Introduction

While substantially contributing to human wellbeing, the ocean is increasingly threatened by local human action and climate change1. Marine protected areas (MPAs) are advocated as a key strategy for simultaneously protecting biodiversity and supporting coastal livelihoods2,3. They are now part of the United Nations Convention on Biological Diversity and Sustainable Development Goals. Their level of protection encompasses fully protected areas where all activities are prohibited to a range of “partially protected MPA” that allow activities to different degrees4,5. The former are known to deliver ecological benefits through exclusion of human activities6,7,8, whereas the latter assume that conservation will be achieved through cooperation in the social space that leads to sustainable use9.

While scientific evidence shows that most benefits, including biodiversity conservation, food provisioning and carbon storage, stem from fully or highly protected areas, most established MPAs are of lower protection levels because of lobbying from current users and political bias towards creating many, rather than highly protected areas6,7,8,10. Also, it has been argued that excluding people who are dependent on those areas for their livelihood might not be socially equitable11, and that cultural and historical assessments should be part of MPA design. Potential benefits and beneficiaries must also be highlighted and understood at a local level to discuss trade-offs and address the ecological, social and economic requirements of sustainability9.

However, guiding principles are lacking on how to manage trade-offs in specific social-ecological systems (SES)12. Indeed, while conceptual models of SES have been elaborated to characterize human-nature interactions and inform decision-making13,14,15,16,17,18,19, and previous works have been developed20,21,22, effective science-policy interfaces in marine environments are scant8. There is, therefore, room for more effective and inclusive science-policy frameworks, including dedicated modeling approaches. Each step of collaborative prospective modeling from elaborating narratives to interpreting simulation results, including model conception, may help explore the ecological, social and economic consequences of management alternatives at a local level and in the context of ongoing climate change.

For decision-makers, there is a growing awareness that integrating valuable scientific knowledge and stakeholders during the management process can offer better outcomes23,24,25,26,27,28,29,30 and is less likely to result in resources’ collapse31,32. However, such integration raises three main challenges for science. First, how to collaboratively develop narratives that break with the usual approach based on ongoing trends—which has failed to mobilize transformative change33—by including stakeholders and scientists from a diversity of disciplines. Second, how to shift from resources toward ecosystem-based management, and addressing interactions among scales within SES34 by using ecosystem-based modeling. Third, how to better align the modeling practice and illustration of trade-offs with the decision-making process, ultimately setting management rules23 by fitting the modeling on MPA management plans.

In this paper, we argue that bridging the gap between what the literature recommends and what is done on the field requires an innovative science-policy framework that identifies potential benefits, tackles necessary trade-offs and promotes collective deliberation on management measures and rules. To test this hypothesis, we hybridized research and decision-making through collaborative prospective modeling in the case of a French Mediterranean MPA (the Natural Marine Park of the Gulf of Lions), in the context of climate change. Climate change impacts on the ocean (e.g., sea level rise, temperature increase, pH decrease, and to a lesser extent, moisture decrease) are expected to alter marine ecosystems functioning35. In the semi-closed Mediterranean Sea, climate change effects on ecosystems are already visible, with most noteworthy impacts reported being oligotrophication and diversity composition change36,37. Hence, scientists, policymakers and stakeholders involved in the management of such MPA were involved in the present transdisciplinary and multi-actors’ research. We followed a three-step process (Fig. 1) over a three-year period (2015-2019), which entailed: (i) conducting three workshops in stakeholders’ groups (see Supplementary Material Note 1); (ii) developing a social-ecological model through an agent-based modeling; (iii) collectively exploring the simulations’ results. The study adds novelty from previous work13,14,15,16,17,18,19,20,21,22 by combining participatory narrative-building with modeling to shape a deliberation tool in the marine environment. Although an economic analysis would be necessary to identify potential benefits and beneficiaries of different scenarios, such an analysis was not developed as it was beyond the scope of our study. Here, we describe how aiming for sustainability requires a framework for continued work that allows us to (i) build contrasting narratives for the future addressing biodiversity conservation, food provisioning and economic activity in the context of climate change; (ii) explore resulting strategies with a science-based SES model illustrating trade-offs; (iii) deliberate about results in order to adjust strategies.

Fig. 1: Key steps of the framework proposed.
figure 1

A three-step process over a three-year period that consists in conducting workshops in stakeholders’ group for building disruptive narratives (step 1), developing a social-ecological model through agent-based modeling for implementing narratives translated into scenarios (step 2) and collectively exploring the simulations results eventually leading to modifying scenarios hypothesis and re-shaping scenarios (step 3). A pre-requisite to the three-step process consists in agreeing collectively on main issues to be addressed.

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Results

Building disruptive narratives to open the range of possible futures

Recent scientific works suggest that we need to move beyond classical scientific studies depicting future trajectories of decline that have failed to mobilize transformative change23. Exploring different futures through narrative scenarios proves to be helpful to address MPA management issues in a constructive manner36. Lubchenco and Gaines notably emphasize how narratives help in framing our thinking and action38. Indeed, as in mythology or literature, narratives act as a reference framework to which one can refer to make decisions adapted to unpredicted but pictured contexts. In the present context, the challenge was to extend or amend our reference scheme by imagining transformative futures.

