Next Article in Journal
Forest Conversion Changes Soil Particulate Organic Carbon and Mineral-Associated Organic Carbon via Plant Inputs and Microbial Processes
Previous Article in Journal
Variation in the Functional Traits of Forest Vegetation along Compound Habitat Gradients in Different Climatic Zones in China
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 14
Issue 6
10.3390/f14061233
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 thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Compound Forest–Medicinal Plant System Enhances Soil Carbon Utilization

by
Yaohong Yu
,
Xi Lin
,
Yundan Guo
,
Zhuizhui Guan
,
Jinhao Tan
,
Dong Chen
,
Yan Su
,
Jiyue Li
,
Quan Qiu
and
Qian He
*
Guangdong Key Laboratory for Innovative Development and Utilization of Forest Plant Germplasm, College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou 510642, China
*
Author to whom correspondence should be addressed.
Forests 2023, 14(6), 1233; https://0-doi-org.brum.beds.ac.uk/10.3390/f14061233
Submission received: 13 May 2023 / Revised: 6 June 2023 / Accepted: 9 June 2023 / Published: 14 June 2023
(This article belongs to the Section Forest Soil)
Browse Figures
Review Reports Versions Notes

Abstract

:
The sensible use of forest resources and the sound management of forests have become increasingly important throughout the years. In keeping with the trend, a composite forestry operation model has emerged. Traditional Chinese culture and forest management are particularly intertwined in China. Thus, use of the forest–medicine compound management model is recommended. The majority of research on the management of forest–medicine compounds has focused on how to grow more effective medicinal plants, ignoring the effects of the chemicals used on the soil environment, particularly the soil micro-environment. A forest–medicine system was established in South China to investigate the impacts of planting Aspidistra elatior on the variety of rhizospheric microorganisms and their ability to use carbon sources. In the plots with or without A. elatior, three dominant plants (Castanopsis hystrix, Psychotria rubra, and Ficus hirta) grew soil rhizosphere microbes, which were analyzed using Biolog EcoPlates. The study found that planting medicinal plants in the understory improved the soil’s nutritional content, increased the inter-root microbial communities of other medicinal plants, and enhanced the microbes’ ability to use soil carbon sources. The forest–medicine complex model, which rationalizes the use of forest clearings and generates economic and ecological benefits, can significantly increase the quantity of dominant microorganisms and enhance the enrichment of other species, resulting in a positive impact on the soil environment. These findings suggest that the forest–medicine compound management model can improve the use of soil carbon sources throughout the forest system.

1. Introduction

Natural resources, including valuable land resources, are deteriorating as a result of the population boom and brisk economic growth. As a result, in recent years, human exploration has focused heavily on how to manage land resources wisely and effectively [1]. One of the United Nations’ suggested methods for economic revitalization is agroforestry, which has significant positive effects on a variety of industries [2]. One crucial agroforestry technique involves managing the composition of forests by encouraging medicinal plants. This method cultivates shade-tolerant medicinal plants under the forest canopy to improve quality of life, increase forest resources, and generate additional economic rewards [3,4]. Agroforestry can increase the variety of wildlife and actively contribute to the preservation of biodiversity in natural ecosystems [5]. Agroforestry responds to extreme climate-related challenges in semi-arid and degraded environments by enhancing soil, microclimates, and agroforestry productivity [6]. Tree diversity and bird species richness are higher in areas near communities that create a greater variety of forest products and plant “restoration trees”, which are helpful in restoring mature forests. Agroforestry is compatible with maintaining the diversity of bird and tree communities [7]. Some studies have demonstrated that intercropping Chinese herbs with forest or agricultural crops improved the yield and quality of Chinese herbs [8], increased soil fertility [9], and improved environmental conditions [10]. However, agroforestry has some drawbacks. According to a few studies, agroforestry’s growth may threaten forests’ health [11]. However, whether agroforestry complex management influences the ecological advantages of forests is becoming a matter of debate. Therefore, communities almost all around the world are looking for in-forest plantation systems that are environmentally and economically appropriate for local environments.
Aspidistra is a native and perennial medicinal plant in Japan that belongs to the lily family in plant taxonomy and is composed of approximately 174 species. Currently, there are approximately 50 Chinese species, mainly distributed across the Yunnan-Guizhou Plateau, the Guangxi Zhuang Autonomous Region, Guangdong Province, and other places in China [12]. In China, it is also known by the folk name “Zhi Zhu Bao Dan”. Recent research into its biological components has primarily concentrated on its steroidal chemicals. The plant has a large content of monohydroxy diosgenin steroidal saponins, Δ25(27)-new pentahydroxyspiro saponins, spiral sterols, and other saponins [13]. Previous studies have realized that steroidal saponins are the major active constituents of Aspidistra. It has been documented that a high-grade drug in Zhiwen-bencao (Questions About Ryukyu Materia Medica) and the stems of Aspidistra have been used to make a functional medicine to treat abscesses, traumatic injuries, pain, and coughing [13].
Soil is an important carrier of plants and is the basis of productive forestry [14]. Soil microorganisms play a critical role in regulating carbon (C) and nitrogen (N) pools and nutrient cycles by promoting the activities of terrestrial ecosystems [15,16,17]. The plant rhizosphere is a soil micro-area where plant roots and microorganisms communicate more actively [18]. In the forest–medicine compound model, the plant rhizosphere is a special micro-ecosystem in which the forest, medicinal plants, soil, and microorganisms interact, which maintains the ecological function of the soil [19,20]. For instance, Zhu et al. showed that the Elaeocarpus decipiens–Polygonatum cystoma composite system was beneficial for the survival of root-soil fungi by studying the composition and diversity of root-soil fungi in four different forest–medicine compound management models [21]. Peng et al. found that the soil microbial composition in the Juglans regia–Salvia miltiorrhiza complex system was ranked as follows: bacteria > actinomycetes > fungi; additionally, the activities of four soil enzymes, including proteases, were greater than those of the monoculture model [22].
Biolog EcoPlates provide a rapid, effective, and affordable method for detecting the functional diversity of the soil microbial community, the utilization availability of carbon sources, and overall soil activity [23,24]. Although there are more studies on plant inter-root soil microbes, in general, high-throughput sequencing [25,26] and plate counting methods [27,28] are the primary research methodologies. The research has focused on the coevolution process of medicinal plants and microorganisms [29,30], the synergistic mechanisms between root secretions and soil microorganisms [31,32], and the relationships between the microbiome and secondary metabolites of medicinal plants [33,34]. However, the capacity of inter-root soil microorganisms to utilize carbon sources and the functional diversity of the soil community need to be explored. As a result, we aim to explore the effects of understory planting of medicinal plants on the carbon source utilization of their own rhizosphere soil microorganisms and those of other plants. We hypothesize that planting A. elatior can improve the number and activity of rhizosphere soil microorganisms in other understory vegetation. Specifically, we hypothesize that (i) planting A. elatior will increase the number of microbial species in the rhizosphere soil of other tree species and the activity of rhizosphere soil microorganisms in carbon source utilization, and (ii) planting medicinal plants in the forest understory will change the ability of dominant plants to access different nutrients in the soil.

2. Materials and Methods

2.1. Sites

The sampling site is located in the Maofeng Mountain Forest Park of Baiyun district, Guangzhou city, Guangdong Province, South China (113°22′15″–113°29′11″ E, 23°15′40″–23°19′46″ N). It has a subtropical monsoon climate with a mean annual temperature of 21.8 °C and a mean annual precipitation range of 1600–1800 mm. The plot is a steep slope of a low hill. The soil is lateritic red soil, is brownish-yellow or grayish-yellow, and the soil layer is thick. The slope is 26° and south oriented. The keystone plant, a 17-year-old Castanopsis hystrix, is used to cultivate large-diameter timber. The number of C. hystrix individuals is 450 trees per hectare, the average diameter at breast height is about 30.9 cm, and the coverage is about 75%. The main grass and shrub species are Psychotria rubra, C. hystrix, Ficus hirta, Evodia lepta, and Dicranopteris dichotoma. The Aspidistra elatior was planted in January 2019, and its density is about 15,000 trees per hectare. We used A. elatior rhizomes with more than eight buds for vegetative reproduction. Base fertilizer was applied before planting, and watering was carried out after planting. After that, fertilization and watering were no longer carried out. Herbaceous plants were artificially removed every season, and only medicinal plants and naturally germinated C. hystrix seedlings were retained. In contrast, the forest nearby (the control group) was not planted with A. elatior, and it was subject to no relative anthropogenic disturbances.

