ArticlePDF Available

Abstract and Figures

Wind is the leading disturbance agent in European forests, and the magnitude of wind impacts on forest mortality has increased over recent decades. However, the atmospheric triggers behind severe winds in Western Europe (large‐scale cyclones) differ from those in Southeastern Europe (small‐scale convective instability). This geographic difference in wind drivers alters the spatial scale of resulting disturbances and potentially the sensitivity to climate change. Over the 20th century, the severity and prevalence of cyclone‐induced windstorms have increased while the prevalence of atmospheric instability has decreased and thus, the trajectory of Europe‐wide windthrow remains uncertain. To better predict forest sensitivity and trends of windthrow disturbance we used dendrochronological methods to reconstruct 140 years of disturbance history in beech‐dominated primary forests of Central and Eastern Europe. We compared generalized linear mixed models of these disturbance time series to determine whether large‐scale cyclones or small‐scale convective storms were more responsible for disturbance severity while also accounting for topography and stand character variables likely to influence windthrow susceptibility. More exposed forests, forests with a longer absence of disturbance, and forests lacking recent high severity disturbance showed increased sensitivity to both wind drivers. Large‐scale cyclone‐induced windstorms were the main driver of disturbance severity at both the plot and stand scale (0.1–∼100 ha) whereas convective instability effects were more localized (0.1 ha). Though the prevalence and severity of cyclone‐induced windstorms have increased over the 20 century, primary beech forests did not display an increase in the severity of windthrow observed over the same period.
This content is subject to copyright. Terms and conditions apply.
1. Introduction
A total of 40% of Europe is covered with forest (182 million ha; Cook,2019), and windthrow is directly
responsible for more than 50% of damage reported in those forested areas each year (18.6 million m3 on
average; Gardiner etal., 2010; Schelhaas, 2008). Furthermore, total wind-induced forest damage has in-
creased since the early 20th century (Schelhaas etal.,2003; Seidl etal.,2017; Usbeck etal.,2010). Shifting
disturbance dynamics and managerial responses to disturbance can alter habitat provisioning (Bengtsson
etal.,2000; Kozák etal.,2018), regulate carbon storage (Burrascano etal.,2013; Carey etal.,2001; Harmon
etal.,1990; Luyssaert etal.,2008; Seedre etal.,2020), and impact the role of forestry in the European econo-
my (Leverkus etal.,2012; Müller etal.,2019). Thus, understanding the drivers of shifting wind disturbance
patterns is needed to facilitate informed decision-making for forest and conservation management, and
additionally quantify the future role of Europe's forests in global biogeochemical cycles.
Abstract Wind is the leading disturbance agent in European forests, and the magnitude of wind
impacts on forest mortality has increased over recent decades. However, the atmospheric triggers
behind severe winds in Western Europe (large-scale cyclones) differ from those in Southeastern Europe
(small-scale convective instability). This geographic difference in wind drivers alters the spatial scale of
resulting disturbances and potentially the sensitivity to climate change. Over the 20th century, the severity
and prevalence of cyclone-induced windstorms have increased while the prevalence of atmospheric
instability has decreased and thus, the trajectory of Europe-wide windthrow remains uncertain. To better
predict forest sensitivity and trends of windthrow disturbance we used dendrochronological methods to
reconstruct 140years of disturbance history in beech-dominated primary forests of Central and Eastern
Europe. We compared generalized linear mixed models of these disturbance time series to determine
whether large-scale cyclones or small-scale convective storms were more responsible for disturbance
severity while also accounting for topography and stand character variables likely to influence windthrow
susceptibility. More exposed forests, forests with a longer absence of disturbance, and forests lacking
recent high severity disturbance showed increased sensitivity to both wind drivers. Large-scale cyclone-
induced windstorms were the main driver of disturbance severity at both the plot and stand scale
(0.1–100ha) whereas convective instability effects were more localized (0.1ha). Though the prevalence
and severity of cyclone-induced windstorms have increased over the 20 century, primary beech forests did
not display an increase in the severity of windthrow observed over the same period.
Plain Language Summary Two main atmospheric patterns are driving European windthrow
with large-scale winter storms being more prevalent in Western Europe and summer thunderstorm-
generated winds being more prevalent in Eastern Europe. In central Europe, most forests display a
mixed-severity disturbance regime indicating that both large- and small-scale disturbances are occurring.
However, few studies have been conducted looking at the prevalence of large-scale winter windstorms
and small-scale summer thunderstorms. Here we found evidence for both, but recently large-scale winter
windstorms have had a greater impact.
© 2021. The Authors.
This is an open access article under
the terms of the Creative Commons
Attribution-NonCommercial License,
which permits use, distribution and
reproduction in any medium, provided
the original work is properly cited and
is not used for commercial purposes.
Both Cyclone-induced and Convective Storms Drive
Disturbance Patterns in European Primary Beech Forests
J. L. Pettit1,2 , J. M. Pettit1,2, P. Janda1, M. Rydval1 , V. Čada1 , J. S. Schurman1,
T. A. Nagel3, R. Bače1, M. Saulnier1 , J. Hofmeister1, R. Matula1, D. Kozák1, M. Frankovič1,
D. O. Turcu4, M. Mikoláš1, and M. Svoboda1
1Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Suchdol, Czech Republic,
2Department of Biology, Minot State University, Minot, ND, USA, 3Department of Forestry and Renewable Forest
Resources, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia, 4Forest Research and Management
Institute – Timişoara Branch (ICAS), Voluntari, Ilfov, Romania
Key Points:
Windstorms and convective
instability drive primary forest
windthrow damage
Topographic exposure and time and
severity of last disturbance moderate
Windstorms create higher severity
windthrow than convective
Supporting Information:
Supporting Information may be found
in the online version of this article.
Correspondence to:
J. L. Pettit,
Pettit, J. L., Pettit, J. M., Janda, P.,
Rydval, M., Čada, V., Schurman, J. S.,
etal. (2021). Both cyclone-induced and
convective storms drive disturbance
patterns in European primary beech
forests. Journal of Geophysical Research:
Atmospheres, 126, e2020JD033929.
Received 21 SEP 2020
Accepted 6 JAN 2021
Author Contributions:
Writing – original draft: J. M. Pettit,
P. Janda, M. Rydval, V. Čada, J. S.
Schurman, T. A. Nagel, R. Bače, J.
Hofmeister, R. Matula, D. Kozák, M.
Frankovič, M. Mikoláš, M. Svoboda
Writing – review & editing: M.
Saulnier, D. O. Turcu
1 of 17
Journal of Geophysical Research: Atmospheres
Extratropical cyclones are one of two meteorological drivers of intense wind in Europe, and these cyclones
can influence forest areas in excess of hundreds of km2 (Brázdil etal.,2004). The temperature, pressure, and
humidity gradients produced by cyclones, and their associated fronts, can induce winds in excess of 20m/s
across large affected areas (Brázdil etal.,2004). As temperature and related atmospheric humidity values
have risen in Europe (Hartmann etal.,2013), fronts associated with cyclones have increased in strength
(Schemm etal.,2017). These fronts have caused windstorms in Western Europe, including several recent
events (e.g., storms Vivian, Lothar, and Martin) each of which disturbed more than 100 million m3 of debris
(Gardiner etal.,2010). The recent increase in strength of cyclones, usually in winter, has led some research-
ers to suggests that cyclone-induced windstorms are increasing the frequency and severity of windthrow
disturbance events (Schelhaas etal.,2003; Usbeck etal.,2010). However, although intense wind prevalence
has increased (Figure1), an increasing disturbance trend is not apparent in disturbance reconstructions
from primary and old-growth forests of Central or Eastern Europe (Čada etal.,2020; Firm et al., 2009;
Schurman etal.,2018; Zielonka etal.,2010).
Certain disturbance reconstructions from these forests show decreasing trends in disturbance severity over
the 20th century. This could be due in part to reductions of other disturbance and mortality agents (Seidl
et al., 2017). However, with the majority of European disturbance agents increasing in intensity (Seidl
etal.,2017) and the fact that mountain regions of Central and Eastern Europe are subject to significant
increases in the prevalence of intense wind speeds (see Figure3 in Donat et al.,2011 and Figure 1 in
this study), we would expect to see increases in disturbance severity if winds produced by cyclonic storms
are drastically increasing. Thus, large-scale cyclones may not be the sole windthrow driver in Central and
2 of 17
Figure 1. Average annual (a) wind speed and (b) convective available potential energy (CAPE) based on 20th century
reanalysis data. Locations of Carpathian Mountain beech plots (the focal forests of this study) are shown as filled dots.
Trends in the prevalence of extreme days per year in Carpathian beech forests plots (open dots) show (c) intense wind
speed has increased and (d) severe CAPE has decreased over the 20th century. Local polynomial regression trend lines
are shown in red.
Journal of Geophysical Research: Atmospheres
Eastern Europe, or some moderator of wind (e.g. stand character and canopy roughness) may be reducing
the influence of these storms.
The second major meteorological driver of intense wind in Europe is atmospheric instability, which gener-
ates strong winds through microbursts and occasionally, tornadoes. Despite the fact that severe convective
storms are common across Europe (Taszarek etal.,2019), studies of convective storms on European forest
disturbance are limited (Brázdil etal.,2018; Furtuna etal.,2018; Nagel etal.,2006,2017). These convective
storm elements can create higher speed winds than extratropical cyclones but at smaller scales and for
shorter periods. For example, over the course of a few days, cyclones can elevate winds across hundreds of
km2; whereas most convective storms usually only have strong winds that affect less than 1km2 over the
course of ca. 1h (Brázdil etal.,2004). Thus, to help differentiate these drivers, we refer to these phenomena
in this text as “large-scale cyclones” and ‘small-scale convective instability'.
Throughout Europe, the temporal and regional patterns of small-scale convective instability differ from
cyclones (Figure1). Atmospheric conditions associated with strong cyclones (elevated atmospheric hu-
midity and subsequent front strength, Schemm etal.,2017) are on the rise, whereas conditions associated
with strong convective storms (high relative humidity which influences air parcel buoyancy; Del Genio
etal.,2007) are decreasing in prevalence as Northern Hemisphere temperatures rise (Hartmann etal.,2013).
In Southeastern Europe convective instability is more common (Furtuna etal., 2018; Nagel et al., 2017;
Taszarek etal.,2019), whereas cyclones dominate in Western Europe. Despite the lower prevalence of cy-
clone-induced winds in Central, Southern, and Eastern Europe, windthrow is still a major disturbance
agent driving forest dynamics there (Nagel etal.,2017; Sommerfeld etal.,2018; Synek etal.,2020). Distur-
bance reconstructions in old-growth European beech (Fagus sylvatica L.) dominated forests from Slovenia
to Montenegro show that windthrow is the most prevalent disturbance agent but large-scale stand-leveling
windstorms are not the norm (Furtuna etal., 2018; Nagel etal.,2017). Instead, smaller-scale windthrow
events (<10ha) are more common, and most occur during the summer suggesting that small-scale convec-
tive instability is the main driver and not large-scale cyclonic storms.
These two drivers of wind form a cline of disturbance size across Europe with large-scale disturbance being
common in Western Europe (e.g., France and Germany) and smaller-scale disturbance more common in
Southeastern Europe (e.g., Slovenia to Montenegro) coincident with the prevalence of intense windstorms
in the west (Bett etal.,2017) and intense convective instability in the southeast (Brooks etal.,2003; Taszarek
etal.,2019). Based on disturbance reconstructions from the Carpathian Mountains at the border of Central
and Eastern Europe, most disturbances are smaller scale (<10ha; Čada etal.,2020) and thus, if large-scale
cyclones are driving windthrow dynamics in these forests, some moderator of wind is increasing forest
resistance to large-scale disturbance. Otherwise, it may be that small-scale convective instability is at least
partially responsible for the smaller disturbance patch size there, just as it is in Southeastern Europe. It
should be noted, however, that most studies describing the large-scale wind disturbance in Western Europe-
an forests come from commercially managed forests where forest structure and composition have been al-
tered from a natural state, which has the potential to temporarily increase forest vulnerability to windthrow,
possibly biasing disturbance events toward the severe end of the spectrum (Everham & Brokaw,1996; Gar-
diner etal.,2005,2010; Quine & Gardiner,2007; Schelhaas etal.,2003). Thus, studies attempting to discern
the relative influence of large-scale cyclones and small-scale convective instability should analyze forests
that lack or control for evidence of management practices including stand thinning and lengthening of
rotation periods to reduce this possible disturbance severity and scale bias.
