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Article

Variations in Acorn Traits in Two Oak Species: Quercus mongolica Fisch. ex Ledeb. and Quercus variabilis Blume

by
Jiacheng Sun
1,
Wenshi Shi
2,
Yanyan Wu
1,
Jing Ji
1,3,
Jian Feng
4,
Jiabing Zhao
1,
Xinru Shi
1,
Changjian Du
1,
Wei Chen
1,
Jianfeng Liu
1,
Zeping Jiang
3 and
Shengqing Shi
1,*
1
State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
2
Department of Achievement Transformation and Industrial Development, Chinese Academy of Forestry, Beijing 100091, China
3
Key Laboratory of Forest Ecology and Environment of National Forestry and Grassland Administration, Research Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing 100091, China
4
Liaoning Academy of Forestry, Shenyang 110032, China
*
Author to whom correspondence should be addressed.
Forests 2021, 12(12), 1755; https://0-doi-org.brum.beds.ac.uk/10.3390/f12121755
Submission received: 25 October 2021 / Revised: 1 December 2021 / Accepted: 9 December 2021 / Published: 12 December 2021
(This article belongs to the Section Forest Ecophysiology and Biology)
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Abstract

:
Quercus mongolica Fisch. ex Ledeb. and Q. variabilis Blume are two main oak species in China, producing large amounts of acorns every year. However, the trait variations in acorns, as a promising energy crop material, are not fully understood, hence we compared the traits of acorns from the different populations with the altered geographic distribution in this study. Thirteen acorn traits, including phenotype, proximate compositions and functional compounds, were analyzed in both Quercus L. species collected from 44 populations across China. The results showed that, except large differences found among accessions in acorn sizes, the starch varied from 140.96–297.09 mg/g in Q. mongolica and 130.99–306.28 mg/g in Q. variabilis, indicating its substantial differences among populations. The total polyphenols, total flavonoids and soluble tannins varied from 41.76–158.92, 23.43–91.94, and 15.11–17.81 mg/g, respectively, in Q. mongolica, 89.36–188.37, 50.59–116.07, 15.24–17.33 mg/g, respectively, in Q. variabilis, demonstrating their large variations in the levels of polyphenols among populations. Moreover, the acorns of Q. mongolica in North China and Q. variabilis in Southwest China had higher levels of starch and polyphenols. As the geographical location approached in the distribution of two Quercus species, the difference in acorn sizes gradually increased, while that in polyphenols were opposite. Principal component analysis and cluster analysis further revealed that the acorn sizes became larger and polyphenols became less with the increasing latitudes in both species. In North China, the acorns of Q. mongolica had small sizes and high polyphenols, which was contrary to those in Q. variabilis. These findings indicated that acorn traits were closely associated with the geographical distribution. Thus, our results will provide references for the selection breeding of acorn with the high starch, high or low polyphenols in the different regions.

1. Introduction

Quercus L. (oaks) is a largest genus with about 450 species around the world in Fagaceae [1,2]. The fruits of oak trees from Fagaceae, commonly known as acorns, can be used as an energy plant and woody food [3,4]. The acorns are very rich in nutrients with carbohydrates, proteins, vitamins and sterols, which have been as a nut food for more than 1000 years [5,6,7], but the exploitation and utilization of acorns is far inferior to other nuts [8]. Nowadays, acorns have been received more and more attention with people’s demand for healthy foods. Compared with flour, acorns not only have the properties of other commonly used substances [9], but also contain high-level functional compounds, such as polyphenols, fiber and resistant starch, which can be developed as new functional foods and industrial products [10]. For example, acorn starch has a high paste consistency used as a thickener and stabilizer in foods [10], and possesses a high percentage of resistant starch grown as a prebiotic promoter [11]. Acorn starch can also be used in other industrial applications such as paper, plastic, textile, pharmaceutical and cosmetic industries [12].
Acorns are also rich in polyphenols, and more than 60 unique phenols have been identified, including ellagic acid, gallic acid derivatives, and flavonoids [12]. Polyphenols are a class of phytochemicals with high antioxidant activity [13,14]. For example, ferulic acid has an inhibitory effect on cancers [15,16]; gallic acid has high scavenging ability of DPPH free radicals [17,18]; chlorogenic acid and caffeic acid can improve lipid metabolism and hormone levels associated with obesity [19]. In addition to the beneficial aspects of polyphenols described above, large amounts of polyphenols or tannins, which can make acorns taste bitter [20], are also commonly used in tanning leather and other industrial applications [21]. Thus, acorns may have very great potentials for the different purposes as a new source of plant active chemicals.
The chemical composition of acorns can reveal the differences between species, even within the same species due to the different effects of soil and climate [22]. A study has shown that Spanish acorns have higher protein and fat contents than Portuguese acorns [23]. Shimada and Saitoh divides acorns into three groups [24]: (1) High levels of tannins and proteins (two North American red oaks, subgenus Erythrobalanus); (2) high levels of tannins but low levels of proteins (two Japanese evergreen oaks, Cyclobalanopsis; three Japanese deciduous oaks, Lepidobalanus; one North American white oak, Lepidobalanus); and (3) low levels of tannins and moderate levels of proteins (one Cyclobalanopsis species; seven Lepidobalanus species), by using cluster analysis. Moreover, acorns from group 3 above are considered to have greater nutritional values [24]. The comparison of different species in Quercus genus demonstrates that Q. faginea Lam. gives the highest α-tocopherol and total tocopherols contents; Q. nigra L. stands out for the contents in carbohydrates; Q. suber L. shows the highest protein levels [21]. Besides, the acorn phenotypic traits and chemical compositions are found to be significantly different in the same species, such as Q. acutissima Carruth., Q. variabilis Blume., and Q. ilex subsp. ballota., from the different populations [25,26,27], as other fruits of woody plants Castanea sativa Mill. [28], Xanthoceras sorbifolium Bunge [29] and Sorbus domestica L. [30].
Two oak species included in this study, Mongolian oak (Q. mongolica Fisch. ex Ledeb.) and Chinese cork oak (Q. variabilis Blume.), are widely distributed in the cold temperate zone and temperate zone in the regions of Northeast and North China [31] and mainly distributed in the temperate and subtropical regions in China [32], respectively. These two oak species are the main components of deciduous broad-leaved forest in the north of China [32,33]. Recent years, the ecophysiological characteristics [34,35,36,37], acorn development and metabolism [38,39,40], acorn predation and population regeneration [41,42], and acorn phenotypic variation [26,43] have been studied in these two oak species. Moreover, the high levels of phenotypic plasticity were found in the acorns of Q. mongolica from 11 populations within Liaoning province [43] and Q. variabilis from 43 populations within 16 provinces [26]. However, the variation of chemical compositions is still not enough to be investigated in the large-scale distribution areas, so that the values and differences for the acorns of these two species are currently underestimated from the different populations. The present work needs to be further addressed whether acorn traits have significant differences among populations, and whether they are affected by geographical distribution. Thus, the objectives of this study are as follows: (1) To comprehensively quantify and compare acorn traits including phenotype (length, width, perimeter, single weight, aspect ratio), proximate compositions (total soluble sugars, starch, total soluble proteins, total amino acids), and functional compounds (total polyphenols, total flavonoids, soluble tannins, vitamin E) from 44 populations, (2) to explore the correlation between total polyphenols contents and total antioxidant capacities based on the characteristics of acorns rich in polyphenols, and (3) to determine the correlation of acorn traits and geographical distribution. This work would provide a better basis for the selection of high-quality oak germplasm resources for the future application in food and industrial products.

