ArticlePDF Available

Abstract and Figures

The growth of past, present, and future forests was, is and will be affected by climate variability. This multifaceted relationship has been assessed in several regional studies, but spatially resolved, large-scale analyses are largely missing so far. Here we estimate recent changes in growth of 5800 beech trees ( Fagus sylvatica L.) from 324 sites, representing the full geographic and climatic range of species. Future growth trends were predicted considering state-of-the-art climate scenarios. The validated models indicate growth declines across large region of the distribution in recent decades, and project severe future growth declines ranging from −20% to more than −50% by 2090, depending on the region and climate change scenario (i.e. CMIP6 SSP1-2.6 and SSP5-8.5). Forecasted forest productivity losses are most striking towards the southern distribution limit of Fagus sylvatica , in regions where persisting atmospheric high-pressure systems are expected to increase drought severity. The projected 21 st century growth changes across Europe indicate serious ecological and economic consequences that require immediate forest adaptation.
This content is subject to copyright. Terms and conditions apply.
Climate-change-driven growth decline of European
beech forests
Edurne Martinez del Castillo 1, Christian S. Zang 2, Allan Buras3, Andrew Hacket-Pain 4, Jan Esper1,5,
Roberto Serrano-Notivoli6, Claudia Hartl 7, Robert Weigel8, Stefan Klesse9, Victor Resco de Dios 10,11,
Tobias Scharnweber 12, Isabel Dorado-Liñán 13, Marieke van der Maaten-Theunissen 14,
Ernst van der Maaten 14, Alistair Jump15, Sjepan Mikac 16, Bat-Enerel Banzragch8, Wolfgang Beck17,
Liam Cavin15, Hugues Claessens18, Vojtěch Čada 19, Katarina Čufar 20, Choimaa Dulamsuren21,
Jozica Gričar22, Eustaquio Gil-Pelegrín23, Pavel Janda19, Marko Kazimirovic 24, Juergen Kreyling 12,
Nicolas Latte18, Christoph Leuschner8, Luis Alberto Longares25, Annette Menzel26, Maks Merela 20,
Renzo Motta27, Lena Mufer8,12, Paola Nola 28, Any Mary Petritan29, Ion Catalin Petritan30, Peter Prislan 22,
Álvaro Rubio-Cuadrado 31, MilošRydval 19, Branko Stajić24, Miroslav Svoboda19, Elvin Toromani32,
Volodymyr Trotsiuk 9, Martin Wilmking 12, Tzvetan Zlatanov 33 & Martin de Luis 25
The growth of past, present, and future forests was, is and will be affected by climate
variability. This multifaceted relationship has been assessed in several regional studies, but
spatially resolved, large-scale analyses are largely missing so far. Here we estimate recent
changes in growth of 5800 beech trees (Fagus sylvatica L.) from 324 sites, representing the
full geographic and climatic range of species. Future growth trends were predicted con-
sidering state-of-the-art climate scenarios. The validated models indicate growth declines
across large region of the distribution in recent decades, and project severe future growth
declines ranging from 20% to more than 50% by 2090, depending on the region and
climate change scenario (i.e. CMIP6 SSP1-2.6 and SSP5-8.5). Forecasted forest productivity
losses are most striking towards the southern distribution limit of Fagus sylvatica, in regions
where persisting atmospheric high-pressure systems are expected to increase drought
severity. The projected 21st century growth changes across Europe indicate serious ecological
and economic consequences that require immediate forest adaptation. OPEN
A full list of author afliations appears at the end of the paper.
COMMUNICATIONS BIOLOGY | (2022) 5:163 | | 1
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Global environmental change is affecting ecosystems in
many regions around the world. Forests are key terrestrial
ecosystems where evidence increasingly points towards
cascading impacts related to anthropogenic-induced climate
change13, including far-reaching consequences for the water and
carbon (C) cycles, and services to society4. Evolving questions
related with those impacts can be best addressed through large-scale
analyses, encompassing the full distribution range of key species3.
There is a long tradition of forest cover prediction research
focus on understanding the links between climate change and
forest presence/abundance5,6. Less knowledge is available on
ecologically-based predictions of species growth performance.
Considering that the stem represents ~70% of the trees biomass7,
secondary growth can be considered a reasonable proxy of total C
sequestration7, and can be effectively used as an indicator of tree
health and performance8.
Dendroecological analyses typically present local data and have
provided valuable regional insight into growth responses to local
habitat conditions and climate change2,9. Despite recent advances
in tree-ring research9, spatio-temporal studies of actual and pre-
dicted growth are uncommon, particularly at scales incorporating
speciesgeographic and climatic distributions10. The tree-ring
community has developed international dendrochronological
databanks, yet these are typically biased or limited for certain taxa,
biomes and trailing-edge populations1113, hindering their value
for ecologically-focused application. If such challenges can be
overcome, the large spatial scale represented by tree-ring net-
works, their annual resolution and the potential for multi-decade
assessment of growth changes present a unique opportunity to
unravel spatial patterns and drivers of recent growth, and predict
future growth dynamics based on climate change scenarios. Such
information would be crucial to estimate species resilience to
warmer and potentially drier future conditions.
Successful upscaling of tree-ring data, however, requires dense
networks covering the full range of bioclimatic and ecological
conditions of the study species. To enable this advance, we
established a dense and species-specic network supportive of
comparative ecological analyses, covering the entire ecological
spectrum of Fagus sylvatica L. (hereafter beech), including over
780,000 ring width measurements from 5800 trees and 324 sam-
pling sites across Europe (Fig. 1).
Beech is one of the most important forest species in Europe
from an ecological (e.g. habitat, biodiversity) and socio-economical
(e.g. timber harvest, recreation) perspective14. Beech played a
dominant role in postglacial reforestation, rapidly spreading from
its Mediterranean refuges to the central and northern regions of the
continent15. Currently, in the face of rapid climate change, beech
may be endangered in its geographical and ecological range16.
However, the speciesresilience to predicted changes and its eco-
logical plasticity across the distribution range are not well
Using this network, we analyse past growth rates of beech and
use this information to project growth variability considering
different climate change scenarios (i.e, representative Shared
Socioeconomic Pathways scenarios of CMIP6 (Coupled Model
Intercomparison Project)) to disentangle 21st century patterns of
the speciesperformance at continental scales. We perform a
comparative analysis among regions in Europe and map forest
growth considering local environmental stresses and dis-
turbances. A generalised linear mixed-effects model (GLMM) is
developed to model tree growth and support spatio-temporal
comparisons across the speciesdistribution range, identifying
regions where growth has increased or declined in recent decades.
The model is validated and used to predict radial growth during
three distinct periods until the late 21st century and the results
discussed considering likely climate change scenarios.
Our study provides evidences of striking changes in growth
patterns of this species during the studied period, especially in the
southern areas. The models showed that growth is being recently
limited due to climate and modulated by site-prevailing condi-
tions. In this sense, forecasted reductions in precipitation and
temperature increments would lead to an overall decrease in tree
productivity, most notably if both conditions occur at the same
time. Interestingly, tree growth could be signicantly enhanced at
high latitudes in the future, even under a worst-case climate
change scenario.
Model development and performance. Among the tested mixed-
effects models, the full model containing all considered and sig-
nicant variables and interactions was the most accurate to pre-
dict the speciesgrowth across Europe, as shown by the Akaike
Information Criterion (AIC) scores (Table 1). AIC measures
the relative goodness of t of a given model; the lower its value,
the more accurate the model is.. Indeed, 86% of growth variability
Fig. 1 Spatial and climatic range of beech sites. a Geographical distribution of the 324 study sites (black dots) in the natural distribution range of European
beech (green area based on the EUFORGEN map65; see Supplementary Data 2 for details). bClimatic envelope of European beech sampling sites,
considering annual temperature and precipitation. Sites are labelled according to the environmental zones detailed in Metzger et al.69.
2COMMUNICATIONS BIOLOGY | (2022) 5:163 | |
Content courtesy of Springer Nature, terms of use apply. Rights reserved
was explained by the model (Supplementary Fig. 1). We modelled
annual basal area increment (BAI) for 324 beech sites across
Europe considering (i) prevailing moisture/aridity conditions, (ii)
elevation and latitude to estimate radiation and photoperiod, and
(iii) seasonally varying climate conditions including precipitation
totals and temperatures from previous-year summer to current-
year autmn (relative to the year of tree-ring formation). The
GLMM included a total of 21 variables organised in three,
interacting variable groups. This resulted in a total of 66 variable
interactions that signicantly contributed to the growth model
(Supplementary Data 1). When tting the GLMM, estimated
previous-year BAI was considered as random factors to account
for size dependency of growth trends.
Application of the GLMM demonstrated that the interaction of
geographical variables as latitud or altitud with the aridity index
(AI) were signicant to explain beech growth variability (i.e. the
effects of e or latitude were different between trees growing in
xeric and mesic climates). Precipitation correlated positively with
tree growth, whereas maximum and minimum temperatures
showed variable effects and depended on the season. Seasonal
temperatures effects were stronger in explaining growth varia-
bility across the distribution range than precipitation.
Contributions of variable interactions to model beech growth
were relevant. Regardless of the specic weight and signicance of
each seasonal climatic variable, our results show that most
sensitivities to annually varying climate are modulated by mean
aridity and the geographic components altitude and latitude. The
nal model was able to reproduce tree-ring growth across Europe
and covering the entire species distribution for every year for the
period 19552016 (Supplementary Fig. 1).
