Environmental Impact of Invertebrates For Biological Control 0f Anthropods: Methods and Risk Assessment

Environmental Impact of Invertebrates for Biological Control of Arthropods

Methods and Risk Assessment

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Environmental Impact of Invertebrates for Biological Control of Arthropods Methods and Risk Assessment

Edited by

Franz Bigler and Dirk Babendreier Agroscope, FAL Reckenholz Swiss Federal Research Station for Agroecology and Agriculture Zürich Switzerland and

Ulrich Kuhlmann CABI Bioscience Switzerland Centre Delémont Switzerland

CABI Publishing

CABI Publishing is a division of CAB International CABI Publishing CAB International Wallingford Oxon OX10 8DE UK

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© CAB International 2006. All rights reserved. No part of this publication may be reproduced in any form or by any means, electronically, mechanically, by photocopying, recording or otherwise, without the prior permission of the copyright owners. A catalogue record for this book is available from the British Library, London, UK. Library of Congress Cataloging-in-Publication Data Environment impact of invertebrates for biological control of arthropods : methods and risk assessment / edited by Franz Bigler and Dirk Babendreier and Ulli Kuhlmann. p. cm. Includes bibliographical references and index. ISBN-13: 978-0-85199-058-3 (alk. paper) ISBN-10: 0-85199-058-4 (alk. paper) 1. Insect pests--Biological control. 2. Arthropod pests--Biological control. 3. Arthropoda as biological pest control agents. 4. Pesticides-Environmental aspects. I. Bigler, Franz. II. Babendreier, Dirk. III. Kuhlmann, Ulli. IV. Title. SB933.3.E58 2006 632⬘.96--dc22 2005020627 ISBN-10: ISBN-13:

0-85199-058-4 978-0-85199-058-3

Typeset by Columns Design Ltd, Reading, UK. Printed and bound in the UK by Cromwell Press, Trowbridge.

Contents

Contributors Foreword Joop C. van Lenteren Preface Acknowledgements

vii xi xiii xv

1

Current Status and Constraints in the Assessment of Non-target Effects Dirk Babendreier, Franz Bigler and Ulrich Kuhlmann

1

2

Selection of Non-target Species for Host Specificity Testing Ulrich Kuhlmann, Urs Schaffner and Peter G. Mason

15

3

Host Specificity in Arthropod Biological Control, Methods for Testing and Interpretation of the Data Joop C. van Lenteren, Matthew J.W. Cock, Thomas S. Hoffmeister and Don P.A. Sands

38

4

Measuring and Predicting Indirect Impacts of Biological Control: Competition, Displacement and Secondary Interactions Russell Messing, Bernard Roitberg and Jacques Brodeur

64

5

Risks of Interbreeding Between Species Used in Biological Control and Native Species, and Methods for Evaluating Their Occurrence and Impact Keith R. Hopper, Seth C. Britch and Eric Wajnberg

78

6

Assessing the Establishment Potential of Inundative Biological Control Agents Guy Boivin, Ursula M. Kölliker-Ott, Jeffrey Bale and Franz Bigler

98

7

Methods for Monitoring the Dispersal of Natural Enemies from Point Source Releases Associated with Augmentative Biological Control Nick J. Mills, Dirk Babendreier and Antoon J.M. Loomans

114

8

Risks of Plant Damage Caused by Natural Enemies Introduced for Arthropod Biological Control Ramon Albajes, Cristina Castañé, Rosa Gabarra and Òscar Alomar

132

v

vi

9

Contents

Methods for Assessment of Contaminants of Invertebrate Biological Control Agents and Associated Risks Mark S. Goettel and G. Douglas Inglis

145

10 Post-release Evaluation of Non-target Effects of Biological Control Agents Barbara I.P. Barratt, Bernd Blossey, Heikki M.T. Hokkanen

166

11 Molecular Methods for the Identification of Biological Control Agents at the Species and Strain Level Richard Stouthamer

187

12 The Usefulness of the Ecoregion Concept for Safer Import of Invertebrate Biological Control Agents Matthew J.W. Cock, Ulrich Kuhlmann, Urs Schaffner, Franz Bigler and Dirk Babendreier

202

13 Statistical Tools to Improve the Quality of Experiments and Data Analysis for Assessing Non-target Effects Thomas S. Hoffmeister, Dirk Babendreier and Eric Wajnberg

222

14 Principles of Environmental Risk Assessment with Emphasis on the New Zealand Perspective Abdul Moeed, Robert Hickson and Barbara I.P. Barratt

241

15 Environmental Risk Assessment: Methods for Comprehensive Evaluation and Quick Scan Joop C. van Lenteren and Antoon J.M. Loomans

254

16 Balancing Environmental Risks and Benefits: a Basic Approach Franz Bigler and Ursula M. Kölliker-Ott

273

Glossary

287

Index

291

Contributors

Albajes, Ramon, Universitat de Lleida, Centre UdL-IRTA, Rovira Roure 191, 25198 Lleida, Spain. Email: [email protected]. Phone number: ⫹34-973-702571. Fax number: ⫹34-973-238301. Alomar, Òscar, IRTA, Centre de Cabrils, 08348 Cabrils (Barcelona), Spain. Email: [email protected]. Phone number: ⫹34-93-750-9961. Fax number: ⫹34-93-7533954. Babendreier, Dirk, Agroscope, FAL Reckenholz, Swiss Federal Research Station for Agroecology and Agriculture, Reckenholzstr. 191, 8046 Zürich, Switzerland. Email: [email protected]. Phone number: ⫹41-44-377-7217. Fax number: ⫹4144-377-7201. Bale, Jeffrey, School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK. Email: [email protected]. Phone number: ⫹44-121-414-5908. Fax number: ⫹44-121-414-5925. Barratt, Barbara, AgResearch, Invermay Agricultural Centre, Private Bag 50034, Mosgiel, New Zealand. Email: [email protected]. Phone number: ⫹64-3-489-9059. Fax number: ⫹64-3-489-3739. Bigler, Franz, Agroscope, FAL Reckenholz, Swiss Federal Research Station for Agroecology and Agriculture, Reckenholzstr. 191, 8046 Zürich, Switzerland. Email: [email protected]. Phone number: ⫹41-44-377-7235. Fax number: ⫹41-44377-7201. Blossey, Bernd, Department of Natural Resources, 122E Fernow Hall, Cornell University, Ithaca, New York 14853, USA. Email: [email protected]. Phone number: ⫹1-607-2555314. Fax number: ⫹1-607-255-0349. Boivin, Guy, Centre de Recherche et de Développement en Horticulture, Agriculture et Agroalimentaire Canada, 430 Boul. Gouin, Saint-Jean-sur-Richelieu, Québec J3B 3E6, Canada. Email: [email protected]. Phone number: ⫹1-450-346-4494. Fax number: ⫹1-450-346-7740. Britch, Seth, Beneficial Insects Introduction Research Laboratory, Agricultural Research Service, USDA, 501 South Chapel Street, Newark, DE 19713, USA. Email: [email protected]. Phone number: ⫹1-302-731-7330 ext. 239. Fax number: ⫹1-302-737-6780. Brodeur, Jacques, Département des Sciences Biologiques, Institut de Recherche en Biologie Végétale, Université de Montréal, 4101, rue Sherbrooke Est, Montréal vii

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Contributors

(Québec), Canada H1X 2B2. Email: [email protected]. Phone number: ⫹1-514-872-4563. Fax number: ⫹1-514-872-9406. Castañé, Cristina, IRTA, Centre de Cabrils, 08348 Cabrils, (Barcelona), Spain. Email: [email protected]. Phone number: ⫹34-93-750-9961. Fax number: ⫹34-93-7533954. Cock, Matthew, CABI Bioscience Switzerland Centre, Rue des Grillons 1, 2800 Delémont, Switzerland. Email: [email protected]. Phone number: ⫹41-32-421-4870. Fax number: ⫹41-32-421-4871. Gabarra, Rosa, IRTA, Centre de Cabrils, 08348 Cabrils (Barcelona), Spain. Email: [email protected]. Phone number: ⫹34-93-750-9976. Fax number: ⫹34-93-7533954. Goettel, Mark, Environmental Health, Agriculture and Agri-Food Canada, Lethbridge Research Centre, PO Box 3000, 5403 – 1st Avenue South, Lethbridge, AB T1J 4B1 Canada. Email: [email protected]. Phone number: ⫹44-403-317-2264. Fax number: ⫹44-403-382-3156. Hickson, Robert, Ministry of Research, Science and Technology, PO Box 5336, Wellington, New Zealand. Email: [email protected]. Phone number: ⫹644-917-2917. Fax number: ⫹64-4-471-1284. Hoffmeister, Thomas, Institute of Ecology and Evolutionary Biology, University of Bremen, Leobener Str. NW2, D-28359 Bremen, Germany. Email: [email protected]. Phone number: ⫹49-421-218-4290. Fax number: ⫹49-421-218-4504. Hokkanen, Heikki, Department of Applied Zoology, University of Helsinki, PO Box 27, 00014 Helsinki, Finland. Email: heikki.hokkanen@helsinki.fi. Phone number: ⫹3589191-58371. Fax number: ⫹358-9191-58463. Hopper, Keith, Beneficial Insects Introduction Research Laboratory, Agricultural Research Service, USDA, 501 South Chapel Street, Newark, DE 19713, USA. Email: [email protected]. Phone number: ⫹1-302-731-7330 ext. 238. Fax number: ⫹1-302737-6780. Inglis, Douglas, Food Safety and Quality, Agriculture and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB T1J 4B1, Canada. Email: [email protected]. Phone number: ⫹1-403-317-3355. Fax number: ⫹1-403-382-3156. Kölliker-Ott, Ursula, Agroscope, FAL Reckenholz, Swiss Federal Research Station for Agroecology and Agriculture, Reckenholzstr. 191, 8046 Zürich, Switzerland. Email: [email protected]. Phone number: ⫹41-44-377-7181. Fax number: ⫹4144-377-7201. Kuhlmann, Ulli, CABI Bioscience Switzerland Centre, Rue des Grillons 1, 2800 Delémont, Switzerland. Email: [email protected]. Phone number: ⫹41-32-4214882. Fax number: ⫹41-32-421-4871. Loomans, Antoon, Plant Protection Service, Section Entomology, PO Box 9102, 6700 HC Wageningen, The Netherlands. Email: [email protected]. Phone number: ⫹31-317-496825. Fax number: ⫹31-317-421701. Mason, Peter, Agriculture and Agri-food Canada, Research Centre, K.W. Neatby Building, Central Experimental Farm, 960, Carling Avenue, Ottawa, Ontario K1A OC6, Canada. Email: [email protected]. Phone number: ⫹1-613-759-1908. Fax number: ⫹1-613-759-170. Messing, Russell, University of Hawaii at Manoa, Kauai Agricultural Research Center, 7370 Kuamoo Road, Kapaa, Hawaii 96746, USA. Email: [email protected]. Phone number: ⫹1-808-822-4984 x223. Fax number: ⫹1-808-822-2190. Mills, Nick, Environmental Science, Policy and Management, 127 Mulford Hall, University of California, Berkeley, CA 94720-3114, USA. Email: [email protected]. Phone number: ⫹1-510-642-1711. Fax number: ⫹1-510-643-5438.

Contributors

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Moeed, Abdul, ERMA New Zealand, PO Box 131, Wellington, New Zealand. Email: [email protected]. Phone number: ⫹64-4-916-2426. Fax number: ⫹64-4914-0433. Roitberg, Bernie, Department of Biological Science, Simon Fraser University, Burnaby, BC, V5A IS6, Canada. Email: [email protected]. Phone number: ⫹1-604-2913585. Fax number: ⫹1-604-291-3496. Sands, Don, CSIRO Entomology, 120 Meiers Road, Indooroopilly, Queensland 4068, Australia. Email: [email protected]. Phone number: ⫹61-403-517224. Schaffner, Urs, CABI Bioscience Switzerland Centre, Rue des Grillons 1, 2800 Delémont, Switzerland. Email: [email protected]. Phone number: ⫹41-32-4214877. Fax number: ⫹41-32-421-4871. Stouthamer, Richard, Department of Entomology, University of California, Riverside, CA 92521, USA. Email: [email protected]. Phone number: ⫹1-951-8272422. Fax number: ⫹1-951-827-3086. van Lenteren, Joop, Laboratory of Entomology, Wageningen University, PO Box 8031, 6700 EH Wageningen, The Netherlands. Email: [email protected]. Phone number: ⫹31-317-482327. Fax number: ⫹31-317-484821. Wajnberg, Eric, INRA, 400, Route des Chappes, BP 167, 06903 Sophia Antipolis Cedex, France. Email: [email protected]. Phone number: ⫹33-4-92-38-6447. Fax number: ⫹33-4-92-38-6557.

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Foreword

Classical biological control of insects, where exotic natural enemies are introduced to control exotic pests, has been applied for more than 120 years, and release of more than 2000 species of natural enemies has resulted in the permanent reduction of at least 165 pest species worldwide. Augmentative biological control, where exotic or native natural enemies are periodically released, has been used for 90 years, and more than 150 species of natural enemy are available on demand for the control of about 100 pest species. Contrary to the thorough environmental risk evaluations applied in the search for natural enemies of weeds, potential risks of biological control agents for arthropod control have not been routinely studied in pre-release evaluations. The reason might be that until now, very few problems have been reported concerning negative effects of releases of invertebrates for control of arthropods, despite there having been well over 5000 introductions that have been made worldwide. It is a well-known fact that intended or accidental invasions by many other exotic organisms have resulted in serious negative environmental and economic effects. However, discussion of the risks of releases of exotic natural enemies for non-target species now takes a prominent place in biological control programmes. On the other hand, one normally tends to forget or even not know the enormous economic and environmental benefits resulting from biological control with introduced exotic organisms. Recent retrospective analyses of biological control projects have provided quantitative data on nontarget effects and illustrated the need for risk assessments to increase the future safety of biological control. Twenty countries have already implemented regulation for release of biological control agents and many other countries are considering regulation. Soon, the International Standard for Phytosanitary Measures (ISPM3) will become the standard for all biological control introductions worldwide, but this standard does not provide methods by which to assess environmental risks. The same can be said about other risk assessments that have previously been used to evaluate exotic natural enemies. In order to fulfil the need of developing environmental risk assessment methods, as well as a framework for a general risk assessment of biological control agents, an international group of scientists first wrote a number of working papers. Next, these were discussed and modified during a week of hard work in the Swiss mountains. Finally, the papers were peer reviewed and rewritten for the current book. The goal of this book is not only to present risk assessment methods, but also to give ample background information relevant for developing and adapting these methods. xi

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Foreword

It is my hope that this book will find its way to scientists, biological control workers and regulators. Intensive collaboration between representatives of these groups will hopefully result in a light and harmonized regulation procedure that is not prohibitive to the biological control industry and will result in the selection of safe natural enemies. Joop C. van Lenteren President of the International Organization for Biological Control (IOBC Global) Professor of Entomology, Laboratory of Entomology, Wageningen University, The Netherlands.

Preface

While safety of biological control was generally not questioned until the beginning of the 1990s, an ongoing debate started shortly after the Rio Convention on Biodiversity was agreed in 1992. Based on this agreement and on an increasing amount of published literature blaming biological control for contributing to biodiversity loss, international organizations and national governments started after the mid-nineties to publish documents in which general principles of guidance and good governance for import and release of invertebrate biological control agents were laid down. None of the international documents was meant to give detailed advice to national regulatory bodies on how to regulate import and release of such organisms, nor did they provide methodologies on how to assess potential effects and how to perform risk and benefit analysis. While the documents specify what information will be needed for risk assessment, they do not give any indication on how to obtain the relevant information, i.e., what methods could best be applied to obtain the needed data to perform risk assessments. This lack of background information and advice on methodology was the starting point of the present book. The idea was born to publish a document that summarizes the present status on risk assessment in biological control of arthropods with invertebrates, and gives guidance on methods to generate data which enable biological control scientists, natural enemy producers, retailers, practitioners and regulators to make informed risk assessments. The guiding principle of the book is to provide a science-based framework for identifying and evaluating relevant environmental effects that could result from import and release of exotic invertebrate biological control agents. It should assist those who are involved in risk and benefit assessment and in regulation of invertebrate biological control agents used against arthropod pests. A literature review has shown us that there is presently very little literature published providing reliable information on standard methods that could be applied to produce data for risk assessment. It is the intention of the present book to set a framework for risk assessment, to discuss strengths, weaknesses and lack of methods and to propose new approaches and practical guidance on how to measure and evaluate effects that contribute to environmental risks and benefits. We are aware that we do not cover all relevant aspects of risk–benefit assessment and regulation, and further efforts will be needed. Nevertheless, we are confident we can offer the reader a range of methods and guidance that will improve and facilitate regulation of invertebrate biological control agents, and contribute to the ongoing debate. xiii

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Preface

Based on previous projects and existing experience on risk assessment and regulation of invertebrate biological control agents, we identified the most critical issues to be considered and addressed in this book. With financial support from the Swiss Agency of Environment, Landscape and Forest and the Swiss Federal Research Station for Agroecology and Agriculture we were able to invite an international group of experts to prepare chapters and to present and discuss them at a workshop held in Engelberg, Switzerland, in 2004. The very open, critical and constructive atmosphere here was the ground for fruitful debates that contributed to improving the chapters and to make the book more comprehensive. The book consists of three parts, namely the major section in which methods for assessing environmental effects of invertebrate biological control agents are reviewed, discussed in the light of risk assessment and, when possible, recommendations on appropriate methods are made. The second section consists of three chapters presenting different technical tools which are extremely important in environmental risk assessment and regulatory procedures, and they belong to the basic prerequisites to evaluate risks. In the third section, the principles of environmental risk assessment are presented together with a case study; two methods on how to perform risk analysis with invertebrate biological control agents are shown with practical examples given, and finally, a risk–benefit assessment together with an example is discussed. As the book is a compilation of the current knowledge of methodology available for assessing non-target effects and risks of invertebrate biological control agents, it shows the arsenal of tools and methods. However, limitations of our understanding of ecological mechanisms and lack of methods to analyse such processes show the obvious gaps. We are far from having answers and solutions to all questions relevant to risks and regulation, and we still need to tackle a number of practical problems. Bearing in mind that improvements can still be made in the future, we should not forget that regulation of biological control agents must be cost effective. Overregulation of biological control would be disastrous because it would prevent progress of biological control and its role in IPM. Regulation of invertebrate biological control agents will certainly undergo changes in the coming years. We expect that national authorities in many countries will be more demanding, with the consequent need for biological control manufacturers to prepare more elaborated dossiers, with more information and data. This will be an additional burden for biological control projects and lead to a longer time period for approval of new organisms. On the other hand, it will give more confidence in biological control and help to maintain and strengthen the good reputation of these pest control methods. We have reached our goals if this book contributes to the better assessment of environmental effects, risks and benefits of invertebrate biological control agents, and if it provides guidance to all those who are involved in biological control and its regulation. Franz Bigler, Dirk Babendreier and Ulli Kuhlmann, June 2005 Zürich and Delémont, Switzerland.

Acknowledgements

This book has been written by authors who have long-standing expertise in biological control and/or regulation of agents introduced and released to this end. First, we would like to thank those authors who participated in the workshop held in 2004 in Engelberg, in the Swiss Alps, where first drafts of the chapters were discussed and critically reviewed in an open and constructive spirit. Special thanks are addressed to the few coauthors who were not able to attend, but still made their invaluable contributions to different chapters. Many colleagues reviewed the chapters and gave their comments and views, provided ideas and insights and helped the authors to achieve a text which will be useful to all stakeholders of biological control. From within the group of workshop participants, we would like to thank Barbara Barratt, Guy Boivin, Jaques Brodeur, Keith Hopper, Doug Inglis, Antoon Loomans, Peter Mason, Russell Messing, Nick Mills, Bernie Roitberg, Richard Stouthamer and Joop van Lenteren. Furthermore, several external reviewers shared their expertise with us, and the following colleagues are particularly acknowledged: Moshe Coll, Eric Conti, Dave Gillespie, George Heimpel, Lia Hemerik, Mark Hoddle, Kim Hoelmer, Larry Lacey, Peter McEvoy, Bill Turnock, Franco Widmer and Robert Wiedenmann. This book is the fruit of a project funded by the Swiss Agency of Environment, Landscape and Forest, the Swiss Federal Research Station for Agroecology and Agriculture and CABI Bioscience Centre, Switzerland. We are thankful for the continuous support by these institutions.

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Current Status and Constraints in the Assessment of Non-target Effects Dirk Babendreier,1 Franz Bigler1 and Ulrich Kuhlmann2

1Agroscope,

FAL Reckenholz, Swiss Federal Research Station for Agroecology and Agriculture, Reckenholzstr. 191, 8046 Zürich, Switzerland (email: [email protected]; [email protected]; fax number: +41-44-3777201); 2CABI Bioscience Centre, Rue des Grillons 1, 2800 Delémont, Switzerland (email: [email protected]; fax number: +41-32-4214871)

Abstract In the last two decades increasing concerns have been expressed regarding potential nontarget effects of invertebrate biological control agents of arthropods. This has led to an increasing number of studies investigating non-target effects in many systems. Several international initiatives aimed at providing guidance for risk assessment of biological control agents are briefly reviewed here. Furthermore, we aim to provide an overview of the current status of non-target testing of arthropod biological control agents, and identify the most recent developments. Most importantly, we aim to identify constraints and unsolved questions which need further research or consideration in the future. Major obstacles encountered include the need for harmonization of regulation and methods, and the increasing costs that are associated with implementing regulation. In addition, statistical analysis, the interpretation of host range tests, and inherent uncertainties associated with non-target testing are major problems currently faced in risk assessment. Finally, this chapter will refer to other chapters of this book that address the identified issues and propose the urgently needed and relevant methodology.

History of Initiatives for Regulation The potential for non-target effects resulting from the release of biological control agents has been recognized for over a hundred years. However, only much later has this question stimulated intensive discussion among scientists and beyond (Howarth, 1983, 1991). Since then, nontarget effects in biological control are increasingly being studied, and a number

of reviews have been published within the last ten years (e.g. Simberloff and Stiling, 1996; Follett et al., 2000; Lockwood et al., 2001; Lynch et al., 2001; Louda et al., 2003). International laws and agreements coupled with an increasing interest in the import and release of exotic biological control agents requires harmonized and appropriate regulation. However, provisions within such legislation vary considerably

©CAB International 2006. Environmental Impact of Invertebrates for Biological Control of Arthropods: Methods and Risk Assessment (eds F. Bigler et al.)

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between countries. A starting point towards international regulation was marked by the FAO Code of Conduct for the Import and Release of Exotic Biological Control Agents; this was adopted in 1995 by the FAO Conference and published in 1996 as the International Standard for Phytosanitary Measures No. 3 (IPPC, 1996). One objective of the Code was to provide a standard for those countries that lack adequate legislation and procedures to regulate importation and to analyse risks related to biological control agents. The document lists in a generic way the responsibilities of the authorities and importers and exporters of biological control agents. The revised version of this Code of Conduct has extended its range from classical biological control to inundative biological control, native natural enemies, microorganisms and other beneficial organisms, and it also includes evaluation of environmental impacts (IPPC, 2005). This standard will certainly continue to provide guidance for countries that are developing their own legislative systems for biological control regulation, and the Code may be seen as a first attempt to globally harmonize regulation of biological control agents. Shortly after the Code’s first publication, the European and Mediterranean Plant Protection Organization (EPPO) together with CABI Bioscience organized a workshop on safety and efficacy of biological control in Europe (EPPO, 1997). This workshop broadly endorsed the FAO Code but recommended that regulation should not slow the importation or import of biological control agents, be it for preliminary research or for subsequent release. The workshop concluded that a certification system should be put in place for Europe instead of a registration procedure to ensure a ‘light’ regulatory system with efficient and rapid mechanisms. The reasoning behind this decision was based on previous experience with the registration system for microbial biological control agents in Europe: the EU Directive and its implementation is so stringent that it is basically impossible to register a new

microorganism in EU countries. An expert panel was established and the results of their meetings were published in two guidance documents and in a ‘positive list’ of organisms for safe use in EPPO countries (EPPO, 1999, 2001, 2002). The two guidelines stress the importance of a two-step system for importation and release, i.e. EU countries should first establish a regulatory process for the import of exotic organisms for research under containment. The results of these investigations will provide the necessary data to make decisions on whether the organism can later be imported for release. In parallel with the EPPO panel activities, the EU-funded research project ERBIC (Evaluating Environmental Risks of Biological Control Introductions into Europe) was executed from 1998 to 2002. One of the main outcomes of the project was a proposal for the environmental risk assessment of exotic natural enemies in inundative biological control (van Lenteren et al., 2003). This represents the first paper with detailed criteria for risk assessment and a ranking system that is based on the quantitative evaluation of more than 30 invertebrate biological control agents used in inundative control in Europe. In 2000, the North American Plant Protection Organization (NAPPO) published its ‘Guidelines for Petition for Release of Exotic Entomophagous Agents for the Biological Control of Pests’ (RSPM No 12, NAPPO, 2000). These guidelines are intended to assist researchers and companies in drafting a petition for release of exotic entomophagous agents for biological control of pest insects and mites. It will also assist reviewers and regulators in assessing the risk of exotic introductions intended for biological control. The guideline specifies the requirement for information on biology of the agent and the target pest(s), the economic impact of the pest, regulatory status, and the quarantine procedures needed for importation of the biological control agent. To some extent there has been some harmonization in data requirements for entomophagous biological control agents in that the three countries

Current Status and Constraints in the Assessment of Non-target Effects

(Canada, USA and Mexico) have agreed to conform to NAPPO guidelines. However, currently the regulatory system within the USA is cumbersome with a mixture of inconsistent Federal and State jurisdiction. The system for biological control regulation in Hawaii, the State where the most rigorous review procedure has been adopted, is worth reviewing. While the system appears to be exhaustive in ensuring environmental safety of biological control, and allows for a degree of public consultation, it is steeped in bureaucracy that results in frustration and lengthy delays for biological control practitioners. Island nations, such as Australia and New Zealand, have the unique situation where shared borders are not an issue, and complete control over imported biological control agents can be achieved. The 1996 Hazardous Substances and New Organisms (HSNO) Act in New Zealand (http://www.legislation.govt.nz) has attracted considerable attention internationally as very environmentally focused legislation, and its implementation by ERMA NZ has been observed with interest (see Moeed et al., Chapter 14 this volume). In Australia, biological control agents are regulated by two agencies under three separate Acts, and have been similarly heralded as a thorough and biosafety-conscious approach. The two systems have some key differences in approach, the most notable ones being the opportunity for public participation and the degree of risk-aversion of the regulatory agencies. An initiative starting from a meeting held in Canada in 1999 resulted in OECD (Organization for Economic Co-operation and Development) member countries developing a harmonized approach for regulation of invertebrate biological control agents. It was agreed that a harmonized regulatory system in the OECD member countries would be beneficial for biological control and that a ‘light’ form of regulation would be appropriate. The development of harmonized guidance for regulation requirements would enable companies to submit the same applications to many countries, and would allow regulatory agencies to benefit

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from each other’s reviews. The document (OECD, 2003) proposes guidance for member countries on information requirements for: a) the characterization and identification of the organism; b) the assessment of safety and effects on human health; c) the assessment of environmental risks; and d) the assessment of efficacy of the organism. With native or established organisms and with those in long-term use in a country, substantially reduced information requirements may be appropriate. In Europe, the biological control industry expressed their concerns when the OECD guidance document was published as the information requirements were considered to be too stringent. As a consequence, the International Biocontrol Manufacturers’ Association (IBMA) proposed to the International Organization for Biological Control (IOBC/WPRS) facilitation of the harmonization among the European regulatory authorities. A Commission for the IOBC/WPRS was established in 2003 and a meeting of scientists, together with the biological control industry and regulators, resulted in the production of a document that gives detailed guidance on regulation procedures for exotic and indigenous biological control agents (Bigler et al., 2005). Most recently, the European Commission released a call for project applications with the aim of developing a balanced system for regulation of biological control agents (micro- and macro-organisms), semiochemicals and botanicals. This specifies that the number of microbiological products on the market in Europe is currently still low compared to other countries, e.g. the USA and Canada. The aim of the task is to review current legislation, guidelines and guidance documents and to compare this with similar legislation in other countries where the introduction of new biopesticides has proved to be more successful. New appropriate and balanced regulatory systems should be designed. It can be expected that within a few years the EU members and other European countries may regulate invertebrate biological control agents under uniform principles.

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From this overview on regulation in different countries it is becoming evident that challenges and opportunities have emerged. The above-mentioned initiatives generally highlight what should be done or what knowledge is required, but they are not designed to provide detailed methods on how one should test for non-target effects. Recently, a guide to best practice of host range testing has been released by Van Driesche and Reardon (2004). In addition, all aspects of non-target testing have recently been addressed in a comprehensive review of the current methods used to assess potential risks of biological control agents (Babendreier et al., 2005). This book attempts to go a step further by providing guidance on methods necessary to assess non-target effects of invertebrate biological control agents of insect pests. The authors feel that the lack of methodology and approaches is a major concern and a bottleneck in environmental risk assessment at the moment, and that these issues need to be tackled.

Status and Important Issues in Assessing Environmental Effects While all documents underline the need for regulation of invertebrate biological control agents, the level of guidance on information needed for risk assessment varies to a great extent between these documents. The OECD guidance document (OECD, 2003) is one of the most comprehensive initiatives to date, as it requires relatively detailed information from the applicant in order to receive an import and release permit, and because the OECD covers a wide geographic area. Based on experience with many other regulatory documents released by the OECD, we assume that this document will be widely adopted internationally, or at least serve as a basis for national regulatory documents. Therefore, this chapter basically follows the issues raised in that document (OECD, 2003). While the first two parts of the document address issues of characterization and identification of organisms as well as

human health and safety, here we will discuss mainly the third part, i.e. the assessment of environmental risks.

Host specificity Host specificity is a key element if nontarget effects of biological control agents are to be assessed, and this is also reflected in the OECD document. Although only information available to identify any potential hazards posed to the environment is currently required under 3.1, data may be required for host specificity testing under 3.2 (Table 1.1). Here, we like to stress that host range assessment does not necessarily mean that tests have to be conducted. Often, published information is sufficient to draw conclusions on the host specificity of the agent. A recent example was provided by De Nardo and Hopper (2004), who conducted a comprehensive literature study for the ichneumonid parasitoid Macrocentrus grandii (Goidanich). These authors stressed that a lot of information can be obtained even from negative observations, i.e. from studies on potential nontarget hosts that did not report the biological control candidate as a natural enemy. Although host specificity testing has been required in weed biological control projects for many decades, it was incorporated into arthropod biological control projects rather recently. For the latter, there are still not many experimental studies available in which host range testing was conducted, though this number increased recently (see Babendreier et al., 2005). There are also several reviews or discussion papers available dealing with topics that need to be addressed in these tests (Sands, 1997, 1998; Van Driesche and Hoddle, 1997; Sands and Van Driesche, 2000; Van Driesche and Murray, 2004a,b; Van Driesche and Reardon, 2004). After the first step, i.e. the collection of all available information (Sands and Van Driesche, 2004) an important subsequent step may be to carry out field surveys in the

Current Status and Constraints in the Assessment of Non-target Effects

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Table 1.1. Information requirements of the OECD document on environmental risk assessment of biological control agents. 3. Information for assessment of environmental risks 3.1 Identify any potential hazards posed to the environment including: (a) available information on the role of organism in original ecosystem, the type of natural enemy (parasitoid, predator, pathogen), type of organisms it attacks, effects of attack on targets and non-targets, intra-guild effects, higher up trophic level effects, effects on ecosystem (b) available information on existing natural enemies of the target organism in the area of release (c) available information on non-target effects from previous use in biological control 3.2 Host specificity testing (a) available information and/or data on possible direct effects: ● on non-target host/prey related to target host (phylogenetically or ecologically related) ● on non-related non-target hosts, such as threatened and endangered species ● concerning competition or displacement of organisms ● concerning potential for interbreeding with indigenous natural enemy strains or biotypes ● on plants (target crop and non-target plants) (b) available information and/or data on potential of establishment and dispersal of biological control agent (c) available information on and/or data on possible indirect effects (d) available information (from rearing facility; in the field) on ability to vector viruses or microorganisms which can negatively affect non-target organisms 3.3 Available information, and/or data on potential host/prey range in areas of release and potential distribution 3.4 Available information on environmental benefits e.g. beneficial effects of release compared to current or alternative control methods 3.5 Summary of information for assessment of environmental risks

country of origin and also to analyse the fauna of the proposed area of introduction (Hoddle, 2004). For those surveys, classical ecological methods or more recently developed molecular methods may be used depending on the organisms (Symondson, 2002; Gariepy et al., 2005). Field surveys are not only an important preliminary step in identifying the species with the most narrow host range out of a pool of species, but they can also provide guidance regarding which species should be included in host specificity tests (see Kuhlmann et al., Chapter 2, this volume). A general problem with field surveys is in defining the limits of the system. Should one collect only species from taxa that contain known hosts or include additional taxa? Creating a list of species that should be tested for acceptance by biological control agents is obviously a difficult task. A general problem, especially for insect biological control, is that the taxonomy of involved groups is often unclear (Van

Driesche and Reardon, 2004). Moreover, the number of species in taxonomic groups is often higher by an order of magnitude compared to plants. Molecular tools are increasingly being used and may help to solve this problem in the future (see Stouthamer, Chapter 11, this volume). Criteria that have been taken into account for creating such lists in arthropod biological control have included geographic distributions, oviposition phenology, number of generations per year, overwintering stage, host-plant preferences, and the type and feeding niche of the host (for a review, see Babendreier et al., 2005). In addition to the ecological criteria mentioned above, the importance and availability of potential non-target species were also considered; some species that would be desirable members of a host range test list may be impossible to find or to rear. However, there appears to be some contradiction as the OECD (2003, see Table 1.1) requires information on rare non-target hosts which, generally, is very difficult or

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impossible to obtain (Barratt, 2004). In this book, Kuhlmann et al. (Chapter 2, this volume) for the first time worked out a general approach that could be applied in creating a list of non-target species used in host-range testing, both for inundative and classical biological control agents targeting insects. The ultimate aim of host range tests is to determine the agent’s ecological host range, i.e. the number of hosts that will be attacked in the field where the biological control agent is to be introduced (Van Driesche and Reardon, 2004). Clearly, laboratory tests have their limitations, as it is extremely difficult to accurately reproduce the cues and stimuli that affect host acceptance of biological control agents in a natural environment (Keller, 1999; Kuhlmann et al., 2000; Sands and Van Driesche, 2000). The interpretation of host specificity tests is a problem and there are ongoing debates regarding how indicative these tests are. A number of studies exist that conducted nochoice tests and choice tests with the same non-target species. The majority of these studies have shown that results from both kinds of tests are in general agreement (Duan and Messing, 2000; Zilahi-Balogh et al., 2002; Mansfield and Mills, 2004). However, Haye (2004) has shown that several non-target species were less preferred in choice tests while target and non-target species were equally parasitized in nochoice tests. Unfortunately, the reverse was also observed, i.e. non-targets and the target were similarly attacked in choice tests while less non-target parasitism was observed in the no-choice test. Whether choice tests are useful or necessary at all is still debated. Guidance on what test should be used and how this should be done is given by van Lenteren et al. (Chapter 3, this volume). Most importantly, however, one likes to know whether results obtained under laboratory (or semi-field) conditions are indicative of what a biological control agent would attack in the field. So far, there is no long track record on the reliability of host specificity testing in arthropod biological control. A pioneering study was conducted by Barratt et al. (1997), who compared results on host specificity of Microctonus

aethiopoides Loan and Microctonus hyperodae Loan (Hymenoptera: Braconidae) obtained in the laboratory with actual field parasitism after the agents were established. The authors basically concluded that tests conducted in the laboratory were in fact indicative of field parasitism. Coombs (2003) reported that the tachinid fly Trichopoda giacomelli (Blanchard) attacked two nontarget hosts after field release in Australia, exactly as was anticipated by host range tests carried out beforehand. However, there are also examples, such as the retrospective case study on the braconid wasp Peristenus digoneutis Loan (Haye et al., 2005), suggesting that physiological host range is often (much) greater than ecological host range. Despite the fact that laboratory tests demonstrated high parasitism levels in non-targets, ecological assessments in both North America and Europe suggested a much lower impact of P. digoneutis on non-target mirids. While some non-targets were not parasitized at all, others showed very low levels of parasitism (below 1% in Europe). Therefore, ecological host range studies in the area of origin provide useful supplementary data for interpretation of pre-release laboratory host range tests. Recently, Withers and Browne (2004) came up with a different approach, aiming the overall objective at maximizing the probability that non-target test species would be accepted during laboratory tests, which resulted in an accurate (although probably overestimated) risk assessment of the invertebrate biological control agent. When relying only on small cage laboratory experiments to assess the maximum host range possible retrospectively, P. digoneutis may have been classified as potentially risky, when in fact laboratory tests may have had a poor predictive value in this case. In general, when and why there is a good match between laboratory and field data remains an open and important question in arthropod biological control.

Competition and indirect effects It is suggested that negative interactions amongst biological control agents and com-

Current Status and Constraints in the Assessment of Non-target Effects

petitors may play a significant role both for the success of biological control projects and for non-target effects (Denoth et al., 2002; Reitz and Trumble, 2002). In fact, some well-documented examples of displacement have occurred among introduced biological control agents, and some of these showed that ecological processes responsible for displacement can be very complex (e.g. Murdoch et al., 1996). Regarding the natural enemy complex of the target, it is obvious that a successful biological control agent by itself may have dramatic consequences on the composition of this complex (e.g. Neuenschwander, 2001). It may be questioned whether displacement of an exotic natural enemy by another exotic, and population changes of native natural enemies associated with the control of the pest, can really be considered relevant non-target effects. Information on indirect effects is now required by the OECD (2003, see Table 1.1), but how this can be achieved is unclear, and it is still debated how an indirect effect can be defined. We believe that a clarifying definition has been provided by Messing et al. (Chapter 4, this volume), which basically distinguishes between direct competitive effects (those in which a natural enemy comes into direct physical contact with a competitor) and indirect competitive effects (in which the interaction among competing natural enemies is mediated via a third organism). While the former part of the definition relates to intra-guild predation (also listed in the OECD document, see Table 1.1), the latter part of the definition relates to all other, sometimes complex, processes. Messing et al. (Chapter 4, this volume) propose that an evaluation of indirect effects should preferentially concentrate on population- and community-level impacts rather than on consequences on individuals, and where possible, should be pursued under field conditions for extended periods of time. These studies typically include prolonged post-release monitoring and are thus labour-intensive and costly. Basic methods used to date include field surveys to compare non-target populations prior to and following release of the biological control agents (Brown, 2003), field

7

cage studies including the biological control agents and a competitor (Schellhorn et al., 2002) or intra-guild experiments in small arenas (e.g. for predators (Burgio et al., 2002); for parasitoids (Wang and Messing, 2002)). A tiered approach, combining laboratory, semi-field and field experiments, was recently applied in order to assess whether mass releases of Trichogramma brassicae Bezdenko (Hymenoptera: Trichogrammatidae) against the European corn borer might have detrimental effects on populations of other natural enemies in maize and adjacent habitats (Babendreier et al., 2003a). Again, the most serious problem with indirect effects is their complexity and the high degree of uncertainty inherently associated with them. Therefore, it is very difficult to incorporate them into risk assessment schemes (see Messing et al., Chapter 4 this volume; van Lenteren and Loomans, Chapter 15, this volume).

Post-release studies Retrospective post-release studies could be especially useful in verifying predictions made from host specificity testing before release of a biological control agent; however, to our knowledge, very few such studies are available (see Barratt et al., Chapter 10, this volume). This is probably due to the fact that most host specificity tests of arthropod biological control agents have been conducted only recently. The paucity of baseline data is a major drawback for postrelease studies. Typically for such studies, one or several non-target species were selected and sampled in areas where the biological control agent was released or was known to occur, and the mortality due to the agent was determined. Another method involves the placement of non-target individuals in the field where the biological control agent is known to occur or has been experimentally released. Life tables have also been shown to be a valuable tool in post-release studies, making it possible to put the observed mortality of the non-targets into context. To date, these studies suggest

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that it is often not the suspected introduced agent, but rather other factors, that were responsible for most of the non-target mortality observed (Barron et al., 2003; Johnson et al., 2005). Alternatively, populations of non-targets could be observed both in areas where the biological control agent is present and in areas where it is absent. Although this approach is being used in New Zealand to detect potential non-target effects in longterm studies (B.I.P. Barratt, Mosgiel, NZ, 2004, personal communication), to date no published reports are available where this method has been applied in arthropod biological control.

Establishment and dispersal Two additional topics, namely the potential for establishment and dispersal, need to be addressed in risk assessment of biological control agents, though they are mainly important in inundative release programmes. Unfortunately, until recently (Babendreier et al., 2005) there have been very few published studies dealing explicitly with these issues in the context of nontarget effects. General methods used were either to expose the agent under outdoor conditions or to assess the agent’s lethal temperature. All methods available are summarized in Boivin et al. (Chapter 6, this volume). A quite different approach that may be useful in predicting the likelihood of establishment of a biological control agent is based on ecoregions (see Cock et al., Chapter 12, this volume). However, even when an exotic biological control agent is not able to establish permanently, seasonal persistence might be possible. This means that potential nontarget effects would be limited in time and dependent on the dispersal abilities of the agent. Despite the large amount of literature on dispersal in general, few studies have been carried out on dispersal of biological control agents specifically to assess non-target effects. The most important details required include the numbers leaving release fields (or the greenhouse), and the densities of agents at certain distances

from the point of release. Suitable methods to gather these data are provided by Mills et al. (Chapter 7, this volume).

Modelling In addition to experimental studies, modelling approaches can also be used to predict potential risks of biological control agent introductions. Using a Nicholson-Bailey model, Lynch et al. (2002) studied whether transient non-target effects can occur at an early stage of a biological control introduction due to the very high target and, consequently, agent populations. Interestingly, this study demonstrated the potential for a strong, transient decline of a non-target host population, even when the biological control agent has a very low acceptance of the non-target species. Recently, another modelling study was conducted with the aim of making predictions for populations of non-targets when these suffered from, for example, 15% parasitism (Barlow et al., 2004). Building upon the vast amount of knowledge on Microctonus spp. introduced in New Zealand, Barlow et al. (2004) used discrete Ricker or continuous logistic models that incorporated density dependence and the intrinsic rate of increase as the key factors. Using the same parasitism rate, the model predicted reductions of two nontarget host populations of 8% or 35%, respectively, and the major factor was found to be the intrinsic rate of increase of populations at different altitudes. We believe that such studies are potentially important in addressing the risks of biological control agents to non-target populations, but on the other hand we feel that the special value of modelling studies will become apparent only when these have been validated with field, or at least experimental, data.

General Considerations Regarding the Regulation of Invertebrate Biological Control Agents Above, we have provided a short overview on the status of non-target testing of arthro-

Current Status and Constraints in the Assessment of Non-target Effects

pod biological control with special emphasis on methodological aspects. We also identified several difficulties encountered and briefly discussed them where appropriate. However, some more general constraints may be important to note as well. First, the statistical analysis of studies testing for non-target effects is sometimes inappropriate (see Hoffmeister et al., Chapter 13, this volume). For instance, there is not enough discussion on the number of replicates that should be carried out in host range testing; often this number is too low. A still unsolved issue is the question of whether one or very few replicates showing negative results are sufficient to conclude that the non-target host is outside of the agent’s host range. Clearly, statistical power decreases if a small number of replicates are carried out, and low power may be especially critical in the context of risk assessment (see Hoffmeister et al., Chapter 13, this volume). Another problem, especially valid for many field studies, is that they often have been limited in time (e.g. one field season only) and space. Longer-term studies may allow more precise conclusions to be drawn on non-target impacts, but have rarely been conducted in the past. The importance of spatial dimension was demonstrated by Follett et al. (2000), who found non-target parasitism to be dependent on the elevation level of Hawaiian Islands. Clearly, to increase the temporal or spatial scale of such studies would increase the costs, a problem that is discussed below. In those cases where parasitism/predation of a non-target host was observed, it is important to know the consequences at the population level. However, impact of biological control agents on field populations of non-target species has rarely been investigated experimentally. Even if effects on the population level have been demonstrated, there is still no consensus as to what a relevant non-target effect is. First approaches have been outlined by Lynch et al. (2001), who suggested a severity index ranging from zero (no negative reports) to nine (large-scale extinction). They concluded that few serious non-target effects

9

were observed if the baseline is the extirpation of host populations on regional or even larger scales. However, biological control agents will already be rejected at a lower level of impact; but at what level of effect to reject a natural enemy is an important and yet unsolved question. As host specificity again (and establishment for inundative releases) will be the central issue(s), the question may finally be: how many non-target species should be in the host range of a biological control agent in order to consider it unsafe (see van Lenteren et al., Chapter 3, this volume)? What about an agent that has the potential to attack some non-target species, but on the other hand also has the potential for large benefits? We believe that such questions will be of increasing importance in the risk assessment of biological control agents and these questions are being addressed by Bigler and Kölliker-Ott (Chapter 16, this volume). One disadvantage of regulating invertebrate biological control agents would be the increased costs and time lag to bring new biological control agents on to the market. While producers of biological control agents must invest more initially to develop new agents, these costs are likely to be passed to growers who buy biological control agents, and ultimately to consumers who want to purchase ‘pesticide-free’ products. There is also the risk that a few producers of biological control agents will dominate the biological control industry and small units will be eliminated. On the other hand it is important that augmentative biological control is not oversold; that is, recommended when unnecessary or when not appropriate. A spin-off benefit of regulating biological control agents will be the increased difficulty of selling products that are ineffective or inappropriate, and which may nevertheless pose risks to the environment. Another potential benefit would be greater protection of intellectual property. Thus, regulations would enhance reputable biological control agent manufacturers and sellers, make biological control more science-based and help to maintain a good image of biological control by the public.

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Given the limited resources available for biological control projects, it was stated that extensive assessment of non-target effects would be unrealistic and impractical (Messing, 2001). If a large number of species are tested with detailed investigations of the host-finding behaviour, and tests are conducted under semi-field conditions, then costs can be substantial. Obviously, the most costly species are those having a relatively wide host range, and it is worth noting that for polyphagous biological control agents, such as most trichogrammatids, a comprehensive list of non-target species may not be manageable. We believe that if polyphagous agents are to be considered at all (e.g. in inundative biological control), other approaches of risk assessment may have to be used. For instance, studies on habitat specificity or dispersal might be more promising than pure host range testing to determine the risk of such agents. One example of what can be done to assess non-target effects of the polyphagous T. brassicae was recently provided by Babendreier et al. (2003b). Another example is nematodes, which are often not restricted in their host range, but hardly any non-target effects due to the release of nematodes have been observed in the past (see Barratt et al., Chapter 10, this volume). If risks are not negligible, a cost–benefit analysis will provide a more accurate and balanced picture of the advantages and disadvantages of releasing an agent; in fact, information on potential benefits is also required by the OECD guidance document (Table 1.1), but to date only limited information on cost–benefit analysis in invertebrate biological control is available. One of the few papers including such information in the context of biological control was recently published by Heimpel et al. (2004) on the risks and benefits of introducing parasitoids for control of soybean aphids. In this book, we shall try to elaborate further on this issue (see Bigler and Kölliker-Ott, Chapter 16, this volume). Regulations will certainly have an impact on the business strategy of biological control manufacturers, particularly when generalist species are involved. Investigations of local

strains of the same or a related species could be encouraged. However, local populations should be used only as source material for laboratory cultures, not as a convenient supply. In North America, the convergent ladybird beetle, Hippodamia convergens Guérin-Meneville, is collected from overwintering aggregations and shipped directly to buyers (Gillespie et al., 2002). This is questionable because this practice has the potential for reducing local biodiversity and for transmitting contaminants (e.g. parasitoids and diseases) to native species in the area of release (see Goettel and Inglis, Chapter 9, this volume). A problem somehow specific to the OECD guidance document is the fact that it often requests ‘available information’. There will immediately be the question of what to do if there are no data available for a specific question. Moreover, the OECD document includes some issues that have received little attention in the past, including the potential for interbreeding (see Hopper et al., Chapter 5, this volume), the potential of damage to non-target plants (see Albajes et al., Chapter 8, this volume) or the potential risk that a biological control agent carries unwanted contaminants (see Goettel and Inglis, Chapter 9, this volume). These topics are addressed in the book and information on how to tackle such questions is provided.

Conclusions Despite the fact that few non-target effects associated with arthropod biological control have been reported, the number of studies that have tested for such effects increased substantially during the last decade. A lot of progress has been achieved and many recent introductions have been accompanied by appropriate host range assessments. Nevertheless, we are still not at the stage where host-range assessment combined with pre- and post-release studies are standard procedures in each biological control project, a suggestion put forward by Barratt et al. (Chapter 10, this volume). We would also like to stress that often only a fraction

Current Status and Constraints in the Assessment of Non-target Effects

of all potential risks have been assessed. This becomes especially obvious when looking at indirect effects where it is clearly not possible to test for all interactions. Although the above-mentioned efforts have already led to increased costs of biological control projects, a recent evaluation of the IPPC Code of Conduct revealed no decrease in the number of introductions of exotic biological control agents, but rather indicated a delay of introductions (Kairo et al., 2003). This, however, may be due to the fact that the IPPC Code was not legally binding to involved parties. If guidance documents (e.g. the OECD document) could find their way into national laws, then this situation may change in the future (i.e. the number of biological control projects and introductions might decrease). However, the application of appropriate regulatory procedures is important in order to maintain public confidence in biological control and to facilitate introductions and the commercial use of exotic biological control agents in the future.

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Since regulation and non-target testing will increase associated costs, it is important to use the available resources as efficiently as possible. Therefore, it is important to provide guidance on testing non-target effects, and for this goal it is extremely valuable to have appropriate methods available. This book aims to contribute to both objectives. We believe that improving non-target testing procedures in arthropod biological control is not only necessary for reducing the potential of adverse effects on non-targets even further, but also for preventing the hurdles that accompany over-regulation. The vast majority of agents used in arthropod biological control have been shown to be safe. Finally, we suggest conducting careful and well-balanced analyses of potential risks and benefits for biological control projects in the future, keeping in mind that all plant protection methods bear risks and benefits which need to be evaluated against each other.

References Babendreier, D., Rostas, M., Hofte, M.C.J., Kuske, S. and Bigler, F. (2003a) Effects of mass releases of Trichogramma brassicae on predatory insects in maize. Entomologia Experimentalis et Applicata 108, 115–124. Babendreier, D., Schoch, D., Kuske, S., Dorn, S. and Bigler, F. (2003b) Non-target habitat exploitation by Trichogramma brassicae (Hym.: Trichogrammatidae): what are the risks for endemic butterflies? Agricultural and Forest Entomology 5, 199–208. Babendreier, D., Bigler, F. and Kuhlmann, U. (2005) Methods used to assess non-target effects of invertebrate biological control agents of insect pests. BioControl 50, 821–870. Barlow, N.D., Barratt, B.I.P., Ferguson, C.M. and Barron, M.C. (2004) Using models to estimate parasitoid impacts on non-target host abundance. Environmental Entomology 33, 941–948. Barratt, B.I.P. (2004) Microctonus parasitoids and New Zealand weevils: comparing laboratory estimates of host ranges to realized host ranges. In: Van Driesche, R.G and Reardon, R. (eds) Assessing Host Ranges for Parasitoids and Predators Used for Classical Biological Control: a Guide to Best Practice. FHTET-2004-03, Forest Health Technology Enterprise Team, Morgantown,West Virginia, USA, pp. 103–120. Barratt, B.I.P., Evans, A.A., Ferguson, C.M., Barker, G.M., McNeill, M.R. and Phillips, C.B. (1997) Laboratory non-target host range of the introduced parasitoids Microctonus aethiopoides and M. hyperodae (Hymenoptera: Braconidae) compared with field parasitism in New Zealand. Environmental Entomology 26, 694–702. Barron, M.C., Barlow, N.D. and Wratten, S.D. (2003) Non-target parasitism of the endemic New Zealand Red Admiral Butterfly (Bassaris gonerilla) by the introduced biological control agent Pteromalus puparum. Biological Control 27, 329–335. Bigler, F., Bale, J., Cock, M., Dreyer, H., GreatRex, R., Kuhlmann, U., Loomans, A. and van Lenteren, J. (2005) Guideline on information requirements for import and release of invertebrate biological control agents in European countries. Biocontrol News and Information 26, 115N-123N. Brown, M.W. (2003) Intraguild responses of aphid predators on apple to the invasion of an exotic species, Harmonia axyridis. BioControl 48, 141–153.

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Burgio, G., Santi, F. and Maini, S. (2002) On intra-guild predation and cannibalism in Harmonia axyridis (Pallas) and Adalia bipunctata L. (Coleoptera: Coccinellidae). Biological Control 24, 110–116. Coombs, M. (2003) Post-release evaluation of Trichopoda giacomellii (Diptera: Tachinidae) for efficacy and non-target effects. In: Van Driesche, R.G. (ed.) Proceedings of the 1st International Symposium on Biological Control of Arthropods, Honolulu, Hawaii, 14–18 January 2002. FHTET-2003-05, United States Department of Agriculture, Forest Service, Morgantown, West Virginia, USA, pp. 399–406. De Nardo, E.A.B. and Hopper, K.R. (2004) Using the literature to evaluate parasitoid host ranges: a case study of Macrocentrus grandii (Hymenoptera: Braconidae) introduced into North America to control Ostrinia nubilalis (Lepidoptera: Crambidae). Biological Control 31, 280–295. Denoth, M., Frid, L. and Myers, J.H. (2002) Multiple agents in biological control: improving the odds? Biological Control 24, 20–30. Duan, J.J. and Messing, R.H. (2000) Evaluating non-target effects of classical biological control: fruit fly parasitoids in Hawaii as a case study. In: Follett, P.A. and Duan, J.J. (eds) Nontarget Effects of Biological Control. Kluwer Academic Publishers, Norwell, Massachusetts, USA, pp. 95–109. EPPO (1997) EPPO/CABI workshop on safety and efficacy of biological control agents in Europe. EPPO Bulletin 27, 1–3. EPPO (1999) First import of exotic biological control agents for research under contained conditions. EPPO Bulletin 29, 271–272. EPPO (2001) Import and release of exotic biological control agents. EPPO Bulletin 31, 33–35. EPPO (2002) List of biological control agents widely used in the EPPO region. EPPO Bulletin 32, 447–461. Follett, P.A., Duan, J.J., Messing, R.H. and Jones, V.P. (2000) Parasitoid drift after biological control introductions: Re-examining Pandora’s Box. American Entomologist 46, 82–94. Gariepy, T.D., Kuhlmann, U., Haye, T., Gillott, C. and Erlandson, M. (2005) A single-step multiplex PCR assay for the detection of European Peristenus spp. (Hymenoptera: Braconidae), parasitoids of Lygus spp. (Hemiptera: Miridae). Biocontrol Science and Technology, 15, 481–495. Gillespie, D.R., Shipp, J.L., Raworth, D.A. and Foottit, R.G. (2002) Aphis gossypii Glover, melon/ cotton aphid, Aulacorthum solani (Kaltenbach), foxglove aphid, Macrosiphum euphorbiae (Thomas), potato aphid, and Myzus persicae (Sulzer), green peach aphid (Homoptera: Aphididae). In: Mason, P.G. and Huber, J.T. (eds) Biological Control Programmes in Canada 1981–2000. CABI Publishing, Wallingford, UK, pp. 44–49. Haye, T. (2004) Studies on the ecology of European Peristenus spp. (Hymenoptera: Braconidae) and their potential for the biological control of Lygus spp. (Hemiptera: Miridae) in Canada. PhD thesis, University of Kiel, Germany. Haye, T., Goulet, H., Mason, P.G. and Kuhlmann, U. (2005) Does fundamental host range match ecological host range of Lygus plant bug parasitoids? A retrospective case study. Biological Control, 35, 55–67. Heimpel, G.E., Ragsdale, D.W., Venette, R., Hopper, K.R., O’Neil, R.J., Rutledge, C.E. and Wu, Z.S. (2004) Prospects for importation biological control of the Soybean aphid: anticipating potential costs and benefits. Annals of the Entomological Society of America 97, 249–258. Hoddle, M.S. (2004) Analysis of fauna in the receiving area for the purpose of identifying native species that exotic natural enemies may potentially attack. In: Van Driesche, R.G. and Reardon, R. (eds) Assessing Host Ranges for Parasitoids and Predators Used for Classical Biological Control: a Guide to Best Practice. Forest Health Technology Enterprise Team, Morgantown, West Virginia, USA, pp. 24–39. Howarth, F.G. (1983) Classical biological control: panacea or Pandora’s box. Proceedings of the Hawaiian Entomological Society 24, 239–244. Howarth, F.G. (1991) Environmental impacts of classical biological control. Annual Review of Entomology 36, 485–509. IPPC (1996) Code of conduct for the import and release of exotic biological control agents. Publication No. 3, FAO, Rome, Italy. IPPC (International Plant Protection Convention) (2005) Guidelines for the export, shipment, import and release of biological control agents and other beneficial organisms. International Standards for Phytosanitary Measures No. 3. https://www.ippc.int/servlet/CDSServlet?status=ND0xMz M5OS43NjA0NyY2PWVuJjMzPXB1YmxpY2F0aW9ucyZzaG93Q2hpbGRyZW49dHJ1ZSYzNz1p bmZv#koinfo (accessed 16 November 2005).

Current Status and Constraints in the Assessment of Non-target Effects

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Johnson, M.T., Follett, P.A., Taylor, A.D. and Jones, V.P. (2005) Impacts of biological control and invasive species on a non-target native Hawaiian insect. Oecologia 142, 529–540. Kairo, M.T.K., Cock, M.J.W. and Quinlan, M.M. (2003) An assessment of the use of the Code of Conduct for the Import and Release of Exotic Biological Control Agents (ISPM No. 3) since its endorsement as an international standard. Biocontrol News and Information 24, 15N–27N. Keller, M. (1999) Understanding host selection behaviour: the key to more effective host specificity testing. In: Withers, T.M. and Stanley, J.N. (eds) Host Specificity Testing in Australasia: Towards Improved Assays for Biological Control. CRC for Tropical Pest Management, Brisbane, Australia, pp. 84–92. Kuhlmann, U., Mason, P.G. and Foottit, R.G. (2000) Host specificity assessment of European Peristenus parasitoids for classical biological control of native Lygus species in North America: use of field host surveys to predict natural enemy habitat and host ranges. In: Van Driesche, R.G., Heard, T.A., McClay, A.S. and Reardon, R. (eds) Proceedings: Host Specificity Testing of Exotic Arthropod Biological Control Agents: the Biological Basis for Improvement in Safety. Xth International symposium on Biological Control of Weeds, July 4–14, 1999, Bozeman, Montana. Bulletin, FHTET-99–1, USDA Forest Service Morgantown, West Virginia, USA, pp. 84–95. Lockwood, J.A., Howarth, F.G. and Purcell, M.F. (2001) Balancing Nature: Assessing the Impact of Importing Non-Native Biological Control Agents (an International Perspective). Thomas Say Publications in Entomology, ESA. Lanham, Maryland, USA, 130 pp. Louda, S.M., Pemberton, R.W., Johnson, M.T. and Follett, P.A. (2003) Nontarget effects – the Achilles’ Heel of biological control? Retrospective analyses to reduce risk associated with biocontrol introductions. Annual Review of Entomology 48, 365–396. Lynch, L.D., Hokkanen, H.M.T., Babendreier, D., Bigler, F., Burgio, G., Gao, Z.H., Kuske, S., Loomans, A., Menzler-Hokkanen, I., Thomas, M.B., Tommasini, G., Waage, J.K., van Lenteren, J.C. and Zeng, Q.-Q. (2001) Insect biological control and non-target effects: a European perspective. In: Wajnberg, E., Scott, J.K. and Quimby, P.C. (eds) Evaluating Indirect Ecological Effects of Biological Control. CABI Publishing, New York, USA, pp. 99–125. Lynch, L.D., Ives, A.R., Waage, J.K., Hochberg, M.E. and Thomas, M.B. (2002) The risks of biocontrol: transient impacts and minimum non-target densities. Ecological Applications 12, 1872–1882. Mansfield, S. and Mills, N.J. (2004) A comparison of methodologies for the assessment of host preference of the gregarious egg parasitoid Trichogramma platneri. Biological Control 29, 332–340. Messing, R.H. (2001) Centrifugal phylogeny as a basis for non-target host testing in biological control: Is it relevant for parasitoids? Phytoparasitica 29, 187–190. Murdoch, W.W., Briggs, C.J. and Nisbet, R.M. (1996) Competitive displacement and biological control in parasitoids: a model. American Naturalist 148, 807–826. NAPPO (2000) Guidelines for petition for release of exotic entomophagous agents for the biological control of pests. Secretariat of North American Plant Protection Organization, Ottawa, Canada. Neuenschwander, P. (2001) Biological control of the cassava mealybug in Africa: A review. Biological Control 21, 214–229. OECD (2003) Guidance for information requirements for regulations of invertebrates as biological control agents. OECD Environment, Health and Safety Publications. Series on Pesticides 21, 22 pp. Reitz, S.R. and Trumble, J.T. (2002) Competitive Displacement among insects and arachnids. Annual Review of Entomology 47, 435–465. Sands, D. (1997) The ‘safety’ of biological control agents: Assessing their impact on beneficial and other non-target hosts. Memoirs of the Museum of Victoria 56, 611–616. Sands, D. (1998) Guidelines for testing host specificity of agents for biological control of arthropod pests. In: Zalucki, M.P., Drew, R.A.I. and White, G.G. (eds). Proceedings of the Sixth Australasian Applied Entomological Research Conference, Volume 1. University of Queensland Press, Brisbane, Australia, pp. 556–560. Sands, D.P.A. and Van Driesche, R.G. (2000) Evaluating the host range of agents for biological control of arthropods: rationale, methodology and interpretation. In: Van Driesche, R.G., Heard, T.A., McClay, A.S. and Reardon, R. (eds) Proceedings: Host Specificity Testing of Exotic Arthropod Biological Control Agents: the Biological Basis for Improvement in Safety. Xth International symposium on Biological Control of Weeds, July 4–14, 1999, Bozeman, Montana. Bulletin, FHTET-99-1, USDA Forest Service Morgantown, West Virginia, USA, pp. 69–83.

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Sands, D.P.A. and Van Driesche, R.G. (2004) Using the scientific literature to estimate the host range of a biological control agent. In: Van Driesche, R.G. and Reardon, R. (eds) Assessing Host Ranges for Parasitoids and Predators Used for Classical Biological Control: a Guide to Best Practice. Forest Health Technology Enterprise Team, Morgantown, West Virginia, USA, pp. 15–23. Schellhorn, N.A., Kuhman, T.R., Olson, A.C. and Ives, A.R. (2002) Competition between native and introduced parasitoids of aphids: non-target effects and biological control. Ecology 83, 2745–2757. Simberloff, D. and Stiling, P. (1996) How risky is biological control? Ecology 77, 1965–1974. Symondson, W.O.C. (2002) Molecular identification of prey in predator diets. Molecular Ecology 11, 627–641. Van Driesche, R.G. and Hoddle, M. (1997) Should arthropod parasitoids and predators be subject to host range testing when used as biological control agents? Agriculture and Human Values 14, 211–226. Van Driesche, R.G. and Murray, T.J. (2004a) Overview of testing schemes and designs used to estimate host ranges. In: Van Driesche, R.G. and Reardon, R. (eds) Assessing Host Ranges for Parasitoids and Predators Used for Classical Biological Control: a Guide to Best Practice. Forest Health Technology Enterprise Team, Morgantown, West Virginia, USA, pp. 68–89. Van Driesche, R.G. and Murray, T. J. (2004b) Parameters used in laboratory host range tests. In: Van Driesche, R.G. and Reardon, R. (eds) Assessing Host Ranges for Parasitoids and Predators Used for Classical Biological Control: a Guide to Best Practice. Forest Health Technology Enterprise Team, Morgantown, West Virginia, USA, pp. 56–67. Van Driesche, R.G. and Reardon, R. (2004) Assessing Host ranges for Parasitoids and Predators Used for Classical Biological Control: a Guide to Best Practice. Forest Health Technology Enterprise Team, Morgantown, West Virginia, USA. van Lenteren, J.C., Babendreier, D., Bigler, F., Burgio, G., Hokkanen, H.M.T., Kuske, S., Loomans, A.J.M., Menzler-Hokkanen, I., van Rijn, P.C.J., Thomas, M.B., Tommasini, M.G. and Zeng, Q.-Q. (2003) Environmental risk assessment of exotic natural enemies used in inundative biological control. BioControl 48, 3–38. Wang, X.G. and Messing, R.H. (2002) Newly imported larval parasitoids pose minimal competitive risk to extant egg-larval parasitoid of tephritid fruit flies in Hawaii. Bulletin of Entomological Research 92, 423–429. Whithers, T.M. and Browne, L.B. (2004) Behavioral and physiological processes affecting outcomes of host range testing. In: Van Driesche, R.G. and Reardon, R. (eds) Assessing Host ranges for Parasitoids and Predators Used for Classical Biological Control: a Guide to Best Practice. Forest Health Technology Enterprise Team, Morgantown, West Virginia, USA, pp. 40–55. Zilahi-Balogh, G.M.G., Kok, L. and Salom, S. (2002) Host specificity of Laricobius nigrinus Fender (Coleoptera: Derodontidae), a potential biological control agent of the hemlock woolly adelgid, Adelges tsugae Annand (Homoptera: Adelgidae). Biological Control 24, 192–198.

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Selection of Non-target Species for Host Specificity Testing

Ulrich Kuhlmann,1 Urs Schaffner1 and Peter G. Mason2 1CABI

Bioscience Switzerland Centre, Rue des Grillons 1, 2800 Delémont, Switzerland (email: [email protected]; fax number: +41-32-4214871); 2Agriculture and Agri-Food Canada, Research Centre, Central Experimental Farm, Ottawa, Ontario, K1A 0C6 Canada (email: [email protected]; fax number: +1-613-7591701)

Abstract We present comprehensive recommendations for setting up test species lists for arthropod biological control programmes that are scientifically based and ensure that all aspects of potential direct impacts are considered. It is proposed that a set of categories, including ecological similarities, phylogenetic/taxonomic affinities and safeguard considerations are applied to ecological host range information to develop an initial test list. This list is then filtered to reduce the number of species to be tested by eliminating those with different spatial, temporal and morphological attributes and those species that are not readily obtained, and thus unlikely to yield scientifically sound data. The revised test list is used for the actual testing but can (and should) be revised if new information obtained indicates that additional or more appropriate species should be included. Use of the recommendations is illustrated by a case study on the host specificity of a tachinid fly Celatoria compressa Wulp, a candidate for use as a biological control agent against the western corn rootworm, Diabrotica virgifera virgifera LeConte.

Introduction Biological control is an environmentally friendly and highly cost-effective strategy for combating pests in agriculture and forest ecosystems. Despite recent concerns about unintended effects, the use of exotic natural enemies against invasive alien species in natural and agricultural habitats remains a key component of integrated pest management. What has changed during the last decade is the importance of scientifically sound decisions for ensuring that exotic biological control agents introduced

into new environments have minimal impact on non-target species. Host-specificity testing of entomophagous biological control agents has lagged behind that of phytophagous biological control agents. In fact, until the warnings by Howarth (1983, 1991), Lockwood (1993a,b, 2000) and Louda et al. (1997), concerns about impacts on non-target species were infrequently considered in entomophagous biological control projects. Lynch et al. (2001) reviewed the published and unpublished European information and determined that a mere 1.5% of entomophagous biological

©CAB International 2006. Environmental Impact of Invertebrates for Biological Control of Arthropods: Methods and Risk Assessment (eds F. Bigler et al.)

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control agents introduced before 1999 appeared to have undergone host specificity analyses, thus the extent of information on non-target impacts, including selection of species to be tested, is limited. Selection of appropriate species for testing potential impacts of candidate biological control agents is the first critical step in the process once the need for pest suppression is justified and one or more potential agents have been identified. Several authors, e.g. Sands (1997, 1998); van Lenteren et al. (2003), have suggested that the centrifugal phylogenetic method of Wapshere (1974) should be the primary method used for selecting non-target species for testing candidate entomophagous biological control agents. However, the centrifugal phylogenetic approach may not always be feasible because of taxonomic uncertainties and the greater number of taxa that could be required in testing compared to weeds (Kuhlmann et al., 2000). Moreover, other parameters such as the feeding niche or the common habitat of target and non-target species may be more meaningful, at least for certain biological control agents (Messing, 2001). In this chapter, we review the current practice of developing test plant lists in weed biological control programmes as a basis for discussion, what determines parasitoid host ranges, and review the approaches taken in recent arthropod biological control programmes. We propose comprehensive recommendations for setting up test species lists for arthropod biological control programmes that are scientifically based and ensure that all aspects of potential direct impacts are considered. At the same time, the recommendations attempt to take into consideration possible practical constraints associated with arthropod host specificity screening (Sands and Van Driesche, 2000). Use of the recommendations is illustrated by a case study on the host specificity of a tachinid fly, Celatoria compressa, a candidate for use as a biological control agent against the chrysomelid Diabrotica virgifera virgifera (Kuhlmann et al., 2005). Although most of

the available information is on parasitoids, the recommendations developed should apply to other invertebrate groups such as arthropod predators and entomopathogenic nematodes.

What can be Learned from Current Practice in Weed Biological Control? For more than 30 years, the screening of the fundamental (= physiological) and the ecological host range of candidate biological control agents has been the most crucial step in pre-release studies of any weed biological control programme (Harris and Zwoelfer, 1968; Zwoelfer and Harris, 1971; Wapshere, 1974). Because of the overriding importance of safety, greatest care is taken in selecting appropriate test plants and in designing meaningful screening tests to accurately predict the host specificity of potential control agents. Host range studies were originally developed to protect agricultural crops from unwanted attack. While taxonomic relatedness provides a starting point, in practice other considerations, such as inclusion of beneficial (i.e. crop) species and those that are aesthetically important (i.e. species at risk), are also considered. At present, the selection of test plants is based on proposals made by Harris and Zwoelfer (1968) Wapshere (1974) and Wapshere (1989). The aim is to select those plant species most likely to be hosts of the organism in question, without undue expansion of the test plant list. The basis of the standard selection protocol is the centrifugal phylogenetic method developed by Wapshere (1974). This method was based on the observation that the host range of specialist herbivores is usually restricted to one or a few phylogenetically related plant taxa. Recent studies have confirmed this pattern for many, but not all, insect herbivore groups (Bernays, 2000; Pemberton, 2000; for groups including biological control agents see Dobler, 2001; Ronquist and Liljeblad, 2001). The centrifugal phylogenetic method involves selecting and testing plants of increasingly distant phylogenetic

Selection of Non-target Species for Host Specificity Testing

relationship to the target weed (Wapshere, 1974; Table 2.1). As a safeguard against failure of the centrifugal phylogenetic method, Wapshere (1974) proposed adding a number of economically important plants to the test plant list, as well as any plant species on which the candidate agent had previously been recorded. In modern weed biological control programmes, additional plants considered in test plant lists are species with phytochemical or morphological features similar to those of the target host; plants known to be attacked by organisms closely related to the candidate biological agent; threatened and endangered species in the same family as the target species; and those occurring in the same habitat (Table 2.2).

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As noted by Briese et al. (2002), while the centrifugal phylogenetic method claims to be phylogenetically based, it used to be – and largely still is to date – based on taxonomic circumscription. For example, it is only recently that comprehensive phylogenies of the species-rich genera Centaurea and Senecio, both of which include invasive weeds, and species in the closely related genera, have been hypothesized using molecular data (Garcia-Jacas et al., 2001; Pelser et al., 2002). The number of plant species that should be included in a non-target list depends mainly on: ● The taxonomic position of the target weed – whether it belongs to an isolated family or to a family with close relations.

Table 2.1. Wapshere’s (1974) centrifugal phylogenetic testing method. Testing sequence

Plants to be tested

Host range determined if plants at that phylogenetic level remain unattacked

1st 2nd 3rd 4th 5th 6th

Other forms (ecotypes/biotypes) of target species Other species of same genus Other members of tribe Other members of subfamily Other members of family Other members of order

Specific to clone Specific to species Specific to genus Specific to tribe Specific to subfamily Specific to family

Table 2.2. Plant categories listed in the Reviewer’s Manual for the Technical Advisory Group for Biological Control of Weeds (USDA/APHIS, Plant Protection and Quarantine) for compilation of a test plant list. Category 1: Category 2: Category 3:

Category 4: Category 5:

Category 6:

Category 7:

Genetic types of the target pest species (genotypes, geographic populations, etc.) Species in the same genus as the target weed, divided by subgenera (if applicable), including economically and environmentally important plants of North America. Species in other genera in the same family as the target weed, divided by subfamily (if applicable), including economically and environmentally important plants of North America. Threatened and endangered species in the same family as the target weed, divided by subgenus, genus and subfamily. Species in other families in the same order that have some phylogenetic, morphological or biochemical similarities to the target weed, or that share the same habitat, including economically and environmentally important plants of North America. Species in other orders that have some morphological or biochemical similarities to the target or that share the same habitat, including economically and environmentally important plants of North America. Any plant species on which the biological control agent or its close relatives (within the same genus) have previously been found or recorded feeding and/or reproducing.

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● The number of closely related cultivated plants, and other-valued wild plants. ● The geographic and/or ecological isolation of the release area. ● Whether or not the candidate biological control organism belongs to a systematic group which is known to be restricted to a small group of closely related plants (genus, subtribe and tribe). In recent examples of host range determination, the number of plant species screened ranges from 40 to more than 100. In host plant lists of modern weed biological control projects, unrelated plant species sharing conspicuous secondary metabolites or morphological characters are represented to a certain extent, but it is usually not known whether the characters selected are indeed of relevance in the host selection behaviour of the candidate weed biological control agent. To increase the chances of detecting disjunct oligophagy, one needs to elucidate the cues used by the candidate species in selecting and accepting host plants (Schaffner, 2001). In a recent review of the relevance of the criteria set up by Wapshere (1974), Briese et al. (2002) argued that none of the safeguard criteria has generated additional insight into the results obtained by applying the centrifugal phylogenetic method. Briese et al. (2002) therefore recommended dropping these safeguard criteria to reduce costs of pre-release studies. However, host range testing with the agromyzid fly Napomyza sp. near lateralis in a biological control project against Russian knapweed, Acroptilon repens (L.) de Candolle, revealed that the only plant species outside the knapweeds (genera Acroptilon and Centaurea) found to be both within the fundamental and ecological host ranges of this species is the distantly related host plant of a sibling agromyzid species (U. Schaffner, Delémont, 2004, unpublished results). Since the addition of a few safeguard species, e.g. in nochoice feeding bioassays, usually does not cause major additional costs in weed biological control programmes, further inclusion of safeguard species may be scientifically and politically justified, despite the fact that they rarely contribute to additional informa-

tion on the host affiliation. While these practices have been developed over time for weed biological control, in arthropod biological control other factors may determine the parasitoid host range. These factors will be outlined in the following section.

What Determines Parasitoid Host Range? Our knowledge of parasitoid host ranges is based primarily on associations made through rearing a limited number of host species. The number of studies exploring the evolutionary and ecological determinants of host use in parasitoids is growing (e.g. Hawkins, 1994; Hawkins and Sheehan, 1994), yet for most groups we have limited information on the relative importance of host habitat, processes of host location, physiological interactions with hosts, host defences or host phylogenetic history in influencing parasitoid host ranges (Stireman and Singer, 2003). Documenting parasitoid host range is far more difficult than collecting data on host parasitoid species load because it involves rearing parasitoids to the adult stage for identification from many different species of hosts rather than rearing or dissecting many individuals of a single host species. Existing data are of two main types: large catalogues of known host associations, frequently, although not always, concerned with selected taxonomic groups of parasitoids and very seldom including any quantitative information; and food webs containing information on all parasitoids attacking a restricted range of hosts, often in a single geographical area (Memmott and Godfray, 1993). Information from host catalogues must be treated with extreme caution (Askew and Shaw, 1986; Noyes, 1994). The difficulties of parasitoid taxonomy, plus the risk of erroneous parasitoidhost associations, render many large catalogues almost useless for ecological studies, but exceptions occur where experts have at least carefully scrutinized host records in the literature (e.g. Boucek and Askew, 1968; Griffiths, 1964–1968).

Selection of Non-target Species for Host Specificity Testing

No parasitoid successfully parasitizes all hosts in the environment, and species that are attacked by the same parasitoid share certain characteristics. The two most important determinants of host range are most probably host taxonomy and shared ecology (Askew and Shaw, 1986; Shaw, 1988). The correlation between host taxonomy and parasitoid range has been demonstrated on numerous occasions (e.g. Askew, 1961; Griffiths, 1964–1968) and these correlations can arise for at least two reasons. First, parasitoids may attack closely related hosts because they share similar physiological properties and defence mechanisms. Second, closely related parasitoids are likely to be biologically similar, for example, they are more likely to feed on hosts using the same host plant or to have similar feeding niches. The importance of shared ecology is best illustrated by examples of unrelated hosts of parasitoids that share host plants or feeding niches and are attacked by the same parasitoid. Hosts that feed on the same food plant frequently share the same parasitoids (e.g. Vinson, 1981, 1985; Fitton et al., 1988). Plant chemistry may influence parasitoid host range if hosts sequester toxins from their food plants. Chemical similarity is known to influence polyphagy at the herbivore trophic level, and chemical diversity has been linked with host range (e.g. Strong et al., 1984), including semiochemicals released when the plant is damaged by herbivore hosts (e.g. Godfray, 1994). Hoffmeister (1992) surveyed the parasitoids attacking seven races or species of tephritid fly feeding in the fleshy seeds of a variety of trees, shrubs and climbers in Europe. He found that host ecology, broadly defined as phenology, feeding habitat and host plant taxonomy, was more important than host taxonomy in determining the make-up of the parasitoid complex. Based on Godfray (1994), some predictions can be made about the relative host ranges of parasitoid species with an intimate biochemical and physiological connection with their hosts (larval koinobiont endoparasitoids), species that do not have to contend with active host defences (larval

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idiobiont ectoparasitoids) and species that attack non-growing host stages. The first group should be relatively specialized and their host range will be strongly influenced by host taxonomy. The last group should be less specialized and their host range will be influenced by both host taxonomy and host ecology. Thus: ● Koinobionts should have fewer hosts than idiobionts. ● Pupal and egg and adult parasitoids should be less specialized than larval parasitoids (Strand, 1986). ● The koinobiont parasitoids of taxonomically isolated hosts should attack few other species. ● The idiobiont parasitoids of ecologically isolated hosts should attack few other species. Idiobiont larval parasitoids more often attack hosts in concealed feeding niches where death or permanent paralysis is less likely to increase the risk of predation (Hawkins, 1990). There will be numerous exceptions to the broad generalizations set up by Godfray (1994). For example, many tachinid flies are koinobiont endoparasitoids, yet can subvert the host immune system of a wide variety of species and thus enjoy a remarkably broad host range (Belshaw, 1994). The suggestion that koinobionts have broader host ranges than idiobionts has some empirical support. In surveys of parasitoids of lepidopteran and hymenopteran leafminers, Askew and Shaw (1986), Pschorn-Walcher and Altenhofer (1989) and Sato (1990) all observed more restricted host ranges among idiobionts than among koinobionts. The importance of shared ecology should not be overemphasized. There are many examples of parasitoids that attack one or a few closely related hosts in a wide variety of habitats (e.g. Price, 1981). Futuyma and Moreno (1988) reviewed a variety of macroevolutionary aspects of parasitoid host range. In some taxa, particular specializations appear to be taxonomically conserved: all Eucharitidae parasitize ants; the complete Opiinae and Alysiinae clade (Braconidae) are restricted to cyclorraphous

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Diptera; and the ichneumonid subfamily Ichneumoninae and the braconid subfamily Microgasterinae parasitize only Lepidoptera (Futuyma and Moreno, 1988). In other groups, for example the Eulophidae and Pteromalidae, nearly all species parasitize a restricted set of hosts, yet the clade is not committed to any particular host group. Generalism also may be phylogenetically conserved. The braconid genus Dacnusa is comprised of many species specialized on particular agromyzid leaf miners, but the few species with wide host ranges are closely related (Godfray, 1994). In summary, although determining parasitoid host ranges is plagued with difficulties (Shaw, 1994), it appears that most parasitoids attack a narrow range of hosts (Memmott et al., 2000). The two principal factors that limit host ranges in parasitoids are thought to be taxonomic relatedness of hosts and host ecology. The effect of host taxonomic affinity is believed to be related primarily to physiological (and morphological) defences of hosts that may require specific adaptations of their parasitoids (Vinson and Iwantsch, 1980; Godfray, 1994). The proposal that physiological defences limit parasitoid host ranges is analogous to arguments concerning the importance of secondary chemicals in the specialization of phytophagous insects on food plants (Ehrlich and Raven, 1964). Ecological characteristics that influence host use by parasitoids include the plants on which a host feeds (Vinson, 1981; Askew, 1994), the microhabitat in which it feeds (Weseloh, 1993), the host’s phenology (Askew, 1961) and the host and parasitoid mobility (Barratt, 2004). Thus, parasitoid host range is determined by biological and ecological factors, often, but not always, associated with related host species.

What Methods Have Been Used So Far for Selecting Non-target Species in Arthropod Biological Control? A review of some recent studies suggests that in practice, criteria for selecting nontarget species for testing can be divided

into five categories: ecological, phylogenetic, socio-economic, biological and availability of test species (Table 2.3). Many studies state the reasons behind selection of the test species, and all but three studies used at least two of the categories in their selection. The numbers of non-target species tested in the laboratory ranged from one to 23 (average 10.5). Although Rutledge and Wiedenmann (1999) and Bourchier (2003) did not actually do any testing, both provided important ideas for selecting test species. Phylogenetic considerations were based on taxonomic relatedness (e.g. same genus, same family, etc.) of test species to target host. Ecological features included overlaps of geographic range, habitat preference and feeding niche of species representing different components of the community. Biological characteristics included known host range, phenological overlap of the target and nontarget species, dispersal capability of the candidate biological control agent (and parasitized host), morphological similarity, behavioural factors (e.g. feeding, oviposition, host location, etc.) and overlap of the physiological host range of biological control agents. Socio-economic factors included whether a potential test species was commercially important (e.g. a pollinator), beneficial (e.g. predator, weed biological control agent) or of conservation importance (e.g. rare or endangered). The availability of non-target material was considered, and sources included commercial or laboratory cultures, field collections and progeny of field-collected individuals. In most examples literature records provided important guidance on at least broad groups, habitats or biological parameters. In one case, surveys by Fuester et al. (2001) in the area of origin of the target species provided information on actual host range that was useful for selecting test lists. Sands et al. (1993) studied the host range of Cotesia erionotae (Wilkinson) (Hymenoptera: Braconidae), a parasitoid of the banana skipper Erionota thrax (L.) (Lepidoptera: Hesperiidae). One non-target species in the same family as the banana skipper and three species that were consid-

Table 2.3. Review of some recent studies suggests that, in practice, criteria for selecting non-target species for testing invertebrate biological control agents for arthropod pests can be divided into five categories: ecological, phylogenetic, socio-economic, biological and availability of test species. Ecological similarity

Phylogenetic affinity Sands et al. (1993)

Socio-economic

Biological

Availability

Andow et al. (1995)

–

Sands et al. (1993)

Neale et al. (1995)

Neale et al. (1995)

Neale et al. (1995)

Duan and Messing (1996, 1997)

Duan and Messing (1996, 1997)

Duan and Messing (1996, 1997)

Duan and Messing (1996, 1997)

Duan and Messing (1996, 1997)

Duan et al. (1997)

Duan et al. (1997)

Duan et al. (1997)

Duan et al. (1997)

Duan et al. (1997)

Cameron and Walker (1997) Barratt et al. (1997, 1998, 2000)

Barratt et al. (1997, 1998, 2000)

Barratt et al. (1997, 1998, 2000)

Kitt and Keller (1998)

Cameron and Walker (1997)

Cameron and Walker (1997)

Barratt et al. (1997, 1998, 2000)

Barratt et al. (1997, 1998, 2000)

Kitt and Keller (1998)

Kitt and Keller (1998)

Orr et al. (2000)

Orr et al. (2000)

Rutledge and Wiedenmann (1999) Porter (2000)

Porter (2000)

Boettner et al. (2000)

Porter (2000) Boettner et al. (2000)

Boettner et al. (2000)

Fuester et al. (2001)

Boettner et al. (2000) Fuester et al. (2001) Mansfield and Mills (2002)

Munro and Henderson (2002)

Munro and Henderson (2002)

Bourchier (2003)

Bourchier (2003)

Babendreier et al. (2003a,b)

Babendreier et al. (2003a,b) Babendreier et al. (2003c)

Selection of Non-target Species for Host Specificity Testing

Andow et al. (1995)

Bourchier (2003) Babendreier et al. (2003a,b) Babendreier et al. (2003c)

Babendreier et al. (2003d)

Babendreier et al. (2003d) Benson et al. (2003) 21

22

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ered of commercial value were selected. Although not stated, it appears that test individuals were obtained commercially or field-collected. The results indicated that none of the non-target species would be attacked. Andow et al. (1995) developed a hypothetical analysis of risks to non-target Lepidoptera after release of Trichogramma nubilale Ertle and Davis (Hymenoptera: Trichogrammatidae) for control of Ostrinia nubilalis Hübner (Lepidoptera: Crambidae). Selection of the non-target Karner Blue Butterfly, Lycaeides melissa samuelis Nabakov, as a test species was based on endangered status, spatial occurrence, known host range of the agent, phenological overlap of the target and non-target species, dispersal of the biological control agent and mortality of the agent during dispersal. Their analysis indicated that populations of L. m. samuelis were unlikely to be reduced by inundative introductions of T. nubilale. Neale et al. (1995) developed a nontarget test list for assessing one encyrtid and two eulophid larval parasitoids of the citrus leafminer, Phyllocnistris citrella Stainton (Lepidoptera: Gracillaridae) in Australia. Although not stated, the test species were probably chosen based on ecological, phylogenetic and socioeconomic criteria. Twelve non-target Lepidoptera species belonging to five families were selected; these were mainly leafminers and gallformers, and included the single native Australian representative of Phyllocnistris and several weed biological control agents. Three gall-forming and one leaf-mining fly species and a single leaf-mining beetle species were also included. The outcome of host range testing indicated that the parasitoids were specific to the target species. Duan and Messing (1996, 1997) and Duan et al. (1997) studied the potential non-target impacts of Dichasmimorpha longicaudata (Ashmead), Dichasmimorpha tryoni (Cameron) and Psytallia fletcheri (Sivestri) (all Hymenoptera: Braconidae), introduced for fruit fly control in Hawaii. Two non-target species were selected based

on a suite of criteria. These included: gall size and shape or feeding niche (of the target and non-target species); relatedness of parasitoid species attacking target and nontarget hosts in the field; and shape of the parasitoid ovipositor and specialized searching behaviour. One of the non-target species studied was a native species collected in the field, and the other was a weed biological control agent that was obtained from an established culture. These studies showed that fruit shape, size and colour are essential stimuli to elicit oviposition by the candidate parasitoids and that these species would only attack fruit fly hosts that live in fruit or fruit-like structures; non-target hosts in a different feeding niche were not impacted by the biological control agents. Cameron and Walker (1997) studied the host specificity of Cotesia rubecula (Marshall) and Cotesia plutellae Kudjumov (Hymenoptera: Braconidae), parasitoids of Pieris rapae L. (Lepidoptera: Pieridae) and Plutella xylostella (L.) (Lepidoptera: Plutellidae). Initial selection of non-target species for host specificity testing was based on literature records and field collections of Lepidoptera from Brassica spp. and Urtica dioica DC in areas where C. rubecula and C. plutellae were abundant in New Zealand and Fiji. The test list was refined using behavioural data on host plant attractiveness to each parasitoid species. These studies determined that C. rubecula was highly specific to P. rapae. In contrast, despite being more attracted to species associated with cabbage volatiles, C. plutellae attacked all species tested, and successfully developed in ten of the 14 non-target species. Barratt et al. (1997, 1998, 2000) studied host specificity of two braconid parasitoids, Microctonus aethiopoides Loan and Microctonus hyperodae Loan (Hymenoptera: Braconidae), of the adult Sitona discoideus Gyllenhal and Listronotus bonariensis (Kuschel) (Coleoptera: Curculionidae), important forage pests in New Zealand. They conducted field surveys (Barratt et al., 1998) of native Curculioniodea to determine which phylogenetic, ecological and behav-

Selection of Non-target Species for Host Specificity Testing

ioural affinities could be used to develop a test list. Of the 85 Curculionoidea species found, 11 were selected, and test material was collected from the field. A combination of phylogenetic and known host range information on M. aethiopoides and M. hyperodae was used to determine which non-target species would potentially be at greatest risk. Additional pest and beneficial (weed biological control agents) species related to the target species found in the surveys were included. Further criteria included similarities in feeding, seasonal abundance and activity patterns. Parasitoid behaviour patterns were also studied to determine if oviposition activities coincided with active cycles of potential non-target hosts. Laboratory results suggested that M. aethiopoides successfully developed in nine of 12, and M. hyperodae in four of 11 species tested. Field studies confirmed that M. aethiopoides parasitized a broader range of species than did M. hyperodae. Kitt and Keller (1998) carried out tests on host plant preferences of the aphid parasitoid Aphidius rosae Haliday (Hymenoptera: Aphidiidae). Results showed that only non-target aphids on roses, the habitat utilized by the target Macrosiphum rosae (L.) (Hemiptera: Aphididae), would be at risk, thereby reducing the list of non-target species to test. Species tested included those collected in sufficient numbers from glasshouses and from the field. They concluded that A. rosae would successfully attack only the target, M. rosae. Rutledge and Wiedenmann (1999) tested the response of Cotesia flavipes Cameron, Cotesia sesamiae (Cameron) and Cotesia chilonus (Matsumura), braconid parasitoids of stem-boring pests of graminaceous plants. Although no non-target testing was conducted, biological characteristics of the parasitoids and responses to a range of host and non-host plant volatiles were used as a theoretical basis for selecting non-target species. It was concluded that for certain parasitoids, testing plant preferences could help determine their ecological host range.

23

Boettner et al. (2000) studied the nontarget effects of the tachinid Compsilura concinnata (Meigen) (Diptera: Tachinidae), introduced for control of the gypsy moth Lymantria dispar (L.) (Lepidoptera: Lymantriidae), and 12 other pest species, including the saturniid moth Hemileuca oliviae Cockerell. Based on the knowledge that C. concinnata has a very broad host range (>180 native North American Lepidoptera spp.), non-target hosts selected for study were species of the same family (Saturniidae) as the target, species that feed on plants found in the same habitat (oak forest) as the main target species and those were obtainable from culture. An additional, threatened, non-target species was collected by chance in the study habitat and was incorporated into the project. These authors found that C. concinnata was responsible for significant parasitism (36% to 81%) of the three non-target species studied. Orr et al. (2000) studied host specificity of Trichogramma brassicae Bezdenko (Hymenoptera: Trichogrammatidae) and used biological and ecological criteria to determine which non-target species to include in evaluations. Based on dispersal behaviour, they determined that Lepidoptera species found in the target habitat (maize) and adjacent habitats were the most appropriate for host range testing. Furthermore, only those Lepidoptera species where eggs were present during periods of T. brassicae release were considered to be potentially vulnerable. Orr et al. (2000) collected and identified Lepidoptera species and estimated their flight period from museum collection data. Flight periods from 22 species overlapped with T. brassicae release periods, and progeny from field-collected material were used for further testing. The authors noted that species not attracted to light traps or not abundant may have been missed, particularly rare species. Of the 22 species tested in the laboratory, 11 were found to be highly suitable hosts for T. brassicae, but in the field, parasitism of these same non-target species was very low, often zero.

24

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Porter (2000) examined the host specificity of Pseudacteon curvatus Borgmeier (Diptera: Phoridae) as a biological control agent for the fire ants Solenopsis invicta Buren and Solenopsis richteri Forel (Hymenoptera: Formicidae) in the southern United States and used phylogenetic and biological information to develop a list of non-target species for testing. Information on the candidate agent indicated that only Formicidae were attacked by Pseudacteon spp., that the ovipositor of this group was highly specialized and that host size was a factor, thus limiting the ability to parasitize other organisms. Material was collected from the field for the 19 species tested. Results confirmed that P. curvatus will only develop in Solenopsis spp., and parasitism of two native species tested was considerably less than for the target species. Fuester et al. (2001) studied the host range of Aphantorhaphopsis samarensis (Villeneuve) (Diptera: Tachinidae), a candidate for biological control of gypsy moth in North America. Ecological and biological information, such as habitat and life history overlap, were considered in the selection of non-target species. Field studies in the area of origin provided information on the realized host range of A. samarensis. Of the 54 species collected in 11 families of Lepidoptera no A. samarensis emerged. Progeny of field-collected individuals (11 species from ten lepidopteran families) were primarily used in laboratory tests, although it was not stated from which habitats these species were collected. Only one non-target species, one of two Lymantriidae tested, was successfully parasitized by A. samarensis. Mansfield and Mills (2002) evaluated the host range of Trichogramma platneri Nagarkatti for control of Cydia pomonella L. (Lepidoptera: Tortricidae). They considered ecological and biological criteria (e.g. known hosts, novel hosts and host egg characteristics) to develop a list of nontarget species for testing. From this list, commercially available species and laboratory cultures that could be easily obtained were chosen for testing. The results indicated that of the 17 species tested, T. plat-

neri successfully emerged from six of 12 Lepidoptera species and the neuropteran, Chrysoperla carnea Stephens. These authors also concluded that larger eggs are generally better hosts for T. platneri. Munro and Henderson (2002) evaluated the tachinid Trigonospila brevifacies (Hardy) a parasitoid of the fruit crop tortricid Epiphyas postvittana Walker. Community-level interactions were considered when selecting non-target test species, and the list was narrowed down to species in families (Tortricidae and Oecophoridae) known to be hosts of the tachinid parasitoid. Test candidates were field-collected in the forest community. Results showed that T. brevifacies was more abundant in the field than all native parasitoids collected, and parasitized more species than did native New Zealand tachinid species. Benson et al. (2003) examined the impact of Cotesia glomerata and C. rubecula parasitoids of P. rapae on non-target Pieris spp. Phylogenetic information and ecological information were used to determine the species to be tested. The results indicated that neither of the two non-target species Pieris virginiensis Edwards and Pieris napi (Scudder), nor the target species P. rapae, were attacked in the habitat occupied by P. virginiensis. Bourchier (2003) developed a list of butterfly species that are potentially at risk if Trichogramma minutum Riley were to be mass-released in maize against Ostrinia nubilalis Hübner in Canada. Using recent taxonomic information and an existing database of 153 species he considered ecological and biological attributes (geographic distributions, oviposition, phenology, number of generations per year, overwintering stage, host-plant preferences and egg-mass type and location) to establish known host ranges of Trichogramma spp. Most species were excluded from the list because of mismatch in the geographic distributions and oviposition phenology, and some species were excluded because their biology was less known. This served as a baseline for selecting a manageable number of nontarget insects that should be subjected to host range testing. Bourchier (2003) sug-

Selection of Non-target Species for Host Specificity Testing

gested that these non-target host selection criteria should be generally applied to inundative and classical biological control agents. Like Orr et al. (2000), Bourchier (2003) noted the difficulty of obtaining rare species, especially those on the ecological vulnerability list. Babendreier et al. (2003a,b) conducted laboratory and field risk assessment studies for T. brassicae using an approach similar to Bourchier (2003), considering ecological information, habitat and temporal overlap of non-target hosts and the biological control agent to select species for testing. For field tests, availability was used to determine the list of non-target species. These authors focused on butterflies as non-targets because their biology was better known, and also because butterfly biodiversity is of great concern in conservation biology. The list of 23 non-target lepidopteran species included nine species on the endangered species list in Switzerland. All species were tested in the laboratory (Babendreier et al. (2003a)) and successful parasitism was documented for 17 of the 23 species. Of the six species tested under field-cage conditions (including two species on the endangered list), all were parasitized by T. brassicae, though only at low levels. A field study with two non-target species revealed that both were parasitized at up to 2 m from the release point but parasitism at 20 m was zero. The work of Babendreier et al. (2003a,b) marks the first instance that rare butterfly species have been included in host specificity testing of biological control agents of arthropods. Babendreier et al. (2003c) studied the potential of T. brassicae to overwinter in eggs of non-target Lepidoptera. Phylogenetic information, representatives of several lepidopteran families and availability of test material were used to select the non-target species studied. T. brassicae successfully overwintered in all of the six species tested. Babendreier et al. (2003d) studied the impacts of T. brassicae on predators associated with maize. In this work non-target species were selected based on ecological

25

considerations and on availability of test material. Representative groups occurring in the target habitat were chosen and species that were commercially available were tested. Two of the four non-target predators were successfully parasitized at high levels in the laboratory, but under field conditions the levels of parasitism were very low and significantly less than for control species. In summary, a variety of strategies has been used to select species for non-target host tests. Although phylogenetic considerations were an underlying criterion (i.e. that a particular parasitoid group attacks certain host groups), ecological, biological and socio-economic information was very important for selecting non-target species for study. Availability of test material was also critical for selection of non-target test species in most studies.

