Introduction to Hierarchical Bayesian Modeling for Ecological Data (Hardback) book cover

Introduction to Hierarchical Bayesian Modeling for Ecological Data

By Eric Parent, Etienne Rivot

© 2012 – Chapman and Hall/CRC

427 pages | 143 B/W Illus.

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About the Book

Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian Modeling for Ecological Data gives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It also helps readers get started on building their own statistical models.

The text begins with simple models that progressively become more complex and realistic through explanatory covariates and intermediate hidden states variables. When fitting the models to data, the authors gradually present the concepts and techniques of the Bayesian paradigm from a practical point of view using real case studies. They emphasize how hierarchical Bayesian modeling supports multidimensional models involving complex interactions between parameters and latent variables. Data sets, exercises, and R and WinBUGS codes are available on the authors’ website.

This book shows how Bayesian statistical modeling provides an intuitive way to organize data, test ideas, investigate competing hypotheses, and assess degrees of confidence of predictions. It also illustrates how conditional reasoning can dismantle a complex reality into more understandable pieces. As conditional reasoning is intimately linked with Bayesian thinking, considering hierarchical models within the Bayesian setting offers a unified and coherent framework for modeling, estimation, and prediction.


"This book is a welcome addition to the Bayesian literature. It is well written and amply illustrates Bayesian methods with practical applications in fisheries management. The programs for data analyses are available on the book’s website, allowing users to get their ‘hands dirty’ and in the process really understand the model construction and the software."

— Subhash R. Lele, Ecology, 95(1), 2014

"The book is well written and easy to read, and the material presented deserves a greater exposure in taught statistics courses. I thoroughly recommend the book and believe that the statistical techniques and their application to quantitative fisheries science could ideally complement a short undergraduate course in applied statistics."

—Carl M. O’Brien, International Statistical Review (2013), 81

Table of Contents

I Basic Blocks of Bayesian Modeling

Bayesian Hierarchical Models in Statistical Ecology

Challenges for statistical ecology

Conditional reasoning, graphs and hierarchical models

Bayesian inferences on hierarchical models

What can be found in this book?

The Beta-Binomial Model

From a scientific question to a Bayesian analysis

What is modeling?

Think conditionally and make a graphical representation

Inference is the reverse way of thinking

Expertise matters

Encoding prior knowledge

The conjugate Beta pdf

Bayesian inference as statistical learning

Bayesian inference as a statistical tool for prediction

Asymptotic behavior of the beta-binomial model

The beta-binomial model with WinBUGS

Further references

The Basic Normal Model

Salmon farm’s pollutants and juvenile growth

A Normal model for the fish length

Normal-gamma as conjugate models to encode expertise

Inference by recourse to conjugate property

Bibliographical notes

Further material

Working with More Than One Beta-Binomial Element

Capture-mark-recapture analysis

Successive removal analysis

Testing a new tag for tuna

Further references

Combining Various Sources of Information

Motivating example

Stochastic model for salmon behavior

Inference with WinBUGS


Discussion and conclusions

The Normal Linear Model

The decrease of Thiof abundance in Senegal

Linear model theory

A linear model for Thiof abundance

Further reading

Nonlinear Models for Stock-Recruitment Analysis

Stock-recruitment motivating example

Searching for a SR model

Which parameters?

Changing the error term from lognormal to gamma

From Ricker to Beverton and Holt

Model choice with informative prior

Conclusions and perspectives

Getting beyond Regression Models

Logistic and probit regressions

Ordered probit model


II More Elaborate Hierarchical Structures

HBM I: Borrowing Strength from Similar Units


HBM for capture-mark-recapture data

Hierarchical stock-recruitment analysis

Further Bayesian comments on exchangeability

HBM II: Piling up Simple Layers

HBM for successive removal data with habitat and year

Electrofishing with successive removals

HBM III: State-Space Modeling


State-space modeling of a biomass production model

State-space modeling of Atlantic salmon life cycle model

A tool of choice for the ecological detective

Decision and Planning



The Sée-Sélune river network

Salmon life cycle dynamics

Long-term behavior: Collapse or equilibrium?

Management reference points

Management rules and implementation error

Economic model



Appendix A: The Normal and Linear Normal Model

Appendix B: Computing Marginal Likelihoods

Appendix C: The Baseball Players’ Historical Example

Appendix D: More on Ricker Stock-Recruitment



About the Authors

Éric Parent is head of the Research Laboratory for Risk Management in Environmental Sciences (Team MORSE) and a professor in applied statistics and probabilistic modeling for environmental engineering at the National Institute for Rural Engineering, Water and Forest Management (ENGREF/AgroParisTech) in Paris, France. Dr. Parent’s research encompasses Bayesian theory and applications, with special emphasis on environmental systems modeling.

Étienne Rivot is a researcher in the Fisheries Ecology Laboratory at Agrocampus Ouest in Rennes, France. Dr. Rivot’s research focuses on the application of Bayesian statistical modeling for the analysis of ecological data, inference, and predictions.

About the Series

Chapman & Hall/CRC Applied Environmental Statistics

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Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General
SCIENCE / Life Sciences / Biology / Marine Biology
TECHNOLOGY & ENGINEERING / Environmental / General