Abstract
Coupled human and natural systems (CHANS) research highlights reciprocal interactions (or feedbacks) between biophysical and socioeconomic variables to explain system dynamics and resilience. Empirical models often are used to test hypotheses and apply theory that represent human behavior. Parameterizing reciprocal interactions presents two challenges for social scientists: (1) how to represent human behavior as influenced by biophysical factors and integrate this into CHANS empirical models; (2) how to organize and function as a multidisciplinary social science team to accomplish that task. We reflect on these challenges regarding our CHANS research that investigated human adaptation to fire-prone landscapes. Our project sought to characterize the forest management activities of land managers and landowners (or “actors”) and their influence on wildfire behavior and landscape outcomes by focusing on biophysical and socioeconomic feedbacks in central Oregon (USA). We used an agent-based model (ABM) to compile biophysical and social information pertaining to actor behavior, and to project future landscape conditions under alternative management scenarios. Project social scientists were tasked with identifying actors’ forest management activities and biophysical and socioeconomic factors that influence them, and with developing decision rules for incorporation into the ABM to represent actor behavior. We (1) briefly summarize what we learned about actor behavior on this fire-prone landscape and how we represented it in an ABM, and (2) more significantly, report our observations about how we organized and functioned as a diverse team of social scientists to fulfill these CHANS research tasks. We highlight several challenges we experienced, involving quantitative versus qualitative data and methods, distilling complex behavior into empirical models, varying sensitivity of biophysical models to social factors, synchronization of research tasks, and the need to substitute spatial for temporal variation in social data and models, among others. We offer recommendations that other research teams might consider when collaborating with social scientists in CHANS research.
Highlights
Coupled human and natural systems (CHANS) research has emerged as a new approach for studying complex social-ecological interactions
We (1) briefly summarize what we learned about actor behavior on this fire-prone landscape and how we represented it in an agent-based model (ABM), and (2) more significantly, report our observations about how we organized and functioned as a diverse team of social scientists to fulfill these CHANS research tasks
We (1) briefly summarize what we learned about actor behavior on this fire-prone landscape and how we represented it in an ABM, and (2) more significantly, report on the challenges we encountered while working as a multidisciplinary team of social scientists to develop data and methods for representing actor behavior in a CHANS ABM
Summary
Coupled human and natural systems (CHANS) research has emerged as a new approach for studying complex social-ecological interactions. We were especially interested in representing the management behavior of agency or other organizational actors because fireprone landscapes commonly include significant public, tribal, or private corporate actors (e.g., Moritz et al 2014) To advance these goals, project social scientists were tasked with identifying and characterizing factors—biophysical versus socioeconomic, internal versus external, and fast versus slow—that influence actors’ forest management and wildfire risk mitigation behavior, and with using this information to parameterize actor decisionmaking in an ABM. Project leaders—a landscape ecologist and an ecological engineer—initially sought social scientist team members who expressed interest in helping develop information concerning actors’ forest management and wildfire risk mitigation behaviors as they are influenced by key biophysical and socioeconomic conditions and processes related to wildfire, for use in developing decision rules for representing actor behavior in the ABM.
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