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Reducing eutrophication increases spatial extent of communities supporting commercial fisheries: a model case study
Stockholm University, Faculty of Science, Stockholm University Baltic Sea Centre.ORCID iD: 0000-0003-2688-2788
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Number of Authors: 92018 (English)In: ICES Journal of Marine Science, ISSN 1054-3139, E-ISSN 1095-9289, Vol. 75, no 4, p. 1306-1317Article in journal (Refereed) Published
Abstract [en]

In this study we investigate if eutrophication management has the potential to substantially affect which areas are going to be most suitable for commercial fishing in the future. We use a spatial ecosystem model, forced by a coupled physical-biogeochemical model, to simulate the spatial distribution of functional groups within a marine ecosystem, which depends on their respective tolerances to abiotic factors, trophic interactions, and fishing. We simulate the future long-term spatial developments of the community composition and their potential implications for fisheries under three different nutrient management scenarios and changing climate. The three nutrient management scenarios result in contrasting developments of bottom oxygen concentrations and phytoplankton abundance, with substantial effects on fish production. Nutrient load reduction increases the spatial extent of the areas suitable for the commercially most valuable demersal fish predator and all types of fisheries. This suggests that strategic planning of fishery management strategies could benefit from considering future changes in species distributions due to changes in eutrophication. We show that combining approaches from climate research, physical oceanography, biogeochemistry, biogeography, and trophic ecology with economical information provides a strong foundation to produce scientific knowledge that can support a multisectoral management of ecosystems.

Place, publisher, year, edition, pages
2018. Vol. 75, no 4, p. 1306-1317
Keywords [en]
ecopath with ecosim, ecospace, ecosystem restoration, eutrophication, fisheries, nutrient management, simulation model, spatial distribution
National Category
Agricultural Science, Forestry and Fisheries Biological Sciences Earth and Related Environmental Sciences
Identifiers
URN: urn:nbn:se:su:diva-159126DOI: 10.1093/icesjms/fsy003ISI: 000439700800009OAI: oai:DiVA.org:su-159126DiVA, id: diva2:1243220
Available from: 2018-08-30 Created: 2018-08-30 Last updated: 2020-03-05Bibliographically approved

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Bauer, BarbaraTomczak, Maciej T.
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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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