapters 4 to 6). Moreover, the accuracy of forecasts about the future distributions (and possible extinction) of species under climate change or any other sort of environmental change are also critically constrained by the quality of their input data (Whittaker et al., 2005; Ladle, 2009). Given the central role of robust biogeographical data in the development of conservation biogeography, efforts to address the Wallacean and Linnean shortfalls are likely to be of continuing importance for a long time to come, although there are several important initiatives in play that hold the promise for rapid advances in the coming years and decades (Chapter 4). One such area is the production of a definitive global species list that can be used to resolve problems such as synonomy. The ‘Catalogue of Life’ (CoL: www. catalogueoflife.org), which aims to become a comprehensive catalogue of all known species of organisms on Earth, now has 1.1 million species on its annual checklist (Thomas, 2009). Species occurrence records are also rapidly accumulating, most notably through the Global Biodiversity Information Facility (GBIF: www. gbif.org), which provides access to 189 million species occurrence records to date. More ambitious and data rich bioinformatics projects are also under way, such as the much vaunted ‘Encyclopedia of Life’ (www. eol.org) project (initiated in 2007) detailed in Chapter 4. Most recently, some conservationists have suggested that the IUCN Red List system needs to be expanded into a project dubbed the ‘Barometer of Life’, which

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focuses on the conservation threat status of each species (Stuart et al., 2010). These global initiatives will, at a conservative estimate, take at least a decade to complete (Thomas, 2009), although this may be either overly pessimistic or optimistic, depending on technological advances and socio-economic trends. The hopeful view is that advances in automated species identification and remote sensing hardware will rapidly accelerate the current rate of data acquisition. A less rose-tinted assessment is that limited funding and the shifting geopolitical climate may critically impede the progress of many initiatives. Although global bioinformatics initiatives such as the GBIF provide important data for research and practice, in general they lack specific tools and applications to assist real-world decisions about the conservation, management and the sustainable use of biodiversity (see also Section 10.2.3 below). Biodiversity information is also increasingly being seen as important for decision-makers in other sectors such as agriculture, fisheries, and tourism. Several countries, realizing the importance of providing high quality biodiversity data to their decisionmakers in a form that is accessible and useful, have responded by creating more spatially focused regional or national biodiversity information management initiatives. These typically tend to concentrate more upon providing the information and tools required for policy, governance and management across different sectors. For example, the recently implemented InterAmerican Biodiversity Information Network (IABIN: www.iabin.net) project aims to: 1 develop an internet-based decentralized network to provide access to scientifically credible biodiversity information that currently exists in individual institutions and agencies in the Americas; 2 provide the tools necessary to draw knowledge from that wealth of resources, which in turn will support sound decision-making concerning the conservation and sustainable use of biodiversity. The eventual impact of these various initiatives on the sum total of global biogeographical knowledge should be immense. By processing and collating the data that already exist in scattered and largely inaccessible form, they pave the way to better forecasts and more informed environmental decision-making about conservation, natural resource management, agriculture, sustainable development, etc. There may also be

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enormous benefits for our conceptual understanding of biogeographical processes and relationships. However, in the excitement generated by the online availability of data sets that, until recently, would take years of study to assemble, it is important not to lose sight of the need for continued efforts to determine taxonomies and phylogenies, to document community ecology and auto-ecology, and to engage in continued field sampling to generate the rigorous, systematic and unbiased data sets required for pure and applied biogeographical analysis. Indeed, one problem inherent in the increasing availability of digital data sets is that it becomes increasingly easy for scientists to undertake sophisticated but poorly founded analyses of inadequately sampled systems with which they are insufficiently familiar, as they have not personally taken years of painstaking efforts to assemble the data. To guard against this prospect, it is important that those building such databases make particular efforts to record meta-data describing the properties of the data sets, and that biogeographers undertake what we may broadly term sensitivity analyses involving the use of null or simulation models, analyses of the spatial structure of their data, application of data quality filtering, etc., to tease out the real structure in their data from the bias and noise that frequently exist (e.g. see Hopkins 2007; Hortal et al., 2007; Feeley & Silman, 2010). Moreover, high-quality biodiversity information systems and biogeographical analyses are not, in themselves, the solution to global conservation problems. The hard decisions about where to invest resources and what aspects of biodiversity and ecosystem function should be prioritized will still be dependent upon a mixture of historical legacy, political necessity and societal consent (see Chapters 2 and 3).

10.2.2 Improving models, simulations and forecasts We previously highlighted four generic themes which, we argued, required concerted attention within conservation biogeography, these were: 1 scale dependency; 2 inadequacies in taxonomic and distributional data; 3 sensitivity analyses to develop improved understanding of the effects of model structure and parameterization (e.g. in relation to predicting future species extinction rates); and

4 development of more refined theoretical models, e.g. of species–area effects (Whittaker et al., 2005; Richardson & Whittaker, 2010). As discussed in these pages, much recent progress has been made in these areas, but they are likely to remain important research foci. We will briefly discuss each in turn. First, scale dependency is of central importance in diverse aspects of conservation biogeography. For instance, our assessments of conservation baselines are strongly influenced by the temporal frame of reference (Chapter 3), yet there remain many areas of the world where we lack clearly resolved long-term perspectives on ecosystem dynamics (e.g. Birks, 2005; Willis et al., 2007). Similarly, assessment of the spatial patterns of diversity, the location of hotspots and the outcomes of strategic conservation planning exercises have also been shown to exhibit scale-dependency (e.g. Lennon et al., 2001; Araújo et al., 2005a). The effects of anthropogenic influences generally, changes arising from the introduction of non-native species (e.g. Olden, 2006; Foxcroft et al., 2009) and the criteria applied to assess the extinction risk assigned to plant or animal species also are sensitive to scale parameters of the system (e.g. Martín, 2009). Second (as highlighted in Section 10.2.1), as more and more genetic, taxonomic and distributional data are becoming available for analysis, we need to develop increasingly sophisticated means of determining which components of diversity variation are artefacts of collecting intensity or of analytical failings, as opposed to the ‘real’ underlying biogeographical pattern. In addition, we are in the midst of a phylogenetic revolution, fuelled by fast, cheap molecular DNAsequencing technologies, which promises not only to continue to refine our knowledge of species identities but also to continue to provide exciting advances in our understanding of evolutionary relationships across even large clades. This in turn opens up new possibilities and challenges in the use of evolutionary distinctiveness indices in conservation planning analyses (e.g. Forest et al., 2007; Cadotte & Davies, 2010). Third, and a key repeated theme, is the need to deploy sensitivity analyses to a wide range of issues in modelling future processes and patterns of diversity change – for example, in respect of the spread of alien species (Gallien et al., 2010; Smolik et al., 2010); the role of spatial autocorrelation in species’ distribution models (Veloz, 2009); and the forecasting of species’

Future directions responses to future climate and land-use change (Araújo et al., 2008; Diniz-Filho et al., 2009). Fourth, there is a tendency for many of us to seek to draw recommendations from particular case studies as we publish them but, as in many areas of human endeavour, a single case study may make for poor guidance. Therefore we highlight a continuing need for efforts to develop and synthesize emerging findings into improved theoretical frameworks and to update conservation biogeography theory for the purpose of revising guidelines to practitioners.

10.2.3 Turning theory into practice Biogeographical science has had a foundational role within conservation biology, as demonstrated by the early attention paid to deriving guidelines for protected area design from island biogeography (Chapter 8). Currently, biogeography is in an exciting phase in which there is greatly enhanced potential to apply the subject to problems in the conservation of biodiversity (Whittaker et al., 2005; Richardson & Whittaker, 2010). An important step in this process is to encourage the teaching of conservation biogeography at university level – a key motivation in the writing of this book. However, even if there is a rapid increase in biogeographical understanding, translating this knowledge into guidelines, protocols, tools and applications that are useful at every level of conservation decisionmaking presents significant challenges. Indeed, many would argue that generating the scientific information is the easy bit, and the difficulties really start in communicating geographically precise information about the current and future status of biodiversity in a form that is accessible and able to influence policy (Kalliola et al., 2008). If societies are to realize the full benefits of conceptual advances and an increase in knowledge, it is therefore vital that new understandings are quickly converted into practical tools. This process can be facilitated in a number of ways: • First, conservation biogeography, like many other ecologically-related disciplines, will progress faster and have more impact if scientists and practitioners make their data freely available and accessible. This will be a challenge. There remains a culture of ‘data hoarding’ in ecological and environmental science

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(Parr & Cummings, 2005; Thomas, 2009). This is understandable from the perspective of those who wish to make further use (i.e. produce more publications) of hard-won, painstakingly compiled data sets prior to releasing them for others to plunder. Costello (2009) has recently argued that many of the perceived barriers will be removed if the idea of ‘data sharing’ is replaced by that of ‘data publication’. However, to make this a reality, online data publication systems and, of more relevance to conservation biogeography, biodiversity information systems (see Section 10.2.1 above) will need to develop mechanisms and protocols for data curation, supporting documentation, citation and data access. • Second, a greater investment needs to be made in producing freely available and user-friendly tools and applications to analyse and visualize biogeographical data. An excellent example of this is the SAM (Spatial Analysis in Macroecology) software that allows biogeographers to correct statistically for the influence of spatial clustering of data points (Rangel et al., 2006). Interestingly, the often powerful and pervasive influence of this sort of clustering (known technically as spatial auto-correlation) on standard statistical tests has only recently been generally recognized (Legendre, 1993), and many studies still ignore it in their analyses. It is therefore particularly encouraging that Rangel and his colleagues have developed and made this powerful statistical package freely available (download from: www.ecoevol.ufg.br/sam). Other similar initiatives are clearly to be welcomed. • Third, greater efforts need to be made to mainstream biodiversity into other sectors of environmental governance. In this context, ‘mainstreaming’ can be defined as the integration of conservation goals and sustainable use of biodiversity into sectors that impact biodiversity (mainly outside of protected areas). Successful mainstreaming of biodiversity requires that high-quality biodiversity data are made available to key decision-makers in forms that they can use. Conservation biogeography can play a vital role in this by providing improved frameworks and concepts that allow biodiversity information to be meaningfully organized and structured. Moreover, conservation biogeographers have the knowledge and skills to produce models and visualization tools that can transform raw data on occurrences and distributions into products that are useful and of genuine practical importance.

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Advances are already being made in this direction; for example, Richardson et al. (2009) recently developed a multidimensional decision-making framework for managed relocation of species as a response to anthropogenic climate change – one of the most radical and potentially socially divisive conservation strategies currently being considered. Their heuristic tool incorporates both ecological and social criteria and, critically, produces outputs that can be depicted in graphical 2-D space, making it easier for diverse stakeholders to interpret and use. In the broader context, effective mainstreaming requires that environmental stakeholders and wider society are made aware of the importance of biodiversity and its current status. This can be achieved through better education and communication.

10.2.4 Education, communication and public engagement In a recent review of the representation of ‘biogeography’ in UK newspapers, websites, and blogs, Ladle (2008, p. 390) concluded that, ‘[the term biogeography has] very little presence in the public sphere, and that this probably reflects a lack of understanding of the subject area and its relevance in contemporary environmental debates’. A lack of popular representation and understanding can often be traced back to a lack of exposure during formal education. It is perhaps revealing that a search for ‘biogeography’ teaching resources in the UK government’s schools National Curriculum website carried out in August 2008 (while the site was still publically available) returned no results, whereas searching for ‘biodiversity’ resources produced 219 separate documents. This suggests that in the UK, at least, biogeography is hardly seen as a discipline at all in schools, let alone one that has a contemporary importance. It should be noted, however, that while the term biogeography is poorly represented, biogeographical subjects are richly covered across the electronic and popular media, especially those related to conservation and the future of the natural world. Unfortunately, the translation of such information into the public domain via these media often misinterprets or sensationalizes findings. An example of this was the coverage of a large study by Thomas et al. (2004), collating bioclimatic envelope modelling efforts for 1,103 species from several

different taxa and from several different regions of the world. The simulations indicated that a potentially substantial proportion (15–37 per cent being the favoured range, but perhaps as low as 5.6 per cent or above 50 per cent) of these species would be ‘committed to extinction’, based on loss of habitat driven by climate change scenarios for 2050 (see Box 7.3 for details). Despite the authors clearly describing these percentages as being ‘an estimate of proportions of species committed to future extinctions as a consequence of climate change over the next 50 years’, and ‘not the number of species that will become extinct during this period’, the global news media almost universally misreported the story. In the UK, 26 out of 29 newspaper reports were factually incorrect (Table 10.2), with the most frequent misrepresentation being that one million species would go extinct by the year 2050 (Ladle et al., 2004). The source of the remarkable claim of one million species being threatened with extinction was the press release issued by the lead author, and it may be derived by picking a value within the wide range of possible values reported in the paper, say 25 per cent, then assuming this proportionate loss can be extrapolated to all species on the planet and then making the further assumption that there may be around 4 million species of land plants and animals on Earth (Ladle et al., 2004). If Thomas had chosen a perfectly reasonable value for global species diversity of, for example 10 million then, by this reasoning, 2.5 million species would have been considered ‘committed to extinction’. Unsurprisingly, the figure of a million threatened species was widely misunderstood, as illustrated by a letter sent to a leading UK newspaper from a staff member of a high-profile national conservation NGO. The letter stated that, ‘The recent report from Professor Chris Thomas and his research team gives further evidence of the fragility of a million known species, as well as probably several million that are still unknown’ (“Revealed: how global warming will cause extinction of a million species”, J. Purvis, The Independent, 8 January, p. 19). The writer was clearly unaware that the million species were substantially made up of the ‘still unknown’. It will be appreciated by readers of this book that there are, of course, several other important assumptions inherent in the extrapolations involved here, not least that the models can be relied upon in their forecasts of species range losses and consequent extinctions, and that the species in the Thomas et al. (2004)

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Table 10.2 Results of a survey of the coverage of the Thomas et al. (2004) article on climate change and extinction by UK national and regional newspapers. Primary reports refer to direct reporting of the study, while secondary reports include letters and commentary. None of the six claims were made in the original scientific study, whose authors went to some length in the original paper or associated press release to stress the three important qualifications listed in the table. Table from Ladle et al. (2004). Primary reports

Secondary items

Claims Million or more species extinct Species extinct by 2050 Quarter of all life forms extinct Quarter of all land animals/plants extinct Third of all life forms extinct Third of all land animals/plants extinct

14 10 1 7 2 1

7 7 2 1 0 0

Qualifications Based on millions of unidentified species Only a few actually extinct by 2050 Phrase ‘committed to extinction’ used

0 2 1

0 0 0

analyses are broadly representative of all terrestrial species. In fact, the selected 1,103 species were all endemics within their respective study areas, mostly having rather restricted geographical ranges and thus comparatively narrow climate envelopes and more vulnerability to climate change (see Box 7.3). Despite the huge degree of uncertainty associated with the projections within the paper, the story made the front pages. The paper has been extremely highly cited since, featuring not just in scientific discourse but also in such places as the Stern Review on the economic implications of climate change, commissioned by the UK government, in which it was used to support an estimate of at least ten per cent species extinctions globally in response to a 1°C global temperature increase. The climate change/extinction story was also actively discussed on the internet, but here the representation was far more variable. Several sites ranked highly on popular search engines were critical of the underlying science, while traditional media sites (e.g. newspapers, newswires, etc.) were, like the print newspapers, generally uncritical and factually incorrect (Ladle et al., 2005). Such polarization of representations and oversimplification, to the point of misrepresentation, has in our view the potential to damage the

credibility of environmental science. It is equally clear that conservation biogeographers will struggle to counter misrepresentations if, like most scientists, they restrict their pronouncements and debates to academic journals and conferences. On the other hand, efforts by scientists to communicate via traditional media often run into the difficulty that these media are not receptive to representing complex and highly technical content without a great deal of simplification, and they are generally highly resistant to scientists checking and correcting the way their information is represented. An approach that one of us has advocated is the engagement of scientists in blogs (also known as weblogs), which are web-based interactive forums in which topical issues can be freely debated by anyone with access to the internet who is interested in the issue (Ashlin & Ladle, 2006). However, uncontrolled, unregulated debating sites on controversial scientific issues often appear to generate more heat than light, and it is by no means clear that they provide the most effective means of scientists engaging in public dissemination and outreach. Devictor et al. (2010) highlight a rather different means of engagement between conservation biogeographers and the public, through the medium of citizen science (Figure 10.1). Citizen science programmes

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Figure 10.1 The role citizen science may play within conservation biogeography, highlighting: (a) the differences between those schemes in which the scientists dictate the programme, and more participative approaches; (b) five key factors in the success of citizen science programmes. Modified slightly from Devictor et al. (2010 – their Figure 1).

refer to data sets collected by the participation of the general public, and they have been more formally defined as ‘a method of integrating public outreach and scientific data collection locally, regionally, and across large geographical scales’ (Cooper et al., 2007). Typically, they involve members of the general public following a simple, standardized sampling protocol to collect data across a large number of sampling points at a particular time of year, repeated over a number of years (e.g. Table 10.3, Figure 10.1). Such data sets are not without their limitations – so, for instance, those species that are particularly difficult to detect and identify are likely to be under-sampled. However, the sheer scale of some of the data sets means that a variety of cross-validation techniques can be employed, and useful results can be extracted. Indeed, Devictor et al. (2010) comment that they know of more than 200 scientific publications resulting wholly or in part from citizen science data sets, with

contributions made to understanding, for example: mechanisms driving species responses to land-use changes; species traits most affected by global warming; impacts of acid rain on birds; changes in plant phenology; and protected area efficiency. Beyond the direct value to science, citizen science programmes have a key role to play in demystifying science, in reinforcing and extending environmental education and in engaging citizens actively in the endeavour of nature conservation. This engagement might extend from the typical top-down approach, whereby scientists dictate the form of the project and are solely responsible for the interpretation, to more participatory, bottom-up forms of engagement, in which: ‘… citizen science could be of great help to promote a conservation biogeography based on local ecological knowledge in several socio-economic contexts (i.e. not limited to the most developed countries)’ (Devictor et al., 2010, p. 360).

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Table 10.3 Some examples of citizen science programmes, (a) highlighting the scale of some prominent schemes; and (b) highlighting the nature of the involvement, skills and training involved and the means of communication of findings (from Devictor et al., 2010, their Tables 1 and 3). (a) Website (all after http//www.)

Issue

Type

Name

Characteristic

a Spatial scale

Regional

Appalachian Mountain Watch

Mountains

outdoors.org/ conservation/ mountainwatch

USA

National

French Garden Butterfly Monitoring

France

noeconservation.org

France

Continental

Spider WebWatch

North America

spiderwebwatch.org

USA, Canada

Worldwide

e-bird

worldwide

ebird.org/content/ ebird

World

Long-term data set

Christmas Bird Count

since 1900

audubon.org/bird/ cbc/

USA, Canada

Medium-term data set

UK Butterfly Monitoring Scheme

since 1976

ukbms.org

UK

Few years old programmes

FrogwatchUSA

since 1998

nwf.org/

USA

High

Nest Watch

>25,000 people

birds.cornell.edu

USA

Very High

Big Garden Bird Watch

>40,0000 people

rspb.org.uk/ birdwatch/

UK

Issue

Type

Name

Characteristic

Website (all after http//www.)

Country

a Skill

Beginner

French Garden Butterfly Observatory

no skill required

noeconservation.org

France

Intermediate

Nocturnal Owl Survey

ability to identify few species

bsc-eoc.org/volunteer

Canada

Confirmed

French Butterfly Monitoring

ability to identify many species

mnhn.fr/vigie-nature

France

Occasional observation

e-bird

no commitment

ebird.org/content/ ebird

World

One-day event per year

Bailly Birdathlon

during a 24-hour period in May, to find as many bird species as possible

bsc-eoc.org/support/ birdathon

USA, Canada

b Temporal scale

c Sample size

Country

(b)

b Time required

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Prospects and challenges

Table 10.3 Continued (b)

Issue

c Education

d Communication

Website (all after http//www.)

Country

ten minutes, one evening a week throughout the summer

mos.org/fireflywatch

USA

UK Butterfly Monitoring Scheme

26 counts a year and 5 hours by count

ukbms.org

UK

Special training tools

Frog Watch

frog calls’ records

naturewatch.ca

Canada

Field training / workshop

Anglers Monitoring Initiative

1 day workshop obligatory

riverflies.org

UK

Special activities for children

Great Lake Worm Watch

downloads of educative games

nrri.umn.edu/worms

USA

Special tools for teachers

RoadKill

12 interactive activities proposed

http:// roadkill.edutel.com/

USA

Special tools for university students

Project BudBurst

exercises of data collection

budburst.ucar.edu/

USA

Tools to analyse its own data

e-bird

access to maps and data important to the observer

ebird.org/content/ ebird

World

Press room

Feeder Watch

free downloads of reports, videos and press articles

birds.cornell.edu/pfw

USA, Canada

Pollution and conservation alert

Anglers Monitoring Initiative

update of species conservation status

riverflies.org

UK

Results online

All of them to varying extents (interactive maps, graphs, reports …)

Type

Name

Characteristic

Regular commitment on a season

Firefly Watch

High commitment

Future directions 10.2.5 Reconciliation ecology and a biogeography of the countryside Conservation philosophy, science, and practice must be framed against the reality of human-dominated ecosystems, rather than the separation of humanity and nature underlying the modern conservation movement. (Western, 2001, p. 5458) Rosenzweig (2001, 2003) has argued persuasively that the most fundamental conservation challenge facing us is to learn ‘how to share anthropogenic habitats with wild species’ (2001, p. 5409). In other words, we need to discover ways to transform and diversify what are often impoverished anthropogenic habitats so that they have the potential to harbour more species. Daily et al. (2001, 2003) make much the same case, arguing for recognition of the importance of making anthropogenic habitats as wildlife-friendly as possible, both for the ecosystem services (e.g. pollination) provided by wild species in these landscapes and because protected area systems alone cannot save enough of wild nature. Daily and colleagues issue their plea under the term ‘countryside biogeography’, while Rosenzweig uses the label ‘reconciliation ecology’ in recognition of the inevitable trade-offs and compromises that need to be made in such an undertaking. Rosenzweig’s case is firmly based on biogeographical principles; he reasons that, because reduction in area of suitable habitat is the key driver of reduction in diversity, we need to halt or reverse this trend (Rosenzweig, 2003; and see Chapter 8). In recent decades, a big part of the effort to do so has been channelled through the creation of protected areas, but at roughly 12 per cent of the terrestrial surface of the Earth under some form of protection it is likely to become increasingly difficult to argue for significant further increases in the terrestrial protected area estate. We may be approaching the limits of this strategy, at least in terms of the more restrictive categories of protected area (e.g. IUCN categories 1–4; Table 2.2). Rosenzweig’s argument is that by changing the way we transform habitats and how we manage already transformed habitats, we can potentially achieve our socio-economic objectives without the accompanying reduction in species ranges. Both countryside biogeography and reconciliation ecology derive directly from an understanding of

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biogeography at the landscape scale, and in particular of the logic of species–area relationships, the role of the transformed matrix (i.e. ‘countryside’), habitat corridors, dispersal and metapopulation dynamics (see Chapter 8). The conservation biogeography toolbox of conceptual frameworks, models and forecasts will thus be central to the success of the reconciliation ecology agenda. There are already many ongoing initiatives that are aligned with this agenda, most notably in modifications to agricultural practices that are extending the geographical area available to many plants and animals, or which allow the passage of species between habitat fragments. Ultimately, the success of reconciliation ecology in ‘pushing’ nature back up the species–area curve will depend on the broad support of many sectors of society for the goals and values encompassed. For large-scale habitat modifications, it will be necessary to engage with a whole range of disciplines and professions, including planners, developers, anthropologists and social scientists, who can both help design effective strategies and also ‘broker the deal’ with the human inhabitants of these landscapes.

10. 3 LOOK I N G T O T H E FU T U R E In Chapter 1 we defined conservation biogeography as ‘the application of biogeographical principles, theories, and analyses, being those concerned with the distributional dynamics of taxa individually and collectively, to problems concerning the conservation of biodiversity’ (original source: Whittaker et al., 2005). However, as outlined in Chapter 2, conservation is a reflection of societal values. Thus, although the models and insights of conservation biogeography may help us better conserve biodiversity, the type of biodiversity we seek to conserve depends upon the values held by the various organizations and institutions that determine conservation policy. These bodies are, in turn, influenced by the values of wider society as a whole. Over time, values change – just consider the change in typical western attitudes to fur coats in the last 50 years – and, while there is no guarantee that society will continue to endorse the current objectives of the conservation movement, it will represent a great failure if conservationists cannot convince society at large that wild nature matters to the human condition and warrants more careful custodianship. Conservation biogeographers have the potential to play an important

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Prospects and challenges

role in ensuring public support for nature conservation if they can succeed in converting their research findings into compelling narratives – modern parables that are in tune with the changing social values of nature (Bowman, 2001). It is an exciting time to be a biogeographer: the discipline is awash with new tools, techniques, and accessible, large-scale data sets, with the promise of more systematic local, regional, and global information systems to come in the near future. Conservation

biogeography, the intersection of a venerable academic discipline with some of the world’s most pressing environmental problems, surely has a vital role to play in informing future debates about the conservation of biodiversity. We hope that this book has given you the sense of a discipline in a state of intellectual ferment, offering promise and potential, as scientists from around the globe tackle some of the biggest issues to face the natural world.

Glossary of Terms

Adaptive ecosystem management: A form of conservation management that integrates scientific knowledge of ecological relationships within a complex socio-political and values framework towards the general goal of protecting native ecosystem integrity over the long term (Grumbine, 1994). Anthropocene: A term used by some scientists to refer to the most recent period of Earth’s history, where humans began to have a significant global impact on the environment and climate system. It has no precise start date, but is often considered to start in the late 18th century. Bioclimate envelope models: See Species distribution models. Biodiversity: A contraction of ‘biological diversity’. Biodiversity has many definitions, one prominent one being ‘[t]he variability of life from all sources, including within species, between species, and of ecosystems’ (Matthews et al., 2001). Some commentators have noted that biodiversity definitions are often closer to subjective ‘value judgement’ concepts such as quality of life than an objective measure of an environmental property. Biodiversity hotspot: An area high in selected biodiversity attributes, such as species richness or endemism; sometimes a biodiversity attributes analysis is combined with a threat criterion to provide a composite hotspot/threatspot analysis. The most prominent ‘hotspots’ scheme in conservation is of the latter form, being that developed by the international NGO Conservation International, whose technical definition of a hotspot is a geographical area that contains at least 0.5% or 1,500 species of vascular plants as endemics, and which has lost at least 70% of its primary vegetation (Myers et al., 2000).

Biological invasion: The process whereby species expand their geographical distribution outside of their natural dispersal range via the actions of humans. (cf. non-native species, naturalized species, invasive species). Biome: A major type of natural vegetation that occurs wherever a particular mix of climatic and edaphic conditions is encountered; or we may equate it with the notion of a major ecosystem type. The latter usage translates rather better into the marine realm than ‘natural vegetation type’. Biophilia: The innate emotional affiliation of humans to other forms of life, which predisposes humanity to value life and living systems. Biotic homogenization: The process by which the genetic, taxonomic or functional similarities of regional biotas increase over time. Climate envelope models: see Species distribution models. Complementarity: The principle that in designing reserve networks to maximize the total number of species ‘saved’ with least effort (expenditure), you should seek sites that complement one another rather than simply designating the sites that are individually most diverse. Conservation area network: A network of areas that perform a conservation function, whether they are strictly protected or not. See Protected area network. Conservation biogeography: ‘[T]he application of biogeographical principles, theories and analyses, being those concerned with the distributional dynamics of taxa individually and collectively, to problems concerning the conservation of biodiversity’ (Whittaker et al., 2005, p. 3).

Conservation Biogeography Edited by Richard J. Ladle and Robert J. Whittaker © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33503-3

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Glossary of terms

Conservation biology: The area of applied research designed to inform management decisions concerning the conservation of biodiversity. The roots of conservation biology lie largely within the mid-20th century, although it was not until the 1970s and early 1980s that it was formally identified as an academic sub-discipline with dedicated journals and textbooks. Conservation values: Beliefs and ideas about nature and wildlife that inform assessments of worth. Convention on Biological Diversity (CBD): The CBD was signed on 5 June 1992 at the United Nations Conference on the Environment and Development in Rio de Janeiro. The Convention has three main aims: the conservation of biodiversity; the sustainable use of its components; and sharing the benefits from the commercial and other utilization of genetic resources in a fair and equitable way. At the heart of the CBD is the acknowledgement that biodiversity is essential for human existence and that the utilitarian (use) value is the key to effective conservation. Cultural landscape: A landscape dominated by anthropogenic habitats, within which may be embedded fragments of less impacted land with higher conservation value. Decision support tools: Computer-based information systems intended to help decision-makers compile and analyse data to help solve conservation problems. Earth Summit: An informal term for the United Nations Conference on Environment and Development (UNCED) held in Rio de Janeiro, Brazil, on 3–14 June 1992. Ecological extinction: Species that are extinct in the wild but which have an extant captive bred population, or are present in the wild but at such low densities that they no longer interact to a meaningful degree with other species in the community. Ecological relict: ‘A community or fragment of one that has survived some important change, often to become in appearance an integral part of the existing vegetation’ (Clements, 1934, p. 42). Ecoregions: ‘Regions of relative homogeneity in ecological systems or in relationships between organisms and their environments’ Omernik (1987, p. 123). Ecosystem resilience: The capacity of ecosystems to absorb disturbance; and their ability to reorganize when a critical threshold is exceeded.

Ecosystem services: The processes or products of natural ecosystems and species that provide a utilitarian value, e.g. natural processes such as pollination and watershed protection which, if removed, would have economic consequences. Edge effects: The ecological influence of the altered physical and biotic properties that typically characterize the edge of a habitat type, and which extend towards the core of the habitat patch. The term is most frequently applied within applied island biogeography when discussing characteristics of habitat islands. Endemism: A species (or other taxonomic entity) is endemic when it is naturally confined to a defined geographical area. Some authors have sought to delimit the term as applying at a particular scale, or with particular physical units of analysis; herein we do not so delimit it. See Range restricted species. Environmental surrogate: A physical or climatic variable used as a proxy to derive ecological classifications. Environmental surrogates can incorporate some biotic variables such as vegetation. Extinction: Used to refer to the disappearance of a species from an area (also termed extirpation) or globally. Various ways the term can be used are described in Table 4.2. Extinction debt: The anticipated eventual species loss from an area due to habitat loss and fragmentation. Fortress conservation: The practice of completely excluding local people from protected areas. The term is often used more broadly to apply to any conservation practice that excludes local people from access to, or exploitation of, natural resources. Habitat island: Areas isolated from other reserves by anthropogenically transformed habitats (sometimes named the ‘matrix’) that are generally unsuitable for the species of conservation concern. Historical (phylogenetic) biogeography: The broad branch of biogeography that concerns itself with historical interpretations of biogeographical patterns. The term can have quite specific meanings, e.g. the derivation of cladograms for areas based on the phylogenies of the organisms inhabiting these areas. Homology/homologous: Characters that are similar in different taxa because they are shared through a common ancestor.

Glossary of terms

Invasive species: A species that expands its population from the site of original arrival into intact or semi-intact vegetation (regardless of demonstrated impacts). Island rule: The general tendency for an ordered size changes of island vertebrate species in relation to mainland congeners, such that large-bodied species tend to get smaller and vice versa. Keystone species: A species that has a disproportionate effect on its environment relative to its biomass. Such organisms typically have a strong influence on many other organisms within an ecosystem and may play an important role in determining the structure of the ecological community. Linnean extinction: Extinctions of undiscovered species inferred from the species–area relationship and estimates of species diversity for a given ecosystem or region. The assumed losses of these inferred species have been termed Centinelan extinctions by Wilson (1992). Linnean shortfall: The discrepancy between the number of species that have been formally described by taxonomists and the number of species that are thought to exist. Named after the father of modern nomenclature, Caroleus Linnaeus (a Latinized form of Carl von Linné). Local extinction: The complete loss of a population of a species within a clearly defined geographical area, but where extant free-living populations still exist outside that area. Macroecology: A top-down and multi-scale approach to analyses of the structure of biotas, focused on the emergent outcomes of statistical analysis of key properties (e.g. species abundance, distribution and diversity) in order to understand the processes involved in structuring ecological systems. It is often, but not necessarily, concerned with analyses of coarse-scale data sets of large spatial extent. Mass extinction event: A major episode of extinction involving many different taxa and occurring fairly suddenly in the fossil record. Matrix (or habitat matrix): Anthropogenically transformed habitats that are generally unsuitable for the species of conservation concern within which habitat islands (which sometimes are also conservation areas) are embedded. Mesopredator release: The increasing number of smaller omnivores and predators due to the absence of larger predators – a phenomenon that can result from habitat fragmentation.

