Mixed-stock analysis of Atlantic herring (Clupea harengus): a tool for identifying management units and complex migration dynamics

Dorte Bekkevold*, Florian Berg, Patrick Polte, Valerio Bartolino, Henn Ojaveer, Henrik Mosegaard, Edward D. Farrell, Jelena Fedotova, Jakob Hemmer-Hansen, Bastian Huwer, Vanessa Trijoulet, Christoffer Moesgaard Albertsen, Angela P. Fuentes-Pardo, Tomas Gröhsler, Mats Pettersson, Teunis Jansen, Arild Folkvord, Leif Andersson

*Corresponding author for this work

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We developed and validated a mixed-stock analysis (MSA) method with 59 single-nucleotide polymorphisms selected from genome-wide data to assign individuals to populations in mixed-stock samples of Atlantic herring from the North and Baltic seas. We analysed 3734 herring from spawning locations and scientific catches of mixed feeding stocks to demonstrate a “one-fits-all” tool with unprecedented accuracy for monitoring spatio-temporal dynamics throughout a large geographical range with complex stock mixing. We re-analysed time-series data (2002–2021) and compared inferences about stock composition with estimates from morphological data. We show that contributions from the western Baltic spring-spawning stock complex, which is under management concern, have likely been overestimated. We also show that a genetically distinctive population of western Baltic autumn spawners, ascribed low fisheries importance, contributes non-negligible and potentially temporally increasing proportions to mixed-stock aggregations, calling for a re-evaluation of stock definitions. MSA data can be implemented in stock assessment and in a variety of applications, including marine ecosystem description, impact assessment of specific fleets, and stock-rebuilding plans.
Original languageEnglish
Article numberfsac223
JournalICES Journal of Marine Science
Issue number1
Pages (from-to)173-184
Number of pages12
Publication statusPublished - 2023


  • Clupeid
  • Genetic stock identification
  • Genomics
  • Management units
  • Migration
  • Mixed stock-analysis
  • Population structure
  • SNP


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