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Article

Effects of Multiple-Metal-Compound Contamination on the Soil Microbial Community in Typical Karst Tea Plantations

by
Xianfei Huang
1,†,
Xingfu Wang
2,†,
Qing Wu
1,
Zhenming Zhang
3,*,
Huili Yang
1 and
Ximei Wen
4,*
1
Guizhou Provincial Key Laboratory for Information Systems of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, Guiyang 550001, China
2
College of Health Management, Guiyang Healthcare Vocational University, Guiyang 550081, China
3
College of Resources and Environmental Engineering, Guizhou University, Guiyang 550025, China
4
Institute of Mountain Resources, Guizhou Academy of Sciences, Guiyang 550001, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2023, 14(9), 1840; https://doi.org/10.3390/f14091840
Submission received: 8 August 2023 / Revised: 4 September 2023 / Accepted: 7 September 2023 / Published: 9 September 2023
(This article belongs to the Section Forest Soil)
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Abstract

:
In this study, the effects of pollution levels and heavy metal pollution on soil microbial diversity in karst tea plantations are reported. Four tea plantations from plateau hills, under forests, by lakesides and on steep slopes in the South China karst were used as research regions. Soil samples were taken from these tea plantations, the soil heavy metals Cd, Cr, Pb, Zn, Ni and Cu were tested using inductively coupled plasma-mass spectrometry, and Hg and As were tested via atomic fluorescence spectrometry. The soil microbes were analyzed via high-throughput sequencing technology. Heavy metal pollution was evaluated via the single factor index and pollution load index, and the correlation between soil heavy metals and the microbial community was analyzed via SPSS 18.0 and Canoco 5.0 software. The results showed that the studied tea plantation soils were greatly polluted by the heavy metals, Cd and Hg, to a low to moderate degree. The comprehensive pollution of multiple heavy metals occurred only in lakeside tea plantations, in which pollution reached a low degree. It is also suggested that Hg and Cd were the major contributors, followed by Cu. The soil microbial diversity in soil samples from lakeside tea plantations was the highest; however, the discrepancy in its dominant species composition was also the highest. When the pollution load index was close to 0.6, the microbial diversity decreased sharply. Afterward, the diversity and heterogeneity generally gently increased, and the dominant composition was more obvious. These results reveal that the impact of heavy metal pollution on soil microbial diversity was not very distinct, but the impact on the dominant microbial community composition was obvious. In addition, the heavy metals, Cd, Hg and Cu, were the key factors that impacted the soil microbial community composition.

1. Introduction

Tea is one of the most popular beverages in the world, and the public consumes tea and its derivatives second only to water [1]. China is a major country in tea production and consumption, with tea planting areas accounting for approximately 60% of the global tea planting area [2,3]. Most tea gardens adopt a single planting mode with only tea trees or a single variety, which leads to an overly simple ecosystem structure in the tea garden and affects the soil properties, structure, and nutrients [4]. Heavy metal pollution originating from the application of industrial fertilizers and pesticides is a critical problem in tea cultivation [5,6]. Soil heavy metal pollution can affect plant growth, soil microbial activity, and ecological rehabilitation [7,8]. These factors affect tea plant growth and development, further restraining tea quality and production [9,10]. In addition, soil heavy metals greatly affect the morphology and cellular metabolism of microorganisms, leading to a decrease in biodiversity [11,12]. Furthermore, heavy metals accumulate in organisms through the food chain, posing a threat to food safety and human health [13,14]. When heavy metal accumulation in the human body is excessive, it may result in chronic illnesses [15]. Long-term consumption of foods with high cadmium content can easily cause various chronic diseases, such as cardiovascular and cerebrovascular diseases, physical deformities, cancer, and others [16]. Because soil microorganisms are much more sensitive to heavy metal stress than animals and plants in the same environment, they represent the most potential indicator for evaluating soil environmental quality. They also serve as a sensitive and straightforward measure for evaluating the impact of soil on human health [15,17,18]. Under heavy metal stress, certain strains can better adapt to and tolerate heavy metal ion toxicity. Over time, these strains gradually replace microorganisms that are sensitive to heavy metals [19]. A soil microbial community that adapts to heavy metal pollution is essential in the bioremediation of heavy metal pollutants and serves as an indicator of environmental evolution [20,21]. There are significant differences in the response of various microbial types to different heavy metals and their concentrations, which can be attributed to variations in cell architecture and function [12,22]. The relationship between soil heavy metals and microorganisms is a popular research topic.
Soil microorganisms are important components of the soil ecosystem and are crucial to the activity and functioning of the soil ecological environment [20]. They are essential in plant growth and the biochemical cycling of soil elements, influencing the cycling of elements such as carbon, nitrogen, oxygen, sulfur, and others, as well as soil fertility, ecosystem stability, and resistance to interference [23,24]. In addition, soil microorganisms participate in soil organic matter degradation and humus formation. It drives the transformation and cycling of soil nutrients and the metabolism of soil organic carbon [14,25]. There is a rich variety and many microorganisms in soil with strong activity, which can degrade and clean toxic substances [26,27]. Li’s research results showed that microorganisms can reduce the utilization efficiency of Pb and Cd heavy metals in plants by redistributing their chemical forms [28]. However, heavy metal pollution can greatly affect microorganism morphology and cellular metabolism, leading to a decrease in biodiversity and Pb and Cd [11,12]. Cd was classified as a class I carcinogen by the International Agency for Research on Cancer [29]. The World Health Organization has listed Pb and Cd among the 10 most harmful toxic substances to human health, ranking them second and seventh, respectively [12,30]. Moreover, a high As concentration can restrain soil microorganism activity [31]. This is because the presence of As impacts the diversity and community structure of microorganisms in the soil [32]. Therefore, there is a negative correlation between As concentration and soil microorganism numbers [33].
Many scholars have studied the soil microorganism response to heavy metals from various perspectives. For example, Zhang et al. researched how radioactivity pollution has affected the soil microbial community structure in uranium mining areas. The results showed that the microbial number, functional diversity of the microbial community, species richness, dominance, and uniformity significantly decreased with increasing radiation value [34]. Radionuclides are the primary factors driving changes in soil microbial community metabolism in the region. Factors such as pH value, total nitrogen, available potassium, and soil organic matter significantly affect the soil microorganism community structure. Notably, Campylobacter chlorophylla has a high tolerance to high concentrations of As pollution [35]. The study conducted by Chen et al. showed a significant correlation between the total concentration of Pb and Bacteroidetes and Firmicutes, active components of Pb and Rubrobacter, total concentration of Cd and Acidiferrobacter, and active components of Cd and Actinobacteria. These findings revealed that these soil microbial types were the dominant species under Pb and Cd pollution [12]. The research conducted by Xing et al. showed that Cd and Cu pollution can hinder the number of microorganisms and community diversity. Conversely, Cr, Pb, and Zn pollution were found to have a promoting effect [15]. Among them, bacteria and actinomycetes were found to be the most sensitive to heavy metals, with fungi being the second-most sensitive. Guo et al. conducted a study on the response of soil microorganism community metabolism to the combined pollution of Cu, Cd, Pb, and Zn. The results showed that under light and moderate pollution, the soil microbial community exhibited an activation effect on carbon source utilization. However, under heavy pollution, it showed an inhibitory effect [36]. Zhang et al. conducted a study on the effects of Cd, Zn, Cr, and Hg pollution on microorganisms in the soil profile. The study showed that Pb is the key factor influencing microbial diversity [37]. In addition, the enzymes in soil mainly come from soil microorganisms. These enzymes are important in the biogeochemical cycle, soil structure maintenance, detoxification metabolism, and reaction processes for pollutants [38,39]. Among the correlations, those between heavy metals and the enzymes, urease, phosphatase, and dehydrogenase, in soil were the most significant [27,40,41].
In summary, the sensitivity of soil microorganism responses to changes in the soil environment can be a sensitive indicator of the soil ecological environment. Soil microorganisms are crucial in promoting soil quality and maintaining structural stability. Additionally, the number and community of soil microbes are influenced by heavy metal pollution. The background values of heavy metals in the Guizhou Karst farmland are relatively higher than those in other places, largely due to a black shale layer in the surface soil [42]. Furthermore, chemicals and pesticides are used in the process of tea plantation management, resulting in a new source of external pollution from heavy metals. However, no studies have reported on the response of soil microorganisms to multiple heavy metals in the soil of karst tea plantations [10,43,44,45,46,47]. Therefore, analyzing the characteristics of multiple heavy pollutants in tea plantation soil and then further analyzing the coupling mechanism between soil heavy metal pollution and soil microorganisms was attempted. The aim was to provide a reference for heavy metal pollution remediation and tea plantation management, thereby promoting tea quality and production in karst areas.

