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Interactions between people and ecological systems, through leisure or tourism activities, form a complex socio-ecological spatial network. The analysis of the benefits people derive from their interactions with nature—also referred to as cultural ecosystem services (CES)—enables a better understanding of these socio-ecological systems. In the age of information, the increasing availability of large social media databases enables a better understanding of complex socio-ecological interactions at an unprecedented spatio-temporal resolution. Within this context, we model and analyze these interactions based on information extracted from geotagged photographs embedded into a multiscale socio-ecological network. We apply this approach to 16 case study sites in Europe using a social media database (Flickr) containing more than 150,000 validated and classified photographs. After evaluating the representativeness of the network, we investigate the impact of visitors’ origin on the distribution of socio-ecological interactions at different scales. First at a global scale, we develop a spatial measure of attractiveness and use this to identify four groups of sites. Then, at a local scale, we explore how the distance traveled by the users to reach a site affects the way they interact with this site in space and time. The approach developed here, integrating social media data into a network-based framework, offers a new way of visualizing and modeling interactions between humans and landscapes. Results provide valuable insights for understanding relationships between social demands for CES and the places of their realization, thus allowing for the development of more efficient conservation and planning strategies.

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Owing to the high rate of global industrialization, widely distributed forest areas have decreased. Few of these natural forests have succeeded to remain untouched by human activities. The primary ecosystems are characterized by advanced age, high biodiversity and climax condition. These virgin forest, are found in Buzau Mountains, in the Eastern Carpathians of Romania. For understanding and develop the functional principles of virgin forests, field information was collected from three permanent research plots of one hectare area. Gini index (G = 0,68-0,84) and Camino index (H = 1,62-1,74) were recorded for all permanent study plots. The obtained values reveal high heterogeneity. Total volume of dead wood is between 54,93 m 3 •ha-1 (Șapte Izvoare) and 123, 34 m 3 •ha-1 (Penteleu-Viforâta 2), most of it came from coniferous species (fir and spruce). There have been analyzed the relationships between dead wood and alive components using different statistical distribution functions (Beta, Gamma, Weibull), and the quantity of dry biomass and CO 2 stock from dead wood was estimated.

The stand structure of a virgin forest situated at an average altitude of 1130 m a.s.l. in the Milea Viforâta Nature Reserve (Southern Carpathians, Romania) was investigated to determine the specific development phases of the forest and understand how they influence the stand structure, with the aim of providing optimal solutions and structural models for sustainable forest management. All trees with breast height diameter (dbh) ≥ 8 cm were inventoried in the study plot (1 ha), and the main dendrometrical variables were measured. Radial increment cores were taken from all the trees and were subsequently processed. A total of 317 trees from three species -European beech (Fagus sylvatica), silver fir (Abies alba) and Norway spruce (Picea abies)were sampled at different development phases (optimum, ageing, breakdown and dieback, rejuvenation). Testing stand structural diversity with the Gini index, a minimal stability was found in the rejuvenation development phase and a maximum stability in the ageing phase. No significant match was found between standard theoretical functions (Normal, Weibull, Gamma and Exponential) and the observed distribution of tree diameter. Also, it was confirmed that dominance of beech in all development phases is a consequence of its high competitive ability and its capacity to endure difficult environmental and biologically stressful conditions. The results revealed a series of structural models specific to these forest ecosystems, which can help managing forests under the selection system.