Abstract

AbstractIn northern temperate forests, ice storms are a common disturbance agent, though climate change may alter their occurrence patterns. Their impact on forest ecosystems is complex, as they influence both structure and processes. In 2014, an ice storm of high intensity and large spatial extent occurred in Slovenia, Central Europe, which enabled a detailed study of ice damage to individual trees across a broader spatial scale. Pre- and post-ice storm measurement data on 11 414 trees on 960 permanent plots were used to examine ice damage patterns on trees in the disturbed forest area (~8700 km2) to determine the predictors of ice damage to trees and to investigate the relative susceptibility of eight groups of tree species in mixed Central European forests. We used a novel approach to modelling ice-storm intensity across the region based on measured data on air temperature, precipitation amount and duration, precipitation intensity and wind speed. The ice storm damaged 31 percent of the analysed trees; high variability in the damage rate was observed across the disturbed area. For the tree species, a susceptibility to ice damage index (SI) ranging between 0 (no damage) and 1 (complete damage) was calculated based on terrestrial assessment of trees. Tree species differed significantly in susceptibility to ice damage: Abies alba (SI = 0.14) and Quercus sp. (SI = 0.11) were rather resistant; Picea abies, Fagus sylvatica and Acer sp. (SI = 0.23–0.28) were moderately to very susceptible; and Pinus sp. (SI = 0.62) was extremely susceptible to ice damage. Eight predictors and three interactions were included in an ordinal logistic regression model of tree damage: ice damage on trees depends mainly on ice-storm intensity, elevation and tree species, whilst tree dbh and social status, tree size and tree species diversity indices and slope were relatively less important. Our study illustrates the complexity of damage patterns on trees due to ice storms and the significance of ice-storm intensity and tree species as predictors when modelling ice damage on individual trees.

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