4.1 Quantitative overview

Network analysis in SES research showed to be a relatively young research field, with the first publication retrieved by our search string dating back to 2006. Particularly since 2012, the annual number of relevant publications rose, reaching more than 16 published studies annually since 2014 and the highest value of 24 studies in 2017 (Figure 5). The first study falling into Type III was published in 2010, and the first Type II in 2013, after which the number of publications of these Types steadily rose (Figure 5). Overall, 78% (n = 107) of the studies fell into category Type I, 12% (n = 16) into Type II and 11% (n = 15) into Type III (Figure 6a). Regarding the type of nodes used in the studies, all studies falling into Types II and III (i.e. 23% of total) included both social and ecological nodes (Figure 6b), while 86% (n = 92) of the studies falling into Type I worked exclusively with social nodes and 13% (n = 14) focused on ecological nodes only. One Type I study (1% of all Type I) used two networks with ecological and social nodes, respectively, while analysis was conducted separatedly.

Since five studies used stylized networks for empirical analysis (i.e. they did not use a specific case study), 133 studies were classified for the system type in which the data were collected (Figure 6c). The majority of studies focused on land-based systems (64%, n = 85 of these 133 studies), followed by marine (24%, n = 32) and freshwater (9%, n = 12) ecosystems; only 3% (n = 4) of the studies included data from more than one system type (Figure 6c).

4.2 Type I network studies for SES research

Type I studies were defined to look at one node type, either from the social or from the ecological realm, and the links (either SS or EE) between them (compare Figures 2 and 3). The literature search identified 107 studies that corresponded to the Type I category (compare Figure 6a; Table 2). Most of these studies represented resource users or actors from the social realm involved in governing environmental problems as nodes. The idea of ‘social capital’ represented the most commonly used theoretical concept in the social setting and was applied, for instance, in the context of managing marine resources in a Kenyan fishing community (Bodin & Crona, 2008), or among fisher cooperatives in Chile to assess post-disaster trajectories (Marín, Bodin, Gelcich, & Crona, 2015). Especially the two aspects ‘collaboration’ and ‘information flows’ among individuals or groups have attracted considerable attention. One study, for example, showed how distinct patterns of collaboration between actors involved in ecosystem-based management in Sweden impact their ability to develop specific environmental policy output (Bodin, Sandström, & Crona, 2016). Another work described how segregation patterns in terms of information exchange emerging from ethnic clustering among fishers was correlated with environmental outcome related to shark bycatch rates (Barnes et al., 2016).

Studies from the ecological realm mainly analysed food webs (i.e. trophic links), for example focusing on the impact of fisheries on the marine food web (Rocchi et al., 2017), on the analysis of the role of keystone species, and/or the impact of invasive species on ecosystems (Ortiz, Rodriguez-Zaragoza, Hermosillo-Nuñez, & Jordán, 2015; Vasas & Jordán, 2006). Another bulk of publications investigated spatial interdependencies between ecosystems (e.g. Banerjee, Banerjee, Mukherjee, Rakshit, & Ray, 2016) as represented by the movement of animals, for example, through ecological corridors and connectivity between places (Mokross, Ryder, Corrêa Côrtes, Wolfe, & Stouffer, 2014), in urban contexts (Aly & Amer, 2010; Calder, Cumming, Maciejewski, & Oschadleus, 2015) or between ecologically different areas subject to pastoralism (Easdale, Aguiar, & Paz, 2016).

Even though most publications discussed implications of their network results for the respectively other (i.e. not specifically incorporated) realm of the SES, only few studies presented a methodological approach for crossing the social–ecological interface. Examples include Ossola, Locke, Lin, and Minor (2019), who analysed tree canopy connectivity with node attributes being human use type, and Genc, Van Capelleveen, Erdis, Yildiz, and Yazan (2019), who applied both ENA and SNA metrics to a social network consisting of waste flows between companies. One study combined social network analysis with agent-based modelling to investigate human behaviour as to inform conservation interventions (Dobson, De Lange, Keane, Ibbett, & Milner-Gulland, 2019). Prager and Pfeifer (2015), as a third methodological example, constructed two separate networks comprising of social and ecological components, respectively, and then compared the findings of structural analysis with a spatial reference. The latter approach could be understood as what Sayles et al. (2019) conceptually term a non-articulated SEN.

