what are the effects of fishing to the environment of the pacific ocean?

Once fishing began in each pelagic ecosystem, immediate declines in the biomass of tunas, sharks, and billfishes in both the ETP (Fig. 7) and CNP (Fig. were observed in both models. These reductions in predator biomass were apparently accompanied by reciprocal increases in the biomass of lower trophic levels. For example, in the ETP model in Fig. 7, the declines in upper trophic levels were mirrored by increases in nontarget Auxis and dorado (Coryphaena spp.). Further, the CNP model predicted analogous increases in the small scombrid and mahi-mahi groups that were mirrored by declines in flying fishes and squids (Fig. 8). The aggregate effect of tuna fishing in both models appears to have been a shift in the distribution of biomass from upper-level predators to their prey.

When all fisheries operated simultaneously, the effects of single fisheries were not readily detectable in Figs. 7 and 8. The large, early declines in sharks and billfishes in the CNP occurred in the absence of purse seining and appeared to result primarily from longline fishing (Fig. 8). However, bycatch from the “other” fishery also contributed to the catch of these apex predators (Fig. 6). When we isolated the effects of purse seining and longlining on the food webs of each model, the direct and indirect effects attributable to individual fisheries emerged.

As stated previously, the effect of an individual fishery was inferred by comparing simulations with all fisheries to simulations with all fisheries minus the particular fishery of interest. Purse seining and longlining, respectively, had system-wide effects that were similar between the ETP and CNP models. Purse-seine fisheries in the ETP reduced the biomass of bigeye, yellowfin, and sharks (Fig. 9), whereas CNP purse seining reduced skipjack biomass by 50% (Fig. 10). The biomass of the small scombrids group that were functionally similar to the Auxis spp. in the ETP indirectly increased in both systems (Figs. 9 and 10). The increase in small scombrids was presumably the result of decreased predation by the targeted tunas, which are important predators of small scombrids. The effects of the model longline fisheries were nearly identical in both food webs. Reductions in the biomass of bigeye, albacore, marlins, and sharks occurred, and the negative trends persisted throughout (Figs. 9 and 10). The model results suggested that the effects of longlining were mainly direct and strongest at the upper trophic levels, whereas the primary indirect response of the lower trophic levels was apparently to purse-seine effort.

The predicted effects of the single fisheries often led to higher levels of biomass reduction than the combined effect of multiple, concurrently operating fisheries. For example, CNP skipjack responded strongly to the purse-seine fishery. If purse seining had been the only fishery in the CNP, skipjack biomass might have been reduced below the level that resulted from both longlining and purse seining. Similar situations were observed for small scombrids, blue marlin, and large sharks in the CNP and for albacore in the ETP (Figs. 9 and 10). In some cases, the aggregate fishery effect was often closely approximated by the effect of only one fishery. The declines in marlin and bigeye were closely approximated by longline fisheries alone in both models. Skipjack and small scombrids biomass trends were driven mainly by purse seining (Figs. 9 and 10).

Alternative management scenarios

In the ETP and CNP, the banning of shallow longline gear and shark finning led to the recovery of the marlin and shark populations, respectively. In the ETP, the ban on shallow longline gear led to increases in marlin biomass of more than 60% (Table 2, Fig. 11A). In the CNP, the ban on shallow longline gear allowed blue marlin biomass to nearly double, and “other billfish,” mostly striped marlin, increased by more than 50% (Figure 12A). Increased marlin stocks then caused minor reductions in other food-web components, notably by preying on and competing with yellowfin tuna and competing with sharks in the ETP. The rest of the food web did not respond strongly to the alteration of the longline fishing strategy.

The ban on shark finning caused increases in all shark groups. Shark biomass doubled in the ETP within 30 yr (Fig. 11B). In the CNP, adult blue sharks, large sharks, and brown sharks all increased by more than 60% (Table 2, Fig. 12B). An indirect effect of the finning ban in both systems was that shark predation increased on marlins and mid-trophic-level species such as tunas. Marlin in both models declined by roughly 10% under the shark finning ban. The rest of the food web did not respond strongly to the change in shark fishing mortality.

Combining the finning ban and the ban on shallow longline gear led to a general recovery of marlin and sharks. In this scenario, marlin recovered to levels slightly lower than if only shallow gear had been banned. The ETP shark responses to the combined ban were generally lower than if only finning had been banned (Fig. 11C). Shark recovery in the CNP was similar to shark recovery under the finning ban only (Fig. 12C). Yellowfin biomass in both systems declined more strongly under the combined scenario than for either alternative fishing strategy alone; this was caused by increased predation by both marlin and sharks on yellowfin. The rest of the food web did not respond strongly to the combined finning ban and shallow-gear ban.

