# Identifying drivers of non-stationary climate-growth relationships of European beech

Christopher Leifsson, Allan Buras, Stefan Klesse, Claudia Baittinger, Banzragch Bat-Enerel, Giovanna Battipaglia, Franco Biondi, Branko Stajić, Marius Budeanu, Vojtěch Čada, et al.

► To cite this version:

Christopher Leifsson, Allan Buras, Stefan Klesse, Claudia Baittinger, Banzragch Bat-Enerel, et al. Identifying drivers of non-stationary climate-growth relationships of European beech. Science of the Total Environment, 2024, 937, pp.173321. 10.1016/j.scitotenv.2024.173321. hal-04684844

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https://hal.inrae.fr/hal-04684844v1

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Distributed under a Creative Commons Attribution 4.0 International License
Identifying drivers of non-stationary
climate-growth relationships of European
beech

Christopher Leifsson¹, *

Allan Buras¹

Stefan Klesse²

Claudia Baittinger³

Banzragch Bat-Enerel⁴,⁵

Giovanna Battipaglia⁶

Franco Biondi⁷

Marius Budeanu⁹

Vojtěch Čada¹⁰

J. Julio Camarero¹¹

Liam Cavin¹²

Hugues Claessens¹³

Katarina Čufar¹⁴

Martin de Luis¹⁵

Isabel Dorado-Liñán¹⁶

Choimaa Dulamsuren⁵

Balázs Garamszegi¹⁷

Michael Grabner¹⁸

Andrew Hacket-Pain¹⁹

Jon Kehlet Hansen²⁰

Claudia Hartl²¹

Weiwei Huang²²,²⁰

Pavel Janda¹⁰

Alistair S. Jump¹²

Marko Kazimirović⁸

Florian Knutzen²³

Jürgen Kreyling²⁴

Alexander Land²⁵

Nicolas Latte¹³

François Lebourgeois²⁶

Christoph Leuschner⁴

Luis A. Longares¹⁵

Edurne Martinez del Castillo²⁷

Annette Menzel²⁸

Renzo Motta²⁹

Lena Muffler-Weigel³⁰

Paola Nola³¹

Momchil Panayatov³²

Any Mary Petritan⁹
Ion Catalin Petritan³³

Ionel Popa⁹,³⁴

Cătălin-Constantin Roibu³⁵

Álvaro Rubio-Cuadrado³⁶

Miloš Rydval¹⁰

Tobias Scharnweber†³⁷

Branko Stajić⁸

Miroslav Svoboda¹⁰

Elvin Toromani³⁸

Volodymyr Trotsiuk²

Marieke van der Maaten-Theunissen³⁹

Ernst van der Maaten³⁹

Robert Weigel³⁰

Martin Wilmking³⁷

Tzvetan Zlatanov⁴⁰

Anja Rammig¹

Christian Zang⁴¹

¹ Technical University of Munich School of Life Sciences Weihenstephan, Professorship for Land Surface-Atmosphere Interactions, Hans-Carl-v.-Carlowitz-Platz 2, 85354 Freising, Germany

² Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland

³ The National Museum of Denmark, Environmental Archaeology and Materials Science, I.C. Modewegs Vej 11, DK-2800 Kgs. Lyngby, Denmark

⁴ Plant Ecology, University of Goettingen, 37073 Goettingen, Germany

⁵ Applied Vegetation Ecology, Faculty of Environment and Natural Resources, University of Freiburg, 79106 Freiburg, Germany

⁶ University of Campania Luigi Vanvitelli, via Vivaldi 43, 8100 Caserta, Italy

⁷ DendroLab, Dept. of Natural Resources and Environmental Science, University of Nevada,
37 Reno, NV 89557, USA

38 8 University of Belgrade, Faculty of Forestry, Belgrade, Serbia

39 9 National Institute for Research and Development in Forestry Marin Dracea, 13 Closca street, Brasov, Romania

41 10 Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, Kamycka 129, Praha 6 Suchdol, 16521, Czech Republic

43 11 Instituto Pirenaico de Ecología (IPE), CSIC, Avda. Montañana 1005, 50080 Zaragoza, Spain

44 12 Biological and Environmental Sciences, University of Stirling, Stirling, FK9 4LA, Scotland, UK

46 13 Forest is Life, ULiège, Passage des Déportés 2, B-5030 Gembloux, Belgium

47 14 University of Ljubljana, Biotechnical Faculty, Department of Wood Science and Technology, Jamnikarjeva 101, 1000 Ljubljana, Slovenia

49 15 Dpto. de Geografía y Ordenación del Territorio. IUCA. Universidad de Zaragoza. C/ Pedro Cerbuna s/n 50009, Zaragoza. Spain

51 16 Departamento de Sistemas y Recursos Naturales, Universidad Politécnica de Madrid, 28040 Madrid, Spain

53 17 Institute of Forest Ecology, University of Natural Resources and Life Sciences, Vienna, Austria

55 18 University of Natural Resources and Life Sciences, Vienna, Austria

56 19 Department of Geography and Planning, School of Environmental Sciences, University of Liverpool, Liverpool, United Kingdom

58 20 Department of Geosciences and Natural Resource Management, University of Copenhagen,
59 Copenhagen, Denmark

60 21 Nature Rings - Environmental Research & Education, 55118 Mainz, Germany

61 22 Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province, Nanjing Forestry University, Nanjing 210037, China

63 23 Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Fischertwiete 1, 20095 Hamburg, Germany

65 24 University of Greifswald, Experimental Plant Ecology, Soldmannstraße 15, 17498 Greifswald, Germany

67 25 University of Hohenheim, Institute of Biology (190a), Garbenstraße 30, 70599 Stuttgart, Germany

69 26 Université de Lorraine, AgroParisTech, INRAE, Silva, F-54000 Nancy, France

70 27 Department of Geography. Johannes Gutenberg University Mainz, Mainz, Germany

71 28 Technical University of Munich School of Life Sciences Weihenstephan, Professorship for Ecoclimatology, Hans-Carl-v.-Carlowitz-Platz 2, 85354 Freising, Germany

73 29 Department of Agricoltural, Forest and Food Sciences, University of Turin, Largo Paolo Braccini 2, 10095 Grugliasco (TO), Italy

75 30 Ecological-Botanical Garden, University of Bayreuth, 95447 Bayreuth, Germany

76 31 Department of Earth and Environmental Sciences, University of Pavia, Via S. Epifanio 14, I-27100, Pavia, Italy

78 32 University of Forestry, Dendrology Department, Forest Faculty, Sofia, Bulgaria

79 33 Faculty of Silviculture and Forest Engineering, Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Transilvania University of Braşov,
81 Brașov, Romania

82 ³⁴ Center for Mountain Economy (CE-MONT), Vatra Dornei, Romania

83 ³⁵ Forest Biometrics Laboratory, Faculty of Forestry, "Stefan cel Mare" University of Suceava,
Universitatii street, no. 13, Suceava, Romania, RO720229

85 ³⁶ Departamento de Sistemas y Recursos Naturales, Escuela Técnica Superior de Ingeniería de Montes, Forestal y del Medio Natural, Universidad Politécnica de Madrid. Ciudad Universitaria s/n, 28040, Madrid, Spain.

