COVID-19 Update: We are currently shipping orders daily. However, due to transit disruptions in some geographies, deliveries may be delayed. To provide all customers with timely access to content, we are offering 50% off Science and Technology Print & eBook bundle options. Terms & conditions.
Spatial Capture-Recapture - 1st Edition - ISBN: 9780124059399, 9780124071520

Spatial Capture-Recapture

1st Edition

Authors: J. Royle Richard B. Chandler Rahel Sollmann Beth Gardner
eBook ISBN: 9780124071520
Hardcover ISBN: 9780124059399
Paperback ISBN: 9780128100127
Imprint: Academic Press
Published Date: 26th August 2013
Page Count: 612
Sales tax will be calculated at check-out Price includes VAT/GST
Price includes VAT/GST

Institutional Subscription

Secure Checkout

Personal information is secured with SSL technology.

Free Shipping

Free global shipping
No minimum order.


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



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




No. of pages:
© Academic Press 2013
26th August 2013
Academic Press
eBook ISBN:
Hardcover ISBN:
Paperback ISBN:

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


"...a book for the DIY quantitative ecologist who wants to understand their data...I enjoyed it tremendously and it already had a strong influence on how I think about some of my current research projects."--Basic and Applied Ecology, Spatial Capture-Recapture

"...a timely and informative contribution that summarizes the history and motivation behind SCR models,...will be a vital addition to wildlife ecologist’s book shelves for many years to come."--The Journal of Wildlife Management, Sep 14

Ratings and Reviews