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Article

Intra-Annual Growth Dynamics and Environmental Response of Leaves, Shoots and Stems in Quercus serrata on Lushan Mountain, Subtropical China

by
Dina Fu
1,
Wenpeng Zhang
1,
Xinsheng Liu
1,2,*,
Yesi Zhao
3,
Lian Sun
1,
Sirui Zhang
1 and
Zilong Chen
1
1
School of Geography and Tourism, Anhui Normal University, Wuhu 241000, China
2
Key Laboratory of Earth Surface Processes and Regional Response in the Yangtze-Huaihe River Basin, Anhui Normal University, Wuhu 241000, China
3
School of Geography, Jiangsu Second Normal University, Nanjing 211200, China
*
Author to whom correspondence should be addressed.
Forests 2025, 16(2), 305; https://doi.org/10.3390/f16020305
Submission received: 7 January 2025 / Revised: 5 February 2025 / Accepted: 7 February 2025 / Published: 10 February 2025
(This article belongs to the Special Issue Drought Impacts on Wood Anatomy and Tree Growth)
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Abstract

:
Primary and secondary growth of trees are key components of carbon sequestration in forest ecosystems. However, the temporal relationships between primary and secondary growth as well as their responses to environmental variations are still poorly understood. Herein, we continuously measured the intra-annual leaf, shoot and stem growth of Quercus serrata for two years on Lushan Mountain, southeastern China. Our results showed that shoots were ranked as the first organ to initiate, peak and cease growth, rather than leaves and stems. Moreover, the phenological stages of shoot growth were negatively associated with those of leaves and stems, whereas there was a weak positive correlation in phenological events between leaves and stems. These temporal connections in phenological events between primary and secondary growth suggest a prioritized carbon allocation to shoot growth and a high dependence of stem growth on carbon from newly developing leaves. Although stem growth started earlier in response to the warmer spring in 2018 compared to the colder spring in 2017, no significant difference in annual increment was observed between years, which was related to the more severe drought condition during the dry season in 2018. At the intra-annual scale, different organs generally had a consistent growth response to temperature variables but showed a divergent response to vapor pressure deficit. Despite a relatively short observational period and potential bias in spatial representativeness, our data provide nuanced knowledge on seasonal growth dynamics in primary and secondary of broadleaved species, underlining the importance of jointly considering intra-seasonal variabilities of environmental conditions in order to correctly predict tree growth response to climate change in subtropical regions.

