Centre d'Ecologie Fonctionnelle et Evolutive, CNRS, Montpellier, France.
Evolution. 2013 Mar;67(3):708-21. doi: 10.1111/j.1558-5646.2012.01809.x. Epub 2012 Oct 11.
Genetic correlations between traits can constrain responses to natural selection. To what extent such correlations limit adaptation depends on patterns of directional selection. I derive the expected rate of adaptation (or evolvability) under randomly changing selection gradients. When directional selection gradients have an arbitrary covariance matrix, the average rate of adaptation depends on genetic correlations between traits, contrary to the isotropic case investigated in previous studies. Adaptation may be faster on average with more genetic correlation between traits, if these traits are selected to change jointly more often than the average pair of traits. However, natural selection maximizes the long-term fitness of a population, not necessarily its rate of adaptation. I therefore derive the average lag load caused by deviations of the mean phenotype from an optimum, under several forms of environmental changes typically experienced by natural populations, both stochastic and deterministic. Simple formulas are produced for how the G matrix affects long-term fitness in these contexts, and I discuss how their parameters can be estimated empirically.
遗传相关性会限制对自然选择的反应。这种相关性在多大程度上限制了适应性,取决于定向选择的模式。我推导出了在随机变化的选择梯度下的预期适应速度(或可进化性)。当定向选择梯度具有任意协方差矩阵时,适应的平均速度取决于性状之间的遗传相关性,这与以前研究中调查的各向同性情况不同。如果这些性状比平均性状对经常一起发生变化,那么性状之间的遗传相关性越高,平均适应速度可能会越快。然而,自然选择的目的是最大化种群的长期适应度,而不一定是其适应速度。因此,我推导出了在自然种群通常经历的几种环境变化形式下,平均表型与最优值的偏差引起的平均滞后负荷,包括随机和确定性变化。针对这些情况下 G 矩阵如何影响长期适应度,我给出了简单的公式,并讨论了如何通过经验估计其参数。