4.3 Type II network studies for SES research

Type II networks were defined to integrate two different types of nodes, that is, from both the social and the ecological realm. Links thus represent either SS and SE edges, or EE and SE interactions, while no direct links between the nodes from the respectively other realm are incorporated (compare Figures 2 and 3). In all, 16 studies fell into this category (compare Figure 6a; Table 2).

A diverse range of conceptualizations was used to construct the Type III networks. Social nodes either represented individual resource users (e.g. fishers) or collective actors (such as NGOs, state agencies) and were interacting with the biological realm of the SES as represented by natural resources (Leventon et al., 2017), compartments of a food web (Dimitriadis, Borthagaray, Vilela, Casadevall, & Carranza, 2016; Levine, Muthukrishna, Chan, & Satterfield, 2015), renewable resources in general (Barfuss, Donges, Wiedermann, & Lucht, 2017), ecosystem services (Alonso Roldán, Villasante, & Outeiro, 2015) or ecological variables as defined by the SES framework of Ostrom (2009; e.g. Delgado-Serrano et al., 2015). Studies then looked at the interaction between resources and institutions in a resource management network, for example, through management actions (e.g. Alonso Roldán et al., 2015; Barfuss et al., 2017; Beilin, Reichelt, King, Long, & Cam, 2013; Leventon et al., 2017).

Other work studied the impact of fishing (i.e. with fishers representing social nodes) on food web dynamics (Dimitriadis et al., 2016; Levine et al., 2015). Haak, Fath, Forbes, Martin, and Pope (2017) investigated the transmission and contagion of aquatic species across ecosystems, operationalizing the nodes of the network as water reservoirs located in the United States connected by movement of anglers between the reservoirs. This was coupled with a trophic model for each reservoir implemented in Ecopath with Ecosim (EwE, Christensen & Walters, 2004), for which the impact of anglers' movement on trophic dynamics was estimated.

With only few exceptions, most studies classified as Type II used descriptive network statistics from classical social network analysis to study relational questions. For example, using network modularity for determining management units (Dimitriadis et al., 2016), or linking networks to ‘qualities of resilience’ (i.e. diversity, modularity, connectivity and feedback loops; Beilin et al., 2013). Only one study incorporated a temporal component into their analysis (Barfuss et al., 2017) while most other studies used network data to represent a status quo. Showing the diversity of applications, the studies' results related to a variety of purposes including the aim to solve questions on collaboration between institutions in resource management, describe effects of actor interactions in networks, identify the most critical variables in a SES and management units to be targeted and enhance understanding of processes and interactions in SESs (see also Table 2).

4.4 Type III network studies for SES research

Type III network studies were defined to consider two node types, that is, entities from both the social and the ecological realm, as well as links between social actors (SS), between ecological units (EE) and bridging the human–nature interface (SE; cf. Figure 3). In all, 15 studies fell into this category (cf. Figure 6a; Table 2), with different analytical network approaches emerging.

Most studies falling into this category framed human–nature relations in the context of landscape management, with forest patches (Bodin, Robins, et al., 2016; Bodin & Tengö, 2012), vegetation clusters (Baggio & Hillis, 2018; Guerrero, Bodin, McAllister, & Wilson, 2015), local urban green spaces (Ernstson, Barthel, Andersson, & Borgström, 2010) or watershed units (Sayles & Baggio, 2017; Zhao, Wei, Wu, Lu, & Fu, 2018) representing ecological nodes managed by different human actors. A second major theme targeted fisheries, with fishers (as social nodes) engaging with their environment through the extraction of species from the ecosystem described as a food web (Barnes et al., 2019; Ortiz & Levins, 2017; Zador et al., 2017).