1. Introduction

Primary (i.e., leaf, shoot and bud development associated with apical meristems) and secondary growth (i.e., cambial activity and xylem differentiation) are key components of tree growth and carbon sequestration processes in forest ecosystems [1,2]. Internal physiological mechanisms [3,4] and external environmental factors [5,6,7,8] jointly regulate the development of primary and secondary growth. Investigating the seasonal growth dynamics of different tree organs is essential for assessing the correlations between primary and secondary growth phenophases and for comprehending their influences on forest carbon sequestration in forests [9,10]. Additionally, such in-depth research can shed new light on growth–climate relationships under a changing climate [11,12].
One of the focal points in tree growth is the temporal correlation between primary and secondary growth (i.e., the impact of previous phenophases in primary growth on subsequent ones in radial growth) [13,14]. Concurrent phenology monitoring of leaves, shoots and stems has revealed diverse phenological time series patterns separating primary from secondary growth across different taxonomic groups. Studies on coniferous species have demonstrated contrasting initiation sequences for primary growth and secondary growth [1,9,13,15], in which cambial activity begins either before [16] or after bud break [9,17]. Likewise, there is no consensus on the sequential order of primary and secondary growth among broadleaved tree species [18,19]. Bud break, leaf elongation and cambial resumption occur almost simultaneously in diffuse-porous species [20,21]. By contrast, ring-porous species exhibit a distinct sequential order, with new xylem cells generally developing before bud break [22,23]. There is not a fixed temporal relationship between primary and secondary growth, which might vary by species and locations. In addition, the temporal correlation between primary and secondary growth across the entire phenological cycle has received little attention, particularly in subtropical regions with a relatively longer growing season.
A dendrometer is a non-destructive device with a high temporal resolution for continuous monitoring of the stem diameter. Dendrometers have been extensively applied to study seasonal stem growth and its response to environmental factors in different species globally [24,25,26,27]. Numerous studies on environmental drivers of tree growth have indicated that temperatures are pivotal in initiating both primary and secondary growth [28,29,30,31], whereas the peak and ending of stem growth are primarily determined by photoperiod [32,33] and moisture conditions [6,34,35], respectively. The environmental drivers of primary and radial growth can also differ among climatic zones. In temperate and cold-limited areas, xylem cell enlargement generally occurs at night; therefore, temperatures act as major constraints on apical and radial meristem growth [36,37]. However, some research in subtropical regions has shown that rainfall and relative humidity can promote radial growth, while vapor pressure deficit and temperatures can limit tree growth [25,27,34,38,39], implying that water stress is the primary factor controlling tree growth. Nonetheless, it remains to be explored whether there is a consistent environmental response of primary and stem radial growth in subtropical regions. Thus, it is necessary to compare how primary and stem radial growth respond to intra-seasonal environment variability.
In the Northern Hemisphere’s temperate regions, a large body of research has found that climate warming-induced advancement of the growing season can promote tree growth in cold and humid areas [33,40], while it can inhibit tree growth in cold and dry areas [5,6,35]. Climate warming has induced more frequent and intense drought in subtropical China in recent decades [41,42,43]. However, tree ring studies on coniferous species have revealed complex growth–climate relationships in subtropical forests, including positive correlations with winter temperatures [44,45] and summer precipitation [45,46,47]. It is worth noting that previous studies were mainly conducted in conifers [25,33,38,39], and observational data regarding how the tree growth of subtropical deciduous species responds to climate warming or increasing drought frequency are limited [48,49,50]. Exploring the environmental forcing of deciduous tree species is fundamental to comprehending how tree growth in subtropical forests adapts to changing climatic conditions.
Quercus serrata, a deciduous broadleaf tree species, is a dominant and important timber species in the evergreen–deciduous broadleaved mixed forests in Lushan Mountain, southeastern China [34]. This study monitored the seasonal dynamics of the primary growth (leaves and shoots) and radial growth of Q. serrata at one site on Lushan Mountain over the growing seasons in 2017 and 2018. Our hypothesis is that the primary growth should take priority over the stem growth in subtropical deciduous tree species, and the stem growth may be more affected by summer drought than spring warming. Our objectives were threefold: (1) to identify the critical phenological stages of primary and secondary growth; (2) to investigate the temporal correlation of the critical phenological stages between primary and secondary growth; and (3) to explore how primary and secondary growth respond to inter- and intra-annual variations in environmental conditions.

2. Materials and Methods

2.1. Study Site and Tree Selection

The study site is situated on the western slope of the Lushan Mountain (921 m a.s.l., 29°33′ N, 115°33′ E), in the northern part of Jiangxi Province, southeastern China (Figure 1). This area is featured by a typical subtropical monsoon climate, with alternating rainy (March to June) and dry seasons (July to November) [34]. According to the long-term meteorological record, the annual mean temperature and precipitation are 11.9 °C and 2010 mm, respectively [25]. Deciduous broadleaf species become increasingly dominant at higher elevations, and thus the evergreen–deciduous broadleaved mixed forest is the predominant vegetation type between 600 and 1000 m on Lushan Mountain. The soil type is classified as mountain yellow–brown forest soil with a high content of organic matter and total nitrogen. The entire soil profile shows a relatively strong acidic reaction [51]. In February 2016, a permanent forest plot on sloping terrain was constructed at an elevation of 921 m, where constructive species include Pinus taiwanensis Hayata, Quercus serrata, Chamaecyparis obtuse, Cryptomeria japonica, Castanopsis eyrei and Quercus myrsinifolia [50]. In this study, four Quercus serrata trees were chosen to monitor the intra-annual growth dynamics in leaves, shoots and stems during the growing seasons in 2017 and 2018. The monitored trees were upright, healthy and similar in dominance. To minimize the potential effects of the tree age on primary and secondary growth, the selected trees had an age of 72 ± 10 years, a diameter at breast height (DBH) of 18.6 ± 2.2 cm and a height of 8.7 ± 2.2 m (Table 1).

