Will tree species experience increased frost damage due to climate change because of changes in leaf phenology?
Bibliographic record
Abstract
In temperate zones, trees tend to unfold their leaves earlier due to climate warming. However, changes in the timing of the bud development also affect the dynamics of the cold-hardening process, which may increase frost injuries endured by trees because new leaves unfold at a period when frost events can still occur. This possible increase in frost damage in response to climate change is known as the “frost-damage hypothesis”. In this study, we have tested this hypothesis by forcing a process-based frost-injury model with process-based phenological models for 22 North American species with two Intergovernmental Panel on Climate Change storylines. Using a simplified parameterization of the frost-injury model, we found that risk of frost injury changed with climate change for all species. In fact, frost injury decreased for the vast majority of the species, but this trend varied across species and throughout each species’ distribution. We further explored the variability of response among species using their phenological and geographic characteristics. The interspecific trends depicted here show what could be the implications of climate change on the ecophysiology of boreal and temperate trees and highlight the importance of process-based models in studying the complexity of long-term impacts of climate change on species biology.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".