Dynamics of the Honeybee (Apis mellifera) Gut Microbiota Throughout the Overwintering Period in Canada
Bibliographic record
Abstract
Microbial symbionts inhabiting the honeybee gut (i.e., gut microbiota) are essential for food digestion, immunity, and gut protection of their host. The taxonomic composition of the gut microbiota is dynamic throughout the honeybee life cycle and the foraging season. However, it remains unclear how drastic changes occurring in winter, such as food shortage and cold weather, impact gut microbiota dynamics. The objective of this study was to characterize the gut microbiota of the honeybee during the overwintering period in a northern temperate climate in Canada. The microbiota of nine honeybee colonies was characterized by metataxonomy of 16S rDNA between September 2017 and June 2018. Overall, the results showed that microbiota taxonomic composition experienced major compositional shifts in fall and spring. From September to November, Enterobacteriaceae decreased, while Neisseriaceae increased. From April to June, Orbaceae increased, whereas Rhizobiaceae nearly disappeared. Bacterial diversity of the gut microbiota decreased drastically before and after overwintering, but it remained stable during winter. We conclude that the honeybee gut microbiota is likely to be impacted by the important meteorological and dietary changes that take place before and after the overwintering period. Laboratory trials are needed to determine how the observed variations affect the honeybee 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 | 0.000 |
| 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".