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 inherent 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-

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.

The Q75 index and the inverse diversity index we use are sensitive to the number of species (functional groups), and we see it as having merit mainly for expressing relative changes for a given model or for models with the same group structure. To focus the index on the exploited part of the ecosystem, i.e., the part for which we have most information and where human impact is most likely to be seen, we limited the groups included to those with a trophic level of three or more. We therefore excluded from the index primary producers and groups that are primarily herbivores or detritivores, e.g., zooplankton and most benthos groups. Average Trophic Level of the Catch.—Previous studies show that fisheries “development” goes hand in hand with the process of fishing down the food web, in which catches initially increase while the average trophic level of the catch decreases (Pauly et al., 1998). We estimate the average trophic level of the catch by fleet on the basis of the biomass-weighted trophic level of the groups caught by each fleet. The trophic levels of the groups are in turn fractional estimates obtained as the average trophic level of their prey plus one. To establish points of reference for the calcula- tions, we set the trophic levels of detritus and primary producers to 1, in accordance with common practice.

TEST CASE: THE GULF OF THAILAND We exemplified the behavior of the ecosystem-policy optimizations through their application to a well-studied and well-documented ecosystem, the shallow (10–50 m depth) Thai section of the Gulf of Thailand. Multispecies/ecosystem models for this area go back to the dawn of their use in fisheries (Pope, 1979; Larkin and Gazey, 1982), and several models of the Ecopath family have been constructed for the area (Pauly and Christensen, 1993; Christensen, 1998; FAO/FISH- CODE, 2001; Vibunpant et al., 2003). For the present study we used the version of the model described in FAO/FISHCODE (2001); it describes the situation of the ecosystem in 1973, and the ecosystem groupings and key parameters are presented in Table 1. We consider the Gulf of Thailand representative of many tropical shelf systems, but it differs from others mainly in having a well-documented fishery. Up to the 1960s the fishery was mainly for coastal and pelagic species, followed by a rapid development of a demersal trawl fishery in the 552 BULLETIN OF MARINE SCIENCE, VOL. 74, NO. 3, 2004

Table 1. Ecosystem groupings, trophic levels as estimated from diet compositions, production-to-biomass (P/B) and consumption-to-biomass (Q/B) ratios, ecotrophic efficiencies (EE), and catches for a model of the 10- to 50-m depth zone of the Thai part of the Gulf of Thailand, 1973 (based on FAO/FISHCODE, 2001). Group name Trophic Biomass P/B Q/B EE Catch levels (mt km−2) (yr−1) (yr−1) (mt km−2 yr−1) Rastrelliger spp. 3.0 0.212 3 12 0.95 0.049 Scomberomorus spp. 3.9 0.016 0.8 4 0.098 0.001 Carangidae 3.6 0.083 1.786 7.142 0.95 0.014 Pomfret 3.5 0.008 0.569 2.845 0.95 0.003 Small pelagic fish 2.9 0.472 3 12 0.95 0.015 False trevally 3.6 0.004 2 10 0.95 0.001 Large piscivores 4.2 0.054 1.2 6 0.287 0.018 Sciaenidae 3.4 0.035 1.5 7.5 0.95 0.040 Saurida spp. 3.9 0.054 2 10 0.497 0.032 Lutjanidae 3.9 0.016 0.8 4 0.518 0.009 Plectorhynchidae 3.2 0.008 0.8 4 0.95 0.003 Priacanthus spp. 3.3 0.071 2 10 0.289 0.029 Sillago 3.2 0.049 2 10 0.95 0.003 Nemipterus spp. 3.0 0.093 2.5 10 0.15 0.034 Ariidae 3.2 0.018 2 10 0.692 0.015 Rays 3.1 0.048 0.5 2.5 0.175 0.013 Sharks 4.5 0.013 0.5 2.5 0.562 0.008 Cephalopods 3.3 0.344 2 8 0.606 0.151 Shrimps 2.3 0.276 5 20 0.95 0.110 Crab, lobster 2.6 0.041 3 12 0.95 0.109 Trash fish 2.6 0.524 4 16 0.963 0.695 Small demersal fish 3.2 0.182 3 12 0.95 0.042 Med. demersal piscivores 3.9 0.024 2 10 0.95 0.001 Med. demersal benthivores 3.2 0.092 2 10 0.664 0.007 Shellfish 2.2 0.169 3 15 0.95 0.000 Jellyfish 3.0 2.0 5 20 0 0.000 Sea cucumber 2.0 1.0 4.5 22.5 0 0.000 Seaweeds 1.0 1.0 15 — 0 0.000 Coastal tuna 4.1 0.019 0.8 4 0.95 0.000 Sergestid shrimp 2.3 0.067 10 40 0.95 0.005 Mammals 3.7 0.1 0.05 30 0 0.000 Pony fishes 2.7 0.077 3.5 14 0.95 0.000 Benthos 2.2 33 5 25 0.548 0.000 Zooplankton 2.0 17.3 40 160 0.201 0.000 Juvenile small pelagics 3.0 0.085 4 16 0.95 0.097 Juvenile Caranx 3.0 0.033 4 16 0.95 0.026 Juvenile Saurida 3.0 0.021 4 16 0.95 0.043 Juvenile Nemipterus 3.0 0.025 4 16 0.95 0.058 Phytoplankton 1.0 30 200 — 0.444 0.000 Detritus 1.0 10,000 — — 0.17 0.000

