Cunningham C W
Zoology Department, Duke University, Durham, North Carolina 27705, USA.
Mol Biol Evol. 1997 Jul;14(7):733-40. doi: 10.1093/oxfordjournals.molbev.a025813.
Advocates of conditional combination have argued that testing for incongruence between data partitions is an important step in data exploration. Unless the partitions have had distinct histories, as in horizontal gene transfer, incongruence means that one or more data support the wrong phylogeny. This study examines the relationship between incongruence and phylogenetic accuracy using three tests of incongruence. These tests were applied to pairs of mitochondrial DNA data partitions from two well-corroborated vertebrate phylogenies. Of the three tests, the most useful was the incongruence length difference test (ILD, also called the partition homogeneity test). This test distinguished between cases in which combining the data generally improved phylogenetic accuracy (P > 0.01) and cases in which accuracy of the combined data suffered relative to the individual partitions (P < 0.001). In contrast, in several cases, the Templeton and Rodrigo tests detected highly significant incongruence (P < 0.001) even though combining the incongruent partitions actually increased phylogenetic accuracy. All three tests identified cases in which improving the reconstruction model would improve the phylogenetic accuracy of the individual partitions.
条件组合的支持者认为，检测数据分区之间的不一致性是数据探索中的重要一步。除非各分区有不同的历史，如水平基因转移的情况，否则不一致意味着一个或多个数据支持错误的系统发育关系。本研究使用三种不一致性检验方法来考察不一致性与系统发育准确性之间的关系。这些检验方法应用于来自两个充分证实的脊椎动物系统发育关系的线粒体DNA数据分区对。在这三种检验方法中，最有用的是不一致长度差异检验（ILD，也称为分区同质性检验）。该检验区分了以下两种情况：一种是合并数据通常能提高系统发育准确性（P > 0.01），另一种是合并后的数据准确性相对于单个分区有所下降（P < 0.001）。相比之下，在一些情况下，Templeton检验和Rodrigo检验检测到高度显著的不一致性（P < 0.001），尽管合并不一致的分区实际上提高了系统发育准确性。所有这三种检验都识别出了通过改进重建模型可提高单个分区系统发育准确性的情况。
