Markerless tracking of an entire honey bee colony

Katarzyna Bozek (), Laetitia Hebert, Yoann Portugal, Alexander S. Mikheyev and Greg J. Stephens
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Katarzyna Bozek: Biological Physics Theory Unit, OIST Graduate University
Laetitia Hebert: Biological Physics Theory Unit, OIST Graduate University
Yoann Portugal: Biological Physics Theory Unit, OIST Graduate University
Alexander S. Mikheyev: Ecology and Evolution Unit, OIST Graduate University
Greg J. Stephens: Biological Physics Theory Unit, OIST Graduate University

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: Abstract From cells in tissue, to bird flocks, to human crowds, living systems display a stunning variety of collective behaviors. Yet quantifying such phenomena first requires tracking a significant fraction of the group members in natural conditions, a substantial and ongoing challenge. We present a comprehensive, computational method for tracking an entire colony of the honey bee Apis mellifera using high-resolution video on a natural honeycomb background. We adapt a convolutional neural network (CNN) segmentation architecture to automatically identify bee and brood cell positions, body orientations and within-cell states. We achieve high accuracy (~10% body width error in position, ~10° error in orientation, and true positive rate > 90%) and demonstrate months-long monitoring of sociometric colony fluctuations. These fluctuations include ~24 h cycles in the counted detections, negative correlation between bee and brood, and nightly enhancement of bees inside comb cells. We combine detected positions with visual features of organism-centered images to track individuals over time and through challenging occluding events, recovering ~79% of bee trajectories from five observation hives over 5 min timespans. The trajectories reveal important individual behaviors, including waggle dances and crawling inside comb cells. Our results provide opportunities for the quantitative study of collective bee behavior and for advancing tracking techniques of crowded systems.

Date: 2021
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DOI: 10.1038/s41467-021-21769-1

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