Flowering Plant Microbiomes and Network Interactions Across an Urban Gradient
Bibliographic record
Abstract
We used flowers to explore how ephemeral anthosphere microbiomes differ among flowering plant species and along an urban gradient. Here, we sequenced 16S rRNA for bacteria, ITS1 for fungi and rbcL for plant DNA from 10 different plant species sampled to characterise anthosphere microbiomes along an urban gradient and identify important network interactions. Bacterial and fungal flower microbiomes significantly differed in diversity across plant species, especially among Asteraceae and Fabaceae. Across all analyses, four taxa, the bacteria Pantoea and Rosenbergiella and the fungi Alternaria and Cladosporium were highly prevalent and contributed to the majority of microbiome composition differences observed between plant species. These four taxa harbour strains or species that may be either pathogenic or beneficial to plants. Across a land use gradient, the plant community bacterial and fungal microbiome was stable and consistent. Flower-plant networks confirmed all focal flower families in abundance on each sampled flower, with the addition of Paulowniaceae, suggesting that pollinators visiting the focal flowers also visit this plant family. Our findings reveal that anthosphere microbiomes are diverse at the plant community level and encouragingly remain robust against urbanisation.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".