# The Key Role of Xylem Hydraulic Vulnerability in Influencing Interannual Growth Variability of Earlywood and Latewood Across the Northern Hemisphere

Mengyun Sun, Lixin Wang, Steven A Kannenberg, Hongyan Liu, Christopher

R Schwalm, Philippe Ciais, Pierre Gentine, William Kolby Smith, Kailiang

Yu, Wen Zhang, et al.

► To cite this version:

Mengyun Sun, Lixin Wang, Steven A Kannenberg, Hongyan Liu, Christopher R Schwalm, et al.. The Key Role of Xylem Hydraulic Vulnerability in Influencing Interannual Growth Variability of Earlywood and Latewood Across the Northern Hemisphere. Journal of Geophysical Research: Biogeosciences, 2025, 130 (8), 10.1029/2025jg008837. hal-05231913

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
JGR Biogeosciences

RESEARCH ARTICLE

10.1029/2025JG008837

**Key Points:**

* Higher interannual variability of earlywood and latewood is mostly observed in arid regions, particularly in the southwestern United States

* Xylem hydraulic vulnerability is the main influencing factor mediating the interannual variability of earlywood and latewood

* The effects of xylem hydraulic vulnerability on the interannual variability of earlywood and latewood increased over time

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

X. Wu,
xiuchen.wu@bnu.edu.cn

**Citation:**

Sun, M., Wang, L., Kannenberg, S. A., Liu, H., Schwalm, C. R., Ciais, P., et al. (2025). The key role of xylem hydraulic vulnerability in influencing interannual growth variability of earlywood and latewood across the Northern Hemisphere.

*Journal of Geophysical Research: Biogeosciences, 130, e2025JG008837.*

https://doi.org/10.1029/2025JG008837

Received 14 FEB 2025

Accepted 29 JUL 2025

**Author Contributions:**

**Conceptualization:** Xiuchen Wu

**Data curation:** Mengyun Sun,
Wen Zhang, Yang Li, Zhenju Chen

**Formal analysis:** Mengyun Sun

**Funding acquisition:** Xiuchen Wu

**Methodology:** Mengyun Sun, Wen Zhang

**Project administration:** Xiuchen Wu

**Supervision:** Xiuchen Wu

**Visualization:** Mengyun Sun, Yang Li

**Writing – original draft:** Mengyun Sun,
Lixin Wang, Steven A. Kannenberg,
Hongyan Liu, Christopher R. Schwalm,
Philippe Ciais, Pierre Gentine, William
Kolby Smith, Kailiang Yu, Wen Zhang,
Yang Li, Yuting Yang, Ximeng Li,
Deliang Chen, Peng Zhang, Xiuchen Wu

The Key Role of Xylem Hydraulic Vulnerability in
Influencing Interannual Growth Variability of Earlywood
and Latewood Across the Northern Hemisphere

Mengyun Sun¹,²,³, Lixin Wang³ ID, Steven A. Kannenberg⁴, Hongyan Liu⁵ ID, Christopher R. Schwalm⁶, Philippe Ciais⁷ ID, Pierre Gentine⁸ ID, William Kolby Smith⁹ ID, Kailiang Yu¹⁰, Wen Zhang⁹, Yang Li⁹, Yuting Yang¹¹, Ximeng Li¹², Zhenju Chen¹³, Deliang Chen¹⁴ ID, Peng Zhang¹⁵,¹⁶, and Xiuchen Wu¹,²,¹⁷ ID

¹State Key Laboratory of Earth Surface Processes and Hazards Risk Governance, Beijing Normal University, Beijing, China, ²Faculty of Geographical Science, Beijing Normal University, Beijing, China, ³Department of Earth and Environmental Sciences, Indiana University Indianapolis, Indianapolis, IN, USA, ⁴Department of Biology, West Virginia University, Morgantown, WV, USA, ⁵College of Urban and Environmental Science, Peking University, Beijing, China, ⁶Woodwell Climate Research Center, Falmouth, MA, USA, ⁷UMR8212-Laboratoire des Sciences du Climat et de l'Environnement (LSCE), CEA-CNRS-UVSQ, Gif-Sur-Yvette, France, ⁸Department of Earth and Environmental Engineering & Climate School, Columbia University, New York, NY, USA, ⁹School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA, ¹⁰Department of Ecology & Evolutionary Biology, Princeton University, Princeton, NJ, USA, ¹¹Department of Hydraulic Engineering, Tsinghua University, Beijing, China, ¹²College of Life and Environmental Science, Minzu University of China, Beijing, China, ¹³College of Forestry, Shenyang Agricultural University, Shenyang, China, ¹⁴Regional Climate Group, Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden, ¹⁵School of Geography and Ocean Science, Nanjing University, Nanjing, China, ¹⁶Frontiers Science Center for Critical Earth Material Cycling, Nanjing University, Nanjing, China, ¹⁷Department of Health and Environmental Sciences, School of Science, Xi'an Jiaotong-Liverpool University, Suzhou, China

**Abstract** Estimating interannual variability of earlywood and latewood growth is crucial for assessing the fluctuations in forest carbon sequestration. However, with amplified climate variations, it remains unclear how earlywood and latewood growth vary across space and over time, as well as the key driving factors that influence these patterns. Here, we quantified the interannual variability of earlywood (EW<sub>CV</sub>) and latewood (LW<sub>CV</sub>) from 1901 to 2013 based on 596 tree-ring chronologies across the Northern Hemisphere and investigated their spatial-temporal patterns. Using Boosted Regression Tree models, we assessed which factors related to climate, hydraulic status, plant structure, and stand properties determine EW<sub>CV</sub> and LW<sub>CV</sub>. We found that both EW<sub>CV</sub> and LW<sub>CV</sub> were higher in drier regions, but they exhibited divergent spatial distribution patterns. This may be because earlywood is more affected by local long-term aridity, whereas latewood appears to be more prone to within-season water availability. Besides, xylem hydraulic vulnerability (P<sub>50</sub>, xylem water potential at 50% loss of hydraulic conductance) emerged as the key influencing factor of growth fluctuation in both earlywood and latewood. These findings suggest that differences in hydraulic functions between earlywood and latewood play a key role in how trees adapt to water stress. Our results illuminate the mechanisms of how earlywood and latewood respond to changing climatic conditions and highlight the crucial role of hydraulic functions in vegetation model prediction of tree growth.

