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Article

Identifying Superior Growth and Photosynthetic Traits in Eighteen Oak Varieties for Southwest China

by
Zengzhen Qi
†,
Xiang Huang
†,
Yang Peng
,
Hongyi Wu
,
Zhenfeng Xu
,
Bo Tan
,
Yu Zhong
,
Peng Zhu
,
Wei Gong
,
Gang Chen
,
Xiaohong Chen
and
Wenkai Hui
*
Key Laboratory of Ecological Forestry Engineering of Sichuan Province, College of Forestry, Sichuan Agricultural University, Chengdu 611130, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2024, 15(11), 2006; https://doi.org/10.3390/f15112006
Submission received: 8 October 2024 / Revised: 8 November 2024 / Accepted: 10 November 2024 / Published: 14 November 2024
(This article belongs to the Special Issue Physiological, Biochemical and Morphological Adaptation Characteristics of Woody Plants to the External Environment)
Browse Figures
Versions Notes

Abstract

:
Quercus, commonly known as oak, has great potential as one of the most widely cultivated plant species. However, the lack of superior varieties is a bottleneck for its usage and application in Southwest China. Here, this study aims to explore the growth and photosynthetic traits of 18 oak varieties with the goal of identifying the adaptable superior varieties for the region, focusing on nutrient growth, leaf morphology, chlorophyll content, and photosynthetic parameters over a 32-week growth period. The results showed that a significant diversity was observed among the varieties. Growth rhythm and fitted curves divided the 18 oak varieties into three patterns. Additionally, for the leaf morphological parameters, Q. denta boasted the maximum leaf area (167.24 cm2), leaf width (13.62 cm), and leaf aspect ratio (156.6); Q. mongo showed the greatest leaf length (20.37 cm); while Q. acutis had the largest leaf form factor (3.44) and leaf gap (0.39). Chlorophyll content was based on three-time-points investigation, with higher levels observed in Q. mongo, Q. robur 4, Q. wutai, Q. denta, Q. acutis, and Q. robur 1. The transpiration rate (E) (5.03 mmol m−2), stomatal conductance (gsw) (0.22 mol m−2 s−1), and total water vapor conductance (gtw) (0.19 mol m−2 s−1) were dominantly obtained in Q. robur 1, while Q. denta exhibited the highest intercellular CO2 concentration (Ci) (564.67 µmol mol−1). Conversely, Q. wutai displayed a significantly higher leaf chamber CO2 concentration (Ca) (502.11 µmol mol−1). Furthermore, growth traits showed a correlation with leaf morphological and photosynthetic traits. PCA analysis grouped the oak varieties into five clusters, with Q. acutis, Q. robur 1, Q. palus 3, Q. denta, Q. nutta, Q. mongo, and Q. wutai identified as superior varieties. These findings not only offer promising oak candidate varieties for Southwest China, but also provide insights for establishing efficient breeding program for other woody plants.

1. Introduction

Quercus, commonly referred to as oak, constitutes the largest group within the Fagaceae family and plays a pivotal role in forest ecosystem [1]. With around 400–600 species, oaks are found in temperate to tropical areas of the Northern Hemisphere [2], holding significant economic and ecological value. In China, particularly in southwest regions, approximately 120 oak species thrive in mountainous areas [3]. Oaks fulfill diverse functions, contributing to wood and charcoal production, while also playing crucial roles in soil conservation and biodiversity preservation [4]. However, oak candidate varieties in Southwest China encounter various challenges, hindering their prosperity. Notably, the scarcity of superior types limits their practical application and utilization. Therefore, it is essential to investigate the growth and physiological attributes of oak species, serving as the scientific groundwork for effective utilizing and cultivating exceptional oak varieties in China.
Tree growth, a widely used metric for assessing plant performance, is also a crucial factor in determining forest productivity and biomass accumulation [5]. Growth traits are integral for the survival of forest trees and influence intraspecific competition, particularly in economically significant tree species. These traits offer valuable insights into identifying optimal timings for byproduct harvesting [6]. A previous study elucidated significant variations in growth rhythms concerning tree height and diameter at breast height (DBH) across diverse tree species [7]. Notably, many species exhibited seasonal growth patterns, with accelerated growth during spring, summer, or autumn, but slower growth during winter, indicating distinct growth strategies in response to seasonal changes [8,9]. Thus, environmental factors, such as temperature and precipitation, exert considerable influence on tree growth rhythms [10,11]. Furthermore, a recent study highlighted the interactions between tree species and environmental conditions in shaping growth rhythms [12], often associated with adaptive responses to local climate variations, soil composition, and elevation [13]. Moreover, previous studies have demonstrated that logistic models were favored for modeling plant growth processes and have been extensively utilized in investigating seedling growth patterns [14]. In sum, understanding the developmental rhythm patterns is crucial for cultivating superior tree species, improving afforestation survival rates, and determining optimal harvesting seasons [15].
Leaves serve as the primary sites for photosynthesis in forest trees, harnessing energy for the ecosystem’s primary producers [14]. Photosynthesis stands as a cornerstone biological process in forest trees, profoundly shaping their lifelong growth and development [16]. By analyzing the photosynthetic and physiological variations of C. oleifera ‘Huaxin’ throughout the year, it becomes feasible to determine the optimal leaf-to-fruit ratio for enhancing fruit productivity [17]. Previous studies have demonstrated that leaf photosynthetic traits influence transpiration regulation [18,19]. In general, tree species with enhanced photosynthetic capabilities exhibit elevated transpiration rates [20]. A comparative analysis of the photosynthetic characteristics of regenerated European beech (Fagus sylvatica L.) and pedunculate oak (Quercus robur L.) plants offers valuable insights into the adaptation mechanisms and physiological roles of these broadleaf species [21]. Furthermore, stomatal conductance (gsw), as the primary gateway for water vapor movement between the plant and the environment, significantly influences the water balance of plants [22]. High water deficiency leads to the closure of stomata to reduce water loss and prevent dehydration [23]. Total water vapor conductance (gtw) encompasses both stomatal and nonstomatal pathways, providing a more comprehensive view of water loss from the plant [24]. A study demonstrated that gtw can have a substantial impact on plant water status, especially when stomata are closed or during periods of water stress [25]. Therefore, the regulation of both is an integral part of the overall mechanism that plants use to optimize photosynthesis under different environmental scenarios. Additionally, leaf traits directly impact forest trees’ photosynthetic potential, with the photosynthetic pigments within playing pivotal roles in the photosynthesis process [26]. Assessing chlorophyll content, a common practice in forestry and ecological studies, holds particularly significance in current research [27]. This assessment, often complemented by other leaf morphological and physiological parameters, facilitates comprehensive evaluations. Research has established a correlation between leaf chlorophyll concentration and plant health levels [28,29]. For instance, reduced chlorophyll concentration indicates decreased forest vitality [30]. Therefore, monitoring chlorophyll levels in leaves provides a valuable means of accurately assessing the overall health of forest trees.
To date, previous studies have predominantly concentrated on tree breeding and stress resistance in oak species [31,32]. However, there remains a notable gap in research concerning the growth and physiological variability of different oak varieties. Furthermore, the current study lacks a comprehensive evaluation of a diverse range of oak varieties, hindering a thorough examination of intervariety differences and the development of superior varieties. The superior varieties were defined as those exhibiting robust growth, high photosynthetic efficiency, and adaptability to local environmental conditions [33]. Therefore, elucidating the growth and photosynthetic traits of oak trees represents an initial step in the cultivation and breeding process, offering a productive approach to fostering the advancement of superior variations within the production domain. Here, the aims of this study were as follows: (1) We collected 18 varieties of common oak seedlings from different countries and regions to investigate the growth traits for as long as 32 weeks; (2) we evaluated the significant differences and correlation of the traits among different oaks, containing nutrient growth, leaf morphology index, chlorophyll content, and photosynthetic indexes; (3) we preliminarily screened the superior materials in Southwest China. These data will provide supporting information for breeding superior oak varieties in Southwest China, and will contribute more references associated with plant growth traits of oaks for the other regions.

