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Record W4409990570 · doi:10.1186/s42408-025-00368-1

Evidence for strong bottom-up controls on fire severity during extreme events

2025· article· en· W4409990570 on OpenAlexaff
Nicholas A. Povak, Susan J. Prichard, Paul F. Hessburg, Vivian Griffey, R. Brion Salter, Tucker J. Furniss, Gina R. Cova, Robert W. Gray

Bibliographic record

VenueFire Ecology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsNative Mental Health Association of Canada
FundersJoint Fire Science Program
KeywordsEnvironmental scienceEcologyGeographyBiology

Abstract

fetched live from OpenAlex

Abstract Background Record fire years in recent decades have challenged post-fire forest recovery in the western United States and beyond. To improve management responses, it is critical that we understand the conditions under which management can mitigate severe wildfire impacts, and when it cannot. Here, we evaluated the influence of top-down and bottom-up fire severity forcings on 17 wildfires occurring during two consecutive record-setting years in the eastern Cascade Mountains of Washington State. Despite much of the area having been burned after an extended period of fire exclusion, nearly one-third of the forested area burned at low severity. Results Using random forest modeling and Shapley local importance measures, we found that weather and fuels were both dominant drivers of fire severity, and past fuel treatments were successful at reducing severity—even during extreme fire progression days. First-entry fires were more typically driven by top-down climate and weather variables, while for reburns (i.e., overlapping fire footprints within the period of record), severity was largely mitigated by reduced fuels and a positive influence of topography (e.g., burning downslope). Likewise, reburns overall exhibited lower fire severity than first entry fires, suggesting strong negative feedbacks associated with past fire footprints. The normalized difference moisture index (NDMI)—an indicator of live fuel loading and moisture levels—was a leading predictor of fire severity for both first-entry fires and reburns. NDMI values < 0 (i.e., low biomass) were associated with reduced fire severity, while values > 0.25 (i.e., high biomass) were associated with increased severity. Forest management was effective across a variety of conditions, especially under low to moderate wind speeds (< 17 m·s −1 ), and where canopy base heights were ≥ 1.3 m. Conclusions Our findings support previous work demonstrating strong top-down weather and climate controls on fire severity along with bottom-up spatial controls of fuels and topography on patterns of fire severity. Local importance measures refined our understanding of the conditions under which bottom-up factors successfully mitigated fire severity. Our results indicate a clear role for fuels and fire management—including wildland fire use—to restore characteristic composition and structure to the landscape and to moderate fire severity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.273
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2025
Admission routes1
Has abstractyes

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