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      Multidisciplinary estimates of connectivity and population structure suggest the use of multiple units for the conservation and management of meagre, Argyrosomus regius

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          Abstract

          Information on population structure and connectivity of targeted species is key for proper implementation of spatial conservation measures. We used a combination of genomics, biophysical modelling, and biotelemetry to infer the population structure and connectivity of Atlantic meagre, an important fisheries resource throughout its distribution. Genetic samples from previously identified Atlantic spawning locations (Gironde, Tejo, Guadalquivir, Banc d’Arguin) and two additional regions (Algarve and Senegal) were analysed using genome-wide SNP-genotyping and mitochondrial DNA analyses. Biophysical models were conducted to investigate larval dispersal and connectivity from the known Atlantic spawning locations. Additionally, thirteen fish were double-tagged with biotelemetry transmitters off the Algarve (Portugal) to assess movement patterns and connectivity of adult individuals. This multidisciplinary approach provided a robust overview of meagre population structure and connectivity in the Atlantic. Nuclear SNP-genotyping showed a clear differentiation between the European and African populations, with significant isolation of the few known Atlantic spawning sites. The limited level of connectivity between these subpopulations is potentially driven by adults, capable of wide-ranging movements and connecting sites 500 km apart, as evidenced by tagging studies, whilst larval dispersal inferred by modelling is much more limited (average of 52 km; 95% of connectivity events up to 174 km). Our results show sufficient evidence of population structure, particularly between Africa and Europe but also within Europe, for the meagre to be managed as separate stocks. Additionally, considering the low degree of larvae connectivity, the implementation of marine protected areas in key spawning sites could be crucial towards species sustainability.

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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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            Inference of Population Structure Using Multilocus Genotype Data

            We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci—e.g., seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/~pritch/home.html.
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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
                davidbecas@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                9 January 2024
                9 January 2024
                2024
                : 14
                : 873
                Affiliations
                [1 ]GRID grid.7157.4, ISNI 0000 0000 9693 350X, CCMAR, Centre of Marine Sciences, , University of Algarve, ; 8005-139 Faro, Portugal
                [2 ]GRID grid.4305.2, ISNI 0000 0004 1936 7988, Royal (Dick) School of Veterinary Studies and the Roslin Institute, , University of Edinburgh, ; Easter Bush Campus, Midlothian, EH25 9RG UK
                [3 ]Institut Mauritanien de Recherches Océanographiques et des Pêches (IMROP), ( https://ror.org/04xghb049) BP 22, Nouadhibou, Cansado Mauritania
                [4 ]Conservation and Research of West African Aquatic Mammals (COREWAM), Dakar, Senegal
                [5 ]GRID grid.7759.c, ISNI 0000000103580096, Departamento de Biología, Facultad de Ciencias del Mar y Ambientales, , Universidad de Cádiz. Campus de Excelencia Internacional del Mar (CEIMAR), ; Avda. República Saharaui, s/n, Puerto Real, 11510 Cádiz, Spain
                [6 ]Instituto Universitario de Investigación Marina (INMAR), Campus de Excelencia Internacional del Mar (CEIMAR), Avda. República Saharaui, S/N, Puerto Real, 11510 Cádiz, Spain
                [7 ]Parc naturel marin de l’estuaire de La Gironde et de la mer des Pertuis, OFB, 17320 Marennes, France
                [8 ]Ocean Sciences Institute (Okeanos), University of the Azores, ( https://ror.org/04276xd64) 9901-862 Horta, Portugal
                [9 ]Institute of Marine Research (IMAR), ( https://ror.org/03r8hgy04) 9901-862 Horta, Portugal
                [10 ]Faculty of Bioscience and Aquaculture, Nord Universitet, ( https://ror.org/030mwrt98) Bodø, Norway
                Article
                50869
                10.1038/s41598-023-50869-9
                10776566
                38195638
                ea45827d-4260-4a0a-a934-5729fe25eb7a
                © The Author(s) 2024

                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
                : 27 March 2023
                : 27 December 2023
                Funding
                Funded by: Fundação para a Ciência e a Tecnologia
                Award ID: UIDB/04326/2020
                Award ID: UIDP/04326/2020
                Award ID: LA/P/0101/2020
                Award ID: PTDC/BIA-CBI/6515/2020
                Award ID: PTDC/BIA-BMA/30278/2017
                Funded by: Fundação para a Ciência e Tecnologia, Portugal
                Award ID: DL57/2016/CP1361/CT0036
                Award ID: UI/BD/151309/2021
                Award ID: DL57/2016/CP1361/CT0035
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                marine biology,conservation biology,animal migration
                Uncategorized
                marine biology, conservation biology, animal migration

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