Short-term survival and crown rebuilding of European broadleaf tree species following a severe ice storm

Abstract

Ice storms cause widespread damage to forests in many temperate regions, leaving behind many live trees with severe crown damage. Following a severe ice storm in 2014 that damaged forests across Slovenia, we examined how tree-level attributes influenced survival and crown rebuilding three growing seasons after the storm. Field sampling was carried out in four mature stands dominated by native broadleaf species. Of the 763 sampled trees, the annual mortality rate following the storm was 2.2%, and nearly all of the trees that died experienced > 75% crown removal. Oak (Quercus petraea) and chestnut (Castanea sativa) had higher rates of mortality than beech (Fagus sylvatica) and maple (Acer pseudoplatanus). Mixed models found that survival significantly increased with tree diameter and decreased with increasing crown damage. Although we observed sprouting across all the dominant species, maple, oak, and chestnut showed a more vigorous response compared to beech, and maple had the fastest sprout growth. Model results showed that sprout density and length increased with level of crown damage. The results indicate that these broadleaf forests are resilient to severe ice damage. Consequently, hasty salvage cutting of trees with canopy damage should be avoided, as many individuals with over 75% crown damage are likely to survive and recover.

The European Forest Condition Monitor: Using Remotely Sensed Forest Greenness to Identify Hot Spots of Forest Decline

“… Q mapped for Slovenia on DOY 113 (A) and 209 (B) . The polygons demarcate areas affected by the ice storm according to Roženbergar et al ( 2020 ). Corresponding values grouped according to affectedness from DOY 97 until DOY 289 are shown in (C) .…”

“…For the ice storm example, we focused on the extreme ice storm that hit Slovenia in February 2014 (Roženbergar et al, 2020 ). Here, we compared the temporal development of Q and PDM from DOY 97 to DOY 289 (i.e., most of the local growing season) in 2005 for areas that were affected or unaffected by the ice storm, respectively.…”

“…Here, we compared the temporal development of Q and PDM from DOY 97 to DOY 289 (i.e., most of the local growing season) in 2005 for areas that were affected or unaffected by the ice storm, respectively. To identify the corresponding areas, we digitized and georeferenced the map shown in Roženbergar et al ( 2020 ) and applied the resulting shapefile to extract Q and PDM for the corresponding EFCM maps and categorized the values as either affected or unaffected. Extracted values were statistically compared for each DOY according to the two categories using Wilcoxon rank-sum test.…”

“…To visualize how extraordinary precipitation sums were in 2011, we used climate station data from the German Meteorological Service (DWD) of the closest station with a continuous precipitation record since 1970, i.e., "Groß Lüsewitz" ∼40 km south of the peninsula. For the ice storm example, we focused on the extreme ice storm that hit Slovenia in February 2014 (Roženbergar et al, 2020). Here, we compared the temporal development of Q and PDM from DOY 97 to DOY 289 (i.e., most of the local growing season) in 2005 for areas that were affected or unaffected by the ice storm, respectively.…”

“…The higher frequency of more extreme climate conditions in course of climate change poses certain threats on forests worldwide (Allen et al, 2010 , 2015 ; Anderegg et al, 2015 ; Vitasse et al, 2019 ; Buras et al, 2020 ; Kannenberg et al, 2020 ). Among potential direct causes of the resulting forest decline and tree mortality are in particular so-called hotter droughts (Allen et al, 2010 , 2015 ; Buras et al, 2018 ; Brun et al, 2020 ; Rita et al, 2020 ; Schuldt et al, 2020 ), late-frost (Menzel et al, 2015 ; Príncipe et al, 2017 ; Vitasse et al, 2019 ), periodically water-logged soils after floods and heavy precipitation (Kreuzwieser and Rennenberg, 2014 ), ice storms (Roženbergar et al, 2020 ), and winter storms (Valinger et al, 2014 ). Since extreme environmental conditions often impair tree defense mechanisms, secondary pathogens such as fungi and beetles frequently amplify tree decline and die-back rates (Bigler et al, 2006 ; Kohler et al, 2010 ; Spruce et al, 2019 ).…”