Trade-Offs in Ecosystem-Scale Optimization of Fisheries Management Policies

BULLETIN OF MARINE SCIENCE, 74(3): 549–562, 2004 MOTE SYMPOSIUM INVITED PAPER TRADE-OFFS IN ECOSYSTEM-SCALE OPTIMIZATION OF FISHERIES MANAGEMENT POLICIES Villy Christensen and Carl J. Walters ABSTRACT Recent applications of ecosystem models have had some apparent success evaluating how fisheries and environmental changes have affected marine populations, and a stage has been reached where ecosystem models can be used to describe agents of mortality and trophic interdependencies in the marine environment with some credibility. This success has raised the stakes for modeling and caused its focus to evolve to include ecosystem-scale optimization policies aimed, modestly, at determining the mix of fishing fleets that will optimize a combination of objectives, subject to the assumptions inher- ent in the model—as is the case with all models. A resemblance between our model predictions and real-world conditions may indicate that trade-offs among economic, social, and ecosystem objectives resulting from optimization for fleet configurations are more pronounced than hitherto recognized. The present paper reports the consequences of such optimizations for a model meant to mimic aspects of the Gulf of Thailand ecosystem, intended to determine how the model reacts to different weightings for the objective functions individually and jointly to examine the trade-offs involved. The results indicate that optimizing for economic profit is consistent with including ecosystem considerations, whereas optimizing landed value is in conflict with profit as well as ecosystem optimization. A number of recent studies (e.g., FAO/FISHCODE, 2001; Cox et al., 2002; Martell, 2002; Martell et al., 2002; Polovina, 2002; Stanford, 2002; Cox and Kitchell, this issue; Martell and Walters, this issue) have used ecosystem models and time-series data to evaluate the degree to which ecosystem changes over time could be attributed to fisheries and/or environmental changes. In the process they have seemingly done a credible job of predicting not the future but recent history for a number of marine ecosystems. We attribute this apparent success to the linkage of the foraging-arena concept (Walters and Juanes, 1993) with trophic mass-balance modeling (Polovina, 1984; Christensen and Pauly, 1992), as implemented in the Ecopath with Ecosim (EwE) approach (Walters et al., 1997, 2000; Pauly et al., 2000; Christensen and Walters, 2004). The approach is presently being tested for a variety of ecosystems including, but not limited to, the eastern Bering Sea, the Aleutian Islands, the Gulf of Alaska, Hecate Strait, the northern California Current, the northern and southern Benguela Current, Atlantic Canada, and the Chesapeake Bay. Although we have no way to predict whether the initial success rate will be maintained, we do conclude that we have reached a stage where ecosystem models can be used to describe with some credibility agents of mortality and trophic interdependencies in the marine environment. With this background, it is time to raise the stakes for such modeling, and the result, in the EwE context, has been the development of a routine for ecosystem-scale policy optimization aimed at determining the mix of effort for fishing fleets that will optimize a combination of objectives (Walters et al., 2002). This routine was the focus of a joint Fisheries and Agriculture Organization/University of British Columbia workshop in July 2000 (Pitcher and Cochrane, 2002), which led to the realization that the trade-offs between economic, social, and ecosystem objectives when the model optimizes fleet con- Bulletin of Marine Science 549 © 2004 Rosenstiel School of Marine and Atmospheric Science of the University of Miami 550 BULLETIN OF MARINE SCIENCE, VOL. 74, NO. 3, 2004 figurations are much more pronounced than usually recognized. Here, we examine the behavior of such optimizations with the aim of exploring the trade-offs involved. We consider the exploration of policy important for testing the behavior of ecosystem models. We stress that models are always wrong; no model can fully represent natural dynamics, so every model will fail if addressing certain questions (unless, of course, the model is the system being managed through an experimental management approach, Walters, 1986). A useful model is one that correctly orders a set of policy choices, i.e., makes correct predictions about the relative values of variables that matter to policy choice. It may also serve to set limits on what is achievable, to explore the trade-offs involved in our interventions, and perhaps most importantly, to make us take into account the possibility of surprising trade-offs in how ecosystems function. This last would include a forewarning that trade-offs undoubtedly exist that we cannot know about in real-world systems. Notable examples involve fisheries and conservation-policy objectives (Okey and Wright, this issue) and unexpected consequences of stock-enhancement programs (Cox and Kitchell, this issue). METHODS