简约而非兼容性将被证明具有误导性的情况
Circumstances in which parsimony but not compatibility will be provably misleading
摘要：
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，标准的统计方法提供了一种有效的策略，用于从比对的序列数据中提取系统发育信息时，每个站点（字符）是一个共同的过程。然而，对于其他类型的数据（例如形态数据），字符可能过于模糊，同质或饱和，无法开发有效捕捉变化的基本过程的模型。为了解决这个问题，我们研究了一个经典的，但被忽视的方法推断分裂的基础树，即最大兼容性的属性。通过采用一个简单而极端的模型，其中每个字符要么完全适合某棵树，要么完全随机（但不知道任何字符属于哪一类），我们能够推导出精确和明确的公式，关于最大兼容性的性能。我们表明，这种方法是能够确定一组非平凡的同质性自由字符，当类群的数量$n$是大的，即使当随机字符的数量是大的。相比之下，我们表明，一种方法，使更均匀地使用所有的数据-最大简约性-可以证明估计树，其中{ \em没有}的原始同质性的字符支持分裂。
英文摘要：
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 $n$ of taxa is large, even when the number of random characters is large. By contrast, we show that a method that makes more uniform use of all the data --- maximum parsimony --- can provably estimate trees in which {\em none} of the original homoplasy-free characters support splits.
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