Umfang:
Online Ressource
ISBN:
9780124071520
,
012407152X
,
9780124059399
Inhalt:
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
Inhalt:
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
Inhalt:
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
Inhalt:
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
Inhalt:
"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 competitors and prey can influence activity patterns and foraging behavior. Further, understanding distribution and spatial variation in abundance is necessary in the conservation and management of populations"--
Anmerkung:
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
,
4.2.4 Example: Black bear study on Fort Drum
,
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
Weitere Ausg.:
ISBN 9780124059399
Sprache:
Englisch
Schlagwort(e):
Forschungsmethode
;
Bayes-Verfahren
;
Wildtiermanagement
;
Fang-Wiederfang-Methode
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