Improving Natural Resource Management E-Book

Timothy C. Haas

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 378

Veröffentlichungsjahr: 2011

Beschreibung

The decision to implement environmental protection options is a political one. These, and other political and social decisions affect the balance of the ecosystem and how the point of equilibrium desired is to be reached. This book develops a stochastic, temporal model of how political processes influence and are influenced by ecosystem processes and looks at how to find the most politically feasible plan for managing an at-risk ecosystem. Finding such a plan is accomplished by first fitting a mechanistic political and ecological model to a data set composed of observations on both political actions that impact an ecosystem and variables that describe the ecosystem. The parameters of this fitted model are perturbed just enough to cause human behaviour to change so that desired ecosystem states occur. This perturbed model gives the ecosystem management plan needed to reach desired ecosystem states. To construct such a set of interacting models, topics from political science, ecology, probability, and statistics are developed and explored.

Key features:

• Explores politically feasible ways to manage at-risk ecosystems.
• Gives agent-based models of how social groups affect ecosystems through time.
• Demonstrates how to fit models of population dynamics to mixtures of wildlife data.
• Presents statistical methods for fitting models of group behaviour to political action data.
• Supported by an accompanying website featuring datasets and JAVA code.

This book will be useful to managers and analysts working in organizations charged with finding practical ways to sustain biodiversity or the physical environment. Furthermore this book also provides a political roadmap to help lawmakers and administrators improve institutional environmental management decision making.

Leseprobe

Contents

Cover

Statistics in Practice

Title Page

Copyright

Dedication

Preface

List of Figures

List of Tables

Nomenclature

Part I: Managing a Political–Ecological System

Chapter 1: Introduction

1.1 The Problem to be Addressed

1.2 The Book's Running Example: East African Cheetah

1.3 The EMT Simulator

1.4 How to Use the EMT to Manage an Ecosystem

1.5 Chapter Topics and Order

1.6 The Book's Accompanying Web Resources

Chapter 2: Simulator architecture, operation, and example output

2.1 Introduction

2.2 Theory for Agent-based Simulation

2.3 Action Messages and IntIDs Model Operation

2.4 A Plot for Displaying an Actions History

2.5 Conclusions

2.6 Exercises

Chapter 3: Blue whale population management

3.1 Introduction

3.2 Current Status of Blue Whales

3.3 Groups that Affect Blue Whale Populations

3.4 Blue Whale Ecosystem ID

3.5 Interactions between IDs

3.6 Data Sets for the Blue Whale EMT

3.7 Main Points of this Chapter's Example

3.8 Exercises

Chapter 4: Finding the most practical ecosystem management plan

4.1 Introduction

4.2 Some Methods for Developing Ecosystem Management Plans

4.3 Overview of the Consistency Analysis Parameter Estimator

4.4 The MPEMP: Definition and Construction

4.5 The MPEMP for East African Cheetah

4.6 Conclusions

4.7 Exercises

Chapter 5: An open, web-based ecosystem management tool

5.1 Introduction

5.2 Components of a politically realistic EMT

5.3 id language and software system

5.4 How the EMT website would be used

Part II: Model Formulation, Estimation, and Reliability

Chapter 6: Influence diagrams of political decision making

6.1 Introduction

6.2 Theories of political decision making

6.3 Architecture of a group decision-making ID

6.4 Related modeling efforts

6.5 Conclusions

6.6 Exercises

Chapter 7: Group IDs for the East African cheetah EMT

7.1 Introduction

7.2 Country Backgrounds

7.3 Selection of Groups to Model

7.4 President IDs

7.5 EPA IDs

7.6 Rural Residents IDs

7.7 Pastoralists IDs

7.8 Conservation NGOs ID

7.9 Conclusions

7.10 Exercises

Chapter 8: Modeling wildlife population dynamics with an influence diagram

8.1 Introduction

8.2 Model of Cheetah and Prey Population Dynamics

8.3 Solving SDEs Within an ID

8.4 Example of Ecosystem ID Output

8.5 Conclusions

8.6 Exercises

Chapter 9: Political action taxonomies, collection protocols, and an actions history example

