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      An integrative re-evaluation of Typhlatya shrimp within the karst aquifer of the Yucatán Peninsula, Mexico

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          Abstract

          The Yucatán Peninsula, Mexico is a carbonate platform well-known for extensive karst networks of densely stratified aquifer ecosystems. This aquifer supports diverse anchialine fauna, including species of the globally distributed anchialine shrimp genus Typhlatya (Atyidae). Four species ( T. campecheae, T. pearsei, T. dzilamensis and T. mitchelli) are endemic to the Peninsula, of which three are federally listed in Mexico. This first integrative evaluation (i.e., molecular, morphological, broad geographic and type locality sampling, and environmental data) of Yucatán Typhlatya reveals considerable species identity conflict in prior phylogenetic assessments, broad species ranges, syntopy within cave systems and five genetic lineages (of which two are new to science). Despite sampling from the type locality of endangered T. campecheae, specimens (and molecular data) were indistinguishable from vulnerable T. pearsei. Ancestral/divergence reconstructions support convergent evolution of a low-salinity ancestor for a post-Paleogene arc Yucatán + Cuba Typhlatya clade within the anchialine Atyidae clade. A secondary adaptation for the coastal-restricted euryhaline (2–37 psu), Typhlatya dzilamensis (unknown conservation status) was identified, while remaining species lineages were low-salinity (< 5 psu) adapted and found within the meteoric lens of inland and coastal caves. This study demonstrates the need for integrative/interdisciplinary approaches when conducting biodiversity assessments in complex and poorly studied aquifers.

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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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              Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data

              Summary: The two main functions of bioinformatics are the organization and analysis of biological data using computational resources. Geneious Basic has been designed to be an easy-to-use and flexible desktop software application framework for the organization and analysis of biological data, with a focus on molecular sequences and related data types. It integrates numerous industry-standard discovery analysis tools, with interactive visualizations to generate publication-ready images. One key contribution to researchers in the life sciences is the Geneious public application programming interface (API) that affords the ability to leverage the existing framework of the Geneious Basic software platform for virtually unlimited extension and customization. The result is an increase in the speed and quality of development of computation tools for the life sciences, due to the functionality and graphical user interface available to the developer through the public API. Geneious Basic represents an ideal platform for the bioinformatics community to leverage existing components and to integrate their own specific requirements for the discovery, analysis and visualization of biological data. Availability and implementation: Binaries and public API freely available for download at http://www.geneious.com/basic, implemented in Java and supported on Linux, Apple OSX and MS Windows. The software is also available from the Bio-Linux package repository at http://nebc.nerc.ac.uk/news/geneiousonbl. Contact: peter@biomatters.com
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                Author and article information

                Contributors
                eborda@tamusa.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                29 March 2022
                29 March 2022
                2022
                : 12
                : 5302
                Affiliations
                [1 ]GRID grid.264764.5, ISNI 0000 0004 0546 4832, Department of Marine Biology, , Texas A&M University at Galveston, ; 200 Seawolf Pkwy, Galveston, TX USA
                [2 ]GRID grid.5326.2, ISNI 0000 0001 1940 4177, Molecular Ecology Group, Water Research Institute, , National Research Council of Italy (IRSA CNR), ; 28922 Pallanza, Italy
                [3 ]Posgrado en Ciencias Biológicas, Unidad de Posgrado, Edificio A, 1er piso, Circuito de Posgrados, Ciudad Universitaria, Coyoacán, Ciudad de México, Mexico
                [4 ]GRID grid.412852.8, ISNI 0000 0001 2192 0509, Instituto de Investigaciones Oceanológicas, , Universidad Autónoma de Baja California, ; Ensenada, Baja California Mexico
                [5 ]GRID grid.453560.1, ISNI 0000 0001 2192 7591, Department of Invertebrate Zoology, Smithsonian Institution, , National Museum of Natural History, ; P.O. Box 37012, Washington D.C., USA
                [6 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Department of Earth, Atmospheric and Planetary Sciences, , Massachusetts Institute of Technology, ; Green Bldg., 77 Massachusetts Ave, Cambridge, MA USA
                [7 ]GRID grid.56466.37, ISNI 0000 0004 0504 7510, Geology & Geophysics Department, , Woods Hole Oceanographic Institution, ; 266 Woods Hole Road, MS #52, Woods Hole, MA USA
                [8 ]GRID grid.131063.6, ISNI 0000 0001 2168 0066, Department of Biological Sciences, , University of Notre Dame, ; 100 Galvin Life Science Center, Notre Dame, IN USA
                [9 ]GRID grid.134563.6, ISNI 0000 0001 2168 186X, School of Anthropology, , University of Arizona, ; Emil W. Haury Anthropology Bldg., 1009 E South Campus Dr., Tucson, AZ USA
                [10 ]GRID grid.9486.3, ISNI 0000 0001 2159 0001, Unidad Multidisciplinaria de Docencia e Investigación, Facultad de Ciencias, , Universidad Nacional Autónoma de México, ; Puerto de Abrigo S/N, Sisal, Yucatán, Mexico
                [11 ]National Coastal Resilience Laboratory (LANRESC), Puerto de Abrigo S/N, Sisal, Yucatán, Mexico
                [12 ]GRID grid.264759.b, ISNI 0000 0000 9880 7531, International Chair for Ocean and Coastal Studies in Mexico, Harte Research Institute, , Texas A&M at Corpus Christi, ; 6300 Ocean Drive, Corpus Christi, TX USA
                [13 ]GRID grid.9486.3, ISNI 0000 0001 2159 0001, Colección Nacional de Crustáceos, Instituto de Biología, , Universidad Nacional Autónoma de México, ; A.P. 70-153, 04510 Coyoacán, México D.F. Mexico
                [14 ]GRID grid.469272.c, ISNI 0000 0001 0180 5693, Department of Life Sciences, , Texas A&M University San Antonio, ; One University Way, San Antonio, TX USA
                Article
                8779
                10.1038/s41598-022-08779-9
                8961266
                35351932
                9c033cfd-19b6-43ab-8930-be9fb03f698e
                © The Author(s) 2022

                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
                : 22 September 2021
                : 7 March 2022
                Funding
                Funded by: Cave Conservancy Foundation
                Funded by: National Science Foundation Graduate Research Fellowship
                Award ID: M1703014
                Award Recipient :
                Funded by: National Science Foundation, Louis Stokes Alliance for Minority Participation
                Award ID: 1612776
                Funded by: National Science Foundation, Research Experiences for Undergraduates
                Award ID: 1560242
                Funded by: Texas A&M-CONACYT
                Funded by: Texas A&M University-San Antonio, Start-up Funds
                Funded by: Texas A&M University-San Antonio, College of Arts and Sciences Summer Research Grant
                Funded by: Texas A&M University-San Antonio, College of Arts and Sciences Summer Research Fellowship
                Categories
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                Custom metadata
                © The Author(s) 2022

                Uncategorized
                evolutionary genetics,phylogenetics,taxonomy,evolutionary biology,biodiversity,conservation biology

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