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Motion Dynamics of Foragers in Honey Bee Colonies

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12421))

Abstract

Information transfer among foragers is key for efficient allocation of work and adaptive responses within a honey bee colony. For information to spread quickly, foragers trying to recruit nestmates via the waggle dance (dancers) must reach as many other non-dancing foragers (followers) as possible. Forager bees may have different drives that influence their motion patterns. For instance, dancer bees need to widely cover the dance floor to recruit nestmates, the more broadly, the higher the food source profitability. Followers may instead move more erratically in the hope of meeting a dance. Overall, a good mixing of individuals is necessary to have flexibility at the level of the colony behavior and optimally respond to changing environmental conditions. We aim to determine the motion pattern that precedes communication events, exploiting a data-driven computational model. To this end, real observation data are used to define nest features such as the dance floor location, shape and size, as well as the foragers’ population size and density distribution. All these characteristics highly correlate with the bees walking pattern and determine the efficiency of information transfer among bees. A simulation environment is deployed to test different mobility patterns and evaluate the adherence with available real-world data. Additionally, we determine under what conditions information transfer is most efficient and effective. Owing to the simulation results, we identify the most plausible mobility pattern to represent the available observations.

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Acknowledgements

This work was partially supported by CONACYT, Mexico, through a grant for postdoctoral stay abroad, scholarship holder No. 272227.

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Correspondence to Fernando Wario .

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Wario, F., Wild, B., Dormagen, D., Landgraf, T., Trianni, V. (2020). Motion Dynamics of Foragers in Honey Bee Colonies. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2020. Lecture Notes in Computer Science(), vol 12421. Springer, Cham. https://doi.org/10.1007/978-3-030-60376-2_16

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  • DOI: https://doi.org/10.1007/978-3-030-60376-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60375-5

  • Online ISBN: 978-3-030-60376-2

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