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Article

Soil Microbial Communities Show Different Patterns under Different Land Use Types in the Coastal Area of Nantong, China

by
Jinbiao Li
1,2,†,
Anyong Hu
1,†,
Xiuping Wang
3,
Chuang Zhao
1,
Jiarui Jin
1,
Guangming Liu
2,*,
Yujie Han
1 and
Bo Liu
1
1
School of Geographic Science, Nantong University, Nantong 226019, China
2
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
3
Institute of Coastal Agriculture, Hebei Academy of Agriculture and Forestry Sciences, Tangshan 063200, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2023, 13(10), 2613; https://doi.org/10.3390/agronomy13102613
Submission received: 19 September 2023 / Revised: 7 October 2023 / Accepted: 11 October 2023 / Published: 13 October 2023
(This article belongs to the Special Issue Sustainable Water-Fertilizer Management for Soil Salinization Amendment and Crop Production in Salt-Affected Agroecosystems)
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Abstract

:
Tidal flats in eastern China have undergone various transformations into other land-use types. Understanding the impact of land-use conversion on soil properties and microbial communities is crucial for effective ecological conservation efforts. In this study, we compared soil chemical properties and the diversity, composition, and ecological functions of soil bacterial and fungal communities across four land-use types: natural bare land (BL), unused reclaimed tidal land (Phragmites, PL), agricultural land (maize, ML), and shelterbelt land (SL), utilizing next-generation sequencing technology. The results indicated that soil electrical conductivity decreased, while soil organic carbon (SOC) and nutrient contents increased in ML and SL compared to BL and PL. The bacterial Chao1 and fungal Chao1 and Shannon values vary across different land-use types. A higher relative abundance of Acidobacteriota, specifically RB41, was found in ML compared to BL. Principal coordinate and PerMANOVA analysis showed that the composition of bacterial and fungal communities differed significantly across the four land-use types. SOC explained the most variance in both bacterial and fungal communities. Carbon-related functional genes and fungal guilds exhibit greater diversity across the four land-use types compared to nitrogen-related functional genes. In conclusion, the transformation of natural land-use types to managed one greatly altered soil chemical and microbial properties. Our study offers foundational insights into the microbial communities in the typical land-use types of Eastern China’s coastal area. Future studies should emphasize the quantification of human interventions and their impact on soil microbial communities and ecological functions.