**Plain Language Summary** Wood formation is the primary biological process of woody plants to store atmospheric carbon dioxide, which is sensitive to climate. With global warming, climate variations are increasing, which likely leads to uneven wood growth and greater uncertainty about forest carbon storage. However, how the patterns and driving factors of uneven wood growth may change with global warming and intensifying water limitation remains poorly understood. Based on the data from 596 tree ring chronologies across the Northern Hemisphere, we identified the spatiotemporal patterns of seasonal wood growth fluctuation from 1901 to 2013 and examined the regulatory effects of climate and physiological traits on wood growth. We found that trees in drier regions, such as the southwestern US, show higher growth fluctuation. Xylem vulnerability to embolism, a water-related tree physiological trait, plays a dominant role in the growth fluctuation of earlywood and latewood. This insight is crucial for improving our understanding of how forests can continue to absorb and store carbon amid changing climates, particularly in regions that are getting drier.
Writing - review & editing:

Mengyun Sun, Lixin Wang, Steven
A. Kannenberg, Christopher R. Schwalm,
Philippe Ciais, Pierre Gentine, William
Kolby Smith, Kailiang Yu, Wen Zhang,
Yang Li, Xiuchen Wu

**1. Introduction**

Forests are facing an increasing risk of climate-induced decline and die-off, which is expected to be one of the greatest uncertainties in the global carbon budget (Anderegg et al., 2020, 2022; Brodribb et al., 2020). Interannual growth variability in tree rings is a critical indicator of how trees respond to various stressors and reflects fluctuations in forests carbon uptake (Babst et al., 2014; Cuny et al., 2015; Ren et al., 2019). Over the recent years, growing evidence shows that tree growth, especially in more arid regions, is increasingly susceptible to climate extremes, such as drought (Meko et al., 2013; Rigling et al., 2003), consequent drought legacy effects (Huang et al., 2018; Wu et al., 2022), heatwave (P. Zhang et al., 2020), and water availability (Dannenberg et al., 2019; Jiang et al., 2019). Climate extremes cause a significant reduction in tree ring width or even the formation of missing rings (Fritts, 1976; Martinez-Sancho et al., 2022), thereby increasing the interannual variability of radial growth, that is, more variable long-term carbon storage. Besides, due to the differences in the functional traits and resource acquisition strategies, large interannual variability in tree rings may also indicate that large fluctuations in available resources (Reich, 2014).

Earlywood and latewood are produced in the spring and summer, respectively, and are sensitive to changes in seasonality (Camarero et al., 2020; Griffin et al., 2011). Currently, with warming-induced earlier spring phenology, longer droughts and higher water stress, the asymmetry in seasonal terrestrial carbon uptake is becoming more obvious (Piao et al., 2020; K. Wang et al., 2022; Wolf et al., 2016). For example, the carbon fluxes in Mediterranean ecosystems have a higher interannual variability in summer and autumn (Allard et al., 2008). One study suggested an increase in the probability of large negative growth anomalies across the US Southwest resulting solely from 20th-century increases in variability of cool-season (October to March) precipitation (Dannenberg et al., 2019). Thus, the interannual variability of earlywood and latewood tree rings is expected to be a vital proxy for detecting climate extremes and their influence on carbon sequestration. The International Tree-Ring Data Bank (ITRDB) provides tree ring measurements with large temporal and spatial coverage (Zhao et al., 2019), making it possible to understand seasonal terrestrial carbon uptake from the perspective of inter-annual variability of earlywood and latewood growth at a global scale.

Plant functional traits constitute a fundamental control of water transport, leaf photosynthetic and plant life-
history strategies by regulating morphological and physiological behaviors (Adler et al., 2014; Choat
et al., 2012; McGill et al., 2006; Mencuccini et al., 2019). Hydraulic traits reflect plants' water-use strategies,
determining how efficiently plants can transport water, resist drought-induced embolism, and ultimately survive
in diverse environments (Kannenberg et al., 2021). There is a trade-off between water transport efficiency and
xylem embolism within species, which is associated with xylem vessel diameter (Olson et al., 2018). Vessels with
larger diameters transport water more efficiently but are also more susceptible to embolism during droughts
(Domec, 2002; Royo-Navascues et al., 2021). Thus, although earlywood transports approximately 90% of the
water (Ewers et al., 1997; Panshin & De Zeeuw, 1964), its vulnerability to embolism could be higher than that of
latewood. Given the increasing water constraints and more frequent hydraulic dysfunction in numerous plant
groups (Blackman et al., 2012; Choat et al., 2007; Jiao et al., 2021), it is both necessary and urgent to detect the
hydraulic trade-off between earlywood and latewood at across broad geographic and temporal ranges and how it
affects the response of trees growth to climate.

Here, we compiled a data set of 596 paired early- and late-wood width chronologies from ITRDB, which were divided into 177 arid and 419 humid sites distributed across the Northern Hemisphere (15°N-75°N). The interannual growth variabilities of earlywood and latewood were calculated as the coefficient of variation (CV) for earlywood and latewood chronologies, respectively. We assessed the spatiotemporal variations in earlywood and latewood and evaluated the contributions of multiple plant functional traits and climatic variables across the study region to address the following questions:

1. How are the spatial and temporal distributions of interannual growth variability of earlywood and latewood in the Northern Hemisphere?

2. Does the interannual growth variability of earlywood and latewood differ in space and time?

3. Which plant functional traits emerge as important factors affecting the variability of earlywood and latewood?

We proposed three corresponding hypotheses: (a) The drier the region, the higher interannual growth variability of earlywood and latewood. (b) The interannual growth variability of earlywood and latewood differed in space and time. (c) Hydraulic traits play an important role in mediating the growth variability of earlywood and
latewood. Addressing these knowledge gaps will enhance our understanding of the underlying relationship between plant functional traits and forest woody growth and strengthen our predictions of future terrestrial carbon sink under a changing climate.