88 ³⁷ Institute for Botany and Landscape Ecology, University Greifswald, 17487 Greifswald, Germany

90 ³⁸ Department of Forestry, Agricultural University Tirana, Tirana, Albania

91 ³⁹ Chair of Forest Growth and Woody Biomass Production, TU Dresden, Dresden, Germany

92 ⁴⁰ Institute of Biodiversity and Ecosystem Research, Bulgarian Academy of Sciences, 2 Gagarin Street, 1113 Sofia, Bulgaria

94 ⁴¹ Weihenstephan-Triesdorf University of Applied Sciences, Department of Forestry, Hans-Carl-v.-Carlowitz-Platz 3, 85354 Freising, Germany

⁹⁶ * Correspondence: Christopher Leifsson <cleifsson@gmail.com>

97 **Keywords:** climate sensitivity, dendroecology, forests, drought, linear mixed-effects models,
Fagus sylvatica

99 **Running title:** Drivers of non-stationarity in European beech
100 Abstract

The future performance of the widely abundant European beech (Fagus sylvatica L.) across its ecological amplitude is uncertain. Although beech is considered drought-sensitive and thus negatively affected by drought events, scientific evidence indicating increasing drought vulnerability under climate change on a cross-regional scale remains elusive. While evaluating changes in climate sensitivity of secondary growth offers a promising avenue, studies from productive, closed-canopy forests suffer from knowledge gaps, especially regarding the natural variability of climate sensitivity and how it relates to radial growth as an indicator of tree vitality. Since beech is sensitive to drought, we in this study use a drought index as a climate variable to account for the combined effects of temperature and water availability and explore how the drought sensitivity of secondary growth varies temporally in dependence on growth variability, growth trends, and climatic water availability across the species' ecological amplitude.

Our results show that drought sensitivity is highly variable and non-stationary, though consistently higher at dry sites compared to moist sites. Increasing drought sensitivity can largely be explained by increasing climatic aridity, especially as it is exacerbated by climate change and trees' rank progression within forest communities, as (co-)dominant trees are more sensitive to extra-canopy climatic conditions than trees embedded in understories. However, during the driest periods of the 20th century, growth showed clear signs of being decoupled from climate. This may indicate fundamental changes in system behavior and be early-warning signals of decreasing drought tolerance. The multiple significant interaction terms in our model eluci-
date the complexity of European beech's drought sensitivity, which needs to be taken into
consideration when assessing this species' response to climate change.

1 Introduction

Comprehensively understanding how forests' growth responds to the increasing frequency, severity, and duration of drought conditions is crucial for assessing their resilience to climate change (Anderegg et al., 2022; McDowell et al., 2020; Wang et al., 2023). The safeguarding of forests is necessary as they provide vital ecosystem services, including being a significant carbon sink of anthropogenic greenhouse gas emissions (Cabon et al., 2022; Friedlingstein et al., 2022; Pugh et al., 2019; Tagesson et al., 2020), contributing to biodiversity (FAO and UNEP, 2020; Leuschner and Ellenberg, 2017), providing biogeophysical feedback effects (Bonan, 2008; Liu et al., 2023; Makarieva and Gorshkov, 2007; Portmann et al., 2022; Smith et al., 2023) and contributing to human welfare and economy (FAO and UNEP, 2020; Hanewinkel et al., 2014). The relatively stable climatic conditions that European forests have evolved under are on an unprecedented trajectory (IPCC, 2021), and the impacts of climate change on forests may be augmented by centuries of management, which has led to non-natural distributions of many species (Brunet et al., 2010; Forest Europe, 2020). Given the long lifespans and slow recruitment of trees, forest management is challenged by selecting tree species for rejuvenation that can cope with the projected climatic conditions in the 21st century and beyond (Anderegg et al., 2022; Berner and Goetz, 2022; Brodribb et al., 2020; Quetin et al., 2023).
To quantify trees' resilience to climate change, approximating annual increments in secondary growth using tree-ring width measurements (TRW) is frequently used. Secondary growth is directly linked to carbon storage and economic value (Fritts, 1976; Gea-Izquierdo and Sánchez-González, 2022; Hanewinkel et al., 2014) and has the advantage of providing spatially and temporally extensive information on acute and chronic growth changes in response to climate (Anderegg et al., 2015; Babst et al., 2013; Kannenberg et al., 2019; Leifsson et al., 2023). Converting TRW to basal area increments (BAI) can further improve the connection to secondary growth, as BAI is a more accurate predictor of actual annual biomass increments (Babst et al., 2014; LeBlanc, 1990; Visser et al., 2023). However, secondary growth has a limited capacity to reveal changes in the underlying mechanisms that govern growth in response to climate change. Recent studies have indicated these mechanisms are better elucidated by quantifying alterations in the sensitivity of growth to climate variables, as expressed by statistically quantifying climate-growth relationships (Heilmayr et al., 2023; Kannenberg and Maxwell, 2022; Peltier and Ogle, 2020) because climate sensitivity of growth varies in accordance with how limiting climate variables are for growth (Stine and Huybers, 2017). If those climatic limitations change, so does the climate sensitivity of growth, potentially revealing changes in the underlying mechanisms irrespective of responses of secondary growth, making it an essential aspect of tree-ring-based assessments of climate-change resilience (Kannenberg and Maxwell, 2022; Peltier and Ogle, 2020).

Climate sensitivity of growth is well understood to vary according to site-specific climatic conditions (Babst et al., 2013; Buras et al., 2018; D'Orangeville et al., 2018; S. Klesse et al.,
2018; Lange et al., 2018; Marchand et al., 2019; Muffler et al., 2020; Zeng et al., 2022) and to structural details of forests, such as strata, density, and composition (Keen et al., 2021; Marchand et al., 2019; Mausolf et al., 2018; Metz et al., 2020; Wang et al., 2023; Zang et al., 2014, 2012). Just as climate sensitivity of growth varies with spatial climate variability, climate also features a temporal variability called non-stationarity (Anderson-Teixeira et al., 2022; Babst et al., 2019; Harvey et al., 2020; Lange et al., 2018; Leifsson et al., 2023; Muffler et al., 2020; Peltier and Ogle, 2020; Szejner et al., 2020; Tumajer et al., 2022; Weigel et al., 2023). Remarkably, Wilmking et al. (2020) showed in their review of almost 2,000 papers that 2/3 had not tested for non-stationary climate-growth relationships, while more than half of the remaining 1/3 did report non-stationary climate-growth relationships. Therefore, climate-change-induced deviations in climate-growth relationships must be quantified against the backdrop of the natural variability of climate-growth relationships.

Large-scale tree-ring networks tend to be composed mainly of samples from dominant trees in proximity to the margins of the respective species' distributional range (Cook and Kairiukstis, 1990; Fritts, 1976; Grissino-Mayer and Fritts, 1997). Consequently, data from ecologically oriented productive and closed-canopy forests are comparatively under-represented (Amoroso et al., 2017; Stefan Klesse et al., 2018; Zhao et al., 2019). However, climate signals are weaker in these environments due to more moderate climatic conditions, more complex structural composition, and greater competition, with the latter two likely also influenced by forest management (Astigarraga et al., 2020; Dulamsuren et al., 2022; Keen et al., 2021; Marqués et al., 2022b; Mausolf et al., 2018; Rollinson et al., 2021; Wang et al., 2023). The implication
for estimating climate-growth relationships in closed-canopy forests is that they are likely
influenced by changes in trees' social status within forest communities due to ontogeny, as
there is a stark contrast between juvenile trees growing in the relatively moist and shaded
understories compared to large (co-)dominant trees which are more exposed to macro-climatic
conditions (Brienen et al., 2022; Leuschner et al., 2023a). The greater exposure to macro-
climate of (co-)dominant trees may make them more susceptible to drought conditions and
drought-induced stress, leading to lower or declining growth rates and higher growth variability
and climate sensitivity (Anderson-Teixeira et al., 2022; Cailleret et al., 2019; Hansen et al.,
2022; Jump et al., 2006; Wang et al., 2023), amplified by increasing climatic stress under
climate change (Buras and Menzel, 2019; Wang et al., 2023). Low and negative growth trends
have been connected to deteriorating tree vitality (Cailleret et al., 2017; Dobbertin, 2005;
Dulamsuren et al., 2022; Klesse et al., 2022; Neycken et al., 2024, 2022; Pedersen, 1998; van
der Maaten et al., 2024), while increasing growth variability and climate sensitivity have been
connected to early warning signals of mortality following drought stress (Cailleret et al., 2019;
Camarero et al., 2015; Keen et al., 2021).

