Data from: Effects on population divergence of within-generational learning about prospective mates
Servedio, Maria R.; Dukas, Reuven (2013), Data from: Effects on population divergence of within-generational learning about prospective mates, Dryad, Dataset, https://doi.org/10.5061/dryad.c0164
Although learned mate preferences are suspected to have important effects during speciation, theoretical models have largely neglected the effects on speciation and population divergence of within-generational learning, that is, learning based upon prior experience with potential mates. Here we use population genetic models to address this deficit. Focussing on the situation of secondary contact between populations that still hybridize, we consider models of learning by females and by males under polygyny. We assess the effects of learning to prefer conspecifics from previous conspecific encounters, learning to avoid heterospecifics from previous heterospecific encounters, and learning to prefer familiar types. We examine the amount of population divergence that results from learning in these models. We also assess the effect of learning on the spread of an allele that strengthens assortative mating in both models. We find that learning can have counterintuitive, but logical and understandable effects that differ with the version of the model assessed. In general, population divergence is expected to increase most consistently when females learn to strengthen their preferences for conspecifics from previous encounters with conspecifics. Our results also suggest that within-generational learning will generally inhibit the spread of alleles strengthening assortative mating.