2. Materials and Methods

2.1. Plant Materials

The acorns were collected from the adult trees in 2018 covering a total of 44 oak natural populations from 10 provinces/municipality in China (Table S1; Figure 1). Representative samples about 5–10 kg were collected from 6–8 adult trees about 30 m apart from each other. Healthy acorns were brought back to the lab for a kill at 105 °C (30 min) and then dried at 60 °C to constant weight, except that parts of them were used for the measurements of phenotypic traits, after being washed clearly. The mixed acorns were randomly divided into 3 replicates, and ground into powder, respectively, for the measurements of proximate compositions and polyphenols.

2.2. Measurements of Acorn Phenotypic Traits

The length (the longitudinal axis; L) and width (the maximum width of longitudinal section; W) of 100 healthy acorns were measured by using an electronic digital vernier caliper in each population. The perimeter (the maximum perimeter of cross section; P) of 100 healthy acorns was measured by using a soft ruler (the precision is 0.1 cm). Single weight (SW) of 100 acorns was measured by a balance (the precision is 0.001 g) in each replicate.

2.3. Determination of Proximate Compositions

Total soluble sugars were determined by the anthrone colorimetric method [44]. Briefly, the powder sample of 0.1 g was put into 1 mL of distilled water, and then incubated at 95 °C in water bath for 10 min, and centrifuged at 8000 g, 25 °C for 10 min after cooling. The supernatant was transferred into a 10 mL test tube, and the distilled water was added up to 10 mL, and then mixed well to be tested at 620 nm.
Starch was determined by the anthrone colorimetric method [45]. Briefly, the powder sample of 0.1 g was put into 1 mL of ethanol, and then extracted at 80 °C in water bath for 30 min. After centrifuged at 3000 g, 25 °C for 5 min, the supernatant was discarded. The distilled water of 0.5 mL was added to the precipitate and incubated in water bath at 95 °C for 15 min after mixed well. Perchloric acid of 0.35 mL was added after cooling, and then extracted at room temperature for 15 min. The distilled water of 0.85 mL was added, and mixed well. After centrifuged at 3000 g and 25 °C for 10 min, the supernatant was used for measurement at 620 nm.
Total soluble proteins were determined by the Bradford method [46]. Briefly, the powder sample of 0.1 g was put into 1 mL of phosphate buffer, and centrifuged at 8000 g, 4 °C for 10 min. The supernatant was used for a measurement at 595 nm.
Total amino acids were determined by the ninhydrin colorimetric method [47]. Briefly, the powder sample of 0.1 g was added into 1 mL of glacial acetic acid, and centrifuged at 8000 g, 4 °C for 10 min. The supernatant of 200 μL was transferred to 1.5 mL Eppendorf tube, and 200 μL of acetic acid was added. The mixed solution was placed in a boiling water bath for 15 min and then cooled to room temperature before being tested at 570 nm.

2.4. Determination of Total Polyphenols and Total Flavonoids

The powder sample of 0.1 g was added into 5 ml of 50% methanol, and extracted with ultrasound for 30 min. After centrifuged at 12,000 rpm, 25 °C for 10 min, the supernatant was used for the determination of total polyphenols by the Folin–Ciocalteu method [48] and total flavonoids extracts by colorimetric method previously described in our previous study [49].

2.5. Determination of Soluble Tannins

The soluble tannins were measured by the method of the kit (Suzhou Keming Biotechnology, Suzhou, China) according to the reaction of tannin with phosphomolybdic acid in an alkaline environment producing blue compounds [49]. Briefly, the powder sample of 0.1 g was added into 1 mL distilled water and extracted at 80 °C in water bath for 30 min. After centrifuged at 8000 g, 25 °C for 10 min, the supernatant solution was used for test. The test solution of 5 μL, 130 μL of distilled water, 35 μL of sodium tungstate, phosphomolybdic acid and phosphoric acid solution were added into a new Eppendorf tube, and mixed well. The mixture was incubated at room temperature for 5 min, and then 30 μL of Na2CO3 was added. The reaction mixture was mixed well and incubated at room temperature for 30 min, and then measured at 760 nm.

2.6. Determination of Vitamin E

Vitamin E was measured by using the kit (Suzhou Keming Biotechnology, Suzhou, China) according to the chemical reaction principle that vitamin E reduces Fe3+ is reduced to Fe2+, and Fe2+ produces a colored complex with 1,10-phenanthroline. Briefly, the powder sample of 0.1 g was added into 1 mL n-heptane-ethanol, and shaken on a vortex mixer for 5 min, then centrifuged at 5000 g, 25 °C for 10 min. The supernatant solution of 100 μL, 20 μL o-phenanthroline-ethanol, 20 μL ferric chloride-ethanol were added to a new tube, mixed well, incubated at room temperature for 5 min. The phosphoric acid-ethanol of 60 μL was added, and mixed well. The mixed solution was measured at 530 nm.

2.7. Measurements of Antioxidant Capability

In order to reveal the correlation between total antioxidant capacity and total polyphenols contents, the samples from three populations with high, medium and low levels of total polyphenols in two studied oak species were selected for the measurements of total antioxidant capacity, using 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) and ferric reducing antioxidant potential (FRAP) assays, respectively.
DPPH assay was referred to the method of Brand-Williams et al. [50]. The 20 μL extract of total polyphenols was added into 180 μL DPPH methanol solution (25 mg/L), and measured at 525 nm after 20 min of light-proof reaction. The methanol solution was used instead of the sample extract as the control.
FRAP assay was referred to the methods of Benzie and Strain [51]. The 20 μL extract of total polyphenols was added to 180 μL FRAP working solution (100 mM), mixed well and reacted at 37 °C for 5 min, and then determined at 593 nm. The ddH2O was used instead of the sample extract as control.
ABTS assay was referred to the method of Re et al. [52]. The 10 μL extract of total polyphenols was mixed with 20 μL of peroxidase, and then added into 170 μL of ABTS working solution. After incubated at 25 °C for 6 min, the mixture solution was determined at 414 nm. The ddH2O was used instead of the sample extract as control.

2.8. Statistical Analysis

Perform variance analysis, correlation analysis and cluster analysis on the data set of 13 characteristics of acorns, including phenotypes (length, width, perimeter, single weight, aspect ratio), proximate com-positions (total soluble sugars, starch, total soluble proteins, total amino acids), and functional compounds (total polyphenols, total flavonoids, soluble tannins, vitamin E), using IBM SPSS 20.0 (IBM Corp, Armonk, NY, USA). Data were expressed as mean ± standard deviation. Significant differences among means were assessed using Duncan’s multiple comparison at p ≤ 0.05. Correlation between phenotypic traits and environmental factors were analyzed using Pearson correlation analysis. Of which, clusters were identified by the hierarchical agglomerative clustering, using Ward’s method with squared Euclidean distance based on 11 indicators, except for the perimeter and total flavonoids content, because both parameters have extremely high correlation with width and total polyphenols content respectively, thereby reducing the influence of too many variables on statistical analysis. Principal component analysis (PCA) was performed using R (version 4.0.0). The Mantel test was performed with the R package “Vegan”. Vegdist function was used to calculate the distance between traits, dist function was used to calculate the distance between environmental factors, distm function in geosphere package was used to calculate the geographical distance, and Pearson method was used to test the correlation.

