Next Article in Journal
A Small-Scale Investigation into the Viability of Detecting Canopy Damage Caused by Acantholyda posticalis Disturbance Using High-Resolution Satellite Imagery in a Managed Pinus sylvestris Stand in Central Poland
Previous Article in Journal
Genome-Wide Identification and Expression Analysis of the AlkB Homolog Gene Family in Tamarix chinensis
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 16
Issue 3
10.3390/f16030471
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Influence of Tree Species Composition on Leaf and Soil Properties and Soil Enzyme Activity in Mixed and Pure Oak (Quercus variabilis) Stands

by
Juan Wang
1,
Chang Liu
1,
Xinliang Shao
2,
Yiting Song
1 and
Xu Wang
3,*
1
College of Forestry, Henan Agricultural University, Zhengzhou 450046, China
2
Key Laboratory of Plant Genetics and Molecular Breeding, Zhoukou Normal University, Zhoukou 466001, China
3
College of Life Sciences, Henan Agricultural University, Zhengzhou 450046, China
*
Author to whom correspondence should be addressed.
Forests 2025, 16(3), 471; https://doi.org/10.3390/f16030471
Submission received: 15 January 2025 / Revised: 28 February 2025 / Accepted: 2 March 2025 / Published: 7 March 2025
(This article belongs to the Section Forest Soil)
Browse Figures
Versions Notes

Abstract

:
There is an increasing number of studies providing evidence that multi-species tree plantations possess more advantages in terms of species-specific tree diameter, growth rates, and soil properties than monocultures. In order to clarify the effect of a multi-species tree plantation on leaf nutrition and soil fertility, a statistical analysis was carried out on the leaf and soil properties, as well as soil enzyme activity, from two repeatedly measured stands in the Qingliangsi Forest District of the Dengfeng Forest Station. For the analysis, the plots were categorized into type A and type B according to the different forest structures. Type A was the mixed plantation of Quercus variabilis and Platycladus orientalis, while type B was a pure stand of Quercus variabilis. The results clearly showed that the leaf water content of P. orientalis was greater than that of Q. variabilis. The total water, free fatty acids, soluble sugar, flavonoid, tannin, lignin, cellulose, and hemicellulose contents of P. orientalis were higher than those of Q. variabilis in type A. Furthermore, the soil moisture of the mixed plantations was apparently higher than that of the pure stand. Soil peroxidase activity was the highest both in type A and type B among the 10 soil enzyme activities. Meanwhile, there was a significant difference between soil catalase and acid phosphatase activities. Soil urease, leucine aminopeptidase, and sucrase were significantly correlated with multiple soil enzyme activities. In addition, based on the correlation analysis results, we noted that type A had more complex relationships than type B in the leaf and soil properties and soil enzyme activity. Compared to the monoculture stands, multi-species tree stands appeared to have more complicated and preferable soil and water conservation capabilities. These results further verify the beneficial role of mixed plantations in water holding capacity and in improving soil quality. Q. variabilis is a broad-leaved deciduous tree species, and P. orientalis is an evergreen coniferous tree species. Our study indicates that these two native Chinese tree species are suitable as the target species when constructing mixed forests. They can increase the interaction of leaf and soil properties, enhance the soil enzyme activities, and improve the soil.

1. Introduction

Severe soil erosion occurred in the hilly area of western Henan Province due to China’s economic policy and deforestation from the 1950s to the 1960s [1,2]. The terrain there was fragmented, with steep slopes and weak soil erosion resistance, leading to a decrease in soil productivity and ecological degradation, seriously hindering social and economic development [1]. Therefore, since the 1970s, China has implemented a series of national forestry ecological engineering programs to improve the ecological environment, including the Natural Forest Protection Project, the Grain for Green Program, the Shelterbelt Forestry Project [3], etc. With the development of afforestation and the gradual restoration of vegetation, biomass carbon and biomass nitrogen in China have also increased [4,5]. Meanwhile, the nutrient contents in forestry ecosystems have been altered due to the changes in land use patterns [6]. Particularly, due to the nationwide Grain for Green Program carried out in the late 1990s, the carbon storage and soil N cycle multi-functionality increased significantly in forest ecosystems [4,5,6,7,8]. However, with the rapid increase in artificial forestation areas, the issues of simple plant community structure, such as poor stability and low resistance to various pests and diseases, have become increasingly visible, which seriously affects productivity and the sustainable management of forestry [9,10,11]. Consequently, many scholars have shown increasing interest in optimizing the afforestation models to assist ecological restoration and achieve sustainable forest management, especially strengthening productivity, resistance, and stability of multi-species tree stands [12,13,14].
In 2019, the Henan Forestry Bureau released the “Recommended List of Main Afforestation Tree Species in Henan Province”, which recorded a total of 70 native tree species in Henan Province [15], including Quercus acutissima Carruth., Quercus variabilis Blume, Robinia pseudoacacia L., Platycladus orientalis (L.) Franco, etc. Quercus, as one of the most important tree species and forest vegetation types in hilly areas, is widely distributed on the north slope of the Funiu Mountains, the Xiong’er Mountains, and the foothills [2]. Platycladus orientalis also became a pioneer tree species in the loess hilly-gully area due to its characteristics, such as drought resistance and a wide adaptation to poor natural environments [2]. Mixed plantations of Q. variabilis and P. orientalis were considered the most important tree species combination in north and central south China [16,17]. Why are mixed plantations of Q. variabilis and P. orientalis considered the major cultivation model for vegetation restoration? What are the advantages of the combination of these two tree species, compared to a single tree species?
Some researchers consider that afforestation, the rational allocation of tree species with different requirements for photosynthetic efficiency, different soil nutrients, etc., can improve the productivity and stability of forest stands and play a protective role in preserving soil water and nutrient contents [18,19]. The leaf is the principal site of photosynthesis, and soil is the substrate for vegetation growth. The nutritional status of leaves can reflect the nutrient absorption efficiency of roots; at the same time, the soil provides most of the nutrients needed for growth [20,21]. In order to match its surroundings, the nutrients and enzyme activities of plants occur as a series of adaptive changes, such as changes in water content, total nitrogen content, urease, and acid phosphatase activity. Wei et al. found that elm, poplar, scotch pine, and larch should be promoted as the most suitable tree species for afforestation, rather than the Amur cork tree and ash, in the Songnen Plain by assaying the pH of soil, water, organic carbon, etc. At the same time, they also found that soil organic carbon and fertility in broad-leaved species were both higher than in coniferous species by measuring the contents of cellulose and lignin [22]. In addition, Bai et al. found that soil pH, organic matter, and nitrogen and phosphorus nutrient content were the key factors driving the changes in enzyme activity of conifer (Pinus massoniana Lamb.)–broadleaf (Castanopsis hytrix Hook. f. & Thomson ex A. DC.) mixed forests but not in their corresponding pure plantations [23]. So, compared to one-tree-species plantations, multi-tree-species plantations can obviously improve the absorption and transformation of nutrients in leaves and soil to regulate overall nutrient cycling in a forest ecosystem [24,25,26,27].
Mixed-species plantations have gradually become an effective approach for enhancing ecosystem functions in forestry production practice. Therefore, we hypothesized that, compared to Q. variabilis pure stands, the leaf and soil properties and soil enzyme activity of mixed-species plantations of Q. variabilis and P. orientalis would be enhanced and demonstrate advantages. In order to explore whether mixed-species plantations of Q. variabilis and P. orientalis had these advantages, the productivity and stability of different stands and their differences in soil and water conservation and soil improvement were discussed. This will provide the basis for selecting tree species of mixed plantations and mixed afforestation mode.

