Prioritizing forest fuels treatments based on the probability of high-severity fire restores adaptive capacity in Sierran forests.

In frequent fire forests of the western United States, a legacy of fire suppression coupled with increases in fire weather severity have altered fire regimes and vegetation dynamics. When coupled with projected climate change, these conditions have the potential to lead to vegetation type change and altered carbon (C) dynamics. In the Sierra Nevada, fuels reduction approaches that include mechanical thinning followed by regular prescribed fire are one approach to restore the ability of the ecosystem to tolerate episodic fire and still sequester C. Yet, the spatial extent of the area requiring treatment makes widespread treatment implementation unlikely. We sought to determine if a priori knowledge of where uncharacteristic wildfire is most probable could be used to optimize the placement of fuels treatments in a Sierra Nevada watershed. We developed two treatment placement strategies: the naive strategy, based on treating all operationally available area and the optimized strategy, which only treated areas where crown-killing fires were most probable. We ran forecast simulations using projected climate data through 2,100 to determine how the treatments differed in terms of C sequestration, fire severity, and C emissions relative to a no-management scenario. We found that in both the short (20 years) and long (100 years) term, both management scenarios increased C stability, reduced burn severity, and consequently emitted less C as a result of wildfires than no-management. Across all metrics, both scenarios performed the same, but the optimized treatment required significantly less C removal (naive=0.42 Tg C, optimized=0.25 Tg C) to achieve the same treatment efficacy. Given the extent of western forests in need of fire restoration, efficiently allocating treatments is a critical task if we are going to restore adaptive capacity in frequent-fire forests.