Among the functions provided by forests, protection has always been considered one of the preeminent in mountain areas. In order to fulfil, maximize, and sustain this function, specific forest structures should be obtained and maintained through properly designed forest management. A specific management goal should be defined with a well-defined forest target against each natural hazard, based on the protection potentially provided by the forest stands, in either an active (e.g. against avalanches) or passive way (e.g. against rockfall). Climate change is forecast to affect both disturbance regimes and forest ecosystems, leading to new challenging issues concerning protection forest management. This paper describes how a forest stand exerts its protective role against rockfalls and the target profile to be reached for sustaining this function. Potential consequences of climate change on forest ecosystems that management will have to face in the near future are also addressed. New perspectives are provided taking into account the knowledge coming from recent research studies and specifically the results obtained in the RockTheAlps project (ASP462), dealing with the assessment of protection forests against rockfall in the Alps.

As a consequence of climate change, the impact of pluvial flooding is expected to increase in the next decades. Despite citizens’ poor knowledge, several types of stormwater infrastructure can be implemented to mitigate the impact of future events. This paper focuses on the implementation of green and grey stormwater interventions (i.e., with or without vegetation) on private properties. Framed by the Protection Motivation Theory, a survey-based case study analysis, carried out in a pluvial flooding-prone area of the Veneto Region (Italy), highlights the main factors driving people’s willingness to implement these interventions. The analysis shows that the implementation of grey stormwater infrastructures is driven by the perceived threat and the amount of past pluvial flooding damage (i.e., the direct experience as a proxy of prior knowledge) while the implementation of green stormwater infrastructures is driven also by additional factors (awareness of these interventions, age and education level of the citizens). Based on these results, lack of knowledge on innovative stormwater interventions represents a critical barrier to their implementation on private properties, and it confirms the need for specific dissemination and information activities.

Worldwide, mountain forests represent a significant factor in reducing rockfall risk over long periods of time on large potential disposition areas. While the economic value of technical protection measures against rockfall can be clearly determined and their benefits indicated, there is no general consensus on the quantification of the protective effect of forests. Experience shows that wherever there is forest, the implementation of technical measures to reduce risk of rockfall might often be dispensable or cheaper, and large deforestations (e.g. after windthrows, forest fires, clear-cuts) often show an increased incidence of rockfall events. This study focussed on how the protective effect of a forest against rockfall can be quantified on an alpine transregional scale. We therefore estimated the runout length, in terms of the angle of reach, of 700 individual rockfall trajectories from 39 release areas from Austria, Germany, Italy and Slovenia. All recorded rockfall events passed through forests which were classified either as coppice forests or, according to the CORINE classification of land cover, as mixed, coniferous or broadleaved dominated high forest stands. For each individual rockfall trajectory, we measured the forest structural parameters stem number, basal area, top height, ratio of shrub to high forest and share of coniferous trees. To quantify the protective effect of forests on rockfall, a hazard reduction factor is introduced, defined as the ratio between an expected angle of reach without forest and the back-calculated forest-influenced angles of reach. The results show that forests significantly reduce the runout length of rockfall. The highest reduction was observed for mixed high forest stands, while the lowest hazard reduction was observed for high forest stands dominated either by coniferous or broadleaved tree species. This implies that as soon as one tree species dominates, the risk reduction factor becomes lower. Coppice forests showed the lowest variability in hazard reduction. Hazard reduction due to forests increases, on average, by 7% for an increase in the stem number by 100 stems per hectare. The proposed concept allows a global view of the effectiveness of protective forests against rockfall processes and thus enable to value forest ecosystem services for future transregional assessments on a European level. Based on our results, general cost–benefit considerations of nature-based solutions against rockfall, such as protective forests as well as first-order evaluations of rockfall hazard reduction effects of silvicultural measures within the different forest types, can be supported.

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Background Brain-computer interfaces (BCIs) are systems capable of translating human brain patterns, measured through electroencephalography (EEG), into commands for an external device. Despite the great advances in machine learning solutions to enhance the performance of BCI decoders, the translational impact of this technology remains elusive. The reliability of BCIs is often unsatisfactory for end-users, limiting their application outside a laboratory environment. Methods We present the analysis on the data acquired from an end-user during the preparation for two Cybathlon competitions, where our pilot won the gold medal twice in a row. These data are of particular interest given the mutual learning approach adopted during the longitudinal training phase (8 months), the long training break in between the two events (1 year) and the demanding evaluation scenario. A multifaceted perspective on long-term user learning is proposed: we enriched the information gathered through conventional metrics (e.g., accuracy, application performances) by investigating novel neural correlates of learning in different neural domains. Results First, we showed that by focusing the training on user learning, the pilot was capable of significantly improving his performance over time even with infrequent decoder re-calibrations. Second, we revealed that the analysis of the within-class modifications of the pilot’s neural patterns in the Riemannian domain is more effective in tracking the acquisition and the stabilization of BCI skills, especially after the 1-year break. These results further confirmed the key role of mutual learning in the acquisition of BCI skills, and particularly highlighted the importance of user learning as a key to enhance BCI reliability. Conclusion We firmly believe that our work may open new perspectives and fuel discussions in the BCI field to shift the focus of future research: not only to the machine learning of the decoder, but also in investigating novel training procedures to boost the user learning and the stability of the BCI skills in the long-term. To this end, the analyses and the metrics proposed could be used to monitor the user learning during training and provide a marker guiding the decoder re-calibration to maximize the mutual adaptation of the user to the BCI system.