Conservation by Proxy: Indicator, Umbrella, Keystone, Flagship, and Other Surrogate Species

conservation by proxy

Large mammalian carnivores, such as this jaguar, are used as proxy species to pursue many different agendas in conservation.

Conservation by Proxy indicator, umbrella, keystone, flagship, and other surrogate species

Tim Caro Illustrated by Sheila Girling

Washington | Covelo | London

Copyright © 2010 Island Press All rights reserved under International and Pan-American Copyright Conventions. No part of this book may be reproduced in any form or by any means without permission in writing from the publisher: Island Press, 1718 Connecticut Avenue NW, Suite 300, Washington, DC 20009. Island Press is a trademark of The Center for Resource Economics.

Library of Congress Cataloging-in-Publication Data Caro, T. M. (Timothy M.) Conservation by Proxy : Indicator, umbrella, keystone, flagship, and other surrogate species / Tim Caro ; Illustrated by Sheila Girling. p. cm. Includes bibliographical references and index. ISBN-13: 978-1-59726-192-0 (cloth : alk. paper) ISBN-10: 1-59726-192-0 (cloth : alk. paper) ISBN-13: 978-1-59726-193-7 (pbk. : alk. paper) ISBN-10: 1-59726-193-9 (pbk. : alk. paper) 1. Conservation of natural resources. 2. Environmental protection. 3. Globalization. I. Girling, Sheila. II. Title. S936.C37 2010 333.95′16—dc22 2010008855

Design and typesetting by Karen Wenk Font: Galliard Printed on recycled, acid-free paper

Manufactured in the United States of America 10 9 8 7 6 5 4 3 2 1

You can’t always get what you want But if you try sometimes you just might find You get what you need. —Mick Jagger and Keith Richards

table of contents

xv

Preface INTRODUCTION Chapter 1

Buzzwords in Conservation Biology Shortcuts Biodiversity Usage Documentation Remarkable species Scale Surrogate species in systematic conservation Taxonomy of surrogate species Other terms Difficulties in surrogate typology Loose definitions Lax terminology Multiple applications and purpose Using the same species for two surrogate tasks Hidden agendas and research displacement activities Summary

1 1 3 3 8 8 9 13 15 16 17 17 22 23 26 26 27

DISTRIBUTION OF BIODIVERSITY Chapter 2

Species Indicators of Biodiversity at a Large Scale A big picture Congruency of species richness Environmental surrogates Higher taxa Congruency of endemism Congruency of rarity Congruency of threatened species

31 31 32 36 37 39 42 43

x Table of Contents Complementarity and congruency Concordance between different measures of biodiversity Global scale Continental scale Complementarity Biodiversity distribution and protected areas Practical application Summary

45 50 50 51 53 53 57 58

RESERVE SITE SELECTION Chapter 3

Species Indicators of Biodiversity in Reserve Selection A smaller scale Cross-taxon congruence of species richness Within-taxon congruence of species richness Taxon subsets Higher taxa Morphospecies Congruency of endemism, congruency of rarity, and congruency of threatened species Concordance between measures of biodiversity Species richness and endemism Species richness and rarity Species richness and threatened species Biodiversity metrics Congruency of complementarity Species richness Other biodiversity measures Persistence Higher taxa Protected area coverage Marine reserve prioritization Environmental surrogates Combining environmental and taxonomic surrogates Practical issues Summary

61 61 62 66 68 68 71 72 74 74 76 78 78 79 79 82 83 84 86 89 90 94 95 96

RESERVE DESIGN AND MANAGEMENT Chapter 4

Umbrella Species and Landscape Species Three conservation goals

99 99

Table of Contents xi Lambeck’s insight Umbrella species by taxon Plants Invertebrates Mammals Birds Choosing an appropriate umbrella species Problems with umbrella species Management implications Landscape species Summary

Chapter 5

Keystone, Engineering, and Foundation Species The keystone species concept Classic keystone species Wider scope Mesopredator release in temperate ecosystems Ecological meltdown in the neotropics Keystone introductions Removing invasive species Problems with using keystone species as a conservation tool Reasons for continuing to use keystone species Ecosystem engineers Mechanisms of habitat modification Examples of ecosystem engineers Difficulties in using ecosystem engineers in conservation Advantages of ecosystem engineers Foundation species Management issues Summary

102 103 103 105 106 108 113 117 119 120 125

127 127 127 129 132 134 136 138 139 142 143 144 146 151 153 153 154 156

SPECIES INDICATORS OF ANTHROPOGENIC CHANGE Chapter 6

Environmental Indicator Species Ecosystem health and biological integrity Environmental indicators Sentinel species Examples of the uses of environmental indicator species Marine pollution Freshwater pollution River modification Marine fisheries

159 159 162 167 169 169 171 174 177

xii Table of Contents

Chapter 7

Climate change in marine ecosystems Proliferation and obfuscation of terms Summary

181 184 185

Ecological-Disturbance Indicator Species

189

Effects of disturbance Proposed criteria for indicator species Single species and species-groups as indicators of disturbance Single species Species-groups Examples of the use of species-groups in documenting effects of land-use change Forest fragmentation: BDFFP Countryside biogeography Tropical plantations Exurban USA Changes in populations over time Determining the number of species-groups Management pointers Summary

Chapter 8

Cross-Taxon-Response Indicator Species Habitat alteration Fora for cross-taxon-response indicator species Land-use changes Agricultural landscapes Management areas Intraguild-response indicator species Population changes Management indicator species Difficulties with the MIS concept Early warnings Substitute species Problems with cross-taxon-response indicator species Summary

189 190 194 194 196 197 197 203 206 209 210 211 213 214

217 217 219 219 224 227 228 229 230 233 234 238 239 242

PROMOTING CONSERVATION Chapter 9

Flagship Species Characteristics of flagship species Multiple objectives

245 245 246

Table of Contents xiii Are flagship species successful? Public awareness Raising funds Reserve establishment Qualities of flagship species Iconic species What next? Summary

249 249 251 251 257 258 259 260

SUMMARY OF CONCEPTS AND COST-EFFECTIVENESS Chapter 10 Surrogate Species in the Real World Surrogate categories Synopsis Multi-surrogacy Predictive power of surrogate species Distribution of biodiversity Reserve site selection Reserve design and management Species indicators of anthropogenic change Promoting conservation Wrap-up Summary

263 263 264 268 270 271 274 277 278 281 283 284

References

287

Scientific Names of Species Mentioned in the Text

355

Subject Index

365

California blue mussels completely cover littoral-zone rock faces, providing shelter for many species of marine invertebrates and microorganisms. Protecting these influential engineering species consequently conserves habitat used by numerous other species. (Drawing by Sheila Girling.)

preface

Mention “umbrella species” or “flagship species” and people will nod their heads sagely, but ask them to tell you what these phrases mean and they will mumble something incoherent. Talk to nature reserve managers about “keystone species” and they will expostulate about important species in the ecosystem. Go to a conservation conference and you will hear “focal” and “indicator species” as catchwords thrown about with panache but little substance. Everyone is using these species-terms as shortcuts to achieving conservation goals, but few really know what they signify or whether they are of any real use as conservation tools. I wanted to write a book about surrogate species—that is, species that are used to represent other species or aspects of the environment to obtain a conservation objective—for three reasons. First, I wished to be clear as to what these buzzwords mean. Beginning in 1999, I had written several papers about surrogate species but had never completely clarified the differences and overlap among these terms. I knew that I was confused, and I suspected others might be too. Second, I wanted to help people evaluate whether conservation shortcuts had sound biological foundations. It is important to clarify what conservation tools can and cannot achieve, because political decisions with long-lasting ramifications for conservation are being made using these ideas and so we need to know their biological limitations. I had a nagging feeling that buzzwords are often used in conservation because they are fashionable and attract attention, but I wanted to see whether they really represented underlying biological patterns. Third, I wanted to remind conservation practitioners of the conservation settings in which these terms are most commonly used, so that they could immediately place them in context when they heard them next. In short, my objective was to write a concise reference book in which conservation biologists and conservation managers could consult theory and evidence underlying surrogate species concepts. The book is timely because of the increasing use and proliferation of surrogate buzzwords in conservation biology, the absence of any central clearing house that defines and

xv

xvi Preface

scrutinizes their usefulness, and the urgent necessity to understand whether decision makers can go on employing these concepts with confidence. I am particularly grateful to Toby Gardner for many e-mail discussions and for reading and commenting on the whole manuscript; the book is much better as a consequence. I also thank Monique Borgerhoff Mulder and Tim Davenport for after-dinner advice; the University of California Davis library system for enabling me to read their reprints from the other side of the world; Barbara Dean, Mike Fleming, Erin Johnson, Maureen Gately, and Sharis Simonian at Island Press for guidance; Thomas Sears for help with the figures; my mother, Sheila Girling, for the charming drawings; the Tanzania Wildlife Research Institute and Commission for Science and Technology for making it possible to live in Tanzania; and the people of Msasani and members of the Gymkhana Squash Club, Dar es Salaam, and regulars at the Drop Shot Bar there for their hospitality while I wrote this book. Tim Caro March 1, 2010

Confusion bedevils surrogate species terminology. Focal species, for instance, is used in a well-defined, specific sense but is also used to refer to any species that is being studied, such as this gray wolf. (Drawing by Sheila Girling.)

Chapter 1

Buzzwords in Conservation Biology

Shortcuts The goal of conservation biology is to stop or delay the extinction of plant and animal populations and to prevent or slow habitat destruction. As populations, species, and habitats are under threat in so many places, we are forced to make difficult decisions about where to focus conservation attention. Ideally, detailed study should precede important decisions, especially those that have long-term ramifications for conservation, but four factors prohibit this: the complexity of nature prevents accurate appraisal of all its aspects, the scale of the biodiversity crisis is vast, political decisions must be made rapidly, and there is a severe shortage of funds. Consequently, conservation scientists are compelled to take shortcuts to identify and solve problems. These involve using satellite imagery to monitor environmental change, modeling population responses to anthropogenic pressure, interviewing people about their activities, garnering expert opinion about species’ distributions and the threats they face, and monitoring subsets of species. Subsets may act as proxies for the presence of others, and may help us in deciding where to set up protected areas, in measuring plant and animal community responses to anthropogenic change, and in raising conservation awareness. They are called surrogate species or surrogate taxa, which I define as “species that are used to represent other species or aspects of the environment to attain a conservation objective” (see also Wiens et al. 2008). 1

2 conservation by proxy

Surrogates may be species that represent the whole pool of species, or those that represent subsets of the species pool, or they may be speciesgroups that represent the species pool (see Fig. 1-1). All of these fall under the rubric of surrogate species in this book. Broadly, surrogates are likely to be most useful when the number of species being protected or monitored is uncertain, or the spatial extent of the task is intermediate in size (Wiens et al. 2008). When the area or number of species is very small, individual species can sometimes be considered one by one, and when the area or number of species is very large, surrogate species may be unable to represent the variety of taxa or habitats present and so may not be helpful. Spatial scale is thus very important in the science of surrogate species. At its heart, the surrogate species concept relies on extrapolation, from group A to group B, from area A to area B, from subgroup to group, from smaller to larger scale, sample to inventory, habitat to inventory, and so on (Hammond 1995). The key issue is whether such extrapolations are valid. Until we know this, the surrogate species concept is a risky gambit because we may be making unreliable approximations of the larger picture. Nevertheless, perhaps because surrogate terms are catchy shorthand expressions and have now become conservation buzzwords, unreliable and ill-conceived surrogate species continue to be proposed in conservation workshops, in meetings, and in the literature, potentially affecting important management decisions without careful thought as to what such terms really signify in nature and without considering the burden that they carry for conserving species and habitats. Moreover, surrogate concepts are often interchanged,

Figure 1-1. The hierarchical relation between the species pool of interest, species groups, and surrogate species. Surrogate species may be used either to represent the species pool as a whole or to represent subsets of that pool designated by species groups, or the groups themselves may be used as distillations of the larger species pool. (Reprinted from Wiens et al. 2008.)

Buzzwords in Conservation Biology 3

elided, or just used incorrectly, generating a loose terminology that is confusing to laypersons, wildlife managers, and conservation scientists alike. In short, buzzwords in conservation are useful only if they are clearly defined, address clear objectives, and prove themselves to be effective. I start this chapter by introducing issues of biodiversity and scale, then outline the variety of ways surrogate species are used in conservation, and present five different problems in their application. This chapter provides a sketch of how surrogate concepts are brought into play and misused in conservation science.

Biodiversity Usage Biological diversity or biodiversity (Wilson 1992; Harper & Hawksworth 1995) “means the variability among living organisms from all sources including, inter alia, terrestrial, marine, and other aquatic systems, and the ecological complexes of which they are part; this includes diversity within species, between species, and of ecosystems” (Heywood 1995, 8; see also OTA 1987; Hunter 1996; Hubbell 2001; Groves et al. 2002; and Magurran 2004). The concept captures variability in biological systems from genes to communities but in practice centers on species richness (i.e., the number of species present) in an area. Species richness is reasonably precise (at least for animals), relatively easy to measure, and is generally assumed to play a positive role in ecosystem dynamics. Some people erroneously equate species richness with α diversity (i.e., the number of species in a homogeneous habitat), but it can also refer to β diversity, the difference in composition of species between habitats located in close proximity in the same landscape (also referred to as species turnover or community dissimilarity, Whittaker 1960; Su et al. 2004). Measures of β diversity can be quantified in several ways, using Jaccard’s coefficient or percentage similarity, for example. Alpha diversity increases with the size of the area sampled, whereas β diversity declines because fewer new species are encountered; γ diversity refers to the total number of species in a given region. Biodiversity is also measured using combinatorial measures of richness and abundance (Maclaurin & Sterelny 2008), allowing species community structure and composition to be assessed using measures such as the Q statistic, Simpson’s index, and Generalized Dissimilarity Modeling (Ferrier et al. 2007).

4 conservation by proxy

Biodiversity is used in other senses as well. Endemic species have relatively small geographic ranges (e.g., an average of 64,561 km2 for 147 endemic Mexican mammals versus 427,183 km2 for 314 nonendemics, Ceballos et al. 1998). They may be restricted to an ecoregion, such as mangrove forests, or to a country—bonobos are endemic to the Democratic Republic of Congo, for instance. Species with narrow geographic ranges are especially susceptible to extinction from disease, invasive species, sustained habitat degradation, climate change, or political instability (Usher 1986; Pimm et al. 1995). Rarity is a component of biodiversity, too. It is important in conservation because rare species are prone to extinction. Rarity can be measured in three orthogonal ways—a wide or narrow geographic distribution, a broad or restricted habitat specificity, or a local population size that is somewhere large or everywhere small (Rabinowitz et al. 1986). These can be combined together to produce seven different forms of rarity and one form of commonness, although in practice rare species are frequently defined by their limited geographic distribution—often as the lowest quartile of species based on their representation on a geographic grid (Gaston 1994; Flather & Sieg 2007)—so in this sense they are similar to narrow endemics. They are also defined as species with absolutely or relatively small population sizes (Andelman & Fagan 2000). Other definitions include habitat specialists and, confusingly, threatened species, although these need not be synonymous (Freitag & van Jaarsveld 1997), as well as combinations of measures (Cofre & Marquet 1999; Marcot & Flather 2007). In ecological surveys, rare species can denote species of which only a single individual is recorded (Novotny & Basset 2000), or those found at only one site (Longino et al. 2002)—such rarity can be more apparent than real because it is affected by sampling effort. Threatened species are yet another aspect of biodiversity. Threat refers to processes that drive loss of biodiversity, such as logging and mining. It can be considered in terms of exposure, or probability of a threatening process affecting an area, or intensity or magnitude, or the impact or outcome of the threat. Vulnerability is a closely related term that often refers to species and populations but also relates to the area of occupancy (Wilson et al. 2005). Conventionally, the extent to which a species is vulnerable is measured using the International Union of Nature and Natural Resources (IUCN) categorization of threatened and endangered species (see Table 1-1a) in which species are ascribed categories using several criteria (see Table 1-1b); sometimes these categories are assigned ordinal measures (e.g., Rey Benayas & de la Montana 2003). The method is transparent and

Not evaluated (NE)

Data deficient (DD)

Least concern (LC)

Near threatened (NT)

Vulnerable (VU)

Extinct (EX) Extinct in the Wild (EW) Critically endangered (CR) Endangered (EN)

(a)

When there is no reasonable doubt that last individual has died. When the species is known only to survive in cultivation, in captivity, or as a naturalized population(s) outside the past range. When the best available evidence indicates that it meets any of the criteria A–E for CR and is therefore thought to be facing an extremely high risk of extinction in the wild. When the best available evidence indicates that it meets any of the criteria A–E for EN and is therefore thought to be facing an extremely high risk of extinction in the wild. When the best available evidence indicates that it meets any of the criteria A–E for VU and is therefore thought to be facing an extremely high risk of extinction in the wild. When it has been evaluated against the criteria but does not qualify for CR, EN, or VU now, but is close to qualifying for or is likely to qualify for a threatened category in the near future. When it has been evaluated against the criteria and does not qualify for CR, EN, VU, or NT. Widespread and abundant taxa are included in this category. When there is inadequate information to make a direct, or indirect, assessment of its risk of extinction based on its distribution and/or population status. When a species has not yet been evaluated against the criteria.

Table 1-1. (a) IUCN Red List categories, (b) a simplified overview of the IUCN Red List Criteria that are used to categorize critically endangered, endangered, and vulnerable species (from Rodrigues et al. 2006; IUCN 2008).

<5,000 km2

<500 km2

<100 km2

<10 km2

Small range (area of occupancy)

≥50%

≥80%

Reduction in population size B. Small range (extent of occurrence)

≥70%

EN

≥90%

CR

A. Reduction in population size

Table 1-1(b).

Table 1-1. Continued

<2,000 km2

<20,000 km2

≥30%

≥50%

VU

Over 10 years / 3 generations in the past where causes of reduction are reversible and understood and have ceased Over 10 years / 3 generations in past, future, or combination Plus 2 of (a) severe fragmentation and/or few locations (1, ≤5, ≤10); (b) continuing decline; (c) extreme fluctuation Plus 2 of (a) severe fragmentation and/or few locations (1, ≤5, ≤10); (b) continuing decline; (c) extreme fluctuation

Notes

<250

<50 N/A

≥50% in 10 years/ 3 generations

C. Small and declining population

D. Very small population Very restricted population

E. Quantitative analysis

≥20% in 20 years/ 5 generations

<250 N/A

<2,500

<1,000 <20 km2 area of occupancy or ≤5 locations ≥10% in 100 years

<10,000

Estimated extinction risk using quantitative models (e.g., population viability analyses)

Mature individuals. Continuing decline either (1) over specified rates and time periods, or (2) with (a) specified population structure or (b) extreme fluctuation Mature individuals Capable of becoming CR or even EX within a very short time frame

8 conservation by proxy

can be updated with new information (IUCN 2008), and changes in species categorization over time enable trends in extinction risk to be tracked (e.g., Butchart et al. 2005). Threatened species can also be defined in terms of combinatorial measures of population size and speed of decline, independent measures including the Food and Agriculture Organization’s data on annual percentage loss of forests over set time intervals, human population density and rates of increase, whether or not species are featured on various other endangered lists, or even the absence of protected areas within a country (Millsap et al. 1990; Master 1991; Sisk et al. 1994; Foose & van Strien 1997).

Documentation The way in which species numbers are tallied affects the total number of endemic, rare, or threatened species that are recorded. When species in an area are counted using geographic range maps, the sum is higher than that based on surveys because species are not found everywhere throughout their geographic range (Rondinini et al. 2005). North American birds are detected on average at only 40.5 percent of sites within their mapped ranges during a single year survey, for instance. Species with low range occupancy are those with low average densities and narrow habitat requirements—species occupying high elevations, for example (Hurlbert & White 2005). Combining survey and range-map distributions conflates measures of differing resolution. Additionally, estimates of species numbers depend on scientific expertise. Well-known groups such as birds and vascular plants can be identified by nonspecialists, but others such as arthropods may be identified only to morphospecies and thus be subject to biases caused by sexual dimorphism and phenotypic developmental changes that will boost species totals, or by cryptic species (different species that look morphologically similar) that will diminish species totals.

Remarkable Species Simple species richness at a site misses the important concept of taxonomic distinctiveness in which distantly related species are thought to be worthy of more conservation effort than closely related species. (Figure 1-2 shows how taxonomic distinctiveness can be calculated.) In triage operations where only a limited number of species can be “saved,” heuristic models

Buzzwords in Conservation Biology 9

Figure 1-2. Taxonomic distinctiveness ∆+ is based on the average pairwise path lengths between species in an assemblage. In this example (based on presence/ absence data and ignoring species abundances), ∆+ values are (a) 3.0; (b) 1.0; (c) 1.56; and (d) 1.2. The four hypothetical assemblages are therefore ranked in an intuitive way such that the greater the distribution of species among higher taxa, the greater the value of the index—in this case species in (a) are most distinct. (Reprinted from Clarke & Warwick 1998.)

have been put forward that maximize the number of speciation events (Vane-Wright et al. 1991) or branch lengths (Faith 1992) within a clade, as opposed to number of species. These demand rather detailed cladograms and knowledge of character state changes that are unavailable for most taxa and so have yet to be formally used in prioritizing sites of high biodiversity (Mace et al. 2003). Where proposals to conserve species richness or taxonomic distinctiveness have been pitted against each other (e.g., Virolainen et al. 2001), outcomes can differ enormously because high species totals often arise from recent adaptive radiations in which species are closely related rather than taxonomically distinct. This discrepancy means the criterion on which reserves are chosen has ramifications for species representation (Forest et al. 2007).

Scale Scale is an important issue in ecology because associations between species richness and altitude, latitude, productivity, and other variables depend on

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Figure 1-3. Conditional variation in hummingbird (Trochilidae) species richness explained by latitude, topography, and the latitude × topography interaction in South America according to size of quadrat in degrees. The y-axis shows the proportion of variance explained by partial correlation analyses measured as r2 values (Reprinted from Rahbek & Graves 2000).

the scale at which species richness is measured (Levin 1992; Schneider 1994; Poiani et al. 2000; Rahbek 2005). For instance, the extent to which hummingbird species richness in South America is predicted by latitude and topography varies with size of grid cell (see Fig. 1-3). In conservation, scale is vital for at least three reasons. First, identifying patterns of biodiversity at a global or continental scale is important for determining which biogeographic units need conservation attention, but practical decisions about land use occur at smaller national, regional, or local levels. In the words of Vane-Wright and colleagues as far back as 1991: Identification of whole countries, geographic regions, islands or large grid squares as critical faunas or floras is a basic step, but such units rarely represent practically conservable areas in terms of current economic resources. Furthermore, within critical areas, all species of interest rarely coexist at single localities. It is therefore necessary to undertake further rounds of analysis, within each priority region, to develop effective action plans for conservation. (249)

Buzzwords in Conservation Biology 11

The second reason for the importance of scale is empirical: some studies indicate that biodiversity is associated closely across taxa at one scale of measurement but not at another, and further studies show that congruency between different biodiversity metrics can be differentially affected by scale. The general consensus is that patterns of biodiversity correspond less at finer scales of resolution (Reid 1998; Garson et al. 2002; Wolters et al. 2006). For example, species richness (Rahbek & Graves 2001) and forest destruction, a proxy for threatened species (Gaston & Blackburn 1996), both increase toward the tropics so these variables are likely to be associated at a global but not at a national scale. Effects of scale vary according to the measure of biodiversity used. In sub-Saharan Africa, grid cell sizes of sizes 1 × 1° up through 8 × 8° show high levels of coincidence in specifying the five (or ten) top-priority grid cells when measures of richness of rare mammal species, mammal species richness, and richness of endangered mammals are considered. There is little concordance between grid squares, however, if complementary species richness is at issue (Larsen & Rahbek 2003). Complementarity refers here to choosing grid squares on the basis of their holding the rarest species of mammal, then adding on squares holding the next rarest species of mammal, and so on—the difficulty being that species that are rare based on small grid cells may not be considered rare when based on large grid squares (Erasmus et al. 1999). More generally, scale is comprised of two components—grain, the size of the observational unit; and extent, the size of the entire study area. Examining these independently, Hess and colleagues (2006) collated data from the mid-Atlantic and Pacific Northwest regions of the United States on amphibian, bird, butterfly, freshwater fish, freshwater mussel, mammal, and reptile species richness at four levels: hexagons (~649 km2), subecoregions (3,800–34,000 km2), ecoregions (8,300–79,000 km2), and geographic regions (315,000–426,000 km2). They examined correlations with varying extent of analysis (grain held constant at the hexagon) and varying grain (extent held constant at the region). The degree to which one taxon correlated with the remaining six taxa combined varied enormously by all three factors—grain, extent, and region of the United States from –0.93 (considering the smallest subecoregion value for the hexagon grain in the mid-Atlantic for mammals) to +0.96 (largest subecoregion value for the hexagon grain in the Pacific Northwest for mammals; see Fig. 1-4). Scale dependency therefore forces us to decide whether we want to maximize species richness at a small scale in our backyards or the county, or in the state, or at a large scale in a country, or even the entire globe (Gardenfors 2001), and scale-dependent differences in congruency dictate that the

Figure 1-4. Spearman’s rank-correlation coefficients (rho) for each taxon for the varying grain (a, b) and varying extent (c, d) analyses for the Mid-Atlantic (a, c) and Pacific Northwest (b, d) regions. Each dot represents a correlation coefficient. In the varying extent analyses (c, d) there is an observation for each taxon in each subecoregion and ecoregion. Bars in the varying extent analyses (c, d) are means of the correlation coefficients for the taxon within the extent. Taxon key: A, amphibian; B, bird; Bu, butterfly; F, fish; M, mammal; Mu, mussel; R, reptile. (Reprinted from Hess et al. 2006.)

Buzzwords in Conservation Biology 13

surrogate taxa by which we might measure this biodiversity will differ, too (Bohring-Gaese 1997). The third reason for considering scale is that the effects of disturbance are scale-dependent. This is because α and β diversity increase with spatial scale at a faster rate in undisturbed habitat than in disturbed habitat because of reduced habitat heterogeneity in the former and abridged spatial autocorrelation of diversity following disturbance (Dumbrell et al. 2008). This results in complex scale-dependent and taxonomic effects. For example, birds show greater species diversity in disturbed than undisturbed forests at a large scale but lower species diversity in disturbed as opposed to undisturbed forest at a small scale. In contrast, butterflies show lower species diversity in disturbed forests at a large scale but higher diversity there at a small scale (Hamer & Hill 2000; Hill & Hamer 2004). Consequently, effects of habitat disturbance should not be evaluated at a single spatial scale.

Surrogate Species in Systematic Conservation The biodiversity crisis has compelled people to think strategically about how to conserve species and habitats. Systematic conservation planning addresses one aspect of conservation—setting up and maintaining reserves that separate biodiversity from the processes that threaten its existence in the wild. In the past, the choice of reserves was often haphazard rather than scientifically driven, and this problem still remains today. Compared to methodically designed reserves, those established in the absence of systematic planning often reach suboptimal solutions to reserve networks. For example, ad hoc reserves result in inappropriately small protected areas—in a total of 222 published conservation targets, those that were policy-platform initiated were significantly smaller than conservation targets emanating from conservation assessments or planning exercises, or from formal analyses that identified thresholds below which fragmentation or habitat loss exerted deleterious effects on biota (Svancara et al. 2005). Margules, Pressey, and Sarkar laid out the key stages to systematic reserve planning: identify and involve stakeholders, assess opportunities and constraints for conservation implementation, compile data on biodiversity using species and biophysical data, formulate conservation targets precisely for reserve planning, review existing conservation areas, select additional conservation areas systematically, assess the prognosis for biodiversity in newly selected areas, refine the network of reserves and examine its feasibility, implement

14 c o n s e r v a t i o n b y p r o x y

Table 1-2. Example of a list of issues that need to be addressed in order to make a comprehensive assessment of the effectiveness of protected areas (from Pressey & Taffs 2001). Number and extent of protected areas Number Total extent Features of protected areas Representativeness Efficiency/sampling bias Vulnerability bias Features that need to be protected Environmental variation within ecosystem or vegetation types Geographical variation within assemblages, species, and populations Design of protected areas Size Configuration Compactness Proximity/connectivity Replication as insurance against major disturbances Alignment with climatic gradients (to allow accommodation to climate change) Defensibility of boundaries Alignment with watershed boundaries Adjacent land uses (present or expected) Proximity to likely illegal access routes Management effectiveness Existence of management plans Management resources Encroachment by inappropriate extractive uses

conservation actions including management, and maintain required values for conservation areas through monitoring (Margules & Pressey 2000; Sarkar et al. 2006; Margules & Sarkar 2007). Many detailed schemes have been devised that lay out the factors that need to be taken into account in order to assess the effectiveness of protected areas (see, for example, Table 1-2) and it is now generally recognized that a combination of the best science coupled with clear objectives and tailored appropriately to the social context is critical to successful conservation

Buzzwords in Conservation Biology 15

planning (Tear et al. 2005; Mace et al. 2006). (Critiques that scientific clarity is unachievable, or fails to identify important areas, or delivers perverse outcomes can all be readily dismissed; see Carwardine et al. 2008). Surrogate species are used at several stages in this process—in documenting biodiversity, in establishing conservation goals, in outlining and refining the reserve network, and in monitoring.

Taxonomy of Surrogate Species Surrogate species can be divided into three major categories with little overlap in objectives: those that help in locating areas of conservation significance, those that help in documenting effects of environmental change on biological systems, and a third group, those employed in public-relations exercises. Specifically, reserve selection and management now necessitate reviewing existing data and deciding whether data sets are sufficiently congruent that one data set can serve as a surrogate for biodiversity across a planning region. Within this new framework of conservation planning, a major dichotomy is whether to focus on species (Wilcove 1993), on habitats (Franklin 1993), or on both (Cowling et al. 2004). Some planners advocate using species distributions to compile databases for identifying conservation priorities (Brooks et al. 2004), and in fact many of the surrogates used to identify areas of biodiversity are taxonomic in nature. (These issues form the core of chapters 2–5.) Others believe that environmental variables such as forest types or ecoregions are appropriate surrogates for taxonomic diversity (Ferrier & Watson 1997), either on their own or combined with species data (Rodrigues & Brooks 2007; Mumby et al. 2008). Surrogate species are used in ways other than simply identifying areas of conservation interest. Cost-effective management of protected areas can benefit from using shortcuts that alert managers to specific causes of environmental perturbation before they become severe (chapters 4 and 5). For instance, the population size or reproductive performance of selected species could act as a shortcut to laborious measurements of environmental change. The second arena in which surrogates are used in conservation is to describe how anthropogenic disturbance has perturbed individual health or behavior, or has affected populations, or communities (chapters 6 and 7). Simply recording environmental change may be insufficient to document the way in which biological systems are affected. In addition, many species’

16 c o n s e r v a t i o n b y p r o x y

responses to environmental change can sometimes be summarized by one species-group’s responses to a changing environment (chapter 8). There are numerous ways in which responses to environmental degradation have been recorded—from the cellular to community level—but these are all essentially surrogates for either biotic or abiotic change. Finally, surrogate species are used to promote public understanding of conservation problems and to raise money (chapter 9). To reiterate, surrogate species map on to a set of biodiversity features that are used to guide conservation planning with the expectation of conserving broader biodiversity (Rodrigues & Brooks 2007), or are used as indicators of the state of the environment or of environmental change (McGeoch 2007), or else are a small number of species that form the centerpiece of conservation action (Favreau et al. 2006). Varying amounts of research effort have been devoted to these different types of surrogate species. Environmental indicator species initially received by far the most attention, followed by reserve selection studies in the last 20 years. Ecological disturbance indicators have been studied piecemeal in many parts of the world; limited work has been conducted on keystone and foundation species, whereas umbrella species (see relevant chapters for definitions) and indicators of biodiversity at a large scale have received relatively little investigation; public-relations species almost none.

Other Terms Targets and target species—terms that are used in many spheres of biology— refer in conservation to abiotic factors such as land clearance or to biotic factors such as ecosystem health, and are also used when referring to species, to their distribution, protection, or population size. Confusingly, target species are sometimes used in the sense of surrogate species when trying to identify which species should be the object of conservation attention (New 1995; Bonn & Schroder 2001) or simply as shorthand for a species under research investigation (Hammond 1995), but I do not use them in these senses in this book. Background species is a term that is sometimes used synonymously with target species but usually in a more narrow sense meaning “species that live in the same geographical area as species that have been used to identify an area of conservation concern” (Caro 2003, 172). Its use is principally restricted to studies involving single surrogate species such as umbrella species (e.g., Roberge & Angelstam 2006).

Buzzwords in Conservation Biology 17

Difficulties in Surrogate Typology Loose Definitions One of the principal impediments to effective use of surrogate terms in conservation is a failure to adhere to clear definitions (Regan et al. 2002; Marcot & Flather 2007; Fleishman & Murphy 2009). Perhaps the most opaque surrogate term of all is focal species. Focal species was defined carefully by Lambeck (1997), specifically in relation to managing a whole community or ecosystem by concentrating on the needs of one or a few species. He defined focal species as a “suite of species, each of which is used to define different spatial and compositional attributes that must be present in a landscape and their appropriate management regimes” (Lambeck 1997, 849). Regarding the landscape reconstruction aspect of management, he thought that individuals would be limited by insufficient area to meet their habitat needs, inability to move to suitable habitat patches, and shortage of critical resources. Regarding management of ecosystem processes, he thought that fire, predators, weeds, and livestock might need consideration, at least in an Australian context (see Fig. 1-5). He then advocated grouping species that were susceptible to similar threatening processes, and for each threat identifying the species that required the most comprehensive response. A landscape designated and managed to meet the needs of these focal species should encompass the requirements of all other species. Reassuringly, some species do have specific habitat requirements that can be identified quantitatively. For example, landscape structure in northern Finland was divided a priori into ten classes thought to be differentially suitable for flying squirrel breeding, or for dispersal, or as being unsuitable. Then land within 1- and 3-km radii of forest sites occupied by flying squirrels was examined on the ground. Sure enough, these areas contained more suitable breeding habitat (mixed spruce-deciduous forests) and dispersal habitat (pine and young forests) than random points in the landscape (Reunanen et al. 2000). Bird communities, too, are tightly correlated with particular forms of agricultural landscape settings in northern Italy (Bani et al. 2006; Padoa-Schioppa et al. 2006), and there are many other examples. Recognizing threatening processes responsible for declining species populations, selecting species most sensitive to each process, and identifying and protecting the habitat that each focal species needs are reasonably precise endeavors and can work well. To take two examples: first, in southeastern Australia, where temperate woodlands have been disturbed by

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Figure 1-5. Schematic representation of the procedure used to identify focal species. The requirements of these species are used to define the spatial, compositional, and management guidelines for the area under consideration. The actual causes of vulnerability may vary from place to place. In this example, taken from the wheat belt of Western Australia, fragmentation, habitat loss, and resource depletion were identified as the limiting factors that require landscape reconstruction. Fire, exotic predators, and weeds represent examples of the types of processes that need to be managed. (Reprinted from Lambeck 1997.)

urbanization and agriculture, woodland birds need protection. Birds were surveyed on 72 remnants of native vegetation, and data on habitat structure were taken as well. Presence/absence data for 31 species of birds were assessed graphically, and two species were identified as especially sensitive— the eastern yellow robin was found in a range of habitats and remnant areas but only in patches close to one another, while the hooded robin was sensi-

Buzzwords in Conservation Biology 19

Figure 1-6. Presence or absence of (left) the Eastern yellow robin, and (right) hooded robin in the northern region of the Australian Capital Territory and bordering area of New South Wales. The habitat complexity score is derived from measures of canopy, tall shrubs, low shrubs, ground herbage, logs and fallen branches, and litter. (Reprinted from Watson et al. 2001.)

tive to both remnant area and habitat complexity but less to isolation (see Fig. 1-6). These two species were then identified as candidate focal species such that if the spatial and habitat compositional requirements of these two species could be met, then the requirements of other bird species would be met as well. Specifically, remnants would have to be 100 ha or more in size, attain a habitat complexity score of 12 or above, and be within 1.5 km of five neighboring patches (Watson et al. 2001). Second, in an office-based study employing the Delphi approach, which relies on expert opinion, focal species were identified in Nova Scotia, Canada. Thirty-three characteristics that identify species as important in the community or that make them vulnerable in anthropogenically altered landscapes were compiled from the literature and distilled into eleven categories. Vertebrates were then scored as affirmative or negative on these by experts. This generated a consensus table of responses (see Table 1-3) that could be used to identify particularly useful focal species, such as Blanding’s

Yes Yes Yes — — — Yes Yes

Blanding’s turtle Wood turtle Northern ribbon snake Bullfrog Northern leopard frog Pickerel frog Blue-spotted salamander Four toed salamander

— denotes not known.

1.1

Species

Yes Yes — — Yes Yes No No

1.2

Yes — — Yes Yes Yes Yes Yes

1.3

Process limited

Management

— — — — — — — —

1.4

Yes — No No No No No No

2.1

Yes Yes Yes — No — Yes Yes

2.2

Yes — Yes No No — Yes Yes

2.3

Area limited

— Yes — — No — — —

2.4

— Yes — — — Yes — —

3.1

Yes — — — — — — —

3.2

Dispersal limited

Structure/Reconstruction

— — — No No No No Yes

4.1

Resource limited

Table 1-3. Limiting factors for reptile and amphibian focal species in Nova Scotia, Canada. Limitation categories (process, area, dispersal, resource) are from Lambeck (1997). Associated characteristics are (1.1) population threatened by direct exploitation, harassment, or ecological interactions; (1.2) sensitive to climate change; (1.3) pollution susceptible/accumulator species; (1.4) sensitive to high annual variation in river/stream flow (applies to freshwater only); (2.1) space-demanding/wide-ranging; (2.2) habitat threatened by loss, conversion, degradation, or fragmentation; (2.3) habitat specialization; (2.4) dependent upon provincially rare habitat; (3.1) limited dispersal power; (3.2) dependent upon unimpeded/unobstructed habitat/watercourse for migration/dispersal; (4.1) dietary and/or reproductive specialization (from Beazley & Cardinal 2004).

Buzzwords in Conservation Biology 21

turtle. There are other examples of the focal species concept (sensu Lambeck 1997) working successfully (e.g., Hess & King 2002; Hess et al. 2006). Nonetheless, the focal species concept does not work in every case. Species of mammals, birds, and reptiles most sensitive to human recreational disturbance, leaf-litter disturbance, wood maturity, and patch area in Monza Park, Northern Italy, showed few positive associations with responses of other species of small mammals, birds, reptiles, and amphibians (Ficetola et al. 2007). In addition, there are methodological hurdles for identifying focal species, including poor overall data quality, limited numbers of species for which data are available, failure of researchers to identify all the limiting factors in a community, and inability to rank every species by each limiting process, as well as certain species being limited by several factors, issues of scale, researcher subjectivity, and labor intensity (Lindenmayer et al. 2002; Lindenmayer & Fischer 2003; Beazley & Cardinal 2004; but see Lambeck 2002). Nonetheless, debate about the import of a surrogate concept is to be expected. The greater problem, however, is that the term focal species is used very loosely indeed—for instance, as a general term for various combinations of vulnerable or sensitive, umbrella, flagship, keystone, and habitat-qualityindicator species (Noss 1990, 1999; Caro 2000; Dale & Beyeler 2001; Zacharias & Roff 2001). Therefore, some scientists use focal species in exactly the same way as surrogate species (Armstrong 2002; Caro 2002a). The origins of this are easy to fathom because some of Lambeck’s limiting factors, such as the area required to satisfy habitat needs, are similar to the stipulation that umbrella species should have specific habitat requirements (see chapter 4); and because being sensitive to environmental stress is also a hallmark of a good indicator of habitat quality (Beazley & Cardinal 2004). Thus, it is a simple step to using these surrogate terms, umbrellas or indicators and so on, as ways to identify focal species. As an illustration, Noss, Carroll, and their coworkers called grizzly bears, wolves, wolverines, and lynxes in the Greater Yellowstone area focal species because they require large areas to support breeding populations, and also because they are sensitive to landscape change (Carroll et al. 2001; Noss et al. 2002). Others use focal species in a sense that is only distantly related to Lambeck’s limiting factors. Kautz and Cox (2001) defined their focal species as indicators of biodiversity that met one or more of the following criteria: feasibility of mapping, wide ranging species, indicators of rare community types, endangered and threatened species due to habitat loss, keystone species, or species associated with habitats supporting diverse wildlife. In other instances, focal species are identified through various statistical analyses

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that assess species’ fidelity and frequency of occurrence across different landscape types (Kintsch & Urban 2002) or associations with particular habitat types (Bani et al. 2002). A third habit of speech is something even more relaxed, amounting to “the species that I am studying.” For example, Rubino and Hess (2002) identified their study animal, the barred owl, as a focal species, simply by stating its “habitat requirements represent bottomland hardwood, riparian, and forested wetland landscapes” (90). Ryti (1992) used the term focal taxon as a collection of species, each of which is represented in at least one reserve. From 400,000 individual arthropod individuals collected, Basset and others (2008) chose 21 taxa on which to concentrate their research attention and called these “focal taxa.” As a result of using the term focal species to refer to the researchers’ focus of interest, or defining it carefully but not according to limiting factors, or using it as an alternative term to surrogate species, Lambeck’s specific use of the term has become diluted to the point at which it no longer has a specific meaning. Use of focal species has to be redefined afresh every time it is used. There is a real danger of this occurring to umbrella species (see chapter 4).

Lax Terminology Even when definitions are clear and rigorously adhered to, some people apply one catchphrase for one type of conservation objective while others use a different expression for the same objective (Regan et al. 2002), a case in point being one type of umbrella species and indicators of biodiversity, both of which are used for reserve site selection (Ryti 1992). Similarly, umbrella species and flagship species are both terms that are sometimes used in planning reserve shape and size (Williams et al. 2000a). A related problem is that some conservation scientists use these buzzwords inappropriately, ascribing to them an objective meaning that differs from the mainstream use of the word. Examples include using the term indicators to describe how woodpecker species’ distributions overlap with areas of high relative abundance of other bird species, when the phrase umbrella species would be more appropriate (Roberge & Angelstam 2006); using the term surrogate taxon for vascular plants employed to select areas of fungal species richness when umbrella species would be more pertinent (Chiarucci et al. 2005); eliding the terms engineering and keystone species (Daily et al. 1993); and ascribing nu-

Buzzwords in Conservation Biology 23

merous meanings to the phrase indicator species, making it one of the most muddled expressions in conservation (Lindenmayer et al. 2000).

Multiple Applications and Purpose Another form of confusion arises when the same term is used to prosecute different conservation objectives (Regan et al. 2002). Indicators have been employed to assess concentrations of pollutants in the environment and their effects on organisms, and they have a long history in ecotoxicology (Newman & Clements 2008). During the last 20 years, however, they have been used to achieve two additional objectives in conservation circles (Pearson 1995). The first is to identify areas of species richness, endemism, rarity, or threatened species, extrapolating from one taxonomic group to another, often from common to rare taxa, or from one familiar species or speciesgroup to other taxa (Hammond 1995). The second objective is to assess changes in the environment; this can be subdivided (Landres et al. 1988) into monitoring environmental health at medium to large spatial scales (Karr 1991; Cairns et al. 1993; Carignan & Villard 2002; chapter 6); assessing changes in species richness or abundance as a result of anthropogenic habitat modification or management practice (Ferris & Humphrey 1999; Basset et al. 2004), and also changes in populations of other species over time (Simberloff 1998; chapter 7); and documenting the responses of other taxa to disturbance (see chapter 8). Indicator species in conservation are therefore highly confusing, not only because of this variety of conservation goals (see Table 1-4), but also because some definitions incorporate several of these objectives (Table 1-4). Indeed, Gregory and colleagues (2005) have embraced this and taken a less categorical approach to indicator species, arguing that they can be more or less predictive of other aspects of biodiversity, and that they have weak or strong links to environmental drivers (see Fig. 1-7). They acknowledge that both variables are on a continuum but it is really only those in cells 3 and 4 that can assess environmental change, and those in cells 2 and 4 that identify the presence of sympatric species. I find it more helpful to divide up conservation objectives into discrete categories. Another complexity is that units of measurement can be presence or absence of species, relative abundances, or densities of single or several species (or even demographic or behavioral measures) recorded at a variety of

24 c o n s e r v a t i o n b y p r o x y

Table 1-4. Definitions of indicator species. Definitions of indicator species that emphasize biodiversity A small set of species with occurrence patterns that functionally are related to species richness of a larger set of organisms (Thomson et al. 2005, 504). Definitions of indicator species that emphasize environmental health In biology an indicator is an organism so intimately associated with particular environmental conditions that its presence indicates the existence of those conditions (Patton 1987, 33). A characteristic of the environment that, when measured, quantifies the magnitude of stress, habitat characteristics, degree of exposure to the stressor, or degree of ecological response to the exposure (Hunsaker & Carpenter 1990, xxiii). Ecological indicators have several purposes. They can be used to assess the condition of the environment or to monitor trends in condition over time. They can provide an early warning signal of changes in the environment, and they can be used to diagnose the cause of an environmental problem (Dale & Beyeler 2001, 4). Definitions of indicator species that emphasize responses to specific taxa A species or group of species that responds predictably, in ways that are readily observed and quantified, to environmental disturbance or to a change in environmental state (McGeoch 2007, 145). Definitions of indicator species that combine environmental health and responses of specific taxa An indicator may be defined as a characteristic that, when measured repeatedly, demonstrates ecological trends, and a measure of the current state or quality of an area (Ferris & Humphrey 1999, 313–14). Definitions of indicator species that combine environmental health and species representing responses of other species in the community Managers use indicators for two different reasons—first because their presence and fluctuations are believed (or hoped) to reflect those of other species in the community; and second because they are believed to reflect chemical and/or physical changes in the environment (Simberloff 1998, 248). Definitions of indicator species that combine environmental health, responses of specific taxa, and species representing responses of other species in the community An indicator is a statistic or parameter that, tracked over time, provides information on trends in the condition of a phenomenon and has significance extending beyond that associated with the properties of the statistic itself. Environmental indicators are selected key statistics that represent or summarize a significant aspect of the state of the environment, natural resource sustainability, and related human activities. They focus on trends in environmental changes, stresses

Table 1-4. Continued causing them, how the ecosystem and its components are responding to these changes, and societal responses to prevent, reduce, or ameliorate these stresses (Vandermeulen 1988, 63–64). Indicators are defined as variables, pointers, or indices of a phenomenon and are widely used for environmental reporting, research, and management support (Jennings 2005, 213). Definitions of indicator species that incorporate biodiversity, environmental health, and responses of specific taxa An indicator species is an organism whose characteristics (e.g., presence or absence, population density, dispersion, reproductive success) are used as an index of attributes too difficult, inconvenient, or expensive to measure for other species or environmental conditions of interest (Landres et al. 1988, 317). Indicators are measurable surrogates for environmental end points such as biodiversity that are assumed to be of value to the public (Noss 1990, 357). Scientifically reliable, cost-effective measures of the states or trend of an environmental phenomenon that is scientifically or logistically challenging to measure directly (Fleishman & Murphy 2009, 1110).

ability to generalize findings to a broader set of biodiversity components and attributes

Figure 1-7. A classification of indicators for biodiversity based on our ability to generalize findings to a broader set of biodiversity components and attributes, and potential links to natural or man-induced drivers. (Reprinted from Gregory et al. 2005.)

26 c o n s e r v a t i o n b y p r o x y

temporal and spatial scales (Lindenmayer et al. 2000). Moreover, forest extent and forest structure (Noss 1999), as well as biogeochemical and climatic variables, can also be used as indicators although, not being species per se, they are not covered in this book.

Using the Same Species for Two Surrogate Tasks Sometimes one species is used as a surrogate for two conservation objectives, which can lead to further confusion (Kremen 1992; Brown & Freitas 2000; Regan et al. 2002; chapter 10). Examples include species as flagship and umbrella species (e.g., jaguar), or biodiversity indicators and ecological disturbance indicator species (e.g., butterflies). The predominant use is context-dependent such that the same species may be used for different objectives in different parts of its range, or in different conservation programs, or at different time periods. Nonetheless, different surrogate terms make different assumptions and are used to pursue different objectives, so it is important that conservation biologists understand how they are using surrogate typology.

Hidden Agendas and Research Displacement Activities Progress in using surrogate species effectively in conservation is further impeded because surrogate species studies often have different objectives, and it is not always made explicit which objective is being addressed in a given piece of work. Broadly, studies of surrogate species can be divided as follows. First, there are academic analyses of species’ distributional data sets using one or more algorithms that generate several outputs—these are not expected to aid in making conservation decisions in the real world. Often such research, typical of reserve selection exercises, ends with a disclaimer such as “our analyses should not be taken as a measurement of how much land must be protected to conserve each taxon but rather as an approximate indication of the success of a reserve network to capture different taxa” (Virolainen et al. 2000, 1144), to pick just one example. Second, there are ecological studies on a particular species that lives in a circumscribed area. A species is studied to see if it has the potential to act as a surrogate in covering the geographic ranges or sufficiently large population sizes of sympatric species. In these circumstances there has been no

Buzzwords in Conservation Biology 27

management request for such information; instead the exploratory exercise is initiated by the researcher’s quest for knowledge. Some umbrella species studies are like this (e.g., Suter et al. 2002). Third, but less often, managers ask for an assessment of the effectiveness of a particular species in fulfilling a surrogate role before it has been awarded conservation effort (e.g., Noon & McKelvey 1996). Fourth, in another twist, a species or species-group may already be the subject of conservation recovery program, and researchers search for tangential benefits that may accrue for background species (e.g., Tushabe et al. 2006). Fifth, conservation biologists or academics will write about the species on which they work or about a certain taxon, suggesting that it could increase public awareness of conservation (e.g., Wallis de Fries 1995). Sometimes a list of ideal surrogate qualities is presented first, and then the study animal is found to possess many of these traits (e.g., Medellin et al. 2000); alternatively, the surrogate qualities of the species are simply listed (e.g., Sparling et al. 2001; Ball 2004). Since a number of these studies do not address the question of whether surrogate species are effective practical conservation tools but instead promote the convenience of particular algorithms, indices, or species, there is not a great deal of empirical evidence that can be marshaled to assess the utility of many types of surrogate species. In truth, the science of surrogate species to date is rather an academic pursuit despite sitting squarely in the ambit of conservation biology—an applied discipline. The majority of the research is not yet aimed at solving conservation problems in the real world and the challenge is to edge it in that direction.

Summary Biodiversity most commonly refers to species richness but encompasses concepts of species communities, endemism, rarity, and threat. Scale is a critical element in conservation science and in the search for surrogate taxa. Systematic conservation planning is now central to mainstream conservation, but identifying important geographic areas for conservation action, managing protected areas, and promoting conservation each demand effective shortcuts involving selected single species or species-groups. Therefore, surrogate species are used to represent other species to attain a conservation objective. Effective use of surrogate species in conservation is impeded by loose definitions—focal species is a case in point, applying

28 c o n s e r v a t i o n b y p r o x y

different terms to reach the same objective and the same term to reach different objectives, using the same species for different conservation tasks, and having hidden agendas that obfuscate clear thinking. The purpose of this book is to clarify the meanings, objectives, and effectiveness of surrogate species terms in conservation science.

Beetles are a species-rich taxon, with the majority of species still not described. Therefore, conservationists are keen to use better-known taxonomic groups containing fewer species to identify regions of high species richness. (Drawing by Sheila Girling.)

Chapter 2

Species Indicators of Biodiversity at a Large Scale

A Big Picture Effective wildlife conservation requires targeting specific areas of the globe for protection and management in order to achieve maximum impact with limited funds. Consequently, several conservation organizations have tried to identify areas of global importance. Birdlife International, for example, has pinpointed endemic bird areas across the world (Bibby et al. 1992; Stattersfield et al. 1998), World Wildlife Fund WWF–USA has identified its Global 200 Ecoregion sites (Olson & Dinerstein 1998; Olson et al. 2001; Burgess et al. 2002), WWF/IUCN has identified Centres of Plant Diversity (WWF/IUCN 1994, 1995, 1997), and Conservation International has recognized 25 global hotspots of biodiversity (Myers et al. 2000), as well as the last remaining wilderness areas (Mittermeier et al. 1998, 1999). These exercises fall into two categories, one of which concentrates on areas of high vulnerability and high irreplaceability such as Madagascar, the other on those of low vulnerability but high irreplaceability such as New Guinea (Brooks et al. 2006). To identify critical areas, these projects collate large databases using metrics such as total number of known species, or number of endemic, rare, or threatened species living in various parts of the world, sometimes additionally combining them with information on human population density and activities (Sanderson et al. 2002c) or ecosystem services (Hoekstra et al. 2005). 31

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Conservation organizations would like to assume that the distribution of species of one or a few animal or plant groups will predict (be an effective surrogate of) the distribution of species in other groups, and a good deal of effort has been put into finding out whether this is the case. Surrogacy might occur across taxa using the same measure of biodiversity, say, between endemic birds and endemic amphibians, or it might occur between measures of biodiversity, say, between endemic birds and rare birds, or it might occur across both, say, between endemic birds and rare amphibians. All these possibilities have been examined at a large scale and it is useful to keep them distinct when searching for generalities. The purpose of this chapter is to explore whether assumptions of surrogacy are valid at a large scale. First I examine the extent of cross-taxon congruency within four commonly used measures of biodiversity. Next I discuss the use of portfolios in global and continental priority setting. Then I examine the extent to which different biodiversity measures can substitute for each other, mostly within but sometimes between taxa. Finally, I look at whether biodiversity is well protected by contemporary reserves. The objectives of delineating the most biologically diverse or most threatened areas worldwide or at a continental scale are, or should be, a precursor to conservation policies crafted at a national or regional level within or occasionally across national boundaries, so there is a natural fissure for considering issues of surrogacy at the priority-setting stage and then again later when selecting reserve sites (Margules & Pressey 2000). The structure of chapters 2 and 3 reflect this division. Subsequently, the configuration of reserves can also be aided using surrogate species (chapter 4).

Congruency of Species Richness Initially, attempts to establish global conservation priorities through identifying areas of high biodiversity using surrogate taxa consisted of documenting the number of species in different taxonomic groups and noting if areas of high and low species richness corresponded across taxa. Generally, taxa are the five classes of vertebrates, along with butterflies, tiger beetles, and flowering plants (Brooks et al. 2004; Wolters et al. 2006)—simply because they are best known. Results look promising. Amphibian, reptile, bird, and mammal species richness are each significantly correlated with remaining vertebrate taxonomic group species richness across 799 WWF terrestrial ecoregions around the globe (Lamoreux et al. 2006); vascular plants are

Species Indicators of Biodiversity at a Large Scale

33

significantly correlated with vertebrate taxa across 296 geographic areas (Qian & Ricklefs 2008); and amphibian, bird, and mammal species distributions are congruent at the smaller observational scale of 1° grid squares (Greyner et al. 2006). At a continental scale, across 1° grid squares in sub-Saharan Africa (Moore et al. 2003) or across 10,000-km2 grid cells in Canada (Warman et al. 2004), mammal and bird species richness are highly and significantly correlated (N = 1957 cells, rs = 0.86; N = 1275 cells, rs = 0.96 respectively), as respectively are mammals with snakes or reptiles (rs = 0.82, 0.75), mammals with amphibians (rs = 0.82, 0.85), birds with snakes or reptiles (rs = 0.71, 0.76), birds with amphibians (rs = 0.76, 0.84), and snakes or reptiles with amphibians (rs = 0.74, 0.83). There are problems with such broad analyses, however: first, adjacent grid squares are not statistically independent because a species found in one grid square is likely to be found in a neighboring one (Carroll & Pearson 1998; Keitt et al. 2002), and second, associations may be driven by environmental variables that have not been measured. Take North America, for example, where removing latitude diminishes the strength of associations between taxa (see Table 2-1; but see Qian & Ricklefs 2008). Third, irrespective of taxonomic identity, species-rich groups perform indicator roles better than taxa with fewer species (Larsen et al. 2009). Fourth, where associations between the same paired taxa are compared on different continents, the strength of the correlations differs substantially (Pearson & Cassola 1992; Harcourt 2000).

Table 2-1. Pearson product-moment correlations (r) of species counts among taxa and with latitude, from Pearson and Cassola (1992). Based on species counts within 207 grid cells (275 km2) overlaid on a map of North America. Correlations below the diagonal (italicized) are partial correlations after removing the effect of latitude. Statistical probabilities are reported parenthetically (from Flather et al. 1997). Tiger beetle

Tiger beetle Bird Butterfly

0.09 (0.20) 0.42 (<0.01)

Bird

Butterfly

Latitude

0.37 (<0.01)

0.75 (<0.01) 0.74 (<0.01)

–0.80 (0.01) –0.040 (<0.01) –0.72 (<0.01)

0.71 (<0.01)

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These issues thwart the quest for a single indicator taxon of global species richness (Flather et al. 1997). If associations within continents are examined in more detail, it appears that certain taxa are effective in predicting overall species richness in some parts of a continent, whereas other taxa predict it well elsewhere (see Figure 2-1). Butterflies, birds, and mammals each predict combined species richness of other groups in southeastern North America; trees, amphibians, and land snails perform well in the southeast and central regions; reptiles and tiger beetles in the southeast United States, the Rocky Mountains, and the West Coast. Given these patterns, it makes greater sense to choose taxa from each group to predict overall biodiversity—in the case of North America, one from each group of, say, butterflies, trees, and reptiles (Ricketts et al. 1999). Attempts have also been made to identify surrogate taxa by focusing on hotspots of species richness (although there are other hotspot measures, Mittermeier et al. 1998), because we do not need to pinpoint coincidence of locations that are depauperate in species but only sites of unusual richness. Yet examination of maps of hotspots of species richness per se shows little congruence in North America between mammals, birds, reptiles, amphibians, tiger beetles, or trees (Flather et al. 1997), and coincidence between hotspots at global and continental scales is only manifest when other sorts of biodiversity measures are examined (see Figure 2-1). There have been large-scale attempts to measure cross-taxon congruency in β diversity—that is, changes in species composition between places. Examining 100-km2 grid squares across the terrestrial western hemisphere revealed that regions of great dissimilarity for amphibians, and for birds, and for mammals all coincided with each other closely, especially in neotropical areas. This is an optimistic conclusion for conservation because if reserves need to be set up close to each other to conserve differing communities, they will perform a good service for all three vertebrate groups (McKnight et al. 2007). In summary, although there are good reasons for believing that species richness of different taxa should increasingly coincide at larger spatial scales (because more species will be included in a grid square or state), empirical investigations have instead painted a mixed picture with positive associations, where they occur, often driven by well-known environmental or habitat variables. This has led to two developments: using large-scale environmental indicators of species richness rather than species-groups, or else eschewing species richness per se as a useful surrogate metric of biodiversity.

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35

Figure 2-1. Maps of the residuals from regressions of overall species richness on individual taxa. Shaded areas do not indicate where the taxon is high in species richness, but rather where the overall richness index is higher than predicted by the N Gi(e) taxon alone. Shading is assigned as an index where Index (e) = 1/n ∑ ——— where i=1 Gi(t) n is the number of taxonomic groups used in the index (here, the remaining 8 other than the individual taxon shown), Gi(e) is the number of species in group i in the ecoregion, and Gi(t) is the total number of species of group i in the database. The index computes, for each taxon, the fraction of North American species that is found in each ecoregion and then averages the fractions across all taxa, giving equal weight to the taxonomic groups. White denotes overall species richness 0–0.05; dispersed stippled, 0.06–0.10; condensed stippled, 0.11–0.15; white dots on black, 0.16–0.20; and black 0.21–0.27. Hatched ecoregions contain no species of the taxon in question. (Reprinted from Ricketts et al. 1999.)

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Environmental Surrogates Ecological theory and data show that gradients of species richness are related to several environmental variables, including lower (e.g., Wolda 1987) or mid-elevations (e.g., Rahbek 1995), higher precipitation (e.g., Reed & Fleagle 1995), or greater solar flux (e.g., Currie 1991)—all benign environmental conditions fostering species coexistence. Climatic variability (e.g., Stevens 1992) and habitat heterogeneity (e.g., Kerr & Packer 1997) that permit specialization in varied habitats (see Begon et al. 1990) also promote species coexistence. Therefore, environmental variables have been used to predict species distributions (Faith & Walker 1996a, b; Faith 2003). Variables approximating temperature gradients or moisture associated with altitude are extracted from principal-components analyses and are checked against biodiversity measures such as species richness on geographic grids. Such attempts have met with varying success, working well for European plants but not for vertebrate classes, for example (Araujo et al. 2001). Many other environmental measures have been employed to pinpoint putative areas of high species richness, including rainfall, distance to water, land cover, elevation, and human population density (Araujo et al. 2001; Loiselle et al. 2003; Bonn & Gaston 2005; Rondinini et al. 2005). Rodrigues and Brooks (2007) considered three general types of environmental surrogates in a meta-analysis that examined 347 tests of surrogacy from 47 different studies: (i) those based on biological assemblages or communities, including vegetation systems or forest types (Ferrier & Watson 1997), ecoregions (Williams et al. 2000a), floristic provinces (Trakhtenbrot & Kadmon 2005), and assemblage diversity (e.g., Araujo et al. 2004); (ii) those in which abiotic data are combined with species’ raw distributional data (Ferrier & Watson 1997); and (iii) those based simply on environmental data such as geology, climate, and altitude (Ferrier & Watson 1997), on environmental diversity (Araujo et al. 2001), or environmental cluster analysis (Trakhtenbrot & Kadmon 2005). Combining studies from different scales, Rodrigues and Brooks found that tests of surrogacy based on abiotic data combined with species’ raw distributional data using abiotic data perform somewhat better (69 percent positive effect, 3 percent negative effect) than those based purely on abiotic data (40 percent positive, 15 percent negative), or simply on biological assemblages (50 percent positive, 34 percent negative). Yet tests using just environmental surrogates actually have lower predictive power (47 percent positive, 22 percent negative) than cross-taxon surrogates (78 percent pos-

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itive, 8 percent negative). So the jury is still out on the efficacy of crosstaxon surrogacy at large spatial scales.

Higher Taxa Vertebrates and plants notwithstanding, species can be very difficult to identify. Instead, higher taxa (genera or families) can be used to pinpoint areas of species richness at a global or continental level. There are often strong positive correlations between species richness and genus richness, as well as species richness and family richness at global (Williams et al. 1994) and continental scales (Williams & Gaston 1994; Grelle 2002). For example, if one can uncover a relationship between, say, generic richness and species richness on a continent where taxa are well known, it might be possible to estimate areas of species richness using genera on a continent where species are more numerous or difficult to sample (Gaston 1996a). This method originates in paleontology where fossil members of families are often easier to identify than species and are more likely to be recovered (Sepkoski 1992). The benefit of this method is that identifications can be made by nonexperts but unfortunately, there are difficulties, too. First, precise relationships between species and higher taxa can differ geographically (Gaston & Blackburn 1995). Neotropical plant diversity is much greater than on other Old World continents, but generic diversity in the New World is only slightly greater, and family diversity is equivalent. Thus the ratio of Old World family-to-species richness reveals little about that in Central and South America (Prance 1995). Similarly, centers of neotropical plant species endemism are not reflected at the family level (Prance 1995). Second, the strength of the relationship between higher taxon richness and species richness declines with the number of species found in the higher taxon, so that a survey of orders that would save more time than a survey of genera provides a weaker indication of species richness than does a census of genera (see Figure 2-2). Third, the predictive power of higher taxa is lowest at species-rich sites, the very places where surveying orders or families would save the most time. Fourth, species-rich families are an important cause of overall species richness, driving log-log plots of species and higher taxon richness to exceed unity, so that particular species-rich families may be a better predictor of overall species richness of an order than the total number of families (Balmford et al. 1996a). These four caveats limit the utility of the higher-taxon approach.

Figure 2-2. Species richness in relation to different levels of higher-taxon richness across tropical protected areas. Correlations weaken at higher taxonomic scales. (a) Angiosperm species vs. genera (r2 = 0.93, n = 16 sites, p < 0.001), (b) Angiosperm species vs. families (r2 = 0.89, n = 16, p < 0.001), (c) Angiosperm species vs. orders (r2 = 0.79, n = 16, p < 0.001), (d) Bird species vs. genera (r2 = 0.99, n = 30, p < 0.001), (e) Bird species vs. families (r2 = 0.66, n = 30, p < 0.001), (f ) Bird species vs. orders (r2 = 0.44, n = 30, p < 0.001), (g) Mammal species vs. genera (r2 = 0.97, n = 10, p < 0.001), (h) Mammal species vs. families (r2 = 0.90, p = 10, p < 0.001), and (i) Mammal species vs. orders (r2 = 0.49, n = 10, p < 0.05). (Reprinted from Balmford et al. 1996a.)

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Congruency of Endemism Endemism originally referred to being restricted to a particular area irrespective of its size, but following an influential paper by Terborgh and Winter (1983), the term was extended to species restricted to a small area (so-called narrow endemics). That study pinpointed sites of avian endemism in Ecuador and Colombia by mapping the geographic distributions of birds with ranges of an arbitrarily chosen area of 50,000 km2 or less and suggested that places where range overlap was greatest might be sites for future reserves. Globally, about one-quarter of all birds are endemic using this criterion, and areas where two or more species of endemic birds are found are now termed endemic bird areas (Bibby et al. 1992; Stattersfield et al. 1998). Interestingly, endemic mammals, categorized in the same way, constitute about 25 percent of mammals. Globally, endemic bird and endemic mammal areas correspond well (Balmford & Long 1995). Applying the definition to amphibians incorporates as many as 60 percent of amphibians, and areas containing two or more endemic amphibians correspond very closely to those of birds and mammals (see Figure 2-3). One study that incorporated endemism to identify global hotspots has received a good deal of attention. Myers and his colleagues at Conservation International (2000) attempted to identify terrestrial global hotspots of biodiversity by focusing on regions of the globe that contained at least 0.5 percent, or 1,500, of the world’s 300,000 vascular plant species that were endemic to a particular country. In addition, to qualify as a hotspot, the area must have already lost 70 percent of its primary vegetation. Twentyfive such hotspots were identified and these contained 44 percent of all plant species worldwide and additionally 35 percent of the documented 27,298 bird, mammal, reptile, and amphibian species. Interestingly, high counts of endemic plants matched those of nationally endemic vertebrates across many of the hotspots, but not all (see Table 2-2), and can therefore be used as a surrogate for vertebrate endemism. The study received publicity because the 25 hotspots of (threatened) endemic richness comprise only 1.4 percent of the land surface and thereby focus conservation attention on a limited set of key areas, particularly Madagascar, the Philippines, Sundaland, Brazil’s Atlantic forest, the Caribbean, Indo-Burma, the western Ghats and Sri Lanka, and the Eastern Arc and coastal forests of Tanzania and Kenya. Although there is dispute over nuances of hotspot classification (Brummitt & Lughadha 2003; Ovadia 2003) and a recognition that conservation of functioning ecosystems, species depauperate areas, and sites of

Figure 2-3. Global maps of areas that hold two or more (a) bird, (b) mammal, or (c) amphibian species with a global range less than 50,000 km2 (endemic species), showing strong congruency of these areas. (Reprinted from Eken et al. 2004.)

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Table 2-2. Congruence between plants and vertebrates (from Myers et al. 2000).

Hotspots

Tropical Andes Mesoamerica Caribbean Brazil’s Atlantic forest Choco/Darien/western Ecuador Brazil’s cerrado Central Chile California floristic province Madagascar Eastern Arc and coastal forests West African forests Cape floristic province Succulent Karoo Mediterranean basin Caucasus Sundaland Wallacea Philippines Indo-Burma South-central China Western Ghats/Sri Lanka SW Australia New Caledonia New Zealand Polynesia/Micronesia

Endemic plants as % of global total (300,000)

Endemic vertebrates as % of global total (27,298)

Congruence (%) (rounded)

6.7 1.7 2.3 2.7 0.8 1.5 0.5 0.7 3.2 0.5 0.8 1.9 0.6 4.3 0.5 5.0 0.5 1.9 2.3 1.2 0.7 1.4 0.9 0.6 1.1

5.7 4.2 2.9 2.1 1.5 0.4 0.2 0.3 2.8 0.4 1.0 0.2 0.2 0.9 0.2 2.6 1.9 1.9 1.9 0.7 1.3 0.4 0.3 0.5 0.8

85 41 79 78 53 27 40 43 88 80 80 11 33 21 40 52 26 100 83 58 54 29 33 83 73

evolutionary potential require attention, too (Karieva & Marvier 2003), global hotspots have received considerable attention and funding as a result of this exercise. Human population pressures have been integrated into the ranking of global hotspots as well (Cincotta et al. 2000; Shi et al. 2005; Luck 2007). A second study using the WWF’s ecoregions found that global patterns of endemism between amphibians, reptiles, birds, and mammals were

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highly and significantly correlated with the remaining three classes lumped together (r = 0.503, 0.587, 0.612, and 0.490 respectively), showing that endemism within well-documented taxa can predict that of vertebrates as a whole, although this is unlikely to be entirely sufficient since planning for one taxon cannot explain all the variance in the rest (Lamoreux et al. 2006). Given that endemism is an important marker of species vulnerability and is likely to be documented by conservation biologists (e.g., de Klerk et al. 2002), and that there are independent reasons for thinking that certain topographies such as mountain tops or areas of inundation provide refugia for species or foster speciation in several taxa concurrently, endemism will go on being used in priority-setting efforts and it has some power of crosstaxon surrogacy.

Congruency of Rarity Since conservation practice tries to prevent population extinctions, it seems logical to focus conservation priorities on rare or threatened taxa. The few studies that have tried to pinpoint areas where taxa are coincidentally rare show mixed results. Globally, rare species, defined as birds, mammals, or amphibians in the lowest quartile of range distributions within their taxon, are poorly associated across 1° grid squares, although these associations become more positive if the size of grid squares is enlarged (Greyner et al. 2006). Findings are the same if hotspots (the richest 5 percent of grid cells) are chosen (Greyner et al. 2006). In another study, mammals with restricted geographic ranges, or else IUCN-listed mammals, performed reasonably well in predicting the location of rare and endangered terrestrial mammals across the South American continent when relatively small 100km2 squares were scrutinized (Tognelli 2005). Empirically, it is too early to say whether certain rare taxa are reliable surrogates for other rare taxa but we do know that whereas amphibian, mammal, and bird rarity are all high on the neotropical mainland, bird rarity is also high on oceanic islands, mammal rarity on continental shelf islands, and amphibian rarity on continental land masses (Greyner et al. 2006), so we can guess that associations are unlikely to be strong. Complex interactions resulting from different causes of rarity, including endemism and differential sensitivity to anthropogenic activities, as yet poorly understood, make it unlikely that rare taxa can stand in for each other.

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Congruency of Threatened Species Globally, threatened birds, mammals, and amphibians (defined as “vulnerable,” “endangered,” or “critically endangered” using IUCN criteria; see Table 1-1) are very poorly associated at the 1° scale, either across all grid cells or across hotspot grid cells (the richest 5 percent) (Greyner et al. 2006). Although habitat loss affects all three taxa, invasive species and overexploitation additionally impact birds; overexploitation adversely affects mammals; and disease, pollution, and land conversion threaten amphibians. These threatening processes are unlikely to be coincident. On continents, the most renowned study to date used species listed under the Federal Endangered Species Act throughout the United States, including all species, subspecies, and populations proposed for listing up to August 1995—a total of 924 species in 2858 counties (Dobson et al. 1997). These were then assigned to each county in the continental United States and Hawaii. Counties housing endangered plants are centered on southern California; those harboring endangered birds are found across the continent but especially in Florida and west of the Mississippi; endangered fish in the southwest, Mississippi Valley, and southeastern coastal states; and mollusks in Appalachia (see Figure 2-4). Consequently, there is little geographic congruence between taxa. For instance, only two counties are hotspots of three endangered taxonomic groups—San Diego and Santa Cruz counties in California—and only nine hotspot counties contain two taxonomic groups. One difficulty with comparing threatened taxa is that poorly studied groups will incorporate fewer threatened species for artifactual reasons alone. Common species tend to be recorded first by scientists and they are necessarily less prone to extinction, so less-studied taxa that are just starting to be researched will contain fewer threatened species. Additionally, lessstudied groups will be less well evaluated for threat than well-studied taxa. Thus, the more species recorded in a taxon, the greater the proportion of threatened species will be documented. Similarly, in areas where studies have been more intensive, there should be more species recorded as threatened or extinct (McKinney 1999). Witness mollusks, crustaceans, and insects that are poorly studied at a global scale; they have up to two orders of magnitude fewer threatened species than mammals, whereas on the betterstudied continents, nations, or regions, proportions of species that are threatened match those of mammals. Therefore, the extent of threatened species in well-known taxa, such as mammals, might or might not predict

Figure 2-4. The geographic distribution of four groups of endangered species in the United States of America: (A) plants, (B) birds, (C) fish, and (D) mollusks. The maps illustrate the number of listed species in each county. Alaska and Hawaii are shown in the bottom lefthand corner of the maps (not to scale). (Reprinted from Dobson et al. 1997.)

Species Indicators of Biodiversity at a Large Scale

Proportion threatened (relative to mammals)

10.0 1.0 0.1

fish reptiles plants

mussels plants insects birds

fish plants birds butterflies

ants butterflies mollusks spiders

45

butterflies dragonflies reptiles plants

reptiles

mollusks crustaceans

0.01

0.001

insects

Global

U.S.

California

U.K.

Suffolk

Area studied

Figure 2-5. Observed relationship between similarity of threat among taxa. All taxa in regions to the right of “Global” on the x-axis are relatively well studied. Log scale on the y-axis measures proportion of threatened species in each taxon divided by proportion of mammals threatened in that region (y = 1 where taxon percentage = mammal percentage). (Reprinted from McKinney 1999.)

poorly known taxa at a global or continental scale, but might underestimate threat in other taxa at the national or county scale, simply on the basis of sampling bias alone (see Figure 2-5).

Complementarity and Congruency In situations in which there is a practical opportunity or, more commonly, the hypothetical possibility of prioritizing several areas of conservation significance, a portfolio of sites can be identified but chosen so as to complement each other (Margules et al. 1988; Pressey et al. 1993; Gaston et al. 2008). Complementarity, “a measure of the extent to which an area, or set of areas, contributes unrepresented features to an existing area or set of areas” (Margules & Pressey 2000, 249), is a reserve design principle that emerged in Australia (Kirkpatrick 1983; Margules et al. 1988), Britain (Ackery & Vane-Wright 1984), and South Africa (Rebelo & Siegfried 1990) at almost the same time (Justus & Sarkar 2002). Complementarity analysis is an iterative process that selects cells or counties in a stepwise fashion such that each new cell includes the greatest number of species not yet represented

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among cells selected thus far (Vane-Wright et al. 1991). The so-called greedy algorithm starts by selecting a grid cell or county with the highest species richness. The remainder of the grid cells are then sorted in terms of species richness of all those species not represented in the first cell, and the computer algorithm selects the richest of these remaining cells. This is then repeated in an iterative fashion until all species are represented at least once in a site (or a given number of times). Ties can be broken according to various rules that lead to a number of potentially different outcomes. This process produces a near-minimum set of sites. Alternatively, the algorithm could be: choose the county or cell with the greatest number of rare species. The algorithm then searches for rare species that are not represented in locations already selected and takes the site that contains the most, repeating this iteratively until all the rare species are captured. There are a great many other algorithms available (e.g., Csuti et al. 1997; Possingham et al. 2000) and several types of software (e.g., WORLDMAP, Williams 1999). Choosing complementary sites produces a species-accumulation curve on the y-axis with the cumulative number of sites selected on the x-axis. Let’s take an example: Ceballos and colleagues (2005) imported range maps for 4,795 species of terrestrial mammals into a database and sought to determine the minimum area necessary to conserve all these species globally, either through identifying the minimum number of 100-km2 grid cells or enough grid cells to preserve a minimum of 10 percent of the range of each species. The minimum criterion demanded 668 cells, or 4.2 percent of the earth’s ice-free land surface. The 10 percent geographic range criterion demanded 1,702 cells or 11 percent of ice-free land surface. Mammals are peculiar because a large number of species have restricted geographic ranges, a characteristic that is known eventually to demand a wide geographic area under complementary algorithms. The interest from a biodiversity-indicator standpoint at a large scale is whether the outcomes of complementarity analyses capture biodiversity better than other methods. The answer is a qualified yes. In Africa, a complementarity analysis using the greedy algorithm that selects 1° grid cells based on species richness shows that selecting just 50 cells (2.5 percent of the continental surface) captures a great many species of vertebrate, and it does this more efficiently than 50 cells chosen at random, or on the basis of simple species richness or the smallest range size (see Table 2-3). In South America, 100 50-km2 cells, picked to represent geographically rare or IUCN-listed species using a complementarity algorithm, managed to incorporate more species than squares

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Table 2-3. The success of four methods for representing the terrestrial vertebrate species of sub-Saharan Africa in sets of 50 1° grid cells, measured by absolute and percentage representation of species. The methods are greedy complementarity (choosing cells that progressively add the greatest number of species to those already represented); choosing the richest cells; choosing the cells with the highest rangesize rarity scores (the sum of reciprocals of the range sizes of all species in the cell); and choosing random cells (±2.5 percent tail). Greedy algorithms perform well (from Brooks et al. 2001). Method of representing cells

Mammals

Birds

Snakes

Amphibians

Species total 50 cells in greedy complementarity set 50 richest cells

940 841 89% 614 65% 709 75% 525±58 56±6%

1,921 1,877 98% 1,414 74% 1,554 81% 1,515±105 79±5%

406 375 84% 249 56% 320 71% 199±30 49±7%

615 552 89% 393 63% 490 79% 225±55 37±10%

50 cells with highest range-size rarity 50 cells chosen at random

chosen using other criteria, such as containing large mammals, or that were selected randomly (Tognelli 2005; see also Williams et al. 2000a). A second question is whether certain taxa, such as mammals, capture more diversity than other taxa using complementarity. Congruence in complementarity shown by different taxa can be assessed by deriving two matrices, one containing dissimilarities in species composition between all pairs of sites based on the surrogate taxon and the other containing dissimilarities based on the target taxon or set of target taxa. The statistical significance of the correlation between these two sets of dissimilarities is examined using Monte Carlo simulations. More formally, the efficiency with which a set of complementary sites chosen for species in one taxon predicts sites that represent the gamut of species in another taxon can be assessed by plotting the percentage of target species that are represented against the total area or total number of sites. Three curves may be generated (see Figure 2-6): The first is an optimal curve using the target taxon to obtain maximal representation of every one of its species. The second is a random curve showing rate of species

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accumulation for the target taxon if sites were chosen on the basis of area starting with the target. The third (surrogate) curve is the rate of target species accumulated based on the sequence of sites chosen to maximize representative species richness in the surrogate taxon. In fact, there is no single random curve because there are many combinations of random sites and there is no single surrogate curve because there may be several equivalent sets of sites maximizing the representation of surrogates, so it can be thought of as a population of optimal solutions with a mean. The three curves necessarily coincide at 100 percent of area when all target species are represented (Figure 2-6).

Figure 2-6. Surrogacy can be assessed by comparing a surrogate curve or band indicating the average percentage of target species represented when selecting conservation areas using surrogate data; an optimal curve, maximizing the number of target species represented; and a random curve or band indicating the average percentage of target species represented when sites are selected randomly. A quantitative comparison can be made by calculating a Species Accumulation Index (SAI) of surrogate efficiency for given area a, using surrogate taxa (point S), a random set R, or an optimal point O. (Reprinted from Rodrigues & Brooks 2007.)

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If the surrogacy curve falls close to the optimum then the taxon can stand in for the target taxon, but if it coincides with the random curve or falls below that, it will have low surrogacy value. Additionally, a quantitative measure can be obtained called the species accumulation index (SAI) where SAI = (S – R) × (O – R) S is the area under the surrogate curve, R is the area under the random curve, and O is the area under the optimal curve. Under perfect surrogacy, SAI = 1; with positive surrogacy 1 > SAI > 0; with neutral surrogacy, surrogate and random curves coincide on average, but SAI can also be negative (Ferrier 2002; Rodrigues & Brooks 2007). Unfortunately, we do not yet have enough global or continent-wide studies to pick out especially reliable taxa. In the study of Federally listed species in the United States in which counties were sequentially selected on the basis of number of listed species, important sites were generated using plant, mollusk, arthropod, fish, herptile (i.e., reptile and amphibian), bird, and mammal complementary data sets (Dobson et al. 1997). Plants captured most species but that was because more counties were needed to protect all endangered U.S. plants. An area-independent index of predictive power showed that birds and herptiles captured most endangered vertebrates on the continent at the U.S. county scale. There are few comparable data sets from other continents to verify the importance of these groups, although the predictive power of complementary networks chosen using different taxa is better understood at a smaller spatial scale (see chapter 3). That said, taxa having many species, or having many plant species, or having small average geographic range sizes are likely to be good surrogates (Ryti 1992; Manne & Williams 2003), whereas taxa containing large carnivores, large-bodied species, and widespread species are likely to be poor (Andelman & Fagan 2000; Linnell et al. 2000; Larsen et al. 2007). While the academic search for good taxonomic indicators of richness, endemism, and rarity using complementarity analyses is likely to go on, the utility of planning a portfolio of biodiversity foci is questionable at a big scale because conservation decisions are mostly formulated nationally or at smaller regional scales within nations. Perhaps only in countries that span continents, such as Russia, Canada, Australia, or the United States, might exposure of biodiversity networks at large scales be of practical use for reserve planners.

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Concordance between Different Measures of Biodiversity Global Scale At a global scale, concordance between the principal measures of biodiversity may be low or high. Two studies examining species distributions of vertebrates worldwide agree that species richness and endemism are poorly correlated. At the observational level of ecoregion, correlation coefficients between these particular biodiversity measures are low (although positive and significant) for amphibians (r = 0.096) and reptiles (r = 0.085) but negative and non-significant for birds (r = –0.068), mammals (r = –0.099), and all four taxa combined (r = –0.025) (Lamoreux et al. 2006). At the level of 1° grid cells and this time focusing on hotspots (the richest 2.5 percent of grid cells), avian hotspots of species richness amount to nine, most of which most are in mountainous regions of continents, whereas avian hotspots of endemism amount to 20 of which fewer than half are in these areas, instead being on islands or island archipelagoes (Orme et al. 2005). In contrast, a study of range-restricted bird species across countries that controlled for area found significant correlations between species richness, threatened species, and, unsurprisingly, single-country endemics for many of the following taxa: birds, mammals, reptiles, amphibians, fish, tiger beetles, swallowtail butterflies, flowering plants, gymnosperms, and ferns. Range-restricted birds—for which there are good data—seem to be valuable indicators of national patterns of species richness, threat, and endemism in other taxa (Balmford & Long 1995). Turning to species richness and threatened status based on IUCN criteria, there is a less optimistic pattern for birds, with nine species-richness hotspots and the ten hotspots of threatened species being poorly associated; the majority of the latter were on islands. Hotspots of endemism and threat were not coincident either (see Figure 2-7). Relaxing the stringent 2.5 percent hotspot criterion had little effect; indeed, overall correlations between different measures of biodiversity across all grid cells were weak for birds. In a parallel mammal exercise looking at 100-km2 squares, hotspots (the top 2.5 percent of grid squares) of species richness, endemism (range-restricted), and threatened species showed relatively high congruence with 444 of 2,833 species (16 percent) being common to all three categories. Twenty-three percent of mammals were found in both species richness and threatened species hotspots; 22 percent in threatened-species and

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Figure 2-7. Extent of congruence between hotspots of avian biodiversity. Venn diagram showing congruence across species-richness hotspots (SR), threat-richness hotspots (TR), and endemic-richness hotspots (ER), where hotspots are the richest 2.5 percent of cells. Figures show number of grid cells and corresponding percentages. (Reprinted from Orme et al. 2005.)

range-restricted hotspots; but 2 percent in range-restricted and species-rich hotspots (Ceballos & Ehrlich 2006). The authors argued that these figures are low because species-richness patterns are influenced by widespread mammals, but range-restricted and threatened mammals have narrow geographic ranges with little overlap. If richness is determined, perhaps, by energy availability, endemism by refugia and allopatric speciation, but threat by diverse anthropogenic drivers in concert with biological susceptibility, a priori it seems improbable that these measures will be coincident and interchangeable for pinpointing sites of conservation effort across the world (Funk & Fa 2010).

Continental Scale There is no pattern of congruency of biodiversity indicators as yet at a continental scale. In an early paper, Kerr (1997) found that mammals, Lasioglossum bee species, Plusiinae moths, and Papilionidae butterflies each showed strong correlations between species richness and endemism over 336 very large (2.5 × 2.5° or 2.5 × 5° quadrats) in North America. Yet in a subsequent study on the same continent, but this time examining 110

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ecoregions, correlations between levels of species richness and two different measures of endemism were low for butterflies, birds, mammals, amphibians, and reptiles (Ricketts 2001). Taxa, choice of geographic unit, definitions of endemism, or limits of the continental scope could all be responsible for inconsistencies. Comparing species richness and rarity, results are similarly inconsistent. On the one hand, primates in Africa show little correlation between species richness and rarity-weighted richness calculated as the sum of rarity scores (i.e., reciprocal of the number of squares in which a species occurs) for all taxa in each 1° grid cell across Africa and Madagascar (Hacker et al. 1998). On the other hand, species richness and rarity of terrestrial birds are tightly associated across similar sized grid cells in Australia (Curnutt et al. 1994). Yet another study from Africa examining mammals, birds, snakes, and amphibians found weak congruence between taxon richness and the top 25 percent of species whose geographic ranges occupied the fewest 1° cells (Moore et al. 2003). Associations between species richness and threatened species are little studied at a continental expanse but results seem positive, at least at a large grain. In the African primate dataset, hotspots (top scoring 5 percent of grid cells for species richness) and risk of extinction were found in similar areas—Cameroon, eastern Democratic Republic of the Congo, and eastern Madagascar (Hacker et al. 1998). In the New World, 611,000-km2 grid squares (n = 116) that contained high avian species richness also held high numbers of BirdLife International’s list of threatened birds (Gaston & Blackburn 1996). Either areas of high species richness are subject to especially threatening processes or these areas contain a high proportion of vulnerable species, or both. Examination of associations between endemism and rarity or threat at a continental scale have yet to be carried out. While it might be assumed that rare and threatened species would be congruent geographically, this seems to depend on the measure of threat. When threat was construed as anthropogenic change over time—changes in human density or land development or urban roads—U.S. counties at the top of these lists were those that contained the most Federally listed threatened and endangered species (Abbitt et al. 2000). Similarly, agricultural activity predicted endangered U.S. plants, mammals, birds, and reptiles, while water use and human population density predicted densities of endangered reptiles (Dobson et al. 1997). Yet in Africa, IUCN-listed threatened species and rarity-weighted primates, those with small geographic ranges (see above), were not found on the same parts of the continent or Madagascar (Hacker et al. 1998).

