# Analysing resilience of European beech tree to recurrent extreme drought events through ring growth, wood anatomy and stable isotopes

Guangqi Zhang, Nathalie Bréda, Nicolas Steil, Pierre-antoine Gaertner, Joseph Levillain, Julien Ruelle, Catherine Massonnet

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## To cite this version:

Guangqi Zhang, Nathalie Bréda, Nicolas Steil, Pierre-antoine Gaertner, Joseph Levillain, et al. Analysing resilience of European beech tree to recurrent extreme drought events through ring growth, wood anatomy and stable isotopes. Journal of Ecology, 2025, 113 (4), pp.955-973. 10.1111/1365-2745.70014 . hal-05077536

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# Analysing resilience of European beech tree to recurrent extreme drought events through ring growth, wood anatomy and stable isotopes

Guangqi Zhang ID

Nathalie Bréda ID

Nicolas Steil

Pierre-Antoine Gaertner ID

Joseph Levillain ID

Julien Ruelle ID

Catherine Massonnet ID

UMR SILVA, Université de Lorraine,
AgroParisTech, INRAE, Centre
de Recherche Grand-Est Nancy,
Champenoux, France

**Correspondence**

Guangqi Zhang
Email: zh.guangqi@gmail.com

**Funding information**

Region Grand-Est, Grant/Award Number:
22-JC-006; French National Research
Agency-LabEx ARBRE, Grant/Award
Number: ANR-11-LABX-0002-01

Handling Editor: Giovanna Battipaglia

**Abstract**

1. Recent extreme drought events in Central Europe have caused widespread forest dieback with detrimental effects on forest functioning and carbon and water balance. This impact has been notable on European beech (Fagus sylvatica L.), particularly at the core of its distribution, causing concern among forest stakeholders and questions about the resilience capacity of beech trees. The objective of this study is to investigate the physiological processes linked to water and carbon constraints involved in the resilience of beech cambial growth to drought.

2. We selected 56 beech trees distributed in four plots in north-eastern France with different soil water deficits characterized retrospectively by a water balance model. Functional traits including tree ring width, wood anatomical traits and stable isotopes (e.g. δ¹³C and δ¹⁸O) were measured to retrospectively assess the effect of recent recurrent drought in 2015, 2018–2020, and 2022.

3. Decreased tree growth and increased δ¹⁸O and intrinsic water use efficiency (iWUE) were observed due to soil water shortage, whereas xylem vessel size and theoretical specific xylem hydraulic conductivity (Kth) did not show obvious changes. Vessel density was negatively correlated with annual ring width index and was highly sensitive to drought. δ¹³C, δ¹⁸O and iWUE were not significantly related to tree ring width index. The plot that experienced the most severe drought intensity in 2018–2020 showed a significant decrease in tree growth resistance and resilience compared to its resistance and resilience during the 2015 drought event. Surprisingly, growth resilience was not associated with tree anatomical and isotopic traits.

4. Synthesis. Our results demonstrate that beech xylem structure responds to drought by adjusting the radial growth of tree rings with a relatively stable vessel diameter. Our study also highlights the impact of consecutive or recurrent drought in reducing beech tree resistance and resilience, particularly at sites with higher drought intensity. Tree resilience does not seem to involve changes

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<footnote>This is an open access article under the terms of the [Creative Commons Attribution-NonCommercial License](http://creativecommons.org/licenses/by-nc), which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

© 2025 The Author(s). *Journal of Ecology* published by John Wiley & Sons Ltd on behalf of British Ecological Society.</footnote>
in traits that would promote the hydraulic functioning to better cope with future
soil water shortages.

KEYWORDS

carbon and oxygen isotope discrimination, recovery, resilience, resistance, soil water deficit,
tree cambial growth, water use efficiency, xylem anatomy

# 1 | INTRODUCTION

Extreme drought events are responsible for widespread forest die-back and large-scale tree mortality events across the globe (Allen et al., 2010; Choat et al., 2018; McDowell et al., 2022), which could have a detrimental impact on both short-term forest functioning (e.g. forest productivity, water and carbon cycles) and long-term ecosystem dynamics (Anderegg et al., 2013; Bréda et al., 2006; McDowell et al., 2020). With the recurrent droughts projected for the future (IPCC, 2021), it is crucial to identify the physiological mechanisms that enable trees to cope with and withstand severe water shortages, in order to better understand long-term changes in growth, vigour and survival (Hartmann et al., 2022; McDowell et al., 2020). This is particularly true in Central Europe, which has experienced an exceptional drought episode from 2018 to 2020, leading to widespread early discoloration and premature leaf fall of dominant deciduous tree species mainly in the summer of 2018 (Frei et al., 2022; Thom et al., 2023; Toreti et al., 2019). That summer total leaf discoloration and defoliation of trees have been attributed to embolism in the water-conducting vessels and leaf petiole, in response to hot temperature and drought, that reduces tree leaf area and transpiration to avoid vessel cavitation and xylem embolism, protecting perennial organs (branches, stems) from hydraulic failure (Arend et al., 2022; Bréda et al., 2006; Schuldt et al., 2020; Wolfe et al., 2016). Understanding the physiological mechanisms of drought-induced tree mortality has been a long-standing challenge addressed largely in the literature by the two main interconnected concepts, hydraulic failure and carbon starvation (Adams et al., 2017; Choat et al., 2012; Chuste et al., 2020; Hammond et al., 2019; Hartmann, 2015; McDowell, 2011; McDowell et al., 2008). It is also important to understand the mechanisms of tree growth recovery after a drought, as trees with lower drought resilience tend to face higher mortality risk from future droughts (DeSoto et al., 2020; Klein et al., 2018; Trugman et al., 2018). A more comprehensive understanding of the processes that distinguish growth resilience and mortality trajectories would facilitate the evaluation of risks to forest ecosystems (Reyer et al., 2015).

Plant responses to drought are mediated by several structural and functional traits that integrate water and carbon processes, such as tree ring width, wood anatomical traits and intrinsic water use efficiency (iWUE) assessed by carbon isotope discrimination (Cailleret et al., 2017; Camarero et al., 2023; Olano et al., 2014; Pellizzari et al., 2016; Ponton et al., 2001; Puchi et al., 2021). Tree

radial growth, which reflects changes in individual vitality, productivity and carbon allocation (Babst et al., 2014; Babst et al., 2018), has been demonstrated to be driven by climate and, in particular, limited by drought (Babst et al., 2019; Camarero et al., 2021). Changes in annual tree-ring widths provide a measure of growth responses to drought over several decades (Cailleret et al., 2019; Camarero et al., 2023; Italiano et al., 2023). In particular, tree growth resilience indices (i.e. growth resistance, recovery and resilience) (Lloret et al., 2011) were widely used in studies focused on growth response to drought (Cabon et al., 2023; DeSoto et al., 2020; Song et al., 2022; Veuillen et al., 2023; Vitasse et al., 2019), although the way to measure ecological resilience is widely debated in the literature (Ingrisch & Bahn, 2018; Schwarz et al., 2020). Growth resistance evaluates the capacity of the tree to maintain its growth level during a disturbance, and growth recovery indicates the capacity of the tree to restore its growth after a disturbance compared to growth during the disturbance, while growth resilience is defined as the capacity of the tree to reach pre-disturbance growth levels. The deterioration of resilience over time due to recurrent drought could be considered an early warning signal of tree decline (DeSoto et al., 2020; Veuillen et al., 2023). Both drought-induced hydraulic failure and tree growth response after drought could be associated with hydraulic conductivity in the xylem, which is mainly coordinated by wood anatomical traits, especially for vessel traits (Arnič et al., 2021; Klein et al., 2018; Pellizzari et al., 2016). Vessels are the main channels for axial water transport in plants, and their traits greatly affect plant hydraulic performance, that is the flow rate of water, maintenance of the water potential gradient and vulnerability to xylem cavitation (Wheeler et al., 2005). Vessel sizes (e.g. diameter) could be used as a proxy of theoretical specific hydraulic conductivity (i.e. conductance per unit xylem surface area), which is widely used in the literature (Poorter et al., 2010; Schuldt et al., 2016; Tyree & Ewers, 1991). However, quantitative wood anatomy has rarely been used to determine the relative importance of physiological mechanisms that contribute to drought-induced die-offs (Italiano et al., 2023).