2. Materials and Methods

2.1. Study Region

The study areas were located in the central part of Guizhou Province with geographic coordinates, 106°07′–107°17′ E and 26°11′–26°55′ N, including the Wudang tea plantation, Kaiyang tea plantation, Qingzhen tea plantation, and Huaxi tea plantation. Guizhou Province is the core area of the karst rocky desertification continuous district in southwestern China [48,49]. Approximately 92.5% of the total area is mountains or hills, and approximately 61.9% of the terrestrial area is karst landforms [50]. The average elevation in the study area is approximately 1100 m, with a relative height difference of 100–200 m. The terrain in the area is continuous and undulating, with hills, basins, valleys, and depressions distributed alternately. This area features a humid monsoon climate and is affected by the quasistationary Guiyang–Kunming front. The average annual temperature is 13 °C, and the average annual rainfall is 1128.5 mm. Based on field investigation and statistical analysis, tree species mainly include Pinus massoniana Lamb., Cunninghamia lanceolata (Lamb.) Hook., Cupressus funebris Endl., shrub species primarily include Camellia sinensis (L.) O. Ktze. and Pyracantha fortuneana (Maxim.) H. L. Li. The land use types mostly include cultivated land, garden land, forestland, and unused land. The soil types primarily include Cab high fertility Orthic Anthrosols, Cab low fertility Orthic Anthrosols, Xan Udic Fernalisols, Black Lithomorphic Isohumisols, Cab Udi Orthic Entisols, and Cab medium fertility Orthic Anthrosols.

2.2. Research Design

2.2.1. Sample Collection

The Wudang tea plantation (plateau hills tea plantation, PH), Kaiyang tea plantation (under forest tea plantation, UF), Qingzhen tea plantation (lakeside tea plantation, LR), and Huaxi tea plantation (steep-slope tea plantation, SS) were used as research regions. Three sample plots were set up in an “S-shape” within each tea plantation, with each plot measuring 20 m × 20 m. The arrangement of sampling spots was in a quincunx pattern within one square meter of the 0–20 cm surface soil layer. The mixed soil samples were taken from five points along the diagonal line and mixed evenly. Finally, an approximately 1 kg soil sample was collected and stored in self-sealed plastic bags. A total of 12 soil samples were obtained from different tea plantations.

2.2.2. Sample Preparation and Test

All soil samples were taken to the laboratory, air-dried, ground, weighed, and prepared as required for laboratory analysis [42]. Soil sample digestion was performed according to the method of Cao et al. [51,52]. Heavy metals Pb, Cd, Cu, Ni, Zn, and Cr were tested via inductively coupled plasma-mass spectrometry (ICP-MS 7900, Agilent Technologies, Inc., Tokyo, Japan), and the elements, As and Hg, were tested via atomic fluorescence spectrometry (AFS-933, Jitian Instrument Co., Ltd, Beijing, China) [42,53,54]. In addition, high-throughput sequencing technology was used to analyze the change characteristics of soil microbial communities [55].

2.3. Evaluation and Analysis

2.3.1. Soil Heavy Metal Pollution Assessment

Single-heavy-metal pollution was evaluated via the single factor index (Pi) method, and the comprehensive pollution of multiple heavy metal elements was assessed via the pollution load index (PLI) [42,56]. The formula is as follows:
P i = C i S i
In the formula, Ci (g/kg) represents the actual content value of heavy metal i, Si (g/kg) represents the reference value of heavy metal i, and Pi represents the pollution index of heavy metal i. The pollution degree was classified into 5 levels: clean (Pi ≤ 1), light (1 < Pi ≤ 2), moderate (2 < Pi ≤ 3), moderate to heavy (3 < Pi ≤ 4), and severe (Pi > 4).
P L I = C F 1 × C F 2 × C F 3 ⋯ C F n 1 n
In the formula, CFi is the ratio of the actual value and reference value of heavy metal i. The pollution degree was classified into 4 levels: nonpollution (0 ≤ PFI < 1), light pollution (1 ≤ PFI < 2), moderate pollution (2 ≤ PFI < 3), and high pollution (PFI ≥ 2).

2.3.2. Soil Microbial Diversity and Community Structure

After sequencing all samples, circular consensus sequencing (CCS) sequences were obtained through barcode recognition. The CCS sequences were screened and clustered to identify effective CCS and then classified as operational taxonomic units (OTUs). OTU clustering involves clustering sequences at a similarity level of 97% using USEARCH (version 10.0) and filtering OTUs with a threshold of 0.005% of all sequences [57,58]. The numbers of phyla, classes, orders, families, genera, and species were determined based on the OTU results, after which the microbial community structure and cluster were analyzed further. QIIME 1.8.0 software was used to analyze alpha diversity, which represents the microbial diversity and abundance within a specific area, single sample, or ecosystem. This analysis included metrics such as Chao1, Ace, Shannon, Simpson index, and others.

2.3.3. Statistical Methods and Software

Software programs such as Excel 2016, SPSS 18.0, R for Windows 3.5.1, Origin 5.3, USEARCH 10.0, QIIME 1.8.0, and Cannon 5.0 were used for analysis and visualization.

3. Results

3.1. Descriptive Statistics of Soil Heavy Metal Content

The distribution characteristics of heavy metal concentrations in different tea plantation types are shown in Figure 1. The Cd and Hg contents were significantly lower than those of other heavy metals in all tea plantations. The descending order of Cd and Hg concentrations in different tea plantations was LR > UF > SS > PH, with only small discrepancies. However, the coefficient of variation for Cd and Hg was higher than that for other heavy metals in each tea plantation. This indicated a high spatial distribution discrepancy of Cd and Hg in different tea plantation soils. There was a greater discrepancy in the levels of Pb, As, Cu, Ni, Zn, and Cr in different tea plantation soils. Among them, the Pb concentration in LR was the highest, followed by UF and SS, with PH having the lowest levels. The As concentration in LR was noticeably higher than that in other tea plantations. There was a distinct discrepancy in Cu concentration in the soil between different tea plantations, with the descending order being SS > LR > PH > UF. The Ni concentration values in LR and SS were significantly higher than those in PH and UF. The distribution characteristics of Zn and Cr concentrations in different tea plantations were similar, showing that Zn and Cr levels in LR were noticeably higher, while levels in UF were distinctly lower than those in other tea plantations. In summary, the heaviest metal elements in LR had a greater concentration value, but in UF, they were generally lower.

3.2. Characteristics of Soil Heavy Metal Pollution in Different Tea Plantation Areas

The soil heavy metal pollution characteristics were evaluated via a single factor index and are shown in Figure 2a. There were some soil heavy metal elements that exceeded the borderline pollution level. The heavy metals, Cd and Hg, exceeded the pollution line in all tea plantation soils, and the descending order was LS > UF > SS > PH. The pollution indices of Cd and Hg in the soil of LR were greater than 2, and they were significantly higher than those in other tea plantations. In addition, the soil heavy metal Cu in tea plantations of SS and LR exceeded the lowest threshold value of pollution, and Cr showed pollution in LR only. However, the pollution degrees of Pb, As, Ni, and Zn were lower than the threshold values in all tea plantation soils, which indicated that those soil heavy metal elements were often at safe levels. In summary, all tea plantations were generally polluted by Cd and Hg, and this pollution was mostly observed in LR. In addition, the comprehensive pollution from multiple heavy metal elements in different tea plantation types is shown in Figure 2b. The PLI in each tea plantation was as follows: PH (0.59), UF (0.57), LR (1.03), and SS (0.78), with respective coefficients of variation of 0.13, 0.21, 0.05, and 0.19. Only LR had a PLI slightly greater than 1, and the other tea plantations had PLI values lower than 1 to different degrees. The coefficient of variation for the PLI in UF and SS reached a moderate variation level, and the PLI in LR was the lowest. These results reveal that the soil in LR was generally polluted by multiple heavy elements, but the occurrence of this situation in PH, UF, and SS was lower than that in LR.

