There are considerable phylogenetic incongruencies between morphological and phylogenomic data for the deep evolution of animals. This has contributed to a heated debate over the earliest-branching lineage of the animal kingdom: The sister to all other Metazoa (SOM). Here we use published phylogenomic datasets (∼45,000-400,000 characters in size with ∼15-100 taxa) that focus on early metazoan phylogeny to evaluate the impact of incorporating morphological datasets (∼15-275 characters). We additionally usesmall exemplar datasets to quantify how increased taxon sampling can help stabilize phylogenetic inferences. We apply a plethora of common methods, i.e. likelihood models and their "equivalent" under parsimony: character weighting schemes. Our results are at odds with the typical view of phylogenomics, i.e., that genomic-scale datasets will swamp out inferences from morphological data. Instead, weighting morphological data 2-10× in both likelihood and parsimony can in some cases "flip" which phylum is inferred to be the SOM. This typically results in the molecular hypothesis of Ctenophora as the SOM flipping to Porifera (or occasionally Placozoa). However, greater taxon sampling improves phylogenetic stability, with some of the larger molecular datasets (>200,000 characters and up to ∼100 taxa) showing node stability even with ≧100× up-weighting of morphological data. Accordingly, our analyses have three strong messages. A) The assumption that genomic data will automatically "swamp out" morphological data is not always true for the SOM question. Morphological data have a strong influence in our analyses of combined datasets, even when outnumbered thousands of times by morphological data. Morphology therefore should not be counted out a priori. B.) We here quantify for the first time how the stability of the SOM node improves for several genomic datasets when the taxon sampling is increased. C.) The patterns of "flipping points" (i.e., the weighting of morphological data it takes to change the inferred SOM) carry information about the phylogenetic stability of matrices. The weighting space is an innovative way to assess comparability of datasets that should be developed into a new sensitivity analysis tool.

中文翻译：

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形态特征可以强烈影响从系统发育数据集推断出的早期动物关系


动物深层进化的形态学和系统发育数据之间存在相当大的系统发育不一致。这引发了关于动物界最早分支谱系的激烈争论：所有其他后生动物的姐妹（SOM）。在这里，我们使用专注于早期后生动物系统发育的已发表的系统发育数据集（约 45,000-400,000 个字符大小，约 15-100 个分类群）来评估合并形态学数据集（约 15-275 个字符）的影响。我们还使用小型样本数据集来量化增加分类单元采样如何帮助稳定系统发育推断。我们应用了大量的常见方法，即似然模型及其在简约下的“等效方法”：字符加权方案。我们的结果与系统基因组学的典型观点不一致，即基因组规模的数据集将淹没形态学数据的推论。相反，在似然性和简约性方面对形态学数据进行 2-10 倍的加权在某些情况下可能会“翻转”哪个门被推断为 SOM。这通常会导致栉水母的分子假说，因为 SOM 翻转为多孔动物（或偶尔为扁动物）。然而，更大的分类单元采样提高了系统发育稳定性，一些较大的分子数据集（> 200,000 个字符和多达 ∼100 个分类单元）即使在形态数据权重增加 ≧100 倍的情况下也显示出节点稳定性。因此，我们的分析传达了三个强烈的信息。 A) 对于 SOM 问题，基因组数据将自动“淹没”形态学数据的假设并不总是正确。即使形态数据的数量是形态数据的数千倍，形态数据对我们对组合数据集的分析也有很大的影响。因此，形态学不应被先验地排除在外。 B.）我们在这里首次量化了当分类单元采样增加时，多个基因组数据集的 SOM 节点的稳定性如何提高。 C.) “翻转点”的模式（即，改变推断的 SOM 所需的形态数据的权重）携带有关矩阵系统发育稳定性的信息。加权空间是一种评估数据集可比性的创新方法，应开发成新的敏感性分析工具。