## 2. Materials and Methods

### 2.1. Tree Ring Data and Processing

Raw earlywood and latewood ring width measurements were mostly obtained from the ITRDB (Zhao et al., 2019) and provided by sites in Northeast China (Jin et al., 2021). For raw ring-width data, detrending is a necessary step to remove ecological “noise,” such as age-related growth trends and other low-frequency signals, to isolate the impact of environmental drivers (Cook & Kairiukstis, 2013). In this study, the ring-width data were detrended using a cubic smoothing spline approach with a 50% frequency response cutoff of 50 years to obtain a standardized ring width index (RWI) (Cook & Kairiukstis, 2013). All detrended RWI series from each site were then averaged to construct the site-level standardized RWI chronology using a bi-weight robust mean (Cook & Kairiukstis, 2013; Fritts & Swetnam, 1989). Detrending and mean site chronology calculations were conducted using the “dplR” package (V.1.7.4) in R (version 4.1.0), following the standard dendrochronological protocol (Bunn, 2008).

All the RWI chronologies met the following criteria before further analyses: (a) the basic metadata (longitude, latitude, species name, and elevation) and raw ring width measurements were available; (b) earlywood and latewood chronologies paired at site level; (c) chronologies covered at least 30 years between 1901 and 2013; (d) the tree core samples were larger than 5. The final sample size of standard RWI chronologies in this study was 596 over the northern hemisphere, encompassing 50 tree species across 6 main genera (Table S1 and Figure S1 in Supporting Information S1).

### 2.2. Climate Data Set

Gridded monthly temperature (TMP), precipitation, potential evapotranspiration, and vapor pressure for the period of 1901–2013 were obtained from the CRU TS v. 4.04 with a spatial resolution of 0.5° (Harris et al., 2020). Monthly climatic water deficit (CWD) was calculated as the difference between monthly precipitation and monthly potential evapotranspiration (Huang et al., 2018). Monthly vapor pressure deficit (VPD) was calculated by the Tetens Formula using the monthly mean temperature and vapor pressure. Monthly incoming solar radiation (SR) was obtained from the CRUNCEP version 7 data sets for the period of 1901–2013 at a spatial resolution of 0.5° (Viovy, 2018).

We consistently divided the tree radial growth activity into three stages based on seasonal patterns and anatomical observations (Table S2 in Supporting Information S1): the previous non-growing season (PN) from the preceding October to March, the earlywood growth period (EP) from April to July, and the latewood growth period (LP) from August to September. Since earlywood and latewood growth are sensitive to climate variability in different seasons, we considered the effects of climate variability in different stages separately. At each study site, we calculated the CV of different climate factors in EP, LP, and PN. To further validate the robustness of our findings, we additionally compared them to the results derived from a different definition of EP (April–June and March–July), LP (July–September) and PN (October–March).

### 2.3. Plant Functional Traits, Soil Properties, and Forest Stand Structure

The xylem water potential at 50% loss of hydraulic conductivity ($P_{50}$), canopy height and maximum rooting depth (rooting depth) are widely used and important functional traits governing water uptake and transportation within the stem/branches. $P_{50}$ was derived from a global xylem hydraulics data set (Choat et al., 2012) and extracted for each site based on species. The gridded maximum rooting depth and canopy height were obtained from the Global Earth Observation for Integrated Water Resource Assessment (Fan et al., 2017) and Global 1 km Forest Canopy Height data set (Simard et al., 2011), respectively. We extracted data from these two data sets for 596 RWI sites based on latitude and longitude.

The cation-exchange capacity (CEC) of the topsoil and the available water-storage capacity (AWC) of the soil were used to characterize soil fertility and AWC, respectively. The two soil properties were obtained from the 0.05° gridded Harmonized World Soil Database v.1.2 (Wieder et al., 2014) and correspond to each site according
to latitude and longitude. Tree density was used to represent the competitive pressure between trees, derived from a global map of tree density (Crowther et al., 2015). The ecological memory represents the growth memory effect, defined here as the first-order autocorrelation in the earlywood/latewood RWI chronologies, which quantifies the effects of radial growth in the preceding year on the subsequent radial growth (Liu et al., 2019; Seddon et al., 2016).

The potential effects of foliar functional traits on the variability of seasonal wood growth were also considered in this study. Three foliar functional traits, including foliar nitrogen concentration per unit dry mass (Nm), foliar phosphorus concentration per unit dry mass (Pm) and specific leaf area (SLA), are closely related to photosynthesis and leaf respiration. All foliar traits were obtained from the TRY database (Butler et al., 2017) and extracted based on latitude and longitude.

## 2.4. Spatiotemporal Pattern in the Seasonal Wood Growth Variability

The study region was categorized into arid regions (aridity index (AI) ≤ 0.65) and humid regions (AI > 0.65) based on an AI (which is defined as the ratio of annual precipitation to annual potential evapotranspiration) (L. Wang & Collins, 2024). The AI values were sourced from the Global AI and Potential Evapotranspiration (ET0) Climate Database v2 (Trabucco & Zomer, 2018). The CV of RWI chronologies was calculated to estimate the annual growth relative variations of earlywood and latewood (EW<sub>CV</sub> and LW<sub>CV</sub>), respectively, in the period of 1901–2013, using the following equation:

$$ EW_{CV_i} \text{ or } LW_{CV_i} = \frac{\sigma_i}{\mu_i} \qquad (1) $$

where *i* represents the *i*th forest site, *σ* and *μ* are the standard deviation and mean of the earlywood or latewood RWI chronologies of the *i*th site, respectively. To evaluate how detrending ring-width series to RWI chronologies affects EW<sub>CV</sub> and LW<sub>CV</sub>, we compared EW<sub>CV</sub> and LW<sub>CV</sub> values with and without detrending, as well as the cubic smoothing spline approach with a modified negative exponential detrending method (Figure S2 in Supporting Information S1). Non-detrended EW<sub>CV</sub> and LW<sub>CV</sub> values had a weak relationship with detrended ones (*R*<sup>2</sup> = 0.41 and 0.74, respectively; Figures S2a and S2b in Supporting Information S1), suggesting the necessity of detrending to remove influences caused by low-frequency trends. Additionally, EW<sub>CV</sub> and LW<sub>CV</sub> values were strongly correlated between the cubic smoothing spline approach and the modified negative exponential approach (*R*<sup>2</sup> = 0.78 and 0.86, respectively; Figures S2c and S2d in Supporting Information S1), suggesting that the patterns of EW<sub>CV</sub> and LW<sub>CV</sub> appear to be broadly aligned across methods. Based on the above analysis, all subsequent EW<sub>CV</sub> and LW<sub>CV</sub> values were calculated using the cubic smoothing spline approach. The EW<sub>CV</sub> and LW<sub>CV</sub> values from 596 sites were grouped into 7 bins (0–0.7) and 3 classes (low: 0–0.2, moderate: 0.2–0.4, high: 0.4–0.7) according to their frequency distribution (Figures 1a and 1b). Additionally, the average EW<sub>CV</sub> and LW<sub>CV</sub> values of each AI interval were calculated and plotted along the AI gradient (Figure 1c).

