44 Fishing policy exploration
The interface for the policy exploration is described in a EwE User Guide chapter.
Background
A central aim of fisheries management is to regulate fishing mortality rates over time so as to achieve economic, social and ecological sustainability objectives. An important dynamic modelling and assessment objective is thus to provide insight about the trade-offs involved in setting such mortality rates, including the impact of how they may vary over time – for instance during development or recovery from past overfishing. Recognizing that the question of what is “optimal” is a question of societal definition and also that we indeed cannot expect models to provide very precise estimates of what may be optimum fishing mortality rates, the ambition level is rather to explore trade-offs as a consequence of alternative management scenarios, rather than provide a prescription for the “optimal policy”.
To this effect, Ecosim implements a formal optimization method that can be used to search for fishing policies that would maximize particular policy goals or “objective functions” for management. This method is an “open loop” policy exploration simulation and optimization that acknowledges that policy may be defined as an approach towards reaching a broadly defined goal, that fisheries policies are often implemented via quotas that are recalculated annually, and through regulation that affects fleet structure and deployment.
Two very different approaches are implemented in the policy search for identification of optimum levels of fishing efforts for multiple fleets that may each exploit multiple species from an ecosystem.
The aim of the “sole owner” approach is to identify a single, overall performance measure for combined value from all fishing operations, then vary the by-fleet efforts so as to try and maximize this performance measure.
A fundamental problem with the “sole owner” approach is the implicit assumption of value and cost pooling. Supposedly “optimum” solutions often involve operating one or more fleets at uneconomic levels, essentially using these fleets to cull some species so as to increase production from other, more valued species.
The “multiple fishing rights” approach treats each fishing fleet (and perhaps non-consumptive stakeholder or user groups as well) as a separate economic industry with some legal right or entitlement, then seek a level for each fleet that optimizes a fleet-specific performance criterion such as total profits or growth until profitability (ratio of profits to income or cost) falls to a typical or reasonable level for economic industries in the economy as a whole. The basic problem in this “multiplayer game” approach is that performances of the fleets are linked through tradeoffs due to bycatch and trophic interaction effects. Growth of some fleets may enhance fishing opportunities for others, (e.g., fishing on piscivores can result in higher net production of planktivores), while growth of other fleets may shunt surplus production away from other fleets, (e.g., fishing on planktivores can reduce production of piscivores and abundances of non-target species that are valued for non-consumptive activities like whale-watching).
When filtered through the complex of ecological interactions involved in a food web, the net effect of any fleet on any other can be quite counter-intuitive. For instance, in development of management policy for red snapper (Lutjanus campechanus) in the Gulf of Mexico, it has been assumed that large bycatches of this species in shrimp trawls have been deleterious to recruitment, and that sustainable harvests of red snapper would be increased if shrimp trawlers were required to use bycatch reduction devices (BRDs). But rather there is evidence that recruitment of the snapper may actually have increased since development of the shrimp fishery, and ecosystem modelling suggest that this may be because shrimp trawling has had a larger negative effect on competitors and predators of juvenile red snapper (and shrimp) than its direct mortality effect on the juveniles.
One approach to multispecies optimization would be to promote selective fishery practices by each fleet (minimize wasteful bycatch with no apparent trophic benefits), then encourage each fleet to develop to an optimum economic level (defined by some criterion like profit or profitability). Then as multiple fleets develop in successive moves of the multiplayer game, cross-impacts (both positive and negative) would be exposed in terms of impacts on catches and costs, and the optimum or target level for each fleet would evolve over time in response to changes in the other fleets. Such a system might or might not approach some multi-fleet bionomic equilibrium (they typically do in Ecosim simulations), but that equilibrium would typically involve considerable erosion in ecosystem structure especially at top trophic levels due to shunting of production into fisheries for species of lower trophic levels.
Trade-offs between fleets
Figure 1. Fleet trade-offs in Anchovy Bay.
As discussed in connection with the multiple fishing rights discussion, there may well be trade-offs between fleets. To explore this independently of the fishing policy search, we suggest you run the “Fleet trade-off analysis”, which can be assessed at Ecosim > Tools > Fleet trade-off.[1]
The fleet trade-off analysis, runs Ecosim once for each fleet in a model. For the first run, the effort of the first fleet is set to 0.9 times the baseline, Ecosim is run, and the value of the landings for each fleet is calculated and stored. In the second run, the effort of the second fleet is reduced, and so forth.
