Chan Yvonne L, Schanzenbach David, Hickerson Michael J
Hawai'i Institute of Marine Biology, School of Ocean and Earth Science and Technology, University of Hawai'i at Manoa
Cyberinfrastructure, University of Hawai'i at Manoa.
Mol Biol Evol. 2014 Sep;31(9):2501-15. doi: 10.1093/molbev/msu187. Epub 2014 Jun 12.
Methods that integrate population-level sampling from multiple taxa into a single community-level analysis are an essential addition to the comparative phylogeographic toolkit. Detecting how species within communities have demographically tracked each other in space and time is important for understanding the effects of future climate and landscape changes and the resulting acceleration of extinctions, biological invasions, and potential surges in adaptive evolution. Here, we present a statistical framework for such an analysis based on hierarchical approximate Bayesian computation (hABC) with the goal of detecting concerted demographic histories across an ecological assemblage. Our method combines population genetic data sets from multiple taxa into a single analysis to estimate: 1) the proportion of a community sample that demographically expanded in a temporally clustered pulse and 2) when the pulse occurred. To validate the accuracy and utility of this new approach, we use simulation cross-validation experiments and subsequently analyze an empirical data set of 32 avian populations from Australia that are hypothesized to have expanded from smaller refugia populations in the late Pleistocene. The method can accommodate data set heterogeneity such as variability in effective population size, mutation rates, and sample sizes across species and exploits the statistical strength from the simultaneous analysis of multiple species. This hABC framework used in a multitaxa demographic context can increase our understanding of the impact of historical climate change by determining what proportion of the community responded in concert or independently and can be used with a wide variety of comparative phylogeographic data sets as biota-wide DNA barcoding data sets accumulate.
将多个分类群的种群水平抽样整合到单一群落水平分析中的方法，是对比较系统地理学工具包的重要补充。检测群落中的物种在时空上如何在人口统计学上相互追踪，对于理解未来气候变化和景观变化的影响以及由此导致的物种灭绝加速、生物入侵和适应性进化的潜在激增至关重要。在这里，我们提出了一个基于分层近似贝叶斯计算（hABC）的统计框架用于此类分析，目的是检测整个生态组合中一致的人口统计学历史。我们的方法将来自多个分类群的种群遗传数据集整合到单一分析中，以估计：1）在时间上聚类脉冲中人口统计学上扩张的群落样本比例，以及2）脉冲发生的时间。为了验证这种新方法的准确性和实用性，我们使用模拟交叉验证实验，随后分析了来自澳大利亚的32个鸟类种群的实证数据集，这些种群被假设在更新世晚期从较小的避难所种群扩张而来。该方法可以适应数据集的异质性，例如物种间有效种群大小、突变率和样本大小的变异性，并利用对多个物种同时分析的统计优势。在多分类群人口统计学背景下使用的这个hABC框架，可以通过确定群落中有多少比例的物种协同或独立响应，来增加我们对历史气候变化影响的理解，并且随着生物群全基因组条形码数据集的积累，可以与各种比较系统地理学数据集一起使用。
