Data from: A new phylogenetic method for identifying exceptional phenotypic diversification

Currently available phylogenetic methods for studying the rate of evolution in a continuously valued character assume that the rate is constant throughout the tree or that it changes along specific branches according to an a priori hypothesis of rate variation provided by the user. Herein, we describe a new method for studying evolutionary rate variation in continuously valued characters given an estimate of the phylogenetic history of the species in our study. According to this method, we propose no specific prior hypothesis for how the variation in evolutionary rate is structured throughout the history of the species in our study. Instead, we use a Bayesian Markov Chain Monte Carlo approach to estimate evolutionary rates and the shift-point between rates on the tree. We do this by simultaneously sampling rates and shift-points in proportion to their posterior probability, and then collapsing the posterior sample into an estimate of the parameters of interest. We use simulation to show that the method is quite successful at identifying the phylogenetic position of a shift in the rate of evolution, and that estimated rates are asymptotically unbiased. We also provide an empirical example of the method using data for Anolis lizards.当前用于探究连续性状演化速率的系统发育方法，要么假定演化速率在整个系统发育树中保持恒定，要么依据用户预先提供的速率变异先验假说，限定速率仅在特定分支上发生改变。本文提出一种全新方法，可在已有研究类群物种系统发育历史估计结果的前提下，分析连续性状的演化速率变异模式。该方法无需预先设定演化速率变异在研究类群物种演化历史中的具体结构框架。取而代之的是，我们采用贝叶斯马尔可夫链蒙特卡洛（Bayesian Markov Chain Monte Carlo）方法，同步估计演化速率以及系统发育树上的速率转换节点：依据参数的后验概率按比例对演化速率与速率转换节点进行采样，随后将后验采样结果整合为目标参数的估计值。我们通过模拟实验验证，该方法可精准识别演化速率转换在系统发育树上的位置，且所估计的演化速率具有渐近无偏性。此外，我们还利用安乐蜥（Anolis）的相关数据，给出了该方法的实际应用案例。