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Information-theoretic approaches to statistical analysis in behavioural ecology: an introduction

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Abstract

Scientific thinking may require the consideration of multiple hypotheses, which often call for complex statistical models at the level of data analysis. The aim of this introduction is to provide a brief overview on how competing hypotheses are evaluated statistically in behavioural ecological studies and to offer potentially fruitful avenues for future methodological developments. Complex models have traditionally been treated by model selection approaches using threshold-based removal of terms, i.e. stepwise selection. A recently introduced method for model selection applies an information-theoretic (IT) approach, which simultaneously evaluates hypotheses by balancing between model complexity and goodness of fit. The IT method has been increasingly propagated in the field of ecology, while a literature survey shows that its spread in behavioural ecology has been much slower, and model simplification using stepwise selection is still more widespread than IT-based model selection. Why has the use of IT methods in behavioural ecology lagged behind other disciplines? This special issue examines the suitability of the IT method for analysing data with multiple predictors, which researchers encounter in our field. The volume brings together different viewpoints to aid behavioural ecologists in understanding the method, with the hope of enhancing the statistical integration of our discipline.

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Acknowledgements

I am grateful to D. R. Anderson, R. Freckleton, F. Guthery, R. Montgomerie, S. Nakagawa, and P. Stephens for their constructive comments at the different stages of the manuscript. Special thanks to all referees that participated in the evaluation of the contributed papers (see details at the end of this volume). P. A. Bednekoff kindly assisted during the editorial process and helped obtain reports from independent referees. During this study, I was supported by a “Ramon y Cajal” research grant from the Spanish National Research Council (Consejo Superior de Investigaciones Científicas–CSIC). The Department of Systematic Zoology and Ecology, Eötvös Loránd University, Hungary provided stimulating working place, for which I am indebted to J. Török.

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  1. Department of Evolutionary Ecology, Estación Biológica de Doñana–CSIC, c/Americo Vespucio, s/n, 41092, Seville, Spain

    László Zsolt Garamszegi

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  1. László Zsolt Garamszegi
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Correspondence to László Zsolt Garamszegi.

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Communicated by P. Bednekoff

This contribution is part of the Special Issue “Model selection, multimodel inference and information-theoretic approaches in behavioural ecology”.

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Garamszegi, L.Z. Information-theoretic approaches to statistical analysis in behavioural ecology: an introduction. Behav Ecol Sociobiol 65, 1–11 (2011). https://doi.org/10.1007/s00265-010-1028-7

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