Cover -- Title page -- Copyright page -- Contents -- List of boxes -- Preface -- Acknowledgements -- Guide to using this book -- Companion website -- PART I.: Introduction To Decision Making -- 1: Introduction: Why a Structured Approach in Natural Resources? -- The role of decision making in natural resource management -- Common mistakes in framing decisions -- Poorly stated objectives -- Prescriptive decisions -- Confusion of values and science -- Poor use of information -- What is structured decision making (SDM)? -- Why should we use a structured approach to decision making? -- Limitations of the structured approach to decision making -- Adaptive resource management -- Summary -- References -- 2: Elements of Structured Decision Making -- First steps: defining the decision problem -- General procedures for structured decision making -- Predictive modeling: linking decisions to objectives prospectively -- Uncertainty and how it affects decision making -- Type of uncertainty -- Dealing with uncertainty in decision making -- Summary -- References -- 3: Identifying and Quantifying Objectives in Natural Resource Management -- Identifying objectives -- Identifying fundamental and means objectives -- Clarifying objectives -- Separating objectives from science -- Barriers to creative decision making -- Types of fundamental objectives -- Identifying decision alternatives -- Quantifying objectives -- Dealing with multiple objectives -- Multi-attribute valuation -- Utility functions -- Determining weights by pricing out or indifference scores -- Marginal gain -- Swing weighting -- Other approaches -- Additional considerations -- Decision, objectives, and predictive modeling -- References -- 4: Working with Stakeholders in Natural Resource Management -- Stakeholders and natural resource decision making -- Why involve stakeholders? -- Stakeholder analysis.

Stakeholder governance -- Working with stakeholders -- Characteristics of good facilitators -- Getting at stakeholder values -- Stakeholder meetings -- The first workshop -- References -- Additional reading -- PART II.: Tools for Decision Making and Analysis -- 5: Statistics and Decision Making -- Basic statistical ideas and terminology -- Probability -- Probability distributions -- Using data in statistical models for description and prediction -- Why you should look at your data? -- Linear models -- Hierarchical models -- Shrinkage estimators, sharing information -- Bayesian inference -- Example - Binomial likelihood with a beta prior on p -- Hierarchical and random effects models -- Resampling and simulation methods -- Statistical significance -- References -- Additional reading -- 6: Modeling the Influence of Decisions -- Structuring decisions -- Influence diagrams -- Frequent mistakes when structuring decisions -- Defining node states -- Decision trees -- Solving a decision model -- Conditional independence and modularity -- Parameterizing decision models -- Elicitation of expert judgment -- Quantifying uncertainty in expert judgment -- Group elicitation -- The care and handling of experts -- References -- Additional reading -- 7: Identifying and Reducing Uncertainty in Decision Making -- Types of uncertainty -- Irreducible uncertainty -- Reducible uncertainty -- Effects of uncertainty on decision making -- Approaches for incorporating uncertainty in decision making -- Sensitivity analysis -- Value of information -- Imperfect information -- Reducing uncertainty -- Traditional approaches to reducing epistemic uncertainty -- Reducing uncertainty through ARM -- References -- Additional reading -- 8: Methods for Obtaining Optimal Decisions -- Overview of optimization -- Factors affecting optimization -- Single vs. multiple decision controls.

Unconstrained vs. constrained optimization -- Static or equilibrium optimization vs. dynamic optimization -- Deterministic vs. stochastic system response -- Multiple attribute objectives and constrained optimization -- Dynamic decisions -- Optimization under uncertainty -- Maximum expected values approach -- Simulation-optimization -- Stochastic dynamic programming -- Analysis of the decision problem -- Sensitivity analysis -- Simulation of optimal decision impacts -- Suboptimal decisions and "satisficing" -- Other problems -- Summary -- References -- PART III.: Applications -- 9: Case Studies -- Case study 1 Adaptive Harvest Management of American Black Ducks -- Background -- Decision problem -- Stakeholders, decision makers, and development of an SDM/ARM approach to black duck harvest management -- Management objectives and decision alternatives -- Model development -- Incorporation of uncertainty -- Evaluation of decision alternatives -- Sensitivity analysis -- Current status of black duck AHM -- Case study 2 Management of Water Resources in the Southeastern US -- Background -- Decision problem -- Stakeholders and decision makers -- Management objectives and decision alternatives -- Model development -- Incorporation of uncertainty -- Evaluation of decision alternatives -- Sensitivity analysis -- Current status of water resource management -- Case study 3 Regulation of Largemouth Bass Sport Fishery in Georgia -- Background and decision problem -- Stakeholders and decision makers -- Management objectives and decision alternatives -- Model development -- Incorporation of uncertainty -- Evaluation of decision alternatives and sensitivity analysis -- Value of information -- Current status of largemouth bass management -- Summary -- References -- 10: Summary, Lessons Learned, and Recommendations -- Summary -- Lessons learned.