2.2. Sampling

Soil sampling was conducted during the dry season of 2021. Three subsample plots (20 m × 20 m) were randomly established in both the planted and control sites, and each one was located at least 20 m away from the others (Figure 1). For each plot, 10 rhizosphere soil sub-samples were removed from the P. rubra, C. hystrix, F. hirta, and A. elatior roots at 10–50 cm depth with a small brush, and the same tree species were mixed to give a single soil sample [35]. At the same elevation, a small amount of soil without roots was mixed as non-rhizosphere soil and collected according to the five-sampling-point method using a ‘Z’ shape. Part of the soil samples was sieved through a 2-mm sieve, placed in a small cooler at 8 °C, and brought back to the laboratory for Biolog detection of the bacterial community function. The experiment was carried out within 48 h of sample collection. Another portion of soil samples was placed in a 4 °C refrigerator and transported to the laboratory within 12 h for determination of the soil carbon source activity. Another portion of the soil samples was dried and ground through a 100-mesh sieve for the determination of soil nutrients.

2.3. Community Level Physiological Profile Determined by Biolog EcoPlates

The experimental equipment was sterilized in advance, and the soil samples were activated at 25 °C for 4 h. About 10 g of soil samples were weighed and placed in a sterilized triangular flask, and 90 mL of sterilized normal saline (0.85% NaCl) was added and sealed with a sterile sealing film. After shaking in the shaker for 30 min and standing for 15 min, the soil suspension was diluted to approximately 10−3 g·ml−1 through the stepwise dilution method [36]. In the ultra-clean bench, 200 μL of the prepared soil suspension was inoculated into the Biolog EcoPlate detection hole with a pipette, and the lid was covered. It was fixed with a rubber band and placed in an incubator at 28 °C for 7 days. Every 12 h, the absorbance was measured using a plate reader at 590 nm and 750 nm. The difference between the two wavelengths was utilized as the measured value to eliminate interference [37].

2.4. Data Analysis

Biolog EcoPlates (Biolog Inc., Hayward, CA, USA) contain three replicate wells for 31 carbon sources and three negative controls with no substrate. Substrates were divided into six substrate categories (polymers, carbohydrates, carboxylic acids, amino acids, amines, and phenolic compounds) according to their chemical nature. Using the difference between the absorbance of each well and the control well at different culture times on each plate, the average well color development (AWCD) of the rhizosphere soil microorganisms of different plants in each culture was calculated. Additionally, the richness index (S), Pielou evenness index (E), Shannon–Wiener index (H), and Simpson index (D) were measured to assess the functional diversity of the rhizosphere microorganisms [38].
The AWCD can characterize the overall utilization of different carbon sources for soil microbial communities and is one of the important indicators to measure the ability of soil microbial communities to use carbon sources [39]. AWCD can reflect soil microbial activity and the microbial community physiological function diversity [38]. The larger the AWCD value, the stronger the activity of microorganisms using carbon sources. The calculation formula is: AWCD = ∑(Ci-R)/n (Ci is the absorbance of the 31 carbon source holes in the culture plate; R is the absorbance value of the control hole; n is the total number of carbon sources in the culture plate, 31 in this study). The richness index (S) is the total number of carbon sources used. In this study, S is equal to the number of holes ((Ci-R) > 0.25 per hole) [40]. It can indirectly reflect the differences in the composition of microbial community structures. The more holes that undergo color change, the more abundant the microbial community species [41]. The Shannon–Wiener diversity index (H) is a comprehensive index for studying the number and distribution of community species and individuals and is one of the most widely used community diversity indexes [42]. The calculation formula is H = −∑Pi·lnP (Pi represents the ratio of the absorbance value in the i th non-control hole to the sum of the absorbance values of all non-control holes). The Pielou evenness index (E) reflects the average microbial utilization of all carbon source mechanisms [43]. The larger the E value, the stronger the average utilization activity of microorganisms for all carbon source mechanisms. The calculation formula is E = H/lnS. The Simpson dominance index (D) is relatively sensitive to enriched species and is used to measure the concentration of microbial diversity. The larger the value, the higher the community diversity [44]. The calculation formula is D = 1 − ∑Pi2.
Differences in soil microbial community structure and functional characteristics between the planted and control group were analyzed and evaluated for significance using one-way analysis of variance (ANOVA), which was carried out using SPSS 26.0 (IBM Company, Armonk, NY, USA) for statistical analysis of the experiment’s results. The figures were generated using Origin Pro 2021. Principal component analysis (PCA) is one of the commonly used methods to analyze the metabolic functional diversity of microbial communities in Biolog EcoPlate technology. The spatial coordinate system was constructed by standardizing the data at the “inflection point” of 31 carbon sources used by microorganisms in different soils [35]. The multivariate vectors of different samples are transformed into uncorrelated principal component vectors, and the metabolic characteristics of different microbial communities can be intuitively reflected by the position of points in the principal component vector space after dimensionality reduction [45]. The principal component analysis diagram of soil microbial communities for different carbon source utilization rates was drawn using Origin Pro 2021. The carbon source utilization heat map (pheatmap package) was drawn using R software. Redundancy analysis (RDA) combined with the Monte Carlo test (CANOCO, Ithaca, NY, USA) was used to determine the relationship between microbial abundance and soil chemical properties [46].

3. Results

3.1. Soil Parameters in Response to Aspidistra Elatior Planting

For soil physicochemical properties, planting A. elatior had significant effects on SOM, TN, TP, TK, AN, AP, and AK to a certain extent, but no significant effect on pH (Table 1). In general, the SOM, TN, TP, TK, AN, AP, and AK contents in the rhizosphere soil of the three dominant plants were significantly higher than those in the control plot. Specifically, regarding SOM content, planting A. elatior had the most significant effect on the rhizosphere soil of C. hystrix and P. rubra, and the SOM content of the rhizosphere soil of C. hystrix and P. rubra increased by 9.5 g·kg−1 after planting A. elatior. In terms of total soil nutrients, the TK content of the three dominating plants’ rhizosphere soil was dramatically raised after planting A. elatior. Following planting, the TK content of the soil in the rhizosphere of C. hystrix, P. rubra, and F. hirta increased by 9.57 g·kg−1, 11.83 g·kg−1, and 12.00 g·kg−1, respectively. For the available nutrients of the soil, AN in the rhizosphere soil of C. hystrix and P. rubra was considerably impacted by the planting of A. elatior. The rhizosphere soil AN content of C. hystrix and P. rubra rose by 33 mg·kg−1 and 34.74 mg·kg−1, respectively, after planting. Additionally, compared to the control group, the rhizosphere soil of A. elatior had considerably greater SOM, TN, TP, TK, AN, AP, and AK contents. In particular, A. elatior’s rhizosphere soil had a SOM content that was 9.14 g·kg−1 higher than that of the control group, a TK content that was 13.51 g·kg−1 higher, and an AK content that was 8.99 mg·kg−1 higher. Moreover, the AP concentration of the soil in the rhizosphere was more than twice as high as that of the soil in the non-rhizosphere.

3.2. Kinetic Analysis of Carbon Source Utilization by Soil Microorganisms

3.2.1. Variation Characteristics of Soil Microbial AWCD

After 7 days of continuous culturing of rhizosphere soil microorganisms, it was found that the AWCD value of different rhizosphere soils increased slowly in the first 48 h, followed by a rapid increase within 48–132 h, after which the value increased slowly and gradually became stable (Figure 2). It showed that soil microorganisms began to utilize carbon sources gradually and recover physiological activity. Moreover, the capacity to utilize carbon sources rose and then the utilization of carbon sources stabilized. The AWCD of the three dominant plants was as follows: planted sites’ soil (rhizosphere) > control sites’ soil (rhizosphere) > non rhizosphere soil; A. elatior rhizosphere soil > A. elatior non-rhizosphere soil.
This study found that the carbon source utilization ability of microbial communities in non-rhizosphere soil samples was significantly lower than that in plant rhizosphere soil samples after 72 h. The carbon source consumption activity of the dominant plant rhizosphere microorganisms in the planted A. elatior plots was considerably higher than that in the non-planted A. elatior plots and non-rhizosphere soils. The carbon source utilization activity of the dominant plant rhizosphere microorganisms in the A. elatior plot was ranked as C. hystrix > P. rubra > F. hirta.