9.1 Introduction

9.2 Political Action Taxonomies

9.3 Adapting the BCOW Taxonomy to Ecosystem Management Actions

9.4 EMAT Coding Protocol

9.5 Actions History Data for the East African Cheetah EMT

9.6 Conclusions

Chapter 10: Ecosystem data

10.1 Introduction

10.2 Wildlife Monitoring

10.3 Wildlife Abundance Estimation Methods

10.4 East African Cheetah and Prey Abundance Data

10.5 Data on Cheetah Habitat Suitability Nodes

10.6 Conclusions

10.7 Exercises

Chapter 11: Statistical fitting of the political–ecological system simulator

11.1 Introduction

11.2 Consistency Analysis Applied to an Actions History

11.3 Consistency Analysis of the East African Cheetah EMT Simulator

11.4 Conclusions and Another Collection Initialization Algorithm

11.5 Exercises

Chapter 12: Assessing the simulator's reliability and improving its construct validity

12.1 Introduction

12.2 Steps for Assessing Simulator Reliability

12.3 Sensitivity Analysis

12.4 One-Step-Ahead Prediction Error Rates

12.5 MC Hypothesis Tests

12.6 Sensitivity to Hidden Bias Analysis

12.7 Conclusions

12.8 Exercises

Part III: Assessment

Chapter 13: Current capabilities and limitations of the politically realistic EMT

13.1 Introduction

13.2 Current capabilities of the EMT

13.3 Current limitations of the EMT

13.4 Supporting the EMT in the real world

13.5 Consequences of using a politically realistic EMT

Appendices

Appendix A: Heuristics used to assign hypothesis values to parameters

Appendix B: Cluster computing version of Hooke and Jeeves search

References

Index

Statistics in Practice

Statistics in Practice

Series Advisors

Human and Biological Sciences

Stephen Senn

University of Glasgow, UK

Earth and Environmental Sciences

Marian Scott

University of Glasgow, UK

Industry, Commerce and Finance

Wolfgang Jank

University of Maryland, USA

Statistics in Practice is an important international series of texts which provide detailed coverage of statistical concepts, methods and worked case studies in specific fields of investigation and study.

With sound motivation and many worked practical examples, the books show in down-to-earth terms how to select and use an appropriate range of statistical techniques in a particular practical field within each title’s special topic area.

The books provide statistical support for professionals and research workers across a range of employment fields and research environments. Subject areas covered include medicine and pharmaceutics; industry, finance and commerce; public services; the earth and environmental sciences, and so on.

The books also provide support to students studying statistical courses applied to the above areas. The demand for graduates to be equipped for thework environment has led to such courses becoming increasingly prevalent at universities and colleges.

It is our aim to present judiciously chosen and well-written workbooks to meet everyday practical needs. Feedback of views from readers will be most valuable to monitor the success of this aim.

A complete list of titles in this series appears at the end of the volume.

This edition first published 2011

© 2011 John Wiley & Sons, Ltd

Registered office

John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, United Kingdom

For details of our global editorial offices, for customer services and for information about how to apply for permission to reuse the copyright material in this book please see our website at www.wiley.com.

The right of the author to be identified as the author of this work has been asserted in accordance with the Copyright, Designs and Patents Act 1988.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher.

Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books.

Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. The publisher is not associated with any product or vendor mentioned in this book. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold on the understanding that the publisher is not engaged in rendering professional services. If professional advice or other expert assistance is required, the services of a competent professional should be sought.

Library of Congress Cataloging-in-Publication Data

Haas, Timothy.

Improving natural resource management : ecological and political models / Timothy Haas.

p. cm.

Includes bibliographical references and index.

ISBN 978-0-470-66113-0 (cloth)

1. Ecosystem management. 2. Ecosystem management–Simulation methods. 3. Ecosystem management–Political aspects. 4. Ecosystem management–Monitoring. 5. Wildlife monitoring.

