Towards an integrated forecasting system for pelagic fisheries

Christensen, A; Butenschon, M; Gurkan, Z; Allen, JI. 2012 Towards an integrated forecasting system for pelagic fisheries. Ocean Science Discussions, 9. 1437 - 1479. 10.5194/osd-9-1437-2012

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Official URL: http://dx.doi.org/10.5194/osd-9-1437-2012

Abstract/Summary

First results of a coupled modeling and forecasting system for the pelagic fisheries are being presented. The system consists currently of three mathematically fundamentally different model subsystems: POLCOMS-ERSEM providing the physical-biogeochemical environment implemented in the domain of the North-West European shelf and the SPAM model which describes sandeel stocks in the North Sea. The third component, the SLAM model, connects POLCOMS-ERSEM and SPAM by computing the physical-biological interaction. Our major experience by the coupling model subsystems is that well-defined and generic model interfaces are very important for a successful and extendable coupled model framework. The integrated approach, simulating ecosystem dynamics from physics to fish, allows for analysis of the pathways in the ecosystem to investigate the propagation of changes in the ocean climate and lower trophic levels to quantify the impacts on the higher trophic level, in this case the sandeel population, demonstrated here on the base of hindcast data. The coupled forecasting system is tested for some typical scientific questions appearing in spatial fish stock management and marine spatial planning, including determination of local and basin scale maximum sustainable yield, stock connectivity and source/sink structure. Our presented simulations indicate that sandeels stocks are currently exploited close to the maximum sustainable yield, but large uncertainty is associated with determining stock maximum sustainable yield due to stock eigen dynamics and climatic variability. Our statistical ensemble simulations indicates that the predictive horizon set by climate interannual variability is 2–6 yr, after which only an asymptotic probability distribution of stock properties, like biomass, are predictable.

Item Type: Publication - Article
Subjects: Data and Information
Fisheries
Marine Sciences
Oceanography
Divisions: Plymouth Marine Laboratory > National Capability categories > Modelling
Depositing User: EPServices Admin
Date made live: 11 Feb 2014 15:57
Last Modified: 06 Jun 2017 16:09
URI: http://plymsea.ac.uk/id/eprint/5305

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