Data from: Examining the full effects of landscape heterogeneity on spatial genetic variation: a multiple matrix regression approach for quantifying geographic and ecological isolation

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（landscape heterogeneity）对空间遗传变异的影响，是景观遗传学（landscape genetics）的核心研究目标之一。生态与地理变量可通过地理隔离（Geographic Isolation）与生态隔离（Ecological Isolation）两种途径塑造种群遗传结构：前者指地理屏障与地理距离限制基因流（gene flow），后者则指栖息于不同环境的种群间，因环境对跨环境扩散个体的选择排斥，而限制了基因交流。尽管学界已开发出多种可细致研究地理隔离的方法，但针对生态隔离的研究却相对匮乏，部分原因在于厘清这两种机制的遗传效应本身极具挑战性。本文提出一种全新的分析方法，借助随机化多重矩阵回归（multiple matrix regression with randomization）量化地理隔离与生态隔离的遗传效应。通过一系列基于个体的模拟（individual-based simulations），本文探索了该方法有效的参数空间，证实其可在广泛的实验条件下准确反映地理隔离与生态隔离的影响。此外，本文还将该方法应用于多组真实世界数据集，结果显示生态隔离常被忽视，但却是影响空间遗传变异格局的重要驱动因子；同时，本分析框架也可为解析景观如何推动自然种群遗传变异的演化提供全新视角。