Bayesian statistics has exploded into biology and its sub-disciplines, such as ecology, over the past decade. The free software program WinBUGS, and its open-source sister OpenBugs, is currently the only flexible and general-purpose program available with which the average ecologist can conduct standard and non-standard Bayesian statistics.

Key Features

  • Comprehensive and richly commented examples illustrate a wide range of models that are most relevant to the research of a modern population ecologist
  • All WinBUGS/OpenBUGS analyses are completely integrated in software R
  • Includes complete documentation of all R and WinBUGS code required to conduct analyses and shows all the necessary steps from having the data in a text file out of Excel to interpreting and processing the output from WinBUGS in R


Professional ecologists, upper-level graduate and graduate ecology students

Table of Contents





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


No. of pages:
© 2012
Academic Press
Electronic ISBN:
Print ISBN:

About the authors

Marc Kery

Dr Kery is a Population Ecologist with the Swiss Ornithological Institute and a courtesy professor ("Privatdozent") at the University of Zürich/Switzerland, from where he received his PhD in Ecology in 2000. He is an expert in the estimation and modeling of abundance, distribution and species richness in "metapopulation designs" (i.e., collections of replicate sites). For most of his work, he uses the Bayesian model fitting software BUGS and JAGS, about which he has published two books with Academic Press (2010 and 2012). He has authored/coauthored 70 peer-reviewed articles and four book chapters. Since 2007, and for a total of 103 days, he has taught 23 statistical modeling workshops about the methods in the proposed book at research institutes and universities all over the world.