Here, we did so by inviting scientists, stakeholders, and decision-makers to participate in three workshops led by a specialist in building prospective scenarios (see Methods). Each time, participants were split into three groups to progressively write a narrative about the Natural Marine Park of the Gulf of Lions by 2050 (see Supplementary Notes 1–2). It led to the writing of three original and transformative narratives (Table 1). 2050 was considered close enough to fit with the real political deadline, i.e., the completion of two management plans, and far enough to deal with some expected effects of climate change, such as the decline of primary production in marine ecosystems.

Table 1 Co-designed visioning narratives for the Natural Marine Park of the Gulf of Lions by 2050 built at the experts’ workshops.
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Ecosystem-based modeling to address SES complexity

Sustainably managing the ocean requires MPA managers to adopt integrated ecosystem-based management (EBM) approaches that consider the entire ecosystem, including humans (Fig. 2). While fishing affects target species, marine food webs and habitats (depending on fishing and anchoring gear), climate change is expected to influence the dynamics of all marine organisms in terms of growth and spatial distribution (including primary production). EBM focuses on maintaining a healthy, productive, and resilient ecosystem so it provides the functions humans want and need. It requires a transdisciplinary approach that encompasses both the natural dimension of ecosystems and the social aspects of drivers, impacts and regulation39.

Fig. 2: The social-ecological system (SES) of the Gulf of Lions marine protected area.
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Representing the Natural Marine Park of the Gulf of Lions as a social-ecological system outlining the main interactions on the territory to be addressed when talking about managing economic activities and environment protection. This representation has been issued through conducting a workshop held around a chronological matrix summarizing the mean features of the territory.

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Whether “end-to-end models” are recommended by marine scientists to study the combined effects of fishing and climate change on marine ecosystems, using one of these tools was beyond the scope of the project (see Methods, Overview of end-to-end models). We therefore looked for alternative approaches and built on knowledge and data from the park management plan and on past research conducted on the area: ecosystem-based quality indexes (EBQI) describing the functioning of specific ecosystems and mass-balance models analyzing the overall ecosystem structure and fishing impacts (Ecopath with Ecosim) (see Methods, Ecosystems description). We mapped four major park habitats (see Fig. 3): “sandy & mud” (31 species), “rock” (18 species), “posidonia” (17 species), and “coralligenous” (15 species). Here, (group of) species are represented in aggregate form (biomass density) and linked together with diet ratios (see Supplementary Tables 1–4).

Fig. 3: The Gulf of Lions marine protected area food web.
figure 3

A snapshot of the trophic flows in the ecosystem during a given period describing the ecological functioning of the Natural Marine Park of the Gulf of Lions.

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This ecosystem-based representation is at the core of our modeling exercise. To simulate ecosystem dynamics, we used the ecosystem food webs as transmission chains for the type of controlling factors described in the narratives:40 bottom-up control (climate, management) and top-down control (fisheries, management). For each (group of) species, biomass variation results from the equal combination of two potential drivers on a yearly basis: the abundance of prey (bottom-up control, positive feedback) and the abundance of predators (top-down control, negative feedback) (see Methods, Food-web modeling). To link this food-web modeling with the driving factors described in the narratives, we adopted an agent-based modeling framework. Agent-based models (ABMs) are already used for SES applications and science-policy dialog (see Methods, Rationale for ABM). We then developed a spatially explicit model for the main dimensions of the MPA described in the narratives. To set up agents and their environments, we used data from the ecosystem-based representation and geographic information systems (GIS) layers provided by the MPA team. To model space, we used a regular grid, the size of each cell being related to the average size of an artificial reef village (0,25 km2). In accordance with our prospective horizon, simulations were run by 2050 with an annual time step.

The food-web model is located at the cell level with the previous year’s outputs as input data for each new year. Other human and non-human agents are also represented at the cell level. At this stage, we modeled temporal dynamics but lacked important spatial dynamics, such as adaptive behaviors of human and nonhuman agents relocating their activities as a result of management measures. For now, interactions between agents are mostly made of spatial-temporal co-occurrence with restricted mobility.

Despite this, we were able to simulate the variation in any group of species in terms of biomass density in the case of a change in primary production, fishing effort, artificial reef planning or reintroduction of species. To disentangle the efficacy of the MPA’s management measures from climate change impacts, we ran each scenario with and without climate change (see Fig. 4). Indeed, the variation in primary production is the only difference among scenarios that does not depend on management choices at the MPA level. We could capture some of their propagation and final effects on indicators similar to those of the park management plan and the ecosystem function and natural resources targeted by the narratives: total biomass, harvested biomass, and diving sites access (see Methods, Modeling of drivers and indicators of ecosystem status). For now, all indicators are expressed in biomass quantity and number/share of accessible diving sites (physical units), not in economic value (monetary units). This would require an accurate economic analysis, which is to be developed in a future experiment.

