Honey bees (Apis cerana) use animal feces as a tool to defend colonies against group attack by giant hornets (Vespa soror)
Bibliographic record
Abstract
Honey bees (genus Apis) are well known for the impressive suite of nest defenses they have evolved to protect their abundant stockpiles of food and the large colonies they sustain. In Asia, honey bees have evolved under tremendous predatory pressure from social wasps in the genus Vespa , the most formidable of which are the giant hornets that attack colonies in groups, kill adult defenders, and prey on brood. We document for the first time an extraordinary collective defense used by Apis cerana against the giant hornet Vespa soror . In response to attack by V . soror , A . cerana workers foraged for and applied spots of animal feces around their nest entrances. Fecal spotting increased after colonies were exposed either to naturally occurring attacks or to chemicals that scout hornets use to target colonies for mass attack. Spotting continued for days after attacks ceased and occurred in response to V . soror , which frequently landed at and chewed on entrances to breach nests, but not Vespa velutina , a smaller hornet that rarely landed at entrances. Moderate to heavy fecal spotting suppressed attempts by V . soror to penetrate nests by lowering the incidence of multiple-hornet attacks and substantially reducing the likelihood of them approaching and chewing on entrances. We argue that A . cerana forages for animal feces because it has properties that repel this deadly predator from nest entrances, providing the first report of tool use by honey bees and the first evidence that they forage for solids that are not derived from plants. Our study describes a remarkable weapon in the already sophisticated portfolio of defenses that honey bees have evolved in response to the predatory threats they face. It also highlights the strong selective pressure honey bees will encounter if giant hornets, recently detected in western North America, become established.
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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".