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      The impact of molecular data on the phylogenetic position of the putative oldest crown crocodilian and the age of the clade

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          Abstract

          The use of molecular data for living groups is vital for interpreting fossils, especially when morphology-only analyses retrieve problematic phylogenies for living forms. These topological discrepancies impact on the inferred phylogenetic position of many fossil taxa. In Crocodylia, morphology-based phylogenetic inferences differ fundamentally in placing Gavialis basal to all other living forms, whereas molecular data consistently unite it with crocodylids. The Cenomanian Portugalosuchus azenhae was recently described as the oldest crown crocodilian, with affinities to Gavialis , based on morphology-only analyses, thus representing a potentially important new molecular clock calibration . Here, we performed analyses incorporating DNA data into these morphological datasets, using scaffold and supermatrix (total evidence) approaches, in order to evaluate the position of basal crocodylians, including Portugalosuchus . Our analyses incorporating DNA data robustly recovered Portugalosuchus outside Crocodylia (as well as thoracosaurs, planocraniids and Borealosuchus spp.), questioning the status of Portugalosuchus as crown crocodilian and any future use as a node calibration in molecular clock studies. Finally, we discuss the impact of ambiguous fossil calibration and how, with the increasing size of phylogenomic datasets, the molecular scaffold might be an efficient (though imperfect) approximation of more rigorous but demanding supermatrix analyses.

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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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            BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis

            Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release.
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              Partitionfinder: combined selection of partitioning schemes and substitution models for phylogenetic analyses.

              In phylogenetic analyses of molecular sequence data, partitioning involves estimating independent models of molecular evolution for different sets of sites in a sequence alignment. Choosing an appropriate partitioning scheme is an important step in most analyses because it can affect the accuracy of phylogenetic reconstruction. Despite this, partitioning schemes are often chosen without explicit statistical justification. Here, we describe two new objective methods for the combined selection of best-fit partitioning schemes and nucleotide substitution models. These methods allow millions of partitioning schemes to be compared in realistic time frames and so permit the objective selection of partitioning schemes even for large multilocus DNA data sets. We demonstrate that these methods significantly outperform previous approaches, including both the ad hoc selection of partitioning schemes (e.g., partitioning by gene or codon position) and a recently proposed hierarchical clustering method. We have implemented these methods in an open-source program, PartitionFinder. This program allows users to select partitioning schemes and substitution models using a range of information-theoretic metrics (e.g., the Bayesian information criterion, akaike information criterion [AIC], and corrected AIC). We hope that PartitionFinder will encourage the objective selection of partitioning schemes and thus lead to improvements in phylogenetic analyses. PartitionFinder is written in Python and runs under Mac OSX 10.4 and above. The program, source code, and a detailed manual are freely available from www.robertlanfear.com/partitionfinder.
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                Author and article information

                Contributors
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                Journal
                Biology Letters
                Biol. Lett.
                The Royal Society
                1744-957X
                February 2022
                February 09 2022
                February 2022
                : 18
                : 2
                Affiliations
                [1 ]Department of Geosciences, Eberhard Karls Universität Tübingen, Hölderlinstraße 12, 72074 Tübingen, Germany
                [2 ]School of Biological Sciences, Flinders University, GPO Box 2100, South Australia 5001, Australia
                [3 ]Australia Earth Sciences Section, South Australian Museum, North Terrace, Adelaide, South Australia 5000, Australia
                [4 ]Dipartimento di Scienze della Terra, Universitàt degli Studi di Torino, Via Valperga Caluso 35, 10125 Torino, Italy
                [5 ]Central Natural Science Collections, Martin-Luther University Halle-Wittenberg, D-06108 Halle (Saale), Germany
                Article
                10.1098/rsbl.2021.0603
                35135314
                f0a7ef60-35d7-4075-b29a-4964d9a48e2b
                © 2022

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