MacPherson Ailene, Hohenlohe Paul A, Nuismer Scott L
Program in Bioinformatics and Computational Biology, University of Idaho, Moscow, ID 83844, USA.
Program in Bioinformatics and Computational Biology, University of Idaho, Moscow, ID 83844, USA Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA.
Proc Biol Sci. 2015 Mar 7;282(1802). doi: 10.1098/rspb.2014.1570.
All species are locked in a continual struggle to adapt to local ecological conditions. In cases where species fail to locally adapt, they face reduced population growth rates, or even local extinction. Traditional explanations for limited local adaptation focus on maladaptive gene flow or homogeneous environmental conditions. These classical explanations have, however, failed to explain variation in the magnitude of local adaptation observed across taxa. Here we show that variable levels of local adaptation are better explained by trait dimensionality. First, we develop and analyse mathematical models that predict levels of local adaptation will increase with the number of traits experiencing spatially variable selection. Next, we test this prediction by estimating the relationship between dimensionality and local adaptation using data from 35 published reciprocal transplant studies. This analysis reveals a strong correlation between dimensionality and degree of local adaptation, and thus provides empirical support for the predictions of our model.
所有物种都陷入了一场持续不断的斗争,以适应当地的生态条件。在物种无法实现局部适应的情况下,它们面临着种群增长率下降,甚至局部灭绝的问题。对于有限的局部适应,传统解释集中在适应不良的基因流动或同质的环境条件上。然而,这些经典解释未能解释不同分类群中观察到的局部适应程度的差异。在这里,我们表明局部适应的不同水平可以更好地由性状维度来解释。首先,我们开发并分析了数学模型,这些模型预测局部适应水平将随着经历空间可变选择的性状数量的增加而提高。接下来,我们通过使用来自35项已发表的相互移植研究的数据估计维度与局部适应之间的关系来检验这一预测。该分析揭示了维度与局部适应程度之间的强相关性,从而为我们模型的预测提供了实证支持。