Reducing purse-seining effort by 50% had no positive effects on marlin in either food web. Sharks increased about 80% in the ETP, where they are a large component of bycatch in purse-seine sets (Fig. 11D). CNP sharks were not influenced by the purse-seine reduction (Fig. 12D). In both systems, tunas responded most strongly to the reduction in purse-seine effort (Table 2). In turn, this led to declines of about 20% in the small scombrids of the CNP because of higher predation by tunas (Fig. 12D).

Reducing longlining effort by 50% caused increases in both billfish and shark biomasses in both food webs (Figs. 11E and 12E). In most cases, however, the group-specific increases were not as strong as those resulting solely from the shark-finning ban, the shallow-gear ban, or the combined strategy. Under the reduced longline effort scenario, bigeye biomass increased because of lower fishing mortality, whereas yellowfin biomass decreased slightly because of higher predation rates.

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DISCUSSION

In the eastern tropical Pacific (ETP) and the central north Pacific (CNP), the development of pelagic tuna fisheries has caused similar changes in the overall structure of the food webs. Simulations suggest that the catches of tunas, sharks, and billfishes have lowered the biomass of the upper trophic levels in both systems, whereas increases in animal numbers at the intermediate and lower trophic levels have accompanied the declines of top predators. Historical simulations and hypothetical management scenarios further demonstrated that the effects of longline and purse-seine fisheries have been strongest in the upper trophic levels, but that lower trophic levels may respond more strongly to purse-seine fisheries. The apex predator guild responded most strongly to longlining. The differences in the food-web effects resulting from longline and purse-seine fisheries further identify trade-offs that could help guide ecosystem-based management actions.

The pathways connecting ETP and CNP fisheries to their primary food webs depend on the different assemblages of tunas that are caught by each fishery, thereby creating fishery-specific food webs. The overlap of primary forage groups among the fishery food webs create conditions that allow the effect of multiple fisheries, each acting on different assemblages of tunas and other large predators, to concentrate indirect effects on the same set of forage groups. A comparison of the ecosystem effects of the historical development of fisheries showed that middle and lower trophic levels in the ETP and CNP may be sensitive to reductions in upper-level predator biomass. In both systems, it was shown that biomass reductions in the upper trophic level were accompanied by increases in the biomass of the lower trophic levels, a finding that is similar to the growing evidence from other fished ecosystems (e.g., Lilly et al. 2000, Carscadden et al. 200l).

The sensitivity of the lower trophic levels in our models was often minor, and these levels frequently exhibited a change of less than 20% in their biomass. As discussed below, given the host of uncertainties associated with ecosystem models, responses of such magnitude should be viewed with some skepticism. However, our results do suggest that the potential for indirect interactions exists and that the primary food webs for each fishery can be relatively simple. Therefore, the propagation of fishery effects throughout the food web should not be dismissed as unlikely because of apparent food-web complexity. Changes in lower trophic levels could happen if the perturbations to the upper trophic levels are sufficiently large, such as those caused by the expansion and intensification of the fishing effort over the last 50 yr.

The simulations also suggested that an increase in biomass caused indirectly by one fishery may buffer the declines driven directly by another fishery. Generally, we expect fishing to increase the total mortality of a species. However, if one fishery tends to remove the predators of the target species of another fishery, then the balance of increased fishing mortality and reduced predation mortality may lead to little net change in biomass for this target species. For example, skipjack in the CNP decline due to targeted fishing by the purse-seine fishery. However, skipjack tend to increase when their predators, i.e., bigeye, are caught by the longline fishery. Combining longline and purse-seine fisheries leads to an intermediate result, which is less extreme than under either individual fishery. For groups that are caught by multiple fisheries, the additive effect of increased fishing mortality likely outweighs any potential declines in predation mortality, and stronger reductions in biomass under combined fisheries than under individual fisheries can result. For example, large bigeye in both systems were targeted by multiple fisheries (Figs. 4 and 5), and their response to combined fishing by all gears was larger than their response to either longlining or purse seining alone.

Alternative management strategies to rebuild shark and billfish populations in each ecosystem revealed differences between (1) purse seines and longlines in terms of their effectiveness for recovering top predators and (2) gear modifications and bycatch laws vs. effort reductions in longlining. Our results suggested that decreases in longline effort made it possible to rebuild the ETP and CNP shark and billfish populations, whereas the reduction in purse-seine effort had little influence on the recovery of top predators in both systems. Thus, the role of longlining generally appears to be more critical to the conservation of sharks and billfish in the Pacific.

