Soil bacterial community assemblage and associated physicochemical characteristics along a moisture gradient of the Wuliangsuhai wetland in China

doi:10.21203/rs.3.rs-4681997/v1

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Soil bacterial community assemblage and associated physicochemical characteristics along a moisture gradient of the Wuliangsuhai wetland in China

https://doi.org/10.21203/rs.3.rs-4681997/v1

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Bacteria are important drivers of biogeochemical cycles and participate in nutrient transformations in wetland ecosystems. We identified 4 sites along a soil moisture gradient of a degraded prairie wetland in Inner Mongolia, China, that could be classified according to dominant vegetation type. Along with various soil physicochemical measurements, the soil bacterial communities were evaluated using 16S rDNA multiplex 454 pyrosequencing. The taxonomic classification revealed significant variation in bacterial community assemblage across the 4 sites. Community diversity was strongly correlated with soil moisture content, with linear relationships in the abundance of certain phyla being observed along the transect in a distance-dependent manner. Furthermore, several bacterial communities were also found to be associated with total nitrogen composition in the soil. Together, these were found to be primary factors influencing bacterial diversity. Additionally, total phosphorous and organic carbon were identified as important limiting resources for the dominant microbial lineages identified.

Earth and environmental sciences/Ecology

Earth and environmental sciences/Environmental sciences

Wuliangsuhai wetland

bacteria

amplicon

pyrosequencing

microbial communities

Natural wetlands are unique ecosystems possessing characteristics of both terrestrial and aquatic environments, providing highly productive and ecologically valuable ecosystem services including nutrient cycling, stormwater runoff storage, sediment accretion, pollution filtration, and erosion control.¹ The Wuliangsuhai, or Ulansuhai Nur, wetland is an inland saline-alkaline wetland listed in the Directory of Wetlands of International Importance in 2002 and is the largest natural wetland following the same latitude on the earth.² In recent years, the growth of industrial and agricultural practices, together with increased population growth and urban development, has resulted in significant degradation of the wetland.³

Soil microorganisms are sensitive to changes in the environment and play critical roles in the response of ecosystems to natural or anthropogenic perturbations.^4,5 The relative abundance and quantitative distribution of species directly or indirectly influences soil maintenance, organic matter decomposition, biogeochemical cycles, and plant nutrient availability.^6,7 Current wetland microbiology studies have focussed on characterizing the microbial community composition in relation to environmental factors or linking important biogeochemical processes to the responsible microbial communities.^8,9 Microbial investigation into wetland soils in China is a relatively recent pursuit, with the majority of studies focussing on the mechanism of microbial decontamination in artificial wetland soils.^10,11

Bacteria play an essential role in the soil and have been studied for several decades by both isolation and molecular methods.^12–16 However, for complex soil environments and their associated genetic diversity, these methods are inefficient for accurately estimating the environmental microbial community.¹⁷ Next generation sequencing (NGS) technologies are increasingly used in environmental studies.^18,19 Roche 454 Pyrosequencing is one of the popular high throughput sequencing systems and is able to generate more than 400,000 effective reads with average read lengths of up to several

hundreds of base pairs.^18,20 This technique has been successfully applied in investigating microbial diversity and abundance in marine water, ocean sediment, soil and the human distal intestine.^17,21–23 It provides sufficient numbers of reads of good length for the accurate identification and estimation of bacterial diversity and richness.²⁴

Several studies have indicated that the bacterial communities along lake wetland shores change in response to numerous environmental factors, including water content,²⁵ availability of C and N^26–28 and pH.²⁹ In fact, these ecosystems might be associated with these environmental factors along a horizontal gradient spanning up to several hundred kilometres.³⁰ Some studies have found the bacterial community composition to differ along a wetland gradient with respect to distance to the water-land junction.³¹ Furthermore, bacterial soil assemblages are increasingly believed to follow a spatially predictable pattern, rather than simply being random.³²

In this study, we aimed to investigate the changes in the microbial bacterial community composition along a soil moisture gradient of a degraded wetland. Four sampling plots were identified along a transect based on their primary vegetation class, and each site was associated with a different soil moisture level (Fig. 1). Sequence tags from the 4 sampling sites were generated and the levels of bacterial diversity at each of the sites were determined. The relationship between various physicochemical properties of the soil, including pH, potassium, magnesium, calcium, and sodium, as well as organic carbon (OC), total phosphorus (TP) and total nitrogen (TN), and bacterial community assemblage, were investigated. This study should improve our understanding of the soil bacterial community composition of degraded wetlands as well as provide new insights into the bacterial community assemblage of the Wuliangsuhai wetland and the associated soil physicochemical characteristics.

