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Article

Distinct Diazotrophic Communities in Water and Sediment of the Sub-Lakes in Poyang Lake, China

by
Qiang Wu
1,2,*,
Zhigang Zhu
1,
Longlingfeng Liu
1,
Yin Qin
1,
Yufang Jiang
1,
Jinfu Liu
1,
Wenxiang Zou
1,
Fei Wang
1 and
Yuwei Chen
1
1
The School of Hydraulic Engineering, Nanchang Institute of Technology, Nanchang 330099, China
2
Jiangxi Key Laboratory of Poyang Lake Water Resources and Environment, Jiangxi Academy of Water Science and Engineering, Nanchang 330029, China
*
Author to whom correspondence should be addressed.
Water 2024, 16(16), 2277; https://doi.org/10.3390/w16162277
Submission received: 29 June 2024 / Revised: 10 August 2024 / Accepted: 11 August 2024 / Published: 13 August 2024
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Abstract

:
The sub-lakes of Poyang Lake have significant ecological value. To date, the diazotrophs in sub-lakes are unknown. Moreover, no study has simultaneously focused on diazotrophic communities in the water and sediment in natural freshwater ecosystems. In this study, we investigated the diazotrophic alpha diversity, structure, abundance, molecular ecological network, and community assembly mechanism in the water and sediment of sub-lakes using Illumina MiSeq sequencing and a quantitative polymerase chain reaction assay. The results showed that the sediment had a greater diversity of diazotrophs than the water. Proteobacteria and Spirochaetes were the dominant diazotrophic phyla in the water, whereas Proteobacteria was the dominant diazotrophic phylum in the sediment. There were significant differences in the composition of diazotrophic communities between the water and sediment. The sediment had a more complex co-occurrence network of diazotrophs than the water. Deterministic processes dominate the community assembly of diazotrophs in both the water and sediment of the sub-lakes, and the relative role of deterministic processes was stronger for sediment than water. Our study is the first to reveal the differences in the diazotrophic communities between the water and sediment in natural freshwater ecosystems and provides the fundamental scientific datasets for understanding the nitrogen fixation process in sub-lakes.

1. Introduction

Poyang Lake, the largest freshwater lake in China, is of global significance for biodiversity conservation and biogeochemical cycles [1]. As the world’s largest bird sanctuary for migratory birds, the lake provides the ideal environmental and climatic conditions for wintering migratory birds [2]. The hydrological characteristics of the lake are dynamically regulated by the five upstream rivers (Gan River, Fu River, Xin River, Rao River, and Xiu River) and the Yangtze River [2], resulting in its dramatic intra-annual water level fluctuations [3]. During the low water period (November to March), the surface water area of the lake shrinks to less than 1000 km2, with more than 100 separated sub-lakes appearing [4]. During the wet season (April to October), the sub-lakes connect with the main lake to form a single lake covering more than 3000 km2 [5]. The sub-lakes have significant ecological value [6]. More than 50% of the submerged vegetation biomass of Poyang Lake is distributed in these sub-lakes [7], providing fish nurseries and breeding grounds, as well as adequate food resources and ideal habitats for migratory birds [8,9,10]. Approximately 70–80% of the total numbers of wintering water birds in Poyang Lake are distributed in these sub-lakes [8].
Biological nitrogen fixation, a microbial-mediated process of reducing atmospheric dinitrogen (N2) to bioavailable nitrogen compounds, stimulates primary production and thus plays an important role in the removal of atmospheric CO2 [11]. Nitrogen fixation occurs widely in diverse aquatic systems, such as oceans [12,13], estuaries [14,15], rivers [16,17], reservoirs [18,19], and lakes [20,21,22]. As an important process in the N cycle, nitrogen fixation provides nutrient resources to primary producers and alleviates N-deficient conditions in freshwater ecosystems [18,23]. The nifH gene, which encodes one of the multisubunit metalloproteins of the nitrogenase enzyme, is highly conserved and has therefore been widely used as a molecular marker to study diazotrophic communities [24,25]. The application of molecular biology techniques based on the nifH gene has revealed the presence of a diverse array of diazotrophic phylotypes in freshwater ecosystems [22,26,27]. These diazotrophic phylotypes include members of the Proteobacteria, Spirochaetes, Cyanobacteria, Euryarchaeota, Bacteroidetes, and Nitrospirae [22,26,28]. It is essential to elucidate the diversity and community structure of diazotrophs in freshwater ecosystems, given the important role of diazotrophs in freshwater biogeochemistry and ecosystem functioning [19]. Previous studies have demonstrated that nitrogen fixation occurs simultaneously in water columns and sediments in freshwater ecosystems [29,30]. Several studies have been carried out on the diversity and community structure of diazotrophs in freshwater columns or sediments [20,21,22,31]. However, to our knowledge, no study has simultaneously concerned itself with the diversity and community structure of diazotrophs in two distinct but closely linked habitats (i.e., the water column and the sediment) in natural freshwater ecosystems.
It is crucial to investigate the diazotrophic communities in the sub-lakes of Poyang Lake, given the significant ecosystem functioning of diazotrophs and the ecological value of the sub-lakes. In the present study, we aimed to (1) investigate the diazotrophic alpha diversity, structure, nifH gene abundance, molecular ecological network, and community assembly process in both the sub-lakes water and sediment, and (2) explore the potential drivers that regulate the variability of the diazotrophic community in these sub-lakes. The results reported here will provide the fundamental scientific datasets for understanding the nitrogen fixation process in the sub-lakes of Poyang Lake and will enrich the biodiversity database of the sub-lakes.