In terms of analysis, the majority of Type III studies investigated the occurrence of micro-structures (also called motifs, building blocks or key configurations) in the network representing all possibilities how a total of four nodes (two social and two ecological entities) could be linked (Barnes et al., 2019; Bodin, Robins, et al., 2016; Bodin & Tengö, 2012; Guerrero et al., 2015; Hamilton, Fischer, & Ager, 2019). Theoretical assumptions were made as to which of all these possible motifs create favourable conditions for effectively dealing with environmental issues. Analysis then focused on the frequency with which each of these favourable building blocks occur (Bodin & Tengö, 2012; Guerrero et al., 2015; Bodin, Robins, et al., 2016; methods and limitations discussed in Bodin, Robins, et al., 2016). For the most part, this approach conceptualized SES as multi-layer networks in which the social and ecological nodes each constituted one layer. Social nodes were connected through collaboration or information between human actors (SS links) and ecological nodes through spatial proximity, dispersal of seeds or the movement of species (EE links), to then, as an example, explore the fit between social and ecological processes in environmental governance (with management of landscape patches conceptualized as SE links). Many examples combined motif analysis with exponential random graph modelling (ERGM; e.g. Barnes et al., 2019; Bodin, Robins, et al., 2016; Bodin & Tengö, 2012; Guerrero et al., 2015; Hamilton et al., 2019) and focused on topological questions of whole network metrics of each of the layers and the cross-layer links. Especially, the recent methodological advances in multi-level ERGM (see Lazega & Snijders, 2016) as used, for example, by Baggio et al. (2016), were discussed as powerful tools for analysing networks in future research. Indeed, also temporal ERGM (see e.g. Hanneke, Wenjie, & Xing, 2010) offer strong potential, since they allow for the analysis of network dynamics over time, an aspect that could often not be captured by more classical network approaches. TERGM, in contrast, allows to study time series of networks, assuming that interactions and interdependencies between actors (and hence broader network structures) arise sequentially, that is, conditional upon the rest of the network (Desmarais & Cranmer, 2012).

The second major approach applied qualitative network modelling (including the Loop Analysis approach, e.g. in Ortiz & Levins, 2017) for the analysis of links between fishers and respective markets (SS links), food web components (EE links) and between both realms through fisheries (SE links). With the aim of testing the stability of the SEN, the propagation of bottom-up and top-down perturbations was simulated through the elimination (Ortiz & Levins, 2017) or disturbance (Zador et al., 2017) of nodes to observe the effects on adjacent nodes and the entire network. The results provided insights on SES behaviour, for example, through the identification of the point at which a network breaks apart or the observation of (predicted) changes in strongly connected network components.

5.1 Understanding human–nature interaction

In an ever-interconnected world, with multiple anthropogenic pressures driving environmental and resource degradation (Giakoumi et al., 2015; Halpern et al., 2008), it becomes imperative to study and understand these complex dynamics. Tackling these problems becomes an ever important endeavour if conceptualizing human–nature interactions in the context of complex SES (Berkes & Folke, 1998). While many different methodological approaches may exist to model SES, including mechanistic and statistical models (Schuwirth et al., 2019), modelling such complex systems requires to include non-linear feedbacks, adaptive processes, different time scales and spatial characteristics, as well as risks and uncertainties, while holding a clear knowledge of the key components of a specific problem (Levin et al., 2013). These aspects pose a challenge to most methodological approaches, but a basic first step should be the understanding of key elements and their interactions. This is why this study was based on two straightforward premises: (a) humans and nature are closely connected in SES (Ostrom, 2009) and (b) network analysis is exceptionally well-suited to understanding relational data (Wasserman & Faust, 1994). Our review indicated the increasing recognition of NA for describing human–nature relations since the mid-2000s, with both the annual number of studies and the level of integration continuously increasing ever since (Figure 5). Other studies exploring temporal patterns of research output related to a specific topic similarly reported increases in publication numbers in recent decades, which can indicate the progressing maturation of a scientific field (increased scientific and/or public interest leading to an intensification of research efforts) but also reflects general trends (acceleration of cooperation and publication processes). This exponential increase in publications makes systematic reviews and meta-analysis necessary tools to generate evidence-based practice and to resolve seemingly contradictory research outcomes in the respective fields (Gurevitch, Koricheva, Nakagawa, & Stewart, 2018), as well as to identify gaps in knowledge for the guidance of future research (Castellanos-Galindo et al., 2020). For example, systematic reviews have covered the topic of marine climate change research (Pedersen et al., 2016), sea grass ecology (Duarte, 1999), coral reef management (Comte & Pendleton, 2018), coastal planning (Sierra-Correa & Cantera Kintz, 2015), ecosystem service evaluation of ‘blue forests’ (i.e. salt marshes, sea grasses, mangroves; Himes-Cornell, Pendleton, & Atiyah, 2018) and mangrove research (Castellanos-Galindo et al., 2020). In our case, we believe the observed pattern is indicative of a field in its early stage of development with large potential for further applications. This is in particular apparent from the ever-increasing number of Type II and III studies (Figure 5). A broad range of methodological approaches were applied (see Sections 4.2, 4.4) emphasizing the value of NA for studying SES. Categorizing existing network studies based on the level of incorporation of social and ecological realms provides the basis for a conscious decision as to design future research studying SES through a NA lens. As further discussed in the following sections, each of the three types of network approaches clearly has advantages and disadvantages, with the research question and case-specific reasoning ideally driving the choice of network operationalization and analysis, and ultimately determining what insights can be drawn from the empirical studies.