I. Title.

QH75.H29 2011

333.95'16–dc22

2010040965

A catalogue record for this book is available from the British Library.

Print ISBN: 978-0-470-66113-0

ePDF ISBN: 978-0-470-97934-1

oBook ISBN: 978-0-470-97933-4

ePub ISBN: 978-0-470-97955-6

Dedicated to the next generation of ecosystem managers

Preface

This is a how-to book for finding the most politically acceptable but effective plan for managing an at-risk ecosystem. In this book, finding such a plan is accomplished by first fitting mechanistic political and ecological models to a data set composed of both observations on political actions that impact an ecosystem and observations on variables that describe the ecological processes that are occurring within it. Then, the parameters of these fitted models are perturbed just enough so as to produce desired ecosystem state endpoints. This perturbed model gives the ecosystem management plan needed to reach the desired ecosystem state. To construct such a set of interacting models, topics from political science, ecology, probability, and statistics are developed. These group decision-making models capture group belief systems in their structure and parameter values. Hence, perturbing parameters to achieve needed shifts in behavior to cause desired ecosystem responses is equivalent to asking the question: ‘What is the smallest change in group belief systems that would cause behavioral changes towards the ecosystem that would, in turn, result in the ecosystem responding in a desired way?’ By focusing on belief system change, the tool is ideally suited to a non command and control ecosystem management system. Such non hierarchical management systems describe many at-risk ecosystems including those that straddle country boundaries. The book's running example is of one such trans-boundary ecosystem management case: conservation of cheetahs across Kenya, Tanzania, and Uganda (East Africa).

To demonstrate the proposed management tool's wide applicability, a sketch of how the tool could be used to manage the world's remaining population of blue whales is given in Chapter 2. These two examples of managing at-risk species are appropriate for a book devoted to managing natural resources when biodiversity is viewed as a natural resource.

Two types of readers will get the most from this book. The first type of reader is a person who is in, or is training for, a job in environmental and/or wildlife management wherein one of the decreed management goals is the protection of some part of the ecosystem, for example, wildlife that is at threat from anthropogenic forces. This first type of reader might be a student in a natural resources management program – or a member of a forestry, fish and game, national park, environmental protection agency, or other conservation-focused agency. This reader might also be employed by a wildlife advocacy organization such as the African Wildlife Foundation or the World Wildlife Fund. The prerequisites needed by this first type of reader are some familiarity with natural resources and elementary statistics. This type of reader should read Chapters 1–5 to acquire a working knowledge of how to use the book's methods to manage an at-risk ecosystem. Section 4.3 in Chapter 4 does, however, contain material that is best understood by a reader possessing a knowledge of calculus-based probability and statistics along with the notion of a vector of random variables.

The second type of reader is one who is trained in both the social sciences and mathematical statistics and is interested in how social science theory, ecology, probability, inferential statistics, and computers can be synthesized to create a decision support system for the scientific management of an at-risk ecosystem. This second type of reader would typically be a student or academic in political science, political economy, ecology, natural resources management, or statistics. This type of reader would be best prepared by having some background in one or more of the areas of political science, ecology, or mathematical statistics. This reader should read all of the book's chapters in order.

This book has the following pedagogical features:

1. The East African cheetah management application of the proposed ecosystem management tool is used as a running example through all of the chapters.

2. Exercises are at the end of most chapters – making the book suitable for a graduate lecture course on natural resource and/or wildlife management.

3. A companion website (www4.uwm.edu/people/haas/cheetah_emt) contains all computer code and data used in this book. Specifically, this website contains the software for, and an example of, the book's main contribution: a web-based Ecosystem Management Tool (EMT). The following can be freely downloaded:

All software described in the book (namely, the id software package) in the form of Java source (.java) and Windows class (.class) files.A user's manual for id.The political actions data set for the East African cheetah EMT along with the data collection protocol and a suite of web-based data acquisition aids.The ecosystem data set for the East African cheetah EMT.Output files from (a) the East African cheetah EMT's ecosystem management plan search, (b) statistical estimation of the EMT simulator, and (c) the simulator's sensitivity analysis.A web-based tutorial that covers the basics of probability, statistics, and influence diagrams.Answers to all of the book's exercises.