3.3. Statistical Analysis of Soil Microbial Numbers at Each Level

There was a discrepancy in the environmental background and topographic characteristics among different tea plantation types. To analyze the variation in soil microbial populations in these plantations, the numbers of phyla, classes, orders, families, genera, and species were counted separately (Table 1).
The total numbers of phyla, classes, orders, families, genera, and species of soil microbes in all tea plantations were 23, 43, 113, 176, 299, and 455, respectively. The numbers of phyla in different tea plantations ranged between 17 and 20, and their average values were 19 (PH), 18 (UF), 19 (LR), and 19 (SS). The numbers of classes in different tea plantations ranged between 30 and 36, and their average values were 31 (PH), 34 (UF), 34 (LR), and 33 (SS). The numbers of orders were between 72 and 92, and their descending order was LR(87) > UF(79) > SS(75) > PH(74). The numbers of families were between 108 and 139, and their descending order was LR(127) > UF(123) > PH(120) > SS(113). The numbers of genera were between 150 and 215, and their descending order was LR(197) > UF(186) > SS(176) > PH(173). The numbers of species were between 192 and 322, and their descending order was LR(296) > UF(272) > SS(246) > PH(241). There was a slight discrepancy in the numbers of phyla, classes, and orders between different tea plantations, but the numbers in LR were generally higher than those in the other tea plantations. There was a greater discrepancy in the numbers of families, genera, and species across different tea plantations. Among these, the range value of families in different tea plantations was 31. There were slight differences in PH, UF, and LR, all of which were significantly higher than SS. The range value of genera in different tea plantations reached 65. There was a slight variation between PH and SS, both of which were lower than LR and UF, which had greater values. There was a greater discrepancy in species numbers across different tea plantations, with the range value reaching 130. The difference in species numbers in PH and SS was relatively low, and they were distinctly lower than those in UF. However, the value in UF was significantly lower than that in PH and SS.

3.4. Distribution Characteristics of Soil Microbial Diversity in Different Tea Plantations

Soil microbial diversity was tested using high-throughput sequencing technology. The average effective data numbers for all soil samples in each tea plantation were as follows: 10,087 (PH), 9938 (UF), 9011 (RL), and 9892 (SS). Sequences were then clustered into OTUs with 97% consistency using QIIME 1.8.0 software. The numbers of OTUs for PH, UF, RL, and SS were 501, 610, 722, and 525, respectively. In terms of OTUs, different types of tea plantations displayed a descending order ranging from RL > UF > SS > PH (Table 2). In addition, the coverage rate of each sample was higher than 0.98. These results indicated that sequencing can accurately reflect the true situation of microbial communities in the soil.
The soil microbial diversity and degree of homogeneity were described by the Shannon index and Simpson index. There was a significant discrepancy in the Shannon index across different tea plantations, with the range value reaching 0.93. The descending order of the Shannon index in all tea plantations was LR > UF > PH > SS, with the Shannon index of LR being significantly higher than that of the other tea plantations. The Shannon index exhibited a gradient descending distribution pattern between UF, PH, and SS. These results indicate that the soil microbial diversity was the highest in LR and the lowest in SS. In addition, a similar distribution pattern of the Simpson index was observed in different tea plantations, similar to the Shannon index. However, there was no discrepancy in UF and LR. These results show that the Shannon index and Simpson index had a higher consistency level than the other indices, indicating a higher soil microbial diversity and good homogeneity across different tea plantations.
The soil microbial community abundance was described by the Chao1 index and ACE index. Higher values of the Chao1 and ACE indices indicated greater soil microbial abundance. There was a similar distribution pattern observed for the Chao1 index and ACE index between different tea plantations, with a descending order of LR > UF > SS > PH. In addition, there was a significant discrepancy in soil microbial community abundance between different tea plantations. Among them, the soil microbial community abundance in LR, UF, and SS showed a gradient descending pattern with significant differences. However, the discrepancy in soil microbial community abundance between SS and PH was lower than that between SS and UF. In summary, similar distribution characteristics were observed in soil microbial diversity and microbial community abundance between different tea plantations. However, there was a slight discrepancy between them, primarily observed in the comparison between SS and PH.

3.5. The Soil Microbial Community Composition and Structure Characteristics Vary in Different Tea Plantations

To analyze the composition of the soil microbial community, the soil microbial features were sorted and annotated using a method that combines a naive Bayes classifier with comparison based on the UNITE database. The composition characteristics of soil microbes in different tea plantation types are shown in Figure 3, and six levels of phylum, class, order, family, genus and species are listed. Soil microbes were distinguished by different colors, where a longer color column represented a greater relative abundance. The top 10 soil microbial species are listed, with all others grouped under the ‘Other’ category. Unclassified represents species without taxonomic annotation. Acidobacteriota, Proteobacteria, Bacteroidota, and Verrucomicrobiota were the dominant phyla in pH. Their relative abundances were 32.1%, 28.7%, 9.6%, and 8.5%, respectively, with an approximate accumulation ratio of 80%. Acidobacteriota, Proteobacteria, Verrucomicrobiota and Unclassified_Bacteria were the dominant phyla in UF, and their relative abundances were 34.4%, 28.2%, 11.5% and 8.9%, respectively. Acidobacteriota, Proteobacteria, Verrucomicrobiota and Bacteroidota were the dominant phyla in LR, and their relative abundances were 33.7%, 28.5%, 10.9% and 7.2%, respectively. Acidobacteriota, Proteobacteria, Chloroflexi and Verrucomicrobia were the dominant phyla in SS, and their relative abundances were 41.6%, 23.8%, 10.3% and 6.7%, respectively. At the phylum level, there were similar composition characteristics in different tea plantation types. Acidobacteriota and Proteobacteria were the dominant phyla in all tea plantation types, and their accumulation ratio of relative abundance exceeded 60% of the total. The descending order of Acidobacteriota in different tea plantations was SS > UF > LR > PH, and the range of relative abundance was 9.5%. The descending order of Proteobacteria was PH > LR > UF > SS, with a range value of 4.9%. There was a slight difference between PH, LR, and UF (Figure 3a). At the class level, Acidobacteria, Alphaproteobacteria, Gammaproteobacteria, and Bacteroidia were the dominant classes in the tea plantations of PH. Their relative abundances were 31.8%, 14.4%, 14.3%, and 9.6%, respectively, with a cumulative value reaching 70.1. There were similar composition characteristics in the UF, LR, and SS tea plantation types. The dominant classes followed a descending order of Acidobacteria, Alphaproteobacteria, Gammaproteobacteria, and Verrucomicrobia. Among the tea plantation types, the relative abundances of the dominant classes in UF were 33.7%, 15.8%, 12.4%, and 11.5%. In LR, they were 32.3%, 13.7%, 14.8%, and 10.7%. In SS, those values were 40.5%, 12.3%, 11.6%, and 6.7%, respectively. The descending order of Acidobacteria in different tea plantations was SS > UF > LR > PH. Among them, SS had a distinctly higher abundance than the others, while the differences between UF, LR, and PH were relatively small. There was a slight difference in the abundance of Alphaproteobacteria between different tea plantations, with the descending order being UF > PH > LR > SS (Figure 3b).
At the order level, the dominant orders in PH were Acidobacteriales, Subgroup_2, Pedosphaerales and Chitinophagales; their relative abundances were 17.1%, 12.0%, 7.3% and 7.1%, respectively, and the accumulation ratio reached 43.5%. The relative abundances of Acidobacteriales and Subgroup_2 were significantly higher than those of Pedosphaerales and Chitinophagales. The dominant orders in UF and LR were Acidobacteriales, Subgroup_2, Unclassified_bacteria, and Pedosphaerales. The descending order of relative abundance in UF was Subgroup_2 (15.8%) > Acidobacteriales (15.3%) > Unclassified_bacteria (8.9%) > Pedosphaerales (6.9%). The descending order in LR was Acidobacteriales (15.4%), Subgroup_2 (13.2%), Pedosphaerales (6.7%), and Unclassified_bacteria (5.5%). Both their accumulation ratios exceeded 40%. Among them, the relative abundances of Acidobacteriales and Subgroup_2 were significantly higher than those of Unclassified_bacteria and Pedosphaerales. The dominant orders in SS were Acidobacteriales, Subgroup_2, Ktedonobacterales, and Pedosphaerales, with relative abundances of 22.3%, 14.7%, 8.8%, and 5.0%, respectively. The accumulation ratio reached 50.8%. The relative abundance of Acidobacteriales was significantly higher than that of Pedosphaerales. There was a small difference in relative abundance between Ktedonobacterales and Pedosphaerales. The descending order of Subgroup_2 in different tea plantations was SS > UF > LR > PH, with SS being distinctly higher than the others. The discrepancy in Acidobacteriales in different tea plantation types was small, with the descending order being PH > LR > UF > SS (Figure 3c). At the family level, the dominant families in PH were Acidobacteriaceae_Subgroup_1, Pedosphaeraceae, Chitinophagaceae, and Uncultured_forest_soil_bacterium, with relative abundances of 12.6%, 7.3%, 7.0%, and 5.2%, respectively. Their accumulation ratio reached 31.2%. Among them, the relative abundance of Acidobacteriaceae_Subgroup_1 was significantly higher than that of the others. The dominant families in UF were Acidobacteriaceae_Subgroup_1, Uncultured_acidobacteria_bacterium, Unclassified_bacteria, and Pedosphaeraceae. There was a small difference between these dominant families, with relative abundances of 10.7%, 9.0%, 8.9%, and 6.9%, respectively. Acidobacteriaceae__subgroup_1, Pedosphaeraceae, Uncultured_acidobacteria_bacterium and Unclassified_bacteria were the dominant families in LR, and their relative abundances were 10.2%, 6.7%, 6.1% and 5.5%, respectively. The relative abundance of Acidobacteriaceae_Subgroup_1 was distinctly higher than that of the others, while there was little difference between Pedosphaeraceae, Uncultured_acidobacteria_bacterium, and Unclassified_bacteria. The dominant families in SS were Uncultured_forest_soil_bacterium, Acidobacteriaceae_Subgroup_1, Uncultured_acidobacteriaceae_bacterium, and Pedosphaeraceae. Their relative abundances were 10.8%, 10.7%, 8.9%, and 5.0%, respectively. The descending order of the first family was PH > SS > UF > LR, and the descending order of the second family was SS > UF > PH > LR (Figure 3d).
At the genus level, the dominant genera in PH were Uncultured_forest_soil_bacterium, Occallatibacter, Uncultured_acidobacteriaceae_bacterium, and Unclassified_pedosphaeraceae. Their relative abundances were 6.2%, 4.8%, 4.6%, and 4.6%, respectively. The accumulation value of the dominant family was only 20.3%, and these values were similar. The dominant genera in UF were Uncultured_acidobacteria_bacterium, Unclassified_bacteria, Unclassified_pedosphaeraceae, and Acidipila_silvibacterium. Their relative abundances were 9.6%, 8.9%, 3.8%, and 3.8%, respectively. The first two were significantly higher than the latter two. The dominant genera in LR were Acidipila_silvibacterium, Uncultured_acidobacteria_bacterium, Unclassified_bacteria, and Uncultured_soil_bacterium. Their relative abundances were 6.7%, 6.4%, 5.5%, and 3.2%, respectively. The dominant genera in SS were Uncultured_forest_soil_bacterium, Uncultured_acidobacteriaceae_bacterium, Granulicella and Acidipila_silvibacterium, and their relative abundances were 11.7%, 8.9%, 3.9% and 3.7%, respectively. The first two were distinctly higher than the latter two, and the difference between the latter two was slight. The descending order of the first genus was SS > UF > LR > PH, and the descending order of the second family was UF > SS > LR > PH (Figure 3e). In terms of species, the dominant species in PH were Uncultured_soil_bacterium, Uncultured_forest_soil_bacterium, Uncultured_acidobacteriaceae_bacterium, and Unclassified_bacteria, with relative abundances of 6.4%, 5.4%, 4.6%, and 3.3%, respectively. The discrepancy of dominant species in PH was approximated, with a range of only 3.1%. The dominant species in UF were Uncultured_Acidobacteria_bacterium, Unclassified_bacteria, Unclassified_elsterales, and Acidobacteriaceae_bacterium_K5, with relative abundances of 9.5%, 8.9%, 5.1%, and 3.1%, respectively. The first two were distinctly higher than the latter two, and there was little variation between the first two. The dominant species in LR were Uncultured_acidobacteria_bacterium, Acidobacteriaceae_bacterium_K5, Unclassified_bacteria, and Unclassified_acidobacteriales, with relative abundances of 6.2%, 6.0%, 5.5%, and 3.6%, respectively. The difference in relative abundance between the dominant species was small. The dominant species in SS were Uncultured_forest_soil_bacterium, Uncultured_acidobacteriaceae_bacterium, Unclassified_bacteria, and Edaphobacter_acidisoli, with relative abundances of 10.9%, 8.9%, 3.3%, and 3.1%, respectively. The first two were distinctly higher than the latter two, and there was little variation between the latter two. The descending order of the first family was SS > UF > PH > LR, and the descending order of the second family was UF > SS > LR > PH (Figure 3f).
In summary, there was a significant discrepancy observed at each level of soil microbial composition. The distribution characteristics of the phylum and class levels of soil microbes in different tea plantations were similar, and the dominant compositions of phylum and class were distinct. There was a greater difference in the distribution characteristics of the composition of dominant orders and families in different tea plantations, and the degree of dominance was not obvious. In addition, there was no distinct distribution pattern of dominant genera and species in different tea plantations, and the relative abundance of dominant genera and species was low. These results revealed that the distribution pattern of different levels of soil microbes in different tea plantation types gradually decreased from phylum to species. Furthermore, the degree of dominance in the composition also gradually decreased.

