Spatial Capture-Recapture

Spatial Capture-Recapture

1st Edition - August 26, 2013

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  • Authors: J. Royle, Richard B. Chandler, Rahel Sollmann, Beth Gardner
  • eBook ISBN: 9780124071520
  • Hardcover ISBN: 9780124059399

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Spatial Capture-Recapture provides a comprehensive how-to manual with detailed examples of spatial capture-recapture models based on current technology and knowledge. Spatial Capture-Recapture provides you with an extensive step-by-step analysis of many data sets using different software implementations. The authors' approach is practical – it embraces Bayesian and classical inference strategies to give the reader different options to get the job done. In addition, Spatial Capture-Recapture provides data sets, sample code and computing scripts in an R package.

Key Features

  • Comprehensive reference on revolutionary new methods in ecology makes this the first and only book on the topic
  • Every methodological element has a detailed worked example with a code template, allowing you to learn by example
  • Includes an R package that contains all computer code and data sets on companion website


Ecologists and biologists

Table of Contents

  • Foreword


    Themes of this book


    Organization of this book


    Part I: Background and Concepts

    Chapter 1. Introduction


    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


    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


    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


    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


    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


    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


    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


    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


    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


    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


    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


    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


    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


    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


    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


    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


    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


    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


    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


    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



Product details

  • No. of pages: 612
  • Language: English
  • Copyright: © Academic Press 2013
  • Published: August 26, 2013
  • Imprint: Academic Press
  • eBook ISBN: 9780124071520
  • Hardcover ISBN: 9780124059399

About the Authors

J. Royle

J. Royle
Dr Royle is a Senior Scientist and Research Statistician at the U.S. Geological Survey's Patuxent Wildlife Research Center. His research is focused on the application of probability and statistics to ecological problems, especially those related to animal sampling and demographic modeling. Much of his research over the last 10 years has been devoted to the development of methods illustrated in our new book. He has authored or coauthored more than 100 journal articles, and co-authored the books Spatial Capture Recapture, Hierarchical Modeling and Inference in Ecology and Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, all published by Academic Press.

Affiliations and Expertise

Research Statistician, U.S. Geological Survey, Patuxent Wildlife Research Center, Laurel, MD, USA

Richard B. Chandler

Affiliations and Expertise

Warnell School of Forestry and Natural Resources, Athens, GA, USA

Rahel Sollmann

Affiliations and Expertise

North Carolina State University, Raleigh, NC, USA

Beth Gardner

Affiliations and Expertise

North Carolina State University, Raleigh, NC, USA

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