Fire severity and the legacy of mountain pine beetle outbreak: high-severity fire peaks with mixed live and dead vegetation
Bibliographic record
Abstract
Bark beetle outbreaks and wildfires are two of the most prevalent disturbances that influence tree mortality, regeneration, and successional trajectories in western North American forests. Subboreal forests have experienced broad overlaps in these disturbances, and recent wildfires have burned through landscapes with substantial tree mortality from prior outbreaks. This study investigated how fuel conditions associated with mountain pine beetle outbreaks influence the probability of high burn severity (i.e. stand-replacing fire) across a range of fire weather conditions in subboreal forests of central interior British Columbia, Canada. We focused on three large fires that occurred in 2012, 2013, and 2014. We characterized outbreak severity, outbreak-influenced prefire vegetation, and subsequent burn severity using Landsat spectral vegetation indices, high-resolution imagery, and field observations. Substantial portions of the prefire landscape contained mixtures of live and dead vegetation created by variable beetle damage and vegetation response—spatial patterns that are related to, but distinct from, peak outbreak severity. We evaluated drivers—fuels, weather, and topography—of high-severity fire under 'extreme,' 'moderate,' and 'benign' fire weather conditions (i.e. burning conditions) using Boosted Regression Trees. While fire weather was a primary driver in most cases, prefire vegetation was an influential predictor variable across all burning conditions, and the probability of high-severity fire was highest when prefire vegetation was a mixture of tree mortality from bark beetles and live vegetation. Thus, while weather and drought are important drivers of wildfires in subboreal forests, bottom-up drivers of elevation and vegetation, including the fuel legacies of bark beetle outbreaks, are crucial factors influencing high-severity burning. The legacy of recent bark beetle outbreaks will continue for decades on these landscapes, affecting fuel structures, future wildfires, forest dynamics, and the broader social-ecological systems of the region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".