Alaska EPSCoR’s interdisciplinary integration core was charged with drawing together information and research from the different disciplines involved in EPSCoR. Integration Core work in Phase III was spearheaded by members of the Resilience and Adaptive Management (RAM) Group at UAA, headed by Phase III co-PI Lil Alessa. RAM is based in social-ecological systems (SES) research, which posits that social and natural systems’ functions are distinct but interacting, working as parts of a single dynamic whole. The Group’s chief accomplishment in Phase III was the creation and refinement of a coupled SES modeling framework that utilizes social, biological, and physical data. This framework was built around a number of cyber-enabled integration tools and methods, including:
- Social-Ecological Hotspots Mapping, through which qualitative data such as social values can be mapped alongside biophysical variables to represent a social-ecological landscape (and which served as a precursor to the U.S. Geological Survey SOLVeS tool)
- SES Typology, a method which uses diagnostic or indicator variables to characterize the resilience trajectory of individual SES’s
- The Arctic Water Resources Vulnerability Index, which uses physical and social data gathered via GIS mapping and community input to assess the vulnerability of a hydrological system;
- Forecasting Environmental Resilience in Arctic Landscapes (FERAL), an agent-based model in which the actions of a given agent create feedbacks within a virtual social-ecological landscape
- Architecture for Integrated and Dynamic Data Analysis (AIDA), a text analysis tool which transforms qualitative information such as interviews into quantitative data by using a logarithm that searches and sorts semantic networks and contexts
The RAM Group’s work yielded valuable information about the social science aspects of resilience theory. This includes data which suggest that human behaviors require significant forcing in order to change patterns of resource use, at thresholds which appear to be greater than the existing literature suggests. Core researchers have also found evidence suggesting that social-ecological systems can be classified based on “sociometabolic” transitions which are dictated by the rates and scales of matter and energy transformations.
A further focus of the RAM Group was the continuing development of techniques and technology to construct “policy simulators” which can be used as decision-support tools by lawmakers and land managers. Through the use of multiscenario, interactive simulations and large-scale video technology, policymakers can simulate the possible results of major land and resource-use choices in order to determine the best adaptive responses to given situations.