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Forest stand growth dynamics in Central Europe have accelerated since 1870

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Forest ecosystems have been exposed to climate change for more than 100 years, whereas the consequences on forest growth remain elusive. Based on the oldest existing experimental forest plots in Central Europe, we show that, currently, the dominant tree species Norway spruce and European beech exhibit significantly faster tree growth (+32 to 77%), stand volume growth (+10 to 30%) and standing stock accumulation (+6 to 7%) than in 1960. Stands still follow similar general allometric rules, but proceed more rapidly through usual trajectories. As forest stands develop faster, tree numbers are currently 17-20% lower than in past same-aged stands. Self-thinning lines remain constant, while growth rates increase indicating the stock of resources have not changed, while growth velocity and turnover have altered. Statistical analyses of the experimental plots, and application of an ecophysiological model, suggest that mainly the rise in temperature and extended growing seasons contribute to increased growth acceleration, particularly on fertile sites.
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ARTICLE
Received 7 Mar 2014 |Accepted 12 Aug 2014 |Published 12 Sep 2014
Forest stand growth dynamics in Central Europe
have accelerated since 1870
Hans Pretzsch1, Peter Biber1, Gerhard Schu¨tze1, Enno Uhl1,2 & Thomas Ro
¨tzer1
Forest ecosystems have been exposed to climate change for more than 100 years, whereas
the consequences on forest growth remain elusive. Based on the oldest existing experimental
forest plots in Central Europe, we show that, currently, the dominant tree species Norway
spruce and European beech exhibit significantly faster tree growth ( þ32 to 77%), stand
volume growth ( þ10 to 30%) and standing stock accumulation ( þ6 to 7%) than in 1960.
Stands still follow similar general allometric rules, but proceed more rapidly through usual
trajectories. As forest stands develop faster, tree numbers are currently 17–20% lower than in
past same-aged stands. Self-thinning lines remain constant, while growth rates increase
indicating the stock of resources have not changed, while growth velocity and turnover have
altered. Statistical analyses of the experimental plots, and application of an ecophysiological
model, suggest that mainly the rise in temperature and extended growing seasons contribute
to increased growth acceleration, particularly on fertile sites.
DOI: 10.1038/ncomms5967 OPEN
1Chair for Forest Growth and Yield Science, Center of Life and Food Sciences Weihenstephan, Technische Universita
¨tMu
¨nchen, Hans-Carl-von-Carlowitz-
Platz 2, 85354 Freising, Germany. 2Bavarian State Institute of Forestry, Hans-Carl-von-Carlowitz-Platz 1, 85354 Freising, Germany. Correspondence and
requests for materials should be addressed to H.P. (email: hans.pretzsch@lrz.tum.de).
NATURE COMMUNICATIONS | 5:4967 | DOI: 10.1038/ncomms5967| www.nature.com/naturecommunications 1
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The lifespan of many tree species is over several hundred
years; therefore, knowledge regarding tree and forest stand
dynamics, and long-term impacts due to environmental
change is largely incomplete. Retrospective tree ring analyses can
only partly close this knowledge gap, as this method indeed offers
insights into tree growth, but not into stand dynamics. Forest
inventories primarily assess managed forests, where the influences
of climate change and thinning might be coalesced, and difficult
to differentiate. Models are frequently used as a means to
circumvent data collection and subsequent analyses1. However,
modelling is no substitute for underlying field data, and the full
potential of any modelling approach is only fulfilled when
feedback between modelling studies and empirical analyses are
achieved. A unique source of information, however, was provided
by long-term experimental plots established in approximately
1872, the founding year of the International Union of Forest
Research Organisations2. These plots, which were surveyed 10–20
times until the present day, provide the longest existing time
series data on forest stand dynamics available, approximately 140
years. Originally, the study stands were established to examine
stand growth principles3, but not for growth trend analysis2.In
addition to the per se uniqueness of the sites, the locations are in
Central European regions where the longest time series on driving
variables (precipitation and temperature) dates back to 1781. The
original data acquisition objective was to support sustainable
forestry at a local scale; however, we subsequently used these
unique records to quantify and characterize changes in Central
European forest growth.
We chose Norway spruce (Picea abies (L.) Karst.) and
European beech (Fagus sylvatica L.) as the study species. These
taxa dominate Central Europe’s forests occupying 30%, that is, a
total area of 14 106ha of all forest areas. The plots selected for
this study represent pure, even-aged stands, which were
established by planting or seeding. Site conditions varied broadly,
and soils ranged from dry silty sands to moist deep silts. Since the
first site observations and records in 1870, the plots were
maintained under continuous scientific control, and surveyed on
a single tree basis. Therefore, investigators excluded plots and
reports impacted by disturbances, including storms or bark beetle
infestations. We included only unmanaged, or at most moderately
thinned, but always fully stocked plots. This selection resulted in a
unique survey data set from 36 spruce and 22 beech plots.
Based on these data we show that both species currently exhibit
significantly faster tree growth, stand volume growth and
standing stock accumulation than still in 1960 and the decades
before. Self-thinning lines remain constant, while growth rates
increase indicating the stock of resources have not changed, while
growth velocity and turnover have altered. This means stands still
follow similar general allometric rules, but proceed more rapidly
through usual trajectories. As we can demonstrate, this results in
stands currently having lower tree numbers per unit area than
past stands at the same age. Our data also reveal that the growth
acceleration is stronger on fertile sites, which is supported by
scenario runs with an ecophysiological growth model.
Results
Changes in stand dynamics and environmental conditions.
First, we pooled our data and compared them to standard yield
tables4,5 (Fig. 1). Yield tables, common forestry tools that tabulate
stand growth age dependently, were developed primarily from
1795 to 1965. Yield table data were derived from long-term plot
field survey data; and thus they served to represent past growth
conditions in this comparison. We found stand growth rates and
standing stocks after 1960 (empty symbols in Fig. 1) exceeded the
yield table ranges by 50–100%, which called the validity of yield
table range data into question, and suggested essential changes in
stand dynamics.
For the same period our plot surveys spanned, we compiled
available data on environmental variables reported to drive forest
growth dynamics (Fig. 2). For Central Europe, forest environ-
mental and growing conditions exhibited significant changes
since the first experimental forest plots were established in 1870
(Fig. 2). During the addressed period, the atmospheric CO
2
concentration rose from 295 p.p.m. in 1900 to approximately 390
p.p.m. in 2010 (refs 6,7; see Matyssek and Sandermann8for the
possible effects of atmospheric composition on trees). This means
an increase of more than 30% within nearly one century. Wet
N-deposition increased by 0.5–1.0 kg ha 1per decade7,9.
Throughout Central Europe, average total N-deposition
increased from approximately 2.5 kg ha 1per year in 1900 to
more than 9 kg ha 1per year in the first decade of the twenty-
first century6. Global average temperature has increased by
roughly 0.7 °C (ref. 7) within the twentieth century. During the
same period, the average temperature in Europe has risen by
0.95 °C (ref. 7). In Germany, the mean annual air temperature
increased by 1.0 °C (ref. 10) during the twentieth century; and
the sum annual precipitation increased by 9% during the same
period. However, the annual distribution varied. During the
winter months, precipitation increased by 19% during the last
century, and rainfall in summer decreased by 3% on average. If
only the second half of the twentieth century is measured,
summer precipitation shows a 16% reduction.
1870−1960
1961−2010
Yield table
0
10
20
30
40
PAIV (m3 ha1 yr1)V (m3 ha1)
Age (years)
Age (years) Age (years)
Age (years)
20 40 60 80 100 120 140 20 40 60 80 100 120 140
500
1,000
1,500
50 100 150
0
5
10
15
20
50 100 150
200
400
600
800
1,000
0
European beech
V (m3 ha1)PAIV (m3 ha1 yr1)
Norway spruce
Figure 1 | Observed versus expected stand growth for Norway spruce
and European beech since 1870. Observed Periodic Annual Volume
Increment (PAIV in m3ha1per year) for Norway spruce (a) and European
beech (c), and wood standing stock volume (Vin m3ha 1) for Norway
spruce (b) and European beech (d) up to 1960 (filled symbols) and
after 1960 (empty symbols) compared with a common yield table for
Norway spruce4(grey section: site index 32–40) and European beech5
(grey section: site index I–IV).