The output of the routine is saved to a CSV-file, which subsequently can be used as datafile to produce output such as in the figure to the right. In the Anchovy Bay case shown on the figure to the right, negative income is shown in read, and positive income in blue. In the example, reduction of trawler effort, has a negative income on itself and on sealers, while the three other fleet benefits. Overall, those benefits do not outweigh the cost to trawlers (the Total is negative). In the Anchovy Bay case, there are no fleets for which a reduction would lead to an overall increase in catch value, but for many other models we have seen cases where this was the case.
R-code for producing fleet trade-off plots can be downloaded from this link (filename: FleetTradeOff_R_Code.zip). Download and unzip the R file, and place it in a directory where you also place the CSV-file downloaded from running the fleet trade-off analysis. Then run the R file.
Open loop
The fishing policy exploration module implements what control systems analysts call an “open loop”[2] simulation that acknowledges that policy may be defined as an approach towards reaching a broadly defined goal, but that actions must vary over time in response to new information.
An “open loop policy” provides a prescription for what to do at different future times without reference to what the system actually ends up doing along the way to those times. It would obviously be wrong to just apply an open loop policy blindly over time, each year committing a fishery to fishing rates calculated at some past time from only the data available as of that time. In practice, actual management needs to be implemented using feedback policies (see MSE chapter) where harvest goals are adjusted over time as new information becomes available and in response to unpredicted ecological changes due to environmental factors.
But in spite of the need for feedback in application, the open loop policy calculations serve a purpose, they can be done regularly over time as new information becomes available, to keep providing a general blueprint (or directional guidance) for where the system can/should be heading and to gain insights about trade-offs associated with alternative management policies. Also, we can often gain valuable insight about the functional form of better feedback policies (how to relate harvest rates to changes in abundance as these changes occur) by examining how the open loop fishing rates vary with changes in abundance, especially when the open loop calculations are done with Ecosim time forcing to represent possible changes in environmental conditions and productivity in the future.
Policy objectives
The policy exploration/optimization has two alternative objective functions, where by default the objective function is defined based on an evaluation of five weighted policy objectives[3] as described below.
The second option implements a risk-averse utility measure that instead of setting relative weights to different objectives as described above, use an alternative objective function that invokes a balanced “investment portfolio” of fishing activities. This method is described in the Risk-averse portfolio utility chapter and invoked by checking the Maximize portfolio utility option as described in the Fishing policy exploration chapter in the EwE User Guide.
Maximize fisheries rent
This objective, maximizing profits or net economic value, R, is based on calculating profits as the value of the catch (catch · price, by fleet and species) less the cost of fishing (fixed + variable costs, scaled proportional to effort). Giving a high weight to this objective often results in phasing out most fleets except the most profitable ones, and the wiping out of ecosystem groups competing with or preying on the more valuable target species.
Maximize social benefits (jobs)
The social benefits objective (J) is represented by the employment supported by each fleet. The benefits are calculated as number of jobs (assumed proportional to the catch value), and are fleet specific. Optimizing efforts often leads to more extreme (with regard to overfishing) fishing scenarios than optimizing for profit.
Maximize mandated rebuilding of species
The mandated rebuilding objective (Blim) can capture that external pressure (or legal decisions) may force policy makers to concentrate on preserving or rebuilding the population of given species in a given area. In Ecosim this corresponds to setting a threshold biomass (relative to the biomass in Ecopath) for one or more species or groups, and optimizing towards the fleet effort structure that will most effectively ensure this objective. The implications of this are case-specific: we are finding that the optimization routine may rigorously hammer (through increased fishing) competitors and predators of the species in question; or at the other extreme that fisheries may be shut down without social or economic consideration (as is indeed often the case when legal considerations take over).
This objective can thus be used to include consideration of biological limits, such as Blim in harvest control rules, but as described above, it may potentially point to measures that are a bit smarter than standard operating procedure: “close the fleet”.