3.2.2. Variation Characteristics of Different Carbon Sources of Soil Microorganisms

Biolog EcoPlate substrates can be divided into six categories: carbohydrates, carboxylic acids, amino acids, polymers, phenolic acids, and amines, according to the properties of the chemical groups of 31 external carbon sources (Table 2) [47]. The AWCD values of the rhizosphere and non-rhizosphere soils of the dominant tree species over a 0–168 h period for the non-A. elatior plot were plotted (Figure 3). We observed that the utilization rate of the six types of carbon sources gradually increased with the increase in culture time. The utilization of the six types of carbon sources by the rhizosphere soil microorganisms of the three major tree species in the control plots varied significantly (p < 0.05). The utilization degree among different treatments ranked from strong to weak is as follows: planting plots > control plots > non-rhizosphere soil. Overall, the utilization activity of different carbon sources in each soil was steadily stable when cultured for 132 h, and it was representative enough to analyze the difference of carbon source utilization at 132 h [45]. Initially, the AWCD of the rhizosphere soil microorganisms of the three dominant tree species in the control plot for the utilization of the six types of carbon sources was low and not very different. Later, with the extension of the culture time, the AWCD grew rapidly until it basically maintained the same level. After that, the AWCD of the dominant plant soil microorganisms in the planting plots was vastly higher than that in the control plots. In general, in the control plots, the order of utilization of six carbon sources by the rhizosphere soil microorganisms of C. hystrix was: amino acids > carboxylic acids > polymers > amines > carbohydrates > phenolic acids. In the planted samples, the order of utilization of the six types of carbon sources by the soil microorganisms of C. hystrix was: amino acids > carboxylic acids > amines > polymers > carbohydrates > phenolic acids. In the control plots, the order of utilization of the six types of carbon sources by soil microorganisms in the P. rubra inter-root area was: carbohydrates > carboxylic acids > amines > amino acids > phenolic acids > polymers. In the planting plots, the order of utilization was: amino acids > polymers > carboxylic acids > amines > carbohydrates > phenolic acids. The order of utilization of six kinds of carbon sources by the rhizosphere soil microorganisms of F. hirta was: carboxylic acids > polymers > amino acids > phenolic acids > carbohydrates > amines. In the planting plot, the order of utilization of six kinds of carbon sources by the rhizosphere soil microorganisms of F. hirta was: carboxylic acids > amino acids > polymers > phenolic acids > carbohydrates > amines. In addition to the utilization of amino acid carbon sources, the rhizosphere soil microorganisms of the three dominant species were ranked as follows: C. hystrix > P. rubra > F. hirta. Their utilization of the other five carbon sources was ranked as follows: C. hystrix > P. rubra > F. hirta.

3.3. Analysis of Soil Microbial Diversity

The microbial diversity index in each soil sample gradually stabilized after 132 h, so the data at 132 h were selected for diversity analysis (Figure 4). The richness index of soil microbial communities of the three dominant plants in the planting plot was significantly higher than that in the control plot. On the whole, there was no significant difference in the Shannon index (S) of the dominant species of rhizosphere microorganisms between the planting plot and the control plot (p > 0.05). The Shannon index of the three dominant plants showed a trend of C. hystrix > F. hirta > P. rubra. There was no significant difference in the microbial community diversity index (H) of the rhizosphere soil of the three dominant plants in the control group and the planting group. The diversity index (H) of the three dominant plant communities showed a trend of C. hystrix > F. hirta > P. rubra. For C. hystrix and P. rubra, the evenness (E) of rhizosphere microorganisms increased significantly in the A. elatior plots, while the evenness (E) of the rhizosphere microorganisms of F. hirta planted with A. elatior was not significantly different from that without A. elatior. The evenness index (E) of the three dominant plant communities showed the trend of P. rubra > C. hystrix > F. hirta. There was a significant difference in the dominance index (D) between the rhizosphere microorganisms of the dominant species in the planting plot and the control plot (e.g., P. rubra > F. hirta).
After comprehensive analysis, it was demonstrated that the number, enrichment degree, and carbon source utilization ability of microorganisms in non-rhizosphere soil are marginally lower than those in rhizosphere soil. The number of microorganisms in the rhizosphere soil of other dominant plants in the planting plot increased, as did their capacity to utilize carbon sources.

3.4. Functional Analysis of Soil Microbial Community

In order to further understand the specific utilization of various carbon sources by soil microbial communities, we used 132 h of data for principal component analysis (Figure 5, Table 3). A total of two principal components (PC1, PC2) were extracted, and their combined content was 38.5% (PC1 was 27.2%, PC2 was 11.3%). It was found that the distribution of the rhizosphere soil of the same plant differed in terms of the principal component system composed of 31 carbon sources in the control plots and the planting plots. All types of carbon sources, with the exception of 4-hydroxybenzoic acid, were distributed in the first and fourth quadrants. The representative points of the rhizosphere soil of C. hystrix in the control plot were concentrated in the first and third quadrants, while C. hystrix representative points in the A. elatior plot were concentrated in the fourth quadrant. In the control plots, the second and third quadrants contained the typical spots of the soil from the P. rubra rhizosphere. The representative points of the rhizosphere soil of F. hirta in the unplanted sample plot were located in the second and third quadrants. The representative points of the rhizosphere soil of F. hirta in the planted sample plots were located in the second and fourth quadrants. This indicates that the rhizosphere soil microorganisms of the same dominant species have different carbon source utilization abilities in the plots with or without A. elatior. In the coordinate system, the largest projections to the first axis are the No.6 (D-cellobiose) and No.27 (L-serine) carbon sources, while the largest projections to the second axis are the No.21 (Itaconic acid) and No.28 (L-threonine) carbon sources.
It is clear that, except for 4-hydroxybenzoic acid, the other 30 carbon sources are positively correlated with PC1, among which Tween 80, L-serine, L-phenylalanine, N-acetyl-D-glucosamine, Tween 40, and PC1 have the largest correlation coefficients, including for one carbohydrate, two amino acids, and two amines (Figure 5). In conjunction with the heat map (Figure 6) drawn according to different carbon source utilization characteristics based on the AWCD values measured at 132 h, it can be concluded that PC1 may be the degree of difference in carbon source utilization by microbial communities in different soils, which are more inclined to use amines, amino acids, and carbohydrates.

3.5. Redundancy Analysis of Soil Microbial Community Function and Environmental Factors

Redundancy analysis (RDA) was performed for microbial community function and environmental factors (i.e., soil properties). The higher the contribution rate of soil chemical indicators, the greater the impact on microbial community function. Table 4 shows that the environmental factors that have the greatest impact on the diversity of microbial metabolic function are total potassium, available phosphorus, and total phosphorus. The contribution rate of total potassium to microbial community function was the highest (46.69%), and was significantly correlated with microbial community function. Additionally, the contribution rate of available phosphorus to microbial community function was 21.60%, and was significantly correlated with microbial community function. Total phosphorus was significantly correlated with microbial community function, and its contribution rate to microbial community function was 11.60 %. There was no statistically significant correlation between other soil physical and chemical indicators and microbial community function. Redundancy analysis (Figure 7) illustrates that the metabolic activity of dominant plants in the planting plot is more closely related to soil total potassium, available potassium, total phosphorus, and available phosphorus (more obvious enrichment of these nutrients). The soil organic matter, total nitrogen, and accessible nitrogen had a stronger relationship with the metabolic activity of the dominant plants in the control plot (more obvious enrichment of these nutrients).

4. Discussion

4.1. Differences in Rhizosphere and Non-Rhizosphere Soil Microbial Communities

Here, our results showed that rhizosphere soil microorganisms were more capable of using carbon sources than non-rhizosphere soil microorganisms. Their diversity index, richness, and evenness dominance were higher than those of non-rhizosphere soil, indicating rhizosphere aggregation. The ability to use six types of carbon sources was higher in rhizosphere soil than in non-rhizosphere soil. This result is similar to those of previous studies [48,49,50]. The reason why plant roots have a significant effect on soil microbial community is that the rhizosphere environment is very conducive to the survival of microorganisms. Thus, the rhizosphere is enriched with more nutrients. In particular, carbon and nitrogen, which are important for microorganisms, are higher in inter-rooted soils than in non-rooted soils, in general, and the microbial community structures between the two soil types differ dramatically [51,52]. In summary, the changed soil microbial community structure will inevitably lead to changes in functional characteristics such as microbial physiological metabolism. These results support our proposed hypothesis (ii).