Fig. 4: Simulation results for the scenario 1 (S1), scenario 2 (S2), scenario 3 (S3) in each ecosystem.
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Those three scenarios correspond to the narratives that emerged from stakeholders’ groups (SG) (see Table 1). Scenarios 1 and 3 make no special provision for the effects of climate change and therefore include an assumption about the effects of climate change (CC) in the form of reduced primary production. They are rated “+ CC”. On the other hand, narrative 2 and thus scenario 2 provides for combating climate change, therefore it does not include an assumption regarding the impacts of climate change and is scored “no CC”. S1 + CC: enhancing total biomass with primary production decreasing due to climate change. S2 no CC: enhancing harvested biomass without decreasing primary production. S3 + CC: enhancing diving sites access with primary production decreasing due to climate change. a Evolution of the total biomass representing the evolution of the sum of biomass of all species in each ecosystem between 2018 and 2050. b Evolution of the living biomass representing the evolution of the difference between the total biomass and the fished species biomass in each ecosystem between 2018 and 2050. c Evolution of the fished biomass representing the evolution of the sum of biomass of all fished species in each ecosystem between 2018 and 2050. d Evolution of the diving sites access in each ecosystem between 2018 and 2050. e Evolution of the share of fully protected areas in each ecosystem between 2018 and 2050.

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Informing management choices based on simulation results

No scenario perfectly reached the objectives it was designed for (Fig. 4). However, they all draw interesting perspectives, such as the occurrence of unexpected co-benefits. In effect, the developed framework allows us to look at the building blocks of the scenarios and the combination of variables to explain the obtained results, as well as proposing explanations and suggesting new hypotheses for enhancing the efficacy of each scenario. Table 2 summarizes the major assumptions of the three scenarios developed by the project team based on the narratives.

Table 2 Overview of the three scenarios.
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Scenario 1, “Enhancing total biomass”, aimed at increasing biodiversity. Simulation results showed that undersea biomass varied little (-0.11%) despite the primary production decrease under climate change (see Supplementary Table 5). However, the trophic chain structure changed with a large increase in important species to local fisheries (see biomass variation of each group in Supplementary Table 6-10). For example, mackerel, whiting, hake, tuna, octopuses, and soles notably increased in muddy and sandy ecosystems; octopuses, seabass, echinoderms, bivalves, and gastropods in the coralligenous ecosystem; echinoderms, octopuses, and conger in the rocky ecosystem; suprabenthos, echinoderms, octopuses, conger, and scorpion fish in the Posidonia ecosystem. The increase in the above listed species is balanced due to the double prey/predator constraint by a decrease in the biomass of other existing species: benthic invertebrates and fish feeding on benthic crustaceans in muddy and sandy ecosystems; benthic macrophytes, scorpion fish, suprabenthos, and lobsters in the coralligenous ecosystem; suprabenthos, salema, seabass, and scorpion fish in the rocky ecosystem; and, worse, Posidonia itself, salema, and crabs in the Posidonia ecosystem. Simulation results also showed that fished biomass drops by 36%, which is consistent with the high share of fully protected areas (FPAs) in the absence of spatial dynamics and fishing effort relocation. Also, most diving sites that are currently appealing will no longer be accessible (-98%), which is expected to support habitat and species biomass regeneration but would mark the end of an attractive activity.

Hence, scenario 1 proposed an extension of FPA up to 30% and localized it on the richest areas in terms of biodiversity, which leads to a sharp drop in the potential fished biomass indicator. While this strong protection may not be sufficient to trigger system recovery as a whole, it greatly changes the trophic chain structure, improving the biomass of some very important targeted fishing species (see Supplementary Table 6-10). This improvement could be seen as a co-benefit aligned with the analysis by Sala et al.10. It opens avenues to move forward in searching for “win-win” strategies and opens a perspective of co-benefits for local fisheries in case spillovers occur and adequate fisheries management rules are to be defined.

Moreover, if coupled with the same kind of measures that allow us to cancel the negative effect of climate change on primary production (as in scenario 2), scenario 1 would exhibit the best results in terms of total and living biomass variation, although these two indicators are insufficient to assess the quality of the ecosystem. Two hypotheses could be further tested: (i) the time horizon may not be sufficient, and/or (ii) the intensity of the reintroduction of grouper as a keystone species is insufficient given its low reproduction rate and longevity. Nevertheless, it would be interesting to review this scenario searching co-benefits strategies. A new version of the model could test pairing spatial use rights and different levels of protection within strategic zoning and a connected MPA network. It could also consider the spillover of marine organisms and the relocation of human activities due to FPA. In this case, it would be important to determine if the spillover of marine species would be enough so that the relocation of the fishing effort would not significantly affect ecosystem functioning of unprotected areas. In a timely manner, additional measures regulating the fishing effort from a strategic planning/zoning perspective should complement the framework.

