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Spatial Capture-Recapture
- 1st Edition - August 26, 2013
- Authors: J. Andrew Royle, Richard B. Chandler, Rahel Sollmann, Beth Gardner
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 1 0 0 1 2 - 7
- Hardback ISBN:9 7 8 - 0 - 1 2 - 4 0 5 9 3 9 - 9
- eBook ISBN:9 7 8 - 0 - 1 2 - 4 0 7 1 5 2 - 0
Spatial Capture-Recapture provides a comprehensive how-to manual with detailed examples of spatial capture-recapture models based on current technology and knowledge. Spatial C… Read more
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Request a sales quoteSpatial 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.
- 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
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
- No. of pages: 612
- Language: English
- Edition: 1
- Published: August 26, 2013
- Imprint: Academic Press
- Paperback ISBN: 9780128100127
- Hardback ISBN: 9780124059399
- eBook ISBN: 9780124071520
JR
J. Andrew 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, USARC
Richard B. Chandler
Affiliations and expertise
Warnell School of Forestry and Natural Resources, Athens, GA, USARS
Rahel Sollmann
Affiliations and expertise
North Carolina State University, Raleigh, NC, USABG
Beth Gardner
Affiliations and expertise
North Carolina State University, Raleigh, NC, USARead Spatial Capture-Recapture on ScienceDirect