The question that follows is if, and then how, growth trends and growth variability influ-
ence the sensitivity of BAI to climatic conditions. Detrending BAI, as done in some studies
(Anderson-Teixeira et al., 2022; Bernal-Escobar et al., 2022; Jump et al., 2006; Pedersen, 1998;
Weigel et al., 2023), leads to quasi-stationary BAI variability, but may mask crucial ecological
information. Other studies limited their analyses to only mature trees after a juvenile period
of rapid growth. However, defining juvenile periods is problematic in closed-canopy forests as
growth patterns are more complex due to dependence on non-climatic environmental factors, such as variations in incoming radiation and resources due to competition. To the best of our knowledge, no study has comprehensively analyzed how climate sensitivity varies with growth rates expressed as BAI in closed-canopy forests.

Therefore, the specific aim of this study is to explore how growth trends, growth variability, and climatic conditions modulate climate sensitivity. For this purpose, we use a drought index as a climate variable, and will consequently, from here on, refer to drought sensitivity instead of climate sensitivity and drought conditions instead of climatic conditions. We furthermore use European beech (Fagus sylvatica L.) tree-ring data from a European-wide network of sites (Dorado-Liñán et al., 2022; Hacket-Pain et al., 2018; Martinez del Castillo et al., 2022). Contrary to the common issue of forest representativeness discussed above, the data in this network has been sampled for ecologically oriented purposes in closed-canopy forests. European beech is a native species across most of the European continent while also being one of the most dominant and economically important tree species (Hanewinkel et al., 2013; Leuschner and Ellenberg, 2017). Since beech is considered drought-sensitive and is widely known to feature a strong dependence on water availability (Dorado-Liñán et al., 2022; Leuschner, 2020; Mazza et al., 2024; Weigel et al., 2023), and has been shown to potentially decline in future abundance (Buras and Menzel, 2019), the species' future under climate change is of great importance for many European ecosystems and economies (Gessler et al., 2007; Leuschner, 2020). With an increasing focus on dendroecological studies in the complex environments of closed-canopy forests, significant knowledge gaps regarding the underlying drivers of altered drought sensitiv-
ity of secondary growth must be addressed, especially on a cross-regional scale, to accurately
understand the species' future under global change.

To improve our understanding of the variability of climate-growth relationships in beech trees
in closed-canopy forests, we tested the following hypotheses. (H1) Drought sensitivity is pos-
itively related to growth variability, and (H2) Drought sensitivity is negatively related to
growth trends. Both of these hypotheses are based on the expectation that growth becomes
increasingly governed by climatic stress due to climate change and due to an increasing in-
fluence of extra-canopy climate conditions when trees reach a (co-)dominant status. (H3a)
Drought sensitivity is positively related to temporal variations in drought conditions since
climatic constraints on tree growth are amplified during drier conditions. Similarly, (H3b)
drought sensitivity is higher at drier sites than wet sites since tree growth is more constrained
by the drier conditions. Consequently, H3a and H3b are distinctly different in that they refer
to temporal and spatial variations in drought conditions, respectively. Since it seems likely
that these effects on drought sensitivity interact, we furthermore explore whether there are
interactive and non-linear effects of the predictor variables growth trends, growth variability,
and water availability that modulate their relationships with drought sensitivity.
239

# 2 Methods and materials

240

## 2.1 Tree-ring data

241

We obtained tree-ring width (TRW) measurements of European beech (Fagus sylvatica L.)

242

from 344 sites from a European tree-ring network (Fig. 1) (Dorado-Liñán et al., 2022; Hacket-

243

Pain et al., 2018; Martinez del Castillo et al., 2022). We calculated basal area increments

244

(BAI) as $BAI_t = \pi R_t^2 - \pi R_{t-1}^2$ where $R_t$ is the stem radius at the end of an annual increment

245

and $R_{t-1}$ is the stem radius at the beginning of the annual increment. For the trees where

246

diameter at breast height (DBH) had been measured, BAIs were calculated by sequentially

247

subtracting from the measured DBH towards the pith. When DBH had not been measured,

248

BAIs were calculated by adding them up sequentially from the pith out. If the former option

249

resulted in BAI being negative (due to a mismatch between measured DBH and sum of TRWs),

250

BAI was calculated according to the second option (see the bai.out function of the dpIR-

251

package for details, Bunn et al., 2020). To retain absolute rates of basal area increments, we

252

did not standardize the sites’ BAI time series. However, they were log-transformed because

253

BAI is multiplicative, and growth variability increases with increasing growth rates. Site-level

254

chronologies were then calculated from the log-transformed BAI time series by robust averaging

255

for all years with at least four trees (Fritts, 1976).
256

## 2.2 Climate data

257

We obtained monthly minimum, mean, and maximum temperature data from 1901 until 2016, the year of the most recent tree-ring, at a spatial resolution of 0.5° from the Climate Research Unit (CRU) TS 4.04 dataset (Harris et al., 2020). Concurrent precipitation data was obtained at the same resolution from the Global Precipitation Climatology Center (GPCC) version 2018 (Schneider et al., 2014). To conform to local topographical effects, these gridded products were downscaled to 30-arc second resolution using CHELSA version 1.2 (Karger et al., 2017).

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We used the drought index Standardized Precipitation-Evapotranspiration Index (SPEI) to represent the major climatic variability controlling tree growth as it accounts for the combined effects of both temperature and precipitation. To calculate SPEI, we first calculated modified Hargreaves potential evapotranspiration (PET) (Droogers and Allen, 2002; Hargreaves, 1994), then subtracted PET from precipitation to calculate climatic water balance ($CWB = P - PET$,

Because light, temperature, and water resources are heterogeneously available between sites, the response of growth to the timing and time scale of these resources also differ between sites (Vicente-Serrano et al., 2013). To account for this variability, we identified the timing and time scale of SPEI that best explained tree-ring growth at each site (Leifsson et al., 2023) by calculating SPEI integrations of 2-12 months for April-September at every site and correlated all these time frames and integrational scales of SPEI with the respective sites’ BAI chronologies. For each site, we then selected the one combination of SPEI timing and scale that showed the highest positive and significant correlation (Fig. S1), using Spearman’s rank
to account for potentially non-normally distributed data. This site-specific combination of
timing and scale (from here on called SPEIx-month) selected for each site was the only one
used for the respective sites for all further analyses.