2.2. Measurements of Leaves’ and Shoots’ Growth and Phenology Observation

The length of new leaves and shoots was measured weekly from March to October in 2017 and 2018. Two or three branches in the mid-canopy of each tree that faced north and south were chosen randomly and marked for observation. A steel ruler, with an accuracy of 1 mm, was used to measure the weekly extension of leaves and shoots on each branch [9,21]. These measurements were deemed indicative of the overall leaf and shoot growth within the tree canopy [9,52,53]. From March to November, the budburst and the coloring evolution (leaf unfolding, senescence and defoliation) of each branch were monitored every 5–7 days.

2.3. Monitoring of Stem Radius Variations

The stem radial growth at breast height (1.3 m above ground) was continuously measured using automatic or manual dendrometers (DRL26C/DB20, Brno, Czech Republic; Table 1). The manual dendrometer recorded the stem radial growth at weekly intervals with a precision of 0.1 mm, while the automatic one logged data hourly with an accuracy of 1 μm. The automatic dendrometer data were processed on a weekly basis to facilitate subsequent analysis. To reduce the hygroscopic effect of dead bark on the dendrometer measurements, necrotic tissue was carefully removed from the bark before installation [25]. The dendrometer was outfitted with a rotating position sensor that measured the circumference of stainless-steel bands mounted on tree trunks. These circumference measurements were further converted into the stem radius. The age of each monitored tree was estimated using the linear relationship between diameter at breast height (DBH) and the tree age for Q. serrata in subtropical China [54].

2.4. Microenvironment Data

A meteorological station (HOBO U30, Onset, Bourne, MA, USA) was installed within the plot to monitor the microenvironmental conditions within a 20 m radius around the sampled trees. The measured microenvironmental variables included the air temperature (Ta, °C; accuracy ± 0.02 °C; range −40–75 °C), relative humidity (RH, %; accuracy ± 0.1%; range 0%–100%) and precipitation (Pr, mm; accuracy ± 0.2 mm). The soil temperature (Ts, °C; accuracy ± 0.03 °C; range −40–100 °C) and volumetric soil water content (SWC, m3 m−3; accuracy ± 0.06%; range 0%–40.5%) were measured at soil depths of 10 cm and 30 cm. Sensor-specific calibration was applied before installation to ensure the reliability of the data. Since January 2017, a data recorder (U30-NRC, Onset, Bourne, MA, USA) has been used to store the data with a recording interval of 30 min. The following formula was used to calculate the vapor pressure deficit (VPD, kPa) [55]:
VPD = 0.611exp [17.502Ta/(Ta + 240.97)](1 − RH/100)
where VPD represents the vapor pressure deficit (kPa), Ta is the air temperature (°C) and RH is the relative humidity (%).

2.5. Modelling of Intra-Annual Leaf, Shoot and Stem Growth and Determination of Phenological Phases

Given that the growth rates of leaves, shoots and stems is uneven over the growing season, exhibiting slow growth in the early stages, rapid growth in the middle stages and slow growth again in the later stages, the S-shaped Gompertz function can be effectively used for fitting [1,36]. To evaluate the differences in the intra-annual growth dynamics among different organs, we defined the critical phenological phases for leaf, shoot and stem growth (i.e., the timing of onset, peak and cessation) and estimated the growth rates and annual increments using data from individual monitoring records. The model equation is as follows:
y = y0 + A exp[– e(β − κt)]
where A and y0 are the upper and lower asymptotes, y represents the weekly stem radius or leaf/shoot length, t is the day of year (DOY), β is the axial position parameter and κ is the rate of change parameter.
We determined the start and end of the growing season (SOG and EOG) for each organ using the fitted curves, whereby SOG and EOG were defined as the dates when the annual increment of the sampled organ reached 5% and 95%, respectively [33]. The duration of growth was determined as the difference between the onset and ending dates. The growth rate and its inflection points can be derived from parameters of the Gompertz function. Specifically, the following formulas were used to calculate the maximum growth rate (Rmax), average growth rate (Rmean, representing 90% of the annual increment) and the timing of the maximum growth (Tp) [56]:
Tp = β/κ
Rmax = κA/e
Rmean ≈ 9/40·eRmax