2. Materials and Methods

2.1. Study Area, Sample Collection, and Physicochemical Analysis

Poyang Lake (28°24′–29°46′ N; 115°49′–116°46′ E) lies on the southern bank of the middle Yangtze River and is located in northern Jiangxi Province (central China). The lake is one of the six major wetland systems in the world [32]. The lake mainly receives freshwater inflow from the five upstream tributaries (Gan River, Fu River, Xin River, Rao River, and Xiu River) and typically flows from south to north, eventually discharging into the Yangtze River [33]. During the low water period (November to March), the surface water area of the lake shrinks to less than 1000 km2, with a narrow meandering channel and more than 100 separate sub-lakes [4]. The sub-lakes are separated by grasslands and mudflats [4]. During the rainy season (April to October), the area floods and sub-lakes within the floodplain are combined into a single lake covering more than 3000 km2 [5].
In March 2023, nine water and nine sediment samples were collected from nine representative sub-lakes: Baishahu Lake (BS), Changhuchi Lake (CC), Changhu Lake (CH), Dachahu Lake (DC), Donghu Lake (DH), Beishenhu Lake (NS), Sanniwan Lake (SN), Zhanbeihu Lake (ZB), and Zhonghuchi Lake (ZC) (Figure 1). There was no rain or only light rain in the 7 days prior to sampling (including the day of sampling). In the middle of each sub-lake, surface water (top 50 cm) and sediments (top 10 cm) were synchronously collected with a 5-L organic glass water sampler and a Peterson’s grab sampler, respectively. Each sediment sample was collected from approximately 1 m deep in the lake. The water and sediment samples were stored on ice in a cooler before being transported to the laboratory for further processing. Each water and sediment sample was separated into two portions: one for physicochemical analysis and one for molecular analysis.

2.2. Physicochemical Analysis

For water samples, water temperature (WT), pH, electrical conductivity (EC), and dissolved oxygen (DO) were measured in the field using a multi-parameter water quality meter (YSI 6600V2, Yellow Springs, OH, USA). The concentrations of total nitrogen (TN), ammonia nitrogen (NH4-N), orthophosphate phosphorous (PO4-P), total phosphorous (TP), and chemical oxygen demand (CODMn) were determined according to the methods of Jin and Tu [34]. For sediment samples, pH was measured with a pH meter (FE20, Mettler Toledo, Greifensee, Switzerland), and EC was measured with a benchtop conductivity meter (Orion 3-Star, Thermo, Waltham, MA, USA). The content of organic matter (OM) was measured according to the literature [30]. We used the alkaline potassium persulfate spectrophotometric method [35] and the molybdenum blue colorimetry method [34] to determine sediment TN and TP content, respectively.

