Code for the population genetic models of the evolution of preference strength
Servedio, Maria; Lerch, Brian; Xu, Kuangyi (2023), Code for the population genetic models of the evolution of preference strength, Dryad, Dataset, https://doi.org/10.5061/dryad.c59zw3rcm
Sexual selection has a rich history of mathematical models that consider why preferences favor one trait phenotype over another (for population genetic models) or what specific trait value is preferred (for quantitative genetic models). Less common is exploration of the evolution of choosiness or preference strength: that is, by how much a trait is preferred. We examine both population and quantitative genetic models of the evolution of preferences, specifically developing “baseline models” of the evolution of preference strength during the Fisher process. Using a population genetic approach based on the classic model of Kirkpatrick (1982), we find selection for stronger and stronger preferences when trait variation is maintained by mutation. However, this force is quite weak and likely to be swamped by drift in moderately-sized populations. In a quantitative genetic model based on Lande (1981), unimodal preferences will generally not evolve to be increasingly strong without bounds when male traits are under stabilizing viability selection, but evolve to extreme values when viability selection is directional. Our results highlight that different shapes of fitness and preference functions lead to qualitatively different trajectories for preference strength evolution ranging from no evolution to extreme evolution of preference strength.
The dataset consists of Mathematica code used to develop and analyze the two locus model, the three locus model, and the model with finite population sizes in the accompanying paper. It also contains a the quasi-linkage equilibrium analysis.
Mathematica is necessary, and a free reader for Mathematica is available online.
National Science Foundation, Award: DEB 1939290
Graduate School, University of North Carolina at Chapel Hill, Award: Royster Fellowship