Regardless of the relative presence of large-scale cyclones or small-scale convective instability, forest expo-
sure and disturbance history can mediate windthrow severity. Variables influencing forest vulnerability to
windthrow that can be estimated or reconstructed across the 20th century include topographic exposure
(Quine & White,1998; Senf & Seidl,2018), and time series of forest disturbance severity and time since the
last disturbance (Janda etal.,2017). Topographic exposure is a measure of shielding based on a virtual hori-
zon angle at a fixed distance from a point on the map. Points at valley bottoms are more shielded from wind
than points at the peak of a mountain. Thus, forests on ridges and mountain peaks are more vulnerable than
those in valleys (Ruel etal.,2002). When measuring the windthrow risk of a particular stand, forest dynam-
ics can also influence susceptibility to windthrow. For example, a recently disturbed forest with few living
stems remaining will need adequate time for trees to regenerate before becoming susceptible to disturbance
3 of 17
Journal of Geophysical Research: Atmospheres
again. Thus, variables like time since the last disturbance and the severity of the last disturbance are likely
to mediate future susceptibility to wind disturbance (Schurman etal.,2018). Also, as we discuss above, lon-
gitude and its association with temperature and humidity patterns can influence windthrow risk through
the relative presence of large-scale cyclone-induced windstorms (more common in Western Europe) versus
small-scale convective instability (more common in Southeastern Europe (Brázdil etal.,2004). Finally, the
presence of a temporal trend in disturbance severity datasets and the differing trends for the two main
drivers of European wind (Figures1c and1d) imply that storm severity or forest vulnerability are changing
through time (Schelhaas etal.,2003; Usbeck etal.,2010). Quantifying the effects of these wind moderat-
ing variables will inform forest susceptibility models of windthrow disturbance and elucidate atmospheric
drivers of wind.
Our objective in this study was to use a large network of primary forest plots, where the main disturbance
agent is wind, to determine the relative influence of wind drivers at two spatial scales and examine trends in
wind-induced forest disturbance over the 20th century. We checked for evidence of large-scale cyclone-in-
duced windstorms and small-scale convective instability in European beech forests at the plot (ca. 0.1ha)
and forest stand scales (ca. 100ha) while also controlling for windthrow susceptibility variables acting at
local and continental scales. By addressing these objectives, we can inform hypotheses presented in the
literature on the reason for recent increases in European windthrow disturbance which include an increase
in intense wind frequency (which we account for) and increases in forest management and cover (which
our methods exclude).
2. Materials and Methods
2.1. Study Plots
We assessed the historical disturbance of 20 beech-dominated primary mixed forests stands within the
Carpathian Mountains of Slovakia and Romania. The presence of primary forests was determined through
forest inventories in Slovakia (Kozák etal.,2018; Mikoláš etal.,2019; Sabatini etal.,2018; also see http:// and Romania (Kozák etal.,2018; Sabatini etal.,2018) and detailed descriptions of these
primary forest inventories can be found in the study by Mikoláš etal.,(2019). Primary forest stands occurred
in four geographic clusters which we refer to as landscapes (West Slovakia, East Slovakia, North Romania,
and South Romania) covering 42°–50° latitude and 14°–25° longitude, with plots ranging in elevation from
615 to 1,324m a.s.l (Figure2a). To obtain historical disturbance data from stands, we collected tree cores
from 280 circular plots randomly positioned within primary forests. We used ArcGIS 10.7 to randomly place
the 280 plots in non-overlapping pairs oriented along topography contours with plot pair centers positioned
80m apart (Figure2b). Only beech-dominated mixed forest plots were included in this study to ensure
that windthrow was the predominant disturbance agent (Nagel etal.,2006) and because beech-dominated
mixed forest is the most abundant forest type across temperate Europe, increasing comparability of these
results to other windthrow studies in Europe. The most common tree species within stands in order of
abundance were Fagus sylvatica (71%), Abies alba (15%), Picea abies (6%), and Acer pseudoplatanus (4%).
2.2. Historical Disturbance Chronology Calculation
Annual records of percent canopy area removed were created based on tree-ring data for each plot and
stand. At each plot, trees were selected for coring based on a hierarchy of size classes in a nested circle
design. All trees 6cm diameter at breast heigh (DBH) were cored up to 8m from the plot center. Also, a
quarter of canopy and subcanopy trees 10–20cm DBH, and all trees 20cm DBH were cored up to 17.84m
from the plot center. These cores were dried prior to mounting and sanding using consecutively finer grit
sandpaper (up to 1000 ANSI grit). Cores were visually crossdated and ring widths were measured using a
Lintab measuring machine and TSAP-Win software. Crossdating was verified with Cofecha and CDendro
software (Holmes,1983; Larsson,2003).
Within crossdated tree-ring series, we used two types of disturbance-indicating growth patterns to re-
construct disturbance events: (1) Rapid early growth of trees established under an open canopy and (2)
abrupt increases in growth, called releases (Altman etal.,2018). Open canopy established trees were
identified as individuals that exceeded a threshold value of mean growth from 5 to 15 years of growth
4 of 17
Journal of Geophysical Research: Atmospheres
(Fraver & White,2005a). These threshold values were calculated based on logistic regressions compar-
ing empirical data from collected plot seedlings growing in the open canopy or closed canopy conditions
(Janda etal.,2017). Separate regressions were performed for common tree species present (Fagus syl-
vatica, Abies alba, Picea abies, Acer pseudoplatanus, and a group of all other species present pooled in
an additional group) in each landscape (i.e., five species groups * 4 landscapes=20 species: landscape
critical values). Releases, our second disturbance indicating growth pattern, were identified using the
absolute increase method (Fraver & White,2005b; Trotsiuk etal., 2014). This method compares the
mean growth in the 10 years before a focal year and the 10 subsequent years including the focal year
at all possible positions along an individual tree growth series. If the absolute increase in growth is
greater than a threshold value, then a release event is recorded at the focal year. We limit recorded re-
leases to one every 20years and no tree may record a release above a DBH where they are considered to
have attained canopy status. Because the average growth of the species within beech-dominated mixed
forests was different, we calculated absolute increase threshold values for each species group in each
landscape separately and the canopy position DBH cutoff for each species individually. Thus, release
events were defined as years when the growth comparisons indicate a growth increase exceeding 1.25
the standard deviation of increases in the landscape-species combination (Fraver & White,2005b; Trot-
siuk etal.,2014).
Both of these disturbance-indicating growth patterns were then transformed to a measure of canopy area
removed using methods of Lorimer and Frelich(1989) and Schurman etal.,(2018). We used power function
models that estimated canopy area from a disturbance indicating the tree's current DBH (Lorimer & Fre-
lich,1989). Power functions were specific to each tree species group in each landscape.
5 of 17
Figure 2. (a) Spatial arrangement of beech dominated primary mixed forest plots in the Carpathian Mountains. Plot colors depict the landscape groupings
used for calculating local disturbance threshold values. Inset map shows Europe with a red bounding box representing the study region. (b) Example
arrangement of plots within one stand showing scale. Notice that the stand footprint is approximately 1km2 (100ha), the maximum size of disturbance usually
recorded from small-scale convective instability. (c) Plot level disturbance reconstruction. Bars represent the canopy area removed estimated from individual
tree disturbance indicating growth patterns and the kernel density smoothing line used to alleviate temporal uncertainty of event timing. (d) Stand level
disturbance reconstruction. Bars represent canopy area removed, estimated from individual tree disturbance indicating growth patterns, and the kernel density
smoothing line calculated at the stand scale.
Journal of Geophysical Research: Atmospheres
To better characterize the plot and stand level timing and severity of all tree level disturbance events of
both disturbance types, we created a raw chronology of pooled canopy area removed before fitting a kernel
density estimation (KDE) function to temporally smooth the disturbance chronology. Yearly values of tree
level canopy area removed were pooled at the plot (Figure2c) and subsequently stand level (Figure2d) to
create raw disturbance chronologies. Then, a kernel density function with a 30-year window was fit to plot
and stand chronologies of raw canopy area removed to create two spatial hierarchies of disturbance chro-
nologies (Trotsiuk etal.,2014).
Peaks in the plot and stand kernel density disturbance chronologies were used for identifying the timing and
severity of disturbance events and for calculating time series describing the time since the last disturbance
and last disturbance severity for each plot and stand (Schurman etal.,2018). We used three criteria for de-
termining a plot peak: (1) The kernel density disturbance chronology had to be increasing for at least the 5
previous years, (2) it had to exceed 10% canopy area removed, and (3) subsequent peaks had to be separated
by at least 10years. Because disturbance chronologies include data from the young growth of trees and
because trees that could record recent disturbance may not have reached our criteria diameter classes at the
time of sampling (median age of all trees in plots at 6cm DBH, the minimum sampling size, was 31years),
we truncated the historical disturbance chronologies so that 1989 represented the most recent year.
2.3. Windthrow Susceptibility Variables
Because past disturbance influences the susceptibility of a stand to future disturbance (Schurman
etal., 2018), we calculated the time series of time since the last disturbance and the severity of that last
disturbance for plots and stands based on kernel density function peaks (FigureS1). Beginning in the year
1600, values of time since the last disturbance and severity of the last disturbance were set to zero, and time
since the last disturbance increased by one every year without disturbance. After detecting the first distur-
bance peak greater than 10% canopy area removed in a kernel density disturbance chronology, the “severity
of last disturbance” variable was set to the peak severity of the disturbance and the “time since last distur-
bance” was reset to zero. After the first disturbance peak, the last disturbance severity and time since the
last disturbance values only changed if a disturbance peak with a magnitude greater than 10% of the canopy
area of the plot or stand occurred more than 10years after the last disturbance peak. We shifted values of
time since last disturbance and severity of last disturbance to 15years after the peak disturbance year (half
the length of the KDE function window; FigureS1), because kernel density peaks occur at the temporal
center of raw disturbance events. Thus, a 15-years shift is necessary to ensure that evidence of previous dis-
turbance, not disturbance during the focal period, was used to predict disturbance severity of the focal year.
The reason for incorporating time since the last disturbance and severity of the last disturbance is twofold.
First, those variables have been shown to influence the susceptibility of forests to future disturbance. Sec-
ond, because disturbance changes the structure of the forest, these variables can be interpreted as a rough
proxy for forest structural complexity. Forests with lower severity disturbance and disturbance that hap-
pened further in the past are more likely to display higher structural complexity (Janda etal.,2017; Meigs
etal.,2017). Thus, we are examining the direct effect of past disturbance on future disturbance as well as
approximating an indirect effect of structure on disturbance susceptibility.
Beyond time since the last disturbance and severity of the last disturbance, wind-induced disturbance in
forests is also likely moderated by factors including topographic exposure, longitude, and time period. Land-
scapes that are more exposed are more likely to experience higher severity disturbance with elevated wind
levels, thus, to account for topographic exposure, we calculated a distance-limited “topex” value for every
plot. Distance limited topex is −1 * the average of the virtual horizon angles up to a distance of 1km from
each plot center at the eight cardinal and intercardinal directions (Quine & White,1998; Ruel etal.,2002;
Schmidt etal.,2010). We calculated topographic exposure values in ArcMap 10.7 based on digital elevation
model layers retrieved from the USGS Earth Explorer ( Using this method,
more positive values occur on mountain tops where trees are more exposed to wind whereas lower val-
ues occur in valleys where trees are more shielded from the wind. Longitude moderates the wind-induced
disturbance because of the strength of various drivers of wind change as the climate becomes more con-
tinental. Areas closer to the west coast experience higher average wind speeds and more intense cyclones
and more continental areas experience more convective instability (Siedlecki,2009). Finally, there is some
6 of 17
Journal of Geophysical Research: Atmospheres
evidence that the effect of wind speed and convection on forest disturbance may vary across the 20th cen-
tury (Gardiner etal.,2010; Schelhaas etal.,2003; Usbeck etal.,2010) possibly due to global change. Thus,
we also account for the influence of “time period” (i.e., year) as an explanatory variable in order to examine
temporal changes in susceptibility to wind.
