A review of the social-ecological systems framework: applications, methods, modifications, and challenges

Abstract

The social-ecological systems framework (SESF) is arguably the most comprehensive conceptual framework for diagnosing interactions and outcomes in social-ecological systems (SES).This article systematically reviews the literature applying and developing the SESF and discusses methodological challenges for its continued use and development.Six types of research approaches using the SESF are identified, as well as the context of application, types of data used, and commonly associated concepts.The frequency of how each second-tier variable is used across articles is analyzed.A summary list of indicators used to measure each second-tier variable is provided.Articles suggesting modifications to the framework are summarized and linked to the specific variables.The discussion reflects on the results and focuses on methodological challenges for applying the framework.First, how the SESF is historically related to commons and collective action research.This affects its continued development in relation to inclusion criteria for variable modification and discourse in the literature.The framework may evolve into separate modified versions for specific resource use sectors (e.g., forestry, fisheries, food production, etc.), and a general framework would aggregate the generalizable commonalities between them.Methodological challenges for applying the SESF are discussed related to research design, transparency, and cross-case comparison.These are referred to as "methodological gaps" that allow the framework to be malleable to context but create transparency, comparability, and data abstraction issues.These include the variable-definition gap, variable-indicator gap, the indicator-measurement gap, and the data transformation gap.A benefit of the framework has been its ability to be malleable and multipurpose, bringing a welcomed pluralism of methods, data, and associated concepts.However, pluralism creates challenges for synthesis, data comparison, and mutually agreed-upon methods for modifications.Databases are a promising direction forward to help solve this problem.In conclusion, future research is discussed by reflecting on the different ways the SESF may continue to be a useful tool through (1) being a general but adaptable framework, (2) enabling comparison, and (3) as a diagnostic tool for theory building.