Quantitative Conservation Biology Theory and Practice of Population Viability Analysis

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Format: Paperback
Pub. Date: 2002-06-22
Publisher(s): Sinauer Associates is an imprint of Oxford University Press
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Summary

Conservation biology relies not only on the general concepts, but on the specific methods, of population ecology to both understand and predict the viability of rare and endangered species and to determine how best to manage these populations. The need to conduct quantitative analyses of viability and management has spawned the field of "population viability analysis," or PVA, which, in turn, has driven much of the recent development of useful and realistic population analysis and modeling in ecology in general. However, despite calls for the increased use of PVA in real-world settings--developing recovery plans for endangered species, for example--a misperception remains among field-oriented conservation biologists that PVA models can only be constructed and understood by a select group of mathematical population ecologists.

Part of the reason for the ongoing gap between conservation practitioners and population modelers has been the lack of an easy-to-understand introduction to PVA for conservation biologists with little prior exposure to mathematical modeling as well as in-depth coverage of the underlying theory and its applications. Quantitative Conservation Biology fills this void through a unified presentation of the three major areas of PVA: count-based, demographic, and multi-site, or metapopulation, models. The authors first present general concepts and approaches to viability assessment. Then, in sections addressing each of the three fields of PVA, they guide the reader from considerations for collection and analysis of data to model construction, analysis, and interpretation, progressing from simple to complex approaches to answering PVA questions. Detailed case studies use data from real endangered species, and computer programs to perform all described analyses accompany the text.

The goal of this book is to provide practical, intelligible, and intuitive explanations of population modeling to empirical ecologists and conservation biologists. Modeling methods that do not require large amounts of data (typically unavailable for endangered species) are emphasized. As such, the book is appropriate for undergraduate and graduate students interested in quantitative conservation biology, managers charged with preserving endangered species, and, in short, for any conservation biologist or ecologist seeking to better understand the analysis and modeling of population data.

RESOURCES

Downloadable MATLAB programs included as boxes in the text are available to students and instructors.

Author Biography


William F. Morris is a Professor in the Biology Department at Duke University.

Daniel F. Doak is a Professor in the Environmental Studies Program at the University of Colorado at Boulder.

Table of Contents

Preface xiii
What is Population Viability Analysis, and How can it be Used in Conservation Decision-Making?
1(14)
Potential Products and Uses of PVA
2(6)
Types of Population Viability Analysis
8(2)
A Roadmap to this Book
10(2)
Our Modeling Philosophy: Keep it Simple
12(3)
The Causes and Quantification of Population Vulnerability
15(36)
Mean Vital Rates and Population Viability
16(2)
Temporal Variability in Vital Rates
18(18)
Other Processes Influencing Viability
36(7)
Quantifying Population Viability
43(8)
Count-Based PVA: Density-Independent Models
51(48)
Population Dynamics in a Random Environment
52(6)
The Relationship between the Probability of Extinction and the Parameters μ and σ2
58(6)
Using Count Data to Estimate the Population Growth Parameters μ and σ2: An Illustration Using the Yellowstone Grizzly Bear Census
64(15)
Using Estimates of μ and σ2 to Calculate Probability of Extinction
79(8)
Using Extinction Time Estimates
87(2)
Key Assumptions of Simple Count-Based PVAs
89(7)
When to Use This Method
96(3)
Count-Based PVA: Incorporating Density Dependence, Demographic Stochasticity, Correlated Environments, Catastrophes, and Bonanzas
99(52)
Density Dependence
100(27)
Combined Effects of Demographic and Environmental Stochasticity
127(6)
Environmental Autocorrelation
133(8)
Catastrophes, Bonanzas, and Other Highly Variable Effects
141(5)
Concluding Remarks
146(1)
Appendix: An Overview of Maximum Likelihood Parameter Estimation
147(4)
Accounting for Observation Error in Count-Based PVAs
151(30)
Potential Sources of Observation Error
152(3)
Considerations for Reducing Observation Error before a Census is Initiated
155(2)
Quantifying Observation Errors while a Census is being Conducted
157(1)
Correcting for Observation Errors after the Census Data have been Collected
158(21)
A Directory to more Advanced Methods for Estimating Parameters in the Face of Observation Error
179(2)
Demographic PVAs: Using Demographic Data to Build Stochastic Projection Matrix Models
181(34)
Overview of Procedures for Building Projection Matrices
182(1)
Step 1: Conducting a Demographic Study
183(3)
Step 2: Establishing Classes
186(10)
Step 3: Estimating Vital Rates
196(8)
Step 4: Building the Projection Matrix
204(6)
Putting it all Together: Estimating Projection Matrices for Mountain Golden Heather
210(5)
Demographic PVAs: Using Projection Matrices to Assess Population Growth and Viability
215(36)
Structured Populations in a Deterministic Environment
217(11)
Growth and Extinction Risk of Structured Populations in a Variable Environment
228(23)
Demographic PVAs Based on Vital Rates: Removing Sampling Variation and Incorporating Large Variance, Correlated Environments, Demographic Stochasticity, and Density Dependence into Matrix Models
251(74)
Estimation and Construction of Stochastic Models Based on Vital Rates
253(21)
Simulations to Estimate Population Rate and Extinction Risk
274(31)
Simulating Demographic Stochasticity
305(5)
Including Density Dependence in Matrix Models
310(15)
Using Demographic PVA Models in Management: Sensitivity Analysis
325(48)
The Basic Idea of Sensitivity Analysis
326(5)
Sensitivity Analysis for Deterministic Matrices
331(20)
Sensitivity Analysis for Stochastic Matrix Models
351(18)
Sensitivity Analysis for Density-Dependent Models
369(4)
Population Dynamics across Multiple Sites: The Interaction of Dispersal and Environmental Correlation
373(30)
Terminology for Multi-Site PVAs
375(1)
Multi-Site Processes and Data Needs
375(23)
A Schematic Breakdown of Multi-Site Situations
398(4)
Using Occam's Razor in Multi-Site PVAs
402(1)
Methods of Viability Analysis for Spatially Structured Populations
403(40)
Patch-Based Approaches
403(20)
Count-Based Approaches
423(9)
Demographic Approaches
432(8)
Using Multi-Site PVAs with Care
440(3)
Critiques and Cautions: when to Perform (and When Not to Perform) a Population Viability Analysis
443(12)
Critiques and Criticisms of PVA
444(6)
General Recommendations and Cautions for Conducting a Population Viability Analysis
450(4)
Closing Remarks
454(1)
Appendix: Mathematical Symbols Used in this Book 455(4)
Literature Cited 459(13)
Index 472

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