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Using multivariate autoregressive state-space models to examine stock structure of Greenland halibut in the North Atlantic

  • J. Úbeda*
  • , A. Nogueira
  • , N. Tolimieri
  • , M. Vihtakari
  • , B. Elvarsson
  • , M. Treble
  • , J. Boje
  • *Corresponding author for this work
  • University of Alicante
  • Greenland Institute of Natural Resources
  • National Oceanic and Atmospheric Administration
  • Institute of Marine Research
  • Marine and Freshwater Research Institute
  • Fisheries and Oceans Canada

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Accurate information on population structure is essential for effective fisheries management. Greenland halibut (Reinhardtius hippoglossoides) in the North Atlantic is managed as four separate offshore stocks. We use Multivariate Autoregressive State-Space (MARSS) models to assess population structure by means of abundance and biomass trends in four regions (Norwegian Sea, Iceland, Southeast Greenland, and Northwest Atlantic) where three offshore stocks are recognized: (1) Baffin Bay–Davis Strait (Northwest Atlantic stock), (2) Southeast Greenland and Iceland (West Nordic stock (WNS)), and (3) the Barents and Norwegian Seas (Northeast Arctic stock). We formulated model alternatives, using bottom trawl survey data from each region for 1996–2019, to evaluate support for different population structures. Abundance and biomass observations from each region were linked to growth rate parameters in MARSS models and the impact of climate (North Atlantic Oscillation Index) and fishing (commercial catches) on stock dynamics was investigated. Top models identified the Northwest Atlantic as an independent population. Best-fit models treated Greenland halibut in the WNS as two independent populations (east and west), with potential connections between eastern Iceland and the western Barents Sea. These results suggest a mismatch between current stock perception and management boundaries in the Northeast Atlantic.
Original languageEnglish
JournalFisheries Management and Ecology
Volume30
Issue number5
Pages (from-to)521-535
Number of pages15
ISSN0969-997X
DOIs
Publication statusPublished - 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 14 - Life Below Water
    SDG 14 Life Below Water

Keywords

  • Multivariate autoregressive state-space models
  • NAO index
  • North Atlantic Ocean
  • Population structure
  • Reinhardtius hippoglossoides
  • Sustainable fisheries management

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