Table of Contents

Part I: INTRODUCTION TO STATISTICAL ANALYSIS OF ECOLOGICAL DATA

1. Introduction
   • Population Ecology
   • Conservation and Management
   • Data and Models
   • Bayesian and Classical Statistical Inference
   • Senescence
   • Summary
   • Further Reading
2. 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
   • Summary
   • Further Reading
3. Classical Inference Based on the Likelihood
   • Introduction
   • Simple Likelihoods
   • Model Selection
   • Maximising Log-Likelihoods
   • Confidence Regions
   • Computer Packages
   • Summary
   • Further Reading

Part II: BAYESIAN TECHNIQUES AND TOOLS

4. Bayesian Inference
   • Introduction
   • Prior Selection and Elicitation
   • Prior Sensitivity Analyses
   • Summarising Posterior Distributions
   • Directed Acyclic Graphs
   • Summary
   • Further Reading
5. Markov Chain Monte Carlo
   • Monte Carlo Integration
   • Markov Chains
   • Markov Chain Monte Carlo (MCMC)
   • Implementing MCMC
   • Summary
   • Further Reading
6. 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
   • Summary
   • Further Reading
7. 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
   • Summary
   • Further Reading

Part III: ECOLOGICAL APPLICATIONS

8. Covariates, Missing Values and Random Effects
   • Introduction
   • Covariates
   • Missing Values
   • Assessing Covariate Dependence
   • Random Effects
   • Prediction
   • Splines
9. Multi-State Models
   • Introduction
   • Missing Covariate/Auxiliary Variable Approach
   • Model Discrimination and Averaging
10. State-Space Modelling
    • Introduction
    • Leslie Matrix-Based Models
    • Non-Leslie-Based Models
    • Capture-Recapture Data
11. Closed Populations
    • Introduction
    • Models and Notation
    • Model Fitting
    • Model Discrimination and Averaging
    • Line Transects

APPENDICES

1. Common Distributions
   • Discrete Distributions
   • Continuous Distributions
2. 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
3. Programming in WinBUGS
   • WinBUGS
   • Calling WinBUGS from R

References

Index