Frontiers in socio-environmental research: components, connections, scale, and context

• Published in: Ecology and society Vol. 23; no. 3; p. 23
• Main Authors: Pulver, Simone, Ulibarri, Nicola, Sobocinski, Kathryn L., Alexander, Steven M., Johnson, Michelle L., McCord, Paul F., Dell'Angelo, Jampel
• Format: Journal Article
• Language: English
• Published: Ottawa Resilience Alliance 01-10-2018
• Subjects: Case studies; components; connections; context; coupled human and natural systems; Cross cutting; Data processing; Ecological monitoring; Ecosystem assessment; Ecosystem services; Ecosystems; Empirical analysis; Environmental research; frameworks; human environment; Natural resource management; resilience; scale; Social-ecological systems; socio-environmental systems; Structure-function relationships; Sustainability; Systems analysis; vulnerability
• ISSN: 1708-3087; 1708-3087
• DOI: 10.5751/ES-10280-230323

The complex and interdisciplinary nature of socio-environmental (SE) problems has led to numerous efforts to develop organizing frameworks to capture the structural and functional elements of SE systems. We evaluate six leading SE frameworks, i.e., human ecosystem framework, resilience, integrated assessment of ecosystem services, vulnerability framework, coupled human-natural systems, and social-ecological systems framework, with the dual goals of (1) investigating the theoretical core of SE systems research emerging across diverse frameworks and (2) highlighting the gaps and research frontiers brought to the fore by a comparative evaluation. The discussion of the emergent theoretical core is centered on four shared structuring elements of SE systems: components, connections, scale, and context. Cross-cutting research frontiers include: moving beyond singular case studies and small-n studies to meta-analytic comparative work on outcomes in related SE systems; combining descriptive and data-driven modeling approaches to SE systems analysis; and promoting the evolution and refinement of frameworks through empirical application and testing, and interframework learning.