Bayesian Population Analysis using WinBUGS

Bayesian Population Analysis using WinBUGS

A Hierarchical Perspective

1st Edition - September 28, 2011

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  • Authors: Marc Kery, Michael Schaub
  • eBook ISBN: 9780123870216
  • Paperback ISBN: 9780123870209

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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

  • Dedication




    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

    5.5. Summary and Outlook

    5.6. Exercises

    Chapter 6. Estimation of the Size of a Closed Population from Capture–Recapture Data

    6.1. Introduction

    6.2. Generation and Analysis of Simulated Data with Data Augmentation

    6.3. Analysis of a Real Data Set: Model Mtbh for Species Richness Estimation

    6.4. Capture–Recapture Models with Individual Covariates: Model Mt+X

    6.5. Summary and Outlook

    6.6. Exercises

    Chapter 7. Estimation of Survival from Capture–Recapture Data Using the Cormack–Jolly–Seber Model

    7.1. Introduction

    7.2. The CJS Model as a State-Space Model

    7.3. Models with Constant Parameters

    7.4. Models with Time-Variation

    7.5. Models with Individual Variation

    7.6. Models with Time and Group Effects

    7.7. Models with Age Effects

    7.8. Immediate Trap Response in Recapture Probability

    7.9. Parameter Identifiability

    7.10. Fitting the CJS to Data in the M-Array Format: the Multinomial Likelihood

    7.11. Analysis of a Real Data Set: Survival of Female Leisler's Bats

    7.12. Summary and Outlook

    7.13. Exercises

    Chapter 8. Estimation of Survival Using Mark-Recovery Data

    8.1. Introduction

    8.2. The Mark-Recovery Model as a State-Space Model

    8.3. The Mark-Recovery Model Fitted with the Multinomial Likelihood

    8.4. Real-Data Example: Age-Dependent Survival in Swiss Red Kites

    8.5. Summary and Outlook

    8.6. Exercises

    Chapter 9. Estimation of Survival and Movement from Capture–Recapture Data Using Multistate Models

    9.1. Introduction

    9.2. Estimation of Movement between Two Sites

    9.3. Accounting for Temporary Emigration

    9.4. Estimation of Age-Specific Probability of First Breeding

    9.5. Joint Analysis of Capture–Recapture and Mark-Recovery Data

    9.6. Estimation of Movement among Three Sites

    9.7. Real-Data Example: The Showy Lady's Slipper

    9.8. Summary and Outlook

    9.9. Exercises

    Chapter 10. Estimation of Survival, Recruitment, and Population Size from Capture–Recapture Data Using the Jolly–Seber Model

    10.1. Introduction

    10.2. The JS Model as a State-Space Model

    10.3. Fitting the JS Model with Data Augmentation

    10.4. Models with Constant Survival and Time-Dependent Entry

    10.5. Models with Individual Capture Heterogeneity

    10.6. Connections between Parameters, Further Quantities and Some Remarks on Identifiability

    10.7. Analysis of a Real Data Set: Survival, Recruitment and Population Size of Leisler's Bats

    10.8. Summary and Outlook

    10.9. Exercises

    Chapter 11. Estimation of Demographic Rates, Population Size, and Projection Matrices from Multiple Data Types Using Integrated Population Models

    11.1. Introduction

    11.2. Developing an Integrated Population Model (IPM)

    11.3. Example of a Simple IPM (Counts, Capture–Recapture, Reproduction)

    11.4. Another Example of an IPM: Estimating Productivity without Explicit Productivity Data

    11.5. IPMs for Population Viability Analysis

    11.6. Real Data Example: Hoopoe Population Dynamics

    11.7. Summary and Outlook

    11.8. Exercises

    Chapter 12. Estimation of Abundance from Counts in Metapopulation Designs Using the Binomial Mixture Model

    12.1. Introduction

    12.2. Generation and Analysis of Simulated Data

    12.3. Analysis of Real Data: Open-Population Binomial Mixture Models

    12.4. Summary and Outlook

    12.5. Exercises

    Chapter 13. Estimation of Occupancy and Species Distributions from Detection/Nondetection Data in Metapopulation Designs Using Site-Occupancy Models

    13.1. Introduction

    13.2. What Happens When p < 1 and Constant and p is Not Accounted for in a Species Distribution Model?

    13.3. Generation and Analysis of Simulated Data for Single-Season Occupancy

    13.4. Analysis of Real Data Set: Single-Season Occupancy Model

    13.5. Dynamic (Multiseason) Site-Occupancy Models

    13.6. Multistate Occupancy Models

    13.7. Summary and Outlook

    13.8. Exercises

    Chapter 14. Concluding Remarks

    14.1. The Power and Beauty of Hierarchical Models

    14.2. The Importance of the Observation Process

    14.3. Where Will We Go?

    14.4. The Importance of Population Analysis for Conservation and Management

    Appendix 1. A List of WinBUGS Tricks

    Appendix 2. Two Further Useful Multistate Capture–Recapture Models



Product details

  • No. of pages: 554
  • Language: English
  • Copyright: © Academic Press 2011
  • Published: September 28, 2011
  • Imprint: Academic Press
  • eBook ISBN: 9780123870216
  • Paperback ISBN: 9780123870209

About the Authors

Marc Kery

Marc Kery
Dr. Marc works as a senior scientist at the Swiss Ornithological Institute, Seerose 1, 6204 Sempach, Switzerland. This is a non-profit NGO with about 160 employees dedicated primarily to bird research, monitoring, and conservation. Marc was trained as a plant population ecologist at the Swiss Universities of Basel and Zuerich. After a 2-year postdoc at the (then) USGS Patuxent Wildlife Center in Laurel, MD. During the last 20 years he has worked at the interface between population ecology, biodiversity monitoring, wildlife management, and statistics. He has published more than 100 peer-reviewed journal articles and five textbooks on applied statistical modeling. He has also been very active in teaching fellow biologists and wildlife managers the concepts and tools of modern statistical analysis in their fields in workshops all over the world, something which goes together with his books, which target the same audiences.

Affiliations and Expertise

Senior Scientist,Swiss Ornithological Institute

Michael Schaub

Michael Schaub
Michael Schaub is the Head of the Ecology Department at the Swiss Ornithological Institute and a courtesy Professor at the University of Bern. His research interests include population dynamics, capture-recapture models, integrated population models, and migratory birds. He has coauthored approximately 130 peer-reviewed journal publications and the book Bayesian Population Analysis using WinBUGS.

Affiliations and Expertise

Head of Ecology, Swiss Ornithological Institute, Sempach, Switzerland

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