The ectoparasitic mite Varroa destructor is considered one of the greatest threats to the honeybee Apis mellifera. To successfully manage mite populations residing in the colony, beekeepers must stay informed of infestation levels in their apiaries. The remote, non-destructive detection of Varroa mites in honeybee hives would therefore be highly desirable.Here we show that an ultra-sensitive (1000 mV/g) accelerometer can detect vibrational waveforms originating from one individual mite. We further focus on a commonly observed pulsing behaviour never before described, characterising its physical features, periodicity and strength. The spectral features of the detected pulses strongly depend on the substrate on which they are produced. The characteristics of the vibrational pulse, particularly its repeatability and strength, indicate that mite vibrations could be successfully detected in a fully populated honeybee hive. These features, combined with the remarkably high varroa muscular power output (up to 810nW) indicate that this pulse may be functional for the mite. Our results uncover an exciting novel behaviour and provide a foundation for the remote detection of mites in beehives using vibration capture.

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Little is known about mite gait, but it has been suggested that there could be greater variation in locomotory styles for arachnids than insects. The Varroa destructor mite is a devastating ectoparasite of the honeybee. We aim to automatically detect Varroa-specific signals in long-term vibrational recordings of honeybee hives and additionally provide the first quantification and characterisation of Varroa gait through the analysis of its unique vibrational trace. These vibrations are used as part of a novel approach to achieve remote, non-invasive Varroa monitoring in honeybee colonies, requiring discrimination between mite and honeybee signals. We measure the vibrations occurring in samples of freshly collected capped brood-comb, and through combined critical listening and video recordings we build a training database for discrimination and classification purposes. In searching for a suitable vibrational feature, we demonstrate the outstanding value of two-dimensional-Fourier-transforms in invertebrate vibration analysis. Discrimination was less reliable when testing datasets comprising of Varroa within capped brood-cells, where Varroa induced signals are weaker than those produced on the cell surface. We here advance knowledge of Varroa vibration and locomotion, whilst expanding upon the remote detection strategies available for its control.

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