## 2.5. Boosted Regression Tree Models

We used Boosted regression tree (BRT) models to identify the effects of various predictors on the EW<sub>CV</sub> and LW<sub>CV</sub> in arid and humid regions during the period of 1901–2013. BRT model is a non-parametric model, which combines regression trees and a boosting framework that does not require prior assumptions. It is therefore relatively robust against missing data, overfitting and collinearity, and can effectively characterize complex nonlinear relations with good prediction performance (Elith et al., 2008). In this study, 18 predictor variables were considered and grouped into four categories: (a) Hydraulic traits: P<sub>50</sub>, rooting depth and canopy height; (b) stand properties: tree density, AWC, CEC, and ecological memory; (c) variability of climate factors: the CV of temperature, climate water deficit, SR and VPD during earlywood growth period (TMP<sub>EP_CV</sub>, CWD<sub>EP_CV</sub>, SR<sub>EP_CV</sub>, VPD<sub>EP_CV</sub>), latewood growth period (TMP<sub>LP_CV</sub>, CWD<sub>LP_CV</sub>, SR<sub>LP_CV</sub>, VPD<sub>LP_CV</sub>), and the previous non-growing-season (TMP<sub>PN_CV</sub>, CWD<sub>PN_CV</sub>, SR<sub>PN_CV</sub>, VPD<sub>PN_CV</sub>); (d) foliar traits: Nm, Pm and SLA. Detailed descriptions, units and data source for the 18 predictor variables can be found in Table S3 in Supporting Information S1.

We parameterized the BRT models setting a tree complexity of 5, a learning rate of 0.005 and a bag fraction of 0.5, using the *gbm* package in R (Elith et al., 2008). We estimated the relative contribution (or importance) of each
<figure><img src="image_2.png" /><figcaption>b Interannual Variability of Latewood</figcaption></figure>

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

**Figure 1.** Spatial patterns in the interannual growth variability of earlywood and latewood in the Northern Hemisphere. Interannual growth variability was evaluated by the coefficient of variation of the corresponding earlywood (EW<sub>CV</sub>) and latewood (LW<sub>CV</sub>) tree-ring chronologies across 596 forest sites in the period of 1901–2013. The insets in (a) and (b) show the frequency distributions of EW<sub>CV</sub> and LW<sub>CV</sub>. Variation of interannual growth variability classified from zero (low), meaning drier regions, to 3.5 (high), meaning wetter regions. The changes of EW<sub>CV</sub> and LW<sub>CV</sub> along with increasing aridity index (AI) (c). Purple and green dots were the mean EW<sub>CV</sub> and LW<sub>CV</sub> per AI interval, respectively. The solid broken red lines showed the trend for EW<sub>CV</sub> and LW<sub>CV</sub>, and the shaded areas were their 95% confidence intervals. The dashed lines were the results of linear regression, and the corresponding *p* values were marked with the same color.

predictor variable based on the number of times a variable was selected for splitting in each BRT model, weighted by the squared model improvement, and averaged across all trees (Friedman & Meulman, 2003). The relative contribution was scaled so that the sum adds to 100, with higher numbers indicating stronger influence on EW<sub>CV</sub> and LW<sub>CV</sub>. To ensure robustness of models' performance, random samplings with replacement were applied by splitting the data set into 80% training and 20% testing sets, with 100 runs performed. During each run, model performance was first estimated by 10-fold cross-validation on training data. The model evaluation was conducted on the test set by calculating R<sup>2</sup> values between tree ring-based growth variability and BRT-predicted growth variability (Figure S3 in Supporting Information S1). Additionally, we explored the relationship between EW<sub>CV</sub> and LW<sub>CV</sub> and predictor variables using partial dependence plots, which are the graphical
visualizations of the marginal effect of a given predictor variable after accounting for the average effects of all other variables in the model (Elith et al., 2008).

The BRT models were further applied to explore the temporal change in the effects of different driving factors on the EW<sub>CV</sub> and LW<sub>CV</sub>. The study period of 1901–2013 was divided into two subperiods: 1901–1945 and 1946–2013. This division was a compromise of balancing the available tree ring measurements and the length of study periods in the two subperiods (Figure S4 in Supporting Information S1). We recalculated EW<sub>CV</sub> and LW<sub>CV</sub> and the effects of factors on EW<sub>CV</sub> and LW<sub>CV</sub> over these two periods. Significant differences (*p* < 0.05) in relative contributions of driving factors between 1901–1945 and 1946–2013 were identified using the Mann-Whitney U test.

## 3. Results

### 3.1. Spatial Patterns of the Seasonal Wood Growth Variability

We first assessed the spatial pattern of EW<sub>CV</sub> and LW<sub>CV</sub> across forest stands within arid and humid regions during the period of 1901–2013 in the Northern Hemisphere (Figure 1). We observed a clear north-south gradient in North America for EW<sub>CV</sub> and LW<sub>CV</sub>. Specifically, the sites with high EW<sub>CV</sub> and LW<sub>CV</sub> (0.4–0.7) were mostly distributed in low latitudes, especially in the arid southwestern United States, whereas the sites with low EW<sub>CV</sub> and LW<sub>CV</sub> (0–0.2) mainly appeared in North American boreal regions (Figures 1a and 1b). However, we observed more of a west-east gradient in Eurasia for EW<sub>CV</sub>, with the sites having moderate EW<sub>CV</sub> (0.2–0.4) mainly found in Siberia, but those with low EW<sub>CV</sub> (0–0.2) mainly found in central Europe (Figure 1a). In addition, most of the LW<sub>CV</sub> (0.2–0.4) sites were moderate in Eurasia, especially in the Mediterranean region (Figure 1b).

