Sap flux and stable isotopes of water show contrasting tree water uptake strategies in two co‐occurring tropical rainforest tree species
Bibliographic record
Abstract
Abstract The short‐term dynamics of tree water use strategies for neighbouring co‐occurring species are poorly understood. Here, we quantify the high frequency changes in water sources and sap flux patterns of two commonly co‐occurring tropical rainforest tree species: Dendrocnide photinophylla (Kunth; Chew) and Argyrodendron peralatum (F.M. Bailey; Edlin ex J.H. Boas). A combination of continuous sap flux measurements and hourly sampling of xylem water stable isotope composition (δD and δ18O) were used to observe water use strategies during a 24‐h transpiration cycle. Sap flux ranged between 2.82 and 28.50 L day−1, with A. peralatum recording a 67% higher rate than D. photinophylla. For both tree species, sap flux increased with tree size and diurnal sap flux increase resulted in more isotopically enriched xylem water. A Bayesian Mixing Model analysis, which used sampled soil water isotopic composition from five soil depths ranging from of 0 to 1 m, revealed that D. photinophylla primarily used water from very shallow depth or soil surface layer (2–60 cm), while A. peralatum sourced its water mostly from deeper layers (60–100 cm). We propose that these differences in species' water consumption patterns are related to plant water storage capacity and wood anatomical features. This research demonstrates that combining xylem isotope composition and sap flux measurements can help reveal species‐specific water use strategies, which can be beneficial for improved process understanding in ecohydrological modelling.
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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 | 0.001 | 0.001 |
| 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".