Te yellow-legged hornet Vespa velutina is an invasive alien species in many areas of the world. In Europe, it is considered a species of Union concern and national authorities have to establish surveillance plans, early warning and rapid response systems or control plans. Tese strategies customarily require the assessment of the areas that could be colonised beyond outbreaks or expanding ranges, so as to establish efcient containment protocols. Te hornet is spreading through a mix of natural diffusion and human-mediated transportation. Despite the latter dispersion mode is hardly predictable, natural diffusion could be modelled from nest data of consecutive years. Te aim of this work is to develop a procedure to predict the spread of the yellow-legged hornet in the short term in order to increase the efciency of control plans to restrain the diffusion of this species. We used data on the mean distances of colonial nests between years to evaluate the probability of yellow-legged hornet dispersal around the areas where the species is present. Te distribution of nests in Italy was mainly explained by elevation (95% of nests located within 521 m a.s.l.) and distance from source sites (previous years’ colonies; 95% within 1.4–6.2 km). Te diffusion models developed with these two variables forecast, with good accuracy, the spread of the species in the short term: 98–100% of nests were found within the predicted area of expansion. A similar approach can be applied in areas invaded by the yellow-legged hornet, in particular beyond new outbreaks and over the border of its expanding range, to implement strategies for its containment. Te spatial application of the models allows the establishment of buffer areas where monitoring and control efforts can be allocated on the basis of the likelihood of the species spreading at progressively greater distances.

Establishing surveillance areas for tackling the invasion of Vespa velutina in outbreaks and over the border of its expanding range

Lioy, Simone;Manino, Aulo;Porporato, Marco;Laurino, Daniela;ROMANO, ANDREA;Bertolino, Sandro
Last
2019-01-01

Abstract

Te yellow-legged hornet Vespa velutina is an invasive alien species in many areas of the world. In Europe, it is considered a species of Union concern and national authorities have to establish surveillance plans, early warning and rapid response systems or control plans. Tese strategies customarily require the assessment of the areas that could be colonised beyond outbreaks or expanding ranges, so as to establish efcient containment protocols. Te hornet is spreading through a mix of natural diffusion and human-mediated transportation. Despite the latter dispersion mode is hardly predictable, natural diffusion could be modelled from nest data of consecutive years. Te aim of this work is to develop a procedure to predict the spread of the yellow-legged hornet in the short term in order to increase the efciency of control plans to restrain the diffusion of this species. We used data on the mean distances of colonial nests between years to evaluate the probability of yellow-legged hornet dispersal around the areas where the species is present. Te distribution of nests in Italy was mainly explained by elevation (95% of nests located within 521 m a.s.l.) and distance from source sites (previous years’ colonies; 95% within 1.4–6.2 km). Te diffusion models developed with these two variables forecast, with good accuracy, the spread of the species in the short term: 98–100% of nests were found within the predicted area of expansion. A similar approach can be applied in areas invaded by the yellow-legged hornet, in particular beyond new outbreaks and over the border of its expanding range, to implement strategies for its containment. Te spatial application of the models allows the establishment of buffer areas where monitoring and control efforts can be allocated on the basis of the likelihood of the species spreading at progressively greater distances.
2019
46
46
51
69
Asian yellow-legged hornet; invasive species; control plans; monitoring; nest distance; predictive models
Lioy, Simone; Manino, Aulo; Porporato, Marco; Laurino, Daniela; Romano, Andrea; Capello, Michela; Bertolino, Sandro
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1704394
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