3. Results

3.1. Phenotypic Variations of Two Acorns from Different Populations

The acorns of Q. mongolica were generally oval, and the acorn sizes reached significant differences among 29 populations (p < 0.05; Table 1) from Northeast (Heilongjiang, Jilin, and Liaoning) and North China (Beijing and Hebei). At the average levels, the length, width, and perimeter were 2.12, 1.63, and 4.98 cm, respectively; single acorn weight was 2.94 g; the aspect ratio of single acorn was 1.31, but there were large variation ranges among populations (Table 1).
The acorn sizes of Q. variabilis reached significant differences among 15 populations (p < 0.05) from Southwest, Central and North China (Table 2). Averagely, the length, width, and perimeter were 2.09, 1.69, and 5.22 cm, respectively; single acorn weight was 3.67 g; the aspect ratio of single acorn was 1.25, but their variation ranges were also very large (Table 2).

3.2. Proximate Composition Variations of Two Acorns from Different Populations

The differences in acorn proximate composition contents of Q. mongolica and Q. variabilis among populations reached a significant level (p < 0.05), respectively. In Q. mongolica acorns, averagely, the contents of total soluble sugars, starch, total soluble proteins, and total amino acids varied from 30.56–87.21, 140.96–297.09, 8.22–15.15, and 10.74–58.27 mg/g, respectively; vitamin E varied from 47.59–69.68 μg/g (Table 1), indicating that high nutrient plant materials could be selected in these populations.
In Q. variabilis acorns, the average contents of total soluble sugars, starch, total soluble proteins, and total amino acids varied from 31.53–61.21, 130.99–306.28, 11.73–28.31, 25.71–48.23 mg/g, respectively; vitamin E (VE) varied from 22.39–69.21 μg/g (Table 2).

3.3. Polyphenols Variations of Two Acorns from Different Populations

The contents of polyphenols of Q. mongolica and Q. variabilis acorns reached significant differences (p < 0.05) among populations (Table 1 and Table 2). The average contents of total polyphenols, total flavonoids and soluble tannins were 74.48, 42.36 and 16.20 mg/g, respectively, in Q. mongolica acorns, and 136.31, 83.26 and 16.34 mg/g, respectively, in Q. variabilis acorns. Moreover, both species had larger variation ranges at the levels of total polyphenols and total flavonoids in the different populations.

3.4. Identification of Antioxidant Capacities of Total Polyphenols Extracts

Totally, the contents of acorn polyphenols in Q. variabilis were higher than those in Q. mongolica (Figure 2A). In these two oak species, the extracts with high polyphenols showed the strongest DPPH scavenging ability, and the highest value was 87.12% for QV12, and the lowest value was 32.7% for QM15 (Figure 2B; Table 1 and Table 2). Moreover, the DPPH scavenging ability in Q. variabilis was always higher than that in Q. mongolica.
Similarly, FRAP and ATBS are also used to evaluate the antioxidant capacity of plant tissue extracts. The changes of FRAP and ABTS showed the same trend as that of DPPH in the acorn extracts of both oak species (Figure 2C,D), further proving that the antioxidant capacity of acorns is positively correlated with the contents of total polyphenols. Our result also demonstrated that the acorn extracts of Q. variabilis had much higher activities than those of Q. mongolica. In addition, it is interesting that the acorn extracts of Q. variabilis with the medium polyphenol level had much higher antioxidant capacity than those of Q. mongolica with the high polyphenol level, indicating much more effective antioxidant components in the former one.

3.5. Multivariate Correlation Analysis

The longitude was positively correlated with acorn length and VE content, and negatively correlated with total soluble proteins and total polyphenols (Table 3). The similar correlation was found for the changes of latitude, except that latitude was positively correlated with total soluble sugars (Table 3). The annual average temperature was negatively correlated with total soluble sugars and VE, while positively correlated with total soluble proteins and total polyphenols. The number of sunshine hours was negatively correlated with single acorn weight, total soluble proteins and total polyphenols, but positively correlated with VE (Table 3).
The analysis of Mantel test showed significant correlations between the acorn traits, geographical and climatic distance matrices (Table 4). The acorn traits had a higher correlation with climate matrices (r = 0.5838, p < 0.001), but a relatively lower correlation with geographical matrices (r = 0.446, p < 0.001). Subsequently, three main categories of the acorn traits were checked again by using the Mantel test, respectively (Table 5). The results showed that functional compounds were significantly correlated with geographical (r = 0.5155, p < 0.001) and climate matrices (r = 0.6927, p < 0.001), but other two traits had very low correlation with geographical and climate matrices, although there was a significant correlation between phenotypes and geographical matrices (r = 0.2199, p = 0.0222).

3.6. Principal Component Analysis

The acorn traits including phenotypes, proximate compositions and functional compounds were separated clearly by PCA in Q. mongolica and Q. variabilis among populations (Figure 3A,B). In the acorns of Q. mongolica from 29 populations, PC1 reflected 33.58% of the information in the total data, including acorn length, width, perimeter, and single acorn weight, which had negative principal component coefficients (Figure 3A; Table S2). We also found that the total polyphenols contents showed a significant negative correlation with acorn sizes (Table S3). Therefore, the smaller the acorn sizes were, the higher the contents of total polyphenols were. PC2 had a 15% contribution to the total data (Figure 3A), and the PC2 score in the figure was negative with the actual score (Table S2). Thus, we believed that the larger the value of PC2 was, the smaller the total amino acid and VE contents were, the greater the total soluble sugars were (Table S3). It can be clearly seen that distribution regions had significant differences on acorn traits revealed by the result of PCA (Figure 3A). The acorn traits in North China were clearly separated from those in Northeast China.
In Q. variabilis, PC1 and PC2 embodied 35.16% and 20.29% of the total data, respectively, from 15 populations (Figure 3B). The higher correlation with PC1 was the acorn phenotypic traits, including length, width, girth, and single acorn weight, while the higher correlation with PC2 was total polyphenols and total flavonoids (Table S4). Moreover, the scores of PC1 and PC2 were positive with the acorn sizes and the total polyphenols, and total soluble sugars and starch have a significant positive correlation with polyphenols (Table S5).
In North China, the acorn traits were also separated clearly by PCA, although both types of oak species are distributed here (Figure 4). The contribution of PC1 to the data was 48.57%, and PC2 was 23.33%. The higher correlation with PC1 was the sizes of acorns, including length, width, perimeter, and single acorn weight, and the score coefficients of these indexes were all negative values. The contents of total soluble proteins and total amino acids were more related to PC2, and the score coefficients of these indicators were all positive (Table S6). Considering about their correlation, the range of values occupied by these two oak species on PC2 was similar, but there was a big difference on PC1. In the same distribution area, Q. variabilis acorns had a larger size and a lower polyphenols level, while Q. mongolica acorns had a smaller size and a higher polyphenols level, indicating that both oak species in North China had significant differences in acorn size and total polyphenols, although other contents of proximate compositions had small differences.

