Constructing a social-ecological economic network of community-based marine restoration initiatives

Governments and environmental agencies across the globe are creating policies and regulations to reduce environmental impacts (IPBES, 2019; European Commission, 2022). Central to this initiative is the call for restoration of ecosystems both in the marine and terrestrial realm. As we continue to progress in a rapidly changing world, many marine and coastal systems have declined in health (Cloern et al., 2016; Halpern et al., 2019). This decline calls for more substantive actions to enhance ecosystem health and resilience while building stronger human-nature relationships to support stewardship towards nature (Samper, 2003; Waltham et al., 2020; European Commission, 2022; Guan et al., 2023). Marine and coastal resources are a vital part of communities, their livelihoods and spiritual and cultural wellbeing. Coastal communities, scientists and environmental activists continually call for action to improve the state of the marine environment (Tedesco et al., 2023). However, there is a need to recognise that windows of opportunity to preserve intact ecosystems are shrinking. More active interventions are necessary to save marine ecosystems and restore their contributions to people. Marine restoration efforts can provide a sense of purpose and bring hope to communities, which is required to help motivate change for better policy and management towards nature (Saunders et al., 2020; Ferretti et al., 2023). Restoration can provide a vehicle for people to reconnect with nature, identify common values and contribute to fixing environmental problems (Davies et al., 2015).

The importance of combining social, ecological, economic and cultural perspectives is increasingly recognised in restoration. Marine and coastal systems are complex social-ecological systems whose dynamics involve interactions at multiple scales connecting people, society (governance and culture) and nature’s benefits to people (Selig et al., 2019). Understanding social-ecological systems can help us shift towards resilience-thinking approaches and holistic ecosystem-based management (e.g., Olsson et al., 2004; Folke, 2006; Wainger et al., 2017). Growing in importance is the need to understand the social and economic benefits gained from restoration to help to funnel more investment into restorative actions. Nevertheless, frameworks demonstrating the context dependency of social-ecological system connections are important because restoration projects often require a lot of time from various individuals, groups and stakeholders and a large budget (Ostrom, 2009; France, 2016; de Juan et al., 2017; Brueckner-Irwin et al., 2019; Lazzari et al., 2021).

To make meaningful ecological changes, we first need to consider how to get people and organisations to understand the importance of restoration actions, and then to act and invest. Restoration action is firstly a human-led action, so understanding the social and political constraints to restoration on scales ranging from local to national or global allows navigation through legal and governance systems (Macpherson et al., 2021). Marine restoration not only should be focused on the outcomes of ecological success, but also should realise that marine restoration can change social and political stances (Suding, 2011; Saunders et al., 2020).

Ecological restoration has been defined as the “process of assisting the recovery of an ecosystem that has been degraded, damaged or destroyed” (Gann et al., 2019, p. S7). There are multiple pathways that communities can take to enhance restoration opportunity and secure the resources necessary to implement action. To choose paths that enhance possibilities of success, we need to understand how restoration activities occur in social-ecological spaces, particularly which actors or processes are enabling or inhibiting the restoration action. While there have been ample conversations that include and provide assessments for analysing the benefits of community-based restoration in the context of restorative economies, no clear linkages have been made between the social, economic and ecological aspects, as they have been analysed mainly in isolation.

There are two main types of ecological restoration: passive and active. Passive restoration focuses primarily on protecting or removing stressors and allowing the ecosystem to recover on its own; this approach can include establishing marine reserves and no-take zones. However, the success of passive restoration efforts depends largely on how degraded the ecosystem is, how well protected it is, and whether legacy effects or cumulative effects exist (Hewitt et al., 2022). Active restoration focuses on reintroducing key species or augmenting habitat to encourage larval and juvenile settlement, which can include re-seeding shellfish (e.g., Benjamin et al., 2023), removing herbivores that inhibit habitat formers (e.g., Miller and Shears, 2023) or replanting flora such as mangroves and seagrass (e.g., Lekammudiyanse et al., 2024). Different active restoration methods can be used in different areas depending on context dependencies, restoration budget and the goals and desires of communities, environmental and conservation managers and investors.