2.6. Statistical Analysis

An independent samples t-test was employed to analyze the differences in the phenological dates of leaf, shoot and stem growth, as well as the annual increments of different organs between 2017 and 2018. Pearson correlation analysis was applied to assess the correlations between the different phenological phases of the three organs using pooled data from two observation years. To investigate the impact of various environmental variables on the growth of organs, we established a linear mixed effects model (LME, lme4 package) and stepwise multivariable regression analysis to explore the relationships between the weekly averaged environmental factors (except for precipitation) preceding the monitoring and the weekly growth rates of leaves, shoots and stems. The models are as follows [57]:
Log(Y + 1) = α + βX + δ + ε
where Y represents each organ’s weekly growth rates, and X represents environmental factors (Pr, RH, VPD, Ta_min, Ta_mean, Ta_maximum, Ts_mean, and SWC). α and β represent the intercepts and coefficients of fixed effect, and δ and ε represent the variation from random effect and residual error.
Log(Y + 1) = β0 + β1X1 +β2X2 + … + βKXk
where X1, X2, …, XK are environmental factors, β1, β2, … , βK are coefficients and β0 is the intercept.
All statistical analyses and visualizations were conducted using R statistical software (R Core Team 2022, version 4.2.2) [58].

3. Results

3.1. Environmental Conditions

Although the annual mean temperature and total precipitation were similar between 2017 and 2018, environmental variables in the rainy and dry seasons varied markedly between years (Figure 2 and Table 2). In 2018, the mean air temperature from March to June increased by 1.8 °C compared to 2017, and the rainy-season precipitation in 2018 was slightly reduced. In contrast, the mean air temperature from July to September was almost identical between years, but the dry-season precipitation in 2018 was greatly reduced by 53% relative to 2017. Additionally, the soil moisture in 2018 was lower than that in 2017 throughout the growing season.

3.2. Seasonal Growth Dynamics of Leaf, Shoot and Stem and Organ Phenology

According to Figure 3 and Figure 4, leaf elongation initiated between 3 April and 18 April (DOY 93–108) and reached its peak approximately seven days later (DOY 110–116). The ending dates of leaf growth, occurring between 2 May and 24 May (DOY 122–144), were matched with those dates of the observed phenological photographs. Shoot elongation started on 16–21 March (DOY 75–102), peaked in the following week (DOY 80–105) and ended in late April (DOY 97–115). The radial growth of the trees began between 20 March and 18 May (DOY 79–138), peaked approximately a month later (DOY 117–170) and ceased between mid-August and mid-September (DOY 222–259).
For leaves, there was no significant difference in the timing of phenological stages or annual increment between years. For shoots, phenological timings in 2018 were significantly delayed compared to 2017, but the annual increment was identical and did not show a significant difference between years (Figure 5). No significant between-year differences in the timings of the peak and cessation as well as the annual increment were detected, even though there was an earlier onset of stem radial growth in 2018 (Figure 5).

3.3. Temporal Correlations of Critical Phenological Phases Among Different Organs

For each organ, a strong positive association was generally found between the onset, peaking and cessation timings for the leaf (r = 0.75–0.97, p < 0.1), shoot (r = 0.88–0.99, p < 0.05) and stem (r = 0.54–0.98) growth phenology (Figure 6). However, there were no consistent temporal correlations of critical phenological phases among different organs. The shoots’ SOG, Tp and EOG negatively affected the phenological events in leaves and stems. In contrast, a weak positive correlation was observed between the phenological events in leaves and stems (Figure 6), indicating that an earlier start in phenological phases in leaves can lead to earlier subsequent phenological phases in stems.

3.4. Response of Leaf, Shoot and Stem Growth to Environmental Variables

The weekly leaf growth was positively correlated with the mean air temperature, but it was negatively related to the minimum air temperature (Figure 7a). The weekly shoot growth was positively related to the vapor pressure deficit and relative humidity. In addition, the soil temperature had a negative impact on shoot growth (Figure 7b). The stem increments of Q. serrata were significantly and negatively affected by the vapor pressure deficit, mean soil temperature and minimum air temperature. However, there was a positive correlation between the mean air temperature and weekly stem increments of Q. serrata (Figure 7c). Overall, a combination of different environmental factors explained 13%–30% of the variations in the growth rates of leaves, shoots and stems during the two growing seasons (Table S1).