Past regional growth changes. To compare beech forest perfor-
mance over past decades, mean growth rates of two consecutive
31-year periods were computed for a population-wide average
beech tree with a xed basal area of 86059.03 (1/10,000 mm2) (i.e.
the average basal area of the entire data set, which is equivalent to
a 80 years old tree) (Fig. 2). This multidecadal aggregation was
chosen as it represents an unprecedented increase in tempera-
tures from 19551985 to 19862016 exceeding 1 °C in many
regions in Europe17. Indeed, the most recent period is the
warmest 31-year period in Europe over the past 500 years, and is
up to 0.45 °C warmer than the second warmest 31-year period,
which occurred from 1750 to 177918.
Our results reveal substantial spatio-temporal differences in
beech growth over the past six decades across Europe (Fig. 3).
Tree growth rate was two to three times higher at low altitudes in
NW and central Europe including coastal sites in Belgium,
Netherlands, Denmark and the British Isles, compared in the
southern distribution limit of beech. Lower tree growth is also
modelled at higher altitudes in central Europe, the Alps, and
along the Carpathians. Growth was lowest at the northernmost
and south-western edge of the speciesdistribution in Sweden and
Spain, respectively, as well as in Italy and south-eastern Europe.
The most recent period showed a similar geographical pattern in
tree growth, compared to 19551985. However, regions of high
growth are overall less extensive and regions of reduced growth
overall larger, and these tendencies are superimposed on a general
decrease in growth magnitude.
The spatial representation of growth differences between 1955-
1985 and 19862016 reveals a notable decline in growth across
most of the area covered by European beech (Fig. 3). The strength
of this decline varies across Europe, being higher at low latitudes
and lower towards north, thereby revealing a distinct latitudinal
gradient of forest growth decline. The sharpest contrast was
recorded between northern areas including Sweden and Norway,
where modelled growth increase up to 20% between the two
periods, and south Europe, where severe growth declines of up to
20% were modelled.
21st century growth responses to climate change scenarios. The
GLMM yields varying BAI trends under the projected climate
change scenarios (Fig. 4). Even under the relatively optimistic
SSP1-2.6 scenario, growth changes across geographic gradients
remain greater in magnitude than the observed changes in growth
between the two historical periods. Growth reductions up to 30%
are projected in southern Europe during the 2020-250, compared
to a baseline of 19862016 (Fig. 4a). This decline increases
northward to reductions of ~10% and then zero change prevailing
in central European sites. On the other hand, growth increases of
~25% are projected in mountainous environments across central
Europe, and ~35% increases are expected for southern Scandi-
navia. Patterns from 20402070 and 20602090 (Fig. 4b, c) are
similar, except for more accentuated growth reductions in
southern Europe including the Balkans, and more polarised
patterns (e.g. in the Apennines, Greece, Romania and Spain
versus the Alps, Sweden and Denmark) towards the end of the
21st century.
The more realistic SSP5-8.5 scenario leads to dramatic decreases
in beech productivity over vast parts of Europe (Fig. 4df). From
2020 to 2050 growth, decreases of 2030% are expected to affects
most forests in central Europe, even including some elevated sites in
northeast France and southern Germany. In southern Europe,
growth reductions may exceed 50%, particularly during the period
20402070. On the other hand, north of 55°N and in mountainous
regions of Central Europe, growth trends are positive. These spatially
varying trends continue throughout the 20402070 and 20602090
periods, though at much accelerated rates. From 2040 to 2070, the
general southeast-to-northwest pattern, modulated by altitude, is
Table 1 Modelsvalidation.
AI Cli Geo RE AIC ΔAIC Chisq Df Pr
Null model –– 642591.5 25404.4 NA NA NA
–– 642509.6 25322.5 83.9 1 5.32E-20
–– ●●642417.9 25230.8 95.8 2 1.61E-21
●●642348.3 25161.2 77.6 4 5.66E-16
624421.0 7233.9 17949 11 0
●● 621819.4 4632.3 2639.6 19 0
●● 618820.9 1633.9 3038.4 20 0
Full model ●● 617187.1 0.0 1647.9 7 0
Each row represents a single model and each colored column represents the inclusion of each group of predictor variables (red, moisture conditions (AI), blue, seasonal climatic temperature and
precipitation variables (Cli), yellow, geographical variables (Geo) and green, random effects (RE)). For each model, Akaike Information Criterion value and difference (AIC, ΔAIC), χ2test value and
degrees of freedom (Df) of the χ2test and p (Chisq, Pr > Chisq)) are shown.
COMMUNICATIONS BIOLOGY | (2022) 5:163 | | 3
Content courtesy of Springer Nature, terms of use apply. Rights reserved
pronounced, including maximum growth reductions >50% in
southern Europe. The dramatic changes modelled from 2060 to
2090 considering SSP5-8.5 should be interpreted with caution, as the
altered climatic conditions in some regions exceed the applicability
domain of the GLMM (Supplementary Fig. 2).
Our study provides evidence of striking changes in growth pat-
terns of a European key tree species over the past 60 years and
upcoming 80 years. Over recent decades, growth declines are
particularly severe towards the southern distribution limits in
Europe, and these general trends will continue as the climate
continues to warm and become drier. GLMM models demon-
strate how spatial differences in growth are predominantly
explained by differences in temperature and water availability, all
modulated by site-prevailing climate conditions. Consequently,
reductions in precipitation or increases in temperature will lead to
an overall decrease in tree productivity, most notably if both
conditions occur simultaneously. Importantly, our results help to
reconcile previous results, which had failed to provide a con-
sistent picture of growth trends in beech across Europe, parti-
cularly in southern Europe where predicted declines were not
consistently reported in site-based analyses. Here we show that
when age/size effects are accounted for, growth declines in beech
are observed across southern Europe, particularly at lower ele-
vation. Furthermore, our results generalise recent reports of
growth declines extending into central Europe. In this sense, our
study reconciles differences across studies and provides a com-
prehensive approach revealing a persistent decrease in beech tree
productivity and C sequestration since the 1980s in all but the
most northern of the species distribution.
Adaptive management is of major importance for preserving
forest viability and mitigating harmful effects of climate change.
To invoke such management policies, we need to assess species-
specic climatic effects at varying spatio-temporal scales19,20.In
this sense, empirical modelling has proven useful to forest man-
agers to anticipate climate impacts on future forest growth,
supporting, for example, species selection in case of tree planta-
tions, or planning assisted migrations21,22. Therefore, den-
droecology combined with modelling is a powerful tool to
evaluate the environmental imprints on mid to long-term forest
dynamics, and opens the possibility to estimate tree productivity,
and associated functioning under projected climate and site
conditions10,23. The GLMM model applied here also addresses
possible sampling biases that commonly apply in eld-based
applications (e.g., the big-tree selection bias24), as it takes into
account and minimise the effects of size and age in statistical
analysis by adding tree size as random factor. Potential biases can
occur, however, if sample sizes are small and if age cohorts were
equal across sites24.
The climatic and geographical variables included in models for
continent-scale growth predictions must be ecologically mean-
ingful and accessible. Although other variables affect growth
variability of beech, such as soil type, nutrient presence, masting
events, competition, and insect infestation2531, most of these are
difcult to predict. Evaluating their impact on growth is also
challenging in spatial modelling, often because of limited spatial
resolution, and may depend directly or be correlated with other
variables. For example, photoperiod determines the seasonality of
<0.4 0.6 0.9 1.1 1.4 1.6 1.9 2.1 2.4 2.7 2.9 >3.2
Fig. 2 Spatial patterns of beech growth during the last decades. Mean estimates of BAI (in mm2) from 19551985 (a) and 19862016 (b), calculated for a
theoretical tree derived from a 324-site chronology network.
<−50 −30 −10 10 30 >50
Fig. 3 The spatial pattern of beech growth changes across Europe. Tree
growth changes are expressed in percent BAI change from 1986 to 2016
relative to the 19551985 period mean.
4COMMUNICATIONS BIOLOGY | (2022) 5:163 | |
Content courtesy of Springer Nature, terms of use apply. Rights reserved
processes in trees, the length of xylogenesis, and therefore the
amount of growth32, yet it is closely correlated with latitude. The
interaction among variables must be considered given the varia-
bility of climate sensitivity of a species across environmental,
altitudinal and latitudinal gradients3336.
The GLMM growth model applied here displays strong geo-
graphical variance and reveals the existence of a regional opti-
mum for beech growth in mountainous areas of central Europe,
under the current climate. The spatial variability of beech growth
across Europe follows an apparent combination of N-S and NW-
SE gradients, combined with an altitudinal gradient. In this sense,
beech is more productive (i.e. produce wider rings) at lower
elevations, particularly in NW Europe. Indeed, beech phenology,
as well as rates and timing of xylogenesis, are affected by
altitude3739, which in turn control tree growth. The evaluation of
the impact of the warmer and drier conditions over recent dec-
ades across the speciesdistribution requires consideration of
local differences from regional climate and site conditions. The
observed N-S and NW-SE growth gradients across Europe may
be affected by prevailing atmospheric circulation patterns, con-
tinentality and photoperiod optimum, but further research is
needed to disentangle the drivers of growth variability across
these large scales, geographic gradients.
