Data from: Disentangling the effects of geographic and ecological isolation on genetic differentiation

Populations can be genetically isolated by both geographic distance and by differences in their ecology or environment that decrease the rate of successful migration. Empirical studies often seek to investigate the relationship between genetic differentiation and some ecological variable(s) while accounting for geographic distance, but common approaches to this problem (such as the partial Mantel test) have a number of drawbacks. In this article, we present a Bayesian method that enables users to quantify the relative contributions of geographic distance and ecological distance to genetic differentiation between sampled populations or individuals. We model the allele frequencies in a set of populations at a set of unlinked loci as spatially correlated Gaussian processes, in which the covariance structure is a decreasing function of both geographic and ecological distance. Parameters of the model are estimated using a Markov chain Monte Carlo algorithm. We call this method Bayesian Estimation of Differentiation in Alleles by Spatial Structure and Local Ecology (BEDASSLE), and have implemented it in a user-friendly format in the statistical platform R. We demonstrate its utility with a simulation study and empirical applications to human and teosinte datasets.种群可因地理距离，以及会降低成功迁移速率的生态或环境差异而发生遗传隔离。实证研究常试图在控制地理距离的前提下，探究遗传分化与特定生态变量之间的关联，但针对该问题的常用方法（如偏曼特尔检验（partial Mantel test））存在诸多缺陷。本文提出一种贝叶斯方法，可帮助研究者量化地理距离与生态距离对抽样种群或个体间遗传分化的相对贡献。我们将多个独立位点上的种群等位基因频率建模为空间相关高斯过程（Gaussian process），其协方差结构随地理与生态距离的增加而呈递减趋势。模型参数通过马尔可夫链蒙特卡洛（Markov chain Monte Carlo）算法进行估计。我们将该方法命名为"基于空间结构与局域生态的等位基因分化贝叶斯估计（Bayesian Estimation of Differentiation in Alleles by Spatial Structure and Local Ecology，BEDASSLE）"，并在统计平台R中以易用的格式实现了该方法。我们通过模拟研究，以及针对人类与类蜀黍（teosinte）数据集的实证应用，验证了该方法的实用价值。