4.2. Effects of Aspidistra Elatior Planting on Rhizosphere Soil Microbial Community of Three Dominant Species

The compound forest–medicine system can significantly improve the nutrient contents of soil, which may be due to the increase in biodiversity caused by the planting of A. elatior in the forest, thus increasing the species and quantity of soil animals and microorganisms. The rate of litter decomposition and soil nutrient content increased with the increase in soil organisms and animals [53]. The utilization activity of carbon sources by rhizosphere microorganisms of dominant plants in the planting plot was significantly higher than that in the control plot. This might be caused by the planting of understory medicinal plants, which boosts the metabolic rate of other dominant plants, strengthens the interaction between plants and microbes, and increases species diversity and the quantity of soil microorganisms [54]. The order of this promoting effect is as follows: C. hystrix > P. rubra > F. hirta. Therefore, planting A. elatior in the forest understory can promote the natural regeneration of trees and improve the carbon source utilization ability of rhizosphere microbial communities of other medicinal plants. Through comprehensive analysis, it can be seen that A. elatior plantings have an obvious impact on how three dominating plants use six different carbon sources, both in terms of order and intensity. These differences ought to be the main reasons for the functional differences of microbial communities in soil. Hence, in the future, medicinal plants can be selected and planted under large-diameter timber forests or similar suitable places. The cultivation of medicinal plants in forest understories can be developed regarding the amount of soil carbon sources to improve the benefits of forest–medicine composite systems. Additionally, the order of utilization of amino acid carbon sources by rhizosphere soil microorganisms of the three dominant species after planting A. elatior was: C. hystrix > P. rubra > F. hirta. The order of utilization of other five carbon sources was: C. hystrix > P. rubra > F. hirta. This shows that the proper retention of C. hystrix seedlings when planting A. elatior can improve the utilization rate of various carbon sources in the soil of the whole forest–medicine composite system. These results support our proposed hypothesis (i).
Our study found that the compound forest–medicine system can enhance the enrichment of other dominant species and increase the quantity of dominant microorganisms. By comparing the four indexes (S, H, E, D) of soil microbial communities of three dominant plants in the control plot and the planting plot, it can be observed that the distribution of microorganisms in the rhizosphere soil of the two plots is similar. However, planting A. elatior can significantly improve the enrichment of microbial species in the rhizosphere soil of other tree species in the plot and increase the activity of rhizosphere soil microorganisms in the rhizosphere soil of C. hystrix and P. rubra, as well as increasing carbon source utilization. It can also significantly enhance the enrichment of other dominant species in the plot. The average activity level and concentration of microorganisms using all carbon source mechanisms in the rhizosphere soil of C. hystrix, P. rubra, and F. hirta can be significantly improved by planting A. elatior in the forest understory. This effect is most pronounced in the rhizosphere soil microorganisms of C. hystrix.

4.3. Rhizosphere Soil Microbial Community Function and Its Relationship with Physicochemical Factors

Principal component analysis of rhizosphere soil microbial communities showed that PC1 may be the degree of difference in carbon source utilization by microbial communities in different soils. The carbon source utilization ability of Tween 80, L-serine, L-phenylalanine, N-acetyl-D-glucosamine, and Tween 40 should be the main reason for the functional differences of microbial communities in various rhizosphere soils. PC2 represents the similarity of carbon source utilization by microbial communities in various soils. Additionally, the utilization of itaconic acid, L-threonine, putrescine, glycyl-L-glutamic acid, and D-galacturonic acid by microorganisms in different rhizosphere soils is similar.
The soil physical and chemical factors that affect the rhizosphere soil microorganisms of other dominant species are different before and after planting A. elatior. The metabolic activity of the dominant plants in the A. elatior plot is more closely related to the total potassium, available potassium, total phosphorus, and available phosphorus in the soil. In contrast, the total potassium, available potassium, total phosphorus, and available phosphorus are more likely to be related to the metabolic activity of the dominant plants in the non-A. elatior plot. Hence, the appropriate planting of medicinal plants in the forest understory can change the soil nutrient indicators that the rhizosphere microbial metabolism of dominant plants require more.

5. Conclusions

Our results showed the forest–medicine compound system can significantly improve soil nutrient content. The ability of rhizosphere soil microorganisms to use carbon sources was stronger than those of non-rhizosphere soil, and the diversity index, richness, evenness, and dominance of rhizosphere soil microbial communities were higher than those of non-rhizosphere soil. Rhizosphere soil outperformed non-rhizosphere soil in terms of its capacity to use six different types of carbon sources. Planting A. elatior in the forest can promote the natural regeneration of trees and improve the ability of other medicinal plants’ rhizosphere microbial communities to utilize carbon sources. In the future, medicinal plants can be selected for cultivation in large-diameter timber forests or similar suitable places, and the cultivation of medicinal plants in forests can be developed in combination with the amount of carbon sources in the soil. Planting A. elatior can stimulate the number of microbial species in the rhizosphere soil of other tree species, increase the activity of carbon source utilization by microorganisms in the rhizosphere soil of C. hystrix and P. rubra, as well as significantly improve the enrichment of other dominant species and increase the number of dominant root microorganisms. C. hystrix (shrubs) planted with A. elatior showed the most noticeable impact on the microbial community in the rhizosphere soil. The appropriate retention of C. hystrix seedlings when planting the medicinal plant A. elatior could improve the utilization rate of various carbon sources in the whole forest–medicine composite system. Furthermore, the ability to use carbon sources such as Tween 80, L-serine, L-phenylalanine, N-acetyl-D-glucosamine, and Tween 40 is the main reason for the functional differences of different soil microbial communities. Overall, proper planting of medicinal plants in the forest understory can change the nutrients required for the rhizosphere microbial metabolisms of dominant plants.

Author Contributions

J.L. and Q.H. mainly contributed to the conceptualization of the work; funding acquisition, Q.H. and J.L.; conceptualization, Y.Y.; methodology, Y.Y., J.T. and Q.H.; Software, Y.G.; Investigation, Y.Y., X.L., J.T. and D.C.; Data curation, Y.Y.; Writing original draft, Y.Y.; Writing review & editing, X.L., Z.G., Y.S., Q.Q. and Q.H.; Supervision, Y.S., J.L. and Q.H.; Project administration, Q.H. All authors have read and agreed to the published version of the manuscript.

Funding

This study was jointly supported by the National Natural Science Foundation of China (32001246) and Guangdong Forestry Science and Technology Innovation Project (2021KJCX011).

