Data from: Investigating evolutionary lag using the species-pairs evolutionary lag test (SPELT)

For traits showing correlated evolution, one trait may evolve more slowly than the other, producing evolutionary lag. The species-pairs evolutionary lag test (SPELT) uses an independent contrasts based approach to detect evolutionary lag on a phylogeny. We investigated the statistical performance of SPELT in relation to degree of lag, sample size (species pairs), and strength of association between traits. We simulated trait evolution under two models: one in which trait X changes during speciation and the lagging trait Y catches up as a function of time since speciation; and another in which trait X evolves in a random walk and the lagging trait Y is a function of X at a previous time period. Type I error rates under “no lag” were close to the expected level of 5%, indicating that the method is not prone to false-positives. Simulation results suggest that reasonable statistical power (80%) is reached with around 140 species pairs, although the degree of lag and trait associations had additional influences on power. We applied the method to two datasets and discuss how estimation of a branch length scaling parameter (κ) can be used with SPELT to detect lag.

对于表现出协同演化的性状而言，其中一个性状的演化速率可能慢于另一个，由此产生演化滞后（evolutionary lag）。物种类对演化滞后检验（species-pairs evolutionary lag test，简称SPELT）采用基于独立对比法（independent contrasts）的策略，在系统发育树上检测演化滞后现象。本研究围绕演化滞后程度、样本量（物种类对数量）与性状间关联强度三个因素，探究了SPELT的统计性能表现。我们采用两种模型模拟性状演化过程：其一为性状X在物种形成事件发生时产生变化，滞后性状Y则会依据物种形成后的时长逐步追上X；其二为性状X以随机游走模式演化，滞后性状Y则为先前某一时间点X的函数。在"无演化滞后"的情景下，一类错误率（Type I error rates）接近预设的5%水平，表明该方法不易产生假阳性结果。模拟结果显示，当物种类对数量约为140时，即可获得较为理想的统计功效（80%），尽管演化滞后程度与性状关联强度仍会对功效产生额外影响。我们将该方法应用于两个数据集，并探讨了如何将分支长度缩放参数（κ）与SPELT结合使用以检测演化滞后。