3.6. Soil Microbial Cluster Characteristics in Different Tea Plantations

To further reveal the similarities and differences in soil microbial composition in different tea plantation types, the different classification levels of soil microbes in all sampling spots were analyzed using heatmap clustering. Vertical clustering represents the similarity in abundance of different species between samples. The closer the distance between two species, the shorter the length of the branch. This indicates that the abundance of these two species is more similar. At the phylum level, there was a greater similarity in the soil microbial composition between SS-1 and SS-2, PH-2 and PH-3, UF-3 and LR-3, and LR-1 and LR-2. The degree of similarity between UF and LR was higher than that of the others. In addition, the distribution characteristics of Fusobacteriota and Chloroflexi, Abditibacteriota and Armatimonadota, Patescibacteria and Proteobacteria, Planctomycetota and Bacteroidota, Methylomirabilota and Bdellovibrionota, Spirochaetota and Elusimicrobiota in different sampling spots were similar (Figure 4a). At the class level, there was a greater similarity in soil microbial composition characteristics between PH-2 and PH-3, UF-1 and UF-3, UF-2 and LR-1, and SS-1 and SS-2. Additionally, UF and LR exhibited greater similarity across all tea plantation types. The distribution characteristics between Dehalococcoidia and Chloroflexia, Armatimonadia and Abditibacteria, Bacilli and Clostridia, Fusobacteriia and Negativicutes, Microgenomatia and Endomicrobia, Phycisphaerae and Spirochaetia, Alphaproteobacteria and Verrucomicrobiac, Oligoflexia and Methylomirabilia, Bacteroidia and Bedellovibrionia, Polyangia and Anaerolineae, and Vampirivibrionia and Gracilibacteria in different sampling spots were similar. There was a greater discrepancy in soil microbial classes between different sampling spots. Among them, the similarity in the distribution characteristics of Spirochaetia between LR-2 and UF-2 was the highest (Figure 4b).
At the order level, the soil microbial composition characteristics between PH-2 and PH-3, LR-1 and LR-2, UF-1 and UF-3, and SS-1 and SS-2 were similar. There were a greater number of similar Bryobacterales and Bedllovibrionales in PH-2 and PH-3, Crtophagales in LR- and LR-2, Tepidisphaerales in UF-1 and UF-3, and Ktedonobacterales in SS-1 and SS-2 (Figure 4c). In terms of family-level classification, there were distinct distribution characteristics in the soil microbial composition between LR-1 and LR-2, PH-2 and PH-3, UF-1 and UF-2, and SS-1 and SS-2. There was a greater similarity in the abundance characteristics of Azospirillaceae between LR-1 and LR-2, ctw_cuo3_e12 between PH-2 and PH-3, Azospirillales_incterae_sedis between UF-1 and UF-2, and Ktedonobacteraceae between SS-1 and SS-2 (Figure 4d).
At the genus level, there were greater similarities in the characteristics of soil microbial composition between SS-1 and SS-2, PH-2 and PH-3, LR-1 and LR-2, and UF-1 and UF-2. Among the soil microbial genera, hsb_of53_f07 in SS-1 and SS-2, Gemmatimonas in PH-2 and PH-3, Acidipila_silvibacterium in LR-1 and LR-2, and p3ob_42 in UF-1 and UF-2 exhibited greater similarities in their abundance characteristics (Figure 4e). At the species level, there were greater similarities in the distribution characteristics of soil microbial composition between sampling spots LR-1 and LR-2, UF-1 and UF-2, SS-1 and SS-2, and PH-2 and PH-3. Among the different sampling spots, the abundance characteristics of Acidobacteriaceae_bacterium_k5 in LR-1 and LR-2, spartobacteria_bacterium_wx31 and chthoniobacter_bacterium_ellin507 in UF-1 and UF-2, acidibacter_bacterium_ellin5264 in SS-1 and SS-2, and Flavitalea_antarctica in PH-2 and PH-3 were significantly higher than those of other species (Figure 4f).

