Soil microbial diversity and network organization respond to land use and agricultural inputs worldwide
Bibliographic record
Abstract
Soil microbiomes are critical for ecosystem functioning, yet the global influences of climate and agricultural practices on their diversity and structure remain incompletely characterized. Here we analyzed 1921 soil samples from 33 countries worldwide across diverse biomes to assess how climate gradients and agricultural inputs, including pesticides and fertilizers, shape prokaryotic and fungal communities. We found that microbial diversity peaks at intermediate temperatures and differs markedly between natural and agricultural soils, with agriculture increasing microbial diversity while altering community composition and ecological guilds. Pesticide use selectively reduced bacterial diversity and shifted fungal guilds, decreasing ectomycorrhizal fungi while increasing saprotrophs, whereas fertilization reduced microbial network cohesion, with organic and inorganic fertilizers eliciting distinct community responses. These findings reveal that climatic factors and agricultural management jointly influence soil microbial diversity, community structure, and network connectivity, with implications for soil health and ecosystem resilience in managed landscapes. Overall, our results demonstrate that agricultural practices, including the use of pesticides and both organic and inorganic fertilizers, act as strong ecological filters that reshape soil microbiomes worldwide-enhancing apparent diversity but driving a functional shift toward less mutualistic, more fragmented, and potentially less resilient communities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".