Study sites and sampling

The Wuliangsuhai wetland is a freshwater prairie lake located in the western part of Inner Mongolia, China (108°43′ E to 108°57′ E; 40°46′ N to 41°03′ N), encompassing an area of approximately 293 km² (Fig. 1). The region is characterized by a temperate, continental monsoon climate with an average annual temperature of 6.7°C. The average annual precipitation is 224 mm, of which 60% constitutes summer rainfall (July–August). Field research was performed in July 2012, which is when the vegetation cover and species diversity is at its greatest. We established 4 primary sampling plots along the south shore of the lake (Fig. 1). These plots were characterized by differences in primary vegetation type and species, with the predominant species in plot 1 being the perennial wetland grass Phragmites australis (Poaceae), an invasive plant that has become increasingly prevalent in the wetland environments in China owing to its conservative use of nutrients, including N and P.³³ Conversely, plot 2 was characterized by the shrubby hyperhalophyte Kalidium foliatum (Amarathaceae), a pioneer species common on the shores of brackish or saline lakes. The predominant species in plot 3 was the native seablite Suaeda salsa (Chenopodiaceae), a euhalophytic herb occurring on saline and alkaline soils on inland lakeshores and the intertidal zone. It has been studied for its potential use as a biomonitor in the restoration of degraded land due to its ability to remove heavy metals and salts from the soil.³⁴ Lastly, plot 4 was characterized by the endemic desert halophyte Nitraria tangutorum (Nitrariaceae) that exhibits high heat, drought, salt and wind tolerance, and is a dominant species that plays an important role in structuring desert plant communities.³⁵ The plots were labelled PA, KF, SS and NT, respectively.

A decreasing soil moisture gradient was observed along the sampling transect from site PA to site NT (Table 1; Fig. 1). Each of the plots corresponding to each vegetation class contained 3 replicate plots, resulting in a total of 12 sampling localities. Sterilized stainless steel Grab Samplers were used to collect soil from the top 20 cm layer. Five quadrats (1 m × 1 m) in each plot were randomly selected and 50 g of soil was collected at the centre of each quadrat. Then, the 5 soil samples were combined in a sterile plastic bag and transported to the laboratory on ice. In the laboratory, the samples from each plot were manually sieved (2 mm) and homogenized. Visible roots, stones, and other unwanted debris were removed prior to further processing. Approximately 30 g of each soil sample was stored at 4°C for physicochemical analysis. The composite samples for pyrosequencing were kept frozen at − 80°C until use.

Table 1

Basic characteristics of the analysed wetland soil samples. The data represent mean ± SD from four studied sites. Abbreviations in table: PA: *Phragmites australis* plot; KF: *Kalidium foliatum* plot; SS: *Suaeda salsa* plot; NT: *Nitraria tangutorum* plot
Transect location	pH	Water content (%)	Na⁺ (mol/Kg)	K⁺ (mol/Kg)	Ca²⁺ (mol/Kg)	Mg²⁺ (mol/Kg)	Organic C (g/Kg)	Total N (g/Kg)	C:N	Total P (g/Kg)	Prokaryotes (10⁶cfu/g dry soil)
PA	9.48 ± 0.02	32.58 ± 1.25	52.98 ± 3.76	0.58 ± 0.02	0.29 ± 0.01	0.41 ± 0.08	2.44 ± 0.09	3.11 ± 0.10	0.78	0.69 ± 0.03	44.67 ± 46.20
KF	9.05 ± 0.07	28.72 ± 2.17	40.58 ± 5.17	0.44 ± 0.01	0.37 ± 0.03	0.15 ± 0.01	1.39 ± 0.11	2.96 ± 0.18	0.47	0.80 ± 0.06	4.67 ± 1.53
SS	9.78 ± 0.09	23.16 ± 0.56	45.17 ± 2.93	1.09 ± 0.03	0.38 ± 0.01	0.91 ± 0.02	3.78 ± 0.16	2.84 ± 0.07	1.33	0.69 ± 0.04	22.67 ± 6.11
NT	9.38 ± 0.03	14.16 ± 2.89	3.22 ± 0.13	0.73 ± 0.02	0.24 ± 0.01	0.76 ± 0.02	3.89 ± 0.27	5.87 ± 0.15	0.66	0.47 ± 0.02	423.67 ± 162