As a proxy for the amount of leaf water lost by plants due to
transpiration controlled by stomatal conductance, per unit of as-
similated carbon, iWUE is an important attribute when determin-
ing the exchange of water between terrestrial ecosystems and the
atmosphere (Seibt et al., 2008). Tree ring carbon isotope compo-
sition (δ¹³C) is widely used to infer iWUE as it provides integrated
information on the growing season scale about the ratio between
the photosynthesis rate and the stomatal conductance rate that is related to intercellular (Cᵢ) and atmospheric (Cₐ) concentrations of CO₂ (Battipaglia et al., 2010; Diao et al., 2023; Pellizzari et al., 2016; Saurer et al., 2004). Complementarily, the oxygen stable isotope composition (δ¹⁸O) provides additional physiological information on the effect of stomatal conductance rate on iWUE (Daux et al., 2018; Nock et al., 2011). Tree-ring δ¹⁸O is related to leaf water δ¹⁸O whose content is controlled by both source water δ¹⁸O content (precipitation and depth to which roots draw water from the soil) and isotopic enrichment during leaf water loss by transpiration (preferential evaporation of the lighter ¹⁶O isotope) (Gessler et al., 2014). For instance, a lower stomatal conductance leads to decreased Cᵢ/Cₐ ratios and thus an increase in iWUE and tree ring δ¹³C. Similarly, an increase in tree ring δ¹⁸O can also be interpreted as a leaf water ¹⁸O enrichment due to a reduced stomatal conductance, which is expected to result in a strong coupling between iWUE (δ¹³C) and δ¹⁸O. Tree ring δ¹³C and δ¹⁸O help to understand retrospectively physiological responses to environmental conditions such as soil water conditions (Gessler et al., 2018). Therefore, water and carbon limitation in drought-induced dysfunctions could be better understood by combining δ¹³C and δ¹⁸O analyses (dual-isotope approach), along with tree ring width and wood anatomical characteristics.

European beech (Fagus sylvatica L.) is an important tree species in temperate European forests (Leuschner & Ellenberg, 2017), with a wide distribution range and a high level of intraspecific plasticity. The radial growth of the species is sensitive to soil water deficit and has been suffering growth decline in Central Europe generally in the last decade and sometimes before, especially after the 2003 dry and hot extreme event (Bréda et al., 2006; Leuschner, 2020). Following the extraordinary drought spell of 2018, especially in Central Europe, beech may have reached a tipping point where many individual trees can no longer survive and are turning to a die-back process. Furthermore, the consecutive drought events in 2019 and 2020 exacerbated the consequences of the extreme drought and temperature anomalies experienced by beech trees in 2018 (Obladen et al., 2021; Thom et al., 2023). Therefore, it is necessary to assess the risk of beech forest decline under future hotter and longer droughts, which may exceed physiological regulation and thus challenge forest managers on the question of the future of beech as a productive species (Hajek et al., 2016).

By retrospectively analysing and comparing structural and functional traits, we specifically aim to investigate: (1) How is tree growth related to tree ring anatomical and isotopic properties, in particular, in response to extreme soil water deficit? We expect that a beech tree with wider rings would have larger vessels and specific hydraulic conductivity but lower vessel density, while narrower tree rings would have higher iWUE and δ¹⁸O. (2) Are tree growth resistance, recovery and resilience affected by recurrent drought events, and how do anatomical and isotopic traits influence tree growth resilience capacity? We expect that tree growth resilience would be limited after experiencing recurrent and prolonged droughts. In addition, we expect that higher vessel diameter and specific hydraulic

conductivity after drought would lead to greater growth resilience, while lower iWUE would lead to lower growth resilience.

## 2 | MATERIALS AND METHODS

### 2.1 | Study sites

This study was conducted in northeastern France, where trees experienced severe drought between 2018 and 2020, as calculated by soil water balance modelling (Figure S1). Four sampling sites dominated by *F. sylvatica* were selected, located in the plains and in the heart of the beech ecological range, in a latitudinal range of 158 km. Specifically, these sites were located in Flavigny-sur-Moselle (FLA), Langensoultzbach (LAN), Bouzemont (BOU) and Chassey-les-montbozon (CHA) (Figure 1). The geographical and climatic information for these four sites is shown in Table 1. The initial site selection was based on the variation between 2019 and 2022 of the average crown leaf loss of the trees at each site as monitored annually by the French forest health division according to ICP forest rating (Eichhorn et al., 2020). The mean leaf loss of trees in the FLA, LAN and BOU sites was greater than 50%, while that of the CHA site was less than 30%, allowing comparison between sites (Table S1).

### 2.2 | Soil water deficit indicators calculation

The retrospective annual soil water deficit at the stand level was characterized by the duration and the intensity of the water deficit, as calculated using the BILJOU© daily water balance model (Granier et al., 1999, https://appgeodb.nancy.inra.fr/biljou/), to objectively identify when trees are submitted to edaphic drought. Soil water deficit is defined as occurring when the daily relative extractable water of soil falls below a 40% threshold, the value inducing regulation of tree transpiration by stomatal closure (Granier et al., 1999). The water deficit is calculated as the sum of daily differences between the relative extractable water and the 0.4 value divided by 0.4. To quantify locally the soil water deficit, characteristics of the forest stand (e.g. coordinates, leaf area index and phenology: dates of budburst and leaf fall), soil properties (e.g. rooting depth, plant extractable soil water content, fine root fraction, soil water content at permanent wilting point and bulk density) and daily meteorological data (e.g. 2 m wind speed, rainfall, average air temperature, average vapour pressure deficit and global radiation) for each selected site were needed to perform the calculation via the BILJOU© model.

For each site, a maximum leaf area index was set based on visual evaluation by experts (here we set at 6 for maximum leaf area index, an average value before decline for beech stands) (Bréda, 2003) and the average budburst and leaf fall day of the year were estimated (the budburst day was set at 112 and leaf fall day was set at 300) (Lebourgeois et al., 2002). Soil profiles were described and soil samples from each soil layer were collected to obtain the soil properties requested to run the water balance model (Table S2). A minimum
<figure><img src="image_5.png" /><figcaption>FIGURE 1 Location of the four study sites in northeastern France. The background map represents the altitude distribution in the zone.</figcaption></figure>

<table><caption>TABLE 1 Mean climate characteristics across growth season (April–September) from 2013 to 2022 for the four sites and mean biometric parameters of beech trees per site in 2022.</caption><thead><tr><th>Site</th><th>Latitude (N)</th><th>Longitude (E)</th><th>Altitude (m)</th><th>T<sub>mean</sub><br>(°C)</th><th>T<sub>max</sub><br>(°C)</th><th>T<sub>min</sub><br>(°C)</th><th>Precipitation<br>(mm)</th><th>VPD<br>(kPa)</th><th>No.</th><th>DBH<br>(cm)</th><th>Age<br>(years)</th></tr></thead><tbody><tr><td>FLA</td><td>48°34′23″</td><td>6°12′29″</td><td>298</td><td>15.9</td><td>27.5</td><td>2.7</td><td>376.4</td><td>0.56</td><td>15</td><td>50.1</td><td>108</td></tr><tr><td>LAN</td><td>48°57′22″</td><td>7°43′01″</td><td>274</td><td>15.8</td><td>27.5</td><td>2.6</td><td>395.0</td><td>0.56</td><td>14</td><td>65.2</td><td>160</td></tr><tr><td>BOU</td><td>48°15′29″</td><td>6°15′15″</td><td>394</td><td>15.3</td><td>26.9</td><td>2.2</td><td>380.0</td><td>0.53</td><td>15</td><td>71.4</td><td>149</td></tr><tr><td>CHA</td><td>47°31′55″</td><td>6°19′05″</td><td>312</td><td>16.4</td><td>27.7</td><td>3.4</td><td>482.4</td><td>0.54</td><td>12</td><td>51.9</td><td>104</td></tr></tbody></table>

Abbreviations: DBH, diameter at breast height; No., number of trees; T<sub>max</sub>, maximum temperature during the growth season; T<sub>mean</sub>, mean temperature during the growth season; T<sub>min</sub>, minimum temperature during the growth season; VPD, vapour pressure deficit during the growth season.

of three soil sampling points were taken to check the homogeneity of the soil sites. We calculated soil available water content and soil humidity at the permanent wilting point of each soil layer using the pedotransfer function of Al Majou et al. (2007) with particle size distribution, organic carbon analysis and bulk density as input data. The daily meteorological data were obtained from the SAFRAN meteorological re-analysis system for French sites (Quintana-Seguí et al., 2008).

