Articles | Volume 9, issue 2
Ocean Sci., 9, 261–279, 2013

Special issue: The MyOcean project: scientific advances for operational ocean...

Ocean Sci., 9, 261–279, 2013

Research article 07 Mar 2013

Research article | 07 Mar 2013

Towards an integrated forecasting system for fisheries on habitat-bound stocks

A. Christensen1, M. Butenschön2, Z. Gürkan1, and I. J. Allen2 A. Christensen et al.
  • 1DTU Aqua, Technical University of Denmark, 2920 Charlottenlund, Denmark
  • 2Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, UK

Abstract. First results of a coupled modelling and forecasting system for fisheries on habitat-bound stocks are being presented. The system consists currently of three mathematically, fundamentally different model subsystems coupled offline: POLCOMS providing the physical environment implemented in the domain of the north-west European shelf, the SPAM model which describes sandeel stocks in the North Sea, and the third component, the SLAM model, which connects POLCOMS 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 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 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 sandeel stocks are currently exploited close to the maximum sustainable yield, even though periodic overfishing seems to have occurred, but large uncertainty is associated with determining stock maximum sustainable yield due to stock inherent 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.