Spatially detailed tree-ring analysis exposes widespread forest growth decline throughout Canada
Bibliographic record
Abstract
Environmental changes across Canada's forests highlight the need to understand long-term growth dynamics and identify areas of decline, essential for predicting ecosystem vulnerability to future vegetation shifts. Here, we analyzed basal area increment trends using tree-ring data from 4410 sites spanning 1950–2018, organized into 647 1° × 1° grid cells. We then analyzed spatial patterns of these changes in relation to the long-term averages of mean annual temperature (MAT) and mean annual precipitation (MAP), the rates of change in MAT and MAP, and tree species dominance. Significant tree growth declines occurred in 42.3% of grid cells, while only 8.3% showed increases. Declines were concentrated in the boreal and montane forests of British Columbia, Alberta, and the southern Northwest Territories, with additional declines in southern Quebec, southern Labrador, and Ontario's boreal mixedwood forest transition zones. The strength of growth declines was moderately dependent on MAT, with cooler regions having more negative trends for Pseudotsuga menziesii, Picea engelmannii, and Picea glauca. Additionally, Abies lasiocarpa and Pseudotsuga menziesii exhibited declining growth in areas of their geographic range experiencing the most rapid warming. These widespread growth declines could signal early stages of forest degradation and highlight strategically timed and targeted need for adaptive forest management.
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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 | 0.002 |
| 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".