Abstract
The waggle dance is one of the most popular examples of animal communication. Forager bees direct their nestmates to profitable resources via a complex motor display. Essentially, the dance encodes the polar coordinates to the resource in the field. Unemployed foragers follow the dancer’s movements and then search for the advertised spots in the field. Throughout the last decades, biologists have employed different techniques to measure key characteristics of the waggle dance and decode the information it conveys. Early techniques involved the use of protractors and stopwatches to measure the dance orientation and duration directly from the observation hive. Recent approaches employ digital video recordings and manual measurements on screen. However, manual approaches are very time-consuming. Most studies, therefore, regard only small numbers of animals in short periods of time. We have developed a system capable of automatically detecting, decoding and mapping communication dances in real-time. In this paper, we describe our recording setup, the image processing steps performed for dance detection and decoding and an algorithm to map dances to the field. The proposed system performs with a detection accuracy of 90.07%. The decoded waggle orientation has an average error of -2.92° (± 7.37°), well within the range of human error. To evaluate and exemplify the system’s performance, a group of bees was trained to an artificial feeder, and all dances in the colony were automatically detected, decoded and mapped. The system presented here is the first of this kind made publicly available, including source code and hardware specifications. We hope this will foster quantitative analyses of the honey bee waggle dance.
Highlights
The honey bee waggle dance is one of the most popular communication systems in the animal world
While the duration of a WR is estimated from the number of frames exported by the attention module (AM), its orientation is computed in a separate processing step here on referred as orientation module (OM), usually performed offline to keep computing resources free for detecting waggle runs
We investigated the possible error sources and visually inspected waggle runs decoded with large error
Summary
The honey bee waggle dance is one of the most popular communication systems in the animal world. Assuming homogeneous object density in all directions, it may be sufficient to use a simple conversion factor obtained from the waggle durations observed in dances for a single feeding location This way, without tracking the foragers’ flights, one can deduce the distribution of foragers in the environment by establishing the distribution of dance-communicated locations. A first group of solutions focused on mapping the bees’ trajectories via tracking algorithms [32,33,34] These trajectories might be analyzed to extract specific features such as waggle run orientation and duration, using either a generic classifier trained on bee dances (see [35,36,37]) or methods based on hand-crafted features such as the specific spectral composition of the trajectory in a short window [6]. Compared to a human observer, the system extracts the Automatic detection and decoding of honey bee waggle dances waggle orientation with an average error of -2.92 ̊ (± 7.37 ̊) well within the range of human error
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