Ecosystem-based fisheries management (EBFM) is considered essential for achieving commercial growth and conserving fragile ecosystems in a changing marine environment (Trochta et al., 2018). Theories regarding holistic fishing patterns are increasingly put forward on managing marine ecosystems at an ecosystem level. For example, “balanced harvesting” calls for proportionally fishing all ecological components according to their productivity (Zhou et al., 2010; Garcia et al., 2012), while “protection of forage fish” aims at preserving the stability of food webs (Essington and Munch, 2014; Hilborn et al., 2017). With the increasing computational power over recent decades, various ecosystem models have been developed to help us understand ecosystem dynamics beneficial to optimizing decision-making processes (Travers et al., 2007). Ecosystem models are increasingly recognized as a helpful tool to facilitate the implementation of EBFM.

Although the benefits of EBFM have been demonstrated in research, few marine ecosystem currently employ any ecosystem-level management schemes. The applications of ecosystem models are often restricted by inadequate data, especially in developing countries (Costello et al., 2012; Hamel and Bryant, 2017). The high expense cost and requirement of complex trade-offs impede tactical applications of EBFM (Hilborn, 2011). Globally, single-species approaches are by far the most common tool used to inform fisheries management. Most traditional single-species approaches fail to account for trophic interactions (Skern-Mauritzen et al., 2016). It may increase the bias associated with model-based decision-making. Implementing single-species fisheries management (SSFM) with integrated trophic interactions is increasingly encouraged as an important step toward EBFM (Zhang et al., 2016).

While the total capture fishery production remains stable in China, many species of high commercial value have been replaced by species from lower trophic levels (Szuwalski et al., 2017). Such “fishing down the food webs” phenomena indicate the unsustainable exploitation (Pauly et al., 1998). Fisheries management agencies in China have installed a series of management measures to mitigate the impact of overfishing (Su et al., 2020). However, fishermen may change harvest strategies to catch non-target species as a way to offset the loss of landings from their primary managed stocks. Current regulations, such as marine protected areas (MPAs), a summer fishing moratorium, and “double control” system, are less efficient in restricting species-specific catches (Shen and Heino, 2014). Shifts in fishing effort can influence interspecies interactions (KC et al., 2017). We hypothesized that SSFM can be more efficient if trophic interactions were incorporated.

Jiaozhou Bay is a temperate bay located in the southeast Shandong Peninsula, China. The ecosystem structure of Jiaozhou Bay has altered over the last two decades due to human activities such as fishing, shoreline modifications, and water pollution. There is a decreasing trend of top predator biomass in recent years (Ma et al., 2018b). Controlling marine fishing vessels and their total engine power has been implemented to relieve fishing pressure on fisheries resources since 1987 in Chinese coastal waters (Huang and He, 2019). We focus on the management strategy that controls fishing effort on target species. Based on a developed end-to-end model OSMOSE-JZB (Object-oriented Simulator of Marine ecOSystEms; OSMOSE) (Xing et al., 2017), we evaluated the impacts of trophic interactions on the effectiveness of managing two commercial species (Oratosquilla oratoria and Sebastes schlegelii) characterized by different growth traits in the Jiaozhou Bay, China. Our analysis can improve our understanding of how varying trophic interaction influences the effectiveness of managing target species.

Trophic interactions play an important role in influencing the fish population (Fu et al., 2020; Xing et al., 2020a). The impacts of fishing other species on the low-trophic-level mantis shrimp O. oratoria were complex. On the one hand, the proportion of large fishes in fish community would increase with the reduced fishing effort (Xing et al., 2021). The increase of large fish biomass was beneficial to age-0 O. oratoria via influencing small fishes who fed on age-0 O. oratoria (Figure 4A). On the other hand, the increased biomass of predators resulted in the decline of age-1 O. oratoria biomass (Figure 4C). As the top predator in the Jiaozhou Bay (Han et al., 2017), age-1 S. schlegelii had a strong resistibility to changes of trophic interactions (Figure 4D). The changes of other species fisheries were more likely to influence age-0 S. schlegelii (Figure 4B). The food condition influences the growth of fish population especially for low-trophic-level species (Fu et al., 2020). There was a positive relationship between O. oratoria biomass and starvation mortality. The starvation mortality of S. schlegelii seemed not to change when the S. schlegelii biomass increased (Figure 5). It was mainly attributed to (1) S. schlegelii population was much smaller than O. oratoria population and (2) S. schlegelii fed on more species in the Jiaozhou Bay (Ma et al., 2018a). The changes in predation mortality of O. oratoria and S. schlegelii were bigger than starvation mortality. It implied the impacts of prey-predator processes on O. oratoria and S. schlegelii were bigger than the food competition.