2.3. DNA Extraction, Sequencing, and Quantitative PCR Analysis

To collect the water microorganisms, approximately 50 mL of the water sample was filtered through a 0.2 µm polycarbonate filter (Isopore Membrane, Millipore, Burlington, MA, USA) using a vacuum pump. Microbial DNA was extracted from the filters and approximately 0.5 g of the sediment sample using the E.Z.N.A. Water DNA kit (Omega, Norcross, GA, USA) and the FastDNA SPIN Kit for Soil (MP Biomedicals, Santa Ana, CA, USA), respectively. The extracted DNA from the water and sediment samples was used to amplify the nifH gene with the primers nifH1 (5′-TGYGAYCCNAARGCNGA-3′) and nifH2 (5′-ADNGCCATCATYTCNCC-3′) [36]. After an initial denaturation step of 95 °C for 5 min, the amplification was carried out with 35 cycles of 30 s at 95 °C, 30 s at 55 °C, and 45 s at 72 °C, plus a final extension at 72 °C for 10 min. Amplicons were visualized on 2% agarose gels and purified with the AxyPrep DNA Gel Extraction Kit (Axygen, Union City, CA, USA). Purified PCR products were quantified by QuantiFluor™-ST (Promega, Madison, WI, USA) and then mixed in equimolar ratios. The pooled PCR products were sequenced using an Illumina MiSeq platform (Illumina, San Diego, CA, USA) according to the manufacturer’s instructions. The obtained raw reads were deposited into the NCBI Sequence Read Archive (SRA) with the accession number PRJNA1127016.
To investigate the nifH gene abundance in the water and sediment of the sub-lakes, quantitative PCR (qPCR) analysis using the above primers nifH1 and nifH2 was carried out with an ABI 7500 real-time PCR system (Applied Biosystems, Carlsbad, CA, USA). The qPCR was performed in triplicate for each standard and for each sample, and the amplification conditions were as follows: 3 min at 95 °C, followed by 40 cycles of 15 s at 95 °C, 20 s at 65 °C, and 20 s at 72 °C. A standard curve was generated by tenfold serial dilution of recombinant plasmids carrying the targeted nifH gene fragment. The correlation coefficient (R2) value of the standard curve was 0.995, and the amplification efficiency was 93.34%.

2.4. Data Processing and Analyses

Raw reads from the high-throughput sequencing were quality-filtered with Trimmomatic [37] and merged with FLASH [38]. Chimeric sequences were checked and discarded by UCHIME [39]. Chimeric-free sequences were assigned to operational taxonomic units (OTUs) at 97% sequence similarity using UPARSE [40]. The representative sequences of each OTU were aligned against the NCBI database to classify them into taxonomic groups [41].
We compared alpha diversity indices and taxonomic differences in the diazotrophic community between the water and sediment using a paired t-test in IBM SPSS 19.0 software (IBM Company, Armonk, NY, USA). Principal coordinate analysis (PCoA) was performed with the vegan package in R 4.3.2 software to compare the composition of the diazotrophic community between the water and sediment, and analysis of similarity (ANOSIM) was carried out to identify significant differences. In order to assess the interactions among diazotrophic taxa in the water and sediment of the sub-lakes, co-occurrence network analyses were performed using the Molecular Ecological Network Analyses pipeline (http://ieg4.rccc.ou.edu/mena/, accessed on 15 May 2024) based on random matrix theory (RMT) [42]. The network visualization was created using the Gephi 0.10.1 interactive platform [43]. The keystone taxa of the network were recognized based on within-module connectivity (Zi) and among-module connectivity (Pi) [44].
To determine the assembly mechanisms of water and sediment diazotrophic communities in the sub-lakes, the neutral community model (NCM) was employed with R software according to the protocol [45], and checkerboard score (C-score) and modified stochasticity ratio (MST) analyses were performed using the EcoSimR package [46] and the NST package [47] in R software, respectively. Mantel tests were performed in R software using the vegan package to explore the possible environmental drivers regulating the variability of the water and sediment diazotrophic communities in the sub-lakes.

