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      Redescription of the skull of the Australian flatback sea turtle, Natator depressus, provides new morphological evidence for phylogenetic relationships among sea turtles (Chelonioidea)

      1 , 2 , 1 , 2 , 1 , 2 , 3 , 4
      Zoological Journal of the Linnean Society
      Oxford University Press (OUP)

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          Abstract

          Chelonioidea (sea turtles) are a group where available morphological evidence for crown-group relationships are incongruent with those established using molecular data. However, morphological surveys of crown-group taxa tend to focus on a recurring subset of the extant species. The Australian flatback sea turtle, Natator depressus, is often excluded from comparisons and it is the most poorly known of the seven extant species of Chelonioidea. Previous descriptions of its skull morphology are limited and conflict. Here we describe three skulls of adult N. depressus and re-examine the phylogenetic relationships according to morphological character data. Using X-ray micro Computed Tomography we describe internal structures of the braincase and identify new phylogenetically informative characters not previously reported. Phylogenetic analysis using a Bayesian approach strongly supports a sister-group relationship between Chelonia mydas and N. depressus, a topology that was not supported by previous analyses of morphological data but one that matches the topology supported by analysis of molecular data. Our results highlight the general need to sample the morphological anatomy of crown-group taxa more thoroughly before concluding that morphological and molecular evidence are incongruous.

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          MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

          Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d N /d S rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.
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            Bayesian phylogenetic analysis of combined data.

            The recent development of Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) techniques has facilitated the exploration of parameter-rich evolutionary models. At the same time, stochastic models have become more realistic (and complex) and have been extended to new types of data, such as morphology. Based on this foundation, we developed a Bayesian MCMC approach to the analysis of combined data sets and explored its utility in inferring relationships among gall wasps based on data from morphology and four genes (nuclear and mitochondrial, ribosomal and protein coding). Examined models range in complexity from those recognizing only a morphological and a molecular partition to those having complex substitution models with independent parameters for each gene. Bayesian MCMC analysis deals efficiently with complex models: convergence occurs faster and more predictably for complex models, mixing is adequate for all parameters even under very complex models, and the parameter update cycle is virtually unaffected by model partitioning across sites. Morphology contributed only 5% of the characters in the data set but nevertheless influenced the combined-data tree, supporting the utility of morphological data in multigene analyses. We used Bayesian criteria (Bayes factors) to show that process heterogeneity across data partitions is a significant model component, although not as important as among-site rate variation. More complex evolutionary models are associated with more topological uncertainty and less conflict between morphology and molecules. Bayes factors sometimes favor simpler models over considerably more parameter-rich models, but the best model overall is also the most complex and Bayes factors do not support exclusion of apparently weak parameters from this model. Thus, Bayes factors appear to be useful for selecting among complex models, but it is still unclear whether their use strikes a reasonable balance between model complexity and error in parameter estimates.
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              A Likelihood Approach to Estimating Phylogeny from Discrete Morphological Character Data

              Paul Lewis (2001)
              Evolutionary biologists have adopted simple likelihood models for purposes of estimating ancestral states and evaluating character independence on specified phylogenies; however, for purposes of estimating phylogenies by using discrete morphological data, maximum parsimony remains the only option. This paper explores the possibility of using standard, well-behaved Markov models for estimating morphological phylogenies (including branch lengths) under the likelihood criterion. An important modification of standard Markov models involves making the likelihood conditional on characters being variable, because constant characters are absent in morphological data sets. Without this modification, branch lengths are often overestimated, resulting in potentially serious biases in tree topology selection. Several new avenues of research are opened by an explicitly model-based approach to phylogenetic analysis of discrete morphological data, including combined-data likelihood analyses (morphology + sequence data), likelihood ratio tests, and Bayesian analyses.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Zoological Journal of the Linnean Society
                Oxford University Press (OUP)
                0024-4082
                1096-3642
                April 01 2021
                March 15 2021
                July 21 2020
                April 01 2021
                March 15 2021
                July 21 2020
                : 191
                : 4
                : 1090-1113
                Affiliations
                [1 ]School of Biological Sciences, University of Adelaide, Adelaide, South Australia, SA, Australia
                [2 ]South Australian Museum, Adelaide, Adelaide, South Australia, SA, Australia
                [3 ]Earth Sciences, Natural History Museum, London, UK
                [4 ]Cell and Developmental Biology, UCL, University College London, London, UK
                Article
                10.1093/zoolinnean/zlaa071
                1e6353d9-4644-4759-92eb-7c32d8177fb7
                © 2020

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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