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      Multilocus phylogeny and historical biogeography of Hypostomus shed light on the processes of fish diversification in La Plata Basin

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          Abstract

          Distribution history of the widespread Neotropical genus Hypostomus was studied to shed light on the processes that shaped species diversity. We inferred a calibrated phylogeny, ancestral habitat preference, ancestral areas distribution, and the history of dispersal and vicariance events of this genus. The phylogenetic and distribution analyses indicate that Hypostomus species inhabiting La Plata Basin do not form a monophyletic clade, suggesting that several unrelated ancestral species colonized this basin in the Miocene. Dispersal to other rivers of La Plata Basin started about 8 Mya, followed by habitat shifts and an increased rate of cladogenesis. Amazonian Hypostomus species colonized La Plata Basin several times in the Middle Miocene, probably via the Upper Paraná and the Paraguay rivers that acted as dispersal corridors. During the Miocene, La Plata Basin experienced marine incursions, and geomorphological and climatic changes that reconfigured its drainage pattern, driving dispersal and diversification of Hypostomus. The Miocene marine incursion was a strong barrier and its retraction triggered Hypostomus dispersal, increased speciation rate and ecological diversification. The timing of hydrogeological changes in La Plata Basin coincides well with Hypostomus cladogenetic events, indicating that the history of this basin has acted on the diversification of its biota.

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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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            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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              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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                Author and article information

                Contributors
                yamilapcardoso@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                3 March 2021
                3 March 2021
                2021
                : 11
                : 5073
                Affiliations
                [1 ]GRID grid.9499.d, ISNI 0000 0001 2097 3940, Laboratorio de Sistemática y Biología Evolutiva, Facultad de Ciencias Naturales y Museo, , Universidad Nacional de La Plata, Consejo Nacional de Investigaciones Científicas y Técnicas, ; Paseo del Bosque S/N, B1900FWA, La Plata, Buenos Aires Argentina
                [2 ]GRID grid.8591.5, ISNI 0000 0001 2322 4988, Department of Genetics and Evolution, , University of Geneva, ; 30 quai Ernest Ansermet, 1211 Geneva 4, Switzerland
                [3 ]GRID grid.8591.5, ISNI 0000 0001 2322 4988, Institute of Genetics and Genomics in Geneva (IGE3), , University of Geneva, ; Geneva, Switzerland
                Author information
                http://orcid.org/0000-0003-3497-4359
                Article
                83464
                10.1038/s41598-021-83464-x
                7930046
                33658600
                3e5e2874-a090-4b49-8230-01c382b23b38
                © 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
                : 16 April 2020
                : 5 January 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003033, Ministerio de Ciencia, Tecnología e Innovación Productiva;
                Award ID: PICT2017-1330
                Funded by: Seed Money Latin 2015
                Funded by: Brazilian–Swiss Joint Research Programme 2015
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                evolutionary theory,phylogenetics,speciation,ichthyology
                Uncategorized
                evolutionary theory, phylogenetics, speciation, ichthyology

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