However, identifying management options for future longline fisheries to achieve shark and billfish recovery was not a straightforward task. Our simulations suggested that a simple reduction of longline effort could allow a larger number of populations to rebuild, but the overall recovery of sharks and billfishes might be less marked and at potentially higher costs to the fishery than under a more conventional management strategy that emphasizes gear modifications and bycatch retention practices. Although the combined prohibition of shallow gear and shark finning was more effective at recovering marlin and sharks than a reduction of longlining effort, few other species benefited. Under both management strategies, increased predation may initiate further declines in yellowfin biomass. Furthermore, enacting a ban only on shark finning may allow for a recovery of shark populations, but may also cause an unintended decline in marlin populations; this represents a bitter twist to a policy intended to conserve pelagic apex predators. Our simulations also suggested that the largest changes in the biomass of any billfish or shark group in response to any fishing strategy were only slightly greater than a doubling of biomass and, more typically, less than an 80% increase. If recovery goals mandate higher equilibrium biomasses for top predators (e.g., Myers and Worm 2003), then a combination of stronger restrictions on gear deployment, bycatch retention, and fishing effort may be required to allow predator stocks to rebuild.

Caveats

When considering an analysis of this type, it is important to take into account the possible shortcomings of a food-web modeling approach. The authors of the original ETP (Olson and Watters 2003) and CNP (Cox et al. 2002b) models both provide thorough discussions of the uncertainties that they felt most affected their initial analyses. Below, we discuss some of those uncertainties with respect to the comparative inferences of this paper.

Ecosystem analyses often rely on an incomplete knowledge of the ecological interactions among a diverse group of species. The models we used represent a hypothesis about system structure and the potential dynamics that arise as a result of that structure. Specifically, the model structure depends on information about diets, catch, and bycatch. Diet data are particularly difficult to quantify in pelagic systems. As diet data and other ecological information for more taxa become better known, the configuration and dynamics of these models will likely change, along with our understanding of how these systems function. In the ETP model, Olson and Watters (2003) found that trophic flows were most sensitive to parameter estimates for Cephalopods and Auxis spp. for which few to no empirical data were available. However, in dynamic simulations under historic patterns of fishing effort, they found that the sensitivity of the model to these two groups affected levels of predicted biomass throughout the food web, but not trends in biomass (Olson and Watters 2003). As such, our comparisons of the magnitude of fishery effects in the ETP and CNP should be viewed with some skepticism, but it remains probable that the overall food-web trends that arise as a result of fisheries effects are reasonable.

Another large source of uncertainty in our comparisons derives from the animal groupings, which range from specific to generic in each model. For some groups, comparisons are fairly straightforward, because a single species was modeled and was common to both models, e.g., yellowfin or bigeye tunas. For other groups such as large sharks, the ETP group did not contain the same species as the CNP group. In general, species with different life histories were often lumped together into functional guilds. The guild members in each model were often reasonably similar and fulfilled functionally equivalent roles, but they were not exact replicas. Thus, it would be false to assume that all the species included in a larger grouping responded in exactly the same manner as the guild, neither within nor between the modeled systems.

Expectations of how food webs will respond to fishing may further depend on the nature of physical forcing. Specifically, the ability to identify the effects of fishing, particularly at lower trophic levels, will almost certainly depend on the strength and frequency of bottom-up forcing that the food web experiences (Watters et al. 2003). Our results from considering only the effects of fisheries suggest that lower trophic levels may respond to fishing, but disentangling environmental signals from fishery and predation signals in data is a task fraught with considerable challenges for management and future studies based on an ecosystem perspective.

A final matter of concern is the vast spatial extent of the modeled ecosystems. The large-scale perspective adopted by approaches that take into account the entire food web does restrict the ability of these models to capture important local interactions among smaller groups of species or between fisheries and their food webs in localized areas. Further, the ETP and CNP models together span the Pacific Ocean (Fig. 1), and there is no true biological boundary that separates the western ETP from the eastern CNP. As such, if animals occupy both regions, it is uncertain which of the two model hypotheses, if not an alternative model, might take precedence. We do not know how important such overlap might be to these ecosystems.

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CONCLUSION

We conclude by summarizing the emergent themes of this comparative analysis of two pelagic ecosystems. First, both pelagic ecosystems were similarly structured, but the individual fisheries that operate in each system rested on dissimilar food webs. Our visualizations identified the primary target and bycatch groups and the important food resources used by those targets. These simplified food webs could be used to distinguish and, perhaps, monitor fishery effects in the future. Second, the main ecosystem effects of tuna fishing in both the ETP and CNP models were similar and can be characterized by (1) the reduction of apex predators, analogous to recent reports of world wide trends (Dayton et al. 2002, Baum et al. 2003), and (2) the increased biomass of some prey species that resulted from the release of predation. The food-web animations presented in Figs. 7 and 8 highlighted these effects. The strongest perturbations to these ecosystems were the simultaneous development and growth of multiple, concurrent tuna fisheries. However, the effects of longlining appear strongest at the top of the food web, whereas the purse-seine fisheries caused larger changes in biomass at lower trophic levels. Attempts to recover top predators by reducing fishing mortality via longline gear modifications and by stricter regulations on shark finning may be as effective as simple reductions in longline fishing effort.