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      Bridgehead effect and multiple introductions shape the global invasion history of a termite

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          Abstract

          Native to eastern Asia, the Formosan subterranean termite Coptotermes formosanus (Shiraki) is recognized as one of the 100 worst invasive pests in the world, with established populations in Japan, Hawaii and the southeastern United States. Despite its importance, the native source(s) of C. formosanus introductions and their invasive pathway out of Asia remain elusive. Using ~22,000 SNPs, we retraced the invasion history of this species through approximate Bayesian computation and assessed the consequences of the invasion on its genetic patterns and demography. We show a complex invasion history, where an initial introduction to Hawaii resulted from two distinct introduction events from eastern Asia and the Hong Kong region. The admixed Hawaiian population subsequently served as the source, through a bridgehead, for one introduction to the southeastern US. A separate introduction event from southcentral China subsequently occurred in Florida showing admixture with the first introduction. Overall, these findings further reinforce the pivotal role of bridgeheads in shaping species distributions in the Anthropocene and illustrate that the global distribution of C. formosanus has been shaped by multiple introductions out of China, which may have prevented and possibly reversed the loss of genetic diversity within its invasive range.

          Abstract

          Blumenfeld and Eyer et al. retrace the invasion history of the termite Coptotermes formosansus from eastern Asia to the US using approximate Bayesian computation. They find a complex invasion history, with multiple introductions originating from eastern Asia, as well as a bridgehead introduction originating from Hawaii, which appears to have mitigated the loss of genetic diversity within the invasive US range.

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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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            The variant call format and VCFtools

            Summary: The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API. Availability: http://vcftools.sourceforge.net Contact: rd@sanger.ac.uk
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              adegenet: a R package for the multivariate analysis of genetic markers.

              The package adegenet for the R software is dedicated to the multivariate analysis of genetic markers. It extends the ade4 package of multivariate methods by implementing formal classes and functions to manipulate and analyse genetic markers. Data can be imported from common population genetics software and exported to other software and R packages. adegenet also implements standard population genetics tools along with more original approaches for spatial genetics and hybridization. Stable version is available from CRAN: http://cran.r-project.org/mirrors.html. Development version is available from adegenet website: http://adegenet.r-forge.r-project.org/. Both versions can be installed directly from R. adegenet is distributed under the GNU General Public Licence (v.2).
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                Author and article information

                Contributors
                alex93@tamu.edu
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                12 February 2021
                12 February 2021
                2021
                : 4
                : 196
                Affiliations
                [1 ]GRID grid.264756.4, ISNI 0000 0004 4687 2082, Department of Entomology, 2143 TAMU, , Texas A&M University, ; College Station, TX USA
                [2 ]GRID grid.250060.1, ISNI 0000 0000 9070 1054, Department of Entomology, , Louisiana State University Agricultural Center, ; Baton Rouge, LA USA
                [3 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Ministry of Agriculture Key Lab of Molecular Biology of Crop Pathogens and Insect Pests, Institute of Insect Sciences, College of Agricultural and Biotechnology, , Zhejiang University, ; Zhejiang, PR China
                [4 ]GRID grid.135963.b, ISNI 0000 0001 2109 0381, Department of Veterinary Sciences, , University of Wyoming, ; Laramie, WY USA
                [5 ]GRID grid.430387.b, ISNI 0000 0004 1936 8796, Department of Entomology, Rutgers, , The State University of New Jersey, ; New Brunswick, NJ USA
                [6 ]GRID grid.410445.0, ISNI 0000 0001 2188 0957, Department of Plant and Environmental Protection Sciences, , University of Hawaii at Manoa, ; Honolulu, HI USA
                [7 ]GRID grid.15276.37, ISNI 0000 0004 1936 8091, Entomology and Nematology Department, Fort Lauderdale Research and Education Center, , Institute of Food and Agricultural Sciences, University of Florida, ; Fort Lauderdale, FL USA
                [8 ]GRID grid.264756.4, ISNI 0000 0004 4687 2082, Texas A&M AgriLife Genomics and Bioinformatics Service, ; College Station, TX USA
                Author information
                http://orcid.org/0000-0002-9019-1693
                http://orcid.org/0000-0002-7801-0466
                Article
                1725
                10.1038/s42003-021-01725-x
                7881189
                33580197
                c1838a38-b236-4385-9423-d94b71e5952e
                © 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 June 2020
                : 19 January 2021
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                © The Author(s) 2021

                population genetics,invasive species,molecular ecology

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