4. Discussion

4.1. The Reliability of Soil Microbial Data Is Dependent on Various Factors

The rarefaction curve and Shannon curve were used to verify whether the amount of sequencing data was sufficient to reflect the species diversity in the sample [59]. Within a certain range, if the curve showed a sharp increase as the number of sequences increased, it indicated that many species had been discovered in the community. When the curve tended to flatten, it indicated that the species in this environment did not significantly increase with the increase in sequencing quantity. The number of OTUs significantly increased before sampling 4000 sequences in different tea plantations; afterward, the rarefaction curve showed a gentle trend. The flatness of the rarefaction curve in different tea plantations (PH > SS > UF > LR) indicated that the existing sequencing data were sufficient to identify new OTUs (Figure 5a). Furthermore, the curve of the Shannon index not only reflected species diversity but also indicated whether the sequencing data volume was sufficient [37]. The Shannon index of all tea plantations quickly increased at first, and then the curve transitioned to a gentle slope (Figure 5b). These results indicated that the sampled sequences were sufficient for data analysis. In addition, the rank curve reflected the abundance and evenness of species. On the horizontal axis, the wider the curve, the greater the richness of species composition. On the vertical axis, the flatter the curve is, the higher the uniformity of species composition [60]. The abundance and evenness of the soil microbial composition in LR were higher than those in the other tea plantations. However, information on these characteristics in PH is relatively limited (Figure 5c).

4.2. Investigating the Response of Soil Microbial Diversity to Heavy Metals

According to the information in Figure 2 and Table 2, the comprehensive pollution of multiple heavy metals in LR was of a light degree, whereas the pollution of multiple heavy metals in PH, UF, and SS was categorized as nonpollution or potential pollution degrees. The descending order of multiple heavy metal pollution in different tea plantations was LR > SS > PH > UF. The distribution characteristics of heavy metal pollution in different tea plantation types were compared with the distribution of soil microbial diversity. The results show that as heavy metal pollution increased, the soil microbial diversity index also increased. The relationship between the soil microbial diversity index and the PLI is listed in Figure 6, which shows that when the PLI value was close to 0.6, the ACE, Chao1, Simpson, and Shannon index values were the lowest. Before reaching the lowest point, the values of the soil microbial diversity index decreased rapidly, and these values quickly increased after this point. This trend extended until approximately 6.5, after which the soil microbial diversity index showed a generally gradual increase. The results indicate that soil heavy metal pollution not only failed to lead to a decrease in soil microbial diversity but also promoted an increase in soil microbial diversity to a certain degree. These results were similar to those of research conducted by Mikiya, which found no influence of heavy metals on certain soil microbes [61].
There was a significant discrepancy in soil microbial composition between different types of tea plantations, and the descending order of the coefficient of variation for the relative abundance of dominant soil microbes in different tea plantations was LR > SS > PH > UF. In addition, the Simpson index of soil microbial diversity in LR was relatively higher than the values observed in the other tea plantations. This indicates a greater discrepancy in soil microbial community composition in LR. These results reveal that soil heavy metal pollution mainly influences the soil microbial composition of dominant species, which in turn impacts soil microbial diversity to a lesser extent. In addition, there was a significant correlation between Cd, Ni, and the ACE index and Chao1 index, as well as between Cd and the Shannon index (Table 3).
According to the information shown in Figure 2a, all different types of tea plantations were generally polluted by Cd and Hg simultaneously. However, LR was also found to be polluted by Cu and Cr. Changes in microbial communities caused by Cd and Cu pollution may affect the diversity of metabolic functions [62]. Cu is typically present in soil as a trace element, serving as a component of microbial enzymes and participating in microbial metabolic processes. Adequate copper levels can promote the growth and metabolic activity of soil microorganisms [27]. The soil microbial genus, Acidipila_silvibacterium, was commonly found in all types of tea plantations. The heavy metal, Cu, was positively impacted by Cd and Hg, and the tea plantation soil was also polluted by Cd and Hg. Therefore, an important reason for the impact of heavy metal pollution on soil microbial diversity is that the heavy metal, Cu, influences the components of microbial enzymes and microbial metabolic processes. In addition, soil heavy metal pollution impacts the proportion of the dominant microbial structure, which in turn further influences soil microbial diversity.

4.3. The Key Factors Impacting the Soil Microbial Community

To discuss the response of dominant soil microbial species and diversity to different heavy metal elements, the correlation between different heavy metals and the top 10 soil microbial genera, as well as the diversity index, was sorted using the redundancy analysis (RDA) method in Canoco 5.0 software (Figure 7). Overall, the dominant genera in all soil samples were Acidobacteriaceae_bacterium_K5, Edaphobacter_acidisoli, Occallatibacter_savannae, Tepidisphaera_mucosa, Acidibacter_bacterium_Ellin5264, Bradyrhizobium_erythrophlei, and ADurb. Bin063_1_bacterium_Ellin5102, Pseudolabrys_taiwanensis, Puia_dinghuensis, and Acidipila_Silvibacterium_bacterium_Ellin310. According to the relationship between line length and included angle between heavy metals and dominant genera [6], there was a significant positional correlation between Puia_din and Cu, Cr, Zn; ADurbBin0 and Ni, Cr, Zn, and Cd; and Acidobac and Cr and Ni. The relationship between Acidipil and Cd, and Occallat and Cr and Zn showed a significant negative correlation (Figure 7a). These results reveal that there were different effects of a particular metal on a particular group of bacteria. Among them, Ni, Cd and Cu significantly positively promoted the abundance of ADurbBin0, Acidobac and Puia_din, respectively; however, the effect of Cd on Acidipil was negative (Figure 7b–e).
For PH, the ACE and Chao1 indices were positively impacted by Cu, Ni, Cr, and Zn. The Shannon index and Simpson index were positively impacted by As. However, there was a negative correlation between the ACE and Chao1 index and Cd and Pb. Similarly, the Shannon index and Simpson index showed a negative correlation with Hg (Figure 8a). In the UF soil samples, there was a significant positive correlation between the Chao1 and ACE indices and Hg. Additionally, there was a significant negative correlation between the Chao1 and ACE indices and As (Figure 8b). In the LR soil samples, the Chao1, ACE, Shannon, and Simpson indices were significantly impacted by Cd and Hg. However, these indices were negatively impacted by the other heavy metals (Figure 8c). In the SS soil samples, all the indices and the heavy metals, Cu, Cd, Ni, Zn, and Cr, showed a negative impact. Additionally, these indices were negatively impacted by Pb (Figure 8d).
In summary, there was a greater discrepancy in the relationship between soil microbial diversity and heavy metals, as well as in the composition of dominant species and heavy metal pollution. The impact of key heavy metals on soil microbial diversity and community composition in different tea plantations was not fully consistent. Overall, all tea plantations were generally polluted by Cd and Hg. However, the comprehensive pollution coupled with multiple heavy metals was of no or a light pollution degree. There was only light pollution observed in LR as a result of multiple heavy metals. Moreover, LR was also lightly polluted by Cu and Cr. The impact of soil heavy metal pollution on soil microbial diversity was not obvious; however, it did affect species diversity by influencing the composition of the soil microbial community. The heavy metals, Cd, Hg, and Cu, were the key factors that impacted the composition of the soil microbial community.

5. Conclusions

The karst landform in Guizhou Province is the core region of the South China Karst. Tea is an important cash crop that is widely planted in karst areas. The major models of tea plantations in the Guizhou karst are found in plateau hills, under forests, by lakesides, and on steep slopes. These tea plantations are widely affected by low to moderate levels of heavy metal pollution, specifically Cd and Hg. Among them, Cd pollution in lakeside tea plantations and Hg pollution in lakeside tea plantations and under forest tea plantations reached a moderate degree. In addition, lakeside tea plantations were lightly polluted by Cu and Cr, while steep-slope tea plantations were also mildly polluted by Cu. In all tea plantations, the pollution degree of Hg was greater than that of Cd. The descending order of heavy metal pollution in lakeside tea plantations was Hg > Cd > Cr > Cu. On the other hand, in steep-slope tea plantations, the order was Hg > Cu > Cd. The comprehensive pollution of multiple heavy metals in lakeside tea plantations reached only a low degree, with Hg and Cd as the major contributors. There was a greater discrepancy in soil microbial diversity and community composition dominance between different tea plantation types, with the descending order being lakeside > under forest > plateau hills > steep slope. The impact of heavy metal pollution on soil microbial diversity was not very distinct; instead, it mainly affected the composition of the microbial community. When the pollution load index approached a value of 0.6, the microbial diversity was at its lowest. Afterward, the diversity and heterogeneity generally increased gradually, and the dominant composition became more apparent. The heavy metals, Cd, Hg, and Cu, were the key factors that impacted the composition of the soil microbial community.

