Towards an integrated forecasting system for pelagic fisheries

Research output: ResearchConference abstract for conference – Annual report year: 2012

View graph of relations

First results of a coupled modelling and forecasting system for pelagic fisheries are presented. The system consists of three mathematically fundamentally different model subsystems: POLCOMSERSEM provides the physical–biogeochemical environment in the northwest European shelf, the Sandeel Population Analysis Model describes sandeel stocks in the North Sea, and the Sandeel Larval Analysis Model connects POLCOMS‐ERSEM and SPAM by computing the physical–biological interaction. Our main findings by 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
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 basis of hindcast data. The coupled forecasting system has been 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 sandeel stocks are currently exploited close to the maximum sustainable yield, but large uncertainty is associated with determining stock maximum sustainable yield due to stock eigendynamics and climatic variability. Our statistical ensemble simulations indicate that the
predictive horizon set by climate interannual variability is 2–6 years, after which only an asymptotic probability distribution of stock properties, such as biomass, is predictable
Original languageEnglish
Publication date2012
StatePublished - 2012
Download as:
Download as PDF
Select render style:
Download as HTML
Select render style:
Download as Word
Select render style:

ID: 123046453