We review observational, experimental, and model results on how plants respond to extreme climatic conditions induced by changing climatic variability. Distinguishing between impacts of changing mean climatic conditions and changing climatic variability on terrestrial ecosystems is generally underrated in current studies. The goals of our review are thus (1) to identify plant processes that are vulnerable to changes in the variability of climatic variables rather than to changes in their mean, and (2) to depict/evaluate available study designs to quantify responses of plants to changing climatic variability. We find that phenology is largely affected by changing mean climate but also that impacts of climatic variability are much less studied, although potentially damaging. We note that plant water relations seem to be very vulnerable to extremes driven by changes in temperature and precipitation and that heat-waves and flooding have stronger impacts on physiological processes than changing mean climate. Moreover, interacting phenological and physiological processes are likely to further complicate plant responses to changing climatic variability. Phenological and physiological processes and their interactions culminate in even more sophisticated responses to changing mean climate and climatic variability at the species and community level. Generally, observational studies are well suited to study plant responses to changing mean climate, but less suitable to gain a mechanistic understanding of plant responses to climatic variability. Experiments seem best suited to simulate extreme events. In models, temporal resolution and model structure are crucial to capture plant responses to changing climatic variability. We highlight that a combination of experimental, observational, and/or modeling studies have the potential to overcome important caveats of the respective individual approaches.

To understand how diversity is distributed in space is a fundamental aim for optimizing future species and community conservation. We examined in parallel species richness and beta diversity components of nine taxonomic groups along a finite space, represented by pastured grasslands along an elevational gradient. Beta diversity, which is assumed to bridge local alpha diversity to regional gamma diversity was partitioned into the two components turnover and nestedness and analyzed at two levels: from the lowest elevation to all other elevations, and between neighboring elevations. Species richness of vascular plants, butterflies, beetles, spiders and earthworms showed a hump-shaped relationship with increasing elevation, while it decreased linearly for grasshoppers and ants, but increased for lichens and bryophytes. For most of the groups, turnover increased with increasing elevational distance along the gradient while nestedness decreased. With regard to step-wise beta diversity, rates of turnover or nestedness did not change notably between neighboring steps for the majority of groups. Our results support the assumption that species communities occupying the same habitat significantly change along elevation, however transition seems to happen continuously and is not detectable between neighboring steps. Our findings, rather than delineating levels of major diversity losses, indicate that conservation actions targeting at a preventive protection for species and their environment in mountainous regions require the consideration of entire spatial settings.

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A synergic integration of Synthetic Aperture Radar (SAR) and optical time series offers an unprecedented opportunity in vegetation phenology monitoring for mountain agriculture management. In this paper, we performed a correlation analysis of radar signal to vegetation and soil conditions by using a time series of Sentinel-1 C-band dual-polarized (VV and VH) SAR images acquired in the South Tyrol region (Italy) from October 2014 to September 2016. Together with Sentinel-1 images, we exploited corresponding Sentinel-2 images and ground measurements. Results show that Sentinel-1 cross-polarized VH backscattering coefficients have a strong vegetation contribution and are well correlated with the Normalized Difference Vegetation Index (NDVI) values retrieved from optical sensors, thus allowing the extraction of meadow phenological phases. Particularly for the Start Of Season (SOS) at low altitudes, the mean difference in days between Sentinel-1 and ground sensors is compatible with the acquisition time of the SAR sensor. However, the results show a decrease in accuracy with increasing altitude. The same trend is observed for senescence. The main outcomes of our investigations in terms of inter-satellite comparison show that Sentinel-1 is less effective than Sentinel-2 in detecting the SOS. At the same time, Sentinel-1 is as robust as Sentinel-2 in defining mowing events. Our study shows that SAR-Optical data integration is a promising approach for phenology detection in mountain regions.