Author Contributions

X.H. and Z.Z. conceived the research idea and designed the sampling plan. X.W. (Xingfu Wang), Q.W., H.Y. and X.W. (Ximei Wen) conducted field data collection, laboratory analysis, and data treatment. X.W. (Xingfu Wang) was responsible for revising the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Planing Project of Guiyang City (no. Zhukehe (2021) 3–30, no. Zhukehe (2021) 3–27 and no. Zhukehe (2023) 3–11), the Doctoral Research Fund of Guiyang Healthcare Vocational University (No. K2023-8), the Collaborative Innovation Center of Biology and Information Technology in Karst Plateau Area of Guizhou Province (no. QJJ (2022) 010), the Central Guidance Local Science and Technology Development Fund (Qianke Zhongyin (2022) 4035), Guiyang Science and Technology Plan Project (Zhuke Contract (2022) No. 3–7), and the Tongren City Science and Technology Support Project ((2021) No. 24).

Institutional Review Board Statement

This study did not involve humans or animals.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Not available.

Conflicts of Interest

The authors declare that they have no known competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.

References

  1. Jurowski, K.; Kondratowicz-Pietruszka, E.; Kroniak, M. The Toxicological Safety Assessment of Heavy Metal Impurities (As, Pb, and Cd) in Mint Tea Infusions (Mentha piperita L.) Available in Polish Markets. Biol. Trace Elem. Res. 2022, 201, 2627–2635. [Google Scholar] [CrossRef] [PubMed]
  2. Gebrewold, A.Z. Review on integrated nutrient management of tea (Camellia sinensis L.). Cogent Food Agric. 2018, 4, 1543536. [Google Scholar] [CrossRef]
  3. Chen, Y.H.; Li, M.J. Evaluation of influencing factors on tea production based on random forest regression and mean impact value. Agric. Econ. 2019, 65, 340–347. [Google Scholar] [CrossRef]
  4. Jia, X.T.; Huang, Y.; Wang, Y.H.; Sun, D.Z. Research on water and fertilizer irrigation system of tea plantation. Int. J. Distrib. Sens. Netw. 2019, 15, 1550147719840182. [Google Scholar] [CrossRef]
  5. Huang, H.B.; Lin, C.Q.; Yu, R.L.; Hu, G.R.; Jiang, S.H.; Qiu, Q.J. Distribution and pollution assessment of heavy metals in soils from Tieguanyin tea garden of Anxi County. Environ. Chem. 2018, 37, 994–1001. [Google Scholar]
  6. Wang, X.F.; Cao, R.S.; Wu, X.L.; Zhang, Z.M.; Huang, X.F. Study on heavy metals pollution assessment method of soil around a wasted mining in Karst mountain area. J. Guizhou Norm. Univ. (Nat. Sci.) 2021, 39, 29–35. [Google Scholar]
  7. Teng, Y.; Luo, Y.M.; Huang, C.Y.; Long, J.; Li, Z.G.; Christie, P. Tolerance of grasses to heavy metals and microbial functional diversity in soils contaminated with copper mine tailings. Pedosphere 2008, 18, 363–370. [Google Scholar] [CrossRef]
  8. Rashed, M.N. Monitoring of contaminated toxic and heavy metals, from mine tailings through age accumulation, in soil and some wild plants at Southeast Egypt. J. Hazard. Mater. 2010, 178, 739–746. [Google Scholar] [CrossRef]
  9. Kumar, S.; Rana, S.S.; Ramesh, A.N.N. Weed managemet in tea with herbicides mixture. Indian J. Weed Sci. 2014, 46, 353–357. [Google Scholar]
  10. Zhu, L.; Zhao, Y.; Yan, X.B.; Sun, S.N. Reasearch Progress on Effects of Tea Plantation Managements on Soil Microbial Community. Chin. J. Soil Sci. 2023, 54, 245–252. [Google Scholar]
  11. Xavier, J.C.; Costa, P.E.S.; Hissa, D.C.; Melo, V.M.M.; Falcão, R.M.; Balbino, V.Q.; Mendonça, L.A.R.; Lima, M.G.S.; Coutinho, H.D.M.; Verde, L.C.L. Evaluation of the microbial diversity and heavy metal resistance genes of a microbial community on contaminated environment. Appl. Geochem. 2019, 105, 1–6. [Google Scholar] [CrossRef]
  12. Chen, R.L.; Cai, X.X.; Zhou, L.H. Characteristics of soil contamination with heavy metals (Pb and Cd) in a smelting plant of Gansu and their effects on microbial community structure. Ecol. Environ. Sci. 2021, 30, 596–603. [Google Scholar]
  13. Klatt, C.G.; Liu, Z.; Ludwig, M.; Kühl, M.; Jensen, S.I.; Bryant, D.A.; Ward, D.M. Temporal metatranscriptomic patterning in phototrophic Chloroflexi inhabiting a microbial mat in a geothermal spring. ISME J. 2013, 7, 1775–1789. [Google Scholar] [CrossRef]
  14. Bian, F.Y.; Zhong, Z.K.; Zhang, X.P.; Yang, C.B.; Su, W.H. Remediation of Heavy Metal Contaminated Soil by Moso Bamboo (Phyllostachys edulis) Intercropping with Sedum plumbizincicola and the Impact on Microbial Community Structure. Sci. Silvae Cinicae 2018, 54, 106–116. [Google Scholar]
  15. Xing, Y.; Si, Y.X.; Hong, C.; Li, Y. Impact of long-term heavy metal pollution on microbial community in iron mine soil. Res. Environ. Sci. 2013, 26, 1201–1211. [Google Scholar]
  16. Jin, Z.G.; Zhou, M.Z. An assessment on contamination and potential ecological risk of cadmium and arsenic in the cultivated soils around the Ni-Mo mining area in Songli, Zunyi, China. J. Agro-Environ. Sci. 2012, 31, 2367–2373. (In Chinese) [Google Scholar]
  17. Xu, Z.Y.; Tang, M.; Chen, H.; Ban, Y.H.; Zhang, H.H. Microbial community structure in the rhizosphere of Sophora viciifolia grown at a lead and zinc mine of northwest China. Sci. Total Environ. 2012, 435/436, 453–464. [Google Scholar] [CrossRef]
  18. Chen, Y.; Jiang, Y.M.; Huang, H.Y.; Mou, L.C.; Ru, J.L.; Zhao, J.H.; Xiao, S. Long-term and high-concentration heavy-metal contamination strongly influences the microbiome and functional genes in Yellow River sediments. Sci. Total Environ. 2018, 637–638, 1400–1412. [Google Scholar] [CrossRef] [PubMed]
  19. Yin, K.; Wang, Q.; Lv, M.; Chen, L. Microorganism remediation strategies towards heavy metals. Chem. Eng. J. 2019, 360, 1553–1563. [Google Scholar] [CrossRef]
  20. Nguyen, J.; Lara-Gutiérrez, J.; Stocker, R. Environmental fluctuations and their effects on microbial communities, populations and individuals. FEMS Microbiol. Rev. 2021, 45, fuaa068. [Google Scholar] [CrossRef]
  21. Kowalchuk, G.A.; Heidelbery, J.F.; Bailey, M.J. Multidisciplinary journal of microbial ecology. ISME J. 2007, 1, 1–3. [Google Scholar] [CrossRef]
  22. Li, J.; Liu, Y.R.; Zhang, L.M.; He, J.Z. Sorption mechanism and distribution of cadmium by different microbial species. J. Environ. Manag. 2019, 237, 552–559. [Google Scholar] [CrossRef]
  23. Richard, J.; Manuela, P.; Antonella, S.; Anna, K.; Stanislav, K. The role of soil microorganisms in plant mineral nutrition—Current knowledge and future directions. Front. Plant Sci. 2017, 8, 1617. [Google Scholar]
  24. Du, Y.Y.; Zhang, D.W.; Chen, H.S.; Wu, J.F.; Zhou, D.G.; Jin, D.C.; Yan, M.L. Effect of cadmium stress on the soil fungal communities of two oilseed rape species. Microbiol. China 2021, 48, 4030–4045. [Google Scholar]
  25. Dick, R.P. Soil enzyme activities as integrative indicators of soil health. In Biological Indicators of Soil Health; CAB International: Wallingford, UK, 1997; pp. 121–156. [Google Scholar]
  26. Khan, M.U.; Malik, R.N.; Muhammad, S. Human health risk from heavy metal via food crops consumption with wastewater irrigation practices in Pakistan. Chemosphere 2013, 93, 2230–2238. [Google Scholar] [CrossRef] [PubMed]
  27. Zhang, X.; Zhang, Q.; Cheng, Y.; Jiang, R. The impact of heavy metal contamination on soil microbial diversity and enzyme activities in a copper mine. Ecol. Environ. Sci. 2016, 25, 517–522. [Google Scholar]
