Dryness limits vegetation pace to cope with temperature change in warm regions
Bibliographic record
Abstract
Abstract Climate change leads to increasing temperature and more extreme hot and drought events. Ecosystem capability to cope with climate warming depends on vegetation's adjusting pace with temperature change. How environmental stresses impair such a vegetation pace has not been carefully investigated. Here we show that dryness substantially dampens vegetation pace in warm regions to adjust the optimal temperature of gross primary production (GPP) () in response to change in temperature over space and time. spatially converges to an increase of 1.01°C (95% CI: 0.97, 1.05) per 1°C increase in the yearly maximum temperature ( T max ) across humid or cold sites worldwide (37 o S–79 o N) but only 0.59°C (95% CI: 0.46, 0.74) per 1°C increase in T max across dry and warm sites. temporally changes by 0.81°C (95% CI: 0.75, 0.87) per 1°C interannual variation in T max at humid or cold sites and 0.42°C (95% CI: 0.17, 0.66) at dry and warm sites. Regardless of the water limitation, the maximum GPP (GPP max ) similarly increases by 0.23 g C m −2 day −1 per 1°C increase in in either humid or dry areas. Our results indicate that the future climate warming likely stimulates vegetation productivity more substantially in humid than water‐limited regions.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".