TY - JOUR
T1 - Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks
AU - Gernat, Tim
AU - Rao, Vikyath D.
AU - Middendorf, Martin
AU - Dankowicz, Harry
AU - Goldenfeld, Nigel
AU - Robinson, Gene E.
N1 - Funding Information:
We thank Reliance Label Solutions for printing bCodes, the School of Life Sciences Machine Shop for constructing bee tracking equipment, the Carl R. Woese Institute for Genomic Biology (IGB) Core Facilities and the Beckman Institute Visualization Laboratory for providing imaging equipment, and the University of Illinois and the National Center for Supercomputing Applications for providing computational resources. We are grateful to T. Kurobe of Denso Wave, Incorporated, for his contribution to the conception of the project; T. Iwahori of Nitto Denko Corporation for contributing to the development of the bee tracking system; C. Nye for bee management; C. Dana, C. Fu, and members of the G.E.R. laboratory, in particular G. Lawrence, P. Kundu, Z. Axelrod, and J. Herman, for assistance with field work; members of the IGB Computer Network Resource Group for assistance with data storage; E. Hadley for assistance with figures; W. Deng and members of the M.M. and G.E.R. laboratories for discussions; and C. Lutz, members of the G.E.R. laboratory, and the reviewers for comments on the manuscript. This material is based on work supported by the National Science Foundation under Grant BCS-1246920 (to H.D. and G.E.R.), a grant from the Christopher Family Foundation (to G.E.R.), National Academies Keck Futures Initiative Grant NAKFI CB4 (to T.G.), and National Institutes of Health Grant R01GM117467 (to G.E.R. and N.G.).
Funding Information:
ACKNOWLEDGMENTS. We thank Reliance Label Solutions for printing bCodes, the School of Life Sciences Machine Shop for constructing bee tracking equipment, the Carl R. Woese Institute for Genomic Biology (IGB) Core Facilities and the Beckman Institute Visualization Laboratory for providing imaging equipment, and the University of Illinois and the National Center for Supercomputing Applications for providing computational resources. We are grateful to T. Kurobe of Denso Wave, Incorporated, for his contribution to the conception of the project; T. Iwahori of Nitto Denko Corporation for contributing to the development of the bee tracking system; C. Nye for bee management; C. Dana, C. Fu, and members of the G.E.R. laboratory, in particular G. Lawrence, P. Kundu, Z. Axelrod, and J. Herman, for assistance with field work; members of the IGB Computer Network Resource Group for assistance with data storage; E. Hadley for assistance with figures; W. Deng and members of the M.M. and G.E.R. laboratories for discussions; and C. Lutz, members of the G.E.R. laboratory, and the reviewers for comments on the manuscript. This material is based on work supported by the National Science Foundation under Grant BCS-1246920 (to H.D. and G.E.R.), a grant from the Christopher Family Foundation (to G.E.R.), National Academies Keck Futures Initiative Grant NAKFI CB4 (to T.G.), and National Institutes of Health Grant R01GM117467 (to G.E.R. and N.G.).
Publisher Copyright:
© 2018 National Academy of Sciences.All Rights Reserved.
PY - 2018/2/13
Y1 - 2018/2/13
N2 - Social networks mediate the spread of information and disease. The dynamics of spreading depends, among other factors, on the distribution of times between successive contacts in the network. Heavy-tailed (bursty) time distributions are characteristic of human communication networks, including face-to-face contacts and electronic communication via mobile phone calls, email, and internet communities. Burstiness has been cited as a possible cause for slow spreading in these networks relative to a randomized reference network. However, it is not known whether burstiness is an epiphenomenon of human-specific patterns of communication. Moreover, theory predicts that fast, bursty communication networks should also exist. Here, we present a high-throughput technology for automated monitoring of social interactions of individual honeybees and the analysis of a rich and detailed dataset consisting of more than 1.2 million interactions in five honeybee colonies. We find that bees, like humans, also interact in bursts but that spreading is significantly faster than in a randomized reference network and remains so even after an experimental demographic perturbation. Thus, while burstiness may be an intrinsic property of social interactions, it does not always inhibit spreading in real-world communication networks. We anticipate that these results will inform future models of large-scale social organization and information and disease transmission, and may impact health management of threatened honeybee populations.
AB - Social networks mediate the spread of information and disease. The dynamics of spreading depends, among other factors, on the distribution of times between successive contacts in the network. Heavy-tailed (bursty) time distributions are characteristic of human communication networks, including face-to-face contacts and electronic communication via mobile phone calls, email, and internet communities. Burstiness has been cited as a possible cause for slow spreading in these networks relative to a randomized reference network. However, it is not known whether burstiness is an epiphenomenon of human-specific patterns of communication. Moreover, theory predicts that fast, bursty communication networks should also exist. Here, we present a high-throughput technology for automated monitoring of social interactions of individual honeybees and the analysis of a rich and detailed dataset consisting of more than 1.2 million interactions in five honeybee colonies. We find that bees, like humans, also interact in bursts but that spreading is significantly faster than in a randomized reference network and remains so even after an experimental demographic perturbation. Thus, while burstiness may be an intrinsic property of social interactions, it does not always inhibit spreading in real-world communication networks. We anticipate that these results will inform future models of large-scale social organization and information and disease transmission, and may impact health management of threatened honeybee populations.
KW - Trophallaxis
KW - barcode
KW - burstiness
KW - temporal network
KW - tracking
UR - http://www.scopus.com/inward/record.url?scp=85041948121&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85041948121&partnerID=8YFLogxK
U2 - 10.1073/pnas.1713568115
DO - 10.1073/pnas.1713568115
M3 - Article
C2 - 29378954
AN - SCOPUS:85041948121
SN - 0027-8424
VL - 115
SP - 1433
EP - 1438
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 7
ER -