Soil physicochemical analyses

Samples were submitted to the Soil Testing Laboratory at the Inner Mongolia Academy of Agricultural and Animal Husbandry Sciences for pH, water content, potassium, magnesium, calcium, and sodium analyses. Organic carbon (OC), total phosphorus (TP) and total nitrogen (TN) analysis was also performed.

Soil samples were prepared by lyophilizing in a freeze drier (Christ Alpha 1/2–4 LSC, Germany) after which the water content of each sample was calculated. Soil pH was determined using a soil to water ratio of 1:2.5 (w/w).³⁶ Organic carbon (OC) was measured using a titration method,³⁷ while total nitrogen was estimated using the Kjeldahl method.³⁸ Total phosphorus was measured colorimetrically after digesting the soil with 60% HClO₄.³⁹ Cations were analyzed by direct current plasma emission spectrometry (DCP).

DNA extraction, amplification of 16S rDNA and pyrosequencing

Soil total DNA was extracted from 0.5 g mixed thawed soil from each the 3 plots across each of the sampling sites using the glass beads/calcium chloride/SDS method as described previously by Li et al.⁴⁰ Universal bacterial primer set 27F (5′-3′ AGAGTTTGATCCTGGCTCAG) and 533R (5′-3′ TTACCGCGGCTGCTGGCAC) covering the V1-V3 regions of SSU were synthesized by Shanghai Majorbio Bio-pharm Technology Co., Ltd (Shanghai, China). As the choice of primer can significantly influence the results of diversity analyses of bacterial communities due to their different target ranges,⁴¹ we decided to conduct a database search to evaluate how universal our primer choice was. We used the bacterial SILVA database (http://www.arb-silva.de) for this purpose.⁴² In order to combine multiple samples for one run of 454 sequencing, a sample tagging approach was employed. Each tag was added to the 5′ end of forward primer. The PCR amplification was performed in an ABI 9700 thermocycler (ABI, Foster City, USA) using the following program: 95°C for 2 min;⁴³ cycles of denaturation (95°C for 0.5 min), annealing (55°C for 0.5 min), and extension (72°C for 0.5 min); followed by the final elongation (72°C for 5 min). All three PCR products were pooled and purified using the AxyPrep DNA Gel Extraction Kit (Axygen, USA). Quantification of the purified PCR products was measured using a QuantiFluor^TM-ST Fluorometer (Promega Corporation, CA, USA). The partial 16S rDNA was then sequenced on a Roche GS-FLX 454 pyrosequencer (Roche, Mannheim, Germany) following the manufacturer’s instructions for amplicon sequencing.

Bioinformatics analysis

It has been recognized that pyrosequencing is subject to error, especially with regards to the interpretation of homopolymeric regions of sequences and in the production of amplicon sequences of anomalous lengths, both of which have the potential to overinflate diversity estimates.⁴⁴ Therefore, it is important that data quality controls and clustering methods are implemented in order to reduce errors on diversity estimates.⁴⁴ Data preprocessing was performed using Mothur software.⁴⁵ Reads that were removed from the dataset included those that were shorter than 200 bp in length. We also removed those reads that had an average quality score less than 25; those that had a number of ambiguous bases greater than 0. Following quality filtering as outlined above, the reads were then subjected to denoising to remove sequencing errors by means of flowgram clustering. The quality-filtered sequences were aligned using a Needleman-Wunsch algorithm and clustered using the SILVA database.^42,46 Then, these sequences were compared using CHOPseq (Majorbio) and clustered into operational taxonomic units (OTUs) using a 97% sequence identity cutoff in Mothur implementing the furthest neighbor method. Rarefaction analysis was performed using Mothur and plot-rarefaction (Majorbio). Statistical analysis of OTU richness via rarefaction, Good’s coverage index^,47 and the Shannon and Simpson diversity indices were calculated using Mothur.