2.3 Moving window analyses

We used a moving window approach to estimate the temporal variability of the BAI chronolo-
gies' drought sensitivity. We set the window length to 31 years to balance having enough data
points for robust estimations and allowing for good temporal variability. Each window was al-
ways moved by one year at a time. Each site was analyzed individually to estimate the following
variables for all available windows: 1) *Drought sensitivity* of BAI to the site-specific combina-
tion of SPEIx-month using Spearman's rank correlation. To avoid inflated correlations due
to collinear overall trends of the relatively short 31-year windows and emphasize correlations
of the high-frequency inter-annual variability, both BAI and SPEIx-month were detrended
by linear regression against time in each window. 2) *Growth trend* of (log-transformed) BAI
calculated as the regression coefficient between BAI and time. Contrary to BAI used for SPEI-
sensitivity correlations; BAI is not detrended against time for the growth trends. 3) *Growth
variability* determined as the standard deviation of the residuals of the growth trend just de-
scribed. 4) *SPEI climatology* determined as the average undetrended SPEI, indicating drier
and wetter climatic periods. Because all sites contained more than 40 years, a minimum of ten
31-year windows could be fit per site, resulting in at least ten of each of the above variables
being calculated per site. Although strong collinearities between these moving window-derived
variables within sites sometimes occurred, there were no overall collinearities across all sites (Fig. S2), supported by variance inflation (VIF) <1.5 for the explanatory variables (Zuur et al., 2010). We additionally performed further data exploration following the protocol of Zuur et al. (2010), ensuring that no assumptions were violated and lowering the risk of type I and type II errors. Finally, the inherent autocorrelation of these variables induced by the moving window method will be accounted for in the linear mixed-effects model explained in section 2.4.

## 2.4 Linear mixed-effects model

The resulting time series of the moving window-derived variables were supplied to a linear mixed-effects model. To answer our hypotheses, growth trends, growth variability, and SPEI climatology were supplied as predictor variables, and drought sensitivity as the response variable. We tested for non-linear relationships between each site's response variable and all predictor variables. Akaike Information Criterion (AIC) of linear and quadratic terms was compared, and it was shown that AIC was more frequently lower for quadratic terms than for linear terms (Fig. S3). Because of this we included non-linear terms of all predictor variables (Anderson-Teixeira et al., 2022). We additionally included all two-way interactions between the predictor variables, as drought conditions can influence both growth trends and variability (Astigarraga et al., 2020; Hansen et al., 2022), and growth trends may influence growth variability (Cailleret et al., 2019; Camarero et al., 2015; S. Klesse et al., 2018; Neycken et al., 2022).
Due to the multiple linear, non-linear, and interaction terms, all variables were z-score standardized (Fig. S4). This improves the model performance by removing collinearities between the parameters of the main effects and their interactions, making them independent and interpretable (Gelman and Hill, 2007; Harrison et al., 2018; Schielzeth, 2010). The standardization was done at the level of each variable so that between-site variations within each variable were retained and could be accounted for in the model. A maximal model was created, including the second-order polynomials of all variables and all two-way interactions. All estimated parameters were significant, except for the interaction between growth trends and growth variability, which was removed (Table S1). This resulted in the optimal model

$$DS_{ij} = \beta_0 + \beta_1 G_{ij} + \beta_2 G_{ij}^2 + \beta_3 V_{ij} + \beta_4 V_{ij}^2 + \beta_5 C_{ij} + \beta_6 C_{ij}^2 + \beta_7 GC_{ij} + \beta_8 VC_{ij} + v_j + \epsilon_{ij} \quad (1)$$

where $DS_{ij}$ is the drought sensitivity of BAI, $G_{ij}$ is the growth trend of BAI, $V_{ij}$ is the growth variability, $C_{ij}$ is the SPEI climatology, while $GC_{ij}$ and $VC_{ij}$ are the respective interactions. In all cases, $i$ refers to an observation at site $j$, and hence $v_j$ refers to the random effect of the site-level intercept while $\epsilon_{ij}$ is the error associated with the $i$th observation at site $j$. $\beta_0$ and $\beta_1, ..., \beta_8$ refer to the fixed effect intercept and the regression coefficients of all main effects, quadratic effects, and interactions common across all sites. An autocorrelation structure with calendar year as a continuous within-site time covariate was included in the model, successfully addressing the considerable autocorrelation in the data induced by the moving window method
333 (Fig. S5). We set the method to maximum likelihood (ML) during the model selection process
334 but changed to restricted maximum likelihood (REML) for parameter estimation following
335 best statistical practice (Harrison et al., 2018; Pinheiro and Bates, 2000; Zuur et al., 2009).

2.5 Assessing the effect of site aridity on model predictions

337 While the model in equation 1 tests for the effect of temporal variations in SPEI climatology
338 within sites, we also assessed how the model predictions differed depending on site climate. To
339 do so, we created two subsets of sites, one with the driest and one with the wettest tertile of
340 sites, based on the sites’ mean annual CWB. The mean annual CWB was calculated over the
341 whole period for which we had climate data (1901 to 2016), irrespective of the length of the
342 TRW-chronologies of the respective sites, to avoid bias. Due to the lack of objective thresholds,
343 we determined the dry and wet subsets based on tertiles. The mean annual CWB of the dry
344 tertile group ranged between -439.5 and 125.8 mm y-1, whereas the wet tertile group ranged
345 between 349.5 and 1555 mm y-1 (Fig. 2). Comparing these sites by mean annual precipitation
346 sum and mean annual temperature showed an overlapping border between the groups caused
347 by the modified Hargreaves PET, taking into account differences in incoming radiation at
348 different latitudes (Fig. 1 and 2).

349 The two subsets of relatively wet and relatively dry sites (high and low mean annual CWB,
350 respectively) were applied to equation 1 individually, allowing group-specific estimations of
351 all parameters (Table S2). The subset of sites with intermediate climatic conditions (middle
tertile) was not analyzed.

## 2.6 Visualization of drought sensitivity responses

To show how drought sensitivity reacts to all three predictor variables, we created full factorial matrices of -2, 0, and +2 standard deviations ($\sigma$) between all three pairwise combinations of growth variability, growth trends, and SPEI climatology, resulting in matrices with nine levels each. Each explanatory variable was combined in its continuous version with the categorical matrix of the other two variables, which were applied to equation 1. This allowed drought sensitivity to vary as a function of the continuous variable for each of the nine configurations of the other two predictor variables. Note that standard deviations are calculated from 31-year averages of all explanatory variables, as per the moving window approach (Section 2.3), and are therefore not equal to standard deviations of the initial input data (e.g. -2 $\sigma$ in SPEI climatology $\neq$ -2 $\sigma$ in SPEI). The predictions were calculated based on the group-specific model parameters to compare dry and wet sites.

# 3 Results

## 3.1 Model overview

Drought sensitivity was most sensitive and negatively related to SPEI climatology (Table 1), indicating drought sensitivity increased with increasingly dry conditions. Drought sensitiv-
ity was positively related and second most sensitive to growth variability, indicating drought sensitivity increased with higher growth variability. Drought sensitivity was least sensitive and negatively related to growth trends, indicating drought sensitivity decreased slightly with increasing growth trends (Table 1). All quadratic terms showed negative signs, indicating concave-down non-linear relationships between drought sensitivity and all explanatory variables. Drought sensitivity was again most sensitive to the quadratic term of SPEI climatology, but less sensitive to the quadratic term of growth variability compared to the respective term of growth trends, in contrast to their linear terms. The interaction between SPEI climatology and growth trends (GC) was negative, meaning that the relationships between these variables and drought sensitivity become more negative with higher (wetter) SPEI climatology and higher growth trends. In comparison, the interaction between SPEI climatology and growth variability (VC) was positive, meaning that the relationships between these variables and drought sensitivity became more positive with increasing (wetter) SPEI climatology and increasing growth variability (Table 1). Consequently, the negative relationship between SPEI climatology and drought sensitivity is influenced in opposite directions by growth trends and growth variability, the former enhancing the negative relationship and the latter mitigating and potentially reversing it. While all parameters in the model were significant, the explained variance was low, with marginal R² ~ 0.08 and conditional R² ~ 0.08 (Nakagawa et al., 2017), indicating high variance in the estimated parameters.
388

## 3.2 Response of drought sensitivity

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391

To visualize how drought sensitivity reacts to all three predictor variables of the model (Table 1), the following three sections display drought sensitivity varying as a function of each continuous variable for each of the nine configurations of the other two predictor variables.

