Norway spruce dominates natural regeneration five years after a large-scale wind disturbance in the higher montane and lower subalpine belts in the eastern Alps

Natural forest regeneration following disturbances is highly variable and often slow in high-altitude mountain forests. Among disturbance types, windstorms have emerged as major drivers of forest dynamics in Europe over recent decades. Post-disturbance forest regeneration is influenced by multiple factors, including salvage logging intensity, browsing pressure, climatic conditions and topography. In 2018, the Vaia storm damaged 1.7 % of the forest area in South Tyrol, providing a unique opportunity to study regeneration dynamics. Here, we assessed natural regeneration five years after the event by analyzing tree species composition, height distribution, and density across windthrow areas. The influence of site factors such as deadwood presence and elevation was evaluated, and terminal shoot browsing was quantified to identify potential deviations from stocking targets. In addition to field measurements, the use of Earth Observation data was tested to distinguish between poorly and well-regenerated sites. The regeneration was dominated by Norway spruce (Picea abies L. Karst) (65 %), followed by pioneer species including rowan (Sorbus aucuparia), willow (Salixs spp.), and birch (Betula pendula) (27 % combined). Seventy-four percent of individuals were ≤ 25 cm tall, and regeneration was concentrated at forest edges, with only 34 % of plots reaching the standard target of 3.500 undamaged individuals per hectare. Deadwood presence negatively affected both tree density and species richness, while lower elevations favored regeneration. Terminal shoot browsing affected 25 % of all small trees, with highest damage observed for Silver fir (Abies alba Mill) (53 %) and willow (60 %). The Enhanced Vegetation Index (EVI) showed a moderate positive correlation with regeneration success (R= 0.58). Our findings highlight critical factors shaping early post-disturbance regeneration and suggest that integrating field data with Earth Observation data can support targeted forest restoration measures.