Abstract ID: 3.12226 | Accepted as Poster | Poster | TBA | TBA

Ecotones, defined as transition areas between two biomes, are often considered as biodiversity hotspots due to the overlap of species from adjacent ecosystems. This is particularly evident in alpine-treeline ecotones, which form the transition between closed forests at lower elevations and open alpine areas at higher altitudes. Their high species richness is further amplified by the complex topography of mountain regions, creating a mosaic of microhabitats with varying climatic and environmental conditions. Treeline ecotones are also highly sensitive to climate change, as rising temperatures drive an upward shift of the forest boundary. Given their ecological significance and vulnerability, accurately mapping and monitoring these areas is crucial for understanding their spatiotemporal dynamics. However, identifying treeline ecotones remains a challenge: since ecotones are dynamic habitats in both space and time, it is not easy to delimit them. To address this, we propose a novel methodology that combines LiDAR data and field surveys to map and characterize treeline ecotones in mountain areas. Three study areas were selected within the French side of the Mont Blanc massif: Loriaz, Peclerey and Plan de l’Aiguille. A hierarchical sampling protocol was designed to capture key vegetation characteristics above and below the treeline. Sampling points were selected using a random stratified approach, based on the habitat map of the Chamonix valley and on the Canopy Height Model (CHM) derived from LiDAR data. Vegetation surveys were carried out within 10 x 10 m plots, where structural and compositional attributes of the vegetation were measured. During the 2024 summer season, 54 field vegetation surveys were conducted along 6 transects within the three study areas. Five EUNIS habitat types were identified: S22 Alpine and subalpine ericoid heath; S25 Subalpine and subarctic deciduous scrub; T311 Alpine and Carpathian subalpine Picea forests and T343 Western Larix, mountain pine and Pinus cembra forests. Preliminary analyses combining field and LiDAR data suggest that LiDAR-derived canopy height appears to be a good predictor of forests and shrubs. These findings highlight the potential of LiDAR data for refining the mapping of treeline ecotones and providing a more reliable framework for long-term monitoring of these sensitive ecosystems.