3.7. Cluster Analysis

The cluster analysis showed that the acorns of Q. mongolica among populations could be divided into four clusters (Figure 5A). The acorns of both cluster I and cluster II were large in sizes and low in total polyphenols. But cluster I had higher total soluble sugars, which mostly distributed in Daxinganling Mountains and Xiaoxinganling Mountains of Northeast China. However, cluster II had higher starch, mainly collecting from Changbai Mountains of Northeast China. Of which, the population of QM13 had the highest total amino acids and the lowest total polyphenols. Cluster III contained high total soluble proteins and VE, mainly collecting from Changbai Mountains. The populations of cluster IV were all in North China, with small acorns, high total polyphenols, and high total soluble sugars. In all populations, QM29 had the smallest size and the highest total polyphenols, and QM27 had the highest total soluble sugars. Therefore, we considered that the acorns of cluster I were more valuable for people to eat, while those of cluster IV had higher values for industrial utilization.
In Q. variabilis, the acorns among populations could be divided into three clusters (Figure 5B). The acorns of cluster I were all from Central and North China, and had large sizes, low total polyphenols; cluster II was those with average performance in North China and Central China. Opposite to cluster I and II, the acorns of cluster III from Southwest China were small in size, high in starch and total polyphenols, and poor in edible quality. However, high starch and total polyphenols in cluster III had extremely high industrial application values. The results of cluster analysis further verified that the acorn characteristics were obviously affected by latitude.

4. Discussion

Over the years, the chemical compositions of woody plant fruits, in terms of carbohydrates, proteins, vitamins, and especially secondary metabolites, have received special attention [53]. In nature, however, the variations of chemical compositions as well as phenotypic changes in these fruits are reported to be closely associated with the geographical distribution [24,25,26,27,28,29,30,43,54,55], even if they are within populations [56,57]. The diversities of these fruit traits are the consequences of multiple evolutionary processes, including inherent genetic factors and external environment pressures [26,57,58,59,60].
The field investigation found that there are significant differences in the phenotypes of acorns from different populations between interspecies or intraspecies, including the two studied species Q. mongolica and Q. variabilis. In this study, the length and width of acorns in Q. mongolica were 1.72–2.34 cm and 1.27–1.95 cm, which were almost consistent with the results of Liang et al. [43], although the maximum value of acorn width was much larger in the present work; those of Q. variabilis were 1.45–2.38 cm and 1.30–1.84 cm, which were also consistent with the results of Gao et al. [26], except one population with a much higher value of 3.17 cm in acorn length [26]. In agreement with other species, the phenotypic diversities of fruits are also widely reported, such as Q. ilex subsp. ballota [27], C. sativa [28,54], X. sorbifolium [29], and S. domestica [30]. These results indicate that the phenotypic variations of acorns between intraspecies, besides internal genetic variation [38,56,57], may reflect the plants’ response to different selection pressures [26,58], such as geographical distribution [26,43] and patterns of acorn predation [41,42,57]. Besides, the distribution of Q. variabilis has larger latitude and longitude ranges than that of Q. mongolica in China. Therefore, our study also showed that the acorns of Q. variabilis had a greater degree of phenotypic variations at the intraspecies level as well as the larger weight, which might be caused by changes in the ecological environments such as global warming [32,61].
Generally, the fruit sizes are considered to be positively associated with the fruit qualities, such as the contents soluble sugars and starch [62,63], although our field investigation found a higher moth-eaten rate in the larger acorns inside, in agreement with a previous study [64]. Based on the characteristics of common distribution between Q. mongolica and Q. variabilis in North China, our further investigation found that the acorn sizes of Q. mongolica became smaller, while those of Q. variabilis became larger, but the sizes tended to be similar, when approaching to the same distribution region of North China (Figure S1). This finding demonstrated that the acorn sizes increased with the increasing latitude. Meanwhile, the differences in total soluble sugars and starch gradually increased, while the differences in total amino acids and total polyphenols gradually decreased, further indicating that the chemical compositions were closely associated with acorn sizes and latitude changes.
The results of PC and cluster analysis also proved that the acorn sizes and chemical compositions had a close relationship with the changes of latitudes in this study. From North China to Northeast China, the acorns of Q. mongolica had larger sizes and higher total soluble sugars, but lower total polyphenols and total amino acids. Similar changes in the phenotype and soluble sugars of acorns are found in Q. ilex subsp. ballota from Andalusia region of southern Spain [27] and four oak species from Mediterranean basin [65]. However, the acorn sizes of Q. variabilis gradually increased, while total soluble sugars, starch and total polyphenols gradually decreased with the increasing latitudes from Southwest to North China, and the latter two compositions were almost in accordance with a previous report [32]. Changes in these compositions, especially secondary metabolites, may be mainly affected by external environment conditions, such as soil compositions, climate changes, and sampling sources [66,67]. Another plausible explanation for our finding might be that, in areas with lower latitudes, acorns are more likely to be caught by herbivores because of the higher diversity of insects, thus, chemical defense plays an important role in self-protection of acorns, although structural defense is the first barrier of herbivores [68]. This was also in line with the theory of co-evolution between plants and herbivores [69]. In the common distribution area of North China, we found that the size of Q. variabilis acorns was larger and the content of polyphenols was lower, while those of Q. mongolica acorns were contrary, but both kinds of acorns differed slightly in other proximate composition contents. Therefore, we considered that acorns for one oak species in low latitudes might protect against herbivorous animals through smaller size and higher polyphenols.
In addition, the polyphenols have received extensive attention from domestic and foreign researchers because of their great roles in human health [13,21], which are also important criteria for evaluating the development potential of acorn food. In this study, all acorn samples for chemical analysis were heat-dried, considering that they are usually stored by air-drying or processed into food by cooking, although functional compounds could be changed. This work further showed that acorns of two studied oak species had higher total polyphenols, thus they need to be de-astringent if processed into food [20]. A higher polyphenols in Q. mongolica acorns from North China resulted in much more bitter, but contrary to those in Northeast China, which are more popular in terms of food consumption in life. However, the acorns of Q. variabilis in Southwest China had the highest levels of polyphenols. These natural antioxidants can be utilized in the food industries, benefiting the prolonged shelf life in food products [70]. In addition, the antioxidant capacity is also an important criterion for evaluating the acorns as functional foods [71]. For example, Q. rubra L. acorns have been used as a potential alternative to cocoa powder in the manufacture of chocolate [72]. In this study, the higher antioxidant activity of Q. variabilis acorns was detected, but its main functional components still needed a further investigation in future work.