  28. Li, X.M.; Peng, W.H.; Jia, Y.Y. Biorem ediation of lead contaminated soil with Rhodobacter sphaeroides. Chemosphere 2016, 156, 228–235. [Google Scholar] [CrossRef]
  29. Vim, A.; Had, A.; Tgca, B. Chronic exposure to lead and cadmium pollution results in genomic instability in a model biomonitor species (Apodemus flavicollis Melchior, 1834). Ecotoxicol. Environ. Saf. 2020, 194, 110413. [Google Scholar] [CrossRef]
  30. Awadalla, A.; Mortada, W.I.; Abol-Enein, H.; Shokeir, A.A. Correlation between blood levels of cadmium and lead and the expression of microRNA-21 in Egyptian bladder cancer patients. Heliyon 2020, 6, e05642. [Google Scholar] [CrossRef] [PubMed]
  31. Sun, Y.Q.; Zhang, X.; Wu, Z.X.; Wu, S.L.; Hu, Y.J.; Xing, Y.; Chen, B.D. Soil microbial community structure and carbon source metabolic diversity in the Realgar mining area. Acta Sci. Circumstantiae 2015, 35, 3669–3678. [Google Scholar]
  32. Wu, J.; Xie, M.J.; Yang, Q.; Tu, S.X. Current researches in microbial remediation of Arsenic pollution. Environ. Sci. 2011, 32, 817–824. [Google Scholar]
  33. Simon, T. The effect of nickel and arsenic on the occurrence and symbiotic abilities of native rhizobia. Rostlinna Vyroba 2000, 46, 63–68. [Google Scholar]
  34. Zhang, L.J.; Wang, N.; Huang, W.; Guli, N.S.; Sha, Y.M.; Luo, Y.; Zheng, G.F.; Wang, W. Effects of Radioactive Contamination on Soil Microbial Community Structure in Uranium Mine Area. J. Microbiol. 2023, 43, 43–50. [Google Scholar]
  35. Li, Y.B.; Zhang, M.M.; Xu, R.; Lin, H.Z.; Sun, X.X.; Xu, F.Q.; Gao, P.; Kong, T.L.; Xiao, E.Z.; Yang, N.; et al. Arsenic and antimony co-contamination influences on soil microbial community composition and functions: Relevance to arsenic resistance and carbon, nitrogen, and sulfur cycling. Environ. Int. 2021, 153, 106522. [Google Scholar] [CrossRef] [PubMed]
  36. Guo, X.L.; Gu, J.; Chen, Z.X.; Gao, H.; Qin, Q.J.; Sun, W.; Zhang, W.J. Effects of heavy metals pollution on soil microbial communities metabolism and soil enzyme activities in coal mining area of Tongchuan, Shaanxi Province of Northwest China. Chin. J. Appl. Ecol. 2012, 23, 798–806. [Google Scholar]
  37. Zhang, H.D.; Liu, X.H.; Wang, Y.J.; Duan, L.S.; Dong, L. Effects of heavy metal pollution on microbial community in soil profiles. Environ. Sci. Technol. 2022, 45, 184–191. [Google Scholar]
  38. Burns, R.G.; Deforest, J.L.; Marxsen, J.; Sinsabaugh, R.L.; Stromberger, M.E.; Wallenstein, M.D.; Weintraub, M.N.; Zoppini, A. Soil enzymes in a changing environment: Current knowledge and future directions. Soil Biol. Biochem. 2013, 58, 216–234. [Google Scholar] [CrossRef]
  39. Zhang, Q.; Zhou, W.; He, P.; Sun, J.W.; Wang, X.b. Distribution of soil nutrients, extracellular enzyme activities and microbial communities across particle-size fractions in a long-term fertilizer experiment. Appl. Soil Ecol. 2015, 94, 59–71. [Google Scholar] [CrossRef]
  40. Mandal, K.; Jyot, G.; Singh, B. Effect of dehydrogenase, phosphatase and urease activity in cotton soil after applying thiamethoxam as seed treatment. Environ. Monit. Assess. 2015, 187, 298. [Google Scholar]
  41. Tejada, M.; Moreno, J.L.; Hernández, M.T.; García, C. Soil amendments with organic wastes reduce the toxicity of nickel to soil enzyme activities. Eur. J. Soil Biol. 2008, 44, 129–140. [Google Scholar] [CrossRef]
  42. Wang, X.F.; Huang, X.F.; Hu, J.W.; Yao, S.M. Cadmium pollution and Bioconcentration characteristic in crops around the abandon Ni-Mo mining area in the karst mountainous. Environ. Chem. 2020, 39, 1872–1882. [Google Scholar]
  43. Ma, L.F.; Yang, X.D.; Shi, Y.Z.; Yi, X.Y.; Ji, L.F.; Cheng, Y.; Ni, K.; Ruan, J.Y. Response of tea yield, quality and soil bacterial characteristics to long-term nitrogen fertilization in an eleven-year field experiment. Appl. Soil Ecol. 2021, 166, 103976. [Google Scholar] [CrossRef]
  44. Sen, S.; Pathak, S.K.; Suiam, M.L. Weed flora of tea plantations of Ri Bhoi district of Meghalaya, India with a glimpse on its ethno biological value. World Sci. News 2016, 56, 82–96. [Google Scholar]
  45. Zhang, X.C.; Jiang, H.G.; Wan, X.C.; Li, Y.W. The effects of different types of mulch on soil properties and tea production and quality. J. Sci. Food Agric. 2020, 100, 5292–5300. [Google Scholar]
  46. Lei, B.N.; Wang, J.; Yao, H.Y. Ecological and Environmental Benefits of Planting Green Manure in Paddy Fields. Agriculture 2022, 12, 223. [Google Scholar] [CrossRef]
  47. Fu, H.P.; Li, H.; Yin, P.; Mei, H.L.; Li, J.J.; Zhou, P.Q.; Wang, Y.J.; Ma, Q.P.; Jeyaraj, A.; Thangara, K.; et al. Integrated Application of Rapeseed Cake and Green Manure Enhances Soil Nutrients and Microbial Communities in Tea Garden Soil. Sustainability 2021, 13, 2967. [Google Scholar] [CrossRef]
  48. Li, Y.; Xiong, K.n.; Liu, Z.Q.; Li, K.; Luo, D. Distribution and influencing factors of soil organic carbon in a typical karst catchment undergoing natural restoration. Catena 2022, 212, 106078. [Google Scholar] [CrossRef]
  49. Lan, J.C.; Long, Q.X.; Huang, M.Z.; Jiang, Y.X.; Hu, N. Afforestation-induced large macroaggregate formation promotes soil organic carbon accumulation in degraded karst area. For. Ecol. Manag. 2022, 505, 119884. [Google Scholar] [CrossRef]
  50. Dai, B.; Peng, T.; Zhang, X.B.; Mo, X.M.; Wang, S.J. Major types and formation mechanism of karst in plateau and discussion on karst landform zoning in Guizhou, China. Mt. Res. 2023, 41, 155–168. [Google Scholar]
  51. Cao, R.S.; Fan, M.Y.; Huang, X.F.; Yang, H.; Zhang, Z.; Hu, J.W.; Qin, F.X. Analysis of organic matter and heavy metals in soils around the coal-fired power plant in Jinsha. Environ. Chem. 2017, 36, 398–407. (In Chinese) [Google Scholar]
  52. Pruvot, C.; Douay, F.; Hervé, F.; Waterlot, C. Heavy metals in soil, crops and grass as a source of human exposure in the former Mining Areas (6 pp). J. Soils Sediments 2006, 6, 215–220. [Google Scholar] [CrossRef]
  53. Zhong, W.S.; Ren, T.; Zhao, L.J. Determination of Pb (lead), Cd (cadmium), Cr (chromium), Cu (copper), and Ni (nickel) in Chinese tea with high-resolution continuum source graphite furnace atomic absorption spectrometry. J. Food Drug Anal. 2016, 24, 46–55. [Google Scholar] [CrossRef] [PubMed]
  54. Hu, J.W.; Liu, F.; Chen, J.A.; Li, C.X.; Wu, D.; Zhang, J.P.; Luo, J. Assessment of selenium pollution in sediments from Hongfeng Lake, China. Environ. Chem. 2013, 32, 1448–1455. [Google Scholar]
  55. Sun, H.; Wu, Z.N.; Miao, T.T.; Liu, J.L.; Su, X.H.; Ding, C.J.; Cao, Z.H.; Yan, C.X.; Wang, T. Effects of Different Fertilization Treatments on Soil Microbial Communities in Poplar Forest Land. Chin. Agric. Sci. Bull. 2023, 39, 36–43. [Google Scholar]
  56. Jorfi, S.; Maleki, R.; Jaafarzadeh, N.; Ahmadi, M. Pollution load index for heavy metals in main-ab plain soil, khuzestan, iran. Data Brief 2017, 15, 584–590. [Google Scholar] [CrossRef] [PubMed]
  57. Edgar, R.C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 2013, 10, 996–998. [Google Scholar] [CrossRef]
  58. Bokulich, N.A.; Subramanian, S.; Faith, J.J.; Gevers, D.; Gordon, G.L.; Knight, R.; Mills, D.A.; Caporaso, J.G. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 2013, 10, 57–59. [Google Scholar] [CrossRef]
  59. Wang, Y.; Sheng, H.F.; He, Y.; Wu, J.Y.; Jiang, Y.X.; Tam, N.F.Y.; Zhou, H.W. Comparison of the levels of bacterial diversity in freshwater, intertidal wetland, and marine sediments by using millions of illumina tags. Appl. Environ. Microbiol. 2012, 78, 8264–8271. [Google Scholar] [CrossRef]
