A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion
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Akaike’s information criterion (AIC) is increasingly being used in analyses in the field of ecology. This measure allows one to compare and rank multiple competing models and to estimate which of them best approximates the “true” process underlying the biological phenomenon under study. Behavioural ecologists have been slow to adopt this statistical tool, perhaps because of unfounded fears regarding the complexity of the technique. Here, we provide, using recent examples from the behavioural ecology literature, a simple introductory guide to AIC: what it is, how and when to apply it and what it achieves. We discuss multimodel inference using AIC—a procedure which should be used where no one model is strongly supported. Finally, we highlight a few of the pitfalls and problems that can be encountered by novice practitioners.
KeywordsAkaike’s information criterion Information theory Model averaging Model selection Multiple regression Statistical methods
This paper originates from a presentation by the first author at the 12th International Behavioral Ecology Congress at Cornell University in the post-conference symposium ‘Advances in statistical philosophy and experimental design in behavioral ecology’ organised by László Garamszegi and Shinichi Nakagawa. We thank László Garamszegi for inviting us to contribute to this issue. We are indebted to Gonçalo Cardoso for allowing us to use and reanalyse his junco data for the example in this paper. We thank six anonymous reviewers for their time and efforts. The members of the Animal Behaviour and Evolution group at the University of Melbourne also provided useful feedback on the manuscript. MRES was financially supported by the Australian Research Council.
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