Harvest-induced changes in forest landscapes does not fully compensate for climate-induced increase in landscape flammability in eastern Canada
Bibliographic record
Abstract
Abstract Context Wildfires are a major natural disturbance in boreal ecosystems, strongly influenced by both climate-driven fire weather and fuel characteristics. While climate change is intensifying fire-prone weather conditions, other natural and human-related disturbances, as well as natural evolution of forest stands, are reshaping forest composition, potentially altering landscape flammability. The net effect of these opposing forces remains uncertain in eastern Canada's boreal forest. Objectives We assess whether changes in the forest fuel landscape as well as changes in fire weather have mitigated or exacerbated the potential behavior of wildfire across Quebec’s commercial forest zone. Specifically, we analyze trends in predicted intensity and speed of potential fires from 1978 to 2023 as well as the effect of harvesting and other disturbances on these trends. Methods Using the Canadian Forest Fire Behavior Prediction (FBP) System, we estimated potential head fire intensity and speed based on reconstructed ERA5 daily fire weather at local solar noon and high-resolution (~ 14 ha) annual fuel maps, from 1978 to 2023. We disentangled climate-driven (top-down) and fuel-driven (bottom-up) influences by comparing scenarios with fixed vs. dynamic fuels and weather conditions. Results Landscape flammability has increased significantly in some regions over the study period, primarily due to rising fire-prone weather conditions. Despite a general long-term decline in flammable fuels associated with timber harvesting and post-disturbance succession, these changes were sometimes insufficient to offset climate-driven increases in potential head fire intensity and spread. The western and northern regions of the study area exhibited the most pronounced trends toward exacerbated fire behavior, aligning with increased temperatures and fire-prone weather. Conclusions Fire weather intensification is emerging as the dominant driver of landscape flammability, overriding mitigation effects from fuel changes in many regions. This suggests that future wildfire risk in eastern Canada will continue to rise under climate change, despite ongoing shifts in forest composition. These findings highlight the need for adaptive fire management strategies that account for both fuel and climate-driven changes in wildfire regimes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".