Late spring freezes coupled with warming winters alter temperate tree phenology and growth
Bibliographic record
Abstract
Spring phenology is advancing with warming but late spring freezes may not advance at the same rate, potentially leading to an increase in freezes after trees initiate budburst. Research suggests warming winters may delay budburst through reduced chilling, which may cause plants to leafout more slowly, thus decreasing spring freeze tolerance. Here, we assessed the effects of late spring freezes and reduced over-winter chilling on sapling phenology, growth and tissue traits, across eight temperate tree and shrub species in a laboratory experiment. We found that spring freezes delayed leafout - extending the period of greatest risk for freeze damage - increased damage to the shoot apical meristem, and decreased leaf toughness and leaf thickness. Longer chilling accelerated budburst and leafout, even under spring freeze conditions. Thus, chilling compensated for the adverse effects of late spring freezes on phenology. Despite the effects of spring freezes and chilling on phenology, we did not see any major reordering in the sequence of species leafout. Our results suggest climate change may impact forest communities not through temporal reassembly, but rather through impacts on phenology and growth from the coupled effects of late spring freezes and decreased over-winter chilling under climate change.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".