Honey bees and wild pollinators play a key role in the pollination of crops and wild plants worldwide. Their populations are currently experiencing drastic declines, due to interactions between multiple stressors: habitat loss and fragmentation, use of pesticides, climate change, pathogens and alien species. The invasion of Europe by the Asian Hornet (Vespa velutina) represents an emerging yet rapidly growing additional threat for pollinators. The hornet has already invaded a large part of France and is now spreading over the rest of Europe at an exceptionally high speed, and it is recognized as a major predator of bees. To improve the prediction of its potential future distribution, with respect to classical Species Distribution Models, I used specifically tailored predictive variables. Instead of the 19 bioclimatic variables classically used for any species, I created original expert knowledge based variables (climatic, land use and biotic inteactions), shaped according to the hornet's biological requirements. I used a combination of metrics to evaluate each variable predictive power. Since each variable type affects invasibility at a different spatial scale, I used a hierarchical procedure, applying variables successively at the scale at which they are the most influential, to obtain the final prediction. This model represents a powerful tool to identify the regions at risk of invasion by the hornet and to to help managers target areas where action is needed in priority.