A1 Refereed original research article in a scientific journal
Comprehensive microsatellite baseline for genetic stock identification of Atlantic salmon (Salmo salar L.) in northernmost Europe
Authors: Ozerov M, Vähä JP, Wennevik V, Niemelä E, Svenning MA, Prusov S, Fernandez RD, Unneland L, Vasemägi A, Falkegard M, Kalske T, Christiansen B
Publisher: OXFORD UNIV PRESS
Publishing place: Oxford
Publication year: 2017
Journal: ICES Journal of Marine Science
Journal name in source: ICES JOURNAL OF MARINE SCIENCE
Journal acronym: ICES J MAR SCI
Volume: 74
Issue: 8
First page : 2159
Last page: 2169
Number of pages: 11
ISSN: 1054-3139
eISSN: 1095-9289
DOI: https://doi.org/10.1093/icesjms/fsx041(external)
Abstract
The rivers in northernmost Europe support many of the largest wild Atlantic salmon stocks supplying up to 60% of the total annual catch in the world, but very little is known about their relative contributions to coastal fisheries. We generated a comprehensive Atlantic salmon genetic baseline consisting of 185 populations along the Norwegian, Barents and White Sea coasts, genotyped for a high number of genetic markers, which has a dense geographical coverage and includes all notable stocks in the region. The hierarchical genetic structure observed was the basis for defining 7 regional and 26 local re3porting groups (RGs) enabling practical implementation of genetic stock identification (GSI). The GSI accuracy varied across populations and the study area, depending on the level of genetic structuring, but a high level of accuracy was achieved when individuals were classified to regional (97%) or to local (90%) RGs. Simulations showed that salmon from the majority of the largest populations in northernmost Europe can be reliably identified in Norwegian and Russian coastal mixed-stock fisheries. The baseline will be an invaluable resource for development of future adaptive management regimes for Atlantic salmon in northern Europe.
The rivers in northernmost Europe support many of the largest wild Atlantic salmon stocks supplying up to 60% of the total annual catch in the world, but very little is known about their relative contributions to coastal fisheries. We generated a comprehensive Atlantic salmon genetic baseline consisting of 185 populations along the Norwegian, Barents and White Sea coasts, genotyped for a high number of genetic markers, which has a dense geographical coverage and includes all notable stocks in the region. The hierarchical genetic structure observed was the basis for defining 7 regional and 26 local re3porting groups (RGs) enabling practical implementation of genetic stock identification (GSI). The GSI accuracy varied across populations and the study area, depending on the level of genetic structuring, but a high level of accuracy was achieved when individuals were classified to regional (97%) or to local (90%) RGs. Simulations showed that salmon from the majority of the largest populations in northernmost Europe can be reliably identified in Norwegian and Russian coastal mixed-stock fisheries. The baseline will be an invaluable resource for development of future adaptive management regimes for Atlantic salmon in northern Europe.
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