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Complementarity When conservation priority portfolios are an option, congruence between sites using different measures of biodiversity is weak at a continental level (see Kershaw et al. 1995). The top ten areas in Africa selected by ranking mammal species richness using the greedy algorithm correspond poorly to those ranked using threatened mammals, and the same holds for birds (see Table 2-4; see also Griffin 1999). More generally, however, a meta-analysis of 228 cross-taxon tests of surrogacy from 23 mostly terrestrial studies (Rodrigues & Brooks 2007) found that complementary sets of sites selected to represent a particular set of species as a surrogate for representing another set of species usually did so positively (78 percent of cases) rather than negatively (8 percent). Congruency within terrestrial, freshwater, or marine realms was greater than between them. Results were more likely to be positive when a complementary set of sites based on one surrogate group was used to predict a group of which it was a subset, or if the surrogate group partially overlapped that of the group for which predictions were being sought. The authors concluded that there was room for cautious optimism in using complementary sets of species from one group to predict sites holding biodiversity of another taxon. Another meta-analysis using many of the same data sets reached a similar conclusion (Cabeza et al. 2008). The difficulty is that both reviews ran together studies conducted at a large scale (continents and globally) with smaller national studies and very small field ecological sites—observational units ranged from 1° cells to 0.25-ha sample plots—and some studies used different biodiversity metrics for surrogate and target taxa whereas others used the same. It might therefore be premature to surmise that complementarity at global and continental scales are congruent across taxa.

Biodiversity Distribution and Protected Areas Setting conservation priorities in the abstract is an important component of systematic conservation planning, but we need to assess whether important areas of biodiversity are already protected and, if not, which parts of the world need new reserves. Progress has been made in this direction using GAP analyses. When the global distributions of mammals, turtles, and amphibians were examined, the ranges of 12 percent, or 1,424 species, were not covered at all by existing protected areas (IUCN categories I–IV)

Cape Town 50% Lowland fynbos and renosterveld

5. N. Bale 34% Ethiopian montane grassland woodland

Soutpansberg 63% S. Africa bushveld highland forest

Eritrea 57% Ethiopian xeric grassland and shrubland

Usambaras 50% Eastern Arc forest

Ruwenzori hills 36% Albertine rift forest

All birds

Drakensberg 40% Mt Cameroon 68% Drakensberg montane grasslands, Cameroonian highland forest woodland, forest

Ruwenzori 34% Albertine rift montane

Mt Cameroon 45% Cameroonian highland forest

Mt Cameroon 20% Cameroonian highlands

4. NE. DR Congo 29% Northeastern Congo lowland forest

Kilimanjaro 32% East African montane forest

2. Mt Cameroon 19% Cameroonian highland forest

N. Udzungwas 11% Eastern Arc forest

Mt Nimba 27% Guinean montane forest

Ruwenzori 23% Albertine Rift montane forest

1. Usambaras 10% Eastern Arc montane forest

Priority threatened birds

3. S. S. Africa 24% Mt Nimba 40% Montane fynbos and renosterveld Guinean montane forest

All mammals

Priority threatened mammals

Table 2-4. Conservation priorities for sub-Saharan African terrestrial vertebrate species. Each list gives the first ten areas selected in greedy complementary sets for the group in question. Also given is the cumulative percentage of species within the group represented by each area’s inclusion in the overall set (from Brooks et al. 2001).

Vumba 54% E. Zimbabwe montane forest grass

Awash 57% Ethiopian montane forest

Mid-Angola 59% Angolan miombo woodland

N. Itombwe 62% Albertine rift montane forest

NE DR Congo 64% Northeastern Congo lowland forest

6. Mau 38% East African montane forest

7. NW Somalia 42% Somali montane xeric woodland

8. N. Tai 45% Western Guinean lowland forest

9. Mt Oku 48% Cameroonian highland forest

10. N. Itombwe 50% Albertine Rift montane forest

Nyungwe 62% Albertine rift montane forest

Mombasa 59% North Zanzibar coastal forest

Gabela 56% Angola montane forest grassland

Itombwe 51% Albertine rift montane forest

Yabello 46% Somali acacia-commiphora

Gambia 80% West Sudanian savanna

Ankober 78% Ethiopian montane grassland and woodland

Cape Town 76% Lowland fynbos and renosterveld

Mt Elgon 74% East African montane forest

Mt Moco 71% Angola montane forest grassland

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(Rodrigues et al. 2004a)! A study of range-restricted birds revealed no association between them and the number of fully or partially protected areas across countries once area had been controlled for (Balmford & Long 1995). (Another exercise conducted at a fine 5-km2 scale and using field collections, plant distributions, and climatic and environmental variables concluded that 43 percent of species were left unprotected [Ferrier et al. 2004].) Interestingly, the percentages of so-called gap species that are not protected across biomes or countries are correlated with levels of endemism, not the percentage of area that is protected, so that conservation policy makers should not necessarily target countries with few protected areas but countries with high endemism (Rodrigues et al. 2004a). In a follow-up analysis, more-demanding inclusion criteria were set because maps of the extent of occurrence overestimate true occurrences and because species may be present in protected areas but not actually protected if management is weak. When representation targets were reset to be 100 percent of geographic ranges for species with very small ranges (<100 km2) down to 10 percent for widespread species, and calculating the irreplaceability value of sites outside protected areas (i.e., the likelihood that the site would be included in an expanded protected area network that represented all species to their representation targets), and weighting these sites by the risk of species extinctions of those species found there, some informative findings emerged. Currently, even with 11.5 percent of terrestrial land surface protected, 74 percent of species do not achieve their representation targets; 89 percent of threatened species are not protected; and 92 percent of critically endangered species do not fall under protection. This exercise pinpoints areas of the globe in need of protection. Overwhelmingly, these fall in tropical and subtropical moist forests. In Asia these are in the Western Ghats, Sri Lanka, the eastern Himalayas, southern and eastern China, the Ryuku Islands, Vietnam, northern Thailand, peninsular Malaysia, the Philippine and Indonesian islands, New Guinea, Pacific islands, New Zealand, and the wet tropics of Queensland, Australia. In Africa, outlying islands such as Sao Tomé, the Seychelles, Comoros, Mauritius, and Reunion, and mountains such as the Kenyan and Ethiopian highlands, the Eastern Arc chain, the Albertine rift, the Cameroonian highlands, and upper Guinea require protection, as well as coastal East African forests, the Cape Floristic region, and Maputaland-Pondoland. Madagascar is critical. In the western hemisphere, the Andes and the Choco and Tumbes forests, the Atlantic forest, the Caribbean, and Central America all need conservation attention (Rodrigues et al. 2004b).

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Other studies have drawn attention to the distribution of reserve sizes, noting that the vast majority are small—in the Western hemisphere, 57 percent of reserves are <10 km2 and 83 percent are <100 km2—too small to maintain viable populations of many vertebrates. Alaska holds 35 percent of the total protected area in North and South America but is depauperate in flora and fauna. For bats that show a marked latitudinal gradient in species richness peaking in the tropics, Alaskan reserves are of little conservation significance, and 82 percent of threatened and small-range bat species are not represented within New World reserves (Andelman & Willig 2003). Exercises that pinpoint gaps in protection are the new frontier in large-scale conservation priority setting and they prepare the ground for reserve prioritization at a finer scale.

Practical Application Given limited funds and opportunities, international conservation organizations need to set priorities for their programs, staff, fund-raising, and onthe-ground conservation efforts. Some of the biggest organizations have research teams that try to document biodiversity across marine or terrestrial ecosystems in order to help them set conservation agendas. As yet, there has been a noticeable absence of breakthroughs in identifying certain taxa that reliably predict biodiversity of other taxa, whether sites of high biodiversity, hotspots, or complementary sets of areas are considered. To date, there seems to be no particular measure of biodiversity nor magic taxonomic group that priority exercises can fall back on. This is a cause for concern. For instance, we may pour conservation money into species-rich nations or provinces while threatened taxa continue to disappear elsewhere. Or we may channel effort into considering hotspots of endemic species but miss sites of species richness. The choices are unfortunate and the precautionary principle dictates it is better to use combinatorial measures that incorporate several dimensions of biodiversity. Indeed, from a practical standpoint, this is the conventional wisdom. The most famous study, of Myers and colleagues (2000), melded areas of plant endemism with threat in the form of the area having lost 70 percent or more of its primary vegetation, in order to be termed a global hotspot. Other studies concur. Key biodiversity areas (KBAs), for example, must adhere to four criteria: globally threatened species must occur in significant numbers; a significant percentage of the global population of one or more

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range-restricted (<50,000 km2) species must be present on a regular basis; a significant proportion of the global population of a congregatory species must occur on a regular basis; and sites must hold a significant proportion of the group of species whose distributions are restricted to a biome or subdivision. The last three criteria are concerned with geographically irreplaceable sites, whereas the first addresses vulnerability. KBAs have been applied to birds for more than 20 years (Eken et al. 2004). Academic explorations of congruence between taxa (e.g., frogs and plants) and between measures of biodiversity (e.g., rarity and richness) will likely continue, but from a practical standpoint it may be productive to take a handful of indicator taxa to triangulate biodiversity hotspots, as suggested by studies such as those of Ricketts and colleagues (1999) (see Figure 2-1). This may have greater merit than using endemic bird distributions that are sometimes used as de facto taxonomic indicators of biodiversity. Another option is to use ecoregions rather than species as surrogates for biodiversity (see chapter 10). Given that many endemics are restricted to certain habitats, a priori this might be a profitable avenue for identifying habitat specialist distributions. For some taxa such as fungi and insects, the higher-taxon approach of documenting the presence or absence of families or orders may be the only realistic way to document their geographic distributions at large scales. In short, despite considerable research effort, there are surprisingly few practical shortcuts to documenting biodiversity across the world’s terrestrial ecosystems. Marine ecosystems are even less studied.

Summary When the objective is to identify areas of conservation significance at a continental or worldwide scale, it may be helpful to use the distribution of one taxon as a surrogate for the distribution of biodiversity in other taxa. Yet a search for cross-taxon congruence of species richness reveals few positive results. Instead, environmental surrogates of species richness have been explored with varying success. Using families or genera to predict species numbers seems promising, although there are statistical quibbles. Crosstaxon congruence in rare and threatened species show few positive findings, but patterns of endemism across taxa do seem reasonably concordant. Some conservation exercises ask for a portfolio of high biodiversity areas. These are identified using complementary algorithms that choose areas in a stepwise iterative fashion so as to include the greatest number of species not yet represented in selected areas. It is not yet established

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whether complementary sets of areas chosen using one taxonomic group reliably predict sets of areas with high biodiversity in other taxa at large scales. Different measures of biodiversity—species richness, rarity, and so on—are not usually congruent across terrestrial global ecosystems, and the same is generally true for continents, although there are exceptions. It is difficult to assess whether different areas chosen on the basis of complementarity are congruent between measures of biodiversity. A practical outcome of these priority exercises has been to show that a high proportion of species are not yet legally protected by reserves, especially species in tropical and subtropical moist forests. Despite considerable research there are currently few practical shortcuts to documenting biodiversity at a large spatial scale.

Butterfly species, such as this monarch, are well known and their presence has been used to document species richness of other taxonomic groups at regional scales. For example, they feed on certain plant species, suggesting that they could represent vascular plant species diversity. (Drawing by Sheila Girling.)

Chapter 3

Species Indicators of Biodiversity in Reserve Selection

A Smaller Scale Having established that a particular country or area of the globe is rich in species or holds high levels of endemism, a nongovernmental organization (NGO) might next want to know where biodiversity peaks at the national or regional level—coarse-level priority areas (areas selected at a large geographic scale) are simply too big to be protected in their entirety (VaneWright et al. 1991). Similarly, a government might wish to establish a reserve in an area of particularly high species richness. Fortunately, there are now systematic procedures for selecting reserve sites, but their outcome depends on data quality, measures of biodiversity, procedures employed to maximize these measures, and how procedures are compared (Margules & Pressey 2000; Cabeza & Moilanen 2001; Sarkar et al. 2006). Owing to the impossibility of documenting all biodiversity at a local— let alone regional—scale, conservation scientists have to employ indicator taxa as shortcuts. This demands congruency in spatial distribution between taxa, driven perhaps by species’ similar responses to environmental gradients, or to biotic interactions between species, or even to local random draws from a regional pool containing a great many species (Gaston & Williams 1996). Unfortunately, there are biological reasons for thinking that taxonomic congruence will be weaker at a regional than at a global or continental scale, because environmental correlates of species richness at 61

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large scales, such as latitude and productivity, do not change markedly within most nations. Moreover, at this smaller scale, species such as plants with low mobility and pronounced habitat specificity will be found at rather few sites, whereas large-bodied species or habitat generalists will be found at many sites, and this will reduce the strength of cross-taxon species correlations (Saetersdal et al. 1993; Csuti et al. 1997). Despite these concerns, numerous attempts have been made to uncover taxonomic predictors of biodiversity at the regional scale, and these constitute the topic of this chapter. First, focusing on species richness, I tackle the extent to which different taxa can stand in for each other, and then, within a taxon, the extent to which subsets can predict richness across a whole group, whether or not higher taxa can predict species numbers, and the use of morphospecies as a surrogate for species. I then look at cross-taxon congruency of other measures of biodiversity—endemism, rarity, and threatened species. Next, I switch gears and examine cross-taxon surrogacy in situations where several reserves can be established at once. I then examine how reserve-selection algorithms are being applied to real-world protected-area coverage and marine reserves. Finally, I consider how environmental surrogates on their own or in concert with species surrogates can be used to select reserves. Studies of indicators of biodiversity at a regional scale concentrate on relatively pristine ecosystems, a portion of which conservation biologists hope to protect. In contrast, studies of indicators of the effects of environmental disturbance on co-occuring taxa are a parallel field of inquiry that focus on anthropogenically altered landscapes (see chapter 8).

Cross-Taxon Congruence of Species Richness Recent discoveries of areas of remarkable species richness, such as the Foja mountains in western New Guinea, where twenty new species of frogs, four new butterfly species, five new palms, and a new bird of paradise were discovered in 2006 (Cryanoski 2006), suggest that cross-taxon biodiversity is associated at a local scale but this is not confirmed by orderly investigation. The most renowned systematic study is by Prendergast and colleagues (1993), who mapped the distribution of British plants and animals onto a 10-km2 national grid. Defining hotspots as the top 5 percent of a given taxon’s records in grid squares, they found very low proportions of overlap between hotspots for butterflies, dragonflies, liverworts, aquatic plants, and breeding birds. In fact, not a single grid square was a hotspot for all five

Species Indicators of Biodiversity in Reserve Selection 63

taxa, and only two squares were a hotspot for four. Defining coldspots as the most species-poor 5 percent of recorded squares, they found that overlap only ever reached a maximum of 24 percent between depauperate areas (for dragonflies and butterflies, see Table 3-1; see also Prendergast & Eversham 1997). While Britain is a highly modified and fragmented landscape with climatic and geological heterogeneity, elsewhere study after study of species richness has uncovered either weakly significant positive or nonsignificant correlations between taxa. As illustrations, only weak associations were found between primates and plants and birds in Borneo (Meijaard & Nijman 2003); between plants, invertebrates, and vertebrates in Uganda (Howard et al. 1998); among terrestrial invertebrates in South Africa (Kotze & Samways 1999; Lovell et al. 2007); among temperate Lepidoptera (Ricketts et al. 2002); among large freshwater invertebrates (Heino et al. 2003); and among bryophytes, fish, and macroinvertebrates in Scandinavia (Heino et al. 2005). In some instances there are significant positive associations in species richness of various taxonomic groups—between amphibians, reptiles, mammals, and vascular plants, as well as, to a lesser extent, birds across 32 provinces in China, albeit a huge area (Xu et al. 2008); between birds and butterflies, and birds and woody plants in Uganda (Tushabe et al. 2006); between butterfly and skipper species richness and hymenopteran species in southern Ontario, Canada (Kerr et al. 2000); between amphibians, reptiles, birds, and mammals in Spain (Rey Benayas & de la Montana 2003); between various taxa in Austria, particularly birds and vascular plants (Sauberer et al. 2004); and between butterflies and vascular plant species in Switzerland (Pearman & Weber 2007). The practical difficulty is that there is no taxonomic or geographical consistency to these findings—for example, plants predict birds in Switzerland (Pearman & Weber 2007), but plants do not predict animal richness in Flanders, Belgium (Maes et al. 2005). More broadly, a meta-analysis of 237 species-richness correlations revealed positive but generally feeble correlations averaging 0.374, most of these being from forest and grassland habitats (Wolters et al. 2006). No obvious explanatory variables could account for the heterogeneity in correlations—spatial scale, taxonomic distance, trophic position, and biome had no influence on congruency. Associations were more notable in tropical than temperate locations, however. One likely underlying cause behind these conflicting and depressing results is that species in different taxa are differentially associated with environmental variables (Vessby et al. 2002; Heino et al. 2003) making it unlikely that species-richness associations will be marked. We might expect correlations between a pair of taxa to

Butterflies Dragonflies Liverworts Aquatic plants Breeding birds

— 0.24 (n = 89)0 0.13 (n = 29)0 0.13 (n = 111) 0.15 (n = 115)

Butterflies

0.34 (n = 99)0 — 0.09 (n = 145) 0.16 (n = 170) 0.22 (n = 88)0

Dragonflies

0.0 (n = 101)0 0.0 (n = 94)00 — 0.11 (n = 142) 0.14 (n = 94)0

Liverworts

0.60 (n = 118) 0.25 (n = 111) 0.03 (n = 98)0 — 0.21 (n = 58)0

Aquatic plants

0.26 (n = 112) 0.12 (n = 116) 0.23 (n = 116) 0.21 (n = 112) —

Breeding birds

Table 3-1. Proportional overlap of hotspots with hotspots (above diagonal) and coldspots with coldspots (below diagonal) for each group of British fauna and flora; n = maximum possible number of overlaps calculated as the smaller of the pair of hotspot (or coldspot) groups adjusted for squares lacking records for either group (from Prendergast et al. 1993).

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be significantly positive if both are widely distributed (Lennon et al. 2004), or if environmental gradients range from favorable to highly disturbed, because taxa often respond similarly to land degradation (e.g., Laurance & Peres 2006; Sodhi et al. 2007), or if they are in a symbiotic or host-plant association (Karban & Baldwin 1997), or if both depend on the same resource (Juutinen et al. 2006; Norden et al. 2007), but these fine-scale explanations have not been used to predict where real-world taxonomic associations will be found. Gaston (1996a) summarized the situation thus: There is as yet minimal evidence for the assumption that spatial variation in the diversity of any particular taxonomic group of organisms can be used reliably and generally to predict spatial variation in the diversity of another such group. It seems more probable that where reasonably good relationships do exist between the diversities of pairs of taxa that they do so only within limited (e.g., biogeographic, bioclimatic) regions and at particular scales, and that over larger areas and at other scales variances become too high for relationships to be of practical use. Congruence will probably be greatest between phylogenetically and ecologically closely related groups of taxa sharing the same general or particular habitat within a closely circumscribed region. (107) A single taxonomic group reliably capable of predicting species richness of other groups at a regional scale seems too ambitious. Nonetheless, the most useful question for conservation purposes is not whether species richness of taxon A predicts species richness of taxon B—a relatively academic question partially tied to understanding ecological and biogeographical relationships—but whether taxon A predicts combined totals of taxa B through Z, or at least B through G. Combined species richness of mammals is predicted by the combined species total of Didelphimorphia in Amazonian sites but not by other orders such as primates or rodents (Sebastiao & Grelle 2009); and between amphibian, or reptile, or bird, or mammal, or vascular plant species richness and the remaining classes across Chinese provinces (Xu et al. 2008). So there are some instances where post hoc choice of a particular indicator taxon can predict a portion of species diversity. Biodiversity can be characterized in ways other than species richness, including community composition, a measure incorporating species identity with species abundance. A handful of studies in both terrestrial and freshwater environments suggest that sites having a similar community composition in one taxon also have similar community compositions of

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Table 3-2. Cross-taxon correlations among bird, butterfly, and plant species richnessa in each meadow; and bird, butterfly, and plant community similarityb between all pairs of meadows in two study areas within the Greater Yellowstone Ecosystem, USA (from Su et al. 2004). Study area and comparison

Gallatins Bird vs. butterfly Bird vs. plant Butterfly vs. plant Tetons Bird vs. butterfly Bird vs. plant Butterfly vs. plant

Species-richness correlationc

Community-similarity correlationd

0.000 –0.113 0.027

0.291 *** 0.371 *** 0.474 ***

–0.007 –0.011 0.478 *

0.661 *** 0.673 *** 0.768 ***

a

Richness is the total number of species recorded in each meadow across all sampling dates. Bray-Curtis similarity. c Spearman’s rho. d Mantel’s calculated using Monte-Carlo permutation tests with 999 permutations. * p < 0.05; ***p = 0.001 b

other taxa (Allen et al. 1999; Kilgour & Barton 1999; Pazkowski & Tonn 2000; Heino et al. 2003; Bilton et al. 2006). In some instances, these correlations between communities from different taxa are more striking than simple species-richness correlations (see Table 3-2). The difficulty is that it is not quite clear what sort of community a conservation planner might want to maximize.

Within-Taxon Congruence of Species Richness In the species-rich tropics, species inventories are especially challenging to compile because of the number of individuals to be collected and identified. Any way to reduce the number of individuals, taxa, or items of information (called inventory abbreviation) is useful (Higgins & Ruokolainen 2004). Tree sampling, for instance, can be abbreviated by limiting collection to large classes of trees, sampling only certain taxa, identifying trees just to genus or family level, reporting density, or using presence-absence data, rather than abundance (e.g., Slik et al. 2008). Around Iquitos, Peru, pres-

Species Indicators of Biodiversity in Reserve Selection 67

ence-absence data were virtually interchangeable with abundance inventories of trees. Genus-level identification preserved approximately 80 percent of the information in the full inventory but reduced the number of taxa sampled by an enormous 78 percent—although it still required a large number of individuals to be sampled. Inventories based on sampling trees of >10-cm diameter at breast height (dbh) captured 79 percent of floristic variation but required sampling just 25 percent of the number of stems per site and 40 percent of the number of taxa. Best of all, restricting sampling to just two families, whichever they might be, generated very high correlations with the full inventory (see Table 3-3; see also Pawar et al. 2007). More generally, three methods have been used to assess species diversity within a taxon using surrogate species from the same taxonomic group.

Table 3-3. Correlations (r) between just two taxa (various pairs) and full tree inventories at nine sites near Iquitos, Peru; most correlations are high (from Higgins & Ruokolainen 2004).

r abundance

r presence/ absence

Mean number stems per site

Tall groups Leguminosae and Lecythidaceae Myristicaceae and Lecythidaceae Myristicaceae and Rubiaceae Myristicaceae and Melastomataceae Burseraceae and Melastomataceae Protium and Guarea Escweilera and Pithecellobium Protium and Pithecellobium Meliaceae and Violaceae

0.89 0.89 0.84 0.85 0.78 0.73 0.84 0.84 0.77

0.82 0.86 0.72 0.71 0.72 0.79 0.85 0.69 0.79

61 52 50 41 31 31 25 22 40

156 51 93 61 69 55 30 38 62

Short groups Annonaceae and Rubiaceae Violaceae and Rubiaceae Meliaceae and Rubiaceae Rubiaceae and Melastomataceae

0.83 0.76 0.73 0.81

0.83 0.79 0.73 0.76

38 38 38 38

136 78 112 96

441

1,190

Taxonomic scope

Full inventorya a

Characteristics of full inventory (all stems ≥ 2.5 cm dbh).

Total number of species sampled

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Taxon Subsets Cross-taxonomic congruence demands that members of species from different taxa respond to the same ecological variables in roughly similar ways. Within one taxon or guild, however, this assumption may be less demanding—related species might be expected to respond to environmental factors in approximately the same fashion (Marcot & Flather 2007). Therefore, it is a less theoretically challenging proposal to use the number of species in an easy-to-identify family as a marker for the number of species within the same order. For example, macrolichens (often the only lichens to be identified) are keen indicators of lichen species richness writ large in Europe (Bergamani et al. 2005); and species richness of Pselaphidae, Scarabaeidae, and Carabidae beetle familes, which are relatively easy to identify, predict overall beetle species richness in New South Wales, Australia (Oliver & Beattie 1996a). It is rather strange that so little research effort has been directed at discovering whether the species richness of certain families is a marker for species richness of orders of which they are a part (but see Cardoso et al. 2004a; Fleishman et al. 2005).

Higher Taxa When the number of species in a taxon is very large, or expertise in species identification is limited or expensive, it may prove more cost-effective to identify biodiversity to genus or family level and use these as approximations for species richness at a regional scale (Balmford et al. 1996a, b). The underlying assumptions are that there are fewer genera and families than species (i.e., fewer monotypic genera); that effort required to secure a representative sample is less for higher taxa than for species, or that identification is easier; and that the distribution of species within genera and families is relatively homogeneous (Mandelik et al. 2007). Given the great diversity of invertebrates, and insects in particular, and the difficulties in identifying them to species level, it is not surprising that the higher-taxon method has attracted entomologists (as well as fungi and lichen systematists). Findings have been striking in group after taxonomic group: number of genera is a very strong predictor of number of species (even after subtracting a number equivalent to the number of genera from the species total; see Villasenor et al. 2005). This is true for vascular flora in Mexico (Villasenor et al. 2005); macrolichen, moss, liverwort, and woody plants in the Indian Himalayas (Negi & Gadgil 2002); ants in New South Wales (Pik et

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al. 1999), spiders in Portugal (Cardoso et al. 2004b); vascular plants, beetles, and moths in Israel (Mandelik et al. 2007); as well as rocky reef fish and intertidal mollusks in southeast Australia (Gladstone & Alexander 2005); and macroinvertebrates and diatoms in Finnish streams (Heino & Soininen 2007). At the family level, correlations with species richness are often strong as well, for instance Finnish macroinvertebrates (Heino & Soininen 2007), Australian mollusks (Gladstone & Alexander 2005), Himalayan plant groups (Negi & Gadgil 2002), and plants and beetles in Israel (Mandelik et al. 2007). Although there are few groups for which the relationship between order richness and species richness has been investigated, among the British macromycete fungi, at least, it is positive and significant (Balmford et al. 2000). Incidentally, this study of fungi illustrates the reduction in strength of correlation coefficients with increasing taxon level at both national and local scales (see Figure 3-1). Close correspondence is likely driven in part by species, genera, and families having similar relationships to common environmental variables (Heino & Soininen 2007). In short, using genera richness to predict species richness appears a promising shortcut for documenting biodiversity in an area; however, there are a number of provisos. First, field collection methods do not distinguish between taxonomic levels—vouchers for all specimens must initially be collected and sorted, only then can the higher-taxon approach reduce the expense of time and money (Lovell et al. 2007). Second, the strength of the correlation between higher taxa and species richness will diminish with sampling intensity, because additional species’ records in heavily sampled sites are likely to come from previously recorded higher-level taxa (Andersen 1995). Third, the same reduction in strength of relationship occurs as the sampling area increases, because additional species are increasingly likely to be members of previously recorded genera (Andersen 1995). This probably contributes to the strong correlations between family and species richness uncovered by Williams and Gaston (1994) for British ferns and butterflies, Australian passerines, and North and Central American bats across large geographic extents using large grain sizes. Fourth, relationships between genera and species richness differ by habitat type (Andersen 1995). Fifth, correlations become weaker as the ratio of higher taxon-to-species number increases (being perfect, 1:1, in monotypic genera) (Lovell et al. 2007). Given these worries, an accurate inventory of the region is necessary prior to selecting a surrogate taxonomic level. Although this could be performed in the office using distribution maps, in practice high-priority survey regions usually have sketchy range maps. Last, the focus on genera or families is conceptually confusing

Figure 3-1. Left column: cross-level congruence in macromycete richness in different parts of Britain, n = 9 areas. (a) log10 species vs. log10 genera, r2 = 0.96, p < 0.001; (b) log10 species vs. log10 families, r2 = 0.70, p < 0.01; (c) log10 species vs. log10 orders, r2 = 0.63, p < 0.01. Right column: cross-level congruence in recorded macromycete richness of sites in the Sheffield area, UK, after 3 visits, n = 19 sites. (a) log10 species vs. log10 genera, r2 = 0.91, p < 0.001; (b) log10 species vs. log10 families, r2 = 0.75, p < 0.001; (c) log10 species vs. log10 orders, r2 = 0.44, p < 0.01. (Reprinted from Balmford et al. 2000.)

Species Indicators of Biodiversity in Reserve Selection 71

because conservationists are usually concerned about species extinctions and a higher-taxon approach does not address these. Moreover, conservation effort is targeted at species rather than at families or genera (although this could change as the extinction crisis deepens). A few studies have tried to meld higher-taxon surrogates and crosstaxon surrogates. For example, in fragments of prairie, sedge meadow, and prairie fen around Chicago, USA, plant species and plant genera richness predict butterfly species richness, leafhopper species richness, and Papaipema moth richness well, but plant family richness does not (Panzer & Schwartz 1998; see also Pawar et al. 2007).

Morphospecies Another avenue for documenting species richness of megadiverse groups is to use morphospecies. People with minimal training in taxonomy can identify morphospecies using external morphological features. In a study of ants, beetles, and spiders in New South Wales, arthropods were collected using pitfall traps and litter samples. Non-specialists were asked to group them into morphospecies; subsequently, specialists produced a corrected morphospecies inventory derived by lumping species that had been identified as two different morphospecies. Finally, specialists examined each specimen, separating them into different bona fide species, splitting and lumping morphospecies where necessary. Total estimates for ant richness were very similar across methods, but beetle and spider richness were higher for morphospecies, indicating that some splitting had occurred (see Table 3-4). That said, morphospecies and species inventories yielded identical rankings across dry, grassy, moist forest and rainforest habitat types, and community assemblages were similar using morphospecies, corrected morphospecies or species (Oliver & Beattie 1996b; see also Pik et al. 1999; Nippress et al.

Table 3-4. Estimates of richness of ants, beetles, and spiders from four forests in New South Wales, Australia, using morphospecies, corrected morphospecies, and species inventories (from Oliver & Beattie 1996a). Inventory

Morphospecies Corrected morphospecies Species

Ants

Beetles

Spiders

92 84 93

431 331 376

146 118 121

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2008). Given that primary sorting can be conducted by non-specialists, with specialists simply verifying vouchers and subsamples, time and consultancy fee savings are considerable (Oliver & Beattie 1996a). Attempts have been made to explore the consequences of dealing with inadequate data—a reduction in survey intensity, curtailment of the extent of a survey, exclusion of some species’ records, or uncertainty about details of species’ distributions (Freitag & van Jaarsveld 1998; Wilson et al. 2005). Encouragingly, a surprising number of these problems have only a muted effect on the outcome of reserve selection algorithms, but the consensus is that inventory efforts should be distributed as widely as possible across taxa and sites.

Congruency of Endemism, Congruency of Rarity, and Congruency of Threatened Species Endemism is a slippery concept at a national or regional scale because species that are narrowly endemic globally may not be endemic to any one country, and species that are endemic to a nation may not be globally narrow endemics (e.g., Saetersdal et al. 1993; Bonn et al. 2002). Some measures can cope with this, such as relative endemicity scores that calculate the proportion of a species’ total continental range or global range that falls within a particular region under consideration (Freitag & van Jaarsveld 1997). Relatively few attempts have been made to explore cross-taxon congruence of endemism at a regional scale. One example is of plants, snails, and vertebrates endemic to the tropical rainforests of Queensland, Australia, that are highly and positively intercorrelated across 23 biogeographic regions (Moritz et al. 2001). Another is amphibians, reptiles, birds, mammals, and vascular plants across 32 provinces in China, where significant correlation coefficients range from 0.48 to 0.89, with those between birds and mammals, and between amphibians and vascular plants, being particularly strong (Xu et al. 2008). In contrast, endemic fish, frogs, tortoises, snakes, birds, and mammals in South Africa show little spatial congruence (Lombard 1995). Species that are endemic to one country attract conservation attention and consequently provide leverage to help conservationists establish reserves (Rebelo & Siegfried 1992), so evidence showing that centers of local endemism transfer across taxonomic boundaries at a regional scale would be helpful. At present, however, knowledge that an area contains two or three endemic species may be all that we have to broker a conservation deal.

Species Indicators of Biodiversity in Reserve Selection 73

One of the problems in looking for congruence between rare taxa is that rarity is defined in many ways—habitat specificity, national geographic distribution, local abundance, even featuring on lists of conservation concern—so that measures of rarity even within a taxon may fail to correspond (e.g., Williams et al. 1996; Kintsch & Urban 2002); thus it is not surprising that searches for cross-taxonomic indicators of rarity at a regional level are uneven at best. In southeast Massachusetts, rare and declining bird species show little geographic overlap with state-listed moth species (Grand et al. 2004); and in Switzerland, red-listed plant, bird, and butterfly distributions show no significant associations; nor, in the main, do areas occupied by Swiss plant, bird, and butterfly species with the lowest 25 percent of occurrences (Pearman & Weber 2007). Cross-taxonomic congruence of Red Data Book–species hotspots in South Africa looks more promising, however (Lombard 1995). In general, sites where rare species in one taxon are found do not coincide very well with sites of rare species in other taxa. Where habitats are being destroyed, it might seem obvious that multiple taxa would be affected similarly and that one group could represent other threatened taxa. The way in which species are categorized as threatened, however, is often using a global metric—the IUCN criteria that incorporate various measures of absolute or changing population size, range size, and even quantitative analyses that can embrace several countries rather than being fixed on responses to habitat alteration at a national level (see Table 1-1). Globally threatened species may not be the most threatened nationally, and nationally threatened species may not be threatened globally (see Table 3-5; also Bonn et al. 2002). Sometimes national Red Lists or algorithms based on species’ national distributions are used instead. Here, there is greater potential for congruency but species can be threatened for many idiosyncratic reasons, including loss of breeding sites, hunting, or drainage of foraging areas. Consequently, threatened taxa may not have overlapping distributions unless defined in relation to specific threats at a local scale, when it is more sensible just to discuss the threat during conservation planning. Indeed, attempts to find congruency of threatened species at a regional scale are few and largely unproductive: along the St Lawrence River in Canada, for example, hotspots of vulnerable or threatened bird and plant species show poor overlap (Tardif & DesGranges 1998); and in Greece 60-km2 cells containing threatened plants and birds (categorized by dividing the number of species in a taxon by the maximum number of species in the same taxon found in any cell, and defining the lowest 20 of these cells as taxon-specific hotspots of threat) overlapped by only 35 percent, plants and mammals by 58 percent, and birds and mammals by 63 percent

74 c o n s e r v a t i o n b y p r o x y

Table 3-5. Reasons for disparities between threatened endemic animal taxa (Critically Endangered, Endangered, and Vulnerable) on the IUCN global Red List and on National Red Lists from Argentina, Bolivia, Ecuador, and Venezuela. The number of threatened taxa on both lists is shown at the top (from Hilton-Taylor et al. 2000).

Number of threatened taxa on both lists Species not assessed by IUCN Species on IUCN but not national lists Sub-specific taxa not assessed by IUCN Sub-specific taxa on IUCN but not national lists Different use of categories in national lists Different use of criteria on national lists Different information used for listings Total number of threatened taxa

M

B

R

A

F

8 1 4 2 2 2 7 3 29

30

1 1

7

1 7 3

4

1

4 15 6 59

1

4

7

All

40 16 7 7 2 7 22 9 11 110

M, mammals; B, birds; R, reptiles; A, amphibians; F, bony fish.

with only 6 out of 104 squares containing all three threatened taxa (Troumbis & Dimitrakopoulos 1998).

Concordance between Measures of Biodiversity Species Richness and Endemism There are bound to be many species in areas where there are many endemics, and national studies do show significant positive correlations between richness and endemism; for example, in mammals across 2° × 2° grid squares in Mexico (Ceballos et al. 1998) and across provinces in China (Xu et al. 2008; see Figure 3-2). In China, amphibian, reptile, bird, mammal, and vascular plant richness are almost all significantly associated with endemism of the same taxon, endemism of other taxa, and overall endemism of all other classes combined even after the effects of latitude and area have been removed. The difficulty is that the top-scoring provinces for species richness or for endemism may not be the top for any individual taxon, so priorities should be based on several taxonomic groups (the “shopping basket approach,” see Niemela & Baur 1998; Kotze & Samways 1999; Virolainen et al. 2000). The idea here is to use two or more taxa in combination

Figure 3-2. Map of overall richness index (top), endemism index (center), and threat index (bottom) in terrestrial ecosystems of China. The maps incorporate the richness, endemism, and threat indices of amphibians, reptiles, birds, mammals, and vascular plants. (Reprinted from Xu et al. 2008.)

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as indicators of regional biodiversity. For example, in 38,600-m2 sites in eastern Austria, two taxon combinations of bryophytes, vascular plants, gastropods, spiders, orthopterans, carabid beetles, ants, and birds showed very high average correlations (>0.68) with remaining overall species richness; specifically, vascular plants and birds, and gastropod and ant combinations were the best predictors of overall richness. In situations where taxa are spatially incongruent, two taxa with different environmental requirements may encompass more overall biodiversity than one taxon occupying a restricted set of niches (see Bani et al. 2002). As yet there is no consensus on which taxa should be used.

Species Richness and Rarity If species richness could predict species rarity, often a forecast of extinction, it would be useful because rare species are difficult to find, owing to restricted geographic ranges or small population sizes. Unfortunately, after ranking species as rare or common on the basis of number of sites occupied, studies of birds in Britain, and South Africa, and of plants, birds, and butterflies in Switzerland all show that rare species account for far less of overall same-taxon species richness than do common ones (Lennon et al. 2004; Perman & Weber 2007). So with hindsight it comes as little surprise that studies find only weak or no association between species richness and rarity. The most well-known such study is of British fauna and flora: 10-km2 grid squares that were hotspots of species richness (in the top 5 percent of records) for butterflies, dragonflies, liverworts, aquatic plants, and breeding birds were not associated with rarity hotspots of the same taxon (rare species were defined as those occupying <16 squares) or uncommonness hotspots of the same taxon (defined as occupying 16–100 squares, see Table 3-6). Williams and colleagues (1996) reanalyzed the British bird data using different criteria of rarity, the 25 percent of species with the fewest records, or Red Data birds, and found the same result—hotspots of species richness and different measures of rarity were weakly associated. Studies from elsewhere in Europe show the same low correlations: for example, between total species richness and Red List rarity in lichens and wood-fungi in southern Sweden (Norden et al. 2007), and within-taxon amphibian, reptile, nesting bird, and mammal richness and rarity in Spain (Rey Benayas & de la Montana 2003; see also van Jaarsveld et al. 1998). Yet there are places where associations are strong. In Australia, 47 percent of bird species-richness hotspots contain at least one rare bird species

Number (proportion) of species hotspots containing no rare species Number (proportion) of species hotspots containing no uncommon species Number (proportion) of rare species occurring in species hotspots Number (proportion) of uncommon species occurring in species hotspots 111 (0.97) 28 (0.25) 1 (0.50) 8 (0.73)

29 (0.24) 2 (1) 9 (0.75)

Dragonflies

108 (0.88)

Butterflies

96 (0.63)

32 (0.52)

0 (0)

54 (0.50)

Liverworts

45 (0.87)

8 (0.57)

3 (0.02)

91 (0.67)

Aquatic plants

40 (0.65)

18 (0.57)

32 (0.27)

90 (0.78)

Breeding birds

Table 3-6. Rare and uncommon species in hotspots of species richness in Britain (from Prendergast et al. 1993).

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(Curnutt et al. 1994), and in Sweden, sites of terrestrial vertebrate species richness and sites containing species with population sizes of <1000 individuals are associated (Berg & Tjernberg 1996). Also, both bird and plant species-richness sites and rarity hotspots are associated on the St Lawrence River, Canada (Tardif & DesGranges 1998); richness hotspots and rarity in South African plants, butterflies, antlions, and buprestid beetles (van Jaarsveld et al. 1998; but see Lombard 1995); and mammalian richness and rarity in Chile (Cofre & Marquet 1999). The issue is therefore complicated and there is an impasse at present. We do know that both common and rare vascular plants, birds, and butterflies are each associated with overall species richness of these same groups at high-elevation sites in Switzerland, but at low-altitude sites only common species are associated (Pearman & Weber 2007). The reasons are unclear but this sort of variability, resulting just from a change in altitude, suggests that the precautionary principle is useful—we simply cannot assume that areas of species richness and rarity are necessarily coincident at a regional scale. At this stage and at this scale, there is a general consensus that species-rich hotspots do not coincide with large numbers of endemic or rare taxa (Lombard 1995).

Species Richness and Threatened Species Turning to associations between species richness and threatened species, no clear picture emerges either. In Greece, hotspots of threat (see p. 73) contain large numbers of species overall (Troumbis & Dimitrakopoulus 1998). In Spain, however, species richness and vulnerability are negatively associated in four classes of terrestrial vertebrates (Rey Benayas & de la Montana 2003). In China, species richness and threatened species are on the whole positively and significantly correlated both across and within amphibians, reptiles, mammals, and vascular plants, but not in birds (Xu et al. 2008). It looks as if the level of threat differs across countries and, at least in China, as if the mechanisms underlying avian threat differ from those that bedevil other taxa.

Biodiversity Metrics Associations between endemic, rare, and threatened species are just as equivocal. In Chinese provinces again, endemic and threatened species are significantly correlated within all taxa except birds. Across taxa, results are

Species Indicators of Biodiversity in Reserve Selection 79

mostly significant but not when reptiles or birds are involved. Across seven regions of Chile, numbers of rare and threatened species of mammals are associated (Cofre & Marquet 1999) as well as across 50-km2 grid cells in Spain (Rey Benayas & de la Montana 2003). In general then, the extent of congruence between biodiversity metrics varies depending on taxa, and likely on the size of the country, on the grain size of measurement (grid-cell size or size of region), on the measures of diversity being compared, and on the way that each measure is derived. The extent to which these factors independently affect the strength of correlations between measures of biodiversity has yet to be explored, although there is some progress—for example, taxonomic diversity of indicator groups seems to have little effect (Bladt et al. 2008)—but until we know the context in which biodiversity of one taxon can reliably stand in for that of others, focusing on a few groups has limited utility—calling the whole idea of single biodiversity indicators into question at this scale. Currently, a popular but still relatively untried idea is to use a “shopping basket approach” where several taxa are simultaneously used to identify important areas for biodiversity.

Congruency of Complementarity Species Richness Where the conservation objective is to identify a set or network of national or regional reserves rather than a single protected area, the reserve system should be representative in order to contain as many elements of biodiversity as possible. A very popular approach is using complementary analyses (see chapter 2) in which two outcomes are usually sought: seeking representation of certain attributes a given number of times in a near-minimum number of areas, or else maximizing representation of attributes in a fixed area or for a fixed cost (Cabeza & Moilanen 2001). Complementarity incorporates principles of flexibility and irreplaceability. Flexibility arises because there are sometimes ties in the choice of the next-most-suitable site, leading to numerous possible site combinations that can help in providing planners with alternative options. Irreplaceability refers to the potential contribution of a given site to a reservation goal, or the extent to which options for a reserve network vanish if that site is lost. An irreplaceable site must be included in a reserve system that represents all species; in a sense it is a site of outstanding importance (Pressey et al. 1993; Williams et al.

80 c o n s e r v a t i o n b y p r o x y

1996). There is an underlying premise to complementary procedures—that reserve sites must be chosen to occupy minimum space to avoid conflict with other land-use practices. From the standpoint of surrogacy, the question is whether sites chosen on the basis of complementarity for one taxonomic group can stand in for sites chosen on the basis of complementarity for other taxa. The answer was notably provided in a study of woody plants, large moths, butterflies, birds, and small mammals in 50 Ugandan forests, where the task was to choose which 20 percent to protect as strict nature reserves (Howard et al. 1998). There was cross-taxon geographic congruence when species totals were tallied, but congruence disappeared once sampling effort had been removed—large forests had been sampled more intensively for all taxa. Yet when the top 20 percent of forest area was picked using complementary sets of birds, or of butterflies, these taxa captured as many species as those using all groups together, and they captured far more than sites starting with the largest areas or selecting sites randomly. This was because there were six different forest types in the 50 reserves resulting in high complementarity between them, whichever taxon was under consideration (see Figure 3-3). To be effective complementarity surrogates, biodiversity-

1.0

1.0

0.8

0.8

0.8

Birds

Moths

0.6

0.6

0.6

0.4

0.4 0.4

0.8 0.6 Plants

1.0

0.4 0.4

0.8 0.6 Plants

1.0

Butterflies

1.0

0.8

Mammals

1.0

0.8

Butterflies

1.0

0.6

0.4

0.8 0.6 Plants

1.0

0.6

0.4

0.4 0.4

0.8 0.6 Plants

1.0

1.0

1.0

0.8

0.8

0.8

0.4

0.8 0.6 Moths

1.0

Mammals

Birds 0.4

0.8 0.6 Moths

1.0

0.4

0.8 0.6 Butterflies

1.0

1.0

0.4

0.8 0.6 Birds

1.0

0.6

0.4

0.4

0.4

0.4

0.4

0.6

0.6

0.6

0.6

0.8 0.6 Moths

Mammals

1.0

0.8

Mammals

1.0

0.8 Birds

1.0

0.4

0.4

0.8 0.6 Butterflies

1.0

Figure 3-3. Cross-taxon congruence in complementarity scores between 49 pairs of forests in Uganda. Scores were calculated as the sum of the number of species found at just one or the other pairs of sites, divided by the combined total found at either or both sites. Only complementarity scores between each site and the next smallest forest are included. (Reprinted from Howard et al. 1998.)

Species Indicators of Biodiversity in Reserve Selection 81

indicator taxa must comprise species that span several habitats (Faith & Walker 1996b). A parallel but temperate study in Denmark using 10-km2 scale data on butterflies, birds, amphibians, reptiles, large moths, bats, and click beetles found high cross-taxon congruence in complementarity-based priority sites, too (Lund & Rahbek 2002). Bats and large moths predicted species richness in other groups well, although birds failed to do so. Bats and large moths may be affected and isolated by agricultural fragmentation in Europe more than are wide-ranging birds. Numerous other attempts at finding cross-taxon congruency in complementarity have proved successful (e.g., woody plants and various plant and animal groups in Greece [Kati et al. 2004a, b]; vascular plants and birds in Austria [Sauberer et al. 2004]; assorted South African taxa [Reyers et al. 2000]). In addition, sets of reserves identified using complementarity normally capture sites of species richness well, which is reassuring (e.g., Reyers et al. 2000; Murray-Smith et al. 2009). There are exceptions, however. Pairwise comparisons of complementary species sets between mammals, birds, plants, butterflies, termites, antlions, scarab beetles, and burpestid beetles across 25-km2 grid cells in South Africa showed low overlap, a mean of only 10 percent and a maximum of 21 percent, so congruence in complementarity is not ubiquitous (van Jaarsveld et al. 1998). Using surrogate taxa in complementarity algorithms is promising, and there may even be some preliminary general rules. Groups that are species rich and occur at many sites will overlap many species from other taxa, as will groups that are not well ordered (or nested) across sites. Think of a perfectly ordered (nested) archipelago in which species of the proposed surrogate taxon always disappear from sites in the same sequence; here a reserve would capture all species in the surrogate group at the most species-rich site and only propose a reserve set very limited in extent. Less vagile species such as plants are distributed across sites, in part by chance, and are hence poorly ordered, so they are likely to be better surrogate taxa than morevagile birds. Unfortunately, a number of factors affect cross-taxon congruency in complementarity exercises. Rarity has a disproportionate effect on sets of sites chosen because common species are rapidly captured early on, whereas rare ones require selection of additional sites (Pressey et al. 1999); thus it is crucial to know if rare species are truly rare (or are vagrants—often found on coastlines), are apparently rare because of the artificial divisions of geopolitical units (Rodrigues & Gaston 2002), or are rare because of biased sampling. In the last instance, disproportionate records of species near

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roads, for instance, inflate the number of sites necessary to protect a full complement of species, because such sampling makes narrowly distributed or habitat-specialist species appear rarer than they really are (Grand et al. 2007), which increases cross-taxon congruency. Another problem is that experts working on two different taxa might each eschew the same difficultto-access habitats for sampling (Williams et al. 2006). Again, this would artificially increase cross-taxon congruency. More optimistically, low sampling effort has relatively little effect on networks identified through complementarity analyses (Gaston & Rodrigues 2002; Vellend et al. 2008).

Other Biodiversity Measures Cross-taxon congruence in complementary sets of reserves based on numbers of endemic species has also been investigated with the goal of pinpointing regional sites of range restriction that do not replicate each other. Success is mixed. In the rainforests of northeast Queensland, Australia, where Moritz and collaborators (2001) restricted their complementarity analyses to species endemic to that region only, correlations between sites chosen using insects, plants, snails, and vertebrates were high. Insects and snails were highly effective surrogate taxa for vertebrates, whereas vertebrates were poor for all other groups. In deciduous woodlands in Norway, however, endemic species (defined as those present at one site only) were poorly matched between vascular plants and birds, in that 75 percent of the area was required to preserve all endemic plant species but only 25 percent was needed for all endemic birds (Saetersdal et al. 1993). Comparing complementarity data sets derived using different measures of biodiversity, results are variable as well. In South Africa and Lesotho, complementary networks of endemic bird species capture low numbers of species overall, whereas those based on threatened avian species encapsulate more but are still not impressive (Bonn et al. 2002). By definition, nationally endemic species are more likely to be found in the center of a country, not in neighboring countries, whereas nationally threatened species often have restricted ranges that are likely to be on a nation’s borders if their geographic range extends in from a neighboring country. Areas holding nationally endemic and threatened species are therefore unlikely to be congruent. Furthermore, any associations with species richness will likely depend on environmental factors that may differ according to region. So in the eastern states of the United States, sites selected to cover as many different

Species Indicators of Biodiversity in Reserve Selection 83

groups of terrestrial and aquatic taxa as possible often captured rather few at-risk species when only the first ten sites were considered, in some cases fewer than sites chosen at random (Lawler et al. 2003). In other parts of the United States, however, sites chosen using habitat specialists, a group likely to be associated with rarity, overlapped with many species, at least in the California coastal scrub but then again not on the Columbia Plateau (Andelman & Fagan 2000). Currently, the possibility that different complementarity measures of biodiversity can substitute for each other remains elusive. A novel attempt to use environmental variation as a surrogate for species diversity has been to configure environmental space as a continuum rather than in discrete categories and let it act as a proxy for species in space. The expected complementarity value of a particular area (i.e., the relative number of additional species that it adds to those already selected) is indicated by how much the addition of that area decreases the sum of the distances of all areas to the nearest already-selected area in multidimensional space. Maximizing the Environmental Diversity (ED) value should maximize the number of species that are protected (Faith & Walker 1996a) and also reflect patterns of β and γ diversity. This system has been heralded as an important step but has not been used extensively in reserve planning.

Persistence Proponents of biodiversity conservation planning all agree that population persistence is critical. Persistence can be promoted locally by selecting areas that are larger than a threshold size, as these are likely to hold larger populations (Rodrigues et al. 2000a). Alternatively, sites can be chosen on the basis of population abundance as measured by number of territories or density (Rodrigues et al. 2000b). Persistence can also be approximated by the length of time over which a species is seen, or how frequently (Rodrigues et al. 2000a), or by choosing sites in the center of species’ geographic ranges, not on their peripheries (Loiselle et al. 2003), or even by conducting population viability analyses (PVAs) for each species at a site (Noss et al. 2002). At a regional level, multiple sites can be chosen in the hope that redundancy can counteract population extinctions. For example, two studies of plant species on the Ingleborough, UK, limestone pavements, 11 years apart, concluded that increasing the number of sites containing populations of species would improve the probability of persistence (see Table

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Table 3-7. Minimum sets of pavements beginning with existing reserves, needed to sample one, two, three, four, and five of the 50 nationally rare and nationally uncommon plant species in 1974, the number of species these pavements still held in 1985, and the extra pavements needed to have been set aside in 1974, with the benefit of hindsight, to ensure the desired of number populations in 1985 (from Margules et al. 1994).

Conservation goal in 1974

At least 1 population At least 2 populations At least 3 populations At least 4 populations At least 5 populations

Number of pavements

Number of sp. meeting conservation goala

Number of extra pavements in 1974 to achieve 1985 goal

Total number of pavements needed in 1974 to achieve conservation goal

18 22 27 35 39

50 39 33 31 26

6 8 9 9 9

24 30 36 44 48

a

This refers to the number of plant species with the number of populations required to meet the conservation goal. Thus, only 39 of the 50 species had 2 or more populations, i.e., 11 species occurred on only one pavement.

3-7). Another possibility is to choose sites that are clumped together in order to facilitate recolonization of empty habitats (Lombard et al. 1997).

Higher Taxa Similarly positive associations between species richness and genera richness are generated through complementarity analyses, as they are by simple siteby-site totals, although species-family relationships are not so promising. For British macrofungi, correlations are as high between complementary sites based on genera as they are based on species (see Figure 3-4), where complementarity between one site and the next smallest is calculated as the sum of genera (or species) found at one site or at the other divided by the combined total found at either or both (Colwell & Coddington 1994). Strong associations between species and genera based on complementarity are also seen between plants, beetles, and moths in Israel (Mandelik et al.

Species Indicators of Biodiversity in Reserve Selection 85

Figure 3-4. Cross-level congruence in the complementarity of pairs of sites in the Sheffield area, UK. (a) Species of macrofungi vs. genera, n = 18, r2 = 0.70, p < 0.001; (b) species vs. families, n = 18, r2 = 0.13, p < 0.1; (c) species vs. orders, n=18, r2 = 0, NS. (Reprinted from Balmford et al. 2000.)

2007). Complementarity based on families approximates complementarity based on species in woody plants in Sri Lankan forests (Balmford et al. 1996b), and spiders in Portugal (Cardoso et al. 2004b), but not in the Israeli taxa (Mandelik et al. 2007). Yet once again associations are not uniformly promising: overlap of genera and species is low for each of

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mammals, birds, plants, butterflies, termites, antlions, scarab beetles, and buprestid beetles in South Africa, with an average of <30 percent congruence and around only 10 percent for species and families (van Jaarsfeld et al. 1998). Preliminary results nonetheless suggest that genera can at least stand in for species in regional prioritization exercises based on complementarity, but with the provisos outlined earlier in the chapter. Finer-scale studies can lead to insights, too. A few studies have found that complementary sites chosen on the basis of subgroups of one taxon, either on a family or a trophic level, can predict complementary sites in a wider sweep of the same taxon (e.g., Pinto et al. 2008) or other taxa (Pawar et al. 2006 to 2007). Species with small geographic ranges, perhaps associated with peculiar feeding habitats or foraging styles, may have large numbers of irreplaceable sites that are not coincident spatially. If these sites are secured, they will also capture diversity of species that are widely distributed or have overlapping geographic ranges.

Protected Area Coverage While many systematic prioritization efforts assume that the region or nation is a blank slate in need of reserves, others try to integrate proposed protected areas with those already in existence. These latter studies fall into two closely related categories: those that draw attention to the shortfall in existing protected-area coverage, and those that show where new reserves should be sited. Studies may use species richness, or rarity, or phylogenetic distinctiveness, as well as hotspots of these measures and also complementarity algorithms to derive ideal reserve networks. In regards to shortfall, there are two issues—efficiency and effectiveness of the systematic conservation plan. Efficiency is a measure of how a reserve system harbors maximum biodiversity in a minimum number of sites or total area (Pressey & Nicholls 1989), and is evaluated relative to the minimum set of sites representing each species in the region at least once based on complementary analyses (see Figure 3.5). Effectiveness, in contrast, compares the number of sites or areas occupied by the existing system with that occupied by the minimum set of sites representing each species once (or some other specified minimum target), again based on complementarity analyses (see Figure 3-5). An existing reserve network can therefore be relatively inefficient but nonetheless effective. Since most reserves were gazetted before the advent of systematic conservation planning, and real-world issues marginalize scientific decision

Species Indicators of Biodiversity in Reserve Selection 87

Figure 3-5. Illustration of the concepts of efficiency and effectiveness. Efficiency is larger when the area or number of sites occupied by a reserve network is smaller. Maximum possible efficiency is the one obtained by the minimum set that attains the total representation target (note that this corresponds to the minimum set that represents each species once only when considering that specific target). Effectiveness is larger when the reserve is closer to attaining the total representation target, that is, when Tgap is smaller. Maximum possible effectiveness is reached by a set of reserves with Tgap = 0. Therefore, whereas efficiency is a measure based on the size of the reserve system (y-axis), effectiveness is a measure based on its performance in terms of achieving a predetermined representation target (x-axis). (Reprinted from Rodrigues et al. 1999.)

making, it is not surprising that reserve networks usually fall short in terms of efficiency (but see Eeley et al. 2001). In some cases, it is actually better to start selection of reserves afresh rather than complementing an existing set if the total reserve area allowed is limited (Pressey 1994; Virolainen et al. 2001)! Rodrigues and colleagues (1999) collated 11 examples of existing reserves falling short of conservation target calculations. For instance, in New Caledonia, an island characterized by enormously high floral endemism, 83 percent of critically endangered, endangered, vulnerable, and conservation-dependent plant species were absent from protected areas, thus calling for a five- to ninefold increase in protected area coverage (Jaffre et al. 1998). The mismatch between ideal positioning of reserves and current reserve networks is driven by reserves having been set up in marginal, higher-elevation or unproductive soil habitats (e.g., in the United States

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[Scott et al. 2001]; or in Mexico [Cantu et al. 2004]), the so-called lands nobody wanted. In the developing world, poor positioning is often the result of historical legacy or contemporary expert-driven decisions—a worrying development. As illustrations of the former, national parks have been fashioned from former big-game hunting areas in parts of Africa (Caro 2003), whereas other reserves are simply sites of topographical significance (e.g., Kilimanjaro National Park, Tanzania). As an illustration of the latter, Important Bird Areas (IBAs) is a program started in 1981 designed to protect birds all over the world. IBAs are crafted using four criteria: the presence of globally threatened species, range-restricted species, biome-restricted species, or significant single- or mixed-species congregations, but IBAs are based on knowledge of individual experts, governments, and NGOs, and are often grafted onto existing protected-area network systems. Under scrutiny, however, IBAs in the Andes are inefficient in representing at-risk species—those species that live in small geographic areas or in low numbers (O’Dea et al. 2006). Some studies have used systematic reserve-site selection algorithms to pinpoint where additional reserves should be located. These have been based on vegetation types (e.g., Fearnside & Ferraz 1995), or on species distributions (e.g., Eeley et al. 2001). For instance, Tognelli and collaborators (2008) divided Chile into 9,190 non-overlapping 100-km2 hexagons and overlaid these with the geographical ranges of 653 terrestrial vertebrate species as well as all 124 existing protected areas of many descriptions. If a species had a geographic range of <1,000 km2 its entire range had to be covered by a protected area; if it was >250,000 km2 only 10 percent had to be covered; interpolations were made for species in between. This allowed the researchers to identify gap species not covered anywhere, partial gap species, and completely covered species. They found 87 gap species were not covered by any protected area, 390 were partially covered, and 176 were entirely covered. Even when additional sites that had been proposed by government experts were added to existing protected areas, gap species amounted to 39, most of which were in northern and central Chile. A disproportionate number of reptiles and amphibians were not subject to protected-area coverage. The authors then used a reserve-selection analysis to select conservation areas that in total would represent target amounts of various species-groups but at the same time forcing their solutions to include existing and proposed reserves; this generated a national map of where new reserves should be sited.

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Marine Reserve Prioritization Point-source pollution, rising sea temperature, and destructive demersal fishing affecting rocky reefs, coral reefs, hard shelf, mangrove, and offshore epipelagic ecosystems (Halpern et al. 2007) have given great impetus to setting up marine protected areas, but the systematic selection and design of these reserves lags behind that of terrestrial protected areas (e.g., McNeill 1994; Roberts 2005; Game et al. 2009). Many criteria have been used in marine protected-area selection—economics, such as the number of fishers dependent on the area; social criteria, including recreational value and aesthetic appeal; scientific considerations, such as current research projects; and feasibility, such as compatibility with existing uses and enforceability (Roberts et al. 2003). In regards to biological criteria, several ideas are on the table, including identifying hotspots of species richness using suitable habitats to supplement poor databases, biogeographical zonation based on differences in community structure, complementarity using species richness, endemism, or rarity—methods that can generate reserves in different locations (e.g., Airame et al. 2003; Beger et al. 2003; Gladstone 2007). As in terrestrial habitats, complementarity is acknowledged as being superior to other prioritization methods, especially if it focuses on core areas of species’ geographic ranges (Turpie et al. 2000; Fox & Beckley 2005). Cross-taxon congruence is limited, however. Even using just one greedy algorithm measure, sites across Indo-Pacific coral reefs chosen on

Table 3-8. Protection levels in percent achieved by different selection methods when selecting the top seven reserve sites at Kimbe Bay, New Britain (20 percent of all 35 sites) (from Beger et al. 2003). Selection method

Fish

Coral

All species

Complementarity (Richness) Complementarity (Rarity) Hotspots (Richness) Hotspots (Rarity) Hotspots (3 eco-habitatsa) Random

76.9 76.9 70.6 76.7 75.8 63.8

84.6 83.4 81.0 81.3 80.9 71.5

78.7 77.5 73.1 77.7 76.1 66.7

a

A spatially distinct group of reefs that exhibit a distinct community structure resulting from different environmental factors, such as proximity to shore.

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the basis of coral complementarity do not predict representative fish species, nor vice versa (Beger et al. 2007); and representative mollusk sites are congruent with representative macroalgae and mollusk sites in southeast Australia but macroalgae are not (Gladstone 2002). Nonetheless, the number of studies that compare marine-reserve selection outputs derived from different taxa is still very limited. Outcomes of complementarity analyses based on different sorts of biodiversity measures differ, too. For example, along the coast of South Africa, endemism is greatest in the center of the coastline but species richness increases from west to east (Awad et al. 2002); and at Kimbe Bay, New Britain, sites with high fish-species richness do not contain many rare species, yet sites containing rare corals are associated with coral richness hotspots (see Table 3-8). An interesting study involving plants, fish, and invertebrates in Jervis Bay, New South Wales, found that the efficacy of surrogates in pinpointing other taxa depended on the level of representation allowed. When 80 percent representation was permitted, cells selected to represent plants, invertebrates, or fish captured similar high proportions of taxa overall, but when only 10 percent was permitted, cells selected to represent plants contained less than 30 percent of other taxa, whereas cells selected to represent fish and invertebrate assemblages captured around 60 percent of taxa (see Figure 3-6). Strength of surrogacy therefore depended in part on the availability of potential reserves. Reserve selection in marine systems is likely to follow different rules from terrestrial systems because of planktonic larval phases, migration, and absence of hard barriers to movement, but implications of these for the utility of biodiversity indicators in marine-reserve design are still poorly understood (Hastings & Botsford 2003).

Environmental Surrogates Given the weak empirical record in using species or taxonomic indicators to identify areas of species richness or hotspots at the regional level, and the fact that species’ distributional survey data are often insufficiently widespread to be used at spatial scales needed for regional conservation planning, along with the onerous task of collecting data on surrogate taxon species distributions over extended areas, there is a strong argument for using environmental information as a proxy for species distributions, either by itself or in conjunction with species data (Ferrier 2002). Environmental variables are often simpler and cheaper to measure than collecting data on re-

Species Indicators of Biodiversity in Reserve Selection 91

Figure 3-6. Percentage of all taxa in grid cells selected to contain 10, 20, 40, 60 and 80 percent of the occurrences of habitat categories or biotic assemblage (plant, fish, or invertebrate). The percentage of grid cells chosen is similar, but not identical, to the chosen level of representation, because each surrogate can use different and variable numbers of grid cells to achieve its target frequency of occurrence. (Reprinted from Ward et al. 1999.)

gional or national biota because they can be mapped remotely (Belbin 1993). They can be divided as follows (Wessels et al. 1999): (i) Climatic attributes include temperature, precipitation, and radiation (e.g., Belbin 1993). (ii) Physio-chemical variables include water chemistry and particle size measured in the field (e.g., Faith & Norris 1989; Hill & Keddy 1992). (iii) Habitat types (obtained from ground-based or sometimes aerial surveys) are areas dominated by particular species such as Scots pine with a particular physical structure, for example, abundant deadwood (e.g., Debinski & Brussard 1994; Simila et al. 2006). (iv) Landform-vegetation classes consist of commonly known habitat types such as coastal vegetation, wetlands, or subalpine vegetation that can be gleaned from satellite imagery and aerial photography (e.g., Breininger et al. 1995; Awimbo et al. 1996). (v) Landscape variables incorporate connectivity and surrounding habitat as well as regeneration and disturbance regimes (e.g., Noss 1987; Maddock & Benn 2000). (vi) Land systems are a group of areas throughout which

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there is a recurring soil, vegetation, and topographic pattern (e.g., Wessels et al. 1999). For example, land systems in western New South Wales are closely associated with many different taxonomic groups and are far simpler to measure (Oliver et al. 2004). (vii) Land classes are geographic subdivisions obtained from maps that can be used at many different scales (e.g., Pressey & Logan 1995); whereas (viii) landscape ecosystems are geographic, volumetric, layered segments of earth whose components include climate landforms, soil, water, and biota (e.g., Lapin & Barnes 1995). (ix) Environmental groups are distinguished by climate, terrain, temperature, precipitation, soils, and forest cover (e.g., Mackey et al. 1989; Bedward et al. 1992). Most studies combine different aspects of these various environmental categories. Advantages of using communities or landscapes or other environmental categories rather than species lists are that such classifications will include ecological processes and unknown species (Noss 1990); environmental knowledge is available across larger areas than taxonomic knowledge; and provided that environmental variables can predict taxonomic distributions, species distribution can be mapped remotely (Ferrier 2002). Environmental-species relationships can be derived using expert opinion (Ferrier 2002), GAP analyses (Scott et al. 1993), or model fitting (Guisan & Zimmerman 2000)—and can then be used to map species distributions in areas where biodiversity has been poorly sampled. It is believed that false absences inherent in inadequate ecological sampling are reduced by using environmental proxies, and false presences inherent in using coarse scale range maps are also reduced (Ferrier 2002). Several evaluations have been made between environmental maps of various sorts and biodiversity assessments but one example may suffice. MacNally and colleagues (2002) examined 80 sites in a box-ironbark system of north-central Victoria, Australia. They divided sites into 14 ecological vegetation classes defined as “one or more floristic communities which exist under a common regime of ecological processes and which are linked to broad landscape features. The similarity of environmental regimes is manifested in comparable life forms, genera, and vegetation structure” (901). They conducted surveys of birds, mammals, reptiles, nocturnal flying invertebrates, terrestrial invertebrates, and tree species, and they took 21 measures of habitat structure. They found significant coherence between vegetation classes and taxonomic or habitat categories in 45 cases, significant incoherence in one case, and random distributions in 47, showing that some aspects of fauna and habitat structure are differentiated across ecological vegetation classes but others are not (see Table 3-9). Patterns of

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Table 3-9. Analyses of coherence within ecological vegetation classes by faunal element, tree species, and habitat structure in Victoria, Australia, based on mean Bray-Curtis similarities (from MacNally et al. 2002).

Ecological Vegetation Class

Sandstone rise broombrush Gravelly sediment mallee Box-ironbark forest Healthy dry forest Healthy woodland Sedge-rich woodland Granitic-hills herb-rich woodland Granitic-hills woodland Metamorphic slopes shrubby woodland Hillcrest herb-rich woodland Alluvial terraces herb-rich woodland Low-rises grassy woodland Creek line grassy woodland Plains grassy woodland

Number of sites

4 6 24 11 3 3 4 2 4 4 5 4 4 2

Ba

M

R

+b x NA + x NA + x x + + x + x x + x NA x + + x x x + + x x x + + + + + NA + + x x + NA

NFI TI

+ + + x x + x x x x x + x x

x x x x x x + x x x + + – x

T HS

x x + + x + + + + + + + + x

+ + + + + + x x + x + x x x

a

B, birds; M, mammals; R, reptiles; NFI, nocturnal flying invertebrates; TI, terrestrial invertebrates; T, tree species; HS, habitat structure. b +, significant coherence; –, significant incoherence; x, random; NA, not applicable.

birds, trees, and mammals showed conformity with each other across sites but these three taxa formed a coherent biota in only 4 out of 14 vegetation classes, covering a total of <20 percent of the box-ironbark forests. Thus ecological vegetation classes could be used as biodiversity management units in relation to birds, mammals, and trees but reptiles and amphibians would have to be left out. Indeed, caution is warranted in almost every study, as not all environmental variables are associated with species diversity. In Moreton Bay, Australia, distance to the ocean explained 18 percent of marine taxa present but ocean currents explained none (Stevens & Connolly 2004). In northern Finland, macroinvertebrate assemblages, species distributions, and taxonomic richness were not associated with particular tributaries or stream types. Rather, many taxa occurred across all stream types; some occurred only sporadically in a given stream type; and few showed strong fidelity for any given stream type (Heino & Mykra 2006). One problem is that widespread species decrease between-group

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heterogeneity, whereas rare species increase noise and decrease withingroup similarity, leading to low correlations between habitats or environments and species distributions. Moreover, if species respond to environmental variation at a spatial scale finer than that of land-class mapping or respond to environmental variables not considered, congruence will be weak. In short, use of environmental surrogates for species distributions carries risks.

Combining Environmental and Taxonomic Surrogates Such worries notwithstanding, if geographic location or environmental and landscape features can act as surrogates for the distribution of taxa in some instances, why can’t we just use environmental surrogates in reserve design? There are at least two reasons: habitats will not necessarily protect very rare species, and second, they cannot ensure persistence of populations. Consequently, there have been a number of attempts to combine environmental and taxonomic approaches in reserve site selection (Pressey 1994, 2004; Tuomisto et al. 1995; Faith et al. 2001) employing vastly different methodologies. One type relies on expert opinion. When trying to develop a wildlife conservation plan in North Carolina, USA, Hess and King (2002) canvassed opinions of 43 people to create a list of species sensitive to threatening processes, identify landscape types and sensitive species associated with them, and create a list of specific threats and species affected by these. Species were given numerical scores weighted by degree of expert knowledge. After several rounds of questions, this generated a final list of landscapes and species for conservation planning. These were extensive undisturbed habitat (characteristic of bobcat and eastern box turtle), riparian and bottomland forest (barred owl and beaver), upland forest (ovenbird and broad-winged hawk), mature forest (pileated woodpecker), pastures and grassy fields (loggerhead shrike), and open and early successional forest (northern bobwhite). Each of these landscapes was believed to encompass other important species. Another method is to use presence or absence of one particular species along with various habitat variables derived from ecological data (e.g., Rothley et al. 2004). A third method imports land-class data derived by intersecting layers of vegetation, climatic zones, geology, and topography into a geographic information system (GIS) and combines it with species distributions (e.g., Lombard et al. 1997, 2003).

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Turning now to the second issue, population persistence, simple PVAs are incorporated into reserve design based on environmental and taxonomic surrogates. In the Greater Yellowstone Ecosystem, Noss and colleagues (2002) used a site-selection algorithm that minimizes the area needed to meet target goals of habitat suitability for grizzly bear, gray wolf, wolverine, lynx, and elk populations and selects planning units that are clustered or adjacent to existing reserves. In addition, however, they used species distribution data based on sightings and radio telemetry, and they performed PVAs on the five mammal species. They also mapped protectedarea locations. The result was a spatially explicit map of source and sink populations of carnivores and their spatial relationships to protected areas. Aside from specifying large areas in need of conservation, the habitat suitability models showed that grizzly bear and wolf locations, in particular, were negatively associated with road and trail density and positively with wilderness areas, providing a management objective for the future (see Carroll et al. 2003). In another exercise, population-viability models were combined with habitat analyses in Florida, USA. First, 54 vertebrate taxa for which maps were available were chosen on the basis of being wide ranging or being associated with rare communities, being endangered, and being ecologically linked to rare species. Next, census population sizes were calculated that would generate effective population sizes of 50, the minimum size approximation to prevent inbreeding depression over the short term. Generally, this was around 200 individuals, but in order to be cautious a conservation goal was set at 10 populations of this size. Distribution maps were then created based on available records and species’ habitat requirements using land-cover data, home-range size, and other variables. These were then overlaid on conservation lands. This allowed researchers to identify where populations of inadequately protected species could be conserved, chosen on the basis of corridors and other criteria. This amounted to 1.95 million hectares, or 13 percent of the land area of the state. This exciting sort of work extends standard GAP analysis by using population viabilities to assess the value of individual habitat patches (Kautz & Cox 2001).

Practical Issues Reserve site selection has principally been an academic exercise aimed at selecting areas for conservation in a modern systematic fashion rather than on an historical ad hoc basis. Yet the task of documenting species diversity over

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wide areas is too extensive, and so shortcuts using indicator taxa are necessary. Unfortunately, cross-taxon congruence at a regional scale of reserve planning (where the goal is to identify areas of biodiversity richness or hotspots) varies greatly depending on the taxa investigated, the biogeographic history of the region concerned, and the spatial scale of analysis. Even though we can now calibrate the power of an indicator group by the extent to which it co-occurs with target species using presence/absence data (Halme et al. 2009), there are no taxonomic or biome-related rules of thumb that can be used to identify appropriate surrogate taxa at this stage (Lindenmayer & Burgman 2005). Pearson (1995) suggested that the most important criterion for a biodiversity indicator was to be well known and taxonomically stable; second, it must be readily surveyed; and third, as this chapter has shown, patterns observed in the indicator taxon must reflect those in other taxa—a key hurdle. Fourth, such taxa should occur over a broad geographic range and across habitat types, so that results are applicable elsewhere. Fifth, the indicator species’ biology and life history must be well known; sixth, indicators must be specialized to a narrow habitat and so must be sensitive to environmental change; and seventh, they must be of economic importance, especially in developing countries. Sadly, we don’t have very much more sophisticated criteria to look to 15 years later. When a network of sites is to be considered, however, surrogate taxa show greater potential and have been used to predict complementary biodiversity. There has been remarkably little practical application of these methods to setting up reserves except in isolated instances in South Africa and Australia (e.g., Lombard et al. 1997; Pressey et al. 2000). Since the number of taxa with solid distributional data is limited and biased toward vertebrates, it may be expedient to employ the “shopping basket” approach of using a small number of well-documented taxa together to choose reserve sites, or to combine environmental and species distributions, given that data on land classes and habitats are increasingly available. Habitats are easily agreed-upon variables that are well known to managers and conservation planners alike.

Summary To prioritize potential reserve sites at a national or regional level, shortcuts are necessary because it is too time-consuming and expensive to draw up distributions of large numbers of species. Some studies of surrogate species

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at these scales have found little cross-taxon congruence of species-diversity distributions; others have found strong correlations, but there is no ecoregional or taxonomic or scale-related consistency to contradictory findings. Other shortcuts to documenting species richness include using the size of one family to predict the number of species in the order; examining genera or families instead of species; and using morphospecies to save identification time. At the regional level, attempts to find cross-taxon congruency between rare species in different taxa, between endemic species across taxa, and threatened species between taxa have met with decidedly mixed success in part because these are relative measures that may not be relevant outside the country. Some studies show that areas of species richness and endemism are congruent, but species richness and rarity, or species richness and threatened species, are sometimes associated, sometimes not. When sets of reserves are under consideration, complementarity algorithms can be used to select a minimum number of sites with all species represented, or to maximize representation of attributes in a fixed area. Generally, there is substantial cross-taxon congruence of areas chosen using complementary species. Congruence is less pronounced in reserves chosen on the basis of complementary rare species, endemic species, or threatened species, however. Reserve-site selection schemes are making attempts to incorporate aspects of population persistence. Choosing complementary reserve networks on the basis of genera and, to a lesser extent, families, seems to work well. Some attempts have been made to show how existing protected-area coverage falls short of ideal reserves based on systematic conservation planning and where new reserves should be sited. Marine site selection falls far behind terrestrial site selection, and greater movement of fauna may involve different rules regarding biodiversity indicators. Environmental surrogates categorized at many different scales of measurement using habitat- and geographic-based data can predict species distributions. When used in concert with selected taxa, these can incorporate aspects of population persistence and include rare species in the absence of detailed data on species distributions. Overall, there has been remarkably little practical application of any of these methods to setting up reserves.

A successful example of a “local umbrella species” is the pygmy owl, breeding sites of which overlap with large numbers of bird, butterfly, and tree species. (Drawing by Sheila Girling.)

Chapter 4

Umbrella Species and Landscape Species

Three Conservation Goals The long-term success of a biological reserve will be judged by whether it can maintain viable populations of its constituent species. Conservationist planners are concerned about the size and shape of a reserve because larger reserves will hold greater populations and some configurations may promote long-term population persistence better than others. Yet it is impossible to assess the sizes and viabilities of many different species’ populations and then relate them to reserve design. Instead, planners sometimes use a single-species shortcut, hoping that the location, size, and shape of the area covered by a viable population of that one “umbrella species” will cover sufficient home ranges of individuals of other species so that these too will have viable populations (Berger 1997; Caro 2003). Thus the area or configuration of areas occupied by the population of one species is used as a shortcut to designate where viable populations of other background species occur. Following Zacharias and Roff (2001), I call these classic umbrella species. Umbrella species are also used in a less demanding way. Here, the area covered by the population of one species is used as a way of delineating the location, size, and shape of a reserve that will contain as many other species as possible. In this exercise, the presence of one particular species is used to predict the presence of many other species irrespective of their population 99

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sizes. I call these local umbrella species to distinguish them from species indicators of biodiversity that are used at a regional or national level. For managers, however, an umbrella species may provide a convenient shortcut for managing a reserve or ecosystem such that if the population of one species can be kept viable through safeguards and judicious interventions then it is hoped that populations of many sympatric species will maintain positive growth rates. I call these management umbrella species. Given that management umbrella species may predict population responses to planned human activity, they bear some resemblance to population responses of certain ecological-disturbance indicator species to unplanned anthropogenic disturbance (see chapter 7). Both are used to monitor changing populations of sympatric species, but management umbrella species are additionally employed as targets of management action (Zacharias & Roff 2001; Roberge & Angelstam 2004). Management umbrella species resemble certain sorts of management indicator species (see chapter 8), one purpose of which is to maintain biodiversity within managed forests. A management indicator species is chosen by authorities charged with managing forests in the United States using one of five selection criteria, one of which is that the chosen species indicates changes in other species’ population sizes. The terms are more or less synonymous as regards to safeguarding sympatric species’ populations, although management indicator species have other uses, too. As umbrella species have been employed for different tasks in conservation, it has been tricky to agree about their utility as a conservation shortcut (Roberge & Angelstam 2004). Umbrella species have been characterized by specific habitat requirements as “species dependent on successional, rare, or unpredictable habitats or resources” (Wilcox 1984, 643); by area requirements as “species with large area requirements, which if given sufficient protected habitat area, will bring many other species under their protection” (Noss 1990, 360); and by the number of species with which they overlap: The umbrella species concept hinges on the assumption that the presence of a certain species in a geographical area indicates that other species will also be present. . . . Conservation of an umbrella species is believed to protect other species, even if relationships between the umbrella and the community type are poorly established. (Zacharias & Roff 2001, 69) At present there is no consensus definition of this multifaceted concept. Roberge and Angelstam (2004) defined umbrella species as “a species

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whose conservation confers protection to a large number of naturally cooccurring species” (Roberge & Angelstam 2004, 77), and they divided these species into those that are used to determine the minimum size and shape (reserve design) of a conservation area and, second, those used to determine the location of sites to be included in reserve networks. These usages reflect different objectives of those conservationists who know where to place a reserve but don’t yet know an appropriate size for it, and those who don’t yet know where to place a reserve but at least agree on the region or habitat in which it should be established. It can quickly be seen that a separate conservation dilemma—not knowing where to place a reserve in a country or region and so requiring distributional information on species richness using indicator species or indicator taxa (see chapter 3), is more of a hypothetical problem, as reserves are rarely established in such an abstract fashion. To be clear, there are at least four differences between umbrella species and species indicators of biodiversity. (i) Studies of umbrella species make explicit attempts to investigate the ramifications of protecting populations of one (or a few species) for the conservation of other species or for the management of other species (at a local level). In contrast, studies of species indicators of biodiversity at a large scale (see chapter 2) or at a regional scale (see chapter 3) utilize distributional data about many species within a taxon to predict geographic distributions of biodiversity. The former umbrella approach is usually more applied in that it involves trapping, marking, and mapping single species; the latter indicator approach is more academic in that it involves scrutinizing range maps. (ii) Umbrella species are either single species, for example, the capercaillie, or a group of hummingbirds, or a small guild, such as saprophyllic beetles, whereas species indicators of biodiversity are usually a whole taxonomic group, such as birds or dragonflies. (iii) Umbrella species are used at a local geographic scale, indicator species at global to regional scales. (iv) Umbrella species may focus on target species’ population sizes, indicator species on target species’ distributions. Nonetheless, the terms umbrella species and species indicators of biodiversity are often incorrectly interchanged at a regional scale. Roberge and Angelstam (2004) did not specifically consider those umbrella species used by managers to maintain a suite of viable target populations but instead added a third category of umbrella species by which they envisaged setting minimum standards for the composition and structure of ecosystems and even ecosystem processes by incorporating ideas of connectivity between reserves based on individual dispersal patterns of umbrella species. Despite some exploration of this topic using, for example, the

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California gnatcatcher, the European nuthatch, Italian carnivores, and the Siberian flying squirrel to plan reserve networks (see, respectively, Fleury et al. 1998; van Langevelde et al. 2000; Bani et al. 2002; Hurme et al. 2008), this concept can be subsumed within the ambit of the classic umbrella concept—for instance, using species’ migratory patterns to delineate reserve boundaries (Pearsall 1957).

Lambeck’s Insight In an influential paper, Lambeck (1997) suggested that it could be more prudent to employ several umbrella species to define spatial and compositional attributes of a landscape and their appropriate management regimes, because no single umbrella species would be the most sensitive to all of these attributes. He suggested identifying one species that requires the most area, another that needs to disperse over the greatest distance, and yet another that is most constrained by a resource bottleneck. These will be different species, and there may be several process-limited species too, each most sensitive to a particular environmental or anthropogenic threat (see Figure 1-5). Species are therefore identified on the basis of threatening processes and can then collectively be used as counsel in identifying reserve location and size and also the species for which management action should be targeted. In short, each threatening process is assigned a “focal species” (see chapter 1) whose “requirements for persistence define the attributes that must be present if [the landscape] is to meet the needs of the remaining biota” (Lambeck 1997, 851). A practical illustration of Lambeck’s idea comes from Watson and colleagues (2001), who carried out this protocol with birds in the northern Australian Capital Territory and New South Wales (see chapter 1). Lambeck’s scheme demands knowledge of the factors limiting species and is therefore labor intensive (Lindenmayer et al. 2001). Perhaps unsurprisingly, use of a single umbrella species or multiple umbrella species yields different outcomes even within the same ecosystem. Take, for instance, the wet heathlands of northern Belgium. There Maes and Van Dyck (2004) chose the Alcon blue butterfly as an umbrella species for selection of sites containing Red Listed species, typical wet heathland species, and areas with biotrope attributes that characterize high-quality wet heathlands (places with sufficient soil humidity, bare ground, scattered trees, moorland pools, particular microtopography [vegetation structure], seepage, and typical mosses, scored on a presence/absence basis and then summed). They chose the Alcon blue because it is an obligate ant-brood

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parasite on the Myrmica ruginodis species and only uses Gentiana pneumonanthe as a larval host plant. The butterfly is threatened, as is its host plant; it has high conservation value; and it is demanding in its habitat requirements (Roberge & Angelstam 2004). Against this they contrasted multispecies umbrellas chosen on the basis of ecosystem attributes: requiring large areas of wet heathland; being sensitive to habitat fragmentation, dessication, and eutrophication; and being dependent on one of the biotrope attributes noted above. From this list, only well-known species, those of intermediate rarity, those sensitive to habitat disturbance, and those that fall into more than one of the ecosystem attributes were chosen. In the end, they arrived at two birds, two butterflies, two plant species, two dragonfly species, and a grasshopper as focal species (sensu Lambeck). Now, contrasting sites where the Alcon blue was present with those where it was absent, and sites with the presence of 5–9 multi-species umbrella species with sites with 0–4 species, yielded quite different results. Sites with the Alcon blue were significantly richer in Red Listed species and typical wet heathland species, but not in overall species diversity, than those without the butterfly. Sites with 5–9 multi-species umbrellas were not significantly richer than those with fewer (0–4) (see Table 4-1). That said, the total number of typical wet heathland species and an index of habitat heterogeneity were significantly positively correlated with the number of species in the multi-species group. While one could argue that these approaches yield different results, one could also plead that single- and multispecies approaches together yield matching information. While umbrella species can certainly be single species or multiple species, perhaps the consensus nowadays is to use several umbrella species simultaneously, thanks to Lambeck’s well-formulated reasoning (Noss et al. 2002; Miller et al. 1999; Chase et al. 2000). I next examine the data on umbrella species taxon by taxon.

Umbrella Species by Taxon Plants Attempts have been made to understand whether protection of vascular plant diversity as a whole might conserve less well-known taxa such as bryophytes, lichens, or fungi, but none have tried to use a single plant population or small collection of plant species as classic umbrella species. Enterprises to date really use plant groups as indicators of biodiversity, except

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Table 4-1. Average number of all species, Red List species, typical wet heathland species, and biotrope attributes in sites (a) with and without the Alcon blue butterfly, and (b) with 5–9 multi-species (high-quality sites) and 0–4 multi-species (low-quality sites) (from Maes & Van Dyck 2005). Present

(a) All species Red-List species Typical wet heathland species Biotrope attributes

170.0 21.6 26.8 4.0

Absent

* **

171.4 17.9 22.4 3.1

Site quality High

(b) All species Red-List species Typical wet heathland species Biotrope attributes

174.2 19.6 21.7 4.4

Low

(*)

158.9 15.7 19.2 3.8

(*) P < 0.1; * P < 0.05; ** P < 0.01

that they are being carried out at a local rather that at a regional scale. To take an illustration, Chiarucci and colleagues (2005) examined 25 forest plots in Tuscany, Italy, and sampled vascular plants, trees, and shrubs (woody plants) to try to locate reserve sites that maximized the number of macrofungal species. They found that neither total plant-species richness nor woody-plant-species richness were strongly associated with fungalspecies richness, probably because fungi rely on decaying-wood habitats. A second, more taxonomically restricted study, this time of lichens and beetles in 15 plots in southern Sweden, showed that the presence of one particular lichen species, Lobaria pulmonaria, was associated with lichens on the Red List and with Red Listed wood beetles dependent on hollow trees (Nilsson et al. 1995). These putative local umbrella species-groups were chosen either on the basis of being easy to identify (vascular plants and trees are well known) or because they are marker for a habitat type (the lichen occurs in very old tree stands in northern Europe). Despite these attractions, data are too few and

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too mixed to allow us to assume that plants will provide umbrellas for populations of other species, either in designing local reserves or in pinpointing locations of taxon-specific species richness. Nevertheless, in some circumstances plants may be a better umbrella taxon than birds. When a simple reserve algorithm was applied to species lists from 18 islands in the Gulf of California, and in 25 canyon fragments in San Diego County, California, plants did a better job of encompassing species richness of mammals, birds, reptiles, and plants than did birds in both archipelagoes (see Table 4-2). Plants were more diverse and less ordered (nested) than birds and as a consequence were able to overlap with more background species (Ryti 1992).