Several studies indicated that 2018 was considered a severe drought year in Central Europe (Schuldt et al., 2020; Thom et al., 2023). However, in order to better identify the periods before,

during and after the drought, and to compare the intensity of soil water deficit among our sites, we calculated drought indicators for each site for the past 10 years from 2013 to 2022. In doing so, we determined that 2015, 2018–2020 and 2022 were severe drought years in the FLA, LAN and BOU sites by comprehensively analysing soil water deficit intensity, temperature and precipitation information of each site and most drought occurred before July (Figure 2; Figure S2). It is worth noting that the FLA site experienced the highest drought intensity in 2018 and is generally the driest site in this study (Figure 2e–h). Based on the annual water deficit intensity, it appears that the CHA site, which had the lowest crown leaf loss among
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>FIGURE 2 Temperature, precipitation (a–d), soil water deficit (e–h) and daily soil relative extractable water (i–l) information for the four sites (FLA, LAN, BOU and CHA) from 2013 to 2022. In (a–d), the orange and light-yellow lines represent the maximum and mean temperature during growth season (from April to September), respectively and the blue bars represent total precipitation during growth season; Past represents the average from 1959 to 1999. In (e–h), the red lines represent water deficit intensity and the yellow bars represent water deficit duration for a certain year calculated with the Biljou© model; The grey dashed line at 40 represents the drought intensity threshold used to separate dry and wet years; The numbers noted above certain yellow bars is the starting date (in Julian day) of drought conditions for the years identified as dry; Past represents the average from 1959 to 1999. In (i–l), the lines of different colours represent different years and allows to point out the starting day of drought; The black line represents the average soil relative extractable water calculated on the 1959–2022 time sequence; The red dashed line at 0.4 represents the threshold of soil water availability.</figcaption></figure>

the four sites, did not experience severe soil water deficit intensity, but the years 2018 and 2022 presented particularly severe anomalies of soil water intensity conditions compared to the usual soil water conditions for this site. Moreover, it still experienced a certain duration of water deficit in 2015, 2018–2020, and 2022 compared to other years (Figure 2h). Therefore, we treat 2015, 2018–2020 and 2022 as dry years across all four sites for better comparison in the following analyses.

## 2.3 | Tree sampling

At each site, we selected 15 dominant trees whose crown condition has been annually monitored since 2019 by the French Forest Health Division. The trees' health status was estimated according to the percentage of crown leaf loss using binoculars according to the ICP Forest rating (Eichhorn et al., 2020). A total of 56 trees were finally sampled during the spring of 2023, as one tree was missing from the LAN site and three trees from the CHA site (Table 1). The French National Forest Health Division requested permission to core trees from the managers or owners of each studied site. The diameter at breast height (DBH) was measured for each sampling tree. Using an increment borer, four cores from bark to pith were taken from each tree at 1.3 m. One core to the pith was used for tree ring width measurement and tree age determination; one core of 15 cm for xylem

anatomical measurement and two cores of 15 cm in two opposite directions were mixed together ring by ring according to cross-dated tree cores for annual carbon and oxygen natural isotope measurements. One of the four cores (core to the pith) was cross-dated against the site master series, and since all four cores were from the same tree, the three remaining cores were cross-dated against the well-dated core to the pith.

## 2.4 | Tree ring width

In the laboratory, the cores used for tree ring width measurement underwent surface treatment using a core-microtome (Gärtner & Nievergelt, 2010). Subsequently, the tree cores were painted black with a marker and the vessels were filled with calcium carbonate powder to clearly distinguish the ring boundaries. Tree core images were acquired with a digital camera (Canon EOS 5DSR, Japan) and tree ring width (TRW; mm) was measured to a precision of 0.01 mm using CooRecorder software (Cybis Electronics, Sweden). The accuracy of visual cross-dating was further checked with the CDendro software (Cybis Electronics, Sweden). We identified the age of each tree according to the sum of years from the cross-dated cores plus the number of rings estimated with pith-offset. We also obtained the basal area increment (BAI) to represent the tree ring area in a given year by converting tree ring width (González de Andrés et al., 2021).
<figure><img src="image_8.png" /></figure>

The standardization to remove age-related growth trends is crucial for assessing the climate-growth relationship, as it allows us to remove all the low-frequency variation. To preserve decadal variability in growth and reflect climatic influences on growth, the TRW series were detrended using the R package 'dpIR' (Bunn, 2008). A cubic smoothing spline with a 50% frequency cut-off at 30 years was fitted to each individual raw series. Then, the ratio between the raw TRW value and the corresponding year's spline value was calculated to obtain the detrended ring width index (RWI) per tree.

To assess stem growth responses to drought, resilience metrics (i.e. resistance, recovery and resilience) based on RWI chronologies were calculated for each tree and for the first two drought events (i.e. 2015 and period 2018 to 2020). The resilience indices were calculated as follows (Lloret et al., 2011):

$$RWI \text{ Resistance} = \frac{RWI_{\text{drought}}}{RWI_{\text{pre}}}, \qquad (1)$$

$$RWI \text{ Recovery} = \frac{RWI_{\text{post}}}{RWI_{\text{drought}}}, \qquad (2)$$

$$RWI \text{ Resilience} = \frac{RWI_{\text{post}}}{RWI_{\text{pre}}}, \qquad (3)$$

where $RWI_{drought}$ is the ring-width index in drought year 2015 or the average value for the drought period 2018 to 2020; $RWI_{pre}$ is the average ring-width index before the drought year or period; and $RWI_{post}$ is the average ring-width index after the drought year or period. However, our choice was limited by the fact that our trees experienced a 2-year interval between droughts, and there are also studies showing that most trees recover from droughts within 2 years (Schwalm et al., 2017). Moreover, as generally observed for beech (Knüver et al., 2022; Lebourgeois et al., 2005; van der Werf et al., 2007), only first-order auto-correlation (AC1, correlation tree ring n and n-1) is significant, while AC2, AC3 and AC4 are non-significant for any of our studied sites. Therefore, we ultimately chose 2 years as our post-drought reference period. We also compared our results with 1 year and 2 years post-drought reference periods, and the results were not significantly different (data not shown). In addition, since the pre-drought period for the 2018 to 2020 drought was also the post-drought period for the 2015 drought (2016–2017), we finally decided to choose the same $RWI_{pre}$ for 2015 drought and the 2018–2020 drought in order to maintain an undisturbed reference period. But tree growth fluctuates significantly if the pre-drought reference period is set to 2 years (2013–2014); therefore, we ended up choosing a 5-year pre-drought reference period (2010–2014) to represent a normal growth pattern before the drought.

## 2.5 | Wood anatomy

Tree cores used for xylem anatomical measurement were placed in a solution of alcohol and water for further analysis. When analysing, 10-cm-long sections were selected (at least the most recent 10 tree

rings were included, which cover different droughts experienced by the tree) and we used a sliding microtome to cut 15–20 µm thick cross-sections. Cross-sections were stained with FASGA, a solution with safranin and Alcian blue, and then dehydrated using 100% ethanol. Permanent slides were prepared using Euparal mounting medium. Finally, high-resolution images of the sections were taken using a light microscope (Zeiss Axio Imager M2, Germany) with a digital camera (Zeiss Axiocam 506 colour, Germany). Image processing was performed using ImageJ 1.52 software (https://imagej.nih.gov/ij/) and several vessel traits were measured within each ring for the period 2013 to 2022.

