Ship-Ship-Hooray! Free Shipping on $25+ Details >

by William F. Morris and Daniel F. Doak

Edition: 02Copyright: 2002

Publisher: Sinauer Associates, Inc.

Published: 2002

International: No

This title is currently not available in digital format.

Well, that's no good. Unfortunately, this edition is currently out of stock. Please check back soon.

Available in the Marketplace starting at $52.00

Price | Condition | Seller | Comments |
---|

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.

**Morris, William F. : Duke University**

William F. Morris is Associate Professor of Biology at Duke University. He received a B.S. in Biology from Cornell University and a Ph.D. in Zoology from the University of Washington (with Peter Kareiva). Dr. Morris's major research interests are the population ecology of plant?insect interactions (including plant?pollinator and plant?herbivore interactions), the ecological dynamics of mutualism, the comparative demography of plants, and population viability analysis. Dr. Morris has conducted a PVA for the endangered Golden Mountain Heather and led a workshop on PVA methods for conservation practitioners of The Nature Conservancy.

**Doak, Daniel F. : University of California, Santa Cruz**

Daniel F. Doak is Associate Professor of Ecology and Evolutionary Biology at the University of California, Santa Cruz. He received a B.A. in Biology from Swarthmore College and a Ph.D. in Zoology from the University of Washington (with Peter Kareiva). Dr. Doak's research includes the application of demographic models to a variety of basic and applied problems for plant and animal populations. His published work includes PVA analyses of grizzly bears, sea otters, desert tortoises, and cheetahs; however, as a field biologist, his focus is the demography of long-lived arctic plants and investigation of factors limiting populations of rare and endangered plants in central California. He has worked with numerous non-profit and governmental organizations to improve conservation of endangered species.

PREFACE

1. What Is PVA, and How Can It Be Used in Conservation Decision-making?

What is Population Viability Analysis?

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

Introduction

Factors Influencing Population Viability

Quantifying Population Viability

3. Count-based PVA: Density-independent Models

Introduction

Population Dynamics in a Random Environment

Using Count Data to Estimate the Population Growth Parameters µ and s2?An Illustration Using the Yellowstone Grizzly Bear Census

Using Estimates of µ and s2 to Calculate the Probability of Extinction

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

Introduction

Density Dependence

Combined Effects of Demographic and Environmental Stochasticity

Environmental Autocorrelation

Incorporating Catastrophes and Bonanzas

Concluding Remarks

5. Accounting for Observation Error in Count-based PVAs

Introduction

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

A Directory to More Advanced Methods for Estimating Parameters in the Face of Observation Error

6. Demographic PVAs: Using Demographic Data to Build Stochastic Projection Matrix Models

Introduction

Overview of Procedures for Building Projection Matrices

Step One: Conducting a Demographic Study

Step Two: Establishing Classes

Step Three: Estimating Vital Rates

Step Four: Building the Projection Matrix

Putting It All Together: Estimating Projection Matrices for Mountain Golden Heather

Summary

7. Demographic PVAs: Using Projection Matrices to Assess Population Growth and Viability

Introduction

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

Introduction

Estimation and Construction of Stochastic Models Based on Vital Rates

Simulations to Estimate Growth and Extinction

Simulating Demographic Stochasticity

Estimation and Simulation of Density Dependence in Vital Rates

Summary

9. Using Demographic PVA Models in Management: Sensitivity and Elasticity Analysis

Introduction

The Basic Idea of Sensitivity Analysis

Sensitivity and Elasticity Analysis for Deterministic Matrices

Sensitivity Analysis for Stochastic Matrix Models

Sensitivity Analysis for Density-dependent Models

Summary

10. Multi-site PVAs: The Interaction of Dispersal and Environmental Correlation

Introduction

Terminology for Multi-site PVAs

Multi-site Processes and Data Needs

A Schematic Breakdown of Multi-site Situations

Summary: Using Occam?s Razor in Multi-site PVAs

11. Multi-site PVAs: Methods of Analysis for Spatially Complex Populations

Introduction

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 PVA

Introduction

Critiques and Criticisms of PVA

General Recommendations and Cautions for Conducting a Population Viability Analysis

REFERENCES

shop us with confidence

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.

