BACKGROUND The yellow‐legged hornet, Vespa velutina nigrithorax, is an invasive alien species (IAS) which was accidentally introduced in Europe from Asia. This social insect preys primarily on honeybees but also on other pollinators and insects. Consequently, the establishment of this pest has a negative impact on biodiversity, pollination and economy. There is no clear coordination and uniformed methods for eradication measures between countries. Here we present the first field study of the strategy of eradication of the pest species V. velutina that has been conducted in the westernmost Mediterranean archipelago. RESULTS We investigated the combination of different eradication methods, such as trapping; the use of the citizen science data for detection of presence, the active search of nests and the removal of nests using mechanical methods. The progression of the number of secondary nests found was 1 (2015), 9 (2016) and 20 (2017), with zero during 2018, 2019 and 2020, and just one embryo nest in 2018. More than half of the nests (58%) were detected thanks to citizen science data. The people sent us adult detections, and we started the triangulation method to find the nests. The last hornet found in the traps was in June 2018. CONCLUSION Early detections of the IAS are crucial to minimise their effects, and citizen science may offer an important source of information to determine the presence and distribution of V. velutina. The findings we present here indicate successful management for this globally significant pest and could contribute to advance the ‘science of eradication’. © 2021 Society of Chemical Industry

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Ecological niche models have proved to be a powerful tool in assessing invasiveness risk of alien species, allowing the optimization of control strategies. Vespa velutina (Hymenoptera: Vespidae) is an invasive species with strong ecological, economical and health impacts in Europe after it was first reported in France in 2004. It was detected for the first time on a Mediterranean island (Mallorca, Balearic Islands, Spain) in 2015, where a single nest was found in the northwest of the island. Immediately, a control plan was implemented. In this study, we analysed 30 occurrence data in Mallorca island to assess the suitability distribution predicted for Mediterranean island conditions using an ensemble of small models. We obtained high values of AUC (0.9165), Somers’ D (0.8331), Boyce (0.7611) and TSS (0.7754) as quality parameters of the final ensembled model. We show for the first time that there are suitable areas where this species can expand and stablish, mainly in steeper slopes and low isothermality zones. Likewise, the distribution suitability of V. velutina for other Mediterranean islands (Ibiza, Formentera, Menorca, Corsica, Sardinia, Sicily, Crete and Cyprus) was also explored, showing potentially suitable zones. This study provides valuable information regarding the areas in the Mediterranean islands under risk of invasion, and it could be used by both scientists and managers for an early detection and control of the invasive species due to its cost-effectiveness in terms of conservation.

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BACKGROUND: The yellow-legged hornet (Vespa velutina) is native to Southeast Asia and is an invasive alien species of concern in many countries. More effective management of populations of V. velutina could be achieved through more widespread and intensive monitoring in the field, however current methods are labor intensive and costly. To address this issue, we have assessed the performance of an optical sensor combined with a machine learning model to classify V. velutina and native wasps/hornets and bees. Our aim is to use the results of the present work as a step towards the development of a monitoring solution for V. velutina in the field.RESULTS: We recorded a total 935 flights from three bee species: Apis mellifera, Bombus terrestris and Osmia bicornis; and four wasp/hornet species: Polistes dominula, Vespula germanica, Vespa crabro and V. velutina. The machine learning model achieved an average accuracy for species classification of 80.1 ± 13.9% and 74.5 ± 7.0% for V. velutina. V. crabro had the highest level of misclassification, confused mainly with V. velutina and P. dominula. These results were obtained using a 14-value peak and valley feature derived from the wingbeat power spectral density.CONCLUSION: This study demonstrates that the wingbeat recordings from a flying insect sensor can be used with machine learning methods to differentiate V. velutina from six other Hymenoptera species in the laboratory and this knowledge could be used to help develop a tool for use in integrated invasive alien species management programs.