We measured the xylem cross-sectional area ($A_s$) and total number of vessels (n) in each ring section. For each vessel, we measured its major ($R_1$), minor ($R_2$) vessel radius and vessel area. Vessel fraction (VF) was defined as the ratio of the sum of vessel area to xylem cross-sectional area. Vessel density (VD; mm⁻²) was counted as the average number of vessels per mm² of xylem cross-section. The diameter of each vessel (D; µm) was calculated according to the equation (Schuldt et al., 2013; White, 1991):

$$D = \sqrt[4]{\frac{32(R_1 R_2)^3}{R_1^2 + R_2^2}}. \qquad (4)$$

The mean vessel diameter (MVD; µm) was then calculated from the individual vessel diameter distribution. Vessel mean hydraulic diameter (MHD; µm) was calculated according to the equation (Scholz et al., 2013):

$$MHD = \sqrt[4]{\frac{\sum D^4}{n}}. \qquad (5)$$

We also calculated the theoretical specific xylem hydraulic conductivity ($K_{th}$; kg m⁻¹ MPa⁻¹ s⁻¹) using the Hagen-Poiseuille equation (Tyree & Ewers, 1991):

$$K_{th} = \frac{\pi \rho \sum D^4}{128 \eta A_s}, \qquad (6)$$

where ρ is the density of water (998.2 kg m⁻³ at 20°C), η is the water viscosity (1.002 × 10⁻⁹ MPas at 20°C), and $A_s$ is the xylem cross-sectional area.

To represent the potential water conductivity capacity of a whole tree ring independent of its width, we also calculated the total ring area hydraulic conductivity ($TK_{th}$; kg m MPa⁻¹ s⁻¹):

$$TK_{th} = BAI \times K_{th}. \qquad (7)$$

## 2.6 | ¹³C and ¹⁸O stable isotope ratios and iWUE

Tree cores used for carbon and oxygen natural isotope analyses were cut first into annual segments following the tree rings spanning the period 2013–2022 under a visual microscope, and 10 segments per core were collected. Segments of two cores per tree from the same years were mixed together, and finally 10 samples per tree
<table><caption>TABLE 2 Tree ring traits of beech from 2013 to 2022 across four sites. Min, Max and Mean are values from all the individual rings; SD and CV were calculated first per tree and then averaged per site.</caption><thead><tr><th>Trait</th><th>Abbreviation</th><th>Unit</th><th>Min</th><th>Max</th><th>Mean</th><th>SD</th><th>CV</th></tr></thead><tbody><tr><td>Tree ring width</td><td>TRW</td><td>mm</td><td>0.08</td><td>3.94</td><td>1.26</td><td>0.55</td><td>0.48</td></tr><tr><td>Ring width index</td><td>RWI</td><td>—</td><td>0.07</td><td>2.31</td><td>0.87</td><td>0.37</td><td>0.45</td></tr><tr><td>Carbon isotope composition</td><td>δ<sup>13</sup>C</td><td>‰</td><td>-30.28</td><td>-23.65</td><td>-26.77</td><td>0.62</td><td>0.02</td></tr><tr><td>Oxygen isotope composition</td><td>δ<sup>18</sup>O</td><td>‰</td><td>20.93</td><td>27.77</td><td>24.50</td><td>0.86</td><td>0.03</td></tr><tr><td>Water use efficiency</td><td>iWUE</td><td>µmol mol<sup>-1</sup></td><td>62.23</td><td>135.28</td><td>73.05</td><td>7.29</td><td>0.07</td></tr><tr><td>Vessel fraction</td><td>VF</td><td>—</td><td>0.16</td><td>0.51</td><td>0.36</td><td>0.03</td><td>0.09</td></tr><tr><td>Mean vessel diameter</td><td>MVD</td><td>µm</td><td>37.14</td><td>62.63</td><td>49.03</td><td>2.60</td><td>0.05</td></tr><tr><td>Vessel density</td><td>VD</td><td>mm<sup>-2</sup></td><td>92.87</td><td>265.58</td><td>168.00</td><td>16.84</td><td>0.10</td></tr><tr><td>Theoretical specific xylem hydraulic conductivity</td><td>K<sub>th</sub></td><td>kg m<sup>-1</sup> MPa<sup>-1</sup> s<sup>-1</sup></td><td>13.53</td><td>71.05</td><td>38.10</td><td>5.67</td><td>0.15</td></tr><tr><td>Total ring area hydraulic conductivity</td><td>TK<sub>th</sub></td><td>kg m MPa<sup>-1</sup> s<sup>-1</sup></td><td>0.0031</td><td>0.2561</td><td>0.0759</td><td>0.0340</td><td>0.50</td></tr></tbody></table>

Abbreviations: CV, coefficient of variation; Max, maximum; Min, minimum; SD, standard deviation.

were collected. Each sample was ground to a fine powder with a mixer mill.

For δ¹³C analysis, wood powder from each sample (1mg) was weighed into a tin foil capsule and was combusted to CO₂ with an excess of oxygen in an elemental analyser (vario ISOTOPE cube, Elementar, Germany) linked to a continuous flow isotope ratio mass spectrometer (Isoprime100, IRMS, Elementar, United Kingdom) available at SILVATECH (Silvatech, INRAE). For δ¹⁸O analysis, wood powder (0.5mg) was weighed into a silver foil capsule and was combusted using an isotope ratio mass spectrometer (Isoprime, IRMS, Elementar, United Kingdom) interfaced with an elemental analyser (vario PYRO cube, Elementar, Germany). Isotope values were expressed as delta values in ‰ relative to the isotope ratio of Vienna Pee Dee Belemnite (VPDB) standards for carbon and Vienna Standard Mean Ocean Water (VSMOW) standards for oxygen. The uncertainty of the measurements is 0.3‰ for carbon and 0.8‰ for oxygen for complex samples (i.e. not a pure molecule). The δ¹³C series were corrected due to fossil fuel burning (McCarroll & Loader, 2004) following:

$$ \delta^{13}\text{C} = \delta^{13}\text{C}_{\text{tree}} - (\delta^{13}\text{C}_{\text{atm}} + 6.4), \quad (8) $$

where δ¹³Ctree is the raw δ¹³C value of tree rings and δ¹³Catm is δ¹³C composition of CO₂ in the atmosphere.

We also used raw δ¹³C values of tree rings to calculate iWUE, which is defined as the ratio between the photosynthesis rate (A) and the stomatal conductance (g_s) (Ehleringer & Vogel, 1993):

$$ \text{iWUE} = \frac{A}{g_s} = \frac{C_a - C_i}{1.6}, \quad (9) $$

where C_a is the concentration of CO₂ in the atmosphere; C_i is the concentration of CO₂ in the intercellular spaces; and 1.6 is the ratio of diffusivities of water and CO₂ in the atmosphere. The C_i was calculated by measuring δ¹³C of the tree ring. According to Farquhar et al. (1982) and Farquhar and Richards (1984)

$$ \delta^{13}\text{C}_{\text{tree}} = \delta^{13}\text{C}_{\text{atm}} - a - \frac{(b-a)C_i}{C_a}, \quad (10) $$

where a is the fractionation factor due to CO₂ diffusion through stomata (4.4‰), b is the fractionation factor due to the Rubisco enzyme during photosynthesis (27‰). Thus, C_i was derived as follows:

$$ C_i = \frac{C_a (\delta^{13}\text{C}_{\text{atm}} - \delta^{13}\text{C}_{\text{tree}} - a)}{b - a}. \quad (11) $$

