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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.
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ARTICLE
Patterns and drivers of recent disturbances across
the temperate forest biome
Andreas Sommerfeld 1, Cornelius Senf 1,2, Brian Buma 3, Anthony W. DAmato 4,
Tiphaine Després 5,6, Ignacio Díaz-Hormazábal7, Shawn Fraver8, Lee E. Frelich 9, Álvaro G. Gutiérrez 7,
Sarah J. Hart10, Brian J. Harvey11, Hong S. He12, TomášHlásny5, Andrés Holz 13, Thomas Kitzberger14,
Dominik Kulakowski 15, David Lindenmayer 16, Akira S. Mori17, Jörg Müller18,19, Juan Paritsis 14,
George L. W. Perry 20, Scott L. Stephens21, Miroslav Svoboda5, Monica G. Turner 22, Thomas T. Veblen23 &
Rupert Seidl 1
Increasing evidence indicates that forest disturbances are changing in response to global
change, yet local variability in disturbance remains high. We quantied this considerable
variability and analyzed whether recent disturbance episodes around the globe were con-
sistently driven by climate, and if human inuence modulates patterns of forest disturbance.
We combined remote sensing data on recent (20012014) 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.
DOI: 10.1038/s41467-018-06788-9 OPEN
1University of Natural Resources and Life Sciences (BOKU) Vienna, Institute of Silviculture, Peter Jordan Straße 82, 1190 Wien, Austria. 2Geography
Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany. 3Dept. of Integrative Biology, University of Colorado, 1151
Arapahoe, Denver, CO 80204, USA. 4University of Vermont, Rubenstein School of Environment and Natural Resources, Aiken Center Room 204E,
Burlington, VT 05495, USA. 5Faculty of Forestry and Wood Sciences, Czech University of Life Sciences in Prague, Kamýcká 129, 165 21 Prague 6, Czech
Republic. 6Institut de Recherche sur les Forêts, Université du Québec en Abitibi-Témiscamingue, 445 boulevard de lUniversité, Rouyn-Noranda, QC J9X
5E4, Canada. 7Facultad de Ciencias Agronómicas, Departamento de Ciencias Ambientales y Recursos Naturales Renovables, Universidad de Chile, Av. Santa
Rosa 11315, La Pintana, 8820808 Santiago, Chile. 8University of Maine, School of Forest Resources, 5755 Nutting Hall, Orono, Maine 04469, USA.
9Department of Forest Resources, University of Minnesota, 1530 Cleveland Ave. N., St.Paul, MN 55108, USA. 10 Department of Forest and Wildlife Ecology,
University of WisconsinMadison, Madison, WI 53706, USA. 11 School of Environmental and Forest Sciences, University of Washington, Seattle, WA 98195,
USA. 12 School of Geographical Sciences, Northeast Normal University, Changchun 130024, China. 13 Department of Geography, Portland State University,
Portland, OR 97201, USA. 14 INIBIOMA, CONICET-Universidad Nacional del Comahue, Quintral 1250, Bariloche, 8400 Rio Negro, Argentina. 15 Clark
University, Graduate School of Geography, Worcester, MA 01602, USA. 16 Fenner School of Environment and Society, The Australian National University,
Canberra, ACT 2601, Australia. 17 Graduate School of Environment and Information Sciences, Yokohama National University, Yokohama 240-8501, Japan.
18 Field Station Fabrikschleichach, Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Glashüttenstraße 5, 96181
Rauhenebrach, Germany. 19 Bavarian Forest National Park, Freyunger Str. 2, 94481 Grafenau, Germany. 20 School of Environment, University of Auckland,
Auckland 1142, New Zealand. 21 Department of Environmental Science, Policy and Management, University of California, Berkeley, CA 94720, USA.
22 Department of Integrative Biology, Birge Hall, University of WisconsinMadison, Madison, WI 53706, USA. 23 Department of Geography, University of
Colorado, Boulder, CO 80309, USA. These authors contributed equally: Andreas Sommerfeld, Cornelius Senf. Correspondence and requests for materials
should be addressed to A.S. (email: andreas.sommerfeld@boku.ac.at)
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Natural disturbances are an essential component of forest
ecosystems1. Yet, forest disturbance regimes are changing
in response to global climate change2. Hotter and pro-
longed droughts3, exceptional bark beetle outbreaks4, and mega-
res5have been increasingly reported in recent years, and are
impacting forest ecosystems across all forested continents2.
Changes in disturbance dynamics can have substantial impacts on
ecosystem services provided by forests, e.g., climate regulation6,7,
provisioning of drinking water8,9, and protection from natural
hazards10, as well as affect conservation of biological diversity11.
Quantifying disturbance patterns (i.e., the size, shape, and pre-
valence of disturbances in forest landscapes) and understanding
their drivers is thus a key challenge for ecological research.
While the potential drivers of the ongoing disturbance change
are global, the responses to these drivers vary considerably at the
local scale. Insights from well-studied systems such as Yellowstone
National Park in North America1, the Bohemian Forest ecosystem
in Europe12, and the OShannassy water catchment in Australia13
have provided important insights into the complex interactions
between climate variability, disturbances, and forest development.
While an in-depth understanding of disturbance dynamics exists
for a growing number of landscapes (i.e., contiguous land areas of
roughly between 103and 106ha) around the globe, their responses
to global drivers have not consistently been compared to date.
Questions such as whether recent bark beetle outbreaks in North
America differ from those in Europe with regard to their climate
sensitivity, or whether recent res in Australia created similar
patterns as those in the Americas remain largely unexplored.
Comparing the variation in disturbance patterns and their rela-
tionship to climate variability among landscapes at subcontinental
to global scales14,15 has the potential to elucidate whether recent
disturbance episodes were consistently driven by climate across
continents, or whether climate sensitivities differ between systems.
Furthermore, such a comparison can shed light on how regional-
to continental-scale drivers such as climate variability interact with
local factors such as the topographic template of a landscape16 and
the inuence of human land use17. A better understanding of
global disturbance patterns and their multi-scale drivers is also
crucial for improving the representation of disturbances in global
vegetation models18,19, and can have important implications for
policy decision making, e.g., in the context of climate change
mitigation20,21.