Differences were observed in the spatial distribution of high EW<sub>CV</sub> and LW<sub>CV</sub>. For example, LW<sub>CV</sub> of the sites in the southwestern United States ranged from 0.5 to 0.7, whereas EW<sub>CV</sub> ranged from 0.4 to 0.5 (Figures 1a and 1b). Notably, EW<sub>CV</sub> showed a significant decreasing trend (*p* < 0.001) along the aridity gradient. LW<sub>CV</sub> also significantly decreased as AI increased (*p* < 0.05) under the drier range (AI < 1.5), although it fluctuated in the wetter region (Figure 1c). Besides, with the temporal change, the sites of LW<sub>CV</sub> showed higher fluctuations compared with the sites of EW<sub>CV</sub>, especially for the southwestern United States and central Europe (Figure S5 in Supporting Information S1).

### 3.2. Influencing Factors for the Interannual Growth Variability

We then identified the main influencing factors of EW<sub>CV</sub> and LW<sub>CV</sub> across different regions based on BRT models. Separate BRT models were applied to attribute the EW<sub>CV</sub> and LW<sub>CV</sub> to 18 diverse factors, including seasonal climate, plant functional traits, and stand properties (Table S3 in Supporting Information S1). The constructed BRT models could effectively capture the interannual growth variability in both earlywood and latewood, with model coefficients of determination R<sup>2</sup> ranging from 0.64 to 0.71 (Figure S3 in Supporting Information S1). We found P<sub>50</sub>, rooting depth, tree density, CEC, CWD<sub>PN_CV</sub>, and TMP<sub>LP_CV</sub> were the six most important variables that contributed relatively large to EW<sub>CV</sub> and LW<sub>CV</sub> (Figure 2).

Among the three types of hydraulic traits considered here, xylem P<sub>50</sub> was most important for mediating the variability of seasonal wood growth (Figure 2) and it was the most critical single factor affecting LW<sub>CV</sub> (Figures 2c and 2d). The median relative contributions of P<sub>50</sub> to LW<sub>CV</sub> in arid and humid regions were 21.1% and 18.2%, respectively, compared to 9.2% and 8.5% for EW<sub>CV</sub> (Figure 2). The EW<sub>CV</sub> and LW<sub>CV</sub> nonlinearly increased with P<sub>50</sub> in both climate regions, with this trend more prominent in LW<sub>CV</sub> (Figures 3a and 3f). EW<sub>CV</sub> and LW<sub>CV</sub> were also affected by rooting depth, especially in arid regions, with median relative contributions of 4.2% and 7.0%, respectively (Figures 2a and 2c). Interestingly, EW<sub>CV</sub> in both arid and humid regions tended to decrease with increasing rooting depth below certain thresholds, whereas an opposite pattern was observed for LW<sub>CV</sub> (Figures 3b and 3g).

Among stand properties, tree density played an important role in determining the EW<sub>CV</sub> and LW<sub>CV</sub>, especially for humid regions (with a median relative contribution of 6.8% and 10.9%, respectively), whereas these effects were much smaller in arid regions (Figure 2). The EW<sub>CV</sub> slightly decreased with increasing tree density in humid regions, whereas the LW<sub>CV</sub> followed a concave-down pattern with increasing tree density, with lower LW<sub>CV</sub> occurring in moderate tree density (Figures 3c and 3h). Furthermore, the effects of AWC were more prominent on EW<sub>CV</sub> compared with LW<sub>CV</sub>, with a median relative contribution of 9.9% in humid regions and 3.6% in arid
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Diverse influencing factors for the interannual growth variability of earlywood and latewood in arid and humid regions. The relative contributions of different predictors in controlling the variability of earlywood (EW<sub>CV</sub>) in arid (a) and humid regions (b) and the variability of latewood (LW<sub>CV</sub>) in arid (c), and humid regions (d). The specific EP, LP, and PN are April to July, August to September and October to March, respectively. Violins show the data distribution of relative contributions for 100 model runs. Different colors of the y-axis legend represent different groups of predictors, including hydraulic traits (blue): the xylem water potential at 50% loss of hydraulic conductivity (P<sub>50</sub>), maximum rooting depth (rooting depth), canopy height; site properties (red): tree density, the available water-storage capacity, the cation-exchange capacity of the topsoil (CEC) and ecological memory; variability of climate factors (green): the coefficient of variation of temperature, climatic water deficit, solar radiation, and vapor pressure deficit during earlywood growth period (TMP<sub>EP_CV</sub>, CWD<sub>EP_CV</sub>, SR<sub>EP_CV</sub>, and VPD<sub>EP_CV</sub>), latewood growth period (TMP<sub>LP_CV</sub>, CWD<sub>LP_CV</sub>, SR<sub>LP_CV</sub>, and VPD<sub>LP_CV</sub>), and the preceding non-growing-season (TMP<sub>PN_CV</sub>, CWD<sub>PN_CV</sub>, SR<sub>PN_CV</sub>, and VPD<sub>PN_CV</sub>); foliar traits (yellow): leaf nitrogen concentration (Nm), leaf phosphorus concentration (Pm) and the specific leaf area. The bright blue bars represent the median.</figcaption></figure>

regions, respectively (Figures 2a and 2c). The effects of CEC on tree growth variability were not prominent except for the LW<sub>CV</sub> in humid regions (Figure 2d), but interestingly, both EW<sub>CV</sub> and LW<sub>CV</sub> followed a concave-down pattern as CEC increased (Figures 3d and 3i).

Across the climate factors, the CWD<sub>PN_CV</sub> (CV of the CWD in the preceding non-growing-season) explained the most variability in EW<sub>CV</sub> in arid regions (with a median relative contribution of 6.4%) (Figure 2a), whereas the TMP<sub>EP_CV</sub> and TMP<sub>LP_CV</sub> (CV of mean temperature in early- and late-growing seasons, respectively) were more important (with a median relative contribution of 6.9% and 8.5%, respectively) for LW<sub>CV</sub> in arid regions (Figure 2c). Higher CWD<sub>PN_CV</sub> and TMP<sub>LP_CV</sub> were associated with lower EW<sub>CV</sub> and LW<sub>CV</sub> in arid regions, whereas an opposite pattern prevailed in humid regions (Figures 3e and 3j). In humid regions, the relative contributions of SR<sub>PN_CV</sub> and SR<sub>EP_CV</sub> (CV of mean SR in the preceding non-growing-season and earlywood growth period, respectively) to EW<sub>CV</sub> and LW<sub>CV</sub> were higher than other climate factors (6.8% and 7.0%, respectively, Figures 2b and 2d). The three leaf functional traits only exerted marginal effects on EW<sub>CV</sub> and LW<sub>CV</sub> in both arid and humid regions (Figure 2).