In this article, we provide a heuristic model to help understand how key actors and processes of community-based restoration affect the social-ecological system and generate different outcomes and benefits. We use a Bayesian network (BN) model to understand social-ecological system connections and behaviour. We use a range of scenarios (different driving actors and/or processes) to explore how different pathways of community-based restoration action can lead to different outcomes and benefits for different participants. The model is comprised of four main groups (colour-coded in Figures 1 and S1) that represent a social-ecological system: social, ecological, economic and outcomes of restoration. BN models have been used widely in environmental interdisciplinary and transdisciplinary research because of their ability to include both qualitative and quantitative data and their usefulness when trying to balance the interests of industry, communities and nature (Marcot et al., 2001; Haines-Young, 2011; Rositano et al., 2017; Siwicka and Thrush, 2020; Bulmer et al., 2022). BN models provide a simple way to model complex relationships, express uncertainty and allow for stakeholder participation (Marcot et al., 2006; Choy et al., 2009).

BN models are a graphical probabilistic dependency tool that can model network relationships. They can be used to represent current knowledge of a system through mapping causal relationships between variables in a network diagram, or they can be used to operationalise knowledge by expressing the strength and certainty we have about the relationships. The networks are comprised of “nodes,” which define the model variables and their states, and unidirectional “links,” which describe the connection or relationship between child and parent nodes but not their feedbacks (Pearl, 2000). Each node can take one of several states or values. The state of a node is determined by the conditional probability (or set of conditions met) of the parent nodes which are displayed in conditional probability tables (CPTs). When a node has no parents, the CPT is its prior probability distribution. CPTs can be generated from a combination of knowledge sources, including expert opinion and empirical data. Our BN model was built considering the multiple actors, processes and relationships that link across societies, from individuals to regional and national governance and international investment and environmental responsibilities, with the aim to inform how larger social processes such as governance and policy can impact smaller/finer ecological processes, such as a species recovery potential (Figures 1 and S1 and Table S1).

The model was constructed and parameterised following the Marcot et al. (2006) framework using Netica software from Norsys (version 5.0.17; Text S1). The initial conceptual model structure was based on marine restoration case studies (e.g., Ayers and Kittinger, 2014; Brown et al., 2014; Kittinger et al., 2016; DeAngelis et al., 2020; Valenzuela et al., 2020), marine social-ecological system literature (Perring et al., 2015; Silver et al., 2015; Caswell et al., 2020; Cisneros-Montemayor et al., 2021; Lazzari et al., 2021; Refulio-Coronado et al., 2021; Tedesco et al., 2023) and expert knowledge. Nodes were selected that would best capture the dynamics of a social-ecological system of community-based restoration actions within a 10-year timeframe (Figure 2 and Table S1). The model timeframe was constrained to reduce uncertainties around political cycles, climate change, ecological recovery processes and stressor timescales.

The social processes and entities reflect the interactions between broader-scale governance, social norms and potential entities involved in restoration efforts. Law and policy (both national and international) play a large role in influencing the governance structure, motivation to restore, how much funding can be accessible and how well the environment is managed (Baker and Eckerberg, 2013; France, 2016; Ounanian et al., 2018; Shumway et al., 2021). The economic processes and outcomes reflect the movement of available funding for restoration through various actions and the economic processes that may slow or boost restoration action, for example, business practices. The ecological nodes are focused on system components that reflect an ecosystem’s ability to recover and the external factors that can affect ecosystem recovery. Active restoration is often designed to overcome bottlenecks in recovery and address hysteresis (Nyström et al., 2012; Silliman et al., 2015; Biggs et al., 2021). The external factors are broad characteristics of stressors (Gladstone-Gallagher et al., 2023; 2024) that reflect how they may impact different components of an ecosystem; the aim was to capture the ecosystem response rather than to provide an exhaustive list of possible stressors. The remaining nodes are benefits (ideal social, ecological and economic outcomes) gained from community-based restoration efforts. These benefits may feed back into motivations to generate higher levels of funding.