Maximize biodiversity
The maximize biodiversity objective (D) captures only a single indicator for biodiversity, the biomass distribution across functional groups in the ecosystem model. The assumption is that as, e.g., exploitation increases, the system will move towards a biomass concentration for a few groups only. In Anchovy Bay, as an example, optimization for jobs (catch value) will result in a system that’s dominated by shrimp and anchovy. The objective is by default calculated using the Shannon diversity measure, with Kempton’s Q index being optional.
Maximize ecosystem structure
The ecosystem structure objective (a component of “ecosystem health” is inspired by E.P. Odum’s[4] description of ecosystem “maturity”, wherein mature ecosystems are dominated by large, long-lived organisms,[5] The default setting for ecosystem structure is the the group-specific biomass/production (B/P, unit: year) ratio as this measure expresses the average longevity by functional group. The ecosystem structure optimization often implies reduction of fishing effort for all fleets except those targeting species with low weighting factors.
In addition to the default setting for average longevity, the “Maximize ecosystem structure” can be used with a range of network analysis indicators that can be defined by functional group, e.g., the trophic level.
Objective function
The policy search optimization by default uses an objective function incorporating the five objectives discussed above, each weighted[6]. The routine will iteratively seek to maximize the objective function by setting relative fishing effort (E) by fleet (fl),
[latex]f(E_{fl}) = \text{Max}(w_1 R + w_2 J +[/latex] \begin{equation} \left\{ \begin{array}{cc} w_3 \cdot (B_{lim} – B) , \text{ if }B<B_{lim} \\ 0 , \text{ if } B \geq B_{lim} \end{array} \right\} \end{equation}[latex]+w_4 D + w_5 \frac{B}{P} )\tag{1}[/latex]
There is an art to using this function in actual implementations: what weights to use? Using the same weight wi on two or more objectives should not be expected to translate to even importance for those objectives. It may for instance be much easier to increase profitability than the biodiversity objective, calling for higher weights on biodiversity for a balanced solution. See the chapter for more discussion of this.
The fishing policy search routine then estimates time series of relative fleet sizes that would maximize this multi-criterion objective function. In Ecosim, the relative fleet sizes are used to calculate relative fishing mortality rates by fleet, assuming the mix of fishing rates over biomass groups remains constant for each fleet type, (i.e., reducing a fleet type by some percentage results in the same percentage decrease in the fishing rates that it causes on all the groups that it catches, i.e. leads to proportional changes in catchabilities). It is, however, possible to consider hyper-stability and hyper-depletion effects by specifying density-dependent catchabilities in Ecosim (Ecosim > Input > Group info > Density-dependent catchability, see the Group info tutorial).
The Risk-averse portfolio utility chapter describes an optimization method based on an alternative objective function.
Acknowledgement
This chapter was developed for the EcoScope project to guide implementation of the EwE Policy Search for the project case studies. EcoScope is funded from the European Commission’s Horizon 2020 Research and Innovation programme under grant agreement No 101000302. Project coordinator: Aristotle University of Thessaloniki, Greece. Part of the text is from the unpublished EwE User Guide: Christensen V, C Walters, D Pauly, R Forrest. Ecopath with Ecosim. User Guide. November 2008.Media Attributions
- Anchovy Bay
- EcoScope logo
- Available from EwE version 6.7.0.19005 on (from Oct 31. 2024). This version is available for download from EII) ↵
- "Open loop" as there is no feedback year-by-year to the policy. Instead, a full run is made and the objective function then evaluated. This is in contrast to a "closed loop" (now known as MSE, see chapter) where there's feed back every year ↵
- for details, see Christensen V, Walters CJ. 2004. Trade-offs in ecosystem-scale optimization of fisheries management policies. Bulletin of Marine Science 74: 549–562. and Christensen, V., Z. Ferdaña, J. Steenbeek. 2009. Spatial optimization of protected area placement incorporating ecological, social and economic criteria. Ecological Modelling 220:2583-2593. https://doi.org/10.1016/j.ecolmodel.2009.06.029 ↵
- Odum, E.P., 1969. The strategy of ecosystem development. Science, 164: 262-270. DOI: 10.1126/science.164.3877.262. ↵
- see Christensen, V. 1995. Ecosystem maturity - towards quantification. Ecol. Modelling. 77:3-32 https://doi.org/10.1016/0304-3800(93)E0073-C. ↵
- Christensen & Walters. 2004. op.cit. ↵