1. Introduction

Soil salinization is a widespread problem in many parts of the world, including coastal areas, arid regions, and agricultural lands [1]. China has about 100 million ha of saline-alkali land, of which about 1.3 million are in coastal regions [2]. Coastal saline soil exhibits elevated levels of salinity and sodicity, as well as a high pH value [3]. It is characterized by suboptimal soil quality and structure, a deficiency in soil organic matter, limited hydraulic conductivity, and reduced efficiency in resource utilization [4,5]. Soil salinity and sodicity are leading to land degradation and poor crop yield of the coastal areas [6]. Excess salts disrupt microbial activity, consequently impacting soil processes and functions that rely on microbes [7].
Various regions across China have been making significant efforts to utilize saline-alkali land to enhance crop yields and increase farmers’ income, driven by China’s policies promoting agricultural modernization. In the coastal areas, soils undergo various transformations during these processes, including natural colonization by salt-tolerant plants, cultivation for agricultural purposes, and tree planting for shelterbelts [8]. Studies have shown that the reclamation of coastal saline soils to different land-use types significantly alters soil physicochemical properties [9]. Land-use changes can lead to substantial and enduring alterations in soil salinity, carbon and nutrient levels, soil texture, and soil pH [10,11,12]. These alterations are primarily driven by shifts in above-ground vegetation and associated land management practices across various land-use types. Understanding the influence of land-use change on soil microbial diversity and composition is crucial for soil nutrient cycling, carbon management, food production, and environmental management to ensure the sustainable use of land [13]. Some studies have found no significant or minimal effects of land-use change on soil microbial diversity [14,15]. Some studies found that changes in land-use type greatly alter soil microbial composition and diversities, especially transforming natural forests into croplands [16]. However, little is known about how changes in land-use types, both natural and human-induced, in coastal areas may affect the soil microbiome due to the complex interactions between soil physicochemical factors and land-use changes.
The factors responsible for soil microbial communities can vary due to different land-use changes. For example, He H. et al. [17] found that electrical conductivity (EC), total nitrogen, soil organic matter, and heavy metals such as copper (Cu) and chromium (Cr) are the primary factors driving bacterial differences in the Yellow River Delta, China, after the long-term conversion of natural wetlands to agricultural fields and artificial woodlands. Similarly, He Z. et al. [13] found that soil clay, pH, and moisture were key factors affecting the diversity of both soil bacterial and fungal communities in river basins. In coastal areas, Zhang et al. [18] revealed that soil EC, pH, reclaimed time, and depth significantly affected soil bacterial communities. However, information in driving factors that shaped microbial composition and diversity across different land-use types in the coastal area is still limited and needs further clarification.
It was reported that less than 1% of the microbes could be cultured by conventional cultural techniques [19]. In the past two decades, the Next-Generation Sequencing (NGS) has revolutionized the study of soil microbial communities [20]. Unlike traditional culture-based methods, NGS provides a high-throughput, culture-independent approach that can reveal the full spectrum of microorganisms, including those that are difficult or impossible to culture in the laboratory. Several tools have emerged for forecasting the ecological roles of microbial taxa identified through amplicon-based next-generation sequencing data. Such data could offer profound insights into microbial ecology research and potentially serve as a cost-effective substitute for metagenomic sequencing. In this study, we applied the NGS techniques to study the microbial communities in the coastal area across different land-use types.
The stable and sustainable development of Jiangsu tidal flat, which is the largest continuous muddy tidal flat wetland in China, with the most diverse ecological characteristics and intricate erosion-deposition dynamics, holds utmost importance. The coastal region of Jiangsu province offers a range of land-use statuses and management histories, creating a continuum of increasing soil disturbance intensity that allows us to compare how land use affects microbial community structure. In this study, we investigated the diversity and composition of soil bacterial and fungal communities across four distinct land-use types before and after reclamation. Our main objectives were to (1) assess soil properties in various land-use types, (2) determine the impact of land-use type on the diversity and composition of soil bacterial and fungal communities, and (3) identify soil chemical properties responsible for changes in microbial communities.

2. Materials and Methods

2.1. Study Area and Sample Collection

This study was conducted in a reclaimed tidal flat coastal area (121.3937° E–121.4145° E, 32.3537° N–32.3556° N) in Rudong County, Nantong City, China. This area has a long history of tidal flat reclamation, and the sampling points were mainly reclaimed during the last two decades [21]. After reclamation, aquaculture ponds and agriculture were the extensive types of land use [22]. However, in this study, to compare the microbial diversity in the terrestrial soil ecosystems, we selected bare tidal land (BL), unused reclaimed tidal land (with Phragmites naturally growing, PL), agricultural land (maize, ML), and shelterbelt land (SL) as the research objects. The study area belongs to a north subtropical humid climate zone. The annual mean temperature in the study area is 16.1 °C, and the annual mean precipitation is 1045 mm [23]. The soil in this area is classified as coastal tideland solonchaks [24]. The BL and PL are rarely affected by anthropogenic activities. Fertilizers with an application rate of ~300 kg N ha−1, ~50 kg P ha−1, 50 kg K ha−1, and some occasional farmyard manure are deposited into ML annually. A small amount of fertilizer (~100 kg N ha−1) was applied to the SL when trees were planted and during annual management. The primary plants in the SL were Populus and Fraxinus chinensis, with some Chinese mugwort and Phragmites [25].
Following a completely randomized design, we collected soil samples from the four land-use types in July 2022. In total, 16 sampling sites (four of each land use type) were selected with a distance > 400 m between each two sites. For sample collection, five soil cores were drilled from the top mineral soil layer (0–20 cm) and then amalgamated into a ziplock bag. All soil samples were stored in a cooler and transferred to the lab. After removing none-soil components, each soil sample was passed through a 2 mm mesh and divided into two parts; one was air-dried and further sieved to analyze soil chemical properties, and another was stored at −80 °C until DNA extraction. For soil total carbon (TC), total nitrogen (TN), and soil organic carbon (SOC), soils passed through a 0.149 mm mesh.