New opportunities for global forest disturbance research arise
from recent advances in remote sensing. Increasingly available
remotely sensed datasets on forest disturbance22 offer high spatial
resolution and are globally consistent, enabling large-scale com-
parative efforts. However, while the global mapping of forest
disturbances is now feasible23,24, attributing disturbance agents
from remote sensing data and distinguishing between natural and
anthropogenic disturbances remains challenging25. Furthermore,
ecological context information such as the prevailing tree species
composition cannot usually be gleaned from space, underlining
the importance of terrestrial information and local ecosystem
understanding for a meaningful interpretation of remotely sensed
disturbance information. Integrating remote sensing analyses
with in-depth knowledge on selected ecosystems across the globe
can provide new insights by combining the consistent synoptic
view of satellite analysis with the expertise and insights gained
from decades of local forest disturbance research.
Our objective was to analyze patterns and drivers of recent
disturbances across temperate forests at the global scale. We
compiled a global network of 50 protected forest landscapes each
with > 2000 ha contiguous forest area (Fig. 1) for which in-depth
local systems knowledge exists. We jointly analyzed severe canopy
disturbances (i.e., complete mortality of all trees taller 5 m within
a 30 m grid cell) in these landscapes using Landsat-derived dis-
turbance maps for 2001201424. Focusing our network of land-
scapes on protected areas and their surroundings allowed us to
isolate patterns of natural disturbances (inside protected areas)
from those of areas where natural and human disturbances
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Mean annual temperature [°C]
Mean annual precipitation [mm]
b
Fig. 1 A network of 50 protected landscapes to understand global patterns and drivers of temperate forest disturbances. aThe geographic location of the
landscapes, and btheir location in climate space. The area of the temperate biome is indicated in green47. See Supplementary Table 1 for more detailed
information on the individual landscapes. Note that the climatic envelope of the biome (green dots in b) is based on a sample of 10,000 4500 m×4500 m
grid cells throughout the biome
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interact (outside protected areas). We concentrated our analysis
on a single biome as we were particularly interested in within-
biome variation in disturbance patterns and drivers, rather than
the comparatively well-studied between-biome differences. We
selected temperate forests as the target of our study as they are
affected by a wide variety of disturbance agents, frequently con-
tain both angiosperm and gymnosperm species (i.e., high trait
variability), and are represented in both the northern and
southern hemisphere, spanning an extensive gradient in envir-
onmental conditions.
We hypothesized that differences in disturbance agents and
tree species are the main determinants of among-landscape var-
iation in disturbance patterns across the globe (H1). Specically,
we expected that areas predominately affected by re as well as
landscapes dominated by tree species with high general suscept-
ibility (i.e., traits such as high maximum tree height and low
wood density) are most affected by disturbances. Our alternative
hypothesis was that spatial proximity of landscapes is a good
indicator of similarities in disturbance patterns, with global dif-
ferences in disturbances mainly explained by geographical loca-
tion (e.g., on different continents or hemispheres). To test these
hypotheses, we calculated four disturbance indicators, whereof
two are landscape-level metrics (percent of landscape disturbed
20012014, edge density) and two are patch-level metrics (area-
weighted mean patch size, area-weighted mean perimeter-area-
ratio), and used cluster analysis to identify patterns among
landscapes indicating differences in recent disturbance activity
(with disturbance activity jointly referring to the prevalence, size,
and shape of disturbances as characterized by our four focal
indicators). Furthermore, we hypothesized that the patterns of
natural disturbances (i.e., those observed in protected areas with
only minimal direct human inuence) differ from the combined
natural and human disturbances outside protected areas (H2).
We expected disturbances in protected areas to be generally
smaller and more complex in shape (i.e., higher perimeter-area-
ratio) compared to those outside of protected areas. This
hypothesis is based on the insights that disturbances outside
protected areas are the result of both natural and human dis-
turbances (which can amplify each other, e.g., when strong winds
uproot edge trees of freshly created clear-cuts), and that
management-related biotic homogenization has the potential to
increase forest susceptibility to disturbances relative to natural
ecosystem development2628. The alternative hypothesis was that
high recent levels of natural disturbance activity supersede the
signal of human land use, with similar disturbance patterns inside
and outside protected areas. Finally, based on local and regional
studies highlighting the importance of climate variability29 and
landscape structure30 for disturbance dynamics, we tested for a
consistent global relationship among climate variability, relative
topographic complexity, and the spatio-temporal dynamics of
forest disturbances. If recent disturbance episodes are responding
consistently to climatic and topographic drivers, we would expect
to nd a non-random signal in a regression analysis across our set
of globally distributed landscapes (H3). Alternatively, if responses
vary among landscapes and cancel each other out at the global
scale, the regression coefcients for these drivers would not differ
signicantly from zero.
Results
Patterns of recent natural disturbances. Disturbance dynamics
between 2001 and 2014 varied strongly across the temperate
forest biome. Unsupervised cluster analysis identied three dis-
tinct groups of landscapes based on their recent disturbance
dynamics (Table 1; Supplementary Figure 1), which we in the
following refer to as low, moderate, and high disturbance activity
clusters. Each cluster comprises a group of landscapes of similar
characteristics with regard to the size and shape of disturbance
patches, the percentage of a landscape disturbed during the study
period, and the average amount of edges created by these dis-
turbances (Supplementary Figure 2). Approximately one-third of
the landscapes studied (representing 19.9% of the forest area) fell
within the low disturbance activity cluster. This group was
characterized by small and complex disturbance patches
(Table 1), with disturbances on average affecting only 0.31% of
the landscapes forest area between 2001 and 2014. Examples of
landscapes with low disturbance activity are the Te Paparahi
Conservation Area (New Zealand), Shiretoko (Japan), Feng Lin
(China), Five Ponds (USA), Hainich (Germany), and Hornopirén
(Chile). The majority of the landscapes (23 landscapes, repre-
senting 30.6% of the forest area studied) fell within the moderate
disturbance activity cluster. This group was characterized by a
roughly 30 times larger area-weighted mean patch size than the
landscapes in the low disturbance activity cluster (Table 1).
