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 use small 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.

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