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All issues Volume 35 (2022) Aquat. Living Resour., 35 (2022) 8 References
Open Access
Issue
Aquat. Living Resour.
Volume 35, 2022
Article Number 8
Number of page(s) 8
DOI https://doi.org/10.1051/alr/2022009
Published online 21 June 2022
  • Almuly R, Poleg-Danin Y, Gorshkov S, Gorshkova G, Rapoport B, Soller M, Funkenstein B. 2005. Characterization of the 5′ flanking region of the growth hormone gene of the marine teleost, gilthead sea bream Sparus aurata: analysis of a polymorphic microsatellite in the proximal promoter. Fish Sci 71: 479–490. [CrossRef] [Google Scholar]
  • Almuly R, Skopal T, Funkenstein B. 2008. Regulatory regions in the promoter and first intron of Sparus aurata growth hormone gene: repression of gene activity by a polymorphic minisatellite. Comp Biochem Physiol Part D Genomics 3: 43–50. [Google Scholar]
  • Antao T., Lopes A., Lopes R.J., Beja-Pereira A., Luikart G. 2008. LOSITAN: a workbench to detect molecular adaptation based on a Fst-outlier method. BMC Bioinform 9: 1–5. [CrossRef] [Google Scholar]
  • Arechavala-Lopez P., Fernandez-Jover D., Black K.D., Ladoukakis E., Bayle-Sempere J.T., Sanchez-Jerez P., Dempster T. 2013. Differentiating the wild or farmed origin of Mediterranean fish: a review of tools for sea bream and sea bass. Rev Aqua 4: 1–21. [Google Scholar]
  • Arechavala-Lopez P., Toledo-Guedes K., Izquierdo-Gomez D., Šegvić-Bubić T., Sanchez-Jerez P. 2018. Implications of sea bream and sea bass escapes for sustainable aquaculture management: a review of interactions, risks and consequences. Rev Fish Sci Aquac 26: 214–234. [CrossRef] [Google Scholar]
  • Astola A., Orti, M., Calduch-Giner J.A., Pérez-Sánchez J., Valdivia M.M. 2003. Isolation of Sparus auratus prolactin gene and activity of the cis-acting regulatory elements. Gen Comp Endocrinol 134: 57–61. [CrossRef] [PubMed] [Google Scholar]
  • Atalah J., Sanchez-Jerez P. 2020. Global assessment of ecological risks associated with farmed fish escapes. Glob Ecol Conserv DOI: 10.1016/j.gecco.2019.e00842. [Google Scholar]
  • Beaumont M.A., Nichols R.A. 1996. Evaluating loci for use in the genetic analysis of population structure. Proc Royal Soc B 263: 1619–1626. [CrossRef] [Google Scholar]
  • Blel H., Panfili J., Guinand B., Berrebic P., Saida K., Durandb J.D. 2010. Selection footprint at the first intron of the Prl gene in natural populations of the flathead mullet (Mugil cephalus, L. 1758). J Exp Mar Biol Ecol 387: 60–67. [CrossRef] [Google Scholar]
  • Brown R.C. 2003. Genetic management and selective breeding in farmed populations of gilthead seabream, Sparus aurata. PhD thesis, University of Stirling, U.K. [Google Scholar]
  • Brown C., Miltiadou D., Tsigenopoulos C.S. 2015. Prevalence and survival of escaped European seabass Dicentrarchus labrax in Cyprus identified using genetic markers. Aquac Environ Interact 7: 49–59. [CrossRef] [MathSciNet] [Google Scholar]
  • Buschmann A.H., Riquelme V.A., Hernández-González M.C., Varela D., Jiménez J.E., Henríquez L.A., Vergara P.A., Guíñez R., Filún L. 2006. A review of the impacts of salmonid farming on marine coastal ecosystems in the southeast Pacific. ICES J Mar Sci 63: 1338–1345. [CrossRef] [Google Scholar]
  • Chaoui L., Gagnaire P.A., Guinand B., Quignard J.P., Tsigenopoulos C., Kara M.H., Bonhomme F. 2012. Microsatellite length variation in candidate genes correlates with habitat in the gilthead sea bream Sparus Aurata. Mol Ecol 21: 5497–5511. [CrossRef] [PubMed] [Google Scholar]
  • Chapuis M.P., Estoup A. 2007. Microsatellite null alleles and estimation of population differentiation. Mol Biol Evol 24: 621–631. [CrossRef] [PubMed] [Google Scholar]