5.2 Potentials and challenges of the different network study approaches

Network analysis assumes, generally speaking, that there are properties and processes emerging from this global view that could not be seen if system components were studied individually, and separately. This consideration calls for the construction of networks as comprehensive as possible, that is, explicitly including in its analysis as many components of the network as possible. Type III network studies—representing social and ecological nodes and the articulated links between both node types (i.e. SS, SE, EE links)—offer therefore a viable pathway for research focusing on these emergent properties at the SES level that can only be explained through the interactions of its parts (Ostrom, 2009). It is, without doubt, methodologically and conceptually interesting to construct Type III networks to advance theory development based on the direct integration of social and ecological entities in the network conceptualization (Barnes et al., 2019; Bodin et al., 2019). The potential to include direct connections and feedback loops to capture the relationship within and across both realms is especially appealing. This allows to assess effects and repercussions for both realms simultaneously and theories from both the social and natural sciences can be integrated and tested using the same dataset and methodology (Bodin, Robins, et al., 2016; Guerrero et al., 2015). An in-depth discussion of the potential of the existing conceptualizations and analytical approaches of Type III networks is found in Sayles et al. (2019). However, it is important to emphasize that Type III network studies face also challenges, and a broad discussion of differently articulated SENs—as done in the present work—is of great value to compare the respective potential and limitations of different Types. For example, Type III networks require high amounts of data and high conceptual effort for constructing the network. The identification of relevant variables and network boundaries becomes even more challenging when one has to choose from a rich pool of possible social, ecological and socio-ecological variables (Bodin & Tengö, 2012). A complicated task for conceptualizing Type III networks also relates to the scale at which social and ecological units should be considered as network components, in particular if multiple link types cross the human–nature interface. Moreover, analysis in Type III network studies typically requires sophisticated mathematical and statistical approaches that may be unnecessary when aiming to resolve determinate questions that concern only parts of the SES. By this, Type III studies allow to enter new territories in terms of theory development (for a recent review and related hypotheses, see Bodin et al., 2019). For example, latest advances in exponential random graph modelling (ML-ERGM, see Wang, Robins, Pattison, & Lazega, 2013) offer an interesting mathematical approach to be used in Type III studies (cf. Section 4.4). However, we would argue that the application of Type III studies is—due to the abovementioned aspects—still rather academic.

In contrast, Type I and Type II network conceptualizations present other advantages while facing challenges too. Clearly, both types are not as elegant since they do not include social and ecological variables as ‘equal’ partners in the conceptual and mathematical formulation of the network. Yet, Type I approaches have the strong advantage of building on long research traditions in ecological (ENA) and social (SNA) network analyses. Numerous theoretical assumptions have been developed in these lines of research that can be operationalized for the context of understanding human–nature relationships (see e.g. Bodin & Crona, 2008; Bodin & Prell, 2011; Fath et al., 2007; Ulanowicz, 2004). This provides for enhanced transferability of established assumptions between case studies and for comparative analysis to draw generalizable conclusions within the different disciplines. As for the social dimension, recent reviews have, for example, elaborated on structural properties (of social networks) that might be linked to sustainability outcomes (Henry & Vollan, 2014), the implications of network structure of and individual actor's positioning within collaborative networks for tackling environmental problems (Bodin, 2017), and how SNA is used in different management contexts (Groce, Farrelly, Jorgensen, & Cook, 2019). Although not operationalized as part of the network and included in the formal network analysis, qualitative or quantitative information on the respective other dimension (e.g. environmental change or ecological conditions in SNA studies) can be included in the study design and analysis to obtain meaningful insights on human–nature relationships; for instance, to obtain understanding of the relation between social structure and the ability of governance actors to successfully deal with environmental change (Bodin, Sandström, et al., 2016; Gorris, Glaser, Idrus, & Yusuf, 2019). The focus on either the social or the ecological dimension limits, however, the discussion of social–ecological feedbacks and outcomes on the SES level to indirect measures and the capability of capturing emergent properties of the system.

