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伪支持率为 99%的引导和刀切法支持非支持支系。

Spurious 99% bootstrap and jackknife support for unsupported clades.

机构信息

Department of Biology, Colorado State University, Fort Collins, CO 80523-1878, USA.

出版信息

Mol Phylogenet Evol. 2011 Oct;61(1):177-91. doi: 10.1016/j.ympev.2011.06.003. Epub 2011 Jun 16.

Abstract

Quantifying branch support using the bootstrap and/or jackknife is generally considered to be an essential component of rigorous parsimony and maximum likelihood phylogenetic analyses. Previous authors have described how application of the frequency-within-replicates approach to treating multiple equally optimal trees found in a given bootstrap pseudoreplicate can provide apparent support for otherwise unsupported clades. We demonstrate how a similar problem may occur when a non-representative subset of equally optimal trees are held per pseudoreplicate, which we term the undersampling-within-replicates artifact. We illustrate the frequency-within-replicates and undersampling-within-replicates bootstrap and jackknife artifacts using both contrived and empirical examples, demonstrate that the artifacts can occur in both parsimony and likelihood analyses, and show that the artifacts occur in outputs from multiple different phylogenetic-inference programs. Based on our results, we make the following five recommendations, which are particularly relevant to supermatrix analyses, but apply to all phylogenetic analyses. First, when two or more optimal trees are found in a given pseudoreplicate they should be summarized using the strict-consensus rather than frequency-within-replicates approach. Second jackknife resampling should be used rather than bootstrap resampling. Third, multiple tree searches while holding multiple trees per search should be conducted in each pseudoreplicate rather than conducting only a single search and holding only a single tree. Fourth, branches with a minimum possible optimized length of zero should be collapsed within each tree search rather than collapsing branches only if their maximum possible optimized length is zero. Fifth, resampling values should be mapped onto the strict consensus of all optimal trees found rather than simply presenting the ≥ 50% bootstrap or jackknife tree or mapping the resampling values onto a single optimal tree.

摘要

使用自举法和/或刀切法来量化分支支持通常被认为是严格简约法和最大似然系统发育分析的重要组成部分。先前的作者已经描述了如何应用重复内频率方法来处理给定自举伪重复中发现的多个同样最优的树,这可以为否则不支持的分支提供明显的支持。我们展示了当每个伪重复中保留一组非代表性的同样最优的树时,可能会出现类似的问题,我们称之为重复内抽样不足的伪像。我们使用人为和经验示例来说明重复内频率和重复内抽样不足的自举和刀切伪像,证明这些伪像可能出现在简约法和似然分析中,并表明这些伪像出现在来自多个不同系统发育推断程序的输出中。基于我们的结果,我们提出了以下五个建议,这些建议特别适用于超级矩阵分析,但也适用于所有系统发育分析。首先,当在给定的伪重复中发现两个或更多的最优树时,应该使用严格一致法而不是重复内频率法来总结。其次,应该使用刀切法而不是自举法进行重采样。第三,应该在每个伪重复中进行多次树搜索,同时保持每个搜索中多个树,而不是仅进行一次搜索并保持单个树。第四,在每个树搜索中,应该将具有最小可能优化长度为零的分支进行折叠,而不是仅在最大可能优化长度为零时才折叠分支。第五,重采样值应该映射到所有找到的最优树的严格一致上,而不是简单地呈现大于等于 50%的自举或刀切树,或者将重采样值映射到单个最优树上。

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