A Negative Fire–Vegetation Feedback Substantially Limits Reburn Extent Across the North American Boreal Biome
Bibliographic record
Abstract
Abstract The North American boreal biome (NAB) is warming at 2–4 times the mean global rate, contributing to increasing wildfire activity. The degree to which this trend alters biome-level feedbacks to global climate depends on how strongly bottom-up feedbacks between fire and vegetation dampen the effects of climate drivers. As young vegetation recovering from fire covers a growing portion of the landscape, it could resist reburning, buffering against further increases in fire. Resistance to reburning could be particularly strong in the NAB, where slow post-fire fuel accumulation is sometimes combined with a fire-driven shift from conifers toward less-flammable, deciduous trees. However, continued warming could eventually override the feedback. To quantify the strength of the feedback throughout the biome, we divided the NAB into 27 Fire Regime Units (FRUs) and used fire data from 1986 to 2018 to determine the area expected to have burned more than once (that is, reburned) within each FRU under the null assumption that recent fire does not influence burn probability. Then, we ran a spatial simulation to quantify the strength of departure from the null value while accounting for variation driven by stochastic fire patterns. Reburn extent was 5 Mha less than expected without the feedback. Departure from the null model was strongest in the most fire-prone FRUs, suggesting that the feedback will continue to dampen climate-driven increases in wildfire activity. These results provide a sound baseline from which to identify potential weakening of the feedback under continued warming, and our approach could be expanded to other biomes.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".