Trophic regulation of soil microbial biomass under nitrogen enrichment: A global meta-analysis
Bibliographic record
Abstract
Eutrophication, including nitrogen (N) enrichment, can affect soil microbial communities through changes in trophic interactions. However, a knowledge gap still exists about how plant resources (‘bottom-up effects’) and microbial predators (‘top-down effects’) regulate the impacts of N enrichment on microbial biomass at the global scale. To address this knowledge gap, we conducted a global meta-analysis using 2885 paired observations from 217 publications to evaluate the regulatory effects of plant biomass and soil nematodes on soil microbial biomass under N enrichment across terrestrial ecosystems. We found that the effects of N enrichment on soil microbial biomass strongly varied across ecosystems. N enrichment decreased the soil microbial biomass of natural grasslands and forests due to soil acidification and the subsequent losses of predatory and microbivorous nematodes stimulating microbial growth. By contrast, N enrichment increased the microbial biomass of managed croplands mainly via increasing plant biomass production. The short-term of N enrichment (experimental duration ≤ 5 years) could reduce microbial biomass via decreasing nematode abundance across diverse ecosystems, whereas the long-term of N enrichment (experimental duration > 5 years) mainly promoted microbial biomass via increasing plant biomass. These findings highlight the critical roles of microbial predators and plant input in shaping microbial responses to N enrichment, which are highly dependent on ecosystem type and the period of N enrichment. Earth system models that predict soil microbial biomass and their linkages to soil functioning should consider the variations in plant biomass and soil nematodes under future scenarios of N deposition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.013 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".