Contrasting stomatal sensitivity to temperature and soil drought in mature alpine conifers
Bibliographic record
Abstract
Abstract Conifers growing at high elevations need to optimize their stomatal conductance ( g s ) for maximizing photosynthetic yield while minimizing water loss under less favourable thermal conditions. Yet the ability of high‐elevation conifers to adjust their g s sensitivity to environmental drivers remains largely unexplored. We used 4 years of sap flow measurements to elucidate intraspecific and interspecific variability of g s in Larix decidua Mill. and Picea abies (L.) Karst along an elevational gradient and contrasting soil moisture conditions. Site‐ and species‐specific g s response to main environmental drivers were examined, including vapour pressure deficit, air temperature, solar irradiance, and soil water potential. Our results indicate that maximum g s of L . decidua is >2 times higher, shows a more plastic response to temperature, and down‐regulates g s stronger during atmospheric drought compared to P . abies . These differences allow L . decidua to exert more efficient water use, adjust to site‐specific thermal conditions, and reduce water loss during drought episodes. The stronger plasticity of g s sensitivity to temperature and higher conductance of L . decidua compared to P . abies provide new insights into species‐specific water use strategies, which affect species' performance and should be considered when predicting terrestrial water dynamics under future climatic 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) | 0.000 | 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 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".