In the fire-prone and fire-adapted landscape of the Rogue River Basin of southwestern Oregon, communities mobilize to prepare, respond, and recover from wildfire while modifying the current social and ecological system.
We present a mixed integer programming model for prioritizing fuel treatments within a landscape fuel break network to maximize protection against wildfires, measured by the total fire size reduction or the sum of Wildland Urban Interface areas avoided from burning. This model uses a large dataset of simulated wildfires in a large landscape to inform fuel break treatment decisions.
Significant resources have been devoted to increasing the supply of data and information products for wildland fire management. There has been comparatively less emphasis on understanding the demand for these products. There are large differences in the number of information sources that fire managers use in decision making.
Environmental governance outcomes hinge on the design and implementation of management decisions. Yet, available research methodologies can be limited in their ability to capture the complexity of decision-making and implementation processes and in turn predict and explain environmental governance outcomes.
Public land management agencies in the US are committed to using science-informed decision making, but there has been little research on the types and topics of science that managers need most to inform their decisions.