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      High levels of genetic diversity and population structure in an endemic and rare species: implications for conservation

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          Abstract

          Petunia secreta is a rare and endemic species, that was found in two different landscapes, approximately 21 Km apart from each other. In this study we showed that P. secreta presented high genetic diversity that was equivalent to or even higher than that of widespread Petunia species. Two evolutionary lineages were found and they are correlated to the different landscapes where P. secreta grows: open areas in conglomerate sandstone towers at an elevation of approximately 300-400 m or along the road growing in an open vegetation flat area. Therefore the major risk to P. secreta maintenance is its rarity, suggesting the necessity of a preservation program.

          Abstract

          The analysis of genetic structure and variability of isolated species is of critical importance in evaluating whether stochastic or human-caused factors are affecting rare species. Low genetic diversity compromises the ability of populations to evolve and reduces their chances of survival under environmental changes. Petunia secreta, a rare and endemic species, is an annual and heliophilous herb that is bee-pollinated and easily recognizable by its purple and salverform corolla. It was described as a new species of the Petunia genus in 2005. Few individuals of P. secreta have been observed in nature and little is known about this species. All the natural populations of P. secreta that were found were studied using 15 microsatellite loci, two intergenic plastid sequences and morphological traits. Statistical analysis was performed to describe the genetic diversity of this rare species and the results compared with those of more widespread and frequent Petunia species from the same geographic area to understand whether factors associated with population size could affect rare species of this genus. The results showed that despite its rarity, P. secreta presented high genetic diversity that was equivalent to or even higher than that of widespread Petunia species. It was shown that this species is divided into two evolutionary lineages, and the genetic differentiation indices between them and other congeneric species presented different patterns. The major risk to P. secreta maintenance is its rarity, suggesting the necessity of a preservation programme and more biological and evolutionary studies that handle the two evolutionary lineages independently.

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          Most cited references27

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          Statistical confidence for likelihood-based paternity inference in natural populations.

          Paternity inference using highly polymorphic codominant markers is becoming common in the study of natural populations. However, multiple males are often found to be genetically compatible with each offspring tested, even when the probability of excluding an unrelated male is high. While various methods exist for evaluating the likelihood of paternity of each nonexcluded male, interpreting these likelihoods has hitherto been difficult, and no method takes account of the incomplete sampling and error-prone genetic data typical of large-scale studies of natural systems. We derive likelihood ratios for paternity inference with codominant markers taking account of typing error, and define a statistic delta for resolving paternity. Using allele frequencies from the study population in question, a simulation program generates criteria for delta that permit assignment of paternity to the most likely male with a known level of statistical confidence. The simulation takes account of the number of candidate males, the proportion of males that are sampled and gaps and errors in genetic data. We explore the potentially confounding effect of relatives and show that the method is robust to their presence under commonly encountered conditions. The method is demonstrated using genetic data from the intensively studied red deer (Cervus elaphus) population on the island of Rum, Scotland. The Windows-based computer program, CERVUS, described in this study is available from the authors. CERVUS can be used to calculate allele frequencies, run simulations and perform parentage analysis using data from all types of codominant markers.
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            Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach.

            A maximum likelihood estimator based on the coalescent for unequal migration rates and different subpopulation sizes is developed. The method uses a Markov chain Monte Carlo approach to investigate possible genealogies with branch lengths and with migration events. Properties of the new method are shown by using simulated data from a four-population n-island model and a source-sink population model. Our estimation method as coded in migrate is tested against genetree; both programs deliver a very similar likelihood surface. The algorithm converges to the estimates fairly quickly, even when the Markov chain is started from unfavorable parameters. The method was used to estimate gene flow in the Nile valley by using mtDNA data from three human populations.
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              Supporting Red List threat assessments with GeoCAT: geospatial conservation assessment tool

              Abstract GeoCAT is an open source, browser based tool that performs rapid geospatial analysis to ease the process of Red Listing taxa. Developed to utilise spatially referenced primary occurrence data, the analysis focuses on two aspects of the geographic range of a taxon: the extent of occurrence (EOO) and the area of occupancy (AOO). These metrics form part of the IUCN Red List categories and criteria and have often proved challenging to obtain in an accurate, consistent and repeatable way. Within a familiar Google Maps environment, GeoCAT users can quickly and easily combine data from multiple sources such as GBIF, Flickr and Scratchpads as well as user generated occurrence data. Analysis is done with the click of a button and is visualised instantly, providing an indication of the Red List threat rating, subject to meeting the full requirements of the criteria. Outputs including the results, data and parameters used for analysis are stored in a GeoCAT file that can be easily reloaded or shared with collaborators. GeoCAT is a first step toward automating the data handling process of Red List assessing and provides a valuable hub from which further developments and enhancements can be spawned.
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                Author and article information

                Journal
                AoB Plants
                AoB Plants
                aobpla
                aobpla
                AoB Plants
                Oxford University Press
                2041-2851
                2016
                14 January 2016
                : 8
                : plw002
                Affiliations
                [1 ]Laboratory of Molecular Evolution, Department of Genetics, Universidade Federal do Rio Grande do Sul , PO Box 15053, Porto Alegre, 91501-970 Rio Grande do Sul, Brazil
                [2 ]Laboratory of Genomics and Molecular Biology, Pontifícia Universidade Católica do Rio Grande do Sul , Av. Ipiranga 6681, Porto Alegre, 90619-900 Rio Grande do Sul, Brazil
                Author notes
                [* ]Corresponding author's e-mail address: loreta.freitas@ 123456ufrgs.br ; loreta@ 123456pq.cnpq.br
                [†]

                These authors contributed equally to this work.

                Associate Editor: Bao-Rong Lu

                Article
                plw002
                10.1093/aobpla/plw002
                4768524
                26768602
                6378fcb6-d142-4b53-91f7-7423185cf678
                Published by Oxford University Press on behalf of the Annals of Botany Company.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 20 August 2015
                : 5 January 2016
                Page count
                Pages: 17
                Funding
                Funded by: Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
                Funded by: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
                Funded by: Programa de Pós-Graduação em Genética e Biologia Molecular da Universidade Federal do Rio Grande do Sul (PPGBM-UFRGS)
                Categories
                1006
                Research Articles

                Plant science & Botany
                conservation,genetic diversity,microendemic,microsatellites,plant evolution,plastid sequences

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