Coastal sand dunes are complex transitional systems hosting high levels of biodiversity and providing important benefits to society. In this paper we aimed to evaluate the multi-service nature of ecosystem services (ES) supply in the dunes of the Italian Adriatic coast within Natura 2000 (N2K) sites. We i) identified ES indicators and assessed the supply capacity (Climate regulation, Protection from wind and aerosol, Erosion regulation, Recreation and Tourism and Existence value of biodiversity) of natural dune ecosystems of European conservation concern; ii) upscaled this data to create an inventory of ES supply for all dune N2K sites in the study area; iii) explored the trade-offs among ES; and iv) summarized and spatially compared the overall multi-service value of the N2K sites. The study provides a method for quantifying the role of N2K sites in supplying benefits for our society. We found that the multi-service capacity of coastal dunes is uneven within sites and within administrative regions. This variability is related to both ecological (e.g. distribution, ecological integrity, extent and conservation status of dune habitats) and administrative (e.g. local implementation of the Habitats Directive) characteristics of the analysed area. ES are not coupled as several sites with high values for one ES show very low values for others. The results suggest that conservation actions should favour restoration of the natural dune zonation, since this underpins multi-service ES supply. The approach can distinguish regions with high ES values and regions where the paucity of protected areas represents a gap in ES supply, fact that offers an incentive to enhance the protection strategy but also suggests an urgent need to improve the N2K network by enlarging existent sites and including new ones.

Coastal areas harbor the most threatened ecosystems on Earth, and cost-effective ways to monitor and protect them are urgently needed, but they represent a challenge for habitat mapping and multi-temporal observations. The availability of open access, remotely sensed data with increasing spatial and spectral resolution is promising in this context. Thus, in a sector of the Mediterranean coast (Lazio region, Italy), we tested the strength of a phenology-based vegetation mapping approach and statistically compared results with previous studies, making use of open source products across all the processing chain. We identified five accurate land cover classes in three hierarchical levels, with good values of agreement with previous studies for the first and the second hierarchical level. The implemented procedure resulted as being effective for mapping a highly fragmented coastal dune system. This is encouraging to take advantage of the earth observation through remote sensing technology in an open source perspective, even at the fine scale of highly fragmented sand dunes landscapes.

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.

Several studies have highlighted the global decline of pollinator insects, with Lepidoptera being particularly affected in large parts of Europe in recent decades. Given the important biogeographic and conservation implications from faunal studies on the large Mediterranean islands, we focused on assessing butterfly diversity within the Culuccia Peninsula in north-eastern Sardinia (Italy). This area, characterized by an environmental mosaic largely untouched by human activities over the last century, is still unknown in terms of flora and fauna. For this reason, we compiled the first butterfly checklist of the Culuccia Peninsula and produced a detailed land cover map of the area to investigate the influence of the land use classes on Lepidoptera communities across seasons. The butterflies were sampled in five sessions from April to October 2022, along seven fixed transects selected to cover land use classes representative of natural and human-exploited areas for agriculture and grazing. Despite the small size of the study area, 23 of the 56 species present in Sardinia were recorded. The butterfly communities presented high dissimilarity across the different land use classes, which was driven primarily by species turnover. Significant differences in Lepidoptera diversity were observed between land cover classes with sparse vegetation and dense maquis, with higher species richness observed in the former. Shrub-dominated land use classes were associated with cooler butterfly communities compared to open environments, suggesting that they could serve as refugia in response to predicted climate change.

Combining field collected and remotely sensed (RS) data represents one of the most promising approaches for an extensive and up-to-date ecosystem assessment. We investigated the potential of the so called spectral variability hypothesis (SVH) in linking field-collected and remote-sensed data in Mediterranean coastal dunes and explored if spectral diversity provides reliable information to monitor floristic diversity, as well as the consistency of such information in altered ecosystems due to plant invasions. We analyzed alpha diversity and beta diversity, integrating floristic field and Remote-Sensing PlanetScope data in the Tyrrhenian coast (Central Italy). We explored the relationship among alpha field diversity (species richness, Shannon index, inverse Simpson index) and spectral variability (distance from the spectral centroid index) through linear regressions. For beta diversity, we implemented a distance decay model (DDM) relating field pairwise (Jaccard similarities index, Bray–Curtis similarities index) and spectral pairwise (Euclidean distance) measures. We observed a positive relationship between alpha diversity and spectral heterogeneity with richness reporting the higher R score. As for DDM, we found a significant relationship between Bray–Curtis floristic similarity and Euclidean spectral distance. We provided a first assessment of the relationship between floristic and spectral RS diversity in Mediterranean coastal dune habitats (i.e., natural or invaded). SVH provided evidence about the potential of RS for estimating diversity in complex and dynamic landscapes.