Data from: A Bayesian approach for detecting the impact of mass-extinction events on molecular phylogenies when rates of lineage diversification may vary

The paleontological record chronicles numerous episodes of mass extinction that severely culled the Tree of Life. Biologists have long sought to assess the extent to which these events may have impacted particular groups. We present a novel method for detecting the impact of mass-extinction events on molecular phylogenies, even in the presence of tree-wide diversification-rate variation and in the absence of additional information from the fossil record. Our approach is based on an episodic stochastic-branching process model in which rates of speciation and extinction are constant between events. We model three types of events: (1) instantaneous tree-wide shifts in speciation rate; (2) instantaneous tree-wide shifts in extinction rate, and; (3) instantaneous tree-wide mass-extinction events. Each type of event is modeled as an independent compound Poisson process (CPP), where the waiting times between events are exponentially distributed with event-specific rate parameters. The magnitude of each event is drawn from an event-specific prior distribution. Parameters of the model are then estimated in a Bayesian statistical framework using a reversible-jump Markov chain Monte Carlo algorithm. This Bayesian approach enables us to distinguish between tree-wide diversification-rate variation and mass-extinction events by specifying a biologically informed prior on the magnitude of mass-extinction events, and empirical hyperpriors on the diversification-rate parameters. We demonstrate via simulation that this method has substantial power to detect the number of mass-extinction events, provides unbiased estimates of the timing of mass-extinction events, while exhibiting an appropriate (i.e., < 5%) false-discovery rate, even when background diversification rates vary. Finally, we provide an empirical demonstration of this approach, which reveals that conifers experienced a major episode of mass extinction ≈ 23 Ma This new approach—the CPP on Mass Extinction Times (CoMET) model—provides an effective tool for detecting the impact of mass-extinction events on molecular phylogenies, even when the history of those groups includes temporal variation in diversification rates and when the fossil history of those groups is poorly known.

古生物记录记载了多起大规模灭绝事件，这些事件曾严重削减了生命之树的演化分支与生物多样性。长期以来，生物学家一直致力于评估此类灭绝事件对特定生物类群的影响程度。我们提出了一种全新方法，可用于检测大规模灭绝事件对分子系统发育树（molecular phylogenies）的影响，即便存在全树尺度的分化速率变异，且缺乏化石记录提供的额外信息时仍可适用。本方法基于间歇式随机分支过程模型（episodic stochastic-branching process model），该模型假设事件之间的成种速率与灭绝速率保持恒定。我们对三类事件进行建模：(1) 全树尺度的成种速率瞬时偏移；(2) 全树尺度的灭绝速率瞬时偏移；以及(3) 全树尺度的瞬时大规模灭绝事件。每一类事件均被建模为独立的复合泊松过程（compound Poisson process, CPP），事件间的等待时间服从指数分布，且各事件拥有专属的速率参数。每类事件的强度均从对应事件专属的先验分布中抽样得到。模型参数可通过贝叶斯统计框架，结合可逆跳跃马尔可夫链蒙特卡洛（reversible-jump Markov chain Monte Carlo）算法进行估计。该贝叶斯方法通过为大规模灭绝事件的强度设定基于生物学先验知识的先验分布，并为分化速率参数设定经验超先验，从而能够区分全树尺度的分化速率变异与大规模灭绝事件。我们通过模拟实验证实，即便背景分化速率存在变异，该方法仍具备较强的检测大规模灭绝事件数量的效能，可对灭绝事件发生时间提供无偏估计，且假发现率控制在合理范围内（即< 5%）。最后，我们通过实证案例展示了该方法的应用效果，结果显示针叶树类（conifers）在约23 Ma曾经历过一次大规模灭绝事件。这一全新方法——即基于大规模灭绝时间的复合泊松过程（compound Poisson process on Mass Extinction Times, CoMET）模型——为检测大规模灭绝事件对分子系统发育树的影响提供了高效工具，即便相关类群的演化历史存在分化速率的时间变异，且该类群的化石记录信息匮乏时仍可适用。