Analysis and management of animal populations : modeling, estimation, and decision making / Byron K. Williams, James D. Nichols, Michael J. Conroy.

Format:

Book

Author/Creator:

Williams, Byron K.

Contributor:

Nichols, James D.

Conroy, Michael J. (Michael James), 1952-

Alumni and Friends Memorial Book Fund.

Language:

English

Subjects (All):

Animal populations--Statistical methods.

Animal populations.

Animals.

Population Surveillance.

Models, Statistical.

Medical Subjects:

Animals.

Population Surveillance.

Models, Statistical.

Physical Description:

xvii, 817 pages : illustrations ; 29 cm

Place of Publication:

San Diego : Academic Press, 2002.

Summary:

Analysis and Management of Animal Populations deals with the processes involved in making informed decisions about the management of animal populations. It covers the modeling of population responses to management actions, the estimation of quantities needed in the modeling effort, and the application of these estimates and models to the development of sound management decisions. The book synthesizes and integrates in a single volume the methods associated with these themes, as they apply to ecological assessment and conservation of animal populations. Key Features* Integrates population modeling, parameter estimation and decision-theoretic approaches to management in a single, cohesive framework * Provides authoritative, state-of-the-art descriptions of quantitative approaches to modeling, estimation and decision-making * Emphasizes the role of mathematical modeling in the conduct of science and management* Utilizes a unifying biological context, consistent mathematical notation, and numerous biological examples

Contents:

Part I Framework for Modeling, Estimation, and Management of Animal Populations

Chapter 1 Introduction to Population Ecology

1.2. Population Dynamics 4

1.3. Factors Affecting Populations 4

1.4. Management of Animal Populations 6

1.5. Individuals, Fitness, and Life History Characteristics 7

1.6. Community Dynamics 9

Chapter 2 Scientific Process in Animal Ecology

2.1. Causation in Animal Ecology 11

2.2. Approaches to the Investigation of Causes 12

2.3. Scientific Methods 13

2.4. Hypothesis Confirmation 16

2.5. Inductive Logic in Scientific Method 17

2.6. Statistical Inference 18

2.7. Investigating Complementary Hypotheses 18

Chapter 3 Models and the Investigation of Populations

3.1. Types of Biological Models 22

3.2. Keys to Successful Model Use 22

3.3. Uses of Models in Population Biology 23

3.4. Determinants of Model Utility 28

3.5. Hypotheses, Models, and Science 30

Chapter 4 Estimation and Hypothesis Testing in Animal Ecology

4.1. Statistical Distributions 34

4.2. Parameter Estimation 42

4.3. Hypothesis Testing 50

4.4. Information-Theoretic Approaches 55

4.5. Bayesian Extension of Likelihood Theory 57

Chapter 5 Survey Sampling and the Estimation of Population Parameters

5.1. Sampling Issues 60

5.2. Features of a Sampling Design 61

5.3. Simple Random and Stratified Random Sampling 62

5.4. Other Sampling Approaches 67

5.5. Common Problems in Sampling Designs 74

Chapter 6 Design of Experiments in Animal Ecology

6.1. Principles of Experimental Design 80

6.2. Completely Randomized Designs 83

6.3. Randomized Block Designs 89

6.4. Covariation and Analysis of Covariance 91

6.5. Hierarchical Designs 92

6.6. Random Effects and Nested Designs 97

6.7. Statistical Power and Experimental Design 100

6.8. Constrained Experimental Designs and Quasi-Experiments 102

Part II Dynamic Modeling of Animal Populations

Chapter 7 Principles of Model Development and Assessment

7.1. Modeling Goals 113

7.2. Attributes of Population Models 114

7.3. Describing Population Models 117

7.4. Constructing a Population Model 122

7.5. Model Assessment 126

7.6. A Systematic Approach to the Modeling of Animal Populations 131

Chapter 8 Traditional Models of Population Dynamics

8.1. Density-Independent Growth

The Exponential Model 136

8.2. Density-Dependent Growth

The Logistic Model 139

8.3. Cohort Models 141

8.4. Models with Age Structure 143

8.5. Models with Size Structure 157

8.6. Models with Geographic Structure 159

8.7. Lotka-Volterra Predator-Prey Models 161

8.8. Models of Competing Populations 164

8.9. A General Model for Interacting Species 170

Chapter 9 Model Identification with Time Series Data

9.1. Model Identification Based on Ordinary Least Squares 174

9.2. Other Measures of Model Fit 176

9.3. Correlated Estimates of Population Size 178

9.4. Optimal Identification 178

9.5. Identifying Models with Population Size as a Function of Time 179

9.6. Identifying Models Using Lagrangian Multipliers 181

9.7. Stability of Parameter Estimates 181

9.8. Identifying System Properties in the Absence of a Specified Model 182

Chapter 10 Stochastic Processes in Population Models

10.1. Bernoulli Counting Processes 189

10.2. Poisson Counting Processes 192

10.3. Discrete Markov Processes 197

10.4. Continuous Markov Processes 202

10.5. Semi-Markov Processes 205

10.6. Markov Decision Processes 207

10.7. Brownian Motion 210

10.8. Other Stochastic Processes 213

Chapter 11 The Use of Models in Conservation and Management

11.1. Dynamics of Harvested Populations 223

11.2. Conservation and Extinction of Populations 231

Part III Estimation Methods for Animal Populations

Chapter 12 Estimating Abundance Based on Counts

12.1. Overview of Abundance Estimation 242

12.2. A Canonical Population Estimator 243

12.3. Population Censuses 245

12.4. Complete Detectability of Individuals on Sample Units of Equal Area 245

12.5. Complete Detectability of Individuals on Sample Units of Unequal Area 247

12.6. Partial Detectability of Individuals on Sample Units 250

12.7. Indices to Population Abundance or Density 257

Chapter 13 Estimating Abundance with Distance-Based Methods

13.1. Point-to-Object Methods 263

13.2. Line Transect Sampling 265

13.3. Point Sampling 278

13.4. Design of Line Transect and Point Sampling Studies 281

Chapter 14 Estimating Abundance for Closed Populations with Mark-Recapture Methods

14.1. Two-Sample Lincoln-Petersen Estimator 290

14.2. K-Sample Capture-Recapture Models 296

14.3. Density Estimation with Capture-Recapture 314

14.4. Removal Methods 320

14.5. Change-in-Ratio Methods 325

Chapter 15 Estimation of Demographic Parameters

15.1. Detectability and Demographic Rate Parameters 334

15.2. Analysis of Age Frequencies 337

15.3. Analysis of Discrete Survival and Nest Success Data 343

15.4. Analysis of Failure Times 351

15.5. Random Effects and Known-Fate Data 361

Chapter 16 Estimation of Survival Rates with Band Recoveries

16.1. Single-Age Models 366

16.2. Multiple-Age Models 383

16.3. Reward Studies for Estimating Reporting Rates 391

16.4. Analysis of Band Recoveries for Nonharvested Species 398

16.5. Poststratification of Recoveries and Analysis of Movements 402

16.6. Design of Banding Studies 406

Chapter 17 Estimating Survival, Movement, and Other State Transitions with Mark-Recapture Methods