Disturbance patches in the moderate cluster were less complex
(i.e., had a lower area-weighted mean perimeter-area-ratio) but
affected a larger forest area (on average 4.61% of the landscape)
between 2001 and 2014. Examples of landscapes in this group are
the Bavarian Forest (Germany), Baxter State Park (USA), Los
Alerces (Argentina), and Nelson Lakes National Park (New
Zealand). Although only 9 of the 50 landscapes analyzed fell
within the high disturbance activity cluster, they accounted for
49.5% of the total forest area under study. Area-weighted mean
patch size was two orders of magnitude larger than in the
moderate disturbance activity cluster, and disturbance patches
were considerably less complex (Table 1). On average, almost one
quarter of the landscapes forested area was affected by dis-
turbances between 2001 and 2014 in the high disturbance activity
cluster, resulting in the highest edge density among all three
Table 1 Characteristics of disturbance clusters
Cluster Low Moderate High
Number of landscapes 18 23 9
Total forest area [ha] 788,986 1,216,364 1,965,572
Mean annual temperature [°C] 6.5 (5.67.5) 5.3 (4.46.2) 3.7 (2.84.6)
Mean annual precipitation [mm] 1393 (12411544) 1222 (10711374) 1197 (10461349)
Mean percent of forest area disturbed 20012014
[%]
0.31 (0.130.48) 4.61 (0.448.79) 21.50 (13.8629.18)
Edge density [m/ha] 2.87 (1.244.50) 21.69 (2.8040.58) 43.22 (25.5360.91)
Area-weighted mean patch size [ha] 0.66 (0.460.85) 24.22 (6.9641.47) 4451.04 (365.248536.84)
Area-weighted mean perimeter-area-ratio [m/ha] 960.09 (905.261014.92) 617.28 (560.74673.82) 215.31 (150.15280.74)
Characteristics of three global clusters of disturbance activity, determined based on satellite-derived disturbance metrics using Gaussian nite mixture models. Values in parentheses indicate the 95%
condence interval
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clusters. Examples for landscapes in this group are Yellowstone
(USA), Puyehue (Chile), and OShannassy (Australia).
The clustering based on the four landscape metrics considered
here did not reveal strong geographical patterns (Supplementary
Figure 3a). Landscapes from at least three continents were present
in each cluster, and all three disturbance activity clusters were
represented in both the southern and northern hemispheres.
Furthermore, no clear pattern emerged when comparing the
clusters in climate space (Supplementary Figure 3b), although a
tendency of cooler landscapes experiencing higher disturbance
activity could be detected (Table 1).
Disturbance activity clusters were associated with different
major disturbance agents and tree genera (Fig. 2a, b, Supple-
mentary Figure 4). Agents differed signicantly among dis-
turbance activity clusters (χ2=37.64, p< 0.01). Landscapes in the
low disturbance activity cluster were frequently affected by
multiple disturbance agents, with windthrow being the most
prevalent agent. Major bark beetle outbreaks were largely absent
from this group of landscapes. In landscapes experiencing
moderate disturbance activity in 20012014, re and bark beetle
outbreaks were more prevalent compared to the low cluster. High
disturbance activity was predominately associated with wildre,
with bark beetle outbreaks and drought also being important
agents in highly disturbed landscapes.
Dominant tree genera (i.e., the genera with the highest
proportion of basal area) differed among disturbance activity
clusters (χ2=69.09, p< 0.001). Low disturbance activity land-
scapes were largely dominated by broadleaved trees from the
genera Nothofagus,Fagus, and Acer, i.e., species that have a
relatively low maximum attainable height but higher wood
density (Fig. 2c). The moderate disturbance activity cluster was
characterized by both broadleaved and coniferous tree species, yet
their average trait characteristics largely resembled those of the
low disturbance activity landscapes. High disturbance activity
landscapes in the northern hemisphere were dominated by the
genera Picea, Abies, Pseudotsuga, and Pinus, which are
Volcanism
Snow & ice
Defoliators
Pathogens
Drought
Bark beetles
Wildfires
Windthrow
Low Moderate High
0.1 0.2 0.3 0.4
a
Tsuga
Acer
Pinus
Eucalyptus
Pseudotsuga
Fagus
Abies
Picea
Nothofagus
Low Moderate High
0.1 0.2 0.3
b
● ●
p = 0.79
p = 0.13
p = 0.04
40
60
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100
Low
Moderate
High
Dominance [%]
c
p = 0.53
p = 0.53
p = 0.57
0
50
100
Low
Moderate
High
Conifers [%]
p = 0.19
p = 0.04
p = 0.19
25
50
75
100
Low
Moderate
High
Maximum tree height [m]
p = 0.76
p = 0.02
p = 0.02
0.4
0.6
0.8
1.0
Low
Moderate
High
Wood density [mg/mm3]
Fig. 2 Distribution of disturbance agents, tree genera, and tree species traits across three global clusters of disturbance activity (cf. Table 1). Bubbles are
scaled relative to the occurrence of the two most important adisturbance agents and btree genera within each cluster. cDominance [%] indicates the
share of the single most prevalent tree species on the overall tree species composition, while conifers [%] indicates the respective share of all conifer
species. Maximum tree height and wood density indicate a weighted trait distribution across landscapes in the respective disturbance activity clusters.
Boxplots denote the median (center line) and interquartile range (box), with whiskers extending to three times the interquartile range and points indicating
values outside this range. Test statistics and p-values are based on approximate KruskalWallis tests with 9999 permutations. For further information on
statistical analyses see Supplementary Table 2
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characterized by a higher maximum attainable tree height and
lower wood density (see Supplementary Table 2 for test statistics
on trait differences between clusters). Conversely, the high
disturbance activity landscapes located in the southern hemi-
sphere were mainly characterized by Nothofagus. The share of the
single most dominant tree species on the overall species
composition did not differ between landscapes in the low and
moderate clusters. In high disturbance activity landscapes,
however, the most important tree species was less dominant
compared to landscapes in low and moderate clusters (Fig. 2c).
Disturbance differences inside and outside protected areas.
Disturbance patches inside protected areasalmost exclusively
inuenced by natural disturbance agents (but see Supplementary
Table 6)were smaller and more complex than disturbance
patches in surrounding areas affected by both human and natural
disturbances in the low and moderate disturbance activity clusters
(Fig. 3, Supplementary Table 3). For landscapes with high dis-
turbance activity, however, the distribution of patch sizes and
perimeter-area-ratios did not differ signicantly between pro-
tected areas and their surroundings. Hence, there is a higher
similarity between disturbances in protected and unprotected
systems in areas that experienced high disturbance activity
recently. Furthermore, patch size and patch complexity differed
more strongly among disturbance activity clusters in protected
systems compared to unprotected systems.