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Metapopulation: A population of geographically separated subpopulations interconnected by patterns of gene flow, extinction and recolonization. Minimum viable population (MVP): The minimum size of a population that will ensure its long-term survival – for instance, providing a 95% probability of persistence for the next 100 or 1,000 years. Monophyletic: A group (taxon or gene) that includes an ancestral group and all of its descendant groups, also referred to as a clade. Naturalistic fallacy: The assumption that, prior to invasion by non-indigenous species, ecosystems must have been natural and pristine, and that this is the ecosystem state that should be preserved. Naturalized species: Species that have selfsustaining populations at the original point of establishment or only in highly modified habitats. Naturdenkmal: Roughly translated as nature monument, a concept that was articulated by the Berlin-based forester Hugo Conwentz in the first decade of the 20th century to mean a place for the study and contemplation of nature, motivated by the belief that monuments of nature have value to human civilization, culture and identity. Nature conservation: A social movement working to develop or reassert certain values in society concerning the human/nature relationship. Non-analogue community: Used in palaeoecology to refer to past communities having a species composition for which there is no contemporary equivalent, such systems providing evidence of the essentially individualistic nature of community assemblage processes. Projected into the future, the term is sometimes used to describe hypothesized future communities assembled under the influence of anthropogenic change processes. Non-native species: Populations that have become established outside the bounds of their native ranges through the action of humanmediated transport. Paraphyletic group: An incomplete evolutionary unit in which one or more descendants of a particular ancestor have been excluded from the group. Phylogeography: The study of the genetic and geographical structure of populations and species. Phylogenetic diversity (PD): A measure of biodiversity that incorporates taxonomic difference between species, e.g. based on estimating the length

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Glossary of terms

of evolutionary pathways that connect a given set of taxa. Phytosociology: A sub-discipline of plant community ecology that seeks to describe and understand plant species co-occurrences at the level of local communities. Phoenix extinction: A species that is extinct in the wild, but for which genetic material is available in the form of stored material or a closely related conspecific or congeneric variety/breed/hybrid, allowing the possibility of a future reintroduction of the species or a functionally equivalent form. Population bottleneck: A severe, but perhaps fleeting, reduction in population number that is often associated with reduced genetic diversity and heterozygosity, and reduced adaptability of that population. Population viability analysis: A sophisticated family of models that use demographic and environmental information to provide an estimate of the probability of a population surviving over a given time period (often 1,000 years). Protected area: An area of land or sea designated in order to conserve or protect attributes of nature valued by society, groups or individuals. Many have been created largely for their biodiversity value and/ or are now managed to maintain or enhance their biodiversity value, often alongside other management goals and considerations. Protected area networks: A number of protected areas within a given geographical area that have been chosen to fulfil a shared conservation goal (e.g. minimizing future biodiversity loss). The constituent protected areas may, or may not, be ecologically connected, although this is often assumed. Examples include the European Natura 2000 network. See Conservation area networks. Range restricted species: Species that occupy a very limited geographical range due to specialized habitat preferences. Reciprocally monophyletic: A term used in phylogenetics to describe the point at which, for a given gene, alleles within closely-related taxa or clades are monophyletic with respect to each other. Red List: A list of species that are considered to be threatened with extinction. The best known example is the IUCN Red List, which provides information on the taxonomic, conservation status and distribution of taxa that are facing a high risk of global extinction.

Redundancy: This term can have varied meanings, one of which is the strategy of choosing multiple protected areas that contain the same species (or other attribute) of conservation interest in order to decrease the long-term probability of its extinction, i.e. to provide a certain level of resilience within a protected area network. Representation principle: The idea that conservation schemes should seek to conserve systems of sites that are representative of a set of community types, major ecosystem types, biogeographical zones, species, etc. Restoration ecology: An attempt to ‘move a damaged system to an ecological state that is within some acceptable limits relative to a less disturbed system’ (Falk et al., 2006). Rewilding: Refers to ‘action on the landscape level with a goal of reducing human control and allowing ecological and evolutionary processes to reassert themselves’ (Klyza, 2001). Sink population: A breeding group that does not produce enough offspring to maintain itself over several generations without immigrants from other populations. Sink habitat: A habitat in which local mortality exceeds local reproductive success for a given species. Source population: A breeding group that produces enough offspring to be self-sustaining, and that often produces excess offspring that augment the population of other areas nearby. Source habitat: A habitat in which local reproductive success exceeds local mortality for a given species. Species distribution models: Species distribution models relate field observations of the presence/ absence of a species to environmental predictor variables, based on statistically or theoretically derived response surfaces, for prediction and inference. The predictor variables are often climatic but can include other environmental variables. Species relaxation: The decline of species number towards an eventual, hypothetical and lower equilibrium, due to the dominance of extinction over immigration after fragmentation. Synanthropic species: Also known as urban exploiters, theses are species that are able to thrive in the novel conditions created as a landscape is urbanized. Systematic conservation planning: A discipline aiming to maximize the efficiency and effectiveness of protected area network design. The key

Glossary of terms

technique of systematic conservation planning is to create a range of hypothetical alternative networks that enable planners to engage with the complexity of multi-sectoral spatial planning. Systematic conservation planning is concerned with the optimal application of spatially-explicit conservation management actions to promote the persistence of biodiversity and other natural features in situ. Trophic cascade: The chain of knock-on extinctions observed or predicted to occur following the

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loss of one or a few species that play a critical role (e.g. as a pollinator) in ecosystem functioning. Wallacean extinction: Species that have not been documented for many years, but in which final extinction is uncertain because populations might survive in areas that have not been surveyed within the potential distributional range. Wallacean shortfall: The inadequacy of scientific knowledge of the geographical distributions of species.

References

Ababneh, L. (2008) Bristlecone pine paleoclimatic model for archeological patterns in the White Mountain of California. Quaternary International, 188, 59–78. Adams, W. (2004) Against extinction: the past and future of conservation. Earthscan, London. Adger, W.N. (2000) Social and ecological resilience: are they related? Progress in Human Geography, 24, 347–364. Agapow, P.M., Bininda-Emonds, O.R.P., Crandall, K.A., Gittleman, J.L., Mace, G.M., Marshall, J.C. & Purvis, A. (2004) The impact of species concept on biodiversity studies. Quarterly Review of Biology, 79, 161–179. Airame, S., Dugan, J.E., Lafferty, K.D., Leslie, H., McArdle, D.A. & Warner, R.R. (2003) Applying ecological criteria to marine reserve design: a case study from the California Channel Islands. Ecological Applications, 13, 170–184. Akçakaya, H.R., Radeloff, V.C., Mladenoff, D.J. & He, H.S. (2004) Integrating landscape and metapopulation modeling approaches: viability of the sharp-tailed grouse in a dynamic landscape. Conservation Biology, 18, 526–537. Alessa, L. & Chapin, F.S., III (2008) Anthropogenic biomes: a key contribution to earth-system science. Trends in Ecology & Evolution, 23, 529–531. Allee, R.J., Dethier, M., Brown, D., Deegan, L., Ford, R.G., Hourigan, T.F., Maragos, J., Schoch, C., Sealey, K., Twilley, R., Weinstein, M.P. & Yoklavich, M. (2000) Marine and estuarine habitat classification. US Department of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Washington, DC. Allen, D.E. (1994) The naturalist in Britain: a social history, 2nd edn. Princeton University Press, Princeton, NJ. Allen, G.R. (2008) Conservation hotspots of biodiversity and endemism for Indo-Pacific coral reef fishes. Aquatic Conservation: Marine and Freshwater Ecosystems, 18, 541–556. Almeida-Neto, M., Guimarães, P., Guimarães, P. R., Jr, Loyola, R.D. & Ulrich, W. (2008) A consistent metric for nestedness analysis in ecological systems: reconciling concept and quantification. Oikos, 117, 1227–1239. Andelman, S.J. & Fagan, W.F. (2000) Umbrellas and flagships: efficient conservation surrogates or expensive mistakes?

Proceedings of the National Academy of Sciences USA, 97, 5954–5959. Ando, A., Camm, J., Polasky, S. & Solow, A. (1998) Species distributions, land values, and efficient conservation. Science, 279, 2126–2128. Angulo, A. & Reichle, S. (2008) Acoustic signals, species diagnosis, and species concepts: the case of a new cryptic species of Leptodactylus (Amphibia, Anura, Leptodactylidae) from the Chapare region, Bolivia. Zoological Journal of the Linnean Society, 152, 59–77. Anon (2001/2002) Which MPA is the oldest? MPA News, 3, 5–6. Araújo, M.B. & Luoto, M. (2007) The importance of biotic interactions for modelling species distributions under climate change. Global Ecology and Biogeography, 16, 743–753. Araújo, M.B. & Williams, P.H. (2000) Selecting areas for species persistence using occurrence data. Biological Conservation, 96, 331–345. Araújo, M.B. & Williams, P.H. (2001) The bias of complementarity hotspots toward marginal populations. Conservation Biology, 15, 1710–1720. Araújo, M.B., Humphries, C.J., Densham, P.J., Lampinen, R., Hagemeijer, W.J.M., Mitchell-Jones, A.J. & Gasc, J.P. (2001) Would environmental diversity be a good surrogate for species diversity? Ecography, 24, 103–110. Araújo, M.B., Williams, P.H. & Turner, A. (2002a) A sequential approach to minimise threats within selected conservation areas. Biodiversity and Conservation, 11, 1011–1024. Araújo, M.B., Williams, P.H. & Fuller, R.J. (2002b) Dynamics of extinction and the selection of nature reserves. Proceedings of the Royal Society B: Biological Sciences, 269, 1971–1980. Araújo, M.B., Densham, P.J. & Humphries, C.J. (2003) Predicting species diversity with ED: the quest for evidence. Ecography, 26, 380–383. Araújo, M.B., Cabeza, M., Thuiller, W., Hannah, L. & Williams, P.H. (2004a) Would climate change drive species out of reserves? An assessment of existing reserve-selection methods. Global Change Biology, 10, 1618–1626.

Conservation Biogeography, First edition. Edited by Richard J. Ladle and Robert J. Whittaker. © 2011 by Blackwell Publishing Ltd. Published 2011 by Blackwell Publishing Ltd.

References

Araújo, M.B., Densham, P.J. & Williams, P.H. (2004b) Representing species in reserves from patterns of assemblage diversity. Journal of Biogeography, 31, 1–14 Araújo, M.B., Thuiller, W., Williams, P.H. & Reginster, I. (2005a) Downscaling European species atlas distributions to a finer resolution: implications for conservation planning. Global Ecology and Biogeography, 14, 17–30. Araújo, M.B., Whittaker, R.J., Ladle, R.J. & Erhard, M. (2005b) Reducing uncertainty in projections of extinction risk from climate change. Global Ecology and Biogeography, 14, 529–538. Araújo, M.B., Nogués-Bravo, D., Reginster, I., Rounsevell, M. & Whittaker, R.J. (2008) Exposure of European biodiversity to changes in human-induced pressures. Environmental Science and Policy, 11, 38–45. Arcese, P. & Sinclair, A.R.E. (1997) The role of protected areas as ecological baselines. Journal of Wildlife Management, 61, 587–602. Armsworth, P.R., Daily, G.C., Kareiva, P. & Sanchirico, J.N. (2006) Land market feedbacks can undermine biodiversity conservation. Proceedings of the National Academy of Sciences USA, 103, 5403–5408. Arndt, H. & Rémy, J.P. (2005) Conserving biodiversity under climate change: the rear edge matters. Ecology Letters, 8, 461–467. Arrhenius, O. (1921) Species and area. Journal of Ecology, 9, 95–99. Ashlin, A. & Ladle, R.J. (2006) Environmental science adrift in the blogosphere. Science, 312, 201. Atmar, W. & Patterson, B.D. (1993) The measure of order and disorder in the distribution of species in fragmented habitat. Oecologia, 96, 373–382. Atmar, W. & Patterson, B.D. (1995) The nestedness temperature calculator: a Visual Basic program, including 294 presence– absence matrices. AICS Research, University Park, NM and Field Museum, Chicago (http://aics-research.com/nestedness/tempcalc.html). Avery, M.L., Greiner, E.C., Lindsay, J.R., Newman, J.R. & Pruett-Jones, S. (2002) Monk parakeet management at electric utility facilities in south Florida. Proceedings of the 20th Vertebrate Pest Conference (ed. by R.M. Timm and R.H. Schmidt), pp. 140–145. University of California Davis, CA. Avise, J. (2000) Phylogeography: the history and formation of species. Harvard University Press, Cambridge, MA. Avise, J.C. (2005) Phylogenetic units and currencies above and below the species level. Phylogeny and conservation (ed. by A. Purvis, J.L. Gittleman and T. Brooks), pp. 76–101. Cambridge University Press, Cambridge, UK. Avise, J.C. (2009) Phylogeography: retrospect and prospect. Journal of Biogeography, 36, 3–15. Avise, J.C. & Hamrick, J.L. (1996) Conservation genetics. Kluwer Academic Publishers, Norwell, MA. Avise, J.C. & Walker, D. (1999) Species realities and numbers in sexual vertebrates: perspectives from an asexually trans-

265

mitted genome. Proceedings of the National Academy of Sciences USA, 96, 992–995. Bailey, R.G. (1983) Delineation of ecosystem regions. Environmental Management, 19, 239–248. Bailey, R.G. (1996) Ecosystem geography. Springer-Verlag, New York. Bailey, R.G. (1998) Ecoregions: the ecosystem geography of the oceans and continents. Springer, New York. Ball, I.R. & Possingham, H.P. (2000) Marxan (v 1.8.6): marine reserve design using spatially explicit annealing. User Manual. University of Queensland, Brisbane. Balogh, B. (2002) Scientific forestry and the roots of the modern American state: Gifford Pinchot’s path to progressive reform. Environmental History, 7, 198– 225. Barnes, J. & de Jager, M. (1996) Economic and financial incentives for wildlife use on private land in Namibia and implications for policy. South African Journal of Wildlife Research, 26, 37–46. Barnosky, A.D., Koch, P.L., Feranec, R.S., Wing, S.L. & Shabel, A.B. (2004) Assessing the causes of late Pleistocene extinctions on the continents. Science, 306, 70–75 Barrett, C.B., Brandon, K., Gibson, C., Gjertsen, H. (2001) Conserving tropical biodiversity amid weak institutions. BioScience, 51, 497–502. Bartels, G.B. & Norton, B.E. (1993) An examination of the carrying capacity concept. Range ecology at disequilibrium (ed. by R.H. Behnke, I. Scoones and C. Kerven), pp. 89–103. ODI / IIED, London. Basse, B. & McLennan, J.A. (2003) Protected areas for kiwi in mainland forests of New Zealand: how large should they be? New Zealand Journal of Ecology, 27, 95–106. Bates, J.M. & Demos, T.C. (2001) Do we need to devalue Amazonia and other large tropical forests? Diversity and Distributions, 7, 249–255. Baum, D.A. & Donoghue, M.J. (1995) Choosing among alternative phylogenetic species concepts. Systematic Botany, 20, 560–573. Baum, J.K., Myers, R.A., Kehler, D.G., Worm, B., Harley, S.J. & Doherty, P.A. (2003) Collapse and conservation of shark populations in the northwest Atlantic. Science, 299, 389–392. Baum, K.A., Haynes, K.J., Dillemuth, F.P., Cronin, J.T. (2004) The matrix enhances the effectiveness of corridors and stepping stones. Ecology, 85, 2671–2676. Beale, C.M., Lennon, J.J. & Gimona, A. (2008) Opening the climate envelope reveals no macroscale associations with climate in European birds. Proceedings of the National Academy of Sciences USA, 105, 14908–14912. Beger, M., Jones, G.P. & Munday, P.L. (2003) Conservation of coral reef biodiversity: a comparison of reserve selection procedures for corals and fishes. Biological Conservation, 111, 53–62. Beger, M., McKenna, S.A. & Possingham, H.P. (2007) Effectiveness of surrogate taxa in the design of coral reef

266

References

reserve systems in the Indo-Pacific. Conservation Biology, 21, 1584–1593. Bellwood, D.R. & Hughes, T.P. (2001) Regional-scale assembly rules and biodiversity of coral reefs. Science, 292, 1532–1534. Bellwood, D.R. & Meyer, C.P. (2009) Searching for heat in a marine biodiversity hotspot. Journal of Biogeography, 36, 569–576. Bellwood, D.R., Hoey, A.S. & Choat, J.H. (2003) Limited functional redundancy in high diversity systems: resilience and ecosystem function on coral reefs. Ecology Letters 6, 281–285. Bellwood, D.R., Hughes, T.P., Folke, C. & Nyström, M. (2004) Confronting the coral reef crisis. Nature, 429, 827–833. Bennun, L. & Fishpool, L.D.C. (2000) The Important Bird Areas programme in Africa: an outline. Ostrich, 71, 150–153. Bergamini, A., Ungricht, S. & Hofmann, H. (2009) An elevational shift of cryophilous bryophytes in the last century – an effect of climate warming? Diversity and Distributions, 15, 871–879. Berkes, F. & Folke, C. (eds) (1998) Linking social and ecological systems: management practices and social mechanisms for building resilience. Cambridge University Press, New York. Berkes, F., Colding, J. & Folke, C. (eds) (2003) Navigating SocialEcological Systems. Cambridge University Press, Cambridge. Bhagwat, S.A. & Rutte, C. (2006) Sacred groves: potential for biodiversity management. Frontiers in Ecology and the Environment, 4, 519–524. Bhagwat, S.A., Willis, K.J., Birks, H.J.B. & Whittaker, R.J. (2008) Agroforestry: a refuge for tropical biodiversity? Trends in Ecology & Evolution, 23, 261–267. Biedermann, R. (2003) Body size and area-incidence relationships: is there a general pattern? Global Ecology and Biogeography, 12, 381–387. Biggs, H.C. & Rogers, K.H. (2003) An adaptive system to link science, monitoring, and management in practice. The Kruger experience: ecology and management of savanna heterogeneity (ed. by J.T. du Toit, K.H. Rogers and H.C. Biggs), pp. 59–80. Island Press, Washington, DC. Birks, H.J.B. (1996) Contributions of Quaternary palaeoecology to nature conservation. Journal of Vegetation Science, 7, 89–98. Birks, H.J.B. (2005) Mind the gap: how open were European primeval forests? Trends in Ecology & Evolution, 20, 154–156. Blackmore, A.C., Mentis, M.T. & Scholes, R.J. (1990) The origin and extent of nutrient-enriched patches within a nutrient-poor savanna in South Africa. Journal of Biogeography, 17, 463–470. Blair, R.B. (1996) Land use and avian species diversity along an urban gradient. Ecological Applications, 6, 506–519. Blair, R.B. (2004) The effects of urban sprawl on birds at multiple levels of biological organization. Ecology and Society, 9, 2 (http://www.ecologyandsociety.org/vol9/iss5/ art2/)

Blake, J.G. (1991) Nested subsets and the distribution of birds on isolated woodlots. Conservation Biology, 5, 58–66. Bloch, C.P., Higgins, C.L. & Willig, M.R. (2007) Effects of large-scale disturbance on metacommunity structure of terrestrial gastropods: temporal trends in nestedness. Oikos, 116, 395–406. Bode, M., Wilson K., McBride M. & Possingham, H.P. (2008) Optimal dynamic allocation of conservation funding among priority regions. Bulletin of Mathematical Biology, 70, 2039–2054. Boecklen, W.J. (1997) Nestedness, biogeographic theory, and the design of nature reserves. Oecologia, 112, 123–142. Boitani, L., Cowling, R.M., Dublin, H.T., Mace, G.M., Parrish, J., Possingham, H.P., Pressey, R.L., Rondinini, C. & Wilson K.A. (2008) Change the IUCN Protected Area categories to reflect biodiversity outcomes. PLoS Biology, 6, e66. doi:10.1371/journal.pbio.0060066 Borges, P.A.V., Cunha, R., Gabriel, R., Martins, A.F., Silva, L. & Vieira, V. (2005) A list of the terrestrial fauna (Mollusca and Arthropoda) and flora (Bryophyta, Pteridophyta and Spermatophyta) from the Azores. Direcção Regional de Ambiente and University of Azores, Horta, Angra do Heroísmo and Ponta Delgada. Botkin, D.B. (1990) Discordant harmonies: a new ecology for the twenty-first century. Oxford University Press, Oxford. Botkin, D.B., Saxe, H., Araújo, M.B., Betts, R., Bradshaw, R.H.W., Cedhagen, T., Chesson, P., Dawson, T.P., Etterson, J.R., Faith, D.P., Ferrier, S., Guisan, A., Hansen, A.S., Hilbert, D.W., Loehle, C., Margules, C., New, M., Sobel, M.J., Stockwell, D.R.B. (2007) Forecasting the effects of global warming on biodiversity. BioScience, 57, 227–236. Botsford, L.W., Micheli, F. & Hastings, A. (2003) Principles for the design of marine reserves. Ecological Applications, 13, 25–31. Bottrill, M. & Pressey, R.L. (2009) Designs for nature: regional conservation planning, implementation and management. IUCN, Gland. Bottrill, M., Joseph, L.N., Carwardine, J., Bode, M., Cook, C., Game, E.T., Grantham, H., Kark, S., Linke, S., McDonaldMadden, E., Pressey, R.L., Walker, S., Wilson, K.A. & Possingham, H.P. (2008) Is conservation triage just smart decision-making? Trends in Ecology and Evolution, 23, 649–654. Bowman, D.M.J.S. (2001) Future eating and country keeping: what role has environmental history in the conservation of biodiversity? Journal of Biogeography, 28, 549–564. Boyd, W.E., Lentfer, C.J. & Parr, J. (2005) Interactions between human activity, volcanic eruptions and vegetation during the Holocene at Garua and Numundo, West New Britain, PNG. Quaternary Research, 64, 384–398. Briggs, J.C. (1974) Marine zoogeography. McGraw-Hill, New York. Brncic, T.M., Willis, D.J. & Washington, R. (2007) Culture or climate? The relative influences of past processes on the composition of the lowland Congo rainforest. Philosophical

References

Transactions of the Royal Society B: Biological Sciences, 362, 229–242. Brook, B.W., Sodhi, N.S. & Ng, P.K.L. (2003) Catastrophic extinctions follow deforestation in Singapore. Nature, 424, 420–423. Brook, B.W., Traill, L.W. & Bradshaw, C.J.A. (2006) Minimum viable population sizes and global extinction risk are unrelated. Ecology Letters, 9, 375–382. Brooks, T.M. & Balmford, A. (1996) Atlantic forest extinctions. Nature, 380, 115. Brooks, T.M., Pimm, S.L. & Collar, N.J. (1997) Deforestation predicts the number of threatened birds in insular Southeast Asia. Conservation Biology, 11, 382–394. Brooks, T.M., Pimm, S.L. & Oyugi, J.O. (1999) Time lag between deforestation and bird extinction in tropical forest fragments. Conservation Biology, 13, 1140–1150. Brooks, T.M., Mittermeier, R.A., Mittermeier, C.G., da Fonseca, G.A.B., Rylands, A.B., Konstant, W.R. Flick, P., Pilgrim, J., Oldfield, S., Magin, G. & Hilton-Taylor, C. (2002) Habitat loss and extinction in the hotspots of biodiversity. Conservation Biology, 16, 909–923. Brooks, T.M., Bakarr, M.I., Boucher, T., da Fonseca, G.A.B., Hilton-Taylor, C., Hoekstra, J.M., Moritz, T., Olivier, S., Parrish, J., Pressey, R.L., Rodrigues, A.S.L., Sechrest, W., Stattersfield, A., Strahm, W. & Stuart, S.N. (2004) Coverage provided by the global protected-area system: is it enough? BioScience, 54, 1081–1091. Brown, J.H. (1971) Mammals on mountaintops: nonequilibrium insular biogeography. American Naturalist, 105, 467–478. Brown, J.H. (1995) Macroecology. The University of Chicago Press, Chicago. Brown, J. H. & Sax, D.F. (2004) An essay on some topics concerning invasive species. Austral Ecology, 29, 530–536. Brown, J. H. & Sax, D.F. (2005) Biological invasions and scientific objectivity: reply to Cassey et al. (2005). Austral Ecology, 30, 481–483. Brown, J.H., Mehlman, D.W. & Stevens, G.C. (1995) Spatial variation in abundance. Ecology, 76, 2028–2043. Brown, J.H., Stevens, G.C. & Kaufman, D.M. (1996) The geographic range: size, shape, boundaries, and internal structure. Annual Review of Ecology and Systematics, 27, 597–623. Bryant, D., Burke, L., McManus, J. & Spalding, M. (1998) Reefs at risk: a map-based indicator of threats to the world’s coral reefs. World Resources Institute, Washington, DC. Buckley, L.B. & Roughgarden, J. (2004) Effects of changes in climate and land use. Nature, 430, 6995. Burke, L., Selig, E. & Spalding, M. (2002) Reefs at risk in Southeast Asia. World Resources Institute, Washington, DC. Burnett, C. & Blaschke, T. (2003) A multi-scale segmentation/ object relationship modelling methodology for landscape analysis. Ecological Modelling, 168, 233–249. Bush, M.B. (1996) Amazonian conservation in a changing world. Biological Conservation, 76, 219–228.

267

Bush M.B. (2002) Distributional change and conservation on the Andean flank: a palaeoecological perspective. Global Ecology and Biogeography, 11, 463–473. Bush, M.B. & Whittaker, R.J. (1991) Krakatau: colonization patterns and hierarchies. Journal of Biogeography, 18, 341–56. Bush, M.B. & Lovejoy, T.E. (2007) Amazonian conservation: pushing the limits of biogeographical knowledge. Journal of Biogeography, 34, 1291–1293. Bush, M.B. & De Oliviera, P.E. (2006) The rise and fall of the Refugial Hypothesis of Amazonian speciation: a paleoecological perspective. Biota Neotropica, 6, doi: 10.1590/ S1676-06032006000100002 Bush, M.B., De Oliveira, P.E., Colinvaux, P.A., Millere, M.C. & Moreno, J.E. (2004) Amazonian paleoecological histories: one hill, three watersheds. Palaeogeography, Palaeoclimatology, Palaeoecology, 214, 359–393. Cadotte, M.W. & Davies, T.J. (2010) Rarest of the rare: advances in combining evolutionary distinctiveness and scarcity to inform conservation at biogeographical scales. Diversity and Distributions, 16, 376–384. de Candolle, A.P. (1855) Géographie botanique raisonnée. Masson, Paris. Carr, M.H., Neigel, J.E., Estes, J.A., Andelman, S., Warner, R.R. & Largier, J.L. (2003) Comparing marine and terrestrial ecosystems: implications for the design of coastal marine reserves. Ecological Applications, 13(supplement), 90–107. Carroll, C., Noss, R.F., Paquet, P.C. & Schumaker, N.H. (2003) Use of population viability analysis and reserve selection algorithms in regional conservation plans. Ecological Applications, 13, 1773–1789. Callicott, J.B., Crowder, L.B. & Mumford, K. (1999) Normative concepts in conservation. Conservation Biology, 13, 22– 35 Carlton, J.T. & Geller, J.B. (1993) Ecological roulette: the global transport of nonindigenous marine organisms. Science, 261, 78–82. Carnaval, A.C., Hickerson, M.J., Haddad, C.F.B., Rodrigues, M.T. & Moritz, C. (2009) Stability predicts genetic diversity in the Brazilian Atlantic forest hotspot. Science, 323, 785–789. Carrión, J.S. & Fernández, S. (2009) The survival of the ‘natural potential vegetation’ concept (or the power of tradition). Journal of Biogeography, 36, 2202–2203. Carrión, J.S., Andrade, A., Bennett, K.D., Munuera, M. & Navarro, C. (2001) Crossing forest thresholds. Inertia and collapse in a Holocene sequence from south-central Spain. The Holocene, 11, 635–653. Carter, R.N. & Prince, S.D. (1981) Epidemic models used to explain biogeographical distribution limits. Nature, 293, 644–645. Carwardine, J., Wilson, K., Watts, M., Etter, A., Klein, C. & Possingham, H. (2008) Avoiding costly conservation mistakes: the importance of defining actions and costs in

268

References

spatial priority setting. PLoS ONE, 3, e2586. doi: 10.1371/ journal.pone.0002586 Carwardine, J., Klein, C.J., Wilson, K.A., Pressey, R.L. & Possingham, H.P. (2009) Hitting the target and missing the point: target-based conservation planning in context. Conservation Letters, 2, 4–11. Cassey, P., Lockwood, J.L., Blackburn, T.M. & Olden, J.D. (2007) Spatial scale and evolutionary history determine the degree of taxonomic homogenization across island bird assemblages. Diversity and Distributions, 13, 458–466. Castro, S.A. & Jaksic, F.M. (2008) How general are global trends in biotic homogenization? Floristic tracking in Chile, South America. Global Ecology and Biogeography, 17, 524–531. Castro, S.A., Muñoz, M. & Jaksic, F.M. (2007) Transit towards floristic homogenization on oceanic islands in the southeastern Pacific: comparing pre-European and current floras. Journal of Biogeography, 34, 213–222. Caughley, G. (1994) Directions in conservation biology. Journal of Animal Ecology, 63, 215–244. Chan, K.M.A., Shaw, M.R., Cameron, D.R., Underwood, E.C. & Daily, G.C. (2006) Conservation planning for ecosystem services. PLoS Biology, 4, 379. Channell, R. & Lomolino, M.V. (2000) Trajectories to extinction: spatial dynamics of the contraction of geographical ranges. Journal of Biogeography, 27, 169–179. Chape, S., Fish, L., Fox, P. & Spalding, M. (2003) United Nations List of Protected Areas. IUCN/UNEP, Gland, Switzerland/ Cambridge, UK. Chape, S., Harrison, J., Spalding, M. & Lysenko, I. (2005) Measuring the extent and effectiveness of protected areas as an indicator for meeting global biodiversity targets. Philosophical Transactions of the Royal Society B: Biological Sciences, 360, 443–455. Chapin, F.S., III, Callaghan, T.V., Bergeron, Y., Fukuda, M., Johnstone, J.F., Juday, G. & Zimov, S.A. (2004) Global change and the boreal forest: thresholds, shifting states or gradual change? Ambio, 33, 361–365. Cheung, C.P.S., Aliño, P.M., Uychiaoco, A.J. & Arceo, H.A. (2002) Marine Protected Areas in Southeast Asia, ASEAN Regional Centre for Biodiversity Conservation, Department of Environment and Natural Resources, Los Banos, Philippines. Cheung, W., Alder, J., Karpouzi, V., Watson, R., Lam, V., Day, C., Kaschner, K. & Pauly, D. (2005) Patterns of species richness in the high seas. Convention on Biological Diversity, Montreal. Chiba, S. (2004) Ecological and morphological patterns in communities of land snails of the genus Mandarina from the Bonin Islands. Journal of Evolutionary Biology, 17, 131–143. Chown, S.L., van Rensburg, B.J., Gaston, K.J., Rodrigues, A.S.L. & van Jaarsveld, A.S. (2003) Energy, species richness, and human population size: conservation implications at a national scale. Ecological Applications, 13, 1233–1241.