2. Materials and Methods

2.1. Sampling Site

The leaves and soil samples were collected in Qingliangsi Forest District of Dengfeng Forest Station. The sampling site was located on the southern slope of the Songshan Mountains (34°23′ to 34°33′ N, 112°53′ to 113°11′ E), Henan, China, where man-made forests of Quercus are the dominant hardwood. This site is located in the temperate continental monsoon climate zone belonging to a transition between warm temperate and subtropical climate areas. The altitude is between 350 and 1512 m, the annual precipitation is between 700 and 800 mm, and the annual average temperature range is 13–15 °C [28,29].
Two kinds of forest types (type A and type B) were divided according to different site conditions and forest structure. Each forest type included three sample plots, for a total of six sample plots (Figure 1). Each sample plot was 20 m × 20 m. The interval between each sample plot exceeded 100 m. Type A was mixed plantations of Platycladus orientalis and Quercus variabilis (punctate mixed patterns), and the quantity ratio of Q. variabilis to P. orientalis was 3:1. Type B was pure stands of Quercus variabilis. The altitude, slope position, slope, and other information of sample plots are shown in Table 1. Both type A and type B had the same associated tree species: Cotinus coggygria, Grewia biloba, and Vitex negundo. Type A had an average coverage of 4.50% for C. coggygria, 12.25% for G. biloba, and 10.91% for V. negundo. Type B had an average coverage of 5.00% for C. coggygria, 11.35% for G. biloba, and 8.90% for V. negundo.

2.2. Leaf and Soil Collection

We chose 3 trees in each sampling plot randomly of the same species as biological replications. The crown of each tree was divided into three equal parts. Mature leaves of four directions (north, south, east, and west) were picked off from upper, middle, and lower crown layers. Twelve leaves of each crown layer from one tree in total were put into an ice box after adding tags. The collected leaves were used to measure the leaf nutrition levels in different tree species.
Three small areas (5 m × 5 m) were selected randomly of each sample plot (20 m × 20 m) with at least 2 m intervals. The soil samples were mixed as one sample of the same tree with a 2 mm sieve. The ecological factors such as gradient and slope position were kept consistent while setting up the sampling plot. Soil in each small area was picked according to five-point sampling. The soil reached a depth of 20 cm [30,31]. The collected soils were used to measure the physiological parameters and enzyme activities.

2.3. Measurement of Leaf Indices

Ten indices were measured to quantify the physiological variations in leaves, including the total water, protein, crude fat, free fatty acids, soluble sugar, lignin, hemicellulose, cellulose, tannin, and flavonoid contents. The content levels of these ten substances were determined using a spectrophotometer (UV-1800PC; Shanghai Mapada Instrument Co., Ltd., Shanghai, China) and the relevant content kit (Suzhou Comin Biotechnology Co., Ltd., Suzhou, China) in accordance with the manufacturers’ instructions [32,33,34].
Leaf water content (%) = (fresh weight − dry weight)/dry weight × 100%
The protein content was determined by the bicinchoninic acid (BCA) method, and its absorbance value was measured at 562 nm. The crude fat was measured by extraction after drying, crushing, and weighing. Free fatty acids reacted with copper salt to form copper soap in weak acid conditions, and we measured the absorption peak of copper soap at 715 nm to calculate free fatty acids content. An anthrone colorimetry method was adopted to determine the concentration of soluble sugar. Cellulose decomposed into β-glucose when heated under acidic conditions. The color depth of derivatives generated by dehydration condensation of β-glucose was used to quantify the cellulose content. Hemicellulose was converted into reducing sugars after acid treatment. Reducing sugars combined with dinitrosalicyclic acid generate a reddish-brown substance, which had a specific absorbing peak at 540 nm. Tannin reacted with phosphomolybdic acid to form a blue compound in an alkaline environment, and this colored compound had a specific absorbing peak at 760 nm. Flavonoid reacted with aluminum ions to form a red compound in alkaline nitrate solution, and this colored compound had a specific absorbing peak at 510 nm.

2.4. Measurement of Soil Indices

Eight indices were measured to quantify the physiological variations in soils, including the soil moisture, pH, total nitrogen, total phosphorus, ammonium nitrogen, nitrate nitrogen, organic matter, and organic carbon contents. The content levels of these eight substances were determined using the relevant content kit (Suzhou Comin Biotechnology Co., Ltd., Suzhou, China) in accordance with the manufacturer’s instructions [35,36]. Determination of soil moisture content was the same as for leaf water content. A pH meter was used for measuring soil pH. Total nitrogen content was determined by using an azotometer. Total phosphorus content was determined by using the Mo-Sb colorimetric method. Ammonium nitrogen in soil created the water-soluble dye indophenol blue in strongly alkaline medium, and this colored compound had a specific absorbing peak at 625 nm. Nitrate nitrogen content was calculated using the colorimetric method by the color depth of the reaction between salicylic acid and nitrate nitrogen under alkaline conditions. Organic matter and organic carbon contents were determined with photometry at 585 nm.
Ten kinds of soil enzyme activities were determined according to each protocol of the kit (S-β-GC, S-NAG, S-ACP, S-POD, S-CAT, S-PPO, S-LAP, S-SC, S-AL, and S-UE) for β-glucosidase, N-acetyl-β-D-glucosidase, acid phosphatase, peroxidase, catalase, polyphenol oxidase, leucine aminopeptidase, sucrase, amylase, and urease, respectively. All of the enzyme activity kits were purchased from the Comin Biotechnology Company (Suzhou, China) [36,37,38].

2.5. Statistical Analysis

Data were initially tested for normality (Kolmogorov–Smirnov test) and homoscedasticity (Levene’s test) before subjecting them to analysis of variance (ANOVA). Statistical analysis of ten leaves’ and eight soils’ physico-chemical indices, as well as ten soil enzyme activity assays in man-made oak forest, was performed using SPSS, version 18.0 (SPSS Inc., Chicago, IL, USA) and ANOVA followed by Tukey’s tests for multiple comparisons to detect significant differences. Pearson correlation analysis was used to determine the relationship among indices and enzyme activities. The stronger the linear correlation is, the closer the correlation coefficient is to 1 or −1. Tests of significance were performed at a 95% confidence level. Data analyses were performed using sigmaplot, version 12.5 (Systat Software Inc., San Jose, CA, USA). All of the data were expressed as the mean ± SE.

3. Results

3.1. Leaf Properties Assays

At a 95% confidence level, the normality and homogeneity of ten leaf properties were all greater than 0.05 (Supplementary Table S1). Properties of the leaves indicated that the indices were variable in mixed plantations of P. orientalis and Q. variabilis and pure stands of Q. variabilis (Figure 2 and Supplementary Table S2). The leaf water content remained in the range 51–74% (Figure 2A). Q. variabilis had the lowest among these tree species. The leaf water content of P. orientalis was 55.32% ± 0.39%, which was higher than that of Q. variabilis (F = 454.817, d.f. = 4, p < 0.001). However, Q. variabilis was higher in crude fat content than P. orientalis (Figure 2B). Between type A and type B, G. biloba and C. coggygria showed a significant difference in crude fat contents (p < 0.01). The free fatty acid content of P. orientalis was 420.95 ± 17.14 nmol/g, but that of Q. variabilis was only 19.65 ± 3.43 nmol/g in type A (Figure 2C). The free fatty acid content of P. orientalis was about 3 times that of C. coggygria, 12 times that of G. biloba, and 22 times that of V. negundo (F = 276.086, d.f. = 4, p < 0.001). Q. variabilis protein content was the highest of the four tree species in type B (F = 117.697, d.f. = 3, p < 0.001) (Figure 2D). By significance analysis, the V. negundo protein content was significantly different between type A and type B (p < 0.05). At the same time, the protein contents of Q. variabilis and P. orientalis were almost 4 times higher than those of G. biloba and V. negundo in type A (F = 128.517, d.f. = 4, p < 0.001). P. orientalis had a maximum value of soluble sugar content both in types A and B (Figure 2E). Except C. coggygria and G. biloba, soluble sugar contents of the other tree species in type A were higher than that in type B. Between type A and type B, the difference in soluble sugar content of V. negundo was significant (p < 0.05). Furthermore, flavonoid contents among the tree species in type B were less than in type A (Figure 2F). G. biloba tannin content was the lowest among all species in type A (F = 22.360, d.f. = 4, p < 0.001) and type B (F = 91.404, d.f. = 3, p < 0.001) (Figure 2G). The lignin content of P. orientalis was 107.90 ± 6.68 mg/g (Figure 2H). There was a significant difference in lignin content of C. coggygria Scop. (p < 0.01) and G. biloba (p < 0.05). Furthermore, significant differences in cellulose contents of Q. variabilis were found between type A and type B (p < 0.01) (Figure 2I). Three groups, Q. variabilis, G. biloba, and V. negundo, showed remarkable differences in hemicellulose contents between types A and B (p < 0.01). Q. variabilis hemicellulose content in type B was lower than that in type A (Figure 2J).