3. Results

3.1. Diazotrophic Alpha Diversity and Abundance

In this study, across all water and sediment samples, we obtained 664,095 high-quality sequences. The results of the rarefaction analyses suggested a sufficient depth of sequencing used in this work (Figure S1). The diazotrophic OTU richness in the water and sediment ranged from 501 to 913 (711 on average) and 750 to 2070 (1288 on average), respectively. The Chao richness estimator of diazotrophs in the water and sediment ranged from 680 to 1214 (929 on average) and 1005 to 2346 (1571 on average), respectively. The Shannon index of diazotrophs in the water and sediment ranged from 3.66 to 5.82 (4.83 on average) and 4.76 to 6.51 (5.67 on average), respectively. Figure 2 reveals that diazotrophic OTU richness, Chao richness estimator, and Shannon index were significantly lower (paired t-test, p < 0.05) in the water than in the sediment.
Table S1 shows the number of nifH genes in the water and sediment of the nine sub-lakes. For the water, the diazotrophic abundance in each sample was in the range of 3.86 × 106 to 6.26 × 107 (2.07 × 107 on average) copies/L. For the sediment, the diazotrophic abundance ranged from 1.20 × 106 to 3.90 × 107 (7.88 × 106 on average) copies/g.

3.2. Comparison of Composition of Water and Sediment Diazotrophic Communities

Taxonomic assignment results showed a total of 24 diazotrophic phyla in all water and sediment samples, including 48 classes, 91 orders, 162 families, and 318 genera. Among these taxonomic groups, 21 phyla, 40 classes, 78 orders, 134 families, and 234 genera were found in the water samples, while 21 phyla, 41 classes, 74 orders, 136 families, and 238 genera were found in the sediment samples. 18 phyla, 33 classes, 61 orders, 108 families, and 154 genera were shared by the water and sediment diazotrophs, representing 75.0%, 68.8%, 67.0%, 66.7%, and 48.4% of all taxonomic groups, respectively. In the present study, we selected 61 and 71 representative OTUs (which accounted for >0.2% of total nifH sequences) in the water and sediment samples, respectively, to construct the phylogenetic tree. The representative OTUs in the water and sediment samples were both grouped into Cluster I and Cluster III (Figures S2 and S3). The proportion of representative OTUs belonging to Cluster I was higher in the water (55/61) than in the sediment (57/71). On the contrary, the proportion of representative OTUs belonging to Cluster III was higher in the sediment (14/71) than in the water (6/61).
Figure 3 reveals that Proteobacteria and Spirochaetes were the main diazotrophic phyla (average relative abundance > 5%) in the sub-lakes water columns, accounting for 80.68 to 97.09% of the total sequences per sample, while Proteobacteria was the main phylum in the sub-lakes sediments. Proteobacteria was the predominant diazotrophic phylum in both the water and sediment, representing 65.31 to 96.36% (85.63% on average) and 76.03 to 94.84% (88.20% on average) of all sequences in each sample, respectively. The proportion of Spirochaetes in the water and sediment ranged from 0.25 to 15.37% (5.84% on average) and 0.39 to 6.63% (2.55% on average), respectively.
We further compared the composition of water and sediment diazotrophic communities at the class level. The main diazotrophic classes (average relative abundance > 5%) in the water were Alphaproteobacteria (38.31%), Betaproteobacteria (33.05%), Deltaproteobacteria (10.19%), and Spirochaetia (5.84%), while Alphaproteobacteria (54.68%), Deltaproteobacteria (17.18%), Betaproteobacteria (9.63%), and Gammaproteobacteria (6.69%) were the main diazotrophs in the sediment (Figure S4). Among these diazotrophic classes, Alphaproteobacteria and Deltaproteobacteria were significantly more abundant (paired t-test, p < 0.05) in the sediment than in the water (Figure 4a,c), while Betaproteobacteria was reversed (Figure 4b).
We also found differences in the diazotrophic community composition at the genus level between the water and sediment. The dominant diazotrophic genera (average relative abundance > 5%) in the water were Rhodopseudomonas (14.63%), Bradyrhizobium (13.81%), Burkholderia (11.40%), Geobacter (8.49%), Paraburkholderia (7.96%), Dechloromonas (6.18%), and Spirochaeta (5.44%), while Bradyrhizobium (40.02%), Geobacter (10.28%), and Methylocystis (9.72%) were the dominant diazotrophs in the sediment. A paired t-test showed that the proportion of six of the above eight dominant diazotrophic genera was significantly different (p < 0.05) between the water and sediment (Figure 5). Among these six dominant diazotrophic genera, Rhodopseudomonas, Burkholderia, Paraburkholderia, Dechloromonas, and Spirochaeta were significantly more abundant in the water than in the sediment, while Bradyrhizobium was reversed.
At the OTU level, PCoA and ANOSIM analyses based on Bray-Curtis distances indicated that the diazotrophic community composition was significantly different between the water and sediment (Figure 6). Furthermore, Figure 6 reveals that the diazotrophic communities were more dispersed in the water and more concentrated in the sediment, suggesting that the variation of diazotrophic community structure among sub-lakes was greater in the water and less in the sediment.

