Land use alters diazotroph community structure by regulating bacterivores in Mollisols in Northeast China
Bibliographic record
Abstract
Changes in land use can generate environmental pressures that influence soil biodiversity, and numerous studies have examined the influences of land use on the soil microbial communities. However, little is known about the effects of land use on ecological interactions of soil microbes and their predators. Diazotrophs are key soil microbes that play important functional roles in fixing atmospheric nitrogen. In this study, we investigated the co-association of diazotroph community members and patterns of diazotroph and bacterivore networks under different long-term land uses including cropland, grassland, and bare land. Diazotroph community was characterized by high-throughput sequencing. The results indicated that land use type influenced the dominant genera of diazotrophs and shaped the occurrence of specific indicator diazotroph taxa. Co-existing pattern analysis of diazotrophs and bacterivores indicated that grassland converted from cropland increased the complexity of diazotroph and bacterivore network structure. The number of nodes for diazotrophs and bacterivores was higher in grassland than in cropland and bare land. Random forest analysis revealed that six bacterivore genera Cephalobus, Protorhabditis, Acrobeloides, Mesorhabditis, Anaplectus, and Monhystera had significant effects on diazotrophs. Bacterivores were found to have predominantly negative effects in bare land. Different bacterivores had differing effects with respect to driving changes in diazotroph community structure. Structural equation model showed that land use could control diazotroph community composition by altering soil properties and regulating abundance of bacterivores. These findings accordingly enhance our current understanding of mechanisms underlying the influence of land use patterns on diazotrophs from the perspective of soil food webs.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".