Subsequent to a tree growth increase during the rst part of
the last century in Europe40, recent studies reported growth
decreases in beech41,42. This decrease was attributed to increas-
ing temperatures, the impact of extreme climatic events and
long-term changes of environmental conditions. Our ndings of
negative beech BAI trends over past decades are inconsistent
with other studies reporting growth increases31,43,44 and spatially
varying growth trends depending on altitude45. However, the
different ndings are mainly due to varying approaches when
dealing with age effects, and whether the results are derived from
repeated diameter measurements and detrended chronologies
instead of raw tree growth increments. Despite methodological
differences, local case studies are relevant as they may account
local trends, which could help to identify research gaps and
further research46. In this sense, our study reconciles differences
across studies and provides a comprehensive approach revealing
a persistent decrease in beech tree productivity and C seques-
tration since the 1980s. Although beech has been reported to be
drought sensitive throughout Europe34,42,47, our simulations
suggest that temperatures may start to gain prominence as a
limiting factor across a large portion of the speciesdistribution
area. Our results support those of Mette et al. (2013)48 showing
that beech growth in central Europe is currently not only limited
by precipitation. The observed and projected temperature
increases foster atmospheric pressure decits, constrain stomata
closure, amplify tree water demand and increase risks of
hydraulic failure41. Drought induced defoliation, extension of
canopy duration and associated limitations of metabolic reserves
(where respiration may exceed photosynthesis49), and higher
turnover rates of ne roots likely contribute to this temperature
sensitivity50. Thus, even though beech is a late-successional
<−50 −40 −30 −20 −10 10 20 30 40 >500
Fig. 4 Relative changes in tree growth. Changes are projected under SSP1-2.6 (ac) and SSP5-8.5 (df) CMIP6 climate scenarios for different periods:
20202050 (a,d), 20402070 (b,e), and 20602090 (c,f) In this panel, BAI changes were expressed in percentage of change compared to the
19862016 period.
COMMUNICATIONS BIOLOGY | (2022) 5:163 | | 5
Content courtesy of Springer Nature, terms of use apply. Rights reserved
species that is considered competitively superior to many other
European tree species51, including broad-leaved Quercus,Acer,
Tilia,Fraxinus and Carpinus42, it is also prone to warming-
induced growth declines.
Our results also demonstrate that the effects of warming
temperatures, especially beyond 1.5 °C, cannot be compensated
without large increases in precipitation (as in the SSP1-2.6 sce-
nario, see Supplementary Fig. 3), except at very high altitudes in
central Europe. Similar conclusions were drawn by Walentowski
et al.20 demonstrating that temperature rises must be compen-
sated by increases in precipitation to maintain tree vitality. Severe
growth reductions are expected if the combination of summer
drought and hotter temperatures becomes prevailing, as is fore-
casted in SSP5-8.5. The cumulative effect of hotter droughts52
might lead to amplied hydraulic failure and dieback of vast
forested areas. The highest vulnerability of beech sites to global
warming is observed at the southern edge of the speciesdis-
tribution range, as shown by Forzieri et al.3.
The projected increase of global mean surface temperature by
the end of the 21st century is expected to range from 1.5 to
5.5 °C (with respect to 19002000) depending on the Shared
Socioeconomic Pathways scenario. The projected changes in
precipitation will not be uniform, but include decreases in
southern Europe and increases in northern latitudes (north of
55°N)1. However, future climate is uncertain, particularly for
precipitation, as the CMIP6 archive might be subject to mul-
tiple sources of error. Our results are likely affected by addi-
tional uncertainties including the role of extreme weather
events (i.e. late spring frost, heat waves, res), soil composition
(i.e. nitrogen, phosphorous, potassium) and tree species com-
petition, all of which complicating species-specictreegrowth
Projected climate change will foster a progressive decrease of
beech growth. As beech is a dominant tree species across large
regions of Europes forests, this indicates an important reduction
in functioning as a carbon sink to mitigate atmospheric CO
increases. Furthermore, as beech is high importance both com-
mercially and environmentally, a long-lasting decrease in pro-
ductivity may be critical at multiple levels. We recommend to
forest managers to consider these results in long-term silviculture
Analysing the drivers of growth across an unprecedented network
of beech sites covering Europe, we report a pervasive growth rate
decline from 1955 to 2016. This decline is widespread in Europe,
except for sites located towards the northern distribution range in
Denmark, Norway and Sweden and at higher elevation in
mountain regions. Recorded growth variations range from
+1020% in the north, to 20% in the south of Europe. By
employing a GLMM, we show that future increases in global
temperature, particularly those exceeding 1.5 °C, lead to a wide-
spread decrease 20 to 40% in beech growth, a situation that
could be further amplied to 50% if drought conditions prevail.
These signicant growth trends point towards increased forest
mortality, as declining growth has been reported as a precursor of
tree die-off2. These ndings challenge recent predictions of
increasing terrestrial carbon stocks under climate change
scenarios53, as the strength of beech forests as a carbon sink will
Tree-ring network. We compiled a network of tree-ring chronologies from closed-
canopy and mature stands dominated by European beech. The databank comprises
324 sites, with ~5800 trees and ~780,000 tree-ring measurements. Geographically,
the networks extends from 5.8 to 28.4°E and 38.8 to 58.5°N, and covers the entire
geographic distribution range of Fagus sylvatica in Europe. Sites also cover the full
climatic range of the species, with annual precipitation and temperature ranging
from 500 to 2000 mm and 3.8 to 13.5 °C, respectively (Fig. 1and Supplementary
Data 2). The selected plots are mostly undisturbed sites, located between 1 and
1900 m a.s.l, covering the full elevation gradient of the species, including beech
treeline sites.
Increment cores were dried and sanded according to standard procedures54 to
enhance the visibility of tree-ring boundaries. Tree-ring widths were measured to
the nearest 0.01 mm and each tree-ring series were crossdated using COFECHA or
CooRecorder software. Classical detrending methods to remove age-related trends
were not applied to support comparisons between different periods. We instead
converted the tree-ring width data into annual basal area increments (BAI), in cm2
per year, as this procedure accounts for the geometrical constraint of adding a
cross-sectional area of wood to a stem of an increasing radius. The BAI series of
each tree were obtained based on the measured diameter at breast height when
sampled, and computed using the bai.out function of the R package dplR (version
1.7.2). The mean BAI of dened periods can be compared over time, as it is not
affected by biological trends55,56.
Climate variables. CHELSAcruts57 was used to extract climate data from gridded
networks. Monthly precipitation and maximum and minimum temperatures were
downloaded and combined to seasonal means covering the period from 1901 to
2016. Prevailing moisture conditions were dened by applying the De Martonne
aridity index58 (AI) as previous studies showed that site-specic moisture condi-
tions modulate the climate sensitivity of trees59. AI was calculated for European
grid cells from 1950 to 2016 using (Eq. 1):
10 þT
where Pis the annual precipitation sum (in mm) and T(in °C) the annual mean air
temperature. The climate types dened by De Martonne range from arid (values
from 0 to 10), semi-arid (1020), Mediterranean (2024), semi-humid (2428),
humid (2835), very humid (3555) to extremely humid (>55).
Predictive growth model. Generalised linear mixed-effects models (GLMM) were
used to estimate the joint effects of climate and geographical variables on tree
growth. In the statistical computing environment R, GLMMs were tted by
maximum likelihood (Adaptive Gauss-Hermite Quadrature) using the R package
lme4 (version 1.121). These models are particularly useful as they combine the
properties of linear mixed models and generalised linear models, allowing the
inclusion of random effects and the analysis of nonnormal data60,61. Mixed models
are suited for studies over time inuenced both by factors that can be assumed to
be similar for many sites (e.g. the effect of climate) and by characteristics that
substantially vary from site to site (e.g. populations)62. In addition, mixed models
explicitly account for the correlations between repeated measurements within each
site. In fact, collinearity among predictor variables can cause problems in models
variables interpretation because those predictors explain some of the same variance
in the response variable, and their effects cannot be estimated independently63.
Since the main objective of model application is the interpretation of the output
(i.e. growth models), nor the inuence of the variables, we included variables based
on AIC values (Table 1).
The model was based on the period 19502016 due to the common availability
of climate and dendrochronological data. We then tted a single GLMM to predict
annual BAI of a tree jin a site iin a year tas a function of prevailing climate,
latitude, altitude, temperature and precipitation (season k), assuming a gamma
distribution of the response variable (Eq. 2):
logðBAIi;j;tÞ¼βþlogðAIiÞþfðLATiÞþfðALTiÞþfðTmaxi;t;kÞþfðTmini;t;kÞþ logðPPi;t;kÞ
þlogðAIiÞ´fðLATiÞþ logðAIiÞ´fðALTiÞþfðLATiÞ´fðALTiÞþ logðAIiÞ
Where βis the intercept, fare smoothing functions and log are logarithms applied
to the variables. All variables were standardised before model constructions to
guarantee a compensated weight and avoid effects related with the range of
variables. The elements included in the model as independent variables were AI
(Aridity Index), LAT (latitude), ALT (altitude), Tmax (maximum seasonal
temperatures from previous to current summer), Tmin (minimum seasonal
temperatures from previous to current summer), and PP (total seasonal
precipitation from previous to current summer), as well as the statistically
signicant interaction between variables. To account for the possible inuence of
age effects (i.e. trends), and particularities of individual trees, we additionally
included a random slope of previous year basal area (BA) of each ring and tree
(Code). Therefore, BA and tree code were included as random factors in the model
to avoid individual inuences on our results. The model was evaluated considering
the dominant paradigm of GLMM validation64, which involves the generation of a
null hypothesis (i.e. null model) to test the selected model through a chi-squared
test (P< 0.05). We thereby evaluated the accuracy of the model (full model) using a
likelihood ratio test by comparing the model with reduced models where
explanatory variables of interest were omitted, before nally a comparison with a
6COMMUNICATIONS BIOLOGY | (2022) 5:163 | |
Content courtesy of Springer Nature, terms of use apply. Rights reserved
nullmodel was performed, where only the intercept term and random effects
were included (Table 1).
Later, the model was applied to each cell of a climatic grid covering the entire
species range, based on EUFORGEN distribution maps65. Annual BAI values from
1950 to 2016 were calculated to compare mean growth rates over Europe, i.e. mean
BAI values of the periods from 1955 to 1985 and 1986 to 2016. The turning point
in the mid-1980s was selected as this represents the onset of an ongoing period of
strong warming17,18. Percentage growth changes31 were calculated for each grid
point by comparing mean growth rates with pre-dened periods. All maps were
produced using R package maps (version 3.3.0).
