Rethinking phylogenetic comparative methods
Rethinking phylogenetic comparative methods
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DOI：
10.1101/222729
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
J. Uyeda;Rosana Zenil‐Ferguson;Matthew W. Pennell
中科院分区：
文献类型：
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作者：
J. Uyeda;Rosana Zenil‐Ferguson;Matthew W. Pennell
作为进化进化过程的结果，密切相关的物种往往在无数不同的方面彼此相似。从统计学的角度来看，这意味着一个物种的特征不会独立于其他物种的特征。自20世纪80年代引入以来，系统发育比较方法(PCM)一直被认为是解决这一问题的一种方法。在这篇文章中，我们认为这种对相变的思考方式是严重误导的。这不仅在文献中播下了关于PCM正在做什么的广泛混乱的种子，而且还引导我们开发了一些方法，这些方法很容易受到我们试图建立防御系统的东西的影响--不可复制的进化事件。通过三个案例研究，我们证明了对奇异事件的易感性确实是比较生物学中一个反复出现的问题，它将几个看似无关的争议联系在一起。在每个案例研究中，我们都提出了问题的潜在解决方案。虽然我们提出的解决方案的细节不同，但它们都有一个共同的主题：将假设检验与数据驱动的方法(我们称之为“系统发育自然史”)统一起来，将单一进化事件的影响与我们正在调查的因素的影响区分开来。更广泛地说，我们认为，在权衡支持因果假设的证据时，我们的领域有时是草率的。我们建议，改进我们推论的一种方法是将系统发育重新想象为概率图形模型；采用这种思维方式将有助于准确地澄清我们正在测试的是什么，以及哪些证据支持我们的主张。
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.