Multi-model inference of non-random mating from an information theoretic approach

Abstract

AbstractNon-random mating has a significant impact on the evolution of organisms. Here, I developed a modelling framework for discrete traits (with any number of phenotypes) to explore different models connecting the non-random mating causes (intra sexual competition and/or mate choice) and their consequences (sexual selection and/or assortative mating).I derived the formulas for the maximum likelihood estimates of each model and used information criteria for performing multimodel inference. Simulation results showed a good performance of both model selection and parameter estimation. The methodology was applied to data from Galician Littorina saxatilis ecotypes, to show that the mating pattern is better described by models with two parameters that involve both mate choice and intrasexual competition, generating positive assortative mating plus female sexual selection.As far as I know, this is the first standardized methodology for model selection and multimodel inference of mating parameters for discrete traits. The advantages of this framework include the ability of setting up models from which the parameters connect causes, as intrasexual competition and mate choice, with their outcome in the form of data patterns of sexual selection and assortative mating. For some models, the parameters may have a double effect i.e. they cause both kind of patterns, while for others models there are separated parameters for one kind of pattern or another.The full methodology was implemented in a software called InfoMating (available at http://acraaj.webs6.uvigo.es/InfoMating/Infomating.htm).