Climate change effects on tree growth from Romanian forest monitoring Level II plots.

Forest health status is negatively influenced by climate change, air pollution and other disturbances. Extreme droughts reduce stand productivity, increase vulnerability to pests, and can even provoke mortality. Growth dynamics at tree and forest stand levels are considered the main indicators of stability and productivity in forest ecosystem structures. The main climate drivers for tree growth were identified using basal area increment (BAI) as a synthetic indicator. BAI chronologies were obtained from increment cores for 1960-2012 period. Six species were analysed in an attempt to identify their growth limiting factors. For the most important oak species in Romania, resilience components were computed in order to analyse their response to drought events. Moreover, growth dynamics were analysed for two species in mixed and monoculture forests. The results suggest that - in comparison to Picea abies and Fagus sylvatica, the sensitivity of Quercus spp. is much higher (0.3-0.47). Oakspecies situated in the most drought-affected areas are sensitive to rainfall values from the previous autumn, current spring, and early summer, with April monthly values having the most significant effect on BAI increment (r = 0.47*) The most sensitive species to drought is Q. cerris and Q. frainetto. Their BAI reduction during drought is >50% compared with the BAI values before the drought period. The recovery capacity of tree growth following drought events is lower for Q. robur and Q. petraea and higher for Q. cerris and Q. frainetto. The mixed forest stands have not showed a constant higher resistance to drought.

Synthetic aperture radar sensitivity to forest changes: A simulations-based study for the Romanian forests.

Natural and anthropogenic disturbances pose a significant threat to forest condition. Continuous, reliable and accurate forest monitoring systems are needed to provide early warning of potential declines in forest condition. To address that need, state-of-the-art simulations models were used to evaluate the utility of C-, L- and P-band synthetic aperture radar (SAR) sensors within an integrated Earth-Observation monitoring system for beech, oak and coniferous forests in Romania. The electromagnetic simulations showed differentiated sensitivity to vegetation water content, leaf area index, and forest disturbance depending on SAR wavelength and forest structure. C-band data was largely influenced by foliage volume and therefore may be useful for monitoring defoliation. Changes in water content modulated the C-band signal by <1 dB which may be insufficient for a meaningful retrieval of drought effects on forest. C-band sensitivity to significant clear-cuts was rather low (1.5 dB). More subtle effects such as selective logging or thinning may not be easily detected using C- or L-band data with the longer P-band needed for retrieving small intensity forest disturbances. Overall, the simulations emphasize that additional effort is needed to overcome current limitations arising from the use of a single frequency, acquisition time and geometry by tapping the advantages of dense time series, and by combining acquisitions from active and passive sensors. The simulation results may be applicable to forests outside of Romania since the forests types used in the study have similar morphological characteristics to forests elsewhere in Europe.

Multiscale socio-ecological networks in the age of information.

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.