Different analytical methods can yield competing interpretations of evolutionary history and, currently, there is no definitive method for phylogenetic reconstruction using morphological data. Parsimony has been the primary method for analysing morphological data, but there has been a resurgence of interest in the likelihood-based Mk-model. Here, we test the performance of the Bayesian implementation of the Mk-model relative to both equal and implied-weight implementations of parsimony. Using simulated morphological data, we demonstrate that the Mk-model outperforms equal-weights parsimony in terms of topological accuracy, and implied-weights performs the most poorly. However, the Mk-model produces phylogenies that have less resolution than parsimony methods. This difference in the accuracy and precision of parsimony and Bayesian approaches to topology estimation needs to be considered when selecting a method for phylogeny reconstruction.

Morphological data provide the only means of classifying the majority of life's history, but the choice between competing phylogenetic methods for the analysis of morphology is unclear. Traditionally, parsimony methods have been favoured but recent studies have shown that these approaches are less accurate than the Bayesian implementation of the Mk model. Here we expand on these findings in several ways: we assess the impact of tree shape and maximum-likelihood estimation using the Mk model, as well as analysing data composed of both binary and multistate characters. We find that all methods struggle to correctly resolve deep clades within asymmetric trees, and when analysing small character matrices. The Bayesian Mk model is the most accurate method for estimating topology, but with lower resolution than other methods. Equal weights parsimony is more accurate than implied weights parsimony, and maximum-likelihood estimation using the Mk model is the least accurate method. We conclude that the Bayesian implementation of the Mk model should be the default method for phylogenetic estimation from phenotype datasets, and we explore the implications of our simulations in reanalysing several empirical morphological character matrices. A consequence of our finding is that high levels of resolution or the ability to classify species or groups with much confidence should not be expected when using small datasets. It is now necessary to depart from the traditional parsimony paradigms of constructing character matrices, towards datasets constructed explicitly for Bayesian methods.

The relationships of crustaceans and hexapods (Pancrustacea) have been much discussed and partially elucidated following the emergence of phylogenomic data sets. However, major uncertainties still remain regarding the position of iconic taxa such as Branchiopoda, Copepoda, Remipedia, and Cephalocarida, and the sister group relationship of hexapods. We assembled the most taxon-rich phylogenomic pancrustacean data set to date and analyzed it using a variety of methodological approaches. We prioritized low levels of missing data and found that some clades were consistently recovered independently of the analytical approach used. These include, for example, Oligostraca and Altocrustacea. Substantial support was also found for Allotriocarida, with Remipedia as the sister of Hexapoda (i.e., Labiocarida), and Branchiopoda as the sister of Labiocarida, a clade that we name Athalassocarida (=”nonmarine shrimps”). Within Allotriocarida, Cephalocarida was found as the sister of Athalassocarida. Finally, moderate support was found for Hexanauplia (Copepoda as sister to Thecostraca) in alliance with Malacostraca. Mapping key crustacean tagmosis patterns and developmental characters across the revised phylogeny suggests that the ancestral pancrustacean was relatively short-bodied, with extreme body elongation and anamorphic development emerging later in pancrustacean evolution.

Our ability to correctly reconstruct a phylogenetic tree is strongly affected by both systematic errors and the amount of phylogenetic signal in the data. Current approaches to tackle tree reconstruction artifacts, such as the use of parameter-rich models, do not translate readily to single-gene alignments. This, coupled with the limited amount of phylogenetic information contained in single-gene alignments, makes gene trees particularly difficult to reconstruct. Opsin phylogeny illustrates this problem clearly. Opsins are G-protein coupled receptors utilized in photoreceptive processes across Metazoa and their protein sequences are roughly 300 amino acids long. A number of incongruent opsin phylogenies have been published and opsin evolution remains poorly understood. Here, we present a novel approach, the canary sequence approach, to investigate and potentially circumvent errors in single-gene phylogenies. First, we demonstrate our approach using two well-understood cases of long-branch attraction in single-gene data sets, and simulations. After that, we apply our approach to a large collection of well-characterized opsins to clarify the relationships of the three main opsin subfamilies.