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| Titel |
Towards an integrated forecasting system for fisheries on habitat-bound stocks |
| VerfasserIn |
A. Christensen, M. Butenschön, Z. Gürkan, I. J. Allen |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1812-0784
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| Digitales Dokument |
URL |
| Erschienen |
In: Ocean Science ; 9, no. 2 ; Nr. 9, no. 2 (2013-03-07), S.261-279 |
| Datensatznummer |
250018035
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| Publikation (Nr.) |
 copernicus.org/os-9-261-2013.pdf |
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| Zusammenfassung |
| 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. |
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