Simulated growth considering climate change scenarios. We used CMIP6
multi-model ensemble means representative of various earth system models for
minimum and maximum temperature (21 models) as well as precipitation (26
models) to project future tree growth, representative of an optimistic (SSP1-2.6)
and a pessimistic (SSP5-8.5) scenario66. To do so, we for each scenario-model
combination computed the difference of variable-specic climatologies between
historic simulations (period 19852014) and future simulations representative of
three distinct periods (20202050, 20402070, 20602090) and averaged those over
all models for each scenario to obtain ensemble means. These ensemble mean
delta-values were then added to the corresponding CHELSAcruts67 climate vari-
ables, to obtain climate data representing simulated climatologies of the corre-
sponding scenario and period.. Therefore, all seasonal climatic variables of the
model were updated to future projected predictions (depending on the SSP),
meanwhile geographic variables and AI index remained stable. Future beech
growth was forecasted by applying the model to projected local climatic conditions,
resulting in six growth variation scenarios.
Given the range of the climate scenarios, we calculated applicability domains
(AD)59,68 of the model for each period (Supplementary Fig. 2). When the range of
future climate variability exceeded the range of past conditions from 1901 to 2016,
the predictive performance of the model becomes uncertain. Therefore, for
different combinations of seasonal climate conditions located within the AD,
growth estimates are expected to be as reliable as those in the training sample.
However, for those pixels outside the AD, the reliability of estimates declines, and
the predicted growth patterns should be interpreted with caution.
Reporting summary. Further information on research design is available in the Nature
Research Reporting Summary linked to this article.
Data availability
The data that support the ndings of this study are available from the corresponding
author and co-authors upon reasonable request. All relevant data for the gures are
included in the supplementary information les.
Received: 16 August 2021; Accepted: 2 February 2022;
1. IPCC. IPCC Fifth Assessment Report (AR5). 1012 (IPCC, 2014).
2. Cailleret, M. et al. A synthesis of radial growth patterns preceding tree
mortality. Glob. Chang. Biol. 23, 16751690 (2017).
3. Forzieri, G. et al. Emergent vulnerability to climate-driven disturbances in
European forests. Nat. Commun. 12,112 (2021).
4. Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate
benets of forests. Science (2008).
5. Buras, A. & Menzel, A. Projecting tree species composition changes of
European forests for 20612090 under RCP 4.5 and RCP 8.5 scenarios. Front.
Plant Sci. 9,113 (2019).
6. van der Maaten, E. et al. Species distribution models predict temporal but not
spatial variation in forest growth. Ecol. Evol. 7, 25852594 (2017).
7. Lebaube, S., Le Goff, N. L., Ottorini, J. M. & Granier, A. Carbon balance and
tree growth in a Fagus sylvatica stand. Ann. Sci. 57,4961 (2000).
8. Dobbertin, M. Tree growth as indicator of tree vitality and of tree reaction to
environmental stress: a review. Eur. J. For. Res. 124, 319333 (2005).
9. Büntgen, U. Re-thinking the boundaries of dendrochronology.
Dendrochronologia 53,14 (2019).
10. Klesse, S. et al. Continental-scale tree-ring-based projection of Douglas-r
growth: Testing the limits of space-for-time substitution. Glob. Chang. Biol.
26, 51465163 (2020).
11. Zhao, S. et al. The International Tree-Ring Data Bank (ITRDB) revisited: data
availability and global ecological representativity. J. Biogeogr. 46, 355368
12. Babst, F. et al. When tree rings go global: challenges and opportunities for
retro- and prospective insight. Quat. Sci. Rev. 197,120 (2018).
13. Klesse, S. et al. Sampling bias overestimates climate change impacts on forest
growth in the southwestern United States. Nat. Commun. 9,19 (2018).
14. Yousefpour, R. et al. Realizing mitigation efciency of European commercial
forests by climate smart forestry. Sci. Rep. 8,111 (2018).
15. Giesecke, T., Hickler, T., Kunkel, T., Sykes, M. T. & Bradshaw, R. H. W.
Towards an understanding of the Holocene distribution of Fagus sylvatica L.J.
Biogeogr. 34, 118131 (2007).
16. Fang, J. & Lechowicz, M. J. Climatic limits for the present distribution of
beech (Fagus L.) species in the world. J. Biogeogr. 33, 18041819 (2006).
17. Luterbacher, J., Dietrich, D., Xoplaki, E., Grosjean, M. & Wanner, H.
European seasonal and annual temperature variability, trends, and extremes
since 1500. Science 303, 14991503 (2004).
18. Luterbacher, J. et al. European summer temperatures since Roman times.
Environ. Res. Lett.11, 24001 (2016).
19. Nabuurs, G. J. et al. By 2050 the mitigation effects of EU forests could nearly
double through climate smart forestry. Forests 8,114 (2017).
20. Walentowski, H. et al. Assessing future suitability of tree species under climate
change by multiple methods: a case study in southern Germany. Ann. Res. 60,
101126 (2017).
21. Mäkelä, A. et al. Process-based models for forest ecosystem management:
current state of the art and challenges for practical implementation. Tree
Physiol. 20, 289298 (2000).
22. Leech, S. M., Almuedo, P. L. & Neill, G. O. Assisted migration: adapting forest
management to a changing climate. BC J. Ecosyst. Manag. 12,1834 (2011).
23. Sass-Klaassen, U. G. W. et al. A tree-centered approach to assess impacts of
extreme climatic events on forests. Front. Plant Sci. 7, 1069 (2016).
24. Bowman, D. M. J. S., Brienen, R. J. W., Gloor, E., Phillips, O. L. & Prior, L. D.
Detecting trends in tree growth: not so simple. Trends Plant Sci. 18,1117
25. Hacket-Pain, A. J. et al. Climatically controlled reproduction drives
interannual growth variability in a temperate tree species. Ecol. Lett. 21,
18331844 (2018).
26. Dorji, Y., Annighöfer, P., Ammer, C. & Seidel, D. Response of beech (Fagus
sylvatica L.) trees to competition-new insights from using fractal analysis.
Remote Sens.11, 2656 (2019).
27. Petit-Cailleux, C. et al. Combining statistical and mechanistic models to
unravel the drivers of mortality within a rear-edge beech population. bioRxiv (2019).
28. Weigel, R., Gilles, J., Klisz, M., Manthey, M. & Kreyling, J. Forest understory
vegetation is more related to soil than to climate towards the cold distribution
margin of European beech. J. Veg. Sci. 30, 746755 (2019).
29. Etzold, S. et al. Nitrogen deposition is the most important environmental
driver of growth of pure, even-aged and managed European forests. Forest
Ecol. Manag. 458, 117762 (2020).
30. Martínez-Sancho, E. et al. The GenTree dendroecological collection, tree-ring
and wood density data from seven tree species across Europe. Sci. Data 7,17
31. Hartl-Meier, C., Dittmar, C., Zang, C. & Rothe, A. Mountain forest growth
response to climate change in the Northern Limestone Alps. Trees 28,
819829 (2014).
32. Way, D. A. & Montgomery, R. A. Photoperiod constraints on tree phenology,
performance and migration in a warming world. Plant Cell Environ. 38,
17251736 (2015).
33. Martínez del Castillo, E. et al. Spatial patterns of climate growth
relationships across species distribution as a forest management tool in
Moncayo Natural Park (Spain). Eur. J. Res. 138, 299 (2019).
34. Hacket-Pain, A. J., Cavin, L., Friend, A. D. & Jump, A. S. Consistent limitation
of growth by high temperature and low precipitation from range core to
southern edge of European beech indicates widespread vulnerability to
changing climate. Eur. J. Res. 135, 897909 (2016).
35. van der Maaten, E. Climate sensitivity of radial growth in European beech
(Fagus sylvatica L.) at different aspects in southwestern Germany. Trees 26,
777788 (2012).
36. Decuyper, M. et al. Spatio-temporal assessment of beech growth in relation to
climate extremes in Slovenia an integrated approach using remote sensing
and tree-ring data. Agric. Meteorol. 287, 107925 (2020).
37. Kraus, C., Zang, C. & Menzel, A. Elevational response in leaf and xylem
phenology reveals different prolongation of growing period of common beech
and Norway spruce under warming conditions in the Bavarian Alps. Eur. J.
Res. 135, 10111023 (2016).
38. Martínez del Castillo, E. et al. Living on the edge: contrasted wood-formation
dynamics in Fagus sylvatica and Pinus sylvestris under mediterranean
conditions. Front. Plant Sci. 7, 370 (2016).
39. Čufar, K. et al. Temporal shifts in leaf phenology of beech (Fagus sylvatica)
depend on elevation. Trees 26, 10911100 (2012).
40. Bontemps, J. D., Hervé, J. C. & Dhôte, J. F. Dominant radial and height growth
reveal comparable historical variations for common beech in north-eastern
France. Forest Ecol. Manag. 259, 14551463 (2010).
COMMUNICATIONS BIOLOGY | (2022) 5:163 | | 7
Content courtesy of Springer Nature, terms of use apply. Rights reserved
41. Latte, N., Lebourgeois, F. & Claessens, H. Increased tree-growth
synchronization of beech (Fagus sylvatica L.) in response to climate change in
northwestern Europe. Dendrochronologia 33,6977 (2015).
42. Zimmermann, J., Hauck, M., Dulamsuren, C. & Leuschner, C. Climate
warming-related growth decline affects Fagus sylvatica, but not other broad-
leaved tree species in central european mixed forests. Ecosystems 18, 560572
43. Tegel, W. et al. A recent growth increase of European beech (Fagus sylvatica
L.) at its Mediterranean distribution limit contradicts drought stress. Eur. J.
Res. 133,6171 (2014).