To reduce the potential impact of autocorrelation in hydraulic trait values across sites, we filtered the dominant species in arid and humid regions for the BRT models. This filtering yielded results consistent with those obtained using the full species data set (Figure S6 in Supporting Information S1 and Figure 2), supporting the robustness of our findings. Similarly, the patterns of relative contributions for most variables remained consistent after excluding *Quercus* and *Castanea*, that is, removing all angiosperms (Figure S7 in Supporting Information S1) and when using a different period for the definition of earlywood and latewood (Figures S8 and S9 in Supporting Information S1).
<figure><img src="image_6.png" /></figure>

**Figure 3.** Partial dependence plots for the marginal effects of six main predictors on the interannual growth variability of earlywood and latewood. Main predictors of the interannual growth variability of earlywood include (a) P<sub>50</sub>, (b) rooting depth, (c) tree density, (d) CEC, and (e) CWD<sub>PN_CV</sub>. Main predictors of the interannual growth variability of latewood include (f) P<sub>50</sub>, (g) rooting depth, (h) tree density, (i) CEC, and (j) TMP<sub>LP_CV</sub>. Lines represent smoothed averages for 100 model runs (Each smoothed line is shown in Figures S11 and S12 in Supporting Information S1), the envelopes around the solid lines represent 95% confidence interval. Yellow and green solid lines represent arid and humid regions, respectively. The ticks at the top and bottom of the figure represent the distribution of predictors in the model, and the colors are consistent with the region represented by the solid lines. Because of stochastic instability at the extreme ends of predictor variables, our interpretation of partial-dependence plots focuses on the regions between the 5th and 95th percentiles of each predictor (yellow and green areas formed by vertical dashed lines).

### 3.3. Temporal Shift in the Main Influencing Factors of Interannual Growth Variability

We then assessed the temporal shifts in the main influencing factors influencing the EW<sub>CV</sub> and LW<sub>CV</sub> by comparing the effects of the main influencing factors in two periods (1901–1945 and 1946–2013). Overall, the importance of P<sub>50</sub> for EW<sub>CV</sub> in arid regions and LW<sub>CV</sub> in humid regions increased over time (Figure 4a), which was consistent with the results using 1940 and 1950 as the mid-points (Figure S10 in Supporting Information S1). Specifically, the median relative contributions of P<sub>50</sub> to EW<sub>CV</sub> in arid regions and LW<sub>CV</sub> in humid regions significantly increased by 4.1% and 0.5%, respectively (Figure 4a). The effects of P<sub>50</sub> on EW<sub>CV</sub> in humid regions and LW<sub>CV</sub> in arid regions were not statistically significant between the two periods (Figure 4a).

The effect of rooting depth on LW<sub>CV</sub> in arid regions significantly decreased by 5.0%, whereas its influence increased slightly (<0.6%) in other situations (Figure 4b). In general, the relative contributions of tree density to the EW<sub>CV</sub> and LW<sub>CV</sub> in arid regions increased significantly by 1.2% and 0.8%, respectively, whereas no significant change was observed in humid regions (Figure 4c). The relative contribution of CEC on LW<sub>CV</sub> in arid regions decreased significantly by 3.4%, with no marked changes in other situations (Figure 4d). In arid regions, the relative contribution of CWD<sub>PN_CV</sub> and TMP<sub>LP_CV</sub> on EW<sub>CV</sub> and LW<sub>CV</sub> increased significantly by 5.1% and 1.0%, respectively, whereas their effects on EW<sub>CV</sub> and LW<sub>CV</sub> in humid regions decreased significantly by 1.6% and 0.5% (Figure 4e).

## 4. Discussion

### 4.1. Implications of Interannual Growth Variability of Earlywood and Latewood Across the Northern Hemisphere

In this study, the CV of tree rings reflects the relative variation of radial growth across years, where a higher CV indicates greater fluctuations in growth magnitude. We found that both EW<sub>CV</sub> and LW<sub>CV</sub> were higher in drier conditions, spatially and along the aridity gradient (Figure 1). These results support our first hypothesis that the drier regions, the higher interannual growth variability of earlywood and latewood (i.e., the greater the fluctuation of earlywood and latewood growth), suggesting that trees in these environments experience more variable and possibly more extreme changes in earlywood and latewood formation. These findings are consistent with
<figure><img src="image_7.png" /><figcaption>**Figure 4.** Temporal changes in the effects of six main predictors on the interannual growth variability of earlywood and latewood in arid and humid regions. Temporal changes of relative contributions of six main predictors to the tree growth variability as estimated by boosted regression tree models dependent on regions between the period 1901–1945 and period of 1946–2013: (a) P₅₀, (b) rooting depth, (c) tree density, (d) CEC, and (e) CWDₚₙ_CV and TMPₚₗ_CV. The yellow and green box plots show the relative contributions during the first period and the second period, respectively. The blue lines represent median values, and blue texts show the specific difference in relative contributions between the two periods. Significant difference (*P* < 0.05) in relative contributions of each predictor between 1901 and 1945 and the period 1946–2013 was identified by the Mann-Whitney U test and was denoted by asterisks.</figcaption></figure>
previous studies that show greater absolute growth variability under drier or drought-prone conditions. For example, Voelker et al. (2018) reported that anisohydric conifers from summer-dry environments exhibited strong growth responses to evaporative demand, leading to larger interannual differences in ring-width. Similarly, Maxwell et al. (2024) found that hydroclimate extremes, particularly drought, induced asymmetric effects on tree growth in the eastern US, resulting in substantial year-to-year variability. In northern China, Han et al. (2025) demonstrated that species in arid regions showed marked growth reductions during drought and variable recovery responses, reinforcing the link between aridity and amplified growth variability.

We found that there were divergent distribution patterns of EW<sub>CV</sub> and LW<sub>CV</sub>. Specifically, EW<sub>CV</sub> was generally low to moderate across space (Figure 1a), but significantly decreased along the entire AI gradient (from dry to wet conditions, Figure 1c). Earlywood is efficient for water transport but also vulnerable to embolism (Bouda et al., 2019; Ewers et al., 1997; Gleason et al., 2016), making it especially sensitive to fluctuations in local long-term water availability, regardless of whether conditions are dry or wet. In dry environments, limited water supply and increased hydraulic risk can restrict earlywood formation and increase its variability. In wet environments, although water is not as limiting generally, year-to-year fluctuations in moisture still influence earlywood growth due to its strong reliance on efficient water transport during the early growing season.