Type II studies analyse the direct relations between the nodes of one realm (either ecological or social) and how this network interacts with the respectively other dimension. This enables to assess the impact of certain actions or changes in one area (either the ecological or social realm) on the other. Based on our results, this type of network has much fewer empirical examples in the literature. Its potential is, for instance, evident in the study by Haak et al. (2017) who analyse the impact of fisheries (as social nodes) on the dynamics of food webs (the ecological network) for exploring different fisheries management regimes, focusing on the expected consequences for food web structure and/or the population dynamics of the main target species. Such Type II studies offer the possibility to directly integrate human–nature relationships in network analysis while facing less of the conceptual and methodological complexity and extensive data needs as Type III studies. At the same time, the studies may be able to better build on the strong theoretical and methodological foundations of the Type I studies than the Type III studies can. For example, when human interaction with a food web is conceptualized as resource extraction (e.g. fishing), then social-to-ecological links are described in the same unit (biomass/energy flow) as ecological-to-ecological links among the biological actors, which facilitates quantitative analysis. Hence, as mentioned above, we argue that especially Type II network research offers an underexplored potential and ample scope for future studies.

In general, the specific research question should drive the decision of how much integration is actually necessary. Approaches that direct their attention to network complexity with, for example, multi-layer network coupling spatial patches, are well described; for example in Pilosof, Porter, Pascual, and Kéfi (2017), who describe multiple approaches for multi-layer networks. Nonetheless, their focus is chiefly on ecological aspects, which would classify all of the mentioned networks into our Type I, irrespective of their complexity. Other multi-layer networks (e.g. Friesen, Martone, Rubidge, Baggio, & Ban, 2019; Geier, Barfuss, Wiedermann, Kurths, & Donges, 2019) could, in contrast, be classified as Type II, emphasizing the need to let research questions drive the structure of the network, and not the other way around. These thoughts are particularly important when limited resources need to be invested in the most efficient way. Davis, Chadès, Rhodes, and Bode (2019) argue that neither ecological nor social information is necessarily most important for studying a SES, but that researchers should rather focus on understanding the ‘primary effects of their management actions’. Bodin, Robins, et al. (2016) comment in this regard that when assessing the necessity (or lack thereof) of increased collaboration in resource management, an excessive complexity can be counterproductive. Since conceptualization and parametrization of nodes and edges are still difficult to standardize among social and ecological subsystems, data requirements and methodological possibilities for analysis are often still limited as to produce case-specific conclusions only. This links also to the work of Bodin et al. (2019) who emphasize the necessity to formulate clear assumptions of causality when constructing networks to study social–ecological interdependencies, which is an especially difficult task for Type III network studies. Given the existing barriers and challenges for the development of Type III studies, a detailed quantitative analysis of one realm using a Type I or II approach might, in comparison, be more suitable and/or relevant to questions related to the impact evaluation of (expected) changes.

5.3 Conceptual and methodological challenges for integrating social and ecological realms

One basic assumption to systematically revise and categorize SENs relates to the definition of network compartments (nodes, links). For this work, we defined links as social, if connecting two social (i.e. human) entities. Similarly, edges were considered ecological, if linking two biological actors, while social–ecological links were conceptualized as to cross the human–nature interface, that is, connecting one social and one ecological node (cf. Section 2). Alternative approaches could engage, in contrast, with the nature of links. In this case, an ecological link might be anything that is derived from nature—be it physically (e.g. the movement of organisms, flow or sharing of biological resources) or theoretically (e.g. ecosystem services). This would imply that two social nodes (e.g. two fishers, or a retailer and a customer) could be connected by an ecological link (e.g. biomass of fish sold). Accordingly, two ecological vertices (e.g. individuals or groups of mammals) could be interconnected by a social action (e.g. the transfer of knowledge). This intriguing approach, however, was not followed in our work due to the difficulty to standardize the intrinsically subjective definitions of social edges, relating to different perspectives on anthropomorphized, human-centred concepts, for example, on what a social action entails. Since the purpose of this review was to present a general conceptualization aimed to engage with an as broad audience as possible, the discussion of these concepts would go beyond the scope of the present work.

