The prediction of swarming in honeybee colonies using vibrational spectra

Ramsey, M-T, Bencsik, M ORCID: https://orcid.org/0000-0002-6278-0378, Newton, MI ORCID: https://orcid.org/0000-0003-4231-1002, Reyes, M, Pioz, M, Crauser, D, Delso, NS and Le Conte, Y, 2020. The prediction of swarming in honeybee colonies using vibrational spectra. Scientific Reports, 10 (1): 9798 (2020.

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Abstract

In this work, we disclose a non-invasive method for the monitoring and predicting of the swarming process within honeybee colonies, using vibro-acoustic information. Two machine learning algorithms are presented for the prediction of swarming, based on vibration data recorded using accelerometers placed in the heart of honeybee hives. Both algorithms successfully discriminate between colonies intending and not intending to swarm with a high degree of accuracy, over 90% for each method, with successful swarming prediction up to 30 days prior to the event. We show that instantaneous vibrational spectra predict the swarming within the swarming season only, and that this limitation can be lifted provided that the history of the evolution of the spectra is accounted for. We also disclose queen toots and quacks, showing statistics of the occurrence of queen pipes over the entire swarming season. From this we were able to determine that (1) tooting always precedes quacking, (2) under natural conditions there is a 4 to 7 day period without queen tooting following the exit of the primary swarm, and (3) human intervention, such as queen clipping and the opening of a hive, causes strong interferences with important mechanisms for the prevention of simultaneous rival queen emergence.

Item Type:	Journal article
Publication Title:	Scientific Reports
Creators:	Ramsey, M.-T., Bencsik, M., Newton, M.I., Reyes, M., Pioz, M., Crauser, D., Delso, N.S. and Le Conte, Y.
Publisher:	Springer
Date:	16 June 2020
Volume:	10
Number:	1
Identifiers:	Number Type 10.1038/s41598-020-66115-5 DOI 1333664 Other
Rights:	© The Author(s) 2020. 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/.
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	19 Jun 2020 11:40
Last Modified:	19 Jun 2020 11:41
URI:	https://irep.ntu.ac.uk/id/eprint/40061

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