Scenario 2, “Enhancing harvested biomass”, aimed at increasing food provisioning. Simulation results showed that total fished biomass increases by 2% with or without considering climate change impacts on primary production, which matches the guideline of the narrative. However, fished biomass increases only in the muddy habitat, by >3%, while it decreases by between -3 and -32% in the other habitats, as a result of the counterbalancing effect of keeping the 2% share of FPA. Interestingly, the total biomass in the rocky habitat decreases less (with climate change) or even increases (without climate change) in scenario 2 compared to scenario 1.

At the same time, while living biomass seems stable when climate change is not included (-0.03%), it will decrease with primary production (-0.89%) in contrast with scenario 1. Indeed, when compared to scenario 1, few species showed significant downward variation, except crabs in the Posidonia ecosystem. Also, even with the smallest FPA’s share, currently appealing diving sites are reduced by 63%, which confirms that most existing diving spots are concentrated in areas of high natural value in or around the existing MPA.

Scenario 2 favors fishing by increasing fishing effort (5%) and limiting FPA (2%). It also supports fishing with the reintroduction of target species and the densification of the species’ habitats. This scenario notably avoids the negative effects of climate change on primary production due to ecological measures taken at the watershed level. However, comparative simulation results illustrate that marine park management measures alone would not generate such an effect. In view of the results, the fishing effort may have been increased too early, thereby canceling out the efforts made elsewhere. Moreover, catches might have been higher if the model had considered a shift of fishing activities from FPA to areas where fishing is allowed. Here, FPAs are located on rocky, Posidonia, and coralligenous habitats, which are areas of greatest natural value (GIS layer). Even if the share of FPA is the lowest in this scenario, almost all of the rocky habitat (excluding artificial reefs) is considered, which is one reason explaining the biomass increase in this habitat. This shows the importance of precise and strategic zoning in determining access rules in MPAs. This is also due to the densification of existing villages of artificial reefs and the creation of new villages in the rocky habitat. Three new hypotheses could be further tested: (i) maintaining the fishing effort at its 2018 level, (ii) increasing the introduction of target species, and (iii) enhancing the functioning of the trophic chain by reintroducing keystone species rather than target species of fishing?

Finally, scenario 3, “Enhancing diving site access”, aimed at increasing eco-tourism. Simulation results indicated that the main objective of the scenario is not achieved since diving access is restricted by 100% and 91% respectively in the coralligenous and rocky habitats, that host most currently appealing diving sites. At the same time, living biomass (and total biomass) decreases more than in scenario 1 (−0.6%) and less than in scenario 2 in the same climatic context of primary production reduction, reflecting the difference in FPA cover of the different scenarios. Interestingly, despite taking for granted the loss of historical ecosystems and traditional economic activities, and including primary production reduction, the total biomass increases by 0.12% in the rocky habitat, which is again a better score than what scenario 1 reached. Finally, fished biomass lowers by 14%, due to a 10% FPA’s share, which is in accordance with a narrative that promotes the creation of alternative economic activities.

Scenario 3 is the scenario that produces the most impressive results since diving site access was in sharp decrease, whereas the scenario was supposed to favor it. These results’ explanation lies in a contradiction between the assumptions of the narrative. In fact, by placing 10% of the territory under full protection and locating these areas on sites of high biodiversity, FPAs are located on the very sites favored by divers. This contradiction between the goal of this narrative and the restricted access to FPA proves to be a determining factor in the success of the scenario. Retrospectively, this may seem obvious, but the exact delimitation of access rules to protected areas remains a hot topic. This scenario is of high interest because it illustrates an actual dilemma and confirms scenario 2’s analysis that access rules need to be aligned and defined with precise and strategic zoning. Other hypotheses to be tested include allowing recreational diving access to FPA, while extractive activities remain prohibited.

Discussion

Our analysis highlights the usefulness of using a three-step (plus one) framework, hybridizing a collaborative modeling approach and a decision-making process (Fig. 1) as a way to identify both the future desired for an MPA and the pathways to get there. Similar collaborative approaches have been developed by the Commod community18. A Commod-type project can focus on the production of knowledge to improve understanding of the actual SES, or it can go further and be part of a concerted effort to transform interaction practices with the resource or forms of socio-economic interactions18. Ours is original as it aims not only to share a common understanding of the SES at present and help solve current challenges, but also to anticipate and create a shared future. Indeed, the proposed framework allows discussion of hypotheses concerning the future of the management area, which enables the reshaping of our thinking and the potential framing of new strategies. The framework acts as a dialog space for people concerned with SES and willing to support the implementation of management plans. This dialog space offers the possibility to realize that there is a difference between expectations or likely effects of management options and the complexity of reality. Indeed, the simulation results only sometimes illustrated the expected effects of the narratives. In this respect, our method paves the way for questioning beliefs, which did not occur in previous similar studies10. It contributes to moving to informed-based strategies, as recommended by Cvitamovic et al.41. The science-policy future experiments we conducted considered place-based issues, participants knowledge, and imaginaries. Scientists coming from ecology and social sciences, decision-makers, and other MPA stakeholders all found the approach to be groundbreaking; by opening the box of scriptwriting, involved stakeholders experienced a way to construct new narratives and broaden solutions for ocean use, as advocated by Lubchenco and Gaines38. However, such an approach must be taken cautiously, as it is time-consuming for all participants. At the beginning of the project, participants shared concerns about the usefulness of a prospective approach not connected to a real political agenda. The mobilization of tools during the workshops (see Methods, Prospective workshops) was beneficial to show how much the approach was anchored in reality, and allowed for creating a common ground. In the end, most participants underlined how instructive it was to meet with each other and exchange viewpoints on challenges concerning the future of the MPA rather than being consulted separately as it usually happens.

