Landscape-level effectiveness of fuel treatments in a forest-dominated ecosystem in the Southern United States - Junyeong Choi; Tomás Lagos; Brittany Segundo; Lina M. Villa-Zapata; Oleg A. Prokopyev; Lewis Ntaimo; Curt W. Stripling; Jianbang Gan (PLoS ONE, 2026)
Wildfire has become an increasing threat to natural ecosystems and human livelihood alike in many parts of the world. Vegetation fuel treatments are considered a viable option for mitigating wildfire risk and damage; yet existing studies have yielded mixed or inconclusive results on fuel treatment effectiveness especially at the landscape level. Using fire behavior simulations and statistical analysis of simulation outputs, we assessed landscape-level effectiveness of prescribed burning (PB) and thinning from below (TFB) relative to their site-level effectiveness in terms of area burned (AB) and total cost of treatment and timber loss (TC) in a forest-dominated ecosystem in the southern United States. We found that effectiveness of a treatment varied with measurement metrics and extent, vegetation characteristics and dynamics, and their interactions with the treatment. PB and TFB were less effective at the landscape level than at the site level where fires burned only inside the treatment area. At both site and landscape levels, the effectiveness of PB and TFB in reducing AB and TC largely depended on the quantity of biomass and fire ignition location. TFB outperformed PB in mitigating both AB and TC with a larger timber volume, a longer delay in fire occurrence after treatment, or a higher uncertainty of fire ignition location. TFB was also more effective than PB in reducing TC at the landscape level. By clarifying the conditions under which a fuel treatment can mitigate the area burned and the total cost, this study advanced knowledge of fuel treatment effectiveness especially at the landscape level. Such knowledge can aid in developing and deploying treatment strategies to minimize fire extent and adverse economic consequences in the study region and beyond.