High-mountain ecosystems are increasingly threatened by climate change, causing biodiversity loss, habitat degradation and landscape modifications. However, very few detailed studies have focussed on plant biodiversity in the high mountains of the Mediterranean. In this study, we investigated the long-term changes that have occurred in the composition, structure and ecology of high-mountain vegetation in the central Apennines (Majella) over the last 42 years. We performed a re-visitation study, using historical and newly collected vegetation data to explore which ecological and structural features have been the most successful in coping with climatic changes. Vegetation changes were analysed by comparing geo-referenced phytosociological relevés collected in high-mountain habitats (dolines, gentle slopes and ridges) on the Majella massif in 1972 and in 2014. Composition analysis was performed by detrended correspondence analysis, followed by an analysis of similarities for statistical significance assessment and by similarity percentage procedure (SIMPER) for identifying which species indicate temporal changes. Changes in ecological and structural indicators were analysed by a permutational multivariate analysis of variance, followed by a post hoc comparison. Over the last 42 years, clear floristic changes and significant ecological and structural variations occurred. We observed a significant increase in the thermophilic and mesonitrophilic plant species and an increment in the frequencies of hemicryptophytes. This re-visitation study in the Apennines agrees with observations in other alpine ecosystems, providing new insights for a better understanding of the effects of global change on Mediterranean high-mountain biodiversity. The observed changes in floristic composition, the thermophilization process and the shift towards a more nutrient-demanding vegetation are likely attributable to the combined effect of higher temperatures and the increase in soil nutrients triggered by global change. The re-visitation approach adopted herein represents a powerful tool for studying climate-related changes in sensitive high-mountain habitats.

Background: High-mountain ecosystems are centres of plant diversity that are particularly sensitive to land-use and climate change. Aims: We investigated the ecological trends associated with land use and climate change since the 1950s in different vegetation types in high-mountain habitats in the central Apennines. Methods: We analysed temporal changes in: Pinus mugo scrub, calcareous subalpine grasslands and alpine scree vegetation, comparing historical and recent vegetation records from vegetation plots from two periods (1955–1980 and 1990–2014) for their ecological indicator values (Landolt temperature and nutrient indicators) and structural traits (growth forms) over time using generalised linear models (GLMs). Results: We observed significant temporal differences in the ecology and structure of the analysed habitats. In the Pinus mugo scrub we detected a reduction of subalpine and herbaceous species and in calcareous alpine screes we observed an increment of the lower montane, montane and subalpine species and of dwarf shrubs. Conversely, subalpine grasslands were stable over time. Conclusions: Ecological changes that have occurred in the Central Apennines, following changes in type and intensity of land use and recent warming are consistent with those observed in other European mountains, for which climate and land-use changes are claimed as the main driving forces.

Remote Sensing (RS) is a useful tool for detecting and mapping Invasive Alien Plants (IAPs). IAPs mapping on dynamic and heterogeneous landscapes, using satellite RS data, is not always feasible. Unmanned aerial vehicles (UAV) with ultra-high spatial resolution data represent a promising tool for IAPs detection and mapping. This work develops an operational workflow for detecting and mapping Acacia saligna invasion along Mediterranean coastal dunes. In particular, it explores and tests the potential of RGB (Red, Green, Blue) and multispectral (Green, Red, Red Edge, Near Infra—Red) UAV images collected in pre-flowering and flowering phenological stages for detecting and mapping A. saligna. After ortho—mosaics generation, we derived from RGB images the DSM (Digital Surface Model) and HIS (Hue, Intensity, Saturation) variables, and we calculated the NDVI (Normalized Difference Vegetation Index). For classifying images of the two phenological stages we built a set of raster stacks which include different combination of variables. For image classification, we used the Geographic Object-Based Image Analysis techniques (GEOBIA) in combination with Random Forest (RF) classifier. All classifications derived from RS information (collected on pre-flowering and flowering stages and using different combinations of variables) produced A. saligna maps with acceptable accuracy values, with higher performances on classification derived from flowering period images, especially using DSM + HIS combination. The adopted approach resulted an efficient method for mapping and early detection of IAPs, also in complex environments offering a sound support to the prioritization of conservation and management actions claimed by the EU IAS Regulation 1143/2014.