4. Discussion

4.1. Temporal Correlations of Phenological Events Differ Between Tree Organs

We found that shoots were ranked as the first organ to start growth and to reach the growth peak and cessation stages, followed by leaves and stems (Figure 3 and Figure 4). This is in contrast to the coniferous and broadleaved tree species in temperate and boreal forests, where cambial activity may start either before or after needle unfolding [9,16,17] and/or bud break [20,22]. Moreover, the seasonal growth dynamics of shoots and leaves showed a high degree of synchrony, which may be due to the fact that Quercus spp. is a ring-porous species. These trees have a short sprouting time and rapid leaf expansion, because the growth of shoots is needed to provide a supportive structure for the unfolding of leaves [59]. The earliest emergence and shortest duration in leaves and shoots rather than stems in Q. serrata highlight the fact that building canopy takes priority over and is a prerequisite for stem growth in deciduous tree species [31,60].
Temporal relationships between the apical and radial growth phenology throughout the growing season can inform the concurrent optimal allocation of photoassimilates and non-structural carbon storage among tree organs, thereby coordinating the tree growth at the whole tree level [1]. In this study, our results revealed a positive (albeit weak) correlation between phenological events in leaves and stems, whereas phenological events in shoots were both negatively correlated with those in leaves and stems (Figure 6). These findings indicate a potential promoting effect of phenological events in leaves on subsequent ones in stems. In contrast, phenological events in shoots can exert a strong inhibitory effect on subsequent phenological stages in leaves and stems. These results agreed with the research of Gričar et al. [61] and Yin et al. [23], in which they reported that ring-porous species, such as Quercus pubescens and Quercus mongolica, showed a close connection in growth phenology between leaf development and stem growth. Physiologically, the radial growth of ring-porous species does not rely on carbohydrate storage but depends more on the leaf photosynthesis of newly emerged leaves [22]. This phenomenon further explains why Q. serrata initiates leaf expansion significantly earlier than cambial resumption. On the other hand, the growing periods for leaves and shoots were highly overlapped, indicating a potential competition for carbohydrate storage and thus leading to negative correlations in phenological events between leaves and shoots. The negative correlations in phenological events between shoots and stems was probably attributed to the indirect inhibiting effect of phenological events in shoots on those in leaves.

4.2. Inter-Annual Environment Variation and Growth Difference Between Years

We found that the phenological phases of the three organs of Q. serrata were generally advanced in the warm year (2018) compared to the cold year (2017), whereas the annual growth rates showed little changes between years (Figure 5). Warmer temperatures would lead to an earlier phenological phase and thus increased annual growth, and our results align with the general notion regarding the impacts of environment change on plant phenology [62,63].
While warmer springs advances the onset timing of tree growth, it does not necessarily enhance the ability of forests to sequester carbon. In this study, the stem growth of Q. serrata began earlier in 2018, but it did not translate into an increase in annual increments, aligning with the finding of Dow et al. [40]. Our results challenge the paradigm that warmer springs and prolonged growing seasons will ultimately lead to higher annual stem growth in mid- and high-latitude forests [63,64]. Warmer spring temperatures can negatively impact growth, especially when water is limited [5,6,38]. In particular, ample early-season water supply can maintain a steady tree water status and thus the production of xylem cells and cell expansion [28,65]. By contrast, during the peak to late growing season, stem growth is more sensitive to water stress and high temperatures [66,67]. The study demonstrates that summer drought during the mid to late growing season can induce the radial growth of Q. serrata to peak and cease earlier (Figure 5f,i), indicating the paramount control of summer drought on stem growth. Previous observations on Pinus taiwanensis Hayata in the studied site further demonstrated that summer drought can negatively affects stem growth via its control on growth cessation [25,34].
Due to the relatively short observational period and limited sampled site, our data could not answer whether and how the tree phenology of subtropical deciduous tree species would respond to long-term climate change. To gain in-depth understandings of tree phenology and the coordination in phenological events between different organs, long-term in situ observations with multiple species and across larger special scales are urgently needed in future research.

