Water-use strategies of coexisting shrub species in the Yellow River Delta, China
Bibliographic record
Abstract
In coastal ecosystems, water availability is limited because of the high soil salinity influenced by sea water intrusion, high soil noncapillary porosity, and significant seasonal fluctuation of precipitation. Therefore, water availability is a key determinant of plant growth and distribution in coastal ecosystems. Tamarix chinensis Lour. and Ziziphus jujuba var. spinosa Hu are two coexisting shrub species growing on Chenier Island in the Yellow River Delta (YRD), China. Our aim was to investigate how the water-use strategies of the two species respond to variations in soil moisture to improve understand of their adaptations to drought stress and their coexistence mechanism. During the growing season, the oxygen stable isotope signatures (δ18O) were measured for soil water in different soil depths (0–20, 20–40, 40–60, and 60–100 cm), shallow groundwater, and xylem water. The proportional contributions of potential water sources for the two species were calculated by using the IsoSource mixing model. The results showed that the δ18O values of the two species showed a clear seasonal difference. When soil moisture was high and air temperature was low, T. chinensis mainly used water from soil depths of 60–100 cm, while Z. jujuba mainly used water from soil depths of 0–40 cm. When soil moisture was low or air temperature was high, T. chinensis mainly used the saline shallow groundwater, while Z. jujuba mainly used water from soil depths of 20–100 cm. When there was a large amount of precipitation, both T. chinensis and Z. jujuba mainly absorbed water from soil depths of 20–40 cm. Tamarix chinensis and Z. jujuba had different water-use patterns during the growing season, which reflected their adaptations to seasonal fluctuations in soil water content within a water-limited coastal ecosystem, while the niche differentiation in water use of the two species clarifies their coexistence mechanism.
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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".