Quantitative conservation biology: theory and practice of population viability analysis / William F. Morris, Daniel F. Doak

Bibliografía: páginas 459-471

Preface.. 1 What Is Population Viability Analysis, and How Can It Be Used in Conservation Decision-Making?.. Potential Products and Uses of PVA.. Types of Population Viability Analysis.. A Roadmap to This Book.. Our Modeling Philosophy: Keep It Simple.. 2 The Causes and Quantification of Population Vulnerability.. Mean Vital Rates and Population Viability.. Temporal Variability in Vital Rates.. Other Processes Influencing Viability.. Quantifying Population Viability.. 3 Count-Based PVA: Density-Independent Models.. Population Dynamics in a Random Environment.. The Relationship between the Probability of Extinction and the Parameters µ and σ2.. Using Count Data to Estimate the Population Growth Parameters µ and σ2: An Illustration Using the Yellowstone Grizzly Bear Census.. Using Estimates of µ and σ2 to Calculate Probability of Extinction.. Using Extinction Time Estimates.. Key Assumptions of Simple Count-Based PVAs.. When to Use This Method.. 4 Count-Based PVA: Incorporating Density Dependence, Demographic Stochasticity, Correlated Environments, Catastrophes, and Bonanzas.. Density Dependence.. Combined Effects of Demographic and Environmental Stochasticity.. Environmental Autocorrelation.. Catastrophes, Bonanzas, and Other Highly Variable Effects.. Concluding Remarks.. Appendix: An Overview of Maximum Likelihood Parameter Estimation.. 5 Accounting for Observation Error in Count-Based PVAs.. Potential Sources of Observation Error.. Considerations for Reducing Observation Error before a Census Is Initiated.. Quantifying Observation Errors while a Census is Being Conducted.. Correcting for Observation Errors after the Census Data Have Been Collected.. More Advanced Methods for Estimating Parameters in the Face of Observation Error.. 6 Demographic PVAs: Using Demographic Data to Build Stochastic Projection Matrix Models.. Overview of Procedures for Building Projection Matrices

Step 1: Conducting a Demographic Study.. Step 2: Establishing Classes.. Step 3: Estimating Vital Rates.. Step 4: Building the Projection Matrix.. Putting It All Together: Estimating Projection Matrices for Mountain Golden Heather.. 7 Demographic PVAs: Using Projection Matrices to Assess Population Growth and Viability.. Structured Populations in a Deterministic Environment.. Growth and Extinction Risk of Structured Populations in a Variable Environment.. 8 Demographic PVAs Based on Vital Rates: Removing Sampling Variation and Incorporating Large Variance, Correlated Environments, Demographic Stochasticity, and Density Dependence into Matrix Models.. Estimation and Construction of Stochastic Models Based on Vital Rates.. Simulations to Estimate Population Rate and Extinction Risk.. Simulating Demographic Stochasticity.. Including Density Dependence in Matrix Models.. 9 Using Demographic PVA Models in Management: Sensitivity Analysis.. The Basic Idea of Sensitivity Analysis.. Sensitivity Analysis for Deterministic Matrices.. Sensitivity Analysis for Stochastic Matrix Models.. Sensitivity Analysis for Density-Dependent Models.. 10 Population Dynamics across Multiple Sites: The Interaction of Dispersal and Environmental Correlation.. Terminology for Multi-Site PVAs.. Multi-Site Processes and Data Needs.. A Schematic Breakdown of Multi-Site Situations.. Using Occam's Razor in Multi-Site PVAs.. 11 Methods of Viability Analysis for Spatially Structured Populations.. Patch-Based Approaches.. Count-Based Approaches.. Demographic Approaches.. Using Multi-Site PVAs with Care.. 12 Critiques and Cautions: When to Perform (and When Not to Perform a Population Viability Analysis.. Critiques and Criticisms of PVA.. General Recommendations and Cautions for Conducting a Population Viability Analysis.. Closing Remarks.. Appendix: Mathematical Symbols Used in This Book.. Literature Cited.. Index

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. eng

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. eng