5. Conclusions

The phenotypic characteristics and chemical composition contents of Q. mongolica and Q. variabilis acorns, collected from 44 populations in China, had wide variations, which is the prerequisite for screening high-quality oak germplasm resources. As the geographical location approached in these two oak species, the acorn shape, total amino acids and total polyphenols tended to be similar, but the acorn sizes, total soluble sugars and starch were more different. In terms of industrial utilization, the acorns of Q. mongolica in North China had high levels of starch and total polyphenols, with the highest polyphenols from QM29 population. While those of Q. variabilis in Southwest China also had high levels of starch and polyphenols, with the highest starch from QV14 population. Thus, these two populations could be used as excellent production areas for industrial raw materials. Moreover, as the latitudes increased, the acorn sizes increased, and the contents of total polyphenols decreased, indicating that the acorn sizes and polyphenols were closely associated with geographic location. To the best of our knowledge, this is the first comprehensive study of the relationship between acorn traits, including phenotypes, proximate compositions and functional compounds, and geographical distribution in China. Our study would give new insight into the selection of excellent resources for the future food processing and industrial application of acorns.

Supplementary Materials

The following are available online at https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/f12121755/s1, Table S1. Sample locations of collected acorns of Quercus mongolica and Q. variabilis. Table S2. Principal component coefficients of Quercus mongolica. Table S3. Correlation coefficients of Quercus mongolica. Table S4. Principal component coefficients of Quercus variabilis. Table S5. Correlation coefficients of Quercus variabilis. Table S6. Principal component coefficients of two Quercus species. Figure S1: Changes in acorn traits when geographical distribution approached in both Quercus species. Blue: Q. mongolica; Red: Q. variabilis. Southwest: Q. variabilis in Southwest China; Northeast: Q. mongolica in Northeast China; North China: Q. mongolica and Q. variabilis in North China.

Author Contributions

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

Funding

This research was supported by ‘the Fundamental Research Funds for the Central Non-profit Research Institution of CAF (CAFYBB2018ZB001-1)’.

Data Availability Statement

The data underlying this article are available in the article and in its online supplementary material.

Acknowledgments

We greatly thank Yongyu Sun, Jingle Zhu, and Yueqiao Li for the help during the collection of acorns. We also greatly thank Chuankui Song for his good suggestions and providing the lab for the determination of polyphenols and their activities. Moreover, we acknowledge the anonymous editor and reviewers who provided many helpful comments and suggestions for improving this manuscript.

Conflicts of Interest

None of the authors have any actual or potential conflicts of interest.

