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

465 Pages·2013·8.23 MB·English

by  Michael J. Conroy

Decision making in natural
resource management
Decision making in natural resource
management: a structured,
adaptive approach
Michael J. Conroy
Warnell School of Forestry and Natural Resources, University of Georgia,
Georgia
James T. Peterson
US Geological Survey, Oregon Cooperative Fish and Wildlife Research Unit,
Oregon State University, Oregon
A John Wiley & Sons, Ltd., Publication
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Library of Congress Cataloging-in-Publication Data
Conroy, Michael J. (Michael James), 1952-
Decision making in natural resource management: a structured, adaptive approach / Michael J.
Conroy, James T. Peterson.
p. cm.
Includes bibliographical references and index.
ISBN 978-0-470-67175-7 (cloth : alk. paper) – ISBN 978-0-470-67174-0 (pbk. : alk. paper)
1. Natural resources–Decision making. 2. Natural resources–Management. I. Peterson,
James T. II. Title.
HC85.C675 2013
333.7–dc23
2012035084
A catalogue record for this book is available from the British Library.
Wiley also publishes its books in a variety of electronic formats. Some content that appears in
print may not be available in electronic books.
Cover image: Beartooth Mountains in Montana. © Colton Stiffler/Shutterstock.com
Cover design by Design Deluxe
Set in 10.5/12 pt ClassicalGaramondBT by Toppan Best-set Premedia Limited
1 2013
Contents
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
The role of decision making in natural resource management 4
Common mistakes in framing decisions 5
What is structured decision making (SDM)? 6
Why should we use a structured approach to decision making? 7
Limitations of the structured approach to decision making 8
Adaptive resource management 9
Summary 10
References 10
2 Elements of Structured Decision Making 13
First steps: defining the decision problem 13
General procedures for structured decision making 15
Predictive modeling: linking decisions to objectives prospectively 17
Uncertainty and how it affects decision making 18
Dealing with uncertainty in decision making 21
Summary 23
References 23
vi Contents
3 Identifying and Quantifying Objectives in Natural
Resource Management 24
Identifying objectives 24
Identifying fundamental and means objectives 25
Clarifying objectives 28
Separating objectives from science 29
Barriers to creative decision making 30
Types of fundamental objectives 32
Identifying decision alternatives 34
Quantifying objectives 38
Dealing with multiple objectives 38
Multi-attribute valuation 41
Utility functions 43
Other approaches 50
Additional considerations 52
Decision, objectives, and predictive modeling 55
References 55
4 Working with Stakeholders in Natural Resource
Management 57
Stakeholders and natural resource decision making 57
Stakeholder analysis 59
Stakeholder governance 62
Working with stakeholders 68
Characteristics of good facilitators 68
Getting at stakeholder values 71
Stakeholder meetings 72
The first workshop 74
References 76
Additional reading 76
PART II. TOOLS FOR DECISION MAKING AND ANALYSIS 77
5 Statistics and Decision Making 79
Basic statistical ideas and terminology 80
Using data in statistical models for description and prediction 100
Linear models 104
Hierarchical models 116
Bayesian inference 129
Resampling and simulation methods 140
Statistical significance 145
References 146
Additional reading 146
Contents vii
6 Modeling the Influence of Decisions 147
Structuring decisions 147
Influence diagrams 148
Frequent mistakes when structuring decisions 153
Defining node states 157
Decision trees 159
Solving a decision model 160
Conditional independence and modularity 164
Parameterizing decision models 165
Elicitation of expert judgment 179
Quantifying uncertainty in expert judgment 188
Group elicitation 189
The care and handling of experts 190
References 191
Additional reading 191
7 Identifying and Reducing Uncertainty in
Decision Making 192
Types of uncertainty 192
Irreducible uncertainty 193
Reducible uncertainty 194
Effects of uncertainty on decision making 197
Sensitivity analysis 203
Value of information 217
Reducing uncertainty 220
References 230
Additional reading 231
8 Methods for Obtaining Optimal Decisions 232
Overview of optimization 233
Factors affecting optimization 234
Multiple attribute objectives and constrained optimization 239
Dynamic decisions 246
Optimization under uncertainty 249
Analysis of the decision problem 253
Suboptimal decisions and “satisficing” 256
Other problems 257
Summary 258
References 258
PART III. APPLICATIONS 261
9 Case Studies 263
Case study 1 Adaptive Harvest Management of American
Black Ducks 263
viii Contents
Case study 2 Management of Water Resources in the
Southeastern US 276
