Criteria for Designing Ecosystem-Based, Experimental Management: Bottom Trawling and the Bering Sea Ecosystem
Thomas A. Okey and Gretchen A. Harrington
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Detailed knowledge of the status and productivity of stocks has been the primary focus of fisheries management in order to maximize resource extraction. Ironically, maximum extraction is usually constrained only by the most optimistic stock scenarios from detailed single species models. This has led to degradation of marine ecosystems because natural variability and effects on nontarget species have not been adequately considered. Furthermore, the very act of gathering knowledge about ecosystem degradation has the potential to cause further damage due to the systemwide scale of fishery management experiments. However, a strategic approach for identifying trade-offs and risks of management experiments can simultaneously optimize knowledge-gathering, conservation goals, and economic sustainability.
In this paper we propose decision criteria for determining the most appropriate type of experimental management designs within the context of ecosystem-based management. The two "types" of experimental management designs are "unconstrained experimental management" and "precautionary experimental management," distinguished by the presence or absence of "stress treatments," in which extraction or anthropogenic disturbance is increased.
These decision criteria are designed to optimize both resource protection and knowledge-gathering in any experimental management situation.
Initiatives to reveal bottom trawling effects on the Bering Sea ecosystem are an opportunity to design ecosystem-based, experimental management programs wherein conservation objectives can be achieved through a well-designed experimental program for gathering knowledge. Conservation and knowledge gathering goals have been viewed as conflicting, or "goals to balance," but we argue that a precautionary experimental management design can be optimal for both purposes, especially when using marine protected areas to increase replication, provide ecological controls, and address statistical detection problems by simplifying multitreatment designs. New approaches to spatially explicit ecosystem modeling can be employed to help design such management experiments and help interpret results within an ecosystem context.
- Item number: AK-SG-99-01af
- Year: 1999
- DOI: https://doi.org/10.4027/eafm.1999.32