Structured decision making for Hector's Dolphin conservation -- Landowner incentives for conservation of early successional habitats in Georgia -- Cahaba shiner -- Other lessons -- References -- PART IV.: Appendices -- Appendix A: Probability and Distributional Relationships -- Probability axioms -- Conditional probability -- Conditional independence -- Expected value of random variables -- Law of total probability -- Bayes' theorem -- Distribution moments -- Sample moments -- Additional reading -- Appendix B: Common Statistical Distributions -- General distribution characteristics -- Distribution parameters -- Distribution quantiles -- Generation of random numbers -- Continuous distributions -- Uniform distribution -- Normal distribution -- Exponential distribution -- Gamma distribution -- Beta distribution -- Distributions of quadratic forms -- Discrete distributions -- Discrete uniform -- Bernoulli and binomial distributions -- Multinomial distribution -- Poisson distribution -- Geometric distribution -- Negative binomial distribution -- Hypergeometric distribution -- Reference -- Additional reading -- Appendix C: Methods for Statistical Estimation -- General principles of estimation -- Properties of estimators -- Method of moments -- Least squares -- Maximum likelihood -- Example: binomial likelihood -- Bayesian approaches -- Binomial likelihood with a beta prior on p -- Example - posterior inference with a vague (non-informative) prior -- Informative prior (I) - prior based on x successes in n initial trials -- Example - posterior inference with prior from a previous study -- Informative prior (II) - prior estimate of mean and variance of p -- Conjugate distributions -- Poisson likelihood-gamma conjugate prior -- Posterior inference with a vague (non-informative) prior -- Normal likelihood - normal conjugate prior.

Example - posterior inference with a vague (non-informative) prior -- Monte Carlo methods for Bayesian inference -- Example -- Example - closed population capture-recapture -- Empirical Bayes estimation -- References -- Appendix D: Parsimony, Prediction, and Multi-Model Inference -- General approaches to multi-model inference -- Model plausibility -- Example -- Multi-model inference and model averaging -- Example -- AIC for least squares -- Adjustment for overdispersion (quasi-likelihood) for count data -- Multi-model Bayesian inference -- Computational approaches for Bayesian MMI -- Reversible jump MCMC -- Bayesian information criteria -- References -- Appendix E: Mathematical Approaches to Optimization -- Review of general optimization principles -- General statement of the optimization problem -- Necessity and sufficiency conditions -- Unconstrained Optimization -- Classical programming -- Bivariate classical programming -- Example - conservation of 2 species -- Multivariate classical programming -- Sensitivity analysis -- Nonlinear programming -- Example: conservation of 2 species with inequality constraints -- Linear programming -- Example - Reserve design -- Dynamic decision problems -- Nonlinear programming -- Example -- The calculus of variations -- Example - Pest control -- Pontryagin's maximum principle -- Dynamic programming -- Discrete time dynamic programming -- Example -- Stochastic dynamic programming -- Example - Harvest of a stochastically growing population -- Transition matrix formulation of SDP -- Example -- Decision making under structural uncertainty -- Reduction of structural uncertainty -- Passive adaptation -- Example - Passive adaptation -- Active adaptation -- Example - Active adaptation -- Generalizations of Markov decision processes -- Heuristic methods -- Simulation-gaming/simulation-optimization.

Genetic algorithms and machine learning.

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. Additional resources for this book can be found at: www.wiley.com/go/conroy/naturalresourcemanagement.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.