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms5967
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In addition, the rise in atmospheric CO
2
concentration, the
higher N-deposition and the increase in air temperature were two
to three times higher in the second half of the twentieth century
compared with the first half. However, the strong temperature
increase during the last 50 years reported for all of Europe7, but
was not reported for Germany. The annual mean temperature
increase for 1950–2000 was equal to the century average at 1.0 °C;
only winter temperatures showed a higher increase in the second
half of the twentieth century10. Higher temperatures will also
extend the growing season11,12. Menzel and Fabian11 reported the
average annual growing season has been extended by 10.8 days,
since the early 1960s. Chmielewski and Ro
¨tzer12 also found the
vegetation period was lengthened between 0.6 and 6.3 days per
decade in different European natural regions during 1969–1998.
Based on temperature data from the four climate stations used in
this study (Supplementary Table 1), the length of the growing
season, defined as the number of days annually with temperatures
above 10 °C, was calculated for the last 110 years. Averaged over
the climate stations, the growing season was extended by 22 days.
The main increase, however, was detected over the last 50 years
(Fig. 2).
This suggests notable wood volume growth rate increases at the
stand level over the last 100 years (Fig. 1), coinciding with an
increase in resource supply (CO
2
, N), together with an extended
growing season accompanied by changes in other climatic
variables (Fig. 2). These observations justified statistical analyses
of growth trends, and model-based examination of the underlying
mechanisms.
Growth trends of key stand variables. First, we employed linear
mixed models (LMMs) to determine whether the most important
stand characteristics were dependent on only stand age, or also on
calendar year. Standing wood volume (V), mean diameter (dq),
dominant height (ho ¼mean height of the 100 tallest trees per
hectare) and mean tree volume (
v) currently grow significantly
faster than in the past (Figs 3,4 and Supplementary Tables 2,3).
Under the environmental conditions of the year 2000, any given
mean diameter was attained following stand establishment up to
more than one decade earlier than its counterpart in 1960 or
before (Fig. 3). Stand volume growth, expressed as periodic
annual increment of volume (PAIV) changed from 1960 to 2000
by, respectively, 10% and 30% for Norway spruce and European
beech (Table 1). Most stands continued to accumulate volume,
and have not reached a final constant yield plateau (Figs 1 and 3).
For a 60-year-old Norway spruce stand, we expected a maximum
standing volume Vof 760 m3ha 1in 1960, and 810 m3ha 1in
2000. In a 130-year-old European beech stand, the expected
maximum volume in 1960 was 630 m3ha 1compared with
700 m3ha 1in 2000 (Fig. 3). A consequence of accelerated stand
development was a more rapid decrease in stand tree number N
per unit area (Fig. 3), and change in tree mortality rate, MORT
(Fig. 4). A comparison between 1960 and 2000 showed a 17%
decrease in Norway spruce tree number and a 21% decrease in
European beech. We did not detect a significant change in Nor-
way spruce mortality rate, however, European beech exhibited a
17% change (Table 1). The calendar year effect on size and
stand growth, and volume accumulation were significantly posi-
tive, and the calendar year effects on tree numbers were sig-
nificantly negative. Significance levels obtained with LMM were at
least Po0.05 with sample sizes n¼157 (V,dq,ho,
v,N) and
n¼141 (PAIV) for Norway spruce, and n¼225 (V,dq,ho,
v,N)
and n¼217 (PAIV) for European beech (see Supplementary
Tables 2, 3). In contrast, the negative calendar year effects on
European beech mortality rates were only significant at the
Po0.1 level (LMM, n¼119), whereas there was even no sig-
nificant effect for Norway spruce (Supplementary Table 3).
We further tested whether stand allometry2,13, the relationship
between average growth rate per tree and mean tree volume
(iv
vrelationship) and the relationship between tree number N
and mean tree volume (N
vrelationship, self-thinning line)
changed over time (Fig. 5). Results indicated self-thinning
parameters did not significantly change with the calendar year
(Fig. 5a,c and Supplementary Table 4). Regarding the iv
v
relationship, the slope remained independent from the calendar
year, however, the level significantly increased from past to present
(Fig. 5b,d and Supplementary Table 4). Parameter estimates
indicated the relative growth rate changed by 25% in Norway
spruce, and 57% in European beech from 1960 to 2000 (Table 1,
Supplementary Table 4. Both species showed similar slopes for the
same allometric relationship (iv
vrelationship and N
v
relationship), as predicted by the Metabolic Scaling Theory13.
Therefore, present forest stands grow more rapidly, and
accumulate a given standing volume earlier than comparable
stands did a century ago. Regression results suggest the stands
grow along a self-thinning line similar in slope and growth levels
as in the past, but pass more rapidly through this usual N
v
trajectory. Consequently, the identified growth trend was
primarily based on a changed relationship between tree size
and growth. Remarkably, the change in the iv
vrelationship
was manifested only in the curve’s level and not in its slope, the
allometric coefficient. In Table 1, we used a 75-year reference age,
which was the approximate harvest rotation age. We showed for
stands of this age, stand characteristics changed from 1960 to
2000. Both years (1960 and 2000), as well as stand age (75 years),
were inserted in the statistically fitted model functions to
quantify the relative changes (equations 1,2 and Supplementary
Tables 2–4). Although tree height increased marginally, mean tree
280
300
320
340
360
380
400
CO2 (p.p.m.)
2
4
6
8
10
Ndeposition (Tg yr−1)
4
5
6
7
8
9
10
a
b
Temperature (°C)
400
600
800
1,000
1,200
1,400
1,600
Precipitation (mm yr−1)
1900 1920 1940 1960 1980 2000
Calendar year
N−deposition CO2
Temperature Precipitation
120
140
160
180
200
220
d yr−1
Calendar year
Number of days per year with T > 10 °C
1900 1920 1940 1960 1980 2000
Figure 2 | Change in growth conditions for Central Europe since 1900.
(a) Trend in mean annual air temperature (dashed), annual precipitation
(dotted), atmospheric CO
2
-concentration (bold black line) and
N-deposition (bold grey). For better trend visualization loess smoothers for
temperature and precipitation have been added (thin solid lines).
(b) Extended annual growing season, expressed by the number of days per
year with a mean temperature 410 °C (solid). The dashed line represents a
loess smoother. Data sources: Churkina et al.6, Scho
¨nwiese et al.10
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diameter and most notably mean tree volume increment showed
accelerated change. Beech stand volume growth ( þ30%) and
standing volume accumulation ( þ7%) proceeded significantly
faster, so that tree number at a given age was already 21% lower
than in the past. As the species’ self-thinning lines showed no
significant upward shift during the whole time span covered by
our data, decreased beech mortality rate ( 17%) cannot be
attributed to delayed mortality. Rather, it can be explained by the
fact that in even-aged stands, mortality rate decreases continu-
ously with stand age under steady-state conditions. Seemingly,
owing to the more rapid growth, decreased mortality rates are
reached significantly earlier than five or more decades ago. The
fact that spruce and beech grew more rapidly, but stands still
followed similar self-thinning became most obvious by 32–77%
increased mean tree volume increments over time, an upward
shift in growth-size allometry by 25–57%, and continued self-
thinning. Our findings that self-thinning remained constant,
while growth rates increased indicated the stock of resources have
not changed, while the growth velocity and turnover have altered
over time. Note, that changes in the reported order of magnitude
are relevant for forest ecology and management.
Site dependency of accelerated stand growth dynamics.To
arrive at a differential diagnosis to explain accelerated forest
dynamics in Central Europe, we first statistically analysed if the
change in iv
vrelationships, clearly responsible for observed
growth trends, were dependent on site quality. The latter was
quantified using a site index (SI) based on actual stand height–age
relationships and standard yield tables. This is common forestry
practice, as the height a forest stand reaches at a given age is a
reliable indicator of the growing conditions at a given location.
The analysis revealed the slope depicting an increased trend in
volume increments was caused by environmental change in both
species, that is, the relative climate change benefits in terms of
gains in stand productivity was significantly greater under better
site conditions compared with poor sites (equation 3, Fig. 6 and
Supplementary Table 5).
