Historical fire regimes in whitebark pine ecosystems of west‐central British Columbia
Bibliographic record
Abstract
Abstract Forest ecosystems across western North America are experiencing increasingly large and severe wildfire disturbances. From 2012 to 2024, approximately 600,000 ha of forest in Tweedsmuir Provincial Park, British Columbia's largest protected area, were impacted by wildfires. These wildfires burned primarily through lower elevation subboreal forests, but high‐severity fire also impacted subalpine and treeline ecosystems across the mountainous provincial park. Whitebark pine is a long‐lived and endangered high‐elevation tree species experiencing extensive mortality throughout western North America from an invasive pathogen and recent large‐scale outbreaks of mountain pine beetle. To understand the impacts of changing fire regimes on subboreal and subalpine whitebark pine ecosystems, we reconstructed the first fire history in North Tweedsmuir Provincial Park. Eleven study sites containing whitebark pine were sampled along lakeshores, islands, knolls, and ridgelines. Our fire history record indicated two key findings. First, fire‐scarred trees provided evidence of low‐severity fire at all 11 study sites. Our dendrochronological record covered 830 years (1190–2020) and included 127 fire scars during a 580‐year period (1377–1957), with a composite mean fire interval of 8 years in the period 1580–1957 recorded across the study area. Second, our results highlight centuries of Indigenous fire stewardship that, combined with lightning, comprised the historical fire regime. Prior to 20th century fire suppression policies, the fire regime was characterized by shorter fire intervals than the contemporary period, effectively reducing available fuels, and creating a mosaic of burned and unburned forests across the landscape. Our research findings highlight the need for proactive and dynamic wildfire management that supports multiple cultural and ecological values across protected areas.
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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 | 0.002 | 0.002 |
| 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) | 0.001 | 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".