Current and projected cumulative impacts of fire, drought, and insects on timber volumes across Canada
Bibliographic record
Abstract
Canada's forests are shaped by disturbances such as fire, insect outbreaks, and droughts that often overlap in time and space. The resulting cumulative disturbance risks and potential impacts on forests are generally not well accounted for by models used to predict future impacts of disturbances on forest. This study aims at projecting future cumulative effects of four main natural disturbances, fire, mountain pine beetle, spruce budworm and drought, on timber volumes across Canada's forests using an approach that accounts for potential overlap among disturbances. Available predictive models for the four natural disturbances were used to project timber volumes at risk under aggressive climate forcing up to 2100. Projections applied to the current vegetation suggest increases of volumes at risk related to fire, mountain pine beetle, and drought over time in many regions of Canada, but a decrease of the volume at risk related to spruce budworm. When disturbance effects are accumulated, important changes in volumes at risk are projected to occur as early as 2011-2041, particularly in central and eastern Canada. In our last simulation period covering 2071-2100, nearly all timber volumes in most of Canada's forest regions could be at risk of being affected by at least one of the four natural disturbances considered in our analysis, a six-fold increase relative to the baseline period (1981-2010). Tree species particularly vulnerable to specific disturbances (e.g., trembling aspen to drought) could suffer disproportionate increases in their volume at risk with potential impacts on forest composition. By 2100, estimated wood volumes not considered to be at risk could be lower than current annual timber harvests in central and eastern Canada. Current level of harvesting could thus be difficult to maintain without the implementation of adaptation measures to cope with these disturbances.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".