Disentangling the Effects of Tree and Soil Properties on the Water Uptake of a Waterlogging Tolerant Tree in the Yangtze River Delta, China
Bibliographic record
Abstract
Waterlogging tolerant tree species exert a critical role in forest preservation and the associated water conservation in flood prone areas. Clarifying the patterns and drivers of water uptake by waterlogging tolerant trees is crucial for forest management in flood-prone areas, especially in the scenario of precipitation changes in the estuary delta. Here, we uploaded the values of δD and δ18O obtained from soil and xylem waters to a Bayesian mixed model (MixSIAR) to determine the water use pattern of Taxodium distichum, a waterlogging tolerant tree, following different magnitudes of rainfall events in three sites of the Yangtze River Delta, China. We further conducted variation partitioning analysis and a random forest model to discern the dominant factor driving plant water uptake. Our results indicated that T. distichum mainly absorbed soil water from shallow soil layers (0–40 cm, 43.63%–74.70%), while the percentage of water uptake from deep soil layers was lower in the Yangtze River Delta (60–100 cm, 13.43%–35.90%), whether in light, moderate, or heavy rainfall conditions. Furthermore, our results demonstrated that tree traits, such as fine root biomass, are dominantly driving plant water uptake. These findings imply that waterlogging tolerant tree species could increase the percentage of water uptake from shallow soils by changing their plant attributes, which would effectively improve the water conservation of forests in the estuary delta.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".