Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS

Distribution Community Least squares. Detection Bugs Density Occurrence Population Dynamics abundance Bayesian inference Binomial Conditional probability Gibbs sampling Poisson Robust design Availability Random effects Generalized linear model Prediction Point process Measurement error Bootstrap Markov chain Monte Carlo Missing Value Distance sampling Multinomial distribution GLM Data simulation Linear model State space model Ecological Process Maximum Likelihood Occupancy Bootstrapping Parameterization Fixed effects WinBUGS Hierarchical model Data Augmentation Conditional Likelihood Detectability Species distribution model Unmarked Posterior Distribution Posterior Predictive Distribution Wagtail Capture-recapture sampling Bayesian p-value Community occupancy model Dail�Madsen model Double observer protocol Community N-mixture model Delta Method Coverage bias False-negative error Detection probability Double-observer sampling Full likelihood false-negative False-positive Generalized linear model (GLM)Goodness of fit GLMM Hierarchical distance sampling hierarchical models Island scrub jay Effective sample area integrated likelihood Linear Predictor Line Transect Marginal likelihood lmm Joint species distribution model Mixed model Markov chain Monte Carlo (MCMC)Mixed model Multispecies occupancy model Metropolis�Hastings Multispecies abundance model Nominal sample area Generalized linear mixed model OpenBUGS Negative Binomial Group Size Observation process Poisson�lognormal Point transect r2 presence/absence Removal sampling JSDM species richness JAGS Link Function N-mixture model Temporary emigration zero-inflated Poisson Parametric Bootstrap Metacommunity Species accumulation Time removal Spatial distance sampling Survival Probability Sunflower effect Vital rates