3.2. Soil Properties Assays

Eight indices of soil in mixed plantations of P. orientalis and Q. variabilis (type A) and pure stands of Q. variabilis (type B) were compared (Supplementary Table S3). Soil indices were different from each other due to the different physical and chemical properties in the two types (Figure 3). Soil pH values were both acidic, with an average of 5.80 ± 0.42 for type A and 5.96 ± 0.37 for type B. Additionally, soil nitrate nitrogen content in type A was more than twice as much as ammonium nitrogen content and more than three times that in type B (Figure 3A). The results showed that the contents of soil total nitrogen and total phosphorus in types A and B had no obvious differences, but soil total nitrogen content was more than 10 times the soil total phosphorus in both types (Figure 3B). Soil in type A was higher in water content than type B and showed remarkable differences (p < 0.05). Although the soil organic matter and organic carbon contents between types A and B had no significant difference, the soil organic matter contents were higher than organic carbon of both types (Figure 3C).

3.3. Soil Enzyme Activity Assays

The normality and homogeneity of ten enzyme activities were all greater than 0.05 (Supplementary Table S4). Soil enzyme activity indices were divided into three parts by unit. Enzyme activities of the soils indicated that the indices were variable in both types A and B (Figure 4). The mean of S-UE in type B was higher than in type A, while the difference between S-UE in types A and B was not significant (Figure 4A). The S-SC activity was lower than those of S-PPO, S-POD, and S-AL in type A (Figure 4B). S-PPO and S-POD activities in type A were higher than those in type B, but S-SC and S-AL activities in type A were lower than those in type B. Except for S-LAP and S-ACP, S-CAT, S-β-GC, and S-NAG in type B were higher than those in type A (Figure 4C). The soil enzyme activities of S-CAT and S-ACP were significantly different between type A and type B according to results of significance analysis (p < 0.05).

3.4. Correlation Analysis Between Leaf and Soil Indices

Correlation analysis was conducted on the leaf and soil properties and soil enzyme activities, respectively (Table 2, Table 3, Table 4, Table 5, Tables S5 and S6). There were two significant pair-wise correlations on leaf properties of Q. variabilis in type A. The correlation of crude fat and lignin content groups and of flavonoid and cellulose content groups reached the significant level (p < 0.05). Through correlation analysis, free fatty acid and lignin contents were significantly correlated with hemicellulose content of P. orientalis in type A. Meanwhile, flavonoid content and cellulose content were found to be correlated (p < 0.05) (Table 2). There were four significant pair-wise correlations: water and tannin content groups, flavonoid and free fatty acid content groups, cellulose and protein content groups, and hemicellulose and cellulose content groups of C. coggygria in type A.
There was a significant correlation between crude fat and flavonoid content of G. biloba (p < 0.05) in type A (Supplementary Table S5). The flavonoid and crude fat content groups, hemicellulose and crude fat content groups, and hemicellulose and flavonoid content groups were significantly correlated (p < 0.01) in V. negundo. Both crude fat content and hemicellulose content had significant negative correlation with flavonoid content. Detailed characteristics are listed in Supplementary Table S5.
There were eight significant pair-wise correlations in leaf properties of type B and only one of them—tannin and flavonoid content—showed highly a significant correlation in C. coggygria (p < 0.01). In type B, tannin contents of Q. variabilis, C. coggygria, and G. biloba were significantly associated with water, crude fat, and hemicellulose content, respectively (Table 2 and S6). Flavonoid content was significantly associated with crude fat content of C. coggygria (Table 3) and with hemicellulose content of G. biloba (Supplementary Table S6). Lignin content had an obvious negative correlation with G. biloba protein content and V. negundo soluble sugar content.
There were 19 significant pair-wise correlations (all positive correlations) in soil in total. Fourteen of the nineteen relevant indicators appeared in type A, and the remaining five appeared in type B (Table 4). Eleven groups of soil properties were highly significant in type A and three groups in type B. Fourteen groups were obviously correlated at the level of 0.01 in types A and B together. Soil pH, soil total nitrogen, soil total phosphorus, soil organic matter, and soil organic carbon were all positively correlated with water content in type A. Soil organic matter and soil organic carbon were the highest relevant indicators associated with soil properties (correlation rate was 1.000) in both types.
There were 23 significant pair-wise correlations of soil enzyme activities in all (Table 5). In particular, for type A we found that S-LAP had a remarkable significantly positive correlation with S-CAT, S-ACP, and S-SC. The analysis found a significant association between S-UE and S-CAT and S-SC in both types (p < 0.01). The difference was that S-UE was significantly correlated with S-LAP in type A and with S-POD in type B. Furthermore, except for S-UE and S-POD groups, the remaining pair-wise correlation groups were all positive. Meanwhile, those soil enzyme activity indices which had pair-wise correlation with S-POD were negative both in types A and B. Eight groups of soil enzyme activity indices were highly significant in type A and seven groups in type B altogether (p < 0.01). The results showed that S-PPO and S-AL had no visible relation to the soil enzyme activity indices of the rest. S-NAG only had an obvious positive correlation with S-β-GC and S-LAP in type B. S-β-GC was positively related to S-UE in type A and to S-NAG in type B. Concurrently, S-β-GC had a negative correlation with S-ACP and S-LAP in type B.

