Phosphorus additions imbalance terrestrial ecosystem C:N:P stoichiometry
Bibliographic record
Abstract
Carbon (C):nitrogen (N):phosphorus (P) stoichiometry in plants, soils, and microbial biomass influences productivity and nutrient cycling in terrestrial ecosystems. Anthropogenic inputs of P to ecosystems are increasing; however, our understanding of the impacts of P addition on terrestrial ecosystem C:N:P ratios remains elusive. By conducting a meta-analysis with 1413 paired observations from 121 publications, we showed that P addition significantly decreased plant, soil, and microbial biomass N:P and C:P ratios, but had negligible effects on C:N ratios. The reductions in N:P and C:P ratios became more evident as the P application rates and experimental duration increased. The P addition effects on terrestrial ecosystem C:N:P stoichiometry did not vary with ecosystem types or climates. Moreover, the responses of N:P and C:P ratios in soil and microbial biomass were associated with the responses of soil pH and fungi:bacteria ratios. Additionally, P additions increased net primary productivity, microbial biomass, soil respiration, N mineralization, and N nitrification, but decreased ammonium and nitrate contents. Decreases in plant N:P and C:P ratios were both negatively correlated to net primary productivity and soil respiration, but positively correlated to ammonium and nitrate contents; microbial biomass, soil respiration, ammonium contents, and nitrate contents all increased with declining soil N:P and C:P ratios. Our findings highlight that P additions could imbalance C:N:P stoichiometry and potentially impact the terrestrial ecosystem functions.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".