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Article

Diversity and Structure of Vegetation Rhizosphere Bacterial Community in Various Habitats of Liaohekou Coastal Wetlands

by
Yinchu Liu
1,
Zhen Guo
2,*,
Peidong Zhang
1,*,
Jun Du
2,
Ping Gao
2 and
Zhiwei Zhang
2
1
College of Environment and Safety Engineering, Qingdao University of Science and Technology, Qingdao 266042, China
2
Coastal Science and Marine Policy Center, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
*
Authors to whom correspondence should be addressed.
Sustainability 2022, 14(24), 16396; https://doi.org/10.3390/su142416396
Submission received: 17 October 2022 / Revised: 29 November 2022 / Accepted: 1 December 2022 / Published: 7 December 2022
Browse Figures
Review Reports Versions Notes

Abstract

:
Coastal wetlands are a type of unique ecosystem, in which rhizosphere microorganisms of vegetation play a significant role in the overall ecology. Rhizosphere soil samples from the vegetation of Liaohekou Coastal Wetlands, Northeast China (40°54′44″ N, 121°47′51″ E), were collected in seven habitats (Suaeda and Phragmites community in different coverage, aquaculture ponds and farmland biotopes) to evaluate the diversity and structure of bacterial community using high throughput sequencing. Soil physicochemical characteristics and bacterial communities were found to be affected by vegetation coverage by ANOVA tests. As and Ni were the main heavy metal variables affecting the bacterial communities as demonstrated by RDA tests, while NO3−-N were important variables in nutrient factors. Proteobacteria was the predominant phylum in all soils. Gillisia and Woeseia were the two most dominant genera peculiarly in Suaeda and Phragmites community. Meanwhile SparCC showed that Woeseia play a dominant role in wetland rhizosphere bacterial communities. The Chemoheterotrophic function was dominant in all communities with FAPROTAX results, while in wetland the cycle of Sulfur and Nitrogen were significantly affected by vegetation type and coverage. In conclusion, this study revealed the structural composition and diversity of rhizosphere bacterial communities under different vegetation types and coverage. This research could help deepen our understanding of the microbial ecology on the wetlands and provide information on bacterial communities in various habitats.

1. Introduction

Soil microorganisms play an important role in ecosystem function [1]. Their diversity can not only describe any complex ecological environment but also reflect the changes of the environment earlier. It has been considered an important biological index in the ecosystem. Among soil microorganisms, bacteria have the largest number and species, the widest distribution, and great metabolic potential [2]. They participate in the carbon and nitrogen material cycle, the formation of soil structure, promoting plant growth, and improving the ecological environment [3]. The bacterial communities in the rhizosphere affect the content and composition of nutrients around the roots of vegetation. They directly or indirectly affect the growth of plants and even interact with each other [4,5]. Soil microorganisms play an indispensable role in plant nutrient absorption and plant community regulation. They not only affect but also simultaneously respond to the changes in the plant growth process [6]. Therefore, studying the diversity and function of the rhizosphere soil bacterial community is helpful to understand the relationship between plants, soil microorganisms, and their environment [7].
The Liaohe Estuary is a typical suaeda-reed coastal wetland area that plays an important role in international biodiversity conservation. It is a sedimentary plain in the lower reaches of the river, with a low and flat terrain and an altitude of 0–6.5 m. However, with the increasing development and utilization of coastal zone in recent years, it has gradually led to a series of problems, such as coastal wetland shrinkage, beach erosion, habitat fragmentation, environmental pollution and so on [8]. Meanwhile, rivers in the Liaohe River Estuary area have been seriously polluted by discharges of wastewater containing petroleum pollutants and nutrients [9]. At the same time, salinity, heavy metals, nutrients and so on can greatly affect and change the microbial community structure of wetlands [10,11]. Recent studies have shown the distribution pattern of the Liaohekou Coastal Wetlands plant and explored the different types of vegetation on soil properties and the influence of the vegetation rhizosphere bacterial communities diversity [12,13]. Studies have also revealed the relationship between the physical and chemical properties of coastal wetlands and the root system microorganisms of Suaeda glauca [14].
Therefore, it is very important to explore the impact of vegetation types on the rhizosphere microbial community of Liaohekou Coastal Wetlands. In this study, we take Liaohekou Coastal Wetland, one of the largest wetland in Asia as typical study area. We preserved and pretreated the wetland vegetation rhizosphere soil after sampling, then extracted total DNA of samples using the DNA Kit, carried out PCR amplification, purification and quantitative operations, and conducted a preliminary study on soil and plant rhizosphere bacteria through high-throughput sequencing platform. This study revealed the structure and abundance characteristics of rhizosphere bacteria in different types of vegetation in wetlands, predicted their community function, and analyzed their relationship with soil environmental factors. The results of this study will provide a perspective of microbial ecology for understanding the current situation of vegetation rhizosphere ecology in different habitats of wetlands and the means of wetland restoration.

