Machine learning reveals the waggle drift’s role in the honey bee dance communication system

The honey bee waggle dance is one of the most prominent examples of abstract communication among animals: successful foragers convey new resource locations to interested followers via characteristic “dance” movements in the nest, where dances advertise different locations on different overlapping su...

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Bibliographic Details
Published in:PNAS nexus 2023-09, Vol.2 (9), p.pgad275-pgad275
Main Authors: Dormagen, David M, Wild, Benjamin, Wario, Fernando, Landgraf, Tim
Format: Article
Language:English
Subjects:
Advertising
Animal behavior
Biological, Health, and Medical Sciences
Dancing
Environmental aspects
Honeybee
Insect communication
Machine learning
Neural networks
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Summary:The honey bee waggle dance is one of the most prominent examples of abstract communication among animals: successful foragers convey new resource locations to interested followers via characteristic “dance” movements in the nest, where dances advertise different locations on different overlapping subregions of the “dance floor.” To this day, this spatial separation has not been described in detail, and it remains unknown how it affects the dance communication. Here, we evaluate long-term recordings of Apis mellifera foraging at natural and artificial food sites. Using machine learning, we detect and decode waggle dances, and we individually identify and track dancers and dance followers in the hive and at artificial feeders. We record more than a hundred thousand waggle phases, and thousands of dances and dance-following interactions to quantitatively describe the spatial separation of dances on the dance floor. We find that the separation of dancers increases throughout a dance and present a motion model based on a positional drift of the dancer between subsequent waggle phases that fits our observations. We show that this separation affects follower bees as well and results in them more likely following subsequent dances to similar food source locations, constituting a positive feedback loop. Our work provides evidence that the positional drift between subsequent waggle phases modulates the information that is available to dance followers, leading to an emergent optimization of the waggle dance communication system.
ISSN:2752-6542
2752-6542
DOI:10.1093/pnasnexus/pgad275