{"product_id":"decision-making-in-natural-resource-management-isbn-9780470671757","title":"Decision Making in Natural Resource Management","description":"\u003cp\u003eThis 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.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAdditional resources for this book can be found at: \u003c\/b\u003e\u003ca href=\"http:\/\/www.wiley.com\/go\/conroy\/naturalresourcemanagement\"\u003e\u003cb\u003ewww.wiley.com\/go\/conroy\/naturalresourcemanagement\u003c\/b\u003e\u003c\/a\u003e.\u003c\/p\u003e \u003cp\u003eList of boxes xi\u003c\/p\u003e \u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003eAcknowledgements xiv\u003c\/p\u003e \u003cp\u003eGuide to using this book xv\u003c\/p\u003e \u003cp\u003eCompanion website xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I. INTRODUCTION TO DECISION MAKING 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction: Why a Structured Approach in Natural Resources? 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe role of decision making in natural resource management 4\u003c\/p\u003e \u003cp\u003eCommon mistakes in framing decisions 5\u003c\/p\u003e \u003cp\u003eWhat is structured decision making (SDM)? 6\u003c\/p\u003e \u003cp\u003eWhy should we use a structured approach to decision making? 7\u003c\/p\u003e \u003cp\u003eLimitations of the structured approach to decision making 8\u003c\/p\u003e \u003cp\u003eAdaptive resource management 9\u003c\/p\u003e \u003cp\u003eSummary 10\u003c\/p\u003e \u003cp\u003eReferences 10\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Elements of Structured Decision Making 13\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFirst steps: defining the decision problem 13\u003c\/p\u003e \u003cp\u003eGeneral procedures for structured decision making 15\u003c\/p\u003e \u003cp\u003ePredictive modeling: linking decisions to objectives prospectively 17\u003c\/p\u003e \u003cp\u003eUncertainty and how it affects decision making 18\u003c\/p\u003e \u003cp\u003eDealing with uncertainty in decision making 21\u003c\/p\u003e \u003cp\u003eSummary 23\u003c\/p\u003e \u003cp\u003eReferences 23\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Identifying and Quantifying Objectives in Natural Resource Management 24\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIdentifying objectives 24\u003c\/p\u003e \u003cp\u003eIdentifying fundamental and means objectives 25\u003c\/p\u003e \u003cp\u003eClarifying objectives 28\u003c\/p\u003e \u003cp\u003eSeparating objectives from science 29\u003c\/p\u003e \u003cp\u003eBarriers to creative decision making 30\u003c\/p\u003e \u003cp\u003eTypes of fundamental objectives 32\u003c\/p\u003e \u003cp\u003eIdentifying decision alternatives 34\u003c\/p\u003e \u003cp\u003eQuantifying objectives 38\u003c\/p\u003e \u003cp\u003eDealing with multiple objectives 38\u003c\/p\u003e \u003cp\u003eMulti-attribute valuation 41\u003c\/p\u003e \u003cp\u003eUtility functions 43\u003c\/p\u003e \u003cp\u003eOther approaches 50\u003c\/p\u003e \u003cp\u003eAdditional considerations 52\u003c\/p\u003e \u003cp\u003eDecision, objectives, and predictive modeling 55\u003c\/p\u003e \u003cp\u003eReferences 55\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Working with Stakeholders in Natural Resource Management 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStakeholders and natural resource decision making 57\u003c\/p\u003e \u003cp\u003eStakeholder analysis 59\u003c\/p\u003e \u003cp\u003eStakeholder governance 62\u003c\/p\u003e \u003cp\u003eWorking with stakeholders 68\u003c\/p\u003e \u003cp\u003eCharacteristics of good facilitators 68\u003c\/p\u003e \u003cp\u003eGetting at stakeholder values 71\u003c\/p\u003e \u003cp\u003eStakeholder meetings 72\u003c\/p\u003e \u003cp\u003eThe first workshop 74\u003c\/p\u003e \u003cp\u003eReferences 76\u003c\/p\u003e \u003cp\u003eAdditional reading 76\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II. TOOLS FOR DECISION MAKING AND ANALYSIS 77\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Statistics and Decision Making 79\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBasic statistical ideas and terminology 80\u003c\/p\u003e \u003cp\u003eUsing data in statistical