High abundance of lactobacilli in the gut microbiome of honey bees during winter
Bibliographic record
Abstract
Honey bee gut microbiota play specific roles in promoting host growth and physiology by regulating the immune system, behavior, metabolism, and neurological processes. While the gut microbiota of honey bee queens, workers, and larvae has been extensively studied, less is known about the composition of gut microbiota in the winter worker bees. This study investigates the dynamics of the gut microbiota in overwintering adult worker bees, focusing on two commercial bee strains: Bolton™ bees and Mann Lake™ bees. These Apis mellifera strains were investigated under different storage conditions (indoor storage at 6 °C and outdoor storage in natural conditions) during the winter months (October, November, and December). Utilizing 16S rRNA gene amplicon sequencing, we characterized the microbial composition of the whole gut. We observed the Lactobacilli dominated in all the overwintering honey bee guts with a significantly higher abundance of unclassified Lactobacillus species in November, while Lactobacillus apis showed significantly higher abundance in October. Bolton bees exhibited significantly higher abundance levels of Bartonella (denoted as uncultured) and Bifidobacterium, along with an unexpected presence of Wolbachia. In contrast, Mann Lake bees demonstrated an increased abundance of Commensalibacter. Our results suggest that Shannon diversity is influenced by the month rather than by the bee strain or storage conditions. We also found significant differences in Bray Curtis diversity index by month. Overall, taxonomical abundance was not affected by whether the hives were stored outside or in constant temperature indoor storage. However, various bacterial species showed differences in abundance across different months, with slight variations observed between bee strains. Given the potential benefits of the honey bee gut microbiome for health and nutrition, our data suggests that the genus Lactobacillus may play a significant role in bee health during winter and overwintering storage.
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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".