Detection of Adaptive Divergence in Populations of the Stream Mayfly Ephemera strigata with Machine Learning
Detection of Adaptive Divergence in Populations of the Stream Mayfly Ephemera strigata with Machine Learning
复制标题
用机器学习检测流蜉蝣种群的自适应发散
DOI：
10.1002/ece3.6398
复制
发表时间：
2020
影响因子：
2.6
通讯作者：
M. Gamboa and K. Watanabe
中科院分区：
文献类型：
--
作者：
Li;B.;S. Yaegashi;T. M. Carvajal;M. Gamboa and K. Watanabe
适应性分化是形成自然种群遗传变异的关键机制。将生态学与进化生物学联系起来的一个核心问题是，空间环境异质性如何导致一个物种内的本地种群之间的适应性差异。在这项研究中，我们使用基因组扫描的方法来检测选择下的候选位点，研究了日本东北部名取河流域的may - yephemera striiga的适应性分化。除了传统的基于距离的冗余分析（dbRDA）之外，我们还应用了一种新的机器学习方法（即随机森林）来研究环境因素与非中性位点的自适应发散之间的关系。采用基于中性位点的空间自相关分析来考察该物种的扩散能力。我们的结论如下：(a)E。分形图显示了名取河流域种群间的高度适应差异；(b)随机森林检测自适应发散的分辨率高于传统统计分析；(c)将所有标记划分为中性和非中性位点，可以充分了解遗传多样性、局部适应和扩散能力等参数。
Adaptive divergence is a key mechanism shaping the genetic variation of natural populations. A central question linking ecology with evolutionary biology is how spatial environmental heterogeneity can lead to adaptive divergence among local populations within a species. In this study, using a genome scan approach to detect candidate loci under selection, we examined adaptive divergence of the stream mayflyEphemera strigatain the Natori River Basin in northeastern Japan. We applied a new machine‐learning method (i.e., random forest) besides traditional distance‐based redundancy analysis (dbRDA) to examine relationships between environmental factors and adaptive divergence at non‐neutral loci. Spatial autocorrelation analysis based on neutral loci was employed to examine the dispersal ability of this species. We conclude the following: (a)E. strigatashow altitudinal adaptive divergence among the populations in the Natori River Basin; (b) random forest showed higher resolution for detecting adaptive divergence than traditional statistical analysis; and (c) separating all markers into neutral and non‐neutral loci could provide full insight into parameters such as genetic diversity, local adaptation, and dispersal ability.