2.2. Soil Chemical Properties Determination

To measure soil electrical conductivity (EC) and pH, soil samples were extracted using a 1:5 soil-to-water (w:v) suspension, centrifuged, and then the supernatant was measured using an F-38 EC meter and an F-20 pH meter (Mettler Toledo Ltd., Shanghai, China), respectively. The SOC was determined using the wet oxidation-redox titration method [26]. Soil TC and TN were determined using the dry combustion method on an elemental analyzer (Elementar Vario EL CUBE, Langenselbold, Germany). Soil available phosphorus (AP) was determined using a molybdenum antimony colorimetric method [27].

2.3. DNA Extraction and Amplicon Sequencing

DNA was extracted using a E.Z.N.A.® soil DNA kit (Omega Bio-tek, Norcross, GA, USA) according to the manufacturer’s instructions and was carried out by Major Bio-pharm Technology Co., Ltd. (Shanghai, China). The concentration and purity of the DNA were assessed using a NanoDrop 2000 spectrophotometer. The V3-V4 hypervariable region of the 16S rRNA gene of bacteria was amplified using primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) [28]. The ITS region of fungi was amplified using primers ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5′-GCTGCGTTCTTCATCGATGC-3′) [29]. Polymerase Chain Reaction (PCR) for 16S was conducted using a TransGen AP221-02 20 μL reaction system, with 4 μL 5× FastPfu Buffer, 2 μL 2.5 mM dNTPs, 0.8 μL Forward Primer (5 μM), 0.8 μL Reverse Primer (5 μM), 0.4 μL FastPfu Polymerase, 0.2 μL BSA, 10 ng Template DAN, and a final volume of 20 μL was made using ddH2O. PCR for ITS was conducted using a TaKaRa rTaq 20 μL reaction system, with 2 μL 10× FastPfu Buffer, 2 μL 2.5 mM dNTPs, 0.8 μL Forward Primer (5 μM), 0.8 μL Reverse Primer (5 μM), 0.2 μL rTaq Polymerase, 0.2 μL BSA, 10 ng Template DAN and a final volume of 20 μL was made using ddH2O. PCR conditions were: 3 min at 95 °C as an initial denaturation step, 35 cycles for 16S (25 cycles for ITS) at 95 °C for 30 s, annealing at 55 °C for 30 s, extension at 72 °C for 45 s, after cycles running, a final elongation step at 72 °C for 10 min. The purified amplicons were pooled in equimolar and sequenced on an Illumina MiSeq PE300 platform (Illumina, San Diego, CA, USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China).

2.4. Sequenced Data Processing

Raw reads of 16S and ITS were processed using Divisive Amplicon Denoising Algorithm 2 (DADA2) package (v1.26.0) [30] in RStudio [31] following a published workflow [32], which includes amplicon denoising, quality inspecting, filtering and trimming, dereplication and sample inference, paired reads merging, chimeras removing and taxonomy assigning amplicon sequence variants (ASVs) and taxonomy files. For bacteria, taxonomy was assigned based on SILVA SSU taxonomic training data formatted for DADA2 [33]. For fungi, taxonomy was assigned based on General FASTA Release files from the UNITE ITS database [34]. The Functional Annotation of Prokaryotic Taxa (FAPROTAX) [35] and the FUNGUild tool [36] were used to annotate bacterial and fungal ecological functions, respectively.

2.5. Statistical Analyses

Statistical analyses were conducted using the microeco package (v0.19.0), following a published workflow [37] which includes stacked bar plots of taxa at phylum rank, heatmap of taxa at genus rank, alpha (α) diversity indices (Chao1 and Shannon values), principal coordinate analyses, and microbial function predictions.