Another interesting point is that the proposed framework fosters anticipatory governance capacity by testing assumptions, understanding interdependencies, and sparking discussions. It avoids policymakers acting in their own jurisdiction generating spillovers that modify the evolutionary pathways of related SESs or constraining the adaptive capacity of other policymakers42. Lack of coordination between policy actors across jurisdictions and incomplete analysis of potential cascading effects in complex policy contexts can lead to maladaptation42. In this regard, our framework can contribute to understanding the marine space as a “commons”43 and to resolving issues facing an MPA as a decentralized governance institution. Marine parks are social constructs that must build on historical legacy and be invested with new commonalities to become legitimate and formulate acceptable, sustainable policies (see Supplementary Note 1).

The framework also allowed to collaboratively explore the impacts of alternative management scenarios on marine SESs considering climate change, identifying benefits and beneficiaries, and resulting trade-offs among ecological functions supporting them. This experience led to interesting conclusions from the simulation results themselves. The latter showed that co-benefits may arise and be favored by a precise and coherent system of rules of access and uses complementing a more physical, biological, and ecological set of measures. Our findings showed that some trade-offs might satisfy several objectives, even if not those targeted first, opening the way to potential co-benefits, as shown by10. For instance, the strong protection extension in scenario 1 changed each species’ biomass distribution within each ecosystem, improving the biomass of some important fished species and opening avenues to search for “win-win” strategies. Similarly, measures allowing us to cancel the negative effect of climate change on primary production proposed in scenario 2 would increase the total biomass together with maintaining biodiversity in scenario 1.

More generally, this research developed a companion modeling framework that would enable us to move forward in the search for win-win strategies by pairing strategic zoning of high protection and access rules. As far as we know, the co-designed model we developed is the only agent-based model combining collaborative and ecosystem-based modeling that can be used as a lab experiment to identify co-benefiting strategies in marine spaces. Nevertheless, some improvements are needed. There are avenues insofar as the model suffers from shortcomings. The first difficulty faced in the modeling exercise was the mismatch between spatial scales of ecological and climate modeling. While the former operates at the habitat scale (1 km2), the latter provides smoothed environmental variables at a resolution larger than 50 km2, unresolving taking into account thresholds leading to life cycle bottlenecks for instance. The latter points to the need to downscale climate projections at relevant scales for ecosystem functioning. Other concern relies on improving the modeling tool by describing spatiotemporal dynamics arising from the spillover of marine organisms44, the resilience brought by population connectivity45, and the relocation of human activities46. Proceeding to a sensitivity analysis, or building alternative outputs indicators, allows disentangling and clarifies the different modeled effects inside each scenario. There is a tension here between re-writing scenarios and pertaining their collaborative scriptwriting which led to the scenarios implemented, very meaningful about the richness of the stakeholder’s engagement.

Finally, marine management should be an inclusive, iterative process, where modeling acts as an ongoing exploratory experiment to identify the conditions under which co-benefits and win-win strategies can be realized. Hence, the modeling process facilitates interactions between participants in a transparent and open process. One can thus imagine working sequentially until satisfactory results are obtained for any stakeholder involved. This search for a hybridized collaboration framework in the construction of policies proves particularly fruitful in creating a shared future and looking for sustainability.

Methods

Prospective workshops

Each of the three groups focused on fostering one of the three ecosystems’ functions considered: production of total biomass, fish stock level for fishing activities or potential access to diving sites. They allow us to work on interactions between biodiversity conservation and economic development. Proxys used and related to these ecosystem functions are also aligned with the ones used in the park management plan, which helps for science-policy dialog.

To reach the objectives of the narratives, participants were especially requested to give indications about considering climate change impact or not, fishing effort evolution, spatial sea-users’ rights (FPA), facilities planning (artificial reefs, floating wind turbines, harbors and breakwaters, multipurpose facilities) and ecological engineering (reintroduction of species), the main features of the social-ecological representation on which we all agreed (see Fig. 2). In order to help envisioning disruptive changes, we decided to draw on possible future land/sea-scapes of the MPA. Here land/sea-scapes are understood in several aspects: coastal viewpoint, marine natural or artificial habitats, above/undersea marine space occupation by humans and non-humans. To do so, we introduced visual tools during the prospective workshops (see Supplementary Note 1):

(i) an archetypal map of the MPA including typical features to recall main territorial issues without being trapped in too specific considerations: a city by the sea, the mouth of a river, an estuary, a rocky coast, a sandy coast;

(ii) tokens related to the available means to reach the narratives’ objectives: ecosystem status (primary production), fisheries evolution (fishing effort), facilities planning & ecological engineering (esthetical artificial reefs, floating wind turbines, harbors and break walls, reintroduction of species), sea-users’ access and regulation (recreational uses and fully protected areas). Tokens were used to inform participants about the localization and intensity of each item, which helped shape the participant’s vision of the future and link with the simulation model.

