Diversity and assembly of the microbiome of a leguminous plant along an urbanization gradient
Bibliographic record
Abstract
Interactions between plants and bacterial communities are essential for host physiology and broader ecosystem functioning, but plant–microbiome interactions can be disrupted by environmental change like urbanization. Here, we evaluated how urbanization affected the diversity and assembly of soil and white clover Trifolium repens microbiome communities. We sampled 35 populations of white clover and associated roots and soil along an urbanization gradient. Soil alpha diversity was greater at the urban and rural limits of the gradient and lower in suburban habitats, while root alpha diversity was not influenced by urbanization. Root and soil bacterial communities had distinct compositions, with greater beta diversity for root compared to soil microbiomes. We found that urbanization directly and indirectly affected soil microbiome assembly, particularly through soil carbon. In contrast, root microbiome assembly was not linked to urbanization, which suggested that the host plant acted as an additional filter on microbiome assembly independent of urbanization. We also found that key pathogenic bacteria like Legionella and Clostridium varied in abundance with urbanization, which has implications for human health. Together, our study underscores the importance of examining how urban‐driven environmental change alters the ecology and function of soil and root microbiomes.
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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 | 0.000 | 0.000 |
| 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".