Depending on the ecosystems and spatial scales subject to restoration, full recovery of long-lived species, biodiversity and ecosystem function may extend well beyond the scope of our model. However, we are at the beginning of restoration interventions; we focus on short-term outcomes as these represent the first steps in a potentially long process. Successfully navigating this first phase of the process is critical. Nevertheless, our model provides insight into longer-term recovery, whether passive or active, through the development of scenarios that reflect long-term changes in society, economies and environment, including those benefits which may change in how they are expressed overtime

Expert elicitation was used to test the model structure and define the relationships based on the four-step elicitation Investigate, Discuss, Estimate and Aggregate (IDEA) protocol (Hemming et al., 2018). Ten researchers with collective expertise in environmental governance, law and policy, marine ecology and environmental economics attended separate workshops for each area of expertise. While all experts are based in New Zealand and conduct marine-based research in New Zealand, they also have collective research experience in Oceania, Europe, South and North America and Southeast Asia. Most continue to work in countries other than New Zealand, including in the Global South, or to pursue comparative international research across the Global North and Global South. Research includes working with community groups and indigenous peoples who aspire to restore nature. Specific experts were brought back for another workshop to assess the overall social-ecological structural integrity of the BN model. The experts answered questions on model relationships and the outcomes of different scenarios. The conditional probability associated with specific relationships was defined independently by each expert, who also provided a probability score on how confident they felt with the nature of the relationship. The experts then discussed the results of their answers and were open to changing their answers after the discussion if they wished.

Following the expert elicitation workshops, individual expert responses were averaged; responses were omitted where the experts felt they could not define the relationship or had low confidence in the relationship. While the strength of certain relationships varied for each expert, the consensus was consistent throughout the relationships discussed. For each elicited probability relationship, the experts provided an explanation of why they thought the relationship was structured in a certain way. This reasoning, alongside literature, was used to cross-check the conditional probabilities. We then used a BN model interpolator, InterBeta BN model CPT Interpolator (https://bayesian-intelligence.com/interpolator/, accessed on December 5, 2023; Mascaro and Woodberry, 2022), to fill in the missing probability rows for each node in the model. The interpolator is parameterised by the bounds of the best- and worst-case scenarios set, alongside the weight of each parent node as an input. We used the elicited probabilities to set these bounds and adjust the weight for each parent. We then compared the interpolator-generated probabilities with the elicited probabilities that were used for each node and used those with similar probabilities for each node.

In our BN model, we selected scenarios that were focused on exploring how different ecological and social starting conditions influence restoration outcomes. Ecological status is the foundation of the restoration process and can impact the goals and expectations of the restoration project set by the restorers and investors. We identified three general ecological conditions: poor, okay and good ecosystem health. These conditions reflect the ability of an ecosystem to naturally recover (where “good” retains the ecological functions that will aid natural recovery) and are characterised by both ecological and stressor principles (Table 1). For each starting condition, we compared how different restoration actions (i.e., active or passive) generate different outputs.

The scenarios were focused on social capital, provisioning services and generating revenue from restoration to better understand the consequence of differently prioritising social and economic benefits versus social-ecological benefits. Social capital is important because it encapsulates the enhancement of community relationships that help society function and work towards shared goals. Generating revenue was targeted as supporting long-term viability of restoration actions, and revenue generated has been identified as important to potential investors (BenDor et al., 2015; Cortés Acosta et al., 2021). Finally, we included provisioning services because not only do they provide the ecological benefits of improving ecosystem structure and function but they also improve human wellbeing (Qiu et al., 2022). Including provisional services was expected to provide insight into what might happen when goals are multi-dimensional in terms of benefits received.

We then ran scenarios selected to enhance the understanding of the effects of maximising social-ecological and or economic actions on restoration outputs (grey boxes in Figures 1 and 2). Within each of the runs, we also recorded how the different restorations actions, that is, passive (e.g., create marine protected area), passive and reduce stressors (e.g., create marine protected area and reduce pollution), active enhancement (e.g., replant shellfish bed), active enhancement and reduce stressors (e.g., replant shellfish bed and reduce pollution) and reduce stressor (e.g., reduce pollution), would differ.

• Scenarios 1–3: Maximising increase in social capital (100%) under (1) poor, (2) okay and (3) good ecological starting conditions.

• Hypothesis A: Social capital is most likely to increase with active restoration because of high levels of local community involvement generating a reconnection of spiritual and cultural ties.

• Scenarios 4–6: Maximising restoration revenue increase (100%) under (1) poor, (2) okay and (3) good ecological starting conditions.

• Hypothesis B: Revenue generated from restoration may be delayed after restoration action; however, new “green/blue” jobs are likely, and employment rates will likely increase. Any revenue from restoration will be strongly affected by the ecological condition.

• Scenarios 7–9: Maximising provisioning services increase (100%) under (1) poor, (2) okay and (3) good ecological starting conditions.

