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Individual deviation from behavioural correlations: a simple approach to study the evolution of behavioural syndromes
Authors:Email author" target="_blank">Gábor?HerczegEmail author  László?Zsolt?Garamszegi
Institution:1.Ecological Genetics Research Unit, Department of Biosciences,University of Helsinki,Helsinki,Finland;2.Behavioural Ecology Group, Department of Systematic Zoology and Ecology,E?tv?s Loránd University,Budapest,Hungary;3.Department of Evolutionary Ecology,Estación Biológica de Do?ana–CSIC,Seville,Spain
Abstract:The study of correlations between different behaviours in a population—referred to as behavioural syndromes—has begun to flourish during recent years. However, the evolutionary mechanisms that cause behavioural traits to vary non-independently from each other are still poorly understood. Here, we bring behavioural syndromes into a new perspective, in which the phenomenon is regarded at the individual level and on a continuous scale instead of as a population-level presence/absence trait. As the correlation between behaviours is never perfect (i.e. r < 1), individuals are likely to vary in how consistently they behave. Therefore, we can predict that if behavioural syndromes at the population level are results of natural selection, the consistency in a suite of behaviours—and not the behavioural configuration per se—should be heritable and involve fitness advantages at the individual level. We define a variable that describes the individual deviation from the hypothetical perfect correlation predicted by the syndrome. The use of such a variable depicting the consistency of behaviours of individuals allows us to make solid evolutionary inferences about correlated behaviours from patterns of individual instead of population variation. We suggest that, by adopting the concept of syndrome deviation, understanding the evolution of behavioural syndromes and, in particular, testing competing evolutionary hypotheses about the origin of behavioural syndromes becomes possible in a more rigorous manner than before.
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