Turning down the heat: vegetation feedbacks limit fire regime responses to global warming
Bibliographic record
Abstract
Background/Question/Methods Boreal wildfire activity is projected to increase dramatically under climate change, raising concerns about ecological and socio-economic consequences. Projections of fire activity have often incorporated only climate-related controls, neglecting biotic feedbacks. This could lead to incorrect projections of fire activity and biased vulnerability assessments to climate change. This would lead policy makers to take wrong directions and adopt inappropriate adaptation policies, with significant ecological and socio-economic costs. We introduced sensitivity to climate- and vegetation-related controls in a landscape fire model using empirical models of fire initiation, escape and spread in temperate and boreal forests of southern Québec, Canada. We coupled the landscape fire model with a dynamic vegetation model to integrate explicitly two biotic feedbacks mechanisms related to post-fire regeneration and successional processes. We constructed a 2x2 simulation experiment where the feedbacks were activated or deactivated under the RCP8.5 scenario of 21 st century climate warming. We calculated various measures of annual fire activity such as fire frequency, the mean annual burn rate, the fire size distribution, to quantify the importance of these feedbacks on projections. Results/Conclusions According to our models, biotic feedbacks would markedly offset expected increases in fire activity under climate change. In a scenario where vegetation was assumed constant throughout the century, the mean annual burn rate is projected to increase by 18 times at the end of the century relative to historical levels, reaching 1.82% per year. In comparison, if both biotic feedbacks were included this increase was reduced to 4 times. Increases in burn rate were due more to increases in the size rather than in the number of fires. Among the four scenarios, the number of fires increased by 4 to 5 times while the average size increased by 2 to 13 times. The differences between the scenarios were related to the amounts of various fuel types, and their relative frequencies of fire ignition and probabilities of fire spread. These findings have implications for fire risk management and adaptation to climate change, as the extensive forest management now being practiced in the region could act as widespread fuels management.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".