• Hypothesis C: Increase in provisioning services is reliant on the return of ecosystem multi-functionality and abundance of species, so will be a reflection of the ecological baseline and the type of restoration action that is carried out.

To represent the model outputs for the selected nodes that were maximised—social capital, restoration revenue and provisioning services—we conducted a sensitivity analysis using Netica to understand which nodes had the most influence on the outcomes (see Text S1 and sensitivity reports in Tables S2–S4).

We used the BN model to investigate how both different ecological starting conditions (Figures 3–5) and restoration actions impact ecological outcomes and alter important social processes and entities, economic processes and restoration outcomes (Figures 6–9). These plots show the relative importance of variables that were revealed to be the most influential to the node that was maximised (social capital, restoration revenue and provisioning services). The relative importance of the nodes was derived from sensitivity analyses (Text S1 and Tables S2–S4); their relative importance does not imply that the rest of the network does not have any influence on the selected node.

The model predicts that poorer ecological conditions require higher levels of collaboration from the local community and government bodies and agencies (assuming that they are actively engaging). High levels of collaboration could therefore lead to more social engagement and interaction in the network during the restoration project and increase social capital. Higher levels of community engagement were also coupled with high levels of reconnection of spiritual and cultural ties (Figure 3). Nevertheless, how reliant, or responsible, a community or individual may feel towards the ecosystem plays an important role in government and community engagement regardless of the state of the ecosystem (Figure 3). Reconnection of spiritual and cultural ties also involves the need for stronger engagement with government bodies and agencies in comparison to okay and good ecological starting conditions.

Under okay ecological conditions, the influence of social, ecological and economic processes are similar (Figure 3), with no one process that is more influential than the others in the network. This similarity may signal that other context dependencies are at play. Under good ecological starting conditions, an ecosystem’s recovery potential is likely to be higher; however, social processes such as community involvement are less significant (Figure 3).

Ecological state had no influence on rates of employment and economic production practices. The model predicts that when the ecosystem is poor, restoration actions would require a high-to-moderate level of investment to continue with the action, with passive restoration actions being the only actions that can be carried out with low levels of investment under all ecosystem states (Figure 3). Economic production practices will likely remain the same until there is government intervention.

This amplification of the role of the community was more evident under active enhancement and reduced stressor restoration methods (Figure 4). Under the poor ecological starting point scenario, restoration actions were more likely to involve “active enhancement” (Figure 4), which requires more human resources. When ecosystem conditions are poor, local community groups that have a high level of connectedness and responsibility towards the ecosystem appear more willing to put in effort towards restoration actions.

The model predicts that restoration outcomes are dependent on both the starting ecological conditions and restoration actions. Under poor ecological conditions and active forms of restoration, social capital is likely to be higher because of the higher levels of community involvement required. We expected high levels of community involvement to have important feedback on restoration success, as a tight community network may help keep the project going once the initial funders have left.

Overall, okay ecological conditions were predicted to have better restoration outcomes than poor, but better social outcomes than good. Under good ecological conditions, passive restoration is more likely because there are higher rates of recovery potential and more ecological processes that are intact to facilitate natural recovery, resulting in comparatively greater/faster ecological restoration outcomes (Figure 5).

• Hypothesis A: Social capital is most likely to increase with active restoration because of high levels of local community involvement generating high levels of reconnection of spiritual and cultural ties.

The model demonstrates that to maximise social capital under poor and okay ecological conditions, social aspects such as reconnection, responsibility and knowledge generation are important and maximisation is likely to occur under active interventions (active and active and reducing stressors) despite the lack of good outcomes for ecosystem resilience (Figure 6A and 6B). Under good starting conditions, ecosystem resilience is likely to increase but is not accompanied by high levels of community involvement. Government involvement becomes important (Figure 6C), probably related to the primary need being to reduce stressors rather than create active enhancement.

• Hypothesis B: Revenue generated from restoration may be delayed after restoration action; however, new “green/blue” jobs are likely, and employment rates will likely increase. Any revenue from restoration will be strongly affected by the ecological condition.

Our hypothesis that employment would also increase was not supported by the model (Figure 7), although our hypothesis that results would be affected by the ecological condition was supported (Figure 8). High levels of return of ecosystem multi-functionality could be achieved when ecosystems were not severely degraded (i.e., had good ecological condition) and either active or active and reducing stressor interventions were involved (Figure 8C). Interestingly, a focus on restoration revenue when ecological conditions were poor or okay reduced the chances to achieve other ecological outcomes, such as generating high abundance of adult species of interest or increased biodiversity (Figure 8A and 8B). Focusing on restoration revenue, regardless of ecological conditions, was unlikely to involve economic production practices (Figure 7).