Invertebrates Butterflies have occupied an important place in investigations of the umbrella species concept—usually as local umbrella species—because they are well known and admired. One of the first studies was on the Bay checkerspot butterfly, which occupies a highly fragmented native grassland habitat in central California. Since the butterfly is protected under the U.S. Endangered Species Act (ESA), Launer and Murphy (1994) wanted to

Table 4-2. Effects of using a bird- or plant-reserve network on the number of species in other taxa included in the reserve network in California, USA (from Ryti 1992).

Gulf Islands Birds Mammals Reptiles Plants San Diego Canyons Birds Rodents & lagomorphs All mammals Plants

Number of species in bird-reserve network

% of total species in taxon

Number of species in plant-reserve network

% of total species in taxon

29 18 45 392

100.0 78.3 61.6 73.5

29 23 71 533

100.0 100.0 97.3 100.0

8 6 12 29

100.0 54.6 70.6 44.8

7 11 17 87

87.5 100.0 100.0 100.0

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examine how its protection would benefit other species. They found a strong association between sites occupied by the butterfly and the presence of serpentine-restricted and serpentine-associated plant species, far fewer of which were found on unoccupied sites. Nonetheless, patch area also had a strong effect, with uncommon plant species generally absent from smaller patches that were occupied by more widespread species. Beetles also feature in local umbrella species studies. Saproxylic beetles feed on mold inside rotten wood and are now endangered in Europe because this type of habitat has declined so severely (Tews et al. 2004). Osmoderma eremita is a saproxylic beetle species given high priority by the European Union’s habitat directive. In Sweden, logs with O. eremita contain significantly more beetle species than those without, probably because of their shared habitat requirements. Interestingly, beetle-rich sites are identified more precisely by using O. eremita than by using coarse measures of trees and stands incorporating trunk diameter and stand density, which might be poor markers for rotting microhabitats within tree trunks (Ranius 2002). Whether insect species-groups in general are a priori likely to be good umbrella species is equivocal. Protecting microclimates occupied by insects likely involves selecting just a limited subset of habitat patches and thus omitting large areas used by sympatric species. Conversely, if invertebrates occupy a unique ecosystem such as a cave or isolated wetland, then sympatric species might benefit as well. Nonetheless, one of the two premises underlying umbrella species—a large area required to maintain the viability of a (vertebrate) population—is not met using small-bodied invertebrates.

Mammals Mammal umbrella studies are split into studies of classic umbrella species and local umbrella species. Conducting a study on mammalian umbrella and target species in Namibia, Berger (1997) was one of the first scientists to investigate the possible benefits of protection afforded by an umbrella species. He estimated the area needed to encompass the ranging patterns of 50 black rhinoceroses in the Kaokoveld and then censused how many gemsbok, zebra, kudu, giraffe, and ostrich actually lived there. He concluded that this area was inadequate because it held no more than 250 individuals of any of these species throughout the harsh dry season; moreover, 250 is not a sufficient number to give these populations a high certainty of persistence. In short, this rather unique study (because it was of popula-

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tions rather than species) suggests that this particular mammalian herbivore is not an umbrella species that southern African conservationists can rely on; and in the rest of Africa the black rhinoceros is now too rare to serve this purpose. In East Africa, where reserves were initially established on the basis of hunters’ preferences for big mammal species that consequently became de facto umbrella species, Caro (2001, 2003) asked whether these reserves still do a good job of conserving other mammal species that hunters did not pursue. He argued that non-game species such as giraffe and cheetah still benefited from the establishment of these parks using umbrella species, although this did not hold for all mammalian taxa (see Table 4-3). In particular, small mammals fare well outside heavily protected areas (set up using umbrella species) for unknown reasons (Keesing 1998; Caro 2002b; see also Fontaine et al. 2007, regarding land snails). In North America, large carnivores are touted as important local umbrella species because their area requirements (i.e., the combined home ranges of a viable population of individuals) are large and will therefore include viable populations of many other species and of natural communities. Noss and colleagues (1996) cited Shaffer’s (1992) grizzly bear recovery plan, which covered 34 percent of the state of Idaho, USA. This area captured 10 percent or more of the statewide ranges of 71 percent of mammal species, 67 percent of avian fauna, and 61 percent of amphibians, although

Table 4-3. Summary of findings about population sizes of mammals around Katavi National Park, Tanzania (from Caro 2003). Population size Taxonomic group

Example

Inside

Outside

Umbrella species* Trophy species Large herbivore Large carnivore

Black Rhinoceros Buffalo Lion

Extinct High High

Extinct Low Low

Background species+ Large herbivore Small carnivore Small mammal

Giraffe Serval Striped grass rat

High High Low

Low Low High

* As based on preferences of twentieth-century expatriate hunters. + Not hunted by expatriate hunters during the last century.

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only 27 percent of reptiles. Sixty-seven percent of the vegetation types in Idaho would have 10 percent or more encompassed by the Shaffer plan. A subsequent study involving grizzly bear, fisher, lynx, and wolverine in the Rocky Mountain region found that predicted suitable habitat, developed from matching satellite imagery with trapping and sightings records, was different for lynx and fisher than for wolverine but that wolverine and grizzly were similar, suggesting several umbrella species would be of service (Carroll et al. 2001). Elsewhere, Thorne and coworkers (2006) suggested that a network of mountain lion core areas and linkages on the central coast of California captured 77 percent of serpentine rock associated with rare plants, 88 percent of old-growth redwood, a majority of three types of oak woodland, and 79 percent of steelhead populations. Both studies yield encouraging results. On the face of it, it is difficult to argue that conservation plans covering such large geographic areas could not have the potential of protecting many other species, yet a reserve network structured around eight large and meso-carnivores in the Canadian Rockies had limited potential in protecting other taxa—8 percent of gastropods, 27 percent of rare mammals, and 38 percent of rare plant communities, for example (Carroll et al. 2003). This is a cautionary tale showing that a focus on carnivores may be too restrictive in identifying areas of species richness in relatively depauperate temperate regions (see also Kerr 1997). Despite these concerns, carnivores are regarded as an attractive group for identifying sizes and configurations of reserves because of their relatively large home ranges and high profile (Ray et al. 2005; Sergio et al. 2008); they will likely go on being used as umbrella species—whatever the data to the contrary! Smaller mammals have been proposed as umbrella species, too. In the northern boreal forests of Finland, Siberian flying squirrel presence is associated with greater amounts of deadwood and higher numbers of polypore species and records. Strangely, flying squirrels do not rely on deadwood— their diet is the leaves and catkins of deciduous trees and the buds and green cones of conifers, and they use cavities in living aspen. Instead, mature boreal forests likely harbor both downed wood and cavity trees (Hurme et al. 2008).

Birds Birds have been studied principally as local umbrella species but also as classic umbrella species when target species’ abundances are measured. Birds

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are an attractive umbrella group because they are well known and relatively easy to monitor—amateur bird watchers help collect routine data (e.g., RSPB 2007). Background species have either been other bird species or small collections of various vertebrates or invertebrates, with outcome variables such as species richness of birds, or other easy-to-measure taxa. Woodpeckers, grouse, raptors, and owls may all be umbrella candidates. Starting with woodpeckers, Mikusinski and collaborators (2001) found that the number of forest birds, particularly forest specialists, that live in closed forests and rarely enter open landscapes were positively associated with the number of woodpecker species found in 10-km2 plots in Poland (see Figure 4-1). Examining woodpeckers again in northern Europe but in a different area, Roberge and Angelstam (2006) found that the presence of green, lesser spotted, and middle spotted woodpeckers was associated with an abundance of other bird species in deciduous forests although not in coniferous forests. Extending woodpeckers’ umbrella role to other taxa and again in northern Europe, Martikainen and coworkers (1998) discovered the number of saproxylic beetle species was greater in areas inhabited by white-backed woodpeckers than elsewhere, while, in central Sweden, Roberge and colleagues (2008) noted more forest birds of conservation

Figure 4-1. Relationship between number of woodpecker species and other forest birds in atlas plots in Poland (n = 2,317; standard error bars included). (Reprinted from Mikusinski et al. 2001.)

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concern where they were present. In all these geographic areas, the association between background species and woodpeckers stems from a reliance on similar resources—deadwood and deciduous trees. The second family is Galliformes. In the Swiss Alps, the presence of capercaillie, the world’s largest grouse, is not associated with avian species richness but it is with mountain-bird species richness and with abundance of both groups (see Figure 4-2). In Finland, areas within 300 m and 1 km

Figure 4-2. Species richness and abundance in the three species groups by studyplot categories of capercaillie occurrence. Ubiquists are birds that have no elevational preference and therefore occur from lowlands to upper treeline; mountain birds occur in the subalpine zone approximately 1000 m above sea level. (1) Capercaillie core area; (2) Capercaillie occurrence outside core area; (3) no Capercaillie present. (Reprinted from Suter et al. 2002.)

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of capercaillie lek sites are associated with greater species richness of breeding forest birds and greater abundances of three-toed woodpeckers, pigmy owls, and red-breasted flycatchers (Pakkala et al. 2003). In the Great Basin of Nevada, USA, sage grouse distribution coincides with the distribution of homeotherms of conservation concern, especially sagebrush obligates like the pigmy rabbit or Brewer’s sparrow, although not with reptiles or amphibians (Rowland et al. 2006). Generally, however, sage grouse habitat coincided surprisingly poorly with habitat occupied by species of concern and with habitats under risk of invasion by cheatgrass. A different study examining prairie chickens in Minnesota concluded that choosing areas on the basis of booming sites was not dramatically better than selecting large sites or even random sites (Poiani et al. 2001). Therefore, despite attention to this family, driven perhaps by hunters’ preferences and edibility, evidence for its umbrella properties is mixed. Predatory birds provide some of the strongest evidence for local umbrella species. Specifically, five species of owl (pigmy, Tengmalm’s, longeared, tawny, and scops owl) as well as goshawks were surveyed in the Italian Alps, and bird-, tree-, and butterfly-species diversity was assessed at their 25 breeding sites. These were compared with 25 randomly chosen sites. In addition, two controls were added: 25 breeding sites of randomly chosen species at a lower trophic level, and 25 breeding sites of lowertrophic-level species with specialized ecological requirements. Compared with the three types of controls, locations occupied by each species of avian predator held greater numbers of bird species, more vulnerable avian species, and more tree species (see Figure 4-3) with only one exception. These sites also held higher densities of birds and more butterfly species. Breeding sites of birds from lower trophic levels did not show this effect. GAP analysis confirmed that top-predator breeding sites coincided with a greater proportion of avian species than control sites (Sergio et al. 2005a, 2006). Similarly, eagle owl presence and eagle owl–suitable habitat held greater combined amphibian, reptile, and bird diversity than did other sites (Sergio et al. 2004; but see Ozaki et al. 2006). Roth and Weber (2007) replicated Sergio and colleagues’ study across the border in Switzerland. In addition to examining red kites, black kites, goshawks, sparrowhawks, common buzzards, kestrels, and tawny owls, they also looked at parids. For all the seven predators, bird-species richness was significantly higher in 1km2 cells where they were found, but the same was true for five of the parid species. Compared to controls, coal tit, crested tit, and willow tit presence was associated with significantly higher butterfly-species richness, whereas red kite, black kite, goshawk, blue tit, great tit, and marsh tit presence was associated with significantly lower butterfly-species richness. Four predator

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Figure 4-3. Biodiversity estimates collected at sites occupied by six top predators, at randomly selected sites (spatial control sites), sites occupied by six randomly selected, lower-trophic-level species (taxonomic A control sites), and sites occupied by six lower-trophic-level species with specialized ecological requirements (taxonomic B control sites). Top-predator sites are shown in black and spatial control sites in white. Values show averages ±1 SE in (a) numbers of all avian species, (b) numbers of avian species classified as vulnerable, and (c) numbers of tree species. (Reprinted from Sergio et al. 2006.)

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species were associated with significantly higher plant-species richness, as were all the tit species. These results question both the import of top predators being viewed as local umbrella species as well as explanations about apex predators’ disproportionate influence on ecosystems (see chapter 5). Parallel umbrella arguments have been put forward for the spotted owl in the Pacific Northwest, USA, made infamous by the controversy between environmentalists and loggers. The owl’s breeding territories are thought to house many old-growth-forest-dependent species (Wilcove 1994; Noon & McKelvey 1996): a reserve network in northern California, established using quantitative information about northern spotted owl distribution and needs, as well as that of other species, protected mollusks and salamanders very well (Dunk et al. 2006). Other studies that have promoted birds as umbrella species show a mixed pattern, although admittedly outcome measures vary widely (see Table 4-4). In general, these findings suggest that places inhabited by certain bird species may be sites of particularly high species richness for birds as well as other taxa. Three ecological reasons may underlie these associations (Sergio et al. 2006, 2008). First, predators may structure ecological communities by facilitating resources essential to other species or by initiating trophic cascades (see chapter 5). Second, species that are top predators or that capture particular prey species may require relatively intact lower trophic levels to survive. Third, if species are habitat specialists, they may be sympatric with other species that require the same resources. Avian predators and habitat specialists may additionally have large area requirements that could be associated with high levels of species richness. Interestingly then, food or habitat requirements may be hallmarks of a successful avian umbrella species and indeed umbrella species in general. Occasionally birds are used as management umbrella species—the kaka in New Zealand has been advanced as a species that can guide large-scale mustelid-trapping restoration exercises, for example (Leech et al. 2008).

Choosing an Appropriate Umbrella Species The most determined efforts to assess the characteristics of effective umbrella species come from Fleishman and her colleagues (Fleishman et al. 2000a, 2001, 2005). Beginning with butterflies in canyons in the Great Basin of Nevada but then extending their studies to other sites and to birds, they contrived an “umbrella index” that included three parameters added together. The first, the mean proportion of co-occurring species, was

Lakes in Central Italian alps

Coastal sage scrub in southern California

Black kite

40 species of bird

40 species of birds and small mammals

Fish species

Selected vertebrates, invertebrates, vascular plants, natural communities

Size of patch, not gnatcatcher, predicts butterfly presence. Nuthatch umbrella can incorporate changing agricultural scenarios. Bottomland forest patches ±86 ha that are owl habitat cover 75% of vertebrate but 0% of invertebrate species. Black kite density associated with fish species richness. Few species associated with species richness. Species of concern not associated with species richness.

Birds associated with specialist butterflies but not generalists.

Finding

1. Swengel & Swengel 1999; 2. Rubinoff 2001; 3. van Langevelde et al. 2000; 4. Rubino & Hess 2002; 5. Sergio et al. 2003; 6. Chase et al. 2000.

North Carolina, USA

3 butterfly species

Coastal sage scrub in southern California Southern Netherlands Forest bird species

Prairie specialist, grassland, and generalist butterflies

Background species

Prairie grasslands in northcentral USA

Location

Barred owl

Nuthatch

Henslow’s sparrow Grasshopper sparrow Dickassel California gnatcatcher

Umbrella

Table 4-4. Examples of birds being used as local umbrella species that are not mentioned in the text.

6

5

4

3

2

1

Source

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quantified on a scale of 0 to 1. The second, the occurrence rate (essentially rarity), was the proportion of sampling locations in which the species were present; being present in either a very low or very high proportion of locations received scores close to 0, but being present in exactly half of the locations received a score of 1. The third parameter incorporated life-history characteristics that potentially influence vulnerability to human activities. For butterflies, this included vagility, reproductive rate, and habitat specificity; for birds, type of nest, number of eggs laid per year, sensitivity to nesting near habitat edges, and so on. For each of these vulnerability variables, species were assigned one of three categories of sensitivity, which were then divided by the maximum score for any species in the assemblage, resulting in a relative scale of 0 (low) to 1 (high). Finally, the three parameters were added together to give each species “an umbrella index.” Across 10 data sets, the proportion of species that were identified as an umbrella species—defined as those that had an “umbrella index” above the mean +1 SD—ranged from 7 to 20 percent (see Table 4-5). If all locations holding one or more umbrella species were conserved, 93 to 100 percent of species would be protected, and between 50 and 100 percent of inventory locations would be protected across data sets. These species, derived from their “umbrella index,” would be better than umbrella species chosen at

Table 4-5. The total number of species in brackets, number of umbrella species, and inventory locations in each of the 10 listed data sets, together with the proportions of species and locations protected using the umbrella species (from Fleishman et al. 2001). Percentage protected Data set

California birds, reference sites (40) California birds, validation sites (53) Ohio birds, reference sites (44) Ohio birds, validation sites (60) California butterflies (26) Ohio butterflies (28) Toiyabe butterflies, canyons (69) Toiyabe butterflies, canyon segments (64) Toquima butterflies, canyons (56) Toquima butterflies, canyon segments (56)

Umbrellas

Locations

Species

Locations

5 10 8 7 4 2 12 11 11 10

6 24 6 24 6 6 19 102 10 49

95 100 98 100 100 93 100 100 100 100

67 96 67 100 67 50 95 98 89 90

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random. Thus Fleishman’s measure stresses the importance of umbrella species being neither too rare nor too ubiquitous, being sensitive to human disturbance, and having local ranges that overlap with many species living in the wider area. The “umbrella index” has been successfully used in other contexts (e.g., Betrus et al. 2005). More generally, we can list criteria proposed for umbrella species (see also Seddon & Leech 2008) used in both reserve design and management. In all cases these criteria pertain to single umbrella species or multi-taxa focal species (sensu Lambeck) (see Table 4-6). First, for each sort of umbrella species, its biology must be reasonably well known so we can be sure that its habitat requirements match those of the general area to be conserved, such as old growth forest or seasonal wetlands, and that its breeding sites, foraging patches, and movements fall within the area of interest. It must have a strong probability of persistence to be used for long-term conservation efforts. The species must be easily sampled or observed in order to make it cost-effective as an umbrella species. It must co-occur with target species of conservation interest, such as habitat specialists or threatened species. Umbrella species should be sensitive to human disturbance so that responses to fragmentation, climate change, and pollution will be incorporated into planning and management decisions about background species. Classic umbrella species should have large habitat requirements, either because they have large home ranges, often associated with large body size or being migratory, or because they are habitat specialists and these habitats are patchily distributed. Some argue that species with disproportionate effects on the community would be useful as classic and management umbrella species. A local umbrella species used to select the site of a reserve should have a wide geographic range in order that that it co-occurs with many sympatric species. For this reason it should not be too rare, but neither should it be found everywhere or else it will have little predictive power for particular species groupings. A wide-ranging human commensal will be a poor marker of more sensitive species’ changing population sizes and geographic distributions. Long generation time and longevity may make management umbrella species particularly sensitive, because these species will take longer to recover than other species following management action. While this long list gives the impression that these criteria have been well thought out and tested, currently most of the proposed features rely on logic rather than empirical data. Table 4-6 shows that different sorts of umbrella species have both similar and dissimilar characteristics, making it

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Table 4-6. Features proposed for umbrella speciesa.

Features

Biology well known Long-term persistence Easily monitored Co-occurrence with species of conservation interest Sensitive to human disturbance Require large amounts of habitat Large home range Large body size Migratory Habitat specialist Keystone species Wide geographic range Neither rare nor ubiquitous Long generation time Long longevity

Classic umbrella species

Local umbrella species

Management umbrella species

X X X

X X X

X X X

X X X X X X X X

X X

X X

X X X X X

a

Adapted from Caro & O’Doherty 1999; Andelman & Fagan 2000; Fleishman et al. 2000a; King & Beazley 2005; Seddon & Leech 2008.

improbable that species employed to, say, design reserve boundaries will be useful in monitoring the long-term fate of species within it.

Problems with Umbrella Species There are several methodological concerns when employing umbrella species and umbrella species-groups as conservation tools. If umbrella species are being used to select a reserve site, the choice will depend on the quality of presence/absence data (Poiani et al. 2001; Thomson et al. 2005). If the objective is to determine the size of a reserve needed to protect cooccurring species, good data are required on the ranging patterns of individuals of the umbrella population (Poiani et al. 2001). Wide-ranging behavior that takes individuals outside the reserve may be problematic if umbrella individuals respond poorly to habitat fragmentation or suffer

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human-induced mortality there (Woodroffe & Ginsberg 1998; Caro 2003; Sergio et al. 2005b). Where the reserve will only be politically acceptable if the umbrella population remains viable, it may be necessary to construct a PVA requiring solid demographic data (e.g., Leech et al. 2008). Methodological problems aside, contemporary empirical data provide no clarity about the context in which umbrella species might operate effectively. Site-selection studies are equivocal, revealing some situations where umbrella species encompass significantly greater species richness than sites chosen at random, but there are other situations where performance is no better. One of the most thorough analyses of the efficacy of umbrella species is by Andelman and Fagan (2000), who investigated proposed umbrella species’ traits systematically although their outcome variable, target species presence, was examined at three scales—local, regional, and continental—and so really only the first pertains to umbrella species. Their study examined data sets consisting of incidence records of endangered, threatened, rare, and “of concern” species in three locations: southern California coastal sage scrub; the Columbia Plateau, a five-state region of northwestern United States; and a county-by-county database of the entire United States. Andelman and Fagan (2000) picked constellations of species out of these data sets that they thought possessed the qualities of viable umbrella species. These included large carnivores that have large area requirements, habitat specialists with narrowly defined habitat needs including sand dunes or caves, species with advanced age at first reproduction that might take a long time to recover from environmental change, long-lived species whose abundance might indicate cumulative habitat degradation, and widespread species. They then plotted the percentage of other species that were found in sites occupied by these species in each of the three geographic data sets, focusing, however, on the presence of the target species, not their population sizes. No clear patterns emerged. On one hand, across all three data sets species with an advanced age at first reproduction protected relatively few species; on the other hand, habitat specialists were quite efficient, with more than 60 percent of background species protected. At a local level in southern California, big carnivores and long-lived species did not embrace a very high proportion of other species, whereas widespread species did so (not a surprising result). In general, umbrella species often did only as well as or worse than sites chosen at random. Andelman and Fagan (2000) concluded that surrogate schemes using umbrella species failed to protect a large proportion of co-occurring species. The power of their analyses of site selec-

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tion was that they asked questions of three data sets at different scales and used many different umbrella-selection criteria. Finally, the data to show that viable populations of candidate umbrellas can encompass viable populations of sympatric species are virtually nil at present but, in the long term, umbrella species will only work if the species that they protect have sufficiently large population sizes.

Management Implications Despite these methodological and empirical uncertainties, it seems inevitable that umbrella species will continue to be used because we do not have sufficient time or funding to determine species presence or absence at even a local geographic level. In any case, identification of biodiversity hotspots is not a proven method of garnering political support for establishment of protected areas; the import of ecosystem services or the presence of a well-known species have greater political leverage. Furthermore, where umbrella species have been used informally to establish reserves, as in East Africa (Neumann 1998; Caro 2003) or in central America (Rabinowitz 1986), the reserves seem to work reasonably well and to hold many of the species that were there originally (but see Soule et al. 1979; Newark 1987). At least that is how it appears now after relatively few years since the reserves were established and with no systematic before-and-after comparative data! So there is a mismatch between the systematic scientific findings that generate decidedly equivocal results, and the apparent success achieved thus far by certain umbrella-based reserves although they are not underpinned by any scientific or systematic method (Caro 2003). Ideally, we need to compare reserves that were set up using umbrella species with those that were set up for other reasons, but in practice this is very difficult. In the absence of further information, the link between a species with specialized habitat requirements and other species with similar requirements is notable (although there is a danger that these umbrella species will conserve only a particular suite of habitat specialists, not biodiversity writ large), and a link between species having large area requirements and many sympatric species holds promise, too, as evidenced in inquiries of some raptor and large carnivore umbrella species. If rules of thumb are required, apex predators or habitat specialists are the safest bets in choosing local and classic umbrella species. The efficacy of umbrella species is not yet proven from a scientific point of view and its continued use needs extreme caution.

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Landscape Species Many of the features proposed for umbrella species (see Table 4-6) still have shortcomings with respect to identifying the location, size, and management of protected areas. Even species with large home ranges may live in those habitats with few other species (e.g., caribou); ecologically important keystone species may not have large home ranges (e.g., termites); and species sensitive to human disturbance may not be charismatic (e.g., freshwater mussels). Moreover, single species are often viewed as inadequate for determining the size of a protected area. Therefore, some conservation biologists have made attempts to bring these concepts together when formulating strategies for reserve establishment and management. Their initiatives try to combine important components of choosing reserve location, particularly reserve design and management in cases where it is already known approximately where the reserve needs to be established. One of these combinatorial attempts has been crafted by the Wildlife Conservation Society, which has coined the term “landscape species.” These are defined as biological species that “use large, ecologically diverse areas and often have significant impacts on the structure and function of natural ecosystems. Their requirements in time and space make landscape species particularly susceptible to human alteration and use of natural landscapes” (Sanderson et al. 2002a, 43; see also Living Landscapes Bulletin 2). The first step in using landscape species involves knowing the location of a general site—say, Montane rainforest in Ecuador or Miombo woodland in Angola (Sanderson et al. 2002a). The next step is proposing a list of candidate species subjectively and then ranking them by five different criteria: area requirements, heterogeneity of habitat, vulnerability, ecological functionality, and socioeconomic significance. These criteria are chosen because species with large home ranges or extensive habitat requirements are more likely to encompass viable populations of less wide-ranging species— a direct link to the umbrella species concept. Those species that need specific habitat requirements to complete their life cycle are more likely to be affected by habitat fragmentation and will require patch connectivity to maintain viable populations. Species that are most sensitive to threat will act as early warnings for less-susceptible species. Species that have strong effects on the structure and function of ecosystems may be instrumental in fostering healthy ecosystems. Finally, wildlife that has socioeconomic or cultural significance has a greater chance of being accepted as an emblem of conservation. Given that no single species can hope to score maximally on

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all five criteria, a suite of landscape species are chosen for the objective of choosing and managing a reserve within the prearranged general area. Procedurally, several factors are used to derive a score for each criterion (see Coppolillo et al. 2004). For each species, area requirements are scored according to home-range size based, if possible, on long-term data from that site or similar sites, as well as dispersal distances (if available), the proportion of target landscape occupied by the species, and the extent to which management units within the landscape need to be connected as determined by expert opinion. For habitat heterogeneity, three measures are appropriate: the proportion of habitat types within the landscape that individuals use, the proportion of jurisdictional types overlapped by the population, and the proportion of habitat types on which the status of the species (or population—it is not clearly defined) depends. For vulnerability, sensitive species must be identified to help formulate maximum acceptable levels of threat, exactly as Lambeck (1997) envisaged. By listing the major land uses in the target landscape and scoring their effect on each species, threats are characterized according to severity (S, on a scale of 0–3), urgency (U, 0–3), the area affected (Pa, 0–4), likely recovery time (R, 0–3), and probability of occurrence (Po, 0–1). These threats are then summed to construct a vulnerability index: ∑[(U + R) × S × Pa × Po] For functionality, species are scored according to whether they have a role as predators, seed dispersers, or pollinators, or whether they disturb the environment mechanically (paying homage to the engineering species idea, chapter 5). For each functional category, species are scored as 0 indicating no role, 1 for suspected or weak function, 2 for clear effects, and 3 for strong effects; subscores are then added together. For socioeconomic significance, the final category, five binary scores are used for the species’ potential as a flagship species (see chapter 9), for having a both a positive and negative local cultural value, and for having both a positive and negative economic value when managed sustainably. Each of the five criteria is then normalized to generate a score between 0 and 1, and they are then added together (see Figure 4-4). Using aggregate scores for between 10 and 25 species in the target ecosystem, species are ranked and the top three to five species are used to identify the final reserve boundaries and how the reserve will be managed. The degree to which species complement each other is an additional consideration: species with the least overlap in spatial distribution and those under threat are added to the

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Figure 4-4. A schematic summary of the data constituting the five selection criteria for landscape species, and their combination to form an aggregate score. (Reprinted from Coppolillo et al. 2004.)

list, and so on. An illustration of the procedure is provided by Coppolillo and colleagues, who applied this method to two areas where a reserve was to be designated, the Bolivian Andes and northern Congo, and they identified five species on which the reserve site and management were to be based (see Table 4-7). Nonetheless, decisions can be made to override the order ranking (for example, see vicuna in Table 4-7). It has also been argued that landscape species need not only be an endpoint of conservation action—they can be used in a monitoring role, too. For example, conservation effort can be mobilized to reduce direct and indirect threats to individual landscape species and their habitats, while longterm data can simultaneously be collected on the landscape species’ population size. This allows conservationists to evaluate the effectiveness of a particular conservation action (Living Landscapes Bulletin 3), not only on

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Table 4-7. Aggregate and individual category scores for species in the Madidi area of the northwestern Bolivian Andes (from Coppolillo et al. 2004). Species

White-lipped peccary Jaguar* Spectacled bear* Condor* Suribi catfish* Pseudoplatysoma spp. Anaconda Tapir Giant river otter Spider monkey Macaw spp. Ara spp. Puma Giant river turtle Vicuna*

H

A

V

F

SE

Aggregate

0.78 0.78 0.74 0.88 0.59 0.67 0.78 0.67 0.67 0.35 1.00 0.53 0.32

1.00 1.00 0.60 1.00 1.00 1.00 0.60 0.60 0.40 0.80 0.80 1.00 0.60

0.86 0.86 0.78 0.28 0.90 0.79 0.70 0.87 1.00 0.85 0.32 0.96 0.31

1.00 0.50 0.50 0.25 0.50 0.50 1.00 0.50 0.50 0.50 0.50 0.00 0.25

0.40 0.80 0.80 1.00 0.40 0.40 0.20 0.60 0.60 0.60 0.40 0.40 1.00

4.03 3.94 3.41 3.41 3.35 3.35 3.28 3.24 3.17 3.10 3.02 2.89 2.48

H, heterogeneity; A, area; V, vulnerability; F, functionality; SE, socio-economic criteria. * Indicates inclusion in the suite of landscape species.

the landscape species but, by proxy, on other members of the community as well. It is not particularly clear, however, how reduction in conflict or monitoring of one species can necessarily solve conflicts for sympatric species or help monitor them. The landscape species concept is important not simply because it is a transparent and (one would hope) replicable method for identifying species around which to “structure site-based conservation,” but also because it combines key surrogate concepts in a single comprehensive conservation strategy. Therefore, in theory, if a single umbrella species is inadequate because it focuses on area requirements to the exclusion of threat, then combining the umbrella-species approach with the ecological-disturbance indicator concept (see chapter 7) should help ameliorate this imperfection. If inadequate attention is paid to cultural significance when considering a species’ ecological role, adding consideration of socioeconomic significance should improve matters. The beauty of the landscape species idea is that it is an attempt to bring these important ideas together in a priority-setting exercise, although its data requirements are quite extensive.

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In reality, however, the exercise is hardly ever used. Landscape species are picked on a whim. Consider, for example, the African wild dog, which is sometimes referred to as a landscape species because it has a very large home range even for a carnivore, and it requires a substantial prey base to support a pack, thus rendering it a candidate umbrella species. This species is also attractive to Western donors and so has fund-raising potential. Another, more systematically developed example, is the jaguar. In a prioritysetting exercise, 35 experts from 12 nations attended a “Jaguars in the New Millenium” workshop to determine where jaguars still live, as well as the probability of populations’ survival in different portions of 36 geographic regions that are home to jaguars (Sanderson et al. 2002b). Although quantitative data were used in this workshop, only two of the five landscape species criteria (see Figure 4-4) were addressed. In neither example is the carnivore a landscape species (sensu Sanderson et al. 2002a), because landscape species pertain to smaller areas than whole continents (a species can qualify as a landscape species in one ecosystem but not in another), and they are chosen on the basis of ranking the five criteria, not chosen a priori, and because they are necessarily part of a suite of species meant to represent landscape use, rather than a single species. The danger here is that a flagship species is picked out of a hat and termed a “landscape species” in the hope that its new moniker will promote its visibility.

Summary The principle behind umbrella species is that by protecting a viable population of one species, populations of many others can be protected, too. Umbrella species are used in three senses: to plan the location of a reserve, to configure its size and shape, and to help manage a reserve. Lambeck combined these objectives by suggesting that several species with differing sensitivities to area or dispersal limitations, or to process constraints, could be used collectively as multi-taxa umbrellas. The concept differs from that of a species indicator of biodiversity because it focuses on the consequences of protection for other species or populations, and so it is more applied, operates at a small scale, and is usually a single or small group of species. Attempts have been made to use plants and invertebrates as umbrella species, but studies are too few to reach definite conclusions. Large mammals seem to be more promising umbrella species because their large area requirements may encompass viable populations of many other species. Woodpeckers, grouse, and avian predators have been advanced as umbrella

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species for other species, especially birds, possibly because they require intact lower-trophic levels or large areas to survive. Many criteria have been proposed for what constitutes a good umbrella species, including being neither too rare nor too common, having large habitat requirements, and being sensitive to human disturbance. Yet a formal exploration of three data sets of different geographic extents found no conclusive evidence that these features were important. In contrast to findings from this systematic investigation, some reserves established using umbrella species—though not systematically—still hold viable populations of many background species. Landscape species combine several surrogate concepts as a tool to design reserves. Landscape species are chosen by quantitatively ranking five criteria: area requirements, heterogeneity of habitat, vulnerability, ecological functionality, and socioeconomic significance. These are then combined into a single metric. Landscape species are used in part as representatives of an ecosystem and in part as a way to design a protected area or a network of protected areas. As yet, they have had very little practical application in reserve design.

Sea otters eat sea urchins, a rapacious consumer of kelp. As kelp provides habitat for a great many species, sea otters have a disproportionate, if indirect, effect on the structure of inshore communities and are an archetypal keystone species. (Drawing by Sheila Girling.)

Chapter 5

Keystone, Engineering, and Foundation Species

The Keystone Species Concept Classic Keystone Species Where the functioning of an ecosystem and the integrity of its constituent species depend in part on the presence of just a single species, there might be an opportunity to maintain the whole ecological community by focusing conservation effort on that one species (Simberloff 1998; Carroll et al. 2001). Ecological theory acknowledges strongly interacting species whose presence or absence influences the distribution and abundance of many others (Soule et al. 2005), the most notable of which are keystone species, an idea first put forward by Paine (1966, 1969). Working in the intertidal zone in Mukkaw Bay, Washington State, USA, he found that the seastar, Pisaster ochraceus, had an enormous effect on macroinvertebrate species diversity because seastars held the dominant detritivore mussel, Mytilus californianus, in check. On the upper sections of the shoreline, Paine observed M. californianus, Balanus cariosus, and Mitella polymerus (a goose-necked barnacle), and lower down on the shoreline, he found these species plus B. glandula, one species of anemone, two chiton species, two limpet species, four benthic algae species, a sponge, and a nudibranch (15 species in total). In experimental areas where he had removed Pisaster, he noted reduced species diversity (only eight species) because swathes of Mytilus and 127

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scattered clumps of Mitella crowded out most of the other species. He remarked: The species composition and physical appearance were greatly modified by the activities of a single native species high in the food web. These individual populations are the keystone of the community’s structure, and the integrity of the community and its unaltered persistence through time, that is, stability, are determined by their activities and abundances. (Paine 1969, 92) Subsequent work at other sites along the Washington and Oregon coastlines confirmed this but found that Pisaster prevented Mytilus invading lower intertidal areas only at wave-exposed headlands. In wave-protected coves, predation was more diffuse, with whelks and other predators involved as well as Pisaster; and in wave-protected sites regularly buried by sand, Pisaster had minimal effect on macroinvertebrate diversity (Menge et al. 1994). A second classic example of a keystone predator also comes from marine ecosystems. Sea otters eat sea urchins, Strongylocentrotus sp., and other sessile marine invertebrates; sea urchins, in turn, are voracious predators of kelp species and other macroalgae (Estes & Palmisano 1974). Comparing the shorelines of Aleutian Islands with and without sea otters, as well as areas that had recently been recolonized by sea otters following recovery from exploitation, Estes and Duggins (1995) found that the presence of sea otters was associated with low biomass of sea urchins and substantial kelp forests, whereas communities lacking sea otters were infested with sea urchins and greatly deforested. At similar sites in southeast Alaska to which otters had been translocated, effects were even more marked because sea otters there eliminated all size classes of urchins after only two years, whereas they took only large urchins in the Aleutians. Pisaster and sea otters are forceful illustrations of the potential keystone role of top predators in marine ecosystems, but the first of the two examples is also a cautionary tale because it shows that effects of keystone species are context dependent. During the 1990s, sea otter populations suffered a marked decline in western Alaska. This was due to predation by killer whales, not only witnessed in direct attacks but more generally by declines in those areas accessible to killer whales (Estes et al. 1998). Endothermy and large size drive enormous energetic demands in killer whales, giving them enough appetite to consume 5 to 7 female sea otters per whale per day! The estimated 170 mammal-eating killer whales in western Alaskan waters might require nearly 300,000 otters per annum, sufficient to drive the otter population to

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Figure 5-1. Sequential collapse of marine mammals in the North Pacific Ocean and southern Bering Sea, all shown as proportions of annual maxima. Data from whale landings; harbor seals—counts and modeling; northern fur seals—pup production; Steller’s sea lions—estimated abundance; and sea otters from counts. (Reprinted from Springer et al. 2003.)

quick extinction (Willams et al. 2004). Recent killer whale predation on sea otters may be a consequence of the loss of great whales as prey and a resulting switch to increasingly smaller mammalian prey (see Figure 5-1). With few mammalian prey left in the north Pacific and the southern Bering Sea, killer whales may soon suffer a severe population decline, or be forced to concentrate solely on fish, or move to new areas. In support of the last scenario, three killer whales from Glacier Bay, Alaska, were sighted attacking gray whales in central California (Williams et al. 2004); if they choose to hunt sea otters there, the 2,500 threatened sea otters in California could disappear within four months, with predictable consequences for sea urchins and kelp.

Wider Scope Originally formulated for top predator species, the keystone concept was extended to include keystone herbivores, such as snow geese in the Hudson

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Bay, Canada, that alter landscapes by consuming plants (Kerbes et al. 1990); keystone prey, such as European rabbits, which provide a prey base for raptor diversity and abundance in southern Spain (Delibes-Mateolis et al. 2007); keystone pathogens that change communities, such as rinderpest, which affects African herbivores (Tompkins et al. 2001) or myxomatosis infecting Australian rabbits (Ratcliffe et al. 1952); keystone competitors or “climax species” that suppress community diversity (Richardson & Bond 1991); keystone mutualists, such as the fig trees on which so many frugivore species depend in the neotropics (Vitousek 1990); keystone dispersers of seeds, such as ants and rodents (Brown & Heske 1990); keystone pollinators, such as fruit bats (Cox et al. 1991); keystone decomposers, such as mycorrhizal fungi (Gilbert 1980); and ecosystem engineers that redistribute earth and nutrients (Mills et al. 1993; Bond 1994). This proliferation of uses became so diverse that the only common threads to keystone species were that they stood out from the majority of other species in their effects on community structure or function, and their disappearance could result in the loss of a considerable portion of the community (Mills et al. 1993; Simberloff 1998). Broadening the concept led to a redefinition of keystone species by Power and colleagues (1996) that extended beyond keystone predators. They defined a keystone species as “one whose impact is large, and disproportionately large relative to its abundance” (Power et al. 1996, 609). Yet despite relaxation of the concept, Power and coworkers thought that only a small proportion of species in a given community would be sufficiently interactive to be a keystone species. Since then, many ecologists have drawn up impressive lists of keystone species. Pace and colleagues (1999) collated representative examples of community changes brought about as a result of loss of a single predator or group of predators (see Table 5-1), showing that trophic cascades occur in all major ecosystems although their strength varies, being more pronounced in lentic and marine benthic systems and weaker in marine plankton and terrestrial food webs (Shurin et al. 2002; see also Strong 1992; Polis & Strong 1996; Chase 2000). Cross-ecosystem comparisons, mostly of invertebrate predators, show that effects of predator removal strongly influence herbivores but have rather less effect on plants, and that this attenuation is even more pronounced if plant biomass is measured. Top-down effects are more easily seen if plant damage is measured; is it possible that plants can tolerate increased herbivory without primary production being affected (Schmitz et al. 2000; Halaj & Wise 2001; Shurin et al. 2002)? Another meta-analysis of 60 terrestrial studies showed that in 45 cases there were marked increases in herbivores in the absence of predator

Table 5-1. Examples of studies identifying trophic cascades (from Pace et al. 1999). Ecosystem

Marine Open ocean

Cascade

Coastal

Salmon-zooplanktonphytoplankton Whales-otter-urchins-kelp

Intertidal

Birds-urchins-macroalgae

Freshwater Streams Shallow lake

Pitcher plants

Trout or galaxidinvertebrates-periphyton Fish-zooplanktonphytoplankton

Mosquitoes-protozoabacteria

Terrestrial Meadow

Lizards-grasshopper-plants

Soybean field

Spiders-insects-soybeans

Oldfield

Mantids-insects-plants

Tropical forests

Beetles-ants-insects-Piper plants

Effect

Twofold higher phytoplankton when salmon abundant. Increased predation by whales on otters leads to increased urchin grazing and up to ten times less kelp. Algal cover is 24-fold higher in presence of birds.

Annual primary production differed by sixfold. Dramatic changes in fish populations because of mortality from low oxygen or poor recruitment leads to shifts in zooplankton size structure and corresponding strong effects on phytoplankton. Strong effects of mosquitoes on protozoan community composition, which in turn affect bacterial biomass and species composition.

Grasshopper density directly related to distance from lizard “sites”; plant biomass declined with distance from lizard “sites.” Leaf damage related to manipulations of spider density. Herbivore load reduced twofold with a corresponding increase in plant biomass. Percent of leaf area eaten four times greater in beetle addition plots than controls.

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Table 5-1. Continued Ecosystem

Boreal forests

Soil

Cascade

Effect

Wolves-moose-balsam fir

Population cycles of wolves, moose, and balsam fir growth on Isle Royale. Entopathogenic nematodes- Presence of nematodes leads to caterpillars-bush lupine low densities of caterpillars; high lupine mortality associated with abundant caterpillars.

species, together with a subsequent increase in plant damage, and, to a lesser extent, declines in plant biomass and plant reproduction (Schmitz et al. 2000). This meta-analysis demonstrated that keystone effects are more prevalent in regards to vertebrate than invertebrate predators. Given such findings and knowledge that some vertebrate predators are attractive to the public as candidates for protection (see chapter 9), conservation scientists have argued that keystone predators, and more generally keystone species, should be special conservation targets (Terborgh 1986; Simberloff 1998; Terborgh et al. 1999), or even a top priority for conservation efforts (Cox et al. 1991). Conservation managers have contended that keystone species should be the focus of protection laws (Rohlf 1991), and restoration ecologists have stated that they should be the centerpiece of efforts to reestablish and maintain ecosystem stability (Conway 1989) or to eradicate invasive species (Rayner et al. 2007). Four classes of studies documenting the effects of predator removals or additions on ecosystem structure and function that follow suggest that there is merit in these suggestions (see also Steneck & Sala 2005; Sergio et al. 2008).

Mesopredator Release in Temperate Ecosystems Mammalian carnivores kill and sometimes consume smaller mid-sized carnivores (Palomares & Caro 1999). Therefore, the presence of a large carnivore may keep mid-sized carnivores at low population densities, resulting in an abundance of the latter’s rodent and small-bird prey. If the top predator is removed, mid-sized carnivores may increase in numbers or expand their distribution or change their behavior (called mesopredator release, Soule et al. 1988; Prugh et al. 2009) leading to a decline in their prey. The

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most highly regarded study is of coyotes as the top predator, and native mesopredators—striped skunk, raccoon, gray fox—and exotic mesopredators—domestic cat and opossum—living in chaparral canyons in and around San Diego, California, USA (Crooks & Soule 1999). Coyote presence and abundance were negatively correlated with abundance of mesopredators, principally cats and raccoons (see Table 5-2), but positively correlated with diversity of eight species of birds that specialize on coastal sage scrub and chaparral habitat. The mechanisms behind these correlations were known: mesocarnivores avoided canyons that coyotes had visited recently and sites where coyotes were active, while cat owners reported an average of 15 birds killed per cat per annum, or roughly 525 birds per canyon, far more than most of these fragmented populations could tolerate. A different study in Michigan, USA, found greater song-sparrow nesting success in years with abundant coyotes, in conjunction with an association between higher nest predation and greater mesopredator abundance (Rogers & Caro 1998; see also Sovada et al. 1995). Similar effects of top-down control by coyotes come from a coyote removal experiment in Texas, USA, that resulted in large increases in abundance of bobcat, badger, striped skunk, and gray fox but a decline in

Table 5-2. Trophic interactions in San Diego chapparal fragments (from Crooks & Soule 1999).

Mesopredators or birds

Trophic mesopredator abundance Gray fox abundance Domestic cat abundance Opossum abundance Raccoon abundance Bird species diversity a

Coyote abundance ra

Coyote presence/ absence tb

–0.569*** –0.597*** –0.375*** –0.611*** –0.264*** 0.452**

–2.463*** –1.090*** –3.344*** –3.220*** –1.908*** 5.580***

Bird diversity rc

–0.539*** –0.361*** –0.635*** –0.464*** –0.487***

Pearson correlations between mean coyote abundance (averaged across quarterly sampling sessions) and mean abundance of mesopredator species (averaged across quarterly sampling sessions) or number of scrub-breeding bird species in each of 28 habitat fragments. b t-test of mean abundance of mesopredator species or bird species diversity as a function of coyote presence or absence in each fragment. c Pearson correlations between number of scrub-breeding bird species per fragment and mean mesopredator abundance. * p < 0.1; ** p < 0.05; *** p < 0.01

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Figure 5-2. Mean (and 1 standard deviation) rodent-species richness for comparison and treatment sites (2 sites each) before and after a seasonal coyote-removal program was initiated in Andrews and Martin Counties, Texas, 1989–1991. (Reprinted from Henke & Bryant 1999.)

rodent species richness (see Figure 5-2). Therefore, efforts to foster conservation of biological communities could be facilitated by conserving top predators. To date, most studies have simply documented the consequences of intraguild predation in mammalian carnivores living in temperate regions, rather than trying to understand the circumstances that facilitate it (see Ritchie & Johnson 2009). A study in Sweden, however, shows that mesopredator release of red foxes by wolves and Eurasian lynx depends on system productivity being high. In low-productivity ecosystems in northern regions, release is muted because of few intraguild interactions or impaired ability of mesopredator populations to benefit from reduced harassment (Elmhagen & Rushton 2007; see also Gehrt & Prange 2007; Prange & Gehrt 2007).

Ecological Meltdown in the Neotropics A second collection of studies of conservation consequence comes from predators and seed-dispersing neotropical mammals that have disappeared as a result of anthropogenic activities (Wright et al. 1994; Terborgh et al. 2008). In the Caroni Valley in Bolivar State, eastern Venezuela, the hydroelectric scheme that created Lago Guri flooded 4,300 km2, generating is-

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lands of differing size. Small islands consist of semi-deciduous tropical dry forest and hold predators of invertebrates, seed-predating small rodents, and herbivores such as howler monkeys, common iguanas, and leaf-cutter ants, Atta spp. and Acromyrmex spp., all living at very high densities. Medium-sized islands additionally hold nine-banded armadillos, agoutis, and capuchin monkeys, but few frugivores and still no vertebrate predators; howlers and iguanas live at high densities, but leaf-cutter ants are less abundant as colonies are predated by armadillos. Large landmasses support most vertebrates. Small and large saplings were found at far lower densities on small islands (37 percent less than on large islands, see Figure 5-3) due to

Figure 5-3. Per-capita mortality plus growth out of a sapling class (left bars) versus per-capita recruitment into the class (right bars) of small (top) and large (bottom) saplings at small (S), medium (M), and large (L) landmasses at Lago Guri, Venezuela. Values shown are means of the proportions that are dying and recruiting at each site. Black portions of bars denote mortality; white portions, growth out of the size class into the next-larger size class; gray, recruitment into the size class from the next smaller. Corresponding bars with different letters denote significant differences using Bonferroni corrected t-tests. (Reprinted from Terborgh et al. 2006.)

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devastation by leaf-cutter ants and folivorous howler monkeys (Terborgh et al. 2001, 2006; see also Feeley & Terborgh 2008). A second example from the neotropics concerns the ubiquitous problem of poachers removing large mammals (Redford 1992) and indicates how complex the consequences of such offtake can be. Wright and coworkers (Wright et al. 2000; Wright & Duber 2001) identified eight forest sites near to and including Barro Colorado Island, Panama, that were subject to differential poaching pressure. Large mammal abundance was inversely related to this pressure. In poached areas, fewer seeds were dispersed from Astrocaryum standleyanum and Attalea butyraceae palm trees by large mammals, and consequently seeds accumulated beneath parent trees. The proportion of dispersed seeds that were destroyed by rodents was lower at sites with a high incidence of poaching, although the reasons were not clear. Missed seeds, however, were instead attacked by brucid beetles, Speciomerus giganteus and Pachymerus cardo. In areas subject to little poaching, seeds were dispersed by large mammals but were then eaten by rodents. Thus, large-mammal removal could decrease, increase, or have no effect on tree regeneration, depending on how each mammal interacts with seeds of each tree species and the relative importance of seed dispersal as well as rodent and brucid beetle predation (see also Wright et al. 2007). These complicated direct and indirect effects may help to explain why partial mammal defaunation increased seedling recruitment in other tropical sites, such as Los Tuxlas, Mexico (Dirzo & Miranda 1991), but decreased it in Central Panama (Asquith et al. 1997) and Peru (Terborgh et al. 2008). Nonetheless, effects of removal of certain top predators and herbivores (putative keystone species) can be extraordinary. Terborgh and colleagues (2006) report that on the small islands in Lago Guri “the understory is almost free of foliage, so that a person standing in the interior sees light streaming in from the edge around the entire perimeter. There is almost no leaf litter, and the ground is bright red from the subsoil brought to the surface by leaf-cutter workers” (261). Implementing the almost impossible task of a moratorium on mammal hunting could shortcut species-byspecies restoration efforts in selected areas of the neotropics.

Keystone Introductions With populations of top predators being eroded from many ecosystems, there is a good deal of discussion about large-carnivore reintroductions (Hayward & Somers 2009). Much anticipation (Smith et al. 2003; Ripple

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& Beschta 2004) and work heralded the reintroduction of wolves back into the Greater Yellowstone ecosystem, USA, in 1995 and 1996 when 31 wolves were released after the species had been absent for 70 years. Wolves principally prey on elk there and their return has resulted in a steady decline in elk numbers (Ripple & Beschta 2007), through predation and through a decline in elk-calf recruitment driven by low progesterone levels (Creel et al. 2007). Elk also show behavioral responses to heightened predation (Creel & Winnie 2005), including habitat shifts to protected wooded areas (Creel et al. 2005), which in turn affect the trees on which elk browse. For example, aspen, a favorite food, is growing faster at riparian sites now spurned by elk than it did previously, and by spring 2006 had surpassed browse-height levels of elk (Ripple et al. 2001; Ripple & Beschta 2007). This mirrors historical changes when aspen recruited only during a short window of time, 1870–1920, when wolves were present (Ripple & Larsen 2000). Historical data show an absence of cottonwood species in the 60 years before wolves were reintroduced to Yellowstone (Beschta 2003). Wolves also prey on moose. Like elk, moose eat willow as their primary food during winter, and willow is an important habitat for birds. Where moose densities are limited by human (rather than wolf) hunting pressure, willows are taller than in protected areas, with positive consequences for avian species richness, nesting densities, and other measures of avian biodiversity (Berger et al. 2001). Indeed, two riparian bird species, gray catbirds and MacGillivray’s warblers, are absent from national park sites where moose densities are high. In short, herbivore protection in the absence of large carnivores has resulted in the decline of selected avian taxa, and reintroduction of wolves into North American ecosystems is likely to restore this. Additionally, wolves kill coyotes, especially transients, and since reintroduction have reduced coyote densities by 39 percent (Berger & Gese 2007). This has led to reductions in pronghorn fawn mortality (Berger et al. 2008). Wolves also provide year-round carcasses for other scavengers (Wilmers et al. 2003), including grizzly bears and ravens, and their return dramatically extends the availability of carrion that used to derive principally from food-stressed elk succumbing in deep snow and hence was available only in winter months. Given that winters are shortening due to climate change, wolves may buffer the effects of global warming for scavengers (Wilmers & Getz 2005). Wolves’ prominent role in structuring ecosystems is not limited to Yellowstone but is also found at other North American sites (Hebblewhite et al. 2005; McLaren & Peterson 1994), and a multiplicity of knock-on effects has been seen following other canid

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reintroductions in temperate areas (Croll et al. 2005). There are strong conservation arguments, therefore, for retaining and reintroducing wolves to North American ecosystems.

Removing Invasive Species Where invasive predators have caused destruction of native fauna on oceanic islands, the solution would seem to be predator eradication, yet knowledge of trophic cascades suggests that this might be too simplistic. For example, on Little Barrier Island off the coast of New Zealand, humanintroduced domestic cats and Polynesian rats were respectively eating adults and chicks of the pelagic and endangered Cook’s petrel. Between 1972 and 1979, breeding success of the petrel was 0.32 chicks per burrow; after an effective cat eradication program, breeding success dropped to 0.09 chicks/burrow (1982–2004), but rebounded to 0.59 chicks per burrow in 2005–2007 when rats, too, were eradicated (see Figure 5-4). Strangely,

Figure 5-4. Proportion of Cook’s petrel burrows at high- and low-altitude study sites (± 1 standard error) that fledged chicks during successive predator regimes on Little Barrier Island (cats and rats before 1980, rats 1980–2005, no predators after 2005). Values for two high-altitude sites are represented by squares and circles; values for low-altitude sites by triangles. (Reprinted from Rayner et al. 2007.)

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these effects were seen only at high altitudes; lower down, rats consumed more vegetation and less animal protein (Rayner et al. 2007). As cats have been introduced to so many islands and threaten so many species (Le Corre 2008), their potential role as keystone players needs serious consideration before decisions are made to remove them alone.

Problems with Using Keystone Species as a Conservation Tool Unfortunately, there are considerable difficulties in using keystone species as conservation tools. First, keystone species are difficult to identify in advance; only after their loss are ecosystem effects felt (Bond 1994; Power et al. 1996; but see Tanner et al. 1994). Also, attempts to identify the characteristics of keystone predators or keystone systems (ecosystems with a putative keystone predator) have foundered. In a comparison of 11 keystone and six diffuse systems (those with several predator species), Menge and coworkers (1994) found that selective or differential predation on dominant prey occurs in both types of system. Similarly, having a principal prey type that is competitively dominant over other prey is an attribute common to both systems. Ideas that the predator is large in relation to prey, has high mobility, or has a large foraging range are not limited to keystone predators, either; nor do keystone predators have indeterminate growth or high numerical responses compared to diffuse predator species. That keystone systems are characterized by prey refuges in space or in size, that subdominant competitors have more rapid recovery rates than dominant competitors, or that keystone systems have high species diversity compared to diffuse systems are not identifying characteristics either. The only distinguishing feature that keystone systems exhibit is high rates of prey production, itself dependent on recruitment and growth rates of prey and, of course, availability of resources. Yet a high rate of prey production is a relative measure with no easily identifiable production threshold to serve as a yardstick. Second, there is the problem of ecological context—under one set of conditions a population may play a keystone role but under another it has no effect on the community. The Oregon Pisaster study showed this clearly but there are a great many other examples where habitat structure, waves, weather, or other factors affect the strength of interactions (see Table 5-3). As an illustration, the Malgas and Marcus Islands off the west coast of South Africa are only 4 km apart yet support radically different benthic communities. Malgas Island is dominated by seaweeds and rock lobsters

Table 5-3. Context dependency in keystone effects, with demonstrated or suspected causal factors (from Power et al. 1996). Habitat

Marine New England rocky New England rocky New Jersey soft bottom Chilean rocky intertidal Oregon rocky intertidal California kelp beds California salt marsh Freshwater Wisconsin lake

California rivers

California rivers

Southeastern U.S. ponds Swedish lakes

Species

Context dependency

Nucella lapillus (carnivorous gastropod) Littorina littorea (herbivorous gastropod) Callinectes sapidus (carnivorous crab) Concholepas concholepas (carnivorous gastropod) Pisaster ochraceus (carnivorous starfish)

Keystone in low but not high waveturbulence areas. Keystone on permanently but not periodically submerged substrata. Keystone in low but not high waveturbulence areas. Keystone in high but not low waveturbulence areas, or where sea squirt dominate. Keystone on wave-exposed headlands but not in wave-sheltered areas. Keystone in areas with little but not a lot of drift kelp.

Strongylocentrotus francisanus (herbivorous sea urchin) Cuscuta salina (parasitic plant)

Stizostedion vitreum Micropterus salmoides Esox luscious (piscivorous fishes) Onchorynchus mykiss Hesperoleucas symmetricus (invertebrate-eating fishes) Onchorynchus mykiss Hesperoleucas symmetricus (invertebrate-eating fishes) Notophthalmus viridescens (carnivorous salamander) Perca fluviatilis (carnivorous fish)

Keystone effect strongest where Salicornia host most dominant.

Keystones when phosphorus inputs low to moderate, but not when high. Keystones following scouring winter floods, not during drought years. Keystones over boulder-bedrock, not over gravel.

Keystone in presence of Siren, not in absence of this predator. Keystone in absence of macrophytes, weaker effects with macrophytes.

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Table 5-3. Continued Habitat

Terrestrial South African shrublands

Species

Anoplolepis custodiens (seed-dispersing ant)

African savannah Loxodonta africana (elephant) South Pacific Islands

Pteropus spp. (large frugivorous bats)

West African villages Gulf of California Islands

Lassa virus (hemorrhagic fever) Metepiera arizonica Argiope argentata (spiders) Southwestern Thomomys bottae (rootU.S. meadows eating pocket gopher)

Arizona desert

Dipodomys spp. (seedeating kangaroo rats)

Context dependency

Keystone in sclerophyll shrublands, not not in other shrublands, grasslands, or savannah. Keystone in fire-disturbed or sparse woodlands, not in dense undisturbed woodlands. Keystone on islands where large frugivorous birds exterminated, not where they remain. Potential keystone where humans contact African brown rat. Spiders suppress herbivores unless parasitized by pompilid wasps. Gophers suppress aspen invasion of meadows except on rocky outcrops. Kangaroo rats prevent transition from shrubland to grassland only at transition of the two vegetation types.

that consume mussels and Burnupena whelks and so prevent their establishment; the only exception is B. papyracea, which is protected by a commensal bryozoan, Alconidium nodosum, covering its shell. Marcus Island, on the other hand, has virtually no seaweeds or rock lobsters but has extensive mussel beds and Burnupena species coverage. When rock lobsters were experimentally transferred to Marcus Island they were rapidly attacked by many whelks that attached themselves to the lobsters and consumed them within an hour! Each island seems to be characterized by a different top predator, possibly a keystone predator in the case of Malgas Island, yet local fisherman reported similar densities of rock lobsters on the two islands just 20 years ago (Barkai & McQuaid 1988). This example forcefully shows how peripatetic keystone populations may be and that keystone species are often a misnomer because high interaction strength is a local community phenomenon, not a species characteristic.

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The island study raises a third problem: keystone identification may hinge on the time that it is investigated. When sea otters feed on sea urchins living at low urchin densities, they take all size classes of prey and keep urchin populations in check; but when sea urchin populations are high, otters predate large-size classes and the smaller urchins are left alone to feed on algae (Harrold & Reed 1985). If prey recruitment varies over time, predators’ impact will vary, too. In essence, annual productivities of community members need to be measured over several years with and without the putative keystone species present in order to verify a keystone system (Hurlbert 1997). Moreover, in an ideal case, researchers should wait to see if other keystone species move in and compensate for the missing keystone (Ernest & Brown 2001). Variation in interaction strengths over space and time mean that empirical studies of keystone species are time-consuming and expensive. For instance, a study showing that three kangaroo rat species had dramatic effects on reducing perennial and annual grasses, increasing bird activity, and keeping grassland rodents in check in the Chihuahuan desert, took 12 years to complete (Brown & Heske 1990). More generally, meta-analyses show that there is enormous variation in the effects of removing or adding predators that may depend on behavior of predator and prey, species diversity, habitat complexity, and primary productivity (Shurin et al. 2002) as well as the identity of predators themselves (Bruno & O’Connor 2005). Finally, definitional issues haunt the keystone species concept and its use in conservation. There is an implied dichotomy between keystone or non-keystone, which is absurd because interaction strengths between any species-pair must lie on a continuum, forcing subjective decision-making. Also, if species are recognized as being keystone simply because they are important for something in the ecological community, the process of identifying keystone species becomes subjective, given both the lack of clear definitions and also difficulties in empirical measurement—so the concept loses its utility as an ecological descriptor and conservation tool (Hurlbert 1997).

Reasons for Continuing to Use Keystone Species Some species do have disproportionate effects on community structure and even population persistence in some contexts, and if these can be identified there is reason to center conservation reintroductions and populationbolstering programs on keystone species (Prugh et al. 2009); there is a

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growing acknowledgment that they are ecologically important (Terborgh & Estes 2010). That said, a great deal of research is needed to establish that a keystone system exists, and any conservation protocol involving keystone species will be temporally and spatially relevant only to a particular population at a certain point in time. There may be (untested) public-relations value in arguing that a particular species has added importance because of its keystone properties (see chapter 10), but such an argument must, by necessity, omit many nuances and provisos in efforts to convince politicians and lay people.

Ecosystem Engineers One particular type of keystone species that might be used to set up a reserve or be included in a protected ecosystem through reintroduction or population enhancement is an ecosystem-engineering species (Miller et al. 2000; Crain & Bertness 2006; Buchman et al. 2007). Physical ecosystem engineers are organisms that directly or indirectly control the availability of resources to other organisms by causing physical-state changes in biotic or abiotic materials. Physical ecosystem engineering by organisms is the physical modification, maintenance, or creation of habitats. Ecological effects of engineers on many other species occur in virtually all ecosystems, because the physical-state changes directly create non-food resources such as living space, directly control abiotic resources, and indirectly modulate abiotic forces that, in turn, affect resource use by other organisms. Trophic interactions, i.e., consumption, decomposition, and resource competition, are not engineering. (Jones et al. 1997, 1947) In two seminal papers, Jones and colleagues (1994, 1997) separated ecosystem engineers into two categories: autogenic engineers that change environments via their own physical structures (i.e., their living and dead tissues), and allogenic engineers that change the environment by transforming living or non-living materials from one physical state to another via mechanical or other means. An example of the former is a tree canopy that stabilizes diel and annual temperatures, influences soil nutrients through leaf fall, reduces evapotranspiration, and stabilizes water flow in streams beneath (Ellison et al. 2005); dead branches provide opportunities

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for birds to excavate nesting cavities (Daily et al. 1993). The quintessential example of an allogenic engineer is the American beaver that, by constructing dams, creates wetlands that may last far longer than the lifetime of the individual beaver. Ecosystem or ecological engineers are a subset of keystone species but differ from them in several regards that are still subject to debate (Cuddington 2007; Jones & Gutierrez 2007; Wilson 2007). First, keystone species have disproportionate effects in comparison to their relative abundance, but this is not necessarily true for ecological engineers, although it can be. Second, keystone species are thought to act alone, whereas several ecosystem engineering species may operate in concert to modify their habitat (Oren et al. 2007), as in species composing a coral reef or forest. Third, keystone species are defined by their effects on the community, whereas ecological engineering species by the mechanisms with which they modify their environment. Additionally, Wilson (2007) argues that ecosystem engineers must have a positive impact on their own population growth as a result of modifying the environment.

Mechanisms of Habitat Modification As the number of organisms that modify biotic and abiotic aspects of their habitat is vast, it is helpful to categorize the mechanisms by which they operate in order to appreciate the different scales over which they act and hence their potential importance in the conservation arena. Generally, physical ecosystem engineering activities can have ramifications for either biochemical heterogeneity or heat transfer in both terrestrial and marine ecosystems (see Table 5-4). Availability of materials may be changed through modifying the fluid dynamics of a patch, through the pumping of fluids, or through material transport. Animal hoofprints, termite mounds with impermeable surfaces, and layers of leaf litter are all examples. Heat transfer can occur through modification of the thermal properties of a patch, convective forcing, or direct heat transfer. Examples include plant litter accumulating on the soil surface, which insulates heat conduction from soil to air or vice versa, or animal burrows enhancing heat convection from deep soils. These mechanisms are varied and potentially very widespread given the abundance of some of these engineering species—ants and trees, for instance. Furthermore, the spatial scale over which an individual or group of engineers operates is extremely broad (see Table 5-5), ranging from 0.01 m2 to 15 km2. The temporal scale can range from less than a

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Table 5-4. Examples of physical ecosystem engineering activities that cause biogeochemical heterogeneity through controls on material flows, controls on heat transfer processes, or both (from Gutierrez & Jones 2006). Activity

Organism

Effect

Acting through controls on material flows Dam building Beavers Decreased phosphorus-processing rates due to anaerobic conditions developed after retention of organic matter and increased microbial respiration. Generation of Riparian trees Increased denitrification due to trapping debris dams and retention of organic matter. Burrowing Fiddler crabs Decreased sulphate-reduction rates in the oxidized layer around burrows, presumably because of intense reoxidation of reduced compounds, as indicated by low pools of reduced sulphide compounds and high iron content. Burrowing and Polychaetes and Availability of oxygen in otherwise anoxic burrow thallasinidean sediments and concomitant enhancement irrigation shrimps of nitrification coupled with nitrate reduction at burrow walls via increased supply of oxic water. Excavation and Pocket gophers Increased rates of nitrogen mineralization deposition and nitrification in mounds due to soil of soils as surloosening and increased air exposure. face mounds Litter burial Earthworms Increased microbial biomass and respira(exotic) tion rates due to burial of litter-derived organic carbon in recently invaded mineral soils with no previous earthworm history. Transport of litWood rats Increased phosphorus release at nests due ter to nests to transport of phosphorus-rich litter. Acting through controls on heat-transfer processes Shading Trees Increased microbial respiration and litter decomposition under tree canopies due to increased soil moisture via reduced evaporation. Shading Prairie grasses Decreased soil temperature and microbial respiration due to insulation.

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Table 5-4. Continued Activity

Tussock formation (accumulation of dead materials in sedge crown) Peat production

Organism

Effect

Tundra sedge

Increased carbon and nutrient cycling in underlying soils due to soil insulation from low atmospheric temperatures.

Peat mosses

Increased soil respiration due to soil insulation from low atmospheric temperatures.

Acting through combined controls on material flows and heat-transfer processes Construction of Muskrats Increased microbial biomass and litter delitter mounds composition within mounds due to litter exposure to aerobic conditions and insulation from external temperatures by the mound itself.

minute to hundreds of millions of years in the case of marine mollusks (Thayer 1979). So should a conservationist try to maximize the temporal or spatial scale, or both, when choosing an ecological engineer around which to design a reserve or restore a habitat? In fact, remarkably little thought has been applied to this; instead, a small number of mammal species are cited repeatedly in the context of using ecosystem engineers in conservation.

Examples of Ecosystem Engineers Far the most-cited example of an ecosystem engineering species is the North American beaver. Beavers cut trees and use them to dam small streams, which opens up the forest canopy and expands wetlands, leading to large accumulations of nutrients and detritus, as well as substantial shifts in anaerobic biogeochemistry. In mid-sized streams, accumulation of cut wood often results in small islands and massive storage of sediment and detritus. In large rivers, beavers construct dams and canals that increase the diversity of wetland habitat. Up to three beaver colonies/km2 can be found in some areas, and small streams can be dammed on average 10.6 times/km (Naiman et al. 1986). This can result in an increase in certain sorts of species richness. For example, in the central Adirondacks of New York State, USA, the proportion

Table 5-5. Examples illustrating how ecosystem engineers create structures that can operate on temporal and spatial scales that differ from direct biotic interactions (Hastings et al. 2007).

Species

Earthworm Martiodrilus carimaguensis Termite Macrotermes viator Termite Macrotermes bellicosus American beaver Lepidopteran Pseudotelphusa sp. Eriophyis mite Desert porcupine Humpback whale Soft shell clam Hardparts of corals, bryozoans, mollusks Cordgrass Botta’s pocket gopher Banner-tailed kangaroo rat Tropical trees Balsam fir

Habitat

Structure

Size of engineered patch (m2)

Persistence (years)

Tropical savannah

Soil casts

0.02

0.2–0.9

Savannah

Mounds

152–224

4000

Savannah/ woodland

Mounds

4.9–5.7

20–25

NE forest

Dam/meadow pond complex Leaf ties

Depends

70

0.01

0.11–0.15

Oak woodland

Tropical agro-forestry Negev desert

Leaf ties

0.002

0.33–0.50

Soil pits

0.0257

20

Pelagic

Bubble nets

7–707

3.9 x 10-6

Intertidal Intertidal/ subtidal

Shell beds Shell beds

15,680,000 Various

100 500 x 106

Intertidal

20–5000?

3–20

Grassland

Sediment hummocks Mounds

707–2000

Few years

Desert

Mounds

7–20

Decades

Neotropical forest Conifer forest

Woody habitat

0.00049–0.1

10

Woody habitat

0.03

100

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Table 5-5. Continued

Species

Tropical trees Centracid sunfish Dasyatid stingray

Habitat

Structure

Size of engineered patch (m2)

Persistence (years)

Neotropical forest Temperate lake

Leaf litter

0.31–10.78

0.62

Nests

0.28–1.22

1

Intertidal

Pits

0.07–0.5

0.003–0.01

of obligate and facultative wetland-plant species is considerably greater in meadow and alder habitats modified by beavers (Wright et al. 2002), although species favoring uplands are reduced. In Alberta, Canada, greater numbers western toads, boreal chorus frogs, and wood frogs less than one year old are found in beaver ponds than in unobstructed streams (Stevens et al. 2007). In the precolonial period, when there was an estimated beaver population of 60–400 million individuals from the Canadian tundra to northern Mexico, the landscape and species composition must have been quite different from today, with our contemporary population of 6–12 million animals (Naiman et al. 1986; Wright et al. 2003). Pocket gophers (Geomyidae) are another oft-cited example of ecosystem engineers. They excavate horizontal tunnels between 6 and 20 cm belowground and deposit soil in old tunnels or in mounds on the surface, with the result that a great deal of soil is moved—(60 m3/ha/year) in species that live in dense colonies. Mounds created by related mole rats can cover 25 percent of the ground surface in parts of South Africa. Mounds differ from surrounding vegetation and although they may be nutrient poor (depending on where the soil comes from), nitrogen is inevitably redistributed and placed in the presence of light (Huntly & Inouye 1988; Reichman & Seabloom 2002). These favorable conditions enable forbs or annuals to grow on the mounds (Huntly & Inouye 1988), and plant biomass to increase nearby (Reichman et al. 1993). Excavating rodents also affect sympatric animal species. Around Gunnison’s prairie dog towns in North American grasslands, arthropod community structure differs between active and inactive colonies and the area in between them (see Figure 5-5a). For black-tailed prairie dogs, bird assemblages differ between prairie dog towns and paired sites (Figure 5-5b).

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Northern Flicker Long-billed Curlew

Golden Eagle

Burrowing Owl

Summer Prairie Dog Towns

Red-tailed Hawk Killdeer Swainson’s Hawk

Scaled Quail Ferruginous Hawk

Fall Prairie Dog Towns

Northern Harrier Prairie Falcon Longspurs

Fall Paired Sites

American Pipit Rough-legged Hawk

Brown-headed Cowbird Dickcissel Turkey Vulture ScissorBarn Swallow tailed Flycatcher Upland Sandpiper Lark Bunting Lark Sparrow Meadowlarks Grasshopper Western Kingbird Sparrow Eastern Kingbird Mourning Dove Common Nighthawk

Horned Lark American Kestrel Loggerhead Shrike

Red-winged Blackbird

White-crowned Sparrow

Northern Mockingbird

Summer Paired Northern Bobwhite Sites Ring-necked Pheasant

Cassin’s Sparrow

Bullock’s Oriole

Figure 5-5. Above: Arthropod community structure in the Petrified Forest National Park, Arizona, USA, plotted against landscape structure with a larger fractal dimension (D) indicating greater prairie dog activity. Arthropod community structure based on one-dimensional NMDS ordination scores. (Reprinted from Bangert & Slobodchikoff 2006.) Below: Differences in assemblages of avian communities on 36 prairie dog towns and 36 paired sites in the Oklahoma Panhandle, USA, during summer and autumn of 1997, 1998, and 1999 combined. Ordinations based on correspondence analysis using presence/absence data and including only those species occurring in at least five sites. Species plotted closest to the macrohabitats in which they occurred most often. (Reprinted from Smith & Lomolino 2004.)

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Another example is that of African elephants, which modify their habitat by knocking over trees and then consuming terminal leaves and branches (Laws 1970; Western 1989; Dublin et al. 1990). Elephants may once have driven vegetation dynamics to oscillate between woodlands and grasslands (Caughley 1976; Pellew 1983), but nowadays elephants live mostly in circumscribed reserves and are unable to migrate, leading to severe habitat degradation in some parts of southern Africa (Cumming 1983; Kerley et al. 2001). Aside from their effect on woodlands, elephants’ influence on other aspects of biodiversity have been little studied. Herremans (1995) showed that habitat modification had rather little effect on bird diversity in Botswana; instead it provided suitable habitat for long-distance migrants in the wet season. Overall, a mosaic of elephant-damaged woodlands and intact woodlands increased bird diversity at a regional scale. This reiterates the point that ecosystem engineers likely enhance species richness at a scale that incorporates both engineered and non-engineered habitats. Ecosystem engineers are found in marine ecosystems, too. Notable are sea grasses and kelps that form canopies in nearshore waters, modifying water flows and providing refuges from predation; mussels and clams that build shellfish beds that, in turn, reduce extreme environmental conditions and fertilize sediments, thus facilitating marine plant growth; corals, oysters, vermetid gastropods, subellid worms, and cructose coralline algae that construct large mineralized reefs; numerous taxa that burrow and excavate, including sea cucumbers and groupers; and dugongs, which bulldoze through sediments and vegetation (Coleman & Williams 2002; Levin & Dayton 2009). And consider the multitudinous effects of oyster reefs: The biogenic structure formed by vertically upright oyster aggregations creates habitat for dense assemblages of mollusks other than oysters, polychaetes, crustaceans, and other resident invertebrates. Juvenile fish and mobile crustaceans also recruit to and utilize oyster reefs as refuge and foraging grounds, so that oyster reefs augment the tertiary productivity of estuaries. (Grabowski & Peterson 2007, 282) Finally, there are many surprising examples of ecosystem engineers, such as pine trees, that increase the likelihood of forest fires by accumulating soils and litter (Whelan 2002) and thereby change selective pressures on themselves and other members of the community (Laland et al. 1999); hummock plants in brackish marshes, which increase soil elevation and oxidation thus allowing other organisms to live in flooded anaerobic conditions (Stribling et al. 2007); American alligators, which construct wallows

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that are used as refuges by aquatic and terrestrial invertebrates and vertebrates (Kushlan 1974); and numerous soil invertebrates, such as earthworms, ants, and termites, that affect patchiness of soil nutrients and hydrology across vast areas (Jouquet et al. 2006; Buchman et al. 2007; Lavelle et al. 2007). Many species can be ecological engineers at least in certain circumstances.

Difficulties in Using Ecosystem Engineers in Conservation Despite the importance of ecosystem engineers in structuring biological communities (Wright & Jones 2006), it is not clear how such species might be used effectively in conservation. First, their efficacy depends on their population size and therefore the natural and anthropogenic factors that affect it (Gurney & Lawton 1996). This must be of concern because there is an increasing roster of abiotic factors acting alone or with human exploitation to remove ecological engineers from ecosystems (e.g., oysters and mussels, Mytilus edulis, Lenihan & Peterson 1998; Altieri & Witman 2006; Grabowski & Peterson 2007). Second, engineered alterations to habitat demand an appropriate constellation of abiotic conditions to be in place (Jones & Gutierrez 2007). Take, for example, hairy armadillo and plains viscacha in Argentina, which create burrows that are used by burrowing owls. Whereas viscacha construct burrows and eat open understory vegetation, armadillos do not alter the vegetation and their burrows are only suitable for owls following fires or human disturbance (Machiote et al. 2004). Temperature is an abiotic factor that affects many rate-limited processes, from the rate that rodents can burrow to the speed at which microbial reactions occur (Gutierrez & Jones 2006), so climate change may enhance or diminish the effects of ecological engineers, depending on latitude and altitude. Third, the ecological context determines the extent to which the effects of engineering species are positive for conservation (Jones & Gutierrez 2007). Some have argued that ecosystem engineers have their greatest impact where they can ameliorate harsh environmental conditions (Crain & Bertness 2006; Bruno et al. 2003); under benign or intermediate environmental conditions they can only provide refuges from predators or competitors. Thus the benefits of ecological engineers are peripatetic. Take, for instance, the hypersaline soils of the southern New England salt marshes caused by high evapotranspiration that marsh elder (Ira) find difficult to tolerate. Black needle rush, Juncus gerardi, is salt tolerant, provides ground

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cover and shade, and reduces evaporation. Juncus extends the distribution of marsh elder seaward into more stressful areas, but at higher salt marsh elevations, which are less saline, Juncus decreases marsh elder growth by competing for resources (Bertness & Hacker 1994). Fourth, the relative area of engineered and non-engineered patches in a landscape is important because it is the heterogeneity of habitat that has the potential for conservation; if all the habitat is engineered, as perhaps in a pocket-sized reserve, patch heterogeneity will be lost and the engineered habitat will be subject simply to species-area considerations. Fifth, many ecosystem engineers have dramatic effects on their environment by changing it (elephants and beavers kill trees) or physically degrading it (Atlantic puffin burrows undermine soil structure at their colonies [Furness 1991]). These effects can be negative if ecological engineers fabricate stressful environments in which only a competitive dominant can flourish. Indeed, there is a litany of exotic engineers negatively affecting species diversity through excessive use of resources such as water, light, or oxygen; donating limiting resources such as nitrogen; promoting or suppressing fire; promoting erosion; colonizing intertidal mudflats and stabilizing sediments and sand; and accumulating or redistributing salt and litter (J. Crooks 2002). Examples include cheatgrass, which replaces shrubs and perennial grasses, which in turn increases fire frequency and so inhibits native plant growth in North America; water hyacinth, which clogs waterways and increases anoxia in Lake Victoria; and marine isopods, which undermine the structure of marine substrates (Talley & Crooks 2007). “Invasive species which change the character, condition, form or nature of ecosystems over a substantial area relative to the extent of that ecosystem” are called transformer species (Richardson et al. 2000). Therefore, the beneficial or costly effects of native or invasive species on other species may be in delicate balance (Pintor & Soluk 2006). Species richness can be enhanced if disturbance increases resources through eliminating competitively dominant species but could be diminished if conditions for competitive dominants are favored. Sixth, in regards to habitat restoration, the beneficial effects of ecosystem engineers may take years to be felt. Specifically, ecosystem engineers may fail to recolonize previously engineered landscapes, especially in circumscribed protected areas (Wright et al. 2003); and degraded habitats that were formerly structured by an ecological engineering species may recover only very slowly (see Raffa & Berryman 1987). Given these potential negative effects, the issue of scale becomes critical. Across a large scale of engineering and non-engineering activity, engineers may enhance species diversity and foster different community struc-

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ture, but at a small scale negative effects may predominate (e.g., elephants packed into a small reserve surrounded by inhospitable habitat). Last, there are many ecosystem-engineering species that the public might balk at supporting in the name of conservation—microalgae on sea lice (Arrigo et al. 1991), European periwinkles (Bertness 1984), or bagworm caterpillars (Wessels & Wessels 1991). More often than not, ecosystem engineers are not attractive mammals but are unalluring invertebrates that are difficult to use in public relations.

Advantages of Ecosystem Engineers Ecosystem engineers are probably critical in maintaining community structure and function in all reserves, so steps should be taken to prevent their disappearance. Yet the task of monitoring them is difficult if they are microbes or earthworms—although easier if pocket gophers. Ecosystem engineers may also be used to restore habitats. Illustrations include aquatic macrophytes stabilizing sediments and reducing turbulent mixing in eutrophic lakes, or salt tolerant plants redistributing salinity in hypersaline pans caused by wheat farming in Australia (Byers et al. 2006)—but note these are being used as restoration tools rather than as surrogate species. Additionally, there is some merit in co-opting ecosystem engineers to promote the conservation agenda (Crain & Bertness 2006) because they can be advertised as species having added importance in providing additional habitat to other species (Buse et al. 2008). That this effect is highly context-dependent is often (wisely!) forgotten in efforts to raise conservation funding.

Foundation Species Foundation species, also termed “dominant species,” are a “group of critical species which define much of the structure of a community” (Dayton 1972, 85). They are species that create stable conditions for other species rather than enhancing biodiversity, either through consuming dominant competitors, as do keystone species, or changing the availability of resources to other species, as do ecosystem engineers. (They also differ from core species [Hanski 1982] that are defined as being locally abundant and regularly common). In Dayton’s original formulation, a foundation species has to influence the community out of proportion to its biomass or local cover, and removal of the species would produce a significant change in

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the community structure that remains. Typically, foundation species occupy low trophic levels and are usually a single plant species that dominates a community and is therefore tightly associated with a specific altitude or water depth, humidity, salinity, and soil type (see Table 5-6). Foundation species are thought to promote biodiversity because, despite excluding competitors and thereby reducing species richness, they provide habitat for a great many smaller species, suggesting that their net effect may be positive. As an illustration, intertidal mussels displace seaweed and barnacles from rocky shorelines but provide habitat for many invertebrate species (Lohse 1993; Seed 1996). Similarly, kelp forests exclude competitors but foster great community diversity, as evidenced by resulting barrens following sea urchin predation on kelp (Witman & Dayton 2001). This last example shows that keystone predators (sea otters in this case) can have dramatic effects on communities if they indirectly or directly affect foundation species. While foundation species are often easy to recognize because of their numerical advantage and diversity, usually we only become aware of their importance following their loss (Ellison et al. 2005; Prevey et al. 2010). Consider the eastern hemlock, a tree that covers 1 million hectares in pure stands in the northeast USA, and in mixed stands along riparian strips in southeastern USA. Since the 1980s it has been attacked by the hemlock woolly adelgid, an exotic pest. Within 4 to 15 years of infestation, hemlock trees of all sizes succumb. Following attack, hemlock is replaced by birch, Betula sp., oaks, Quercus sp., and maples, Acer sp. in the north, and by yellow poplar, Liriodendron sp., in the south. These changes are accompanied by loss of ant and bird species, changes in soil ecosystem processes, altered hydrology, and loss of aquatic invertebrate biodiversity in streams (Jenkins et al. 1999; Snyder et al. 2002; Tingley et al. 2002). It was difficult to foresee these ecosystem changes resulting from the loss of eastern hemlock. A large number of ecosystems are characterized using foundation species, including kelp beds, mangrove forests, mussel shoals, and redwood forests, so the public is familiar with these names. No wonder that conservation efforts are often based around them.

Management Issues Reserve designers might want to incorporate keystone, engineering, and foundation species into their plans because there are sound ecological reasons for thinking that these species sometimes have enormous effects on

Table 5-6. Examples of trees as foundation species (from Ellison et al. 2005). Bald cypress Dominates deepwater swamps of southeastern North America. Its presence and density affect the water table and flow of sediment and nutrients. Douglas fir Dominates young and old-growth forests at low and mid-elevations west of the Cascade range, and at higher elevations in the interior of the Pacific Northwest of North America. Live trees and fallen logs provide unique habitat for wildlife including the spotted owl. The evergreen foliage controls light levels, microclimate, and gas exchange from forest floor to canopy. Fraser fir A locally abundant endemic in six high-altitude areas in the southern Appalachians, USA. It has strong associations with animals and plants. Jarrah A unique Australian forest type composed mainly of Eucalyptus marginata. Facilitates woody perennial species in the understory. Port-Orford cedar Endemic to southwestern Oregon and northern California, USA in riparian and upland sites. It recycles calcium to surface soils, provides shade, and stabilizes soil streams and banks. Eastern hemlock Southern Appalachians to southern Canada and west to the Great Lakes region of North America. Deep shade and slowly decomposing litter results in slow rates of nitrogen cycling and habitat for salamanders, fish, and freshwater invertebrates that are intolerant of seasonal drying. Whitebark pine High-elevation forests of Rocky Mountains, USA. Retards snowmelt at high elevations; at low elevations provides shade and cool soil, facilitating establishment of diverse plant communities and associated invertebrates and microbes. American chestnut Co-dominant with oak in southern Appalachians. Affects decomposition, nutrient cycling, and productivity in forests. Mangroves (Rhizophora spp.) Form dense monospecific stands in estuarine and coastal forests throughout the tropics. Mangroves are highly productive and prevent soil erosion and sedimentation onto nearby coral reefs.

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the structure of local communities. It is often difficult to identify keystone species in advance, however, and the effects of all three types of surrogate are context-specific and may wane or increase with time. Some reserves have been established for keystone species, such as the sea otter reserve at Monterey Bay, California, but this was done because the otter was endangered, not because it is a keystone species; and for engineering species, such as wildebeest in Serengeti, but in this case the reserves were created because of its migration. There are few real-world examples of reserves designed to protect ecologically important species per se. Managers of reserves may enhance community diversity by bolstering or reintroducing populations of ecosystem engineers, but the effects of engineering species are best felt when there is a heterogeneous mix of engineered and non-engineered habitat. The use of ecologically important species may carry conservation weight in fund raising. It would be helpful to show that the public gives more money or that politicians are made more aware of conservation issues through keystone, engineering, or foundation species, as this is the belief among NGO marketing departments.

Summary Where the presence of one species disproportionately influences the distribution and abundance of many others, there may be merit in focusing conservation attention on that species. Such keystone species include predators, mutualists, herbivores, and pathogens, but the most frequently cited examples are mammalian predators. Larger predators promoting prey populations by limiting their mesopredators; loss of many species where large, seed-eating mammals have been hunted or removed; the ecological consequences of reintroducing large predators; and control of invasive rats and rabbits by island domestic cats all point to benefits of using keystone species as shortcuts to maintain and restore communities. Unfortunately, it is difficult to identify keystone species in advance since they have no special characteristics, keystone properties vary over space and time, and definitions of keystone species are somewhat subjective. Despite these problems, they may have public-relations value. Ecosystem-engineering species modify the habitats in which they live by means of their own physical structure or by transforming living or nonliving material from one physical state to another. They can therefore alter the habitat of sympatric species. Examples include beavers, which change

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stream hydrology; pocket gophers, which redistribute soil and nutrients; African elephants, which convert woodland to grassland; and marine invertebrates, which build coral reefs. The conservation efficacy of an engineering species depends on its population size, appropriate abiotic and local conditions, the ratio of engineered to non-engineered habitat, whether it transforms habitat in a beneficial or detrimental way, and the time frame and spatial scale over which it operates. Foundation species create stable conditions for other species by stabilizing fundamental ecosystem processes. Many ecosystems are named after foundation species, such as redwood forests or mussel shoals. There has been very little practical application of these ecological ideas to conservation beyond NGOs trying to capitalize on them to raise funds.