We further complemented our empirical studies with simula-
tion experiments based on the ecophysiological model BAL-
ANCE1. We simulated forest growth under historical and recent
environmental conditions, with the former as a reference.
Simulations showed standing volume stock clearly decreased for
scenarios where past climate conditions (1901–1930) were
substituted by recent climate conditions (1981–2010); results
were detected in both species, but most notably in Norway spruce
(Fig. 7). Model results revealed climate change alone, that is,
changes in temperature and precipitation, including an extended
growing season, did not fully explain the observed growth trend
in Norway spruce and European beech. However, if changes in air
chemistry were considered in addition, the simulated stand
volume and average annual stand volume increment in beech and
spruce for recent Central European conditions exceeded the
values for past environmental conditions, as empirically
20 30 40 50 60
0
5
10
15
20
25
30
Age (years)
Age (years) Age (years) Age (years) Age (years)
Age (years) Age (years) Age (years)
20 30 40 50 60
0
10
20
30
40
20 30 40 50 60 20 30 40 50 60
40 60 80 100 120 40 60 80 100 120 40 60 80 100 120 40 60 80 100 120
1870−1960
1961−2010
1900
1960
2000
200
400
600
800
0
2,000
4,000
6,000
8,000
0
10
20
30
40
0
5
10
15
20
0
200
400
600
800
0
2,000
4,000
6,000
8,000
0
dq (cm) PAIV (m3 ha1 yr1)V (m3 ha1)N (ha1)
European beech
PAIV (m3 ha1 yr1)dq (cm) V (m3 ha1)N (ha1)
Norway spruce
Figure 3 | Statistical analysis of tree and stand dynamic changes since 1870. Trends in (a,e) mean stem diameter dq;(b,f) stand periodic annual
volume increment (PAIV); (c,g) standing volume Vand (d,h) tree number N; for Norway spruce (ad) and European beech (eh) age ranges. Observations
before 1960 (filled symbols); after 1960 (empty symbols); predictions with our fitted linear mixed models (LMM) for 2000 (dashed line); for 1960 (solid
line) and as a reference for 1900 (dotted line). The grey-shaded areas illustrate the prediction standard error. Although the error bands
partly overlap, all illustrated calendar year trends were significant at Po0.05 (LMM), with n¼157 (a,c,d); n¼141 (b); n¼225 (e,g,h) and n¼217 (f).
Note that positions on the error bands were not independent, for example, a prediction on the lower edge of the confidence band for one calendar year
would be on the lower edge for all other calendar years.
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms5967
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&2014 Macmillan Publishers Limited. All rights reserved.
demonstrated. On fertile sites, the observed environmental
change patterns resulted in increased growth acceleration,
facilitating defined forest stand sizes, standing stock and
developmental stages decades earlier than in the beginning of
the twentieth century. In contrast, on sites with mineral nutrient
limitations, environmental changes accelerated growth to a lesser
extent. Thus, long-term survey data statistical analyses (Fig. 6 and
Supplementary Table 5) and modelling scenario analyses (Fig. 7)
suggested current increased temperatures and extended growing
season contributed more to growth acceleration when site mineral
nutrient supply was greater.
Discussion
Studies in long-term ecosystem dynamics and change should
consider past and present anthropogenic and natural causes.
Long-term time series analyses on ecological processes and
climate were applied to better understand ecosystem behaviour in
the American Southwest14. Based on tree ring analysis, Swetnam
et al. revealed an unprecedented ramp in tree growth since the
mid-1970s, and attributed the observations to recovery from a
1950s extreme drought period, anomalous warming and mild wet
winters associated with El Nin
˜o events14. The study did not
completely rule out anthropogenic effects, such as CO
2
enrichment.
20 30 40 50 60
Age (years)
A
g
e (
y
ears) A
g
e (
y
ears) A
g
e (
y
ears) A
g
e (
y
ears)
Age (years) Age (years) Age (years)
All years
20 30 40 50 60 20 30 40 50 60 20 30 40 50 60
40 60 80 100 120 40 60 80 100 120 40 60 80 100 120 40 60 80 100 120
0.000
0.005
0.010
0.015
0.020
0.025
0.00
0.01
0.02
0.03
0.04
5
10
15
20
25
0
0.2
0.4
0.6
0.8
0.0
2
4
6
10
0
8
10
20
30
40
0
0.5
1.0
1.5
2.0
0.0
5
10
15
20
0
MORT (% yr1)
i
v (m3 yr1)
v
(m3)
European beech
ho (m)
Norway spruce
v
(m3)ho (m) i
v (m3 yr1)MORT (% yr1)
1882−1960
1961−2012
1900
1960
2000
1870−1960
1961−2010
1900
1960
2000
Figure 4 | Additional statistical scrutiny of tree and stand dynamic changes since 1870. Trends in (a,e) dominant height ho;(b,f) mean tree
volume
v;(c,g) mean tree annual volume increment iv; and (d,h) relative tree mortality rate, MORT; for Norway spruce (ad) and European beech
(eh) age ranges. Observations before 1960 (filled symbols); after 1960 (empty symbols); predictions with our fitted linear mixed models (LMM) for 2000
(dashed line); for 1960 (solid line); and as a reference for 1900 (dotted line). The grey-shaded areas illustrate the prediction standard error.
Although the error bands partially overlap, all illustrated calendar year trends are statistically significant at a minimum of Po0.05 (LMM), with n¼157
(a,b); n¼141 (c); n¼225 (e,f); n¼217 (g), with the exception of MORT in European beech (h) with Po0.1 (LMM, n¼119) and no significance in
Norway spruce (d). Note that positions on these error bands are not independent, for example, a prediction on the lower edge of the confidence band for
one calendar year would be on the lower edge for all other calendar years.
Table 1 | Change of the characteristics of 75-year-old forest
stands 2000 in relation to 1960.
Forest stand attribute Change from 1960–2000 in %
N. spruce E. beech
Dominant tree height, ho þ6þ9
Mean tree diameter, dq þ9þ14
Mean tree volume,
vþ34 þ20
Stand volume growth, PAIV þ10 þ30
Standing volume stock, Vþ6þ7
Tree number, N17 21
Mortality rate, MORT NS 17
Mean tree volume
increment iv þ32 þ77
Shift of iv
v-allometry þ25 þ57
Shift of N
v-allometry NS NS
E. beech, European beech; N. spruce, Norway spruce; PAIV, periodic annual increment of
volume.
Comparative changes between 2000 and 1960 determined from our fitted linear mixed models
(LMMs). We only report changes based on significant calendar year effects; bold numbers:
Po0.05 (LMM); normal number: Po0.10 (LMM). Sample sizes for Norway spruce: n¼157 (ho,
dq,
v, V, N, N
v-allometry), n¼141 (PAIV, iv,iv
v-allometry), n¼90 (MORT). Sample sizes
for European beech: n¼225 (ho, dq,
v, V, N, N
v-allometry), n¼217 (PAIV, iv,iv
v-allometry),
n¼119 (MORT). The crucial calendar year effects for a given forest stand attribute might result
from one or two significant parameter estimates.
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The increased stand growth revealed in our study surprisingly
occurred during the period when acid rain (1970–1990) and
drought episodes (1976 and 2003) suggest decreased productivity
should have occurred. One possible explanation for these results
includes acid rain, after long-distance transport, only affected
rather restricted areas of the Central European highland
mountain tops (for example, Ore Mountains, Black Forest,
Bavarian Forest or Bohemian Forest), but rarely the lowland
forests, where the experimental areas used in this study are
located15,16. The 1976 and 2003 droughts were the most severe in
Europe’s recent climate history. However, both droughts were
rather short lived, and an upward growth trend occurred
immediately following each drought17. Longer drought periods,
as expected under future climatic conditions7,18, might cause
much longer lags in tree growth.