Case study 3 Regulation of Largemouth Bass Sport Fishery
in Georgia 284
Summary 291
References 291
10 Summary, Lessons Learned, and
Recommendations 294
Summary 294
Lessons learned 294
Structured decision making for Hector’s Dolphin conservation 295
Landowner incentives for conservation of early successional
habitats in Georgia 298
Cahaba shiner 299
Other lessons 303
References 304
PART IV. APPENDICES 307
Appendix A Probability and Distributional
Relationships 309
Probability axioms 309
Conditional probability 309
Conditional independence 310
Expected value of random variables 311
Law of total probability 311
Bayes’ theorem 312
Distribution moments 313
Sample moments 316
Additional reading 316
Appendix B Common Statistical Distributions 317
General distribution characteristics 317
Continuous distributions 320
Discrete distributions 329
Reference 338
Additional Reading 338
Appendix C Methods for Statistical Estimation 339
General principles of estimation 339
Method of moments 342
Least squares 343
Contents ix
Maximum likelihood 346
Bayesian approaches 353
References 372
Appendix D Parsimony, Prediction, and
Multi-Model Inference 373
General approaches to multi-model inference 373
Multi-model inference and model averaging 376
Multi-model Bayesian inference 380
References 383
Appendix E Mathematical Approaches to Optimization 384
Review of general optimization principles 385
Classical programming 392
Nonlinear programming 397
Linear programming 399
Dynamic decision problems 402
Decision making under structural uncertainty 419
Generalizations of Markov decision processes 427
Heuristic methods 427
References 429
Appendix F Guide to Software 430
Appendix G Electronic Companion to Book 432
Glossary 433
Index 449
List of boxes
Box 3.1 Utility function with proportional scoring: NZ conservation
example 45
Box 3.2 Utility function with weights based on pricing out: NZ
conservation example 47
Box 3.3 Utility function with weights based on swing weights: NZ
conservation example 48
Box 3.4 Non-additive utility function: harvest of American black
ducks incorporating a population value 50
Box 3.5 Cost ratio utility function with constraints: NZ conservation
example 52
Box 3.6 Multi-attribute valuation with ranked outcomes 53
Box 4.1 Stakeholder analysis example: water management and mussel
conservation in the southeastern US 61
Box 4.2 R.L. Harris Dam stakeholder governance 65
Box 4.3 Example agenda for first stakeholder meeting 75
Box 5.1 Modeling continuous outcomes: the normal distribution 88
Box 5.2 Modeling integer (count) outcomes: the Poisson distribution 91
Box 5.3 Modeling “success/failure” outcomes: the binomial
distribution 93
Box 5.4 Summarizing and manipulating data using R 101
Box 5.5 Building a linear model in R 106
Box 5.6 Generalized linear models and multiple predictor models
in R 109
Box 5.7 Evaluating and comparing multiple models 114
Box 5.8 Linear hierarchical modeling and partitioning of variance
components 122
Box 5.9 Bayesian example: binomial likelihood with a beta prior 132
Box 5.10 Modeling a random effect using WinBUGS: Binomial
success with random variation 137
Box 5.11 Hierarchical model with random effects in WinBUGS 138
xii List of Boxes
Box 5.12 Jackknife and bootstrap estimation of variance and bias 141
Box 5.13 Parametric bootstrap estimation of confidence intervals 143
Box 6.1 Creating an influence diagram with Netica ® software 166
Box 6.2 Parameterizing an influence diagram with empirical
data and regression models 172
Box 6.3 Parameterizing an influence diagram with output from
a simulation model 177
Box 6.4 Elicitation of expert information and the construction
of probability distributions 181
Box 7.1 Creating a risk profile using a utility curve 201
Box 7.2 An illustration of one-way sensitivity analysis 205
Box 7.3 An illustration of two-way sensitivity analysis 210
Box 7.4 An illustration of response profile sensitivity analysis 213
Box 7.5 An illustration of indifference curves 215
Box 7.6 An illustration of adaptive resource management process 223
Box 8.1 Unconstrained single-control optimization: MSY 238
Box 8.2 Unconstrained optimization with 2 controls: conservation
of 2 species 240
Box 8.3 Constrained optimization with 2 controls – conservation
of 2 species 242
Box 8.4 Constrained optimization with 2 controls – conservation
of 2 species: inequality constraints 243
Box 8.5 Linear objective and constraints: optimal reserve design 244
Box 8.6 Dynamic optimization: Harvest under the discrete
logistic growth model 247
Box 8.7 Simulation-optimization: Conservation of 2 species 251
Box 8.8 SDP: Partial controllability of harvest 252
Box 8.9 Sensitivity analysis: conservation of 2 species 254
Box 8.10 Simulating the optimal decision: Partial controllability
of harvest 256
Box 8.11 Satisficing: sub-MSY management 257