Amphibians are in decline worldwide as a result of multiple factors, including contaminants, disease, and land-use changes, acting alone or in concert. Thus amphibians are indicators of environmental conditions as well as species requiring close monitoring in their own right. (Drawing by Sheila Girling.)

Chapter 6

Environmental Indicator Species

Ecosystem Health and Biological Integrity When a marine, freshwater, or terrestrial habitat is degraded or suffers from pollution (i.e., the release of a potentially harmful chemical, physical, or biological agent into the environment as a result of human activity), we may want to ask whether these perturbations affect ecosystem health (Rapport et al. 1998). “An ecosystem is healthy and free from ‘distress syndrome’ if it is stable and sustainable—that is, if it is active and maintains its organization and autonomy over time and is resilient to stress” (Haskell et al. 1992, 9; see also Karr et al. 1986). Ecosystem or environmental health (used here synonymously) denotes many different qualities. It can emphasize homeostasis, where any change beyond a normal range of variation is regarded as problematic; or the absence of disease; or species diversity or ecosystem complexity; or health as being resilience to stressors; or a capacity for the ecosystem to grow vigorously; or a balance between different ecological or biogeochemical components within the ecosystem (Constanza 1992). The accent placed on these objectives or combinations of them affects the type of measurements taken and hence which indicators of ecosystem health are most suitable. For example, a more dynamic ecological measure might incorporate measures of succession, whereas a more static version might measure species’

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numbers and relative abundances (Schaeffer et al. 1988); a health index may quantitatively combine indices of vigor, organization, and resilience. Ecosystem health is an awkward concept because it can incorporate not only ecological integrity (i.e., maintaining structure and function) but human values, too, and thus it has attracted the attention of philosophers, economists, and managers as well as ecologists, who have all attempted to define it (Rapport 1989; Norton 1991; Haskell et al. 1992; Karr 1999). Some definitions make a point of integrating the interests of several disciplines. For example, Boulton’s (1999) overview incorporates ecological and societal issues relating to goods and services (see Figure 6-1). Biological integrity is a closely related concept to ecosystem or environmental health. Karr and Dudley (1981) define it as “the capability of supporting and maintaining a balanced, integrated, adaptive community of organisms having a species composition, diversity, and functional organization comparable to that of natural habitat in the region” (56). Correct physiochemical conditions such as pH; trophic bases such as productivity; habitat such as vegetation height and form; temporal variation in, for instance, weather; and biotic interactions including predation and disease are all components of biological integrity (Angemeier & Karr 1994). (Biological integrity differs from biological diversity because it emphasizes processes

Figure 6-1. Schematic representation of the concept of river health. (Reprinted from Boulton 1999.)

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and refers to natural systems—biological diversity may be increased by alien species). I treat ecosystem health and biological integrity synonymously because they share the concepts of wholeness, resistance, and resilience (Noss 2004). Wholeness refers to a complete assemblage of species’ communities and genotypes, intact food webs, and natural disturbance regimes. Resistance is the ability of an ecosystem to remain unchanged when subjected to stress. Resilience is the rate at which an ecosystem returns to the reference state following disturbance. The three need not be correlated (Pimm 1991). Partly because ecosystem health and biological integrity have been difficult to define precisely, a great many methods have been devised to measure them. Ecotoxicological investigations about ecosystem health examine molecular, cellular, biochemical, and physiological responses (called biomarkers) of species subject to xenobiotic exposure (Handy & Depledge 1999; Jamil 2001; Lam & Gray 2003); other studies examine survival and reproduction of individuals, or population or community changes that are of greater concern to conservation biologists, restoration biologists, and wildlife managers (Spellerberg 1991; Groom et al. 2006; Hunter & Gibbs 2007). Some of these environmental indicators are therefore species (also confusingly called “bioindicator species” by some) and can help resolve questions about the extent to which an ecosystem can withstand environmental insult before it becomes distressed (Adams 2002). I treat environmental indicators and indicators of ecosystem health as synonymous. Indicator species may also be employed in a more specific fashion, however—to indirectly document changes in sympatric species’ identities or numbers or population sizes in response to anthropogenically induced environmental change. These are often termed “ecological indicator species” (McGeoch 1998) but I call them cross-taxon-response indicator species for clarity and cover them in chapter 8. Alternatively, scientists and managers may simply want to describe the consequences of environmental change for a given target species or species-group so that the indicator itself becomes the endpoint (Kelly & Harwell 1990; Rice 2003). I call these ecologicaldisturbance indicators and deal with them in chapter 7. To reiterate, the target of an environmental indicator is therefore the ecosystem; the target of an ecological-response indicator is that species or species-group itself; and the target of a cross-taxon-response indicator is other sympatric species. In this chapter, I first summarize the conceptual basis for measuring ecosystem health using environmental indicator species and sentinel species. Then, by way of examples taken from outstanding categories of contemporary anthropogenic disturbance in aquatic ecosystems, I show how environmental indicator species are used.

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Environmental Indicators Using Hunsaker and Carpenter’s (1990) definition of an indicator (see Table 1-5), Cairns and colleagues (1993) constructed a framework for developing indicators of ecosystem health. They divided indicators into compliance indicators, diagnostic indicators, and early-warning indicators. Compliance indicators are used to judge the attainment and maintenance of ecosystem objectives related to the restoration and continuation of environmental quality, so here data are needed to assess current environmental conditions and document trends over time. Diagnostic indicators identify specific changes that are capable of isolating specific stress effects on compliance indicators. Early-warning indicators or sentinels (see pages 167–169) allow management decisions to be implemented before compliance indicators are affected and involve anticipating hazardous conditions in advance of their occurrence, principally in relation to human health. While these three sorts of indicators are very different, they have some or all of the characteristics shown in Table 6-1. Cairns and colleagues also used an orthogonal analysis to categorize indicators into physiochemical (e.g., measurements of habitat, water level, and temperature, and so on), biological, and socioeconomic (e.g., human health, human use, and perceptions of environmental quality). Biological indicators are important because measuring only chemical or physical attributes of a system fails to capture effects of interactions between toxicants, the effects of water quality on the expression of toxicity, and the adverse biological reactions that can occur below detectable concentrations (Cairns & van der Schalie 1980). They categorized biological indicators as either measurements of individuals that are relevant to diagnostic and early warning indicators, or as measurements of populations, community, or ecosystems that are relevant to compliance indicators. I now examine these in turn. Individual organisms may be assessed for their ability to sequester chemicals in measurable quantities in situ (bioaccumulators or accumulators, sensu Jorgensen et al. 2005) and may be monitored for the effects that these chemicals have on the morphology, individual growth rates, susceptibility to disease, behavior, and other aspects of their phenotype (Little 2002). Such biological indicators or bioindicators of aquatic pollution have a long history in environmental toxicology (Kolkwitz & Marsson 1908; Cairns & Pratt 1993), and single species or species-groups include protozoa; invertebrates comprising rotifera, crustaceans, insects, nematodes, nemertine and annelid worms, and mollusks; vertebrates, principally fish and amphibians; and plants—all of which have been used repeatedly as indica-

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Table 6-1. Attributes of ideal environmental indicators (from Cairns et al. 1993). 1. Biologically relevant, i.e., important in maintaining a balanced community. 2. Socially relevant, i.e., of obvious value to an observer by shareholders, or predictive of a measure that is of obvious value. 3. Sensitive to stressors without an all-or-none response or extreme natural variability. 4. Broadly applicable to many stressors and sites. 5. Diagnostic of the particular stressor causing the problem. 6. Measurable, i.e., capable of being operationally defined and measured using a standard procedure with documented performance and low measurement error. 7. Interpretable, i.e., capable of distinguishing acceptable and unacceptable conditions in a scientifically and legally defensible way. 8. Cost-effective, i.e., inexpensive to measure, providing the maximum amount of information per unit of effort. 9. Integrative, i.e., summarizing information from many unmeasured indicators. 10. Historical data are available to define nominative variability, trends, and possibly acceptable and unacceptable conditions. 11. Anticipatory, i.e., capable of providing an indication of degradation before serious harm has occurred; early warning. 12. Nondestructive of the ecosystem. 13. Potential for continuity of measurement over time. 14. Of an appropriate scale to the management problem being addressed. 15. Not redundant with other measured indicators, i.e., providing unique information. 16. Timely, i.e., providing information quickly enough to initiate effective management action before unacceptable damage has occurred.

tors of stream and river ecosystem health (Jamil 2001). For example, freshwater mollusks, snails, and bivalves are well suited to monitoring contaminants because of their limited mobility and large size. Their soft tissues readily accumulate metals, and their ability to survive stress is related to uptake of metals and aromatic compounds (Hellou & Law 2003). Individual organisms may also be used in laboratory tests (bioassay organisms) where biochemical effects, carcinogenesis, or feminization might be measured. At the population level, indicators are employed to track progress in restoring habitats, to monitor certain species of commercial value, or to follow populations of species known to be especially sensitive to individual stressors (Power 2002), simply by repeatedly sampling their population

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sizes. Conversely, exploiter species, often exotics, are a special sort of indicator species because they flourish in disturbed systems and may compete with native species through predation, competition, or the introduction of diseases and parasites. Exploiters have a wide range of environmental tolerance and their presence or absence can therefore indicate habitat disturbance. For example, five alien fish species in southeastern Queensland rivers in Australia—eastern gambusia, swordtail, platy, European carp, and goldfish—were associated with numerous aspects of disturbance, including the percentage of upstream catchment that was urban, cleared, and planted with crops. These disturbed sites had a wider diel range of dissolved oxygen concentrations, higher conductivity, higher muddy substrates, and greater infestations of aquatic macrophages and filamentous algae, yet lower riparian cover (Kennard et al. 2005). The presence of alien species depends, of course, on type and number of introductions, the nature and diversity of native species, and numerous habitat variables, but their presence signifies both a symptom and often a cause of the decline of river and general ecosystem health (Moyle & Light 1996). At the community level, indicators include documenting the number of species as well as their relative abundance or dominance, biomass, guild structure, size spectra and trophic structure (Attrill 2002). Measures of species richness, a simple and uncontroversial measure, or species evenness (e.g., Shannon-Weiner index) are not related to ecosystem stress in a simple fashion because degraded habitats often differ from undisturbed habitats in holding species that are tolerant of perturbation. Measures of the dominance of different species (e.g., Simpson or Berger-Parker indices) may provide a better clue to disturbance (Marques et al. 2005). For example, dominance curves for biomass and abundance of different taxa can be plotted on a cumulative scale on the y-axis against species’ order of importance on the x-axis, using logarithmic scales. In undisturbed communities, large organisms maintain the biomass curve entirely above the abundance curve; in disturbed communities, the abundance curve lies above the dominants because there are so many small individuals; in moderately disturbed communities, the curves cross each other (Warwick et al. 1987). In marine benthic communities, disturbance results in a shift in the relative proportion of large- to small-bodied species, such as polychaete worms (Warwick & Clarke 1994). Studies at the community level are restricted principally to aquatic habitats (Clements & Newman 2002). Attempts have been made to combine suites of these different types of indicators into indices of biological integrity or ecosystem health because reliance on a single taxonomic group is problematic—it might be insensi-

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tive to particular changes in stream chemistry, for instance (Cairns 1986). Karr (1981, 1991) devised an index of biotic integrity (IBI) that incorporated 12 attributes of a fish community (see Table 6-2), including species richness and composition, trophic composition, and fish abundance. Sites in the stream of interest are evaluated against expected richness from similar

Table 6-2. Metrics used to assess biological integrity of fish communities, based on Index of Biological Integrity (IBI). Ratings 5, 3, and 1 are respectively assigned to each metric according to whether its value approximates, deviates somewhat from, or deviates strongly from the value expected at a comparable site that is relatively undisturbed (from Karr 1991). Ratings of metric* Metrics

Species richness and composition 1. Total number of fish native species* 2. No. and identity of darter sp. (Benthic sp.) 3. No. and identity of sunfish sp. (watercolumn sp.) 4. No. and identity of sucker sp. (long-lived sp.) 5. No. and identity of intolerant sp. 6. Percentage of individuals as green sunfish (tolerant sp.) Trophic composition 7. Percent individuals as omnivores 8. Percent individuals as insectivorous cyprinids (insectivores) 9. Percent individuals as piscivores (top carnivores) Fish abundance and condition 10. Number of individuals in sample 11. Percent individuals as hybrids, exotics, lithophils 12. Percent individuals with disease, tumors, damage, anomalies

5

3

1

Expectations for metrics 1–5 vary with stream size and region <5

5–20

>20

<20 >45

20–45 45–20

>45 <20

>5

5–1

<1

Expectations vary with stream size 0 >0–1 >1 0–2

>2–5

>5

* Original IBI metrics for the Midwest of the United States. Note, total score reflects integrity class of site: 58–60 excellent; 48–52 good; 40–44 fair; 28– 34 poor; 12–22 very poor.

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undisturbed sites; a site is assigned a score of 1 if it deviates strongly, 3 if it deviates moderately, and 5 if it deviates little from the reference site. Measures of trophic composition, fish abundance, and condition are assigned scores, too (as in Table 6-2). These are eventually summed to give a total index of biological integrity (IBI) that approximates the integrity of a site. The advantages of an IBI are that it uses professional judgment, it is quantitative, it retains information for each metric, it uses a relatively undisturbed comparative baseline, and it can monitor changes over time if used repeatedly. However, it cannot precisely identify the cause of a problem, such as changes in flow versus presence of xenobiotics. It has been adapted to watersheds (e.g., Gammon et al. 2003; Simon & Exl 2003) outside the U.S. Midwest, where it was developed, focusing on different fish species (e.g., Steedman 1988; Lydy et al. 2003) and on aquatic invertebrates (e.g., Kerans & Karr 1994; Fore et al. 1996). Indicators of environmental health have been categorized in other ways as well, but these classifications similarly range from specific to general. Jorgensen and others (2005) considered eight levels: level 1 addresses the presence of species and type of species; level 2, ratios of classes of organisms; level 3, concentrations of chemical compounds; level 4, the concentration of a trophic level such as phytoplankton; level 5, process rates such as primary production; level 6, composite indicators that might include biomass, respiration, and mineral cycles; level 7 encompasses holistic indicators such as resilience or carbon turnover; and level 8 includes measures of energy. Indicators of biological integrity imply that there is a natural reference condition to which the polluted or disturbed habitat can be compared (Stoddard et al. 2006). Yet in 2010 finding such a habitat is acknowledged as difficult or impossible (Noble & Dirzo 1997; Sandin et al. 2008). At minimum, atmospheric pollution and climate change will have some effect on freshwater ecosystems (Davies & Jackson 2006), while predator loss and severe overfishing have probably affected all marine ecosystems (Jackson et al. 2001; Lotze & Worm 2009). Reference conditions can be minimally disturbed conditions if available, or historical conditions based on pre-intensive agriculture in the European context, pre-Columbian in North America, and pre-European in Australia. Alternatively, reference points can be a least-disturbed condition defined quantitatively (i.e., <3 percent agriculture), or even best-attainable conditions, or the ecological condition if best-management practices were brought to bear on the least-disturbed sites. These conditions are on a continuum (see Figure 6-2) and so require clarification beforehand. Situations in which ecosystems have been enormously altered but to an unknown degree pose contentious problems for

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Figure 6-2. The differing levels of human disturbance of the landscape in different ecological regions (or in different stream types or stream sizes) create a situation where the least-disturbed sites remaining in each region describe very different definitions of “reference condition.” (The best attainable condition will never be better than the minimally disturbed condition or worse than the least-disturbed condition but may be equivalent to either, depending on the level of human disturbance in the region). Here, stream group B is distinguished from stream group C in that the level of degradation is greater for C, and that a reasonable goal or “reference condition” for C might be a condition that does not presently exist but could be achieved with reasonable management (illustrated as “best attainable”). In contrast, the condition at the least-disturbed sites for group B might be a reasonable goal or reference condition for these streams. (Reprinted from Stoddard et al. 2006.)

planners seeking to set aside reserves in, for example, vastly altered tall-grass prairie or Caribbean marine habitat, or for restoration biologists attempting to reconstitute functioning ecosystems that predate human arrival (Caro 2007).

Sentinel Species For some, the idea of a sentinel species denotes a species whose attributes enable us to detect an environmental contaminant early on, before it becomes harmful. Originally the term sentinel referred to an early warning of the potential risks before disease develops in human populations (Stahl 1997), but has since also taken on the connotation of indicating harm to

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the environment irrespective of whether humans are impacted (Phillips & Segar 1986; O’Brien et al. 1993; van der Schalie et al. 1999). Beeby (2001) refined the sentinel species concept providing a definition that did not include humans at all: “Sentinel species are biological monitors that accumulate a pollutant in their tissues without adverse effects. Primarily used to measure the amount of a pollutant that is biologically available, they may also increase the sensitivity of an analytical procedure or summarize a complex pollution signal” (285). He suggested that effective sentinel species must be insensitive to the pollutant over the normal range, that they must integrate a pollution signal spatially or temporally, and must show a simple correspondence between ambient contaminant levels and their tissues. Their uses, he surmised, were as accumulators that could summarize a complex pollution signal, as integrators over time or space, and as a measure of bioavailability of a pollutant at a source. Several authors have distilled the characteristics most frequently outlined for an “ideal” sentinel species (see Table 6-3). A sentinel species needs to be calibrated, measuring the correspondence between the sentinel and

Table 6-3. Characteristics most frequently specified to facilitate the easy collection and interpretation of sentinel data (from Beeby 2001). Characteristics of “ideal” sentinel species:

Rapid equilibration with source. A linear relationship with source over the range of ambient concentrations. The relationship between the tissue and source concentrations should be the same at all sites studied. Abundant species from which large numbers can be taken without altering the age structure or having some other significant effect on the population. Easily identified and easily aged. Large body of knowledge about the species’ physiology, including the effects of age, size, season, and reproductive activity on the assimilation of the pollutant. Large body (to provide abundant tissue for analysis). Long-lived (allowing integration of the pollutant over long periods). Sedentary or with a limited (and/or well-defined) home range. Uptake is from a well-defined pollutant source—e.g., litter, soil solution.

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either its source or other species; and needs to be validated, that is, it must be demonstrated that the species maintains this correspondence over a range of pollutant concentrations and environmental conditions. These provisions also apply to environmental indicator species and once the issue of harm to human health has been relaxed, there is very little to separate sentinel species from indicators of ecosystem or environmental health. Indeed, sentinel species are used inconsistently among ecotoxicologists and conservation biologists because they are interchanged with biological indicator species, monitoring species, and bioaccumulators, thus weakening the meaning of the term. I therefore regard sentinel species in conservation science as virtually synonymous with environmental indicator species. In regards to human health, it could be argued that sentinel species need to be sufficiently sensitive to environmental change that they are more likely to give false alarms than to miss a dangerous event, given that the costs of these two types of errors are not symmetrical. “Costs” here refer to the consequences of medical or managerial authorities not acting when they should, versus acting unnecessarily (Rice 2003). Using the precautionary principle, many conservation scientists and managers would argue that the conservation costs are asymmetrical, too, and that sentinel or environmental indicator species must be chosen to show great sensitivity.

Examples of the Uses of Environmental Indicator Species Marine Pollution The world’s oceans are very large, so marine environmental pollution— heavy-metal deposition, acid precipitation, sewage-sludge, industrial effluent, crude oil, pulp-mill effluent, thermal effluent, eutrophication, and disturbance (Williams & Mackenzie 2003; see also Weilgart 2007)—may be difficult to detect, because dilution and dispersion are rapid. Instead, marine organisms that accumulate pollutants can be used to assess concentrations that are too dilute to be measured economically. Even in coastal and estuarine ecosystems with nearby human populations and activities, where absolute concentrations of pollutants are higher than out at sea and can be directly detected more easily, marine organisms allow us to measure bioavailability (and hence noxiousness) (Giere 1993). Literally hundreds of indicator species and more complicated biological constructs have been proposed for marine ecosystems.

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An environmental indicator species may be the exploited species itself or represent the status of the ecosystem, either because it plays a central role (see chapter 5) or because it is thought to be typical of a group of species. Examples range from the bottom of the food chain to the top, where toxins accumulate in predators and humans. There are microalgae (<42 µm), meiofauna (>42 µm and <500 µm), and macroalgae (>500 µm) that accumulate heavy metals, persistent organic pollutants, polycyclic aromatic hydrocarbons, and pesticides (Kennedy & Jacoby 1999; Torres et al. 2008). These organisms feed on detritus, diatoms, and algal mats and are also a food source for benthic macrofauna as well as commercially important fish, and they have huge biomass. They play a major role in nutrient and pollutant cycling. They are ubiquitous, easy to measure, respond to perturbation rapidly because of their short generation time, and in some cases are moderately sedentary, thereby reliably indicating site-specific environmental perturbations (Hallegraeff 1993). Parasites are also used to monitor chemical pollutants in marine ecosystems, specifically trichodinids as indicators of eutrophication, gyrodactylids to detect hydrocarbons in marine sediments, and adult acanthocephalans and tapeworms to determine heavy metals. Shellfish are sedentary as adults and accumulate heavy metals, polycyclic aromatic hydrocarbons, poly-chloro-diphenyls and dichlor-diphenylchloroethanes in their soft tissues—and they are consumed by people. Ecosystem health is assessed by measuring mollusk tissue at the molecular, cellular, and physiological levels and the condition of individuals and their responses to standardized stress tests (Hellou & Law 2003). Under the “mussel watch” program, contaminant concentrations have been monitored over space and time on every continent (e.g., O’Connor 1998; Claisse 1989). Fish have been used to monitor global concentrations of dioxins, taking advantage of the worldwide fisheries industry. For instance, dioxins are found in larger concentrations in the muscle of skipjack tuna from the East and South China Seas than elsewhere as a result of industrialization (Ueno et al. 2005). At the top end of the food chain, bird diversity and abundance have been used to document ecosystem recovery over time. For example, following the Exxon Valdez oil spill (a sudden short-term perturbation), species that bred on beaches as well as winter residents suffered disproportionately, whereas solitary fish-eating, diving, migratory, or pelagic species suffered few effects. Two years later there was little signature of the disaster (Wiens et al. 1996).

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Sometimes several taxonomically unrelated species are used together. Along the northern Chilean coast, copper mine tailings in seawater drive lower algal and filter feeder abundance and consequent domination by one species of alga, Enteromorpha compressa, which results in more dipteran flies. The upshot is changes in abundance of consumer species with more reptiles, crustaceans, and intertidal fishes with larger body masses but notably fewer birds (Farina et al. 2003), all of which can be used to track tailings. There are many other biological indices of marine pollution, as discussed earlier (Rice 2000, 2003). These include diversity indices such as the Bray-Curtis similarity index, which compares species’ composition and abundances in two areas, say, polluted and unpolluted, on a scale of 1 to 100. Pollution often causes loss of species intolerant of chemical pollutants, but allows tolerant ones to thrive (Spellerberg 1991). Then there are ordination techniques that classify community structure—these measures include principal components analysis (PCA), non-metric multi-dimensional scaling (MDS), and correspondence analysis. There are also aggregated indicators, such as size-spectra and dominance curves, where species are ranked by their abundances because perturbations will allow some species to dominate, driving the curve upward (Clarke 1990). Further, there are whole-ecosystem models, also called “emergent property metrics,” which include mass-balance models that show whether biomass is redistributed among trophic levels in response to pollution (Dalsgaard et al. 1998). Last, non-biological variables such as water quality and leisure opportunities can be used as indicators of environmental quality from a recreational standpoint (Ward & Jacoby 1992).

Freshwater Pollution Freshwater pollution has occupied scientists for a century because of its effects on human health. The problem of river pollution became acute with the increasing use of insecticides during the 1930s. It eventually led societies to acknowledge the environmental consequences of widespread organochloride insecticides on biological systems and subsequently to ban DDT in the 1970s, at least in the industrialized world (Carson 1962). Insecticides are still used, of course, and nowadays the chief ones are organophosphorus, carbamate, and pyrethroid compounds. Heavy-metal pollution of waterways is a common problem worldwide (e.g., Malm et al. 1995). The traditional means of biomonitoring waterways is to compare

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communities at upstream unpolluted reference sites with polluted sites or recovering sites downstream. Unfortunately, these comparisons may not be valid in that local conditions upstream may change downstream and also that statistical independence may be compromised (Clements & Newman 2002). A great many taxonomic groups have been employed to assess contaminants in freshwater aquatic communities (Simon 2003). Macroinvertebrates, including gastropods, amphipods, insects, and oligochaetes, are commonly used as surrogate species to monitor environmental pollution in freshwater systems (Rosenberg & Resh 1993), often incorporating indices that include the total number of invertebrate taxa, the number of taxa in selected groups, their relative abundances, and the relative abundances of pollution-tolerant taxa (Pinel-Alloul et al. 1996). Fish accumulate heavy metals and organic compounds, so they are sampled from different parts of rivers and streams, especially near point-source pollution, and their physiology is examined in the laboratory. For example, in response to environmental stresses, circulating corticosteroids or catecholamines (primary responses), or changes in blood glucose, lactic acid, or tissue glycogen (secondary responses), or tertiary changes such as lengthweight relationships or organ-to-mass ratios, are measured (Barton et al. 2002). Tissues are also examined: lead concentration in livers of brown trout are correlated with lead concentrations in sediments, for instance (Linde et al. 1998). Pollutants have diverse sublethal immunological and physiological effects on fishes, too, and a battery of standardized tests have been developed as indicators of environmental pollution. These include developmental rates deriving from changes in life-history variables such as age at maturity, fecundity, gonad size, and body size, all indicative of nutrient enrichment (Greeley 2002); developmental anomalies such as fluctuating asymmetry; pathologies such as tissue lesions, alterations in fin morphology, liver color, and the abundance of fat; parasite infestations; carcinomas; and numerous immunological responses involving lymphocyte suppression of T-cell responses (Clarke 1993; Barton et al. 2002; Myers & Fournie 2002; Rice & Arkoosh 2002). Exercise performance and aerobic metabolic rate are routine biomarkers. Critical swimming speed, Ucrit, is sensitive to pollutants such as metals, ammonia, organochlorine fungicides, organophosphate insecticides, bleach, and low pH (Randall & Brauner 1991). Aerobic metabolic rate measured as oxygen consumption is an indicator of stress in fishes and is elevated by organochlorine pesticides, herbicides, and methylmercury (Rice 1990). In a standardized procedure, salmonid species are exposed to a sin-

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gle toxicant, and swimming performance, Ucrit, and maximum oxygen uptake are each measured across two sequential tests, with a brief recovery period in between (Jain et al. 1998). The ratio of measurements between the two tests is used to gauge specific aspects of pollution in the laboratory, but it has also been transferred to the field (McKenzie et al. 2007). In summary, assays of biomarkers of sub-lethal toxic stress should be cheap and reliable, sensitive to pollution exposure, have a well-defined baseline against which contaminant-induced stress can be measured, have recognized and understood confounding variables, an established mechanism, and known longterm influences on the organism, that is, have a known connection between physiological response and life history (Stegeman et al. 2002; van der Oost et al. 2003). Species that live both in freshwater and on land are sometimes employed as surrogates to examine ecosystem health, but studies of wild terrestrial vertebrates often focus on the adverse effects of contaminants on those taxa per se (e.g., Delibes et al. 2009). This is a repeated trend in studies of anthropogenic disturbance in terrestrial ecosystems (see chapter 7). In amphibians, a case in point, pesticide use has been linked to population declines in the western United States (Sparling et al. 2001). At a physiological level, endocrine disruption has been linked to atrizine, a herbicide that is the most common contaminant of drinking, ground, and surface water in the United States. Atrizine induces aromatase, an enzyme that converts testosterone to estradiol in mammals and amphibians, and consequently it feminizes male frogs. Males develop testicular oocytes, the frequency of which is associated with atrizine concentrations in laboratory African clawed toads and wild leopard frogs (Hayes 2004). In addition, atrizine, together with phosphate, both used in corn and sorghum production, increase frog susceptibility to parasitic trematodes by augmenting snailintermediate hosts and suppressing amphibian immunity (Rohr et al. 2008). Trematodes cause limb deformities and liver damage. Species associated with freshwater habitats but not breeding in them become contaminated, too. Fish-eating birds have been a model guild for examining the effects of organochlorines in the Great Lakes region of the United States more than 30 years (see Fox 2001; Weseloh et al. 2002; Newman et al. 2007). Herring gulls, Caspian terns, double-crested cormorants, and bald eagles show low reproductive success, resulting from embryonic mortality and deformities at contaminated sites. In addition, they show physiological changes such as altered enzyme function, thyroid function, and T-cell-mediated immunity, as well as increases in vulnerability to bacterial, viral, and parasitic infections (Luebke et al. 1997). In both

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the amphibian and avian studies, the emphasis is on either reducing or halting the use of contaminants, both to prevent environmental deterioration and to stop declines in the indicator species.

River Modification In many parts of the industrialized world, aquatic vegetation and natural dams made of debris have been removed from rivers and streams in order to increase water flow, which diminishes the channel’s ability to retain particulate organic matter. In contrast, artificial dams reduce flow, and landscape modification in riparian habitat increases sedimentation. These alterations affect diverse channel and floodplain habitats in natural river systems (see Figure 6-3). Upstream in the fast-flowing rhithron, small fish live in well-oxygenated riffles while larger insectivorous species live in pools; both are susceptible to dessication and anoxia caused by channel widening. In the lowland potamon, lenthic species move between floodplain pools and are tolerant of low-oxygenated water (pleisopotamonic species) or no oxygen (paleopotamonic and annual species); the former are in danger of being cut off from the main channel through levee construction. Lotic species are migrants moving up and down tributaries in the potamonic region; their breeding is vulnerable to damming if access to the floodplain is cut off. Eurytopic guilds are either benthic-generalized adaptable species that live in the center of the main channel and are thus sensitive to siltation; or they are riparian species tolerating substantial habitat modification. Whereas estuarine and lagoon species tolerate salinity, the freshwater guild is negatively affected by reduced river flow that allows saline waters to penetrate upstream (Welcomme et al. 2006). Differential sensitivity of these guilds enables managers to choose the species with which to monitor effects of river alteration. Bunn and Arthington (2002) have outlined the diverse biological consequences of changing flow regimes for aquatic plant and animal life (see Table 6-4). Given the plethora of effects that even relatively simple river modification can have on plants and animals (as shown in Table 6-4), there are a number of methods for monitoring the effects of river alteration. At a large continental or global scale, the presence or absence of aquatic species can be monitored over time using, for instance, the IUCN Red List of threatened species (IUCN 2003). Habitat change can also be followed using remote sensing (e.g., Dahl 2000; UNEP/DEWA 2001), but this is more difficult for rivers and lakes where issues of discharge, chemical contamination, and

Figure 6-3. Diagram of different channel and floodplain habitats in the upstream (rhithron), lowland (potamon), and estuarine areas of a river. (Reprinted from Welcomme et al. 2006.)

Table 6-4. Summary of biotic responses to altered flow regimes in relation to flow-induced changes in habitat (from Bunn & Arthington 2002). Increased stability of baseflow and reduction of flow variability Excessive growth of aquatic macrophytes; proliferation of larval blackflies; reduction in fish populations; increased standing crop and reduced diversity of macroinvertebrates; conditions favoring populations of exotic fish species (carp, mosquitofish). Erratic (diurnal) patterns in flow below hydroelectric dams Reduction in species richness of benthic macroinvertebrates; reduction in standing crop of benthic macroinvertebrates; stranding of macroinvertebrates; stranding of fish. Modified temperature regimes below dams Delayed spawning in fish; disrupted insect-emergence patterns; reduced benthic standing crop; elimination of temperature-specific species of fish. Conversion of lotic habitat to lentic Decline of populations of riverine crayfish and snails; elimination of salmonids and pelagic spawning fishes and dominance of generalist fish species; loss of fishes adapted to turbid river habitats; loss of fishes due to inundation of spawning grounds; proliferation of exotic fish species. Human alteration of timing of spates Reduced survivorship of larval atyid shrimps and recruitment of riverine fish; stable low flows required for spawning and recruitment of riverine fish. Reduced seasonality Reduced synchrony of breeding in gammarid shrimps. Human alteration of timing of rising flows Loss of cues for fish spawning and migration. Water abstraction and interbasin transfers of water Reduction in migrating shrimp larvae; transfer of schistosomiasis; translocation of fish species. Presence of in-stream barriers Increased predation on juvenile migrating shrimp; loss of migratory fish species. Reduced frequency, duration, and area of inundation of floodplain wetlands Reduced spawning areas and/or recruitment success of lowland river fish; decline in waterbird species richness and abundance; decline in wetland vegetation.

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eutrophication are important. At an ecological level, wetland and riparian systems are monitored using integrative methods that include nonbiological indicators such as flow regime, water quality, land use, and aspects of river diversity as well as biological indicator species. Species that are chosen often include aquatic and riparian plant species whose biology is well known and which cannot move to escape negative impacts (Innes et al. 2000). Fish diversity is also recorded; for example, alien fish species are indicative of riparian degradation, sedimentation, and urbanization because they thrive in turbid streams with fine sediment beds (Walters et al. 2003; Kennard et al. 2005). Other taxa include invertebrates because density and diversity of benthic macroinvertebrates in pools and riffles is adversely affected by fine sediment deposition—invertebrates live between stones in coarse beds (Doledec et al. 1999; Muotka et al. 2002; Roy et al. 2003)—and many freshwater invertebrates are specialized and therefore sensitive (Berkman et al. 1986). Nematodes can indicate change in sediments because certain species flourish at degraded sites (Boyd et al. 2000). Water-loving birds can be used to follow wetland and riparian degradation; neotropical migrants with specific habitat requirements for breeding are sensitive to residential and agricultural disturbance, whereas edge-tolerant and exotic species are found in greater numbers in disturbed habitat (Croonquist & Brooks 1991). Amphibians that are sensitive to hydrological and chemical alterations and are easily identifiable may be monitored in wetland habitats. There are advantages and shortcomings to these different taxonomic groups of organisms (see Table 6-5).

Marine Fisheries Indicators have been used very extensively to detect the impact of fishing on fish stocks (Walters 2003; Jennings 2005; Jennings & Dulvy 2005; Quirijns et al. 2008), but fishing mortality has wider consequences for the community, too (see Figure 6-4). For example, top-predator removal is a worry because it is thought to have dramatic consequences for lower trophic levels, operating directly through lethal effects and indirectly by changing the distribution and behavior of meso-consumers (Heithaus et al. 2008; see chapter 5). Indeed, reduction in species diversity, decline in mean trophic level, increased by-catch, greater annual variation in populations, changes in resilience and resistance to disturbance, and adverse effects on the viability of ecologically important species that are not fished can all

Table 6-5. Indicators used in riparian and wetland ecological assessment (from Innis et al. 2000). Advantages

Vegetation Life-history requirements and successional pathways well known for most species. Large amount of existing data on many riparian and wetland vegetative communities. Sessile; cannot avoid negative impacts. Invertebrates Often highly specialized and therefore sensitive. Short generation time allows for rapid response that is easily identified. Life histories and ecology often well understood. Birds and mammals High societal value. Ecology and life history well worked out. Good landscape integrator present in all riparian and wetland classes. Amphibians Few species, many easily identifiable. Especially sensitive to hydrological and chemical alteration. Most highly dependent on riparian or wetland areas for some portion of life history. Hydrologic patterns Long-term data often available from gauging stations. Hydraulic alteration often an underlying cause for observed changes in biotic indicators.

Disadvantages

Long recovery times for some species following disturbance. Successional stage of vegetative community naturally variable; in dynamic systems, endpoints are not always realistic or optimal.

Low perceived societal value. Communities may be foraged selectively by fish or birds.

Mobility and seasonality create spatial and temporal variability. Difficult to assess causes of impacts for migratory or wide-ranging species.

Great inherent variation in dispersal. Seasonal variation in abundance.

Does not provide information about biotic response to alterations; may not be sensitive to acute impacts from short-term events.

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Figure 6-4. Theoretical direct and indirect effects of fishing on fish populations and communities (N, abundance; B, biomass). (Reprinted from Shin et al. 2005.)

occur (Murawski 2000; Nicholson & Jennings 2004). Recently, environmental indicators have been used to understand these effects (Pauly et al. 1998; Essington et al. 2006). These include measuring alterations in species composition, proportion of piscivorous species (Murawski 2000), mean trophic level of the catch (Cury et al. 2005), mean length of fish at maturity (Shin et al. 2005), and proportion of noncommercial species (Trenkel & Rochet 2003). The key is not to develop more indicators but to select those most appropriate for a specific objective by using criteria such as strong theoretical underpinnings, public support, cost, ease and accuracy of measurement, sensitivity, responsiveness, and specificity, as well as historical data upon which to base comparisons (Rice & Rochet 2005). Indicators are also applied to monitor the effectiveness of marine protected areas. Here, before-and-after protection comparisons are made, or exploited and unexploited marine reserves are compared. Commercial species, in particular top trophic-level large species, especially large individuals, are seen as the principal beneficiaries of protection (see Figure 6-5). Studies of such groups have shown that protection may only exert its effects several years after the protected area has been established, and that responses are variable, with some species’ abundances declining under protection due to enhanced predation or competition (Micheli et al. 2004; Claudet et al. 2006), and other (noncommercial) species being unaffected by protection.

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Figure 6-5. Mean ± SE density (numbers/1000 m2) of large predatory reef fish (families Carangidae, Serranidae [Epinephelinae], as well as Lutjanidae and Lethrinidae as a group) at four sites at the seven sampling times. Solid arrows indicate when fishing began, and open arrows indicate when fishing stopped at the two Sumilion sites. Fishing stopped at Apo reserve in 1982, and the site remained unfished throughout. Apo non-reserve was open to fishing throughout. (Reprinted from Russ & Alcala 1996).

Also, it is not always clear whether large piscivores are good indicators of the effects of protection on the community, or whether the principal consequence of marine reserves is just to increase population densities of large predatory fish. This highlights the fine line between indicators of ecosystem health and species-specific responses to environmental change mentioned at the start of the chapter.

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Climate Change in Marine Ecosystems Rising atmospheric carbon dioxide concentrations during the twentieth century have increased the acidity of sea water by 0.1 pH unit and have driven average global temperatures up, including that of the sea, by an average of 0.74°C. As a consequence, sea levels have risen by 17 cm due to thermal expansion, melting glaciers, and retreating ice sheets (IPCC 2007). Complex changes are expected in at least four major marine arenas: a reconfiguration of ecosystems in temperate seas, loss of coral reefs, changes in fish populations, and constriction of polar ecosystems (Hoegh-Guldberg 2005). In each case biological indicators are being marshaled to synthesize difficult-to-measure, nuanced, and often nonspecific changes in these ecosystems. Demersal fish assemblages in the North Sea are being used to develop an indicator of ecosystem responses to a consistent 0.2 to 0.6°C warming over the last three decades. Some of these 28 species surveyed by the English Groundfish Survey have warm thermal preferences, others cold; some have broad thermal ranges, others narrow. The survey trawled fish from 84 localities in the North Sea at different latitudes and depths from 1980 to 2004 (Dulvy et al. 2008). Over this period most species have sunk deeper on average by 5.5 m per decade, 11 significantly. The exceptions are warmtolerant, small-bodied non-exploited species with a northern range boundary in the North Sea (see Figure 6-6a). Some assemblages moved northward, particularly abundant, widespread, warm-tolerant specialist species (see Figure 6-6b), but other species shifted south. The deepening response, with its sensitivity and specificity to climate change (not fishing), could be a promising indicator of thermal changes in semi-enclosed seas (see also Perry et al. 2005). Coral reefs cover 0.09 percent of the area of the marine biome but hold 25 percent of its species. When sea temperatures climb and reach >1°C higher than summer maxima, scleractinian corals expel symbiotic zooxanthellae or else the zooxanthellae lose their pigments, and the corals die—a phenomenon known as bleaching (Glynn 1993). Other factors, including UV radiation, decreased alkalinity, hurricanes, and rising sea levels may all be involved, too (Hoegh-Guldberg 1999; Gardner et al. 2005). Photosynthetic activity of zooxanthellae is the chief energy source for calcification and total productivity of coral reef ecosystems. Bleaching events have dramatically increased in frequency from 1979 onward; a mass bleaching event in 1997/8 affected 16 percent of coral reefs worldwide (Wilkinson 2002). Corals do not suffer uniformly—instead, finely branched staghorn corals are most sensitive whereas encrusting and massive corals survive

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Figure 6-6. Trends in geographic response of different demersal fish assemblages over a 24-year period, (a) mean depth, (b) mean latitude. Black and grey points indicate significance at p < 0.001 and p < 0.01 respectively. The x-axis represents the direction and strength of geographic response over time. Panel (a): positive values denote moving to shallow water, negative values moving to deeper water. Panel (b): positive values denote moving to a more northerly distribution, negative values to a more southerly distribution. (Reprinted from Dulvy et al. 2008.)

(Loya et al. 2001). Simple models indicate that the bleaching “threshold” will be chronically exceeded as oceanic temperatures rise during the next 50 years, leading to massive losses of all corals (Hughes et al. 2003). Fish abundance is closely tied to coral abundance (Jones et al. 2004; Munday 2004)—across studies, there is a correlation between decline in live coral and percentage change in fish-species richness (see Figure 6-7) several years after coral bleaching—so fish can be indicators of coral system health. For example, at Mafia Island Marine Park, Tanzania, abundance and taxonomic richness of fishes declined greatly six years after a bleaching event, even though they increased temporarily immediately afterward (Garpe et al. 2006). Generalist fish species increased but specialist coral associated fishes declined following bleaching on the Great Barrier Reef, Australia (Bellwood et al. 2006). Seabirds can also be used as indicators of ecosystem responses to global warming, because their breeding success on land is easier to monitor than trophic changes at sea. As illustrations, least auklets nesting on the Pribilof Islands specialize on large calanoid copepod species. When sea-water temperature rises, copepod reproduction declines, leading to a reduction

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Figure 6-7. Relationship between coral decline and species richness of fish assemblages on coral reefs. (Reprinted from Wilson et al. 2006.)

in the amount that auklet parents can regurgitate to chicks and an associated rise in corticosterone levels indicative of nutritional stress (Springer et al. 2007). Similarly, tufted puffin off the coast of British Columbia breed earlier each year as sea-surface temperatures rise, but their chick growth rates decline as their chief prey, northern sand lance, moves to deeper waters. When breeding-season sea-surface temperatures averages exceed 9.9°C, growth rates drop to <5 g/day and young fail to fledge (Gjerdrum et al. 2003). In polar ecosystems where global warming is having strong effects, high-profile marine mammals are being tried as indicators of ecosystem disturbance—notably to monitor the biological effects of reduction in sea ice, changing size and location of polynyas (areas of open water surrounded by sea ice), and changes in sea-floor environment. After describing the natural history of seven arctic and four subarctic marine mammals and their biological and demographic responses to climate change, Laidre and colleagues (2008) constructed a subjective but ordinal sensitivity index using nine variables (see Table 6-6). The index suggests that narrowly distributed and specialized feeders such as narwhal and walrus, icedependent species that use the marginal ice zone (hooded and harp seals), and species reliant on annual sea ice as predator refuges or hunting platforms (e.g., polar bears), are especially sensitive to changing conditions; ringed and bearded seals were least sensitive. This attempt provides an entry point into investigating the complex repercussions of climate change on arctic species and topography.

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Table 6-6. Sensitivity index for arctic marine mammals (from Laidre et al. 2008). Species

Aa

B

C

D

E

F

G

H

I

SUM

Arctic Beluga Narwhal Bowhead Ringed seal Bearded seal Walrus Polar bear

2b 1i 1i 3i 2i 2i 1i

2 1 2 3 3 2 3

2 1 2 3 2 2 2

2 1 2 3 3 2 1

2 1 1 3 3 2 2

1 1 2 3 3 2 2

3 3 3 1 1 1 1

3 2 2 3 3 3 1

1 1 1 3 3 2 1

18 12 16 25 23 18 14

Subarctic Spotted seal Ribbon seal Harp seal Hooded seal

2i 2i 3i 2i

1 1 1 1

3 3 3 2

3 2 3 2

2 2 1 1

2 3 1 1

1 1 1 1

2 2 3 2

3 3 3 3

19 19 19 15

a

A, Population size; B, Geographic distribution; C, Habitat specificity; D, Diet diversity; E, Migration; F, Site fidelity; G, Sea ice changes; H, Trophic web changes; I, Rmax; SUM, Total b 1, Highly sensitive; 2, Moderately sensitive; 3, Least sensitive

Proliferation and Obfuscation of Terms Assessing the biological effects of chemical pollutants on biological systems beyond measuring water chemistry is important because biological effects may be manifest below normal analytical capabilities; the effects of interactions between pollutants cannot be predicted through chemical measurements alone; and the physical and chemical attributes of water affect expression of toxicity on organisms (Cairns & van der Schalie 1980). Unfortunately, there has been a proliferation of terms involving measurement of biological responses to anthropogenic disturbance in aquatic environments. Instead of arguing over ecosystem health or ecosystem integrity, over ecological stressors or pollutants, sentinels or bioindicators, bioaccumulators or sensitive species, it is more productive to state the objectives of a study clearly. Is the goal to compare sites spatially or over time; to assess the effects of a single pollutant or the interactions of several on an organism; to gauge the effects of pollution on human health, single species, guilds, communities, or ecosystem function; to monitor mortality, repro-

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duction, or physiological responses, or to investigate the relationship between them? Most studies do specify objectives, often justify the use of single or constellations of indicator species, and then present the data, but they additionally pepper the paper or report with ill-defined terms that are used interchangeably. Future studies should attempt to clarify goals without recourse to jargon or shorthand buzzwords that serve only to obfuscate the uncovering of the biological consequences of pollution and perturbation.

Summary Ecosystem health has many different definitions that include homeostasis, absence of disease, species diversity, or community resilience to stress. Biological integrity is a similar concept to ecosystem health because both emphasize community wholeness, resistance, and resilience. Species that are indicators of environmental health are called environmental indicator species (a type of biological indicator) and are central to ecotoxicology. They include individuals that sequester chemicals, indicators of population change, indicators of community structure, and indicators of ecosystem processes. Indices of biological integrity (IBIs) combine many of these facets into a quantitative measure but require unpolluted or minimally disturbed reference conditions for comparison. Most examples of indicators being used to assess environmental pollution come from freshwater and marine ecosystems, but there is an unfortunate proliferation of terms. For example, sentinel species is inconsistently used by ecotoxicologists and conservation biologists but it is very similar to environmental indicator species. Environmental indicator species of marine pollution include algae, mollusks that accumulate pollutants in soft tissues, and fish species. When several species are used, sophisticated analytical models can be used to compare community structures of polluted and unpolluted areas. Freshwater pollution can be monitored using macroinvertebrate and fish species. Fish responses include endocrine changes and alterations in blood chemistry or organ mass, while batteries of tests have been developed around tissue lesions, carcinomas, immune responses, and exercise performance measures. Terrestrial vertebrates are used not so much as surrogates to measure environmental health but as emblematic victims of environmental pollution. Many river systems have been modified by channeling and dams, the effects of which are sometimes monitored using fish and invertebrate communities. In the fishing industry, principal biological indicators relate to

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exploited species’ catches, but indicators of the community effects of fishing and the benefits of marine protected areas are being developed. Indicators of climate change in marine ecosystems include fish with varying thermal tolerances, coral reef die-offs, seabird reproduction on land, and possibly marine mammals living in the arctic.

Primary and secondary forests are being converted to oil palm plantations in many parts of Southeast Asia with a concomitant loss of biodiversity. (Drawing by Sheila Girling.)

Chapter 7

Ecological-Disturbance Indicator Species

Effects of Disturbance One of the main challenges for conservation scientists is to understand how land-use change affects plants and animals. The simplest way to address this is to compare the presence or population size of a species in a disturbed habitat to that in an undisturbed habitat. This, however, can lead to overly optimistic or pessimistic conclusions about the biological effects of habitat alteration if the species is either particularly tolerant or else sensitive to environmental change. It is customary, therefore, to monitor several species within one guild or taxon, or several taxa, by reporting their presence or absence, their abundances, community composition, community evenness, and so on. Such comparisons give an indication of the magnitude of the ecological effects of disturbance, and hence I will call them ecologicaldisturbance indicator species. They have been defined by McGeoch (2007) as “a species or group of species that demonstrates the effects of environmental change (such as habitat alteration and fragmentation and climate change) on biota or biotic systems” (145). The more species or taxa or guilds that are monitored, the more confident we can be of how wildlife is responding to change. The response of an individual species to anything but severe environmental change is unlikely to mirror that of other sympatric species for several reasons: species of different body size have different-sized home ranges 189

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and consequently experience the landscape at different spatial scales; species have different life histories and therefore experience different temporal scales; they have different foraging and breeding requirements and thus use different resources and habitats within the landscape; and for physiological and trophic reasons may have differential sensitivities to landscape alteration. Thus a search for cross-taxon congruency in response to habitat modification is likely to be far less profitable than simply documenting responses to habitat modification across multiple groups. Moreover, the latter objective is of greater direct consequence to conservation because it informs management policy about land-use development and the way to mitigate its effects. The principal drivers of environmental disturbance in terrestrial ecosystems in the twentieth century have arguably been land degradation, agricultural practices, and urbanization, whereas in aquatic ecosystems they have been various sorts of pollution, targeted fisheries, and whaling. Indicators of aquatic pollution include physiological responses, changes in population size, changes in community structure, and many combinatorial measures (see chapter 6), but indicators of disturbance in terrestrial ecosystems historically have been changes in species richness, in community composition, and in population sizes, the last being the most sensitive as it can forewarn of impending local extinction. In this chapter, I restrict discussion to terrestrial ecosystems, focusing on criteria for selecting ecological-disturbance indicator species, on types of indicator, and on attempts to measure ecological disturbance in selected forested, agricultural, and urban landscapes, but not on how they might additionally be a sign of other species’ responses to environmental change, which I deal with in chapter 8.

Proposed Criteria for Indicator Species Sensitivity to ecological disturbance is a critical attribute of a good ecological indicator species and can be calculated in a number of ways. To take an early method, TWINSPAN (Hill 1979) constructs a site typology and uses it to obtain a species classification based on the fidelity of species to groups of sites. Sites are classified by dividing them into two subsets based on their positive or negative sign on the first axis of a correspondence analysis, and this is then reiterated by dividing sites into increasingly smaller subsets. At each step, each species is scored for its “preference” for one side or the other. These preferences produce a refined site ordination with species receiving a presence/absence categorization for different levels of relative

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abundance. So, depending on the relative-abundance cutoffs that are decided upon, a species with a relative abundance of 30 percent would fall into the >0 and >5 and >26 percent categories, making it possible to use relative abundances as a measure of indicator power at different sites. A more commonly used method, IndVal (Dufrene & Legendre 1997), combines measurements of the degree of specificity of each species to a type of habitat or environmental state (classified subjectively or quantitatively) and also its frequency of occurrence within that habitat or state. It is calculated as a specificity measure Aij = Nindividualsij /Nindividualsi where Nindividualsij is the mean number of individuals of species i across sites of group j and Nindividualsi is the sum of the mean numbers of individuals of species i over all groups. Aij is maximum when species i is only present in cluster j. Also there is a fidelity measure Bij = Nsitesij /Nsitesj where Nsitesij is the number of sites in cluster or habitat j where species i is present, and Nsitesj is the total number of sites in that cluster. Bij is maximum when species i is present in all objects of cluster j. The percentage indicator value for species i in that cluster or habitat j is then IndValij = Aij × Bij × 100. Out of these calculations emerge two sorts of indicator (see Figure 7-1). A characteristic indicator species, defined as having both high specificity and fidelity in a particular habitat type, is a reliable indicator species because it is specific to a locality and because it has a high probability of being sampled in that locality during monitoring and assessment. A detector species that spans a range of habitats (and does not have high specificity) is likely to shift in a particular direction following ecological change (i.e., will move in fidelity). Species with high IndVals of >70 percent may be classified as characteristic species, those with intermediate values of 50 to 70 percent as detector species. These two sorts of indicators compliment each other with detector species facilitating long-term assessment and direction of environmental change, and characteristic species highlighting changes in a particular habitat (McGeoch et al. 2002; Alves da Mata et al. 2008). There are many other methods of assessing sensitivity, including k-dominance curves,

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Figure 7-1. Species characterized by a combination of their degree of environmental specificity and fidelity. (Reprinted from McGeoch et al. 2002.)

rarefaction techniques, correspondence analysis, and probability-based indicators of ecological disturbance (Magurran 2004; Howe et al. 2007; Halme et al. 2009). In general, an indicator group, such as birds, is first selected and a subset of species in the group is chosen for detecting certain disturbances. Given the huge number of species that could stand in as indicators, there have been efforts to limit the scope using measurement, life-history, and economic filters. Hilty and Merenlender (2000), for example, summarized such tries but in so doing drew together criteria for indicators of sites of species richness (e.g., Dudley et al. 2005; chapter 2), indicators of ecosystem health (e.g., Carignan & Villard 2002; chapter 6), and indicators of habitat quality and of population trends (e.g., Landres et al. 1988; this chapter), although these indicators do not always have the same attributes (McGeoch 1998). Focusing on the last sort, one can draw up a list of criteria that an ideal indicator species should possess (Herricks & Schaefer 1985; see Table 7-1). Species should be easy to find and cheap to sample repeatedly, encouraging non-specialists to participate in monitoring their environment even when constrained by low budgets. Species should be identifiable by nonsystematists (Lawton et al. 1988; Gardner et al. 2008a). To standardize comparisons across sites, species with wide geographic distributions are helpful (but see below). Species need to have limited mobility so that they cannot avoid adverse conditions through movement or migration (i.e., dispersal-limited species, Norden & Appelqvist 2001). Species responding most rapidly to change might be small and have small home ranges (Landres et al. 1988), and those with specialized dietary or habitat requirements would be unable to switch. Species with specific requirements such as par-

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Table 7-1. Attributes of indicators of the effects habitat disturbance and environmental change over space or time. X indicates that the criterion applies. Response to:

Criteria

Measurement attributes Easy to locate Cheap to monitor Easy to identify Wide geographic distribution Life-history characteristics Low mobility Small body size Small home range Food or habitat specialist Resource limited Process limited High reproductive rate Population considerations Low population fluctuation Responds predictably Anticipatory response Exists across a wide range of stress Known cause and effect Other variables Economically important Threatened Can double as other sorts of surrogate

Spatial disturbance

Temporal change

X X X

X X X X

X X X X X X

X X X X X X X

X X

X X X X X

X X X

X X X

X

Based on Soule 1985; Hellawell 1986; Landres et al. 1988; Kelly & Harwell 1990; Noss 1990; Regier 1990; Pearson & Cassola 1992; Johnson et al. 1993; Kremen 1994; Caro & O’Doherty 1999; Hilty & Merenlender 2000; Dale & Beyeler 2001; Carignan & Villard 2002.

ticular nest sites, or those demanding an ecological process (such as flooding or fire) to occur at a certain time will be especially sensitive. Species with high reproductive rates will react quickly to change, too. From a population standpoint, population stability is important because any change will be noticed rapidly. Populations need to respond predictably and early, and to provide continuous assessment over a wide range

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and intensity of stresses in order to reduce concerns about threshold effects and allow several impacts to be measured simultaneously. Importantly, without a known cause-and-effect relationship between the indicator population size and a particular environmental variable, differences in abundance or species presence over space or time will simply indicate that something unidentifiable has changed. Other secondary criteria include species that are economically important so as to attract people’s interest in monitoring them. Threatened species may also focus the public’s attention. Species that serve other conservation goals, such as flagship species or umbrella species, could lend added political clout to conservation efforts (Brown & Freitas 2000; chapter 10). To date, there have been no attempts to assess which of these ideas are more important than others. For example, Hilty and Merenlender (2000) argued that many of these suggestions are in conflict: species with large geographic ranges are not necessarily habitat specialists; cosmopolitan species may be tolerant of a wide range of environments and hence lack sensitivity; specialists may not be abundant and may therefore be difficult to monitor; species with low population variability are unlikely to react swiftly to environmental change; species that fulfill a number of roles in conservation may accomplish none particularly well; and threatened species are often hard to locate and live at low densities, making monitoring difficult. Empirical data are needed to address these concerns. A sensible way to deal with these conflicts is to use several species simultaneously in order to grasp the consequences of environmental change but to keep the number fairly limited. Hilty and Merenlender (2000) put forward a decision-making framework for selecting indicator taxa and tried to attach qualitative yardsticks to each criterion to help managers make practical choices (see Table 7-2). It looks promising but has yet to be tried.

Single Species and Species-Groups as Indicators of Disturbance Single Species Once we have information about the decline (or increase [McGeoch & Chown 1998]) of a population, we need to determine its causes in order to make informed policy decisions, and a simple way is to match changes in abundance or reproductive parameters to aspects of land use or exploitation. Alternatively, we may first become alert to an anthropogenically

Ecological-Disturbance Indicator Species 195

Table 7-2. Stepwise decision-making framework for selecting environmental-indicator taxa (from Hilty & Merenlender 2000). Step 0 Step 1 Step 2 Step 3 Step 4 Optional step

Step 5

Decide what ecosystem attribute(s) indicator taxa should reflect. Make a list of all species in the area that best satisfy the baseline information criteria. From this initial list, retain species that best meet the suggested niche and life-history criteria. Remove species that may respond to changes occurring outside the system of interest. Use only those species that can be easily detected and monitored with available funds. Reduce the list further by deleting taxa in the list with cosmopolitan distributions and/or those that represent other agendas of interest. Select a set of complementary indicator taxa from different taxonomic groups so that all selection criteria are met by more than one taxon.

driven environmental change and want to determine how it affects a species that lives in that environment. Often managers are aware of both population and environmental changes simultaneously. In the case of the northern spotted owl in the Pacific Northwest, USA, populations were known to be in decline, and by 1950 almost all the old-growth forest on which the owl was supposed to depend had been felled on private lands, so it was presumed that these changes were linked. The extent and type of cutting of old-growth forest on public lands that could be tolerated without jeopardizing the owl’s long-term population viability, therefore, became a management issue and generated a tremendous amount of research at several scales—at a home-range span, in forest stands, and across the landscape (e.g., Noon & McKelvey 1996). Environmental change need not be detrimental, of course, and restoration projects, especially those targeted at particular species, often judge their success on population recovery of just one species. As an illustration, one of the two rare and phylogenetically unique tuatara species lives on tiny islands off North Island, New Zealand, including the Hen and Chicken Islands—there the effects of a Pacific-rat eradication program have been monitored. Comparison of rat-infested and rat-free islands demonstrated a change in adult tuatara condition (adjusted mass) for the better, as well as increases in the proportion of juveniles on the three islands where rats had

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been removed, with predicted increases in tuatara densities (Towns et al. 2007).

Species-Groups Species-groups from one or more taxa are used far more commonly than a single species to monitor the effects of ecological disturbance. Their varied population responses paint a larger and more subtle picture, and can uncover ways in which species can substitute for each other’s role in an ecosystem (Elmqvist et al. 2003). For example, Medellin and colleagues (2000) caught 2,413 individual bats comprising 34 species from four different habitats—forest, cacao, young and old fields, and cornfields in Chiapas, Mexico, over a ten-month period. In addition, they took standard vegetation measures in each habitat that revealed the relative degree of disturbance. Now they could relate bat metric values to disturbance: bat species richness, the number of rare species, and a bat diversity index were all linked to reduced habitat disruption, whereas relative abundance of the most common bat species was associated with increased anthropogenic activity. Dung beetles are increasingly regarded as an important ecologicaldisturbance indicator species-group in degraded tropical forests (Hill 1996; Halffter & Arellano 2002; Pineda et al. 2005). The logic is that forest destruction results in a changed vertebrate fauna and consequently altered dung production that affects dung beetle community structure and composition (see Figure 7-2). When findings from 33 studies were collated across nine different habitat types and metrics of biodiversity were examined, some striking patterns emerged. Compared to intact primary forest, total species richness occurs at intermediate levels in all modified habitats, whereas the percentage of species found in intact forests declines in a stepwise manner, first to selectively logged, secondary, and agroforests; then to tree plantations, annual crops, and pasture; and then finally clear-cuts (see Figure 7-3a). Total dung beetle abundance varies substantially across modified landscapes, showing that this is a poor ecological-disturbance metric but that the abundance of intact forest species declines in a more directed fashion (see Figure 7-3b). Community evenness declines relative to intact forests, especially in tree plantations, cattle pastures, and clear-cuts (see Figure 7-3c). Community similarity is low in most modified habitats and reaches nearly zero in treeless maize fields, cattle pastures, and clear-cuts (see Figure 7-3d). Dung beetle species are thus surprisingly sensitive to different forms of land use, even to the point of distinguishing between selective logging of less or more than four trees/ha, the point at which indepen-

Ecological-Disturbance Indicator Species 197

Figure 7-2. Illustrated hypothetical linkages from dung beetle to remote sensor: (1) Forest management can change forest structural heterogeneity. (2) Forest structural heterogeneity and its change can be measured by remote sensing by using semivariography of vegetation indices. (3) Forest structural heterogeneity influences habitat quantity and quality for forest vertebrate fauna. (4) Forest vertebrate fauna composition influences dung production and thus dung-beetle-community structure and composition. (5) The link between forest structural heterogeneity as measured by satellite, and dung-beetle-community species diversity and composition. (Reprinted from Aguilar-Amuchastegui & Henebry 2007.)

dent vegetation measures show that forest structure starts to change (Davis et al. 2001; Scheffler 2005; Aguilar-Amuchastegui & Henebry 2007). More commonly, however, studies use a number of species from a variety of taxa rather than just one taxon to gauge effects of ecological change on flora and fauna. Four of these contemporary studies are now outlined.

Examples of the Use of Species-Groups in Documenting Effects of Land-Use Change Forest Fragmentation: BDFFP Starting out as an attempt to verify whether island biogeography theory applied to terrestrial forest islands in a sea of agriculture, in 1979 a large-scale

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Figure 7-3. (a-d) Influence of habitat modification on standardized dung beetlecommunity parameters in tropical forest. Habitat abbreviation: intact forest (I); selectively logged forest (SL), late secondary forest (LS), early secondary forest (ES), agroforests (AF), tree plantations (TP), annually cropped fields (AC), pastures (PAS), and clear-cuts (CC). N = number of sites. (a) Stotal (total species richness) and Sintact (richness of the intact species), (b) Ntotal (total abundance) and Nintact (abundance of the intact forest species); * denotes outliers removed, (c) EH (Shannon evenness index) and (d) CMH (Morista Horn index of community similarity, relative to intact forest). (Reprinted from Nichols et al. 2007.)

experiment was initiated in Amazonian rainforest 80 km north of Manaus, Brazil. Five 1-ha, four 10-ha, and two 100-ha fragments were isolated from surrounding forest by clearing for cattle pastures. Twelve reserves ranging from 1 to 1,000 ha served as controls in nearby continuous forest (see Figure 7-4). Teams of researchers then sampled animal and plant indicators of fragmentation in different-sized fragments, and at differing distances from fragment edges, before and at varying times after isolation. Three major themes have emerged from this long-term Biological Dynamics of Forest

Ecological-Disturbance Indicator Species 199

Figure 7-4. The Biological Dynamics of Forest Fragments Project study area in central Amazonia, showing locations of forest fragments and control sites in intact forest. (Reprinted from Laurance et al. 2002.)

Fragments Project (BDFFP): fragment-area effects, edge effects, and the extent and nature of the intervening matrix, although the importance of these effects varies enormously by taxon (Laurance et al. 2002). It comes as no surprise that species richness of most taxonomic groups is positively associated with fragment size around Manaus, and that continuous forest holds more species than isolated forest reserves. Large mammals, understory birds, and insect taxa such as beetles are sensitive to fragment area, with small fragments losing species very rapidly, only 2 to 6 years after isolation (Stratford & Stouffer 1999; Ferraz et al. 2003) and larger fragments losing species in a predictable order (Stouffer et al. 2009). Species richness of some taxa have remained stable or even increased after forest fragmentation, however; these include rainforest frogs, butterflies, and small mammals. Hummingbirds, for instance, can maintain their abundance in fragments by moving freely through secondary forest to reach them, even appearing in fragments for the first time after isolation, although this may reflect a failure to detect them initially (Stouffer & Bierregaard 1995b). So responses differ by indicator taxon, but they also vary within a single taxonomic class: insectivorous birds that feed on and follow army ant swarms are the first group to be lost following isolation because they are reluctant to cross open areas, but they later recolonize fragments surrounded by Cecropia secondary growth. Species that live in mixed-species flocks are

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Table 7-3. Number of bird species 6–9 years after isolation for nine forest fragments (A-I). Pre-isolation total is the number of species in the group in pre-isolation samples. Time 4 total is the species richness 6–9 years after isolation for all fragments combined (from Stouffer & Bierregaard 1995a). Cecropia secondary growth

Vismia secondary growth

Group

Ant followers Flocking Dropouts Non-dropouts Solitary Gap Arboreal Terrestrial

Preisolation total

A

B

C

D

E

F

G

H

I

Time 4

3

0

0

0

0

2

2

3

3

1

3

3 10

2 0

1 0

1 0

3 0

3 2

3 1

3 0

3 8

3 8

3 9

2 10 7

2 0 1

2 2 1

2 2 0

2 2 0

2 4 0

2 4 0

2 3 1

0 6 2

2 4 0

2 10 4

1 ha

10 ha

1 ha

10 ha

quickly lost from 1- and 10-ha fragments that are smaller than the size of a flock territory, but species whose flocks disintegrate immediately after isolation persist in fragments (see dropouts in Table 7-3; Van Houtan et al. 2006). Solitary arboreal insectivores that use gaps in the forest show no decline, although the majority of arboreal birds decrease dramatically following isolation. Finally, terrestrial birds decline markedly and appear very vulnerable to forest fragmentation, perhaps because they are unable to meet their energy requirements (Stouffer & Bierregaard 1995a; Stouffer et al. 2006). The edges of forest fragments exhibit a host of physical changes following isolation: they receive more light, experience increased temperature, have lower relative humidity and soil moisture, and suffer increased wind disturbance—effects that penetrate to differing extents. Large-seeded, slow-growing, old-growth trees suffer pronounced mortality at fragment edges due to physiological stress, wind, and parasitic lianas, which results in a reduction in living biomass at forest edges (see Figure 7-5). Some animal taxa decline at forest edges, including hymenoptera, beetles, ants, and butterflies, whereas others respond positively, such as termites, leaf hoppers, scale insects, light-loving butterflies, frugivorous bats, and insectivorous

-1

-1

Biomass change (tons ha year )

Ecological-Disturbance Indicator Species 201 5 0 -5

-10 -15 40

100

1000

Distance to forest edge (m)

Figure 7-5. Rate of change in above-ground tree biomass as a function of distance of the plots from the forest edge. The solid line is an exponential curve fitted to the data, and the dashed lines are 95 percent confidence intervals for 26 forest-interior plots (>500 m from edge) (Reprinted from Laurance et al. 1997.)

marsupials (Laurance et al. 2002). Indicator responses, then, are idiosyncratic and when combined with fragment size result in a bewildering array of effects within even one taxonomic group. Among beetles, some families respond to edges but not fragment area, others respond to area but not edges, others to both, and still others to neither (see Table 7-4). Some Araptus beetle species showed three different responses to changes associated with forest fragmentation! The third component, the nature of the matrix in between fragments, has varied consequences, too. Clearings of just 15–100 m are insurmountable barriers for dung and carrion beetle species, some understory bird species, and arboreal mammals. While large mammals such as primates and peccaries eschew clearings, others such as jaguars traverse them easily. As secondary growth returned to the BDFFP cattle pasture matrix, birds started to return to and build up abundance in fragments, especially when Cecropia, a tall, closed-canopy tree returns, although not when Vismia spp. grow following fire and pasture abandonment (Stouffer et al. 2006); some frog and ant species use the habitat matrix, too. Indeed, between 8 and 25 percent of all frog, bird, small mammal, and ant species are exclusively associated with the matrix (Gascon et al. 1999). The BDFFP started out as an academic exercise, but it has great applied significance because so much of formerly continuous tropical rainforest is

Table 7-4. A classification of beetle-species responses to forest fragmentation. X shows species absent (from Didham et al. 1998). Species absent from samples taken in forest fragments of size: Family

Genus

Species responding to edge effects only (area-insensitive) Edge avoiders Staphylinidae Piestus sp. Curculionidae Araptus sp. Edge specialists Staphylinidae ?genus Hydrophilidae Phaenostoma sp. Curculionidae Araptus sp. Species responding to area effects only (edge-insensitive) Large-area specialists Leiodidae Adelopsis sp. Staphylinidae Jubus sp. Curculionidae Hypothenemus sp. Curculionidae Araptus sp. Curculionidae ?genus Small-area specialists Empty category Species responding to both edge and area effects Large-area, edge avoiders Staphylinidae Coproporus sp. Carabidae Tachys sp. Small-area, edge avoiders Hydrophilidae Phaenostoma sp. Large-area, edge specialists Carabidae Tachys sp. Staphylinidae Baeocera sp. Small-area, edge specialists Empty category Species showing no response to edge or area effects Staphylinidae Carpelimus sp. Leiodidae Adelopsis sp. Dytiscidae Copelatus sp. Leiodidae ?Agathidium sp.

1 ha

10 ha

X

X

100 ha

X X

X X X X X

X X

X X

X X X

X X X

X X

X

X

X

X

X

X

Ecological-Disturbance Indicator Species 203

Table 7-4. Continued Species absent from samples taken in forest fragments of size: Family

Nitidulidae Leiodidae Ptiliidae Scydmaenidae Staphylinidae Staphylinidae Staphylinidae Staphylinidae Curculionidae Curculionidae Curculionidae Curculionidae Curculionidae

Genus

Stelidota sp. Aglyptinus sp. ?Ptenidium sp. Euconnus sp. Globa sp. Goniacerus sp. Phalepsiodes sp. Tuberoplectus sp. Araptus sp. Araptus sp. Araptus sp. ?genus ?genus

1 ha

10 ha

100 ha

X

X

X

being fragmented; now we understand that the response of different taxa to this form of ecological disturbance is highly eclectic (Collinge 2009). This is further exacerbated by synergistic effects of both fire (Cochrane & Laurance 2002) and hunting interacting with fragmentation (Peres & Michalski 2006; see also Brook et al. 2008; Laurance & Useche 2009). Responses are so variable taxonomically, spatially, and temporally that it becomes foolhardy to attempt to use even a single species-group as an indicator of the extent of ecological disturbance in contemporary Amazonia.

Countryside Biogeography Rather than simply concentrate research effort on characterizing degraded tropical forest and its effects on biodiversity, we need to document how various forms of agricultural practice impact wildlife. The rationale for this avenue of research is that many pristine ecosystems will soon be cleared, planted, and grazed, that ecosystem services within these altered landscapes will likely depend in part on native wildlife, and that the more we understand these changes the better we can craft conservation incentives to

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protect key resources that nurture biodiversity. The study of diversity, abundance, conservation, and restoration of species in rural and other human-dominated landscapes has been termed countryside biogeography (Daily 1997), and an instructive example comes from southwestern Costa Rica, although parallel studies have been conducted in many parts of the Americas and elsewhere. These projects convey the message that diverse indicator taxa must be monitored if we are to gain insights into the consequences of anthropogenic disturbance. Around Las Cruces Reserve (227 ha), Coto Brus, Costa Rica, approximately 73 percent of the area has been deforested and replaced with smallscale coffee and cattle production varying from <2 to 50 ha in size and interspersed with <0.5-ha fields of banana, bean, sugar cane, taro, yucca, and other crops, as well as similarly small-sized residential gardens and native forest patches. Within this landscape, deforested habitats support 37 to 42 percent of the herbaceous and scrubby species inventory, showing a substantial amount of native flora still exists 40 years after clearance. Generally, a different, non-overlapping set of 24 to 45 percent of all herbaceous and scrubby plants are restricted to forested habitats (Mayfield & Daily 2005). In this environment, moths, butterflies, mammals, and birds were sampled in the “control” Las Cruces Reserve site and in coffee plots, pastures, and forest remnants of different sizes and at different distances from the reserve. As might be expected, different indicator taxa respond to anthropogenic alteration idiosyncratically (see Table 7-5), but some patterns emerge. Coffee plots with adjacent forest support high butterfly and mammal but not moth diversity. Distance from the reserve had negative consequences for the proportion of unique species of moth found, irrespective of habitat type (Ricketts et al. 2001), and for species richness of birds, although here sites of high agricultural intensity had an additional adverse effect (Luck & Daily 2003). Other former Central American forest habitats in Mexico, Guatemala, Nicaragua, and Panama demonstrate that live fences and riparian strips harbor high bird (Estrada et al. 2000), bat (Harvey et al. 2006), and non-flying mammal (Estrada et al. 1994) species richness, that distance from forest fragments reduces bird species richness (Estrada et al. 1997), that long-distance migrants fare well in disturbed habitats (Hutto 1989)— especially around remaining Acacia pennatula trees (Greenberg et al. 1997a)—and that shade coffee benefits woodland-resident birds, migrant birds, and vulnerable birds (Greenberg et al. 1997b; Petit & Petit 2003). More broadly, across plant and many animal taxa, forest-specialist species richness declines from mature forests to shade coffee plantations to

Ecological-Disturbance Indicator Species 205

Table 7-5. Some of the principal findings regarding indicators of ecological disturbance in southern Costa Rica. Birds Species richness falls off from large to small forest fragments.1 Fewer understory insectivorous birds occur in small forest fragments because they cannot cross deforested areas.2 Substantial proportion of birds occur in the agricultural landscape.1 Remnant trees, riparian strips, and small forest patches are important for nesting and foraging.3 Mammals Coffee-forest remnants have similar species richness to the large forest site.4 Coffee-forest remnants have higher species richness than coffee sites.4 Pasture-forest remnants do not differ in species richness from pasture remnants.4 Moths Species richness is higher near the large forest fragment.5 Species richness and abundance do not differ across different types of agricultural sites.5 Butterflies Only the large forest site supports local endemics.6 Sites near the center of the forest are more similar in species composition than other sites.6 Coffee-forest habitats have higher species richness and abundance than coffee sites.6 Coffee-forest and coffee sites near the forest reserve have different species compositions than those far from the forest reserve.6 1. Daily et al. 2001; 2. Sekercioglu et al. 2002; 3. Sekercioglu et al. 2007; 4. Daily et al. 2003; 5. Ricketts et al. 2001; 6. Horner-Devine et al. 2003.

second-growth forests, with coffee grown in the sun suffering greater losses than that grown under native forest canopies (Philpott et al. 2008). In contrast, forest-generalist species richness is greater in modified landscapes than in mature forests, at least in the neotropics (Pardini et al. 2009). The applied significance of these and other studies using several indicator groups is that small changes in land use, such as protecting primary forest remnants, maintaining live fences, and protecting isolated fruiting trees, could have strong positive effects on diversity in this highly altered landscape (Luck & Daily 2003; Faria et al. 2007; Tscharntke et al. 2008).

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Tropical Plantations Although thousands of small landholders cut tropical forest to plant maize, coffee, sugar cane, and cacao in the neotropics, large-scale forestry plantations and soybean plantations are increasingly responsible for rainforest conversion (Butler & Laurance 2008). Tree plantations have increased from 17 Mha in 1980 to ~70 Mha in 2000 (Brown 2000), 50 percent of which are Eucalyptus species; in Brazil alone, Eucalyptus coverage has increased 17fold since 1980. Barlow, Gardner, Peres, and colleagues conducted an extensive study that compared several taxonomic indicator groups in Eucalyptus plantations and primary rainforest but also in secondary forest at Jari, northeast Brazil. Secondary forests on cleared smallholder and plantation land have reclaimed one sixth of all primary forest that was clear cut in the 1990s, and there is an intense debate as to the value of these forests as repositories of biodiversity (Wright & Muller-Landau 2006; Brook et al. 2006; Gardner et al. 2007a). The Jari project sampled animals in 53,000 ha of plantation and a similar amount of secondary forest embedded in a 500,000 ha primary-forest matrix. Secondary forest is hotter, has lower basal tree area, and has a characteristically denser understory than primary forest, but the same amounts of canopy openness and fallen leaf litter. Eucalyptus forest has the highest temperatures, the most open understories and canopies, and the lowest leaf fall (Barlow et al. 2007a). Once again, the way that different taxonomic groups respond to these and other habitat differences is complex and varied (see Table 7-6). The expected gradient in species richness from primary to secondary to Eucalyptus was manifested only in trees and lianas, birds, fruit-feeding butterflies, and leaf-litter amphibians. Epigeic arachnids, lizards, dung beetles, and bats showed equivalent species richness in secondary forest and plantation. Large mammals exhibited equivalent richness in primary and secondary forests. Five other taxa had similar levels of species richness in all three habitats: moths, grasshoppers, fruit flies, scavenger flies, and orchid bees. The percentage of species unique to each habitat was enormously variable, too: in primary forest, 57 percent of trees and lianas were unique but only 5 percent of orchid bees. The secondary forest and plantation held the same percentage of unique bat species, but the former housed more unique moths yet fewer unique large mammals than the latter (see Figure 7-6). Community structure differed significantly for most habitat comparisons across the 15 taxa. Therefore, choice of the value metric and taxon has a great effect on messages about land-use change in Amazonia. An overall conclusion from Jari is that surprisingly high numbers of forest species are found in

Table 7-6. Summary of the major findings to emerge from the Jari biodiversity project. Birds1,2 Species richness P > S > E.a Species abundances in P did not predict that in S or E. Community structure different in the 3 habitats, with few species in common. Habitat specialists P > S > E. Riparian forest strips within E contain forest birds. Leaf-litter herpetofauna3 Species richness P > S > E for amphibians, P > S = E for lizards. Species abundances P = S = E. Community structure different in the 3 habitats. E has wide-ranging habitat generalists. S subset of lizards and amphibians found in P. E very depauperate. Butterflies4,5 Species richness P > S > E. Species abundance E > S > P. Community structure different in 3 habitats. Species richness S > P in dry season, P > S in wet season. Native understory important for fruit-feeding species. Moths6 Species richness P = S = E. Species abundance P = S > E. Community structure different in 3 habitats. P affected by basal area of lianas. S affected by basal area of live trees and canopy cover. E affected by canopy cover, number of saplings and lianas. Dung beetles7 Species richness P > S = E. Species abundance P > S = E. Larger dung beetles prone to local extinctions. Epigeic arachnids8 Species richness P > S = E. Rank order species abundance P = S but abundant E species rare in P or S. Community structure different in 3 habitats. 1. Barlow et al. 2007b; 2. Hawes et al. 2008; 3. Gardner et al. 2007b; 4. Barlow et al. 2007c; 5. Barlow et al. 2008; 6. Hawes et al. 2009; Gardner et al. 2008a; 8. Lo-Man-Hung et al. 2008. a P, primary forest; S, secondary forest; E, Eucalyptus plantation

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Figure 7-6. The percentage of species unique to primary, secondary, and plantation forests (A), and the percentage of species recorded in primary forest that were also recorded in secondary forest and plantations (B). Primary, secondary, and plantation forests are represented by gray bars, black circles, and white circles, respectively. (Reprinted from Barlow et al. 2007d.)

secondary and plantation forests if they are adjacent to and surrounded by primary forest (Barlow et al. 2007d). This result parallels one of the conclusions from neotropical countryside biogeography—that nearby or adjacent primary-forest patches enhance the conservation value of degraded forest habitats. Oil palm plantations are another form of disturbance in low-lying tropical areas. Malaysia and Indonesia together produce 80 percent of the world’s palm oil, and between 1990 and 2005 this resulted in an estimated loss of 1.0 million and 1.7 to 3.0 million ha of forest in each country, respectively. Palm oil production is currently increasing by 9 percent each year to supply biofuel markets in the European Union and food products in China, India, Indonesia, and the United States. Oil palm grows well in lowlying tropical areas and competes with species-rich tropical rainforests. Many parts of South America, Africa, and Southeast Asia are potentially suitable for oil palm plantations, so understanding its effects on biodiversity is an important goal. The prognosis seems bleak—oil palm plantations have a uniform age structure, little undergrowth, low canopy, and frequent human disturbance, and they are replanted every 25 to 30 years (Fitzherbert et al. 2008; Loh et al. 2009).

Ecological-Disturbance Indicator Species 209

Across studies, total vertebrate species richness in oil plantations was 38 percent that of natural forest. Only 22 percent of vertebrates found in forests were found in plantations, with just a 29 percent similarity in community composition—oil palm plantations were dominated by only a few vertebrate species. In contrast, total invertebrate species richness was 89 percent of natural forest, although only 31 percent of species found in forests were also found in plantations, and community similarity dipped to 21 percent. In short, in plantations forest fauna was lost and replaced by a few largely non-forest-generalist species of little conservation concern. Flora was similarly impoverished (Danielsen et al. 2009). In conclusion, and in contrast to the case studies documented earlier in this chapter, ecological-disturbance indicator studies show consistent rather than varied reductions in three aspects of biodiversity in the altered habitat.

Exurban USA A final example of the use of ecological indicators comes from the industrial world. As urban human populations increase, residential housing and recreational facilities are expanding into former farming, forest, or ranching areas (urban fringe development), and an increasing number of people are building houses in rural areas away from cities (rural residential development, Hansen et al. 2005). These residences often lie near protected areas or are situated in valley bottoms or riparian habitats, both of which hold diverse biological communities (Knight et al. 1995). A fast pace of exurban development since the 1950s has prompted research on species richness and abundance along rural-urban gradients, especially in the western part of the United States, where 13 percent of land area is now influenced by human activities (Leu et al. 2008). Two key findings emerge from these studies: there is no consistent change in ecological-disturbance indicator metrics with intensity of human land use, and taxonomic reactions to urbanization are inconsistent. Blair (1996, 1999) studied summer-resident birds on a preserve, on open space, on a golf course, in a residential neighborhood, in an office park, and in the business district in Santa Clara County, California, USA, in a locale formerly covered by oak woodland. He found first that while moderate levels of development increase overall species richness and abundance, they reduce native bird diversity because widely distributed species flourish at the expense of natives. As development becomes more severe, both total and native diversity declines.

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Second, response patterns to ecological disturbance are heterogeneous, even within a taxon. Many species decline in abundance with increasing land-use intensity: of 21 bird species in the Jasper Ridge Biological Preserve, 14 were found in the open-space recreational area, 12 on the golf course, 10 in the residential area, 9 in the office park, and 3 in the business district. Other studies show that some species thrive in residential areas, such as American robins, American crows, black-billed magpies, scrub jays, and exotics like European starlings. Still other birds peak in abundance at intermediate disturbance levels, such as spotted towhees or Brewer’s sparrows (Maestas et al. 2001; Hansen et al. 2005). These findings are mirrored in mammals—human-sensitive, also called urban-avoider species, include deer mice and red-backed voles; human-adapted or urban-exploiter species consist of species such as raccoons and opossums; and suburban “adaptables” like coyotes and white-tailed deer peak in abundance on moderately disturbed ranchland (Racey & Euler 1982; Vogel 1989; Odell & Knight 2001; McKinney 2002). In sum, the consequences of exurban development for biological communities are not easily tracked by any single ecological-disturbance indicator. Some generalizations have emerged about land use in the U.S. Mountain West—the states of Arizona, Colorado, Idaho, Montana, Nevada, Wyoming, Utah, and New Mexico, all of which have rapidly expanding exurban populations. Studies in Colorado indicate that ranches are more similar to protected areas than exurban development because ranches and protected areas have similar populations of native wildlife species of conservation concern and human-adapted species (Maestas et al. 2003). Moreover, ranches often lie at lower elevations on better soil than marginalized protected areas, so they are worthy of conservation effort. In exurban settings, however, human influence extends more than 300 m from a house due to recreation and exotic predators like domestic cats and dogs (Lenth et al. 2006) and has considerable adverse impacts on wildlife.

Changes in Populations over Time Despite the importance of using several indicator taxa, single-species monitoring programs are still enormously important in conservation (Caughley & Gunn 1996) because they help document rates of decline and so alert local and national authorities and NGOs to problems, help place species in IUCN categories of vulnerability, and can be instrumental in forging political initiatives. When changes in population size are coupled with life-

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history data, population viability analyses (PVAs) can be formulated and can pinpoint factors limiting population growth rates. For example, the consequences of reduced sightings of individually recognized North Atlantic right whale mothers were estimated to be sufficient to account for the downturn in the total whale population size (Fujiwara & Caswell 2001). Collisions with large vessels or entanglement in fishing gear were implicated in maternal mortalities and have since been reduced in some parts of their range (Vanderlaan & Taggart 2009). If the objective is to understand how communities change over time, several species should be monitored simultaneously; there are long-term data sets on many sympatric bird populations in Europe, the United States, and Japan that can be related to patterns of land use (e.g., Yamaura et al. 2009). But to take an example from the developing world, repeated aerial censuses of 11 to 18 large mammal species over 12 years in Tanzania allowed researchers to gain a general understanding of the well-being of eight protected ecosystems. In four large protected-area landscapes across the country, more than 60 percent of these mammal species showed persistent declines during the 1990s—in some cases related to an influx of people fleeing civil unrest, in others to confinement to a national park, to diversion of water for irrigation outside the park, or to poaching (Stoner et al. 2006). In rare instances, long-term population-monitoring programs are carried out across different types of land use to provide a spatio-temporal picture of the effects of land-use change or management action. When the Tanzania large-mammal aerial census data were broken down by management type into heavily protected national parks, game reserves for tourist hunters, game-controlled areas permitting resident hunting, mining, and timber extraction, and other areas where people live in scattered settlements, the percentage of species faring well—that is, increasing significantly or showing no change over a decade—was significantly lower in game-controlled areas and other areas but not in national parks or game reserves (see Figure 7-7). The take-home message is that game-controlled areas need to be upgraded if large-mammal populations are to recover.

