Oil spill fishery impact assessment modeling: The fisheries recruitment problem
A model to assess the impact of oil spill on fisheries, consisting of an oil spill fates model, a continental shelf hydrodynamics model, an ichthyoplankton transport and fates model, and a fish population model, has been applied to the Georges Bank-Gulf of Maine region to estimate the impact of oil...
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Veröffentlicht in: | Estuarine, coastal and shelf science coastal and shelf science, 1984-01, Vol.19 (6), p.591-610 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | A model to assess the impact of oil spill on fisheries, consisting of an oil spill fates model, a continental shelf hydrodynamics model, an ichthyoplankton transport and fates model, and a fish population model, has been applied to the Georges Bank-Gulf of Maine region to estimate the impact of oil spills on several important commercial fisheries. The model addresses direct impacts of oil on a fishery through hydrocarbon-induced egg and larval mortality. This early life stage mortality is estimated by dynamically mapping the spatial intersection of the surface and subsurface oil concentrations resulting from the spill with the developing eggs and larvae. Ichthyoplankton entering an area with hydrocarbon concentrations in excess of a specified threshold are assumed lost. Model output is given in terms of differential catch, comparing the non-impacted and the hydrocarbon impacted fisheries. Difficulties in establishing stock-recruit relationships, and the inability to predict first year survival even one year ahead make the quantification of absolute catch losses impossible. Output of the model system discussed here is therefore limited to relative rather than absolute catch losses.
The paper is organized to demonstrate first the importance of the recruitment question to impact estimation, second that a modeling methodology is necessary to evaluate impacts given the magnitude of unexplained observed recruitment variability, and third a stochastic solution to the problem which places impact estimates in the context of a probability distribution. Lastly, the model system is applied to the problem of attaining better early life history mortality estimates, to ultimately improve impact estimation capabilities. |
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ISSN: | 0272-7714 1096-0015 |
DOI: | 10.1016/0272-7714(84)90017-9 |