  • Cossu P., Scarpa F., Sanna D., Lai T., Dedola G.L., Curini-Galletti M., Mura L., Fois N., Casu M. 2019. Influence of genetic drift on patterns of genetic variation: the footprint of aquaculture practices in Sparus aurata (Teleostei: Sparidae). Mol Ecol 28: 3012–3024. [CrossRef] [PubMed] [Google Scholar]
  • Diserud O.H., Hedger R., Finstad B., Hendrichsen D., Jensen A.J., Ugedal O. 2020. Salmon louse infestation in wild brown trout populations generates multi-modal mixture distributions. Aquac Environ Interact 12: 447–456. [CrossRef] [Google Scholar]
  • Earl D.A., von Holdt B.M. 2012. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4: 359–361. [CrossRef] [Google Scholar]
  • FEAP. 2020. Federation of European Aquaculture Producers, annual report. [Google Scholar]
  • Fleming I.A., Hindar K., Mjölneröd I.B., Jonsson B., Balstad T., Lamberg A. 2000. Lifetime success and interactions of farm salmon invading a native population. Proc Royal Soc B 267: 1517–1523. [CrossRef] [PubMed] [Google Scholar]
  • Ford J.S., Myers R.A. 2008. A global assessment of salmon aquaculture impacts on wild salmonids. PLoS Biol DOI: 10.1371/journal.pbio.0060033. [PubMed] [Google Scholar]
  • García-Fernández C., Sánchez J.A., Blanco G. 2018. Early assessment of gilthead sea bream (Sparus aurata) spawning dynamics by mini-broodstocks. Aquac Res 49: 36–47. [CrossRef] [Google Scholar]
  • Glover K.A., Quintela M., Wennevik V., Besnier F., Sørvik A.G., Skaala Ø. 2012. Three decades of farmed escapees in the wild: a spatio-temporal analysis of Atlantic salmon population genetic structure throughout Norway. PLoS One DOI: 10.1371/journal.pone.0043129. [Google Scholar]
  • Glover K.A., Pertoldi C., Besnier F., Wennevik V., Kent M., Skaala Ø. 2013. Atlantic salmon populations invaded by farmed escapees: quantifying genetic introgression with a Bayesian approach and SNPs. BMC Genet 14: 1–19. [PubMed] [Google Scholar]
  • Goudet J. 2002. FSTAT, a program to estimate and test gene diversities and fixation indices (version 2.9.3.2) https://www2.unil.ch/popgen/softwares/fstat.htm [Google Scholar]
  • Griot R., Allal F., Phocas F., Brard-Fudulea S., Morvezen R., Haffray P., François Y., Morin T., Bestin A., Bruan, J.-S., Cariou S., Peyrou B., Brunier J., Vandeputte M. 2021. Optimization of genomic selection to improve disease resistance in two marine fishes, the European Sea Bass (Dicentrarchus labrax) and the Gilthead Sea Bream (Sparus aurata). Front Genet 12: 1294. [Google Scholar]
  • Guinand B., Chauvel C., Lechene M., Tournois J., Tsigenopoulos C.S., Darnaude A.M., Gagnaire P.A. 2016. Candidate gene variation in gilthead sea bream reveals complex spatiotemporal selection patterns between marine and lagoon habitats. Mar Ecol Prog Ser 558: 115–127. [CrossRef] [Google Scholar]
  • He X.P., Xia J.H., Wang C.M., Pang H.Y., Yue G.H. 2012. Significant associations of polymorphisms in the prolactin gene with growth traits in Asian seabass (Lates calcarifer). Anim Genet 43: 233–236. [CrossRef] [PubMed] [Google Scholar]
  • Izquierdo-Gomez D., Sanchez-Jerez P. 2016. Management of fish escapes from Mediterranean Sea cage aquaculture through artisanal fisheries. Ocean Coast Manag 122: 57–63. [CrossRef] [Google Scholar]
  • Jackson D., Drumm A., McEvoy S., Jensen Ø., Mendiola D., Gabiña G., Borg J.A., Papageorgiou N., Karakassis Y., Black K.D. 2015. A pan-European valuation of the extent, causes and cost of escape events from sea cage fish farming. Aquaculture 436: 21–26. [CrossRef] [Google Scholar]
  • Jakobsson M., Rosenberg N.A. 2007. CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23: 1801–1806. [CrossRef] [PubMed] [Google Scholar]
  • Janssen K., Chavanne H., Berentsen P., Komen H. 2017. Impact of selective breeding on European aquaculture. Aquaculture 472: 8–16. [CrossRef] [Google Scholar]