Recommendations for Selecting a Species List for Host Specificity Testing using Invertebrates in Biological Control of Arthropods It is apparent that the criteria used in weed biological control are unlikely to provide all the necessary information that would enable development of a meaningful nontarget test list for entomophagous biological control agents. Arguments that have been brought forward in support of this include: ● Arthropods often outnumber plant species in communities by an order of magnitude (e.g. Kuhlmann et al., 2000; Messing, 2001). ● There is a significant lack of knowledge of arthropod phylogeny (e.g. Sands and Van Driesche, 2000; Messing, 2001). ● Natural enemies of arthropod pests respond to two trophic levels, i.e. the host and its host plant(s) (e.g. Godfray, 1994). ● Disjunct host ranges appear to be the rule with parasitoids, rather than the exception as in herbivores (Messing, 2001).

26

U. Kuhlmann et al.

● The fact that it is much more difficult and time consuming to rear a large number of test arthropod species than test plant species (Kuhlmann et al., 1998; Sands and Van Driesche, 2000). A central question with regard to the selection of test species is whether the host range of parasitoids considered for use in biological control programmes is restricted to one or a few closely related groups of herbivorous insects, or whether phylogenetic disjunction in host range, i.e. a host range that includes phylogenetically unrelated species, is the rule, rather than the exception. There seems to be consensus among arthropod biological control scientists that phylogeny is a valuable starting point for predicting and assessing the host range of parasitoids but that other criteria (e.g. ecological similarities and safeguard considerations) are also of high relevance, even more so than in host range assessment of herbivores. Thus, the selection of nontarget test species has to be carried out on a case-by-case basis. Recent studies to determine the host range of candidate entomophagous biological control agents have used an array of criteria to develop lists of species for testing the agent’s host range, as shown above in the review of methods used to date. However, there is currently no standard protocol which has been developed for test species selection. Here, we provide recommendations for developing a test list for host specificity of entomophagous arthropods (Fig. 2.1). As a first step, the information available on the recorded field hosts of the candidate biological control agent, as well as of closely related species, should be collected (see De Nardo and Hopper, 2004). Although literature reports or museum collections are important, this information should be viewed with caution, and the quality of the data assessed with a taxonomic expert. Also, it should be noted that host records tend to be primarily from agricultural and forest habitats and from economically more important species. There is general consensus that experiments are required to thoroughly determine the ecological host range of a potential biological

control agent (Hopper, 2001). Within the first step, the ecological (or realized) host range of the candidate species should be assessed through carefully planned field studies of the parasitoid–host complexes in the area of origin of the candidate biological control agent. Knowledge of the host species attacked by the candidate agent and its close relatives in the native range will facilitate the selection of appropriate test species for host range testing in the proposed area of introduction (Kuhlmann et al., 2000; Kuhlmann and Mason, 2003). In addition, comparable field studies in the area of introduction would generate valuable insight into which herbivore species would be exposed to the candidate biological control agent, both in space and time. If little is known about the target pest (see Barratt, 2004), initial studies need to be carried out to develop the information required for selection of appropriate non-target test species. Based on the knowledge of the ecological host range of the candidate biological control agent in its native range, an initial test species list should be established. We propose that this list be compiled by selecting species from three different categories, which need not be followed in any particular sequence: Category 1: Ecological Similarities: Species which live in the same/adjacent habitat (e.g. on arable land and adjacent field margins) or feed in the same microhabitat (e.g. on same plant species, or in galls) as the target species; Category 2: Phylogenetic/Taxonomic Affinities: Species which are taxonomically/phylogenetically related to the candidate biological control agent (according to modern weed biological control programmes); Category 3: Safeguard Considerations: ‘Safeguard species’, which are either beneficial insects (e.g. pollinators, other biological control agents) or rare and endangered species that belong to the same family or order. Additionally, host species of congeneric species of the candidate biological control agent could be selected when appropriate.

Selection of Non-target Species for Host Specificity Testing

27

Ecological Host Range Information

Category 1: Ecological Similarities

Category 2: Phylogenetic/ Taxonomic Affinities

Category 3: Safeguard Considerations

Initial Test List

Filter 1: Spatial, Temporal and Morphological Attributes

Filter 2: Accessibility and Availability

Revised Test List

New Information

Host Specificity Testing

Fig. 2.1. Recommendations for the selection of non-target species for a test list to be applied in host specificity testing of invertebrates for biological control of arthropods.

Depending on the information available, one may prioritize in the test list either species related to the target host or species that feed in the same microhabitat. Priority should be given to selecting species that are associated with more than one category. The initial list may consist of 50 or more test species and may be comparable to the final test plant list in a weed biological control programme. However, it is much more laborious and time-consuming to rear 50 or more insect species than it is

to grow plant species. Collection of suitable stages of test species may be possible, but requires evidence that the collected stages are not already parasitized or diseased. Holding field-collected individuals for non-target testing in a laboratory colony is recommended to ensure that any field parasitism or natural disease runs its course. Sands (1997) stated that testing of more than ten species of non-target arthropods may be impractical and often unnecessary. Further, Sands (1998) suggested

28

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that carefully designed tests on a few species related to the target will provide adequate information relating to the host specificity of candidate agents. We therefore propose reduction of the list of test species by filtering out those species with attributes which do not overlap with those of the target species. Attributes that may lead to a species being discarded from the test list are non-overlapping geographical distribution, different climate requirements, phenological asynchronization or host size which is outside the range that is accepted by the candidate biological control agent (Filter 1 in Fig. 2.1). The latter attribute can be tested by offering target species or other host species of different size classes to the candidate biological control agent. Other attributes may be investigated by studying the herbivore complex that inhabits the area into which the candidate biological control agent is to be released. Some of the species remaining in the test list are not available or accessible in large enough numbers and they should not be considered for host specificity testing as an adequate number of replicates cannot be conducted (Filter 2 in Fig. 2.1). In the case of rare and endangered species consideration can be given to testing congenors as surrogates. The revised test species list may then include some ten to 20 test species. Although this revised list would be appropriate for starting host specificity testing, it should not necessarily be considered as the final test list. Results from ongoing host specificity testing and parallel studies that aim to assess the chemical, visual and tactile cues emitted by the host or its hostplant(s), and involved in the agent’s host-selection behaviour, may shed new light on which non-target species may be at risk of being attacked by the candidate biological control agent. We therefore propose that the revised test species list should be periodically revisited during the prerelease studies of arthropod biological control programmes (indicated by the feedback loop in Figure 2.1). New information gath-

ered during the pre-release studies may lead to scientifically based justification for removal or addition of test species. Such a scenario is also applied in weed biological control programmes. In North America, test plant lists that have been submitted to and approved by the Technical Advisory Group at the beginning of a programme may be subject to well-founded revision during later stages of the pre-release studies. However, we believe that this reiterative process is of greater relevance in arthropod biological control programmes because of the need to keep the test list as short as possible, while still providing a reliable host range profile for the candidate biological control agent.

Selection of Non-target Species for a Test List: a Case Study Celatoria compressa, an adult parasitoid of species in the subtribe Diabroticina in North America, was selected as a candidate for classical biological control of Diabrotica virgifera virgifera (Coleoptera: Chrysomelidae: Galerucinae) in Europe. Prior to its potential release, host specificity testing was conducted to evaluate the potential impacts of C. compressa on European indigenous Coleoptera species. The non-target species selection recommendations described above were applied to select appropriate indigenous non-target species for host specificity testing of C. compressa under quarantine conditions.

Ecological host range information Information was compiled about the known field host ranges of Celatoria species, such as C. compressa, C. bosqi Blanchard, C. diabroticae (Shimer) and C. setosa (Coquillet), based on published host–parasitoid rearing records from North, Central and South America. Based on literature records, the known ecological host range of the three betterknown Celatoria species (C. bosqi, C. diabroticae and C. setosa) is restricted to the

Selection of Non-target Species for Host Specificity Testing

subtribe Diabroticina within the tribe Luperini of the subfamily Galerucinae. Celatoria bosqi, present in South America, is known to parasitize Diabrotica speciosa (Germar) (Blanchard, 1937; Heineck-Leonel and Salles, 1997), D. sp. nr. fulvofasciata Jacoby and D. viridula (F.) (G. Cabrera Walsh, Buenos Aires, 2003, personal communication) and the chrysomelid Cerotoma arcuata Olivier (Magalhães and Quintela, 1987). The ecological host range from the North American C. diabroticae is restricted to D. undecimpunctata howardi Barber, D. undecimpunctata undecimpunctata Mannerheim, D. longicornis (Say) and D. v. virgifera (Fisher, 1983). Although recorded hosts of the North American C. setosa include Diabrotica species (Arnaud, 1978), field and experimental data indicated that it was almost exclusively a parasitoid of Acalymma species, such as the chrysomelids Acalymma blandula LeConte, A. trivittata (Mannerheim) and A. vittata (F.) (Fischer, 1981, 1983). As ecological host range information about C. compressa was mostly not available, field host range surveys were carried out in Mexico, the area of origin. Celatoria compressa was found to only parasitize D. v. virgifera, D. balteata LeConte, D. porracea Harold, D. scutellata Baly, D. tibialis Baly, D. viridula, Acalymma blomorum Munroe and Smith, A. fairmairei (F.), A. innubum (F.), A. trivittata, Gynandrobrotica spp. and Cerotoma atrofasciata Jacoby (Eben and Barbercheck, 1996; A. Eben, Xalapa, Mexico, 2003, personal communication). The information on the ecological host range provided evidence that C. bosqi, C. diabroticae and C. setosa, as well as C. compressa, are highly specialized. Thus, most probably all species in the genus Celatoria parasitize only adults of single or related genera within the subfamily Galerucinae (most probably at the tribe level of Luperini), or Alticinae in the family Chrysomelidae (see Cox, 1994 and publications mentioned above). Additionally, it was reported by Fischer (1983) for C. diabroticae and C. setosa, and by Zhang et al. (2003) for C. compressa, that females use a

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piercing ovipositor to successfully parasitize hosts. Therefore, it is likely that Celatoria species have a high degree of host specificity compared to many other tachinids due to the elaborately modified piercing ovipositor (Belshaw, 1994; J. O’Hara, Ottawa, 2000, personal communication). Based on these findings, the selection of non-target coleopteran species for testing should be limited to the family Chrysomelidae. Ecological similarities (Category 1) Literature records were used to compile a list of the Coleoptera species which occur in selected European agricultural habitats such as maize (Zea mays L.), lucerne (Medicago sativa L.), pumpkin (Cucurbita maxima Duch.), wheat (Triticum aestivum L.) and sunflower (Helianthus annuus L.), as well as in adjacent field margin habitats. These habitats were selected because they are commonly present in the area invaded by the target. A total of 185 coleopteran species (belonging to 14 families) were found to be associated with these selected habitats in Europe. From these 185 species, three Galerucinae, 22 Alticinae, six Chrysomelinae, five Criocerina and two Cassidinae species (all in the family Chrysomelidae) were included (in total, 38 species for the initial test list). With regard to species living in the same microhabitat (same host plant) no obvious candidates were found but the cereal leaf beetle, Oulema melanopus (L.), which occasionally feeds in the same microhabitat (maize), and has been considered in the selection process. Phylogenetic/Taxonomic affinities (Category 2) The phylogenetic relationship of the nontarget species to the target was checked to ensure that European species of related subfamily, genera or subtribes of the target were added to the non-target list. In addition, a representative non-target species from a genus in a different family within the same order (outgroup) was selected.

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As reported before, D. v. virgifera belongs to the tribe Luperini (subtribe Diabroticina) within the subfamily Galerucinae (Wilcox, 1972), therefore, representative species closely related to D. v. virgifera were considered. Further, phylogenetic studies by Hsiao (1994) have shown that the subfamily Galerucinae is closely related to the subfamilies Criocerinae, Chrysomelinae and Alticinae, and relatively distant from Cassidinae. In this case study, representative non-target species selected belonged to the subfamilies Criocerinae (e.g. O. melanopus), Chrysomelinae (e.g. Gastrophysa viridula Deg. and Gonioctena fornicata Brüggemann) and Cassidinae (e.g. Cassida rubiginosa Müller). Within the subfamily Galerucinae, other representative non-target species in the tribe Galerucini, such as Galerucella pusilla Duft and Pyrrhalta luteola (Müller), were chosen. In addition, a species in the tribe Luperini, Aulacophora foveicollis Lucas (subtribe Aulacophorinia), which represents a species of the genus Diabrotica in the Old World (Maulik, 1936), was selected. Besides this chrysomelid, the pea weevil, Sitona lineatus L., was selected as the outgroup representative of a different and not closely related Coleoptera family (Coleoptera: Curculionidae); this is a common species present in Diabrotica-invaded areas (in total, two additional species for the initial test list).

Safeguard considerations (Category 3) Representatives of beneficial insect families such as Coccinellidae or Carabidae, as well as weed biological control agents, were included to avoid non-target impacts on these organisms. In addition, rare and endangered species were considered for selection. The two-spotted ladybird beetle, Adalia bipunctata L., was added to the non-target list as a representative of beneficial Coccinellinae (Coleoptera: Coccinellidae), and the golden loosestrife beetle, G. pusilla, considered as an important species for the control of the weed, purple loosestrife (Lythrum salicariae L.) in Europe (two additional species for the initial test list).

Initial test list Taking the above results, a total of 42 species were included in the initial test list (38 (category 1) + 2 (category 2) + 2 (category 3) = 42 species). It should be noted that G. pusilla was selected under both categories 2 and 3, and O. melanopus was selected under categories 1 and 2, illustrating that species can fulfil multiple information requirements. Spatial, temporal and morphological attributes (Filter 1) The initial test list was progressively filtered (reduced), due to the fact that nontarget species potentially at risk need to have ecological and biological attributes that may or may not overlap with those of the target species. In this case, geographical distribution and climate requirements (European continent excluding UK and Scandinavia), temporal pattern of adult occurrence in the field (June till October) and similarity in size (3–10 mm required for parasitoid development within the host; Eben and Barbercheck, 1996) were used. As a result of Filter 1, 21 chrysomelid species were excluded due to body size (>10 mm and <3 mm) and one chrysomelid species was excluded due to its different temporal pattern to D. v. virgifera. At this point 20 (42 minus 22) potential non-target Coleopteran species remained on the test list. Accessibility and availability (Filter 2) With the regard to the accessibility and availability, the goal not only was to further reduce the number of non-target species to be tested but also to maintain representative species from each of the subfamilies of the Chrysomelidae that were included in the initial test list. Field surveys were conducted in southern Hungary over a two-year period to identify the availability of the 20 test species in maize, alfalfa, sunflower, wheat and adjacent field margin habitats. Based on this filter, two closely related species of Galerucinae, P. luteola and G. pusilla and two representatives of Chrysomelinae, G. viridula and G. fornicata, were chosen. One representative species of the subfamily Cassidinae, C.

Selection of Non-target Species for Host Specificity Testing

rubiginosa, was included, as well as a representative from the subfamily Criocerinae, O. melanopus. Lack of information about the biology and rearing methods for many of the nontarget insects chosen made it impractical to assemble sets of laboratory-reared nontarget species for testing. Therefore, 100 to 120 adults of each non-target species were dissected after each field collection to assess naturally occurring field parasitism. For the tests, only specimens from non-target populations free of parasitism were used. As a result of Filter 2, nine species were selected for host specificity testing (seven chrysomelids, S. lineatus and A. foveicollis). Revised test list As a result of applying the recommendations outlined for the selection of non-target species for a test list, the revised test

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list for host specificity testing of the tachinid fly C. compressa, a candidate biological control agent of the western corn rootworm, D. v. virgifera, comprises nine Coleoptera species (Table 2.4). As mentioned above, this revised list should not necessarily be considered as the final test list as new results from ongoing host specificity testing and parallel biological studies may shed new light on which non-target species may be at risk of being attacked by the candidate biological control agent.

Conclusions Selecting non-target species for inclusion in host range testing for exotic entomophagous biological control agents must be done carefully to ensure that appropriate species are chosen. While the centrifugal–phylogenetic method used for selecting test species in weed biological control is a useful starting

Table 2.4. Revised test list for host specificity testing of the tachinid Celatoria compressa, a candidate biological control agent of the western corn rootworm, Diabrotica virgifera virgifera LeConte. Family Subfamily Tribe

Species

Host plants hosts

Aulacophora foveicollis Lucas

Pumpkin, Cucurbita maxima Duch.

Galerucinae Galerucini

Galerucella pusilla Duft

Purple loosestrife, Lythrum salicaria L.

Galerucinae Galerucini

Pyrrhalta luteola (Mueller)

Elm, Ulmus spp.

Chrysomelinae

Gastrophysa viridula Deg.

Sorrel, Rumex spp.

Chrysomelinae

Gonioctena fornicata Brueggemann

Lucerne, Medicago sativa L.

Criocerinae

Oulema melanopus (L.)

Wheat, Triticum aestivum L.

Cassidinae

Cassida rubiginosa Mueller

Thistle, Cirsium arvense (L.) Sop.

COCCINELIDAE Coccinellinae

Adalia bipunctata L.

Flour moth eggs, Ephestia spp.

CURCULIONIDAE Brachyderinae

Sitona lineatus Linnaeus

White clover, Trifolium repens L. Lucerne, Medicago sativa L.

CHRYSOMELIDAE Galerucinae Luperini

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point, other attributes such as ecological similarities, biological properties, socioeconomic considerations and availability of test species are of primary importance and have been used in the limited number of studies conducted to date. In fact, recent work in host range testing for weed biological control agents has included phylogenetically unrelated plant species that share conspicuous biological attributes relevant to host selection behaviour of phytophagous species. The number of plant species screened in weed biological control ranges from 40 to 100, including ‘safeguard’ species, but testing these numbers of species would be prohibitive for entomophagous biological control agents. Thus, one of the key aspects in host specificity testing in arthropod biological control programmes lies in setting up a test species list that is both scientifically sound and manageable. This is a challenging task,

bearing in mind that host selection by parasitoids is often triggered by an additional trophic level (host and host–plant) than that by herbivores. The recommendations proposed are intended to further stimulate and help improve the host specificity testing of entomophagous biological control agents (see van Lenteren et al., Chapter 3, this volume). In fact, the process of compiling a test species list is already a valuable step by itself in the pre-release assessment because it provides a mechanism for assembling and synthesizing relevant information and knowledge. Hopefully, new evidence from thorough host specificity tests will accumulate relatively quickly so that the proposed recommendations for the non-target selection procedure, which are based on a relatively small data set of experimental parasitoid host range assessments, can be soon revisited and refined if necessary.

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Price, P. (1981) Semiochemicals in evolutionary time. In: Nordlund, D.A., Jones, R.L. and Lewis, W.J. (eds) Semiochemicals, their Role in Pest Control. John Wiley and Sons, New York, pp. 251–279. Pschorn-Walcher, H. and Altenhofer, E. (1989) The parasitoid community of leaf-mining sawflies (Fenusini and Heterarthrini): a comparative analysis. Zoologischer Anzeiger 222, 37–57. Ronquist, F. and Liljeblad, J. (2001) Evolution of the gall wasp–host plant association. Evolution 55, 2503–2522. Rutledge, C.E. and Wiedenmann, R.N. (1999) Habitat preferences of three congeneric braconid parasitoids: Implications for host-range testing in biological control. Biological Control 16, 144–154. Sands, D.P.A. (1997) The ‘safety’ of biological control agents: assessing their impact on beneficial and other non-target hosts. Memoirs of the Museum of Victoria 56, 611–615. Sands, D.P.A. (1998) Guidelines for testing host specificity of agents for biological control of arthropod pests. Sixth Australian Applied Entomological Research Conference, The University of Queensland, Brisbane, Australia, Volume I. The University of Queensland, Australia, pp. 556–560. Sands, D.P.A. and Van Driesche, R.G. (2000) Evaluating the host range of agents for biological control of arthropods: rationale, methodology and interpretation. In: Van Driesche, R.G., Heard, T.A., McClay, A.S. and Reardon, R. (eds) Proceedings: Host Specificity Testing of Exotic Arthropod Biological Control Agents: The Biological Basis for Improvement in Safety. Xth International Symposium on Biological Control of Weeds, Bozeman, Montana, July 4–14, 1999. FHTET-99-1, USDA Forest Service, Forest Health Technology Enterprise Team, Morgantown, West Virginia, pp. 69–83. Sands, D.P.A., Bakker, P. and Dori, F.M. (1993) Cotesia erionotae (Wilkinson) (Hymenoptera: Braconidae) for biological control of banana skipper, Erionota thrax (L.) (Lepidoptera: Hesperiidae) in Papua New Guinea. Micronesia, Supplement 4, 99–105. Sato, H. (1990) Parasitoid complexes of lepidopteran leafminers on oaks (Quercus dentata and Quercus mongolica) in Hokkaido, Japan. Ecological Research 5, 1–8. Schaffner, U. (2001) Host range testing of insects for biological weed control: how can it be better interpreted? BioScience 51, 1–9. Shaw, S.R. (1988) Euphorine phylogeny: The evolution of diversity in host-utilization by parasitoid wasps (Hymenoptera: Braconidae). Ecological Entomology 13, 323–335. Shaw, M.R. (1994) Parasitoid host ranges. In: Hawkins, B.A. and Sheehan, W. (eds) Parasitoid Community Ecology. Oxford University Press, Oxford, UK, pp. 111–144. Stireman, J.O. and Singer, M.S. (2003) Determinants of parasitoid–host associations: insights from a natural tachinid–lepidopteran community. Ecology 84, 296–310. Strand, M.R. (1986) The physiological interactions of parasitoids with their hosts and their influence on reproductive strategies. In: Waage, J.K. and Greathead, D. (eds) Insect Parasitoids. Academic Press, London, pp. 97–136. Strong, D.R., Lawton, J.H. and Southwood, T.R.E. (1984) Insects on Plants. Blackwell Scientific, Oxford, UK. van Lenteren, J.C., Babendreier, D., Bigler, F., Burgio, G., Hokkanen, H.M.T., Kuske, S., Loomans, A.J.M., Menzler-Hokkanen, I., van Rijn, P.C.J., Thomas, M.B., Tommasini, M.G. and Zeng, Q.-Q. (2003) Environmental risk assessment of exotic natural enemies used in inundative biological control. BioControl 48, 3–38. Vinson, S.B. (1981) Habitat location. In: Nordlund, D.A., Jones, R.L. and Lewis, W.J. (eds) Semiochemicals, their Role in Pest Control. John Wiley and Sons, New York, pp. 51–78. Vinson, S.B. (1985) The behaviour of parasitoids. In: Kerkut, G.A. and Gilbert, L.I. (eds) Comprehensive Insect Physiology, Biochemistry and Pharmacology. Pergamon Press, New York, pp. 417–469. Vinson, S.B. and Iwantsch, G.F. (1980) Host suitability for insect parasitoids. Annual Review of Entomology 25, 397–419. Wapshere, A.J. (1974) A strategy for evaluating the safety of organisms for biological weed control. Annals of Applied Biology 77, 201–211. Wapshere, A. (1989) A testing sequence for reducing rejection of potential biological control agents for weeds. Annals of Applied Biology 114, 515–526. Weseloh, R.M. (1993) Potential effects of parasitoids on the evolution of caterpillar foraging behavior. In: Stamp, N.E. and Casey, T.M. (eds) Caterpillars: Ecological and Evolutionary Constraints on Foraging. Chapman and Hall, New York, pp. 203–223. Wilcox, J.A. (1972) Chrysomelidae, Galerucinae, Luperini. Coleopterum Catalogus Supplementa. Pars 78, Fasc, 3. Junk’s, Gravenhagen, The Netherlands.

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Zhang, F., Toepfer, S., Riley, K. and Kuhlmann, U. (2003) Basic biology and small-scale production of Celatoria compressa (Diptera: Tachinidae), a parasitoid of Diabrotica virgifera virgifera (Coleoptera: Chrysomelidae). Bulletin of Entomological Research 93, 569–575. Zwoelfer, H. and Harris, P. (1971) Host specificity determination of insects for biological control of weeds. Annual Review of Entomology 16, 159–178.

3

Host Specificity in Arthropod Biological Control, Methods for Testing and Interpretation of the Data

Joop C. van Lenteren,1 Matthew J.W. Cock,2 Thomas S. Hoffmeister3 and Don P.A. Sands4 1Laboratory of Entomology, Wageningen University, P.O. Box 8031, 6700 EH Wageningen, The Netherlands (email: [email protected]; fax number: +31-317-484821); 2CABI Bioscience Centre, Rue des Grillons 1, 2800 Delémont, Switzerland (email: [email protected]; fax number: +41-32-421-4871); 3Institute of Ecology and Evolutionary Biology, University of Bremen, Leobener Strasse NW2, 28359 Bremen, Germany (email: [email protected]; fax number: +49-421-218-4504); 4CSIRO Entomology, 120 Meiers Road, Indooroopilly, Queensland 4068, Australia (email: [email protected])

Abstract Potentially, the introduction of exotic natural enemies or mass release of biological control agents may lead to unwanted non-target effects. Whether or not such effects occur will depend mainly upon the host range of the biological control agent and the presence of non-target species in the areas of release and dispersal. To predict non-target effects, risk assessments for release of exotic natural enemies have been developed and applied during the modern era of biological control. Although methods to determine host ranges of natural enemies have been proposed during the past decades, decisions about release of exotic natural enemies are often still based on short-term decisions strongly influenced by financial benefit and tend to ignore environmental ethics, especially where risks are difficult to quantify. Here, we propose a framework for host-range testing of arthropod biological control agents, and suggest methods for evaluating possible effects on those non-target species considered to be at risk. Several factors should be incorporated into a host-range assessment, including literature and museum records, field observations in the area of origin, as well as physiological, behavioural and ecological observations and experiments. Usually, laboratory-based manipulative experiments will form the core of host-range assessments. In this chapter we concentrate on the question of how to determine host ranges. Several important considerations involved in designing host-range testing are presented. Next, a framework for step-wise host-range testing is given with levels of increasing complexity that should allow over- and underestimation of the host range of a biological control agent to be avoided. Finally, the interpretation of data obtained with host-range testing is discussed and conclusions are drawn about the importance of hostrange testing within the framework of future biological control projects. 38

©CAB International 2006. Environmental Impact of Invertebrates for Biological Control of Arthropods: Methods and Risk Assessment (eds F. Bigler et al.)

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Introduction Despite the thorough host-range evaluations applied in the evaluation of potential natural enemies of weeds (Wapshere, 1974, 1975), it was unusual for host ranges of biological control agents for insect or mite control to be extensively studied until recently (Kuhlmann and Mason, 2003). The earlier lack of concern for non-target effects, combined with the fact that very few non-target effects were ever found in insect biological control, resulted in hardly any host-range assessment or screening studies before the 1990s, except in Australia, where they were started in the 1980s (see glossary in this book for definition of host range and host specificity; in this chapter the word host is often treated as synonymous with prey). However, it was not the relative lack of host-range assessment, so much as the almost complete lack of integration of modern natural enemy biology into such tests, that alarmed us when preparing this chapter. We will thus have to summarize some of this knowledge

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before designs of host-range testing can be discussed. Several publications have appeared in which ideas or methods for host-range testing are presented (Table 3.1; Barratt et al., 1997, 2003; Sands, 1998; Hopper, 2001; Kuhlmann and Mason, 2003; van Lenteren et al., 2003). Aspects of risk assessments have been developed and applied during the past two decades, though often in a preliminary way and not always satisfactorily. Decisions about release of exotic natural enemies are often still strongly influenced by financial and social benefits reflecting national priorities (see e.g. Neuenschwander and Markham, 2001; Cock, 2003) and tend to ignore environmental ethics, especially where risks are difficult to quantify (van Lenteren, 1997). However, there are several positive developments taking place currently. A recent review, in which the implementation and use of the IPPC Code of Conduct (CoC) (IPPC, 1996) is evaluated (Kairo et al., 2003), contains a number of very important conclusions. These include:

Table 3.1. Approaches for host-range testing presented in the literature. No-choice, ‘black box’ host-range test, small scale: does biological control agent prey on or parasitize non-target host? (Sands, 1998; Babendreier et al., 2003a; van Lenteren et al., 2003.) No-choice, behavioural observation host-range test, small scale: does biological control agent attack or parasitize non-target host consistently? (Sands, 1998; van Lenteren et al., 2003.) No-choice sequential host-range test with behavioural observation, small scale: does biological control agent attack or parasitize non-target host consistently? (Sands, 1998; van Lenteren et al., 2003.) Choice, behavioural observation host-range test, small scale: does the biological control agent attack the non-target when the target species is present? (Sands, 1998; van Lenteren et al., 2003.) Choice, behavioural observation host-range test, large scale semi-field testing: does the biological control agent attack the non-target when the target species is present in a semi-natural situation? (Sands, 1998; van Lenteren et al., 2003.) Choice, black box host-range test, large scale semi-field testing: does the biological control agent attack the non-target when the target species is present in a semi-natural situation? (Sands, 1998; Babendreier et al., 2003b; van Lenteren et al., 2003.) Choice, black box host-range test, field testing: does the biological control agent attack the non-target when the target species is present in a natural situation? (Sands, 1998; van Lenteren et al., 2003.) Pre-introduction field determination of host range in area of origin of natural enemy. (Barratt et al., 2003; Kenis et al., 2003; Kuhlmann and Mason, 2003.) Post-introduction field determination of host range in area of release of natural enemy. (Barratt et al., 1997; Coombs, 2003.) Other approaches mentioned in literature: Fecundity, sex ratio, emergence of biological control agent, selective exploitation of hosts. (Mansfield and Mills, 2004.)

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● The current wide use of the CoC. ● With the CoC, several requests for importation could be rejected based on good reasons. ● The CoC made evaluation procedures generally more rigorous and lengthy, but did not necessarily lead to fewer introductions. ● Most respondents were positive about the implementation of the CoC, but also expressed concerns that the CoC lacks procedures for, amongst others, hostrange assessment schemes and hostrange testing methods that need to be developed with high priority (Kairo et al., 2003; Quinlan et al., 2003). Although there is still much debate on how to test host specificity, several protocols for host-range determination have been designed and used during the past decade (Barratt et al., 1997; Sands, 1998; van Lenteren et al., 2003). An important conclusion from recent papers on risks of releasing exotic biological control agents is that host-range assessment should form the focus of every natural enemy risk assessment. This needs to be combined with the potential spread of an introduced classical biological control agent, or in the case of augmentative biological control, considered together with the numbers of natural enemies that are released and the dispersal capacity of the natural enemy (see Mills et al., Chapter 7, this volume), to determine the probability that non-target effects will occur. Several sources of information may be incorporated into a host-range assessment, including literature records, field observations in the area of origin, and physiological, behavioural and ecological observations and experiments. Usually though, laboratory-based manipulative experiments to test host range will be performed. Developing a list of appropriate non-target species is a difficult task and is discussed in detail by Kuhlmann et al. (Chapter 2, this volume). In this chapter we will concentrate on the question of how to test the host specificity of arthropod biological control

agents. First we illustrate that next to hostrange testing, other factors can and should be incorporated into a host-range assessment. Then we discuss a number of important considerations involved in designing host-specificity testing, and a framework for host-range testing is presented. Next we discuss the interpretation of data obtained with host-range testing and finally some conclusions are drawn.

Host-range Assessment Host-specificity testing is an important aspect of host-range assessment – perhaps the most important, and the easiest conceptually for regulators. However, the purpose of host-specificity testing is to determine experimentally the potential (= physiological) host range of a biological control agent, in order to assess the risks that it presents to the environment and to human interests. There are other approaches that also provide evidence regarding the potential host range of a biological control agent, and which can be used to make predictions about the risks that it presents. Sometimes this evidence may be rather conclusive, but at other times it will provide supporting, corroborative evidence. It should be remembered that many early studies of classical biological control have included detailed studies on the pest and its natural enemies in the pest’s area of origin – in many cases in much more detail than is normally undertaken today. This detailed information inevitably generates insight into the potential host range of the biological control agents being studied, so that many introductions were made knowing the likely host range of the introduced biological control agent. Some of the best documented examples relate to the work carried out by the pioneering group of entomologists working in Fiji in the 1920s and 1930s (Paine, 1994), e.g. against the coconut moth, Levuana iridescens Bethune-Baker (Tothill et al., 1930) (the tachinid parasitoid of this zygaenid moth was known to attack species of Zygaenidae, Noctuidae and

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Pyralidae in its native range, and not surprisingly subsequently attacked several non-target species when introduced into Fiji), the coconut spike moth, Tirathaba rufivena (Walker) (Paine, 1935), and the coconut scale, Aspidiotus destructor Signoret (Taylor, 1935). Though several researchers proposed to release only hostspecific natural enemies, others supported the idea of releasing polyphagous natural enemies in classical biological control programmes to increase the probability of establishment (for a discussion see Turnbull and Chant, 1961). The number of recent studies (i.e. postIPPC (1996)), including quantitative data on the pest and its natural enemies in their area of origin, which have been published is still quite limited, so it is difficult to suggest standard methods for assessing host ranges. The examples mentioned below may be considered as case studies, and as more accumulate, standard methods should be easier to derive. Even in isolation, host-range information can go a long way towards providing an assessment of host specificity. Field studies have the advantage that laboratory constraints are not involved in changing behaviour and apparent specificity. On the other hand, only non-target species indigenous to the source area can be assessed, and specific information relating to non-target species restricted to the target area can only be generated in containment (e.g. NAPPO, 2004), and therefore will almost certainly require experimental testing methods at much smaller scales.

Literature records If the source area of the potential biological control agents is one where the local ecology has been intensively studied, then there will be accumulated host records, often catalogued, giving a strong indication that a particular parasitoid or predator is exclusively associated with particular hosts/prey. See section ‘choice of nontarget species’ for more information.

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Taxonomic extrapolation The biology of some groups is sufficiently well known that particular parasitoid species can be predicted as being restricted to certain host groups based on their taxonomic affinities, e.g. a particular genus may be known only as parasitoids of mealybugs. This argument was used recently in the case of Anagyrus kamali Moursi, widely introduced in the Caribbean area for control of pink hibiscus mealybug, Maconellicoccus hirsutus (Green) (Kairo et al., 2000), and can be applied to several groups of parasitoids.

Field surveys If the ecology and host or prey associations are not rigorously known then it is possible to make targeted surveys to assess the utilization of species related to the target species (e.g. Kuhlmann and Mason, 2003; Lopez and Kairo, 2003).

Behavioural studies One of the prerequisites for effective and realistic host-specificity testing is a good understanding of the biology of the host– parasitoid or predator–prey interaction, including host/prey location, searching behaviour, oviposition behaviour etc. These aspects in themselves may give very strong indications of host specificity, and can be particularly helpful in assessing the potential prey range of predators. For example, the coccinellid predator Hyperaspis pantherina Fürsch was shown to lay almost all of its eggs on the fluted egg sac of its normal prey, Orthezia insignis Browne (Booth et al., 1995), indicating a close co-evolved relationship. Nephaspis spp. (Coccinellidae) seem to be stimulated to oviposit in the presence of whitefly flocculence (Lopez and Kairo, 2003). A clear example of a behaviour-based specificity is provided by Teretrius nigrescens (Lewis), the histerid predator of larger grain borer, Prostephanus truncates (Horn), which was shown to locate its prey’s breeding sites by attraction to the aggregation

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pheromone of the target beetle (Rees et al., 1990; Boeye et al., 1992; Borgemeister et al., 2003) – again convincing evidence of a close co-evolved relationship.

Morphological constraints Limits to the size of biological control agents may also have clear implications regarding potential host range. Potential biological control agents may be demonstrably too big, too small or morphologically ill-equipped to attack particular non-targets, or their ovipositor may be too short to reach them, too flimsy to penetrate them, etc. There will doubtless be other mechanisms not considered here, which demonstrates the need for assessing each case on the available evidence.

Considerations when Developing Host-range Testing Hypotheses about host ranges of natural enemies generated from the literature and field surveys can be tested in formal laboratory host-range tests (Sands, 1998). Hostrange tests aim to demonstrate whether a natural enemy can feed, develop or reproduce on a non-target species. Laboratory testing can become quite complicated as a result of multitrophic chemical communication, learning and wide host ranges involving many host plant species. Host preferences are determined not only by the choice of species offered, but also by the physiological condition and experience of the natural enemy included. Host-range testing is relevant only if proper experimental controls are included. Hence, before a specific testing scheme is designed, several points need to be considered in order to make the tests meaningful (Table 3.2).

Knowledge of natural enemy foraging behaviour The host-finding behaviour of natural enemies is usually separated into the follow-

ing phases: host-habitat searching, host searching and host evaluation, with eventually host acceptance (Doutt, 1959). A predator will eat the accepted prey, a parasitoid may parasitize a host and/or feed from it (host feeding). Only those hosts that provide the natural enemy with possibilities for development and reproduction are considered suitable. To be able to show the full range of host-finding behaviour, the natural enemy needs to be exposed to the complete plant–host complex, but in laboratory tests often only subsets are offered, or hosts are offered on unnatural host plants and in abnormal distribution patterns, which may result in altered hostrange profiles (van Dijken et al., 1986; Conti et al., 2004). The main aspects affecting foraging behaviour are as follows: ● Plants affect host suitability through food and secondary plant substances (Schoonhoven et al., 1998), and thereby influence foraging efficiency and fitness of natural enemies (Vet, 2001; Harvey et al., 2003). Plant nutrition and diseases, and plant contamination with non-host or prey species, or with higher-order predators or their signals, should be considered as well. ● Plant anatomical, morphological and architectural characteristics also influence foraging behaviour of natural enemies; e.g. plant hairs often reduce search efficiency (Kareiva and Sahakian, 1990; van Lenteren and de Ponti, 1990; Dicke, 1999). ● Plant odour, taste, colour, shape and touch may each influence attraction or repellence of natural enemies (e.g. Dicke, 1999; Vet, 2001). ● It has been extensively demonstrated that many plants, after being attacked by herbivores, start producing volatiles that attract natural enemies, the so-called herbivore-induced synomones or HIS (Dicke and Vet, 1999). Based on these HIS volatiles, some natural enemies can assess host presence from a distance (Dicke and Vet, 1999). ● Host cues and other host characteristics affect natural enemy behaviour and should thus be offered in a natural set-

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Table 3.2. Points to consider and specify in host-range testing. Arenas/cages Should be clean and free of deterrent chemicals. Should have control of light, temperature and humidity. The right colour, pattern and size should be considered as it should allow for mating and normal search behaviour. Should allow for provision of full set of (infected) host plant, host and natural enemy stimuli. Host plant, host and natural enemy Strains of biological materials used should be characterized, preferably field-collected; healthy material should be used to prevent genetic deterioration; if laboratory rearing is needed, genetic changes that influence host preference should be monitored. If rearing of plant, host and/or natural enemy is based on artificial media and diets, the effects on host preference should be evaluated. How host-plant and host stimuli are included in the test situation should be descibed. Host plant Should not contain any residues of pesticides or other negatively interfering chemical materials. Should be in optimal condition. Should be herbivore infested sufficiently long enough before host-range testing to allow for production of host-induced synomones. Effect of, e.g. host plant colour, shape, odour, taste and structure may all influence host acceptance. Choice of host-plant species should be extensively described. Host/prey Laboratory rearing effects should be considered that might influence acceptance by natural enemy. Should be healthy, in appropriate stage for predation or parasitism and in sufficient numbers. Should be offered to natural enemy on its natural host plant and in normal host distribution pattern. Host colour, shape, odour, taste and structure should be controlled for, since all may influence host acceptance. Choice of non-target species should be extensively described. Natural enemy Possibility of diapausing by natural enemy should be considered. Laboratory rearing effects that might influence preference should be considered. Intra-specific variation in host preference should be considered. Should be healthy, in right physiological condition. Conditioning and learning effects that might negatively interfere with host preference should be prevented. Artificial selection that results in changed host-preference patterns should be prevented. Opportunity for host to feed should be ensured. The natural enemy strain that is used in host-range testing should be characterized and voucher material of the tested natural enemy strain(s) should be retained. Multitrophic aspects Should allow for normal set of stimuli to be provided by all organisms relevant for host-range testing (including host plants). Test should always include positive and negative controls. After testing, stocks of the natural enemy should be replaced instead of releasing laboratory-adapted, genetically bottlenecked stock.

ting. A recent study clearly shows how important the role of volatile and contact host infochemicals are in host location and host recognition (Conti et al., 2004). For example, in the laboratory, when contact infochemicals of non-co-evolved

hosts were presented, a partial new association was obtained. But this association is unlikely to occur in the field as the parasitoid did not respond to the volatile cues of the new host. This observation strongly indicates how carefully

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laboratory and semi-field experiments should be designed in order to avoid the overestimation or underestimation of the risk of non-target effects. ● Many generalist natural enemy species change their location and host-selection behaviour after finding a certain host in a certain habitat, because they quickly learn to associate host availability with host plant and host cues (Vet et al., 1995). Learning can play a strong role during all phases of host finding (host community location, microhabitat location, habitat acceptance, host detection and host acceptance; for examples see Vet et al. (1995). One of the important effects of learning is that habitat and host preferences can be altered and temporary specializations to preferred stimuli may arise. Field experiments by Papaj and Vet (1990) revealed that female Leptopilina heterotoma (Thomson), experienced with host-infested food substrates such as mushrooms or fermenting apples, were more likely to find a host-food substrate and found it faster than naive females, i.e. learning reduced travel times. In addition, learning greatly influenced the choice of substrate. As a result of learning, the host range may change, and this is another problem which often cannot be accounted for in small-scale laboratory tests. Thus, it is essential to study and describe the general host-finding behaviour of a natural enemy before designing hostrange testing protocols. This is particularly important in small-scale laboratory tests.

Quality and rearing conditions of the host plant, host and natural enemy The rearing of the host plant, host and natural enemy species prior to testing should be described in a detailed way, as well as the host-range testing procedures, in order to be able to trace the effects of conditioning, learning and multitrophic chemical communication. During rearing and testing, use of pesticides and other chemicals that might interfere with host preference should be avoided. Day length, humidity and temperature should be appropriate to

the latitude and ecoregion of release. Temperatures should be adjusted, either to the mean or to appropriate diurnal temperature cycles of the receiving country. However, if diapause in non-target test organisms or agents is known or suspected, it may be necessary to regulate laboratory environmental conditions to avoid suspended development.

Unnatural hosts, artificial diets and effects of particular phenomena like diapause Rearing on unnatural hosts/prey or under unnatural conditions may cause behavioural changes in immature stages and adults (Morrison and King, 1977; Grenier and DeClerq, 2003; Vet et al., 2003). Reduced vigour can occur when natural enemies are reared on unnatural hosts or when natural enemies are reared on hosts that are reared on an unnatural host diet. Rearing on artificial diets involves the risk of changing natural enemy host preferences, because they are no longer exposed to their natural set of infochemicals and other stimuli (Grenier and DeClerq, 2003; van Lenteren, 2003; Vet et al., 2003). An aspect that is often difficult to consider, if one is unfamiliar with the biology of the natural enemy, is the potential effect of diapausing natural enemies on hostspecificity evaluation. Parasitoid eggs or early instar larvae may remain in diapause until the host reaches the developmental stage that stimulates development of the natural enemy. This could result in an underestimation of host range if the diapause leads to unrecognized parasitization in non-target hosts. The effect can be suspected only if diapause occurs in the known target host. For example, after egg hatch, the first-instar larvae of a pteromalid, Scutellista caerulea (Fonscolombe), remain in diapause beneath coccid hosts until they begin to oviposit, sometimes following a period of parasitoid dormancy extending for several months. As soon as oviposition commences the parasitoid larvae begin feeding on the eggs and complete develop-

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ment without feeding on the body of the host (Sands et al., 1986). Likewise, firstinstar larvae of an encyrtid, Anicetus communis Annecke, hatch in autumn and overwinter without feeding in the body tissues of third-instar hosts, but they develop rapidly as soon as the host changes to the fourth instar (Waterhouse and Sands, 2001).

Host or natural enemy infection by pathogens Laboratory-reared insects can be infected by pathogens (Bjørnson and Schütte, 2003). These could lead to high mortality, reduced fecundity, prolonged development, small adults or wide fluctuations in the quality of insects. Goodwin (1984), Shapiro (1984), Sikorowski (1984), Singh and Moore (1985), Bjørnson and Schütte (2003) and Stouthamer (2003) give information on the effects of microorganisms on insect cultures and the measures available to minimize or eliminate the pathogens or contaminations. Further, they discuss the recognition of diseases and microorganisms in insect rearing and the common sources of such microbial contaminants (see also Goettel and Inglis, Chapter 9, this volume). The most common microbial contaminants encountered in insect rearing are fungi, followed by bacteria, viruses, protozoa (particularly microsporidia) and nematodes. The field-collected insects that are used to start a laboratory colony are a major source of microbial contaminants. The second main source is the various dietary ingredients. Disinfection of insects and dietary ingredients are recommended to prevent such contamination. The causes of microbial contamination can usually be quickly found, but elimination of pathogens from insect colonies is difficult (Bartlett, 1984a; Bjørnson and Schütte, 2003). Diseased hosts and/or diseased natural enemies may result in changed host preferences. Fungusinfected whiteflies, for example, at a certain stage are no longer accepted as hosts for parasitism by Encarsia formosa Gahan (Fransen and van Lenteren, 1993).

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Behavioural variation in natural enemies The variation and changes in behaviour of natural enemies that can be caused by rearing conditions are manifold, and may lead to rather unexpected changes in host preference (Vet et al., 2003). This issue, together with a thorough theoretical background, is discussed by Lewis et al. (2003) and Vet et al. (2003). Most ecologists are aware that variability in natural enemy behaviour occurs frequently and that it is important to know the sources of variability in order to prevent mistakes, e.g. during host-range testing. The sources of intrinsic variation in foraging behaviour (genetic, phenotypic and those related to the physiological state) are not mutually exclusive but overlap extensively, even within a single individual. The eventual foraging effectiveness and host acceptance of a natural enemy is determined by how well the natural enemy’s net intrinsic condition is matched with the foraging environment in which it operates.

Managing genetic qualities Host-range studies should be done with well-characterized strains of a natural enemy species, preferably based on genetic identification methods (see Hopper et al., Chapter 5, this volume; Stouthamer, Chapter 11, this volume). When selecting between strains of natural enemies, ensure that the traits of the natural enemies are appropriately matched with the targeted use situations in the field. Reliable genetic characterization is of particular importance when using strains of polyphagous species.

Managing phenotypic qualities Without care, insectary environments lead to agents developing weak or distorted responses. Understanding the sources and mechanism of natural enemy learning allows the provision of an appropriate level of experience before testing the natural enemies. Pre-release exposure to important stimuli can help improve the responses of

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natural enemies through associative learning, leading to reduction in escape response, increased arrestment in target areas and, thus, a lower risk of non-target effects. Managing physical and physiological qualities Natural enemies should be tested in the physiological state in which they are most responsive to herbivore or plant stimuli and will not be hindered in their responses by deprivations that interfere with host searching and acceptance. Natural enemies face varying situations in meeting their food, mating, reproductive and safety requirements. Presence of strong chemical, visual or auditory cues, cues related to presence of enemies of the natural enemy, and (temporary) egg depletion can all reduce or disrupt the response to cues used to find hosts (Heimpel and Rosenheim, 1998; Lewis et al., 2003). For example, hunger may result in increased foraging for food and decreased attention to hosts in insect parasitoids (Waeckers, 2003). In that case, the reaction to food and host cues will be different from when the natural enemy is well fed. In view of the above discussion, it is best to rear natural enemies in as natural a situation as possible to obtain reliable hostrange data.

Intraspecific variation: biotypes and their different host ranges Intraspecific, allopatric biotypes of parasitoids with differing host ranges are well known and may be important when selecting the most effective agents. Biotypes can exhibit dissimilar host ranges and host specificity, comparable to separate species. For example, different biotypes of Comperiella bifasciata Howard parasitize yellow scale, Aonidiella citrina (Coquillet) and red scale, Aonidiella aurantii (Maskell). The yellow scale biotype will oviposit in red scale but many of the parasitoid eggs and some larvae become encapsulated without any parasitoid development (Brewer, 1971). However, the red scale bio-

type of C. bifasciata parasitizes up to 80% of adult females of A. aurantii (Smith et al., 1997). Similarly, larvae of different biotypes of a pteromalid egg predator, Scutellista caerulea (Fonscolombe), are known to attack different species of coccid prey (Waterhouse and Sands, 2001). Host-range tests must therefore be conducted with individual natural enemies representing the same geographical origin as those intended for making releases.

Genetic changes, inbreeding and replenishment of breeding stock Inbreeding problems may follow continued laboratory culture after several or many generations while testing, or simply when building up numbers. There is no accepted guide to how many generations are likely to result in decline in genetic quality, as many factors contribute, including the number of individuals used for breeding. A useful ‘rule of thumb’ is to add newly imported individuals (not before rearing through at least one generation) to the culture kept in containment, or to replace the culture entirely after about four generations of rearing in the laboratory. Caution must be exercised not to overlook hyperparasitoids or diseases such as pathogenic microsporidia introduced with the freshly imported material. When rearing host and natural enemy in the laboratory for several generations, genetic changes may influence host preference. Those host ranges determined with laboratory-reared material may, therefore, not be representative. Bartlett (1984a,b, 1985) discusses what happens to genetic variability in the process of domestication, what factors might change variability and which ones might be expected to have little or no effect. In laboratory domestication the insects that survive and reproduce have suitable genotypes for survival in this new environment, a process called winnowing by Spurway (1955) or, more widely, but less appropriately, ‘forcing insects through a bottleneck’ (e.g. Boller, 1979).

Host Specificity in Arthropod Biological Control

Variability in performance traits is usually abundantly present in natural populations (Prakash, 1973; Hoekstra, 2003; Nunney, 2003) and can remain great even in inbred populations (Yamazaki, 1972). The size of the founder population will directly affect how much variation will be retained from the native gene pool (Hoekstra, 2003; Nunney, 2003) and differences between field and laboratory environments will eventually result in differences in variability. Although there is no agreement on the size of founder populations needed for starting a mass production, a minimum number of a thousand individuals is suggested (Bartlett, 1985; van Lenteren, 2003). Founder populations are usually much smaller, creating a serious risk with regard to whether hostrange assessment is representative for the species in question. It is often difficult to produce the desired numbers of individual agents for release, and the only option may be to rear many generations in the laboratory, with the resultant risks of inbreeding. Another obstacle for laboratory production is the lack of techniques for preventing selection pressures leading to genetic deterioration of organisms. Through such deterioration, natural enemies may also show different host preference patterns. Characterization of natural enemy strains by DNA fingerprinting may help in identification of certain strains of natural enemies and in following up changes in laboratory populations of field-collected material (see Stouthamer, Chapter 11, this volume). Benchmark testing of host preference with a certain set of non-target hosts may also help in discovering changes.

Relevant multitrophic perspective for testing Natural enemy behaviour is influenced by other trophic levels (Price et al., 1980; Vet and Dicke, 1992; Dicke et al., 2003). The natural environment of a biological control agent is composed of relevant constitutive and induced chemicals, as well as

47

irrelevant chemicals, the so-called noise (Dicke, 2000). Composition of infochemicals may vary with genotype of the producer (host plant or host), with biotic, and with abiotic conditions (Dicke and van Loon, 2000). The value of a certain infochemical may also depend on the simultaneous presence of other cues (Dicke, 2000). This all stresses the importance of a proper set of (infested) host plant and host stimuli to be provided in order to obtain reliable host preference data. During testing, the target and non-target hosts should be offered in a natural host distribution pattern, on the natural host plant or part of that or on an alternative host plant, which is not repellent to the natural enemy (van Dijken et al., 1986; Sands, 1998; Follett et al., 2000).

Choice of non-target species The choice of non-target species is difficult but critical. For a detailed discussion of this issue, see Kuhlmann et al. (Chapter 2, this volume). In addition to what is presented there, we would like to stress that available knowledge about the ecological and physiological attributes that are explored by a natural enemy in its native area can help in narrowing down the number of non-targets for which tests need to be designed (Waterhouse and Sands, 2001; Kenis et al., 2003; Kuhlmann and Mason, 2003). Host–parasitoid and predator–prey associations have been catalogued from publications of the periods 1913–1937 (Thompson, 1943–1965) and 1938–1962 (Herting, 1971–1982). Major sources of information for parasitoids, predators, their hosts and prey can also be found in the Review of Agricultural Entomology (formerly Review of Applied Entomology, Series A) and electronic databases, including CAB Abstracts, Agricola, Biosis and Zoological Record. Books summarizing biological control programmes may include much relevant information, e.g. global (Clausen, 1978), Canada (Mason and Huber, 2002), USA (Coulsen et al., 2000), Caribbean (Cock, 1985), Europe

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(Greathead, 1976), Africa (Greathead, 1971; Neuenschwander et al., 2003), Pacific (Waterhouse and Norris, 1987), South-east Asia (Waterhouse, 1998a), China (Waterhouse, 1998b), Australia (Waterhouse and Sands, 2001) and New Zealand (Cameron et al., 1989). Early literature may have useful information. For example, Compsilura concinnata (Meigen), introduced from Europe into the USA against Gypsy moth, Porthetria dispar (L.), was known to develop on many non-target species in Europe before it was released in the USA. More specialized literature on parasitoids, for example, Austin and Dowton (2000), often provides details of hosts for natural enemies in their countries of origin. Several early authors (e.g. in Clausen, 1978) discussed alternative hosts of parasitoids and prey as part of the biology of the agents in their native range. These records when available give an indication of potential host range but often only pest species were documented and other, nontarget, hosts were unrecorded. Only occasionally has the host range of a parasitoid been intentionally evaluated in the country of origin before its introduction somewhere else. For example, Jones and Sands (1999) tested a eulophid larval parasitoid (Euplectrus melanocephalus Girault) of fruit-piercing moths (Eudocima spp.) with other non-target noctuid moth larvae to determine if it was suitable as an agent for introduction into the Pacific region against the major pest species, Eudocima fullonia (Clerck). National, regional and global taxonomic works and databases should be consulted, and will often provide useful supporting evidence and clues for appropriate hostspecificity test organisms. Examples of important sources include Krombein et al. (1979) on North American Hymenoptera, Noyes (1998) on global Chalcidoidea, Yu and Horstmann (1997) on global Ichneumonidae, etc. The availability of this type of information on the internet is constantly and rapidly growing, and a search will reveal important new and relevant sources.

Special consideration should be given to designing tests for prey ranges of polyphagous predators, where host size and location might be a better guideline than phylogenetic relatedness. Moreover, a wider prey range needs to be tested than with many parasitoids because more intraguild predation is expected, as well as higher up trophic level effects (DeClerq, 2002; van Lenteren et al., 2003). Also, some adult predators accept a wider range of prey than do their immature stages. Others may have different prey requirements or preferences to their immature stages and require separate prey-range tests if being considered as biological control agents. For example, Causton et al. (2004) tested, separately, adults and larvae of the coccinellid, Rodolia cardinalis (Mulsant), with a range of potential prey including the target – cottony cushion scale, Icerya purchasi Maskell. They found that neonate larvae of the predator were able to prey on only one of the non-target species tested, a fluted scale, Margarodes similis Morrison, but larvae were unable to moult to second instar or survive to complete development. In contrast, adults of this predator were able to survive on this non-target species of prey for periods of up to 13 days and have been known to subsist on a wider range of other insects and nectar for up to three months (Sands and Van Driesche, 2003). Other categories needing care with testing are generalist parasitoids and (facultative) hyperparasitoids (e.g. Sands and Van Driesche, 2003).

Framework for Host-range Testing All the above considerations may lead to the conclusion that host-range testing is too complicated and produces unreliable results. But based on the very limited number of negative non-target effects known, we may conclude that biological control workers have generally done an excellent job in making predictions about such effects in the past. That such predictions were, in addition to knowledge of systematics and field studies, often based on gut

Host Specificity in Arthropod Biological Control

feeling, green fingers and informed guesses of biological control experts, does not lessen our respect for our predecessors. Below and in Figure 3.1 we present a design for a testing scheme to determine host ranges of insect natural enemies. Because of the large variation in natural enemy-host relationships, this testing sequence should be considered as a basic approach, which will need to be adapted Step 1: Small arena no-choice black-box test Are non-target species attacked?

49

for specific situations. For example, steps 1 and 2 can often be combined. Further, when the behaviour of the natural enemy is known, observations for steps 2 and 3 can be automated, thereby reducing costs of observation and data analysis. Additionally, the test sequence we present may be simplified if this can be based on the biology of the natural enemy (e.g. Babendreier et al., 2003a,b). Depending on

No

Insignificant NT effect

Yes

Step 2: Small arena no-choice behavioural test Are non-target species attacked?

No, or at end of observation

Insignificant NT effect

No, low rate, or no switching

Insignificant NT effect

Yes, consistently

Step 3: Large arena choice behavioural test Are non-target species attacked?

Yes, consistently

Significant NT effect

Next step only for inundative control with native species or exotics that cannot establish Step 4: Field test Are non-target species attacked?

Yes

No

Insignificant NT effect

Significant NT effect

Fig. 3.1. Flow chart summarizing host-range assessment (testing does not necessarily start at step 1). NT = non-target.

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the multitrophic system under consideration, one does not necessarily have to start with step 1, but can start with approaches in e.g. large arenas that allow a much more precise estimate of the host range. Host-range testing can be carried out either as no-choice or choice. No-choice tests produce results that can easily be analysed statistically, whereas choice test are more complicated to analyse. Attack rates on targets and non-targets are not independent data, and the encounter rate with targets and non-targets depends upon the depletion of the available hosts (unless they are replaced as they are attacked). Thus the ratio of target to non-target hosts does not remain stable over the course of the experiment. If only one target host remains in the arena and 20 non-target hosts are still available, the situation cannot be compared with a situation where both kinds of host would be available in equal numbers. On the other hand, nochoice tests may lead to the acceptance of non-target hosts under situations where no attack would occur in the presence of target species. This would represent situations where the target pest species in the field are not yet present or have disappeared, or where natural enemies would disperse into habitats where only non-target species occur. Thus this approach might considerably overestimate the risk of attack on non-target species under field conditions. The tests described below are examples. There are a great many potential designs, and these will be determined by the nature of the interaction between the natural enemy (parasitism, predation) and the habitat occupied by the organism. For all tests, careful consideration of the number of replicates is essential (see Hoffmeister et al., Chapter 13, this volume). This is of particular importance for behavioural and host-choice tests. Step 1: Small arena no-choice black-box test The aim of this test is to answer the question: does the biological control agent attack the non-target organism in the

appropriate stage on the relevant part (e.g. the leaf or a root) of its natural host plant? A positive control is performed with the target species; a negative control is performed with the target and non-target species without the natural enemy to check survival etc. of target species under test conditions. Detailed behavioural observations are not performed in step 1, but it is suggested to check the activity (searching or not) of the natural enemy at the start of testing, and after a certain interval (e.g. about 30 minutes) to be sure that lack of attack in tests is not the effect of poor condition of natural enemies, but of rejection of the non-target. Consider that extensive stinging and superparasitism can lead to host mortality and prevent parasitoid development, and thus potentially underestimation of the host range. For predators, consider the effect of cannibalism on prey range. PARAMETERS TO BE MEASURED FOR PARASITOIDS.

● Number of hosts killed and not killed (predation, stinging, host feeding). ● Number of hosts parasitized and not parasitized (dissection, emergence of adult parasitoids (emergence data may underestimate the number of hosts parasitized because of egg/larval/pupal mortality and encapsulation of hosts)). ● Host suitability for parasitoid (attack versus development). For predators, analogous variables, such as number of prey attacked, development of immature stages, longevity and production of offspring can be measured. Simple statistical tests suffice to show significant differences in host or prey attack.

METHODS FOR ANALYSIS.

If none of the non-targets is attacked (with use of a sufficiently high number of replicates (see Hoffmeister et al., Chapter 13, this volume)) and the target species (= positive control = pest species) is attacked at a rate approaching that in the field, one can stop testing, because no direct effects on the tested non-target

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Host Specificity in Arthropod Biological Control

species in field are expected. If non-target hosts are attacked, even at very low rates, further testing is mandatory (see Step 2). Step 2: Small arena no-choice behavioural test The aim of this test is to answer the question: does the biological control agent consistently attack the non-target organism on the appropriate substrate of its natural host plant? A positive control is performed with the target species; a negative control is done with the target and non-target species without the natural enemy. Superparasitism in the confines of a small arena may lead to unnatural mortality of the host. Therefore, special precautions may be necessary to deprive individual hosts of repeated oviposition after first oviposition to avoid host mortality. For example, after first oviposition by C. erionotae in a larva of Erionota thrax, repeated oviposition by parasitoids killed the host, thus preventing any assessment for parasitoid development (D.P.A. Sands, unpublished results). With predators, the possibility of cannibalism in small arenas needs to be taken into account. This no-choice test can overestimate the risk of including the non-target species in the host range of the natural enemy. Large arena tests with entire host plants (see below) can safeguard against this overestimated hazard. Alternatives are to apply sequential alternate exposure of target and non-target species to avoid overestimating risk (Sands and Coombs, 1999), and to compare ovipositional display of a parasitoid on its known host with its behaviour on a non-target species (Sands and Van Driesche, 2003). TO BE MEASURED

● Number of hosts killed and not killed (predation, stinging, host feeding). ● Number of hosts parasitized and not parasitized (dissection, emergence of adult parasitoids). ● Host suitability for parasitoid (attack versus development).

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● Encounter and attack rate over time for non-target species to determine possible increase in acceptance due to increasing oviposition/predation pressure. ● Latency time to first attack. ● Adapt variables to be measured for predators. A comparison of the proportion of target and non-target hosts killed is best performed with a generalized linear model with binomial distribution and logit link function (see Hoffmeister et al., Chapter 13, this volume). The positive control acts as a statistical control against the treatment and the negative control. Alternatively, proportional values might be arcsine transformed and analysed with an ANOVA-like approach. The latency times until the first target and non-target hosts, respectively, are attacked can be analysed with survival analysis (Cox proportional hazard model (see Hoffmeister et al., Chapter 13, this volume)). The attack rates over time should be analysed as number of accepted hosts vs number of rejected hosts for different time intervals of the experiment. Since more than one data point from each individual enters the analysis, such data are appropriately analysed with a Generalized Estimating Equations (GEE) generalized linear model for repeated measurements.

METHODS FOR ANALYSIS.

If the target host (= positive control = pest species) is attacked at a rate approaching that in the field, and the nontarget host is not attacked at all (with a sufficiently high number of replicates (see Hoffmeister et al., Chapter 13, this volume)), one can stop testing, because no direct effects on non-target species in the field are expected. If attack rates are above zero for target and non-target hosts, but the attack on non-target hosts is significantly lower than on target hosts, the hazard to non-target hosts under field conditions might be low to acceptable, and further testing should be considered. If non-targets are attacked only at the end of the observation period (long latency time), then the risk of direct effects on these species is small. If non-target species are consistently attacked, with a

INTERPRETATION.

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latency time similar to the target, and attack rates on target and non-target hosts do not differ significantly, non-target effects might be considerable and further testing is mandatory. Step 3: Large arena choice behavioural test The aim of this test is to answer the question: does the biological control agent attack non-targets when target and nontarget species are present in a semi-natural situation on their natural host plants? Present multiple host plants each with their own non-target species and the target species in a large arena; offer target and non-target hosts in as natural a situation as possible and on their natural host plants; positive controls are done in the same type of cage with the natural enemy and the target host only, and the natural enemy and the non-target host only; a negative control is performed with the target species and non-target species, but without the natural enemy. Care should be taken that the same number of total hosts is present at the start of each treatment. The experiments should be terminated before the target host is eliminated, or in case of parasitoids, before most target hosts are parasitized. Consider that extensive stinging and superparasitism can lead to host mortality and prevent parasitoid development, and thus to potential underestimation of the host range. TO BE MEASURED

● Number of target and non-target hosts killed and not killed (predation, stinging, host feeding). ● Number of target and non-target hosts parasitized and unparasitized (dissection, emergence of adult parasitoids). If behavioural observation of the natural enemy is feasible, the latency time to attack, as well as encounter and attack rates over time, should be noted in order to determine host preference, eventual changes in preference and a possible increasing attack pressure of normally non-attacked hosts when the preferred host is less available at the end of the observation period.

● Adapt variables to be measured for predators. The negative control is used to correct for mortality of target and non-target hosts that is independent of the natural enemy under study. Using a generalized linear model with binomial distribution and logit link function (see Hoffmeister et al., Chapter 13, this volume), the proportion of non-target hosts killed in the choice test is compared to the proportion of target hosts killed in the nochoice control and to the proportion of non-target hosts killed in the no-choice control. Note that the attack rate on target hosts in the choice test is not used in order to achieve independent data. The latency times until the first target and non-target hosts are attacked can be analysed with survival analysis (Cox proportional hazard model (see Hoffmeister et al., Chapter 13, this volume)). Again, latency times on nontarget hosts in the choice test are compared with latency times on non-target and target hosts in no-choice controls. The attack rates over time should be analysed as number of accepted hosts vs number of rejected hosts for different time intervals of the experiment. Since more than one data point from each individual enters the analysis, such data are appropriately analysed with a GEE generalized linear model for repeated measurements. In the choice test, only the data of the non-target species are used because of the dependency of data from target and non-target species within the same cage. For predators, special considerations apply, related to cannibalism and mutual predation between non-target/target and the predator. METHODS FOR ANALYSIS.

Non-target species that are easily attacked on their natural host plants, i.e. with similar latency times to target hosts and with similar attack rates, pose a high risk for non-target effects. If latency times of attack on a non-target species are much higher and attack rates are much lower than in the target control, the natural enemy displays a strong preference for the target species, but may be prone to attack

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Host Specificity in Arthropod Biological Control

the non-target species under situations where the target species is not present. If latency times in the choice test and the non-target control are much higher than in the target control and the attack rates are much lower in the choice test and nontarget control than in the target control, the risk of direct effects on the non-target species under field conditions is small. Step 4: Field test The aim of this test is to answer the question: does the biological control agent attack the non-target when the non-target and the target species are present in their respective habitats? This test can only be done safely in the area of release if the biological control agent cannot establish in this area (e.g. agents from tropical areas to be used in greenhouses in temperate climates). The test can be done in the native area of the natural enemy if the non-target species also occur in this area. Sometimes surrogate species, i.e. close relatives of the non-target species that occurs in the planned area of release, can be used for testing, but it must be remembered that some agents exhibit a high degree of specificity and the surrogate may not necessarily be a potential host. Release the natural enemy in the nontarget habitat, and determine if there is any attack of non-target species. Control: put target species on target host plant in the non-target habitat. Replicate the approach in a number of plots. TO BE MEASURED. Number of hosts killed (predation, stinging, host feeding) and not killed, number of hosts parasitized and unparasitized (dissection, emergence of adult parasitoids). Adapt variables to be measured for predators. FOR ANALYSIS. Use generalized linear models with binomial distribution and logit link function for analysis of mortality rates of target and non-target hosts. Use plot as factor to control for the deviance that is explained by the plot per se (prevalence of natural enemy).

METHODS

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If target species is easily attacked, and no or low attack of non-target species occurs, a low risk for direct effects on non-target species is expected. If the biological control agent easily attacks nontarget species on their host plants in their natural habitat, it poses a very high risk for non-target effects.

INTERPRETATION.

Interpretation of Data Obtained with Host-range Testing The first thing to consider when interpreting host-range data is whether there might be any confusing effects of test conditions (van Dijken et al., 1986; Sands and Van Driesche, 2000). Regularly observed confusing effects of test design are: ● Overestimated host ranges, in which non-hosts are used by agents when deprived for long periods of their normal hosts. ● Overestimated host ranges in which non-hosts are used when in close proximity to the normal host due to transference of stimuli. ● Underestimated host ranges in which valid, but less preferred, hosts are ignored in the presence of a more preferred host. This is one reason why we do not suggest choice tests in small arenas and why a nontarget control is essential in a large arena test. The disruption to insect behaviour when they are held in confinement, or outdoors in cages, is well known for biological control agents generally (Sands, 1993) and more especially for arthropod agents (Sands and Papacek, 1993). Sometimes a particular host will be accepted in laboratory trials but when released into the field, the agent will ignore it. This anomaly commonly leads to overestimated host-range predictions for an agent and may lead to discontinuation of evaluation studies which, if continued, might have shown high degrees of host specificity. Laboratory evaluation for host preferences, in contrast to host acceptance, is even more difficult for accurate predictions

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for an effective agent. For example, the ladybird beetle, Curinus coeruleus (Mulsant), was introduced into Hawaii from Mexico in 1922 as a general predator primarily to control a coconut mealybug, laboratory tests having shown the agent as thriving on this prey. Although this ladybird beetle became established, it remained scarce and ineffective as an agent against the target mealybug. However, in 1984 the ladybird beetle suddenly increased in abundance following introduction from Central America of a more suitable host, a psyllid, Heteropsylla cubana Crawford. Curinus coeruleus has since been recognized as a most important agent almost specifically adapted to H. cubana in many countries where the latter has become a pest (Waterhouse and Norris, 1987). Not all potential agents are affected by confinement during tests for host preference or specificity but it is important to be wary of this problem arising and, depending on the suspected nature of the problem, to adjust the design of experiments to minimize or prevent overestimated host ranges in agents. If laboratory host-range tests remain inconclusive, decisions whether or not to release an agent may depend on information from its native range or from countries where it has already been introduced. Next, there is the problem of when to reject a natural enemy for release. How many non-target species should be in the host range of a biological control agent in order to decide it is unsafe? How much population reduction of a non-target can be accepted before it should be rejected for release? For mono- or slightly oligophagous, and for clearly polyphagous, biological control agents, the above host-range testing framework will usually lead to clear answers about risks for non-target species. Indeed, in a number of cases, host-specificity data from mono- or slightly oligophagous species found in the literature were confirmed when exposed to new non-target host species (e.g. Cameron and Walker, 1997), but exceptions do occur. For example, natural enemy species that were considered to be monophagous, or to have

a rather restricted host range, were found to attack a number of other species in the area of release (e.g. Brower, 1991; Barratt et al., 1997). Perhaps even more unanticipated was the finding that a natural enemy, shown to be polyphagous at one location, appeared to perform as a monophagous natural enemy after introduction in another region (Salerno et al., 2002). Conclusions about host specificity can, therefore, seldom be made purely on data collected in the area of origin of the biological control agent, although this is an important first step (Kuhlmann et al., 2000). The most difficult group for interpretation of host-range data will be the more pronounced oligophagous and slightly polyphagous biological control agents. These agents might first of all not be the most efficient natural enemies and result in intermediate or partial control, and may also show more severe non-target effects when compared to strongly polyphagous species. However, hosts in the native range may all be closely related to the target species and closely related species may be absent in a receiving country, greatly reducing the risks of non-target attack. This group of natural enemies needs to be studied carefully, and more case studies are needed. Host-range data have earlier been used to reject introductions. For example, Sands and Van Driesche (2000) reported that four egg parasitoids in the genus Ooencyrtus were not released in the USA for control of Nezara viridula because they were shown to attack at least 20 species of unrelated native Heteroptera. The decision not to release them was based on their wide host ranges and lack of evidence that they were effective in suppressing the target pest in their native ranges (Jones, 1988). We do not know of any other clear examples stressing the importance of efficacy in selecting or rejecting a natural enemy for release (but see van Lenteren and Woets, 1988), and we propose that this point should be considered more seriously in future evaluation programmes. In another case, host-range data were used to release parasitoids in some coun-

Host Specificity in Arthropod Biological Control

tries, but not in others. Two egg parasitoids from Papua New Guinea (Telenomus lucullus Nixon and Ooencyrtus sp. papilionis species group) were evaluated in containment for their suitability as biological control agents for the fruit-piercing moth, Eudocima fullonia, in Australia and the Pacific region. Studies on the host specificity of both parasitoids indicated that their host range was confined to noctuid species belonging to the genus Eudocima, of which several were also pest species. Both egg parasitoids were shown to complete development on the common pest species of Eudocima, but a rare Australian species, E. iridescens, could not be obtained for testing (D.P.A. Sands, unpublished results). The decision not to release the egg parasitoids in Australia was made even though the rare non-target species, E. iridescens, was also indigenous to Papua New Guinea. In contrast, the two parasitoids were approved for introduction into Samoa, Fiji and Tonga (Sands et al., 1993), countries where E. iridescens does not occur. The parasitoids subsequently became established without any observed detrimental impacts on non-target species in those three countries. This is not to say that host-range expansions, host shifts, host race formation or speciation cannot occur in introduced biological control agents. While, to our knowledge, no recent example is available for introduced insect parasitoids, there are reports of relatively generalist indigenous parasitoids adapting over several years to attack introduced pests. Equally, some herbivorous insects such as tephritid fruit flies provide a well-known example of evolutionary host race formation in ecological time (Berlocher and Feder, 2002). Apple maggot flies, Rhagoletis pomonella (Walsh), seem to have switched to cherries within the last century (Jones et al., 1989). There are several similar examples from the pest literature, e.g. a castniid, Telchin licus (Drury), now known as the large moth borer of sugar cane, adapted to sugar cane as a new host in Guyana at the beginning of the 20th century, about two centuries after sugar cane was first introduced to the

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region (González and Cock, 2004). Nevertheless, such host-range expansions, host shifts, or host race formations seem not to occur so often that they represent a major concern for the release of otherwise host-specific insectan natural enemies.

Conclusions Determination of host specificity of (generalist) natural enemies will always be a complicated and time-consuming affair. First, there is the problem of the selection of appropriate non-target species to be tested (see Kuhlmann et al., Chapter 2, this volume). Next, a set of tests needs to be chosen which is suitable, and thus usually quite specific to the natural enemy under evaluation. The sequential host-range assessment design presented in this chapter is new, although it is constructed from elements that have been developed and tested earlier. Step 1 and 2 tests have previously been used in decision-making about release or not. Step 3 tests have been used in a few cases only. Step 4 tests have not yet been used and we would expect their use to be infrequent. We propose to use this sequential test when the environmental risks of new exotic natural enemies need to be determined. We have already indicated that, depending on the type of natural enemy and the ecosystem where it will be released, the testing sequence might need to be adapted. We also realize that this sequential design will undergo changes with growing experience. After host-range testing, there is the issue of interpretation of data obtained from the various tests. For all these phases, arthropod biological control workers have just started to develop a theoretical and methodological background. Finally, the risk posed by and the benefits resulting from the release of the exotic biological control agent should be weighted against the risks and benefits of any other control method under consideration (van Lenteren et al., 2003; van Lenteren and Loomans, Chapter 15, this volume; Bigler and Kölliker-Ott, Chapter 16, this volume).

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Several reviews address the issue of non-target impacts in biological control (e.g. Howarth, 1983, 1991; Pimentel et al., 1984; Harris, 1990; Lockwood, 1996; Simberloff and Stiling, 1996; Samways, 1997; Stiling and Simberloff, 1999; Louda et al., 2003; van Lenteren et al., 2006). Earlier papers raised questions regarding specific biological control introductions, and brought up many conceptual issues regarding the potential complexity of such effects (e.g. Howarth, 1983, 1991; Harris, 1990). More recent papers, such as Stiling and Simberloff (1999), begin to tackle the problem in a more quantitative manner. However, there is still a need for further quantitative analysis, for the bringing together of a more exhaustive list of examples, and in general to go further beyond the anecdotal. Most would agree that the examples discussed in Howarth (1991), Simberloff and Stiling (1996), Louda et al. (2003), and in other similar papers, while they highlight the potential pitfalls of biological control and the potential complexity of non-target effects in practice, do not provide enough evidence to make rational assessments about non-target effects (Lynch et al., 2001; van Lenteren et al., 2003). An extensive evaluation of data of hundreds of biological control projects by Lynch et al. (2001) showed the following with regard to host specificity and nontarget effects: ● Of the more than 5000 classical introductions of insects against insects, 80 (i.e. 1.5%) cases had one or more nontarget effect record associated with them. It should be realized, however, that only a minority of these cases included a careful evaluation of nontarget effects. On the other hand, if strong non-target effects had appeared, they would have been perceived and recorded. ● These cases suggest that most of the agents used in classical biological control which utilized non-target hosts, did so at a low level, and did not gen-

erate non-target mortality high enough to imply a population-level impact. Less than 10% of these agents are estimated to have caused a populationlevel impact. ● In augmentative/inundative types of biological control many of the natural enemies used are generalists, but cannot establish and, therefore, their use is considered sufficiently safe because they only cause transitory effects. Still, several quite serious local population effects were found. ● A large proportion (about 35%) of cases where agents have established on the target, but have not led to a biological control success, have been recorded as inducing non-target effects or minor impact (meaning some attack of nontargets, but without reducing population densities of the non-target). The exhaustive data search of Lynch et al. (2001), in which more than 5000 recorded biological control cases were analysed and 30 international biological control experts were contacted for additional information, has underlined our ignorance of the degree to which non-target effects occur. Hostrange testing, combined with pre- and post-release studies, need to become standard procedures in each biological control project (Coombs, 2003). That this does not necessarily result in fewer introductions of exotic biological control agents has been shown by the recent evaluation of the IPPC Code of Conduct (Kairo et al., 2003), but it does lead to higher costs and delay of introduction. However, if higher costs and later introduction do result in fewer serious mistakes, the investments are certainly justified.

Acknowledgements Peter Mason, David Gillespie and Eric Conti are thanked for reviewing this chapter and for suggesting several important improvements.

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Smith, D., Beattie, G.A.C and Broadley, R. (1997) Citrus Pests and their Natural Enemies. Department of Primary Industries, Brisbane, Australia. Spurway, H. (1955) The causes of domestication: an attempt to integrate some ideas of Konrad Lorenz with evolution theory. Journal of Genetics 53, 325–362. Stiling, P. and Simberloff, D. (1999) The frequency and strength of non-target effects of invertebrate biological control agents of plant pests and weeds. In: Follett, P.A. and Duan, J.J. (eds) Nontarget Effects of Biological Control. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 31–44. Stouthamer, R. (2003) The use of unisexual wasps in biological control. In: van Lenteren, J.C. (ed.) Quality Control and Production of Biological Control Agents: Theory and Testing Procedures. CABI Publishing, Wallingford, UK, pp. 93–113. Taylor, T.H.C. (1935) The campaign against Aspidiotus destructor in Fiji. Bulletin of Entomological Research 26, 1–102. Thompson, W.R. (1943–1965) A Catalogue of the Parasites and Predators of Insect Pests. 18 parts in 4 sections. Commonwealth Agricultural Bureaux, Farnham Royal, UK. Tothill, J.D., Taylor, T.H.C. and Paine, R.W. (1930) The Coconut Moth in Fiji. A History of its Control by Means of Parasites. Imperial Bureau of Entomology, London, UK. Turnbull, A.L. and Chant, D.A. (1961) The practice and theory of biological control of insects in Canada. Canadian Journal of Zoology 39, 697–753. van Dijken, M.J., Kole, M., van Lenteren, J.C. and Brand, A.M. (1986) Host-preference studies with Trichogramma evanescens Westwood (Hym., Trichogrammatidae) for Mamestra brassicae, Pieris brassicae and Pieris rapae. Journal of Applied Entomology 101, 64–85. van Lenteren, J.C. (1997) From Homo economicus to Homo ecologicus: towards environmentally safe pest control. In: Rosen, D., Tel-Or, E., Hadar, Y. and Chen, Y. (eds) Modern Agriculture and the Environment. Kluwer Acadamic Publishers, Dordrecht, The Netherlands, pp. 17–31. van Lenteren, J.C. (2003) Quality Control and Production of Biological Control Agents: Theory and Testing Procedures. CABI Publishing, Wallingford, UK. van Lenteren, J.C. and de Ponti, O.M.B. (1990) Plant-leaf morphology, host-plant resistance and biological control. Proceedings of 7th International Symposium of Insect-Plant Relationships, 3–8 June 1989, Budapest, Hungary. Sympia Biologia Hungarica 39, 365–386. van Lenteren, J.C. and Woets, J. (1988) Biological and integrated pest control in greenhouses. Annual Review of Entomology 33, 239–269. van Lenteren, J.C., Babendreier, D., Bigler, F., Burgio, G., Hokkanen, H.M.T., Kuske, S., Loomans, A.J.M., Menzler-Hokkanen, I., Rijn, van P.C.J., Thomas, M.B., Tomassini, M.C. and Zeng, Q.-Q. (2003) Environmental risk assessment of exotic natural enemies used in inundative biological control. BioControl 48, 3–38. van Lenteren, J.C., Bale, J., Bigler, F., Hokkanen, H.M.T. and Loomans, A.J.M. (2006) Assessing risks of releasing exotic biological control agents. Annual Review of Entomology 51, 609–634. Vet, L.E.M. (2001) Parasitoid foraging efficiency links behaviour to populations processes. Applied Entomology and Zoology 36, 399–408. Vet, L.E.M. and Dicke, M. (1992) Ecology of infochemical use by natural enemies in a tritrophic context. Annual Review of Entomology 37, 141–172. Vet, L.E.M., Lewis, W.J. and Cardé, R.T. (1995) Parasitoid foraging and learning. In: Cardé, R.T. and Bell, W.J. (eds) Chemical Ecology of Insects 2. Chapman and Hall, New York, pp. 65–101. Vet, L.E.M., Lewis, W.J., Papaj, D.R. and van Lenteren, J.C. (2003) A variable-response model for parasitoid foraging behaviour. In: van Lenteren, J.C. (ed.) Quality Control and Production of Biological Control Agents: Theory and Testing Procedures. CABI Publishing, Wallingford, UK, pp. 25–39. Waeckers, F.L. (2003) The parasitoids’ need for sweets: sugars in mass rearing and biological control. In: van Lenteren, J.C. (ed.) Quality Control and Production of Biological Control Agents: Theory and Testing Procedures. CABI Publishing, Wallingford, UK, pp. 59–72. Wapshere, A.J. (1974) A strategy for evaluating the safety of organisms for biological weed control. Annals of Applied Biology 77, 201–211. Wapshere, A.J. (1975) A protocol for programmes for biological control of weeds. Pest Articles and News Summaries (PANS) 21, 295–305. Waterhouse, D.F. (1998a) Biological Control of Insect Pests: Southeast Asian Prospects. ACIAR Monograph No 51. Australian Centre for International Agricultural Research, Canberra, Australia.

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Measuring and Predicting Indirect Impacts of Biological Control: Competition, Displacement and Secondary Interactions Russell Messing,1 Bernard Roitberg2 and Jacques Brodeur3 1University

of Hawaii, Kauai Agricultural Research Station, 7370 Kuamoo Rd., Kapaa, Hawaii, 96746 USA (email: [email protected]; fax number: +1-808-822-2190); 2Biological Sciences, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada (email: [email protected]; fax number: +1-604-291-3496); 3Institut de Recherche en Biologie Végétale, Université de Montréal, 4101, rue Sherbrooke Est, Montréal (Québec), H1X 2B2, Canada (email: [email protected]; fax number: +1-514-872-9406)

Abstract In recent years concern over the potential environmental impact of biological control agents has broadened to include indirect ecological interactions with other species, such as competition and apparent competition. It is extremely difficult to predict the outcome of such relationships based on pre-release quarantine testing. Nevertheless, regulators increasingly ask for such data, and biological control practitioners must be prepared to address the issue. In this chapter we review the best available current methods to measure and predict indirect impacts resulting from competition, displacement and other subtle secondary interactions of newly imported biological control agents. We provide a framework for considering both top-down and bottom-up effects, and we review how descriptive studies (using small arenas, focal observations, and molecular and biochemical tools) and manipulative experiments (including large-cage trials and surrogate experiments) can complement historical, theoretical and phylogenetic considerations to provide a comprehensive overview of the organism’s role in the ecological community.

Introduction Until the mid-1980s, it was widely accepted that the practice of biological pest control was so far superior to chemical control in terms of environmental safety that it was considered a ‘safe’ technology (Coulson et al., 1991; DeBach and Rosen, 1991; Greathead, 1995). In the relatively 64

short time since then, as circumstantial evidence mounted indicating that introduced predators and parasitoids have at least the potential to significantly impact non-target species (Howarth, 1990; Simberloff and Stiling, 1996), the applied sub-discipline of natural enemy risk analysis has begun to come to terms with the difficulties inherent in extrapolating from

©CAB International 2006. Environmental Impact of Invertebrates for Biological Control of Arthropods: Methods and Risk Assessment (eds F. Bigler et al.)

Measuring and Predicting Indirect Impacts of Biological Control

highly simplified, structured, artificial quarantine environments to complex, dynamic, natural ecosystems. Increasing numbers of research papers, funded grants, review articles and books (Follett and Duan, 2000; Lockwood et al., 2001; Wajnberg et al., 2001; Louda et al., 2002) attest to the commitment with which biological control practitioners and applied ecologists have addressed risk analysis in recent years. Yet, despite these efforts, it must be admitted that the majority of studies to date have focused on simple two-species interactions. Researchers have barely begun to come to grips with the complexities of subtle, indirect interactions such as competition and apparent competition. The nature of competition in itself has long been a source of debate and controversy for those studying the ecology of organisms, both in native habitats and following invasions (Connell, 1980, 1983; Cooper, 1993). Given the difficulties inherent in quantifying, or even documenting, the existence of competition in the field, it is perhaps not surprising that the ability to predict the effects of competition and other indirect effects of newly introduced organisms has proved extremely frustrating, as the following quotes attest: ‘the ability to predict indirect effects, given that the quantification of direct interactions is so intractable, seems a long way off.’ (Lonsdale et al., 2001.) ‘the best data require years of painstaking field work … such data are simply not yet widely available.’ (Stiling and Simberloff, 2000.) ‘most species live in a complex web of interactions … this makes it difficult to predict the response of even well-understood systems … some ecologists even despair of finding general patterns.’ (Holt and Hochberg, 2001.)

Despite the acknowledged difficulty in predicting the outcome of competition and other indirect ecological interactions, critics of biological control continue to press the case that these may be responsible for profound negative ecological impacts. In Hawaii, introduced natural enemies have

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been called a form of ‘biological pollution’ (Howarth, 1991), and the decline in some endangered avian species has been blamed in part on introduced arthropod natural enemies competing with native forest birds for Lepidopterous prey (Howarth, 1990). Extrapolations from simple malaise trap collections of introduced parasitoids in Hawaiian rainforests have led some to the conclusion that native parasitoids are declining due to competition, and that ‘the purposeful introduction of parasitoid and predatory insects into Hawaii should be discontinued’ (Asquith and Miramontes, 2001). Even host-specific natural enemies have been implicated in negative environmental impacts, via mechanisms such as ecological replacement, compensatory responses and food web interactions (Pearson and Callaway, 2003). Practitioners and regulators of biological control are thus faced with a daunting task: while theoretical and empirical studies provide the merest beginnings of a framework to guide the prediction and evaluation of indirect non-target impacts and risk analysis, concrete and responsible decisions must nevertheless be made if classical and augmentative biological control programmes are to continue as viable and respected components of integrated pest management. As an example, a recent permit application to introduce host-specific parasitoids of Mediterranean fruit fly, Ceratitis capitata (Wiedemann) (Diptera: Tephritidae), into Hawaii was required to provide data on potential interactions of the new parasitoid with extant fruit fly parasitoids, and with other tephritids used in biological control of weeds (BokononGanta et al., 2005). It is our goal in this chapter to focus on the best available current methods for investigators for measuring and predicting possible indirect impacts on non-target species resulting from competition, displacement and other secondary interactions of newly imported or released biological control agents. The emphasis is necessarily on approaches that can be used in a quarantine facility prior to field release of newly imported organisms, as it is generally

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accepted that once an arthropod is released in the field it is extremely difficult, if not impossible, to mitigate or eradicate it should unacceptable consequences ensue. However, we also discuss novel methods of investigation that, while not yet normally applied to non-target risk analysis, nevertheless have potential for application in this context to circumvent some of the limitations and pitfalls of the restrictive quarantine environment. We offer and discuss a preliminary conceptual classification of the types of interaction that may occur, review experimental techniques with appropriate examples, and give some indication of the feasibility and relative predictive capability of each of these methods. Historically speaking, concern over nontarget impacts (both direct and indirect) has been much greater for plants than for arthropods; this is based in large part on the more easily recognized economic value of many plant species (i.e. food, forage, fibre crops and ornamentals). Arthropods, except for very few well-recognized pollinators and biological control agents, were generally not considered from a conservation perspective. Therefore, practitioners of weed biological control usually had more experience and a better established framework for dealing with non-target issues than had practitioners of insect and mite

(A) Predation Carnivore/ Herbivore

3

Herbivore

2

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1

biological control. However, in this chapter (due in part to the authors’ own experience and background) we concentrate more on the arthropod biological control perspective, recognizing that many of the principles and techniques can be adapted across the board to any organism.

A Synthesis of Approaches In considering the possibilities of competition and displacement among natural enemies in the biological control of weeds, the emphasis has generally been on a bottomup approach: i.e. looking at effects mediated through a common host plant. In a frequently cited paper, McEvoy and Coombs (2000) used the following framework to characterize the types of interactions of concern among weed biological control agents (Fig. 4.1). Of particular note is the fact that all interactions in McEvoy and Coombs (2000) framework are either directly between the biological control agents themselves, or else are a function of the agents’ effects on their food source; higher trophic levels are not considered (Fig. 4.1). Note that the arrows point up, the traditional direction of energy flow in trophic webs, indicating a bottom-up approach.

(B) Interference

(C) Exploitation

2

2

3

1

3

1

Fig. 4.1. Interactions between populations in a weed biological control system including (A) predation, (B) interference competition and (C) exploitation competition. Populations are represented as circles, a positive effect of one population on another is represented by an arrow while a negative effect is represented by a filled circle. No arrow means no direct interaction between two variables.

Measuring and Predicting Indirect Impacts of Biological Control

In contrast to this, and from a perspective focused particularly on biological control of arthropods rather than weeds, Holt and Hochberg (2001) use a different conceptual framework, in which interactions are influenced primarily by higher trophic levels (Fig. 4.2). Note that arrows point down, emphasizing a top-down approach. In complex ecosystems, interactions can occur at all trophic levels simultaneously (in ecological time), and a comprehensive risk analysis must consider both bottom-up and top-down effects. The approaches of McEvoy and Coombs (2000) and of Holt and Hochberg (2001) can be combined to give us a more unified perspective that allows us to view all direct and indirect non-target effects in a single framework, regardless of the type of biological control agent that is being imported (Fig. 4.3). In this framework we attempt to standardize the terminology across weed and arthropod sub-disciplines (i.e. rather than the term ‘predation’ of McEvoy and Coombs (2000), we use the term ‘killing’ in recognition of the fact that larval para-

(a)

sitoids may kill their competitors through direct combat without necessarily feeding on the killed competitor). In terms of competition, we distinguish between direct competitive effects (those in which a natural enemy or its semiochemicals (synthetic or metabolic products) come into direct physical contact with a competitor) and indirect competitive effects (in which the interaction among competing natural enemies is mediated via a third organism). The direct impacts (killing and interference competition) act on natural enemies in the same trophic level, in contrast to exploitation competition (which is mediated via a lower trophic level) and apparent competition (which is mediated through a higher trophic level). The category of circuitous competition is, admittedly, rather ill-defined, and is used to refer to more complicated interactions that may involve multiple trophic levels (both higher and lower) and permutations of interaction that are clearly recognized as feasible, if not well documented.

Agent Target

(b)

Shared predation (apparent competition)

Non-target Agent

Target (c)

Non-target Agent

Target (d)

Competition

Mixed predation and competition

Specialist consumer Non-target

Agent

Exploitative competition

Native predator Enrichment

Target (e)

Non-target Agent

Target

Hidden natural enemy Non-target

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Intra-guild predation

Fig. 4.2. Holt and Hochberg’s (2001) framework for depicting ‘community module’ interactions in arthropod biological control.

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Mechanism

Effect

Trophic level

Apparency

Risk

Killing

direct

same

high

high

Interference competition

direct

same

Host discrimination (general usage)

Exploitation competition

indirect

lower

Extrinsic competition (general usage)

Apparent competition

indirect

higher

Circuitous competition

indirect

mixed

low

low

Terms used in literature Predation (McEvoy and Coombs, 2000) Lethal interference (Collier et al., 2002) Intra-guild predation (Rosenheim et al., 1995) Intrinsic competition (general usage) Multi-parasitism (general usage)

Enrichment (Holt and Hochberg, 2001)

Fig. 4.3. Comprehensive framework for direct and indirect non-target effects, encompassing terminology of both weed and arthropod biological control.

