1. Phylogenetic comparative methods are becoming increasingly popular for investigating evolutionary patterns and processes. However, these methods are not infallible – they suffer from biases and make assumptions like all other statistical methods.
2. Unfortunately, although these limitations are generally well known in the phylogenetic comparative methods community, they are often inadequately assessed in empirical studies leading to misinterpreted results and poor model fits. Here, we explore reasons for the communication gap dividing those developing new methods and those using them.
3. We suggest that some important pieces of information are missing from the literature and that others are difficult to extract from long, technical papers. We also highlight problems with users jumping straight into software implementations of methods (e.g. in r) that may lack documentation on biases and assumptions that are mentioned in the original papers.
4. To help solve these problems, we make a number of suggestions including providing blog posts or videos to explain new methods in less technical terms, encouraging reproducibility and code sharing, making wiki‐style pages summarising the literature on popular methods, more careful consideration and testing of whether a method is appropriate for a given question/data set, increased collaboration, and a shift from publishing purely novel methods to publishing improvements to existing methods and ways of detecting biases or testing model fit. Many of these points are applicable across methods in ecology and evolution, not just phylogenetic comparative methods.

中文翻译：

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揭示系统发育比较方法的“黑暗面”。

1. 
   系统发育比较方法在研究进化模式和过程方面变得越来越流行。然而，这些方法并非万无一失——它们存在偏差，并像所有其他统计方法一样做出假设。
2. 
   不幸的是，尽管这些局限性在系统发育比较方法界中众所周知，但在实证研究中常常对它们进行不充分的评估，从而导致结果误解和模型拟合不佳。在这里，我们探讨了开发新方法和使用新方法的人之间沟通差距的原因。
3. 
   我们认为文献中缺少一些重要的信息，而其他信息则很难从冗长的技术论文中提取。我们还强调了用户直接跳入方法的软件实现（例如在r中）的问题，这些方法可能缺乏原始论文中提到的偏差和假设的文档。
4. 
   为了帮助解决这些问题，我们提出了一些建议，包括提供博客文章或视频来用不太技术性的术语解释新方法、鼓励可重复性和代码共享、制作维基风格的页面来总结流行方法的文献、更仔细的考虑和测试一种方法是否适合给定的问题/数据集，增加协作，以及从发布纯粹新颖的方法到发布对现有方法和检测偏差或测试模型拟合的方法的改进的转变。其中许多观点适用于生态学和进化论的各种方法，而不仅仅是系统发育比较方法。