Author Bio

**Morris, William F. : Duke University**

William F. Morris is Associate Professor of Biology at Duke University. He received a B.S. in Biology from Cornell University and a Ph.D. in Zoology from the University of Washington (with Peter Kareiva). Dr. Morris's major research interests are the population ecology of plant?insect interactions (including plant?pollinator and plant?herbivore interactions), the ecological dynamics of mutualism, the comparative demography of plants, and population viability analysis. Dr. Morris has conducted a PVA for the endangered Golden Mountain Heather and led a workshop on PVA methods for conservation practitioners of The Nature Conservancy.

**Doak, Daniel F. : University of California, Santa Cruz**

Daniel F. Doak is Associate Professor of Ecology and Evolutionary Biology at the University of California, Santa Cruz. He received a B.A. in Biology from Swarthmore College and a Ph.D. in Zoology from the University of Washington (with Peter Kareiva). Dr. Doak's research includes the application of demographic models to a variety of basic and applied problems for plant and animal populations. His published work includes PVA analyses of grizzly bears, sea otters, desert tortoises, and cheetahs; however, as a field biologist, his focus is the demography of long-lived arctic plants and investigation of factors limiting populations of rare and endangered plants in central California. He has worked with numerous non-profit and governmental organizations to improve conservation of endangered species.

Table of Contents

PREFACE

1. What Is PVA, and How Can It Be Used in Conservation Decision-making?

What is Population Viability Analysis?

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

Introduction

Factors Influencing Population Viability

Quantifying Population Viability

3. Count-based PVA: Density-independent Models

Introduction

Population Dynamics in a Random Environment

Using Count Data to Estimate the Population Growth Parameters µ and s2?An Illustration Using the Yellowstone Grizzly Bear Census

Using Estimates of µ and s2 to Calculate the Probability of Extinction

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

Introduction

Density Dependence

Combined Effects of Demographic and Environmental Stochasticity

Environmental Autocorrelation

Incorporating Catastrophes and Bonanzas

Concluding Remarks

5. Accounting for Observation Error in Count-based PVAs

Introduction

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

A Directory to More Advanced Methods for Estimating Parameters in the Face of Observation Error

6. Demographic PVAs: Using Demographic Data to Build Stochastic Projection Matrix Models

Introduction

Overview of Procedures for Building Projection Matrices

Step One: Conducting a Demographic Study

Step Two: Establishing Classes

Step Three: Estimating Vital Rates

Step Four: Building the Projection Matrix

Putting It All Together: Estimating Projection Matrices for Mountain Golden Heather

Summary

7. Demographic PVAs: Using Projection Matrices to Assess Population Growth and Viability

Introduction

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

Introduction

Estimation and Construction of Stochastic Models Based on Vital Rates

Simulations to Estimate Growth and Extinction

Simulating Demographic Stochasticity

Estimation and Simulation of Density Dependence in Vital Rates

Summary

9. Using Demographic PVA Models in Management: Sensitivity and Elasticity Analysis

Introduction

The Basic Idea of Sensitivity Analysis

Sensitivity and Elasticity Analysis for Deterministic Matrices

Sensitivity Analysis for Stochastic Matrix Models

Sensitivity Analysis for Density-dependent Models

Summary

10. Multi-site PVAs: The Interaction of Dispersal and Environmental Correlation

Introduction

Terminology for Multi-site PVAs

Multi-site Processes and Data Needs

A Schematic Breakdown of Multi-site Situations

Summary: Using Occam?s Razor in Multi-site PVAs

11. Multi-site PVAs: Methods of Analysis for Spatially Complex Populations

Introduction

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 PVA

Introduction

Critiques and Criticisms of PVA

General Recommendations and Cautions for Conducting a Population Viability Analysis

REFERENCES

Publisher Info

Publisher: Sinauer Associates, Inc.

