Accelerating effects of growing-season warming on tree seasonal activities are progressively disappearing
Bibliographic record
Abstract
The phenological changes induced by climate warming have profound effects on water, energy, and carbon cycling in forest ecosystems. In addition to pre-season warming, growing-season warming may drive tree phenology by altering photosynthetic carbon uptake. It has been reported that the effect of pre-season warming on tree phenology is decreasing. However, temporal change in the effect of growing-season warming on tree phenology is not yet clear. Combining long-term ground observations and remote-sensing data, here we show that spring and autumn phenology were advanced by growing-season warming, while the accelerating effects of growing-season warming on tree phenology were progressively disappearing, manifesting as phenological events converted from being advanced to being delayed, in the temperate deciduous broadleaved forests across the Northern Hemisphere between 1983 and 2014. We further observed that the effect of growing-season warming on photosynthetic productivity showed a synchronized decline over the same period. The responses of phenology and photosynthetic productivity had a strong linear relationship with each other, and both showed significant negative correlations with the elevated temperature and vapor pressure deficit during the growing season. These findings indicate that warming-induced water stress may drive the observed decline in the responses of tree phenology to growing-season warming by decelerating photosynthetic productivity. Our results not only demonstrate a close link between photosynthetic carbon uptake and tree seasonal activities but also provide a physiological perspective of the nonlinear phenological responses to climate warming.
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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".