MICHAEL J. CONROY is a Senior Research Scientist for the Warnell School of Forestry and Natural Resources at the University of Georgia. He is the author of three previous books and has over thirty years' experience in the application of quantitative solutions to problems in natural resource management. He teaches workshops in modeling, statistical estimation, and structured decision making for 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 students and professionals working in natural resources and related disciplines for more than a decade.

“An easily readable and coherent account, this book has a definite role on the shelf (and its outline content in the minds) of conservation decision-makers and advisors.”  (African Journal of Range & Forage Science, 1 October 2015)“This is one of the best resources on structured decision-making I have found – specifically tailored for those working in or studying in the fields of ecology, NRM, land management and conservation biology.”  (Ecological Management & Restoration, 20 January 2015)“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)

• List of boxes xiPreface xiiiAcknowledgements xivGuide to using this book xvCompanion website xviiPART I. INTRODUCTION TO DECISION MAKING 11 Introduction: Why a Structured Approach in Natural Resources? 3The role of decision making in natural resource management 4Common mistakes in framing decisions 5What is structured decision making (SDM)? 6Why should we use a structured approach to decision making? 7Limitations of the structured approach to decision making 8Adaptive resource management 9Summary 10References 102 Elements of Structured Decision Making 13First steps: defining the decision problem 13General procedures for structured decision making 15Predictive modeling: linking decisions to objectives prospectively 17Uncertainty and how it affects decision making 18Dealing with uncertainty in decision making 21Summary 23References 233 Identifying and Quantifying Objectives in Natural Resource Management 24Identifying objectives 24Identifying fundamental and means objectives 25Clarifying objectives 28Separating objectives from science 29Barriers to creative decision making 30Types of fundamental objectives 32Identifying decision alternatives 34Quantifying objectives 38Dealing with multiple objectives 38Multi-attribute valuation 41Utility functions 43Other approaches 50Additional considerations 52Decision, objectives, and predictive modeling 55References 554 Working with Stakeholders in Natural Resource Management 57Stakeholders and natural resource decision making 57Stakeholder analysis 59Stakeholder governance 62Working with stakeholders 68Characteristics of good facilitators 68Getting at stakeholder values 71Stakeholder meetings 72The first workshop 74References 76Additional reading 76PART II. TOOLS FOR DECISION MAKING AND ANALYSIS 775 Statistics and Decision Making 79Basic statistical ideas and terminology 80Using data in statistical models for description and prediction 100Linear models 104Hierarchical models 116Bayesian inference 129Resampling and simulation methods 140Statistical significance 145References 146Additional reading 1466 Modeling the Influence of Decisions 147Structuring decisions 147Influence diagrams 148Frequent mistakes when structuring decisions 153Defining node states 157Decision trees 159Solving a decision model 160Conditional independence and modularity 164Parameterizing decision models 165Elicitation of expert judgment 179Quantifying uncertainty in expert judgment 188Group elicitation 189The care and handling of experts 190References 191Additional reading 1917 Identifying and Reducing Uncertainty inDecision Making 192Types of uncertainty 192Irreducible uncertainty 193Reducible uncertainty 194Effects of uncertainty on decision making 197Sensitivity analysis 203Value of information 217Reducing uncertainty 220References 230Additional reading 2318 Methods for Obtaining Optimal Decisions 232Overview of optimization 233Factors affecting optimization 234Multiple attribute objectives and constrained optimization 239Dynamic decisions 246Optimization under uncertainty 249Analysis of the decision problem 253Suboptimal decisions and “satisficing” 256Other problems 257Summary 258References 258PART III. APPLICATIONS 2619 Case Studies 263Case study 1 Adaptive Harvest Management of American Black Ducks 263Case study 2 Management of Water Resources in the Southeastern US 276Case study 3 Regulation of Largemouth Bass Sport Fishery in Georgia 284Summary 291References 29110 Summary, Lessons Learned, and Recommendations 294Summary 294Lessons learned 294Structured decision making for Hector’s Dolphin conservation 295Landowner incentives for conservation of early successional habitats in Georgia 298Cahaba shiner 299Other lessons 303References 304PART IV. APPENDICES 307Appendix A Probability and Distributional Relationships 309Probability axioms 309Conditional probability 309Conditional independence 310Expected value of random variables 311Law of total probability 311Bayes’ theorem 312Distribution moments 313Sample moments 316Additional reading 316Appendix B Common Statistical Distributions 317General distribution characteristics 317Continuous distributions 320Discrete distributions 329Reference 338Additional Reading 338Appendix C Methods for Statistical Estimation 339General principles of estimation 339Method of moments 342Least squares 343Maximum likelihood 346Bayesian approaches 353References 372Appendix D Parsimony, Prediction, and Multi-Model Inference 373General approaches to multi-model inference 373Multi-model inference and model averaging 376Multi-model Bayesian inference 380References 383Appendix E Mathematical Approaches to Optimization 384Review of general optimization principles 385Classical programming 392Nonlinear programming 397Linear programming 399Dynamic decision problems 402Decision making under structural uncertainty 419Generalizations of Markov decision processes 427Heuristic methods 427References 429Appendix F Guide to Software 430Appendix G Electronic Companion to Book 432Glossary 433Index 449