Crandall Eric D, Toonen Robert J, Selkoe Kimberly A
School of Natural Sciences California State University, Monterey Bay Seaside California.
School of Ocean and Earth Science and Technology, Hawai'i Institute of Marine Biology University of Hawai'i at Manoa Kane'ohe Hawaii.
Evol Appl. 2018 Oct 15;12(2):255-265. doi: 10.1111/eva.12712. eCollection 2019 Feb.
Assessing the geographic structure of populations has relied heavily on Sewell Wright's -statistics and their numerous analogues for many decades. However, it is well appreciated that, due to their nonlinear relationship with gene flow, -statistics frequently fail to reject the null model of panmixia in species with relatively high levels of gene flow and large population sizes. Coalescent genealogy samplers instead allow a model-selection approach to the characterization of population structure, thereby providing the opportunity for stronger inference. Here, we validate the use of coalescent samplers in a high gene flow context using simulations of a stepping-stone model. In an example case study, we then re-analyze genetic datasets from 41 marine species sampled from throughout the Hawaiian archipelago using coalescent model selection. Due to the archipelago's linear nature, it is expected that most species will conform to some sort of stepping-stone model (leading to an expected pattern of isolation by distance), but -statistics have only supported this inference in ~10% of these datasets. Our simulation analysis shows that a coalescent sampler can make a correct inference of stepping-stone gene flow in nearly 100% of cases where gene flow is ≤100 migrants per generation (equivalent to = 0.002), while -statistics had mixed results. Our re-analysis of empirical datasets found that nearly 70% of datasets with an unambiguous result fit a stepping-stone model with varying population sizes and rates of gene flow, although 37% of datasets yielded ambiguous results. Together, our results demonstrate that coalescent samplers hold great promise for detecting weak but meaningful population structure, and defining appropriate management units.
几十年来，评估种群的地理结构在很大程度上依赖于休厄尔·赖特的F统计量及其众多类似方法。然而，人们普遍认识到，由于F统计量与基因流之间的非线性关系，在基因流水平相对较高且种群规模较大的物种中，F统计量常常无法拒绝随机交配的零模型。相反，溯祖谱系采样器允许采用模型选择方法来表征种群结构，从而提供了进行更强有力推断的机会。在这里，我们通过模拟 stepping-stone 模型，验证了溯祖采样器在高基因流背景下的使用。在一个案例研究中，我们随后使用溯祖模型选择重新分析了从整个夏威夷群岛采样的41个海洋物种的遗传数据集。由于该群岛呈线性，预计大多数物种将符合某种 stepping-stone 模型（导致预期的距离隔离模式），但F统计量仅在约10%的这些数据集中支持了这一推断。我们的模拟分析表明，在基因流≤100个迁移个体/世代（相当于m = 0.002）的几乎100%的情况下，溯祖采样器能够正确推断 stepping-stone 基因流，而F统计量的结果则好坏参半。我们对实证数据集的重新分析发现，近70%结果明确的数据集符合具有不同种群规模和基因流速率的 stepping-stone 模型，尽管37%的数据集产生了不明确的结果。总之，我们的结果表明，溯祖采样器在检测微弱但有意义的种群结构以及定义合适的管理单元方面具有很大的潜力。
