Wind and snow significantly impact Austrian forests, affecting structure, community composition, and ecosystem services. From 2002–2023, salvage cuts due to wind and snow ranged from 0.5 to 10.3 million m³ , with bark beetle infestations adding 0.6–5.0 million m³ of damage. Accurate estimation of damaged wood is crucial, making disturbance models essential for effective forest management in Austria. This paper aims to develop predictive models for estimating salvage cuts in coniferous, broad-leaved, and mixed species stands. It focuses on predicting the probability of wind/snow breakage at both the plot and individual-tree levels, as well as the survival chances of damaged trees. Using a dataset of 343,722 trees from 9532 plots in the Austrian National Forest Inventory (1981–2021), we categorized disturbance-related damages as "events." Sixteen logistic mixedeffect models were employed to analyze climate, site, stand, and individual-tree data to assess event probabilities. Findings show strong correlations between wind speed, drought index, snow, and random event occurrences. The probability of tree breakage increases with stand height and snow’s interaction with tree height. The height-diameter ratio is a crucial variable affecting breakage risks. Topographic exposure indices from digital terrain models also influence event occurrences. Forest edge structure increases random event occurrence and tree breakage probability. Thinning can temporarily increase wind and snow damage risk but enhances longterm stability and forest resistance. Larger trees with larger crowns have higher survival rates when damaged. These models assist forest managers in developing adaptive strategies to enhance forest resilience under changing conditions, including appropriate timing and severity of thinning operations to foster tree stability against snow breakage. Keywords: Mixed effect models; Logistic models; Fagus sylvatica L.; Picea abies [L.] Karst.; Austrian forests; Climate change; Storm