Kahle et al. provided evidence for growth trends at the tree
level19, however, our approach showed the relevance of growth
trends for stand level productivity. A broad scientific community
was made aware of such trends in the 1990s20, however, to date,
statistical inference beyond the case study level was missing. Our
data, which covered an observational time span of more than one
century, and even included records from subsequent stands at the
same locations, revealed statistically significant growth changes
from past to present. These unique time series analyses
and results reflected how climate change actually modified the
various components of forest stand dynamics. Tree and stand
development are driven by resources rather than mere age13, and
because of increased resource availability, both aged faster than in
the past. As predicted by allometric theory13, the stands passed
along continuous self-thinning lines21, which reflected site
carrying capacity22. However, to date, because of increased
growth rates, stands achieved defined sizes, standing stock and
stand development stages significantly earlier than in the past. In
other words, an average tree exhibited accelerated growth, but at
any given average tree size, the maximum tree packing density
per unit area did not change.
Our simulation results were consistent with biosphere model
evaluations in response to climate variability, nitrogen and CO
2
concentration changes23–26. Despite high variability in published
models, overall results exhibit congruencies. The dependencies
between carbon, water and nitrogen cycles as depicted in the
model simulations are also obvious in empirical studies. For
example, boreal Norway spruce growth was not increased due to
higher CO
2
concentrations, unless nutrients were supplied27.
Further, FACE-experiments indicated that enhanced CO
2
concentrations also affect nitrogen availability and plant water
supply25,28, whereas the effect of elevated CO
2
concentrations on
forest stand growth is still unclear, especially under long-term
conditions29–32. The enhanced forest growth within the last
decades found empirically and by model simulations within this
study is in line with other results reported in the literature33,34.
Most likely, the observed climate change patterns including
extended growing seasons11,12, combined with higher
N-depositions caused this increased growth33,34. Other nutrient
0.02
a
c
b
d
0.05 0.10 0.20 0.50 0.02 0.05 0.10 0.20 0.50
0.005 0.020 0.100 0.500 2.000
200
500
1,000
2,000
5,000
0.005 0.020 0.100 0.500 2.000
0.0005
0.002
0.005
0.02
1882−1960
1961−2012
Time invariant
1870−1960
1961−2010
1900
1960
2000
10,000
5,000
2,000
1,000
0.020
0.010
0.005
0.05
v
(m
3
)v
(m
3
)
v
(m
3
)v
(m
3
)
Norway spruce
N (ha
1
)i
v (m
3
yr
1
)
European beech
N (ha
1
)i
v (m
3
yr
1
)
Figure 5 | Stand allometry in past and present for Norway spruce and
European beech. Relationships between (a,c) tree number Nand mean tree
volume
v; and (b,d) mean annual volume growth iv and mean tree volume
v
in a double-logarithmic scale for Norway spruce (a,b) and European beech
(c,d). Filled symbols: observations up to 1960; empty symbols: after 1960.
Predictions derived from our fitted linear mixed models (LMMs) do not
change with calendar year for the N
vrelationship (solid lines in a,c),
whereas they do for the iv
vrelationship. We show the predictions for the
years 2000 (dashed lines in b,d), 1960 (solid lines in b,d) and the 1900
reference (dotted lines in b,d). The grey-shaded
areas illustrate the prediction standard error. Although the error bands
partially overlap, all illustrated calendar year trends were significant at
Po0.01 (LMM, n¼141 and n¼217 for Norway spruce and European
beech). Note that positions on these error bands are not independent, for
example, a prediction on the lower edge of the confidence band for one
calendar year would be on the lower edge for all other calendar years.
0.0
ab
0.2 0.4 0.6 0.8
0.005
0.010
0.015
0.020
0.025
0.030
0.0 0.5 1.0 1.5 2.0 2.5 3.0
0.00
0.01
0.02
0.03
0.04
0.05
0.06
1882−1960
1961−2012
1960
2000
SI 41 m
SI 29 m
1860−1960
1961−2012
1960
2000
SI 37 m
SI 22 m
v
(m3)v
(m3)
Norway spruce European beech
i
v (m3 yr1)i
v (m3 yr1)
Figure 6 | Site-dependent allometry change. The iv
vrelationship’s
change (iv: mean annual volume growth;
v: mean tree volume) depended on
site quality as expressed by the site index (SI) for Norway spruce (a) and
European beech (b). SI is the yield-table expectation for stand height at 100
years of age. Filled symbols: observations up to 1960; empty symbols: after
1960. Dashed lines represent the estimated curves obtained from fitted
linear mixed models (LMM) for the calendar year 2000; solid lines for
1960. Light grey and black lines: curves for the lowest and highest SI
represented by our data, respectively. The grey-shaded areas illustrate the
prediction standard error. Although the error bands partly overlap, the
illustrated interactions of calendar year and site quality were significant at
Po0.01 (LMM, n¼141 and n¼217 for Norway spruce and European
beech). Note that positions on these error bands were not independent, for
example, a prediction on the lower edge of the confidence band for a given
calendar year and a given site quality would be on the lower edge for all
other calendar years and SI’s.
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supplies in addition to N (ref. 35), extreme events24, soil
conditions36, elevated ozone37, stand structure, that is, species
composition38 and scaling to the landscape level39 can further
modify the effect of higher temperature, and changed
precipitation patterns on stand productivity.
The accelerated tree growth and forest ageing requires
conformance of all associated organisms, including humans.
Plants and animals inhabit these habitats, and depend on special
phases in stand development and structure; faster growth means
interference in species living conditions, and demands for higher
mobility40. European beech profited more from changed growing
conditions, therefore, Norway spruce might become a weaker
competitor against beech and hence lose ground on the long run.
In forestry practices, more rapid growth in size can result in
earlier harvest threshold diameters and rotation because of
increased stand productivity, which can raise the annual cut.
Recent growth trends allow foresters to maintain much higher
standing stocks. However, strong thinnings, which use earlier
conditions as a guideline, might reduce stand density such that
the actual growth potential is not fully realized. For a specific
mean tree size, standing stock and mortality rate can be achieved
one or more decades earlier, this leaves age-based experience
values, widely used yield tables and other models, and many
traditional management guidelines to become obsolete1.In
addition, a shortened rotation period can mean reduced risk in
terms of forest damage, including bark beetle infestation,
windthrow and/or snow breakage.
Assuming growth acceleration is caused by higher resource
supplies during a lengthened growing season, similar growth
trends can be expected for a forest area of more than 45 106ha
from Northern Germany to Slovenia, and France to Hungary7.
Because our findings were based on continuously unthinned or at
most moderately thinned forest stands, but always fully stocked
experimental stands, progress in silvicultural practices can be
excluded as cause for the observed growth trend. In the primarily
intensively thinned stands from routine forest practices in Central
Europe, the positive effects of thinning on tree and stand growth
might even contribute to a climate change-induced growth trend.
Tree breeding can also be excluded as a cause for observed growth
trends, as on our research plots in many cases subsequent stands
at the same location are of the same genotype. Other relevant
species in this area, including Sessile oak (Quercus petraea L.) and
Scots pine (Pinus sylvestris L.) dominate on less fertile sites than
those typically stocked with Norway spruce and European beech,
so that the benefit of the additional resource supply and growth
acceleration might be even higher41. The increased growth rate,
harvest and standing stock accumulation can be expected to
heighten the carbon turnover rate in Central European forests.
We roughly estimated the wood-related share in this rate by
assuming an additional annual volume growth of 3 m3ha 1per
year (B0.75 t C) on a 45 106ha area, which resulted in
34 106t C per year additionally stored in wood at first.
However, whether this accelerated turnover translates into actual
additional C sequestration by increasing forests’ standing stock or
the stock of long-living wood products is highly questionable and
cannot be answered with our data. Raising standing stocks may
be undesired in managed forests as higher stand densities
correlate with a higher risk of storm or snow damage.
Intensified harvest, on the other hand, might result in new local
problems because of mineral nutrient export comparable to
former litter raking. Yet, it offers a chance for substituting fossil-
based products in a C-neutral way, even if wood is only put into
0
20
40
60
80
100
120
140
160
Past
climate
Recent
climate
Recent
climate +
CO2 + N
PAIV
(%)
60
70
80
90
100
110
120
130
140
Age (years)
Recent climate
Recent climate + CO2 + N
60
70
80
90
100
110
120
130
140
Age (years)
Recent climate
Recent climate + CO2 + N
Past
climate
Recent
climate
Recent
climate +
CO2 + N
PAIV
(%)
0
20
40
60
80
100
120
140
160
35 40 45 50 55 60 65
35 40 45 50 55 60 65
Relative V
(%)
Relative V
(%)
Figure 7 | Scenario analysis based on the ecophysiological growth model BALANCE. Simulated relative standing volume (V;a,c) and average relative
stand periodic annual volume increment (PAIV;b,d) with standard errors (n¼4) for European beech (a,b) and Norway spruce (c,d) over 30 years
for recent climatic conditions (1981–2010), recent climate with additional increased CO
2
concentrations, and N deposition (100% base: past climate
1901–1930).
