Scotland Robert W, Steel Mike
Department of Plant Sciences, Oxford University, Oxford OX1 3RB, UK;
Biomathematics Research Centre, University of Canterbury, Christchurch, New Zealand
Syst Biol. 2015 May;64(3):492-504. doi: 10.1093/sysbio/syv008. Epub 2015 Jan 28.
Phylogenetic methods typically rely on an appropriate model of how data evolved in order to infer an accurate phylogenetic tree. For molecular data, standard statistical methods have provided an effective strategy for extracting phylogenetic information from aligned sequence data when each site (character) is subject to a common process. However, for other types of data (e.g., morphological data), characters can be too ambiguous, homoplastic, or saturated to develop models that are effective at capturing the underlying process of change. To address this, we examine the properties of a classic but neglected method for inferring splits in an underlying tree, namely, maximum compatibility. By adopting a simple and extreme model in which each character either fits perfectly on some tree, or is entirely random (but it is not known which class any character belongs to) we are able to derive exact and explicit formulae regarding the performance of maximum compatibility. We show that this method is able to identify a set of non-trivial homoplasy-free characters, when the number [Formula: see text] of taxa is large, even when the number of random characters is large. In contrast, we show that a method that makes more uniform use of all the data-maximum parsimony-can provably estimate trees in which none of the original homoplasy-free characters support splits.
系统发育方法通常依赖于数据如何进化的适当模型，以便推断出准确的系统发育树。对于分子数据，当每个位点（特征）遵循共同过程时，标准统计方法提供了一种从比对序列数据中提取系统发育信息的有效策略。然而，对于其他类型的数据（例如形态数据），特征可能过于模糊、同塑性或饱和，以至于无法开发出有效捕捉潜在变化过程的模型。为了解决这个问题，我们研究了一种经典但被忽视的推断基础树中分支的方法的性质，即最大兼容性。通过采用一个简单且极端的模型，其中每个特征要么完美地符合某棵树，要么完全是随机的（但不知道任何特征属于哪一类），我们能够推导出关于最大兼容性性能的精确且明确的公式。我们表明，当分类单元数量[公式：见正文]很大时，即使随机特征数量很大，这种方法也能够识别出一组非平凡的无同塑性特征。相比之下，我们表明一种更均匀地使用所有数据的方法——最大简约法——可以证明估计出的树中，没有一个原始的无同塑性特征支持分支。