List of Figures

1.1 Area of East Africa that is the subject of the politically realistic East African cheetah EMT

2.1 Illustrative example of a decision-making ID

2.2 Schematic of the IntIDs model of interacting political and ecological processes

2.3 Sequential updating scheme of an IntIDs model consisting of m groups and an ecosystem

2.4 East African cheetah EMT simulator output over the year 2004

3.1 Blue whale sightings between the years 1965 and 2010

3.2 Anti-whaling complex of the US group ID

3.3 Pro-whaling complex of Japan group ID

3.4 Ecosystem ID in the blue whale EMT simulator

4.1 A political–ecological data set

4.2 Simulator output over the time period of 2010 through 2060 using consistent parameter values

4.3 Simulator predictions for 2010–2060 using MPEMP parameter values

5.1 Directory tree of a typical EMT website

5.2 id language file for modeling NO3 deposition through precipitation

6.1 Architecture of the group decision-making ID

7.1 Kenya president ID

7.2 KenyaEPAID

7.3 Kenya rural resident ID

7.4 Kenya pastoralist ID

7.5 Conservation NGOs ID

8.1 Ecosystem ID for the East African cheetah EMT

8.2 Kenya's regions of approximately homogeneous climate and vegetation regimes

8.3 District boundaries for Tanzania

8.4 District boundaries for Uganda

8.5 East African cheetah ecosystem ID output

10.1 Cheetah sightings reported by tourists in Tanzania over the time period April 1, 2003 to December 1, 2008

10.2 Climate zones of Kenya, Tanzania, and Uganda

10.3 Protected areas maintained by the governments of Kenya, Tanzania, and Uganda

10.4 Unprotected area landuse regions of Kenya, Tanzania, and Uganda

11.1 Simulator actions output under βC values over the time period 1997 to 2009

List of Tables

2.1 CPT for the Goal: Feed Family node

2.2 CPT for the Goal: Avoid Prosecution node

2.3 Relative importance weights for the Utility: Overall Goal Attainment node

2.4 Reassembling the middle pentad in a three-pentad sequence

3.1 Example actions data for the blue whale EMT

5.1 Qualifiers and relations for the id language words influence diagram and node

5.2 Qualifiers and relations for the id language word context

5.3 Qualifiers and relations for the id language word report

5.4 Qualifiers and relations for the id language word: report, continued

6.1 Definition of symbols used to express nodes in the Situation subID of the group decision-making ID

6.2 Definition of symbols used to express nodes in the Scenario subID of the group decision-making ID

7.1 Input actions that affect economic and/or militaristic resource nodes in a president ID

7.2 Actions and targets for a typical president ID

8.1 Hypothesis values of parameters that define the population dynamics SDE system

8.2 Hypothesis values of parameters that define areal fraction detection chance nodes of the ecosystem ID

9.1 Some BCOW actions and their codes

9.2 Fields for coding an observed action for entry into an EMAT database

10.1 Summary of wildlife abundance estimators

10.2 Transect survey sightings reported by Maddox (2003)

10.3 Nonsurvey sightings from tourists in Tanzania over the time period April 1, 2003 to December 1, 2008

10.4 Areal detection fraction data on cheetahs in Kenya

10.5 Areal detection fraction observations computed from herbivore abundance data reported by Mbugua (1986) and Peden (1984)

10.6 Line transect survey prey counts, estimated values of survey halfwidths (μ), total transect length (L), and region areas (A) as reported by Maddox (2003)

10.7 Nighttime prey sightings reported by Wambua (2008) in the Kenyan districts of Machakos and Makueni

11.1 Nodes that represent evaluation dimensions

11.2 A truth table to relate Situation Change and Scenario Change node value combinations to values of a Scenario Goal node