2.4. Historical Meteorology Data
We use the 20th Century Reanalysis version 3 data set as a source of estimated atmospheric and wind
conditions from 1850 to 1989 (Compo etal.,2006,2011). At the time of writing, this reanalysis product
represents the most temporally extensive data set with the highest resolution which models wind speed
and convective instability. This reanalysis has been tested for accuracy (Slivinski etal.,2019) and has been
compared to other reanalysis datasets (Bett etal.,2017). Values for surface u-wind (zonal or east-west), sur-
face v-wind (meridional or north-south), and convective available potential energy (CAPE), a measure of
small-scale convective instability, were extracted at each plot and stand centroid for all 3-h time steps from
January 1, 1850 to December 31, 1989. Measures of vector wind were calculated from u-wind and v-wind
values at every time step as these are estimated from the temperature and pressure gradients produced by
large-scale patterns including extratropical cyclone windstorms (Leckebusch & Ulbrich,2004). Because
CAPE and wind speed values within the 20th century reanalysis data set are estimates from an ensemble
of models (Compo etal.,2006) and because trees in each area are likely adapted to the prevailing average
wind and storminess (Quine & Gardiner,2007), we create an annual time series of the number of time
steps where wind speed or CAPE were greater than two standard deviations above the mean for that area.
This method is analogous to using the 98th percentile as a threshold as Leckebusch etal.(2008) and Donat
etal.(2011) did when examining the influence of extreme storms on regions of Europe. Also, the number of
extreme wind speed and CAPE periods have previously been shown to correspond to storminess (Anyomi
etal.,2016; Leckebusch etal.,2008; Usbeck etal.,2010).
2.5. Models
Some uncertainty in the timing of disturbance events exists in the reconstructed disturbance dataset be-
cause there are lags in tree growth and recruitment after disturbance. We chose to address this uncertainty
by analyzing the historic dataset in 5-year periods. Our dependent variable, proportion canopy area re-
moved, and independent variables, time since last disturbance (10% canopy area removed), the severity
of last disturbance (10% canopy area removed), number of high windspeed 3-h intervals, and number of
high CAPE 3-h intervals were averaged into a time series of 5-year periods. Values for distance-limited top-
ographic exposure and longitude (additional explanatory variables) were static through time.
To address our primary goal of describing the influence of wind speed and storminess on primary mixed
forest disturbance, we fit seven univariate and four multivariate Bayesian regression models (Table1) to
the historical disturbance data set and compared models using leave-one-out cross validation which ranks
models based on predictive ability using an information theory approach. Because many plots had 5-year
periods without observed disturbance and because certain susceptibility criteria must be met for a plot to be
vulnerable to wind disturbance (e.g., just after a large disturbance there may not be live trees in the plot and
thus the plot will not be able to record disturbance regardless of wind speeds) we chose to use a two-part
zero-inflated beta model structure. This structure allows us to model and account for variables that increase
the probability of observing disturbance as well as modeling variables influencing disturbance severity. In
the disturbance presence portion of the model, we use a beta distribution model because we are modeling
disturbance severity as the proportion of plot or stand canopy area disturbed, which is bound between zero
and one. The model formula for all models tested can be summarized with the following equations:
i ii
Y ZIBeta p ;
i p pi
log |
x stand
7 of 17
Journal of Geophysical Research: Atmospheres
student t 3, 0,10
x student t 3, 0,10
Equation1 represents the full two-part mixture model where Yi is the modeled disturbance severity of
period i, pi is the probability that a period will exhibit zero disturbance, and ϕi is the mean of the beta
distribution model which estimates disturbance severity given certain forest and atmospheric conditions.
Equation2 and3 represent the respective forms of the zero-inflated and beta portions of the model where
the terms αp and αϕ are the zero-inflated and beta intercepts, the βpxi and βϕxi terms represent a general-form
parameter estimate for the independent variables in the models, and (1|stand) represents the random fac-
tor of stand used to account for our nested design. Equation4,5, and6 are prior distributions used for the
intercept (α) and parameter estimates (β) for the zero-inflated and beta portions of the model (Equation2
and3). All variable 5-year period averages were scaled to z-scores to increase computational efficiency and
so that a flat prior with a half Student's t distribution with 3 degrees of freedom and a scale parameter of
10 could be used in Bayesian regression models. Our four multivariate models (table1) can be described as
(1) the susceptibility variables model, where we account only for factors influencing forest susceptibility to
the wind (see windthrow susceptibility variables section above) but no wind or convective-instability term
is present. This model can be interpreted as a test of the influence of wind on forest disturbance. If this
is the best model, neither CAPE nor wind speed significantly influences forest disturbance. Multivariate
model 2 is the wind speed+susceptibility model, where the wind speed and all two-way interactions with
wind speed are added to the zero-inflation and beta portion of the model. This model tests for the influence
of extratropical cyclones. Multivariate model 3 is the convective instability+susceptibility model which
matches the form of the wind speed+susceptibility model with CAPE substituted for wind speed and it
tests for the influence of convective instability on forest disturbance. Multivariate model 4 is the global mod-
el which includes all terms from the wind speed model and convective instability model but also includes
8 of 17
Model Form
Time since last dist. only α+β time since dist.
Severity of last dist. only α+β dist. severity
Topographic exposure only α+β topographic exposure
Time period only α+β time period
Lng. only α+β lng.
Cyclone wind only α+β WSPD
Convective inst. only α+β CAPE
Susceptibility vars. α+β time since dist.+β dist. severity+β topographic exposure+β
lng.+β time period
Cyclone wind+susceptibility α+(β time since dist.+β dist. severity+β topographic exposure+β
lng.+β time period) * β WSPD
Convective inst.+sus. α+(β time since dist.+β dist. severity+β topographic exposure+β
lng.+β time period) * β CAPE
Global α+(β time since dist.+β dist. severity+β topographic exposure+β
lng.+β time period) * (β WSPD+β CAPE)+β WSPD:β CAPE
Note. WSPD represents the number 3-h periods with intense cyclone-induced wind speeds and CAPE represents the
number of 3-h periods with intense convective instability.
Table 1
Fixed Effect Structure of both Zero-inflated and Beta Portions of Models Predicting disturbance Severity in Beech
Dominated Primary Mixed Forests of Central and Eastern Europe
Journal of Geophysical Research: Atmospheres
the two-way interaction between wind speed and CAPE. These same model forms were fit to both the plot
level and stand level datasets.
3. Results
3.1. Disturbance Reconstruction
All plots within mixed primary forests showed evidence of disturbance based on our reconstruction meth-
ods. When tree level disturbance events were aggregated to the stand level, reconstructions showed evi-
dence of disturbances covering larger areas but the maximum severity observed, measured as canopy area
removed, was 60% lower at the coarser stand level than at the plot level (see variability in Figure3 and Fig-
ureS2). The 5-year interval with the most severe disturbance event on the plot level was one in which 99%
of the canopy area was removed. This occurred in a plot within the Vihorlat stand in Slovakia during the
1885–1890 period. At the stand level, the largest reconstructed disturbance only removed 39% of the canopy
area and occurred in the Belia stand of Romania from 1895 to 1900 (FigureS2).
9 of 17
Figure 3. Raw disturbance data observed in beech dominated mixed primary forest plots (blue dots) and stands (red dots) in comparison to hypothesized wind
disturbance moderating forest and location traits. Beta regression and logit β values are given for zero-inflated beta mixture models. The model predicted values
are displayed as a line with a 95% credible interval.
Journal of Geophysical Research: Atmospheres
Even though the range of maximum disturbance severity differed between plots and stands, many other
aspects of the plot and stand disturbance regimes were very similar. Plots had an average of 2.03±0.91 (1
SD) disturbance peaks from 1850 to 1989 where stands averaged 2.21±1.03. This implies a disturbance
return interval of 68years on the plot level and 62years on the stand level. Plot level disturbance averaged
over all plots from 1850 to 1989 was 7.5%±12.4% canopy area removed, and plot disturbance was below
10% canopy area removed for 75.7%±0.1% of the analyzed period. Stand level disturbance severity averaged
over all stands from 1850 to 1989 was 7.5%±6.2% canopy area removed and stand disturbance was below
10% canopy area removed for 75.0%±0.1 of the analyzed period.
3.2. Wind Disturbance Models
In our model comparison of wind drivers (e.g., large-scale extratropical cyclones and small-scale convec-
tive instability) and susceptibility variables, multivariate models that included both wind drivers and their
interactions with susceptibility variables outperformed models without wind as well as single predictor
models (Table2). Single predictor models (e.g., wind speed only, time since disturbance only, etc.) were
outperformed in every instance by multivariate models when predicting patterns of disturbance in forests of
Central and Eastern Europe. At the plot level, the best model overall was the global model which included
both cyclonic and convective wind drivers, the interactions between them, and the interactions between
drivers and susceptibility variables. A measure of model quality, the expected log predictive density (ELPD)
of the best model, estimated using leave-one-out cross-validation was >2.9 times better than the next best
model which only contained susceptibility variables and wind speed. However, when aggregating evidence
of disturbance to the stand level, two models show almost equal evidence toward best-describing patterns in
disturbance. These models are the global model and the wind speed and susceptibility model. Both models
include wind speed driven by large-scale cyclone-induced windstorms and two-way interactions between
wind speed and susceptibility variables, but the global model also contained convective instability and inter-
actions. Because the global model explains more variance, has a lower leave-one-out information criterion
score, shows a slightly better ELPD, and can be used to interpretall interactions, we focus discussion on
this model.
Examining the influence of susceptibility variables on historical disturbance severity, independent of chang-
es in wind driver variables, we saw that topographic exposure, the severity of the last disturbance, time
since last disturbance, and time interval were strong predictors of the presence and severity of plot level
disturbance (Figure3). Longitude, independent of wind variables, was not a good predictor of disturbance
10 of 17
Plot level Stand level
Model ELPD ± SE R2Model ELPD ±SE R2
Global 0.0 ±0.0 0.05 Global 0.0 ± 0.0 0.27
Windspeed and susceptibility −25.3 ±8.6 0.04 Windspeed and susceptibility −1.8 ± 4.9 0.25
Convection and susceptibility −88.7 ±14.3 0.03 Convection and susceptibility −29.2 ± 10.0 0.15
Susceptibility vars. −112.6 ±16.4 0.02 Susceptibility vars. −46.1 ± 12.0 0.08
Times since last dist. only −161.2 ±19.7 0.01 Severity of last dist. only −46.8 ± 12.3 0.07
Severity of last dist. only −232.1 ±22.9 0.02 Time period only −49.0 ± 12.9 0.06
Time period only −273.1 ±25.6 0.01 Times since last dist. only −52.5 ± 13.5 0.05
Wind speed only −284.2 ±26.2 0.01 Intercept only −53.9 ± 13.7 0.05
Topographic exposure only −287.3 ±26.3 0.01 Topographic exposure only −54.0 ± 13.7 0.05
Longitude only −290.4 ±26.5 0.01 Wind speed only −54.5 ± 13.6 0.05
Convection only −292.3 ±26.5 0.01 Longitude only −54.6 ± 13.7 0.05
Intercept only −293.3 ±26.5 0.01 Convection only −55.2 ± 13.6 0.05
Note. Models predict disturbance severity in beech-dominated mixed primary forests of Central and Eastern Europe.
