April M. Wright’s research while affiliated with Southeastern Louisiana University and other places

Reconstructing the evolutionary history of different groups of organisms provides insight into how life originated and diversified on Earth. Phylogenetic trees are commonly used to estimate this evolutionary history. Within Bayesian phylogenetics a major step in estimating a tree is in choosing an appropriate model of character evolution. While the most common character data used is molecular sequence data, morphological data remains a vital source of information. The use of morphological characters allows for the incorporation fossil taxa, and despite advances in molecular sequencing, continues to play a significant role in neontology. Moreover, it is the main data source that allows us to unite extinct and extant taxa directly under the same generating process. We therefore require suitable models of morphological character evolution, the most common being the Mk Lewis model. While it is frequently used in both palaeobiology and neontology, it is not known whether the simple Mk substitution model, or any extensions to it, provide a sufficiently good description of the process of morphological evolution. In this study we investigate the impact of different morphological models on empirical tetrapod data sets. Specifically, we compare unpartitioned Mk models with those where characters are partitioned by the number of observed states, both with and without allowing for rate variation across sites and accounting for ascertainment bias. We show that the choice of substitution model has an impact on both topology and branch lengths, highlighting the importance of model choice. Through simulations, we validate the use of the model adequacy approach, posterior predictive simulations, for choosing an appropriate model. Additionally, we compare the performance of model adequacy with Bayesian model selection. We demonstrate how model selection approaches based on marginal likelihoods are not appropriate for choosing between models with partition schemes that vary in character state space (i.e., that vary in Q-matrix state size). Using posterior predictive simulations, we found that current variations of the Mk model are often performing adequately in capturing the evolutionary dynamics that generated our data. We do not find any preference for a particular model extension across multiple data sets, indicating that there is no ‘one size fits all’ when it comes to morphological data and that careful consideration should be given to choosing models of discrete character evolution. By using suitable models of character evolution, we can increase our confidence in our phylogenetic estimates, which should in turn allow us to gain more accurate insights into the evolutionary history of both extinct and extant taxa.

Mammalian origin is often traced back to the Therapsida, a clade of diverse synapsids that emerged during the early-middle Permian from “pelycosaur” grade ancestors. The six major therapsid groups (Biarmosuchia, Dinocephalia, Anomodontia, Gorgonopsia, Therocephalia, and Cynodontia) evolved rapidly between the early and middle Permian. This fast radiation has generated uncertainty at the base of Therapsida, resulting in conflicting topologies that make it difficult to assess the order and timing of the acquisition of classically mammalian traits. We performed both cladistic and tip-dated Bayesian phylogenetic analyses using a new morphological character matrix, which includes both external and internal cranial characters, determined through the application of micro-CT scanning. The bayesian analysis was performed using Revbayes and MrBayes, where a Fossilized Birth-death (FBD) model was employed. In both the cladistic and bayesian analyses, Biseridens is more closely related to the newly recovered monophyletic clade that includes Biarmosuchia and Dinocephalia, rather than to the Anomodontia. Sinophoneus is recovered as an basal offshoot of dinocephalians, thus questioning the monophyly of the Anteosauria clade. Additionally, Therocephalia is recovered as paraphyletic, filling up the mid-Permian ghost lineage at the base of the Cynodontia. The FBD estimates an origin time for Therapsida at 286 Ma, which is 8 Ma prior to the Olson’s Gap (278 to 269 Ma), previously hypothesized to be an extinction. A rapid radiation of synapsid lineages following the origin of Therapsida suggests that Olson’s Gap may actually be a period of adaptive radiation.

Phylogenetic estimation is, and has always been, a complex endeavor. Estimating a phylogenetic tree involves evaluating many possible solutions and possible evolutionary histories that could explain a set of observed data, typically by using a model of evolution. Values for all model parameters need to be evaluated as well. Modern statistical methods involve not just the estimation of a tree, but also solutions to more complex models involving fossil record information and other data sources. Markov chain Monte Carlo (MCMC) is a leading method for approximating the posterior distribution of parameters in a mathematical model. It is deployed in all Bayesian phylogenetic tree estimation software. While many researchers use MCMC in phylogenetic analyses, interpreting results and diagnosing problems with MCMC remain vexing issues to many biologists. In this manuscript, we will offer an overview of how MCMC is used in Bayesian phylogenetic inference, with a particular emphasis on complex hierarchical models, such as the fossilized birth-death (FBD) model. We will discuss strategies to diagnose common MCMC problems and troubleshoot difficult analyses, in particular convergence issues. We will show how the study design, the choice of models and priors, but also technical features of the inference tools themselves can all be adjusted to obtain the best results. Finally, we will also discuss the unique challenges created by the incorporation of fossil information in phylogenetic inference, and present tips to address them.