2. Materials and Methods

2.1. Study Site Information

This study was performed in the forestry base of Sichuan Agricultural University, located at Wenjiang District, Chengdu City, Sichuan Province (30.60° N, 103.65° E) [34]. The average elevation was 511 m, and the soil types of the nursery site was a 5:1 mixture of local soil and Hogwarts brand plant nutrient organic soil. The climate here is humid subtropical, with an annual average sunshine of >1160 h, mean annual rainfall of 1012 mm, and an average annual temperature of 22 °C, with four distinct seasons, mild climate, and abundant rainfall [34].

2.2. Experimental Material Information

Oak seedlings of two or three years old were selected to perform this study. All the seedlings were planted in pots with about 1 m in diameter to provide ample growing space, and the nutrient soil was mixed with local soil (50:50) for cultivating the seedlings [35]. The information of 18 selected oak varieties were shown in Table 1. The local soil texture was classified as sandy loam, possessing the following chemical properties: a pH of 7.51, organic matter content of 16.6 g·kg−1, total nitrogen (N) of 1.0 g·kg−1, alkali hydrolysable N of 80.3 mg·kg−1, total potassium (K) of 23.9 g·kg−1, total phosphorus (P) of 0.5 g·kg−1 (note: typically, total P would be measured in grams per kilogram rather than milligrams, so this may be a typo or special case), available P of 22.7 mg·kg−1, available K of 90.8 mg·kg−1, and a field capacity moisture content of 25.2% [36].

2.3. Growing Traits Measurement

Five well-grown seedlings were, respectively, selected from each of 18 oak varieties for seeding height and ground diameter measurements, which were conducted approximately every two weeks from 9 March 2023 until 20 October 2023 (Table S1). In this study, the seedling height and ground diameter were investigated as the key growing traits of each oak. Seedling height refers to the vertical height of oak seedlings [37], and the height of the aboveground portion of the plant was measured with a tape measure [38]. Ground diameters at a height of 3–5 cm from the soil surface were taken twice and averaged to obtain the precise data using vernier calipers.

2.4. Leaf Morphometric Determination

During the peak growing season of oak trees, on 30 July 2023, three mature leaves were randomly sampled from each oak tree. There were more than 10 oak trees per species, with one tree planted per pot. A total of six leaf morphology parameters were measured using a portable leaf area meter survey (C1-203, Camas, NE, USA), containing leaf length, leaf width, leaf area, leaf aspect ratio, leaf form factor, and leaf gap [39]. Every leaf underwent three measurements. Subsequently, statistically analysis was conducted to evaluate the variation in leaf morphology among the different oak varieties.

2.5. Photosynthesis Parameter Investigation

Eleven candidate oak varieties were selected for photosynthesis parameters investigation based on their superior growth characteristics. In total, six traits were investigated weekly from 19 July to 30 August 2023, during clear weather, between 9:30 a.m. and 12:30 p.m. All the parameters were detected using a portable photosynthesizer, LI-6800 (LI-COR, Inc., Lincoln, NE, USA), positioned in the middle section of the plant canopy, including transpiration rate (E), intercellular CO2 concentration (Ci), leaf chamber CO2 concentration (Ca), stomatal conductance (gsw), and total water vapor conductance (gtw) of the mature functional leaves [40]. The reference chamber CO2 concentration was set at 400 μmol/mol−1, with leaf chamber temperature set to 30 °C and a relative humidity of 50%. Light intensity was maintained to match ambient conditions at 1000 μmol m−2 s−1. The leaves were selected from the same orientation as the mature functional leaves [41]. Measurements were conducted on three plants, with three leaves sampled from each plant.

2.6. Chlorophyll Content Investigation

On 16 July, 31 July, and 16 August 2023, the mature and healthy leaves of similar location were, respectively, collected from the 11 candidate oak varieties in the middle of the canopy (SPAD-502 Plus, Tokyo, Japan). In each investigation, three leaves were sampled and weighed for each variety, and the total chlorophyll content was determined by a chlorophyll meter [42].