2. Materials and Methods

2.1. Study Area

The research site was located in the estuary area of Liaoning Liaohekou National Nature Reserve (121°28′ E–121°58′ E, 40°45′ N–41°05′ N) (Figure 1). Located at the top of Liaodong Bay in the Bohai Sea and the center of Liaohe River Delta, it’s formed by the alluvial deposits of Liaohe River, Daling River, Xiaoling River, and many other rivers. Liaohekou National Nature Reserve in Liaoning Province has a temperate semi-humid monsoon climate, with an average annual precipitation of 650 mm and an average annual temperature of 8.5 °C. With a lot of nutrients depositing in the soil, Liaohekou Coastal Wetland is a kind of estuary wetland that is suitable for the reproduction of various organisms. The ecological types are mainly reed swamp, river water area, shallow beach, and sea area, and the main wetland plants are Phragmites australis, Suaeda glauca, Typha orientalis Presl, Pinellia ternata and so on. Four replicate plots (1 m × 1 m) at Liaohekou Coastal Wetlands, Northeast China (40°54′44″ N, 121°47′51″ E) were randomly selected as the wetland vegetation study area.

2.2. Soil Samples Collection

To study and compare the bacterial community diversity of Suaeda glauca and Phragmites australis under different coverage. The rhizosphere soil was collected separately (approximately 100 g) at the different sites from Suaeda glauca and Phragmites australis community on 11 September 2020 (Figure 1). Meanwhile, the vegetation rhizosphere soil in the surrounding aquaculture ponds and farmland biotopes were collected under similar longitude and latitude for control groups (Figure 1). Soils attached to the roots (5–10 cm depth) were placed in plastic sampling bag by shovels, and each replicate contained approximately 100 g (fresh weight) soils pooled fromseveral sampling points. One part of the soils for physicochemical analysis was dried at room temperature, and the other part for bacterial community analysis was stored in a dark environment at −80 °C. All the samples were respectively abbreviated as high coverage Suaeda glauca community (HC_Su), high coverage Phragmites australis community (HC_Ph), low coverage Suaeda glauca community (LC_Su), low coverage Phragmites australis community (LC_Ph), Suaeda glauca and Phragmites australis mixed community (MIX), aquaculture ponds community (PAD), farmland biotopes (CUL). For convenience, the following charts, pictures, texts will use the above abbreviations to replace the original community name.

2.3. Physicochemical Analysis

Soil pH was determined using the 1:10 soil-to-water ratio [15] by a PHSJ-3F pH meter (Shanghai INESA Scientific Instrument Co., Ltd., Shanghai, China). Determination of total carbon (TC) was performed on a Vario EL elemental analyzer (Elementar Co., Langenselbold, Germany). Ammonia (NH4+-N), nitrate (NO3−-N), and nitrite (NO2−-N) in soils were extracted with 2 mol·L−1 KCl, and then determined by a SAN++ continuous flow analyzer (Skalar Co., Delft, Dutch). Soil Cr, Cd, Pb, Ni, Hg, and As were digested with HCl–HNO3–HClO4 on a hot plate, and then measured by inductively coupled plasma-optical emission spectrometry (ICP-OES). Statistical significance of the soil properties was examined by the Shapiro-Wilk and Levene tests before the one-way analysis of variance (ANOVA). The p value less than 0.05 suggested significance in ANOVA results.