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Figure 1. Geographical positions of the collected acorns in Quercus mongolica Fisch. ex Ledeb. and Q. variabilis Blume. The populations were marked with dots in colors. Red: Q. mongolica; Green: Q. variabilis. The dashed line indicated the distribution range of these two oak species.
Figure 1. Geographical positions of the collected acorns in Quercus mongolica Fisch. ex Ledeb. and Q. variabilis Blume. The populations were marked with dots in colors. Red: Q. mongolica; Green: Q. variabilis. The dashed line indicated the distribution range of these two oak species.
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Figure 2. Antioxidant activities of acorns in Quercus mongolica and Q. variabilis from the different populations. (A): The contents of total polyphenols in two oak species of three designated populations; (B–D): The antioxidant capacity revealed by the methods of DPPH, FRAP and ABTS, respectively. Blue: Q. mongolica; Red: Q. variabilis. H: The population with the highest total polyphenols; M: The population with a medium total polyphenols; L: The acorn with the least total polyphenols. The letters in the figures indicated the significance.
Figure 2. Antioxidant activities of acorns in Quercus mongolica and Q. variabilis from the different populations. (A): The contents of total polyphenols in two oak species of three designated populations; (B–D): The antioxidant capacity revealed by the methods of DPPH, FRAP and ABTS, respectively. Blue: Q. mongolica; Red: Q. variabilis. H: The population with the highest total polyphenols; M: The population with a medium total polyphenols; L: The acorn with the least total polyphenols. The letters in the figures indicated the significance.
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Figure 3. PCA of phenotypes, proximate compositions and functional components in the acorns of Quercus mongolica and Q. variabilis. (A) Q. mongolica. Green: Acorns from Heilongjiang of Northeast China; Yellow: Acorns from Jilin and Liaoning of Northeast China; Red: Acorns in North China. (B) Q. variabilis. Green: Acorns in North China; Yellow: Acorns in Central China; Red: Acorns in Southwest China.
Figure 3. PCA of phenotypes, proximate compositions and functional components in the acorns of Quercus mongolica and Q. variabilis. (A) Q. mongolica. Green: Acorns from Heilongjiang of Northeast China; Yellow: Acorns from Jilin and Liaoning of Northeast China; Red: Acorns in North China. (B) Q. variabilis. Green: Acorns in North China; Yellow: Acorns in Central China; Red: Acorns in Southwest China.
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Figure 4. PCA of acorn traits in both oak species of North China. Acorn traits included phenotypes, proximate compositions, and functional compounds. Red: Q. mongolica; Green: Q. variabilis.
Figure 4. PCA of acorn traits in both oak species of North China. Acorn traits included phenotypes, proximate compositions, and functional compounds. Red: Q. mongolica; Green: Q. variabilis.
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Figure 5. Cluster analysis of acorn traits in Quercus mongolica (A) and Q. variabilis (B) from the different populations. Acorn traits included phenotypes, proximate compositions, and functional compounds.
Figure 5. Cluster analysis of acorn traits in Quercus mongolica (A) and Q. variabilis (B) from the different populations. Acorn traits included phenotypes, proximate compositions, and functional compounds.
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Table
Table 1. Acorn traits including phenotypes, proximate compositions and functional compounds of Quercus mongolica Fisch. ex Ledeb. from 29 populations. Acorn phenotype: length, width, perimeter, single weight, and aspect ratio; proximate compositions: total soluble sugars, starch, total soluble proteins, and total amino acids; functional compounds: total polyphenols, total flavonoids, soluble tannins, and vitamin E. Data were expressed as mean ± standard deviation.
Table 1. Acorn traits including phenotypes, proximate compositions and functional compounds of Quercus mongolica Fisch. ex Ledeb. from 29 populations. Acorn phenotype: length, width, perimeter, single weight, and aspect ratio; proximate compositions: total soluble sugars, starch, total soluble proteins, and total amino acids; functional compounds: total polyphenols, total flavonoids, soluble tannins, and vitamin E. Data were expressed as mean ± standard deviation.
NO.L (cm)W (cm)P (cm)SW (g)ARTS (mg/g)S (mg/g)TP (mg/g)TAA (mg/g)TPS (mg/g)TF (mg/g)ST (mg/g)VE (µg/g)
QM12.29 ± 0.101.57 ± 0.045.00 ± 0.133.03 ± 0.391.46 ± 0.0369.27 ± 6.88242.49 ± 27.4110.18 ± 0.8115.19 ± 5.4966.32 ± 10.336.07 ± 4.7915.52 ± 1.5250.58 ± 0.44
QM22.21 ± 0.071.65 ± 0.045.23 ± 0.143.03 ± 0.361.34 ± 0.0651.96 ± 5.17196.56 ± 10.9112.50 ± 0.5510.74 ± 1.1376.09 ± 7.1235.03 ± 1.616.37 ± 0.2950.59 ± 2.55
QM31.99 ± 0.041.61 ± 0.215.07 ± 0.602.78 ± 0.771.26 ± 0.1755.81 ± 1.81199.22 ± 12.4512.38 ± 1.0535.02 ± 6.6857.02 ± 6.3133.09 ± 9.0416.11 ± 0.1848.99 ± 2.22
QM42.08 ± 0.021.62 ± 0.015.08 ± 0.062.96 ± 0.141.29 ± 0.0259.08 ± 3.49205.53 ± 21.5511.60 ± 1.8531.06 ± 11.3874.40 ± 3.5536.55 ± 2.1115.80 ± 0.8358.47 ± 3.71
QM52.15 ± 0.091.56 ± 0.104.84 ± 0.262.66 ± 0.301.40 ± 0.0659.6 ± 2.64194.76 ± 14.2511.89 ± 1.1422.47 ± 2.2560.35 ± 2.9532.52 ± 1.9115.52 ± 1.2153.55 ± 3.47
QM62.18 ± 0.191.89 ± 0.085.93 ± 0.323.93 ± 0.631.15 ± 0.0570.92 ± 12.26213.93 ± 19.2411.85 ± 1.3618.62 ± 4.4757.64 ± 14.0630.83 ± 4.8615.58 ± 0.7853.99 ± 4.78
QM72.11 ± 0.101.73 ± 0.085.40 ± 0.223.45 ± 0.551.23 ± 0.0856.28 ± 5.28224.37 ± 19.768.22 ± 3.2816.66 ± 2.2754.31 ± 3.6926.53 ± 3.7916.65 ± 0.1552.32 ± 2.45
QM81.98 ± 0.071.64 ± 0.055.11 ± 0.102.79 ± 0.331.21 ± 0.0464.03 ± 5.11208.48 ± 28.0911.14 ± 0.3819.09 ± 2.3356.26 ± 7.2730.07 ± 3.8215.11 ± 0.7853.47 ± 1.7
