Key message: Forecasting annual seed production will improve the management of forests across Europe. The foreMast R package we developed predicts current year masting probability in beech (Fagus sylvatica L.) using climate data easily accessible by any stakeholder. Context: Modelling and predicting forest masting is one of the most challenging tasks in forest management, as it is a strategy shared by several species, very important for tree dispersion and forest regeneration, mainly related to climate and ecological processes. Aims: As many studies focus on European beech (Fagus sylvatica L.) masting without simple practical implementations, we developed a tool capable of predicting beech masting years. Methods: The tool is an R package (foreMast) made by three functions, which relies mainly on climate data. The algorithm performance is compared with the records of the MASTREE database, which gather several beech seed production series for various sites across European countries. Results: Overall, the results show a tight correlation with the compared sites (ρ = 0.50 to 0.61, p-value < 0.0001, respectively), especially when temperatures weigh three times more than precipitation. Nevertheless, in some sites, seed production seems to be more related to precipitation dynamics than to temperatures. Conclusion: foreMast can be used both for studying changes in mast events in relation to climate changes and in operative forest management and planning. It is flexible and thus amenable to future implementation of additional predicting variables or target species. © 2021, INRAE and Springer-Verlag France SAS, part of Springer Nature.

In this work, we evaluated the influence of root structure on shallow landslide distribution. Root density measurements were acquired in the field and the corresponding root cohesion was estimated. Data were acquired from 150 hillslope deposit trenches dug in areas either devoid or affected by shallow landslides within the Garfagnana Valley (northern Tuscany, Italy). Results highlighted a correlation between the root reinforcement and the location of measurement sites. Namely, lower root density was detected within shallow landslides, with respect to neighboring areas. Root area ratio (RAR) data allowed us to estimate root cohesion by the application of the revised version of the Wu and Waldron Model. Then, we propose a new method for the assimilation of the lateral root reinforcement into the infinite slope model and the limit equilibrium approach by introducing the equivalent root cohesion parameter. The results fall within the range of root cohesion values adopted in most of the physically based shallow landslide susceptibility models known in the literature (mean values ranging between ca. 2 and 3 kPa). Moreover, the results are in line with the scientific literature that has demonstrated the link between root mechanical properties, spatial variability of root reinforcement, and shallow landslide locations. © 2023 by the authors.