Statistical analysis

Correlation analysis was used to determine correlations between the biogeochemical characteristics at each of the sampling sites, as well as to investigate the relationships between these biogeochemical characteristics and the bacterial composition of the sites. Spearman’s rank was used to test for significant relationships between the variables, and significance was estimated at the 5% and 1% levels. SPSS V. 18 (SPSS Inc., Chicago, IL, USA) was used for this purpose. Canonical correspondence analysis (CCA) was employed to examine the influence of the physicochemical properties on bacterial composition. Significance was tested using Monte Carlo permutation tests in the program CANOCO V. 5.4

The 16S rDNA sequences derived from pyrosequencing were deposited in the NCBI Sequence Read Archive under the accession number SRA 091095.

Soil properties

The biogeochemical characteristics of the transect soils are summarized in Table 1. The sites exhibited a narrow range of pH values and all the samples had alkaline pHs (9.05–9.78). The sites varied widely with respect to water content, nutrient, cation concentrations and prokaryotes. The soil water contents, ranging from 33–14%, decreased incrementally from plot PA to NT. The organic carbon concentration varied from 1.39 to 3.89 g/kg dry soil, and the highest value was recorded at site NT. The highest total nitrogen concentration (5.87 g/kg dry soil) was also recorded at site NT, while the highest total phosphorus (0.80 g/kg dry soil) was associated with site KF. Laboratory DCP analyses determined the concentrations of major cations, all of which contained sodium as the dominant cation. The Na⁺ concentrations were high, ranging from 3.22 to 52.98 mol/kg. The K⁺ and Ca²⁺ concentrations in the soils were 0.44 to 1.09 mol/kg and 0.24 to 0.38 mol/kg, respectively, while the Mg²⁺ concentration was in the range of 0.15 to 0.91 mol/kg. Site KF possessed the highest K⁺, Ca²⁺ and Mg²⁺ concentration. Information regarding the abundance of prokaryotes at each site is also presented in Table 1, and the highest value was recorded at site NT.

Community composition, diversity and estimated richness

Based on our database search, the non-coverage rates for 27F and 533R were low, the primer pairs chosen in this study appear to be a good choice for the assessment of bacterial diversity, as they targeted a wide range of taxa and exhibited suitable coverage, and can thus be considered universal.

Across all samples, 36,749 sequences were filtered from 41,642 reads (average read length of ~ 478 bp) and were classified as being of bacterial origin. The 454 sequence libraries ranged in size from 6,692 sequences at site SS to 13,573 sequences at site KF and contained between 1,236 OTUs at site SS and 3,303 OTUs at site KF (Table 2). Overall, 7,277 unique OTUs were identified among the 454 sequence libraries.

The rarefaction curves that we generated for each sampling site are presented in Fig. 2. The curves constructed for the samples from site NT generated the steepest slope, suggesting that there was greatest bacterial diversity at this site.

Table 2

Summary of sequence library sizes, operational taxonomic units (OTUs), and diversity and richness estimates
Transect location	Sequence library size	Number of OTUs^*	Shannon(H’)	Simpson	Coverage (%)
PA	8707	2511	6.94	0.004	84
KF	13573	3303	7.10	0.004	87
SS	6692	1236	5.75	0.02	92
NT	7777	2538	7.01	0.004	78
All sites	36749	7277

^* The number of OTUs identified at ‘all sites’ is not equivalent to the sum of OTUs across the study transect, as some OTUs were found at multiple locations.

At a genetic distance of 3%, the Shannon H’ index ranged from 5.75 at site SS to 7.10 at site KF. The sample from site KF had the lowest pH, but indicated the highest predicted diversity of all the samples. With the exception of the relatively small SS 454 sequence library, the H’ values did not indicate any significant gradient changes along the decreasingly waterlogged site. Good’s coverage index revealed that these libraries represented the majority of bacterial 16S rDNA sequences present in each soil sample, with values ranging from 78–92%. All estimators revealed that the bacterial richness in the samples from the KF site were the highest out of all three samples.