44. Hacket-Pain, A. J. & Friend, A. D. Increased growth and reduced summer
drought limitation at the southern limit of Fagus sylvatica L.,
despite regionally warmer and drier conditions. Dendrochronologia 44,2230
45. Dulamsuren, C., Hauck, M., Kopp, G., Ruff, M. & Leuschner, C. European
beech responds to climate change with growth decline at lower, and growth
increase at higher elevations in the center of its distribution range (SW
Germany). Trees 31, 673686 (2017).
46. Spiecker, H., Mielikäinen, K., Köhl, M. & Skovsgaard, J. P. Growth trends in
European forests: studies from 12 countries.European Forest Institute Research
Report (1996).
47. Cavin, L. & Jump, A. S. Highest drought sensitivity and lowest
resistance to growth suppression are found in the range core of the
tree Fagus sylvatica L. not the equatorial range edge. Glob. Chang. Biol. 23,
118 (2016).
48. Mette, T. et al. Climatic turning point for beech and oak under climate change
in Central Europe. Ecosphere 4,119 (2013).
49. Michelot, A., Simard, S., Rathgeber, C. B. K., Dufrêne, E. & Damesin, C.
Comparing the intra-annual wood formation of three European species (Fagus
sylvatica, Quercus petraea and Pinus sylvestris) as related to leaf phenology
and non-structural carbohydrate dynamics. Tree Physiol. 32, 10331045
50. Meier, I. C. & Leuschner, C. Belowground drought response of European
beech: Fine root biomass and carbon partitioning in 14 mature stands across a
precipitation gradient. Glob. Chang. Biol. 14, 20812095 (2008).
51. Leuschner, C. & Ellenberg, H. Ecology of Central European Forests.Vegetation
Ecology of Central Europe. Vol. I (Springer, 2017).
52. Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of
global vulnerability to tree mortality and forest die-off from hotter drought in
the Anthropocene. Ecosphere.6,1
55 (2015).
53. Pechanec, V., Purkyt, J., Benc, A., Nwaogu, C. & Lenka, Š. Ecological
Informatics Modelling of the carbon sequestration and its prediction under
climate change. (2017).
54. Speer, J. H. Fundamentals of Tree-Ring Research (University of Arizona Press,
55. Biondi, F. & Qeadan, F. A theory-driven approach to tree-ring
standardization: dening the biological trend from expected basal area
increment. Tree-Ring Res. 64,8196 (2008).
56. Biondi, F. & Qeadan, F. Removing the tree-ring width biological trend using
expected basal area increment. in USDA Forest Service RMRS-P-55 124131
57. Karger, D. N. et al. Climatologies at high resolution for the earths land surface
areas. Sci. Data 4,120 (2017).
58. De Martonne, E. Une nouvelle fonction climatologique: Lindice daridité. La
Meteorol.2, 449458 (1926).
59. Martínez del Castillo, E., Longares, L. A., Serrano-Notivoli, R. & de Luis, M.
Modeling tree-growth: assessing climate suitability of temperate forests
growing in Moncayo Natural Park (Spain). Ecol. Manag. 435, 128137 (2019).
60. Bolker, B. M. et al. Generalized linear mixed models: a practical guide for
ecology and evolution. Trends Ecol. Evol. 24, 127135 (2009).
61. Calcagno, V. & Mazancourt, C. De. glmulti: an R package for easy automated
model selection with (generalized) linear models. J. Stat. Softw. 34,129
62. Detry, M. A. & Ma, Y. Analyzing repeated measurements using mixed models.
JAMA J. Am. Med. Assoc. 315, 407 (2016).
63. Harrison, X. A. et al. A brief introduction to mixed effects modelling and
multi-model inference in ecology. PeerJ 2018,132 (2018).
64. Johnson, J. B. & Omland, K. S. Model selection in ecology and evolution.
Trends Ecol. Evol. 19, 101108 (2004).
65. Caudullo, G., Welk, E. & San-Miguel-Ayanz, J. Chorological maps for the
main European woody species. Data Brief 12, 662666 (2017).
66. Meinshausen, M. et al. The shared socio-economic pathway (SSP) greenhouse
gas concentrations and their extensions to 2500. Geosci. Model Dev. 13,
35713605 (2020).
67. Karger, D. N. & Zimmermann, N. E. CHELSAcruts - High Resolution
Temperature And Precipitation Timeseries For The 20th Century And Beyond. (2018).
68. Norinder, U., Rybacka, A. & Andersson, P. L. Conformal prediction to dene
applicability domain - a case study on predicting ER and AR binding. SAR
QSAR Environ. Res. 27, 303316 (2016).
69. Metzger, M. J., Bunce, R. G. H., Jongman, R. H. G., Mücher, C. A. & Watkins,
J. W. A climatic stratication of the environment of Europe. Glob. Ecol.
Biogeogr. 14, 549563 (2005).
EMdC was supported and nanced by the Alexander von Humboldt Foundation. CSZ
and AB acknowledge funding by the Bavarian Ministry of Science and Arts from the
Bavarian Climate Research Network (BayKliF); J.E. by the ERC advance project
Monostar (AdG 882727) and SustES project (CZ.02.1.01/0.0/0.0/16_019/0000797); C.H.
by the German Research Foundation (HA 8048/1-1); I.D.L. by Fundació La Caixa
through the Junior Leader Program (LCF/BQ/LR18/11640004); S.M. by European
Regional Development Fund (KK.; K.C., M.M., J.G., and P.P. by Slovenian
Research Agency ARRS, programs P4-0015 and P40107 and project J4-2541; B.S. by the
Ministry of Education and Science of the Republic of Serbia (Project 451-02-68/2020/14/
2000169); I.C.P. was supported by Romanian Ministry of Education and Research grant
CNCS-UEFISCDI, project number PN-III-P4-ID-PCE-2020-2696, within PNCDI III.
We thank the World Climate Research Programme and Earth System Grid Federation
for hosting and promoting CMIP6, and Wolfram Elling and Christoph Dittmar for beech
tree-ring data.
Author contributions
E.M.d.C. and M.d.L. conceived the study and conducted rst drafts and analyses. C.S.Z.,
A.H.-P., C.H., R.W., R.S.-N. and S.K. contributed critically to the drafts, conceived new
ideas and designed nal methodology. A.B. pre-processed and contributed the CMIP6
data. E.M.d.C. analysed the data, drafted and led the writing of the manuscript with
inputs from A.H.-P., J.E., I.D.-L., T.S., S.M., V.R.d.D. and A.J. All authors, i.e. E.M.d.C,
C.S.Z, A.B., A.H.-P., J.E., R.S.-N., C.H., R.W., S.K., V.R.d.D., T.S., I.D.-L., M.v.d.M.-T.,
E.v.d.M., A.J., S.M., B.-E.B., W.B., L.C., H.C., V.Č., K.Č., C.D., J.G., E.G.-P., P.J., M.K.,
J.K., N.L., C.L., L.A.L., A.M., M.M., R.M., L.M., P.N., A.M.P., I.C.P., P.P., A.R.-C., M.R.,
B.S., M.S., E.T., V.T., M.W., T.Z. and M.d.L. implemented eldwork, collected the tree-
ring data, actively contributed to the manuscript, and gave nal approval for its
Open Access funding enabled and organized by Projekt DEAL.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary material
available at
Correspondence and requests for materials should be addressed to Edurne Martinez del
Peer review information Communications Biology thanks Donald Falk and Mizanur
Rahman for their contribution to the peer review of this work. Primary Handling Editor:
Caitlin Karniski.
Reprints and permission information is available at
Publishers note Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional afliations.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons license, and indicate if changes were made. The images or other third party
material in this article are included in the articles Creative Commons license, unless
indicated otherwise in a credit line to the material. If material is not included in the
articles Creative Commons license and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this license, visit
© The Author(s) 2022
8COMMUNICATIONS BIOLOGY | (2022) 5:163 | |
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Department of Geography, Johannes Gutenberg University, Mainz, Germany.
Department of Forestry, University of Applied Sciences
Weihenstephan-Triesdorf, Triesdorf, Germany.
Land Surface-Atmosphere Interactions, Technical University Munich, Freising, Germany.
Department of Geography and Planning, School of Environmental Sciences, University of Liverpool, Liverpool, UK.
Global Change Research
Institute of the Czech Academy of Sciences (CzechGlobe), Brno, Czech Republic.
Department of Geography, Autonomous University of Madrid,
Madrid, Spain.
Nature Rings Environmental Research and Education, Mainz, Germany.
Plant Ecology, Albrecht-von-Haller-Institute for Plant
Sciences, University of Goettingen, Goettingen, Germany.
Forest Dynamics, Swiss Federal Research Institute for Forest, Snow and Landscape
WSL, Birmendorf, Switzerland.
School of Life Science and Engineering, Southwest University of Science and Technology, Mianyang, China.
Department of Crop and Forest Sciences and Joint Research Unit CTFC-AGROTECNIO CERCA Center, University of Lleida, Lleida, Spain.
Institute for Botany and Landscape Ecology, University Greifswald, Greifswald, Germany.
Systems and Natural Resources Department,
Universidad Politécnica de Madrid, Madrid, Spain.
Chair of Forest Growth and Woody Biomass Production, TU Dresden, Tharandt, Germany.
Biological and Environmental Sciences, Faculty of Natural Sciences, University of Stirling, Stirling, Scotland.
Faculty of Forestry and Wood
Technology, University of Zagreb, Zagreb, Croatia.
Institute of Forest Ecosystems, Thünen Institute, Eberswalde, Germany.
TERRA Teaching
and Research Centre (Forest Is Life), Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium.
Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences, Prague, Czech Republic.
Biotechnical Faculty, Department of Wood Science and Technology, University of
Ljubljana, Ljubljana, Slovenia.
