Large, near-term increases in climate-induced tree growth may help offset impacts of increasing disturbance across Canadian boreal forest
Bibliographic record
Abstract
Abstract Large projected increases in forest disturbance pose a major threat to future wood fibre supply and carbon sequestration in the cold-limited, Canadian boreal forest ecosystem 1–4. Given the large sensitivity of tree growth to temperature, warming-induced increases in forest productivity have the potential to reduce these threats, but research efforts to date have yielded contradictory results attributed to limited data availability, methodological biases, and regional variability in forest dynamics 5–8. Here we apply a novel machine-learning algorithm to an unprecedented network of over 1 million tree growth records from 18,715 permanent sample plots distributed across both Canada and the US, spanning a 16.5°C climatic gradient, and project the near-term (2050s time period) growth of the six most abundant tree species in the Canadian boreal forest. Our results reveal a large, positive effect of increasing thermal energy on tree growth for most of the target species, leading to 40-52% projected gains in growth with climate change under RCP 4.5 and 8.5. The magnitude of these gains, which peak in the colder and wetter regions of the boreal forest, suggests warming-induced growth increases should no longer be considered marginal, but may in fact significantly offset some of the negative impacts of projected increases in drought and wildfire on wood supply and carbon sequestration and have major implications on ecological forecasts and the global economy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".