1960s as a result of a German fisheries development project. The fishery depleted the larger species within a few years (Pauly, 1979), reached a catch level of around 800,000 mt, and came to rely on catches of invertebrates and “trash fish.” An interesting aspect of the Gulf of Thailand is that indications exist that the trends in catch level and composition can be explained on the basis of changes in fishing pressure without consideration of environmental forcing factors (Christensen, 1998). For the present study we ran most simulations with a weighting factor of 0.2 for the optimization objectives that were kept constant in a given run and raised the variable objective(s) from 0 to 1.0 in steps of 0.1. For the simulations varying only the weightings for one objective, a series of CHRISTENSEN AND WALTERS: OPTIMIZATION POLICIES 553 runs (from 10 to 30) were made with initial fishing efforts drawn at random for all fleets. The vul- nerability settings for predator-prey interactions (to which Ecosim simulations are very sensitive) were, for the case study, set so as to optimize fit with time-series data (FAO/FISHCODE, 2001).

RESULTS AND DISCUSSION

PROFIT Optimization for profit can be achieved in two ways with the Ecosim optimization routine, distinguished by whether or not they allow nonprofitable fleets to continue operating in order to improve profits in other fleets. Optimizing for profits leads to ecosystems where emphasis is on maintaining productive stocks of profitable species, at the expense of competitors and predators. Optimizing for profits can thus be seen as an “agricultural configuration,” in which the system is modified so as to produce what yields the highest profits. This configuration is not necessarily a monoculture system, however; achieve- ment of monoculture may require directed intensive effort, which is very seldom, if ever, profitable. Yet, in the case study of optimization for profits, we found that effort was shifted to produce a structure in which more than half of the total value of the catch was derived from shrimps. Overall, the profit optimization was predicted to lead to a 54% increase in profit through a 4% decrease in effort associated with a 3% increase in value of the catch (or jobs; Table 2). Interestingly the catch was predicted to decrease 21%, so the value increase was due to increased landing of high-value groups.

VALUE OF CATCH CONSIDERATIONS In the Gulf of Thailand simulations, optimization for value of catch led to a 77% increase in value but only a 7% increase in overall catch (Table 2). This result was associated with a strong increase in effort and, consequently, unprofitable fisheries. The catch composition was dominated by “trash fish” and shrimps, at the expense of larger fishes, and shrimps contributed nearly two-thirds of the total landed value. The system was, however, not driven fully toward a monoculture system, as considerable diversity persisted in landings. Even if the optimal fishing effort included a considerable increase in effort, the effort was predicted to be concentrated largely in two fleets, pair trawls and other gear, while the effort for the remaining fleets decreased. Thus, value optimization was associated with fleet specialization rather than with diversification.

ECOLOGICAL CONSIDERATIONS When the Gulf of Thailand model optimized for longevity-weighted biomass of vertebrates, the result was a major reduction in effort; overall exploitation rates were reduced to one-third to one-ﬁfth of their values in the baseline run (Table 2). The two ﬂeets that were allowed the highest relative effort were the purse seiners, which operate at a

Table 2. Key results from single-objective optimizations. Values are expressed relative to baseline values (from Ecopath model); >> indicates a high positive value. Index\optimization Proﬁts Value of landings Ecosystem structure Proﬁts (%) 154 −84 18 Value (%) 103 177 24 Effort (%) 96 >> 24 Catch (%) 79 107 30 554 BULLETIN OF MARINE SCIENCE, VOL. 74, NO. 3, 2004

Figure 1. Gulf of Thailand fleet effort configurations resulting from optimization for profit, value of landings, and ecosystem structure (“ecology”); shown as averages plus or minus one standard deviation (n = 15–20; 20-yr run). The weight for the objective being optimized was kept at 1.0, and the two other objectives at 0.2. All effort levels are expressed relative to the Ecopath baseline run. relatively high trophic level (take a substantial biomass of predatory pelagics) and the “other gears,” which take a substantial biomass of invertebrates that are not counted in the ecosystem structure measure and that may serve as competitors (as well as prey) for the vertebrates. The ecological structure optimization, as intended, resulted in increased biomass of most fishes (notably of large piscivores, rays, sharks, catfish, and snappers), but at the expense of others (notably scads, grunts, and tunas).