• Hypothesis C: Increase in provisioning services is reliant on the return of ecosystem multi-functionality and abundance of species, thus a reflection of ecological condition and the types of restoration action.

Maximum increases in provisioning services reflected ecological condition and the types of restoration action, occurring with good ecological conditions; active and active and reduce stressor actions provided higher levels of abundance for the species/community of interest (Figure 9). However, restoration actions were least likely to be influenced by multi-functionality. Indeed, in poor and okay ecological conditions, other processes such as biodiversity, multi-functionality and recovery potential were unlikely to be influenced (Figure 9A and 9B).

Marine restoration action has focused largely on generating positive ecological outcomes. Here, our BN model broadens the conversation to provide insights into how marine ecological restoration can also lead to social or economic benefits (Martin and Lyons, 2018; Saunders et al., 2020) and provide a blueprint for how to carry out restoration actions, set goals and understand potential outcomes throughout the lifespan of a restoration project (Figure 10). Our results highlight that ecologically poor conditions have the potential to generate higher levels of social returns. This finding demonstrates that community-based restoration is driven by various social and ecological dependencies, including ecosystem recovery potential, goals set and desired outcomes. These context-dependent results also highlight the importance of goal setting for desirable outcomes for restoration action. For example, while funding can limit the type of restoration possible, focusing on maximising revenue likely results in poorer ecological outcomes. This result emphasises the importance of developing processes to navigate restorative practices and adapt to changing circumstances. Understanding the desirable goals and current ecosystem conditions are important conversation points within communities both for setting expectations as well as expanding opportunities for ongoing investment.

The model demonstrates that the present ecosystem condition plays a critical role in the outcome of restoration action. For example, an ecosystem in poor condition is more likely to deliver higher rates of social capital, because of the resources (human and financial) required to kickstart the system in comparison to an ecosystem that still has some functionality intact. These context dependencies differentially weigh which processes are important, which in turn influences goals and outcomes.

Constructing the BN model required discussion across multiple disciplines. As such, it became a useful exercise for identifying the social-ecological and economic processes that play an important role in community-based restoration activities and whether these were correlated. Interestingly, our model suggested that even when ecological benefits are slow, social and economic benefits can still be obtained. In fact, they may be larger when ecological condition is poor, as the need for extra effort and co-operation increases social capital. In such cases, action plans need to address managing expectations by recognising social and economic benefits and identifying positive milestones in ecological restorations to boost morale (DeAngelis et al., 2020). In comparison, having a healthier ecosystem may require fewer resources and shorter recovery times for comparable ecological communities (Hewitt et al., 2022). While focusing on the complexity of restoration may be easy, ecological understanding of place and its context dependencies are crucial when establishing a plan of action for restoration. In particular, the model demonstrates, regardless of ecological success or failure, that restoration action can have positive social benefits (Valenzuela et al., 2020).

Alongside ecological context dependencies, there is also a need to consider the social and economic context that shapes outcomes. Importantly, the model highlights that there is no single process or stakeholder of particular importance; communities wanting to move forward still need to consider the social and economic processes that influence the speed of project development. For example, law and policy that inhibit restoration or restrict restoration action may arise because management agencies require expensive and time-consuming consents for restoration to take place. While law and policy and its operations within society can be complex, if there is great enough desire then communities are able to proceed with restoration actions, and legislative change can evolve in parallel (Everard et al., 2016).

Goal setting is a highly important part of any restoration project as it structures the direction and speed of a project alongside the current state of the ecology. It involves considering why we need to restore from ecological, social and cultural perspectives. The model demonstrates that when we maximise priorities such as revenue generation or social capital, we can depress other benefits. For example, fixating on revenue generation could encourage worse economic production practices and, therefore, worse outcomes for ecological benefits. In comparison, when we focus on maximising provisioning services (e.g., food provisioning services such as aquaculture) other social and economic benefits tend to be enhanced. While setting both short and long-term goals is important to keeping morale high and keeping the project going, prioritisation of short-term goals over long-term goals can create a false sense of long achievements, which impacts long-term goals (Caswell et al., 2020). For example, in Galway Bay, Ireland, a growing population led to an increase in demand for food; to help with this concern, technological advancements that provided swift economic growth were prioritised, leading to overexploitation at local and regional scales (Caswell et al., 2020).