  60. Kõljalg, U.; Nilsson, R.H.; Abarenkov, K.; Tedersoo, L.; Taylor, A.F.; Bahram, M.; Bates, S.T.; Bruns, T.D.; Bengtsson-Palme, J.; Callaghan, T.M.; et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 2013, 22, 5271–5277. [Google Scholar] [CrossRef]
  61. Hiroki, M. Effects of Heavy metal contamination on soil microbial population. Soil Sci. Plant Nutr. 1992, 38, 141–147. [Google Scholar] [CrossRef]
  62. Wang, X.L.; Xu, J.M.; Yao, H.Y.; Xie, Z.M. Effects of Cu Zn Cd and Pb compound contamination on soil microbial community. Acta Sci. Circumstantiae 2003, 23, 22–27. [Google Scholar]
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Figure 1. Distribution of heavy metal concentrations in different tea plantation soils. (Note: PH is the tea plantation on plateau hills, UF is the tea plantation under forests, LR is the tea plantation surrounding the lakeside, and SS is the tea plantation on steep slopes. The same below).
Figure 1. Distribution of heavy metal concentrations in different tea plantation soils. (Note: PH is the tea plantation on plateau hills, UF is the tea plantation under forests, LR is the tea plantation surrounding the lakeside, and SS is the tea plantation on steep slopes. The same below).
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Figure 2. Characteristics of soil heavy metal pollution distribution in different tea plantations: (a) the single heavy metal contamination, (b) the multiple-metal-compound contamination.
Figure 2. Characteristics of soil heavy metal pollution distribution in different tea plantations: (a) the single heavy metal contamination, (b) the multiple-metal-compound contamination.
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Figure 3. The distribution characteristics of the soil microbial community composition in different tea plantation types are as follows: (a–f) represent the soil microbial phylum, class, order, family, genus, and species, respectively.
Figure 3. The distribution characteristics of the soil microbial community composition in different tea plantation types are as follows: (a–f) represent the soil microbial phylum, class, order, family, genus, and species, respectively.
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Figure 4. The abundance heatmap represents different levels of soil microbial composition. In the map, (a–f) correspond to the soil microbial phylum, class, order, family, genus, and species, respectively.
Figure 4. The abundance heatmap represents different levels of soil microbial composition. In the map, (a–f) correspond to the soil microbial phylum, class, order, family, genus, and species, respectively.
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Figure 5. Soil microbial data analysis in different tea plantation types: (a) the rarefaction curve, (b) the Shannon index curve, (c) the rank abundance curve.
Figure 5. Soil microbial data analysis in different tea plantation types: (a) the rarefaction curve, (b) the Shannon index curve, (c) the rank abundance curve.
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Figure 6. Soil microbial diversity index response to comprehensive pollution caused by multiple heavy metal elements; (a–d) are the ACE, Chao1, Simpson and Shannon indices, respectively.
Figure 6. Soil microbial diversity index response to comprehensive pollution caused by multiple heavy metal elements; (a–d) are the ACE, Chao1, Simpson and Shannon indices, respectively.
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Figure 7. The effect of a particular metal on a particular microbial genus: (a) The top 10 soil microbial genera in response to heavy metals in all samples. (b–e) Represent the correlation between a particular metal and a particular microbial genus.
Figure 7. The effect of a particular metal on a particular microbial genus: (a) The top 10 soil microbial genera in response to heavy metals in all samples. (b–e) Represent the correlation between a particular metal and a particular microbial genus.
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Figure 8. The ranking of the correlations between soil microbial genera and heavy metal elements in different tea plantations is as follows: (a–d) represent the correlation between the soil microbial diversity index and heavy metals in different types of tea plantations, namely, PH, UF, LR, and SS.
Figure 8. The ranking of the correlations between soil microbial genera and heavy metal elements in different tea plantations is as follows: (a–d) represent the correlation between the soil microbial diversity index and heavy metals in different types of tea plantations, namely, PH, UF, LR, and SS.
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Table 1. The statistics of soil microbial numbers at each level.
Table 1. The statistics of soil microbial numbers at each level.
Research AreaPhylaClassesOrdersFamiliesGeneraSpecies
PH19 ± 131 ± 174 ± 2120 ± 10173 ± 21241 ± 46
UF18 ± 134 ± 179 ± 5123 ± 1186 ± 7272 ± 14
LR19 ± 234 ± 287 ± 4127 ± 10197 ± 15296 ± 23
SS19 ± 133 ± 375 ± 2113 ± 4176 ± 13246 ± 26
Total2343113176299455
Table 2. The soil microbial diversity index varies in different types of tea plantations.
Table 2. The soil microbial diversity index varies in different types of tea plantations.
Tea Plantation TypeOTUs NumberShannon IndexSimpson
Index		Chao1
Index		ACE
Index
PH501 ± 1537.20 ± 0.440.98 ± 0.00598.36 ± 170.33583.14 ± 170.89
UF610 ± 447.55 ± 0.130.99 ± 0.00716.49 ± 43.87718.70 ± 53.50
LR722 ± 3237.96 ± 0.140.99 ± 0.00814.81 ± 11.95812.72 ± 26.27
SS525 ± 217.03 ± 0.620.97 ± 0.02631.56 ± 34.33620.70 ± 32.70
Average590 ± 1147.43 ± 0.500.98 ± 0.01690.31 ± 116.30683.77 ± 121.89
Table 3. Pearson correlations between soil microbial diversity indices and heavy metals.
Table 3. Pearson correlations between soil microbial diversity indices and heavy metals.
ACEChao1Simpson IndexShannon IndexPbCdHgAsCuNiZn
Chao10.99 **1
Simpson index0.390.371
Shannon index0.81 **0.84 **0.80 **1
Pb0.390.340.520.571
Cd0.68 *0.70 *0.280.62 *0.431
Hg−0.03−0.010.060.040.210.511
As0.040.050.410.26−0.31−0.41−0.61 *1
Cu0.120.160.170.20−0.110.270.35−0.081
Ni0.66 *0.69 *0.150.520.060.70 *0.40−0.220.58 *1
Zn0.430.460.080.39−0.010.64 *0.35−0.050.530.81 **1
Cr0.440.470.110.400.030.66 *0.40−0.100.58 *0.84 **0.99 **
Note: ** Correlation is significant at the 0.01 level, * Correlation is significant at the 0.05 level.
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Huang, X.; Wang, X.; Wu, Q.; Zhang, Z.; Yang, H.; Wen, X. Effects of Multiple-Metal-Compound Contamination on the Soil Microbial Community in Typical Karst Tea Plantations. Forests 2023, 14, 1840. https://doi.org/10.3390/f14091840

AMA Style

Huang X, Wang X, Wu Q, Zhang Z, Yang H, Wen X. Effects of Multiple-Metal-Compound Contamination on the Soil Microbial Community in Typical Karst Tea Plantations. Forests. 2023; 14(9):1840. https://doi.org/10.3390/f14091840

Chicago/Turabian Style

Huang, Xianfei, Xingfu Wang, Qing Wu, Zhenming Zhang, Huili Yang, and Ximei Wen. 2023. "Effects of Multiple-Metal-Compound Contamination on the Soil Microbial Community in Typical Karst Tea Plantations" Forests 14, no. 9: 1840. https://doi.org/10.3390/f14091840

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