Determining the Number of Species-Groups The ecological-disturbance indicator concept is attractive because it is a commonsense alternative to the impossibility of monitoring everything in the environment, but as most of the examples outlined in this chapter have shown, species-groups do not respond in the same way to a given form of

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Figure 7-7. Average percentages of species faring well (significantly increasing or showing no significant change) (dotted) versus poorly (significantly declining or occupying too few pixels to detect a decline) (striped) across (a) wet-season comparisons and (b) dry season comparisons. NP, national parks; GR, game reserves; GCA, game controlled areas; other, other areas with virtually no protection (number of zones surveyed in parentheses). (Reprinted from Stoner et al. 2007.)

land-use change (Basset et al. 2001, 2008). One solution is to sample more than one species-group at a range of sites across the landscape but still keep the number of species-groups limited (Simberloff 1998). Manley and colleagues (2004) set out to test the feasibility of such an approach by asking the question, for a hypothetical survey set up at sites across an ecoregion at two points in time: For which vertebrates could we detect a minimum magnitude of change in occurrence at a minimum level of precision? Their geographic area was the Sierra Nevada and Southern Cascade mountain ranges of California, USA. They considered eight standard sampling methods: point counts for birds; broadcast calling for nocturnal birds; Sherman and Tomahawk live trapping for small- and medium-sized mammals, camera track stations for carnivores; area searches for amphibians, reptiles, large rodents, and ungulates; mist netting for bats; and visual encounters for aquatic amphibians, reptiles, and mammals conducted over a regular sampling grid. Specifically, they wanted to estimate the number of species that had at least the minimum number of sample points within their range sufficient to detect a magnitude of change of ≥20 percent between two points in time at a set level of precision. To do this, they estimated the probability of a species being present at a sampling site as the proportion of the species’ range in the study area, which comprised suitable habitat, and they placed these into low, moderate, and high categories. They also estimated the probability of detection at

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a site as high, moderate, or low based on visibility, ease of identification, and expert opinion. Multiplying these two probabilities together yielded the probability of detection during a sampling period and allowed them to calculate the minimum number of sampling sites necessary to detect a 20 percent change between two points in time. Of 465 vertebrate species evaluated in this way, 76 percent would be adequately detected, that is, have a presence-detection product of ≥0.5. Of the 110 species not adequately detected, 27 had ranges encompassing fewer than 13 sample points and could not be surveyed accurately; the rest would require more sampling points to be detected adequately. The proportion of different taxa for which a 20 percent or greater change could be detected over time was 83 percent for birds, 76 percent for mammals, 65 percent for reptiles, and 44 percent for amphibians. Common species (90 percent) and habitat generalists (98 percent) were easily detected, but only 68 percent of habitat specialists and 33 percent of rare species, the latter principally due to having small ranges in the Sierra Nevada. The exercise demonstrates quantitatively that standard sampling techniques can detect changes in occurrence for three-quarters of the species from four different vertebrate classes. The expense of such a program matches that of vegetation sampling and is within the fiscal and institutional bracket of land-management agencies. Thus there is a practical middle ground that has hitherto been largely ignored in conceptual discussions of ecological-disturbance indicators but that has, interestingly, been taken for granted by Rapid Assessment Teams as well as many ecologists making quantitative descriptions of ecological communities. In short, it is sufficient to sample a few well-known and carefully chosen taxonomic groups to obtain a preliminary understanding of a biological community.

Management Pointers Effects of land-use change on biota are assessed by comparing presence or abundance of single species, or preferably many species, to that found in relatively untouched habitats. Similarly, stewards of multiple-use areas or agricultural land can gauge the consequences of their actions on the landscape by monitoring presence or abundance of multiple species by employing simple measures or more complex indices of biodiversity. Wildlife managers keen to improve game species’ habitats have been doing this for centuries, but they had a sharper focus—on their economically important target species and its prey or breeding habitat rather than on taxonomic

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diversity. There is as yet no recipe for choosing the appropriate group of species to monitor, since they will differ by region, nature of land-use change, agricultural activity, and so on. Nonetheless, there is an unwritten consensus, still under debate, that vascular plants (Juutinen & Monkkonen 2004), birds (Gray et al. 2007; Najera & Simonetti 2010), butterflies (Thomas 2005; Koh 2007), and dung beetles (Nichols et al. 2007; Gardner et al. 2008b) are promising ecological-disturbance indicators—they are attractive, diverse, and reasonably easy to identify, and they are responsive to ecological change.

Summary Species discussed in this chapter are not proxies for other species but are yardsticks for the effects of land-use change on biotic communities. Many species are affected by land-use change resulting from human activity but often in different ways, so it is customary to monitor a number of species in order to assess the effects of disturbance. TWINSPAN and IndVal are methods that assess species’ sensitivities to disturbance. There are many checklists of attributes that species should possess to be good ecological disturbance indicators; however, it is critical to understand the mechanism by which environmental change affects species in order to formulate appropriate mitigation. Sometimes single species are used to assess the effects of ecological disturbance but species-groups provide a deeper understanding. Some examples of land-use change in tropical and temperate ecosystems are given. The Biological Dynamics of Forest Fragments Project in Manaus, Brazil, shows that fragment size, edge effects, and the type of intervening matrix all influence biotic communities, but responses of individual species and species-groups are idiosyncratic. Species in patches of agricultural land in Central America also show individualistic responses when compared to rain forests nearby. Again, in comparisons between Eucalyptus plantations, secondary forest, and primary forest in Amazonia, taxonomic responses vary considerably. In temperate regions, the consequences of exurban housing are manifested in many ways, depending on the species— some of them are adaptable, others are human-sensitive. Monitoring changes in population size over time is the backbone of conservation biology because it alerts management to upcoming problems, but it is always important to monitor more than one species-group in order to judge the effects of anthropogenic habitat alteration.

Many European farmland and wading birds, such as these common snipe, have suffered population declines during the last 40 years due to modernization of agricultural practice. Changes in one species mirror those of others in the same guild. (Drawing by Sheila Girling.)

Chapter 8

Cross-Taxon-Response Indicator Species

Habitat Alteration Pristine habitat is initially transformed by small-scale farmers moving in to plant crops, graze livestock, and hunt, and by large-scale projects such as dams, plantations, and agribusiness. Conservationists want to know how species’ composition and abundances are affected by such changes. Subsequently, as the landscape becomes increasingly human-dominated, we may want to know how different forms of agriculture and urbanization affect biota. These questions can be answered directly by sampling multiple taxa across gradients of disturbance (as discussed in chapter 7), or possibly by sampling a restricted subset of taxa that will be a proxy for the responses of other species (as indicated by the dashed line in Figure 8-1). The former question has been addressed a great number of times (e.g., Forman et al. 2003; Laurance & Peres 2006; chapter 7) but the latter only rarely and sometimes only as an afterthought—having measured presence or abundance of several taxa in different land-use categories, academics sometimes then ask whether taxonomic responses are correlated (e.g., Klein et al. 2002). Accordingly, they have set about trying to identify one species-group that can, at least on a case-by-case basis, give a general indication of the consequences of anthropogenic disturbance across a snapshot in time. These

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Figure 8-1. Conceptual model illustrating use of indicator species to infer population trends and habitat suitability for other species of interest. Solid lines show simple cause and effect or direct influence of environmental factors; dashed line shows influence or extrapolation of population attributes or environmental conditions from the indicator species to the species of interest. (Reprinted from Landres et al. 1988.)

species-groups are sometimes termed ecological indicator species but for the sake of clarity, I will call them cross-taxon-response indicator species, because their presence or population size may predict the response of other taxa to environmental change. Cross-taxon-response indicator species differ from local or classic umbrella species because the former is a tool for documenting responses to ecological change, the latter a tool for selecting reserves of appropriate design. To date, there has not been a great deal of empirical work on crosstaxon-response indicator species, perhaps because their identification involves scientists sampling a great many taxa in a given landscape. Current evidence is neither overwhelmingly in favor nor uniformly against indicators being effective surrogates of other species’ responses to ecological change, and the mechanisms behind any positive associations are usually opaque. In this chapter I describe various implementations of the crosstaxon-response indicator species concept in three spheres of conservation. Then I examine three surrogate concepts related to cross-taxon response indicators: management indicator species, early-warning species, and substitute species. Finally, I assess challenges in using cross-taxon-response indicator species.

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Fora for Cross-Taxon-Response Indicator Species Land-Use Changes Empirical studies of taxonomic congruence across a gradient of disturbance have generated mixed but on the whole negative findings. An early and influential study by Lawton and colleagues (1998) examined eight taxonomic groups across six sorts of habitat types in Mbalmayo Forest Reserve, Cameroon (near primary forest, old-growth secondary forest, partial manual clearance, partial mechanical clearance, complete clearance, and manually cleared farm fallow). They found rather variable changes in species richness with increasing disturbance (see Figure 8-2). As a consequence, out of a total of 36 possible correlations, only six proved significant and two of these were negative. They concluded “by using changes in species richness of familiar and well-studied groups (such as birds or butterflies) as indicators of changes in the diversity of other taxa gives a highly misleading picture of overall faunal changes” (74). Working in Sulawesi, Schultze and colleagues (2004) sampled trees, understory plants, birds, butterflies, and dung beetles across a gradient of near-primary to secondary forests to agroforestry systems to annual crops. Here all the categories of fauna and flora declined along the gradient of disturbance (see Figure 8-2) and, in complete contrast to Lawton and colleagues’ work, an extraordinary number of taxonomic pair-wise comparisons were significantly positively correlated (36 out of 38) across 10 to 20 sites. This particular study notwithstanding, taxon congruency across ten studies is not impressive (see Table 8-1). Many factors could be responsible for a lack of consistency in these studies, including differences among biomes, habitats, patterns of land-use change, types of alteration, and differential uses of habitat within a given landscape by different species (Gardner et al. 2009). Vertebrates may be rather poor predictors of other vertebrates’ responses to fragmentation, because studies regularly uncover differential sensitivity among related species. For example, as forest woodlots decline in size in western-central Indiana, USA, southern flying squirrels are lost first, then gray squirrels, then red squirrels, then fox squirrels, and finally eastern chipmunks (Nupp & Swihart 2000). In a woodlot of a given size, there remains a nested subset of small mammals found in continuous forest. This pattern of highly ordered nested subsets where species are lost in a predictable stepwise fashion has been found repeatedly in fragmented habitats

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Figure 8-2. Species richness of animal and plant groups along a gradient of increasing habitat modification. Left eight panels (Cameroon): NP, national park; OS, oldgrowth secondary; PManC, partial manual clearance plus plantation; PMechC, partial mechanical clearance plus plantation; CC, complete clearance with young plantation; FF, manually cleared farm fallow. (a) birds (with mean habitat scores [open circles] on right ordinate), (b) butterflies, (c) flying beetles (filled circles, malaise traps; open circles, flight interception traps), (d) canopy beetles, (e) canopy ants, (f) leaf-litter ants, (g) termites (two measures), and (h) soil nematodes with 95 percent confidence (two additional data points from heavily disturbed sites). (Reprinted from Lawton et al. 1998.) Right ten panels (Sulawesi): NF, near-primary forest OSF, old secondary forest; YSF, young secondary forest; AF, agroforestry sys-

tems; AC, annual culture. Means and 95 percent confidence intervals of true number of species (b–f and j) or interpolated data from rarefaction curves where sampling effort differed between land-use types (a, g–i). Within each panel, data points with the same letter are not significantly different at P < 0.05. Additionally, open circles give total species richness as estimated by the first-order jackknife method. (Reprinted from Schulze et al. 2004.)

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Table 8-1. Levels of multi-taxon congruency in response patterns to landscape change in tropical forests (from Gardner et al. 2009). Multi-taxon congruency

Taxonomic groups

Birds, butterflies, flying beetles, canopy beetles, canopy ants, leaf-litter ants, termites, soil nematodes1 Trees, understory plants, birds, butterflies, dung beetles2 Birds, bats, butterflies, dung beetles3 Ferns, frogs, lizards, birds, bats4 Bats, birds, dung beetles, epigenic arachnids, fruit flies, fruit-feeding butterflies, grasshoppers, large mammals, leaf-litter amphibians, lizards, moths, orchid bees, scavenger flies, small mammals, trees, lianas5 21 taxa from across 7 orders of arthropod: Coleoptera, Diptera, Hemiptera, Hymenoptera, Mantodea, Neuroptera, Orthoptera6 Trees, lianas, herbs, epiphytic liverworts, birds, butterflies, lower canopy ants and beetles, dung beetles, bees and wasps and their parasitoids7

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Table 8-1. Continued Multi-taxon congruency

Taxonomic groups

Fruit-feeding butterflies and 5 other order/families of arthropod (Araneae, Carabidae, Scarabaeidae, Staphylinidae, epigaeic Coleoptera excluding above)8 Ferns, trees, frugivorous butterflies, leaf-litter frogs and lizards, bats, small mammals and birds9

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* Mean correlation coefficient of species richness responses. + Mean correlation of community structure responses (based on Mantel tests of similarity matrices). # Number and taxon in brackets denotes the level of cross-taxon-response congruency for the highest-performing single taxon. 1. Lawton et al. 1998; 2. Schultze et al. 2004 ; 3. Harvey et al. 2006; 4. Faria et al. 2007; 5. Barlow et al. 2007d; 6. Bassett et al. 2008; 7. Kessler et al. 2009; 8. Uehara-Prado et al. 2009; 9. Pardini et al. 2009.

(Brown 1971; Patterson 1984; Wright et al. 1998; K. Crooks 2002), so that presence of the most-sensitive species will always predict the presence of others, but the presence of the least-sensitive species is unlikely to predict the presence of others. Insects, on the other hand, might be expected to have greater predictive power—at least in relation to the plants on which they feed. In New Zealand, for instance, the number of beetle morphospecies is associated with the proportion of native as opposed to introduced plant species across seven different habitat types (Crisp et al. 1998); and in Venezuela, different sorts of disturbed forest have characteristic tiger beetle assemblages (Rodriguez et al. 1998). In these and similar studies it is not necessarily clear which taxa constitute the conservation shortcut since all have to be inventoried! Outside academia in large-scale inventory surveys, it is worthwhile first to conduct a cost-benefit analysis of ease of inventorying particular taxa (see chapter 10). For instance, in the Eastern deciduous forests of the

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United States, forest moths were chosen to predict overall lepidopteran species richness (not the other way around) because they exhibited a subjectively higher cumulative “indicator score” based on species diversity, wellknown taxonomy, ease of identification, known natural history, ease of survey, and ecological fidelity (Summerville et al. 2004). It is difficult to construct generalizations about species’ or speciesgroups’ congruency value—there are so many factors to consider. For birds and butterflies, the most important aspect affecting species richness near Madrid, Spain, is landscape heterogeneity, with specific aspects of land use being less critical. For amphibians and reptiles, however, it is the proportion of certain land-use types (Atauri & de Lucio 2001). For mammals, the probability of occurrence can be related to both fragment isolation and size. K. Crooks (2002) examined carnivore responses to habitat fragmentation in California. Across 29 urban fragments and 10 unfragmented control sites of differing area and isolation, he found differential sensitivity to urbanization. Whereas spotted skunks, long-tailed weasels, and American badgers only occurred in larger habitat blocks, coyotes, opossums, gray foxes, domestic cats, striped skunks, and raccoons were detected in most urban fragments. Bobcats were detected in 9 out of 10 controls but only 2 of 29 urban fragments, mountain lions in 7 control fragments and no urban fragments. Crooks could determine that coyotes would occur with a 50 percent probability in fragments of 1.8 km2 in size, mountain lions in 23-km2 patches. Looking at it the other way, domestic cats dropped below a 50 percent likelihood in fragments larger than 1.4 km2 (see Figure 8-3). Similarly, coyotes had a 50 percent probability of occurrence in fragments isolated by 833 m, but bobcats by only 6 m, meaning that they required fragments to be very close together. As species dropped out of the community in a predictable order, the presence of certain species such as mountain lions meant that most of the other carnivores would be present, although the presence of others, such as raccoons, had poor predictive power. These examples show that correct choice of taxon and even species depends on sound ecological knowledge of responses to anthropogenic change and that choice of indicator species cannot be guided by “fishing expeditions” or advocating a special taxon based on familiarity or a whim.

Agricultural Landscapes Cross-taxon-response indicator species do not work effectively in humandominated rural landscapes. In Sweden, comparisons across fields sown

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Figure 8-3. Logistic regression models of the probability of occurrence of native (solid lines) and exotic (dashed line) carnivores as a function of (A) fragment area, and (B) isolation. Area and isolation curves were constructed after the other independent variable was held constant by substituting its median values into a two-way (area × isolation) logistic regression model. Only species with significant area and isolation effects are shown. Dotted line represents 50 percent probability of occurrence. (Reprinted from K. Crooks 2002.)

with different crops, and between mown and unmown fields left fallow for different periods, reveal that trap-nesting Hymenopteran diversity correlates with plant-species richness, thereby providing a shortcut for assessing plant diversity (Tscharntke et al. 1998). Yet attempts to use birds to identify the conservation value of semi-natural pastures under different forms of management fail when all avian species, or species with decreasing populations, or dry-pasture species with declining populations, are each tried as cross-taxon indicators (Part & Soderstrom 1999). Then, in Venezuela, comparisons between tropical montane cloud-forest fragments and coffee plantations demonstrate a reduction in frog-species richness in the coffee,

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an increase in dung beetle diversity, but no difference in bat-species totals (Pineda et al. 2005). While a great number of studies demonstrate responses to agriculture (e.g., Pimental et al. 1992; Daily 1997; Duelli & Obrist 1998; Klein et al. 2002; Weibull et al. 2003; Bhagwat et al. 2008), there is simply no overwhelming evidence that population responses are sufficiently correlated to enable the use of certain species as reliable crosstaxon-response indicators at a small scale. Across a broader area, the downbeat message is the same. Take, for instance, a study involving 25 agricultural 16-km2 landscapes carried out across seven European countries that estimated total species richness of vascular plants, birds, and five arthropod groups in addition to measuring area, diversity, and spatial arrangement of landscape elements, as well as crop diversity, livestock density, and fertilizer and pesticide use (Billetter et al. 2008). No taxon predicted another with any certainty, although there were some associations: bees with herbs, spiders with birds, carabid beetles with horseflies. Yet neither woody plants nor bugs could be predicted by any taxon whatsoever. In contrast, there were several associations between various taxa and landscape and land-use variables. In particular, the share of seminatural habitat in the study site that was positively associated with species richness for vascular plants, birds, and arthropods, as well as habitat diversity, was associated with bee species richness. Scale is critical in interpreting the efficacy of cross-taxon indicator species because the spatial requirements of taxa are so variable. Take, for example, responses of different bee species to intensively managed agricultural areas and patchily distributed fragments of forests in Lower Saxony, Germany (Steffan-Dewenter et al. 2002). There, species richness and abundance of wild solitary bees show a positive correlation with the percentage of seminatural habitats at small scale (of up to 750 m away), but bumble bees and honey bees that have a larger foraging range do not respond to landscape alterations at this scale. Flower-visiting honey-bee abundance actually rose with decreasing proportion of seminatural habitats at a radius of 3,000 m (Ricketts et al. 2001; Steffan-Dewenter et al. 2002). Furthermore, issues of target taxa being generalists or specialists (Steffan-Dewenter 2003) and trophic level affect these scale-dependent responses. With trophic level, the strength of species-area relationships should increase with trophic rank, because predatory species have larger home ranges than herbivores, and thus carnivores should be lost more quickly from small farms than should herbivores. (We might expect fewer trophic-level differences with regards to faunal relaxation on large farms or habitat fragments, however [Holt et al. 1999]). There is some evidence for these ef-

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fects, because predatory species richness is very sensitive to landscape simplification, phytophages are less sensitive, and omnivores are insensitive, suggesting that higher predatory guilds may be the most suitable ecological indicators of the sort of habitat disturbance that involves ecological changes related to patch size (see Figure 8-4). Overall, issues of scale and trophic level, coupled with differential responses to the type of crops planted and to various agricultural practices (Weibull et al. 2000), make generalizations about congruency in species’ responses to agricultural landscapes very difficult indeed.

Management Areas Sometimes conservation managers want to understand how different forms of legal protection affect wildlife, and a very small number of studies have examined several taxa simultaneously across legal boundaries at an ecological scale (see Carrillo et al. 2000). In the Bushbuckridge area of Mpumalanga Province of South Africa, comparisons of species diversity between a protected research area and adjacent communal rangelands with high human population density and pressure on natural resources demonstrated that responses to disturbance were taxon-specific. Lizards showed higher

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species richness and were often more abundant on communal lands (Smart et al. 2005), as was plant diversity (Shackleton 2000); grasshopper diversity was equivalent (Prendini et al. 1996), whereas bird diversity was higher in the protected area (Lewis 1997). Staying in Africa, this time in and around Katavi National Park in western Tanzania, large-mammal abundance but not species richness declined precipitously from heavy to partial to no protection-management areas; small-mammal species richness as well as amphibian and bird diversities declined with increasing human activity but then rose again on village land; but butterflies and trees showed no clear pattern (Gardner et al. 2007c). Across Europe, a meta-analysis of 120 comparisons of remnant unmanaged forests to managed forests showed that species dependant on continuity of forest cover, large trees, and deadwood, such as bryophytes, lichens, fungi and saproxylic beetles, as well as carabid beetles, were negatively affected by forest management, whereas vascular plant species were favored. The responses of bird species were heterogeneous (Paillet et al. 2010). All three studies suggest that one taxonomic group has limited ability to predict reliably responses of other taxa to different management regimes, although formal correlations were not conducted in any case.

Intraguild-Response Indicator Species A variant of the cross-taxon indicator is the more restrictive intraguildresponse indicator, which may hold some promise—because if two species from the same guild rely on the same resources, and if those resources disappear, it seems likely that the presence of one species should predict that of another. Canterbury and colleagues (2000) examined birds in 197 plots in loblolly shortleaf pine forests from Georgia to Virginia, USA, classifying them into foraging, nesting, and habitat functional groups or communities. In the habitat assemblage group, the presence or absence of individual bird species showed particularly strong positive correlations with each other. Within this particular assemblage, shrubland species and mature-forest species respectively showed decreasing and increasing probability of occurrence with the corresponding percentage of canopy cover across sites. Furthermore, canopy cover itself and tree basal area were both negatively associated with subjective levels of disturbance. Therefore, the bird community index apparently reflects avifaunal responses to disturbance. Next, using a principal components analysis of vegetation measures, Canterbury and coworkers (2000) constructed a habitat index that ac-

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counted for 62 percent of the variation in the bird community index and was correlated with subjective disturbance measures. Therefore, these habitat and bird-community indices are both strong predictors of forest condition—from fragmentation to clear-cutting to agriculture to residential use, at least in the southeastern United States. More generally, bird-community membership has been used to set regional planning priorities because birds’ spatial distribution is often indicative of remaining habitat availability and quality, more than is the case for single species or even overall species richness (Bishop & Meyers 2005).

Population Changes When conservation ecologists want to monitor changes in several populations over time, they may try to choose a species or taxon that will alert them to coincident changes in sympatric species. Indicators of population change are most expeditious where other populations are logistically difficult to monitor if they live in marine or freshwater environments—or if they require more-costly sampling techniques than do the indicator species or taxon. For instance, bird-population sizes are easier to monitor than populations of small mammals. A striking example of population-change indicators comes again from changes that have occurred in bird communities. Since the 1970s, European farmland bird populations started to decline rapidly as a result of agricultural intensification, including loss of hedgerows, land drainage, increased mechanization, fertilizer and pesticide use, reduction in spring cultivation, simplification of crop rotations, changes in crop use, and loss of farm diversity (Aebischer et al. 2000; Vickery et al. 2004). These have reduced nesting success in several species, such as corn buntings, and depressed survival in other species, such as house sparrows. That these changes are tied to agricultural intensification can be seen by increases in farmland birds in the accession countries since the breakup of the East European bloc and the concomitant reduction in agricultural intensity there, in contrast to the continued decline of these birds in western Europe (see Figure 8-5a). Woodland bird populations showed less of a decline (see Figure 8-5b). Birds are a very promising indicator taxon because they fit almost all of the criteria shown in Table 7-1, and there are a great many birdmonitoring schemes established throughout Europe. Changes in farmland bird populations have been accompanied by declines in populations of several other taxa that are arguably more difficult to monitor (see Table 8-2).

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Figure 8-5. Supra-national, multi-species indicators of European bird populations 1980–2002 (a) European farmland birds (countries = 18, species = 23, ±1.96 S.E.) and (b) European woodland, park and garden birds (countries = 18, species = 24, ±1.96 S.E.) the index for the base year (1990) is set to 100. (Reprinted from Gregory et al. 2005.)

Management Indicator Species There is a long history of using indicator species in forestry management and conservation, with different types of indicators conveying information on the current condition of a forest resource, on the level of pressure that impacts that resource, and on the institutional plans to improve the condition of the resource (Hagan & Whitman 2006). The resource may be biodi-

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Table 8-2. Evidence showing concomitant declines of farmland biodiversity in the United Kingdom and in continental Europe (from Gregory et al. 2005). Significant changes in invertebrate and plant populations with a preponderance of declines. Plant diversity, abundance, and seed bank have declined. Trends in invertebrate populations have been stable or have declined (Donald 1998). Plant and arthropods associated with farmland. Many arable plants have become rare, but some attained pest status. Many arthropods have declined (Southerton & Self 2000). Widespread declines in many plant, bird, butterfly, bee, and dung beetle taxa, with some extinctions (Pitkanen & Tiainen 2001). Temporal links between declines of farmland bird and arthropod populations and changes in agricultural practices (Benton et al. 2002). Plants, invertebrates, reptiles, birds, and mammals show widespread population declines in farmland. Marked loss of specialized taxa in favor of generalist taxa (Robinson & Sutherland 2002). Plants, birds, and butterflies: respectively 28 percent, 54 percent, and 71 percent of their species had declined in range size (Thomas et al. 2004). Parallel declines in bird and butterfly species (van Strien et al. 2004).

versity within managed forests, or ancient woods where many species are now threatened (or it may be a forest type of economic value or large trees to be harvested, although these are more peripheral to conservation). Management indicator species (MIS), defined “as any species, group of species, or species habitat elements selected to focus management attention for the purpose of resource production, population recovery, maintenance of population viability, or ecosystem diversity” (USDA Forest Service 1984, Amendment 260-91-8), emerged during the mid-1970s and 1980s, when their application became formally integrated into procedures used by the USDA Forest Service (USFS) and U.S. Fish and Wildlife Service (USFWS) to set management goals. Indeed, National Forests are now mandated to incorporate MIS in the analysis of management-plan alternatives during forest planning (Everest et al. 1997). There are five classes of MIS: endangered species, species with special habitat needs, game species, non-game species of special interest, and plant and animal species selected because their population changes are believed to indicate the effects of management activities on other species of selected biological communities or on water quality (Patton 1987). These were later condensed into recovery species, featured species, sensitive species, and

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ecological indicators (Code of Federal Regulations 1985; see Landres 1992). (Note, a similar development was initiated by the USFWS under its habitat-evaluation procedures [HEP] to document the quality and quantity of available habitat for “evaluation species”—species of high public interest, or those sensitive to specific environmental factors, or keystone species, or a single species representative of a guild). The reasons to consider using MIS are therefore multifaceted and include indicating the effects of human disturbance on other species (a sort of cross-taxon-response indicator, and hence their inclusion here) or on the environment, and in maintaining populations of certain species in their own right (i.e., ecological-disturbance indicators). Note, however, that MIS are also very similar to management umbrella species (see chapter 4), and it may be helpful to think of the latter as a subset of the former, both in the sense that MIS may be chosen on the basis of other criteria, too, and because MIS are used for resource production as well as conservation tasks whereas management umbrella species are used solely for conservation objectives. Despite conceptual overlap with at least three other surrogate typologies, MIS are still chosen by the authorities for monitoring because their population trends are believed to indicate the effects of management activities in particular U.S. forests. As an illustration, in southern California, USA (see Table 8-3), management is charged with maintaining and im-

Table 8-3. Management indicator species for southern California forests (from USDA 2005). Species

White fir Coulter pine Bigcone Douglas fir Blue oak Engelmann oak California black oak Valley oak Arroyo toad Song sparrow California spotted owl Mountain lion Mule deer

Indicator of management

Montane coniferous forest Coulter pine forest Bigcone Douglas fir forest Oak regeneration Oak regeneration Oak regeneration Oak regeneration Aquatic habitat Riparian habitat Montane coniferous forest Fragmentation Healthy, diverse habitats

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proving habitat functions for the Arroyo toad, including taking care of its primary feeding areas, winter range, breeding areas, movement corridors, and habitat linkages, and with monitoring trends in its abundance, distribution, and habitat conditions, in the belief (and this is critical) that this will benefit other aquatic and riparian fish and game species. But MIS are also chosen to be watched carefully so that action can be taken to prevent their population decline; for example, improving regeneration prospects of Engelmann oak (see Table 8-3). Given these dual tasks, lack of precision, and the number of underlying assumptions about the predictive power of indicators, MIS have been roundly criticized (Landres et al. 1988; Lindenmayer et al. 2000; Rolstad et al. 2002).

Difficulties with the MIS Concept Niemi and coworkers (1997) examined the ability of 25 bird species selected as MIS to predict habitat associations or other species of bird in the Chequamegon Natural Forest of northern Wisconsin, USA. Of the 18 possible MIS listed by the Land and Resource Management Plan for that forest, only seven were sufficiently abundant to be surveyed and only two species showed a nonrandom distribution with respect to forest type. There was no consistency in how these seven species were associated with other bird species in different sections of the study area. Over time, only one significant association, that between the yellow-bellied flycatcher and the black and white warbler, remained consistent over a seven-year period. In short, MIS did not predict specific forest types, and species associations were not maintained spatially or temporarily, thus casting grave doubts on the utility of using “representative” forest species. In another test, the northern flying squirrel, a species proposed as a MIS of temperate rain forest of southeastern Alaska, USA, was examined in relation to 26 vegetative and structural habitat variables and also multivariate habitat factors generated by factor analysis (Smith et al. 2005). The multivariate factors were poorly associated with presence of flying squirrels, suggesting that squirrel home ranges did not consist of multiple lateseral forest attributes occurring together. Rather, two single variables better explained their presence: large >74-cm dbh trees and understory Vaccinium cover, each of which are easier to measure than the squirrels themselves! This raises an important and somewhat obvious issue, namely that it may be easier to identify old-growth forests through direct measurement. Certainly, forest structural diversity can be assessed by measuring

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heterogeneity (i.e., the relative abundance of structural components in the vertical and horizontal planes), complexity (i.e., the absolute abundance of individual structural components), and scale (i.e., variation as a result of the size of the area). These give some indication of the number of niches for microbial plant and animal communities (Ferris & Humphrey 1999). Documenting forest structure itself is relatively straightforward and involves recording the number of trees of different sizes classes, the number of tree species, the size and height of canopy, stand density and amount of deadwood, and so on—deadwood, in particular, is recognized as a key indicator in forests because it fosters a wide range of species (Franklin et al. 1981). Nonetheless, as in the case of MIS, there are shortcomings: habitat variables are often poor predictors of species abundance (Lindenmayer et al. 2002), and they are greatly affected by the rules chosen to classify habitat. The coarse-grained land-cover maps favored by managers explain very little variance in bird abundance, for example. Moreover, some bird guilds are well explained by a given habitat classification, whereas others are explained with little confidence (Cushman et al. 2008). To discover that red wattlebirds are more likely to be found where eucalypt cover is consolidated while crimson rosellas are found where the same cover is dispersed is sobering (Lindenmayer et al. 2002). In reality, identifying forests of high biodiversity requires knowledge not only of species composition, vascular plants, bryophytes, lichens, fungi, butterflies, carabid beetles, woodpeckers, and so forth (Ferris & Humphrey 1999), but also forest structural and functional components; so it may be most expeditious to measure abundance of a few selected species in combination with some structural elements (Noss 1990; chapter 10).

Early Warnings There is a special sort of cross-taxon-response indicator species that, in theory, would be extraordinarily useful in conservation: indicators that could give early warning of an impending problem for other species. One hundred years ago, canaries (actually lemon-breasted seedeaters) were taken down into coal mines to inform miners working underground that carbon monoxide levels had risen to lethal levels that would soon endanger their health; when the canary stopped singing it was time to leave the mine (Burrell & Seibert 1916). The concept has been borrowed for assessing ecosystem health by ecotoxicologists (see chapter 6), but conservation biologists would also like to find analogous species to provide an early warning of en-

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vironmental disturbance that will soon affect populations of other species over space or time. There are very few concrete examples of these extra-sensitive species, again sometimes called sentinel species (borrowing and changing the term from ecotoxicology and medicine), that can predict in advance sympatric species’ responses to environmental change. One example is the percentage of grass cover being an early warning of desertification in the Chihuahuan desert (de Soyza et al. 1998). Usually, however, the logical sequence of events runs in other directions (see Table 8-4). Often, for example, declines are noticed in some populations, as was the case in southern Asia with populations of oriental white-backed, long-billed, and slender-billed vultures that have declined by more that 95 percent since the early 1990s (Oaks et al. 2004). Later these were followed by declines in Egyptian and redheaded vultures (Cuthbert et al. 2006), and other species such as marabou storks are now suspected of being in trouble (Cuthbert et al. 2007). These declines were caused by scavenging from livestock carcasses treated with diclofenac, a non-steroid anti-inflammatory drug, and, to a lesser extent,

Table 8-4. Common causal chains in uncovering a conservation problem. Early warning species are shown at the bottom. First event

Second event

Species found to be declining.

Populations of other species found to be declining. Species found to be Environmental declining. change acknowledged. Environmental Populations found change recognized. to be declining.

Environmental Species identified change recognized. as likely to be at risk. Environmental Species found to change anticipated. be declining.

Measures

Example

Steps taken to stop all declines.

Asian vultures

Steps taken to identify specific aspects of change. Specific aspects of change specified and restoration initiated. Legal steps taken to protect them.

Amphibians

Steps taken to stop decline of that and other species.

European farm birds

Polar bear

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carpofen and fluinixen, shortly before the cow died. Further research has now identified meloxicam as a veterinary alternative to diclofenac that does not result in kidney damage, visceral gout, and death in vultures, and the Indian Government has banned the sale of diclofenac (Swan et al. 2006). Another alternative sequence of events is the simultaneous discovery of several species in decline and then making concerted attempts to uncover the cause. A current disturbing example is the global decline in amphibians (Lips et al. 2006; Pounds et al. 2006), which has resulted in nine species of amphibian becoming extinct since 1980 and another 113 possibly extinct. This crisis led to intense research that uncovered the pathogen chytridiomycosis as being an important source of mortality (Rachowicz et al. 2005), although several other factors, including climate change, contribute as well (Collins & Storfer 2003). At present the most promising short-term solution is captive breeding and reintroductions (Griffiths & Pavajeau 2008). A third avenue is simultaneously recognizing that environmental change is underway and that some populations are in decline, and then, through research, linking the two to determine the specific aspects of change that are responsible in the hope of ameliorating them. Changing farming practices in Britain, coupled with declines in farmland birds, drove research that showed that loss of nest sites and reduction in winter seeds and summer invertebrates for feeding nestlings were responsible for lowered avian reproduction and survival. This further led to Biodiversity Action Plans for individual species and schemes to increase wet habitats (Bradbury & Kirby 2006) and provide supplemental winter seed (Siriwardena et al. 2007). A fourth route is where well-understood environmental changes are predicted to affect certain species that then become a focus of conservation attention. There are a number of transformations anticipated as a consequence of climate change for ice-obligate mammals that rely on sea-ice platforms, ice-associated cetaceans that are adapted to sea-ice-dominated ecosystems, and seasonally migrant species for which sea ice can act as a barrier (see Table 8-5). Melting arctic ice due to global warming has resulted in the listing of the polar bear under the Endangered Species Act, although little can be done about stemming its decline in the short term. Thus, although early-warning species would be a tremendous asset in theory—forecasting cross-taxon responses in advance at the ecological scale—in practice the sequence of discovery is more convoluted. If we knew that a particular target species was likely to decline in the near future despite having healthy populations now, we would have an op-

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Table 8-5. Anticipated climate-related changes for ice-obligate, iceassociated, and seasonally migrant marine mammals in the arctic (from Moore & Huntingdon 2008). Species category Species

Ice-obligate Polar bear Walrus Bearded seal Ringed seal Ice-associated Bowhead whale Beluga Narwhal Seasonally migrant Gray whale Harbor seal

Anticipated change

Declines in recruitment and body condition

Migration alteration and occupation of new feeding areas

Novel occupation of Arctic latitudes and longer residence times

portunity to protect it or to address the factors that we suspect will affect it adversely. There is now some opportunity to do this—but only for taxa for which we have good life-history and ecological data. The opportunity presents itself because extinction risk is not only a reflection of extrinsic anthropogenic drivers but of species’ intrinsic biological attributes. For example, carnivores and primates that occupy small geographic ranges, occur at low population densities, occupy a high trophic level, and exhibit low reproductive rates are prone to extinction (Purvis et al. 2000). The importance of intrinsic factors varies even within mammalian clades (Fisher et al. 2003), so if traits such as these evolve along branches of a phylogenetic tree, then close relatives of threatened species are expected to be at greater risk than distant relatives, allowing us to make predictions about extinction risk in the absence of data on levels of extrinsic threat (Mace et al. 2003). It is helpful to think of this as a discrepancy between predicted extinction risk and present extinction risk. Cardillo and others (2006) converted the World Conservation Union Red List into a 0 to 5 scale using only species listed under criterion A—those that have a recent or ongoing decline in population size (see Table 1-1). This they correlated with numerous life-history and ecological variables, such as gestation length and arboreality, for all major mammalian clades, using phylogenetically independent contrasts, which

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yielded a set of independent biological predictors of extinction risk. They then fitted the same set of predictors in standard non-phylogenetic regressions and used these to calculate predicted values of extinction risk. Latent extinction risk was calculated as predicted-minus-current risk of extinction based on the Red List. It can be thought of as the potential of a species to decline rapidly if subject to the human impact equivalent to the average that other species experience. When they plotted the geographic ranges of those terrestrial mammals with high latent extinction risks on a world map, two areas stood out: northern boreal forests of the New World, and an arc of islands from the Andaman and Nicobar Islands through Indonesia, New Guinea, and Melanesia. These are areas where human impact is arguably low but where mammal species are inherently sensitive to human disturbance. Similar algorithms have been applied to other groups and have yielded surprising results. Hylid and Bufonid frogs both seem predisposed to chytridiomycosis infections and climate change, with the former clade concentrated in Central and South America and Australia (Corey & Waite 2008). Given the population declines in closely related British birds, the now-common blackbird seems destined for an imminent population reduction (Thomas 2008). Based on projected human population increases, African viverrids seem particularly susceptible to risk of extinction because of their life-history traits (Cardillo et al. 2004). Now there is an argument for watching these species currently listed as “of least concern” or “greenlisted species” very carefully indeed because of these early warnings.

Substitute Species Some researchers have proposed using behavioral indicators of environmental change in common species as substitutes for parallel behavioral changes in other, rarer species. Behavioral responses are common outcome variables in ecotoxicological studies and are usually observed in species thought to be under environmental stress, but here it is proposed that the behavior of one species is used as an indicator of behavioral responses in others. This is because it may be difficult to locate and observe rare or narrow endemics, sample sizes may be inadequate, and conducting experimental tests on endangered species may be problematic. Substitute species have been defined (very broadly) as “species or populations that are studied on the assumption that they show how populations of conservation concern might respond to environmental disturbance” (Caro et al. 2005, 1822) and

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are therefore cross-taxon-response indicators but the focus is on behavioral rather than population change. Substitute species are chosen either on the basis of genetic or ecological similarity to the target so as to approximate its response to disturbance; or they may be chosen because they are members of a group of species along with the target (see Dearborn et al. 2001) and so can provide a consensus response close to that of the target. Or they may be chosen because they help identify a relationship between the response and some aspect of the phenotype. A practical example comes from using the presence of the relatively common bronze darter to predict the response of the amber darter, a species that is restricted to just two rivers in the southeastern United States, to aspects of urbanization (Wenger 2008). The difficulty with substitute species is that the endangered species is likely to be subject to greater disturbance than other, more-common species or is more sensitive to a given level of disturbance (Caro et al. 2005). In fact, the number of studies showing that different species exhibit differential sensitivity (whether behavioral or not) to landscape change is enormous—insects (Collinge 2000) or carnivores (Gehring & Swihart 2003; Riley et al. 2003) in fragmented North American landscapes, to mention only two. Therefore the use of cross-taxon-response behavioral indicators may be limited.

Problems with Cross-Taxon-Response Indicator Species There is a conundrum: on one hand, “the concept of indicator species remains an appealing and potentially important one because of the impossibility of monitoring everything” (Lindemayer et al. 2000, 945); on the other hand, there are a great many difficulties in using indicators at the species or species-group level. For convenience, we can classify these conceptual problems into those that pertain within a single habitat and those that apply across habitats. First, it is difficult to believe that two species should respond in exactly the same fashion to environmental change. Species are influenced by disease, predation, weather, refuge availability, breeding sites, and inter- and intraspecific competition, but to differing degrees, so it seems improbable that any two species would respond in exactly the same way to a given environmental challenge. Species’ responses to climate change are a case in point (Root & Schneider 2002; Lindenmayer & Fischer 2003; Root et al. 2003; Jonzen et al. 2006).

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Nonetheless, there are situations where populations do change in parallel, such as declining European farmland birds, because constellations of species are similarly limited, some by shortage of nest sites, others by shortage of winter feed, others by shortage of food for offspring—often these species belong to the same foraging guild. Similar to Lambeck’s focal species, the challenge is to choose cross-taxon-response indicator species knowing that the limiting factors are shared by other sympatric species and suspecting that one of these factors is changing for the worse. Therefore, considerable background knowledge is required, which limits the usefulness of the cross-taxon indicator as a shortcut. Second, given that so many different factors affect populations, managing an area for a particular species in the hope that benign environmental conditions will be maintained for others is optimistic, especially if the management protocol becomes overly focused on a particular species’ requirements (Landres 1992). In brief, suggesting that the demands of a single species might satisfy those of an entire community is wishful thinking (Cairns 1986). Moving on to the idea of using the same cross-taxon-response indicator species or species-group in different habitats, there are additional conceptual difficulties. First, different populations of the same species respond to multiple independent factors in different ways at different locations. Of 47 bird species recorded in the North American Breeding Bird Survey that occurred in more than one region in the United States, 77 percent showed a decline in at least one region but increases in at least one other. Only 23 percent showed consistent increases or consistent declines across all regions in which they were found. Just as worrying, in 77 percent of 22 regions, there were statistically significant increases in at least one species but significant declines in at least one other. These findings question whether there can ever be a common species that we can always rely on to monitor effects of environmental disturbance on other species (Taper et al. 1995). Second, in comparing disturbance regimes, species nestedness can be important because sensitive species that drop out of the community early should be good candidates for assessing the presence of others. Yet in those studies that have examined the consistency of nestedness there are queries related to both space and time. Among amphibians in Ontario, Canada, or bryophytes and lichens in coniferous spruce forests in Norway, species nested at one set of sites have a low probability of being nested at other sites (Hecnar & M’Closkey 1997; Saetersdal et al. 2005). Saetersdal and coresearchers volunteer two reasons for this lack of consistency: enormous variation in individual species’ population densities across a landscape and poor dispersal ability, both resulting in some species not being present at a

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given site. Both factors would produce inconsistencies in nestedness. In a separate appraisal, repeated inventories of butterflies over time in Nevada, USA, revealed no change in overall nestedness but the degree of nestedness of individual species did change (Fleishman et al. 2000b). Third, there is the issue of scale when attempting to generalize whether cross-taxon-response indicator species are effective across sites. As area increases, empirical data show that rates at which new species from different taxa are added together differs. Consequently, correlations between species vary from place to place and decline with increasing area (Weaver 1995), making it problematic to use the same cross-taxon-response indicator at different scales (see also Murphy & Wilcox 1986; Flather et al. 1997). Fourth, cross-taxon indicators are task specific. Around the Great Lakes region of Ontario, Canada, sites suffering from air pollution from smelter and mining activities, but now naturally recovering, showed different species associations than sites enjoying active restoration that involved fertilizing, seeding, and planting native and nonnative species (Anand et al. 2005). In naturally recovering sites, vascular-plant richness was correlated with other groups, as was the vascular- and non-vascular-plant Shannon diversity index, but in restored sites none were significant (see Table 8-6). Vascular plants therefore predict overall diversity but only in natural environments, raising the possibility that surrogates may be less than useful in monitoring progress of restoration projects. Landres ended his 1992 article thus: “The current use of ecological indicators to assess population trends and habitat quality for other species of interest is financially not practical, conceptually inappropriate, and empirically unsupported, potentially leading to inaccurate long-term management and assessment decisions” (1313). Cross-taxon-response indicators of

Table 8-6. Correlations between diversity of taxonomic groups in naturally recovering and restored sites (from Anand et al. 2005). Naturally recovering sites

Vascular plants/nonvascular plants Vascular plants/bryophytes Vascular plants/lichen Bryophytes/lichens * p < 0.05, ** p < 0.02, ***p < 0.001

Restored sites

Taxa richness

Shannon diversity

Taxa richness

Shannon diversity

0.96*** 0.37*** 0.56*** 0 ***

0.96*** 0.83*** 0.77*** 0.44***

0.29 0.01 0.09 0.21

0.15 0.11 0.23 0.15

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disturbance have a shaky conceptual scientific basis but are nonetheless embedded in agency management and conservation practice. Recognizing this, Landres advised always stating management goals clearly, using (crosstaxon-response) indicator species only as a last resort, and choosing them only if they are in accord with assessment goals, which requires knowledge of the biology of the indicator species in advance. At present, the field is an awkward marriage of administrative inertia using unsubstantiated science and the necessity of developing monitoring schemes as new environmental problems appear. Management projects that plan on using cross-taxonresponse indicators need to be very circumspect.

Summary By focusing on one or a few species, conservationists may be able to assess many species’ responses to anthropogenic activities, but examination of empirical data suggests that this is tricky. First, species’ responses to land degradation and urbanization are idiosyncratic. Second, species sensitive to habitat change can predict responses of species that are less sensitive to habitat change—but not vice versa. Third, some studies in agricultural landscapes demonstrate cross-taxon-response congruency, but they are scale-dependent. Last, areas under various sorts of legal management affect different taxonomic groups in dissimilar ways. Despite these difficulties, there is evidence that bird-community types predict structural vegetation changes inherent in forestry practice and in agricultural intensification. Management Indicator Species (MIS) are used in forestry management to maintain biodiversity, to identify old forests, and to try to maintain suitable harvests; there are five classes of MIS. Scrutiny of the MIS concept has raised doubts about its utility in predicting other aspects of forestcommunity diversity, and it may instead be more judicious to measure a few selected species and forest structural elements together. It would be expeditious if certain species could give advance warning of declines in other species. In reality, however, it is more common for conservation biologists to discover a species declining, thus alerting them to a broader problem of other species backsliding at the same time, or to changes in the environment. In other situations, environmental changes are recognized and populations are then found to be in decline or else are predicted to decline shortly. Some advance predictions can be made, however, because closely related species share traits, some of which make them susceptible to extinction. Thus, common species with threatened relatives may

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be at risk. Sometimes behavioral responses to environmental change in common species are used as a substitute for examining behavioral changes in endangered species, but this strategy is questionable. Cross-taxon congruency in response to environmental change is somewhat improbable where change is not too harsh—sympatric populations are not necessarily expected to change in parallel, given that they are subject to so many different abiotic and biotic influences. Also, across habitats, species show idiosyncratic population trajectories; species nestedness is inconsistent; increasing area reduces correlations between sites; and congruency varies even between naturally recovering and actively restored sites. Thus, the science underpinning the use of cross-taxon-response indicator species is not well substantiated, but administrative inertia ensures that these species will continue to be used.

The jaguar is an interesting example of a flagship species because it has been used in at least two capacities—to set up reserves in the neotropics and to raise money for conservation organizations in the developed world. (Drawing by Sheila Girling.)

Chapter 9

Flagship Species

Characteristics of Flagship Species When a local conservation group, NGO, or government wants to educate the public about conservation or promote a conservation program, or when a conservation organization or zoological institution aims to raise its profile, increase its membership, or obtain funds, it will often use a single charismatic species as an emblem and rallying point for the conservation campaign, as a catalyst for conservation action, and for its power to draw in money. The underlying reason for using just a single species, or occasionally a small collection of species, is that the public is assumed to have inadequate knowledge of ecological or biodiversity complexities (Leader-Williams & Dublin 2000), or has a short attention span. The reason for choosing certain species over others is that they have particular ecological, aesthetic, or visceral appeal (Lorimer 2007) and are thus attractive and memorable. These are flagship species, “chosen for their charisma, to increase public awareness of conservation issues and rally support for the protection of that species’ habitat. Protection of other species is accomplished through the umbrella effect of the flagship species” (Favreau et al. 2006, 3,951; see also Heywood 1995; Meffe & Carroll 1997; Simberloff 1998). Flagship species are usually homeotherms—large, endangered, toppredatory mammals, or else piscivorous or omnivorous large birds (Clucas et al. 2008; see also Ward et al. 1998; Gunnthorsdottir 2001; White et al. 245

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2001). Presumably these are the traits that resonate with the public (in both the industrialized and developing world), as many people are in awe of large bird and mammal species out of respect or fear; in the urban sectors of the industrialized world, at least, people are thrilled by witnessing an act of predation, and they are concerned about species disappearance. Indeed, 8 out of the top 12 species featured on U.S. conservation and nature magazine covers over a decade were of large carnivores (Clucas et al. 2008). Nevertheless, occasionally poikilotherm vertebrates (Walpole & LeaderWilliams 2002), invertebrates (Samways 1994), or plants (Farjon et al. 2004) are chosen as flagship species. Here I discuss the different ways in which flagship species are used as surrogates in conservation, efforts to measure their success, and their principal characteristics.

Multiple Objectives User groups employ flagship species to deliver subtly different conservation messages. First, NGOs or local conservation organizations try to raise public awareness about habitat and species losses by using striking and memorable species. In billboards, posters, brochures, or stickers, eye-catching pictures are interwoven with short texts about the ecology of an area or the habits of a species. For instance, the Natural Resources Defense Council has used the white “ghost” or “spirit” bear (actually a white morph of brown bears) as a symbol for conserving temperate rainforest along the coast of British Columbia, Canada. Conservation groups mounted a local awareness campaign using the golden lion tamarin to increase awareness of this species and its dwindling Atlantic rainforest habitat in Rio de Janeiro and São Paulo, Brazil (Dietz et al. 1994). The Colobus Trust in southeast Kenya uses the Angolan black and white colobus as an emblem to conserve habitat for primate species and to advertise its willingness to give veterinary attention to primates hit by vehicles (Anderson et al. 2007). The target audience for these educational tools or advertisements can also be the wider international community. For example, Greenpeace uses whales to educate the public in industrialized nations about whaling, to build support for a boycott of nations that continue to whale (such as Norway), and to raise funds for their efforts to halt whaling and to support other Greenpeace activities. Also, flagship taxa such as butterflies are used to educate the public about biodiversity—insects, for example (New 1995). Second, and related to conservation enlightenment as discussed above, governments use flagship species to symbolize a country’s natural heritage and to increase environmental awareness. For instance, almost every coun-

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try has a national bird or mammal that is protected by law (e.g., Uganda has the crowned crane). When the Government of India mounted “Project Tiger” in 1972, annexing 15 tiger reserves that aimed to conserve the species, its prey, and its habitat, they were doing this in part to save a quintessential Indian species (Panwar 1982). Third, NGOs focus attention on their own organizations using flagship species. For example, the Worldwide Fund for Nature (WWF) uses the giant panda as its logo (Dietz et al. 1994), while the Flora and Fauna Preservation Society’s logo is the Arabian oryx—their most famous project was “Operation Oryx,” which reintroduced this species back to the Arabian peninsula (Ostrowski et al. 1998). Flagship species featured in conservation organizations’ magazines are used to maintain the public’s interest in ongoing projects. Fourth, flagship species are enlisted to raise money for a conservation organization by capitalizing on a high-profile species with which donors sympathize. For example, Defenders of Wildlife uses stories of wolf persecution in its fund-raising efforts to run its offices and legal projects. Also, zoological institutions like to exhibit high-profile species such as giant pandas, or to breed attractive species, such as cheetahs, for the purposes of promoting themselves, attracting more visitors, and raising money (Christie 2007). Related to this, flagship species may be used to encourage tourists to visit particular places (Kruger 2005; Verissimo et al. 2009) Fifth, flagship species are used to establish reserves (see Table 9-1). If the population of a charismatic species is thought to be worthy of protection for its own sake, and a reserve is especially set up for this purpose, the reserve will necessarily harbor other species; consequently these flagship species assume an umbrella role. For example, mountain tapirs have been used to set up reserves in the high Andes (Downer 1986). In the marine realm, some conservation efforts use charismatic megafauna, such as whales, seals, and migratory seabirds, as flagship species to identify where to set up marine protected areas. (Often these species occupy distinctive habitats whose communities are substantially different from the surround, [Roff & Evans 2002]). Interestingly, some characteristics of flagship species, such as large body size and being a predator, overlap with useful traits of umbrella species—large species are likely to have large home ranges, for instance (see chapter 4). Writers sometimes apply the terms flagship species and umbrella species interchangeably or attribute both meanings to the same species (e.g., Johnsingh & Joshua 1994), although originally flagship species were a public relations tool while classic umbrella species had an ecological basis (Simberloff 1988; Caro & O’Doherty 1999). To avoid semantic quibbles, I

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Table 9-1. Use of flagship species in establishing reserves (from Caro et al. 2004). Species

Reserve

Plants Redwoods

Avenue of Giants Redwood State and National Parks, California, USA

Arthropods Monarch butterflies

El Rosario Monarch Butterfly Sanctuary, Michoacan, Mexico

Birds Brown pelicans Flamingoes

Pelican Island National Wildlife Reserve, Florida, USA Lake Nakuru National Park, Kenya

Mammals Jaguar Tiger Javan rhinoceros and Javan tiger African elephant Baird’s tapir

Cockscomb Jaguar Nature Reserve, Belize 15 reserves in India Ujung Kulon National Park, Indonesia Addo National Park, South Africa Tapir Mountain Nature Reserve, Belize

Table 9-2. Principal proponents and objectives for which flagship species are used. Promote conservation awareness

NGOs/Local groups Zoos Governments

X X

Selfpromotion

Raise funds

Set up a reserve

X X

X X

X X

X shows that the objective is relevant.

will simply call these flagship umbrella species; I cover them in this chapter rather than chapter 4 only for convenience; the emphasis on the first and second word in the term depends simply on the use to which the species is put in a particular project. In summary, flagship species are used to achieve at least four objectives in conservation (see Table 9-2), and species that are useful in one arena may not necessarily be useful in another.

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Are Flagship Species Successful? Public Awareness Case studies provide anecdotal evidence that flagship species do enhance public awareness. Dietz and coworkers (1994) describe how three liontamarin species (golden-headed, golden lion, and black lion tamarins), all rare, were used to build public support for a habitat-conservation plan in Brazil. The project focused on residents of Rio de Janeiro and São Paulo who were purchasing wild animals illegally and on government bureaucrats and politicians. The program educated them about environmental protection, tamarins, and reserve establishment. Results after two years were impressive (see Table 9-3) because people had generalized their environmental awareness, suggesting that conservation-education programs centered on individual species helped to change local attitudes about forest conservation. In other examples, captive Asian elephants were used in Aceh, Indonesia, to raise awareness in villages and thereby encourage people to patrol forests, detect illegal logging, and control crop-raiding by wild elephants.

Table 9-3. Responses to some questions used in interviews of adults before and after two years of the golden lion tamarin conservationeducation project (from Dietz et al. 1994). Responses to questions

Question

Before project activities

After project activities

What is the name of this animal? (photo of golden lion tamarin)

Correct

59% (518)a

79% (497)

How does the tamarin live?

Correct

24% (140)

55% (406)

Is the golden lion tamarin important or beneficial?

Don’t know Yes

77% (515) 14%

22% (400) 62%

What would you do with a little bird you found in the woods?

Raise at home Leave it alone

55% (512) 44%

29% (499) 69%

What would you do with a snake you found in the woods?

Kill it Leave it alone

73% (512) 25%

55% (499) 32%

a

Number of responses.

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On Pemba Island, Tanzania, the endemic Pemba flying fox was used as a symbol to educate children about habitat conservation and to change hunting regulations. In southern Belize, the ceiba or kapok tree that plays a central role in the folklore of Mayan communities was used as an emblem to conserve forest in the Golden Stream Corridor Reserve. All three projects were apparently successful (Bowen-Jones & Entwistle 2002). In contrast, Linnell and others (2000) report that attempts to use large carnivores as flagship species in Sweden were problematic because hunters viewed wolves and brown bears as competitors for ungulate prey, while sheep farmers and reindeer herders complained about predation. Linnell and colleagues argue that negative attitudes of rural inhabitants mean that large carnivores are inappropriate flagship species in this part of Scandinavia. Actually, much has been made of context in choosing flagship species. Species that are attractive for urban folk far removed from wilderness areas may be unattractive for rural populations living near these animals; species respected in the industrialized world may be scorned in the developing world (Leader-Willams & Dublin 2000; Woodroffe et al. 2005). For example, schoolchildren in rural Guyana are most concerned about the huge Arapaima fish that are eaten locally, while tapirs, anacondas, jaguars, harpy eagles, giant anteaters, and red howler monkeys— attractive to people from industrialized nations—were classified in the same category as tarantulas, black caimans, frogs, freshwater stingrays, and bullet ants (Borgerhoff-Mulder et al. 2009). Flagship species in the developing world must be chosen carefully. Writing about what they term culturally defined “keystone species,” which from their definition can be construed as flagship species—“plant and animal species whose existence and symbolic value are essential to the stability of a cultural group over time”(3)—Cristancho and Vining (2004) stipulated seven conditions for their successful use in conservation. The story of the species’ origin is tied to the myths, ancestors, or origins of the culture; the species is central to the transmission of cultural knowledge; it is indispensable in the major rituals on which the community’s stability depends; it is either related to or used in activities intended to supply the basic needs of the community such as getting food, constructing shelters, and curing illnesses; the species has significant spiritual or religious value for the culture in which it is embedded; it exists physically within the territory that the cultural group inhabits or to which it has access; and the cultural group refers to the species as one of the most important species. Possible examples include coca for the Letuama Indians, the jaguar for the Tukano Indians, and pigs for Tsembaga of New Guinea. As many of these species are likely to be

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domesticates—for instance, corn for the Hopi Indians—the merit of conserving culturally defined species must lie indirectly in helping to preserve the well-being of indigenous communities that are caring for local native species.

Raising Funds Surveys and willingness-to-pay (WTP) analyses show that flagship species can be enlisted to raise conservation funding (Kontoleon & Swanson 2003; Verissimo et al. 2009). In parts of the industrialized world, the public values species that provide ecological services (Montgomery 2002), as well as endangered species (Tisdell et al. 2007) and endemic species (Meuser et al. 2009), suggesting these are useful attributes for flagship species. For instance, correspondents are willing to pay reasonable sums of money to conserve North American threatened and endangered species (see Table 9-4) and would pay more by means of a one-off payment than they would annually, more if they have visited places where the species lived, and more for marine mammals (Loomis & White 1996). Flagship species can be specifically used to raise money for habitat conservation. Kontoleon and Swanson (2003) asked 305 non-Chinese correspondents whether they would be willing to pay for conservation of the giant panda. Respondents were informed about the plight of the carnivore and that its best hope for survival lay in the Wolong Reserve, where 200 free-living and caged pandas are found. People were given the option of contributing money toward boosting the population by 300 animals that were either to be kept in 100-m2 cages, in 5,000-m2 pens, or in their natural habitat. Mean WTP was $3.90, $8.43, and $14.86, respectively, showing that people not only wanted to conserve pandas but to protect them in situ, thereby contributing to habitat conservation.

Reserve Establishment High-profile species have been used to set up protected areas. A prominent example of these flagship umbrella species is the northern spotted owl, which has come to symbolize North American old-growth forest. Studies conducted in Washington, Oregon, and California showed that owls nest and roost in conifer-dominated stands characterized by large, old trees and closed canopies that are less fragmented than are surrounding landscapes.

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Table 9-4. Review of 20 studies reporting economic values of rare, threatened, or endangered species using WTP estimates (in 1993 US$) (from Loomis & White 1996).

Species

Studies reporting annual WTP Northern spotted owl Pacific salmon/steelhead Grizzly bear Whooping crane Red-cockaded woodpecker Sea otter Gray whale Bald eagle Bighorn sheep Sea turtle Atlantic salmon Squawfish Striped shiner Studies reporting lump sum WTP Bald eagle Humpback whale Monk seal Gray wolf Arctic grayling/Cutthroat trout

Low value

High value

$44 31

$95 88

10

15

17 15 12

33 33 30

7

8

178

254

16 13

118 17

Average of all studies

$70 63 46 35 13 29 24 24 21 13 8 8 6

216 173 120 67 15

There is great variation in home-range size: 450 ha in California and up to 3,200 ha in Washington (Hunter et al. 1995; Lehmkuhl & Raphael 1993; Ripple et al. 1996), which relates to abundance of medium-sized prey and dusky-footed and bushy-tailed woodrats (although home-range size decreases with increasing old-growth forest in areas where owls eat northern flying squirrels [Carey et al. 1992]). Knowledge of home-range size and habitat attributes was used to model owl population sizes under various tree-harvest regimes, resulting in a number of proposals (USDA [90], USDI [93]): maintaining 40-ha reserves around known nesting sites; 46–91-m riparian buffers along all streams; dispersal corridors where 50 percent of the land needs to hold tree diameters >28 cm and canopy clo-

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sure of >40 percent; and so on. Although it has been very difficult to reach a consensus on tree-cutting practices in this region of the United States, owls are both a flagship for remaining old-growth forests and an umbrella for undisturbed or lightly disturbed redwood forests. Some studies have specifically tried to assess whether flagship umbrella species could prove useful in reserve establishment either at a local scale or at a regional or continental scale. (Almost all focus is on the presence or absence of background species, not on their population sizes or viability.) In the central-eastern Italian Alps, numbers of avian species, vulnerable bird species, and tree diversity, as well as the total number of birds and butterflies, were greater in six avian predator species’ breeding territories than at control sites (Sergio et al. 2005a, 2006; see Figure 4-3). In contrast, a study of goshawks in Hokkaido, Japan, found no association between their home ranges and bird, butterfly, carabid beetle, and forest-floor plant species richness or abundance, except that goshawk avian-prey abundance was higher in their home ranges (Ozaki et al. 2006; see also Roth & Weber 2008). Certainly, one could argue that top avian predators are charismatic and could be called flagship species, and in Italy they seem to be associated with some aspects of biodiversity and so could aid in conserving wildlife at a local scale. Switching to mammals, species that are used as flagships in other public relations contexts have been noted as having wide ramifications for ecosystems. Ripple and Beschta (2006) reported that the cougar population in Zion National Park, Utah, USA, declined as a consequence of increased tourist traffic. This resulted in an increase in mule deer densities, higher browsing intensities, and reduced recruitment of cottonwood trees, leading to bank erosion and reductions in terrestrial and aquatic species abundance. Similarly, Berger and colleagues (2001) found that wolf and grizzly bear extinction in the southern Greater Yellowstone Ecosystem triggered an eruption of the moose population that caused a decline in vegetation density, particularly that of willow. This was in turn associated with a reduction in breeding-bird densities. In both cases, this cascade is in line with keystone ideas advanced for large carnivores (Terborgh et al. 1999; Ray et al. 2005). Other studies have reached opposing conclusions. Caro and colleagues (2004) tried to determine whether flagship umbrella species could be useful tools in establishing small reserves in subtropical rainforest in Belize, specifically asking whether locations where flagships were commonly seen were areas that harbored high vertebrate biodiversity. They identified 1-km2 areas where flagship species—jaguar and Baird’s tapir—were frequently seen and

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two other 1-km2 areas where non-flagship large mammals—white-lipped peccary and spider monkey—were seen. To examine whether flagship centers of activity coincided with vertebrate diversity, species richness and abundance of frogs, phyllostomatid bats, small terrestrial mammals, scansorial mammals, and birds were sampled at the four sites. They found no significant differences in species richness across sites for any taxon except that more frog species were found at the tapir site along the river. Similarly, there were no strong differences in abundance of taxa except that more frogs were found at the tapir site and fewest frog, bat, and bird individuals at the spidermonkey site. The message to emerge was that no small area chosen due to flagship species usage was substantially better at incorporating vertebrate species richness or abundance than any other area. A similar study that focused on flagship species’ daily activity, this time of European otters, was conducted in western France. Areas of the river that had been frequented by otters for the previous 20 years did not support greater bird-, amphibian-, and mollusk-species richness than areas that were not frequented by otters (Bifolchi & Lode 2005). Finally, in this list of ecological tests of flagship umbrella species, Berger (1997) asked whether a black rhinoceros population of 50 individuals in the Namib desert, Namibia, could encompass sufficiently large populations of background species. He found that dry-season population sizes of gemsbok, zebra, kudu, giraffe, and ostrich did not reach 250 individuals in the area used by rhinoceroses, indicating that the size and characteristics of the area were inappropriate for long-term viability of these background species. This study is particularly interesting because population sizes of background species, rather than presence/absence, was the outcome variable, speaking directly to merits of long-term capacity of a reserve to support populations. There have also been attempts to fathom how flagship species’ presence predicts biodiversity at a larger scale. Andelman and Fagan (2000) examined overlap between biodiversity and flagship species presence in 785 25km2 areas in the coastal sage-scrub communities in southern California, in 1,241 sites across five U.S. states in the Columbia River area of the northwest United States, and at 2,856 sites across U.S. counties. They defined flagships in two ways: large carnivores and unspecified charismatic species. Results were disappointing. Large carnivores protected less than 50 percent of background species in the sage-scrub and Columbia plateau data sets, although close to 90 percent across the country. Their charismatic species showed similar patterns of poor overlap with background species in the three areas. In most cases, overlap figures were lower than that of 20 species

Flagship Species 255

chosen randomly as representative candidates for the presence of other species. In a hypothetical money-saving enterprise, the number of sites was reduced to protecting one, three, five, or ten occurrences of charismatic species or big carnivores, rather than every occurrence, but now invariably less than 20 percent of background species coincided with them. Andelman and Fagan concluded that flagships do not serve in a competent umbrella capacity across these three scales. In another large-scale analysis, Kerr (1997) examined large 2.5° × 2.5° quadrats covering North America to determine whether large-carnivore presence predicted invertebrate diversity. Only four quadrats contained large carnivores and, if protected, they would conserve 51.9 percent of Lasiglossum, 77.8 percent of Papilionidae, and 31.4 percent of Plusinae species, or only 43.5 percent of invertebrate diversity in total. Again, hardly impressive. Working with an African database, Williams and colleagues (2000a) investigated a data set of large-mammal and bird distributions in 1° grid cells across the continent. They picked two different sets of flagship species, the first being “well-known” species (two species of rhinoceros, elephant, gorilla, common chimpanzee, and bonobo); the second being the classic sportsmen’s bag of the “big five” (lion, leopard, buffalo, elephant, and [two species of] rhinoceros), and determined whether the presence of these species in a grid cell coincided with species richness. They discovered rather poor species representation in cells covered by each sort of charismatic species compared to what was actually on the ground. They attributed this failure to considerable overlap in flagship species’ distributions, particularly in African savannah and woodland biomes. Indeed, the best surrogate species for mammal and bird diversity was a combined distribution of a bat, gerbil, rat, crake, barn owl, and oriole—hardly charismatic taxa (see Table 9-5). In a similar vein, but here using Primates, Carnivora, Proboscidae, Perissodactyla, and Artiodactyla flagship taxa (228 mammal species in total), Williams and colleagues (2000b) found that these overlapped the distribution of only 50 percent or 54 percent of the other 937 African mammal species, depending on whether the top 50 species-rich grid squares were considered, or whether the top 50 grid squares with endemic flagship species were used (endemic being defined as the top 25 percent of species with the most restricted ranges). Neither method performed better than species chosen at random. To summarize, there are now a handful of quantitative empirical studies that have attempted to see whether the presence of flagship umbrella species in an area, or combined individual home ranges of a subpopulation

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Table 9-5. Patterns of co-occupancy of grid cells (overlap) for species within the groups of six species used to select areas together with numbers of species of all sub-Saharan mammals and breeding birds in example sets of 50 grid cells selected using the hotspotscomplementary-richness method (from Williams et al. 2000a).

Groups of 6 species used for area selection

“Well-known” species “Big Five” species “Best” 6 species from random draws Totala a

Number of ecoregions within combined range of 6 species

Mean number of background species per cell from the group of 6 species

Median number of mammal and bird species represented using repeated runs

125 757

81 95

1.50 2.63

1,755 1,675

13 1,961

94 98

1.35 6.00

2,121 2,687

Median range size (cells)

Gives an estimate of data if everything were counted.

of flagship umbrella species, or daily activities of individuals of a flagship umbrella species overlap with large numbers of other species, or many individuals of other species (see chapter 4). The majority of these studies has found no strong associations, exceptions being that the presence of certain raptors and some North American carnivores can signal local species richness at an ecological scale, and so are worth examining. There are several possibilities as to why top predators in temperate regions might be associated with species richness: (a) Raptors might select breeding sites on the basis of ecosystem productivity that is associated with high biodiversity in some ecosystems (Worm et al. 2003). (b) Top predators have large area requirements that encompass territories of less-demanding species (the classic umbrella species argument). (c) Apex predators may be sensitive to ecosystem dysfunction so that their continued presence signifies a functioning ecosystem. (d) Top predators select sites with high topographic and habitat complexity that favors high biodiversity. And (e) they feed on a variety of prey species that must be present (Gaston 1996a; Carroll et al. 2001; Gittleman et al. 2001; Sergio et al. 2006). Alternatively, top predators might themselves be responsible for maintaining high biodiversity (Sergio et al. 2008). For example, they (a) can provide carrion for sympatric species (Wilmers et al. 2003); (b) can protect lower trophic levels by keeping other predators out of the area (Quinn &

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Kokorev 2002); (c) can alter the environment, thereby providing refuges for other species (Craighead 1968); and (d) be responsible for trophic cascades (Palomares et al. 1995; Rogers & Caro 1998; Soule et al. 1988; Estes et al. 1998; Crooks & Soule 1999; Berger et al. 2001, Ripple & Beschta 2006; chapter 5). Given these possible mechanisms, it seems strange that so few investigations of flagship umbrella species have been positive.

Qualities of Flagship Species Attempts have been made to list criteria that make a successful flagship species (Bowen-Jones & Entwistle 2002; Farjon et al. 2004), but there is no consensus (Favreau et al. 2006). In part, the criteria must depend on conservation goals that should be specified in advance (see Table 9-6). When a flagship species is used to raise awareness of conservation issues in the

Table 9-6. Helpful features for different uses of flagship species. Raise awareness in

Features

Well known Well liked Homeotherm Large Carnivore Charismatic Endangered Culturally significant Requires large area Important ecologically Confined to one habitat Indicates species richness Sensitive to disturbance Utility Scientific uniqueness

industrialized world

developing world

X X X

X

X X X

Self-promotion or raise money in industrialized world

Help establish reserve

X X X X X X X X

X X

X X X X

X

X X X

X denotes applicable to use (from Dietz et al. 1994; Caro & O’Doherty 1999; LeaderWilliams & Dublin 2000; Zacharias & Roff 2001; Bowen-Jones & Entwistle 2002; Caro et al. 2004; Farjon et al. 2004; Sergio et al. 2006; Clucas et al. 2008; Verissimo et al. 2009).

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industrialized world, it should attract attention by being charismatic and should live in one sort of habitat with which it can be easily associated. It may have additional significance and educational value if it plays an important ecological role in a specified habitat or is sensitive to disturbance (as discussed in chapters 5 through 7). In the developing world, flagship species should be well known and liked (more likely if the species also have utilitarian value); they should be charismatic, possibly endangered (although many national emblems are not), and culturally significant, especially if used locally. Utility and scientific uniqueness can help sell the conservation idea. When a flagship species is used to raise funds in the industrialized world it should be well known and liked—this often means a homeotherm that is large; is a carnivore, making it charismatic; typically has a vulnerable or endangered conservation status, perhaps due to endemism; likely requires a large area if it is a large mammal or bird; and perhaps is sensitive to disturbance. These traits are some of the classic hallmarks of a flagship species. Finally, if flagships are being employed to set up reserves, subpopulations should require a large area in which to live so that they encompass sufficient area to protect viable populations of background species. Some conservationists have even argued that flagship species should be migratory (Zacharias & Roff 2001). They should overlap with a large amount of biodiversity, perhaps because they are in a commanding ecological position that structures the ecological community. From a pragmatic standpoint, species that are culturally significant will be better placed in helping to establish a reserve.

Iconic Species Species that are famous because of peculiar traits, because they live in particular habitats, or are closely associated with a certain country (often a combination of these) are sometimes called iconic species (see Table 9-7). The duck-billed platypus is renowned because it is a mammal that lays eggs, has a singular snout, and is restricted to Australia. The polar bear has a uniform white pelage and lives in the arctic. The Beccariophoenix madagascarensis palm is a large, majestic, and critically endangered tree and is endemic to Madagascar. Iconic status can also come about because a species is extraordinarily rare or has been the object of repeated expeditions to find it. An air of mystery surrounds the ivory-billed woodpecker, believed until very recently to be extinct (Hill 2007). In conservation, iconic status can be used to attract public attention that will help save the species itself, as in the case of trying to mitigate the

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Table 9-7. Examples of species that have been termed iconic in the literature. Species

Traits

Habitat

American chestnut Beccariophoenix madagascarensis Salt-water crocodile

Large, majestic

Eastern forests

Large, majestic Very large

Salt water

Duck-billed platypus Tasmanian devil Polar bear

Egg-laying Carnivorous Largest bear, white Flightless

Kaka

Country

Source

USA

1

Madagascar Australia and New Guinea Australia Tasmania

2 3

New Zealand

7

Arctic

4 5 6

1. Wheeler & Sederoff 2009; 2. Shapcott et al. 2007; 3. Bradshaw et al. 2006; 4. Grutzner et al. 2008; 5. Jones et al. 2008; 6. O’Neill et al. 2008; 7. Leech et al. 2008.

impact of fatal facial tumor disease in Tasmanian devils (Jones et al. 2008). Alternatively, it can be used to marshal public attention to the habitat in which the species lives—for example, the fate of polar bears is largely dependent on summer arctic sea ice, which is disappearing (Slocum 2004). As with flagship species, there are no distinct categories of iconic and non-iconic species, and iconic status is promulgated by biologists intent on attributing added importance to their study animal. There is no quantitative evidence that iconic status increases likelihood of funding or public awareness. Iconic species are only useful if they are extant. The iconic dodo is extinct and of questionable help to conservation efforts. Mythical animals such as the yeti, unicorn, or phoenix are of no conservation service because arguments to conserve the habitat in which they “live” are baseless.

What Next? In the developing world there is a clear need to educate the public about habitat loss and species’ declines, and if this task is accomplished more easily by using figureheads than by explaining complex conservation issues and anthropogenic pressures in detail, then flagship species are a conservation necessity. Inevitably, this means that the public will continue to have to

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swallow simplistic prose accompanied by pictures and logos of charismatic species. The need to dumb down the quality and quantity of information is taken as given (Leader-Williams & Dublin 2000). Public conservation education is not keeping up with advances in conservation biology and management, which recognizes the importance of community-based conservation organizations and working with plantation industries to find compromises to biodiversity loss, to take just two examples. The utility of using species to perform double duty as emblems and umbrella species is mixed. On one hand, there is a suggestion that top predators may serve as local umbrella species for species diversity in terrestrial ecosystems. On the other hand, these species receive undue conservation attention. Listings of species under the U.S. Endangered Species Act (ESA) are slanted heavily toward birds and mammals with, in order, the bald eagle, northern spotted owl, Florida scrub jay, West Indian manatee, redcockaded woodpecker, Florida panther, grizzly or brown bear, Least Bell’s vireo, American peregrine falcon, and whooping crane receiving 54 percent of the total U.S. Federal and state ESA spending between 1989 and 1991, and each species receiving >$10 million. Being large, endangered, and taxonomically unique, or being a bird, a mammal, a reptile, or an amphibian, all increased the likelihood of being listed; while being large and being a mammal were associated with greater spending (Metrick & Weitzman 1996). Given that listing depends in part on the amount of background research carried out on a species, and that, in the main, researchers prefer to work on attractive species (Lorimer 2007), and that Federal spending is a zero-sum game, implicitly or explicitly the U.S. government is concentrating on conserving species with flagship species qualities. There are complaints about the disproportionate use of large birds and mammals in conservation at the expense of non-charismatic species (Wilson 1987; Munoz 2007), so there is a burden of proof on conservationists to demonstrate clear benefits of using flagship species in an umbrella role in conservation, not just for fund raising and education.

Summary Flagship species are popular charismatic species that serve as symbols and rallying points to stimulate conservation awareness and action. They are used to educate the public about conservation, to advertise conservation NGOs, to raise funds for conservation organizations and zoos, and to facilitate the establishment of reserves. The entities that use flagship spe-

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cies are local conservation groups, zoological institutions, NGOs, and governments. The success of flagship species has been assessed in two contexts: as public-relations tools and in the establishment of reserves. Willingness-topay studies and largely anecdotal case histories suggest that flagship species have been important in conservation education and awareness building, but also that flagship species must be chosen with care—human perceptions of species differ geographically. Studies in Italy show that biodiversity is higher in raptor territories although the mechanistic basis for this is unclear; and the absence of charismatic large carnivores has detrimental effects on lower trophic levels in North American ecosystems. In contrast, studies of jaguars, tapirs, European otters, and black rhinoceroses did not find an association between centers of flagship species activity and various biodiversity measures of background species. At a larger scale, the presence of flagship species such as large carnivores or apes was not associated with greater numbers of background species across North America or Africa, so the benefits of using flagship species in reserve design are not assured. Iconic species are famous because of their unique traits or well-known endemism.

Rainforests have been used to attract public attention to habitat destruction in the tropics and can supplement or replace the use of surrogate species as tools to raise conservation awareness. (Drawing by Sheila Girling.)

Chapter 10

Surrogate Species in the Real World

Surrogate Categories Two key points emerge from this book. The first is that there are several shortcuts to achieving conservation goals. Surrogate species can be broadly divided into three major categories with little overlap in objectives: those that can help to identify the location of areas of conservation significance, those that can help to document the effects of environmental change on biological systems; and those that can be used in public-relations exercises. Within the first major category, there are surrogates employed at a large global or continental scale, others at a regional or national level, and still others at a more local level that are used to help define the shape and size of reserves. There are two orthogonal divisions here: between global prioritization, national reserve selection, or local reserve design; and between using a taxonomic group or a single species. In the second major category, there is a historical scientific divide between indicators of perturbation in aquatic and in terrestrial ecosystems, and another division between species or taxa that document environmental change directly and those that reflect the responses of other taxa to environmental change. The third major category is relatively straightforward: single species chosen to excite public interest. The second point is that the biological foundations of surrogate species are insecure. There are very few arenas in which we can say that studies have 263

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unequivocally demonstrated that certain surrogate species or speciesgroups represent the distribution of other taxa, or the responses of other taxa to environmental change. In this chapter, I first briefly revisit surrogate typology with a view to clarifying the different objectives for which they are used. Next, I examine conceptually awkward but useful situations where species can be used for several conservation tasks simultaneously. In the second part, I try to suggest future directions in using surrogate species, focusing especially on how environmental and economic information is being drawn into conservation surrogacy.

Synopsis Indicators of biodiversity may be used to determine large areas of current biological significance at a global or continental scale using one taxonomic group (table 10-1). The targets are other taxonomic groups whose distributions or identities are more difficult to ascertain, considered either separately or combined together. A second class of biodiversity indicators is used to identify smaller areas of conservation concern at a regional or national level, again using one taxonomic group. Less commonly, one or a few species are used—called local umbrella species—although other sorts of umbrella species have different objectives. When the conservation goal is to plan the shape and size of a reserve most appropriate for other species, a single species, often with a large home range or specific habitat requirements, may be used as a proxy for the distribution of populations of other species (classic umbrella species, or landscape species), or several species may be used ( focal species, sensu Lambeck). When the objective is to maintain a functional community in or outside a reserve, species with disproportionate ecological influence may need inclusion in the plan (keystone, engineering, or foundation species). Similarly, certain species may be appropriate as a central point of management attention because of their large impact on local ecology (management umbrella, keystone, engineering, and foundation species). The second major category of surrogate species are indicators of environmental change; nowadays this is almost entirely rapid, anthropogenically induced change. Thus the word indicator is unfortunately used in two senses—in relation to the distribution of biodiversity, and with regard to environmental transformation. Anthropogenic change in freshwater systems is frequently caused by pollution. Indicators of aquatic pollution are well established in ecotoxicology and range from the cellular to community

Keystone species

Engineering species

Conserve populations

Classic umbrella species Focal species sensu Lambeckb Management umbrella species Landscape species

One taxon

Regional biodiversity indicator Local umbrella species

One or few species

One species

One or few populations One or few populations Populations of several species One or few populations A few species

One taxon

Taxonomic group used

Biodiversity indicator

Surrogate species

Identify location, size of reserve and manage it Conserve populations

Identify areas of biological significance Identify areas of biological significance Identify location, size, shape of a reserve Determine size and shape of a reserve Determine most limiting factors Manage populations

Principal conservation objective

Table 10-1. Surrogate species in conservation biology.

Other taxa, all other taxa Other taxa, all other taxa Other taxa, all other taxa Other species’ populations Other species’ populations Other species’ populations Other species, & populations Other species or populations Other species or populations

Target or background species

(P)

National

Regional

Regional

(A)

(A)

(A)

A, P

National

Regional, national

(A)

A, (P)

A, (P)

A

Principal applicationa

National

National

Regional, national

Global, continental

Spatial scale

Assess other species’ responses to environmental change Assess effects of management on that species and others Assess other species’ responses to environmental change Usually one species

Substitute speciesg

One or few species

Several species

One or few species

Ecologicaldisturbance indicator species Cross-taxon-response indicator speciese

Assess effects of disturbance on species

Usually one species

One or few species

One species

Taxonomic group used

Management indicator speciesf

Environmental indicatorc species Sentinel speciesd

Assess extent of disturbance

Assess extent of disturbance

Foundation species

Surrogate species

Conserve populations

Principal conservation objective

Table 10-1. Continued

Behavior of other species

That or other species’ populations

Other species

Other species or populations Environmental change Environmental or change other species Environmental change

Target or background species

Land-use system

Terrestrial ecosystem

Terrestrial ecosystem

Land-use system

Aquatic or terrestrial ecosystem

Aquatic ecosystem

Regional

Spatial scale

(A)

P

(A)

A, (P)

(A)

P

(A)

Principal applicationa

Habitat

One species

One species

Iconic species

Habitat, that species

Habitat

One species

Flagship umbrella speciesh Flagship species

Regional, national

Regional, national, local Global, regional, national, local

(P)

P

(P)

b

A, usually academic; (A), academic but used infrequently; P, usually practical; (P), practical but used infrequently. , easily abused term; c, used in pollution studies; d, similar to environmental indicator species; e, subset of ecological-disturbance indicator species; f, similar to ecological-disturbance indicator species, cross-taxon-response indicator species, management umbrella species; g, similar to cross-taxon-response indicator species; h, similar to classic umbrella species.

a

Raise political will for a reserve Raise conservation awareness and funds Raise conservation awareness and funds

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level. Anthropogenic change in terrestrial ecosystems is usually due to forms of land conversion, and several species may be monitored at once to assess how such disturbance affects biological systems. Sentinel species is an ill-defined term used in both pollution and habitat-alteration contexts that sometimes refers to classic environmental- or ecological-disturbance indicator species, or to species that are particularly sensitive to anthropogenic change. Confusingly, there is another type of (cross-taxon-response) indicator species that may be indicative of both habitat disturbance and other species’ responses to habitat disturbance. Management indicator species are chosen with a hodgepodge of objectives, including monitoring that and other species’ responses to management activities. Substitute species focus on the behavior of one species being a marker for anthropogenically induced behavioral change in other species. Charismatic species employed in public relations, the third major category, are called flagship species. They are used to educate the public, raise money, or to establish a protected area—those involved in the last task I call flagship umbrella species. Iconic species are single species famous because of their biological characteristics or geographic location.

Multi-Surrogacy One source of confusion over the terminology of surrogate species is that some species can be legitimately used for several conservation objectives simultaneously. Large, charismatic, wide-ranging, threatened species—many large whales, for instance—are sometimes variously called flagship, classic umbrella, and ecological-disturbance indicator species at the same time (see Noss et al. 1996; Dalerum et al. 2008; Sergio et al. 2008). To take a specific example, in an attempt to identify marine protected area sites in the ScotiaFundy Region of Canada, King and Beazley (2005) consulted with experts and used the literature to identify 29 species that were endangered, threatened, or of special concern according to Canadian authorities, and were subject to known threats or limiting factors. Grouping them into keystone, umbrella, indicator, vulnerable and sensitive, or flagship categories, they found that many of these species fill several roles (see Table 10-2). King and Beazley singled out the North Atlantic right whale and deep-sea corals as being particularly well spread across surrogate categories. These therefore carry considerable weight when conservationists design a marine protectedarea network. Large mammalian carnivores are used in multiple surrogate roles as well. Cougars, jaguars, tigers, grizzly bears, and giant pandas are

Table 10-2. Characteristics of species identified for representing potential marine protected areas in Nova Scotia, Canada (from King & Beazley 2005). Speciesab

Keystone Presence is critical to maintaining community organization and diversity. Functionally important predator, prey, plant, link, or modifier. Umbrella Require large amounts of habitat or several specific habitat types. Established habitat association. Indicator Sensitive to human activities. Presence implies pristine or undisturbed habitat. Vulnerable and sensitive Vulnerable Listed as endangered, threatened, or of special concern by COSEWIC. c Listed as a species at risk by an international body (e.g., IUCN). Reduced or declining population size. Sensitive Low genetic variation. Poor dispersal ability. Low fecundity. Dependent on patchy or unpredictable resources. Congregate in large groups. Long-distance migrations. Long-lived. Large-bodied.

M, N, V, X K, M, N, O, P, Q, T, X

A, C, E, F, K, L, O, S, T, b, c B, F, N, W, X,

A, B, D, F, G, H, I, J, K, L, M, O, Q, R, S, T, U, V, Y, b, c F, V

A, B, C, D, E, F, G, H, I, J, K, Y, b A, B, C, D, E, F, K, O, Q, S, U, b, c A, B, C, D, E, F, G, H, I, J, K, O, Q, R, T, U, V, W, Y, b, c A, B, C, D, Y A, B, C, E, S, T, U, Y, Z, a, b, c A, C, E, Y, b, c A, F, K, L, M, N, O, P, R, W, X, Z, a, c A, C, E, F, L, M, S, T, U, Y, Z, a, b, c A, B, C, E, I, Q, S, T, U, V, W, b, c A, B, C, D, E, I, Q, S, T, U, b, c

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Table 10-2. Continued Speciesab

Flagship Charismatic species. Large vertebrate.

A, B, C, D, E, F, V, b, c A, B, C, D, E, F, G, H, J, K, O, P, Q, R, S, T, U, b, c

Commercial or recreational harvested species.

