Diverse Diets with Consistent Core Microbiome in Wild Bee Pollen Provisions
Bibliographic record
Abstract
Bees collect pollen from flowers for their offspring, and by doing so contribute critical pollination services for our crops and ecosystems. Unlike many managed bee species, wild bees are thought to obtain much of their microbiome from the environment. However, we know surprisingly little about what plant species bees visit and the microbes associated with the collected pollen. Here, we addressed the hypothesis that the pollen and microbial components of bee diets would change across the range of the bee, by amplicon sequencing pollen provisions of a widespread small carpenter bee, Ceratina calcarata, across three populations. Ceratina calcarata was found to use a diversity of floral resources across its range, but the bacterial genera associated with pollen provisions were very consistent. Acinetobacter, Erwinia, Lactobacillus, Sodalis, Sphingomonas and Wolbachia were among the top ten bacterial genera across all sites. Ceratina calcarata uses both raspberry (Rubus) and sumac (Rhus) stems as nesting substrates, however nests within these plants showed no preference for host plant pollen. Significant correlations in plant and bacterial co-occurrence differed between sites, indicating that many of the most common bacterial genera have either regional or transitory floral associations. This range-wide study suggests microbes present in brood provisions are conserved within a bee species, rather than mediated by climate or pollen composition. Moving forward, this has important implications for how these core bacteria affect larval health and whether these functions vary across space and diet. These data increase our understanding of how pollinators interact with and adjust to their changing environment.
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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".