Phylogenetic trees establish a historical context for the study of organismal form and function. Most phylogenetic trees are estimated using a model of evolution. For molecular data, modeling evolution is often based on biochemical observations about changes between character states. For example, there are four nucleotides, and we can make assumptions about the probability of transitions between them. By contrast, for morphological characters, we may not know a priori how many characters states there are per character, as both extant sampling and the fossil record may be highly incomplete, which leads to an observer bias. For a given character, the state space may be larger than what has been observed in the sample of taxa collected by the researcher. In this case, how many evolutionary rates are needed to even describe transitions between morphological character states may not be clear, potentially leading to model misspecification. To explore the impact of this model misspecification, we simulated character data with varying numbers of character states per character. We then used the data to estimate phylogenetic trees using models of evolution with the correct number of character states and an incorrect number of character states. The results of this study indicate that this observer bias may lead to phylogenetic error, particularly in the branch lengths of trees. If the state space is wrongly assumed to be too large, then we underestimate the branch lengths, and the opposite occurs when the state space is wrongly assumed to be too small.

A bstract In recent years, there has been an increased interest in modeling morphological traits using Bayesian methods. Much of the work associated with modeling these characters has focused on the substitution or evolutionary model employed in the analysis. However, there are many other assumptions that researchers make in the modeling process that are consequential to estimated phylogenetic trees. One of these is how among-character rate variation (ACRV) is parameterized. In molecular data, a discretized gamma distribution is often used to allow different characters to have different rates of evolution. Morphological data are collected in ways that fundamentally differ from molecular data. In this paper, we appraise the use of standard parameters for ACRV and provide recommendations to researchers who work with morphological data in a Bayesian framework.

Phylogenetic estimation is, and has always been, a complex endeavor. Estimating a phylogenetic tree involves evaluating many possible solutions and possible evolutionary histories that could explain a set of observed data, typically by using a model of evolution. Modern statistical methods involve not just the estimation of a tree, but also solutions to more complex models involving fossil record information and other data sources. Markov Chain Monte Carlo (MCMC) is a leading method for approximating the posterior distribution of parameters in a mathematical model. It is deployed in all Bayesian phylogenetic tree estimation software. While many researchers use MCMC in phylogenetic analyses, interpreting results and diagnosing problems with MCMC remain vexing issues to many biologists. In this manuscript, we will offer an overview of how MCMC is used in Bayesian phylogenetic inference, with a particular emphasis on complex hierarchical models, such as the fossilized birth-death (FBD) model. We will discuss strategies to diagnose common MCMC problems and troubleshoot difficult analyses, in particular convergence issues. We will show how the study design, the choice of models and priors, but also technical features of the inference tools themselves can all be adjusted to obtain the best results. Finally, we will also discuss the unique challenges created by the incorporation of fossil information in phylogenetic inference, and present tips to address them.

The Dicynodontia (Therapsida: Anomodontia) is one of the most successful Permo-Triassic terrestrial tetrapod clades and the oldest specimens are recorded from the middle Permian Eodicynodon Assemblage Zone of South Africa. Their fossil record is abundant and species-rich across Pangea. By contrast, the fossil record of the basal-most anomodonts, which includes non-dicynodont anomodonts and early forms of dicynodonts, is patchy and their morphology and phylogeny are deduced from relatively few specimens. Discovered in 1982 and described in 1990, the holotype of Eodicynodon oelofseni (NMQR 2913) is one of the better-preserved early anomodont specimens. However, it has been suggested that E. oelofseni does not belong to the genus Eodicynodon. Here, using CT-scanning and 3D modeling, the skull of Eodicynodon oelofseni, Patranomodon nyaphulii and Eodicynodon oosthuizeni are redescribed. In the framework of this study, the application of 3D scanning technology to describe anatomical structures which were previously inaccessible in these fossils has enabled detailed redescription of the cranial morphology of the basal anomodonts Patranomodon, Eodicynodon oelofseni and E. oosthuizeni and led to a greater understanding of their cranial morphology and phylogenetic relationships. Based on an anatomical comparison and phylogenetic analyses (Bayesian and cladistics) the phylogenetic relationships of basal anomodonts are reassessed and it is suggested that NMQR 2913 does not belong to the genus Eodicynodon but likely represents a separate genus basal to other dicynodonts. A new genus is erected for NMQR 2913. This presents one of the first applications of Bayesian Inference of phylogeny on Therapsida.

