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Abstract 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. 

Abstract 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 datasets. 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 datasets, 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. 

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 \textit{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. 

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 PermianEodicynodonAssemblage 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 ofEodicynodon oelofseni(NMQR 2913) is one of the better-preserved early anomodont specimens. However, it has been suggested thatE. oelofsenidoes not belong to the genusEodicynodon. Here, using CT-scanning and 3D modeling, the skull ofEodicynodon oelofseni,Patranomodon nyaphuliiandEodicynodon oosthuizeniare 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 anomodontsPatranomodon,Eodicynodon oelofseniandE. oosthuizeniand 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 genusEodicynodonbut 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. 

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. 

Over the past decade, a new set of methods for estimating dated trees has emerged. Originally referred to as the fossilized birth–death (FBD) process, this single model has expanded to a family of models that allows researchers to coestimate evolutionary parameters (e.g., diversification, sampling) and patterns alongside divergence times for a variety of applications from paleobiology to real-time epidemiology. We provide an overview of this family of models. We explore the ways in which these models correspond to methods in quantitative paleobiology, as the FBD process provides a framework through which neontological and paleontological approaches to phylogenetics and macroevolution can be unified. We also provide an overview of challenges associated with applying FBD models, particularly with an eye toward the fossil record. We conclude this review by discussing several exciting avenues for the inclusion of fossil data in phylogenetic analyses. 

No abstract available. 

Poeciliopsis (Cyprinodontiformes: Poeciliidae) is a genus comprised of 25 species of freshwater fishes. Several well-known taxonomic uncertainties exist within the genus, especially in relation to the taxonomic status of Poeciliopsis pleurospilus and P . gracilis . However, to date, no studies have been conducted to specifically address the taxonomic status of these two species. The goal of this study was to examine the taxonomic validity of P . pleurospilus and P . gracilis using genomic data (ddRADseq) in phylogenetic, population genetic, and species delimitation frameworks. Multiple analyses support the recognition of both taxa as distinct species and also permits us to revise their respective distributions. A species delimitation analysis indicates that P . pleurospilus and P . gracilis are distinct species, each of which consists of two distinct lineages that are geographically structured. Phylogenetic and population genetic analyses provide clear evidence that individuals of P . gracilis are distributed north and west of the Isthmus of Tehuantepec in both Pacific and Atlantic river systems in Mexico, whereas individuals of P . pleurospilus are distributed in both Atlantic and Pacific river systems south and east of the Isthmus of Tehuantepec, from southern Mexico to Honduras.