Table 2
Model Comparison Organized by Expected Log Predictive Density Based on Leave-One-Out Cross-validataion
Journal of Geophysical Research: Atmospheres
severity in plots as both zero-inflated and beta parameter estimate credible intervals overlapped zero. High
values of topographic exposure increased the probability of disturbance. High values of time since the last
disturbance increased disturbance probability and severity. High values of the severity of the last distur-
bance and time period decreased the probability and severity of disturbance. At the stand level, only severity
of the last disturbance, time since last disturbance, and time period increased disturbance severity. The
directions of these relationships were the same as when analyzing at the plot level: high values of previous
disturbance severity, recent time periods, and longer times since last disturbance increased disturbance
severity observed.
3.3. Wind Moderators
When interactions between susceptibility variables and wind drivers were considered, we saw some gener-
alizable patterns. Regardless of the wind driver or the scale, intense wind conditions lost their impact over
time. This trend was much more pronounced for the “CAPE:time period” interaction than for the “wind
speed:time period” interaction. When the intense cyclone-induced wind was observed in plots or stands
lacking previous high severity disturbance, predicted disturbance severity was higher. Strong interactions
of wind drivers with time since the last disturbance was apparent at the plot level only. When increased
time since the last disturbance was paired with intense wind or CAPE conditions, disturbance severity in-
creased. When greater topographic exposure was paired with intense wind or CAPE conditions, the result
was greater severity or probability of disturbance at the plot level, but when plot level estimates of exposure
were averaged to the stand level, parameter estimate credible intervals began to overlap zero (FigureS3
andS4). Interestingly, the influence of high wind speeds produced from extratropical cyclones did not
taper with longitude as expected, but the influence of convective instability did increase with longitude as
When we plotted conditional effects of wind interactions with multiple forest susceptibility traits (e.g., time
since last disturbance, severity of last disturbance, and topographic exposure), it was apparent that winds
from large-scale cyclone-induced windstorms were more influential to disturbance severity in mixed beech
primary forests of Central and Eastern Europe than winds from small-scale convective instability, espe-
cially in susceptible plots and stands. Susceptible plots were defined as those with susceptibility variables
one standard deviation higher than the mean (e.g., plots with >77years since the last disturbance [66 for
stands], the severity of last disturbance removed>50% of the canopy area [26% for stands], and had an
exposure value of>−1.6° [−4.17° for stands]). At the plot and stand levels, the slope of the “wind speed-sus-
ceptible” predicted line was greater than that of the “convective instability-susceptible” line and higher
values of disturbance severity were reached (ca. 5% more severe at 20 severe days per year; see Figure4).
Wind from cyclone-induced windstorms was interacting with forest susceptibility at the plot level, and this
interaction was maintained even when aggregating to the stand level so that regardless of scale, higher cy-
clone-induced wind speeds caused higher severity disturbance in susceptible forests. However, when exam-
ining how convective instability interacts with forest susceptibility, more susceptible plots exhibited higher
severity disturbance when instability was high (Figure4a), but at the stand level, the impact of convective
storms were reduced (Figure4c).
4. Discussion
In this study, we showed that intense wind speed prevalence, driven by large-scale extratropical cy-
clones, was the main driver of windthrow disturbance especially in susceptible plots and stands of pri-
mary mixed forest in Central and Eastern Europe. The influence of smaller-scale convective instability
on disturbance was also supported by models. Based on these data, increases in intense wind speed
prevalence observed over the 20th century have not resulted in an increase in the scale of disturbance
events observed. Additionally, coincident reductions in the prevalence of intense convective storms,
have resulted in a net decline of disturbance severity. Because this study was conducted in fixed area
plots located only in primary forests, changes in forest area and management do not influence these
11 of 17
Journal of Geophysical Research: Atmospheres
4.1. Intense Convective Instability
The highest-ranked models for plots and stands both included evidence of convective storms which indi-
cates that, in mixed forests of Central and Eastern Europe, thunderstorms with microbursts and tornadoes
are significantly impacting forest dynamics, a fact that has largely been ignored in the European windthrow
literature (Antonescu etal.,2017; Gardiner etal.,2010; Schelhaas etal.,2003; Usbeck etal.,2010). The influ-
ence of small-scale convective storms on forest disturbance has only been recorded in mixed forests of the
Dinaric Mountains of Southeastern Europe (Nagel etal.,2017), mixed forests of the Romanian Carpathian
Mountains (Furtuna etal.,2018), and in the deciduous forests of North America (Canham et al.,2001;
Peterson & Pickett,1991). Interestingly, these studies as well as the current study all focus on mixed forests
dominated by a deciduous species which likely reduces the susceptibility of forests to windthrow in winter
when trees lack leaves. Thus, we would not expect to see the same relative influence of convective storms
in coniferous forests lacking deciduous trees. Additionally, the lack of convective forest disturbance studies
is probably due to the smaller and more heterogeneous average footprint of convective-induced windthrow
events, usually much less than 1km2 (Canham etal.,2001; Nagel etal.,2017). Thus, because the average
area covered by stands analyzed here was 1km2, the influence of convective winds was not as influential
at the stand scale. Disturbance from small-scale convective storms may have only affected one plot or a
few trees and could be averaged out when calculating the disturbance severity at the stand scale. However,
small-scale disturbance dynamics such as those created by small-scale convective storms cannot be ignored
as they create forests of high structural complexity by opening up gaps in which recruitment can diversify
the age and size profile of the forest (Franklin etal.,2002; Lorimer & Halpin,2014; Meigs etal.,2017; Tepley
etal.,2013). These localized events increase forest horizontal and vertical heterogeneity by creating gaps for
seedling/sapling recruitment.
12 of 17
Figure 4. Predicted disturbance values per number of intense wind speed (panel a and c) and CAPE days (panels b
and d) from the global zero-inflated beta models fit to plot (panels a and c) and stand (panels b and d) data from beech
dominated mixed primary forests of Central and Eastern Europe. Susceptible plot and stand lines represent those
that have a topographic exposure and time since last disturbance values one sd above the mean and severity of last
disturbance one sd below the respective mean observed for those variables. CAPE: convective available potential energy.
Journal of Geophysical Research: Atmospheres
In addition to the smaller size of the intense wind footprint of convective storms, dynamics specific to con-
vective instability also played a part in reducing the predicted impact of convective instability in forests. The
CAPE values from 20th century reanalysis data used as our measure of convective instability are modeled
on a 1°×1° grid and represent conditions favorable for producing uplift in the atmosphere but should not
be interpreted as implying that a disturbance-inducing storm will form or that a storm will cover the entire
grid cell. Thus, high values of CAPE are not always associated with forest disturbance and only increase the
probability of exposure to high winds. We accounted for this in two ways: (1) We used zero-inflated models
in order to account for the probability of observing no disturbance despite having elevated CAPE and (2) we
used the number of extreme events per year as an independent variable which has been shown to influence
windthrow (Leckebusch etal.,2008; Usbeck etal.,2010).
Convective storms had a greater influence with increasing longitude, potentially indicating the influence
of a more continental climate. Also, there was a strong and consistent interaction between longitude and
intense CAPE observed in the plot level global model (FigureS4). Thus, the forests of Romania are more
vulnerable to convective storms, probably because convective instability is more likely to be intense in many
areas of Romania compared to Slovakia (Taszarek etal., 2019). This pattern of more intense conditions
with continentality has been noted in previous research on storm prevalence across Europe (Antonescu
etal.,2017; Brooks etal.,2003) and is probably playing a role in driving this pattern. However, this is one of
the first studies to show this pattern with empirical forest disturbance data.
4.2. Intense Wind Speed
Intense wind speeds were a primary driver of disturbance severity regardless of the scale measured. This
pattern was true across the longitudinal cline of this study making this one of a limited number of studies at-
tributing large-scale cyclone-induced windstorms to forest disturbance in Romania (Gardiner etal.,2010).
The interactions between the prevalence of intense cyclone-induced wind speeds and susceptibility varia-
bles were also stronger and more consistent than those of the smaller scale intense winds created by small-
scale convective instability, likely reflecting the larger footprint of cyclones (Brázdil etal.,2018; Gardiner
etal.,2010; Leckebusch etal.,2008; Leckebusch & Ulbrich,2004; Usbeck etal.,2010). It is even likely that
this pattern would have been observed at the landscape scale based on the average size of large-scale cy-
clone-induced windstorms (Brázdil etal.,2004) and the strength of these patterns in these data.
The fact that cyclone-induced windstorms are driving disturbance dynamics at stand scales has implications
for stand structure. Large severe disturbances can reduce local structural complexity (Janda etal.,2017;
Meigs etal.,2017), however, even the most severe stand level disturbance observed here would only be
classified as low or possibly moderate severity in the global/European context (see predicted disturbance
severities around 0.15 proportion canopy area removed in Figure4). So, even the stand scale disturbances
observed here are still increasing structural complexity of stands through gap creation and patch dynamics,
only the gap sizes are likely a bit larger than those produced through convective instability.
4.3. Trends Over Time
The link between both convective storms and cyclone-induced windstorms and forest disturbance sever-
ity has weakened over the 20th century. This trend is more understandable for intense CAPE, which has
decreased in prevalence over the 20th century and will likely continue to do so (Figure1). Reductions in
CAPE values are caused by reductions in relative humidity with warming and the subsequent changes in
air parcel buoyancy based on adiabatic lapse rates (Riemann-Campe etal.,2009). As temperatures continue
to increase, atmospheric humidity values will increase but relative humidity values will continue to decline
(Hartmann etal.,2013). Rising atmospheric humidity will cause intense wind speed prevalence to rise,
though some uncertainty in the extent of this pattern exists due to changes in cyclone storm path changes
(Sepp etal.,2005). Despite the observed increases in intense wind speed prevalence over the 20th century,
we observed a reduction in the influence of wind on disturbance severity (i.e. the same prevalence of intense
wind causes lower severity disturbance today than in the past). This result is in direct contrast to hypoth-
eses made in previous studies of windthrow disturbance in European forests that, based on data from sal-
vage logging and reported damage, surmise increasing prevalence of intense winds may be responsible for
13 of 17
Journal of Geophysical Research: Atmospheres
disproportionate increases in catastrophic windthrow damage (Gardiner etal.,2010; Schelhaas etal.,2003;
Usbeck etal.,2010). However, these studies rightly list the caveat that windthrow and salvage logging re-
ports are spatially and temporally inconsistent and reports may be disproportionately more common from
intensively managed stands and in recent periods, reducing the applicability of their results for unmanaged
primary forest ecosystems (Everham & Brokaw,1996). These studies, however, are able to incorporate data
from recent periods which our study was not able to do, due to the ca. 30years time delay required when
using dendrochronological methods to reconstruct disturbance history. Thus, we could not incorporate data
from recent severe windstorms like Lothar, Martin, and Kyrill, which would have almost certainly inflated
the impact of recent wind speed increases on forest disturbance. All time periods analyzed here maintain
a sufficient presence of trees with the potential to record disturbance (FigureS5). Based on the number of
trees with the ability to record disturbance, we see that the recent portion of the 20th century that we ana-
lyzed was well represented and if the disturbance was present, it could have been recorded by trees.
Other hypotheses given for the elevated levels of recently reported damage in Western Europe windthrow
studies are an increase in forest cover across Europe (Gardiner etal.,2010) and increases in the age of the
average European forest (Schelhaas etal., 2003). Increases in forest area likely led to increases in forest
disturbance, however by using data from a plot census in this study, the area represented by disturbance re-
cording trees has not drastically changed. Thus, our data do not directly test this hypothesis but do exclude
forest cover increases as a potential bias. As for the increasing age of European forests due to the length-
ening of rotation periods, this may make even-aged managed forests more vulnerable to wind disturbance.
However, primary forest plots are usually older on average and their lack of stand-leveling disturbance
events implies that older trees remain present within forests. Despite this presence of old trees, which sug-
gests primary forests should be more vulnerable, we observed a decrease in disturbance severity. Thus,
based on these data, it may be more likely that disproportionate increases in wind disturbance observed in
Western Europe may be due both to forest area increases and the prevalence of maturing, even-aged mon-
ocultures in commercial forests there.