3.3. Comparison of Co-Occurrence Network of Water and Sediment Diazotrophic Communities

The diazotrophic co-occurrence network structure in the sub-lakes sediment was more complex compared to its corresponding water (Figure 7 and Table 1). The size of the co-occurrence network in the sediment (266 nodes) was larger than that in the water (158 nodes). In addition, only 34 nodes (which accounted for 8.7% of all nodes) were shared by the water and sediment co-occurrence networks, suggesting that the network structure of diazotrophs in the water was markedly different from that in the sediment. The number of edges was 56.2% higher in the sediment than in the water (Table 1). The ratio of positive edges to negative edges in the water and sediment was 0.94 and 0.97, respectively, suggesting that the balance between cooperation and competition among diazotrophic taxa occurred in both the water and sediment. The modularity and number of modules were lower in the water than in the sediment (Table 1). Moreover, the diazotrophic network in the sediment had higher average clustering coefficients and a lower average path distance compared to its counterpart in the water (Table 1), suggesting that the diazotrophic taxa in the sediment were more interconnected and more closely related.
We further compared the keystone taxa of the diazotroph network between the water and sediment of the sub-lakes (Figure 8). For the water, two module hubs (one Geobacter and one Burkholderia) were presented in the co-occurrence network. For the sediment, two module hubs (one Methylocystis and one Bradyrhizobium) and nine connectors (one Methylocystis, one Ensifer, six Bradyrhizobium, and one Geobacter) were presented in the co-occurrence network.

3.4. Assembly Mechanisms of Water and Sediment Diazotrophic Communities

We determined the relative importance of neutral processes in the assembly of water and sediment diazotrophic communities using the neutral community model (Figure 9). Figure 9 shows low explained variation of the diazotrophic community for both the water (R2 = 0.001) and sediment (R2 = 0.013), suggesting that stochastic processes were unimportant in shaping both the water and sediment diazotrophic communities. We performed checkerboard score (C-score) and modified stochasticity ratio (MST) analyses to qualitatively and quantitatively assess the relative role of deterministic and stochastic processes in the assembly of water and sediment diazotrophic communities (Table 2 and Figure 10). The value of the observed C-score was significantly greater than the value of the simulated C-score in both the water (p < 0.001) and sediment (p = 0.015), and standardized effect size (SES) values were greater than 2 (Table 2), suggesting that diazotrophic species were non-randomly distributed, i.e., the assembly of the diazotrophic community in both the water and sediment was dominated by deterministic processes. The MST of the diazotrophic community in the water was mainly distributed below 0.5, and in the sediment, it was all distributed below 0.5 (Figure 10). This supported the same conclusion as the C-score analyses. Furthermore, the mean MST in the sediment (0.16) was lower than that in the water (0.36), indicating that the relative role of deterministic processes in shaping the diazotrophic community was stronger for the sediment than the water.
In order to explore the potential factors of deterministic processes in regulating the variability of the diazotrophic community, we examined the relationships between the diazotrophic community structure and environmental variables in the water and sediment using the Mantel test (Table 3 and Table 4). For the water, TP was significantly related to the community structure of diazotrophs at the genus level (Table 3). We further examined the correlations between the proportion of abundant diazotrophic genera (average relative abundance >1%) and TP using Spearman correlation analysis. TP was significantly and positively correlated with Methylocystis and Afipia (r = 0.695, p < 0.05, and r = 0.753, p < 0.05, respectively), but negatively correlated with Stenotrophomonas (r = −0.778, p < 0.05). For the sediment, TN, organic matter (OM), and the N:P molar ratio were significantly associated with the composition of the diazotrophic community at the phylum level (Table 4). The correlations between the proportion of abundant diazotrophic phyla (average relative abundance > 1%) and the above influential factors were tested using Spearman correlation analysis. TN and molar ratio of N:P were significantly and negatively associated with Bacteroidetes (r = −0.783, p < 0.05, and r = −0.850, p < 0.01, respectively). OM was significantly and positively correlated with Proteobacteria (r = 0.80, p = 0.01).

