Remote and automated detection of Asian hornets (Vespa velutina nigrithorax) at an apiary, using spectral features of their hovering flight sounds

• Honeybees and hornets are monitored at hives, using a camcorder and microphone. • Their flight sounds differ and can be discriminated with machine learning. • Two-dimensional-Fourier-transform analysis promotes the discrimination success. • The hovering of hornets during predation is key to acoustic discrimination success. Asian hornets ( Vespa velutina nigrithorax ) are an invasive species that have spread across Europe since 2004. As V.velutina largely predate on honeybees, assessing their presence at apiaries would be useful for invasive species control programmes and beekeepers to help protect their hives. At present, hornet monitoring techniques are both costly and time consuming. A promising alternative is a remote detection strategy at apiaries, which would promote straightforward, non-invasive data acquisition. The remote capture of flight acoustics should benefit hornet detection as wingbeat frequencies have previously been described as ‘the fingerprint’ of some flying invertebrate species. We here demonstrate a non-invasive method of V.velutina detection using their hovering flight sounds, captured by microphones that can be left at an apiary over the long-term. Paired with a training algorithm (principal component analysis and discriminant function analysis) that discriminates between hornet flight and other external noises (honeybee flight sounds and general background noise), we demonstrate that hornet hovering acoustics exhibit specific spectral features that promote the detection of individuals at an apiary. The training algorithm in our study was highly accurate (98.7 %) when testing just under 1-hour of apiary recordings. Utilising two-dimensional-Fourier-transforms has also benefited this algorithm, as the analysis technique is ideal for identifying repeating features in sound/vibrational data, which are an inherent consequence of hovering hornet sounds. The experimental design and training algorithm used in this study have demonstrated excellent potential for hornet detection in the field, and are now ready to be tested on long-term, continuous data to further assess their success.