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Missing data, incomplete taxa, and phylogenetic accuracy

机译:数据丢失,分类单元不完整和系统发育准确性

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摘要

The problem of missing data is often considered to be the most important obstacle in reconstructing the phylogeny of fossil taxa and in combining data from diverse characters and taxa for phylogenetic analysis. Empirical and theoretical studies show that including highly incomplete taxa can lead to multiple equally parsimonious trees, poorly resolved consensus trees, and decreased phylogenetic accuracy. However, the mechanisms that cause incomplete taxa to be problematic have remained unclear. It has been widely assumed that incomplete taxa are problematic because of the proportion or amount of missing data that they bear. In this study, I use simulations to show that the reduced accuracy associated with including incomplete taxa is caused by these taxa bearing too few complete characters rather than too many missing data cells. This seemingly subtle distinction has a number of important implications. First, the so-called missing data problem for incomplete taxa is, paradoxically, not directly related to their amount or proportion of missing data. Thus, the level of completeness alone should not guide the exclusion of taxa (contrary to common practice), and these results may explain why empirical studies have sometimes found little relationship between the completeness of a taxon and its impact on an analysis. These results also (1) suggest a more effective strategy for dealing with incomplete taxa, (2) call into question a justification of the controversial phylogenetic supertree approach, and (3) show the potential for the accurate phylogenetic placement of highly incomplete taxa, both when combining diverse data sets and when analyzing relationships of fossil taxa.
机译:数据丢失的问题通常被认为是重建化石类群的系统发育以及将来自不同特征和类群的数据进行系统发育分析的最重要的障碍。实证和理论研究表明,包括高度不完整的分类单元可能会导致多个同等的简约树,较差的共识树并导致系统发育准确性下降。但是,导致不完整分类群成问题的机制仍不清楚。人们普遍认为,不完整的分类单元是有问题的,因为它们所拥有的丢失数据的比例或数量。在这项研究中,我使用模拟显示与包含不完整的分类单元相关的准确性降低是由于这些分类单元的完整字符太少而不是丢失的数据单元太多。这种看似微妙的区别具有许多重要含义。首先,矛盾的是,所谓的不完整类群的缺失数据问题与缺失数据的数量或比例没有直接关系。因此,仅完整性级别不能指导排除分类单元(与常规做法相反),这些结果可以解释为什么经验研究有时发现分类单元的完整性及其对分析的影响之间几乎没有关系。这些结果还(1)提出了一种更有效的策略来处理不完整的分类单元;(2)引起争议的系统进化树方法的合理性受到质疑;(3)显示了高度不完整的分类单元的正确系统发育定位的潜力,两者结合各种数据集并分析化石分类群的关系时。

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