4. Discussion

Forest soil is the important basis material of forest ecosystems, which not only plays a role in water and soil conservation but also provides essential nutrients for forest growth [39]. Due to the changes in properties of forest soil, there are differences in soil nutrients in different spatial positions, that is, there is spatial heterogeneity of nutrients [40]. When in the same geographical environment, the spatial heterogeneity of soil nutrients is affected by stand types, tree species composition, community structure, and human interference [41]. Compared with pure stands, a mixed plantation composed of two or more tree species can more fully utilize nutrient space, improve site conditions, and regulate microclimate [24,42]. Choosing appropriate mixed tree species is the key to efficient nutrients supply [43]. Therefore, efforts should be made to coordinate the growth characteristics and ecological requirements of mixed tree species as much as possible, while also considering “tree species in the suitable sites” issues, such as combining coniferous and broad-leaved trees [44,45], or combining evergreen and deciduous tree species [46], and fully utilizing resources to promote the formation of a stable ecosystem [47].
Q. variabilis is endemic to China, which is a tree species belonging to the Quercus genus in the Fagaceae family. It is one of the main tree species in China’s warm temperate deciduous broad-leaved forests. Meanwhile, P. orientalis is an evergreen coniferous tree species belonging to the Platycladus genus in the Cupressaceae family. P. orientalis and Q. variabilis are typical plantation tree species in northern China, and the mixture of these two tree species is an important cultivation model for vegetation restoration in rocky mountainous areas [2,17,29,48]. Our results showed that the cellulose content of Q. variabilis leaves in mixed plantations was lower than that in pure plantations, while the hemicellulose content was lower in pure plantations than that in mixed plantations. These may be related to its resistance. Cellulose participates in important processes such as material transport and water conduction, greatly enhancing the mechanical strength of plant cell walls [49,50]. And hemicellulose plays a role in filling and connecting cellulose fibers, improving the flexibility and stability of the plant cell walls [51]. Studies have found that the mechanical toughness of plant leaves is positively correlated with cellulose content [52]. That is to say, the toughness of Q. variabilis leaves is enhanced in pure plantations. Most studies characterized the physical defense ability of plants by analyzing their cellulose and hemicellulose contents, which not only enhanced their mechanical strength but also had strong antifungal ability [52,53]. Therefore, Q. variabilis enhanced its resistance to the environment by regulating the contents of cellulose and hemicellulose. From the perspective of tree species characteristics, Q. variabilis is the top-level tree species, while P. orientalis is the pioneer tree species [16,54]. Top-level tree species are often those with strong adaptability, rapid growth, and high resistance to environmental pressure. However, pioneer tree species usually have strong update capability and competitive adaptability and can survive and reproduce in harsh environments. In China, the research on drought tolerance, insect resistance, nutrient utilization, and site conditions of mixed plantations of Q. variabilis and P. orientalis has become a hot topic of concern for researchers to clarify the role of this mixed model in vegetation restoration and forest management [17,30,55].
These two tree species could change their water source based on soil moisture [56]. As we saw in the results, soil moisture of mixed plantations had a higher level than in pure plantations, and the difference between mixed plantations and pure plantations is significant. The main factors causing this phenomenon may be related to the root distribution of tree species and the distribution of soil moisture in different seasons [16,57]. P. orientalis largely absorbs water from middle and deep soil layers during the dry season and switches its main water source to surface layers during the wet season. Contrary to P. orientalis, Q. variabilis mainly absorbs water from surface and middle soil layers during the dry season and changes its dominant water sources to the deep soil layer throughout the wet season [16,58,59]. At the same time, P. orientalis is very sensitive to soil moisture during its growth, showing strong drought resistance as an excellent pioneer tree species [60]. Therefore, P. orientalis and Q. variabilis show the opposite characteristics of water utilization, which suggests that these two species are suitable for a mixed forest vegetation when selecting plant species for afforestation [57].
Affected by forest stand, microclimate, tree species composition, and other factors, the changes in plant leaf and soil are still very complex, although the rules of plant material cycle and energy flow are basically the same. In Beijing’s mountainous areas, the topography was the dominant factor affecting the forest productivity of P. orientalis plantations, followed by stand and soil nutrient factors [48]. The interaction of tree species had significant effects on β-glucosidase, β-N-acetylglucosaminidase, leucine aminopeptidase, and acid phosphatase enzyme, which were the main factors affecting soil enzyme activity in tropical and subtropical forests [61]. In our experiment, soil moisture in type A had an obvious significant correlation with soil pH, total nitrogen, soil organic matter, and soil organic carbon, but soil moisture in type B had no correlation among all soil properties indices. In addition, significant differences between type A and type B were found in soil catalase and acid phosphatase. Moreover, soil urease and soil leucine aminopeptidase had a greater impact on soil enzyme activity than others both in type A and type B. These differences may be caused by differences in the amount and composition of microbes but also by other factors like microclimate, ground vegetation, and litter [62].
Soil enzymes play an important role in the cycling of biogenic elements [5,24,28]. The activity of enzymes is an important measure of soil quality [61,63]. The stand with a mixing proportion of 2:1 of Pinus yunnanensis Franch. and Alnus nepalensis D. Don, Celtis tetrandra Roxb., and Quercus acutissima had higher moisture content, total nitrogen, total phosphorus, sucrase, urease, and catalase enzyme activities; therefore, compared with pure plantations, the mixed P. yunnanensis plantation could improve the soil quality, especially its chemical properties and enzymes [64]. Furthermore, soil enzymes, such as lignin peroxidases and cellulases, are primarily produced by fungi and bacteria [64,65]. Microorganisms can obtain restricted nutrient elements by producing extracellular enzymes according to their own nutrient resources (such as C, N, P). Sheng et al. studied the effect of subtropical mixed Pinus massoniana plantations on soil extracellular enzyme activity and found that hydrolase activity decreased, but phenol oxidase activity increased, indicating the impact of tree species mixing on regulating microbial decomposition and anabolism [66]. These enzymes play a vital role in the decomposition and cycling of soil organic matter, including complex compounds like lignin, cellulose, and hemicellulose [67]. Our results showed that soil urease, leucine aminopeptidase, and sucrase were significantly correlated with multiple soil enzyme activities. Furthermore, soil enzyme activity indicators had more complex correlations in mixed plantations. Thus, compared to monoculture stands, multi-tree-species forest stands are increasingly being studied because of their potential benefits in adjusting ecosystems; and these benefits include but are not limited to microbial regulation of soil enzymes, improvement of soil structure, full utilization of nutrition space, and increased resistance.

5. Conclusions

Our study found that Q. variabilis in pure plantations enhanced resistance by regulating its own leaf cellulose and hemicellulose content. Compared with pure Q. variabilis plantations, mixed plantations of Q. variabilis and P. orientalis significantly improved soil nutrients, with soil urease, leucine aminopeptidase, and sucrase playing important roles; in addition, the difference in soil water between Q. variabilis and P. orientalis indicated that deciduous broad-leaved Q. variabilis and evergreen coniferous P. orientalis were suitable tree species for mixed plantation construction. This is one of the reasons why the mixed plantation of Q. variabilis and P. orientalis was considered as a major cultivation model for vegetation restoration in north and central south China. As further research on soil nutrient improvement in mixed plantations of Q. variabilis and P. orientalis, we will continue to investigate regulation and control of soil enzyme activity by soil fungi and bacteria in order to provide a reliable theoretical basis for mixed plantation construction.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f16030471/s1, Table S1: Normality and homoscedasticity results of ten leaf indices; Table S2: Significance analysis of leaf properties among different tree species between mixed plantations of P. orientalis and Q. variabilis (type A) and pure stands of Q. variabilis (type B); Table S3: Normality and homoscedasticity results of eight soil indices; Table S4: Normality and homoscedasticity results of ten soil enzyme activities; Table S5: Correlation analysis of leaf properties among different shrub species in mixed plantations of P. orientalis and Q. variabilis (type A); Table S6: Correlation analysis of leaf properties among different shrub species in pure stands of Q. variabilis (type B).

Author Contributions

Conceptualization, J.W. and X.S.; Data curation, J.W., C.L. and Y.S.; Investigation, J.W. and C.L.; Supervision, X.S. and X.W.; Writing—original draft, J.W., C.L. and Y.S.; Writing—review and editing, X.S. and X.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program of China, grant number 2018YFD0600204-04; Key Research Projects of Henan Provincial Universities, grant number 21A220005; the Doctoral Scientific Research Foundation of Henan Agricultural University, grant number 30500627; the Teaching Reform Research and Practice Project of Henan Agricultural University, grant number 2023XJGLX071.