  • Jombart T. 2008. Adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24: 1403–1405. [Google Scholar]
  • Jonsson B., Jonsson N. 2006. Cultured Atlantic salmon in nature: a review of their ecology and interaction with wild fish. ICES J Mar Sci 63: 1162–1181. [CrossRef] [Google Scholar]
  • Kalinowski S.T., Taper M.L., Marshall T.C. 2007. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol Ecol 16: 1099–1106. [CrossRef] [PubMed] [Google Scholar]
  • Karlsson S., Moen T., Lien S., Glover K.A., Hindar K. 2011. Generic genetic differences between farmed and wild Atlantic salmon identified from a 7K SNP‐chip. Mol Ecol Resour 11: 247–253. [CrossRef] [PubMed] [Google Scholar]
  • Karlsson S., Diserud O.H., Moen T., Hindar K. 2014. A. standardized method for quantifying unidirectional genetic introgression. Ecol Evol 4: 3256–3263. [CrossRef] [PubMed] [Google Scholar]
  • Karlsson S., Diserud O.H., Fiske P., Hindar K. 2016. Widespread genetic introgression of escaped farmed Atlantic salmon in wild salmon populations. ICES J Mar Sci 73: 2488–2498. [CrossRef] [Google Scholar]
  • Laird P.W., Zijderveld A., Linders K., Rudnicki M.A., Jaenisch R., Berns A. 1991. Simplified mammalian DNA isolation procedure. Nucleic Acids Res 19: 4293. [CrossRef] [PubMed] [Google Scholar]
  • Launey S., Krieg F., Haffray P. 2003. Twelve new microsatellite markers for gilthead seabream (Sparus aurata L.): characterization, polymorphism and linkage. Mar Environ Res 3: 457–459. [Google Scholar]
  • Le Féon S., Dubois T., Jaeger C., Wilfart A., Akkal-Corfini N., Bacenetti J., Costantini M., Aubin J. 2021. DEXiAqua, a model to assess the sustainability of aquaculture systems: methodological development and application to a French Salmon Farm. Sustainability DOI: 10.3390/su13147779. [Google Scholar]
  • Lee-Montero I., Navarro A., Borrell Y., García-Celdrán M., Martín N., Negrín-Báez D., Blanco G., Armero E., Berbel C., Zamorano M.J., Sánchez J.J., Estévez A., Ramis G., Manchado M., Afonso J.M. 2013. Development of the first standardised panel of two new microsatellite multiplex PCRs for gilthead seabream (Sparus aurata L.). Anim Genet 44: 533–546. [CrossRef] [PubMed] [Google Scholar]
  • Loukovitis D., Sarropoulou E., Vogiatzi E., Tsigenopoulos C.S., Kotoulas G., Magoulas A., Chatziplis D. 2012. Genetic variation in farmed populations of the gilthead sea bream Sparus aurata in Greece using microsatellite DNA markers. Aquac Res 43: 239–246. [CrossRef] [Google Scholar]
  • Peñaloza C., Manousaki T., Franch R., Tsakogiannis A., Sonesson A.K., Aslam M.L., Allal F., Bargelloni L., Houston R.D., Tsigenopoulos C.S. 2021. Development and testing of a combined species SNP array for the European seabass (Dicentrarchus labrax) and gilthead seabream (Sparus aurata). Genomics 113: 2096–2107. [CrossRef] [PubMed] [Google Scholar]
  • Polovina E.-S., Kourkouni E., Tsigenopoulos C.S., Sanchez-Jerez P., Ladoukakis E.D. 2020. Genetic structuring in farmed and wild Gilthead seabream and European seabass in the Mediterranean Sea: implementations for detection of escapees. Aquat. Living Resour. 33: 7 [Google Scholar]
  • Pritchard J.K., Stephens M., Donnelly P. 2000. Inference of population structure using multilocus genotype data. Genetics 155: 945–959. [Google Scholar]
  • Raymond M., Rousset F. 2003. A population genetic software for exact test and ecumenicism: GENEPOP, version 3.4. Heredity 68: 248–249. [Google Scholar]
  • Rice W.R. 1989. Analyzing tables of statistical tests. Evolution (N. Y) 43: 223–225. [Google Scholar]
  • Rosenberg N.A., Calabrese P.P. 2004. Polyploid and multilocus extensions of the Wahlund inequality. Theor Popul Biol 66: 381–391. [CrossRef] [PubMed] [Google Scholar]
