A case for microbial therapeutics to bolster colony health and performance of honey bees
Bibliographic record
Abstract
The holobiont theory of evolution explains how individuals are deeply symbiotic with their gut microbes, such that microbes are adapted to influence host metabolism, immunity and behaviour, as signalled from the gut to the brain. For eusocial taxa like the Western honey bee (Apis mellifera), this brain-gut axis may scale up from the individual to affect entire colonies. Here, we examine how microbial supplementation of honey bee feeds could manipulate the brain-gut axis to affect hygienic and other social behaviours relevant to beekeeping, such as foraging, recruitment (dance language) and defence. To illustrate this concept, we focus on various lactic acid-producing bacteria that can synthesize neurotransmitters such as octopamine, dopamine, serotonin and γ-aminobutyric acid, which can influence an individual bee’s behavioural cycles and responsiveness to environmental cues. If the behaviour of a worker bee can be deliberately manipulated, and this effect multiplied across many workers, microbial neurotherapeutics could conceivably render colonies more behaviourally responsive to symptoms of disease, or more motivated to forage or possibly less aggressive towards beekeepers. Drawing from the scientific literature, we infer how microbial supplements, such as neurostimulatory or neurosuppressive probiotics, could be applied or even engineered to co-opt the brain-gut axis to bolster colony health or improve performance. The mechanistic link between the gut microbiota and the collective social behaviour of single colonies remains an understudied aspect of honey bee social biology with relevance to apiculture.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".