Natural selection stops the evolution of male attractiveness

Abstract

Sexual selection in natural populations acts on highly heritable traits and tends to be relatively strong, implicating sexual selection as a causal agent in many phenotypic radiations. Sexual selection appears to be ineffectual in promoting phenotypic divergence among contemporary natural populations, however, and there is little evidence from artificial selection experiments that sexual fitness can evolve. Here, we demonstrate that a multivariate male trait preferred by Drosophila serrata females can respond to selection and results in the maintenance of male mating success. The response to selection was associated with a gene of major effect increasing in frequency from 12 to 35% in seven generations. No further response to selection, or increase in frequency of the major gene, was observed between generations 7 and 11, indicating an evolutionary limit had been reached. Genetic analyses excluded both depletion of genetic variation and overdominance as causes of the evolutionary limit. Relaxing artificial selection resulted in the loss of 52% of the selection response after a further five generations, demonstrating that the response under artificial sexual selection was opposed by antagonistic natural selection. We conclude that male D. serrata sexually selected traits, and attractiveness to D. serrata females conferred by these traits, were held at an evolutionary limit by the lack of genetic variation that would allow an increase in sexual fitness while simultaneously maintaining nonsexual fitness. Our results suggest that sexual selection is unlikely to cause divergence among natural populations without a concomitant change in natural selection, a conclusion consistent with observational evidence from natural populations.genetic variance | genetic constraint

Comparing G: multivariate analysis of genetic variation in multiple populations

“…First, our goal in this paper has been to supply a series of approaches that facilitate the biological interpretation of variation in G. All approaches focus on a change in genetic variance, rather than other metrics that attempt to describe more holistic ways in which matrices may differ (for example, proportionality, equality or matrix correlation). This is important because it is ultimately the level of genetic variance in particular directions that result in the biologically important consequences of differences in G, such as influencing the response to selection (Chenoweth et al, 2010), or the evolution of genetic variance itself (Hine et al, 2011).…”

“…The eigenanalyses of these eigentensors revealed that the leading eigenvectors of E 1 and E 2 accounted for 73% and 70% of the variation in these eigentensors, lc2-lc9 are the log-contrasts of the ratios to Z,Z-5,9-C 24:2 of, respectively: Z,Z-5,9-C 25:2 , Z-9-C 25:1 , Z-9-C 26:1 , 2-Me-C 26 , Z,Z-5,9-C 27:2 , 2-Me-C 28 , Z,Z-5,9-C 29:2 , 2-Me-C 30 . See Hine et al (2011) for full details of these traits. r 1 is the first eigenvector of the R matrix for an unstructured experimental design.…”

“…The decomposition of the multivariate breeders' equation The vector of directional sexual gradients was determined in the base population of the selection experiment (I ss in Hine et al (2011)). Using this as our estimate of b in the multivariate breeders' equation, we determined if there was any significant difference between any pair of the populations in the predicted response to selection of any individual traits.…”

“…The example data set we use is a subset of an experiment reported in Hine et al (2011), where specific details of the experimental design and laboratory procedures can be found. Briefly, an artificial selection experiment was conducted on eight traits (cuticular hydrocarbons) in Drosophila serrata.…”

“…Although G-matrices are highly conserved among some populations (see Arnold et al (2008)), they have also been demonstrated to rapidly diverge among both natural populations and experimental treatments (Cano et al, 2004;Doroszuk et al, 2008;Hine et al, 2009;Johansson et al, 2012). Laboratory manipulations have demonstrated that G can evolve rapidly in response to drift (Phillips et al, 2001), and that selection can drive rapid and repeatable evolution of G (Blows and Higgie, 2003;Hine et al, 2011). Further, the reproduction by experimental evolution of patterns observed in G among natural populations (Blows and Higgie, 2003), and the observations that the strength (Hunt et al, 2007) and pattern (Roff and Fairbairn, 2012) of selection are associated with levels of genetic variation in populations suggest selection might have a discernible effect on G.…”