In broad terms, direct mechanisms (in particular killing) are more visible to the scientific observer and thus more readily observed, measured and analysed by researchers than are indirect mechanisms. We posit a gradient of apparency such that the ease in experimentally analysing and, hence, reliably predicting these types of species interactions increases as one moves from the bottom to the top of Fig. 4.3 (dark arrow). This implies that regulators may place more confidence in predictions of direct impact than of indirect impact, and certainly there are more reliable laboratory methods established for the former category (see below). As a working hypothesis, we very tentatively suggest that direct mechanisms have a more frequent or profound impact on population levels of competing species than do mechanisms mediated through increasing levels of trophic interaction (dashed arrow in Fig. 4.3). We recognize that the greater apparency referred to above imputes a built-in bias to generalizations regarding the empirical record when comparing documented examples of natural enemy competition. We clearly acknow-

ledge that the unique and idiosyncratic nature of multi-species interactions will provide numerous exceptions to most general patterns that we can outline, particularly in an emerging field such as non-target risk analysis. Certainly we can document scenarios in which apparent competition has overwhelming importance in determining the competitive outcome of interactions. A great deal more experimentation and targeted retrospective analyses are necessary to determine if patterns such as these can serve as useful guidelines for decision-makers and regulatory officials concerned with evaluating new biological control programmes. Further complicating any analysis of impact is the relationship between individual and population performance. Even in cases where researchers demonstrate competitive harm to individuals in laboratory or field bioassays, it does not necessarily mean that significant harm will occur at the population level (i.e. a population may, or may not, suffer from removal of its members). This can easily be demonstrated with a simple but more realistic form of the usual logistic-type model:

Measuring and Predicting Indirect Impacts of Biological Control

γ  dN N  = N 1 –    r K  dt 

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Descriptive studies (1)

where: K is carrying capacity, r is intrinsic growth rate and ␥ is a shape parameter. This simple model assumes that intraspecific competition occurs, but that the strength of such competition depends upon population size. Thus, there are at least two ways in which a population’s performance may be seemingly impervious to loss of its individual members. First, when a population is large in number (relative to the carrying capacity of its prey population), removal of an individual barely impacts per capita performance. Second, when gamma values are large, reduction of population size may be compensated for by increased performance of survivors. This may occur either simultaneously, or subsequently, with population reduction (May et al., 1981). Of course, these are simple situations; Holt and Hochberg (2001) provide a number of less obvious examples where impacts on individuals do not translate into population level effects. It is crucial that regulators do not simply extrapolate from observations of feeding or parasitism on individual arthropods in a laboratory cohort and erroneously conclude that this implies a significant population effect. There are a number of approaches available for explicating indirect interactions. These include descriptive studies, manipulative experiments and theoretical models. These are complementary methods that work best when developed hand in hand. Not all of these approaches will be applicable in any given project, or for all interaction categories. Below, we briefly describe and provide examples of the most commonly used methods. Some approaches (e.g. Petri dish studies) will generate data to help make an informed decision about whether or not to release a particular candidate agent; post-release field studies, on the other hand, will give us broader temporal and spatial perspectives to help inform future decision making.

Direct observations and predation/parasitism tests conducted in the laboratory, in cages or under field conditions can provide a preliminary assessment of interactions among competing species. These may include assays to measure foraging parameters (handling time, patch time allocation, etc.), discriminating capacity and prey/host acceptance. Ecosystem size and complexity are the primary constraints restricting the extent of pre-release (quarantine) studies: any size arena that can reasonably fit inside a quarantine facility can be used in the target country. Petri dish Petri dish experiments are commonly used to establish diet breadth and trophic relationships among biological control agents. The two-dimensional arena reduces or eliminates to a very large extent the hostfinding component, and maximizes potential interactions among individuals (Lucas et al., 1998; Wang and Messing, 2002, 2003). These simple experiments may provide opportunities for identifying underlying mechanisms. This is particularly true for choice experiments in which attack rates are variable and depend upon interactions with other species, such as when choice varies in the presence or absence of a natural enemy (Schmitz et al., 1997). Because there is an almost inexhaustible set of contexts for such choice tests, this kind of work should be done in tandem with theoretical considerations that identify critical switch points (see Roitberg, 2000; Holt and Hochberg, 2001). To identify indirect interactions, the procedure should incorporate a wide range of non-target organisms, including those from the same trophic level (guild members). For example, parasitism bioassays conducted in Petri dishes allowed PérezLachaud et al. (2004) to identify complex trophic and guild interactions in five species of indigenous and introduced bethylid wasps deployed to control the coffee berry borer and lepidopteran pests of

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coconuts and almonds. They showed that Cephalonomia hyalinipennis Ashmead (Hymenoptera: Bethylidae) not only displays conspecific and allospecific ovicide and larvicide, but may also develop as a facultative hyperparasitoid, thereby threatening the establishment and survivorship of other bethylid species. Likewise, Wang and Messing (2004a,b) showed that the ectoparasitic pupal parasitoids Pachycrepoideus vindemmiae (Rondani) (Hymenoptera: Pteromalidae) and Dirhinus giffardii Silvestri (Hymenoptera: Chalcididae) not only compete with each other within the same guild, but can also develop as facultative hyperparasitoids on the primary braconid parasitoids that contribute to the biological control of tephritid fruit flies in Hawaii. These small-scale and short-term experiments have, however, several drawbacks, as they are conducted in an artificial environment that has little in common with the natural foraging conditions usually experienced by natural enemies in the field, and they do not allow the complete repertoire of behaviours to be expressed. Furthermore, such simplified environments may prevent the expression of mediated interactions and population level processes. They are unlikely, therefore, to provide sufficient information for the accurate prediction of risk associated with some of the more complex types of competitive interaction. Yet, in smaller quarantine facilities they are often the most that can be accomplished on site. Focal observations Although labour intensive, continuous tracking of a freely foraging natural enemy helps to characterize the ecological niche of a species by determining its distribution in the habitat, its patterns of activity and diet. This approach has been used with a wide range of arthropods (Kareiva and Odell, 1987; Heimpel et al., 1997; Casas, 2000). When restricted to a quarantine setting, cage studies should be designed to replicate as much as possible the essential features of an oviposition environment,

and should allow for construction of quantitative ethograms that may facilitate understanding of competitive interactions with similarly behaving species (Wang and Messing, 2002, 2003). Once again, however, this approach runs the risk of oversimplifying a range of mitigating factors. More realistic behaviours can be tracked in natural settings in the country of origin, albeit with a different suite of associated species. Focal observations circumvent the problem of confinement associated with Petri dish or cage experiments. One good example comes from the study of Cisneros and Rosenheim (1998), who characterized the diet of nymphs and adults of Zelus renardii Konelati (Heteroptera: Reduviidae), a generalist predatory bug in cotton fields. Using a behavioural event recorder, they observed predators foraging freely in the field for a total of 94 h and noted information about their activity, distribution on the plant and type of prey attacked. They quantified ontogenetic changes in within-plant distribution of the predators and showed how these changes modulated the prevalence of trophic and guild interactions. A similar example that focused on Hemipteran predators of spider mites can be found in Rosenheim (2005). Time-lapse video recording may also be used to reduce the drawbacks of direct observations, such as intensity of labour and observer-caused disturbance (Jervis and Kidd, 1996). Empirical observations are also essential for determining mechanisms that underlie complex competitive interactions. This would be especially true of trait-mediated effects (Schmitz et al., 1997), wherein the impact of a biological control agent is brought about by altering one or more traits of the focal organism. For example, in the presence of an introduced predator a native herbivore might reduce its range of host plants, thereby altering inter-specific competition as well as predator–prey interactions. Further, as noted by Lima (1998), a very common response by prey to the presence of natural enemies is to reduce activity levels. This will probably mean a reduction of interactions with other mem-

Measuring and Predicting Indirect Impacts of Biological Control

bers of the community, including nontarget species. How might one deal with this problem? Observing rates of change is one valuable way of predicting overall effects (i.e. don’t use observation to document the phenomenon, but rather to quantify it). However, because such effects are context dependent, it is unlikely that observations alone will provide data sufficient to determine degree of risk from release of an introduction. As mentioned above, a well-honed theory will determine what sorts of effects are critical and where to look for them. Molecular and biochemical studies Dissections of a predator’s gut, serology (e.g. enzyme-linked immunosorbent assay (ELISA)), prey labelling and electrophoresis have been used for several decades to identify prey and to detect parasitoids within their hosts (reviewed in Powell et al., 1996; Symondson, 2002). New molecular approaches using polymerase chain reaction (PCR) primers have been developed to amplify the DNA of prey species from the gut contents of predators. These approaches increase both the probability and duration of prey detection (Hoogendoorn and Heimpel, 2001). To our knowledge, none of these techniques have been employed to characterize guild interactions within a community of natural enemies. However, they can be powerful adjuncts in demonstrating that predatory or parasitic interactions are in fact occurring, and at rates sufficient to cause (or alleviate) concern.

Manipulative experiments A second, powerful way to identify and analyse direct and indirect interactions in an arthropod community is to run inclusion/exclusion experiments in large cages, to use surrogates, or to conduct surveys or manipulative experiments in the area of origin of a potential new natural enemy before its introduction. The principle behind this approach is to compare interac-

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tions at the level of the population (e.g. survivorship rates of natural enemies, growth rates of herbivore populations) among treatments, including different combinations of natural enemies and herbivorous pests. Large-cage experiments These kinds of experiments have great potential for identifying risk, so long as they provide sufficient scale and complexity to afford ample expression of predation and oviposition behaviours. Attack rate and distribution of a nabid predator, for example, were shown to depend strongly upon the spatial scale of the experiment (Ostman and Ives, 2003). Conducting a macrocosm experiment under large-cage conditions does allow for the control of certain parameters (e.g. presence/absence, population densities, etc.) that are particularly useful in predicting indirect effects. This can be especially important when predation rates are frequency and/or density dependent (Abrams, 2004). For example, the tendency of predators to express switching behaviour may be density dependent; thus, simple experiments that work with a small set of densities may cause researchers to predict strong interactions between target and non-target species in the absence of switching, and vice versa (Abrams and Matsuda, 2003). Again, this is particularly important when complex nonlinear interactions occur (Peacor, 2002). Finally, Holt (1995) suggests that we break down complex communities into smaller, manageable modules for study. Large-cage (macrocosm) studies would allow for isolation of modules for semi-field study. Although not in common usage, largerscale arenas or microcosms could be constructed within quarantine settings, finances permitting, and would allow more realistic extrapolations and risk analyses prior to natural enemy release. Different types of devices have been employed to exclude all natural enemies or to prevent specific groups of natural enemies from interacting with focal organisms (e.g. aerial or surface-dwelling arthropods):

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mesh cages placed over plants, branches, leaves; clip cages; or other physical barriers (Jervis and Kidd, 1996). Rosenheim et al. (1993) used field cages in cotton fields to explore the effect of combining the green lacewing Chrysoperla carnea (Stephens) (Neuroptera: Chrysopidae) with three species of hemipteran predators on population densities of the cotton aphid, Aphis gossypii Glover (Homoptera: Aphididae). Aphids were controlled less effectively by a combination of predators than when C. carnea was released alone. This nonadditive effect was caused by predatory bugs feeding on lacewings. Similar experiments were performed by Colfer and Rosenheim (2000), who examined indirect interactions between the convergent lady beetle Hippodamia convergens GuérinMéneville (Coleoptera: Coccinellidae) and the braconid Lysiphlebus testaceipes Cresson (Hymenoptera: Braconidae) on caged cotton plants. Intra-guild predation played an important role in decreasing parasitoid densities, as coccinellids destroyed a large proportion of mummified aphids. In another study, Hoogendoorn and Heimpel (2004) showed that the native coccinellid Coleomegilla maculata (DeGeer) (Coleoptera: Coccinellidae) re-located to different parts of a plant in response to the presence of the introduced coccinellid Harmonia axyridis Pallas (Coleoptera: Coccinellidae). This movement was presumed to decrease the amount of intraguild predation between the two lady beetles, but may have led C. maculata to occupy less rewarding foraging patches. Field-cage (i.e. mesh bags) studies were also used by Borer et al. (2004) to explain how parasitoids of California red scale could co-exist in citrus groves. There have been a few precedents, particularly in Hawaii, for regulatory authorities to allow release of a new natural enemy from quarantine solely for the purpose of further testing in laboratory and field cage studies. This approach recognizes the increased, intermediate level of risk that ensues from quarantine removal and restriction to caged environments (in the laboratory or in the field); truly estab-

lished populations in nature can only rarely be eradicated, but mitigation may be possible in carefully controlled circumstances. Manipulation of a community to quantify interactions among populations can also be achieved in open-plot experiments, either post-release or in the country of origin, where insecticides are employed to selectively suppress species or groups of natural enemies. The imported red fire ant Solenopsis invicta Buren (Hymenoptera: Formicidae), an invasive species in the southern United States, is a voracious generalist predator that threatens native invertebrates and vertebrates. To experimentally document interference between ants and other natural enemies, Eubanks et al. (2002) suppressed fire ant populations in large cotton plots (>1.2 ha) with insecticide baits. They compared densities of arthropod predators throughout the growing season in treated vs control plots, and were able to quantify the predators’ susceptibility to predation by the fire ant. Colfer et al. (2003) recently used a similar approach to examine the role of naturally occurring generalist arthropod predators on the establishment and efficacy of a predatory mite, Galendromus occidentalis (Nesbitt) (Acarina: Phytoseiidae), that is commonly released for the control of the spider mite Tetranychus urticae Koch (Acarina: Tetranychidae). They first used small field cages to test interactions with different combinations of predators over short-term periods in cotton fields. Next, they employed insecticide manipulations to examine long-term interactions on a larger spatial scale. They concluded that hemipteran predators have a negative impact on predatory mite populations via intra-guild predation. However, generalist predators did not curtail the long-term biological control of spider mites, because their addition to the T. urticae–G. occidentalis system caused a significant overall reduction in pest densities. To a large extent the methods referred to above cannot be used in the target country prior to release of a particular natural enemy in question. They can, however, be

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of great value when conducted post-release to help establish patterns and principles that guide overall analyses of indirect impact and risk assessment. For individual arthropod introductions it may be possible to conduct similar studies in the country of origin, prior to collection and importation. In one example, Liu et al. (2004) obtained baseline information on species interactions with natural enemies of the soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphidae), in China, prior to their importation to North America. While not giving the kinds of detailed information alluded to above (because some of the species from the target country will not be present in the country of origin), nevertheless some basic patterns may be discerned. Biological control projects are practical experiments in applied ecology, and the extent of pre-release testing is invariably limited by logistical considerations such as funding, time constraints, manpower and political realities. In many cases it will be logistically or economically impossible to conduct the extent of research desired in the country of origin. In recent explorations for Mediterranean fruit fly parasitoids in Kenya, for example, our research efforts were pummelled by floods, impassable roads, worker illness and lethal terrorist attacks. Nevertheless, even when logistical considerations preclude complex manipulative experiments, simple surveys of diet breadth and habitat use in the country of origin can provide invaluable information that should not be overlooked.

Surrogate experiments Even with the seemingly intractable problems associated with predicting competitive interactions prior to the release of a new biological control agent into a complex ecosystem, it may be possible in some situations to use surrogate species in field experiments. These types of experiments have great potential after theoretical explorations have identified critical features of the interaction, and subsequent analyses demonstrate that the surrogate is a biologically

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close and realistic stand-in for the natural enemy in question. For example, substantial differences in the intrinsic rate of increase between the surrogate and focal species could render such experiments moot. In an elegant field demonstration of indirect impacts, van Nouhuys and Hanski (2000) showed how apparent competition mediated by a generalist hyperparasitoid might be predicted using a surrogate primary parasitoid. The specialist braconid wasp Cotesia melitaearum (Wilkinson) (Ichneumonoidae: Braconidae), which attacks the Granville fritillary butterfly Melitaea cinxia (Linnaeus) (Lepidoptera: Nymphalidae) on islands near Finland, is itself attacked by the generalist ichneumonid hyperparasitoid Gelis agilis (Fabricius) (Ichneumonoidae: Cryptinae). The researchers experimentally added to the system a second braconid host of G. agilis (Cotesia glomerata (L.)), which does not compete with C. melitaearum for resources (thus controlling for any effects of exploitation competition). Their replicated experiments in the field were able convincingly to demonstrate that the addition of the new primary parasitoid increased extant populations of the hyperparasitoid, which subsequently reduced populations of C. melitaearum in all replicates (so much so, in fact, that two of the original populations were driven to extinction). One can envisage how, in a similar manner, surrogates could be used to test for possible indirect impacts of introducing a new parasitoid in addition to an existing, partially effective, parasitoid in a biological control programme. The key, of course, is having an available surrogate species that already exists in the target region (i.e. does not itself have to go through quarantine), and one that is biologically similar to the prospective import. While it is unlikely that the risk of apparent competition could be quantified definitively using this method, it could add a valuable dimension to a comprehensive risk analysis, and offer guidelines for post-release follow-up studies should release of the new parasitoid be approved.

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Rules of Thumb Biological control risk analysis is a very new discipline, and is still struggling to come to terms with methods for measuring even direct impacts of predators and parasitoids. For subtle indirect effects, there is still much that needs to be learned in order to improve, integrate and make best use of the methods discussed above. In the near future the choice, outcome and interpretation of these types of tests will necessarily have to be integrated with common sense and our best current understanding of parasitoid (or predator) ecology and community dynamics. The current biological control-permitting system in most countries makes use, implicitly, of expert opinion in providing guidance to decision-makers. It may be advantageous to make this approach explicit, and to formalize the ‘expert systems’ approach so as to take fullest advantage of our current knowledge, imperfect as it may be. There is ample precedent for using expert systems in other aspects of integrated pest management (e.g. Messing et al., 1989). In very general terms there may be some ‘rules of thumb’ based on arthropod life history parameters that can help decision-makers generalize to some extent about the risk of particular importations. Most obvious is the breadth of host (or prey) range (see van Lenteren et al., Chapter 3, this volume); highly polyphagous species will generally be riskier than monophagous or stenophagous ones. Hymenoptera that can act as facultative hyperparasitoids are more likely to have indirect effects than are obligate primary parasitoids (Brodeur, 2000). Some parasitoid taxa are known to be more vulnerable to hyperparasitism; these indicate an increased risk for apparent competition with native parasitoids (Heimpel et al., 2004). While bearing in mind these considerations, however, one must guard against oversimplification, and recognize that the idiosyncratic nature of extremely diverse parasitoid life

histories will always require detailed analyses of individual species and even of sub-species genetic cohorts.

Conclusions All of the techniques that have been mentioned in this chapter have their own advantages and their own limitations. None of them, in themselves, are able to predict accurately the full extent of competitive and other indirect interactions that will follow upon the introduction of a new species into an existing community matrix. Natural ecosystems, and even simplified agricultural environments, are usually too complex and their relationships too subtle for us to know in advance to what extent new species will alter existing patterns. Nevertheless, as in many fields of human endeavour, decisions must be made even though our knowledge is imperfect. Biological control is but one option for managing invasive species, and these invasives often have significant negative environmental and economic impacts. Alternative choices for pest management, including the choice to do nothing, have their own risks and also proceed with imperfect knowledge. In order to minimize risk when introducing new biological control agents, we suggest that a comprehensive overview of the organism’s role in the ecological community be outlined, using a combination of the techniques mentioned here along with a thorough literature evaluation of the species and its congeners in the area of origin. We also argue that a sustained and well-funded effort to retrospectively evaluate the case histories from prior biological control programmes would help shore up the empirical data base upon which theory and modelling can build a broader picture of community dynamics and the response to insertion of new species. While risk cannot be eliminated, it can be managed with increasing confidence as our understanding of community dynamics grows incrementally.

Measuring and Predicting Indirect Impacts of Biological Control

Acknowledgements This work was supported in part by USDAARS grant No. 5853208147 to RHM and

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NSERC Operating grants to BDR and JB. We also thank George Heimpel and an anonymous reviewer for constructive comments on an earlier version of this chapter.

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5

Risks of Interbreeding Between Species Used in Biological Control and Native Species, and Methods for Evaluating Their Occurrence and Impact Keith R. Hopper,1 Seth C. Britch1 and Eric Wajnberg2 1USDA,

ARS, Beneficial Insects Introduction Research Laboratory, 501 South Chapel Street, Newark, Delaware 19713, USA (email: [email protected]; [email protected]; fax number: +1-302-737-6780); 2INRA, 400 Route des Chappes, BP 167, 06903 Sophia-Antipolis Cedex, France (email: [email protected]; fax number: +33-4-92-38-6557)

Abstract Insect species introduced or augmented for biological control of insect pests may interbreed with native species, which may change fitness or cause evolution, which may in turn alter abundances. By ‘interbreeding’, we mean any reproductive interactions between species. We review the literature on factors affecting the likelihood of interbreeding between insect species and the impacts when these occur. We discuss phylogenetic relatedness, geographical distribution, spatial and temporal barriers to mating, mate recognition, copulation and sperm use, hybrid inviability and sterility, hybrid speciation, reproductive character displacement and introgression. We concentrate on the risks from introduced species, but we also address the risks from augmentation of native species. We propose methods for pre-introduction or pre-augmentation assessment of the likelihood and potential impact of interbreeding between native species and insects used in biological control. Finally, we propose methods for evaluating the occurrence and impact of interbreeding after insect species are introduced or augmented.

Introduction Exotic species introduced into a new region may court, mate, hybridize or introgress with native species, and these interactions may change fitness (Oliver, 1979; Rawlings, 1985; Presgraves, 2002) or cause evolution (Ewel et al., 1999; Cox, 2004), which may in turn alter abundances 78

(Simberloff and Stiling, 1996). Native species augmented in abundance for biological control may also court, mate or hybridize with other native species, which may cause changes in their fitness and alter their abundances (Pinto et al., 2003). By ‘court’, we mean recognize one another as potential mates, but not necessarily sufficiently to copulate. By ‘mate’ we mean

©CAB International 2006. Environmental Impact of Invertebrates for Biological Control of Arthropods: Methods and Risk Assessment (eds F. Bigler et al.)

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copulation, with or without sperm transfer, which may or may not produce progeny. By ‘hybridize’, we mean produce hybrid progeny, which may or may not be viable or fertile. By ‘introgress’, we mean transfer of DNA sequences between species, which may or may not affect fitness, behaviour or ecology, and may or may not persist and spread in the receiving species. By ‘interbreeding’, we mean any or all such reproductive interactions between species. The risks of interbreeding apply to intentional introductions, like those in the horticultural and pet trades (Frank and McCoy, 1995; Young et al., 1999; Walker et al., 2002), as well as to accidental introductions (Yukawa, 1996; Sagarra and Peterkin, 1999; Swanson et al., 2000). Insect species introduced for biological control of insect pests may interbreed with native species (Huxel, 1999; Mooney and Cleland, 2001), although we have found few studies on such interbreeding, and only one potential case of hybridization (Yara et al., 2000). Here we review the literature on factors affecting the likelihood of interbreeding between insect species and the impacts when these occur. We concentrate on the risks from introduced species, but we also discuss the risks from augmentation of native species. We propose methods for pre-introduction or pre-augmentation assessment of the likelihood and potential impact of interbreeding between native species and insects used in biological control. Finally, we propose methods for evaluating the occurrence and impact of interbreeding after insect species have been introduced or augmented. We deal primarily with interbreeding among species, rather than groups below the level of species, because we assume that introductions of subspecies or populations of species that already occur in the target region are rare except for multiple introductions of exotic agents. Distinguishing closely related species can be difficult, particularly among parasitic Hymenoptera (Davis et al., 1987; Danforth et al., 1998; Hoy et al., 2000). Indeed, taxa described as species have been found to be complexes of sibling species which

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differ in traits like host specificity (Davis et al., 1987; Nyman, 2002). Native sibling species are unlikely to be at risk from biological control introductions because candidates for introduction would not be considered if what appeared to be the same species already occurred in the target region. However, native species might be at risk from interbreeding with sibling species augmented for biological control. Because data on interbreeding as a result of biological control introductions or augmentations are rare, we draw on the broader literature about interbreeding among insect species, while concentrating on evidence from the orders most frequently used to control insect pests: Diptera and Hymenoptera (Clausen, 1978). Research on species in the genus Trichogramma (Hymenoptera: Trichogrammatidae) provides examples on how to measure several attributes important in risk assessment of interbreeding. Thus, we use this research as a case history throughout the paper. Although we cover published literature in our review, useful data (e.g. concerning geographic distribution and habitats and hosts used) for particular projects can be obtained from museum collections and other unpublished sources (project reports, quarantine records). Encounters between introduced and native insect species in biological control are necessarily contacts between newly sympatric species that were previously allopatric. Thus, the literature on interbreeding between allopatric species after secondary contact (Mayr, 1963; Dobzhansky, 1970; Kohlmann and Shaw, 1991; Brennan and Fairbairn, 1995; Shoemaker et al., 1996; Sperling et al., 1996; Willett et al., 1997) is directly relevant. However, we do not mean to say that introduced and native species arose by allopatric speciation. Indeed, native versus introduced species considered in biological control could have arisen by any of the proposed mechanisms of speciation: sympatric, allopatric, peripatric or parapatric; mediated by ecological selection, sexual selection, allopolyploidy, genetic drift or symbiotes. The literature on hybrid zones (Endler

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1977; Moore, 1977; Barton and Hewitt, 1981, 1985; Harrison, 1986; Howard, 1986; Barton and Gale, 1993) is relevant to interbreeding between native species and insects introduced or augmented for biological control. For introduced insects, the appropriate hybrid zone model is likely to change with time after introduction. Initially, the distribution of introduced populations may overlap completely with native species at risk of interbreeding, but native species may have large regions of allopatry. This is very different from the usual models of hybrid zones where both species have regions of allopatry with a more or less narrow region of sympatry where hybridization can occur. Further along after introduction, introduced species may spread throughout the region where target pests occur and may broadly overlap the distribution of native species. On the other hand, introduced species may not initially overlap distributions of some native species and may only come into contact with them after some period of spread. The width and stability of hybrid zones will depend on dispersal, habitat specificity and the relative fitness of hybrids versus parental populations. For augmented native species, the distribution of hybrid zones will depend on the spatial distribution of releases and on dispersal rates.

edness and the likelihood or impact of interbreeding varies among taxa and depends as well on whether species are allopatric versus sympatric, or ecologically and behaviourally similar versus dissimilar (Coyne and Orr, 1997). In insects, all reported cases of hybridization are among species in a single genus or in a species complex within a genus (Table 5.1). Lack of observations of interbreeding among more distantly related species may arise from a bias towards searching for such interactions only where one expects to find them. Nonetheless, effort on interbreeding in biological control should concentrate on closely related species in the same complex or genus. A centrifugal approach, like that used in host range testing, may prove useful (see van Lenteren et al., Chapter 3, this volume). Case history Pinto et al. (1992) examined molecular genetic differences among 22 cultures of the Trichogramma minutum Riley complex and established phylogenetic groups that strongly predicted reproductive compatibility. In contrast, Pinto et al. (1991) showed that taxonomic grouping of T. pretiosum Riley, T. deion Pinto and Oatman and T. minutum, based on morphology, correlated poorly with reproductive compatibility, even among populations considered conspecific.

Factors Affecting Interbreeding Geographic distribution Phylogenetic relatedness Species that are phylogenetically close are more likely to interbreed than species that are phylogenetically distant (Coyne and Orr, 1997). Phylogenetic relatedness can be determined using molecular, behavioural and morphological data. Thus, one could perhaps delimit native species at risk for interbreeding with introduced species based on phylogenetic proximity. However, for many taxa, we have no phylogenies, only taxonomic keys based on morphology and only loosely related to phylogeny. Furthermore, the relationship between relat-

Climatic, habitat and geographic barriers to the spread of the introduced species may prevent sympatry with some native species, even after introduction. Thus, whether a particular native species is at risk for interbreeding depends on its distribution, the climatic tolerances of the introduced species and the ability of the introduced species to disperse across habitat and geographic barriers like grasslands, deserts and mountains. For augmentative releases, the geographic distribution of augmentation programmes will determine which native species are at risk of interbreeding.

Table 5.1. Studies showing hybrids between insect species in orders with taxa used for biological control. Family

Species

Diptera

Chironomidae Chironomidae

Drosophilidae Tephritidae Tephritidae Tephritidae Tephritidae

Glyptotendipes pallens Meigen, G. glaucus Meigen Chironomus usenicus Loginiva and Belyanina (C. plumosus Linnaeus ⫻ C. behningi Goetghebuer) Anopheles gambiae Giles, A. arabiensis Patton Culex pipiens Linnaeus, C. quinquefasciatus Say Anopheles hyrcanus Pallas group Aedes triseriatus Say, A. brelandi Zavortink, A. hendersoni Cockerell Aedes triseriatus Say, A. zoosophus Dyar and Knab Anopheles bwambae White, A. gambiae Giles Aedes scutellaris Walker group (A. cooki Belkin ⫻ A. kesseli Huang and Hitchcock) Drosophila spp. Eurosta solidaginis Fitch Anastrepha sororcula Zucchi, A. obliqua Macquart Bactrocera tryoni Froggatt, B. neohumeralis Hardy Anastrepha fraterculus Wiedemann complex

Aphelinidae Apidae Cynipidae Formicidae Formicidae Pteromalidae Torymidae Trichogrammatidae

Aphytis spp., Aphelinus spp. Apis spp. Andricus kollari Hartig spp. Solenopsis invicta Buren, S. richteri Forel Acanthomyops spp. Catolaccus grandis Burks Torymus sinensis Kamijo, T. beneficus Yasumatsu and Kamijo Trichogramma minutum Riley spp.

Culicidae Culicidae Culicidae Culicidae Culicidae Culicidae Culicidae

Hymenoptera

Lab.

Field

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

Publication (Michailova, 1998) (Polukonova and Beljanina, 2002) (Besansky et al., 1997) (Cornel et al., 2003) (Miao et al., 1988) (Taylor and Craig, 1985) (Taylor, 1990) (Thelwell et al., 2000) (Sherron and Rai, 1984) (Coyne and Orr, 1997) (Craig et al., 1997) (Dos Santos et al., 2001) (Pike et al., 2003) (Selivon et al., 1999) (Rao and DeBach, 1969) (Clarke et al., 2002) (Walker et al., 2002) (Shoemaker et al., 1996) (Umphrey and Danzmann, 1998) (Morales-Ramos et al., 2000) (Yara et al., 2000) (Pinto et al., 2003)

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Order

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Methods for prediction Field data on climatic distributions of candidates for introduction and native species at risk should allow assessment of whether they will become sympatric after introduction, assuming no barriers to dispersal. Computer programs, such as BIOCLIM (Nix, 1986) and GARP (Chen and Peterson, 2000), have been developed for climate matching and these may be useful for predicting distributions of biological control agents after introduction. If climatic distributions overlap but there are geographic or habitat barriers between the region of introduction and the region harbouring a native species at risk, the dispersal capacity of the candidate for introduction must be assessed. Given that climatic tolerances of introduced species may evolve, it would be useful to measure genetic variation in climatic tolerances in the material to be introduced. Methods for detection Field data on actual distribution of a biological control agent after introduction and spread will reveal which closely related native species are actually at risk of interbreeding. Case history Pak and Oatman (1982) and Glenn et al. (1997) measured development times for several populations of Trichogramma across California and Australia, respectively, and showed that species pairs or complexes were differently adapted to temperature regimes or to temperature requirements of their hosts.

Spatial and temporal barriers to mating Sympatric species that mate in different seasons or in different habitats will rarely, if ever, interbreed because they rarely meet (Feder et al., 1994; Bush and Smith, 1998; Tilmon et al., 1998). Temporal isolation could extend to differences in diurnal rhythm of courtship (Wood and Guttman,

1982; Feder et al., 1993; Morrow et al., 2000), and habitat specificity can involve differences in choice of host plant species for mating (Feder and Bush, 1989; Wood et al., 1999). Some courtship and mating may happen even with very little spatial or temporal overlap (Deverno et al., 1998; Haegele, 1999). Although rare courtship and mating are unlikely to affect demography, they may lead to introgression. Whether rare mating leads to introgression depends on post-zygotic barriers (inviability, sterility and reduced fitness of hybrids) and on the selective advantage or disadvantage conferred by introgressed sequences. New introgression is unlikely for augmentation of native species, unless the augmentation is in seasons or habitats where the augmented species does not ordinarily occur. Methods for prediction Field data on habitat and seasonal distributions of candidates for introduction and native species at risk will allow assessment of whether they will encounter one another in sympatry. For augmentative releases, the habitat and seasonal distribution of augmentation programmes can be used to predict which native species are at risk of interbreeding. Field and laboratory data on diurnal periodicity and on host plant fidelity of mating behaviour could show whether individuals of different species will encounter one another at smaller spatial and temporal scales. Methods for detection Field data on habitat and seasonal distributions of introduced agents and native species will show whether they are likely to encounter one another in sympatry. Because introgression may occur even with rare matings, survey for such introgression may prove useful. Techniques for detecting introgression are discussed below. Case history To assay seasonal activity of several species of Trichogramma over two years,

Risks of Interbreeding Between Species

Thorpe (1984) placed Heliothis virescens Fabricius (Lepidoptera: Noctuidae) eggs in experimental plots of soybean, weedy vegetation and trees. Parasitism was temporally partitioned among four Trichogramma species in the first year, but was more evenly distributed in the second year.

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court and couple only with conspecifics in the laboratory, this is likely to hold in the field as well. However, if candidate and native species court or couple with one another, this may or may not mean they will do so in the field. Methods for detection

Mate recognition Species whose mating periods and habitats more or less overlap must still recognize one another as potential mates. Mate recognition cues include colours, shapes, scents, songs and dances (Cibrian and Mitchell, 1991; Heady and Denno, 1991; Monti et al., 1995; Haegele, 1999; Dos Santos et al., 2001; Deering and Scriber, 2002). To what extent such cues are recognized between species varies with taxonomic group, phylogenetic proximity and whether species are sympatric or allopatric (Coyne and Orr, 1997). Partial recognition of cues which does not lead to mating or hybridization can still mean time wasted courting. Methods for prediction Field and laboratory data on mate recognition cues will reveal whether species are likely to recognize one another as mates. Laboratory mating trials will show whether they do indeed recognize one another as mates, how frequently they do so and to what degree (courtship could reach various levels of completion up to copulation). Candidates for introduction must be reared for at least one generation in the laboratory for host-range testing, identification, and clearing of hyperparasitoids and pathogens. Thus, introduction candidates include only species that will mate in the laboratory. Candidates for augmentation also must mate in the laboratory, otherwise they cannot be augmented. Given that native species at risk are closely related to candidates for introduction or augmentation, it is likely that they will mate in the laboratory as well. In any case, within-species crosses can serve as controls for between-species crosses. If candidate and native species

Field observations of courtship and mating in areas where introduced or augmented species and native species commonly cooccur would show whether they recognize one another as mates. Such observations are difficult with small, possibly nocturnal insects, especially if one or the other species is rare. Traps baited with virgin females (Davis et al., 1987; Brodeur and McNeil, 1994) may help in showing whether scents or songs are recognized between species. If interspecific courtship or mating is common enough to affect population dynamics, the effects may be measured by comparing reproductive rates or stage distributions before versus after introduction or augmentation, or in areas with versus without the introduced or augmented species. Case history Production of female offspring in matings between T. minutum and Trichogramma platneri Nagarkatti from different geographic regions is rare (Pinto et al., 2003). Stouthamer et al. (2000) measured mate choice between T. minutum and T. platneri in the laboratory by observing female behaviour in the presence of conspecific and heterospecific males. Although no female offspring resulted from mating with heterospecific males, females did not prefer males of either species. Thus releasing one species in the range of the other might affect population dynamics (Pinto et al., 2003).

Copulation and sperm use Species may recognize one another as mates sufficiently to attempt copulation,

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but be unable to copulate normally because of morphological differences in genitalia (Arnqvist, 1998). Such differences can lead to injury or death of the female, or death of both partners if they become locked in copula (Sota and Kubota, 1998). Even if the partners survive, females may act as if they have been mated, even though no sperm has been transferred. If sperm is transferred, it may not fertilize heterospecific eggs (Jamart et al., 1995) or may be less competitive in multiple-mated females (Robinson et al., 1994; Howard, 1999). Females of some insect species cease to court, or reject males, after copulation or insemination (Allen et al., 1994; Fleischmann et al., 2001; Jang, 2002). Thus, if inseminated with heterospecific sperm first, these females may have reduced or no receptivity towards conspecific males. Interspecific mating may have no measurable effects, especially if females readily remate (Albuquerque et al., 1996). However, if interspecific matings are common, they could reduce the net reproductive rates of one or both species. The worst-case scenario would be where an introduced species, ineffective at controlling an abundant pest, was maintained at high numbers and mated with a rare native species whose females did not remate. This could lead to the native species being swamped with interspecific matings. Augmentative releases could have this effect where density was increased, but the effect should not extend beyond the dispersal range of the augmented species. A second scenario would be where an introduced species, while still rare, mated with a common native species, so that the introduced species was swamped with interspecific matings. At low densities, Allee effects, for example from failure to find appropriate mates, might become important and extinction possible (Hopper and Roush, 1993). In the second scenario, this would mean effort wasted in a failed introduction; in the first scenario, this would mean the loss of a native species. Such interactions are like pest control using sterile males, which has been effective in several cases (Gould and Schliekelman, 2004).

Methods for prediction Examination of genitalia might reveal whether morphological incompatibilities would be likely between candidates for introduction or augmentation and native species (but see Porter and Shapiro, 1990; Goulson, 1993; Eberhard, 2001). Laboratory crosses would show whether interspecific mating compromised intraspecific receptiveness and whether sperm was transferred and used interspecifically. Methods for detection To determine whether native or introduced females were sterilized without sperm transfer, one could collect females in the field, test whether they are receptive to mating with conspecific males in the laboratory, and then dissect the females to determine whether they carry sperm. To determine whether native or introduced females were sterilized by copulations with sperm transfer, one could collect females in the field, allow them to oviposit in the laboratory, and then dissect them to determine whether they carried sperm. Eggs which produced no progeny (or only male progeny for haplodiploids) would reveal that females had been sterilized. One would need to test females mated with conspecific males (either in the laboratory or in regions without the other species) to control for levels of sterility/infertility within species. As with courtship or mating without copulation or sperm transfer, one could measure demographic impacts by comparing population dynamics before versus after introduction or augmentation or in areas with versus without introduced or augmented species. Case history Genital morphology, correlated with reproductive incompatibility between Trichogramma species (Rohi and Pintureau, 2003), was used to estimate interspecific divergences among species groups (Pintureau, 1993). On the other hand, Nagarkatti and Fazaluddin (1973) found production of hybrids in the laboratory did not correlate with morphological, and in

Risks of Interbreeding Between Species

particular genitalic, similarity, geographic proximity or habitat similarity. Although heterogamic insemination was frequent, production of interspecific hybrids was rare, and most cases of hybrid progeny were unidirectional. Stouthamer et al. (2000) describe methods for observing sperm transfer, and Damiens et al. (2002) describe a method for measuring viability of sperm in spermathecae of females.

Impacts of Interbreeding Hybrid progeny: inviability and sterility If species mate and reproduce, the hybrid progeny may show reduced viability or fertility. If interspecific matings are common, production of inviable progeny could affect population dynamics like copulation, which effectively sterilizes females. Thus, the two scenarios described under the section on copulation apply here as well: (1) a rare native species being swamped by hybridization with an ineffective introduced biological control agent or with an augmented native species, or (2) a newly introduced species being swamped by hybridization with a common native species. If hybrid progeny are viable but sterile, these two scenarios would be worsened because hybrids would also mate with, and thus effectively sterilize, individuals of one or both species. The most abundant species would mostly mate intraspecifically, but the least abundant species would either mate interspecifically or with hybrids. This interaction would be like the project to control Heliothis virescens using sterile-male hybrids from crosses between H. virescens and H. subflexa Guenée (King et al., 1985; Proshold et al., 1986). Although this project had mixed success, it showed sufficient promise at suppression to be pursued for a decade, suggesting that interspecies hybridization might reduce abundances of either native species or introduced biological control agents. The Heliothis-hybrid project produced a series of mathematical models (Roush and Schneider, 1985; Laster et al., 1996) that could be used to analyse

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the likely effects of sterile hybrids between introduced or augmented species and native species. Methods for prediction Laboratory crosses could show whether hybrid progeny are produced and whether these progeny are inviable or sterile. However, fertility and survival may be intermediate, with either all hybrids showing intermediate fertility or survival or with some crosses producing inviable/sterile hybrids and others producing fit hybrids (Oliver, 1979; Collins, 1997; Coyne and Orr, 1997; Presgraves, 2002). Withinspecies crosses would provide controls for level and between-family distribution of survival or fertility. Methods for detection To determine whether native or introduced females are producing inviable hybrid progeny, one could collect females from the field, allow them to oviposit in the laboratory, dissect the females to determine whether mated, and then measure the number of progeny (or female progeny for haplodiploids) reaching adulthood. One would need controls of females known to be mated with conspecific males. These could be obtained from areas where the species did not overlap or from laboratory crosses. To determine whether native or introduced females are producing viable hybrids, one could search for hybrids in field collections. Hybrids would be easiest to find where introduced and native species are about equal in abundance. Hybrids could be identified either by phenotype using, for instance, the hybrid character index (Anderson, 1936; Howard et al., 1993) or genotype (Nason and Ellstrand, 1993; Anderson and Thompson, 2002). If hybrids show readily identifiable morphological phenotypes, clearly distinguishable from both introduced and native parents, screening by phenotype might be simplest. On the other hand, hybrids between closely related species might be difficult to identify by

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phenotype. In this case, hybrids could be detected as heterozygotes of fixed molecular differences between native species and introduced or augmented species. Once fixed molecular differences between native and introduced or augmented species are established, the presence of such heterozygotes could easily be detected. Insertions/deletions in nuclear ribosomal genes like ITS1 and ITS2, or in introns flanked by conserved exons, might be easiest to detect because they would not require sequencing (e.g. Zhu et al., 2000). Single nucleotide polymorphisms (SNPs) that distinguish native versus introduced or augmented haplotypes could also be detected without sequencing (e.g. Morlais and Severson, 2002). Rare hybrids would be difficult to detect by either phenotypic or genotypic screening. However, for hybridization without introgression to affect population dynamics of native or introduced species, hybrids would have to be common. One could measure effects of introduced or augmented species on dynamics of native species by comparing populations before versus after introduction or augmentation, or in areas with versus without the introduced or augmented species. Measuring effects of native species on demography of introduced species would be more difficult, unless there were areas in the region of introduction where hybridization was absent or at least less common. In the latter case, one could compare introduced species dynamics with and without hybridization with the native species. Case history Although interspecies hybrids were rare, Nagarkatti and Fazaluddin (1973) found in all instances where hybrids were produced, hybrids were viable and fertile.

so even if hybrids are sterile. The most worrisome shift in host use would be to attack species used by the native species yielding the hybrid. Methods for prediction If the hosts of the native species producing hybrids are of concern, the host range of hybrids could be measured in laboratory experiments like those for evaluation of host use by candidates for introduction or augmentation (see van Lenteren et al., Chapter 3, this volume). Methods for detection Collecting from hosts of native species at risk for interbreeding should reveal whether hybrids are parasitizing these hosts. The techniques for detecting such hybrids are discussed above under ‘Hybrid progeny: inviability and sterility’.

Hybrid speciation Hybrids between species may not cross with parental species but cross among themselves, which could give rise to a new species (Arnold, 1997; Barton, 2001). Such hybrid speciation is much more likely in plants than in insects (Rieseberg et al., 1995; Rieseberg, 1997), but is being discovered in a growing number of animals (Bullini, 1994). Augmentation of native species is unlikely to produce new, hybrid species. If a hybrid species were to result from a biological control introduction, it would be as if two species had been introduced, one with known traits and the other with some combination of traits from the introduced and native parents. Methods for prediction

Hybrid progeny: host range shifts If viable hybrids are produced, they could affect unexpected non-target species if hybrid host range differed from that of the introduced or augmented species. This is

Laboratory crosses among hybrids and between hybrids and their parental species could show whether hybrids would be most likely to cross among themselves or backcross to the parental species after introduction.

Risks of Interbreeding Between Species

Methods for detection To determine whether hybrids were breeding among themselves or backcrossing to the introduced or native parents, one could collect insects from the field and screen for hybrid phenotypes and genotypes. Hybrids mating among themselves would show segregation of genes from both parents. Segregation should be detectable with phenotypic and molecular markers, even if some hybrid genotypes are more favoured than others.

Reproductive character displacement If species commonly hybridize, but hybrids have lower fitness than either parental species, reproductive traits of one or both species may diverge (Dobzhansky, 1940). Such reproductive character displacement has often been invoked in discussions of sympatric speciation and reinforcement after secondary contact between allopatric species (McLain, 1986; Bordenstein et al., 2000; Kawano, 2002). Some are sceptical about the likelihood of reinforcement (Moore, 1957; Mayr, 1963; Barton and Hewitt, 1981), but recent models and data of sympatric speciation (Via, 2001) and of reinforcement (Howard, 1993) suggest that reproductive character displacement may be more common than many have thought. Post-introduction evolution in reproductive traits of introduced species is probably not of concern, unless it would affect success in biological control, which seems unlikely. On the other hand, some would consider evolution in reproductive traits of native species undesirable (Simberloff and Stiling, 1996; Mooney and Cleland, 2001), although such evolution would not necessarily mean changes in abundance.

Introgression Fertile hybrids between species may backcross to either parental species and thus introgress DNA sequences from one species into another (Anderson, 1953; Dowling and

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Secor, 1997). This is true even if hybrid fitness is low and successful backcrosses rare (Barton, 2001). However, the genomes of species sufficiently close to hybridize are quite similar (Hewitt, 1988; Barton, 2001), so that many introgressed sequences will have no effect, either not changing sequences or not changing function, and thus not changing fitness. Augmentation of native species is unlikely to increase introgression much. However, native and introduced species presumably differ, at least in host range or impact on the target pest; otherwise the candidate for introduction would not be under consideration. The fate and impact of introgressed sequences depends on the selective advantage or disadvantage they confer, the frequency of introgression, and dispersal rates (Barton and Gale, 1993; Barton, 2001). If hybrids and backcrosses are common, neutral and even mildly deleterious genes could become common in the area of contact, although they would be unlikely to spread far beyond the hybrid zone (Barton and Hewitt, 1981). As discussed above under ‘Hybrid progeny: inviability and sterility’, which species would be most affected depends on relative abundances. A rare native, swamped by backcrosses with hybrids from a common introduced species, could have high levels of introgression, and the same applies to a rare introduced species swamped by backcrosses with hybrids from a common native. High levels of introgression would be relatively easy to detect using molecular markers. Introgressed sequences that are strongly deleterious, either through direct effects on traits fitness components or through breakup of co-adapted gene complexes, would be strongly selected against and thus unlikely to persist or spread. Thus, the major effect of introgression of deleterious sequences would be demographic. If hybrids and successful backcrosses are rare, introgressed sequences would be unlikely to persist or spread unless they are selectively advantageous (Barton and Hewitt, 1985; Barton and Gale, 1993; Linder et al., 1998). However, if an introgressed sequence is selectively advantageous, it

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could rapidly sweep to fixation with modest levels of selective advantage and dispersal (Barton, 2001). Unfortunately, such a selective sweep could be very difficult to detect if it involved a small, unknown sequence affecting a trait not previously measured. On the other hand, introgression of sequences affecting previously measured traits, like host specificity or climatic tolerances, would be relatively easy to detect. Introgression of sequences affecting traits like host use or climatic tolerances could have major, and perhaps unwanted, consequences. Introgression of sequences affecting climatic tolerances or diapause conditions could allow range expansions of native species or increase the realized ranges of an introduced species. On the other hand, introgressed genes affecting climatic tolerances could act like conditional lethals, spreading because of fitness advantage under current conditions and then causing heavy mortality when conditions change. Introgression of such genes resembles proposals for genetic control, which although seldom implemented, show much promise for pest management (Gould and Schliekelman, 2004). Nonetheless, such a catastrophic outcome seems unlikely by chance given the similarity between genomes of species that will hybridize. Introgression of sequences affecting host range raises the most worrisome and plausible scenarios for interbreeding. Sequences introgressed from an introduced species into a native species could allow the native species to attack species beyond its original range, including the target pest. The latter would not be bad in itself, and indeed might provide useful control, but some hold that any such introgression-driven evolution is a form of environmental pollution and thus should be avoided (Mooney and Cleland, 2001; Allendorf and Lundquist, 2003). Sequences introgressed from a native species into an introduced species could cause a rapid shift in host range, allowing the introduced species to attack hosts of the native species. Given that host specificity is essential for the safety of biological control

introductions, introgression-driven evolution of host range, especially to attack native species, is clearly undesirable. The likelihood of such introgressive changes in host range is unknown; no examples are available in the literature. Methods for prediction Laboratory crosses could show with what frequency backcross progeny are produced from crosses of hybrids with either candidates for introduction or native species. If backcross progeny are produced, one could measure their host range, climatic tolerances, mating behaviour and other traits of interest. Within-species crosses would be needed as controls for expected levels of traits. Methods for detection If backcrosses are common, one could measure introgression using molecular markers. If backcrosses are rare, it will be difficult to measure introgression using molecular markers, unless one has markers for specific genes of interest. For rare introgression, measurement of phenotypic changes will be easier. Beside changes in host range and climatic tolerances measured in the laboratory crosses, one could measure morphological traits after various generations of backcrossing to determine whether introgression could be detected by screening morphological phenotypes. One could measure demographic effects of introgression from introduced species into native species by comparing population dynamics before versus after introduction, or in populations where introgression had or had not occurred. The latter approach depends on being able to detect introgression. Measuring demographic effects of introgression from native species into introduced species would be more difficult, unless there were areas in the region of introduction where introgression was absent. In this case, one could compare introduced species dynamics with and without introgression with the native species.

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Case history

Recommendations and Conclusions

Because T. minutum and T. platneri hybridized occasionally in the laboratory and there was concern about gene flow between these species in the wild, Pinto et al. (2003) tested for introgression where these species are sympatric in the Pacific North-west and found no introgression of species-specific alleles at the Pgm locus. Although hybrids are expected to differ phenotypically from parents and thus be detectable, Nagarkatti and Fazaluddin (1973) found hybrids invariably resembled the maternal parent. As evidence that introgression could be deleterious, hybridizing geographical populations of Trichogramma and selecting hybrids for tolerance of temperature extremes produced a weak response and actually reduced parasitism in the laboratory (Ashley et al., 1974).

We organized the tests described above into flowcharts for predicting the risks of interbreeding from introduction (Fig. 5.1) and augmentation (Fig. 5.2), and for assessing impacts of interbreeding with introduced (Fig. 5.3) or augmented species (Fig. 5.4). Decision makers must realize that these flowcharts are extremely schematic. The details of the biology of each biological control candidate, and what is known about that biology, may require modifications in the flowcharts or in the tests proposed above. The major differences between procedures for introductions (Figs 5.1 and 5.3) and augmentation (Figs 5.2 and 5.4) are that those for introductions address the risk of introgression of novel genes, while those for augmentation concentrate on the demographic effects of mating and hybridization. The

Closely related (<same genus)?

No

Yes Likely geographical overlap?

No

Yes Likely habitat/seasonal overlap?

No

Yes Recognize as mates?

No

Yes Hybridize?

No

Yes Hybridize often (>1/10)? No

Yes

Hybrids viable and fertile? Yes

Further study No Accept

Further study

Fig. 5.1. Pre-introduction tests to predict interbreeding between species introduced for biological control and native species. See text for description of tests.

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Closely related (<same genus)?

No

Yes Likely geographical overlap?

No

Yes Likely habitat/seasonal overlap?

No

Yes Recognize as mates?

No

Yes Hybridize?

No

Yes Hybridize often (>1/2)? Yes

No Accept

Further study

Fig. 5.2. Tests to predict interbreeding between native species augmented for biological control and other native species. See text for description of tests.

major difference between predicting impacts (Figs 5.1 and 5.2) and assessing impacts (Figs 5.3 and 5.4) is that the latter involve field measurements of mating, hybridization and introgression. Where post-introduction tests show effects on populations of nontarget species, further releases of introduced species and augmentation of native species should be stopped, and similar candidates should be avoided in the future. To illustrate how one might proceed with these flowcharts, we will use our results on the Aphelinus varipes complex (Hymenoptera: Aphelinidae) (K.R. Hopper, J.B. Woolley, J.M. Heraty, A.M.I. Farias, S.C. Britch, unpublished results). The A. varipes complex comprises a group of sibling species in Eurasia. One species from the Republic of Georgia parasitizes Diuraphis noxia (Mordvilko) (Hemiptera: Aphididae), the Russian wheat aphid. If D. noxia were accidentally introduced into Japan and became a pest (as has occurred in the United States), this Georgian species would be a candidate for introduction into

Japan. However, a species in the A. varipes complex, which does not parasitize D. noxia, already occurs in Japan. Because the Georgian and Japanese species are closely related, the answer to the first question in Fig. 5.1 would be ‘Yes’. The climate in Georgia where the parasitoids were collected matches fairly well the target climate in Japan (Walter and Lieth, 1967), so the species are likely to overlap in geographical range after introduction, and the answer to the second question in Fig. 5.1 would be ‘Yes’. Because they overlap broadly in host range, with the exception of D. noxia, they are also likely to occur in the same habitats, and the answer to the third question in Fig. 5.1 would be ‘Yes’. Their DNA sequences differ across several genes, indicating they have had separate evolutionary histories for several hundred thousand years, but these species readily mate and produce viable offspring in laboratory experiments. Thus, the answers to the remaining questions in Fig. 5.1 would also be ‘Yes’. Given that the introgression

Risks of Interbreeding Between Species

Closely related (<same genus)?

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No

Yes Geographical overlap?

No

Yes Habitat/seasonal overlap?

No

Yes Recognize as mates in field? Yes

No

Effects on populations?

Hybridize in field?

No No

Yes No

Hybridize often (>1/10)? No

Yes

Effects on populations?

Hybrids viable and fertile?

No No

Yes Introgression occurs? Yes

No

Effects on populations?

No No impact detected

Fig. 5.3. Post-introduction tests to measure occurrence and impact of interbreeding between species introduced for biological control and native species. See text for description of tests. Where there are effects on populations of non-target species, further releases should be stopped and similar candidates should be avoided in the future.

seems likely, further study would be needed according to Fig. 5.1. However, because the species differ in host use and introgression could lead to an evolutionary shift in host use, in either the introduced or native species, we would recommend against releasing the Georgian species in Japan, particularly because there are other candidates with narrower host ranges that do not mate with the Japanese species. In our opinion, the risks are small of large impacts from interbreeding between native species and insects used in biological control. But data are lacking about both the likelihood and impact of interbreeding, so more research is needed. Fortunately,

this research will not only improve the safety of biological control, but will also shed light on the behaviour, ecology and genetics of courtship, mating and hybridization, and thus on the mechanisms of speciation.

Acknowledgements We thank the participants of the Engelberg workshop of June 2004 for providing insightful discussions, and an anonymous reviewer for comments on the manuscript, and USDA-ARS and INRA for their support during the preparation of this chapter.

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Closely related (<same genus)?

No

Yes Geographical overlap?

No

Yes Habitat/seasonal overlap?

No

Yes Recognize as mates in field? Yes

Effects on populations?

Hybridize in field?

No No No

Yes Hybridize often (>1/10)?

No

Yes

No

Effects on populations?

No impact detected

Fig. 5.4. Tests to measure occurrence and impact of interbreeding between species augmented for biological control and native species. See text for description of tests. Where there are effects on populations of non-target species, augmentation should be stopped and further studies conducted.

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6

Assessing the Establishment Potential of Inundative Biological Control Agents

Guy Boivin,1 Ursula M. Kölliker-Ott,2 Jeffrey Bale3 and Franz Bigler2 1Horticultural

Research and Development Center, Agriculture and Agrifood Canada, 430 Boul. Gouin, St-Jean-sur-Richelieu, Québec, J3B 3E6 Canada (email: [email protected]; fax number: +1-450-346-7740); 2Agroscope FAL Reckenholz, Swiss Federal Research Station for Agroecology and Agriculture, Reckenholzstrasse 191, 8046 Zürich, Switzerland (email: [email protected]; [email protected]; fax number: +41-44-377-7201); 3School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK (email: [email protected]; fax number: +44-121-414-5925)

Abstract Establishment of exotic natural enemies in the area of release is not a desirable attribute in inundative releases as it increases the risks of non-target effects on native species. To evaluate the risks of non-target effects, this chapter focuses on factors which may limit the establishment of introduced natural enemies, either for a season or permanently. From a risk assessment perspective, the risk associated with the release of a species with seasonal persistence capacity is limited in time. The establishment of natural enemies in a novel habitat depends on several factors, some abiotic and some biotic. Among the abiotic factors, climate is a major factor. Temperature and humidity, especially when soil moisture is considered in species that spend part of their development in the soil, are the components of weather that have the major impact on the survival and establishment of exotic species. Biotic factors, and especially the occurrence of alternate host/prey, also play an important role in the probability that an organism will become established. We describe in this chapter the methods that should be used to assess the probability that exotic natural enemies can become established, based on these factors. We recommend first evaluating to what extent temperature may limit establishment. Only where the risk of establishment based on thermal requirements is determined to be higher than ‘insignificant’, should the availability and suitability of host or prey for overwintering in the nontarget habitat or the impact of humidity be investigated.

Introduction In contrast to classical biological control or inoculative releases, the ability of an exotic natural enemy to establish in the area of 98

release is not a desirable attribute in inundative releases. Establishment of an introduced organism increases the risks of non-target effects on native species. Such risks include displacement of native preda-

©CAB International 2006. Environmental Impact of Invertebrates for Biological Control of Arthropods: Methods and Risk Assessment (eds F. Bigler et al.)

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tor or parasitoid species and a decrease in the population density of native species used as prey or host. To evaluate the risks of non-target effects, this chapter focuses on factors which may limit the establishment of introduced natural enemies. One of the risks that has to be assessed before releasing a biological control agent is the potential for the establishment of a natural enemy in areas where it is not indigenous. For example, a risk of establishment of exotic natural enemies is present if individuals escape from greenhouses in which inoculative or inundative releases are made. Mass release of predators or parasitoids in the field may also result in the establishment of these species, either for a season or permanently. Seasonal persistence is the survival and reproduction of a species throughout one season, with seasonally occurring conditions preventing further survival. Inability to overwinter due to the occurrence of low temperatures is probably the most frequent reason for failure to establish long-term populations. Permanent establishment is the survival of a species for several years. From a risk assessment perspective, the risks associated with the release of a species with seasonal persistence capacity is limited in time. If negative effects are found after the release, these effects will last for only one season. For species with the capacity to establish permanently, any damage will also be permanent. The risk factors linked to these two types of establishment should therefore be different. The establishment of natural enemies in a novel habitat depends on several factors, some abiotic and some biotic. Among the abiotic factors, climate is a major factor, and it has long been advocated that climate matching of the recipient system and the native range should help predict dispersal and potential geographic spread (Louda et al., 2003; Cock et al., Chapter 12, this volume). Temperature is the component of weather that has the major impact on the survival and establishment of exotic species. Humidity can also be a limiting factor, especially when soil moisture is considered in species that spend part of their development in the soil. These abiotic

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factors are well characterized in most areas and the response of natural enemies to the conditions expected in the area of release can be tested. In fact, the impact of factors such as temperature and humidity should be among the first ones to be tested, especially in areas where extreme conditions are expected. The importance of abiotic factors, and mostly temperature, on the probability of establishment of a natural enemy is highlighted by the fact that most of the successes in classical biological control programmes have occurred in warm climates (DeBach, 1964). Data on temperature and humidity can be used to predict the distribution of species that have been previously introduced. However, in situations in which outdoor establishment is undesirable, such as with escapes from greenhouses, it is now apparent that climate matching between native and introduced ranges may not be a sound basis for predicting long term survival (Hart et al., 2002a,b), and more comprehensive analyses of thermal tolerance are required. Biotic factors also play an important role in the probability that an organism will become established. The occurrence of alternate host/prey, the presence of competitors or natural enemies and, finally, access to other food sources, are all factors that are important in the capacity for an organism to establish itself in an area. These factors are more difficult to assess than the abiotic factors and, for many organisms, the information available from both the area of origin and the area of introduction is often lacking or patchy. Finally, the interaction of abiotic and biotic factors will act together on both the host/prey and the natural enemy. The probability of establishment of organisms in temperate climates is affected both by mortality due to climatic extremes and by the difficulty in adjusting and synchronizing their lifecycle with that of their host, especially when these hosts enter quiescence or diapause stages during the season when extremes are reached (Bale, 1991a). This ability to synchronize with their host/prey is especially critical in specialist natural enemies because they cannot use alternate

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hosts/prey to either survive until emergence or sustain populations between generations of the host/prey. The current trend toward using specialist species is sound, both from an environmental perspective and because it decreases the probability of establishment. The use of specialist species is thus generally preferable, and is particularly useful for greenhouses, where their external establishment is undesirable. We will consider in this chapter only factors that could prevent establishment of a natural enemy. Factors that affect the efficacy of the introduced organism but that will not prevent a species from establishing will not be covered. The occurrence of food sources is one such factor. The availability of an adequate food source is known to increase parasitoid efficacy but it may be impossible to demonstrate that no food source is available and that this absence will render establishment impossible. Other factors not likely to be decisive in preventing establishment include competition with other natural enemies and the presence of predators or hyperparasitoids. Therefore, we will concentrate on temperature and humidity among the abiotic factors, and on the presence of host/prey in the area of release among biotic factors. In this chapter we will briefly describe the methods that can be used to quantify these factors and ultimately determine the risk of establishment.

Factors Preventing Establishment Abiotic factors The methods that can be used to measure abiotic factors that may limit the establishment of exotic natural enemies are summarized in Table 6.1. The table includes a short description of the methods, the information gained from the experiments, and lists the equipment needed to perform the tasks. Temperature Temperature influences the probability of establishment of insect natural enemies and

the range of their distribution once established. Both low and high temperatures are to be considered, although the meaning of ‘high’ and ‘low’ will vary according to the area of origin of the organism. Temperature can affect the probability of establishment either through the thermal budget of an organism or through direct mortality caused by exposure to low or high temperature. The thermal budget refers to the accumulation of day degrees necessary to complete a generation and can be used to assess the number of generations that are theoretically possible per year. When the total number of day degrees available in an area is either below the minimum needed for a generation, or is such that at the end of summer the organism is in a stage where it cannot survive winter, establishment is unlikely. Direct mortality attributable to temperature can be due either to short exposure to lethal temperatures or to prolonged exposure to sub-optimal temperatures that become lethal over time. Most insect species have a thermal optimum at which survival and development are normal. As the temperature decreases, the insect eventually enters a sub-optimal zone, where mortality will occur after a certain time at that temperature. Below that temperature, the insect enters the temporary cold stupor zone, where vital functions such as feeding and mating are strongly reduced. Finally, it enters the chill coma, where movement becomes slower and eventually stops (Vannier, 1994). When the temperature of the insect body falls below 0°C, the haemolymph (or other tissues) eventually freezes at the supercooling point. A similar gradation can be found as the temperature increases. The insect will enter the temporary heat stupor zone, where it shows loss of coordination and short episodes of lethargy. As the temperature increases, the insect becomes motionless in the thermostupor zone (heat coma). The insect eventually dies when the temperature reaches the upper thermal death point (Vannier, 1994). As for all invertebrates, the rate of development of insects varies with temper-

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ature. Several models have been used to describe the relationship between temperature and the developmental rate of insects but most have in common that above a certain temperature, the base temperature, day degrees start to accumulate. Above the base temperature, rate of development increases gradually, often with a positive slope, up to a certain temperature where the slope becomes negative. This sigmoid curve reaches a peak temperature at which the rate of development is at a maximum. Above this temperature, rate of development decreases, often quite rapidly, down to the point where no development occurs at all (Fig. 6.1). Two factors are important from the perspective of establishment. The first is the base temperature, as it is needed to calculate the accumulation of day degrees, and the second is the thermal budget, which is the number of day degrees necessary for an organism to complete a generation. Exposure to low temperature can kill insects either by freezing or by cumulative cold damage, without freezing. Two strategies have been described by which insects survive at low temperature: freeze tolerance and freeze intolerance. Freeze-tolerant species generally freeze at relatively high temperatures (above ⫺10°C) and can recover when they thaw. In these species,

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the presence of polyols is common, and these products protect the frozen tissues from frost damage. The majority of overwintering insects are freeze-intolerant (Bale, 1991b) and are killed at the moment they freeze at the supercooling point. These species must avoid freezing, either by behavioural or physiological adaptations. Behavioural adaptations include selection of protected overwintering sites and migration away from the geographical area where temperatures lower than the supercooling point occur. Physiological adaptations involve emptying the gut to avoid the presence of ice-nucleating particles and synthesis of cryoprotectants. The use of the supercooling point to assess the cold-hardiness of a species, and therefore its probability of establishment in an area based on the lowest temperature occurring in this area, is relevant only for species where winter mortality occurs predominantly at or close to the freezing temperature of the insect. A well-known example is the autumnal moth, Epirrita autumnata (Borkhausen) (Lepidoptera: Geometridae), that occurs on mountain birch in northern Europe. The overwintering eggs of this species have a mean supercooling point of ⫺35.9°C and egg survival correlates well with the lowest tempera-

Fig. 6.1. Example of a temperature response curve (based on the equation of Brière et al., 1999).

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tures during winter (Tenow and Nilssen, 1990), an indication that for this species the supercooling point is an accurate measure of cold hardiness. Classification of insects as freeze-tolerant or freeze-intolerant takes into account only freezing as a cause of death. Although this factor is relevant for species in temperate or subarctic climates that survive well at low temperatures above their supercooling point, for most species, mortality caused by low temperature occurs at temperatures much higher than the supercooling point (Bale and Walters, 2001). Exposure at temperatures above the supercooling point induces mortality, following cumulative-cold injuries, that is proportional to both the temperature and the duration of the exposure. This mortality appears to result from membrane phase transitions and protein conformational changes at low temperature (Sinclair et al., 2003). For these species, the supercooling point is an unreliable index of cold-tolerance and the impact of sub-freezing temperature must be measured. In tropical species, cumulative-cold damage may occur even at temperatures above 0°C. While the damage caused by brief periods of chill coma is readily reversible, long periods of low temperature may prove fatal (Denlinger and Lee, 1998). Some of the damage caused by low-temperature exposure can be reduced if the organism is exposed to pulses of higher temperature. These periods at higher temperature could enable insects to regenerate certain energy resources or cryoprotectants that are progressively depleted at low temperature (Denlinger and Lee, 1998). METHODS OF ASSESSING TEMPERATURE EFFECTS.

When the effect of low-temperature exposure is measured, the timing of the observation is important. Mortality can increase progressively when the individuals are returned to favourable conditions and therefore an early mortality assessment can underestimate the effect of the cold exposure (Bale, 1991a). In addition, the effect of cold exposure can be apparent only at a later stage of development, either when the

individual may die or when sub-lethal effects that reduce the fitness of the individual appear, such as reduced fecundity. Depending on the climate of the areas of origin and on the introduction of a natural enemy, different cold-related indices have been proposed to assess the probability of establishment of alien species in the UK (Bale and Walters, 2001). When these indices are used, it can be informative to test both the natural enemy to be introduced and a native related species. The native species is known to survive in the area of introduction and results may differentiate between this species and the exotic species, in which case an assessment can be made of the likelihood of establishment of the non-native species. If the climate in the area of origin differs greatly from the area of introduction, then establishment of the introduced organism in the release area may be unlikely. Information on the biology (e.g. overwintering stages, diapause characteristics), ecology (e.g. overwintering sites, migratory performance) and thermal requirement (e.g. base temperature, thermal budget) of the introduced organisms (if available) may also help to determine their potential to establish in a specific area. This information should also cover all aspects related to the capacity of the organism to adapt to a new environment, including its response to humidity. For example, the ability of an organism to enter diapause in its area of origin is likely to increase the chance of establishment in the area of introduction. LITERATURE STUDY.

DEVELOPMENTAL THRESHOLDS. The lower developmental threshold, or base temperature, is the temperature below which no development occurs. This temperature is established by obtaining the rate of development of the organism at different temperatures and then calculating the temperature at which the development rate is zero. No development will occur during periods where the maximum daily temperature is below the base temperature. The upper developmental threshold

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is the temperature above which no development occurs. These thresholds can be used to calculate the number of day degrees required for a species to complete a generation. Day degrees have been used quite extensively to express insect development over the growing season. Most published estimates of day degree accumulation include the lower developmental threshold temperature (Tbase), but only a few include the optimum or higher developmental threshold temperature (Tsup). The development rate of an organism (i.e. 1/day) in relation to temperature is generally linear over the optimum temperature range but becomes curvilinear close to the Tbase and Tsup (Fig. 6.1). The use of a linear regression to estimate the Tbase (the intercept on the x-axis) may thus lead to important errors at temperatures close to the thermal extremes of the organism. Nonlinear equations are therefore preferable to express development rate as a function of temperature. These can be classified into three broad categories based on their capacity to determine Tbase and Tsup as summarized below: (i) direct estimations of Tbase and Tsup (e.g. Brière et al., 1999), (ii) indirect estimations of Tbase and Tsup (e.g. Duthie, 1997) and (iii) indirect estimations of Tbase and direct estimation of Tsup (e.g. Lactin et al., 1995). In most cases, these equations provide accurate estimates of optimum temperature when appropriate data of insect development as a function of temperature are available. The thermal budget is the number of day degrees required by a species to complete a generation. The base temperature (developmental threshold) must be established before this index can be calculated. When the annual accumulation of day degrees in an area is below that required to complete a generation, a natural enemy will not be able to establish. In addition, if the day degree accumulation permits the development of partial generations, the impact could be either to reduce the size of the population or even to pre-

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vent its establishment. When it is of interest to consider partial development (i.e. number of day degrees necessary to complete diapause or specific stages of the life cycle), a similar approach is used and the accumulation of day degrees recorded each day until the desired phase of development is completed. POINT. The supercooling point (SCP) is the temperature at which an individual freezes. For freeze-intolerant species, death occurs at or above this temperature. Although is it recognized that death can occur at temperatures much above the supercooling point, this temperature is still relevant, especially for species originating from cold areas. Also, the supercooling point indicates the temperature above which the incidence of prefreeze mortality can be investigated. The supercooling point is determined by detecting the small increase of temperature resulting from the release of latent heat when body water freezes. The organism is cooled at a constant rate (typically 1°C/min) while its temperature is continuously recorded. Microthermocouples, of type T (copper-constantan) or K (chromel– alumel), are normally used and temperature recording done on either a paper chart or a data logger (Hance and Boivin, 1993). Preparation of the organism is also critical as age, feeding status, surface particles or water film can modify the freezing temperature through the presence of ice nucleators or ice crystals. In addition, although a cooling rate of 1°C/min is usually used, this rate is much higher than occurs in natural situations (Sinclair, 2001). Variation in cooling rate has little effect on the SCP, but may modify the ability of insects to survive the freezing event. Cooling at a constant rate can be achieved by apparatus using a watercooled Peltier effect module linked to an electronic control unit (Bale et al., 1984; Panneton et al., 1995). Such systems control the temperature within ± 0.2°C. It is also possible to achieve an approximately linear decrease of temperature by placing the insect in an insulated container within SUPERCOOLING

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a large freezer at ⫺30°C (Hance and Boivin, 1993). However, the decrease in temperature tends to become curvilinear as the temperature within the insulated container approaches the temperature of the freezer. Care must be taken to test the developmental stage of the organism that will normally overwinter. Organisms that overwinter in diapause must also be in diapause when tested, as diapause often changes the supercooling point or, more importantly, the cold tolerance. LETHAL TEMPERATURE 50 (LTEMP50). The LTemp50 is the temperature at which 50% of a population dies. However, in some species, the determination of this temperature depends on the duration of exposure. As exposure time decreases, the LTemp50 may be closer to the supercooling point. Since the purpose of estimating the LTemp50 is to identify the temperature at which 50% of a population are killed, organisms are usually cooled at 1°C/min and exposed to a series of decreasing subzero minimum temperatures for 1 min with mortality assessed 24 and 48 h after exposure. The organism should be placed in a closed container, to avoid damage through desiccation and to buffer the decrease and increase in temperature that will occur at both the beginning and the end of the experiment. The range of temperatures to be tested should be based on the low temperatures experienced by the species in its natural habitat, but should be above the measured supercooling point. Ideally the organism should be in a state compatible to that which overwinters, i.e. for many species low-temperature exposure occurs when the insect is in forms of dormancy, such as diapause or quiescence. The data are obtained as percentage mortality at decreasing temperatures and are then analysed by probit or logit to derive an estimate of the LTemp50. It is important to recognize that this index assesses mortality but does not take into account any sublethal effects such as reduction of longevity, fecundity or modification of behaviour that may occur in surviving insects.

LETHAL TIME 50 (LT50). This index is based on the duration of exposure at a given temperature sufficient to cause 50% mortality in a population. In a sense it is the reverse of LTemp50 but with constant exposure temperatures. Choosing the temperatures to be tested may, however, prove difficult. These temperatures should be chosen so as to be similar to the low temperatures likely to be experienced in the area where the natural enemy will be released. Several containers containing individuals to be tested are placed at the selected temperatures and, at intervals, replicate samples are removed, placed at a standard temperature (i.e. 20°C or 25°C) and survival of the individuals is assessed. Survival should be assessed 24 h and 48 h after removal from the low-temperature environment, and the data analysed by probit or logit. Although low temperature can affect longer-term survival or reproduction (Bale, 1991a), experiments that can detect such effects are long and costly and unlikely to be performed on a routine basis.

If local regulations permit, outdoor cages should be used to assess winter survival. Survival of a species under semi-natural conditions in an outdoor cage is as close an approximation of natural conditions as can be obtained. Multiple cages should be used as independent replicates. However, it should be noted that conditions within the cages may differ from the habitat where the release will be made, and that atypical winters may over- or underestimate mortality. Temperature recording inside the outdoor cages may help to explain unexpected results. The results from outdoor cage tests can be used to verify the findings obtained in the laboratory tests and the predictions on winter survival and hence the likelihood of permanent establishment. As an example, outdoor cage studies were performed with Trichogramma brassicae Bezd. to assess overwintering in six different host eggs under natural conditions in Switzerland (Babendreier et al., 2003). A summary of the methods discussed above is tabulated in Table 6.1.

OUTDOOR CAGE TESTS.

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Table 6.1. Summary of methods that can be used to assess the likelihood of establishment of an introduced natural enemy based on abiotic factors. Assessment

Description of method

Temperature Literature study

Climate matching between the area of origin and introduction; ecological information on the organism Developmental The organisms are reared threshold at different temperatures Thermal budget The organisms are reared at different temperatures Supercooling point The organism is cooled at constant rate (1°C/min) while its temperature is recorded

Lethal temperature Mortality at 24 or 48 h at a specific temperature

Lethal time

Field cage tests Humidity

Equipment needed

Information gained

No special equipment needed

If climates differ then establishment is unlikely; thermal requirements of the organism (if available)

Climatic chamber with controlled temperature Climatic chamber with controlled temperature Microthermocouples, temperature recording either on a paper chart or a data logger, watercooled Peltier effect module controlled by an electronic control unit or insulated container within a large freezer at ⫺30°C Climatic chamber with controlled temperature

Base temperature

Mortality after certain lapse of time at a specific temperature Mortality

Climatic chamber with controlled temperature Field or outdoor cages

Assess survival, fecundity etc. at different temperatures and humidities

Climatic chamber with controlled temperature, desiccators with salt solutions

Humidity While temperature is usually the predominant abiotic factor preventing establishment, humidity may influence long-term survival of introduced species if the area of origin and introduction differ in humidity conditions. Evaluating performance and survival at low and high humidities may help to predict limitations to the establishment of introduced natural enemies. While temperature directly influences development and survival, humidity effects may be less pronounced since organisms have the ability to regulate their body water content to some extent. Hadley (1994) describes the mechanisms used by

Day degree requirement to complete one generation Cold tolerance, acclimation ability

Cold tolerance as a function of exposure temperature; acclimation ability Cold tolerance as a function of exposure time; acclimation ability Ability to overwinter under near field conditions Tolerance to different temperature/humidity combinations

terrestrial arthropods to maintain water balance. Water is gained by drinking, eating, metabolizing food items and absorbing vapour from the atmosphere. For the majority of species, water in the diet is sufficient to balance losses. The processes by which water is lost include cuticular and respiratory transpiration, passive diffusion from oral and anal openings and water loss associated with excretion. Cuticular transpiration constitutes the major avenue of water loss despite the presence of a highly waterproofed integument in most species. In terrestrial arthropods, the epicuticle provides the principal barrier to water loss. Quantitative differences in cuticular lipids

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may contribute to the lower water loss rates and hence increased desiccation resistance of some species (Hadley, 1994). For most species, absolute water loss increases at lower humidities as a result of the lower saturation of the surrounding air. In contrast, the calculated permeability of the cuticle (corrected for saturation deficit) often increases as humidities rise. This may facilitate water loss and thus prevent the arthropod from becoming overhydrated (Hadley, 1994). If the humidity conditions become unfavourable for species survival, individuals of the free-living stages may be able to reduce or avoid dehydration stress by clustering (Yoder and Barcelona, 1995; Yoder and Smith, 1997), by moving into microhabitats more suitable for survival, or by using avoidance behaviours such as burrowing and nocturnal activity. The desiccating conditions present on the surface can largely be avoided by moving a few centimetres into the soil, or by restricting the surface activity to night-time hours when humidities are higher (Hadley, 1994). Humidity can also be higher in aboveground microshelters, e.g. condensation on the undersides of rocks may provide water. Soil moisture may influence survival of natural enemies with life stages living in the soil. The diameter of pores between soil particles decreases with increasing depth. A portion of the pore system is often filled with water that accumulates from rainfall or rises as capillary groundwater. The remainder of the pore system contains air that is saturated with water vapour (Eisenbeis and Wichard, 1987). While mobile life stages may undertake horizontal and vertical migrations to retain access to moisture during hot and dry periods, immobile life stages (i.e. eggs, pupae) may be affected by changes of humidity within soil pores. A range of relative humidities can be generated using saturated salt solutions (Winston and Bates, 1960) or glycerol–water mixtures (Johnson, 1940). Calcium sulphate (Drierite) provides 0% relative humidity. The relative

METHODS OF ASSESSING HUMIDITY EFFECTS.

humidities, as well as the temperatures chosen for the experiment, should reflect the conditions in the non-target habitat when humidity conditions become extreme. Desiccation tolerance is influenced by a variety of factors including age, sex and life history stage (Eckstrand and Richardson, 1980; Lamb, 1984; Hadley, 1994). The free-living stages may be more susceptible to humidity extremes than stages protected within hosts. These facts should be taken into account when selecting individuals for the test. Experiments assessing humidity effects on introduced species should include a taxonomically related resident species as a control (as was done for temperature in Bale and Walters (2001)) and for comparison of results. If, for example, the local species dies at the humidity extremes naturally occurring in the non-target area, then protected microhabitats with more favourable environmental conditions may be available for survival during unfavourable environmental conditions. In order to assess if local humidity conditions can cause lethal or sub-lethal effects on the exotic natural enemy, the introduced species may be reared at or exposed to different temperatures and humidities in small chambers (incubators). For economic reasons, not all temperature/humidity combinations that occur in the area of release can be tested in practice, hence we propose that the most current extremes of temperature and humidity should be identified (from climate records) and the relevant stages tested under these conditions. The parameters used to assess humidity effects may include egg hatch rate, development time, pre-imaginal survival, fecundity or longevity.

Biotic factors Host/prey Among other factors, establishment of an exotic natural enemy depends on the availability and suitability of hosts or prey and their spatial and temporal synchronization

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with the introduced organism. First, a list of potential hosts or prey that are taxonomically related and occur in habitats similar to that in which the new agents would be released has to be established. Then, it has to be determined which of these hosts or prey match in space and time with the introduced organism, e.g. do they occur at the same altitude and time as the natural enemy (for more details see Kuhlmann et al., Chapter 2, this volume). Next, the acceptance and suitability of the remaining species can be assessed in host specificity tests (for more details see van Lenteren et al., Chapter 3, this volume). Permanent establishment is only possible if overwintering hosts are available. The indigenous alternate hosts or prey may not be adequate for overwintering of the natural enemy, either because of their intrinsic capacity to survive harsh conditions at the stage during which they are attacked, because of a lack of synchronization with the exotic natural enemy, or because its type of dormancy is unsuitable for the parasitoid or predator. When the host is not at a suitable stage for winter survival at the time the natural enemy is preparing for winter, no permanent establishment will occur. This is the case for several Trichogramma species that need diapausing eggs of their host to survive winter (Boivin, 1994). METHODS OF ASSESSING HOST/PREY EFFECTS.

The methods for obtaining a list of potential hosts or prey and for testing their suitability for survival and reproduction of the natural enemy are described by Kuhlmann et al. (Chapter 2, this volume) and by van Lenteren et al. (Chapter 3, this volume). After a list of potential hosts or prey has been established, the availability and suitability for overwintering of the introduced organism and the abundance and ecological significance of these species can be determined. The methods described in the temperature and humidity sections should be used to assess survival of the exotic natural enemy in the overwintering stage of the potential hosts or prey. Care must be taken to use the appropriate developmental stage

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of both the host and the natural enemy and to make sure that they are in dormancy if they overwinter in this condition.

Case Studies Using Temperature to Assess the Establishment Potential of Non-native Biological Control Agents in the UK The protocols for the practical assessment of establishment potential of non-native invertebrate biological control agents can be developed from a theoretical analysis of the requirements of such species when introduced into a new environment. In simple terms, if a non-native species is introduced into a greenhouse ecosystem, in a region with a winter season, for outdoor establishment to occur, any escaping individuals will require a combination of (i) a thermal budget above the developmental threshold sufficient to complete at least one generation per year, (ii) one or more life stages able to survive at low temperature, (iii) the ability to enter quiescent or diapause states, and (iv) sources of host or prey. In the context of this chapter, the interrelationships between temperature and development and winter survival have recently been investigated in a number of insect and mite greenhouse biological control agents introduced into the UK over the past 15 years. The work was conducted to seek ecophysiological explanations for the ‘unexpected’ establishment of some introductions and, in turn, to develop experimental approaches that could be used to assess the establishment potential of candidate species under current or future consideration for import and release. The predatory mite Neoseiulus (Amblyseius) californicus (McGregor) (Acari: Phytoseiidae) was first released in UK greenhouses in 1991 and within ten years was reported to have established wild populations in areas close to release sites. The predatory mirid Macrolophus caliginosus Wagner (Heteroptera: Miridae) was released in 1995 and has been observed outside of greenhouses at different times of the year, though establishment

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has not yet been confirmed. Two other species, the parasitoid Eretmocerus eremicus Rose and Zolnerowich (Hymenoptera: Aphelinidae) and the predatory ladybird Delphastus catalinae (Horn) (Coleoptera: Coccinellidae), are both licensed for release in the UK, with no reports of winter survival or establishment. Currently, another predatory mite, Typhlodromips montdorensis (Schicha) (Acari: Phytoseiidae), is being considered as a candidate species for release. A range of experimental procedures have been applied to these species to determine their developmental threshold temperature, thermal budget (day degree) requirement per generation, potential annual voltinism, cold tolerance (freezing temperature, lethal temperatures and times) and acclimation ability, and response to diapause-inducing cues and winter field survival. Using M. caliginosus as a case study to exemplify this approach (Hart et al., 2002a), the threshold temperature for development from egg to adult was estimated to be 8.4°C (simple linear regression) and 7.7°C (weighted linear regression) with thermal budget requirements of 472 and 495 day degrees, respectively, above the threshold. Analysis of developmental data for a range of species suggests that a line derived from simple linear regression does not fit closely with data points at the lowest experimental temperature, sometimes resulting in an inaccurate estimate of the threshold temperature; in most cases, this problem can be overcome by the application of weighted linear regression. For some species it may be valuable to examine differences in threshold temperatures between different life cycle stages to identify possible ‘rate-limiting’ stages. For example, the threshold temperatures for the egg and nymphs of M. caliginosus calculated by weighted linear regression are 8.7° and 7.2°C, respectively. Estimates of the developmental temperature and thermal budget can then be related to climate records for any intended release site to determine the likely annual voltinism. For instance, in the Midlands area of the UK, the annual number of day

degrees above the developmental threshold of 7.7°C varied from 1059 to 1347 (mean 1253) over the ten-year period from 1991 to 2000, indicating that in all but one year, M. caliginosus would have been able to complete two (but never three) generations. By inspection of the monthly totals of available day degrees it is possible to determine whether development is restricted to the summer months, or can proceed through winter. Also, the development data for a particular species or strain can be related to climate records for any release site, in different countries or regions of the world. Cold-tolerance assessments were made on two age groups: first/second instar nymphs and fifth instar/adults. The likelihood of winter survival is increased if individuals escaping from greenhouses are able to acclimate at lower temperatures. In most insects capable of an acclimation response, significant changes in one or more indices of cold tolerance are usually detectable after seven to ten days at 5–10°C. Acclimation regimes are therefore intended to detect the ability to acclimate rather than to produce ‘fully acclimated winterhardy’ populations. The mean supercooling points of the two tested age groups of M. caliginosus with and without acclimation at 10°C for seven days in a 12:12 LD cycle varied from ⫺19.0 ± 0.6° to ⫺20.3 ± 0.3°C, with no significant difference between the groups. Supercooling points of many insects lie in the range of ⫺15° to ⫺25°C, but low freezing temperatures are not, in isolation, a reliable indicator of cold tolerance. Lethal temperatures (LTemp) of M. caliginosus were calculated by cooling replicate samples of the four treatment groups at 0.5°C/min to temperatures between ⫺5° and ⫺19°C (mean supercooling point of the ‘least’ cold-hardy age group), with exposure of 1 min at the minimum temperature. Probit analysis of the data provides estimates of the temperatures required to kill given proportions of each treatment group, typically, 10, 50 and 90%. The lethal temperatures of the two age groups were similar, with no acclimation response and LTemp50 values consistently around ⫺15°C,

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indicating some ‘pre-freeze’ mortality. Estimates of supercooling points and lethal temperatures often show values that are lower than the minimum temperatures likely to be experienced in the region or country of release; for instance, temperatures of ⫺15°C or lower rarely occur in the UK. However, the supercooling point and LTemp50 are both indices that are measured after very brief exposures. It is likely, therefore, that estimates of the duration of survival at less severe temperatures will provide a more informative guide to survival under field conditions. When replicate samples of the different M. caliginosus age and acclimation groups were exposed at ⫺5°, 0° and 5°C for increasing periods of time, the lethal time (LT) values increased at the higher temperatures, with LT50 values at 5°C of around 20–30 days for the different groups. These extended exposure experiments are more realistic in terms of natural conditions, but other factors, such as starvation, may affect the observed survival. During intermittent periods of higher winter temperatures, natural enemy species may search for hosts and prey and extend their survival. Laboratory experiments should therefore include treatments that provide access to prey. When the LT experiments at 5°C were repeated with greenhouse whitefly, Trialeurodes vaporariorum (Westwood) (Homoptera: Aleyrodidae), added as prey, 50% survival time of ‘fed adults’ increased from 30 to 50 days, with 10% still alive after 75–80 days. Whilst these ‘time’ experiments approximate more to field situations, insects and mites will usually be subject to fluctuating temperatures. There are now many examples where duration of survival is increased when insects, kept at constant low and stressful temperatures (often in chill coma), are periodically transferred to higher ‘recovery’ temperatures, and thus able to move and feed. For these reasons it is possible that laboratory exposures, even at 5°C, will underestimate the field survival of insects and mites originating from Mediterranean or tropical climates, especially during mild winters. Assessment of survival in the field is there-

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fore an essential component in the risk assessment of establishment potential. Biological control agents may escape from greenhouse environments at any time of the year, and therefore encounter conditions that may be temporarily favourable or more or less immediately lethal. More specifically, organisms that escape at the end of the summer will have to survive in the field for six months or longer before favourable conditions return, whereas, for those escaping in mid-winter, the cold and starvation stress will be less prolonged. Whilst this difference in the time of escape is unlikely to affect permanent establishment, it may allow a species to persist in the field until the next winter. When nymphs and adults of M. caliginosus (with no whitefly prey) were placed in the field in November and January (to represent early- and mid-winter escapes from greenhouses), there was a progressive decline in survival, with 100% mortality after 40 and 60 days for nymphs and adults, respectively, with the microhabitat temperature rarely falling below 0°C. A similar pattern was observed in the following winter, with maximum nymph and adult survival times of 40–60 days with the temperature falling to ⫺5°C on some occasions. However, in the same winter, when M. caliginosus were provided with whitefly prey, adult survival increased to 75 days, and more importantly, some nymphs developed in the field and were still alive after 200 days, the duration of a full temperate winter (Hart et al., 2002a). We conclude from these case studies that there are other considerations to take into account in the planning and interpretation of field experiments. First, if a species is able to enter a diapause state, which is usually associated with increased cold tolerance and the ability to withstand starvation, winter survival and long-term establishment is more likely to occur. The diapause trait in some source populations of N. californicus is a major contributing factor to its establishment in the UK. There is, though, a second important factor, also exemplified by N. californicus. Some insect and mite biological control agents have

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rapid generation times, such that, even at lower temperatures, the individual that starts the winter will never survive until the end of winter. However, if escaping individuals can reproduce in the field, their progeny may be able to sustain the population until the following spring which, in turn, obviates the need to enter a diapause state (Hart et al., 2002b). The application of the experimental protocol described for M. caliginosus to other species provides an opportunity to investigate the combined datasets to identify laboratory indices that are reliable predictors of field survival in winter. This has been done for M. caliginosus (Hart et al., 2002a), N. californicus (Hart et al., 2002b), E. eremicus (Tullett et al., 2004), T. montdorensis (Hatherly et al., 2004) and D. catalinae. For these species, a strong correlation has been found between the LT50 at 5°C in the laboratory and the duration of winter field survival (Fig. 6.2). On the basis of this relationship, it appears that a reliable prediction of winter field survival can be

obtained from a relatively rapid laboratory assay. Clearly, this approach is likely to be attractive to biological control companies in the production of the environmental risk assessment dossier that accompanies a licence application, as it focuses limited research and development budgets on a critical range of experiments. In this respect, whilst the ‘LT50 prediction’ provides an accurate ‘retrospective’ ecophysiological explanation for the establishment success and failure of a range of species released in the UK over the past 15 years, there also some caveats to be considered at this time. First, whilst the species so far investigated are drawn from different taxonomic groups and from different trophic guilds (predators and parasitoids), it is likely that there will be some exceptions that will not conform with the emerging laboratory–field relationship described in Fig. 6.2. It is probably too early for regulatory authorities and biological control companies to rely exclusively on the laboratory LT50 to predict establishment potential; it

Fig. 6.2. Relationship between LT50 in the laboratory and field survival of the same life cycle stages of five non-native biological control agents.

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would be valuable to apply the full range of approaches described in the risk assessment protocol to further species in order to gain confidence in the predictive power of the laboratory experiments. Secondly, the LT50 winter field survival relationship is not intended to predict with ‘precise’ accuracy the maximum survival times of escaped populations of non-native biological control agents. Rather, the system should be viewed as a mechanism by which to categorize candidate biological control agents into different ‘risk’ groups. Thus with reference to Fig. 6.2, E. eremicus, D. catalinae and T. montdorensis comprise a ‘low or no risk’ group, where all field populations die out in winter after approximately one month. Macrolophus caliginosus is in a ‘marginal risk’ group, where extended survival in winter could be expected, but long-term and widespread establishment may not occur. The ability to move flexibly in winter between the greenhouse and outdoor locations (as is believed to be the case with M. caliginosus) would increase the occurrence of such species outdoors. Finally, N. californicus falls into a ‘high risk’ group, where establishment is likely, attributable to both the cold-hardy diapause strains and non-diapause populations that are sufficiently cold hardy to develop and reproduce in winter, at least in a temperate climate. Of course, as emphasized at the outset, if the species is sufficiently cold hardy to survive through winter, establishment will then depend on access to host or prey. In a wider perspective, knowledge that establishment is likely to occur is in itself not a reason to prohibit the import and release of a non-native species. It is the acquisition of that knowledge that allows a rational and informed decision to be made after an appropriate evaluation of the risks and benefits.

Conclusions and Recommendations When assessing the establishment potential of an exotic natural enemy, we recommend first evaluating to what extent temperature may limit establishment. Only where the

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risk of establishment based on thermal requirements is determined to be higher than ‘insignificant’ (for definition of ‘insignificant’ see below), should the availability and suitability of host or prey for overwintering in the non-target habitat or the impact of humidity be investigated. We suggest that experiments should start with temperature as a limiting factor for the following reasons: (i) temperature is the most likely abiotic factor to limit establishment; (ii) temperature data for the release area are often available from meteorological offices and their acquisition requires minimal effort; (iii) testing for the temperature requirements and limitations is simpler than testing for host or prey specificity; (iv) humidity alone is seldom a limiting factor; most often it acts together with temperature; (v) other biotic factors such as food sources (pollen, nectar, etc.), or competition with other natural enemies, have rarely been reported to be the sole factors preventing establishment; and (vi) a combination of thermal budget, lethal temperature (LT50) and outdoor cage experiments may provide reliable data with adequate effort for predicting establishment. However, outdoor cage tests with exotic natural enemies may require ‘contained release’ licences from the national regulatory authority, which may not be granted. As Hart et al. (2002a,b) and Tullett et al. (2004) have shown, LT50 by itself may be a reliable predictor of field survival (Fig. 6.2). However, more tests are needed to confirm the power of prediction using LT50 in isolation from other experiments. Based on the comparison of the thermal requirements and tolerances of the exotic natural enemy to the temperature in the area of introduction, the likelihood and magnitude for establishment in non-target habitats can be categorized. The likelihood of establishment can be classified as ‘very unlikely’, ‘unlikely’, ‘possible’, ‘likely’ or ‘very likely’, and the magnitude as ‘minimal’, ‘minor’, ‘moderate’, ‘major’ or ‘massive’ (for description of risk classes see van Lenteren et al. (2003), van Lenteren and Loomans (Chapter 15, this volume, Tables 15.1, 15.2 and 15.3)).

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Based on the temperature requirements of a species, the likelihood and magnitude of establishment can be assessed qualitatively and combined in a risk matrix, resulting in risk levels of ‘insignificant’, ‘low’, ‘medium’ and ‘high’ (van Lenteren and Loomans, Chapter 15, this volume, Table 15.1). The matrix can be used as a tool by the risk assessment authorities to conclude whether and to what extent temperature conditions are

limiting establishment. For organisms with seasonal persistence, the probability for permanent establishment is categorized as ‘very unlikely’ and the magnitude as ‘minimal’, therefore the risk is ‘insignificant’. If the risk of establishment is categorized as ‘low’, ‘medium’ or ‘high’, evaluation of other factors limiting establishment, such as availability, acceptance and suitability of overwintering hosts, will be needed.

