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Markerless tracking of an entire honey bee colony

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 compre...

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Autores principales: Bozek, Katarzyna, Hebert, Laetitia, Portugal, Yoann, Mikheyev, Alexander S., Stephens, Greg J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7979864/
https://www.ncbi.nlm.nih.gov/pubmed/33741938
http://dx.doi.org/10.1038/s41467-021-21769-1
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author Bozek, Katarzyna
Hebert, Laetitia
Portugal, Yoann
Mikheyev, Alexander S.
Stephens, Greg J.
author_facet Bozek, Katarzyna
Hebert, Laetitia
Portugal, Yoann
Mikheyev, Alexander S.
Stephens, Greg J.
author_sort Bozek, Katarzyna
collection PubMed
description 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.
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spelling pubmed-79798642021-04-16 Markerless tracking of an entire honey bee colony Bozek, Katarzyna Hebert, Laetitia Portugal, Yoann Mikheyev, Alexander S. Stephens, Greg J. Nat Commun Article 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. Nature Publishing Group UK 2021-03-19 /pmc/articles/PMC7979864/ /pubmed/33741938 http://dx.doi.org/10.1038/s41467-021-21769-1 Text en © The Author(s) 2021, corrected publication 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Bozek, Katarzyna
Hebert, Laetitia
Portugal, Yoann
Mikheyev, Alexander S.
Stephens, Greg J.
Markerless tracking of an entire honey bee colony
title Markerless tracking of an entire honey bee colony
title_full Markerless tracking of an entire honey bee colony
title_fullStr Markerless tracking of an entire honey bee colony
title_full_unstemmed Markerless tracking of an entire honey bee colony
title_short Markerless tracking of an entire honey bee colony
title_sort markerless tracking of an entire honey bee colony
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7979864/
https://www.ncbi.nlm.nih.gov/pubmed/33741938
http://dx.doi.org/10.1038/s41467-021-21769-1
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