models for description and prediction 100\u003c\/p\u003e \u003cp\u003eLinear models 104\u003c\/p\u003e \u003cp\u003eHierarchical models 116\u003c\/p\u003e \u003cp\u003eBayesian inference 129\u003c\/p\u003e \u003cp\u003eResampling and simulation methods 140\u003c\/p\u003e \u003cp\u003eStatistical significance 145\u003c\/p\u003e \u003cp\u003eReferences 146\u003c\/p\u003e \u003cp\u003eAdditional reading 146\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Modeling the Influence of Decisions 147\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStructuring decisions 147\u003c\/p\u003e \u003cp\u003eInfluence diagrams 148\u003c\/p\u003e \u003cp\u003eFrequent mistakes when structuring decisions 153\u003c\/p\u003e \u003cp\u003eDefining node states 157\u003c\/p\u003e \u003cp\u003eDecision trees 159\u003c\/p\u003e \u003cp\u003eSolving a decision model 160\u003c\/p\u003e \u003cp\u003eConditional independence and modularity 164\u003c\/p\u003e \u003cp\u003eParameterizing decision models 165\u003c\/p\u003e \u003cp\u003eElicitation of expert judgment 179\u003c\/p\u003e \u003cp\u003eQuantifying uncertainty in expert judgment 188\u003c\/p\u003e \u003cp\u003eGroup elicitation 189\u003c\/p\u003e \u003cp\u003eThe care and handling of experts 190\u003c\/p\u003e \u003cp\u003eReferences 191\u003c\/p\u003e \u003cp\u003eAdditional reading 191\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Identifying and Reducing Uncertainty in\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDecision Making 192\u003c\/p\u003e \u003cp\u003eTypes of uncertainty 192\u003c\/p\u003e \u003cp\u003eIrreducible uncertainty 193\u003c\/p\u003e \u003cp\u003eReducible uncertainty 194\u003c\/p\u003e \u003cp\u003eEffects of uncertainty on decision making 197\u003c\/p\u003e \u003cp\u003eSensitivity analysis 203\u003c\/p\u003e \u003cp\u003eValue of information 217\u003c\/p\u003e \u003cp\u003eReducing uncertainty 220\u003c\/p\u003e \u003cp\u003eReferences 230\u003c\/p\u003e \u003cp\u003eAdditional reading 231\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Methods for Obtaining Optimal Decisions 232\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOverview of optimization 233\u003c\/p\u003e \u003cp\u003eFactors affecting optimization 234\u003c\/p\u003e \u003cp\u003eMultiple attribute objectives and constrained optimization 239\u003c\/p\u003e \u003cp\u003eDynamic decisions 246\u003c\/p\u003e \u003cp\u003eOptimization under uncertainty 249\u003c\/p\u003e \u003cp\u003eAnalysis of the decision problem 253\u003c\/p\u003e \u003cp\u003eSuboptimal decisions and “satisficing” 256\u003c\/p\u003e \u003cp\u003eOther problems 257\u003c\/p\u003e \u003cp\u003eSummary 258\u003c\/p\u003e \u003cp\u003eReferences 258\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART III. APPLICATIONS 261\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Case Studies 263\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCase study 1 Adaptive Harvest Management of American Black Ducks 263\u003c\/p\u003e \u003cp\u003eCase study 2 Management of Water Resources in the Southeastern US 276\u003c\/p\u003e \u003cp\u003eCase study 3 Regulation of Largemouth Bass Sport Fishery in Georgia 284\u003c\/p\u003e \u003cp\u003eSummary 291\u003c\/p\u003e \u003cp\u003eReferences 291\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Summary, Lessons Learned, and Recommendations 294\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 294\u003c\/p\u003e \u003cp\u003eLessons learned 294\u003c\/p\u003e \u003cp\u003eStructured decision making for Hector’s Dolphin conservation 295\u003c\/p\u003e \u003cp\u003eLandowner incentives for conservation of early successional habitats in Georgia 298\u003c\/p\u003e \u003cp\u003eCahaba shiner 299\u003c\/p\u003e \u003cp\u003eOther lessons 303\u003c\/p\u003e \u003cp\u003eReferences 304\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART IV. APPENDICES 307\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Probability and Distributional Relationships 309\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eProbability axioms 309\u003c\/p\u003e \u003cp\u003eConditional probability 309\u003c\/p\u003e \u003cp\u003eConditional independence 310\u003c\/p\u003e \u003cp\u003eExpected