(iii) cards describing real-world examples of what tokens stand for. They were used to broaden the participants’ thinking scope by introducing stories in foreign places and at different times. Here, they helped illustrate alternative options among scenarios.

Overview of end-to-end models

End-to-end models represent the different ecosystem components from primary producers to top predators, linked through trophic interactions and affected by the abiotic environment47. They allow the study of the combined effects of fishing and climate change on marine ecosystems by coupling hydrodynamic, biogeochemical, biological and fisheries models. Some are suited to explore the impact of management measures on fisheries dynamics with an explicit description of fishing stocks’ spatial and seasonal dynamics, fishing activities and access rights (ISIS-Fish)48,49,50 but they do not represent environmental conditions or trophic interactions, so their capacity to simulate the impact of fisheries management on ecosystem dynamics and possible feedbacks is limited.

Others explicitly model trophic interactions between uniform ecological groups with biomass flows based on diet matrixes (Ecopath with Ecosim51, Atlantis). They rely on the assumption that major features of marine ecosystems depend on their trophic structure; thus, there is no need to detail each species to describe the state and dynamics of the ecosystem. They can be used to explore the evolution of the system under variations in biological or fishery conditions but may lack flexibility to simulate regime shifts due to radical variations in such conditions.

Some others do not set a priori trophic interactions, which are considered too rigid to explore the nonlinear effects of both fishing and change in primary production. They describe predation as an opportunistic process that depends on spatial co-occurrence and size adequacy between a predator and its prey (OSMOSE). Due to the simulation of emergent trophic interactions, it is particularly relevant to explore the single or combined effects of fishing, management and climate change on ecosystem dynamics. However, they do not properly describe fisheries dynamics (fixed fishing mortality) and must be coupled with fleet dynamics models (dynamic effort allocation)52.

Ecosystems description

First, we selected three publications describing the specific ecosystem functioning associated with marine park habitats: the Mediterranean seagrass ecosystem53, the coralligenous ecosystem54 and the algae-dominated rocky reef ecosystem55.

Second, we selected two publications using the same mass-balance model (EwE) to analyze the overall ecosystem structure and fishing impacts in the Gulf of Lion56 and the northwestern Mediterranean Sea57. They both provide a snapshot of the trophic flows in the ecosystem during a given period, which is based on a consistent set of detailed data for each group of species: biomass density, food requirements (diet matrix), mortality by predation and mortality by fishing. The former focuses on the Gulf of Lion but depicts a larger area than that of the park in terms of distance to the shore and especially depth (-2500 m against -1200 m). Thus, the rocky reef ecosystem that exists within the park is “masked” by the prevalence of sandy/muddy habitats. The latter depicts a wider part of the Mediterranean Sea but is comparable to the park in terms of depth (−1000 m against −1200 m) and provides useful information on the rocky reef ecosystem.

Each ecosystem represents the following proportion of the whole system: muddy = 85.57%, sandy = 12.23%, rocky = 1.75, posidonia = 0.23 and coralligenous = 0.22%. For “rocky”, “posidonia” and “coralligenous”, we selected corresponding ecological groups and associated data (Ewe) from functional compartments (EBQI). For “sandy&muddy”, we created an ad hoc conceptual model of the ecosystem functioning from the Gulf of Lion trophic chain (Ewe).

Food-web modeling

For each related group of species, the variation in the average density results from the equal combination of two potential drivers on a yearly basis: the abundance of prey (bottom-up control, positive feedback) and the abundance of predators (top-down control, negative feedback). To do so, we use data from the EwE publications listed above: biomass density, food requirements (diet matrix), mortality by predation and by fishing (see Supplementary Tables 11–14). For one species, the White gorgonian (Eunicella Singularis), we use site-specific data produced during the RocConnect project (http://isidoredd.documentation.developpement-durable.gouv.fr/document.xsp?id=Temis-0084332).

To model the effect of prey abundance on their predators, the biomass of each group of species is described as the sum of its annual food requirements, detailing each prey (see Supplementary Tables 1–4). While nothing happens to a prey species, there is no change in prey abundance, and the biomass of each predator species remains the same. If anything happens to a prey species, this translates into that species density, which then reflects its availability for feeding predators and eventually affects the biomass of predator species. The effect on the biomass of predator species is proportional to the change in prey species density and to the specific weight of prey species in each predator’s diet. In other words, the more prey there is at the beginning of the period, the more of its predators there could be at the end.

