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ARTICLE
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 Muffler8,12, 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.
https://doi.org/10.1038/s42003-022-03107-3 OPEN
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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
change1–3, 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 tree’s 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
species’geographic 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 populations11–13, 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-specific 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 species’resilience to predicted changes and its eco-
logical plasticity across the distribution range are not well
understood.
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 species’performance 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 species’distribution 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 significantly enhanced at
high latitudes in the future, even under a worst-case climate
change scenario.
Results
Model development and performance. Among the tested mixed-
effects models, the full model containing all considered and sig-
nificant variables and interactions was the most accurate to pre-
dict the species’growth across Europe, as shown by the Akaike
Information Criterion (AIC) scores (Table 1). AIC measures
the relative goodness of fit of a given model; the lower its value,
the more accurate the model is.. Indeed, 86% of growth variability
ab
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.
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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 significantly contributed to the growth model
(Supplementary Data 1). When fitting 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 significant 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 specific weight and significance 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
final model was able to reproduce tree-ring growth across Europe
and covering the entire species distribution for every year for the
period 1955–2016 (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 fixed 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 1955–1985 to 1986–2016 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 species’distribution 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 1955–1985. 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 1986–2016 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 1986–2016 (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 2040–2070 and 2060–2090 (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. 4d–f). From
2020 to 2050 growth, decreases of 20–30% 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
2040–2070. 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 2040–2070 and 2060–2090
periods, though at much accelerated rates. From 2040 to 2070, the
general southeast-to-northwest pattern, modulated by altitude, is
Table 1 Models’validation.
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.
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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).
Discussion
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-
specific 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 field-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 infestation25–31, most of these are
difficult 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
a
<0.4 0.6 0.9 1.1 1.4 1.6 1.9 2.1 2.4 2.7 2.9 >3.2
b
Fig. 2 Spatial patterns of beech growth during the last decades. Mean estimates of BAI (in mm2) from 1955–1985 (a) and 1986–2016 (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 1955–1985 period mean.
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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 gradients33–36.
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
altitude37–39, which in turn control tree growth. The evaluation of
the impact of the warmer and drier conditions over recent dec-
ades across the species’distribution 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 first 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 findings 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 findings 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 species’distribution
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 deficits, 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 fine roots likely contribute to this temperature
sensitivity50. Thus, even though beech is a late-successional
abc
def
<−50 −40 −30 −20 −10 10 20 30 40 >500
Fig. 4 Relative changes in tree growth. Changes are projected under SSP1-2.6 (a–c) and SSP5-8.5 (d–f) CMIP6 climate scenarios for different periods:
2020–2050 (a,d), 2040–2070 (b,e), and 2060–2090 (c,f) In this panel, BAI changes were expressed in percentage of change compared to the
1986–2016 period.
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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 droughts”52
might lead to amplified 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 species’dis-
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 1900–2000) 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, fires), soil composition
(i.e. nitrogen, phosphorous, potassium) and tree species com-
petition, all of which complicating species-specifictreegrowth
projections.
Projected climate change will foster a progressive decrease of
beech growth. As beech is a dominant tree species across large
regions of Europe’s forests, this indicates an important reduction
in functioning as a carbon sink to mitigate atmospheric CO
2
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
plannings.
Conclusions
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
+10–20% 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 amplified to −50% if drought conditions prevail.
These significant growth trends point towards increased forest
mortality, as declining growth has been reported as a precursor of
tree die-off2. These findings challenge recent predictions of
increasing terrestrial carbon stocks under climate change
scenarios53, as the strength of beech forests as a carbon sink will
decrease.
Methods
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 defined 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 defined by applying the De Martonne
aridity index58 (AI) as previous studies showed that site-specific moisture condi-
tions modulate the climate sensitivity of trees59. AI was calculated for European
grid cells from 1950 to 2016 using (Eq. 1):
AI ¼P
10 þT
where Pis the annual precipitation sum (in mm) and T(in °C) the annual mean air
temperature. The climate types defined by De Martonne range from arid (values
from 0 to 10), semi-arid (10–20), Mediterranean (20–24), semi-humid (24–28),
humid (28–35), very humid (35–55) 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 fitted by
maximum likelihood (Adaptive Gauss-Hermite Quadrature) using the R package
lme4 (version 1.1–21). 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 influenced 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 model’s
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 influence of the variables, we included variables based
on AIC values (Table 1).
