Automatic methods for long-term tracking and the detection and decoding of communication dances in honeybees

By Fernando Wario, Benjamin Wild, M. Couvillon, Raúl Rojas, Tim Landgraf. 2015

75 citations recorded by the source corpus.

The honeybee waggle dance communication system is an intriguing example of abstract animal communication and has been investigated thoroughly throughout the last seven decades. Typically, observables such as durations or angles are extracted manually directly from the observation hive or from video recordings to quantify dance properties, particularly to determine where bees have foraged. In recent years, biology has profited from automation, improving measurement precision, removing human bias, and accelerating data collection. As a further step, we have developed technologies to track all individuals of a honeybee colony and detect and decode communication dances automatically. In strong contrast to conventional approaches that focus on a small subset of the hive life, whether this regards time, space, or animal identity, our more inclusive system will help the understanding of the dance comprehensively in its spatial, temporal, and social context. In this contribution, we present full specifications of the recording setup and the software for automatic recognition and decoding of tags and dances, and we discuss potential research directions that may benefit from automation. Lastly, to exemplify the power of the methodology, we show experimental data and respective analyses for a continuous, experimental recording of nine weeks duration.

Subfields: Ecology, Evolution, Behavior and Systematics, Genetics, Insect Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology, Evolution, Behavior and Systematics

1. Tim Landgraf
2. Benjamin Wild
3. Leon Sixt

In Genetics

1. Tim Landgraf
2. Benjamin Wild
3. Leon Sixt

In Insect Science

1. Tim Landgraf
2. Benjamin Wild
3. Leon Sixt

Citations in the corpus

• RenderGAN: Generating Realistic Labeled Data (2016, methodology): “The previous BeesBook vision system (Wario et al., 2015) was tailored to specifically track all animals of small honeybee colonies over their entire lifetime.”