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Sukisno; Widiatmaka; Purwanto, M.Y.J.; Noorachmat, B.P.; Munibah, K. Determination of sustainable land resources management key factors in Musi Hydropower Plant Catchment Area of Bengkulu Province. IOP Conf. Ser. Earth Environ. Sci. 2022, 950, 012102. [Google Scholar] [CrossRef]
  2. David, S. Agroforestry: New hope for subsistence farmers. Nature 1979, 280, 533–534. [Google Scholar] [CrossRef] [Green Version]
  3. Kaoma, H.; Shackleton, C.M. The direct-use value of urban tree non-timber forest products to household income in poorer suburbs in South African towns. For. Policy Econ. 2015, 61, 104–112. [Google Scholar] [CrossRef]
  4. Lessmeister, A.; Heubach, K.; Lykke, A.M.; Thiombiano, A.; Wittig, R.; Hahn, K. The contribution of non-timber forest products (NTFPs) to rural household revenues in two villages in south-eastern Burkina Faso. Agrofor. Syst. 2018, 92, 139–155. [Google Scholar] [CrossRef] [Green Version]
  5. Jeffrey, A.M.; Götz, S. Agroforestry and biodiversity conservation—Traditional practices, present dynamics, and lessons for the future. Biodivers. Conserv. 2006, 15, 549–554. [Google Scholar]
  6. Fahad, S.; Chavan, S.B.; Chichaghare, A.R.; Uthappa, A.R.; Kumar, M.; Kakade, V.; Pradhan, A.; Jinger, D.; Rawale, G.; Yadav, D.K.; et al. Agroforestry systems for soil health improvement and maintenance. Sustain. Sci. 2022, 14, 14877. [Google Scholar] [CrossRef]
  7. Bohn, J.L.; Diemont, S.A.W.; Gibbs, J.P.; Stehman, S.V.; Mendoza Vega, J. Implications of Mayan agroforestry for biodiversity conservation in the Calakmul Biosphere Reserve. Agrofor. Syst. 2014, 88, 269–285. [Google Scholar] [CrossRef]
  8. Verma, R.K.; Chauhan, A.; Verma, R.S.; Rahman, L.-U.; Bisht, A. Improving production potential and resources use efficiency of peppermint (Mentha piperita L.) intercropped with geranium (Pelargonium graveolens L. Herit ex Ait) under different plant density. Ind. Crops Prod. 2013, 44, 577–582. [Google Scholar] [CrossRef]
  9. Song, H.Y.; Chen, D.; Sun, S.X.; Li, J.; Tu, M.Y.; Xu, Z.H.; Gong, R.G.; Jiang, G.L. Peach-Morchella intercropping mode affects soil properties and fungal composition. PeerJ 2021, 9, e11705. [Google Scholar] [CrossRef]
  10. Zhang, X.P.; Gao, G.B.; Wu, Z.Z.; Wen, X.; Zhong, H.; Zhong, Z.K.; Bian, F.Y.; Gai, X. Agroforestry alters the rhizosphere soil bacterial and fungal communities of moso bamboo plantations in subtropical China. Agriculture 2019, 143, 192–200. [Google Scholar] [CrossRef]
  11. Li, S.F.; Gong, S.S.; Hou, Y.H.; Li, X.N.; Wang, C. The impacts of agroforestry on soil multi-functionality depending on practices and duration. Sci. Total Environ. 2022, 847, 157438. [Google Scholar] [CrossRef] [PubMed]
  12. Chen, M.J.; Liang, S.Y. Distribution of steroidal glucosides in Aspidistra. Chin. Bull. Bot. 1999, 16, 610–613. [Google Scholar]
  13. Peng, J.; Zhao, Y.; Xu, L.; Kang, L.-P.; Cui, J.-M.; Yu, H.-S.; Zhang, J.; Ma, B.-P. Metabolite profiling of steroidal glycosides in the rhizome of Aspidistra sichuanensis using UPLC/Q-TOF MSE. Sci. Total Environ. 2017, 415, 63–84. [Google Scholar] [CrossRef]
  14. Zheng, M.M.; Wang, C.; Li, W.X.; Song, W.F.; Shen, R.F. Soil nutrients drive function and composition of phoC -Harboring bacterial community in acidic soils of southern China. Front. Microbiol. 2019, 10, 2654. [Google Scholar] [CrossRef] [Green Version]
  15. Kardol, P.; Bezemer, T.M.; van der Putten, W.H. Temporal variation in plant-soil feedback controls succession. Ecol. Lett. 2006, 9, 1080–1088. [Google Scholar] [CrossRef]
  16. McMahen, K.; Guichon, S.H.A.; Anglin, C.D.; Lavkulich, L.M.; Grayston, S.J.; Simard, S.W. Soil microbial legacies influence plant survival and growth in mine reclamation. Ecol. Evol. 2022, 12, e9473. [Google Scholar] [CrossRef]
  17. Van der Heijden, M.G.A.; Bardgett, R.D.; Van Straalen, N.M. The unseen majority: Soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol. Lett. 2008, 11, 296–310. [Google Scholar] [CrossRef]
  18. Yakov, K. Priming effects: Interactions between living and dead organic matter. Soil Biol. Biochem. 2010, 42, 1363–1371. [Google Scholar] [CrossRef]
  19. Olanrewaju, O.S.; Ayangbenro, A.S.; Glick, B.R.; Babalola, O.O. Plant health: Feedback effect of root exudates-rhizobiome interactions. Appl. Microbiol. Biotechnol. 2019, 103, 1155–1166. [Google Scholar] [CrossRef] [Green Version]
  20. Nihorimbere, V.; Ongena, M.; Smargiassi, M.; Thonart, P. Beneficial effect of the rhizosphere microbial community for plant growth and health. Biotechnol. Agron. Soc. 2011, 15, 327–337. [Google Scholar]
  21. Zhu, L.; Hu, X.; Xie, X.; Zhu, P. Analysis on rhizosphere soil fungal community composition and diversity under different forest-medicine intercropping patterns. Modern Agric. Sci. Technol. 2021, 12, 139–142. (In Chinese) [Google Scholar]
  22. Peng, X. Study on microbial quantity characteristics and enzyme activity of Salvia miltiorrhiza rhizosphere soil in Juglans regia-Salvia miltiorrhiza complex ecosystem. Shaanxi J. 2016, 62, 17–21. (In Chinese) [Google Scholar]
  23. Jay, L.G. Analytical approaches to the characterization of samples of microbial communities using patterns of potential C source utilization. SBB 1996, 28, 213–221. [Google Scholar] [CrossRef]
  24. Kela, P.W.; Raymond, L.L. Dynamics in the bacterial community-level physiological profiles and hydrological characteristics of constructed wetland mesocosms during start-up. Ecol. Eng. 2010, 37, 666–677. [Google Scholar] [CrossRef]
  25. Qin, S.; Li, J.; Chen, H.; Zhao, G.; Zhu, W.; Jiang, C.; Xu, L.; Li, W. Isolation, diversity, and antimicrobial activity of rare actinobacteria from medicinal plants of tropical rain forests in Xishuangbanna, China. Appl. Environ. Microb. 2009, 75, 6176–6186. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Zhao, K.; Penttinen, P.; Chen, Q.; Guan, T.; Lindström, K.; Ao, X.; Zhang, L.; Zhang, X. The rhizospheres of traditional medicinal plants in Panxi, China, host a diverse selection of actinobacteria with antimicrobial properties. Appl. Microbiol. Biotechnol. 2012, 94, 1321–1335. (In Chinese) [Google Scholar] [CrossRef] [PubMed]
  27. Clayton, S.J.; Clegg, C.D.; Murray, P.J.; Gregory, P.J. Determination of the impact of continuous defoliation of Lolium perenne and Trifolium repens on bacterial and fungal community structure in rhizosphere soil. Biol. Fertil. Soils. 2005, 41, 109–115. [Google Scholar] [CrossRef]
  28. Chen, J.; Zhang, X.S.; Yang, J.X.; Jiao, X.L.; Gao, W.W. Effect of continuous cropping and soil treatment on rhizosphere fungal community of Panax quinquefolium. Chin. J. Chin. Mater. Med. 2012, 37, 3531–3535. (In Chinese) [Google Scholar]
  29. Spor, A.; Roucou, A.; Mounier, A.; Bru, D.; Breuil, M.; Fort, F.; Vile, D.; Roumet, P.; Philippot, L.; Violle, C. Domestication-driven changes in plant traits associated with changes in the assembly of the rhizosphere microbiota in tetraploid wheat. Sci. Rep. 2020, 10, 12234. [Google Scholar] [CrossRef]