The model was based on the period 1950–2016 due to the common availability
of climate and dendrochronological data. We then fitted 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
´fðTmaxi;t;kÞ;fðTmini;t;kÞ;logðPPi;t;kÞþfðLATiÞ´fðTmaxi;t;kÞ;fðTmini;t;kÞ;logðPPi;t;kÞ
þfðALTiÞ´fðTmaxi;t;kÞ;fðTmini;t;kÞ;logðPPi;t;kÞþðBAj;i;tjCodejÞ
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
significant interaction between variables. To account for the possible influence 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 influences 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 finally a comparison with a
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“null”model 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-defined 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-specific climatologies between
historic simulations (period 1985–2014) and future simulations representative of
three distinct periods (2020–2050, 2040–2070, 2060–2090) 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 findings of this study are available from the corresponding
author and co-authors upon reasonable request. All relevant data for the figures are
included in the supplementary information files.
Received: 16 August 2021; Accepted: 2 February 2022;
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Acknowledgements
EMdC was supported and financed 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.05.1.1.02); K.C., M.M., J.G., and P.P. by Slovenian
Research Agency ARRS, programs P4-0015 and P4–0107 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 first 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 final 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 fieldwork, collected the tree-
ring data, actively contributed to the manuscript, and gave final approval for its
publication.
Funding
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 https://doi.org/10.1038/s42003-022-03107-3.
Correspondence and requests for materials should be addressed to Edurne Martinez del
Castillo.
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.
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1
Department of Geography, Johannes Gutenberg University, Mainz, Germany.
2
Department of Forestry, University of Applied Sciences
Weihenstephan-Triesdorf, Triesdorf, Germany.
3
Land Surface-Atmosphere Interactions, Technical University Munich, Freising, Germany.
4
Department of Geography and Planning, School of Environmental Sciences, University of Liverpool, Liverpool, UK.
5
Global Change Research
Institute of the Czech Academy of Sciences (CzechGlobe), Brno, Czech Republic.
6
Department of Geography, Autonomous University of Madrid,
Madrid, Spain.
7
Nature Rings –Environmental Research and Education, Mainz, Germany.
8
Plant Ecology, Albrecht-von-Haller-Institute for Plant
Sciences, University of Goettingen, Goettingen, Germany.
9
Forest Dynamics, Swiss Federal Research Institute for Forest, Snow and Landscape
WSL, Birmendorf, Switzerland.
10
School of Life Science and Engineering, Southwest University of Science and Technology, Mianyang, China.
11
Department of Crop and Forest Sciences and Joint Research Unit CTFC-AGROTECNIO CERCA Center, University of Lleida, Lleida, Spain.
12
Institute for Botany and Landscape Ecology, University Greifswald, Greifswald, Germany.
13
Systems and Natural Resources Department,
Universidad Politécnica de Madrid, Madrid, Spain.
14
Chair of Forest Growth and Woody Biomass Production, TU Dresden, Tharandt, Germany.
15
Biological and Environmental Sciences, Faculty of Natural Sciences, University of Stirling, Stirling, Scotland.
16
Faculty of Forestry and Wood
Technology, University of Zagreb, Zagreb, Croatia.
17
Institute of Forest Ecosystems, Thünen Institute, Eberswalde, Germany.
18
TERRA Teaching
and Research Centre (Forest Is Life), Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium.
19
Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences, Prague, Czech Republic.
20
Biotechnical Faculty, Department of Wood Science and Technology, University of
Ljubljana, Ljubljana, Slovenia.
21
Applied Vegetation Ecology, Faculty of Environment and Natural Resources, University of Freiburg,
Freiburg, Germany.
22
Slovenian Forestry Institute, Ljubljana, Slovenia.
23
Forest Resources Department, Centro de Investigación y Tecnología
Agroalimentaria de Aragón (CITA), Zaragoza, Spain.
24
University of Belgrade –Faculty of Forestry, Belgrade, Serbia.
25
Department of Geography
and Regional Planning, University of Zaragoza, Zaragoza, Spain.
26
TUM School of Life Sciences/Ecoclimatology, Technical University of Munich,
Munich, Germany.
27
Department of Agriculture, Forestry and Food Sciences, University of Turin, Grugliasco, Italy.
28
Department of Earth and
Environmental Sciences, University of Pavia, Pavia, Italy.
29
National Institute for Research and Development in Forestry “Marin Dracea”,
Voluntari, Romania.
30
Faculty of Silviculture and Forest Engineering, University of Brasov, Brașov, Romania.
31
Departamento de Sistemas y
Recursos Naturales, Escuela Técnica Superior de Ingeniería de Montes, Forestal y del Medio Natural, Universidad Politécnica de Madrid,
Madrid, Spain.
32
Faculty of Forestry Sciences, Agricultural University of Tirana, Koder-Kamez, Albania.
33
Institute of Biodiversity and Ecosystem
Research, Bulgarian Academy of Sciences, Sofia, Bulgaria. ✉email: Emdc85@gmail.com
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1.
2.
3.
4.
5.
6.
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