ASPECTS OF ONE-OBJECTIVE OPTIMIZATIONS Characteristics of Favored Fleets.—Optimizations for the three objectives (profit, value, and “ecology”) resulted in different fleet configurations, but all were character- ized by fleet specialization rather than diversification. In addition, the profit and value configurations were much more similar to one another than either was to the ecological configuration (Fig. 1). Careful examinations of the reasons for changes in fleet size dur- ing optimization for individual objectives do not lead to any simple answers. The most profitable fleets were often, but not necessarily, favored in profit optimizations. Trophic level of the catch was not a dominant factor in determining which fleets would be maintained when optimization was for longevity-weighted biomass, even if longevity and trophic level were correlated. Also, the fleets that increased their effort when optimization was for landed value were not necessarily those with highest catch value at baseline. We conclude that the optimizations do not “get stuck” in local minima. CHRISTENSEN AND WALTERS: OPTIMIZATION POLICIES 555

Figure 2. Biomass diversity index as a function of weight placed on proﬁt, landed value, and ecosystem structure (“ecology”) (evaluated as summed biomass weighted after longevity). Weights for the two constant objectives (e.g., value and ecosystem structure when weight for proﬁt is varied) are set to 0.2 in all simulations. The simulations are for the Gulf of Thailand 1973 ecosystem model with n = 233–330 and with individual counts between 19 and 30. Average values plus or minus one standard deviation are indicated.

Impact on Biomass Diversity.—We use biomass diversity as one of many potential measures of ecosystem structure, expecting a connection between resilience and even biomass distribution, i.e., distribution in which most groups are well represented and no small set of groups dominates. We demonstrate how the three objectives behaved in this regard in Figure 2. For comparison, the biomass diversity index for the 1973 model was about 0.75, and all of the optimizations led to biomass distributions that were more even than this starting point. Comparisons of objectives show that increasing weight on ecosystem structure led to increase in the biomass diversity index, whereas emphasizing profit led to a gradual decline in the index; optimizing for value led to a somewhat stron- ger decline than optimization for profit. Overall, we conclude that the biodiversity index behaves as expected and may therefore be worth further consideration. Trophic Level of the Catch.—Placing more emphasis on optimizing for profits led to reduced catch and a higher average trophic level of the catch (Fig. 3A). In contrast, and as expected, optimizing for value led to a steady decline of the average trophic level of the catch as the weight on value was increased (Fig. 3B). Optimizing for ecosystem structure initially led to a small increase in the average trophic level of the catch. For high weights the picture changed, however, and the trophic level dropped considerably, as did the catch (Fig. 3C). The marked difference was associated with a shift in fleet- specific effort; notably, otter-trawl and “other gear” moved from increased effort to near zero effort while beam-trawl effort increased. The overall result was a change between 556 BULLETIN OF MARINE SCIENCE, VOL. 74, NO. 3, 2004

Figure 3. Trade-off between total catch and trophic level of the catch (mt km−2 yr−1) when the Gulf of Thailand ecosystem fleet configuration is optimized for (A) profit, (B) value of landings, and (C) ecosystem structure, estimated as longevity-weighted biomass of vertebrates. Point labels indicate the weight used, and arrows the directions taken as weights are increased.

two different ecosystem states associated with marked increases in the large piscivores, sharks, and rays. When catches were plotted against trophic levels of the catch (Fig. 3), the results were not backward-bending curves like those apparent in many ﬁshing-down-the-food-web studies (Pauly et al., 1998). Such curves should not be expected, however, as the plots show effect of weightings, not time series of data.