  30. Phillips, D.A.; Joseph, C.M.; Maxwell, C.A. Trigonelline and Stachydrine Released from Alfalfa Seeds Activate NodD2 Protein in Rhizobium meliloti. Plant Physiol. 1992, 99, 1526. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Turrà, D.; El, G.M.; Rossi, F.; Di Pietro, A. Fungal pathogen uses sex pheromone receptor for chemotropic sensing of host plant signals. Nature 2015, 527, 521–524. [Google Scholar] [CrossRef]
  32. Hu, L.F.; Robert, C.A.M.; Cadot, S.; Zhang, X.; Ye, M.; Li, B.; Manzo, D.; Chervet, N.; Steigner, T.; van der Heijden, M.G.A.; et al. Root exudate metabolites drive plant-soil feedbacks on growth and defense by shaping the rhizosphere microbiota. Nat. Commun. 2018, 9, 2738. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Arora, M.; Saxena, P.; Choudhary, D.K.; Abdin, M.Z.; Varma, A. Dual symbiosis between Piriformospora indica and Azotobacter chroococcum enhances the artemisinin content in Artemisia annua L. World J. Microbiol. Biotechnol. 2016, 32, 19. [Google Scholar] [CrossRef] [PubMed]
  34. Maggini, V.; De Leo, M.; Mengoni, A.; Gallo, E.R.; Miceli, E.; Reidel, R.V.B.; Biffi, S.; Pistelli, L.; Fani, R.; Firenzuoli, F.; et al. Plant-endophytes interaction influences the secondary metabolism in Echinacea purpurea (L.) Moench: An in vitro model. Sci. Rep. 2017, 32, 19. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Gao, X.; Xiao, N.; Ye, Y.; Fu, M.; Li, J. Analysis of microbial community functional diversity in the Changqing Oilfield based on Biology-ECO method eld based on Biology-ECO method. Appl. Environ. Microbiol. 2014, 20, 913–918. (In Chinese) [Google Scholar]
  36. Rutgers, M.; Wouterse, M.; Drost, S.M.; Breure, A.M.; Mulder, C.; Stone, D.; Creamer, R.E.; Winding, A.; Bloem, J. Monitoring soil bacteria with community-level physiological profiles using Biolog (TM) ECO-plates in the Netherlands and Europe. Agric. 2016, 97, 23–35. [Google Scholar] [CrossRef]
  37. Mary, S.; Richard, D. Shifts in substrate utilization potential and structure of soil microbial communities in response to carbon substrates. Soil Biol. Biochem. 2001, 33, 1481–1491. [Google Scholar] [CrossRef]
  38. Jiang, Z.; Li, P.; Wang, Y.; Li, B.; Wang, Y. Effects of roxarsone on the functional diversity of soil microbial community. Int. Biodeterior. 2013, 76, 32–35. [Google Scholar] [CrossRef]
  39. Garland, J.L.; Mills, A.L. Classification and characterization of heterotrophic microbial communities on the basis of patterns of community-level sole-carbon-source utilization. Appl. Environ. 1991, 57, 2351–2359. [Google Scholar] [CrossRef] [Green Version]
  40. Rogers, B.F.; Tate, R.L. Temporal analysis of the soil microbial community along a toposequence in Pineland soils. Soil Biol. Biochem. 2001, 33, 1389–1401. [Google Scholar] [CrossRef]
  41. Zhang, H.F.; Li, G.; Song, X.L.; Yang, D.L.; Li, Y.J.; Qiao, J.; Zhang, J.N.; Zhao, S.L. Changes in soil microbial functional diversity under different vegetation restoration patterns for Hulunbeier Sandy Land. Acta Ecol. Sin. 2013, 33, 38–44. [Google Scholar] [CrossRef]
  42. Monaci, E.; Beldomenico, I.; Buongarzone, E.; Casucci, C.; Cecca, G.S.; Ciani, M.; Perucci, P.; Santilocchi, R.; Vischetti, C. A laboratory study to evaluate the agronomic utilization of industrial lump sulphur by-product in alkaline soil. Agrochimica 2009, 53, 238–249. [Google Scholar]
  43. Song, X.C.; Wang, H.L.; Qin, W.D.; Deng, X.J.; Tian, H.D.; Tan, Y.B.; Wang, S.N.; Cao, J.Z. Effects of stand type of artificial forests on soil microbial functional diversity. J. Appl. Ecol. 2019, 30, 841–848. [Google Scholar] [CrossRef]
  44. Yin, Y.C.; Yan, Z.Z. Variations of soil bacterial diversity and metabolic function with tidal flat elevation gradient in an artificial mangrove wetland. Sci. Total Environ. 2020, 718, 137385. [Google Scholar] [CrossRef] [PubMed]
  45. Li, H.; Li, X.; Yao, Q.; Li, Q. Biolog-ECO analysis of rhizosphere soil microbial community characteristics of five different plants in two different grasslands. Microbiol. Ch. 2020, 47, 2947–2958. (In Chinese) [Google Scholar]
  46. Craig, M. Multivariate analysis of ecological data Using Canoco 5, 2nd edition. Afr. J. Range Forage Sci. 2015, 32, 289–290. [Google Scholar] [CrossRef] [Green Version]
  47. Li, C.; Liu, X.; Meng, M.; Zhai, L.; Zhang, B.; Jia, Z.; Gu, Z.; Liu, Q.; Zhang, Y.; Zhang, J. The use of Biolog Eco microplates to compare the effects of sulfuric and nitric acid rain on the metabolic functions of soil microbial communities in a subtropical plantation within the Yangtze River Delta region. Catena 2021, 198, 105039. [Google Scholar] [CrossRef]
  48. Xu, H.Y.; Lv, J.; Yu, C. Combined phosphate-solubilizing microorganisms jointly promote Pinus massoniana growth by modulating rhizosphere environment and key biological pathways in seedlings. Ind. Crops Prod. 2023, 191, 116005. [Google Scholar] [CrossRef]
  49. Xie, B.; Chen, Y.; Cheng, C.; Ma, R.; Zhao, D.; Li, Z.; Li, Y.; An, X.; Yang, X. Long-term soil management practices influence the rhizosphere microbial community structure and bacterial function of hilly apple orchard soil. Agric. Ecosyst. 2022, 180, 104627. [Google Scholar] [CrossRef]
  50. Qian, F.H.; Huang, X.J.; Su, X.M.; Bao, Y.Y. Responses of microbial communities and metabolic profiles to the rhizosphere of Tamarix ramosissima in soils contaminated by multiple heavy metal. J. Hazard. Mater. 2022, 438, 129469. [Google Scholar] [CrossRef]
  51. Barber, D.A.; Lynch, J.M. Microbial growth in the rhizosphere. Soil Biol. Biochem. 1977, 9, 305–308. [Google Scholar] [CrossRef]
  52. Foster, R.C. Microenvironments of soil microorganisms. Biol. Fertil. Soils 1988, 6, 189–203. [Google Scholar] [CrossRef]
  53. Xiao, W.Y.; Chen, C.; Chen, X.L.; Huang, Z.Q.; Chen, H.Y.H. Functional and phylogenetic diversity promote litter decomposition across terrestrial ecosystems. Glob. Ecol. Biogeogr. 2020, 29, 2261–2272. [Google Scholar] [CrossRef]
  54. Al-Taweel, L.S.; Al-Budairy, Z.J. Influence of vermicompost, seaweed extract and nitrogen fertilisers on Maize (Zea mays L.) soil rhizosphere microbes Asian Journal of Water. Int. J. Environ. Pollut. 2021, 18, 79–85. [Google Scholar]
Forests 14 01233 g001 550
Figure 1. The study area (a) and sampling site (b) are located in Hat Peak Mountain Forest, Guangzhou, Guangdong, China. The dark brown block represents the large diameter planting belt of C. hystrix, and the dark brown color indicates the A. elatior planting row.
Figure 1. The study area (a) and sampling site (b) are located in Hat Peak Mountain Forest, Guangzhou, Guangdong, China. The dark brown block represents the large diameter planting belt of C. hystrix, and the dark brown color indicates the A. elatior planting row.
Forests 14 01233 g001
Forests 14 01233 g002 550
