Morphological Characters Can Strongly Influence Early Animal Relationships Inferred from Phylogenomic Data Sets

Abstract

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 data sets ($\sim $45,000-400,000 characters in size with $\sim $15-100 taxa) that focus on early metazoan phylogeny to evaluate the impact of incorporating morphological data sets ($\sim $15-275 characters). We additionally use small exemplar data sets to quantify how increased taxon sampling can help stabilize phylogenetic inferences. We apply a plethora of common methods, that is, likelihood models and their "equivalent" under parsimony: character weighting schemes. Our results are at odds with the typical view of phylogenomics, that is, that genomic-scale data sets will swamp out inferences from morphological data. Instead, weighting morphological data 2-10$\times $ 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 data sets ($>$200,000 characters and up to $\sim $100 taxa) showing node stability even with $\geqq100\times $ upweighting of morphological data. Accordingly, our analyses have three strong messages. 1) 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 data sets, even when outnumbered thousands of times by molecular data. Morphology therefore should not be counted out a priori. 2) We here quantify for the first time how the stability of the SOM node improves for several genomic data sets when the taxon sampling is increased. 3) 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 data sets that could be developed into a new sensitivity analysis tool. [Metazoa; Morphology; Phylogenomics; Weighting.].

Figure 1.

Molecular topology stability heat maps for the likelihood analyses using combined exemplar molecular (C10)/morphology (Mk) data sets for six (a, left) and 11 (b, right) taxa. The stability of the molecular topology is greater when the flip from Ctenophora to another SOM requires a higher weighting of morphological characters.

Figure 2.

Molecular topology stability heat maps for the parsimony analyses using combined exemplar molecular (LGX2)/morphology (no transformation) data sets for six (a, left) and 11 (b, right) taxa. The stability of the molecular topology is greater when the flip from Ctenophora to another SOM requires a higher weighting of morphological characters.

Figure 3.

Molecular topology stability heat maps comparing the weights at which the flip from Ctenophora to another SOM occurs for the “full” taxa data sets for the C10 model with Mk for morphology (a, left, likelihood) and the LGX2 transformation matrix with no transformation for morphology (b, right, parsimony). The stability of the molecular topology is greater when the flip requires a higher weighting of morphological characters.

Figure 4.

Molecular topology stability heat maps comparing the weights at which the flip from Ctenophora to another SOM occurs when combined with our three curated morphological matrices Com (our combined matrix), PL1 containing only characters that support Placozoa as the SOM, and PO1 containing only characters that support Porifera as the SOM (see Materials and Methods section). Shown are our analyses for six taxa (a), 11 taxa (b), and “full” taxa (c), each for likelihood using the C10 model on the left, and for parsimony using the LGX2 amino acid transformation matrix on the right. The stability of the molecular topology is greater when the flip requires a higher weighting of morphological characters.

Figure 5.

The inferred SOM when flipped by combined parsimony analysis using the LGX2 amino acid transformation matrix and weighting morphology by , or . White indicates that no flip occurred (as in the Si2 and Si3 combined matrices) or that the flip was to Bilateria (as in the Ch4 matrix). Results for the exemplar taxon sets are shown in a () and b (), and the results for the “full” taxon data set are shown in c.