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      Disentangling the Anacondas: Revealing a New Green Species and Rethinking Yellows

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          Abstract

          Anacondas, genus Eunectes, are a group of aquatic snakes with a wide distribution in South America. The taxonomic status of several species has been uncertain and/or controversial. Using genetic data from four recognized anaconda species across nine countries, this study investigates the phylogenetic relationships within the genus Eunectes. A key finding was the identification of two distinct clades within Eunectes murinus, revealing two species as cryptic yet genetically deeply divergent. This has led to the recognition of the Northern Green Anaconda as a separate species (Eunectes akayima sp. nov), distinct from its southern counterpart (E. murinus), the Southern Green Anaconda. Additionally, our data challenge the current understanding of Yellow Anaconda species by proposing the unification of Eunectes deschauenseei and Eunectes beniensis into a single species with Eunectes notaeus. This reclassification is based on comprehensive genetic and phylogeographic analyses, suggesting closer relationships than previously recognized and the realization that our understanding of their geographic ranges is insufficient to justify its use as a separation criterion. We also present a phylogeographic hypothesis that traces the Miocene diversification of anacondas in western South America. Beyond its academic significance, this study has vital implications for the conservation of these iconic reptile species, highlighting our lack of knowledge about the diversity of the South American fauna and the need for revised strategies to conserve the newly identified and reclassified species.

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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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              MEGA11: Molecular Evolutionary Genetics Analysis Version 11

              The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens, and gene families using rapid relaxed-clock methods. Methods for estimating divergence times and confidence intervals are implemented to use probability densities for calibration constraints for node-dating and sequence sampling dates for tip-dating analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor , and an extended Tree Explorer to display timetrees. Also added is a Bayesian method for estimating neutral evolutionary probabilities of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihood analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net .
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                Journal
                DIVEC6
                Diversity
                Diversity
                MDPI AG
                1424-2818
                February 2024
                February 16 2024
                : 16
                : 2
                : 127
                Article
                10.3390/d16020127
                84bfe16b-add8-41f8-b55d-24849d2162d0
                © 2024

                https://creativecommons.org/licenses/by/4.0/

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