References Babendreier, D., Kuske, S. and Bigler, F. (2003) Overwintering of the egg parasitioid Trichogramma brassica in Northern Switzerland. BioControl 48, 261–273. Bale, J.S. (1991a) Implications of cold hardiness for pest management. In: Lee, R.E. and Denlinger, D.L. (eds) Insects at Low Temperature. Chapman and Hall, New York, pp. 461–498. Bale, J.S. (1991b) Insects at low temperature: a predictable relationship? Functional Ecology 5, 291–298. Bale, J.S. and Walters, K.F.A. (2001) Overwintering biology as a guide to the establishment potential of non-native arthropods in the UK. In: Atkinson, D. and Thorndyke, M. (eds) Environment and Animal Development. Genes Life Histories and Plasticity. Bios, Oxford, UK, pp. 343–354. Bale, J.S., O’Doherty, R., Atkinson, H.J. and Stevenson, R. (1984) An automatic thermoelectric cooling method and computer-based recording system for supercooling point studies on small invertebrates. Cryobiology 21, 340–347. Boivin, G. (1994) Overwintering strategies of egg parasitoids. In: Wajnberg, E. and Hassan, S.A. (eds) Biological Control with Egg Parasitoids. CABI Publishing, Wallingford, UK, pp. 219–244. Brière, J.F., Pracros, P., Le Roux, A.Y. and Pierre, J.S. (1999) A novel rate model of temperaturedependent development for arthropods. Environmental Entomology 28, 22–29. DeBach, P. (1964) Biological Control of Insect Pests and Weeds. Chapman and Hall, London, UK. Denlinger, D.L. and Lee, R.E. (1998) Physiology of cold sensitivity. In: Hallman, G.J. and Denlinger, D.L. (eds) Temperature Sensitivity in Insects and Application in Integrated Pest Management. Westview Press, Boulder Colorado, pp. 55–96. Duthie, J.A. (1997) Models of the response of foliar parasites to the combined effects of temperature and duration of wetness. Phytopathology 87, 1088–1095. Eckstrand, I.A. and Richardson, R.H. (1980) Comparison of some water balance characteristics in several Drosophila species which differ in habitat. Environmental Entomology 9, 716–720. Eisenbeis, G. and Wichard, W. (1987) Atlas on the Biology of Soil Arthropods. Springer Verlag, Berlin, Germany. Hadley, N.F. (1994) Water Relations of Terrestrial Arthropods. Academic Press, New York. Hance, T. and Boivin, G. (1993) Effect of parasitism by Anaphes sp. (Hymenoptera: Mymaridae) on the cold hardiness of Listronotus oregonensis (Coleoptera: Curculionidae) eggs. Canadian Journal of Zoology 71, 759–764. Hart, A.J., Bale, J.S., Tullett, A.G., Worland, M.R. and Walters, K.F.A. (2002a) Effects of temperature on the establishment potential of the predatory mite Amblyseius californicus McGregor (Acari: Phytoseiidae) in the UK. Journal of Insect Physiology 48, 593–599. Hart, A.J., Tullett, A.G., Bale, J.S. and Walters, K.F.A. (2002b) Effects of temperature on the establishment potential in the UK of the non-native glasshouse biocontrol agent Macrolophus caliginosus. Physiological Entomology 27, 112–123. Hatherly, I.S., Bale, J.S., Walters, K.F.A. and Worland, M.R. (2004) Thermal biology of Typhlodromips montdorensis: implications for its introduction as a glasshouse biological control agent in the UK. Entomologia Experimentalis et Applicata 111, 97–109. Johnson, C.G. (1940) The maintenance of high atmospheric humidities for entomological work with glycerol-water mixtures. Annals of Applied Biology 27, 295–299.

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Lactin, D.J., Holliday, N.J., Johnson, D.L. and Craigen, R. (1995) Improved rate model of temperaturedependent development by arthropods. Environmental Entomology 24, 68–75. Lamb, M.J. (1984) Age related changes in the rate of water loss and survival time in dry air of active Drosophila melanogaster. Journal of Insect Physiology 30, 967–973. Louda, S.M., Pemberton, R.W., Johnson, M.T. and Follett, P.A. (2003) Nontarget effects – the Achilles’ heel of biological control? Retrospective analyses to reduce risk associated with biocontrol introductions. Annual Review of Entomology 48, 365–396. Panneton, B., St-Laurent, G. and Boivin, G. (1995) Un générateur de fonction de température pour l’étude de la résistance au froid des insectes. Canadian Agricultural Engineering 37, 287–293. Sinclair, B.L. (2001) Field ecology of freeze tolerance: interannual variation in cooling rates, freeze–thaw and thermal stress in the microhabitat of the alpine cockroach Celatoblatta quinquemaculata. Oikos 93, 286–293. Sinclair, B.J., Vernon, P., Klok, C.J. and Chown, S.L. (2003) Insects at low temperature: an ecological perspective. Trends in Ecology and Evolution 18, 257–262. Tenow, O. and Nilssen, A. (1990) Egg cold hardiness and topoclimatic limitations to outbreaks of Epirrita autumnata in northern Fennoscandia. Journal of Applied Ecology 27, 723–734. Tullett, A.G., Hart, A.J., Worland, M.R. and Bale, J.S. (2004) Assessing the effects of low temperature on the establishment potential in Britain of the non-native biological control agent Eretmocerus eremicus. Physiological Entomology 29, 1–9. van Lenteren, J.C., Babendreier, D., Bigler, F., Burgio, G., Hokkanen, H.M.T., Kuske, S., Loomans, A.J.M., Menzler-Hokkanen, I., van Rijn, P.J.C., Thomas, M.B., Tommasini, M.G. and Zeng, Q.-Q. (2003) Environmental risk assessment of exotic natural enemies used in inundative biological control. BioControl 48, 3–38. Vannier, G. (1994) The thermobiological limits of some freezing intolerant insects: the supercooling and thermostupor points. Acta Oecologica 15, 31–42. Winston, P.W. and Bates, D.H. (1960) Saturated solutions for the control of humidity in biological research. Ecology 41, 232–237. Yoder, J.A. and Barcelona, J.C. (1995) Food and water-resources used by the Madagascan hissingcockroach mite, Gromphadorholaelaps schaeferi. Experimental and Applied Acarology 19, 259–273. Yoder, J.A. and Smith, B.E. (1997) Enhanced water conservation in clusters of convergent lady beetles, Hippodamia convergens. Entomologia Experimentalis et Applicata 85, 87–89.

7 Methods for Monitoring the Dispersal of Natural Enemies from Point Source Releases Associated with Augmentative Biological Control Nick J. Mills,1 Dirk Babendreier 2 and Antoon J.M. Loomans 3 1Environmental

Science, Policy and Management, 127 Mulford Hall, University of California, Berkeley, CA 94720-3114, USA (email: [email protected]; fax number: +1-510-643-5438); 2Agroscope FAL Reckenholz, Swiss Federal Research Station for Agroecology and Agriculture, Reckenholzstr. 191, 8046 Zürich, Switzerland (email: [email protected]; fax number: +41-44-377-7201); 3Plant Protection Service, Section Entomology, PO Box 9102, 6700 HC Wageningen, The Netherlands (email: [email protected]; fax number: +31-317-421701)

Abstract Mark–release–recapture (MRR) experiments are considered the best approach to use in monitoring the dispersal of natural enemies from the target environment, in an assessment of the risk of non-target impacts from augmentative releases. Starting from some general considerations of the difficulties of using MRR, we specifically address marking techniques, the design of recapture grids and the limitations imposed by different sampling strategies for the recapture of the natural enemies released. Subsequently, we describe both an exponential and a diffusion model for dispersal that can be used to analyse the time-integrated density–distance data generated from MRR experiments, pointing out the need to examine and correct the data for directionality, if possible, or to use a diffusion model with displacement when correction is not possible. The application of the exponential and diffusion models of dispersal to the estimation of dispersal distance and density, the two most important metrics to consider in a risk assessment of non-target impacts of augmented natural enemies, is also discussed. Finally, we present a case study of an inundative release of Trichogramma brassicae in a meadow in Switzerland to illustrate how the data from an MRR experiment can be fitted to a dispersal model to estimate dispersal distance and the density of dispersing individuals at different distances from the release point.

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Introduction The risk of non-target impacts has become of increasing concern in the biological control of arthropod pests (Simberloff and Stiling, 1996; Follett and Duan, 2000; Louda et al., 2003). The focus of attention has been on understanding the host range of imported natural enemies used for the control of invasive pests, the potential for evolutionary host range expansion of imported natural enemy populations and the consequences of natural enemy impacts on non-target species at the population level (Hoddle, 2004). Although the risk of non-target impacts from imported natural enemies poses the greatest concern to ecologists and environmentalists, inundative biological control agents, or those natural enemies that are mass reared to locally inundate managed ecosystems for the control of arthropod pests (Daane et al., 2002), also have the potential to cause non-target impacts in the surrounding landscape (Lynch et al., 2001). With this in mind, an initial step in the evaluation of the environmental risks of augmentative biological control has been made by van Lenteren et al. (2003), who recently developed a risk index for the commercially available inundative control agents used in greenhouse or open-field crops in Europe. Augmentative biological control includes both the inoculative release of smaller numbers of natural enemies for season-long control of arthropod pests and the inundative release of very large numbers of mass-produced natural enemies for the rapid, but only temporary, suppression of arthropod pests (Daane et al., 2002). The natural enemies used in augmentative biological control may either be indigenous or exotic, and while all groups of arthropod natural enemies have been considered, we will focus here on invertebrate natural enemies in accordance with the OECD (2004) Guidance for Regulation of Invertebrates as Biological Control Agents (IBCAs). Insect parasitoids, insect and mite predators and

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entomopathogenic nematodes are mass produced by an increasing number of commercial suppliers and government research institutes worldwide, and are widely available for augmentative release for the suppression of a variety of arthropod pests of managed crops and livestock production (van Lenteren, 2003). Using the approach of Hickson et al. (2000), van Lenteren et al. (2003) proposed that non-target impacts of augmentative biological control agents be assessed from the likelihood (probability) and the magnitude (consequences) of adverse effects based on the following five risks relating to the ecology of the natural enemy: (i) establishment in the target region if the natural enemy is exotic, (ii) dispersal from the target environment, (iii) host range, (iv) direct effects on non-target organisms and (v) indirect effects on other organisms in the target environment. In this chapter we will focus exclusively on dispersal from the target environment, and discuss methods used to quantify and analyse the dispersal of natural enemies from a central release point, taking Trichogramma brassicae Bezdenko as a case study.

Potential for Adverse Impacts of Natural Enemies on Non-targets from Dispersal Dispersal is the exploratory, undirected movement of individuals away from the habitat of origin (den Boer, 1990). In the context of environmental impacts of augmentative biological control, this represents the undirected movement of natural enemies away from the release site and into the surrounding landscape. In most cases, the dispersal of natural enemies will be by flight in the adult stage, as the movement of juvenile stages is restricted to a very local scale. There are two interesting exceptions, however, one in which dispersal of juvenile entomopathogenic nematodes occurs through flight of the adult

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host (Lacey et al., 1995), and a second in which dispersal of adult female parasitoids (T. brassicae) is facilitated through a phoretic association with adult female butterflies (Fatouros et al., 2005). In considering the risk of non-target impacts from the dispersal of natural enemies released for augmentative biological control, two factors of potential concern are the likely distance of dispersal and the density of natural enemies at given distances from the release point. Although dispersal distance is potentially a species-specific trait, as it is dependent upon longevity and power of flight, there is often an overriding influence of the abiotic and biotic characteristics of the surrounding landscape. In contrast, the number of natural enemies dispersing, and thus their density at a given distance from the release point, is primarily influenced by the number of natural enemies released, and the abundance of the pest relative to the foraging requirements of the released natural enemies. As a result, inoculative releases of natural enemies often pose a much reduced environmental risk in comparison to inundative releases, by virtue of the far smaller numbers of natural enemies released at a site.

Approaches to Quantifying Movement There have been three different approaches used to quantify the dispersal of insects (Fagan, 1997; Turchin, 1998): (i) the analysis of density curves in relation to distance from a release point, (ii) the analysis of fluxes of individuals crossing a boundary and (iii) the analysis of movement paths. The recapture of marked individuals in traps placed at successive distances from a release point, and the subsequent analysis of density–distance curves, were pioneered by Dobzhanzky and Wright (1943) in a study of the movement of Drosophila species. Mark-release-recapture (MRR) has been the most widely used approach in the analysis of dispersal and has been applied to insects of all sizes. The second approach is based on observations of the cumulative

count or flux of marked individuals caught at a delimited boundary surrounding a central release point. Fagan (1997) used this technique to determine the dispersal rate of mantids as they moved out from a central point and were caught on tanglefoot bands at the perimeter of square plots. Although this is an interesting alternative approach, it has yet to be used more extensively and is more complex, but may be particularly well suited to the measurement of biases in dispersal (Turchin, 1998). The third approach is to record the movement of individual insects, to map their paths, and to use temporal and spatial coordinates to estimate dispersal rates. Although this is a powerful approach (Turchin, 1998), and has been used for a number of larger insects, particularly butterflies (e.g. Turchin et al., 1991), it is not suitable for monitoring dispersal of small insects and is better applied to investigations of the effects of environmental heterogeneity on movement. Thus, the methodology that is best suited for the assessment of dispersal as an environmental risk of mass releases of invertebrate natural enemies is the analysis of MRR experiments.

General considerations for mark–release– recapture (MRR) experiments In estimating the pattern of dispersal of natural enemies from a central release point through time in MRR studies, there are several key issues that need to be considered: ● It is essential to be able to distinguish the dispersing natural enemies from individuals in the wild population. This is not a problem if the natural enemy is an exotic species without locally established populations or, if as happens in some cases, that wild populations of an indigenous species are absent in the target region. In many cases, however, indigenous natural enemies will have wild-type counterparts in the field, and distinguishing between mass-released and wild individuals will be a major concern requiring use of some form of natural or applied marker.

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● Dispersal from a central point is subject to an area-dilution effect, whereby as individuals move further away from a central release point they are spread over a progressively greater area and consequently become more difficult to recapture (Turchin, 1998). To improve the accuracy of recapture data a greater number of recapture points some distance from the release point could be used, although it is often impractical to monitor a greater number of traps effectively. Alternatively, baits, in the form of foods, hosts, or kairomones, have been used to increase the attractiveness of recapture points, and provide a more practical approach to counteracting the dilution effect. Some caveats, however, are that baits often have an unknown sphere of attraction, which may affect the dispersal of individuals in unknown

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ways, and can lead to interference if their range of attraction is greater than the distance between recapture points. In addition, recapturing too great a number of individuals before they have completed their dispersal can itself bias the dispersal process, posing a dilemma in terms of the trade-off between sampling efficiency and bias (Yamamura et al., 2003). ● Time is an important variable in any MRR experiment, and its influence on the analysis of dispersal data has often been underestimated. Dispersal is a continuous process and the pattern of recaptures in relation to distance from a central release point changes dramatically with time (Fig. 7.1). Some recapture techniques, such as sweep netting or traps deployed for very short time intervals (<4 h), provide instantaneous

Time, t

Ra

dia

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ist

an ce

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Survivorship curve integrated over distance

Instantaneous density –distance curves

Density–distance curve integrated over time

Fig. 7.1. A schematic representation of the pattern of recaptures in a mark–release–recapture experiment in relation to both distance from the release point and time since release. Integrating the recaptures over distance provides an estimate of the survivorship curve for the released individuals, and integration over time provides a time-integrated density–distance curve.

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estimates of density versus distance at intervals over the course of the experiment. However, most MRR experiments make use of traps or trap hosts that are monitored at daily intervals or longer, and generate partial cumulative recaptures that need to be integrated (summed) over the full lifespan of the dispersing individuals. ● Male insects tend to be much wider ranging in their dispersal than females, and thus it is essential to distinguish the sexes when recoveries are made from the traps (e.g. Bellamy and Byrne, 2001). This latter point is of particular concern in the context of the environmental risk of dispersal of mass-released parasitoids as it is only the movement of females that poses a risk to non-target hosts.

Markers A variety of markers can be used to distinguish released natural enemies from individuals in the wild population, and these have been extensively reviewed by Hagler and Jackson (2001) and Lavandero et al. (2004). As external markers, tags and mutilation marking have been used extensively in vertebrate studies. However, they are of far less value for invertebrates, particularly insects, as only the larger, more robust species such as bees, carabid beetles and butterflies can be successfully marked this way, and the method is too time consuming for marking large groups of insects for MRR. Visible genetic markers, such as eye colour, have been used as natural markers (e.g. Dobzansky and Wright, 1943), but are unlikely to be available for most commercial natural enemies. Paints have been used both to mark individual insects, and also to mark larger groups of insects (Jones et al., 1996; van der Werf et al., 2000), but again the approach is better suited to larger insects and must first be tested for potential toxicity. More commonly, fluorescent dusts have been used to mark insects for dispersal studies (Corbett and Rosenheim, 1996; Prasifka et al., 1999). These dusts are available in a variety of colours, they are

inexpensive to use and their detection can be enhanced by use of UV light. Larger insects can be tumbled in the dusts to mark them, while more delicate insects need a more cautious approach. Drawbacks of using fluorescent dusts are that they have been found to reduce longevity in some insect parasitoids (Messing et al., 1993; N.J. Mills, unpublished results), and the dusts can be transferred to unmarked individuals following release in the field. As internal markers, oil-soluble dyes have been used extensively, as they can be incorporated into the diet of insects at low cost. However, they are not always easily detectable, and many have proved to be toxic to insects. Nonetheless, acridine orange has been recommended for marking adult Hymenoptera and Diptera through incorporation into honey (Strand et al., 1990), and a resin-based dye has been used to mark parasitoids of diamondback moth under field conditions (Schellhorn et al., 2004). Trace element markers, particularly rubidium and strontium, have also been used extensively to mark many types of insects, including insect parasitoids (Corbett et al., 1996; Fernandes et al., 1997; Gu et al., 2001; Hougardy et al., 2003). By incorporating RbCl into the diet of predators, and into either host diets or adult foods for parasitoids, natural enemies can be marked in large numbers, although the detection of elemental markers through atomic absorption spectroscopy is both time-consuming and expensive. More recently, immunological or protein markers have been developed for use either internally or externally on the surface of insects. Vertebrate immunoglobulins (IgG) have been used most frequently and have proved effective for parasitoids of all sizes, as well as for a number of insect predators (Hagler and Jackson, 1998; Hagler et al., 2002a). Internal marking is achieved through incorporation of the proteins into the diet, and external marking by fogging with either an atomizer for larger insects or a nebulizer in the case of small parasitoids. The marker is detected through use of a sandwich enzyme-linked immunosorbent assay based on vertebrate-specific antibod-

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ies. This marking procedure has the advantages of being quick and easy to implement, but one drawback is that the cost of the IgG proteins is relatively high for use in MRR studies. Similarly, while the marking of natural enemies with genetically engineered proteins is not practical at the current time, due to regulatory constraints, it may prove to be an effective marking procedure for MRR studies in the future. Whatever marker is used, there is always a concern that the marker may reduce longevity or influence dispersal ability. While it is easy to compare the longevity of marked and unmarked individuals it is less easy to determine effects on dispersal ability. One approach to addressing this problem is double marking, using two different marking procedures to mark two groups of individuals and releasing both groups simultaneously to monitor their patterns of dispersal. Two different marking techniques are unlikely to influence the natural enemies in the same way, and thus it can be concluded that marking has no influence on dispersal ability if both groups show similar patterns of behaviour.

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Recapture grids In all MRR experiments it is necessary to recapture the marked and released individuals at various distances from the release point over time. Configurations of trapping or sampling grids (Fig. 7.2) for monitoring dispersal have varied from linear transects (Kuske et al., 2003) to simple orthogonal transects (Dobzhansky and Wright, 1943; McDougall and Mills, 1997), rectangular or square lattices (Plant and Cunningham, 1991; Corbett and Rosenheim, 1996; Schneider, 1999) and radiating (so-called wagon wheel) designs with either linear (Turchin and Theony, 1993; van der Werf et al., 2000) or curved arms (Messing et al., 1995). The two most important aspects of the design of a recapture grid are that it extends far enough, and recapture points are located at uniform distances. Clearly, it is wasteful if recapture locations are so far from the release point that no marked individuals are recaptured, but it is best to extend the grid far enough that no more than 10% of dispersers are able to extend beyond the bounds of the grid (Turchin, 1998). In many cases, the likely dispersal distance may not be known initially, and thus it may be necessary to run a preliminary pilot test.

Fig. 7.2. Examples of the recapture grids that have frequently been used in mark–release–recapture experiments, showing (a) linear, (b) orthogonal, (c) lattice and (d) radiating or wagon wheel designs.

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In addition to the extent of the grid, there must be a sufficient number of recapture points through the grid for distance from the release point, as the dependent variable, to fully characterize the dispersal pattern. It is often considered that the number of arms in a wagon-wheel design might be important, but in reality it is the number of regularly spaced recapture points along an arm of the wheel that will provide a better description of dispersal. It is best to use at least six distance points in a MRR grid, and to use either a concentric circle or regular lattice design.

Sampling strategies and trap types The aim of MRR experiments is to obtain an estimate of the spatial density of marked individuals throughout the recapture grid. Thus, recaptures of marked individuals over time can be achieved either through sampling of absolute numbers per unit area, using techniques such as sweep nets (e.g. van der Werf et al., 2000), or through sampling of relative numbers per unit area as in the case of traps. The reader is referred to Sutherland (1996), Southwood and Henderson (2000) and Leather (2005) for further details of sampling techniques. As the monitoring of natural enemy dispersal through MRR experiments will, in most cases, involve the use of traps, we will focus on this sampling strategy for the remainder of the chapter, and a brief discussion of trap types is included here. Pitfall traps have been used extensively for monitoring the dispersal of epigeal invertebrates, and have been used extensively in MRR experiments for carabids (Garcia et al., 2000; Raworth and Choi, 2001). However, for natural enemies that disperse by flight, a greater range of trap types have been used, including intercept traps, attractant traps and sentinel host traps. Intercept traps consist of transparent plastic sheets (McDermott and Hoy, 1997) or large Petri dishes (Messing et al., 1994) that are coated with glue or oil to intercept and trap the aerial dispersal of insects and mites. Intercept traps have the advantage that they do not influence the flight or dis-

persal behaviour of the insects and mites that they trap, but as a result the number of individuals trapped tends to be very low. Intercept traps have been used successfully to monitor the dispersal of parasitoids (Messing et al., 1994; Kuske et al., 2003) and mite predators (Charles and White, 1988; McDermott and Hoy, 1997). An interesting variant on an intercept trap is the use of small suction traps (Hagler et al., 2002b). These have proved to be effective in trapping small parasitoids and may also be applicable to other small arthropods, such as insect and mite predators. As an alternative to intercept traps, traps with various forms of attractants can be used, such as colour, shape and various forms of bait, including food and infochemicals. Yellow or white sticky or water traps have frequently been used to trap arthropod natural enemies (e.g. Corbett and Rosenheim, 1996), and sticky spheres rather than flat sheets may be particularly effective for parasitoids of fruit-boring pests (e.g. Messing et al., 1995). Examples of the use of various baits for monitoring natural enemy dispersal include use of natural enemy sex pheromones (e.g. Suckling et al., 2002), synomones in the form of pumpkin puree for tephritid fruit fly parasitoids (e.g. Messing et al., 1995), kairomones in the form of host sex pheromones for parasitoids of scales and aphids (e.g. Gabrys et al., 1997) and aggregation pheromones and host tree volatiles for predators of scolytids (e.g. Mills and Schlup, 1989). While these various forms of attractants can help to trap sufficient numbers of dispersing natural enemies for quantitative analysis of dispersal behaviour, it must be remembered that the attractive range of these attractants is generally unknown with regard to interference between traps in the grid, and that it is also unknown whether the attractants influence the normal dispersal behaviour of the natural enemy. Sentinel or trap hosts have also been widely used to monitor dispersal of insect parasitoids. Non-feeding host stages, including eggs, puparia and pupae, have been used most frequently in MRR studies as they can easily be placed in natural set-

Methods for Monitoring the Dispersal of Natural Enemies

tings in the field, and recoveries are generally good if the sentinel hosts are protected in some way from predation. Deployment of potted trap plants with feeding stages of sedentary hosts such as aphids (Muratori et al., 2000), whiteflies (Loomans, 2002) and diamondback moth (Mitchell et al., 1999) has also proved effective for monitoring parasitoid dispersal. Host eggs have frequently been used to monitor dispersal of Trichogramma species (McDougall and Mills, 1997; Fournier and Boivin, 2000), Trissolcus basalis (Justo et al., 1997) and host puparia for parasitoids of filth flies (Floate et al., 2000; Skovgård, 2002). However, trap hosts differ from other forms of traps for monitoring dispersal in that they monitor parasitism rather than parasitoid numbers. This has one advantage in that it monitors the dispersal of females only, but significant disadvantages include the indirect nature of the measurement variable, trap saturation and that trapping efficiency that varies with time. Parasitism is an indirect measurement of dispersal and can only be used as a presence/absence measurement, as it is impossible to separate the number of adult parasitoids visiting the trap from the number of hosts parasitized by each parasitoid female (Gross and Ives, 1999). Thus, in place of a density–distance curve, trap hosts provide a probability of an encounter–distance curve that is not applicable to the diffusion models discussed below. In addition, it seems likely that traps placed closer to the central release point would experience greater saturation than other forms of trap, and that ageing of trap hosts during the monitoring interval could lead to variation in susceptibility to parasitism.

Models for Analysing Density–Distance Curves Generated by MRR Data Exponential model Assuming a lack of directionality in the data, either empirical or diffusion models can be used to describe the density–

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distance curves generated by MRR experiments. Taylor (1978) investigated the suitability of a wide range of empirical models for describing the decline in density with distance and found that the following special case of the gamma distribution provided the best fit to a variety of insect dispersal data: C(r) = exp[a + br 0.5]

(1)

where C(r) is the mean number of individuals per trap at radial distance r from the release point, and a and b are fitted constants. This exponential model has also been found to provide a good description of density–distance curves by a number of other authors (Freeman, 1977; Plant and Cunningham, 1991; Kishita et al., 2003). It should be noted that time is not explicit in this model, and thus separate models can be fitted for each recapture interval of the experiment (Plant and Cunningham, 1991), or a single model can be fitted to a timeintegrated metric such as the total recaptures per trap over the course of the experiment recovered at each radial distance from the release point (Kishita et al., 2003). The two constants of the exponential model are more accurately estimated using a least-squares fitting procedure, rather than using linear regression of lnC(r) on r. The exponential model can also be used to describe probability of encounter–distance curves generated through the use of trap hosts. However, its greatest drawback is that the fitted constants have no interpretable biological meaning.

Diffusion model As an alternative to the exponential model, dispersal can be modelled as a simple diffusion process, where movement of individuals is assumed to be radially symmetric and to occur at a constant rate in a homogeneous environment (Okubo, 1980; Kareira, 1983; Turchin, 1998). It has been shown that dispersal of a range of phytophagous insects can adequately be described by diffusion (Kareiva, 1983), which generates a density–distance curve of the form:

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C (r,t) = (␣No/4␲Dt)exp[(⫺r 2/4Dt) – ␦t]

N.J. Mills et al.

(2)

where C(r,t) is the mean number of individuals recaptured per trap at radial distance r from the release point at time t, No is the number of marked individuals released, ␣ is a constant recapture efficiency of the traps used, D the diffusion coefficient or rate of dispersal, and ␦ is a constant rate of disappearance of individuals due to a combination of mortality during dispersal and settlement of individuals at sources of suitable hosts. Thus the diffusion model generates a time-specific density–distance curve, and each one of its parameters has a clear biological interpretation. As noted by Turchin (1998), however, most MRR experiments use traps to recapture the dispersing individuals, and traps accumulate individuals either continuously or over fixed periods of time. Thus, MRR experiments using traps require a time-integrated approach to quantifying dispersal and, as shown by Turchin and Thoeny (1993), the time-integrated form of equation (2) can be represented as: C(r) = Ar⫺0.5exp(⫺r/B)

(3)

where C(r) is the mean cumulative recaptures per trap at radial distance r from the release point over the course of the experiment, A = (␣No)/[(8␲)0.5(D 3␦)0.25] and B = (D/␦)0.5. Similarly to exponential model (1), the time-integrated diffusion model (3) is also exponential, but has the advantage that the parameters have clear biological interpretation. A is a scale parameter proportional to the total number of marked individuals released and the recapture efficiency of the traps, and B represents the spatial scale of dispersal as determined by the rate of dispersal and disappearance of released individuals. Although the model can be linearized by taking logs, a more accurate estimate of the constants A and B is obtained using a non-linear least-squares fitting procedure. It should be noted that if too many natural enemies disperse beyond the end of the recapture grid, such that the cumulative density–distance curve remains too far above the x axis at the greatest radial distances, then the value of B will be large and cannot be accurately estimated.

As suggested by Plant and Cunningham (1991), the rate of disappearance of individuals over the course of a MRR experiment (␦) can be estimated from a survivorship curve (Fig. 7.1), relating the total trap catch from all traps in the recapture grid (C(t)) in relation to time interval since release (t), using the simple exponential model: C(t) = aexp(⫺␦t)

(4)

In addition, we can also use this relationship to estimate the recapture efficiency (␣) of the traps in the recapture grid from ␣ = a/N0, where the fitted constant a estimates the total number of individuals that would have been trappable within the recapture grid in the absence of disappearance. Using these two estimated constants, the rate of dispersal (D) of the natural enemies can be estimated either from B, if the cumulative density–distance curve approaches the x axis, or from A. Although not yet used for natural enemies, the time-integrated diffusion model has been used effectively to quantify dispersal of bark beetles (Turchin and Thoeny, 1993), tobacco budworm (Schneider, 1999), a stem-galling fly (Cronin et al., 2001) and glassywing sharpshooter (Blackmer et al., 2004).

The problem of directionality One potential problem associated with the estimation of dispersal from MRR experiments through the analysis of density–distance curves is that dispersal is assumed to occur at random and to be radially symmetrical. In reality, however, the dispersal of natural enemies may be influenced by environmental factors such as wind direction, or linear features of the landscape such as hedgerows. One useful way in which MRR data can be examined for evidence of directionality is to examine the mean displacement of recaptures along two orthogonal axes and to test the two mean displacements for departure from zero with a t-test (Turchin and Thoeny, 1993; Blackmer et al., 2004). The mean displacement (x–) along the x axis for a particular replicate of a MRR experiment is given by:

Methods for Monitoring the Dispersal of Natural Enemies

x = ∑1 x i C i n

/∑ C n 1

i

(5)

where xi is the x coordinate of the location of trap i from the central release point, Ci is the number of individuals recaptured in trap i and n is the number of traps in the experiment. The mean displacement (y៮) along the y axis can be calculated similarly, and then both sets of displacements tested for departure from zero to check for significant directionality. Turchin (1998) notes that this t-test can generate a significant difference even when the magnitude of the departure is small relative to the overall scale of the dispersal, and suggests ignoring directionality if the mean displacement is less than 10% of the root mean square of the dispersal distance. If the directionality is moderate, it may be possible to identify and exclude individual replicates from the data set, or if the problem is restricted to particular traps then these traps, plus equivalent traps located at the same distance in the opposite direction, can be removed from the data set (Turchin, 1998). However, in some cases, directionality is unavoidable, and the analysis of the MRR data must be modified to allow for a directional tendency. This general problem has been of particular importance in the analysis of the passive dispersal of atmospheric pollutants, pollen and spores, and has been resolved by incorporating wind speed and direction into the diffusion model to generate Gaussian plume or tilted plume models of directed dispersal (Okubo and Levin, 1989; Turchin, 1998). This approach has been used in the development of a risk analysis for fungal spores used in the biological control of weeds (de Jong et al., 1999, 2002) and for dispersal of predatory mites (Jung and Croft, 2001). For most invertebrate natural enemies, however, dispersal is an active process and any directionality in dispersal may be driven as much by landscape features as by wind. In this case, the diffusion model can be modified to allow for displacement to give the following density–distance function: C(x,y,t) = (␣No/4␲Dt)exp{⫺␦t ⫺[((ct ⫺xcos␾ (6) ⫺ysin␾)2 + (⫺xsin␾ + ycos␾)2)/4Dt]}

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where C(x,y,t) is the number of individuals trapped at coordinates x and y from a central release point at time t, and c is the rate and ␾ the angle of displacement. This latter model has been used to analyse dispersal of Mediterranean fruit flies (Plant and Cunningham, 1991) and leafhopper egg parasitoids (Corbett and Rosenheim, 1996). While the disappearance rate (␦) and recapture efficiency (␣) can be estimated independently from the associated survivorship curve, as noted above for the symmetrical diffusion model, the diffusion rate (D), displacement rate (c) and angle (␾) must be estimated using least squares or maximum likelihood techniques.

Applying Models to the Estimation of Dispersal Distance and Density of Dispersers In assessing non-target impacts of natural enemy augmentation, the distance dispersed and the density at a given distance from the release point are the factors of interest. For the distance dispersed, one option in analysing data collected from a MRR experiment is to use the greatest distance at which an individual was recaptured to define the radius of potential non-target impacts in an environmental risk analysis. In some cases, the maximum distance reached by an individual may be of particular importance, such as when exotic natural enemies from a Mediterranean climate are employed in augmentation programmes for glasshouse pests in a temperate region. The escape and long-distance dispersal of even a single exotic natural enemy into a climatic region where it is able to persist in the natural environment poses a potential risk. Whether such long-distance dispersal can be detected in MRR experiments is questionable however, due to the impracticality of extending recapture grids to sufficient distances to recapture these individuals, and to the severe dilution effect that reduces the probability of recapture at greater distances. In contrast, for indigenous natural enemies, long-distance dispersal is

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less relevant, with the dispersal distance of the majority of the dispersers being more representative of the likelihood of non-target impacts. Alternative dispersal distance metrics that may be more indicative of the likely sphere of non-target impacts include the mean distance from the point of release (Hawkes, 1972; Turchin and Thoeny, 1993; Kishita et al., 2003) and the median, or some other quantile, distance representing the radius of a circle that encloses a specific proportion of dispersing individuals (Plant and Cunningham, 1991; Turchin and Thoeny, 1993; Schneider, 1999; Smith et al., 2001; Blackmer et al., 2004). The mean dispersal distance is given by: ∞

r = ∫0 2π r 2C(r ) dr

∞

∫0 2π rC (r )dr

(8)

where C (r) is the recaptures per trap at radial distance r. This equation simplifies to r៮ = 20/b2 for the exponential model (1), and to r៮ = 1.5B for the time-integrated diffusion model (3). Alternatively, any quantile dispersal distance r(p) is given by: r ( p)

p = ∫0

2π rC (r ) dr

∞

∫0 2π rC (r )dr

(9)

where p is the chosen proportion of the population of dispersers and r(p) is the estimated radial distance of the circle that encloses that proportion of dispersers (Turchin and Thoeny, 1993), such that p = 0.5 for estimation of the median radial distance dispersed, or p = 0.95 for estimation of the 95th quantile radial distance that encloses 95% of the dispersing individuals. Either the exponential model (1) or the diffusion model (3) can be used to substitute for C(r) in equation (9), and the equation must be solved numerically using any mathematical software package. It may also be of value in the context of augmentative releases of natural enemies to use equation (9) to estimate p⬘, the proportion of individuals that pass beyond a specified radial distance r(p⬘) from the central release point. In this case, the integral for the numerator in equation (9) would run from r (p⬘) to ∞ rather than from 0 to r (p), and the equation would be solved for p⬘. There is no direct equivalent for the mean or median dispersal distance for the diffusion

model with displacement (6), as the extent of dispersal is not symmetrical and depends on the strength of the directionality. In this case, the full two-dimensional pattern of dispersal must be analysed and the rate of diffusion (dispersal) estimated from the fitted model, as illustrated by Plant and Cunningham (1991) and Corbett and Rosenheim (1996). The density of natural enemies at a given radial distance from the release point can similarly be estimated from the density– distance curve. It may be valuable to consider the mean density of natural enemies enclosed within a circle of radial distance r(p) from the central release point during the dispersal period, which is given by: Nr (p) = N0p/␲r(p)2

(10)

where N0 is the initial number released and p is the proportion of the population enclosed within the circle from equation (9). Equivalently, the mean density of natural enemies dispersing beyond a circle of radial distance r(p⬘) during the dispersal period is: Nr (p⬘) = N0p⬘/␲ (rmax2 ⫺r(p⬘)2)

(11)

where rmax is either the greatest observed distance dispersed by the natural enemy or some arbitrary outer radial distance of interest. Then finally, to estimate more generally the density of dispersing natural enemies within a concentric ring of width x, at a particular radial distance r from the central release point, is given by: N r = N 0  ∫r −0.5 2π rC (r ) dr  x r + 0.5x

/π [(r + 0.5x )2 − (r − 0.5x )2 ]

∞



∫0 2π rC (r )dr  (12)

Trichogramma as a Case Study of Dispersal in the Context of Non-target Impacts A full assessment of the environmental risks from dispersal of mass-released natural enemies would need to consider a number of components, including the following dispersal-related questions: (i) how far do natural enemies fly from a central

Methods for Monitoring the Dispersal of Natural Enemies

Number of recaptured females per trap, C(r )

release point and how does their density decline with distance? (ii) do they pass potential barriers to dispersal such as hedgerows? and (iii) do they move into and settle in non-target habitats? For the purposes of this chapter, we concentrate on the first question only, and use data from the dispersal of Trichogramma brassicae as an example, a biological control agent widely used for inundative releases against European cornborer. A set of 100,000 unmarked adult T. brassicae, 59% female, were released from a point source in an extensively managed meadow near Zürich in Switzerland in June, 2000, where wild T. brassicae were virtually absent. Using an orthogonal transect design, sticky traps were installed above the vegetation at distances of 2, 4, 8, 16, 32 and 64 m from the central release point. Traps consisted of a plastic transparent sheet (30 ⫻ 21 cm) which was sprayed with glue (Soveurode®) on both sides. At each distance for each of the four directions, we installed one ‘low’ trap at 40–70 cm height and one ‘high’ trap at 100–130 cm. All traps were replaced daily over a period of four

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days, and the number of T. brassicae males and females on each side of each trap was counted under a dissecting microscope. For simplicity, we consider here only the cumulative number of recaptured female parasitoids from both sides of the two traps (high and low) at each location. The recapture data for T. brassicae females showed significant directionality, with a mean displacement of 4.2 m in the westerly direction (t = 3.33, P < 0.003), although no significant displacement occurred along the north–south transect. The directionality was caused primarily by the long distance dispersal of a few individuals trapped 64 m to the west of the release point, and was reduced to less than 10% of the root mean square displacement by excluding, as outliers, three of the eight traps from 64 m west and, equivalently, three of the eight traps from 64 m east (as recommended by Turchin, 1998). The recaptures per trap, integrated over the four-day trapping period, were then fitted to both the exponential model (1) and the time-integrated diffusion model (3). Both models provided a good description of the

120 100

C(r ) = exp(7.05 – 2.34r 0.5), R 2 = 0.99

80

C(r ) = 166.08 r –0.5 exp(r/1.94), R 2 = 0.99

60 40 20 0 0

10

20

30

40

50

60

70

Radial distance (m) from release point, r Fig. 7.3. The observed time-integrated pattern of recaptures per trap in relation to radial distance from the central release point for female Trichogramma brassicae from the MRR experiment (solid circles). Both an exponential model (black) and a diffusion model (grey) provide an excellent fit to the data, and the projections are almost identical, such that the diffusion curves mostly obscure the exponential curve.

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data (Fig. 7.3), accounting for 99% of the variation in observed densities in relation to radial distance from the release point. Using equations (8) and (9), the mean and quantile dispersal distances of female T. brassicae were calculated for each of the two fitted models (Table 7.1). In general, the dispersal distance of the parasitoids was limited, with the exponential model predicting slightly greater distances than the diffusion model. Thus, 67% of the dispersing individuals did not travel further than 3.3–3.7 m within four days and 95% were

contained by radii of 7.6–11.0 m depending on the model. Examination of the total trap recaptures in relation to days since release (Fig. 7.4), generated an estimate of the rate of disappearance of 1.34 ± 0.06 per day during the course of the dispersal experiment. Using this estimate of the disappearance rate, the diffusion rate calculated from equation (3) was 5.04 m2/day. These estimates indicate that the dispersal distance of T. brassicae is limited and characterized by a very low rate of dispersal and a high rate of disappearance. While part of the disap-

Table 7.1. Estimating dispersal distance and density of females for a release of Trichogramma brassicae from a central release point using both the exponential and diffusion models of dispersal. Dispersal distance in m was estimated as the median r(0.5), 67th quantile r(0.67), 95th quantile r(0.95) and mean r៮. Densities per m2 were estimated both for parasitoids contained within circles Nr (p) represented by these radial distances and for parasitoids outside of these circles Nr (p ⬘) and within an arbitrary outer circle of 30 m. The density estimates also include an arbitrary radius of 5 m. Parameter

Exponential model Estimate

a or A b or B

7.05 2.34

Diffusion model

SE

Estimate

0.42 0.28

166.08 1.94

SE 33.69 0.35

Distance

Nr (p)

Nr (p⬘)

Nr (p)

Nr (p⬘)

r (0.5) r (0.67) r (0.95)

2.46 3.78 10.95

1555.85 881.56 148.71

10.50 7.07 1.20

Distance 2.30 3.30 7.58

1781.42 1152.65 310.19

10.49 7.04 1.11

r៮ r=5m

3.64

923.94 576.10

7.35 5.00

2.91

1348.12 630.27

8.25 3.46

Total number recaptured, C(t )

200 160 C(t ) = 669.54 exp(–1.34t ), R 2 = 0.99 120 80 40 0 0

1

2

3

4

5

Days since first released Fig. 7.4. The observed distance-integrated estimate of the survivorship curve for female Trichogramma brassicae from the mark–release–recapture experiment. An exponential mortality model provides an excellent fit to the data.

Methods for Monitoring the Dispersal of Natural Enemies

represented by these dispersal radii were much lower. For example, the density of dispersers found outside of a circle representing the median dispersal distance and within an arbitrary larger circle of 30 m radial distance from the release point was only 10.5 per m2 (Table 7.1). From this analysis we can conclude that dispersal of female T. brassicae from a central release point where 59,000 females were released was extremely limited in both distance and time. With few individuals dispersing beyond a radial distance of 10 m, the risk of non-target impacts from a release of this size are unlikely to be of significance.

Density of T. brassicae females per m2

pearance will have been due to settling of individuals during the dispersal process, the greatest loss is probably due to mortality, as the longevity of Trichogramma adults under field conditions is often short (e.g. Mansfield and Mills, 2002). In addition, these results confirm that the sticky panels had a very low trapping efficiency of 0.011 (estimated from equation (4)) as would be expected of an intercept trap in the absence of any attractant. The mean density of T. brassicae females per m2 in relation to radial distance from the release point was estimated from equation (12) using diffusion model (3). While the estimated densities were high at distances very close to the release point, they declined rapidly to a negligible level at a distance of greater than 10 m from the release point (Fig. 7.5). Similarly, the mean density of parasitoid females enclosed within circles of different radial distances from the central release point were as large as 1555–1781 per m2 for the median distance travelled, but dropped to 148–310 per m2 for the radial distance travelled by 95% of the population (Table 7.1). It is worth noting that for a natural enemy with such limited dispersal, the corresponding mean densities over the period outside of the area

5000

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Conclusions and Recommendations The quantification of dispersal by natural enemies from a central release point presents many practical difficulties, as noted in the discussion above. Dispersal continues to be one of the most difficult population parameters to estimate accurately, but recent advances in marking techniques and improvements in quantification of dispersal, through use of variants of the diffusion model, have greatly improved our ability

30 25

4000

20 15

3000 10 5

2000

0 0

5

10

15

20

1000

0 0

10

20

30

40

50

60

70

Radial distance (m) from release point, r Fig. 7.5. The estimated density of female Trichogramma brassicae per m2 in relation to the radial distance from the release point in the MRR experiment. The inset graph provides finer detail of densities at distances from 6–20 m.

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to conduct dispersal experiments more effectively. Nonetheless, it is important to understand that estimates of dispersal distance and density can be strongly influenced by the type of landscape, the intervening landscape matrix and the climatic conditions prevailing at the time of the experiment. Thus we recommend the following set of procedures for an assessment of dispersal by natural enemies in the context of non-target impacts from augmentative releases of biological control agents: ● Use MRR as the most effective approach to the study of the dispersal of natural enemies. ● Conduct MRR experiments in a homogeneous landscape with low-growing vegetation, such as a meadow, to provide maximum estimates of dispersal potential. ● Use a non-disturbing marking technique, such as an immunological marker, that can be used to mark a large number of individuals quickly and effectively. ● Use a lattice or wagon-wheel grid to recapture the released natural enemies

● ●

●

●

and ensure that it extends a sufficient distance from the central release point, with traps placed at regular distances within the grid. Analyse the recapture data for evidence of directionality. If directionality can be ignored or corrected, use either the exponential model (1) or the diffusion model (3) to analyse the time-integrated recapture data and estimate dispersal distance and density. If directionality is strong, use the diffusion model with displacement (6) to determine the dispersal rate of the natural enemy, and consider which climatic or landscape factors might have influenced the displacement. Whenever possible, conduct a series of replicate MRR experiments to permit quantification of the variation (SD or SE) in estimates of dispersal distance and density for the natural enemy, and to relate the individual estimates from each replicate to the specific climatic conditions (e.g, wind speed, temperature) that prevailed during the course of each experiment.

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Risks of Plant Damage Caused by Natural Enemies Introduced for Arthropod Biological Control

Ramon Albajes,1 Cristina Castañé,2 Rosa Gabarra2 and Òscar Alomar2 1Universitat

de Lleida, Centre UdL-IRTA, Rovira Roure 191, 25198 Lleida, Spain (email: [email protected]; fax number: +34-973-238301); 2IRTA, Centre de Cabrils, 08348 Cabrils (Barcelona), Spain (email: [email protected]; [email protected]; [email protected]; fax number: +34-937-533954)

Abstract Although the capacity to feed on both prey and plants is relatively widespread among pest natural enemies, mostly in predators, crop damage has rarely been reported. Little is known about the mechanisms governing crop damage occurrence by predators that can also facultatively feed on plants. This chapter aims to provide guidance on how to assess the risks of crop damage by introduced invertebrate biological control agents. Risks of crop damage do not seem to be characteristic and constant for each species but variable, depending on external factors. Based on the experience gained with the management of native facultative predators in conservation biological control, we discuss the role of variables linked to the predator, the crop plant and the target habitat when assessing risks of crop damage by introduced natural enemies. Among the variables related to the predator, capacity to consume and to injure plants and to vector plant pathogens are associated most with high risk of crop damage. When this information is not reliably available in the literature, pre-release tests must be carried out. These should include observations on plant-feeding activity and its confirmation by using chemical or immunological markers and gut content analyses. Specific trials, designed to test whether damage is caused, should take into account the variables linked to the crop that may affect its susceptibility: crop species, cultivar, growth stage and tissue. Risks posed by plant-feeding predators should be analysed in relation to the potential benefits expected from such predators for biological control.

Introduction It has been commonly thought that natural enemies introduced to control arthropod pests are safe for crop plants, and hence 132

risks of crop damage have rarely been considered in the protocols for the evaluation of potential biological control agents. The growing realization that there are predaceous arthropods and parasitoids that can

©CAB International 2006. Environmental Impact of Invertebrates for Biological Control of Arthropods: Methods and Risk Assessment (eds F. Bigler et al.)

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occasionally or regularly feed on plants or plant products has become a concern, as if plant feeding would inevitably lead to crop damage. However, the facultative herbivory in natural enemies does not mean that they will necessarily feed on plants and, even if they do, that they will injure the plant or that injury will result in yield loss. Fortunately, plant damage occurs only in very few situations, despite the fact that many biological control agents regularly feed on plants. For instance, in a thorough review of most predator groups by Hagen et al. (1999), plant feeding is mentioned several times, but crop damage is very rarely noticed. Crop damage is the result of complex interactions between the morphological, physiological and behavioural traits of the natural enemy, and some environmental features. The objective in this chapter is to provide guidance on how to assess plant feeding and damage by invertebrate biological control agents introduced for arthropod control. In order to predict the effects of plant feeding by natural enemies it is necessary first to determine the factors responsible for facultative phytophagy, then to assess the possible resultant yield loss, and finally to identify the level of phytophagy by the natural enemy in the target agricultural ecosystem. At the end of the chapter we propose some criteria and procedures that may help to assess risks of negative effects on plants caused by candidate natural enemies.

Feeding Habits of Arthropod Natural Enemies Arthropods have been considered to belong to a unique trophic level, that is, to be either herbivores or carnivores. However, insect feeding studies are increasingly showing that many insect species are instead omnivores, that is, they can use foods of more than one trophic level (Pimm and Lawton, 1978). The capacity to feed on both prey and plants (zoophytophagy) is a special case of omnivory, called true omnivory by some authors. True omnivory

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has received attention due to its implications for biological and chemical control of agricultural pests (Alomar and Wiedenmann, 1996; Coll and Guershon, 2002). The alternation of prey- and plant-feeding stages during the life cycle of arthropods is a common feature. For example, many predatory insects feed upon plants at the adult stage by consuming floral or extrafloral nectar, pollen, seeds, plant saps and other plant materials, whereas they are carnivorous in juvenile stages. This is the case with many predators and parasitoids that have been used successfully in biological control. Less frequently, but not rarely, insect predators may feed on plants and/or on prey at the same developmental stage. These so-called ‘facultative predators’ may switch from prey feeding to plant feeding and vice versa (Albajes and Alomar, 2004). Due to the difficulty in assigning the relative position within a range stretching from strict zoophagy to strict phytophagy, there are many terms found in the literature related to facultative predators, such as zoophytophages, phytozoophages, plantfeeding omnivores, facultative phytophagous predators, facultative herbivores and opportunistic predators (Alomar, 2002). In this chapter we will consider plantfeeding predators as being those that can alternate between zoophagy and phytophagy in different developmental stages, as well as those that may feed facultatively on plants and prey in the same developmental stage. The term ‘plant-feeding predators’ therefore includes true omnivores, zoophytophages, facultative predators and equivalent terms. Insects that feed on plants and prey in different stages are represented in most insect orders, even if herbivores with cannibalistic habits are not considered. Most parasitoids feed on plant-derived products at the adult stage. Several families of insect and mite natural enemies may ingest plant products in at least one developmental stage (Hagen et al., 1999). This is the case with many predatory Heteroptera (Anthocoridae, Miridae, Nabidae, Geocoridae, Pentatomidae), Thysanoptera

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(Aeolothripidae), Neuroptera (both green and brown lacewings), Coleoptera (Carabidae, Coccinellidae), Diptera (Cecidomyiidae, Syrphidae), Hymenoptera (Formicidae, Vespidae and many parasitoid families) and of at least four arachnid families, among which the Phytoseiidae are prominent predators. Facultative predation is relatively common among arthropods. For example, Albajes and Alomar (2004) give a list of taxa including at least one species with facultative predation habits. It contains 18 orders and 84 families of mostly insects, but some arachnids are also included. Cannibalism, haematophagy and host feeding by parasitoids, but not saprophagy, are excluded from the list. Many of the original references reviewed in the literature of facultative predation are only occasional observations, and a more careful study of the feeding behaviour of predatory arthropods could greatly increase the list. Zoophytophagy could have special structural and behavioural traits adapted to omnivory or to blend characteristics of both phytophagous and carnivorous insects. Omnivory has probably evolved from strict carnivores, but also from original herbivores (Whitman et al., 1994). Few studies, however, have been devoted to the evolution of morphological, physiological and behavioural traits associated with omnivory, and whether they have evolved in correlation. This lack of studies prevents us from unequivocally associating certain morphological structures, physiological characteristics and behavioural patterns of natural enemies with plant feeding or risk to the crop plant. Cohen (1996) and Coll and Guershon (2002) (the former restricted to Heteroptera) reviewed the most common traits associated with plant-feeding habits in predatory arthropods. The morphology of mouthparts is closely related to feeding regimes. For example, zoophagous heteropterans are armed with back-curving teeth, whereas phytophagous heteropterans have mandibular teeth that curve forward towards the food, or may have no teeth at all. Another differential morphological trait

concerns salivary glands. Heteropteran phytophages have less complex salivary glands in comparison with true predatory organisms. In general among insects, predatory species have a shorter and simpler midgut than herbivorous species. Most physiological and biochemical adaptations to omnivory involve digestive enzymatic traits. Because of the differences in the composition of prey- and plant-based diets, in particular in the protein:carbohydrate ratio, different spectra of enzymes are found in phytophages and carnivores, and a simultaneous occurrence of several types in omnivores can be expected. Proteases and phospholypases, for example, are more common among predatory insects, whereas pectinases and amylases are more likely to occur in plant feeders. The presence of symbionts in the gut has been associated with plant-feeding insects, to which they provide nutritional factors that are depauperate in plant tissues. Behavioural traits of plant-feeding predators are linked to their capacity to respond to stimuli from plants, although for some authors other non-nutritive factors linked to predator foraging behaviour may help in understanding the causes and consequences of omnivory.

Plant-feeding Predators in Biological Control The ecological role of true omnivory Most investigations on plant-feeding predators have emphasized the benefits and costs for predator fitness derived from mixed diets. However, understanding the full ecological relevance of true omnivory may help to incorporate it into the theory of population and food web dynamics, and to gain more predictability of the benefits and risks of plant-feeding predators in biological control. The benefits may stem, first, from nutritional considerations. Plant food tends to be more abundant, stable, aggregated and easier to obtain than prey food, particularly in agricultural ecosystems. Furthermore, it has been widely reported that predators

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that are able to complement or supplement their carnivorous diet with plant materials enhance one or more of their fitness components, such as developmental rate, survival, fecundity or longevity. But other non-nutritive factors may also explain the advantages of omnivory. Although they have historically received little attention in ecological research, advantages such as reduced competition for prey with other predators, toxin dilution and reduction of predation risks have been cited as benefits obtained from mixed food regimes. The costs for predators of feeding on plants are traditionally said to be a consequence of the poor quality of plant food in comparison with prey food; plant tissues are, in general, poorer in their C:N ratio than those at higher trophic levels, even when particularly N-rich plant tissues (e.g. pollen, seeds, flowers, fruit) are selected (Denno and Fagan, 2003). This could diminish some of the above-mentioned fitness components and counterbalance their benefits. Increased food intake may compensate for lower food quality, although this also has some potential limitations and disadvantages. Feeding strategies in true omnivores thus respond to the compromise between benefits and costs of zoophytophagy within the limits established by phylogenetic constraints.

suppression show that the result of feeding on plants by predators is uncertain and depends on many factors (Eubanks and Denno, 2000). However, plant-feeding predators offer advantages for use in biological control, so management programmes must minimize risks while maximizing their benefits (Alomar, 2002). There is some experimental evidence – still poorly investigated – that plant-feeding predators are able to establish themselves on the crop early in the season even before colonization by pests, a particularly positive trait in annual crops that have to be recolonized every season. Once they are established, the possibility of feeding on plant tissues may allow predators to remain on the crop even in the absence or shortage of prey. This ability, however, may be greatly influenced by the plant. The need for pollen, for example, causes Orius spp. (Heteroptera: Anthocoridae) to leave low pollen-producing cucumber varieties, whereas they can remain on the crop at low densities in varieties with higher pollen production. Plant characteristics may mediate prey preferences by predators, which may avoid foraging or ovipositing on certain plant species or varieties. Again, Orius spp. are rarely observed on tomato even if it hosts high numbers of thrips, their target prey.

Limitations and advantages of plantfeeding predators for biological control

Damage to Crop Plants

The potential of plant-feeding predators for biological pest control has traditionally been neglected, mainly due to the risk that feeding on crop plants may result in economic damage as a direct consequence either of injuries to plant tissues or indirectly by inoculating pathogens that cause crop diseases. Another reason is their common nature as generalist predators, a group of natural enemies believed to be less effective in maintaining pests under economic thresholds, and in this case even less effective because it is said that plant consumption may decrease prey ingestion. Recent studies on the role of omnivory in pest

Nutrients obtained from plants by plantfeeding predators To answer the question of which nutrients or, at least, which type of nutrients plantfeeding predators obtain when feeding on plants, it is necessary to explain the behaviour of facultative predators, which may help us to understand the extent of plant feeding and therefore the risk of damage. However, studies in this field are extremely rare and most of them refer to the specific case of Heteroptera. Predaceous Heteroptera need a substantial amount of water in order to feed on their prey due to their extra-oral digestion. In

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this type of feeding, also called ‘flush and macerate’, the insect injects into the prey a considerable amount of digestive enzymes from its salivary glands. These enzymes are diluted in water and, together with the action of the insect’s stylets, they pierce prey tissues and macerate them with the watery saliva, forming slurry that will be ingested and finally digested in the gut (Cohen, 1995). This process involves a great demand for water. The high investment in enzymes that this type of feeding involves means that they must be collected back from the prey or plant, resulting in very efficient predators in relation to prey consumption, because they consume most of the food contents. Moreover, water is additionally needed to maintain their physiological status. Due to this high demand for water that Heteropteran predators derive mainly from plant tissues, phytophagy in these predators may be considered not as facultative but compulsory (Gillespie and McGregor, 2000; Sinia et al., 2004). Facultative predators obtain not only water from plants but also other substances that allow them to maintain their fitness. What they obtain varies to a great extent according to the predator and the plant (and plant part) considered. Whereas certain plant species can sustain predator development, and even some reproduction in the absence of prey, other plants are only able to maintain the adults alive for a period of time (Stoner, 1970; Naranjo and Stimac, 1985; Perdikis and Lykouressis, 2000; Sanchez et al., 2004). Also, different plant parts may have different nutritive values. While seeds and pollen frequently contain up to 10% nitrogen, leaves often contain as little as 0.7%, and phloem and xylem tissues even less (<0.005%) (Eubanks et al., 2003). It is not clear to what extent plant feeding by some predators is characteristic and constant for each species, or is variable depending on external factors. For example, injury to tomato fruits by Dicyphus tamaninii Wagner (Heteroptera: Miridae) is more likely to occur under prey shortage (Salamero et al., 1987; Alomar and Albajes,

1996), a hypothesis that led to the implementation of a predator/prey management programme on field tomatoes aimed at avoiding the coincidence of high predator populations with low prey (greenhouse whitefly) densities (Albajes and Alomar, 1999; Lucas and Alomar, 2002). However, other behavioural studies conducted on D. tamaninii have shown that time spent feeding on leaves does not vary with prey density (Montserrat et al., 2004). This suggests different behaviour patterns of plant feeding for leaves and fruits in this predator. On the other hand, prey feeding enhances feeding on leaves in another predator of the same genus, D. hesperus Knight (Sinia et al., 2004). In the latter, water has been reported as the main factor obtained from plant feeding (Gillespie and McGregor, 2000), although the risk of damaging fruits is low (McGregor et al., 2000), because these bugs prefer to feed on leaves. Gillespie and McGregor (2000) proposed considering three categories of functional relationships between prey and plant feeding that might result in different risks of crop damage. The negative relationship – plant feeding decreases as prey feeding increases – represents a favourable situation for using facultative predators in biological control. The positive relationship occurs when water, or any other resource limiting predation, is the main component of the plant diet in the predator. As more prey is ingested, more water is needed for digestion and other physiological functions of the prey. This situation may or may not lead to crop damage, depending on the plant part used as water source. A third category is when plant and prey feeding are independent; this scenario occurs when the predator needs to consume plant resources to acquire a certain amount of a critical element not available in the prey and not directly related to foraging, ingesting and digesting the prey. Only if the amount of the critical element needed by the predator is high may this functional relationship limit the use of the predator in biological control.

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Plant injury by plant-feeding predators Heteroptera have been much used both in conservation and augmentation biological control, and provide a useful framework for discussion of the complexity of the factors involved in damage (Alomar, 2002). Injuries caused by plant-feeding predators can affect plant growing tissues, stems, leaves or fruits. The damage is speciesspecific, that is, a predatory species may damage certain crops, but not others. Campyloma verbasci (Meyer) (Heteroptera: Miridae) is responsible for damage on apples, but very rarely on pears, in Canada (Thistlewood and Smith, 1996); Dicyphus tamaninii may cause damage to tomatoes under prey shortage but not to cucumbers or melons (Alomar and Albajes, 1996; Castañé et al., 2000; Alomar et al., 2003), and the same has been observed with Macrolophus caliginosus Wagner (Heteroptera: Miridae) for courgettes or cherry tomatoes, whereas it is safe for regular tomatoes (Lucas and Alomar, 2002; Castañé et al., 2003). Crop susceptibility to predator feeding may change even with the crop cultivar or crop growth stage. Damage caused by C. verbasci is concentrated during the blooming period, early in the season, and is greater in ‘Delicious’ than in other apple varieties such as ‘McIntosh’ (Reding and Beers, 1996). The local conditions can also alter the damage potential of a plant-feeding predator; while damage by M. caliginosus to tomatoes has been claimed by English growers (Sampson and Jacobson, 1999), the bug has been widely introduced by growers in continental European greenhouses without major reported problems. Similarly, Nesidiocoris tenuis (Reuter) (Heteroptera: Miridae) may severely injure tomato plants in the south of France and in Sicily, while in the Spanish areas of Valencia, Murcia and the Canary Islands damage is only occasionally observed, and the predator is now commercially produced and released in vegetable greenhouses in southern Spain (CarneroHernández et al., 2000; BioBulletin, 2004). In the case of Heteroptera, the type of injury that is observed externally varies

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from simple punctures in the green fruit, which become discoloured spots in the mature fruit (tomatoes) (Alomar and Albajes, 1996), to diverse depressions (pits) and scars, which are very well described in apples (Boivin and Stewart, 1982). In many cases, injured tissues show a brownish area internally, as in the case of the mirid bug N. tenuis, which may feed on the vascular tissues of tomato plants. This type of damage (toxaemia) is the result of several concurrent processes, such as the mechanical destruction of cells by the stylet, together with the action of the salivary enzymes on the tissues and the wound-response reaction by the plant. This response is characterized by the release of phenolic compounds that are oxidized to quinones and subsequently form non-toxic polymers, producing the characteristic brown discolouration of wounded tissues (Raman et al., 1984). This type of internal tissue lesion has been associated with other predaceous and phytophagous Heteroptera (Schaefer and Panizzi, 2000; Wheeler, 2001).

Criteria for Risk Assessment Plant damage assessment within current regulations Until now, regulations on the importation and release of exotic biological control agents have mostly been aimed at limiting the risks for native non-target species, in addition to characterizing biological control agents and their efficacy. This is the case for most of the currently available guidance documents produced by several international organizations, including FAO (1996), EPPO (2002), OECD (2004), and by state administrations or scientists (van Lenteren et al., 2003). The FAO Code of Conduct for the Import and Release of Exotic Biological Control Agents and EPPO Standards PM 6/1 and 6/2 provide guidelines for assessing and reducing the risks associated with release of invertebrate natural enemies, but say nothing specific about the risks of damage to crop plants.

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The OECD’s Guidance for Information Requirements for Regulation of Invertebrates as Biological Control Agents is the only document that specifically requests that ‘effects on plants should be provided if the biological control agent is potentially a facultative herbivore and if there is a potential for phytotoxic effects’. Finally, van Lenteren et al. (2003) refer to omnivory by saying that ‘some natural enemies also feed on plant materials during part of their life cycle … and information on the effect on plants by these agents should be provided’, but nothing is said about how to proceed in assessing risks of crop damage. In conclusion, very little control is mandatory on the interactions between the biological control agent and the plant in any of the current regulatory documents relating to natural enemies.

Variables and Associated Values for Assessing Risks of Crop Damage In the preceding sections we discussed the variables that may be involved in determining crop damage by plant-feeding predators released for biological control purposes. In this section each of the variables is associated with risk levels in order to predict the conditions in which damage to crops is more likely to occur. Risk assessments ideally operate with predictive models; for model building we need to establish clearly quantitative or, at least, qualitative relationships between variables (causes) and response (crop damage), and estimate the likelihood of the response occurring. As mentioned, these relationships between predator, crop and habitat variables and crop damage risks are fairly unknown and no formal models are available. However, assessments may still be made less formally from the list of the most relevant variables and variable traits included in Table 8.1, which are associated with risk levels. Risks are categorized as low (+), moderate (++) or high (+++) for each of the variable traits. Data on the traits should be surveyed first in the literature, and when they are not available they must be acquired through pre-release evaluations.

Variables related to the predator (or parasitoid) include its taxonomical affinity with species with proven plant-feeding activity, adaptation of its morphology and physiology to ingesting and digesting plant tissues, its measured capacity to consume and to injure plants and, finally, its capacity to vector plant pathogens or to cause plant toxaemias. These produce symptoms of plant disease induced from the effects of salivary compounds (phytotoxins) introduced by insect feeding. The closer the taxonomical affinity between the candidate species and other species with proven damage potential, the higher is the risk of damage to the crop. This implies the correct identification of the predator: even the identification of the biotype may be necessary as some predators may show different feeding behaviour according to their origin. The availability of fully developed and reliable molecular tools may facilitate and speed up the correct identification of the candidate (Symondson and Hemingway, 1997). However, crop damage risks are difficult to assess only on the basis of a taxonomical affinity. Some morphological and physiological traits of the predator may complement indications provided by taxonomical affinity. Some of them are easy to observe, while others may be difficult to check for non-specialists. While each of the morphological and physiological traits by itself represents a low risk of crop damage, co-occurrence of all or most of the traits may lead to a moderate or even high risk. The predator’s capacities for feeding on plants and injuring them are of course the most relevant variables for risk assessment. If a predator can ingest crop plant materials, this does not necessarily lead to injury. Only when injury actually occurs is the risk of crop damage really high. In addition to the intrinsic capacity of the predator to injure the plant, other variables related to predator (e.g. physiological status, age), predator population (density) or predator diet requirements (functional relationship between plant and prey feeding) may complementarily determine the amount and

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Table 8.1. Variables and their traits involved in risks of crop damage caused by introduced biological control agents. Risks of crop damage by the plant-feeding activity of the predator may be low (+), moderate (++) or high (+++). Variable relating to the:

Variable

Predator

Taxonomical affinity

Records of crop injury by individuals of the same family (+), genus (++) or species (+++)

Morphology and physiology

Hypognathous head (+) Mouthpart traits associated with herbivory (+) Less chitinized and toothed fore-gut (+) Complex mid-gut (+) Presence of symbionts (+) Presence of amylases and pectinases (+)

Capacity to feed on plants and to injure them

Ingestion of plant materials (++) Injury to crop plants (+++)

Capacity to vector plant pathogens and to produce plant toxaemias

Sucking mouthparts (++) High frequency of transmission/toxaemia records in individuals of the same family (+), genus (++) or species (+++) High damage potential (+++)

Crop susceptibility

Records of crop damage by the predator have been recorded in plants of the same family (+), genus (++), species (+++) and variety (+++) Presence of susceptible plant tissues and growth stages (++)

Crop cycle

Possible co-occurrence of susceptible growth stage and high predator density (++)

Favourability for predator establishment

High ecological compatibility for predator establishment (+)

Favourability for epidemics of diseases vectored by the predator

Presence of non-agricultural pathogensusceptible plants (++) Large amount of disease inoculum (+++)

Crop

Habitat

severity of the injuries. Other variables modulating the influence of predator capacity for injuring plants on crop damage risks are related to the crop (see below). The release of natural enemies that are able to vector plant diseases or to cause phytotoxaemias is particularly risky. However, records in the literature of plant-feeding predators that have caused crop damage by vectored plant diseases or by phytotoxaemias are very rare, although this may be potentially suspected in predators with sucking mouthparts or in those that are taxonomically close to proven efficient vectors. Based on this low record frequency, risks of crop damage by plant diseases vec-

Traits and level of associated risk of crop damage

tored by plant-feeding predators could be considered as low, but in the case of pathogens with high damaging potential these risks are high. Crop susceptibility and favourable conditions in the habitat for disease development lead to higher risks. There are two main variables related to the crop to be considered in risk assessment: its susceptibility and its phenology. Crop susceptibility may affect crop response to feeding injuries. This feature is variable within the same plant family, but also within the same genus, and even in the same species, depending on to the cultivar. Given a predator, the risk of crop damage on a certain crop ranges from low

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to high as damage is recorded in plants of the same family, genus, species and variety, respectively. The predator may also display preferences for certain tissues within a plant, and risks of crop damage may be high if the preferred tissue is the marketed one (e.g. fruit) or one that greatly contributes to yield (young leaves). As crop plants differ in their susceptibility to crop damage by plant-feeding predators during their growth, the risk varies with the crop growth stage. Crop cycles leading to high probability of co-occurrence of susceptible crop growth stages and of high predator densities are more at risk. The favourability of the habitat for predator establishment increases the risk of crop damage whenever injury might be caused. Conditions for natural enemy establishment are discussed by Boivin et al. (Chapter 6, this volume); here, it is remarked only that the establishment of potentially injurious predators in the new habitat increases the risk of crop damage as the time of plant exposure to the potential hazard is higher, and the range of plants exposed may include more susceptible plants than if the predator activity is restricted to one crop season. For predators that can potentially transmit plant diseases, their presence in the habitat of plants susceptible to the disease and a high amount of inoculum increase the risk of damage to the crop where the predator has to be released. Data on most of the variable traits included in Table 8.1 are seldom available in the literature, and at least some of them will have to be obtained by specific procedures before importation of natural enemies for classical biological control. Pre-release screenings cannot cover all the items mentioned in Table 8.1, and they thus have to be prioritized. As there are more indications in the literature of crop damage risks by a candidate, more prerelease trials have to be carried out. Direct observation of plant feeding, plant injury and derived damage caused by a candidate gives by far the best evidence of crop damage risk.

Testing procedures The proposed methods do not differ substantially from common practice in applied entomology, but researchers should take into consideration those factors (such as plant species, plant growth stage and amount of prey) that may affect plant feeding, injury and damage. The book by Dent and Walton (1997) may be consulted in selecting the methods to follow for measuring some of the traits included in Table 8.1. There now follow some indications about how to proceed for testing plant feeding and injuring capabilities in candidates for importation. To confirm or reject the plant-feeding capability in the predator, simple tests can be performed in the laboratory with small cages. Food-deprived (24 h) individuals of the predator (immatures and females) can be caged on different parts and tissues of the plant, which must be devoid of prey in order to force the predator to feed. During the trial period, which can take a variable number of days depending on the environmental conditions and predator, but which has to be not less than 15 days, survival and presence of injury on the plant must be periodically checked. Control cages, (a) with plants but without predators, and (b) with no plants but large numbers of prey, should be set up. The trial should be carried out for two crop growth stages per crop screened. Crops to be screened should be selected according both to their economic importance in the release area and to their taxonomic affinity to the plant that hosted the predator in the original habitat. Chemical (e.g. rubidium) or immunological markers and gut content analysis will confirm whether the individuals have ingested plant materials. Other methods developed for detecting prey consumption (Agustí et al., 2003) or dispersal (e.g. Silberbauer et al., 2004) may also be used to identify plant feeding. Predator capacity to transmit plant diseases or phytotoxaemias may be tested with common procedures used in this type of study. Even pathogens may be used as markers of plant feeding by the predator.

Risks of Plant Damage Caused by Natural Enemies

Whenever plant feeding is confirmed, specific trials for potential injury to the crop are needed. Damage to the plant may become apparent long after injury has been caused. Therefore, these trials should be done in large exclusion cages during a sufficient period of time. Crops to be tested should follow the same criteria as above. The following factors should be considered: plant (species, cultivar, growth stage and plant part or tissue), developmental stage and density of the candidate predator, and type and amount of prey. The most important factors should be selected and tested according to prior information. Injuries to the tissues that are directly marketed or that have a high contribution to the yield pose greater risks than those to less valuable tissues, and should always, therefore, be included in the trials.

Conclusions Recent awareness of risks involved in the practice of biological control against invertebrate pests has dealt mainly with the potential impacts of introduced natural enemies on native fauna. This is increasingly taken into consideration in the international and national regulations concerning importation of natural enemies for biological pest control. Much less attention has been paid to the potential negative effects on crop plants caused by introduced natural enemies. This is probably because there are few records in the literature of negative effects of intentionally released invertebrate biological control agents on crop plants. Even in the case of some predators introduced into Europe for inoculative biological control that have proved to be feeding on plants, like Podisus maculiventris (Say) (Heteroptera: Pentatomidae) and Orius insidiosus (Say) (Heteroptera: Anthocoridae), no crop damage has been reported. Much of the experience gained with the use of facultative predators in biological control comes from the management

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of populations of native predators such as Dicyphus tamaninii and Macrolophus caliginosus in southern Europe, and D. hesperus in Canada. It is an important fact that neither the likelihood nor the magnitude of recorded yield losses by those predators seems to indicate a major cause for concern of immediate risk. However, based on our experience, caution is needed when requesting importation of plant-feeding predators for release. In spite of the amount of information acquired in recent years, little is still known about the mechanisms governing crop damage by some plant-feeding predators. We feel that the growing realization of the importance of generalist predators in Conservation Biological Control will also provide more detailed insights as to the real risks posed by some omnivores. Therefore, we suggest that augmentation of omnivores in their native habitats should also include follow-up schemes that allow identification and quantification of damage risks under realistic conditions. These data can then be used when defining risks for importation. Particularly significant is the fact that the risk varies according to the geographic location: for example Nesidiocoris tenuis is considered as a pest in northern Mediterranean areas, whereas it is mass reared and sold for inoculative biological control in greenhouses further south with no reported damage to crops. More data are needed to predict when, where and how some predators switch from prey to plant feeding in order to obtain more precise risk assessment methods. Meanwhile, regulations on importing and releasing natural enemies should take into consideration predators that can potentially feed on plants as relatively non-risky, except when several of the risky traits co-occur in the predator, the targeted crop and the habitat. In addition, when a plant-feeding predator is being considered for introduction into a new area, the risks of crop damage must be analysed in relation to the potential benefits expected from introduction of such predators.

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Hagen, K.S., Mills, N.J., Gordh, G. and McMurtry, J.A. (1999) Terrestrial arthropod predators of insect and mite pests. In: Bellows, T.S. and Fisher, T.W. (eds) Handbook of Biological Control. Academic Press, San Diego, California, pp. 383–503. Lucas, E. and Alomar, O. (2002) Impact of Macrolophus caliginosus presence on damage production by Dicyphus tamaninii (Heteroptera: Miridae) on tomato fruits. Journal of Economic Entomology 95, 1123–1129. McGregor, R.R., Gillespie, D.R., Park, C.G., Quiring, D.M.J. and Foisy, M.R.J. (2000) Leaves or fruit? The potential for damage fruits by the omnivorous predator, Dicyphus hesperus. Entomologia Experimentalis et Applicata 95, 325–328. Montserrat, M., Albajes, R. and Castañé, C. (2004) Behavioral responses of three plant-inhabiting predators to different prey densities. Biological Control 30, 256–264. Naranjo, S.E. and Stimac, J.L. (1985) Development, survival and reproduction of Geocoris punctipes (Hemiptera: Lygaeidae): effects of plant feeding on soybean and associated weeds. Environmental Entomology 14, 523–530. OECD (2004) Guidance for Information Requirements for Regulation of Invertebrates as Biological Control Agents (IBCAs). OECD Environment, Health and Safety Publications, Paris, France, http://www.oecd.org/dataoecd/6/20/28725175.pdf (accessed 26 March 2005). Perdikis, D. and Lykouressis, D. (2000) Effects of various items, host plants, and temperatures on the development and survival of Macrolophus pygmaeus Rambur (Hemiptera: Miridae). Biological Control 17, 55–60. Pimm, S.L. and Lawton, J.H. (1978) On feeding on more than one trophic level. Nature 275, 542–544. Raman, K., Sanjayan, K.P. and Suresh, G. (1984) Impact of feeding injury of Cyrtopeltis tenuis Reut. (Hemiptera: Miridae) on some biochemical changes in Lycopersicon esculentum Mill. (Solanaceae). Current Science 53, 1092–1093. Reding, M.E. and Beers, E.H. (1996) Influence of prey availability on survival of Campylomma verbasci (Hemiptera: Miridae) and factors influencing efficacy of chemical control on apples. In: Alomar, O. and Wiedenmann, R.N. (eds) Zoophytophagous Heteroptera: Implications for Life History and Integrated Pest Management. Thomas Say Publications in Entomology, ESA, Lanham, Maryland, pp. 141–154. Salamero, A., Gabarra, R. and Albajes, R. (1987) Observations on predatory and phytophagous habits of Dicyphus tamaninii. IOBC/WPRS Bulletin 1987/X/2, 165–169. Sampson, C. and Jacobson, R.J. (1999) Macrolophus caliginosus Wagner (Heteroptera: Miridae): a predator causing damage to UK tomatoes. IOBC/WPRS Bulletin 22(1), 213–216. Sanchez, J.A., Gillespie, D.R. and McGregor, R.R. (2004) Plant preference in relation to life history traits in the zoophytophagous predator Dicyphus hesperus. Entomologia Experimentalis et Applicata 112, 7–19. Schaefer, C.W. and Panizzi, A.R. (2000) Heteroptera of Economic Importance. CRC Press, Boca Raton, Florida. Silberbauer, L., Yee, M., Del Socorro, A., Wratten, S., Gregg, P. and Bowie, M. (2004) Pollen grains as markers to track the movements of generalist predatory insects in agroecosystems. International Journal of Pest Management 50, 165–171. Sinia, A., Roitberg, B., McGregor, R.R. and Gillespie, D.R. (2004) Prey feeding increases water stress in the omnivorous predator Dicyphus hesperus. Entomologia Experimentalis et Applicata 110, 243–248. Stoner, A. (1970) Plant feeding by a predaceous insect, Geocoris punctipes. Journal of Economic Entomology 63, 1911–1915. Symondson, W.O.C. and Hemingway, J. (1997) Biochemical and molecular techniques. In: Dent, D.R. and Walton, M.P. (eds) Methods in Ecological and Agricultural Entomology. CABI Publishing, Wallingford, UK, pp. 293–340. Thistlewood, H.M.A. and Smith, R.F. (1996) Management of the mullein bug, Campyloma verbasci (Heteroptera: Miridae), in pome fruit orchards of Canada. In: Alomar, O. and Wiedenmann, R.N. (eds) Zoophytophagous Heteroptera: Implications for Life History and Integrated Pest Management. Thomas Say Publications in Entomology, ESA, Lanham, Maryland, pp. 119–140. van Lenteren, J.C., Babendreier, D., Bigler, F., Burgio, G., Hokkanen, H.M.T., Kuske, S., Loomans, A.J.M., Menzler-Hokkanen, I., van Rijn, P.C.J., Thomas, M.B., Tomassini, M.G. and Zeng, Q.-Q. (2003) Environmental risk assessment of exotic natural enemies used in inundative biological control. BioControl 48, 3–38.

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Methods for Assessment of Contaminants of Invertebrate Biological Control Agents and Associated Risks Mark S. Goettel and G. Douglas Inglis Lethbridge Research Centre, Agriculture and Agri-Food Canada, 5403–1st Avenue South, Lethbridge, Alberta T1J 4B1, Canada (email: [email protected]; [email protected]; fax number: +1-403-382-3156)

Abstract With the importation or transport of any commodity, there exists the hazard that unwanted organisms or substances (i.e. ‘contaminants’) will be conveyed and introduced. Invertebrate biological control agents (IBCAs) can be contaminated with numerous biotic and abiotic entities such as parasitoids, hyperparasitoids, pathogenic and/or non-pathogenic microorganisms, other organisms, pesticide residues, unwanted packaging materials, etc. Therefore, assessment of the risk posed by the contaminant must be addressed in the commerce of IBCAs. In this chapter, we provide an overview of possible contaminants of IBCAs and of the methods used to detect them. We consider two major factors when assessing risk. These are: (i) whether the IBCA is field collected or insectary reared; and (ii) whether the IBCA is exotic, being introduced for classical biological control or is indigenous and to be used for inundative biological control. We conclude that minimal risk is posed by contaminants of commercially mass-produced IBCAs, that are established in the area of use and are to be used inundatively. For such IBCAs, we recommend that the standards established for importation of most commodities, such as many foodstuffs, plants, vegetables, fruits etc. (i.e. quality control assurances by the producers) be adopted. Field-collected IBCAs, on the other hand, have a much higher potential for harbouring unknown contaminants that may represent a risk. We recommend that feral IBCAs to be released outside of the area from which they were collected should be kept for at least one generation under quarantine, if at all possible, and that the appropriate quarantine protocols are applied. This would allow the detection and elimination of biotic contaminants. We stress that the key to the regulation of IBCAs is to address the extent of the possibility that a contaminant could pose a hazard to the commodity or to the environment of the commodities’ final destination, and, if warranted, to ensure that such harm does not take place. The extent to which measures for prevention of transfer of contaminants are implemented must be weighed in relation to the present transfer of unknown or unwanted substances by other means. If the cautionary approach is strictly implemented for all possible contaminants, then almost certainly the international movement of IBCAs would grind to a halt. The ramifications of this must be weighed against the presently known benefits of IBCAs in our agriculture and forestry industries. ©CAB International 2006. Environmental Impact of Invertebrates for Biological Control of Arthropods: Methods and Risk Assessment (eds F. Bigler et al.)

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Introduction In the importation or transport of any commodity, there is always a concern that ‘contaminants’ will be conveyed and introduced. Before we proceed, it is first necessary to define what constitutes a contaminant. According to MerriamWebster’s Medical Dictionary (2003), a contaminant is defined as: ‘a substance that contaminates; to contaminate is to soil, stain or corrupt by contact; to tarnish; to pollute; contamination is the act of contaminating or polluting including (either intentionally or accidentally) unwanted substances or factors.’ The key word in this definition is ‘unwanted’. Restricting the definition to unwanted presents a cadre of problems. What is ‘unwanted’ as far as an invertebrate biological control agent (IBCA) is concerned? Human-pathogenic bacteria associated with IBCAs may be unwanted, yet the introduction of a small number of cells of a relatively weak human-pathogenic microorganism does not necessarily pose a serious threat. Microorganisms are ubiquitous and no invertebrates are devoid of them unless special measures are taken. How does one determine whether a microorganism falls into the ‘unwanted’ category? To determine this, the definition of contaminant must also address risk, and this paper defines contamination as the inclusion of any unwanted substance or factor (i.e. a contaminant) in the commerce of IBCAs that poses an unacceptable risk. In defining unacceptable risk, we limit our discussion to the impacts of contaminants on the health of the IBCAs or on humans, and their potential impact on ecosystems (e.g. introduction of non-indigenous microorganisms). We also compare risk assessments applied to other invertebrates in some OECD (Organization for Economic Cooperation and Development) countries.

Contaminants Associated with Invertebrates Using the above definitions, a contaminant could be one of numerous factors that

could affect the IBCA’s efficacy, the health of the user, or which could become established or pollute the new environment. Possibilities include pathogenic or nonpathogenic microorganisms, parasitoids, hyperparasitoids, misidentified invertebrates, pesticide residues, unwanted packaging materials, etc. In this chapter, we characterize possible contaminants as either microorganisms, invertebrates or abiotic agents.

Microorganisms Microorganisms are ubiquitous and they are always found in association with both field-collected and mass-reared invertebrates, including IBCAs. Their associations are complex, and their associations with IBCAs can be considered as either incidental, mutualistic, pathogenic or commensalistic. It is important to emphasize that these categories are not necessarily exclusive of each other. A number of arthropods vector mammalian (e.g. West Nile Virus) and plant pathogens. Furthermore, microorganisms associated with IBCAs may be pathogens of invertebrates, plants or vertebrates, including humans. Groups of microorganisms dealt with in this chapter include the viruses, bacteria, fungi and protozoa. For convenience, we also include the nematodes in this section. Numerous examples of pathogens of beneficial arthropods are provided by Vinson (1990), and of mass-produced IBCAs by Bjørnson and Schütte (2003). Viruses Viruses are obligate, intracellular pathogens that consist of double-stranded or single-stranded nucleic acid (DNA or RNA) encased in a protective coating called a capsid. Collectively, the nucleic acid and capsule are termed a nucleocapsid. Depending on the virus, some nucleocapsids are enclosed within a lipid envelope. Virions are the infectious unit of a virus. In enveloped viruses, the virions consist of the nucleocapsid (i.e. nucleic

Methods for Assessment of Contaminants of Invertebrate BCAs

acid and capsid) and the envelope. In nonenveloped viruses, the virions are comprised only of the nucleocapsid. Viruses do not possess the ability to replicate themselves independently of a living host, and thus cannot be cultured on microbiological media. They, in essence, highjack the metabolic machinery of the host cell and trick it into producing progeny viruses. A number of entomopathogenic viruses produce occlusion bodies (OBs), in which the virions are embedded within a paracrystalline protein matrix. The OBs protect the virions (i.e. from ultraviolet light) and increase persistence of the virions outside of the host; their formation has important consequences for their disease-producing potential. They may also serve an important function in the infection process. Most of the viruses associated with insects belong to one of 12 viral families, but many remain unclassified. Of particular concern to IBCAs are viruses in the families Baculoviridae, Poxviridae, Parvoviridae, Reoviridae and Polydnaviridae. Some of these viruses possess restricted host ranges affecting insects in a specific genus, whereas others can affect a variety of hosts belonging to different orders. For most viruses of IBCAs, the primary route of infection is through the alimentary canal after ingestion. However, other routes of infection do occur (e.g. mechanical introduction of virions on infested ovipositors). In some instances, viruses are restricted to specific tissues (e.g. midgut epithelium), but other viruses spread systemically, thus affecting the entire body. As a general rule, viruses that are restricted to specific tissues incite chronic disease, whereas systemically transmitted viruses often cause acute disease. Virions are typically released into the environment in frass or from cadavers. Epizootics in natural populations are common and, periodically, entire colonies can be wiped out in mass-rearing operations. Viruses are often intimately associated with both parasitoids and predators and their hosts and prey (Vinson, 1990). Parasitoids are often implicated in the transmission of the virus to the host. For example, an ascovirus can be transmitted

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by the hymenopteran parasitoid, Cotesia marginiventris (Cresson) (Hymenoptera: Braconidae), from infected to healthy Spodoptera larvae (Lepidoptera: Noctuidae) (Hamm et al., 1985). Viral pathogens are also present in numerous mite and insect species that are used in biological control (Bjørnson and Schütte, 2003). For instance, cytovirus and nuclearpolyhedroviruses are known from the aphid predator, Chrysoperla (Neuroptera: Chrysopidae) (Martignoni and Iwai, 1986). Unidentified, non-occluded virus particles were observed in the yolk of predatory Neoseiulus cucumeris (Oudemans) (Mesostigmata: Phytoseiidae) and Phytoseiulus persimilis Athias-Henriot (Mesostigmata: Phytoseiidae) mites; however, the effects of these on predatory efficacy were not established (Bjørnson et al., 1997). More information on entomopathogenic viruses can be found in Granados and Federici (1986), Adams and Bonami (1991a), Tanada and Kaya (1993), Miller (1997), Hunter-Fujita et al. (1998) and Miller and Ball (1998). Bacteria The bacteria represent a very large and diverse group of prokaryotes. There are two main types of prokaryotes, the archaeabacteria and the eubacteria (collectively referred to as bacteria). Although all bacteria lack a nucleus and organelles, they are very diverse, both in morphology and physiology. Some are single-celled, while others form filaments or aggregates. They may be spherical, rod-shaped, spiral or lobed. Their size varies in diameter from 0.1 to more than 15 ␮m (filaments up to 200 ␮m). Most produce a well-defined cell wall. The archaebacteria differ from the eubacteria in many important respects, such as: (i) their cell walls lack the carbohydrate, peptidoglycan; (ii) their lipid bilayer membranes consist of branched chain hydrocarbons linked by ether linkages to glycerol; and (iii) many archaebacteria live in extreme environments and are very difficult to culture. Phylogenetic studies indicate that the archaeabacteria are

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close relatives of the eukaryotes. The vast majority of bacteria associated with IBCAs are eubacteria, and they can be divided into two groups based on cell wall morphology (i.e. Gram positive or negative). Most eubacteria are saprotrophs, but some are facultative or obligate pathogens, and some form mutualistic symbioses with IBCAs. A number of bacteria associated with arthropods are pathogenic to vertebrates, including humans. Normally, IBCAs are never devoid of bacteria, whether they are feral or reared in captivity. Saprotrophs catabolize nonliving organic matter and are common within the alimentary canal of IBCAs, as well as on their external integuments. Although they may be commensalistic, in some instances they have been shown to be beneficial to the arthropod, providing a degree of protection from pathogenic microorganisms. They may also be pathogens themselves (i.e. facultative), able to infect arthropods under stress. This is a common scenario in rearing settings (Inglis and Sikorowski, 2005a). Other bacteria are more specialized pathogens. The best known of insect-pathogenic bacteria is the spore-forming Bacillus thuringiensis (Bt) Berliner (Baciliales: Bacillaceae). Other entomopathogenic bacteria are found in the genera Bacillus, Aeromonas, Clostridium, Paenibacillus, Photorhabdus, Pseudomonas, Rickettsia, Rickettsiella, Serratia, Wolbachia and Xenorhabdus. Some are opportunistic pathogens, whereas others are highly evolved pathogens. Other bacterial taxa also form symbioses with arthropods, but their effect is beneficial to the host and the bacterium (i.e. a mutualistic symbiosis); in many situations, the presence of the bacterium is essential to the survival of the arthropod. Wolbachia is commonly associated with a diverse array of organisms. It is an intracellular parasite, and it may have profound negative effects on the reproductive fitness of IBCAs, although not necessarily on host fitness (Zchori-Fein et al., 2000). Among beneficials, Wolbachia is ubiquitous and it has been found in parasitoids such as Aphytis, Encarsia, Lysiphlebus,

Muscidifurax, Nasonia and Trichogramma, and in predators such as Adalia, Phytoseiulus, Neoseiulus and Metaseiulus (Stouthamer et al., 1999). In a survey of pest and beneficial arthropods studied by researchers at Agriculture and Agri-Food Canada, infections of Wolbachia were detected in 40 of the 65 species examined. Taxa within the Acari (Tetranychidae), Anoplura (Haematopinidae, Linognathidae, Pediculidae) Coleoptera (Chrysomelidae, Curculionidae), Diptera (Muscidae, Calliphoridae), Hymenoptera (Braconidae, Encyrtidae, Pteromalidae, Trichogrammatidae), Mallophaga and Siphonaptera (Pulicidae) were all infected (G. Kyei-Poku and K. Floate, Alberta, 2004, personal communication). Serratia marcescens Bizio (Enterobacteriales: Enterobacteraceae) is a common contaminant in reared insects. In general, it is not a very virulent pathogen, causing disease only when insect vigour is greatly reduced (Sikorowski and Lawrence, 1997). For example, Lighthart et al. (1988) found that a high-temperature pulse (i.e. a physiological stressor) before inoculation with S. marcescens greatly increased the susceptibility of Metaseiulus occidentalis (Nesbitt) (Mesostigmata: Phytoseiidae) to the bacterium. Greany et al. (1977) found that optimizing Opious longicaudatus Ashmead (Hymenoptera: Braconidae) hostrearing conditions greatly reduced parasitoid mortality attributed to bacteria, including S. marcescens. Readers are referred to Tanada and Kaya (1993), Charles et al. (2000), Glare and O’Callaghan (2000) and Siegel (2000) for more information on entomopathogenic bacteria. Fungi Fungi represent a diverse assemblage of non-phylogenetically related microorganisms (representing at least three Kingdoms). They are grouped together since they are all eukaryotes, they usually produce hyphae and possess rigid cell walls, and they are all heterotrophs (i.e. organisms that utilize organic matter as a

Methods for Assessment of Contaminants of Invertebrate BCAs

source of energy). Some fungi are adapted to existence in liquid environments and produce unicellular growth forms (i.e. yeasts). Reproduction may be sexual or asexual; some types of fungi produce both types of spores, others produce either sexual or asexual spores. Fungi are primarily decomposers of non-living organic matter, but some have evolved highly specialized relationships with arthropods. Some form mutualistic relationships with those such as leafcutting ants or Ambrosia beetles, which cultivate fungi as a source of food, and with polyphagous chrysopid adults, which utilize yeasts to provide essential nutrients. Other relationships with fungi are benign or detrimental. Most entomopathogenic fungi are members of two divisions, the Zygomycota and the Ascomycota. Some are obligate pathogens infecting specific species, some are less specialized, able to infect a variety of host species, whereas others are facultative pathogens, only able to infect insects that are immunocompromised. Fungal epizootics are common in some insect species, while others are rarely affected. Fungi are unique among the insect pathogens as their primary route of entry into the host is via the external integument. Many beneficial invertebrates are susceptible to entomopathogenic fungi (Goettel et al., 1990; Bjørnson and Schütte, 2003; Vestergaard et al., 2003). For instance, Neozygites spp. have been found infecting Neoseiulus and Macrochelus. Some species, such as Beauveria bassiana Balsamo (Vuillemin) (Hypocreales: Clavicipitaceae), are ubiquitous and have a very wide host range, infecting many IBCAs. This fungus is often found infecting overwintering coccinelids. Some fungal taxa may also adversely affect humans, but this is primarily restricted to rearing settings in which facultative pathogens colonize organic materials (e.g. insect diets) and propagules are released into the rearing environment (Inglis and Sikorowski, 2005a,b). Readers are referred to Samson et al. (1988), Tanada and Kaya (1993) and Butt et al. (2001) for more information on entomopathogenic fungi.

