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Detecting the historical signature of key innovations using stochastic models of character evolution and cladogenesis

机译:使用角色演变和成败的随机模型检测关键创新的历史特征

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Phylogenetic evidence for biological traits that increase the net diversification rate of lineages (key innovations) is most commonly drawn from comparisons of clade size. This can work well for ancient, unreversed traits and for correlating multiple trait origins with higher diversification rates, but it is less suitable for unique events, recently evolved innovations, and traits that exhibit homoplasy. Here I present a new method for detecting the phylogenetic signature of key innovations that tests whether the evolutionary history of the candidate trait is associated with shorter waiting times between cladogenesis events. The method employs stochastic models of character evolution and cladogenesis and integrates well into a Bayesian framework in which uncertainty in historical inferences (such as phylogenetic relationships) is allowed. Applied to a well-known example in plants, nectar spurs in columbines, the method gives much stronger support to the key innovation hypothesis than previous tests.
机译:进化谱系大小的比较最常见的是生物学特性的系统发育证据,这些生物学特性会增加谱系的净多样化速率(关键创新)。这对于古老的,不可逆转的性状以及将多个性状起源与更高的多样化率相关联的效果很好,但是它不太适合于独特事件,最近发展的创新以及表现出同质性的性状。在这里,我提出了一种用于检测关键创新的系统进化特征的新方法,该方法可以测试候选性状的进化史是否与较短的基因发生事件之间的等待时间相关。该方法采用了字符进化和进化的随机模型,并很好地集成了贝叶斯框架,该框架允许历史推断的不确定性(例如系统发育关系)。与以前的测试相比,该方法应用于植物中的一个众所周知的例子,即哥伦比的花蜜刺,为关键的创新假设提供了更强大的支持。

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