2.7. Statistical Analysis

Data were statistically processed and analyzed using Excel 2018 and SPSS Statistics 24.0, Origin 2018 was employed to draw the figures. One-way analysis of variance (ANOVA) and least significant difference (LSD) (p < 0.05) tests were performed to assess variance among the samples of oaks growth and photosynthesis, presented as mean with standard error of the mean (mean ± SEM) [43]. Pearson’s correlation analysis was utilized to estimate correlation coefficients and relationships between growth, leaf, and photosynthesis traits. Principal component analysis (PCA) was conducted to evaluate the oak candidate varieties and identify the superior members.

3. Results

3.1. Growth Traits Investigation of the 18 Oak Trees

Growth traits play a crucial role in forests evaluation, serving as indicators of stand productivity and facilitating predictions of overall growth and succession [44]. The seedling height and ground diameter of the 18 oak trees exhibited similar growth patterns, characterized by distinctive “S”-shaped curves (Figure 1). However, notable differences emerged during their developmental stages. Seedling height experiences an initial growth phase from May to early June, followed by a rapid growth period from mid-June to late July, and a subsequent tapering of growth from August to early October, ceasing altogether by mid-October (Figure 1a); in particular, Qm (72.41–243.23 cm) and Qr2 (101.92–258.77 cm) were relatively significant. In contrast, ground diameter showed early growth from June to early July, followed by a rapid growth period from mid-July to mid-August, and a gradual tapering of growth from late August to late October(Figure 1b), especially in Qd (10.29–24.63 cm) and Qm (10.11–28.30 cm).

3.2. Growth Patterns Analysis of the 18 Oak Trees

The net increase in seedling growth serves as a reliable foundation for evaluating their growth rate. Understanding the growth patterns of oak trees can provide a theoretical basis for scientific management and cultivation [45]. This study discovered a consistent developmental pattern in the net rise of seedling height and ground diameter of oaks throughout the annual growing season. While both measurements can be classified into three groups, there may be slight variations among different individuals.
The height growth patterns of 18 oak seedlings were divided into three types, including H-I, H-II, and H-III types (Figure 2 and Figure S1). In the H-I type, oaks exhibited biannual growth periods, with the most substantial growth between spring and summer, particularly in June–August, characterized by vigorous growth (Figure 2a,b and Figure S1a–d). As observed in Qr1, during this period, the average net growth of height reached its peak at 27.51 cm. Therefore, it is crucial to fertilize the oak trees during this period to ensure their development. Additionally, oak trees of H-II type exhibited three growth periods annually, primarily in March, June, and August (Figure 2c,d and Figure S1e–h). Growth in this type may be impeded by rainfall or high temperatures, possibly explaining the slowed growth observed in May and July; for instance, Qd’s average net growth was the lowest, with only 0.51 cm and 0.35 cm, respectively. Finally, the H-III type oak trees thrived predominantly in the first half of the year (Figure 2e,f and Figure S1i–l), specifically from March to May, with a significant slowdown in height growth during the latter half of the year. As for Qru2, it reached a peak in net growth in March with 11.81 cm, and subsequently experienced a slowdown in growth. Thus, it is essential to apply adequate fertilizers and ensure nutrient availability during the spring growth period.
Similarly, the ground diameter growth patterns of 18 oak seedlings could also be divided into three types, including D-I, D-II, and D-III types (Figure 3 and Figure S2). In the D-I types, oaks exhibited steady and continuous growth periods, particularly in summer–autumn (Figure 3a,b and Figure S3a–c), especially in Qr4, which reached its average highest growth value of 2.09 cm in August. Hence, it is vital to timely administer fertilizer during this period to ensure that the oak trees receive adequate nutrients. Moreover, the oak tree growth of the D-II type fluctuated dramatically throughout the investigated months (Figure 3c,d and Figure S3d–k). As observed in Qd, despite frequent fluctuations in growth, a peak in average growth still occurred in August (1.52 cm). Finally, the D-III type oak trees exhibited two rapid-growth phases, in May–June and August–September, respectively, just as the average growth peak for Qw occurred in June (2.17 cm). Therefore, it is advisable to apply fertilizer and watering promptly during this period in oak trees to ensure their strong and consistent development.

3.3. Growth Curve Fitting of the 18 Oak Trees

The growth curves of oak trees were modeled using the logistic equation employing observed values of oak growth traits. The R2 values for all oak trees, except Qru1, exceeded 0.9, indicating an excellent fit of the logistic model and a highly significant correlation across these varieties. Each oak variety displayed distinct S-shaped fitted curves, offering valuable insights into their growth patterns and predictive capabilities.
The height of seedlings can be classified into three patterns based on the onset of tachyphylaxis in different oak trees (Figure 4a–c and Figure S3). The first pattern, labeled as Qd (Figure 4a), demonstrates a pronounced period of rapid growth that persists for an extended duration, exhibiting nearly uninterrupted expansion throughout the analyzed timeframe. The second growth phase, represented by Qm (Figure 4b), initiates after 60 days of growth and undergoes a period of rapid increase. This growth is sustained for a certain duration before gradually declining, indicating eventual stagnation. The third pattern, known as Qru2 (Figure 4c), begins its rapid growth phase at an earlier stage when the seedling is 30 days old. However, this phase is shorter in duration, after which the growth rate gradually decreases until it stops completely.
While the ground diameter fitting curves of most oak trees exhibit similarities, they can be classified into three distinct groups based on the slope of the curves (Figure 4d–f and Figure S4). The first category, exemplified by Qd (Figure 4d), displayed a consistent pattern of continuous expansion, characterized by a gentle slope and a steady rate of growth. The second category, illustrated by the Qw (Figure 4e), is marked by a “slow–fast–slow” growth pattern. After 80 days, they transitioned into a phase of rapid growth, which persisted for a specific duration before gradually diminishing. In the third category, such as Qru2 (Figure 4f), the oak trees entered a fast-growth phase around 30 days, although this phase was short-lived. Subsequently, the rate of growth gradually declined, resulting in a plateau stage.
Overall, the rapid development phase of oak seedlings, comprising approximately 40% or more of the entire growth cycle, holds a crucial role in oak tree growth. This period exerts a significant influence on the overall growth trajectory of the seedlings. Therefore, it is imperative to optimize water and fertilizer management as the seedlings approach this phase of rapid growth. Timely application of fertilizer is essential to ensure that the seedlings receive sufficient nutrients for their rapid development.