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&2014 Macmillan Publishers Limited. All rights reserved.
short-lived use like energy provision. Currently, it is unclear
whether growth trends will remain positive under future climate
change7. Detection, analysis and understanding can only be
gained from continued long-term observation plots.
Hans Carl von Carlowitz (1645–1714) initiated long-term
plots, and other founding forestry fathers followed by quantifying
sustainable forestry practices at regional levels. Unfortunately,
most plots were viewed as out-dated when it was time to
inventory forests, considered too expensive and abandoned.
However, our study emphasized the unique contribution of long-
term observational plots to regional and global ecosystem
monitoring, ecological research and environmental policy. Even
300 years after von Carlowitz proposed the concept of sustainable
forests by publishing Sylvicultura Oeconomica42 in 1713, long-
term plots remain the ultimate arbiters of human footprints on
forest ecosystems.
Methods
Observational plots used in this study.Our empirical data were derived from 58
long-term observational plots in Germany; 36 supported Norway spruce and 22
European beech stands (Supplementary Tables 6–10). The plots cover a range of
northern latitudes between 47.78°and 51.63°, and eastern longitudes between 7.92°
and 13.31°(Supplementary Fig. 1). They belong to an extensive network of long-
term research plots, which is scientifically maintained under the first author’s and
his group’s responsibility (see Supplementary Note 1 for background information).
All 58 plots selected for this study were either unthinned or moderately thinned,
but always fully stocked. This focus on stands with no or minor silvicultural impact
on stand dynamics, which were strictly and consistently maintained under scien-
tific control, excluded treatment and treatment changes as reasons for the observed
growth trends.
The unthinned (‘A-grade’43) experimental plots established in the nineteenth
century possess considerable value and information potential for eco-monitoring.
In Europe’s entirely managed forests, the unthinned plots represent an exceptional
case of 140 years of unmanaged ecosystem development, where growth trends are
not confounded by any silvicultural treatment effects. The potential for slight
interference on A-grade plots because of forest protection occurs, however, if any,
only dead and suppressed dying trees with negligible influence on stand dynamics
are removed43. In addition, this study also used so-called B-grade plots, where the
living basal area is slightly reduced, but without impact to the crown layer43. This is
understood as a slight advance on natural mortality. A similar concept has been
applied on the ‘80%-plots’ used in this study, where stand basal area is never
reduced to less than 80% of an accompanying A-grade plot. Supplementary
Table 10 provides a plot-wise list of thinning intensities. We exclusively selected
stands established with high initial stand densities (tree number per hectare
45,000) to assure only fully stocked stands were included. All stands are
monospecific and even-aged; they originated from planting or seeding. We used
such long-term observational plots to determine whether growth rate changed over
time, and how changes affected tree growth, standing volume, stand density,
mortality and other stand attributes.
The selected plots represent growth conditions in the plains and highlands of
Middle and Central Germany (Supplementary Fig. 1). The localities occur from 330
to 843 m above sea level. Although European beech plots dominate the Atlantic
plains and highlands climate, Norway spruce plots are located in submontane and
montane highlands, and the pre-alpine mountain zone (Supplementary Tables 6–
8). The long-term mean temperature and annual precipitation exhibits a broad
range across both species (5.7–8.5 °C and 605–1,369 mm per year), as well as for
each species separately (for example, for Norway spruce, 6.9–8.3 °C and 812–
1,256 mm per year). The plot distribution over 13 eco-regions and 11 geological
zones is reflected by the broad spectrum of soil types. The poorest soils are podsols
derived from sandstone and cretaceous material in the Palatinate and Upper
Palatinate regions; the most fertile soils are parabrown soils from diluvial loess-
loam in the pre-alpine highlands. The majority of stands are supported on soils of
mediocre fertility on Triassic, Jurassic and Cretaceous formations between
Frankfurt and Munich (Supplementary Fig. 1, see Supplementary Tables 6 and 8
for further details).
The data set comprises plots in present day mature stands surveyed up to 18
times since 1870, but also in young stands established in the last decade and only
surveyed twice. Hence, the plots cover both historic and recent growth behaviour
under respective environmental conditions. The broad variation of stand age
(21–188 years), dominant height (10.7–44.4 m), tree number per hectare
(133–11,238 trees ha 1) and quadratic mean tree diameter (5.4–54.4 cm) show the
plots represent a rather wide range of stand developmental stages. Total yield
(50 2,459 m3ha 1), standing volume (50–1,637 m3ha 1) and periodic
annual volume incremen t (7.1–41.5 m3ha 1per year), as well as SI (19.8–43.1 m)
emphasize the wide spectrum of site conditions and productivity levels
(cf. Supplementary Table 9).
Survey and evaluation of long-term observational plots.We based our analyses
on the International Union of Forest Research Organisations2standard variables,
which quantified above ground mean tree and stand stem volume, rather than
single tree volume or biomass. Therefore, additional assumptions for scaling from
volume to mass were avoided; however, it required the following variable
definitions: (i) all stand variables relate to a unit area of 1 ha (104m2); (ii) tree
diameter, dq (cm), refers to the quadratic mean diameter at breast height (1.30 m)
for all trees per plot; (iii) dominant height, ho (m) is the mean height of the 100
tallest trees per hectare; (iv) mean tree volume,
v(m3) is the arithmetic mean stem
volume; (v) annual tree volume growth, iv (m3yr 1) is the mean annual volume
growth of the mean trees with volume
v; (vi) PAIV (m3ha 1per year) refers to the
entire stand’s mean annual stem volume growth during a period between two
surveys; (vii) standing stand volume, V(m3ha 1) is the accumulated stem volume
per hectare; (viii) the successive surveys of remaining, dead and harvested trees
generates the current tree number per unit area, N(ha 1), and enables the
calculation of the annual tree mortality rate, MORT (% per year); and (ix) the plots’
SI is expressed as measured or expected stand height at age 100 years. We used the
prevalent yield tables by Assmann and Franz4, and Schober5for Norway spruce
and European beech, respectively. For complementary details, see Supplementary
Methods.
Dependency of stand variables on age and calendar year.We examined
whether stand development on observational plots reflects any long-term growth
trends by modelling stand characteristics dependent on stand age and calendar
year. Certainly, stand characteristics from successive surveys (for example, PAIV,
standing volume (V) and tree mortality rate (MORT) depend on age. An additional
calendar year effect on stand characteristics would indicate a growth trend; if
stands at a defined age perform differently in different calendar periods or decades,
this will indicate a change in growth and site conditions. For investigating this, the
following basic LMM structure was the most appropriate:
Yijt ¼b0þb1Aijt þb2yearijt þb3Aijt yearijt þbiþbij þeijt ð1Þ
Variable Yrepresents the stand characteristic of interest (for example, PAIV, Vand
so on), untransformed or logarithmized, depending on whether the logarithmic
transformation rendered a better model fit. Similarly, Arepresents stand age,
untransformed or its logarithm. Choosing appropriate combinations of Yand A
logarithmic and untransformed values allowed us to sufficiently cover nonlinear
age-dependent relationships with a linear regression model. The second explana-
tory variable, calendar year, corresponding to a given observation, is indicated by
the variable year.
The indices i,jand trepresent the location an observational plot is included, the
plot itself and the point of time a plot survey has occurred. Fixed effects parameters
are b
0
b
3
, whereas b
i
and b
ij
are location and plot random effects (b
i
BN(0,t
1
2),
b
ij
BN(0,t
2
2)). Including these random effects, we avoid biased results due to the
plot-specific and possibly also location-specific autocorrelation among the
observations. Finally, e
ijt
denotes i.i.d. errors (e
ijt
BN(0,s2)).
