Mapping N deposition impacts on soil microbial biomass across global terrestrial ecosystems
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it