Reconstructing the evolutionary history of different groups of organisms provides insight into how life originated and diversified on Earth. Phylogenetic trees are commonly used to estimate this evolutionary history, providing a hypothesis of the events. Within Bayesian phylogenetics a major step in estimating a tree is in choosing an appropriate model of character evolution. In the case of most extinct species, our only source of information to decipher their phylogenetic relationships is through the morphology of fossils. We therefore use a model of morphological character evolution, the most common of which being the Mk Lewis model. While it is frequently used in palaeobiology, it is not known whether the simple Mk substitution model, or any extensions to it, provide a sufficiently good description of the process of morphological evolution. To determine whether or not the Mk model is appropriate for fossil data we used posterior predictive simulations, a model adequacy approach, to estimate absolute fit of the model to morphological data sets. We first investigate the impact that different versions of the Mk model have on key parameter estimates using tetrapod data sets. We show that choice of substitution model has an impact on both topology and branch lengths, highlighting the importance of model choice. Next, we use simulations to investigate the power of posterior predictive simulations for morphology. Having validated this approach we show that current variations of the Mk model are in fact performing adequately in capturing the evolutionary dynamics that generated our data. We do not find any preference for a particular model extension across multiple data sets, indicating that there is no ‘one size fits all’ when it comes to morphological data and that careful consideration should be given to choosing models of discrete character evolution. By using suitable models of character evolution, we can increase our confidence in our phylogenetic estimates, which should in turn allow us to gain more accurate insights into the evolutionary history of both extinct and extant taxa.

The eastern Pinesnake (Pituophis melanoleucus) is found throughout eastern United States. Taxonomy in this group has been controversial with several conflicting species designations. Three subspecies of the eastern Pinesnake have prevailed in the literature to their geographic locations and scale coloration: the northern Pinesnake (P. m. melanoleucus), the Florida Pinesnake (P. m. mugitus), and the Black Pinesnake (P. m. lodingi). Within the region, there are several major barriers to dispersal, particularly major river drainage systems and human modification of the longleaf pine habitat. Consistently, a lack of phylogenetic resolution has plagued these taxa in prior studies. The goal of this study was to examine the taxonomic validity of the eastern Pinesnake complex using single nucleotide polymorphisms (SNPs) isolated from ultra-conserved elements (UCEs) in phylogenetic and population genetic approaches. Molecular species delimitation approaches indicated that the population of eastern Pinesnake exhibits population structure across its range that may rise to the level of being new species.

Phylogenetic estimation is, and has always been, a complex endeavor. Estimating a phylogenetic tree involves evaluating many possible solutions and possible evolutionary histories that could explain a set of observed data, typically by using a model of evolution. Modern statistical methods involve not just the estimation of a tree, but also solutions to more complex models involving fossil record information and other data sources. Markov Chain Monte Carlo (MCMC) is a leading method for approximating the posterior distribution of parameters in a mathematical model. It is deployed in all Bayesian phylogenetic tree estimation software. While many researchers use MCMC in phylogenetic analyses, interpreting results and diagnosing problems with MCMC remain vexing issues to many biologists. In this manuscript, we will offer an overview of how MCMC is used in Bayesian phylogenetic inference, with a particular emphasis on complex hierarchical models, such as the fossilized birth-death (FBD) model. We will discuss strategies to diagnose common MCMC problems and troubleshoot difficult analyses, in particular convergence issues. We will show how the study design, the choice of models and priors, but also technical features of the inference tools themselves can all be adjusted to obtain the best results. Finally, we will also discuss the unique challenges created by the incorporation of fossil information in phylogenetic inference, and present tips to address them.