Linking stomatal size and density to water use efficiency and leaf carbon isotope ratio in juvenile and mature trees
Bibliographic record
Abstract
Abstract Water‐use efficiency (WUE) is affected by multiple leaf traits, including stomatal morphology. However, the impact of stomatal morphology on WUE across different ontogenetic stages of tree species is not well‐documented. Here, we investigated the relationship between stomatal morphology, intrinsic water‐use efficiency (iWUE) and leaf carbon isotope ratio (δ 13 C). We sampled 190 individuals, including juvenile and mature trees belonging to 18 temperate broadleaved tree species and 9 genera. We measured guard cell length (GCL), stomatal density (SD), specific leaf area (SLA), iWUE and bulk leaf δ 13 C as a proxy for long‐term WUE. Leaf δ 13 C correlated positively with iWUE across species in both juvenile and mature trees, while GCL showed a negative and SD a positive effect on iWUE and leaf δ 13 C. Within species, however, only GCL was significantly associated with iWUE and leaf δ 13 C. SLA had a minor negative influence on iWUE and leaf δ 13 C, but this effect was inconsistent between juvenile and mature trees. We conclude that GCL and SD can be considered functional morphological traits related to the iWUE and leaf δ 13 C of trees, highlighting their potential for rapid phenotyping approaches in ecological studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".