3.4. Leaf Morphological Investigation of the 18 Oak Trees

Leaves play a vital role in regulating plant growth. This study investigates the leaf morphological treats of 18 oak varieties to gain deeper insights into the factors influencing variations in their growth. Our phenotypic observations revealed significant differences in oak leaf morphology (Figure 5), while further findings suggest that the significant variation was observed in the leaf morphology indices among different oaks (Figure 6). Qd demonstrated the highest values for leaf area (167.24 cm2), leaf width (13.62 cm), and leaf aspect ratio (156.6), while Qm showed the largest leaf length (20.37 cm), suggesting their robust leaf development, which augments photosynthetic capacity (Figure 6a,c,d). Conversely, Qc and Qr3 exhibited lower values across these indices, suggesting suboptimal leaf growth, thereby impacting the photosynthetic capacity adversely (Figure 6a,c,d). Furthermore, in terms of leaf form factor and leaf gap, Qa exhibited the highest value (3.44, 0.39), indicating its superior capacity to enhance transpiration and photosynthesis by enlarging stomata (Figure 6e,f).

3.5. Photosynthetic Traits Investigation of the 18 Oak Trees

Based on the above growth traits investigation and analysis, we selected 11 oak varieties to uncover the patterns of photosynthetic traits, including Q1, Qa, Qd, Qm, Qn, Qp3, Qr1, Qr4, Qrb, Qru2, and Qw.
Chloroplast pigments serve as the fundamental components for photosynthesis in forest trees, and the fluctuation in chlorophyll content indicates the photosynthetic capacity of forest trees [46]. Significant differences in chlorophyll content were observed among the 11 oak varieties (Figure 7a, Table S1). On 16 July, Qr4 exhibited the highest chlorophyll content, surpassing Qn by 77.99%. By 30 July, while Qn still retained the lowest chlorophyll content, Qm emerged with the highest value, representing a 76.65% increase. On 16 August, Qr1 displayed the highest chlorophyll content, surpassing Qn by 82.12%. The collective findings from the 3-day survey reveal that Qm, Qr4, Qw, Qd, Qa, and Qr1 consistently maintained high chlorophyll levels, suggesting their superior photosynthesis potential. Conversely, Qn and Qru2 exhibited consistently low chlorophyll content, suggesting a diminished photosynthetic capacity.
Significance variations in photosynthetic traits were observed among the 11 oak varieties (Figure 7b–f, Table S2). Qr1 exhibited the highest transpiration rate (E) (5.03 mmol m−2), stomatal conductance (gsw) (0.22 mol m−2 s−1), and total water vapor conductance (gtw) (0.19 mol m−2 s−1), indicating a superior photosynthetic capacity. These values were found to be 86.35%, 61.71%, and 76.72%, respectively, higher than the lowest values recorded in Qr4 (Figure 7b,e,f). Qd exhibited the highest intercellular CO2 concentration (Ci) (564.67 µmol mol−1), surpassing Qp3 by 60.09% (Figure 7c). Conversely, Qw displayed a significantly higher leaf chamber CO2 concentration (Ca) (502.11 µmol mol−1) compared to the other oak varieties, with a relative increase of 40.17% compared to the lowest recorded in Qp3 (Figure 7d). However, there was little significant difference between this indicator among other oak varieties.

3.6. Correlation Analysis of Growth and Photosynthetic Traits

A Pearson’s correlation analysis was conducted to elucidate the relationship between growth and photosynthetic traits (Figure 8, Table S3). Seedling height showed a significant positive correlation with E, gsw, and gtw (p < 0.05), while ground diameter was positively correlated with chlorophyll content, leaf area, and leaf length (p < 0.05). Furthermore, a very significant positive correlation was observed among leaf length, leaf width, leaf area, and leaf aspect ratio (p < 0.05), whereas negative correlations were evident in leaf form factor, leaf aspect ratio, leaf gap, and leaf width (p < 0.05). Moreover, most photosynthetic traits showed negative correlations, except for those among gtw, gsw, and E, indicating that stomatal conductance adjustment is essential to sustain the total water equilibrium and to ensure optimal photosynthesis in forest trees.

3.7. Comprehensive Evaluation of Oak Varieties

Based on the correlation analysis, eight traits were selected to conduct principal component analysis (Table S4), including ground diameter, seedling height, leaf length, leaf area, E, chlorophyll content, gsw, and gtw. The first two principal components contributed variance rates of 42.30% and 26.61%, respectively, totaling 68.91%, signifying their ability to encapsulate the majority of the information from the original eight indexes (Table 2). Consequently, these two primary components were selected for a comprehensive evaluation of the 11 oak varieties. Furthermore, the eigenvalues of the two principal components were 3.384 and 2.129, respectively (Table 2). The most significant contributors to PC1 are seedling height (X2) and E (X4), with contribution rates of 0.452 and 0.479, respectively (Table S3). Conversely, in PC2, leaf length (X5) and leaf area (X6) emerged as the primary contributors, with contribution rates of 0.620 and 0.651, respectively (Table S3). All of these traits play pivotal roles in oak growth and can be used to conduct a comprehensive evaluation.
The ratio of the single PC contribution rate to the cumulative contribution rate was used as the weight for calculating the comprehensive evaluation scores of different oak varieties. The final evaluation scores of the 11 oak varieties were ranked as follows: Qa > Qn > Qw > Qm > Qr1 > Qp3 > Qd > Qr4 > Qrb > Q1 > Qru2 (Table 3). In the loading plots (Figure 9), Qa achieved the highest composite score, indicating its superior adaptation to the climatic conditions in Southwest China. Conversely, Qrb, Ql, and Qru2 obtained lower composite scores, indicating their unsuitability for cultivation in Southwest China due to inferior adaptability. Qm and Qw attained the highest overall scores after Qa, suggesting their potential for cultivation and favorable suitability. However, Qn exhibited a significantly higher PC1 value than PC2, indicating superior seedling height and transpiration rate, while Qr4 displayed a slightly higher PC2 value, suggesting robust leaf length and leaf area growth. Despite this, the composite score of Qn was 57.10% higher than that of Qr4, implying its superior adaptability and overall score. Additionally, Qr1, Qp3, and Qd, located at the center of the plot, had more average contributions, with composite scores ranking in the upper range. This suggests their growth potential and adaptability to the cultivating environments in Southwest China. To sum up, Qa, Qr1, Qp3, and Qd, along with Qn, Qm, and Qw, can be considered ideal candidates for cultivating superior oak varieties in further studies.
To evaluate the superior candidate varieties, the average values were compared between 18 clones and superior varieties. All superior varieties demonstrated enhanced characteristics compared to the other clones (Table 4). Particularly, there was a significant increase in total water vapor conductance (gtw) (33.33%), while substantial improvements were found in the transpiration rate (E) (22.47%), stomatal conductance (gsw) (22.22%), and ground diameter (21.44%). These results indicate that the selection of superior varieties is beneficial for the future breeding of oaks and will contribute theoretically to the strategic development of oak forests in China.