Applied Vegetation Ecology, Faculty of Environment and Natural Resources, University of Freiburg,
Freiburg, Germany.
Slovenian Forestry Institute, Ljubljana, Slovenia.
Forest Resources Department, Centro de Investigación y Tecnología
Agroalimentaria de Aragón (CITA), Zaragoza, Spain.
University of Belgrade Faculty of Forestry, Belgrade, Serbia.
Department of Geography
and Regional Planning, University of Zaragoza, Zaragoza, Spain.
TUM School of Life Sciences/Ecoclimatology, Technical University of Munich,
Munich, Germany.
Department of Agriculture, Forestry and Food Sciences, University of Turin, Grugliasco, Italy.
Department of Earth and
Environmental Sciences, University of Pavia, Pavia, Italy.
National Institute for Research and Development in Forestry Marin Dracea,
Voluntari, Romania.
Faculty of Silviculture and Forest Engineering, University of Brasov, Brașov, Romania.
Departamento de Sistemas y
Recursos Naturales, Escuela Técnica Superior de Ingeniería de Montes, Forestal y del Medio Natural, Universidad Politécnica de Madrid,
Madrid, Spain.
Faculty of Forestry Sciences, Agricultural University of Tirana, Koder-Kamez, Albania.
Institute of Biodiversity and Ecosystem
Research, Bulgarian Academy of Sciences, Soa, Bulgaria. email:
COMMUNICATIONS BIOLOGY | (2022) 5:163 | | 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
... However, climate change puts pressure on the dominant position of beech in central and southern Europe (Allen et al., 2015;Reyer et al., 2013). Many studies highlight the drought sensitivity of beech (Brinkmann et al., 2019;Cavin and Jump, 2017;Köcher et al., 2009;Leuschner, 2020;Leuschner et al., 2019;Scharnweber et al., 2011;Scherrer et al., 2011) while other sources emphasize the resilience of beech or put this impact into perspective, making it highly dependent on the followed climate scenario and the specific stand characteristics (Dyderski et al., 2018;Leuschner, 2020;Martinez del Castillo et al., 2022). ...
... The precipitation data from April-May and July-August two years ago were also added as these can have an effect on mast behavior (Bajocco et al., 2021). All variables were standardized before model construction and BAI values were logtransformed (Martinez del Castillo et al., 2022). Tree ID and year were added as random effects. ...
... These findings are similar to a study on beech trees from the Meerdaal forest and Sonian forest by Vannoppen et al. (2018). However, most studies on beech in Western Europe find a growth decline in the last few decades Charru et al., 2017Charru et al., , 2010Dittmar et al., 2003;Kint et al., 2012;Latte et al., 2015;Martinez del Castillo et al., 2022;Vannoppen et al., 2018), largely attributed to the effects of climate change. The same is true for the southern range limit of beech like Spain and Italy, where climate conditions are sub-optimal (Jump et al., 2006;Peñuelas et al., 2008;Piovesan et al., 2008;Rozas et al., 2015;Rubio-Cuadrado et al., 2021;Serra-Maluquer et al., 2019). ...
Common beech (Fagus sylvatica) is one of the most important deciduous tree species in European forests. However, climate-change-induced drought may threaten its dominant position. The Sonian Forest close to Brussels (Belgium) is home to some of the largest beech trees in the world. This UNESCO world heritage site is famous for its high density of very large beech trees as a result of its climatic suitability, fertile soil conditions, and past management. Here we utilized tree-ring data from increment cores to investigate the growth of these old and monumental beech trees, evaluating their growth trends, response to past climate, and the effect of mast years on 39 living and 16 recently wind-thrown trees. Our analysis reveals that the sampled trees were generally sensitive to spring and summer droughts but recovered quickly after such an extreme climatic event. The growth trend of living trees has remained high and only shows a slight, statistically insignificant, decline over the past 50 years. Although the overall growth rate remains strong (BAI 50 cm2/year), the past five decades have shown strong inter-annual growth variations due to frequent and more intense droughts combined with an increased frequency of mast years. We also found notable differences in growth patterns between the living trees and those that had recently been wind-thrown. While there were no significant differences between living and wind-thrown trees in response to droughts, heatwaves, or mast years when examining year-to-year growth changes, the wind-thrown trees did exhibit considerably lower overall growth rates and a significant downward trend in growth (BAI -0.57 cm2/year). This difference in growth trends has been apparent since at least the 1980s. Overall, the findings of this study can provide valuable insights for understanding the long-term dynamics of lowland beech forests and their responses to climate change.
... Stand level growth data include mortality at the tree level, growth reactions on stress events, and growth decline of individual trees. Most dendrochronological or dendroecological studies do not scale up to absolute growth rates at the stand level 21,44 . Studies of individual tree growth or mortality hardly consider that surviving trees can partly buffer growth losses and mortality 45 . ...
Full-text available
Forests cover about one-third of Europe’s surface and their growth is essential for climate protection through carbon sequestration and many other economic, environmental, and sociocultural ecosystem services. However, reports on how climate change affects forest growth are contradictory, even for same regions. We used 415 unique long-term experiments including 642 plots across Europe covering seven tree species and surveys from 1878 to 2016, and showed that on average forest growth strongly accelerated since the earliest surveys. Based on a subset of 189 plots in Scots pine (the most widespread tree species in Europe) and high-resolution climate data, we identified clear large-regional differences; growth is strongly increasing in Northern Europe and decreasing in the Southwest. A less pronounced increase, which is probably not mainly driven by climate, prevails on large areas of Western, Central and Eastern Europe. The identified regional growth trends suggest adaptive management on regional level for achieving climate-smart forests.
... The negative effects of climate warming have been documented for species with the greatest ecological and economic importance in Europe, i.e. Fagus sylvatica L. (Martinez del Castillo et al., 2022), Picea abies (L.) H.Karst. (Bosela et al., 2021), and Quercus robur L. (Doležal et al., 2010). Therefore, assessing the potential forest tree habitat availability under a changing climate is urgent. ...
Alien tree species are considered both a threat to nature conservation and a base for forest management. We compiled species occurrences from biodiversity databases, forest inventories, and literature data. We modeled the availability of potential niches using the MaxEnt method and bioclimatic variables for current conditions, 2041–2060, and 2061–2080 periods. We used four climate scenarios: SSP126, SSP245, SSP370, and SSP485. The results confirm our hypotheses that, (i) coniferous species will contract, and deciduous trees will expand their climatic niche, (ii) a significant part of the areas where the studied species currently occur will be outside their climatic optimum in the coming decades; (iii) changes in the climatic optimum distribution will be greater in the 2041–2060 period than in 2061–2080. These predicted shifts are relevant for evidence-based management in sites already occupied by the studied alien trees. Our results are also relevant to the development of prevention and early detection measures in areas predicted to become climatically suitable for the studied species.
... Despite its broad ecological niche, beech is a drought-sensitive species (Leuschner, 2020) with a high capacity to recover even from persistent drought stress (Pretzsch et al., 2020), particularly when mixed with other species (Pretzsch et al., 2013). However, future growth decline is expected throughout much of its distribution range (Martinez del Castillo et al., 2022), with the centre of its range have been proven to be particularly vulnerable in the past (Cavin and Jump, 2016). The high vulnerability to prolonged drought stress can be attributed to its high transpirational water loss, which persists even with substantial levels of embolism and defoliation (Walthert et al., 2021). ...
Full-text available
During the summer of 2022, an acute drought once more afflicted central and southern Europe. This marked the third episode (after 2015 and 2018) of severe aridity in large parts of Germany within the last decade, leading to increased soil water depletion. Consequently, from July 2022 onward, European beech trees (Fagus sylvatica L.) exhibited early withering and pronounced premature defoliation. Nevertheless, crown defoliation exhibited substantial variation among trees within the same forest stands, prompting questions regarding the causal factors. In our study, we scrutinized twelve mature drought-impacted, beech-dominated forest stands in northern Bavaria, arranged along a gradient of different nutrient regime levels (base-rich, intermediate, base-poor), with co-occurring vital (≤40% crown defoliation) and declining (≥60% crown defoliation) trees. Within each stand, we selected an equal number of vital and declining trees, culminating in a total of 332 target trees. Dendrochronological patterns were analyzed to identify a potential timing of growth separation between vitality classes. Moreover, we used a Bayesian modelling framework to discern whether disparities in tree vitality hinged on competition, structure, small-scale differences in plant-available water capacity, and spatial clustering of declining competitors. We further explored the factors influencing the magnitude of growth decline post-2018 and how these were modulated by the site's nutrient regime. Our study unveiled that (i) low competition with increased size diversity bolstered tree vitality; (ii) declining trees were spatially aggregated; (iii) vital and declining trees exhibited strikingly similar growth trajectories in the past, which underwent a drastic shift following 2018, indicating a potential for a rapid vitality decline under recurrent severe drought stress; (iv) plant-available water capacity emerged as a crucial determinant of vitality and growth subsequent to 2018; (v) growth decline was most pronounced at base-poor and intermediate sites. Our findings underscore the importance of accommodating small-scale differences in soil and stand characteristics and advocate for silvicultural guidance towards reduced stand densities in combination with a more heterogenous structure to mitigate beech dieback in drought-prone forest stands.
... At the same time, there is a reduction in humidity and precipitation, exacerbating the effects of the high temperatures. These conditions have been demonstrated to favor numerous impacts in the region, including the reduction of glacier extension to their minimum levels (Alonso-González et al., 2020), the creation of ideal conditions to forest fires (Resco de Dios et al., 2022), reduced vegetative activity due to hydric stress (Martinez del Castillo et al., 2022), affections to crops (Trnka et al., 2014), and an increase in heat-related deaths (Tobías et al., 2021), etc. Despite being potentially anomalous in Spain's climatic history, extreme summer conditions are expected to recur with increasing frequency in the future (Lorenzo et al., 2021;Markonis et al., 2021;Ionita and Nagavciuc, 2021;Hari et al., 2020). ...