392

### 3.2.1 Effects of growth variability on drought sensitivity

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406

Drought sensitivity generally increased with increasing growth variability, with a comparably small quadratic effect due to an order of magnitude difference between the two terms (Table 1, Fig. 3). However, while the coupling of the positive relationship increased with wetter-than-average SPEI climatology, it decreased with drier-than-average SPEI climatology to the point that the relationship between growth variability and drought sensitivity was lost during the driest conditions, as indicated by the flat slope of the yellow lines in Fig. 3. The difference in drought sensitivity between the lower and upper ends of the growth variability range was 0.02, 0.12, and 0.22 for dry, average, and wet SPEI climatology, respectively. In comparison, the difference in mean drought sensitivity between dry and wet periods increased with increasing growth trends, as indicated by the increasing distance between lines between the facets in Fig. 3. During wet periods, mean drought sensitivity decreased significantly with increasing growth trends, 0.26, 0.19, and 0.04 for -2, 0, and +2 σ, respectively. During dry periods, the difference was not as large and instead increased slightly with growth trends, 0.29, 0.37, and 0.38 for -2, 0, and +2 σ, respectively. Consequently, while mean drought sensitivity increased
407 with declining SPEI climatology (drier periods), this pattern was broken at low growth trends
408 (Fig. 3a).

3.2.2 Effects of growth trends on drought sensitivity

410 The relationship between drought sensitivity and growth trends was relatively weak compared
to the other variables. However, the quadratic term and interaction term with SPEI climatol-
411 ogy were higher compared to the corresponding terms for growth variability (Table 1). Overall,
412 drought sensitivity decreased with increasing growth trends and more so with higher (wetter)
413 SPEI climatology (Fig. 4). The difference in drought sensitivity between -2 and +2 σ of
414 growth trends was -0.07 and -0.22 for average and wet SPEI climatology, respectively (cyan
415 and purple lines in Fig 4a compared to Fig. 4c). However, the relationship between growth
416 trends and drought sensitivity switched to become positive during dry climatic periods, with a
417 corresponding difference in drought sensitivity of 0.09 (yellow lines in Fig. 4a compared to Fig.
418 4c). This was caused by the relatively high estimate of the interaction term between growth
419 trends and SPEI climatology compared to the linear and quadratic terms of growth trends
420 (Table 1). A consequence of this is that under average SPEI climatology, the highest drought
421 sensitivity is found around a growth trend of approximately zero. In contrast, it is found
422 at declining growth trends at wetter-than-average periods and at increasing growth trends at
423 drier-than-average periods (Fig. 4).

425 The negative relationship between growth trends and drought sensitivity became more neg-
ative with increasing SPEI climatology. The difference in mean drought sensitivity between
-2 and +2 σ of SPEI climatology was 0.28 and 0.09 for -2 and +2 σ of growth variability,
respectively, although mean drought sensitivity was higher in the latter case. Furthermore,
the interaction between growth trends and SPEI climatology led to a change from a negative
to a positive relationship between growth trends and drought sensitivity in drier-than-average
periods (yellow line compared to cyan and purple lines in Fig. 4).

3.2.3 Effects of SPEI climatology on drought sensitivity

Drought sensitivity was most sensitive to the respective linear and quadratic terms of SPEI
climatology, in addition to SPEI climatology being the only explanatory variable that signif-
icantly interacted with the other explanatory variables (Table 1). SPEI climatology had an
overall negative relationship with drought sensitivity, though it reversed to a positive relation-
ship due to interactions (Fig. 5). As indicated in Fig. 3 and 4, drought sensitivity became
insensitive to variations in growth variability and growth trends at very low (dry) SPEI clima-
tology (left edges of facets in Fig. 5). At low (dry) SPEI climatology, the difference in drought
sensitivity between -2 and +2 σ of growth variability was 0.02 for all values of growth trends.
This was in contrast to high (wet) SPEI climatology (right edges of facets in Fig. 5), where
drought sensitivity declined rapidly with decreasing growth variability and increasing growth
trends, resulting in a corresponding difference in drought sensitivity of 0.22. Consequently,
there was a shift in maximum drought sensitivity towards higher (wetter) SPEI climatology
with increasing growth variability and decreasing growth trends. At below-average growth
variability and above-average growth trends (purple and orange lines in Fig. 5a), maximum drought sensitivity was found at <-2 σ of the SPEI climatology distribution (i.e., more negative than the minimum SPEI climatology shown in Fig. 5a). At above-average growth variability and below-average growth trends, this point shifted to inside the ± 2 σ distribution of SPEI climatology (i.e., inside Fig. 5c). At -2 σ of growth trends and +2 σ of growth variability (cyan line in Fig. 5c), maximum drought sensitivity occurred at wetter-than-average SPEI climatology, with the consequence of a strong decline in drought sensitivity towards drier-than-average periods.

### 3.2.4 Effect of site aridity on drought sensitivity

The model parameters created for dry and wet tertiles of sites were largely similar but with some differences (Fig. 6, Table S2). The more notable difference was a large difference in intercept, which was positive for dry sites but negative for wet sites. The consequence was a considerably higher mean drought sensitivity for dry sites than wet sites (Fig. S6a and S7-S9). Other differences in model parameters between dry and wet tertiles of sites were higher effects for the linear term for growth variability (V), the quadratic term for growth trends (G²), and the interaction between growth variability and SPEI climatology (VC), as well as a somewhat higher effect of the linear term for SPEI climatology (C), in dry sites compared to wet sites (Fig. 6). The model for dry sites did not show that the quadratic term for growth variability was significant, though the effect was still marginally higher than that for wet sites (Fig. 6). These effects were underlined by slight positive shifts in the distributions of growth trends and
growth variability for dry sites compared to wet (Fig. S6b and c).

The greater effect of the linear term for growth variability for dry sites resulted in an overall much more sensitive relationship with drought sensitivity (Fig. S7). However, the interaction between growth trends and SPEI climatology was also greater, resulting in a similar relationship between growth variability and drought sensitivity for dry and wet sites at the driest distribution range of SPEI climatology (compare the increasing difference in sensitivity between yellow and purple lines in Fig. S7).

Comparing the relationship between growth trends and drought sensitivity between dry and wet sites showed that, on the one hand, the linear term was higher for wet sites, but on the other hand, the quadratic term was higher for dry sites (Fig. S8). The relationship consequently showed a more pronounced peak drought sensitivity for the dry sites, especially for average and wetter-than-average SPEI climatology, which more frequently occurred within ± 2 σ of the growth trends distribution, in contrast to the wet sites where it occurred at lower growth trends than -2 σ (Fig. S8).

