Data from: Evidence for climate-driven diversification? A caution for interpreting ABC inferences of simultaneous historical events

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（Approximate Bayesian computation, ABC）正快速在群体遗传学领域得到广泛应用。其中，msBayes方法可用于推断成对类群的分化时间分布，使系统地理研究者能够检验共分布生物类群分化的历史成因假说。本研究借助msBayes方法，对分布于菲律宾群岛的22对脊椎动物种群的分化时间分布展开推断，以检验更新世时期的海平面波动是否引发了群岛间的物种分化。为辅助解读研究结果，我们开展了一系列基于模拟的功效分析。本研究的经验结果强烈支持全部22对类群均发生过近期同步分化事件，这与更新世驱动物种分化假说的预测相一致。然而，本研究的经验估计结果对先验分布的变动较为敏感；同时模拟结果显示，该方法检测分化时间随机变异的功效较低，且存在偏向支持聚类分化的偏差。本研究结果表明，在开展近似贝叶斯计算的模型选择推断时，应同步开展功效分析与先验敏感性分析。本研究指出的问题可通过以下途径潜在缓解：为分化模型（而非模型类别）设置均匀先验，同时为种群统计与分化时间参数设置更为灵活的先验分布。