A guide to Bayesian model selection for ecologists

Authors

  • M. B. Hooten,

    Corresponding author
    1. U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Colorado State University, Fort Collins, Colorado 80523-1484 USA
    2. Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523-1484 USA
    3. Department of Statistics, Colorado State University, Fort Collins, Colorado 80523-1484 USA
    4. Graduate Degree Program in Ecology, Colorado State University, Fort Collins, Colorado 80523-1484 USA
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  • N. T. Hobbs

    1. Graduate Degree Program in Ecology, Colorado State University, Fort Collins, Colorado 80523-1484 USA
    2. Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, Colorado 80523-1484 USA
    3. Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, Colorado 80523-1484 USA
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  • Corresponding Editor: A. M. Ellison.

  • AP: The first character of Eq. 23 and of the sentence following it is a lightface script L. This character also appears in the sentence before Eq. 25, two paragraphs before Eq. 46, and in Eqs. 46 and 47.

Abstract

The steady upward trend in the use of model selection and Bayesian methods in ecological research has made it clear that both approaches to inference are important for modern analysis of models and data. However, in teaching Bayesian methods and in working with our research colleagues, we have noticed a general dissatisfaction with the available literature on Bayesian model selection and multimodel inference. Students and researchers new to Bayesian methods quickly find that the published advice on model selection is often preferential in its treatment of options for analysis, frequently advocating one particular method above others. The recent appearance of many articles and textbooks on Bayesian modeling has provided welcome background on relevant approaches to model selection in the Bayesian framework, but most of these are either very narrowly focused in scope or inaccessible to ecologists. Moreover, the methodological details of Bayesian model selection approaches are spread thinly throughout the literature, appearing in journals from many different fields. Our aim with this guide is to condense the large body of literature on Bayesian approaches to model selection and multimodel inference and present it specifically for quantitative ecologists as neutrally as possible. We also bring to light a few important and fundamental concepts relating directly to model selection that seem to have gone unnoticed in the ecological literature. Throughout, we provide only a minimal discussion of philosophy, preferring instead to examine the breadth of approaches as well as their practical advantages and disadvantages. This guide serves as a reference for ecologists using Bayesian methods, so that they can better understand their options and can make an informed choice that is best aligned with their goals for inference.

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