Decision Making in Natural Resource Management: A Structured, Adaptive Approach

This book is intended for use by natural resource managers and scientists, and students in the fields of natural resource management, ecology, and conservation biology, who are confronted with complex and difficult decision making problems. The book takes readers through the process of developing a structured approach to decision making, by firstly deconstructing decisions into component parts, which are each fully analyzed and then reassembled to form a working decision model.  The book integrates common–sense ideas about problem definitions, such as the need for decisions to be driven by explicit objectives, with sophisticated approaches for modeling decision influence and incorporating feedback from monitoring programs into decision making via adaptive management. Numerous worked examples are provided for illustration, along with detailed case studies illustrating the authors’ experience in applying structured approaches. There is also a series of detailed technical appendices.  An accompanying website provides computer code and data used in the worked examples.

List of boxes xi Preface xiii Acknowledgements xiv Guide to using this book xv Companion website xvii PART I. INTRODUCTION TO DECISION MAKING 1 1 Introduction: Why a Structured Approach in Natural Resources? 3 2 Elements of Structured Decision Making 13 3 Identifying and Quantifying Objectives in Natural Resource Management 24 4 Working with Stakeholders in Natural Resource Management 57 PART II. TOOLS FOR DECISION MAKING AND ANALYSIS 77 5 Statistics and Decision Making 79 6 Modeling the Infl uence of Decisions 147 7 Identifying and Reducing Uncertainty in Decision Making 192 8 Methods for Obtaining Optimal Decisions 232 PART III. APPLICATIONS 261 9 Case Studies 263 10 Summary, Lessons Learned, and Recommendations 294 PART IV. APPENDICES 307 Appendix A Probability and Distributional Relationships 309 Appendix B Common Statistical Distributions 317 Appendix C Methods for Statistical Estimation 339 Appendix D Parsimony, Prediction, and Multi–Model Inference 373 Appendix E Mathematical Approaches to Optimization 384 Appendix F Guide to Software 430 Appendix G Electronic Companion to Book 432 Glossary 433 Index 449

Michael J. Conroy is a Senior Research Scientist in the Warnell School of Forestry and Natural Resources at the University of Georgia. He has over thirty years experience in applications of quantitative approaches to solving problems in natural resource management and is the author of three previous books.  He teaches and runs workshops in modeling, statistical estimation, and structured decision making for undergraduate and graduate students and professionals both in the US and internationally. James T. Peterson is the Assistant Unit Leader and Associate Professor for the USGS Oregon Cooperative Fish and Wildlife Research Unit at Oregon State University. He has been developing and teaching courses in applied quantitative decision making to undergraduate and graduate students and professionals in natural resource and related disciplines for more than a decade.

“I highly recommend this book to resource managers, scientists, students, and anyone who faces difficult, complex, or uncertain decisions that would benefit from adopting a structured approach to decision making.”  ( The Journal of Wildlife Management , 8 November 2013) “I highly recommend the very results oriented and working model based book Decision Making in Natural Resource Management: A Structured, Adaptive Approach by Michael J. Conroy and James T. Peterson, to any natural resource managers, scientists, government policy makers, business leaders, conservation groups, and students of natural resource management, ecology, and conservation biology who are seeking a complete guide to structured and effective decision making in the area of natural resource management. This book will guide leaders toward better decisions, through a more integrated examination of the real problems to find viable and effective solutions.”  ( Blog Business World , 5 April 2013)