4.4. Structural Complexity
Previous studies have hypothesized the potential for stands high in structural complexity to have increased
resistance to windthrow (Everham & Brokaw,1996; Gardiner etal.,2010; Mitchell,2013). Stands in this
study show increasing resistance to windthrow and likely have high structural complexity induced by
changes in the mixed-severity disturbance regime (Figures 3 b and 3c). Across the time period analyzed, dis-
turbance severity decreased and the time since disturbance increased on average. Both of these variable tra-
jectories can be interpreted as increases in forest structural complexity based on previous research in spruce
forests (Janda etal.,2017; Meigs etal.,2017). Also, as the forests in this study are aging and increasingly
exhibit old-growth structure traits (e.g. increased canopy height roughness), their resistance to wind distur-
bance may be increasing as well (Mitchell,1995). However, the link between structure and disturbance was
not directly measured here and should be interpreted with caution. Previous studies that have attempted
to account for stand structure as a susceptibility variable have not shown a clear and obvious influence
of structure on windthrow susceptibility (Barry Gardiner etal., 2005; Mitchell,2013). Regardless, the fact
that disturbance probability is lower recently even when the intense wind is controlled for (Figure3e), is
evidence that something about these forests has changed and increased resistance. Thus, future research on
the influence of structural complexity on windthrow vulnerability is warranted.
4.5. Susceptibility to Windthrow
Here we controlled for five variables that we hypothesized moderated wind-induced disturbance in beech
dominated mixed forests of Central and Eastern Europe, all influenced disturbance in the hypothesized
manner, but patterns were scale specific or only interacted with one of the two wind drivers. Longitude
only interacted with small-scale convective instability at smaller, plot scales. The fact that longitude did
not show strong interactions with the larger-scale cyclone-induced windstorms indicates that intense
winds were ubiquitous across the Carpathian Mountains and the influence of intense wind speeds was
consistent across the longitudinal gradient. Despite evidence that extratropical cyclones are not likely to
reach deep into continental Europe due to the East-Central European High (Di Rita etal.,2018) and the
14 of 17
Journal of Geophysical Research: Atmospheres
poleward movement trend of extratropical cyclones making landfall in Europe (Leckebusch etal.,2008;
Sepp etal.,2005), the absolute area of elevated wind speeds that cyclones produce covers the full gradient of
longitudes studied here (14°–25°). Topographic exposure was consistent as a moderator of wind disturbance
regardless of the wind driver but only at the plot scale. This result has been observed before and, thus, many
windthrow susceptibility models include distance-limited topographic exposure as a predictor (Quine &
White,1998; Ruel etal.,2002; Schmidt etal.,2010). The reduced interaction between winds and topograph-
ic exposure at the stand scale was expected because stand measures of topographic exposure represented
an average of plot level exposure, which reduced differences between stand measurements. Beyond topo-
graphic exposure, topographic roughness has been shown to moderate the scale of disturbances, which may
be an additional reason that we did not observe severe disturbances at the larger stand scale as our stands
are mostly in or near mountainous areas (Senf & Seidl,2018). Previous disturbance also influenced future
disturbance severity as expected, with shorter time interval since the last disturbance and higher severity of
the last disturbance associated with higher severity forest leveling, i.e., when more trees are present, more
forest can be disturbed (Janda etal.,2017; Meigs etal.,2017).
There are variables that we do not include in this analysis that are known moderators of windthrow distur-
bance. These include soil depth, soil moisture, tree height, and stand density (Canham etal.,2001; Gardiner
etal.,2008; Nicoll etal.,2008; Usbeck etal.,2010), yet reliably reconstructing these variables over the anal-
ysis period is a large feat outside of the scope of this analysis. Because our main goals were to determine
if large-scale cyclone-induced storms were the only source of wind disturbance in Central and Eastern
European forests and to describe changes in the influence of wind drivers over the 20th century, maximiz-
ing model fit was not essential to achieve these goals. Inclusion of other drivers and moderators of forest
dynamics would almost certainly increase the fit of models, and the low estimated R2 values observed for
plot scale models are evidence of this, but finding strong and significant trends of cyclones, convection, and
wind moderators across the 20th century had never been accomplished prior to this study. Thus, this study
provides essential information based on readily available forest positions and past disturbance data that can
be used to predict the risk of future windthrow disturbance.
Though large-scale cyclone-induced windstorms are driving small- and large-scale disturbances in primary
forests, the influence of convective storms cannot be ignored. The fact that both intense windstorms and
intense convective instability are less influential in recent years may be evidence that primary forests are
less susceptible to windthrow due to changes in the disturbance regime. Forest susceptibility variables used
here can be extrapolated to many forests. Forest exposure, time since the last disturbance, and severity of
the last disturbance can be incorporated into censuses to map and monitor forest vulnerability in response
to predicted increases in intense cyclone activity.
Data Availability Statement
Datasets for this research are available in these in-text data citation references (Pettit,2020), with CC 4.0
Altman, J., Ukhvatkina, O. N., Omelko, A. M., Macek, M., Plener, T., Pejcha, V., etal. (2018). Poleward migration of the destructive effects
of tropical cyclones during the 20th century. Proceedings of the National Academy of Sciences 115, 11543–11548.
Antonescu, B., Schultz, D. M., Holzer, A., & Groenemeijer, P. (2017). Tornadoes in Europe: An underestimated threat. Bulletin of the Amer-
ican Meteorological Society, 98(4), 713–728.
Anyomi, K. A., Mitchell, S. J., & Ruel, J.-C. (2016). Windthrow modeling in old-growth and multi-layered boreal forests. Ecological Model-
ling, 327, 105–114.
Bengtsson, J., Nilsson, S. G., Franc, A., & Menozzi, P. (2000). Biodiversity, disturbances, ecosystem function and management of European
forests. Forest Ecology and Management, 132(1), 39–50.
Bett, P. E., Thornton, H. E., & Clark, R. T. (2017). Using the twentieth century reanalysis to assess climate variability for the European wind
industry. Theoretical and Applied Climatology, 127(1–2), 61–80.
Brázdil, R., Blennow, K., Dobrovolný, P., Štekl, J., Kotyza, O., Valášek, H., & Jež, J. (2004). History of weather and climate in the Czech lands
VI: Strong winds.
Brázdil, R., Stucki, P., Szabó, P., Řezníčková, L., Dolák, L., Dobrovolný, P., etal. (2018). Windstorms and forest disturbances in the Czech
Lands: 1801-2015. Agricultural and Forest Meteorology, 250–251, 47–63.
15 of 17
We would like to thank the Maramureş
Mountains Nature Park and Semen-
ic-Caraș Gorge National Park for their
assistance with site access and surveys.
Journal of Geophysical Research: Atmospheres
Brooks, H. E., Lee, J. W., & Craven, J. P. (2003). The spatial distribution of severe thunderstorm and tornado environments from global
reanalysis data. Atmospheric Research, 67–68(68), 73–94.
Burrascano, S., Keeton, W. S., Sabatini, F. M., & Blasi, C. (2013). Commonality and variability in the structural attributes of moist temperate
old-growth forests: A global review. Forest Ecology and Management, 291, 458–479.
Čada, V., Trotsiuk, V., Janda, P., Mikoláš, M., Bače, R., Nagel, T. A., etal. (2020). Quantifying natural disturbances using a large-scale den-
drochronological reconstruction to guide forest management Ecological Applications.
Canham, C. D., Papaik, M. J., & Latty, E. F. (2001). Interspecif ic variation in susceptibility to windthrow as a function of tree size and storm
severity for northern temperate tree species. Canadian Journal of Forestry and Research, 31(1), 1–10.
Carey, E. V., Sala, A., Keane, R., & Callaway, R. M. (2001). Are old forests underestimated as global carbon sinks? Global Change Biology,
7(4), 339–344.
Compo, G. P. J. S., & Whitaker, P. D. (2006). Feasibility of a 100-year reanalysis using only surface pressure data. Bulletin of American Me-
teorological Society, 87(2), 175–190.
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J., Yin, X., etal. (2011). The Twentieth Century Reanalysis Project.
Quarterly Journal of the Royal Meteorological Society, 137(654), 1–28.
Cook, E. (Ed.). (2019). Agriculture, forestry and fishery statistics, 2019 edition. Publications Office of the European Union.
Del Genio, A. D., Yao, M.-S., & Jonas, J. (2007). Will moist convection be stronger in a warmer climate?: Convection strength in a warmer
climate. Geophysical Research Letters, 34(16).
Di Rita, F., Fletcher, W. J., Aranbarri, J., Margaritelli, G., Lirer, F., & Magri, D. (2018). Holocene forest dynamics in central and west-
ern Mediterranean: Periodicity, spatio-temporal patterns and climate influence. Scientific Reports, 8(1).
Donat, M. G., Renggli, D., Wild, S., Alexander, L. V., Leckebusch, G. C., & Ulbrich, U. (2011). Reanalysis suggests long-term upward trends
in European storminess since 1871. Geophysical Research Letters, 38(14).
Everham, E. M., & Brokaw, N. V. L. (1996). Forest damage and recovery from catastrophic wind. The Botanical Review, 62(2), 113–185.
Firm, D., Nagel, T. A., & Diaci, J. (2009). Disturbance history and dynamics of an old-growth mixed species mountain forest in the Slove-
nian Alps. Forest Ecology and Management, 257(9), 1893–1901.
Franklin, J. F., Spies, T. A., Pelt, R. V., Carey, A. B., Thornburgh, D. A., Berg, D. R., etal. (2002). Disturbances and structural development
of natural forest ecosystems with silvicultural implications, using Douglas-fir forests as an example. Forest Ecology and Management,
155(1–3), 399–423.
Fraver, S., & White, A. S. (2005a). Disturbance dynamics of old-growth Picea rubens forests of northern Maine. Journal of Vegetation Sci-
ence, 16(5), 597.
Fraver, S., & White, A. S. (2005b). Identifying growth releases in dendrochronological studies of forest disturbance. Canadian Journal of
Forestry and Research, 35(7), 1648–1656.
Furtuna, P., Haidu, I., & Maier, N. (2018). Synoptic processes generating windthrow. A case study for Apuseni mountains (Romania).
Geographia Technica, 13(2), 52–61.
Gardiner, B, Blennow, K., Carnus, J.-M., Fleischer, P., Ingemarson, F., Landmann, G., etal. (2010). Destructive storms in European forests:
Past and Forthcoming impacts, 138.
Gardiner, B., Byrne, K., Hale, S., Kamimura, K., Mitchell, S. J., Peltola, H., & Ruel, J.-C. (2008). A review of mechanistic modeling of wind
damage risk to forests. Forestry, 81(3), 447–463.
Gardiner, B., Marshall, B., Achim, A., Belcher, R., & Wood, C. (2005). The stability of different silvicultural systems: a wind-tunnel investi-
gation. Forestry: An International Journal of Forest Research, 78(5), 471–484.
Harmon, M. E., Ferrell, W. K., & Franklin, J. F. (1990). Effects on carbon storage of conversion of old-growth forests to young forests. Sci-
ence, 247(4943), 699–702.
Hartmann, D. L., Tank, A. M. K., Rusticucci, M., Alexander, L. V., Brönnimann, S., Charabi, Y. A. R., etal. (2013). In Climate change 2013
the physical science basis: Working group I contribution to the fifth assessment report of the intergovernmental panel on climate change (pp.
159–254). Cambridge University Press. Observations: Atmosphere and surface.
Holmes, R. L. (1983). Computer-assisted quality control in tree-ring dating and measurement. Tree-Ring Bulletin, 43, 69–78.
Janda, P., Trotsiuk, V., Mikoláš, M., Bače, R., Nagel, T. A., Seidl, R., etal. (2017). The historical disturbance regime of mountain Norway
spruce forests in the Western Carpathians and its influence on current forest structure and composition. Forest Ecology and Manage-
ment, 388, 67–78.
Kozák, D., Mikoláš, M., Svitok, M., Bače, R., Paillet, Y., Larrieu, L., etal. (2018). Profile of tree-related microhabitats in European primary
beech-dominated forests. Forest Ecology and Management, 429, 363–374.
Larsson, L. (2003). CDendro: Cybis Dendro dating program. öbaden,Cybis Elektronik & Data AB.
