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      Unveiling the unknown phylogenetic position of the scallop Austrochlamys natans and its implications for marine stewardship in the Magallanes Province

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          Abstract

          Two species of scallop, Austrochlamys natans (“ Ostión del Sur”) and Zygochlamys patagonica (“ Ostión patagonico”) are presently exploited in the southern part of the Magallanes Province (MP). The lack of clarity in taxonomic identification and ecological aspects is generating both erroneous extraction statistics and an unperceived harvesting pressure on A. natans and Z. patagonica. We aim to discriminate these Magallanes scallops accurately, improve our understanding of their complex natural history and discuss possible implications for their management and conservation status, given the current fisheries statistics. To achieve these goals, we present a complete review of the historical identification of the Magallanes scallop and a multi-locus molecular phylogeny which allowed us to recover the phylogenetic position of A. natans. We sampled 54 individuals from five localities across the southern Pacific coast of the MP. We calculated the depth of the byssal notch (BND) and shell height (VH) ratio from morphological characters and conducted phylogenetic reconstructions with mitochondrial (12S and 16S) and nuclear markers (28S) using Bayesian and maximum likelihood analyses. Both morphology and molecular phylogeny identified two distinct entities, Z. patagonica and a distinct, highly divergent lineage that corresponds to A. natans. Our study provides integrative evidence to alert the current fishery management and the need for further conservation studies.

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          MUSCLE: multiple sequence alignment with high accuracy and high throughput.

          We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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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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              New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0.

              PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from http://www.atgc-montpellier.fr/phyml/.
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                Author and article information

                Contributors
                sebastian.rosenfeld@umag.cl
                karin.gerard@umag.cl
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                31 March 2021
                31 March 2021
                2021
                : 11
                : 7241
                Affiliations
                [1 ]GRID grid.443909.3, ISNI 0000 0004 0385 4466, Laboratorio de Ecología Molecular, Departamento de Ciencias Ecológicas, Facultad de Ciencias, , Universidad de Chile, ; Las Palmeras # 3425, Ñuñoa, Santiago Chile
                [2 ]GRID grid.442242.6, ISNI 0000 0001 2287 1761, Laboratorio de Ecosistemas Marinos Antárticos Y Subantárticos, , Universidad de Magallanes, ; Avenida Bulnes 01890, Punta Arenas, Chile
                [3 ]Instituto de Ecología Y Biodiversidad, Las Palmeras 3425, Ñuñoa, Santiago Chile
                [4 ]GRID grid.442242.6, ISNI 0000 0001 2287 1761, Centro de Investigación Gaia-Antártica, , Universidad de Magallanes, ; Avenida Bulnes 01855, Punta Arenas, Chile
                [5 ]GRID grid.7119.e, ISNI 0000 0004 0487 459X, Research Center Dynamics of High Latitude Marine, Ecosystem (Fondap-IDEAL), , Universidad Austral de Chile, ; Casilla # 567, Valdivia, Chile
                [6 ]GRID grid.143640.4, ISNI 0000 0004 1936 9465, School of Environmental Studies, , University of Victoria, ; 3800 Finnerty Road, Victoria, BC Canada
                Article
                86492
                10.1038/s41598-021-86492-9
                8012595
                33790335
                99aab9bd-6d9b-48ac-b1c8-4d09dc4d1b0d
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 December 2020
                : 15 March 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100002850, Fondo Nacional de Desarrollo Científico y Tecnológico;
                Award ID: 1180433
                Award ID: 1161358
                Funded by: PIA-CONICYT
                Award ID: ACT172065
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                phylogenetics,evolution,taxonomy,ecology,biodiversity
                Uncategorized
                phylogenetics, evolution, taxonomy, ecology, biodiversity

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