11.3 Situation subID to Scenario subID relationships that produce three unique out-combinations

11.4 Initial collection match statistics

11.5Initialize step match statistics

11.6 Consistency analysis agreement function values

12.1 Observed and one-step-ahead predictions on the cheetah nonsurvey sightings node

13.1 Fields where modern ecosystem managers need in-depth knowledge along with suggested texts

Nomenclature

AERActual Error RateAERSActual Error Rate SumASCIIAmerican Standard Code for Information InterchangeBCOWBehavioral Correlates of WarCITESConvention on International Trade in Endangered SpeciesCPTConditional Probability TableDANDocument Archive NumberDBODesires, Beliefs, and OpportunitiesDM-groupDecision-Making GroupDSADeterministic Sensitivity AnalysisECAEmpirically Calibrated Agent-based modelEERExpected Error RateEMATEcosystem Management Actions TaxonomyEMTEcosystem Management ToolEOSEarth Observation SystemEPAEnvironmental Protection AgencyFAOFood and Agriculture OrganizationGBIFGlobal Biodiversity Information FacilityGCMGlobal Climate ModelGDPGross Domestic ProductGISGeographic Information SystemGUIGraphical User InterfaceGWGlobal WorkspaceIDInfluence DiagramIntIDsInteracting Influence DiagramsIQRInterquartile RangeIUCNInternational Union for Conservation of NatureIWCInternational Whaling CommissionMCMonte CarloMCMCMarkov Chain Monte CarloMDASMultiple Dimensions Ahead SearchMITMassachusetts Institute of TechnologyMPEMPMost Practical Ecosystem Management PlanNGONongovernmental OrganizationOGAOverall Goal AttainmentOSAPAEROne-Step-Ahead Predicted Actions Error RateOSARMSPEOne-Step-Ahead Root Mean Squared Prediction ErrorPBSSParallel Best Step SearchPDPFProbability Density Probability FunctionPOMPPrivately Optimal Management ProblemPRAProbabilistic Reduction ApproachPSOSVParallel Search Over Subsets of VariablesRMSPERoot Mean Squared Prediction ErrorSASimulated AnnealingSDEstochastic differential equationSEMStructural Equation ModelSMLSimulated Maximum LikelihoodSOMPSocially Optimal Management ProblemsubIDSub-influence DiagramTEEBThe Economics of Ecosystems and Biodiversity (Project)UNUnited NationsUSAUnited States of AmericaUSDAUS Department of Agriculture

Part I

Managing a Political–Ecological System

Chapter 1

Introduction

1.1 The Problem to be Addressed

In this book, biodiversity is considered a nonrenewable natural resource (USAID 2005, p. 6, USGS 1997, Wikipedia 2010, UFZ 2008). Many species are headed for extinction in habitats that straddle two or more developing countries. With our current understanding of biological processes (circa 2010), the loss of a species is irreversible. Because of this irreversibility, it can be argued that this problem should be of high priority to all countries. This book gives one way to address this problem.

Two characteristics of this problem make solutions difficult to find. First, within developed countries, constituencies prefer their policy makers to spend most of their conservation budget on internal conservation programs. Because of this internal focus, developing countries, with inadequate budgets for conservation programs, can expect to receive (currently) only modest supplemental conservation resources from developed countries. Second, because the habitat of many at-risk species straddles the political boundaries of several developing countries, conventional wildlife conservation strategies (such as government-run command and control programs) may not be implemented with sufficient completeness to achieve a species' long-term survival.

These considerations have motivated the development here of an approach to ecosystem management that does not assume central control but instead, after building scientific models of both the political processes at work in the habitat-hosting countries and the dynamics of the ecosystem in which the managed species is a participant, searches for politically feasible management plans. In other words, this book proposes a two-step procedure: first understand how the political–ecological system works at a mechanistic level and only then begin a search for management plans that require the least change in human belief systems in order to effect behavioral changes that result in a sequence of actions that leads to the survival of the species being managed. The term is used rather than to emphasize the active, institution- and ecosystem-changing tendencies of human groups across an ecosystem.