Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, 02138.
Evolution. 2013 Dec;67(12):3403-11. doi: 10.1111/evo.12134. Epub 2013 May 11.
Understanding the effects of landscape heterogeneity on spatial genetic variation is a primary goal of landscape genetics. Ecological and geographic variables can contribute to genetic structure through geographic isolation, in which geographic barriers and distances restrict gene flow, and ecological isolation, in which gene flow among populations inhabiting different environments is limited by selection against dispersers moving between them. Although methods have been developed to study geographic isolation in detail, ecological isolation has received much less attention, partly because disentangling the effects of these mechanisms is inherently difficult. Here, I describe a novel approach for quantifying the effects of geographic and ecological isolation using multiple matrix regression with randomization. I explored the parameter space over which this method is effective using a series of individual-based simulations and found that it accurately describes the effects of geographic and ecological isolation over a wide range of conditions. I also applied this method to a set of real-world datasets to show that ecological isolation is an often overlooked but important contributor to patterns of spatial genetic variation and to demonstrate how this analysis can provide new insights into how landscapes contribute to the evolution of genetic variation in nature.
理解景观异质性对空间遗传变异的影响是景观遗传学的主要目标。生态和地理变量可以通过地理隔离和生态隔离来影响遗传结构，其中地理障碍和距离限制基因流动，而居住在不同环境中的种群之间的基因流动则受到选择的限制，这些选择会阻止它们之间的扩散者移动。尽管已经开发出了详细研究地理隔离的方法，但生态隔离受到的关注要少得多，部分原因是分离这些机制的影响本质上很困难。在这里，我描述了一种使用随机化多元矩阵回归来量化地理和生态隔离影响的新方法。我使用一系列基于个体的模拟探索了该方法的有效参数空间，并发现它可以在广泛的条件下准确描述地理和生态隔离的影响。我还将该方法应用于一组真实数据集，以表明生态隔离是造成空间遗传变异模式的一个经常被忽视但很重要的因素，并展示这种分析如何为了解景观如何促进自然中遗传变异的进化提供新的见解。