2.4. High-Throughput Sequencing

DNA from different samples was extracted using the E.Z.N.A. ®Stool DNA Kit (D4015, Omega, Inc., Syracuse, NY, USA) according to the manufacturer’s instructions. PCR amplifications were carried out using primer 341F (5’-CCTACGGGNGGCWGCAG-3’)and 805R (5’-GACTACHVGGGTATCTAATCC-3’) targeting the V3-V4 fragments of 16S rRNA gene. Then, an Illumina NovaSeq platform was used for the high-throughput sequencing at LC-Bio Technology Co., Ltd., Hang Zhou, Zhejiang Province, China. After sequencing, Paired-end reads were assigned to samples based on their unique barcode and truncated by cutting off the barcode and primer sequence. Paired-end reads were merged using FLASH. Quality filtering on the raw reads was performed under specific filtering conditions to obtain high-quality clean tags according to the fqtrim (v0.94). Chimeric sequences were filtered using Vsearch software (v2.3.4). After dereplication using DADA2, the feature table and feature sequence were obtained.

2.5. Data Analysis

Alpha diversity and beta diversity were calculated by normalizing to the same sequences randomly. Then according to SILVA (release 132) classifier, feature abundance was normalized using the relative abundance of each sample. Alpha diversity is applied in analyzing the complexity of species diversity for a sample through 5 indices, including Chao1, Observed species, Goods coverage, Shannon, Simpson, and all these indices in our samples were calculated with QIIME2. The violin figure about Shannon index was made using Kruskal-Wallis test. Beta diversity was calculated by QIIME2. Blast was used for sequence alignment, and the feature sequences were annotated with the SILVA database for each representative sequence. SparCC was used to calculate the correlation between species through the abundance and change relationship of different species in each sample, and find the related species through screening under certain conditions to help us better understand the relationship between dominant genera. SparCC fits the observed data to the Dirichlet distribution, then calculates the proportion and correlation of species repeatedly [16]. The final correlation is the median of the distribution (rho). We used RDA analysis to explore the relationship between bacterial community and environmental factors. Mantel tests were conducted simultaneously with RDA analysis to test the correlation between environmental factor variables and community structure changes. FAPROTAX was used for acquiring more accurate bacterial community function prediction. The relative abundances of bacterial community functional groups per sample were calculated as the OTUs cumulative abundance allocated to each functional group. OTUs without any functional annotation were excluded from the analysis and then inquired about potential functional groups involved in soil environmental conditions to get the final grouping information table. All the bioinformatic analysis was performed using the online OmicStudio tools (https://www.omicstudio.cn/tool (accessed on 1 June 2022)) (R version 4.0.3). The raw sequencing data have been submitted to NCBI Sequence Read Archive (SRA) with the BioProject number PRJNA823859.

3. Results and Discussion

3.1. Soil Physicochemical Properties

As shown in Table 1, the soil properties exhibited significant variations among the different habitats and vegetation coverage. Soil pH was significantly different among the different coverage vegetation with the highest value detected in HC (8.78), followed by PAD (8.48), which could be one of the factors leading to the difference in rhizosphere microbial community [12]. The level of TC in the soils was 7.57–12.09 g·kg−1 (in dry weight basis, similarly hereinafter), respectively, which were more plentiful in HC and MIX. This should be caused by higher vegetation density of MIX community and HC community. The ANOVA results show that there are no significant differences in NH4+-N among all samples (p > 0.05), although results revealed that NO3−-N differed greatly from groups (p < 0.001) (Table S1). This may indicate that NH4+-N is a more stable and main form of nitrogen in wetland soil. Similar findings also demonstrated the dominance of NH4+-N among inorganic nitrogen in the soils from Chinese estuarine wetlands [15]. Also in the wetland soils, HC and MIX had significantly higher inorganic nitrogen content compared to others, a phenomenon that is more favorable to nitrogen cycling in their rhizosphere soil [17]. NO3−-N were more enriched in CUL, which could be caused by the agricultural planting mode [18]. The relatively large methane emission from aquaculture ponds may be the reason for the low level of NH4+-N in PAD [19]. Plant species richness could reduce the uptake of mineral nitrogen to microorganisms, so the mineral nitrogen in the soil of HC and MIX was relatively high [20].