QM92.11 ± 0.031.63 ± 0.125.08 ± 0.312.76 ± 0.541.31 ± 0.1266.87 ± 1.95188.89 ± 4.311.24 ± 0.4025.81 ± 1.1994.99 ± 9.948.75 ± 1.9816.30 ± 0.8157.95 ± 3.04
QM102.20 ± 0.101.58 ± 0.024.87 ± 0.092.68 ± 0.211.40 ± 0.0553.22 ± 1.8276.70 ± 94.0310.09 ± 0.1426.11 ± 4.0259.55 ± 3.2135.75 ± 8.1416.07 ± 0.4753.57 ± 4.88
QM112.31 ± 0.051.61 ± 0.035.07 ± 0.073.59 ± 0.091.44 ± 0.0161.81 ± 1.48245.35 ± 31.4510.19 ± 0.5815.02 ± 4.4282.07 ± 6.4452.66 ± 3.8816.38 ± 0.1847.59 ± 1.66
QM122.22 ± 0.081.68 ± 0.035.29 ± 0.103.73 ± 0.231.33 ± 0.0738.56 ± 5.07211.92 ± 32.218.70 ± 0.8640.52 ± 2.4445.56 ± 9.3931.96 ± 8.215.69 ± 0.652.18 ± 3.33
QM131.94 ± 0.051.58 ± 0.064.98 ± 0.172.88 ± 0.101.23 ± 0.0730.56 ± 2.08294.29 ± 13.138.75 ± 1.0858.27 ± 2.4153.37 ± 4.6834.53 ± 2.9715.88 ± 0.353.09 ± 3.1
QM142.21 ± 0.051.71 ± 0.125.32 ± 0.363.93 ± 0.531.30 ± 0.0935.77 ± 0.68263.11 ± 8.798.92 ± 0.4442.48 ± 3.4980.50 ± 12.4550.55 ± 2.5716.09 ± 0.2551.21 ± 5.5
QM152.16 ± 0.061.74 ± 0.035.33 ± 0.093.98 ± 0.201.25 ± 0.0444.35 ± 6.68198.23 ± 17.3210.26 ± 1.2442.90 ± 1.2141.76 ± 5.7723.43 ± 1.3716.67 ± 0.0660.24 ± 1.83
QM162.09 ± 0.021.66 ± 0.085.13 ± 0.233.54 ± 0.271.26 ± 0.0836.17 ± 1.76252.06 ± 3.158.89 ± 0.3243.56 ± 2.5887.08 ± 7.4345.65 ± 1.4616.20 ± 0.1463.52 ± 4.14
QM172.23 ± 0.221.74 ± 0.204.98 ± 0.143.28 ± 0.051.29 ± 0.0932.5 ± 4.88239.22 ± 57.9310.44 ± 0.3545.38 ± 2.8569.14 ± 3.2134.61 ± 2.6815.84 ± 0.6163.42 ± 2.6
QM182.34 ± 0.091.95 ± 0.064.50 ± 0.172.25 ± 0.181.20 ± 0.0742.46 ± 5.86178.42 ± 47.6113.87 ± 2.4948.62 ± 2.6588.15 ± 7.144.45 ± 1.2116.56 ± 0.663.63 ± 1.62
QM192.13 ± 0.131.71 ± 0.125.48 ± 0.403.57 ± 0.711.25 ± 0.0144.38 ± 3.89140.96 ± 30.9812.85 ± 1.8953.62 ± 1.1670.12 ± 2.544.29 ± 5.5215.69 ± 1.1260.85 ± 2.83
QM202.26 ± 0.151.65 ± 0.125.13 ± 0.283.42 ± 0.681.38 ± 0.0840.6 ± 6.98222.37 ± 18.5715.15 ± 2.0755.32 ± 8.3262.94 ± 5.0140.03 ± 7.0115.97 ± 0.5669.68 ± 2.49
QM212.23 ± 0.061.53 ± 0.074.85 ± 0.162.68 ± 0.501.46 ± 0.0543.96 ± 4.04254.16 ± 20.1514.51 ± 1.533.43 ± 6.1253.97 ± 3.9837.64 ± 2.9816.04 ± 0.1268.79 ± 0.7
QM222.14 ± 0.091.60 ± 0.134.95 ± 0.353.30 ± 0.641.35 ± 0.0636.48 ± 0.94297.09 ± 51.510.34 ± 0.3341.02 ± 1.4764.80 ± 2.7734.84 ± 2.8716.52 ± 0.6247.70 ± 1.54
QM232.03 ± 0.051.57 ± 0.094.87 ± 0.232.73 ± 0.371.30 ± 0.0449.77 ± 1.02206.79 ± 70.2513.30 ± 0.8144.98 ± 2.2362.27 ± 2.5833.76 ± 5.8116.39 ± 0.8456.46 ± 0.87
QM242.26 ± 0.051.47 ± 0.044.62 ± 0.052.68 ± 0.071.54 ± 0.0661.81 ± 2.47189.63 ± 13.1911.38 ± 1.8931.59 ± 2.0883.46 ± 2.1340.86 ± 5.4516.14 ± 1.0957.35 ± 2.05
QM252.03 ± 0.071.62 ± 0.065.08 ± 0.192.99 ± 0.321.25 ± 0.0260.38 ± 0.79181.32 ± 31.7311.50 ± 0.9444.05 ± 2.63106.69 ± 4.8356.46 ± 5.6516.91 ± 0.1253.71 ± 5.99
QM262.01 ± 0.101.52 ± 0.064.77 ± 0.192.57 ± 0.251.34 ± 0.1041.82 ± 2.09242.36 ± 22.8510.42 ± 1.1538.33 ± 3.1283.97 ± 6.6258.05 ± 20.116.54 ± 0.2552.02 ± 2.45
QM272.11 ± 0.241.62 ± 0.293.96 ± 0.161.34 ± 0.271.32 ± 0.1087.21 ± 4.31291.63 ± 36.0510.03 ± 0.3132.60 ± 1.42102.73 ± 7.5963.39 ± 18.8216.68 ± 0.1160.35 ± 2.41
QM281.78 ± 0.071.46 ± 0.084.58 ± 0.231.73 ± 0.191.22 ± 0.0465.90 ± 3.42287.51 ± 10.3111.64 ± 1.6435.51 ± 5.57105.48 ± 2.7564.08 ± 8.9116.73 ± 0.6654.70 ± 3.03
QM291.72 ± 0.061.27 ± 0.033.97 ± 0.060.92 ± 0.141.36 ± 0.0374.45 ± 1.94268.33 ± 35.3512.24 ± 1.135.19 ± 1.90158.92 ± 4.891.94 ± 14.117.81 ± 0.5659.66 ± 0.18
Average2.12 ± 0.091.63 ± 0.084.98 ± 0.202.94 ± 0.351.31 ± 0.0652.97 ± 3.68228.13 ± 27.7411.19 ± 1.134.59 ± 3.4274.48 ± 5.8842.36 ± 5.6416.2 ± 0.5456.19 ± 2.67
F6.137 **4.368 **8.727 **9.768 **4.932 **30.459 **4.112 **5.232 **28.902 **39.152 **11.207 **1.962 *11.237 **
Note: L: Length of longitudinal axis; W: Maximum lateral width; P: Perimeter; SW: Single acorn weight; AR: Aspect ratio; TS: Total soluble sugars; S: Starch; TP: Total soluble proteins; TAA: Total amino acids; TPS: Total polyphenols; TF: Total flavonoids; ST: Soluble tannins; VE: Vitamin E. * p < 0.05, and ** p < 0.01.
Table
Table 2. Acorn traits including phenotypes, proximate compositions, and functional compounds of Quercus variabilis Blume from 15 populations. Acorn phenotypes: length, width, perimeter, single weight, and aspect ratio; proximate compositions: total soluble sugars, starch, total soluble proteins, and total amino acids; functional compounds: total polyphenols, total flavonoids, soluble tannins, and vitamin E. Data were expressed as mean ± standard deviation.
Table 2. Acorn traits including phenotypes, proximate compositions, and functional compounds of Quercus variabilis Blume from 15 populations. Acorn phenotypes: length, width, perimeter, single weight, and aspect ratio; proximate compositions: total soluble sugars, starch, total soluble proteins, and total amino acids; functional compounds: total polyphenols, total flavonoids, soluble tannins, and vitamin E. Data were expressed as mean ± standard deviation.
NO.L (cm)W (cm)P (cm)SW (g)ARTS (mg/g)S (mg/g)TP (mg/g)TAA (mg/g)TPS (mg/g)TF (mg/g)ST (mg/g)VE (µg/g)
QV12.14 ± 0.161.71 ± 0.185.27 ± 0.573.87 ± 1.121.26 ± 0.0731.53 ± 6.18165.10 ± 11.9711.73 ± 1.3431.92 ± 0.9889.36 ± 10.0150.59 ± 9.6116.16 ± 0.3258.73 ± 2.1
QV21.98 ± 0.11.54 ± 0.114.78 ± 0.292.35 ± 0.521.29 ± 0.1341.7 ± 4.55174.8 ± 35.2924.95 ± 0.4443.59 ± 2.59102.28 ± 8.165.78 ± 4.0315.24 ± 0.8341.22 ± 1.13
QV32.21 ± 0.21.84 ± 0.135.65 ± 0.314.56 ± 0.581.21 ± 0.1537.41 ± 4.15192.74 ± 29.7412.47 ± 1.4325.71 ± 0.2391.79 ± 6.7958.48 ± 4.5316.55 ± 0.550.61 ± 2.07
QV42.27 ± 0.161.72 ± 0.145.29 ± 0.423.75 ± 1.021.32 ± 0.139.01 ± 5.66130.99 ± 6.0318.31 ± 0.3645.31 ± 0.86106.62 ± 4.768.13 ± 2.3916.37 ± 0.3164.01 ± 2.1
QV52.13 ± 0.221.71 ± 0.085.43 ± 0.213.86 ± 0.561.24 ± 0.0942.83 ± 2.72145.04 ± 9.315.32 ± 0.5542.08 ± 0.61126.87 ± 12.4871.65 ± 8.6216.53 ± 0.269.21 ± 1.52
QV62.12 ± 0.131.67 ± 0.115.19 ± 0.323.67 ± 0.61.27 ± 0.0738.26 ± 6.01167.58 ± 12.7228.31 ± 13.2241.91 ± 1.37116.09 ± 9.6574.91 ± 2.1816.27 ± 0.2645.49 ± 3.99
