Analysis and management of animal populations : modeling, estimation, and decision making /

Preface p. xiii
Acknowledgments p. xvii
Part I Framework for Modeling, Estimation, and Management of Animal Populations
Chapter 1 Introduction to Population Ecology
1.1. Some Definitions p. 3
1.2. Population Dynamics p. 4
1.3. Factors Affecting Populations p. 4
1.4. Management of Animal Populations p. 6
1.5. Individuals, Fitness, and Life History Characteristics p. 7
1.6. Community Dynamics p. 9
1.7. Discussion p. 9
Chapter 2 Scientific Process in Animal Ecology
2.1. Causation in Animal Ecology p. 11
2.2. Approaches to the Investigation of Causes p. 12
2.3. Scientific Methods p. 13
2.4. Hypothesis Confirmation p. 16
2.5. Inductive Logic in Scientific Method p. 17
2.6. Statistical Inference p. 18
2.7. Investigating Complementary Hypotheses p. 18
2.8. Discussion p. 19
Chapter 3 Models and the Investigation of Populations
3.1. Types of Biological Models p. 22
3.2. Keys to Successful Model Use p. 22
3.3. Uses of Models in Population Biology p. 23
3.4. Determinants of Model Utility p. 28
3.5. Hypotheses, Models, and Science p. 30
3.6. Discussion p. 31
Chapter 4 Estimation and Hypothesis Testing in Animal Ecology
4.1. Statistical Distributions p. 34
4.2. Parameter Estimation p. 42
4.3. Hypothesis Testing p. 50
4.4. Information-Theoretic Approaches p. 55
4.5. Bayesian Extension of Likelihood Theory p. 57
4.6. Discussion p. 58
Chapter 5 Survey Sampling and the Estimation of Population Parameters
5.1. Sampling Issues p. 60
5.2. Features of a Sampling Design p. 61
5.3. Simple Random and Stratified Random Sampling p. 62
5.4. Other Sampling Approaches p. 67
5.5. Common Problems in Sampling Designs p. 74
5.6. Discussion p. 76
Chapter 6 Design of Experiments in Animal Ecology
6.1. Principles of Experimental Design p. 80
6.2. Completely Randomized Designs p. 83
6.3. Randomized Block Designs p. 89
6.4. Covariation and Analysis of Covariance p. 91
6.5. Hierarchical Designs p. 92
6.6. Random Effects and Nested Designs p. 97
6.7. Statistical Power and Experimental Design p. 100
6.8. Constrained Experimental Designs and Quasi-Experiments p. 102
6.9. Discussion p. 106
Part II Dynamic Modeling of Animal Populations
Chapter 7 Principles of Model Development and Assessment
7.1. Modeling Goals p. 113
7.2. Attributes of Population Models p. 114
7.3. Describing Population Models p. 117
7.4. Constructing a Population Model p. 122
7.5. Model Assessment p. 126
7.6. A Systematic Approach to the Modeling of Animal Populations p. 131
7.7. Discussion p. 134
Chapter 8 Traditional Models of Population Dynamics
8.1. Density-Independent Growth--The Exponential Model p. 136
8.2. Density-Dependent Growth--The Logistic Model p. 139
8.3. Cohort Models p. 141
8.4. Models with Age Structure p. 143
8.5. Models with Size Structure p. 157
8.6. Models with Geographic Structure p. 159
8.7. Lotka-Volterra Predator-Prey Models p. 161
8.8. Models of Competing Populations p. 164
8.9. A General Model for Interacting Species p. 170
8.10. Discussion p. 171
Chapter 9 Model Identification with Time Series Data
9.1. Model Identification Based on Ordinary Least Squares p. 174
9.2. Other Measures of Model Fit p. 176
9.3. Correlated Estimates of Population Size p. 178
9.4. Optimal Identification p. 178
9.5. Identifying Models with Population Size as a Function of Time p. 179
9.6. Identifying Models Using Lagrangian Multipliers p. 181
9.7. Stability of Parameter Estimates p. 181
9.8. Identifying System Properties in the Absence of a Specified Model p. 182
9.9. Discussion p. 184
Chapter 10 Stochastic Processes in Population Models
10.1. Bernoulli Counting Processes p. 189
10.2. Poisson Counting Processes p. 192
10.3. Discrete Markov Processes p. 197
10.4. Continuous Markov Processes p. 202
10.5. Semi-Markov Processes p. 205
10.6. Markov Decision Processes p. 207
10.7. Brownian Motion p. 210
10.8. Other Stochastic Processes p. 213
10.9. Discussion p. 220
Chapter 11 The Use of Models in Conservation and Management
11.1. Dynamics of Harvested Populations p. 223
11.2. Conservation and Extinction of Populations p. 231
11.3. Discussion p. 237
Part III Estimation Methods for Animal Populations
Chapter 12 Estimating Abundance Based on Counts
12.1. Overview of Abundance Estimation p. 242
12.2. A Canonical Population Estimator p. 243
12.3. Population Censuses p. 245
12.4. Complete Detectability of Individuals on Sample Units of Equal Area p. 245
12.5. Complete Detectability of Individuals on Sample Units of Unequal Area p. 247
12.6. Partial Detectability of Individuals on Sample Units p. 250
12.7. Indices to Population Abundance or Density p. 257
12.8. Discussion p. 261
Chapter 13 Estimating Abundance with Distance-Based Methods
13.1. Point-to-Object Methods p. 263
13.2. Line Transect Sampling p. 265
13.3. Point Sampling p. 278
13.4. Design of Line Transect and Point Sampling Studies p. 281
13.5. Other Issues p. 286
13.6. Discussion p. 287
