No growth stimulation of Canada’s boreal forest under half-century of combined warming and CO2fertilization
Bibliographic record
Abstract
Considerable evidence exists that current global temperatures are higher than at any time during the past millennium. However, the long-term impacts of rising temperatures and associated shifts in the hydrological cycle on the productivity of ecosystems remain poorly understood for mid to high northern latitudes. Here, we quantify species-specific spatiotemporal variability in terrestrial aboveground biomass stem growth across Canada’s boreal forests from 1950 to the present. We use 873 newly developed tree-ring chronologies from Canada’s National Forest Inventory, representing an unprecedented degree of sampling standardization for a large-scale dendrochronological study. We find significant regional- and species-related trends in growth, but the positive and negative trends compensate each other to yield no strong overall trend in forest growth when averaged across the Canadian boreal forest. The spatial patterns of growth trends identified in our analysis were to some extent coherent with trends estimated by remote sensing, but there are wide areas where remote-sensing information did not match the forest growth trends. Quantifications of tree growth variability as a function of climate factors and atmospheric CO2 concentration reveal strong negative temperature and positive moisture controls on spatial patterns of tree growth rates, emphasizing the ecological sensitivity to regime shifts in the hydrological cycle. An enhanced dependence of forest growth on soil moisture during the late-20th century coincides with a rapid rise in summer temperatures and occurs despite potential compensating effects from increased atmospheric CO2 concentration.
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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 | 0.000 | 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) | 0.002 | 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".