Small-scale fishing villages populate the Gulf of Nicoya coastline in Costa Rica. When comparing many case studies of community-based natural resource use around the world, such as those in the Gulf of Nicoya, patterns of interacting variables can be identified as consistently shaping outcomes. Leadership, social capital and the importance of a resource, for example, are a commonly recurring set of interacting variables.
Photo credit: Stefan Partelow.

By Stefan Partelow, Sergio Villamayor-Tomas, Klaus Eisenack, Graham Epstein, Elke Kellner, Matteo Roggero, and Maurice Tschopp.

Read the full paper here.

Many studies examine variables linking people and nature together to shape sustainability outcomes. One important approach is social-ecological systems (SES) analysis. Because the SES field deals with explaining complex systems, each individual study tends to examine a different set of variables – sometimes several dozen – to explore these dynamics. There is no standardized protocol for SES research beyond general frameworks, which provide common variables but not common methods. This creates a challenge for synthesizing knowledge across all the studies when each study uses different variable definitions and methods.

Our meta-analysis is one of the few studies that attempts to systematically synthesize many diverse SES case studies to examine which variables occur most frequently together across cases, which includes how they co-shape social and ecological outcomes. This is important for building theory because the purpose of theory is to develop some more general explanations of how patterns and mechanisms shape outcomes with some degree of certainty.

Our study is a starting point for identifying what we call the building blocks of SES theory. We need to start somewhere, and our study identifies the most frequent pairs of two and three variables across 71 models from different SES case studies. This is useful because we can then take this set of frequent pairs and see what qualitative mechanisms explain them in different case contexts, and whether they are consistent or different. It also allows other scientists to test their presence and mechanisms in their own cases. When we can show that a certain pair is regularly recurring and a consistent causal mechanism explains the recurrence, it will allow us to support a theory that can more accurately inform how we govern those interactions to shape the outcomes we want. Overall, we don’t just want to assume that systems are complex without data-driven explanations. This study provides a foundation for doing that more systematically by synthesizing knowledge.