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      Genus Hyalella (Amphipoda: Hyalellidae) in Humid Pampas: molecular diversity and a provisional new species

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      Arthropod Systematics & Phylogeny
      Pensoft Publishers

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          Abstract

          Hyalella is a genus of epigean freshwater amphipods endemic to the Americas. The study of morphological characters alone has traditionally dominated the description of new species. Recently, molecular systematics tools have contributed to identifying many cryptic species and a high level of convergent evolution in species complexes from North America and the South American highlands. In this study, we evaluate for the first time the molecular diversity in Hyalella spp. in Uruguay, a country located in the humid pampa ecoregion, based on four molecular markers. Thus, we investigate the systematic position of H. curvispina in the context of the available phylogenetic hypothesis for the genus. Phylogenetic and morphological analyses confirm that there is a “curvispina complex”. This complex includes H. curvispina and several similar morphological forms but is paraphyletic in relation to some altiplano species. In addition, we found one provisional new species. The results obtained are contrasted with previous studies to help understand the mechanisms of genetic differentiation and speciation of the genus, which seems to have a strong tendency towards morphological convergence.

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          MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

          The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.
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            IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

            Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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              jModelTest 2: more models, new heuristics and parallel computing.

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                Author and article information

                Journal
                Arthropod Systematics & Phylogeny
                ASP
                Pensoft Publishers
                1864-8312
                1863-7221
                June 28 2022
                June 28 2022
                : 80
                : 261-278
                Article
                10.3897/asp.80.e79498
                65be1bb1-5e99-4b49-9506-3baf9ab37922
                © 2022

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

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