QV72.1 ± 0.181.76 ± 0.145.49 ± 0.413.73 ± 0.791.19 ± 0.142.07 ± 1.63169.56 ± 28.2424.22 ± 1.9628.72 ± 1.23161.65 ± 12.38116.07 ± 20.0515.85 ± 0.9542.47 ± 3.85
QV82.26 ± 0.141.82 ± 0.145.6 ± 0.444.62 ± 0.811.25 ± 0.140.65 ± 5.28168.84 ± 20.0322.84 ± 1.4726.61 ± 1.42127.3 ± 34.4191.68 ± 1.9415.73 ± 1.2340.2 ± 1.33
QV91.97 ± 0.121.7 ± 0.15.27 ± 0.323.61 ± 0.591.16 ± 0.0742.68 ± 3.24224.5 ± 51.9223.28 ± 0.5929.79 ± 1.21143.15 ± 4.5283.67 ± 9.9715.56 ± 1.1955.41 ± 2.27
QV102.27 ± 0.181.68 ± 0.175.16 ± 0.53.97 ± 0.971.36 ± 0.1446.56 ± 5.95228.7 ± 56.7223.52 ± 0.6532.91 ± 3.81123.98 ± 20.9983.48 ± 3.2516.22 ± 0.4444.73 ± 0.79
QV112.02 ± 0.141.46 ± 0.144.53 ± 0.362.47 ± 0.531.39 ± 0.1361.21 ± 6.03253.03 ± 4.5720.35 ± 0.0841.91 ± 2.54151.5 ± 6.8587.09 ± 5.4517.22 ± 0.5422.39 ± 2.7
QV122.26 ± 0.131.83 ± 0.175.62 ± 0.514.74 ± 1.141.24 ± 0.1143.07 ± 1.66203.41 ± 24.1311.84 ± 1.6848.23 ± 8.44188.37 ± 5.9799.97 ± 5.3716.72 ± 0.5744.3 ± 3.35
QV132.38 ± 0.141.77 ± 0.115.48 ± 0.354.67 ± 0.691.35 ± 0.0840.19 ± 1.22189.04 ± 26.0414.58 ± 0.5344.96 ± 2.55156.32 ± 7.8688.86 ± 6.2716.39 ± 0.9739.03 ± 3.29
QV141.45 ± 0.151.3 ± 0.084.1 ± 0.241.57 ± 0.331.11 ± 0.0947.87 ± 5.47306.28 ± 66.318.53 ± 0.6626.31 ± 1.37178.26 ± 6.14100.49 ± 18.1217.33 ± 0.0731.2 ± 1.28
QV151.82 ± 0.221.76 ± 0.175.46 ± 0.553.63 ± 1.081.04 ± 0.0749.49 ± 2.59246.25 ± 33.9319.6 ± 0.6629.32 ± 0.87181.12 ± 0.65107.96 ± 1.6217.02 ± 0.636.46 ± 2.03
Average2.09 ± 0.161.69 ± 0.135.22 ± 0.393.67 ± 0.761.25 ± 0.142.97 ± 4.16197.73 ± 27.8219.32 ± 1.7135.95 ± 2.01136.31 ± 10.183.26 ± 6.8916.34 ± 0.645.7 ± 2.25
F20.122 **11.889 **11.869 **13.141 **8.39 **6.6 **5.9 **6.494 **25.554 **19.249 **13.321 **2.209 *75.806 **
Note: L: Length of longitudinal axis; W: Maximum lateral width; P: Perimeter; SW: Single acorn weight; AR: Aspect ratio; TS: Total soluble sugars; S: Starch; TP: Total soluble proteins; TAA: Total amino acids; TPS: Total polyphenols; TF: Total flavonoids; ST: Soluble tannins; VE: Vitamin E. * p < 0.05, and ** p < 0.01.
Table
Table 3. Correlation analysis of geographical factors and acorn traits.
Table 3. Correlation analysis of geographical factors and acorn traits.
VariableLWPSWARTSSTPTAATPSTFSTVE
Longitude0.346 *0.0070.034−0.1320.409 **0.1560.021−0.652 **−0.13−0.889 **−0.901 **−0.406 **0.562 **
Latitude0.313 *−0.023−0.018−0.1850.417 **0.35 *−0.008−0.638 **−0.267−0.817 **−0.843 **−0.378 *0.472 **
AAT−0.1670.0760.0960.277−0.301 *−0.394 **−0.1540.699 **0.2530.787 **0.837 **0.305 *−0.459 **
SH−0.088−0.208−0.3 *−0.399 **0.1660.2950.029−0.359 *−0.253−0.519 **−0.502 **−0.1270.352 *
AAT: Annual average temperature (°C); SH: Sunshine hour (h); L: Length of longitudinal axis (cm); W: Maximum lateral width (cm); P: Perimeter (cm); SW: Single acorn weight, (g); AR: Aspect ratio; TS: Total soluble sugars (mg/g); S: Starch (mg/g); TP: Total soluble proteins (mg/g); TAA: Total amino acids (mg/g); TPS: Total polyphenols (mg/g); TF: Total flavonoids (mg/g); ST: Soluble tannins (mg/g); VE: Vitamin E (μg/g). * p < 0.05 and ** p < 0.01.
Table
Table 4. Correlations between traits (L, W, P, SW, AR, TS, S, TP, TAA, TPS, TF, ST, VE), climatic (AAT, SH) and geographic (Longitude, Latitude) factors.
Table 4. Correlations between traits (L, W, P, SW, AR, TS, S, TP, TAA, TPS, TF, ST, VE), climatic (AAT, SH) and geographic (Longitude, Latitude) factors.
Comparisonrp-Value
Traits, Geographic factors0.4461.00 × 10−4
Traits, Climatic factors0.58381.00 × 10−4
AAT: Annual average temperature (°C); SH: Sunshine hour (h); L: Length of longitudinal axis (cm); W: Maximum lateral width (cm); P: Perimeter (cm); SW: Single acorn weight, (g); AR: Aspect ratio; TS: Total soluble sugars (mg/g); S: Starch (mg/g); TP: Total soluble proteins (mg/g); TAA: Total amino acids (mg/g); TPS: Total polyphenols (mg/g); TF: Total flavonoids (mg/g); ST: Soluble tannins (mg/g); VE: Vitamin E (μg/g).
Table
Table 5. Correlations between phenotypes (L, W, P, SW, AR), proximate compositions (TS, S, TP, TAA), functional compounds (TPS, TF, ST, VE), climatic (AAT, SH) and geographic (Longitude, Latitude) factors.
Table 5. Correlations between phenotypes (L, W, P, SW, AR), proximate compositions (TS, S, TP, TAA), functional compounds (TPS, TF, ST, VE), climatic (AAT, SH) and geographic (Longitude, Latitude) factors.
PhenotypesProximate CompositionsFunctional Compounds
rp-Valuerp-Valuerp-Value
Geographic factors0.21990.02220.052580.20830.51551.00 × 10−4
Climatic factors0.071320.16610.056550.1620.69271.00 × 10−4
AAT: Annual average temperature (°C); SH: Sunshine hour (h); L: Length of longitudinal axis (cm); W: Maximum lateral width (cm); P: Perimeter (cm); SW: Single acorn weight, (g); AR: Aspect ratio; TS: Total soluble sugars (mg/g); S: Starch (mg/g); TP: Total soluble proteins (mg/g); TAA: Total amino acids (mg/g); TPS: Total polyphenols (mg/g); TF: Total flavonoids (mg/g); ST: Soluble tannins (mg/g); VE: Vitamin E (μg/g).
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Sun, J.; Shi, W.; Wu, Y.; Ji, J.; Feng, J.; Zhao, J.; Shi, X.; Du, C.; Chen, W.; Liu, J.; et al. Variations in Acorn Traits in Two Oak Species: Quercus mongolica Fisch. ex Ledeb. and Quercus variabilis Blume. Forests 2021, 12, 1755. https://0-doi-org.brum.beds.ac.uk/10.3390/f12121755

AMA Style

Sun J, Shi W, Wu Y, Ji J, Feng J, Zhao J, Shi X, Du C, Chen W, Liu J, et al. Variations in Acorn Traits in Two Oak Species: Quercus mongolica Fisch. ex Ledeb. and Quercus variabilis Blume. Forests. 2021; 12(12):1755. https://0-doi-org.brum.beds.ac.uk/10.3390/f12121755

Chicago/Turabian Style

Sun, Jiacheng, Wenshi Shi, Yanyan Wu, Jing Ji, Jian Feng, Jiabing Zhao, Xinru Shi, Changjian Du, Wei Chen, Jianfeng Liu, and et al. 2021. "Variations in Acorn Traits in Two Oak Species: Quercus mongolica Fisch. ex Ledeb. and Quercus variabilis Blume" Forests 12, no. 12: 1755. https://0-doi-org.brum.beds.ac.uk/10.3390/f12121755

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