Exploration of beta diversity across altitude gradients in an Alpine region in Trentino using FOSS4G and a historical floristic archive - F. Geri; F. Zottele; N. La Porta; C. Tattoni; P. Zatelli; M. Ciolli; M. Ciolli (The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences)
In the changing alpine mountain environment, beta diversity plays a crucial role in understanding ecosystem dynamics and guiding conservation efforts. This study wants to demonstrate the FOSS4G capabilities presenting a preliminary exploration of species turnover across environmental gradients in the Province of Trento, a highly biodiverse region in the northeastern Italian Alps, using a large database of vegetation surveys from the 1970s that was digitized and organized into a geodatabase using FOSS4G in the FORCING project. GRASS, QGIS, PostGIS, and R were used to process data from 517 linear transects, encompassing 190,761 species records. Beta diversity was assessed in relation to environmental factors such as altitude and slope, with statistical tests performed using Pearson correlations and Sørensen’s similarity coefficient. Variance partitioning was conducted via redundancy analysis (RDA) in R’s Vegan package. Results indicate that species richness and beta diversity increase with greater altitude and slope variation along transects, confirming that more heterogeneous environments support higher species turnover. Sørensen’s coefficient revealed that species similarity declines with altitude separation, particularly beyond 1,500 meters. Variance partitioning identified altitude range as the most influential factor, with combined effects from slope and elevation contributing significantly to beta diversity. This study demonstrates the effectiveness of FOSS4G software for spatial statistical analyses in biodiversity research, highlighting its capability to integrate numerical and geostatistical approaches. Future research will compare historical and contemporary floristic data, apply alternative statistical methods, and incorporate remote sensing for enhanced biodiversity assessments.