decision making

The sagebrush biome in the western United States is a focus of widespread conservation concern due to multiple interacting threats including larger, more severe wildfires. Given the immense scale of the region and limited resources, prioritizing restoration treatments is essential for optimizing risk reduction and managing for resilient ecosystems.

Effective interventions to prevent human-caused ignitions on public lands play a critical role in social and ecological adaption to wildfire. While wildfire prevention spending generates a high return on investment, funding and capacity to support such programing within federal, state, and local land and fire management agencies remains limited.

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.

Background: Climate change is a strong contributing factor in the lengthening and intensification of wildfire seasons, with warmer and often drier conditions associated with increasingly severe impacts.

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.

Aim