Emerging trend of increasing spring frost damage for beech at higher elevations in the Jura Mountains: evidence from tree‐ring data
Bibliographic record
Abstract
Late spring frost (LSF) severely impacts tree growth and forest productivity, with global warming potentially altering LSF risk due to asymmetric changes in vegetation onset and frost timing. However, reconstructing past frost regimes with climatic and phenological data remains challenging. Using phenological models, high-resolution climate and tree-ring data, we identified damaging LSF on European beech at two sites in the Swiss Jura mountains over nine decades. A novel tree-ring indicator, comparing frost-sensitive beech with evergreen Norway spruce, allowed us to isolate LSF impacts from background climate signals. While no significant long-term trend in the safety margin between leaf-out and LSF was detected since 1930, negative margins - that is, leaf-out preceding last frost - were frequent at the high-elevation site in the last two decades. Our tree-ring approach identified six damaging LSF events since 1991, exceeding twice the long-term return rate. No lag effects of LSF were found, suggesting beech can tolerate episodic frosts. These findings demonstrate the potential of tree rings as bioindicators of past LSF events, offering an alternative to climatic and phenological records, which contain uncertainties that hamper LSF reconstructions. The increasing frequency of damaging LSFs raises concern about future frost risks in mountainous areas under climate change.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".