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Protozoa The protozoa are also a diverse assemblage of non-phylogenetically related eukaryotic microorganisms. They can exist in mutualistic symbioses with insects. For example, the hindgut in termites houses protozoa that hydrolyse cellulose. This is an obligate symbiosis, and neither the protozoa nor the termites can survive without each other. Protozoan pathogens of arthropods are typically single-celled organisms possessing varied characteristics and little taxonomic affinity among groups (Solter and Becnel, 2000). Many species are obligate pathogens and have complicated life cycles, some with intermediate hosts. Most infections are chronic and non-lethal, but typically result in reduced fecundity. The phylum Apicomplexa contains the insect-pathogenic gregarines. The most commonly encountered are eugregarines with species within Gregarina, whereas Farinocystis, Mattesia and Ophryocystis are neogregarines commonly producing lethal infections in dipteran, coleopteran and hemipteran hosts. Most neogregarines have narrow host ranges; however, others, such as Farinocystis tribolii Weiser and Mattesia grandis McLaughlin (Neogregarinorida: Lipotrophidae), have a relatively wide host range. Many neogregarines are found as contaminants in insectaries. Members of the phylum Ciliophora are usually found in the larval and adult stages of dipterans. The two most common genera are Lambornella and Tetrahymena. The phylum Rhizopoda includes the amoebas such as Malameba locustae (King and Taylor) – infecting acridids, and Malpighamoeba mellificae Prell (Amoebida: Amoebidae) – infecting honeybees. The phylum Microsporidia have traditionally been considered to be primitive protozoa. However, recent evidence indicates that they are actually highly evolved intracellular fungi (Keeling and Fast, 2002). Nonetheless, we discuss them here as protozoa, largely because they are traditionally handled with this phylogenetically diverse group of microorganisms. The microsporidia is a large group (approximately 1000 species) of obligate intracellular pathogens affecting a

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variety of vertebrates and invertebrates. Approximately 600 species have been reported as infecting insects. Insects in virtually all orders possess members in which microsporidial infections have been documented. Some species of entomopathogenic microsporidia possess a narrow host range (e.g. one host species), whereas other species possess a wide host range, which includes vertebrates. Protozoa are of concern as pathogens of IBCAs; they primarily incite sublethal, debilitating disease, although acute disease may occur in some instances and they are common in mass-reared IBCAs. The most common entomopathogenic genus is Nosema, which has more than 150 described species reported from at least 12 insect orders. For instance, N. muscidifuracis Becnel and Geden (Microsporidia: Nosematidae) is prevalent in mass-reared Muscidifurax used for fly control (Geden et al., 1995). Infected parasitoids have an extended developmental time, are shorter lived and have a much reduced fecundity. Microsporidians have been found in many genera of beneficial insects, including Coccinella, Cotesia, Encarsia, and Phytoseiulus (Bjørnson and Shütte, 2003), Metaseiulus (Olsen and Hoy, 2002) and Tachinaephagus (Ferreira del Almeida et al., 2002). Recently, a microsporidian was found to be responsible for the decline of the weed biological control weevils, Neochetina eichorniae Warner and N. bruchi Hustache (Coleoptera: Erirhinidae), originally introduced from South America and mass produced in Florida for control of water hyacinth (ARS, 2004). This microsporidian was found to decrease survival rates of the weevils by 30%, and their reproductive capacity by 60 to 70%. The original source of this contaminant within the rearing facility is not known. Nematodes Thirty families of nematodes within six orders are associated with insects. The most common association between nematodes and insects is apparently commensalistic. Such nematodes can be found

externally on the exoskeleton or internally in the reproductive, respiratory, digestive or excretory systems, or within the haemocoel, where they subsist causing very little or no apparent damage to their host. Some of these commensal nematodes are phoretic, utilizing insects for dispersal. Other nematodes, including free-living nematodes, are saprotrophs, and may utilize insect cadavers merely as a nutrient source. Many nematodes are animal and plant parasites that use the insects as vectors. Examples include those nematodes responsible for onchocerciasis, eyeworm and elephantiasis in humans, and for canine heartworm, while plant-parasitic nematodes vectored by insects include those responsible for pine wilt and red ring disease of coconut. The most commonly encountered entomopathogenic nematodes are included in three major families, the Mermithidae, Steinernematidae and Heterorhabditidae. All are obligate insect pathogens and gain entry through the cuticle, spiracles, mouth, anus or via mechanical injury. Although steinernematid and heterorhabditid nematodes are generally nonspecific insect pathogens, natural epizootics caused by entomopathogenic nematodes are relatively rare in nature, and nematodes are not commonly encountered in rearing settings. In addition, infections in field populations of beneficial insects such as predators and parasitoids are rare, even after inundative nematode applications (Akhurst, 1990).

Invertebrates Invertebrates are almost always associated in one way or another with other insects. Sweep samples from field collections are an attestation to the biodiversity of insects within ecosystems. Insects can harbour commensals such as phoretic mites; pseudoscorpions and the like; ectoparasites such as parasitic mites; and endoparasitoids such as tachinid flies, or endohyperparasitoids such as braconid wasps. Insect–insect associations can be mutualis-

Methods for Assessment of Contaminants of Invertebrate BCAs

tic, such as the classic relationship between aphids and some ants. Hyperparasitoids often occur in IBCAs. For instance, Mesochorus curvulus Thompson (Hymenoptera: Ichneumonidae) is a hyperparasitoid of Peristenus spp. (Hymenoptera: Braconidae), a biological control agent of European lygus bugs, Lygus rugulipennis Poppius and L. pratensis Linnaeus (Hemiptera: Miridae) (Day, 2002); various aphidiine braconids and aphelinids in the genera Aphelinus, Aphidius, Ephedrus, Lysiphlebus and Trioxys are parasitized by a variety of hyperparasitoids; many Encarsia species are facultative hyperparasitoids of other primary parasitoids; and convergent ladybird beetles field-collected in California and exported as IBCAs may be parasitized by Dinocampus spp. (Hymenoptera: Braconidae). IBCAs can be contaminated with species of similar appearance or a species can be shipped in error. For instance, predatory mite species in the genus Amblyseius (Acari: Phytoseiidae) look very much alike, even to taxonomic specialists. European species of Orius are very similar to species such as Orius insidiosus (Say) (Hemiptera: Anthocoridae), and could easily be shipped mistakenly to an importer who might not recognize the error. Whenever any host plant material has to be shipped with insects, it can be difficult to find and exclude eggs or young instars of predators that are small, inconspicuous and/or hidden in leaf folds, under veins or in debris. In shipments of aphids, such problem predators have included syrphids and cecidomyiids, in particular. Another problem that may occur is hitch-hiking facultatively polyphagous or saprophytic mites on field-collected host material, sometimes phoretic on the target insects. It can be very difficult to exclude all of these from every shipment. Such arthropods have caused problems from time to time in the quarantine cultures of olive fruit flies and artificial diets for other insects at the USDA European Biological Control Laboratory in Montpellier, France. Finally, the species used as a host in the mass production of the IBCA can be a con-

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taminant, especially if it is a species that is not the same target host species, or if it significantly increases the pest population in the area of introduction. For example, whitefly puparia can accompany shipments of Encarsia puparia. In addition, other incidental species could co-occur with the IBCA. For instance, a number of soil-dwelling mites could co-occur in shipments of the predatory soil mites, Hypoaspis spp.

Abiotic contaminants Inanimate compounds or agents that are either detrimental to the efficacy of the agent or to the safety of the user or environment of introduction could conceivably accompany biological control agents. The list of possible inanimate contaminants is limitless. Unless intentionally introduced, existence of contaminants that may harm the biological agent itself are more probable than those that may harm the environment or user. Such compounds could include chemically contaminated packaging material, inappropriate substrates that harbour the insects, pesticide residues, etc. Inanimate contaminants that harm the environment or user are more difficult to contemplate. However, some possible contaminants in this category could include toxic compounds used to rear or decontaminate the insects from microorganisms. For example, fumigation of leafcutting bee cocoons with paraformaldehyde is carried out prior to export, in order to decontaminate the cells of spores of Ascosphaera aggregata Skou (Ascomycota: Ascosphaeraceae). Improper aeration after fumigation could result in the build-up of potentially dangerous levels of formalin gases within the shipping containers, which in turn could potentially be harmful to the recipient of the shipment.

Diagnostic and Detection Techniques The ability to detect and intercept the importation or transfer of contaminants

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associated with IBCAs will very much depend on the diagnostic and detection techniques available for the specific contaminants in question. Here, we provide an overview of the techniques that are presently available for the detection of such contaminants.

Microorganisms Many commercialized techniques have been developed for quick diagnosis of certain microorganisms, mostly for rapid and routine diagnosis for presence of pathogens of human concern. They can be divided into methods involving: microscopy; classical microbiology; physiological characters; protein detection and characterization; and nucleic acid detection and characterization. These methods can be applied in vitro (if a microorganism is culturable) and/or in vivo. Since many of the microorganisms of concern incite disease, diagnostic methods involved with pathogenesis have been developed. They are: (i) differential diagnosis, where signs and symptoms and postmortem changes are compared in a systemic manner between different diseases to distinguish one disease from another; (ii) preliminary diagnosis, which is the first cursory examination of a diseased insect; (iii) tentative diagnosis, which is made after general macroscopic and microscopic examination and some cursory laboratory tests; and (iv) definitive diagnosis, in which a final conclusion is made, and the disease-causing organism is identified. Facts to be collected on which to base the definitive diagnosis include: (i) history of disease; (ii) physical examination; and (iii) ancillary examination. It is also important to stress that quantitative assessments (i.e. how many microorganisms are present) are critical in many risk-assessment schemes. Common symptoms of the presence of pathogens within IBCAs include sluggishness, reduced or cessation of feeding, colour change and reduced fecundity. Unfortunately, many of these symptoms are also associated with the moulting process,

and moulting insects are often erroneously suspected of being diseased. Insects suspected of being diseased should first be examined externally, followed by dissection and macroscopic examination of the internal organs and tissues, at first with the naked eye, and thence with the aid of a stereomicroscope. Although this can provide valuable information on the identity of the pathogen, it is usually insufficient to make a conclusive identification. Detailed observations involving microscopic examination of suspect tissues are usually required. Non-destructive diagnosis can sometimes be made by examination of the haemolymph, faecal pellets or meconium. Microscopic examinations are made using light or electron microscopy. The first procedure usually entails use of a wetmount, wherein the whole insect, or specific tissues, are gently crushed in a drop of water between the microscope slide and cover slip. Some entomopathogens can be easily observed at the light microscope level using many of the light microscopy techniques available. In some instances, tissues must be prepared for histological examination using standard sectioning and staining techniques for light and electron microscopy. While nematodes, fungi, bacteria and protozoa can be observed readily at the light microscopy level with phase contrast without the use of stains, use of differential stains can greatly enhance visualization of these, as well as other, pathogens. For more information on histological techniques used to diagnose entomopathogens, the reader is referred to Becnel (1997) and Evans and Shapiro (1997). The reader is referred to Lacey and Brooks (1997) for a key to the major groups of entomopathogens. Molecular methods for detecting the presence of microorganisms have advanced tremendously in recent years and are routinely being used to detect microorganisms in the agri-food, veterinary and medical sciences and related industries. The two primary strategies use either immunological or nucleic acid-based methodologies. Immunological detection of insect

Methods for Assessment of Contaminants of Invertebrate BCAs

pathogens relies on the application of mono- or polyclonal antibodies to antigens of the microorganism produced in mammals or birds. Methods such as enzymelinked immunosorbent assay (ELISA) are frequently used. In ELISA, the breakdown of a substrate bound to the antibody by an enzyme causes a colour change, indicating the presence of the antigen. One of the major problems with immunological methods is poor sensitivity to low amounts of antigens. Nucleic acid-based methods for microorganism detection can be much more sensitive. Initially, hybridization methods, including Northern (i.e. RNA) and Southern (i.e. DNA) blots were used to qualitatively detect insect pathogens (St Leger and Joshi, 1997, and references therein). Another, more powerful, method that relies on hybridization is fluorescence in situ hybridization (FISH). The most powerful and widely adopted method uses the polymerase chain reaction (PCR). PCR multiplies specific regions of nucleic acid. Following the amplification, nucleic acid specific to the microorganism can be detected by electrophoresis with or without hybridization. The use of nested PCR is often necessary to obtain adequate levels of sensitivity while providing specific amplification. Although PCR can be exceptionally sensitive and specific, it must be stressed that extreme care must be taken in developing PCR methods. The inclusion of an internal amplification control (IAC) is becoming mandatory for diagnostic PCR; an IAC is a non-target nucleic acid sequence present in the same sample reaction tube, which is coamplified simultaneously with the target sequence. If an IAC is not included, it is unknown whether a negative response represents a true or false negative (i.e. the reaction could be inhibited due to malfunction of the thermal cycler, incorrect PCR mixture, poor polymerase activity and/or the presence of inhibitory materials). Recently, the application of real-time quantitative PCR (RTQPCR), in which the amplification process is monitored in real time, has made it is possible to estimate the initial quantity of a

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specific template (e.g. a gene). This can then be extrapolated to numbers of pathogens (e.g. virions) present. The reader is referred to reviews of Innis et al. (1990), Persing (1996) and Caetano-Anollés and Gresshoff (1997) for more information on PCR-based detection of pathogens. In the following sections, we briefly outline the techniques presently available for detection of specific groups of microorganisms from invertebrates. For detailed procedures, the readers are referred to Poinar and Thomas (1984), Lacey (1997) and Inglis and Sikorowski (2005a,b), and references therein. Viruses All viruses are obligate parasites and they are typically detected and/or quantified in situ or following extraction of virions from insect tissues. The application of molecular detection methods targeting viral proteins or nucleic acids is now commonplace. However, microscopic examination using light or electron microscopy can still provide valuable information on the aetiology of viral diseases. Light microscopy can be used to visualize occlusion bodies, but is limited to observing tissue abnormalities (e.g. hypertrophy of the midgut epithelium) for viral diseases in which occlusion bodies are not produced. More specific protocols for virus diagnosis and identification can be found in Adams and Bonami (1991b), Tompkins (1991), Evans and Shapiro (1997) and Inglis and Sikorowski (2005a). Bacteria Bacteria associated with arthropods are primarily saprotrophs (including facultative pathogens), but some are obligate parasites. Detection and quantification of saprotrophic bacteria is often accomplished by isolation of cells using selective or nonselective media. For qualitative assessments of bacteria, insects are typically homogenized and the homogenate plated on an appropriate agar medium. Individual colonies are then subcultured to ensure

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purity. Once in pure culture, bacteria are typically identified based on morphological (e.g. cell shape and cell wall structure), physiological (e.g. assimilation of carbohydrates) and/or molecular (e.g. 16S rDNA sequence) characters. Quantitative assessments of cell densities using microbiological methods typically involve the use of the dilution spread-plate or most probable number methods. Detection of intracellular obligate parasites can typically be made only by using molecular techniques. This usually involves the visualization of cells of a particular taxon by microscopy using in situ hybridization, or the application of conventional PCR-based detection methods. For conventional PCR, taxon-specific primers (i.e. short segments of DNA that anneal to complementary sequences in the target nucleic acid) using universal genes (e.g. 16S rRNA genes), or specific to genes unique to the taxon of interest, are used. For example, to detect Wolbachia infections, primers specific to genes encoding proteins on the surface of the cell wall are used (Zhou et al., 1998; Stouthamer et al., 1999; Kyei-Poku et al., 2003). The use of real-time quantitative PCR is becoming more popular in detection of bacteria in situ; this method also allows for the quantification of fastidious bacteria and obligate parasites. More information on entomopathogenic bacteria and their detection can be found in Tanada and Kaya (1993), Klein (1997), Thiery and Frachon (1997), Charles et al. (2000) and Inglis and Sikorowski (2005a). Fungi Entomopathogenic fungi are most easily diagnosed on insect cadavers. If an entomopathogenic fungus is suspected, and no outward growth of mycelia is visible on the cadaver, placement of the cadaver in a sterile humid chamber will induce outward growth of mycelia and production of conidia on the cadaver surface. The fungus can then be isolated into pure culture by aseptically transferring the conidia or hyphae to an appropriate agar medium. Care must be taken to ensure that the cadaver is not

incubated for more than several days, as eventually saprotrophic fungi will overcome any insect cadaver placed under humid conditions. However, if an entomopathogen was the cause of death, it will usually surface on the cadaver before saprotrophic fungi do. Some entomophthoralean fungi do not readily grow in culture, and therefore identification must be made from material obtained directly from the cadaver. If diagnosis is necessary prior to death, the insect can be sacrificed and a wet mount of the haemocoel can be examined for the presence of hyphal bodies (sometimes termed ‘blastospores’), which are essentially short fragments of mycelium. The appearance and size of the hyphal bodies may provide some evidence as to the type of fungus involved; however, positive identification to the genus level is usually not possible. Many of the fungi associated with arthropods are saprotrophic, and thus they can be cultured on microbiological media. As with the bacteria, fungi are typically isolated on selective or non-selective media. Identifications of filamentous fungi (i.e. fungi producing hyphae) are primarily based on sporogenesis. In contrast, the identification of yeasts primarily relies on physiological characters. However, molecular methods are becoming more widely used to characterize fungi. The most commonly used genes are the 18S and 26S rRNA genes and regions between them. Accurate quantification of filamentous fungi using microbiological media is problematic given their growth form. For example, many fungi are r-selection organisms producing copious quantities of asexual spores. Each propagule is capable of producing a colony on an agar medium, and therefore microbiological quantification methods grossly overestimate biomass of such fungi. Some fungi associated with arthropods (e.g. many entomophthoralean and all laboulbinalean fungi) cannot be cultured on agar media. Therefore, they must be allowed to sporulate directly on the cadaver before they can be identified microscopically.

Methods for Assessment of Contaminants of Invertebrate BCAs

For more information on entomopathogenic fungi and their diagnosis, readers are referred to Poinar and Thomas (1984), Samson et al. (1988), Goettel and Inglis (1997), Humber (1997), Lacey and Brooks (1997), Papierok and Hajek (1997), Butt et al. (2001) and Inglis and Sikorowski (2005a). Protozoa Characterization of entomopathogenic protozoa is difficult and currently relies almost exclusively on microscopic characters. However, detection of spores within the insect is relatively easy. It is often possible to make preliminary diagnoses through direct observation of the squashed cadaver in wet mounts, where large numbers of spores are usually visible. Spores can readily be detected in smears using Giemsa, Trichome or Buffalo Black stains with bright-field microscopy, or using calcofluor with fluorescence microscopy (Vavra and Chalupsky, 1982; Didier et al., 1994; Inglis and Sikorowski, 2005a). In addition, there have been efforts to develop indirect antibody detection of microsporidia (Didier et al., 1994; Green et al., 2000). Development of molecular characters to detect entomopathogenic protozoa in situ would simplify diagnostics; however, this area of investigation is still in its infancy. Primers to detect specific human-pathogenic enteric microsporidia either in water or in faecal samples have been developed (Muller et al., 2001; Dowd et al., 2003). However, the tremendous diversity of entomopathogenic microsporidia and the lack of economic incentive to develop primers have hindered the development and application of PCR-based detection methods for entomopathogenic taxa. One example where PCR has been applied for detection of an entomopathogenic microsporidian is Thelohania solenopsae Knell, Allen and Hazard (Microsporidia: Thelohaniidae) infections of red imported fire ants, Solenopsis invicta Buren (Hymenoptera: Formicidae) (Valles et al., 2002; Milks et al., 2004). As with other taxa of

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microsporidia (Weiss and Vossbrinck, 1999), the most likely target for primers to detect entomopathogenic microsporidia is the universal 18S rRNA gene. Primers to other genes have been used to detect human-pathogenic taxa, and these may prove useful in detecting some entomopathogenic microsporidia as well (Weiss and Vossbrinck, 1999). Keys to entomopathogenic protozoa and more details on classical diagnosis of infected insects can be obtained by consulting Brooks (1988), Undeen and Vávra (1997), Becnel and Andreadis (1999), Solter and Becnel (2000) and Inglis and Sikorowski (2005a). Nematodes Nematodes can be easily visualized under magnifications of 10 to 100 ⫻ under a stereomicroscope. In some cases, the nematodes can be seen within the body of the intact insect. The insect can be dissected to liberate the nematodes, but identification of most species of entomopathogenic nematodes requires the adult stage. However, the stages that are present within, or emerge from, the host are usually not the adult stage and must be held under appropriate conditions until they mature. The reader is referred to Gaugler and Kaya (1990) and Kaya and Stock (1997) for more details and for a key to entomopathogenic nematodes.

Invertebrates Most invertebrate contaminants such as ectoparasitoids, commensals and incidentals are visible to the naked eye. Consequently, close examination of the shipment or anaesthetized biological control agents with the aid of a hand lens would reveal the presence of such invertebrates. Early stages of parasitism are very difficult to detect and may be facilitated by molecular methods. For instance, Ratcliffe et al. (2002) used PCR to detect the presence of early-stage parasitoids in fly pupae. On the other hand, it is possible to detect

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later stages through dissection and examination, by the naked eye or under magnification. However, it is difficult to observe hyperparasitoids through dissection, as their larvae are inside their primary parasitoid, which is inside the primary host. Another method would be to rear the insects through a generation, as virtually all invertebrate parasites will have completed their life cycle and emerged as adults within the lifespan of their host. For instance, European Peristenus species are obtained as cocoons that have emerged from parasitized, field-collected lygus nymphs and these are shipped to North America. The cocoons usually require overwintering in quarantine in North America before emerging the following spring. The hyperparasitoid Mesochorus is screened out at emergence from the Peristenus. Recently, Ashfaq et al. (2005) developed PCR primers which were used successfully to detect Mesochorus spp. within Peristenus within the lygus primary hosts. Proper recognition and identification of the IBCA is necessary to prevent accidental introduction of similar-appearing species. Voucher specimens can be sent to specialists for taxonomic verification (see Stouthamer, Chapter 11, this volume). Observations on behaviour and life history attributes can also often signal the possibility that one is dealing with a contaminating species.

sible contamination of IBCAs by abiotic contaminants. Such contamination would be very difficult to predict a priori.

Defining the Risk Posed by IBCA Contaminants In risk assessment, risk is usually defined as ‘hazard ⫻ probability’ (Zadoks, 1998). A hazard is any imaginable adverse effect that can be identified. Once a hazard has been identified, it is then necessary to assign a probability or likelihood of occurrence. With biological entities such as microorganisms, hazards typically remain imprecise. Furthermore, assigning a probability that the hazard will occur to beneficial organisms is difficult. As a result, decisions regarding risks associated with biological organisms are rarely based purely on scientific data. Nevertheless, relatively strict approval standards are currently imposed on the application of microorganisms (e.g. plant protection products) in many jurisdictions throughout the world, and guidelines for commerce in IBCAs are being considered for implementation. Therefore, one way or another, risk assessments must be made.

Microorganisms Pathogens of invertebrates or plants

Abiotic contaminants As mentioned above, the types or nature of conceivable inanimate contaminants that could potentially affect the agent’s efficacy or harm the environment of introduction are virtually limitless, especially if the contamination is intentional. Detection of abiotic agents, such as toxins and poisons, that may affect an agent’s efficacy is difficult; however, these often can be narrowed down in many cases to suspected sources of contamination (e.g. fumigation at point of arrival, etc.). It is beyond the scope of this chapter to cover the methods that would be required to determine the pos-

Many microorganisms have been developed as commercial microbial control agents, or have been used in the classical biological control of pest insects, with no or minimal impact on biodiversity or environmental health (Laird et al., 1990; Goettel and Hajek, 2001; Goettel et al., 2001; Hokkanen and Hajek, 2003). There are also numerous examples of how entomopathogens can be used safely in conjunction with IBCAs (e.g. Laird et al., 1990; Hokkanen and Hajek, 2003). Microorganisms pathogenic to IBCAs are a primary concern as far as the efficacy of the IBCAs themselves is concerned. Disease incited by pathogens is often detri-

Methods for Assessment of Contaminants of Invertebrate BCAs

mental to the host, resulting in reduced longevity and death. Pathogenic microorganisms are divided into obligate or facultative pathogens. As a general rule, obligate invertebrate pathogens possess narrow host ranges, whereas facultative pathogens infrequently incite disease in vertebrate hosts. Furthermore, facultative pathogens are ubiquitous, whereas obligate pathogens are typically intimately associated with their hosts. In addition to direct effects on IBCAs, the possibility of transmission to other invertebrates exists for pathogens possessing wide host ranges. IBCAs can conceivably be contaminated with plant pathogens, especially if host plant material is transported along with the IBCA. For instance, concerns have been raised regarding the possibility of Macrolophus caliginosus Wagner (Hemiptera: Miridae) transmitting pepino mosaic virus to tomatoes (Bolckmans, 2003). The risk is dramatically greater if the contaminating microorganism is exotic (i.e. it does not already occur in the area of introduction) rather than indigenous (i.e. it is already present in the area of introduction), and it is capable of establishing itself in the area of introduction. Certainly, exotic pathogens as contaminants of IBCAs are a potential hazard, and therefore pose a higher risk and should be avoided. The difficulty lies in assessing the probability of their occurrence and of quantifying the hazard they represent. The reader is referred to Cook et al. (1996), Goettel and Hajek (2001) and Hajek et al. (2003) for further discussions on the potential risks of the introduction of exotic pathogens. Pathogens of vertebrates Insects reared in captivity frequently possess a bacterial microflora more typical of that found associated with humans. Since humans typically carry human-pathogenic bacteria within their gastro-intestinal tracts, respiratory organs, skin or hair, it is not surprising that insects reared in captivity also carry human-pathogenic microorganisms. Human pathogens are classified

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into four categories based on the threat they represent to human and animal health (Health Canada, 2004). These include: (i) level 2 pathogens, which represent a moderate individual risk and limited community risk; (ii) level 3 pathogens, which represent a high individual risk but a low community risk; and (iii) level 4 pathogens, which represent a high individual risk and a high community risk. Of the human pathogens, level 2 microorganisms are most commonly found associated with invertebrates. These include representatives of bacteria, fungi, protozoa, nematodes and other parasitic microorganisms. Examples of level 2 pathogens sometimes found in association with invertebrates include: Aspergillus spp., Bacillus cereus Frankland and Frankland (Bacillales: Bacillaceae), Clostridium spp., Cryptococcus spp., Enterobacter spp., Lactobacillus spp., Micrococcus spp., Pseudomonas spp., Salmonella spp., Serratia spp., Staphylococcus aureus Rosenbach (Bacillales: Staphylococcaceae), Streptococcus spp., and Yersinia spp. Although they are capable of inciting disease in animals, level 2 pathogens are unlikely to be a serious hazard to healthy humans, the community, livestock or the environment (Health Canada, 2004). In healthy animals, exposure levels required to incite infection are typically high. Furthermore, effective treatment and preventive measures are available and the risk of spread is limited. Therefore, the risk posed by level 2 pathogens associated with IBCAs is very low. For instance, some entomopathogenic microsporidia can infect vertebrates (e.g. a Nosema species that infects mosquitoes also infects the cooler body parts of mice such as the tail, ears and feet), but the zoonotic risk of insectpathogenic microsporidia is considered minimal at present. Most fungi associated with reared insects originate from decomposing vegetation. Some are human pathogens, and their proliferation on organic matter (e.g. artificial diets) and subsequent liberation of large numbers of propagules can impact negatively on the health of insectary work-

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ers. Fungi such as Aspergillus, Penicillium, Rhizopus and a variety of yeast and yeastlike organisms can colonize insect diets and may be hazardous to employees. Such fungi may be capable of infecting humans directly, they may produce secondary metabolites which can be toxic to humans if they are ingested, or they can act as allergens. Inhalation of airborne fungal propagules can cause allergic rhinitis or sinusitis, hypersensitivity pneumonitis due to sensitization to fungal spores, and/or organic dust syndrome caused by inhalation of large quantities of toxin-containing microbial particles. Although such problems may be apparent in insectaries, they should normally not pose a problem as far as contaminants of IBCAs are concerned. Unless contaminated diet is present with the IBCA, quantities of fungal propagules carried on the external exoskeletons of insects or in their alimentary canal would typically be small. In some instances, insects that come in contact with faeces from humans or livestock may be contaminated with more serious pathogens (e.g. verotoxigenic Escherichia coli (Migula) Castellani and Chalmers (Enterobacteriales: Enterobacteriaceae) or Campylobacter jejuni (Jones et al.) Véron and Chatelain (Campylobacterales: Campylobacteraceae). Such pathogens would be more prevalent in feral insects, but normally this would not occur in reared insects. Competitive displacement Evidence indicates that introduction of most microorganisms into an ecosystem (e.g. soil) has only a transient effect on the indigenous microflora (Alabouvette and Steinberg, 1998). The most common microorganisms associated with mass-rearing of insects (see above) are ubiquitous and would not normally pose a hazard to the microbial flora if conveyed with the IBCA. However, concerns have been raised regarding possible displacement of indigenous obligate insect pathogens. For instance, Lockwood (1993) suggested that the introduction of an exotic obligate fungal

pathogen of grasshoppers, Entomophaga praxibuli Humber, Milner and Soper (Entomophthorales: Entomophthoraceae), in a classical biological control programme for control of native grasshoppers in North America, might competitively displace or even cause extinction of the native Entomophaga grasshopper pathogens. However, in reality, infection levels were low and declining, suggesting that the pathogen had little chance of establishing itself (Bidochka et al., 1996). To date, there is no evidence of displacement of an indigenous pathogen due to introduction of a microorganism for classical biological control. The advent of molecular diagnostic techniques that enable one to track particular genotypes of a pathogen provides an opportunity to conduct more detailed studies on the potential of competitive displacement of native entomopathogenic microorganisms by non-indigenous ones.

Invertebrates Throughout history, many invertebrates have been either intentionally or unintentionally introduced into new ecosystems, where they have caused detrimental effects or become serious pests (Pimentel, 2002). Furthermore, most predators and parasitoids have the potential to seriously affect the efficacy of the IBCA in question. Consequently, every effort must be made to avoid the presence of unknown invertebrate contaminants in shipments of IBCAs.

Abiotic contaminants As mentioned above, contamination due to abiotic elements would be very difficult to predict a priori. Unless intentional, it is difficult to conceive that such contaminants would pose a hazard beyond that of the user or immediate vicinity of use. Details on source and treatment of the IBCAs prior to shipment would aid in the identification for possible presence of contaminants. By and large, abiotic contaminants should pose a minimum risk.

Methods for Assessment of Contaminants of Invertebrate BCAs

Guidelines for Assessing the Risk Although risk cannot be scientifically defined, standards based on the precautionary principle and familiarity are typically relied upon. However, application of such standards may not be relevant to contaminants associated with IBCAs. The amount of effort used to detect potential contaminants should be in direct proportion to the risks they pose to the user, to the environment and to the IBCA itself. A number of recommendations regarding risk assessment of microorganisms were agreed upon by participants of the ‘Microbiological Plant Protection Products Workshop on the Scientific Basis for Risk Assessment’, held in Stockholm, Sweden (Anon, 1998). One of the six points agreed upon is directly relevant to contaminants of IBCAs. Within the production control heading, it was indicated that the ‘level of acceptable contaminants should be judged from a “risk acceptance” point of view’. The goal is to define ‘risk acceptance’ with respect to contaminants associated with IBCAs. Guidelines for regulation of IBCAs must be addressed and implemented relative to the commerce of other commodities, including invertebrates. For instance, currently there are no regulations for the importation of many invertebrates such as all species of aquatic snails, leeches, scorpions, spiders, the German cockroach, the Russian cockroach and Drosophila melanogaster Meigen (Diptera: Drosophilidae) into many countries such as Canada. As far as amphibians and reptiles are concerned, the present Canadian Food Inspection Agency (CFIA) policy reads ‘Please be advised that amphibians and reptiles (excluding turtles and tortoises) are no longer regulated under the Health of Animals Regulations and as a result, no CFIA import permit is required, nor a health certificate and no inspection will normally be done at the border. Imports are permitted from any country, for any use, to any destination in Canada’ (CFIA, 2004). Even in countries with very strict quarantine standards, such as

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Australia (AQIS, 2004), quarantine standards for importation of living organisms are generally specific to recognized pathogens, and do not encompass nonpathogenic contaminants. In considering risk acceptance of contaminants in IBCAs, the following points should be considered. ● IBCAs used are diverse and their production involves substantially different methods. ● Microbial contaminants associated with insects are diverse and, in many instances, their biology is poorly understood. ● The risk of introduction of an obligate pathogen of an IBCA is higher if the IBCA is field-collected than if it was laboratory-reared. ● There are currently no quality control (QC) standards for contaminants associated with IBCAs. ● Given the diversity of contaminants encountered, logistics of testing are difficult. ● Where testing of IBCA for contaminants is applied, the methods and comprehensiveness of testing vary tremendously. ● Contaminants associated with the IBCA typically occur in relatively small numbers. ● Epizootics of disease in an insect population are dependent on more than simply dose. ● There is global commerce in plants and animals, yet for the most part, no or minimal standards exist for contaminants associated with these entities. ● There is global movement of people with no standards applied as far as human pathogens or commensal microorganisms are concerned.

Recommendations We consider that routine contamination by incidental or commensal microorganisms is to be expected and no additional precautions are needed. Although such organisms may not necessarily be

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‘wanted’, they are inevitable and in most instances should pose a minimal risk. Such organisms would normally not be very different to those that are found in numerous commodities that are exported in large quantities around the world. In addition, abiotic contaminants should also be of minimal risk and should not normally warrant special consideration. Exceptions would be in situations where there is fear of deliberate sabotage (e.g. bioterrorism). The contaminants which warrant consideration are biotic agents that pose a threat to the IBCAs themselves, or to the ecosystem of introduction. We consider two factors that could affect the risk posed by a contaminant in an IBCA that could be used when assessing risk. These are: (i) whether the IBCA is field-collected or insectary-reared; and (ii) whether the insect is exotic, being introduced primarily for classical biological control or is indigenous and used primarily for inundative biological control. Risk of presence of pathogens within commercially produced IBCAs should presumably be low, as good QC and pathogen management should be an integral part of mass rearing (Inglis and Sikorowski, 2005a). In contrast, use of feral insects provides an increased risk, as it is difficult to predict or detect the presence of pathogens or parasitoids in feral populations. Although no recognized standards exist to date, there are attempts to adopt international QC standards for massproduced IBCAs (van Lenteren, 2003). Adoption of such QC standards will facilitate the detection of contaminants associated with mass-reared IBCAs that may have a detrimental impact on their efficacy, especially if sound detection methodologies for microorganisms are applied (Inglis and Sikorowski, 2005a,b). As part of QC, natural enemies of the IBCAs should be routinely screened for in commercial rearing insectaries. The impacts on success of the IBCA, and therefore of customer satisfaction, are such that the IBCA producers and

exporters must be very vigilant regarding contaminants that affect efficacy of their product. Furthermore, many massproduced IBCAs destined for export are relatively cosmopolitan species for use against cosmopolitan pests. Therefore we consider that minimal risk is posed by contaminants of mass-produced IBCAs that are established in the area of use and are to be used inundatively. It is expected that there is minimum concern regarding contaminants if the IBCAs come from a well-established and reputable rearing source. However, we recommend that the principles established for importation of most commodities such as many foodstuffs, plants, vegetables, fruits, etc. be adopted for reared IBCAs (i.e. mandatory documentation of the QC status of IBCAs with regard to contaminants). This would eliminate the need to scrutinize every shipment. With respect to contaminants and risk, the key factors to consider are: (i) correct identity of the IBCAs; (ii) QC data on natural enemies of the IBCAs (e.g. pathogenic microorganisms and parasitoids); and (iii) information on the rearing systems used (e.g. source of insects, quarantine status, rearing systems, etc.), which would be important in gleaning information on the potential for contamination (e.g. plant material used to rear IBCAs that may be potentially contaminated by phytopathogens). Field-collected IBCAs, in contrast to insectary-reared IBCAs where appropriate QC standards have been applied, have a much higher potential for harbouring unknown parasitoids, pathogens or other contaminants. There is also a much greater potential that these may include misidentified contaminants (i.e. similarappearing species). Certainly, every effort must be made to prevent introduction of contaminants that could affect the IBCA itself, or that could become established and become a pest per se. Consequently, unless already well established in the area of introduction, field-collected IBCAs warrant much stricter scrutiny for contaminants. It is standard practice in many

Methods for Assessment of Contaminants of Invertebrate BCAs

countries that such agents be held in an approved containment or quarantine facility (e.g. ARS, 1991) prior to release, and we recommend that this practice be adopted for most field-collected IBCAs being introduced for the first time to an eco-region. Ideally, such insects should be kept for at least one generation under quarantine. This would allow detection and elimination of any parasitoids and/or pathogens that may have been included with the imported insects. For insects that can not be reared or for which there are limited numbers, representative samples plus suspect individuals can be sacrificed for a contaminant (i.e. pathogen) check (see Inglis and Sikorowski, 2005a for the strategies used to detect and eliminate entomopathogens from insectary-reared IBCAs). The numbers used would depend upon their relative risk of harbouring pathogens or parasites. Such information may be obtained from monitoring the parent feral populations. Strict QC and monitoring of viability will facilitate the elimination of entomopathogens. However, the detection of entomopathogens inciting disease in IBCAs as part of QC protocol is often overlooked. Furthermore, the detection and assessment of the risk represented by a contaminant requires considerable expertise. We recommend that insect-rearing personnel obtain the appropriate training in the methodologies used for diagnosis, and to assess the potential risk posed by contaminates, or alternatively, to obtain assistance from specialists. This is not only essential in QC of IBCAs to be used in biological control programmes, but is a prerequisite for applying strategies within rearing settings for management of entomopathogens and disease.

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commodity final destination, and, if warranted, ensure that such harm does not take place. The extent to which measures for prevention of transfer of contaminants are implemented must be weighed in relation to the present transfer of unknown or unwanted substances by other means. For example, presently there are no regulations for the importation of many invertebrates. Consequently, one must compare the possibility of introduction of contaminants via IBCAs with other methods (i.e. transportation of people, forestry and agricultural products, etc.). Certainly, regulations regarding importation of invertebrates to be used in biological control must not be more stringent than those for other organisms, as far as most contaminants are concerned. Exceptions may be those substances, more specifically microbial and other living organisms that may be detrimental to the environment of introduction, especially those that could become established. We have identified two major points that need to be considered in assessing potential risk: (i) whether IBCAs are fieldcollected or mass-reared in an insectary; and (ii) whether they are indigenous and destined for use primarily in inundative biological control, or whether they are exotic and destined for use primarily in classical biological control. As a minimum, it is evident that QC procedures for commercialized IBCAs should include monitoring for entomopathogens. Fieldcollected IBCAs destined for use in classical biological control warrant a higher degree of scrutiny.

Acknowledgements Conclusions The key to regulation of IBCAs is to address the extent of the possibility that a contaminant could pose a hazard to the commodity, or to the environment of the

We wish to thank the following for their help in providing information and suggestions in completing this chapter: James Becnel, Dave Gillespie, Kim Hoelmer, Jeff Littlefield, Charles Pickett and Charles Vossbrink.

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Inglis, G.D. and Sikorowski, P.P. (2005a) Entomopathogens and their impact on insect rearing. In: Schneider, J.C. (ed.) Principles and Procedures for Rearing Quality Insects. Mississippi State University, Massachusetts (in press). Inglis, G.D. and Sikorowski, P.P. (2005b) Microbial contaminants and their impact on insect rearing. In: Schneider, J.C. (ed.) Principles and Procedures for Rearing Quality Insects. Mississippi State University, Massachusetts (in press). Innis, M.A., Gelfand, D.H., Sninski, J.J. and White, T.J. (1990) PCR Protocols: A Guide to Methods and Applications. Academic Press, San Diego, California. Kaya, H.K. and Stock, S.P. (1997) Techniques in insect nematology. In: Lacey, L.A. (ed.) Manual of Techniques in Insect Pathology. Academic Press, San Diego, California, pp. 281–324. Keeling, P.J. and Fast, N.M. (2002) Microsporidia: biology and evolution of highly reduced intracellular parasites. Annual Review of Microbiology 56, 93–116. Klein, M.G. (1997) Bacteria of soil-inhabiting insects. In: Lacey, L.A. (ed.) Manual of Techniques in Insect Pathology. Academic Press, San Diego, California, pp. 101–116. Kyei-Poku, G., Benkel, B., Goettel, M.S. and Floate, K. (2003) Elimination of Wolbachia from Urolepis rufipes (Ashmead) (Hymenoptera: Pteromalidae) with heat and antibiotic treatments: implications for host reproduction. Biocontrol Science and Technology 13, 341–354. Lacey, L.A. (1997) Manual of Techniques in Insect Pathology. Academic Press, San Diego, California. Lacey, L.A. and Brooks, W.M. (1997) Initial handling and diagnosis of diseased insects. In: Lacey, L.A. (ed.) Manual of Techniques in Insect Pathology. Academic Press, San Diego, California, pp. 1–15. Laird, M., Lacey, L.A. and Davidson, E.W. (1990) Safety of Microbial Insecticides. CRC Press, Boca Raton, Florida. Lighthart, B., Sewall, D. and Thomas, D.R. (1988) Effect of several stress factors on the susceptibility of the predatory mite, Metaseiulus occidentalis (Acari: Phytoseiidae), to the weak bacterial pathogen Serratia marcescens. Journal of Invertebrate Pathology 52, 33–42. Lockwood, J.A. (1993) Environmental issues involved in biological control of rangeland grasshoppers (Orthoptera: Acrididae) with exotic agents. Environmental Entomology 22, 503–518. Martignoni, M.E. and Iwai, P.J. (1986) A Catalogue of Viral Disease of Insects, Mites, and Ticks. US Department of Agriculture, Forest Service, Pacific Northwest Research Station, General Technical Report PNW-195. Merriam-Webster Medical Dictionary (2003) http://www.intelihealth.com/IH/ihtIH/WSIHW000/ 9276/9276.html (accessed 26 May 2005). Milks, M.L., Sokolova, Y.Y., Isakova, I.A., Fuxa, J.R., Mitchell, F., Snowden, K.F. and Vinson, S.B. (2004) Comparative effectiveness of light-microscope techniques and PCR in detecting Thelohania solenopsae (Microsporidia) infections in red imported fire ants (Solenopsis invicta). Journal of Eukaryote Microbiology 51, 187–191. Miller, L.K. (1997) The Baculoviruses. Plenum Press, New York. Miller, L.K. and Ball, L.A. (1998) The Insect Viruses. Plenum Press, New York. Muller, A., Bialek, R., Kamper, A., Fatkenheuer, G., Salzberger, B. and Franzen, C. (2001) Detection of Microsporidia in travelers with diarrhea. Journal of Clinical Microbiology 39, 1630–1632. Olsen, L.E. and Hoy, M.A. (2002) Heat curing Metaseiulus occidentalis (Nesbitt) (Acari: Phytoseiidae) of a fitness-reducing microsporidium. Journal of Invertebrate Pathology 79, 173–178. Papierok, B. and Hajek, A.E. (1997) Fungi: Entomophthorales. In: Lacey, L.A. (ed.) Manual of Techniques in Insect Pathology. Academic Press, San Diego, California, pp. 187–212. Persing, D.H. (1996) PCR Protocols for Emerging Infectious Diseases. ASM Press, Washington DC. Pimentel, D. (2002) Biological Invasions. CRC Press, Boca Raton, Florida. Poinar, G.O. Jr. and Thomas, G.M. (1984) Laboratory Guide to Insect Pathogens and Parasites. Plenum Press, New York. Ratcliffe, S.T., Robertson, H.M., Jones, C.J., Bollero, G.A. and Weinzieri, R.A. (2002) Assessment of parasitism of house fly and stable fly (Diptera: Muscidae) pupae by pteromalid (Hymenoptera: Pteromalidae) parasitoids using polymerase chain reaction assay. Journal of Medical Entomology 39, 52–60. Samson, R.A., Evans, H.C. and Latgé, J-.P. (1988) Atlas of Entomopathogenic Fungi. Springer-Verlag, Berlin, Germany. Siegel, J.P. (2000) Bacteria. In: Lacey, L.A. (ed.) Field Manual of Techniques in Invertebrate Pathology. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 209–230.

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Post-release Evaluation of Non-target Effects of Biological Control Agents

Barbara I.P. Barratt,1 Bernd Blossey2 and Heikki M.T. Hokkanen3 1AgResearch Invermay, Private Bag 50034, Mosgiel, New Zealand (email: [email protected]; fax number: +64-3-489-3739); 2Department of Natural Resources, Fernow Hall, Cornell University, Ithaca, New York 14853, USA (email: [email protected]; fax number: +1-607-255-0349); 3Department of Applied Zoology, University of Helsinki, PO Box 27, 00014 Helsinki, Finland (email: heikki.hokkanen@helsinki.fi; fax number: +358-9191-58463)

Abstract In this chapter, post-release evaluation of non-target impacts of introduced biological control agents is discussed, with emphasis on parasitoids used for biological control, but examples are also given for insect pathogens and herbivores for weed biological control where they provide illustrative examples and useful comparisons. The scope of nontarget effects of biological control agents is discussed in relation to: direct effects on native non-target species; direct effects on beneficial or valued exotic species; direct effects on non-target pest species; competition with, or displacement of other natural enemies; and indirect effects on the same or other trophic levels. Three case studies from recent and on-going research on post-release impacts are presented. These include first, a classical biological control release of the braconid, Microctonus aethiopoides, introduced to control a pest weevil in New Zealand. Secondly, examples of inundative application of entomopathogenic nematodes are given which highlight aspects of non-target effects on insect populations in the field, competition between endemic and exotic species at the application site, and post-application persistence or dispersal. The third case study describes indirect effects of biological control of spotted knapweed in North America. Finally, a summary of possible approaches to post-release monitoring and impact assessment of a biological control release is presented.

Introduction Other chapters have been directed mainly at pre-release methods and risk assessment for arthropod biological control agents. In this chapter, however, we have changed the emphasis to post-release impacts of biological control agents, particularly methods 166

that have been or could be used to measure non-target impacts. While regulators have a keen interest in this area because it will enable them to validate their own prerelease predictions and inform future decisions, they are often not able to fund this research, or require that post-release monitoring be carried out. Exceptions to this can

©CAB International 2006. Environmental Impact of Invertebrates for Biological Control of Arthropods: Methods and Risk Assessment (eds F. Bigler et al.)

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occur in New Zealand where the regulatory agency, the Environmental Risk Management Authority (ERMA New Zealand), can, under some circumstances, approve a biological control release on condition that post-release monitoring on nontarget impacts is carried out. Also the USDA ARS policy commits researchers to monitoring non-target effects of introduced biological control agents (Delfosse, 2000; Van Driesche, 2004). However, the actual implementation and extent of monitoring vary considerably among programmes. Concerns over potential non-target impacts of introduced biological control agents have been expressed since the early implementation of control programmes (Perkins, 1897), but particularly so in recent years (e.g. Howarth, 1983; Lockwood, 1993; Simberloff and Stiling, 1996). However, study of such effects has received very little attention from biological control researchers or ecologists; indeed, information on post-release impacts on target effects is often minimal. Much anecdotal evidence for dwindling numbers of particular, sometimes iconic, species after biological control agents have been introduced has been provided (Gibbs, 1980; Howarth, 1983; Boettner et al., 2000), but until recently little quantitative evidence was available to substantiate these reports. Investigations which suggest that population decline of charismatic nontarget species is the result of biological control releases are, in fact, sometimes the result of unrelated, but coincidental species decline (Johnson et al., 2005). Many studies have shown that nontarget species are attacked in the field by introduced biological control agents (particularly parasitoids), but Hopper (1998) drew attention to the fact that few studies demonstrated that such attack had any impact on population density of a nontarget species. Stanley and Julien (1998) pointed out that too few biological control programmes continued their efforts to the point where pre-release predictions were validated by post-release studies of biological control agents. Holt and Hochberg (2001) concluded from theoretical studies

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that the impact on a non-target species would be greater if, in addition to sustaining a higher attack rate, it had a lower intrinsic rate of increase than the target, so clearly attack rates alone do not adequately describe risk to non-target species. The European Union funded a four-year project on ‘Evaluating environmental risks of biological control’, which aimed to review current and past practices as well as to develop guidelines for improved biological control practices in the future. A literature review for biological control of insects showed that data on post-release impacts were reported for less than 2% (of over 5000) of classical biological control introductions (Lynch et al., 2001). Extrapolating from their data, they estimated that just under 10% of instances of non-target attack may have led to population impacts, and hence over 600 non-target insect species may have been affected at the population level throughout the history of biological control. The authors of the review suggested that there exists major under-reporting of adverse effects of biological control agent introductions. For weed biological control, the safety record appears to be very high, with only two of over 300 species that have been released worldwide affecting non-target species at the population level (see below). Post-release monitoring is not routine in weed biological control programmes, and so under-reporting of non-target effects may exist; however, herbivores and their impact on plants are easier to assess than effects of parasitoids, invertebrate predators or entomopathogens. In this contribution the scope of nontarget effects is reviewed and discussed, with emphasis on parasitoids used for classical, inundative and inoculative biological control, but reference to insect pathogens and herbivores for weed biological control will be made where they provide illustrative examples and useful comparisons. Examples of case studies from recent and ongoing research are provided, and finally, suggestions for protocol development of post-release studies and monitoring are presented.

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Scope of Non-target Effects Impacts of biological control agents on non-target species are often classified as direct, or indirect. Direct effects are those exerted by the biological control agent on species other than the target host. Nontarget species may include native species, introduced beneficials or other pest species. Indirect effects are much more difficult to classify, since they can involve effects at the same trophic level, such as natural enemy displacement by an introduced parasitoid, or wider ecological impacts resulting from changes in food web composition and structure (Lynch and Ives, 1999; see Messing et al., Chapter 4, this volume), some of which can be entirely unpredictable (Polis and Strong, 1996; Polis, 1998). For this discussion on evaluation of non-target effects five categories will be adopted: ● Direct effects on native non-target species. ● Direct effects on beneficial or valued exotic species. ● Direct effects on other pests. ● Competition with or displacement of other natural enemies. ● Indirect effects on the same or other trophic levels.

Direct effects on native non-target species Most research on non-target effects of biological control agents has been carried out on weed biological control agents (Pemberton, 2000; Blossey et al., 2001), despite the fact that only 300–400 herbivores have been used for weed biological control (Julien and Griffiths, 1998), compared with over 2000 species of parasitoids and predators released for classical biological control of insect pests (Greathead and Greathead, 1992). However, less than 50 exotic pathogens have been released for biological control (Fuxa, 1987). Much of the evidence on post-release non-target impacts has come from retrospective investigation of biological control

releases. These studies can provide valuable information, although often lack comparative pre-release data. Consequently, there is no real baseline for comparison, particularly for attempts at measuring impacts on population density of nontarget species or food webs. Therefore, retrospective studies have been used mostly for comparing pre-release predictions with realized post-release impacts (Barratt et al., 2000a), cataloguing numbers of non-target species attacked in the field by a biological control agent (Barratt et al., 1997; Stiling and Simberloff, 2000; Asquith and Miramontes, 2001) and quantifying the proportion of a non-target species population attacked by an introduced biological control agent (Barratt et al., 2000a,b). Louda et al. (2003) analysed characteristics of non-target effects from retrospective studies of herbivorous and entomophagous (parasitoids and predators) biological control agents where quantitative data were available, and found that several patterns emerged. These led the authors to make a number of recommendations including: the avoidance of generalist or adventive biological control agents; more extensive host range testing; consideration of ecological risk; prioritization of agents; and consideration of potential for genetic adaptation. Faunal surveys are another way in which non-target effects of biological control agents have been studied. In a survey of ichneumonids and braconids of rainforest in Hawaii, a total of 17 parasitoid species was collected. Only two were deliberately introduced parasitoid species, but they represented about 45% of the total catch, and they were known to utilize native hosts. Of 32 biological control species of ichneumonids and braconids in Hawaii, seven have now been found to have additional recorded hosts (Asquith and Miramontes, 2001). Observations on decline of conspicuous or iconic species have prompted some investigations of non-target impacts of biological control agents. One such case in New Zealand was the observation that the numbers of the native red admiral butterfly, Bassaris gonerilla (F.), had declined since

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the release of pupal parasitoids to control white butterfly, Pieris rapae (L.) (Gibbs, 1980). This led to a field study by Barron et al. (2003) who found, in fact, that pupal mortality of B. gonerilla from P. rapae parasitoids was relatively low compared with egg parasitism by a (possibly native) scelionid, Telonomus sp., and pupal mortality by an accidentally introduced ichneumonid, Echthromorpha intricatoria (F.). Similarly, the decline in numbers of the native koa bug, Coleotichus blackburniae White, in Hawaii was attributed to the introduction of parasitoids for the southern green stink bug, Nezara viridula (L.). A life table analysis showed that the role of these parasitoids was minor in comparison with a complex of egg predators, and it was also concluded that habitat loss has been a major factor in koa bug decline (Johnson et al., 2005). Transient effects can occur very soon after biological control agent release, resulting from high densities of the biological control agent which have developed on the target host, subsequently attacking nontarget species, even if they are less preferred than the target (Holt and Hochberg, 2001; Lynch et al., 2002). The latter authors point out that in theoretical models such effects can be significant, even causing local extinctions of non-target populations, and recommend that monitoring programmes should be in place to measure transient effects before the release of the biological control agent. Brief transient effects have been reported in weed biological control programmes, when large herbivore populations build up and deplete the target plant resource. However, the nontarget plants attacked in this situation can be entirely unpredictable. For example, Cactoblastis cactorum (Berg) feeding on Opuntia species in Australia was found attacking tomatoes and melons when host populations collapsed and larvae ran out of food. Similarly, Galerucella calmariensis L., a biological control agent of purple loosestrife (Lythrum salicaria L.), was found on a number of plant species growing in the vicinity of emerging teneral adults (Blossey et al., 2001).

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Direct non-target impacts of microbial insecticides using insect pathogens for biological control, and methods of assessing them, have been studied and reviewed extensively (e.g. Miller, 2000; Goettel and Hajek, 2001; Glare and O’Callaghan, 2003; Hokkanen and Hajek, 2003; Lacey and Merritt, 2003). To date there is no evidence of substantial mortality in a non-target species, or other environmental impacts from these introductions (Goettel and Hajek, 2001), possibly because pathogens used for classical introductions are typically relatively host specific, and usually high numbers of inocula are required for any infection. Mass applications of Bacillus thuringiensis kurstaki in forest ecosystems for gypsy moth control in Oregon resulted in significant decreases in the abundance (55–86%) and species richness (20–67%) of non-target Lepidoptera during the year of application and the following year, returning close to normal (0–44% reductions) in the second year post-treatment (Miller, 2000). Goettel and Hajek (2001) point out that sometimes abundance and species richness of non-target Lepidoptera can increase (Sample et al., 1996), possibly due to reduced competition from the target pest, in this case gypsy moth. One of the best-studied cases concerning direct non-target effects of classical introductions of microbial agents is that of Entomophthora maimaiga Humber, Shimazu, Soper and Hajek, a fungal pathogen of Lepidoptera in the tussock moth family (Lymantriidae) (for a review see Hajek et al., 2003). Comprehensive non-target studies in the laboratory and in the field were carried out in 1997–2001, involving 50 endemic lymantriid species and related Lepidoptera. Only four nontarget species were found infected, usually single individuals among the large numbers collected (typically 0.3–1.0% infection levels). However, one non-target species, Dasychira obliquata (Grote and Robinson) was on one occasion found to suffer 35.7% infection, the highest non-target rate observed (Hajek et al., 2003). The evidence for non-target effects in over 1200 weed control programmes conducted

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worldwide, targeting 133 plant species by releasing 350 species of insects and pathogens (Julien and Griffiths, 1998), has been reviewed extensively (McFadyen, 1998; Pemberton, 2000; Blossey et al., 2001; Gassmann and Louda, 2001). While the existence of hidden or unreported non-target effects cannot be excluded, available data suggest that host-range tests are scientifically reliable, and evidence for non-target effects is extremely limited. Throughout the history of weed biological control, concerns that weed biological control agents may become less host specific and attack non-target species (Secord and Kareiva, 1996; Simberloff and Stiling, 1996; Louda et al., 1997) have not materialized. However, two ‘high-profile cases’ involve non-target impacts of the seedfeeding weevil, Rhinocyllus conicus Fröhlich, attacking native North American Cirsium species (Louda et al., 1997), and of the moth, Cactoblastis cactorum Berg, whose larvae are now attacking rare native Opuntia species in Florida (Simberloff and Stiling, 1996). The currently reported adverse environmental impacts of these two species are the result of the poor decision-making processes in place before 1970. Such decisions resulted in the release of R. conicus, which was known not to be host-specific, and C. cactorum in the Caribbean, despite frequent imports of horticultural plant material from the area into southern Florida and the potential for natural dispersal (Simberloff and Stiling, 1996; Pemberton, 2000; Gassmann and Louda, 2001). In neither instance has pre-release study failed to predict attack on non-target plants (in fact both species are known to have broad host ranges), but decisions to import and release were made by regulators despite the evidence. Current regulations (USDA, 1999) incorporate measures to avoid similar mistakes occurring in the future (Gassmann and Louda, 2001). Inundative and inoculative forms of biological control have several advantages in terms of non-target concerns. Several mechanisms can be employed to minimize the possibility of unwanted consequences, including the timing and location of control agent releases, and the mode of application.

For example, releases of Trichogramma egg parasitoids in maize can be regionally restricted if there is evidence that there is a possible threat to a refuge of rare butterflies. Similarly, applications of the fungus Beauveria bassiana (Balsamo) Vuillemin could be avoided on flowering crops favoured by bumblebees, and in some cases, spray applications of entomopathogens could be replaced by soil applications to minimize non-target impacts. Ultimately, negative impacts of inoculative or inundative releases of biological control agents can be avoided simply by discontinuing the use of those particular agents.

Direct effects on beneficial or valued exotic species Parasitoids have been known to attack beneficial insects, including species introduced themselves as biological control agents, particularly for weeds. This is an unfortunate situation that does little to persuade the public that biological control practitioners are competent. In New Zealand, Microctonus aethiopoides Loan introduced to control the lucerne pest Sitona discoideus Gyllenhal, has been found in the field to successfully attack R. conicus, the nodding thistle receptacle weevil (Ferguson et al., 1999; Murray et al., 2002). Despite the fact that R. conicus was included as a test species when M. aethiopoides was being evaluated in quarantine in the early 1980s, it was not found to be attacked. A possible explanation for this is that quarantine tests with R. conicus were carried out in autumn, when the weevils were in diapause and had become quiescent. Microtonus aethiopoides requires a mobile host in order to oviposit successfully, and so the attack upon R. conicus may not have occurred in these tests. This highlights the importance of understanding the biology/phenology of the organisms concerned in the programme. Entomopathogens with a broad ability to infect insects sometimes have the potential directly to infect beneficial insects, such as

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other natural enemies of pest species. Natural infections by B. bassiana in ladybird beetles are well known, but their incidence is usually low (Hokkanen et al., 2003a). Natural infections in other predatory beetles have been studied (e.g. Steenberg et al., 1995; Hokkanen et al., 2003a), but applications of fungi or nematodes in the field have been shown not to result in increased incidence of infections in these non-target species (Hokkanen et al., 2003a). Interestingly, it appears that predators are more resistant to infections by entomopathogenic fungi than their herbivorous counterparts, and that the degree of herbivory (in omnivores) may predict the susceptibility to the pathogen (Hokkanen and Zeng, 2001; Hokkanen et al., 2003a). Insect parasitoids are sometimes affected by microbial pesticides to the same extent as their hosts (Husberg and Hokkanen, 2001; Hokkanen et al., 2003a). Honeybees have been shown to be relatively safe from fungal infections partly due to high hive temperatures, but bumblebees may be at risk, especially from B. bassiana treatments, because their hive temperature is lower (Hokkanen et al., 2003b).

Direct effects on other pests (fortuitous biological control) When a biological control agent attacks another pest after release, this has been termed ‘fortuitous’ biological control, or a positive non-target effect (Ehler, 2000). There are examples of this in the literature, but few have been quantified. For example, M. aethiopoides was introduced to control S. discoideus, but it also attacks Listronotus bonariensis (Kuschel), another introduced weevil pest (McNeill et al., 1993); however, levels of parasitism rarely reach more than 10% in the field. The encyrtid parasitoid Anagyrus indicus Shafee, Alam and Agarwal, although not a deliberate introduction, has provided effective biological control of the spherical mealybug, Nipaeococcus viridis (Newstead) on Guam and the Marianas Islands (Nechols, 2003).

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An example from weed biological control is the accidental introduction of the aquatic moth, Acentria ephemerella Dennis and Schiffermüller, in north-eastern North America. This species attacks a number of submersed aquatic plants, but the strong preference for Eurasian watermilfoil (Myriophyllum spicatum L.) has resulted in substantial control of M. spicatum in central New York (Johnson and Blossey, 2002). Fortuitous biological control arose from using the fungus Lecanicillium lecanii (Zimmermann) Zare and Gams, which was introduced into Seychelles to successfully control Lecaniinae scales. This resulted in gradual disappearance of the small, black ant, Technomyrmex albipes (Smith) a household pest, possibly as a consequence of the lower numbers of scale-insects (Hajek et al., 2003). There is some evidence that entomophagous biological control agents can become adapted to hosts in their new range; for example, the parasitoid Bathyplectes curculionis Thoms was introduced to California to control the alfalfa weevil Hypera postica (Gyllenhal). It also attacked a related pest species, Hypera brunneipennis (Boheman), and initially encapsulation rates were very high, but after a number of years the parasitoid developed successfully in this host 95% of the time (Salt and van den Bosch, 1967). Furthermore, Berberet et al. (2003) showed that, over a 28-year period, the effectiveness of encapsulation in protecting H. postica from parasitism by B. curculionis has diminished. The authors considered that strain or biotype variability accounted for some of these differences in host immunosuppression ability. There have been no reported cases of evolution of host specificity in herbivorous biological control agents.

Competition with, or displacement of, other natural enemies Bennett (1993) noted that few pre-release studies have been carried out which would enable competitive displacement of a

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natural enemy by an introduced biological control agent to be verified; however, he provided several examples where there is strong evidence to suggest that this has occurred. Shiga (1999) showed that the parasitoid Torymus sinensis Kamijo, introduced to control Dryocosmus kuriphilus Yasumatsu (chestnut gall wasp) in Japan, partially displaced a polyphagous native parasitoid, Megastigmus nipponicus Yasumatsu and Kamijo, which had become a primary parasitoid of the gall wasp, and almost eliminated the congeneric native parasitoid Torymus beneficus Yasumatsu from chestnut groves. This interaction was further complicated by hybridization between the two Torymus species, which may have repressed T. sinensis’ reproductive capability in the early stages of establishment. Wang and Messing (2002) gave examples of competitive displacement of fruit fly parasitoids in Hawaii, evidence which has led to tighter regulation of biological control for fruit fly management. Goldson et al. (2003) demonstrated in quarantine laboratory experiments that the introduction of a European strain of M. aethiopoides to control Sitona lepidus Gyllenhal, in addition to the Moroccan strain already established in New Zealand for S. discoideus management, could lead to hybridization between the strains. The results from the experiments showed that the efficacy of the hybrids was reduced, suggesting that introducing the European strain would potentially reduce the efficacy of both strains on their intended target hosts. This has led to a proposal to release a parthenogenetic strain to avoid compromising existing and future biological control efficacy (S.L. Goldson, personal communication). Risks to non-target natural enemies from hybridization are discussed in detail by Hopper et al. (Chapter 5, this volume). In biological control programmes where multiple species are introduced to control a single pest, competitive interactions are likely to occur, raising concerns that less successful species may suppress more successful ones. This is well established for

insect biological control programmes where intra-guild predation may lead to reduced herbivore suppression (Rosenheim, 1998; Rosenheim et al., 1999). There is no published evidence for replacement of a successful control agent by an unsuccessful agent in weed biological control. Instead, the success in reducing weed populations increases with the increased number of control agents that are released (Denoth et al., 2002).

Indirect effects on other trophic levels Biological control agent release can lead to complex and unpredictable impacts on ecosystems, and several authors have suggested ways in which this can be approached. Simberloff and Stiling (1996) suggested that the keystone-species concept might be useful in predicting environmental impacts of species used for biological control agents. Those species which are likely to eliminate a taxon, which in turn might make a substantial change to the habitat, can possibly be identified pre-release (see van Lenteren et al., Chapter 3, this volume). Alternatively, the future development and monitoring of bioindicators that are identified locally or regionally (National Research Council, 2000) might allow broad-scale assessments to be made of indirect effects of biological control, and a better understanding of the true ecological and economic costs and benefits of biological control to be realized. Waage (2001), in introducing a symposium on the subject of the indirect effects of biological control agents, suggested that our ability to predict the outcome of biological control programmes depended upon experience from past programmes, retrospective hypothesis testing and the application of sound pre-release principles and biological control agent selection. In the same volume, Holt and Hochberg (2001) consider that fundamental principles of community assembly are essential in evaluating indirect impacts of biological control: establishment, impact and landscape context.

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Lockwood (2000) argued that we should not be trying to measure non-target effects only on species, but also on ecological processes. While he admitted that this can be extremely difficult, and subject to lags, he argued that just as pest control should be measured in terms of reduction of pest damage rather than by reduction in pest numbers, impacts of biological control should be measured in terms, not of reduction of non-target species, but of impacts on ecological processes. The construction of food webs may provide a better understanding of the ecology of biological control, the impact that biological control agents have on community and ecosystem function, and trophic relationships (Memmott, 2000). In a study carried out in a wilderness preserve in Hawaii, a quantitative food web was constructed for plants, Lepidoptera and parasitoids, which showed that 83% of parasitoid individuals reared from Lepidoptera were biological control agents, and 3% were native (Henneman and Memmott, 2001). The use of stable isotopes holds additional promise for measuring how resource flows are redirected in response to invasions or biological control releases (e.g. McCutchan et al., 2003). Any serious evaluation of the impacts of invasive plants or insects on native species and ecosystems, as well as an assessment of how biological control programmes affect the species composition and functioning of ecosystems, has to rely on longterm data on species abundance and ecosystem function at various spatial scales (Blossey, 1999). In the USA, monitoring programmes associated with agriculture have an extensive history. One of the most sophisticated of these is the National Resource Inventory (NRI), administered every five years through the Natural Resource Conservation Service (NRCS). The NRI relies increasingly on remotesensing techniques (Nusser and Goebel, 1997), but it does not collect information about the presence or abundance of any plant or animal species other than crop species. Therefore, the long-term monitoring activities of the NRI have so far not

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been able to provide sufficient or relevant data for environmental assessments or effective natural resource management (Goebel, 1998). The dearth of information internationally about the status of ecological and natural resources presents a serious problem for any evaluation of effects associated with any human activity, including the release of biological control agents.

Case Studies The three case studies described below provide examples of direct non-target impacts of a parasitoid used in classical biological control, non-target effects of entomopathogenic nematodes used in inundative biological control, and indirect post-release impacts of weed biological control.

Case study 1: post-release impact of the parasitoid Microctonus aethiopoides The braconid M. aethiopoides was introduced into New Zealand in 1982 to control the forage pest weevil S. discoideus. Microctonus aethiopoides is a solitary, koinobiont endoparasitoid of the adult stage of the target weevil. It was selected as a retrospective case study along with another braconid in the same genus, M. hyperodae (introduced to control L. bonariensis), with the objective of investigating and improving upon the value of quarantine testing for predicting post-release nontarget effects (Barratt et al., 1997, 2000a,b, 2003). Surveys throughout New Zealand showed that M. aethiopoides had become well established in S. discoideus populations in lucerne-growing areas (Stufkens et al., 1987; Ferguson et al., 1994), where it had been shown to suppress S. discoideus populations (Goldson et al., 1993). Microctonus aethiopoides was, however, released with limited host range testing in quarantine, which revealed no evidence of attack on non-target species (M. Stufkens, personal communication). Since its release,

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M. aethiopoides has been found to be relatively polyphagous. In the field the wasp had been found to parasitize 13 New Zealand and four exotic species of weevils from five tribes and two subfamilies of Curculionidae (Barratt et al., 2000b; B.I.P. Barratt, unpublished results). The native weevil species which appeared to be most at risk from non-target attack by M. aethiopoides were in the subfamily Entiminae. This finding has led to concern in New Zealand about the impacts of biological control agents on non-target species (e.g. Barratt et al., 2000a,b), particularly given the 90% endemism at the species level of indigenous Coleoptera found in New Zealand (Klimaszewski and Watt, 1997). Research aimed at evaluating postrelease impacts of M. aethiopoides has progressed along the following four main lines of investigation. Seasonality of parasitism in non-target weevil populations Regular sampling of several native weevil populations allowed the phenology of nontarget species to be determined, along with the seasonality of parasitism by M. aethiopoides (Barratt et al., 2000b). The study showed that the main period of reproductive activity of some native weevils was asynchronous with periods of peak non-target parasitism (when the target host was absent), and probably reduced the non-target impact as a consequence. However, other weevil species with different phenologies were more heavily parasitized during their periods of reproductive activity. Introduction of Microctonus aethiopoides into new environment A release of M. aethiopoides was made in an upland area of native grassland where it was thought not to be established, but where long-term data on native weevil population abundance were known. Three spatially separated release sites were matched with three control sites with simi-

lar vegetation composition. Three years post-release, parasitism levels in non-target species are very low, but should this build up to higher levels; data from this study will be used to test the model outlined below. Modelling non-target impacts A population model has been developed (Barlow et al., 2004) to quantify the impact of M. aethiopoides on the abundance of a non-target host, based on the host intrinsic rate of increase, the average abundance of the host in the presence of parasitism and the estimated mortality caused by the parasitoid. The data were taken from the two studies outlined above. The non-target host population was modelled in the presence of parasitism, and the impact quantified as the increase in equilibrium host density when parasitism was removed from the model. In low-altitude pasture, parasitism over three years averaged 15%, and the model estimated an 8% reduction of the host. In higher-altitude native grassland the method was applied in reverse to predict the parasitoid’s impact if it did establish, and reached 15% parasitism. Here, the model predicted a 30% suppression of population density. The host’s intrinsic rate of increase, rm, estimated from both sites, accounted for this difference in predicted impact. The impact was small at the low-altitude area where rm was high, and larger at the higher altitude site where rm was lower.

Spatial distribution of non-target parasitism A final study, still under investigation, examines the altitudinal distribution of non-target parasitism by M. aethiopoides from valley-floor forage systems up to native grassland. Native weevil density and parasitism is being measured, and will be analysed using the model above to estimate impacts. In summary, the approaches taken to determine post-release impacts of M. aethiopoides have been to investigate seasonality and phenology of non-target hosts

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with parasitoid activity in the field, to determine whether M. aethiopoides can establish in native grassland environments some distance from the target host habitat and to use the data to develop a predictive model of non-target population density reduction. This work has involved considerable resources, which will not always be available for biological control agent postrelease impact investigations, but might facilitate future studies and suggest possible approaches.

Case study 2: non-target impacts of inundative application of entomopathogenic nematodes Insect-killing nematodes of the genera Steinernema and Heterorhabditis are widely used against a variety of horticultural and ornamental crop pests, as well as on turf grass and home gardens. These are usually inundative applications with high numbers of individuals, which decline to low population levels or die out within a few weeks or months. Non-target effects of such applications have been studied intensively in the laboratory and in the field (Bathon, 1996; Ehlers and Hokkanen, 1996; Peters, 1996; Barbercheck and Millar, 2000; Ehlers, 2003), but there appears to be no single, representative case study which has examined more than one of the critical features at a time. The most important of these studies in the context of this chapter have focussed on the reduction of non-target insect populations in the field, on competition between endemic and exotic species at the application site, and on post-application persistence or dispersal, which can be used as proxy measures for non-target safety (Hokkanen et al., 2003c). In order to cover these aspects, we look briefly here at several case studies involving entomopathogenic nematodes. Direct post-application impacts The most comprehensive investigations have involved Steinernema feltiae (Filipjev), Heterorhabditis bacteriophora

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Poinar and Heterorhabditis megidis Poinar, Jackson and Klein, and were conducted by Rethmeyer (1991), Buck and Bathon (1993) and Koch and Bathon (1993) over a threeyear period on 100 m2 plots in different habitats, to investigate non-target impacts, but with no clear target pest. The results have been summarized by Bathon (1996). A total of approximately 400,000 arthropod specimens were collected from the release plots, and the treatment impacts evaluated. Densities of a few species were reduced (although some increased) after the entomopathogenic nematode (EPN) application; however, reductions were temporally and spatially restricted. In general, the impact on the non-target populations was negligible. In cases of effective target pest control by EPNs, reductions in populations of specific natural enemies of the pest can be expected. Indeed, H.M.T. Hokkanen (unpublished results) obtained a 94% reduction in pollen beetle, Meligethes aeneus (F.), numbers by inundative release of S. feltiae in oilseed rape, but observed a similar reduction in the numbers of the specific parasitoid, Phradis morionellus (Holmgren), emerging from the treated plots. Reductions in the numbers of other non-target insects did not occur. Barbercheck and Millar (2000) studied the possible competition between inundated exotic EPNs and naturally occurring, local species. They introduced the exotic Steinernema riobravis Cabanillas, Poinar and Raulston from Texas on plots in North Carolina containing endemic populations of S. carpocapsae (Weiser) and H. bacteriophora. The introduction resulted in a reduction of insect mortality caused by the endemic species when soil samples were baited with Galleria mellonella (L.). Their data suggest that coexistence of the three nematode species in the field was possible, and that the risk for local extinction of the native nematodes was minimal. However, the results also indicate that the exotic species can cause a (often transient) reduction in local EPN populations. If the exotic species does become established it will most likely co-exist with the local species,

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assisted by the typically highly aggregated distribution of EPN populations. Their relatively low mobility is likely to result in fragmented populations with an aggregated distribution (Barbercheck and Millar, 2000), which will ensure that parts of the population will survive while other parts might be transiently eliminated by an introduction of exotic populations. These metapopulation dynamics are of major importance for the survival and coexistence of species (Harrison and Taylor, 1997). Persistence of EPNs If an introduced biological control agent cannot persist in the new environment, its non-target impacts, if any, will be transient. Therefore, post-release monitoring of EPN persistence at the release site can give some indication of the possibility for nontarget effects. Successful establishment of EPNs requires optimal environmental conditions during application and the continuous presence of susceptible hosts (Ehlers, 2003). Even then, the majority of the applied nematodes do not survive for long. Smits (1996) recorded 70% loss of the applied nematode population after one week, and 90% loss two weeks following an application on turf. The potential to persist can, however, differ between species. Strong (2002) estimated that heterorhabditid populations have a half-life of approximately one month, whereas the half-life for steinernematids usually exceeds one month. Some exotic EPN species are able to establish in new environments. Parkman et al. (1993) released the South American nematode species Steinernema scapterisci Nguyen and Smart to control exotic mole crickets in Florida, and reported successful establishment at all sites, and persistence for over five years. The continuous presence of potential hosts seems to be essential for successful establishment. Dispersal of EPNs EPNs have been considered exceptionally safe, partly due to their relative immobility in the release area (Ehlers and Hokkanen,

1996). For example, S. scapterisci was recorded as moving 10 cm in five days after application to the soil surface (Nguyen and Smart, 1990). EPNs can, however, be spread passively by other organisms, and the potential for this should be considered when investigating post-release non-target effects. Parkman et al. (1993) recorded a mean maximum cumulative distance of dispersal of 60 m and a cumulative area occupied by S. scapterisci of 4.2 ha, recorded 21 months after application. The possibility of phorecy via infected hosts has been shown several times, for example by Timper et al. (1988), who recorded that adult noctuids (Spodoptera exigua (Hübner)) infected with S. carpocapsae dispersed up to 11 m from the site of infection. Lacey et al. (1995) demonstrated the potential of infected adult scarabaeids (Popillia japonica (Newman)) to disperse Steinernema glaseri (Steiner) by flight, and in Florida, mole crickets (Scapteriscus spp.) infected with the exotic nematode S. scapterisci were collected in sound traps 23 km from the nearest release site, indicating long-distance dispersal and area-wide establishment. Since its release, no detrimental effects on non-target organisms have been recorded (Parkman and Smart, 1996). In conclusion, numerous studies have shown that since the first application of the EPN S. glaseri against the white grub P. japonica in New Jersey (USA) (Glaser and Farrell, 1935), no ecological problems caused by inundative applications of nematodes are evident. Several factors contribute to this, including poor persistence after application, low ability to disperse and the high density of infective juveniles necessary over a large area for an impact at the population level. So far, only one case of a permanent impact of EPNs on the density of an insect is known: the lowering of mole cricket populations in Florida (artificially high population densities of exotic Gryllotalpa species), caused by the exotic nematode S. scapterisci, resembling a classical introduction of a biological control agent rather than a typical inundative application, with no known non-target effects reported.

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Case study 3: indirect effects of knapweed biological control – how biological control agents may affect human health Centaurea maculosa Lamarck (spotted knapweed) is a herbaceous Eurasian perennial weed now widespread in temperate grasslands across North America. Dry rangelands in western North America are particularly vulnerable to invasion by spotted knapweed, which affects native species (Callaway et al., 2003) and cattle and sheep ranchers (Story, 2002). Of the many herbivore species attacking C. maculosa in Europe, 12 insect species were screened and released in North America (Story, 2002). Two tephritid flies, Urophora quadrifasciata (Meigen) and Urophora affinis Frauenfeld, have become particularly widespread and very abundant in knapweed sites across North America. Larvae overwinter in the galled flower heads, until they pupate and emerge in the spring; however, these seed-feeders are not affecting knapweed abundance despite reducing seed output by 40% or more (Story, 2002). The abundance of fly larvae in spotted knapweed stands has attracted secondary consumers, including a number of birds, deer, chipmunks and mice (Pearson et al., 2000). One of the most abundant species using this newly available resource is the deer mouse (Peromyscus maniculatus). This omnivorous rodent has a diverse diet of grains, nuts, fruits and invertebrates, but particularly over the winter may consume several hundred Urophora larvae/day (Pearson et al., 2000), constituting its major food item (>80%). Mouse populations are up to threefold larger in invaded habitats compared to sites with native vegetation, and over-winter mortality of mice is greatly reduced in the presence of knapweed (Ortega et al., 2004). However, spotted knapweed sites cannot support similarly high mouse populations during the summer mouse-breeding season when no fly larvae are available, because alternative food sources provided by native species are diminished by spotted knapweed invasion (Ortega et al., 2004). But it is clear that

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introduced gall fly biological control agents subsidize native deer mouse populations. Increasing the functional complexity (diversity and trophic linkages) is generally considered to improve ecosystem health and stability (Neutel et al., 2002). It has been argued (Blossey, 2003) that the introduction of biological control agents, even if unsuccessful in controlling the target weed, may constitute an improvement in ecosystem health because of the ability of higher trophic levels to exploit a new resource, and not as a negative non-target effect. Alternatively, (Pearson et al., 2000) and (Ortega et al., 2004) argue that subsidies provided by Urophora gall flies, and many other biological control agents that build large populations but fail to control their host, may have strongly negative effects on associated arthropod communities and potential human health. Deer mice consume many other invertebrates and seeds; mouse populations with access to fly larvae in the winter may build up large populations that may overexploit other prey items, potentially resulting in population collapse. While the authors offer no data to support this hypothesis, it is clear from experiments using a closely related species, the whitefooted mouse (Peromyscus leucopus), that subsidized rodent populations may exert strong predation pressures on arthropods. Experiments in oak forests in eastern North America revealed complicated trophic linkages between mast fruiting events of oaks, gypsy moth (Lymantria dispar), mice, deer and ticks, which are the main vector of Lyme disease (Jones et al., 1998). In years following high acorn production, mice were able completely to suppress gypsy moth outbreaks, but incidence of Lyme disease vectors was high when rodent populations were high. In the case of spotted knapweed flies subsidizing deer mouse populations, concerns are not only that rippling effects may have negative consequences for ecosystems and native taxa through direct mouse predation, but also that human health may be affected (Ortega et al., 2004). Deer mice are the main carrier of Sin Nombre hantavirus, which can cause significant human mortality (Childs et al., 1994).

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The linkages between knapweed flies and increased deer mouse populations have been well established; however, assessing whether this strong pairwise interaction results in associated arthropod declines or even an increase in hantavirus incidence in human populations requires additional experimental work. The study linking oaks, moths, mice, deer and ticks (Jones et al., 1998) provides an inspiring example of how such work can be conducted. At a minimum it is clear that the complicated linkages (direct and indirect) in food webs are affected by introduction of biological control agents. Often such linkages may result in unpredictable and surprising effects. Awareness of the potential for such effects, while still unpredictable in direction (negative or positive) or magnitude, could guide future investigations towards the direct and indirect effects of biological control introductions. A true assessment of the magnitude of changes in food webs as a result of biological control introductions needs access to pre-pest invasion data. For example, in the knapweed fly system, knapweed invasion reduced plant diversity, and most likely seed output and associated invertebrate communities. Whether mouse populations, now supported through overwintering fly larvae, are indeed higher than before knapweed invasion remains unclear.

Recommendations for Evaluating Non-target Impacts Using previously published information and case studies as outlined above, a series of recommendations can be made for postrelease studies of impacts of biological control agents. In practical terms, a logical method of evaluating non-target effects is to combine this with measuring target effects using similar methods as far as possible. This can be appropriate when target and potential non-target hosts are in the same environment: for example, in early post-release studies and in cases where the biological control agent is very immobile; however, impacts often extend beyond the

target host environment, requiring a different approach. An outline is given below for possible approaches for measuring postrelease impacts of biological control agents using the categories of non-target effects discussed above.

Direct effects on non-target, beneficial or valued species In preparation for post-release studies, prerelease opportunities can be taken to enhance robust post-release investigations: ● Population monitoring of a range of potential ‘high risk’ non-target species to give a baseline for post-release evaluation. The species list can come from: – knowledge of host range in country of origin; – quarantine host range studies in country of proposed new release which indicate potential non-target hosts (see van Lenteren et al., Chapter 3, this volume); – literature on known non-target hosts from releases elsewhere; – knowledge of phylogenetic and ecological affinities of target with fauna in country of proposed release; – known beneficial species in proposed country of release; – surveys of fauna and ecosystem processes in proposed country of release. See also Kuhlmann et al. (Chapter 2, this volume). ● Life table analysis for one or two ‘high risk’ non-target species so that postrelease impacts can be quantified. Candidate species can be selected from quarantine data where potential nontarget effects have been determined. ● Surveys of potential non-target species in the target host environment. ● Surveys to determine if and where the target pest occurs, outside of the environment within which it is known as a pest, can indicate potential environments in which non-target effects could occur.

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● Similarly, surveys outside of the target host environment can be useful for collecting information on the range and distribution of native hosts phylogenetically related to the target host. ● For some biological control agents, phylogenetic ‘relatedness’ to potential nontarget species is less important than habitat, e.g. some leaf-miner parasitoids are able successfully to attack leaf miners from a number of insect orders, but might show specificity to the host plant of the leaf miner complex. ● Information on the mobility of the proposed biological control agent and the target host is useful. Even if the biological control agent is relatively immobile, a host capable of wide dispersal can potentially carry the agent to new habitats. Then post-release: ● Determine which, if any, non-target species (including beneficials and other pest species where appropriate) are attacked in the field by sampling in the target pest habitat and beyond; determine which species and the proportion of non-target populations being attacked. ● Field evaluation of non-target attack should incorporate appropriate spatial, temporal and seasonal scales. ● For predators, gut analysis methods can be used to determine diet breadth (e.g. Hoogendorn and Heimpel, 2003). ● For herbivorous control agents, longterm monitoring using standardized techniques plus regular observations. ● Development of food web models using stable isotope ratios may be helpful in quantifying how invasions and biological control introductions at various trophic levels affect resource flows in different habitats. If non-target impact is identified in the field: ● Regular sampling programme is useful for determining comparative phenology of the target host and one or more identified non-target species to help predict impact.

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● Once a biological control agent is established in a non-target population, life table analysis is ideal for estimating impact if feasible. The dynamics here are likely to change over time and space. In weed-control programmes, long-term monitoring of target and non-target populations is essential. Attack alone does not imply that a population level effect will materialize, but declines or increases in plant populations should be recorded. ● Consider developing and testing a predictive model of population impact. ● Match pre-release predictions with postrelease evaluation. If they match up poorly, ascertaining reasons for this is of value to practitioners and regulators for future biological control proposals. ● If a beneficial species is attacked, it might be necessary to determine how this affects the benefits for which the species is valued. If non-target impact is not identified in the field: ● Maintain a low-intensity monitoring programme if possible, e.g. annual sampling at a small number of key sites near such release sites, or at areas of high target impact, and presumably of high biological control agent activity. If a biological control agent is very effective in reducing target populations, there may be a period when the agent is under pressure to locate suitable alternative hosts.

Competition with or displacement of other natural enemies ● Pre-release information on existing natural enemies (parasitoids, pathogens, predators and herbivores) of the target host, and particularly on identified potential non-target hosts, is useful, so that indirect effects can be ascertained post-release. ● Post-release, non-target hosts can be sampled over time to determine the extent of displacement of natural

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enemies by the newly released biological control agent and these results compared with pre-release data. This can be integrated with the investigations of direct non-target effects, above. See Messing et al. (Chapter 4, this volume) for further information.

Indirect effects on other trophic levels and food webs Specific recommendations in this area are extremely difficult, and studies need to be designed on a case-by-case basis. In general, the ecology of the system within which non-target parasitism is occurring needs to be very well known if realistic indirect effects are to be measured. The ultimate goal of the release of biological control agents is the restoration of invaded ecosystems. Uninvaded reference sites or long-term documentation of communities before release of biological control agents would provide useful benchmarks (Blossey, 1999). The current poor availability of biological inventories will make true assessments of indirect impacts on food webs and species difficult. Monitoring protocols need to be able to detect the extent to which the release of biological control agents can drive population fluctuations or changes in ecosystem function. Natural ecosystems are immensely complex, although invaded systems may have lost a degree of their original complexity. However, the prevalence of organism interactions makes it difficult to predict the response of even well-understood systems to environmental change or perturbations (Yodzis, 1988; Polis and Strong, 1996). For example, large fluctuations in the populations of birds, insects and mammals can be associated with the North Atlantic Oscillation and the El Niño Southern Oscillation (Sillett et al., 2000; Mysterud et al., 2001). Consequently, arguing with confidence that conditions have ecologically improved or deteriorated, or are simply different due to changes associated with spread of biological control

agents, is impossible unless such impacts can reliably be distinguished from natural oscillations or plant succession. Lag effects make the detection and mitigation of impacts even more challenging (Parker et al., 1999; Byers and Goldwasser, 2001). Laboratory and small-scale field experiments can not adequately replicate interactions that occur in the field. The only way to capture the full range of ecological effects of the release of biological control agents is by observations in actual ecosystems.

Conclusions Monitoring non-target impacts of biological control agents is likely to be the most effective means by which real progress can be made in improving the pre-release decision-making process. Only by field-testing assumptions made in the artificial environment of laboratories or quarantine facilities can the level of scientific uncertainty be reduced, and the confidence of biological control practitioners and regulators improve in the future. Given that in the foreseeable future we will never achieve complete certainty of knowledge of the extremely complex ramifications of releasing a new species into any new environment, there is potential for a progressive improvement that can be attained by feeding back information from field releases to each new biological control proposal. The significance of this improvement will depend upon the quality, scale and timescale of post-release information that can be obtained, and upon the availability of funding for long-term monitoring. At the present time, we have no accepted standards and approaches for post-release evaluations in biological control, and the development of such techniques is of high priority. Investigations to determine whether keystone species or bioindicator approaches may provide useful information need to be implemented so that these approaches can be refined over time. Any assessment of the true ecological and economical impacts of biological control will

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require sophisticated monitoring programmes to provide meaningful data. Most importantly, while the focus of this book is on non-target impacts, monitoring techniques developed to assess direct and indirect effects of biological control will also allow us to assess potential ecological benefits. These benefits may outweigh potential and realized non-target effects, but at present we lack the ability to assess these. Ideally, such monitoring efforts would be embedded in nationally or internationally organized and implemented environ-

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mental monitoring programmes to provide sufficient detail to detect environmental changes precipitated by biological control. Such monitoring programmes and standards are in place, for example for marine environments and monitoring of greenhouse gases. For monitoring impacts of biological control this situation seems to be a long way off, and so our recommendations for post-release monitoring are, by default, second best. However, given appropriate and well-resourced effort, it might be possible to move the goalposts slightly nearer.

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Entomological Research Conference. The Cooperative Research Centre for Tropical Pest Management, Brisbane, Australia, pp. 561–564. Steenberg, T., Langer, V. and Esbjerg, P. (1995) Entomopathogenic fungi in predatory beetles (Col: Carabidae and Staphylinidae) from agricultural fields. Entomophaga 40, 77–85. Stiling, P. and Simberloff, D. (2000) The frequency and strength of nontarget effects of invertebrate biological control agents of plant pests and weeds. In: Follett, P.A. and Duan, J.J. (eds) Nontarget Effects of Biological Control Introductions. Kluwer Academic Publishers, Norwell, Massachusetts, pp. 31–43. Story, J. (2002) Spotted knapweed. In: Van Driesche, R., Blossey, B., Hoddle, M., Lyon, S. and Rearden, R. (eds). In: Biological Control of Invasive Plants in the Eastern United States. Forest Health Technology Enterprise Team, Morgantown, West Virginia, pp. 169–180. Strong, D.R. (2002) Populations of entomopathogenic nematodes in foodwebs. In: Gaugler, R. (ed.), Entomopathogenic Nematology. CABI Publishing, Wallingford, UK, pp. 225–240. Stufkens, M.W., Farrell, J.A. and Goldson, S.L. (1987) Establishment of Microctonus aethiopoides, a parasitoid of the sitona weevil in New Zealand. In: Popay, A.J. (ed.) Proceedings of the 40th New Zealand Weed and Pest Control Conference. The New Zealand Weed and Pest Control Society, Quality Inn, Nelson, New Zealand, pp. 31–32. Timper, P., Kaya, H.K. and Gaugler, R. (1988) Dispersal of the entomogenous nematode Steinernema feltiae (Rhabditida: Steinernematidae) by infected adult insects. Environmental Entomology 17, 546–550. USDA (1999) Reviewers Manual for the Technical Advisory Group for Biological Control Agents of Weeds. Manual Unit of Plant Protection and Quarantine, Animal Plant Health Inspection Service (APHIS), United States Department of Agriculture, Annapolis, Maryland. Van Driesche, R. (2004) Predicting host ranges of parasitoids and predacious insects – what are the issues. In: Van Driesche, R. and Reardon, R. (eds) Assessing Host Ranges for Parasitoids and Predators Used for Classical Biological Control: a Guide to Best Practice. USDA Forest Service. Morgantown, West Virginia, pp. 1–3. Waage, J.K. (2001) Indirect ecological effects of biological control: the challenge and the opportunity. In: Wajnberg, E., Scott, J.K. and Quimby, P.C. (eds) Evaluating Indirect Ecological Effects of Biological Control. CABI Publishing, Wallingford, UK, pp. 1–12. Wang, X.G. and Messing, R.H. (2002) Newly imported larval parasitoids pose minimal competitive risk to extant egg–larval parasitoid of tephritid fruit flies in Hawaii. Bulletin of Entomological Research 92, 423–429. Yodzis, P. (1988) The indeterminancy of ecological interactions. Ecology 69, 508–515.

11

Molecular Methods for the Identification of Biological Control Agents at the Species and Strain Level Richard Stouthamer Department of Entomology, University of California, Riverside, CA 92521, USA (email: [email protected]; fax number: +1-951-827-3086)

Abstract Natural enemies used in biological control programmes are sometimes difficult to identify because of their small size and lack of distinguishing morphological characters. This applies in particular to parasitoid wasps, which form the most important group of natural enemies. There are two aspects of identification that are important: the correct recognition of a taxon and its name. With the current availability of molecular methods, our ability unambiguously to recognize an individual as belonging to a particular taxon has vastly improved. These methods can now be applied by people with little training in insect taxonomy for determining if an unknown specimen belongs to a molecularly known taxon or not. However, the formal naming and description of taxa does require the specialized knowledge of insect systematists. Descriptions and cost estimates are given for the use of: Randomly Amplified Polymorphic DNA (RAPD), Microsatellite DNA, Inter Simple Sequence Repeats (ISSR), the Internal Transcribed Spacers (ITS1 and ITS2), the D2 expansion regions of the 28s ribosomal gene and the Cytochrome Oxidase I and II of the mitochondria. Examples are given of the application of these techniques in biological control projects such as: development of molecular keys to recognize different taxa; methods for the recognition of only a single species when the knowledge of the identity of other species is not important; recognition of released wasps in an already established population of the same species; and recognition of contamination in mass rearing.

Introduction Identification of natural enemies used in biological control programmes is often problematic. For many reasons it can be difficult to name a natural enemy: (i) the systematics of the group to which the species belongs is not well developed; (ii) the species is new to science; or (iii) the

species is difficult to distinguish from other species in the genus. Two aspects of the recognition of the potential biological control agent are important: the unambiguous recognition of the taxon, and its name. Much is gained if the name of a species is known because it will allow access to the existing knowledge of the species in the literature. Furthermore, the name of the

©CAB International 2006. Environmental Impact of Invertebrates for Biological Control of Arthropods: Methods and Risk Assessment (eds F. Bigler et al.)

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species is often required for importation and release permits. While it is desirable to be able to recognize and have a name for the natural enemy, for biological control purposes it is enough to have an unambiguous recognition system and to wait for the systematics of the group to catch up with our ability to recognize the taxon. When the recognition of species was purely based on morphological characters, the involvement of taxonomists was needed. However, with the arrival of a vast array of molecular methods it is now possible to develop a system unambiguously to recognize different taxa without necessarily finding morphological characters that distinguish them from closely related taxa. In this chapter I will review a number of methods that have recently been used for the identification of different insect species. This review is based to a large extent on the work that has been done on the recognition of Trichogramma species using molecular markers (Pinto et al., 1997; Silva et al., 1999; Stouthamer et al., 1999; Stouthamer et al., 2000a,b; Ciociola et al., 2001a,b; Pinto et al., 2002, 2003; Borghuis et al., 2003; de Almeida and Stouthamer, 2003). Trichogramma are minute parasitoid wasps that parasitize the eggs of mainly Lepidoptera (Pinto and Stouthamer, 1994). The wasps of this genus are applied in large numbers in biological control programmes of moths and butterflies (Smith, 1996). The genus is distributed worldwide and at least 200 species have been described. Both in North America and in Europe, systematic work has been done on this genus; most recently, the North American fauna has been treated thoroughly by Pinto (1999). Their small size and the lack of distinguishing morphological features make them difficult to identify to the species level. Characters that are used for their identification are largely based on the structure of the male genitalia (Nagarkatti and Nagaraja, 1971). Extensive preparation of specimens is required to make these structures visible (Platner et al., 1999). This identification system poses a number of problems from the applied point of view. If wasps are released for biological control, the identification of

field collected material requires a substantial involvement of taxonomists who are not really waiting to do this routine identification. Also, because this identification system is based on male characters only, many of the collected specimens (females) cannot be identified. Consequently, additional characters have been developed to identify the species, such as allozymes (Pinto et al., 1992; Pintureau, 1993) and the DNA sequences of various genes (VanlerbergheMasutti, 1994; Stouthamer et al., 1999). The genus Trichogramma is a good test case for developing and testing the usefulness of molecular identification systems because thorough morphological and crossing studies have been done for the species in this genus (Pinto, 1999). This allows for a comparison between the molecular data and the characters used in the traditional approach to species recognition. In addition, Trichogramma species have been sampled from many different populations and sometimes also over a wide geographical range, which allows the determination of the stability of the molecular identification system. While the emphasis in this chapter is on Trichogramma, the same approach can be used for other biological control agents. Here, the different molecular methods for the recognition of species will be discussed followed by examples of: (i) how to develop a molecular key for a group of taxa, (ii) how molecular methods can be used to specifically discriminate one taxon from other closely related taxa, and (iii) how released wasps of a particular species can be distinguished from the resident population of that species.

Molecular Methods Used to Recognize Different Natural Enemy Species and Strains When two populations are separated from each other and cannot interbreed, they will no longer exchange genes. If one of the populations experiences a random mutation, then the chance that the same mutation happens in the second population is negligible. The longer the populations are

Molecular Methods for the Identification of BCAs

isolated from each other, the more differences accumulate. At some point, these differences become so large that these two different populations are now considered to be two different species. DNA sequences can then be used to differentiate between these taxa. Some genes accumulate mutations more rapidly than do other genes. Consequently, the DNA sequence of almost any gene can be used to tell two unrelated species apart, but for closely related species only rapidly evolving genes can be used. Here, we will discuss the most commonly used genes for distinguishing taxa, from the most rapidly evolving genes to the more slowly evolving genes.