3.2. Diversity Indexes and Differences in Bacterial Community

A total of 1,862,656 clean sequences were obtained from 28 soil samples by high-throughput sequencing, and the observed OTUs ranged from 2024 to 4271 with Good’s coverage over 0.99 across all samples, suggesting that the sequencing results can well represent the real situation of the bacterial community (Table S2). Rank abundance analysis generally reflects the good species abundance and evenness from the level of whole OTUs (Figure S1), while the Simpson index in all samples valuing close to 1 also proved that (Table S2). Chao1 values demonstrated that the taxa richness of CUL and PAD were higher than that of the wetland vegetation. The Shannon index varied from 7.74 to 11.11 and showed significant differences among communities (Kruskal-Wallis, p = 0.016), which could indicate that the variety of vegetation type and coverage lead to the diversity of rhizosphere bacterial community (Figure S2).
The results of the beta-diversity were obtained by PCA (Figure 2a) and PCoA (Figure 2b) analysis, which showed that rhizosphere bacteria had a certain separation during the CUL, PAD and wetland communities, but the overall wetland vegetation samples has comparatively little difference in distribution. NMDS (Figure 2c) shows similar results to the above, with further possessing a more evident and distinct dissimilarity degree (stress function < 0.2). Bray-Curtis distance (Figure S3) revealed that the community similarity between the MIX and Ph is higher, and they had a conspicuous difference with Su, which could indicate that the composition of soil bacteria in the suaeda-reed mixed community is more inclined to phragmites community. ANOSIM and Adonis results (Table S3) both explained the remarkable distinction between groups (p < 0.01).

3.3. Composition and Structure of the Bacterial Community

The 16S rRNA gene sequencing tags were phylogenetically classified into 69 phyla, 197 classes, 815 families, and 1860 genera. At the phylum level classification, Proteobacteria has a significant advantage with an average abundance of 45.57%, with the other dominant phylum mainly including Bacteroidetes, Actinobacteria, Gemmatimonadetes, Acidobacteria, Chloroflexi, Planctomycetes, Firmicutes, and Verrucomicrobia (Figure 3). The sum of the relative abundances of the above phylum accounted for more than 90% in all samples, indicating their stability in the wetland. A similar phylum composition structure was found in previous wetland soil investigations in the Jiulong River estuary [21] and intertidal sediments in the Yellow Sea [22]. As the dominant phylum in soil samples, Proteobacteria was involved in nitrification, denitrification, and sulphuration [23,24]. Playing the leading role in a large number of processes related to the nitrogen cycle and sulfur cycle, it means that Proteobacteria was the core rhizosphere bacterial community in the microbial community structure of wetland vegetation. The relative abundance of Bacteroidetes was higher in wetland habitats (10.91–19.95%), but lower in CUL (2.91%) and PAD (6.90%), which indicated that the diversity of plant rhizosphere bacterial community is closely related to habitat types. In particular, Bacteroidetes plays a major role in the carbon cycle, which is specifically embodied in the degradation of organic matter, the absorption and utilization of organic matter [25]. Actinobacteria is the main bacterial community related to the function of degrading complex polymers [26], and Gemmatimonadetes have been proved to be closely related to phototrophy [27], so they also play an important role in the overall ecological function of wetland.
For the genus level, significant differences were reflected among habitats. Gillisia (7.48% on average) of Bacteroidetes and Woeseia (6.32% on average) of Proteobacteria were the two species with the highest relative abundance in wetland habitats (Figure S3), which is highly similar to the results of the study in Shuangtaizi River Estuarine wetland [15]. Figure 4 indicated that Woeseia strongly correlated with multiple dominant genera proving that it may play a dominant role in wetland rhizosphere bacterial community, and has a positive correlation with Gillisia (rho > 0.4). Gillisia was discovered possibly to be interrelated with the hormetic responses of soil alkaline phosphatase in some conditions [28]. Also, recent studies inferred that Gillisia could contribute to the bioremediation of organic pollutants [15]. Woeseia has been widely found in the rhizosphere of various vegetation in estuary wetlands in recent years [29,30], with research finding its pivotal participation in biogeochemical cycling [31]. Combined with their positive correlation and relative abundance advantages, it is speculated that Woeseia and Gillisia played a core and positive role in wetland rhizosphere ecology. Moreover, Thiobacillus was found to be more enriched in CUL (1.41%) and PAD (4.62%) than wetland vegetation (0.33% on average), which indicated that bacterial community of wetland vegetation has relatively inferior chemoautotrophy ability.