Christie, M., Hanley, N., Warren, J., Murphy, K., Wright, R. & Hyde, T. (2005) Valuing the diversity of biodiversity. Ecological Economics, 58, 304–317. Christie, P. (2004) Marine protected areas as biological successes and social failures in Southeast Asia. American Fisheries Society Symposium, 42, 155–164. Clavero, M. & García-Berthou, E. (2005) Invasive species are a leading cause of animal extinctions. Trends in Ecology & Evolution, 12, 10. Clavero, M. & García-Berthou, E. (2006) Homogenization dynamics and introduction routes of invasive freshwater fish in the Iberian Peninsula. Ecological Applications, 16, 2313–2324. Clements, F.E. (1916) Plant succession: an analysis of the development of vegetation. Carnegie Institution, Washington, DC. Clements, F.E. (1934) The relict method in dynamic ecology. Journal of Ecology, 22, 39–68. Clements, F.E. & Shelford, V.E. (1939) Bio-ecology. Chapman and Hall, New York. Clergeau, P., Croci, S., Jokimäki, J., Kaisanlahti-Jokimäki, M.L. & Dinetti, M. (2006) Avifauna homogenization by urbanisation: analysis at different European latitudes. Biological Conservation, 127, 336–344. Cocks, K.D. & Baird, I.A. (1989) Using mathematical programming to address the multiple reserve selection problem: an example from the Eyre Peninsula, South Australia. Biological Conservation, 49, 113–130. Cody, M.L. (2006) Plants on islands: diversity and dynamics on a continental archipelago. University of California Press, Berkeley, CA. Cohen, A.N. & Carlton, J.T. (1998) Accelerating invasion rate in a highly invaded estuary. Science, 279, 555–557. Colautti, R.I. & MacIsaac, H.J. (2004) A neutral terminology to define ‘invasive’ species. Diversity and Distributions, 10, 135–141. Colby, M.E. (1991) Environmental management in development: the evolution of paradigms. Ecological Economics, 3, 193–213. Colding, J. & Folke, C. (2001) Social taboos: ‘invisible’ systems of local resource management and biological conservation. Ecological Applications, 11, 584–600. Colinvaux, P.A. & De Oliveira, P.E. (2000) Palaeoecology and climate of the Amazon basin during the last glacial cycle. Journal of Quaternary Science, 15, 347–356. Collar, N.J. & Andrew, P.A. (1988) Birds to watch: the ICBP world checklist of threatened birds. ICBP technical publications No. 8, International Council for Bird Preservation, Cambridge, UK. Collen, B., Ram, M., Zamin, T. & McRae, L. (2008) The tropical biodiversity data gap: addressing disparity in global monitoring. Tropical Conservation Science, 1, 75–88. Collingham, Y.C., Wadsworth, R.A., Huntley, B. & Hulme, P.E. (2000) Predicting the spatial distribution of non-

References

indigenous riparian weeds: issues of spatial scale and extent. Journal of Applied Ecology, 37, 13–27. Connor, D.W., Allen, J.H., Golding, N., Howell, K.L., Lieberknect, L.M., Northen, K.O. & Reker, J.B. (2004) The Marine Habitat Classification for Britain and Ireland, Version 04.05. JNCC, Peterborough, UK. Connor, E.F. & McCoy, E.D. (1979) The statistics and biology of the species–area relationship. American Naturalist, 113, 791–833. Conservation International. (2004) Conserving Earth’s living heritage: a proposed framework for designing biodiversity conservation strategies. Conservation International, Washington, DC. Conwentz, H. (1909) The care of natural monuments. Cambridge University Press, Cambridge. Cooper, C.B., Dickinson, J., Phillips, T. & Bonney, R. (2007). Citizen science as a tool for conservation in residential ecosystems. Ecology and Society, 12, 11. [online] URL: http:// www.ecologyandsociety.org/vol12/iss2/art11/. Costanza, R., d’Arpe, R., de Groot, R., Farber, S., Grasso, M., Hannon, B., Litnburg, K., Naeem, S., O’Neill, R.V., Paruelo, G., Raskin, R.G., Sutton, P. & van den Belt, M. (1997) The value of the world’s ecosystem services and natural capital. Nature, 387, 253–260. Costanza, R., Andrade, F., Antunes, P., van den Belt, M., Boersma, D., Boesch, D.F., Catarino, F., Hanna, S., Limberg, K., Low, B., Molitor, M., Pereira, J.G., Rayner, S., Santos, R., Wilson, J. & Young, M. (1998) Principles for sustainable governance of the oceans. Science, 281, 198–199. Costello, C. & Polasky, S. (2004) Dynamic reserve site selection. Resource and Energy Economics, 26, 157–174. Costello, M.J. (2009) Motivating online publication of data. BioScience, 59, 418–427. Cotgrove, S. &. Duff, A. (1991) Environmentalism, values, and social change. The British Journal of Sociology, 32, 92–110. Cowen, R.K., Paris, C.B. & Srinivasan, A. (2006) Scaling of connectivity in marine populations. Science, 311, 522–527. Cowling, R.M., Pressey, R.L., Lombard, A.T., Desmet, P.G. & Ellis, A.G. (1999) From representation to persistence: requirements for a sustainable system of conservation areas in the species-rich mediterranean-climate desert of southern Africa. Diversity and Distributions, 5, 51–71. Cowling, R.M., Pressey, R.L., Rouget, M. & Lombard, A.T. (2003) A conservation plan for a global biodiversity hotspot – the Cape Floristic Region, South Africa. Biological Conservation, 112, 191–216. Coughenour, M.B. & Ellis, J.E. (1993) Landscape and climatic control of woody vegetation in a dry tropical ecosystem: Turkana District, Kenya. Journal of Biogeography, 20, 383–398. Cox, C.B. (2001) The biogeographic regions reconsidered. Journal of Biogeography, 28, 511–523.

269

Crandall, K.A., Posada, D. & Vasco, D. (1999) Effective population sizes: missing measures and missing concepts. Animal Conservation, 2, 317–319. Crandall, K.A., Bininda-Emonds, O.R.P., Mace, G.M. & Wayne, R.K. (2000) Considering evolutionary processes in conservation biology. Trends in Ecology & Evolution, 15, 290– 295. Crawford, B., Kasmidi, M., Korompis, F. & Pollnac, R.B. (2006) Factors influencing progress in establishing communitybased marine protected areas in Indonesia. Coastal Management, 34, 39–64. Crawley, M.J. & Harral, J.E. (2001) Scale dependence in plant biodiversity. Science, 291, 864–868. Crisci, J.V. (2001) The voice of historical biogeography. Journal of Biogeography, 28, 157–168. Crisci, J.V. & Katinas, L. (2009) Darwin, historical biogeography, and the importance of overcoming binary opposites. Journal of Biogeography, 36, 1027–1032. Cronon, W. (1996) The trouble with wilderness: or getting back to the wrong nature. Environmental History, 1, 7–28. Crowley, J.M. (1967) Biogeography. Canadian Geographer, 11, 312–26. Curnutt, J.L., Pimm, S.L. & Maurer, B.A. (1996) Population variability of sparrows in space and time. Oikos, 76, 131–144. Curran, L.M., Caniago, I., Paoli, G.D., Astianti, D., Kusneti, M., Leighton, M., Nirarita, C.E. & Haeruman, H. (1999) Impact of El Niño and logging on canopy tree recruitment in Borneo. Science, 286, 2184–2188. Curtis, T.P., Head, I.M., Lunn, M., Woodcock, S., Schloss, P.D. & Sloan, W.T. (2006) What is the extent of prokaryotic diversity? Philosophical Transactions of the Royal Society B: Biological Sciences, 361, 2023–2037. Daily, G.C., Ehrlich, P.R. & Sánchez-Azofeifa, A. (2001) Countryside biogeography: use of human-dominated habitats by the avifauna of southern Costa Rica. Ecological Applications, 11, 1–13. Daily, G.C., Ceballos, G., Pacheco, J., Suzán, G. & SánchezAzofeifa, A. (2003) Countryside biogeography of neotropical mammals: conservation opportunities in agricultural landscapes of Costa Rica. Conservation Biology, 17, 1814–1826. Daily, G.C., Polasky, S., Goldstein, J., Kareiva, P.M., Mooney, H.A., Pejchar, L., Ricketts, T.H., Salzman, J. & Shallenberger, R. (2009) Ecosystem services in decision making: time to deliver. Frontiers in Ecology and the Environment, 7, 21–28. Dammerman, K.W. (1929) Preservation of wildlife and nature reserves in the Netherlands Indies. Fourth Pacific Science Congress, Bandung. Darlington, P.J. (1957) Zoogeography: the geographical distribution of animals. Wiley, New York. Darwin, C. (1859) On the origin of species by means of natural selection, 1st edn. John Murray, London.

270

References

Darwin, C. (1872) On the origin of species, 6th edn. Mentor, New York. Dasmann, R.F. (1972) Towards a system for classifying natural regions of the world and their representation by national parks and reserves. Biological Conservation, 4, 247–255. Dasmann, R.F. (1973) A system for defining and classifying natural regions for the purposes of conservation. IUCN, Morges, Switzerland. Davis, A.J., Jenkinson, L.S., Lawton, J.H., Shorrocks, B. & Wood, S. (1998) Making mistakes when predicting shifts in species range in response to global warming. Nature, 391, 783–786. Davis, G.E. (2005) Science and society: marine reserve design for the California Channel Islands. Conservation Biology, 19, 1745–1751. Davis, M.B. (1981) Quaternary history and the stability of forest communities. Forest succession: concepts and application (ed. by D.C. West), pp. 132–153. Springer-Verlag, Berlin. Davis, M.B. & Shaw, R.G. (2001) Range shifts and adaptive responses to Quaternary climate change. Science, 292, 673–679. Dawson, D. (1994) Are habitat corridors conduits for animals and plants in a fragmented landscape? A review of the scientific evidence. English Nature Research Reports, No. 94. Day, J.C. & Roff, J.C. (2000) Planning for representative marine protected areas: a framework for Canada’s oceans. World Wildlife Fund Canada, Toronto. Dearing, J.A. (2008) Landscape change and resilience theory: a palaeoenvironmental assessment from Yunnan, SW China. The Holocene, 18, 117–127. Dehnen-Schmutz, K. (2004) Alien species reflecting history: medieval castles in Germany. Diversity and Distributions, 10, 147–151. Delcourt, H. & Delcourt, P. (1991) Quaternary ecology: a paleoecological perspective. Chapman and Hall, London. Desmet, P. & Cowling, R.M. (2004) Using the species–area relationship to set baseline targets for conservation. Ecology and Society, 9, 11 (http://www.ecologyandsociety.org/vol9/ iss2/art11). Devictor, V., Whittaker, R.J. & Beltrame, C. (2010) Beyond scarcity: citizen science programmes as useful tools for conservation biogeography. Diversity and Distributions, 16, 354–362. Diamond, J.M. (1972) Biogeographical kinetics: estimation of relaxation times for avifaunas of southwest Pacific islands. Proceedings of the National Academy of Sciences USA, 69, 3199–3203. Diamond, J.M. (1975a) The island dilemma: lessons of modern biogeographic studies for the design of natural preserves. Biological Conservation, 7, 129–146. Diamond, J.M. (1975b) Assembly of species communities. Ecology and evolution of communities (ed. by M.L. Cody, and J.M. Diamond), pp. 342–444. Harvard University Press, Cambridge, MA.

Diamond, J.M. (2004) Collapse: how societies choose to fail or succeed. Viking Press, London. Diamond, J.M. (2007) Easter Island revisited. Nature, 317, 1692–1694. Diamond, J.M. & May, R.M. (1981) Island biogeography and the design of nature reserves. Theoretical ecology, 2nd edn (ed. by R.M. May), pp. 228–252. Blackwell, Oxford. Diaz, R.J. (2008) Spreading dead zones and consequences for marine ecosystems. Science, 321, 926–929. Dietrich, G. (1963) General oceanography. John Wiley and Sons, London. Dillon, S. & Fjeldså, J. (2005) The implications of different species concepts for describing biodiversity patterns and assessing conservation needs for African birds. Ecography, 28, 682–692. Dinerstein, E. & Irvin, W.R. (2005) Re-wilding: no need for exotics as natives return. Nature, 437, 476. Dinerstein, E., Olson, D.M., Graham, D.J., Webster, A.L., Pimm, S.A., Bookbinder, M.A. & Ledec, G. (1995) A conservation assessment of the terrestrial ecoregions of Latin America and the Caribbean. The World Bank, Washington, DC. Diniz-Filho, J.A.F., Bini, L.M., Rangel, T.F., Loyola, R.D., Hof, C., Nogués-Bravo, D. & Aráujo, M.B. (2009) Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change. Ecography, 32, 897–906. Dimitrakopoulos, P.G., Memtsas, D. & Troumbis, A.Y. (2004) Questioning the effectiveness of the Natura 2000 Special Areas of Conservation strategy: the case of Crete. Global Ecology and Biogeography, 13, 199–207. Dmowski, K. & Kozakiewicz, M. (1990) Influence of a shrub corridor on movements of passerine birds to a lake littoral zone. Landscape Ecology, 4, 99–108. Doak, D.F. & Mills, L.S. (1994) A useful role for theory in conservation. Ecology, 75, 615–626. Doak, D.F., Marino, P.C. & Kareiva, P.M. (1992) Spatial scale mediates the influence of habitat fragmentation on dispersal success: implications for conservation. Theoretical Population Biology, 41, 315–336. Dobson, A., Lodge, D., Alder, J., Cumming, G., Keymer, J., McGlade, J., Mooney, H., Rusak, J.A., Sala, O., Wolters, V., Wall, D., Winfree, R. & Xenopoulos, M. (2006) Habitat loss, trophic collapse and the decline of ecosystem services. Ecology, 87, 1915–1924. Donald, P.F. & Greenwood, J.J.D. (2001) Spatial patterns of range contraction in British breeding birds. Ibis, 143, 593–601. Donlan, J. (2007) Restoring America’s big, wild animals. Scientific American, June 2007, 70–77. Donlan, J., Greene, H.W., Berger, J., Bock, C.E., Bock, J.H., Burney, D.A., Estes, J.A., Foreman, D., Martin, P.S., Roemer, G.W., Smith, F.A. & Soulé, M.E. (2005) Re-wilding North America, Nature, 436, 913–914. Doolittle, W.F. & Zhaxybayeva, O. (2009) On the origin of prokaryotic species. Genome Research, 19, 744–756.

References

Drake, J.M. & Lodge, D.M. (2004) Global hot spots of biological invasions: evaluating options for ballast-water management. Proceedings of the Royal Society, B: Biological Sciences, 271, 575–580. Drechsler, M. (2005) Probabilistic approaches to scheduling reserve selection. Biological Conservation, 122, 253– 262. Drechsler, M., Lourival, R. & Possingham, H. P. (2009) Conservation planning for successional landscapes. Ecological Modelling, 220, 438–450. Driver, A., Cowling, R.M. & Maze, K. (2003) Planning for living landscapes: perspectives and lessons from South Africa. Conservation International and Botanical Society of South Africa, Washington, DC and Cape Town. Dublin, H.T., Sinclair, A. & McGlade, J. (1990) Elephants and fire as causes of multiple stable states in the Serengeti– Mara woodlands. Journal of Animal Ecology, 59, 1147– 1164. Dudley, N. (ed.) (2008) Guidelines for applying protected area management categories. IUCN, Gland, Switzerland. Dumaup, J.N.B., Cola, R.M., Trono, R.B., Ingles, J.A., Miclat, E.F.B. & Ibuna, N.P. (2004) Conservation plan for SuluSulawesi marine ecoregion. WWF Sulu-Sulawesi Marine Ecoregion Program, Quezon City, Philippines. Eagleton, T. (1983) Literary theory: an introduction. University of Minnesota Press, Minneapolis, MN. Early, R., Anderson, B. & Thomas, C.D. (2008) Using habitat distribution models to evaluate large-scale landscape priorities for spatially dynamic species. Journal of Applied Ecology, 45, 228–238. Ebach, M.C. & Goujet, D.F. (2006) The first biogeographical map. Journal of Biogeography, 33, 761–769. Eberle, M.E. & Channell, R.B. (2006) Homogenization of fish faunas in two categories of streams in a single basin in Kansas and the choice of similarity coefficients. Transactions of the Kansas Academy of Science, 109, 41–46. Edwards, S.V. (2009) Is a new and general theory of molecular systematics emerging? Evolution, 63, 1–19. Egerton, F.N. (1973) Changing concepts of the balance of nature. Quarterly Review of Biology, 48, 322–350. Egoh, B., Rouget, M., Reyers, B., Knight, A. T., Cowling, R. M., van Jaarsveld, A. & Welz, A. (2007) Integrating ecosystem services into conservation assessments: a review. Ecological Economics, 63, 714–721. Ehrlich, P.R. & Ehrlich, A.H. (1992) The value of biodiversity. Ambio, 21, 219–226. Ehrlich, P.R. & Wilson, E.O. (1991) Biodiversity studies: science and policy. Science, 253, 758–762. Eken, G., Bennun, L., Brooks, T.M., Darwall, W., Fishpool, L.D.C., Foster, M., Knox, D., Langhammer, P., Matiku, P. Radford, E., Salaman, P., Sechrest, W., Smith, M.L., Spector, S. & Tordoff, A. (2004) Key biodiversity areas as site conservation targets. BioScience, 54, 1110–1118. Ekman, S. (1953) Zoogeography of the sea. Sidgwick and Jackson, London.

271

Ellis, E.C. & Ramankutty, N. (2008) Putting people in the map: anthropogenic biomes of the world. Frontiers in Ecology and the Environment, 6, 439–447. Elton, C.S. (1930) Animal ecology and evolution. Oxford University Press, Oxford. Elton, C.S. (1958) The ecology of invasions by animals and plants. Methuen, London. Emerson, B.C. & Gillespie, R.G. (2008) Phylogenetic analysis of community assembly and structure over space and time. Trends in Ecology & Evolution, 23, 619–630. Environment Australia (1998) Interim marine and coastal regionalisation for Australia. Department of Environment and Heritage, Canberra. Available from http://www.environment.gov.au/coasts/mbp/publications/imcra/pubs/ imcra3-3.pdf (accessed July 2009). Erwin, T.L. (1983) Beetles and other insects of tropical forest canopies at Manaus, Brazil, sampled by insecticidal fogging. Tropical rain forest ecology and management (ed. by S.L. Sutton, T.C. Whitmore and A.C. Chadwick), pp. 59–75. Blackwell Scientific Publications, Oxford. Estes, J.A., Duggins, D.O. & Rathbun, G.B. (1989) The ecology of extinctions in kelp forest communities. Conservation Biology, 3, 252–264. Evans, I.M., Summers, R.W., O’Toole, L., Orr-Ewing D.C., Evans, R., Snell, N. & Smith, J. (1999) Evaluating the success of translocating red kites Milvus milvus to the UK. Bird Study, 46, 129–144. Evans, K.L., Gaston, K.J., Frantz, A.C., Simeoni, M., Sharp, S.P., McGowan, A., Dawson, D.A., Walasz, K., Partecke, J., Burke, T. & Hatchwell, B.J. (2009) Independent colonization of multiple urban centres by a formerly forest specialist bird species. Proceedings of the Royal Society B: Biological Sciences, 276, 2403–2410. Ewald, J. (2003) A critique for phytosociology. Journal of Vegetation Science, 14, 291–296. Ewers, R.M. & Didham, R.K. (2006) Confounding factors in the detection of species responses to habitat fragmentation. Biological Reviews, 81, 117–142. Ewers, R.M., Kliskey, A., Walker, S., Rutledge, D., Harding J. & Didham, R.K. (2006) Past and future trajectories of forest loss in New Zealand. Biological Conservation, 133, 312–325. Faith, D.P. (2006) The role of the phylogenetic diversity measure, PD, in bio-informatics: getting the definition right. Evolutionary Bioinformatics Online, 2, 301–307. Faith, D.P. (2009) Phylogenetic triage, efficiency and risk aversion. Trends in Ecology & Evolution, 24, 182. Faith, D.P. & Walker, P.A. (1996) Environmental diversity: on the best-possible use of surrogate data for assessing the relative biodiversity of sets of areas. Biodiversity and Conservation, 5, 399–415. Faith, D.P., Ferrier, S. & Walker, P.A. (2004a) The ED strategy: how species-level surrogates indicate general biodiversity patterns through an ‘environmental diversity’ perspective. Journal of Biogeography, 31, 1207–1217.

272

References

Faith, D.P., Reid, C.A.M. & Hunter, J. (2004b) Integrating phylogenetic diversity, complementarity, and endemism for conservation assessment. Conservation Biology, 18, 255–261. Fahrig, L. & Merriam, G. (1994) Conservation of fragmented populations. Conservation Biology, 8, 50–59. Fahrig, L. & Paloheimo, J. (1988) Determinants of local population size in patchy habitats. Theoretical Population Biology, 34, 194–213 Falk, D.A., Palmer, M., Zedler, J. & Hobbs, R.J. (2006) Foundations of restoration ecology. Island Press, London. Farnsworth, E.J. & Ogurcak, D.E. (2006) Biogeography and decline of rare plants in New England: historical evidence and contemporary monitoring. Ecological Applications, 16, 1327–1337. Feeley, K.J. (2003) Analysis of avian communities in Lake Guri, Venezuela, using multiple assembly rule models. Oecologia, 137, 104–113. Feeley, K.J. & Silman, M.R. (2010) Modelling the responses of Andean and Amazonian plant species to climate change: the effects of georeferencing errors and the importance of data filtering. Journal of Biogeography, 37, 733–740. Felton, A., Fischer, J., Lindenmayer, D.B., Montague-Drake, R., Lowe, A.R., Saunders, D., Felton, A.M., Steffen, W., Munro, N.T., Youngentob, K., Gillen, J., Gibbons, P., Bruzgul, J.E., Fazey, I., Bond, S.J., Elliott, C.P., Macdonald, B.C.T., Porfirio, L.L., Westgate, M. & Worthy, M. (2009) Climate change, conservation and management: an assessment of the peerreviewed scientific journal literature. Biodiversity and Conservation, 18, 2243–2253. Fernandes, L., Day, J., Lewis, A., Slegers, S., Kerrigan, B., Breen, D., Cameron, D., Jago, B., Hall, J., Lowe, D., Innes, J., Tanzer, J., Chadwick, V., Thompson, L., Gorman, K., Simmons, M., Barnett, B., Sampson, K., De’ath, G., Mapstone, B., Marsh, H., Possingham, H. P., Ball, I., Ward, T., Dobbs, K., Aumend, J., Slater, D. & Stapleton, K. (2005) Establishing representative no-take areas in the Great Barrier Reef: large-scale implementation of theory on marine protected areas. Conservation Biology, 19, 1733–1744. Fernández-Palacios, J.M., Arévalo, J.R., Delgado, J.D. & Otto, R. (2004) Canarias, ecologıá medio ambiente y desarrollo. Centro de la Cultura Popular de Canarias, La Laguna. Ferrier, S. (2002) Mapping spatial pattern in biodiversity for regional conservation planning: where to from here? Systematic Biology, 51, 331–363. Ferrier, S., Pressey, R.L. & Barrett, T.W. (2000) A new predictor of the irreplaceability of areas for achieving a conservation goal, its application to real-world planning, and a research agenda for further refinement. Biological Conservation, 93, 303–325. Field, R., O’Brien, E.M. & Whittaker, R.J. (2005) Global models for predicting woody plant richness from climate: development and evaluation. Ecology, 86, 2263–2277. Fischer, J. & Lindenmayer, D.B. (2005) Perfectly nested or significantly nested an important difference for conservation management. Oikos, 109, 485–494.

Fishpool, L.D.C. & Evans, M.I. (2001) Important Bird Areas in Africa and associated islands, BirdLife International, Cambridge, UK. Fitter, A.H. & Fitter, R.S.R. (2002) Rapid changes in flowering time in British plants. Science, 296, 1689–1691. Fitzsimmons, J.A. & Wescott, G. (2004) The classification of lands managed for conservation: existing and proposed frameworks, with particular reference to Australia. Environmental Science and Policy, 7, 477–486. Fjeldså, J. (2000) The relevance of systematics in choosing priority areas for global conservation. Environmental Conservation, 27, 67–75. Fleishman, E., Murphy, D.D. & Brussard, P.F. (2000) A new method for selection of umbrella species for conservation planning. Ecological Applications, 10, 569–579. Fleishman, E., MacNally, R., Fay, J.P. & Murphy, D.D. (2001) Modeling and predicting species occurrence using broad scale environmental variables: an example with butterflies of the Great Basin. Conservation Biology, 15, 1674–1685. Fleishman, E., Donnelly, R., Fay, J.P. & Reeves, R. (2007) Applications of nestedness analyses to biodiversity conservation in developing landscapes. Landscape Urban Planning, 81, 271–281. Fogarty, M.J. & Botsford, L.W. (2007) Population connectivity and spatial management of marine fisheries. Oceanography, 20, 112–123. Folke, C., Carpenter, S., Walker, B., Scheffer, M., Elmqvist, T., Gunderson, L. & Holling, C.S. (2004) Regime shifts, resilience, and biodiversity in ecosystem management. Annual Review of Ecology, Evolution, and Systematics, 35, 557–581. de Fontaubert, A.C. (2001) Legal and political considerations. The status of natural resources on the high seas (ed. by WWF/ IUCN/WCPA), pp. 1–68. WWF/IUCN, Gland, Switzerland. Forest, F., Grenyer, R., Rouget, M., Davies, T.J., Cowling, R.M., Faith, D.P., Balmford, A., Manning, J.C., Proches, S., van der Bank, M., Reeves, G., Hedderson, T.A.J. & Savolainen, V. (2007) Preserving the evolutionary potential of floras in biodiversity hotspots. Nature, 455, 757–760. Forshaw, N. (1998) Protea Atlas Project. Available from http:// protea.worldonline.co.za. Forster, J.R. (1778) Observations made during a voyage round the world, on physical geography, natural history and ethic philosophy. G. Robinson, London. Foxcroft, L.C., Richardson, D.M., Rouget, M. & MacFadyen, S. (2009) Patterns of alien plant distribution at multiple spatial scales in a large national park: implications for ecology, management and monitoring. Diversity and Distributions, 15, 367–378. Frank, D.J., Hironaka, A., Meyer, J.W., Schoffer, E. & Tuma, N.B. (1999) The rationalization and organisation of nature in world culture. Constructing world culture: international non-government organisations since 1875, (ed. by J. Boli and G.M. Thomas), pp. 81–96. Stanford University Press, Stanford, CA.

References

Frank, K.T., Petrie, B., Choi, J.S. & Leggett, W.C. (2005) Trophic cascades in a formerly cod-dominated ecosystem. Science, 308, 1621–1623. Frankham, R., Ballou, J.D. & Briscoe, D.A. (2002) Introduction to conservation genetics. Cambridge University Press, Cambridge. Freese, C.H. Aune, K.E., Boyd, D.P., Derr, J.N., Forrest, S.C., Gates, C.C., Gogan, P.J.P., Grassel, S.M., Halbert, N.D., Kunkel, K. & Redford, K.H. (2007) Second chance for the plains bison. Biological Conservation, 136, 175– 184. Frissell, C.A., Liss, W.J., Warren, C.E. & Hurley, M.D. (1986) A hierarchical framework for stream habitat classification: viewing streams in a watershed context. Environmental Management, 10, 199–214. Funtowicz, S. & Ravetz, J. (1994) Uncertainty, complexity and post-normal science. Environmental Toxicology and Chemistry, 13, 1881–1885. Gallien, L., Münkemüller, T., Albert, C.H., Boulangeat, I. & Thuiller, W. (2010) Predicting potential distributions of invasive species: where to go from here? Diversity and Distributions, 16, 331–342. Game, E.T., Watts, M.E., Wooldridge, S. & Possingham, H.P. (2008) Planning for persistence in marine reserves: a question of catastrophic importance. Ecological Applications, 18, 670–680. Game, E.T., Grantham, H.S., Hobday, A.J., Pressey, R.L., Lombard, A.T., Beckley, L.E., Gjerde, K., Bustamante, R., Possingham, H.P. & Richardson, A.J. (2009) Pelagic protected areas: the missing dimension in ocean conservation. Trends in Ecology & Evolution, 24, 360–369. Gascon, C., Lovejoy, T.E., Bierregaard, R.O., Jr, Malcolm, J.R., Stouffer, P.C., Vasconcelos, H., Laurance, W.F., Zimmerman, B., Tocher, M. & Borges, S. (1999) Matrix habitat and species persistence in tropical forest remnants. Biological Conservation, 91, 223–230. Gaston, K.J. (1991) How large is a species’ geographic range? Oikos, 61, 434. Gaston, K.J. (1994) Measuring geographic range sizes. Ecography, 17, 198–205. Gaston, K.J. (1996) Species-range-size distributions: patterns, mechanisms and implications. Trends in Ecology & Evolution, 11, 197–201. Gaston, K.J. (2003) The structure and dynamics of geographic ranges. Oxford University Press, Oxford. Gaston, K.J. & Blackburn, T.M. (1994) Are newly discovered bird species small-bodied? Biodiversity Letters, 2, 16–20. Gaston, K.J. & Blackburn, T.M. (2000) Pattern and process in macroecology. Blackwell Science, Oxford. Gaston, K.J. & Fuller, R.A. (2007) Biodiversity and extinction: losing the common and the widespread. Progress in Physical Geography, 31, 213–225. Gaston, K.J. & Fuller, R.A. (2008) Commonness, population depletion and conservation biology. Trends in Ecology & Evolution, 23, 14–19.

273

Gaston, K.J. & Fuller, R.A. (2009) The sizes of species’ geographic ranges. Journal of Applied Ecology, 46, 1–9. Gaston, K.J. & O’Neill, M.A. (2004) Automated species identification: why not? Philosophical Transactions of the Royal Society B: Biological Sciences, 359, 655–667. Gaston, K.J., Rodrigues, A.S.L., van Rensburg, B.J., Koleff, P. & Chown, S.L. (2001) Complementary representation and zones of ecological transition. Ecology Letters, 4, 4–9. Gaston, K.J., Pressey, R.L. & Margules, C.R. (2002) Persistence and vulnerability: retaining biodiversity in the landscape and in protected areas. Journal of Biosciences, 27, 361–384. Gaston, K.J., Jackson, S.F., Cantú-Salazar, L. & Cruz-Piñón, G. (2008) The ecological performance of protected areas. Annual Review of Ecology, Evolution, and Systematics, 39, 93–113. Gates, S. & Donald, P. F. (2000) Local extinction of British farmland birds and the prediction of further loss. Journal of Applied Ecology, 37, 806–820. Gillespie, R.G. (2004) Community assembly through adaptive radiation in Hawaiian spiders. Science, 303, 356–359. Gillson, L. (2004a) Testing non-equilibrium theories in savannas: 1400 years of vegetation change in Tsavo National Park, Kenya. Ecological Complexity, 1, 281–298. Gillson, L. (2004b) Evidence of hierarchical patch dynamics in an East African savanna? Landscape Ecology, 19, 883–894. Gillson, L. & Duffin, K.I. (2007) Thresholds of potential concern as benchmarks in the management of African savannahs. Philosophical Transactions of the Royal Society B: Biological Sciences, 362, 309–319. Gillson, L. & Willis, K.J. (2004) ‘As Earth’s testimonies tell’: wilderness conservation in a changing world. Ecology Letters, 7, 990–998. Gilmore, S., Mackey, B. & Berry, S. (2007) The extent of dispersive movement behaviour in Australian vertebrate animals, possible causes, and some implications for conservation. Pacific Conservation Biology, 13, 93–103. Gilpin, M.E. (1988) Extinction in subdivided habitats – comment. Conservation Biology, 2, 290–292. Gleason, H.A. (1922) On the relation between species and area. Ecology, 3, 158–162. Gleason, H.A. (1926) The individualistic concept of the plant association. Bulletin of the Torrey Botanical Club, 53, 7–26. Godefroid, S. & Koedam, N. (2003) How important are large vs. small forest remnants for the conservation of the woodland flora in an urban context. Global Ecology and Biogeography, 12, 287–298. Gotelli, N.J. (1991) Metapopulation models: the rescue effect, the propagule rain, and the core-satellite hypothesis. American Naturalist, 138, 768–776. Good, R. (1947–1974) The geography of flowering plants. Longmans, London. Graham, C.H. & Hijmans, R.J. (2006) A comparison of methods for mapping species ranges and species richness. Global Ecology and Biogeography, 15, 578–587.