Han et al. (2017) reported the negative relationship between demersal fishes (e.g., J. belangerii) and O. oratoria. The linear regression analysis showed the increase of J. belangerii fishing mortality had a bigger positive impact on O. oratoria compared to other species fisheries (Figure 7A). Small fishes can influence large juvenile fish via trophic interactions (Vergnon et al., 2008). S. schlegelii was more sensitive to the change of P. fangi fishery (Figure 7B). The modeled spawning season of S. schlegelii was set from May to June based on the study of Lin (2013). The P. fangi biomass markedly increased since March and peaked in June (Xing et al., 2017). A large number of P. fangi in May and June certainly had a negative impact on the recruitment of S. schlegelii stock. There was an overlap of trophic niches between S. schlegelii and J. belangerii (Ma et al., 2015). Interestingly, S. schlegelii gained benefits from the decline of fishing pressure to J. belangerii. The low-trophic-level species have been dominant in the Jiaozhou Bay as a long-term overfishing. The food competition between S. schlegelii and J. belangerii was low as abundant prey biomass and their small population size (Xing et al., 2017). Moreover, the increase of J. belangerii biomass could reduce the impact of small fish on age-0 S. schlegelii via predator-controlled processes.

It is a consensus of implementing fisheries management in an ecosystem context (Herrón et al., 2019). Successful SSFM is built on the understanding of ecological dynamics as well as fishing (Hilborn, 2011). Stock assessment may be biased without considering possible ecological processes (Fu et al., 2018). Our study highlighted identifying fisheries management strategies should incorporate a broader consideration of species-specific characteristics. The “fishing less” and “fishing more” are potential adaptive harvest strategies in response to the decline of natural resources (Cinner et al., 2011). Catch reduction and high labor cost will result in the decline of fishing effort. Han et al. (2018) reported the high-trophic-level species biomass increased and low-trophic-level species declined with removing fishing. The fast growth and high reproduction supported O. oratoria to recover quickly from heavy fishing. However, O. oratoria would be at a disadvantage with competing with other species when the fishing effort on other species is reduced. This might result in the further decline of O. oratoria stock. Thus, the fishery management may be less effective in the case of simply emphasizing the direct fishing impact on O. oratoria. S. schlegelii have a stronger resistibility to the increase of other species biomass. S. schlegelii fishery management should pay more attention to the direct fishing impact. Growing market demand and subsidy policy encourage fishermen to catch more high-trophic-level species with a high commercial value. It will be a risk to S. schlegelii fishery when J. belangerii fishery increased and P. fangi fishery decreased.

Complex and hierarchical model structure inevitably introduces uncertainty into modeling processes that weaken the confidence of stakeholders and hampers their applications in tactical management (Moffit et al., 2016). We quantified uncertainty in fishing efforts to solidify our findings. Similar to findings in S1 and S2, the results of S3 and S4 suggested that intensifying the extent of fishing efforts exposed to other species could result in a larger variation on target species biomass. But the understanding of the impacts of core parameters on model outputs is still needed (Xing et al., 2020b). Except for overfishing, climate change threatens the sustainable development of fisheries. Moullec et al. (2019) report the sea warming will result in a decline in the proportion of large fishes. Climate change can influence fishes by changing the primary productivity (Fu et al., 2020). The changes in fishing and environmental factors will likely influence the population processes such as fish growth and recruitment (Szuwalski and Hollowed, 2016; Martino et al., 2019). Fisheries management can benefit from consideration of both interspecies interactions and climate change (Travers-Trolet et al., 2014). The future application of our model should further investigate the impact of changed trophic interactions resulting from fishing impacts and environment variability.

Single-species approaches may perform inefficiently for ecosystem-level management because of ignoring comprehensive fishing impacts on the ecosystem (Jacobsen et al., 2017). End-to-end modeling approaches like the one used in this study can be used to investigate the impact of changed trophic interactions. As the specificity of ecosystem and complex interspecies interactions, the understanding of trophic dynamics in a particular ecosystem is important for an effective decision-making process. The simulations demonstrate fisheries management should be considered in a more comprehensive perspective instead of only emphasizing direct fishing impacts on managed fish stocks. Admittedly, some of our conclusions and consequences are similar to the studies in other fisheries ecosystems. The study presents an ecosystem approach aiming to facilitate the implementation of SSFM in an ecosystem context, and is an attempt in narrowing the gap in using the ecosystem modeling approach to advise fisheries management.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

LX: conceptualization, data curation, formal analysis, methodology, software, and writing – original draft. YC: conceptualization, methodology, supervision, and writing – review and editing. BL, KT, and RB: methodology and writing – review and editing. YR: data curation, supervision, and writing – review and editing. BX: data curation, supervision, and funding acquisition. All authors fully participated in the project, accepted the responsibility for the work, and agreed to the submission.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.