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Because wildfire size and frequency are expected to increase in many forested areas in the United States, organizations involved in forest and wildfire management could arguably benefit from working together and sharing information to develop strategies for how to adapt to this increasing risk. Social capital theory suggests that actors in cohesive networks are positioned to build trust and mutual understanding of problems and act collectively to address these problems, and that actors engaged with diverse partners are positioned to access new information and resources that are important for innovation and complex problem solving. We investigated the patterns of interaction within a network of organizations involved in forest and wildfire management in Oregon, USA, for evidence of structural conditions that create opportunities for collective action and learning. We used descriptive statistical analysis of social network data gathered through interviews to characterize the structure of the network and exponential random graph modeling to identify key factors in the formation of network ties. We interpreted our findings through the lens of social capital theory to identify implications for the network's capacity to engage in collective action and complex problem-solving about how to adapt to environmental change. We found that tendencies to associate with others with similar management goals, geographic emphases, and attitudes toward wildfire were strong mechanisms shaping network structure, potentially constraining interactions among organizations with diverse information and resources and limiting opportunities for learning and complex problem-solving needed for adaptation. In particular, we found that organizations with fire protection and forest restoration goals comprised distinct networks despite sharing concern about the problem of increasing wildfire risk. [ABSTRACT FROM AUTHOR]

Although people and organizations in the Great Lakes region, USA take seriously their role as stewards of natural resources, many lack capacity to fulfill that role in a meaningful way. Stepping into that gap, The Stewardship Network (TSN) envisions "a world of empowered, connected communities caring for land and water, now and forever," and fulfills that vision through its mission to "connect, equip, and mobilize people and organizations to care for land and water in their communities." TSN uses a scalable model of linked local and regional capacity building, science communication, civic engagement, and on-the-ground stewardship activities to achieve these goals. The model engages local and regional groups in an ongoing process of learning around conservation and restoration that improves social and ecological knowledge. I share the story of TSN to demonstrate how transdisciplinary science can take hold locally and expand regionally to bring people from diverse disciplines and functional roles together to solve common problems. I demonstrate how researchers and practitioners can collaborate to create enduring mechanisms of social and ecological change. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Fire-prone landscapes are not well studied as coupled human and natural systems (CHANS) and present many challenges for understanding and promoting adaptive behaviors and institutions. Here, we explore how heterogeneity, feedbacks, and external drivers in this type of natural hazard system can lead to complexity and can limit the development of more adaptive approaches to policy and management. Institutions and social networks can counter these limitations and promote adaptation. We also develop a conceptual model that includes a robust characterization of social subsystems for a fire-prone landscape in Oregon and describe how we are building an agent-based model to promote understanding of this social-ecological system. Our agent-based model, which incorporates existing ecological models of vegetation and fire and is based on empirical studies of landowner decision-making, will be used to explore alternative management and fire scenarios with land managers and various public entities. We expect that the development of CHANS frameworks and the application of a simulation model in a collaborative setting will facilitate the development of more effective policies and practices for fire-prone landscapes. [ABSTRACT FROM AUTHOR]