Comparative metagenomics reveals expanded insights into intra- and interspecific variation among wild bee microbiomes
Bibliographic record
Abstract
The holobiont approach proposes that species are most fully understood within the context of their associated microbiomes, and that both host and microbial community are locked in a mutual circuit of co-evolutionary selection. Bees are an ideal group for this approach, as they comprise a critical group of pollinators that contribute to both ecological and agricultural health worldwide. Metagenomic analyses offer comprehensive insights into an organism's microbiome, diet, and viral load, but remain largely unapplied to wild bees. Here, we present metagenomic data from three species of carpenter bees sampled from around the globe, representative of the first ever carpenter bee core microbiome. Machine learning, co-occurrence, and network analyses reveal that wild bee metagenomes are unique to host species. Further, we find that microbiomes are likely strongly affected by features of their local environment, and feature evidence of plant pathogens previously known only in honey bees. Performing the most comprehensive comparative analysis of bee microbiomes to date we discover that microbiome diversity is inversely proportional to host species social complexity. Our study helps to establish some of the first wild bee hologenomic data while offering powerful empirical insights into the biology and health of vital pollinators.
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 | 0.001 |
| 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 | 0.001 |
| 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".