Randomly amplified polymorphic DNA (RAPD) RAPD PCR (Polymerase Chain Reaction) is based on the use of short, ten base pair (bp)-long, primers. Sets of primers with different sequences can be bought and tested. No a priori knowledge is needed of the genome of the insect to be studied. The primers will amplify a stretch of DNA from those places in the genome where a priming site on the forward and on the reverse strands occur less than approximately 2500 bp from each other. Per primer, several products of different sizes will be amplified. If there is variation in the genome of the species for these priming sites, different banding patterns will be visible after electrophoresis. The RAPD technique is inexpensive to develop and may give information on the species status of individuals if species-specific differences in RAPD profiles exist (Kazmer et al., 1995). The drawback of this identification system is the often poor reproducibility of the results. Because of these reproducibility problems this technique is used less and less.

Microsatellite DNA A large part of the genome of insects consists of repetitive DNA, where a DNA sequence is repeated many times.

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Microsatellite DNA consists of short repeats (two to six base pairs) that are tandemly repeated ten to 100 times. These microsatellite loci are found throughout the genome of most insect species. Many microsatellite loci have a large number of alleles that differ from each other in the number of repeats. This allelic polymorphism makes microsatellites such good markers for determining, for instance, the mating structure of a population. PCR is used to amplify the microsatellite locus, and the PCR product then needs to be analysed on an automatic sequencer to determine its exact size. Finding microsatellite loci in the genome of a species is labour intensive. In general, it involves extracting the genomic DNA of the species, followed by a step to enrich the DNA for the presence of microsatellite repeats and cloning this enriched DNA and, finally, determining the sequences of the cloned DNA. Those clones that contain microsatellite sequences are then used to design primers. Subsequently, the primers are tested and those that consistently amplify the microsatellite are then used to determine if the population shows variation in that microsatellite locus. Developing microsatellite primers is a long process and, while in principle it is not difficult, having this done by a specialist company saves both money and time. At present, the cost for developing microsatellites by a commercial laboratory is approximately $10,000–12,000. The cost of determining the genotype on an individual is high because in addition to doing a PCR reaction per individual, using labelled primers, the PCR product needs to be analysed on an automated sequencer. Therefore, the cost per individual equals the number of different microsatellite PCR reactions performed per individual plus the cost of determining the size of the PCR products on an automated sequencer. Microsatellites are applied for population studies and can answer such questions as the area of origin of a particular population from the total geographic area in which a species occurs. Additionally, they can be used to determine how much an augmentative release improves the immediate parasitization of a pest (see below).

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Inter simple sequence repeats (ISSR) ISSR is the ‘poor man’s’ version of microsatellites. To apply this method, no sequence information is needed of the genome of the species to be studied. It uses primers that consist of an anchored microsatellite repeat. The five prime end of the primer consists of a microsatellite repeat while the three prime end has a non-repetitive sequence, for instance ATATATATATATGT, etc. Sets of these primers can be bought and they need to be tested in order to select the ones that show the appropriate level of variation. Similarly to RAPD PCR, only a single primer is used, and it will bind to those places in the genome where the binding sites on the forward and on the reverse strand are separated from each other by less than 2500 bp. The PCR products of ISSR PCR are made visible on agarose gels. ISSRs can be used for population studies, similarly to RAPD studies. The advantage of ISSR PCR over RAPD PCR is that there are less repeatability problems with this technique. The costs of applying this technique are the cost of extracting DNA from individual insects, performing a PCR reaction and determining the size of the bands on an agarose gel.

Internal transcribed spacers (ITS1 and ITS2) While the previous methods are used mainly for population studies, the determination of DNA sequences of individuals is

Internal transcribed spacer 1

Intergenic spacer

28s

generally utilized to ask questions about the species/biotype status of different individuals. ITS1 and ITS2 are the internal transcribed spacers of the ribosomal cistron. The ribosomal cistron consists of the genes that code for the RNA that forms the backbone of ribosomes. It consists of three genes that actually code for the ribosomal RNA (18s, 5.8s and 28s), two spacers (ITS1 and ITS2) that separate these genes and a spacer (Intergenic spacer) that separates the cistron from the next cistron (Fig. 11.1). The genes coding for the ribosomal RNA are highly conserved: this means that there are not many differences in the DNA sequences of these genes between unrelated groups of animals. Consequently, these genes are not very useful for telling the difference between closely related species. However, the spacers do not code for any RNA that ends up in the ribosomes, although the spacers do play some role in allowing the ribosomal RNA to fold properly. Mutations in these spacers are not selected against very strongly and, consequently, large differences exist in these sequences when different species are compared. Spacers are very suitable for telling differences between species. Per genome, up to 1000 copies of the ribosomal cistron can exist (Collins et al., 1989) in an area of the chromosome called the nuclear organizing region (NOR). NORs are found either on a single chromosome or on a number of different chromosomes. The different copies of the ribosomal cistron remain very similar through a process called ‘concerted evolution’ (Li and Graur,

Internal transcribed spacer 2

Intergenic spacer

18s

5.8s

28s

rRNA

rRNA

rRNA

18s

Fig. 11.1. Organization of the ribosomal cistron consisting of the intergenic spacer, 18s rRNA gene, internal transcribed spacer 1, 5.8s rRNA gene, internal transcribed spacer 2 and the 28s rRNA gene. This ribosomal cistron is tandemly repeated hundreds of times.

Molecular Methods for the Identification of BCAs

1991). While in general this appears to work for all the copies of this cistron in the genome of an individual, sometimes the copies on different chromosomes will differ somewhat from each other. In that way two slightly different ‘gene families’ will occur in the same individual. For instance, copies of the ITS2 of the deer tick, Ixodes ricinus (L.) (Acari: Ixodidae), differed by up to 4% of the nucleotides (Rich et al., 1997). ITS1 and ITS2 are very useful for creating keys for the identification of arthropods because these spacer regions do not vary only in DNA sequence between species, but often also in their length. The length of a spacer is a good trait because it can be determined easily by simply doing gel electophoresis using the PCR product of the particular spacer. Closely related species can have spacers that are very different in length. However, even if species have the same length of spacer region, they generally differ substantially in DNA sequence. To determine the sequence of ITS products, it is necessary to clone the PCR product first before it can be sequenced. The reason for this is that individuals can harbour several slightly different ITS sequences. If a PCR product, containing slightly different sequences, is sequenced directly, this mixture of sequences can not be read reliably by the automated sequencer. The cost of determining a sequence for a spacer consists of: (i) performing a PCR reaction; (ii) the cost of cloning the PCR product; (iii) performing a second PCR reaction on the cloned DNA; and (iv) sending the purified PCR product to the automated sequencer for two sequencing reactions.

Expansion domain D2 of the ribosomal 28s The 28s ribosomal RNA consists of core areas and a series of expansion regions. These expansion regions are numbered D1, D2, etc. While the core regions are defined as those regions of the ribosomal RNA that are also found in prokaryotes, the eukaryotic RNA has expanded from the prokary-

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otic RNA though the expansion regions (Linares et al., 1991). The core regions are highly conserved, but the expansion regions are less conserved. The expansion regions that are often used for identifying species or higher taxa are the D2 and D3. In Hymenoptera the D2 sequence is sometimes used for species identification (Babcock and Heraty, 2000). Within an individual, the sequence of D2 does not vary so the cost of determining the sequences consists of: (i) PCR reaction; (ii) purifying the PCR product; and (iii) the cost of two sequencing reactions on the automated sequencer.

Cytochrome oxydase (COI and COII) In contrast to the ribosomal spacers and expansion regions that are found on the nuclear chromosomes, the Cytochrome Oxydase I and II are located in the mitochondrial genome of the organism. Mitochondria are maternally inherited. Although only a single variant of these genes is present in the mitochondrial genome, sometimes copies of these genes have been transferred to the nuclear genome. These nuclear genes of mitochondrial origin (pseudogenes) generally lose their original function and will rapidly accumulate mutations. Pseudogenes can be recognized when the amino acid sequences of these genes are compared with known functional copies of COI and COII. The pseudogenes will often have stop codons in their sequence. COI and COII are used for species identification. Some withinspecies variation exists, which may make their application for identification difficult. Within an individual the sequence of COI and COII will not vary (except if pseudogenes are amplified as well), so the cost of determining the sequences consists of: (i) PCR reaction; (ii) purifying the PCR product; and (iii) the cost of two sequence reactions on an automated sequencer. If pseudogenes are also amplified, a cloning step needs to be inserted in the procedure, and several clones may need to be sequenced to find the ‘true’ sequence.

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Primers for the different applications Table 11.1 gives an overview of which DNA sequences and methods are the most suitable for different biological control applications. Primers can be found for many of the different gene regions described here in the following publications: Brower and DeSalle (1994) – primers for nuclear genes such as ITS1, ITS2, D2; Simon et al. (1994) – primers for mitochondrial genes COI, COII, etc. Small amounts of primers for many different gene regions can be bought from different vendors. The University of British Columbia sells sets of primers for RAPD, ISSR, mitochondrial DNA and nuclear DNA of insects (http://www.michaelsmith.ubc.ca/services/NAPS/Primer_Sets/).

Molecular Recognition of Taxa If the species in a group are well characterized and there is no question regarding the species identity of individuals, molecular methods for identification may still be advantageous, for instance, to identify larval stages or adults if the morphological identification requires extensive specimen preparation. Different genes may be most suitable for different taxa. Two different type of genes can be used, i.e. single-copy genes (i.e. one copy of the gene per haploid set) or multi-copy genes (i.e. many copies per haploid set or many copies in the cytoplasm of the cell). Multi-copy genes have

the advantage that much more template is present in an individual insect, and consequently even in somewhat degraded DNA it is still possible to amplify these genes. Examples of multi-copy genes are the genes and spacers in the ribosomal cistron and genes on mitochondria. The ribosomal spacers (genus Nasonia, Trichogramma, etc.) (Campbell et al., 1994; Stouthamer et al., 1999) and the D2 region of the 28s rRNA (genus Encarsia) (Babcock and Heraty, 2000) have been used successfully for the recognition of different species of parasitoid wasps. For most genes, differences will be found between species; however, if the goal of the identification method is to develop an inexpensive way for reliably identifying the species then the spacer regions are often most suitable. The spacers (ITS1 and ITS2) have the advantage that they not only differ in sequence between species, but also in size, and because many copies exist per haploid set they are relatively easy to amplify. Most of the genes encoding for proteins such as the mitochondrial COI and COII, and the ribosomal regions such as D2, differ in sequence but not in size. It is much more inexpensive to determine the size of a PCR product than to determine its sequence. There can be considerable difference in the size of the spacers between species, for instance in the North American Trichogramma species the ITS2 spacer varies in size from 389 in Trichogramma itsybitsi to 510 in Trichogramma interius (Pinto et al., 2002). Similarly, there is large

Table 11.1. Molecular methods that are most suitable for different biological control applications. Application

Method

Population studies Mating structure Geographic origin of populations Recognition of released specimens

Microsatellites, ISSR Microsatellites, ISSR, COI, COII Microsatellite, ISSR

Identification of different species Species recognition Identification key

ITS1, ITS2, D2, COI, COII ITS1, ITS2

Phylogeny

COI, COII, D2

Quality control: species ID of reared material

ITS1, ITS2

Molecular Methods for the Identification of BCAs

variation in the size of the ITS1 sequence in many closely related species (Chang et al., 2001). Once a gene has been chosen, many individuals of each species need to be sequenced to establish that the variation in the DNA sequence within a species is low and that consistent differences exist between species. In addition, for multicopy genes (such as the ribosomal spacers) several clones of a PCR product of a single individual need to be sequenced because within-individual variation may exist (Rich et al., 1997; Fenton et al., 1998). The information presented above is based on the assumption that the species are well characterized. However, the same approach can be applied if the group is not well known and consists of one or several species. To accomplish this, initially sequence several genes, for instance a ribosomal spacers (ITS1 and ITS2) and a mitochondrial gene (COI or COII) of many different collections, and determine if groups (= taxa) can be recognized in this assemblage. Next, try to establish several lines of each of the taxa that have been recognized using the sequences and perform crossing experiments to verify crossing compatibility between the different putative species. Once consistency is found between crossing compatibility and sequence groups, the taxa belonging to a different sequence group can be assumed to be different species. If two lines that share the same sequence are incompatible in the crossing experiments, they should be tested to exclude the involvement of microbial symbionts as a cause of the incompatibility (Stouthamer, 2004).

Development of molecular keys to identify different species Once the ITS sequences are known, a molecular key based on the PCR product can be constructed using as characters: (i) the size of the PCR product; and (ii) differences based on the size fragments following digestion of the PCR product with restriction enzymes.

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In general, the size character is useful if the size of the PCR product of the species differs by at least 20 nucleotides for PCR products in the range of 300–600. The larger the PCR product, the larger the difference in size needs to be for a reliable distinction of the products using agarose gels. If all the species in the group differ by more than 20 nucleotides in size then the species in the group can be distinguished simply based on the size character. In a number of cases this is as far as one needs to go. For instance, Alvarez and Hoy (2002) found that the two Ageniaspis species, released in Florida for the biological control of the citrus leaf miner, differed in the size of their ITS2 by 300 nucleotides. Sometimes different species have a similar-sized PCR product. To be able still to distinguish them, the PCR product may be cut by restriction enzymes into differentsized fragments. For the restriction fragment characters we need to determine the locations within each of the sequences where the restriction enzymes cut. This can be done using computer programs that are available free on the web. For instance, the sequence alignment editor BioEdit (www.mbio.ncsu.edu/BioEdit/bioedit.htm) contains links to the program WebCutter (www.firstmarket.com/cutter/cut2.html), that can be used to generate restriction maps of the sequences. These programs have as output the size of the fragments resulting from the cutting action of a particular restriction enzyme. Once the restriction enzymes have been identified that allow the distinction of species with similar-sized PCR products, their price per unit should be checked. The price of some restriction enzymes prohibits their routine application. The best restriction enzyme for distinguishing two species with a similar-sized PCR product is one that will cut both PCR products into differently sized fragments. Such restriction enzymes have the advantage that it is immediately clear that, indeed, the restriction reaction has worked. Sometimes it is necessary to choose a restriction enzyme that will cut the product of only one species. This situation is less desirable because if a PCR

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product is not cut then the question remains whether the restriction digest has failed or whether it is, indeed, the species that lacks the restriction site for that particular restriction enzyme.

Case study: the development of a molecular recognition system for species of the genus Trichogramma In close cooperation with Dr J.D. Pinto, we have developed a molecular-based system to identify the different species of North American Trichogramma species. This system is based on the sequences of the Internally Transcribed Spacer 2 (ITS2) of the ribosomal cistron. As a first test of the molecular identification system, we compared the ITS2 sequences of closely related Trichogramma species of the pretiosum group. This group consists of five closely related species that are difficult to distinguish morphologically. Using the ITS2 sequence, consistent differences were found between these species. The ITS2 differed not only in sequence but also in the size of the ITS2 spacer in some of the species. Two traits of the ITS2 were used to design a key for these species: (i) the size of the PCR product obtained from them, as can be visualized on an agarose gel; and (ii) the ability of different restriction enzymes to cut the PCR product in different-sized bands (Table 11.2).

These spacers can only be used if the variation of the ITS2 sequence within a species is limited so that all sequences derived from that species are more similar to each other than to the sequences from other species. Two types of within-species sequence variation are commonly encountered: (i) the presence of two slightly different gene families; and (ii) variation in the number of microsatellite repeats within the sequence. An example of different gene families within an individual is found in the species Trichogramma kaykai (Pinto et al., 1997; Stouthamer et al., 1999). These wasps are polymorphic for an ITS2 sequence that either does or does not contain an EcoR1 restriction site (Table 11.2). Both sequences are found within some individuals and restricting the ITS2 PCR product of this species with the enzyme EcoR1 results in a banding pattern that consists of three bands: one band for the uncut PCR product (i.e. the sequence that lacks the EcoR1 site), and two bands for the PCR product (i.e. the sequence with the EcoR1 site) that is cut at the restriction site. A similar polymorphism was found in the green peach aphid (Fenton et al., 1998). A much more common pattern of variation within the ITS2 of a species is the presence of microsatellite repeats of different copy numbers. Microsatellite repeats are tandem repeats of short DNA sequences, for instance: ATATATAT or

Table 11.2. Size (in number of nucleotides) of the PCR product of the ITS2 and flanking regions of the 5.8s and 28s rDNA genes, and the restriction fragments generated by the restriction enzymes Mse1 and EcoR1 of several species belonging to the Trichogramma pretiosum complex. (Modified from Stouthamer et al., 1999.)

Trichogramma species T. deion collection 1 T. deion collection 2 T. kaykai collection 1 T. kaykai collection 2 T. sathon collection T. pratti collection T. pretiosum collection 1

Size of PCR product 511 517 582 575 553 569 522

Fragment sizes after digestion with Mse1

EcoR1

383, 61, 67 389, 128 359, 223 352, 223 424, 129 569 522

511 517 312, 279 575 553 313, 256 522

Molecular Methods for the Identification of BCAs

GATGATGAT, etc. Variation in the number of repeats is commonly found in ITS sequences of a single species; for instance, Trichogramma deion commonly has seven TC repeats starting at position 24, but in some cases six, and in others nine (Table 11.3) (Stouthamer et al., 1999). From the size and restriction data (Table 11.2) we can develop a molecular key (Table 11.4). Initially, the size character is used to separate all taxa in groups that can be distinguished by size, and for those cases where the size criterion alone does not work (i.e. the combinations T. pretiosum/T. deion and T. kaykai/T. pratti) we also use the restriction enzymes.

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Examples of keys developed for Trichogramma species are: (i) those occurring in orchards in North America (Pinto et al., 2002); (ii) the species occurring in Portugal (Silva et al., 1999); and (iii) the species known from Brazil (Ciociola et al., 2001a). Similar ITS-based keys have also been developed for the ticks of North America (Poucher et al., 1999). All Trichogramma species that can be distinguished using morphological characters can also be distinguished using ITS2 sequences. However, the morphologically indistinguishable species T. minutum and T. platneri also have indistinguishable ITS2 sequences (Stouthamer et al., 2000a).

Table 11.3. Aligned sequences of a part of the ITS2 of several collections of Trichogramma deion (modified from Stouthamer et al., 1999). The shaded area shows the variation in the number of microsatellite repeats. Dashes indicate gaps in the aligned sequence. Name of T. deion line Partial ITS2 sequence DRIV DIRV DEUR DMEN DTSN DSHE DLC1 DPTL DCLO DRV1 DSVP DMRY DPIN

GTTTATAAAAACGAACCCGACTGCTCTCTCTCTCTCTCTCTCGCAAGAGAAA– –GAGAG GTTTATAAAAACGAACCCGACTGCTCTCTCTCTCTCTCTCTCGCAAGAGAAA– –GAGAG GTTTATAAAAACGAACCCGACTGCTCTCTCTCTCTCTC– – – – GCAAGAGAAAGAGAGAG GTTTATAAAAACGAACCCGACTGCTCTCTCTCTCTCTC– – – – GCAAGAGAAA– –GAGAG GTTTATAAAAACGAACCCGACTGCTCTCTCTCTCTCTC– – – – GCAAGAGAAAGAGAGAG GTTTATAAAAACGAACCCGACTGCTCTCTCACTCTCTC– – – – GCAAGAGAAAGAGAGAG GTTTATAAAAACGAACCCGACTGCTCTCTCACTCTCTC– – – – GCAAGAGAAAGAGAGAG GTTTATAAAAACGAACCCGACTGCTCTCTCTCTCTCTC– – – – GCAAGAGAAA– GAGAG GTTTATAAAAACGAACCCGACTGCTCTCTCTCTCTCTC– – – – GCAAGAGA– AGAGAGAG GTTTATAAAAACGAACCCGACTGCTCTCTCTCTCTCTC– – – – GCAAGAG–GAGAGAGAG GTTTATAAAAACGAACCCGACTGCTCTCTCTCTCTC– – – – – – GCAAGAGA–AGAGAGAG GTTTATAAAAACGAACCCGACTGCTCTCTCTCTCTC– – – – – – GCAAGAGA–AGAGAGAG GTTTATAAAAACGAACCCGACTGCTCTCTCTCTCTC– – – – – – GCAAGAGAAAGAGAGAG

Table 11.4. Molecular key to the species of the T. pretiosum complex based on the ITS2 PCR product and restriction digest with the enzymes EcoR1 and Mse1 (modified from Stouthamer et al., 1999). 1. Size of the PCR product greater than 540bp Size of the PCR product less than 540bp 2. PCR product not cut by EcoR1 PCR product cut or partially cut by EcoR1 3. Size of the PCR product 580 bp Size of the PCR product 550 bp 4. PCR product restricted with Mse1 gives two bands PCR product not restricted by Mse1 5. PCR product restricted with Mse1 gives two or three bands PCR product restricted with Mse1 gives one band

2 5 3 4 T. kaykai T. sathon T. kaykai T. pratti T. deion T. pretiosum

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These two species differ in their distribution, with T. minutum occurring generally in the eastern part of North America and T. platneri occurring to the west of the Rocky Mountains. In Idaho and Washington their ranges overlap (Pinto et al., 2003). These species are incompatible: in crosses between the species the fertilized eggs die. There does not appear to be any prezygotic isolation between them. When a female is placed with males from both species, the mating appears to be random (Stouthamer et al., 2000b). Because these two taxa are clearly different species we determined the sequence of the mitochondrial COII as a species-specific marker. For the COII the species differed consistently in five of the 365 nucleotides of the COII fragment that we amplified. Based on these differences, a restriction enzyme was found that was able to distinguish the two species from each other (Borghuis et al., 2003).

Modifications of the identification method While in the key above only the ITS2 was used, it is possible to make the identification method more user friendly by performing a multiplex PCR reaction, which allows for the identification of several species in a single step. Multiplex PCR is a PCR reaction using primers that amplify more than a single gene region. For instance, one could put in the PCR reaction mix both ITS1 and ITS2 primers, which results in two PCR products in the same reaction – one for ITS1 and one for ITS2. Since both spacers vary in size, these sizes can be used as an additional character that may allow us to distinguish species that have an ITS2 product of the same size, but differ in the size of the ITS1.

Methods for the recognition of only a single species when the knowledge of the identity of other species is not important Often, it is important to know if a newly released species persists in an area where closely related species of the same genus

also occur. As an example we could use the release of Trichogramma brassicae Bezdenko in maize in Switzerland, where it is important to know if the released wasps are also found in the surrounding natural areas. For the wasps collected from those areas it may not be important to know their identity: the only information we want to learn is if the collected wasps are T. brassicae or not. Under such circumstances specific primers can be designed that will amplify only a gene region of T. brassicae. To do this we will need to know the ITS2 sequence of the other Trichogramma species that may occur in that area. Species-specific primers can be developed by first aligning all the ITS2 sequences of the species found in the region, in order to identify those parts of the sequence where the species differ from each other. Primers can then be designed for these regions, but care should be taken to avoid problems caused by primer dimers, hairpins etc. (Sambrook and Russell, 2001). It is also possible to use primer design computer programs. Commercial programs are available for primer design such as Primer Premier® (Biosoft International) and Oligo® (Molecular Biology Insights). Files can be submitted to these programs that contain the DNA sequences of the different species occurring in a particular area, and the program will select primers that will amplify only one of the species. These commercial programs are expensive. Free web-based primer design programs are also available, but in general these do not allow the submission of more than a single sequence for analysis. An example of the use of specific primers is given in Zhu et al. (2000). Specific primers were designed that amplify only the DNA from the imported aphid parasitoids Aphelinus hordei Kurdjumov (Hymenoptera: Aphelinidae) and Aphidius colemani Viereck (Hymenoptera: Braconidae), and not the DNA from the native aphid parasitoid species in North America. These primers allowed the identification of the parasitoid inside the aphid mummies, thus reducing the time and effort in keeping the parasitized aphids in the laboratory until the parasitoids emerged.

Molecular Methods for the Identification of BCAs

Recognition of released wasps in an already established population of the same species In augmentative biological control it is often important to know the effectiveness of released wasps in already established populations of the same species. Different methods have been used, such as the marking of the released individuals using fluorescent dust; however, such releases give an insight only into the presence and dispersal of the released individuals. These marking methods cannot tell us much about the number of hosts parasitized by the released individuals. The use of genetic markers can give us the same information as the fluorescent markers, but in addition we can determine the effectiveness of the released wasps, because we can recognize their offspring. If we are releasing individuals into a sexual population, then the marked individuals will interbreed with the established population and the direct effect of the release can only be measured in the generation immediately following the release. To find a suitable genetic marker for the recognition of wasps to be released, one needs to find a genetic variant of, for instance, a microsatellite or an allozyme that is rare in the established population. Next, individuals that carry the rare variant need to be cultured to generate offspring that are homozygous for the rare marker. These offspring will then be used to create a mass rearing for release in the field. After the release of the marked parasitoids, hosts can be collected from the field to determine what fraction of the population carries the rare marker. This approach has been used by Kazmer and Luck (1995) to determine the importance of size for the success of Trichogramma in inundative releases. Lines were created that were homozygous for rare allozyme variants, which occurred only at low frequencies in the established population. Releases were done in tomato fields where trap cards containing host eggs had been distributed. These trap cards were replaced daily. In the laboratory all of the wasps emerging from the trap cards were tested to determine the allozyme variant that they carried. This allowed for an estimation

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of the relative importance of wasp size for the success of inundative biological control using T. pretiosum. In principle, any marker gene can be used for these experiments, including mitochondrial variants and even the presence or absence of symbionts; however, for this type of application, microsatellite DNA markers have special advantages. First of all, there are many different alleles per microsatellite locus, which necessarily results in many rare alleles. Therefore, it is possible to generate many different unique lines with rare markers that may be used for release in the field. Such marked lines can be used in succession in field releases.

Contamination of mass rearing Molecular methods can be used to check if contaminations have taken place in mass rearing. Particularly, in species that are small, contamination of mass rearing by a different species has taken place in the past, resulting in the unintended release of the wrong species for biological control (Rosen and DeBach, 1977). These contamination problems are not trivial. In North America, T. minutum and T. platneri are commonly used for the biological control of moth pests in orchards, and these species are often reared in the same insectary. These species are exceedingly difficult to identify, are morphologically identical and have the same ITS2 sequences. They can be recognized using the mitochondrial COII sequences. The geographic distribution of these species is such that they do not overlap, except for areas in Idaho and Washington. However, these species are completely incompatible, and releasing a species in the native area of the other will result in a suppression of the wasps, counteracting the intended biological control (Stouthamer et al., 2000b).

Vouching specimens for DNA analysis It is also important to vouch specimens of species that are released for biological

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control purposes in such a way that their DNA can be studied at later dates. It is important to do this, even for species whose identity seems to be clear. In some cases we later discover that a single species consists of two or more morphologically indistinguishable cryptic species. Specimens for DNA analysis should be preserved by keeping them in 95% ethanol in the refrigerator or freezer.

Cost of Molecular Identification In the past, many of these molecular techniques were very expensive to apply because both the machines and the chemicals needed to do the work were expensive. Many of these costs have now reduced, and PCR machines are now commonly found in laboratories.

approximately $20. Each PCR reaction that is performed costs approximately $1–2 in supplies.

Cost of developing microsatellites for a species Commercial laboratories will perform the work for approximately $10,000 and guarantee you some primer pairs that will amplify microsatellite regions within a period of two months. The work to accomplish this can be done without too much trouble in a wellequipped laboratory for approximately $5000 in supplies, but the amount of time it takes in labour makes the commercial price very competitive. The cost of determining the size of the microsatellites, i.e the allelic profile, ranges from $1 (in-house price at universities) to $3 per individual for commercial laboratories.

Cost of developing an ITS-based identification system

Discussion

If we assume that the group is completely unknown, then we need to collect at least 50 different lines of the species from the field. Determine the ITS2 sequence of each of these lines. Cost for three ITS sequences per line is approximately $30 in supplies and sequencing cost; for the 50 lines it will, therefore, be $1500. Next, these sequences should be imported into a sequence editor and similar sequences should be grouped. Depending on the number of different groups that are found, additional lines can be collected to improve the possibility that all species which are present are detected. Once all the groups have been identified and the variation within the groups is clear, a key can be developed. Initially, the sizes of the PCR products for the individual groups can be compared and, if need be, restriction enzymes can be identified and tested to distinguish species with a similar-sized PCR product. The cost of the restriction enzyme reaction can vary from ten cents per digest to several dollars. Specific primers can be developed and ordered, and the price of a primer pair is

In the past, biological control workers had to rely completely on taxonomists specialized in the natural enemy that was to be considered for biological control introductions. This has led to circumstances where biological control workers, without much formal, systematic training, became the taxonomic specialists for particular groups. This has not always led to a clear and stable taxonomy of the taxa that were considered. For instance, as recently as 1968, only ten Trichogramma species were recognized (Flanders, 1968). The species identification was based on the colour of the wasps. Now we know that there are at least 200 species of Trichogramma and the colour of individuals of a species depends to some extent on the rearing temperature of the wasps (Pinto, 1999). Nowadays, many molecular methods are available for characterizing insects that are new to science or that are difficult to identify using morphological characters. These tools can now be used by scientists to group individuals according to the sequence of particular genes. If distinctly different groups are

Molecular Methods for the Identification of BCAs

found then there is a good chance that these are indeed different species. This assumption should next be verified by crossing experiments both between the groups and within the groups. If there is consistent compatibility within the groups, and incompatibility between groups, these two groups represent different species. To recognize these species from field samples, PCR reactions can be developed that either amplify only the species of interest, or will allow for the identification of all closely related species. Such methods are not difficult to develop, are inexpensive and have many advantages over morphological methods. For identification based on morphology, adult specimens, often of a particular sex, are needed. Sometimes this requires the expensive maintenance of the field-collected material until the natural

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enemy has reached adulthood. Using molecular methods for identification, both sexes and all stages of the insect can be recognized. This can save a lot of expense, and can also give more accurate estimates of parasitization rates from field-collected samples. The development of recognition systems for natural enemies used in biological control is already at an advanced stage; however, for release permits it is still necessary to have a name attached to the taxon that we may want to release. It would be of considerable help if the regulatory agencies would recognize the value of these new methods that can now be as precise, or even more precise, in the recognition of species than some of the morphological characters upon which we relied previously.

References Alvarez, J.M. and Hoy, M.A. (2002) Evaluation of the ribosomal ITS2 DNA sequences in separating closely related populations of the parasitoid Ageniaspis. Annals of the Entomological Society of America 95, 250–256. Babcock, C.S. and Heraty, J.M. (2000) Molecular markers distinguishing Encarsia formosa and Encarsia luteola (Hymenoptera: Aphelinidae). Annals of the Entomological Society of America 93, 738–744. Borghuis, A., Pinto, J.D., Platner, G.R. and Stouthamer, R. (2003) Partial cytochrome oxidase II sequences distinguish the sibling species Trichogramma minutum Riley and Trichogramma platneri Nagarkatti. Biological Control 30, 90–94. Brower, A.V.Z. and DeSalle, R. (1994) Practical and theoretical considerations for choice of a DNA sequence region in insect molecular systematics, with a short review of published studies using nuclear gene regions. Annals of the Entomological Society of America 8, 702–716. Campbell, B.C., Steffen-Campbell, J.D. and Werren, J.D. (1994) Phylogeny of the Nasonia species complex as inferred from an internal transcribed spacer and 28srDNA. Insect Molecular Biology 2, 225–237. Chang, S.C., Hu, N.T., Hsun, C.Y. and Sun, C.N. (2001) Characterisation of differences between two Trichogramma wasps by molecular markers. Biological Control 21, 75–78. Ciociola, A.I., Querino, R.B., Zucchi, R.A. and Stouthamer, R. (2001a) Molecular tool for identification of closely related species of Trichogramma (Hymenoptera: Trichogrammatidae): T. rojasi Nagaraja and Nagarkatti and T. lasallei Pinto. Neotropical Entomology 30, 575–578. Ciociola, A.I., Zucchi, R.A. and Stouthamer, R. (2001b) Molecular key to seven Brazilian species of Trichogramma (Hymenoptera: Trichogrammatidae) using sequences of the ITS2 region and restriction analysis. Neotropical Entomology 30, 259–262. Collins, F.H., Paskewich, S.M. and Finnerty, V. (1989) Ribosomal RNA genes of the Anopheles gambiae species complex. Advances in the Disease Vector Research 6, 1–28. de Almeida, R.A. and Stouthamer, R. (2003) Molecular identification of Trichogramma cacoeciae Marchal (Hymenoptera: Trichogrammatidae): A new record for Peru. Neotropical Entomology 32, 269–272. Fenton, B., Malloch, G. and Germa, F. (1998) A study of variation in rDNA ITS regions shows that two haplotypes coexist within a single aphid genome. Genome 41, 337–345.

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Flanders, S.E. (1968) The validity of Trichogramma pretiosum. Annals of the Entomological Society of America 61, 1122–1124. Kazmer, D.J. and Luck, R.F. (1995) Field tests of the size-fitness hypothesis in the egg parasitoid Trichogramma pretiosum. Ecology 76, 412–425. Kazmer, D.J., Hopper, K.R., Coutinot, D.M. and Heckel, D.G. (1995) Suitability of random amplified polymorphic DNA for genetic markers in the aphid parasitoid, Aphelinus asychis Walker. Biological Control 5, 503–512. Li, W.-H. and Graur, D. (1991) Fundamentals of Molecular Evolution. Sinauer, Sunderland, Massachusetts. Linares, A.R., Hancock, J.M. and Dover, G.A. (1991) Secondary structure constraints on the evolution of Drosophila 28S Ribosomal RNA expansion segments. Journal of Molecular Biology 219, 381–390. Nagarkatti, S. and Nagaraja, H. (1971) Redescription of some known species of Trichogramma, showing the importance of male genitalia as a diagnostic character. Bulletin of Entomological Research 61, 13–31. Pinto, J.D. (1999) Systematics of the North American species of Trichogramma Westwood (Hymenoptera: Trichogrammatidae). Memoirs of the Entomological Society of Washington 22, 1–287. Pinto, J.D. and Stouthamer, R. (1994) Systematics of the Trichogrammatidae with emphasis on Trichogramma. In: Wajnberg, E. and Hassan, S.A. (eds) Trichogramma and other Egg Parasitoids. CABI Publishing, London, UK, pp. 1–36. Pinto, J.D., Kazmer, D.J., Platner, G.R. and Sassaman, C.A. (1992) The taxonomy of the Trichogramma minutum complex: allozymic variation and its relationship to reproductive and geographic data. Annals of the Entomological Society of America. 85, 413–422. Pinto, J.D., Stouthamer, R. and Platner, G.R. (1997) A new cryptic species of Trichogramma (Hymenoptera: Trichogrammatidae) from the Mojave Desert of California as determined by morphological, reproductive and molecular data. Proceedings of the Entomological Society of Washington 99, 238–247. Pinto, J.D., Koopmanschap, A.B., Platner, G.R. and Stouthamer, R. (2002) The North American Trichogramma (Hymenoptera: Trichogrammatidae) parasitizing certain Tortricidae (Lepidoptera) on apple and pear, with ITS2 DNA characterizations and description of a new species. Biological Control 23, 134–142. Pinto, J.D., Platner, G.R. and Stouthamer, R. (2003) The systematics of the Trichogramma minutum species complex (Hymenoptera: Trichogrammatidae), a group of important North American biological control agents: the evidence from reproductive compatibility and allozymes. Biological Control 27, 167–180. Pintureau, B. (1993) Enzymatic analysis of the genus Trichogramma (Hym.: Trichogrammatidae) in Europe. Entomophaga 38, 411–431. Platner, G.R., Velten, R.K., Planoutene, M. and Pinto, J.D. (1999) Slide-mounting techniques for Trichogramma (Trichogrammatidae) and other minute parasitic Hymenoptera. Entomological News 110, 56–64. Poucher, K.L., Hutcheson, H.J., Keirans, J.E., Durden, L.A. and Black, W.C. (1999) Molecular genetic key for the identification of 17 Ixodes species of the United States (Acari: Ixodidae): A methods model. Journal of Parasitology 85, 623–629. Rich, S.M., Rosenthal, B.M., Telford, S.R., Spielman, A., Hartl, D.L. and Ayala, F.J. (1997) Heterogeneity of the internal transcribed spacer (ITS-2) region within individual deer ticks. Insect Molecular Biology 6, 123–129. Rosen, D. and DeBach, P. (1977) Use of scale insect parasites in Coccoidea systematics. Virginia Polytechnic and State University, Research Division Bulletin 127, 5–21. Sambrook, J. and Russell, D.W. (2001) Molecular Cloning: a Laboratory Manual. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York. Silva, I.M.M.S., Honda, J., van Kan, F.J.P.M., Hu, J., Neto, L., Pintureau, B. and Stouthamer, R. (1999) Molecular differentiation of five Trichogramma species occurring in Portugal. Biological Control 16, 177–184. Simon, C., Frati, F., Beckenbach, A., Crespi, B., Liu, H. and Flook, P. (1994) Evolution, weighting, and phylogenetic utility of mitochondrial gene sequences and a compilation of conserved polymerase chain reaction primers. Annals of the Entomological Society of America 87, 651–701.

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Smith, S.M. (1996) Biological control with Trichogramma: Advances, successes, and potential of their use. Annual Review of Entomology 41, 375–406. Stouthamer, R. (2004) Sex ratio distorters and other selfish genetic elements: Implications for biological control. In: Ehler, L., Sforza, R. and Mateille, T. (eds) Genetics, Evolution and Biological Control. CABI Publishing, Wallingford, UK, pp. 235–252. Stouthamer, R., Hu, J., van Kan, F.J.P.M., Platner, G.R. and Pinto, J.D. (1999) The utility of internally transcribed spacer 2 DNA sequences of the nuclear ribosomal gene for distinguishing sibling species of Trichogramma. BioControl 43, 421–440. Stouthamer, R., Gai, Y., Koopmanschap, A.B., Platner, G.R. and Pinto, J.D. (2000a) ITS-2 sequences do not differ for the closely related species Trichogramma minutum and T. platneri. Entomologia Experimentalis et Applicata 95, 105–111. Stouthamer, R., Jochemsen, P., Platner, G.R. and Pinto, J.D. (2000b) Crossing incompatibility between Trichogramma minutum and T. platneri (Hymenoptera: Trichogrammatidae): Implications for application in biological control. Environmental Entomology 29, 832–837. Vanlerberghe-Masutti, F. (1994) Molecular identification and phylogeny of parasitic wasp species (Hymenoptera: Trichogrammatidae) by mitochondrial DNA RFLP and RAPD markers. Insect Molecular Biology 3, 229–237. Zhu, Y.C., Burd, J.D., Elliott, N.C. and Greenstone, M.H. (2000) Specific ribosomal DNA marker for early polymerase chain reaction detection of Aphelinus hordei and Aphidius colemani from Diuraphis noxia. Annals of the Entomological Society of America 93, 486–491.

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The Usefulness of the Ecoregion Concept for Safer Import of Invertebrate Biological Control Agents

Matthew J.W. Cock,1 Ulrich Kuhlmann,1 Urs Schaffner,1 Franz Bigler2 and Dirk Babendreier2 1CABI Bioscience Switzerland Centre, Rue des Grillons 1, 2800 Delémont, Switzerland (email: [email protected]; [email protected]; [email protected]; fax number: +41-32-421-4871); 2Agroscope FAL Reckenholz, Swiss Federal Research Station for Agroecology and Agriculture, Reckenholzstrasse 191, 8046 Zürich, Switzerland (email: [email protected]; [email protected]; fax number: +41-44-377-7201)

Abstract From a scientific perspective it is clear that ecological boundaries are more relevant than national boundaries when assessing the hazards and risks of non-target effects of biological control agents, including invertebrate biological control agents (IBCAs). Different published approaches to categorizing terrestrial ecoregions are introduced and discussed. Movement of species between countries in the same ecoregion is clearly less risky than moving species between disjunct similar ecoregions, and the risk increases the further the separation between disjunct similar ecoregions, e.g. whether on the same continent or on different continents. The implications for movement of IBCAs within Europe are discussed in light of ecoregion classifications and examples of natural and human-mediated spread of introduced insects in the same region. In order to safely regulate the introduction of biological control agents, there needs to be consultation within an ecoregion. An ecoregion approach can be useful in predicting the likelihood that a classical IBCA will become established, but cannot robustly predict that an inundative IBCA will fail to become established. An ecosystem approach can be useful for assessing and managing risks associated with moving organisms within a contiguous land mass.

Introduction In this chapter, the core focus is on invertebrate biological control agents (IBCAs) for controlling arthropods and, specifically in this chapter, also weeds. Although we 202

consider that the ecoregion approach can be applied to all biological control agents, we would not necessarily extend all arguments to pathogen biological control agents, without further consideration of the issues.

©CAB International 2006. Environmental Impact of Invertebrates for Biological Control of Arthropods: Methods and Risk Assessment (eds F. Bigler et al.)

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Decisions regarding the approval of introduction of biological control agents, including IBCAs, are usually made at the national level. There are good reasons for this being related to national sovereignty and responsibility, clearly defined geographical boundaries and (in many cases), decision-making systems already in place. On the other hand, it is well known that biological control agents, once established, will spread to the limits of their ecological tolerance. In doing so, they encounter new habitats, species, climates, etc. and this can change the hazards and risks of non-target impact. From a scientific perspective it is clear that ecological boundaries are more relevant than national boundaries when assessing the hazards and risks of nontarget effects of biological control agents. Ecoregion is a term that ecologists can understand quite well at an intuitive level, but which can be very difficult to derive and use objectively. Ecoregion could be a useful concept in aspects of biological control, although different definitions do appear in the literature. Ecoregion has been defined as a physical region that is defined by its ecology, which includes meteorological factors, elevation, plant and animal speciation, landscape position and soils (US-EPA, 1996). A comparable definition is an area of similar climate, landform, soil, potential natural vegetation, hydrology, or other ecologically relevant variables (Clark et al., 1998). Related terms include ecoarea and ecosystem, but we will use the term ecoregion throughout this chapter except where referring to work which utilizes other specific terms. The ecoregion concept was developed based on natural distribution patterns of species, but we recognize that this pattern has been overlaid, and sometimes obscured, by land-use change, particularly the development of agriculture, as well as by humanmediated introductions of alien species. We are also aware that global warming will affect ecoregions, but do not consider the implications of this in detail here. We suggest that for the assemblage of species that characterize an ecoregion, the combined impact of global warming will be similar to

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that on individual species. Individual species will adapt to changing local conditions (Bale et al., 2002), or they will move to areas that match their existing optimum conditions (Coope 1978, 1995; Parmesan et al., 1999), or they will become extinct (Thomas et al., 2004). Similarly, ecoregions will change, move or disappear. In general, we expect that arthropods will change their range in response to global warming, and broadly speaking will move with their existing preferred ecoregion (Coope, 1978), so that risks to new non-target species encountered will not necessarily be great. Compared to the overall impact of global warming on humans and ecosystems, we consider this potentially increased risk to be trivial. In this chapter we shall explore the extent to which the ecoregion concept can be useful in aspects of the science of biological control. We start by reviewing some of the ecoregion classifications that have been proposed. We have included figures in shades of grey of part or all of these classifications to illustrate the scale and detail of mapping, illustrate particular aspects, and to explore the particular situation in central Europe, but for practical use, the original colour figures will need to be consulted. We then consider current usage of the ecoregion approach in biological control, the spread of alien insects in Europe in relation to ecoregions and the pathways they define, leading to a working model for quarantine needs when moving insects within Europe for study purposes, and finally draw some conclusions.

Ecoregion Classifications Various workers have attempted ecoregion classifications at the regional or global scale, for a variety of reasons. Although there are different systems for terrestrial, freshwater and marine environments, we restrict our consideration to terrestrial systems, as these cover almost all targets hitherto considered for biological control. In our analysis, for pragmatic reasons, we will focus primarily on Eurasia, but the arguments can be extended to other regions.

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Usually, the first parameter considered in an ecoregion classification is climate, particularly patterns of temperature and rainfall. This classification can be modified by important factors such as altitude, soil and vegetation systems. Alternatively, vegetation, which can be considered to reflect the ecological parameters, can be taken as the starting point. Very few systems attempt to include animal associations, and it can be argued that the animal associations will be a function of plant associations. Depending upon the weighting applied to different factors, some classifications show high concurrence with each other, but others can be very different. The larger the scale of analysis, the fewer categories there will be in the classification and the closer the concurrence is likely to be between systems. The four ecoclimatic zones, as illustrated by Bailey (1996) are not very contentious (Fig. 12.1). Europe to the Urals is mostly ‘humid temperate’ and similar, but disjunct, areas occur in Asia, North America, South America, southern Africa and south-eastern Australia. Thus, the

defined regions are conceptually straightforward on this broad scale. At the next level down, there can be many more ecoregions. Differences are apparent between different approaches, and patterns can become less clear. Bailey (1996) synthesizes the ecoregion approach to a global scale in his ‘Ecosystem Geography’, and provides a global analysis at the division level, i.e. continental scale (see also Bailey, 1998). The Times (1998) presents a vegetation-based analysis based partially on the work of P.E. James (e.g. James, 1966), which tends to highlight mountainous areas. Compare Bailey’s (1996) treatments of the division-level ecoregions of Eurasia with that of The Times (1998), as shown in Fig. 12.2. There are substantial similarities, but minor differences. Cleland et al. (1997), presenting a national hierarchical framework of ecological units for the USA, recognize three categories of ecoregion: domains at the global scale, divisions at the continental scale and provinces at the regional scale, as pre-

Fig. 12.1. The four ecoclimatic zones of the earth (Bailey, 1996).

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a

b

Fig. 12.2. Ecoregions of Eurasia: (a) after Bailey (1996) and (b) after The Times (1998). The numbers in the figure from Bailey (1996) refer to his ecoregions; thus, those from 100 refer to the Polar Domain (e.g. 120, Tundra Division; 130, Subarctic Division), those from 200 refer to the Humid Temperate Domain (e.g. 210, Warm Continental Division; 220, Hot Continental Division; 230, Marine Division; 240, Marine Division Mountains; 250, Prairie Division; 260, Mediterranean Division); those from 300 to the Dry Domain (e.g. 330, Temperate Steppe Division); and those with the prefix M denoting Mountain areas. Note how the Urals (1) separate European Russia (2) from Asian Russia (3), the Caucasus region (4) separates southern Russia (5) from south-west Asia (6), and the mountains running from Pakistan to north-east Russia (7) separate China (8) from the rest of temperate Asia (9).

sented by Bailey (2001) (Fig. 12.3). The subdivision of ecological regions can continue downwards into smaller and more homogeneous units, and Cleland et al. (1997) recognize nine levels in their hierarchy. The hierarchical nesting of ecoregion classifications is a useful feature, enabling the scale to be rapidly changed within the same classification. Comparing the three levels of scale in Fig. 12.3 shows

that only at the domain level is it apparent that there are similarities between eastern and western USA, separated by a different ecoregion; at the domain and province levels this aspect is partially obscured. Recent initiatives have been driven by the conservation movement, which needed objective methods in order to classify the world’s biodiversity as a starting point for

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(a)

(b)

(c)

Fig. 12.3. (a)–(c): Three hierarchical levels of ecoregion for the USA: domains, divisions and provinces (Bailey, 2001).

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identifying priorities for conservation of ecosystems. One of the early attempts in this area was that of the International Union for Conservation of Nature and Natural Resources (IUCN) (Dasmann, 1973, 1974; Udvardy, 1975), which identified 193 distinct biotas on a global scale, and was an important starting point for some of the more recent systems, e.g. Olson et al. (2001) below. For Europe, there are two useful recent classifications: The Digital Map of European Ecological Regions (ETCNPB, 2000) and the Biogeographical Regions of Europe, 2001, prepared by the European Environment Agency (EEA, 2003). The latter is based on earlier vegetation mapping (Noirfalise, 1987; Bohn et al., 2000), and recognizes official delineations used in the Habitats Directive (92/43/EEC) and for the EMERALD Network set up under the Convention on the Conservation of European Wildlife and Natural Habitats (Bern Convention) (Roekaerts, 2002). Building on these earlier works, Olson et al. (2001) have attempted a new global synthesis of terrestrial ecoregions in support of conservation of biodiversity, resulting in 867 distinct ecoregion units. These units are categorized into 14 hierarchical biomes and eight biogeographic realms. The biogeographical realms Nearctic, Neotropic, Palaearctic, Afrotropic, Indo-Malay and Australasia (Sclater, 1858; Wallace, 1876), together with Oceania and Antarctic (Fig. 12.5), are well established. The 14 biomes offer a level of detail, slightly less than the Biogeographical Regions of Europe (EEA, 2003), and intermediate between Bailey’s four ecoclimatic zones (Fig. 12.1) and the more detailed ecoregions of Bailey (1996), DMEER (ETCNPB, 2000) and Olson et al. (2001). The detailed mapping into 867 terrestrial ecosystems of Olson et al. (2001) provides a level of resolution that we shall show may be more detailed than is useful for most biological control purposes. Nevertheless, as the starting point for a global hierarchical classification, this seems a major step forward.

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The Use of Ecoregions in Current Biological Control Practice It is incumbent on a country considering the introduction of a biological control agent to consult its neighbours. For example, NAPPO (North American Plant Protection Organization) maintains a Biological Control Panel (NAPPO, 2004), one assignment of which is to exchange information on biological control activities in the three NAPPO countries (Canada, Mexico, USA). The International Plant Protection Convention addresses this in International Standards for Phytosanitary Measures (ISPM) No. 3, ‘Code of Conduct for the Import and Release of Exotic Biological Control Agents’, section 3.1.12: ‘Consult with authorities in neighbouring countries within the same ecoarea and with relevant regional organizations to clarify and resolve any potential conflicts of interest that may arise between countries’ (IPPC, 1996). Ecoarea is defined in ISPM No. 3 as ‘an area with similar fauna, flora and climate and hence similar concerns about the introduction of biological control agents’. The use of the term ecoarea seems to originate in this document and be more or less restricted to it and to other documents based on it. Thus, it is not clearly and objectively defined, nor is it clear how to use this term in practice. In the absence of a precise practical definition we shall treat the term ecoarea as synonymous with ecoregion, and explore further below how the concept can be used in aspects of the science of biological control. ISPM No. 3 has recently been revised and the new version is available on the internet, although it has not yet been formally published (IPPC, 2005). The revised standard focuses on the pest risk assessment process, and does not specifically use the term ecoarea, or advise countries to consult their neighbours. However, this advice from the original standard remains relevant today, and demonstrates that more practical guidelines on biological control procedures with practical advice will be needed to help inexperienced countries carry out biological control responsibly.

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(a)

(b)

Fig. 12.4. Two non-hierarchical levels of ecoregion for Europe: (a) Biogeographical Regions of Europe (EEA, 2003); in addition to the Alps, the so called ‘Alpine Region’ (indicated by an ‘A’) includes the Pyrenees, Scandinavian mountain chain, from the Alps south-west to Macedonia, a small part of the Appeninno Abruzzese in Central Italy, the mountains of Bulgaria, the Carpathians, the Urals and the Caucasus and Transcaucasus Mountains; (b) DMEER, Digital Map of European Ecological Regions (ETCNPB, 2000); the ‘Alps conifer and mixed forests’ region is restricted to the Alps (see Fig. 12.8(a)).

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Fig. 12.5. The eight biogeographic realms and 14 hierarchical biomes as presented by Olson et al. (2001). The biomes are shown in shades of grey, and the original colour publication should be consulted for detailed use.

Ecoregions and the Distribution and Spread of Insects It is obvious and well known that oceans present the most substantial barriers to the movement and introduction of alien terrestrial species. Furthermore, the majority of damaging introductions have involved the transfer of species across oceans. Such movements can still be intra-national, e.g. between mainland USA and Hawaii, or between France and France d’Outre-mers (i.e. Guadeloupe, Martinique, Guyane, La Réunion and territories, which are politically part of France), and such introductions should be regulated, most especially when there are similar climates or broad-scale ecoregions (Bailey’s (1996) ecoclimatic zones or Olson et al.’s (2001) biomes) in both areas. However, we do not focus on this point here, but rather focus on the less obvious risks associated with the spread of species within a land mass, and the introduction of species from another part of the same land mass. At the broadest scale of ecoregion classification into four ecoclimatic zones (Fig. 12.1), it is noteworthy that the Americas are divided north to south, whereas Eurasia and Africa are divided east to west. This remains apparent, e.g. in the schemes of Bailey (1996)

and of The Times (1998) shown in Fig. 12.2 for Eurasia, but becomes increasingly obscure as finer-scale divisions are considered (e.g. see Fig. 12.3). The north–south barriers in the Americas reflect the presence of major mountain barriers, whereas the east–west corridors in Eurasia reflect climate differences as one moves south or north. The Biogeographical Regions of Europe (EEA, 2003; Fig. 12.4a) shows that most biogeographical regions of Europe form contiguous units. In contrast, the presence of similar ecoclimatic zones in the east and west of North America, divided by physical (mountain) and ecological barriers, means that moving organisms between these coasts can lead to the establishment of damaging alien species. For example, a cicadellid, the glassy-winged sharpshooter, Homalodisca coagulata (Say), which is a vector of the bacterium Xylella fastidiosa, a plant pathogen that causes a variety of plant diseases – including phony peach disease of peach and Pierce’s disease of grape – was accidentally introduced from south-eastern USA to the west coast of USA, and is now a major agricultural pest problem (Purcell and Saunders, 1999). Hence, one may generalize that for the northern hemisphere temperate ecoregions, moving organisms between east

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and west in North America would be a higher risk than moving organisms within Europe, and to a lesser extent between Europe and central Asia. Unlike other Biogeographical Regions, the so-called ‘Alpine’ Biogeographical Region (Fig. 12.4a) is not contiguous within Europe. In addition to the Alps, this region includes the Pyrenees, Scandinavian mountain chain, from the Alps south-west to Macedonia, a small part of the Appeninno Abruzzese in Central Italy, the mountains of Bulgaria, the Carpathians, the Urals and the Caucasus and Transcaucasus Mountains. Movement of organisms between these similar, but disjunct, areas, must carry a higher degree of risk than between parts of other, contiguous Biogeographical Regions of Europe. The significant differences in the flora and fauna of distinct Eurasian mountain regions are best shown in the ecoregion classification of DMEER (ETCNPB, 2000) (Fig. 12.4b), whereas the similarities are evident in the Biogeographical Regions of Europe (EEA, 2003) (Fig. 12.4b). We conclude that disjunct similar ecoregions are a potential source of alien species that can become established, whereas within continuous ecosystems and contiguous dissimilar ecoregions this is much less likely to occur. From where do alien invasive species originate, and what can this tell us about the barriers which ecoregions may present to their natural dispersal and spread? The majority of aliens are extra-continental, but some are intra-continental (both categories can be inter-national or intra-national). A recent analysis of the sources of alien insects established in Switzerland (M. Kenis, CABI Switzerland, 2004, personal communication) shows that of the 304 alien insect species provisionally listed for Switzerland, the great majority are inter-continental introductions. However, four are considered to have come from eastern Europe and 39 from the Mediterranean region, but none from the remainder of Europe. Furthermore, the spread of most of these species from within Europe is associated with human activities, e.g. glasshouse pests, pests of exotic ornamentals, pests of human habitations and stored products. For example, the firethorn

leaf miner, Phyllonoryctor leucographella (Zeller) (Lepidoptera: Gracillariidae) has spread through Europe in the last 20–30 years on its Mediterranean hosts, Pyracantha spp., which are widely planted as exotic ornamentals (Nash et al., 1995; UK Moths, 2005). This pattern of spread of species within Europe will now be interpreted in terms of the ecoregion classification systems available, particularly the barriers and corridors which ecoregions may present for colonization and spread (Figs 12.2, 12.4). There is a noteworthy homogeneity from northern Spain to Denmark, and from lowland Switzerland, in both Bailey (1996) and The Times (1998) (Fig. 12.2b), and northern Italy is similar but disjunct. Bailey (Fig. 12.2a) shows differences further east, although The Times (Fig. 12.2b) shows similar vegetation extending to the Urals. Movement of organisms between lowland areas and mountain areas throughout this region would be low risk – if they could have spread, they probably would have, or soon will through man’s activities. The Mediterranean region is distinct from, but contiguous with, the rest of Europe at the level of the Biogeographical Region (Fig. 12.4) or of the biome (Fig. 12.5). The ability of species to spread within the Mediterranean region is currently being shown by the geranium blue butterfly, Cacyreus marshalli Butler (Lepidoptera: Lycaenidae), which was accidentally introduced from South Africa and is spreading steadily. It was introduced into Mallorca in the Balearic Islands in 1987, and was found on Spain’s Mediterranean coast in 1992 (Sarto i Monteys, 1992). Since then it has spread to the Canary Islands, Portugal, France, Italy and Morocco. A summer population in England was eradicated, but probably would not have survived the winter, and winter temperatures will probably limit the final distribution to the warmer parts of Europe. Movement of organisms between the Mediterranean region and the rest of Europe is relatively low risk – again, if it were possible, it would probably have happened already. Thus, those species limited to the Mediterranean region are likely to be climate

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limited, if there are not other more fundamental constraints, such as availability of food plants. As noted, most species from the Mediterranean that have spread into Europe have been able to do so because man’s activities have provided suitable habitats – alien plants, glasshouses, habitations, etc. Alien insects have shown how open the corridors of movement are within Europe. For example, the western corn rootworm, Diabrotica virgifera virgifera LeConte (Coleoptera: Chrysomelidae), has spread from its initial appearance in Serbia in 1992 within ten years as far as France, and looks set to reach all major maize-growing areas in the next few years (Fig. 12.6), unless it is limited by climatic factors (Hemerik et al., 2004). The horse chestnut leafminer, Cameraria ohridella Deschka and Dimic (Lepidoptera: Gracillariidae), has also moved rapidly through Europe (Fig. 12.7) through a combination of natural local dispersal and human-facilitated long-distance

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movement, probably as immatures stages carried in leaves by road and rail transport (Gilbert et al., 2004). We conclude that alien species that are suited to Europe north of the Mediterranean region can be expected to spread throughout mainland Europe with little hindrance from natural barriers. Thanks to human movement and trade, even the English Channel has not stopped the horse chestnut leafminer (Fig. 12.7), and already the western corn rootworm has been reported in the UK (Fig. 12.6). The small-scale ecoregions, e.g. DMEER (ETCNPB, 2000) (Fig. 12.4b), show no correlation with the spread of the two species considered above. The medium-scale systems (Fig. 12.2) of Bailey (1996) and The Times (1998) are also not very useful. It is really only at the scale of biomes (Olson et al., 2001; Fig. 12.5) or of ecoclimatic zones (Bailey, 1996; Fig. 12.1) that the limits to spread start to become apparent. This is not to say that the small-scale DMEER ecore-

Fig. 12.6. The spread of Diabrotica virgifera virgifera LeConte in Europe since first found in 1992 (FAO/Kiss and Edwards, 2004).

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Fig. 12.7. The spread of horse chestnut leafminer, Cameraria ohridella Deschka and Dimic, since it was first found in Europe in 1984 (M. Kenis, CABI Switzerland, 2004, personal communication).

gions would not be useful for predicting the spread of a more specialized alien species, e.g. one tied to a particular host plant of limited distribution. However, when it comes to species of major economic importance, or those with the ability to change ecosystem functioning, such species are likely to be less specific in their ecological needs and so the broader scale ecoregion classifications should be more appropriate. The examples presented above illustrate that species which in recent years have caused major concern and evident impact have spread rather freely within Europe. Invasive species such as western corn rootworm and horse chestnut leafminer are often characterized by a set of species traits that favour high population growth rates and a rapid spread (Kolar and Lodge, 2001). In the classical biological control approach,

IBCAs should also be able to build up outbreak populations and to spread quite easily, so are comparable with invasive species. Introduced IBCAs that are able to establish in one part of Europe can therefore be expected to disperse widely within their ecological limits over relatively few years. For those exotic IBCAs that are used in inundative biological control, much depends on their potential to establish (see Boivin et al., Chapter 6, this volume) and disperse (see Mills et al., Chapter 7, this volume). Often, these IBCAs have limited dispersal capacities but if they can establish, this will only slow down their spread without reducing the area finally covered. It should also be considered that the enormous volume of movement of materials by road and rail transport, particularly in the more developed and politically united con-

Usefulness of the Ecoregion Concept for Safer Import of Invertebrate BCAs

tinents, can frequently facilitate the rapid long-distance spread of species, and that physical barriers such as mountains and rivers are no barrier to this movement. Therefore it is not sensible for countries within Europe to act in isolation, and a common facilitating procedure would be appropriate, similar to the plant pest quarantine system. The same conclusion can be applied to other large land masses, such as North America and Africa, where there is already a more or less firm basis for consultation through the North American Plant Protection Organization and the InterAfrican Phytosanitary Council, respectively.

Ecoregions and Movement of Arthropods for Scientific Study Research organizations, producers and distributors of IBCAs may wish to import exotic species to investigate their prospects as biological control agents. Import of such organisms may carry risks to the environment if not handled under appropriate conditions that prevent escape. At the time of import, many biological and ecological characteristics of the organism may not be known and the purpose of import may be to investigate this. In particular, if the organism has been collected in the wild and the organism is not or little known, it should be kept in containment (e.g. under quarantine) in order to identify and eliminate contaminants (see Goettel and Inglis, Chapter 9, this volume). Thus, scientists and biological control practitioners need to move arthropods around to study them as potential IBCAs. Often, this will involve relatively short field trips to collect material for study, but sometimes populations are easier to collect at greater distances from the laboratory, e.g. species associated with particular crops not grown locally, or species associated with native plants that are only common in parts of their range. In a continent of small countries, such as Europe, this might well involve movement of arthropods across borders. Does this need to be regulated, and if so, how? It is also likely that scientists and amateur entomologists regularly move insects

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between neighbouring countries or further. All this should be considered against a background in which human movement and trade is high volume and long distance within Europe; things will be redistributed freely and easily, and many species have the opportunity to extend their range with unintentional human assistance if they are able to do so. In practice, relatively few do. What are the implications of movement of insects for study within Europe? At one extreme, movement of a few kilometres across common land borders within the same ecoregion is trivial and does not merit regulation. At the other extreme, movement of species between disjunct similar ecoregions carries a risk and should be regulated. What regulations are needed, and what guidelines to inform them? Movement of known exotic pests within Europe to areas where they do not occur is obviously not to be countenanced and should not be permitted except under permit under appropriate quarantine conditions. Nevertheless, it would be pragmatic to recognize that if an exotic insect in Europe can spread and establish more widely, it will do so in time. As noted above, movement between east and west coastal North America, and between east and west Russia, i.e. within the same country, is likely to be associated with higher risks than many movements within Europe, i.e. across national borders. Would an openborders policy for living material within Europe be the simplest and most costeffective option, or are regulations needed? We explore this question by discussing the specific case of Switzerland, below. In order to take the origin of IBCAs, potential hazard and adequate containment into account when importing IBCAs to Switzerland, CABI Bioscience Switzerland Centre and Agroscope FAL Reckenholz conducted two short workshops in 2002. The outcome is the proposal of a matrix with five classes of origin of the organism, three groups of expected hazard if the organism should escape and establish in Switzerland and four classes of containment options to handle the organism after import. These proposals have led to the matrix shown in the Case Study below (Box 12.1).

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Box 12.1 CASE STUDY: A model for the movement of insects for study within Europe. The following protocol is under discussion in Switzerland with regard to the movement of insects within Europe for purposes of scientific study. Containment options for insects for study based on a classification according to origin and perceived hazard.

Class

1 2a 2b 3 4 5

Origin of organism

Native to Switzerland and to countries close to it Populations of species native to class (1) countries, imported from other European countries Populations of species native to class (1) countries, imported from outside Europe Native to Europe, but not to class 1 countries Non-native residents Exotic

Hazard (low to high) and containment (1 to 4) Low

Medium

High

1

na

na

1

2

na

1 1 1 1–2

2–3 2 2–3 3–4

4 3 4 4

na This combination is not anticipated. Explanatory Notes Origin of organism Class 1: Switzerland, mainland France, Belgium, The Netherlands, Luxemburg, Germany, Denmark, Czech Republic, Austria, Liechtenstein, northern Italy. Class 2: Populations of species native to class 1 countries, but collected outside that range of distribution. Class 3: ‘Europe’ includes Russia as far east as the Ural Mountains, and south to the Caucasus Mountains (but including neither mountain range), the Mediterranean islands and the European part of Turkey, but does not include the Atlantic off-shore islands: Madeira, Azores, Canaries, Iceland, Greenland. Class 4: Non-native species that are established as resident in class 1 countries (the level of hazard would depend partially on upon how close the nearest resident populations were to the research facility). Class 5: Not native to Europe (i.e. class 1 and class 3 countries). Hazard (any imaginable adverse effect of escape by the organism) Low: Biology/ecology of species is known and safe (e.g. narrowly host specific, cannot establish/survive winters in Switzerland, already widely established in the proposed area of study). Medium: Biology/ecology of species/strain is only partially known, but hazard appears to be low. High: Known adverse effects or biology/ecology of species unknown and adverse effects possible. Containment options Option 1: Experimental use of imported organism without containment. Option 2: Experimental use of imported organism in open laboratory and greenhouse. Option 3: Experimental use of imported organism in closed laboratory/climatic chamber only. There is much that could be done under level 3 without going to level 4 ‘full quarantine’: e.g. increase the number of doors between the facility and the outside, use of handling boxes in the laboratory, safe disposal of associated materials, etc. Option 4: Experimental use of imported organism only in a suitable quarantine facility as permitted by the appropriate government authority.

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Let us re-examine this approach in light of the ecoregion classification schemes discussed above. What scale of ecoregion concept is being proposed? Can the designated areas be better defined in terms of one of the ecoregion schemes? Switzerland (Fig. 12.8a) comprises part of two DMEER (ETCNPB, 2000) ecoregions: (1) ‘Alps conifer and mixed forests’ and (2) ‘Western European broadleaf forests’. It would be entirely reasonable to anticipate that, within an ecoregion, species that are able to disperse will spread widely with no barriers, i.e. there would be no quarantine risks to Switzerland from within these two ecoregions. The two ecoregions which comprise Switzerland extend to south and central Germany, much of the Czech Republic, most of Austria, part of Slovenia, the far north of Italy, much of eastern France, Luxembourg and southern Belgium. One ecoregion that almost extends into the Swiss Ticino (3) ‘Po Basin mixed forests’, is limited to northern Italy. This group of three ecoregions correlates closely with the original class 1 countries in the matrix. If two more ecoregions were added to this group – (4) ‘Southern temperate Atlantic’ and (5) ‘Northern temperate Atlantic’, it would closely approximate the class 1 countries proposed, and justify the use of these political units. An argument can also be made that if a species can cross from one ecoregion to the next, i.e. along a common border, there is nothing that would have stopped that species from spreading into and through the second ecoregion if it could. On this basis, in addition to the three ecoregions mentioned above, the two ecoregions which cover Switzerland themselves border five more DMEER regions: (6) ‘Central European mixed forests’; (7) ‘Pannonian mixed forests’; (8) ‘Dinaric mountains mixed forest’; (9) ‘Italian sclerophyllous and semi-deciduous forests’; and (10) ‘North-eastern Spain and southern France Mediterranean’. This area would extend the area from which invasions would have happened if they could have happened: to most of Germany; all the Czech Republic;

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nearly all of Poland; Lithuania and southern Estonia; southern Moldavia; a small part of western Russia; most of western Ukraine; western Slovakia; Hungary; western Bulgaria; Slovenia; Croatia; northern and western Serbia; most of Bosnia; Montenegro; most of Italy; all mainland France except the Pyrenees; Belgium; Netherlands; and western Denmark. The addition of the small ecoregion (11) ‘Baltic mixed forests’ in eastern Denmark, northeastern Germany and north-western Poland would simplify applying the ecoregions along political boundaries, although there is no scientific justification for this beyond the fact that most of these ecoregions are varieties of mixed forest. Note that the Pyrenees, Italian mountains and Carpathian mountains are still excluded. Thus, this grouping falls between class (1) and class (3) of the suggested system, but highlights the fact that disjunct mountain systems may represent a risk. Turning to Bailey’s (1996) ecoregions (Figs 12.2a and 12.8a), Switzerland is comprised of groups 240 and 240M, i.e. ‘Marine division’ and ‘Marine Regime Mountains’. Ecoregion 240 is closest to the outer ring of DMEER categories and rather close to the class (1) countries of the matrix. It includes northern Spain, much of the British Isles and southern Scandinavia – which were previously excluded, but does not extend as far to the east as the outer DMEER grouping. Adding ecoregions 210 (‘Warm Continental Division’), 220 (‘Hot Continental Division’) and 250 (‘Prairie Division’) to this group would bring the area in line with Vegetation Type (6) Broadleaf Forest (deciduous) of The Times (1998) (Fig. 12.2b), and extend the region in a narrow tongue as far as the Urals. The situation for Switzerland with regard to the Biogeographical Regions of the European Environment Agency (EEA, 2003) is instructive (Fig. 12.8b) – on the one hand it includes part of the Continental Region, which extends from France to the Urals to Bulgaria, but on the other hand it includes the Alpine Region, which groups the main mountain ranges of

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(a)

(b)

Fig. 12.8. Ecoregions around Switzerland: (a) DMEER (ETCNPB, 2000) regions around Switzerland (cf. Fig. 12.4(b): 1, Alps conifer and mixed forests; 2, Western European broadleaf forests; 3, Po Basin mixed forests; 4, Southern temperate Atlantic; 5, Northern temperate Atlantic; 6, Central European mixed forests; 7, Pannonian mixed forests; 8, Dinaric mountains mixed forest; 9, Italian sclerophyllous and semi-deciduous forests; 10, North-eastern Spain and southern France Mediterranean; 11, Baltic mixed forests; (b) Biogeographical Regions of Europe (EEA, 2003) around Switzerland (cf. Fig. 12.4(a): 1, Alpine; 2, Continental; 3, Atlantic; 4, Mediterranean; 5, Pannonian.

Usefulness of the Ecoregion Concept for Safer Import of Invertebrate BCAs

Europe. Thus, movement of organisms within the Continental Region would be low risk, movement between adjacent biogeographic regions (which would include most of Europe) would be a slightly higher risk, whereas movement of organisms between the disjunct mountainous areas of the ‘Alpine’ Biogeographical Region would be undesirable. Combining the two Vegetation Types (6) ‘Broadleaf Forest (deciduous)’ and (1) ‘Mountain Vegetation’ from The Times (1998) (Fig. 12.2b) would produce an area very similar to the European section of the ‘Humid Temperate’ zone in Bailey’s four ecoclimatic zones (Fig. 12.1). This is still less than all of Europe, which includes two other ecoclimatic zones: ‘Polar’ and ‘Dry’. Whether insects from these two ecoclimatic zones could represent a possible threat to the rest of Europe is not clear, but a priori it seems unlikely that they could survive if they were not already widespread. So, in general, looking at the position of Switzerland in relation to these different ecoregion schemes available, this broadly supports the intuitive suggestions proposed based on political boundaries. However, the ecoregion approach now shows that it would be appropriate to exercise caution and regulate the movement of insects from the disjunct mountain ranges of Europe (Pyrenees, Italy, Carpathians, Scandinavia, Urals, Caucacus, etc.) to Switzerland, whereas, for lower-altitude areas the risks seem relatively small, and regulation, at least for contiguous and adjacent ecoregions, need be little more than a notification process. Similar analyses can be performed for other countries in Europe or elsewhere. Movement within contiguous, similar ecoregions carries minimal risk, and will seldom merit substantial regulation, even if cross-border movements are involved. In contrast, movement of material between similar, but disjunct, ecoregions will always carry risks and needs to be regulated, even if the movements are within one country.

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Discussion and Conclusions An ecoregion approach is more scientifically valid than one based simply on political boundaries. Selection of the appropriate scale is critical for making the approach work, but interpretation will always be needed – i.e. there are unlikely to be simple decision rules. The minimum inclusive group within which a country should consult regarding a possible biological control release is probably the biome (Olson et al., 2001) or Biogeographic Region (EEA, 2003). This suggests that within Europe there is a role for a regional consultation process. Although there is no such process in place at present for IBCAs, future discussion of mechanisms and regulation of the introduction of biological control agents will need to address this aspect. There are other approaches to ecological classification that may be at least as relevant for biological control purposes as the ecoregion approach. For example, to predict the spread of a phytophagous insect, the distribution of its potential host plants is obviously a useful parameter. No phytophagous insect can become established in an area where there are no suitable host plants. Equally, insects can be limited by factors other than food plants, so that there may be extensive areas of apparently suitable food plants where phytophagous insects are unable to persist. Specialist rare insects are especially likely to be very much more limited than the mere availability of their food plants would suggest. Conversely, adaptable and economically damaging insects may well spread to the limits of their available food plants (see above discussion of western corn rootworm and horse chestnut leafminer). In addition to vegetation, climate can be a powerful predictor of distribution. The computer programme CLIMEX has been developed as a climate-matching tool (Sutherst and Maywald, 1985; Sutherst, 1991, 2003; Sutherst et al., 1999), and has been used quite extensively in biological control. Most studies are either retrospective and explanatory (e.g. Byrne et al., 2002), attempt to predict spread of

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introduced pests or biological control agents (e.g. Julien et al., 1995; Scott and Yeoh, 1999; Mason et al., 2003) or select areas in the native range for surveys with a climate that is similar to the invaded range (e.g. Goolsby et al., 2003). There is a shortage of studies evaluating predictions in light of subsequent events (e.g. Goolsby et al., 2005), but over time, data will accumulate on the accuracy of this approach so that it can be refined. Using a general climate model misses the opportunity to identify which of the many climatic factors may be the key to the potential distribution of a species; supplementary studies can be carried out to look at some of the more obvious physiological parameters, in order to refine and improve predictions (e.g. Byrne et al., 2002; see also Boivin et al., Chapter 6, this volume). The INSIM programme (Mols and Diederik, 1996) is a simpler approach, based upon actual weather data and the day-degree requirements for a species to complete at least one generation per year. Hemerik et al. (2004) use this approach to predict the natural rate of spread and limits to spread of the western corn rootworm, Diabrotica virgifera virgifera in Europe (cf. Fig. 12.6). They conclude that the number of generations will decrease as the beetle spreads north and into the mountainous areas, such as the Alps, until it reaches the limit where it is unable to complete a generation in a year. Obviously, predictions using this approach can only be based on climatic parameters – altitude, soil, vegetation and other biotic factors are not considered, and these factors can cause errors in predictions (Samways et al., 1999; National Research Council, 2002; Samways, 2003). Using ecoregions to predict the ability of an insect to become established may well have some advantages over these approaches, since ecoregions are a way of combining vegetation and climate data. Matching ecoregions for the source area of classical biological control agents could optimize the chances of establishment in the target area. An ecoregion approach may thus be considered as one tool for predict-

ing optimum strategies for introduction of classical biological control agents in order to obtain establishment, but it is less likely to be useful in predicting subsequent spread. It is also important to consider whether an ecoregions approach could be used for predicting whether an exotic inundative IBCA would fail to become established where released. This is generally unwanted, and incorrectly predicting that a species would fail to become established could lead to serious non-target effects. In practice, establishment and subsequent spread is likely to be limited by specific factors, or a combination of factors, e.g. minimum winter temperature or availability of a particular host plant, which is likely to differ from specific vegetation, climate or ecoregion classifications. We therefore consider an ecoregions approach unsuitable for making robust predictions that an introduced IBCA cannot become established in a particular area. However, an ecoregion approach offers a useful tool for interpreting and managing the risks of moving organisms within a country, or within a continent. The matrix model developed for this purpose in Switzerland (Box 12.1), which is actually based on countries, is reasonably compatible with the biome (Olsen et al., 2001; Fig. 12.5) or biogeographical region (EEA, 2003; Fig. 12.4a) level of ecoregion classification. Thus, the matrix can be used to combine the relevance of an ecoregion approach with the practicalities of using political boundaries.

Acknowledgements The development of the ‘matrix model’ presented in Box 12.1 benefited from interactive discussions with Dr Hans Dreyer and Dr Alfred Klay of the Swiss Federal Office of Agriculture. Discussions at the Engelberg workshop, and specific comments by Dr Guy Boivin and Dr Kim Hoelmer helped us crystallize our ideas. We thank Dr Robert G. Bailey and Springer for permission to use Figs 12.1

Usefulness of the Ecoregion Concept for Safer Import of Invertebrate BCAs

and 12.2 (upper), Professor J. Kiss for permission to use Fig. 12.6 and Dr Marc Kenis for allowing us to use data from his unpublished inventory of alien insects in Switzerland and Fig. 12.7, hitherto unpublished. We appreciate and thank the United States Department of Agriculture, European Environment Agency, European Topic Centre on Nature Protection and

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Biodiversity and the World-wide Fund For Nature for making publicly available Figs 12.3, 12.4a, 12.4b and 12.5, respectively. We also thank Dr Tim Haye (CABI Bioscience Switzerland Centre) for helping to redraw Fig. 12.2b from The Times (1998) and edit Fig. 12.4a, and Riccardo de Filippi (Agroscope FAL Reckenholz) for help in editing Fig. 12.5.

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FAO / Kiss, J. and Edwards, C.R. (2004) Spread of western corn rootworm in Europe 1992–2003, FAO WCR Network. WCR Net: Information page on the Western Corn Rootworm (Diabrotica virgifera virgifera LeConte) supported by the Food and Agriculture Organization (FAO) of the United Nations, http://www.mkk.szie.hu/dep/nvtt/wcrnet/wcrnet-2.htm (accessed 27 May 2005). Gilbert, M., Grégoire, J.-C., Freise, J.F. and Heitland, W. (2004) Long-distance dispersal and human population density allow the prediction of invasive patterns in the horse chestnut leafminer Cameraria ohridella. Journal of Animal Ecology 73, 459–468. Goolsby, J.A., Wright, A.D. and Pemberton, R.W. (2003) Exploratory surveys in Australia and Asia for natural enemies of Old World climbing fern, Lygodium microphyllum: Lygodiaceae. Biological Control 28, 33–46. Goolsby, J.A., De Barro, P., Kirk, A.A., Sutherst, R., Ciomperlik, M., Ellsworth, P., Gould, J., Hoelmer, K., Naranjo, S., Rose, M., Roltsch, W., Ruiz, R., Pickett, C. and Vacek, D. (2005) Post-release evaluation of the biological control of Bemisia tabaci biotype ‘B’ in the USA and the development of predictive tools to guide introductions for other countries. Biological Control 32, 70–77. Hemerik, L., Busstra, C. and Mols, P. (2004) Predicting the temperature-dependent natural population expansion of the western corn rootworm, Diabrotica virgifera. Entomologia Experimentalis et Applicata 111, 59–69. IPPC (International Plant Protection Convention) (1996) Code of Conduct for the Import and Release of Exotic Biological Control Agents. International Standards for Phytosanitary Measures. Part 1 – Import Regulations. Food and Agriculture Organization of the United Nations, Rome, Italy. IPPC (International Plant Protection Convention) (2005) Guidelines for the export, shipment, import and release of biological control agents and other beneficial organisms. International Standards for Phytosanitary Measures. No. 3. https://www.ippc.int/servlet/CDSServlet?status=ND0xMz M5OS43NjA0NyY2PWVuJjMzPXB1YmxpY2F0aW9ucyZzaG93Q2hpbGRyZW49dHJ1ZSYzNz1p bmZv#koinfo (accessed 16 November 2005). James, P.E. (1966) A Geography of Man. Ginn-Blaisdell Publishing, Waltham, Massachusetts. Julien, M.H., Skarratt, B. and Maywald, G.F. (1995) Potential geographical distribution of alligator weed and its biological control by Agasicles hygrophila. Journal of Aquatic Plant Management 33, 55–60. Kolar, C.S. and Lodge, D.M. (2001) Progress in invasion biology: predicting invaders. Trends in Ecology and Evolution 16, 199–204. Mason, P.G., Olfert, O., Sluchinski, L., Weiss, R., Boudreault, C., Grossrieder, M. and Kuhlmann, U. (2003) Actual and potential distribution of an invasive canola pest, Meligethes viridescens (Coleoptera: Nitidulidae), in Canada. Canadian Entomologist 135, 405–413. Mols, P.J.M. and Diederik, D. (1996) INSIM a simulation environment for pest forecasting and simulation of pest natural enemy interaction. Acta Horticultura 416, 255–262. NAPPO (North American Plant Protection Organization) (2004) Nappo panel reports for 2003–2004. NAPPO, 21 pp. http://www.nappo.org/Reports/Reports-04-03-e.pdf (accessed 27 May 2005). Nash, D.R., Agassiz, D.J.L., Godfray, H.C.J. and Lawton, J.H. (1995) The pattern of spread of invading species: two leaf-mining moths colonizing Great Britain. Journal of Animal Ecology 64, 225–233. National Research Council (Committee on the Scientific Basis for Predicting the Invasive Potential of Nonindigenous Plants and Plant Pests in the United States) (2002) Predicting Invasions of Nonindigenous Plants and Plant Pests. National Academy Press, Washington DC. Noirfalise, A. (1987) Map of the Natural Vegetation of the Member Countries of the European Community and of the Council of Europe. Office for Official Publications of the European Communities, Luxembourg. Olson, D.M., Dinerstein, E., Wikramanayake, E.D., Burgess, N.D., Powell, G.V.N., Underwood, E.C., D’Amico, J.A., Itoua, I., Strand, H.E., Morrison, J.C., Loucks, C.J., Allnutt, T.F., Ricketts, T.H., Kura, Y., Lamoreux, J.F., Wettengel, W.W., Hedao, P. and Kassem, K.R. (2001) Terrestrial ecoregions of the world: a new map of life on Earth. BioScience 51, 933–938. (see also http://www.worldwildlife.org/science/pubs/bioscience.pdf (accessed 26 May 2005)). Parmesan, C., Ryrholm, N., Stefanescu, C., Hill, J.K., Thomas, C.D., Descimon, H., Huntley, B., Kaila, L., Kullberg, J., Tammaru, T., Tennent, W.J., Thomas, J.A. and Warren, M. (1999) Poleward shifts in geographical ranges of butterfly species associated with regional warming. Nature 399, 579–583. Purcell, A.H. and Saunders, S.R. (1999) Glassywinged sharpshooter expected to increase plant disease. California Agriculture 53, 26–27.

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13

Statistical Tools to Improve the Quality of Experiments and Data Analysis for Assessing Non-target Effects Thomas S. Hoffmeister,1 Dirk Babendreier2 and Eric Wajnberg3 1Institute

of Ecology and Evolutionary Biology, University of Bremen, Leobener Str. NW2, D-28359 Bremen, Germany (email: [email protected]; fax number: +49-421-218-4504); 2Agroscope FAL Reckenholz, Reckenholzstr. 191, 8046 Zürich, Switzerland (email: [email protected]; fax number: +41-44-377-7201); 3INRA, 400 Route des Chappes, BP 167, 06903 Sophia-Antipolis Cedex, France (email: [email protected]; fax number: +33-4-92-38-6557)

Abstract When testing non-target effects of biological control agents, it is essential that conclusions can be drawn with high precision and confidence. However, testing non-target effects confronts the experimenter with a number of difficulties. First of all, biologically positive cases of not finding any non-target effect are more difficult to substantiate, since in standard statistical hypothesis testing, we can only associate a precise probability to err with rejecting the null hypothesis that assumes no effect, but not with accepting it. The main problem here is the effect size, i.e. the difference from the null hypothesis that is considered biologically meaningful. Secondly, there will usually be a trade-off between the costs associated with increased sample sizes and the confidence of the results of non-target effects testing. Often, sample size will be a limiting factor due to a shortage of animals, space for testing arenas, research funding, etc. Thus, it becomes especially important to optimize the experimental design and to use the most powerful statistical tools to obtain maximum confidence in the test results. Here, we will briefly (i) introduce the reader to common pitfalls of experimental design, (ii) explain the nature of errors in statistical testing, (iii) point towards methods that determine the power of statistical tests, (iv) explain the distribution of the most commonly encountered types of data, and (v) provide an introduction to powerful statistical tests for such data.

Introduction The last two decades have seen almost a revolution in statistical methods used in ecological investigations, as can be witnessed from a number of recent textbooks 222

on design and statistical approaches in the life sciences (e.g. Crawley, 1993; Hilborn and Mangel, 1997; Crawley, 2002; Grafen and Hails, 2002; Quinn and Keough, 2002; Ruxton and Colegrave, 2003), and from changes in approaches used in more recent

©CAB International 2006. Environmental Impact of Invertebrates for Biological Control of Arthropods: Methods and Risk Assessment (eds F. Bigler et al.)

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publications. This reflects both the increased awareness that conclusions in ecological studies need to be drawn in a quantitative manner with high precision and confidence, and that, for a number of reasons, large sample sizes are often difficult to obtain. Thus, the need for powerful statistical tools that allow precise analysis from limited sample sizes is evident. Formerly, the statistical analysis of data in ecological investigations has been fraught with the difficulty that many, if not most, of the data sampled for this purpose are not normally distributed, and are thus not suitable for the parametric ‘standard’ approaches of Analysis of Variance (ANOVA) and Student t-tests. Instead, non-parametric statistics such as, e.g. Kruskal-Wallis and MannWhitney U-Tests, have been used that are known to be less powerful. In theory, the lack of power of non-parametric statistics may be compensated by larger sample sizes. However, an increase in sample size is often unfeasible for agricultural entomologists, who are usually limited by the time that can be invested, the money that can be spent on experiments, and/or the number of replicates that can be obtained, through a shortage of either experimental fields or insects to work with. Besides such restrictions, several other problems might arise, most of which can be well illustrated by the following example. A couple of years ago, one of the authors of the present chapter heard a talk at an entomological conference, where an investigation into the possible side-effects of genetically modified organisms (GMO) on biodiversity in crop fields was presented. The authors did not find significant treatment effects in most of their tests, but we found it difficult to decide whether the lack of treatment effects was due to a non-optimal experimental design and analysis of the data or whether the conclusion of no effect could be drawn with confidence. Non-target effects of GMOs are an issue of risk assessment that corresponds well with investigations on non-target effects of natural enemies, and thus is used here for an illustration of general problems in design and analysis of risk assessment studies.

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This example inspired us to use a computer-generated data set in this chapter to elucidate some of the problems of design and analysis of non-target effect studies, the non-independence of data that leads to pseudoreplicates, the lack of statistical power and the difference between powerful and less powerful statistical techniques. Imagine the following research question and set-up: we wanted to know whether planting genetically modified plants that are resistant to a target pest species would affect the biodiversity of non-target insects in the crop field. For this, we were allowed to do our experiments on a single large field. Imagine further that we partitioned our field into three sections; thus, we had one section with the GMO treatment, adjacent to the section with the conventional crop (serving as control), and on the last section an isoline of the genetically modified crop, which does not express the resistance against the herbivore pest (serving as a second control), was sown. We sampled the biodiversity of nontarget insects at ten spots within each of the field sections. Altogether, we received ten data points for each of the three treatments. Imagine we found that the biodiversity of non-target insects in one treatment, e.g. the GMO treatment, was significantly lower. Can we conclude with confidence that the GMO crop affects the biodiversity of nontarget insects negatively? Not necessarily. Remember that all the samples for the GMO treatment came from one region of the field. It is possible that the biodiversity of non-target insects had been lower on this side of the field, e.g. due to its proximity to a road. Thus, our spatial clustering of samples has made it impossible to attribute the biodiversity effect to the GMO treatment with confidence, and our ten samples per field section must be considered as being pseudoreplicates. Now, assume we had chosen to do our experiment in 30 fields, each allotted to one of the three treatments at random, such that we obtained ten fields per treatment. We then find a small trend of decreased biodiversity in the GMO treatment compared with the two control treatments. However, using a Kruskal-Wallis test (because data are

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not normally distributed), this trend does not appear to be statistically significant. Can we conclude with confidence that the GMO treatment had no negative effect? To elucidate this, we turned our investigation upside down. Let us assume now that we have an effect of the GMO treatment that reduces the biodiversity by 20%. Using a sample size of ten randomly drawn data points from a Poisson distribution (note that our index of biodiversity is based on species counts, and that counts are usually Poisson distributed), with appropriate means for each of our three treatments, how often would we find a statistically significant difference using a Kruskal-Wallis test? In fact, we would find a significant difference in only about 23 out of 100 cases. Thus, the power of this test is relatively low. Using more powerful statistical tests would increase the power slightly: using a Generalized Linear Model with appropriate Poisson distribution we would find a significant difference in about 27 out of 100 cases. Even if powerful statistical approaches are employed, the amount of replicates necessary to allow conclusions with high precision can be enormous. In our example given here, 126 instead of 30 fields would have to be studied to detect a reduction of 20% in biodiversity with confidence. In the largest study conducted so far on the side-effects of GMO, a power analysis has suggested that 60 fields per crop had to be sampled across three years to detect effects of ecological significance (Perry et al., 2003; Rothery et al., 2003). An experimental design of this extent will perhaps be impossible in most cases where we wish to test possible non-target effects of biological control agents, and it will not even always be necessary. What will be necessary, instead, is a robust design and the decision by the researchers about what magnitude of an effect is desirable to be detected. This requires knowledge of the power of the statistical testing procedures applied, and in the case of insignificant results, stating the power of the statistical test used. It is only then that we can evaluate whether an insignificant finding is likely to mean that there is no ecological effect, or whether the data are not strong enough to

support such a conclusion. This piece of information is still stated only rarely in research papers, and powerful statistics are not yet always employed or even available. Therefore, in the present chapter, we will briefly outline the logic of statistical testing and point towards important advances in statistical techniques for the testing of nontarget effects. We will refer to many of the measurement variables mentioned in other chapters of this book and provide suggestions for their analysis. That does not say that we can and do cover everything of importance for the design and analysis of testing non-target effects. However, if we can increase awareness of possible pitfalls of experimental design and point towards solutions or refer to some of the excellent statistics primers, this chapter might help to improve the precision and accuracy of such experiments. Though this chapter focuses on non-target effects of biological control agents, we would further like to stress its relevance for other studies dealing with risk assessment, e.g. non-target effects of pesticides or GMOs. In the following sections, we will start by reviewing the very basics of statistical testing, i.e. the hypotheses involved in statistical testing and the errors associated with accepting or rejecting those hypotheses. Subsequently, we will discuss the effect size and power of statistical tests, measurements that are of high relevance given a statistical test does not return significant results. Further, the need to obtain independent data for statistical testing and the danger of pseudoreplication will be explained, and also how randomization can prevent pseudoreplication. Building upon this, we present powerful statistical tools, such as Generalized Linear Models and Cox regressions, for the analysis of the kind of data that will typically be generated when assessing non-target effects.

Two Ways to Err in Statistical Testing (α- and β-errors) By performing an experiment it remains impossible to prove, for example, that a nat-

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ural enemy will never attack a non-target host or prey. Using a sound experimental design, we can aim only at achieving high accuracy and precision in what we conclude from the sample that we have tested. Yet, using standard statistical procedures, there is always some possibility that our interpretation of the data is wrong. This is due to the fact that all the measurement variables we are interested in are usually subject to random variation (i.e. variation between sample units that cannot account for a treatment factor under consideration), and that our conclusion is based on a sample rather than on the entire population. Since we conclude from a statistical test either that the null hypothesis (H0) is wrong, and can thus be rejected, or that the alternative hypothesis (H1) is wrong, and thus H0 cannot be

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rejected, we have two ways to err (Table 13.1). An α-error (also called Type I error) occurs if our experimental results suggest there is an effect of the factor of interest on the variable we wish to explain (the socalled ‘dependent variable’) when in fact there is none, thus if we reject H0 provided that H0 is correct. A β-error (also called Type II error) occurs if there is a true effect of the factor in question, but our experiment fails to detect this effect, thus if we do not accept H1 when H0 is wrong (Fig. 13.1, Table 13.1). Only the α-error can be immediately quantified: the P-value associated with a test statistic immediately provides the probability of committing an α-error. Usually, the null hypothesis is rejected if the probability of committing an α-error is 0.05 or less. In that case, the alternative hypothesis is accepted.

Interpretation: Do not reject H0 Do not accept H1

␤-error

Reject H0 Accept H1

␣-error

Fig. 13.1. Graphical representation of α-error (area hatched in white and black) and β -error (area hatched in grey and black) probabilities, using a one-sided t-test, comparing, e.g. encounter rates of biological control agents with non-target hosts. The curves on the left (for the null hypothesis) and right (for a specified alternative hypothesis) represent the probability sampling distribution of the statistical test done. Note that, usually, the alternative hypothesis is not specified, i.e. H1 is just different from H0, and the probability distribution of the statistical test done for H1 is unknown (modified from Quinn and Keough, 2002). Table 13.1. Hypothesis testing: the truth associated with a decision derived from a statistical test when the null hypothesis is in fact true or not true. Decision

Truth according to model

H0 true H0 not true

H0 is not rejected

H0 is rejected

correct β-error (type II)

α -error (type I) correct

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It should be noted, however, that while the statistical test returns a precise error probability for rejecting H0, it is not possible to associate a precise error probability with accepting H1 (see Fig. 13.1, where the α-error is associated with the probability distribution of H0 and not with H1). Moreover, it is important to mention that the α-level is equal to the P-value of the test only if we perform a single test on a given set of data. If we wish to perform multiple pairwise comparisons between, e.g. means from an experiment with more than two treatments, the probability of making at least one α-error by chance among those tests increases with the number of tests performed. This probability of making one or more α-error is called the family-wise α-error rate. When such tests are not independent from each other, e.g. if one data set is used more than once in a test, the family-wise α-error rate becomes difficult, if not impossible to calculate precisely. Yet, several procedures have been put forward to correct for multiple testing. The best known is the Bonferroni procedure, where the αerror is divided by the number of tests performed to obtain a new significance threshold and to keep a global α-error for the whole testing procedure. However, this procedure is overly conservative, i.e. in danger of committing β-errors (to elucidate this, imagine shifting the border between accepting and rejecting H0 in Fig. 13.1 to the right; while α-error decreases, β-error increases). The standard procedure for correcting for multiple testing is the sequential Bonferroni procedure suggested by Holm (1979), where P-values of all m tests are ranked from largest to smallest: the smallest P-value is tested at α/m, the second smallest is tested at α/(m⫺1) and so on, until the first non-significant result occurs. Recently, this procedure has also been criticized for being too conservative (Moran, 2003), and there is an ongoing discussion about the optimal way to correct for multiple testing (Garcia, 2004; Neuhäuser, 2004; Verhoeven et al., 2005). For a good overview on this topic, we recommend the reader consults Quinn and Keough (2002). An important aspect that needs particular attention when testing for non-target effects, if we want to err on the

side of caution, is that it might be more important to know the probability that an effect actually exists, given we did not find an effect (the β-error), than accurately quantifying the α-error. An α-error fixed at 0.05 is not necessarily meaningful. What we need to know instead is the power of the statistical test (see next section, below), which might lead us even to compromise between α- and β-errors (see below).