274

References

Grainger, A. (2008) Difficulties in tracking the long-term global trend in tropical forest area. Proceedings of the National Academy of Sciences USA, 105, 818–823. Grantham, H.S., Petersen, S.L. & Possingham, H.P. (2008) Reducing bycatch in the South African pelagic longline fishery: the utility of different approaches to fisheries closures. Endangered Species Research, 5, 291–299. Grantham, H.S., Wilson, K.A., Moilanen, A., Rebelo, T. & Possingham, H.P. (2009) Delaying conservation actions for improved knowledge: how long should we wait? Ecology Letters, 12, 293–301. Grantham, H.S., Bode, M., McDonald-Madden, E., Game, E.T., Knight, A.T. & Possingham, H.P. (2009) Effective conservation planning requires learning and adaptation. Frontiers in Ecology and the Environment 8, 431–437. Green, A. & Sheppard, S. (2005) Delineating the eastern side of the Coral Triangle, its ecoregions and functional seascapes. The Nature Conservancy, Newmarket, UK. Green, R.E., Collingham, Y.C., Willis, S.G., Gregory, R.D., Smith, K.W. & Huntley, B. (2008) Performance of climate envelope models in retrodicting recent changes in bird population size from observed climatic change. Biology Letters, 4, 599–602. Gregory, R.D., Wilkinson, N.I., Noble, D.G., Robinson, R.A., Brown, A.F., Hughes, J., Procter, D.A., Gibbons, D.W. & Galbraith, C.A. (2002) The population status of birds in the United Kingdom, Channel Islands and Isle of Man: an analysis of conservation concern 2002–2007. British Birds, 95, 410–448. Grehan, J.R. (1993) Conservation biogeography and the biodiversity crisis: a global problem in space/time. Biodiversity Letters, 1, 134–140. Griffiths, C.J., Jones, C.G., Hansen, D.M., Puttoo, M., Tatayah, R.V., Müller, C.B. & Harris, S. (2009) The use of extant nonindigenous tortoises as a restoration tool to replace extinct ecosystem engineers. Restoration Ecology, 18, 1–7. Grimmett, R.F. & Jones, T. (1989) Important Bird Areas in Europe. International Council for Bird Preservation, Cambridge, UK. GRN [Government of the Republic of Namibia] (1996) Nature Conservation Amendment Act, 1996. Government Gazette No. 1333, Windhoek. Groombridge, B. (ed.) (1992) Global biodiversity: status of the Earth’s living resources. A report compiled by the World Conservation Monitoring Centre. Chapman and Hall: London. Groombridge, B. & Jenkins, M. D. (2002) World atlas of biodiversity: Earth’s living resources in the 21st century. University of California Press, Berkeley, CA. Gropp, R.E. (2003) Are university natural science collections going extinct? BioScience, 53, 550–550. Grossman, D. H., Faber-Langendoen, D., Weakley, A. S., Anderson, M., Bourgeron, P., Crawford, R., Goodin, K., Landaal, S., Metzler, K., Patterson, K.D., Pyne, M., Reid, M. & Sneddon, L. (1998) International classification of ecological

communities: terrestrial vegetation of the United States. Volume I. The National Vegetation Classification System: development, status, and applications. The Nature Conservancy, Arlington, Virginia, USA. Also available at: http://biology.usgs.gov/ npsveg. Grove, R. (1996) Green imperialism: colonial expansion, tropical island Edens and the origins of environmentalism, 1600– 1860. Cambridge University Press, New York. Groves, C.R., Jensen, D.B., Valutis, L.L., Redford, K.H., Shaffer, M.L., Scott, J.M., Baumgartner, J.V., Higgins, J.V., Beck, M.W. & Anderson, M.G. (2002) Planning for biodiversity conservation: putting conservation science into practice. BioScience, 52, 499–512. Grumbine, R.E. (1994) What is ecosystem management? Conservation Biology, 8, 27–38. Grumbine, R.E. (1997) Reflections on ecosystem management. Conservation Biology, 11, 41–47. Guisan, A. & Zimmermann, N.E. (2000) Predictive habitat distribution models in ecology. Ecological Modelling, 135, 147–186. Gunderson, L. & Folke, C. (2003) Toward a ‘science of the long view’. Conservation Ecology, 7, 15. Gunderson, L.H. & Holling, C.S. (eds) (2001) Panarchy: understanding transformations in human and natural systems. Island Press, Washington, DC. Gurevitch, J. & Padilla, D.K. (2004) Are invasive species a major cause of extinctions? Trends in Ecology & Evolution, 19, 470–474. Gustafson, E. J. & Gardner, R H. (1996) The effect of landscape heterogeneity on the probability of patch colonization. Ecology, 77, 94–107. Hadly, E.A., Ramakrishnan, U., Chan, Y.L., van Tuinen, M., O’Keefe, K., Spaeth, P.A. & Conroy, C.J. (2004) Genetic response to climatic change: insights from ancient DNA and phylochronology. PLoS Biology, 2, 1600–1609. Hafner, D.J. & Riddle, B.R. (in press) Boundaries and barriers of North American warm deserts: an evolutionary perspective. Palaeogeography and palaeobiogeography: biodiversity in space and time. Systematics Association special volumes (ed. by P. Upchurch, A. McGowan and C. Slater). CRC Press, Boca Raton, FL. Haila, Y.A. (2002) A conceptual genealogy of fragmentation research: from island biogeography to landscape ecology. Ecological Applications, 12, 321–334. Hall, B.P. & Moreau, R.E. (1962) A study of rare birds in Africa. Bulletin of the British Museum of Natural History, 8, 313–378. Hall, J.E., Kirby, K.J. & Whitbread, A.M. (2001) National vegetation classification: field guide to woodland. Joint Nature Conservation Committee, Peterborough. Available from: www.jncc.gov.uk/PDF/fieldguidetowoodland.pdf. Halpern, B.S. & Warner, R.R. (2003) Matching marine reserve design to reserve objectives. Proceedings of the Royal Society B: Biological Sciences, 270, 1871–1878.

References

Halpern, B.S., Walbridge, S., Selkoe, K.A., Kappel, C.V., Micheli, F., D’Agrosa, C., Bruno, J.F., Casey, K.S., Ebert, C., Fox, H.E., Fujita, R., Heinemann, D., Lenihan, H.S., Madin, E.M.P., Terry, M.P., Selig, E.R., Spalding, M., Steneck, R. & Watson, R. (2008) A global map of human impact on marine ecosystems. Science, 319, 948–952. Hannah, L. & Hansen, L. (2005) Designing landscapes and seascapes for change. Climate change and biodiversity (ed. by T. Lovejoy and L. Hannah), pp. 329–341. Yale University Press, New Haven and London. Hannah, L., Midgley, G.F., Lovejoy, T., Bond, W.J., Bush, M.B., Lovett, J.C., Scott, D. & Woodward, F.I. (2002a) Conservation of biodiversity in a changing climate. Conservation Biology, 16, 264–268. Hannah, L., Midgley, G.F. & Millar, D. (2002b) Climate changeintegrated conservation strategies. Global Ecology and Biogeography, 11, 485–495. Hannah, L., Midgley, G., Andelman, S., Araújo, M., Hughes, G., Martinez-Meyer, E., Pearson, R. & Williams, P. (2007) Protected area needs in a changing climate. Frontiers in Ecology and the Environment, 5, 131–138. Hansen, M.C., Shimabukuro, Y.E., Potapov, P. & Pittman, K. (2008) Comparing annual MODIS and PRODES forest cover change data for advancing monitoring of Brazilian forest cover. Remote Sensing of Environment, 112, 3784–3793. Hanski, I. (1999) Metapopulation ecology. Oxford University Press, Oxford. Hanski, I. & Gilpin, M. (1991) Metapopulation dynamics: brief history and conceptual domain. Biological Journal of the Linnean Society, 42, 3–16. Harris, G. & Pimm, S.L. (2008) Range size and extinction risk in forest birds. Conservation Biology, 22, 163–171. Harris, L.D. (1984) The fragmented forest: island biogeography theory and the preservation of biotic diversity. University of Chicago Press, Chicago. Harris, P.T. (2007) High seas and marine protected areas. AusGeo News, 86, 1–3. Harris, P.T. & Whiteway, T. (2009) High seas marine protected areas: benthic environmental conservation priorities from a GIS analysis of global ocean biophysical data. Ocean and Coastal Management, 52, 22–38. Harrison, S. (1991) Local extinction in a metapopulation context: an empirical evaluation. Biological Journal of the Linnean Society, 42, 73–88. Harrison, S. & Taylor, A.D. (1997) Empirical evidence for metapopulation dynamics. Metapopulation dynamics: ecology, genetics and evolution (ed. by I. Hanski and M.E. Gilpin), pp. 27–42. Academic Press, New York. Harrison, S., Stahl, A. & Doak, D. (1993) Spatial models and spotted owls: exploring some biological issues behind recent events. Conservation Biology, 7, 950–953. Harrison, S.P., Braconnot, P., Joussaume, S., Hewitt, C. & Stouffer, R.J. (2002) Comparison of palaeoclimate simulations enhances confidence in models. Transactions of the American Geophysical Union, 83, 447.

275

Hayden, B.P., Ray, G.C. & Dolan, R. (1984) Classification of coastal and marine environments. Environmental Conservation, 11, 199–207. Heath, M.F., Evans, M.I., Hoccom, D.G., Payne, A.J. & Peet, N.B. (2000) Important Bird Areas in Europe: priority sites for conservation, BirdLife International, Cambridge, UK. Hebert, P.D.N., Cywinska, A., Ball, S.L. & Dewaard, J.R. (2003) Biological identifications through DNA barcodes. Proceedings of the Royal Society B: Biological Sciences, 270, 313–321. Hedgepeth, J.W. (ed.) (1957) Treatise on marine ecology and paleoecology. The Geological Society of America Memoir 67. Geological Society of America, NY. Hennig, W. (1966) Phylogenetic systematics. The University of Illinois Press, Urbana, IL. Hernandez, P.A., Graham, C.H., Master, L.L. & Albert, D.L. (2006) The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography, 29, 773–785. Hewitt, G. (2000) The genetic legacy of the Quaternary ice ages. Nature, 405, 907–913. Heywood, V.H., Mace, G.M., May, R.M., Stuart, S.N. (1994) Uncertainties in extinction rates. Nature, 368, 105. Hickling, R., Roy, D.B., Hill, J.K., Fox, R. & Thomas, C.D. (2006) The distributions of a wide range of taxonomic groups are expanding polewards. Global Change Biology, 12, 450–455. Hicks, A.P. (1989) Whatever happened to the Allegheny woodrat? The Conservationist, 43, 34–38. Higgins, C.L., Willig, M.R. & Strauss, R.E. (2006) The role of stochastic processes in producing nested patterns of species distributions. Oikos, 114, 159–167. Hilborn, R., Stokes, K., Maguire, J.-J., Smith, T., Botsford, L.W., Mangel, M., Orensanz, J., Parma, A., Rice, J., Bell, J., Cochrane, K.L., Garcia, S., Hall, S.J., Kirkwood, G.P., Sainsbury, K., Stefansson, G. & Walters, C. (2004) When can marine reserves improve fisheries management? Ocean and Coastal Management, 47, 197–205. Hinsley, S.A., Bellamy, P.E., Newton, I. & Sparks, T.H. (1996) Influences of population size and woodland area on bird species distribution in small woods. Oecologia, 105, 100–106. Hislop, C. (2007) High seas marine protected area policy development: macro-goals or micro-actions? The Environmentalist, 27, 119–129. Hoagstrom, C.W., Wall, S.S., Kral, J.G., Blackwell, B.G. & Berry, C.R. (2007) Zoogeographic patterns and faunal change of South Dakota fishes. Western North American Naturalist, 67, 161–184. Hobbs, R.J. (1992) The role of corridors in conservation: solution or bandwagon? Trends in Ecology & Evolution, 7, 389–392. Hobbs, R.J., Arico, S., Aronson, J., Baron, J.S., Bridgewater, P., Cramer, V.A., Epstein, P.R., Ewel, J.J., Klink, C.A., Lugo, A.E., Norton, D., Ojima, D., Richardson, D.M., Sanderson, E.W.,

276

References

Valladares, F., Vilà, M., Zamora, R. & Zobel, M. (2006) Novel ecosystems: theoretical and management aspects of the new ecological world order. Global Ecology and Biogeography, 15, 1–7. Hoegh-Guldberg, O., Hughes, L., McIntyre, S., Lindenmayer, D.B., Parmesan, C., Possingham, H.P. & Thomas, C.D. (2008) Assisted colonization and rapid climate change. Science, 321, 345–346. Hof, J. & Raphael, M.G. (1997) Optimization of habitat placement: a case study of the northern spotted owl in the Olympic Peninsula. Ecological Applications, 7, 1160– 1169. Hof, J., Bevers, M., Joyce, L. & Kent, B. (1994) An integer programming approach for spatially and temporally optimizing wildlife populations. Forest Science, 40, 177–91. Högmander, H. & Møller, J. (1995) Estimating distribution maps from atlas data using methods of statistical imageanalysis. Biometrics, 51, 393–404. Holdgate, M. (1999) The green web: a union for world conservation. Earthscan and IUCN, London. Holling, C.S. (1973) Resilience and stability of ecological systems. Annual Review of Ecology and Systematics, 4, 1–23. Holling, C.S., Gunderson, L.H. & Ludwig, D. (2001) In search of a theory of adaptive change. Panarchy: understanding transformations in human and natural systems (ed. by L.H. Gunderson and C.S. Holling), pp. 3–24. Island Press, Washington, DC. Holloway, S. (1996) Historical atlas of breeding birds in Britain and Ireland: 1875–1900. T. and A.D. Poyser Ltd, Calton, UK. Holway, D.A. & Suarez, A.V. (2006) Homogenization of ant communities in Mediterranean California: the effects of urbanization and invasion. Biological Conservation, 127, 319–326. Hoopes, M.F. & Harrison, S. (1998) Metapopulation, sourcesink and disturbance dynamics. Conservation science and action (ed. by W.J. Sutherland), pp. 135–151. Blackwell, Oxford, UK. Hopkins, M.J.G. (2007) Modelling the known and unknown plant biodiversity of the Amazon Basin. Journal of Biogeography, 34, 1400–1411. Hortal, J., Lobo, J.M. & Jiménez-Valverde, A. (2007) Limitations of biodiversity databases: case study on seed-plant diversity in Tenerife, Canary Islands. Conservation Biology, 21, 853–856. Huggett, R.J. (2004) Fundamentals of biogeography, 2nd edn, Routledge Fundamentals of Physical Geography Series, London and New York. Hughes, J.B., Daily, G.C. & Ehrlich, P.R. (2002) Conservation of tropical forest birds in countryside habitats. Ecology Letters, 5, 121–129 Hughes, J.D. & Chandran, M.D.S. (1998) Sacred groves around the earth: an overview. Conserving the sacred for biodiversity management (ed. by P.S. Ramakrishnan, K.G. Saxena and U.M. Chandrashekara), pp. 69–86. Oxford and India Book House, New Delhi, India.

Hughes, T.P., Bellwood, D.R. & Connolly, S.R. (2002) Biodiversity hotspots, centres of endemicity, and the conservation of coral reefs. Ecology Letters, 5, 775–784. Hughes, T.P., Bellwood, D.R., Folke, C., Steneck, R.S. & Wilson, J. (2005) New paradigms for supporting the resilience of marine ecosystems. Trends in Ecology & Evolution, 20, 380–386. Hughes, T.P., Bellwood, D.R., Folke, C.S., McCook, L.J. & Pandolfi, J.M. (2007) No take areas, herbivory, and coral reef resilience. Trends in Ecology & Evolution, 22, 1–3. Hulme, P.E. (2009) Trade, transport and trouble: managing invasive species pathways in an era of globalization. Journal of Applied Ecology, 46, 10–18. Hunt, T.L. (2006) Rethinking the fall of Easter Island. American Scientist, 94, 412–419. Hunt, T.L. & Lipo, C.P. (2006) Late colonization of Easter Island. Science, 311, 1603–1606. Hunter, M.L., Jacobson Jr., G.L. & Webb, T., III, (1988) Paleoecology and the coarse-filter approach to maintaining biological diversity. Conservation Biology, 2, 375–385. Huntley, B. (1999) Climatic change and reconstruction. Journal of Quaternary Science, 14, 513–520. Huntley, B.J. & Birks, H.J.B. (1983) An atlas of past and present pollen maps for Europe: 0–13,000 B.P. Cambridge University Press, Cambridge. Hurlbert, A.H. & Jetz, W. (2007) Species richness, hotspots, and the scale dependence of range maps in ecology and conservation. Proceedings of the National Academy of Sciences USA, 104, 13384–13389. Hutchings, J.A. (2000) Collapse and recovery of marine fishes. Nature, 406, 882–885. Hutchinson, G.E. (1957) Concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology, 22, 415–427. Hyrenbach, K.D., Forney, K.A. & Dayton, P.K. (2000) Marine protected areas and ocean basin management. Aquatic Conservation: Marine and Freshwater Ecosystems, 10, 437–458. ICBP (1992) Putting biodiversity on the map: priority areas for global conservation. International Council for Bird Preservation, Cambridge, UK. IPCC (2007) Climate change 2007: impacts, adaptation and vulnerability. Summary for policymakers. Cambridge University Press, Cambridge. Isaac, N.J.B., Mallet, J. & Mace, G.M. (2004) Taxonomic inflation: its influence on macroecology and conservation. Trends in Ecology & Evolution, 19, 464–469. IUCN (1994) Guidelines for Protected Area Management Categories. CNPPA with the assistance of WCMC. IUCN, Gland, Switzerland and Cambridge, UK. IUCN (2001) IUCN Red List categories. IUCN, Gland, Switzerland and Cambridge, UK. IUCN (2003) Speaking a common language: an assessment of the application of the IUCN system of categorising protected areas – background. Available at: http://www.iucn.org/ themes/wcpa/wpc2003/pdfs/outputs/pascat/pascatrev_ bpaper.pdf.

References

IUCN (2008) IUCN Red List. http://www.iucnredlist.org/. Izquierdo, I., Martín, J. L., Zurita, N., & Arechavaleta, M. (eds) (2004) Lista de especies silvestres de Canarias (hongos, plantas y animales terrestres) 2004. Consejería de Medio Ambiente y Ordenación Territorial, Gobierno de Canarias, Santa Cruz de Tenerife. Jackson, J.B.C. (1992) Pleistocene perspectives on coral reef community structure. American Zoologist, 32, 719– 731. Jackson, J.B.C. (2001) What was natural in the coastal oceans? Proceedings of the National Academy of Sciences USA, 98, 5411–5418. Jackson, J.B.C. (2008) Ecological extinction and evolution in the brave new ocean. Proceedings of the National Academy of Sciences USA, 105 (Supp. 1), 11458–11465. Jackson, J.B.C., Kirby, M.X., Berger, W.H., Bjorndal, K.A., Botsford, L.W., Bourque, B.J., Bradbury, R.H., Cooke, R., Erlandson, J., Estes, J.A., Hughes, T.P., Kidwell, S., Lange, C.B., Lenihan, H.S., Pandolfi, J.M., Peterson, C.H., Steneck, R.S., Tegner, M.J. & Warner, R.R. (2001) Historical overfishing and the recent collapse of coastal ecosystems. Science, 293, 629–637. Jameson, S.C., Tupper, M.H. & Ridley, J.M. (2002) The three screen doors: can marine protected areas be effective? Marine Pollution Bulletin, 44, 1177–1183. Jeffries, M.J. (1997) Biodiversity and conservation, Routledge, London. Jepson, P. (2001) Biodiversity and protected area policy: why is it failing in Indonesia? Unpublished Doctoral Thesis, University of Oxford, Oxford. Jepson, P. & Canney, S. (2001) Biodiversity hotspots: hot for what? Global Ecology and Biogeography, 10, 225–227. Jepson, P. & Canney, S. (2003) Values-led conservation. Global Ecology and Biogeography, 12, 271–274. Jepson, P. & Ladle, R. (2010) Conservation: a beginner’s guide. Oneworld, Oxford. Jepson, P. & Whittaker, R.J. (2002a) Histories of protected areas: internationalism of conservationist values and their adoption in the Netherlands Indies (Indonesia). Environment and History, 8, 129–172. Jepson, P. & Whittaker, R.J. (2002b) Ecoregions in context: a critique with special reference to Indonesia. Conservation Biology, 16, 42–57. Jepson, P., Brickle, N. & Chayadin, Y. (2001a) The conservation status of Tanimbar corella and blue-streaked lory on the Tanimbar Islands, Indonesia: results of a rapid contextual survey. Oryx, 35, 224–235. Jepson, P., Jarvie, J.K., MacKinnon, K. & Monk, K.A. (2001b) The end for Indonesia’s lowland forests? Science, 292, 859–861. Jeschke, J.M. & Strayer, D.L. (2005) Invasion success of vertebrates in Europe and North America. Proceedings of the National Academy of Sciences USA, 102, 7198–7202. Jetz, W., Wilcove, D.S. & Dobson, A.P. (2007) Projected impacts of climate and land-use change on the global diversity of birds. PLoS Biology, 5, 1211–1219.

277

Jiménez-Valverde, A., Lobo, J.M. & Hortal, J. (2008) Not as good as they seem: the importance of concepts in species distribution modelling. Diversity and Distributions, 14, 885–890. Johannes, R.E. (1978) Traditional marine conservation methods in Oceania and their demise. Annual Review of Ecology and Systematics, 9, 349–364. Johnston, R.F. (2001) Synanthropic birds of North America. Avian ecology and conservation in an urbanizing world (ed. by J.M. Marzluff, R. Bowman and R. Donnelly), pp. 49–67. Kluwer Academic Press, Norwell. Jones, B.T.B. & Barnes, J.I. (2006) Human wildlife conflict study: Namibian case study. Unpublished Report: www. worldwildlife.org/species/WWFBinaryitem9110.pdf. Joppa, L.N. & Pfaff, A. (2009) High and far: biases in the location of protected areas. PLoS ONE, 4, e8273. Joseph, L.N., Maloney, R.F. & Possingham, H.P. (2009) Optimal allocation of resources among threatened species: a project prioritization protocol. Conservation Biology, 23, 328–338. Judd, S., Watson, J.E.M. & Watson, A.W.T. (2008) Diversity of a semi-arid, intact Mediterranean ecosystem in southwest Australia. Web Ecology, 8, 84–93. Juniper, T. (2003) Spix’s macaw: the race to save the world’s rarest bird. Atria. London. Kalam, M.A. (1996) Sacred groves in Kodagu district of Karnataka (south India): a sociohistorical study. Institut Français de Pondicherry, Pondicherry, India. Kalamandeen, M. & Gillson, L. (2007) Demything ‘wilderness’: implications for protected area designation and management. Biodiversity and Conservation, 16, 165– 182. Kalliola, R., Toivonen, T., Miyakawa, V. & Mavila, M. (2008) Open access to information bridges science and development in Amazonia: lessons of the SIAMAZONIA service. Environmental Research Letters, 3, 034004; doi: 10.1088/ 1748-9326/3/3/034004 Kark, S., Hadany, L., Safriel, U.N., Noy-Meir, I., Eldredge, N., Tabarroni, C. & Randi, E. (2008) How does genetic diversity change towards the range periphery? An empirical and theoretical test. Evolutionary Ecology Research, 10, 391– 414. Kappelle, M., Van Vuuren, M.M.I. & Baas, P. (1999) Effects of climate change on biodiversity: a review and identification of key research issues. Biodiversity and Conservation, 8, 1383–1397. Kareiva, P. & Marvier, M. (2003) Conserving biodiversity coldspots. American Scientist, 91, 344–351. Karl, S.A. & Bowen, B.W. (1999) Evolutionary significant units versus geopolitical taxonomy: molecular systematics of an endangered sea turtle (genus Chelonia). Conservation Biology, 13, 990–999. Karanth, K.K., Nichols, J.D., Sauer, J.R. & Hines, J.E. (2006) Comparative dynamics of avian communities across edges and interiors of North American ecoregions. Journal of Biogeography, 33, 674–682.

278

References

Kearney, M. & Porter, W.P. (2004) Mapping the fundamental niche: physiology, climate, and the distribution of a nocturnal lizard. Ecology, 85, 3119–3131. Keith, D.A., Akçakaya, H.R., Thuiller, W., Midgley, G.F., Pearson, R.G., Phillips, S.J., Regan, H.M., Araújo, M.B. & Rebelo, T.G. (2008) Predicting extinction risks under climate change: coupling stochastic population models with dynamic bioclimatic habitat models. Biology Letters, 4, 560–563. Keitt, T.H., Lewis, M.A. & Holt, R.D. (2004) Allee effects, invasion pinning and species’ borders. American Naturalist, 157, 203–216. Kelleher, G., Bleakley, C. & Wells, S. (eds) (1995) A global system of marine protected areas. Great Barrier Reef Marine Park Authority, World Bank, World Conservation Union, Washington, DC. Keller, L.F., Arcese, P., Smith, J.N.M., Hochachka, W.M. & Stearns, S.C. (1994) Selection against inbred song sparrows during a natural population bottleneck. Nature, 372, 356–357. Kellman, M. (1996) Redefining roles: plant community reorganization and species preservation in fragmented systems. Global Ecology and Biogeography Letters, 5, 111–116. Kelt, D.A. & Brown, J.H. (2000) Species as units of analysis in ecology and biogeography: are the blind leading the blind? Global Ecology and Biogeography, 9, 213–217. Kendle, A. D. & Rose, J.E. (2000) The aliens have landed! What are the justifications for ‘native only’ policies in landscape plantings? Landscape and Urban Planning, 47, 19–31. Keymer, J.E., Marquet, P.A., Velasco-Hernandez, J.X. & Levin, S.A. (2000) Extinction thresholds and metapopulation persistence in dynamics landscapes. American Naturalist, 156, 478–494. Kier, G., Mutke, J., Dinerstein, E., Ricketts, T.H., Küper, W., Kreft, H. & Barthlott, W. (2005) Global patterns of plant diversity and floristic knowledge. Journal of Biogeography, 32, 1107–1116. Kindvall, O. & Ahlén, I. (1992) Geometrical factors and metapopulation dynamics of the bush cricket, Metrioptera bicolor Philippi (Orthoptera: Tettigoniidae). Conservation Biology, 6, 520–529. Kirkpatrick, J. B. (1983) An iterative method for establishing priorities for the selection of nature reserves: an example from Tasmania. Biological Conservation, 25, 127–134. Klaassen, M., Bauer, S., Madsen, J. & Possingham, H. (2008) Optimal management of a goose flyway: migrant management at minimum cost. Journal of Applied Ecology, 45, 1446–1452. Klein, C.J., Chan, A., Kircher, L., Cundiff, A.J., Gardner, N., Hrovat, Y., Scholz, A., Kendall, B.E. & Airamé, S. (2008a) Striking a balance between biodiversity conservation and socioeconomic viability in the design of marine protected areas. Conservation Biology, 22, 691–700.

Klein, C.J., Steinback, C., Scholz, A.J. & Possingham, H.P. (2008b) Effectiveness of marine reserve networks in representing biodiversity and minimizing impact to fishermen: a comparison of two approaches used in California. Conservation Letters, 1, 44–51. Klein, C.J., Wilson, K., Watts, M., Stein, J., Berry, S., Carwardine, J., Smith, M.S., Mackey, B. & Possingham, H.P. (2009) Incorporating ecological and evolutionary processes into large-scale conservation planning. Ecological Applications, 19, 206–217. Klyza, C.M. (2001) Wilderness comes home: rewilding the northeast. Middlebury College Press, Hanover. Knapp, S. (2003) Dynamic diversity. Nature, 422, 475. Knapp, S. (2008) Species concepts and floras: what are species for? Biological Journal of the Linnean Society, 95, 17–25. Knight, A.T. & Cowling, R.M. (2003) Conserving South Africa’s ‘lost’ biome: a framework for securing effective regional conservation planning in the subtropical thicket biome. Terrestrial Ecology Research Unit, University of Port Elizabeth, South Africa. Knight, A.T. & Cowling, R.M. (2007) Embracing opportunism in the selection of priority conservation areas. Conservation Biology, 21, 1124–1126. Knight, A.T., Cowling, R.M. & Campbell, B.M. (2006) An operational model for implementing conservation action. Conservation Biology, 20, 408–419. Knight, A.T., Smith, R.J., Cowling, R.M., Desmet, P.G., Faith, D.P., Ferrier, S., Gelderblom, C.M., Grantham, H., Lombard, A.T., Maze, K., Nel, J.L., Parrish, J.D., Pence, G.Q.K., Possingham, H.P., Reyers, B., Rouget, M., Roux, D. & Wilson, K.A. (2007) Improving the key biodiversity areas approach for effective conservation planning. BioScience, 57, 256–261. Knight, A.T., Cowling, R.M., Possingham, H.P. & Wilson, K.A. (2009) From theory to practice: designing and situating spatial prioritization approaches to better implement conservation action. Spatial conservation prioritization: quantitative methods and computational tools (ed. by A. Moilanen, K. A. Wilson and H. P. Possingham), pp. 249– 259. Oxford University Press, Oxford. Koh, L.P., Dunn, R.R., Sodhi, N., Colwell, R., Proctor, H.C. & Smith, V.S. (2004) Species coextinctions and the biodiversity crisis. Science, 305, 1632–1634. Kotiaho, J.S., Kaitala, V., Komonen, A. & Päivinen, J. (2005) Predicting the risk of extinction from shared ecological characteristics. Proceedings of the National Academy of Sciences USA, 102, 1963–1967. Kreft, H. & Jetz, W. (2010) A framework for delineating biogeographical regions based on species distributions. Journal of Biogeography, 37, 2029–2053. Kremen, C., Cameron, A., Moilanen, A., Phillips, S.J., Thomas, C.D., Beentje, H., Dransfield, J., Fisher, B.L., Glaw, F., Good, T.C., Harper, G.J., Hijmans, R.J., Lees, D.C., Louis, E., Jr, Nussbaum, R.A., Raxworthy, C.J., Razafimpahanana, A.,

References

Schatz, G.E., Vences, M., Vieites, D.R., Wright, P.C. & Zjhra, M.L. (2008) Aligning conservation priorities across taxa in Madagascar with high-resolution planning tools. Science, 320, 222–226. Krishnamurthy, K.V. (2003) Textbook of biodiversity. Science Publishers, Enfield, NH. Krug, A.Z., Jablonski, D. & Valentine, J.W. (2009) Signature of the end-Cretaceous mass extinction in the modern biota. Science, 323, 767–771. Kühn, I. & Klotz, S. (2006) Urbanization and homogenization – Comparing the floras and urban and rural areas in Germany. Biological Conservation, 127, 292–300. Kunin, W.E. (1998) Extrapolating species abundance across spatial scales. Science, 281, 1513–1515. Kunstler, J.H. (1993) The geography of nowhere. Simon and Schuster, New York. Ladle, R.J. (2008) Catching fairies and the public representation of biogeography. Journal of Biogeography, 35, 388–391. Ladle, R.J. (2009) Forecasting extinctions: uncertainties and limitations. Diversity, 1, 133–150 Ladle, R.J. & Gillson, L. (2009) The (im)balance of nature: a public perception time-lag? Public Understanding of Science, 18, 229–242. Ladle, R.J. & Jepson, P. (2008) Toward a biocultural theory of avoided extinction. Conservation Letters, 1, 111–118. Ladle, R.J. & Malhado, A.C.M. (2007) Responding to biodiversity loss. Companion encyclopedia of geography, 2nd edn (ed. by I. Douglas, R. Huggett and C. Perkins), pp. 821–834. Routledge, London. Ladle, R.J., Jepson, P., Araújo, M.B. & Whittaker, R.J. (2004) Dangers of crying wolf over risk of extinctions. Nature, 482, 799. Ladle, R.J., Jepson, P. & Whittaker, R.J. (2005) Scientists and the media: the struggle for legitimacy in climate change and conservation science. Interdisciplinary Science Reviews, 30, 231–240. LaMarche, V.C. (1974) Paleoclimatic inferences from long tree-ring records. Science, 183, 1043–1048. Lambeck, R.J. (1997) Focal species: a multispecies umbrella for nature conservation. Conservation Biology, 11, 849–856. Lamberson, R.H., McKelvey, R., Noon, B.R. & Voss, C. (1992) A dynamic analysis of northern spotted owl viability in a fragmented forest landscape. Conservation Biology, 6, 505–512. Lambshead, P.J.D. & Boucher, G. (2003) Marine nematode deep-sea biodiversity – hyperdiverse or hype? Journal of Biogeography, 30, 475–485. Lande, R., Engen, S. & Sæther, B.-E. (2003) Stochastic population dynamics in ecology and conservation. Oxford University Press, Oxford. Landres, P.B., Morgan, P. & Swanson, F.J. (1999) Overview of the use of natural variability concepts in managing ecological systems. Ecological Applications, 9, 1179–1188.

