Integrated Population Models

Theory and ecological applications with R and JAGS

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Book description

Integrated Population Models: Theory and Ecological Applications with R and JAGS is the first book of its kind on integrated population models (IPMs), which constitute a powerful, unifying framework for combining multiple population- and individual-level data sets to estimate demographic parameters, population sizes, and trends. These models identify the drivers of population dynamics and forecast population composition and size.

Written by two population ecologists and experts in integrated population modeling, this book presents a comprehensive synthesis of the theory of integrated population models and provides an extensive overview of practical applications using Bayesian methods with case studies. The book contains fully documented, complete code for fitting many integrated population models with free software, R and JAGS. It also includes all the required code for pre-model-fitting and post-model-fitting analysis.

Integrated Population Models is an invaluable reference for researchers and practitioners involved in population analysis and for graduate-level students in ecology, conservation biology, wildlife management, and related fields. The text is ideal for self-study and advanced graduate-level courses. It also has an associated website where all this material is presented and is accompanied by its own R package on CRAN.

The authors

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.

Marc Kéry is a Population Ecologist with the Swiss Ornithological Institute and a courtesy Professor at the University of Zürich. He is an expert in the estimation and modeling of abundance, distribution, and species richness in animal and plant populations and has coauthored approximately 100 peer-reviewed articles and five books.

 

Table of contents

Foreword
Preface
Acknowlagements
1 introduction

PART 1 THEORY OF INTEGRATED POPULATION MODELS
2. Bayesian Statistical Modeling Using JAGS
3. Introduction to Stage-Structured Population Models
4. Components of Integrated Population Models
5. Introduction to Integrated Population Models
6. Benefits of Integrated Population Modeling
7. Assessment of Integrated Population Models
8. Integrated Population Models With Density Dependence
9. Retrospective Population Analyses
10. Population Viability Analysis

PART 2 INTEGRATED POPULATION MODELS IN PRACTICE (fully analysed case studies)
11. Woodchat Shrike
12. Peregrine Falcon
13. Horseshoe Bat
14. Hoopoe
15. Black Grouse
16. Barn Swallow
17. Elk
18. Cormorant
19. Gray Catbird
20. Kestrel
21. Black Bear
22. Conclusions

References
Author Index
Subject Index

Code

The complete code of the book is available here.
The R package IPMbook for the book can be downloaded from CRAN, or directly in your R console by writing install.packages('IPMbook'). A big THANKS to Mike Meredith for both!

 

Supplementary resources and information

The following links contain supplementary documents to the book, workshop announcements and further resources.

Solutions to exercises

The solutions to all exercises in the book are organised by chapter. They contain richly commented code.

 

Errata

This pdf file contains a list of errors that we have detected since October 31 2021. If you find other errors, we would be very grateful if you can send us an email.

Workshops

We regularly conduct weekly workshops that cover the material of the book. If you are interested in hosting such a workshop, please send us an e-mail.

All the workshops will be advertised here.

Hierarchical modeling

Hierarchical models are commonplace in population studies. Here is a landing page that leads you to additional resources dealing with hierarchical modeling.