Leckebusch, G. C., & Ulbrich, U. (2004). On the relationship between cyclones and extreme windstorm events over Europe under climate
change. Global and Planetary Change, 44(1–4), 181–193.
Leckebusch, G. C., Weimer, A., Pinto, J. G., Reyers, M., & Speth, P. (2008). Extreme wind storms over Europe in present and future climate:
A cluster analysis approach. Metz, 17(1), 67–82.
Leverkus, A. B., Puerta-Piñero, C., Guzmán-Álvarez, J. R., Navarro, J., & Castro, J. (2012). Post-fire salvage logging increases restoration
costs in a Mediterranean mountain ecosystem. New Forests, 43(5–6), 601–613.
Lorimer, C. G., & Frelich, L. E. (1989). A methodology for estimating canopy disturbance frequency and intensity in dense temperate
forests. Canadian Journal of Forest and Research, 20(5), 615615.
Lorimer, C. G., & Halpin, C. R. (2014). Classification and dynamics of developmental stages in late-successional temperate forests. Forest
Ecology and Management, 334, 344–357.
Luyssaert, S., Schulze, E.-D., Börner, A., Knohl, A., Hessenmöller, D., Law, B. E., etal. (2008). Old-growth forests as global carbon sinks.
Nature, 455(7210), 213–215.
Meigs, G. W., Morrissey, R. C., Bače, R., Chaskovskyy, O., Čada, V., Després, T., etal. (2017). More ways than one: Mixed-severity dis-
turbance regimes foster structural complexity via multiple developmental pathways. Forest Ecology and Management, 406, 410–426.
Mikoláš, M., Ujházy, K., Jasík, M., Wiezik, M., Gallay, I., Polák, P., et al. (2019). Primary forest distribution and representation
in a Central European landscape: Results of a large-scale field-based census. Forest Ecology and Management, 449, 117466.
16 of 17
Journal of Geophysical Research: Atmospheres
Mitchell, S. J. (1995). The windthrow triangle: A relative windthrow hazard assessment procedure for forest managers. The Forestry Chron-
icle, 71(4), 446–450.
Mitchell, S. J. (2013). Wind as a natural disturbance agent in forests: A synthesis. Forestry, 86(2), 147–157.
Müller, J., Noss, R. F., Thorn, S., Bässler, C., Leverkus, A. B., & Lindenmayer, D. (2019). Increasing disturbance demands new policies to
conserve intact forest. Conservation Letters, 12(1), e12449.
Nagel, T. A., Mikac, S., Dolinar, M., Klopcic, M., Keren, S., Svoboda, M., etal. (2017). The natural disturbance regime in forests of the
Dinaric Mountains: A synthesis of evidence. Forest Ecology and Management, 388, 29–42.
Nagel, T. A., Svoboda, M., & Diaci, J. (2006). Regeneration patterns after intermediate wind disturbance in an old-growth Fagus-Abies
forest in southeastern Slovenia. Forest Ecology and Management, 226(1–3), 268–278.
Nicoll, B. C., Gardiner, B. A., & Peace, A. J. (2008). Improvements in anchorage provided by the acclimation of forest trees to wind stress.
Forestry, 81(3), 389–398.
Peterson, C. J., & Pickett, S. T. A. (1991). Treefall and resprouting following catastrophic windthrow in an old-growth hemlock-hardwoods
forest. Forest Ecology and Management, 42(3–4), 205–217.
Pettit, J. (2020). climdf.csv.
Quine, C., & White, I. M. S. (1998). The potential of distance-limited topex in the prediction of site windiness. Forestry, 71(4), 325–332.
Quine, & Gardiner, B. A. (2007). Understanding how the interaction of wind and trees results in windthrow, stem breakage, and canopy
gap formation. In Plant Disturbance Ecology (pp. 103–155). Elsevier.
Riemann-Campe, K., Fraedrich, K., & Lunkeit, F. (2009). Global climatology of convective available potential energy (CAPE) and con-
vective inhibition (CIN) in ERA-40 reanalysis. Atmospheric Research, 93(1–3), 534–545.
Ruel, J.-C., Mitchell, S. J., & Dornier, M. (2002). A GIS based approach to map wind exposure for windthrow hazard rating. Northern Jour-
nal of Applied Forestry, 19(4), 183–187.
Sabatini, F. M., Burrascano, S., Keeton, W. S., Levers, C., Lindner, M., Pötzschner, F., etal. (2018). Where are Europe's last primary forests?
Diversity and Distributions, 24(10), 1426–1439.
Schelhaas, M.-J. (2008). Impacts of natural disturbances on the development of European forest resources: Application of model approach-
es from tree and stand levels to large-scale scenarios. Dissertationes Forestales, 2008(56).
Schelhaas, M.-J., Nabuurs, G.-J., & Schuck, A. (2003). Natural disturbances in the European forests in the 19th and 20th centuries. Global
Change Biology, 9(11), 1620–1633.
Schemm, S., Sprenger, M., Martius, O., Wernli, H., & Zimmer, M. (2017). Increase in the number of extremely strong fronts over Europe? A
study based on ERA-Interim reanalysis (1979-2014). Geophysical Research Letters, 44(1), 553–561.
Schmidt, M., Hanewinkel, M., Kändler, G., Kublin, E., & Kohnle, U. (2010). An inventory-based approach for modeling single-tree storm
damage - experiences with the winter storm of 1999 in southwestern Germany. Canadian Journal of Forestry and Research, 40(8),
Schurman, J. S., Trotsiuk, V., Bače, R., Čada, V., Fraver, S., Janda, P., etal. (2018). Large-scale disturbance legacies and the climate sensitiv-
ity of primary Picea abies forests. Global Change Biology, 24(5), 2169–2181.
Seedre, M., Janda, P., Trotsiuk, V., Hedwall, P.-O., Morrissey, R. C., Mikoláš, M., etal. (2020). Biomass carbon accumulation patterns
throughout stand development in primary uneven-aged forest driven by mixed-severity natural disturbances. Forest Ecology and Man-
agement, 455, 117676.
Seidl, R., Thom, D., Kautz, M., Martin-Benito, D., Peltoniemi, M., Vacchiano, G., etal. (2017). Forest disturbances under climate change.
Nature Climate Change, 7(6), 395–402.
Senf, C., & Seidl, R. (2018). Natural disturbances are spatially diverse but temporally synchronized across temperate forest landscapes in
Europe. Global Change Biology, 24(3), 1201–1211.
Sepp, M., Post, P., & Jaagus, J. (2005). Long-term changes in the frequency of cyclones and their trajectories in Central and Northern Eu-
rope. Hydrology Research, 36(4–5), 297–309.
Siedlecki, M. (2009). Selected instability indices in Europe. Theoretical and Applied Climatology, 96(1–2), 85–94.
Slivinski, L. C., Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Giese, B. S., McColl, C., etal. (2019). Toward a more reliable historical
reanalysis: Improvements for version 3 of the twentieth century reanalysis system. Quarterly Journal of the Royal Meteorological Society,
145(724), 2876–2908.
Sommerfeld, A., Senf, C., Buma, B., D'Amato, A. W., Després, T., Díaz-Hormazábal, I., etal. (2018). Patterns and drivers of recent distur-
bances across the temperate forest biome. Nature Communications, 9(1).
Synek, M., Janda, P., Mikoláš, M., Nagel, T. A., Schurman, J. S., Pettit, J. L., etal. (2020). Contrasting patterns of natural mortality in primary
Picea forests of the Carpathian mountains. Forest Ecology and Management, 457, 117734.
Taszarek, M., Allen, J., Púčik, T., Groenemeijer, P., Czernecki, B., Kolendowicz, L., etal. (2019). A climatology of thunderstorms across
Europe from a synthesis of multiple data sources. Journal of Climate, 32(6), 1813–1837.
Tepley, A. J., Swanson, F. J., & Spies, T. A. (2013). Fire-mediated pathways of stand development in Douglas-fir/western hemlock forests of
the Pacific Northwest, USA. Ecology, 94(8), 1729–1743.
Trotsiuk, V., Svoboda, M., Janda, P., Mikolas, M., Bace, R., Rejzek, J., etal. (2014). A mixed severity disturbance regime in the primary
Picea abies (L.) Karst. forests of the Ukrainian Carpathians. Forest Ecology and Management, 334, 144–153.
Usbeck, T., Wohlgemuth, T., Pfister, C., Volz, R., Beniston, M., & Dobbertin, M. (2010). Wind speed measurements and forest damage in
Canton Zurich (Central Europe) from 1891 to winter 2007. International Journal of Climatology.
Zielonka, T., Holeksa, J., Fleischer, P., & Kapusta, P. (2010). A tree-ring reconstruction of wind disturbances in a forest of the Slovakian
Tatra Mountains, Western Carpathians. Journal of Vegetation Science, 21(1), 31–42.
17 of 17
... Wind damage in Europe is caused by two phenomena: Extratropical cyclones and convective storms (Pettit et al., 2021). Extratropical cyclones develop in Europe during autumn and winter months and are defined by strong winds that increase their speed quickly into violent gusts (Martínez-Alvarado et al., 2014). ...
... The other driver of intense wind in Europe is convective storms, which are more common in the Southeastern part of the continent . They usually occur during summer months and create speed winds that are higher than extratropical cyclones but affect smaller areas for shorter periods (Pettit et al., 2021). ...
Full-text available
It is expected that European Boreal and Temperate forests will be greatly affected by climate change, causing natural disturbances to increase in frequency and severity. To detangle how, through forest management, we can make forests less vulnerable to the impact of natural disturbances, we need to include the risks of such disturbances in our decision-making tools. The present review investigates: i) how the most important forestry-related natural disturbances are linked to climate change, and ii) different modelling approaches that assess the risks of natural disturbances and their applicability for large-scale forest management planning. Global warming will decrease frozen soil periods, which increases root rot, snow, ice and wind damage, cascading into an increment of bark beetle damage. Central Europe will experience a decrease in precipitation and increase in temperature, which lowers tree defenses against bark beetles and increases root rot infestations. Ice and wet snow damages are expected to increase in Northern Boreal forests, and to reduce in Temperate and Southern Boreal forests. However, lack of snow cover may increase cases of frost-damaged seedlings. The increased temperatures and drought periods, together with a fuel increment from other disturbances, likely enhance wildfire risk, especially for Temperate forests. For the review of European modelling approaches, thirty-nine disturbance models were assessed and categorized according to their required input variables and to the models' outputs. Probability models are usually common for all disturbance model approaches, however, models that predict disturbance effects seem to be scarce.
... The disturbance regime across the study region is complex, ranging from frequent tree fall gaps to rare stand-replacing disturbances covering tens to hundreds of hectares with return intervals of several centuries (Čada et al., 2020;Frankovič et al., 2020;Meigs et al., 2017;Nagel et al., 2017). The regime includes a number of different disturbance agents, such as wind from summer and winter storms, bark beetle outbreaks in spruce dominate stands, ice storms, and heavy snow (Čada et al., 2016;Frankovič et al., 2020;Nagel et al., 2017;Pettit et al., 2021). The extensive dataset used in this study is a part of the REMOTE network (for further information, see of sampling plots, aiming to study the long-term dynamics of European primary forests. ...
Canopy accession strategies reveal much about tree life histories and forest stand dynamics. However, the protracted nature of ascending to the canopy makes direct observation challenging. We use a reconstructive approach based on an extensive tree ring database to study the variability of canopy accession patterns of dominant tree species (Abies alba, Acer pseudoplatanus, Fagus sylvatica, Picea abies) in temperate mountain forests of Europe and elucidate how disturbance histories, climate, and topography affect canopy accession. All four species exhibited high variability of radial growth histories leading to canopy accession and indicated varying levels of shade tolerance. Individuals of all four species survived at least 100 years of initial suppression. Fir and particularly beech, however, survived longer periods of initial suppression, exhibited more release events, and reached the canopy later on average, with a larger share of trees accessing the canopy after initially suppressed growth. These results indicate the superior shade tolerance of beech and fir compared to spruce and maple. The two less shade-tolerant species conversely relied on faster growth rates, revealing their competitive advantage in non-suppressed conditions. Additionally, spruce from higher-elevation spruce-dominated forests survived shorter periods of initial shading and exhibited fewer releases, with a larger share of trees reaching the canopy after open canopy recruitment (i.e. in absence of suppression) and no subsequent releases compared to spruce growing in lower-elevation mixed forests. Finally, disturbance factors were identified as the primary driver of canopy accession, whereby disturbances accelerate canopy accession and consequently regulate competitive interactions. Intensifying disturbance regimes could thus promote shifts in species composition, particularly in favour of faster-growing, more light-demanding species.
