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Spatial capture-recapture /
"Space plays a vital role in virtually all ecological processes (Tilman and Kareiva, 1997; Hanski, 1999; Clobert et al., 2001). The spatial arrangement of habitat can influence movement patterns during dispersal, habitat selection, and survival. The distance between an organism and its competit...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boston :
Elsevier,
2013.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Half Title; Title Page; Copyright; Contents; Foreword; Preface; Acknowledgments; PART I: Background and Concepts; 1 Introduction; 1.1 The study of populations by capture-recapture; 1.2 Lions and Tigers and Bears, oh my: Genesis of Spatial; 1.2.1 Camera trapping; 1.2.2 DNA sampling; 1.2.3 Acoustic sampling; 1.2.4 Search-encounter methods; 1.3 Capture-Recapture for Modeling Encounter Probability; 1.3.1 Example: Fort Drum bear study; 1.3.2 Inadequacy of non-spatial capture-recapture; 1.4 Historical Context: a Brief Synopsis; 1.4.1 Buffering; 1.4.2 Temporary emigration.
- 1.5 Extension of Closed Population Models1.5.1 Toward spatial explicitness: Efford's formulation; 1.5.2 Abundance as the aggregation of a point process; 1.5.3 The activity center concept; 1.5.4 The state-space; 1.5.5 Abundance and density; 1.6 Characterization of SCR Models; 1.7 Summary and Outlook; 2 Statistical Models and SCR; 2.1 Random Variables and Probability Distributions; 2.1.1 Stochasticity in ecology; 2.1.2 Properties of probability distributions; 2.2 Common Probability Distributions; 2.2.1 The binomial distribution; 2.2.2 The Bernoulli distribution.
- 2.2.3 The multinomial and categorical distributions2.2.4 The Poisson distribution; 2.2.5 The uniform distribution; 2.2.6 Other 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; 3 GLMs and Bayesian Analysis; 3.1 GLMs and GLMMs; 3.2 Bayesian Analysis; 3.2.1 Bayes' rule; 3.2.2 Principles of Bayesian inference; 3.2.3 Prior distributions; 3.2.4 Posterior inference; 3.2.5 Small sample inference.
- 3.3 Characterizing Posterior Distributions by MCMC Simulation3.3.1 What goes on under the MCMC hood; 3.3.2 Rules for constructing full conditional distributions; 3.3.3 Metropolis-Hastings algorithm; 3.4 Bayesian Analysis Using the BUGS Language; 3.4.1 Linear regression in WinBUGS; 3.5 Practical Bayesian Analysis and MCMC; 3.5.1 Choice of prior distributions; 3.5.2 Convergence and so forth; 3.5.3 Bayesian confidence intervals; 3.5.4 Estimating functions of parameters; 3.6 Poisson GLMs; 3.6.1 North American breeding bird survey data; 3.6.2 Poisson GLM in WinBUGS.
- 3.6.3 Constructing your own MCMC algorithm3.7 Poisson GLM with Random Effects; 3.8 Binomial GLMs; 3.8.1 Binomial regression; 3.8.2 North American waterfowl banding data; 3.9 Bayesian Model Checking and Selection; 3.9.1 Goodness-of-fit; 3.9.2 Model selection; 3.10 Summary and Outlook; 4 Closed Population Models; 4.1 The Simplest Closed Population Model: Model M0; 4.1.1 The core capture-recapture assumptions; 4.1.2 Conditional likelihood; 4.2 Data Augmentation; 4.2.1 DA links occupancy models and closed population models; 4.2.2 Model M0 in BUGS; 4.2.3 Remarks on data augmentation.