Global evidence on nitrogen saturation of terrestrial ecosystem net primary productivity
Bibliographic record
Abstract
The continually increasing nitrogen (N) deposition is expected to increase ecosystem aboveground net primary production (ANPP) until it exceeds plant N demand, causing a nonlinear response and N saturation for ANPP. However, the nonlinear response of ANPP to N addition gradient and the N saturation threshold have not been comprehensively quantified yet for terrestrial ecosystems. In this study, we compiled a global dataset of 44 experimental studies with at least three levels of N treatment. Nitrogen response efficiency (NRE, ANPP response per unit N addition) and the difference in NRE between N levels (ΔNRE) were quantified to test the nonlinearity in ANPP response. We found a universal response pattern of N saturation for ANPP with N addition gradient across all the studies and in different ecosystems. An averaged N saturation threshold for ANPP nonlinearity was found at the N addition rates of 5–6 g m −2 yr −1 . The extent to which ANPP approaches N saturation varied with ecosystem type, N addition rate and environmental factors. ANPP in grasslands had lower NRE than those in forests and wetlands. Plant NRE decreased with reduced soil C:N ratio, and was the highest at intermediate levels of rainfall and temperature. These findings suggest that ANPP in grassland or the ecosystems with low soil C:N ratio (or low and high rainfall or temperature) is easier to be saturated with N enrichment. Overall, these results indicate that the beneficial effect of N deposition on plant productivity likely diminishes with continuous N enrichment when N loading surpasses the N saturation threshold for ANPP nonlinearity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".