... Based on our research and observations, we assert that these causes are multifaceted. Natural factors, such as severe storms [110][111][112], causing windthrows (the latest one occurred on 17 September 2017), as mentioned in the PNA environmental reports [113][114][115][116][117][118], and pests, such as the European spruce bark beetle (Ips typographus) [119,120], have contributed to this decline. Furthermore, the damaged wood provides an opportunity for exploitation, even in protected areas, as allowed by Romanian laws. ...
Full-text available
The assessment of changes in forest coverage is crucial for managing protected forest areas, particularly in the face of climate change. This study monitored forest cover dynamics in a 6535 ha mountain area located in north-west Romania as part of the Apuseni Natural Park from 2003 to 2019. Two approaches were used: vectorization from orthophotos and Google Earth images (in 2003, 2005, 2009, 2012, 2014, 2016, 2017, and 2019) and satellite imagery (Landsat 5 TM, 7 ETM, and 8 OLI) pre-processed to Surface Reflectance (SR) format from the same years. We employed four standard classifiers: Support Vector Machine (SVM), Random Forest (RF), Maximum Likelihood Classification (MLC), Spectral Angle Mapper (SAM), and three combined methods: Linear Spectral Unmixing (LSU) with Natural Breaks (NB), Otsu Method (OM) and SVM, to extract and classify forest areas. Our study had two objectives: 1) to accurately assess changes in forest cover over a 17-year period and 2) to determine the most efficient methods for extracting and classifying forest areas. We validated the results using performance metrics that quantify both thematic and spatial accuracy. Our results indicate a 9% loss of forest cover in the study area, representing 577 ha with an average decrease ratio of 33.9 ha/year−1. Of all the methods used, SVM produced the best results (with an average score of 88% for Overall Quality (OQ)), followed by RF (with a mean value of 86% for OQ).
... Dendrochronological data can be used as a proxy for wind exposure by highlighting windthrow disturbances (e.g. Pettit et al., 2021), but tree coring and laboratory analysis are time-consuming and thus costly. Lightning strikes creating scars in trunks are very rare stochastic events. ...
Tree-related microhabitats (TreMs) have been identified as key features for forest-dwelling taxa and are often employed as measures for biodiversity conservation in integrative forest management. However, managing forests to ensure an uninterrupted resource supply for TreM-dwelling taxa is challenging since TreMs are structures with a limited availability, some of which are triggered by stochastic events or require a long time to develop. At the tree scale, the role of tree species, diameter at breast height (dbh) and status (i.e. living vs standing dead) for favouring TreM occurrence has been quantified and modelled in several studies, since these tree features are routinely recorded in the field. However, TreM occurrence remains difficult to predict, hampering the elaboration of applicable management strategies that consider TreMs. Using an international database encompassing 110,000 trees, we quantified the explanatory power of tree species, dbh, status, time since last harvest and plot context for predicting TreM occurrence at the tree level. Plot context is so far a “black box” that combines local environmental conditions, past and current management legacies, with local biotic features that have high explanatory power for predicting TreM occurrence. Then, based on the literature, we established a set of 21 factors related to site, stand and tree features for which there is a strong assumption that they play a key role in TreM formation. Finally, we identified a sub-set of nine features that should be recorded in the future to provide additional information to enable better prediction of the occurrence of particular TreMs: (i) at plot level: slope, exposure, altitude and presence of cliffs; and (ii) at tree level: bark features, phyllotaxis and compartmentalization capacity of the tree species, plus ontogenic stage and physiological state of the individual tree sampled.
Understanding temporal and spatial variations in historical disturbance regimes across intact, continuous, and altitudinally diverse primary forest landscapes is imperative to help forecast forest development and adapt forest management in an era of rapid environmental change. Because few complex primary forest landscapes remain in Europe, previous research has largely described disturbance regimes for individual forest types and smaller isolated stands. We studied the largest but still largely unprotected mountain primary forest landscape in temperate Europe, the Fagaraș Mountains of Romania. To describe historical disturbance regimes and synchronicity in disturbance activity and trends between two widespread forest community types, dominated by Norway spruce (Picea abies (L.) Karst.) and European beech (Fagus sylvatica L.), we established 191 permanent study plots (70 beech; 121 spruce) across 11 valleys, thereby providing information at both stand and landscape levels. We used a dendrochronological approach to reconstruct and describe the spatiotemporal patterns of historical disturbances. We observed a diverse spectrum of disturbance severities and timing across the forest landscape. High-severity disturbances created periods of synchrony in disturbance activity at the landscape scale, while moderate- and low-severity disturbances were asynchronous and random in both spruce- and beech-dominated primary forests. We detected a peak of canopy disturbance across the region at the end of the nineteenth century, with the most important periods of disturbance between the 1890s and 1910s. At the stand scale, we observed periods of synchronised disturbances with varying severities across both forest types. The level of disturbance synchrony varied widely among the stands. The beta regression showed that spruce forests had significantly higher average synchrony and higher between-stand variability of synchrony than the beech-dominated forests. Synchronised disturbances with higher severity were infrequent, but they were critical as drivers of subsequent forest development pathways and dynamics across both forest types. Our results provide valuable insight into future resilience to climate-driven alterations of disturbance regimes in spruce- and beech-dominated mountain temperate forests in the Carpathians. We suggest that conservation efforts should recognize strictly protecting large continuous and altitudinally diversified forest landscapes such as Fagaraș Mts. as a necessary measure to tackle climate change and ensure temporal and spatial structural heterogeneity driven by a wide range of disturbances. The diverse and synchronous disturbance activity among two interconnected forest vegetation types highlights the need for complex spatiotemporal forest management approaches that emulate disturbance synchronicity to foster biodiversity across multiple forest vegetation types within forest landscapes.
Protecting structural features, such as tree-related microhabitats (TreMs), is a cost-effective tool crucial for biodiversity conservation applicable to large forested landscapes. While the development of TreMs is influenced by tree diameter, species, and vitality, the relationships between tree age and TreM profile remain poorly understood. Using a tree-ring based approach and a large data set of 8038 trees, we modeled the effects of tree age, diameter, and site characteristics on TreM richness and occurrence across some of the most intact primary temperate forests in Europe, including mixed beech and spruce forests. We observed an overall increase in TreM richness on old and large trees in both forest types. The Occurrence of specific TreM groups was variably related to tree age and diameter, but some TreM groups (e.g., epiphytes) had a stronger positive relationship with tree species and elevation. While many TreM groups were positively associated with tree age and diameter, only 2 TreM groups in spruce stands reacted exclusively to tree age (insect galleries and exposed sapwood) without responding to diameter. Thus, the retention of trees for conservation purposes based on tree diameter appears to be a generally feasible approach with rather low risk of underrepresentation of TreMs. Because greater tree age and diameter positively affected TreM development, placing a greater emphasis on conserving large trees and allowing them to reach older ages, for example, through establishment of conservation reserves, would better maintain the continuity of TreM resource and associated biodiversity. However, this approach may be difficult due to the widespread intensification of forest management and global climate change. Article Impact Statement: Conservation of habitat trees based on size, without considering tree age, may impair landscape-level biodiversity potential. This article is protected by copyright. All rights reserved.
European beech is the dominant native tree species in sub-mountain and mountain zones of the European continent and provides multiple ecosystem services to European society. However, the mountain zone is strongly affected by environmental change. Here, we explore the long-term natural dynamics of European beech-dominated old-growth forests in the Western Carpathians, Central Europe. We use fifty-year measurements of forest stand dynamics from eleven nature reserves strictly protected from any management (some of them included in the UNESCO natural heritage program). The study sites cover a typical range of natural conditions of the beech-dominated altitudinal zone in the Western Carpathians. The database includes long-term pair-wise non-destructive measurements of nearly 3.000 beeches from 1970 through 2019. We aim to investigate three crucial processes of beech forest dynamics: increment, ingrowth, and mortality of beech trees. The results, besides expected interactions, indicated a statistically significant change in radial increment and tree mortality during the study period. The radial increment increased, whereas tree mortality decreased during the past decades. Our findings indicate that European beech has strengthened the role of the most vital and competitive tree species in the elevational range from sub-mountain to mountain zone of Central European mountains during the last 50 years. The species increases the potential of enlarging its current distribution and outcompeting other species at the upper as well as lower elevational range edges. The assumed reasons for such development considering postglacial vegetation development in Europe and upcoming climate change are discussed. Possible implications and ways how to utilize the observed findings in close-to-nature forest management are suggested.
Full-text available
Windstorms may have negative consequences on forest ecosystems, industries, and societies. Extreme events related to extra-tropical cyclonic systems remind us that better recognition and understanding of the factors driving forest damage are needed for more efficient risk management and planning. In the present study, we statistically modelled forest damage caused by the windstorm Klaus in southwest France. This event occurred on 24 January 2009 and caused severe damage to maritime pine (Pinus pinaster) forest stands. We aimed at isolating the best potential predictors that can help to build better predictive models of forest damage. We applied the random forest (RF) technique to find the best classifiers of the forest damage binary response variable. Five-fold spatial block cross-validation, repeated five times, and forward feature selection (FFS) were applied to the control for model over-fitting. In addition, variable importance (VI) and accumulated local effect (ALE) plots were used as model performance metrics. The best RF model was used for spatial prediction and forest damage probability mapping. The ROC AUC of the best RF model was 0.895 and 0.899 for the training and test set, respectively, while the accuracy of the RF model was 0.820 for the training and 0.837 for the test set. The FFS allowed us to isolate the most important predictors, which were the distance from the windstorm trajectory, soil sand fraction content, the MODIS normalized difference vegetation index (NDVI), and the wind exposure index (WEI). In general, their influence on the forest damage probability was positive for a wide range of the observed values. The area of applicability (AOA) confirmed that the RF model can be used to construct a probability map for almost the entire study area.
Full-text available
The climatology of (severe) thunderstorm days is investigated on a pan European scale for the period of 1979-2017. For this purpose, sounding measurements, surface observations, ZEUS and EUCLID lightning data, ERA-Interim reanalysis and severe weather reports are compared and their respective strengths and weaknesses are discussed. The research focuses on the annual cycles in thunderstorm activity and their spatial variability. According to all datasets thunderstorms are the most frequent in the central Mediterranean, Alps, Balkan Peninsula and Carpathians. Proxies for severe thunderstorm environments show similar patterns, but severe weather reports instead have their highest frequency over Central Europe. Annual peak thunderstorm activity is in July and August over northern, eastern and central Europe, contrasting with peaks in May and June over western and southeastern Europe. The Mediterranean, driven by the warm waters, has predominant activity in the fall (western part) and winter (eastern part) while the nearby Iberian Peninsula and eastern Turkey have peaks in April and May. Trend analysis of the mean annual number of days with thunderstorms since 1979 indicates an increase over Alps, central, southeastern and eastern Europe with a decrease over southwest. Multiannual changes refer also to changes in the pattern of the annual cycle. Comparison of different data sources revealed that although lightning data provides the most objective sampling of thunderstorm activity, short operating periods and areas devoid of sensors limits their utility. In contrast, reanalysis complements these disadvantages to provide a longer climatology, but is prone to errors related to modeling thunderstorm occurrence and numerical simulation itself.