The 454 libraries detected a greater variety of bacterial taxa members of 41 bacterial phyla along the study transect. With over 44% of the total bacterial sequences, the Proteobacteria represented the dominant phylum in each soil (Fig. 3). The Deltaproteobacteria were one of the dominant classes among the Proteobacteria in the PA and KF samples, while Epsilonproteobacteria and Gammaproteobacteria were the dominant classes at the SS and NT sites, respectively. Comprising 6–11% of the bacterial sequences in each sample, the second most abundant phylum in all four soils was the Bacteriodetes. Other abundant phyla included Actinobacteria (2–16%) and the Chloroflexi (4–10%). However, the abundant phyla differed across the sites. Specifically, in the sample from site SS, Firmicutes represented 13% of the total sequences, while at site NT, the phyla Gemmatimonadetes and Acidobacteria represented 11% and 4% of the total sequences respectively. At site PA, nearly 3% of the sequences were classified as belonging to the phylum Planctomycetes. Approximately 2–5% of the sequences from each sample remained unclassified.

A heat map (Fig. 4) representing the top 10 most abundant OTUs from each sample site was generated in r V. 3.3.1 with the package “gplots” using the default settings.^49,50 In terms of sequence identity, sample sites PA and KF (representing the sites with the highest moisture content) were most closely associated. Interestingly, site SS (representing the third highest soil moisture content) was the most distantly associated to the other sites in terms of bacterial community (Fig. 4).

At the genus level, the relative sequence abundance revealed substantial differences among the soil bacterial communities of the different sites. Sulfurimonas was the most abundant genus in all soil samples. Desulforhopalus, Thioalkalivibrio and Truepera showed a higher relative abundance in the samples from the sites PA and KF than the other two sites. Conversely, Paracoccus, Ochrobactrum and Propionibacterium were present in higher proportions in the sample from site SS, while Pseudomonas, Halomonas and Enterobacter were predominant in the sample from the site NT.

Microbial community correlations with physicochemical characteristics

Correlations between bacterial community parameters and soil properties are presented (Table 3). Correlation analysis showed that at the phylum and Proteobacterial class level, the relative abundances of Actinobacteria (R² = − 0.959), Bacteroidetes (R² = 0.945), Alphaproteobacteria (R² = − 0.973) and Unclassified Proteobacteria (R² = 0.918) in the soil were significantly correlated with water content (p < 0.01), as was Deltaproteobacteria (p < 0.05). Bacteroidetes (R² = − 0.969), Gemmatimonadetes (R² = 0.991) and Gammaproteobacteria (R² = 0.910) were significantly correlated with TN (p < 0.01), while Bacteroidetes (R² = 0.930) were significantly correlated with TP (p < 0.01). We also found negative correlations between TP and the relative abundances of Actinobacteria (R² = − 0.813) and Gemmatimonadetes (R² = − 0.877), and between TN and Epsilonproteobacteria (R² = − 0.855). The abundances of Deltaproteobacteria (R² = − 0.886) and Unclassified Proteobacteria (R² = − 0.829) were negatively correlated with soil OC, whereas the abundances of Actinobacteria (R² = 0.893) were positively associated with OC (p < 0.05). Additionally, no significant correlations were found between Chloroflexi, Betaproteobacteria and any factors measured in this study. Canonical correspondence analysis indicated that among all the parameters examined, water content and TN had the greatest influence on variability in the bacterial community (Fig. 7).

The relative abundance of nitrifying and denitrifying bacteria in the samples involved in nitrogen cycle are also related to environmental factors, especially water content. In this study, eight nitrifying bacteria genera and fifty-eight denitrifying bacteria genera were deteceted(Fig. 5, 6).. The total percentage of nitrifying bacteria in PA, KF, SS and NT were 0.18%, 0.18%, 0.11% and 1.97% respectively. Nitrifying bacteria were detectable in each sample and most abundant in NT. Nitrosomonadaceae_uncultured showed a higher relative abundance in the SS and NT than other samples and it had the highest abundances in NT. Also, many denitrifying bacterial were detected. Paracoccus and Pseudomonas bacteria increased with decreasing soil moisture and a higher abundance in SS and NT soils than PA and KF. Paracoccus had the highest abundances in SS and Pseudomonas had the highest abundances in NT. However, Desulfarculaceae_uncultured and Azoarcus had a higher abundance in PA and KF soils than other samples.

