Tree cover response to climate change in the forest-tundra of north-central Canada: fire-driven decline, not northward advance
Bibliographic record
Abstract
Climate-vegetation models predict rapid northward advance of the subarctic forest-tundra in the coming century, although modelled responses may not be congruent with field data. This study aimed to determine how forest-tundra vegetation has responded to climate change in north-central Canada. Vegetation cover and gradients were mapped and compared to changes in climate parameters between 1955 and 2006. Increased aridity and annual and July warming corresponded to spatial isotherm shifts of one-half the width of the forest-tundra transition. Over the 51-year period, the areal extent of live trees decreased 26% (5227 km2) while the areal extent of recently-burned trees increased 16-fold (7768 km2). Changes in the areal extent of treeless wetland, tall shrubs, and upland tundra were non-significant. There was significant forest loss in the southern forest-tundra and modest forest gain in the northern forest-tundra. Overall, forest loss outpaced forest gain. The forest-tundra increased in areal extent by ~6% via an overall broadening of the transition region. Contrary to model predictions, no appreciable northward migration of the forest-tundra was detected over the 51-year period despite significant climate change. Increased wildfire activity and moisture stress may limit the potential of tree vegetation to expand northward under a warming climate.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".