Shedding light on the 'dark side' of phylogenetic comparative methods.
Shedding light on the 'dark side' of phylogenetic comparative methods.
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DOI：
10.1111/2041-210x.12533
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发表时间：
2016-06
影响因子：
6.6
通讯作者：
FitzJohn RG
中科院分区：
文献类型：
--
作者：
Cooper N;Thomas GH;FitzJohn RG
系统发育比较方法在研究进化模式和过程中越来越受欢迎。然而，这些方法并不是绝对可靠的-它们像所有其他统计方法一样存在偏见和假设。不幸的是，虽然这些限制是众所周知的系统发育比较方法社区，他们往往是不充分的经验研究，导致误解的结果和穷人的模型拟合评估。在这里，我们探讨了那些开发新方法和使用它们的人之间存在沟通鸿沟的原因。我们认为，文献中缺少一些重要的信息，而另一些信息则很难从冗长的技术论文中提取出来。我们还强调了用户直接跳到方法的软件实现（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见和假设的文档。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，以较少的技术术语解释新方法，鼓励可重复性和代码共享，制作wiki风格的页面总结流行方法的文献，更仔细地考虑和测试方法是否适用于给定的问题/数据集，增加合作，从发表纯粹的新方法转向发表对现有方法的改进，以及检测偏差或测试模型拟合的方法。其中许多观点适用于生态学和进化的方法，而不仅仅是系统发育比较方法。
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. 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. 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. 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.