Full-text available
The warming of the global climate system is expected to result in significant socioeconomic stress, primarily through the occurrence of extreme weather and climate events, with the potential for severe impacts on societies. This was evidenced by the vulnerability of European nations during the 2003 summer heatwave, which resulted in the death of tens of thousands of individuals due to heat-related complications. In this analysis, we examine the summer of 2022 in Spain, a Mediterranean country that is among the most impacted by the effects of climate change. A distinct pattern of the subtropical ridge in the 500 hPa geopotential height, which is typically linked to the occurrence of heatwaves in the Iberian Peninsula (IP), and the atmospheric blocking in the North Atlantic region facilitated the southerly flow of exceptionally warm air masses from Africa towards the IP, contributing to the sustained high temperatures throughout the summer season. Our results show that Spain experienced record-breaking temperatures in nearly half of the country that favored more frequent, intense, and longer-lasting heatwaves compared to previous historical records available from 1893. In general, despite normal rainfall conditions, the extremely high temperatures led to intense drought conditions in most areas. Finally, the pa-leoclimatic records suggest that the average summer temperature of 2022 was unprecedented within the last 700 years, and the driest within the last 279 in NE Spain. These findings highlight the need for measures to mitigate the effects of heat on at-risk populations, and to increase resilience and adaptation to climate change in the future.
Global climate change is exacerbating drought pressure on forests. However, the response patterns and physiological mechanisms of conifer species to drought, specifically in terms of radial growth, ecological resilience and soil water utilization, are not clearly understood. This study aims to quantify the effects of resilience on radial growth and identify the role of soil moisture utilization strategies in the resilience of species under drought intensities. We focus on two conifer species, Picea crassifolia (spruce) and Pinus tabuliformis (pine), located on the southern edge of the Tengger Desert in northwestern China. The dynamics of radial growth and ecological resilience were identified, and the seasonal growth rates of species based on soil water were simulated using the VS-oscilloscope model under varying drought stress. The results showed that spruce growth and recovery contributed by soil water were suppressed with frequent severe droughts, leading to a decline in growth (-0.5 cm2 year-1/10a, p < 0.05), despite its greater resistance to mild and moderate drought (-4.63 %). However, pine exhibited a stronger recovery (+40.25 %, p < 0.05) and higher variation in growth (-0.3 cm2 year-1/10a, p < 0.05) under soil moisture stress, despite its weaker resistance to drought (-23.53 %, p < 0.05). These findings provide insights into the growth, resilience, and water adaptation mechanisms of species under drought events, and theoretical support for the conservation and management of conifer diversity and forest ecosystem stability in climate-sensitive regions.
Isotopic signatures of xylem water in different tree compartments such as roots, boles and branches, differ due to physiological and physical processes occurring inside trees. Accordingly, we hypothesised that the extent of such differences among the isotopic signatures of tree compartments is coherent with the distance travelled by the water inside trees and to its residence time. To test this, we compared the O–H isotopic composition of xylem water collected using an in‐situ water extraction method from roots, boles and branches of Fagus sylvatica trees growing on three geomorphological units of the Weierbach experimental catchment, Luxembourg. There was progressive ¹⁸ O and ² H enrichment in xylem waters along the root–branch flow path for all the studied trees. Three explanations could be considered for this progressive enrichment: internal fractionation by xylem–phloem water exchange, chemical reactions of metabolic pathways and variable ages of water retained in the xylem, reflecting historical variation in isotopic composition of uptake water. Support for the hypothesis of isotopic fractionation linked to xylem–phloem water exchange and chemical reactions is that enrichment was generally consistent with the distance travelled by the water and to its residence time inside the trees. However, the relative enrichment of ² H and ¹⁸ O was not consistent along the flow path, with Δ ² H/Δ ¹⁸ O ~7.5 from the soil into the roots and bole and ~4.7–6.5 for pathways that included smaller branches. This contrast suggests different processes controlling above‐ground isotopic enrichment. In particular, the slope of ~7.5 in the lower tree is also consistent with variation in tree water uptake varying along the local meteoric water line, with water in the roots being closer to the composition of rainfall close to the time of sampling and water in the bole being closer to the composition of rainfall from the previous summer. The timing of root and bole sampling in early spring, just before leaf‐out, means sap flow was very slow and makes it plausible that varying‐age water was present in the tree at that time. We also compared the O–H isotopic composition of those samples with the ones of the potential water sources to identify the origin of the water uptaken. The latter varied during the 3 years of sampling, with a preferential uptake from near‐surface waters. Our results suggest multiple biochemical, physiological and physical processes may play fundamental roles in the isotopic composition of xylem water within trees.
Full-text available
Key message Considering their drought tolerance and growth characteristics, rare native tree species are well-suited admixed species for the development of climate-stable forests in Central Europe. Abstract In our study, we assessed the growth and drought reaction of the four rare native tree species European hornbeam (Carpinus betulus L.), European white elm (Ulmus laevis Pall.), field maple (Acer campestre L.), and wild service tree (Sorbus torminalis (L.) Crantz). Based on tree-ring data, we (I) evaluated their species-specific growth characteristics and variability and examined the influencing site and tree characteristics on annual growth. (II) We quantified their reaction to single drought events, also depending on site and tree variables. (III) We compared our results to oak (Quercus robur L., Quercus petraea (Matt.) Liebl.) and European beech (Fagus sylvatica L.). As they are well-known Central European tree species, there is a broad knowledge about their growth and drought response across wide geographical ranges available. Bringing the results of European beech and oak in relation with the rare native species, it allows to categorise their growth and drought reaction and to contextualise their performance. Our results show, that besides European white elm, the rare species showed an overall lower annual growth with a higher variability than European beech and oak. However, especially field maple and wild service tree were better adapted to drought than European beech and partially even recovered better than oak. Combining the aspects of growth stability and drought tolerance, we conclude that rare native tree species are well suited as admixed species in future forest stands. European hornbeam is a suitable match for European beech on wetter sites, while field maple and wild service tree are a sensible complement for the climate stable oak on drier sites.
Full-text available
Forest disturbance regimes are expected to intensify as Earth’s climate changes. Quantifying forest vulnerability to disturbances and understanding the underlying mechanisms is crucial to develop mitigation and adaptation strategies. However, observational evidence is largely missing at regional to continental scales. Here, we quantify the vulnerability of European forests to fires, windthrows and insect outbreaks during the period 1979–2018 by integrating machine learning with disturbance data and satellite products. We show that about 33.4 billion tonnes of forest biomass could be seriously affected by these disturbances, with higher relative losses when exposed to windthrows (40%) and fires (34%) compared to insect outbreaks (26%). The spatial pattern in vulnerability is strongly controlled by the interplay between forest characteristics and background climate. Hotspot regions for vulnerability are located at the borders of the climate envelope, in both southern and northern Europe. There is a clear trend in overall forest vulnerability that is driven by a warming-induced reduction in plant defence mechanisms to insect outbreaks, especially at high latitudes. Natural disturbances imperil healthy and productive forests, but quantifying their effects at large scales is challenging. Here the authors apply machine learning to disturbance records and satellite data to quantify and map European forest vulnerability to fires, windthrows, and insect outbreaks through 1979-2018.
Full-text available
Anthropogenic increases in atmospheric greenhouse gas concentrations are the main driver of current and future climate change. The integrated assessment community has quantified anthropogenic emissions for the shared socio-economic pathway (SSP) scenarios, each of which represents a different future socio-economic projection and political environment. Here, we provide the greenhouse gas concentrations for these SSP scenarios – using the reduced-complexity climate–carbon-cycle model MAGICC7.0. We extend historical, observationally based concentration data with SSP concentration projections from 2015 to 2500 for 43 greenhouse gases with monthly and latitudinal resolution. CO2 concentrations by 2100 range from 393 to 1135 ppm for the lowest (SSP1-1.9) and highest (SSP5-8.5) emission scenarios, respectively. We also provide the concentration extensions beyond 2100 based on assumptions regarding the trajectories of fossil fuels and land use change emissions, net negative emissions, and the fraction of non-CO2 emissions. By 2150, CO2 concentrations in the lowest emission scenario are approximately 350 ppm and approximately plateau at that level until 2500, whereas the highest fossil-fuel-driven scenario projects CO2 concentrations of 1737 ppm and reaches concentrations beyond 2000 ppm by 2250. We estimate that the share of CO2 in the total radiative forcing contribution of all considered 43 long-lived greenhouse gases increases from 66 % for the present day to roughly 68 % to 85 % by the time of maximum forcing in the 21st century. For this estimation, we updated simple radiative forcing parameterizations that reflect the Oslo Line-By-Line model results. In comparison to the representative concentration pathways (RCPs), the five main SSPs (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) are more evenly spaced and extend to lower 2100 radiative forcing and temperatures. Performing two pairs of six-member historical ensembles with CESM1.2.2, we estimate the effect on surface air temperatures of applying latitudinally and seasonally resolved GHG concentrations. We find that the ensemble differences in the March–April–May (MAM) season provide a regional warming in higher northern latitudes of up to 0.4 K over the historical period, latitudinally averaged of about 0.1 K, which we estimate to be comparable to the upper bound (∼5 % level) of natural variability. In comparison to the comparatively straight line of the last 2000 years, the greenhouse gas concentrations since the onset of the industrial period and this studies' projections over the next 100 to 500 years unequivocally depict a “hockey-stick” upwards shape. The SSP concentration time series derived in this study provide a harmonized set of input assumptions for long-term climate science analysis; they also provide an indication of the wide set of futures that societal developments and policy implementations can lead to – ranging from multiple degrees of future warming on the one side to approximately 1.5 ∘C warming on the other.
