Nitrogen and phosphorus addition differentially affect plant ecological stoichiometry in desert grassland
Bibliographic record
Abstract
Abstract Plant C:N:P stoichiometric relations drive powerful constraints on ecological interactions and processes. However, information about plant stoichiometric responses to N and P availability in desert grassland is limited. We conducted two field experiments with 7 levels of N (from 0.5 g to 24 g N ∙ m−2 yr−1) and P (from 0.05 g to 3.2 g P ∙ m−2 yr−1) additions in a desert grassland of Kunlun Mountain in the northwest of China to investigate the effects of these addition rates on the N and P stoichiometry of the dominant grass species Seriphidium korovinii. Nitrogen and P additions both affected plant stoichiometry. N addition suppressed P concentrations, whereas P addition had no effect on plant N concentrations. The N:P ratios of green aboveground biomass (AGB) were positively correlated with N addition ranging from 14.73 to 29.08, whereas those for P additions decreased ranging from 14.73 to 8.29. N concentrations were positively correlated with soil available N:P ratios, whereas, P concentrations were negatively correlated with soil availably N:P. Our results suggest that chemistry and stoichiometry of S. korovinii was directly affected by soil nutrient availability. Soil N availability affects S. korovinii stoichiometry to a greater extent that does soil P availability in this ecosystem. These findings suggest that N-deposition could affect the stoichiometry of this desert grassland ecosystem, and thereby potentially alter litter decomposition, plant community composition, nutrient cycling, and food-web dynamics of these desert ecosystems.
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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 | 0.000 | 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".