4.5 Article

Sexual imprinting leads to speciation in locally adapted populations

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ECOLOGY AND EVOLUTION
卷 12, 期 11, 页码 -

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WILEY
DOI: 10.1002/ece3.9479

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assortative mating; genetic polymorphism; migration selection balance; parapatric speciation; sexual imprinting

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Sexual imprinting is a widespread phenomenon in birds and other species, but its existence requires explanation. Research suggests that if a neutral mating cue, such as novel plumage coloration, arises through mutation, sexual imprinting can lead to speciation in locally-adapted populations.
Sexual imprinting is widespread in birds and other species but its existence requires explanation. Our results suggest that sexual imprinting leads to speciation in locally-adapted populations if a neutral mating cue-e.g., novel plumage coloration-arises through mutation. Importantly, the mating cue locus is not linked to adaptation loci. Local adaptation is a necessary precursor to speciation and occurs when evolution results in stable genetic polymorphisms with one allele predominating in some areas while others predominate elsewhere. Here we use a deterministic two-niche population genetic model to map the set of migration and selection rates for which polymorphic evolutionary outcomes, i.e., local adaptations, can occur. Approximate equations for the boundaries of the set of polymorphic evolutionary outcomes were derived by Bulmer (American Naturalist, 106, 254, 1972), but our results, obtained by deterministic simulation of the evolutionary process, show that one of Bulmer's equations is inaccurate except when the level of dominance is 0.5, and fails if one of the alleles is dominant. Having an accurate map of the set of migration and selection rates for which polymorphic evolutionary outcomes can occur, we then show using the model of Sibly et al. (Ecology and Evolution, 9, 13506, 2019) that local adaptation in all analyzed cases leads to speciation if a new neutral mating cue arises by mutation. We finish by considering how genome sequencing makes possible testing our model and its predictions.

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