Recruitment-dance signals draw larger audiences when honey bee colonies have multiple patrilines
Bibliographic record
Abstract
Honey bee queens (Apis mellifera) who mate with multiple males produce colonies that are filled with numerous genetically distinct patrilines of workers. A genetically diverse colony benefits from an enhanced foraging effort, fuelled in part by an increase in the number of recruitment signals that are produced by foragers. However, the influence of patriline diversity on the attention paid to these signals by audiences of potentially receptive workers remains unexplored. To determine whether recruitment dances performed by foragers in multiple-patriline colonies attract a greater number of dance followers than dances in colonies that lack patriline diversity, we trained workers from multiple- and single-patriline colonies to forage in a greenhouse and monitored their dance-following activity back in the hives. On average, more workers followed a dance if it was performed in a multiple-patriline colony rather than a single-patriline colony (33% increase), and for a greater number of dance circuits per follower. Furthermore, dance-following workers in multiple-patriline colonies were more likely to exit their hive after following a dance, although this did not translate to a difference in colony-level exit rates between treatment types. Recruiting nest mates to profitable food sources through dance communication is critical to a colony's foraging success and long-term fitness; polyandrous queens produce colonies that benefit not only from increased recruitment signalling, but also from the generation of larger and more attentive audiences of signal receivers. This study highlights the importance of integrating responses of both signal senders and receivers to understand more fully the success of animal-communication systems.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".