Factors affecting heat resilience of drone honey bees (Apis mellifera) and their sperm
Bibliographic record
Abstract
Extreme temperatures associated with climate change are expected to impact the physiology and fertility of a variety of insects, including honey bees. Most previous work on this topic has focused on female honey bees (workers and queens), and comparatively little research has investigated how heat exposure affects males (drones). To address this gap, we tested body mass, viral infections, and population origin as predictors of drone survival and sperm viability in a series of heat challenge assays. We found that individual body mass was highly influential, with heavier drones being more likely to survive a heat challenge (4 h at 42°C) than smaller drones. In a separate experiment, we compared the survival of Northern California and Southern California drones in response to the same heat challenge (4 h at 42°C), and found that Southern Californian drones - which are enriched for African ancestry - were more likely to survive a heat challenge than drones originating from Northern California. To avoid survivor bias, we conducted sperm heat challenges using in vitro assays and found remarkable variation in sperm heat resilience among drones sourced from different commercial beekeeping operations, with some exhibiting no reduction in sperm viability after heat challenge and others exhibiting a 75% reduction in sperm viability. Further investigating potential causal factors for such variation, we found no association between drone mass and viability of sperm in in vitro sperm heat challenge assays, but virus inoculation (with Israeli acute paralysis virus) exacerbated the negative effect of heat on sperm viability. These experiments establish a vital framework for understanding the importance of population origin and comorbidities for drone heat sensitivity.
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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".