Full-text available
Windstorms are among the main factors causing damages to forest ecosystems. These meteorological phenomena cannot be predicted, prevented or controlled. They are occurring rapidly and take place in a meteorological context characterized by high velocities of the air currents. This paper analyses the characteristics of the severe meteorological events on 20 July 2011 which have led to windthrows on extended areas within the Apuseni Mountains in the Romanian Carpathians. The study highlights the evolution of the synoptic processes both at regional scale and at mesoscale. The meteorological analysis is carried out based on synoptic maps and by means of the data coming from the doppler WSR-98D radar in Bobohalma. The main results indicate the occurrence of the severe meteorological phenomena against the background of alternating atmospheric circulation types. The main cause is the transition of the zonal circulation into maritime tropical circulation. This contributed to the destabilizing of the warm and moist air masses that have generated strong storms at national level.
Full-text available
Increasing evidence indicates that forest disturbances are changing in response to global change, yet local variability in disturbance remains high. We quantified this considerable variability and analyzed whether recent disturbance episodes around the globe were consistently driven by climate, and if human influence modulates patterns of forest disturbance. We combined remote sensing data on recent (2001–2014) disturbances with in-depth local information for 50 protected landscapes and their surroundings across the temperate biome. Disturbance patterns are highly variable, and shaped by variation in disturbance agents and traits of prevailing tree species. However, high disturbance activity is consistently linked to warmer and drier than average conditions across the globe. Disturbances in protected areas are smaller and more complex in shape compared to their surroundings affected by human land use. This signal disappears in areas with high recent natural disturbance activity, underlining the potential of climate-mediated disturbance to transform forest landscapes.
Estimates of historical disturbance patterns are essential to guide forest management aimed at ensuring the sustainability of ecosystem functions and biodiversity. However, quantitative estimates of various disturbance characteristics required in management applications are rare in longer‐term historical studies. Thus, our objectives were to: (1) quantify past disturbance severity, patch size, and stand proportion disturbed, and (2) test for temporal and sub‐regional differences in these characteristics. We developed a comprehensive dendrochronological method to evaluate an approximately two‐century‐long disturbance record in the remaining Central and Eastern European primary mountain spruce forests, where wind and bark beetles are the predominant disturbance agents. We used an unprecedented large‐scale nested design dataset of 541 plots located within 44 stands and 6 sub‐regions. To quantify individual disturbance events, we used tree‐ring proxies, which were aggregated at plot and stand levels by smoothing and detecting peaks in their distributions. The spatial aggregation of disturbance events was used to estimate patch sizes. Data exhibited continuous gradients from low‐ to high‐severity and small‐ to large‐size disturbance events. In addition to the importance of small disturbance events, moderate‐scale (25‐75% of the stand disturbed, >10 ha patch size) and moderate‐severity (25‐75% of canopy disturbed) events were also common. Moderate disturbances represented more than 50% of the total disturbed area and their rotation periods ranged from one to several hundred years, which is within the lifespan of local tree species. Disturbance severities differed among sub‐regions, whereas the stand proportion disturbed varied significantly over time. This indicates partially independent variations among disturbance characteristics. Our quantitative estimates of disturbance severity, patch size, stand proportion disturbed, and associated rotation periods provide rigorous baseline data for future ecological research, decisions within biodiversity conservation, and silviculture intended to maintain native biodiversity and ecosystem functions. These results highlight a need for sufficiently large and adequately connected networks of strict reserves, more complex silvicultural treatments that emulate the natural disturbance spectrum in harvest rotation times, sizes, and intensities, and higher levels of tree and structural legacy retention.
Mortality, driven by both climate and disturbance legacies, is a key process shaping forest dynamics. Understanding the mortality patterns in primary forests in the absence of severe disturbances provides information on background natural dynamics of a given forest type under ongoing climate change. This can then be compared to mortality rates in severely-disturbed stands. Using a large number of sample plots along a gradient from low to high disturbance, we examined the mortality rates and composition of mortality agents in primary mountain Norway spruce (Picea abies (L.) Karst.) forests on different spatial scales. We evaluated the mortality rates and causes of mortality in 28 stands across a large geographical gradient spanning over 1000 km. We resampled (five-year period) 371 plots (16,287 living trees) in primary Norway spruce forests along the Carpathian mountain chain. The estimated overall annual mortality rate was within the previously reported range of background (ambient) mortality, however, stand-level and plot-level mortality rates varied substantially. Over 18% of plots displayed more than 2% annual mortality and 6% of plots even exceeded 10% per year. Stands in the Western Carpathians showed the highest variability in the mortality rate, with 30% of the stands in this region showing annual mortality rates over 5%. At the plot level, mixed-severity disturbances increased variability of mortality rates within most localities. Overall mortality was evenly distributed among size classes up to 50 cm diameter at breast height (DBH). However, the distributions differ for individual mortality agents. Mortality modes were classified into six categories (broken crown, broken stem, uprooted, competition, bark beetle/fungi, climatic extremes). Bark beetle (Ips typographus L.) infestation was the most frequent mortality agent in all stands, whereas the influence of competition as a mortality agent varied substantially. Mortality from abiotically-caused physical damage was similar to that from competition, yet the distribution among modes of physical damage (uprooted, crown, or stem breakage) varied. The lack of clear evidence of mortality agents in some locations implies that many tree deaths are caused by a combination of contributing factors. The results suggest the role of bark beetle as a mortality agent does not equate to severe mortality at large scales. Prevalence of different size classes affected by individual mortality agents underline the high complexity of the mortality process in primary forests.
Accurate estimations of changes in the forest carbon (C) pools over time are essential for predicting the future forest C balance and its part in the global C cycle. While the overall understanding of global forest C dynamics has improved, some significant forest ecosystem processes have been largely overlooked, resulting in possible biases. As an example, the effects of low and moderate severity disturbances have received disproportionately little attention. In this study, we use an extensive database of 9610 tree increment cores from 400 plots in primary uneven-aged Norway spruce (Picea abies) forests in the Carpathian Mountains, to explore the dynamics of live and dead wood C after disturbance. The data represents a chronosequence of more than 250 years since disturbance, varying highly in severity. We found that disturbance severity had a substantial impact on the post-disturbance long-term accumulation of C. Initially, live tree C accumulated at a similar rate independent of disturbance severity. However, the increase in C leveled off earlier after low disturbance severity while the most heavily disturbed forests continued to accumulate C to the latest stages of stand development. These results stress the importance of taking disturbance severity into account when predicting the long-term dynamics of C storage in forests under climate change. The results also highlight the importance of these forests as significant C pools. If harvested and turned into managed forest they would not reach their maximum C storing capacity.
Given the global intensification of forest management and climate change, protecting and studying forests that develop free of direct human intervention-also known as primary forests-are becoming increasingly important. Yet, most countries still lack data regarding primary forest distribution. Previous studies have tested remote sensing approaches as a promising tool for identifying primary forests. However, their precision is highly dependent on data quality and resolution, which vary considerably. This has led to underestimation of primary forest abundance and distribution in some regions, such as the temperate zone of Europe. Field-based inventories of primary forests and methodologies to conduct these assessments are inconsistent; incomplete or inaccurate mapping increases the vulnerability of primary forest systems to continued loss from clearing and land-use change. We developed a comprehensive methodological approach for identifying primary forests, and tested it within one of Europe's hotspots of primary forest abundance: the Carpathian Mountains. From 2009 to 2015, we conducted the first national-scale primary forest census covering the entire 49,036 km 2 area of the Slovak Republic. We analyzed primary forest distribution patterns and the representativeness of potential vegetation types within primary forest remnants. We further evaluated the conservation status and extent of primary forest loss. Remaining primary forests are small, fragmented, and often do not represent the potential natural vegetation. We identified 261 primary forest localities. However, they represent only 0.47% of the total forested area, which is 0.21% of the country's land area. The spatial pattern of primary forests was clustered. Primary forests have tended to escape anthropogenic disturbance on sites with higher elevations, steeper slopes, rugged terrain, and greater distances from roads and settlements. Primary forest stands of montane mixed and subalpine spruce forests are more abundant compared to broadleaved forests. Notably, several habitat types are completely missing within primary forests (e.g., floodplain forests). More than 30% of the remaining primary forests are not strictly protected, and harvesting occurred at 32 primary forest localities within the study period. Almost all logging of primary forests was conducted inside of protected areas, underscoring the critical status of primary forest distribution in this part of Europe. Effective conservation strategies are urgently needed to stop the rapid loss and fragmentation of the remaining primary forests. Our approach based on precise, field-based surveys is widely applicable and transferrable to other fragmented forest landscapes.
Historical reanalyses that span more than a century are needed for a wide range of studies, from understanding large‐scale climate trends to diagnosing the impacts of individual historical extreme weather events. The Twentieth Century Reanalysis (20CR) Project is an effort to fill this need. It is supported by the National Oceanic and Atmospheric Administration (NOAA), the Cooperative Institute for Research in Environmental Sciences (CIRES), and the Department of Energy (DOE), and is facilitated by collaboration with the international Atmospheric Circulation Reconstructions over the Earth initiative. 20CR is the first ensemble of sub‐daily global atmospheric conditions spanning over 100 years. This provides a best estimate of the weather at any given place and time as well as an estimate of its confidence and uncertainty. While extremely useful, version 2c of this dataset (20CRv2c) has several significant issues, including inaccurate estimates of confidence and a global sea level pressure bias in the mid‐19th century. These and other issues can reduce the effectiveness of studies at many spatial and temporal scales. Therefore, the 20CR system underwent a series of developments to generate a significant new version of the reanalysis. The version 3 system (NOAA‐CIRES‐DOE 20CRv3) uses upgraded data assimilation methods including an adaptive inflation algorithm; has a newer, higher‐resolution forecast model that specifies dry air mass; and assimilates a larger set of pressure observations. These changes have improved the ensemble‐based estimates of confidence, removed spin‐up effects in the precipitation fields, and diminished the sea level pressure bias. Other improvements include more accurate representations of storm intensity, smaller errors, and large‐scale reductions in model bias. The 20CRv3 system is comprehensively reviewed, focusing on the aspects that have ameliorated issues in 20CRv2c. Despite the many improvements, some challenges remain, including a systematic bias in tropical precipitation and time‐varying biases in southern high latitude pressure fields. This article is protected by copyright. All rights reserved.
Significance Long-term variability in tropical cyclone (TC) activity is of high relevance for the development of adaptation and mitigation strategies; however, our current knowledge is based mostly on short-term records, with strong discrepancies among various datasets. We used tree-ring records of past forest disturbances to show rapid increases in the destructive effects of TCs during the 20th century. Long-term changes in TC activity imply that the recent poleward migration of TCs is not within the range of long-term natural variability and may be associated with climate change. Our findings are important, as affected regions were formerly situated at the edge of areas affected by TCs, and these areas are more sensitive to TC hazards because of a lack of experience-based adaptation strategies.
Tree-related microhabitats (TreMs) are important features for the conservation of biodiversity in forest ecosystems. Although other structural indicators of forest biodiversity have been extensively studied in recent decades, TreMs have often been overlooked, either due to the absence of a consensual definition or a lack of knowledge. Despite the increased number of TreM studies in the last decade, the role of drivers of TreM profile in primary forests and across different geographical regions is still unknown. To evaluate the main drivers of TreM density and diversity, we conducted the first large-scale study of TreMs across European primary forests. We established 146 plots in eight primary forests dominated by European beech (Fagus sylvatica L.) in the Carpathian and Dinaric mountain ranges. Generalized linear mixed effect models were used to test the effect of local plot characteristics and spatial variability on the density and diversity (alpha, beta, and gamma) of TreMs. Total TreM density and diversity were significantly positively related with tree species richness and the proportion of snags. Root mean square tree diameters were significantly related to alpha and gamma diversity of TreMs. Both regions reached similarly high values of total TreM densities and total TreM densities and diversity were not significantly different between the two regions; however, we observed between the two regions significant differences in the densities of two TreM groups, conks of fungi and epiphytes. The density and diversity of TreMs were very high in beech-dominated mountain primary forests, but their occurrence and diversity was highly variable within the landscapes over relatively short spatial gradients (plot and stand levels). Understanding these profile provides a benchmark for further comparisons, such as with young forest reserves, or for improving forest management practices that promote biodiversity.