Temperature governs the relative contributions of cuticle and stomata to leaf minimum conductance
Bibliographic record
Abstract
Summary During periods of stomatal closure, such as drought, plant leaves continue to lose water at a rate determined by the minimum leaf conductance, gmin. Although gmin varies with temperature, less is known about what drives this variation, including how the pathways of water loss (cuticle or stomata) vary with temperature. We used gas exchange and bench drying methods to measure gmin and cuticular conductance, gcw, across a wide temperature range (20–50°C) in 11 broadleaf species. Vapour pressure deficit (VPD) covaried with temperature from 0.83 to 10.7 kPa. The dominant pathway of water loss for gmin shifted from stomatal transpiration towards cuticular transpiration as temperature increased. Leaf traits had variable, temperature‐dependent relationships with gmin and gcw, with trait–conductance relationships being generally stronger at higher temperatures. Cuticular thickness varied inversely with high‐temperature gcw. Simulation results showed that gcw may impact photosynthetic capacity estimates, particularly in species with low stomatal conductance. The pathways of water loss in leaves during times of stomatal closure depend strongly on temperature. This effect may have large implications for landscape‐scale water balance modelling and improving gas exchange measurements. We propose variation in VPD as a potential contributing factor in gmin and gcw variation among studies.
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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".