3.4. Correlation Analysis of Environmental Factors

RDA assessed the linkages between soil properties and bacterial community structure (Figure 5). In nonmetal environmental factors, The ANOVA tests identified that NO3−-N (F = 41.709, p < 0.05), pH (F = 4.072, p < 0.05), NO2−-N (F = 4.113, p < 0.05) were the most significant variables in the assembly of bacterial communities (Figure 5a). Mantel tests shows that only NO3−-N (p < 0.05) and NO2−-N (p < 0.01) had significant effects on the structure of bacterial community while pH (rho < 0) presenting negatively correlated with the bacterial community (Table S4). NO3−-N was considered to be one of the principal factors controlling the microbial community assembly in the estuarine wetland [32]. In the previous study on the rhizosphere soil bacterial community of wetland, it was found that NO3−-N had a significant effect on the rhizosphere bacterial community structure of Suaeda [33]. Previous studies likewise determined that NO2−-N could be a major driver of rhizosphere microbial changes in coastal ecosystems [34], which has similarly been verified in our research. Meanwhile, NH4+-N could also be the main controlling factor for the difference in the community structure of bacterial community in certain wetland communities [35]. Therefore, that inorganic nitrogen significantly affects rhizosphere bacterial communities of different types and coverage vegetation in wetland ecology could be inferred.
For the part on heavy metal factors, As and Ni were considered to be the most significant control factors (Figure 5a). Meanwhile, As (F = 17.128, p < 0.01) and Ni (F = 8.911, p < 0.01) showed similar results in the ANOVA test (Table S5), while Mantel tests were partially consistent with RDA results manifesting that Ni (p < 0.01) and Cr (p < 0.01) were the profound influence factors in structuring bacterial community assemblages (Table S4). It is worth mentioning that As (p < 0.05, rho > 0) also played a significant and positive role in Mantel tests. Heavy metals have been considered as one of the important factors affecting microbial activities and community diversity in the rhizosphere of vegetation and have been verified in many studies [36,37]. Previous studies have shown that the concentration of As in soil was directly related to the species and abundance of certain bacterial community [38]. Ni and Cr were also proved to have obvious interaction with the Phragmites community [39]. The significant relationship between some heavy metals and the rhizosphere bacterial community of wetland vegetation may provide new ideas for vegetation coverage and species selection of constructed wetlands [40].