F, H, I, J, K, L, M, O, P, Q, R, S, T, W

a

Species (followed by total number of affirmative consensus responses in brackets). Mammals: A, North Atlantic right whale (14); B, northern bottlenose whale (11); C, blue whale (12); D, harbor porpoise (8); E, fin whale (11). Fish: F, Atlantic salmon (12); G, spotted wolfish (4); H, northern wolfish (5); I, cusk (6); J, Atlantic wolfish (5); K, Atlantic cod (9); L, American shad (5); M, Atlantic herring (6); N, northern sand lance (4); O, haddock (8); P, pollock (4); Q, Atlantic halibut (8); R, winter flounder (4); S, spiny dogfish shark (10); T, porbeagle shark (7); U, barndoor shark (8). Invertebrates: V, deep-sea corals (6); W, sea scallop (5); X, krill (4). Birds: Y, roseate tern (7); Z, red-necked phalarope (3); a, razorbill (3). Reptiles: b, leatherback turtle (12); c, loggerhead turtle (12). b Absence may be due to lack of knowledge rather than the species not displaying the characteristic. c Committee on the Status of Endangered Wildlife in Canada.

frequently touted as fulfilling several objectives by conservation organizations—ecological-disturbance indicators, keystone, flagship, and umbrella species (but see Linnell et al. 2000). It is advantageous to use the same species for different conservation objectives because there is an economy of scale—radio-tracking of cougars, for instance, can yield information on ranging (umbrella) and food habits (keystone), and is appealing research (flagship characteristics). Problems arise when some stakeholders view the species as fulfilling one conservation goal while others regard it as a relatively unhelpful tool in another context—leopards are alluring but poor indicators of anthropogenic disturbance. An additional problem is miscommunication over the precise conservation objectives for which the species is being used (as discussed in chapter 1).

Predictive Power of Surrogate Species The typology of surrogate species in Table 10-1 may change as new objectives arise and new buzzwords are added to the conservation lexicon. To

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date, however, there is surprisingly little hard evidence that surrogate species taken as a whole have a great deal of predictive power about biological systems, despite an extensive amount of research. In a qualitative and limited review of surrogate species conducted in 2004, no firm conclusions could be drawn about where and when surrogate species, meaning flagship, focal (sensu Lambeck), indicator (ecological-disturbance and cross-taxonresponse types), keystone, and classic umbrella species were effective in achieving conservation goals (Favreau et al. 2006). The difficulty was that the methods, temporal scales, geographic contexts, and taxonomic groups were all so diverse. Instead, it was suggested that methods be established to facilitate comparisons between studies, that data-rich areas be mined for multi-scale case studies and at varying temporal scales, and that long-term post-implementation monitoring be initiated. The issues notwithstanding, some positive highlights have emerged in the last two decades. These are (i) Large-scale investigations consistently identify the same areas of the globe where biodiversity is high. (ii) Endemic species in one taxon, especially birds, coincide with those in other taxa at a large scale. (iii) Families and genera predict species richness at large and small scales. (iv) Cross-taxon congruence in species richness is high when a network of regional reserves is chosen with the goal of representing all the species in sympatric taxa. (v) Fish and invertebrate communities predict environmental pollution and perturbation in freshwater habitats. (vi) Landuse change is best monitored using many taxa simultaneously. (vii) Bird communities are good predictors of structural changes in vegetation in forest and agricultural landscapes, and have power to predict responses of other bird species within the same guild. (viii) Flagship species are likely to help raise conservation funds. In conclusion, the search for reliable surrogates has been keen but has not yielded striking breakthroughs. Yet our inability to find consistent and hence reliable associations may matter less as economic concerns and dwindling conservation opportunities take a more prominent role in conservation planning, reserve management, ecological monitoring, and fund raising.

Distribution of Biodiversity In this section I show how conservation objectives can be pursued without recourse to using surrogate species. Already there is a strong precedent for using environmental variables to pinpoint important areas of biodiversity,

272 c o n s e r v a t i o n b y p r o x y

thus circumventing uncertainties of using surrogate species. A handful of ambitious non-species-based approaches have been made to identify terrestrial areas of conservation significance at a global scale, such as WWF’s Global Ecoregion Project, in which 223 areas were classified by their biological distinctiveness as globally outstanding, regionally outstanding, bioregionally outstanding, or locally important (Olson et al. 2001); Conservation International’s Wilderness Areas of >10,000 km2 with <5 people/km2 and >70 percent intactness (Mittermeier et al. 2004); and the Wildlife Conservation Society’s Last of the Wild exercise, which mapped the most remote 10 percent of the globe, based on human population density, land transformation, accessibility, and electrical power infrastructure (Sanderson et al. 2002c). There is a good deal of consensus—areas such as the Eastern Arc Mountains, Tropical Andes, Madagascar, and Indonesia occur repeatedly in these priority-setting exercises that are conducted without recourse to using species inventories or surrogate taxa. Similarly, many measures of threat rely on habitat information rather than impending species’ extinctions. For example, it is now possible to calculate, at a 1-km2 grain, the proportion of different biomes turned over to cultivation or that are managed: nearly 50 percent of temperate grasslands, tropical dry forest, and temperate broadleaf forests have been converted so far, with remarkably small proportions of them under protection. Biomes with very high and very low levels of habitat conversion have only limited protection (see Figure 10-1). Surprisingly, temperate grasslands and Mediterranean forests are under greater threat than tropical moist forests— another finding made without recourse to relying on species data. Other measures, such as hotspots identified by Conservation International, melded species-inventory information with quantitative measures of habitat loss (Myers et al. 2000). In conclusion, large-scale conservation priority-setting has been successfully carried out on land, and now the most promising avenue of research in global prioritization exercises is matching areas of importance to networks of existing protected areas in order to show where new reserves need to be established to conserve threatened species (Hoekstra et al. 2005). These ventures can be performed with existing plant and vertebrate data sets and do not need to incorporate species surrogacy to move forward. In the marine biome, however, rather little progress has been made on prioritization, and surrogate taxa could be extremely useful in pinpointing locations of high biodiversity, given that it is difficult to sample marine taxa systematically. Therefore, there is a call to investigate between-taxon con-

Surrogate Species in the Real World 273

Figure 10-1. Habitat conversion and protection in the world’s 13 terrestrial biomes. Biomes are ordered by their Conversion Risk Index. (CRI is the ratio of the percent area converted to the percent area protected—an index of relative risk of biome-wide biodiversity loss.) (Reprinted from Hoekstra et al. 2005.)

gruency in species richness within oceans (lack of data makes it difficult to gather information on endemism or rarity). Turning to economics, reserve planners have, until recently, focused on the benefits of protecting species in terms of complementary species richness, or conserving hotspots of rarity, and so on, either minimizing the

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number of reserve sites but encompassing all species, or maximizing coverage subject to constraints. But protection costs money—at the start of the century it was estimated as $6–6.5 billion globally (James et al. 2001), with a funding shortfall of perhaps $1.3 billion in developing countries (Bruner et al. 2004). Expanding the protected-area network, the central goal of reserve selection and design studies, incurs survey, transaction, social, acquisition, damage, opportunity, management, and stewardship costs, which vary independently (Naidoo et al. 2006), and these are now being incorporated into models of reserve selection planning, at least at the regional scale, with a key element being recognition that all of these costs vary spatially. In conclusion, there is a noticeable shift in emphasis from biodiversity inventories to cost evaluation.

Reserve Selection Reserve site selection can benefit from incorporating environmental variables into planning. For example, forest type, structural diversity, treespecies richness, vegetation density, and canopy cover are fairly standard environmental surrogates describing species richness and species rarity of diverse insect groups (Fraser et al. 2009), so there is a precedent for broadening the use of these environmental measures to predict other forest fauna. It is the acknowledgment of economics, however, that is transforming reserve-selection procedures. At a national level, it has been recognized for some time that biodiversity surveys are costly but nonetheless economically worthwhile. Collating national surveys from eight countries, Balmford and Gaston (1999) showed that biodiversity surveys paid off in economic terms. They calculated costs of land purchase in 1990 $US and costs of reserve maintenance discounted into the future at annual rates of 5, 10, or 20 percent. Assuming that a survey allowed reserve networks to be planned in a complementary fashion rather than based on land purchased simply because of species richness or availability, and assuming too that this amounted to a minimum of 5 percent in purchase savings, they reckoned that savings averaged $1,320/km2 for five developing countries and $5,946/ km2 for three industrialized countries, far greater values than average survey costs calculated from three developing countries or two industrialized countries ($333/km2 and $753/km2, respectively). At a regional scale, there are also recognized gains in efficiency from incorporating the spatial distribution of costs when planning reserve networks. For example, Ando and colleagues (1998) reanalyzed Dobson and

Surrogate Species in the Real World 275

collaborators’ 1997 data on 2,851 U.S. county-level tallies of 911 endangered species, but they also put county-by-county land-use values into the mix in order to compare the costs of covering the same number of species under a budget-constrained versus a site-constrained approach. The budget-constrained method was more efficient; for example, 453 species could be covered at 30 percent of the cost of achieving the same species coverage through minimizing the number of sites. Similar results have been arrived at using land acquisition prices in Oregon, USA (Polasky et al. 2001, 2008), in Africa (Moore et al. 2004), and in Southeast Asia (Wilson et al. 2006). These are welcome steps toward starting to place ivory-tower reserve-planning exercises into a more realistic economic framework. An additional injection of realism comes from the recognition that reserves are not all acquired simultaneously, because there are normally insufficient funds to put all reserve sites under protection at once (Pressey & Taffs 2001). Unprotected sites may be targeted by property developers in the interim, so it may be worth protecting a relatively valueless reserve at high risk rather than a species-rich reserve at low risk. Existing reserveselection algorithms ignore uncertainty about the fate of as-yet unprotected areas, and this is exacerbated in complementary scenarios because choosing the next reserve depends not only on the species pool in already-protected sites but also the pool in unprotected sites if the goal is, say, to represent each species once within the reserve network. Uncertainty about unprotected areas can be incorporated into reservenetwork planning using stochastic dynamic programming that looks at the whole period over which choices are made, or using an “informed myopic” algorithm that accounts for the possibility of development following each round of choice (Costello & Polasky 2004; Drechsler 2005). Some of these exercises are sophisticated: Meir and colleagues (2004), using stochastic dynamic programming, built an idealized bird-reserve network on the Columbia Plateau, USA, that incorporated the probability of sites becoming available for acquisition, the probability of species being extirpated at a site, and two levels of site costs. They concluded that simple rules of thumb, such as protect the available site with the highest amount of irreplaceability or the greatest species richness, were most effective (see also Turner & Wilcove 2006). Other models are able to incorporate incomplete species inventories (Grantham et al. 2008), additional parcels becoming available in the future (Grantham et al. 2009; McDonald-Madden et al. 2009), spatial constraints in site selection (Hof & Bevers 1998), trade-offs with other non-conservation objectives (Hof & Joyce 1992), and off-reserve proenvironmental management activities (Wilson et al. 2007).

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Socioeconomic factors and the realism that they bring to the table are gaining greater ascendancy in conservation decision making because monetary costs are usually easier to estimate than species totals. Moreover, planners can incorporate species into reserves with greater cost-efficiency than selection procedures that eschew financial accounting (Perhans et al. 2008). For example, in an interesting exercise, Bode and colleagues (2008) determined efficient funding-allocation schedules for each of 34 of the world’s terrestrial biodiversity hotspots by integrating conservation costs of establishing new protected areas and habitat-loss rates from quantitative predictions of extinction risk for endemic vertebrates, and by using seven different surrogate taxonomic groups within a dynamic decision making framework. On average, two-thirds of funds based on any particular taxon would be allocated in the same way whichever surrogate taxon was considered (see Table 10-3). Five hotspots—areas with low cost and high threat— were given funds no matter which taxon was used as a biodiversity surrogate. When threat and cost were ignored (as they have been in so many reserve-selection models of the last decade), conservation schedules were

Table 10-3. Similarity of different funding schedules based on the taxa shown in each row and column, measured as the proportion of funding that is directed to the same hotspots in both schedules, expressed as a percentage. Top lines: funding schedules that include socioeconomic data and biodiversity variation. Bottom lines: funding schedules that do not consider regional variation in socioeconomic factors (from Bode et al. 2008).

Mammals Birds Amphibians Reptiles Freshwater fishes Tiger beetles

Birds

Amphibians

Reptiles

Freshwater fishes

Tiger beetles

Vascular plants

73.7 38.2 — — — — — — — — — —

73.2 8.8 90.6 8.8 — — — — — — — —

53.4 0. 54.5 14.7 54.2 67.7 — — — — — —

57.3 0. 48.3 0. 57.0 23.6 64.2 0. — — — —

64.5 0. 55.8 14.7 64.7 23.6 71.5 21.2 76.0 78.8 — —

78.9 0. 81.4 32.5 81.0 0 55.8 21.2 49.4 0. 64.2 21.2

Surrogate Species in the Real World 277

now hugely affected by choice of taxon (see Table 10-3): only 20.5 percent, on average, shared funding, with no hotspot being allocated money using more than four of the seven taxa. If these results hold up with models incorporating other forms of realism, and at smaller regional scales, the need for effective species-indicators of biodiversity may be less pressing than was previously supposed.

Reserve Design and Management Environmental variables are very effective in capturing the distributions of species and communities at a regional level (Lindenmayer et al. 2002). Hess and colleagues (2006) compared the effectiveness of forest plans in the Triangle Region of North Carolina that were devised in four different ways. The first used six focal species (sensu Lambeck)—bobcat, eastern box turtle, barred owl, ovenbird, broad-winged hawk, and pileated woodpecker—chosen because they were extremely sensitive to area, dispersal, resources, or processes (see Table 10-4). The second mapped areas close to wetlands and riparian areas. The third located diverse forest types—wetland, mixed, evergreen, and deciduous. The fourth simply found large forest patches. Using North Carolina’s Natural Heritage Program, which contains point locations of species and natural communities, the effectiveness of each plan could be evaluated in three different ways (see Table 10-4).

Table 10-4. Three measures of effectiveness for focal species and environmental plans: the proportion of species and communities of conservation concern captured at least once (representation), the proportion of element occurrences captured (completeness), and the proportion of land in the inventory-based plan that was included (overlap) (from Hess et al. 2006).

Plan (area)

Focal species Close wetlands and riparian areas Diverse forest types Largest forested patches

Representation: species and communities captured (%)

Completeness: element occurrences captured (%)

Overlap: area of inventorybased plan captured

87 90 94 84

68 81 86 64

58 49 52 50

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Certainly, all four methods were equivalent in terms of the proportion of species and communities that occurred at least once in the 2,446-km2 area (see representation). Interestingly, places close to water or with diverse forest types had a greater proportion of forest species and community occurrences than areas chosen using focal species (completeness). Focal species outperformed other methods with regard to the proportion of significant Natural Heritage area land that was covered, but not greatly (overlap). Umbrella species, focal species (sensu Lambeck), and landscape species need to be assessed with regard to how they promote financially sensible decision making. For instance, careful accounting shows that conserving wild dogs in large protected areas is far more cost-efficient than setting up a meta-population on private reserves (Lindsey et al. 2005), a finding that suggests that wild dogs would have use as a landscape species. Whether a species has keystone or engineering properties is so dependent on spatial and temporal context that there may be little point in formally incorporating these concepts explicitly into reserve design. In contrast, managers need to pay attention to population sizes of these species because they can, in some circumstances, dramatically affect ecological community structure, and this will demand sensitive monitoring of the protected area on a case-by-case basis. Foundation, keystone, and engineering species may have an as-yet undetermined role in public relations.

Species Indicators of Anthropogenic Change Environmental, ecological-disturbance, and management indicator species can sometimes be bypassed by using environmental measures to assess pollution, species presence, or sustainable forest management. Respectively, robust measures of the effects of pollution include indices of biological integrity that combine environmental metrics, limited species richness and composition, trophic composition, fish abundance, and condition (Karr 1991); forest structural, functional, and compositional attributes including canopy cover and age structure that can predict plant and animal biodiversity (e.g., Smith et al. 2008); and logging intensity, length of rotation cycle, area of native forest, and fragmentation are standard forestry metrics (Gardner 2010). Noss (1990, 1999) made a forceful case for monitoring not only species in order to assess environmental changes but for monitoring environmental factors, too. Since we know some of the adverse affects of fragmentation on species diversity, and the effects of habitat patch size on

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population size, it is an obvious step simply to use aerial photography and satellite technology and GIS to monitor regional landscape change. On the ground, forest conditions, for example, can be measured by recording tree age-class ratios, structural complexity, blocks of continuous forest, road networks, exotic species, their abundances, or even recreational use of forests. Recently, the financial costs of biological surveys have been carefully documented in an effort to make species-monitoring more effective. For instance, radio telemetry and transect surveys can both be applied to determine habitat selection in birds. In a study of lesser kestrels in a Portuguese agricultural landscape, Franco and colleagues (2007) found that although telemetry set-up costs were higher than conducting transects, and day-today activities more expensive in terms of time and money, the area covered was larger and was unconstrained by access to roads (see Table 10-5).

Table 10-5. Cost-benefit analysis of radio-telemetry and transect surveys for foraging and habitat selection studies of lesser kestrels (from Franco et al. 2007). Costs/requirements

Field work period Number of field days Number of people needed per day Survey time per day per person (h) Surveyed area (km2) Access requirements (roads) Species handling/disturbance Identification skills and experience Factors affecting locations error Total number of points obtained People required for set up Set up time (days per person) =) Set up cost (C Set up knowledge =) (based on 0.25= Daily cost (C C /km) Time cost spent (h) =) Total costs (set up and daily cost) (C Significant differences obtained Time spent per significant difference (h) =) Cost per significant difference (C

Telemetry

Transcects

13 Mar–10 Jun 22 2 4 63 Small Yes No Weather 195 2 17 5,850 Large 3.7 312 8,115 26

8 Mar–10 Jun 24 1 4 34.6 Large No Yes Observer 209 1 2 790 Small 10 112 1,815 22

12 312

5 82.5

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Assessing biodiversity-survey costs across three forest types in Brazil— primary, secondary, and eucalypt plantations—Gardner and coworkers (2008a) took stock of the monetary cost of sampling different taxa by adding up costs of equipment and salaries of field assistants, relative time invested including worker hours in laboratory and field, and expertise needed to identify specimens. The last measure varied hugely with some groups such as birds, large mammals, and bats being identified to the species level, others such as arachnids being identified mostly to morphospecies; for some taxa, species-level guides were available; for some there were many experts (e.g., for fruit-feeding butterflies), for others such as orchid bees, only a handful of experts (see Table 10-6). Examining costs of surveying each taxon in each of the habitats (sampling only the equivalent number of individuals to that of the least effectively sampled taxon) revealed that small mammals were consistently expensive to survey every-

Table 10-6. Study taxa (vertebrates, invertebrates, trees, and lianas) sampled in Jari, northeast Brazilian Amazonia (from Gardner et al. 2008a). Number of Taxon

Leaf-litter amphibians Lizards Large mammals Small mammals Bats Birds Scavenger flies Fruit-feeding butterflies Dung beetles Epigeic arachnids Fruit flies Orchid bees Moths Trees and lianasc a,b

individuals

1,739 1,937 280 219 4,125 6,865 5,365 10,588 9,203 3,176 5,085 2,363 1,848 7,600

% morphospecies species

23 30 19 32 54 255 30 130 85 116 38 22 335 219

8.6 3.3 0. 9.4 0. 0. 20.0 0.8 35.3 75.9 5.3 18.2 50.7 NA

Species guide?

No Yes Yes No No Yes No No No No No Yes No NA

Experts globala

Brazilb

20 10 20–30 8 >250 >150 20 10 30 10 >50 15–20 NA 5 >50 20 3 1 30 20 4 3 30 10 15 3 NA NA

Estimate of the number of experts (a) in the world, or (b) in Brazil who would be able to identify samples of their taxonomic group from Jari to the level of species or morphospecies without extensive consultation with reference material or other scientists. c Only genus-level information included for trees and lianas.

Surrogate Species in the Real World 281

where, as to a lesser extent were moths and arachnids, while expenses for some taxa, such as fruit flies, depended on the habitat in which they were sampled. Using the IndVal method to assess ecological-indicator value, or average correlation coefficients between species’ abundance matrices for each taxon and all the remaining others, no one taxon was clearly superior in reflecting response patterns of others to different types of forest (although some were poorer, such as amphibians, bats, and arachnids, depending on the method). There was a strong negative relationship between taxonspecific cost and benefit for each forest type, however. Taxa that were cheapest to survey, such as birds and dung beetles, were the most effective ecological indicators, whereas expensive-to-survey taxa such as small mammals and moths were poor at discriminating other taxa’s responses to habitat modification (see Figure 10-2)—an optimistic finding. Furthermore, knowing taxon-specific costs allowed cost reductions of up to 16 percent by surveying certain taxa simultaneously—leaf-litter amphibians and lizards, small mammals and herpetofauna, arachnids and herpetofauna, and dung beetles and blow flies. Information about net-indicator benefits and doubling up on field surveys gives a fresh and tighter focus to the choices that survey teams can make regarding indicator taxa.

Promoting Conservation Large charismatic rare species continue to attract the attention of the public and are used to galvanize political will to set up reserves. Flagship species are not the sole vehicle to promote conservation, however—threatened biomes such as the warming arctic and disappearing rainforest are familiar hooks to attract public attention in the industrialized world. Specific habitats such as the Everglades or the Serengeti plains appear to work well as conservation rallying points, too. The idea of focusing attention on habitats using environmentally memorable features rather than using species proxies is not new. In lieu of the fact that the public is becoming more sophisticated in its understanding of conservation problems through increasing media attention, it would be instructive to know how much detailed conservation knowledge different sectors of society can assimilate and respond to, and how much NGOs need to worry about viewer fatigue with regard to using the same species repeatedly and the same stories of doom and gloom.

12

(a)

10

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Significant ecological indicator species (indicator species as % of total for a given taxon, averaged across forests)

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Figure 10-2. Cost-effectiveness of surveying 14 higher taxa in the Brazilian Amazon. The relationship between the standardized cost and ecological-indicator value of a variety of focal taxa sampled in (a) primary, (b) secondary, and (c) plantation forests. Note log scale on x-axis. BI, birds; DB, dung beetles; LI, lizards; TR, trees and lianas; BU, butterflies; AM, amphibians; BA, bats; SF, scavenger flies; LM, large mammals; OB, orchid bees; FF, fruit flies; AR, arachnids; MO, moths; SM, small mammals. (Reprinted from Gardner et al. 2008a.)

Surrogate Species in the Real World 283

Effective conservation public relations needs to move from conventional wisdom to evidence-based strategizing. Some evidence suggests that flagship species do not transfer easily from the industrialized- to the developing-world. Research is needed into national perceptions and receptivity to conservation messages in the developing world. Furthermore, it is not clear whether the flagship-species concept works well in human communities that live in contact with flagship species, given wildlife-human conflict and opportunities for exploitation (Woodroffe et al. 2005).

Wrap-Up Surrogate species are a necessary shortcut to pursuing conservation objectives, given the shortage of conservation funding and time, coupled with the complexities of species distributions and the complicated way that different species respond to environmental perturbation. The efficacy of surrogate species and surrogate taxa therefore needs to be assessed carefully in a world where strategic conservation planning is now paramount. Twenty years ago few would have guessed how much thought, computer time, and researchers’ salaries would have been spent on finessing surrogate methods, especially in regards to reserve-site selection and environmental indicators—effort and money that could arguably have been directed toward practical solutions. While some recognize that conservation science is in an “implementation crisis” and needs to move into planning and management phases (Knight et al. 2006), much of the surrogate-species literature is still firmly entrenched in the ivory tower of academia—“journals like Ecology still packed with papers describing more and more sophisticated analyses applied to more and more trivial problems” (Ehrlich 1997, 111). The next step must be to apply studies to real-life situations by working with stakeholders and land-use planners—work that involves socioeconomic knowledge, flexibility, and sensitivity. It is sad that the science of surrogate species is still a cottage industry of scientific-paper production with only sporadic relevance to solving conservation challenges successfully in the real world. Hopefully, pleas for relevance in conservation science (Salafsky et al. 2002; Sutherland et al. 2004; Cleary 2006; Ferraro & Pattanayak 2006) will galvanize practical application of surrogate species concepts.

284 c o n s e r v a t i o n b y p r o x y

Summary Surrogate-species terminology can be clarified by qualifying surrogatespecies phrases more precisely and by stating conservation objectives more clearly. Some species can be used for different conservation goals simultaneously. In general, surrogate species or species-groups do not have a great deal of power to predict the presence of target taxa or their responses to environmental change, although there are some encouraging exceptions. The distribution of terrestrial biodiversity can be mapped globally using environmental variables without recourse to using surrogate taxa, although the latter may still be necessary for mapping marine biodiversity. Reserveplanning procedures now incorporate costs of reserve acquisition and uncertainty about reserve availability in the future, and these factors can relieve the pressure on choosing appropriate surrogate taxa. Environmental variables can be used with great effect in reserve design and in signifying anthropogenic change. Ecological monitoring and surveys can be made more cost-effective with simple financial auditing. Conservation can be promoted using biomes and habitats as well as single species. Therefore, environmental and economic considerations hold promise for bypassing the use of surrogate species as conservation tools.

Large beetle larvae are a delicacy among the Pimbwe people of western Tanzania. They risk arrest to go into Katavi National Park to find Acacia trees where these larvae—called madime in the Kipimbwe language—are found. This raises questions about which species might best foster a conservation ethic in the area and more generally about culturally appropriate flagship species. (Drawing by Sheila Girling.)

references

Abbitt, R. J. F., J. M. Scott, and D. S. Wilcove. 2000. The geography of vulnerability: incorporating species geography and human development patterns into conservation planning. Biological Conservation 96:169–75. Ackery, P. R., and R. I. Vane-Wright. 1984. Milkweed Butterflies. Cornell: Cornell University Press. Adams, S. M. 2002., ed. Biological Indicators of Aquatic Ecosystem Stress. Bethseda, MD: American Fisheries Society. Aebischer, N. J., A. D. Evans, P. V. Price, and J. A. Vickery. 2000. Ecology and Conservation of Lowland Farmland Birds. Tring, UK: British Ornithologists Union. Aguilar-Amuchastegui, N., and Henebry, G. M. 2007. Assessing sustainability indicators for tropical forests: spatio-temporal heterogeneity, logging intensity, and dung beetle communities. Forest Ecology and Management 253:56–67. Airame, S., J. E. Dugan, K. D. Laferty, H. Leslie, D. A. McArdle, and R. R. Warner. 2003. Applying ecological criteria to marine reserve design: a case study from the California Channel Islands. Ecological Applications 13:S170–84. Allen, A. P., T. R. Whittier, D. P. Larsen, P. R. Kaufmann, R. J. O’Connor, R. M. Hughes, R. S. Stemberger, S. S. Dixit, R. O. Brinhurst, A. T. Hirlihy, and S. G. Paulsen. 1999. Concordance of taxonomic richness patterns across multiple assemblages in lakes of the north-eastern United States. Canadian Journal of Fish and Aquatic Sciences 56:739–47. Altieri, A. H., and J. D. Witman. 2006. Local extinction of a foundation species in a hypoxic estuary: integrating individuals to ecosystem. Ecology 87:717–30. Alves da Mata, R., M. McGeoch, and R. Tidon. 2008. Drosophilid assemblages as a bioindicator system of human disturbance in the Brazilian savanna. Biodiversity and Conservation 17:2899–916. Anand, M., S. Laurence, and B. Rayfield. 2005. Diversity relationships among taxonomic groups in recovering and restored forests. Conservation Biology 19:955–62. Andelman, S. J., and W.F. Fagan. 2000. Umbrellas and flagships: efficient conservation surrogates or expensive mistakes? Proceedings of the National Academy of Sciences 97:5954–59. Andelman, S. J., and M. R. Willig. 2003. Present patterns and future prospects for biodiversity in the Western Hemisphere. Ecology Letters 6:818–24.

287

288 References Andersen, A. N. 1995. Measuring more of biodiversity: genus richness as a surrogate for species richness in Australian ant faunas. Biological Conservation 73:39– 43. Anderson, J., J. M. Rowcliffe, and G. Cowlishaw. 2007. The Angola black-andwhite colobus (Colobus angolensis palliates) in Kenya: historical range contraction and current conservation status . American Journal of Primatology 69:664– 80. Ando, A., J. Camm, S. Polasky, and A. Solow. 1998. Species distribution, land values, and efficient conservation. Science 279:2126–28. Angermeier, P. L., and J. R. Karr. 1994. Biological integrity versus biological diversity as policy directives. BioScience 44:690–97. Armstrong, D. 2002. Focal and surrogate species: getting the language right. Conservation Biology 16:285–86. Arrigo, K. R., C. W. Sullivan, and J. N. Kremer. 1991. A bio-optical model of Antarctic sea lice. Journal of Geophysical Research 96 C6:10581–92. Araujo, M. B., C. J. Humphries, P. J. Densham, R. Lampinen, W. J. M. Hagemeijer, A. J. Mitchell-Jones, and J. P. Gase. 2001. Would environmental diversity be a good surrogate for species diversity? Ecography 24:103–10. Araujo, M. B., P. J. Densham, and P. H. Williams. 2004. Representing species in reserves from patterns of assemblage diversity. Journal of Biogeography 31:1037– 50. Asquith, N. M., S. J. Wright, and M. J. Claus. 1997. Does mammal community composition control seedling recruitment in neotropical forests? Evidence from islands in central Panama. Ecology 78:941–46. Atauri, J. A., and J. V. de Lucio. 2001. The role of landscape structure in species richness distribution of birds, amphibians, reptiles and lepidoterans in Mediterranean landscapes. Landscape Ecology 16:147–59. Attrill, M. J. 2002. Community-level indicators of stress in aquatic ecosystems. In Biological Indicators of Aquatic Ecosystem Stress, ed. S. M. Adams, 473–508. Bethseda, MD: American Fisheries Society. Awad, A. A., C. L. Griffiths, and J. K. Turpie. 2002. Distribution of South African marine benthic invertebrates applied to the selection of priority conservation areas. Diversity and Distributions 8:129–45. Awimbo, J. A., D. A. Norton, and F. B. Overmars. 1996. An evaluation of representativeness for nature conservation, Hokitika Ecological District, New Zealand. Biological Conservation 75:177–86. Ball, S. M. J. 2004. Stocks and exploitation of East African blackwood Dalbergia melanoxylon: a flagship species for Tanzania’s Miombo woodlands? Oryx 38:266–72. Balmford, A., and K. J. Gaston. 1999. Why biodiversity surveys are good value. Nature 398:204–5. Balmford, A., M. J. B. Green, and M. G. Murray. 1996a. Using higher-taxon rich-

References 289 ness as a surrogate for species richness: I. Regional tests. Proceedings of the Royal Society London B 263:1267–74. Balmford, A., A. H. M. Jayasuriya, and M. J. B. Green 1996b. Using higher-taxon richness as a surrogate for species richness: II. Local applications. Proceedings of the Royal Society of London B 263:1571–75. Balmford, A., and A. Long. 1995. Across-country analyses of biodiversity congruence and current conservation effort in the tropics. Conservation Biology 9:1539–47. Balmford, A., A. J. E. Lyon, and R. M. Lang. 2000. Testing the higher-taxon approach to conservation planning in a megadiverse group: the macrofungi. Biological Conservation 93:209–17. Bangert, R. K., and C. N. Slobodchikoff. 2006. Conservation of prairie dog ecosystem engineering may support arthropod beta and gamma diversity. Journal of Arid Environments 67:100–115. Bani, L., M. Baietto, L. Bottoni, and R. Massa. 2002. The use of focal species in designing a habitat network for a lowland area of Lombardy, Italy. Conservation Biology 16:826–31. Bani, L., D. Massimino, L. Bottoni, and R. Massa. 2006. A multiscale method for selecting indicator species and priority conservation areas: a case study for broadleaved forests in Lombardy, Italy. Conservation Biology 20:512–26. Barkai, A., and C. McQuaid. 1988. Predator-prey role reversal in a marine benthic ecosystem. Science 242:62–64. Barlow, J., W. L. Overal, I. S. Araujo, T. A. Gardner, and C. A. Peres. 2007c. The value of primary, secondary and plantation forests for fruit-feeding butterflies in the Brazilian Amazon. Journal of Applied Ecology 44:1001–12. Barlow, J., T. A. Gardner, A. I. Araujo, T. C. Avila-Pires, A. B. Bonaldo, J. E. Costa, M. C. Esposito, L. V. Ferreira, J. Hawes, M. I. M. Hernandez, M. S. Hoogmoed, R. N. Leite, N. F. Lo-Man-Hung, J. R. Malcolm, M. B. Martins, L. A. M. da Silva, C. da Silva Motta, and C. A. Peres. 2007d. Quantifying the biodiversity value of tropical primary, secondary, and plantation forests. Proceedings of the National Academy of Sciences 104:18555–60. Barlow, J., T. A. Gardner, L. V. Ferreira, and C. A. Peres. 2007a. Litter fall and decomposition in primary, secondary and plantation forests in the Brazilian Amazon. Forest Ecology and Management 247:91–97. Barlow, J., L. A. M. Maestre, T. A. Gardner, and C. A. Peres. 2007b.The value of primary, secondary and plantation forests for Amazonian birds. Biological Conservation 136:212–31. Barlow, J., I. S. Araujo, W. L. Overal, T. A. Gardner, F. da Silva Mendes, I. R. Lake, C. A. Peres. 2008. Diversity and composition of fruit-feeding butterflies in tropical Eucalyptus plantations. Biodiversity and Conservation 17:1089–104. Barton, B. A., J. D. Morgan, and M. M. Vijayan. 2002. Physiological and condition-related indicators of environmental stresses in fish. In Biological Indicators

290 References of Aquatic Ecosystem Stress, ed. S. M. Adams, 111–48. Bethseda, MD: American Fisheries Society. Basset, Y., E. Charles, D. S. Hammond, and V. K. Brown. 2001. Short-term effects of canopy openness on insect herbivores in a rain forest in Guyana. Journal of Applied Ecology 38:1045–58. Basset, Y., J. F. Mavoungou, J. B. Mikissa, O. Mikissa, S. E. Miller, R. L. Kitching, and A. Alonso. 2004. Discriminatory power of different arthropod data sets for the biological monitoring of anthropogenic disturbance in tropical forests. Biodiversity and Conservation 13:709–32. Basset, Y., O. Missa, A. Alonso, S. E. Miller, G. Curletti, M. de Meyer, C. Eardley, O. T. Lewis, M. W. Mansell, V. Novotny, and T. Wagner. 2008. Changes in arthropod assemblages along a wide gradient of disturbance in Gabon. Conservation Biology 22:1552–63. Beazley, K., and N. Cardinal. 2004. A systematic approach for selecting focal species for conservation in the forests of Nova Scotia and Maine. Environmental Conservation 31:91–101. Bedward, M., R. L. Pressey, and D. A. Keith. 1992. A new approach for selecting fully representative reserve networks: addressing efficiency, reserve design and land suitability with an iterative analysis. Biological Conservation 62:115–25. Beeby, A. 2001. What do sentinels stand for? Environmental Pollution 112:285–98. Beger, M., G. P. Jones, and P. L. Munday. 2003. Conservation of coral reef biodiversity: a comparison of reserve selection procedures for corals and fishes. Biological Conservation 111:53–62. Beger, M., S. A. McKenna, and H. P. Possingham. 2007. Effectiveness of surrogate taxa in the design of coral reef reserve systems in the Indo-Pacific. Conservation Biology 21:1584–93. Begon, M., J. L. Harper, and C. R. Townsend. 1990. Ecology, Individuals, Populations and Communities (2nd ed). Oxford, UK: Blackwell. Belbin, L. 1993. Environmental representativeness: regional partitioning and reserve selection. Biological Conservation 66:223–30. Bellwood, D. R., A. S. Hoey, J. L. Ackerman, and M. Depczynski. 2006. Coral bleaching, reef fish community phase shifts and the resilience of coral reefs. Global Change Biology 12:1587–94. Benton, T. G., D. M. Bryant, L. Cole, and H. Q. P. Crick. 2002. Linking agricultural practice to insect and bird populations: a historical study over three decades. Journal of Applied Ecology 39:673–87. Berg, A., and M. Tjernberg. 1996. Common and rare Swedish vertebrates—distribution and habitat preferences. Biodiversity and Conservation 5:101–28. Bergamini, A., C. Sheidegger, S. Stofer, P. Carvalho, S. Davey, M. Dietrich, F. Dubs, E. Farkas, U. Groner, K. Karkkainen, C. Keller, L. Lokos, S. Lommi, C. Maguas, R. Mitchell, P. Pinho, V. J. Rico, G. Aragon, A.-M. Truscott, P. Wolseley, and A. Watt. 2005. Performance of macrolichens and lichen genera

References 291 as indicators of lichen species richness and composition. Conservation Biology 19:1051–62. Berger, J. 1997. Population constraints associated with the use of black rhinos as an umbrella species for desert herbivores. Conservation Biology 11:69–78. Berger, J., P. B. Stacey, L. Bellis, and M. P. Johnson. 2001. A mammalian predatorprey imbalance: grizzly bear and wolf extinction affect avian neotropical migrants. Ecological Applications 11:947–60. Berger, K. M., and E. M. Gese. 2007. Does intereference competition with wolves limit the distribution and abundance of coyotes? Journal of Animal Ecology 76:1075–85. Berger, K. M., E. M. Gese, and J. Berger. 2008. Indirect effects and traditional trophic cascades: a test involving wolves, coyotes, and pronghorn. Ecology 89:818–28. Berkman, H. E., C. F. Rabeni, and T. P. Boyle. 1986. Biomonitors of stream quality in agricultural areas: fish versus invertebrates. Environmental Management 10:413–19. Bertness, M. D. 1984. Habitat and community modification by an introduced herbivorous snail. Ecology 65:370–81. Bertness, M. D., and S. D. Hacker. 1994. Physical stress and positive associations among marsh plants. American Naturalist 144:363–72. Beschta, R. L. 2003. Cottonwoods, elk, and wolves in the Lamar Valley of Yellowstone National Park. Ecological Applications 13:1295–309. Betrus, C. J., E. Fleishman, and R. B. Blair. 2005. Cross-taxonomic potential and spatial transferability of an umbrella species index. Journal of Environmental Management 74:79–87. Bhagwat, S.A., K.J. Willis, H.J.B. Birks, and R.J. Whittaker. 2008. Agroforestry: a refuge for tropical biodiversity? Trends in Ecology and Evolution 23:261–267. Bibby C. J., N. J. Collar, M. J. Crosby, M. F. Heath, Ch. Imboden, T. H. Johnson, A. J. Long, A. J. Stattersfield, and S. J. Thirgood.1992. Putting Biodiversity on the Map: Priority Areas for Global Conservation. Cambridge, UK: International Council for Bird Preservation. Bifolchi, A., and T. Lode. 2005. Efficiency of conservation shortcuts: an investigation with otters as umbrella species. Biological Conservation 126:523–27. Billeter, R., J. Liira, D. Bailey, R. Bugter, P. Arens, I Augenstein, S. Aviron, J. Baudry, R. Bukacek, F. Burel, M. Cerney, G. De Blust, R, De Cock, T. Diekotter, H. Dietz, J. Dirksen, C. Dormann, W. Durka, M. Frenzel, R. Hamersky, F. Hendrickx, F. Herzog, S. Klotz, B. Koolstra, A. Lausch, D. Le Coeur, J. P. Maelfait, P. Opdam, M. Roubalova, A. Schermann, N. Schermann, T. Schmidt, O. Schweiger, M. J. M. Smulders, M. Speelmans, P. Simova, J. Verboom, W. K. R. E. van Wingereden, M. Zobel, and P. J. Edwards. 2008. Indicators for biodiversity in agricultural landscapes: a pan-European study. Journal of Applied Ecology 45:141–50.

292 References Bilton, D. T., L. Mcabendroth, A. Bedford, and P. M. Ramsay. 2006. How wide to cast the net? Cross-taxon congruence of species richness, community similarity and indicator taxa in ponds. Freshwater Biology 51:578–90. Bishop, J. A., and W. L. Myers. 2005. Associations between avian functional guild response and regional landscape properties for conservation planning. Ecological Indicators 5:33–48. Bladt, J., F. W. Larsen, and C. Rahbek. 2008. Does taxonomic diversity in indicator groups influence their effectiveness in identifying priority areas for species conservation? Animal Conservation 11:546–54. Blair, R. B. 1996. Land use and avian species diversity along an urban gradient. Ecological Applications 6:506–19. Blair, R. B. 1999. Birds and butterflies along an urban gradient: surrogate taxa for assessing biodiversity? Ecological Applications 9:164–70. Bode, M., K. A. Wilson, T. M. Brooks, W. R. Turner, R. A. Mittermeier, M. F. McBride, E. C. Underwood, and H. P. Possingham. 2008. Cost-effective global conservation spending is robust to taxonomic group. Proceedings of the the National Academy of Sciences 105:6498–501. Bohning-Gaese, K. 1997. Determinants of avian species richness at different spatial scales. Journal of Biogeography 24:49–60. Bond, W. J. 1994. Keystone species. In Biodiversity and Ecosystem Function, ed. E.-D. Schulze and H. A. Mooney, 237–53. Berlin: Springer-Verlag. Bonn, A., and K. J. Gaston 2005. Capturing biodiversity: selecting priority areas for conservation using different criteria. Biodiversity and Conservation 14:1083– 1100. Bonn, A., A. S. L. Rodrigues, and K. J. Gaston. 2002. Threatened and endemic species: are they good indicators of patterns of biodiversity on a national scale? Ecology Letters 5:733–41. Bonn, A., and B. Schroder. 2001. Habitat models and their transfer for single and multi species groups: a case study of carabids in an alluvial forest. Ecography 24:483–96. Borgerhoff-Mulder, M., R. Schacht, T. Caro, J. Schacht, and B. Caro. 2009. Knowledge and attitudes of children of the Rupununi: implications for conservation in Guyana. Biological Conservation 142:879–87. Boulton, A. J. 1999. An overview of river health assessment: philosophies, practice, problems and prognosis. Freshwater Biology 41:469–79. Bowen-Jones, E., and A. Entwistle. 2002. Identifying appropriate flagship species: the importance of culture and local contexts. Oryx 36:189–95. Boyd, S. E., H. L. Rees, and C. A. Richardson. 2000. Nematodes as sensitive indicators of change at dredge material disposal sites. Estuarine, Coastal, and Shelf Science 51:805–19. Bradbury, R. B., and W. B. Kirby. 2006. Farmland birds and resource protection in the UK: cross-cutting solutions for multi-functional farming? Biological Conservation 129:530–42.

References 293 Bradshaw, C. J. A., Y. Fukuda, M. Letnic, and B. W. Brook. 2006. Incorporating known sources of uncertainty to determine precautionary harvests of saltwater crocodiles. Ecological Applications 16:1436–48. Breininger, D. R., V. L. Larson, B. W. Duncan, R. B. Smith, D. M. Oddy, and M. F. Goodchild. 1995. Landscape patterns of Florida scrub jay habitat use and demographic success. Conservation Biology 9:1442–53. Bried, J. T., B. D. Herman, and G. N. Ervin. 2007. Umbrella potential of plants and dragonflies for wetland conservation: a quantitative case study using the umbrella index. Journal of Applied Ecology 44:833–42. Brook, B. W., C. J. A. Bradshaw, L. P. Koh, and N. S. Sodhi. 2006. Momentum drives the crash: mass extinction in the tropics. Biotropica 38:302–5. Brook, B. W., N. S. Sodhi, and C. J. A. Bradshaw. 2008. Synergies among extinction drivers under global change. Trends in Ecology and Evolution 23:453–60. Brooks, T., A. Balmford, N. Burgess, J. Fjeldsa, L. A. Hansen, J. Moore, C. Rahbek, and P. Williams. 2001. Toward a blueprint for conservation in Africa. BioScience 51:613–24. Brooks, T., G. A. B. da Fonseca, and A. S. L. Rodrigues. 2004. Species, data, and conservation planning. Conservation Biology 18:1682–88. Brooks, T. M., R. A. Mittermeier, G. A. B. da Fonseca, J. Gerlach, M. Hoffmann, J. F. Lamoreux, C. G. Mittermeier, J. D. Pilgrim, and A. S. L. Rodrigues. 2006. Global biodiversity conservation priorities. Science 313:58–61. Brown, C. 2000. The global outlook for present and future wood supply from forest plantation. Working Paper No. GFPOS/WP/03. FAO. Brown, J. H. 1971. Mammals on mountaintops: non-equilibrium insular biogeography. American Naturalist 105:467–78. Brown, J. H., and E. J. Heske. 1990. Control of a desert-grassland transition by a keystone rodent guild. Science 250:1705–7. Brown, K. S. and A. V. L. Freitas. 2000. Atlantic forest butterflies: indicators for landscape conservation. Biotropica 32:934–56. Brummitt, N., and E. N. Lughadha. 2003. Biodiversity: where’s hot and where’s not? Conservation Biology 17:1442–48. Bruner, A. G., R. E. Gullison, and A. Balmford. 2004. Financial costs and shortfalls of managing and expanding protected-area systems in developing countries. BioScience 54:1119–26. Bruno, J. F., and M. I. O’Connor. 2005. Cascading effects of predator diversity and omnivory in a marine food web. Ecology Letters 8:1048–56. Bruno, J. F., J. J. Stachowicz, and M. D. Bertness. 2003. Inclusion of facilitation into ecological theory. Trends in Ecology and Evolution 18:119–25. Buchman, N., K. Cuddington, and J. Lambrinos. 2007. A historical perspective on ecosystem engineering. In Ecosystem Engineers: Plants to Protists, eds. K. Cuddington, J. E. Byers, W. G. Wilson, and A. Hastings, 25–46. Amsterdam: Elsevier. Bunn, S. E., and A. H. Arthington. 2002. Basic principles and ecological

294 References consequences of altered flow regimes for aquatic biodiversity. Environmental Management 30:492–507. Burgess, N. D., C. Rahbek, F. W. Larsen, P. Williams, and A. Balmford. 2002. How much of the vertebrate diversity of sub-Saharan Africa is catered for by recent conservation proposals? Biological Conservation 107:327–39. Burrell, G. A., and F. M. Seibert. 1916. Gases found in coal mines. Miners’ Circular 14. Washington, DC: Bureau of Mines, Department of the Interior. Buse, J., T. Ranius, and T. Assmann. 2008. An endangered longhorn beetle associated with old oaks and its possible role as an ecosystem engineer. Conservation Biology 22:329–37. Butchart, S. H. M., A. J. Stattersfield, J. Baillie, L. A. Bennun, S. N. Stuart, H. R. Akcakaya, C. Hilton-Taylor, and G. M. Mace. 2005. Using Red List Indices to measure progress towards the 2010 target and beyond. Philosophical Transactions of the Royal Society of London B 360:255–68. Butler, R. A., and W. F. Laurance. 2008. New strategies for conserving tropical forests. Trends in Ecology and Evolution 23:469–72. Byers, J.E., K. Cuddington, C.G. Jones, T.S.Talley, A Hastings, J.G. Lambrinos, J.A. Crookks, and W.G. Wilson. 2006. Using ecosystem engineers to restore ecological systems. Trends in Ecology and Evolution 21:493–500. Cabeza, M., A. Arponen, and A. Van Teeffelen. 2008. Top predators: hot or not? A call for systematic assessment of biodiversity surrogates. Journal of Applied Ecology 45:976–980. Cabeza, M., and A. Moilanen. 2001. Design of reserve networks and the persistence of biodiversity. Trends in Ecology and Evolution 16:242–48. Cairns, J., Jr. 1986. The myth of the most sensitive species. BioScience 36:670–72. Cairns, J., P. V. McCormick, and B. R. Niederlehner. 1993. A proposed framework for developing indicators of ecosystem health. Hydrobiologica 236:1–44. Cairns, J., Jr., and J. R. Pratt. 1993. A history of biological monitoring using benthic macroinvertebrates. In Freshwater Biomonitoring and Benthic Macroinvertebrates, eds. D. M. Rosenberg and V. H. Resh, 10–27. New York: Chapman & Hall. Cairns, J., Jr., and W. H. van der Schalie. 1980. Biological monitoring Part I—early warning systems. Water Research 14:1179–96. Canterbury, G. E., T. E. Martin, D. R. Petit, L. J. Petit, and D. F. Bradford. 2000. Bird communities and habitat as ecological indicators of forest condition in regional monitoring. Conservation Biology 14:544–58. Cantu, C., R. G. Wright, J. M. Scott, and E. Strand. 2004. Assessment of current and proposed nature reserves of Mexico based on their capacity to protect geophysical features and biodiversity. Biological Conservation 115:411–17. Cardillo, M., G. M. Mace, J. L. Gittleman, and A. Purvis. 2006. Latent extinction risk and the future battlegrounds of mammal conservation. Proceedings of the National Academy of Sciences 103:4157–61. Cardillo, M., A. Purvis, W. Sechrest, J. L. Gittleman, J. Bielby, and G. M. Mace.

References 295 2004. Human population density and extinction risk in the world’s carnivores. PLoS Biology 2:909–13. Cardoso, P., I. Silva, N. G. de Oliveira, and A. R. M. Serrano. 2004a. Indicator taxa of spider (Araneae) diversity and their efficiency in conservation. Biological Conservation 120:517–24. Cardoso, P., I. Silva, N. G. de Oliveira, and A. R. M. Serrano. 2004b. Higher taxa surrogates of spider (Araneae) diversity and their efficiency in conservation. Biological Conservation 117:453–59. Carey, A. B., S. P. Horton, and B. L. Biswell. 1992. Northern spotted owls: influence of prey base and landscape character. Ecological Monographs 62:223–50. Carignan, V., and M.-A. Villard. 2002. Selecting indicator species to monitor ecological integrity: a review. Environmental Monitoring and Assessment 78:45–61. Caro, T. M. 2000. Focal species. Conservation Biology 14:1569–70. Caro, T. M. 2001. Species richness and abundance of small mammals inside and outside an African national park. Biological Conservation 98:251–57. Caro, T. M. 2002a. Focal and surrogate species: getting the language right. Conservation Biology 16:286–87. Caro, T. M. 2002b. Factors affecting the small mammal community inside and outside Katavi National Park, Tanzania. Biotropica 34:310–18. Caro, T. M. 2003. Umbrella species: critique and lessons from East Africa. Animal Conservation 6:171–81. Caro, T. 2007. The Pleistocene rewilding gambit. Trends in Ecology and Evolution 22:281–83. Caro, T., J. Eadie, and A. Sih. 2005. Use of substitute species in conservation biology. Conservation Biology 19:1821–26. Caro, T., A. Engilis Jr., E. Fitzherbert, and T. Gardner 2004. Preliminary assessment of the flagship species concept at a small scale. Animal Conservation 7:63–70. Caro, T. M., and G. O’Doherty. 1999. On the use of surrogate species in conservation biology. Conservation Biology 13:805–14. Carrillo, E., G. Wong, and A. D. Cuaron. 2000. Monitoring mammal populations in Costa Rican protected areas under different hunting restrictions. Conservation Biology 14:1580–91. Carroll, C., R. F. Noss, and P. C. Paquet. 2001. Carnivores as focal species for conservation planning in the Rocky Mountain region. Ecological Applications 11:961–80. Carroll, C., R. F. Noss, P. C. Paquet, and N. H. Schumaker. 2003. Use of population viability analysis and reserve selection algorithms in regional conservation plans. Ecological Applications 13:1773–89. Carroll, S. S., and D. L. Pearson. 1998. Spatial modeling of butterfly species richness using tiger beetles (Cicindelidae) as a bioindicator taxon. Ecological Applications 8:534–43. Carson, R. 1962. Silent Spring. Boston, MA: Houghton Mifflin. Carwardine, J., C. J. Klein, K. A. Wilson, R. L. Pressey, and H. Possingham. Hitting

296 References the target and missing the point: target-based conservation planning in context. Conservation Letters 2:3–10. Caughley, G., and A. Gunn. 1996. Conservation Biology in Theory and Practice. Cambridge, MA: Blackwell Science. Caughley, H. 1976. The elephant problem—an alternative hypothesis. East African Wildlife Journal 14:265–83. Ceballos, G., and P. R. Ehrlich. 2006. Global mammal distributions, biodiversity hotspots, and conservation. Proceedings of the National Academy of Sciences 103:19374–79. Ceballos, G., P. R. Ehrlich, J. Soberon, I. Salazar, and J. P. Fay. 2005. Global mammal conservation: what must we manage? Science 309:603–7. Ceballos, G., P. Rodriguez, and R. A. Medellin. 1998. Assessing conservation priorities in megadiverse Mexico: mammalian diversity, endemicity, and endangerment. Ecological Applications 8:8–17. Chase, J. M. 2000. Are there real differences among aquatic and terrestrial food webs? Trends in Ecology and Evolution 15:408–12. Chase, M. K., W. B. Kristan III, A. J. Lynam, M. V. Price, and J. T. Rotenberry. 2000. Single species as indicators of species richness and composition in California coastal sage scrub birds and small mammals. Conservation Biology 14:474–87. Chiarucci, A., F. D’Auria, V. de Dominicis, A. Lagana, C. Perini, and E. Salerni. 2005. Using vascular plants as a surrogate taxon to maximize species richness in reserve design. Conservation Biology 19:1644–52. Christie, S. 2007. Zoo-based fundraising for in situ wildlife conservation. In Zoos in the 21st Century: Catalysts for Conservation? eds. A. Zimmermann, M. Hatchwell, L. Dickie, and C. West, 257–74. Cambridge, UK: Cambridge University Press. Cincotta, R. P., J. Wisnewski, and R. Engelman. 2000. Human population in the biodiversity hotspots. Nature 404:990–92. Claisse, D. 1989. Chemical contamination of French coasts—the results of a tenyear mussel watch. Marine Pollution Bulletin 20:523–28. Clarke, G. M. 1993. Fluctuations asymmetry of invertebrate populations as a biological indicator of environmental quality. Environmental Pollution 82:207–11. Clarke, K. R. 1990. Comparisons of dominance curves. Journal of Experimental Marine Biology and Ecology 138:143–57. Clarke, K. R., and R. M. Warwick 1998. A taxonomic distinctness index and its statistical properties. Journal of Applied Ecology 35:523–31. Claudet, J., D. Pelletier, J.-Y. Jouvenel, F. Bachet, and R. Galzin. 2006. Assessing the effects of marine protected area (MPA) on a reef fish assemblage in a northwestern Mediterranean marine reserve: identifying community-based indicators. Biological Conservation 130:349–69. Cleary, D. 2006. The questionable effectiveness of science spending by international conservation organizations in the tropics. Conservation Biology 20:733–38.

References 297 Clements, W. H., and M. C. Newman. 2002. Community Ecotoxicology. Chichester, Sussex: John Wiley & Sons. Clucas, B., K. McHugh, and T. Caro. 2008. Use of flagship species by U.S. conservation and nature magazines. Biodiversity and Conservation. 17:1517–28. Cochrane, M. A., and W. F. Laurance 2002. Fire as large-scale edge effect in Amazonian forests. Journal of Tropical Ecology 18:311–25. Code of Federal Regulations 1985. Title 36, Code of Federal Regulations, Chapter II. Part 219.19, subpart 64. Washington, DC: U.S. Government Printing Office. Cofre, H., and P. A. Marquet. 1999. Conservation status, rarity, and geographic priorities for conservation of Chilean mammals: an assessment. Biological Conservation 88:53–68. Coleman, F. C., and S. L. Williams. 2002. Overexploiting marine ecosystem engineers: potential consequences for biodiversity. Trends in Ecology and Evolution 17:40–44. Collinge, S. K. 2000. Effects of grassland fragmentation on insect species loss, colonization, and movement patterns. Ecology 81:2211–26. Collinge, S. K. 2009. Ecology of Fragmented Landscapes. Baltimore, MD: Johns Hopkins University Press. Collins, J. P., and A. Storfer. 2003. Global amphibian declines: sorting the hypotheses. Diversity and Distributions 9:89–98. Colwell, R. K., and J. A. Coddington. 1994. Estimating terrestrial biodiversity. Philosophical Transactions of the Royal Society of London B 345:101–8. Constanza, R. 1992. Can ecosystems be healthy? Critical consideration of concepts. Journal of Aquatic Ecosystem Health 1:1–15. Conway, W. G. 1989. The prospects for sustaining species and their evolution. In Conservation for the Twenty-first Century, eds. D. Western and M. C. Pearl, 199– 209. New York: Oxford University Press. Coppolillo, P., H. Gomez, F. Maisels, and R. Wallace. 2004. Selection criteria for suites of landscape species as a basis for site-based conservation. Biological Conservation 115:419–30. Corey, S. J., and T. A. Waite. 2008. Phylogenetic autocorrelation of extinction threat in globally imperiled amphibians. Diversity and Distributions 14:614–29. Cornutt, J., J. Lockwood, H. Luh, P. Nott, and G. Russell. 1994. Hotspots and species diversity. Nature 367:326–27. Costello, C., and S. Polasky. 2004. Dynamic reserve site selection. Resource and Energy Economics 26:157–74. Cowling, R. M., A. T. Knight, D. P. Faith, S. Ferrier, A. T. Lombard, A. Driver, M. Rouget, K. Maze, and P. G. Desmet. 2004. Nature conservation requires more than a passion for species. Conservation Biology 18:1674–77. Cox, P. A., T. Elmqvist, E. D. Pierson, and W. E. Rainey. 1991. Flying foxes as strong interactors in south Pacific Island ecosystems: a conservation hypothesis. Conservation Biology 5:448–54.

298 References Craighead, F. C. 1968. The role of the alligator in shaping plant communities and maintaining wildlife in the southern Everglades. Florida Naturalist 41, 2–7, 69–74, 94. Crain, C. M., and M. D. Bertness. 2006. Ecosystem engineering across environmental gradients: implications for conservation and management. BioScience 56:211–18. Creel, S., D. Christianson, S. Liley, and J. A. Winnie Jr. 2007. Predation risk affects reproductive physiology and demography of elk. Science 315:960. Creel, S., and J. A. Winnie. 2005. Responses of elk herd size to fine-scale spatial and temporal variation in the risk of predation by wolves. Animal Behaviour 69:1181–89. Creel, S., J. Winnie Jr., B. Maxwell, K. Hamlin, and M. Creel. 2005. Elk alter habitat selection as an antipredator response to wolves. Ecology 86:3387–97. Crisp, P. N., K. J. M. Dickinson, and G. W. Gibbs. 1998. Does native invertebrate diversity reflect native plant diversity? A case study from New Zealand and implications for conservation. Biological Conservation 83:209–20. Cristancho, S., and J. Vining. 2004. Culturally defined keystone species. Human Ecology Review 11:153–64. Croll, D. A., J. L. Maron, J. A. Estes, E. M. Danner, and G. V. Byrd. 2005. Introduced predators transform subarctic islands from grassland to tundra. Science 307:1959–61. Crooks, J. A. 2002. Characterizing ecosystem-level consequences of biological invasions: the role of ecosystem engineers. Oikos 97:153–66. Crooks, K. R. 2002. Relative sensitivities of mammalian carnivores to habitat fragmentation. Conservation Biology 16:482–502. Crooks, K. R., and M. E. Soule. 1999. Mesopredator release and avifaunal extinctions in a fragmented system. Nature 400:563–66. Croonquist, M. J., and R. P. Brooks. 1991. Use of avian and mammalian guilds as indicators of cumulative impacts in riparian-wetland areas. Environmental Management 15:701–14. Cryanoski, D. 2006. Calls to conserve biodiversity hotspots. Nature 439:774. Csuti, B., S. Polasky, P. H. Williams, R. L. Pressey, J. D. Camm, M. Kershaw, A. R. Kiester, B. Downs, R. Hamilton, M. Huso, and K. Sahr. 1997. A comparison of reserve selection algorithms using data on terrestrial vertebrates in Oregon. Biological Conservation 80:83–97. Cuddington, K. 2007. Ecosystem engineering: utility, contention, and progress. In Ecosystem Engineers: Plants to Protists, eds. K. Cuddington, J. E. Byers, W. G. Wilson, and A. Hastings, 69–74. Amsterdam: Elsevier. Cumming, C. H. M. 1983. The decision-making framework with regard to the culling of large mammals in Zimbabwe. In Management of Large Mammals in African Conservation Areas, ed. R.O. Owen-Smith, 173–86. Pretoria: Haum. Curnutt, J., J. Lockwood, H. Luh, P. Nott, and G. Russell. 1994. Hotspots and species diversity. Nature 367:326-327.

References 299 Currie, D. J. 1991. Energy and large-scale patterns of animal- and plant-species richness. American Naturalist 137:27–49. Cury, P. M., L. J. Shannon, J.-P. Roux, G. M. Daskalov, A. Jarre, C. L. Moloney, and D. Pauly. 2005. Trophodynamic indicators for an ecosystem approach to fisheries. ICES Journal of Marine Science 62:430–42. Cushman, S. A., K. S. McKelvey, C. H. Flather, and K. McGarigal. 2008. Do forest community types provide a sufficient basis to evaluate biological diversity? Frontiers in Ecology and the Environment 6:13–17. Cuthbert, R., R. E. Green, S. Ranade, S. Saravanan, D. J. Pain, V. Prakash, and A. A. Cunningham. 2006. Rapid population declines of Egyptian vulture (Neophron percnopterus) and red-headed vulture (Sarcogyps calvus) in India. Animal Conservation 9:349–54. Cuthbert, R., J. Parry-Jones, R. E. Green, and D. J. Pain. 2007. NSAIDs and scavenging birds: potential impacts beyond Asia’s critically endangered vultures. Biology Letters 3:90–93. Dahl, T. E. 2000. Status and Trends of Wetlands in the Conterminous United States 1986 to 1997. Washington, DC: U.S. Department of the Interior, Fish and Wildlife Service. Daily, G. C. 1997. Countryside biogeography and the provision of ecosystem services. In Nature and Human Society: The Quest for a Sustainable World, ed. P. H. Raven, 104–13. Washington, DC: National Research Council, National Academy Press. Daily, G. C., G. Ceballos, J. Pacheco, G. Suzan, and A. Sanchez-Azofeifa. 2003. Countryside biogeography of neotropical mammals: conservation opportunities in agricultural landscapes of Costa Rica. Conservation Biology 17:1814–26. Daily, G. C., P. R. Ehrlich, and N. M. Haddad. 1993. Double keystone bird in a keystone species complex. Proceedings of the National Academy of Sciences 90:592–94. Daily, G. C., P. R. Ehrlich, and G. A. Sanchez-Azofeifa. 2001. Countryside biogeography: use of human-domanated habitats by the avifauna of southern Costa Rica. Ecological Applications 11:1–13. Dale, V. H. and S. C. Beyeler. 2001. Challenges in the development and use of ecological indicators. Ecological Indicators 1:3–10. Dalerum, F., M. J. Somers, K. E. Kunkel, and E. Z. Cameron. 2008. The potential for large carnivores to act as biodiversity surrogates in southern Africa. Biodiversity and Conservation 17:2939–49. Dalsgaard, J., S. S. Wallace, S. Salas, and D. Preikshot. 1998. Mass-balance model reconstruction of the Strait of Georgia: the present, one hundred, and five hundred years ago. UBC Fisheries Centre Research Reports 6:72–91. Danielsen, F., H. Beukema, N. D. Burgess, F. Parish, C. A. Bruhl, P. F. Donald, D. Murdiyarso, B. Phalan, L. Reijnders, M. Struebig, and E. B. Fitzherbert. 2009. Biofuel plantations on forested lands: double jeopardy for biodiversity and climate. Conservation Biology 23:348–58.

300 References Davies, S.P., and S.K. Jackson. 2006. The biological-condition gradient: a descriptive model for interpreting change in aquatic ecosystems. Ecological Applications 16:1251-1266. Davis, A.J., J.D. Holloway, H. Huijbregts, J. Krikken, A.H. Kirk-Spriggs, and S,L. Sutton. 2001. Dung beetles as indicators of change in the forests of northern Borneo. Journal of Applied Ecology 38:593-616. Dayton, P. K. 1972. Toward an understanding of community resilience and the potential effects of enrichment to the benthos of McMurdo Sound, Antarctica. In Proceedings of the Colloquium on Conservation Problems in Antarctica, ed. B. C. Parker, 81–95. Lawrence, KS: Allen Press. de Klerk, H. M., T. M. Crowe, J. Fjeldsa, and N. D. Burgess. 2002. Patterns of species richness and narrow endemism of terrestrial bird species in the Afrotropical region. Journal of Zoology London 256:327–42. de Soyza, A. G., W. G. Whitford, J. E. Herrick, J. W. Van Zee, and K. M. Havstad. 1998. Early warning indicators of desertification: examples of tests in the Chihuahuan Desert. Journal of Arid Environments 39:101–12. Dearborn, D. C., A. D. Anders, and E. N. Flint. 2001. Trends in reproductive success of Hawaiian seabirds: is guild membership a good criterion for choosing indicator species? Biological Conservation 10:97–103. Debinski, D. M., and P. F. Brussard. 1994. Using biodiversity data to assess specieshabitat relationships in Glacier National Park, Montana. Ecological Applications 4:833–43. Delibes, M., S. Cabezas, B. Jiménez, and M. J. Gonzalez. 2009. Animal decisions and conservation: the recolonization of a severely polluted river by the European otter. Animal Conservation 12:400–407. Delibes-Mateos, M., S. M. Redpath, E. Angulo, P. Ferreras, and R. Villafuerte. 2007. Rabbits as a keystone species in southern Europe. Biological Conservation 137:149–56. Didham, R. K., P. H. Hammond, J. H. Lawton, P. Eggleton, and N. E. Stork. 1998. Beetle species responses to tropical forest fragmentation. Ecological Monographs 68:295–323. Dietz, J. M., L. A. Dietz, and E. Y. Nagagata. 1994. The effective use of flagship species for conservation of biodiversity: the example of lion tamarins in Brazil. In Creative conservation: Interactive management of wild and captive animals, ed. P. J. S. Olney, G. M. Mace, and T. C. Feistner, 32–49. London: Chapman and Hall. Dirzo, R., and A. Miranda. 1991. Altered patterns of herbivory and diversity in the forest understory: a case study of the posible consequences of contemporary defaunation. In Plant-Animal Interactions: Evolutionary Ecology in Tropical and Temperate Regions, eds. P. W. Price, T. M. Lewinsohn, G. W. Fernandes, and W. W. Bronson, 273–87. New York, NY: John Wiley & Sons. Dobson, A. P., J. P. Rodriguez, W. M. Roberts, and D. S. Wilcove. 1997. Geographic distribution of endangered species in the United States. Science 275:550–53.

References 301 Doledec, S., B. Statzner, and M. Bournard. 1999. Species traits for future biomonitoring across ecoregions: patterns along a human-impacted river. Freshwater Biology 42:737–58. Donald, P. F. 1998. Changes in the abundance of invertebrates and plants on British farmland. British Wildlife 9:279–89. Downer, C. C. 1996. The mountain tapir, endangered “flagship” species of the high Andes. Oryx 30:45–58. Drechler, M. 2005. Probablistic approaches to scheduling reserve selection. Biological Conservation 122:253–62. Dublin, H. T., A. R. E. Sinclair, and J. MacGladie. 1990. Elephant and fire as causes of multiple stable states in Serengeti-Mara woodlands. Journal of Animal Ecology 59:1147–64. Dudley, N., D. Baldock, R. Nasi, and S. Stolton. 2005. Measuring biodiversity and sustainable management in forests and agricultural landscapes. Philosophiocal Transactions of the Royal Society B 360:457-70 Duelli, P., and M. K. Obrist. 1998. In search of the best correlates for local organismal biodiversity in cultivated areas. Biodiversity and Conservation 7:297–309. DuFrene, M., and P. Legendre 1997. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecological Applications 67:345– 66. Dulvy, N. K., S. I. Rogers, S. Jennings, V. Stelzenmuller, S. R. Dye, and H. R. Skjoldal. 2008. Climate change and deepening of the North Sea fish assemblage: a biotic indicator of warming seas. Journal of Applied Ecology 45:1029– 39. Dumbrell, A. J., E. J. Clark, G. A. Frost, T. E. Randell, J. W. Pitchford, and J. K. Hill. 2008. Changes in species diversity following habitat disturbance are dependent on spatial scale: theoretical and empirical evidence. Journal of Applied Ecology 45:1531–39. Dunk, J. R., W. J. Zielinski, and H. H. Welsh Jr. 2006. Evaluating reserves for species richness and representation in northern California. Diversity and Distributions 12:434–42. Eeley, H. A. C., M. J. Lawes, and B. Reyers. 2001. Priority areas for the conservation of subtropical indigenous forest in southern Africa: a case study from KwaZulu-Natal. Biodiversity and Conservation 10:1221–46. Ehrlich, P. R. 1997. A World of Wounds: Ecologists and the Human Dilemma. Oldenorf/Kuhe, Germany: Ecology Institute. Eken, G., L. Bennun, T. M. Brooks, W. Darwall, L. D. C. Fishpool, M. Foster, D. Knox, P. Langhammer, P. Matiku, E. Radford, P. Salaman, W. Sechrest, M. L. Smith, S. Spector, and A. Tordoff. 2004. Key biodiversity areas as site conservation targets. BioScience 54:1110–18. Ellison, A. M., M. S. Bank, B. D. Clinton, E. A. Colburn, K. Elliott, C. R. Ford, D. R. Foster, B. D. Kloeppel, J. D. Knoepp, G. M. Lovett, J. Mohan, D. A. Orwig, N. L. Rodenhouse, W. V. Sobczak, K. A. Stinson, J. K. Stone, C. M. Swan, J. Thompson, B.Von Holle, and J. R. Webster. 2005. Loss of foundation

302 References species: consequences for the structure and dynamics of forested ecosystems. Frontiers in Ecology and the Environment 3:479–86. Elmhagen, B., and S. P. Rushton. 2007. Trophic control of mesopredators in terrestrial ecosystems: top-down or bottom-up? Ecology Letters 10:197–206. Elmqvist, T., C. Folke, M. Nystrom, G. Peterson, J. Bengtsson, B. Walker, and J. Norberg. 2003. Response diversity, ecosystem change, and resilience. Frontiers in Ecology and the the Environment 1:488–94. Erasmus, B. F. N., S. Freitag, K. J. Gaston, B. H. Erasmus, and A. S. van Jaarsveld. 1999. Scale and conservation planning in the real world. Proceedings of the Royal Society of London B 266:315–19. Ernest, S. K. M., and J. H. Brown. 2001. Delayed compensation for missing keystone species by colonization. Science 292:101–4. Essington, T. E., A. H. Beaudreau, and J. Wiedenmann. 2006. Fishing through marine food webs. Proceedings of the National Academy of Sciences 103:3171–75. Estes, J. A., and D. O. Duggins. 1995. Sea otters and kelp in Alaska: generality and variation in a community ecological paradigm. Ecological Monographs 65:75– 100. Estes, J. A., and J. F. Palmisano. 1974. Sea otters: their role in structuring nearshore communities. Science185:1058–60. Estes, J. A., M. T. Tinker, T. M. Williams, and D. F. Doak. 1998. Killer whale predation on sea otters linking oceanic and nearshore ecosystems. Science 282:473– 76. Estrada, A., P. Cammarano, and R. Coates-Estrada. 2000. Bird species richness in vegetation fences and in strips of residual rain forest vegetation at Los Tuxlas, Mexico. Biodiversity and Conservation 9:1399–416. Estrada, A., R. Coates–Estrada, and D. A. Merritt Jr. 1994. Non flying mammals and landscape changes in the tropical rain forest region of Los Tuxtlas, Mexico. Ecography 17:229–41. Estrada, A., R. Coates-Estrada, and D. A. Merritt Jr. 1997. Anthropogenic landscape changes and avian diversity at Los Tuxtlas, Mexico. Biodiversity and Conservation 6:19–43. Everest, F. H., D. N. Swanston, C. G. Shaw III, W. P. Smith, K. R. Julin, and S. D. Allen. 1997. Evaluation of the use of scientific information in developing the 1997 Forest Plan for the Tongass National Forest, USDA Forest Service General Technical Report PNW-GTR-415, Portland, OR. Faith, D. P. 1992. Conservation evaluation and phylogenetic diversity. Biological Conservation 61:1–10. Faith, D. P. 2003. Environmental diversity (ED) as surrogate information for species-level biodiversity. Ecography 26:374–79. Faith, D. P., and R. H. Norris. 1989. Correlation of environmental variables with patterns of distribution and abundance of common and rare freshwater macroinvertebrates. Biological Conservation 50:77–98. Faith, D. P., and P. A. Walker. 1996a. Environmental diversity: on the best-possible

References 303 use of surrogate data for assessing the relative biodiversity of sets of areas. Biodiversity and Conservation 5:399–415. Faith, D. P., and P. A. Walker. 1996b. How do indicator groups provide information about the relative biodiversity of different sets of areas? On hotspots, complementarity and pattern-based approaches. Biodiversity Letters 3:18–25. Faith, D. P., P. A. Walker, and C. R. Margules. 2001. Some future prospects for systematic biodiversity planning in Papua New Guinea—and for biodiversity planning in general. Pacific Conservation Biology 6:325–43. Faria, D., M. L. B. Paciencia, M. Dixo, R. R. Laps, and J. Baumgarten. 2007. Ferns, frogs, lizards, birds and bats in forest fragments and shade cacao plantations in two contrasting landscapes in the Atlantic forest, Brazil. Biodiversity and Conservation 16:2335–57. Farina, J. M., J. C. Castilla, and F. P. Ojeda. 2003. The “idiosyncratic” effect of a “sentinel” species on contaminated rocky intertidal communities. Ecological Applications 13:1533–52. Farjon, A., P. Thomas, and N. T. D. Luu. 2004. Conifer conservation in Vietnam: three potential flagship species. Oryx 38:257–265. Favreau, J. M., C. A. Drew, G. R. Hess, M. J. Rubino, F. H. Koch, and K. A. Eschelbach. 2006. Recommendations for assessing the effectiveness of surrogate species approaches. Biodiversity and Conservation 15:3949–69. Fearnside, P. M., and J. Ferraz. 1995. A conservation gap analysis of Brazil’s Amazonian vegetation. Conservation Biology 9:1134–47. Feeley, K. J., and J. W. Terborgh. 2008. Direct versus indirect effects of habitat reduction on the loss of avian species from tropical forest fragments. Animal Conservation 11:353–60. Ferraro, P. J., and S. K. Pattanayak. 2006. Money for nothing? A call for empirical evaluation of biodiversity conservation investments. PLoS 4:482–88. Ferraz, G., G. J. Russell, P. C. Stouffer, R. O. Bierregaard Jr., S. L. Pimm, and T. E. Lovejoy. 2003. Rates of species loss from Amazonian forest fragments. Proceedings of the National Academy of Sciences 100:14069–73. Ferrier, S. 2002. Mapping spatial pattern in biodiversity for regional conservation planning: where to from here? Systematic Biology 51:331–63. Ferrier, S., G. V. N. Powell, K. S. Richardson, G. Manion, J. M. Overton, T. A. Allnut, S. E. Cameron, K. Mantle, N. D. Burgess, D. P. Faith, J. F. Lamoreux, G. Kier, R. J. Hijmans, V. A. Funk, G. A. Cassis, B. L. Fisher, P. Flemons, D. Lees, J. C. Lovett, and R. S. A. R. Van Rompaey. 2004. Mapping more of terrestrial biodiversity for global conservation assessment. BioScience 54:1101–9. Ferrier, S., G. Manion, J. Elith, and K. Richardson. 2007. Using generalized dissimilarity modeling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Diversity and Distributions 13:252–64. Ferrier, S., and G. Watson. 1997. An evaluation of the effectiveness of environmental surrogates and modeling techniques in predicting the distribution of

304 References biological diversity. Environment Australia, Canberra. http://www.deh.gov.au/ biodiversity/publications/technical/surrogates/. Accessed 15 May 2009. Ferris, R., and J. W. Humphrey. 1999. A review of potential biodiversity indicators for application in British forests. Forestry 72:313–28. Ficetola, G. F., R. Sacchi, S. Scali, A. Gentilli, F. De Bernardi, and P. Galeotti. 2007. Vertebrates respond differently to human disturbance: implications for the use of a focal species approach. Acta Oecologia 31:109–18. Fisher, D. O., S. P. Blomberg, and I. P. F. Owens. 2003. Extrinsic versus intrinsic factors in the decline and extinction of Australian marsupials. Proceedings of the Royal Society of London B 270:1801–8. Fitzherbert, E. B., M. J. Struebig, A. Morel, F. Danielsen, C. A. Bruhl, P. F. Donald, and B. Phalan. 2008. How will oil palm expansion affect biodiversity? Trends in Ecology and Evolution 23:538–45. Flather, C. H., and C. H. Sieg. 2007. Species rarity: definition, causes, and classification. In Conservation of Rare or Little-Known Species: Biological, Social, and Economic Considerations, eds. M. G. Raphael and R. Molina, 40–66. Washington, DC: Island Press. Flather, C. H., K. R. Wilson, D. J. Dean, and W. C. McComb. 1997. Identifying gaps in conservation networks: of indicators and uncertainty in geographicbased analyses. Ecological Applications 7:531–42. Fleishman, E. R., B. Blair, and D. D. Murphy. 2001. Empirical validation of a method for umbrella species selection. Ecological Applications 11:1489–501. Fleishman, E., B. G. Jonsson, and P. Sjogren-Gulve. 2000b. Focal species modeling for biodiversity conservation. Ecological Bulletins 48:85–99. Fleishman, E., and D. D. Murphy. 2009. A realistic assessment of the indicator potential of butterflies and other charismatic taxonomic groups. Conservation Biology 23:1109–16. Fleishman, E., D. D. Murphy, and P. F. Brussard. 2000a. A new method for selection of umbrella species for conservation planning. Ecological Applications 10:569–79. Fleishman, E., J. R. Thomson, R. Mac Nally, D. D. Murphy, and J. P. Fay. 2005. Using indicator species to predict species richness of multiple taxonomic groups. Conservation Biology 19:1125–37. Fleury, S. A., P. J. Mock, and J. F. O’Leary. 1998. Is the California gnatcatcher a good umbrella species? Western Birds 29:453–67. Fontaine, B., O. Gargominy, and E. Neubert. 2007. Priority sites for conservation of land snails in Gabon: testing the umbrella species concept. Diversity and Distributions 13:725–34. Foose, T. J., and N. van Strien, eds. 1997. Asian Rhinos: Status and Conservation Action Plan (New Edition). IUCN/SSC Asian Rhino Specialist Group. Gland, Switzerland. Fore, L. S., J. R. Karr, and R. W. Wisseman. 1996. Assessing invertebrate responses to human activities: evaluating alternative approaches. Journal of the North American Benthological Society 15:212–31.

References 305 Forest, F., R. Greyner, M. Rouget, T. J. Davies, R. M. Cowling, D. P. Faith, A. Balmford, J. C. Manning, S. Proches, M. van der Bank, G. Reeves, T. A. J. Hedderson, and V. Savolainen. 2007. Preserving the evolutionary potential of floras in biodiversity hotspots. Nature 445, 757–60. Forman, R. T. T., D. Sperling, J. A. Bissonette, A. P. Clevenger, C. D. Cutshall, V. H. Dale, L. Fahrig, R. France, C. R. Goldman, K. Meanue, J. A. Jones, F. J. Swanson, T. Turrentine, and T. C. Winter. 2003. Road Ecology: Science and Solutions. Washington, DC: Island Press. Fox, G. A. 2001. Wildlife as sentinels of human health effects in the Great Lakes–St. Lawrence Basin. Environmental Health Perspectives 109:853–61. Fox, N. J., and L. E. Beckley. 2005. Priority areas for conservation of Western Australian coastal fishes: a comparison of hotspot, biogeographical and complementarity approaches. Biological Conservation 125:399–410. Franco, A. M. A., J. M. Palmeirim, and W. J. Sutherland. 2007. A method for comparing effectiveness of research techniques in conservation and applied ecology. Biological Conservation 134:96–105. Franklin, J. F. 1993. Preserving biodiversity: species, ecosystems, or landscapes? Ecological Applications 3:202–5. Franklin, J. F., K. Cromack, W. Denison, A. McKee, C. Maser, J. Sedell, F. Swanson, and G. Juday. 1981. Ecological characteristics of old-growth Douglas-fir forests. USDA Forest Service General Technical Report PNW-118. Pacific Northwest Forest and Range Experiment Station, Portland, OR. Fraser, S. E. M., A. E. Beresford, J. Peters, J. W. Redhead, A. J. Welch, P. J. Mayhew, and C. Dytham. 2009. Effectiveness of vegetation surrogates for parasitoid wasps in reserve selection. Conservation Biology 23:142–50. Freitag, S., and A. S. van Jaarsveld. 1997. Relative occupancy, endemism, taxonomic distinctiveness and vulnerability: prioritizing regional conservation actions. Biodiversity and Conservation 6:211–32. Freitag, S., and A. S. van Jaarsveld. 1998. Sensitivity of selection procedures for priority conservation areas to survey extent, survey intensity and taxonomic knowledge. Proceedings of the Royal Society of London B 265:1475–82. Fujiwara, M., and H. Caswell. 2001. Demography of the endangered North Atlantic right whale. Nature 414:537–41. Funk, S. M., and J. E. Fa. 2010. Ecoregion prioritization suggests an armoury not a silver bullet for conservation planning. PLoS ONE 5:e8923. Furness, R. W. 1991. The occurrence of burrow-nesting among birds and its influence on soil fertility and stability. Symposium of the Zoological Society of London 63:53–67. Game, E. T., H. S. Grantham, A. J. Hobday, R. L. Pressey, A. T. Lombard, L. E. Beckley, K. Gjerde, R. Bustamante, H. P. Possingham, and A. J. Richardson. 2009. Pelagic protected areas: the missing dimension in ocean conservation. Trends in Ecology and Evolution 24:360–69. Gammon, J. R., W. C. Fatz, and T. P. Simon. 2003. Patterns in water quality and fish assemblages in three central Indiana streams with emphasis on animal feed lot

306 References operations. In Biological Response Signatures: Indicator Patterns Using Aquatic Communities, ed. T. P. Simon, 374–417. Boca Raton, FL: CRC Press. Gardenfors, U. 2001. Classifying threatened species at national versus global levels. Trends in Ecology and Evolution 16:511–16. Gardner, T. A. 2010. Monitoring Forest Biodiversity: Improving Conservation through Ecologically Responsible Management. London: Earthscan. Gardner, T. A., J. Barlow, I. S. Araujo, T. C. Avila-Pires, A. B. Bonaldo, J. E. Costa, M. C. Esposito, L. V. Ferreira, J. Hawes, M. I. M. Hernandez, M. S. Hoormoed, R. N. Leite, N. F. Lo-Nan-Hung, J. R. Malcolm, M. B. Martins, L. A. M. Mestre, R. Miranda-Santos, W. L. Overal, L. Parry, S. L. Peters, M. A. Ribeiro-Junior, M. N. F. da Silva, C. da Silva Motta, and C. A. Peres. 2008a. The cost-effectiveness of biodiversity surveys in tropical forests. Ecology Letters 11:139–50. Gardner, T. A., J. Barlow, R. Chazdon, R. Ewers, C. A. Harvey, C. A. Peres, and N. Sodhi. 2009. Prospects for tropical forest biodiversity in a human-modified world. Ecology Letters 12:561–82. Gardner, T. A., J. Barlow, L. T. W. Parry, and C. A. Peres. 2007a. Predicting the uncertain future of tropical forest species in a data vacuum. Biotropica 39:25–30. Gardner, T. A., I. M. Cote, J. A. Gill, A. Grant, and A. R. Watkinson. 2005. Hurricanes and Caribbean coral reefs: impacts, recovery patterns, and role in long term decline. Ecology 86:174–84. Gardner, T. A., M. I. M. Hernandez, J. S. Barlow, and C. A. Peres. 2008b. Understanding the biodiversity consequences of habitat change: the value of secondary and plantation forests for neotropical dung beetles. Journal of Applied Ecology 45:883–93. Gardner, T. A, T. Caro, E. Fitzherbert, T. Banda, and P. Lalbhai. 2007c. Conservation value of multiple use areas in East Africa. Conservation Biology 21:1516– 25. Gardner, T. A., M. A. Ribeiro-Junior, J. Barlow, T. C. S. Avila-Pires, M. S. Hoogmoed, and C. A. Peres. 2007b. The value of primary, secondary, and plantation forests for a neotropical herpetofauna. Conservation Biology 21:775–87. Garpe, K. C., S. A. S. Yahya, U. Lindahl, and M. C. Ohman. 2006. Long-term effects of the 1998 coral bleaching event on reef fish assemblages. Marine Ecology Progress Series 315:237–47. Garson, J., A. Aggarwal, and S. Sarkar. 2002. Birds as surrogates for biodiversity: an analysis of a data set from southern Quebec. Journal of Bioscience 27:347–60. Gascon, C., T. E. Lovejoy, R. O. Bierregaard, J. R. Malcolm, P. C. Stouffer, H. Vasconcelos, W. F. Laurance, B. Zimmerman, M. Tocher, and S. Borges. 1999. Matrix habitat and species persistence in tropical forest remnants. Biological Conservation 91:223–29. Gaston, K. J. 1994. Rarity. London: Chapman and Hall, Gaston, K. J. 1996a. Biodiversity: A Biology of Numbers and Difference. Oxford, UK: Blackwell Science.

