The contribution of sexual selection and genetic constraints to phenotypic divergence among natural populations of Drosophila subquinaria.

Authors

Will M C Jarvis, Howard D Rundle

Published in

Journal of evolutionary biology. Sep 21, 2026. Epub Sep 21, 2026.

Abstract

Evolutionary trajectories are determined by selection and the underlying genetic architecture of additive genetic variances and covariances for the traits. To better understand divergence in multivariate phenotypes among natural populations, we need population-specific estimates of selection and genetic architecture of the traits involved; such extensive data are challenging to acquire and few examples exist. Here we do this using males from six populations of the mushroom-feeding fly Drosophila subquinaria. We take advantage of a naturally occurring pattern of reproductive character displacement involving male chemical display traits (cuticular hydrocarbons/CHCs) between populations that are sympatric vs. allopatric with the closely related Drosophila recens. Genetic (co)variance structures for male CHCs from three populations from each region were previously quantified; here we provide novel estimates of sexual selection on these traits within each population and quantify the extent to which this differs between regions. Then, using all these data we implement a comparative approach investigating how differences in sexual selection and genetic architecture can explain the observed pattern of CHC divergence, extending the method beyond past applications to incorporate uncertainty in estimates of selection and genetic (co)variances. We find modest differences in sexual selection between regions, in contrast to past studies, and that divergence is not well explained by this alone. However, explanatory ability improves substantially when genetic (co)variances among the traits are accounted for. We also find that predicted evolutionary responses to sexual selection in sympatry are likely constrained as the genetic "line of least resistance" is not well aligned with selection.

PMID:
42765274
Bibliographic data and abstract were imported from PubMed on 21 Sep 2026.

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