Short-interval wildfire and drought overwhelm boreal forest resilience
Bibliographic record
Abstract
The size and frequency of large wildfires in western North America have increased in recent years, a trend climate change is likely to exacerbate. Due to fuel limitations, recently burned forests resist burning for upwards of 30 years; however, extreme fire-conducive weather enables reburning at shorter fire-free intervals than expected. This research quantifies the outcomes of short-interval reburns in upland and wetland environments of northwestern Canadian boreal forests and identifies an interactive effect of post-fire drought. Despite adaptations to wildfire amongst boreal plants, post-fire forests at paired short- and long-interval sites were significantly different, with short-interval sites having lower stem densities of trees due to reduced conifer recruitment, a higher proportion of broadleaf trees, less residual organic material, and reduced herbaceous vegetation cover. Drought reinforced changes in proportions of tree species and decreases in tree recruitment, reinforcing non-resilient responses to short-interval reburning. Drier and warmer weather will increase the incidence of short-interval reburning and amplify the ecological changes such events cause, as wildfire activity and post-fire drought increase synergistically. These interacting disturbances will accelerate climate-driven changes in boreal forest structure and composition. Our findings identify processes of ongoing and future change in a climate-sensitive biome.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".