4.3. Growth–Environment Relationships

Investigating the impact of environmental factors on seasonal primary and secondary growth is critical for comprehending the responses of tree growth to short-term variation in environmental conditions [25,31]. Temperature is a critical factor in the initial stages of primary and secondary growth, as evidenced by much research on the growth–environment relationships [29,30,68]. Our results showed that the growth of different organs responded consistently to temperature variables, whereas their responses to moisture variables were highly organ-specific (Figure 7). Our study found a positive relationship between the leaf and stem growth and the mean air temperature, indicating that favorable air temperatures may enhance the growth processes of leaves and stems. This is because optimal temperature conditions can stimulate metabolic processes such as photosynthesis, cell division and lignification and in turn leaf and stem growth [69]. In contrast, the leaf and stem growth was negatively correlated with the soil temperature and minimum air temperature, which could be attributed to the fact that higher nighttime and/or soil temperatures can induce excessive energy and nutrient consumption through respiration, thereby negatively affecting the growth of leaves and stems [70].
The stem growth had a negative correlation with the VPD, but the shoot growth was positively associated with the VPD, indicating that the impact of VPD on tree growth varies by organs. This finding is consistent with those reports by Liu et al. [25], Meng et al. [38] and Zhou et al. [27]. The significant effect of the VPD on shoot growth at the seasonal scale was further supported by the fact that the growth maxima generally corresponded to the highest VPD (Figure 2 and Figure 3). Given that the seasonal shoot growth occurred primarily in the spring and early summer, a moderate VPD may increase the transpiration rates and nutrient transport efficiency, thus promoting shoot growth. However, a higher VPD during the dry season could lead to excessive water loss and stomatal closure, causing water stress in trees and hence inhibiting stem growth [71,72]. The significance of the water–heat balance in subtropical regions with a hot summer and a mild winter is demonstrated by the seasonal VPD effect on the tree growth of Quercus spp. [73]. In addition, the dominant role of VPD on tree growth can help us to gain a better understanding of the long-term paleoenvironment evolution in southeastern China by providing valuable insights into past environment reconstruction from broadleaf tree species in subtropical areas [46,47,50,74,75].

5. Conclusions

In this study, we investigated the temporal relationships of phenological phases between the primary and secondary growth of Quercus serrata and the growth responses to intra- and inter-annual variabilities in environmental conditions. We found that shoot growth preceded leaf and stem growth in every year. Moreover, our results revealed that the timings of the phenological phases in shoots were both negatively linked to those in leaves and stems, but the linkage became positive between leaves and stems. Together, our findings indicate a potential competition in resource allocation between shoots and other organs during the early growing season and a high dependence of stem growth on newly developing leaves. The stem growth did not benefit from a warmer spring in 2018, despite initiating earlier than that in a colder spring year (2017), emphasizing the importance of considering the summer drought on stem growth [27,69]. In general, different organs had a consistent growth response to intra-annual variabilities in temperature variables but had a divergent response to the vapor pressure deficit. These results allow the dynamics of primary and secondary growth phenology to be explored in detail, providing key knowledge on between-organ growth phenology linkages and growth–environment relationships, which will help us to assess potential environmental change effects on forest productivity as well as past environment reconstruction in subtropical regions.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/f16020305/s1, Table S1: Stepwise multivariable regression models relating weekly growth rates of each organ to environmental factors. See text for abbreviations for environmental factors.

Author Contributions

Conceptualization, X.L.; methodology, D.F. and W.Z.; software, D.F. and W.Z.; validation, X.L., Y.Z. and L.S.; formal analysis, D.F.; investigation, D.F., W.Z., S.Z. and Z.C.; data curation, X.L.; writing—original draft preparation, D.F.; writing—review and editing, X.L., Y.Z. and L.S.; visualization, D.F.; supervision, X.L.; project administration, X.L.; funding acquisition, X.L. and Y.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Natural Science Foundation of Anhui Province (2408085MD096), the National Natural Science Foundation of China (41961008, 42201014), the University Natural Science Research Project of Anhui Province (2023AH040020) and the Anhui New Era Education Quality Project (2023dshwyx011). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the funding agencies and supporting institutions.

Data Availability Statement

The data underlying this article will be shared on reasonable request to the corresponding author.