Compared with EW<sub>CV</sub>, there were more sites with higher LW<sub>CV</sub>, especially in the southwestern US (Figures 1a and 1b). Additionally, LWCV decreased only under the dry conditions, as AI values increased from 0 to 1.5 (Figure 1c). Since latewood grows in summer and autumn, it may be more sensitive to climate variability at the end of the growing season, rather than average aridity levels (Antony et al., 2012; Cartenì et al., 2018; Fritts, 1976; Lebourgeois, 2000). Since research suggests that seasonal variations in climate are increasing (Dannenberg et al., 2019; P. Zhang et al., 2020) and vegetation growth in semi-arid and arid areas have faced more pressing water limitation and more severe and frequent extremes (Green, 2024; L. Wang et al., 2022; Wu et al., 2022), our results suggest that latewood may be increasingly sensitive to seasonal water stress. The high growth variability of latewood in the southwestern US also supports this conclusion (Figures 1a and 1b). The southwestern US has a history of severe drought and a large precipitation variability in summer and autumn (Cayan et al., 2010; Dannenberg et al., 2019; F. Zhang et al., 2021). These patterns of climate extremes in the late growing season likely influence latewood response to climate to show high variability.

## 4.2. Xylem Vulnerability Plays an Important Role in Mediating Seasonal Wood Growth Variability

P<sub>50</sub> reflects xylem vulnerability to embolism; it is a key hydraulic trait that reflects the ability of trees to resist drought (Choat et al., 2012; Rodriguez-Zaccaro & Groover, 2019). Our results showed that the relative contribution of P<sub>50</sub> on both EW<sub>CV</sub> and LW<sub>CV</sub> was higher than other variables (Figure 2) and EW<sub>CV</sub> and LW<sub>CV</sub> increased in concert with P<sub>50</sub> (Figures 3a and 3f), providing support for the third hypothesis that P<sub>50</sub> plays a key role in mediating the variability of earlywood and latewood. Although P50 is closely related to photosynthetic processes and gross primary productivity (GPP) through its control over leaf water supply (Sperry et al., 2002), recent studies have shown that GPP is not always tightly coupled with growth (Cabon et al., 2022), particularly during drought (Kannenberg et al., 2022). This decoupling can be explained by the fact that growth is more directly constrained by hydraulic functioning than by carbon assimilation per se. During water stress, trees may maintain photosynthesis for some time using stored water or through partial stomatal closure (McDowell et al., 2008), but radial growth, which relies on sustained cell expansion, is highly sensitive to turgor loss and xylem dysfunction (Hsiao, 1973; Korner, 2015). Thus, P<sub>50</sub> connects to growth not only via its role in supporting photosynthesis, but more critically by affecting whether water transport can be maintained at levels sufficient to support cambial activity and cell enlargement.

The removal of *Quercus* and *Castanea* (i.e., all angiosperms) had little influence on the relative contributions of P<sub>50</sub> and the overall patterns of influencing factors (Figure S7 in Supporting Information S1). This result suggests that key hydraulic traits such as P<sub>50</sub> may play a similarly important role in shaping growth variability across both angiosperms and gymnosperms, despite differences in xylem anatomy. Oaks, for example, possess ring-porous xylem and often display anisohydric water-use strategies, which allow for sustained gas exchange under drying conditions, increasing their susceptibility to embolism (Novick et al., 2022). In line with this functional profile, Maxwell et al. (2024) found that oaks are generally less sensitive to moderate and extreme drought than more mesic species such as Acer and Liriodendron, which have become increasingly dominant in eastern US
forests. However, given the limited sample size, we acknowledge that further species-specific analyses would be necessary to directly test whether oaks functionally align with conifers in their drought response.

Additionally, the effects of P₅₀ on LWCV were higher than EWCV (Figures 2, 3a, and 3f). This could be because latewood mainly forms at the end of the growing season (summer), which is the period when drought is more likely to occur, especially in the dry regions (Swain & Hayhoe, 2014; Welp et al., 2007). For example, under the Mediterranean climate, the latewood growth period is limited by water stress (Balzano et al., 2018). During summer drought, although the earlywood is more vulnerable to embolism than the latewood, the consequence is that the growth of the latewood is inhibited, so its growth variability showed a stronger response to P₅₀. With more frequent and severe droughts in the dry regions, extensive embolism formation in the xylem leads to more plants suffering from hydraulic dysfunction or even mortality (Lens et al., 2013; Michaelian et al., 2011; Nardini et al., 2013), potentially resulting in the increasing fluctuation of tree rings. This largely explains why sites with high interannual variability were distributed in arid regions (Figure 1) and the relative contribution of EWCV and LWCV in arid regions was higher than that of humid regions (Figures 2a and 2c).

### 4.3. The Effects of P₅₀ on Interannual Growth Variability Generally Increased Over Time

As increasing water constraints on vegetation are observed globally (Jiao et al., 2021; Li et al., 2022), both arid regions and humid regions may be increasingly at risk of embolism (Dannenberg et al., 2019; Guan et al., 2014; F. Zhang et al., 2021). This explains why the role of P₅₀ becomes increasingly important (Figure 4a). The increasing importance of P₅₀ over time also suggests that maintenance of embolism resistance could be a key factor for interannual growth variability (Figure 4a). One possible mechanistic explanation underpinning this finding can lie in the “cavitation fatigue,” whereby tree xylem, following drought-induced embolism (cavitation) and repair (refilling), may weaken resistance to future drought events (Anderegg et al., 2013; Hacke et al., 2001). The cause of cavitation fatigue may be that prior embolism leads to irreversible rupture of the pit membrane and stretching of the meshwork of microfibrils (F. Feng et al., 2015). The pit membrane rupture makes the refilled vessels less resistant to cavitation than they first emerge from the vascular cambium, and the stretching of the meshwork of microfibrils increases the probability of air-seeding (Hacke et al., 2001; Hillabrand et al., 2016; Sperry & Tyree, 1990). Additionally, small air bubbles left behind after embolism repair may nucleate the embolus during successive cycles (Stiller & Sperry, 2002). The cause of cavitation fatigue has not yet been well understood, but it lends strong support that hydraulic properties play an increasingly important role in regulating changes in tree growth.

