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Forest insect pests represent a serious threat to E uropean forests and their negative effects could be exacerbated by climate change. This paper illustrates how species distribution modelling integrated with host tree species distribution data can be used to assess forest vulnerability to this threat. Two case studies are used: large pine weevil ( H ylobius abietis L ) and horse‐chestnut leaf miner ( C ameraria ohridella D eschka & D imič) both at pan‐ E uropean level. The proposed approach integrates information from different sources. Occurrence data of insect pests were collected from the G lobal B iodiversity I nformation F acility ( GBIF ), climatic variables for present climate and future scenarios were sourced, respectively, from W orld C lim and from the R esearch P rogram on C limate C hange, A griculture and F ood S ecurity ( CCAFS ), and distributional data of host tree species were obtained from the E uropean F orest D ata C entre ( EFDAC ), within the F orest I nformation S ystem for E urope ( FISE ). The potential habitat of the target pests was calculated using the machine learning algorithm of M axent model. On the one hand, the results highlight the potential of species distribution modelling as a valuable tool for decision makers. On the other hand, they stress how this approach can be limited by poor pest data availability, emphasizing the need to establish a harmonised open E uropean database of geo‐referenced insect pest distribution data.
Barredo et al. (2015) studied this question.