Example Taking one of our above-mentioned data sets about the effects of GM-plants on the biodiversity of non-target insects, our null hypothesis would be that in plots with all three treatments (GMO, non-GM isoline and conventional crop) the insect biodiversity would be the same. Now, we will not use a Kruskal Wallis test (K-W-test) as in the introduction, because it would not be easy to calculate the β-error associated with the K-W-test. Instead, by using an ANOVA on square-root transformed data (to achieve Gaussian distribution of data), we find that the α-error is P = 0.584. Thus, rejecting the null hypothesis and accepting that there is an effect of plant treatment on biodiversity, one would err in 58.4% of the cases. Using a programme for Power analysis (see below) one can calculate the β-error. In our case, the β-error is 0.768, if we wish to be able to detect a 20% difference in biodiversity of non-target insects. Thus, by not rejecting the null hypothesis, and consequently, by not accepting the alternative hypothesis, one would err in 76.8% of the cases. Obviously, this data set is insufficient for either accepting the alternative hypothesis or for not rejecting the null hypothesis with confidence.

Ecological Effect Size, Replicate Number and the Power of Statistical Tests Statistical power is the probability that a given test will result in rejection of the null hypothesis when that null hypothesis is,

Statistical Tools to Improve the Quality of Experiments

indeed, false. Hence, power = 1⫺β. For any particular test, power is dependent on the α-level, the sample size, the sampling variance and the so-called ‘effect size’ (ES). The ES can be regarded as the magnitude of the departure from the null hypothesis (observed ES), or as the difference between the values considered in the null and the alternative hypotheses (see Fig. 13.1 and below). There are two general approaches in Power Analysis (PA). The first one is a priori PA, where one aims to estimate the number of replicates necessary to reach a given power in an experiment. This can be done by specifying the effect size, the αlevel, the desired power and (dependent on the type of analysis) the standard deviation, which has to be estimated from preliminary experiments or from the literature. It should be stressed, however, that estimates for the assumed variance of the data are crucial. Carey and Keough (2002) have shown that the calculated sample size can vary by an order of magnitude depending on what dataset was used as a baseline for variance. The second approach is a post hoc analysis, where the researcher calculates the power achieved in an experiment where the null hypothesis could not be rejected. While general agreement exists on the importance of a priori PA, there is considerable debate on the value of post hoc PA. In particular, parameters are estimated based on the sample data in post hoc PA and are therefore interdependent. Since these estimates are subject to sampling error, the computed values for power are also subject to error and thus should be viewed with some caution. Obviously, the statistical ability to detect an effect (i.e. the power) increases with the size of that effect and, in fact, power is extremely sensitive to one’s choice of effect size (Cohen, 1988). There are several approaches for calculating post hoc power, and the effect size plays a crucial role in all of them. The first approach is to use the observed effect size, e.g. taking the difference between the control and the treatment from the data, and variance. However, this has clear flaws

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which form the basis of large parts of the criticism of post hoc PA (Hoenig and Heisey, 2001; Di Stefano, 2003). Actually, the P-value and power are dependent on the observed effect size such that tests with high P-values tend to have lower power, and vice versa. Therefore, calculating power based on observed effect size and variance adds no new information to the analysis (Thomas, 1997). The second approach is to use a predefined effect size and observed variance. Although it can be often difficult to define effect size properly, a useful approach, especially in the context of assessing nontarget effects, has been to estimate an effect that can be considered biologically significant. For instance, if an earlier study showed that 40% mortality caused populations to decrease in a wider context, this figure could be used as effect size for another study. As was shown in detail and exemplified with an example by Thomas (1997), this second approach appears valuable and allows one to evaluate whether the sample size and α-level were likely to result in detection of a biologically meaningful effect. A third approach is to establish an effect size based on the null and the alternative hypotheses. However, in this case the latter needs to be formulated quantitatively, which is only possible in certain instances. In the absence of any strong arguments that are independent of the hypothesis being tested, the selection of an effect size becomes arbitrary. However, in the case that effect size could neither be calculated based on biological significance nor from the alternative hypothesis, some conventions can be used that were established by Cohen (1998). He suggested using large, medium or small effects as a convention, but the exact size of these effects depends on the type of statistical analysis used. Many software packages readily provide the standardized effect, which is basically the difference between H0 and H1 divided by the standard deviation of the data. Although this avoids specifying the sampling variance, we feel it unwise to use the standardized effect, because it is poorly related to any biologically meaningful

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effect. Rather, we recommend calculating effect size based on either biological significance or on a quantitative alternative hypothesis, but we also believe that it is useful to put the ES of a study into context and to compare it to the procedure proposed by Cohen (1998). As a consequence of the importance of the ES outlined above, we also recommend strongly to report in detail on the ES underlying the analysis, rather than giving only a figure for β or power (cf. Steidl et al., 1997). As a special case of PA, the maximum detectable effect size could be calculated; this can be performed easily by fixing the power and the α-level appropriately. For instance, a researcher might wish to know about the effects he/she would have been able to detect given that the power is 0.8, a figure that has often been used. What constitutes a sufficient power is not absolutely fixed, though conventions of 0.8 or 0.95 have been suggested in the literature as high power (Cohen, 1988). However, in studies on environmental impact it is debatable why one should be satisfied with accepting a four-times higher β- than αerror, which is the case when using the 0.8 value. In contrast, one would like to be at least as confident in avoiding β-errors and α-errors alike in such investigations. Thus, a researcher conducting experiments on potential non-target effects of a biological control agent could ask what maximum possible effect size is consistent with α = β. In this context, it is important to note that in studies dealing with non-target effects, it may be reasonable to increase the α-level, thereby increasing power. Eventually, it depends on the costs associated with specific non-target effects. If the costs of committing β-errors are especially high, PA allows one to adjust α/β to reflect those costs (Rotenberry and Wiens, 1985). As an alternative to classical PA, the application of confidence intervals and equivalence testing has been suggested recently (Hoenig and Heisey, 2001; Andow, 2003). Demonstrating such equivalence requires reversing the traditional burden of proof. In equivalence testing, the null hypothesis states that a large effect exists

in either direction, i.e. the actual treatment effect (D) is larger than a predefined δ (H0:|D| > δ ). The alternative hypothesis is the hypothesis of equivalence, or H1:|D| = δ. Again, this kind of analysis depends on the knowledge of what a large (biologically meaningful) effect is, and the determination of delta is similarly as difficult as determination of the effect size, as discussed above. Given the large uncertainty in this area, it is difficult to give advice on this, though the general idea is appealing for decision-makers in risk assessment (Peterman, 1990). In conclusion, a priori PA can be a valuable aid in the design of any study and, in particular, for monitoring programmes (see Barratt et al., Chapter 10, this volume). In addition to the information on sample size necessary to detect a given effect, it is also very valuable for reducing the cost of largescale programmes as far as possible. Depending on the research question, post hoc PA also can be very useful, particularly because it is not always possible to conduct an ideally high number of replicates. It should be stressed that it is not possible with PA to associate an unambiguous probability of being correct in not rejecting the null hypothesis although, unfortunately, this has been done quite often in the past (see Peterman, 1990). Instead, it is only possible to argue that, with a probability of (1⫺β), there is no difference from the H0 greater than the effect size. If both the ES and β are small (and consequently the power is high), it is reasonable to conclude that the effect is negligible. It is particularly important in studies on non-target effects that a conclusion from a non-significant statistical result should be subject to the same stringent probability standards as a positive conclusion from a significant statistical result. Power analysis could be used to provide these standards.

Programs available A comprehensive review on this topic was written by Thomas and Krebs (1997), and we do not attempt to provide a similar

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detailed compilation here. Instead, we would like to refer to some published information – also on the internet – and highlight a few recent developments. Since the influential paper by Thomas and Krebs (1997), some significant advances have been made, wherein some programs are able to calculate the power for regressions, comparisons of means (ANOVA and General Linear Models) or proportions (χ 2 tests), for correlation tests and survival analysis. However, there are still several statistical tests for which PA is not available and, unfortunately, this includes the Generalized Linear Models, which can be a very powerful statistical tool for data that do not follow a Gaussian distribution. There are also possibilities for calculating power for other tests, but efforts to do this can vary from relatively simple to challenging. For instance, Monte Carlo simulations can be used to calculate power for non-parametric tests (Peterman, 1990). Alternatively, data have to be transformed to fit the assumptions of tests that allow PA, e.g. log-transformation or squareroot transformation for count data, arc sine square-root transformation for proportions (see, e.g. Quinn and Keough, 2002, or another standard statistics textbook, for further information). Information on programs and their strengths and weaknesses can be also obtained from the following homepages: List of programs (from 1996) (http://www.insp.mx/dinf/stat_list.html) and paper by Thomas and Krebs (1997), (http://www.zoology.ubc.ca/~krebs/power. html).

able to detect a 20% loss of biodiversity (i.e. 6.4 species on average), the resulting transformed means for species numbers would be 3, 3, and 2.72 for the three treatments, respectively, and the standard deviation would be approximately 0.5 for all treatments. A simple ANOVA did not detect a significant effect. Entering the above-mentioned values in a programme for PA returns an effect size of ES = 0.2828, and thus what is conventionally described as medium effect size. With a total sample size of 30 the power is (1⫺β) = 0.2397. How many replicates would be needed to achieve a power of 0.8 with such an effect size? Using an a priori test in the programme for PA we receive a necessary sample size of n = 126. Thus, to demonstrate with high confidence that no effect exists would require a much larger study (see, e.g. Lang, 2004 for an estimate of necessary sample sizes for non-target effects of Bt-plants). Using another example, let us see how large the sample size should be in a nontarget effects study of an insect natural enemy. Using the above-mentioned example of Thomas (1997), where the non-target population would be affected only if the mortality were higher than 40%, we can use 0.4 as effect size in an a priori test. If we were to achieve a power of 0.8, the necessary sample size in an experiment with two treatments would be n = 52.

Examples

In a seminal paper, Hurlbert (1984) published a review with respect to proper replication of 176 field experiments covering 156 papers published in ecological journals between 1960 and 1983. Disturbingly, he found that of the 101 studies applying inferential statistics, 48% contained pseudoreplication. Pseudoreplication occurs whenever ‘inferential statistics are used to test for treatment effects with data from experiments where either treatments are not replicated (though samples may be) or replicates are not statistically independent’ (Hurlbert,

Let us, again, take a look at the example data provided in the introduction. Using ten fields for each treatment, the effect of GM plants on insect biodiversity was tested. If we were to analyse those data with ANOVA, we would have to transform the species numbers to receive data with Gaussian distribution. Square root transformation (y ⬘= √៮៮៮៮៮ y+1) could be favourable in our case. If our control plots could harbour eight non-target species and we wish to be

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1984). Statistical independence means that each individual data point might positively or negatively deviate from the population average due to random variation not related to the deviation of another point. An example of lack of statistical independence is given in the introduction, where samples of a study on effects of GMOs on biodiversity were segregated by treatment and, thus, differences attributed towards the treatment could equally well have been attributed to some factor typical for the section of the field the samples came from. In this case, the effects of treatments are potentially confounded with inherent differences between field plots. Although the awareness of researchers of avoiding pseudoreplication has increased and fewer studies contain analyses with pseudoreplicated samples, Heffner et al. (1996) and Ramirez et al. (2000) found, in a recent study on pseudoreplication in experiments on the olfactory response of insects, that an alarming 46% of 105 studies were pseudoreplicated, because of either a lack of independence in the stimulus or the experimental device, the repeated use of experimental animals or the use of groups of animals. Thus, pseudoreplication is still an issue in the design of experiments, and much care has to be taken to avoid any spatial or temporal segregation of samples from different treatments. For example, when testing the host specificity of biological control agents, it is essential that insects for the tests on non-target hosts do not come from one rearing container or incubator and control animals (for the test on target hosts) come from another, or that non-target hosts are always tested in the same container or field cage or on the same plant while target hosts are tested in another cage or on another plant. Equally, positions of experimental units within an experimental chamber or on a field plot need to be switched between treatments to avoid confounding effects of differences in temperature and light conditions, etc. In the same manner, the full set of trials on non-target hosts should not be conducted before tests with target hosts are carried out. Randomization of testing order, or random assignment to

plants or test cages, ensures that pseudoreplication can be avoided. For further reading, we encourage the reader to take a look at the section on pseudoreplication in Ruxton and Colegrave (2003).

Experimental Design: is Randomization Feasible? Basic textbooks on statistics always stress the point that, in order to draw relevant conclusions from an experiment, all treatments, replicates, etc., should be randomized. But what does that mean? Randomization is a process that assigns each replicate of each measured unit (animal, field, species, etc.) to each treatment in a random order, rather than by choice. By doing this, any effect observed will be unequivocally attributed to the treatment studied, and not to lurking variables or uncontrolled factors which might vary over the length of the experiment. For example, if one was interested in estimating the host-range specificity of different potential biological control agents for a pre-release evaluation of non-target risks, he/she would sequentially offer several potential host species to the different biological control agents studied (see van Lenteren et al., Chapter 3, this volume for a detailed description of the proposed method to be used). In this case, it would be preferable to: (i) test the different host species in a random order for the different biological control agents, and (ii) test each host species, with the different biological control agents taken in random order as well. Indeed, in the case where the different host species are always tested in the same order, uncontrolled factors varying with the duration of the experiment could influence the results and lead to differences that might be wrongly interpreted as being due to differences between species. Also, if all potential host species are tested successively on each biological control species, a difference observed between biological control species might simply be due to uncontrolled factors varying with the total duration of the experiment.

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The goal of randomization is to produce comparable groups of replicates in terms of general animal, field, etc., characteristics and other key factors that might affect the outcome of the result obtained. In this way, all groups of replicates are as similar as possible at the start of the study. At the end of the study, if group outcomes differ between each other, the investigators can conclude with some confidence that the treatment tested really influenced the results obtained. Most of the time, randomization is performed by means of a computer program, coin flips or a table of random numbers to assign each measured unit to a particular treatment. Advanced additional methods are sometimes used. Is randomization always feasible, especially in evaluating non-target risk in biological control programmes? Unfortunately, the answer is likely to be ‘no’. In the example given above, where we wanted to estimate the host-range specificity of different potential biological control agents, it would probably be unrealistic to design an experiment in which all host species tested and all potential biological control agents compared were randomized. Regarding the fact that the experimental scheme is based on a succession of different measures (see van Lenteren et al., Chapter 3, this volume), having everything randomized would indeed imply having available, during the total duration of the experiment, a sufficient number of all host and biological control agent species at the right stage. In most cases this would simply be not feasible for economic or spatial reasons. All of this should be kept in mind and, if real randomization appears not feasible, results of the experiments should thus be interpreted with caution.

A Unified Approach Instead of a Menu of Tests, General and Generalized Linear Models When the traits to be analysed follow a Gaussian (also called ‘Normal’) distribution, standard t-tests, ANOVA or regression

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analyses can be used to statistically test the effect of a treatment. All these different ‘classical’ methods assume that the distribution of residuals around the fitted model (i.e. the error distribution) is normal (Gaussian). These different methods, which most readers will be familiar with, are called ‘General Linear Models’, since in its simplest form, a linear model specifies the (linear) relationship between the variable (or response) y, to be explained (the socalled ‘dependent’ variable), and a set of predictors, independent variables, the xs, such that E(y) = b0 + b1x1 + b2x2 + … + bkxk

(1)

In this equation, b0 is the regression coefficient for the intercept and the bi values are the regression coefficients (for variables x1 to xk) computed from the data. So, for example, one could estimate (i.e. predict) the weight of a parasitoid female as a function of the type and number of hosts it feeds on. For many data analysis problems, estimates of the linear relationships between variables are adequate to describe the observed data, and to make reasonable predictions for new observations. However, as we have seen previously (see Box 13.1), most of the biological traits that have to be measured to estimate non-target risks of biological control agents do not necessarily follow a Gaussian distribution. In such cases, the relationship between the variable (or response) y to be explained cannot adequately be summarized by a simple linear equation, for two major reasons: OF THE DEPENDENT VARIABLE. First, the dependent variable of interest may have a non-continuous distribution and, thus, the predicted values of the statistical model should also follow the respective distribution. Any other predicted values are not logically possible. For example, an investigator may be interested in predicting one of two possible discrete outcomes (e.g. a host is accepted or not). In that case, the dependent variable can take on only two distinct values, and the distribution of the dependent variable is said to be binomial. Another example would be to predict how

DISTRIBUTION

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Box 13.1. Measurement variables and their distribution Many ‘classical’ statistical approaches rely upon the assumption that the probability distribution of data from samples and the error terms of the statistical analyses (the residuals) are distributed normally, i.e. Gaussian. With many of the measurement variables we collect in non-target testing of biological control agents, these assumptions are not met. Count data such as, e.g. number of mature eggs of a female, are usually Poisson distributed, data for percentages are Binomial, and data for longevity are usually Exponential or sometimes Gamma distributed. In theory, it is possible to transform many kinds of data such that the assumptions of parametric tests are met, and those tests are also robust against small deviations from the assumptions; but first of all it is hard to estimate the extent of the robustness against deviations from normality in error terms and, secondly, it is often advisable to use actual data rather than transformed data to meet assumptions. The probable most commonly collected types of data are listed in the table below. Note that deviations from the distributions mentioned in the table might occur in individual cases and that, in general in statistical testing, residuals should always be inspected for the adequacy of the model. Measurement variables often found in non-target testing of biological control agents and their distribution. Measurement variable

Distribution (most likely)

attack rate (per unit time) dispersal capacity diurnal periodicity egg load encounter rate (per unit time) fecundity frequency of mating growth rate host acceptance insertion/deletion of genes latency to attack morphology rate of development rate of predation/parasitism spatial distribution (i.e. counts) survivorship/mortality thermal budget (degree-days)

Gaussian Gaussian, or Poisson if counts Gaussian Poisson if counts or Binomial if proportion Gaussian Poisson if counts or Binomial if proportion Poisson if counts or Binomial if proportion Gaussian Binomial Poisson Gamma Gaussian Gaussian Binomial Poisson or Negative binomial Gamma Gaussian

many females a male can mate with. If we were to study actual numbers and not average number of matings per male, the dependent variable (i.e. number of females mated) is discrete (i.e. a male can mate with one, two or three females and so on, but cannot mate with 3.46 females or with fewer than 0 females), and most likely the distribution of that variable is highly skewed (i.e. most males will mate with one, two or three females, fewer will mate with four or five, very few will mate with six or seven, and so on). In this case it would be reasonable to assume that the dependent variable follows a so-called Poisson distribution.

A second reason why a simple linear model might be inadequate to describe a particular relationship is that the effect of the predictors on the dependent variable may not be linear in nature. For example, the relationship between the fecundity of a synovigenic parasitoid female and its age is most likely not linear in nature. Under standardized conditions, fecundity will not markedly differ between females of one or two days of age, whereas such a difference will probably be greater between older females, even with only one day’s age difference. Probably some kind of a power function would be adequate to

LINK FUNCTION.

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describe the relationship between females’ age and fecundity, so that each increment in days of age at older ages will have greater impact on females’ fecundity, as compared to each increment in days of age during early adult life. Put in other words, the link between age and fecundity is best described as non-linear, or rather as a power relationship in this particular example. Generalized Linear Models are a generalization of general linear models and can be used to predict responses both for dependent variables that are not normally distributed and for dependent variables which are non-linearly related to the predictors. Actually, general linear models can be considered as special cases of the generalized linear models. In general, in linear models, the dependent variable values have a normal distribution and the link function, which ‘connects’ the dependent variable to a linear combination of predictor variables, is a simple identity function (i.e. the linear combination of values for the predictor variables is not transformed). To illustrate this, equation (1) gave the general linear model linearly associating a response variable y with values on the x variables, while the relationship in the generalized linear model is assumed to be E(y) = g(b0 + b1x1 + b2x2 + … + bkxk)

(2)

where g(…) is a function. Formally, the inverse function of g(…), say f(…), is called the link function, so that f(E(y)) = b0 + b1x1 + b2x2 + … + bkxk

Various link functions (see McCullagh and Nelder, 1989) can be chosen, depending on the assumed distribution of the y variable values. Table 13.2 gives the four main Generalized Linear Models that can be used in experiments performed to estimate non-target risks of biological control agents. The values of the regression parameters (and their variance and covariance) in the Generalized Linear Model are obtained by a so-called maximum likelihood estimation, which requires iterative computational procedures. Several statistics packages are currently available for doing this. Then, tests of the significance of the effects in the model can be performed via the Wald statistic, the likelihood ratio or score statistic. Detailed descriptions of these tests can be found in McCullagh and Nelder (1989). In summary, Generalized Linear Models are powerful and efficient tools for analysing the sort of data collected in experiments performed to estimate nontarget risks of biological control agents. Just a brief overview has been provided here, and there are several textbooks that provide a thorough description of this sort of statistical modelling approach (e.g. Hosmer and Lemeshow, 1989; McCullagh and Nelder, 1989). We strongly recommend readers of this chapter to consult them.

Examples

(3)

where E(y) stands for the expected value of y.

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Using again our example from the introduction, we may analyse one of our computer-

Table 13.2. List of the main Generalized Linear Models that can be used in experiments performed to estimate non-target risk of biological control agents. Link functions indicated are the most ‘popular’ ones. Others can be used in particular cases (see McCullagh and Nelder, 1989 for an exhaustive description). Distribution

Model description

Appropriate link function

Type of data analysed

Normal

Traditional linear model

identity: ƒ(y) = y

Normally distributed traits

Binomial

Logistic regression

logit: ƒ(y) = log{y/(1⫺y)}

Fractions (proportions)

Poisson

Log-linear model

log: ƒ(y) = log(y)

Counts

Gamma

Gamma model with inverse link

inverse: ƒ(y) = 1/y

Time durations

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generated data sets using a Generalized Linear Model. Since we count the number of species in each field plot, our data are most likely Poisson distributed. Specifying a Generalized Linear Model with Poisson distribution and log link function, and using the number of species per plot as response variable and the crop treatment (GM-plants, non-GM isoline and conventional crop) as factor, we find a P-value of 0.0962; thus, there is an insignificant trend in the data (Fig. 13.2a). An analysis of these data using an ANOVA on square root-transformed data yields a P-value of 0.147. A visual comparison (Fig. 13.2b and c) and statistical tests of the normality of the standardized residuals from both analyses (P = 0.515 and P = 0.474, respectively) suggest that the Generalized Linear Model is the slightly more adequate approach to analyse these data. Note that in both cases the statistical result is insignificant and, thus, the null hypothesis of no effect cannot be rejected, but also that the power analysis suggests a lack of power to conclude with confidence that there is no effect. As a second example, imagine a large arena choice test as suggested by van Lenteren et al. (Chapter 3, this volume). We have three different treatments, with ten field cages each: (1) with the target host (or prey, which is used synonymously here) and non-target host present in the same field cage together with the natural enemy,

(2) with only the non-target host and the natural enemy in the same field cage, and (3) with only the target host and the natural enemy in the same field cage. We are interested in whether the target host is killed at a higher rate than the non-target host and whether the mortality of the non-target host depends upon the fact of whether the target host is available to the natural enemy or not. We will not test whether the mortality rates of target and non-target host are equal within treatment (1), because these data would not be independent. Rather, we will test whether the mortality of non-target hosts in treatment (1) is equal to the mortality of non-target hosts in treatment (2) and equal to that of the target hosts in treatment (3) (this is our null hypothesis). Again, we will use computer-generated data. Given that the mortality rates found were 4.1%, 10.6% and 50.5% in (1), (2) and (3), respectively, we use a Generalized Linear Model with binomial distribution and logit link and find a significant effect overall and also between treatments (Table 13.3). Thus, in this example, the non-target host is attacked at a relatively low rate, and even less so when target hosts are available. This result is visible from the estimates in Table 13.3, where the estimate for mortality is positive and thus higher in treatment (2) than in treatment (1), and much higher (more than three times higher) in treatment (3) than in treatment (1).

Fig. 13.2. Simulated average (+ SE) effect of plant treatment on non-target insect species (panel a). The computer-generated data were analysed by means of a Generalized Linear Model with a log link function and ANOVA on square root transformed values, respectively. Panels (b) and (c) show Normality Plots for the standardized residuals of the respective tests. The relationship in panel (b) shows a slightly better fit with normality assumptions than in panel (c).

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Table 13.3. Results of a Generalized Linear Model on computer-generated data for the mortality rates of target and non-target hosts in large arena choice tests, using an experimental set-up as suggested by van Lenteren et al. (Chapter 3, this volume) (for details, see text). Parameter

Treatment

Estimate

DF

χ-Square

Pr > ChiSq

(3) (2) (1)

⫺3.2591 3.2511 1.0839 0

1 1 1 0

378.47 329.63 30.14 0.0000

<0.0001 <0.0001 <0.0001

Intercept Target host Non-target host in no-choice test Non-target host in choice test*

* In the statistics package SAS, which was used here, the last treatment (in this case (1)) is set to zero by convention and the difference between the last and all other treatments (2) and (3) is tested.

Repeated Measurements in Generalized Linear Models Sometimes, the same individual insect or the same experimental plot is systematically sampled more than once in the course of an experiment. Data from such samples violate the assumption of the independence of data points since they do not have an equal probability of deviating positively or negatively from the population average, but contain some variation due to inherent properties of the individual animal or experimental plot. They can thus be considered pseudoreplicates that cannot be entered into statistical tests as independent data points. Liang and Zeger (1986) introduced Generalized Estimating Equations (GEE) to Generalized Linear Models as a method of dealing with such correlated data. GEE is not available in all statistical packages that provide Generalized Linear Models, but at least SAS (procedure Genmod) and S-plus/R provide GEE. They require that a variable identifies the repeated subject and that the model state-

ment refers to this variable as repeated. More details about GEE can be found, e.g. in Quinn and Keough (2002).

Example Imagine the following field experiment (see van Lenteren et al., Chapter 3, this volume for the rationale of a field test on non-target effects of a biological control agent): we wish to monitor the mortality induced by the natural enemy on the target and nontarget hosts across a time period after the release of the natural enemy. We are especially interested in whether the attack rate on non-target hosts depends upon the density of the target host, which may decrease over the course of the experiment. Again, we will use computer-generated data. In our computer program, we select ten different field plots that we resample at five different times. Over time, the number of target hosts per field plot decreases while the mortality of the non-target hosts increases (Table 13.4). However, in order to

Table 13.4. Computer-generated data for a field test on nontarget effects as a function of time (sampling date) and density of target hosts. Means and standard errors of ten field plots. Sample 1 2 3 4 5

Density of target host

Mortality of non-target host

996.6 ± 10.9 493.4 ± 7.86 289.9 ± 5.78 172.0 ± 2.78 102.3 ± 3.65

1 ± 0.4294 38 ± 0.516 5.6 ± 0.872 7.3 ± 0.870 11.8 ± 1.572

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elucidate the effect of target host density, we enter sampling date and density of the target host as covariates in the model. The Generalized Linear Model allows us to separate the effect of sampling times and target host density. The GEE model for repeated measurements takes care of the fact that we resample the same field plots, and thus target host densities and the mortality rate of the non-target hosts in each plot are not independent. With both variables, sampling date and the density of the target host, in the model we do not find a significant effect (Table 13.5). However, by removing the variable with the least explanatory power from the model (i.e. sampling date), we find that the density of the target host affects the mortality rate of the non-target host (Table 13.5). Estimates from the model show that mortality of non-target hosts increases with decreasing density of the target host, indicating a switch of the natural enemy to a non-preferred host when the preferred host is less available.

Time as a Measurement Variable: Cox Regression and Survival Analysis To estimate the potential impact of natural enemies on their host and potential nontarget host populations, it is often useful to acquire knowledge about the survival times of such insects. Survival data of insects are not normally distributed, but rather the probability λ of an insect being dead at time t, in the simplest case, can be considered to be constant. This leads to an exponential distribution of the data with mean survival time 1/λ, well known from the decay of radioactive particles and a series of population dynamics models, e.g. Ricker fishery models. Here, the arithmetic mean survival time is a poor predictor of the longevity and, usually, the median is used. Besides considering an exponential distribution of the survival times, predictors of a generalized linear model with the more general Gamma distribution and inverse link function give, as this was stated in the previous

Table 13.5. Analysis of Generalized Estimating Equations (GEE) parameter estimates of a Generalized Linear Model for repeated measurements of the mortality rate of non-target hosts in a field test (data from Table 13.4). The upper part of the table shows the analysis with both sample date and density of target host as explanatory variable, which results in an insignificant model. Removing the variable with least significance (i.e. ‘sample date’) leads to a model that demonstrates a significant and negative relationship between the density of the target host and the mortality of the non-target host (lower part of the table). Empirical Standard Error Estimates Parameter Intercept Sample date Density target host

Estimate ⫺3.2420 0.2715 ⫺0.0016

Standard Error 0.9599 0.1917 0.0011

Z of Wald test ⫺3.38 1.42 ⫺1.44

Pr > Z 0.0007 0.1568 0.1503

Score Statistics For Type 3 GEE Analysis Source Sample date Density target host

DF 1 1

Chi-Square 1.44 2.12

Pr > ChiSq 0.2304 0.1451

Empirical Standard Error Estimates Parameter Intercept Density target host

Estimate ⫺1.8476 ⫺0.0031

Standard Error 0.1351 0.0005

Z of Wald test ⫺13.68 ⫺5.99

Score Statistics for Type 3 GEE Analysis Source Density target host

DF 1

Chi-Square 9.14

Pr > ChiSq 0.0025

Pr > Z <0.0001 <0.0001

Statistical Tools to Improve the Quality of Experiments

section, accurate results. Since Generalized Linear Models are fully parametric, they are the most powerful solution for survival analysis, even though in several statistical packages the user may find other types of analyses that are mostly non- or semi-parametric in the menu for survival analysis. However, there is – at least – one possible impediment to using Generalized Linear Models for survival analyses. Imagine a test performed to evaluate the effect of insecticide residues on survival times. While all insects in the treatment group (insecticide) are dead at day 10, 8% of the specimens in the control group are still alive at day 20, the planned end of the observation. What should be done with the data points from this 8% of the control group? Should they be left out, since no data for their longevity have been measured? This would lead to a loss of biologically meaningful data and, even more disturbing, to a bias in the interpretation, since we know that those individuals survived until at least day 20. The only thing we do not know is for how much longer they would have lived. These data points are called ‘right-censored’. A so-called log-rank test, or, more generally, a Cox regression model (= proportional hazards model), can adequately deal with censored survival data (Cox, 1972). Recently, a plethora of different studies have used such a statistical analysis for ecological investigations on insects (e.g. van Alphen et al., 2003). Besides using this sort of analysis to study changes in survival time, survival analysis can also be used when it comes, e.g. to testing residence times or withdrawal times of natural enemies on patches with target and non-target hosts, or when testing the latency until a natural enemy attacks a host or prey (see van Lenteren et al., Chapter 3, this volume). Briefly, the probability of dying, leaving a patch or attacking, λ, can be modified in the course of time by covariates and the Cox regression provides estimates for how the covariates, i.e. treatment effects, modify the baseline hazard of dying, leaving a patch or performing an attack. For further information, we recommend readers to consult papers that provide a thorough description of the method (e.g.

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Haccou and Hemerik, 1985; Haccou and Meelis, 1992; Wajnberg et al., 1999; van Alphen et al., 2003).

Example Imagine a small arena no-choice test with behavioural observation of a candidate natural enemy on either target or non-target hosts (see van Lenteren et al., Chapter 3, this volume for the setup). Observations are limited to one hour, after which almost all of the target hosts were attacked, and 56.7% of the non-target hosts. However, it seems that while target hosts are attacked almost immediately, the natural enemies attack non-target hosts only after a rather long period of searching the small arena, from which they cannot escape. The acceptance of non-target hosts is probably an overestimation of the host range of the natural enemy (see van Lenteren et al., Chapter 3, this volume) and we thus test the latency until the host is attacked. This will elucidate whether there is a significant effect of the host species on the acceptance pattern of the natural enemy. In the Cox regression, the 43.3% of non-target hosts that remained unattacked are entered as censored observations. The Cox regression returns a highly significant (P < 0.001) effect of host species on the probability of being attacked. To elucidate this in detail, we plot the cumulative hazard function. This function gives the cumulated instantaneous potential for the event (i.e. the attack) to occur, given it has not yet occurred. The cumulative hazard function is thus a useful measurement of the danger of being attacked at any point in time. Here, it indicates that the probability of being attacked is 15.733 times higher per unit time for target hosts compared with non-target hosts (Fig. 13.3).

Conclusions Conducting experiments for the assessment of non-target effects of biological control agents will be costly in terms of the man-

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Fig. 13.3. Cumulative hazard functions for the latency until target hosts (solid line) and non-target hosts (dashed line) are attacked. Target hosts have a much higher probability per unit time of being attacked than non-target hosts.

power involved, the specimens provided for testing and the plants or plant parts needed for, e.g. host specificity tests etc. Thus, there is a high premium on using the best experimental design and the most powerful statistical methods, in order to obtain reliable test results from a reasonable amount of replicates. This is especially so, since the result we are most interested in, i.e. the probability that non-target effects do not exist, is not directly testable. What we can test is whether the null hypothesis of no effect on non-target species is wrong. If we do not find a significant effect, it very much depends upon the power of the test to decide with some confidence that no effect exists. Therefore, great care should be taken to determine the appropriate replicate number of tests. A priori power analyses, as

pointed out in this chapter, are the appropriate approach here, and whenever nonsignificant results are stated, the power and the associated effect size should be stated in order to provide the reader with information about the degree of confidence of the results. Furthermore, the experiments should be planned in detail to ensure that no pseudoreplication occurs. Recent analyses of research papers in ecology have found a relatively high prevalence of pseudoreplication (Heffner et al., 1996; Ramirez et al., 2000), in spite of Hurlbert’s (1984) seminal paper. Thus, the importance of avoiding pseudoreplication must be stressed here, and randomization should be used wherever possible to avoid interdependency. Fortunately, very powerful statistical techniques like Generalized Linear Models and survival analyses have become available and are now widely used in a variety of biological disciplines (e.g. Garrett et al., 2004). They not only help to increase the precision of testing results but also the accuracy of tests, since they can adequately deal with non-normally distributed data that we frequently encounter in non-target effects testing. With this chapter we hope to improve the awareness of the problems, and have indicated solutions suitable for improving the quality of experiments assessing non-target effects of biological control agents.

Acknowledgements We are grateful to B.D. Roitberg, L. Hemerik and U. Kuhlmann for reviewing an earlier version of this chapter and for their helpful comments that led to important improvements.

References Andow, D.A. (2003) Negative and positive data, statistical power, and confidence intervals. Environmental Biosafety Research, 2, 75–80. Carey, J.M. and Keough, M.J. (2002) The Variability of Estimates of Variance, and Its Effect on Power Analysis in Monitoring Design. Environmental Monitoring and Assessment 74, 225–241. Cohen, J. (1998) Statistical Power Analysis for the Behavioural Sciences. Lawrence Erlbaum, Hillsdale, New Jersery.

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Cox, D.R. (1972) Regression models and life-tables. Journal of the Royal Statistical Society B 34, 187–220. Crawley, M.J. (1993) GLIM for Ecologists. Blackwell Scientific Publications, Oxford, UK. Crawley, M.J. (2002) Statistical Computing: An Introduction to Data Analysis Using S-Plus. John Wiley and Sons Ltd, Chichester, UK. Di Stefano, J. (2003) How much power is enough? Against the development of an arbitrary convention for statistical power calculations. Functional Ecology 17, 707–709. Garcia, L.V. (2004) Escaping the Bonferroni iron claw in ecological studies. Oikos 105, 657–663. Garrett, K.A., Madden, L.V., Hughes, G. and Pfender, W.F. (2004) New applications of statistical tools in plant pathology. Phytopathology 94, 999–1003. Grafen, A. and Hails, R. (2002) Modern Statistics for the Life Sciences. Oxford University Press, Oxford, UK. Haccou, P. and Hemerik, L. (1985) The influence of larval dispersal in the cinnabarmoth (Tyria jacobaea) on predation by the red wood ant (Formica polyctena). An analysis based on the proportional hazards model. Journal of Animal Ecology 54, 755–769. Haccou, P. and Meelis, E. (1992) Statistical Analysis of Behavioural Data. An Approach Based on Time-Structured Models. Oxford University Press, Oxford, UK. Heffner, R.A., Butler, M.J. and Reilly, C.K. (1996) Pseudoreplication revisited. Ecology 77, 2558–2562. Hilborn, R. and Mangel, M. (1997) The Ecological Detective. Confronting Models with Data. Princeton University Press, Princeton, New Jersey. Hoenig, J.M. and Heisey, D.M. (2001) The abuse of power: the pervasive fallacy of power calculations for data analysis. American Statistician 55, 19–24. Holm, S. (1979) A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70. Hosmer, D.W. and Lemeshow, S. (1989) Applied Logistic Regression. John Wiley and Sons Inc., New York. Hurlbert, S.H. (1984) Pseudoreplication and the design of ecological field experiments. Ecological Monographs 54, 187–211. Lang, A. (2004) Monitoring the impact of Bt maize on butterflies in the field: estimation of required sample size. Environmental Biosafety Research 3, 55–66. Liang, K.-Y. and Zeger, S.L. (1986) Longitudinal data analysis using generalized linear models. Biometrika 73, 13–22. McCullagh, P. and Nelder, J. (1989) Generalized Linear Models. Chapman and Hall, New York. Moran, M.D. (2003) Arguments for rejecting the sequential Bonferroni in eccological studies. Oikos 100, 403–405. Neuhäuser, M. (2004) Testing whether any of the significant tests within a table are indeed significant. Oikos 106, 409–410. Perry, J.N., Rothery, P., Clark, S.J., Heard, M.S. and Hawes, C. (2003) Design, analysis and statistical power of the Farm-Scale Evaluations of genetically modified herbicide-tolerant crops. Journal of Applied Ecology 40, 17–31. Peterman, R.M. (1990) Statistical power analysis can improve fisheries research and management. Canadian Journal of Fisheries and Aquatic Sciences 47, 2–15. Quinn, G.P. and Keough, M.J. (2002) Experimental Design and Data Analysis for Biologists. Cambridge University Press, Cambridge, UK. Ramirez, C.C., Fuentes-Contreras, E., Rodriguez, L.C. and Niemeyer, H.M. (2000) Pseudoreplication and its frequency in olfactometric laboratory studies. Journal of Chemical Ecology 26, 1423–1431. Rotenberry, J.T. and Wiens, J.A. (1985) Statistical power analysis and community-wide patterns. American Naturalist 125, 164–168. Rothery, P., Clark, S.J. and Perry, J.N. (2003) Design of the farm-scale evaluations of genetically modified herbicide-tolerant crops. Environmetrics 14, 711–717. Ruxton, G.D. and Colegrave, N. (2003) Experimental Design for the Life Sciences. Oxford University Press, Oxford, UK. Steidl, R.J., Hayes, J.P. and Schauber, E. (1997) Statistical power analysis in wildlife research. Journal of Wildlife Management 61, 270–279. Thomas, L. (1997) Retrospective power analysis. Conservation Biology 11, 276–280.

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Thomas, L. and Krebs, C.J. (1997) A review of statistical power analysis software. Bulletin of the Ecological Society of America 78, 126–139. van Alphen, J.J.M., Bernstein, C. and Driessen, G. (2003) Information acquisition and time allocation in insect parasitoids. Trends in Ecology and Evolution 18, 81–87. Verhoeven, K.J.F., Simonsen, K.L. and McIntyre, L.M. (2005) Implementing false discovery rate control: Increasing your power. Oikos 108, 643–647. Wajnberg, E., Rosi, M.C. and Colazza, S. (1999) Genetic variation in patch time allocation in a parasitic wasp. Journal of Animal Ecology 68, 121–133.

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Principles of Environmental Risk Assessment with Emphasis on the New Zealand Perspective Abdul Moeed,1 Robert Hickson2 and Barbara I.P. Barratt3 1ERMA

New Zealand, Environmental Risk Management Authority, PO Box 131, Wellington, New Zealand (email: [email protected]; fax number: +64-4-914-0433); 2Ministry of Research, Science and Technology, PO Box 5336, Wellington, New Zealand (email: [email protected]; fax number: +64-4-471-1284); 3AgResearch, Invermay Agricultural Centre, Private Bag 50034, Mosgiel, New Zealand (email: [email protected]; fax number: +64-3-489-3739)

Abstract Principles of risk assessment and management for release of biological control agents are explained. An example of the application of risk assessment and management is given based on the New Zealand practice and experience. Prior to introducing any new organism into New Zealand, it is important to assess and evaluate potential adverse effects on environment and people. This paper outlines the historical basis and current legislative regime for the management of potential effects of invertebrate organisms proposed for release as biological control agents for arthropods. It describes the basis of the two main pieces of legislation – the Hazardous Substances and New Organisms (HSNO) Act 1996 and the Biosecurity Act 1993 – in the management of environmental effects of introduced organisms. The purpose of the HSNO Act is to protect the environment, and the health and safety of people and communities, by preventing or managing the adverse effects of hazardous substances and new organisms. The intentional release, in addition to importation, development, field testing or conditional release, of all new organisms is managed under the HSNO Act. This Act is implemented by an independent agency, the Environmental Risk Management Authority (ERMA New Zealand). The prior approach taken to determine the likely environmental effects of new organisms is outlined against the criteria in the HSNO Act, as well as the risk assessment and management framework. A case study involving the release of an invertebrate biological control agent is mentioned as an example of the risk assessment framework used in New Zealand.

©CAB International 2006. Environmental Impact of Invertebrates for Biological Control of Arthropods: Methods and Risk Assessment (eds F. Bigler et al.)

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Introduction Release of biological control agents has a long history, so why are principles of environmental risk assessment needed? Internationally there is increasing caution over the use of biological control agents. This is due both to historical and scientific factors, and is leading to increasingly complex risk assessment processes (Sheppard et al., 2003). Principles help establish good practice, and this chapter identifies key aspects of risk assessment (and risk management) that should be considered before a new biological control agent is released, regardless of whether all such steps are required by law. The principles outlined here are generally applicable to any biological control agent, and not just to invertebrates. The principles are illustrated by a case study involving the risk assessment of a biological control agent in New Zealand under the Hazardous Substances and New Organisms (HSNO) Act 1996.

Principles of Environmental Risk Assessment

Identifying risks Identifying risks requires identifying what can happen, when, and how. The objective is to identify reasonably foreseeable risks and benefits. This involves determining sources of risks (for example host range), areas of impact (for example native non-target arthropods or species providing economic benefits), incidents

ESTABLISH THE CONTEXT

IDENTIFY RISKS ASSESS RISKS

COMMUNICATION AND CONSULTATION

Risk assessment involves several components and is usually part of a larger risk

management framework (Fig. 14.1). Technically, risk assessment involves the analysis and evaluation of risks, but the discussion below also covers identifying and managing risks, since these will also be central parts of an application for release of a biological control agent. A systematic and transparent approach to identifying and assessing risks is required to reduce the unknown risks, and to identify effective risk management options. The following focuses on risks, but the same principles apply to the identification and assessment of benefits associated with the control agent (see Bigler and Kölliker-Ott, Chapter 16, this volume). Discussion of the potential benefits, as well as the potential risks, will be necessary for informed decision-making.

MONITOR ANALYSE RISKS AND REVIEW EVALUATE and RANK RISKS

TREAT RISKS

Fig. 14.1. The risk management framework taken from the Australian and New Zealand risk management standard AS/NZS 4360, http://www.standards.co.nz (with permission from Standards New Zealand).

Principles of Environmental Risk Assessment: the New Zealand Perspective

that may release the hazard’s potential (such as establishment in other habitats, the availability of alternative hosts) and the exposure pathway (i.e. how the incidents may occur). It is critical for risk assessment to identify as comprehensively as possible the potential risks associated with the release of a biological control agent, since all subsequent steps address what is identified. Poor risk identification will lead to poor risk assessment. An important part of the process is to be transparent in how risks were identified and assessed so that a constructive discussion can occur on the assumptions and interpretations. Risks that are identified but not considered to be reasonable should also be noted in the process, with a justification as to why they are not considered reasonable. It is important to identify risks that are direct, i.e. those that flow immediately from the use of the biological control agent, such as risks related to non-target species, and those that are indirect, i.e. those that are a consequence of secondary effects of using the control agent, such as how the control agent may affect the ability of another species to control the same pest. A distinction between monetary and non-monetary risks may also need to be made. There is a range of means of identifying risks. These include checklists based on prior experience, brainstorming sessions, scenario analyses, interviews or consultations. A combination of ways may be used, and the techniques used should be identified. Brainstorming and discussions with others unfamiliar with the specifics of the pest or control agent can be particularly useful for identifying risks not obvious to those involved in the pest control operation. Some risk assessment processes may require identification and assessment of social and/or cultural risks as well as biophysical risks, such as effects on air, water and soil. Social and cultural risks can be particularly difficult to evaluate and will require the involvement of people with expertise in these areas.

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Analysing risks Analysis involves determining the likelihood of events occurring and the magnitude or consequences if they occur. For example, how likely is it for a non-target host to be parasitized, and if it occurred what would be the environmental and/or other consequences? There may be a chain of events required before an impact occurs, and therefore the likelihood and magnitude of effects need to be analysed. Analysis is based upon professional judgement and information from other sources. Likelihoods and magnitudes can be estimated qualitatively or quantitatively, although for organisms qualitative scales are usually chosen since quantitative information may be difficult to collect. Some risks may be amenable to experimental assessment (such as testing host/prey preferences and host specificity in a laboratory) and/or from observations in ‘the wild’. Others may be inferred from known biological characteristics and past experiences. It is important to realize that, in addition to assigning likelihood and magnitude to each risk, values and judgements will influence how individuals perceive those risks. Analysis of the risks may therefore need to take account of a range of factors, such as: ● The degree to which exposure to the risk is involuntary. ● The extent to which the risk will persist or be controllable. ● The extent to which the risk is not known or poorly understood. ● Experience in managing such potential adverse effects. Even where experimental or other rigorous data are available, there will usually be uncertainty associated with risk analysis, since behaviour in a new environment cannot be completely inferred from laboratory or other studies, or because there may be variability in the extent and quality of the available data, or the assumptions and models used differ to some degree from reality. A key part of the assessment

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process is to identify where key uncertainties lie, and the nature of these uncertainties. Some uncertainties may be able to be reduced by additional research, and these need to be identified. Some commentators (e.g. Wynne, 1992; O’Riordan and Cox, 2001) make a distinction between uncertainty, ignorance, indeterminacy and/or ambiguity. Uncertainty is where likelihood is not known but there may be some understanding of potential impacts. Ignorance is often where ‘we don’t know what we don’t know’ or where there is very little information to estimate likelihood and impact. Indeterminacy is an overlap between uncertainty and ignorance. Ambiguity is where the impacts are unclear but likelihood of events may be able to be estimated. Having clarity about the reliability and relevance of information and the types of uncertainty is the hallmark of good risk assessment.

Evaluating risks Evaluation of risks involves determining what the risk management priorities are. This requires a consideration of both the likelihood and magnitude of specific adverse effects to determine the significance or importance of the risk. Risks of very low likelihood and very little impact will rank very low in significance or priority and may thus be largely ignored, while adverse effects that are almost certain to occur and will have significant adverse effects will rank very high in priority. There will, however, be a range of other risks in between these extremes and it can be harder to determine which ones may require more attention or will be more likely to influence decision-makers. For example, how should the following two types of risk be ranked or compared? ● An adverse effect that is relatively unlikely to occur but if it did would have a substantial impact. ● An adverse effect that is more likely to occur but would have a relatively lower impact.

There may be set criteria (such as predetermined standards, or target risk levels) against which the risks can be evaluated, or the priorities may be based on professional judgement. Generally, magnitude of an adverse effect will be weighted more highly than likelihood, so in the example above the first risk would probably receive a higher priority. The case study given later in this chapter shows how qualitative scales are used to evaluate risks. Criteria and judgement will reflect the approach to risk – that is how risk averse or risk tolerant the person, committee, organisation or legislation is. As with risk identification, transparency and justification of the risk evaluation process is necessary for a robust risk assessment. Some form of risk (or cost) benefit analysis may often be required in an application to release a biological control agent (see Bigler and Kölliker-Ott, Chapter 16, this volume).

Managing risks Risk management is usually the primary responsibility of the decision-maker, but it is important for those seeking to release biological control agents to identify and discuss options to manage identified risks. Some risks could be managed by conducting more research to reduce specific uncertainties, while some may be considered to have only minor effects so that no management is necessary. Other risks may be managed by controlling the timing, size and/or places for the release of the agent, or by requiring post-release monitoring (see Barrat et al., Chapter 10, this volume). However, whether post-release management options can be used will depend on legislative requirements in the country or countries where the release is planned. Some management options may not be practical due to their complexity, efficacy, cost, or because they will reduce the effectiveness of the biological control agent. A critical factor associated with management of risks is the approach to risk. The decision-maker may be risk averse or may

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tolerate some risks that are more significant. The nature and distribution of benefits associated with release of the biological control agent will often influence the decision-making process so, as noted earlier, identification and assessment of benefits need to be handled with the same rigour as risks.

Communication of risks Release of a biological control agent is likely to affect a range of groups or communities. Consequently, it is advisable to engage with such groups early and openly during the risk assessment process. This, however, can result in additional time and costs and so these factors may need to be accounted for in the project. A range of papers and texts discusses the principles and practice of risk communication, and can be accessed at http://www.centerforriskcommunication.com

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world, as well as in New Zealand, has gone through phases of increased scrutiny, starting from a cursory consideration of environmental effects to an establishment of a comprehensive risk assessment and management framework that is supported by statutes. New Zealand has had a history of introductions of new organisms to control introduced pests that have become established in the environment. In this context, a new organism is one that is not found in New Zealand’s natural environment. During the last two decades, potential environmental effects of new organisms have gained greater awareness prior to their release, primarily because some of the past introductions have become pests of national significance.

The Hazardous Substances and New Organisms (HSNO) Act Roles and components of the Act

Risk Assessment Framework in New Zealand Requirements for risk assessments vary between countries, and even between states in a country. Sheppard et al. (2003) recently compared requirements for releasing biological control agents in Australia, Canada, New Zealand, South Africa and the United States, and the reader is referred to this paper for more information. While they all tend to share some common features, the requirements can vary markedly between these countries. The risk management process in New Zealand comes closest to a flawless democratic and complete process, but some concerns have been expressed about the time and cost of the process as implemented there (Sheppard et al., 2003). Invasive species are considered to pose a significant threat to native biodiversity (Pimentel, 2002). To overcome this threat, other organisms have been introduced as biological control agents. Introduction of these organisms in many parts of the

The HSNO Act 1996 (http://www.legislation.govt.nz) provides a framework for assessment and approval of applications to import, develop, test and conditionally release. It applies to microorganisms, plants and animals, including genetically modified organisms (GMOs). The purpose of the Act is to protect the environment, and the health and safety of people and communities, by preventing or managing the adverse effects of hazardous substances and new organisms. In achieving the purpose, there are principles to be recognized and provided for, for example, safeguarding of the life-supporting capacity of air, water, soil and ecosystems; and maintenance and enhancement of the capacity of people and communities to provide for their own economic, social and cultural well-being and for the reasonably foreseeable needs of future generations. The agency that implements this Act is the Environmental Risk Management Authority (the Authority). This is a quasijudicial body, whose members are selected to represent a ‘balanced mix of knowledge

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and experience’ in the appropriate areas. The Authority is supported by the staff and infrastructure of the government Agency, and together, the Authority and the Agency form the Environmental Risk Management Authority (ERMA New Zealand). In consideration of applications for release of a biological control agent, the Act requires consideration of effects on the four issues: (i) Environment, (ii) Human health, (iii) Economy and (iv) Cultural, social and community aspects. Through a public consultation process a consistent methodology (which includes an assessment of monetary and non-monetary costs and benefits) for making decisions has been established. The Authority is required to take into account the need for caution in managing adverse effects where there is scientific and technical uncertainty about those effects. Examples of ways of identifying risks are provided in ERMA New Zealand’s technical guide on identifying, assessing and evaluating risks costs and benefits (ERMA NZ, 2004). In the first instance, risks are identified, assessed,

and analysed by the applicant and then by the submitters (e.g. public, stakeholders), agency, and by the Authority (Table 14.1). The agency, and any additional experts contracted on a case-by-case basis, evaluate the information in the application as well as in submissions, and then advise the Authority. Criteria considered and information required in risk assessment The HSNO Act framework for assessment of risk through its various provisions and explanations is outlined below. THE SUSTAINABILITY OF ALL NATIVE AND VALUED

The criteria are aimed at consideration of the effect new introductions might have on the continued survival, at or close to population densities that existed prior to the introduction. The key element is the sustainability of existing biota, meaning that organisms need a certain threshold of population density to be able to continue unaided existence in the

INTRODUCED FAUNA.

Table 14.1. Roles and components in the risk management process under the Hazardous Substances and New Organisms (HSNO) Act in New Zealand. Risk

Applicant

Submitters

Agency

Identification

Determination of what can happen, when, and how

Analysis

Systematic Primary Optional determination of the responsibility likelihood of events and the magnitude of their consequences

Evaluation and review

Evaluation and review

Evaluation

Determination of risk Optional management priorities by comparing the level of risk against predetermined standards, target risk levels or other criteria

Optional

Evaluation and advice

Primary responsibility

Optional

Evaluation and advice

Primary responsibility

Management Selection and implementation of appropriate options for dealing with risk

Primary Primary Evaluation responsibility responsibility and review plus any additions required

Authority

Optional

Evaluation and review

Principles of Environmental Risk Assessment: the New Zealand Perspective

environment. This population density should be at a level that would sustain the effects of natural population fluctuations and perturbations as a result, for example, of fluctuations in breeding performance, food supply or environmental variables. Population densities naturally fluctuate between years and are at times affected by abiotic factors such as climatic variables of temperature and rainfall. However, resilient populations normally sustain these fluctuations and perturbations and therefore it is anticipated that new introductions, if approved, would not affect the existing native and valued populations in such a way as to jeopardize their continued existence. A number of attributes could be used as a guide to determining whether a particular new organism could become a problem by affecting New Zealand’s inherent biodiversity:

EFFECT ON INHERENT BIODIVERSITY.

● ● ● ● ● ● ● ● ●

Ability to disperse widely. Ability to reproduce rapidly. Life expectancy. Reproduction capacity. Niche requirements. Geographical distribution. Ability to hybridize. Competition. Occurrence of natural enemies of the new organism.

The evaluation of the above attributes will depend on the availability of information such as taxonomic classification, distribution and habitat requirements, characteristics for establishment, competitors and life cycle properties. THE INTRINSIC VALUE OF ECOSYSTEMS. The HSNO Act defines intrinsic value with respect to ecosystems as those aspects of ecosystems and their constituent parts which have value in their own right, including their biological and genetic diversity and the essential characteristics that determine an ecosystem’s integrity, form, functioning and resilience. In this context, constituent parts are the biotic

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microorganisms, plants and animals, and abiotic water, air and substrates such as soil, that are integral parts of the ecosystem. Any marked change in ecosystem functions resulting from new introduction is likely to be considered as affecting the ecosystem’s intrinsic value. This relates to the ability of the new organisms to sting, be a vector of disease or form swarms of an unacceptable nuisance level such as to affect people’s well-being.

PUBLIC HEALTH.

IMPACT ON ORGANISMS AND ECOSYSTEMS OF CUL-

This provision is for the consideration of the effects that the proposed organism’s introduction is likely to have on people’s values and their way of life. It is therefore important that people are consulted for their views on the application to import new organisms into New Zealand.

TURAL VALUE.

THE ECONOMIC AND RELATED BENEFITS TO BE DERIVED FROM THE USE OF A NEW ORGANISM.

This provision is for the risk, cost and benefit analysis of the proposed importation of an organism into New Zealand. The applicants are required to present their analysis and conclusions in support of their case for the importation. The inherent premise is that the benefit (monetary and non-monetary) of having the organism in New Zealand should outweigh its adverse effects. With respect to new organisms in general, New Zealand is a party to many international agreements and therefore obliged to comply with their requirements.

INTERNATIONAL OBLIGATIONS.

CONSIDERATIONS. The HSNO Act requires the Authority to consider the following for a rapid assessment of an application for release of a new organism. In general, this provision is for the consideration of new organisms that are obviously low risk and would not normally require full assessment. The organism’s release is unlikely to succeed under the rapid assessment provisions if the organism is likely to

OTHER

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displace or markedly reduce the numbers of an existing valued species so as to cause deterioration to natural habitats where the organism establishes. Public involvement An underlying aspect for public involvement is to balance the right of public access to information against the need for confidentiality for valid commercial reasons. Public involvement in the process is important because people’s values or quality of life may be affected by the decisions taken. It is considered important that, in the assessment of new organisms, information relevant to the identification of effects and benefits should be available to interested parties, unless there are compelling reasons for withholding it, to ensure public confidence in the process. However, it is also important that industry should have confidence that the process contains provisions that permit legitimate confidential information to be protected. Risk assessment under the HSNO Act The assessment of risk requires consideration of the likelihood and magnitude of an effect (risk analysis) and evaluation of risk management priorities (risk evaluation). Analysis and evaluation are based on professional judgement and information derived from other sources, such as other

studies and experiences. In a majority of cases, even when the available information is relevant and accurate, there will be some residual uncertainty in risk assessment and in respect of new organisms. Qualitative descriptors of risk are generally used because there is insufficient information for quantitative assessment. Tables 14.2 to 14.4 illustrate qualitative matrices for prioritizing environmental effects and risks, and for identifying any risks that may be considered unacceptable on a case-by-case basis. The measure of the level of risk (combination of likelihood and magnitude) is specific to the application. It would therefore be unadvisable to compare measures of level of risk between applications. Risk management under the HSNO Act Under the HSNO Act, the Authority is responsible for the management of risk. The management of risk is influenced by the approach to risk – for example, whether only insignificant risks are acceptable or if higher level of risks can be tolerated. Since the HSNO Act framework reported in Hickson et al. (2000), the Act has been amended to provide for a conditional release option to allow for conditions or controls to be imposed on a release approval. However, this can only occur if the application was specifically made for a conditional release and, conversely, if application was made for a release without condition then no conditions can be

Table 14.2. Qualitative scales for estimating magnitude of adverse environmental effects used by ERMA New Zealand. Description

Examples

Minimal

Highly localized and contained environmental impact, affecting a few individuals of communities of flora or fauna; no discernible ecosystem impact

Minor

Localized and contained reversible environmental impact, some local plant or animal communities temporarily damaged; no discernible ecosystem impact or species damage

Moderate

Measurable long-term damage to local plant and animal communities, but no obvious spread beyond defined boundaries; medium-term individual ecosystem damage; no species loss

Major

Long-term/irreversible damage to localized ecosystem but no species loss

Massive

Extensive irreversible ecosystem damage, including species loss

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scales used to characterize adverse environmental effects can be applied to qualify beneficial effects. Likelihood in the context of risk assessment applies to the composite likelihood of the end effect and may be expressed as a frequency or a probability of something happening. Qualitative scales for determining likelihood of adverse effects as used now by ERMA NZ are presented in Table 14.3. The set of generic likelihood descriptors for adverse effects has evolved from a five- to a seven-scale table since its publication by Hickson et al. (2000). Using the above qualitative descriptors for magnitude of effects and likelihood of the event occurring, an additional two-way table representing a level of risk (combined likelihood and magnitude) can be constructed, as shown in Table 14.4. Here, seven levels of effect are allocated (A to G). These terms are used to emphasize that the matrix is a device for determining which risks (and benefits) require further analysis to determine significance for decision-making.

attached to that approval. The Authority has discretion to decline applications if it considers that release without condition is inappropriate. Hickson et al. (2000) gave an overview of the risk management framework implemented by the Authority under the HSNO Act. Under criteria-based risk assessment of the HSNO Act, a package of biophysical effects were considered for the assessment and estimation of risks and benefits based on a qualitative estimate of magnitude and likelihood of occurrence. Assessment and evaluation of effects on any application and its related information are based on professional judgement of the reviewers and decision-makers. As noted in Hickson et al. (2000), in relation to risks associated with introduction of new organisms, qualitative descriptions are generally used. Since that publication, the qualitative scales used for characterization of effects and risks have evolved and the current practice is outlined below as examples (Tables 14.2 to 14.4). The same qualitative

Table 14.3. Qualitative scales for determining likelihood of adverse environmental effects used by ERMA New Zealand. Descriptor

Description

Highly improbable Improbable (remote) Very unlikely Unlikely (occasional) Likely Very likely Extremely likely

Almost certainly not occurring, but cannot be totally ruled out. Only occurring in very exceptional circumstances. Considered only to occur in very unusual circumstances. Could occur, but is not expected to occur under normal operating conditions. A good chance that it may occur under normal operating conditions. Expected to occur if all conditions are met. Almost certain.

Table 14.4. Qualitative scale for evaluating the level of risk by combining likelihood and magnitude of effects used by ERMA New Zealand. Magnitude of effect Likelihood Highly improbable Improbable Very unlikely Unlikely Likely Very likely Extremely likely

Minimal

Minor

Moderate

Major

Massive

A A B C D E E

A B C D E E F

B C D E E F F

C D E E F F G

D E E F F G G

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The lowest level (A) is deemed equivalent to insignificant, and in cases of uncertainty it may be preferable to split this into sub-categories, with A1 being deemed to equate to insignificant. The ERMA New Zealand process The process implemented under the HSNO Act encourages applicants to consult ERMA NZ staff prior to submitting their applications. Once an application to release a new organism is formally received, it is publicly notified in the press and on the ERMA NZ web site (http://www.ermanz.govt.nz). A summary of the application is sent to major stakeholders, and a copy of the full application (except for any confidential information) is available from ERMA NZ and from its web site. Submissions are invited from the public and stakeholders, including the Department of Conservation. A project team within ERMA NZ, including external scientific advisors (if required), is set up to produce an Evaluation and Review (E&R) report for the Authority. The E&R report evaluates all the information available and takes account of submissions received. In its evaluation, the E&R report identifies the strengths and weaknesses of a particular case but does not make recommendations regarding a decision to the Authority. The purpose of the E&R report is to assist and support decision-making by the Authority by consolidating the information provided by the applicant, submitters and from other sources into a common format which enables conflicting and consistent information to be readily identified. The report presents the relevant information in a format and sequence which meets the decisionmaking requirements of the HSNO Act. The report provides an evaluation of the risk identification and assessments provided in the application, to give an opinion on its quality and credibility, and to identify gaps. The advice contained in the E&R report is given solely on the basis of an objective and expert review of the application and assessments of risks, costs and benefits.

A public hearing is held if any person interested in a particular application wishes to be heard. This has occurred for all biological control agent release applications considered by the Authority to date. The Authority then reaches a decision on the application and the applicant is informed and given a full explanation of the issues considered in making the decision. The process must be completed within 100 days of formal notification of the application, unless it has been extended in order that further information that can be provided by the applicant is sought.

Case study: application to introduce a biological control agent for obscure mealybug in New Zealand A case study is described here which highlights the particular issues which arose during the decision-making process for this particular application. Further case studies for weed biological control agents that have been processed under the HSNO Act are given in Barratt and Moeed (2005). The obscure mealybug, Pseudococcus viburni (Signoret) (Hemiptera: Pseudococcidae), first identified in New Zealand in 1922, is a pest of pipfruit. Growers find it difficult to control this insect using pesticides because of its cryptic nature, making it difficult to achieve adequate spray coverage. Furthermore, pesticide resistance problems have become evident. The pipfruit industry group submitted an application to ERMA NZ to introduce the parasitoid Pseudaphycus maculipennis (Mercet) (Hymenoptera: Encyrtidae) for biological control of obscure mealybug, after a research team in the Horticulture and Food Research Institute of New Zealand had completed a risk assessment programme and also assisted in the preparation of the application. The applicant stated that Hymenoptera in the family Encyrtidae, to which the genus Pseudaphycus belongs, were among the most host-specific parasitoids known, especially those that attack mealybugs (Moore, 1988). Worldwide, all species of parasitoids

Principles of Environmental Risk Assessment: the New Zealand Perspective

in the genus Pseudaphycus were restricted to hosts in the family Pseudococcidae, to which P. viburni belongs. P. maculipennis had only been recorded as parasitizing P. viburni in the field, and based on this evidence the applicant asserted that P. maculipennis was essentially monophagous. The applicant further stated that field studies had shown that exotic mealybugs were only attacked by exotic parasitoids, while native mealybugs were attacked only by native parasitoids. These data (Charles, 1993) and those of Noyes (1988), based on almost 100 years of collecting, indicate that monophagy, or very narrow oligophagy, was the norm among encyrtids that attack mealybugs. Host-specificity tests had been carried out using 17 test species in 15 genera in Europe, Australia and New Zealand, and had confirmed that P. maculipennis was effectively monophagous, conforming to the global norm for Encyrtidae which attack mealybugs. The only other species found to be attacked during host specificity testing in New Zealand was another exotic pest mealybug, the long-tailed mealybug Pseudococcus longispinus (TargioniTozzetti). Field studies in France and Australia provided further evidence that P. maculipennis was almost completely hostspecific to obscure mealybug. The key issues that were identified and addressed in the consideration of this case were the following: Parasitoid efficacy Uncertainty exists about the likelihood of P. maculipennis establishing and the extent of its effect on P. viburni, including the time that it might take for the potential benefits to be realized. The applicant had provided information on P. maculipennis biological programmes overseas and results of biological control programmes involving Encyrtidae in general, in support of the claim that a degree of biological control (either complete, substantial or partial) is likely within five years of establishment and continuing in the long term. If the parasitoid failed to establish, obviously no adverse effects or benefits were expected.

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The project team agreed with the applicant’s assessment of the likely effectiveness of the parasitoid and the Authority concluded that it was likely that the parasitoid would establish and exert a degree of biological control over P. viburni. At least 25% control of P. viburni infestation five years after release was considered plausible, and likely to continue in the long term. Human health effects It was recognized that environmental benefits could be realized if biological control of P. viburni was effective, leading to a reduction in insecticide use. In turn, this could result in reduced environmental pollution, and reduced adverse effects on non-target organisms. The Authority noted that as long as the parasitoid exerted some degree of control over P. viburni, benefits would arise from reduced insecticide use, resulting in reduced pesticide residues on pipfruit for consumption, and reduced risk from spray-drift from orchards. Because of the known characteristics of the organism, the Authority concluded that there was negligible risk from the parasitoid biting or stinging humans, or vectoring plant, human or other animal diseases. Environmental effects The Authority was concerned about potential adverse effects on the endemic mealybug, Pseudococcus zelandicus Cox, the only native species in this genus in New Zealand. In host-range tests conducted in New Zealand (Cox, 1987), the applicant failed to include this species since, despite considerable collecting effort, they were unable to locate a source of specimens. The project team advised that a key issue determining the relative effect on non-target hosts was the degree of environmental overlap in space (habitat, latitude and altitude), as this influences the degree of exposure of non-target hosts to the parasitoid. The applicant asserted that P. viburni and P. zelandicus had no recorded host plants in common and were generally found in different habitats. Since 1922, P. viburni had

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been recorded only from exotic plants in modified habitats. In contrast, P. zelandicus had only been recorded on native plants in sub-alpine habitats. Pseudococcus viburni had never been recorded on any hosts in the same family as these native plants. The Authority concluded that because of the clearly demonstrated likelihood of habitat separation between the target host and P. zelandicus, non-target parasitism would be very unlikely. The Authority recognized the concerns of some submitters about additional non-target effects and the incomplete state of knowledge of the insect fauna of New Zealand – for example, the possibility of undiscovered and/or undescribed species of Pseudococcus might occur in addition to P. zelandicus. The Authority considered that in this case it must reach a decision on the basis of the known, but incomplete, taxonomy and description of New Zealand’s invertebrate fauna. It was considered very unlikely that P. maculipennis would interbreed with other insect species in New Zealand, given there were no native or introduced species of Pseudaphycus reported to be present in New Zealand. It was also considered very unlikely that P. maculipennis would compete with or displace any native natural enemies of mealybugs since existing exotic parasitoids have never been reported from native mealybugs. There were no records that P. viburni was attacked by specialist natural enemies in New Zealand, and although generalist predators may occasionally attack P. viburni, it was not considered that a generalist insect would be dependent on P. viburni for population survival. The Authority acknowledged that it was impossible to predict the rate of evolution of host range for biological control agents in the long term. Evolution in the environment of new behaviours, development of physiological compatibility, and establishment of new genetic traits within a parasitoid population would be important in determining host range extension. This would be more likely to include species that were closely related to existing hosts, and occur in the same habitat. However, the Authority considered it was very unlikely that there

would be any adverse effect on native mealybugs in the long term, due to the high degree of host-specificity of the parasitoid. Cultural effects The project team noted that whilst impacts on native and valued introduced biota were pertinent to assessing the risks to Ma៮ ori, the obligations under the Act require specific and separate consideration. Issues that might concern Ma៮ ori are potential impacts on the sustainability of native and valued introduced biota, the impact on species particularly valued by Ma៮ ori and the effects on Ma៮ ori culture and traditions. Since the project team could find no information about whether native mealybugs had particular cultural or traditional value, the project team and the Authority concluded that it was unlikely that the release of the parasitoid would adversely affect the relationship between Ma៮ ori, or their culture and traditions, with valued flora and fauna. Benefits It was noted that direct monetary benefits from the biological control programme would accrue to pipfruit growers and the pipfruit industry. Although the efficacy of the parasitoid was uncertain, the economic case for the introduction of the parasitoid appeared strong, and the benefits from the release of P. maculipennis would be environmental and economic. The Authority considered that there was currently a risk to biodiversity, including populations of beneficial and native insects, arising from the particular insecticide used to control P. viburni and patterns of its use (several sprays each season). Use of broadspectrum organophosphates in the pipfruit industry was expected to decline in the future due to the development of insecticide resistance in P. maculipennis. Hence adverse environmental effects of spray programmes to control obscure mealybug were likely to decline in future unless replacement sprays had similar, or worse, environmental effects. Nevertheless, the Authority considered that,

Principles of Environmental Risk Assessment: the New Zealand Perspective

as long as the parasitoid exerted some degree of control over P. viburni, reduced insecticide use in the pipfruit industry was likely, resulting in both reduced environmental pollution and reduced adverse effects on nontarget organisms. Other considerations The project team considered that the biological control agent was correctly identified, and that appropriate provisions had been made to ensure that no hyperparasites or pathogens had contaminated the populations to be released from quarantine. The Authority considered all the environmental risks outlined above, as well as other cultural, human health and economic effects, and concluded that the potential positive effects of releasing P. maculipennis outweighed the potential adverse effects, and therefore approved the release of P. maculipennis.

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Conclusions The HSNO Act provides a transparent, consistent and criteria-based framework for making decisions on applications to introduce biological control agents on a case-bycase basis. However, this transparency and consistency increases costs to the applicants involved in biological control programmes. The requirement under the HSNO Act to consult fully with Ma៮ ori before the application is formally submitted, the public consultation process, and the cost of holding public hearings, can be costly. Costs payable to ERMA New Zealand for processing an application have been fixed at NZ$30,000. However, the preparation of the application and required consultations are additional costs. It is therefore advisable for the potential applicants to consider and include regulatory costs in their overall management of the biological control programmes.

References Barratt, B.I.P. and Moeed, A. (2005) Environmental safety of biological control: policy and practice in New Zealand. Biological Control 35, 247–252. Charles, J.G. (1993) A survey of mealybugs and their natural enemies in horticultural crops in North Island, New Zealand, with implications for biological control. Biocontrol Science and Technology 3, 405–418. Cox, J.M. (1987) Pseudococcidae (Insecta: Hymenoptera). Fauna of New Zealand No. 11. DSIR Science Information Publishing Centre, Wellington, New Zealand. ERMA NZ (2004) Decision making: A technical guide to identifying, assessing and evaluating risks, costs and benefits. March 2004. Environmental Risk Management Authority, Wellington, New Zealand, 61 pp. Hickson, R., Moeed, A. and Hannah, D. (2000) HSNO, ERMA and risk management. New Zealand Science Review 57, 72–77. Moore, D. (1988) Agents used for biological control of mealybugs (Pseudococcidae). Biocontrol News and Information 9, 209N–225N. Noyes, J.S. (1988) Encyrtidae (Insecta: Hymenoptera). Fauna of New Zealand No. 13. DSIR Science Information Publishing Centre, Wellington, New Zealand. O’Riordan, T. and Cox, P. (2001) Science, Risk, Uncertainty and Precaution. University of Cambridge Programme for Industry, Cambridge, UK. Pimentel, D. (2002) Biological Invasions: Economic and Environmental Costs of Alien Plants, Animals, and Microbe Species. CRC Press, Boca Raton, Florida. Sheppard, A.W., Hill, R., DeClerck-Floate, R.A., McClay, A., Olckers, T., Quimby, P.C. and Zimmermann, H.G. (2003) A global review of risk-benefit-cost analysis for the introduction of classical biological control agents against weeds: a crisis in the making? Biocontrol News and Information 24, 91N–108N. Wynne, B. (1992) Uncertainty and environmental learning: reconceiving science and policy in the preventive paradigm. Global Environmental Change 2, 111–127.

15

Environmental Risk Assessment: Methods for Comprehensive Evaluation and Quick Scan Joop C. van Lenteren1 and Antoon J.M. Loomans2 1Laboratory

of Entomology, Wageningen University, PO Box 8031, 6700 EH Wageningen, The Netherlands (email: [email protected]; fax number: +31-317-484821); 2Section Entomology, Plant Protection Service, PO Box 9102, 6700 HC Wageningen, The Netherlands. (email: [email protected]; fax number: +31-317-421701)

Abstract In this chapter, we first summarize the international situation with respect to environmental risk assessment for biological control agents. Next, we present the risk assessment procedure previously developed in the OECD and EU-ERBIC projects. Then, we propose a new, comprehensive risk evaluation method consisting of a stepwise procedure, which can be used for all types of biological control agents, used in augmentative and classical biological control programmes, for species or biotypes, and for native, established exotics or as yet unestablished exotics. This new comprehensive method solves weaknesses that we encountered when using the previous assessment methods: decision criteria are more clear and the decision to advise a release is taken at relevant steps in the process, thus preventing unnecessary research. We applied the new procedure to the 92 species of natural enemies mentioned in the EPPO list of commercially available biological control agents. The elimination of obviously risky species early in the process, and the acceptance of other species that previously scored a high index, clearly show the improvements achieved the new procedure. For those natural enemies that have been in use for many years in certain ecoregions of the world we propose that environmental risks are evaluated by using a quick scan method, based on available information only. We have applied this method to all 150 species of natural enemies that are currently commercially available in north-western Europe and concluded that about 5% of these (exotic) species were considered too risky for release in this region, while information was not sufficient for another 15%. However, the applicant could still try to undergo the comprehensive approach in order to get a permit for release.

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©CAB International 2006. Environmental Impact of Invertebrates for Biological Control of Arthropods: Methods and Risk Assessment (eds F. Bigler et al.)

Environmental Risk Assessment

Introduction In the past 100 years many exotic natural enemies have been imported, mass reared and released as biological control agents for pest control (AAFC, 1962–1991; Albajes et al., 1999; Lynch et al., 2000; van Lenteren, 2000, 2003; USDA, 2001; Mason and Huber, 2002; Copping, 2004). Recently, several examples have been reported concerning negative effects of these releases (e.g. Boettner et al., 2000; Follett and Duan, 2000; Wajnberg et al., 2000; Louda et al., 2003). The current popularity of biological control may in the coming years result in more problems than before, as more new agents will become available and commercial biological control activities are executed by an increasing number of persons. Various organizations have developed standards, including guidelines for the export, import, shipment, evaluation and release of biological control agents and beneficial organisms (e.g. EPPO, 2002; IPPC, 2005). Environmental effects of biological control agents form a central element of these guidelines and a growing number of countries already apply risk assessment procedures prior to the import and release of a new natural enemy. Within the EU-funded ERBIC project (ERBIC = Evaluating Environmental Risks of Biological Control Introductions into Europe; van Lenteren et al., 2003) and an OECD working group (Anonymous, 2004), guidelines have been developed to harmonize information requirements for import and release of invertebrate biological control agents used in inoculation and inundation biological control (Eilenberg et al., 2001). Procedures to assess natural enemies currently used by about 25 countries and codes of conduct or guidelines produced by various organisations (e.g. FAOIPPC, EPPO, NAPPO, CABI) were collected, studied and summarized. The guideline and risk assessment procedure produced by the OECD and EU-ERBIC

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group is based on, and partly similar to, the FAO and EPPO guidelines, but the criteria and methodology in the former two documents are more detailed than in the latter two (van Lenteren et al., 2003; Anonymous, 2004). The OECD and EU-ERBIC work resulted in proposals to standardize the information required for (i) a comprehensive risk assessment of natural enemies that are proposed for import and release and (ii) a quick risk assessment of natural enemies that have already been used in certain ecoregions (a region with similar fauna, flora and climate: FAO, 2002) for biological control over several years. In this chapter we first summarize the risk evaluation method developed previously in the EU-ERBIC project and we propose a new, comprehensive method based on experiences gained with the EU-ERBIC risk evaluation method. Subsequently, we will present ideas for a quick scan to be used for natural enemies that are already in use. In this way, we hope to provide biological control experts and risk assessors with the tools for a proper and uniform evaluation of the information provided in the application.

The ERBIC/OECD Comprehensive Environmental Risk Assessment of New Natural Enemies The risk assessment procedure initiated during the ERBIC project and further developed in the OECD project is characterized by questions on four issues: ● Characterization and identification of biological control agent (see Stouthamer et al., Chapter 11, this volume). ● Health risks. ● Environmental risks (see Moeed et al., Chapter 14, this volume). ● Efficacy. In this chapter we will concentrate on the third issue. Assessment of risks related to

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releases of natural enemies demands integration of many aspects of their biology, as well as information on ecological interactions. A comprehensive risk assessment comprises steps such as: ● Identification and evaluation of potential risk of releasing a natural enemy. ● A plan to minimize risk and mitigate unwanted effects of biological control agents (see also Moeed et al., Chapter 14, this volume). ● A risk/benefit analysis of the proposed release of the natural enemy, together with risk/benefit analyses of current and alternative pest management methods (see Bigler and Kölliker-Ott, Chapter 15, this volume). The last step is essential, because the risk/benefit posed by the release of an exotic natural enemy might be considered particularly acceptable in comparison with the risks posed by other control methods. For definition of terms used in this chapter, we refer to the glossary in this book and to Anonymous (2003a).

Risk identification and calculation of risk index Normally, for a risk assessment, one will identify and evaluate the potential negative effects, and determine the probabilities that these will materialize (see also Moeed et al., Chapter 14, this volume). The negative impacts of a biological control agent can be defined as any negative effect which can be named and measured, such as direct and indirect negative effects on non-target organisms and negative effects on the environment. The risk of negative effects of the release of a biological control agent is the product of the likelihood (= probability) of impact and the magnitude of impact. The probability and magnitude of five groups (ecological determinants) of risks are considered: establishment, dispersal, host range, direct effects and indirect non-target effects. Next, qualitative scales for probability and magnitude are described (Table 15.1), after which we attempted to quantify the scales for probability (Table 15.2) and magnitude (Table 15.3).

Table 15.1. Qualitative scales for probability (a), magnitude (b) and level of risk of adverse effects (c) (after Hickson et al., 2000). (a) Probability Very unlikely Unlikely Possible Likely Very likely

Description Not impossible but only occurring in exceptional circumstances Could occur but is not expected to occur under normal conditions Equally likely or unlikely Will probably occur at some time Is expected to occur

(b) Magnitude Minimal Minor Moderate Major Massive

Description Insignificant (repairable or reversible) environmental impact Reversible environmental impact Slight effect on native species Irreversible environmental effects but no species loss, remedial action available Extensive irreversible environmental effects

(c) Level of risk of adverse effect Magnitude Probability

Minimal

Minor

Moderate

Major

Massive

Very unlikely Unlikely Possible Likely Very likely

Insignificant Insignificant Low Low Medium

Insignificant Low Low Low Medium

Low Low Medium Medium High

Medium Medium Medium High High

Medium High High High High

Environmental Risk Assessment

Finally, a numerical value is added to each descriptor of probability and magnitude to be able to quantify risk: Probability Magnitude very unlikely = 1 minimal = 1 unlikely = 2 minor = 2 possible = 3 moderate = 3 likely = 4 major = 4 very likely = 5 massive = 5

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The overall risk index for each natural enemy is obtained by first multiplying the values obtained for probability and magnitude, followed by summing up the resulting values obtained for establishment, dispersal, host range and direct and indirect effects. The minimum score, therefore, is 5 (5 times 1 ⫻ 1), and the maximum score 125 (5 times 5 ⫻ 5). In a first application of

Table 15.2. Descriptions of probability for establishment, dispersal, host range, direct and indirect effects (after van Lenteren et al., 2003; *as in Hickson et al., 2000). Establishmenta* in non-target habitat

Dispersalb potential

Host rangec

Direct* and indirect effects

Very unlikely Unlikely Possible Likely Very likely

<10 m <100 m <1,000 m <10,000 m >10,000 m

0 species 1–3 species 4–10 species 11–30 species >30 species

Very unlikely Unlikely Possible Likely Very likely

aThe

propensity to overcome adverse conditions (winter or summer: physical requirements) and availability of refuges. bDistance moved per release (take number of generations per season into account); determine dispersal curve, sampling points at 10, 100 and 1000 m, sampling period is 50% life span. cThe propensity to realize its ecological host range in the release area.

Table 15.3. Descriptions of magnitude for establishment, dispersal, host range and direct and indirect effects (after van Lenteren et al., 2003). Establishmenta in non-target habitat

Dispersalb potential

Host rangec

Directd and indirecte effects

Minimal

local (transient in time and space)

<1%

species

<5% mortality

Minor

<10%

<5%

genus

<40% mortality

Moderate

10–25%

<10%

family

<40% mortality and/or >10% short-term population suppression

Major

25–50%

<25%

order

>40% short-term population suppression, or >10% permanent population suppression

Massive

>50%

>25%

none

>40% long-term population suppression or local extinction

Magnitude

aPercentage

of potential non-target habitat where biological control agent may establish. of released biological control agent dispersing from target release area. cTaxon range that biological control agent attacks. dDirect effect: mortality, population suppression or local extinction of directly affected non-target organisms; see Lynch et al. (2001) for details. eIndirect effect: mortality, population suppression or local extinction of one or more species of non-target species that are indirectly influenced by the released biological control agent. bPercentage

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this methodology, van Lenteren et al. (2003) analysed 31 cases of natural enemy introductions. The risk indices obtained ranged from 7 to 105 (van Lenteren et al., 2003). This risk assessment methodology also points out clearly that different values for the same organism will be obtained when evaluated for different release areas. For example, the lowest risk index (7) found was for Thripobius semiluteus Boucek, an inundative biological control agent used in greenhouses (no establishment, poor dispersal outside greenhouse, monophagous, no direct or indirect non-target effects), but there was a slight increase to 12 when used in the field (some establishment, considerable dispersal, monophagous, no direct or indirect non-target effects). Also, the predatory mite, Phytoseiulus persimilis AthiasHenriot, had a higher risk index when released in the open field in Mediterranean Italy (24) than when released in greenhouses in temperate-climate countries (10) (for details about each of the criteria see van Lenteren et al., 2003). In the two previous examples, the natural enemies still fell into the low risk category (see below), but

there are also cases where the natural enemy moved from a lower to a higher risk category. For example, Eretmocerus eremicus Rose and Zolnerowich has a risk index of 19 when used in greenhouses in northern Europe because it cannot establish and hardly disperses (lowest risk category), but the risk index increases to 51 (intermediate risk) when used in greenhouses in Mediterranean Europe because of the possibility of establishment, more dispersal and, thus, a higher risk of direct and indirect non-target effects. Likewise, Encarsia pergandiella Howard moved from the intermediate risk category (risk index 49 when applied in greenhouses in northern Europe) to the highest risk category (risk index 73) when used in the field in the Mediterranean (Table 15.4). Based on the evaluation of 31 cases of natural enemy introductions by the EUERBIC project, we proposed to use the following risk categories: ● Low risk category: risk indices lower than 35 points; for organisms falling into this category, a proposal of no objection against release of the agent can usually be issued.

Table 15.4. Risk indices for Encarsia pergandiella when used in northern European greenhouses and in greenhouses and fields in Mediterranean Europe. North Criterion

Likelihood

Magnitude

P ⫻ Ma

1 3 4 5 5

1 1 5 4 1

1 3 20 20 5 49

Likelihood

Magnitude

P⫻M

5 3 4 5 5

5 1 5 4 1

25 3 20 20 5 73

Establishment Dispersal Host range Direct effects Indirect effects SUM = risk index

South Criterion Establishment Dispersal Host range Direct effects Indirect effects SUM = risk index aP

= probability, M = magnitude.

Environmental Risk Assessment

● Intermediate risk category: risk indices between 35 and 70 points; for organisms falling into this category, advice will be issued to come up with specific additional information before a conclusion concerning release can be drawn. ● High risk category: risk indices higher than 70 points; for organisms falling into this category, generally a proposal not to release the agent will be issued. Low risk indices (below 35) were found for many parasitoids, several predatory mites and one predatory insect. Intermediate risk indices (between 35 and 70) were found for all guilds of natural enemies: parasitoids, predatory insects, predatory mites, parasitic nematodes and entomopathogenic fungi. Entomopathogens (Beauveria, Metarhizium and Steinernema) all score intermediate because of their broad host range, but their very limited dispersal capacities strongly reduce risk. The highest risk indices were found for predatory insects (Harmonia axyridis Pallas, Hippodamia convergens Guârin-Meneville, Podisus maculiventris (Say), Orius insidiosus (Say) and parasitoids (E. pergandiella, Trichogramma brassicae Bezdenko and Cales noacki Howard). This was not a surprise as they would all be classified by biological control experts in the high risk category based on what is known of their biology. Because this is the first quantitative risk assessment developed, we expected that the quantification system might have to be adapted based on growing experience. The main problems we encountered were the following: ● Information for the probability and magnitude of all five areas of assessment needed to be available before an evaluation could be made. This makes the assessment in a number of cases unnecessarily costly. ● Candidate natural enemies that appear to be clearly unacceptable for import and release based on data for one group of risks should be identified as early as

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possible in the risk assessment procedure to prevent unnecessary data collection. ● The numerical values calculated by this assessment do not allow a very clear separation between risk categories. This may result in interpretation and decision-making that can be easily manipulated. ● The overall risk index is obtained by adding five different categories, which are, in fact, not completely independent from each other and should not be rated equally. ● The overall score of a certain species for a certain ecoregion might lead to establishing an absolute value and unnecessarily strict administrative requirement for measures. In addition, classical biological control was not explicitly included in the ERBIC risk evaluation. In this chapter we propose a new environmental risk assessment, which includes augmentative as well as classical biological control approaches. It is now a stepwise procedure, which includes weight factors for solving the problems mentioned above.

Risk management The next step in a risk assessment process is to discuss risk management, including risk mitigation and risk reduction. If an exotic biological control agent is expected to cause significant adverse effects on non-target organisms, a permit for releases will not be issued. In some cases, risks may be minimized by imposing restrictions concerning, for example, the types of crops on which the use of the organism is or is not allowed (e.g. treatment of flowering plants with a myco-insecticide), by requesting specific application techniques (e.g. soil incorporation only for insect pathogenic nematodes), or by specifying the ecoregions where the organism is allowed for use (e.g. use of tropical natural enemies in greenhouses in temperate climates).

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Risk/benefit analysis The last step in making a justified environmental risk analysis for a new biological control agent is to conduct a risk/benefit analysis, which should include a comparative performance of pest management methods. The environmental benefits of use of the proposed biological control agent should be compared to environmental effects of currently used and other alternative control methods. Then, the environmental risk analysis is used in the overall risk/benefit assessment where the data concerning characterization, health risks, environmental risks and efficacy of all the control methods for a specific pest will be compared (for details see van Lenteren et al., 2003; Bigler and KöllikerOtt, Chapter 16, this volume).

New, Comprehensive Environmental Risk Assessment for Classical and Augmentative Biological Control Agents New, stepwise risk assessment procedure An environmental risk assessment method consisting of a stepwise procedure is proposed and should be useful for all types of invertebrate biological control agents in augmentative and classical biological control, for species or biotypes (relevant, e.g. in the case of biotypes that diapause or not, or biotypes with and without wings), whether they are native, established exotics or as yet unestablished exotics (Table 15.5, summarized in Fig. 15.1). Native species are included in the evalua-

Table 15.5. Schedule for an environmental risk assessment of an invertebrate biological control agent in a certain area of release. The determinants of the Environmental Risk Index (ERI = Probability ⫻ Magnitude) should be calculated per step as indicated by van Lenteren et al. (2003), and where appropriate with weight factors as given in Fig. 15.2. 1 Origin – native Origin – exotic, either absent OR present in target area 2 Augmentative Biological Control (ABC) programme – establishment not intended Classical Biological Control (CBC) programme – establishment intended 3 Establishment unlikely (likelihood L = 1–2) no weight factor included Establishment possible to very likely (L = 3–5), apply magnitude (M) as a weight factor – if risk threshold not crossed (ERI = less than 12) – if risk threshold crossed (ERI = 12 or more) (upon request of applicant, GO TO 4) 4 If monophagous OR if oligophagous/polyphagous AND only related AND no valued non-targets attacked If oligophagous/polyphagous AND related and unrelated non-targets attacked AND/OR valued non-targets attacked (upon request of applicant, GO TO 5) 5 Dispersal local (L = 1–2) Dispersal outside target area (L = 3 or more) AND extensive (M = 2 or more) apply magnitude (M) as a weight factor – if risk threshold is not crossed (ERI = 5 or less) – if risk threshold is crossed (ERI = 6 or more) 6 Direct and indirect effects inside dispersal area of natural enemy unlikely (L = 1–2) AND at most transient and limited (M = 1–2) Direct and indirect effects inside ‘dispersal area’ likely (L = 3–5) OR permanent (M = 3–5)

GO TO 6 GO TO 2 GO TO 3 GO TO 4 GO TO 6

GO TO 4 No release

Release No release GO TO 6

GO TO 6 No release Release No release

Environmental Risk Assessment

tion procedure as well: when natural enemies are released in very large numbers for immediate control of the target pest, as in inundative biological control, direct dispersal (overflow, drift) from the release area into the surrounding environment is of great concern for direct non-target effects, irrespective of whether the natural enemy species is exotic or not. Contrary to the ERBIC/OECD risk assessment described in the previous section, here, the decision to advise release or not is taken at relevant steps in the process, thus preventing unnecessary research and resulting in early elimination of clearly risky natural enemies. Definitions for terms used in the evaluation process are given in Table 15.6.

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At step 1, exotic and native natural enemies are distinguished. For native natural enemies only, one more step (6) in the procedure needs to be followed. Dispersal (step 5) of native agents may be an important issue to be considered in order to address step 6 accordingly; in particular, this is true for the evaluation of exotic species. For example, direct and indirect effects of a polyphagous biological control agent may be strongly limited because of dispersal. However, because experimental procedures to establish the dispersal potential of natural enemies might be quite lengthy, this is not included here as a standard procedure for native natural enemies. For exotic natural enemies, whether

Fig. 15.1. Simplified scheme of an environmental risk assessment of an invertebrate biological control agent. R, NR: release, no release, is recommended, respectively.

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Table 15.6. Definitions of terms used in environmental risk assessment. Term

Definition

exotic

non-indigenous to the country of release, i.e. originating from the same geographical area or from a different one restricted to the vicinity (<100 m) of the target area (establishment, dispersal) restricted to only the season of release (establishment, direct and indirect effects) effect expected to occur during many seasons/years no non-targets attacked (likelihood = 1) 1–10 non-targets attacked (likelihood = 2 or 3) >10 species attacked (likelihood = 4 or 5) within same genus

local transient permanent monophagous oligophagous polyphagous related

already present or absent in the target area, this and further steps need to be followed. At step 2, natural enemies that are destined for augmentative biological control (ABC) programmes, where establishment of the organism in the area of release is not intended, are separated from natural enemies destined for classical biological control (CBC), where establishment is the aim. For ABC natural enemies one then needs only to demonstrate that they cannot establish in step 3. If they cannot establish (step 3, L = 1–2), one more step of the procedure (6) needs to be followed. However, if they can establish, the Environmental Risk Index (ERI = Likelihood ⫻ Magnitude) should be calculated for establishment (see Tables 15.2 and 15.3 and Fig. 15.2a). If a risk threshold is crossed (L = 3–5 AND M = 3–5, Fig. 15.2a), the natural enemy cannot be released, and is thus eliminated early in the evaluation process. However, if the applicant desires, they can provide data from studies on host range (step 4), dispersal (step 5) and direct/indirect non-target effects (step 6) and ask to have the decision reconsidered. If the risk threshold is not crossed, the same procedure needs to be followed as for CBC natural enemies in step 4. At step 4, the host range issue (see van Lenteren et al., Chapter 3, this volume) is addressed. If the ABC or CBC agent is either monophagous or oligophagous/polyphagous and attacks only related AND not-valued non-targets, i.e. species not of conservation concern, it should be considered for release. On the other hand, if the agent is

oligophagous/polyphagous and does attack related and unrelated non-targets AND/OR valued non-targets, the agent should not be considered for release. However, if the applicant desires, they can provide data from studies on both dispersal (step 5) and direct/indirect non-target effects, and ask to have the decision reconsidered. In that case, continue with step 5. On request, dispersal can be considered relevant for risk assessment of augmentative releases (see Mills et al., Chapter 7, this volume). At step 5, questions about dispersal of ABC and CBC (where appropriate and on request) agents are addressed. If dispersal is local and mainly in the area of release (L = 1 or 2, see Tables 15.2 and 15.3 and Fig. 15.2b), the procedure can be continued at step 6. But, if dispersal is outside target area (L = 3 or more) AND extensive (M = 2 or more), and thus the environmental risk index (ERI) crosses the value of 6 (Fig. 15.2b), the agent should not be released. If the ERI is 5 or less, the procedure can be continued at step 6. At step 6, issues related to direct and indirect non-target effects are addressed, as releases of exotic agents may negatively affect the abundance of native non-target species or other natural enemies that exploit the same resource (for precise details see Messing et al., Chapter 4, this volume). If direct and indirect effects inside the ‘dispersal area’ are unlikely (L = 1–2) AND at most transient and limited levels (M = 1–2), the agent can be released. However, if direct and indirect effects inside the ‘dispersal area’ are likely (L =

Environmental Risk Assessment

3–5) OR permanent (M = 3–5), the agent should not be released (Fig. 15.2c). To calculate risk levels for establishment, dispersal and direct/indirect nontarget effects, the criteria are applied as shown in van Lenteren et al. (2003), but weight factors are added, and the resultant values can easily be obtained from Fig. 15.2. If the ERI is below the risk threshold, the value will be in a white box (= continue procedure/release recommended). When the ERI is above the threshold, the value will be in a grey box (= discontinue procedure/no release recommended). Although threshold values as indicated in Fig. 15.2 are currently still based largely on expert judgement, these values need justification and fine-tuning. This will probably evolve when more data become available through experimental research. The final part of this new risk assessment, i.e. the risk management and the risk/benefit analysis, is the same as described in the previous section.