The relationship between SPEI climatology and drought sensitivity showed the least difference between the dry and wet sites' respective model terms (Fig. S9). The greatest difference was the greater interaction term between SPEI climatology and growth variability for dry sites, which was most pronounced at the lower range of the distribution of growth variability, where the relationship between growth variability and drought sensitivity was more negative than for wet sites (compare the steepness of the slopes of dry and wet sites in Fig. S9a). As a result,
the difference in drought sensitivity between dry and wet sites increased with drying SPEI
climatology, which is also evident in the corresponding figure for growth variability (compare
the distance between dashed and solid lines in Fig. S7a to Fig. S7c).

3.3 Temporal trends of variables

The distributions of the four variables, drought sensitivity of growth (DS), growth trends
(G), growth variability (V), and SPEI climatology (C), all showed different temporal trends
(Fig. S10). The response variable, drought sensitivity, showed a significant positive trend (P
< 0.01), as did growth variability (P = 0.019). Growth trends showed a positive trend until
approximately 1960 (P < 0.001), after which it shifted to a negative trend that continued until
the present (P < 0.001). SPEI climatology exhibited a more complex long-term fluctuation.
In the 1930s-50s, the distributions of values had medians slightly lower than 0, indicating
that most sites were slightly drier than on average. This shifted towards the 1970s-80s when
the medians of the distributions were slightly greater than 0, indicating that most sites were
slightly wetter than on average. Since the 1980s, the distributions trended towards average
climatic conditions, with the most recent median being approximately 0 (Fig. S10). Note that
the SPEI climatology shown here is that of the analyzed sites (Fig. 1) and is not representative
of the region that makes up the ecological amplitude of beech, let alone Europe as a whole.
Furthermore, the annual distributions of variable values do not reveal potential trends within
individual sites.
505 **4 Discussion**

506 **4.1 Effects of growth variability on drought sensitivity (H1)**

507 The model identified growth variability as having an overall positive influence on drought sen-
sitivity (i.e., a tightened coupling) in accordance with our first hypothesis (H1). This is in
508 line with the central claim in dendroclimatology that increased inter-annual growth variability
509 is driven by a stronger translation of climatic variability into growth variability, i.e., drought
sensitivity, rather than originating from internal processes decoupled from climate (Bunn et
510 al., 2013; Fritts, 1976; LaMarche, 1978). The relationship can partly be explained by changes
511 in trees’ social status within forest communities due to ontogeny and partly by increasingly
512 dry climatic conditions. Juvenile trees are embedded in the relatively moist micro-climate
513 of the understory, preventing a strong influence of ambient climate outside the canopy layer.
514

515 This understory embedding is removed as the trees reach a (co-)dominant status once their
516 crowns become increasingly exposed to incoming radiation and extra-canopy climatic condi-
tions (Brienen et al., 2022; Leuschner et al., 2023a). Furthermore, growth variability is neg-
517 atively correlated with SPEI climatology (Fig. S2), indicating that increasingly dry climatic
518 conditions (i.e., decreasing SPEI climatology) lead to increasing growth variability and thereby
519 indirectly influencing drought sensitivity. The increasing influence of climate on growth equals
520 greater drought sensitivity and consequently inter-annual growth variability (Bunn et al., 2013;
521 Carrer and Urbinati, 2004; Leuschner et al., 2023a; Thom et al., 2023; Weigel et al., 2023; Zang
522 et al., 2014). This may further explain the more sensitive relationship between growth variabil-
523
ity and drought sensitivity at drier sites compared to wetter sites during wet climatic periods (Fig. S7), as the dry sites are in greater need of water due to typically dry conditions.

However, the non-linear relationship tapers off with increasing growth variability, eventually reaching a saturation point and suspending the translation between climate variability and growth variability (Bunn et al., 2013). This occurs at lower values of growth variability with increasingly dry conditions, when mean drought sensitivity is also higher, possibly due to greater exposure to extra-canopy climatic conditions. The likely conclusion is that a climatic threshold has been passed beyond which the physiological mechanisms can tolerate (i.e., beyond an optimum response curve discussed in Wilmking et al. 2020). This is in contrast to the increasing climate sensitivity expected if the climate is more growth-limiting, as is the case for most of the SPEI climatology range (Fig. 5). This explains the higher mean drought sensitivity during wetter-than-average periods and low and declining growth trends (purple and teal lines in Fig. 3a and 4) as such growth rates may be related to stressed trees, which are more sensitive to drought conditions (Neycken et al., 2022), raising drought sensitivity under wetter-than-average conditions but lowering drought sensitivity under dry conditions. Likewise, the lower mean drought sensitivity during wetter-than-average periods and high growth trends (purple and teal lines in Fig. 3c and 4) also concur with trees’ social status within forest communities. High growth rates are more likely to be related to juvenile trees, which are more likely to be embedded in the moist and shaded understory where they are less responsive to inter-annual variability of climate during wet periods but better able to utilize temporarily warm periods compared to declining trees (Thom et al., 2023; Wang et al., 2023).
The lack of a clear relationship between drought sensitivity and growth variability (i.e., decoupling) during dry periods (yellow lines in Fig. 3) may also be an effect of increasing climatic stress due to climate change. This might explain why mean drought sensitivity is lower at low growth trends, as trees with these growth rates may be more vulnerable to increased climate stress. In comparison, while the relationship between drought sensitivity and growth variability is also decoupled at high growth trends, the higher mean drought sensitivity may be explained by the considerable buffering capacity against extra-canopy climate in understories of beech forests (Leuschner et al., 2023a). Lastly, when comparing dry and wet sites during dry periods, there is no apparent difference in the sensitivity of the relationship between growth variability and drought sensitivity. In contrast, it is considerably more sensitive in dry sites than in wet sites during wet periods (Fig. S7). This may suggest that beech in dry sites, while generally more sensitive to inter-annual drought conditions, have also adapted to the locally dry conditions.

## 4.2 Effects of SPEI climatology on drought sensitivity (H3)

In agreement with H3a, the relationship between SPEI climatology and drought sensitivity was generally negative and in line with Liebig's law of the minimum (Stine and Huybers, 2017). However, the higher non-linear effect of SPEI climatology had the effect that peak drought sensitivity more frequently occurred within ±2 σ of the SPEI climatology distribution with higher growth variability and lower growth trends (Fig. 5). These results show that during the dry end of the climatic conditions over the course of the 20th century, maximum
drought sensitivity was surpassed, resulting in a reversal and decline in drought sensitivity, in contrast to H3a. This is in general accordance with the optimum response curves presented by Wilmking et al. (2020) as well as other studies showing that drought sensitivity of secondary tree growth is non-stationary (Anderson-Teixeira et al., 2022; Babst et al., 2019; Mazza et al., 2024; Tumajer et al., 2022; van der Maaten et al., 2024; Weigel et al., 2023). The decline in the climate signal towards drier conditions may indicate an early-warning signal for an approaching tipping point (Duffy et al., 2021), e.g., due to cavitation fatigue (Hacke et al., 2001). This is corroborated by the decoupling in climate signal of growth with decreasing growth trends (cyan line in Fig. 5c). Consequently, a continued drying of the European climate may lead to further fatigue and detrimental effects due to insufficient time to recover, especially if trees are also faced with potential legacy effects following drought events (Leifsson et al., 2023).