4. Discussion

Forest growth encompasses a set of core attributes potentially influencing the survival, growth, and mortality of forest trees, indicating their adaptive responses to the environment [47]. Variations in growth traits among various oak species have been observed in several studies [48,49], reflecting differences in initial habitats, experimental locations, and the inherent adaptability of oak trees. In this study, we found a significant association (R2 > 0.9) between seedling height, ground diameter, and predicted growth value, aligning with “slow–fast–slow” in the “S” growth curve pattern. Similar results have been reported in other species, such as in Morus spp. [50] and Chinese fir. [51]. Our results found that height and diameter of oak trees increased consistently from April to August, crucial for optimizing growing conditions and maximizing light absorption during initial growth stages [45]. Rapid height attainment during the seedling’s initial growth stages is essential for securing a favorable environment and subsequent development. Following a period of predevelopment, significant nutrient accumulation primes seedlings for rapid growth [9]. Growth–climate correlations revealed that oak growth was mainly driven by the climatic conditions (March–July precipitation and June maximum temperature) associated with water deficit around the summer solstice [52]. Previous studies have reported that rapid growth of oak species is more related to precipitation than to temperature [53]. Climatic conditions, such as drought and heat, are known to fit to local temperature and seasonality on physiological and morphological traits of oaks [54]. This is consistent with this study. For instance, when spring precipitation increases, most oak species enter a rapid growth phase, experiencing substantial growth. Conversely, high summer temperatures and high evapotranspiration rates may lead to reduced growth due to decreased soil moisture levels. Even during the rapid growth phase, oaks may exhibit minor fluctuations in growth deceleration under extreme high temperatures in July and August. Similarly, an increase in spring precipitation triggers a rapid growth phase in most oak species, resulting in substantial growth, as observed in H-I and H-II oaks. Despite the rapid growth phase of several oak trees constituting less than 2/5 of the developmental stage, it contributed to more than half of the total growth volume, underscoring its vital role in oak tree growth. However, this study noted an uneven growth pattern between height and diameter, common among closely related oak species [55], likely due to long-term competition for survival. The pregrowth phase of seedlings favors sunlight capture and space acquisition, while later phases denote lignification and bud formation [15]. Tailored management strategies, prioritizing water and fertilizer management during the seedling’s vertical growth phase, are crucial for stimulating accelerated growth and ensuring seedling quality. Extensive fertilizer application during later stages could further bolster plant resistance and enhance stand quality [56].
Photosynthesis is a crucial aspect of forest tree growth and serves as a significant indicator of its capacity to adapt to the environment [17]. Oak trees, being photophilous plants, rely on an adequate amount of sunlight to accumulate nutrients [3]. Therefore, the photosynthetic traits of oak trees play an integral part in their successful transition in Southwest China [57]. Among the several oak species studied, there was a substantial correlation between the growth and photosynthetic characters that was confirmed in this study [58]. We found that increased stomatal conductance (gsw) strongly and positively correlated with higher transpiration rate (E), which is consistent with previous findings [59], and we suggest that increased gsw strongly and positively correlated with higher gtw, which revealed a coordinated fashion in these two attributes. Additionally, previous research emphasized that the positive correlation between gsw and gtw serves as a significant mechanism through which plants adapt to climate change, mirroring the way in which plants coordinately regulate their water balance and gas exchange within the context of fluctuating environmental conditions [60]. Altogether, under favorable environmental conditions, such correlation enables oaks to perform photosynthesis and utilize water with high efficiency, thus facilitating growth. In this study, Qr1 had the highest values of E and gsw, indicating their self-regulation and superior photosynthetic capacity at the ambient condition. In actuality, the variation of light intensity has obvious effects on leaf external morphology, physiological characteristics, and, especially, photosynthetic parameters [61]. In the case of insufficient light, such as during cloudy days or in shaded areas, the photosynthetic rate can be severely affected. Under the condition of light intensity being 10% of full-day sunlight, the study in Mahonia bodinieri (Gagnep.) Laferr. observed the lowest net photosynthetic rate, stomatal conductance, and transpiration rate values [62]. Similarly, the photosynthetic rate data in our research is in this situation; variations in light intensity resulting from weather factors can significantly affect the quality of the measurement of photosynthetic data.
Chlorophyll content is closely related to photosynthesis since chlorophyll provides the photosynthetic apparatus that allows plants to absorb light energy in a time-efficient manner [63]. Research has revealed that, under drought conditions, both beech (Fagus sylvatica L.) and downy oak (Quercus pubescens Willd.) trees exhibit impaired photosynthetic function, with beech displaying a more pronounced effect due to its heightened sensitivity to low-moisture conditions. Indeed, they will close their stomates at less negative leaf water potentials and are generally assumed to be negatively affected by drought [64]. Furthermore, a study emphasized that downy oak (Quercus pubescens Willd.) will benefit from prolonged warming by taking advantage of more efficient photosynthetic responses, which could enhance CO2 uptake [65]. However, in our study, we noticed similar patterns across three time points and significant trends in other photosynthetic parameters, suggesting that the less satisfactory 19 July data may be due to weather conditions. Our study revealed that Qm, Qr4, Qw, Qd, Qa, and Qr1 consistently maintained high chlorophyll levels, suggesting their superior photosynthesis potential. Overall, the current study showed that differences in photosynthetic traits could be attributed to other factors such as number and density of stomata, concentration of CO2 in the atmosphere, etc. [66]. However, our findings suggest that the variations in growth traits were greater than the photosynthetic traits among the oaks. For instance, leaf morphological traits (e.g., leaf area and leaf gap) also differed significantly in many cases. A previous study found differential responses between different varieties of Camellia sinensi [1]. This seems to imply that growth traits may be the first to be noticed and selected for in the selection and cultivation process of superior oak varieties. In conclusion, the observed rapid growth phases and nutrient accumulation in oak trees are orchestrated by a multifaceted interplay of physiological and environmental factors. Elucidating the mechanisms by which these factors modulate photosynthetic efficiency is pivotal for accurately predicting growth patterns and nutrient dynamics within oak tree populations.
Principal component analysis is a statistical tool used to explain differences between samples and obtain more information about variables, leading to a better understanding and assessment of the samples [67]. It simplifies complex work and provides more accurate results [68]. PCA has been widely employed in the adaptive investigation of various forest trees in different regions, such as Douglas fir [69] and Picea abies [70]. In our study, we used PCA to analyze the growth and photosynthetic traits of 11 important oak species, effectively evaluating how well they adapt to the local environment. For oak varieties with high comprehensive adaptability (e.g., Qa, Qw, Qm), we could consider breeding them to maximize their benefits. Additionally, for varieties excelling in specific traits (e.g., Qn and Qd), we might explore hybridization to create varieties with greater adaptability. Additionally, PCA results can help prioritize oak growth efforts and guide varietal enhancement towards emphasizing key traits.
In summary, this study focused on analyzing the growth and physiological traits of oak seedlings. Through a series of phenotypic analyses, we gained valuable insights into the nutritional growth, leaf morphology, chlorophyll content, and photosynthetic traits of different oaks for significance over a 32-week growth period, and, ultimately, in combination with PCA, we performed a comprehensive assessment and selection of superior varieties adapted to the region. Moreover, genotypic selection has significantly advanced in the selection of candidate individuals in the past two decades, effectively accelerating genetic gains in plant breeding [71]. A previous study measured 57 L. kaempferi clones over many different growth years, revealing that there were significant positive age–age correlations in growth traits among different growth years, and suggesting that short-term growth data can, indeed, be a reliable predictor for long-term productivity outcomes [72]. Simultaneously, in another study regularly measuring the growth traits of 78 clones of L. kaempferi from 2 to 15 years old, it was found that the tree height was significantly correlated at each age, and the optimal age for early selection was 2 years old [73]. To sum up, early selection can identify individuals with superior traits during the early growth stage of trees, thus enabling better prediction of future productivity. Consequently, our approach primarily focused on phenotypic evaluation in various oak species and selected the superior oak candidates for Southwestern China, especially the Chengdu region. More different geographic conditions, such as climate, altitude, and soil properties, could also be comprehensively considered in further studies to perform the multisite selections of phenotype and genotype and enrich our understanding of oak breeding. Moreover, efforts to collect new germplasm resources, investigate more photosynthetic parameters, and evaluate different-years-old seedlings could facilitate more detailed information, ultimately contributing to the cultivation of superior-quality oaks in Southwestern China. Notwithstanding some limitations, our results could provide encouraging prospects and supports for selection breeding of oaks.

