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

Richards, S.A.; Whittingham, M.J.; Stephens, P.A.

Authors

S.A. Richards

M.J. Whittingham



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.

Citation

Richards, S., Whittingham, M., & Stephens, P. (2011). Model selection and model averaging in behavioural ecology: the utility of the IT-AIC framework. Behavioral Ecology and Sociobiology, 65(1), 77-89. https://doi.org/10.1007/s00265-010-1035-8

Journal Article Type Article
Publication Date 2011-01
Deposit Date Aug 23, 2010
Journal Behavioral Ecology and Sociobiology
Print ISSN 0340-5443
Electronic ISSN 1432-0762
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 65
Issue 1
Pages 77-89
DOI https://doi.org/10.1007/s00265-010-1035-8
Keywords Effect size, Inference, Model weighting, Null hypothesis, Process-based models, Statistics
Public URL https://durham-repository.worktribe.com/output/1549934