3. Results

3.1. Characteristics of Soils

The soil EC, pH, TC, TN, SOC, and AP were significantly different in different land-use types (Table 1). Soil EC was the highest in BL. Soil pH was the lowest in ML. Soil TC, TN, and SOC were significantly (p < 0.05) higher in SL and ML than in BL and PL. Soil AP was significantly (p < 0.05) higher in ML than in BL.

3.2. Bacterial and Fungal Taxa in Soils of Different Land-Use Types

A data set of 383,099 (bacteria) and 801,560 (fungi) quality sequences was produced from the 16 soil samples. Among the four land-use types, there were 62 shared bacterial ASVs, accounting for 9.6% of the total bacterial sequences, and 40 shared fungal ASVs, accounting for 34.6% of the total fungal sequences, respectively (Figure S1).
At the phylum rank, the bacterial communities were dominated by Proteobacteria (relative abundance of 27.89%, on average, same below), Actinobacteriota (19.41%), Chloroflexi (14.23%), and Acidobacteriota (14.07%) (Figure 1A). The relative abundance of Actinobacteriota and Acidobacteriota was higher in ML than in other land-use types (Figure 1A). The relative abundance of Firmicutes was higher in BL and PL than in ML and SL (Figure 1A). The fungal communities were dominated by Ascomycota (63.16%), Basidiomycota (8.22%), Mortierellomycota (7.84%), and Glomeromycota (1.65%) (Figure 1B). The relative abundance of Basidiomycota and Mortierellomycota was higher in ML and SL than in BL and PL (Figure 1B).
At the genus rank, the bacterial composition was similar in BL and PL, with Bacillus and Pseudarthrobacter having a higher relative abundance. While Pseudarthrobacter and RB41 had a higher relative abundance in ML, no dominant bacterial genera were found in SL (Figure S2A). Similarly, the fungal composition was much similar in BL and PL, with Alternaria, Plectosphaerella, and Giberella having a higher relative abundance. Additionally, in ML and SL, Mortierella had the highest relative abundance (Figure S2B).

3.3. Alpha and Beta Diversity of the Microbial Communities

Chao1 and Shannon values were calculated to compare the richness and diversity in soils of different land-use types in the coastal area (Figure 2). The bacterial Chao1 value was the highest in SL, significantly higher than in PL and ML (Figure 2A). The fungal Chao1 value was the highest in ML and the lowest in SL (Figure 2B). The bacterial Shannon value was not significantly different in the four land-use types (Figure 2C). The fungal Shannon value was the highest in ML, significantly higher than in BL and SL (Figure 2D).
Principal coordinate analysis was conducted to explore the difference in microbial communities of different land-use types. The first two axes of the PCoA explained 50.1% and 34.1% of the variations in the bacterial and fungal communities, respectively (Figure 3). On the PCoA plots, the samples were clearly clustered together based on the land-use types. The PerMANOVA results showed that the bacterial composition was significantly different among the four land-use types (R2 = 0.63, p = 0.001), as well as the fungal composition (R2 = 0.46, p = 0.001).

3.4. Relationship between Soil Properties and the Microbial Communities

The effects of soil chemical properties on bacterial and fungal communities were identified using RDA (Figure 4). The six selected soil properties explained 79.6% of the total variance in bacterial communities and 79.5% in fungal communities, respectively. Furthermore, SOC independently explained 61.0% of the variance in bacterial communities (padj = 0.002), followed by TN (59.1%, padj = 0.002), TC (59.0%, padj = 0.002), and EC (27.3%, padj = 0.027) (Table S1). Similarly, SOC (57.0%, padj = 0.002), TC (52.9%, padj = 0.002), and TN (51.2%, padj = 0.002) significantly independently explained variance in the fungal communities (Table S1).

3.5. Bacterial and Fungal Ecological Functions

The FAPROTAX analysis predicted that most carbon cycle function genes were significantly different among the four land-use types (Figure 5A). PL had more abundant chemoheterotrophic and aerobic chemoheterotrophic bacterial groups than BL. On the other hand, SL had more abundant phototrophic, photoheterotrophic, and oxygenic photoautotrophic bacterial groups than the other land-use types (Figure 5A). BL had more abundant nitrate reduction bacterial groups than the other three land-use types (Figure 5B). The FUNGUild analysis predicted that most of the fungal ecological guilds were significantly different among the four land-use types (Figure 5C). Furthermore, most of these guilds, such as saprotroph, arbuscular mycorrhizal, and animal pathogen, exhibited higher relative abundances in PL, ML, and SL compared to BL (Figure 5C).

