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27 results for “multivariate selection”
Data from: Evolutionary rates for multivariate traits: the role of selection and genetic variation
A fundamental question in evolutionary biology is the relative importance of selection and genetic architecture in determining evolutionary rates. Adaptive evolution can be described by the multivariate breeders' equation, which predicts evolutionary change for a suite of phenotypic traits as a product of directional selection acting on them (β) and the genetic variance–covariance matrix for those traits (G). Despite being empirically challenging to estimate, there are enough published estimates of G and β to allow for synthesis of general patterns across species. We use published estimates to test the hypotheses that there are systematic differences in the rate of evolution among trait types, and that these differences are, in part, due to genetic architecture. We find some evidence that sexually selected traits exhibit faster rates of evolution compared with life-history or morphological traits. This difference does not appear to be related to stronger selection on sexually selected traits. Using numerous proposed approaches to quantifying the shape, size and structure of G, we examine how these parameters relate to one another, and how they vary among taxonomic and trait groupings. Despite considerable variation, they do not explain the observed differences in evolutionary rates.
Data from: Multivariate selection and intersexual genetic constraints in a wild bird population
When traits are genetically correlated between the sexes, the response to selection in one sex can be altered by indirect selection in the other sex, a type of genetic constraint commonly referred to as intralocus sexual conflict (ISC). While potentially common, ISC has rarely been studied in wild populations. In this study, we applied a multivariate framework to quantify the microevolutionary impacts of ISC over a set of morphological traits (wing length, tarsus length, bill depth, and bill length) in a wild population of great tits (Parus major) from Wytham Woods, UK. Specifically, we quantified the impact of cross-sex genetic covariances (the B matrix) on the additive genetic variance for relative fitness expected to be generated by directional selection and additive genetic (co)variance. Together, multivariate sex-specific selection and additive genetic (co)variance were expected to generate additive genetic variance for relative fitness that was uncorrelated between the sexes (cross-sex genetic correlation = -0.003, 95% CI = -0.83, 0.83). Gender load, defined as the expected reduction in additive genetic variance for relative fitness generated by the traits under study due to sex-specific effects, was estimated at 50% (95% CI = 13%, 86%). This study provides novel insights into the evolution of sexual dimorphism in great tits and illustrates how quantitative genetics and selection analyses can be combined in a multivariate framework to quantify the expected microevolutionary impacts of ISC.
Data from: Environmental drivers of varying selective optima in a small passerine: a multivariate, multiepisodic approach
In changing environments, phenotypic traits are shaped by numerous agents of selection. The optimal phenotypic value maximizing the fitness of an individual thus varies through time and space with various environmental covariates. Selection may differ between different life cycle stages and act on correlated traits inducing changes in the distribution of several traits simultaneously. Despite increasing interests in environmental sensitivity of phenotypic selection, estimating varying selective optima on various traits throughout the life cycle, while considering (a)biotic factors as potential selective agents has remained challenging. Here, we provide a statistical model to measure varying selective optima from longitudinal data. We apply our approach to analyse environmental sensitivity of phenotypic selection on egg-laying date and clutch size throughout the life cycle of a white-throated dipper population. We show the presence of a joint optimal phenotype that varies over the 35-yr period, being dependent on altitude and temperature. We also find that optimal laying date is density-dependent, with high population density favoring earlier laying dates. By providing a flexible approach, widely applicable to free-ranging populations for which long-term data on individual phenotypes, fitness and environmental factors are available, our study improves the understanding of phenotypic selection in varying environments.
Data from: Evolutionary rates for multivariate traits: the role of selection and genetic variation
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Data from: Environmental drivers of varying selective optima in a small passerine: a multivariate, multiepisodic approach
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Data from: Multivariate selection and intersexual genetic constraints in a wild bird population
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Data from: Environmental heterogeneity, multivariate sexual selection and genetic constraints on cuticular hydrocarbons in Drosophila simulans
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