The prediction of swarming in honeybee colonies using vibrational spectra

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 accelero...

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Autores principales:	Michael-Thomas Ramsey, Martin Bencsik, Michael Ian Newton, Maritza Reyes, Maryline Pioz, Didier Crauser, Noa Simon Delso, Yves Le Conte
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/60221c413d164242b3b09b8b70fddf30
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spelling	oai:doaj.org-article:60221c413d164242b3b09b8b70fddf302021-12-02T17:41:08ZThe prediction of swarming in honeybee colonies using vibrational spectra10.1038/s41598-020-66115-52045-2322https://doaj.org/article/60221c413d164242b3b09b8b70fddf302020-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-66115-5https://doaj.org/toc/2045-2322Abstract 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.Michael-Thomas RamseyMartin BencsikMichael Ian NewtonMaritza ReyesMaryline PiozDidier CrauserNoa Simon DelsoYves Le ConteNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-17 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Michael-Thomas Ramsey Martin Bencsik Michael Ian Newton Maritza Reyes Maryline Pioz Didier Crauser Noa Simon Delso Yves Le Conte The prediction of swarming in honeybee colonies using vibrational spectra
description	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.
format	article
author	Michael-Thomas Ramsey Martin Bencsik Michael Ian Newton Maritza Reyes Maryline Pioz Didier Crauser Noa Simon Delso Yves Le Conte
author_facet	Michael-Thomas Ramsey Martin Bencsik Michael Ian Newton Maritza Reyes Maryline Pioz Didier Crauser Noa Simon Delso Yves Le Conte
author_sort	Michael-Thomas Ramsey
title	The prediction of swarming in honeybee colonies using vibrational spectra
title_short	The prediction of swarming in honeybee colonies using vibrational spectra
title_full	The prediction of swarming in honeybee colonies using vibrational spectra
title_fullStr	The prediction of swarming in honeybee colonies using vibrational spectra
title_full_unstemmed	The prediction of swarming in honeybee colonies using vibrational spectra
title_sort	prediction of swarming in honeybee colonies using vibrational spectra
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/60221c413d164242b3b09b8b70fddf30
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The prediction of swarming in honeybee colonies using vibrational spectra

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