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The analysis of sexual selection classically relies on the regression of individual phenotypes against the marginal sums of a males × females matrix of pairwise reproductive success, assessed by genetic parentage analysis. When the matrix is binarized, the marginal sums give the individual mating success. Because such analysis treats male and female mating/reproductive success independently, it ignores that the success of a male × female sexual interaction can be attributable to the phenotype of both individuals. Also, because it is based on genetic data only, it is oblivious to unproductive matings, which may be documented by behavioral observations. To solve these problems, we propose a statistical approach which combines matrices of offspring numbers and behavioral observations. It models reproduction on each mating occasion of a mating season as three stochastic and interdependent pairwise processes, each potentially affected by the phenotype of both individuals and by random individual effect: visit of a female by a male, concomitant gamete emission, and offspring production. Applied to data from a mating experiment on brown trout, the model yielded different results from the classical regression analysis, with only a negative effect of female body size on the probability of visit and gamete release, while the classical approach based on regression found a positive effect of male size on the number of both visits and offspring, and no effect of female size. Because the general structure of the model can be adapted to other partitioning schemes of the reproductive process, it can be used for a variety of biological systems where behavioral and genetic data are available.

With our powers combined: integrating behavioral and genetic data to estimate mating success and sexual selection