Table 3

Correlations between the relative abundances of the most abundant bacterial phyla and proteobacterial classes and the soil properties in Wuliangsuhai wetland soil
	pH	Water content	OC	TN	C:N	TP
Actinobacteria	0.431	-0.959^**	0.899^*	0.715	0.365	-0.813^*
Bacteroidetes	-0.014	0.945^**	-0.666	-0.969^**	0.133	0.930^**
Chloroflexi	-0.520	0.693	-0.703	-0.187	-0.637	0.302
Firmicutes	0.668	0.212	0.199	-0.685	0.858^*	0.458
Gemmatimonadetes	-0.246	-0.774	0.417	0.991^**	-0.438	-0.877^*
Alphaproteobacteria	0.362	-0.973^**	0.855^*	0.716	0.317	-0.783
Betaproteobacteria	0.625	-0.744	0.827	0.283	0.685	-0.444
Deltaproteobacteria	-0.522	0.893^*	-0.886^*	-0.536	-0.518	0.660
Epsilonproteobacteria	0.489	0.449	-0.074	-0.855^*	0.706	0.682
Gammaproteobacteria	-0.479	-0.576	0.147	0.910^**	-0.670	-0.726
Unclassified Proteobacteria	-0.420	0.918^**	-0.829^*	-0.561	-0.434	0.641
Unclassified bacteria	-0.563	-0.280	-0.077	0.749	-0.781	-0.562
Significance at α = 0.05 level and *α = 0.01 level

Although culture-independent studies of microbial communities have been performed in Wuliangsuhai wetland soils and sediments in the past,^51,52 this is the first to have employed a high throughput 454 pyrosequencing technique to analyse microbial communities and their relationship with soil characteristics. Compared with previous studies,^51,52 we identified a far more diverse bacterial assemblage. Between 6,692 and 13,573 pyrosequences per site were obtained from the bacterial DNA extracted from the 4 sampling sites. Furthermore, Good’s coverage index suggests that sampling to this depth captured the vast majority of the dominant taxa present in the samples. This is the most robust examination of bacterial diversity in the Wuliangsuhai wetland to date.

We found that the bacterial community structure differed between the 4 different dominant vegetation plots. The relative abundance of Actinobacteria increased from 1.85% (PA) to 16.39% (NT), whereas that of Bacteroidetes decreased from 11.39% (PA) to 6.45% (NT), implying that these phyla increased/decreased in a distance-dependent manner along the transect. Proteobacteria, Bacteroidetes, Actinobacteria, Chloroflexi (Fig. 2), which represented more than 70% of all sequences, have also been identified as dominant microflora in previous studies.^53,54 While Janssen⁵⁵ reported that Proteobacteria, Acidobacteria and Actinobacteria were often the most abundant phyla in soils, and emphasized that members of Bacteroidetes and Chloroflexi were generally less abundant, this is in sharp contrast to our findings. Our analyses indicated that class Deltaproteobacteria, Epsilonproteobacteria and Gammaproteobacteria, derived from Proteobacteria phyla, were the predominant phylogenetic groups in our study area, and differ from the dominant taxa found in forest and grassland soils (Alphaproteobacteria and Betaoproteobacteria).⁵⁶ At the genus level, a comparison of the relative abundances revealed significant bacterial community differences between the different vegetation plots. We discovered that Sulfurimonas (Helicobacteraceae: Epsilonproteobacteria) was the most abundant genus in all soil samples. This is not surprising as the ecological description of Epsilonproteobacteria species suggests that they are dominant components in oil fields, which are also generally associated with poor soil health.⁵⁷ Additionally, high proportions of Desulforhopalus, Thioalkalivibrio and Truepera were detected in this survey. The genera Paracoccus and Halomonas, ranked the second most abundant genera in wetland sample SS and NT respectively, have not been found in previous studies of wetlands.^58,59 This is therefore a novel finding, indicating important but previously unknown ecological roles of these genera in wetland systems. In the cluster analysis (Fig. 4), the two most waterlogged sites clustered together, and differed from the other two sites positioned at the water-land junction in terms of community composition, providing support for previous suggestions that bacterial communities are structured along horizontal soil gradients, following a spatially predictable pattern.