4. Discussion

Our results showed that land-use type had significant effects on soil properties and microbial communities.
Land-use patterns have been verified as an important factor affecting soil properties in the studied area [38]. The BL and PL exhibited higher salt content (EC) and lower levels of SOC and nutrients (TC, TN, and AP) compared to ML and SL (Table 1). Compared to ML and SL, BL and PL had higher salinity (EC values); one of the reasons is that BL and PL are frequently influenced by seawater intrusion. On the other hand, to protect the agricultural and shelterbelt areas, a seawall was built to cut off the recharge of saline phreatic water in the studied area, which lowered the salinity in the soils [38]. BL and PL had lower SOC and soil nutrient content, mainly because no management and no fertilization were applied [39]. Furthermore, the significantly higher SOC in SL than in ML is probably due to the different plant roots and litter deposition of the vegetation [40]. Farmers usually remove crop residuals to prepare the tillage area for the next cropping season. Additionally, the SOC turnover rate also greatly depended on the land cover types, with agricultural land consistently having a higher rate than forest land, which resulted in a lower SOC stock [41].
The soil nutrient cycle is mainly driven by soil microbes, and changes in microbial community composition and diversity could be driven by changes in land-use type [16,42,43]. An increase in Chao1 and Shannon values indicates a more diverse community. Despite variations in microbial Chao1 values, the bacterial Shannon value remained unchanged, while the fungal Shannon value significantly increased only in ML compared to BL in our study (Figure 2), suggesting the fungal community has become more diverse in ML. This could be attributed to cultivation practices, such as the use of farmyard manure, which contribute to higher Shannon values [44]. On the other hand, it has been reported that habitat conversion can lead to biodiversity declines, especially when human activities are involved [45]. This supports the observation that bacterial Chao1 values declined in ML compared to BL, given the involvement of human activities in tillage, fertilization, and crop species [45]. However, the fungal Chao1 value increased in ML but declined in SL, likely due to the interaction between plant detritus production and resource limitations in these two land-use types [46]. There are also many other reasons, such as pollution [47] and non-native species [48], that can cause declines in microbial diversity. In the case of SL, their proximity to the road makes them particularly susceptible to industrial pollutants, such as vehicular emissions [49].
According to PCoA and PerMANOVA analyses, the bacterial and fungal community compositions were largely different among the four land-use types. Regarding bacterial composition, Proteobacteria, Actinobacteriota, Chloroflexi, Acidobacteriota, Firmicutes, and Bacteroidota representing more than 75% of the total bacterial sequences; this result was consistent with Wang et al. [50], who found these six phyla represent more than 70% of the total bacterial sequences in a reclaimed mudflat. Furthermore, the relative abundance of Proteobacteria decreased, and the relative abundance of Acidobacteriota increased in managed soils (ML and SL) compared to natural soils (BL and PL). Wang et al. [50] found a similar trend following mudflats reclamation over time, which could be attributed to the intensity of disturbance caused by human activities. However, Acidobacteriota, typically known as oligotrophic bacteria, were enriched in ML. This result is similar to that of Chen et al. [51], who found that Acidobacteriota were highly enriched in farmland. Furthermore, the genus RB41, which belongs to Acidobacteriota, was abundant in ML (Figure S2). This genus plays a key role in the soil carbon cycle [52].
Regarding fungal composition, the majority of ASVs in the studied area belonged to Ascomycota, Basidiomycota, Mortierellomycota, and Glomeromycota. This finding was consistent with an investigation conducted on reclaimed land in the coastal area of Eastern China, even though the aboveground plant species may differ [53]. Furthermore, studies have shown that in several ecosystems, fungi remained relatively stable, and their diversity and composition were not as sensitive as those of bacteria [10,43,54]. However, significant modifications in salinity and plant species contribute to changes in fungal communities. Compared to BL and PL, ML and SL exhibited higher relative abundances of the fungal phyla Basidiomycota and Mortierellomycota, as well as the fungal genera Mortierella. These fungi are diverse fungal phyla that include many wood-decomposing and organic matter-degrading groups [55,56,57]. This functional diversity may help explain their higher relative abundance in relatively nutrient-rich soils. Besides, the relative abundance of fungal genera Metarhizium (Ascomycota) and Humicola (Ascomycota) had higher relative abundance in ML than in other land-use types (Figure S2). Metarhizium is well-known for its ability to infect and kill many arthropods [58]. Its higher abundance in ML is probably related to the higher number of arthropods. Humicola can degrade phenolic compounds [59], and its higher relative abundance in ML may be related to pesticide usage.
Most of the carbon cycle functional genes and fungal functional guilds differed significantly among the four land-use types, unlike the nitrogen cycle genes (Figure 5). This further indicates that carbon-related microorganisms have undergone more noticeable changes. The RDA results also indicated that SOC explained the most variance in soil microbial communities. Due to plant growth and agricultural practices, significant changes have occurred in the status of soil organic carbon and its relationship with soil microorganisms [60]. However, predicting the direct relationship between SOC status and soil microorganisms is challenging due to the complex interactions with salinity and plant factors in the studied area. Future studies may focus on conducting in-depth investigations into the effects of land use on carbon-related functional microorganisms.

