Spatial Capture-Recapture

Foreword

Preface

Themes of this book

Computing

Organization of this book

Acknowledgments

Part I: Background and Concepts

Chapter 1. Introduction

Abstract

1.1 The study of populations by capture-recapture

1.2 Lions and tigers and bears, oh my: genesis of spatial capture-recapture data

1.3 Capture-recapture for modeling encounter probability

1.4 Historical context: a brief synopsis

1.5 Extension of closed population models

1.6 Ecological focus of SCR models

1.7 Summary and outlook

Chapter 2. Statistical Models and SCR

Abstract

2.1 Random variables and probability distributions

2.2 Common probability distributions

2.3 Statistical inference and parameter estimation

2.4 Joint, marginal, and conditional distributions

2.5 Hierarchical models and inference

2.6 Characterization of SCR models

2.7 Summary and outlook

Chapter 3. GLMs and Bayesian Analysis

Abstract

3.1 GLMs and GLMMs

3.2 Bayesian analysis

3.3 Characterizing posterior distributions by MCMC simulation

3.4 Bayesian analysis using the BUGS language

3.5 Practical Bayesian analysis and MCMC

3.6 Poisson GLMs

3.7 Poisson GLM with random effects

3.8 Binomial GLMs

3.9 Bayesian model checking and selection

3.10 Summary and outlook

Chapter 4. Closed Population Models

Abstract

4.1 The simplest closed population model: model

4.2 Data augmentation

4.3 Temporally varying and behavioral effects

4.4 Models with individual heterogeneity

4.5 Individual covariate models: toward spatial capture-recapture

4.6 Distance sampling: a primitive SCR model

4.7 Summary and outlook

Part II: Basic SCR Models

Chapter 5. Fully Spatial Capture-Recapture Models

Abstract

5.1 Sampling design and data structure

5.2 The binomial observation model

5.3 The binomial point process model

5.4 The implied model of space usage

5.5 Simulating SCR data

5.6 Fitting model SCR0 in BUGS

5.7 Unknown N

5.8 The core SCR assumptions

5.9 Wolverine camera trapping study

5.10 Using a discrete habitat mask

5.11 Summarizing density and activity center locations

5.12 Effective sample area

5.13 Summary and outlook

Chapter 6. Likelihood Analysis of Spatial Capture-Recapture Models

Abstract

6.1 MLE for SCR with known N

6.2 MLE when N is unknown

6.3 Classical model selection and assessment

6.4 Likelihood analysis of the wolverine camera trapping data

6.5 DENSITY and the R package secr

6.6 Summary and outlook

Chapter 7. Modeling Variation in Encounter Probability

Abstract

7.1 Encounter probability models

7.2 Modeling covariate effects

7.3 Individual heterogeneity

7.4 Likelihood analysis in secr

7.5 Summary and outlook

Chapter 8. Model Selection and Assessment

Abstract

8.1 Model selection by AIC

8.2 Bayesian model selection

8.3 Evaluating goodness-of-fit

8.4 The two components of model fit

8.5 Quantifying lack-of-fit and remediation

8.6 Summary and outlook

Chapter 9. Alternative Observation Models

Abstract

9.1 Poisson observation model

9.2 Independent multinomial observations

9.3 Single-catch traps

9.4 Acoustic sampling

9.5 Summary and outlook

Chapter 10. Sampling Design

Abstract

10.1 General considerations

10.2 Study design for (spatial) capture-recapture

10.3 Trap spacing and array size relative to animal movement

10.4 Sampling over large areas

10.5 Model-based spatial design

10.6 Temporal aspects of study design

10.7 Summary and outlook

Part III: Advanced SCR Models

Chapter 11. Modeling Spatial Variation in Density

Abstract

11.1 Homogeneous point process revisited

11.2 Inhomogeneous point processes

11.3 Observed point processes

11.4 Fitting inhomogeneous point process SCR models

11.5 Argentina jaguar study

11.6 Summary and outlook

Chapter 12. Modeling Landscape Connectivity

Abstract

12.1 Shortcomings of Euclidean distance models

12.2 Least-cost path distance

12.3 Simulating SCR data using ecological distance

12.4 Likelihood analysis of ecological distance models

12.5 Bayesian analysis

12.6 Simulation evaluation of the MLE

12.7 Distance in an irregular patch

12.8 Ecological distance and density covariates

12.9 Summary and outlook

Chapter 13. Integrating Resource Selection with Spatial Capture-Recapture Models

Abstract

13.1 A model of space usage

13.2 Integrating capture-recapture data

13.3 SW New York black bear study

13.4 Simulation study

13.5 Relevance and relaxation of assumptions

13.6 Summary and outlook

Chapter 14. Stratified Populations: Multi-Session and Multi-Site Data

Abstract

14.1 Stratified data structure

14.2 Multinomial abundance models

14.3 Other approaches to multi-session models

14.4 Application to spatial capture-recapture

14.5 Spatial or temporal dependence

14.6 Summary and outlook

Chapter 15. Models for Search-Encounter Data

Abstract

15.1 Search-encounter designs

15.2 A model for fixed search path data

15.3 Unstructured spatial surveys

15.4 Design 2: Uniform search intensity

15.5 Partial information designs

15.6 Summary and outlook

Chapter 16. Open Population Models

Abstract

16.1 Background

16.2 Jolly-Seber models

16.3 Cormack-Jolly-Seber models

16.4 Modeling movement and dispersal dynamics

16.5 Summary and outlook

Part IV: Super - Advanced SCR Models

Chapter 17. Developing Markov Chain Monte Carlo Samplers

Abstract

17.1 Why build your own MCMC algorithm?

17.2 MCMC and posterior distributions

17.3 Types of MCMC sampling

17.4 MCMC for closed capture-recapture model

17.5 MCMC algorithm for model SCR0

17.6 Looking at model output

17.7 Manipulating the state-space

17.8 Increasing computational speed

17.9 Summary and outlook

Chapter 18. Unmarked Populations

Abstract

18.1 Existing models for inference about density in unmarked populations

18.2 Spatial correlation in count data

18.3 Spatial count model

18.4 How much correlation is enough?

18.5 Applications

18.6 Extensions of the spatial count model

18.7 Summary and outlook

Chapter 19. Spatial Mark-Resight Models

Abstract

19.1 Background

19.2 Known number of marked individuals

19.3 Unknown number of marked individuals

19.4 Imperfect identification of marked individuals

19.5 How much information do marked and unmarked individuals contribute?

19.6 Incorporating telemetry data

19.7 Point process models for marked individuals

19.8 Summary and outlook

Chapter 20. 2012: A Spatial Capture-Recapture Odyssey

Abstract

20.1 Emerging topics

20.2 Final remarks

Part V: Appendix

Appendix. I—Useful Software and R Packages

20.3 WinBUGS

20.4 OpenBUGS

20.5 JAGS

20.6 R

Bibliography

Index