Figure 2. Effects of planting A. elatior on the change trend of carbon source utilization in C. hystrix (a), P. rubra (b), F. hirta (c), and its own (d) rhizosphere soil. CK C.: C. hystrix rhizosphere soil of non-A. elatior plots; NR: non-rhizosphere soil; PA C.: C. hystrix rhizosphere soil of A. elatior plots; CK P.: P. rubra rhizosphere soil of non-A. elatior plots; PA P.: P. rubra rhizosphere soil of A. elatior plots; CK F.: F. hirta rhizosphere soil of non-A. elatior plots; PA F.: F. hirta rhizosphere soil of A. elatior plots; A.: A. elatior rhizosphere soil; AWCD: average well color development.
Figure 2. Effects of planting A. elatior on the change trend of carbon source utilization in C. hystrix (a), P. rubra (b), F. hirta (c), and its own (d) rhizosphere soil. CK C.: C. hystrix rhizosphere soil of non-A. elatior plots; NR: non-rhizosphere soil; PA C.: C. hystrix rhizosphere soil of A. elatior plots; CK P.: P. rubra rhizosphere soil of non-A. elatior plots; PA P.: P. rubra rhizosphere soil of A. elatior plots; CK F.: F. hirta rhizosphere soil of non-A. elatior plots; PA F.: F. hirta rhizosphere soil of A. elatior plots; A.: A. elatior rhizosphere soil; AWCD: average well color development.
Forests 14 01233 g002
Forests 14 01233 g003 550
Figure 3. Variation characteristics of different carbon sources of soil microorganisms. CK C.: C. hystrix rhizosphere soil of non-A. elatior plots; NR: non-rhizosphere soil; PA C.: C. hystrix rhizosphere soil of A. elatior plots; CK P.: P. rubra rhizosphere soil of non-A. elatior plots; PA P.: P. rubra rhizosphere soil of A. elatior plots; CK F.: F. hirta rhizosphere soil of non-A. elatior plots; PA F.: F. hirta rhizosphere soil of A. elatior plots; A.: A. elatior rhizosphere soil; AWCD: average well color development. Different lowercase letters in the figure indicate that different soil microorganisms have significant differences in carbon source utilization at the same time (p < 0.05).
Figure 3. Variation characteristics of different carbon sources of soil microorganisms. CK C.: C. hystrix rhizosphere soil of non-A. elatior plots; NR: non-rhizosphere soil; PA C.: C. hystrix rhizosphere soil of A. elatior plots; CK P.: P. rubra rhizosphere soil of non-A. elatior plots; PA P.: P. rubra rhizosphere soil of A. elatior plots; CK F.: F. hirta rhizosphere soil of non-A. elatior plots; PA F.: F. hirta rhizosphere soil of A. elatior plots; A.: A. elatior rhizosphere soil; AWCD: average well color development. Different lowercase letters in the figure indicate that different soil microorganisms have significant differences in carbon source utilization at the same time (p < 0.05).
Forests 14 01233 g003
Forests 14 01233 g004 550
Figure 4. Changes of each index with culture time. CK C.: C. hystrix rhizosphere soil of non-A. elatior plots; NR: non-rhizosphere soil; PA C.: C. hystrix rhizosphere soil of A. elatior plots; CK P.: P. rubra rhizosphere soil of non-A. elatior plots; PA P.: P. rubra rhizosphere soil of A. elatior plots; CK F.: F. hirta rhizosphere soil of non-A. elatior plots; PA F.: F. hirta rhizosphere soil of A. elatior plots; S: richness index; H: Shannon–Wiener index; E: evenness index; D: Simpson index.
Figure 4. Changes of each index with culture time. CK C.: C. hystrix rhizosphere soil of non-A. elatior plots; NR: non-rhizosphere soil; PA C.: C. hystrix rhizosphere soil of A. elatior plots; CK P.: P. rubra rhizosphere soil of non-A. elatior plots; PA P.: P. rubra rhizosphere soil of A. elatior plots; CK F.: F. hirta rhizosphere soil of non-A. elatior plots; PA F.: F. hirta rhizosphere soil of A. elatior plots; S: richness index; H: Shannon–Wiener index; E: evenness index; D: Simpson index.
Forests 14 01233 g004
Forests 14 01233 g005 550
Figure 5. Principal component analysis of soil microbial communities for different carbon source utilization rates. CK C.: C. hystrix rhizosphere soil of non-A. elatior plots; NR: non-rhizosphere soil; PA C.: C. hystrix rhizosphere soil of A. elatior plots; CK P.: P. rubra rhizosphere soil of non-A. elatior plots; PA P.: P. rubra rhizosphere soil of A. elatior plots; CK F.: F. hirta rhizosphere soil of non-A. elatior plots; PA F.: F. hirta rhizosphere soil of A. elatior plots; Numbers 1–31 in the diagram represent 31 different carbon sources. (1. Methyl Pyruvate, 2. Tween 40, 3. Tween 80, 4. α-Cyclodextrin, 5. Glycogen, 6. D-Cellobiose, 7. α-D-Galactose, 8. β-Methyl-D-Glucoside, 9. D-Xylose/Aldopentose, 10. i-Erythritol, 11. D-Mannitol, 12. N-acetyl-D-glucosamine, 13. D-Glucosamic acid, 14. 1-Phosphate Dextrose, 15. D,L-α-phosphoglycerol, 16. D-Galactonic acid γ-Lactone, 17. D-Galacturonic acid, 18. 2-Hydroxybenzoic acid, 19. 4-Hydroxybenzoic acid, 20. γ-Hydroxybutyric acid, 21. Itaconic acid, 22. α-Ketobutyric acid, 23. D-Malic acid, 24. L-Arginine, 25. L-Asparagine, 26. L-Phenylalanine, 27. L-Serine, 28. L-Threonine, 29. Glycyl-L-Glutamic acid, 30. Phenylethylamine, 31. Putrescine).
Figure 5. Principal component analysis of soil microbial communities for different carbon source utilization rates. CK C.: C. hystrix rhizosphere soil of non-A. elatior plots; NR: non-rhizosphere soil; PA C.: C. hystrix rhizosphere soil of A. elatior plots; CK P.: P. rubra rhizosphere soil of non-A. elatior plots; PA P.: P. rubra rhizosphere soil of A. elatior plots; CK F.: F. hirta rhizosphere soil of non-A. elatior plots; PA F.: F. hirta rhizosphere soil of A. elatior plots; Numbers 1–31 in the diagram represent 31 different carbon sources. (1. Methyl Pyruvate, 2. Tween 40, 3. Tween 80, 4. α-Cyclodextrin, 5. Glycogen, 6. D-Cellobiose, 7. α-D-Galactose, 8. β-Methyl-D-Glucoside, 9. D-Xylose/Aldopentose, 10. i-Erythritol, 11. D-Mannitol, 12. N-acetyl-D-glucosamine, 13. D-Glucosamic acid, 14. 1-Phosphate Dextrose, 15. D,L-α-phosphoglycerol, 16. D-Galactonic acid γ-Lactone, 17. D-Galacturonic acid, 18. 2-Hydroxybenzoic acid, 19. 4-Hydroxybenzoic acid, 20. γ-Hydroxybutyric acid, 21. Itaconic acid, 22. α-Ketobutyric acid, 23. D-Malic acid, 24. L-Arginine, 25. L-Asparagine, 26. L-Phenylalanine, 27. L-Serine, 28. L-Threonine, 29. Glycyl-L-Glutamic acid, 30. Phenylethylamine, 31. Putrescine).
Forests 14 01233 g005
Forests 14 01233 g006 550
Figure 6. The utilization characteristics of different carbon sources by rhizosphere soil microorganisms of different plants. CK C.: C. hystrix rhizosphere soil of non-A. elatior plots; NR: non-rhizosphere soil; PA C.: C. hystrix rhizosphere soil of A. elatior plots; CK P.: P. rubra rhizosphere soil of non-A. elatior plots; PA P.: P. rubra rhizosphere soil of A. elatior plots; CK F.: F. hirta rhizosphere soil of non-A. elatior plots; PA F.: F. hirta rhizosphere soil of A. elatior plots. p < 0.05 represents significant correlation and p < 0.01 represents extremely significant correlation. The red lattice represents positive correlation and the blue lattice represents negative correlation.