The C_a and δ¹³Catm values were obtained from the published paper (Belmecheri & Lavergne, 2020) and were extended to 2022 based on existing data records (Table S3). Finally, the iWUE was calculated as follows:

$$ \text{iWUE} = \frac{C_a - C_i}{1.6} = \frac{C_a(b - \delta^{13}\text{C}_{\text{atm}} + \delta^{13}\text{C}_{\text{tree}})}{1.6(b - a)}. \quad (12) $$

## 2.7 | Statistical analyses

In this study, the minimum, maximum, mean, standard deviation and coefficient of variation of each tree ring trait across all sites and in each site were calculated (Table 2; Table S4). We also calculated the inter-series correlation (Rbar) of measured tree ring traits to evaluate the quality of the master (regional) TRW chronology (Table S5). The data were transformed using natural log to homogenise variance in the following analyses when necessary. For data with 0 and negative values, we performed log conversion after adding a constant. A separate analysis of variance (ANOVA) test was performed with a linear mixed effects model to test the effects of years and sites on tree ring traits. A tree individual random effect was introduced in models, and multiple comparison Tukey honestly significant differences (HSD) post hoc tests were then performed when significant differences were found. We used Pearson's correlation analysis to determine the relationships between tree growth and anatomical and isotopic traits. The traits of each tree ring were averaged at the site level to avoid pseudo-replication. We also performed a principal components analysis (PCA) on tree ring traits to examine overall trait coordination. Pearson correlation analyses were carried out between tree ring traits and environmental factors (mean, maximum and minimum temperatures, precipitation, vapour pressure deficit
<figure><img src="image_9.png" /></figure>

during growth season and soil water deficit intensity) and leaf loss conditions. Separate ANOVA tests with a linear mixed effects model were also used to detect the difference in the resilience metrics for tree growth between the drought year 2015 and the drought period 2018 to 2022. Linear regressions were used to determine the relationships between the resilience metrics of tree growth and anatomical and isotopic traits. All statistical analyses were performed in R version 4.2.2 (R Core Team, 2022), using the packages *dplR* for Rbar calculation (Bunn, 2008), *agricolae* for HSD post hoc tests (Mendiburu, 2019), *lme4* for LMMs (Bates, 2010) and *vegan* for PCA analysis (Dixon, 2003).

## 3 | RESULTS

### 3.1 | Relationships between tree growth and isotopic composition and wood anatomical traits

There were no significant relationships between RWI and δ¹³C, δ¹⁸O and iWUE at all sites (Figure 3a-c). We found that δ¹⁸O was positively related to δ¹³C and iWUE at all sites ($r=0.65$ and 0.67, respectively, Figure S3). Anatomical variables were generally more strongly correlated with RWI than isotopes. Mean vessel diameter and mean hydraulic diameter were positively related to RWI ($r=0.62$ and 0.68, respectively), while vessel density was negatively related to RWI at all sites ($r=-0.75$) (Figure 3d-f). $K_{\text{th}}$ was weakly and positively related to RWI ($r=0.35$), while $TK_{\text{th}}$ was strongly and positively related to RWI ($r=0.93$) at all sites (Figure 3g,h). The PCA showed that the first PCA axis indicated a hydraulic spectrum from mean vessel diameter, $K_{\text{th}}$, and vessel fraction to the left and δ¹⁸O to the right, explaining 36.4% of the trait variation, while the second PCA axis was defined by RWI up and vessel density down, explaining 26% of the trait variation. iWUE was not strongly associated with either axis (Figure S4).

### 3.2 | Dynamics of beech tree-ring growth, anatomy and stable isotopes over the 2013–2022 decade

Thanks to the water balance model, we identified dry years, that is when relative soil water content was below 40% during a long period (Figure 2). We compared tree response in 2015 and 2022, isolated one-year droughts, and in 2018–2020, a recurrent drought period, to wet years (2013–2014, 2016–2017 and 2021) over the last decade (Figure 4; Figure S4). The RWI of the CHA site was relatively stable except for a decrease in the years 2020 and 2021. In the three other sites, tree growth experienced a significant decline in 2014 (RWI=0.8), an increase in 2015 and a new strong decrease during the two following years, reaching the low RWI value of 0.5 in 2017. Then, tree growth presented different trajectories according to the sites: RWI of FLA's trees remained low between 2017 and 2020 (RWI<0.5) whereas LAN and BOU presented an increasing

RWI in 2018, and in 2019 for LAN, followed by a decrease in 2020. In almost all sites, 2020 was the year with one of the lowest RWI values (between 0.5 and 0.7) (Figure 4). The sites FLA, LAN and BOU presented similar within-site inter-series correlation (Rbars between 0.482 and 0.612) but very low values were measured for the site CHA (Rbar = 0.057, Table S5).

There were significant differences ($p<0.05$) between drought and wet years for δ¹³C, δ¹⁸O and iWUE with higher δ¹³C, δ¹⁸O and iWUE in drought years (e.g. 2015, 2019, 2020, 2022) while lower values were observed in wet years (e.g. 2016, 2021) (Figure 4). Whatever the years, trees of BOU and particularly those from the CHA site presented significantly lower values ($p<0.05$) of δ¹³C, δ¹⁸O and iWUE than the trees of FLA and LAN. The inter-series correlation in all sites was higher for δ¹⁸O than for the other traits (Rbar = 0.612). For wood anatomical properties, few significant changes were observed for MVD and $K_{\text{th}}$ due to drought, especially at the CHA site, while VD increased in the years after the 2015 drought and remained at a higher value during the drought period from 2018 to 2020 in all sites (Figure 5). In addition, $TK_{\text{th}}$ showed a similar variation trend to RWI (Figure 3) and the coefficient of variation of these two traits was the largest among the measured traits, with 0.58 for TRW and 0.65 for $TK_{\text{th}}$ (Table 2).

When exploring the relationship between environmental factors and tree ring traits, we found that isotopic traits (δ¹³C, δ¹⁸O and iWUE) were relatively sensitive traits and showed stronger correlations ($p<0.05$) with climate factors of the year such as temperature, precipitation, vapour pressure deficit and water deficit intensity. In addition, RWI, $TK_{\text{th}}$ and VD showed stronger correlations ($p<0.05$) with precipitation, especially in drier areas such as FLA and BOU sites (Figure S6). Overall leaf loss conditions were also significantly related to δ¹³C, iWUE and VD, with a higher leaf loss percentage leading to higher δ¹³C, iWUE and VD (Table S6). When reviewing the relationship between RWI and water deficit intensity over the past few decades, we found that at sites such as FLA, LAN and BOU, water deficit intensity and RWI showed strong negative correlations ($R^2=0.20, 0.08$ and 0.12, respectively) (Figure S5c).

### 3.3 | Tree growth resistance, recovery and resilience to drought, and its relationships with tree ring traits

Tree growth resistance to the 2015 drought was similar to or significantly greater than 1 in all sites, showing that tree growth was maintained during this drought episode. But this was no longer the case for trees in response to the recurrent 2018–2020 drought, with trees showing a growth resistance divided by two at the FLA site and lower than 1 at the CHA and BOU sites (Figure 6a). Following the drought of 2015, tree growth recovery was significantly lower than 1 overall in the FLA, LAN and BOU sites, where the recovery was 0.73, 0.63 and 0.67, respectively. The FLA, LAN and BOU sites had greater tree growth recovery to the 2018–2020 drought than that of tree growth recovery to the 2015 drought, but it was
<figure><img src="image_10.png" /><figcaption>FIGURE 3 Relationships between tree ring width index and (a-c) isotopic and (d-h) anatomical traits in rings of beech tree across four sites. Orange points represent the average tree ring values from FLA site; purple points represent the average tree ring values from LAN site; blue points represent the average tree ring values from BOU site and green points represent the average tree ring values from CHA site. Pearson correlation coefficient *r* and *p* value are given and regression lines are shown when the correlation is significant. TK~th~ is total ring area hydraulic conductivity; K~th~ is theoretical specific xylem hydraulic conductivity; iWUE is intrinsic water use efficiency. N=40 (4 sites × 10 years).</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>FIGURE 4 Variation of tree ring width index and isotopic traits from 2013 to 2022 for beech tree from four sites (FLA, LAN, BOU and CHA). Orange colour represents traits from FLA site; purple colour represents traits from the LAN site; blue colour represents traits from the BOU site; and green colour represents traits from the CHA site. Different lowercase letters indicate significant differences among years (p-value < 0.05) in each site. The '*' indicates significant differences among site in a given year, while 'ns' indicates no significant differences among site in a given year (p-value < 0.05). Data are mean and standard error. N = 12-15 trees per site. iWUE is intrinsic water use efficiency.</figcaption></figure>