ONE FOR ALL OR FLEET BY FLEET? Should all fleets be treated as equal? In view of the difficulty of regulating fishing effort in any case, it is worth considering how our model behaved in two different scenarios for ecosystem-wide fishing polices: (1) using the same relative change in effort for all fleets (“one for all” solidarity) and allowing fishing effort to vary for each fleet (“survival of the fittest”). Intuitively we would expect schemes in which effort is set individually for different fleets to do better than “one for all” scenarios. For example, increasing effort by fleets that remove top predators might relieve predation pressure, thus potentially increasing catch through a (controlled!) process of fishing down the food web. Sets of at least 10 runs with random seeds for each of the three types of optimizations produced the results in Figure 4. Although the differences were small, the “fleet by fleet” scenarios did perform better in more cases than not. CHRISTENSEN AND WALTERS: OPTIMIZATION POLICIES 557

Figure 4. Effect of running optimization using the same relative effort change for all fleets (left- hand bars, marked “One for all”) and optimization using relative effort changes by fleet (right- hand bars, marked “Fleet by fleet”). Three sets of optimizations were run for each configuration one each for ecosystem structure (“ecology”), value of landings, and profit. Effort is summed on the basis of cost of fishing. All values are expressed relative to the baseline run. Standard devia- tions were omitted as they were very close to zero.

The scenarios showed some realistic aspects. The baseline (values of unity) was for the Gulf of Thailand in 1973, a decade after extensive demersal trawl fleets started operating and a decade before the resources were totally exhausted. The optimizations predicted that total catch level would not increase beyond the 1973 level, and it has not; reported catches in 1973 were 831,000 mt, and the highest catch levels on record were 848,000 mt for 1977. Interestingly, the optimization routine indicated that, although the overall catch level would not increase, the value of the catches could be nearly doubled relative to the 1973 level by increase in effort and what amounts to a more vigorous process of fishing down the food web, driving the system toward higher production of notably shrimps and cephalopods. The results also served to indicate that the fishing mortalities that resulted from optimization for value were excessive. We obtained system-wide estimates of fishing mortality of 1.3 yr−1 for the “one for all” scenario and 0.95 yr−1 for the “fleet by fleet” scenario, whereas baseline fishing mortalities were 0.73 yr−1. Such excessive fishing mortalities are risky and may be driving the system toward undesirable configurations.

BALANCING TRADE-OFFS In the simulations discussed here, optimizations were based on two objectives com- bined. Weighting factors for the two objectives were varied in steps of 0.1 from 0.0 to 2.0, so 400 runs were included for each series of optimization. Other weighting factors were set to zero for all runs. Although optimizing on the basis of weights applied to seemingly incompatible objectives may seem questionable, such trade-offs are sometimes inherent in real-world systems. We standardized the starting values of the objective functions by 558 BULLETIN OF MARINE SCIENCE, VOL. 74, NO. 3, 2004

Figure 5. The trade-off between optimization for proﬁts and optimization for value of landings in the Gulf of Thailand ecosystem. The plots show change relative to the baseline Ecopath with Ecosim run for (A) proﬁt; (B) value; (C) ecosystem structure estimated as weighted biomass of vertebrates; (D) diversity; (E) average trophic level of the catch; and (F) total catch.

Figure 6. The trade-off between optimization for proﬁts and optimization for ecosystem structure (“ecology”) (weighted biomass of vertebrates) in the Gulf of Thailand ecosystem. The plots show change relative to the baseline Ecopath with Ecosim run for (A) proﬁt; (B) value of landings; (C) ecosystem structure; (D) diversity; (E) average trophic level of the catch; and (F) total catch. CHRISTENSEN AND WALTERS: OPTIMIZATION POLICIES 559