Drivers of spatio-temporal disturbance dynamics. Inter-annual
climate variability was an important driver of temporal dis-
turbance dynamics in all three disturbance activity clusters. The
full model (including both temperature/precipitation anomalies
and topographic complexity as predictors) was more strongly
supported by the data than the spatial-only model (including only
topographic complexity) and the Null model (likelihood-ratio
tests; all p-values < 0.01; see Supplementary Table 4). However,
the direction and strength of effects, as well as the lag time of
climate effects, varied among clusters (Supplementary Figure 5;
Fig. 4). A 2-year and 3-year lag was most strongly supported by
the data for the low and moderate disturbance activity clusters,
respectively. Conversely, climate variability had an immediate
inuence on disturbances (i.e., zero lag) in the high disturbance
activity cluster (Supplementary Figure 5). For landscapes with low
disturbance activity, we found a signicant but moderately
negative effect of temperature anomaly (GLMM; β=0.20, std.
error =0.07, p=0.01; see Supplementary Table 4). That is,
warmer than average temperatures decreased the probability of
disturbance in the following years (Fig. 4a). This effect of tem-
perature was independent of variation in precipitation. Pre-
cipitation anomalies had a signicant positive direct effect on
disturbance probability (GLMM; β=0.33, std. error =0.07, p<
0.01; Supplementary Table 4), with wetter conditions increasing
disturbance probability in the low disturbance activity cluster
(Fig. 4a). Conversely, for landscapes in the high disturbance
activity cluster, we found a signicant positive effect of tem-
perature (GLMM; β=0.59, std. error =0.01, p< 0.01; Supple-
mentary Table 4), with a higher disturbance probability in years
with above average temperature (Fig. 4c). This effect was further
modulated by precipitation, and was strongly amplied if warm
years coincided with drier than average conditions (GLMM; β=
0.43, std. error < 0.01, p< 0.01; Supplementary Table 4). While
landscapes in the moderate disturbance activity cluster showed an
overall negative effect of temperature on disturbance probability
(GLMM; β=0.30, std. error =0.02, p< 0.01; Supplementary
Table 4), the effect was also signicantly inuenced by pre-
cipitation (GLMM; β=0.17, std. error =0.03, p< 0.01; Sup-
plementary Table 4). In addition, disturbance probability
increased following years with above average temperature and
below average precipitation (Fig. 4b).
Relative topographic complexity signicantly affected spatial
disturbance patterns, with different effects across disturbance
activity clusters (Supplementary Table 4). In the predominately
wind-inuenced low disturbance activity landscapes, disturbance
probability increased by ~7.5% with an increase in topographic
complexity by one standard deviation (GLMM; β=0.30, std.
error =0.04, p< 0.01; Supplementary Table 4). In contrast,
disturbance probability decreased by ~1.25% (GLMM; β=
0.05, std. error =0.01, p< 0.01; Supplementary Table 4) and
2.5% (GLMM; β=0.10, std. error < 0.01, p< 0.01; Supplemen-
tary Table 4) with the same amount of change in topographic
complexity in the mainly beetle-driven and re-driven landscapes
of the moderate and high disturbance activity clusters.
Discussion
We present a quantitative analysis of recent disturbance dynamics
across the temperate forest biome. Variation in recent forest
disturbance activity across the globe was considerable, spanning a
p < 0.01 p < 0.01 p = 1
1
100
10,000
Low Moderate High
Area−weighted mean
patch size [ha]
a
p < 0.01 p < 0.01 p = 0.48
0.00
0.04
0.08
0.12
Low Moderate High
Area−weighted mean
perimeter−area−ratio [m/sqm]
b
Outside
Inside
Fig. 3 Comparison of disturbance patterns inside and outside protected areas. aArea-weighted mean patch size and barea-weighted mean perimeter-
area-ratio are compared for areas inside and outside protected areas for three global clusters of disturbance activity (cf. Table 1). Boxplots denote the
median (center line) and interquartile range (box), with whiskers extending to three times the interquartile range and points indicating values outside this
range. Test statistics and p-values are based on approximate KruskalWallis tests with 9999 permutations
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large gradient of patch sizes and landscape area affected by dis-
turbance. Our results highlight that while extensive disturbances
such as massive bark beetle outbreaks or severe large-scale res
have garnered considerable attention from researchers and the
public recently, many temperate forests are dominated by small-
scale disturbance events. This nding underscores the importance
of a consistent quantication and analysis of disturbance
dynamics across systems. The main disturbance agent affecting a
system was more indicative of within-biome variation in dis-
turbance activity than geographical proximity of landscapes or
their location on the same continent or hemisphere (H1). Spe-
cically, wind was an important agent responsible for small-scale
disturbances in temperate forests31. Wildres and bark beetle
outbreaks, on the other hand, were the two most prominent
agents associated with large and severe disturbances in recent
years. However, the fact that both wind and wildres occurred in
all three clusters of disturbance activity highlights the consider-
able within-biome variability even within disturbance regimes
characterized by the same agent.
Tree species composition was related to global differences in
recent disturbances, with Northern Hemisphere temperate forests
dominated by conifers experiencing elevated disturbance activity.
This pattern can partly be explained by the general life-history
strategy of conifers and their extensive coevolution with dis-
turbances32, as many conifers are well adapted to either tolerate
disturbances or swiftly recolonize disturbed areas. An unexpected
nding was that the dominance of the single most prevalent tree
species was lower in landscapes with high disturbance activity
compared to those with low and moderate disturbance activity.
This contrasts with previous suggestions that disturbance risk
increases if landscapes are dominated by a small number of tree
species33. However, disturbances themselves can have a positive
effect on tree species diversity34,35. Consequently, higher evenness
in systems strongly affected by disturbancesas found here
could be a consequence of disturbances, rather than being cau-
sally related to them.
In large parts of the temperate forest biome, human dis-
turbances dominate the landscape. Consequently, the disturbance
patterns of unprotected systems differed from the natural
disturbance regime observed in protected areas. In the majority
of human-dominated landscapes outside protected areas, dis-
turbance patches were larger and less complex than in protected
areas, supporting our expectation (H2). In landscapes affected by
large-scale disturbances, however, the patterns in protected areas
and their surroundings were similar. This suggests that large
natural disturbances can override the effect of human land use
and dominate landscape patterns in forest ecosystems. As these
events have been found to be particularly climate sensitive
(Fig. 4c), future conditions could produce more coarse-grained
landscape patterns (i.e., landscapes characterized by larger patch
sizes) in temperate forests36.
An important caveat associated with our analysis is that we
could not consider vegetation structure and disturbance history as
potential covariates in our analyses, resulting from the lack of a
globally consistent data set on these variables. It therefore remains
unclear whether the differences between protected and unpro-
tected areas arise from a higher disturbance susceptibility of the
latter systems, or whether they reect management activities such
as clearcutting in areas outside protected landscapes. Disturbance
history and ecological legacies can exert an important inuence
on current disturbance activity37,38. This is important as most of
our study landscapes have been protected only for a few decades,
and legacies from former land use might still persist. Further-
more, although our 50 protected landscapes cover the climatic
envelope of temperate forests well (Fig. 1b), they are not neces-
sarily representative of the full range of ecological conditions of
the entire temperate forest biome. Selecting areas with a long
history of forest dynamics research enabled us to consistently
compare patterns and responses across locally well-researched
systems, but might also be a source of bias that should be con-
sidered in interpreting our results. The nding of similar dis-
turbance patterns inside and outside of protected areas in the
high disturbance activity cluster may, for instance, partly result
from the generally remote location of these particular landscapes,
with limited human activity in their surroundings. Future ana-
lyses explicitly contrasting disturbances inside and outside pro-
tected areas could help to better understand the effect of human
activity on disturbances, and quantify the impact of anthro-
pogenically altered disturbance regimes on biodiversity25,39.
Furthermore, we here focused on severe canopy disturbances with
complete canopy mortality of all trees taller 5 m within a 30 × 30
m grid cell24. Consequently, low severity disturbances and
understory tree mortality were not considered, potentially leading
to an underestimation of forest disturbance activity. However, the
0.000
0.003
0.006
0.009
0.012
−2 −1 0 1 2
Low
0.000
0.020
0.040
0.060
−2 −1 0 1 2
Moderate
0.000
0.200
0.400
0.600
0.800
−2 −1 0 1 2
High
Temperature anomaly
P (disturbance)
Precipitation
anomaly
−2
0
2
abc
Fig. 4 Predicted response of disturbance probability to temperature anomaly, modulated by precipitation anomaly. acThe climate sensitivity of
disturbances separately for three global clusters of disturbance activity (cf. Table 1). Anomaly values are units of standard deviation with zero indicating the
long-term mean. Y-axes are scaled differently across the three panels for clarity of presentation. Prediction uncertainty was estimated from 9999 model
simulations, with the lower and upper limit representing the 2.5 and 97.5% quantile of all simulations. We note that prediction intervals include both
parameter and model uncertainty, and overlapping prediction intervals can occur despite signicant differences in parameter values. For parameter
estimates and standard errors, see Supplementary Table 4. The number of experimental replicates equals the number of study sites per cluster (Low: 18,
Moderate: 23, High: 9)
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-06788-9
6NATURE COMMUNICATIONS | (2018) 9:4355 | DOI: 10.1038/s41467-018-06788-9 | www.nature.com/naturecommunications
Content courtesy of Springer Nature, terms of use apply. Rights reserved
severe canopy disturbances examined here are generally well
represented by the data set used: For example, Buma and Bar-
rett40 found an overall agreement of 91% when comparing the
global disturbance data set used here to high-resolution imagery
in southern Alaska. Furthermore, Borelli et al.41 determined an
overall accuracy of 81% in an independent evaluation of the data
across Europe. We are thus condent that the data set used here
is able to capture the variability in severe canopy disturbances
across the temperate forest biome.
A remaining limitation of our analysis is the relatively short
duration of our study period. The currently available disturbance
time series from satellite data remain too short to characterize
disturbance regimes1satisfactorily, and preclude the assessment of
temporal trends in disturbance7,42. Consequently, we focused only
on the effect of inter-annual climate variability rather than on long-
term trends, using temperature and precipitation anomalies as
predictors. Future work should also consider the effect of climatic
extremes and intra-annual climate patterns for rening our
understanding of climatedisturbance relationships29.Inaddition,
process-based simulation modeling43 could be employed to obtain
a more dynamic and long-term perspective on global disturbance
regimes and their responses to a changing climate.
Here we provide evidence that high recent disturbance activity
in temperate forest ecosystems across the globe was strongly
related to the joint occurrence of warmer and drier than average
climate conditions (H3). These global scale ndings are in general
agreement with local studies4,14,42,44,45, particularly considering
that our high disturbance activity cluster was dominated by
wildres, bark beetle outbreaks, and drought. Our results there-
fore suggest that a warming climate could facilitate large-scale
disturbances in temperate forest ecosystems in the future2,46. Our
ndings also show that climate sensitivity can, to some degree, be
buffered by heterogeneous topography, which impedes the spread
of disturbances and/ or increases the complexity of disturbed
patches25. Interestingly, our analysis suggests that both the effect
of climate and the effect of topography reversed for landscapes
characterized by low disturbance activity, compared to those with
high disturbance activity. For the former, which are pre-
dominately driven by wind disturbance, cooler and wetter con-
ditions as well as higher relative topographic complexity
increased disturbance probability31, which is consistent with
decreased tree anchorage (soil wetness) and increased wind sus-
ceptibility (exposed ridges, funnel effects) under such conditions.
However, the signals detected for low disturbance activity areas
were generally weak, underlining the highly stochastic nature of
small-scale mortality events in forest ecosystems.
We conclude by emphasizing the importance of protected areas
for understanding changes in forest landscapes in the absence of
direct human inuences. Furthermore, our work highlights the
importance of consistent global information for characterizing
patterns and identifying drivers of important ecological processes
such as disturbances. Quantitative baselines acknowledging the
substantial spatio-temporal variability in ecosystems are needed to
identify, monitor, and attribute changes in ecological processes.
The analyses presented here are an important step towards such an
improved quantitative characterization of forest disturbances at the
global scale, combining large-scale remote sensing data with eco-
logical context information from local experts. An improved
quantitative characterization of forest disturbances at the global
scale can, for instance, inform the development and application of
global vegetation models, which largely ignore the impacts of
disturbances to date, or only consider a highly simplied repre-
sentation of disturbance processes18,19. An improved consideration
of disturbance processes in future projections is important as our
results highlight the considerable sensitivity of disturbances to the
ongoing changes in the climate system. We conclude that the
resilience and adaptive capacity of ecosystems to disturbances
remain important priorities of forest research and management.
Methods
A biome-wide network of protected forest landscapes. We compiled a network
of study landscapes distributed throughout the temperate forest biome as dened
by Olson et al.47. (see also Fig. 1). Selection criteria were that the landscapes are
protected (i.e., IUCN Cat. I and Cat. II), and have a minimum of 2000 ha of
contiguous forest area. Studying protected areas allowed us to largely control for
anthropogenic disturbances and focus our main analyses on natural disturbances.
We analyzed 50 landscapes distributed across 16 countries on ve continents,
representing a forest area of 3.9 Mill. ha (median landscape size: 30,889 ha; see
Supplementary Table 1 for details). The study landscapes cover a wide climatic
gradient of the temperate forest biome, with mean annual temperatures ranging
from 0.3 °C to 14.8 °C, and mean annual precipitation sums between 517 mm
and 2315 mm (Supplementary Table 1 and Fig. 1b).
Disturbance data and landscape pattern analysis. We acquired forest cover and
annual disturbance maps (20012014) from Hansen et al.24 (Version 1.2) at 30 m
spatial resolution. A disturbance was dened as a severe canopy disturbance,
meaning the complete mortality of all trees taller 5 m within a pixel24. Only dis-
turbance events that occurred between 2001 and 2014 were considered. To char-
acterize disturbance patterns, we calculated two landscape-level metrics and two
patch-level metrics for each study landscape, using an eight-neighbor rule for
dening adjacency and considering disturbances throughout the entire study
period. The landscape-level metrics were: (i) percent of landscape disturbed
20012014, and (ii) edge density of all disturbed patches within the forest area of a
landscape; with the patch-level metrics being (iii) area-weighted mean patch size,
and (iv) area-weighted mean perimeter-area-ratio. To identify differences and
similarities in recent disturbance patterns across the temperate forest biome, we
used Gaussian nite mixture models, as implemented in the R package mclust48
(version 5.4). Gaussian nite mixture models are an approach for unsupervised
clustering, used here to identify groups of landscapes with similar disturbance
patterns. The optimal number of cluster centers was determined by maximizing the
Bayesian Information Criteria (BIC). Robust statistics for characterizing each
cluster were derived by using parametric bootstrapping (9999 replications). Sub-
sequently, the clusters were characterized with regard to their main disturbance
agents and tree genera (i.e., the two most important agents and genera for a
landscape during the period 20012014; see below for details). To describe
potential functional differences between the clusters, we included plant traits in our
analysis. After an initial screening and analysis of multicollinearity, we focused on
two complementary plant traits corresponding to disturbance resistance and sus-
ceptibility, i.e., maximum potential tree height and mean wood density, extracted
from the TRY database49. Plant height directly increases susceptibility to wind
disturbance and is also a proxy for biomass accumulation potential, which is
related to fuel load in the context of disturbances by wildre. Wood density is
positively correlated with the ability of trees to resist physical forces such as wind
and drought. For each landscape, weighted trait means based on tree species shares
were calculated. Differences in agents and tree genera among clusters were tested
using approximate Pearson χ2tests of homogeneity with 9999 permutations. Dif-
ferences in traits among clusters were tested using two-tailed pairwise approximate
KruskalWallis tests with 9999 permutations, applying false discovery rate cor-
rection. All test procedures were used as implemented in the coin50 package
(version 1.2-1) in R51.
Expert-based information on local ecological context. Remote sensing provides
a consistent estimate of disturbances across the biome, yet the ecological context of
these disturbances, such as the dominant disturbance agents and the tree species
affected, cannot be inferred from space. We thus consulted local experts for all 50
landscapes, collecting ecological context information via a questionnaire (see
Supplementary Table 5). The questionnaire included four questions, two of which
were multiple choice questions with the opportunity to add additional answers.
They focused on determining the dominant tree species, the main disturbance
agents affecting a landscape between 2001 and 2014, and the impact of dis-
turbances on particular tree species. Values of tree species dominance in a given
landscape were estimated as basal area shares. Local experts were identied via
their publication record on the topic of forest dynamics for the selected areas. All
consulted experts also contributed to the analysis and interpretation of the data,
and are identied individually in Supplementary Table 1.
Difference inside and outside protected areas. After focusing on natural dis-
turbance dynamics in protected areas in the previous analysis steps, we asked how
protected areas differed from the disturbance dynamics in the unprotected systems
surrounding our study landscapes. To compare spatial disturbance patterns inside
vs. outside protected areas, we extracted all forest disturbances in a buffer sur-
rounding the protected landscapes. The buffer size was selected proportional to the
landscape size, and was set to the diagonal of the minimum bounding rectangle of
each study landscape. We compared area-weighted mean patch size and area-
weighted mean perimeter-area-ratio between strata (i.e., inside vs. outside
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Content courtesy of Springer Nature, terms of use apply. Rights reserved
protected areas) using boxplots, and tested differences using two-tailed approx-
imate KruskalWallis tests with 9999 permutations.
Drivers of spatio-temporal disturbance dynamics. We used generalized linear
mixed effects models52 (GLMMs) to test the inuence of relative topographic
complexity and climatic variability on spatial and temporal disturbance dynamics.
Analyses were run separately for each disturbance activity cluster, modeling the
annual probability of disturbance at the pixel level. As response variable we used
annual binary disturbance maps indicating whether a pixel was disturbed in a given
year or not. Consequently, the GLMMs were specied with a binomial error dis-
tribution and a logit link-function. As measure of topographic complexity we used
the topographic ruggedness index53 (TRI), which was calculated from a 30 m
digital elevation model obtained from the Shuttle Radar Topographic Mission
(SRTM). We calculated the TRI using a window size of 7 × 7 pixels, depicting the
topographic complexity within a radius of ~100 m around a focal pixel. TRI values
were subsequently scaled to zero mean and a standard deviation of one for each
landscape, with negative values indicating a lower than average relative topographic
complexity, and positive values indicating a higher than average relative topo-
graphic complexity. As a measure of climate variability we obtained time series of
mean annual temperature and annual precipitation sum from FetchClimate54,
which are based on daily climate data from the NCEP/NCAR Reanalysis 1 data-
base. We calculated climate anomalies by scaling the time series to zero mean and a
standard deviation of one for each landscape, with negative values indicating
colder/ dryer than average years, and positive values indicating hotter/ wetter than
average years. As previous studies suggest variable lag times between climate
anomalies and disturbance signals determined from remote sensing12,16, we tested
variable lags ranging from 0 years (i.e., relating the climate anomaly of the current
year to the disturbance in the current year) to 3 years (i.e., relating the climate
anomaly 3 years prior to the disturbance in the current year). The lag best sup-
ported by the data within each cluster was identied using Akaikes Information
Criterion (AIC)16,55. Furthermore, we allowed for an interaction term between
precipitation and temperature to account for potential modulating effects between
these two variables56. Using GLMMs enabled us to incorporate factors on different
hierarchical levels into a combined modeling framework. In particular, TRI values
were available at the pixel level, but remain constant across years. In contrast, the
temperature and precipitation anomalies varied among years, but did not differ
spatially in a study landscape. Hence, we used relative topographic complexity to
explain the spatial variation in disturbance probability, while climate variability was
related to temporal variation of disturbances in our model. In addition, the GLMM
framework allowed us to account for random variation in the model intercept
among study landscapes within a cluster. As sample sizes were very large (several
millions of 30 m pixels), we randomly sampled 10% of the pixels per landscape. As
disturbances were rare in many landscapes, we employed a case-control sampling
design57, that is we randomly down-sampled the absence class (no disturbance) to
approximately the same size of the presence class (disturbance). This design has the
advantage that model estimates are unbiased (with the exception of the intercept),
and the intercept can easily be corrected using the known true proportion of
disturbance presence/absence in the population. Finally, we compared three can-
didate models per cluster: a full model containing predictors of spatial (TRI) and
temporal (precipitation and temperature anomalies as well as their interaction)
variation, a model containing TRI only (spatial-only model), and a null model
(containing only an intercept while maintaining the random effects structure of the
GLMMs). Model comparison was done using the AIC and log- likelihood tests. For
the model most strongly supported by the data we created response curves by
drawing 9999 random simulation from the model (i.e., accounting for parameter
and model uncertainty), as suggested by Gelman and Hill58. All models and cal-
culations were implemented using the lme4 package59 (version 1.1-14) in R.
Data availability
Data on forest disturbances were derived from the global forest change data set24,
available at https://earthenginepartners.appspot.com/science-2013-global-forest.
Data on tree species traits were derived from the plant trait database TRY49,
available at https://www.try-db.org/TryWeb/Home.php (doi: 10.17871/TRY.3).
Landscape-level data on ecological context variables was derived by means of a
questionnaire (see Supplementary Table 5), and is published in full in
Supplementary Table 1.
Received: 28 March 2018 Accepted: 28 September 2018
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Acknowledgements
A.S. and R.S. acknowledge support from the Austrian Science Fund (FWF) through
START grant Y895-B25. C.S. acknowledges funding from the German Academic
Exchange Service (DAAD) with funds from the German Federal Ministry of Education
and Research (BMBF) and the People Programme (Marie Curie Actions) of the European
Unions Seventh Framework Programme (FP7/20072013) under REA grant agreement
Nr. 605728 (P.R.I.M.E.Postdoctoral Researchers International Mobility Experience). T.
D. acknowledges funding from the Fonds institutionnel de recherche de lUniversitédu
Qué
bec en Abitibi-Té
miscamingue, the Natural Sciences and Engineering Research
Council of Canada (NSERC), Tembec, and EACOM Timber Corporation. Á.G.G. was
supported by FONDECYT 11150835. S.J.H. and T.T.V. acknowledge NSF Award
1262687. A.H. was partially supported by NSF (award #1738104). D.K. acknowledges
support from the US NSF. D.L. was supported by an Australian Research Council
Laureate Fellowship. A.S.M. was supported by the Environment Research and Tech-
nology Development Fund (S-14) of the Japanese Ministry of the Environment and by
the Grants-in-Aid for Scientic Research of the Japan Society for the Promotion of
Science (15KK0022). G.L.W.P. acknowledges support from a Royal Society of New
Zealand Marsden Fund grant. S.L.S. acknowledges funds from the US Joint Fire Sciences
Program (project number 14-1-06-22) and UC ANR competitive grants. M.S. and T.H.
acknowledges support from the institutional project MSMT CZ.02.1.01/0.0/0.0/16_019/
0000803. M.G.T. acknowledges funding from the University of Wisconsin-Madison Vilas
Trust and the US Joint Fire Science Program (project numbers 09-1-06-3, 12-3-01-3, and
16-3-01-4). The study used data from the TRY initiative on plant traits (http://www.try-
db.org). The TRY initiative and database is hosted, developed and maintained by J.
Kattge and G. Boenisch (Max Planck Institute for Biogeochemistry, Jena, Germany). TRY
is currently supported by Future Earth/bioDISCOVERY and the German Centre for
Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig.
Author contributions
A.S., C.S., and R.S. designed the study, analyzed the data and wrote the paper. B.B., A.W.
D., T.D., I.D.-H., S.F., L.E.F., Á.G.G., S.J.H., B.J.H., H.S.H., T.H., A.H., T.K., D.K., D.L., A.
S.M., J.M., J.P., G.L.W.P, S.L.S., M.S., M.G.T., and T.T.V. contributed data and com-
mented on the manuscript.
Additional information
Supplementary Information accompanies this paper at https://doi.org/10.1038/s41467-
018-06788-9.
Competing interests: The authors declare no competing interests.
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Many global change stresses on terrestrial and marine ecosystems affect not only ecosystem services that are essential to humankind, but also the trajectory of future climate by altering energy and mass exchanges with the atmosphere. Earth system models, which simulate terrestrial and marine ecosystems and biogeochemical cycles, offer a common framework for ecological research related to climate processes; analyses of vulnerability, impacts, and adaptation; and climate change mitigation. They provide an opportunity to move beyond physical descriptors of atmospheric and oceanic states to societally relevant quantities such as wildfire risk, habitat loss, water availability, and crop, fishery, and timber yields. To achieve this, the science of climate prediction must be extended to a more multifaceted Earth system prediction that includes the biosphere and its resources.
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Biotic disturbances (BDs, e.g., insects, pathogens and wildlife herbivory) substantially affect boreal and temperate forest ecosystems globally. However, accurate impact assessments comprising larger spatial scales are lacking to date, although these are critically needed given the expected disturbance intensification under a warming climate. Hence, our quantitative knowledge on current and future BD impacts, e.g., on forest carbon
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Natural disturbance regimes are changing substantially in forests around the globe. However, large-scale disturbance change is modulated by a considerable spatiotemporal variation within biomes. This variation remains incompletely understood particularly in the temperate forests of Europe, for which consistent large-scale disturbance information is lacking. Here our aim was to quantify the spatiotemporal patterns of forest disturbances across temperate forest landscapes in Europe using remote sensing data, and determine their underlying drivers. Specifically, we tested two hypotheses: (1) Topography determines the spatial patterns of disturbance, and (2) climatic extremes synchronize natural disturbances across the biome. We used novel Landsat-based maps of forest disturbances 1986-2016 in combination with landscape analysis to compare spatial disturbance patterns across five unmanaged forest landscapes with varying topographic complexity. Furthermore, we analyzed annual estimates of disturbance change for synchronies and tested the influence of climatic extremes on temporal disturbance patterns. Spatial variation in disturbance patterns was substantial across temperate forest landscapes. With increasing topographic complexity, natural disturbance patches were smaller, more complex in shape, more dispersed, and affected a smaller portion of the landscape. Temporal disturbance patterns, however, were strongly synchronized across all landscapes, with three distinct waves of high disturbance activity between 1986 and 2016. All three waves followed years of pronounced drought and high peak wind speeds. Natural disturbances in temperate forest landscapes of Europe are thus spatially diverse but temporally synchronized. We conclude that the ecological effect of natural disturbances (i.e., whether they are homogenizing a landscape or increasing its heterogeneity) is strongly determined by the topographic template. Furthermore, as the strong biome-wide synchronization of disturbances was closely linked to climatic extremes, large-scale disturbance episodes are likely in Europe's temperate forests under climate changes. This article is protected by copyright. All rights reserved.
Article
Currently, the temperate forest biome cools the earth's climate and dampens anthropogenic climate change. However, climate change will substantially alter forest dynamics in the future, affecting the climate regulation function of forests. Increasing natural disturbances can reduce carbon uptake and evaporative cooling, but at the same time increase the albedo of a landscape. Simultaneous changes in vegetation composition can mitigate disturbance impacts, but also influence climate regulation directly (e.g., via albedo changes). As a result of a number of interactive drivers (changes in climate, vegetation, and disturbance) and their simultaneous effects on climate-relevant processes (carbon exchange, albedo, latent heat flux) the future climate regulation function of forests remains highly uncertain. Here we address these complex interactions to assess the effect of future forest dynamics on the climate system. Our specific objectives were (i) to investigate the long-term interactions between changing vegetation composition and disturbance regimes under climate change, (ii) to quantify the response of climate regulation to changes in forest dynamics, and (iii) to identify the main drivers of the future influence of forests on the climate system. We investigated these issues using the individual-based forest landscape and disturbance model (iLand). Simulations were run over 200 years for Kalkalpen National Park (Austria), assuming different future climate projections, and incorporating dynamically responding wind and bark beetle disturbances. To consistently assess the net effect on climate the simulated responses of carbon exchange, albedo, and latent heat flux were expressed as contributions to radiative forcing. We found that climate change increased disturbances (+27.7% over 200 years) and specifically bark beetle activity during the 21st century. However, negative feedbacks from a simultaneously changing tree species composition (+28.0% broadleaved species) decreased disturbance activity in the long run (–10.1%), mainly by reducing the host trees available for bark beetles. Climate change and the resulting future forest dynamics significantly reduced the climate regulation function of the landscape, increasing radiative forcing by up to +10.2% on average over 200 years. Overall, radiative forcing was most strongly driven by carbon exchange. We conclude that future changes in forest dynamics can cause amplifying climate feedbacks from temperate forest ecosystems. This article is protected by copyright. All rights reserved.
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Remote sensing is a key information source for improving the spatiotemporal understanding of forest ecosystem dynamics. Yet, the mapping and attribution of forest change remains challenging, particularly in areas where a number of interacting disturbance agents simultaneously affect forest development. The forest ecosystems of Central Europe are coupled human and natural systems, with natural and human disturbances affecting forests both individually and in combination. To better understand the complex forest disturbance dynamics in such systems, we utilize 32-year Landsat time series to map forest disturbances in five sites across Austria, the Czech Republic, Germany, Poland, and Slovakia. All sites consisted of a National Park and the surrounding forests, reflecting three management zones of different levels of human influence (managed, protected, strictly protected). This allowed for a comparison of spectral, temporal , and spatial disturbance patterns across a gradient from natural to coupled human and natural disturbances. Disturbance maps achieved overall accuracies ranging from 81% to 93%. Disturbance patches were generally small, with 95% of the disturbances being smaller than 10 ha. Disturbance rates ranged from 0.29% yr À1 to 0.95% yr À1 , and differed substantially among management zones and study sites. Natural disturbances in strictly protected areas were longer in duration (median of 8 years) and slightly less variable in magnitude compared to human-dominated disturbances in managed forests (median duration of 1 year). However, temporal dynamics between natural and human-dominated disturbances showed strong synchrony, suggesting that disturbance peaks are driven by natural events affecting managed and unmanaged areas simultaneously. Our study demonstrates the potential of remote sensing for mapping forest disturbances in coupled human and natural systems, such as the forests of Central Europe. Yet, we also highlight the complexity of such systems in terms of agent attribution, as many natural disturbances are modified by management responding to them outside protected areas. Ó
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Forest disturbances are sensitive to climate. However, our understanding of disturbance dynamics in response to climatic changes remains incomplete, particularly regarding large-scale patterns, interaction effects and dampening feedbacks. Here we provide a global synthesis of climate change effects on important abiotic (fire, drought, wind, snow and ice) and biotic (insects and pathogens) disturbance agents. Warmer and drier conditions particularly facilitate fire, drought and insect disturbances, while warmer and wetter conditions increase disturbances from wind and pathogens. Widespread interactions between agents are likely to amplify disturbances, while indirect climate effects such as vegetation changes can dampen long-term disturbance sensitivities to climate. Future changes in disturbance are likely to be most pronounced in coniferous forests and the boreal biome. We conclude that both ecosystems and society should be prepared for an increasingly disturbed future of forests.
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Tree mortality is an important process in forest ecosystems, frequently hypothesized to be highly climate sensitive. Yet, tree death remains one of the least understood processes of forest dynamics. Recently, changes in tree mortality have been observed in forests around the globe, which could profoundly affect ecosystem functioning and services provisioning to society. We describe continental-scale patterns of recent tree mortality from the only consistent pan-European forest monitoring network, identifying recent mortality hotspots in southern and northern Europe. Analyzing 925,462 annual observations of 235,895 trees between 2000 and 2012, we determine the influence of climate variability and tree age on inter-annual variation in tree mortality using Cox proportional hazard models. Warm summers as well as high seasonal variability in precipitation increased the likelihood of tree death. However, our data also suggest that reduced cold-induced mortality could compensate increased mortality related to peak temperatures in a warming climate. Besides climate variability, age was an important driver of tree mortality, with individual mortality probability decreasing with age over the first century of a trees life. A considerable portion of the observed variation in tree mortality could be explained by satellite-derived net primary productivity, suggesting that widely available remote sensing products can be used as an early warning indicator of widespread tree mortality. Our findings advance the understanding of patterns of large-scale tree mortality by demonstrating the influence of seasonal and diurnal climate variation, and highlight the potential of state-of-the-art remote sensing to anticipate an increased likelihood of tree mortality in space and time. This article is protected by copyright. All rights reserved.