A limitation of our study is that it cannot distinguish the effects of either juvenile trees that are exposed to extra-canopy climate, such as in the case of gaps in the canopy, or the influence of larger and deeper root systems of larger trees (Annighöfer, 2018; Brinkmann et al., 2019; but see also Gessler et al., 2022). Regarding the juvenile trees, a problem arises from the bias of the data against such occurrences in favor of closed-canopy conditions because of the ecologically oriented sampling. Regarding both the juvenile and large trees, the respective effects are masked in the current results. In the case of juvenile trees, it would require knowing how the gaps affect growth trends and variability, which, however, is unknown. In the case of large trees, better access to (deeper) groundwater would lower the drought sensitivity during dry SPEI climatology, a partial influence that is likewise unknown. It should, however, also
587

prevent the declining drought sensitivity towards the drier SPEI climatology, which is not the
case (Fig. 5).

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607

The predicted drought sensitivity was considerably higher for dry sites than wet sites, agreeing
with H3b (Fig. S6 and S9). Consequently, the impact of site aridity on mean drought sensi-
tivity overruled the corresponding effect of temporal SPEI climatology. The exceptions, when
drought sensitivity was relatively similar between dry and wet sites, were at very low growth
variability and high growth trends (orange lines in Fig. S9a). Assuming that these conditions
relate to moist and shaded understories as discussed above, it indicates that juvenile trees
are relatively equally sensitive to extra-canopy climatic conditions across beech's ecological
amplitude, despite a divergence towards lower growth trends. In the case of the remainder of
the data, the higher drought sensitivity at dry sites agrees with many reports (Knutzen et al.,
2017; Weber et al., 2013; Weigel et al., 2023; Zimmermann et al., 2015), though notably there
are many reports of the opposite (Cavin and Jump, 2017; Friedrichs et al., 2009; Muffler et
al., 2020). In contrast, Hacket-Pain et al. (2016), the only other study covering a comparable
climatological range as our study, reported a lack of difference between dry and wet sites, as
did van der Maaten-Theunissen et al. (2016) in their comparison of two sites of contrasting
available soil water holding capacity. However, except for Hacket-Pain et al. (2016), all of the
referenced studies performed their analyses on a more limited climatological and geographical
scale, and it may be that some of these studies reveal a more pronounced influence of spe-
cific local details, e.g., topography (Didion-Gency et al., 2021; Miranda et al., 2022), forest
composition (Didion-Gency et al., 2021), and edaphic factors (Leuschner, 2020; Weigel et al.,
609 **4.3 Effects of growth trends on drought sensitivity (H2)**

610 Our results showed a generally negative relationship between growth trends and drought sen-
sitivity. Interestingly, however, this was not always the case because of the non-linear and
611 interaction effects, in addition to differences between dry and wet sites, providing a more
612 nuanced perspective on the relationship (Fig. 4 and S8). Consequently, there was divergent
613 support for H2. As with site climate, the literature is equivocal about whether negative growth
614 trends are related to increasing climate stress. In agreement with the overall effect of our re-
sults, negative growth trends have been reported to occur in conjunction with increasingly
615 dry conditions (Jump et al., 2006; Neycken et al., 2024; Piovesan et al., 2008; Weigel et al.,
616 2023). However, Diers et al. (2022) found a decline in drought sensitivity with declining
617 growth trends, which in our results was the case during drier-than-average periods, especially
618 at drier sites (yellow lines in Fig. 4 and S9). Marqués et al. (2022a) and Mazza et al. (2024)
619 found that beech with declining growth rates showed lower sensitivity to precipitation and
620 an intensified negative sensitivity to temperature, contrary to Knutzen et al. (2017), who
621 found an increased sensitivity to precipitation with declining growth rates. All of these results
622 can also be discerned under specific conditions in our results. Neycken et al. (2022) and van
623 der Maaten et al. (2024) did not find clear differences in climate sensitivity between vital
624 and declining beech. Hacket-Pain and Friend (2017) found climate sensitivity to decrease for
625 both decreasing and increasing growth rates, while Camarero et al. (2021) reported declining
626
627
growth rates to occur in conjunction with both declining and unchanging sensitivity to precip-
itation. The relationship between growth trends and climate sensitivity in beech is evidently
complicated and highly variable. However, because of the non-linear terms and interactions in
our model, many of these responses can be found in our results depending on the details of all
variables and site climate. This study is, to the authors' knowledge, the most comprehensive
analysis of the relationship between growth trends and climate sensitivity, and the results
shown here were only revealed through our multivariate and multidimensional approach where
we analyzed the effects of growth variability, growth trends and SPEI climatology on drought
sensitivity simultaneously.

4.4 Decoupled relationships with drought sensitivity

The dry climatic conditions our sites experienced in the 20th century led to drought sensitivity
decoupling from growth variability, and the relationship with growth trends reversed from
increasing to decreasing under low and negative growth trends. Such indications may be
signs of early-warning signals prior to drought-induced mortality (Cailleret et al., 2019, 2017).
While the evidence of such a decoupling is not consistent across angiosperms (Cailleret et al.,
2019), beech may be assumed to be more sensitive compared to other angiosperms because
of its comparably more anisohydric hydraulic strategy (Gessler et al., 2007; Leuschner et al.,
2021; Walthert et al., 2021) which allows for maintained carbon assimilation despite drought
stress (Gebhardt et al., 2023; Motte et al., 2023; Zang et al., 2014). However, the observed
decoupling of growth variability and growth trends from drought sensitivity may likewise be
due to adaptation mechanisms, as beech has been reported to have a good ability to acclimate (Arend et al., 2022; Leuschner et al., 2023b). Still, growth rates in beech trees have been reported to decline following drought events (Camarero et al., 2021; Meyer et al., 2020; Schmied et al., 2023), as has mortality (Buras et al., 2020; Frei et al., 2022; Schuldt et al., 2020) which drier climatic conditions may worsen (Frei et al., 2022).

## Conclusion

This study has elucidated the complex nature of climate sensitivity of secondary growth in European beech across its geographical and climatological distribution. Our novel approach allowed us to determine the influence of growth trends, growth variability, and spatial and temporal SPEI climatology on drought sensitivity at unprecedented detail, dealing with all individual and interactive components simultaneously. The main conclusion to be drawn is the highly non-stationary nature of drought sensitivity of growth, which depends on not only SPEI climatology but also growth characteristics, including their interactions. While drought sensitivity increases with growth variability and decreases with growth trends, increasingly dry climatic conditions also increase drought sensitivity, potentially intensified by climate change. However, drought sensitivity is decoupled from growth variability and growth trends during dry periods.

While other studies have reported non-stationary climate sensitivity of secondary growth, to the authors' knowledge, none have clearly shown the continuous, non-linearity presented in this
study. Our results can largely be explained by both increasingly dry climatic conditions due
to climate change and by trees' rank progression within forest communities. However, drought
sensitivity declined during the driest periods the trees experienced in the 20th century. This
may indicate a tipping point and that further drying of European climate could lead to a
continued decline of climate sensitivity and a critical slowing down due to an accumulation of
legacy effects caused by continually dry conditions and insufficient recovery time.

The results of this study improve our understanding of how climate-growth relationships vary
in closed-canopy European beech forests and reveal underlying causes. This possibly explains
the widely diverging observations on climate sensitivity and growth characteristics found in
the literature and may consequently guide future research toward a unified theory of European
beech's drought sensitivity in a changing climate.