expressing them relative to their base values (from Ecopath). Equal weighting therefore means, in essence, that an increase in profit of, e.g., 10% is considered to have the same value to society as a 10% increase in dock-side value or ecosystem structure. Value or Profits?—A mapping of the trade-offs that result from assigning variable weights to profits and value in optimizations is presented in Figure 5. It is immediately clear that no area in the objective parameter space is optimal for both measures and therefore that choices must be made (on the assumption, which may or may not be valid, that the model accurately reflected the real-world system). Take as a point of departure a situation with equal weightings for profits and value (i.e., any point on the 1:1 line in each of the six plots in Fig. 5). There, plots A and B show that values may increase slightly relative to the baseline while profits increase a bit more. This result does not come at the expense of the ecosystem, as plot C shows that the summed longevity-weighted biomasses in the system will remain steady. Further, plot D indicates rather constant biomass diversity, (which is actually “preserving present misery,” as the biomass diversity measure in the baseline run is 30% below that obtained from a balanced optimization (see Fig. 2). Plot E shows that the average trophic level of the catches would increase, and plot F that the overall catch level would increase slightly. These results show that the equal weighting of profits and value is far from the actual 1973 situation (where weighting was on value of the catch, rather than on system-wide profits optimization). Placing higher weightings on value than on profits (moving toward the upper left cor- ners of the plots) results in higher value of the landings, lower profits (indeed no profits, as shown by light-colored areas), and fairly constant biomass structure, as the removal of long-lived species is about balanced by higher biomasses of very small fish; the biomass diversity may increase, although this result is not consistent, as indicated by the spotty nature of plot D. It is clearer that one result is lower average trophic level of the catch, due to increased dominance by high-value invertebrates. Taking the opposite direction from the even profits and value weighting diagonal, the most marked consequences of increasing the weight on profits in Figure 5 (moving toward the lower right on the plots) are increased profits with maintained value, associated with increase in the biomass of long-lived groups and with higher average trophic level of the catches. All indications are that this would have been a much more profitable and ecologically responsible direction to take. Profits and Ecology!—In contrast to the almost complete mutual exclusivity between value and profit, when equal weights are given to profits and “ecosystem structure” (“ecology,” Fig. 6), the result is a clear (50%) improvement in profits (plot A). At the same time, ecosystem structure improves by a similar percentage (plot C), and not at the cost of reduced value of the landings (plot B). Landings do not decrease (plot F), and the trophic level of the catches is predicted to increase (plot E). Moving from equal objective weightings toward higher weighting on ecosystem structure results (from the 2:1 weighting-ratio level, upper left corner) in a marked drop-off in profits (plot A), value (plot B), trophic level of catches (plot E), and landings (plot F); only the biomass diversity index remains fairly constant (plot D). In the opposite direction, toward higher weight on profits only, the biomass diversity index (plot D) shows a noteworthy pattern. A ridge around the ratio of 3:1, profits to ecology, shows a marked increase in biomass diversity, indicating an area worth studying in more detail. Overall the profits/ecology simulations seem to indicate that you can have your cake and eat it too; at some areas in parameter space, values of all objectives may increase. 560 BULLETIN OF MARINE SCIENCE, VOL. 74, NO. 3, 2004

Figure 7. The trade-off between optimization for landed value and optimization for ecosystem structure (“ecology”) (weighted biomass of vertebrates) in the Gulf of Thailand ecosystem. The plots show change relative to the baseline Ecopath with Ecosim run for (A) proﬁt; (B) value; (C) ecosystem structure; (D) diversity; (E) average trophic level of the catch; and (F) total catch.

Value or Ecology, No Profits.—The choice between value of catch and “ecology” can be illustrated by division of the parameter space in Figure 7 into three zones roughly separated by the 2:1 and 1:2 ratios between the two objectives. High value weightings result in high value of the landings (plot B) and landings (plot F), whereas all other indices diminish. On the other hand, high “ecology” weightings result in high abundance of long-lived groups and high biomass diversity (plot D), whereas, expectedly, the value and amount of landings decrease, and surprisingly, so does the average trophic level of the catches (plot E), especially around the 4:1 value:ecology weighting ratio. The intermediate area (between the 2:1 and 1:2 weightings ratio) is where the basis for compromise is hidden. There, the values of both landings and the ecosystem structure increase, and most other indices show intermediate (close-to-baseline) values. The one exception in all cases is profit. The plot in (A) is not an error; placing weight on value of landings and/or ecology only leads, in all cases, to a situation where fisheries are unprofitable. The sporadic bits of shading serve to illustrate that the optimizations start from random efforts in each simulation and that in only about 1% of runs does a situation with a small profit emerge. The plots in Figures 5–7 represent the first application of objective weighting per- formance for the EwE policy-optimization tools. Given that the starting point for all optimizations was a random set of fishing efforts, it is clear that the optimization routine is rather well behaved; maintaining a given ratio between two weights results in quite similar results. This consistency lends some credibility to the approach, and we argue that it should continue to be explored. CHRISTENSEN AND WALTERS: OPTIMIZATION POLICIES 561

ACKNOWLEDGMENTS

This contribution is a result of the “Sea Around Us” project (http://www.seaaroundus.org) initi- ated and funded by the Pew Charitable Trusts (www.pewtrusts.com). C.J.W. also acknowledges a Pew Marine Conservation Fellowship and support from Canadaʼs National Scientiﬁc and Engi- neering Research Council. We thank M. Supongpan and colleagues for cooperation on modeling of the Gulf of Thailand and D. Pauly, R. Watson, and D. Zeller for discussions that contributed to the shaping of this contribution. We also thank C. W. Fowler and two anonymous reviewers for a variety of suggestions that improved this contribution.

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