The calendar year effect and its interaction with age (represented by b
2
and b
3
parameters) were only maintained in the model when they were statistically
significant. Otherwise, the model was reduced accordingly and fitted again. If the
interaction was significant, but not the isolated year effect, both were maintained in
the model44.
High stand ages in our data primarily occurred with recent calendar years only,
therefore, we excluded specific observations beyond a certain age to develop a
balance of age-calendar year combination data set. Our models were fitted for 60
years and younger stand ages in Norway spruce, and 130 years and younger in
European beech. For most stand characteristics as response variables this resulted
in a sample size of n¼157 and n¼225 for Norway spruce and European beech,
respectively. For the growth variables PAIV and iv the sample size reduced to
n¼141 (spruce) and n¼217 (beech) as there is no increment information
available for the plots’ last surveys. The mortality rate, MORT, could be
meaningfully analysed for the completely unthinned plots only which results in
n¼90 (spruce) and n¼119 (beech). All models were fitted by maximizing the
restricted maximum likelihood criterion (cf. Zuur et al.44).
Allometric relationships of stand growth and size variables.The relationships
between mean tree growth and mean tree size (iv versus
v), and tree number per
unit area and mean size (Nversus
v) are cornerstones of allometric theory45–49.
In the double logarithmic scale, both relationships follow a straight line
ln(y)¼aþbln(x) (equivalent to y¼eaxb) with rather general and species-
overarching values for the slope b. However, it is widely accepted that line levels,
represented by intercept a, depend on environmental conditions and species21,50,51.
We used a LMM, which is very similar to the basic model shown above to test the
extent both allometric relationships are influenced by calendar-year-dependent
trends:
lnðyijtÞ¼b0þb1lnðxijt Þþb2yearijt þb3lnðxijtÞyear ijt þbiþbij þeijt ð2Þ
where yand xrepresent iv and
v,orNand
v, respectively. The variable and index
names are defined the same as in equations 1. We employed exactly the same data
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&2014 Macmillan Publishers Limited. All rights reserved.
used in fitting the age trend models, including only stands younger than 61
(Norway spruce) and 131 (European beech) years.
Site dependency of the iv
vrelationship’s temporal trend.Results of the
previous analyses suggested an upward shift with time (significant parameter b
2
in
equation 2) in the allometric relationship between iv and
vas the common
mechanism underlying the observed growth trends. With the same data, we tested
any change in allometry dependent on site conditions by formulating the following
linear mixed regression model:
lnðivijt Þ¼b0þb1lnð
vijtÞþb2year ijt þb3yearijt SIij þbiþbij þeijt ð3Þ
where SI is the respective plot’s SI, expressed as an expected stand height at an age
of 100 years (see above). The other variable meanings and names are defined
exactly the same as above. If parameter b
3
differs significantly from zero, this
indicates the allometric shift depends on site quality. All statistical analyses were
performed with R 3.0.2 (ref. 52).
Growth trend and changed arrival age at threshold values.To quantify how
stand characteristics changed, we choose stand age 75 years, calculated how the
stands perform at that age in 2000, and divided that by stand performance at the
same age in 1960. For this purpose, both values were derived for the respective
stand variables from the fitted model equations using the fixed effects parameter
estimates (equations 1,2 and Supplementary Tables 2–4) while setting the random
effects to zero. For PAIV, for example, this procedure yields PAIV
age 75,2000
and
PAIV
age 75,1960
, and the ratio RPAIV
age 75,2000/1960
¼PAIV
age 75,2000
/PAIV
age 75,1960
,
which reflects the growth trend since 1960.
The age when a mean tree variable or a stand characteristic arrives at a defined
threshold value is a practical and relevant measure. Assume the fixed effects
parameters of equations 1 have been estimated, we have the following equation for
estimating ^
Y, which is the general expected value for a given stand characteristic or
its logarithm:
^
Y¼b0þb1Aþb2year þb3Ayear ð4Þ
This can be rearranged as
A¼
^
Yb0b2year
b1þb3year ð5Þ
which allows us to estimate A, the mean age (or its logarithm) when a certain
threshold value ^
Yis reached under the environmental conditions of a given year.
Process-based modelling.The physiological growth model BALANCE53,54 we
used for scenario analyses simulates the three-dimensional development of
individual trees in a stand, and estimates the consequences of environmental
influences. Tree development is calculated as a response to individual
environmental conditions, and as environmental conditions change with individual
tree development, the influences of competition, stand structure, species mixture
and management options can be assessed with the model (Supplementary Fig. 2).
Initial tree biomass is calculated from the dimensional variables tree height,
height to crown base, diameter at breast height, tree position and crown radii.
Biomass increase is the result of the interaction between physiological processes,
which are dependent on the physical and chemical microenvironment. These are in
turn influenced by stand spatial structure. Asymmetric crown shapes are included,
and generate a spatially explicit representation of the environment. The calculation
levels vary from stand level to individual trees, from tree components (that is,
foliage, branches, stems, and fine and coarse roots) to crown and root layers, which
are spatially subdivided into segments. Consequently, an increase in biomass is
simulated based on the carbon and nitrogen uptake from each segment, depending
on its energy supply and resource availability.
By using weather data, microclimate and water balance are simulated for
each layer and segment, respectively. Air temperature and radiation within the
stand is calculated for every crown layer of every tree on the basis of leaf area
distribution for the respective tree and its competitors. The spatial distribution of
light and water availability is estimated on a daily basis. Water balance simulation
examines soil conditions in different soil layers, where vertical and horizontal water
flows between rooted and non-rooted fractions are considered. Based on the
Penman-Monteith55 approach, potential evapotranspiration is estimated, from
which the actual evapotranspiration of a tree is calculated using maximum water
uptake derived from water content within soil volume pervaded by fine roots.
Water can be exchanged between rooted and un-rooted soil layers. Total soil water
content is reduced by drainage, which is equivalent to percolation from the deepest
soil layers.
Foliage biomass and leaf area as well as light availability and photosynthetically
active radiation (PAR) absorption change with the onset of bud burst. A tree’s
foliage emergence date determines its assimilation and respiration rate, but also
alters the environmental conditions in the immediate surrounding area. Bud burst
of a tree species is estimated using an air temperature sum model, whereas foliage
senescence is simulated depending on the respiration sum for each segment of a
tree. Based on the aggregated driving variables, all physiological processes, that is,
assimilation, respiration, nutrient uptake, growth, senescence and allocation can be
calculated for each individual tree. Nutrient uptake is the result of demand, supply
and absorption capacity, whereby demand is based on the difference between the
actual nitrogen concentration, and a given optimal concentration. Supply is defined
by soil characteristics of the rooted volume, uptake capacity by root surface, and its
specific absorption rate.
Physiological processes are calculated in 10-day time steps using aggregated
results of daily environmental conditions. Gross primary production is estimated
depending on leaf surface, absorbed PAR, temperature, CO
2
concentration, water
and nitrogen supply (Supplementary Fig. 3).
Total respiration is the sum of maintenance losses and growth respiration.
Maintenance respiration is calculated for each segment as a function of biomass,
specific respiration rate and temperature. Growth respiration is estimated as a
constant fraction of maximum photosynthesis. The fixed carbon not required for
respiration is distributed into plant compartments, including foliage, branches,
stems and roots. The available carbon for allocation is apportioned into different
compartments according to growth and respiration demands. Carbon allocation is
defined by the relationships between the compartments according to the functional
carbon balance theory56, and the pipe model theory57. Consequently, all tissues
within a segment, that is, foliage and branches, or fine and coarse roots, as well as
the amount of stem wood, are mechanistically linked to each other. Dimensional
tree growth is estimated annually, based on biomass accumulation during that year.
Volume expansion depends on the necessary amount of twigs and transport
branches, and the amount of coarse roots within root segments. Therefore, crown
development is preferred in the direction of best assimilation conditions during the
previous year. If net assimilation rates are negative, the crown segment is regarded
as dead. If no segments contain living biomass, the tree is assumed dead and
removed from calculations.
