The ethology of honeybees (Apis mellifera) studied using accelerometer technology

Ramsey, M.-T., 2018. The ethology of honeybees (Apis mellifera) studied using accelerometer technology. PhD, Nottingham Trent University.

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Abstract

While the significance of vibrational communication across insect taxa has been fairly well studied, the substrate-borne vibrations of honeybees remains largely unexplored. Within this thesis I have monitored honeybees with a new method, that of logging their short pulsed vibrations on the long term, and I have started the longstanding endeavour of underpinning the applications of it. The use of advanced spectral analysis and machine learning techniques as part of this new method has revealed exciting statistics that challenges previous expert’s interpretations.

This work is comprised of three results chapters with the aim of determining (1) what can the in-situ monitoring of specific honeybee pulsed vibrations tell us about the status of a colony? (2) What long term statistics be can identified to help to disentangle the function of two specific pulses of vibrations? (3) How effective is honeycomb-embedded accelerometer technology at assessing the ethology of honeybee colonies?

In the first results chapter, I explore the contributions of developing pupae and larvae to accelerometer datasets by monitoring brood frames isolated from the colony with embedded accelerometers. From this, I show that very little vibrational information is obtainable from capped brood using accelerometer. However, I am able to showcase the quantitation of specific vibrational waveforms that are indicative of brood emergence from the honeycomb.

In the second results chapter, the automated detection of honeybee whooping signals was achieved with an 83% accuracy, revealing never-before-seen long-term statistics of vibration, once thought to be an inhibitory or food request signal. Statistics show that this pulse is very common, highly repeatable, occurs mainly at night with a distinct decrease towards midday, is correlated with the brood cycle, and can be elicited en masse as a startle response by bees following the gentle knocking of the hive.

Through synchronisation of high-definition video and accelerometer data, the honeybee dorso ventral abdominal shaking (DVA) signal has been physically quantitated, for the first time, giving a one to-one association between behaviour and intra-comb vibrations. From this, a novel method for the continuous in-situ non-invasive automated detection was developed for a honeybee signal previously thought to have no vibratory component. I show that the signal is detected with high frequency and repeatability, occurring mostly at night with a minimum towards mid-afternoon; inverse to that of the signal's amplitude over an average day. An unprecedented increase in the cumulative amplitude of DVA signals occurs in the hours preceding and following a primary swarm. These statistics suggests that the DVA signal may have additional functions other than as a foraging activation signal, and that the amplitude of the signal might be indicative of the switching of its dual, and potentially multiple functions.
This work has pioneered the use of accelerometer technology for the long-term monitoring of honeybee pulsed vibrations, making significant contributions to the emerging field of biotremology. The applications of this work, however, go far beyond the realms of the honeybee. The methods developed throughout this thesis could easily be adapted for the automated in-situ monitoring of biologically relevant vibroacoustics of multiple wild taxonomic groups, including wasps, termites, elephants and bats, as well as for replacing the need for visually compiled ethograms within lab-based manipulative experiments.

Item Type: Thesis
Creators: Ramsey, M.-T.
Date: July 2018
Rights: This work is the intellectual property of the author. You may copy up to 5% of this work for private study, or personal, non-commercial research. Any re-use of the information contained within this document should be fully referenced, quoting the author, title, university, degree level and pagination. Queries or requests for any other use, or if a more substantial copy is required, should be directed in the owner of the Intellectual Property Rights.
Divisions: Schools > School of Science and Technology
Depositing User: Linda Sullivan
Date Added: 07 Jan 2019 12:22
Last Modified: 08 Jan 2019 08:37
URI: http://irep.ntu.ac.uk/id/eprint/35491

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