Regional disturbance predisposition assessment to support management in Norway spruce-dominated mountain forests

Mountain forests provide essential ecosystem services, including protection against natural hazards. However, climate change and the predominance of structurally homogeneous Norway spruce stands increase their predisposition to disturbances such as windthrow, bark beetle infestation, and snow breakage. Given limited resources for proactive and reactive forest management, prioritizing silvicultural interventions is crucial. We present a framework for the spatially explicit assessment of forest predisposition to snow breakage, windthrow, and bark beetle infestation at 10 m resolution across regional scales. The approach integrates high-resolution forest structural and site factor attributes primarily derived from nationally available airborne laser scanning (ALS) data. These attributes are weighted using an expert-based model according to their influence on disturbance predisposition. Weighted values are aggregated and classified into four predisposition classes, from low (< 50th) to extreme (> 95th percentile). Validation of local foresters at 142 reference sites showed significant correlations between forester-rated and modeled predisposition for windthrow and bark beetle, but not for snow breakage. New bark beetle infestations were strongly associated with increasing predisposition classes. Forest structural parameters such as forest mixture and spruce share, development stage, and canopy layering were key predictors and could be determined with high accuracy, enabling detailed and accurate predisposition mapping even in steep terrain. Assessing predisposition to snow breakage remains challenging due to the high stochastic variability in the location, characteristics, and amount of snow fall. As the main predictors are derived from nationwide ALS data, the methodology is transferable to other mountain regions. Developed in close collaboration with practitioners, the framework supports operational forest management by improving prioritization of interventions. • Framework for spatially explicit predisposition mapping in mountain forests. • Use of detailed forest structure parameters based on nationwide airborne laser scanning. • High correlation between predisposition mapping and on-site forester assessment. • Increase of detected bark beetle nests with increasing predisposition class. • Support of spatial prioritization of forest management interventions.