Chapter 14 Estimating Abundance for Closed Populations with Mark-Recapture Methods
14.1. Two-Sample Lincoln-Petersen Estimator p. 290
14.2. K-Sample Capture-Recapture Models p. 296
14.3. Density Estimation with Capture-Recapture p. 314
14.4. Removal Methods p. 320
14.5. Change-in-Ratio Methods p. 325
14.6. Discussion p. 331
Chapter 15 Estimation of Demographic Parameters
15.1. Detectability and Demographic Rate Parameters p. 334
15.2. Analysis of Age Frequencies p. 337
15.3. Analysis of Discrete Survival and Nest Success Data p. 343
15.4. Analysis of Failure Times p. 351
15.5. Random Effects and Known-Fate Data p. 361
15.6. Discussion p. 362
Chapter 16 Estimation of Survival Rates with Band Recoveries
16.1. Single-Age Models p. 366
16.2. Multiple-Age Models p. 383
16.3. Reward Studies for Estimating Reporting Rates p. 391
16.4. Analysis of Band Recoveries for Nonharvested Species p. 398
16.5. Poststratification of Recoveries and Analysis of Movements p. 402
16.6. Design of Banding Studies p. 406
16.7. Discussion p. 414
Chapter 17 Estimating Survival, Movement, and Other State Transitions with Mark-Recapture Methods
17.1. Single-Age Models p. 418
17.2. Multiple-Age Models p. 438
17.3. Multistate Models p. 454
17.4. Reverse-Time Models p. 468
17.5. Mark-Recapture with Auxiliary Data p. 476
17.6. Study Design p. 489
17.7. Discussion p. 492
Chapter 18 Estimating Abundance and Recruitment with Mark-Recapture Methods
18.1. Data Structure p. 496
18.2. Jolly-Seber Approach p. 497
18.3. Superpopulation Approach p. 508
18.4. Pradel's Temporal Symmetry Approach p. 511
18.5. Relationships among Approaches p. 518
18.6. Study Design p. 520
18.7. Discussion p. 522
Chapter 19 Combining Closed and Open Mark-Recapture Models: The Robust Design
19.1. Data Structure p. 524
19.2. Ad Hoc Approach p. 529
19.3. Likelihood-Based Approach p. 535
19.4. Special Estimation Problems p. 538
19.5. Study Design p. 552
19.6. Discussion p. 553
Chapter 20 Estimation of Community Parameters
20.1. An Analogy between Populations and Communities p. 556
20.2. Estimation of Species Richness p. 557
20.3. Estimating Parameters of Community Dynamics p. 561
20.4. Discussion p. 572
Part IV Decision Analysis for Animal Populations
Chapter 21 Optimal Decision Making in Population Biology
21.1. Optimization and Population Dynamics p. 578
21.2. Objective Functions p. 579
21.3. Stationary Optimization under Equilibrium Conditions p. 579
21.4. Stationary Optimization under Nonequilibrium Conditions p. 580
21.5. Discussion p. 581
Chapter 22 Traditional Approaches to Optimal Decision Analysis
22.1. The Geometry of Optimization p. 584
22.2. Unconstrained Optimization p. 585
22.3. Classical Programming p. 593
22.4. Nonlinear Programming p. 597
22.5. Linear Programming p. 601
22.6. Discussion p. 606
Chapter 23 Modern Approaches to Optimal Decision Analysis
23.1. Calculus of Variations p. 608
23.2. Pontryagin's Maximum Principle p. 618
23.3. Dynamic Programming p. 627
23.4. Heuristic Approaches p. 638
23.5. Discussion p. 639
Chapter 24 Uncertainty, Learning, and Decision Analysis
24.1. Decision Analysis in Natural Resource Conservation p. 644
24.2. General Framework for Decision Analysis p. 649
24.3. Uncertainty and the Control of Dynamic Resources p. 650
24.4. Optimal Control with a Single Model p. 651
24.5. Optimal Control with Multiple Models p. 652
24.6. Adaptive Optimization and Learning p. 653
24.7. Expected Value of Perfect Information p. 654
24.8. Partial Observability p. 655
24.9. Generalizations of Adaptive Optimization p. 656
24.10. Accounting for All Sources of Uncertainty p. 658
24.11. "Passive" Adaptive Optimization p. 658
24.12. Discussion p. 660
Chapter 25 Case Study: Management of the Sport Harvest of North American Waterfowl
25.1. Background and History p. 664
25.2. Components of a Regulatory Process p. 667
25.3. Adaptive Harvest Management p. 671
25.4. Modeling Population Dynamics p. 672
25.5. Harvest Objectives p. 676
25.6. Regulatory Alternatives p. 677
25.7. Identifying Optimal Regulations p. 679
25.8. Some Ongoing Issues in Waterfowl Harvest Management p. 680
25.9. Discussion p. 684
Appendix A Conditional Probability and Bayes' Theorem p. 685
Appendix B Matrix Algebra p. 687
B.1. Definitions
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 p. 693
C.1. First-Order Linear Homogeneous Equations
C.2. Nonlinear Homogeneous Equations--Stability Analysis
C.3. Graphical Methods
Appendix D Difference Equations p. 709
D.1. First-Order Linear Homogeneous Equations
D.2. Nonlinear Homogeneous Equations--Stability Analysis
Appendix E Some Probability Distributions and Their Properties p. 721
E.1. Discrete Distributions
E.2. Continuous Distributions
Appendix F Methods for Estimating Statistical Variation p. 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 p. 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 p. 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
References p. 767
Index p. 793