In New Zealand, restoration is heavily community-driven, whereas government agencies often play key roles in funding and operational activities in Australia, the United States and parts of Europe. Community groups may lead restoration projects because they have a desire to restore an ecosystem that has provided and is still providing an important place for food gathering or meaningful place of spirituality and connection. Keeping morale high when signs of ecological improvement are limited emphasises the need to discuss expectations of how long recovery may take and what may be early indicators of success, both in terms of ecology but also social and cultural perspectives. Having different phases of restoration could help break down the process and create milestones to help keep projects running and participants engaged. For example, restoration on a small reef in mainland Hawaii (Kittinger et al., 2016) began with removal of pest seaweed. This removal of seaweed created an opportunity for a new composting business to sell fertiliser to farmers. Adaptive management and goal-setting strategies for marine restoration efforts have provided some insight into helping generate different pathways of success. What needs to remain clear is the need to put the ecology first and then consider how people can be moved into action with the finite resources that are available.

Entrepreneurs and other investors have the potential to fill the large funding gap that is required for restoration (Iftekhar et al., 2017; Bennett et al., 2019; Vanderklift et al., 2019). While global processes such as economic demand were not considered in our model, we recognise that they play an important component to finding different windows of investment opportunity. Global processes such as economic demand and market prices play a large role in how businesses continue to practice and exploit natural resources; however, with the climate, biodiversity and sustainability crises at hand, businesses are faced with a growing demand to take responsibility (Bennett et al., 2019; Tang et al., 2023). Opportunities and catalysts for change in the business space have arisen along with public demand to know that their purchases are environmentally friendly.

Nature-related financial disclosures and reporting have introduced the prospect of producing financial returns to nature, for example, through a taskforce for such disclosures (Taskforce on Nature-related Financial Disclosures, 2023), blue bonds and other green/blue investment. Research has been conducted on what investment funders need to invest into restoration; however, how restorative economies could help bolster or upscale small-scale restoration efforts still needs to be investigated (DeAngelis et al., 2020; Cortés Acosta et al., 2021), including how these investments are being passed down to the local community groups doing the restoration action.

Restoration activities are often initiated to take advantage of discrete windows of opportunity; once started, there is opportunity to reflect on progress. Our model provides a useful heuristic to open the dialogue on what next steps are needed and whether definitions (sometimes unvoiced) of social, economic and ecological success are realistic or need modification. Restored ecologies accrue benefits to people such as improved ecosystem services, cultural benefits and development of restorative economies. The model provides a map for considering the different ecological, social and economic place-based context dependencies, and in particular how bettering ecological conditions, especially in places that are ecologically poor, can generate benefits.

The supplemental files for this article can be found as follows:

Figure S1. Tables S1–S4. Text S1. docx

We thank the experts who contributed their knowledge to the workshops and the BN development: Richard Bulmer, Conrad Pilditch, Eva Siwicka, Silvia de Juan, Eric Jorgensen, Elizabeth Macpherson, Cara Chrichton and Gemma Couzens.

This research was funded by the Sustainable Seas National Science Challenge—project “Modelling the social-ecological outcomes of community-based interventions” (MBIE C01X1901).

The authors declare that they have no competing interests.

• Contributed to conception and design: JL, KF, NL, ST

• Contributed to acquisition of data: JL, JRH, ST

• Contributed to BN development and testing: JL, JEH

• Contributed to analysis and interpretation of data: JL, JEH, JRH, NL, KF, ST

• Drafted and/or revised the article: JL, JEH, JRH, KF, NL, ST

• Approved the submitted version for publication: JL, JEH, JRH, NL, KF, ST

How to cite this article: Low, JML, Fisher, KT, Lewis, NI, Hewitt, JE, Hillman, JR, Thrush, SF. 2025. Constructing a social-ecological economic network of community-based marine restoration initiatives. Elementa: Science of the Anthropocene 13(1). DOI: https://doi.org/10.1525/elementa.2024.00039

Domain Editor-in-Chief: Jody W. Deming, University of Washington, Seattle, WA, USA

Knowledge Domain: Ocean Science