Mapping N deposition impacts on soil microbial biomass across global terrestrial ecosystems
Bibliographic record
Abstract
Soil microorganisms are key for biodiversity and ecosystem processes. Recent meta-analyses based on nitrogen (N) addition experiments reported an overall negative impact of elevated N on soil microbial biomass on a global scale. However, individual studies have reported divergent effects of N addition, ranging from strongly negative to even positive. Moreover, N deposition varies temporally and spatially worldwide. It remains uncertain how the effects of N deposition on soil microbial biomass vary across global terrestrial ecosystems over time. Through the synthesis of 374 N addition experiments across six continents, we revealed that low quantities of N increased the soil microbial biomass, but high N amounts strongly reduced it. Moreover, the N addition effects were strongly contingent on the ecosystem type, being highly negative in grasslands (−19.3 ± 6.2%, mean and 95% confidence intervals), negative in forests (−8.6 ± 4.2%), and positive in croplands (15.1 ± 12.3%). Further, the soil microbial biomass was most negatively affected by N addition in acidic soils. By combining our meta-analysis results from N addition experiments and global N deposition data, we revealed that the global soil microbial biomass increased by 10.0% in response to cumulative N deposition from 2000–2020. However, regions encompassing the Eastern U.S., Southern Brazil, Europe, and Eastern Asia, with high N deposition rates and large forested areas of acidic soils, were hotspots for microbial biomass loss. Our findings challenge the long-held notion that N deposition has universal negative impacts on soil microbial biomass. Instead, we show that the N deposition impacts on soil microbial biomass are dependent on the amounts of elevated N, ecosystem type, and soil pH, for which N-deposition-induced soil acidification acts as an internal mechanism.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".