Pathogen spillover from honey bees (Apis mellifera L.) to wild bees in North America
Bibliographic record
Abstract
The decline of wild bee populations in North America is worrisome. Honey bee ( Apis mellifera ) pathogens have been mentioned as one factor that may be implicated in these declines. This review analyses evidence of pathogen spillover from Apis mellifera to wild bee species, the mechanisms involved, the role of migratory beekeeping, the different pathogens associated with spillover cases, the impact of pathogens on wild bees, and a few strategies to mitigate the issue. Honey bee pathogens have been detected in more than 50 species within five families of bees in North American countries. Data on pathogen prevalence and phylogeny strongly indicate spillover from honey bees to wild bees, as well as spillback events. Most pathogens studied are viruses, but bacteria, fungi, and protozoa causing diseases in honey bees have been also found to replicate in wild bees and, in some cases, cause damage and shorten the lifespan of the insects. Regulated movement of hives and effective control of honey bee diseases could reduce the frequency of pathogen spillover to wild bee communities because these measures would decrease the risk of transmission. Additionally, the increased use of native bees and habitat restoration could reduce the risk of pathogen spillover from honey bees to wild bees. Studies focussing on the epidemiology and effects of pathogens on wild bee species are urgently needed to develop strategies for the optimal management of honey bees and wild bee species, to protect biodiversity and ecosystems, and to ensure adequate pollination services in North America.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".