References 307 Gaston, K. J. 1996b. Biodiversity—congruence. Progress in Physical Geography 20:105–12. Gaston, K. J., and T. M. Blackburn. 1995. Mapping biodiversity using surrogates for species richness: macro-scales and New World birds. Proceedings of the Royal Society of London B 262:335–41. Gaston, K. J., and T. M. Blackburn. 1996. The spatial distribution of threatened species: macroscales and New World birds. Proceedings of the Royal Society of London B 263:235–40. Gaston, K. J., S. F. Jackson, A. Nagy, L. Cantu-Salazar, and M. Johnson. 2008. Protected areas in Europe: Principle and Practice. Annals of the New York Academy of Sciences 1134:97–119. Gaston, K. J., and A. S. L. Rodrigues. 2002. Reserve selection in regions with poor biological data. Conservation Biology 17:188–95. Gaston, K. J., and P. H. Williams. 1996. Spatial patterns in taxonomic diversity. In Biodiversity: A Biology of Numbers and Difference, ed. K. J. Gaston, 202–29. Oxford, UK: Blackwell Science. Gehring, T. M., and R. K. Swihart. 2003. Body size, niche breadth, and ecologically scaled responses to habitat fragmentation: mammalian predators in an agricultural landscape. Biological Conservation 109:283–95. Gehrt, S. D., and S. Prange. 2007. Interference competition between coyotes and raccoons: a test of the mesopredator release hypothesis. Behavioral Ecology 18:204–14. Giere, O. 1993. Meiobenthology: The Microscopic Fauna in Aquatic Sediments. Berlin, Germany: Springer-Verlag. Gilbert, L. E. 1980. Food web organization and the conservation of neotropical diversity. In Conservation Biology: An Evolutionary-Ecological Perspective, eds. M. E. Soule and B. A. Wilcox, 11–33. Sutherland, MA: Sinaeur Associates. Gittleman, J. L., S. M. Funk, D. Macdonald, and R. K. Wayne. 2001. Carnivore Conservation. Cambridge, UK: Cambridge University Press. Gjerdrum, C., A. M. J. Vallee, C. Cassidy St. Clair, D. F. Bertram, J. L. Ryder, and G. S. Blackburn. 2003. Tufted puffin reproduction reveals ocean climate variability. Proceedings of the National Academy of Sciences 100:9377–82. Gladstone, W. 2002. The potential value of indicator groups in the selection of marine reserves. Biological Conservation 104:211–20. Gladstone, W. 2007. Requirements for marine protected areas to conserve the biodiversity of rocky reef fishes. Aquatic Conservation: Marine and Freshwater Ecosystems 17:71–87. Gladstone, W., and T. Alexander. 2005. A test of the higher-taxon approach in the identification of candidate sites for marine reserves. Biodiversity and Conservation 14:3151–68. Glynn, P. W. 1993. Coral reef bleaching: ecological perspectives. Coral Reefs 12:1– 17. Grabowski, J. H., and C. H. Peterson. 2007. Restoring oyster reefs to recover

308 References ecosystem services. In Ecosystem Engineers: Plants to Protists, eds. K. Cuddington, J. E. Byers, W. G. Wilson, and A. Hastings, 281–98. Amsterdam: Elsevier. Grand, J., J. Buonaccorsi, S. A. Cushman, C. R. Griffin, and M. C. Neel. 2004. A multiscale landscape approach to predicting bird and moth rarity hotspots in a threatened pitch pine-scrub oak community. Conservation Biology 18:1063–77. Grand, J., M. P. Cummings, T. G. Rebelo, T. H. Ricketts, and M. C. Neel. 2007. Biased data reduce efficiency and effectiveness of conservation reserve networks. Ecology Letters 10:364–74. Grantham, H. S., A. Moilanen, K. A. Wilson, R. L. Pressey, T. G. Rebelo, and H. P. Possingham. 2008. Diminishing return on investment for biodiversity data in conservation planning. Conservation Letters 1:190–98. Grantham, H. S., K. A. Wilson, A. Moilanen, T. Rebelo, and H. P. Possingham. 2009. Delaying conservation actions for improved knowledge: how long should we wait? Ecology Letters 12:293–301. Gray, M. A., S. L. Baldauf, P. J. Mayhew, and J. K. Hill. 2007. The response of avian feeding guilds to tropical forest disturbance. Conservation Biology 21:133–41. Greeley, M. S., Jr. 2002. Reproductive indicators of environmental stress in fish. In Biological Indicators of Aquatic Ecosystem Stress, ed. S. M. Adams, 321–77. Bethseda, MD: American Fisheries Society. Greenberg, R., P. Bichier, A. C. Angon, and R. Reitsma. 1997b. Bird populations in shade and sun coffee plantations in Central Guatemala. Conservation Biology 11:448–59. Greenberg, R., P. Bichier, and J. Sterling. 1997a. Acacia, cattle and migratory birds in southeastern Mexico. Biological Conservation 80:235–47. Gregory, R. D., A. van Strien, P. Vorisek, A. W. Gmelig Meyling, D. G. Noble, R. P. B. Foppen, and D. W. Gibbons. 2005. Developing indicators for European birds. Philisophical Transactions of the Royal Society of London B 360:269–88. Grelle, C. E. V. 2002. Is higher-taxon analysis an useful surrogate of species richness in studies of neotropical mammal diversity? Biological Conservation 108:101–6. Greyner, R., C. D. L. Orme, S. F. Jackson, G. H. Thomas, R. G. Davies, T. J. Davies, K. E. Jones, V. A. Olson, R. S. Ridgely, P. S. Rasmussen, T.-S. Ding, P. M. Bennett, T. M. Blackburn, K. J. Gaston, J. L. Gittleman, and I. P. F. Owens. 2006. Global distribution and conservation of rare and threatened vertebrates. Nature 444:93–96. Griffin, P. C. 1999. Endangered species diversity “hot spots” in Russia and centers of endemism. Biodiversity and Conservation 8:497–511. Griffiths, R. A., and L. Pavajeau. 2008. Captive breeding, reintroduction, and the conservation of amphibians. Conservation Biology 22:852–61. Groom, M. J., G. K. Meffe, and C. R. Carroll. 2006. Principles of Conservation Biology, 3rd edition. New Malden, MA: Sinaeur Associates. Groves, C. R., D. B. Jensen, L. A. Valutis, K. H. Redford, M. L. Shaffer, J. M. Scott, J. V. Baumgartner, J. V. Higgins, M. W. Beck, and M. G. Andrson. 2002. Planning for biodiversity conservation: putting conservation science into practice. BioScience 52:499–512.

References 309 Grutzner, F., B. Nixon, and R. C. Jones. 2008. Reproductive biology in egg-laying mammals. Sexual Development 2:115–27. Guisan, A., and N. E. Zimmermann. 2000. Predictive habitat distribution models in ecology. Ecological Modeling 135:147–86. Gunnthorsdottir, A. 2001. Physical attractiveness of an animal species as a decision factor for its preservation. Anthrozoos 14:204–16. Gurney, W. S. C., and J. H. Lawton. 1996. The population dynamics of ecosystem engineers. Oikos 76:273–83. Gutierrez, J. L., and C. G. Jones. 2006. Physical ecosystem engineers as agents of biogeochemical heterogeneity. BioScience 56:227–36. Hacker, J. E., G. Colishaw, and P. H. Williams. 1998. Patterns of African primate diversity and their evaluation for the selection of conservation areas. Biological Conservation 84:251–62. Hagan, J. M., and A. A. Whitman. 2006. Biodiversity indicators for sustainable forestry: simplifying complexity. Journal of Forestry (June):203–10. Halaj, J., and D. H. Wise. 2001. Terrestrial trophic cascades: how much do they trickle? American Naturalist 157:262–81. Halffter, G., and L. Arellano. 2002. Response of dung beetle diversity to humaninduced changes in a tropical landscape. Biotropica 34:144–54. Hallegraeff, G. M. 1993. A review of harmful algal blooms and their apparent global increase. Phycologia 32:79–99. Halme, P., M. Monkkonen, J. S. Kotiaho, A.-L. Ylisirnio, and A. Markkanen. 2009. Quantifying the indicator power of an indicator species. Conservation Biology 23:1008–16. Halpern, B. S., K. A. Selkoe, F. Micheli, and C. V. Kappel. 2007. Evaluating and ranking the vulnerability of global marine ecosystems to anthropogenic threats. Conservation Biology 21:1301–15. Hamer, K. C., and J. K. Hill. 2000. Scale-dependent effects of habitat disturbance on species richness in tropical forests. Conservation Biology 14:1435–40. Hammond, P. M. 1995. Practical approaches to the estimation of the extent of biodiversity in speciose groups. In Biodiversity: Measurement and Estimation, ed. D. L. Hawksworth, 119–36. London: Royal Society Publications. Handy, R. D., and M. H. Depledge. 1999. Physiological responses: their measurement and use as environmental biomarkers in ecotoxicology. Ecotoxiocology 8:329–49. Hansen, A. J., R. L. Knight, J. M. Marzluff, S. Powell, K. Brown, P. H. Gude, and K. Jones. 2005. Effects of exurban development on biodiversity: patterns, mechanisms, and research needs. Ecological Applications 15:1893–905. Hanski, I. 1982. Dynamics of regional distribution: the core and satellite species hypothesis. Oikos 38:210–21. Harcourt, A. H. 2000. Coincidence and mismatch of biodiversity hotspots: a global survey of the order, primates. Biological Conservation 93:163–75. Harper, J. L., and D. L. Hawksworth. 1995. Preface. In Biodiversity: Measurement and Estimation, ed. D. L. Hawksworth, 5–12. London: Chapman and Hall.

310 References Harrold, C., and D. C. Reed. 1985. Food availability, sea urchin grazing, and kelp forest community structure. Ecology 66:1160–69. Harvey, C. A., A. Medina, D. M. Sanchez, S. Vilchez, B. Hernandez, J. C. Saenz, J. M. Maes, F. Casanoves, and F. L. Sinclair. 2006. Patterns of animal diversity in different forms of tree cover in agricultural landscapes. Ecological Applications 16:1986–99. Haskell, B. D., B. G. Norton, and R. Constanza. 1992. What is ecosystem health and why should we worry about it? In Ecosystem Health: New Goals for Environmental Management, eds. R. Constanza, B. G. Norton, and B. D. Haskell, 3– 20. Washington, DC: Island Press. Hastings, A., and L. W. Botsford. 2003. Comparing designs of marine reserves for fisheries and for biodiversity. Ecological Applications 13:S65–70. Hastings, A., J. E. Byers, J. A. Crooks, K. Cuddington, C. G. Jones, J. G. Lambrinos, T. S. Talley, and W. G. Wilson. 2007. Ecosystem engineering in space and time. Ecology Letters 10:153–64. Hawes, J., J. Barlow, T. A. Gardner, and C. A. Peres. 2008. The value of forest strips for understorey birds in an Amazonian plantation landscape. Biological Conservation 141:2262–78. Hawes, J., C. da Silva Motta, W. L. Overal, J. Barlow, T. A. Gardner, and C. Peres. 2009. Diversity and composition of Amazonian moths in primary, secondary and plantation forests. Journal of Tropical Ecology 25:281–300. Hayes, T. B. 2004. There is no denying this: defusing the confusion about atrizine. BioScience 54:1138–49. Hayward, M. W., and M. J. Somers, eds. 2009. Reintroduction of Top-order Predators. Oxford, UK: Wiley-Blackwell. Hebblewhite, M., C. A. White, C. G. Nietvelt, J. A. McKenzie, T. E. Hurd, J. M. Fryxell, S. E. Bayley, and P. C. Paquet. 2005. Human activity mediates a trophic cascade caused by wolves. Ecology 86:2135–44. Hecnar, S. J., and R. T. M. M’Closkey. 1997. Patterns of nestednesss and species association in a pond-dwelling amphibian fauna. Oikos 80:371–81. Heino, J., T. Muotka, R. Paavola, and L. Paasivirta. 2003. Among-taxon congruence in biodiversity patterns: can stream insect diversity be predicted using single taxonomic groups? Canadian Journal of Fish and Aquatic Sciences 60:1039– 49. Heino, J., and H. Mykra. 2006. Assessing physical surrogates for biodiversity: do tributary and stream type classifications reflect macroinvertebrate assemblage diversity in running waters? Biological Conservation 129:418–26. Heino, J., R. Paavola, R. Virtanen, and T. Muotka. 2005. Searching for biodiversity indicators in running waters: do bryophytes, macroinvertebrates, and fish show congruent diversity patterns? Biodiversity and Conservation 14:415–28. Heino, J., and J. Soininen. 2007. Are higher taxa adequate surrogates for specieslevel assemblage patterns and richness in stream organisms? Biological Conservation 137:78–89.

References 311 Heithaus, M. R., A. Frid, A. J. Wirsing, and B. Worm 2008. Predicting ecological consequences of marine top predator declines. Trends in Ecology and Evolution 23:202–10. Hellawell, J. M. 1986. Biological Indicator of Freshwater Pollution and Environmental Management. London: Elsevier Applied Science Publishers. Hellou, J., and R. J. Law. 2003. Stress on stress response of wild mussels, Mytilus edulis and Mytilus trossulus, as an indicator of ecosystem health. Environmental Pollution 126:407–16. Henke, S. E., and F. C. Bryant. 1999. Effects of coyote removal on the faunal community in western Texas. Journal of Wildlife Management 63:1066–81. Herremans, M. 1995. Effects of woodland modification by African elephant Loxodonta africana on bird diversity in northern Botswana. Ecography 18:440– 54. Herricks, E. E., and Schaeffer, D. J. 1985. Can we optimize biomonitoring? Environmental Management 9:487–92. Hess, G. R., R. A. Bartel, A. K. Leidner, K. M. Rosenfeld, M. J. Rubino, S. B. Snider, and T. H. Ricketts. 2006. Effectiveness of biodiversity indicators varies with extent, grain, and region. Biological Conservation 132:448–57. Hess, G. R., and T. J. King. 2002. Planning open spaces for wildlife I. Selecting focal species using a Delphi survey approach. Landscape and Urban Planning 58:25–40. Hess, G. R., F. H. Koch, M. J. Rubino, K. A. Eschelbach, C. A. Drew, and J. M. Favreau. 2006. Comparing the potential effectiveness of conservation planning approaches in central North Carolina. Biological Conservation 128:358– 68. Heywood, V. H., ed. 1995. Global Biodiversity Assessment. Cambridge, UK: Cambridge University Press. Higgins, M. A., and K. Ruokolainen. 2004. Rapid tropical forest inventory: a comparison of techniques based on inventory data from western Amazonia. Conservation Biology 18:799–811. Hill, C. J. 1996. Habitat specificity and food preferences of an assemblage of tropical Australian dung beetles. Journal of Tropical Ecology 12:449–60. Hill, G. E. 2007. Ivorybill Hunters: The Search for Proof in a Flooded Wilderness. New York: Oxford University Press. Hill, J. K., and K. C. Hamer 2004. Determining impacts of habitat modification on diversity of tropical forest fauna: the importance of spatial scale. Journal of Applied Ecology 41:744–54. Hill, M. O. 1979. TWINSPAN: A Fortran Program for Arranging Multivariate Data in an Ordered Two-way Table by Classification of the Individuals and Attributes. Ithaca, NY: Cornell University Press. Hill, N. M., and P. A. Keddy. 1992. Prediction of rarities from habitat variables: coastal plain plants on Nova Scotia lakeshores. Ecology 73:1852–59. Hilton-Taylor, C., G. M. Mace, D. R. Capper, N. J. Collar, S. N. Stuart, C. J. Bibby,

312 References C. Pollock, and J. B. Thomsen. 2000. Assessment mismatches must be sorted out: they leave species at risk. Nature 404:541. Hilty, J., and A. Merenlender. 2000. Faunal indicator taxa selection for monitoring ecosystem health. Biological Conservation 92:185–97. Hoegh-Guldberg, O. 1999. Climate change, coral bleaching, and the future of the world’s coral reefs. Marine Freshwater Research 50:839–66. Hoegh-Guldberg, O. 2005. Climate change and marine ecosystems. Climate Change and Biodiversity , eds. T. E. Lovejoy and L. Hannah, 256–73. New Haven, CT: Yale University Press. Hoekstra, J. M., T. M. Boucher, T. H. Ricketts, and C. Roberts. 2005. Confronting a biome crisis: global disparities of habitat loss and protection. Ecology Letters 8:23–29. Hof, J. G., and M. Bevers. 1998. Spatial Optimization for Managed Ecosystems. New York: Columbia University Press. Hof, J. G., and L. A. Joyce. 1992. Spatial optimization for wildlife and timber in managed forest ecosystems. Forest Science 38:489–508. Holt, R. D., J. H. Lawton, G. A. Polis, and N. D. Martinez. 1999. Trophic rank and the species-area relationship. Ecology 80:1495–504. Horner-Devine, M. C., G. C. Daily, P. R. Ehrlich, and C. L. Boggs. 2003. Countryside biogeography of tropical butterflies. Conservation Biology 17:168–77. Howard, P. C., P. Viskanic, T. R. B. Davenport, F. W. Kigenyi, M. Baltzer, C. J. Dickinson, J. S. Lwanga, R. A. Matthews, and A. Balmford. 1998. Complementarity and the use of indicator groups for reserve selection in Uganda. Nature 394:472–75. Howe, R. W., R. R. Regal, G. J. Niemi, N. P. Danz, and J. M. Hanowski. 2007. A probability-based indicator of ecological condition. Ecological Indicators 7:793– 806. Hubbell, S. P. 2001. The Unified Neutral Theory of Biodiversity and Biogeography. Princeton, NJ: Princeton University Press. Hughes, T. P., A. H. Baird, D. R. Bellwood, M. Card, S. R. Connolly, C. Folke, R. Grosberg, O. Hoegh-Guldberg, J. B. C. Jackson, J. Kleypas, J. M. Lough, P. Marshall, M. Nystrom, S. R. Palumbi, J. M. Pandolfi, B. Rosen, and J. Roughgarden. 2003. Climate change, human impacts, and the resilience of coral reefs. Science 301:929–33. Hunsaker, C. T., and D. E. Carpenter, eds. 1990. Environmental monitoring and assessment program: ecological indicators. Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC. Hunter, J. E., R. J. Gutierrez, and A. B. Franklin. 1995. Habitat configuration around spotted owl sites in northwestern California. Condor 97:684–93. Hunter, M. 1996. Fundamentals of Conservation Biology. Cambridge, MA: Blackwell Science. Hunter, M. L., and J. P. Gibbs. 2007. Fundamentals of Conservation Biology, 3rd ed. Malden, MA: Blackwell.

References 313 Huntly, N., and R. Inouye. 1988. Pocket gophers in ecosystems: patterns and mechanisms. BioScience 38:786–93. Hurlbert, A. H., and E. P. White. 2005. Disparity between range map- and surveybased analyses of species richness: patterns, processes and implications. Ecology Letters 8:319–27. Hurlbert, S. H. 1997. Functional importance vs kestoneness: reformulating some questions in theoretical biocenology. Australian Journal of Ecology 22:369–82. Hurme, E., M. Monkkonen, A.-L. Sippola, H. Ylinen, and M. Pentinsaari. 2008. Role of the Siberian flying squirrel as an umbrella species for biodiversity in northern boreal forests. Ecological Indicators 8:246–55. Hutto, R. L. 1989. The effect of habitat alteration on migratory land birds in a West Mexican tropical deciduous forest: a conservation perspective. Conservation Biology 3:138–48. Innis, S. A., R. J. Naiman, and S. R. Elliott. 2000. Indicators and assessment methods for measuring the ecological integrity of semi-aquatic terrestrial environment. Hydrobiologia 422/423:111–30. IPCC 2007. Climate Change 2007: The Physical Sciences Basis. The Contribution of the Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press. IUCN 2003. IUCN Red List of Threatened Species. Gland, Switzerland: IUCN. IUCN 2008. Guidelines for Using the IUCN Red List Categories and Criteria: Version 7.0., Gland, Switzerland, and Cambridge, UK: Species Survival Commission, International Union of the Conservation of Nature and Natural Resources. Jackson, J. B. C., M. X. Kirby, W. H. Berger, K. A. Bjorndal, L. W. Botsford, B. J. Bourque, R. H. Bradbury, R. Cooke, J. Erlandson, J. A. Estes, T. P. Hughes, S. Kidwell, C. B. Lange, H. S. Lenihan, J. M. Pandolfi, C. H. Peterson, R. S. Steneck, M. J. Tegner, and R. R. Warner. 2001. Historical overfishing and the recent collapse of coastal ecosystems. Science 293:629–38. Jaffre, T., P. Bouchet, and J.-M. Veillon. 1998. Threatened plants of New Caledonia: is the system of protected areas adequate? Biodiversity and Conservation 7:109–35. Jain, K. E., I. K. Birtwell, and A. P. Farrell. 1998. Repeat swimming performance of mature sockeye salmon following a brief recovery period: a proposed measure of fish health and water quality. Canadian Journal of Zoology 76:1488–96. James, A., K. Gaston, and A. Balmford. 2001. Can we afford to conserve biodiversity? BioScience 51:43–52. Jamil, K. 2001. Bioindicators and Biomarkers of Environmental Pollution and Risk Asessment. Enfield, NH: Science Publishers, Inc. Jenkins, J., J. D. Aber, and C. D. Canham. 1999. Hemlock woolly adelgid impacts on community structure and N cycling rates in eastern hemlock forests. Canadian Journal of Forest Research 29:630–45. Jennings, S. 2005. Indicators to support an ecosystem approach to fisheries. Fish and Fisheries 8:212–32.

314 References Jennings, S., and N. K. Dulvy. 2005. Reference points and reference directions for size-based indicators of community structure. ICES Journal of Marine Science 62:397–404. Johnsingh, A. J. T., and J. Joshua. 1994. Conserving Rajaji and Corbett National Parks—the elephant as a flagship species. Oryx 28:135–140. Johnson, R. K., T. Widerholm, and D. M. Rosenberg. 1993. Freshwater biomonitoring using individual organisms, populations, and species assemblages of benthic macroinvertebrates. In Freshwater Biomonitoring and Benthic Macroinvertebrates, eds. D. M. Rosenberg and V. H. Resh, 40–158. New York: Chapman and Hall. Jones, C. G., and J. L. Gutierrez. 2007. On the purpose, meaning, and usage of the physical ecosystem engineering concept. In Ecosystem Engineers: Plants to Protists, eds. K. Cuddington, J. E. Byers, W. G. Wilson, and A. Hastings, 3–24. Amsterdam: Elsevier. Jones, C. G., J. H. Lawton, and M. Shachak. 1994. Organisms as ecosystem engineers. Oikos 69:373–86. Jones, C. G., J. H. Lawton, and M. Shachak. 1997. Positive and negative effects of organisms as ecosystem engineers. Ecology 78:1946–57. Jones, G. P., M. I. McCormick, M. Srinivasan, J. V. Eagle. 2004. Coral decline threatens fish biodiversity in marine reserves. Proceedings of the National Academy of Sciences 101:8251–53. Jones, M. E., A. Cockburn, R. Hamede, C. Hawkins, H. Hesterman, S. Lachish, D. Mann, H. McCallum, and D. Pemberton. 2008. Life-history change in disease-ravaged Tasmanian devil populations. Proceedings of the National Academy of Sciences 105:10023–27. Jonzen, N., A. Linden, T. Ergon, E. Knudsen, J. O. Vik, D. Rubolini, D. Piacentini, C. Brinch, F. Spina, L. Karlsson, M. Stervander, A. Andersson, J. Waldenstrom, A. Lehikoinen, E. Edvardsen, R. Solvang, and N. C. Stenseth. 2006. Rapid advance of spring arrival dates in long-distance migratory birds. Science 312:1959–61. Jorgensen, S. E., F.-Lu. Xu, F. Salas, and J. C. Marques. 2005. Application of indicators for the assessment of ecosystem health. In Handbook of Ecological Indicators for Assessment of Ecosystem Health, eds. S. E. Jorgensen, R. Constanza, and F.-L. Xu, 5–66. Boca Raton, FL: CRC Press. Jouquet, P., J. Dauber, J. Lagerlof, P. Lavelle, and M. Lepage. 2006. Soil invertebrates as ecosystem engineers: intended and accidental effects on soil land feedback loops. Applied Soil Ecology 32:153–64. Justus, J., and S. Sarkar. 2002. The principle of complimentarity in the design of reserve networks to conserve biodiversity: a preliminary history. Journal of Biosciences 27:421–435. Juutinen, A., and M. Monkkonen. 2004. Testing alternative indicators for biodiversity conservation in old-growth boreal forests: ecology and economics. Ecological Economics 50:35–48.

References 315 Juutinen, A., M. Monkkonen, and A.-L. Sippola. 2006. Cost-efficiency of decaying wood as a surrogate for overall species richness in boreal forests. Conservation Biology 20:74–84. Karban, R., and I. T. Baldwin. 1997. Induced Responses to Herbivory. Chicago: University of Chicago Press. Kareiva, P., and M. Marvier. 2003. Conserving biodiversity coldspots. American Scientist 91:344–51. Karr, J. R. 1981. Assessment of biotic integrity using fish communities. Fisheries 6:21–27. Karr, J. R. 1991. Biological integrity: a long-neglected aspect of water resource management. Ecological Applications 1:66–84. Karr, J. R. 1999. Defining and measuring river health. Freshwater Biology 41:221– 34. Karr, J. R., and D. R. Dudley. 1981. Ecological perspectives on water quality goals. Environmental Management 5:55–68. Karr, J. R., K. D. Fausch, P. L. Angermeier, P. R. Yant, and I. J. Schlosser. 1986. Assessing the Biological Integrity in Running Waters: A Method and Its Rationale. Special Publication 5. Champaign, IL: Illinois Natural History Survey. Kati, V., P. Devillers, M. Dufrene, A. Legakis, D. Voukou, and P. Lebrun. 2004a. Hotspots, complementarity or representativeness? Designing optimal smallscale reserves for biodiversity conservation. Biological Conservation 120:471–80. Kati, V., P. Devillers, M. Dufrene, A. Legakis, D. Vokou, and P. Lebrun. 2004b. Testing the value of six taxonomic groups as biodiversity indicators at a local scale. Conservation Biology 18:667–75. Kautz, R. S., and J. A. Cox. 2001. Strategic habitats for biodiversity conservation in Florida. Conservation Biology 15:55–77. Keesing, F. 1998. Impacts of ungulates on the demography and diversity of small mammals in central Kenya. Oecologia 116:381–89. Keitt, T. H., O. N. Bjornstad, P. M. Dixon, and S. Citron-Pousty. 2002. Accounting for spatial pattern when modeling organism-environment interactions. Ecography 25:616–25. Kelly, J. R. and M. A. Harwell. 1990. Indicators of ecosystem recovery. Environmental Management 14:527–45. Kennard, M. J., A. H. Arthington, B. J. Pusey, and B. D. Harch. 2005. Are alien fish a reliable indicator of river health? Freshwater Biology 50:174–93. Kennedy, A. D., and C. A. Jacoby. 1999. Biological indicators of marine environmental health: meiofauna—a neglected benthic community? Environmental Monitoring and Assessment 54:47–68. Kerans, B. L., and J. R. Karr. 1994. A benthic index of biotic integrity (B–IBI) for rivers of the Tennessee Valley. Ecological Applications 4:768–85. Kerbes, R. H., P. M. Kotanen, and R. L. Jefferies. 1990. Destruction of wetland habitats by lesser snow geese: a keystone species on the west coast of Hudson Bay. Journal of Applied Ecology 27:242–58.

316 References Kerley, G. I. H., M. Landman, L. Kruger, N. Owen-Smith, D. Balfour, W. F. de Boer, A. Gaylard, K. Lindsay, and R. Slotow. 2001. Effects of elephants on ecosystems and biodiversity. In Elephant Management: A Scientific Assessment for South Africa, eds. R. J. Scholes and K. G. Mennell, 146–205. Johannesburg: Wits University Press. Kerr, J. T. 1997. Species richness, endemism, and the choice of areas for conservation. Conservation Biology 11:1094–1100. Kerr, J. T., and L. Packer. 1997. Habitat heterogeneity as a determinant of mammal species richness in high-energy regions. Nature 385:252–54. Kerr, J. T., A. Sugar, and L. Packer. 2000. Indicator taxa, rapid biodiversity assessment, and nestedness in an endangered ecosystem. Conservation Biology 14:1726–34. Kershaw, M., G. M. Mace, and P. H. Williams. 1995. Threatened staus, rarity, and diversity as alternative selection measures for protected areas—a test using Afrotropical antelopes. Conservation Biology 9:324–34. Kessler, M., S. Abrahamczyk, M. Bos, D. Buchori, D. Dwi Putra, S. R. Gradstein, P. Hohn, J. Kluge, F. Orend, R. Pitopang, S. Saleh, C. H. Schulze, S. G. Sporn, I. Steffan-Dewenter, S. S. Tjitrosoedirdjo, and T. Tscharntke. 2009. Alpha and Beta diversity of plants and animals along a tropical land-use gradient. Ecological Applications 19:2142–56. Kilgour, B. W., and D. R. Barton. 1999. Associations between stream fish and benthos across environmental gradients in southern Ontario, Canada. Freshwater Biology 41:553–66. King, M. C., and K. F. Beazley. 2005. Selecting focal species for marine protected area network planning in the Scotia-Fundy region of Atlantic Canada. Aquatic conservation: Marine and Freshwater Ecosystems 15:367–85. Kintsch, J. A., and D. L. Urban. 2002. Focal species, community representation, and physical proxies as conservation strategies: a case study in the Amphibolite mountains, North Carolina, U.S.A. Conservation Biology 16:936–47. Kirkpatrick, J. B. 1983. An iterative method for establishing priorities for the selection of nature reserves: an example from Tasmania. Biological Conservation 25:127–34. Klein, A.-M., I. Steffan-Dewenter, D. Buchori, and T. Tscharntke. 2002. Effects of land-use intensity in tropical agroforestry systems on coffee flower-visiting and trap-nesting bees and wasps. Conservation Biology 16:1003–14. Knight, R. L., R. M. Cowling, and B. M. Campbell. 2006. An operational model for implementing conservation action. 2006. Conservation Biology 20:408–19. Knight, R. L., G. N. Wallace, and W. E. Riebsame. 1995. Ranching the view: subdivisions versus agriculture. Conservation Biology 9:459–61. Koh, L.P. 2007. Impacts of land use change on South-east Asian forest butterflies: a review. Journal of Applied Ecology 44:703–13. Kolkwitz, R., and M. Marsson. 1908. Okologie der pflanzlichen Saprobien. Berlin Dt. Bot. Ger. 26:505–19.

References 317 Kontoleon, A., and T. Swanson. 2003. The willingness to pay for property rights for the giant panda: can a charismatic species be an instrument for nature conservation? Land Economics 79:483–99. Kotze, D. J., and M. J. Samways. 1999. Support for the multi-taxa approach in biodiversity assessment, as shown by epigaeic in an Afromontane forest archipelago. Journal of Insect Conservation 3:125–43. Kremen, C. 1992. Assessing the indicator properties of species assemblages for natural areas monitoring. Ecological Applications 2:203–16. Kremen, C. 1994. Biological inventory using target taxa: a case study of butterflies of Madagascar. Ecological Applications 4:407–22. Kruger, O. 2005. The role of ecotourism in conservation: panacea or Pandora’s box? Biodiversity and Conservation 14:579–600. Kushlan, J. A. 1974. Observations on the role of the American alligator (Alligator mississippiensis) in the southern Florida wetlands. Copiea 31:993–96. Laidre, K. L., I. Stirling, L. F. Lowry, O. Wiig, M. P. Heide-Jorgensen, and S. H. Fergusen. 2008. Quantifying the sensitivity of arctic marine mammals to climate-induced habitat change. Ecological Applications 18:S97–125. Laland, K. N., F. J. Odling-Smee, and M. W. Feldman. 1999. Evolutionary consequences of niche construction and their implications for ecology. Proceedings of the National Academy of Sciences 96:10242–47. Lam, P. K. S., and J. S. Gray. 2003. The use of biomarkers in environmental monitoring programmes. Marine Pollution Bulletin 46:182–86. Lambeck, R. J. 1997. Focal species: a multi-species umbrella for nature conservation. Conservation Biology 11:849–57. Lambeck, R. J. 1999. Landscape planning for biodiversity conservation in agricultural regions: a case study from the wheatbelt of Western Australia. Biodiversity technical paper 2. Canberra: Environment Australia. Lambeck, R. J. 2002. Focal species and restoration ecology: response to Lindenmayer et al. Conservation Biology 16:549–51. Lamoreux, J. F., J. C. Morrison, T. H. Ricketts, D. M. Olson, E. Dinerstein, M. W. McKnight, and H. H. Shugart. 2006. Global tests of biodiversity concordance and the importance of endemism. Nature 440:212–14 Landres, P. B. 1992. Ecological indicators: panacea or liability? In Ecological Indicators, vols 1 & 2, eds. D. H. McKenzie, D. E. Hyatt, and V. J. McDonald, 1295– 318. New York: Elsevier Science Publications. Landres, P. B., J. Verner, and J. W. Thomas. 1988. Ecological uses of vertebrate indicator species: a critique. Conservation Biology 2:316–27. Lapin, M., and B. V. Barnes. 1995. Using the landscape ecosystem approach to assess species and ecosystem diversity. Conservation Biology 9:1148–58. Larsen, F. W., J. Bladt, and C. Rahbek. 2007. Improving the performance of indicator groups for the identification of important areas for species conservation. Conservation Biology 21:731–40. Larsen, F. W., J. Bladt, and C. Rahbek. 2009. Indicator taxa revisited: useful for conservation planning? Diversity and Distributions 15:70–79.

318 References Larsen, F. W., and C. Rahbek. 2003. Influence of scale on conservation priority setting—a test on African mammals. Biodiversity and Conservation 12:599–614. Launer, A. E., and D. D. Murphy. 1994. Umbrella species and the conservation of habitat fragments: a case of a threatened butterfly and a vanishing grassland ecosystem. Biological Conservation 69:143–53. Laurance, W. F., S. G. Laurance, L. V. Ferreira, J. M. Rankin-de Merona, C. Gascon, and T. E. Lovejoy. 1997. Biomass collapse in Amazonian forest fragments. Science 278:1117–18. Laurance, W. F., T. E. Lovejoy, H. L. Vasconcelos, E. M. Bruna, R. K. Didham, P. C. Stouffer, C. Gascon, R. O. Bierregaard, S. G. Laurance, and E. Sampaio. 2002. Ecosystem decay of Amazonian forest fragments: a 22-year investigation. Conservation Biology 16:605–18. Laurance, W. F., and C. A. Peres eds. 2006. Emerging Threats to Tropical Forests. Chicago: University of Chicago Press. Laurance, W. F., and D. C. Useche. 2009. Environmental synergisms and extinctions of tropical species. Conservation Biology 23:1427–37. Lavelle, P., S. Barot, M. Blouin, T. Decaens, J. J. Jiminez, and P. Jouquet. 2007. Earthworms as key actors in self-organizing soil systems. In Ecosystem Engineers: Plants to Protists, eds. K. Cuddington, J. E. Byers, W. G. Wilson, and A. Hastings, 77–106. Amsterdam: Elsevier. Lawler, J. J., D. White, J. C. Sifneos, and L. L. Master. 2003. Rare species and the use of indicator groups for conservation planning. Conservation Biology 17:875–82. Laws, R. M. 1970. Elephants as agents of habitat and landscape change in East Africa. Oikos 21:1–15. Lawton, J. H., D. E. Bignell, B. Bolton, G. F. Bloemers, P. Eggleton, P. M. Hammond, M. Hodda, R. D. Holt, T. B. Larsen, N. A. Mawdsley, N. E. Stork, D. S. Srivastava, and A. D. Watt. 1998. Biodiversity inventories, indicator taxa and effects of habitat modification in tropical forest. Nature 391:72–76. Le Corre, M. 2008. Cats, rats and seabirds. Nature 451:134–35. Leader-Williams, N., and H. T. Dublin. 2000. Charismatic megafauna as “flagship” species. In Has the Panda Had Its Day? Future Priorities for the Conservation of Mammal Diversity, eds. A. Entwistle and N. Dunstone, 53–81. Cambridge, UK: Cambridge University Press. Leech, T. J., A. M. Gormley, and P. J. Seddon. 2008. Estimating the minimum viable population size of kaka (Nestor meridionalis), a potential surrogate species in New Zealand lowland forest. Biological Conservation 141:681–91. Lehmkuhl, J. F., and M. G. Raphael. 1993. Habitat pattern around northern spotted owl locations on the Olympic Peninsula, Washington. Journal of Wildlife Management 57:302–14. Lenihan, H. S., and C. H. Peterson. 1998. How habitat degredation through fishery disturbance enhances impacts of hypoxia on oyster reefs. Ecological Applications 8:128–40.

References 319 Lennon, J. J., P. Koleff, J. J. D. Greenwood, and K.J. Gaston. 2004. Contribution of rarity and commonness to patterns of species richness. Ecology Letters 7:81–87. Lenth, B. A., R. L. Knight, and W. C. Gilbert. 2006. Conservation value of clustered housing developments. Conservation Biology 20:1445–56. Leu, M., S. E. Hanser, and S. T. Knick. 2008. The human footprint in the west: a large-scale analysis of anthropogenic impacts. Ecological Applications 18:1119– 39. Levin, L. L., and P. K. Dayton. 2009. Ecological theory and continental margins: where shallow meets deep. Trends in Ecology and Evolution 24:606–17. Levin, S. A. 1992. The problem of pattern and scale in ecology. Ecology 73:1943– 67. Lewis, S. N. W. 1997. Birds as indicators of biodiversity: a study of avifaunal diversity and composition in two contrasting land-use types in the Eastern Transvaal Lowveld, South Africa. MSc dissertation, University of East Anglia, Norwich, UK. Linde, A. R., S. Sanchez-Galan, J. I. Izquierdo, P. Arribas, E. Maranon, and E. Garcia-Vasquez. 1998. Brown trout as biomonitor of heavy metal pollution: effect of age on the reliability of the assessment. Ecotoxicology and Environmental Safety 40:120–25. Lindenmayer, D., and M. A. Burgman. 2005. Practical Conservation Biology. Collingwood, Australia: CSIRO Publishing. Lindenmayer, D. B., and J. Fischer. 2003. Sound science or social hook—a response to Brooker’s application of the focal species approach. Landscape and Urban Planning 62:149–58. Lindenmayer, D. B., A. D. Manning, P. L. Smith, H. P. Possingham, F. Fischer, I. Oliver, and M. A. McCarthy. 2002. The focal-species approach and landscape restoration: a critique. Conservation Biology 16:338–45. Lindenmayer, D. B., C. R. Margules, and D. B. Botkin. 2000. Indicators of biodiversity for ecologically sustainable forest management. Conservation Biology 14:941–50. Lindsey, P. A., R. Alexnder, J. T. du Toit, and M. G. L. Mills. 2005. The cost efficiency of wild dog conservation in South Africa. Conservation Biology 19:1205– 14. Linnell, J. D. C., J. E. Swenson, and R. Andersen. 2000. Conservation of biodiversity in Scandinavian boreal forests: large carnivores as flagships, umbrellas, indicators, or keystones. Biodiversity and Conservation 9:857–68. Lips, K. R., F. Brem, R. Brenes, J. D. Reeve, R. A. Alford, J. Voyles, C. Carey, L. Livo, A. P. Pessier, and J. P. Collins. 2006. Emerging infectious disease and the loss of biodiversity in a neotropical amphibian community. Proceedings of the National Academy of Sciences 103:3165–70. Little, E. E. 2002. Behavioral measures of environmental stressors. In Biological Indicators of Aquatic Ecosystem Stress, ed. S. M. Adams, 431–72. Bethseda, MD: American Fisheries Society.

320 References Living Landscapes Bulletin 2. September 2001. The landscape species approach—a tool for site-based conservation. New York: Wildlife Conservation Society. Living Landscapes Bulletin 3. May 2002. The role of landscape species in site-based conservation. New York: Wildlife Conservation Society. Lo-Man-Hung, N. F., T. A. Gardner, M. A. Ribeiro-Junior, J. Barlow, and A. B. Bonaldo. 2008. The value of primary, secondary and plantation forests for neotropical epigeic arachnids. Journal of Arachnology 36:394–401. Loh, L. P., P. Levang, and J. Ghazoul. 2009. Designer landscapes for sustainable biofuels. Trends in Ecology and Evolution 24:431–38. Lohse, D. P. 1993. The importance of secondary substratum in a rocky intertidal community. Journal of Experimental Marine Biology and Ecology 166:1–17. Loiselle, B. A., C. A. Howell, C. H. Graham, J. M. Goerck, T. Brooks, K. G. Smith, and P. H. Williams. 2003. Avoiding pitfalls of using species distribution models in conservation planning. Conservation Biology 17:1591–600. Lombard, A. T. 1995. The problems with multi-species conservation: do hotspots, ideal reserves and existing reserves coincide? South African Journal of Zoology 30:145–63. Lombard, A. T., R. M. Cowling, R. L. Pressey, and P. J. Mustart. 1997. Reserve selection in a species-rich and fragmented landscape on the Agulhas Plain, South Africa. Conservation Biology 11:1101–16. Lombard, A. T., R. M. Cowling, R. L. Pressey, and A. G. Rebelo. 2003. Effectiveness of land classes as surrogates for species in conservation planning for the Cape Floristic Region. Biological Conservation 112:45–62. Longino, J. T., J. Coddington, and R. K. Cowell. 2002. The ant fauna of a tropical rainforest: estimating species richness three different ways. Ecology 83:689– 702. Loomis, J. B., and D. S. White. 1996. Economic benefits of rare and endangered species: summary and meta-analysis. Ecological Economics 18:197–206. Lorimer, J. 2007. Nonhuman charisma. Environment and Planning D: Society and Space 25:911–32. Lotze, H. K., and B. Worm. 2009. Historical baselines for large marine animals. Trends in Ecology and Evolution 24:254–62. Lovell, S., M. Hamer, R. Slotow, and D. Herbert. 2007. Assessment of congruency across invertebrate taxa and taxonomic levels to identify potential surrogates. Biological Conservation 139:113–25. Loya, Y., K. Sakai, K. Yamazato, Y. Nakano, H. Sambali, and R. van Woesik. 2001. Coral bleaching: the winners and losers. Ecology Letters 4:122–31. Luck, G. W. 2007. A review of the relationships between human population density and biodiversity. Biological Reviews 82:607–45. Luck, G. W., and G. C. Daily. 2003. Tropical countryside bird assemblages: richness, composition, and foraging differ by landscape context. Ecological Applications 13:235–47. Luebke, R. W., P. V. Hodson, M. Faisal, P. S. Ross, K. A. Grasman, and J. Zelikoff.

References 321 1997. Aquatic pollution-induced immunotoxicity in wildlife species. Fundamental and Applied Toxicology 37:1–15. Lund, M. P., and C. Rahbek. 2002. Cross-taxon congruence in complementarity and conservation of temperate biodiversity. Animal Conservation 5:163–71. Lydy, M. J., P. M. Stewart, and T. P. Simon. 2003. Relationships between fish assemblages and organochloride insecticides in sediment and fish tissue in south central Kansas. In Biological Response Signatures: Indicator Patterns Using Aquatic Communities, ed. T. P. Simon, 313–323. Boca Raton, FL: CRC Press. Mace, G. M., J. L. Gittleman, and A. Purvis. 2003. Preserving the tree of life. Science 300:1707–9. Mace, G. M., H. P. Possingham, and N. Leader-Williams. 2006. Prioritizing choices in conservation. In Key Topics in Conservation Biology, eds. D. Macdonald and K. Service, 17–34. Oxford, UK: Blackwell. Machiote, M., L. C. Branch, and D. Villarreal. 2004. Burrowing owls and burrowing animals: are ecosystem engineers interchangeable as facilitators? Oikos 106:527–35. Mackey, B. G., H. A. Nix, J. A. Stein, and S. E. Cork. 1989. Assessing the representativeness of the wet tropics of Queensland World Heritage Property. Biological Conservation 50:279–303. Maclaurin, J., and K. Sterelny. 2008. What Is Biodiversity? Chicago: University of Chicago Press. MacNally, R., A. F. Bennett, G. W. Brown, L. F. Lumsden, A.Yen, S. Hinkley, P. Lillywhite, and D. Ward. 2002. How well do ecosystem-based planning units represent different components of biodiversity? Ecological Applications 12:900– 912. Maddock, A., and G. A. Benn. 2000. Identification of conservation-worthy areas in Northern Zululand, South Africa. Conservation Biology 14:155–66. Maes, D., D. Bauwens, L. de Bruyn, A. Anselin, G. Vermeersch, W. de Landuyt, G. de Knijf, and M. Gilbert. 2005. Species richness coincidence: conservation strategies based on predictive modeling. Biodiversity and Conservation 14:1345–64. Maes, D., and H. van Dyck. 2005. Habitat quality and biodiversity indicator performances of a threatened butterfly versus a multispecies group for wet heathlands in Belgium. Biological Conservation 123:177–87. Maestas, J. D., R. L. Knight, and W. C. Gilgert. 2001. Biodiversity and land-use change in the American Mountain West. Geographical Review 91:509–24. Maestas, J. D., R. L. Knight, and W. C. Gilgert. 2003. Biodiversity across a rural land-use gradient. Conservation Biology 17:1425–34. Magurran, A. E. 2004. Measuring Biological Diversity. Malden, MA: Blackwell. Malm, O., M. B. Castro, W. R. Bastos, F. J. P. Branches, J. R. D. Guimaraes, C. E. Zuffo, and W. C. Pfeiffer. 1995. An assessment of Hg pollution in different goldmining areas, Amazon Brazil. The Science of the Total Environment 175:127–40.

322 References Mandelik, Y., T. Dayan, V. Chikatunov, and V. Kravchenko. 2007. Reliability of a higher-taxon approach to richness, rarity, and composition assessments at the local scale. Conservation Biology 21:1506–15. Manley, P. N., W. J. Zielinski, M. D. Schlesinger, and S. R. Mori. 2004. Evaluation of a multiple-species approach to monitoring species at the ecoregional scale. Ecological Applications 14:296–310. Manne, L. L., and P. H. Williams. 2003. Building indicator groups based on species characteristics can improve conservation planning. Animal Conservation 6:291–97. Marcot, B. G., and C. H. Flather. 2007. Species-level strategies for conserving rare or little-known species. In Conservation of Rare or Little-Known Species: Biological, Social, and Economic Considerations, eds. M. G. Raphael and R. Molina, 125–64. Washington, DC: Island Press. Margules, C. R., A. O. Nicholls, and R. L. Pressey. 1988. Selecting networks of reserves to maximize biological diversity. Biological Conservation 43:63–70. Margules, C. R., A. O. Nicholls, and M. B. Usher 1994. Apparent species turnover, probability of extinction and the selection of nature reserves: a case study of the Ingleborough Limestone pavements. Conservation Biology 8:398–409. Margules, C. R., and R. L. Pressey. 2000. Systematic conservation planning. Nature 405:243–53. Margules, C., and S. Darkar. 2007. Systematic Conservation Planning. Cambridge, UK: Cambridge University Press. Marques, J. C., F. Salas, J. M. Patricio, and M. A. Pardal. 2005. Application of ecological indicators to assess environmental quality in coastal zones and transitional waters: two case studies. In Handbook of Ecological Indicators for Asessment of Ecosystem Health, eds. S. E. Jorgensen, R. Constanza, and F.-L. Xu, 67–104. Boca Raton, FL: CRC Press. Martikainen, P., L. Kaila, and Y. Haila. 1998. Threatened beetles in white-backed woodpecker habitats. Conservation Biology 12:293–301. Master, L. L. 1991. Assessing threats and setting conservastion priorities. Conservation Biology 5:559–63. Mayfield, M. M., and G. C. Daily. 2005. Countryside biogeography of neotropical herbaceous and shrubby plants. Ecological Applications 15:423–39. McDonald-Madden, E., M. Bode, E. T. Game, H. Grantham, and H. P. Possingham. 2009. The need for speed: informed land acquisitions for conservation in a dynamic property market. Ecology Letters 11:1169–77. McGeoch, M. A. 1998. The selection, testing and application of terrestrial insects as bioindicators. Biological Reviews 73:181–201. McGeoch, M. A. 2007. Insects and bioindication: theory and progress. In Insect Conservation Biology, eds. A. J. A. Stewart, T. R. New, and O. T. Lewis, 144–74. London: The Royal Entomological Society. McGeoch, M. A., and S. L. Chown. 1998. Scaling up the value of bioindicators. Trends in Ecology and Evolution 13:46–47.

References 323 McGeoch, M. A., B. J. van Rensburg, and A. Botes. 2002. The verification and application of bioindicators: a case study of dung beetles in a savanna ecosystem. Journal of Applied Ecology 39:661–72. McKenzie, D. J., E. Garofalo, M. J. Winter, S. Ceradini, F. Verweij, N. Day, R. Hayes, R. van der Oost, P. J. Butler, J. K. Chipman, and E. W. Taylor. 2007. Complex physiological traits as biomarkers of the sub-lethal toxicological effects of pollutant exposure in fishes. Philosophical Transactions of the Royal Society 362:2043–59. McKinney, M. L. 1999. High rates of extinction and threat in poorly studied taxa. Conservation Biology 13:1273–81. McKinney, M. L. 2002. Urbanization, biodiversity, and conservation. BioScience 52:883–90. McKnight, M. W., P. S. White, R. I. McDonald, J. F. Lamoreux, W. Sechrest, R. Ridgely, and S. N. Stuart. 2007. Putting beta-diversity on the map: broadscale congruence and coincidence in the extremes. PLoS Biology 5:2424–32. McLaren, B. E., and R. O. Peterson. 1994. Wolves, moose and tree rings on Isle Royale. Science 266:1555–58. McNeill, S. E. 1994. The selection and design of marine protected areas: Australia as a case study. Biodiversity and Conservation 3:586–605. Medellin, R. A., M. Equihua, and M. A. Amin. 2000. Bat diversity and abundance as indicators of disturbance in neotropical rainforests. Conservation Biology 14:1666–75. Meffe, G. K., and C. R. Carroll. 1997. Principles of Conservation Biology. Sunderland, MA: Sinauer Associates. Meijaard, E., and V. Nijman. 2003. Primate hotspots on Borneo: predictive value for general biodiversity and the effects of taxonomy. Conservation Biology 17:725–32. Meir, E., S. Andelman, and H. P. Possingham. 2004. Does conservation planning matter in a dynamic and uncertain world? Ecology Letters 7:615–22. Menge, B. A., E. L. Berlow, C. A. Blanchette, S. A. Navarette, and S. B. Yamada. 1994. The keystone species concept: variation in interaction strength in a rocky intertidal habitat. Ecological Monographs 64:249–86. Metrick, A., and M. L. Weitzman. 1996. Patterns of behavior in endangered species preservation. Land Economics 72:1–16. Meuser, E., H. W. Harshaw, and A. O. Mooers. 2009. Public preference for endemism over other conservation-related species attributes. Conservation Biology 23:1041–46. Micheli, F., B. J. Halpern, L. W. Botsford, and R. R. Warner. 2004. Trajectories and correlates of community change in no-take marine reserves. Ecological Applications 14:1709–23. Mikusinski, G., M. Gromadzki, and P. Chylarecki. 2001. Woodpeckers as indicators of forest bird diversity. Conservation Biology 15:208–17. Miller, B., R. Reading, J. Hoogland, T. Clark, G. Ceballos, R. List, S. Forrest,

324 References L. Hanebury, P. Manzano, J. Pacheco, and D. Uresk. 2000. The role of prairie dogs as a keystone species: response to Stapp. Conservation Biology 14:318– 21. Miller, B., R. Reading, J. Strittholt, C. Carroll, R. Noss, M. Soule, O. Sanchez, J. Terborgh, D. Brightsmith, T. Cheeseman, and D. Foreman. 1999. Using focal species in the design of nature reserve networks. Wild Earth 1998/00:81–92. Mills, L. S., M. E. Soule, and D. F. Doak. 1993. The keystone-species concept in ecology and conservation. BioScience 43:219–24. Millsap, B. A., J. A. Gore, D. E. Runde, and S. I. Cerulean. 1990. Setting priorities for conservation of fish and wildlife species in Florida. Wildlife Monographs 111:1–57. Mittermeier, R. A. 1986. Primate conservation priorities in the neotropical region. In Primates: The Road to Self-Sustaining Populations, ed. K. Benirschke, 221–40. New York: Springer-Verlag. Mittermeier, R. A., N. Myers, P. R. Gil, and C. G. Mittermeier 1999. Hotspots: Earth’s Biologically Richest and Most Endangered Terrestrial Ecoregions. Monterrey, Mexico: Cemex, Conservation International, and Agrupacion Sierra Madre. Mittermeier, R. A., N. Myers, J. B. Thomsen, G. A. B. da Fonseca, and S. Olivieri.1998. Biodiversity hotspots and major tropical wilderness areas: approaches to setting conservation priorities. Conservation Biology 12:516–20. Mittermeier, R.A., P.R. Gil, M. Hoffmann, J. Pilgrim, T. Brooks, C.G. Mittermeier, J. Lamoreux, and C.A.B. da Fonseca. 2004. Hotspots Revisited: Earth’s biologically richest and most endangered terrestrial ecoregions. Monterrey, Mexico, Cemex, Conservation International, Agrupacion Sierra Madre, University of Virginia. Montgomery, C. A. 2002. Ranking the benefits of biodiversity: an exploration of relative values. Journal of Environmental Management 65:313–26. Moore, J., A. Balmford, T. Allnutt, and N. Burgess. 2004. Integrating costs into conservation planning across Africa. Biological Conservation 117:343–50. Moore, S. E., and H. P. Huntingdon. 2008. Arctic marine mammals and climate change: impacts and resilience. Ecological Applications 18:suppl. S157–65. Moore, J. L., A. Balmford, T. Brooks, N. D. Burgess, L. A. Hansen, C. Rahbek, and P. H. Williams. 2003. Performance of sub-Saharan vertebrates and indicator groups for identifying priority areas for conservation. Conservation Biology 17:207–18. Moritz, C., K. S. Richardson, S. Ferrier, G. B. Monteith, J. Stanisic, S. E. Williams, and T. Whiffin. 2001. Biogeographical concordance and efficiency of taxon indicators for establishing conservation priority in a tropical rainforest biota. Proceedings of the Royal Society of London B 268:1875–81. Moyle, P. B., and T. Light. 1996. Biological invasions of freshwater: empirical rules and assembly theory. Biological Conservation 78:149–61. Mumby, P. J., K. Broad, D. R. Brumbaugh, C. P. Dahlgren, A. R. Harbone, A. Hastings, K. E. Holmes, C. V. Kappel, F. Micheli, and J. N. Sanchirico. 2008.

References 325 Coral reef habitats as surrogates of species, ecological functions, and ecosystem services. Conservation Biology 22:941–51. Munday, P. L. 2004. Habitat loss, resource specialization, and extinction on coral reefs. Global Change Biology 10:1642–47. Munoz, J. 2007. Biodiversity conservation including uncharismatic species. Biodiversity and Conservation 16:2233–35. Muotka, T., R. Paavola, A. Haapala, M. Novikmec, and P. Laasonen. 2002. Longterm recovery of stream habitat structure and benthic invertebrate communities from in-stream restoration. Biological Conservation 105:243–53. Murawski, S. A. 2000. Definitions of overfishing from an ecosystem perspective. ICES Journal of Marine Science 57:649–58. Murphy, D. D., and B. A. Wilcox. 1986. Butterfly diversity in natural habitat fragments: a test of the validity of vertebrate-based management. In Wildlife 2000: Modeling Habitat Relations of Terrestrial Vertebrates, eds. J. Verner, M. L. Morrison, and C. J. Ralph, 287–92. Madison, WI: University of Wisconsin Press. Murray-Smith, C., N. A. Brummitt, A. T. Oliveira-Filho, S. Bachman, E. Moat, E. M. Nic Lughadha, and E. J. Lucas. 2009. Plant diversity hotspots in the Atlantic coastal forests of Brazil. Conservation Biology 23:151–63. Myers, M. S., and J. W. Fournie. 2002. Histopathological biomarkers as integrators of anthropogenic and environmental stressors. In Biological Indicators of Aquatic Ecosystem Stress, ed. S. M. Adams, 221–87. Bethseda, MD: American Fisheries Society. Myers, N., R. A. Mittermeier, C. G. Millermeier, G. A. B. da Fonseca, and J. Kent. 2000. Biodiversity hotspots for conservation priorities. Nature 403:853–58. Naidoo, R., A, Balmford, P. J. Ferraro, S. Polasky, T. H. Ricketts, and M. Rouget. 2006. Integrating economic costs into conservation planning. Trends in Ecology and Evolution 21:681–87. Naiman, R. J., J. M. Melillo, and J. E. Hobbie. 1986. Ecosystem alteration of boreal forest streams by beaver (Castor canadensis). Ecology 67:1254–69. Najera, A., and J. A. Simonetti. 2010. Enhancing avifauna in commercial plantations. Conservation Biology 24:319–24. Negi, H. R., and M. Gadgil. 2002. Cross-taxon surrogacy of biodiversity in the Indian Garhwal Himalaya. Biological Conservation 105:143–55. Neumann, R. P. 1998. Imposing Wilderness: Struggles over Livelihood and Nature Preservation in Africa. Berkeley, CA: University of California Press. New, T. R. 1995. Introduction to Invertebrate Conservation Biology. Oxford, UK: Oxford University Press. Newman, M. C., and W. H. Clements. 2008. Ecotoxicology: A Comprehensive Treatment. Boca Raton, FL: CRC Press. Newman, S. C., A. Chmura, K. Converse, A. M. Kilpatrick, N. Patel, E. Lammers, and P. Daszak. 2007. Aquatic bird disease and mortality as an indicator of changing ecosystem health. Marine Ecology Progress Series 352:299–309. Newmark, W. D. 1987. A land-bridge island perspective on mammalian extinctions in western North American parks. Nature 325:430–32.

326 References Nichols, E., T. Larsen, S. Spector, A. L. Davis, F. Escobar, M. Favila, K. Vulinec, the Scarabaeinae Research Network. 2007. Global dung beetle response to tropical forest modification and fragmentation: a quantitative literature review and meta-analysis. Biological Conservation 137:1–19. Nicholson, M. D., and S. Jennings. 2004. Testing candidate indicators to support ecosystem-based management: the power of monitoring surveys to detect temporal trends in fish community metrics. ICES Journal of Marine Sciences 61:35– 42. Niemela, J., and B. Baur. 1998. Threatened species in a vanishing habitat: plants and invertebrates in calcareous grasslands in the Swiss Jura mountains. Biodiversity and Conservation 7:1407–16. Niemi, G. J., J. M. Hanowski, A. R. Lima, T. Nicholls, and N. Weiland. 1997. A critical analysis on the use of indicator species in management. Journal of Wildlife Management 61:1240–52. Nilsson, S. G., U. Arup, R. Baranowski, and S. Ekman. 1995. Tree-dependent lichens and beetles as indicators in conservation forests. Conservation Biology 9:1208–15. Nippress, D. A., A. N. Andersen, A. J. Pik, R. Bramble, P. Wilson, and A. J. Beattie. 2008. The influence of spatial scale on the congruence of classifications circumscribing morphological units of biodiversity. Diversity and Distributions 14:917–24. Noble, I. R., and R. Dirzo. 1997. Forests as human-dominated ecosystems. Science 277:522–25. Noon, B. R., and K. S. McKelvey. 1996. Management of the spotted owl: a case history in conservation biology. Annual Review of Ecology and Systematics 27:135– 62. Norden, B., and T. Appelqvist. 2001. Conceptual problems of ecological continuity and its bioindicators. Biodiversity and Conservation 10:779–91. Norden, B., H. Paltto, F. Gotmark, and K. Wallin. 2007. Indicators of biodiversity, what do they indicate? Lessons for conservation of cryptograms in oak-rich forest. Biological Conservation 135:369–79. Norton, B. G. 1991. Ecosystem health and sustainable resource management. In Ecological Economics: The Science and Management of Sustainability, ed. R. Constanza, 102–17. New York: Columbia University Press. Noss, R. F. 1987. From plant communities to landscapes in conservation inventories: a look at The Nature Conservancy. Biological Conservation 41:11–37. Noss, R. F. 1990. Indicators for monitoring biodiversity: a hierarchical approach. Conservation Biology 4:355–64. Noss, R. F. 1999. Assessing and monitoring forest biodiversity: a suggested framework and indicators. Forest Ecology and Management 115:135–46. Noss, R. F. 2004. Some suggestions for keeping national wildlife refuges healthy and whole. Natural Resources Journal 44:1093–111. Noss, R. F., C. Carroll, K. Vance-Borland, and G. Wuerthner. 2002. A multicriteria

References 327 assessment of the irreplaceability and vulnerability of sites in the Greater Yellowstone Ecosystem. Conservation Biology 16:895–908. Noss, R. F., H. B. Quigley, M. G. Hornocker, T. Merrill, and P. C. Paquet. 1996. Conservation biology and carnivore conservation in the Rocky Mountains. Conservation Biology 10:949–63. Novotny, V., and Y. Basset. 2000. Ecological characteristics of rare species in communities of tropical insect herbivores: pondering the mystery of singletons. Oikos 89:564–72. Nupp, T. E., and R. K. Swihart. 2000. Landscape-level correlates of small-mammal assemblages in forest fragments of farmland. Journal of Mammalogy 81:512– 26. Oaks, J. L., M. Gilbert, M. Z. Virani, R. T. Watson, C. U. Meteyer, B. A. Rideout, H. L. Shivaprasad, S. Ahmed, M. J. I. Chaudhry, M. Arshad, S. Mahmood, A. Ali, and A. A. Khan. 2004. Diclofenac residues as the cause of vulture population decline in Pakistan. Nature 427:630–33. O’Brien, D. J., J. B. Kaneene, and R. H. Poppenga. 1993. The use of mammals as sentinels for human exposure to toxic contaminants in the environment. Environmental Health Perspectives 99:351–68. O’Connor, T. P. 1998. Mussel watch results from 1986 to 1996. Marine Pollution Bulletin 37:14–19. O’Dea, N., M. B. Araujo, and R. J. Whittaker. 2006. How well do Important Bird Areas represent species and minimize conservation conflict in the tropical Andes? Diversity and Distributions 12:205–16. Odell, E. A., and R. L. Knight. 2001. Songbird and medium-sized mammal communities associated with exurban development in Pitkin County, Colorado. Conservation Biology 15:1143–50. Office of Technology Assessment (OTA). 1987. Technologies to maintain biological diversity. Washington, DC: U.S. Government Printing Office. Oliver, I., and A. J. Beattie. 1996a. Designing a cost-effective invertebrate survey: a test of methods for rapid assessment of biodiversity. Ecological Applications 6:594–607. Oliver, I., and A. J. Beattie. 1996b. Invertebrate morphospecies as surrogates for species: a case study. Conservation Biology 10:99–109. Oliver, I., A. Holmes, J. M. Dangerfield, M. Gillings, A. J. Pik, D. R. Britton, M. Holley, M. E. Montgomery, M. Raison, V. Logan, R. L. Pressey, and A. J. Beattie. 2004. Land systems as surrogates for biodiversity in conservation planning. Ecological Applications 14:485–503. Olson, D. M., and E. Dinerstein. 1998. The Global 200: a representation approach to conserving the earth’s most biologically valuable ecoregions. Conservation Biology 12:502–15. Olson, D. M., E. Dinerstein, E. D. Wikramanayake, N. D. Burgess, G. V. N. Powell, E. C. Underwood, J. A. D’Amico, I. Itoua, H. E. Strand, J. C. Morrison, C. J. Loucks, T. F. Allnutt, T. H. Ricketts, Y. Kura, J. F. Lamoreux, W. W. Wettengel,

328 References P. Hedao, and K. R. Kassem. 2001. Terrestrial ecoregions of the world: a new map of life on earth. BioScience 51:933–38. O’Neill, S. J., T. J. Osborn, M. Hulme, I. Lorenzoni, and A. R. Watkinson. 2008. Using expert knowledge to assess uncertainties in future polar bear populations under climate change. Journal of Applied Ecology 45:1649–59. Oren, Y., A. Perevolotsky, S. Brand, and M. Shachak. 2007. Livestock and engineering network in the Israels Negev: implications for ecosystem management. In Ecosystem Engineers: Plants to Protists, eds. K. Cuddington, J. E. Byers, W. G. Wilson, and A. Hastings, 323–42. Amsterdam: Elsevier. Orme, C. D. L., R. G. Davies, M. Burgess, F. Eigenbrod, N. Pickup, V. A. Olson, A. J. Webster, T.-S. Ding, P. C. Rasmussen, R. S. Ridgely, A. J. Stattersfield, P. M. Bennett, T. M. Blackburn, K. J. Gaston, and I. P. F. Owens. 2005. Global hotspots of species richness are not congruent with endemism or threat. Nature 436:1016–19. Ostrowski, S., E. Bedin, D. M. Lenain, and A. H. Abuzinada. 1998. Ten years of Arabian oryx conservation breeding in Saudia Arabia—achievements and regional perspectives. Oryx 32:209–22. Ovadia, O. 2003. Ranking hotspots of varying sizes: a lesson from the nonlinerality of the species-area relationship. Conservation Biology 17:1440–41. Ozaki, K., M. Isono, T. Kawahara, S. Iida, T. Kudo, and K. Fukuyama. 2006. A mechanistic approach to evaluation of umbrella species as conservation surrogates. Conservation Biology 20:1507–15. Pace, M. L., J. J. Cole, S. R. Carpenter, and J. F. Kitchell. 1999. Trophic cascades in diverse ecosystems. Trends in Ecology and Evolution 14:483–88. Padoa-Schioppa, E., M. Baietto, R. Massa, and L. Bottoni. 2006. Bird communities as bioindicators: the focal species concept in agricultural landscapes. Ecological Indicators 6:83–93. Paillet, Y., L. Berges, J. Hjalten, P. Odor, C. Avon, M. Bernhardt-Romermann, R.-J. Bijlsma, L. de Bruyn, M. Fuhr, U. Grandin, R. Kanka, L. Lundin, S. Luque, T. Magura, S. Matesanz, I. Meszaros, M.-T. Sebastia, W. Schmidt, T. Sandovar, B. Tothmeresz, A. Uotila, F. Valladares, K. Vellak, and R. Virtanen. 2010. Biodiversity differences between managed and unmanaged forests: meta-analysis of species richness in Europe. Conservation Biology 24:101–12. Paine, R. T. 1966. Food web complexity and species diversity. American Naturalist 100:65–75. Paine, R. T. 1969. A note on trophic complexity and community stability. American Naturalist 103:91–93. Pakkala, T., J. Pellikka, and H. Linden. 2003. Capercaillie Tetrao urogallus—a good candidate for an umbrella species in taiga forests. Wildlife Biology 9:309–16. Palomares, F., and T. M. Caro. 1999. Interspecific killing among mammalian carnivores. American Naturalist 153:492–508. Palomares, F., P. Gaona, P. Ferraras, and M. Delibes. 1995. Positive effects on game species of top predators by controlling smaller predator populations: an example with lynx, mongooses, and rabbits. Conservation Biology 9:295–305.

References 329 Panwar, H. S. 1982. What to do when you’ve succeeded: Project Tiger, ten years later. In National Parks, Conservation and Development: The Role of Protected Areas in Sustaining Society, ed. A. Jeffrey, 183–89. Washington, DC: Smithsonian Institute Press. Panzer, R., and M. W. Schwartz. 1998. Effectiveness of vegetation-based approach to insect conservation. Conservation Biology 12:693–702. Pardini, R., G. M. Accacio, R. R. Laps, E. Mariano, M. L. B. Paciencia, M. Dixo, J. Baumgarten, and D. Faria. 2009. The challenge of maintaining biodiversity in the Atlantic forest: a multi-taxa conservation assessment of an agro-forestry mosaic in southern Bahia. Biological Conservation 142:1178–90. Part, T. and B. Soderstrom. 1999. Conservation value of semi-natural pastures in Sweden: contrasting botanical and avian measures. Conservation Biology 13:755–65. Paszkowski, C. A., and W. M. Tonn. 2000. Community concordance between fish and aquatic birds of lakes in northern Alberta: the relative importance of environmental and biotic factors. Freshwater Biology 43:421–37. Patterson, B. D. 1984. Mammalian extinction and biogeography in the southern Rocky Mountains. In Extinctions, ed. M. H. Nitecki, 247–93. Chicago: University of Chicago Press. Patton, D. R. 1987. Is the use of “management indicator species” feasible? Western Journal of Applied Forestry 2:33–34. Pauly, D., V. Christensen, J. Dalsgaard, R. Froese, and F. Torres Jr. 1998. Fishing down marine food webs. Science 279:860–63. Pawar, S.P., A.C. Birand, M.F. Ahmed, S. Sengupta, and T.R.S. Raman. 2007. Conservation biogeography in north-east India: hierarchical analysis of cross-taxon distributional congruence. Diversity and Distributions 13:53–65. ———. 2006. Conservation biogeography in north-east India: hierarchical analysis of cross-taxon distributional congruence. Diversity and Distributions 13:53–65. Pearman, P. B., and D. Weber. 2007. Common species determine richness patterns in biodiversity indicator taxa. Biological Conservation 138:109–19. Pearsall, W. H. 1957. Report on an ecological survey of the Serengeti National Park, Tanganyika. Oryx 4:71–136. Pearson, D. L. 1995. Selecting indicator taxa for the quantitative assessment of biodiversity. In Biodiversity: Measurement and estimation, ed. D. L. Hawksworth, 75–79. London: Royal Society Publications. Pearson, D. L., and F. Cassola. 1992. World-wide species richness patterns of tiger beetles (Coleoptera Cicindelidae) indicator taxon for biodiversity and conservation studies. Conservation Biology 6:376–91. Pellew, R. A. 1983. The impacts of elephant, giraffe and fire upon the Acacia tortolis woodlands of the Serengeti. African Journal of Ecology 21:41–74. Peres, C. A., and F. Michalski. 2006. Synergistic effects of habitat disturbance and hunting in Amazonian forest vertebrates. In Emerging Threats to Tropical Forests, eds. W. F. Laurance and C. A. Peres, 105–27. Chicago: University of Chicago Press.

330 References Perhans, K., C. Kindstrand, M. Boman, L. B. Djupstrom, L. Gustafsson, L. Mattsson, L. M. Schroeder, J. Weslien, and S. Wikberg. 2008. Conservation goals and the relative importance of costs and benefits in reserve selection. Conservation Biology 22:1331–39. Perry, A. L., P. J. Low, J. R. Ellis, and J. D. Reynolds. 2005. Climate change and distribution shifts in marine fishes. Science 308:1912–15. Petit, L. J., and D. R. Petit. 2003. Evaluating the importance of human-modified lands for neotropical bird conservation. Conservation Biology 17:687–94. Phillips, D. J. H., and D. A. Segar. 1986. Use of bio-monitors in monitoring conservative contaminants: programme design imperatives. Marine Pollution Bulletin 17:10–17. Philpott, S. M., W. J. Arendt, I. Armbrecht, P. Bichier, T. V. Dietsch, C. Gordon, R. Greenberg, I. Perfecto, R. Reynoso-Santos, L. Soto-Pinto, C. Tejeda-Cruz, G. Williams-Linera, J. Valenzuela, and J. M. Zolotoff. 2008. Biodiversity loss in Latin American coffee landscapes: review of the evidence on ants, birds, and trees. Conservation Biology 22:1093–105. Pik, A. J., I. Oliver, and A. J. Beattie. 1999. Taxonomic sufficiency in ecological studies of terrestrial invertebrates. Australian Journal of Ecology 24:555–62. Pimental, D., U. Stachow, D.A. Takacs, H.W. Brubaker, A.R. Dumas, J.J. Meaney, J.A.S. O’Neill, D.E. Onsi, and D.B. Corzilius. 1992. Conserving biological diversityin agricultural/forestry systems: most biological diversity exists in human-managed ecosystems. BioScience 42:354–362. Pimm, S. L. 1991. The Balance of Nature? Ecological Issues in the Conservation of Species and Communities. Chicago: University of Chicago Press. Pimm, S. L., G. J. Russell, J. L. Gittleman, and T. M. Brooks. 1995. The future of biodiversity. Science 269:347–50. Pineda, E., C. Moreno, F. Escobar, and G. Halffter. 2005. Frog, bat, and dung beetle diversity in the cloud forest and coffee agroecosystems of Veracruz, Mexico. Conservation Biology 19:400–410. Pinel-Alloul, B., G. Methot, L. Lapierre, and A. Willsie. 1996. Macroinvertebrate community as a biological indicator of ecological and toxicological factors in Lake Saint-Francois (Quebec). 1996. Environmental Pollution 91:65–87. Pinto, M. P., J. A. F. Diniz-Filho, L. M. Bini, D. Blamires, and T. F. L. V. B. Rangel. 2008. Biodiversity surrogate groups and conservation priority areas: birds of the Brazilian Cerrado. Diversity and Distributions 14:78–86. Pintor, L. M., and D. A. Soluk. 2006. Evaluating the non-consumptive, positive effects of a predator in the persistence of an endangered species. Biological Conservation 130:584–91. Pitkanen, M., and J. Tiainen. 2001. Biodiversity of Agricultural Landscapes in Finland. Birdlife Conservation Series (No. 3). Helsinki, Finland: Birdlife. Poiani, K. A., M. D. Merrill, and K. A. Chapman. 2001. Identifying conservationpriority areas in a fragmented Minnessota landscape based on the umbrella species concept and selection of large patches of natural vegetation. Conservation Biology 15:513–22.

References 331 Poiani, K. A. B. D. Richter, M. G. Anderson, and H. E. Richter. 2000. Biodiversity conservation at multiple scales: functional sites, landscapes, and networks. BioScience 50:133–46. Polasky, S., E. Nelson, J. Camm, B. Csuti, P. Fackler, E. Lonsdorf, C. Montgomery, D. White, J. Arthur, B. Garber-Yonts, R. Haight, J. Kagan, A. Starfield, and C. Tobalske. 2008. Where to put things? Spatial land management to sustain biodiversity and economic returns. Biological Conservation 141:1505–24. Polasky, S., J. D. Camm, and B. Garber-Yonts. 2001. Selecting biological reserves cost-effectively: an application to terrestrial vertebrate conservation in Oregon. Land Economics 77:68–78. Polis, G. A., and D. R. Strong. 1996. Food web complexity and community dynamics. American Naturalist 147:813–46. Possingham, H., I. Ball, and S. Andelman. 2000. Mathematical models for identifying representative reserve networks. In Quantitative Methods for Conservation Biology, eds. S. Ferson and M. Burgman, 291–306. New York: Springer-Verlag. Pounds, J. A., M. R. Bustamante, L. A. Coloma, J. A. Consuegra, M. P. Fogden, P. N. Foster, E. La Marca, K. L. Masters, A. Merino-Viteri, R. Puschendorf, S. R. Ron, G. A. Sanchez-Azofeifa, C. J. Still, and B. E. Young. 2006. Widespread amphibian extinctions from epidemic disease driven by global warming. Nature 439:161–67. Power, M. 2002. Assessing fish population responses to stress. In Biological Indicators of Aquatic Ecosystem Stress, ed. S. M. Adams, 379–421. Bethseda, MD: American Fisheries Society. Power, M. E., D. Tilman, J. A. Estes, B. A. Menge, W. J. Bond, L. Scott Mils, G. Daily, J. C. Castilla, J. Lubchenco, and R. T. Paine. 1996. Challenges in the quest for keystones. BioScience 46:609–20. Prance, G. T. 1995. A comparison of the efficacy of higher taxa and species numbers in the assessment of the biodiversity in the neotropics. In Biodiversity: Measurement and Estimation, ed. D. L. Hawksworth, 89–99. London: Royal Society Publications. Prange, S., and S. D. Gehrt. 2007. Response of skunks to a simulated increase in coyote activity. Journal of Mammalogy 88:1040–49. Prendergast, J. R., and B. C. Eversham. 1997. Species richness covariance in higher taxa: empirical tests of the biodiversity indicator concept. Ecography 20:210– 16. Prendergast, J. R., R. M. Quinn, J. H. Lawton, B. C. Eversham, and D. W. Gibbons. 1993. Rare species, the coincidence of diversity hotspots and conservation strategies. Nature 365:335–37. Prendrini, L., L. Theron, K. van der Merwe, N. Owen-Smith. 1996. Abundance and guild structure of grasshoppers (Orthoptera: Acridiodea) in communally grazed and protected savanna. South African Journal of Zoology 31:120–29. Pressey, R. L. 1994. Ad hoc reservations: forward or backward steps in developing representative reserve systems? Conservation Biology 8:662–68.

332 References Pressey, R. L. 2004. Conservation planning and biodiversity: assembling the best data for the job. Conservation Biology 18:1677–81. Pressey, R. L., T. C. Hagar, K. M. Ryan, J. Schwarz, S. Wall, S. Ferrier, and P. M. Creaser. 2000. Using abiotic data for conservation assessments over extensive regions: quantitative methods applied across New South Wales, Australia. Biological Conservation 96:55–82. Pressey, R. L., C. J. Humphries, C. R. Margules, R. I. Vane-Wright, and P. H. Williams. 1993. Beyond opportunism: key principles for systematic reserve selection. Trends in Ecology and Evolution 8:124–28. Pressey, R. L., and V. S. Logan. 1995. Reserve coverage and requirements in relation to partitioning and generalization of land classes: analyses for western New South Wales. Conservation Biology 9:1506–17. Pressey, R. L., and A. O. Nicholls. 1989. Efficiency in conservation evaluation: scoring vs. iterative procedures. Biological Conservation 50:199–218. Pressey, R. L., H. P. Possingham, V. S. Logan, J. R. Day, and P. H. Williams. 1999. Effects of data characteristics on the results of reserve selection algorithms. Journal of Biogeography 26:179–91. Pressey, R. L., and K. H. Taffs. 2001. Sampling of land types by protected areas: three measures of effectiveness applied to western New South Wales. Biological Conservation 101:105–17. Prevey, J. S., M. J. Germino, N. J. Huntly, and R. S. Inouye. 2010. Exotic plants increase and native plants decrease with loss of foundation species in sagebrush steppe. Plant Ecology 207:39–51. Prugh, L. R., C. J. Stoner, C. W. Epps, W. T. Bean, A. S. Laliberte, and J. S. Brashares. 2009. The rise of the mesopredator. BioScience 59:779–91. Purtauf, T., J. Dauber, and V. Wolters. 2005. The response of carabids to landscape simplification differs between trophic groups. Oecologia 142:458–64. Purvis, A. J., L. Gittlemam, G. Cowlishaw, and G. M. Mace. 2000. Predicting extinction risk in declining species. Proceedings of the Royal Society of London B 267:1947–52. Qian, H., and R. E. Ricklefs. 2008. Global concordance in diversity patterns of vascular plants and terrestrial vertebrates. Ecology Letters 11:547–53. Quinn, J. L., and Y. Kokorev. 2002. Trading-off risks from predators and from aggressive hosts. Behavioral Ecology and Sociobiology 51:455–60. Quirijns, F. J., J. J. Poos, and A. D. Rijnsdorp. 2008. Standardizing commercial CPUE data in monitoring stock dynamics: accpunting for targeting behaviour in mixed fisheries. Fisheries Research 89:1–8. Rabinowitz, A. 1986. Jaguar. New York: Random House. Rabinowitz, D., S. Cairns, and T. Dillon. 1986. Seven forms of raity and their frequency in the flora of the British Isles. In Conservation Biology: The Science of Scarcity and Diversity, ed. M. E. Soule, 182–204. Sunderland, MA: Sinauer Assoc. Racey, G. D., and D. L. Euler. 1982. Small mammal and habitat response to shore-

References 333 line cottage development in central Ontario. Canadian Journal of Zoology 60:865–80. Rachowicz, L. J., J. M. Hero, R. A. Alford, J. W. Taylor, J. A. T. Morgan, V. T. Vredenburg, J. P. Collins, and C. J. Briggs. 2005. The novel and endemic pathogen hypotheses: competing explanations for the origin of emerging infectious diseases of wildlife. Conservation Biology 19:1441–48. Raffa, K. F., and A. A. Berryman. 1987. Interacting selective pressures in coniferbark beetle systems: a basis for reciprocal adaptations? American Naturalist 129:234–62. Rahbek, C. 1995. The elevational gradient of species richness: a uniform pattern? Ecography 18:200–205. Rahbek, C. 2005. The role of spatial scale and the perception of large-scale speciesrichness patterns. Ecology Letters 8:224–39. Rahbek, C., and G. R. Graves. 2000. Detection of macro-ecological patterns in South American hummingbirds is affected by spatial scale. Proceedings of the Royal Society London B 267:2259–65. Rahbek, C., and G. R. Graves. 2001. Multiscale assessment of patterns of avian species richness. Proceedings of the National Academy of Sciences 98:4534–39. Randall, D. J., and SC. Brauner. 1991. Effects of environmental factors on exercise in fish. Journal of Experimental Biology 160:113–26. Ranius, T. 2002. Osmoderma eremita as an indicator of species richness of beetles in tree hollows. Biodiversity and Conservation 11:931–41. Rapport, D. J. 1989. What constitutes ecosystem health? Perspectives in Biology and Medicine 33:120–32. Rapport, D. J., C. Gaudet, J. R. Karr, J. S. Baron, C. Bohlen, W. Jackson, B. Jones, R. J. Naiman, B. Norton, and M. M. Pollock. 1998. Evaluating landscape health: integrating societal goals and biophysical processes. Journal of Environmental Management 53:1–15. Ratcliffe, F. N., K. Myers, B. V. Fennessy, and J. H. Calaby. 1952. Myxomatosis in Australia: a step towards the biological control of the rabbit. Nature 170:1– 13. Ray, J. C., K. H. Redford, R. S. Steneck, and J. Berger. 2005. Large Carnivores and the Conservation of Biodiversity.Washington, DC: Island Press. Rayner, M. J., M. E. Hauber, M. J. Imber, R. K. Stamp, and M. N. Clout. 2007. Spatial heterogeneity of mesopredator release within an oceanic island system. Proceedings of the National Academy of Sciences 104:20862–65. Rebelo, A. G., and W. R. Siegfried. 1990. Protection of Fynbos vegetation: ideal and real-world options. Biological Conservation 54:15–31. Rebelo, A. G., and W. R. Siegfried. 1992. Where should nature reserves be located in the Cape Floristic Region, South Africa? Models for the spatial configuration of a reserve network aimed at maximizing the protection of floral diversity. Conservation Biology 6:243–52. Redford, K. H. 1992. The empty forest. BioScience 42:412–22.

334 References Reed, K. E., and J. G. Fleagle. 1995. Geographic and climatic control of primate diversity. Proccedings of the National Academy of Sciences. 92:7874–76. Regan, H. M., M. Colyvan, and M. A. Burgman 2002. A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecological Applications 12:618–28. Regier, H. 1990. Workshop issue paper: indicators and assessment of the state of fisheries. Environmental Monitoring and Asessment 15:289–94 Reichman, O. J., J. H. Benedix, and T. R. Seastedt. 1993. Distinct animal-generated edge effects in a tallgrass prairie community. Ecology 74:1281–85. Reichman, O. J., and E. W. Seabloom. 2002. The role of pocket gophers as subterranean ecosystem engineers. Trends in Ecology and Evolution 17:44–49. Reid, W. V. 1998. Biodiversity hotspots. Trends in Ecology and Evolution 13:275– 280. Reunanen, P., M. Monkkonen, and A. Nikula. 2000. Managing boreal forest landscapes for flying squirrels. Conservation Biology 14:218–26. Rey Benayas, J. M., and E. de la Montana. 2003. Identifying areas of high-value vertebrate diversity for strengthening conservation. Biological Conservation 114:357–70. Reyers, B., A. S. van Jaarsveld, and M. Kruger. 2000. Complementarity as a biodiversity indicator strategy. Proceedings of the Royal Society of London B 267:505– 13. Rice, C. D., and M. R. Arkoosh. 2002. Immunological indicators of environmental stress and disease susceptibility in fishes. In Biological Indicators of Aquatic Ecosystem Stress, ed. S. M. Adams, 187–220. Bethseda, MD: American Fisheries Society. Rice, J. 2003. Environmental health indicators. Ocean & Coastal Management 46:235–59. Rice, J. A. 1990. Bioenergetics modeling approaches to evaluation of stress in fishes. American Fisheries Society Symposium 8:90–92. Rice, J. C. 2000. Evaluating fishery impacts using metrics of community structure. ICES Journal of Marine Science 57:682–88. Rice, J. C., and M.-J. Rochet. 2005. A framework for selecting a suite of indicators for fisheries management. ICES Journal of Marine Science 62:516–27. Richardson, D., and W. J. Bond. 1991. Determinants of plant distribution: evidence from pine invasions. American Naturalist 137:639–68. Richardson, D. M., P. Pysek, M. Rejmanek, M. G. Barbour, F. D. Panetta, and C. J. West. 2000. Naturalization and invasion of alien plants: concepts and definitions. Diversity and Distributions 6:93–107. Ricketts, T. H. 2001. Aligning conservation goals: are patterns of species richness and endemism concordant at regional scales? Animal Biodiversity and Conservation 24:91–99. Ricketts, T. H., G. C. Daily, and P. R. Ehrlich. 2002. Does butterfly diversity predict moth diversity? Testing a popular indicator taxon at local scales. Biological Conservation 103:361–70.

References 335 Ricketts, T. H., G. C. Daily, P. R. Ehrlich, J. P. Fay. 2001. Countryside biogeography of moths in a fragmented landscape: biodiversity in native and agricultural habitats. Conservation Biology 15:378–88. Ricketts, T. H., E. Dinerstein, D. M. Olson, and C. Loucks. 1999. Who’s where in North America? BioScience 49:369–81. Riley, S. P. D., R. M. Sauvajot, T. K. Fuller, E. C. York, D. A. Kamradt, C. Bromley, and R. K. Wayne. 2003. Effects of urbanization and habitat fragmentation of bobcats and coyotes in southern California. Conservation Biology 17:566–76. Ripple, W. J., and R. L. Beschta. 2004. Wolves and the ecology of fear: can predation risk structure ecosystems? BioScience 54:755–66. Ripple, W. J., and R. L. Beschta. 2006. Linking a cougar decline, trophic cascade, and catastrophic regime shift in Zion National Park. Biological Conservation 133:397–408. Ripple, W. J., and R. L. Beschta. 2007. Restoring Yellowstone’s aspen with wolves. Biological Conservation 138:514–19. Ripple, W. J., and E. J. Larsen. 2000. Historic aspen recruitment, elk, and wolves in northern Yellowstone National Park. Biological Conservation 95:361–70. Ripple, W. J., E. J. Larsen, R. A. Renkin, and D. W. Smith. 2001. Trophic cascades among wolves, elk and aspen on Yellowstone National Parks’s northern range. Biological Conservation 102:227–34. Ripple, W. J., P. D. Lattin, K. T. Hershey, F. F. Wagner, and E. C. Meslow. 1996. Landscape patterns around northern spotted owl nest sites in southwestern Oregon. Journal of Wildlife Management 61:151–58. Ritchie, E. G., and C. N. Johnson. 2009. Predator interactions, mesopredator release and biodiversity conservation. Ecology Letters 12:982–98. Roberge, J.-M., and P. Angelstam. 2004. Usefulness of the umbrella species concept as a conservation tool. Conservation Biology 18:76–85. Roberge, J.-M., and P. Angelstam 2006. Indicator species among resident forest birds—a cross-regional evaluation in northern Europe. Biological Conservation 130:134–47. Roberge, J.-M., G. Mikusinski, and S. Svensson. 2008. The white-backed woodpecker: umbrella species for forest conservation planning? Biodiversity and Conservation 17:2479–94. Roberts, C. 2005. Marine protected areas and biodiversity conservation. In Marine Conservation Biology: The Science of Maintaining the Sea’s Biodiversity, eds. E. A. Norse, and L. B. Crowder, 265–79. Washington DC: Island Press. Roberts, C. M., S. Andelman, G. Branch, R. H. Bustamante, J. C. Castilla, J. Dugan, B. S. Halpern, K. D. Laferty, H. Leslie, J. Lubchenco, D. McArdle, H. P. Possingham, M. Rucklelshaus, and R. R. Warner. 2003. Ecological criteria for evaluating candidate sites for marine reserves. Ecological Applications 13:S199– 214. Robinson, R. A., and W. J. Sutherland, 2002. Post-war changes in arable farming and biodiversity in Great Britain. Journal of Applied Ecology 39:157–76. Rodrigues, A. S. L., H. R. Akcakaya, S. J. Andelman, M. I. Bakarr, L. Boitani, T. M.

