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.
1. Chapter 1. Introduction - p. XXIX 2. Chapter 2. Statistical Models and SCR - p. 19 3. Chapter 3. GLMs and Bayesian Analysis - p. 46 4. Chapter 4. Closed Population Models - p. 85 5. Part II: Basic SCR Models - p. 121 6. Chapter 5. Fully Spatial Capture-Recapture Models - p. 121 7. Chapter 6. Likelihood Analysis of Spatial Capture-Recapture Models - p. 170 8. Chapter 7. Modeling Variation in Encounter Probability - p. 196 9. Chapter 8. Model Selection and Assessment - p. 218 10. Chapter 9. Alternative Observation Models - p. 243 11. Chapter 10. Sampling Design - p. 275 12. Part III: Advanced SCR Models - p. 303 13. Chapter 11. Modeling Spatial Variation in Density - p. 303 14. Chapter 12. Modeling Landscape Connectivity - p. 327 15. Chapter 13. Integrating Resource Selection with Spatial Capture-Recapture Models - p. 347 16. Chapter 14. Stratified Populations: Multi-Session and Multi-Site Data - p. 363 17. Chapter 15. Models for Search-Encounter Data - p. 379 18. Chapter 16. Open Population Models - p. 399 19. Part IV: Super - Advanced SCR Models - p. 430 20. Chapter 17. Developing Markov Chain Monte Carlo Samplers - p. 430 21. Chapter 18. Unmarked Populations - p. 471 22. Chapter 19. Spatial Mark-Resight Models - p. 495 23. Chapter 20. 2012: A Spatial Capture-Recapture Odyssey - p. 526 24. Part V: Appendix - p. 535 25. Appendix. I—Useful Software and R Packages - p. 535 26. Bibliography - p. 544 27. Index - p. 544
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.
1. Chapter 1. Introduction - p. XXIX 2. Chapter 2. Statistical Models and SCR - p. 19 3. Chapter 3. GLMs and Bayesian Analysis - p. 46 4. Chapter 4. Closed Population Models - p. 85 5. Part II: Basic SCR Models - p. 121 6. Chapter 5. Fully Spatial Capture-Recapture Models - p. 121 7. Chapter 6. Likelihood Analysis of Spatial Capture-Recapture Models - p. 170 8. Chapter 7. Modeling Variation in Encounter Probability - p. 196 9. Chapter 8. Model Selection and Assessment - p. 218 10. Chapter 9. Alternative Observation Models - p. 243 11. Chapter 10. Sampling Design - p. 275 12. Part III: Advanced SCR Models - p. 303 13. Chapter 11. Modeling Spatial Variation in Density - p. 303 14. Chapter 12. Modeling Landscape Connectivity - p. 327 15. Chapter 13. Integrating Resource Selection with Spatial Capture-Recapture Models - p. 347 16. Chapter 14. Stratified Populations: Multi-Session and Multi-Site Data - p. 363 17. Chapter 15. Models for Search-Encounter Data - p. 379 18. Chapter 16. Open Population Models - p. 399 19. Part IV: Super - Advanced SCR Models - p. 430 20. Chapter 17. Developing Markov Chain Monte Carlo Samplers - p. 430 21. Chapter 18. Unmarked Populations - p. 471 22. Chapter 19. Spatial Mark-Resight Models - p. 495 23. Chapter 20. 2012: A Spatial Capture-Recapture Odyssey - p. 526 24. Part V: Appendix - p. 535 25. Appendix. I—Useful Software and R Packages - p. 535 26. Bibliography - p. 544 27. Index - p. 544