5. Conclusions

This study concentrated on oak, an economically important tree species with ecological and productive value. We investigated significant variations in the growth and physiological traits of several oak varieties. Qr1, Qm, and Qw exhibited substantial growth advantages, although Qm and Qa showed greater dominance in leaf shape, and Qr1, Qa, Qm, and Qr4 displayed advantages in chlorophyll content. The transpiration rate (E), stomatal conductance (gsw), and total water vapor conductance (gtw) were highest in Qr1, while intercellular CO2 concentration (Ci) was highest in Qd, while Qw displayed a significantly higher leaf chamber CO2 concentration (Ca). When combined with principal component analysis, Qa, Qr1, Qp3, and Qd, along with Qn, Qm, and Qw, were found to be more suitable for the high summer temperatures and low rainfall. The findings of this study highlight that it can be utilized to identify superior oak species that are ideally suited for local cultivation. This selection process has the potential to enhance both growth and photosynthetic traits simultaneously, with implications on the economic viability and sustainability of oak stands. In future studies, plenty of data of more photosynthetic parameters with different-years-old seedlings will be collected and will reveal more important results, ultimately contributing to the cultivation of superior-quality oaks in Southwest China.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f15112006/s1, Figure S1. Primary patterns of growth rhythm in oak seedling height across the annual growing season in this study. (a–d) are the representative varieties in Pattern H-I: two growth periods mainly in spring and summer, especially from June to August with vigorous growth; (e–j) are the representative varieties in Pattern H-II: the three distinct growth periods occurring in March, June, and August. Growth significantly slows during the periods of heavy rainfall and high temperatures. (k,l) are the representative varieties in Pattern H-III: rapid growth in the first half of the year around March to May. Different lines mean the individual trees investigated in each variety. Figure S2. Primary patterns of growth rhythm in oak ground diameter across the annual growing season in this study. (a–c) are the representative varieties in Pattern D-I: consistent growth throughout the investigated months, notably with rapid growth from summer to autumn. (d–k) are the representative varieties in Pattern D-II: a fluctuating growth throughout the investigated months. (l) is the representative varieties in Pattern D-III: two rapid growth periods, respectively from May to June and August to September. Different lines mean the individual trees investigated in each variety. Figure S3. Fitted curve of dynamic growth related to oak seedling height. (a–n) three mainly patterns of fitted curves for oak seedling height. All fitted curves were estimated based on the 200 days investigation. Figure S4. Fitted curve of dynamic growth related to oak ground diameter. (a–n): two mainly patterns of fitted curves for oak ground diameter. All fitted curves were estimated based on the 200 days investigation. Table S1. The investigation Duration. Table S2. Analysis of photosynthetic parameters for 11 oak candidates. Table S3. Correlation analysis of growth and photosynthetic traits. Table S4. Principal component loading matrices and variance contributions of eight feature indices.

