Data from: Phylogenetic assessment of molecular and morphological data for eutherian mammals

The interordinal relationships of eutherian (placental) mammals were evaluated by a phylogenetic analysis of four published data sets (three sequence and one morphological). The nature and degree of support and conflict for particular groups were assessed by separate bootstrap and homogeneity tests that were followed by combined analyses of the sequence and morphological data. Between orders, strong support (i.e., >95% bootstrap scores) was found for a paraphyletic Artiodactyla (relative to Cetacea) and a monophyletic Cetartiodactyla (Artiodactyla and Cetacea) and Paenungulata (Hyracoidea, Proboscidea, and Sirenia). In turn, some reasonable to strong evidence (>85%) was obtained for Hyracoidea with Sirenia, Dermoptera with Scandentia, Glires (Lagomorpha with Rodentia), and Afrotheria (Amblysomus, Macroscelidea, Paenungulata, and Tubulidentata). Otherwise, no other interordinal clades were supported at these reasonable to strong levels. This overall lack of resolution for eutherian interordinal clusters agrees with other studies that further progress will continue to be slow and difficult. Further resolution will require the integration of more recently published data, the continued sampling of taxa and characters, and the use of more powerful methods of data analysis.本研究通过对4套已发表数据集（3套序列数据与1套形态学数据）进行的系统发育分析（phylogenetic analysis），评估了真兽类哺乳动物（eutherian (placental) mammals）的目间系统发育关系。针对特定类群的支持模式与冲突程度的性质及强弱，本研究通过独立的自举检验（bootstrap）与同质性检验（homogeneity tests）进行了评估，随后对序列数据与形态学数据开展联合分析。在目级阶元间，偶蹄目（Artiodactyla，相对于鲸目（Cetacea））为并系群（paraphyletic）、鲸偶蹄目（Cetartiodactyla，包含偶蹄目与鲸目）以及近蹄类（Paenungulata，包含蹄兔目（Hyracoidea）、长鼻目（Proboscidea）与海牛目（Sirenia））得到了强支持（即自举检验得分>95%）。此外，蹄兔目与海牛目、皮翼目（Dermoptera）与树鼩目（Scandentia）、啮齿兔形类（Glires，包含兔形目（Lagomorpha）与啮齿目（Rodentia））以及非洲兽总目（Afrotheria，包含金毛鼹属（Amblysomus）、象鼩目（Macroscelidea）、近蹄类与管齿目（Tubulidentata））则获得了中等至强的支持证据（自举检验得分>85%）。除此之外，其余目间演化支（clades）均未达到上述中等至强的支持水平。本研究中真兽类目级类群整体分辨率不足的结果，与其他相关研究结论一致，即该领域的后续研究进展仍将缓慢且艰难。若要进一步提升类群分辨率，则需要整合最新发表的数据、持续扩充类群（taxa）与性状（characters）的采样规模，并采用更为高效强大的数据分析方法。