### 4.4. Factors That Play a Secondary Role in Regulating Interannual Growth Variability

Other factors also played a non-negligible role in regulating the growth variability. We found that maximum rooting depth has strong effects on the variability of earlywood and latewood in arid regions (Figures 2a and 2c). Root development and interactions with the below-ground environment, such as nutrient availability, soil, and lithology, have been shown to alter the spatiotemporal evolution of plant water availability, which in turn affects plant growth (Agee et al., 2021; Guswa, 2012; Lanning et al., 2020). Deeper rooting depth likely has a more stable water source and thus reduces growth interannual variability (Figure 3b). However, the variability of latewood was higher where the maximum rooting depth was deeper, and we speculated that this may be related to a more volatile environment at the end of the growing season. However, inferences about plant water sources and their belowground behavior are often limited by current models and tools (Brunner et al., 2015; Phillips et al., 2016; Warren et al., 2015). This leaves the extent to which plant physiology, belowground biological interactions, hydrological and soil/lithological factors intersect with concepts of plant water use strategies underexplored and an area ripe for future research (Kannenberg et al., 2021).

Tree density and CEC had a greater impact on seasonal wood variability in humid regions than arid regions (Figures 2b and 2d). This may be related to the life history strategies of trees living in different regions. For example, water and nutrients are limited in arid regions (L. Wang et al., 2009), trees are more conservative in their use of water and nutrients, whereas trees in humid regions tend to have a profligate strategy (Ludlow, 1989; Nambiar & Sands, 1993). Therefore, trees in humid regions are more sensitive to changes in nutrient availability and tree density. We also found that the lower tree density and CEC, the greater interannual growth variability (Figures 3c, 3d, 3h, and 3i). Although the competition intensity among individual trees is lower under lower tree density, lower tree density indicates a harsh and variable growing environment. As such, the growth variation
increased (Dorman et al., 2015; Seghieri et al., 2005). As water stress and nutrient limitation were relieved, tree
density increased, and growth variability gradually decreased (Figures 3c, 3d, 3h, and 3i). However, beyond
specific thresholds, the variability of latewood continued to increase (Figures 3h and 3i), which may be caused by
the climate extremes and thus more intense competition during the growth period of latewood (Crowther
et al., 2015; H. Feng & Zhang, 2015; Wolf et al., 2016).

The growth variability of earlywood and latewood in different regions was regulated by distinct climatic factors (Figure 2). In arid regions, EW<sub>CV</sub> was strongly linked to CWD<sub>PN_CV</sub> (Figure 2a), whereas LW<sub>CV</sub> was more associated with TMP<sub>LP_CV</sub> (Figure 2c), suggesting the climatic legacy effects on subsequent tree growth (Bose et al., 2020; Scharnweber et al., 2020; Szejner et al., 2020; Wu et al., 2022). Critically, the relative contributions increased over time (Figure 4e), indicating that the impact of legacy effects is increasing. In humid regions, early-season SR (SR<sub>PN_CV</sub> and SR<sub>EP_CV</sub>) had a more pronounced impact on EW<sub>CV</sub> and LW<sub>CV</sub> due to canopy gap dynamics (Brienen et al., 2010). However, with increasing drought stress, these light-limited forests may gradually transition to water limitation (Brienen et al., 2022). It is worth noting that the growth variability of both earlywood and latewood consistently decreased with higher variability in preceding hot and dry conditions in arid regions but increased in humid regions (Figures 3e and 3j). This is likely attributed to regional differences in the eco-physiological adaptation of forests to climate perturbations. Forests in arid regions are physiologically adapted to frequent drought stress, which may help to stabilize interannual wood growth (Jiang et al., 2020; Matheny et al., 2017). In contrast, moist forests are less resistant to drought stress and struggle to maintain stable growth during drought events.

**5. Conclusions**

The study is a significant step toward improving our ability to predict earlywood and latewood growth responses to more extreme climate regimes at a pan-continental scale. Our findings highlighted that: (a) The drier the region, the higher interannual growth variability of earlywood and latewood. (b) The interannual growth variability of earlywood and latewood differed in space and time. (c) Hydraulic traits, particularly P<sub>50</sub>, play an important role in influencing the growth variability of earlywood and latewood. However, some data limitations may lead to uncertainties. For example, hydraulic traits are mostly species-specific, and currently, few global data sets exist for water potential across diverse species, both spatially and temporally (Kannenberg et al., 2021), making it difficult to fully validate the output of plant hydraulic models. Additionally, although this analysis relied on the best-available aggregated data for plant traits, there is a spatial mismatch between individual tree-ring and grid measurement. Their contribution would likely be stronger if plant traits data from the actual trees could be collected. Last but not least, the “unexplained variance” of the BRT model may reflect the effects of pests, pathogens, and non-structural carbohydrates on forest stability (Adams et al., 2017; Anderegg et al., 2020; Bardgett et al., 2008; Seidl et al., 2018), which represents a significant source of uncertainty in carbon-climate feedback. Thus, more species-based research on tree radial growth and hydraulic functions, particularly relying on expanded and long-term field observation networks and higher-resolution remote sensing products, is urgently needed to enhance understanding of tree growth adaptations and improve predictions of carbon sequestration dynamics.

**Conflict of Interest**

The authors declare no conflicts of interest relevant to this study.

**Data Availability Statement**

The tree-ring width time series on which this article is based are available in Zhao et al. (2019). Aridity index is available in Trabucco and Zomer (2018). Climate variables are fully available in Harris et al. (2020) and Viovy (2018). Hydraulic traits, including P₅₀, canopy height and maximum rooting depth, on which this paper is based, can be found in Choat et al. (2012), Simard et al. (2011), and Fan et al. (2017). Tree density is available in Crowther et al. (2015). AWC and CEC are available in Wieder et al. (2014). The foliar traits, including Nm, Pm, and SLA, are fully available in Butler et al. (2017). The processing code for tree-ring data, BRT models, and main figures, along with the integrated dataset, including EW CV and LW CV (calculated from RWI values), as well as
Acknowledgments

X.W. received support from the State Key Research and Development Program (2022YFF0801802) and the National Natural Science Foundation of China (42171050). We thank all contributors to the ITRDB for making this analysis feasible.

weather, soil, and plant functional traits, have been deposited on Zenodo Sun (2024) and are available at https://github.com/Sunmengyun666/ForestVariabilityResponse.

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