significantly greater than 1 only in the BOU site, resulting in a sig-
nificant tree growth recovery difference between the two drought
episodes (Figure 6b). Tree growth resilience to the 2015 drought
and the 2018–2020 drought was almost all significantly lower than
1 overall in the FLA, LAN and BOU sites, but only in the FLA site
the tree growth resilience to 2018–2020 drought was significantly
lower than tree growth resilience to the 2015 drought (Figure 6c).
Tree growth resistance, recovery and resilience metrics in the CHA
site were all maintained close to 1 with no significant difference
between both drought episodes (Figure 6). In addition, we found
positive relationships between growth resilience and recovery or
resistance to both drought periods and tree growth resistance to
the 2015 drought was also positively related to growth resistance
to the 2018–2020 drought (Figure S7). Surprisingly, tree growth re-
sistance, recovery and resilience to both the 2015 and 2018–2020
droughts were not significantly related to anatomical traits and

isotopic composition (Figures S8-S11), except for δ¹³C and iWUE,
which were significantly positive with growth resistance to the 2015
drought (Figure S10a,c). Similarly, tree growth resistance, recovery
and resilience to both the 2015 and 2018-2020 droughts were not
significantly related to leaf loss conditions (Figures S12 and S13).

4 | DISCUSSION

4.1 | Responses of beech ring anatomical traits to drought

In partial agreement with our first hypothesis, we found that ves-
sel traits were closely related to tree ring width, with wider tree rings
having larger mean vessel size, and narrower tree rings having greater
vessel density. Several studies showed similar results and revealed a
<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /><figcaption>FIGURE 5 Variation of tree ring anatomical traits of vessels from 2013 to 2022 for beech tree from four sites (FLA, LAN, BOU and CHA). Orange colour represents traits from FLA site; purple colour represents traits from the LAN site; blue colour represents traits from the BOU site; and green colour represents traits from the CHA site. For each site, different lowercase letters indicate significant differences among years (p-value < 0.05). The '*' indicates significant differences among site in a given year, while 'ns' indicates no significant differences among site in a given year (p-value < 0.05). Data are mean and standard error. N = 12–15 trees per site. TK<sub>th</sub> is total ring area hydraulic conductivity; K<sub>th</sub> is theoretical specific xylem hydraulic conductivity.</figcaption></figure>

negative relationship between tree ring width and vessel density, which
are both highly dependent on environmental factors in beech trees
(Arnič et al., 2021; Diaconu et al., 2016; Oladi et al., 2014). It is widely

acknowledged that tree ring width is significantly influenced by the
external environment (García-Suárez et al., 2009; Gazol et al., 2018),
particularly during periods of extreme drought, which can lead to a
<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /><figcaption>FIGURE 6 (a-c) Tree growth resistance, recovery and resilience of beech in four sites (FLA, LAN, BOU and CHA) to 2015 and 2018-2020 drought. Asterisks represent significant differences between both drought periods with *p* < 0.001 if **\*\*\*** and *p* > 0.01 if **, 'ns'** means nonsignificant difference. RWI, ring width index. The grey dashed line represents the 1.0 value, that is, a total resistance, recovery or resilience of the variable to the drought.</figcaption></figure>

reduction in tree growth (Camarero et al., 2023; Dittmar et al., 2003; Islam et al., 2019; Lebourgeois et al., 2005) going sometimes even to missing tree rings in suppressed beech trees (Grundmann et al., 2008). Our study also confirmed a significant decrease in beech tree growth during and following a period of drought, particularly at the driest site. Additionally, vessel density was observed to change in response to soil water deficit or reduced precipitation, which is consistent with findings from other studies (Islam et al., 2019; Oladi et al., 2014; Pourtahmasi et al., 2011). Furthermore, the principal component analysis shows that tree ring width index and vessel density are represented on the same axis but with opposite directions, indicating a negative correlation between them and may stand for 'plastic' traits that are more susceptible to the influence of exogenous factors (Oladi et al., 2014). The higher vessel density in narrower tree rings could be a normal feature, since narrow rings usually have predominant earlywood, whose function is to conduct water efficiently, while the reduction in vessel density in wider tree rings may be due to the fact that wider tree rings have a higher proportion of latewood, which generally has a lower number of vessels (Oladi et al., 2014), although sometimes the separation between early and latewood is not viewable for diffuse porous wood such as beech.

In comparison to vessel density, mean vessel diameter did not change significantly with the occurrence of drought events and environmental factors, although it exhibited a positive relationship with ring width index. Several studies have reported that vessel diameter is less sensitive to changes in exogenous factors (Carrer et al., 2015; Oladi et al., 2014; Schmitz et al., 2006) and relies more on endogenous signals, such as auxin and cell turgor (Hacke et al., 2017). During vessel formation, the primary cell walls, which contain a high proportion of hemicellulose, are pre-softened under the control of auxin. This enables the vessel to expand further when turgor pressure decreases and more water enters (Sass & Eckstein, 1995). In addition, the positive relationship between vessel diameter and ring width index is probably the result of the strongly inverse relationship between vessel size and density, which is considered a proxy for the trade-off between hydraulic efficiency and safety (Islam et al., 2019; Zanne et al., 2010). The lower vessel density in wider tree rings is often associated with larger vessel sizes, which improves hydraulic efficiency but also increases vulnerability to embolism.

Similarly, the higher vessel density in narrow tree rings is accompanied by reduced vessel sizes, which decreases potential water flow but also reduces the risk of cavitation (Loepfe et al., 2007; Sperry et al., 2008). This ecological trade-off observed across a large panel of species seems also to be verified within beech species.

Similar to the mean vessel diameter, theoretical specific xylem hydraulic conductivity ($K_{th}$) did not exhibit significant changes under drought conditions but demonstrated a significant positive correlation with the ring width index. Since the hydraulic conductivity grows with the fourth power of the vessel diameter (Tyree & Ewers, 1991; Zimmermann, 1983), it is not surprising that $K_{th}$ and mean vessel diameter show similar trends. However, the insignificant variations in $K_{th}$ over different time periods do not necessarily imply that the potential of tree rings to conduct water is unaffected by drought. The $K_{th}$ value represents the specific hydraulic conductivity within a relative xylem area, while the amount of water transported from the roots to the leaves depends on the total tree ring area, which is determined not only by the width of the rings but also by the circumferential growth of the tree in a given year (Oladi et al., 2014). Therefore, considering the total tree ring specific hydraulic conductivity ($TK_{th}$) could offer complementary information when studying the effect of drought on water transport efficiency (Guada et al., 2021). Nevertheless, determining the conductive function of the xylem in diffuse porous species, where there is no clear heartwood/sapwood delimitation, is challenging. According to our results, the $TK_{th}$ followed a similar variation trend to tree ring width with significant declines after drought, again proving the sensitivity to drought of European beech from a hydraulic point of view. There was also evidence that $TK_{th}$ was closely related to exogenous factors, with identical relationships to other environmentally controlled traits such as tree ring width and vessel density.

