Groundwater Depths Affect Phosphorus and Potassium Resorption but Not Their Utilization in a Desert Phreatophyte in Its Hyper-Arid Environment
Bibliographic record
Abstract
Nutrients are vital for plant subsistence and growth in nutrient-poor and arid ecosystems. The deep roots of phreatophytic plants are necessary to access groundwater, which is the major source of nutrients for phreatophytes in an arid desert ecosystem. However, the mechanisms through which changes in groundwater depth affect nutrient cycles of phreatophytic plants are still poorly understood. This study was performed to reveal the adaptive strategies involving the nutrient use efficiency (NUE) and nutrient resorption efficiency (NRE) of desert phreatophytes as affected by different groundwater depths. This work investigated the nitrogen (N), phosphorus (P), and potassium (K) concentrations in leaf, stem, and assimilating branch, as well as the NUE and NRE of the phreatophytic Alhagi sparsifolia. The plant was grown at groundwater depths of 2.5, 4.5, and 11.0 m during 2015 and 2016 in a desert-oasis transition ecotone at the southern rim of the Taklimakan Desert in northwestern China. Results show that the leaf, stem, and assimilating branch P concentrations of A. sparsifolia at 4.5 m groundwater depth were significantly lower than those at 2.5 and 11.0 m groundwater depths. The K concentrations in different tissues of A. sparsifolia at 4.5 m groundwater depth were significantly higher than those at 2.5 and 11.0 m groundwater depths. Conversely, the NRE of P in A. sparsifolia was the highest among the three groundwater depths, while that of K in A. sparsifolia was the lowest among the three groundwater depths in 2015 and 2016. The N concentration and NUE of N, P, and K in A. sparsifolia, however, were not influenced by groundwater depth. Further analyses using structural equation models showed that groundwater depth had significant effects on the P and K resorption of A. sparsifolia by changing soil P and senescent leaf K concentrations. Overall, our results suggest groundwater depths affect P and K concentrations and resorption but not their utilization in a desert phreatophyte in its hyper-arid environment. This study provides a new insight into the phreatophytic plant nutrient cycle strategy under a changing external environment in a hyper-arid ecosystem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".