BALANCE has been extensively validated for basic micro-meteorological and
physiological processes, for water balance, annual tree development and entire
stand development54,58. Detailed descriptions of BALANCE, and single modules
can be obtained from Grote and Pretzsch53,orRo
¨tzer et al.54
Scenarios calculated with BALANCE.Climate data from four German climate
stations with daily time series for more than 100 years formed the foundation of
the growth simulations using BALANCE. Supplementary Table 1 shows the geo-
graphical coordinates, mean air temperature and precipitation values for the
chosen simulation periods. These four stations are representative of most climate
regions in Central Europe. We chose a sandy loam soil type with an available field
capacity of 186 mm to a maximum rooting depth of 1 m. Field capacity decreases
from 35 to 25 vol% with increasing soil depth, whereas the wilting point was set
constant at 8 vol%. Plant available N was assumed low at the beginning of the
simulation. This was justified because historically, forests in Germany were
displaced by agriculture to nutrient poor sites.
Growth development of a 30-year-old Norway spruce and 35-year-old
European beech stand (Supplementary Table 11) was simulated for time spans
1901–1930 and 1981–2001. The first simulation scenario (reference) was the
1901–1930 period using the daily climate record obtained from each station, and
continuously increasing atmospheric CO
2
concentrations from 295 p.p.m. to
307.p.p.m., and N-depositions from 6 kg N ha 1per year to 7 kg N ha 1per year.
The second scenario reproduced the recent climate conditions from 1981 to 2010
while keeping N-depositions and CO
2
concentration on the previous level. In
scenario 3, an increase of atmospheric CO
2
concentrations from 338 p.p.m. from
1981 to 389 p.p.m. in 2010, and increased N-deposition from 15 kg N ha 1per
year in 1981 to 20 kg N ha 1per year in 2010 was included in the model. Scenario
3 thus integrates all recent environmental conditions. Atmospheric CO
2
concentration and N-deposition data were derived from Churkina et al.6In this
way, the single and overall influences of climate, CO
2
and N-deposition were
analysed.
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Acknowledgements
This work was supported by grants from the Deutsche Forschungsgemeinschaft (PR 292/
12-1), the European Commission (FP1206 and IRSES-GA-2011-295136) and the
Bavarian State Ministry for Nutrition, Agriculture and Forestry (7831-23953-2014, W07).
Author contributions
H.P. initiated the study, interpreted the data and wrote the paper. P.B. performed
statistical analyses, interpreted the data and wrote the paper. G.S. compiled the data.
E.U. interpreted the data and revised the manuscript. T.R. performed and interpreted
simulation runs and wrote the paper.
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How to cite this article: Pretzsch, H. et al. Forest stand growth dynamics in Central
Europe have accelerated since 1870. Nat. Commun. 5:4967 doi: 10.1038/ncomms5967
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ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms5967
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Los anillos de crecimiento son un registro histórico natural de procesos ecológicos, que incluye las respuestas fisiológicas y estructurales del xilema, a factores abióticos, interacciones bióticas y prácticas de manejo forestal. El objetivo general del presente trabajo fue evaluar los impactos multiescalares de las prácticas de manejo forestal sobre el crecimiento radial de bosques de coníferas del Estado de México al interior de la ecorregión del Eje Volcánico Transmexicano. El crecimiento de un árbol puede abordarse desde diferentes escalas que involucran los niveles rodal—árbol individual—anillo de crecimiento—célula del xilema. Las técnicas dendroecológicas permiten evaluar el crecimiento de los árboles remanentes después de intervenciones de manejo en escalas de tiempo amplias. Se analizó el efecto de una corta de saneamiento sobre el crecimiento radial del arbolado residual de Pinus hartwegii, y se encontró una respuesta favorable a la intervención con un aumento estadísticamente significativo del incremento de área basal explicado por una disminución de la competencia. Adicionalmente, se evaluó el efecto de un aprovechamiento forestal sobre la formación de madera en árboles remanentes de Abies religiosa, se encontró que las anchuras de anillo aumentaron significativamente después de la corta. La madera tardía después de la intervención presentó traqueidas con lúmenes de mayor diámetro y paredes celulares de menor grosor, estos cambios suscitaron variaciones en la microdensidad de la madera, con un aumento en la densidad mínima y una disminución de la densidad máxima. El conocimiento dendroecológico de un bosque contribuye a optimizar la producción maderable y favorecer la conservación de los ecosistemas forestales.
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... Norway spruce (Picea abies [L.] Karst; mean age in 2020: 69 ± 4; Pretzsch et al., 2014). Long-time average (1971 -2000) mean annual air temperature and mean annual precipitation, and mean air temperature and precipitation during the growing season (May to September) are 7.8°C and 13.8°C, and 750-800 mm year -1 and 460-500 mm yr -1 , respectively. ...
... (Grams et al., 2021. In the frame of the 'Kranzberg Forest Roof experiment' (KRoof; Goisser et al., 2016;Grams et al., 2021, Pretzsch et al., 2014Pretzsch et al., 2016), the study site has been divided into twelve 110 to 200 m² plots, each containing three to seven F. sylvatica and three to seven P. abies mature trees. In spring 2010, the plots were trenched down to 1m soil depth, where a dense layer of tertiary sediments was reached. ...
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Climate change is expected to increase the frequency and intensity of summer droughts. Sufficient drought resistance, the ability to acclimate to and/or recover after drought, is thus crucial for forest tree species. However, studies on the hydraulics of mature trees during and after drought in natura are scarce. In this study, we analyzed trunk water content (electrical resistivity ER) and further hydraulic (water potential, sap flow density, specific hydraulic conductivity, vulnerability to embolism) as well as wood anatomical traits (tree ring width, conduit diameter, conduit wall reinforcement) of drought‐stressed (artificially induced summer drought via throughfall‐exclusion) and unstressed Picea abies and Fagus sylvatica trees. In P. abies, ER indicated a strong reduction in trunk water content after five years of summer drought, corresponding to significantly lower pre‐dawn leaf water potential and xylem sap flow density. Vulnerability to embolism tended to be higher in drought‐stressed trees. In F. sylvatica, only small differences between drought‐stressed and control trees were observed. Re‐watering led to a rapid increase in water potentials and xylem sap flow of drought‐stressed trees, and to increased growth rates in the next growing season ER analyses revealed lower trunk water content in P. abies trees growing on throughfall‐exclusion plots even one year after re‐watering, indicating a limited capability to restore internal water reservoirs. Results demonstrated P. abies to be more susceptible to recurrent summer drought than F. sylvatica, and to exhibit long lasting and pronounced legacy effects in trunk water reservoirs.
... For Central European forests a generally positive long-term growth trend was found in recent studies [5,6], that may contribute to the mitigation of advancing climate change. In this context, mixed stands of various tree species were regularly more productive than monospecific stands [7][8][9]. ...
... The present study corroborates the finding of several existing studies showing a positive tree and stand growth development in recent decades [5,6,60,61]. For example, the growth increased from 1960 to 2000 over 10% and 30% for monospecific Norway spruce and European beech, respectively [6]. ...
... The present study corroborates the finding of several existing studies showing a positive tree and stand growth development in recent decades [5,6,60,61]. For example, the growth increased from 1960 to 2000 over 10% and 30% for monospecific Norway spruce and European beech, respectively [6]. The same was partly true for other important tree species [5,62]. ...
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... Although these methods can give a good indication of either current biomass availability or future growth trends, they fail to make accurate projections on future biomass availability under changing environmental conditions (Pretzsch et al., 2014). ...
... Empirical growth tables are derived from statistical correlations between measurements of tree species-specific growth and site conditions. While this leads to accurate growth predictions under the observed conditions, they lose accuracy when extrapolated to different environments (Fontes et al., 2010;Pretzsch et al., 2014). PBMs can better meet this challenge by describing the underlying mechanisms of forest growth. ...