279

La Sorte, F.A. & Boecklen, W.J. (2005) Changes in the diversity structure of avian assemblages in North America. Global Ecology and Biogeography, 14, 367–378. La Sorte, F.A. & McKinney, M.L. (2006) Compositional similarity and the distribution of geographical range size for assemblages of native and non-native species in urban floras. Diversity and Distributions, 12, 679–686. La Sorte, F.A. & Thompson, F.R. (2007) Poleward shifts in winter ranges of North American birds. Ecology, 88, 1803–1812. Laurance, W.F. (2000) Do edge effects occur over large spatial scales? Trends in Ecology & Evolution, 15, 134–135. Laurance, W.F. (2002) Hyperdynamism in fragmented habitats. Journal of Vegetation Science, 13, 595–602. Laurance, W.F. (2007) Have we overstated the tropical biodiversity crisis? Trends in Ecology & Evolution, 22, 65–70. Laurance, W.F. (2008) Theory meets reality: how habitat fragmentation research has transcended island biogeographic theory. Biological Conservation, 141, 1731–1744. Laurance, W.F., Albernaz, A.K.M., Schroth, G., Fearnside, P.M., Bergen, S., Venticinque, E.M. & Da Costa, C. (2002) Predictors of deforestation in the Brazilian Amazon. Journal of Biogeography, 29, 737–748. Laurel, B.J. & Bradbury, I.R. (2006) ‘Big’ concerns with high latitude marine protected areas (MPAs): trends in connectivity and MPA size. Canadian Journal of Fisheries and Aquatic Sciences, 63, 2603–2607. Lawton, J.H. (1993) Range, population abundance and conservation. Trends in Ecology & Evolution, 8, 409–413. Lawton, J.H. (1995) Population dynamic principles. Extinction rates (ed. by J.H. Lawton and R.M. May), pp. 147–163. Oxford University Press, Oxford. Lawton, J.H. & May, R.M. (eds) (1995) Extinction rates. Oxford University Press, Oxford. Leberg, P.L. (1991) Effects of genetic variation on the growth of fish populations: conservation Implications. Journal of Fish Biology, 37 (Suppl. A), 193–195. Lee, C.E. (2002) Evolutionary genetics of invasive species. Trends in Ecology & Evolution, 17, 386–391. Lee, T.M. & Jetz, W. (2008) Future battlegrounds for conservation under global change. Proceedings of the Royal Society B: Biological Sciences, 275, 1261–1270. Legendre, P. (1993) Spatial autocorrelation: trouble or new paradigm? Ecology, 74, 1659–1673. Lei, G.-C. & Hanski, I. (1997) Metapopulation structure of Cotesia melitaearum, a specialist parasitoid of the butterfly Melitaea cinxia. Oikos, 78, 91–100. Lemieux, A.M. & Clarke, R.V. (2009) The international ban on ivory sales and its effects on elephant poaching in Africa. British Journal of Criminology, 49, 451–471. Lennon, J.J., Koleff, P., Greenwood, J.J.D. & Gaston, K.J. (2001) The geographical structure of British bird distributions: diversity, spatial turnover and scale. Journal of Animal Ecology, 70, 966–979.

280

References

Leprieur, F., Beauchard, O., Hugueny, B., Grenouillet, G. & Brosse, S. (2008) Null model of biotic homogenization: a test with the European freshwater fish fauna. Diversity and Distributions, 14, 291–300. Leroux, S.J., Schmiegelow, F.K.A., Lessard, R.B. & Cumming, S.G. (2007) Minimum dynamic reserves: a framework for determining reserve size in ecosystems structured by large disturbances. Biological Conservation, 138, 464–473. Leslie, J.F. & Bowden, R.L. (2008) Fusarium graminearum: when species concepts collide. Cereal Research Communications, 36, 609–615. Levin, S. (1992) The problem of pattern and scale in ecology. Ecology, 73, 1943–1967. Levins, R. (1970) Extinction. Some mathematical problems in biology (ed. by M. Gesternhaber), pp. 77–107. American Mathematical Society, Providence, RI. Liebhold, A.M., Work, T.T., McCullough, D.G. & Cavey, J.F. (2006) Airline baggage as a pathway for alien insect species invading the United States. American Entomologist, 52, 48–54. Linklater, W.L. (2003) Science and management in a conservation crisis: a case study with rhinoceros. Conservation Biology, 17, 968–975. Lindenmayer, D.B. & Fischer, J. (2006) Habitat fragmentation and landscape change: an ecological and conservation synthesis. Island Press, Washington, DC. Lindenmayer, D.B. & Franklin, J.F. (2002) Conserving forest biodiversity: a comprehensive multiscale approach, Island Press, Washington, DC. Lindenmayer, D.B., Manning, A.D., Smith, P.L., Possingham, H.P., Fischer, J., Oliver, I. & McCarthy, M.A. (2002) The focal-species approach and landscape restoration: a critique. Conservation Biology, 16, 338–345. Lindenmayer, D.B. & Possingham, H.P. (1995) Modelling the impacts of wildfire on the viability of metapopulations of the endangered Australian species of arboreal marsupial, Leadbeater’s possum. Forest Ecology and Management, 74, 197–222. Lingard, M., Raharison, N., Rabakonandrianina, E., Rakotoarisoa, J-A. & Elmqvist, T. (2003) The role of local taboos in conservation and management of species: the radiated tortoise in southern Madagascar. Conservation and Society, 1, 223–46. Lockwood, J.A. & Debrey, L.D. (1990) A solution for the sudden and unexplained extinction of the Rocky Mountain grasshopper (Orthoptera: Acrididae). Environmental Entomology, 19, 1194–1205. Lockwood, J.L. & Moulton, M.P. (1994) Ecomorphological pattern in Bermuda birds: the influence of competition and implications for nature preserves. Evolutionary Ecology, 8, 53–60. Lockwood, J.L., Hoopes, M.F. & Marchetti, M.P. (2007) Invasion ecology. Blackwell Publishing, London.

LoGiudice, K. (2006) Toward a synthetic view of extinction: a history lesson from a North American rodent. BioScience, 56, 687–693. Loiselle, B.A., Howell, C.A., Graham, C.H., Goerck, J.M., Brooks, T., Smith, K.G. & Williams, P.H. (2003) Avoiding pitfalls of using species distribution models in conservation planning. Conservation Biology, 17, 1591–1600. Lombard, A.T., Cowling, R.M., Pressey, R.L. & Rebelo, A.G. (2003) Effectiveness of land classes as surrogates for species in conservation planning for the Cape Floristic Region. Biological Conservation, 112, 45–62. Lomolino, M.V. (1985) Body size of mammals on islands: the island rule reexamined. American Naturalist, 125, 310– 316. Lomolino, M.V. (2001) The species–area relationship: new challenges for an old pattern. Progress in Physical Geography, 25, 1–21. Lomolino, M.V. (2004) Conservation biogeography. Frontiers of biogeography: new directions in the geography of nature (ed. by M.V. Lomolino and L.R. Heaney), pp. 293–296. Sinauer Associates, Sunderland, MA. Lomolino, M.V. (2005) Body size evolution in insular vertebrates: generality of the island rule. Journal of Biogeography, 32, 1683–1699. Lomolino, M.V. (2006) Space, time, and conservation biogeography. The endangered species act at thirty: conserving biodiversity in human-dominated landscapes (ed. by J.M. Scott, D.D. Goble and F.W. Davis), pp. 61–69. Island Press, Washington, DC. Lomolino, M.V. & Channell, R. (1995) Splendid isolation: patterns of geographic range collapse in endangered mammals. Journal of Mammalogy, 76, 335–347. Lomolino, M.V. & Davis, R. (1997) Biogeographic scale and biodiversity of mountain forest mammals of western North America. Global Ecology and Biogeography Letters, 6, 57–76. Lomolino, M.V. & Heaney, L.R. (eds) (2004) Frontiers of Biogeography: new directions in the geography of nature, Sinauer Associates, Sunderland, MA. Lomolino, M.V. & Weiser, M.D. (2001) Towards a more general species–area relationship: diversity on all islands, great and small. Journal of Biogeography, 28, 431–445. Lomolino, M.V., Sax, D. & Brown, J.H. (eds) (2004) Foundations of Biogeography. Chicago University Press, Chicago. Lomolino, M.V., Riddle, B.R. & Brown, J.H. (2006) Biogeography, 3rd edn. Sinauer, Sunderland, MA. Lomolino, M.V., Riddle, B.R., Whittaker, R.J. & Brown, J.H. (2010) Biogeography, 4th edn. Sinauer, Sunderland, MA. Long, A.J., Crosby, M.J., Stattersfield, A.J. & Wege, D.C. (1996) Towards a global map of biodiversity: patterns in the distribution of restricted-range birds. Global Ecology and Biogeography Letters, 5, 281–304. Longhurst, A. (1998) Ecological geography of the sea. Academic Press, San Diego, CA.

References

Losos, J.B. & Schluter, D. (2000) Analysis of an evolutionary species-area relationship. Nature, 408, 847–850. Losos, J.D. & Ricklefs, R.E. (eds) (2010) The theory of island biogeography revisited. Princeton University Press, Princeton, NJ. Losos, J.B., Jackman, T.R., Larson, A., de Queiroz, K. & Rodríguez-Schettino, L. (1998) Historical contingency and determinism in replicated adaptive radiations of island lizards. Science, 279, 2115–2118. Loucks, C., Ricketts, T.H., Naidoo, R., Lamoreux, J. & Hoekstra, J. (2008) Explaining the global pattern of protected area coverage: relative importance of vertebrate biodiversity, human activities and agricultural suitability. Journal of Biogeography, 35, 1337–1348. Lougheed, V.L., McIntosh, M.D., Parker, C.A. & Stevenson, R.J. (2008) Wetland degradation leads to homogenization of the biota at local and landscape scales. Freshwater Biology, 12, 2402–2413. Lounibos, L.P. (2002) Invasions by insect vectors of human disease. Annual Review of Entomology, 47, 233–266. Lourie, S.A. & Vincent, A.C.J. (2004) Using biogeography to help set priorities in marine conservation. Conservation Biology, 18, 1004–1020. Lovejoy, T.E. (1980) A projection of species extinctions. The Global 200 report to the president, Vol. 2 (ed. by G.O. Barney, Council on Environmental Quality (US), United States Department of State), pp. 328–331. Pergamon Press, New York. Low, B.S. (1996) Behavioral ecology of conservation in traditional societies. Human Nature, 7, 353–379. Lowe, P.D. (1983) Values and institutions in the history of British nature conservation. Conservation in perspective (ed. by A. Warren and F.B. Goldsmith), pp. 329–352. John Wiley and Sons Ltd, Chichester. Lozier, J.D., Aniello, P. & Hickerson, M.J. (2009) Predicting the distribution of Sasquatch in western North America: anything goes with ecological niche modelling. Journal of Biogeography, 36, 1623–1627. Lubchenco, J., Palumbi, S.R., Gaines, S.D. & Andelman, S. (2003) Plugging a hole in the ocean: the emerging science of marine reserves. Ecological Applications, 13 (Supplement), S3–S7. Lugo, A.E. (1988a) Diversity of tropical species: questions that elude answers. Biology International, Special Issue 19. Lugo, A.E. (1988b) Estimating reductions in the diversity of tropical forest species. Biodiversity (ed. by E.O. Wilson and F.M. Peters), pp. 58–70. National Academy Press, Washington, DC. Luniak, M., Mulsow, R. & Walasz, K. (1990) Urbanization of the European blackbird – expansion and adaptations of urban population. Urban ecological studies in central and eastern Europe (ed. by M. Luniak), pp. 186–199. Ossolineum, Wroclaw.

281

MacArthur, R.H. (1972) Geographical ecology: patterns in the distribution of species, Harper and Row, New York. MacArthur, R.H. & Wilson, E.O. (1963) An equilibrium theory of insular zoogeography. Evolution, 17, 373–87. MacArthur, R.H. & Wilson, E.O. (1967) The theory of island biogeography. Princeton University Press, Princeton, NJ. Mace, G.M. (2004) The role of taxonomy in species conservation. Philosophical Transactions of the Royal Society B: Biological Sciences, 359, 711–719. Mace, G.M. & Collar, N.J. (1995) Extinction risk assessment for birds through quantitative criteria. Ibis, 137, 240–246. Mace, G.M. & Kunin, W. (1994) Classifying threatened species – means and ends. Philosophical Transactions of the Royal Society B: Biological Sciences, 344, 91–97. Mace, G.M. & Lande, R. (1991) Assessing extinction threats: toward a reevaluation of IUCN threatened species categories. Conservation Biology, 5, 148–157. Mace, G.M., Balmford, A., Boitani, L., Cowlishaw, G., Dobson, A.P., Faith, D.P., Gaston, K.J., Humphries, C.J., Vane-Wright, R.I., Williams, P.H., Lawton, J.H., Margules, C.R., May, R.M., Nicholls, A.O., Possingham, H.P., Rahbek, C. & van Jaarsveld, A.S. (2000) It’s time to work together and stop duplicating conservation efforts. Nature, 405, 393. MacIsaac, H.J. (1996) Potential abiotic and biotic impacts of zebra mussels on the inland waters of North America. American Zoologist, 36, 287–299. MacKenzie, D.I., Nichols, J.D., Royle, J.A., Pollock, K.H., Bailey, L.L. & Hines, J.E. (2006) Occupancy estimation and modelling, Academic Press, San Diego, CA. MacKenzie, J.M. (1988) The empire of nature: hunting, conservation and British imperialism. University of Manchester Press, Manchester. Mackey, B.G., Watson, J.E.M., Hope, G. & Gilmore, S. (2008) Climate change, biodiversity conservation, and the role of protected areas: an Australian perspective. Biodiversity, 9, 11–18. MacKinnon, J. (ed.) (1997) Protected area systems review of the Indo-Malayan realm. The Asian Bureau for Conservation Limited, Canterbury. MacKinnon, J. & Artha, M.B. (1982) National conservation plan for Indonesia, Volumes I–VIII. Field reports 17, 18, 34, 35, 36, 39, 44 FO/INS/78/061 available from Food and Agriculture Organization of the United Nations, Rome. MacKinnon, J. & MacKinnon, K. (1986a) Review of the protected area systems of the Afrotropical realm. IUCN and UNDP, Gland, Switzerland. MacKinnon, J. & MacKinnon, K. (1986b) Review of the protected areas system in the Indo-Malayan Realm. IUCN, CNPPA and UNEP, Cambridge and Gland. MacKinnon, J. & Wind, J. (1981) Birds of Indonesia, Special report F0/INS/78/061, available from Food and Agriculture Organization of the United Nations, Rome. Magle, S.B., Theobald, D.M. & Crooks, K.R. (2009) A comparison of metrics predicting landscape connectivity for a

282

References

highly interactive species along an urban gradient in Colorado, USA. Landscape Ecology, 24, 267–280. Magnusson, W.E. (2004) Ecoregion as a pragmatic tool. Conservation Biology, 18, 4–5. Mangel, M. & Tier, C. (1994) Four facts every population biologist should know about persistence. Ecology, 75, 607–614. Manne, L.L. & Pimm, S.L. (2001) Beyond eight forms of rarity: which species are threatened and which will be next? Animal Conservation, 4, 221–229. Mannle, K. & Ladle, R.J. (in press) Specific-species taboos and biodiversity conservation in northern Madagascar. Sacred species and sites: guardians of biocultural diversity (ed. by G. Pungetti, G. Oviedo and D. Hooke). Cambridge University Press, Cambridge. Marchetti, M.P., Light, T., Feliciano, J., Armstrong, T., Hogan, Z., Viers, J. & Moyle, P.B. (2001) Homogenization of California’s fish fauna through abiotic change. Biotic homogenization (ed. by J.L. Lockwood and M.L. McKinney), pp. 259–278. Kluwer Academic/Plenum Publishers, New York. Marchetti, M.P., Lockwood, J.L. & Light, T. (2006) Effects of urbanization on California’s fish diversity: differentiation, homogenization and the influence of spatial scale. Biological Conservation, 127, 310–318. Margules, C.R. & Pressey, R.L. (2000) Systematic conservation planning. Nature, 405, 243–253. Margules, C. & Sarkar, S. (2007) Systematic conservation planning. Cambridge University Press, Cambridge. Margules, C. & Usher, M.B. (1981) Criteria used in assessing wildlife conservation potential: a review. Biological Conservation, 21, 79–109. Margules, C.R., Pressey, R.L. & Nicholls, A.O. (1991) Selecting nature reserves. Nature conservation: cost effective biological surveys and data analysis (ed. by C.R. Margules and M.P. Austin), pp. 90–97. CSIRO, Canberra. Margules, C., Pressey, R.L. & Williams, P.H. (2002) Representing biodiversity: data and procedures for identifying priority areas for conservation. Journal of Biosciences, 27, 309–326 Margulis, S. (2004) Causes of deforestation of the Brazilian Amazon (World Bank Working Paper, 22). World Bank Publications, Washington, DC. Maroo, S. & Yalden, D.W. (2000) The Mesolithic mammal fauna of Great Britain. Mammal Review, 30, 243–248. Martín, J.L. (2009) Are the IUCN standard home-range thresholds for species a good indicator to prioritise conservation urgency in small islands? A case study in the Canary Islands (Spain). Journal for Nature Conservation, 17, 87–98. Martín-Esquivel, J.L., García, H., Redondo, C., García, I. & Carralero, I. (1995) La red Canaria de espacios naturales protegidos. Viceconsejería de Medio Ambiente, Gobierno de Canarias. Santa Cruz de Tenerife. Matthews, J. & Matthews, C. (2002) Taliesen, the last Celtic shaman. Inner Traditions International, Rochester, VT.

Matthews, J.A. (1996) Classics in physical geography revisited: Gleason, H.A. 1939: The individualistic concept of the plant association. The American Midland Naturalist 21, 92– 110. Progress in Physical Geography, 20, 193–203. Matthews, J.A., Bridges, E.M., Caseldine, C.J., Luckman, A.J., Owen, G., Perry, A.H., Shakesby, R.A., Walsh, R.P.D., Whittaker, R.J. & Willis, K.J. (eds) (2001) The encyclopaedic dictionary of environmental change. Arnold, London. Massyn, P.J. (2007) Communal land reform and tourism investment in Namibia’s communal areas: a question of unfinished business? Development Southern Africa, 24, 381–392. Maurer, B.A. & Brown, J.H. (1989) Distributional consequences of spatial variation in local demographic processes. Annales Zoologici Fennici, 26, 121–131. Mawdsley, N.A., Compton, S.G. & Whittaker, R.J. (1998) Population persistence, pollination mutualisms, and figs in fragmented tropical landscapes. Conservation Biology, 12, 1416–1420. Maxted, N., Dulloo, E., Ford-Lloyd, B.V., Iriondo, J.M. & Jarvis, A. (2008) Gap analysis: a tool for complementary genetic conservation assessment. Diversity and Distributions, 14, 1018–1030. May, R.M., Lawton, J.H., Stork, N.E. (1995) Assessing extinction rates. Extinction rates (ed. by J.H. Lawton and R.M. May), pp. 1–24. Oxford University Press, Oxford. Mayden, R.L. (1997) A hierarchy of species concepts: the denouement in the saga of the species problem. Species: the units of biodiversity (ed. by M.F. Clardige, H.A. Dawah and M.R. Wilson), pp. 381–424. Chapman and Hall, London. Mayr, E. (1942) Systematics and the origin of species. Columbia University Press, New York. McCarthy, M.A., Thompson, C.J. & Possingham, H.P. (2005) Theory for designing nature reserves for single species. American Naturalist, 165, 250–257. McClanahan, T.R. & Arthur, R. (2001) The effects of marine reserves and habitat on East African coral reef fishes. Ecological Applications, 11, 559–569. McCullough, D.G., Work, T.T., Cavey, J.F., Liebhold, A.M. & Marshall, D. (2006) Interceptions of nonindigenous plant pests at US ports of entry and border crossings over a 17year period. Biological Invasions, 8, 611–630. McDonald, K.A. & Brown, J.H. (1992) Using montane mammals to model extinctions due to global change. Conservation Biology, 6, 409–415. McDonald, R.I., Kareiva, P. & Forman, R.T.T. (2008) The implications of current and future urbanization for global protected areas and biodiversity conservation. Biological Conservation, 141, 1695–1703. McDonald-Madden, E., Bode, M., Game, E., Grantham, H. & Possingham, H. (2008) The need for speed: opportunity costs in dynamic conservation. Ecology Letters, 11, 1169–1177. McIntyre, S. & Hobbs, R. (1999) A framework for conceptualizing human effects on landscapes and its relevance to

References

management and research models. Conservation Biology, 13, 1282–1292. McKinney, M.L. (2004) Measuring floristic homogenization by non-native plants in North America. Global Ecology and Biogeography, 13, 47–53. McKinney, M.L. (2005) Species introduced from nearby sources have a more homogenizing effect than species from distant sources: evidence from plants and fishes in the USA. Diversity and Distributions, 11, 367–374. McKinney, M.L. (2006) Urbanization as a major cause of biotic homogenization. Biological Conservation, 127, 247–260. McKinney, M.L. & Lockwood, J.L. (1999) Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends in Ecology & Evolution, 14, 450–453. McLachlan, J.S., Clark, J.S. & Manos, P.S. (2005) Molecular indicators of tree migration capacity under rapid climate change. Ecology, 86, 2088–2098. McNeely, J. A. (1994) Protected areas for the 21st century: working to provide benefits for society. Biodiversity and Conservation, 3, 3–20. McPherson, J.M., Jetz, W. & Rogers, D.J. (2006) Using coarsegrained occurrence data to predict species distributions at finer spatial resolutions – possibilities and limitation. Ecological Modelling, 192, 499–522. Medellín, R.A. & Soberón, J. (1999) Predictions of mammal diversity on four land masses. Conservation Biology, 13, 143–149. Mehlman, D.W. (1997) Change in avian abundance across the geographic range in response to environmental change. Ecological Applications, 7, 614–624. Meir, E., Andelman, S. & Possingham, H.P. (2004) Does conservation planning matter in a dynamic and uncertain world? Ecology Letters, 7, 615–622. Meiri, S., Dayan, T. & Simberloff, D. (2006) The generality of the island rule re-examined. Journal of Biogeography, 33, 1571–1577. Merriam, G. & Lanoue, A. (1990) Corridor use by small mammals: field measurement for three experimental types of Peromyscus leucopus. Landscape Ecology, 4, 123–131. Mertz, O., Ravnborg, H.M., Lövei, G.L., Nielsen, I. & Konijnendijk, C.C. (2007) Ecosystem services and biodiversity in developing countries. Biodiversity Conservation, 16, 2729–2737. Millennium Ecosystem Assessment (2005) Ecosystems and human well-being: general synthesis. Island Press and World Resources Institute, Washington, DC. Miller, W., Drautz, D.I., Ratan, A., Pusey, B., Qi, J., Lesk, A.M., Tomsho, L.P., Packard, M.D., Zhao, F., Sher, A., Tikhonov, A., Raney, B., Patterson, N., Lindblad-Toh, K., Lander, E.S., Knight, J.R., Irzyk, G.P., Fredrikson, K.M., Harkins, T.T., Sheridan, S., Pringle, T. & Schuster, S.C. (2008) Sequencing the nuclear genome of the extinct woolly mammoth. Nature, 456, 387–390.

283

Mills, L.S., Soulé, M.E. & Doak, D.F. (1993) The keystonespecies concept in ecology and conservation. BioScience, 43, 219–224. Mitikka, V., Heikkinen, R.K., Luoto, M., Araújo, M.B., Saarinen, K., Pöyry, J. & Fronzek, S. (2008) Predicting range expansion of the map butterfly in Northern Europe using bioclimatic models. Biodiversity and Conservation, 17, 623–641. Mittermeier, R.A., Myers, N., Gil, P.R. & Mittermeier, C.G., (1999) Hotspots: Earth’s richest and most endangered terrestrial ecoregions, Agrupación Sierra Madre, S.C., Mexico City. Mittermeier, R.A., Gil, P.R., Hoffmann, M., Pilgrim, J., Brooks, T., Mittermeier, C.G., Lamoreux, J. & da Fonseca, G.A.B. (2004) Hotspots revisited: Earth’s biologically richest and most endangered terrestrial ecoregions, Agrupación Sierra Madre, S.C., Mexico City. Also see: http://www.biodiversityhotspots. org/xp/Hotspots/hotspotsScience/ [visited July 2009] Moilanen, A. (2002) Implications of empirical data quality to metapopulation model parameter estimation and application. Oikos, 96, 516–530. Moilanen, A. (2008) Generalized complementarity and mapping of the concepts of systematic conservation planning. Conservation Biology, 22, 1655–1658. Moilanen, A. & Hanski, I. (2006) Connectivity and metapopulation dynamics in highly fragmented landscapes. Connectivity conservation (ed. by Crooks, K.R. and M. Sanjayan) pp. 44–71. Cambridge University Press, Cambridge. Moilanen, A. & Wintle, B.A. (2006) Uncertainty analysis favours selection of spatially aggregated reserve networks. Biological Conservation, 129, 427–434. Moilanen, A., Wilson, K.A. & Possingham, H.P. (2009) Spatial conservation prioritization quantitative methods and computational tools. Oxford University Press, Oxford. Mooers, A.Ø. (2007) Conservation biology: the diversity of biodiversity. Nature, 445, 717–718. Mora, C.S., Andrefouet, S., Costello, M.J., Kranenburg, C., Rollo, A., Veron, J.E.N., Gaston, K.J. & Myers, R.A. (2006) Coral reefs and the global network of marine protected areas. Science, 312, 1750–1751. Morin, X., Augspurger, C. & Chuine, I. (2007) Process-based modelling of species’ distributions: what limits temperate tree species’ range boundaries? Ecology, 88, 2280–2291. Morrone, J.J. (2002) Biogeographical regions under track and cladistic scrutiny. Journal of Biogeography, 29, 149– 152. Moritz, C. (1994) Defining ‘evolutionarily significant units’ for conservation. Trends in Ecology & Evolution, 9, 373–375. Moritz, C., Patton, J.L., Schneider, C.J. & Smith, T.B. (2000) Diversification of rainforest faunas: an integrated molecular approach. Annual Review of Ecology and Systematics, 31, 533–563. Moritz, C., Hoskin, C., Graham, C.H., Hugall, A. & Moussalli, A. (2005) Historical biogeography, diversity and conservation of Australia’s tropical rainforest herpetofauna.

284

References

Phylogeny and conservation (ed. by A. Purvis, J.L. Gittleman and T. Brooks), pp. 243–264. Cambridge University Press, Cambridge. Myers, N. (1979) The sinking ark: a new look at the problem of disappearing species. Pergamon Press, New York. Myers, N. (1985) A look at the present extinction spasm. Animal Extinctions: what everyone should know (ed. by R.J. Hoage), pp. 47–57. Smithsonian Institution Press, Washington, DC and London. Myers, N. (1988) Threatened biotas: ‘hot spots’ in tropical forests. The Environmentalist, 8, 1–20. Myers, N. (1990) The biodiversity challenge: expanded ‘hotspots’ analysis. The Environmentalist, 10, 243–256. Myers, N. (2003) Biodiversity hotspots revisited. BioScience, 53, 796–797. Myers, N., Mittermeier, R.A., Mittermeier, C.G., da Fonseca, G.A.B. & Kent, J. (2000) Biodiversity hotspots for conservation priorities. Nature, 403, 853–858. Naidoo, R. & Ricketts, T.H. (2006) Mapping the economic costs and benefits of conservation. PLoS Biology, 4, 2153–2164. Naidoo, R., Balmford, A., Ferraro, P.J., Polasky, S., Ricketts, T.H. & Rouget, M. (2006) Integrating economic costs into conservation planning. Trends in Ecology & Evolution, 21, 681–687. Naidoo, R., Balmford, A., Costanza, R., Fisher, B., Green, R.E., Lehner, B., Malcolm, T.R. & Ricketts, T.H. (2008) Global mapping of ecosystem services and conservation priorities. Proceedings of the National Academy of Sciences USA, 105, 9495–9500. de Nascimento, L., Willis, K.J., Fernández-Palacios, J.M., Criado, C. & Whittaker, R.J. (2009) The long-term ecology of the lost forests of La Laguna, Tenerife (Canary Islands). Journal of Biogeography, 36, 499–514. Nash, R. (1982) Wilderness and the American mind. Yale University Press, New Haven. NCC [Nature Conservancy Council] (1989) Guidelines for Selection of Biological SSSIs. Nature Conservancy Council, Peterborough. Nelson, B.W., Fereira, A.C., da Silva, M.F. & Kawasaki, M.L. (1990) Endemism centres, refugia and botanical collection density in Brazilian Amazonia. Nature, 345, 714–716. Newmark, W.D. (1991) Tropical forest fragmentation and the local extinction of understorey birds in the Eastern Usambara Mountains, Tanzania. Conservation Biology, 5, 67–78. Nicholls, A. O. & Margules, C. (1993) An upgraded reserve selection algorithm. Biological Conservation, 64, 165–169. Nicholson, E. & Possingham, H.P. (2006) Objectives for multiple species conservation planning. Conservation Biology, 20, 871–881. Nogués-Bravo, D., Rodríguez, J., Hortal, J., Batra, P. & Araújo, M.B. (2008) Climate change, humans, and the extinction of the mammoth. PLoS Biology, 6, e79. doi: 10.1371/ journal.pbio.0060079

Noss, R. (1990) Indicators for monitoring biodiversity: a hierarchical approach. Conservation Biology, 4, 355–364. Noss, R.F. (2004) Conservation targets and information needs for regional conservation planning. Natural Areas Journal, 24, 223–231. O’Dea, N., Araújo, M.B. & Whittaker, R.J. (2006) How well do Important Bird Areas represent species and minimise conservation conflict in the tropical Andes? Diversity and Distributions, 12, 205–214. Ødegaard, F., Diserud, O.H., Engen, S. & Aagaard, K. (2000) The magnitude of local host specificity for phytophagous insects and its implications for estimates of global species richness. Conservation Biology, 14, 1182–1186. Oksanen, L. & Oksanen, T. (2000) The logic and realism of the hypothesis of exploitation ecosystems. American Naturalist, 155, 703–723 Olden, J.D. (2006) Biotic homogenization: a new research agenda for conservation biogeography. Journal of Biogeography, 33, 2027–2039. Olden, J.D. & Poff, N.L. (2003) Toward a mechanistic understanding and prediction of biotic homogenization. American Naturalist, 162, 442–460. Olden, J.D. & Rooney, T.P. (2006) On defining and quantifying biotic homogenization. Global Ecology and Biogeography, 15, 113–120. Olden, J.D., Poff, N.L., Douglas, M.R., Douglas, M.E. & Fausch, K.D. (2004) Ecological and evolutionary consequences of biotic homogenization. Trends in Ecology & Evolution, 19, 18–24. Olden, J.D., Douglas, M.E. & Douglas, M.R. (2005) The human dimensions of biotic homogenization. Conservation Biology, 19, 2036–2038. Olden, J.D., Kennard, M.J. & Pusey, B.J. (2008) Species invasions and the changing biogeography of Australian freshwater fishes. Global Ecology and Biogeography, 17, 25–37. Oliver, M.J. & Irwin, A.J. (2008) Objective global ocean biogeographic provinces. Geophysical Research Letters, 35 (L15601), 1–6. Olsen, S.B. (2003) Frameworks and indicators for assessing progress in integrated coastal management initiatives. Ocean and Coastal Management, 46, 347–361. Olson, D.N. & Dinerstein, E. (1998) The Global 200: a representation approach to conserving the Earth’s most biologically valuable ecoregions. Conservation Biology, 12, 502–515. Olson, D.M. & Dinerstein, E. (2002) The Global 200: priority ecoregions for global conservation. Annals of the Missouri Botanical Garden, 89, 199–224. Olson, D.M., Dinerstein, E., Wikramanayake, E.D., Burgess, N.D., Powell, G.V.N., Underwood, E.C., D’Amico, J.A., Itoua, I., Strand, H.E., Morrison, J.C., Loucks, C.J., Allnutt, T.F., Ricketts, T.H., Kura, Y., Lamoreux, J.F., Wettengel, W.W., Hedao, P. & Kassem, K.R. (2001) Terrestrial ecoregions of the world: a new map of life on Earth. BioScience, 51, 933–938.