Previous studies have also demonstrated that soil properties such as pH, texture or chemical composition are important factors influencing bacterial community structure.^53,56,60,61 Similarly, in our study, the Actinobacteria and Alphaproteobacteria indicated a significant correlation with water content (P < 0.01), with higher abundances identified in soils with low water contents. Conversely, Gemmatimonadetes and Gammaproteobacteria revealed significant correlations to total N (P < 0.01), with higher abundances detected in soils with high total N content. Water content and TN were thus found to be the primary drivers impacting the variability in bacterial composition of the Wuliangsuhai wetland. This is in accordance with other surveys,^62,63 which have indicated that water content and TN may be the principal factors in determining bacterial community composition in wetlands. Additionally, TP and OC were identified as important limiting resources for the dominant bacterial lineages, as the abundance of Bacteroidetes increased in soils with high TP content, whereas Deltaproteobacteria declined in soils with high OC. This is somewhat contrasted to other studies that have found increased OC to be associated with increased bacterial biomass.²⁸

Many nitrifying and denitrifying bacteria have been found in natural wetland in previous studies.^64–67 The influence of soil moisture on active communities is consistent with previous studies that found soil moisture was a major influence regulating functional expression of soil microbial communities.⁴³ An increase in soil moisture triggers both microbial activity and reduced transport of oxygen, resulting in decreased oxygen availability, promoting the growth of microbes capable of using alternative electron acceptors such as NO₃⁻.⁶⁸ For instance, previous research has shown that reduced precipitation decreases soil moisture and O₂ diffusion, which stimulates the activity of nitrifiers(AOA/AOB) and nitrification.⁶⁹ Nitrosomonadaceae_uncultured had higher abundances in NT sample, suggesting that aerobic ammonium-oxidizing bacteria are well adapted to aerobic habitat in NT sample.⁷⁰ The higher abundance of Paracoccus and Pseudomonas in SS and NT suggests that aerobic denitrifying bacteria were found to have higher denitrification rate under aerobic conditions.^71,72 Moreover, in previous studies on denitrification in environmental samples, narG and nosZ were employed more frequently as genetic markers.⁷³ The higher abundance of Paracoccus and Pseudomonas in SS and NT belong to nosZ-containing bacteria.⁷⁴. Previous studies have demonstrated nosZ was significantly correlated to water content, and nosZ-containing bacteria were increased with moisture decreasing.⁷³ Similarly to this survey. Thus, the nitrifying and denitrifying bacteria may have a higher response to soil moisture, which determines oxygen availability and related gene.

In this study, bacterial communities were found to vary along a lakeshore moisture gradient. Several associations between soil physicochemical properties and bacterial community were observed. Moisture and TN were the primary predictors of bacterial community assemblage. Additionally, TP and OC were identified as important limiting resources for the dominant bacterial lineages. These findings have enhanced our understanding of the relationships between bacterial communities in degraded wetland shores, as well as their associations with soil physicochemical properties.

Data availability

The 16S rDNA sequences derived from pyrosequencing were deposited in the NCBI Sequence Read Archive under the accession number SRA 091095.

Author contributions

Manping Zhao: Writing – original draft, Methodology, Investigation, Data curation, Conceptualization. Yongman Wang: Visualization, Resources, Conceptualization. Junyi Li: Methodology, Investigation. Jialin Zhao: Conceptualization. Ji Zhao: Supervision, Funding acquisition. Linhui Wu: Writing – review & editing, Supervision, Funding acquisition.

Conflicts of interest

There are no conflicts to declare.

Acknowledgments:

This work was supported by the National Natural Science Foundation of China (NSFC) (No. 42067037, No. 41701281, No. 32160279, and No. 32161143025), and the Science and Technology Major Project of Inner Mongolia (No. 2022YFHH0017), and the Natural Science Foundation of Inner Mongolia (No.2024MS03027).

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Soil bacterial community assemblage and associated physicochemical characteristics along a moisture gradient of the Wuliangsuhai wetland in China

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Abstract

Figures

Introduction

Materials and methods

Study sites and sampling

Soil physicochemical analyses

DNA extraction, amplification of 16S rDNA and pyrosequencing

Bioinformatics analysis

Statistical analysis

Results

Soil properties

Community composition, diversity and estimated richness

Microbial community correlations with physicochemical characteristics

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1