Wildfire‐mediated vegetation change in boreal forests of Alberta, Canada
Bibliographic record
Abstract
Abstract Climate‐induced vegetation change may be delayed in the absence of disturbance catalysts. However, increases in wildfire activity may accelerate these transitions in many areas, including the western boreal region of Canada. To better understand factors influencing decadal‐scale changes in upland boreal forest vegetation, we developed a hybrid modeling approach that constrains projections of climate‐driven vegetation change based on topo‐edaphic conditions coupled with weather‐ and fuel‐based simulations of future wildfires using Burn‐P3, a spatial fire simulation model. We evaluated eighteen scenarios based on all possible combinations of three fuel assumptions (static, fire‐mediated, and climate‐driven), two fire‐regime assumptions (constrained and unconstrained), and three global climate models. We simulated scenarios of fire‐mediated change in forest composition over the next century, concluding that, even under conservative assumptions about future fire regimes, wildfire activity could hasten the conversion of approximately half of Alberta's upland mixedwood and conifer forest to more climatically suited deciduous woodland and grassland by 2100. When fire‐regime parameter inputs (number of fire ignitions and duration of burning) were modified based on future fire weather projections, the simulated area burned was almost enough to facilitate a complete transition to climate‐predicted vegetation types. However, when fire‐regime parameters were held constant at their current values, the rate of increase in fire probability diminished, suggesting a negative feedback by which a short‐term increase in less‐flammable deciduous forest leads to a long‐term reduction in area burned. Our spatially explicit simulations of fire‐mediated vegetation change provide managers with scenarios that can be used to plan for a range of alternative landscape conditions.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".