Source code for dynamic models and simulations of mate sampling behavior
Watts, James (2022), Source code for dynamic models and simulations of mate sampling behavior, Dryad, Dataset, https://doi.org/10.5061/dryad.1c59zw3xd
Theory predicts that the strength of sexual selection (i.e., how well a trait predicts mating or fertilization success) should increase with population density, yet empirical support remains mixed. We explore how this discrepancy might reflect a disconnect between current theory and our understanding of the strategies individuals use to choose mates. We demonstrate that the density-dependence of sexual selection predicted by previous theory arises from the assumption that individuals automatically sample more potential mates at higher densities. We provide an updated theoretical framework for the density-dependence of sexual selection by (1) developing models that clarify the mechanisms through which density-dependent mate sampling strategies might be favored by selection and (2) using simulations to determine how sexual selection changes with population density when individuals use those strategies. We find that sexual selection may increase strongly with density if sampling strategies change adaptively in response to density-dependent sampling costs, whereas within-individual plasticity in sampling over time (e.g., due to adaptation to increasing sampling costs as the breeding season progresses) produces weaker density-dependent sexual selection. Our findings suggest that density-dependence of sexual selection depends on the ecological context in which mate sampling has evolved.
These files contain the R source code used to produce the figures and analysis presented in the manuscript. Please see the associated README for a description of each file's function.
The associated README file includes a description of each file and the specific workflow used to produce the results in the manuscript. Where appropriate, any code chunks within a single file that are used to produce different figures or analyses are clearly labeled with the corresponding figure or analysis in the manuscript.