Muniz, Danilo G.
Santos, Eduardo S. A.
Guimaraes, Jr., Paulo R.
Número total de Autores: 5
Afiliação do(s) autor(es):
 Univ Sao Paulo, Inst Biosci, Dept Ecol, LAGE Lab, Sao Paulo - Brazil
 Univ Sao Paulo, Inst Biosci, Dept Ecol, Postgrad Programme Ecol, Sao Paulo - Brazil
 Univ Sao Paulo, Inst Biosci, Dept Zool, BECO Lab, Sao Paulo - Brazil
 Univ New South Wales, Evolut & Ecol Res Ctr, Sydney, NSW - Australia
 Univ New South Wales, Sch Biol Earth & Environm Sci, Sydney, NSW - Australia
Número total de Afiliações: 5
Tipo de documento:
METHODS IN ECOLOGY AND EVOLUTION;
Citações Web of Science:
Mate sampling, whereby individuals cannot access all potential mating partners in a population, is a ubiquitous yet poorly explored process. Ignoring mate sampling may underestimate female choice because the smaller the sample taken by individuals of the choosing sex, the weaker the correlation between sexually selected traits and the mating success among individuals of the chosen sex. A main factor promoting mate sampling is the spatial distribution of individuals. Thus, including distances in models of mate choice should improve estimates of mate choosiness. However, spatial distances between individuals are pairwise variables and cannot be readily included in the models commonly used to investigate mate choice. We address this limitation by proposing a multinomial network (MN) model of mate choice, and comparing its performance with a previously published binomial generalized linear mixed model. Both models allow the inclusion of pairwise predictors, accommodating spatial distances between individuals in analyses of mate choice. We evaluated the performance of these models in detecting directional and assortative mate choice using different simulated datasets: with and without spatial information, and with and without spatial autocorrelation of male and female traits. We also took samples of different sizes from the simulated datasets to evaluate the models' performance when data are incomplete. Using both models, the exclusion of spatial information underestimated mate choice. Small sample sizes from the simulated populations led to underestimated directional mate choice, whereas assortative choice estimates were unbiased. Taking larger samples increased statistical power, and confidence interval coverage of both models. Spatial autocorrelation decreased the power of both models, but the MN model was less affected by it. We conclude that including space in analyses of mate choice increases our ability to detect and accurately estimate mate choice using observational data. The MN model is a powerful and flexible tool that should be used in studies of mate choice in spatially structured populations. Moreover, the model can be used to investigate choice in other contexts, such as floral constancy by pollinators and host plant selection by phytophagous insects. (AU)