Tutorial: Policy exploration procedure

When running the policy optimization for Anchovy Bay with objective weights = the inverse of ranges from Table 1, the results in the table below are obtained.

Table 2. Objective function values and fleet effort for Anchovy Bay with weights set to inverse range of objectives

Local maxima

As part of the exploratory analysis, it is important to check whether the maximization search is impacted by the start point, i.e. whether the optimization solutions are unique. By default the optimization routine will start with the fishing rates defined by the Ecopath baseline. It’s possible, however, to instead using random fleet effort (Random F’s in the policy interface) to check if the optimization routine is likely to get stuck at local maxima.  All optimization routines are impacted by this, the ones in EwE being no exceptions.

We illustrate this for Anchovy Bay by running five^[2] optimizations for each of the objective-by-objective optimizations in Table 1. The outcome of that exploratory analysis is presented in Table 3.

Table 3. Policy optimization objective ranges for five runs for each objective, all with random starting efforts.  The last five columns gives the effort by fleet for each optimization run. 

Table 2 has four sections, one for each of the objective-by-objective optimizations. As an example, the first sections shows the outcome of the five runs with random starting point effort when optimizing for Net economic value only. The objective function stopped at a very similar value (2.31) in all five runs, and the effort is very similar for each fleet for all runs, with one exception. The effort for Sealers is 0.95 in the first run, and 1.9 – 2.2 in the four others. This may seem like a big difference, but in the optimization, all five effort levels result in the same basic ecological outcome: a total collapse of the target species, seals.

Examining the entire Table 2, it is clear that that in this example, there is very little tendency for the optimization to get stuck on local maxima. The variation in the objective estimates and effort patterns are very similar across each optimization type, with only a few runs indicating presence of local maxima. The fourth run for Ecosystem structure is the only run that seems to differ in any substantial way, indicative of the optimization being unable to find any unique fishing efforts to optimize this objective (which should not surprise you since it is quite a vague objective in the first place).

The conclusion is that policy optimizations for Anchovy Bay are not very prone to get stuck on local maxima. This is also what we have found for many other ecosystem model optimizations, giving some comfort that the starting point isn’t very critical. Still, that needs to be checked for all models, so including a search with random Starting F’s should be included in all more serious policy explorations.

Fleet trade-off analysis

Figure 1. Fleet trade-off analysis for Anchovy Bay showing impact a 10% reduction in effort for the fleet listed in rows has on the fleets listed above columns. Negative impacts are in red, and positive in blue. 

A next step of exploratory analysis is the fleet trade-off analysis described in the Fishing policy chapter (link to fleet trade-off). We refer to that section for description, including for code to produce plots.

We suggest that you perform the fleet trade-off analysis for your model and explore the outcome. For Anchovy Bay (Figure 1), the plot shows the impact that a 10% reduction for the fleets mentioned to the left, are predicted to have in the value of the catch for all fleets. Red circles indicate reduction in value, and blue increase.  The impacts are displayed so that the circle areas are proportional to the changes in value of the catch, and are thus comparable across fleets^[3].

For Anchovy Bay, the fleet trade-off analysis shows some both straightforward and more complex patterns. Notice for instance that a reduction in Bait boats’ effort will have a positive impact on Seiners. That makes sense since the two fleets both catch anchovy. But conversely, a reduction in Seiners’ effort lead to a small reduction in landed value for the Bait boats. Why? Seiners also catch mackerel, and the reduced effort will lead to more mackerel, which in turn will have a negative impact on anchovy, and hence on Bait boats, (which only catch anchovy).

The strongest impact of effort reduction is for Trawlers and Shrimpers, the two fleets that have the highest value in the model base year. Reduction in Trawlers’ effort has a considerable negative impact on Trawlers, but also an almost corresponding negative impact on Shrimpers. That makes sense as the reduction should lead to more cod and whiting, both of which eat shrimp.  But conversely, reducing Shrimpers’ effort leads to an increase in shrimp landings (so they must be overexploited in the baseline – given the baseline predator-prey conditions though). But why does this not lead to an increase in the value of Trawlers’ landings?  The reason is that shrimp have a positive impact on whiting and mackerel, but a negative impact on cod. Notably, the increase in whiting impacts cod negatively.  Those relationships becomes clearer if you check out the Mixed Trophic Impact analysis (Ecopath > Output > Tools > Network Analysis > Mixed trophic impact > Impact data), which shows that Shrimpers have opposite impact on cod versus whiting and mackerel.

Your policy questions?

Having explored the behaviour of your model, e.g., as described above, the next issue is to clearly define what questions you are asking for your model. An example of this is Alms et al. 2022, ^[4] who compared output from three defined Ecosim scenarios, (1) ban on shrimp trawling, (2) gill net effort reduction of 25%, and (3) the combination of (1) and (2), and then compared these to the output of single- and multi-objective policy optimizations. Here, the multi-objective optimizations were designed to serve as more balanced solutions.  See the paper for details.

While the complex patterns in Figure 1 can be explained as done above, they raise some questions. There is a big negative impact of reducing Trawlers’ effort and a big positive impact of reducing Shrimpers’ effort.  Is this then what we should explore? Well, it’s certainly interesting scenarios, but there are some complications, and it is in line with what actually happened in Anchovy Bay. Effort of trawlers indeed increased 2-3 times over time, but the shrimp effort increased by almost an order of magnitude.  How can that make sense when the fleet trade-off analysis indicate that shrimp catches would increase if Shrimpers’ effort was reduced?  That finding is not wrong, but it doesn’t not consider that the increase in the Trawlers’ effort reduced the abundance of cod and whiting, which in turn lead to many more shrimps being available for the Shrimpers. Predator-release!

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.  Parts of the text are from the unpublished EwE User Guide: Christensen V, C Walters, D Pauly, R Forrest. Ecopath with Ecosim. User Guide. November 2008.