Lyons-Weiler J, Hoelzer G A
Graduate Program in Ecology, Evolution and Conservation Biology, University of Nevada, Reno 89557, USA.
Mol Phylogenet Evol. 1997 Dec;8(3):375-84. doi: 10.1006/mpev.1997.0450.
Long branches in a true phylogeny tend to disrupt hierarchical character covariation (phylogenetic signal) in the distribution of traits among organisms. The distortion of hierarchical structure in character-state matrices can lead to errors in the estimation of phylogenetic relationships and inconsistency of methods of phylogenetic inference. Examination of trees distorted by long-branch attraction will not reveal the identities of problematic taxa, in part because the distortion can mask long branches by reducing inferred branch lengths and through errors in branching order. Here we present a simple method for the detection of taxa whose placement in evolutionary trees is made difficult by the effects of long-branch attraction. The method is an extension of a tree-independent conceptual framework of phylogenetic data exploration (RASA). Taxa that are likely to attract are revealed because long branches leave distinct footprints in the distribution of character states among taxa, and these traces can be directly observed in the error structure of the RASA regression. Problematic taxa are identified using a new diagnostic plot called the taxon variance plot, in which the apparent cladistic and phenetic variances contributed by individual taxa are compared. The procedure for identifying long edges employs algorithms solved in polynomial time and can be applied to morphological, molecular, and mixed characters. The efficacy of the method is demonstrated using simulated evolution and empirical evidence of long branches in a set of recently published sequences. We show that the accuracy of evolutionary trees can be improved by detecting and combating the potentially misleading influences of long-branch taxa.
在真实的系统发育中，长分支往往会扰乱生物间性状分布中的层次性状共变（系统发育信号）。性状状态矩阵中层次结构的扭曲会导致系统发育关系估计中的错误以及系统发育推断方法的不一致。对因长枝吸引而扭曲的树进行检查无法揭示有问题分类单元的身份，部分原因是这种扭曲会通过缩短推断的分支长度和分支顺序错误来掩盖长分支。在此，我们提出一种简单方法，用于检测那些因长枝吸引效应而在进化树中难以确定位置的分类单元。该方法是系统发育数据探索（RASA）的一个与树无关的概念框架的扩展。可能具有吸引作用的分类单元会被揭示出来，因为长分支在分类单元间的性状状态分布中留下了独特的痕迹，并且这些痕迹可以在RASA回归的误差结构中直接观察到。使用一种名为分类单元方差图的新诊断图来识别有问题的分类单元，其中比较了各个分类单元所贡献的明显分支和表型方差。识别长边缘的过程采用多项式时间内求解的算法，并且可以应用于形态学、分子和混合性状。通过模拟进化以及一组最近发表序列中长分支的经验证据证明了该方法的有效性。我们表明，通过检测和对抗长分支分类单元的潜在误导性影响，可以提高进化树的准确性。
