Department of Ecology and Evolutionary Biology, Biodiversity Institute, University of Kansas, Lawrence, Kansas 66045, USA.
Evolution. 2013 Apr;67(4):991-1010. doi: 10.1111/j.1558-5646.2012.01840.x. Epub 2012 Dec 11.
Approximate Bayesian computation (ABC) is rapidly gaining popularity in population genetics. One example, msBayes, infers the distribution of divergence times among pairs of taxa, allowing phylogeographers to test hypotheses about historical causes of diversification in co-distributed groups of organisms. Using msBayes, we infer the distribution of divergence times among 22 pairs of populations of vertebrates distributed across the Philippine Archipelago. Our objective was to test whether sea-level oscillations during the Pleistocene caused diversification across the islands. To guide interpretation of our results, we perform a suite of simulation-based power analyses. Our empirical results strongly support a recent simultaneous divergence event for all 22 taxon pairs, consistent with the prediction of the Pleistocene-driven diversification hypothesis. However, our empirical estimates are sensitive to changes in prior distributions, and our simulations reveal low power of the method to detect random variation in divergence times and bias toward supporting clustered divergences. Our results demonstrate that analyses exploring power and prior sensitivity should accompany ABC model selection inferences. The problems we identify are potentially mitigable with uniform priors over divergence models (rather than classes of models) and more flexible prior distributions on demographic and divergence-time parameters.
近似贝叶斯计算(ABC)在群体遗传学中迅速流行。一个例子是 msBayes,它推断了-taxa 对之间的分歧时间分布,使系统地理学家能够测试关于生物在分布集中的群体中多样化的历史原因的假设。使用 msBayes,我们推断了分布在菲律宾群岛的 22 对脊椎动物种群之间的分歧时间分布。我们的目标是检验更新世海平面波动是否导致了岛屿之间的多样化。为了指导我们结果的解释,我们进行了一系列基于模拟的功效分析。我们的实证结果强烈支持所有 22 个分类单元对的最近同时分歧事件,这与更新世驱动多样化假说的预测一致。然而,我们的实证估计对先验分布的变化很敏感,我们的模拟显示该方法检测分歧时间随机变化的能力较低,并且偏向于支持聚类分歧。我们的结果表明,在 ABC 模型选择推断中,应该伴随着探索功效和先验敏感性的分析。我们确定的问题可以通过在分歧模型(而不是模型类)上使用统一先验和对人口和分歧时间参数的更灵活先验分布来减轻。