In the early 2000s, beech forests in Western Europe suffered from a so far unexplained burst of mortality. Necroses, ambrosia‐beetle and fungal attacks were observed on the trunks. The symptoms were similar to previous events reported throughout the 20th Century. One current hypothesis is that these phenomena were related to early frost events for which the trees were physiologically unprepared and which made them vulnerable to biotic attacks. In the present study, we aimed to test this hypothesis further, by retrospective meteorological analyses and also by an experimental approach. Our meteorological analyses highlighted the occurrence of cold waves a year before the beech declines were reported in 1929, 1942 and 1998. In our experimental approach, frost injuries were inflicted to mature trees in a beech stand using dry ice. The treated trees were more attractive to insects than untreated controls. Insect attacks were observed in the treated zones on the trees but colonization was not very successful. The galleries had aborted most of the time with only a few larval chambers. Very few insects were caught in emergence traps. The results of these two approaches support and strengthen the hypothesis that frost induced beech dieback. Frost injuries increased tree attraction to ambrosia beetles to the point of inducing attacks. However, the overall success of these attacks was much lower than that observed in the 2000s. These differences might reflect limitations in our experimental approach, where frost wounding was applied locally to the trees.

Mapping species spatial distribution using spatial inference and prediction requires a lot of data. Occurrence data are generally not easily available from the literature and are very time-consuming to collect in the field. For that reason, we designed a survey to explore to which extent large-scale databases such as Google maps and Google street view could be used to derive valid occurrence data. We worked with the Pine Processionary Moth (PPM) Thaumetopoea pityocampa because the larvae of that moth build silk nests that are easily visible. The presence of the species at one location can therefore be inferred from visual records derived from the panoramic views available from Google street view. We designed a standardized procedure allowing evaluating the presence of the PPM on a sampling grid covering the landscape under study. The outputs were compared to field data. We investigated two landscapes using grids of different extent and mesh size. Data derived from Google street view were highly similar to field data in the large-scale analysis based on a square grid with a mesh of 16 km (96% of matching records). Using a 2 km mesh size led to a strong divergence between field and Google-derived data (46% of matching records). We conclude that Google database might provide useful occurrence data for mapping the distribution of species which presence can be visually evaluated such as the PPM. However, the accuracy of the output strongly depends on the spatial scales considered and on the sampling grid used. Other factors such as the coverage of Google street view network with regards to sampling grid size and the spatial distribution of host trees with regards to road network may also be determinant.