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Social networks predict the life and death of honey bees

View ORCID ProfileBenjamin Wild, View ORCID ProfileDavid M Dormagen, View ORCID ProfileAdrian Zachariae, View ORCID ProfileMichael L Smith, View ORCID ProfileKirsten S Traynor, View ORCID ProfileDirk Brockmann, View ORCID ProfileIain D Couzin, View ORCID ProfileTim Landgraf
doi: https://doi.org/10.1101/2020.05.06.076943
Benjamin Wild
1Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
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  • For correspondence: b.w@fu-berlin.de tim.landgraf@fu-berlin.de
David M Dormagen
1Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
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Adrian Zachariae
2Robert Koch Institute, Berlin, Germany
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Michael L Smith
4Department of Collective Behavior, Max Planck Institute of Animal Behavior, Konstanz, Germany
5Centre for the Advanced Study of Collective Behavior, University of Konstanz, Konstanz, Germany
6Department of Biology, University of Konstanz, Konstanz, Germany
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Kirsten S Traynor
7Global Biosocial Complexity Initiative, Arizona State University, Tempe, USA
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Dirk Brockmann
2Robert Koch Institute, Berlin, Germany
3Institute for Theoretical Biology, Humboldt University Berlin, Berlin, Germany
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Iain D Couzin
4Department of Collective Behavior, Max Planck Institute of Animal Behavior, Konstanz, Germany
5Centre for the Advanced Study of Collective Behavior, University of Konstanz, Konstanz, Germany
6Department of Biology, University of Konstanz, Konstanz, Germany
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Tim Landgraf
1Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
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  • For correspondence: b.w@fu-berlin.de tim.landgraf@fu-berlin.de
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Abstract

In many social systems, an individual’s role is reflected by its interactions with other members of the group (Gordon 2010, Pinter-Wollmann et al. 2014, Krause 2015, Farine & Whitehead 2015). In honey bee colonies (Apis mellifera), workers generally perform different tasks as they age, yet there is high behavioral variation in same-aged bees (Seeley 1982, Robinson 1992, Huang and Robinson 1996, Johnson 2010). It is unknown how social interactions within the colony relate to an individual’s tasks throughout her life. We propose a new method to extract a single number from each individual’s interaction patterns in multimodal social networks that captures her current role in the colony. This “network age” is better than biological age at predicting task allocation (+99%), survival (+157%), and activity patterns (+44-108%) and even predicts task allocation up to one week (around a sixth of her typical lifespan) into the future. Network age identifies distinct developmental paths and task changes throughout a bee’s life: We show that individuals change tasks gradually and exhibit high task repeatability, and that same aged bees form stable behavioral subgroups in which they predominantly interact with one another. While we derived interaction networks by automatically tracking a fully tagged colony, we show that tracking only 5% of the bees is sufficient to extract a meaningful representation of the individuals’ interaction patterns, demonstrating the feasibility of our method for detecting complex social structures with reduced experimental effort. Since network age more accurately predicts task allocation than biological age, it could be used in experimental manipulations to quantify shifts in the timing of task transitions as a response. We extend our method to extract interaction patterns relevant to other attributes of the individuals, such as their mortality, opening up a broad range of possible applications. Our approach is a scalable instrument to study individual behavior through the lens of social interactions over time in honey bees and other complex social systems.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted May 06, 2020.
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Social networks predict the life and death of honey bees
Benjamin Wild, David M Dormagen, Adrian Zachariae, Michael L Smith, Kirsten S Traynor, Dirk Brockmann, Iain D Couzin, Tim Landgraf
bioRxiv 2020.05.06.076943; doi: https://doi.org/10.1101/2020.05.06.076943
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Social networks predict the life and death of honey bees
Benjamin Wild, David M Dormagen, Adrian Zachariae, Michael L Smith, Kirsten S Traynor, Dirk Brockmann, Iain D Couzin, Tim Landgraf
bioRxiv 2020.05.06.076943; doi: https://doi.org/10.1101/2020.05.06.076943

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