References

Omernik, J.M. (1987) Ecoregions of the conterminous United States. Annals of the Association of American Geographers, 77, 118–125. Önal, H. & Briers, R.A. (2003) Selection of a minimumboundary reserve network using integer programming. Proceedings of the Royal Society B: Biological Sciences, 270, 1487–1491. O’Neill, R.V., De Angelis, D., Waide, J. & Allen, T. (1986) A hierarchical concept of ecosystems. Princeton University Press, Princeton, NJ. Opdam, P., Luque, S. & Jones, K. (2009) Changing landscapes to accommodate for climate change impacts: a call for landscape ecology. Landscape Ecology, 24, 715–721. Orians, G.H. (1993) Endangered at what level? Ecological Applications, 3, 206–208. Orton, J. (1869) The great auk. The American Naturalist, 3, 539–542. Paine, R.T. (1995) A conversation on refining the concept of keystone species. Conservation Biology, 9, 962–964. Palumbi, S.R. (2003) Population genetics, demographic connectivity, and the design of marine reserves. Ecological Applications, 13, S146–S158. Parmesan, C. (1996) Climate and species’ range. Nature, 382, 765–766. Parmesan, C. (2006) Ecological and evolutionary responses to recent climate change. Annual Review of Ecology, Evolution, and Systematics, 37, 637–669. Parmesan, C. & Yohe, G. (2003) A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421, 37–42. Parmesan, C., Gains, S., Gonzalez, L., Kaufman, D.M., Kingsolver, J., Peterson, A.T. & Sagarin, R. (2005) Empirical perspectives on species borders: from traditional biogeography to global change. Oikos, 108, 58–75. Parr, C.S. & Cummings, M.P. (2005) Data sharing in ecology and evolution. Trends in Ecology & Evolution, 20, 362–363. Patlis, J.M., Makinuddin, N., Pratikto, W.A. & Tighe, S.A. (2005) Indonesia: policy transition from islands to ICM. Coastal zone 05. Proceedings of the 14th Biennial Coastal Zone Conference, New Orleans, LA, July 17–21, 2005. NOAA/CSC/20518-CD. CD-ROM. U.S. National Oceanic and Atmospheric Administration, Coastal Services Center, Charleston, SC. Patterson, B.D. (1990) On the temporal development of nested subset patterns of species composition. Oikos, 59, 330–342. Patterson, B.D. (2000) Patterns and trends in the discovery of new neotropical mammals. Diversity and Distributions, 8, 145–151. Patterson, B.D. (2001) Fathoming tropical biodiversity: the continuing discovery of Neotropical mammals. Diversity and Distributions, 7, 191–196. Patterson, B.D. & Atmar, W. (1986) Nested subsets and the structure of insular mammalian faunas and archipelagos. Biological Journal of the Linnean Society, 28, 65–82.

285

Pauly, D., Christensen, V., Dalsgaaard, J., Froese, R. & Torres, F.J. (1998) Fishing down marine food webs. Science, 279, 860–863. Pautasso, M. (2007) Scale dependence of the correlation between human population presence and vertebrate and plant species richness. Ecology Letters, 10, 16–24. Pearce, J.L., Kirk, D.A., Lane, C.P., Mahr, M.H., Walmsley, J., Casey, D., Muir, J.E., Hannon, S., Hansen, A. & Jones, K. (2008) Prioritizing avian conservation areas for the Yellowstone to Yukon Region of North America. Biological Conservation, 141, 908–924. Pearl, R. & Reed L.J. (1920) On the rate of growth of the population of the United States since 1790 and its mathematical representation. Proceedings of the National Academy of Science USA, 6, 275–288. Pearson, R.G. (2006) Climate change and the migration capacity of species. Trends in Ecology & Evolution, 21, 111–113. Pearson, R.G. & Dawson, T.P. (2003) Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecology and Biogeography, 12, 361–371. Pearson, R.G., Raxworthy, C.J., Nakamura, M. & Peterson, A.T. (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. Journal of Biogeography, 34, 102–117. Peluso, N.L. & Vandergeest, P. (2001) Genealogies of the political forest and customary rights in Indonesia, Malaysia and Thailand. Journal of Asian Studies, 60, 761–812. Penn, D.J. (2003) The evolutionary roots of our environmental problems: toward a Darwinian ecology. Quarterly Review of Biology, 78, 275–301. Perrings, C., Dehnen-Schmutz, K., Touza, J. & Williamson, M. (2005) How to manage biological invasions under globalization. Trends in Ecology & Evolution, 20, 212–215. Perry, A.L., Low, P.J., Ellis, J.R. & Reynolds, J.D. (2005) Climate change and distribution shifts in marine fishes. Science, 308, 1912–1915. Peters, R.L. & Darling, J.D.S. (1985) The greenhouse effect and nature reserves. BioScience, 35, 707–717. Peterson, A.T. & Navarro-Siguenza, A.G. (1999) Alternate species concepts as bases for determining priority conservation areas. Conservation Biology, 13, 427–431 Peterson, A.T., Ortega-Huerta, M.A., Bartley, J., SánchezCordero, V., Soberón, J., Buddemeier, R.H. & Stockwell, D.R.B. (2002) Future projections for Mexican faunas under global climate change scenarios. Nature, 416, 626–629. Phillips, S.J. & Dudík, M. (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31, 161–175. Pickett, S.T.A. & Ostfeld, R.S. (1995) The shifting paradigm in ecology. A new century for natural resources management (ed. by R.L. Knight, and S. F. Bates), pp. 261–278. Island Press, Washington, DC.

286

References

Pickett, S.T.A. & Thompson, J.N. (1978) Patch dynamics and the design of nature reserves. Biological Conservation, 13, 27–37. Pickett, S.T.A., Collins, S. & Armesto, J. (1987) A hierarchical consideration of causes and mechanisms of succession. Vegetatio, 69, 109–114. Pickett, S.T.A., Kolasa, J., Armesto, J. & Collins, S. (1989) The ecological concept of disturbance and its expression at various hierarchical levels. Oikos, 54, 129–136. Pickett, S.T.A., Parker, V.T. & Fiedler, P.L. (1992) The new paradigm in ecology: implications for conservation biology above the species level. Conservation biology (ed. by P.L. Fiedler and S.K. Jain), pp. 65–88. Chapman and Hall, New York. Pimentel, D., Zuniga, R. & Morrison, D. (2006) Update on the environmental and ecological costs associated with alieninvasive species in the United States. Ecological Economics, 52, 273–288. Pimm, S.L. (2008) Biodiversity: climate change or habitat loss – which will kill more species? Current Biology, 18, R117–R119. Pimm, S.L. & Askins, R.A. (1995) Forest losses predict bird extinctions in eastern North America. Proceedings of the National Academy of Sciences USA, 92, 9343–9347. Pinchot, G. (1910) The fight for conservation. University of Washington Press, Seattle, WA. Poland, T.M. & McCollough, D.G. (2006) Emerald ash borer: invasion of the urban forest and the threat to North America’s ash resource. Journal of Forestry, April/May, 118–124. Polasky, S., Nelson, E., Camm, J., Csuti, B., Fackler, P., Lonsdorf, E., Montgomery, C., White, D., Arthur, J., Garber-Yonts, B., Haight, R., Kagan, J., Starfield, A. & Tobalske, C. (2008) Where to put things? Spatial land management to sustain biodiversity and economic returns. Biological Conservation, 141, 1505–1524. Pontecorvo, G. (2001) Supply side uncertainty and the management of commercial fisheries: Peruvian Anchovetta, an illustration. Marine Policy, 25, 169–172. Popper, K.R. (1959) The logic of scientific discovery. Science Editions, New York. Popy, S., Bordignon, L. & Prodon, R. (2010) A weak upward elevational shift in the distributions of breeding birds in the Italian Alps. Journal of Biogeography, 37, 57–67. Porter, W.P., Sabo, J.L., Tracy, C.R., Reichman, O.J. & Ramankutty, N. (2002) Physiology on a landscape scale. Integrative and Comparative Biology, 42, 431–453. Possingham, H.P., Day, J., Goldfinch, M. & Salzborn, F. (1993) The mathematics of designing a network of protected areas for conservation. Decision Sciences: Tools for Today, 12th Australian Operations Research Conference (ed. by D. Sutton, E. Cousins and C. Pearce), pp 536–545. ASOR, University of Adelaide, Adelaide. Possingham, H.P., Andelman, S.J., Noon, B.R., Trombulak, S. & Pulliam, H.R. (2001) Making smart conservation decisions. Conservation biology: research priorities for the next

decade (ed. by M.E. Soulé and G.H. Orians), pp. 225–244. Island Press, Washington, DC. Possingham, H.P., Franklin, J., Wilson, K. & Regan, T.J. (2005) The roles of spatial heterogeneity and ecological processes in conservation planning. Ecosystem function in heterogeneous landscapes (ed. by G.M. Lovett, C.G. Jones, M.G. Turner and K.C. Weathers), pp. 389–406. Springer-Verlag, New York. Possingham, H.P., Wilson, K., Andelman, S. & Vynne, C. (2006) Protected areas: goals, limitations, and design. Principles of conservation biology (ed. by M.J. Groom, G.K. Meffe and C.R. Carroll), pp. 509–533. Sinauer Associates Inc, Sunderland, MA. Potts, G.R. (1986) The partridge: herbicides, predation and conservation. Collins, London. Power, M. E., Tilman, D., Estes, J. A., Menge, B. A., Bond, W. J., Mills, L. S., Daily, G., Castilla, J. C., Lubchenco, J. & Paine, R.T. (1996) Challenges in the quest for a keystone. BioScience, 46, 609–620. Prendergast, D.K. & Adams, W.M. (2003) Colonial wildlife conservation and the origins of the Society for the Preservation of the Wild Fauna of the Empire (1903– 1914). Oryx, 37, 251–260. Pressey, R.L. (1994) Ad hoc reservations: forward or backward steps in developing representative reserve systems? Conservation Biology, 8, 662–668. Pressey, R.L. (1997) Priority conservation areas: towards an operational definition for regional assessments. National parks and protected areas: selection, delimitation, and management (ed. by J.J. Pigram and R.C. Sundell), pp. 337–357. Centre for Water Policy Research, Armidale, New South Wales. Pressey, R.L. (1998) Algorithms, politics and timber: an example of the role of science in a public, political negotiation process over new conservation areas in production forests. Ecology for everyone: communicating ecology to scientists, the public and the politicians (ed. by R.T. Wills and R.J. Hobbs), pp. 73–87. Surrey Beatty and Sons, Chipping Norton, NSW. Pressey, R.L. & Bottrill, M.C. (2009) Approaches to landscapeand seascape-scale conservation planning: convergence, contrasts and challenges. Oryx, 43, 464–475. Pressey, R.L. & Cowling, R.M. (2001) Reserve selection algorithms and the real world. Conservation Biology, 15, 275–277. Pressey, R.L. & Nicholls, A.O. (1989) Efficiency in conservation evaluation: scoring versus iterative approaches. Biological Conservation, 50, 199–218. Pressey, R.L., Humphries, C.R., Margules, C.R., Vane-Wright, R.I. & Williams, P.H. (1993) Beyond opportunism: key principles for systematic reserve selection. Trends in Ecology & Evolution, 8, 124–128. Pressey, R.L., Johnson, I.R. & Wilson, P.D. (1994) Shades of irreplaceability – towards a measure of the contribution of sites to a reservation goal. Biodiversity and Conservation, 3, 242–262.

References

Pressey, R.L., Whish, G.L., Barret, T.W. & Watts, M.E. (2002) Effectiveness of protected areas in north-eastern New South Wales: recent trends in six measures. Biological Conservation, 106, 57–69. Pressey, R.L., Cabeza, M., Watts, M.E., Cowling, R.M. & Wilson, K.A. (2007) Conservation planning in a changing world. Trends in Ecology & Evolution, 22, 583–592. Preston, F.W. (1948) The commonness, and rarity, of species. Ecology, 48, 254–283. Preston, F.W. (1960) Time and space and variation of species. Ecology, 41, 611–627. Preston, F.W. (1962) The canonical distribution of commonness and rarity. Ecology, 43, part I, pp. 185–215; part II, pp. 410–432. Primack, R.B. (1993) Essentials of conservation biology. Sinauer Associates, Sunderland, MA. Primack, R.B. (2002) Essentials of conservation biology, 3rd edn. Sinauer Press, Inc., Sunderland, MA. Prugh, L.R., Hodges, K.E., Sinclair, A.R.E. & Brashares, J.S. (2008) Effect of habitat area and isolation on fragmented animal populations. Proceedings of the National Academy of Sciences USA, 105, 20770–20775. Purvis, A. (2008) Phylogenetic approaches to the study of extinction. Annual Review of Ecology, Evolution, and Systematics, 39, 301–319. Purvis, A., Agapow, P.M., Gittleman, J.L. & Mace, G.M. (2000) Nonrandom extinction and the loss of evolutionary history. Science, 288, 328–330. Pyšek, P., Richardson, D.M. & Williamson, M. (2004) Predicting and explaining plant invasion through analysis of source area floras: some critical considerations. Diversity and Distributions, 10, 179–187. Qian, H. & Ricklefs, R.E. (2006) The role of exotic species in homogenizing the North American flora. Ecology Letters, 9, 1293–1298. de Queiroz, K. (2007) Species concepts and species delimitation. Systematic Biology, 56, 879–886. Quinn, J.F. & Hastings, A. (1987) Extinction in subdivided habitats. Conservation Biology, 1, 198–208. Rabinowitz, D. (1981) Seven forms of rarity. The biological aspects of rare plant conservation (ed. by H. Synge), pp. 205– 217. Wiley, Chichester. Radomski, P.J. & Goeman, T.J. (1995) The homogenizing of Minnesota lake fish assemblages. Fisheries, 20, 20– 23. Rahbek, C. & Graves, G.R. (2000) Detection of macroecological patterns in South American hummingbirds is affected by spatial scale. Proceedings of the Royal Society B: Biological Sciences, 267, 2259–2265. Raheem, D.C., Naggs, F., Chimonides, J., Preece, R.C. & Eggleton, P. (2009) Fragmentation and pre-existing species turnover determines land-snail assemblages of tropical rain forest. Journal of Biogeography, 36, 1923– 1938. Rahel, F.J. (2000) Homogenization of fish faunas across the United States. Science, 288, 854–856.

287

Rahel, F.J. & Olden, J.D. (2008) Assessing the effects of climate change on aquatic invasive species. Conservation Biology, 22, 521–533. Rangel, T.F.L.V.B., Diniz-Filho, J.A.F. & Bini, L.M. (2006) Towards an integrated computational tool for spatial analysis in macroecology and biogeography. Global Ecology and Biogeography, 15, 321–327. Raup, D.M. (1991) Extinction: bad genes or bad luck? Norton and Co, New York. Raven, P.H. (1988) Our diminishing tropical forests. Biodiversity (ed. by E.O. Wilson and F.M. Peters), pp. 119– 122. National Academy Press, Washington, DC. Raven, P.H. (2004) Taxonomy: where are we now? Philosophical Transactions of the Royal Society B: Biological Sciences, 359, 729–730. Raven, P.H. & Wilson, E.O. (1992) A 50-year plan for biodiversity surveys. Science, 258, 1099–1100. Ravetz, J. & Funtowicz, S. (1999) Post-normal science – an insight now maturing. Futures, 31, 641–646. Ray, G.C. (1975) A preliminary classification of coastal and marine environments. IUCN Occasional Paper No. 14. IUCN, Morges, Switzerland. Ray, G.C. (1999) Coastal-marine protected areas: agonies of choice. Aquatic Conservation: Marine and Freshwater Ecosystems, 9, 607–614. Ray, G.C. & Hayden, B.P. (1993) Marine biogeographic provinces of the Bering, Chukchi and Beaufort seas. Large Marine Ecosystems: stress, mitigation and sustainability (ed. by K. Sherman, L.M. Alexander and B.D. Gold), pp. 175– 184. American Association for the Advancement of Science Press, Washington, DC. Reaka, M.J., Rodgers, P.J. & Kudla, A.U. (2008) Patterns of biodiversity and endemism on Indo-West Pacific coral reefs. Proceedings of the National Academy of Sciences USA, 105, 11474–11481. Real, L.A. & Brown, J.H. (eds) (1991) Foundations of ecology: classic papers with commentaries. The University of Chicago Press, Chicago. Recher, H.F. (2004) Wild Country. Pacific Conservation Biology, 10, 221–222. Redford, K.H., Coppolillo, P., Sanderson, E.W., da Fonseca, G.A.B., Dinerstein, E., Groves, C., Mace, G., Maginnis, S., Mittermeier, R.A., Noss, R., Olson, D., Robinson, J.G., Vedder, A. & Wright, M. (2003) Mapping the conservation landscape. Conservation Biology, 17, 116–131. Reed, D.H., O’Grady, J.J., Brook, B.W., Ballou, J.D. & Frankham, R. (2003) Estimates of minimum viable population sizes for vertebrates and factors influencing those estimates. Biological Conservation, 113, 23–34. Rees, D.J., Emerson, B.C., Oromí, P. & Hewitt, G.M. (2001) The diversification of the genus Nesotes (Coleoptera: Tenebrionidae) in the Canary Islands: evidence from mtDNA. Molecular Phylogenetics and Evolution, 21, 321–326. Regan, H.M., Ben-Haim, Y., Langford, B., Wilson, W.G., Lundberg, P., Andelman, S.J. & Burgman, M.A. (2005)

288

References

Robust decision-making under severe uncertainty for conservation management. Ecological Applications, 15, 1471–1477. Regan, H.M., Ensbey, M.J. & Burgman, M.A. (2009) Conservation prioritization and uncertainty in planning inputs. Spatial conservation prioritisation: quantitative methods and computational tools (ed. by A. Moilanen, K.A. Wilson and H.P. Possingham), pp. 145–157. Oxford University Press, Oxford. Régnier, C., Fontaine, B. & Bouchet, P. (2009) Not knowing, not recording, not listing: numerous unnoticed mollusk extinctions. Conservation Biology, 23, 1214–1221. Reid, W.V. & Miller, K.R. (1989) Keeping options alive: the scientific basis for conserving biodiversity, World Resources Institute, Washington, DC. Reijnen, R., Foppen, R. & Meeuwsen, H. (1996) The effects of traffic on the density of breeding birds in Dutch agricultural grasslands. Biological Conservation, 75, 255–260. Rejmánek, M. (2000) A must look for North American biogeographers. Diversity and Distributions, 6, 208–211. Remmert, H. (Ed) (1991) The mosaic-cycle concept of ecosystems. Springer-Verlag, Berlin. Renema, W., Bellwood, D.R., Hughes, T.P., Braga, J.C., Bromfield, K., Hall, R., Johnson, K.G., Lunt, P., Meyer, C.P., McMonagle, L.B., Morley, R.J., O’Dea, A., Todd, J.A., Wesselingh, F.P., Wilson, M.E.J. & Pandolfi, J.M. (2008) Hopping hotspots: global shifts in marine biodiversity. Science, 321, 654–657. Rhymer, J.M. & Simberloff, D.S. (1996) Extinction by hybridization and introgression. Annual Review of Ecology and Systematics, 27, 83–109. Ribon, R., Simon, J.E. & de Mattos, G. . (2003) Bird extinctions in Atlantic forest fragments of the Viçosa region, southeastern Brazil. Conservation Biology, 17, 1827–1839. Ricciardi, A. (2007) Are modern biological invasions an unprecedented form of global change? Conservation Biology, 21, 329–336. Richardson, D.M. & Whittaker, R.J. (2010) Conservation biogeography – foundations, concepts and challenges. Diversity and Distributions, 16, 313–320. Richardson, D.M., Pyšek, P., Rejmánek, M., Barbour, M.G., Panetta, F.D. & West, C.J. (2000) Naturalization and invasion of alien plants: concepts and definitions. Diversity and Distributions, 6, 93–107. Richardson, D.M., Hellmann, J.J., McLachlan, J.S., Sax, D.F., Schwartz, M.W., Gonzalez, P., Brennan, E.J., Camacho, A., Root, T.L., Sala, O.E., Schneider, S.H., Ashe, D.M., Clark, J.R., Early, R., Etterson, J.R., Fielder, E.D., Gill, J.L., Minteer, B.A., Polasky, S., Safford, H.D., Thompson, A.R. & Vellend, M. (2009) Multidimensional evaluation of managed relocation. Proceedings of the National Academy of Sciences USA, 106, 9721–9724. Ricketts, T.H. (2001) The matrix matters: effective isolation in fragmented landscapes. The American Naturalist, 158, 87–99.

Ricketts, T.H., Dinerstein, E., Olson, D.M., Loucks, C.J., Eichbaum, W., DellaSala, D., Kavanagh, K., Hedao, P., Hurly, P.T., Carney, K.M., Abell, R. & Walters, S. (1999) Terrestrial ecoregions of North America: a conservation assessment, Island Press, Washington, DC. Ricklefs, R.E. (2000) Rarity and diversity in Amazonian forest trees. Trends in Ecology & Evolution, 15, 83–84. Riddle, B.R. & Hafner, D.J. (1999) Species as units of analysis in ecology and biogeography: time to take the blinders off. Global Ecology and Biogeography, 8, 433–441. Riddle, B.R. & Hafner, D.J. (2006) Phylogeography in historical biogeography: investigating the biogeographic histories of populations, species, and young biotas. Biogeography in a changing world (ed. by M.C. Ebach and R.S. Tangney), pp. 161–176. CRC Press, Boca Raton, FL. Riddle, B.R., Hafner, D.J., Alexander, L.F. & Jaeger, J.R. (2000) Cryptic vicariance in the historical assembly of a Baja California peninsular desert biota. Proceedings of the National Academy of Sciences USA, 97, 14438–14443 Riddle, B.R., Dawson, M.N., Hadly, E.A., Hafner, D.J., Hickerson, M.J., Mantooth, S.J. & Yoder, A.D. (2008) The role of molecular genetics in sculpting the future of integrative biogeography. Progress in Physical Geography, 32, 173– 202. Riitters, K.H., Wickham, J.D. & Wade, T.G. (2006) Evaluating ecoregions for sampling and mapping land-cover patterns. Photogrammetric Engineering and Remote Sensing, 72, 781–788. Roberts, C.M., McClean, C.J., Veron, J.E.N., Hawkins, J.P., Allen, G.R., McAllister, D.E., Mittermeier, C.G., Schueler, G.W., Spalding, M., Wells, F., Vynne, C. & Werner, T.B. (2002) Marine biodiversity hotspots and conservation priorities for tropical reefs. Science, 295, 1280–1284. Roberts, C.M., Andelman, S., Branch, G., Bustamante, R.H., Castilla, J.C., Dugan, J., Halpern, B.S., Lafferty, K.D., Leslie, H., Lubchenco, J., McArdle, D., Possingham, H.P., Ruckelshaus, M. & Warner, R.A. (2003) Ecological criteria for evaluating candidate sites for marine reserves. Ecological Applications, 13, S199–S214. Roberts, C.M., Hawkins, J.P. & Gell, F.R. (2005) The role of marine reserves in achieving sustainable fisheries. Philosophical Transactions of the Royal Society B: Biological Sciences, 360, 123–132. Roberts, C.M., Mason, L. & Hawkins, J.P. (2006) Roadmap to recovery: a global network of marine reserves, Greenpeace International, Amsterdam. Robinson, W.D. (1999) Long-term changes in the avifauna of Barro Colorado Island, Panama, a tropical forest isolate. Conservation Biology, 13, 85–97. Rocha, L.A., Craig, M.T. & Bowen, B.W. (2007) Phylogeography and the conservation of coral reef fishes. Coral Reefs, 26, 501–512. Rodrigues, A.S.L., Gaston, K.J. & Gregory, R.D. (2000) Using presence-absence data to establish reserve selection proce-

References

dures that are robust to temporal species turnover. Biological Sciences, 267, 897–902. Rodrigues, A.S.L., Andelman, S.J., Bakarr, M.I., Boltani, L., Brooks, T.M., Cowling, R.M., Fishpool, L.D.C., da Fonseca, G.A.B., Gaston, K.J., Hoffmann, M., Long, J.S., Marquet, P.A., Pilgrim, J.D., Pressey, R.L., Schipper, J., Sechrest, W., Stuart, S.N., Underhill, L.G., Waller, R.W., Watts, M.E.J. & Yan, X. (2004a) Effectiveness of the global protected area network in representing species diversity. Nature, 428, 640–643. Rodrigues, A.S.L., Akcakaya, H.R., Andelman, S.J., Bakarr, M.I., Boitani, L., Brooks, T.M., Chanson, J.S., Fishpool, L.D.C., da Fonseca, G.A.B., Gaston, K.J., Hoffmann, M., Marquet, P.A., Pilgrim, J.D., Pressey, R.L., Schipper, J., Sechrest, W., Stuart, S.N., Underhill, L.G., Waller, R.W., Watts, M.E.J. & Yan, X. (2004b) Global gap analysis: priority regions for expanding the global protected-area network. BioScience, 54, 1092–1100. Rodrigues, A.S.L., Pilgrim, J.D., Lamoreux, J.F., Hoffmann, M. & Brooks, T.M. (2006) The value of the IUCN Red List for conservation. Trends in Ecology & Evolution, 21, 71–76. Rodwell, J.S. ed. (1991–2000) British plant communities, vols. 1–5. Cambridge University Press, Cambridge, UK. Roff, J.C., Taylor, M.E. & Laughren, J. (2003) Geophysical approaches to the classification, delineation and monitoring of marine habitats and their communities. Aquatic Conservation: Marine and Freshwater Ecosystems, 13, 77–90. Rondinini, C., Stuart, S. & Boitani, L. (2005) Habitat suitability models and the shortfall in conservation planning for African vertebrates. Conservation Biology, 19, 1488–1497. Rondinini, C., Wilson, K.A., Boitani, L., Grantham, H. & Possingham, H.P. (2006) Tradeoffs of different types of species occurrence data for use in systematic conservation planning. Ecology Letters, 9, 1136–1145. Rooney, T.P., Wiegmann, S.M., Rogers, D.A. & Waller, D.M. (2004) Biotic impoverishment and homogenization in unfragmented forest understory communities. Conservation Biology, 18, 787–798. Rooney, T.P., Olden, J.D., Leach, M.K. & Rogers, D.A. (2007) Biotic homogenization and conservation prioritization. Biological Conservation, 134, 447–450. Root, T. (1988) Atlas of wintering North American birds. University of Chicago Press, Chicago. Root, T.L., Price, J.T., Hall, K.R., Schneider, S.H., Rosenzweig, C. & Pounds, J.A. (2003) Fingerprints of global warming on wild animals and plants. Nature, 421, 57–60. Rosenfield, J.A. (2002) Pattern and process in the geographical range of freshwater fishes. Global Ecology and Biogeography, 11, 323–332. Rosenzweig, M.L. (1995) Species diversity in space and time, Cambridge University Press, Cambridge, UK. Rosenzweig, M.L. (2001) Loss of speciation rate will impoverish future diversity. Proceedings of the National Academy of Sciences USA, 98, 5404–5410.

289

Rosenzweig, M.L. (2003) Win-win ecology: how the Earth’s species can survive in the midst of human enterprise. Oxford University Press, New York. Rosenzweig, M.L. (2004) Applying species–area relationships to the conservation of diversity. Frontiers of biogeography: new directions in the geography of Nature (ed. by M.V. Lomolino and L.R. Heaney), pp. 325–343. Sinauer Associates, Sunderland, MA. Rouget, M., Cowling, R.M., Pressey, R.L. & Richardson, D.M. (2003) Identifying spatial components of ecological and evolutionary processes for regional conservation planning in the Cape Floristic Region, South Africa. Diversity and Distributions, 9, 191–210. Rouget, M., Cowling, R.M., Lombard, A.T., Knight, A.T. & Kerley, G.I.H. (2006) Designing large-scale conservation corridors for pattern and process. Conservation Biology, 20, 549–561. Rubenstein, D.R., Rubenstein, D.I., Sherman, P.W. & Gavin, T.A. (2006) Pleistocene Park: does re-wilding North America represent sound conservation for the 21st century? Biological Conservation, 132, 232–238. Rudel, T.K., DeFries, R., Asner, G.P. & Laurance, W.F. (2009) Changing drivers of deforestation and new opportunities for conservation. Conservation Biology, 23, 1396– 1405. Ruttan, L.M. & Borgerhoff-Mulder, M. (1999) Are East African pastoralists truly conservationists? Current Anthropology, 40, 621–652. Ruud, J. (1960) Taboo: a study of Malagasy customs and beliefs. George Allen and Unwin, London. Ryan, S.J. (2006) The role of culture in conservation planning for small or endangered populations. Conservation Biology, 20, 1321–1324. Ryder, O.A. (1986) Species conservation and systematics – the dilemma of subspecies. Trends in Ecology & Evolution, 1, 9–10. Sabine, E., Schreiber, G., Bearlin, A.R., Nicol, S.J. & Todd, C.R. (2004) Adaptive management: a synthesis of current understanding and effective application. Ecological Management and Restoration, 5, 177–182. Sagoff, M. (2005) Do non-native species threaten the natural environment? Journal of Agricultural and Environmental Ethics, 18, 215–236. Sakai, A.K., Allendorf, F.W., Holt, J.S., Lodge, D.M., Molofsky, J., With, K.A., Baughman, S., Cabin, R.J., Cohen, J.E., Ellstrand, N.C., McCauley, D.E., O’Neil, P., Parker, I.M., Thompson, J.N. & Weller, S.G. (2001) The population biology of invasive species. Annual Review of Ecology and Systematics, 32, 305–332. Sala, E., Abuto-Oropeza, O., Paredes, G., Parra, I., Barrera, J.C. & Dayton, P.K. (2002) A general model for designing networks of marine protected areas. Science, 298, 1991–1993. Salafsky, N., Margoluis, R., Redford, K.H. & Robinson, J.G. (2002) Improving the practice of conservation: a

290

References

conceptual framework and research agenda for conservation science. Conservation Biology, 16, 1469–1479. Salm, R.V., Clark, J.R. & Siirila, E. (2000) Marine and Coastal Protected Areas: a guide for planners and managers, IUCN, Washington, DC. Salzer, M.W. & Hughes, M.K. (2007) Bristlecone pine tree rings and volcanic eruptions over the last 5000 yr. Quaternary Research, 67, 57–68. Sanderson, E.W., Jaiteh, M., Levy, M.A., Redford, K.H., Wannebo, A.V. & Woolmer, G. (2002) The human footprint and the last of the wild. BioScience, 52, 891–904. Sarakinos, H., Nicholls, A.O., Tubert, A., Aggarwal, A., Margules, C.R. & Sarkar, S. (2001) Area prioritisation for biodiversity conservation in Québec on the basis of species distributions: a preliminary analysis. Biodiversity and Conservation, 10, 1419–1472. Saunders, D. & Hobbs, R.J. (1989) Corridors for conservation. New Scientist, 28 January, 63–8. Sax, D.F., Stachowicz, J.J., Brown, J.H., Bruno, J.F., Dawson, M.N., Gaines, S.D., Grosberg, R.K., Hastings, A., Holt, R.D., Mayfield, M.M., O’Connor, M.I. & Rice, W.R. (2007) Ecological and evolutionary insights from species invasions. Trends in Ecology & Evolution, 22, 465–471. Saxon, E.B., Baker, B., Hargrove, W., Hoffman, F. & Zganjar, C. (2005) Mapping environments at risk under different climate change scenarios. Ecology Letters, 8, 53–60. Schama, S. (1995) Landscape and memory. Harper Collins, London. Scheffer, M., Carpenter, S., Foley, J., Folke, C. & Walker, B. (2001) Catastrophic shifts in ecosystems. Nature 413, 591–596. Schlaepfer, M., Aukema, J.E., Grant, J., Halpern, B., Hoekstra, J., Kareiva, P., Lawler, J., Manolis, J.C., Nelson, C.R., Olden, J.D., Silliman, B., Stephens, S., Wiens, Zaradic, J. & P. (2005) Re-wilding: a bold plan that needs native megafauna. Nature, 437, 951–951. Schlipp, P.A. (1973) Albert Einstein: philosopher-scientist. Open Court Publishing, La Salle, IL. Schmidt, N.M. & Jensen, P.M. (2003) Changes in mammalian body length over 175 years – adaptations to a fragmented landscape? Conservation Ecology, 7, 6. Schulman, L., Toivonen, T. & Ruokolainen, K. (2007) Analysing botanical collecting effort in Amazonia and correcting for it in species range estimation. Journal of Biogeography, 34, 1388–1399. Schwartz, M.W., Thorne, J.H. & Viers, J.H. (2006) Biotic homogenization of the California flora in urban and urbanizing regions. Biological Conservation, 127, 282–291. Sclater, P.L. (1858) On the general geographical distribution of the members of the class Aves. Zoological Journal of the Linnean Society, 2, 130–145. Schmida, A. & Wilson, M.V. (1985) Biological determinants of species diversity. Journal of Biogeography, 12, 1–20. Scott, M.C. & Helfman, G.S. (2001) Native invasions, homogenization, and the mismeasure of integrity of fish assemblages. Fisheries, 26, 6–15.