To model the effect of predator abundance on their prey, we follow the reciprocal reasoning of the above mechanism. Here, the biomass of each group of species is described as the sum of its annual catches by each other species (see Supplementary Tables 1–4). Here again, while nothing happens to a predator’s species, there is no change in predator abundance, and the biomass of each prey species remains the same. If anything happens to a predator’s species, this translates into that species density, which is then reflected in its food requirements and eventually affects the biomass of prey species. However, this time, the effect on the biomass of prey species is inversely proportional to the change in predator species density and to the specific weight of predator species in each prey’s mortality. In other words, the more predators there are at the beginning of the period, the less prey there could be at the end.

There are only two exceptions to this rule: phytoplankton and detritus. The production of phytoplankton relies on photosynthesis, which requires water, light, carbon dioxide and mineral nutrients. These elements are beyond our representation, so we impose the value of the phytoplankton biomass density at each time step. Additionally, the value of phytoplankton biomass density is the variable used to represent the expected effect of climate change on primary production. The production of detritus comes from three sources: natural detritus, discards and bycatch of sea turtles, seabirds and cetaceans. In other words, the amount of detritus depends on the activity of other marine organisms. Here, we model the amount of detritus as a constant share of the total annual biomass.

Rationale for ABM

Most studies on MPA analyze how they succeed from an ecological point of view56. Few others argue about the conditions under which they succeed from a socio-economical or cultural point of view (refs. 3,58,59,60,61,62,63). Little work embraces both aspects of MPA64,65,66. Currently, agent-based models (ABMs) are convenient methods to integrate ecological and socioeconomic dynamics and are already used by researchers in ecology or economics for ecosystem management67,68,69. ABMs allow the consideration of any kind of agent with different functioning and organization levels69,70, including human activities, marine food webs and facilities planning. ABMs are also usually spatially explicit, which favors connecting with narratives that are spatially explicit too. Basically, an agent is a computer system that is located in an environment and that acts autonomously to meet its objectives. Here, environment means any natural and/or social phenomena that potentially have an impact on the agent.

For these reasons, ABMs are convenient methods to deal with SESs. The possibility of providing each kind of agent with a representation of the environment, according to specific perception criteria, is particularly interesting for applications in the field of renewable resource management19. The ABM developed for SES management usually integrates an explicit representation of space: a grid with each cell corresponding to a homogeneous portion of space. Time is generally segmented into regular time steps. The simulation horizon (total time steps) corresponds to the prospective horizon.

Modeling of drivers and indicators of ecosystem status

In the Mediterranean Sea, current scientific consensus outlines a reduction of the primary production and changes in species composition in the ecosystems as an effect of climate change. However, trophic network re-organization linked to these species’ composition changes is still an open debate. Hence, to model the expected effect of climate change on the ecosystems Natural Marine Park of the Gulf of Lions, we build on IPCC projections that consider a 10% to 20% decrease in net primary production under low latitudes by 2100 due to reduced vertical nutrient supply71,72. Indeed, combined consequences of climate change like water temperature increase and hydric stress act synergistically to reduce primary production. The former reinforces stratification of surface waters resulting in a reduction in the supply of nutrients which leads to a decrease in primary production. The second also leads to a decrease in nutrients from the rivers to the sea impacting primary production Applied to our simulation horizon, this can be translated into an annual steady decrease of up to -4% in phytoplankton biomass density between 2018 and 2050.

To model fisheries, we use the same rule as to model the effect of predator abundance on their prey, but here, this represents the effect of fishing effort on harvested species. As our entry point is traditional small-scale multispecies fisheries, we do not directly modify fishing effort by species but rather by fishing gear56. A change in the fishing effort of a given fishing gear first affects the total biomass of its harvested species and then is allocated between each species after the fishing ratio from the base year. Thanks to the EwE publication on the Gulf of Lion and included data on landings by gear and by species56, we were able to distinguish 4 fishing gear: trawls, tuna seiners, lamparos (traditional kind of night-time fishing using light to attract small pelagics), and other artisanal fishing gear. It does not include recreational fishing. To spatialize fisheries, we do not associate each fishing gear with specific locations or habitats: fishing effort by fishing gear is the same all over the area, with two exceptions. The former refers to FPA areas where any kind of fishing is forbidden (Cerbère-Banyuls Natural Marine Reserve). The latter refers to trawls and artisanal fisheries whose activity is constrained by practical or legal concerns. First, it is known that artisanal fisheries work mostly near the coast up to a maximum distance of 6 nautical miles and a maximum depth of -200 meters. Second, trawls are prohibited between 0 and 3 nautical miles (2013 Trawl Management Plan). Here, we do not model transfer effects between sites or towards new sites.

To model diving, we use a GIS layer indicating the most popular diving sites in the park. With each diving site, we associate an annual number of visitors that fits known trends. Here, changes in diver attendance depend on the extent of fully protected areas prohibiting this practice. Here, again, we do not model transfer effects between sites or towards new sites.

