Computational approaches to the analysis of collective behavior in social insects increasingly rely on motion paths as an intermediate data layer from which one can infer individual behaviors or social interactions. Honey bees are a popular model for learning and memory. Previous experience has been shown to affect and modulate future social interactions. So far, no lifetime history observations have been reported for all bees of a colony. In a previous work we introduced a recording setup customized to track up to 4,000 marked bees over several weeks. Due to detection and decoding errors of the bee markers, linking the correct correspondences through time is non-trivial. In this contribution we present an in-depth description of the underlying multi-step algorithm which produces motion paths, and also improves the marker decoding accuracy significantly. The proposed solution employs two classifiers to predict the correspondence of two consecutive detections in the first step, and two tracklets in the second. We automatically tracked ∼2,000 marked honey bees over 10 weeks with inexpensive recording hardware using markers without any error correction bits. We found that the proposed two-step tracking reduced incorrect ID decodings from initially ∼13% to around 2% post-tracking. Alongside this paper, we publish the first trajectory dataset for all bees in a colony, extracted from ∼3 million images covering 3 days. We invite researchers to join the collective scientific effort to investigate this intriguing animal system. All components of our system are open-source.

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

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.

No abstract

As the study of honey bee health has gained attention in the biology community, researchers have looked for new, non-invasive methods to monitor the health status of the colony. Since the beehive sound alters when the colony is exposed to stressors, analysis of the acoustic response of the colony has been used as a method to identify the type of stressor, whether it is chemical, pest, or disease. So far, two feature sets have been successfully used for this kind of analysis, being these low-level signal features and Mel Frequency Cepstral Coefficients (MFCC). Here we propose using soundscape indices, developed initially to delineate acoustic diversity in ecosystems, as an alternative to now used features. In our study, we examine the beehive acoustic response to trichloromethane laced-air and blank air and compare the performance of all three feature sets to discern the colony's sound between the hive being exposed to the chemical and not. Our results show that sound indices overperform the alternative features sets on this task. Based on these findings, we consider sound indices to be a valid set of features for beehive sound analysis and present our results to call the attention of the community on this fact.