Abstracts (first author)

Poster 

Sexual selection of choosiness under direct benefits

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Author(s): Etienne L, Rousset F, Godelle B, Courtiol A

Summary:

Most theoretical research in sexual selection have studied the evolution of mate choice by indirect selection (i.e. when selection is only present on ornament and/or quality, but not directly on genes responsible for mate choice). However, empirical studies have not brought strong support to indirect selection. A less controversial finding is that choice is related to direct benefits and costs that exert a strong influence on the evolution of mate choice. We present an analytical model in which unilateral (female or male) choosiness evolves only according to such benefits and costs, i.e. only by direct sexual selection. We show that this simple model is sufficient to predict the evolution of all possible levels of choosiness when only four parameters are considered: the encounter rate, the lifetime and the length of unavailability after mating for males and females. This is because these parameters influence the trade-off between direct benefits in terms of the quality of mates and costs in terms of the quantity of mates. We further identify the sensitivity of the relative searching time (RST, i.e. the proportion of lifetime devoted to searching for mates) as a key variable allowing to predict the qualitative effect of any life history trait on the evolution of choosiness. Contrary to other predictors identified by previous models, the sensitivity of the RST encompasses the effect of all life history traits and constitutes an empirically accessible metric. The RST should allow a better understanding of the links between life history and mate choice in the future and thus provide new insights on the evolution of sex roles.



Contacts

Chairman: Octávio S. Paulo
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Address

XIV Congress of the European Society for Evolutionary Biology

Organization Team
Department of Animal Biology (DBA)
Faculty of Sciences of the University of Lisbon
P-1749-016 Lisbon
Portugal

Website

Computational Biology & Population Genomics Group 
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