Incorporating variability in honey bee waggle dance decoding improves the mapping of communicated resource locations.

Honey bees communicate to nestmates locations of resources, including food, water, tree resin and nest sites, by making waggle dances. Dances are composed of repeated waggle runs, which encode the distance and direction vector from the hive or swarm to the resource. Distance is encoded in the duration of the waggle run, and direction is encoded in the angle of the dancer's body relative to vertical. Glass-walled observation hives enable researchers to observe or video, and decode waggle runs. However, variation in these signals makes it impossible to determine exact locations advertised. We present a Bayesian duration to distance calibration curve using Markov Chain Monte Carlo simulations that allows us to quantify how accurately distance to a food resource can be predicted from waggle run durations within a single dance. An angular calibration shows that angular precision does not change over distance, resulting in spatial scatter proportional to distance. We demonstrate how to combine distance and direction to produce a spatial probability distribution of the resource location advertised by the dance. Finally, we show how to map honey bee foraging and discuss how our approach can be integrated with Geographic Information Systems to better understand honey bee foraging ecology.