How plant water status drives tree source water partitioning
Bibliographic record
Abstract
Abstract. The stable isotopes of oxygen and hydrogen (δ2H and δ18O) have been widely used to investigate plant water source partitioning. These tracers have shed new light on patterns of plant water use in time and space. However, this black box approach has limited our source water interpretations and mechanistic understanding. Here, we combine measurements of stable isotope composition in xylem and soil water pools with measurements of plant hydraulics, fine root distribution and soil matric potential to investigate mechanism(s) driving tree water source partitioning. We used a 2 m3 lysimeter planted with a small willow tree (Salix viminalis) to conduct a high spatial-temporal resolution experiment. We found that tree water source partitioning was driven mainly by tree water status and not by patterns of fine root distribution. Source water partitioning was regulated by plant hydraulic response to changing atmospheric demand and soil matric potential. The depth distribution of soil matric potential appeared to be the largest control on the patterns of soil water partitioning during periods of tree water deficit. Contrary to the common steady state assumption in ecohydrological source water investigations, our results show that tree water use is a dynamic process, driven by tree water deficit. Overall, our findings suggest new research foci for future plant water isotopic investigations, highlighting the importance of hydrometric measurements from the plant perspective.
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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 | 0.001 | 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".