Wild bee and pollen microbiomes across an urban–rural divide
Bibliographic record
Abstract
Wild pollinators and their microbiota are sensitive to land use changes from anthropogenic activities that disrupt landscape and environmental features. As urbanization and agriculture affect bee habitats, human-led disturbances are driving changes in bee microbiomes, potentially leading to dysbiosis detrimental to bee fitness. This study examines the bacterial, fungal, and plant compositions of the small carpenter bee, Ceratina calcarata, and its pollen provisions across an urban-rural divide. We performed metabarcoding of C. calcarata and provisions in Toronto by targeting the 16S rRNA, ITS, and rbcL regions. Despite similar plant composition and diversity across bees and their provisions, there was a greater microbial diversity in pollen provisions than in bees. By characterizing the differences in land use, climate, and pesticide residues that differentiate urban and rural landscapes, we find that urban areas support elevated levels of microbial diversity and more complex networks between microbes and plants than rural areas. However, urban areas may lead to lower relative abundances of known beneficial symbionts and increased levels of pathogens, such as Ascosphaera and Alternaria fungi. Further, rural pollen provisions indicate elevated pesticide residues that may dysregulate symbiosis. As anthropogenic activities continue to alter land use, ever changing environments threaten microbiota crucial in maintaining bee health.
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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 | 0.001 | 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".