Bayesian Population Analysis using WinBUGS

Distribution Bird Survival Emigration Immigration Ecology Forecasting Management Recruitment Conservation Data processing Population biology Recovery Population ecology Dispersal Capture Classical Occurrence Population Dynamics Shrinkage abundance Probability trend butterfly Confidence interval Capture-recapture Species distribution Factor Model selection Bayesian Poisson Counts Incidence Random effects Variability GLM Environmental stochasticity Study design State space model Correlation Process Variation Productivity Ecological Process Population Size Prior Fixed effects MCMC Entry Hierarchical model Survival estimation Data Augmentation Posterior Predictive Distribution abundance estimation age at first reproduction Animal counts Age effects Borrowing strength Band recovery Bernoulli-Bernoulli mixture Bayesian p-value covariate binomial mixture model Cormack-Jolly-Seber Design Matrix Credible Interval Detection probability Closed population closed captures combination of information Frequentist dead recovery individual covariates integrated likelihood Linear Predictor Metapopulation Model Mixed model Leslie matrix movement probability multinomial likelihood Multistate model mark recovery Model Matrix Observation Error Observation process population analysis Population model Projection Matrix population growth rate Poisson-log-normal posterior presence/absence species richness reproductive output ring recovery temporal variation immediate trap effect individual variation true survival zero-inflated model Link Function N-mixture model Temporary emigration zero-inflated Poisson metapopulation design pen shell Posterior predictive checking Transition Probability superpopulation Poisson-Binomial Scope of inference Pseudoreplication Survival Probability site-occupancy model