Nitrogen addition mediates the response of foliar stoichiometry to phosphorus addition: a meta-analysis
Bibliographic record
Abstract
Abstract Background Changes in foliar nitrogen (N) and phosphorus (P) stoichiometry play important roles in predicting the effects of global change on ecosystem structure and function. However, there is substantial debate on the effects of P addition on foliar N and P stoichiometry, particularly under different levels of N addition. Thus, we conducted a global meta-analysis to investigate how N addition alters the effects of P addition on foliar N and P stoichiometry across different rates and durations of P addition and plant growth types based on more than 1150 observations. Results We found that P addition without N addition increased foliar N concentrations, whereas P addition with N addition had no effect. The positive effects of P addition on foliar P concentrations were greater without N addition than with N addition. Additionally, the effects of P addition on foliar N, P and N:P ratios varied with the rate and duration of P addition. In particular, short-term or low-dose P addition with and without N addition increased foliar N concentration, and the positive effects of short-term or low-dose P addition on foliar P concentrations were greater without N addition than with N addition. The responses of foliar N and P stoichiometry of evergreen plants to P addition were greater without N addition than with N addition. Moreover, regardless of N addition, soil P availability was more effective than P resorption efficiency in predicting the changes in foliar N and P stoichiometry in response to P addition. Conclusions Our results highlight that increasing N deposition might alter the response of foliar N and P stoichiometry to P addition and demonstrate the important effect of the experimental environment on the results. These results advance our understanding of the response of plant nutrient use efficiency to P addition with increasing N deposition.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.031 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".