Honeybee swarms are a landmark example of collective behavior. To become a coherent swarm, bees locate their queen by tracking her pheromones. But how can distant individuals exploit these chemical signals, which decay rapidly in space and time? Here, we combine a behavioral assay with the machine vision detection of organism location and scenting (pheromone propagation via wing fanning) behavior to track the search and aggregation dynamics of the honeybee Apis mellifera L. We find that bees collectively create a scenting-mediated communication network by arranging in a specific spatial distribution where there is a characteristic distance between individuals and directional signaling away from the queen. To better understand such a flow-mediated directional communication strategy, we developed an agent-based model where bee agents obeying simple, local behavioral rules exist in a flow environment in which the chemical signals diffuse and decay. Our model serves as a guide to exploring how physical parameters affect the collective scenting behavior and shows that increased directional bias in scenting leads to a more efficient aggregation process that avoids local equilibrium configurations of isotropic (nondirectional and axisymmetric) communication, such as small bee clusters that persist throughout the simulation. Our results highlight an example of extended classical stigmergy: Rather than depositing static information in the environment, individual bees locally sense and globally manipulate the physical fields of chemical concentration and airflow.
|Number of pages||8|
|Journal||Proceedings of the National Academy of Sciences of the United States of America|
|Publication status||Published - 30 Mar 2021|
Bibliographical noteFunding Information:
ACKNOWLEDGMENTS. This work was supported by the NSF Graduate Research Fellowship under Grant DGE 1650115 (D.M.T.N.), and NSF Physics of Living Systems Grant 2014212 (O.P.). Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. We also acknowledge funding from the University of Colorado Boulder, BioFrontiers Institute (internal funds), the Interdisciplinary Research Theme on Autonomous Systems (O.P.), Okinawa Institute of Science and Technology Graduate University Graduate University (K.B. and G.J.S.), and Vrije Universiteit Amsterdam (G.J.S.). We thank Seneca Kristjonsdottir and Christopher Borke for bee management, Gary Nave and Michael Neuder for assistance in developing image analysis pipeline, Aubrey Kroger and Emily Walker for annotating images, and Raphael Sarfati and Chantal Nguyen for reading and commenting on the manuscript. We thank Prof. L. Mahadevan, Prof. Massimo Vergassola, Prof. Gene E. Robinson, Prof. Olav Rueppell, and members of the O.P. laboratory for insightful feedback and discussions.
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Copyright 2021 Elsevier B.V., All rights reserved.
- Agent-based model
- Computer vision
- Olfactory communication
- Signal propagation