336 References Brooks, J. S. Chanson, L. D. C. Fishpool, G. A. B. da Fonseca, K. J. Gaston, M. Hoffmann. P. A. Marquet, J. D. Pilgrim, R. L. Pressey, J. Schipper, W. Sechrest, S. N. Stuart, L. G. Underhill, R. W. Waller, M. E. J. Watts, and X. Yan. 2004b. Global gap analysis: priority regions for expanding the global protected-area network. BioScience 54:1092–100. Rodrigues, A. S. L., S. J. Andelman, M. I. Bakarr, L. Boitani, T. M. Brooks, R. M. Cowling, L. D. C. Fishpool, G. A. B. da Fonseca, K. J. Gaston, M. Hoffmann. J. S. Long, P. A. Marquet, J. D. Pilgrim, R. L. Pressey, J. Schipper, W. Sechrest, S. N. Stuart, L. G. Underhill, R. W. Waller, M. E. J. Watts, and X. Yan. 2004a. Effectiveness of the global protected area network in representing species diversity. Nature 428:640–42. Rodrigues, A. S. L., and T. M. Brooks. 2007. Shortcuts for biodiversity conservation planning: the effectiveness of surrogates. Annual Review of Ecology, Evolution and Systematics 38:713–37. Rodrigues, A. S. L., and K. J. Gaston. 2002. Rarity and conservation planning across geopolitical units. Conservation Biology 16:674–82. Rodrigues, A. S. L., K. J. Gaston, and R. D. Gregory. 2000a. Using presenceabsence data to establish reserve selection procedures that are robust to temporal species turnover. Proceedings of the Royal Society B 267:897–902. Rodrigues, A. S. L., R. D. Gregory, and K. J. Gaston. 2000b. Robustness of reserve selection procedures under temporal species turnover. Proceedings of the Royal Society of London B 267:49–55. Rodrigues, A. S. L., J. D. Pilgrim, J. F. Lamoreux, M. Hoffmann, and T. M. Brooks. 2006. The value of the IUCN Red List for conservation. Trends in Ecology and Evolution 21:71–76. Rodrigues, A. S. L., R. Tratt, B. D. Wheeler, and K. J. Gaston. 1999. The performance of existing networks of conservation areas in representing biodiversity. Proceedings of the Royal Society of London B 266:1453–60. Rodriguez, J. P., D. L. Pearson, and R. Barrera. 1998. A test for the adequacy of bioindicator taxa: are tiger beetles (Coleoptera: Cicindelidae) appropriate indicators for monitoring the degradation of tropical forests in Venezuela? Biological Conservation 83:69–76. Roff, J. C., and S. M. J. Evans. 2002. Frameworks for marine conservation—nonhierarchical approaches and distinctive habitats. Aquatic Conservation: Marine and Freshwater Ecosystems 12:635–48. Rogers, C. M., and M. J. Caro. 1998. Song sparrows, top carnivores, and nest predation: a test of the mesopredator release hypothesis. Oecologia 116:227–33. Rohlf, D. J. 1991. Six biological reasons why the endangered species act doesn’t work and what to do about it. Conservation Biology 5:273–82. Rohr, J. R., A. M. Schotthoefer, T. R. Raffel, H. J. Carrick, N. Halstead, J. T. Hoverman, C. M. Johnson, L. B. Johnson, C. Lieske, M. D. Piwoni, P. K. Schoff, and V. R. Beasely. 2008. Agrochemicals increase trematode infections in a declining amphibian species. Nature 455:1235–39.

References 337 Rolstad, J., I. Gjerde, V. S. Gundersen, and M. Saetersdal. 2002. Use of indicator species to assess forest continuity: a critique. Conservation Biology 16:253–57. Rondinini, C., S. Stuart, and L. Boitani. 2005. Habitat suitability models and the shortfall in conservation planning for African vertebrates. Conservation Biology 19:1488–97. Root, T. L., J. T. Price, K. R. Hall, S. H. Schneider, C. Rozenzweig, and J. A. Pounds. 2003. Fingerprints of global warming on wild animals and plants. Nature 421:57–60. Root, T. L., and S. H. Schneider. 2002. Climate change: overview and implications for wildlife. In Wildlife Responses to Climate Change: North American Case Studies, eds. S. H. Schneider and T. L. Root, 1–56. Washington, DC: Island Press. Rosenberg, D. M., and V. H. Resh, eds. 1993. Freshwater Biomonitoring and Benthic Macroinvertebrates. New York: Chapman & Hall. Roth, T., and D. Weber. 2008. Top predators as indicators for species richness? Prey species are just as useful. Journal of Applied Ecology 45:987–91. Rothley, K. D., C. N. Berger, C. Gonzalez, E. M. Webster, and D. I. Rubenstein. 2004. Combining strategies to select reserves in fragmented landscapes. Conservation Biology 18:1121–31. Rowland, M. M., M. J. Wisdom, L. H. Suring, and C. W. Meinke. 2006. Greater sage-grouse as an umbrella species for sagebrush-associated vertebrates. Biological Conservation 129:323–35. Roy, A. H., A. D. Rosemond, D. S. Leigh, M. J. Paul, and J. B. Wallace. 2003. Habitat-specific responses of stream insects to land cover disturbance: biological consequences and monitoring implications. Journal of North American Benthological Society 22:292–307. RSPB. 2007. Conservation Science 2007 in the RSPB. London: Royal Society for the Protection of Birds Reports. Rubino, M. J., and G. R. Hess. 2002. Planning open spaces for wildlife 2: modeling and verifying focal species habitat. Landscape and Urban Planning 64:89–104. Rubinoff, D. 2001. Evaluating the California gnatcatcher as an umbrella species for conservation of southern California coastal sage scrub. Conservation Biology 15:1374–83. Russ, G. R., and A. C. Alcala. 1996. Marine reserves: rates and patterns of recovery and decline of large predatory fish. Ecological Applications 6:947–61. Ryti, R. T. 1992. Effect of the focal taxon on the selection of nature reserves. Ecological Applications 2:404–10. Saetersdal, M., I. Gjerde, and H. H. Blom. 2005. Indicator species and the problem of spatial inconsistency in nestedness patterns. Biological Conservation 122:305–16. Saetersdal, M., J. M. Line, and H. J. B. Birks. 1993. How to maximize biological diversity in nature reserve selection: vascular plants and breeding birds in deciduous woodlands, western Norway. Biological Conservation 66:131–38. Safalsky, N., R. Margoulis, K. H. Redford, and J. G. Robinson. 2002. Improving

the practice of conservation: a conceptual framework and research agenda for conservation science. Conservation Biology 16:1469–79. Samways, M. J. 1994. Insect Conservation Biology. London: Chapman and Hall. Sanderson, E. W., M Jaiteh, M. A. Levy, K. H. Redford, A. V. Wannebo, and G. Woolmer. 2002c. The human footprint and the last of the wild. BioScience 52:891–904. Sanderson, E. W., K. H. Redford, C.-L. B. Chetkiewicz, R. A. Medellin, A. R. Rabinowitz, J. G. Robinson, and A. B. Taber. 2002b. Planning to save a species: the jaguar as a model. Conservation Biology 16:58–72. Sanderson, E. W., K. H. Redford, A. Vedder, P. B. Coppolillo, and S. E. Ward. 2002a. A conceptual model for conservation planning based on landscape species requirements. Landscape and Urban Planning 58:41–56. Sandin, S. A., J. E. Smith, E. E. DeMartini, E. A. Dinsdale, S. D. Donner, A. M. Friedlander, T. Konotchick, M. Malay, J. E. Maragos, D. Obura, O. Pantos, G. Pauly, M. Ritchie, F. Rohwer, R. E. Schroeder, S. Walsh, J. B. C. Jackson, N. Knowlton, and E. Sala. 2008. Baselines and degradation of coral reefs in the Northern Line Islands. PLoS ONE 3:e1548. Sarkar, S., R. L. Pressey, D. P. Faith, C. R. Margules, T. Fuller, D. M. Stoms, A. Moffett, K. A. Wilson, K. J. Williams, P. H. Williams, and S. Andelman. 2006. Biodiversity conservation planning tools: present status and challenges for the future. Annual Reviews of Environment and Resources 31:123–59. Sauberer, N., K. P. Zulka, M. Abensperg-Traun, H.-M. Berg, G. Bieringer, N. Milasowszky, D. Moser, C. Plutzar, M. Pollheimer, C. Storch, R. Trostl, H. Zechmeister, G. Grabherr. 2004. Surrogate taxa for biodiversity in agricultural landscapes of eastern Austria. Biological Conservation 117:181–90. Schaeffer, D. J., E. E. Herricks, and H. W. Kerster. 1988. Ecosystem health: 1. Measuring ecosystem health. Environmental Management 12:445–55. Scheffler, P.Y. 2005. Dung beetle (Coleoptera: Scarabaeidae) diversity and community structure across three disturbance regimes in eastern Amazonia. Journal of Tropical Ecology 21:9–19. Schmitz, O. J., P. A. Hamback, and A. Beckerman. 2000. Trophic cascades in terrestrial systems: a review of the effects of carnivore removals on plants. American Naturalist 155:141–53. Schneider, D. C. 1994. Quantitative Ecology: Spatial and Temporal Scaling. San Diego, CA: Academic Press. Schulze, C. H., M. Waltert, P. J. A. Hessler, R. Pitopang, Shahabuddin, D. Veddeler, M. Muhlenberg, S. R. Gradstein, C. Leuschner, I. Steffan-Dewenter, and T. Tscharntke. 2004. Biodiversity indicator groups of tropical land-use systems: comparing plants, birds, and insects. Ecological Applications 14:1321–33. Scott, J. M., F. Davis, B. Csuti, R. Noss, B. Butterfield, C. Groves, H. Anderson, S. Caicco, F. D’Erchia, T. C. Edwards Jr., J. Ullman, and R. G. Wright. 1993.

References 339 Scott, J. M., F. W. Davis, R. G. McGhie, R. G. Wright, C. Groves, and J. Estes. 2001. Nature reserves: do they capture the full range of America’s biological diversity? Ecological Applications 11:99–1007. Sebastiao, H., and C. E. V. Grelle. 2009. Taxon surrogates among Amazonian mammals: can total species richness be predicted by single orders? Ecological indicators 9:160–66. Seddon, P. J., and T. Leech. 2008. Conservation short cut, or long and winding road? A critique of umbrella species criteria. Oryx 42:240–45. Seed, R. 1996. Patterns of biodiversity in the macro-invertebrate fauna associated with mussel patches on rocky shores. Journal of Marine Biology Association 76:203–10. Sekercioglu, C. H., P. R. Ehrlich, G. C. Daily, D. Aygen, and R. F. Sandi. 2002. Disappearance of insectivorous birds from tropical forest fragments. Proceedings of the National Academy of Sciences 99:263–67. Sekercioglu, C. H., S. R. Loarie, F. O. Brenes, P. R. Ehrlich, and G. C. Daily. 2007. Persistence of forest birds in the Costa Rican agricultural countryside. Conservation Biology 21:482–94. Sepkoski, J. J., Jr. 1992. Phylogenetic and ecological patterns in Phanerozoic history of marine biodiversity. In Systematics, Ecology, and the Biodiversity Crisis, ed. N. Eldridge, 77–100. New York: Columbia University Press. Sergio, F., J. Blas, M. Forero, N. Fernandez, J. A. Donazar, and F. Hiraldo. 2005b. Preservation of wide-ranging top predators by site-protection: black and red kites in Donana National Park. Biological Conservation 125:11–21. Sergio, F., T. Caro, D. Brown, B. Clucas, J. Hunter, J. Ketchum, K. McHugh, and F. Hiraldo. 2008. Top predators as conservation tools: ecological rationale, assumptions and efficacy. Annual Review of Evolution, Ecology and Systematics 39:1–19. Sergio, F., L. Marchesi, and P. Pedrini. 2004. Integrating individual habitat choices and regional distribution of a biodiversity indicator and top predator. Journal of Biogeogaphy 31:619–28. Sergio, F., I. Newton, and L. Marchesi. 2005a. Top predators and biodiversity. Nature 436:192. Sergio, F., I. Newton, L. Marchesi, and P. Pedrini. 2006. Ecologically justified charisma: preservation of top predators delivers biodiversity conservation. Journal of Applied Ecology 43:1049–55. Sergio, F., P. Pedrini, and L. Marchesi. 2003. Reconciling the dichotomy between single species and ecosystem conservation: black kites (Milvus migrans) and eutrophication in pre-Alpine lakes. Biological Conservation 110:101–11. Shackelton, C. M. 2000. Comparison of plant diversity in protected and communal lands in the Bushbuckridge lowveld savanna, South Africa. Biological Conservation 94:273–85. Shaffer, M. L. 1992. Keeping the Grizzly Bear in the American West: A Strategy for Real Recovery. Washington, DC: Wilderness Society.

340 References Shapcott, A., M. Rakotoarinivo, R. J. Smith, G. Lysakova, M. F. Fay, and J. Dransfield. 2007. Can we bring Madagascar’s critically endangered palms back from the brink? Genetics, ecology and conservation of the critically endangered palm Beccariophoenix madagarascensis. Botanical Journal of the Linnean Society 154:589–608. Shi, H., A. Singh, S. Kant, Z. Zhu and E. Waller. 2005. Integrating habitat status, human population pressure, and protection status into biodiversity conservation priority setting. Conservation Biology 19:1273–85. Shin, Y.-J., M.-J. Rocet, S. Jennings, J. G. Field, and H. Gislason. 2005. Using sizebased indicators to evaluate the ecosystem effects of fishing. ICES Journal of Marine Science 62:384–96. Shurin, J. B., E. T. Borer, E. W. Seabloom, K. Anderson, C. A. Blanchette, B. Broitman, S. D. Cooper, and B. S. Halpern. 2002. A cross-ecosystem comparison of the strength of trophic cascades. Ecology Letters 5:785–91. Simberloff, D. 1998. Flagships, umbrellas and keystones: is single-species management passé in the landscape era? Biological Conservation 83:247–57. Simila, M., J. Kouki, M. Monkkonen, A.-L. Sippola, and E. Huhta. 2006. Covariation and indicators of species diversity: Can richness of forest-dwelling species be predicted in northern boreal forests? Ecological Indicators 6:686– 700. Simon, T. P., ed. 2003. Biological Response Signatures: Indicator Patterns Using Aquatic Communities. Boca Raton, FL: CRC Press. Simon, T. P., and J. A. Exl. 2003. Effects of silviculture of biotic integrity for benthic macroinvertebrate and fish assemblages in northeastern Minnesota’s northern lakes and forest ecoregion (USA). In Biological Response Signatures: Indicator Patterns Using Aquatic Communities, ed. T. P. Simon, 325–46. Boca Raton, FL: CRC Press. Siriwardena, G. M., D. K. Stevens, G. Q. A. Anderson, J. A. Vickery, N. A. Calbrade, and S. Dodd. 2007. The effect of supplementary winter seed food on breeding populations of farmland birds: evidence from two large-scale experiments. Journal of Applied Ecology 44:920–32. Sisk, T. D., A. E. Launer, K. R. Switky, and P. R. Ehrlich. 1994. Identifying extinction threats. BioScience 44:592–604. Slik, J. W. F., C. S. Bernard, F. C. Breman, M. Van Beek, A. Salim, and D. Sheil. 2008. Wood density as a conservation tool: quantification of disturbance and identification of conservation-priority areas in tropical forests. Conservation Biology 22:1299–308. Slocum, R. 2004. Polar bears and energy-efficient lightbulbs: strategies to bring climate change home. Environment and Planning D 22:413–38. Smart, R., M. J. Whiting, and W. Twine. 2005. Lizards and landscapes: integrating field surveys and interviews to assess the impact of human disturbance on lizard assemblages and selected reptiles in a savanna in South Africa. Biological Conservation 122:23–31.

References 341 Smith, D. W., R. O. Peterson, and D. B. Houston. 2003. Yellowstone after wolves. BioScience 53:330–40. Smith, G. A., and M. V. Lomolino. 2004. Black-tailed prairie dogs and the structure of avian communities on the shortgrass plains. Oecologia 138:592–602. Smith, G. F., T. Gittings, M. Wilson, L. French, A. Oxbrough, S. O’Donoghue, J. O’Halloran, D. L. Kelly, F. J. G. Mitchell, T. Kelly, S. Iremonger, A.-M. McKee, and P. Giller. 2008. Identifying practical indicators of biodiversity for stand-level management of plantation forests. Biodiversity and Conservation 17:991–1015. Smith, W. P., S. M. Gende, and J. V. Nichols. 2005. The northern flying squirrel as an indicator species of temperate rain forest: test of an hypothesis. Ecological Applications 15:689–700. Snyder, C. D., J. A. Young, D. P. Lemarie, and D. R. Smith. 2002. Influence of eastern hemlock (Tsuga canadensis) forests on aquatic invertebrate assemblages in headwater streams. Canadian Journal of Fish and Aquatic Science 59:262– 75. Sodhi, N. S., B. W. Brook, and C. J. A. Bradshaw. 2007. Tropical Conservation Biology. Malden, MA: Blackwell Publishing. Sotherton, N. W., and M. J. Self. 2000. Changes in plant and arthropod diversity on lowland farmland: an overview. In Ecology and Conservation of Lowland Farmland Birds, eds. N. J. Aebischer, A. D. Evans, P. V. Grice and J. A. Vickery, 26– 35. Tring, UK: British Ornithologists Union. Soule, M. E. 1985. Biodiversity indicators in California: taking nature’s temperature. California Agriculture 49:40–44. Soule, M. E., D. T. Bolger, A. C. Alberts, R. Sauvajot, J. Wright, M. Sorice, and S. Hill. 1988. Reconstructed dynamics of rapid extinctions of chaparral-requiring birds in urban habitat islands. Conservation Biology 2:75–92. Soule, M. E., J. A. Estes, B. Miller, and D. L. Honnold. 2005. Strongly interacting species: conservation policy, management, and ethics. BioScience 55:168–76. Soule, M. E., B. A. Wilcox, and C. Holtby. 1979. Benign neglect: a model of faunal collapse in the game reserves of East Africa. Biological Conservation 15:259–72. Sovada, M. A., A. B. Sargeant, and J. W. Grier. 1995. Differential effects of coyotes and red foxes on duck nest success. Journal of Wildlife Management 59:1–9. Sparling, D. W., G. M. Fellers, and L. L. McConnell. 2001. Pesticides and amphibian population declines in California. Environmental Toxicology and Chemistry 20:1591–95. Spellerberg, I. F. 1991. Monitoring Ecological Change. Cambridge: Cambridge University Press. Springer, A. M., G. V. Byrd, and S. J. Iverson. 2007. Hot oceanography: planktivorous seabirds reveal ecosystem responses to warming of the Bering Sea. Marine Ecology Progress Series 352:289–97. Springer, A. M., J. A. Estes, G. B. van Vilet, T. M. Williams, D. F. Doak, E. M. Danner, K. A. Forney, and B. Pfister. 2003. Sequential megafaunal collapse in the

342 References north Pacific ocean: an ongoing legacy of industrial whaling? Proceedings of the National Academy of Sciences 100:12223–28. Stahl, R. G., Jr. 1997. Can mammalian and non-mammalian “sentinel species” data be used to evaluate the human health implications of environmental contaminants? Human Ecological Risk Assessment 3:329–35. Stattersfield, A. M., M. J. Crosby, A. J. Long, and D. C. Wege. 1998. Endemic Bird Areas of the World: Priorities for Biodiversity Conservation. Cambridge, UK: Birdlife International. Steedman, R. J. 1988. Modification and assessment of an index of biotic integrity to quantify stream quality in southern Ontario. Canadian Journal of Fisheries and Aquatic Sciences 45:492–501. Steffan-Dewenter, I. 2003. Importance of habitat area and landscape context for species richness of bees and wasps in fragmented orchard meadows. Conservation Biology 17:1036–44. Steffan-Dewenter, I., U. Munzenberg, C. Burger, C. Thies, and T. Tscharntke. 2002. Scale-dependent effects of landscape context on three pollinator guilds. Ecology 83:1421–32. Stegemann, J. J., K. W. Rento, B. R. Woodin, Y.-S. Zhang, and R. F. Addison. 2002. Molecular responses to environmental contamination: enzyme and protein systems as indicators of chemical exposure and effect. In Biomarkers: Biochemical, Physiological and Histological Markers of Anthropogenic Stress, eds. R. J. Hugget, R. A. Kimerly, M. Mehrle, and H. L. Bergman, 235–65. Chelsea, MI: Lewis Publishers. Steneck, R. S., and E. Sala. 2005. Large marine carnivores: trophic cascades and top-down controls in coastal ecosystems past and present. In Large Carnivores and the Conservation of Biodiversity, eds. J. C. Ray, K. H. Redford, R. S. Steneck, and J. Berger, 110–37. Washington, DC: Island Press. Stevens, C. E., C. A. Paszkowski, and A. L. Foote. 2007. Beaver (Castor canadensis) as a surrogate species for conserving anuran amphibians on boreal streams in Alberta, Canada. Biological Conservation 134:1–13. Stevens, G. C. 1992. The elevational gradient in altitudinal range: an extension of Rapoport’s latitudinal rule to altitude. American Naturalist 140:893–911. Stevens, T., and R. M. Connolly. 2004. Testing the utility of abiotic surrogates for marine habitat mapping at scales relevant to management. Biological Conservation 119:351–62. Stoddard, J. L., D. P. Larsen, C. P. Hawkins, R. K. Johnson, and R. H. Norris. 2006. Setting expectations for the ecological condition of streams: the concept of reference condition. Ecological Applications 16:1267–76. Stoner, C., T. Caro, S. Mduma, C. Mlingwa, G. Sabuni, and M. Borner. 2007. Assessment of effectiveness of protection strategies in Tanzania based on a decade of survey data for large herbivores. Conservation Biology 21:635–46. Stoner, C., T. Caro, S. Mduma, C. Mlingwa, G. Sabuni, M. Borner, and C. Schel-

References 343 ten. 2006. Changes in large herbivore populations across large areas of Tanzania. African Journal of Ecology 45:202–15. Stouffer, P. C., and R. O. Bierregaard Jr. 1995a. Use of Amazonian forest fragments by understorey insectivorous birds. Ecology 76:2429–45. Stouffer, P. C., and R. O. Bierregaard Jr. 1995b. Effects of forest fragmentation on understorey hummingbirds in Amazonian Brazil. Conservation Biology 9:1085– 94. Stouffer, P. C., R. O. Bierregaard Jr., C. Strong, and T. E. Lovejoy. 2006. Long-term landscape change and bird abundance in Amazonian rainforest fragments. Conservation Biology 20:1212–23. Stouffer, P. C., C. Strong, and L. N. Naka. 2009. Twenty years of understorey bird extinctions from Amazonian rain forest fragments: consistent trends and landscape-mediated dynamics. Diversity and Distributions 15:88–97. Stratford, J. A., and P. C. Stouffer. 1999. Local extinctions of terrestrial insectivorous birds in a fragmented landscape near Manaus, Brazil. Conservation Biology 13:1416–23. Stribling, J. M., J. C. Cornwell, and O. A. Glahn. 2007. Microtopography in tidal marshes: ecosystem engineering by vegetation? Estuaries and Coasts 30:1007– 15. Strong, D. R. 1992. Are trophic casdcades all wet? The redundant differentiation in trophic architecture of high diversity ecosystems. Ecology 73:747–54. Su, J., D. M. Debinski, M. E. Jakubauskas, and K. Kindscher. 2004. Beyond species richness: community similarity as a measure of cross-taxon congruence for coarse-filter conservation. Conservation Biology 18:167–73. Summerville, K. S., L. M. Ritter, and T. O. Crist. 2004. Forest moth taxa as indicators of lepidopteran richness and habitat disturbance: a preliminary assessment. Biological Conservation 116:9–18. Suter, W., R. F. Graf., and R. Hess. 2002. Capercaille (Tetrao urogallus) and avian biodiversity: testing the umbrella-species concept. Conservation Biology 16:778– 88. Sutherland, W. J., A. S. Pullin, P. M. Dolman, and T. M. Knight. 2004. The need for evidence-based conservation. Trends in Ecology and Evolution 19:305–8. Svancara, L. K., R. Brannon, J. M. Scott, C. R. Groves, R. F. Noss, and R. L. Pressey. 2005. Policy-driven versus evidence-based conservation: a review of political targets and biological needs. BioScience 55:989–95. Swan, G. V. Naidoo, R. Cuthbert, R. E. Green, D. J. Pain, D. Swarup, V. Prakash, M. Taggart, L. Bekker, D. Das, J. Diekmann, M. Diekmann, E. Killian, A. Meharg, R. C. Patra, M. Saini, and K. Wolter. 2006. Removing the threat of diclofenac to critically endangered Asian vultures. PloS Biology 4:395–402. Swengel, S. R., and A. B. Swengel. 1999. Correlations in abundance of grassland songbirds and prairie butterflies. Biological Conservation 90:1–11. Talley, T. S., and J. A. Crooks. 2007. Habitat conversion associated with bioeroding

344 References marine isopods. In Ecosystem Engineers: Plants to Protists, eds. K. Cuddington, J. E. Byers, W. G. Wilson, and A. Hastings, 185–202. Amsterdam: Elsevier. Tanner, J. E., T. P. Hughes, and J. H. Connell. 1994. Species coexistence, keystone species, and succession: a sensitivity analysis. Ecology 75:2204–19. Taper, M. L., K. Bohning-Gaese, and J. H. Brown. 1995. Individualistic responses of bird species to environmental change. Oecologia 101:478–86. Tardif, B., and J.-L. DesGranges. 1998. Correspondence between bird and plant hotspots of the St. Lawrence River and influence of scale on their location. Biological Conservation 84:53–63. Tear, T. H., P. Kareiva, P. L. Angermeier, P. Comer, B., Czech, R. Kautz, L. Landon, D. Mehlman, K. Murphy, M. Ruckelshaus, J. M. Scott, and G. Wilhere. 2005. How much is enough? The recurrent problem of setting measurable objectives in conservation. BioScience 55:835–49. Terborgh, J. 1986. Keystone plant resources in the tropical forest. In Conservation Biology: the Science of Scarcity and Diversity, ed. M. E. Soule, 330–44. Sunderland, MA: Sinaeuer Associates. Terborgh, J., F. A. Estes, P. Paquet, K. Ralls, D. Boyd-Heger, B. J. Miller, and R. F. Noss. 1999. The role of top carnivores in regulating terrestrial ecosystems. In Continental Conservation: Scientific Foundations of Regional Reserve Networks, eds. M. E. Soule and J. Terborgh, 39–64. Washington, DC: Island Press. Terborgh, J., K. Feeley, M. Silman, P. Nunez, and B. Balukjian. 2006. Vegetation dynamics of predator-free land-bridge islands. Journal of Applied Ecology 94:253–63. Terborgh, J., L. Lopez, P. Nunez, M. Rao, G. Shahabuddin, G. Orijuela, M. Riveros, R. Ascanio, G. H. Adler, T. D. Lambert, and L. Balbas. 2001. Ecological meltdown in predator-free forest fragments. Science 294:1923–26. Terborgh, J., G. Nunez-Iturri, N. C. A. Pitma, F. H. Cornejo Valverde, P. Alvarez, V. Swamy, E. G. Pringle, and C. E. T. Paine. 2008. Tree recruitment in an empty forest. Ecology 89:1757–68. Terborgh, J., and B. Winter. 1983. A method for siting parks and reserves with special reference to Colombia and Ecuador. Biological Conservation 27:45–58. Tews, J., U. Brose, V. Grimm, K. Tielborger, M. C. Wichmann, M. Schwager, and F. Jeltsch. 2004. Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures. Journal of Biogeography 31:79– 92. Thayer, C. W. 1979. Biological bulldozers and the evolution of marine benthic communities. Science 203:458–61. Thomas, G. H. 2008. Phylogenetic distributions of British birds of conservation concern. Proceedings of the Royal Society of London B 275:2077–83. Thomas, J. A. 2005. Monitoring change in the abundance and distribution of insects using butterflies and other indicator groups. Philosophical Transactions of the Royal Society B 360:339–57. Thomas, J. A., M. G. Telfer, D. B. Roy, C. D. Preston, J. J. D. Greenwood, J. Asher,

References 345 R. Fox, R. T. Clarke, and J. H. Lawton. 2004. Comparative losses of British butterflies, birds, and plants and the global extinction crisis. Science 303:1879– 81. Thomson, J. R., E. Fleishman, R. M. MacNally, and D. S. Dobkin. 2005. Influence of the temporal resolution of data on the success of indicator species models of species richness across multiple taxonomic groups. Biological Conservation 124:503–18. Thorne, J. H., D. Cameron, and J. F. Quinn. 2006. A conservation design for the central coast of California and the evaluation of mountain lion as an umbrella species. Natural Areas Journal 26:137–48. Tingley, M. W., D. A. Orwig, R. Field, and G. Motzkin. 2002. Avian response to removal of a forest dominant: consequences of hemlock woolly adelgid infestations. Journal of Biogeography 29:1505–16. Tisdell, C., H. S. Nantha, and C. Wilson. 2007. Endangerment and likeability of wildlife species: how important are they for payments proposed for conservation? Ecological Economics 60:627–33. Tognelli, M. F. 2005. Assessing the utility of indicator groups for the conservation of South American terrestrial mammals. Biological Conservation 121:409–17. Tognelli, M. F., P. I. Ramirez de Arellano, and P. A. Marquet. 2008. How well do the existing and proposed reserve networks represent vertebrate species in Chile? Diversity and Distributions 14:148–58. Tompkins, D. M., A. P. Dobson, P. Arneberg, M. E. Begon, I. M. Cattadori, J. V. Greenman, J. A. P. Heesterbeek, P. J. Hudson, D. Newborn, A. Pugliese, A. P. Rizzoli, R. Rosa, F. Rosso, and K. Wilson. 2001. Parasites and host population dynamics. In The Ecology of Wildlife Diseases, eds. P. A. Hudson, A. Rizzoli, B. T. Grenfell, H. Heesterbeck, and A. P. Dobson, 45–62. Oxford, UK: Oxford University Press. Torres, M. A., M. P. Barros, S. C. G. Campos, E. Pinto, S. Rajamani, R. T. Sayre, and P. Colepicolo. 2008. Ecotoxicology and Environmental Safety 71:1–15. Towns, D. R., G. R. Parrish, C. L. Tyrrell, G. T. Ussher, A. Cree, D. G. Newman, A. H. Whitaker, and I. Westbrooke. 2006. Responses of tuatara (Sphenodon punctatus) to removal of introduced Pacific rats from islands. Conservation Biology 21:1021–31. Trakhtenbrot, A., and R. Kadmon. 2005. Environmental cluster analysis as a tool for selecting complementary networks of conservation sites. Ecological Applications 15:335–45. Trenkel, V. M., and M.-J. Rochet. 2003. Performance of indicators derived from abundance estimates for detecting the impact of fishing on a fish community. Canadian Journal of Fisheries and Aquatic Sciences 60:67–85. Troumbis, A. Y., and P. G. Dimitrakopoulos. 1998. Geographic coincidence of diversity threatspots for three taxa and conservation planning in Greece. Biological Conservation 84:1–6. Tscharntke, T., A. Gathmann, and I. Steffan-Dewenter. 1998. Bioindication using

346 References trap-nesting bees and wasps and their natural enemies: community structure and interactions. Journal of Applied Ecology 35:708–19. Tscharntke, T., C. H. Sekercioglu, T. V. Dietsch, N. S. Sodhi, P. Hoehn, and J. M. Tylianakis. 2008. Landscape constraints on functional diversity of birds and insects in tropical agroecosystems. Ecology 89:944–51. Tuomisto, H., K. Ruokolainen, R. Kalliola, A. Linna, W. Danjoy, and Z. Rodriguez. 1995. Dissecting Amazonian biodiversity. Science 269:63–66. Turner, W. R., and D. S. Wilcove. 2006. Adaptive decision rules for acquisition of nature reserve. Conservation Biology 20:527–37. Turpie, J. K., L. E. Beckley, and S. M. Katua. 2000. Biogeography and the selection of priority areas for conservation of South African coastal fishes. Biological Conservation 92:59–72. Tushabe, H., J. Kalema, A. Byaruhanga, J. Asasira, P. Ssegawa, A. Balmford, T. Davenport, J. Fjeldsa, I. Friis, D. Pain, D. Pomeroy, P. Williams, and C. Williams. 2006. A nationwide assessment of the biodiversity value of Uganda’s Important Bird Area network. Conservation Biology 20:85–99. Uehara-Prado, M., J. D. Fernandes, A. D. Bello, G. Machado, A. J. Santos, F. Z. Vaz-de-Mello, and A. V. L. Freitas. 2009. Selecting terrestrial arthropods as indicators of small-scale disturbance: a first approach in Brazilian Atlantic forest. Biological Conservation 142:1220–28. Ueno, D., M. Watanabe, A. Subramanian, H. Tanaka, G. Fillman, P. K. S. Lam, G. J. Zheng, M. Muchtar, H. Razak, M. Prudente, K.-H. Chung, and S. Tanabe. 2005. Global pollution monitoring of polychlorinated dibenzo-p-dioxins (PCDDs), furans (PCDFs) and coplanar polychlorinated biphenyls (coplanar PCBs) using skipjack tuna as bioindicator. Environmental Pollution 136:303– 13. UNEP/DEWA 2001. The Mesopotamian Marshlands: Demise of an Ecosystem. Early warning and Assessment. Technical Report No. TR.01-3. Nairobi, Kenya: UNEP. USDA Forest Service. 1984. Wildlife, fish, and sensitive plant habitat management. Title 2600. Amendment No. 48. Washington, DC: USDA Forest Service. USDA Forest Service. 2005. Cleveland National Forest Land Management Plan, September. USDA Forest Service, Pacific Southwest. USDA, Forest Service, U.S. Fish and Wildlife Service, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, National Park Service, Bureau of Land Management, and Environmental Protection Agency. 1993. Forest ecosystem management: an ecological, economic, and social assessment: report of the Forest Ecosystem Management Assessment Team. Portland, OR. U.S. Department of Interior. 1992. Final Draft Recovery Plan for the Northern Spotted Owl, vol. 2. Portland, OR: U.S. Fish and Wildlife Service.. Usher, M. B. 1986. Wildlife conservation evaluation: attributes, criteria and values. In Wildlife Conserrvation Evaluation, ed. M. B. Usher, 3–44. London: Chapman & Hall.

References 347 van der Schalie, W. H., H. S. Gardner, J. A. Bantle, C. T. De Rosa, R. A. Finch, J. S. Reif, R. H. Reuter, L. C. Bakker, J. Burger, L. C. Folmar, and W. S. Stokes. 1999. Animals as sentinels of human health hazards of environmental chemicals. Environmental Health Perspectives 107:309–15. van Houtan, K. S., S. L. Pimm, T. O. Bierregaard Jr., T. E. Lovejoy, and P. C. Stouffer. 2006. Local extinctions in flocking birds in Amazonian forest fragments. Evolutionary Ecology Research 8:129–48. van Jaarsveld, A. S., S. Freitag, S. L. Chown, C. Muller, S. Koch, H. Kull, C. Bellamy, M. Kruger, S. Endrody-Younga, M. W. Mansell, and C. H. Scholtz. 1998. Biodiversity assessments and conservation strategies. Science 279:2106– 8. van Langevelde, F., A. Schotman, F. Claasen, and G. Sparenburg. 2000. Competing land use in the reserve site selection problem. Landscape Ecology 15:243–56. van der Oost, R., J. Beyer, and N. P. E. Vermeulen. 2003. Fish bioaccumulation and biomarkers in environmental risk assessment: a review. Environmental Toxicology and Pharmacology 13:57–149. van Strien, A. J., O. Knol, L. van Duuren, B. ten Cate, and R. Leewis. 2004. Natuurcompendium 2004. Natuur in ciifers, Milieu- en Natuurolanbureau en CBS 2004. http://www.natuurcompendium.nl. accessed 15 May 2009. Vanderlaan, A. S. M., and C. T. Taggart. 2009. Efficacy of a voluntary area to be avoided to reduce risk of lethal vessel strikes to endangered whales. Conservation Biology 23:1467–74. Vandermeulen, H. 1998. The development of marine indicators for coastal zone management. Ocean and Coastal Management 39:63–71. Vane-Wright, R. I., C. J. Humphries, and P. H. Williams. 1991. What to protect?— Systematics and the agony of choice. Biological Conservation 55:235–54. Vellend, M., P. L. Lilley, and B. M. Starzomski. 2008. Using subsets of species in biodiversity surveys. Journal of Applied Ecology 45:161–69. Verissimo, D., I. Fraser, J. Groombridge, R. Bristol, and D. C. MacMillan. 2009. Birds as tourism flagship species: a case study of tropical islands. Animal Conservation 12:549–88. Vessby, K., B. Soderstrom, A. Glimskar, and B. Svensson. 2002. Species-richness correlations of six different taxa in Swedish seminatural grasslands. Conservation Biology 16:430–39. Vickery, J. A., A. D. Evans, P. Grice, R. Brand-Hardy, and N. A. Aebischer. 2004. Ecology and conservation of lowland farmland birds II: the road to recovery. Ibis 146 (suppl 2):1–258. Villasenor, J. L., G. Ibarra-Manriquez, J. A. Meave, and E. Ortiz. 2005. Higher taxa as surrogates of plant biodiversity in a megadiverse country. Conservation Biology 19:232–38. Virolainen, K. M., P. Ahlroth, E. Hyvarinen, E. Korkeamaki, J. Mattila, J. Paivinen, T. Rintala, T. Suomi, and J. Suhonen. 2000. Hot spots, indicatopr taxa, complementarity and optimal networks of taiga. Proceedings of the Royal Society of London B 267:1143–47.

348 References Virolainen, K. M., K. Nattinen, J. Suhonen, and M. Kuitunen. 2001. Selecting herb-rich forest networks to protect different measures of biodiversity. Ecological Applications 11:411–20. Vitousek, P. M. 1990. Biological invasions and ecosystem processes: towards an integration of population biology and ecosystem studies. Oikos 57:7–13. Vogel, W. O. 1989. Response of deer to density and distribution of housing in Montana. Wildlife Society Bulletin 17:406–13. Wallis de Vries, M. F. 1995. Large herbivores and the design of large-scale nature reserves in western Europe. Conservation Biology 9:25–33. Walpole, M. J., and N. Leader-Williams. 2002. Tourism and flagship species in conservation. Biodiversity and Conservation 11:543–47. Walters, C. J. 2003. Folly and fantasy in the analysis of spatial catch-rate data. Canadian Journal of Fisheries and Aquatic Sciences 60:1433–36. Walters, D. M., D. S. Leigh, and A. B. Bearden. 2003. Urbanization, sedimentation, and the homogenization of fish assemblages in the Etowah River Basin. Hydrobiologia 494:5–10. Ward, P. I., N. Mosberger, C. Kistler, and O. Fischer. 1998. The relationship between popularity and body size in zoo animals. Conservation Biology 12:1408– 11. Ward, T. J., and C. A. Jacoby. 1992. A strategy for assessment and management of marine ecosystems: baseline and monitoring studies in Jervis Bay, a temperate Australian embayment. Marine Pollution Bulletin 25:163–71. Ward, T. J., M. A. Vanderklift, A. O. Nicholls, and R. A. Kenchington. 1999. Selecting marine reserves using habitats and species assemblages as surrogates for biological diversity. Ecological Applications 9:691–98. Warman, L. D., D. M. Forsyth, A. R. E. Sinclair, K. Freemark, H. D. Moore, T. W. Barrett, R. L. Pressey, and D. White. 2004. Species distributions, surrogacy, and important conservation regions in Canada. Ecology Letters 7:374–79. Warwick, R. M., and K. R. Clarke. 1994. Relearning the ABC: taxonomic changes and abundance/biomass relationships in distributed benthic communities. Marine Biology 118:739–44. Warwick, R. M., T. H. Pearson, and Ruswahyuni. 1987. Detection of pollution effects on marine macrobenthos: further evaluation of the species abundance/ biomass method. Marine Biology 95:193–200. Watson, J., D. Freudenberger, and D. Paull. 2001. An assessment of the focalspecies approach for conserving birds in variegated landscapes in southeastern Australia. Conservation Biology 15:1364–73. Weaver, J. C. 1995. Indicator species and scale of observation. Conservation Biology 9:939–42. Weibull, A.-C., J. Bengtsson, and E. Nohlgren. 2000. Diversity of butterflies in the agricultural landscape: the role of farming system and landscape heterogeneity. Ecography 23:743–50. Weibull, A.-C., O. Ostman, and A. Granqvist. 2003. Species richness in agroecosys-

References 349 tems: the effect of landscape, habitat, and farm management. Biodiversity and Conservation 12:1335–55. Weilgart, L. S. 2007. The impacts of anthropogenic ocean noise on cetaceans and implications for management. Canadian Journal of Zoology 85:1091–116. Welcomme, R. L., K. O. Winemiller, and I. G. Cowx. 2006. Fish environmental guilds as a tool for assessment of ecological condition of rivers. River Research and Applications 22:377–96. Wenger, S. J. 2008. Use of surrogates to predict the stressor response of imperiled species. Conservation Biology 22:1564–71. Weseloh, D. V., K. D. Holmes, P. J. Ewins, D. Best, T. Kubiak, and M. C. Sheildcastle. 2002. Herring gulls and great black-backed gulls as indicators of contaminants in bald eagles in Lake Ontario, Canada. Environmental Toxicology and Chemistry 21:1015–25. Wessels, K. J., S. Freitag, and A. S. van Jaarsveld. 1999. The use of land facets as biodiversity surrogates during reserve selection at a local scale. Biological Conservation 89:21–38. Wessels, D. C. J., and L.-A. Wessels. 1991. Erosion of biogenically weathered Clarens sandstone by lichenophagius barworm larvae (Lepidotera; Psychidae). Lichenology 23:283–91. Western, D. 1989. Africa’s elephants and rhinos: flagships in crisis. Trends in Ecology and Evolution 2:343–46. Whelan, R.J. 2002. Managing fire regimes for conservation and property protection: an Australian response. Conservation Biology 16:1659–1661. Wheeler, N., and R. Sederoff. 2009. The role of genomics in the potential restoration of the American chestnut. Tree Genetics and Genomics 5:181–87. White, P. C. L., A. C. Bennett, and E. J. V. Hayes. 2001. The use of willingness-topay approaches in mammal conservation. Mammal Review 31:151–67. Whittaker, R.H. 1960. Vegetation of the Siskiyou Mountains, Oregon and California. Ecological Monographs 30:279–338. Wiens, J. A., T. O. Crist, R. H. Day, S. M. Murphy, and G. D. Hayward. 1996. Effects of the Exxon Valdez oil spill on marine bird communities in Prince William Sound, Alaska. Ecological Applications 6:828–41. Wiens, J. A., G. D. Howard, R. S. Holthausen, and M. J. Wisdom. 2008. Using surrogate species and groups for conservation planning and management. BioScience 58:241–52. Wilcove, D. 1993. Getting ahead of the extinction curve. Ecological Applications 3:218–20. Wilcove, D. S. 1994. Turning conservation goals into tangible results: the case of the spotted owl and old-growth forests. In Large-scale Ecology and Conservation Biology, eds. P. J. Edwards, R. M. May, and N. R. Web, 313–29. Oxford, UK: Blackwell Scientific. Wilcox, B. A. 1984. In situ conservation of genetic resources: determinants of minimum area requirements. In National Parks, Conservation, and Development:

350 References Proceedings of the World Congress on National Parks, eds. J. A. McNeely, and K. R. Miller, 18–30. Washington, DC: Smithsonian Institution Press. Wilkinson, C. 2002. Status of Coral Reefs of the World. Townsville, Queensland: Australian Institute of Marine Science. Williams, H. H., and K. Mackenzie. 2003. Marine parasites as pollution indicators: an update. Parasitology 126:S27–41. Williams, P., D. Gibbons, C. Margules, A. Rebelo, C. Humphries, and R. Pressey. 1996. A comparison of richness hotspots, rarity hotspots, and complementary areas for conserving diversity of British birds. Conservation Biology 10:155– 74. Williams, P. H. 1999. WORLDMAP 4 Windows: Software and Help Document 4.19. London. http://www.nhm.ac.uk/science/projects/worldmap/. 15 May 2009. Williams, P. H., N. D. Burgess, and C. Rahbek. 2000a. Flagship species, ecological complementarity and conserving the diversity of mammals and birds in sub-Saharan Africa. Animal Conservation 3:249–60. Williams, P. H., N. Burgess, and C. Rahbek. 2000b. Assessing large “flagship” for representing the diversity of sub-Saharan mammals. In Has the Panda Had Its Day? Future Priorities for the Conservation of Mammal Diversity, eds. A. Entwistle and N. Dunstone, 85–99. Cambridge, UK: Cambridge University Press. Williams, P., D. Faith, L. Manne, W. Sechrest, and C. Preston. 2006. Complementarity analysis: mapping the performance of surrogates for biodiversity. Biological Conservation 128:253–64. Williams, P. H., and K. J. Gaston. 1994. Measuring more of biodiversity: can higher-taxon richness predict wholesale species richness? Biological Conservation 67:211–17. Williams, P. H., C. J. Humprhies, and K. J. Gaston. 1994. Centres of seed-plant diversity: the family way. Proceedings of the Royal Society of London B 256:67–70. Williams, T. M., J. A. Estes, D. F. Doak, and A. M. Springer. 2004. Killer appetities: assessing the role of predators in ecological communities. Ecology 85:3373–84. Wilmers, C. C., R. L. Crabtree, D. W. Smith, K. M. Murphy, and W. M. Getz. 2003. Trophic facilitation by introduced top predators: grey wolf subsidies to scavengers in Yellowstone National Park. Journal of Animal Ecology 72:909–16. Wilmers, C. C., and W. M. Getz. 2005. Gray wolves as climate change buffers in Yellowstone. PLoS Biology 3:571–76. Wilson, E. O. 1987. The little things that run the world (the importance and conservation of invertebrates). Conservation Biology 1:344–46. Wilson, E. O. 1992. The Diversity of Life. Cambridge, MA: Harvard University Press. Wilson, K., R. L. Pressey, A. Newton, M. Burgman, H. Possingham, and C. Weston. 2005. Measuring and incorporating vulnerability into conservation planning. Environmental Management 35:527–43. Wilson, K. A., M. F. McBride, M. Bode, and H. P. Possingham. 2006. Prioritizing global conservation efforts. Nature 440:337–40.

References 351 Wilson, K. A., E. C. Underwood, S. A. Morrison, K. R. Klausmeyer, W. W. Murdoch, B. Reyers, G. Wardell-Johnson, P. A. Marquet, P. W. Rundel, M. F. McBride, R. L. Pressey, M. Bode, J. M. Hoekstra, S. Andelman, M. Looker, C. Rondinini, P. Karieva, M. R. Shaw, and H. P. Possingham. 2007. Conserving biodiversity efficiently: what to do, where, and when. PLoS Biology 5:1850–61. Wilson, K. A., M. I. Westphal, H. P. Possingham, and J. Elith. 2004. Sensitivity of conservation planning to different approaches to using predicted species distribution data. Biological Conservation 122:99–112. Wilson, S. K., N. A. J. Graham, M. S. Pratchett, G. P. Jones, and N. V. C. Polunin. 2006. Multiple disturbances and the global degradation of coral reefs: are reef fishes at risk or resilient? Global Change Biology 12:2220–34. Wilson, W. G. 2007. A new spirit and concept for ecosystem engineering? In Ecosystem Engineers: Plants to Protists, eds. K. Cuddington, J. E. Byers, W. G. Wilson, and A. Hastings, 47–68. Amsterdam: Elsevier. Witman, J. D., and P. K. Dayton. 2001. Rocky subtidal communities. In Marine Community Ecology, eds. M. D. Bertness, S. D. Gaines, and M. E. Hay, 339–66. Sunderland, MA: Sinauer Associates. Wolda, H. 1987. Altitude, habitat and tropical insect diversity. Biological Journal of the Linnean Society 30:313–23. Wolters, V., J. Bengtsson, and A. S. Zaitsev. 2006. Relationship among the species richness of different taxa. Ecology 87:1886–95. Woodroffe, R., and J. R. Ginsberg. 1998. Edge effects and the extinction of populations inside protected areas. Science 280:2126–28. Woodroffe, R., S. Thirgood, and A. Rabinowitz. 2005. People and Wildlife: Conflict or Coexistence? Cambridge, UK: Cambridge University Press. World Wildlife Fund and World Conservation Union. 1994. Europe, Africa, South West Asia and the Middle East. Vol. 1 of Centres of Plant Diversity: A Guide and Strategy for Their Conservation. Cambridge, UK: World Conservation Union Publications. World Wildlife Fund and World Conservation Union. 1995. Asia, Australasia and the Pacific. Vol. 2 of Centres of Plant Diversity: A Guide and Strategy for Their Conservation. Cambridge, UK: World Conservation Union Publications. World Wildlife Fund and World Conservation Union. 1997. The Americas. Vol. 3 of Centres of plant diversity: A Guide and Strategy for Their Conservation. Cambridge, UK: World Conservation Union Publications. Worm, B., H. K. Loetze, and R. A. Myers. 2003. Predator diversity hotspots in the blue ocean. Proceedings of the National Academy of Sciences 100:9884–88. Wright, D. H., B. D. Patterson, G. M. Mikkelson, A. Cutler, and W. Atmar. 1998. A comparative analysis of nested subset patterns of species composition. Oecologia 113:1–20. Wright, J. P., A. S. Flecker, and C. G. Jones. 2003. Local vs landscape controls on plant species richness in beaver meadows. Ecology 84:3162–73. Wright, J. P., and C. G. Jones. 2006. The concept of organisms as ecosystem engineers ten years on: progress, limitations, and challenges. BioScience 56:203–9.

352 References Wright, J. P., C. G. Jones, and A. S. Flecker. 2002. An ecosystem engineer, the beaver, increases species richness at the landscape scale. Oecologia 132:96–101. Wright, S. J., and H. C. Duber. 2001. Poachers and forest fragmentation alter seed dispersal, seed survival, and seedling recruitment in the palm Attalea butyraceae, with implications for tropical tree diversity. Biotropica 33:583–95. Wright, S. J., M. E. Gompper, and B. DeLeon. 1994. Are large predators keystone species in neotropical forests? The evidence from Barro Colorado Island. Oikos 71:279–94. Wright, S. J., A. Hernandez, and R. Condit. 2007. The bushmeat harvest alters seedling banks by favoring lianas, large seeds, and seeds dispersed by bats, birds, and wind. Biotropica 39:363–71. Wright, S. J., and H. C. Muller-Landau. 2006. The future of tropical forest species. Biotropica 38:207–301. Wright, S. J., H. Zeballos, I. Dominguez, M. M. Gallardo, M. C. Moreno, and R. Ibanez. 2000. Poachers alter mammal abundance, seed dispersal, and seed predation in a neotropical forest. Conservation Biology 14:227–39. Xu, H., J. Wu, Y. Liu, H. Ding, M. Zhang, Y. Wu, Q. Xi, and L. Wang. 2008. Biodiversity congruence and conservation strategies: a national test. BioScience 58:632–39. Yamaura, Y., T. Amano, T. Koizumi, Y. Mitsuda, H. Taki, and K. Okabe. 2009. Does land-use change affect biodiversity dynamics at a macroecological scale? A case study of birds over the past 20 years in Japan. Animal Conservation 12:110–19. Zacharias, M. A., and J. C. Roff. 2001. Use of focal species in marine conservation and management: a review and critique. Aquatic Conservation: Marine and Freshwater Ecosystems 11:59–76.

In one of the first studies of umbrella species, the number of large sympatric mammals and birds was surveyed in an area used by a small population of black rhinoceros living in Namibia. (Drawing by Sheila Girling.)

scientific names of species mentioned in the text

Plants Port Orford cedar Redwood Bald cypress Balsam fir White fir Fraser fir Whitebark pine Coulter pine Scotch pine Bigcone Douglas fir Douglas fir Eastern hemlock Oil palm Cheatgrass Cordgrass Black needle rush Water hyacinth Corn Ceiba or kapok tree Cottonwood Aspen American chestnut Blue oak Engelmann oak California black oak Valley oak Yellow poplar Blackwood

Chamaecyparis lawsoniana Sequoia sempervirens Taxodium distichum Abies balsamea Abies concolor Abies fraseri Pinus albicaulis Pinus coulteri Pinus sylvestris Pseudotsuga macrocarpa Pseudotsuga menziesii Tsuga canadensis Elaeis guineensis Bromus tectorum Spartina anglica Juncus gerardi Eichhornia crassipes Zea mays Ceiba pentandra Populus angustifolia, P. trichocarpa Populus tremula, P. tremuloides Castanea dentate Quercus douglasii Quercus engelmannii Quercus kelloggii Quercus lobata Liriodendron tulipifera Acacia melanoxylon 355

356 Scientific Names of Species Mentioned in the Text

Acacia Coca Jarrah

Acacia pennatula Erythroxylum coca Eucalyptus marginata

Invertebrates Deep-sea corals

Paragorgia arborea, Primnoa resedaeformis, Lophelia pertusa Littorina littorea Mytilus californianus Mytilus edulis Crassostrea virginica Mya arenaria Placopecten magellanicus Paraponera clavata Adelges tsugae Danaus plexippus Mitella polymerus Balanus cariosus Meganyctiphanes norvegica Jasus lalandii Avicularia avicularia Calacarus flagelliseta Pistaster ochraceus

European periwinkle California blue mussel Mussel Oyster Soft shell clam Sea scallop Bullet ant Woolly adelgid Monarch butterfly Goose-necked barnacle Acorn barnacle Krill Rock lobster Tarantula Eriophyis mite Starfish

Fish Porbeagle shark Silky shark Dusky shark Whitetip shark Spiny dogfish shark Barndoor shark Freshwater stingray Arapaima Atlantic herring American shad

Lamna nasus Carcharhinus falciformis Carcharhinus obscurus Carcharhinus longimanus Squalus acanthias Raja laevis Potamotrygon humerosa Arapaima gigas Clupea harengus Alosa sapidissima

Scientific Names of Species Mentioned in the Text 357

Goldfish European carp Striped shiner Squawfish Amber darter Bronze darter Arctic grayling/Cutthroat trout Brown trout Atlantic salmon Pacific salmon/steelhead Cusk Atlantic cod Haddock Pollock Eastern gambusia Swordtail Platy Northern wolffish Atlantic wolffish Spotted wolffish Northern sand lance Skipjack tuna Atlantic halibut Winter flounder

Carassius auratus Cyprinus carpio Luxilus chrysocephalus Ptychocheilus lucius Percina antesella Percina palmaris Thymallus arcticus Salmo trutta Salmo salar Onchorhynchus mykiss Brosme brosme Gadus morhua Melanogrammus aeglefinus Pollachius virens Gambusia holbrooki Xiphophorus helleri Xiphophorus maculates Anarhichas denticulatus Anarhichas lupus Anarhichas minor Ammodytes dubius Kastuwonus pelamis Hippoglossus hippoglossus Pseudopleuronectes americanus

Amphibians Western toad Arroyo toad African clawed toad Boreal chorus frog Leopard frog Wood frog

Bufo boreas Bufo californicus Xenopus laveis Pseudacris maculata Rana pipiens Rana sylvatica

Reptiles Blanding’s turtle Eastern box turtle

Emydoidea blandingi Terrapene carolina

358 Scientific Names of Species Mentioned in the Text

Giant river turtle Loggerhead turtle Leatherback turtle Tuatara Common iguana Anaconda American alligator Black caiman Salt-water or estuarine crocodile

Podocnemis expansa Caretta caretta Dermochelys coriacea Sphenodon punctatus Iguana iguana Eunectes murinus Alligator mississippiensis Melanosuchus niger Crocodylus porosus

Mammals Duck-billed platypus Virginia opossum Tasmanian devil Large hairy armadillo Nine-banded armadillo Giant anteater Pemba flying fox Gland-tailed free-tailed bat Golden-headed lion tamarin Golden-rumped or black lion tamarin Golden lion tamarin Howler monkey, red howler monkey Central American spider monkey Black spider monkey Capuchin monkey Angolan black and white colobus Gorilla Bonobo Chimpanzee Coyote Wolf Domestic dog Gray fox Red fox Domestic cat Eurasian or European lynx Bobcat

Ornithorhynchus anatinus Didelphis virginianus Sarcophilus harrisii Chaetophractus villosus Dasypus novemcinctus Myrmecophaga tridactyla Pteropus voeltzkowi Chaerephon bemmeleni Leontopithecus chrysomelas Leontopithecus chrysopygus Leontopithecus rosalia Alouatta seniculus Ateles geoffroyi Ateles paniscus Cebus olivaceus Colobus angolensis Gorilla gorilla Pan paniscus Pan troglodytes Canis latrans Canis lupus Canis familiaris Urocyon cineroargenteus Vulpes vulpes Felis catus Lynx lynx Lynx rufus

Scientific Names of Species Mentioned in the Text 359

Mountain lion, Cougar, Florida panther Lion Jaguar Leopard Tiger Sea otter European otter Giant river otter Striped skunk Spotted skunk Wolverine Long-tailed weasel American badger Walrus Northern fur seal Steller’s sea lion Bearded seal Harp seal Ringed seal Hooded seal Harbor seal Monk seal Raccoon Giant panda Spectacled bear European brown bear, brown bear, grizzly bear Polar bear Bowhead whale North Atlantic right whale Blue whale Fin whale Humpback whale Gray whale Killer whale Beluga Narwhal Harbor porpoise Northern bottlenose whale West Indian manatee

Felis concolor Panthera leo Panthera onca Panthera pardus Panthera tigris Edhydra lutris Lutra lutra Pteronura brasiliensis Mephitis mephitis Splilogale gracilis Gulo gulo Mustela frenata Taxidea taxus Odobenus rosmarus Callorhinus ursinus Eumetopias jubatus Erignathus barbatus Pagophilus groenlandicus Phoca hispida Cystophora cristata Phoca vitulina Monachus sp. Procyon lotor Ailuropoda melanoleuca Tremarctos ornatus Ursus arctos Ursus maritimus Baleana mysticetus Eubalaena glacialis Balaenoptera musculus Balaenoptera physalus Megaptera novaeangliae Eschrichtius robustus Orcinus orca Delphinapterus leucas Monodon monoceros Phocoena phocoena Hyperoodon ampullatus Trichechus manatus

360 Scientific Names of Species Mentioned in the Text

Asian elephant African elephant Mountain zebra South American Tapir Baird’s tapir Mountain tapir White rhinoceros Black rhinoceros Javan rhinoceros White-lipped peccary Vicuña Giraffe Elk Moose Mule deer White-tailed deer Reindeer, caribou Pronghorn Greater kudu African buffalo Bighorn sheep Domestic sheep Gemsbok Arabian oryx Gunnison’s prairie dog Black-tailed prairie dog Gray squirrel Fox squirrel Eastern chipmunk Red squirrel Northern flying squirrel Southern flying squirrel Siberian flying squirrel American beaver Botta’s pocket gopher Banner-tailed kangaroo rat Red-backed vole Somali pygmy gerbil Tinfield’s rock rat Pacific or Polynesian rat

Elephas maximus Loxodonta africana Equus zebra Tapirus terrestris Tapirus bairdii Tapirus pinchaque Ceratotherium simum Diceros bicornis Rhinoceros sondaicus Tayassu pecari Vicugna vicugna Giraffa camelopardalis Cervus elaphus Alces alces Odocoileus hemionus Odocoileus virginianus Rangifer tarandus Antilocapra americana Tragelaphus strepsiceros Syncerus caffer Ovis canadensis Ovis domesticus Oryx gazella Oryx leucoryx Cynomys gunnisoni Cynomys ludovicianus Sciurus carolinensis Sciurus niger Tamias striatus Tamiasciurus hudsonicus Glaucomys sabrinus Glaucomys volans Pteromys volans Castor canadensis Thomomys bottae Dipodomys spectabilis Clethrionomys gapperi Microdillus peeli Aethomys stannarius Rattus exulans

Scientific Names of Species Mentioned in the Text 361

Bushy-tailed woodrat Dusky-footed woodrat Deer mouse Indian crested or desert porcupine Plains viscacha Agouti European rabbit

Neotoma cinerea Neotoma fuscipes Peromyscus maniculatus Hystrix indica Lagostomus maximus Dasyprocta leporine Oryctolagus cuniculus

Birds Ostrich Black-footed albatross Laysan albatross Cook’s petrel Balearic shearwater Red-tailed tropicbird Red-footed booby Double-crested cormorant Brown pelican Snow goose Greater flamingo Lesser flamingo Andean condor Red kite Black kite Bald eagle Marabou stork Egyptian vulture White-rumped vulture Long-billed vulture Slender-billed vulture Red-headed vulture Northern harrier Northern goshawk Eurasian sparrowhawk Broad-winged hawk Common buzzard Harpy eagle Lesser kestrel

Struthio camelus Diomedea nigripes Diomedea immutabilis Pterodroma cookii Puffinus mauretanicus Phaethon rubricauda Sula sula Phalacrocorax auritus Pelecanus occidentalis Anser caerulescens Phoenicopterus ruber Phoenicopterus minor Vultur gryphus Milvus milvus Milvus migrans Haliaeetus leucophalus Leptoptilus crumeniferus Neophron percnopterus Gyps bengalensis Gyps indicus Gyps tenuirostris Sarcogyps calvus Circus cyaneus Accipter gentilis Accipter nisus Buteo platypterus Buteo buteo Harpia harpyja Falco naumanni

362 Scientific Names of Species Mentioned in the Text

Eurasian kestrel Peregrine falcon Baillon’s crake Whooping crane Grey crowned crane Common snipe Red-necked phalarope Herring gull Caspian tern Common-white tern Roseate tern Black noddy Razorbill Atlantic puffin Tufted puffin Least auklet Dodo Kaka Crimson rosella Barn owl European scops owl Eurasian eagle owl Tawny owl Spotted owl Barred owl Eurasian pigmy owl Burrowing owl Tengmalm’s or boreal owl Long-eared owl Red-cockaded woodpecker Three-toed woodpecker Pileated woodpecker Ivory-billed woodpecker Yellow-bellied flycatcher Red wattlebird Hooded robin Eastern yellow robin Florida scrub jay Black-billed magpie American crow

Falco tinnunculus Falco peregrinus Porzana pusilla Grus americana Balearica regulorum Gallinago gallinago Phalaropus lobatus Larus argentatus Sterna caspia Gygis alba Sterna dougallii Anous minutus Alca torda Fratercula arctica Fratercula cirrhata Aethia pusilla Raphus cucullatus Nestor meridionalis Platycercus elegans Tyto alba Otus scops Bubo bubo Strix aluco Strix occidentalis Strix varia Glaucidium passerium Athene cunicularia Aegolius funereus Asio otus Picoides borealis Picoides tridactylus Dryocopus pileatus Camperphilus principalis Empidonax flaviventris Anthochaera carunculata Melanodryas cucullata Eopsaltria australis Aphelocoma coerulescens Pica hudsonia Corvus brachyrhyncos

Scientific Names of Species Mentioned in the Text 363

Raven African black-headed oriole Least Bell’s vireo European blackbird American robin European starling Red-breasted flycatcher Marsh tit Willow tit Coal tit Crested tit Great tit Blue tit House sparrow Lemon-breasted seedeater Black and white warbler Ovenbird Corn bunting Brewer’s sparrow Spotted towhee

Corvus corax Oriolus larvatus Vireo bellii Turdus merula Turdus migratorius Sturnus vulgaris Fidecula parva Parus palustris Parus montanus Parus ater Parus cristatus Parus major Parus caerulus Passer domesticus Serinus citrinipectus Mniotilta varia Seiurus aurocapillus Miliaria calandra Spizella breweri Pipilo maculates

The American beaver modifies its environment by cutting trees and damming watercourses. This ecosystem engineer formerly lived at much higher population densities than today and must have had a great impact on the terrestrial landscape of North America. (Drawing by Sheila Girling.)

index

Accumulators, 167 Africa, see also South Africa. complimentarity and, 46–47 keystone species and, 130 rarity and, 52 scale and, 11 species richness and, 33, 52 threatened species and, 52 umbrella species and, 106–107 African wild dog, 124 Albertine rift, 56 Algae, biodiversity and, 90 environmental indicator species as, 170, 171 Alpha diversity. 3, 13. See also Species richness. Amphibians, biodiversity and, 20, 21, 32, 33, 34, 35, 39, 40, 31–42, 43, 47, 49, 50, 52, 63, 65, 72, 74, 76, 81 cross-taxon-response indicator species as, 225–226 decline in, 235–236 ecological-disturbance indicator species as, 199, 201, 206–208 environmental indicator species as, 158, 173, 177–178 flagship species and, 254 umbrella species and, 113 Andes, 247, 272 Angiosperms, 38 Antlions, 78, 81, 86 Ants, see also Hymenoptera. biodiversity and, 68, 71, 74 ecological-disturbance indicator species as, 200–201

Angola, 120 Aquatic plants, biodiversity and, 62–64, 76 environmental indicator species as, 178 Argentina, 74 Arroyo toad, 232–233 Atlantic forest, 39, 56 Atrizine, 173 Australia, 238 endemic species and, 72, 82 environmental surrogates and, 92– 93 focal species and, 17–19, 102 iconic species and, 258 keystone species and, 130 rarity and, 52, 76–77 taxon subsets and, 68 Austria, 63, 76, 81 Background species, 109 definition 16, 27 Baselines, 166–167, 172 Bats, biodiversity and, 57, 81 cross-taxon-response indicator species as, 225–226 ecological-disturbance indicator species as, 196, 200 flagship species and, 254 Bear, brown, 246 grizzly, 21, 95, 107–108, 253 polar, 183, 258, 259 Beaver, American, 94 ecosystem engineer as, 144, 146– 148 365

366 Index Beccariophoenix madagascarensis, 258 Bees, 206–208, 226. See also Hymenoptera. Beetles 30, 68, 101, 286. See also Dung beetles. biodiversity and, 33, 34, 35, 69, 71, 74, 78, 81, 84, 86 cross-taxon-response indicator species as, 227 ecological-disturbance indicator species as, 199, 201–203 umbrella species and, 104, 106 Behavior, 238–239 Belgium, 63, 102 Belize, 250, 253–254 Berger-Parker index, 164 Beta diversity. 3, 13, 83. See also Species richness. cross-taxon congruency in, 34 Bioaccumulators 162, 184 Biodiversity, 3–8. See also Species indicators of biodiversity. definition, 3 distribution of, 271–274 Bioindicator species, 161. See also Environmental indicators. Biological diversity. See Biodiversity. Biological Dynamics of Forest Fragmentation, 197–203 Biological indicators. See Environmental indicators. Biological integrity, 160–161 Biomarkers, 173 Birdlife International, 31, 52 Birds, biodiversity and, 21, 32, 33, 54, 35, 38, 39, 40, 41–42, 43–44, 47, 49, 50, 52, 53, 56, 62–64, 65, 66, 72, 73, 74, 76, 80, 81, 86 cross-taxon-response indicator species as, 225, 229–231, 240 decline in, 235–236 ecological-disturbance indicator species as, 199–201, 204–205, 206– 207, 209–210, 214 endangered species act and, 260 environmental indicator species as, 170, 173, 177–178, 182–183

flagship species and, 253, 254 intraguild-response indicator species as, 228–229 latent risk of extinction and, 238 umbrella species and, 101, 105, 110– 113 Bobcat, 94 Bobwhite, northern, 94 Bolivia, 74, 122 Borneo, 63 Bray-Curtis similarity index, 171 Brazil, 197–203, 206–208, 249, 280– 282 Britain, 69, 83–84, 85 congruency in, 62–64, 69–70, 76–77 Bryophytes, biodiversity and, 63, 74 Burma, 39 Butterfly Alcon blue, 102–104 Bay checkerspot, 105–106 biodiversity and, 33, 34, 35, 51, 62– 64, 66, 70, 71, 73, 76, 78, 80, 81, 86 cross-taxon-response indicator species as, 223–224 ecological-disturbance indicator species as, 199, 200, 206–207, 214 flagship species and, 253, 255 Monarch, 60 umbrella species and, 105–106 Buzzard, common, 111–113 Buzzwords. See Conservation buzzwords Cameroon 52, 56, 219–222 Canada. See also North America. cross-taxon-response indicator species and, 240, 241 engineering species and, 148 flagship species and, 246 focal species and, 19–21 keystone species and, 130 multi-surrogacy and, 268–270 rarity and, 78 species richness and, 33, 63, 78 threatened species and, 73 Cape floristic region, 56

Index 367 Capercaillie, 101, 110–111 Caribbean, 39, 56 Caribou, 120 Carnivores, flagship species and, 246, 250, 254– 255, 256–257 latent risk of extinction and, 237 mesopredators, 132–134 multi-surrogacy and, 268–270 reintroductions, 136–138, 224–225 response to fragmentation, 224–225 umbrella species and, 107–108 Charismatic species. See Flagship species. Cheetah, 107, 247 Chile, 78, 79, 88 China, 56, 250 endemic species and, 72, 74–75, 78 species richness and, 63, 65, 74–75 threatened species and, 75–78 Classic umbrella species, 99, 264–265 criteria for, 116–117 cross-taxon-response indicator species and, 218 mammals and, 106–107 Climate change, coral reefs and, 181 fish assemblages and, 181 marine ecosystems and, 181–184, 236–237 Coca, 250 Coffee, 204–205, 225–226 Colobus, black and white, 246 Coldspots, 63 Columbia, 39 Community similarity, 65–66 Comoros, 56 Complementarity, 45–49, 53, 79–86 definition, 45, 79 flexibility and, 79 irreplaceability and, 79 scale and, 11 Compliance indicators, 162–163 Concordance. See Congruency. Congo, 122 Congruency, surrogacy curve and, 47–49 Congruency at a large scale

endemic species, 39–42 rarity, 42 species richness, 32–35 threatened species, 43–45 Congruency at a small scale, endemic species, 72, 74–76, 78–79 rarity, 73, 76–78 species richness, 74–79 threatened species, 73, 78–79 Conservation buzzwords, xv, 2–3, 184–185 Conservation International, 31, 272 Conservation proxy. See Conservation shortcuts. Conservation shortcuts, xv, 1–3, 15–16 Corals, 268–269 biodiversity and, 90 environmental indicator species as, 181–183 Core species, 153 Corn, 251 Cost evaluation, anthropogenic change, measurement of, 280–282 biodiversity inventories, 273–274 reserve selection, 274–277 species monitoring of, 279 Costa Rica, 204–205 Cougar, 253, 270 Countryside biogeography, 203–205, 208 Coyote, 132–134 Crane, crowned, 247 Cross-taxon-response indicator species, 266, 268 confusion over terminology, 24–25, 217–218 definition, 24–25, 190 problems with, 239–242 Crustaceans, 43 Darter, 239 Defenders of Wildlife, 247 Delphi approach, 19–24, 94 Democratic Republic of Congo, 52 Denmark, 81 Diagnostic indicators, 162–163 Diatoms, 69

368 Index Dodo, 259 Dominant species. See Foundation species. Dragonflies, biodiversity and, 62–64, 76 umbrella species and, 101 Duck-billed platypus, 258 Dung beetles, 196–197, 206–208, 214, 225–226 Early warning indicators. See Sentinel species. Early warnings. See Sentinel species. Eastern Arc mountains, 39, 56, 272 Ecological baselines. See Baselines Ecological engineering species. See Ecosystem engineers. Ecological indicator species. See Crosstaxon-response indicator species. Ecological integrity, 160 Ecological meltdown, 134–136 Ecological-disturbance indicator species, 266, 268 confusion over terminology, 26, 161, 190 criteria for, 190–195 definition, 24–25, 189 management umbrella species and, 100 research effort and, 16 single species, 194–196 species-groups, 196–197 Ecosystem engineers, 130, 143–153, 278 advantages of, 153 confusion over terminology, 22 definition, 143 keystone species and, 144 management and, 154–155 marine ecosystems in, 150 mechanisms of operation, 144–146 problems with, 151–153 Ecosystem health, definition, 159, 184 measurement, 161–167 Ecuador, 39, 74, 120 Edge effects, 200–201

Elephant, African, 150, 255–256 Asian, 249 Elk, 95 Endangered species, act, 43, 105, 260 definition, 5–7 flagship species and, 251 Endemic bird areas, 39 Endemic species birds, 29–40, 58 congruency, 39–42, 72, 83 definition, 4, 39, 72 documentation of, 8 species richness and, 50–52, 74–76, 83 threatened species and, 50–51, 78 Endemism. See Endemic species. Engineering species. See Ecosystem engineers. Englemann oak, 232–233 Environmental baselines. See Baselines. Environmental indicator species, 266, 268 definition, 24–25 research effort and, 16 Environmental health. See Ecosystem health. Environmental indicators, 162–167, 184 Environmental surrogates, 15, 36–37, 90–95, 274 complementarity and, 83 Environmental variables, 91–92, 274 anthropogenic change and, 278– 279 biodiversity inventories and, 271– 273 reserve design and, 277–278 Ethiopia, 56 Eucalyptus, 206–208 Europe, 228, 229–231 Evaluation species, 232 Exploiter species, 164 Extinct species, 5–7 Exurban development, 209–210

Index 369 Families. See Higher taxa. Fauna and Flora Preservation Society, 247 Featured species, 231 Finland, 69, 93 Fish, biodiversity and, 43–44, 49, 63, 69, 72, 74, 90–91 environmental indicator species as, 170, 172, 177–180, 181 Fisher, 108 Flagship species, 27, 267–268. See also Flagship umbrella species. alternatives to, 281–282 characteristics of , 245–246, 251, 257–258 confusion over terminology, 22, 26 definition, 245 ecosystem engineers and, 153, 156 keystone species and, 143, 156 landscape species and, 124 research effort and, 16 umbrella species and, 247 Flagship umbrella species, examples of, 247–248 tests of, 251–257 utility of, 260 Flying fox, 250 Focal species, 17–21, 264–265 confusion over definition, 21–22 definition of Lambeck, 17–18, 102 landscape species and, 121 umbrella species and, 116, 277– 278 Foundation species, 153–155, 264, 266, 278 definition, 153 management and, 154–155 research effort and, 16 France, 254 Fragmentation, 132–134, 197–203, 224–225, 228–229, 239 Freshwater pollution, 171–174 Frogs, biodiversity and, 72 flagship species and, 254 latent risk of extinction and, 238

Fungi, biodiversity and, 69–70, 76, 84–85 umbrella species and, 104 Galliformes, 110–111 Gamma diversity, 2, 83. See also Species richness. Gastropods, 74, 108 Genera. See Higher taxa Ghats, Western, 39, 56 Gnatcatcher, California, 102 Goshawk, 111–113, 253 Governments, 247–248 Greece, 73–74, 78, 81 Greenpeace, 246 Guatemala, 204 Guinea, 56 Guyana, 250 Habitat evaluation procedures, 232 Hawk, broad winged, 94 Hemlock, eastern, 154 Higher taxa, 37–38, 58, 66–71, 84– 86 Himalayas, 56, 68, 69 Hotspots, biodiversity of, 31, 276–277 species richness of, 34, 52, 62–64, 76–77 endemic species of, 39–41 rarity of, 42, 76–77 Hymenoptera, 224–225 Hummingbirds, 101 ecological-disturbance indicator species as, 199 Iconic species, 258–259, 267–268 Important bird areas, 88 Index of biological integrity, 164– 166 India, 247 Indicator species, confusion over terminology, 22– 26 definition, 24–25 Indonesia 39, 56, 209, 249, 272 IndVal, 191–192, 281

370 Index Insects, biodiversity and, 43, 49, 82 cross-taxon-response indicator species as, 223–224 flagship species as, 246 Intraguild predation. See Mesopredator release. Intraguild-response indicator species, 228–229 Invasive species, 138–139, 152 Inventory abbreviation, 66–67 Invertebrates, biodiversity and, 63, 69, 90–91, 93 environmental indicator species as, 172, 177–178, 200, 206–209 umbrella species and, 105–106 Israel, 69 Italy 21, 104, 111, 253 IUCN criteria, 5–7, 73, 74 Jaguar, iv, 124, 201, 250, 253–254 Japan, 253 Kaka, 113 Kenya, 39, 56, 246 Kestrel, 111–113, 279 Key biodiversity areas, 57–58 Keystone species, 264–265, 278 advantages of, 142–143 confusion over terminology, 22 context dependency, 139–142 definition of, 130, 142 examples of, 127–132 introductions and, 136–138 management and, 154–155 problems with, 139–142 research effort and, 16 Kite, 111–113 Landscape species, 120–124, 264–265, 278 criteria for, 120–122 definition, 120 monitoring and, 122–123 Latent risk of extinction, 237–238 Leafhoppers, biodiversity and, 71

ecological-disturbance indicator species as, 200 Leopard, 270 Lichens, 62–64, 68, 76 Liverworts, biodiversity and, 68 umbrella species and, 104 Local umbrella species, 99–100, 264– 265 birds and, 108–114 criteria for, 116–117 cross-taxon-response indicator species and, 218 land snails and, 107 mammals and, 107–108 Lynx, 95, 21, 108 Madagascar, 31, 39, 52, 56, 258, 272 Malaysia, 209 Mammals, biodiversity and, 21, 32, 33, 34, 35, 38, 39, 40, 41–42, 43, 46, 47, 49, 51, 52, 53, 65, 72, 74, 76, 80, 81, 86 ecological-disturbance indicator species as, 199, 200–201, 204–205, 210 endangered species act and, 260 environmental indicator species as, 183–184 flagship species and, 253, 254 umbrella species and, 105 Management areas, 211–212, 227– 228 Management indicator species, 230– 234, 266, 268 cross-taxon-response indicator species and, 232 definition, 231 ecological-disturbance indicator species and, 232 management umbrella species and, 100, 232 problems with, 233–234 Management umbrella species, 100, 113, 264–265 criteria for, 116–117

Index 371 Marine pollution, 169–171 indices of, 171 Matrix between fragments, 201 Mauritius, 56 Measurement, detection probability, 212–213 higher taxa, 37, 69–70 surrogate species, 23–26 threatened taxa, 43–45 Mesopredator release, 132–134 Mexico, 68, 136, 196, 204 Mollusks, biodiversity and, 43–44, 69, 90 environmental indicator species as, 163, 170 flagship species and, 254 umbrella species and, 113 Morphospecies, 71–72 Moss, 68 Moths, biodiversity and, 69, 71, 73, 80, 81, 84 cross-taxon-response indicator species as, 223–224 ecological-disturbance indicator species as, 204–205, 206–207 Mussels, xiv, 120, 127, 154, 170 Mythical animals, 259 Namibia, 254 Natural Resources Defense Council, 246 Nestedness, 81, 105, 224–225, 240– 241 New Britain, 90 New Caledonia, 87 New Guinea, 31, 56, 62 New Zealand, 56, 138–139, 195–196, 223 NGOs, 246–248, 281 Nicaragua, 204 Norway, 82, 246 North America. See also Canada, United States of America. engineering species and, 146–149, 152 higher taxa, 69

reintroductions, 136–138 species richness and, 33–35 threatened species and, 43–44, 49, 51–52 Nuthatch, 102 Oil palm, 188, 208–209 Orca. See Whale, killer Orthopterans, 74 Oryx, Arabian, 247 Otter, European, 254 Ovenbird, 94 Owl, barred, 22, 94 eagle, 111–113, 253 long-eared, 111–113, 253 pygmy, 98, 111–113, 253 scops, 111–113, 253 spotted, 113, 195, 251–253 tawny, 111–113, 253 Tengmalm’s, 111–113, 253 Panama, 136, 204 Panda, giant, 247, 251 Parasites, 170 Parids. See Tits. Peccary, white-lipped, 253–254 Peru, 66–67 Philippines, 39, 56 Pig, 250 Pisaster, 127–129, 139 Plantations, 206–209 Plants. See Vascular plants. See also Aquatic plants, Woody plants. Pocket gopher, 148 Poland, 109 Pollution, 169. See also Freshwater pollution, Marine pollution Population, monitoring, 210–211, 213–214, 229–230, 240 persistence, 83–84 Portfolios. See Complementarity. Portugal, 69, 85, 279 Prairie dog, 148–149 Primates biodiversity and, 52, 63, 65

372 Index Primates (continued) ecological-disturbance indicator species as, 201 flagship species and, 255–256 latent risk of extinction and, 237 Protected areas, biodiversity and, 53–56 coverage, 86–88 endemism and, 56 marine 89–90, 179–180, 268–270 size, 57 Proxy. See Conservation shortcuts. Rainforests, 197–209, 262 Rapid Assessment Team, 213 Rarity, congruency, 42, 73 definition, 4, 73 documentation of, 8 measurement of, 4, 81–82 species richness and, 52, 76–78 threatened species and, 52, 79 Recovery species, 231 Red-listed species, 103–104, 237 Reintroductions, 136–138 Reptiles, biodiversity and, 20, 21, 32, 34, 35, 39, 41–42, 49, 50, 63, 65, 72, 74, 76, 81 ecological-disturbance indicator species as, 206–208 umbrella species and, 105 Reserve selection, 26 research effort and, 16 umbrella species and, 101 uncertainty and, 275 socioeconomics and, 274–277 Restoration, 241 Resilience, 160–161 Resistance, 161 Reunion, 56 Rhinoceros, black, 106–107, 254, 354 River flow regime, 174–176 River modification, 174–177 Robin, 18–19 Rodents, 65 Ryuku Islands, 56

Sage grouse, 111 Salamanders, 113 Sao Tome, 56 Scale, 2, 42 cross-taxon-response indicator species and, 241 extent, 11–13, 79 grain, 11–13, 79 importance of, 9–13 Scandinavia, 63 Sea otter, 126, 128–129, 154, 156 Sensitive species, 231 Sentinel species, 167–169, 184, 235, 266, 268 definition, 168 criteria for, 168–169 Seychelles, 56 Shannon-Weiner index, 164 Shopping basket approach, 79, 96 Shortcuts. See Conservation shortcuts. Shrike, loggerhead, 94 Simpson index, 164 Snails, 34, 35, 72, 82 Snakes, 33, 47, 52 Snipe, common, 216 South Africa, 56, 81, 96, 227–228 endemism and, 72 rarity and, 73, 78 species richness and, 63, 78, 85– 86 South America, 42, 238 complementarity and, 46–47 Spain, 63, 76, 79, 224 Sparrowhawk, 111–113 Species acculumlation curve, 49 Species indicators of biodiversity, 264– 265 confusion over terminology, 26 definitions, 24–25 research effort and, 16 umbrella species and, 101 Species richness, congruency, 32–35, 45–47, 66–67, 79–82 definition, 3 documentation of, 8 endemism and, 50–52, 74–76

Index 373 rarity and, 52, 76–78 threatened species and, 50–55, 78 Spider monkey, 253–254 Spiders biodiversity and, 69, 71, 74, 85 ecological-disturbance indicator species as, 207 Squirrel, 219 flying, 17, 102, 108, 223 Sri Lanka, 39, 56, 85 Strongly interacting species. See Keystone species. Substitute species, 238–239, 266, 268 definition, 238 Sulawesi, 219–222 Sundaland, 39 Surrogacy curve, 47–49 Surrogate species, 1–3 assessment of success, 270–271 confusion over terminology, 22–27 definition, xv, 1–2, 16 examination of, 32 multi-surrogacy, 268–270 taxonomy of, 15–16, 263–268 use in conservation planning, 15, 283 Surrogate taxa. See Surrogate species. Sweden, 76, 78, 104, 106, 134, 224– 225, 250 Switzerland, 63, 110, 111 Systematic conservation planning, 13– 15, 283 effectiveness, 86–88 efficiency, 86–88 Target species, 16 Tamarins, 246, 249 Tanzania, 39, 88, 211–212, 228, 250 Tapir, Baird’s, 253–254 mountain, 247 Tasmanian devil, 259 Taxonomic distinctiveness, 8–9 Termites, 120 biodiversity and, 81, 86 Thailand, 56

Threatened species, congruency, 43–45, 73–74, 82 definition, 4, 5–7, 73 documentation of, 8 endemism and, 50–51, 74, 78 proxy for, 11 rarity and, 52, 79 species richness and, 50–53, 78 Tiger, 247 Tits, 111–113 Tortoises, 72 Trees. See also Woody plants. biodiversity and, 34, 35 ceiba, 250 ecological-disturbance indicator species as, 200 flagship species and, 253 management indicator species and, 233–234 umbrella species and, 112 Transformer species, 152 Trophic cascades, 130–132, 253 Tuatara, 195–196 Turtle, Blanding’s, 19 eastern box, 94 TWINSPAN, 190–191 Ungulates, 106–107, 255 Uganda, 63, 80 Umbrella index, 113–116 Umbrella species. 26–27. See also Classic umbrella species, Flagship umbrella species, Local umbrella species, Management umbrella species. birds, 108–116 butterflies, 113–116 confusion over definition, 22 definition, 100–101 habitat specialists, 118–119 indicators of biodiversity and, 101 invertebrates, 105–106 mammals, 106–108 multi-species, 102–104, 116 plants, 103–105 problems with, 117–119

374 Index Umbrella species (continued) research effort and, 16 top predators and, 113, 118–119 United States of America. See also North America. congruency at a small scale, 82–83 cross-taxon-response indicator species, 224–225, 241 ecological-disturbance indicator species, 209–210 endangered species, 43–44 engineering species, 147–148 environmental surrogates, 95 flagship species, 251–253, 254–255 keystone species, 127–129 management indicator species, 232– 233 reserve selection, 274–275 sampling methods and, 212–213 substitute species, 239 umbrella species, 105–106, 107– 108, 111, 118–119 Vascular plants, biodiversity and, 32, 35, 37, 39, 40, 44, 49, 63, 65, 66, 68, 69, 71, 72, 73, 74, 78, 81, 82, 84, 90–91 cross-taxon-response indicator species as, 241 ecological-disturbance indicator species as, 214 umbrella species and, 105 Venezuela, 74, 134–136, 225–226. Vertebrates, See also taxonomic names. biodiversity and, 63, 72, 82, 88, 95

cross-taxon-response indicator species as, 219–223 ecological-disturbance indicator species as, 209 Vicuna, 122 Vietnam, 56 Viverrids, 238 Vulnerability. See Vulnerable species. Vulnerable species, 4, 5–7 Vultures, 235–236 Whale, 246 killer, 128–129 North Atlantic right, 211, 268–269 Wholeness, 161 Wildlife Conservation Society, 120, 272 Willingness-to-pay, 251–252 Wolf, xvii, 21, 95, 253 reintroductions, 136–138 Wolverine, 21, 95, 108 Woodpecker, 109–110 green, 109 ivory-billed, 258 lesser spotted, 109 middle spotted, 109 pileated, 94 Woody plants, biodiversity and, 68, 80, 85 umbrella species and, 104 World Wildlife Fund, 247 ecoregions, 31, 32, 41–42, 58, 272 Zoos, 245, 247–248