value of random variables 311\u003c\/p\u003e \u003cp\u003eLaw of total probability 311\u003c\/p\u003e \u003cp\u003eBayes’ theorem 312\u003c\/p\u003e \u003cp\u003eDistribution moments 313\u003c\/p\u003e \u003cp\u003eSample moments 316\u003c\/p\u003e \u003cp\u003eAdditional reading 316\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B Common Statistical Distributions 317\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGeneral distribution characteristics 317\u003c\/p\u003e \u003cp\u003eContinuous distributions 320\u003c\/p\u003e \u003cp\u003eDiscrete distributions 329\u003c\/p\u003e \u003cp\u003eReference 338\u003c\/p\u003e \u003cp\u003eAdditional Reading 338\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix C Methods for Statistical Estimation 339\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGeneral principles of estimation 339\u003c\/p\u003e \u003cp\u003eMethod of moments 342\u003c\/p\u003e \u003cp\u003eLeast squares 343\u003c\/p\u003e \u003cp\u003eMaximum likelihood 346\u003c\/p\u003e \u003cp\u003eBayesian approaches 353\u003c\/p\u003e \u003cp\u003eReferences 372\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix D Parsimony, Prediction, and Multi-Model Inference 373\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGeneral approaches to multi-model inference 373\u003c\/p\u003e \u003cp\u003eMulti-model inference and model averaging 376\u003c\/p\u003e \u003cp\u003eMulti-model Bayesian inference 380\u003c\/p\u003e \u003cp\u003eReferences 383\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix E Mathematical Approaches to Optimization 384\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReview of general optimization principles 385\u003c\/p\u003e \u003cp\u003eClassical programming 392\u003c\/p\u003e \u003cp\u003eNonlinear programming 397\u003c\/p\u003e \u003cp\u003eLinear programming 399\u003c\/p\u003e \u003cp\u003eDynamic decision problems 402\u003c\/p\u003e \u003cp\u003eDecision making under structural uncertainty 419\u003c\/p\u003e \u003cp\u003eGeneralizations of Markov decision processes 427\u003c\/p\u003e \u003cp\u003eHeuristic methods 427\u003c\/p\u003e \u003cp\u003eReferences 429\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix F Guide to Software 430\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix G Electronic Companion to Book 432\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGlossary 433\u003c\/p\u003e \u003cp\u003eIndex 449\u003c\/p\u003e \u003cp\u003e“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.”  (\u003ci\u003eAfrican Journal of Range \u0026amp; Forage Science\u003c\/i\u003e, 1 October 2015)\u003c\/p\u003e \u003cp\u003e“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.”  (\u003ci\u003eEcological Management \u0026amp; Restoration\u003c\/i\u003e\u003ci\u003e, \u003c\/i\u003e20 January 2015)\u003c\/p\u003e \u003cp\u003e“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.”  (\u003ci\u003eThe Journal of Wildlife Management\u003c\/i\u003e, 8 November 2013)\u003c\/p\u003e \u003cp\u003e“I highly recommend the very results oriented and working model based book \u003ci\u003e\u003ca href=\"http:\/\/www.wiley.com\/WileyCDA\/WileyTitle\/productCd-0470671742.html\"\u003eDecision Making in Natural Resource Management: A Structured, Adaptive Approach\u003c\/a\u003e\u003c\/i\u003e 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.”  (\u003ci\u003eBlog Business World\u003c\/i\u003e\u003ci\u003e,\u003c\/i\u003e 5 April 2013)\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e  \u003cb\u003eMichael J. Conroy\u003c\/b\u003e 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.  \u003cp\u003e\u003cb\u003eJames T. Peterson\u003c\/b\u003e 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.\u003c\/p\u003e  \u003cp\u003eThis 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.\u003c\/p\u003e","brand":"Wiley-Blackwell","offers":[{"title":"Default Title","offer_id":47989030551781,"sku":"NP9780470671757","price":153.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470671757.jpg?v=1761782509","url":"https:\/\/k12savings.com\/es\/products\/decision-making-in-natural-resource-management-isbn-9780470671757","provider":"K12savings","version":"1.0","type":"link"}