5. Conclusions

Soil properties underwent significant changes due to land-use types in the coastal area. Soil EC was lower in managed soil (ML and SL) than in natural soil (BL and PL), whereas SOC and nutrient contents were higher in managed soil than in natural soil. Soil bacterial and fungal communities’ composition was significantly shifted by land-use type. SOC explained the most variance in both bacterial and fungal communities across the four different land-use types. The carbon cycle functional genes undergo more changes than the nitrogen cycle functional genes after BL is transformed to other land-use types. This study mainly qualitatively examines the impact of different land-use practices on microbial communities. The quantification of human interventions and their impact on soil microbial communities may be a key focus in the future.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy13102613/s1, Figure S1: Venn diagram of soil bacterial (A) and fungal (B) amplicon sequence variants (ASVs); Figure S2: The relative abundance of taxonomic composition at the genus rank for bacteria (A) and fungi (B) across all soil samples; Table S1: The variance each soil chemical variable can explain independently from others in soil bacterial and fungal communities.

Author Contributions

Conceptualization, J.L. and A.H.; data curation, C.Z. and J.J.; funding acquisition, G.L.; investigation, A.H.; methodology, J.L.; supervision, G.L. and B.L.; validation, Y.H. and B.L.; writing—original draft, J.L. and A.H.; writing—review and editing, X.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program (2022YFD1900104), the Science and Technology Cooperation Project of Inner Mongolia (2021CG0037), and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (23KJB210010).