In our literature review, most studies that fell into Types I and II analysed primarily social nodes (Figure 6b). This can likely be explained with the different terminology authors of network studies with an ecological focus commonly use. Author's choices of terminology for both network components and the framing of the studies reflects how authors classify their research, that is, not necessarily as studying human–nature interaction and/or analysing SES, albeit they often could be understood as such, especially when social agents are integrated in the respective network analysis. This argument is supported by the relatively small number of identified studies using food-web models. For example, out of the several hundred models developed with the EwE modelling framework (Christensen & Pauly, 1992; Christensen & Walters, 2004; Colléter et al., 2015), only a few were encountered by our search that tried to capture empirical studies on human–nature relationships in SES, and none in the recent review by Sayles et al. (2019). In theory, the EwE framework is based on network analytic concepts and allows for building ecological networks of marine food webs (Type I), integrating the effects of fisheries on ecological networks (Type II, cf. Bacalso & Wolff, 2014; Kluger, Taylor, Mendo, Tam, & Wolff, 2016) and tracking the biomass flow from the ecosystem to the consumers of fishery resources (Type III, cf. Christensen, de la Puente, Sueiro, Steenbeek, & Majluf, 2014; Christensen, Steenbeek, & Failler, 2011). Thus, this tool harbours great potential to advance SEN analysis. The absence of these studies in the present work is likely related to our inductive method only capturing research that self-identified as a network study tackling human–nature interaction (compare Table 1). Hence, although the graph theoretic foundations of network research do offer common ground for a joint scientific terminology (Janssen et al., 2006), there is room for improvement in terms of building a common language that could better integrate ecological research in SEN research. This ‘disciplinary fragmentation’ (sensu Gregr & Chan, 2015) is also common to other scientific topics. As an example, Gregr and Chan (2015) found in their review only a 5% overlap of papers being captured by different search strings related to (marine) ecosystem modelling tools, as well as a lack of cross-referencing. We would similarly argue that while adding the disciplinary foci and methods to SES research might help in deepening the understanding of behaviour and dynamics of single system components, true social–ecological approaches should, however, necessarily be of inter- and transdisciplinary nature. Hence, a common language—to which we hope to have contributed with the present work—is an indispensable first step to structuring a joint research agenda.

In terms of methodology, a certain degree of overlap was identified with respect to topics and methods covered by the different Types. For example, analysis of the network topology is widely applied in all three categories, for example, the analysis of node removal was used to assess network-wide impact in Type I (e.g. Rocchi et al., 2017) but also Type III (e.g. Ortiz & Levins, 2017; Zador et al., 2017) cases. Multi-layer network approaches were found for Type I (e.g. Prager & Pfeifer, 2015), Type II (e.g. Geier et al., 2019; Haak et al., 2017) and Type III (e.g. Sayles & Baggio, 2017) studies. Some authors even combine approaches, for example node removal on multi-layer networks (Baggio et al., 2016). In terms of topics addressed, the food-web effects of human action (represented as fishing or harvesting resources) were assessed using Type II (e.g. Dimitriadis et al., 2016) and Type III but also using Type I (Rocchi et al., 2017) network approaches; while issues of spatial misfits were addressed using Type I (Easdale et al., 2016), Type II (Bergsten, Galafassi, & Bodin, 2014) or Type III (Bodin, Robins, et al., 2016; Ernstson et al., 2010). This exemplifies that several topics can be successfully approached by a range of network parametrization possibilities representing different levels of social–ecological integration, and also by using different analytical techniques. However, a particular challenge lies in the varying conceptualizations of nodes and links used in different NA studies because this complicates the comparison across cases, even though this theoretically represents one of the strengths of network analysis (Bodin et al., 2019). Similarly, the scale at which the social and ecological units that are included as nodes are defined (e.g. local to global), further hampers useful cross-case comparisons.

Our results show that the majority of case studies, for which networks (of all Types I–III) were constructed, were from the terrestrial context (Figure 6c). An increasing focus on marine SESs, however, is especially important considering that 40% of the world's population lives within 100 km of the coast with ever-growing demands for marine biotic resources and increasing pressure on coastal marine ecosystems (UN, 2017). Studies addressing marine systems were proportionally more represented in Types II and III than in Type I, possibly hinting at that these systems hold high potential for implementation of Type III studies, maybe because of the data availability, or because of the different characteristics of human–nature interaction at sea compared to terrestrial human–nature interactions. However, a large number of marine food-web models (i.e. networks of Type I) already exists but has not been captured by our literature review, rendering this comparison difficult. Based on these food webs, the connection to the social realm can conceptually relatively easy be included using fishing or other types of resource use for representing the social dimension (i.e. for construction networks of Type II or III). An example for this is the recent study by Kluger, Scotti, Vivar, and Wolff (2019) presenting a multiplex, multi-layer SEN for a SES in which small-scale fisheries and aquaculture represent important contributions to local livelihoods. For this, social science data collection to study the dynamics within the fisheries value chain were combined with existing food webs (based on Kluger et al., 2016). In addition to the importance of an increased focus on coastal marine SES, the comparison across a wide range of different system types (terrestrial, marine, freshwater) holds strong potential to identify common relational features, but has not been opted for in the reviewed studies.