3.5. Metabolic Function Analysis of Bacterial Community

We used FAPROTAX to conduct functional prediction analysis on environmental samples. At present, the commonly used databases such as KEGG database have some limitations, which may lead to the important biological processes such as nitrification and ammonia oxidation cannot be better reflected in the prediction of community function [41]. FAPROTAX can be used for a fast-functional screening or grouping of 16S-derived bacterial data from terrestrial ecosystems [42]. According to the classification annotation results of 16S rDNA sequences, the data were divided into two groups by FAPROTAX, and 60 and 65 functional groups were obtained respectively (Tables S4 and S5). The dominant functions were aerobic chemoheterotrophy and chemoheterotrophy. In particular, all the community functions related to the nitrogen cycle and sulfur cycle were selected to make the heatmap (Figure 6).
The obtained functional components were divided into nitrogen cycle and sulfur cycle, and the abundance of functional bacterial community of different vegetation types was compared horizontally. In wetland vegetation, the relative abundance of respiration of sulfate and sulfur compounds is high in all communities (Figure 6a). Among the functional groups related to the sulfur cycle, the abundance of colonies with the respiration of sulfur compounds and sulfite functions was found to be higher in HC and MIX, while the abundance of colonies with thiosulfate respiration function was higher in LC (Table S4). This is because sulfite and thiosulfate may interfere with and inhibit each other during respiration, a phenomenon that has been confirmed in recent studies [43]. In the nitrogen cycle part, the Ph community had a higher abundance of nitrification and nitrate reduction function-related bacterial community, while the Su and MIX communities had a higher abundance of nitrogen fixation function-related bacterial community than Ph, which similar findings also demonstrated [44]. It was also found that inter-rooted bacterial communities of wetland vegetation with a high cover were more dominant in the abundance of bacterial communities with nitrate respiration, nitrite respiration and denitrification functions while these processes could contribute to the efficient removal of nitrogen in saline wetlands [45]. Exploration of the rhizosphere bacteria community of wetland vegetation and other types of vegetation, such as CUL and PAD, revealed significant differences in functions related to nitrogen and sulfur metabolism, which are mainly reflected in the dark oxidation of sulfur compounds and nitrification (Figure 6b). Combined with the lower chemoheterotrophy and aerobic chemoheterotrophy abundances of PAD and CUL relative to wetland habitats, differences in metabolic types of bacterial communities (chemoheterotrophy and chemoautotrophy, aerobic and anaerobic) may determine differences in the abundance of bacteria such as Nitrosomonas, Nitrobacter, and Thiobacillus, resulting in significant abundance differences in dark oxidation of sulfur compounds and nitrification among the three habitats [46,47]. In addition to the processes related to the nitrogen cycle and sulfur cycle, studies found that HC has a higher abundance of fermentation-related bacterial community (Table S4), which may be conducive to the partial-denitrification of the bacterial community [48]. Moreover, PH shows better performance in aromatic compound degradation than Su (Table S4), and PAH-degrading bacteria is also found in other estuarine-related studies [49].

4. Conclusions

In this study, 16S rRNA gene sequencing was used to investigate the diversity of bacterial communities according to the vegetation types and habitat types of coastal wetlands. Soil nitrogen nutrients that may determine the change of bacterial community varied significantly with vegetation types. Proteobacteria was the main component at the phylum level of wetland rhizosphere bacteria. Woeseia and Gillisia were the dominant genera, which are also the characteristic genus of wetland vegetation. As and Ni were the environmental factors that significantly affect rhizosphere community in heavy metals, meanwhile NO3−-N is the main controlling factor for the difference of rhizosphere community in soil properties. The bacterial community structure of wetland characteristic vegetation varied significantly with the coverage and type of vegetation. In different habitats, the structure of bacterial communities varied greatly due to vegetation types and physicochemical properties of the habitat.
From FAPROTAX analysis, the composition and structure of the rhizosphere community determine its metabolic type and function. The results showed that although the vegetation type led to structural and morphological differences in the rhizosphere bacterial community, the metabolic type and function of the rhizosphere community would affect the important element cycle in the habitat, which also had a decisive impact on the vegetation. The microbial metabolic type and capacity of the Liaohe estuary wetland are closely related to vegetation species and vegetation coverage. Although many studies are focusing on the estuarine wetland in recent years, there is still a lack of research on wetland microbial community ecology. These findings could help deepen our understanding of the microbial ecology on the wetlands and provide information on bacterial communities in various habitats. The novel models extended from this basic research may be more conducive to natural wetland protection and constructing wetland planning.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su142416396/s1, Table S1. Shapiro-Wilk, Levene tests and One-way ANOVA of soil properties in wetlands. Table S2. Estimation of bacterial community sequencing data and alpha diversity indices. Data are presented as mean ± SD (n = 4) in dry weight basis (except goods’coverage and simpson). Table S3. The non-parametric multivariate statistical tests of the microbial communities. Table S4. Mantel test of heavy metals and environmental factors among groups. Table S5. One-way ANOVA test of heavy metals among groups. Table S6. OTUs perfunctional group by FAPROTAX in grouping of wetland vegetation under different coverage. Table S7. OTUs perfunctional group by FAPROTAX in different habitats groups. Figure S1. rank abundance graph of OTUs. Figure S2. violin plot of shannon. Figure S3. Bary-Curtis distance and relative abundance of genus in bacterial community.