Figure 6. The utilization characteristics of different carbon sources by rhizosphere soil microorganisms of different plants. CK C.: C. hystrix rhizosphere soil of non-A. elatior plots; NR: non-rhizosphere soil; PA C.: C. hystrix rhizosphere soil of A. elatior plots; CK P.: P. rubra rhizosphere soil of non-A. elatior plots; PA P.: P. rubra rhizosphere soil of A. elatior plots; CK F.: F. hirta rhizosphere soil of non-A. elatior plots; PA F.: F. hirta rhizosphere soil of A. elatior plots. p < 0.05 represents significant correlation and p < 0.01 represents extremely significant correlation. The red lattice represents positive correlation and the blue lattice represents negative correlation.
Forests 14 01233 g006
Forests 14 01233 g007 550
Figure 7. Redundancy analysis of microbial community function and environmental factors. CK C.: C. hystrix rhizosphere soil of non-A. elatior plots; NR: non-rhizosphere soil; PA C.: C. hystrix rhizosphere soil of A. elatior plots; CK P.: P. rubra rhizosphere soil of non-A. elatior plots; PA P.: P. rubra rhizosphere soil of A. elatior plots; CK F.: F. hirta rhizosphere soil of non-A. elatior plots; PA F.: F. hirta rhizosphere soil of A. elatior plots. OM: soil organic matter; SOC: soil organic carbon; TN: total nitrogen; TP: total phosphorus; TK: total potassium; AN: available nitrogen; AP: available phosphorus; AK: available potassium; The numbers 1–31 in the above figure represent 31 different carbon sources, and the corresponding carbon sources of each label are the same as Figure 5.
Figure 7. Redundancy analysis of microbial community function and environmental factors. CK C.: C. hystrix rhizosphere soil of non-A. elatior plots; NR: non-rhizosphere soil; PA C.: C. hystrix rhizosphere soil of A. elatior plots; CK P.: P. rubra rhizosphere soil of non-A. elatior plots; PA P.: P. rubra rhizosphere soil of A. elatior plots; CK F.: F. hirta rhizosphere soil of non-A. elatior plots; PA F.: F. hirta rhizosphere soil of A. elatior plots. OM: soil organic matter; SOC: soil organic carbon; TN: total nitrogen; TP: total phosphorus; TK: total potassium; AN: available nitrogen; AP: available phosphorus; AK: available potassium; The numbers 1–31 in the above figure represent 31 different carbon sources, and the corresponding carbon sources of each label are the same as Figure 5.
Forests 14 01233 g007
Table
Table 1. Soil nutrient index.
Table 1. Soil nutrient index.
Soil PropertiesC. hystrixP. rubraF. hirtaA. elatiorNon-Rhizosphere Soil
Control SitesPlanted SitesControl SitesPlanted SitesControl SitesPlanted Sites
pH(H2O)4.31 ± 0.03 a4.32 ± 0.01 a4.30 ± 0.02 a4.32 ± 0.01 a4.28 ± 0.02 a4.29 ± 0.01 a4.30 ± 0.014.29 ± 0.01
SOM(g·kg−1)48.07 ± 0.68 b57.57 ± 0.96 a44.93 ± 0.66 b57.03 ± 0.49 a44.47 ± 0.70 b50.83 ± 1.21 a55.80 ± 0.3246.66 ± 1.92
TN(g·kg−1)1.93 ± 0.01 b2.16 ± 0.00 a1.85 ± 0.01 b2.10 ± 0.01 a1.79 ± 0.01 b2.83 ± 0.03 a2.29 ± 0.021.78 ± 0.12
TP(g·kg−1)0.22 ± 0.01 b0.28 ± 0.01 a0.22 ± 0.00 b0.34 ± 0.00 a0.27 ± 0.01 b0.36 ± 0.00 a0.32 ± 0.000.17 ± 0.04
TK(g·kg−1)15.50 ± 0.06 b25.07 ± 0.06 a14.90 ± 0.10 b26.73 ± 0.37 a15.00 ± 0.32 b27.00 ± 0.07 a26.77 ± 0.1813.26 ± 4.13
AN(mg·kg−1)96.57 ± 7.85 b129.67 ± 2.60 a92.93 ± 0.27 b127.67 ± 1.20 a117.67 ± 8.65 a136.67 ± 9.61 a118.67 ± 2.73114.78 ± 31.0
AP(mg·kg−1)2.49 ± 0.23 a3.63 ± 0.94 a2.45 ± 0.21 b4.82 ± 1.45 a2.44 ± 0.17 b4.97 ± 1.17 a4.46 ± 0.362.00 ± 0.25
AK(mg·kg−1)42.40 ± 2.43 b51.30 ± 1.38 a40.90 ± 1.79 a47.73 ± 2.15 a52.47 ± 4.92 a54.83 ± 8.92 a52.23 ± 1.1343.24 ± 6.84
Note: Different lowercase letters in the figure indicate that the rhizosphere soil nutrients of the same plant have significant differences between the control group and the planting group (p < 0.05).
Table
Table 2. Carbon source type of Biolog EcoPlate cultures.
Table 2. Carbon source type of Biolog EcoPlate cultures.
Carbon Source Classification
Carbohydrate-basedβ-Methyl-d-Glucoside, D-Xylose/Pentose, i-Erythritol, D-Mannitol, N-Acetyl-D-Glucosamine, D-Cellobiose, α-D-Lactose, D-Galactose Acid, γ-Lactone, D, L-α-Glycerophosphate, 1-Phosphate, D-Galacturonic Acid
Amino acidsl-Arginine, l-Asparagine, l-Phenylalanine, l-Serine, l-Threonine, Glycyl-L-Glutamic Acid, D-Glucosamine Acid
Carboxylic acids γ-Hydroxybutyric Acid, α-Ketobutyric Acid, D-Malic Acid, Methyl Pyruvate, Itaconic Acid
Multi-clusteringTween 40, Tween 80, α-Cyclodextrin, Glycogen
phenolic acids2-Hydroxybenzoic Acid, 4-Hydroxybenzoic Acid
AminePhenylethylamine, Putrescine
Table
Table 3. Correlation coefficient between 31 carbon sources and 2 principal components.
Table 3. Correlation coefficient between 31 carbon sources and 2 principal components.
Types of Carbon SourcesPC1PC2
Carbohydrate-basedD-Cellobiose0.211340.04132
α-D-Galactose0.212790.11272
β-Methyl-D-Glucoside0.15267−0.06404
D-Xylose/Aldopentose0.085240.25795
i-Erythritol0.113950.26926
D-Mannitol0.140450.07713
N-acetyl-D-Glucosamine0.23613−0.11154
1-Phosphate Dextrose0.22212−0.15159
α-D-Galactose0.20848−0.08522
D-Galactonic acid γ-Lactone0.21352−0.185
D-Galacturonic acid0.15138−0.28473
Amino acidsL-Arginine0.206210.00726
L-Asparagine0.1207−0.17831
L-Phenylalanine0.25007−0.03064
L-Serine0.26381−0.12592
L-Threonine0.168680.31485
D- -Glucosaminic acid0.21941−0.18406
Glycyl-L-Glutamic acid0.224350.29608
Carboxylic acid groupMethyl Pyruvate0.199570.12887
γ-Hydroxybutyric acid0.04675−0.16125
Itaconic acid0.117630.31996
α- Ketobutyric acid0.055270.16371
D-Malic acid0.17018−0.22713
Multi-clusteringα-Cyclodextrin0.188160.16236
Glycogen0.110690.15114
Tween 400.22817−0.01281
Tween 800.279790.02984
Phenolic acids2-Hydroxybenzoic acid0.133840.11432
4-Hydroxybenzoic acid0.050950.13108
AminePhenylethylamine0.086440.12074
Putrescine0.14038−0.30124
Table
Table 4. Contribution rate and significance of environmental factors to microbial community function.
Table 4. Contribution rate and significance of environmental factors to microbial community function.
Environmental FactorContribution Rate (%)Significance
TK46.690.002 **
AP21.610.006 **
TP11.600.076
pH6.330.376
AK4.840.570
OM4.010.704
TN3.880.708
AN2.450.960
** represents the significant correlation between environmental factors and microbial community function.
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

Yu, Y.; Lin, X.; Guo, Y.; Guan, Z.; Tan, J.; Chen, D.; Su, Y.; Li, J.; Qiu, Q.; He, Q. The Compound Forest–Medicinal Plant System Enhances Soil Carbon Utilization. Forests 2023, 14, 1233. https://0-doi-org.brum.beds.ac.uk/10.3390/f14061233

AMA Style

Yu Y, Lin X, Guo Y, Guan Z, Tan J, Chen D, Su Y, Li J, Qiu Q, He Q. The Compound Forest–Medicinal Plant System Enhances Soil Carbon Utilization. Forests. 2023; 14(6):1233. https://0-doi-org.brum.beds.ac.uk/10.3390/f14061233

Chicago/Turabian Style

Yu, Yaohong, Xi Lin, Yundan Guo, Zhuizhui Guan, Jinhao Tan, Dong Chen, Yan Su, Jiyue Li, Quan Qiu, and Qian He. 2023. "The Compound Forest–Medicinal Plant System Enhances Soil Carbon Utilization" Forests 14, no. 6: 1233. https://0-doi-org.brum.beds.ac.uk/10.3390/f14061233

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