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Gu, Z.; Ji, K.; Yi, Q.; Cao, S.; Li, P.; Feng, D. Quantitative evaluation of soil erosion in loess hilly area of western Henan based on sampling approach. Water 2024, 16, 2895. [Google Scholar] [CrossRef]
  2. Wang, Y.; Liu, L.; Li, Z.; Shi, X.; Yang, X.; Shangguan, Z. Storage of carbon and nitrogen in Quercus and Platycladus orientalis plantations at different ages in the hilly area of western Henan province, China. Chin. J. Appl. Ecol. 2018, 29, 25–32. [Google Scholar]
  3. Hou, G.; Delang, C.O.; Lu, X.; Olschewski, R. Optimizing rotation periods of forest plantations: The effects of carbon accounting regimes. For. Policy Econ. 2020, 118, 102263. [Google Scholar] [CrossRef]
  4. Fan, Z.; Wang, J.; Lv, D.; Li, S.; Miao, Y.; Hu, M.; Wu, D.; Liu, F.; Wang, D. Effects of cropland-to-orchard conversion on soil multifunctionality, particularly nitrogen cycling in the eastern Loess Plateau. Front. Microbiol. 2024, 15, 1471329. [Google Scholar] [CrossRef]
  5. Wang, J.; Shi, X.; Lucas-Borja, M.E.; Lam, S.K.; Wang, Z.; Huang, Z. Plants, soil properties and microbes directly and positively drive ecosystem multifunctionality in a plantation chronosequence. Land Degrad. Dev. 2022, 33, 3049–3057. [Google Scholar] [CrossRef]
  6. Tan, S.; Cao, Y.; Zhang, Z.; Zhou, L.; Li, X. The Grain for green program promotes soil organic matter accumulation and improves soil fungal diversity in the southwestern Karst region. Forests 2025, 16, 121. [Google Scholar] [CrossRef]
  7. Xie, L.; Zeng, B.; Jiang, L.; Xu, J. Conservation payments, off-farm labor, and ethnic minorities: Participation and impact of the Grain for green program in China. Sustainability 2018, 10, 1183. [Google Scholar] [CrossRef]
  8. Chen, X.; Xie, J.; Wu, Q.; Zhang, H.; Li, Y. Climate and soil explain contrasting intraspecific trait variability of widespread species over a large environmental gradient. Glob. Ecol. Conserv. 2024, 56, e03338. [Google Scholar] [CrossRef]
  9. Zheng, F.; Gu, J.; Lu, D.; Yang, J.; Shuai, X.; Li, C.; Chen, H. Mixing with native broadleaf trees modified soil microbial communities of Cunninghamia lanceolata monocultures in South China. Front. Microbiol. 2024, 15, 1372128. [Google Scholar] [CrossRef] [PubMed]
  10. Aldea, J.; Ruiz-Peinado, R.; del Río, M.; Pretzsch, H.; Heym, M.; Brazaitis, G.; Jansons, A.; Metslaid, M.; Barbeito, I.; Bielak, K.; et al. Timing and duration of drought modulate tree growth response in pure and mixed stands of Scots pine and Norway spruce. J. Ecol. 2022, 110, 2673–2683. [Google Scholar] [CrossRef]
  11. van Halder, I.; Castagneyrol, B.; Ordóñez, C.; Bravo, F.; del Río, M.; Perrot, L.; Jactel, H. Tree diversity reduces pine infestation by mistletoe. Forest Ecol Manag. 2019, 449, 117470. [Google Scholar] [CrossRef]
  12. Toledo-Aceves, T.; Toledo-Garibaldi, M. Tree species diversity increases carbon stocks in tropical montane cloud forests across successional stages. Forest Ecol. Manag. 2025, 578, 122480. [Google Scholar] [CrossRef]
  13. Hou, J.; Zhou, H.; Hu, J.; Yu, H.; Hu, H. A multi-Scale convolution and multi-Layer fusion network for remote sensing forest tree species recognition. Remote Sens. 2023, 15, 4732. [Google Scholar] [CrossRef]
  14. Fornoff, F.; Klein, A.M.; Blüthgen, N.; Staab, M. Tree diversity increases robustness of multi-trophic interactions. Proc. R. Soc. B Biol. Sci. 2019, 286, 20182399. [Google Scholar] [CrossRef]
  15. National Forestry and Grassland Administration. Available online: https://www.forestry.gov.cn/search/368265 (accessed on 12 April 2023).
  16. Niu, Y.; Jia, G.; Liu, Z.; Wang, X.; Liu, Z. Soil moisture absorption and utilization of Quercus variabilis in Beijing mountain area. J. Beijing For. Univ. 2022, 44, 16–24. [Google Scholar]
  17. Niu, C.; Shou, W.; Yang, X.; Zhang, Z.; Qiao, Y. Study on adaptation strategies of Quercus variabilis, Robinia pseudoacacia and Platycladus orientalis to drought in Taihang mountain. For. Res. 2022, 35, 73–82. [Google Scholar]
  18. Han, Y.; Wang, B.; Sun, H. Effects of species mixing on maximum size-density relationships in Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.)-dominated mixed forests converted from even-aged pure stands. Front. Plant. Sci. 2024, 15, 1342307. [Google Scholar] [CrossRef]
  19. Versace, S.; Garfi, V.; Dalponte, M.; Di Febbraro, M.; Frizzera, L.; Gianelle, D.; Tognetti, R. Species interactions in pure and mixed-species stands of silver fir and European beech in Mediterranean mountains. iForest 2021, 14, 1–11. [Google Scholar] [CrossRef]
  20. Wójcik, P.; Filipczak, J. Prognosis of the nutritional status of apple trees based on prebloom leaves and flowers. J. Plant Nutr. 2019, 42, 2003–2009. [Google Scholar] [CrossRef]
  21. Duan, B.; Man, X.; Cai, T.; Xiao, R.; Ge, Z. Increasing soil organic carbon and nitrogen stocks along with secondary forest succession in permafrost region of the Daxing’an mountains, northeast China. Glob. Ecol. Conserv. 2020, 24, e01258. [Google Scholar] [CrossRef]
  22. Wei, C. A Preliminary Study on the Effect Mechanism of Tree Species on Soil Carbon Sequestration and Fertility. Master’s Thesis, Northeast Forestry University, Harbin, China, 2016. [Google Scholar]
  23. Bai, Y.; Liu, H.; Su, J.; Shen, W. Effects of tree species mixing on bulk and rhizosphere soil microbial resource limitation in stands of Pinus massoniana and Castanopsis hystrix. Acta Ecol. Sin. 2024, 44, 10770–10781. [Google Scholar]
  24. Gong, C.; Tan, Q.; Liu, G.; Xu, M. Impacts of species mixture on soil nitrogen stocks in the Loess Plateau of China. For. Ecol. Manag. 2021, 491, 119145. [Google Scholar] [CrossRef]
  25. Guo, J.; Feng, H.; McNie, P.; Liu, Q.; Xu, X.; Pan, C.; Yan, K.; Feng, L.; Goitom, E.A.; Yu, Y. Species mixing improves soil properties and enzymatic activities in Chinese fir plantations: A meta-analysis. Catena 2023, 220, 106723. [Google Scholar] [CrossRef]
  26. Gong, C.; Tan, Q.; Liu, G.; Xu, M. Mixed-species plantations enhance soil carbon stocks on the loess plateau of China. Plant Soil 2021, 464, 13–28. [Google Scholar] [CrossRef]
  27. Xu, J.; Liu, B.; Qu, Z.; Ma, Y.; Sun, H. Age and species of eucalyptus plantations affect soil microbial biomass and enzymatic activities. Microorganisms 2020, 8, 811. [Google Scholar] [CrossRef]
