Improving network approaches to the study of complex social-ecological interdependencies

Abstract

Achieving effective, sustainable environmental governance requires a better understanding of the causes and consequences of the complex patterns of interdependencies connecting people and ecosystems within and across scales. Network approaches for conceptualizing and analyzing these interdependencies offer one promising solution. Here, we present two advances we argue are needed to further this area of research: (i) a typology of causal assumptions explicating the causal aims of any given network-centric study of social-ecological interdependencies; (ii) unifying research design considerations that facilitate conceptualizing exactly what is interdependent, through what types of relationships, and in relation to what kinds of environmental problems. The latter builds on the appreciation that many environmental problems draw from a set of core challenges that re-occur across contexts. We demonstrate how these advances combine into a comparative heuristic that facilitates leveraging case-specific findings of social-ecological interdependencies to generalizable, yet context-sensitive, theories based on explicit assumptions of causal relationships.

Fig. 1.. Describing social-ecological systems as social-ecological networks.

(a) An example from Madagascar, where actors (red nodes) represents clans, and where forest patches (green nodes) represent the forest patches that the actors are set to manage/utilize. The links represent social ties based on e.g., kinship (red), ecological interdependency through species dispersal across the landscape (green), and social-ecological ties based on ownership or management authority of certain forest patches (blue). (b) A more abstract and aggregated description of a social-ecological system (coastal California, Oregon, and Washington, adapted from ref.) where social nodes (red- white triangles) are defined as an institution/policy devised to address certain problems, and ecological nodes (blue circles and green hexagons) are defined as key components of the ecological system entangled with other key components. The key components are conceived as management and governance targets; thus they constitute a series of interdependent ‘issues’ forming a ‘issue network’.

Fig 2.. A heuristic for facilitating comparable social-ecological network studies.

The matrix serves as an initial foundation, together with the typology of causal relationships in Table 1, for gathering and synthesizing studies across contexts in an effort to develop empirically informed insights regarding the causal relationships between social-ecological structures, processes, and outcomes. The columns capture different core governance challenges, and the rows different levels of aggregation. Comparisons within a matrix element (i.e., a row and a column) can reveal insights across different contexts for a given level of aggregation and core governance challenge. Comparisons across core governance challenges for a given level of aggregation (an entire row, or certain sets of individual core challenges) can reveal generic insights valid across different core governance challenges. Comparison across levels of aggregation for a given type of core governance challenge can provide insights both within and across aggregation levels, and also get at cross-level interdependencies (or identify possible “scale breaks” where e.g. insights applicable at a certain level do not hold as the level of aggregation changes). There are more core governance challenges than what is depicted here, and future research might described/categorized them in other ways. Further, any given case often experiences more than one core governance challenge; thus some cases will apply to (and thus appear in) more than one column (observe that a case might appear in two core challenges not next to each other as they are visually presented here).

Fig 3.. Social-ecological alignment in social-ecological networks.

(a) represents an example of a social-ecological network (social nodes are red, ecological nodes are green), and (b) and (c) represent two micro-level configurations present in the network that capture distinct aspects of social- ecological alignment. In (b), two linked social entities are separately linked to two interdependent ecological entities, hence this configuration represents a closed loop where social- and ecological links are aligned horizontally (closed four cycle). In (c), the same social entity is linked to two interdependent ecological entities, thus forming a closed loop (closed triangle, representing vertical alignment). The histograms in (b) and (c) depict how frequent these configurations appear in the social-ecological network compared to what we would expect by chance (i.e., the blue bars represent the results from a large number of simulated random networks, and the red bar represents the empirical network). In (d), a more simplistic social-ecological network is formed, where the nodes represent both social and ecological entities, but the social and ecological links are preserved as separate links (here laid out in the two planes named ‘ecological connectivity’ and ‘social connectivity’). The top plane captures to what extent the social and ecological links are aligned, i.e., the degree of social-ecological alignment, where an orange link implies a high degree of mismatch.