A new empirical framework to quantify the hydraulic effects of soil and atmospheric drivers on plant water status
Bibliographic record
Abstract
Metrics to quantify regulation of plant water status at the daily as opposed to the seasonal scale do not presently exist. This gap is significant since plants are hypothesised to regulate their water potential not only with respect to slowly changing soil drought but also with respect to faster changes in air vapour pressure deficit (VPD), a variable whose importance for plant physiology is expected to grow because of higher temperatures in the coming decades. We present a metric, the stringency of water potential regulation, that can be employed at the daily scale and quantifies the effects exerted on plants by the separate and combined effect of soil and atmospheric drought. We test our theory using datasets from two experiments where air temperature and VPD were experimentally manipulated. In contrast to existing metrics based on soil drought that can only be applied at the seasonal scale, our metric successfully detects the impact of atmospheric warming on the regulation of plant water status. We show that the thermodynamic effect of VPD on plant water status can be isolated and compared against that exerted by soil drought and the covariation between VPD and soil drought. Furthermore, in three of three cases, VPD accounted for more than 5 MPa of potential effect on leaf water potential. We explore the significance of our findings in the context of potential future applications of this metric from plant to ecosystem scale.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".