Bayesian Population Analysis Using WinBUGS: A Hierarchical PerspectiveBayesian statistics has exploded into biology and its subdisciplines, such as ecology, over the past decade. The free software program WinBUGS, and its opensource sister OpenBugs, is currently the only flexible and generalpurpose program available with which the average ecologist can conduct standard and nonstandard Bayesian statistics.

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Contents
2 Brief Introduction to Bayesian Statistical Modeling  23 
The Simplest Model for Count Data  47 
Conventional Poisson GLMM for Count Data  73 
5 StateSpace Models for Population Counts  115 
6 Estimation of the Size of a Closed Population from CaptureRecapture Data  133 
7 Estimation of Survival from CaptureRecapture Data Using the CormackJollySeber Model  171 
8 Estimation of Survival Using MarkRecovery Data  241 
9 Estimation of Survival and Movement from CaptureRecapture Data Using Multistate Models  263 
11 Estimation of Demographic Rates Population Size and Projection Matrices from Multiple Data Types Using Integrated Population Models  347 
12 Estimation of Abundance from Counts in Metapopulation Designs Using the Binomial Mixture Model  383 
13 Estimation of Occupancy and Species Distributions from DetectionNondetection Data in Metapopulation Designs Using SiteOccupancy Models  413 
14 Concluding Remarks  463 
A List of WinBUGS Tricks  479 
Two Further Useful Multistate CaptureRecapture Models  487 
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10 Estimation of Survival Recruitment and Population Size from CaptureRecapture Data Using the JollySeber Model  315 
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Common terms and phrases
abundance adults age classes Bayesian Bernoulli trial beta1 binomial mixture model BUGS language bugs.dir bugs.directory Call WinBUGS capture capturehistories capture–recapture data CJS model covariate data set debug Define demographic rates detection probability dnorm(0 dunif dunif(0 dynamics ecological example frequentist function getwd hierarchical models individual Initial values inits juvenile Kéry latent linear model logit marray main diagonal markrecovery Markov chains matrix MCMC settings mean sd 2.5 mean.p mean.phi metapopulation mixture model model in BUGS multistate models n.burnin n.chains n.iter n.occasions n.occasions−1 n.thin ncol normal distribution nrow observation error observation process occasion OpenBUGS Parameters monitored parameters params Poisson posterior distribution prior distribution Prior for mean Priors and constraints random effects randomeffects models recapture probability replicate Rhat n.eff Royle and Dorazio runif(1 sampling simulated data siteoccupancy model species Specify model statespace model survival probability tion TRUE vague priors variable win.data working.directory xlab ylab