Social attraction mediates collective foraging decisions in invasive hornets
Bibliographic record
Abstract
Abstract Group-living animals commonly use social information to better locate and exploit resources. In many insects, birds, fish and mammals, this can lead to collective foraging decisions by which animals share a single food source among alternatives of equal qualities. Here, we report collective foraging decisions in a social wasp, the yellow-legged hornet Vespa velutina nigrithorax , a major predator of bees and other terrestrial invertebrates invasive across Asia, Europe and North America. When given a choice between two identical liquid food sources (feeders or traps containing sugar solutions), wild hornets distributed asymmetrically on the two options, and this phenomenon was more frequent as group size increased. Priming one of the food sources with dead hornets predictably biased the collective choices towards this particular option, irrespective of whether the dead insects were conspecifics or hornets from a closely related species. Inter-attraction in yellow-legged hornets is thus a passive and non-specific mechanism, possibly mediated by visual or chemical cues displayed by dead hornets. This collective behaviour may provide important foraging advantages to hornets invading new territories and bring new perspectives for population control.
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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".