Sellars, R.W. (1997) Preserving nature in the national parks: a history. Yale University Press, New Haven, CT. Sekercioglu, C.H., Schneider, S.H., Fay, J.P. & Loarie, S.R. (2008) Climate change, elevational range shifts, and bird extinctions. Conservation Biology, 22, 140–150. Shafer, C.L. (1990) Nature reserves: island theory and conservation practice. Smithsonian Institute Press, Washington, DC. Shafer, C.L. (1995) Values and shortcomings of small reserves. BioScience, 45, 80–88. Shafer, C.L. (1997) Terrestrial nature reserve design at the urban/rural interface. Conservation in highly fragmented landscapes (ed. by M.W. Schwartz), pp. 345–378. Chapman and Hall, New York, NY. Shaffer, M.L. (1981) Minimum population sizes for species conservation. BioScience, 31, 131–134. Shanks, A.L., Grantham, B.A. & Carr, M. (2003) Propagule dispersal distance and the size and spacing of marine reserves. Ecological Applications, 13, S159–S169. Sharp, B.R. & Bowman, D.M.J.S. (2004) Patterns of long-term woody vegetation change in a sandstone-plateau savanna woodland, Northern Territory, Australia. Journal of Tropical Ecology, 20, 1–12. Sharp, B.R. & Whittaker, R.J. (2003) The irreversible cattledriven transformation of a seasonally flooded Australian savanna. Journal of Biogeography, 30, 783–802. Shears, N.T., Smith, F., Babcock, R.C., Duffy, C.A.J. & Villouta, E. (2008) Evaluation of biogeographic classification schemes for conservation planning: application to New Zealand’s coastal marine environment. Conservation Biology, 22, 467–481. Sherman, K. (1993) Large Marine Ecosystems as global units for marine resources management – an ecological perspective. Large Marine Ecosystems: stress, mitigation and sustainability (ed. by K. Sherman, L.M. Alexander and B.D. Gold), pp. 3–14. American Association for the Advancement of Science Press, Washington, D.C. Sherman, K. & Alexander, L.M. (1989) Biomass yields and geography of Large Marine Ecosystems. Westview Press, Boulder, CO. Sherman, K., Alexander, L.M. & Gold, B.D. (eds) (1990) Large Marine Ecosystems: patterns, processes and yields. American Association for the Advancement of Science, Washington, DC. Sherman, K., Aquarone, M.-C. & Adams, S. (2007) Global applications of the Large Marine Ecosystem concept 20072010, NOAA Technical Memorandum NMFS-NE-208. Shimabukuro, Y.E., Duarte, V., Anderson, L.O., Valeriano, D.M., Arai, E., Freitas, R.M., Rudorff, B.F.T. & Moreira, M.A. (2006) Near real time detection of deforestation in the Brazilian Amazon using MODIS imagery. Revista AmbiÁgua, 1, 37–47. Shugart, H. (1984) Theory of forest dynamics. Springer, New York. Sidway, R. (2006) Alladale’s fenced wilderness – making a breakthrough? Ecos, 27, 30–35.

References

da Silva, J.M.C., Cardoso de Sousa, M. & Castelleti, C.H.M. (2004) Areas of endemism for passerine birds in the Atlantic forest, South America. Global Ecology and Biogeography, 13, 85–92. Simberloff, D. (1986) The proximate causes of extinction. Patterns and processes in the history of life (ed. by D.M. Raup and D. Jablonski), pp. 259–276. Springer-Verlag, Berlin. Simberloff, D. (2001) Eradication of island invasives: practical actions and results achieved. Trends in Ecology & Evolution, 16, 273–274. Simberloff, D. & Abele, L.G. (1982) Island biogeography and conservation: strategy and limitations. Science, 93, 1032. Simberloff, D. & Martin, J.-L. (1991) Nestedness of insular avifaunas: simple summary statistics masking complex species patterns. Ornis Fennica, 68, 178–192. Simberloff, D., Farr, J.A., Cox, J. & Mehlman, D.W. (1992) Movement corridors: conservation bargains or poor investments? Conservation Biology, 6, 493–504. Simberloff, D., Parker, I.M. & Windle, P.N. (2005) Introduced species: policy, management, and future research needs. Frontiers in Ecology and the Environment, 3, 12–20. Sites, J.W. & Marshall, J.C. (2004) Operational criteria for delimiting species. Annual Review of Ecology, Evolution, and Systematics, 35, 199–227. Skelly, D.K., Joseph, L.N., Possingham, H.P., Freidenburg, L.K., Farrugia, T.J., Kinnison, M.T. & Hendry, A.P. (2007) Evolutionary responses to climate change. Conservation Biology, 21, 1353–1355. Smart, S.M., Thompson, K., Marrs, R.H., Le Duc, M.G., Maskell, L.C. & Firbank, L.G. (2006) Biotic homogenization and changes in species diversity across human-modified ecosystems. Proceedings of the Royal Society B: Biological Sciences, 273, 2659–2665. Smith, C. I. (2005) Re-wilding: introduction could reduce biodiversity. Nature, 437, 318. Smith, F.A., Betancourt, J.L. & Brown, J.H. (1995) Evolution of body size in the woodrat over the past 25,000 years of climate change. Science, 270, 2012–2014. Smith, K.G. (2006) Patterns of nonindigenous herpetofaunal richness and biotic homogenization among Florida counties. Biological Conservation, 127, 327–335. Smith, P.G.R. & Theberge, J.B. (1986) A review of criteria for evaluating natural areas. Environmental Management, 10, 715–734. Smith, R.J., Goodman, P.S. & Matthews, W.S. (2006) Systematic conservation planning: a review of perceived limitations and an illustration of the benefits, using a case study from Maputaland, South Africa. Oryx, 40, 400–410. Smith, R.J., Easton, J., Nhancale, B.A., Armstrong, A.J., Culverwell, J., Dlamini, S.D., Goodman, P.S., Loffler, L., Matthews, W.S., Monadjem, A., Mulqueeny, C.M., Ngwenya, P., Ntumi, C. P., Soto, B. & Leader-Williams, N. (2008) Designing a transfrontier conservation landscape for the Maputaland centre of endemism using biodiversity, economic and threat data. Biological Conservation, 141, 2127–2138.

291

Smol, J.P., Wolfe, A.P., Birks, H.J.B., Douglas, M.S.V., Jones, V.J., Korhola, A., Pienitz, R., Rühland, K., Sorvari, S., Antoniades, D., Brooks, S.J., Fallu, M.-A., Hughes, M., Keatley, B.E., Laing, T.E., Michelutti, N., Nazarova, L., Nyman, M., Paterson, A.M., Perren, B., Quinlan, R., Rautio, M., Saulnier-Talbot, É, Siitonen, S., Solovieva, N. & Weckström, J. (2005) Climate-driven regime shifts in the biological communities of arctic lakes. Proceedings of the National Academy of Sciences USA, 102, 4397–4402. Smolik, M.G., Dullinger, S., Essl, F., Kleinbauer, I., Leitner, M. Peterseil, J., Stadler, L.-M. & Vogl, G. (2010) Integrating species distribution models and interacting particle systems to predict the spread of an invasive alien plant. Journal of Biogeography, 37, 411–422. Soulé, M.E. (1985) What is conservation biology? BioScience, 35, 727–734. Soulé, M.E. (ed.) (1986) Conservation biology: the science of scarcity and diversity. Sinauer Press, Inc., Sunderland, MA. Soulé, M.E. (1990) The real work of systematics. Annals of the Missouri Botanical Garden, 77, 4–12. Soulé, M.E. & Simberloff, D.S. (1986) What do genetics and ecology tell us about the design of nature reserves? Biological Conservation, 35, 19–40. Soulé, M.E. & Terborgh, J. (eds) (1999) Continental conservation: scientific foundations of regional reserve networks. Island Press, Washington, DC. Soulé, M.E. & Wilcox, B.A. (1980) Conservation biology: an evolutionary-ecological perspective. Sinauer, Sunderland, MA. Soulé, M.E., Bolger, D. T., Alberts, A. C., Wright, J., Sorice, M. & Hill, S. (1988) Reconstructed dynamics of rapid extinctions of chaparral-requiring birds in urban habitat islands. Conservation Biology, 2, 75–92. Soulé, M.E., Mackey, B. G., Recher, H. F., Williams, J. E., Woinarski, J. C. Z., Driscoll, D., Dennison, W. G. & Jones, M. E. (2004) The role of connectivity in Australian conservation. Pacific Conservation Biology, 10, 266–279. Sousa, W.P. (1984) The role of disturbance in natural communities. Annual Review of Ecology and Systematics, 15, 353–391. Soutullo, A., De Castro, M. & Urios, V. (2008) Linking political and scientifically derived targets for global biodiversity conservation: implications for the expansion of the global network of protected areas. Diversity and Distributions, 14, 604–613. Spalding, M.D., Ravilious, C. & Green, E.P. (2001) World atlas of coral reefs. University of California Press. Berkeley, USA. Spalding, M.D., Fox, H.E., Allen, G.R., Davidson, N., Ferdaña, Z.A., Finlayson, M., Halpern, B.S., Jorge, M.A., Lombana, A., Lourie, S.A., Martin, K.D., McManus, E., Molnar, J., Recchia, C.A. & Robertson, J. (2007) Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. BioScience, 57, 573–583. Spalding, M.D., Fish, L. & Wood, L.J. (2008) Toward representative protection of the world’s coasts and oceans – progress, gaps and opportunities. Conservation Letters, 1, 217–226.

292

References

Spear, D. & Chown, S.L. (2008) Taxonomic homogenization in ungulates: patterns and mechanisms at local and global scales. Journal of Biogeography, 35, 1962–1975. Stattersfield, A.J., Crosby, M.J., Long, A.J. & Wedge, D.C. (1998) Endemic bird areas of the world: priorities for biodiversity conservation. BirdLife International (BirdLife Conservation Series No. 7), Cambridge, UK. Steadman, D.W. (1997) Human-caused extinctions of birds. Biodiversity II: understanding and protecting our biological resources (ed. by M.L. Reaka-Kudla, D.E. Wilson and E.O. Wilson), pp. 139–61. Joseph Henry Press, Washington, DC. Steadman, D.W. (2006) Extinction and biogeography of tropical Pacific birds. The University of Chicago Press, Chicago and London. Steenkamp, Y., Van Wyk, B., Victor, J., Hoare, D., Smith, G., Dold, T. & Cowling, R. (2004) Maputaland-PondolandAlbany. Hotspots revisited: Earth’s biologically richest and most endangered ecoregions (ed. by R.A. Mittermeier, P. R. Gil, M. Hoffmann, J.D. Pilgrim, T.M. Brooks, C.G. Mittermeier and G.A.B. da Fonseca), pp. 219–228. CEMEX, Monterrey, Mexico. Steneck, R.S. & Dethier, M.N. (1994) A functional group approach to the structure of algal-dominated communities. Oikos 69, 476–498. Stevens, T. & Connolly, R.M. (2004) Testing the utility of abiotic surrogates for marine habitat mapping at scales relevant to management. Biological Conservation, 119, 351–362. Stiles, D. (2004) The ivory trade and elephant conservation. Environmental Conservation, 31, 309–321. Stockwell, C.A., Mulvey, M. & Vinyard, G.L. (1996) Translocations and the preservation of allelic diversity. Conservation Biology, 10, 1133–1141. Stockwell, D. (2007) Niche modelling: predictions from statistical distributions. Chapman and Hall, Boca Raton, FL. Stoms, D.M. & Estes, J.E. (1993) A remote sensing research agenda for mapping and monitoring biodiversity. International Journal of Remote Sensing, 14, 1839–1860. Stott, P. (1998) Biogeography and ecology in crisis: the urgent need for a new metalanguage. Journal of Biogeography, 25, 1–2. Strange, N., Thorsen, B.J. & Bladt, J. (2006) Optimal reserve selection in a dynamic world. Biological Conservation, 131, 33–41. Strayer, D.L., Eviner, V.T., Jeschke, J.M. & Pace, M.L. (2006) Understanding the long-term effects of species invasions. Trends in Ecology & Evolution, 21, 645–651. Stuart, S.N., Wilson, E.O., McNeeley, J.A., Mittermeier, R.A., Rodríguez, J.P. (2010) The Barometer of Life. Science, 328, 177. Suding, K.N. & Hobbs, R.J. (2009) Threshold models in restoration and conservation: a developing framework. Trends in Ecology & Evolution, 24, 271–279. Sutherland, W.J. (2002) Openness in management. Nature, 482, 834–835.

Swetnam, T.W., Allen, C.D. & Betancourt, J.L. (1999) Applied historical ecology: using the past to manage for the future. Ecological Applications, 9, 1189–1206. Takacs, D. (1996) The idea of biodiversity: philosophies of paradise. John Hopkins Press, Baltimore, MD. Takhtajan, A. (1986) Floristic regions of the world, University of California Press, Berkeley, CA. Tansley, A.G. (1935) The use and abuse of vegetational terms and concepts. Ecology, 16, 284–307. Tansley, A.G. (1939) The British Islands and their vegetation. Cambridge University Press, Cambridge. Tatem, A.J. (2009) The worldwide airline network and the dispersal of exotic species. Oikos, 32, 94–102. Tatem, A.J. & Hay, S.I. (2007) Climatic similarity and biological exchange in the worldwide airline transportation network. Proceedings of the Royal Society B: Biological Sciences, 274, 1489–1496. Taylor, E.B. (2004) An analysis of homogenization and differentiation of Canadian freshwater fish faunas with an emphasis on British Columbia. Canadian Journal of Fisheries and Aquatic Sciences, 61, 68–79. Terborgh, J. (2010) The trophic cascade on islands. The theory of island biogeography revisited (ed. by J.B. Losos and R.E. Ricklefs), pp. 116–142. Princeton University Press, Princeton, NJ. Terborgh, J. & Winter, B. (1983) A method for siting parks and reserves with special reference to Colombia and Ecuador. Biological Conservation, 27, 45–58. Terborgh, J., Lopez, L., Nuñez, V. P., Rao, M., Shahabuddin, G., Orihuela, G., Riveros, M., Ascanio, R., Adler, G.H., Lambert, T.D. & Balbas, L. (2001) Ecological meltdown in predator-free forest fragments. Science, 294, 1923– 1926. Terborgh, J., Nuñez V.P., Balukjian, B. & Silman, M. (2006) Vegetation dynamics of predator-free land-bridge islands. Journal of Ecology, 94, 253–263. Theodoropoulos, D.I. (2003) Invasion biology; critique of a pseudoscience. Avvar Books, Blythe, CA. Theurillat, J.P. & Guisan, A. (2001) Potential impact of climate change on vegetation in the European Alps: a review. Climatic Change, 50, 77–109. Thomas, C. (2009) Biodiversity databases spread, prompting unification call. Science, 324, 1632–1633. Thomas, C.D. & Hanski, I. (1997) Butterfly metapopulations. Metapopulation biology: ecology, genetics and evolution (ed. by I. Hanski and M. Gilpin), pp. 359–386. Academic Press, London. Thomas, C.D., Cameron, A., Green, R.E., Bakkenes, M., Beaumont, L.J., Collingham, Y.C., Erasmus, B.F.N., Ferreira de Siqueira, M., Grainger, A., Hannah, L., Hughes, L., Huntley, B., van Jaarsveld, A.S., Midgley, G.F., Miles, L., Ortega-Huerta, M.A., Townsend Peterson, A., Phillips, O.L. & Williams, S.E. (2004) Extinction risk from climate change. Nature, 427, 145–148. Thomson, K. (1991) Real world biogeography. Journal of Biogeography, 18, 475–477.

References

Thornton, I.W.B., Zann, R.A. & van Balen, S. (1993) Colonization of Rakata (Krakatau Is.) by non-migrant land birds from 1883 to 1992 and implications for the value of island equilibrium theory. Journal of Biogeography, 20, 441–52. Thuiller, W., Araújo, M.B., Pearson, R.G., Whittaker, R.J., Brotons, L. & Lavorel, S. (2004) Uncertainty in predictions of extinction risk. Nature 430, doi: 10.1038/nature02716 Tilman, D., May, R.M., Lehman, C.L. & Nowak, M.A. (1994) Habitat destruction and the extinction debt. Nature, 371, 65–66. Tjørve, E. (2009) Shapes and functions of species–area curves (II): a review of new models and parameterizations. Journal of Biogeography, 36, 1435–1445. du Toit, J.T., Rogers, K.H. & Biggs, H.C. (eds) (2003) The Kruger experience: ecology and management of savanna heterogeneity. Island Press, Washington, DC. Tolley, K.A., Makokha, J.S., Houniet, D.T., Swart, B.L. & Matthee, C.A. (2009) The potential for predicted climate shifts to impact genetic landscapes of lizards in the South African Cape Floristic Region. Molecular Phylogenetics and Evolution, 51, 120–130. Triantis, K.A., Vardinoyannis, K., Tsolaki, E., Botsaris, I., Lika, K. & Mylonas, M. (2006) Re-approaching the small island effect. Journal of Biogeography, 33, 914–923. Triantis, K.A., Mylonas, M. & Whittaker, R.J. (2008) Evolutionary species–area curves as revealed by singleisland endemics: insights for the interprovincial species– area relationship. Ecography, 31, 401–407. Triantis, K.A., Borges, P.A.V., Ladle, R.J., Hortal, J., Cardoso, P., Gaspar, C., Dinis, F., Mendonça, E., Silveira, L.M.A., Gabriel, R., Melo, C., Santos, A.M.C., Amorim, I.R., Ribeiro, S.P., Serrano, A.R.M., Quartau, J.A. & Whittaker, R.J. (2010) Predicting extinctions on oceanic islands. Ecography, 33, 285–294. Trudgill, S. (2001) Psychobiogeography: meanings of nature and motivations for a democratized conservation ethic. Journal of Biogeography, 28, 677–698. Tsing, A.L., Brosius, J.P. & Zerner, C. (1999) Assessing community-based natural-resource management. Ambio, 28, 197–198. Turner, M.G. & Dale, V.H. (1998) Comparing large, infrequent disturbances: what have we learned? Ecosystems, 1, 493–496. Turner, W.R., Brandon, K., Brooks, T.M., Costanza, R., da Fonseca, G.A.B. & Portela, R. (2007) Global conservation of biodiversity and ecosystem services. BioScience, 57, 868–873. Turvey, S.T., Pitman R.L., Taylor B.L., Barlow, J., Akamatsu, T., Barrett, L.A., Zhao, X., Reeves, R.R., Stewart, B.S., Wang, K., Wei, Z., Zhang, X., Pusser, L.T., Richlen, M., Brandon, J.R. & Wang, D. (2007) First human-caused extinction of a cetacean species? Biological Letters, 3, 537–540. Udvardy, M.D.F. (1969) Dynamic zoogeography, with special reference to land animals. Van Nostrand Reinhold, New York.

293

Udvardy, M.D.F. (1975) A classification of the biogeographical provinces of the world. IUCN, Morges, Switzerland. Ulrich, W. & Gotelli, N.J. (2007) Null model analysis of species nestedness patterns. Ecology, 88, 1824–1831 Ulrich, W. & Zalewski, M. (2007) Are ground beetles neutral? Basic and Applied Ecology, 8, 411–420. Ulrich, W., Almeida-Neto, M. & Gotelli, N.C. (2009) A consumer’s guide to nestedness analysis. Oikos, 118, 3–17. UNESCO (2009) Global Open Oceans and Deep Seabed (GOODS) – biogeographic classification. UNESCO-IOC, Paris. Urban, D., O’Neill, R.V. & Shugart, H.H., Jr (1987) Landscape ecology. A hierarchical perspective can help scientists understand spatial patterns. BioScience, 37, 119–127. Vandermeer, J. & Lin, B.B. (2008) The importance of matrix quality in fragmented landscapes: understanding ecosystem collapse through a combination of deterministic and stochastic forces. Ecological Complexity, 5, 222–227. Van den Belt, H. (2004) Networking nature, or Serengeti behind the dykes. History and Technology, 20, 311–333. Van Dyke, F. (2003) Conservation biology: foundations, concepts, applications. McGraw Hill, New York. Van Turnhout, C.A.M., Foppen, R.P.B., Leuven, R., Siepel, H. & Esselink, H. (2007) Scale-dependent homogenization: changes in breeding bird diversity in the Netherlands over a 25-year period. Biological Conservation, 134, 505–516. Van Wieren, S.T. (1991) The management of populations of large animals. The scientific management of temperate communities for conservation (ed. by I.F. Spellerberg, G.M. Goldsmith and M.G. Morris), pp. 103–127. Blackwell Scientific Publications, Oxford. Vellend, M., Verheyen, K., Jacquemyn, H., Kolb, A., Calster, A.V., Peterken, G. & Hermy, M. (2006) Extinction debt of forest plants persists for more than a century following habitat fragmentation. Ecology, 87, 542–548. Veloz, S.D. (2009) Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models. Journal of Biogeography, 36, 2290–2299. Venter, O., Meijaard, E., Possingham, H., Dennis, R., Sheil, D., Wich, S. & Wilson, K. (2009) Carbon payments as a safeguard for threatened tropical mammals. Conservation Letters, 2, 123–129. Vera, F.W.M. (2000) Grazing ecology and forest history. CABI Publishing, Wallingford. Verboom, G.A., Dreyer, L.L. & Savolainen, V. (2009) Understanding the origins and evolution of the world’s biodiversity hotspots: the biota of the African ‘Cape Floristic Region’ as a case study preface. Molecular Phylogenetics and Evolution, 51, 1–4. Veron, J. (2000) Corals of the world. Australian Institute of Marine Science, Townsville, Australia. Vermeij, G. (2005) Invasion as expectation: a historical fact of life. Species invasions: insights into ecology, evolution and biogeography (ed. by D.F. Sax, J.J. Stachowicz and S.D. Gaines), pp. 315–340. Sinauer Press, Sunderland, MA. Virah-Sawmy, M., Willis, K.J. & Gillson, L. (2010) Evidence for drought and forest declines during the recent megafaunal

294

References

extinctions in Madagascar. Journal of Biogeography, 37, 506–519. Vogler, A.P. & Desalle, R. (1994) Diagnosing units of conservation management. Conservation Biology, 8, 354–363 Von Holle, B., Delcourt, H.R. & Simberloff, D. (2003) The importance of biological inertia in plant community resistance to invasion. Journal of Vegetation Science, 14, 425–432. Vrba, E. S. & DeGusta, D. (2004) Do species populations really start small? New perspectives from the Late Neogene fossil record of African mammals. Philosophical Transactions of the Royal Society B: Biological Sciences, 359, 285–293. Walker, B.H. (1992) Biodiversity and ecological redundancy. Conservation Biology, 6, 18–23 Wallace, A.R. (1852) On the monkeys of the Amazon. Proceedings of the Zoological Society of London, 20, 107–110. Wallace, A.R. (1876) The geographical distribution of animals. Macmillan, London. Waples, R.S. (1991) Pacific salmon, Oncorhynchus spp., and the definition of a ‘species’ under the Endangered Species Act. Marine Fisheries Review, 53, 11–22 Wares, J.P., Goldwater, D.S., Kong, B.Y. & Cunningham, C.W. (2002) Refuting a controversial case of human-mediated marine species introduction. Ecology Letters, 5, 577–584. Watson, H.C. (1859) Cybele Britannica, or British plants and their geographical relations. Longman and Company, London. Watson, J., Freudenberger, D. & Paull, D. (2001) An assessment of the focal species approach for conserving birds in variegated landscapes in southeastern Australia. Conservation Biology, 15, 1364–1373. Watson, J.E.M., Whittaker, R.J. & Dawson, T.P. (2004) Habitat structure and proximity to forest edge affect the abundance and distribution of forest-dependent birds in tropical coastal forests of southeastern Madagascar. Biological Conservation, 120, 311–327. Watson, J.E.M., Whittaker, R.J. & Freudenberger, D. (2005) Bird community responses to habitat fragmentation: how consistent are they across landscapes? Journal of Biogeography, 32, 1353–1370. Watson, J., Fuller, R.A. & Barr, L. (2008) Why are we still using a ‘one size fits all’ philosophy for systematic reserve planning in Australia? Pacific Conservation Biology, 14, 233–235. Watson, J.E.M., Fuller, R.A., Watson, A.W.T., Mackey, B.G., Wilson, K.A., Grantham, H.S., Turner, M., Klein, C.J., Carwardine, J., Joseph, L.N. & Possingham, H.P. (2009) Wilderness and future conservation priorities in Australia. Diversity and Distributions, 15, 1028–1036. Watt, A.S. (1947) Pattern and process in the plant community. Journal of Ecology, 35, 1–22. Watts, M.E., Ball, I.R., Stewart, R.S., Klein, C.A., Wilson, K.A., Steinback, C., Lourival, R., Kircher, L. & Possingham, H.P. (2009) Marxan with Zones – software for optimal conservation-based land- and sea-use zoning. Environmental Modelling and Software, 24, 1513–1521.

Weaver, J. & Clements, F.E. (1938) Plant ecology, 2nd edn. Macmillian, London. Webb, C.O., Ackerly, D.D., McPeek, M.A. & Donoghue, M.J. (2002) Phylogenies and community ecology. Annual Review of Ecology and Systematics, 33, 475–505. Webb, T.J. & Gaston, K.J. (2000) Geographic range size and evolutionary age in birds. Proceedings of the Royal Society B: Biological Sciences, 267, 1843–1850. Weitzman, M.L. (1993) What to preserve? An application of diversity theory to crane conservation. Quarterly Journal of Economics, 108, 157–183. Welch, D.M. & Meselson, M. (2000) Evidence for the evolution of bdelloid rotifers without sexual reproduction or genetic exchange. Science, 288, 1211–1215. Western, D. (2001) Human-modified ecosystems and future evolution. Proceedings of the National Academy of Sciences, USA, 98, 5458–5465. Westoby, M., Walker, B. & Noy-Meir, I. (1989) Opportunistic management for rangelands not at equilibrium. Journal of Range Management, 42, 266–274. Westphal, M.I., Pickett, M., Getz, W.M. & Possingham, H.P. (2003) The use of stochastic dynamic programming in optimal landscape reconstruction for metapopulations. Ecological Applications, 13, 543–555. Wheeler, Q.D. & Meier, R. (2000) Species concepts and phylogenetic theory: a debate. Columbia University Press, New York. Whiten, A. & Boesch, C. (2001) The cultures of chimpanzees. Scientific American, 284, 60–67. Whitfield, J. (2009) Bigfoot study highlights habitat modelling flaws. Nature News, doi: 10.1038/news.2009.641 Whittaker, R.H. (1953) A consideration of climax theory: the climax as a population and pattern. Ecological Monographs, 23, 41–78. Whittaker, R.H. (1960) Vegetation of the Siskiyou Mountains, Oregon and California. Ecological Monographs, 30, 279–338. Whittaker, R. H. (1972) Evolution and measurement of species diversity. Taxon, 21, 213–251. Whittaker, R.H. (1975) Communities and ecosystems, 2nd edn. Macmillan, New York. Whittaker, R.H. (1977) Evolution of species diversity in land communities. Evolutionary biology (ed. by M.K. Hecht, W.C. Steere and B. Wallace), pp. 250–268. Plenum Press, New York. Whittaker, R.J. & Fernández-Palacios, J.M. (2007) Island biogeography: ecology, evolution, and conservation, 2nd edn. Oxford University Press, Oxford. Whittaker, R.J., Willis, K.J. & Field, R. (2001) Scale and species richness: towards a general, hierarchical theory of species diversity. Journal of Biogeography, 28, 453–470. Whittaker, R.J., Araújo, M.B., Jepson, P., Ladle, R.J., Watson, J.E.M. & Willis, K.J. (2005) Conservation biogeography: assessment and prospect. Diversity and Distributions, 11, 3–23.