  28. Duan, Y.; Guo, H.; Zhang, L.; Dong, A. Impacts of tourism disturbance on soil properties at different elevations in Songshan scenic area. J. Henan Agric. Univ. 2016, 50, 103–109. [Google Scholar]
  29. Li, Y. The Geological Features and Tourism Resource Protection Development of Songshan World Geopark. Ph.D. Thesis, China University of Geosciences, Wuhan, China, 2014. [Google Scholar]
  30. Guo, Y.; Wang, Y.; Yu, C.; Xu, S.; Wei, L. Effects of mixed decomposition of Platycladus orientalis and Quercus variabilis fine roots on soil organic carbon sequestration. Chin. J. Appl. Ecol. 2024, 30, 707–714. [Google Scholar]
  31. Ma, R. Responses of Functional Traits of Shurbs and Grass of Quercus variabilis Plantation to Thinned and Its Environmental Interpretation. Ph.D. Thesis, Henan Agricultural University, Zhengzhou, China, 2021. [Google Scholar]
  32. Chen, Y.; Yan, L.; Liu, J.; Liu, Z.; Yu, J.; Zheng, Z.; Zhu, H. Developmental changes of okra fruits in cell wall composition, saccharometabolism and related enzymatic activities. Jiangsu J. Agri. Sci. 2023, 39, 1217–1224. [Google Scholar]
  33. Xu, S.; Yuan, X.; Ji, C.; Wang, M.; Zhang, Y.; Jiang, S.; Niu, S.; Cui, B. The nutrients and antioxidant activities of eight crabapple cultivars leaves. Food Ferment. Ind. 2022, 48, 196–201. [Google Scholar]
  34. Pu, H.; Zhi, J.; Zeng, G.; Yue, W. Effects of different induction treatments on contents of nutrient and activities of defense enzymes in the leaves of tomato plant. J. Environ. Entomol. 2019, 41, 405–412. [Google Scholar]
  35. Qin, B. The Characteristics of Soil Quality, Carbon and Nitrogen Conversion of Double-Cropping Rice with the Application of Exogenous Organic Carbon and Its Microbial Regulatory Pathways. Master’s Thesis, Guizhou University, Guiyang, China, 2024. [Google Scholar]
  36. Zhao, W. Study on the Effect of Rhizosphere Soil on Medicinal Components of Paeonia ludlowii in Different Areas of Xizang. Ph.D. Thesis, Tibet University, Lhasa, China, 2024. [Google Scholar]
  37. Shen, Y.; Li, X.; Cai, H.; Xu, T.; Li, J.; Chen, K. Post-fire changes in soil extracellular enzyme activities and their influencing factors in the permafrost region of the Da Xing’anling Mountains, Northeast China. Chin. J. Appl. Ecol. 2025, 36, 497–503. [Google Scholar]
  38. Cao, L.; Jin, D.; Jiang, J.; Li, T. Analysis of bacterial and fungal community composition and soil enzyme activities in the rhizosphere of transgenic Betula platyphylla. J. Nanjing For. Univ. 2024, 48, 129–137. [Google Scholar]
  39. Yuan, X.; Zhu, N.; Guo, Q.; Zhou, G.; Meng, M.; Wu, S.; Yang, L. Effects of different plantations on soil physical and chemical properties and soil quality evaluation in South subtropical zone. For. Res. 2022, 35, 112–122. [Google Scholar]
  40. Amoo, A.E.; Delgado-Baquerizo, M.; Babalola, O.O. Forest plantations reduce soil functioning in terrestrial ecosystems from South Africa. Pedobiologia 2021, 89, 150757. [Google Scholar] [CrossRef]
  41. Zhao, W.; Li, K.; Shen, J.; Zhu, C.; Lin, X.; He, Z.; Ding, G. Effects of slope position and gradient on spatial distribution of soil nutrients in Acacia melanoxylon plantation. JNE For. Univ. 2022, 50, 78–84. [Google Scholar]
  42. Ojeda-González, P.; Donoso, P.J.; Erlwein, A. Synergy in mixed Nothofagus spp. plantations: The effect of deciduous evergreen neighbourhood on tree growth in the Chilean Andes. N. Z. J. For. Sci. 2020, 50, 11. [Google Scholar] [CrossRef]
  43. Chen, R.; Guo, X.; Li, Q.; Ming, A.; Min, H.; Shen, W. Tree species mixing effects on the radial growth of Pinus massoniana and Castanopsis hystrix: A comparison between even-aged and uneven-aged stands. For. Ecol. Manag. 2024, 566, 122058. [Google Scholar] [CrossRef]
  44. Wu, D.; Dou, X.; Tang, M. Relationship between carbon stock and the structure of coniferous and broad-leaved mixed forest in Tianmu Mountains, China. Chin. J. Appl. Ecol. 2023, 34, 2029–2038. [Google Scholar]
  45. Zhu, S.; Wang, R.; Wang, Q.; Lei, T.; Cui, G. Coniferous and broad-leaved mixed forest has the optimal forest therapy environment among stand types in Xinjiang. Ecol. Indic. 2024, 169, 112950. [Google Scholar] [CrossRef]
  46. Qi, J.; Fan, Z.; Fu, P.; Zhang, Y.; Sterck, F. Differential determinants of growth rates in subtropical evergreen and deciduous juvenile trees: Carbon gain, hydraulics and nutrient-use efficiencies. Tree Physiol. 2021, 41, 12–23. [Google Scholar] [CrossRef]
  47. Ding, K.; Zhang, Y.; Wang, L.; Ge, S.; Zhang, Y.; Yang, Q.; Huang, H.; Tong, Z.; Zhang, J. Forest conversion from pure to mixed Cunninghamia lanceolata plantations enhances soil multifunctionality, stochastic processes, and stability of bacterial networks in subtropical southern China. Plant Soil 2023, 488, 411–429. [Google Scholar] [CrossRef]
  48. Zhang, L.; Qi, S.; Li, P.; Zhou, P. Influence of stand and environmental factors on forest productivity of Platycladus orientalis plantations in Beijing’s mountainous areas. Ecol. Indic. 2024, 158, 111385. [Google Scholar] [CrossRef]
  49. Zhao, J.; Yang, Y.; Shen, F.; Meng, J.; Yang, L. Dynamic characteristics of litter decomposition and nutrient release in 4 species of Larix plantations. J. Cent. South Univ. For. Technol. 2024, 44, 119–129. [Google Scholar]
  50. Li, X.; Cui, N.; Zhang, Y.; Qin, Y.; Zhang, J. Mixed Effects on Cellulose, Total phenols and Condensed Tannins Degradation in the Litter Leaves of Pinus massoniana and Native Broad-leaved Tree Species. Ecol. Environ. Sci. 2023, 31, 1813–1822. [Google Scholar]
  51. Chang, C.; Zhu, B.; Zhu, J.; Ji, C.; Yang, W. Review on the study of forest coarse woody debris decomposition. Chin. J. Plant Ecol. 2024, 48, 541–560. [Google Scholar]
  52. Wang, X. Latitudinal Variation Pattern of Defense Strategies of Quercus variabilis Blume. Ph.D. Thesis, Chinese Academy of Forestry, Beijing, China, 2016. [Google Scholar]
  53. Zheng, P. Coupling of Carbon-Nitrogen Cycles and Hydrological Process of Typical Forest Ecosystems in Beijing Mountainous Area. Ph.D. Thesis, Beijing Forestry University, Beijing, China, 2022. [Google Scholar]
  54. Liu, Y.; Jin, Y.; Wang, P.; Wang, M.; Cheng, Z.; Jia, Z. Analyses on phylogenetic structures and interspecific associations between main tree species in natural regeneration communities of three plantations in Xiaoxishan of Beijing. J. Plant Res. Environ. 2023, 32, 54–62. [Google Scholar]