Full-text available
A central challenge in global change research is the projection of the future behavior of a system based upon past observations. Tree‐ring data have been used increasingly over the last decade to project tree growth and forest ecosystem vulnerability under future climate conditions. But how can the response of tree growth to past climate variation predict the future, when the future does not look like the past? Space‐for‐time substitution (SFTS) is one way to overcome the problem of extrapolation: the response at a given location in a warmer future is assumed to follow the response at a warmer location today. Here we evaluated an SFTS approach to projecting future growth of Douglas‐fir (Pseudotsuga menziesii ), a species that occupies an exceptionally large environmental space in North America. We fit a hierarchical mixed‐effects model to capture ring‐width variability in response to spatial and temporal variation in climate. We found opposing gradients for productivity and climate sensitivity with highest growth rates and weakest response to interannual climate variation in the mesic coastal part of Douglas‐fir’s range; narrower rings and stronger climate sensitivity occurred across the semi‐arid interior. Ring‐width response to spatial vs. temporal temperature variation was opposite in sign, suggesting that spatial variation in productivity, caused by local adaptation and other slow processes, cannot be used to anticipate changes in productivity caused by rapid climate change. We thus substituted only climate sensitivities when projecting future tree growth. Growth declines were projected across much of Douglas‐fir’s distribution, with largest relative decreases in the semiarid U.S. Interior West and smallest in the mesic Pacific Northwest. We further highlight the strengths of mixed‐effects modeling for reviving a conceptual cornerstone of dendroecology, Cook’s 1987 aggregate growth model, and the great potential to use tree‐ring networks and results as a calibration target for next‐generation vegetation models.
Full-text available
Climate change is predicted to affect tree growth due to increased frequency and intensity of extreme events such as ice storms, droughts and heatwaves. Yet, there is still a lot of uncertainty on how trees respond to an increase in frequency of extreme events. Use of both ground-based wood increment (i.e. ring width) and remotely sensed data (i.e. vegetation indices) can be used to scale-up ground measurements, where there is a link between the two, but this has only been demonstrated in a few studies. We used tree-ring data together with crown features derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess the effect of extreme climate events on the growth of beech (Fagus sylvatica L.) in Slovenia. We found evidence that years with climate extremes during the growing season (drought, high temperatures) had a lower ring width index (RWI) but we could not find such evidence for the remotely sensed EVI (Enhanced Vegetation Index). However, when assessing specific events where leaf burning or wilting has been reported (e.g. August 2011) we did see large EVI anomalies. This implies that the impact of drought or heatwave events cannot be captured by EVI anomalies until physical damage on the canopy is caused. This also means that upscaling the effect of climate extremes on RWI by using EVI anomalies is not straightforward. An exception is the 2014 ice storm that caused a large decline in both RWI and EVI. Extreme climatic parameters explained just a small part of the variation in both RWI and EVI by, which could indicate an effect of other climate variables (e.g. late frost) or biotic stressors such as insect outbreaks. Furthermore, we found that RWI was lower in the year after a climate extreme occurred in the late summer. Most likely due to the gradual increase in temperature and more frequent drought we found negative trends in RWI and EVI. EVI maps could indicate where beech is sensitive to climate changes and could be used for planning mitigation interventions. Logical next steps should focus on a tree-based understanding of the short-and long-term effects of climate extremes on tree growth and survival, taking into account differential carbon allocation to the crown (EVI) and to wood-based variables. This research highlights the value of an integrated approach for upscaling tree-based knowledge to the forest level.
Full-text available
The dataset presented here was collected by the GenTree project (EU-Horizon 2020), which aims to improve the use of forest genetic resources across Europe by better understanding how trees adapt to their local environment. This dataset of individual tree-core characteristics including ring-width series and whole-core wood density was collected for seven ecologically and economically important European tree species: silver birch (Betula pendula), European beech (Fagus sylvatica), Norway spruce (Picea abies), European black poplar (Populus nigra), maritime pine (Pinus pinaster), Scots pine (Pinus sylvestris), and sessile oak (Quercus petraea). Tree-ring width measurements were obtained from 3600 trees in 142 populations and whole-core wood density was measured for 3098 trees in 125 populations. This dataset covers most of the geographical and climatic range occupied by the selected species. The potential use of it will be highly valuable for assessing ecological and evolutionary responses to environmental conditions as well as for model development and parameterization, to predict adaptability under climate change scenarios.
Full-text available
Individual tree architecture and the composition of tree species play a vital role for many ecosystem functions and services provided by a forest, such as timber value, habitat diversity, and ecosystem resilience. However, knowledge is limited when it comes to understanding how tree architecture changes in response to competition. Using 3D-laser scanning data from the German Biodiversity Exploratories, we investigated the detailed three-dimensional architecture of 24 beech (Fagus sylvatica L.) trees that grew under different levels of competition pressure. We created detailed quantitative structure models (QSMs) for all study trees to describe their branching architecture. Furthermore, structural complexity and architectural self-similarity were measured using the box-dimension approach from fractal analysis. Relating these measures to the strength of competition, the trees are exposed to reveal strong responses for a wide range of tree architectural measures indicating that competition strongly changes the branching architecture of trees. The strongest response to competition (rho = −0.78) was observed for a new measure introduced here, the intercept of the regression used to determine the box-dimension. This measure was discovered as an integrating descriptor of the size of the complexity-bearing part of the tree, namely the crown, and proven to be even more sensitive to competition than the box-dimension itself. Future studies may use fractal analysis to investigate and quantify the response of tree individuals to competition.
Full-text available
Since several studies have been reporting an increase in the decline of forests, a major issue in ecology is to better understand and predict tree mortality. The interactions between the different factors and the physiological processes giving rise tree mortality, as well as the individual variability in mortality risk, still need to be better assessed. This study investigates mortality in a rear-edge population of European beech ( Fagus sylvatica L.) using a combination of statistical and process-based modelling approaches. Based on a survey of 4323 adult beeches since 2002 within a natural reserve, we first used statistical models to quantify the effects of competition, tree growth, size, defoliation and fungi presence on mortality. Secondly, we used an ecophysiological process-based model (PBM) to separate out the different mechanisms giving rise to temporal and inter-individual variations in mortality by simulating depletion of carbon stocks, loss of hydraulic conductance and damage due to late frosts in response to climate. The combination of all these simulated processes was associated with the temporal variations in the population mortality rate. The individual probability of mortality decreased with increasing mean growth, and increased with increasing crown defoliation, earliness of budburst, fungi presence and increasing competition, in the statistical model. Moreover, the interaction between tree size and defoliation was significant, indicating a stronger increase in mortality associated to defoliation in smaller than larger trees. Finally, the PBM predicted a higher conductance loss together with a higher level of carbon reserves for trees with earlier budburst, while the ability to defoliate the crown was found to limit the impact of hydraulic stress at the expense of the accumulation of carbon reserves. We discuss the convergences and divergences obtained between statistical and process-based approaches and we highlight the importance of combining them to characterize the different processes underlying mortality, and the factors modulating individual vulnerability to mortality.
Full-text available
Question It is debated whether forest understory communities will be sensitive to projected climate change or inert due to the regulating effect of local site conditions and soil parameters. A distinction between the relative importance of climate or soil is often hardly possible because both factors usually change at different spatial scales in forests. Here, we compare the relative influence of climate and soil on the forest understory vegetation in lowland beech forest ecosystems (Fagus sylvatica), which were selected for their ecological homogeneity. Location Nine sites along a strong temperature gradient (ΔT = 4 K from east to west in winter, south to north in summer) between Rostock (Germany) and Gdańsk (Poland) in a Baltic Quaternary ground moraine landscape. Methods We conducted a vegetation survey in 60 vegetation plots (80 m² each) across nine forest sites mono‐dominated by European beech and analysed how much variation in understory plant composition is explained by climate and soil parameters. Results Soil explained 32 % of the compositional variation of understory vegetation across sites, climate 22 %, and their interaction 14 %. Topsoil pH, subsoil organic matter content, and subsoil C/N ratio were the most important soil variables; growing season temperature and annual water availability were the most important climatic variables. Conclusion The strong dependence on soil properties could moderate the response of the forest understory vegetation to projected climate change. Forest soil properties, however, also depend on the dominant tree species and the macroclimate. To predict climate change impacts on forest understory vegetation, climate change assessments should consider indirect climate change effects as well as interactions between climate and soil. This article is protected by copyright. All rights reserved.
Changing environmental conditions may substantially interact with site quality and forest stand characteristics, and impact forest growth and carbon sequestration. Understanding the impact of the various drivers of forest growth is therefore critical to predict how forest ecosystems can respond to climate change. We conducted a continental-scale analysis of recent (1995–2010) forest volume increment data (ΔVol, m³ ha⁻¹ yr⁻¹), obtained from ca. 100,000 coniferous and broadleaved trees in 442 even-aged, single-species stands across 23 European countries. We used multivariate statistical approaches, such as mixed effects models and structural equation modelling to investigate how European forest growth respond to changes in 11 predictors, including stand characteristics, climate conditions, air and site quality, as well as their interactions. We found that, despite the large environmental gradients encompassed by the forests examined, stand density and age were key drivers of forest growth. We further detected a positive, in some cases non-linear effect of N deposition, most pronounced for beech forests, with a tipping point at ca. 30 kg N ha⁻¹ yr⁻¹. With the exception of a consistent temperature signal on Norway spruce, climate-related predictors and ground-level ozone showed much less generalized relationships with ΔVol. Our results show that, together with the driving forces exerted by stand density and age, N deposition is at least as important as climate to modulate forest growth at continental scale in Europe, with a potential negative effect at sites with high N deposition.