  • Ryman N., Palm S. 2006. POWSIM: a computer program for assessing statistical power when testing for genetic differentiation. Mol Ecol Notes 6: 600–602. [CrossRef] [Google Scholar]
  • Solberg M.F., Robertsen G., Sundt-Hansen L.E., Hindar K., Glover K.A. 2020. Domestication leads to increased predation susceptibility. Sci Rep 10: 1–11. [Google Scholar]
  • Šegvić-Bubić T., Grubišić L., Karaman N., Tičina V., Mišlov Jelavić K., Katavić I. 2011a. Damages on mussel farms potentially caused by fish predation-Self service on the ropes? Aquaculture 319: 497–504. [CrossRef] [Google Scholar]
  • Šegvić-Bubić T., Lepen I., Trumbić Ž., Ljubković J., Sutlović D., Matić-Skoko S., Grubišić L., Glamuzina B., Mladineo I. 2011b. Population genetic structure of reared and wild gilthead sea bream (Sparus aurata) in the Adriatic Sea inferred with microsatellite loci. Aquaculture 318: 309–315. [CrossRef] [Google Scholar]
  • Šegvić-Bubić T., Talijančić I., Grubišić L., Izquierdo-Gomez D., Katavić I. 2014. Morphological and molecular differentiation of wild and farmed gilthead sea bream Sparus aurata: implications for management. Aquac Environ Interact 6: 43–54. [CrossRef] [Google Scholar]
  • Šegvić-Bubić T., Grubišić L., Trumbić Ž., Stanić R., Ljubković J., Maršić-Lučić J., Katavić I. 2017. Genetic characterization of wild and farmed European seabass in the Adriatic Sea: assessment of farmed escapees using a Bayesian approach. ICES J Mar Sci 74: 369–378. [CrossRef] [Google Scholar]
  • Šegvić-Bubić T., Talijančić I., Vulić L., Šegvić B., Žužul I., Radonić I., Grubišić L. 2020. Assignment of gilthead seabream Sparus aurata to its origin through scale shape and microchemistry composition: management implications for aquaculture escapees. Water DOI: 10.3390/w12113186. [Google Scholar]
  • Talijančić I., Šegvić-Bubić T., Žužul I., Džoić T., Maršić-Lučić J., Grubišić L. 2019. Interactions between wild gilthead seabream Sparus aurata and tuna farms in the Adriatic Sea: morphological and ecophysiological fish adaptations. Aquac Environ Interact 11: 97–110. [CrossRef] [Google Scholar]
  • Talijančić I., Žužul I., Kiridžija V., Šiljić J., Pleadin J., Grubišić L., Šegvić-Bubić T. 2021. Plastic responses of gilthead seabream Sparus aurata to wild and aquaculture pressured environments. Front Mar Sci DOI: 10.3389/fmars.2021.694627 [Google Scholar]
  • Toledo-Guedes K., Brito A., Garcia de Leaniz C. 2021. Phenotypic convergence in sea bass (Dicentrarchus labrax) escaping from fish farms: the onset of feralization? Front Mar Sci 8: 674635. [CrossRef] [Google Scholar]
  • Van Oosterhout C., Hutchinson W.F., Wilis D.P.M., Shipley P. 2004. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol 4: 535–538. [CrossRef] [Google Scholar]
  • Waples R.S., Hindar K., Karlsson S., Hard J.J. 2016. Evaluating the Ryman-Laikre effect for marine stock enhancement and aquaculture. Curr Zoo. 62: 617–627. [CrossRef] [PubMed] [Google Scholar]
  • Yeh F., Yang R., Boyle T. 2002. POPGENE, Version 1.32: A Microsoft Windows-Based Freeware for Population Genetic Analysis. Molecular Biology and Biotechnology Centre, University of Alberta, Edmonton, Canada. http://www.ualberta.ca/~fyeh/info.htm [Google Scholar]
  • Yue G. 2014. Recent advances of genome mapping and marker-assisted selection in aquaculture. Fish Fish 15: 376–396. [CrossRef] [Google Scholar]
  • Žužul I., Šegvić-Bubić T., Talijančić I., Džoić T., Lepen Pleić I., Beg Paklar G., Ivatek-Šahdan S., Katavić I., Grubišić L. 2019. Spatial connectivity pattern of expanding gilthead seabream populations and its interactions with aquaculture sites: a combined population genetic and physical modelling approach. Sci Rep 9: 1–14. [Google Scholar]

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