Evolutionary Sample Size and Consilience in Phylogenetic Comparative Analysis

10.408 Q1

 Syst Biol

. 2021 Mar 15;syab017.

 doi: 10.1093/sysbio/syab017. Online ahead of print.

第一作者：Jacob D Gardner

第一单位：蒙大拿州立大学

通讯作者： Chris L Organ

 Abstract 

    Phylogenetic comparative methods (PCMs) are commonly used to study evolution and adaptation. However, frequently used PCMs for discrete traits mishandle single evolutionary transitions. They erroneously detect correlated evolution in these situations. For example, hair and mammary glands cannot be said to have evolved in a correlated fashion because each evolved only once in mammals, but a commonly used model (Pagel’s Discrete) statistically supports correlated (dependent) evolution. Using simulations, we find that rate parameter estimation, which is central for model selection, is poor in these scenarios due to small effective (evolutionary) sample sizes of independent character state change. Pagel’s Discrete model also tends to favor dependent evolution in these scenarios, in part, because it forces evolution through state combinations unobserved in the tip data. This model prohibits simultaneous dual transitions along branches. Models with underlying continuous data distributions (e.g., Threshold and GLMM) are less prone to favor correlated evolution, but are still susceptible when evolutionary sample sizes are small. We provide three general recommendations for researchers who encounter these common situations: 1) Create study designs that evaluate a priori hypotheses and maximize evolutionary sample sizes;

2) assess the suitability of evolutionary models—for discrete traits, we introduce the phylogenetic imbalance ratio; and 

3) evaluate evolutionary hypotheses with a consilience of evidence from disparate fields, like biogeography and developmental biology. 

Consilience plays a central role in hypothesis testing within the historical sciences where experiments are difficult or impossible to conduct, such as many hypotheses about correlated evolution. These recommendations are useful for investigations that employ any type of PCM.

 摘 要 

   系统发育比较方法（PCM）通常用于研究进化和适应。但是，用于离散特征的常用PCM处理单个进化转换的方式不正确。在这些情况下，他们错误地检测了相关的进化。例如，不能说毛发和乳腺已经以相关的方式进化，因为它们在哺乳动物中仅进化了一次，但是常用的模型（Pagel的离散）在统计学上支持相关的（依存的）进化。通过仿真，我们发现在这些情况下，速率参数估计是模型选择的中心，但由于独立字符状态变化的有效（演化）样本量较小，因此速率参数估计较差。Pagel的Discrete模型在这些情况下也倾向于依赖于进化，部分原因是，因为它通过尖端数据中未观察到的状态组合来强制进化。该模型禁止沿分支同时进行双重过渡。具有基础连续数据分布的模型（例如，阈值和GLMM）较不倾向于支持相关进化，但是当进化样本量较小时，仍然容易受到影响。对于遇到以下常见情况的研究人员，我们提供了三项一般性建议：1）创建评估研究设计先验假设并最大化进化样本数量；2）评估进化模型的适用性-对于离散性状，我们介绍系统发育不平衡率；和3）用来自不同领域（如生物地理学和发育生物学）的证据的一致性来评估进化假说。一致性在难以或不可能进行实验的历史科学中的假设检验中起着核心作用，例如关于相关进化的许多假设。这些建议对于采用任何类型PCM的调查非常有用。

 通讯作者 

**  Chris L Organ **

E-mail: organ@montana.edu