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Robust fisheries management strategies under deep uncertainty
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Number of Authors: 62024 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 14, article id 16863Article in journal (Refereed) Published
Abstract [en]

Fisheries worldwide face uncertain futures as climate change manifests in environmental effects of hitherto unseen strengths. Developing climate-ready management strategies traditionally requires a good mechanistic understanding of stock response to climate change in order to build projection models for testing different exploitation levels. Unfortunately, model-based projections of fish stocks are severely limited by large uncertainties in the recruitment process, as the required stock-recruitment relationship is usually not well represented by data. An alternative is to shift focus to improving the decision-making process, as postulated by the decision-making under deep uncertainty (DMDU) framework. Robust Decision Making (RDM), a key DMDU concept, aims at identifying management decisions that are robust to a vast range of uncertain scenarios. Here we employ RDM to investigate the capability of North Sea cod to support a sustainable and economically viable fishery under future climate change. We projected the stock under 40,000 combinations of exploitation levels, emission scenarios and stock-recruitment parameterizations and found that model uncertainties and exploitation have similar importance for model outcomes. Our study revealed that no management strategy exists that is fully robust to the uncertainty in relation to model parameterization and future climate change. We instead propose a risk assessment that accounts for the trade-offs between stock conservation and profitability under deep uncertainty.

Place, publisher, year, edition, pages
2024. Vol. 14, article id 16863
National Category
Fish and Wildlife Management Fish and Aquacultural Science Climate Science
Identifiers
URN: urn:nbn:se:su:diva-237016DOI: 10.1038/s41598-024-68006-5ISI: 001275770300103PubMedID: 39043856Scopus ID: 2-s2.0-85199250241OAI: oai:DiVA.org:su-237016DiVA, id: diva2:1920779
Available from: 2024-12-12 Created: 2024-12-12 Last updated: 2025-02-01Bibliographically approved

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Blenckner, Thorsten

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