CNH2-L: Developing adaptive capacity in wildfire-prone regions

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The social and environmental costs of wildfires have grown dramatically in recent decades, and more information is needed to understand how communities can reorganize themselves in the face of this growing hazard. This project explores the potential for adaptive social networks of actors in fire-prone regions to improve their ability to cope with increasingly large and intense wildfires. This is achieved first by exploring and summarizing key adaptation lessons from four western US study areas and vetting them via a stakeholder council. Next, the most promising social network structures from these study areas are tested in a well-documented focal area, where many alternative futures are simulated under widely varying future climate scenarios. A stakeholder council is then convened with leading national and state wildfire learning networks to distill, consider, and disseminate findings with the participation of young, next-generation science leaders. Finally, the entire approach is captured in a first-of-its-kind computer model, extending and sharing the tools so that others may use them to expand adaptation efforts elsewhere. Lessons and outcomes will be disseminated to create more resilient landscapes and communities in the face of increasing wildfires through collaborative engagement with students, educators, stakeholders, policymakers, and resource managers.This project explores how social network topology shapes the ability of communities in regions affected by wildfires to adaptively reorganize in response to fires that are more severe than people have previously experienced. The main goal is to identify replicable network building blocks that can enhance adaptive capacity in a variety of fire-prone landscapes. Once these network structures have been identified, they are used to validate an innovative computational platform linking adaptive social network, biophysical, and agent-based simulation models. The modeling system is then applied in dynamic, mapped representations of the focal area landscape and its wildfire actors (e.g., landowners, organizations, and policy makers) to test the limits of different social network topologies to respond to intensifying wildfire regimes. Together, these efforts enable (1) the creation of a generalizable theory of when, where, why, and how adaptive changes arise through social network reorganization in wildfire-prone social-environmental systems experiencing intensifying fire regimes; 2) the synthesis of empirical and modeling insights to distill representations of people's and organization's relationships that increase adaptive capacity with respect to wildfires; and 3) exploration and testing of how the identified relationships may evolve over time under projected future disturbance regimes to alter adaptive capacity and constrain risk in relation to wildfires.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.