Data from: Rethinking phylogenetic comparative methods

As a result of the process of descent with modification, closely related species tend to be similar to one another in a myriad different ways. In statistical terms, this means that traits measured on one species will not be independent of traits measured on others. Since their introduction in the 1980s, phylogenetic comparative methods (PCMs) have been framed as a solution to this problem. In this paper, we argue that this way of thinking about PCMs is deeply misleading. Not only has this sowed widespread confusion in the literature about what PCMs are doing but has led us to develop methods that are susceptible to the very thing we sought to build defenses against --- unreplicated evolutionary events. Through three Case Studies, we demonstrate that the susceptibility to singular events is indeed a recurring problem in comparative biology that links several seemingly unrelated controversies. In each Case Study we propose a potential solution to the problem. While the details of our proposed solutions differ, they share a common theme: unifying hypothesis testing with data-driven approaches (which we term ``phylogenetic natural history'') to disentangle the impact of singular evolutionary events from that of the factors we are investigating. More broadly, we argue that our field has, at times, been sloppy when weighing evidence in support of causal hypotheses. We suggest that one way to refine our inferences is to re-imagine phylogenies as probabilistic graphical models; adopting this way of thinking will help clarify precisely what we are testing and what evidence supports our claims.

受遗传变异演化（descent with modification）过程的影响，近缘物种往往在诸多方面彼此相似。从统计学视角来看，这意味着针对某一物种测得的性状，与其他物种的性状并非相互独立。自20世纪80年代被提出以来，系统发育比较方法（phylogenetic comparative methods, PCMs）一直被视作解决该问题的方案。在本文中，我们指出这种对系统发育比较方法的认知存在根本性误导。这一认知不仅在学术文献中引发了关于系统发育比较方法实际作用的广泛困惑，还导致我们开发出的方法极易受到我们本欲抵御的问题——未重复演化事件——的影响。通过三项案例研究，我们证明了对单次演化事件的易感性确实是比较生物学中反复出现的问题，该问题关联了数起看似无关的学术争议。在每项案例研究中，我们都针对该问题提出了潜在解决方案。尽管各方案的细节存在差异，但它们拥有一个共同核心：将假设检验与数据驱动方法（我们将其称为"系统发育自然历史"）相结合，以区分单次演化事件与我们所研究因素各自产生的影响。从更宏观的层面来看，我们认为本领域在评估支撑因果假设的证据时，有时存在草率疏漏。我们提出，优化研究推论的一种途径是将系统发育树重新构想为概率图模型（probabilistic graphical models）；采用这种认知视角，将有助于明确我们究竟在检验什么，以及何种证据能够支撑我们的研究结论。