All analyses reported here were carried out with the “optimum policy search” module of the EwE software (freely available at www.ecopath.org). For the present study, the module was re- fined to include a batch mode of operation and to calculate a variety of indices concurrently. The policy module uses the efficient, nonlinear Davidson-Fletcher-Powell (Fletcher, 1987) optimization procedure to improve an objective function by changing relative fishing rates itera- tively. This procedure uses a “conjugate-gradient” method, testing alternative parameter values to approximate the objective function locally as a quadratic function of the parameter values and using this approximation to update parameters stepwise. Nonlinear optimization can easily hang up on local maxima and can give unrealistic, extreme answers as a result of inappropriate objective functions. We therefore began all optimizations with random fleet efforts and repeated all simulations at least 10 times with an array of weighting factors. OBJECTIVES As soon as we begin considering policy optimization at the ecosystem level, we are faced with the problem of evaluating across what may seem incompatible objectives. We used three objectives (out of the four included in the EwE optimization routine) to define an objectivity function and estimate a weighted total for each simulation. The objectives are (1) maximize economic profit, (2) maximize landed value of the catch, and (3) maximize “ecosystem structure.” We see these objectives, properly parameterized, as capturing some important aspects of societyʼs inter- est in exploitation of the living resources of the marine environment, noting that we expect landed value of the catch for a fishery to be correlated with the employment it provides. We hasten to point out that the weights and trade-offs are functions of our model and may not reflect the relative importance of such factors in reality. Our point is to explore the ways trade-offs are revealed in the model to illustrate the principle of their occurrence in real-world systems. Long-term trade- offs involving genetic effects of harvesting, for example, are ignored in our model. Many others cannot be known. Profit and Catch Value.—We estimate profit as the difference between the value of the catch (dockside revenue) and the cost of fishing. In the Gulf of Thailand case study (see below) the values and costs were based on FAO/FISHCODE (2001) and were representative for the late 1990s rather than 1973, described by the model. Profitability may well have decreased over this time span. If higher profits had been used, the optimizations would probably have resulted in somewhat higher fleet efforts when the model optimized for profit and possibly when it optimized for value of landings as well. CHRISTENSEN AND WALTERS: OPTIMIZATION POLICIES 551 Ecosystem Structure.—We explored two aspects of ecosystem structure, an index based on one of Odumʼs (1969) measures of ecosystem maturity, calculated as the longevity-weighted summed biomass over ecosystem groupings, and a biomass diversity index. The longevity index is used as one of the objectives for ecosystem policy, whereas the diversity index was used strictly as an index for studying model behavior or perhaps system response to the policy measures. A third index, the average trophic level of the catch, was used to describe how the fishery and ecosystem may interact as a result of modeled policy measures. Maximizing Longevity-Weighted Biomass.—Ratios of production to biomass are available for all ecosystem groupings as part of the standard Ecopath parameters (Table 1). The inverse ratio, biomass/production, expresses average longevity (unit year) and is used as a biomass weighting factor for optimization of “ecosystem structure.” Increasing the index amounts to increasing the summed weighted abundance of long-lived organisms. In the present case study, we excluded all invertebrate groups from the index in order to focus the ecosystem structure objective on the higher trophic levels. Biomass Diversity Index.—A relative index of biomass diversity is calculated with a modified version of Kemptonʼs Q75 index originally developed for expressing species diversity (Kempton, 2002). The index is estimated as Q75 = S/[2 log (N0.25·S/N0.75·S)], where S is the number of species (here functional groups) and Ni·S is the number of individuals (here biomass) in the sample of the (i·S)th most common species (or of a weighted average of the species closest if i·S is not an integer). The Q75 index thus describes the slope of the cumulative species-abundance curve between the lowest and highest quartiles. A sample with high diversity will have a low slope, so an increase in diversity will manifest itself through a lower Q75 index. To reverse this relationship, and to make the Q75 index relative to the baseline run in the EwE simulations, we expressed the biodiversity index as (2 − Qrun/Qbaserun), truncating the index at zero in the unlikely case that the Q75 index should more than double.

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