Data from: Tamm Review: a meta-analysis of thinning, prescribed fire, and wildfire effects on subsequent wildfire severity in conifer dominated forests of the Western US
收藏DataCite Commons2025-05-01 更新2025-05-10 收录
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https://datadryad.org/dataset/doi:10.5061/dryad.zcrjdfnkp
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资源简介:
Increased understanding of how active forest management (i.e., mechanical
thinning, prescribed burning, and managed wildfire) affects subsequent
wildfire severity is urgently needed as people and forests face a growing
wildfire crisis. In response, we reviewed scientific literature for the US
West and completed a meta-analysis that answered three questions: (1) How
much do treatments reduce wildfire severity within treated areas? (2) How
do the effects vary with treatment type, treatment age, and forest type?
(3) How does fire weather moderate the effects of treatments? We found
overwhelming evidence that mechanical thinning with prescribed burning,
mechanical thinning with pile burning, and prescribed burning only are
effective at reducing subsequent wildfire severity, resulting in
reductions in severity from 62% to 72% relative to untreated areas. In
comparison, thinning only was less effective – underscoring the importance
of treating surface fuels when mitigating wildfire severity is the
management goal. The efficacy of these treatments did not vary among
forest types assessed in this study and was high across a range of fire
weather conditions. Prior wildfire had more complex impacts on subsequent
wildfire severity, which varied with forest type and initial wildfire
severity. Across treatment types, we found that effectiveness of
treatments declined over time, with the mean reduction in wildfire
severity decreasing nearly threefold when wildfire occurred greater than
10 years after initial treatment. Our meta-analysis provides up-to-date
information on the extent to which active forest management reduces
wildfire severity and facilitates better outcomes for people and forests
during future wildfire events.
提供机构:
Dryad
创建时间:
2024-05-02