Acknowledgments

We are grateful to Chunsong Wang for assistance in field management and sampling.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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Figure 1. (a) The location of the study site at Lushan Mountain in Southeastern China (The location of Lushan Mt. in Jiangxi Province is indicated by a red pentagram in the inset). (b,c) Instrumented trees with band dendrometers. (d,e) Pictures of key phenological stages of primary growth in Quercus serrata, in which sampled leaf/branch is marked with a rope or red marker. (d) Leaf unfolding (15 April 2017); (e) new shoot growth (24 March 2017); (f) leaf senescence (4 November 2017).
Figure 1. (a) The location of the study site at Lushan Mountain in Southeastern China (The location of Lushan Mt. in Jiangxi Province is indicated by a red pentagram in the inset). (b,c) Instrumented trees with band dendrometers. (d,e) Pictures of key phenological stages of primary growth in Quercus serrata, in which sampled leaf/branch is marked with a rope or red marker. (d) Leaf unfolding (15 April 2017); (e) new shoot growth (24 March 2017); (f) leaf senescence (4 November 2017).
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Figure 2. Annual courses of (a) mean daily air temperature (Ta_mean; maximum and minimum air temperatures are depicted in gray shadings) and daily mean soil temperature (Ts_mean), (b) daily total precipitation (Pr) and mean daily soil water content (SWC) and (c) mean daily vapor pressure deficit (VPD) at the study site in Lushan Mountain during 2017 and 2018.
Figure 2. Annual courses of (a) mean daily air temperature (Ta_mean; maximum and minimum air temperatures are depicted in gray shadings) and daily mean soil temperature (Ts_mean), (b) daily total precipitation (Pr) and mean daily soil water content (SWC) and (c) mean daily vapor pressure deficit (VPD) at the study site in Lushan Mountain during 2017 and 2018.
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Figure 3. Seasonal growth dynamics (a,c) and daily growth rates (b,d) of leaves, shoots and stems of Quercus serrata during 2017 and 2018. Closed points represent measured data, and fitting curves are modeled by applying Gompertz function (see Table 3 for parameters).
Figure 3. Seasonal growth dynamics (a,c) and daily growth rates (b,d) of leaves, shoots and stems of Quercus serrata during 2017 and 2018. Closed points represent measured data, and fitting curves are modeled by applying Gompertz function (see Table 3 for parameters).
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Figure 4. Comparisons of critical timings of phenological phases (a) and growth durations (b; diamond symbols) for primary and secondary growth of Quercus serrata in 2017 and 2018. Leaf unfolding, senescence and defoliation (diamond symbols in the upper panel) are defined based on observational photos. SOG, start of growth; Tp, timing of peak growth; EOG, end of growth.
Figure 4. Comparisons of critical timings of phenological phases (a) and growth durations (b; diamond symbols) for primary and secondary growth of Quercus serrata in 2017 and 2018. Leaf unfolding, senescence and defoliation (diamond symbols in the upper panel) are defined based on observational photos. SOG, start of growth; Tp, timing of peak growth; EOG, end of growth.
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Figure 5. Timings of (a–i) key phenological phases and (j–l) annual increments (mean ± SD) of leaves, shoots and stems of Quercus serrata during the growing seasons of 2017 and 2018. Different lowercase letters indicate significant differences in phenological phases and annual increments between years (p < 0.05).
Figure 5. Timings of (a–i) key phenological phases and (j–l) annual increments (mean ± SD) of leaves, shoots and stems of Quercus serrata during the growing seasons of 2017 and 2018. Different lowercase letters indicate significant differences in phenological phases and annual increments between years (p < 0.05).
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Figure 6. A correlation heatmap of phenological phases among different organs of Quercus serrata during the growing seasons of 2017 and 2018. Darker colors indicate stronger correlations, while lighter colors indicate weaker correlations. *, ** and *** indicate significance levels at 0.1, 0.05 and 0.01, respectively.