## 4.2 | Sensitivity of stable isotope ratios to drought

Consistent with our first hypothesis, we also found that during drought years, $\delta^{13}$C, $\delta^{18}$O and iWUE were significantly higher than during wet years. It is a well-established fact that tree species adapt
to increasing atmospheric CO₂ concentration (and therefore photo-synthetic performance) by increasing their iWUE, as evidenced by increases in tree-ring δ¹³C (Adams et al., 2020; Duquesnay et al., 1998; Mathias & Thomas, 2021; Rahman et al., 2019). Similarly, the increase in δ¹³C and iWUE may also be related to the decrease in stomatal conductance (e.g. partial stomatal closure), which is associated with low soil water content or high atmospheric vapour pressure deficit during the extreme drought period (Martínez-Sancho et al., 2022; Pellizzari et al., 2016). In addition, the δ¹⁸O can serve as a time-integrated estimate of relative humidity or stomatal conductance, which is supported by a negative correlation between δ¹⁸O and stomatal conductance (Barbour et al., 2000; Farquhar & Lloyd, 1993; Grams et al., 2007). Indeed, the δ¹⁸O value measured in tree rings is typically affected by the δ¹⁸O of source water and the evaporative enrichment at the leaf level, which is also strongly affected by changes in stomatal conductance (Barbour, 2007). Therefore, the higher δ¹⁸O observed in tree rings formed during a drought period may indicate a greater reduction in stomatal conductance and transpiration, which contributed to the enhancement of iWUE (δ¹³C) (Battipaglia et al., 2014). Thus, the combination of carbon and oxygen isotope analysis can indicate whether the observed changes in iWUE (δ¹³C) and δ¹⁸O were caused by modified stomatal conductance and/or photosynthesis. According to the dual-isotope conceptual model (Grams et al., 2007; Scheidegger et al., 2000), the combined increase in δ¹³C and δ¹⁸O in our study would imply a decrease in stomatal conductance with a relatively constant photosynthetic rate. The strong stomatal control of iWUE observed here is in discrepancy with the anisohydric behaviour (Tardieu & Simonneau, 1998) known of beech leaves (Leuschner, 2020). However, tree response to drought is complex and can vary along a continuum from isohydric (avoidance) to anisohydric (resistance) according to provenances (Nguyen et al., 2017), to seasons with a diversity of traits combination (Leuschner et al., 2022). In arid regions, water savings may take priority over carbon gain, meaning that the increase in iWUE (δ¹³C) is more driven by stomatal closure (Kannenberg et al., 2021). It seems also to be the case in our region in the core distribution area.

Although isotopic traits show obvious sensitivity to drought and environmental factors of the year, they are not significantly related to tree ring width index, particularly for iWUE. Some studies have shown that increased iWUE is often accompanied by tree growth enhancement, as higher atmospheric CO₂ concentrations increase tree growth by enhancing photosynthesis, resulting in an increase in iWUE (Ainsworth & Rogers, 2007; Keenan et al., 2013). On the contrary, some studies have suggested that despite an increase in iWUE, tree growth has remained stable or even declined for some species in dry areas, indicating that local stress factors may have overridden the expected CO₂-induced growth increase (Liu et al., 2014; Nock et al., 2011; Peñuelas et al., 2011). Our results are consistent with the latter; that is, increased iWUE does not lead to enhanced growth, and the increase in iWUE during drought periods may be mainly due to the closure of partial stomata (Lévesque et al., 2014). In our relatively short-term study, the CO₂ concentration did not seem to increase too much, so it may not cause enhanced photosynthesis

and thus minimally affect the tree ring growth. Inconsistent with our hypothesis, we did not find a negative correlation between δ¹⁸O and tree ring width index. In general, higher δ¹⁸O is associated with lower stomatal conductance, resulting in lower respiration and growth rates, and therefore narrower tree rings. This different result may be because wood δ¹⁸O values result from different sources of variability (e.g. similar source water and atmospheric vapour δ¹⁸O inputs, evaporative exchange between the leaf and atmosphere) and their interpretation is complex (Roden et al., 2022).

## 4.3 | Effects of drought recurrence on tree growth resilience metrics

At all sites, beech demonstrated drought resistance to the 2015 drought greater than 1, which may be related to the timing of the drought. In our study, most of the drought occurred in late June, and the radial growth of beech trees was 70% complete at this time (Etzold et al., 2022). Moreover, the decline in tree growth prior to the onset of the 2015 drought (e.g. growth declined in 2014) also contributed to the greater resistance to drought. Although 2014 was not a drought year, our Figure 2 demonstrated that the soil relative extractable water in 2014 also experienced a relatively low time during the year and occurred even earlier in the year, which may have led to the decline in tree growth. The 2015 drought recovery and resilience were all less than 1 (except for the site CHA where the drought intensity was relatively mild), indicating a persistently detrimental one-year lagged effect of drought on subsequent growth (Song et al., 2022; Tallieu et al., 2020). In addition, the reduction in growth due to a very wet 2017 beginning of the season may have been a contributing factor to the observed decrease in resilience. This phenomenon is illustrated in Figure 2, which demonstrates that the soil relative extractable water remained at a high point until mid-June 2017. The beech tree has been observed to demonstrate a limited tolerance for excess water, with its water relations, photosynthesis (Dreyer, 1994) and growth (Scharnweber et al., 2013) being constrained. When another extreme drought with higher intensity and longer duration occurred (2018–2020), the resistance of the tree growth to drought at site FLA decreased significantly, indicating the impact of the recurrence of soil water deficits on tree resistance, particularly in shallow soil where trees experienced exceptionally longer and severe drought events. This impact is particularly important in the core of the tree distribution area where precipitations are usually sufficient and when dry years suddenly constitute an extreme climate anomaly for trees. In addition, the level of the hazard should also be considered, as the resilience of the tree growth to drought is also highly dependent on the level of the drought they have submitted. As site FLA experienced the highest soil water deficit in 2018, the tree recovery and resilience to drought in this site was also lower than 1 and the legacy effects on tree growth persist. However, the recovery from the 2018–2020 drought in other sites was almost all greater than 1, indicating that trees in these sites appear to be able to adapt and cope with periods of water shortage when faced with drought recurrence (Gazol et al., 2017). This underlines the importance of using soil water balance
and not only precipitation or SPEI index to characterize drought intensity in a stand: including soil properties, climate and tree transpiration and its regulation allows a process-based assessment of water shortage for the trees. In addition, we observed that growth resilience was correlated to growth recovery and resistance, indicating both resistance and recovery ability are all important for tree resilience (Gazol et al., 2017; Song et al., 2022). However, it is not yet clear how the trees' response to the first drought prepares them for future droughts. Some studies suggest that the resistance to a second stress is better for species with a slow recovery trajectory from a first stress because these species invest in long-term metabolic adjustment and adaptation rather than in rapid repair (Camarero, 2023; Mu et al., 2022). However, these trajectories are also strongly dependent on the intensity and duration of soil water deficit (Ponton et al., 2019).

Furthermore, we hypothesised that tree growth resilience could be associated with tree anatomical and isotopic traits, as plant responses to drought are mediated by a range of anatomical and physiological traits (Choat et al., 2018). However, we found that growth resilience cannot be explained by anatomical and isotopic traits, despite vessel density and isotopic traits showing high variability and plasticity in response to drought. A possible explanation is that other factors such as stomatal conductance, carbohydrate storage and rooting depth play a more significant role in explaining growth resistance, recovery and resilience (Chaves et al., 2003). The only exception is that we found a positive correlation between iWUE (δ¹³C) and growth resistance to the 2015 drought. High δ¹³C and iWUE mean that the external environment is more severe (Martínez-Sancho et al., 2022; Pellizzari et al., 2016), and trees reduce stomatal conductance to cope with drought, which reflects their ability to resist drought. In addition, leaf loss conditions may also have an impact on growth resistance. Trees with high leaf loss rates have poor tree crown conditions, which will inevitably affect their ability to resist drought (Frei et al., 2022; Mathes et al., 2024).

