INTRODUCTION TO STATISTICAL ANALYSIS OF ECOLOGICAL DATA Introduction Population Ecology Conservation and Management Data and Models Bayesian and Classical Statistical Inference Senescence Data, Models and Likelihoods Introduction Population Data Modelling Survival Multi-Site, Multi-State and Movement Data Covariates and Large Data Sets; Senescence Combining Information Modelling Productivity Parameter Redundancy Classical Inference Based on the Likelihood Introduction Simple Likelihoods Model Selection Maximising Log-Likelihoods Confidence Regions Computer Packages BAYESIAN TECHNIQUES AND TOOLS Bayesian Inference Introduction Prior Selection and Elicitation Prior Sensitivity Analyses Summarising Posterior Distributions Directed Acyclic Graphs Markov Chain Monte Carlo Monte Carlo Integration Markov Chains Markov Chain Monte Carlo (MCMC) Implementing MCMC Model Discrimination Introduction Bayesian Model Discrimination Estimating Posterior Model Probabilities Prior Sensitivity Model Averaging Marginal Posterior Distributions Assessing Temporal/Age Dependence Improving and Checking Performance Additional Computational Techniques MCMC and RJMCMC Computer Programs R Code (MCMC) for Dipper Data WinBUGS Code (MCMC) for Dipper Data MCMC within the Computer Package MARK R code (RJMCMC) for Model Uncertainty WinBUGS Code (RJMCMC) for Model Uncertainty ECOLOGICAL APPLICATIONS Covariates, Missing Values and Random Effects Introduction Covariates Missing Values Assessing Covariate Dependence Random Effects Prediction Splines Multi-State Models Introduction Missing Covariate/Auxiliary Variable Approach Model Discrimination and Averaging State-Space Modelling Introduction Leslie Matrix-Based Models Non-Leslie-Based Models Capture-Recapture Data Closed Populations Introduction Models and Notation Model Fitting Model Discrimination and Averaging Line Transects Appendix A: Common Distributions Discrete Distributions Continuous Distributions Appendix B: Programming in R Getting Started in R Useful R Commands Writing (RJ)MCMC Functions R Code for Model C/C R Code for White Stork Covariate Analysis Appendix C: Programming in WinBUGS WinBUGS Calling WinBUGS from R References Index A Summary, Further Reading, and Exercises appear at the end of most chapters. Model Selection Maximising Log-Likelihoods Confidence Regions Computer Packages BAYESIAN TECHNIQUES AND TOOLS Bayesian Inference Introduction Prior Selection and Elicitation Prior Sensitivity Analyses Summarising Posterior Distributions Directed Acyclic Graphs Markov Chain Monte Carlo Monte Carlo Integration Markov Chains Markov Chain Monte Carlo (MCMC) Implementing MCMC Model Discrimination Introduction Bayesian Model Discrimination Estimating Posterior Model Probabilities Prior Sensitivity Model Averaging Marginal Posterior Distributions Assessing Temporal/Age Dependence Improving and Checking Performance Additional Computational Techniques MCMC and RJMCMC Computer Programs R Code (MCMC) for Dipper Data WinBUGS Code (MCMC) for Dipper Data MCMC within the Computer Package MARK R code (RJMCMC) for Model Uncertainty WinBUGS Code (RJMCMC) for Model Uncertainty ECOLOGICAL APPLICATIONS Covariates, Missing Values and Random Effects Introduction Covariates Missing Values Assessing Covariate Dependence Random Effects Prediction Splines Multi-State Models Introduction Missing Covariate/Auxiliary Variable Approach Model Discrimination and Averaging State-Space Modelling Introduction Leslie Matrix-Based Models Non-Leslie-Based Models Capture-Recapture Data Closed Populations Introduction Models and Notation Model Fitting Model Discrimination and Averaging Line Transects Appendix A: Common Distributions Discrete Distributions Continuous Distributions Appendix B: Programming in R Getting Started in R Useful R Commands Writing (RJ)MCMC Functions R Code for Model C/C R Code for White Stork Covariate Analysis Appendix C: Programming in WinBUGS WinBUGS Calling WinBUGS from R References Index A Summary, Further Reading, and Exercises appear at the end of most chapters. robabilities Prior Sensitivity Model Averaging Marginal Posterior Distributions Assessing Temporal/Age Dependence Improving and Checking Performance Additional Computational Techniques MCMC and RJMCMC Computer Programs R Code (MCMC) for Dipper Data WinBUGS Code (MCMC) for Dipper Data MCMC within the Computer Package MARK R code (RJMCMC) for Model Uncertainty WinBUGS Code (RJMCMC) for Model Uncertainty ECOLOGICAL APPLICATIONS Covariates, Missing Values and Random Effects Introduction Covariates Missing Values Assessing Covariate Dependence Random Effects Prediction Splines Multi-State Models Introduction Missing Covariate/Auxiliary Variable Approach Model Discrimination and Averaging State-Space Modelling Introduction Leslie Matrix-Based Models Non-Leslie-Based Models Capture-Recapture Data Closed Populations Introduction Models and Notation Model Fitting Model Discrimination and Averaging Line Transects Appendix A: Common Distributions Discrete Distributions Continuous Distributions Appendix B: Programming in R Getting Started in R Useful R Commands Writing (RJ)MCMC Functions R Code for Model C/C R Code for White Stork Covariate Analysis Appendix C: Programming in WinBUGS WinBUGS Calling WinBUGS from R References Index A Summary, Further Reading, and Exercises appear at the end of most chapters. endence Random Effects Prediction Splines Multi-State Models Introduction Missing Covariate/Auxiliary Variable Approach Model Discrimination and Averaging State-Space Modelling Introduction Leslie Matrix-Based Models Non-Leslie-Based Models Capture-Recapture Data Closed Populations Introduction Models and Notation Model Fitting Model Discrimination and Averaging Line Transects Appendix A: Common Distributions Discrete Distributions Continuous Distributions Appendix B: Programming in R Getting Started in R Useful R Commands Writing (RJ)MCMC Functions R Code for Model C/C R Code for White Stork Covariate Analysis Appendix C: Programming in WinBUGS WinBUGS Calling WinBUGS from R References Index A Summary, Further Reading, and Exercises appear at the end of most chapters. P> Appendix B: Programming in R Getting Started in R Useful R Commands Writing (RJ)MCMC Functions R Code for Model C/C R Code for White Stork Covariate Analysis Appendix C: Programming in WinBUGS WinBUGS Calling WinBUGS from R References Index A Summary, Further Reading, and Exercises appear at the end of most chapters.