Impacts of noise pollution from high-speed rail and road on bird diversity: a case study in a protected area of Italy.

The disturbance of infrastructures may affect biological communities that are exposed to them. This study assesses the impact of high-speed (highway and railway) infrastructures in a protected study site, the Natural Reserve Fontanili di Corte Valle Re (Emilia-Romagna, Italy). We compared bird diversity with sound intensity and frequency in three sampling areas, increasingly distant from the infrastructures at the border with the reserve, during the last 4 years (2019-2022), monitoring sedentary, nesting, and migratory bird species. We hypothesize a decreasing diversity closer to the source of disturbance, which is mostly attributable to noise pollution. Our findings confirmed this trend, and we show that, in particular, disturbance seems to influence species richness more than the total abundance of birds. We also discovered that highway disturbance was much higher than railway in terms of frequency and duration. In light of these results, we suggest that some species, which have a behavioral ecology strongly based on singing to communicate with each other for their reproductive and defensive strategies, may suffer more from constant acoustic disturbance. The installation of effective noise barriers to shield the sound produced by the highways should be considered a mandatory request not only in proximity to houses but also in the vicinity of protected areas.

Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach.

The ecosystem services offered by pollinators are vital for supporting agriculture and ecosystem functioning, with bees standing out as especially valuable contributors among these insects. Threats such as habitat fragmentation, intensive agriculture, and climate change are contributing to the decline of natural bee populations. Remote sensing could be a useful tool to identify sites of high diversity before investing into more expensive field survey. In this study, the ability of Unoccupied Aerial Vehicles (UAV) images to estimate biodiversity at a local scale has been assessed while testing the concept of the Height Variation Hypothesis (HVH). This hypothesis states that the higher the vegetation height heterogeneity (HH) measured by remote sensing information, the higher the vegetation vertical complexity and the associated species diversity. In this study, the concept has been further developed to understand if vegetation HH can also be considered a proxy for bee diversity and abundance. We tested this approach in 30 grasslands in the South of the Netherlands, where an intensive field data campaign (collection of flower and bee diversity and abundance) was carried out in 2021, along with a UAV campaign (collection of true color-RGB-images at high spatial resolution). Canopy Height Models (CHM) of the grasslands were derived using the photogrammetry technique "Structure from Motion" (SfM) with horizontal resolution (spatial) of 10 cm, 25 cm, and 50 cm. The accuracy of the CHM derived from UAV photogrammetry was assessed by comparing them through linear regression against local CHM LiDAR (Light Detection and Ranging) data derived from an Airborne Laser Scanner campaign completed in 2020/2021, yielding an [Formula: see text] of 0.71. Subsequently, the HH assessed on the CHMs at the three spatial resolutions, using four different heterogeneity indices (Rao's Q, Coefficient of Variation, Berger-Parker index, and Simpson's D index), was correlated with the ground-based flower and bee diversity and bee abundance data. The Rao's Q index was the most effective heterogeneity index, reaching high correlations with the ground-based data (0.44 for flower diversity, 0.47 for bee diversity, and 0.34 for bee abundance). Interestingly, the correlations were not significantly influenced by the spatial resolution of the CHM derived from UAV photogrammetry. Our results suggest that vegetation height heterogeneity can be used as a proxy for large-scale, standardized, and cost-effective inference of flower diversity and habitat quality for bees.