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The relative abundance of taxonomic composition at the phylum rank for bacteria (A) and fungi (B) across all soil samples. Abbreviations: BL, bare land; PL, Phragmites land; ML, Maize land; SL, Shelterbelt.
Figure 1. The relative abundance of taxonomic composition at the phylum rank for bacteria (A) and fungi (B) across all soil samples. Abbreviations: BL, bare land; PL, Phragmites land; ML, Maize land; SL, Shelterbelt.
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Figure 2. Soil Chao1 and Shannon diversity indices for bacterial (A,C) and fungal (B,D) in different land-use types. Treatments not sharing any lowercase letters are significantly different in each figure. Abbreviations: BL, bare land; PL, Phragmites land; ML, Maize land; SL, Shelterbelt.
Figure 2. Soil Chao1 and Shannon diversity indices for bacterial (A,C) and fungal (B,D) in different land-use types. Treatments not sharing any lowercase letters are significantly different in each figure. Abbreviations: BL, bare land; PL, Phragmites land; ML, Maize land; SL, Shelterbelt.
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Figure 3. Principal coordinate analysis (PCoA) based on Bray–Curtis dissimilarity matrix for bacterial (A) and fungal (B) community. Abbreviations: BL, bare land; PL, Phragmites land; ML, Maize land; SL, Shelterbelt.
Figure 3. Principal coordinate analysis (PCoA) based on Bray–Curtis dissimilarity matrix for bacterial (A) and fungal (B) community. Abbreviations: BL, bare land; PL, Phragmites land; ML, Maize land; SL, Shelterbelt.
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Figure 4. Redundancy analysis (RDA) for bacterial (A) and fungal (B) community. Abbreviations: EC, electrical conductivity; SOC, soil organic carbon, TC, total carbon; TN, total nitrogen; AP, available phosphate; BL, bare land; PL, Phragmites land; ML, Maize land; SL, Shelterbelt.
Figure 4. Redundancy analysis (RDA) for bacterial (A) and fungal (B) community. Abbreviations: EC, electrical conductivity; SOC, soil organic carbon, TC, total carbon; TN, total nitrogen; AP, available phosphate; BL, bare land; PL, Phragmites land; ML, Maize land; SL, Shelterbelt.
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Figure 5. Function predicted by FAPROTAX (A,B) and FUNGUild (C). (A), bacterial functions related to carbon cycle; (B), bacterial functions related to nitrogen cycle; (C), fungal functions. * indicates a significant difference among the four land-use types at p < 0.05.
Figure 5. Function predicted by FAPROTAX (A,B) and FUNGUild (C). (A), bacterial functions related to carbon cycle; (B), bacterial functions related to nitrogen cycle; (C), fungal functions. * indicates a significant difference among the four land-use types at p < 0.05.
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Table 1. Soil chemical properties of samples collected from different land-use types in coastal areas.
Table 1. Soil chemical properties of samples collected from different land-use types in coastal areas.
Land Use 1BLPLMLSL
EC
(mS cm−1)		14.52 ± 0.81 a1.80 ± 1.15 b0.73 ± 0.17 bc0.17 ± 0.08 c
pH8.48 ± 0.30 ab8.93 ± 0.36 a7.99 ± 0.20 b8.45 ± 0.09 ab
TC
(g kg−1)		12.18 ± 0.57 c11.66 ± 0.35 c14.71 ± 0.24 b16.69 ± 0.91 a
TN
(g kg−1)		0.34 ± 0.02 c0.32 ± 0.02 c0.78 ± 0.08 b0.98 ± 0.08 a
SOC
(g kg−1)		4.35 ± 0.74 c2.94 ± 0.56 d6.26 ± 0.33 b10.09 ± 0.84 a
AP
(mg kg−1)		40.07 ± 3.97 b41.79 ± 5.20 ab49.70 ± 4.40 a43.16 ± 0.95 ab
1 Data are mean ± SD values. Means in each row not sharing any lowercase letters are significantly different (p < 0.05, Tukey’s HSD test). Abbreviations: BL, bare land; PL, Phragmites land; ML, Maize land; SL, Shelterbelt.
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Li, J.; Hu, A.; Wang, X.; Zhao, C.; Jin, J.; Liu, G.; Han, Y.; Liu, B. Soil Microbial Communities Show Different Patterns under Different Land Use Types in the Coastal Area of Nantong, China. Agronomy 2023, 13, 2613. https://doi.org/10.3390/agronomy13102613

AMA Style

Li J, Hu A, Wang X, Zhao C, Jin J, Liu G, Han Y, Liu B. Soil Microbial Communities Show Different Patterns under Different Land Use Types in the Coastal Area of Nantong, China. Agronomy. 2023; 13(10):2613. https://doi.org/10.3390/agronomy13102613

Chicago/Turabian Style

Li, Jinbiao, Anyong Hu, Xiuping Wang, Chuang Zhao, Jiarui Jin, Guangming Liu, Yujie Han, and Bo Liu. 2023. "Soil Microbial Communities Show Different Patterns under Different Land Use Types in the Coastal Area of Nantong, China" Agronomy 13, no. 10: 2613. https://doi.org/10.3390/agronomy13102613

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