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      Phylogeny of the supertribe Nebriitae (Coleoptera, Carabidae) based on analyses of DNA sequence data

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          The phylogeny of the carabid beetle supertribe Nebriitae is inferred from analyses of DNA sequence data from eight gene fragments including one nuclear ribosomal gene (28S), four nuclear-protein coding genes (CAD, topoisomerase 1, PEPCK, and wingless), and three mitochondrial gene fragments (16S + tRNA-Leu + ND1, COI (“barcode” region) and COI (“Pat/Jer” region)). Our taxon sample included 264 exemplars representing 241 species and subspecies (25% of the known nebriite fauna), 39 of 41 currently accepted genera and subgenera (all except Notiokasis and Archileistobrius ), and eight outgroup taxa. Separate maximum likelihood (ML) analyses of individual genes, combined ML analyses of nuclear, nuclear protein-coding, and mitochondrial genes, and combined ML and Bayesian analyses of the eight-gene-fragment matrix resulted in a well-resolved phylogeny of the supertribe, with most nodes in the tree strongly supported. Within Nebriitae , 167 internal nodes of the tree (out of the maximum possible 255) are supported by maximum-likelihood bootstrap values of 90% or more. The tribes Notiophilini , Opisthiini , Pelophilini , and Nebriini are well supported as monophyletic but relationships among these are not well resolved. Nippononebria is a distinct genus more closely related to Leistus than Nebria . Archastes , Oreonebria , Spelaeonebria , and Eurynebria , previously treated as distinct genera by some authors, are all nested within a monophyletic genus Nebria. Within Nebria , four major clades are recognized: (1) the Oreonebria Series, including eight subgenera arrayed in two subgeneric complexes (the Eonebria and Oreonebria Complexes); (2) the Nebriola Series, including only subgenus Nebriola ; (3) the Nebria Series, including ten subgenera arrayed in two subgeneric complexes, the Boreonebria and Nebria Complexes, with the latter further subdivided into three subgeneric subcomplexes (the Nebria , Epinebriola , and Eunebria Subcomplexes)); and (4) the Catonebria Series, including seven subgenera arrayed in two subgeneric complexes (the Reductonebria and Catonebria Complexes). A strong concordance of biogeography with the inferred phylogeny is noted and some evident vicariance patterns are highlighted. A revised classification, mainly within the Nebriini , is proposed to reflect the inferred phylogeny. Three genus-group taxa ( Nippononebria , Vancouveria and Archastes ) are given revised status and seven are recognized as new synonymies ( Nebriorites Jeannel, 1941 and Marggia Huber, 2014 = Oreonebria Daniel, 1903; Pseudonebriola Ledoux & Roux, 1989 = Boreonebria Jeannel, 1937; Patrobonebria Bänninger, 1923, Paranebria Jeannel, 1937 and Barbonebriola Huber & Schmidt, 2017 = Epinebriola Daniel & Daniel, 1904; and Asionebria Shilenkov, 1982 = Psilonebria Andrewes, 1923). Six new subgenera are proposed and described for newly recognized clades: Parepinebriola Kavanaugh subgen. nov. (type species: Nebria delicata Huber & Schmidt, 2017), Insulanebria Kavanaugh subgen. nov. (type species: Nebria carbonaria Eschscholtz, 1829), Erwinebria Kavanaugh subgen. nov. (type species Nebria sahlbergii Fischer von Waldheim, 1828), Nivalonebria Kavanaugh subgen. nov. (type species: Nebria paradisi Darlington, 1931), Neaptenonebria Kavanaugh subgen. nov. (type species: Nebria ovipennis LeConte, 1878), and Palaptenonebria Kavanaugh subgen. nov. (type species: Nebria mellyi Gebler, 1847). Future efforts to better understand relationships within the supertribe should aim to expand the taxon sampling of DNA sequence data, particularly within subgenera Leistus and Evanoleist us of genus Leistus and the Nebria Complex of genus Nebria .

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          RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

          Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
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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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              MrBayes 3: Bayesian phylogenetic inference under mixed models.

              MrBayes 3 performs Bayesian phylogenetic analysis combining information from different data partitions or subsets evolving under different stochastic evolutionary models. This allows the user to analyze heterogeneous data sets consisting of different data types-e.g. morphological, nucleotide, and protein-and to explore a wide variety of structured models mixing partition-unique and shared parameters. The program employs MPI to parallelize Metropolis coupling on Macintosh or UNIX clusters.

                Author and article information

                Pensoft Publishers
                16 June 2021
                : 1044
                : 41-152
                [1 ] Department of Entomology, California Academy of Sciences, 55 Music Concourse Drive, San Francisco, CA 94118, USA
                [2 ] Department of Integrative Biology, Oregon State University, Corvallis, OR 97331, USA
                [3 ] Center for Comparative Genomics, California Academy of Sciences, 55 Music Concourse Drive, San Francisco, CA 94118, USA
                [4 ] Department of Entomology, University of Wisconsin, Madison, WI 53706, USA
                [5 ] Institute of Biosciences, University of Rostock, Universitätsplatz 2, D-18055 Rostock, Germany
                [6 ] Department of Entomology, Coleoptera, Stuttgart State Museum of Natural History, Rosenstein 1, 70191 Stuttgart, Germany
                [7 ] Department of Entomology, University of Arizona, Tucson, AZ 85721-0036, USA
                [8 ] University of California, Berkeley, 142 Weill Hall #3200, Berkeley, CA 94720, USA
                [9 ] University of California, Santa Cruz, Long Marine Lab, 117 McAllister Way, Santa Cruz, CA 95060, USA
                Author notes
                Corresponding author: David H. Kavanaugh ( dkavanaugh@ 123456calacademy.org )

                Academic editor: T. Assmann

                David H. Kavanaugh, David R. Maddison, W. Brian Simison, Sean D. Schoville, Joachim Schmidt, Arnaud Faille, Wendy Moore, James M. Pflug, Sophie L. Archambeault, Tinya Hoang, Jei-Ying Chen

                This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                U.S. National Science Foundation German Research Council
                Research Article


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