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      Does the genetic diversity among pubescent white oaks in southern Italy, Sicily and Sardinia islands support the current taxonomic classification?

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          Abstract

          Molecular diversity analysis of deciduous pubescent oaks was conducted for populations from Calabria, Sicily and Sardinia. The aims of this study were twofold. First, to provide data on the genetic diversity of pubescent oaks from an understudied area which currently exhibits one of the highest concentrations of pubescent oak species in Europe. Second, to verify if these groups of oaks are genetically distinct and if their identification is in accordance with the current taxonomic classification. Molecular analyses of leaf material of 480 trees from seventeen populations belonging to putatively different pubescent oak species ( Quercus amplifolia, Q. congesta, Q. dalechampii, Q. ichnusae, Q. leptobalanos, Q. virgiliana) were performed. Twelve gene-based Expressed Sequence Tag-Simple Sequence Repeat markers were selected, and genetic diversity and differentiation were calculated. The results showed relatively high values of allelic richness, heterozygosity and number of private alleles for the populations investigated. A weak but positive correlation between geographical and genetic distance was detected. Genetic assignment (STRUCTURE) and principle coordinate analyses exhibited a weak separation into two genetic groups which, however, did not correspond to the taxonomic, chorological and ecological features of the populations investigated. Sardinian populations formed one group which was separated from the Calabrian and Sicilian populations. In light of the results obtained, the taxonomic classification for the pubescent white oaks currently reported in the major Italian floras and checklists for the study area was not confirmed by molecular analyses.

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          MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

          We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.
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            Detecting the number of clusters of individuals using the software structure: a simulation study

            The identification of genetically homogeneous groups of individuals is a long standing issue in population genetics. A recent Bayesian algorithm implemented in the software STRUCTURE allows the identification of such groups. However, the ability of this algorithm to detect the true number of clusters (K) in a sample of individuals when patterns of dispersal among populations are not homogeneous has not been tested. The goal of this study is to carry out such tests, using various dispersal scenarios from data generated with an individual-based model. We found that in most cases the estimated 'log probability of data' does not provide a correct estimation of the number of clusters, K. However, using an ad hoc statistic DeltaK based on the rate of change in the log probability of data between successive K values, we found that STRUCTURE accurately detects the uppermost hierarchical level of structure for the scenarios we tested. As might be expected, the results are sensitive to the type of genetic marker used (AFLP vs. microsatellite), the number of loci scored, the number of populations sampled, and the number of individuals typed in each sample.
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              Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows.

              We present here a new version of the Arlequin program available under three different forms: a Windows graphical version (Winarl35), a console version of Arlequin (arlecore), and a specific console version to compute summary statistics (arlsumstat). The command-line versions run under both Linux and Windows. The main innovations of the new version include enhanced outputs in XML format, the possibility to embed graphics displaying computation results directly into output files, and the implementation of a new method to detect loci under selection from genome scans. Command-line versions are designed to handle large series of files, and arlsumstat can be used to generate summary statistics from simulated data sets within an Approximate Bayesian Computation framework. © 2010 Blackwell Publishing Ltd.
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                Journal
                European Journal of Forest Research
                Eur J Forest Res
                Springer Science and Business Media LLC
                1612-4669
                1612-4677
                April 2021
                December 12 2020
                April 2021
                : 140
                : 2
                : 355-371
                Article
                10.1007/s10342-020-01334-z
                f45ccd95-8dc1-458b-a73f-b41542bea9b8
                © 2021

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

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

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