Drone Honey Bees (Apis Mellifera) are Disproportionately Sensitive to Abiotic Stressors Despite Expressing High Levels of Stress Response Proteins
Bibliographic record
Abstract
Abstract Drone honey bees (haploid males) are the obligate sexual partners of queens, and the availability of healthy, high-quality drones directly affects a queen’s fecundity and productivity of her subsequent colony. Yet, our understanding of how stressors affect drone fecundity and physiology is presently limited. We investigated sex biases in susceptibility to abiotic stressors (cold stress, topical imidacloprid exposure, and topical exposure to a realistic cocktail of pesticides), and we found that drones were more sensitive to cold and imidacloprid exposure but the cocktail was not toxic at the concentrations tested. We corroborated this lack of apparent toxicity with in-hive cocktail exposures via pollen feeding. We then used quantitative proteomics to investigate protein expression profiles in the hemolymph of topically exposed workers and drones, and we show that drones express surprisingly high levels of putative stress response proteins relative to workers. Drones apparently invest in strong constitutive expression of damage-mitigating proteins for a wide range of stressors, yet they are still sensitive to stress when challenged. The robust expression of stress-response proteins suggests that drone stress tolerance systems are fundamentally rewired relative to workers, and their susceptibility to stress depends on more than simply gene dose or deleterious recessive alleles.
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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) | 0.002 | 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".