## 4.4 | Ecological implications and further perspectives

Our study illustrates the response of European beech to the recurrence of high-intensity and prolonged droughts in hydraulic and isotopic terms. In contrast to the sensitivity of isotopic traits to climate, tree anatomical and hydraulic traits are not decisive factors in explaining growth resilience to drought, particularly, they are not acclimation traits in response to drought recurrence. This information will be particularly valuable for the conservation of tree species such as *F. sylvatica*, one of the most emblematic trees of temperate European forests, especially in the context of global climate change. For studies of European beech coping with drought, finding research directions beyond just hydraulics may provide a more theoretical basis for future forest management. Nevertheless, further research with a larger number of species and sites on the variation of interring characteristics is still needed. This study focused on the intraring variation across years, but the earlywood and latewood in a tree ring may provide different information on drought response. In

particular, the earlywood anatomy reflects the response to drought at the beginning of tree growth and could be a sensitive proxy for drought stress (Camarero et al., 2023). Furthermore, the expression of drought resistance and tree growth resilience through Lloret's index is restrictive, particularly when the timing of drought onset becomes a dominant factor affecting tree growth. Consequently, in the future, the design of more applicable resilience indices, such as those mentioned by Schwarz et al. (2020), will greatly improve our understanding of the effect of the timing of drought and the selection of drought reference periods on tree growth resilience.

## 5 | CONCLUSIONS

We retrospectively analysed multiple traits including tree cambial growth, tree ring anatomy, and isotopic composition to assess the physiological response involved in the resilience trajectory of the beech trees to extreme and prolonged drought episodes. Over the past 10 years, we determined that 2015, 2018–2020 and 2022 were drought years by calculating annual soil water deficits at the stand level. The cumulative intensity of soil water deficit severely affected the resistance of tree growth and the post-drought resilience. Our study highlights the sensitivity of beech trees in the core of their distribution range, where trees are acclimated to moderate summer drought but are particularly threatened by the recurrence of extreme drought anomalies. Furthermore, our findings regarding the relationships between resilience, anatomical and isotopic traits indicate that beech tree resilience does not seem to involve adaptations to traits that would promote the hydraulic functioning of the trees and therefore, that the threat of death in beech may not be solely in the hydraulics. Overall, our work contributes to the understanding of how drought-sensitive trees cope with extreme drought events in terms of their carbon-water relations in the context of climate change.

### AUTHOR CONTRIBUTIONS

Catherine Massonnet, Nathalie Bréda and Guangqi Zhang planned and designed the research. Guangqi Zhang, Nicolas Steil, Pierre-Antoine Gaertner, Joseph Levillain and Catherine Massonnet conducted fieldwork. Guangqi Zhang, Pierre-Antoine Gaertner and Nicolas Steil performed laboratory analyses. Julien Ruelle provides software, script and technical help. Guangqi Zhang analysed data. Catherine Massonnet and Nathalie Bréda provide supervision. Guangqi Zhang wrote the first draft of the manuscript. All the authors contributed critically to the drafts and gave final approval for publication.

### ACKNOWLEDGEMENTS

This work and a part of the Guangqi Zhang' salary was supported by a grant overseen by the French National Research Agency (ANR) as part of the 'Investissements d'Avenir' program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE, RiskForBeech and IsotopeANA projects). Another part of the Guangqi Zhang' salary was supported
by a grant from the Region Grand-Est (HeRRIGE project, 22-JC-006). We are grateful to Patrick Behr and Erwin Thirion for their help in field work. We also thank to François-Xavier Saintonge and refer- ent experts in each forest from French Forestry Health Division for supporting the survey of forest crown condition. We would like to thank SILVATECH (Silvatech, INRAE, 2018, Structural and functional analysis of tree and wood facility, https://doi.org/10.15454/1.55724 00113627854E12) from UMR 1454 SILVA, 1136IAM, 1138 BEF and 4370 EA LERMAB research center INRAE Nancy-Lorraine for its contributions to isotopic analysis (Loïc Louis and Christian Hossann) and for its help in the preparation of samples for anatomical meas- urements (Maryline Harroue and Adeline Motz). SILVATECH facility is supported by the French Research Agency through Laboratory of Excellence ARBRE (ANR-11-LABX-0002-01). We finally thank Cyrille Rathgeber and Stéphane Ponton for their help in the early discussions about this paper.

## CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

## PEER REVIEW

The peer review history for this article is available at https://www. webofscience.com/api/gateway/wos/peer-review/10.1111/1365- 2745.70014.

## DATA AVAILABILITY STATEMENT

The data described in this paper will be freely and openly accessi- ble on Portail Data INRAE under https://doi.org/10.57745/TDTU3I (Zhang, 2025).

## ORCID

Guangqi Zhang https://orcid.org/0000-0002-7151-2885

Nathalie Bréda https://orcid.org/0000-0002-4550-5710

Pierre-Antoine Gaertner https://orcid.org/0009-0004-2689-0774

Joseph Levillain https://orcid.org/0000-0002-9644-7696

Julien Ruelle https://orcid.org/0000-0001-8780-5650

Catherine Massonnet https://orcid.org/0000-0003-2954-8099

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SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**Figure S1:** Anomaly of soil water deficit in France as compared to the average (1961–1990 reference period), as computed for a deciduous canopy cover (leaf area index = 6, extractable water = 150 mm).

**Figure S2:** Temporal changes of temperature, precipitation and soil water deficit intensity for the four sites (FLA, LAN, BOU and CHA) from 1959 to 2022.

**Figure S3:** Relationships between δ¹⁸O and δ¹³C (a) and intrinsic water use efficiency (iWUE) (b) in tree rings across four sites.

**Figure S4:** Principal component analysis (PCA) of tree ring traits in beech tree across four sites.

**Figure S5:** Average basal area increment (BAI) of beech tree (a) and water deficit intensity (WDI) (b), and the relationships between ring width index (RWI) and WDI (c) in four studied sites (FLA, LAN, BOU and CHA) from 1959 to 2020.

**Figure S6:** Pearson correlations between climate factors of each year and tree ring traits across 2013 to 2022 for the four sites and for all sites.

**Figure S7:** Relationships between tree growth resistance and recovery, growth recovery and resilience, growth resistance and resilience to 2015 drought (a-c) and 2018-2020 drought (d-f); and between 2015 drought resistance, recovery, resilience and 2018-2020 drought resistance, recovery, resilience (g-i) for the four sites.

**Figure S8:** Relationships between tree growth resistance (a-d), recovery (e-h) and resilience (i-l) to 2015 drought and anatomical traits for the four sites.

**Figure S9:** Relationships between tree growth resistance (a-d), recovery (e-h) and resilience (i-l) to 2018-2020 drought and anatomical traits for the four sites.

**Figure S10:** Relationships between tree growth resistance (a-c), recovery (d-f) and resilience (g-i) to 2015 drought and isotopic traits for the four sites.

**Figure S11:** Relationships between tree growth resistance (a-c), recovery (d-f) and resilience (g-i) to 2018-2020 drought and isotopic traits for the four sites.

**Figure S12:** Relationships between tree growth resistance (a-c), recovery (d-f) and resilience (g-i) to 2015 drought and leaf loss of 2019 to 2022 for the four sites.

**Figure S13:** Relationships between tree growth resistance (a-c), recovery (d-f) and resilience (g-i) to 2018-2020 drought and leaf loss of 2019 to 2022 for the four sites.

**Table S1:** Leaf loss (LL, %) evolution of beech trees from 2019 to 2022 at four sites.

**Table S2:** Soil properties used in BILJOU© model for four sites.

**Table S3:** Atmosphere CO₂ concentration (Cₐ) and stable carbon isotope (δ¹³Cₐt) from 2013 to 2022.

**Table S4:** Tree ring traits of beech from 2013 to 2022 in each site.

**Table S5:** Mean inter-series correlation (Rbar) of measured tree ring traits within sites and across all sites over the common 2013-2022 period.

**Table S6:** Pearson correlations between Leaf loss and tree ring traits from 2019 to 2022 considering the 56 trees sampled across the four sites.

**How to cite this article:** Zhang, G., Bréda, N., Steil, N., Gaertner, P.-A., Levillain, J., Ruelle, J., & Massonnet, C. (2025). Analysing resilience of European beech tree to recurrent extreme drought events through ring growth, wood anatomy and stable isotopes. *Journal of Ecology*, *113*, 955–973. https://doi.org/10.1111/1365-2745.70014