To model FPA and access rights, we use a GIS layer indicating the boundaries of the existing FPA (Cerbère-Banyuls Natural Marine Reserve). There fishing is prohibited. To model the creation of the new FPA, we target important natural areas. To do so, we use a GIS layer corresponding to a map from the park management plan that indicates important natural areas (see Supplementary Fig. 1a, b). More precisely, the map scales areas after their natural value using a “heat gradient” (see management plan for details). To reach the level of protection expected in each scenario, we downgraded the level of natural value required to be designated an FPA every 5 years between 2020 and 2030. Here, these levels of natural value are chosen to get closer to the expected level of protection. Areas to be protected are designated after their natural value, but the rules of attribution slightly change among scenarios. When protecting a large portion of the MPA (scenario 1, Supplementary Fig. 2), there is no need to first target a specific area: one is sure that all areas of great natural value will be included in the protected perimeter. Here, we seek to make progress on the overall MPA, and the only criterion to be designated a protected area refers to the level of natural value. When protecting a small portion of the MPA (scenario 2), one may want to make sure to protect consistent areas of great natural value rather than sparse micro hot points. To do so, we target the existing Marine Reserve and let new protected areas develop in its surroundings. When protecting a medium portion of the MPA (scenario 3), we use a combination of the two previous rules: in 2020, we target the surroundings of the Marine Reserve to be sure to protect this area of greatest natural value, while in 2025 and 2030, we also let protected areas develop elsewhere, after the local level of natural value. Concerning access rights, fully protected areas were intended as “no go, no take” zones/integral reserves during the workshops. Thus, we prohibit fishing and diving in the corresponding perimeters.

To model facilities planning, we select artificial reefs and floating wind turbines. We do not represent harbors and break walls, as they were much likely associated with sea level rise during the workshops. This is a major issue but beyond the scope of this ecosystem-based modeling.

To model ecological engineering and artificial reefs implementation, we use a GIS layer indicating their location, and we assume that they are comparable to natural rocky reefs73. Thus, existing artificial reefs are associated with the same food web as the Rock ecosystem cited above. According to expert opinion, the occupancy rate of existing artificial reef villages inside the park is ~12%. To model their densification, we impose a steady annual increase in the biomass of each species until it reaches the equivalent of a 50% occupancy rate by 2050. To model the installation of new reefs in new villages, we replace a portion of sandy habitat with rocky habitat corresponding to an occupancy rate of 50%. Then, we describe a three-step colonization by marine organisms: (i) a pioneer phase of 1 year with the development of phytoplankton, zooplankton, detritus, macroalgae and worms; (ii) a maturation phase of 2–5 years with the development of supra-benthos, gorgonians, benthic invertebrates, sea urchins, octopuses and bivalve gastropods; (iii) a completion phase after 5 years, with the development of salema, sparidae, seabream, conger, seabass, scorpion fish, and picarel73.

To model floating wind turbines, we create a GIS layer from a map used by the management team of the park to initiate debates with stakeholders on possible locations of already approved experimental turbines and possible new commercial ones. During the workshops and the project team meetings, two possible adverse effects of floating turbines on the ecosystem were discussed. Some determined that the floating base and the anchorages would have a sort of “fish aggregating device” effect, while the location area would be prohibited from fishing. Other thought antifouling paint would prevent such an effect, while ultrasounds due to the functioning of turbines would trouble cetaceans. Here, we do not model these alternative effects because of time constraints and lack of scientific evidence and data to our knowledge. We model their possible progressive development every five years between 2020 and 2045 around the “overall” and “most acceptable” areas designated by the map using a propagation rule in the surroundings of already approved experimental turbines.

To model multipurpose facilities, we add attendance indicators to artificial reefs and floating turbines in some cases. In scenarios 2 and 3, the development of a commercial wind farm is associated with the development of a touristic dedicated activity consisting of sea-visiting the area, explaining its purpose and possible effects on ecosystems. With each turbine, we associate an annual number of visitors deduced from assumptions on the number of opening days by year, number of visits by day, and number of passengers by visit. Here, visitor attendance follows from the development of a commercial wind farm. In scenario 3, a few artificial reefs are developed with both ecological and esthetic concerns and are associated with the development of a dedicated diving activity. With each reef, we associate an annual number of divers deduced from assumptions on the number of opening days by year, number of visits by day, and number of divers by visit. Here, visitors’ attendance follows from recreational reef development. Two esthetic artificial reef villages are being developed in 2025 and 2035.

To model the reintroduction of species, we focus on one heritage species in scenario 1 (grouper) and on two commercial species in scenario 2 (seabass and dentex). Concerning sites of reintroduction, we targeted rocky ecosystems and specifically existing artificial reef villages. Each year between 2020 and 2025, we repopulate from juveniles and adult individuals expressed in biomass equivalents. Here, priority is given to meeting the food needs of reintroduced species, corresponding to their estimated biomass levels, even if at the expense of the already established species. As biomass levels of reintroduced species are of the same order as those of top predators already represented in the rock ecosystem, this hypothetical situation calls for a later more complex representation of their competition for food.