Dataset Information

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 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.

SUBMITTER: Ramsey MT

PROVIDER: S-EPMC7298004 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The prediction of swarming in honeybee colonies using vibrational spectra.

Ramsey Michael-Thomas MT Bencsik Martin M Newton Michael Ian MI Reyes Maritza M Pioz Maryline M Crauser Didier D Delso Noa Simon NS Le Conte Yves Y

Scientific reports 20200616 1

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 succ ...[more]

PMID: 32546693

Similar Datasets

Project description:Paenibacillus larvae, the causative agent of American foulbrood (AFB), is the primary bacterial pathogen affecting honeybees and beekeeping. The main methods for controlling AFB are incineration of diseased colonies or prophylactic antibiotic treatment (e.g., with tylosin), neither of which is fully satisfactory. The search for superior means for controlling AFB has led to an increased interest in the natural relationships between the honeybee-pathogenic and mutualistic microorganisms and, in particular, the antagonistic effects of honeybee-specific lactic acid bacteria (hbs-LAB) against P. larvae These effects have been demonstrated only on individual larvae in controlled laboratory bioassays. Here we investigated whether supplemental administration of hbs-LAB had a similar beneficial effect on P. larvae infection at colony level. We compared experimentally AFB-infected colonies treated with hbs-LAB supplements to untreated and tylosin-treated colonies and recorded AFB symptoms, bacterial spore levels, and two measures of colony health. To account for the complexity of a bee colony, we focused on (Bayesian) probabilities and magnitudes of effect sizes. Tylosin reduced AFB disease symptoms but also had a negative effect on colony strength. The tylosin treatment did not, however, affect P. larvae spore levels and might therefore "mask" the potential for disease. hbs-LAB tended to reduce brood size in the short term but was unlikely to affect AFB symptoms or spores. These results do not contradict demonstrated antagonistic effects of hbs-LAB against P. larvae at the individual bee level but rather suggest that supplementary administration of hbs-LAB may not be the most effective way to harness these beneficial effects at the colony level.IMPORTANCE The previously demonstrated antagonistic effects of honeybee-derived bacterial microbiota on the infectivity and pathogenicity of P. larvae in laboratory bioassays have identified a possible new approach to AFB control. However, honeybee colonies are complex superorganisms where social immune defenses play a major role in resistance against disease at the colony level. Few studies have investigated the effect of beneficial microorganisms on bee diseases at the colony level. Effects observed at the individual bee level do not necessarily translate into similar effects at the colony level. This study partially fills this gap by showing that, unlike at the individual level, hbs-LAB supplements did not affect AFB symptoms at the colony level. The inference is that the mechanisms regulating the honeybee microbial dynamics within a colony are too strong to manipulate positively through supplemental feeding of live hbs-LAB and that new potential remedies identified through laboratory research have to be tested thoroughly in situ, in colonies.

Dataset Information

The prediction of swarming in honeybee colonies using vibrational spectra.

Publications

The prediction of swarming in honeybee colonies using vibrational spectra.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets