Bayesian Population Analysis using WinBUGS

Dedication

Foreword

Preface

Acknowledgments

Chapter 1. Introduction

1.1. Ecology: The Study of Distribution and Abundance and of the Mechanisms Driving Their Change

1.2. Genesis of Ecological Observations

1.3. The Binomial Distribution as a Canonical Description of the Observation Process

1.4. Structure and Overview of the Contents of this Book

1.5. Benefits of Analyzing Simulated Data Sets: An Example of Bias and Precision

1.6. Summary and Outlook

1.7. Exercises

Chapter 2. Brief Introduction to Bayesian Statistical Modeling

2.1. Introduction

2.2. Role of Models in Science

2.3. Statistical Models

2.4. Frequentist and Bayesian Analysis of Statistical Models

2.5. Bayesian Computation

2.6. WinBUGS

2.7. Advantages and Disadvantages of Bayesian Analyses by Posterior Sampling

2.8. Hierarchical Models

2.9. Summary and Outlook

Chapter 3. Introduction to the Generalized Linear Model

3.1. Introduction

3.2. Statistical Models: Response = Signal + Noise

3.3. Poisson GLM in R and WinBUGS for Modeling Time Series of Counts

3.4. Poisson GLM for Modeling Fecundity

3.5. Binomial GLM for Modeling Bounded Counts or Proportions

3.6. Summary and Outlook

3.7. Exercises

Chapter 4. Introduction to Random Effects

4.1. Introduction

4.2. Accounting for Overdispersion by Random Effects-Modeling in R and WinBUGS

4.3. Mixed Models with Random Effects for Variability among Groups (Site and Year Effects)

4.4. Summary and Outlook

4.5. Exercises

Chapter 5. State-Space Models for Population Counts

5.1. Introduction

5.2. A Simple Model

5.3. Systematic Bias in the Observation Process

5.4. Real Example: House Martin Population Counts in the Village of Magden