Author Contributions

W.H. and X.C. formulated and designed the experiments; W.H., X.C., Y.Z. and P.Z. collected materials; Z.Q., X.H., Y.P. and G.C. performed the experiments; Z.Q. analyzed the data and drew the figures; Z.Q. and W.H. wrote the paper; H.W., W.G., Z.X., B.T. and W.H. revised and proofread the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Sichuan Science and Technology Program (2024YFNH0028).

Data Availability Statement

The data supporting the results presented in this article are included as Supplementary Files.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Figure 1. The average growth patterns of seedling height (a) and ground diameter (b) across the annual growing season for 18 oak varieties.
Figure 1. The average growth patterns of seedling height (a) and ground diameter (b) across the annual growing season for 18 oak varieties.
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Figure 2. Primary patterns of growth rhythm in oak seedling height across the annual growing season in this study. Panels (a,b) are the representative varieties in Pattern H-I: two growth periods mainly in spring and summer, especially from June to August with vigorous growth; (c,d) are the representative varieties in Pattern H-II: the three distinct growth periods occurring in March, June, and August. Growth significantly slows during the periods of heavy rainfall and high temperatures. Panels (e,f) are the representative varieties in Pattern H-III: rapid growth in the first half of the year around March to May. Different lines mean the individual trees investigated in each variety.
Figure 2. Primary patterns of growth rhythm in oak seedling height across the annual growing season in this study. Panels (a,b) are the representative varieties in Pattern H-I: two growth periods mainly in spring and summer, especially from June to August with vigorous growth; (c,d) are the representative varieties in Pattern H-II: the three distinct growth periods occurring in March, June, and August. Growth significantly slows during the periods of heavy rainfall and high temperatures. Panels (e,f) are the representative varieties in Pattern H-III: rapid growth in the first half of the year around March to May. Different lines mean the individual trees investigated in each variety.
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Figure 3. Primary patterns of growth rhythm in oak ground diameter across the annual growing season in this study. Panels (a,b) are the representative varieties in Pattern D-I: consistent growth throughout the investigated months, notably with rapid growth from summer to autumn; (c,d) are the representative varieties in Pattern D-II: a fluctuating growth throughout the investigated months; (e,f) are the representative varieties in Pattern D-III: two rapid growth periods, respectively, from May to June and August to September. Different lines mean the individual trees investigated in each variety.
Figure 3. Primary patterns of growth rhythm in oak ground diameter across the annual growing season in this study. Panels (a,b) are the representative varieties in Pattern D-I: consistent growth throughout the investigated months, notably with rapid growth from summer to autumn; (c,d) are the representative varieties in Pattern D-II: a fluctuating growth throughout the investigated months; (e,f) are the representative varieties in Pattern D-III: two rapid growth periods, respectively, from May to June and August to September. Different lines mean the individual trees investigated in each variety.
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Figure 4. Fitted curve of dynamic growth related to oak seedling height and ground diameter. (a–c) Three main patterns of fitted curves for oak seedling height. (d–f) Three main patterns of fitted curves for oak ground diameter. All fitted curves were estimated based on the 200-day investigation.
Figure 4. Fitted curve of dynamic growth related to oak seedling height and ground diameter. (a–c) Three main patterns of fitted curves for oak seedling height. (d–f) Three main patterns of fitted curves for oak ground diameter. All fitted curves were estimated based on the 200-day investigation.
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Figure 5. Leaf phenotypes of 18 different oak varieties. The bar is 5 cm.
Figure 5. Leaf phenotypes of 18 different oak varieties. The bar is 5 cm.
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Figure 6. Leaf morphological investigation and analysis in 18 oak varieties. (a–f) respectively represent Leaf area, Leaf length, Leaf width, Leaf aspect ratio, Leaf form factor and Leaf gap. Different lower-case letters indicate significant differences among different varieties, p < 0.05.
Figure 6. Leaf morphological investigation and analysis in 18 oak varieties. (a–f) respectively represent Leaf area, Leaf length, Leaf width, Leaf aspect ratio, Leaf form factor and Leaf gap. Different lower-case letters indicate significant differences among different varieties, p < 0.05.
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Figure 7. Analysis of photosynthetic parameters for 11 oak candidates. (a) represent chlorophyll content; E: the transpiration rate (b), Ci: intercellular CO2 concentration (c), Ca: leaf chamber CO2 concentration (d), gsw: stomatal con-ductance (e), gtw: total vapor conductance (f). Different lowercase letters indicate significant differences among the 11 oak candidates; p < 0.05.
Figure 7. Analysis of photosynthetic parameters for 11 oak candidates. (a) represent chlorophyll content; E: the transpiration rate (b), Ci: intercellular CO2 concentration (c), Ca: leaf chamber CO2 concentration (d), gsw: stomatal con-ductance (e), gtw: total vapor conductance (f). Different lowercase letters indicate significant differences among the 11 oak candidates; p < 0.05.
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Figure 8. Correlation analysis between different traits investigated from 11 selected oak candidates. Red and blue indicate the positive and negative correlations, respectively. The darker color indicates more significant correlation between the traits (p < 0.05), and the size of the pies represents the correlation coefficient. The lines, respectively, highlight the significant correlation with seedling height and ground diameter, and purple is a positive correlation.
Figure 8. Correlation analysis between different traits investigated from 11 selected oak candidates. Red and blue indicate the positive and negative correlations, respectively. The darker color indicates more significant correlation between the traits (p < 0.05), and the size of the pies represents the correlation coefficient. The lines, respectively, highlight the significant correlation with seedling height and ground diameter, and purple is a positive correlation.
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Figure 9. Principal component analysis to identify excellent varieties from 11 selected oak candidates. PC1 and PC2 are principal component 1 and principal component 2; X1–X8 are the loading scores of various investigated traits. Different colored pies show the distribution of 11 oak candidates, which are divided by the red circles.
Figure 9. Principal component analysis to identify excellent varieties from 11 selected oak candidates. PC1 and PC2 are principal component 1 and principal component 2; X1–X8 are the loading scores of various investigated traits. Different colored pies show the distribution of 11 oak candidates, which are divided by the red circles.
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Table 1. The origin information of 18 oak varieties in this study.
Table 1. The origin information of 18 oak varieties in this study.
No.Oak Variety NameOriginAbbreviation
1Quercus lyrata Walt.Henan, ChinaQl
2Quercus acutissima Carruth.Shandong, ChinaQa
3Quercus coccinea Muench.Shandong, ChinaQc
4Quercus dentata Thunb.Shandong, ChinaQd
5Quercus mongolica Fisch. ex Ledeb.Shandong, ChinaQm
6Quercus nuttallii Nutt. Chongqing, ChinaQn
7Quercus palustris Münchh.Shandong, ChinaQp0
8Quercus palustris Münchh. (No.1)Shandong, ChinaQp1
9Quercus palustris Münchh. (No.2)Shandong, ChinaQp2
10Quercus palustris Münchh. (No.3)Shandong, ChinaQp3
11Quercus robur L. (Shandong)Shandong, ChinaQr1
12Quercus robur L. (England)Chengdu, ChinaQr2
13Quercus robur L. (Helsinki)Chengdu, ChinaQr3
14Quercus robur L. (Xinjiang)Xinjiang, ChinaQr4
15Quercus robur × bicolor ‘Nadler’.Shandong, ChinaQrb
16Quercus rubra L. (Shandong No.1)Shandong, ChinaQru1
17Quercus rubra L. (Shandong No.2)Shandong, ChinaQru2
18Quercus wutaishansea Mary.Shandong, ChinaQw
Table 2. The results of principal component analysis among 11 oak candidates.
Table 2. The results of principal component analysis among 11 oak candidates.
Principal ComponentEigenvaluePercentage of Variance (%)Cumulative (%)
Principal component 13.38442.29942.299
Principal component 22.12926.60868.907
Principal component 31.03712.96281.869
Principal component 40.5757.19189.060
Principal component 50.5266.57695.636
Principal component 60.2633.28398.919
Principal component 70.0620.78099.699
Principal component 80.0240.301100
Table 3. Comprehensive evaluation of growth and photosynthetic traits of 11 oak candidates.
Table 3. Comprehensive evaluation of growth and photosynthetic traits of 11 oak candidates.
SpeciesPrincipal Component ValuePC1
PC1PC2Total Score
Qa2.0560.5171.0071
Qn2.312−0.4590.8562
Qw0.865−0.1600.3243
Qm0.943−0.7080.2114
Qr1−0.0150.3120.0775
Qp30.865−0.1600.3246
Qd0.085−0.538−0.1087
Qr4−1.5280.656−0.4728
Qrb−0.564−0.823−0.4579
Ql−0.891−0.426−0.49010
Qru2−0.665−1.447−0.66611
Total score is calculated by summing the product of the percentage of variance and the principal component value, e.g., total score of Qa = 42.299% × 2.056 + 26.608% × 0.517 ≈ 1.007.
Table 4. Comprehensive evaluation of growth increment of superior varieties.
Table 4. Comprehensive evaluation of growth increment of superior varieties.
Investigate TraitsAverage Value of
Superior Varieties		Average Value of
All Varieties		Growth Increment/%
Ground diameter/cm15.35 ± 4.3812.64 ± 3.4221.44
Seedling height/cm125.44 ± 29.10118.60 ± 27.035.77
Leaf length/cm16.05 ± 1.6415.02 ± 4.096.86
Leaf area/cm78.83 ± 13.6974.76 ± 40.445.44
E/mmol m−23.27 ± 0.022.67 ± 0.0922.47
Chlorophyll content/µg mL−141.51 ± 3.9138.30 ± 4.968.45
gsw/mol m−2 s−10.11 ± 0.020.09 ± 0.0822.22
gtw/mol m−2 s−10.12 ± 0.090.09 ± 0.4933.33
Growth increment is calculated by subtracting the average value of all varieties from the average value of superior varieties and dividing by the average value of all varieties multiplied by 100 per cent, e.g., growth increment of ground diameter = (15.35 − 12.64)/12.64% ≈ 21.44%.
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MDPI and ACS Style

