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Improving data analysis in herpetology: Using Akaike's information criterion (AIC) to assess the strength of biological hypotheses

Amphibia-Reptilia

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DOI: 10.1163/156853806777239922

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Abstract

In ecology, researchers frequently use observational studies to explain a given pattern, such as the number of individuals in a habitat patch, with a large number of explanatory (i.e., independent) variables. To elucidate such relationships, ecologists have long relied on hypothesis testing to include or exclude variables in regression models, although the conclusions often depend on the approach used (e.g., forward, backward, stepwise selection). Though better tools have surfaced in the mid 1970's, they are still underutilized in certain fields, particularly in herpetology. This is the case of the Akaike information criterion (AIC) which is remarkably superior in model selection (i.e., variable selection) than hypothesis-based approaches. It is simple to compute and easy to understand, but more importantly, for a given data set, it provides a measure of the strength of evidence for each model that represents a plausible biological hypothesis relative to the entire set of models considered. Using this approach, one can then compute a weighted average of the estimate and standard error for any given variable of interest across all the models considered. This procedure, termed model-averaging or multimodel inference, yields precise and robust estimates. In this paper, I illustrate the use of the AIC in model selection and inference, as well as the interpretation of results analysed in this framework with two real herpetological data sets. The AIC and measures derived from it is should be routinely adopted by herpetologists. ?? Koninklijke Brill NV 2006.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Improving data analysis in herpetology: Using Akaike's information criterion (AIC) to assess the strength of biological hypotheses
Series title:
Amphibia-Reptilia
DOI:
10.1163/156853806777239922
Volume
27
Issue:
2
Year Published:
2006
Language:
English
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
First page:
169
Last page:
180
Number of Pages:
12