  55. Cheng, K. The Influence of Site Conditions and Stand Structure on the Damage Degree of Leaf-Eating Insect in Quercus variabilis Pure Forest and Quercus variabilis–Platycladus orientalis Mixed Forest. Master’s Thesis, Henan Agricultural University, Zhengzhou, China, 2021. [Google Scholar]
  56. Liu, Z.; Yu, X.; Jia, G. Water uptake by coniferous and broad-leaved forest in a rocky mountainous area of northern China. Agric. For. Meteorol. 2019, 265, 381–389. [Google Scholar] [CrossRef]
  57. Liu, Z.; Yu, X.; Jia, G. Water utilization characteristics of typical vegetation in the rocky mountain area of Beijing, China. Ecol. Indic. 2018, 91, 249–258. [Google Scholar] [CrossRef]
  58. Wu, Y.; Han, L.; Liu, K.; Hu, X.; Fu, Z.; Chen, L. Water source of Robinia pseudoacacia and Platycladus orientalis plantations under different soil moisture conditions in the Loess Plateau of Western Shanxi, China. Chin. J. Appl. Ecol. 2023, 34, 588–596. [Google Scholar]
  59. Zhang, X.; Yu, X.; Ding, B.; Liu, Z.; Jia, G. Water Storage and Use by Platycladus orientalis under different rainfall conditions in the rocky mountainous area of northern China. Forests 2022, 13, 1761. [Google Scholar] [CrossRef]
  60. Deng, W.; Jia, G.; Liu, Y.; Chen, Q.; Huang, J.; Wen, L.; Zhang, L.; Liu, X.; Jia, J.; Peng, S. Long-term study on the seasonal water uptake of Platycladus orientalis in the Beijing mountain area, northern China. Agric. For. Meteorol. 2021, 307, 108531. [Google Scholar]
  61. Wang, J.; Huang, G.; Lv, K.; Su, T. Soil enzyme activity and stoichiometry of different vegetation types in tropical and subtropical forests. Chin. J. App. Environ. Biol. 2023, 29, 423–431. [Google Scholar]
  62. Tian, S.; Xia, Y.; Yu, Z.; Zhou, H.; Wu, S.; Zhang, N.; Yue, X.; Deng, Y.; Xia, Y. Improvement and the relationship between chemical properties and microbial communities in secondary salinization of soils induced by rotating vegetables. Sci. Total Environ. 2024, 921, 171019. [Google Scholar] [CrossRef]
  63. Yang, K.; Zhu, J.; Zhang, W.; Gu, J.; Wang, Z.; Xu, S. Comparison of soil chemical and microbial properties in monoculture larch and mixed plantations in a temperate forest ecosystem in Northeast China. Ecol. Process. 2022, 11, 12. [Google Scholar] [CrossRef]
  64. Khan, M.F.; Chowdhary, S.; Koksch, B.; Murphy, C.D. Biodegradation of amphipathic fluorinated peptides reveals a new bacterial defluorinating activity and a new source of natural organofluorine compounds. Environ. Sci. Technol. 2023, 57, 9762–9772. [Google Scholar] [CrossRef]
  65. Khan, M.F.; Hof, C.; Niemcová, P.; Murphy, C.D. Recent advances in fungal xenobiotic metabolism: Enzymes and applications. World J. Microbiol. Biotechnol. 2023, 39, 296. [Google Scholar] [CrossRef] [PubMed]
  66. Sheng, M.; Chen, X.; Yu, X.; Yan, J.; Zhang, X.; Hamel, C.; Sheng, Y.; Tang, M. Neighborhood effects on soil properties, mycorrhizal attributes, tree growth, and nutrient status in afforested zones. Restor. Ecol. 2020, 28, 459–467. [Google Scholar] [CrossRef]
  67. Sharker, B.; Islam, M.A.; Hossain, M.A.A.; Ahmad, I.; Al Mamun, A.; Ghosh, S.; Rahman, A.; Hossain, M.S.; Ashik, M.A.; Hoque, M.R.; et al. Characterization of lignin and hemicellulose degrading bacteria isolated from cow rumen and forest soil: Unveiling a novel enzymatic model for rice straw deconstruction. Sci. Total Environ. 2023, 904, 166704. [Google Scholar] [CrossRef]
Forests 16 00471 g001
Figure 1. Location of study area and sampling plots.
Figure 1. Location of study area and sampling plots.
Forests 16 00471 g001
Forests 16 00471 g002aForests 16 00471 g002b
Figure 2. Leaf properties among different tree species in mixed plantations of P. orientalis and Q. variabilis (type A), and pure stands of Q. variabilis (type B). “*” means value p < 0.05, “**” means value p < 0.01. (A–J) show the content of water (A), crude fat (B), free fatty acids (C), protein (D), soluble sugar (E), flavonoid (F), tannin (G), lignin (H), cellulose (I), and hemicellulose (J), respectively.
Figure 2. Leaf properties among different tree species in mixed plantations of P. orientalis and Q. variabilis (type A), and pure stands of Q. variabilis (type B). “*” means value p < 0.05, “**” means value p < 0.01. (A–J) show the content of water (A), crude fat (B), free fatty acids (C), protein (D), soluble sugar (E), flavonoid (F), tannin (G), lignin (H), cellulose (I), and hemicellulose (J), respectively.
Forests 16 00471 g002aForests 16 00471 g002b
Forests 16 00471 g003
Figure 3. Soil properties in mixed plantations of P. orientalis and Q. variabilis (type A) and pure stands of Q. variabilis (type B). “*” means value p < 0.05. (A) shows the content of nitrate nitrogen and ammonium nitrogen. (B) shows the content of total nitrogen and total phosphorus. (C) shows the content of soil water, soil organic matter, and soil organic carbon, respectively.
Figure 3. Soil properties in mixed plantations of P. orientalis and Q. variabilis (type A) and pure stands of Q. variabilis (type B). “*” means value p < 0.05. (A) shows the content of nitrate nitrogen and ammonium nitrogen. (B) shows the content of total nitrogen and total phosphorus. (C) shows the content of soil water, soil organic matter, and soil organic carbon, respectively.
Forests 16 00471 g003
Forests 16 00471 g004
Figure 4. Changes in soil enzyme activities in mixed plantations of P. orientalis and Q. variabilis (type A) and pure stands of Q. variabilis (type B). “*” means value p < 0.05. (A) shows the urease activity. (B) shows the activities of sucrase, polyphenol oxidase, peroxidase, and amylase, respectively. (C) shows the activities of β-glucosidase, N-acetyl-β-D-glucosidase, catalase, leucine aminopeptidase, and acid phosphatase, respectively.
Figure 4. Changes in soil enzyme activities in mixed plantations of P. orientalis and Q. variabilis (type A) and pure stands of Q. variabilis (type B). “*” means value p < 0.05. (A) shows the urease activity. (B) shows the activities of sucrase, polyphenol oxidase, peroxidase, and amylase, respectively. (C) shows the activities of β-glucosidase, N-acetyl-β-D-glucosidase, catalase, leucine aminopeptidase, and acid phosphatase, respectively.
Forests 16 00471 g004
Table 1. Basic situation of stands.
Table 1. Basic situation of stands.
Stand TypeAltitude (m)Slope (°)AspectCanopy Density (%)Stand Density (Plants·hm−2)
Type A
(mixed plantations of P. orientalis and Q. variabilis)		639.749.74Sunny slope76.911423
Type B
(pure stands of Q. variabilis)		602.148.97Sunny slope74.681426
