Impact of Honey Bee Migratory Management on Pathogen Loads and Immune Gene Expression is Affected by Complex Interactions With Environment, Worker Life History, and Season
Bibliographic record
Abstract
The effects of honey bee management, such as intensive migratory beekeeping, are part of the ongoing debate concerning causes of colony health problems. Even though comparisons of disease and pathogen loads among differently managed colonies indicate some effects, the direct impact of migratory practices on honey bee pathogens is poorly understood. To test long- and short-term impacts of managed migration on pathogen loads and immunity, experimental honey bee colonies were maintained with or without migratory movement. Individuals that experienced migration as juveniles (e.g., larval and pupal development), as adults, or both were compared to control colonies that remained stationary and therefore did not experience migratory relocation. Samples at different ages and life-history stages (hive bees or foragers), taken at the beginning and end of the active season, were analyzed for pathogen loads and physiological markers of health. Bees exposed to migratory management during adulthood had increased levels of the AKI virus complex (Acute bee paralysis, Kashmir bee, and Israeli acute bee paralysis viruses) and decreased levels of antiviral gene expression (dicer-like). However, those in stationary management as adults had elevated gut parasites (i.e. trypanosomes). Effects of environment during juvenile development were more complex and interacted with life-history stage and season. Age at collection, life-history stage, and season all influenced numerous factors from viral load to immune gene expression. Although the factors that we examined are not independent, the results illuminate potential factors in both migratory and nonmigratory beekeeping that are likely to contribute to colony stress, and also indicate potential mitigation measures.
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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) | 0.000 | 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".