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Pattern-based control rules for fisheries management


Fisheries that are managed by conventional standards are often managed by regulating catches through what are frequently referred to as control rules. These rules involve three components, each of which are treated as distinct issues in this paper:

  1. reduction in the abundance of the population of fish being harvested,
  2. changes in suggested harvest rates depending on the abundance of the resource population, and
  3. the magnitude of these variable harvest rates.

Of course, these components are not isolated from each other and, in their implementation, they are used in combination. Overall, the application of control rules involves lines of reasoning based on our scientific understanding of at least a few of the basic principles of population dynamics. In part, control rules represent attempts to avoid recognized risks of overharvesting.

In this paper, we make use of information from natural patterns to address each of these three elements of control rules to illustrate how each can be treated in a way that fully accounts for all of the complexity involved (i.e., the infinite set of relevant factors). Specifically, macroecological patterns are used to identify some of the problems caused (rather than solved) through the conventional application of control rules. We find that 1) harvested populations should be maintained at levels from 60-100% larger than what is often considered desirable in conventional management, 2) control rules should be curvilinear (rather than involving two linear segments) with harvests at levels that are between 56% and 72% of maximum when resources populations are at 40% of unharvested levels, and 3) harvest rates commonly need to be less than 10% of the magnitude of those used in much of today's management.

Our approach to arriving at these findings is based on information that is holistic in nature; among the things that get taken into account are the full complexity of evosystems, ecosystems, and all of their associated dynamics—including all associated risks. We emphasize the importance of our work by providing examples of the kind of information that achieves this holism. Key in this regard is the match between the natural patterns used for guidance and the management question being addressed. The discrepancies noted for the conventional application of the three components of control rules on which we chose to focus, can, in large part, be attributed to ignoring the importance of this match. Although full holism can be achieved for each management question addressed with empirical patterns, the degree to which full holism is achieved more generally depends on the extent to which other management questions are asked and addressed, and management action taken, following the examples provided both in this paper and elsewhere.

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