Author Contributions

Y.L., Conceptualization, Formal analysis, Investigation, Methodology, Data curation, Writing original draft, Editing. Z.G., Methodology, Resources, Supervision, Writing review, Funding acquisition. P.Z., Investigation, Supervision, Resources, Editing. J.D., Project administration, Supervision. P.G., Data curation, Investigation. Z.Z., Project administration, Investigation. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Natural Sciences Foundation of China (42171292), and National Coastal Wetland Resources Investigation & Evaluation Project (ZD0421003). Special Fund for Asian Regional Cooperation from China Ministry of Foreign Affairs (WJ0922011).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here: [NCBI (https://www.ncbi.nlm.nih.gov/) Sequence Read Archive (SRA): BioProject number PRJNA823859].

Acknowledgments

We thank the two anonymous reviewers for their valuable suggestions on improving the quality of this paper.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Sampling site in Liaohekou Coastal Wetlands, Northeast China. The picture on the right side were Suaeda glauca community (Su), Phragmites australis community (Ph), aquaculture ponds community (PAD) and farmland biotopes (CUL).
Figure 1. Sampling site in Liaohekou Coastal Wetlands, Northeast China. The picture on the right side were Suaeda glauca community (Su), Phragmites australis community (Ph), aquaculture ponds community (PAD) and farmland biotopes (CUL).
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Figure 2. Beta diversity analyses. (a) PCA; (b) PCoA; (c) NMDS.
Figure 2. Beta diversity analyses. (a) PCA; (b) PCoA; (c) NMDS.
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Figure 3. Relative abundances of dominant phyla in a stacked bar chart. High coverage Suaeda glauca community (HC_Su), high coverage Phragmites australis community (HC_Ph), low coverage Suaeda glauca community (LC_Su), low coverage Phragmites australis community (LC_Ph), Suaeda glauca and Phragmites australis mixed community (MIX), aquaculture ponds community (PAD), farmland biotopes (CUL).
Figure 3. Relative abundances of dominant phyla in a stacked bar chart. High coverage Suaeda glauca community (HC_Su), high coverage Phragmites australis community (HC_Ph), low coverage Suaeda glauca community (LC_Su), low coverage Phragmites australis community (LC_Ph), Suaeda glauca and Phragmites australis mixed community (MIX), aquaculture ponds community (PAD), farmland biotopes (CUL).
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Figure 4. SparCC plot of the 30 most abundant genera in soils.Different nodes in the network diagram represent different dominant genera. The connection between nodes indicates that there is a correlation between the two genera. This figure shows the relationship pair of correlation coefficient |rho| > 0.4. The thickness of the line indicates the strength of the correlation. The solid line indicates a positive correlation and the dotted line indicates a negative correlation. The size of nodes indicates the abundance of bacterial community.
Figure 4. SparCC plot of the 30 most abundant genera in soils.Different nodes in the network diagram represent different dominant genera. The connection between nodes indicates that there is a correlation between the two genera. This figure shows the relationship pair of correlation coefficient |rho| > 0.4. The thickness of the line indicates the strength of the correlation. The solid line indicates a positive correlation and the dotted line indicates a negative correlation. The size of nodes indicates the abundance of bacterial community.
