Selection and evolution of causally-covarying traits
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When traits cause variation in fitness, the distribution of phenotype, weighted by fitness, necessarily changes. The degree to which traits cause fitness variation is therefore of central importance to evolutionary biology. Multivariate selection gradients are the main quantity used to describe components of trait-fitness covariation, but they quantify the direct effects of traits on (relative) fitness, which are not necessarily the total effects of traits on fitness. Despite considerable use in evolutionary ecology, path analytic characterizations of the total effects of traits on fitness have not been formally incorporated into quantitative genetic theory. By formally defining “extended” selection gradients, which are the total effects of traits on fitness, as opposed to the existing definition of selection gradients, a more intuitive scheme for characterizing selection is obtained. Extended selection gradients are distinct quantities, differing from the standard definition of selection gradients not only in the statistical means by which they may be assessed and the assumptions required for their estimation from observational data, but also in their fundamental biological meaning. Like direct selection gradients, extended selection gradients can be combined with genetic inference of multivariate phenotypic variation to provide quantitative prediction of microevolutionary trajectories.
Morrissey , M B 2014 , ' Selection and evolution of causally-covarying traits ' Evolution , vol 68 , no. 6 , pp. 1748–1761 . DOI: 10.1111/evo.12385
Copyright 2014 The Author(s). Evolution Copyright 2014 The Society for the Study of Evolution. This is the accepted version of the following article: Selection and evolution of causally-covarying traits. Morrissey, M. B. Jun 2014 In : Evolution. 68, 6, p. 1748–1761, which has been published in final form at http://dx.doi.org/10.1111/evo.12385
The collection of the Soay sheep data is supported by the National Trust for Scotland and QinetQ, with funding from NERC, the Royal Society, and the Leverhulme Trust.
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