17.1. Single-Age Models 418

17.2. Multiple-Age Models 438

17.3. Multistate Models 454

17.4. Reverse-Time Models 468

17.5. Mark-Recapture with Auxiliary Data 476

17.6. Study Design 489

Chapter 18 Estimating Abundance and Recruitment with Mark-Recapture Methods

18.1. Data Structure 496

18.2. Jolly-Seber Approach 497

18.3. Superpopulation Approach 508

18.4. Pradel's Temporal Symmetry Approach 511

18.5. Relationships among Approaches 518

18.6. Study Design 520

Chapter 19 Combining Closed and Open Mark-Recapture Models: The Robust Design

19.1. Data Structure 524

19.2. Ad Hoc Approach 529

19.3. Likelihood-Based Approach 535

19.4. Special Estimation Problems 538

19.5. Study Design 552

Chapter 20 Estimation of Community Parameters

20.1. An Analogy between Populations and Communities 556

20.2. Estimation of Species Richness 557

20.3. Estimating Parameters of Community Dynamics 561

Part IV Decision Analysis for Animal Populations

Chapter 21 Optimal Decision Making in Population Biology

21.1. Optimization and Population Dynamics 578

21.2. Objective Functions 579

21.3. Stationary Optimization under Equilibrium Conditions 579

21.4. Stationary Optimization under Nonequilibrium Conditions 580

Chapter 22 Traditional Approaches to Optimal Decision Analysis

22.1. The Geometry of Optimization 584

22.2. Unconstrained Optimization 585

22.3. Classical Programming 593

22.4. Nonlinear Programming 597

22.5. Linear Programming 601

Chapter 23 Modern Approaches to Optimal Decision Analysis

23.1. Calculus of Variations 608

23.2. Pontryagin's Maximum Principle 618

23.3. Dynamic Programming 627

23.4. Heuristic Approaches 638

Chapter 24 Uncertainty, Learning, and Decision Analysis

24.1. Decision Analysis in Natural Resource Conservation 644

24.2. General Framework for Decision Analysis 649

24.3. Uncertainty and the Control of Dynamic Resources 650

24.4. Optimal Control with a Single Model 651

24.5. Optimal Control with Multiple Models 652

24.6. Adaptive Optimization and Learning 653

24.7. Expected Value of Perfect Information 654

24.8. Partial Observability 655

24.9. Generalizations of Adaptive Optimization 656

24.10. Accounting for All Sources of Uncertainty 658

24.11. "Passive" Adaptive Optimization 658

Chapter 25 Case Study: Management of the Sport Harvest of

North American Waterfowl

25.1. Background and History 664

25.2. Components of a Regulatory Process 667

25.3. Adaptive Harvest Management 671

25.4. Modeling Population Dynamics 672

25.5. Harvest Objectives 676

25.6. Regulatory Alternatives 677

25.7. Identifying Optimal Regulations 679

25.8. Some Ongoing Issues in Waterfowl Harvest Management 680

Appendix A Conditional Probability and Bayes' Theorem 685

Appendix B Matrix Algebra 687

B.2. Matrix Addition and Multiplication

B.3. Matrix Determinants

B.4. Inverse of a Matrix

B.5. Orthogonal and Orthonormal Matrices

B.6. Trace of a Matrix

B.7. Eigenvectors and Eigenvalues

B.8. Linear and Quadratic Forms

B.9. Positive-Definite and Semidefinite Matrices

B.10. Matrix Differentiation

Appendix C Differential Equations 693

C.1. First-Order Linear Homogeneous Equations

C.2. Nonlinear Homogeneous Equations

Stability Analysis

C.3. Graphical Methods

Appendix D Difference Equations 709

D.1. First-Order Linear Homogeneous Equations

D.2. Nonlinear Homogeneous Equations

Appendix E Some Probability Distributions and Their Properties 721

E.1. Discrete Distributions

E.2. Continuous Distributions

Appendix F Methods for Estimating Statistical Variation 733

F.1. Distribution-Based Variance Estimation

F.2. Empirical Variance Estimation

F.3. Estimating Variances and Covariances with the Information Matrix

F.4. Approximating Variance with the Delta Method

F.5. Jackknife Estimators of Mean and Variance

F.6. Bootstrap Estimation

Appendix G Computer Software for Population and Community Estimation 739

G.1. Estimation of Abundance and Density for Closed Populations

G.2. Estimation of Abundance and Demographic Parameters for Open Populations

G.3. Estimation of Community Parameters

G.4. Software Availability

Appendix H The Mathematics of Optimization 745

H.1. Unconstrained Optimization

H.2. Classical Programming

H.3. Nonlinear Programming

H.4. Linear Programming

H.5. Calculus of Variations

H.6. Pontryagin's Maximum Principle

H.7. Dynamic Programming.

Local Notes:

Acquired for the Penn Libraries with assistance from the Alumni and Friends Memorial Book Fund.

ISBN:

0127544062

OCLC:

49666129