Qi, Z.; Huang, X.; Peng, Y.; Wu, H.; Xu, Z.; Tan, B.; Zhong, Y.; Zhu, P.; Gong, W.; Chen, G.; et al. Identifying Superior Growth and Photosynthetic Traits in Eighteen Oak Varieties for Southwest China. Forests 2024, 15, 2006. https://doi.org/10.3390/f15112006

AMA Style

Qi Z, Huang X, Peng Y, Wu H, Xu Z, Tan B, Zhong Y, Zhu P, Gong W, Chen G, et al. Identifying Superior Growth and Photosynthetic Traits in Eighteen Oak Varieties for Southwest China. Forests. 2024; 15(11):2006. https://doi.org/10.3390/f15112006

Chicago/Turabian Style

Qi, Zengzhen, Xiang Huang, Yang Peng, Hongyi Wu, Zhenfeng Xu, Bo Tan, Yu Zhong, Peng Zhu, Wei Gong, Gang Chen, and et al. 2024. "Identifying Superior Growth and Photosynthetic Traits in Eighteen Oak Varieties for Southwest China" Forests 15, no. 11: 2006. https://doi.org/10.3390/f15112006

APA Style

Qi, Z., Huang, X., Peng, Y., Wu, H., Xu, Z., Tan, B., Zhong, Y., Zhu, P., Gong, W., Chen, G., Chen, X., & Hui, W. (2024). Identifying Superior Growth and Photosynthetic Traits in Eighteen Oak Varieties for Southwest China. Forests, 15(11), 2006. https://doi.org/10.3390/f15112006

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