Published: 2002

International: No

Published: 2002

International: No

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.

**Morris, William F. : Duke University**

William F. Morris is Associate Professor of Biology at Duke University. He received a B.S. in Biology from Cornell University and a Ph.D. in Zoology from the University of Washington (with Peter Kareiva). Dr. Morris's major research interests are the population ecology of plant?insect interactions (including plant?pollinator and plant?herbivore interactions), the ecological dynamics of mutualism, the comparative demography of plants, and population viability analysis. Dr. Morris has conducted a PVA for the endangered Golden Mountain Heather and led a workshop on PVA methods for conservation practitioners of The Nature Conservancy.

**Doak, Daniel F. : University of California, Santa Cruz**

Daniel F. Doak is Associate Professor of Ecology and Evolutionary Biology at the University of California, Santa Cruz. He received a B.A. in Biology from Swarthmore College and a Ph.D. in Zoology from the University of Washington (with Peter Kareiva). Dr. Doak's research includes the application of demographic models to a variety of basic and applied problems for plant and animal populations. His published work includes PVA analyses of grizzly bears, sea otters, desert tortoises, and cheetahs; however, as a field biologist, his focus is the demography of long-lived arctic plants and investigation of factors limiting populations of rare and endangered plants in central California. He has worked with numerous non-profit and governmental organizations to improve conservation of endangered species.

1. What Is PVA, and How Can It Be Used in Conservation Decision-making?

What is Population Viability Analysis?

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

Introduction

Factors Influencing Population Viability

Quantifying Population Viability

3. Count-based PVA: Density-independent Models

Introduction

Population Dynamics in a Random Environment

Using Count Data to Estimate the Population Growth Parameters µ and s2?An Illustration Using the Yellowstone Grizzly Bear Census

Using Estimates of µ and s2 to Calculate the Probability of Extinction

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

Introduction

Density Dependence

Combined Effects of Demographic and Environmental Stochasticity

Environmental Autocorrelation

Incorporating Catastrophes and Bonanzas

Concluding Remarks

5. Accounting for Observation Error in Count-based PVAs

Introduction

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

A Directory to More Advanced Methods for Estimating Parameters in the Face of Observation Error

6. Demographic PVAs: Using Demographic Data to Build Stochastic Projection Matrix Models

Introduction

Overview of Procedures for Building Projection Matrices

Step One: Conducting a Demographic Study

Step Two: Establishing Classes

Step Three: Estimating Vital Rates

Step Four: Building the Projection Matrix

Putting It All Together: Estimating Projection Matrices for Mountain Golden Heather

Summary

7. Demographic PVAs: Using Projection Matrices to Assess Population Growth and Viability

Introduction

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

Introduction

Estimation and Construction of Stochastic Models Based on Vital Rates

Simulations to Estimate Growth and Extinction

Simulating Demographic Stochasticity

Estimation and Simulation of Density Dependence in Vital Rates

Summary

9. Using Demographic PVA Models in Management: Sensitivity and Elasticity Analysis

Introduction

The Basic Idea of Sensitivity Analysis

Sensitivity and Elasticity Analysis for Deterministic Matrices

Sensitivity Analysis for Stochastic Matrix Models

Sensitivity Analysis for Density-dependent Models

Summary

10. Multi-site PVAs: The Interaction of Dispersal and Environmental Correlation

Introduction

Terminology for Multi-site PVAs

Multi-site Processes and Data Needs

A Schematic Breakdown of Multi-site Situations

Summary: Using Occam?s Razor in Multi-site PVAs

11. Multi-site PVAs: Methods of Analysis for Spatially Complex Populations

Introduction

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 PVA

Introduction

Critiques and Criticisms of PVA

General Recommendations and Cautions for Conducting a Population Viability Analysis

REFERENCES