Figure 6. A correlation heatmap of phenological phases among different organs of Quercus serrata during the growing seasons of 2017 and 2018. Darker colors indicate stronger correlations, while lighter colors indicate weaker correlations. *, ** and *** indicate significance levels at 0.1, 0.05 and 0.01, respectively.
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Figure 7. Results of linear mixed analysis of effects of weekly environmental factors prior to the investigated date on weekly increments of leaves (a), shoots (b) and stems (c) for Quercus serrata during 2017 and 2018. Significant levels (p < 0.05) are depicted in red, while gray indicates nonsignificant levels.
Figure 7. Results of linear mixed analysis of effects of weekly environmental factors prior to the investigated date on weekly increments of leaves (a), shoots (b) and stems (c) for Quercus serrata during 2017 and 2018. Significant levels (p < 0.05) are depicted in red, while gray indicates nonsignificant levels.
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Table 1. The diameter at breast height (DBH), height and age for selected Quercus serrata trees (n = 4) at the study site.
Table 1. The diameter at breast height (DBH), height and age for selected Quercus serrata trees (n = 4) at the study site.
SpeciesTree no.DBH (cm)Height (m)Age (yr.) c
Quercus serrata1 a16.48.264
2 a16.36.763
3 b20.412.280
4 b21.17.582
a Manual band dendrometers. b Automatic band dendrometers. c Cambial age was estimated according to the DBH–Age relationship for Quercus serrata.
Table 2. Environmental variables at the study site in 2017 and 2018.
Table 2. Environmental variables at the study site in 2017 and 2018.
MonthsYearTa (°C)Ts (°C)Pr (mm)SWC (m3 m−3)VPD (kPa)
Jan–Dec201713.30.1714810.170.26
201813.10.1711670.150.24
Mar–Jun201714.10.207100.210.26
201815.90.195590.180.27
Jul–Sep201722.50.175950.150.33
201822.10.162800.110.30
Ta (mean daily air temperature); Ts (mean daily soil temperature at 10 and 30 cm depth); Pr (total precipitation); SWC (mean daily soil water content at 10 and 30 cm depth); VPD (vapor pressure deficit).
Table 3. Parameters and R2 of Gompertz functions for intra-annual leaf, shoot and stem growth dynamics of Quercus serrata in 2017 and 2018.
Table 3. Parameters and R2 of Gompertz functions for intra-annual leaf, shoot and stem growth dynamics of Quercus serrata in 2017 and 2018.
OrganYearAβκy0R2Rmax (mm d−1)Rmean (mm d−1)
Leaf20177.033 ± 1.0781.164 ± 0.0240.129 ± 0.0090.009 ± 0.0120.9990.333 ± 0.0590.203 ± 0.035
20188.221 ± 2.4041.199 ± 0.0770.147 ± 0.0240.051 ± 0.0420.9970.449 ± 0.1600.270 ± 0.010
Shoot20177.995 ± 5.8431.336 ± 0.2190.265 ± 0.1140.426 ± 0.0710.9970.894 ± 0.7780.546 ± 0.475
20183.472 ± 2.5936.881 ± 1.3180.164 ± 0.0240.329 ± 0.0550.985 0.225 ± 0.1830.138 ± 0.112
Stem20171.226 ± 0.5522.082 ± 0.2930.031 ± 0.0021.806 ± 1.3360.9940.014 ± 0.0070.006 ± 0.006
20181.180 ± 0.5091.615 ± 0.2070.026 ± 0.0052.974 ± 1.1360.9940.012 ± 0.0060.007 ± 0.004
A, upper asymptote; β, x-axis placement; κ, rate of change; y0, lower asymptotes; Rmax, maximum growth rate; Rmean, mean growth rate. Values are mean ± standard deviations (SD).
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Fu, D.; Zhang, W.; Liu, X.; Zhao, Y.; Sun, L.; Zhang, S.; Chen, Z. Intra-Annual Growth Dynamics and Environmental Response of Leaves, Shoots and Stems in Quercus serrata on Lushan Mountain, Subtropical China. Forests 2025, 16, 305. https://doi.org/10.3390/f16020305

AMA Style

Fu D, Zhang W, Liu X, Zhao Y, Sun L, Zhang S, Chen Z. Intra-Annual Growth Dynamics and Environmental Response of Leaves, Shoots and Stems in Quercus serrata on Lushan Mountain, Subtropical China. Forests. 2025; 16(2):305. https://doi.org/10.3390/f16020305

Chicago/Turabian Style

Fu, Dina, Wenpeng Zhang, Xinsheng Liu, Yesi Zhao, Lian Sun, Sirui Zhang, and Zilong Chen. 2025. "Intra-Annual Growth Dynamics and Environmental Response of Leaves, Shoots and Stems in Quercus serrata on Lushan Mountain, Subtropical China" Forests 16, no. 2: 305. https://doi.org/10.3390/f16020305

APA Style

Fu, D., Zhang, W., Liu, X., Zhao, Y., Sun, L., Zhang, S., & Chen, Z. (2025). Intra-Annual Growth Dynamics and Environmental Response of Leaves, Shoots and Stems in Quercus serrata on Lushan Mountain, Subtropical China. Forests, 16(2), 305. https://doi.org/10.3390/f16020305

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