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Figure 5. db−RDA of nutrient factors (a) and heavy metals (b). The lengths of arrows indicate the impact of soil properties on bacterial communities.
Figure 5. db−RDA of nutrient factors (a) and heavy metals (b). The lengths of arrows indicate the impact of soil properties on bacterial communities.
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Figure 6. Distribution of bacterial function in the FaProTax database for different land use patterns by heatmap: (A) Different wetland vegetation and coverage; (B) Different habitats. Different colors indicate the relative abundance of groups in the individual samples. The samples are grouped according to the similarity among them, and the clustering results are arranged horizontally according to the clustering results. In the figure, red represents the function with higher abundance in the corresponding sample, and blue represents the function with lower abundance.
Figure 6. Distribution of bacterial function in the FaProTax database for different land use patterns by heatmap: (A) Different wetland vegetation and coverage; (B) Different habitats. Different colors indicate the relative abundance of groups in the individual samples. The samples are grouped according to the similarity among them, and the clustering results are arranged horizontally according to the clustering results. In the figure, red represents the function with higher abundance in the corresponding sample, and blue represents the function with lower abundance.
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Table
Table 1. Soil properties in wetlands *.
Table 1. Soil properties in wetlands *.
Soil PropertiesHC_PhHC_SuLC_PhLC_SuMIXPADCUL
pH8.86 ± 0.28 a8.70 ± 0.39 ab8.20 ± 0.12 c8.39 ± 0.15 bc8.37 ± 0.40 bc8.48 ± 0.06 abc8.10 ± 0.23 c
TC (g·kg−1)11.61 ± 1.59 a12.09 ± 2.30 a10.47 ± 0.33 a9.75 ± 1.21 ab11.94 ± 2.16 a7.57 ± 0.29 b10.73 ± 2.10 a
NO3−-N (mg·kg−1)12.80 ± 1.15 c13.17 ± 0.27 c11.86 ± 0.34 c12.52 ± 3.13 c12.98 ± 0.91 c19.13 ± 1.51 b25.36 ± 1.58 a
NO2−-N (mg·kg−1)2.02 ± 0.95 abc2.48 ± 0.60 ab2.45 ± 1.13 ab1.52 ± 0.15 bc2.86 ± 1.01 a1.21 ± 0.11 c0.90 ± 0.13 c
NH4+-N (mg·kg−1)32.02 ± 10.7029.40 ± 5.3429.56 ± 8.4820.65 ± 3.0632.51 ± 3.4619.87 ± 0.4627.34 ± 9.58
* Data are presented as mean ± SD (n = 4) on a dry weight basis. The significant differences are denoted by different letters (one-way ANOVA, p < 0.05).
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Liu, Y.; Guo, Z.; Zhang, P.; Du, J.; Gao, P.; Zhang, Z. Diversity and Structure of Vegetation Rhizosphere Bacterial Community in Various Habitats of Liaohekou Coastal Wetlands. Sustainability 2022, 14, 16396. https://doi.org/10.3390/su142416396

AMA Style

Liu Y, Guo Z, Zhang P, Du J, Gao P, Zhang Z. Diversity and Structure of Vegetation Rhizosphere Bacterial Community in Various Habitats of Liaohekou Coastal Wetlands. Sustainability. 2022; 14(24):16396. https://doi.org/10.3390/su142416396

Chicago/Turabian Style

Liu, Yinchu, Zhen Guo, Peidong Zhang, Jun Du, Ping Gao, and Zhiwei Zhang. 2022. "Diversity and Structure of Vegetation Rhizosphere Bacterial Community in Various Habitats of Liaohekou Coastal Wetlands" Sustainability 14, no. 24: 16396. https://doi.org/10.3390/su142416396

APA Style

Liu, Y., Guo, Z., Zhang, P., Du, J., Gao, P., & Zhang, Z. (2022). Diversity and Structure of Vegetation Rhizosphere Bacterial Community in Various Habitats of Liaohekou Coastal Wetlands. Sustainability, 14(24), 16396. https://doi.org/10.3390/su142416396

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