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Forests and wood products play a major role in climate change mitigation strategies and the transition from a fossil-based economy to a circular bioeconomy. Accurate estimates of future forest productivity are crucial to predict the carbon sequestration and wood provision potential of forests. Since long, forest managers have used empirical yield tables as a cost-effective and reliable way to predict forest growth. However, recent climate change-induced growth shifts raised doubts about the long-term validity of these yield tables. In this study, we propose a methodology to improve available yield tables of 11 tree species in the Netherlands and Flanders, Belgium. The methodology uses scaling functions derived from climate-sensitive process-based modelling (PBM) that reflect state-of-the-art projections of future growth trends. Combining PBM and stand information from the empirical yield tables for the region of Flanders, we found that for the period 1987–2016 stand productivity has on average increased by 13% compared to 1961–1990. Furthermore, simulations indicate that this positive growth trend is most likely to persist in the coming decades, for all considered species, climate or site conditions. Nonetheless, results showed that local site variability is equally important to consider as the in- or exclusion of the CO2 fertilization effect or different climate projections, when assessing the magnitude of forests' response to climate change. Our projections suggest that incorporating these climate change-related productivity changes lead to a 7% increase in standing stock and a 22% increase in sustainably potentially harvestable woody biomass by 2050. The proposed methodology and resulting estimates of climate-sensitive projections of future woody biomass stocks will facilitate the further incorporation of forests and their products in global and regional strategies for the transition to a climate-smart circular bioeconomy.
... The substitution of untoward properties is manifested by similar plant growth simultaneously in favourable and unfavourable conditions. Therefore, the estimation of the combined interaction effects between atmospheric and soil properties indicate ways of living, and community adaptation to environmental change [3]. ...
... Otherwise, eutrophication causes an imbalance between individual substance sources, reducing plant growth [23]. Nevertheless, the growth of the main management tree species at nutrient-rich sites has influenced an increase in the overall increment of European forests since the end of the Little Ice Age [3]. The diameter increment of boreal forests increases by 25% at optimal soil conditions under 2 × CO 2 atmosphere [24]. ...
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Environmental properties affect growth of forest tree species contradictorily. The antagonistic effects of variable environmental properties classify forest response according to various tree compositions among different sites. The division of the forest response was assessed in 52 stands arranged to 26 types of 13 site management populations (MPs) in 5 areas on the territory of the Czech Republic. The assessment was performed using time-series multiple regressions of basal area increment from pure immature stands of Norway spruce (Picea abies), Scots pine (Pinus sylvestris), oaks (Quercus sp.), ash (Fraxinus excelsior) and willows (Salix sp.) dependent on the interpolated average temperatures, annual precipitation, atmospheric concentrations of SO2, NOx and O3 and soil properties over the period 1971–2008 at p < 0.05. Site MPs differentiated forest response to a greater extent than tree species. The response of the forests was significantly distributed by means of the montane, upland and waterlogged sites. The multiple determination index (r2) ≥ 0.6 indicated adaptable tree increment but interval of r2 between 0.80–0.92 implied forest sensitivity to variability in environmental properties on non-waterlogged sites. The index r2< 0.6 suggested the fluctuating forest increment that reflects environmental variability inconsistently. The fluctuating increment most affected the spruce and pine stands grown from upland to submontane locations. Montane spruce stands as well as rock pines appeared to be one of the most sensitive ones to the environmental change. Floodplain forests seemed as adaptable to variable environmental properties.
... Over recent decades, the effects of climate change and anthropogenic and biogenic influences on tree growth have been commonly discussed (Dittmar et al., 2003;Pretzsch et al., 2014;Spiecker, 1996). The majority of research concerns the study of annual growth in natural stands, while studies concerning urban stands are less numerous and also highly contradictory. ...
... Most studies describe the negative influence of the urban environment on the radial growth of trees because of (1) air and environmental pollution (de Bauer and Krupa, 1990;Guardans, 2002;Juknys et al., 2003;Kint et al., 2012), (2) moisture deficiency in the soil (Kint et al., 2012;Scharnweber et al., 2011), (3) disturbance of soil composition and structure (Dittmar et al., 2003;Penninckx et al., 1999) and (4) pest and disease infestation (Chappelka and Grulke, 2016;de Bauer and Krupa, 1990;Juknys et al., 2003). At the same time, some studies (Bytnerowicz et al., 2007;Fang et al., 2014;Pretzsch et al., 2014;Spiecker, 1996) suggest that positive effects concerning the growth of trees arise from climate change conditions and some types of environmental pollution in urban forests. ...
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Eleven stands of Scots pine (Pinus sylvestris L.) from the city of Ekaterinburg and its surroundings were sampled and analyzed using dendrochronological methods to detect the effects of climate, biotic and anthropogenic factors on the annual growth of trees. Tree-ring chronologies were developed for six sites within the city and for five control sites. All chronologies were highly and positively correlated before the 1940s. However, after this period, there was a significant decrease in the correlation among chronologies from urban and rural sites. Divergence lasted about 20 years. This firstly has an anthropogenic cause, mainly due to the evacuation in 1941 of more than 60 industrial factories to Sverdlovsk (now Ekaterinburg), which generated a significant increase in air pollution. Environmental pollution seems to negatively affect tree growth. In the early 1950s, trees in the region also suffered from severe droughts. The results of climate and historical data analysis suggest that the trees on urban sites were weakened by both climate and air pollution factors, which led to a massive nun moth (Lymantria monacha L.) infestation of trees. Defoliation led to a drastic reduction in tree-ring width and, in some cases, to the complete loss of annual rings. The recovery period lasted 10 to 15 years on average. Rural populations were much less affected by the insect outbreak. After urban populations of pine recovered in the 1960s, radial growth of urban and rural populations became synchronized again.
... Reflecting the empirical basis of SwissStandSim, rising annual mean temperatures slightly elevated the productivity of beech and thus facilitated forest restructuring and increased biodiversity especially in spruce-dominated stands. These findings correspond to retrospective studies from Forrester et al. (2021) who reported an increased tree growth in beech-and spruce-dominated stands in Switzerland that was mainly temperature-driven, and Pretzsch et al. (2014) who demonstrated that growth of beech and spruce accelerated in Central Europe over the last 100 + years. In our simulations, this productivity boost in turn led to an increase in timber production under the climate scenarios, which was also reported in other forward-looking studies (e.g. ...
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Climate-adaptive forest management aims to sustain the provision of multiple forest ecosystem services and biodiversity (ESB). However, it remains largely unknown how changes in adaptive silvicultural interventions affect trade-offs and synergies among ESB in the long term. We used a simulation-based sensitivity analysis to evaluate popular adaptive forest management interventions in representative Swiss low- to mid-elevation beech- and spruce-dominated forest stands. We predicted stand development across the twenty-first century using a novel empirical and temperature-sensitive single-tree forest stand simulator in a fully crossed experimental design to analyse the effects of (1) planting mixtures of Douglas-fir, oak and silver fir, (2) thinning intensity, and (3) harvesting intensity on timber production, carbon storage and biodiversity under three climate scenarios. Simulation results were evaluated in terms of multiple ESB provision, trade-offs and synergies, and individual effects of the adaptive interventions. Timber production increased on average by 45% in scenarios that included tree planting. Tree planting led to pronounced synergies among all ESBs towards the end of the twenty-first century. Increasing the thinning and harvesting intensity affected ESB provision negatively. Our simulations indicated a temperature-driven increase in growth in beech- (+ 12.5%) and spruce-dominated stands (+ 3.7%), but could not account for drought effects on forest dynamics. Our study demonstrates the advantages of multi-scenario sensitivity analysis that enables quantifying effect sizes and directions of management impacts. We showed that admixing new tree species is promising to enhance future ESB provision and synergies among them. These results support strategic decision making in forestry.
... Warming has exacerbated drought stress on trees in forests, causing tree death and forest reduction at many high elevations and high latitudes and in dry areas (Hogg et al. 2017;Huang et al. 2021;Jiao et al. 2016;Zhou et al. 2020;Chen et al. 2017). Conversely, a warmer climate could also lengthen growing seasons in cold humid areas, thereby increasing the photosynthetic rate of trees and producing more carbohydrates for growth (Liu 2019;Pretzsch et al. 2014;Bosela et al. 2018;Gao et al. 2022;Guo et al. 2019). Since the 1950s, there has been significant intermittent warming in northwest China, with a rapid temperature increase in the 1980s, followed by a temperature hiatus in the early twentieth century (Edenhofer et al. 2014;Chen et al. 2020;Guan et al. 2015). ...
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... According to current climate projections (IPCC 2015), the growth conditions for forests will change markedly in the future, while Pretzsch et al. (2014) showed that stand growth dynamics of forests in Central Europe already have changed since 1960 in comparison to the period 1870-1960. The years 2018 and 2019 might be seen as heralds of a changed climate. ...
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