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Model selection and model averaging in behavioural ecology: the utility of the IT-AIC framework

Lookup NU author(s): Professor Mark WhittinghamORCiD

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Abstract

Behavioural ecologists often study complex systems in which multiple hypotheses could be proposed to explain observed phenomena. For some systems, simple controlled experiments can be employed to reveal part of the complexity; often, however, observational studies that incorporate a multitude of causal factors may be the only (or preferred) avenue of study. We assess the value of recently advocated approaches to inference in both contexts. Specifically, we examine the use of information theoretic (IT) model selection using Akaike's information criterion (AIC). We find that, for simple analyses, the advantages of switching to an IT-AIC approach are likely to be slight, especially given recent emphasis on biological rather than statistical significance. By contrast, the model selection approach embodied by IT approaches offers significant advantages when applied to problems of more complex causality. Model averaging is an intuitively appealing extension to model selection. However, we were unable to demonstrate consistent improvements in prediction accuracy when using model averaging with IT-AIC; our equivocal results suggest that more research is needed on its utility. We illustrate our arguments with worked examples from behavioural experiments.

Publication metadata

Author(s): Richards SA, Whittingham MJ, Stephens PA

Publication type: Article

Publication status: Published

Journal: Behavioral Ecology and Sociobiology

Year: 2011

Volume: 65

Issue: 1

Pages: 77-89

Print publication date: 19/08/2010

ISSN (print): 0340-5443

ISSN (electronic): 1432-0762

Publisher: Springer

URL: http://dx.doi.org/10.1007/s00265-010-1035-8

DOI: 10.1007/s00265-010-1035-8

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