Temperate forest herbs are adapted to high air humidity — evidence from climate chamber and humidity manipulation experiments in the field
Bibliographic record
Abstract
How growth and morphology of wild plants are controlled by the water vapor saturation deficit of the air (vapor pressure deficit, VPD) is not sufficiently understood. We tested the hypothesis that VPD acts on temperate woodland herbs independently from soil moisture by exposing two species ( Mercurialis perennis L. and Stachys sylvatica Torr.) to variable VPD levels in climate chambers and in open-top chambers on the forest floor. A decrease in air humidity from 85% to 40% in the climate chamber experiment, which simulates a microclimate change after canopy gap creation, resulted in a 40% decrease in biomass production of both species when grown in hydroponic culture. This result is supported by the more realistic open-top chamber experiment, which showed a productivity decrease by approximately 25% when plants were continuously exposed to an atmosphere with 15% lower air humidity than ambient. Elevated VPD levels reduced biomass production through either a reduced leaf expansion rate or a lower number of formed leaf buds. We conclude that many woodland herbs require sufficiently high air humidity for optimal growth; permanently decreased air humidity, as may occur in a future drier climate, after gap creation, or after clear-cutting of the forest, may threaten the vitality and survival of woodland herbs.
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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".