Table 2. Correlation analysis of leaf properties among different arbor species in mixed plantations of P. orientalis and Q. variabilis (type A).
Table 2. Correlation analysis of leaf properties among different arbor species in mixed plantations of P. orientalis and Q. variabilis (type A).
WaterCrude FatFree Fatty AcidProteinSoluble SugarTanninFlavonoidLigninCelluloseHemicellulose
Water1.0000.9430.409−0.336−0.803−1.000 *0.4540.852−0.295−0.219
Crude fat0.5251.0000.690−0.003−0.559−0.9490.7250.9780.0400.118
0.474
Free fatty acid−0.5620.4091.0000.7220.215−0.4250.999 *0.8260.7510.801
0.3110.984
Protein−0.976−0.6990.3661.0000.8310.3190.6870.2070.999 *0.993
−0.952−0.180−0.003
Soluble sugar−0.076−0.889−0.7820.2931.0000.7930.166−0.3730.8060.757
−0.3250.6790.7980.600
Tannin0.751−0.169−0.968−0.5870.6021.000−0.470−0.8610.2780.202
−0.102−0.924−0.977−0.209−0.908
Flavonoid−0.8440.0150.9180.705−0.472−0.9881.0000.8530.7180.770
0.715−0.277−0.277−0.895−0.8930.623
Lignin0.5710.998 *0.358−0.738−0.862−0.114−0.0411.0000.2490.324
−0.234−0.967−0.997−0.076−0.8440.9910.512
Cellulose0.805−0.082−0.943−0.6550.5300.996−0.998 *−0.0271.0000.997 *
−0.6940.3050.4690.8820.906−0.646−1.000 *−0.537
Hemicellulose−0.230.7080.9340.01−0.953−0.8160.7170.667−0.7621.000
0.984−0.975−0.999 *−0.043−0.8250.9860.4830.999 *−0.509
Significant coefficients appear with underline in bold. “*” means a significant correlation at the 0.05 level. Q. variabilis appears in the first row at the bottom left. P. orientalis appears in the second row at the bottom left. C. coggygria appears in the upper right.
Table 3. Correlation analysis of leaf properties among different arbor species in pure stands of Q. variabilis (type B).
Table 3. Correlation analysis of leaf properties among different arbor species in pure stands of Q. variabilis (type B).
WaterCrude FatFree Fatty AcidProteinSoluble SugarTanninFlavonoidLigninCelluloseHemicellulose
Water1.0000.4220.5260.333−0.9250.456−0.446−0.8290.0840.985
Crude fat−0.9191.0000.993−0.715−0.0470.999 *−1.000 *−0.8570.9390.574
Free fatty acid0.806−0.9741.000−0.627−0.1650.997−0.996−0.9120.8910.667
Protein0.22−0.5870.7551.000−0.665−0.6880.6960.252−0.9120.162
Soluble sugar−0.5720.849−0.947−0.9261.000−0.0840.0730.5550.300−0.845
Tannin1.000 *−0.9080.790.194−0.551.000−1.000 **−0.8760.9250.605
Flavonoid0.967−0.7870.627−0.037−0.3430.9731.0000.870−0.929−0.596
Lignin−0.7890.483−0.2720.426−0.052−0.805−0.921.000−0.627−0.914
Cellulose−0.9940.956−0.865−0.3240.657−0.991−0.9330.7181.0000.257
Hemicellulose0.988−0.8470.7050.066−0.4380.9920.995−0.875−0.9661.000
Significant coefficients appear with underline in bold. “*” means a significant correlation at the 0.05 level. “**” means a significant correlation at the 0.01 level. Q. variabilis appears at the bottom left. C. coggygria appears in the upper right.
Table 4. Correlation analysis of soil properties in mixed plantations of P. orientalis and Q. variabilis (type A) and pure stands of Q. variabilis (type B).
Table 4. Correlation analysis of soil properties in mixed plantations of P. orientalis and Q. variabilis (type A) and pure stands of Q. variabilis (type B).
WaterpHTotal NitrogenTotal PhosphorusOrganic MatterOrganic CarbonAmmonium NitrogenNitrate Nitrogen
Water1.0000.5140.4480.3240.3010.301−0.3150.420
pH0.813 **1.0000.5060.698 *0.4000.4000.2290.712 *
Total nitrogen0.942 **0.802 **1.0000.3690.944 **0.944 **0.2210.505
Total phosphorus0.766 *0.827 **0.6511.0000.2030.2030.0630.550
Organic matter0.911 **0.847 **0.972 **0.711 *1.0001.000 **0.3290.387
Organic carbon0.911 **0.847 **0.972 **0.711 *1.000 **1.0000.3290.387
Ammonium nitrogen−0.465−0.485−0.400−0.300−0.501−0.5011.0000.438
Nitrate nitrogen−0.333−0.34−0.139−0.498−0.081−0.081−0.1051.000
Significant coefficients appear in bold. “*” means a significant correlation at the 0.05 level. “**” means a significant correlation at the 0.01 level. Soil of type A appears at the bottom left, and type B is in the upper right.
Table 5. Correlation analysis of soil enzyme activities in mixed plantations of P. orientalis and Q. variabilis (type A) and pure stands of Q. variabilis (type B).
Table 5. Correlation analysis of soil enzyme activities in mixed plantations of P. orientalis and Q. variabilis (type A) and pure stands of Q. variabilis (type B).
S-PPOS-PODS-CATS-ACPS-SCS-ALS-β-GCS-NAGS-LAPS-UE
S-PPO1.000−0.4340.6350.5740.521−0.0180.032−0.0390.0100.652
S-POD0.1451.000−0.653−0.330−0.863 **0.576−0.088−0.0390.191−0.818 **
S-CAT0.410−0.728 *1.0000.3410.827 **−0.092−0.0970.1390.0270.866 **
S-ACP−0.121−0.828 **0.706 *1.0000.4060.200−0.675 *−0.6560.674 *0.404
S-SC0.506−0.6630.938 **0.739 *1.000−0.374−0.0260.183−0.0230.967 **
S-AL0.1570.105−0.235−0.072−0.0531.000−0.551−0.4690.657−0.359
S-β-GC0.381−0.4480.6350.360.6250.3931.0000.834 **−0.953 **0.027
S-NAG0.205−0.0480.4480.250.4220.1260.3861.000−0.787 *0.210
S-LAP0.315−0.748 *0.882 **0.829 **0.924 **0.0980.580.5471.000−0.097
S-UE0.560−0.5860.811 **0.6320.923 **0.3180.736 *0.4790.918 **1.000
Significant coefficients appear in bold. “*” means a significant correlation at the 0.05 level. “**” means a significant correlation at the 0.01 level. Soil enzyme activity of type A appears at the bottom left, and type B is in the upper right.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wang, J.; Liu, C.; Shao, X.; Song, Y.; Wang, X. Influence of Tree Species Composition on Leaf and Soil Properties and Soil Enzyme Activity in Mixed and Pure Oak (Quercus variabilis) Stands. Forests 2025, 16, 471. https://doi.org/10.3390/f16030471

AMA Style

Wang J, Liu C, Shao X, Song Y, Wang X. Influence of Tree Species Composition on Leaf and Soil Properties and Soil Enzyme Activity in Mixed and Pure Oak (Quercus variabilis) Stands. Forests. 2025; 16(3):471. https://doi.org/10.3390/f16030471

Chicago/Turabian Style

Wang, Juan, Chang Liu, Xinliang Shao, Yiting Song, and Xu Wang. 2025. "Influence of Tree Species Composition on Leaf and Soil Properties and Soil Enzyme Activity in Mixed and Pure Oak (Quercus variabilis) Stands" Forests 16, no. 3: 471. https://doi.org/10.3390/f16030471

APA Style

Wang, J., Liu, C., Shao, X., Song, Y., & Wang, X. (2025). Influence of Tree Species Composition on Leaf and Soil Properties and Soil Enzyme Activity in Mixed and Pure Oak (Quercus variabilis) Stands. Forests, 16(3), 471. https://doi.org/10.3390/f16030471

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop