Cold-season freeze frequency is a pervasive driver of subcontinental forest growth
Bibliographic record
Abstract
As northern latitudes experience rapid winter warming, there is an urgent need to assess the effect of varying winter conditions on tree growth and forest carbon sequestration potential. We examined tree growth responses to variability in cold-season (November–April) frequency of freeze days (FFD) over 1951 to 2018 using tree-ring data from 35,217 trees and 57 species at 4,375 sites distributed across Canada. We found that annual radial growth responses to FFD varied by species, with some commonalities across genera and clades. The growth of gymnosperms with late spring leaf-out strategies was negatively related to FFD; years with high FFD were most detrimental to the annual growth of Pinus banksiana, Pinus contorta, Larix lyalli, Abies amabilis, and Abies lasiocarpa. In contrast, the growth of angiosperms with early leaf-out strategies, namely, Populus tremuloides and Betula papyrifera, was better in the coldest years, and gymnosperms with intermediate leaf-out timing, such as widespread Picea mariana and Picea glauca, had no consistent relationship to FFD. Tree growth responses to FFD were further modulated by tree size, tree age, regional climate (i.e., mean cold-season temperature), and local site conditions. Overall, our results suggest that moderately warming winters may temporarily improve the growth of widespread pines and some high-elevation conifers in western Canada, whereas warming winters may be detrimental to the growth of widespread boreal angiosperms. Our findings also highlight the value of using species-specific climate-growth relationships to refine predictions of forest carbon dynamics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 teacher head, 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".