Data from: Artificial selection to increase the phenotypic variance in gmax fails
Sztepanacz, Jacqueline L.; Blows, Mark W. (2017), Data from: Artificial selection to increase the phenotypic variance in gmax fails, Dryad, Dataset, https://doi.org/10.5061/dryad.rn160
Stabilizing selection is important in evolutionary theories of the maintenance of genetic variance, and has been invoked as the key process determining macro-evolutionary patterns of trait evolution. However, manipulative evidence for the extent of stabilizing selection, particularly on multivariate trait combinations is lacking. We used artificial disruptive selection in Drosophila serrata as a tool to determine the relative strength of stabilizing selection experienced by multivariate trait combinations with contrasting levels of genetic and mutational variance. Contrary to expectation, when disruptive selection was applied to the major axis of standing genetic variance, we observed a significant and repeatable decrease in its phenotypic variance. In contrast, the trait combination predicted to be under strong stabilizing selection, showed a significant and repeatable increase in its phenotypic variance. Correlated responses were observed in all selection treatments, and viability selection operating on extreme phenotypes of traits genetically correlated with those directly selected upon limited our ability to increase their phenotypic range. Our manipulation revealed that multivariate trait combinations were subject to stabilizing selection; however, we did not observe a direct relationship between the strength of stabilizing selection and the levels of standing genetic variance. Contrasting patterns of allele frequencies underlying traits with high vs. low levels of standing genetic variance may be implicated in determining the response to artificial selection in multivariate trait combinations.