References

Whittaker, R.J., Triantis, K.A. & Ladle, R.J. (2008) A general dynamic theory of oceanic island biogeography. Journal of Biogeography, 35, 977–994. Whittaker, R.J., Triantis, K.A. & Ladle, R.J. (2010) A general dynamic theory of oceanic island biogeography: extending the MacArthur–Wilson theory to accommodate the rise and fall of volcanic islands. The theory of island biogeography revisited (ed. by J.B. Losos and R.E. Ricklefs), pp. 88–115. Princeton University Press, Princeton, NJ. Wikramanayake, E.D., Dinerstein, E., Loucks, C., Olson, D., Morrison, J., Lamoreux, J., McKnight, M. & Hedao, P. (2001) Terrestrial ecoregions of the Indo-Pacific: a conservation assessment. Island Press, Washington, DC. Wikramanayake, E.D., Dinerstein, E., Loucks, C., Olson, D., Morrison, J., Lamoreux, J., McKnight, M. & Hedao, P. (2002) Ecoregions in ascendance: reply to Jepson and Whittaker. Conservation Biology, 16, 238–243. Wilcove, D.S., McLellan, C.H. & Dobson, A.P. (1986) Habitat fragmentation in the temperate zone. Conservation biology: the science of scarcity and diversity (ed. by M. Soulé), pp. 237–256. Sinauer Associates., Sunderland, MA. Wilcove, D.S. (1987) From fragmentation to extinction. Natural Areas Journal, 7, 23–29. Wilcox, B.A. (1980) Insular ecology and conservation. Conservation biology: an evolutionary-ecological perspective (ed. by M.E. Soulé and B.A. Wilcox), pp. 95–117. Sinauer Associates, Sunderland, MA. Wilkinson, C. (ed.) (2000) Status of coral reefs of the world. AIMS, Townsville, Australia. Williams, D.M. & Reid, G. (2009) New species in the genus Colliculoamphora Williams and Reid (Bacillariophyceae) with commentary on species concepts in diatom taxonomy. Nova Hedwigia, 135, 185–199. Williams, J.W. & Jackson, S.T. (2007) Novel climates, noanalog communities, and ecological surprises. Frontiers in Ecology and the Environment, 5, 475–482. Williams, J.W., Jackson, S.T. & Kutzbach, J.E. (2007) Projected distribution of novel and disappearing climates by 2100 AD. Proceedings of the National Academy of Sciences USA, 104, 5738–5742. Williams, M.R., Lamont, B.B., Henstridge, J.D. (2009) Species– area functions revisited. Journal of Biogeography, 36, 1994–2004. Williams, N.S.G., Morgan, J.W., McCarthy, M.A., McDonnell, M.J. (2006) Local extinction of grassland plants: the landscape matrix is more important than patch attributes. Ecology, 87, 3000–3006. Williams, P.H. (1998) Key sites for conservation: areaselection methods for biodiversity. Conservation in a changing world: integrating process into priorities for action (ed. by G.M. Mace, A. Balmford and J.R. Ginsberg), pp. 211–249. Cambridge University Press, Cambridge. Williams, P.H. & Araújo, M.B. (2002) Apples, oranges and probabilities: integrating multiple factors into biodiversity

295

conservation with consistency. Environmental Modelling and Assessment, 7, 139–151. Williams, P.H., Humphries, C., Araújo, M.B., Lampinen, R., Hagemeijer, W., Gasc, J.-P. & Mitchell-Jones, T. (2000) Endemism and important areas for representing European biodiversity: a preliminary exploration of atlas data for plants and terrestrial vertebrates. Belgian Journal of Entomology, 2, 221–246. Williams, S.P., Gibbons, D., Margules, C., Rebelo, A., Humphries, C. & Pressey, R. (1996) A comparison of richness hotspots, rarity hotspots, and complementary areas for conserving diversity of British birds. Conservation Biology, 10, 155–174. Williamson, M.H. (1988) Relationship of species number to area, distance and other variables. Analytical biogeography, an integrated approach to the study of animal and plant distributions (ed. by A.A. Myers and P.S. Giller), pp. 91–115. Chapman & Hall, London. Williamson, M.H. (1996) Biological invasions. Chapman and Hall, London. Willis, E.O. (1979) The composition of avian communities in remanescent woodlots in southern Brazil. Papeis Avulsos do Departamento de Zoologia, 33, 1–25. Willis, K.J. & Bennett, K.D. (1995) Mass extinction, punctuated equilibrium and the fossil plant record. Trends in Ecology & Evolution, 10, 308–309. Willis, K.J. & Bhagwat, S.A. (2009) Biodiversity and climate change. Science, 326, 806–807. Willis, K.J. & Birks, H.J.B. (2006) What is natural? The need for a long-term perspective in biodiversity conservation. Science, 314, 1261–1265. Willis, K.J. & McElwain, J.C. (2002) The evolution of plants. Oxford University Press, Oxford. Willis, K.J., Gillson, L. & Knapp, S. (2007) Biodiversity hotspots through time: an introduction. Philosophical Transactions of the Royal Society B: Biological Sciences, 362, 169–174. Wilson, E.O. (1969) The species equilibrium. Brookhaven Symposia in Biology, 22, 38–47. Wilson, E.O. (1984) Biophilia. Harvard University Press, Cambridge, MA. Wilson, E.O. (ed) (1988a) Biodiversity. National Academy Press, Washington, DC. Wilson, E.O. (1988b) The current state of biological diversity. Biodiversity (ed. by E.O. Wilson and F.M. Peters), pp. 3–18. National Academy Press, Washington, DC. Wilson, E.O. (1992) The diversity of life. Belknap Press, Cambridge, MA. Wilson, E.O. (1993) Biophilia and the conservation ethic. The Biophilia Hypothesis (ed. by S.R. Kellert and E.O. Wilson), pp. 31–41. Island Press, Washington, DC. Wilson, E.O. (2003) The Encyclopedia of Life. Trends in Ecology & Evolution, 18, 77–80. Wilson, E.O. & Willis, E.O. (1975) Applied biogeography. Ecology and evolution of communities (ed. by M.L. Cody and

296

References

J.M. Diamond), pp. 522–534. Harvard University Press, Cambridge, MA. Wilson, J.R.U., Dormontt, E.E., Prentis, P.J., Lowe, A.J. & Richardson, D.M. (2009) Something in the way you move: dispersal pathways affect invasion success. Trends in Ecology & Evolution, 24, 136–144. Wilson, K.A., McBride, M.F., Bode, M. & Possingham, H.P. (2006) Prioritising global conservation efforts. Nature, 440, 337–340. Wilson, K.A., Underwood, E.C., Morrison, S.A., Klausmeyer, K.R., Murdoch, W.W., Reyers, B., Wardell-Johnson, G., Marquet, P.A., Rundel, P.W., McBride, M.F., Pressey, R.L., Bode, M., Hoekstra, J.M., Andelman, S.J., Looker, M., Rondinini, C., Kareiva, P., Shaw, M.R. & Possingham, H.P. (2007) Conserving biodiversity efficiently: what to do, where and when. PLoS Biology, 5, e223. Wilson, K.A., Carwardine, J. & Possingham, H.P. (2009) Setting conservation priorities. Annals of the New York Academy of Sciences, 1162, 237–264. Wilson, M.H., Kepler, C.B., Snyder, N.F.R., Derrickson, S.R., Dein, F.J., Wiley, J.W., Wunderle, J.M., Lugo, A.E., Graham, D.L. & Toone, W.D. (1994) Puerto Rican parrots and potential limitations of the metapopulation approach to species conservation. Conservation Biology, 8, 114–123. Wilson, R.J., Gutiérrez, D., Gutiérrez, J., Martínez, D., Agudo, R. & Monserrat, V.J. (2005) Changes to the elevational limits and extent of species ranges associated with climate change. Ecology Letters, 8, 1138–1146. Winter, M., Kuhn, I., Nentwig, W. & Klotz, S. (2008) Spatial aspects of trait homogenization within the German flora. Journal of Biogeography, 35, 2289–2297. Wittmeyer, G., Elsen, P., Bean, W.T., Burton, A.C.O. & Brashares, J.S. (2008) Accelerated human population growth at protected area edges. Science, 321, 123–126. Woinarksi, J.C.Z., Mackey, B.G., Nix, H. & Traill, B.J. (2007) The nature of Northern Australia: natural values, ecological processes and future prospects. ANU Press, Canberra. Wonham, M.J., Lewis, M.A. & MacIsaac, H.J. (2005) Minimizing invasion risk by reducing propagule pressure: a model for ballast water exchange. Frontiers in Ecology and the Environment, 3, 473–478. Wood, L.J. (2007) MPA Global: a database of the world’s marine protected areas. Sea Around Us Project, UNEP-WCMC and WWF. (http://www.mpaglobal.org) Wood, L.J., Fish, L., Laughren, J. & Pauly, D. (2008) Assessing progress towards global marine protection targets: shortfalls in information and action. Fauna and Flora International, 42, 340–351. Woodruff, D.S. (2001) Declines of biomes and biotas and the future of evolution. Proceedings of the National Academy of Sciences USA, 98, 5471–5476. Worm, B.H., Sandow, M., Oschlies, A., Lotze, H. & Myers, R.A. (2005) Global patterns of predator diversity in the open ocean. Science, 309, 1365–1369.

Worthen, W.B. (1996) Community composition and nestedsubset analyses: basic descriptors for community ecology. Oikos, 76, 417–426. Wright, D.H. & Reeves, J.H. (1992) On the meaning and measurement of nestedness of species assemblages. Oecologia, 92, 416–428. Wright, D.H., Patterson, B.D., Mikkelson, G.M., Cutler, A. & Atmar, W. (1998) A comparative analysis of nested subset patterns of species composition. Oecologia, 113, 1–20. Wright, D.J. & Heyman, W.D. (2008) Introduction to the special issue: marine and coastal GIS for geomorphology, habitat mapping and marine reserves. Marine Geodesy, 31, 223–230. Wright, S.J. & Muller-Landau, H.C. (2006) The future of tropical forest species. Biotropica, 38, 287–301. Wu, J. (1999) Hierarchy and scaling: extrapolating information along a scaling ladder. Canadian Journal of Remote Sensing, 25, 367–380. Wu, J. & David, J.L. (2002) A spatially explicit hierarchical approach to modeling complex ecological systems: theory and applications. Ecological Modelling, 153, 7–26. Wu, J. & Loucks, O. (1995) From balance of nature to hierarchical patch dynamics: a paradigm shift in ecology. Quarterly Review of Biology, 70, 439–66. Wu, J. & Vankat, J.L. (1995) Island biogeography: theory and applications. Encyclopedia of environmental biology (ed. by W.A. Nierenberg), pp. 371–379. Academic Press, San Diego, CA. WWF [World Wildlife Fund] (2000) The Global 200 ecoregions: a user’s guide. World Wildlife Fund, Washington, DC. WWF [World-wide Fund for Nature] (2007) High conservation value forests: the concept in theory and practice. WWF Forests for Life Programme, Gland, Switzerland (available at www. hcvnetwork.org). Yan, X., Zhenyu, L., Gregg, W.P. & Dianmo, L. (2001) Invasive species in China – an overview. Biodiversity and Conservation, 10, 1317–1341. Zacharias, M.A., Howe, D.E., Harper, J.R., & Wainwright, P. (1998) The British Columbia marine ecosystem classification: rationale, development and verification. Coastal Management, 26, 105–124. Zimmerman, B.L. & Bierregaard, R.O., Jr (1986) Relevance of the equilibrium theory of island biogeography and species– area relations to conservation with a case from Amazonia. Journal of Biogeography, 13, 133–143. Zimov, S.A. (2005) Pleistocene park: return of the mammoth’s ecosystem. Science, 308, 796–798. Zink, R.M. (2004) The role of subspecies in obscuring avian biological diversity and misleading conservation policy. Proceedings of the Royal Society B: Biological Sciences, 271, 561–564. Zink, R.M. & Barrowclough, G.F. (2008) Mitochondrial DNA under siege in avian phylogeography. Molecular Ecology, 17, 2107–2121.

index

adaptive cycles 37 alien species 27 anthropogenic biomes 81 anthropogenic extinction 47, 58 archipelagic scale of SAR 195 Area of Outstanding Natural Beauty 93 assembly rules 221–222 assisted migration 185–188 Atlas Florae Europaeæ 57 Avise, John 72 azonal protected areas 95 hotspots concept 96–98 important areas 97 Azores extinction debt 204–206 Barcode of Life Initiative 53 baselines defining and using 38 derived from long-term ecology 39–41 derived from relict pristine systems 38–39 rapid environmental change 42 rewilding 41–42 bigfoot 70–72 bioclimatic envelope modelling (BEMs) 69–70 biodiversity 47 fundamental taxonomic units 62 Evolutionary Significant Units (ESUs) 64–65 other units 65 species versus other geneticallybased units 62–64 incomplete knowledge 47–48

knowledge shortfalls extinction estimate shortfall 58–62 Linnean shortfall 49–54 Wallacean shortfall 54–58 mapping function 76 biomes, ecosystems and communities 76–81 ecoregions 82–83, 87–91, 96, 101–103, 111, 113–117 mapping, reasons for 48 explaining species types in geographical areas 49 reconstruction of historical development 49 region classification based upon biotas 49 marine realm 83–91 predicting change 176–177 modelling current distributions 177–180 modelling range shifts 180–183 spatial distributions biogeographical regions 75–76, 77 endemism 74–75 mapping species 65–72 phylogeography 72–74 biodiversity conservation 6 Biodiversity Information Standards 53 biogeographical provinces 105, 106 biogeographical realms 105 biological species concept (BSC) 63 biomes 78–81 modelling current distributions 177–180

Conservation Biogeography Edited by Richard J. Ladle and Robert J. Whittaker © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33503-3

biotic homogenization 229–230 conservation 240–241 environmental and human drivers 238–240 manifestations 230–232 novel assemblages 241–242 patterns 232 birds 235–237 fishes 232–235 mammals 237–238 plants 237 process 230 biotic realms 105 biounits 105 Birdlife International 103, 107 Birds Directive (EU) 23, 117 body sizes on islands 221 Boone and Crockett Club (B&CC) 20 carrying capacity 33 Catalogue of Life 53 centres of richness and endemism (CORE) 74 cheese cutter spatial division 97, 98 cherry picking spatial division 97, 98 citizen science 254 climate 33 climate change 185–188 climatic envelope modelling (CEMs) 69–70 climax theory 33 Coastal Zone Management (CZM) 132 Commons Preservation Society 18 communication of biogeographic ideas 252–256 communities 78 community conservation areas 22

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Index

Community Conserved Areas (CCAs) 22 Community-Based Natural Resource Management (CBNRM) 22 complementarity 95, 138, 151, 241 compositionalism 31–32 conservation biogeography areas of current research 248 challenges 248 education, communication and public engagement 252–256 filling Wallacean and Linnean shortfalls 248–250 improving models, simulations and forecasts 250–251 reconciliation ecology 257 turning theory into practice 251–252 definition 3–4 emergence 4–7 future directions 257–258 need for 247–248 scope 7 diversity 8 scale 8 taxonomic units 62 Evolutionary Significant Units (ESUs) 64–65 other units 65 species versus other geneticallybased units 62–64 conservation biology 4–7 general characteristics 6 Conservation International (CI) hotspots 74–75, 97–98, 109–113 conservation movement 5 foundational values 16 conservation practice aims 31 social values 26 attitudes to non-native species 26–28 restoration and rewilding 28–29 conservation resource allocation problem 154 contagion hypothesis 174 Convention Concerning the Protection of World Cultural and Natural Heritage 23 Convention on Biological Diversity (CBD) 10, 18, 22–23 Conwentz, Hugo 19 cookie cutter spatial division 97, 98 correlative species distribution 178 corridors 217–218

country parks 18–19 countryside biogeography 257 critical seascapes 132 cryptozoogeographical distributions 70–72 Dasmann–Udvardy scheme 104–106 decision support tools for planning 152–155 demographical hypothesis 172 differentiation diversity 8 Directive on the Conservation of Wild Birds (EU) 117 dispersal events 226 distribution maps 55–56 diversity scale 9 dynamic arenas 115 Earth Summit 18 ecological biogeography 5 ecological extinctions 59, 60, 61 ecological surprises 34 ecoregions 82 ecosystems 76–78 adaptive management 42–44 balance versus flux 32–34 composition and function 31–32 flux 34–37 predicting the future 168–169 edge effects 216–217 education of biogeographic ideas 252–256 efficiency 139–140 plan development 151–152 Elton, Charles 34 Encyclopedia of Life (EOL) project 52, 53 Endemic Bird Areas (EBAs) 103, 106–108 endemism 8, 74–75 environmental change, rapid 42 environmental surrogates 144–146 Equilibrium Theory of Island Biogeography (ETIB) 190, 191 design guidelines 192 eukaryotes world distribution 51 European Distributed Institute of Taxonomy (EDIT) 53 Evolutionary Significant Units (ESUs) 64–65 extinction debt 200–203 Azores 204–206 extinction estimate shortfall 58–62 extinctions ecological 59, 60, 61 Linnean 58–59, 60

local 59, 60, 61 phoenix 59, 60, 61 predicting 181–183, 198 global species loss 199 time to extinction 202 rates 58 true 59, 60, 61–62 Wallacean 59–61 feedback 34, 37 flexibility 140 plan development 152 focal scale 8 focal species 141 forest reserves 94 forests, High Conservation Value approach 99–100 fragmentation 212 functional homogenization 232 functionalism 31–32 fundamental niche 179 game reserves 17–18, 94 gap analysis 124–125 genetic homogenization 231–232 geographical extent 8 Geographical Information System (GIS) 124 geographical range collapse 170–176 dynamic nature 172–174 geographical scale 7 Global Biodiversity Information Facility (GBIF) 53, 249 habitat corridors 217–218 habitat islands 137, 190 ecosystem collapse 203–208 life history 220 habitat/special management area, IUCN category 24 Habitats Directive (EU) 23 habitats modelling current distributions 177–180 Hierarchical Patch Dynamics Paradigm (HPDP) 34–35 hierarchy of processes 36 High Conservation Value approach 99–100 high seas protected areas 132–134 historical biogeography 4 homogenization of species 229–230 conservation 240–241 environmental and human drivers 238–240 manifestations 230–232 novel assemblages 241–242

Index

patterns 232 birds 235–237 fishes 232–235 mammals 237–238 plants 237 process 230 hotspots 74–75, 96–98, 103, 107, 109–113, 126, 130–132 human population density and species richness 240 human-assisted dispersal of species 226–227 airline traffic 228 ship traffic 227–228 Important Bird Areas (IBA) programme 117–119 incidence functions 210–211 instrumental values 14 Inter-American Biodiversity Information Network (IABIN) 249 Intergovernmental Panel on Climate Change (IPCC) 42 International Council for Bird Preservation (ICBP) 106–107, 117–118 International Institute for Species Exploration 53 International Union for the Conservation of Nature and Natural Resources (IUCN) 23–25 Biogeographical Regions 104–106 protected area categories 24 interprovincial scale of SAR 195 intraprovincial scale of SAR 195 intrinsic values 14 invasive species 27, 224 biogeography economic and ecological impacts 227–229 human-assisted versus prehistoric 226–227 process of invasion 224–226 irreplaceability of sites 152 island biogeography 190–194 guidelines for conservation 219–222 implications of habitat loss and fragmentation 194 ecosystem collapse 203–208 relaxation and extinction debt 199–203 species–area relationships 194–199 nestedness 213–216 edge effects 216–217 habitat corridors 217–218 landscape context 218–219

species incidence metapopulation dynamics 211–213 minimum viable populations 208–211 island species–area relationships (ISARs) 196–197 Jurassic Coast World Heritage site 23 Key Biodiversity Areas (KBAs) 119–121 keystone species 141 landscape scale 7 large marine ecosystems (LMEs) 130–131 latitudinal changes 176 Legacy Infrastructure Network for Natural Environments (LINNE) 53 Leopold, Aldo 21 Linne, Karl von 49–50 Linnean extinctions 58–59, 60 Linnean shortfall 49–54 filling 248–250 local extinctions 59, 60, 61 logistic curve 33 London Convention on African Wildlife 20 Major Ecosystem Types (METs) 48 major habitat type (MHT) 116 marine 131 management unit (MU) 65 mapping function 76 biomes, ecosystems and communities 76–81 ecoregions 82 protocol 65–72 purposes 102 Marine Ecoregions of the World (MEOW) 125 marine realm biodiversity 83–91 conservation initiatives 127–129 protected areas (MPAs) Coastal Zone Management and critical seascapes 132 connectivity networks 133 global representation system 123–126 high seas protected areas 132–134 large marine ecosystems 130–131 origins and expansion 122–123 reefs at risk 126–130

299

status 121–122 WWF Global 200 131–132 Systematic Conservation Planning 147–148 mathematical modelling current distribution of species, habitats and biomes 177–180 range shifts 180–183 Systematic Conservation Planning 152–155 matrix effects 218–219, 222 maximal coverage conservation prioritization problem 153 maximum sustainable yield 33 mesoecosystems 115 metapopulation dynamics 211–213 minimum set problem 153 minimum viable area (MVA) 209–210 minimum viable population (MVP) 193, 208–211 mitochondrial DNA (mtDNA) 65 Muir, John 20–21 national parks 21, 94 IUCN category 24 National Vegetation Classification (NVC) 39, 79–80, 100–101 Natura 2000 (EU) 23 natural disturbances 41 natural monument, IUCN category 24 natural sacred sites 17 Naturdenkmal 94 nature conservation 6 nature movements 19 nature reserves 19 nestedness 213–216 non-analogue communities 40 non-native species, attitudes to 26–28 novel assemblages 241–242 operational geographical units (OGUs) 75 operational model for pragmatic conservation planning 157 palaeoecology 4, 40 Partnership for Enhancing Expertise in Taxonomy (PEET) 52, 53 persistence (adequacy) 139, 163–164, 188, 191 dynamic conservation planning 183 biotic and abiotic processes 183–185 climate change and assisted migration 185–188 long-term ecological insights 184 socio-economic factors 185

300

Index

past, present and future 164–167 geographical range collapse 170–176 integrating evolutionary considerations 166–167 interpreting recent trends 169–170 predicting future ecosystems 168–169 plan development 146–151 predicting biodiversity change 176–177 modelling current distributions 177–180 modelling range shifts 180–183 phoenix extinctions 59, 60, 61 phylogenetic difference 143 phylogenetic diversity (PD) 166 phylogeography 49, 72–74 phytosociology 39 Pinchot, Gifford 18 Planetary Biodiversity Inventories (PBI) 53 pollen record analysis 171 population changes from human activities 169–170 population scale 7 population viability analysis (PVA) 209 populations 33 pristine systems, relict 38–39 prokaryotes, world distribution 51 protected area with sustainable use of natural resources, IUCN category 24 protected areas 14–16 community conservation areas 22 current trends and future directions 134–135 factors influencing establishment 15 framework typology 95–97 biogeographical versus ecological approaches 100–102 spatial classification 97–100 strategic goals 102–104 growth 15 international categorization system 23–25 marine schemes Coastal Zone Management and critical seascapes 132 connectivity networks 133 global representation system 123–126 high seas protected areas 132–134 large marine ecosystems 130–131 origins and expansion 122–123 reefs at risk 126–130 status 121–122 WWF Global 200 131–132

national parks 21 nature movements and nature reserves 19 origins 93–95 resource and game reserves 17–18 sacred sites 16–17 social purpose classification 25 state and country parks 18–19 terrestrial schemes 104 Conservation International (CI) hotspots 109–113 Endemic Bird Areas 106–108 Important Bird Areas (IBA) programme 117–119 IUCN Biogeographical Regions scheme 104–106 Key Biodiversity Areas (KBAs) 119–121 WWF Ecoregions scheme 113–117 wilderness areas 20–21 wildlife sanctuaries and refuges 19–20 protected landscape/seascape, IUCN category 24 public engagement with biogeographic ideas 252–256 Rabinowitz’s seven forms of rarity 67–69 range restriction 67–69 range shifts collapse 170–176 modelling 180–183 rapid environmental change 42 rarity of species 67–69 realized niche 179 reconciliation ecology 257 Red List 67–69, 128 reefs at risk 126–130 relaxation 199–201 relict pristine systems 38–39 representation (principle) 96–98, 101, 103–105, 123–125, 131 representativeness 138–139, 191 plan development 140 environmental surrogates 144–146 species-based surrogates 140–144 reserve designations, international 22–23 resource areas 17–18 restoration of habitats 28–29 rewilding 28–29, 41–42 sacred sites 16–17 sasquatch 70–72 scientific benchmark sites 94 self-organization 35 Sierra Club 20–21

Single Large Or Several Small (SLOSS) reserves debate 193 single-island endemics (SIEs) 197 sinks 218 Sites of Special Scientific Interest (SSSIs) 22, 39 small island effect 221 social purpose classification of protected areas 25 social values 13–14, 29–30 conservation practice 26 attitudes to non-native species 26–28 restoration and rewilding 28–29 international reserve designations 22–23 international system for categorizing protected areas 23–25 protected areas 14–16 community conservation areas 22 factors influencing establishment 15 growth 15 national parks 21 nature movements and nature reserves 19 resource and game reserves 17–18 sacred sites 16–17 state and country parks 18–19 wilderness areas 20–21 wildlife sanctuaries and refuges 19–20 Society for the Preservation of the Wild Fauna of the Empire (SPWFE) 20 socio-economic factors in dynamic conservation planning 185 reserve network design 186–187 spatial scale 8 Special Areas of Conservation 23 Special Protection Areas (SPAs) 23 species major concepts 63 mapping 65–72 modelling current distributions 177–180 species accumulation curves (SACs) 196 species–area relationships (SAR) 194–199 scales 195–197 species-based surrogates 140–144 species density 8 species description cumulative curves 51 species distribution models 69–72, 181–183 species diversity 8 species richness 8 human population density 240 species turnover 8

Index

state parks 18–19 stochastic variation 34 Strategic Adaptive Management 43 strict nature reserve, IUCN category 24 Systematic Conservation Planning 95, 136–137 concepts and principles efficiency 139–140 flexibility 140 persistence (adequacy) 139 representativeness 138–139 consultation and implementation 155–156 decision support tools 152–155 definition 138 development 140 achieving efficiency 151–152 achieving flexibility 152 achieving persistence 146–151 achieving representation 140 future directions 156–157 attainability of persistence 159 better economics and socio-economics 158 changing assets with time 158 dealing with uncertainty 158–159

dynamic problem 158 investment 159 mix of actions 158 threats 159 taboos 17 taxonomic units of conservation biogeography 62 Evolutionary Significant Units (ESUs) 64–65 other units 65 species versus other genetically-based units 62–64 Thoreau, Henry David 20 threatened species 47, 142, 202–203 threshold behaviour 35 thresholds 220–221 Thresholds of Potential Concern (TPC) 42–44 time to extinction 202 transition 35–37 true extinctions 59, 60, 61–62 umbrella species 141 urban/rural gradient studies 239–240

301

Vera, Franz 41 Wallace, Alfred Russel 48–49, 54–55 Wallacean extinctions 59–61 Wallacean shortfall 54–58 filling 248–250 Watt, Alexander 34 wetland degradation 239 wilderness areas 20–21 IUCN category 24 Wilderness Society 21 wildlife conservation 6 wildlife sanctuaries and refuges 19–20, 94 World Wildlife Fund (WWF) 22 Ecoregions scheme 111, 113–117 Global 200 programme 131–132 Yellowstone National Park 20 Yosemite 20 zonal protected areas 95 functional approach 96–97 representation–compositionalist approach 96

Plate 3.4 Landscape stability in alternative steady states. Superimposed lines show possible non-linear change from a nondegraded ‘steady state’ before 1430 cal yr BP, through a 600-year transition period leading to the modern degraded ‘steady state’ after 800 cal yr BP. T1 and T2 represent likely positions of major thresholds in the system. The dashed arrows from T2 show possible future trajectories of landscape recovery. From Dearing (2008).

Conservation Biogeography Edited by Richard J. Ladle and Robert J. Whittaker © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33503-3

(a)

(e)

(b)

(f)

(c)

(g)

(d)

(h)

Plate 4.2 Steps in producing hypothetical distribution maps, illustrated (panels a–d) for a species with a restricted distribution (Inga plumifera), and (panels e–h) for a species with a widespread distribution (Inga capitata). (a) and (e) the degree squares with confirmed occurrences. (b) and (f) the contours of the predicted probability of occurrence, using a probability of occurrence in adjacent degree squares of 0.5 and allowing this effect to accumulate for 5 degree squares. (c) and (g) the hypothetical distribution deduced by accepting a probability of occurrence of greater than 0.5 in any degree square. (d) and (h) the degree squares for each species. Summing these values across all species modelled in the exercise allows the estimation of the total number of species hypothetically occurring in any one degree square. From Hopkins (2007).

(a)

(c)

(b)

(d)

Plate 4.3 Known and unknown plant diversity of the Amazon Basin, based on species occurrence data for 1,584 monographed species. Major rivers of the Amazon Basin are shown in grey. Deeper shades of blue indicate higher numbers of species per 0.5 degree grid cell; yellow the lowest values; brown areas represent land > 1000 m. (a) The distribution of information of species occurrences. (b) The distribution of the expected diversity as predicted by a bootstrap model that compares the contents of the checklists within a circle with a radius of five degree squares of the focal square. (c) The distribution of the diversity that can be explained by modelling the distributions of the 1,584 species as predicted by assuming that each has a likelihood of occurrence of 50% in degree squares adjacent to those where they are already known to occur, and this additive effect extends within a radius of five degree squares. (d) The modelled distribution of incompleteness of knowledge, derived as the difference between layers b and c. Major rivers of the Amazon Basin are shown in grey. From Hopkins (2007).

Plate 4.14 A map of anthropogenic biomes, attempting to show the present day ecological reality rather than the hypothetical or potential ‘natural’ biomes of F.E. Clements and others. Source: Alessa & Chapin (2008), after Ellis & Ramankutty (2008).

Plate 4.15 Partial illustration of eco-biogeographical classification schemes in the central Indo-Pacific, assessed during the compilation of the MEOW – Marine Ecoregions of the World (Spalding et al., 2007). (a) shows two schemes: expert-derived map of biogeographical zones and subzones (pastel shades; Kelleher et al., 1995), and bathymetry, hydrography and productivity-based Large Marine Ecosystems (blue lines and cross-hatching; Sherman & Alexander, 1989). (b) shows three schemes: prevailing wind and chlorophyll-based (pastel blocks; Longhurst, 1998), expert-derived map of the Coral Triangle and its ecoregions, based on biological and physical characteristics (green lines; Green & Sheppard, 2005), and coral distribution-based (blue lines; Veron, 2000). Some biogeographical regionalization schemes consider the entire area as part of a single Indo-Pacific province, lacking internal divisions (e.g. Briggs, 1974; Hayden et al., 1984), but this may reflect the limited data available for what is an extremely biotically diverse and complex biogeographical region. (c) shows the final MEOW classification: provinces (colour-coded) with ecoregion subdivisions. The scheme is largely based on Green & Sheppard (2005) in the east, and on Longhurst (1998) and Kelleher et al. (1995) in the west, with minor adjustments based on expert regional advice.

Plate 5.9 The ‘Global 200’ ecoregions, being those deemed ‘most important’ by WWF. Source: www.panda.org/who_we_ are/history/wwf_conservation_1961_2006/

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Plate B7.1a Genus richness and phylogenetic diversity in the Cape flora: (a) genus richness (ten quantile intervals from yellow to deep red); (b) phylogenetic diversity (PD) per cell calculated using absolute age estimates in million years (colour code as for a); (c) residuals from a loess regression of PD on genus richness. Cells with negative residuals are indicated in blue, and those with positive residuals are shown in red (shading increments of half a standard deviation); (d) the distribution of unusual PD values, as assessed by comparing the observed PD in each cell with 10,000 PD values calculated by random selection of the same number of genera from the Cape flora. Cells with significantly lower PD (P < .0.05, two-tailed) than expected are shaded in blue. Figure from Forest et al. (2007).

Plate 7.2 Geographic range change in the grasshopper warbler (Locustella naevia) in Britain. (a) Probability of occurrence in 1968–1972, estimated using the pattern of contagion among records, with blue being minimum (non-zero) probability of occurrence and warm colours high probabilities (b) The probability of extinction by 1988– 1991, where blue represents low probabilities of extinction and warm colours high probabilities. Actual occurrences in 1988–1981 are shown by black dots. Occurrence records without probability values represent range expansions. Cells of high probabilities of extinction and lacking occurrence records are concentrated towards the margins of the 1968–1972 geographic range, although other species showed markedly different patterns (and contrast with Box 7.2). Reproduced from Araújo et al. (2002b).

Plate B7.5a Four sets of complementarity areas, for plants, breeding birds, mammals and amphibians and reptiles combined, using a 50 km grid resolution, based on a maximum coverage set algorithm and with a target of identifying the best performing 10% of grid cells. The complementarity areas, shown by the open cells, are overlapped on a map of urban land use change intensity for 2020–2050 based on one of four IPCC land use change scenarios examined: the A1FI scenario, a fossil fuel-intensive world of rapid economic growth, low population growth and rapid introduction of new and more efficient technologies. The complementarity area solutions depicted on the maps are illustrative and hypothetical networks of high priority areas for each taxon. Based on Figure 2 from Araújo et al. (2008).

Plate B9.1a (a) The frequency of commercial shipping traffic along shipping routes around the world, ranging from low (blue) to high (red). From Halpern et al. (2008). (b) Global hotspots for biological invasion from ballast water, ranging from low (blue) to high (red). From Drake and Lodge (2004).