678 Acknowledgments

679 This project is funded by the Bavarian Ministry of Science and the Arts in the context of the
680 Bavarian Climate Research Network (BayKliF). The following authors acknowledge funding
681 support. ASJ: Supported by the UK Natural Environment Research Council under grants
682 NE/V00929X/1 and NE/S010041/1. BS: Supported by the Ministry of Education, Science
683 and Technological Development of the Republic of Serbia within the framework of the pro-
684 gram technological development [451-03-65/2024-03/200169]. CCR: Grant of the Ministry
685 of Research, Innovation and Digitization, under the "Romania's National Recovery and Re-
silience Plan - Founded by EU -NextGenerationEU" program, project "Compound extreme
events from a long-term perspective and their impact on forest growth dynamics (CExForD)"
number 760074/23.05.2023, code 287/30.11.2022, within Pillar III, Component C9, Invest-
ment 8. FB: Funded by the Nevada Agricultural Experiment Station through USDA Hatch
and McIntire-Stennis programs. IP: CNCS-UEFISCDI, project number PN-III-P4-PCE-2021-
1002. IDL: Proyectos de Generación de Conocimiento, MICIN (#PID2021-128759OA-I00) and
Proyectos de Consolidación Investigadora 2022 (#CNS2022-135228). JJC: Projects PID2021-
123675OB-C43 and TED2021-129770B-C21 (Spanish Ministry of Science and Innovation). JK:
DFK GRK 2010 Response. KC: ARIS -Slovenian Research and Innovation Agency, program
P4-0015. LMW: DFK GRK 2010 Response. MB: MCID nucleu program, project PN23090303.
MK: Supported by the Ministry of Education, Science and Technological Development of the
Republic of Serbia within the framework of the program technological development [451-03-
65/2024-03/200169] and Science Fond of the Republic of Serbia [6686]. MP: Supported by
University of Forestry, Sofia, Project No. B1219. MS: Czech Science and the project TRANS-
FER. TZ - D01-320/30.11.2023 LTER-BG. PJ: Czech Science and the project TRANSFER. TZ
- D01-320/30.11.2023 LTER-BG. TZ: "D01-320/30.11.2023 LTER-BG". WH: National Natural
Science Foundation of China (No. 32271973)
703 **Data availability statement**

704 Gridded historical temperature data is available at the Climatic Research Unit https:

705 // rudata.uea.ac.uk/cru/data/hrg/ and gridded historical precipitation data is available at

706 the Global Precipitation Climatology Centre https://opendata.dwd.de/climate_environment/

707 GPCC/html/. European beech tree-ring data is not publicly available, contact Christian

708 Zang (christian.zang[at]hswt.de) for inquiries.

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<table><caption>Table 1: Estimated parameters of the restricted maximum likelihood (REML) best model. G is growth trends, V is growth variability, and C is SPEI climatology. V², G², and C² are the quadratic terms of the respective linear terms. GC and VC are the interactions between the respective linear terms.</caption><thead><tr><th>Parameter</th><th>Value</th><th>Std.Error</th><th>t-value</th><th>p-value</th></tr></thead><tbody><tr><td>Intercept</td><td>0.083</td><td>0.038</td><td>2.184</td><td>0.029</td></tr><tr><td>G</td><td>-0.080</td><td>0.012</td><td>-6.686</td><td>0.000</td></tr><tr><td>G<sup>2</sup></td><td>-0.045</td><td>0.004</td><td>-10.608</td><td>0.000</td></tr><tr><td>V</td><td>0.148</td><td>0.010</td><td>14.847</td><td>0.000</td></tr><tr><td>V<sup>2</sup></td><td>-0.016</td><td>0.003</td><td>-5.225</td><td>0.000</td></tr><tr><td>C</td><td>-0.219</td><td>0.008</td><td>-27.742</td><td>0.000</td></tr><tr><td>C<sup>2</sup></td><td>-0.060</td><td>0.004</td><td>-14.384</td><td>0.000</td></tr><tr><td>GC</td><td>-0.093</td><td>0.007</td><td>-13.236</td><td>0.000</td></tr><tr><td>VC</td><td>0.059</td><td>0.006</td><td>9.505</td><td>0.000</td></tr></tbody></table>
<figure><img src="image_2.png" /><figcaption>Figure 1: Analyzed sites colored according to subsetted dry, intermediate, and wet tertile groups based on mean annual climatic water balance (see section 2.5 for details). Topography is a major factor in determining these categories and explains the close spatial proximity of dry and wet sites. The green background area indicates European beech's continuous geographical distribution (Caudullo et al., 2017).</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>Figure 2: Dry, intermediate, and wet tertile subsets of sites used to compare effects of site climate. Climatic conditions between 1901 and 2016 are shown as (a) mean annual climatic water balance (CWB) and (b) within their climate envelope. The overlap of groups in (b) is due to CWB accounting for differences in incoming radiation depending on latitude.</figcaption></figure>
<figure><img src="image_4.png" /></figure>

Figure 3: Drought sensitivity (DS) as a function of growth variability (V) for -2, mean, and +2 standard deviations of SPEI climatology (C, colors; yellow, teal, and purple, respectively) and growth trend (G, facets; (a), (b) and (c), respectively). The left and right edges of the facets represent -2 and +2 standard deviations of the distribution of growth variability values, respectively. X- and y-axes are presented as non-standardized variables to improve interpretation. Predictions based on the x-axis variable outside ±2 standard deviations have been cut to adhere to the same distribution range as the categorized variables (colors and facets). The black vertical line indicates the mean of the distribution of growth variability. The data shown are fixed effects level predictions. The corresponding figure distinguishing between dry and wet sites can be found in Fig. S7.
<figure><img src="image_5.png" /><figcaption>Figure 4: Drought sensitivity (DS) as a function of growth trend (G) for -2, mean, and +2 standard deviations of SPEI climatology (C, colors; yellow, teal, and purple, respectively) and growth variability (V, facets; (a), (b) and (c), respectively). The left and right edges of the facets represent -2 and +2 standard deviations, respectively, of the distribution of growth trend values. X- and y-axes are presented as non-standardized variables to improve interpretation. Predictions based on the x-axis variable outside ±2 standard deviations have been cut to adhere to the same distribution range as the categorized variables (colors and facets). The black vertical line indicates the mean of the distribution of growth trends, while the gray vertical line indicates the point of no growth trend, separating increasing and decreasing trends (corresponding to -0.73 standard deviations from the mean). The data shown are fixed effects level predictions. The corresponding figure distinguishing between dry and wet sites can be found in Fig. S8.</figcaption></figure>
<figure><img src="image_6.png" /></figure>

Figure 5: Drought sensitivity (DS) as a function of SPEI climatology (C) for -2, mean, and +2 standard deviations of growth trend (G, colors; turquoise, black, and orange, respectively) and growth variability (V, facets; (a), (b) and (c), respectively). The left and right edges of the facets represent -2 and +2 standard deviations, respectively, of the distribution of SPEI climatology values. X- and y-axes are presented as non-standardized variables to improve interpretation. Predictions based on the x-axis variable outside ±2 standard deviations have been cut to adhere to the same distribution range as the categorized variables (colors and facets). The black vertical line indicates the mean of the distribution of SPEI climatology. The data shown are fixed effects level predictions. The corresponding figure distinguishing between dry and wet sites can be found in Fig. S9.
<figure><img src="image_7.png" /><figcaption>Figure 6: Comparison of parameter effects for dry and wet subsets of sites after being supplied to equation 1 (Table S2). Non-significant parameters are indicated by a '+' and were kept as the model was created based on all data (see section 2.4).</figcaption></figure>