43 Fishing policy exploration

Ecosim contains a formal optimization routine (i.e., an “open loop” simulation^[1]) that allows users to search for fisheries policies that would maximize long-term management goals.^[2] The routine uses a multi-criterion objective function^[3] representing five common fisheries management goals:

• Maximize fisheries rent (net economic value, R): Where profits are calculated as a function of the value of the catch minus the cost of fishing.
• Maximize fisheries social benefits (J): Where social benefits are expressed as the employment supported by the fleet, such that the number of jobs per ton of fish caught per fleet are proportional to fishing effort.
• Maximize mandated rebuilding of a functional group (B_lim): Where rebuilding targets are set by describing a threshold biomass (relative to the biomass on the base Ecopath model) for a functional group whose biomass is low or declining.
• Maximize species diversity (D): Where diversity is approximated using Kempton’s Q75 index.
• Maximize ecosystem structure or “health” (B/P): Where average longevity across functional groups is regarded as a proxy for ecosystem maturity. Thus, ecosystem configurations that favour higher biomasses for groups with low Production/Biomass ratios are regarded as more desirable.

This routine works by affecting relative fishing effort levels (E) by fleet type (fl). Using a nonlinear optimization procedure, it seeks to iteratively improve the objective function by changing relative fishing rates (by producing time series of relative fleet sizes).^[4]

[latex]f(E_{fl}) = \text{Max}(w_1 R + w_2 J + \begin{equation} \left\{\begin{array}{cc} W_3 \cdot (B_{lim} - B) , \text{ if } B^[5] so that, for example, some functional groups might retain high fishing rates even at lower levels of biomass.

The optimization routine used by EwE allows for maximizing economic objectives under scenarios of full cooperation (i.e., all incomes and costs are pooled, and profits are shared among all fishers and across fleets), constrained cooperation (i.e., maximizing profits across all fleets but where each fleet has to remain economically viable on its own), and full competition (i.e., treating each fleet as a separate economic entity, and seeking to maximize fleet-specific rent).^{[6] [7] [8]} [39–41]. Additionally, it allows users to explore changes in trade-off schedules by: (i) varying discount rates in the net present value calculation,^{[9] [10]} and (ii) incorporating data from the value chain plugin (i.e., fleet level vs supply chain level consequences in terms of net present value and jobs).

Finally, there is an alternative search procedure for optimum fishing patterns that maximize a logarithm-based portfolio utility function. When applied, this portfolio utility function embodies a risk-adverse objective function, as its logarithmic configurations heavily penalizes low values (e.g., years with low economic rent or contributions to employment).

Explore this tool further through the Trade-offs between policy objectives tutorial.