Proposed risk assessment procedure applied to commercial natural enemies widely used in Europe We have applied the stepwise risk assessment procedure described above to the natural enemies in the EPPO list of commercially available agents (EPPO, 2002). This list contains 92 species, of which 34 species are native to The Netherlands and the EPPO region, 22 are native to the EPPO region but not native to The Netherlands, and 36 species are of exotic origin. Of the 30 exotic species that have established in the EPPO region, two species have established in The Netherlands (Fig. 15.3). Let us assume that these natural enemies were evaluated for release in The Netherlands. We start with 92 species: ● Step 1 (n = 92 species): 34 species are native (go to 6), 58 (36 outside EPPO region, 22 from EPPO region but not native to The Netherlands) are exotic (go to 2).

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● Step 2 (n = 58 species): all 58 species destined for augmentative biological control (go to 3), no species destined for classical biological control. ● Step 3 (n = 58 species): for 51 species it is unlikely that they will establish (go to 6); of the remaining seven species (six exotic, one southern EPPO), two exotic species have an ERI of 12 or more, therefore the risk threshold is crossed and these species cannot be released; the other five species have an ERI lower than 12 (go to 4). ● Step 4 (n = 5 species): one species is monophagous or oligophagous/polyphagous with attack of only related, but no valued non-targets and can be released; all other four species are oligophagous/polyphagous, potentially could attack related and unrelated nontargets and/or valued non-targets (no release OR upon request go to 5). ● Step 5 (n = 4 species): none of the species show limited dispersal and in small numbers; it is likely that all four species disperse out of the local area in large numbers and the risk threshold is crossed, and they should not be released (at this point all the exotic organisms have been evaluated). ● Step 6 (n = 85 species; 34 are native species coming from step 1, 51 are exotic species coming from step 3): for all these species direct and indirect effects inside the dispersal area are unlikely and, at most, transient and limited, so they can be released. Some conclusions can be drawn at this point: ● All 34 native species that were evaluated are considered safe for release. ● Exotic species intended for use in augmentative biological control that are likely to establish and cross the risk threshold are detected very early in the evaluation process, and will be excluded from release without the need for studying host range, dispersal and direct/indirect non-target effects. ● Exotic species that are monophagous, or oligophagous/polyphagous with a history

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Magnitude (a) Establishment

local

Likelihood

#

very unlikely

1

unlikely

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possible

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likely

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<10%

10–25%

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>50%

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% Area

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Magnitude (c) Effects

Likelihood

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Effects on <5%mort. <40%mort. >40%mort. >40%sps >40%lps non-target populations 21 22 23 24 20 n.w.

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Fig. 15.2. Ecological Risk Index matrix to determine the level of risk of adverse effects of an IBCA for three ecological determinants: (a) establishment, (b) dispersal and (c) direct and indirect effects. Ecological Risk Indices calculated as Likelihood (vertical) ⫻ Magnitude (horizontal) with their respective calculation factors: 1–5 for likelihood, 2x as a weight factor for magnitude; n.w. = no weight factor included, mort. = mortality, sps = short-term population suppression, lps = long-term population suppression (see Tables 15.2 and 15.3 for descriptions of determinants). White = below threshold, grey = above threshold.

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37%

39%

Native + NL Native – NL Exotic

24% Fig. 15.3. Origin of commercially used biological control agents in EPPO region 2002, n = 92 species, The Netherlands (EPPO, 2002).

of attack of only related, and no attack of valued non-targets, are also detected early in the evaluation without the need for studying dispersal and direct/indirect non-target effects; they can be released. ● Exotic species that are oligophagous/ polyphagous and attack related and unrelated non-targets, and/or valued non-targets, will be excluded from release without the need for studying dispersal and direct/indirect non-target effects. Some exotic IBCAs that are not on the EPPO list, but are actually released commercially in Europe (e.g. H. axyridis, H. convergens and O. insidiosus), had a high ecological risk index in our previous assessment (see van Lenteren et al., 2003), indicating a high potential risk. When we evaluate these exotic IBCAs for release using the proposed assessment procedure, they are considered unsuitable for release at steps 3 or 4. On the other hand, a species such as T. brassicae, also with a high risk index in our previous assessment (see van Lenteren et al., 2003), is not eliminated early in the new procedure and can be released (establishment possible, polyphagous, but dispersal is local and direct and indirect effects within dispersal area unlikely (see Babendreier et al., 2003; Kuske et al., 2003; Mills et al., Chapter 7, this volume)). The early elimination of obviously risky species, and the acceptance of other species that scored – erroneously – a high index in the previous assessment by

van Lenteren et al. (2003), clearly show improvements in the assessment procedure proposed here.

Quick Scan Method for Environmental Risk Assessment of Natural Enemies Already in Use About 150 species of natural enemies have been in use for many years in certain ecoregions of the world (EPPO, 2002; Anonymous, 2003b; van Lenteren, 2003; ANBP, 2004; Copping, 2004). We propose that these species be exempted from a comprehensive environmental risk analysis for these ecoregions, but should be evaluated with a quick scan method for estimation of potential adverse environmental effects. Although a quick scan is based on the same environmental determinants and information requirements as a comprehensive full scan (Table 15.7), there is a basic difference in approach between the two tools. The quick scan method is based on information which is already available, whereas for a comprehensive evaluation specific data would have to be generated as well. In the comprehensive evaluation the lead consideration is ‘whether or not there is sufficient and reliable information to issue a permit for import and release’, and is based on a quantitative evaluation. On the other hand, when using the quick scan method, the question is ‘are there good reasons (e.g. are there any non-target effects and environ-

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mental risks known elsewhere and/or expected in the area of release) to stop continuation of release?’, and is thus based on a qualitative evaluation. The results of a quick scan could help in establishing lists of species (‘white lists’) that can be used in certain, specified regions or (parts of) ecoregions of the world. This would result in strongly reduced costs for regulation of the major part of biological control agents currently used. We have applied this quick scan method to all the species of natural enemies that are currently commercially available in north-western Europe (EPPO, 2002; Loomans, 2004; producers information on

the web; producers price lists). Based on a thorough review of the information which is currently available (Table 15.7), potential risks of all species were evaluated. Application of the quick scan method to natural enemies already in use results in the conclusion that 5% of the species are considered too risky for release, that for 15% of the species more information was needed before being able to conclude that they may be released or not (see below), and that the use of the remaining 80% of the species releases could be continued directly (Fig. 15.4). Organisms belonging to the latter group that show non-significant environmental risks are listed and are

Table 15.7. Information requirements for a quick scan evaluation procedure of natural enemies (IBCAs = invertebrate biological control agents) already in use; updated OECD guidance document (Anonymous, 2004). Available information on the following issues on the IBCA involved should be provided: Information on characterisation and identification Identity Available information on biology and ecology Available information on effects on human health and safety Available information on environmental risks Available information on host/prey range (direct effects) Available information on potential of establishment and dispersal Available information on indirect effects Available information on environmental benefits of release Available information on efficacy

Quick scan results more info. required

rejected 5%

Quick scan approved – origin of IBCAs

exotic 35%

15%

approved 80%

native NWE 45%

native EU 20%

Fig. 15.4. Outcome of the quick scan of IBCAs produced or used in north-western Europe in 2003. Left: percentage approved, in doubt and rejected categories (n = 150). Right: origin of approved IBCAs in percentages (n = 123).

Environmental Risk Assessment

exempted from further evaluation. Organisms that are considered too risky for release after a quick scan can be proposed by the applicant for a comprehensive evaluation and might, based on the provision of a complete set of data, still be granted a permit for release. However, there was a group of organisms already in use (the above-mentioned 15%), for which the information currently available is either inadequate, inappropriate or lacking for completion of a quick scan risk assessment. Several questions, excluding the use of old synonyms or misspelling of names, remain about their status: ● Questions concerning the taxonomic status of the group pending up to date revision (e.g. the genus Trichogramma in North America (Pinto, 1999) and Europe (see Stouthamer, Chapter 11, this volume). ● Questions concerning the exact identity of the organism: the species name is not (yet) indicated exactly, but referred to as ‘cf.’, ‘nr’ or only a genus name and ‘sp.’. ● Questions concerning the proper nomenclature of the species: from the species name given it is not clear which species is actually involved. For instance, whether Delphastus pusillus LeConte is involved or D. catalinae Horn (see discussion by Booth and Polaszek, 1996; Hoelmer and Pickett, 2003). Another example is E. eremicus, which has long been indicated as Eretmocerus nr. californicus, but is also referred to as Eretmocerus californicus Howard: does the application concern E. eremicus or, indeed, E. californicus? ● Questions concerning the origin of strains of a species: which subspecies, ecotype or strain of a species is involved? A species which is currently used and non-native to the ecoregion of release might indicate no substantial risks. However, replacement of commercial strains in the future, by ecotypes better adapted to local (ecoregional) conditions, might affect the outcome of the risk assessment procedure. For instance, strains of Aphidius colemani Viereck currently used largely originate

●

●

●

●

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from the Middle East, but recently other species-group members, originating from Chile and East Africa, are known to have established in the Czech Republic (Stáry, 1999) and Germany (Adisu et al., 2002), respectively. Similarly, release of a species which is native to the ecoregion of release might have no or low direct risks. Replacing native strains by other exotic strains of the same species, however, might change the risk potential and therefore the outcome of the procedure as well. Questions concerning the biology and ecology of a species: even for some species in use for minor applications for a number of years, there is a lack of knowledge on the biology, ecology and efficacy, and nothing is known about potential non-target effects and environmental risks. Questions concerning mass release of a native (e.g. Chrysoperla carnea Stephens) or established exotic species (T. brassicae) in the vicinity of a protected area where red list species, endangered species or protected species are present. Questions about translocation of an exotic species with known non-target effects into parts of the same ecoregion, where it does not yet occur naturally. Questions concerning species widely distributed in part of a certain region (translocation of species within the same continent): species with known non-target effects which have not (yet) expanded their natural area of distribution to the areas of release, and which often enter these areas passively or accidentally (as eggs or larvae) in agricultural or horticultural commodities.

Most of these questions could be overcome through the time-consuming process of collecting detailed information by the regulating authority. But, as data collection is not the authority’s duty, a quick scan procedure would proceed much faster if this information were provided by the producers and retailers (see Table 15.7). In the particular situation of The Netherlands, when the above-mentioned questions were

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addressed, it was shown that one species was not able to establish and, for the others, no evidence was available on significant non-target effects. As a consequence, it was advised that most species continue to be released. In 2005, 134 species were thus placed on a ‘white list’, which will be exempted from further regulatory measures in The Netherlands. Future releases of all other species, biological control agents and other beneficial organisms will need authorization by derogation, i.e. conditional licences (Loomans and Sütterlin, 2005). The problems mentioned above illustrate that a proper identification of massproduced natural enemies is of constant concern for commercial producers, users and regulators. Correct identification of a biological control agent is essential to ensure quality control (van Lenteren, 2003), the presence of contaminants and to validate biological studies and efficacy trials (Hoelmer and Pickett, 2003). Also for experts assessing environmental risks, and for regulators providing lists of approved or refused biological control agents and other beneficial organisms, a correct identification is critical. An exact and unambiguous description of morphological, biochemical or molecular characterisation, as appropriate, and an accession number to a voucher specimen or culture deposited in a museum or culture collection can overcome most of these taxonomic ambiguities mentioned under points 1, 2 and 3 (Anonymous, 2004). A second point of consideration is that a quick scan, resulting in exemption of a species, e.g. A. colemani (point 3), might have the possibility of introducing ecotypes which are much better adapted – but also more risky – to a specific ecoregion. For organisms as mentioned under points 4 and 5, future releases could be restricted to a permit for that specific strain, or by subjecting the species to a comprehensive evaluation procedure. Potential increase of risks as a result of mass release of biological control agents (point 5) in the vicinity of protected areas could be overcome by restricting such releases to regions away from such areas. After answering these questions, a quick

scan will ultimately result in a list of species exempt from further evaluation. Those species considered too risky for release are all exotic. Of the seven species for which more information is needed before an issue for release can be provided, one is native and six are exotic (originating either from different ecoregions in Europe or from the rest of the world). Of the 134 species that are considered safe for use, 45% are native to the area of release, while the remaining 55% are considered exotic (originating either from different ecoregions in Europe of from the rest of the world). Another interesting question is whether we can draw conclusions about the guilds of natural enemies that create most problems. From the category of risky species, 87% consist of polyphagous predators (Heteroptera and Coleoptera) and 13% of polyphagous Hymenoptera. The category for which more information is needed is made up of polyphagous predators (50%), polyphagous and hyperparasitic Hymenoptera (45%) and polyphagous entomopathogenic nematodes (5%). However, these last percentages do not necessarily indicate the level of risk of certain natural enemy guilds, as these species are often subjected to further investigation simply because very few data about their biology are available. Provision of a few more data might move several of these species to the category of ‘safe for use’.

Discussion In this chapter we have summarized the current situation concerning environmental risk assessment of natural enemies, and we have proposed a comprehensive evaluation method, as well as a quick scan for future use. The risk assessment methods and procedures proposed here have a number of strengths, but still need improvement. We have gradually shifted, coming from a descriptive, more qualitative framework – largely based on expert judgement in general (e.g. Hickson et al., 2000), via an overall qualitative and quantitative method

Environmental Risk Assessment

(van Lenteren et al., 2003), to a stepwise evaluation procedure, using quantitative information when needed and where possible. This not only allows better insight into relevant ecological factors, but also constitutes a more objective approach for evaluating the risks of biological control agents. However, thresholds and decision levels are currently still largely based on expert judgement. The quantitative parameters used in the comprehensive evaluation were chosen based on the scientific information available and are subject to revision as additional scientific information becomes available. Methods to determine establishment, dispersal, host range, and direct and indirect effects on non-target organisms are discussed elsewhere in this book. We expect that a lot of new research will be generated in the coming years to validate and optimize evaluation methods. Contrary to the previous EU- ERBIC assessment (van Lenteren et al., 2003), in these new, stepwise procedures, decisions are taken at relevant steps in the process, thus preventing unnecessary research and resulting in early elimination of clearly risky natural enemies. Also, the decision criteria are clearer than those used in earlier assessments. This stepwise risk assessment procedure was then applied to the 92 species of natural enemies mentioned in the EPPO (2002) list of commercially available biological control agents. The early elimination of obviously risky species, but the acceptance of other species that – wrongly – scored a high index in the EU-ERBIC assessment, clearly show improvements of the new assessment procedure. We propose that the approximate number of 150 species of natural enemies, in use for many years in certain ecoregions of the world, be exposed to the quick scan based on available information only, instead of being evaluated with the comprehensive method. We have applied the quick scan to all the 150 species of natural enemies that are currently commercially available in northwestern Europe and concluded that about 5% of these species (all exotic) are considered too risky for release in this region. The topic of implementation of a regis-

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tration procedure for natural enemies is currently hotly debated by the biological control industry, scientists and regulators (Blum et al., 2003; GreatRex, 2003; Hokkanen, 2003; van Lenteren et al., 2003; Anonymous, 2004). The biological control industry foresees lengthy, cumbersome procedures leading to high costs and, thus, in some cases the impossibility of marketing an interesting natural enemy because of excessive costs. It is not easy to estimate costs at this point, as limited information is as yet available about natural enemy evaluations for registration or regulation. Such costs will depend largely on the biological and ecological characteristics of a natural enemy. When dealing with a natural enemy that has a very narrow host range, testing can be limited to several person-months. In such a case, preparing a dossier, including testing, would not take more than six person-months. However, preparation of a dossier for an exotic polyphagous natural enemy that is able to establish could take up to several years, particularly if experiments on dispersal and indirect ecological effects are needed. We estimate that a comprehensive dossier could be appraised within six person-weeks by governmental agencies. Based on the experience with classical biological control agents reviewed by peers, those evaluations, however, take at least six months to complete (Sheppard et al., 2003). Regulators within ministries of environment and agriculture want to prevent unnecessary and risky releases of exotic organisms. The history of arthropod biological control shows that very few mistakes have been made to the present. This is a point in favour for the biological control industry, and is in strong contrast to the problems that have been created by accidental importation of pests and diseases by those other than biological control workers. Current activities will, hopefully, result in a light and harmonized registration procedure that is not prohibitive for the biological control industry and will result in the pre-selection of safe natural enemies. The proposed quick scan method, for organisms already in use, should be considered as a kick-start

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from a situation with no regulations for the use of biological control agents, to one where import and release are regulated to ensure complete safety. Based on Findings of No Significant Impacts (FONSI) – similar to our suggested quick scan method – USDA-APHIS has put up a list of beneficial organisms that are permitted for import into the USA. From 38 biological control agents, 50% are native and 50% exotic to the USA (ANBP, 2004). The quick scan method applied for north-western Europe also results in the continuation of release of a large number of exotic species. At this moment, risk assessment procedures described in this chapter are being considered for implementation by several countries. More than 25 countries already apply a form of regulation of biological control agents. We recommend that countries starting with regulating biological control should not apply the comprehensive evaluation summarized in this chapter to the (roughly) 150 species of natural enemies that are already in use, for a considerable amount of time. In these cases the ‘quick scan’ method should be used to estimate potential adverse environmental effects based on available information only. Ongoing successful and safe biological control programmes can then be continued without interruption, and thus obviate the resultant risk of falling back on chemical control programmes. We estimate that

preparation of a dossier for a quick scan will take two person-weeks, and appraisal one to six person-days per biological control agent. The end result of such a quick scan method, applied in various countries, could result in lists of species that can be used in certain, specified regions (ecoregions) of the world. These species would be exempted from a comprehensive environmental risk analysis. The comprehensive environmental risk analysis should be applied to new species only. A similar procedure with a quick scan can be applied to species that have already been used for several years in classical biological control programmes, thus leading to lists of supposedly safe species – the so-named ‘white lists’ – for certain ecoregions. The availability of regularly updated ‘white lists’ might stimulate the application of biological control worldwide. All information related to regulation of natural enemies leads to the conclusion that it is best (i) to look first for native natural enemies and (ii) to use host-preyspecific natural enemies.

Acknowledgements Peter Mason and Franz Bigler are thanked for their thorough editing and excellent suggestions for improving this paper.

References AAFC, 1962–1991. Insect Liberations in Canada. Research Branch, Agriculture Canada Liberation Bulletin No. 25–54, Canada. Adisu, B., Stáry, P., Freier, B. and Büttner, C. (2002) Aphidius colemani Vier. (Hymenoptera, Braconidae, Aphidiinae) detected in cereal fields in Germany. Anzeiger für Schädlingskunde 75, 89–94. Albajes, R., Gullino, M.L., van Lenteren, J.C. and Elad, Y. (1999) Integrated Pest and Disease Management in Greenhouse Crops. Kluwer Publishers, Dordrecht, The Netherlands. ANBP (2004) USDA-APHIS permitted beneficials imported into the USA from other countries as of 27 Feb 2004. Available at http://www.anbp.org/beneficial%20list.htm) Anonymous (2003a) Glossary of Terms. The Second Report on Harmonisation of Risk Assessment Procedures, Appendix 2. Scientific Steering Committee, European Commission, 7 pp. Anonymous (2003b) 2004 Directory of least-toxic pest control products. The IPM Practitioner 25 (11/12), 1–40. Anonymous (2004) Guidance for Information Requirements for Regulation of Invertebrates as Biological Control Agents. OECD Series on Pesticides, 21. Available at http://www.oecd.org/ dataoecd/6/20/28725175.pdf

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Babendreier, D., Kuske, S. and Bigler, F. (2003) Parasitism of non-target butterflies by Trichogramma brassicae Bezdenko (Hymenoptera: Trichogrammatidae) under field cage and field conditions. Biological Control 26, 139–145. Blum, B., Ehlers, R., Haukeland-Salinas, S., Hokkanen, H., Jung, K., Kuhlmann, U., MenzlerHokkanen, I., Ravensberg, W., Strasser, H., Warrior, P. and Wilson, M. (2003) Letter to the editor. Biological control: Safety and regulatory policy. BioControl 48, 477–484. Boettner, G.H., Elkinton, J.S. and Boettner, C.J. (2000) Effects of a biological control introduction on three nontarget native species of Saturniid moths. Conservation Biology 14, 1798–1806. Booth, R.G. and Polaszek, A. (1996) The identities of ladybird beetle predators used for whitefly control, with notes on some white-fly parasitoids, in Europe. Brighton Crop Protection Conference – Pests and Diseases, 69–74. Copping, L.G. (2004) The Manual of Biocontrol Agents. BCPC Publications Sales, Alton, UK. Eilenberg, J., Hajek, A. and Lomer, C. (2001) Suggestions for unifying the terminology in biological control. BioControl 46, 387–400. EPPO (2002) List of biological control agents widely used in the EPPO region. PM6/3(2). Bulletin OEPP/EPPO Bulletin 32(3): 447–461. Available at http://www.eppo.org/standards/biocontrol/ bio_list.htm FAO (2002) Glossary of Phytosanitary Terms. International Plant Protection Convention. International Standards of Phytosanitary Measures No. 5. Available at https://www.ippc.int/IPP/En/default.jsp Follett, P.A. and Duan, J.J. (2000) Nontarget Effects of Biological Control. Kluwer Academic Publishers, Dordrecht, The Netherlands. GreatRex, R. (2003) Comments on the OECD proposal for ‘Guidance for Registration Requirements for Invertebrates as Biological Control Agents’. Available at http://www.ibma.ch/pdf/ comments_on_the_oecd_proposal.pdf Hickson, R., Moeed, A. and Hannah, D. (2000) HSNO, ERMA and risk management. New Zealand Science Review 57, 72–77. Hoelmer, K.A. and Pickett, C.H. (2003) Geographic origin and taxonomic history of Delphastus spp. (Coleoptera: Coccinellidae) in commercial culture. Biocontrol Science and Technology 13, 529–535. Hokkanen, H.M.T. (2003) Demonstrating the safety of biocontrol. BioControl 48, 1. IPPC (International Plant Protection Convention) (2005) Guidelines for the export, shipment, import and release of biological control agents and other beneficial organisms. International Standards for Phytosanitary Measures. No. 3. https://www.ippc.int/servlet/CDSServlet?status=ND0x MzM5OS43NjA0NyY2PWVuJjMzPXB1YmxpY2F0aW9ucyZzaG93Q2hpbGRyZW49dHJ1ZSYzN z1pbmZv#koinfo (accessed 16 November 2005). Kuske, S., Widmer, F., Edwards, P.J., Turlings, T.C.J., Babendreier, D. and Bigler, F. (2003) Dispersal and persistence of mass released Trichogramma brassicae (Hymenoptera: Trichogrammatidae) in non-target habitats. Biological Control 27, 181–193. Loomans, A.J.M. (2004) Biologische bestrijders en de Flora- en Faunawet: criteria voor risicoinschatting en toelating biologische bestrijders in Nederland. Gewasbescherming 35(1), 33–37. Loomans, A.J.M and Sütterlin, S. (2005) Regulation of invertebrate biological control agents: international context and situation in The Netherlands. IOBC/WPRS Bulletin 28(1), 179–182. Louda, S.M., Pemberton, R.W., Johnson, M.T. and Follett, P.A. (2003) Nontarget effects: the Achilles heel of biological control? Retrospective analyses to reduce risk associated with biocontrol introductions. Annual Review of Entomology 48, 365–396. Lynch, L.D., Hokkanen, H.M.T., Babendreier, D., Bigler, F., Burgio, G., Gao, Z.-H., Kuske, S., Loomans, A., Menzler-Hokkanen, I., Thomas, M.B., Tommasini, G., Waage, J., Lenteren, J.C. van and Zeng, Q.-Q. (2000) Indirect effects in the biological control of arthropods with arthropods. In: Wajnberg, E., Scott, J.C. and Quimby, P.C. (eds) Evaluating Indirect Ecological Effects of Biological Control. CABI Publishing, Wallingford, UK, pp. 99–125. Mason, P.G. and Huber, J.T. (2002) Biological Control Programmes in Canada, 1981–2000. CABI Publishing, New York. Pinto, J.D. (1999) Systematics of the North American species of Trichogramma Westwood (Hymenoptera: Trichogrammatidae). Memoirs of the Entomological Society of Washington 22, 1–287. Sheppard, A.W., Hill, R., DeClerck-Floate, R.A., McClay, A., Olckers, T., Quimby, P.C. and Zimmermann, H.G. (2003) A global review of risk-benefit-cost analysis for the introduction of

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classical biological control against weeds: a crisis in the making? Biocontrol News and Information 24, 91N–108N. Stáry, P. (1999) Parasitoids and biocontrol of Russian wheat aphid, Diuraphis noxia (Kurdj.) expanding in central Europe. Journal of Applied Entomology 123, 273–279. USDA (2001) The ROBO Database. Available at http://www.ars-grin.gov/nigrp/robo.html van Lenteren, J.C. (2000) Measures of success in biological control of arthropods by augmentation of natural enemies. In: Gurr, G. and Wratten, S. (eds) Measures of Success in Biological Control. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 77–103. van Lenteren, J.C. (2003) Quality Control and Production of Biological Control Agents: Theory and Testing Procedures. CABI Publishing, Wallingford, UK. van Lenteren, J.C., Babendreier, D., Bigler, F., Burgio, G., Hokkanen, H.M.T., Kuske, S., Loomans, A.J.M., Menzler-Hokkanen, I., van Rijn, P.C.J., Thomas, M.B., Tommasini, M.G. and Zeng, Q.-Q. (2003) Environmental risk assessment of exotic natural enemies used in inundative biological control. BioControl 48, 3–38. Wajnberg, E., Scott, J.C. and Quimby, P.C. (2000) Evaluating Indirect Ecological Effects of Biological Control. CABI Publishing, Wallingford, UK.

16

Balancing Environmental Risks and Benefits: a Basic Approach Franz Bigler and Ursula Kölliker-Ott

Agroscope FAL Reckenholz, Swiss Federal Research Station for Agroecology and Agriculture, Reckenholzstrasse 191, 8046 Zürich, Switzerland (email: [email protected]; [email protected]; fax number: +41-44-377-7201)

Abstract Risk–cost–benefit assessment of a biological control agent is a complex task, given that it should take into account expected risks, costs and benefits of economic values, human and animal health, and the environment. Environmental impacts can not usually be assessed in monetary terms, so therefore they are analysed in a qualitative manner. The proposed procedure for environmental risk–benefit assessment consists of identifying, analysing and evaluating (weighing up) risks and benefits. During the evaluation phase, risks are balanced against benefits by ranking them separately in decreasing order of significance. The highest ranked adverse effects are then compared to the highest ranked benefits. Even though adverse effects of biological control agents are mostly limited to effects on non-target arthropods, uncertainties of effects and the potential long-term and area-wide impacts greatly complicate risk–benefit assessments. Uncertainties are caused by insufficient data, measurement errors, lack of understanding of ecological systems, environmental stochasticity and implementation errors. An example of an environmental risk–benefit assessment demonstrates that the benefits of replacing the insecticide deltamethrin by releases of the egg parasitoid Trichogramma brassicae outweigh the risks posed by the biological control agent itself.

Introduction The final step in the decision-making process of whether or not to introduce and release an organism in a new environment is to identify, assess and weigh up all adverse and beneficial effects in a risk–cost–benefit assessment (ERMA NZ, 2004; OECD, 2004). Risks and costs are balanced against benefits, and if benefits out-

weigh risks and costs, the biological control agent may be approved, otherwise an application would be declined. Adverse effects associated with introductions have been assessed in terms of probability and magnitude and rated as risks in the preceding risk assessment procedure (see Moeed et al., Chapter 14, this volume, and van Lenteren and Loomans, Chapter 15, this volume). Beneficial effects of release of a

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biological control agent are assessed in comparison to currently used or alternative pest management methods (OECD, 2004). Thus, risks, costs and benefits are the values that can be assigned to particular adverse and beneficial effects that would arise as a result of introducing or not introducing an organism (ERMA NZ, 2000). A comprehensive assessment of risks, costs and benefits associated with the release of a biological control agent should be made by the applicant and then by the regulatory authority, and should include all reasonably foreseeable direct and indirect, monetary and non-monetary, private and public risks, costs and benefits, taking into account when and where such risks, costs and benefits might accrue. In the regulatory context, the applicant and the authority share responsibility for identifying, analysing and controlling risks, costs and benefits. The applicant’s primary task is to identify and assess risks, costs and benefits, while the authority has responsibility for evaluating risks and making decisions on the basis of a combined consideration of risks, costs and benefits (see also Table 14.1 in Moeed et al., Chapter 14, this volume). As applicants and authorities share the responsibility of decisions taken on the basis of available information, it is in their mutual interest to establish a collaborative decision-making framework in which the applicant provides relevant and sufficient information and the authority provides a transparent manner of evaluation and decision-making. This chapter first presents an overview of the categories of costs and benefits of using invertebrate biological control agents. Furthermore, uncertainties in risk–cost–benefit assessments are discussed. The main part of the chapter focuses on presenting a generic procedure for balancing environmental risks and benefits by discussing identification, analysis and evaluation (weighing up) of adverse and beneficial effects. Subsequently, the proposed procedure will be followed by an example that should contribute to a better understanding of the evaluation process, and thus facilitate decision-making in the regulation of biological control agents.

Categories of Costs and Benefits Cost–benefit assessment, preceding approval or rejection of an application for a biological control agent, is a very complex task, given that it should take into account expected costs and benefits on economic values, human and animal health, the environment, as well as on social and ethical aspects. Thus, a structured and systematic approach will facilitate compilation of relevant data and information by the applicant, and enable the authority to make an informed decision and a transparent communication to the stakeholders, including the public. Costs and benefits of any pest control method are manifold and can be categorized as positive and negative effects on economy, human/animal health and the environment (Table 16.1). The tool of cost–benefit assessment may be used either analytically or descriptively.

Economic costs and benefits Economic costs and benefits, be they direct or indirect, can usually be quantified in monetary units that are determined by the market. Although many uncertainties may exist about such values, estimations and figures from previous experience can often be given, and may serve as the best possible indicators of the expected economics of a new agent. The aim of assessing economic costs and benefits is to calculate the monetary values of changes that will result for all actors (applicant, farmer, consumer, society) if an application is approved and a new organism replaces or supplements an existing pest control strategy. That is, costs and benefits of the control options that will be replaced or supplemented must also be known or estimated, and compared to those of the new agent. The difference between the costs and benefits in the comparative scenarios will give a projection of the overall net costs or benefits of releasing the new agent. An important economic aspect of cost–benefit assessment is the expected or experimentally proven efficacy of the agent

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Table 16.1. Categories of costs and benefits of using invertebrate biological control agents. Category Economy Applicant/ distributor Farmer

Consumer Society Human and animal health Environment Soil, water, air Biodiversity and ecosystems

Costs

Benefits

Development of agent (research, rearing, dossier for application, marketing) Market price of agent and its application Higher prices and apparent lower quality of product (food, fibres, etc.) Agent costs subsidized by government

Sales of agent, profits, sustainable business (estimate potential markets in space and time) Control of pest with adequate efficacy, higher yield and quality of product, higher revenue Lower prices and apparent higher quality of product (food, fibres, etc.) Control of pest with no/few risks to humans, animals and environment

Allergies Stings or bites Nuisance

No hazards (exposure of users and residues in food and feed) from other pest control options (e.g. pesticides)

No costs

Prevents pollution by alternative control options (e.g. pesticides) Control of pest with no/little effects on plants, animals, microorganisms and their functions Replacement of control options with high impacts on environment

Adverse effects on plants, animals, microorganisms and on ecosystem functions Introduced species cannot be eradicated if established

compared with the ongoing efficacy of the current pest control method. Efficacy assessment of biological control agents is a requirement listed by the OECD (2004). The role of information on efficacy in the regulation process is to enable the regulatory authority to assess the effectiveness of the biological control agent and to prevent the introduction and release of ineffective biological control agents. A biological control agent is considered to be effective if it can cause a statistically significant reduction in the number of pest organisms, of direct and indirect crop damage, or of yield loss. The applicant has to prove in the submitted dossiers, either by experimental data or by plausible assumptions, that the agent will contribute to the control of a pest in a way that justifies the risks of its release. Furthermore, it is important to postulate a scenario for what is expected to happen if the organism is not released – with its economic consequences. If, for example, the currently used pest control method is a chemical prone to induce resistance in the target pest, it will become

less efficient in controlling the pest, and more chemical will have to be sprayed in the future. A direct comparison of efficacy between chemical and biological control is often difficult and does not give a conclusive answer, because of the different modes of action and time scales of controlling a pest. Nevertheless, both options may contribute to satisfactory control of the target pest if integrated in specific pest management options. Economic costs and benefits are determined by the market and can be combined easily by using monetary units. The most common approach is to combine all values which are in similar units, to calculate the expected values and then to adopt an economic cost–benefit framework. However, accurate prospective economic values of whether or not to release a biological control agent are difficult to gauge, and in most situations only best estimates can be made. Often, it is not possible to combine costs and benefits because either some or all of the data are qualitative, or the units are dissimilar.

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Costs and benefits with respect to human and animal health Costs to human health can occur as a consequence of direct impacts on personnel during the production and release process of an inundatively used agent (e.g. allergies from inhalation of scales from lepidopterans used in mass production of insect eggs). Alternatively, costs can take the form of being simply a nuisance to the public if, for example, introduced exotic agents multiply very successfully in nature and aggregate in buildings or other places, then becoming a temporary nuisance. An example of an introduced agent that became a transient nuisance is Harmonia axyridis Pallas (Coleoptera: Coccinellidae), the Multicolored Asian Lady beetle in North America. This beetle is an important biological control agent, but in autumn it can become annoying when it aggregates in large numbers on buildings and crawls into houses (Koch, 2003), and large populations of migrating adults that feed on ripened grapes are harvested along with the grapes and taint the wine produced. Indirect benefits may stem from the release of a biological control agent that replaces or reduces, for example, the use of insecticides which can potentially harm farm workers and animals, and which contribute to pesticide residues in food and feed. Such benefits can be valued in monetary revenue because a proportion of consumers will buy food at a higher price if health benefits may result.

Environmental costs and benefits Environmental costs and benefits, which are the main focus of this chapter, include the valuation of changes in safeguard subjects like water, soil, air, biodiversity and ecosystem functions. Invertebrate biological control agents do not pollute water, soil or air (Greathead, 1995), and assessment of potential environmental effects may, in the first place, consider biodiversity issues. In most cases it is difficult or impossible to express environmental changes in monetary units, and applicants will only be able to

provide qualitative estimates of environmental risks and benefits using categories such as ‘low’, ‘medium’ and ‘high’ (US EPA, 1998). As it is extremely difficult to assign monetary values to the loss of species or ecosystem functions (Simberloff and Stiling, 1996; Thomas and Willis, 1998), it is inevitable that environmental effects are discusssed in a qualitative manner. The scenarios of these estimates should be qualified by a description and made transparent. For example, if the release of a new agent will reduce pesticide use by an amount which can not be indicated by weight of product or active ingredient, it might be possible to give figures on the expected reduction in the number of pesticide treatments, of the acreage no longer treated with pesticides or a combination of both. New pesticides may be expected to have less environmental effects in general than older ones, and consequently, replacing new chemicals may result only in a ‘medium’ environmental benefit, whereas a ‘high’ benefit would accrue by replacing old ones. Another possible scenario would be that one agent replaces or complements other agents within an IPM system which may be less efficacious or have slightly adverse effects on biodiversity. More careful analysis is needed if the risk of the introduced agent becoming established is high, and adverse effects would be irreversible since established species cannot be eradicated. For further details on risks, costs and benefits we recommend consulting the technical guides ‘Preparing information on risks, costs and benefits for applications under the Hazardous Substances and New Organisms Act 1996 (ERMA NZ, 2000) and ‘A technical guide to identifying, assessing and evaluating risks, costs and benefits’ (ERMA NZ, 2004), published by the Environmental Risk Management Authority of New Zealand.

Uncertainties Applicants and authorities will usually have to deal with uncertainties in considering risks, costs and benefits. Harwell and

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Harwell (1989) and Harwood and Stokes (2003) discuss four different categories of uncertainties: Insufficient data Insufficient data, observation or measurement errors and lack of understanding of ecological systems and processes are intrinsic to complex systems in general. For most ecosystems, a thorough knowledge of the biota present, their direct and indirect interactions, and their responses to environmental conditions are usually lacking. Fully characterizing an ecosystem and its potential modifications related to the introduction of a new biological control agent would be extremely demanding of resources and time, an option that is not possible for virtually any single ecosystem, including less complex systems such as agricultural crops. However, expert knowledge and historical data (including those from other systems) may substitute for the lack of current data and system understanding. Extrapolation Uncertainties stemming from the first category lead directly to the second uncertainty, the necessity for extrapolation. Limited information and data gained for particular conditions and ecosystems are used to predict processes in time and scale for related systems. Extrapolation based upon laboratory and field bioassays has its limitations because test designs and methodologies reflect single components of an ecosystem under specific environmental conditions, not taking into account the multitude of species interactions and population dynamics. All models are simplifications of reality and thus provide an incomplete and potentially misleading representation of systems, with the consequence of inducing further errors if models are used for forecasting. Environmental stochasticity Uncertainties are associated with demographic and environmental stochasticity,

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and the apparently random behaviour of systems that have chaotic dynamics. This is sometimes referred to as natural variation (Harwood and Stokes, 2003). Variations in environmental conditions and rare events like drought, inundations, etc. are, in principle, not predictable in the long term and such uncertainties are inherent in all ecosystems. Implementation errors For managed systems, implementation errors must be taken into account. In the context of biological control, this might include unpredictable policy implementation, or changes in market forces that alter the incentives for farmers and biological control practitioners. The introduction of political and economic effects adds a human dimension to uncertainty. For example, new and more stringent regulation of biological control agents might delay or prevent new organisms being released, which might increase the costs due to extended application dossiers and possible crop losses because of inappropriate or no control of the pest. Promotion of environmentally friendly production by government subsidies could change the competitiveness of biological control in comparison to other control methods. Each of the four categories of uncertainties (insufficient data and information, extrapolation from one system to another, stochasticity and natural variation, and implementation errors due to policy and market forces) has its own characteristics. While simplification and lack of knowledge may be unavoidable, applicants and regulators should document what is known, justify the assumptions and include indications of the confidence levels pertaining to the estimations (US EPA, 1998; ERMA NZ, 2000). Uncertainty will require applicants and authorities to make subjective judgements based on available information and best assumptions taking into account the nature and extent of the uncertainty. Risks, costs and benefits of biological control may have long lead

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times, and this makes the assumptions even more unpredictable in agricultural systems in which high management dynamics are inherent. As uncertainties in risks, cost and benefit assessments are perceived and valued differently by stakeholders – including the public, there is a tendency in some societies to take a cautious approach. Thus, when an activity raises threats of harm to human health or the environment, precautionary measures are taken even if a cause and effect relationship is not fully established scientifically (Wingspread Statement on the Precautionary Principle, 1998). This approach, however, should not lead to the interpretation that lack of full scientific certainty should be used as a reason for declining an application or for postponing decisions about applications of biological control agents.

Environmental Risk–Benefit Assessment A full environmental risk–benefit framework involves basically the same components as the risk assessment framework (Hickson et al., 2000; Moeed et al., Chapter 14, this volume), and the consecutive steps include identification, analysis and evaluation (weighing up) of adverse and beneficial effects.

Baseline scenario As a risk–benefit assessment is a comparative tool (Lockwood, 1993; ERMA NZ, 2004), all relevant risks and benefits should be estimated against a baseline scenario for comparison (ERMA NZ, 2000). The baseline scenario may consist of one or more current pest management methods. In practice, the baseline scenario may often reflect spraying pesticides, as this is the most frequently practised control option. If other control options such as the use of semiochemicals (pheromones), resistant plants or other natural enemies are currently used, incremental risks and

benefits of using the new biological control agent against these options need to be estimated. Furthermore, it is important to postulate a scenario for what is expected to happen if the organism is not released or if no control is taking place. In natural and less disturbed ecosystems where invasive organisms became harmful to native flora and fauna, biological control may be the only realistic option for control of the invaded species. The baseline scenario for this situation would be to compare biological control with no control of the pest, with possible consequences for native biota. Baseline scenarios may be dynamic and risks and benefits may change over time. For example, as crop management practice evolves, new pests may arise, or formerly successful pest control may be eroded, e.g. through resistance build-up by the target pest, and thus become inefficient. This will make innovative control strategies necessary and may open doors for biological control with new organisms. Similarly, new chemicals with less adverse environmental effects may come on the market and set a new baseline if the new agent is then compared to previously used pesticides. These scenarios include specifying an appropriate timeframe and scale, e.g. the number of years the organism will be released until it will be replaced by another control option. The dynamics of baseline scenarios with their related assumptions make risk–benefit assessment extremely difficult and volatile, and they contribute to the uncertainties discussed above.

Identification In the identification phase, all potential risks and benefits of releasing the new organism compared to the current pest control methods should be listed, be they direct or indirect, monetary or nonmonetary, or occurring at different times. As risks and benefits may accrue to private and public entities, it is important to assign values to them as much as possible.

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Where non-monetary risks and benefits are involved, qualitative estimates indicating relative size of values may be used instead of quantitative values. Beneficial effects on the natural environment may be presented in the form of reduced risks. As an example, an environmental benefit might be claimed for a biological control agent in terms of the reduced use of a pesticide. Identification of environmental risks and benefits includes safeguard subjects such as air, water, soil, biodiversity and ecosystem functions (see also Table 16.2). It can be useful to make a preliminary analysis to decide which risks and benefits need to be addressed further. In some cases, risks or benefits associated with adverse or beneficial effects of the organism are so low that further consideration is not needed. In addition to published data, expert knowledge, brainstorming, scenario analysis and life cycle assessment may provide information to help in identifying risks and benefits.

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Analysis Analysis involves determining the likelihood of assigned risks and benefits occuring, and the magnitude if they occur, when releasing the agent compared to the baseline scenario. For each effect, the combination of probability and magnitude determines the level of risk associated with that effect. Similarly to adverse effects, beneficial effects are not certain, and therefore they will also have a probability as well as a magnitude component (see also Moeed et al., Chapter 14, this volume). Based on the level of risks or benefits, the effects can be assigned to qualitative categories. These categories may include the effect classes ‘insignificant’, ‘low’, ‘medium’ and ‘high’, according to the level of the adverse or beneficial effect (see also Table 15.1 in van Lenteren and Loomans, Chapter 15, this volume). The accuracy of the assignment is a function of the quantity and quality of the infor-

Table 16.2. Environmental risks posed by the use of the egg parasitoid Trichogramma brassicae and the insecticide deltamethrin to control the European corn borer, Ostrinia nubilalis, in maize in central Europe.

Fate and behaviour in soil water air

T. brassicae

Deltamethrin

0 0 0

low 0 0

0 0 0 0 0 0 0 0 low 0 0 0

0 0 low low 0 0 0 low high 0 0 0

Effects on non-target organisms mammals birds fish aquatic invertebrates algae sediment-dwelling organisms aquatic plants honeybees other invertebrates (incl. nematodes) earthworms soil microorganisms plants

0: no or insignificant risks; low: low risk; medium: medium risk; high: high risk. For descriptions of risk levels see van Lenteren and Loomans (Chapter 15, this volume).

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mation available and, hence, uncertainties about risks and benefits are variable. Uncertainties are characterized and qualified during the analysis phase. As many of the identified risks and benefits are based on assumptions and prospective estimates, there will be considerable uncertainty about the realization of the qualitative values. The uncertainty bounds on the information contained in the analysis should be expressed quantitatively where possible, but otherwise through narrative statements. Uncertainties should be analysed, or at least described in terms of whether the uncertainty relates to the probability of occurrence, to the magnitude of the effect or to both, and of the source of uncertainty, if known. Uncertainties in risk and benefit assessments can be taken into account in two ways (ERMA NZ, 2004). First, when calculating the level of risk or benefit (see also Table 15.1 in van Lenteren and Loomans, Chapter 15, this volume), a range of descriptors instead of a single descriptor may be used. For example, the probability of an effect could be described as ranging from ‘unlikely’ to ‘possible’, and the magnitude from ‘minor’ to ‘moderate’. This would put the range of risk as ‘low’ to ‘medium’. Alternatively, the level of risk or benefit may be adjusted after it has been estimated, on the grounds of uncertainty. For example, a risk may be deemed to be ‘low’, but with high uncertainty. A practical application of a precautionary approach could consist of revising the level of risk from ‘low’ to ‘medium’. The way in which uncertainty is addressed, either during the allocation

process or after the estimation of the level of risk, should be specified in each case. Compilation, interpretation and categorizing information from different sources is a very critical step in the process, and uncertainties can be substantially reduced if specialists from the different disciplines (e.g. ecotoxicologists, environmental chemists, biological control scientists) are working together as an expert team.

Evaluation The next step of a risk–benefit assessment involves balancing the risks against the benefits of releasing a biological control agent, compared to the current control methods. As environmental effects are usually described in a qualitative manner, they are best compared by using a ranking system. This involves ranking adverse and beneficial effects separately, by listing them in descending order of significance or risk/benefit level in two side-by-side columns (see also Table 16.3). After the adverse and beneficial effects have been ranked, they need to be compared in order to evaluate whether the beneficial effects outweigh the adverse effects. It is best to start by comparing the risk and the benefit with the highest rank. If the benefit with the highest rank exceeds the adverse effect with the highest rank, then the next step is to determine if the highest ranked benefit is greater than the combination of highest and second highest adverse effects. If, for example, the highest ranked beneficial effect is determined to be lower

Table 16.3. Ranking risks and benefits of releasing Trichogramma brassicae in maize compared to spraying with the insecticide deltamethrin. Risks

Benefits

Rank

Rating

Description

Rank

Rating

Description

1

low

Effect on non-target invertebrates

1

high

Less toxic to non-target invertebrates

2 2 2 2

low low low low

Not toxic to aquatic invertebrates Not toxic to honeybees Not toxic to fish No pollution of soil

Balancing Environmental Risks and Benefits

than the combination of the three highest ranked adverse effects, the second highest beneficial effect needs to be included in the comparison, and so forth, until all risks have been outweighed by beneficial effects. If, after ranking the effects, it is clear that one or more effects effectively dominate all other effects, a simple comparison of the dominant adverse and beneficial effects may be sufficient to weigh up the risks and benefits. Ranking techniques can be used to translate qualitative judgment into a ‘mathematical’ comparison. For example, Harris et al. (1994) evaluated risk reduction in Green Bay (Lake Michigan), employing an expert panel to compare and rank the relative risks of several stressors against their potential effects. The great difficulty of comparing environmental effects is to weigh up risks and benefits of short-term effects of pesticides against possible long-term effects of natural enemies on non-target organisms such as arthropods. The distribution of risks and benefits should be analysed in terms of time and space, and the results of this analysis should be taken into consideration in the weighing-up process. In most projects, the risks and benefits will need to be estimated over a limited time span, e.g. five to ten years. Generally, adverse environmental effects caused by chemical sprays differ from those caused by biological control agents in two ways: (i) Different systems (soil, water and air) and organism groups are potentially affected. In a general context, pesticides are known to have the potential for temporary and/or persistent adverse effects on the biophysical environment, to flora, fauna and ecosystem functions. In contrast, the chance of hazard occurring from invertebrate biological control agents to the biophysical environment is in general negligible, except perhaps for soil and water when exotic nematodes containing entomopathogenic bacteria are released in high numbers. Potential negative impacts of biological control agents on non-target organisms are limited to nontarget arthropods, and in exceptional cases they can also include plants (see Albajes et al., Chapter 8, this volume). (ii) The distri-

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butional effects of risks and benefits over time and space differ between pesticides and natural enemies. Pesticides can have toxic effects on several non-target groups, and many species in the sprayed area are killed. However, negative effects are limited to the sprayed and ‘drift-covered’ area, and after the chemical has degraded, beneficial species will reinvade the field from adjacent untreated habitats. Therefore pesticides usually induce transient effects, although repeated use (e.g. several times a season) may create long-term effects. While pesticide effects are most often limited in space and time, biological control agents may establish and thus potentially cause long-term and area-wide effects. Experience with invading species has shown that eradication is seldom feasible once an organism has spread beyond its point of entry. Since established species generally cannot be eradicated, the effects are irreversible. The scenario of not releasing the new organism in case the application is declined should be evaluated as well, as this may result in increased pesticide use in the future with possible consequences for the environment.

Decision-making by the regulatory authority The final step is the decision-making by the authority based on the information gained in the risk assessment and evaluation process. The decision-making process requires that the regulatory authority understands the nature of the effects, related risks, costs and benefits, and is able to make judgements about their relative significance. Many, if not most, predicted risks, costs and benefits that need to be considered by the regulatory authority are not certain for a variety of reasons, and the quality of the information may vary significantly. It is therefore important that risks, costs and benefit estimations include indications of the confidence levels pertaining to the estimate and that all assumptions made in the estimation are known.

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Release of Trichogramma brassicae to control Ostrinia nubilalis in maize – an environmental risk–benefit assessment A practical example of assessing environmental risks and benefits is shown in Tables 16.2 and 16.3. In this example we analyse and evaluate the environmental risks and benefits of releasing the biological control agent, Trichogramma brassicae Bezdenko (Hymenoptera: Trichogrammatidae), to control the European corn borer (ECB), Ostrinia nubilalis Hübner (Lepidoptera: Crambidae), in maize in central Europe, where the pest insect completes one generation per year. We will follow the generic procedure discussed in the preceding sections of this chapter, by first choosing a baseline scenario, then identifying, assessing, and finally weighing up risks and benefits. Balancing adverse and beneficial effects will reveal whether the benefits of releasing T. brassicae outweigh the risks and, therefore, whether a release would be justified.

Background The egg parasitoid T. brassicae has been commercially used in central Europe since the early 1980s to control the ECB in maize (Bigler, 1986). The strain of the egg parasitoid T. brassicae used in Europe originates from Moldavia (northern Black Sea region) and was introduced in the early 1970s to France. Nowadays, approximately 120,000 female T. brassicae per ha are released per pest generation depending on the density of pest populations and the type of maize crop (sweet, seed, grain maize). There are approximately 100,000 ha of maize treated in Europe each year (F. Kabiri, Valbonne, 2004, personal communication).

Baseline scenario Besides the use of T. brassicae, one of the current methods used to control the ECB in central Europe is spraying insecticides containing the active ingredient deltamethrin (pyrethroid). Application of deltamethrin will therefore serve as the baseline sce-

nario. Deltamethrin is generally applied against the ECB with one application per pest generation. Deltamethrin is a fastacting, non-systemic insecticide with contact and stomach action. Like all pyrethroids, it prevents the transmission of nerve impulses and thus rapidly paralyses the insects. Deltamethrin is effective against a wide range of pests (EXTOXNET, 1995). Identification A list of environmental risks and benefits associated with releasing T. brassicae compared to spraying deltamethrin is presented in Table 16.2. This comparison is based on the EU-Directives 95/36/EC (1995) concerning fate and behaviour of pesticides in the environment, and 96/12/EC (1996) concerning the ecotoxicological studies of pesticides (active substances). Both directives with their annexes are an amendment of Council Directive 91/414/EEC, which concerns the placing of plant protection products on the market (EU-Directive 91/414/EEC, 1991) and specifies the environmental safeguards and ecotoxicological profiles for official pesticide registration in the European Union. Other official documents may serve as a basis for a comparative assessment depending on the regulatory requirements of a particular country. The list presented in Table 16.2 includes potential pollution of soil, water and air, and adverse effects on those groups of non-target organisms that are considered in the above-mentioned pesticide registration directives. These include mammals, birds, aquatic and sediment-dwelling organisms, honeybees and other invertebrates, earthworms, soil microorganisms and plants. The selection of non-target organisms is not based on scientific evidence alone, and it is far from being exhaustive; however, it forms a basis that has been used for decades with a good track record of risks and benefits. The list may be extended to other groups of organisms if there is good evidence and justification, but comparison to pesticides may be difficult as data on pesticide effects are not available for other groups of organisms.

Balancing Environmental Risks and Benefits

Analysis The next step involves analysing the risks and benefits in terms of likelihood of occurrence and magnitude of consequence. Risks and benefits can be roughly evaluated and assigned to qualitative categories. These categories may include the effect classes ‘insignificant’, ‘low’, ‘medium’ and ‘high’. The information on environmental effects of T. brassicae is based on data gained in the EU-funded project ‘Evaluation of Environmental Effects of Biological Control Introductions into Europe’ (ERBIC) from 1998 to 2002. The relevant information has been published by Babendreier et al. (2003a,b,c,d) and Kuske et al. (2003, 2004). Mass release of T. brassicae for biological control in maize has no adverse environmental effects except for the potential hazard to non-target insects (Table 16.2). This tiny egg parasitoid is polyphagous, with its major hosts belonging to the Lepidoptera, though with some probability that its host range may extend to other insect orders. However, the results of the ERBIC project show that parasitism of non-target insects (Lepidoptera and different natural enemies inhabiting maize), assessed in different habitat types, is very low under field conditions. Experiments have substantiated that female T. brassicae have a low host-searching efficiency on plants other than maize, resulting in the extremely low parasitism of non-target host eggs. Although the parasitoid has established in its new environment, data from field surveys performed in areas where releases have been made annually since 1990 demonstrate that the introduced species coexists at low population levels with native Trichogramma species (e.g. T. semblidis Aurivilius, T. evanescens Westwood (Kuske et al., 2003)). Substantial populations in semi-natural habitats were observed only temporarily shortly after mass releases. Information gained from the risk assessment experiments and postrelease surveys let us conclude that, because of the low searching efficacy of T. brassicae in non-target habitats, the risks posed by T. brassicae to non-target insects are low or even negligible.

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Toxicological information on deltamethrin can be found in ‘The Pesticide Manual’ (Tomlin, 2000), the ‘Review report for the active substance deltamethrin’ (European Commission, 2002) and in the ‘Pesticide Information Profile’ on deltamethrin provided by the ‘Extension Toxicology Network’ (EXTOXNET, 1995). When categorizing risks posed by deltamethrin, the risk level is determined directly, since in the literature on pesticides no distinction between probability and magnitude of effects is made. In Table 16.2, adverse environmental effects of deltamethrin have been assigned to risk categories. In soil, deltamethrin undergoes microbial degradation within one to two weeks. Since it disappears from the environment within a short period of time, the risk of soil pollution is considered to be low. Deltamethrin in pond water is rapidly adsorbed, mostly by sediment in addition to uptake by plants and evaporation into the air, therefore the risk of water pollution under good agricultural practice is considered to be insignificant. The risk of air pollution is considered to be insignificant since deltamethrin is a lipophilic compound of high molecular weight and consequent low volatility. Deltamethrin does not or insignificantly affects mammals, birds, aquatic plants, soil microorganisms and earthworms. There is no known phytotoxicity to crops. However, it can negatively impact aquatic organisms, bees and other non-target arthropods. Although fishes and aquatic invertebrates are sensitive to deltamethrin in the laboratory, the risk of damage in the field is low because the likelihood of exposure is very low under good agricultural practice. Deltamethrin is highly toxic for honeybees under laboratory conditions (Tomlin, 2000). However, these values are not translated to significant hazards under good agricultural practice because maize is most often treated before pollen is shed, i.e. when the crop is less attractive for honeybees, and thus exposure to deltamethrin will not occur. Moreover, this compound is repellent for bees and thus prevents pollen foraging in

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the crop to some degree shortly after treatment. Deltamethrin has a very broad activity spectrum and poses a high risk to non-target arthropods. The two control methods have been evaluated thoroughly over the last thirty years and the remaining uncertainties are relatively few. When decisions were made whether to spray deltamethrin on maize, some environmental effects of this compound were known, since it had undergone the registration process. The situation was different for the parasitoid as regulatory procedures for natural enemies were not in place in Europe when it was introduced and mass released, and it was more luck than wisdom that this introduction has not caused significant environmental effects. Evaluation After adverse and beneficial effects of the two control methods have been rated (Table 16.2), risks and benefits are balanced against each other. In Table 16.3, risks and benefits of releasing T. brassicae compared to spraying deltamethrin are listed in decreasing order of significance. The main risk – as well as the main benefit – of releasing T. brassicae relates to the organism group ‘non-target invertebrates’, whereas the risks have been rated as ‘low’ and the benefits ‘high’. Since the main benefit outweighs the main risk, a release is justified. The release of T. brassicae has no adverse effects on the environment in general, with low or negligible risks for a few non-target insect species. Given that deltamethrin is a broad-spectrum insecticide, it is obvious that non-target insects and other arthropods are strongly affected when they are exposed. As a consequence of deltamethrin sprays in maize, secondary pest outbreaks (e.g. aphids, spider mites) may occur and other pesticide treatments may be needed to control these pests. The mechanisms behind such pest outbreaks can be explained by the high sensitivity of predators and parasitoids occurring naturally in maize at the time of deltamethrin treatments. While most of the sensitive natural enemies are killed by this insecticide,

the less sensitive or less exposed pests can build up high populations in the absence of natural enemies and reduce the yield of the crop, which translates into economic losses. Pesticide spray can drift into adjacent off-crop sites and affect ecologically more valuable habitats that may serve as reservoirs of natural enemies, and from where it is expected that natural enemies will re-invade crops. The overall environmental risks would increase substantially if secondary pests have to be controlled with other pesticides as a consequence of deltamethrin sprays against the ECB. The interpretation of this result leads to the conclusion that the environmental risks of controlling the ECB with deltamethrin are higher than releasing the egg parasitoid. The assessment of the total environmental risks and benefits of using T. brassicae instead of deltamethrin for ECB control shows that benefits of the egg parasitoid outweigh the risks of the minimal damage caused by the parasitoid to non-target insects. Therefore, it would be environmentally safer if deltamethrin were replaced by T. brassicae.

Conclusions A comprehensive cost–benefit assessment to satisfy requirements for use of invertebrate biological control agents is a complex and ambitious matter. Applicants and regulatory authorities should initiate communication and cooperation at an early stage in the process to find consensus on the type of information and data needed and how to perform the assessment. This strategy should prevent all parties from performing redundant work, reduce costs and accelerate the regulatory process. Regulators and external experts should request only information that is absolutely necessary, i.e. separate data requested for curiosity from those required to perform the evaluation. The majority of applicants are small companies (inundative release) or public research institutions (classical biological control), and both have, in general, very limited resources to spend on research and development of new agents.

Balancing Environmental Risks and Benefits

The interpretation of the precautionary approach, as has become common practice in many countries, should not lead to applications being rejected in the absence of full scientific information and evidence. Using expert knowledge from different disciplines might be the most efficient way to perform the risk–cost–benefit assessment in a pragmatic way and with great accuracy. Where scientific information is otherwise lacking, data generated by the applicant and expert consultations should substitute them. Regulation of biological control agents is a balancing act because

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biological control is now often promoted by government policy and considered as an alternative to existing pest control options; on the other hand, more demanding societies are requesting more safety and better protection of the environment. This opens new avenues for biological control, but may lead to more stringent regulation, with all its drawbacks for developing and implementing new biological control organisms. Although risk–benefit analyses contribute to an increasing degree of safety demanded by our societies, they do render regulation of biological control agents more complex.

References Babendreier, D., Kuske, S. and Bigler, F. (2003a) Non-target host acceptance and parasitism by Trichogramma brassicae Bezdenko (Hymenoptera: Trichogrammatidae) in the laboratory. Biological Control 26, 128–138. Babendreier, D., Kuske, S. and Bigler, F. (2003b) Parasitism of non-target butterflies by Trichogramma brassicae Bezdenko (Hymenoptera: Trichogrammatidae) under field cage and field conditions. Biological Control 26, 139–145. Babendreier, D., Rostas, M., Hofte, M.C.J., Kuske, S. and Bigler, F. (2003c) Effects of mass releases of Trichogramma brassicae on predatory insects in maize. Entomologia Experimentalis et Applicata 108, 115–124. Babendreier, D., Schoch, D., Kuske, S., Dorn, S. and Bigler, F. (2003d) Non-target habitat exploitation by Trichogramma brassicae (Hymenoptera: Trichogrammatidae): what are the risks for endemic butterflies? Agricultural and Forest Entomology 5, 199–208. Bigler, F. (1986) Mass production of Trichogramma maidis Pint. et Voeg. and its field application against Ostrinia nubilalis Hbn. in Switzerland. Journal of Applied Entomology 101, 23–29. ERMA NZ (2000) Preparing information on risks, costs and benefits for applications under the Hazardous Substances and New Organisms Act 1996 (ER-TG-03–1 7/00). http://www.ermanz.govt.nz/resources/publications/pdfs/ER-TG-03-1.pdf (accessed 17 December 2004). ERMA NZ (2004) A technical guide to identifying, assessing and evaluating risks, costs and benefits (ER-TG-05-1 03/04) http://www.ermanz.govt.nz/resources/publications/pdfs/ER-TG-05-01% 200304%20DM%20Tech%20Gde.pdf (accessed 17 December 2004). EU-Directive 91/414/EEC (1991) Council Directive of 15 July 1991 concerning the placing of plant protection products on the market. Official Journal of the European Communities L 230–1. EU-Directive 95/36/EC (1995) Commission Directive 95/36/EC of 14 July 1995 amending Council Directive 91/414/EEC concerning the placing of plant protection products on the market. Official Journal of the European Communities L 172–8. EU-Directive 96/12/EC (1996) Commission Directive 96/12/EC of 8 March 1996 amending Council Directive 91/414/EEC concerning the placing of plant protection products on the market. Official Journal of the European Communities L 65. European Commission (2002) Review report for the active substance deltamethrin. http://europa.eu.int/comm/food/plant/protection/evaluation/existactive/list1-31_en.pdf (accessed 20 December 2004). EXTOXNET (1995) Pesticide information profile on deltamethrin. http://pmep.cce.cornell.edu/profiles/extoxnet/carbaryl-dicrotophos/deltamethrin-ext.html (accessed 13 June 2005). Greathead, D.J. (1995) Benefits and risks of classical biological control. In: Hokkanen, H.M.T. and Lynch, J.M. (eds) Biological Control: Benefits and Risks. Cambridge University Press, Cambridge, UK, pp. 53–63.

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Harris, H.J., Wenger, R.B., Harris, V.A. and Devault, D.S. (1994) A method for assessing environmental risk: a case study of Green Bay, Lake Michigan, USA. Environmental Management 18, 295–306. Harwell, M.A. and Harwell, C.C. (1989) Environmental decision making in the presence of uncertainty. In: Levin, S.A., Harwell, M.A., Kelly, J.R. and Kimball, K.D. (eds) Ecotoxicology: Problems and Approaches. Springer, New York, pp. 517–540. Harwood, J. and Stokes, K. (2003) Coping with uncertainty in ecological advice: lessons from fisheries. Trends in Ecology and Evolution 18, 617–622. Hickson, R., Moeed, A. and Hannah, D. (2000) HSNO, ERMA and risk management. New Zealand Science Review 57, 72–77. Koch, R.L. (2003) The multicolored Asian lady beetle, Harmonia axyridis: A review of its biology, uses in biological control, and non-target impacts. Journal of Insect Science 3, 32. (also see http://www.insectscience.org/3.32/Koch_JIS_3_32_2003.pdf (accessed 23 May 2005)). Kuske, S., Widmer, F., Edwards, P.J., Turlings, T.C.J., Babendreier, D. and Bigler, F. (2003) Dispersal and persistence of mass released Trichogramma brassicae (Hymenoptera: Trichogrammatidae) in non-target habitats. Biological Control 27, 181–193. Kuske, S., Babendreier, D., Edwards, P.J., Turlings, T.C.J. and Bigler, F. (2004) Parasitism of non-target lepidoptera by mass released Trichogramma brassicae and its implication for the larval parasitoid Lydella thompsoni. BioControl 49, 1–19. Lockwood, J.A. (1993) Environmental issues involved in biological control of rangeland grasshoppers (Orthoptera: Acrididae) with exotic agents. Environmental Entomology 22, 503–518. OECD (2004) Guidance for information requirements for regulation of invertebrates as biological control agents (IBCAs). http://www.oecd.org/dataoecd/6/20/28725175.pdf (accessed 13 January 2005). Simberloff, D. and Stiling, P. (1996) Risks of species introduced for biological control. Biological Conservation 78, 185–192. Thomas, M.B. and Willis, A.J. (1998) Biocontrol – risky but necessary? Trends in Ecology and Evolution 13, 325–329. Tomlin, C.D.S. (2000) The Pesticide Manual. The British Crop Protection Council, Bear Farm, Binfield, Bracknell, Berks RG42 5QE, UK. US EPA (1998) Guidelines for ecological risk assessment (EPA/630/R-95/002F). http://cfpub2.epa.gov/ncea/cfm/recordisplay.cfm?deid=12460 (accessed 20 December 2004). Wingspread Statement on the Precautionary Principle (1998). http://www.sehn.org/precaution.html (accessed 12 May 2005).

Glossary

Adverse environmental effects: changes that are considered undesirable because they alter valued structural or functional characteristics of ecosystems or their components. Augmentative releases: either inundative or seasonal inoculative releases, i.e. those forms of biological control where mass-produced biological control agents are released to reduce a pest population without necessarily achieving continuing impact or establishment. Base temperature: the temperature above which degree-days start to accumulate. Below that temperature no development occurs. Beneficial organism: any organism directly or indirectly advantageous to plants or plant products, including biological control agents. Benefit (in risk–benefit assessment): the value of a particular positive effect expressed in monetary or non-monetary terms. Biological control: pest management strategy making use of living natural enemies, antagonists or competitors and other self-replicating biotic entities. Biological control agent: a natural enemy, antagonist or competitor, and other selfreplicating biotic entity used for pest management. Classical biological control: the intentional introduction and permanent establishment of an exotic biological agent for long-term pest suppression. Commensalism: an association between two organisms of different species in which one derives some benefit while the other is unaffected. Competitor: an organism which competes with other organisms for essential resources (e.g. food, shelter) in the environment. Contaminants (for the introduction of invertebrate biological control agents): inclusion of any unwanted organisms or substances in the commerce of IBCAs that poses a risk to the health of IBCAs, humans and/or to ecosystems. Cost (in risk assessment): the value of a particular adverse effect expressed in monetary or non-monetary terms. Direct effect of introduction of exotic biological control agent: involves physical interaction between the biological control agent and target or non-target organisms (effects can be positive, negative or neutral). Ecological host range: the range of species a natural enemy parasitizes/feeds on/infects in nature (but see ‘physiological (= fundamental) host range’).

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Ecoregion: an area with similar fauna, flora and climate and hence similar concerns about the introduction of biological control agents. Ecosystem: a complex of organisms and their environment, interacting as a defined ecological unit (natural or modified by human activity, e.g. agroecosystem), irrespective of political boundaries. Efficacy of a biological control agent: the ability to cause a statistically significant reduction in number of pest organisms, of direct and indirect crop damage, or of yield loss. Entomophagous: organisms that eat insects. Environmental risk assessment: the process that analyses the likelihood of occurrence and magnitude of consequences of an adverse environmental effect. Establishment: successful long-term survival and reproduction of a species after introduction into a new area (but see ‘seasonal persistence’). Exotic: not native to a particular country, ecosystem or ecoregion. Fundamental host range: see ‘physiological host range’. Generalist: see host specificity. Hazard: any potential adverse effect which can be named and measured (e.g. in biological control: direct and indirect adverse effects on non-target organisms and ecosystems). Host range: set of species that allow survival and reproduction of a natural enemy (see also ‘physiological (= fundamental) host range’ or ‘ecological host range’). Host specificity: a measure of the host range of a biological control agent on a scale ranging from an extreme specialist able only to complete development on a single species or strain of its host (monophagous), to a generalist with many hosts ranging over several groups of organisms (polyphagous). Hybrid: the offspring of genetically dissimilar parents or stock, especially the offspring produced by plants or animals of different varieties, species or races. Hyperparasitoid: a parasitoid that uses another parasitoid as a host. Import permit: an official document authorizing importation (of a biological control agent) in accordance with specified requirements. Inbreeding: the mating of genetically related individuals; mating between relatives. Indirect effect of introduction of exotic biological control agent: effect of introduction on other organisms not involving physical interaction with biological control agent (effects can be positive, negative or neutral). Infochemical: chemical that conveys information in an interaction between individuals, evoking in the receiver a behavioural or physiological response that is adaptive to either one of the interacts or to both. Inoculative release: the introduction of a biological control agent with the aim of obtaining establishment and long-term pest suppression e.g. classical biological control. Integrated Pest Management (IPM): a pest population management system that utilises all suitable techniques in a compatible manner to reduce pest populations and maintains them at levels below those causing economic injury. Interbreeding: breeding between different species. Intraguild predation: the killing and eating of species that otherwise use similar resources. Introduction (of a biological control agent): the release of a biological control agent into an ecoregion where it did not exist previously. Inundative release: the release of very large numbers of a mass-produced biological control agent in the expectation of achieving a rapid reduction of a pest population without necessarily achieving continuing impact or establishment. Invertebrate Biological Control Agent (= IBCA): an invertebrate natural enemy used for pest control, including entomopathogenic nematodes. Learning: an adaptive change in behaviour after experience. Legislation: any act, law, regulation, guideline or other administrative order promulgated by a government.

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Lethal temperature50: temperature during a specific duration of exposure at which 50% of the organisms are killed. Lethal time50: duration of exposure to a specific temperature at which 50% of the organisms are killed. Likelihood (in risk assessment): a qualitative description of probability or frequency, in relation to how likely it is that something will occur (see also ‘risk’). Magnitude (in risk assessment): a qualitative descriptor of the size of the consequences if adverse or beneficial effects occur (see also ‘risk’). Magnitude of risk of establishment: the area within which the introduced natural enemy is potentially able to establish, as a percentage of the area in which the exotic natural enemy will be licensed (e.g. a whole country or part of it). Microbial control: the use of microorganisms (including viruses) as biological control agents. Microorganism: a protozoan, fungus, bacterium, virus or other microscopic self-replicating biotic entity. Monophagous: organism that attacks one host species = species specific. Mutualism: an association between organisms of two different species in which each member benefits. Native: naturally occurring in the area of proposed releases. Natural enemy: an organism which lives at the expense of another organism and which may help to limit the population of this other organism; the term natural enemy includes parasitoids, parasites, predators and pathogens. Negligible risks: risks which are of such little significance in terms of their likelihood and magnitude that they do not require active management and/or after the application of risk management do not need to be justified by counterbalancing benefits. Non-target organism: all organisms except the target organism. Oligophagous: organism that attacks a limited group of related hosts in the same genus or subfamily. Parasite: an organism which lives on or in a larger organism, feeding upon it. Parasitoid: an insect parasitic only in its immature stages, killing its host in the process of its development, and free living as an adult. Pathogen: microorganism causing disease. Pest: any species, strain or biotype of plant, animal or pathogenic agent injurious to cropped plants or plant products. Physiological (= fundamental) host range: the range of species a natural enemy can parasitize/feed on/infect in the laboratory (but see ‘ecological host range’). Polyphagous: organism that attacks a wide range of hosts from different (sub-) families. Predator: a natural enemy that preys and feeds on other animal organisms, more than one of which are killed during its lifetime. Quarantine (of a biological control agent): official confinement of biological control agents subject to phytosanitary regulations for observation and research, or for further inspection and/or testing. Release (into the environment): intentional liberation of an organism into the environment. Release (of a consignment): authorization for entry after clearance. Risk: the combination of the likelihood of occurrence and magnitude of consequences should the effects occur. Risk assessment: a process of identifying, analysing and evaluating risks, costs or benefits associated with the introduction of a biological control agent. Risk evaluation: the evaluation by the authority of the combined assessments of risks, costs and benefits for the purposes of deciding whether the application should be approved or declined.

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Glossary

Risk management options: risk reduction actions that may be selected, alone or in combination, to reduce identified risks to an acceptable level (= risk mitigation). Risk mitigation: see ‘risk management options’. Seasonal inoculative releases: the release of mass-produced biological control agents in the expectation of achieving a reduction of a pest population during several generations without necessarily achieving continuing impact or establishment. Seasonal persistence: survival of a population is limited to one growing season. Specialist: see host specificity. Supercooling point: temperature at which an organism freezes; for freeze-intolerant species, instantaneous death occurs at that temperature. Symbiosis: a close, prolonged association between organisms of different species that may, but does not necessarily, benefit each member. Synomone: an allelochemical that is pertinent to the biology of an organism (organism 1) that evokes in the receiver (organism 2) a behavioural or physiological response that is adaptively favourable to both organisms 1 and 2. Thermal budget: accumulation of day-degrees necessary to complete a generation. Trophic levels: a functional classification of taxa within a community that is based on feeding relationships. Unacceptable risks: risks of a type or level which the authority will not accept irrespective of any benefits that might accrue. Uncertainty: the estimated amount by which an observed value may differ from the true value due to incomplete or wrong information.

References Dicke, M. and Sabelis, M.W. (1988) Infochemical terminology: based on cost-benefit analysis rather than origin of compounds. Functional Ecology 2, 131–139. ERMA NZ (2000) Preparing information on risks, costs and benefits for applications under the Hazardous Substances and New Organisms Act 1996 (ER-TG-03-1 7/00). http:// www.ermanz.govt.nz/resources/publications/pdfs/ER-TG-03-1.pdf (accessed 17 December 2004). ERMA NZ (2004) A technical guide to identifying, assessing and evaluating risks, costs and benefits (ER-TG-05-1 03/04). http://www.ermanz.govt.nz/resources/publications/pdfs/ER-TG-0501%200304%20DM%20Tech%20Gde.pdf (accessed 17 December 2004). GuruNet (2005) Answers.com – Online Encyclopedia, Thesaurus, Dictionary definitions. http://www.answers.com (accessed 4 July 2005). IPPC (1996) Code of conduct for the import and release of exotic biological control agents. International Standards for Phytosanitary Measures No. 3. International Plant Protection Convention. Food and Agricultural Organization of the United Nations, Rome, Italy, 23 pp. IPPC (2005) Guidelines for the export, shipment, import and release of biological control agents and other beneficial organisms. http://www.ippc.int/servlet/BinaryDownloaderServlet/76047_ ICPM_7_report_ISPM_0.pdf?filename=1118408473107_ISPM3_2005.pdf&refID=76047 (accessed 1 July 2005). NAPPO (2000) Guidelines for petition for release of exotic entomophagous agents for the biological control of pests, RSPM No. 12. Available at http://www.nappo.org/Standards/OLDSTDS/ RSPM12-e.pdf NAPPO (2004) NAPPO glossary of phytosanitary terms, RSPM No. 5. http://www.nappo.org/ Standards/REVIEW/RSPM5-e.pdf (accessed 1 July 2005). OECD (2004) Guidance for information requirements for regulation of invertebrates as biological control agents (IBCAs). http://www.oecd.org/dataoecd/6/20/28725175.pdf (accessed 13 January 2005). US EPA (1998) Guidelines for ecological risk assessment (EPA/630/R-95/002F). http://cfpub2. epa.gov/ncea/cfm/recordisplay.cfm?deid=12460 (accessed 20 December 2004).

Index

abundance: effect of interbreeding 78, 85 Acentria ephemerella 171 adaptation 55, 171 allopatry 79, 80 amphibians: import regulations 159 Anagyrus indicus 171 analysis, data see statistics: methods Aphanotorhaphopsis samarensis 24 Aphidus rosae 23 Aphis glycines 73 Aphis gossypii 72 Apicomplexa 149 Australia 3, 55, 159 avoidance behaviour 106

bacteria 147–148, 153–154 Bassaris gonerilla 168–169 Bathyplectes curculionis 171 Beauveria bassiana 170, 171 behaviour: of parasites and prey 41–42 avoidance 106 changes caused by rearing conditions 45–46 natural enemy foraging behaviour 42–44 benefits: risk/benefit analysis see under risk assessment and management BIOCLIM 82 Bioedit sequence alignment editor 193

braconid wasps 6 budget, thermal 103 butterflies: rare species in host specificity testing 25

Cactoblastis cactorum 170 Cameraria ohridella 211, 212(fig) Campyloma verbasci 137 Canada 2–3, 159 Celatoria compressa candidate for control of Diabrotica virgifera 27–28 ecological host range 28–31 centrifugal phylogenetic method for test species selection (Wapshere) 16–17, 18 Cephalonomia hyalinipennis 70 Chrysoperla carnea 72, 147 Ciliophora 149 Cirsium spp. 170 climate ecoclimatic zones 204(fig) and species distribution 82, 217–218 effects of introgression 88 Coleomegilla maculata 72 Coleotichus blackburniae 169 competition and other interactions difficulty of prediction 65 direct vs indirect effects 7 displacement 7, 158, 171–172 291

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competition and other interactions continued killing and predation 66(fig), 67, 70, 71 assessment by community manipulation 72 costs to predators of feeding on plants 135 facultative predation 133–134 intra-guild predation 72 methods of assessment focal observations 70–71 large-cage experiments 71–73 molecular and biochemical studies 71 Petri dish experiments 69–70 surrogate experiments 73 post-release studies 171–172, 179–180 threat to endangered species 65 types 67–69 in weed biological control 66 Compsilura concinnata 23 contamination: of biological control agents abiotic contaminants 151, 156 bacteria 147–148, 153–154 definition 146 diagnosis and detection 152–156 fungi 148–149, 154–155 invertebrates 150–151, 155–156 in mass rearing 197 nematodes 150, 155 protozoa 149–150, 155 risk assessment 156–158 guidelines and recommendations 159–161 in test species 45 viruses 146–147, 153 cost–benefit analysis see under risk assessment and management Cotesia spp. 20, 22, 23, 24, 73, 147 courting definition 78 and diurnal rhythms 82 crops: damage by biological control agents see under plants Curinus coeruleus 54 cuticle: lipid content 105–106 Cydia pomonella 24 cytochrome oxidase: gene sequences 191

data: statistical analysis see statistics: methods deer mice 177–178 Delphastus catalinae 110(fig), 111 deltamethrin: environmental risks 279(tab) desiccation tolerance 106 development: thresholds 102–103 Diabrotica virgifera 28, 211, 218 diagnosis: types and methods 152, 154 diapause: effect on host range assessment 44–45 Dichasmimorpha spp. 22 Dicyphus tamaninii 136, 137 diets, artificial: for test species 44 directionality, problem of 122–123 Dirhinus giffardii 70 dispersal 8 mechanisms 115–116 methods of assessment 116 mark-release-recapture (MRR) experiments see mark–release– recapture (MRR) experiments models 121–124 nematodes 176 in risk assessment process 261–262 Trichogramma case study 124–127 displacement 7, 158, 171–172 diurnal rhythms 82 DNA markers 153, 154, 155, 189–192 Dryocosmus kuriphilus 172

ecological host range see under host range and specificity ecology, shared 19–20, 26 ecoregions biogeographic realms and hierarchical biomes 209(fig) classification 203–207 concept and definitions 203 movement of arthropods for scientific study 213–217 use in current biological control practice 207 effect size 227–228 efficacy: assessment 274–275 ELISA (enzyme-linked immunosorbent assay) 153 Encarsia pergandiella 258(tab) Entomophaga maimaiga 169

Index

293

Entomophaga praxibuli 158 enzymes, restriction 193–194 Epiphyas postvittana 24 Epirrita autumnata 101–102 EPPO (European and Mediterranean Plant Protection Organization) workshop on biological control in Europe (1997) 2 risk assessment of agents on EPPO list 263, 265 equivalence testing 228 ERBIC (Evaluating Environmental Risks of Biological Control Introductions into Europe) research project (EU, 1998–2002) 2 ERBIC/OECD risk assessment procedure 255–260 Eretmocerus eremicus 110(fig), 111 Erionota thrax 20, 22 errors of implementation in managed systems 277–278 in statistical testing 224–226, 229–230 establishment 8 abiotic factors 99, 105(tab) humidity 105–106 temperature 100–104, 107–111 biotic factors 99 host/prey effects 106–107 and crop damage potential 140 not desirable in inundative releases 98–99 recommendations for assessing potential 111–112 seasonal persistence vs permanent establishment 99 Eudocima fullonia 55 eugregarines 149 Eurasia: ecoregions 205(fig) insect spread in mountain regions 209–210 Europe ecoregions 208(fig), 215, 216(fig), 217 insect distribution and spread 209–213 movement of arthropods for scientific study 213–217 proposals for regulation and risk assessment 2, 3

risk assessment of commercial natural enemies 263–268, 265–268 European Union project: Evaluating environmental risks of biological control 167 review of current regulatory status 3 extrapolation: factor of uncertainty 277

facultative hyperparasitoids 70, 74, 151 FAO Code of Conduct for the Import and Release of Exotic Biological Control Agents 137 see IPPC Code of Conduct feeding: insect habits 133–134 field surveys see surveys, field Fiji 40–41 food webs 173 foraging: behaviour of natural enemies 42–44 freeze tolerance and intolerance 101, 102 fruit flies 22, 172 fungi 148–149, 154–155, 171

Galendromus occidentalis 72 GARP 82 Gelis agilis 73 Generalized Estimating Equations 235, 236 Generalized Linear Models 233–234 repeated measurements 235–236 gregarines 149 grids, recapture 119–120 Guam: fortuitous biological control 171 gypsy moth 24

habitats 82 habits, feeding 133–134 Harmonia axyridis 72, 265 Hawaii 3, 54, 65, 169, 172, 173 Hazardous Substances and New Organisms (HSNO) Act (New Zealand, 1996) 3 criteria considered and information required 246–250 roles and components 245–246

294

Index

hazards: OECD information requirements 5(tab) health, human 177–178, 251, 276 Heliothis virescens 85 herbivore-induced synomones 24 Heteropsylla cubana 54 Heteroptera injury caused to plants 137 requirement for water 135–136 Heterorhabditis spp. 175–176 Hippodamia convergens 72, 265 host range and specificity behavioural data required 41–42 natural enemy foraging behaviour 42–44 determining factors 19–20 difficulties of data collection 18 ecological host range determination 26, 28–29 effects of introgression 88 field surveys see surveys, field host specificity: purpose of testing 40 information from classical biological control 40–41 literature 4, 56 morphological constraints 42 OECD information requirements 5(tab) physiological host range 40 rarely considered in the past 15–16 in risk assessment process 262 selection of non-target test species 5–6, 47–48 case study: Celatoria compressa 27–31 centrifugal phylogenetic method 16–17, 18 lessons from weed biological control 16–18 literature review of criteria 20–25 recommendations 25–27 shifted in hybrid progeny 86 taxonomic extrapolation 41 test interpretation 6, 53–55 test methodology approaches presented in the literature 39(tab) field test 53 flow chart for test scheme design 49(fig)

large arena choice behavioural test 52–53, 234, 235(tab) no-choice vs choice tests 6, 50 points to consider in test development 42–45 reviews 4 small arena no-choice behavioural test 51–52, 237, 238(fig) small arena no-choice black-box test 50–51 test reliability 6 hosts: effect of characteristics on natural enemy foraging 42–44 humans health effects of biological control 177–178, 251, 276 risk assessment of pathogens 157–158 humidity: determines establishment potential 105–106 hybrid zones 80 hybridization, nucleic acid 153 hybridization: of progeny definition 79 host range shifts 86 hybrid speciation 86–87 introgression through backcrossing of hybrids 87–89 non-viable and sterile progeny 85–86 and phylogenetic relatedness 80, 81(tab) reproductive character displacement 87 Hypera brunneipennis 171 Hypera postica 171 hyperparasitoids 70, 74, 151

idiobionts 19 import: regulations 159, 207, 217 inbreeding: of test species 46–47 index, risk see under risk assessment and management infection: of test species 45 infochemicals 43, 47 information from early studies of classical biological control 40–41 inadequacy of 267–268, 277 OECD requirements see OECD guidance document

Index

295

inter simple sequence repeats 190 interactions see competition and other interactions interbreeding can affect abundance 78, 85 impacts host range shifts 86 hybrid speciation 86–87 non-viable or sterile progeny 85–86 reproductive character displacement 87 relevant factors copulation and sperm use 83–85 geographic distribution 80, 82 mate recognition 83 phylogenetic relatedness 80, 81(tab) spatial and temporal barriers 82–83 test flowcharts 89–91, 92(fig) types 78–79 internal amplification control (IAC) 153 internal transcribed spacers (ITS) 190–191, 194–196, 198 International Standard for Phytosanitary Measures No. 3 see IPPC Code of Conduct introgression definition 79 as a result of rare courtship and mating 82 through backcrossing of fertile hybrids 87–89 invertebrates as contaminants 150–151, 155–156 import regulations 159 risk assessment of pathogens 156–157 IOBC/WPRS Commission document 3 IPPC Code of Conduct (1996, revised 2005) 2, 207 results in delayed introduction 11 review of implementation and use 39–40 ISSR PCR 190 ITS1 and ITS2 (internal transcribed spacers) 190–191, 194–196, 198

Japan 172

keys, molecular 193–194, 195–196 killing see under competition and other interactions koa bugs 169 koinobionts 19

larval vs non-larval parasitoids 19 learning: role in host-finding 44 Lecanicillium lecanii 171 Leptopilina heterotoma: effect of learning 44 link functions 232–233 lipids, cuticular 105–106 Listronotus bonariensis 22–23, 171 Lysiphlebus testaceipes 72

Macrolophus caliginosus 137, 157 cold tolerance 108–109, 110(fig) development threshold temperature 108 introduction into UK 107–108 Macrosiphum rosae 23 mark–release–recapture (MRR) experiments data analysis dispersal distance and disperser density 123–124 models 121–122 problem of directionality 122–123 key issues to consider 116–118 markers used 118–119 pattern of recaptures 117(fig) recapture grids 119–120 recommendations 127–128 sampling strategies and traps 120–121 Trichogramma case study 124–127 markers cost of molecular identification 198 DNA markers 189–192 molecular recognition of taxa 192–194 Trichogramma case study 194–198 in recapture experiments 118–119 unambiguity of molecular methods 188 mating definition 78–79 mate recognition 83 physical incompatibilites 83–85

296

Index

mating continued spatial and temporal barriers 82–83 Mediterranean region: species spread 210 Megastigmus nipponicus 172 Melitaea cinxia 73 Mexico 2–3 Microctonus aethiopoides 22–23, 170, 171, 172 post-release impact 173–175 Microctonus hyperodae 22–23 microorganisms see under contamination microsatellites 189, 194–195, 198 microscopy 152, 153, 155 microsporidians 149–150 mitochondria: DNA sequences 191 modelling 8 in MRR experiments 121–124 non-target impacts of M. aethiopoides 174 monitoring, post-release 7–8 case studies inundative release of nematodes 175–176 knapweed biological control and human health 177–178 Microctonus aethiopoides 173–175 direct effects on beneficial or valued exotic species 170–171 direct effects on non-target native species 168–170 fortuitous biological control 171 to identify competition or displacement 171–172 indirect effects 172–173 recommendations 178–180 regulatory situation 166–167 mountains: ecoregions and insect movement 209–210 MRR experiments see mark–release– recapture (MRR) experiments multiplex PCR 196 Myriophyllum spicatum 171

NAPPO (North American Plant Protection Organization) guidelines for release of entomophagous agents for biological control 2–3, 207

National Resource Inventory (USA) 173 nematodes 150, 155, 171 case study of inundative release 175–176 neogregarines 149 Neoseiulus californicus 110(fig), 111 Neoseiulus cucumeris 147 Nesidiocoris tenuis 137 New Zealand adverse impacts on valued exotic species 170 case study of process of new organism introduction 250–253 competitive displacement 172 Hazardous Substances and New Organisms (HSNO) Act (1996) 3 post-release impact of Microctonus aethiopoides 173–175 post-release monitoring 166–167 risk assessment Hazardous Substances and New Organisms (HSNO) Act 245–250 scales for estimating adverse environmental effects 248(fig) survey of non-target impacts 168–169 Nezara viridula 169 Nipaecoccus viridis 171 Nosema spp. 150

oceans: main barriers to movement 207, 209 OECD guidance document 3, 4, 138, 255–260 information requirements 5(tab) omnivory, true 133, 134–135 Ooencyrtus spp. 54–55 Opuntia spp. 170 Orius insidiosus 265 Ostrinia nubilalis 22, 24, 279(tab) overwintering 101–102, 107

Pachycrepoideus vindemmiae 70 pathogens: risk assessment 156–158 PCR see polymerase chain reaction (PCR) Peristenus digoneutis 6 persistence, seasonal 99 Phyllocnistris citrella 22

Index

297

Phyllonoryctor leucographella 210 Phytoseiulus persimilis 147, 258 Pieris rapae 22, 169 Pieris spp. 24 plants effect of characteristics on natural enemy foraging 42 plant-feeding predators ecological role and use in biological control 134–135 injury caused to plants 137 nutrients obtained from plants 135–136 risk assessment criteria 137–138 risk assessment testing procedures 140–141 risk assessment variables 138–141 risk assessment of pathogens 157 simulation of treatment on nontarget insects 234(fig) and species distribution 217 Plutella xylostella 22 pollution, biological 65 polymerase chain reaction (PCR) 153, 154, 155 ISSR PCR 190 of ITS sequences 190–191, 194–196, 198 of microsatellite DNA 189 of mitochondrial DNA 191 multiplex PCR 196 overview of methods 192 RAPD PCR 155, 189 in the recognition of taxa 192–194 of ribosomal spacers 191 use of restriction enzymes 193–194 populations non-target species 9, 167 not affected by harm to individuals 68–69 types of interactions 66–69 post-release monitoring see monitoring, post-release power, statistical see statistics: methods precautionary principle 285 predation see under competition and other interactions primers: design 196 priorities, national 39 protozoa 149–150, 155 Pseudacteon curvatus 24

Pseudococcus viburni 250–253 Pseudococcus zelandicus 251–252 Pseudophycus maculipennis 250–253 pseudoreplication 229–230 Psytallia fletcheri 22 Pyracantha spp. 210

quality control 160–161

randomization: feasibility 230–231 range: of hosts see host range and specificity RAPD (randomly amplified polymorphic DNA) PCR 189 rearing: of test species effects of conditions on behaviour 45–46 laboratory conditions 44–45 problems due to infection 45 problems of inbreeding 46–47 recapture experiments see mark–release– recapture (MRR) experiments recognition: of mates 83 reptiles: import regulations 159 restriction enzymes 193–194 Rhinocyllus conicus 170 Rhizopoda 149 ribosomes: 28S RNA 191 risk assessment and management concept 242 ERBIC/OECD procedure 255–260 harmonization 255 Hazardous Substances and New Organisms (HSNO) Act (New Zealand, 1996) 245–250 scales for estimating adverse environmental effects 248(fig), 249(fig) identification of risks 242–243, 256–257 process in New Zealand 250–253 proposed stepwise procedure 260–263, 268–270 applied to commercial natural enemies 263–265 quick scan method for agents already in use 265–268 risk analysis 243–244 subject of increasing attention 64–65

298

Index

risk assessment and management continued risk evaluation 244 risk index calculation 257–259, 260(fig), 262, 264(fig) risk management and communication 244–245, 259 risk/benefit analysis 10, 260 analysis and evaluation 279–281, 283–284 baseline scenario 278, 282 categories of costs and benefits 275(tab) economic costs and benefits 274–275 environmental costs and benefits 276 health costs and benefits 276 identification of potential risks and benefits 278–279, 282 regulatory decisions 281 Trichogramma brassicae example 282–284 uncertainties 276–278, 280

safeguard species 26–27, 30–31 seasonal persistence 99 Serratia marcescens 148 Seychelles: fortuitous biological control 171 Sitona discoideus 22–23, 170, 171, 172, 173 Sitona lepidus 172 soil: moisture 106 Solenopsis spp. 24, 72 speciation 79 in hybrids 86–87 specificity, host see host range and specificity Spodoptera spp. 147 statistics: methods ␣- and ß-errors in testing 224–226 Generalized Linear Models 233–234 repeated measurements 235–236 power analysis 226–228 examples 229 software 228–229 power vs replicates required 223–224

pseudoreplication 229–230 randomization in experimental design 230–231 shortcomings of non-parametric tests 223 time as a measurement variable 236–237 unified approach to testing 231–233 variables and their distribution 232(box) Steinernema spp. 175–176 sterility: from interbreeding 85–86 stochasticity 277 supercooling point 101–102, 103–104 surveys, field 4–6 faunal 168 problems of time limits 9 susceptibility, crop 139–140 Switzerland movement of arthropods for scientific study 213–217 sources of alien insects 210 Trichogramma brassicae mark– release–recapture study 124–127 synomones, herbivore-induced 24

taxonomy and crop damage potential 138 difficulties of parasitoid taxonomy 5, 18, 79 extrapolation from parasitoid taxonomy 41 host taxonomy as host range determinant 19 Technomyrmex albipes 171 Telenomus lucullus 55 temperature adaptation of Trichogramma spp. 82 factor determining establishment potential 100–102 developmental thresholds 102–103 lethal temperature 104, 110–111 outdoor cage tests 104 supercooling point 101–102, 103–104 thermal budget 103 UK case studies 107–111 and susceptibility to bacteria 148

Index

299

tests: host range and specificity see under host range and specificity Tetranychus urticae 72 thermal budget 103 thresholds, developmental 102–103 Thripobius semiluteus 258 ticks 177 Torymus benficus 172 Torymus sinensis 172 traps: in recapture experiments 120–121 Trichogramma brassicae 7, 265 environmental risk–benefit assessment 282–284 comparison with deltamethrin 279(tab), 280(tab) host specificity 23 host specificity testing 25 mark–release–recapture study 124–127 Trichogramma minutum: host range testing 24–25 Trichogramma nubilale: risks to nontarget Lepidoptera 22 Trichogramma platneri: host range 24 Trichogramma spp. adaptation to temperature 82 development of molecular recognition system 194–198 female mating preferences 83 genital incompatibility between species 84–85 introgression 89 problems of identification 188 regionally restricted releases 170 reproductive compatibility 80 seasonal activity 82–83 Trichopoda giacomelli 6 Trignospila brevifacies 24 Typhlodromips montdorensis 110(fig), 111

United Kingdom: case studies on establishment 107–111 United States adverse impacts on non-target plants 170 agreement to NAPPO guidelines 2–3 ecoregions 206(fig) insect distribution and spread 209 fortuitous biological control 171 gypsy moth control 169 inconsistent Federal and State jurisdiction 3 National Resource Inventory 173 post-release monitoring 167 rejected introductions 54 Urophora spp. 177

variables, measurement 232(box) time 236–237 variation, intraspecific 46 vectors: of plant diseases 139 vertebrates: risk assessment of pathogens 157–158 viability: effect of interbreeding 85–86 viruses 146–147, 153 and human disease 177

water: requirement for feeding 135–136 WebCutter 193 webs, food 173 weeds: species for host specificity testing 16–18 Wolbachia spp. 148

Zelus renardii 70 zoophytophagy see omnivory, true