Morphological Phylogenetics Evaluated Using Novel Evolutionary Simulations

Abstract

Evolutionary inferences require reliable phylogenies. Morphological data have traditionally been analyzed using maximum parsimony, but recent simulation studies have suggested that Bayesian analyses yield more accurate trees. This debate is ongoing, in part, because of ambiguity over modes of morphological evolution and a lack of appropriate models. Here, we investigate phylogenetic methods using two novel simulation models—one in which morphological characters evolve stochastically along lineages and another in which individuals undergo selection. Both models generate character data and lineage splitting simultaneously: the resulting trees are an emergent property, rather than a fixed parameter. Standard consensus methods for Bayesian searches (Mki) yield fewer incorrect nodes and quartets than the standard consensus trees recovered using equal weighting and implied weighting parsimony searches. Distances between the pool of derived trees (most parsimonious or posterior distribution) and the true trees—measured using Robinson-Foulds (RF), subtree prune and regraft (SPR), and tree bisection reconnection (TBR) metrics—demonstrate that this is related to the search strategy and consensus method of each technique. The amount and structure of homoplasy in character data differ between models. Morphological coherence, which has previously not been considered in this context, proves to be a more important factor for phylogenetic accuracy than homoplasy. Selection-based models exhibit relatively lower homoplasy, lower morphological coherence, and higher inaccuracy in inferred trees. Selection is a dominant driver of morphological evolution, but we demonstrate that it has a confounding effect on numerous character properties which are fundamental to phylogenetic inference. We suggest that the current debate should move beyond considerations of parsimony versus Bayesian, toward identifying modes of morphological evolution and using these to build models for probabilistic search methods. [Bayesian; evolution; morphology; parsimony; phylogenetics; selection; simulation.]

Sphenodontian phylogeny and the impact of model choice in Bayesian morphological clock estimates of divergence times and evolutionary rates

“…Despite the oversimplistic substitution model utilized to handle morphological data in probabilistic approaches to phylogenetics (the Mk model [24]), a wave of recent studies have demonstrated that even in such circumstances, probabilistic methods still reach higher accuracy levels than previous criteria (maximum parsimony), with Bayesian inference performing the best regardless of missing data, characters and taxon number (e.g., [23,26,27]). Additionally, the several advances in modeling relaxed clocks in the last decade have made it possible to incorporate a variety of macroevolutionary parameters for the analysis of morphological data in Bayesian phylogenetics.…”

“…Importantly, several features of morphological datasets make them quite distinct from molecular datasets. Such features include the following: the greater influence of natural selection on the phenotype and its potential impact on developing realistic morphological models [23]; the lack of direct comparability of character states among different characters (as opposed to nucleotides in molecular sequences), thus hampering the development of more complex morphological substitution models [24]; and the much smaller size of the vast majority of morphological datasets compared to molecular sequences. Therefore, it is expected that the impact of model specification on tree topology, divergence times, and evolutionary rates on morphological datasets may vary substantially from molecular datasets.…”

“…Recently, some studies have started to direct their attention to the performance of morphology only datasets in a probabilistic framework, especially so as to compare the performance of software and optimality criteria under various conditions using non-clock Bayesian inference-e.g., [23,[25][26][27][28]. However, very few studies have directed their attention to the performance of model choice using relaxed Bayesian clocks with morphological data only.…”

The origin of Rhinocerotoidea and phylogeny of Ceratomorpha (Mammalia, Perissodactyla)

“…Amynodontidae is closer to the Eggysodontidae-Paraceratheriidae-Rhinocerotidae clade than is Hyracodontidae in the MPT; however, the BIT suggests a closer relationship between the latter two clades. The preference of different topologies generated by the parsimony and Bayesian analysis for morphological data are ongoing debate 67,68 , and it seems that both have advantages and disadvantages for morphological data 69,70 . The parsimony method only provides a point estimate (the MPTs) while Bayesian inference averages over the uncertainties of the topologies by summarizing a majorityrule consensus tree.…”

“…Nevertheless, the taxa or clade contradictory in both methods indicate that the data might not contain enough information to draw firm conclusions about their relationships 71 . With more fossils and more complete data added in the matrix in combination with improvements of algorithms and parameters 70,72,73 , two methods probably converge to more compatible results.…”