Leaves as bottlenecks: The contribution of tree leaves to hydraulic resistance within the soil−plant−atmosphere continuum
Bibliographic record
Abstract
Abstract Within vascular plants, the partitioning of hydraulic resistance along the soil‐to‐leaf continuum affects transpiration and its response to environmental conditions. In trees, the fractional contribution of leaf hydraulic resistance (R leaf ) to total soil‐to‐leaf hydraulic resistance (R total ), or fR leaf (=R leaf /R total ), is thought to be large, but this has not been tested comprehensively. We compiled a multibiome data set of fR leaf using new and previously published measurements of pressure differences within trees in situ. Across 80 samples, fR leaf averaged 0.51 (95% confidence interval [CI] = 0.46−0.57) and it declined with tree height. We also used the allometric relationship between field‐based measurements of soil‐to‐leaf hydraulic conductance and laboratory‐based measurements of leaf hydraulic conductance to compute the average fR leaf for 19 tree samples, which was 0.40 (95% CI = 0.29−0.56). The in situ technique produces a more accurate descriptor of fR leaf because it accounts for dynamic leaf hydraulic conductance. Both approaches demonstrate the outsized role of leaves in controlling tree hydrodynamics. A larger fR leaf may help stems from loss of hydraulic conductance. Thus, the decline in fR leaf with tree height would contribute to greater drought vulnerability in taller trees and potentially to their observed disproportionate drought mortality.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".