4. Discussion

4.1. Diazotrophic Composition in the Water and Sediment of the Sub-Lakes

In the present study, Proteobacteria and Spirochaetes were the main diazotrophic phyla in the sub-lakes water columns, whereas Proteobacteria was the main diazotrophic phylum in the sub-lakes sediments. Proteobacteria have been found to be the main components of diazotrophs in the water columns of Lake Fuxian (China) and in the sediments of Lake Taihu (China) [20,48]. Our recent study has also indicated that the diazotrophic community in Poyang Lake sediments is dominated by Proteobacteria [22]. Spirochaetes has been reported to be present in the diazotrophic communities in the Dongzhen Reservoir (China) water columns [49], and its relative abundance is comparable to that in the sub-lakes water columns (mean value 5.84%).
In this study, seven genera, including Rhodopseudomonas and Bradyrhizobium belonging to Alphaproteobacteria, Burkholderia, Paraburkholderia, and Dechloromonas belonging to Betaproteobacteria, Geobacter belonging to Deltaproteobacteria, and Spirochaeta belonging to Spirochaetes, were the dominant diazotrophic genera in water samples. Three genera, including Bradyrhizobium, Methylocystis, assigned to Alphaproteobacteria, and Geobacter, were the dominant diazotrophic genera in sediment samples. Thus, the dominant diazotrophs in the water and sediment of the sub-lakes showed different compositions at the genus level, with the exception of the dominance of the genera Bradyrhizobium and Geobacter in both the water and sediment. Bradyrhizobium has also been reported to be the dominant diazotrophic genus in the Dongzhen Reservoir water columns and in the Lake Taihu sediments [27,49]. Geobacter has also been found to be the main diazotrophic phylotype in the water columns of Trout Bog Lake (United States) and in the Poyang Lake sediments [22,28]. In addition, Liu et al. [50] have reported that Rhodopseudomonas and Paraburkholderia are the dominant diazotrophic genera in the groundwater of the Hetao Plain (China). Geisler et al. [26] have found that Dechloromonas is the major diazotroph in the Qishon River water during the winter. We have reported that Methylocystis is the abundant diazotrophic genera in Poyang Lake sediments in the previous study [22].

4.2. Distinct Diazotrophic Communities in the Water and Sediment of the Sub-Lakes

Our data showed that diazotrophic communities in the sediments of the sub-lakes had significantly higher alpha diversity indices compared to their counterparts in the water columns (Figure 2). To date, there is a lack of information on the comparison of the alpha diversity of water and sediment diazotrophic communities in freshwater ecosystems. Previous studies have revealed that the microorganisms in sediments are more diverse than those in water columns in freshwater ecosystems [51,52,53], and the major explanation for this might be related to the presence of abundant nutrients and microorganisms protected from predation in sediments [54]. It could be speculated that these advantages might allow for a greater diversity of diazotrophs in the sediments of the sub-lakes compared to their corresponding water columns. However, the speculation needed to be further investigation.
In the present study, we observed significant differences in the composition of diazotrophic communities between the water columns and sediments of the sub-lakes. To some extent, this was consistent with a previous report on the distinct structure of diazotrophic communities between groundwater and in situ sediment “trap” samples from groundwater monitoring wells in the Illinois Basin (USA) [55]. Ren et al. [52] have also found that the composition of microbial communities in the water columns differs from that in the sediments in Poyang Lake. It is well known that water and sediment are two different habitats in freshwater ecosystems. Numerous studies have shown that habitat changes can lead to changes in the structure of diazotrophic communities in aquatic environments [20,22,31,56,57]. Thus, the different structures of diazotrophic communities between the water columns and sediments of the sub-lakes could be related to the habitat differences.
In this study, we found that the structure of the diazotrophic co-occurrence network in the sub-lakes sediment was more complex compared to its corresponding water (Figure 7 and Table 1). The network complexity has been suggested to be related to the microbial alpha diversity, i.e., higher alpha diversity could lead to more complex microbial interactions [58,59]. As mentioned above, our data showed that the diazotrophic alpha diversity was significantly higher in the sediment than in the water. Therefore, a possible reason for the difference in complexity of the diazotrophic network between water and sediment in the sub-lakes would be related to the difference in alpha diversity. In general, a microbial network with a larger network size (nodes), more modules, and a lower average degree (avgK) is more resilient to disturbances [60,61]. In this sense, the diazotrophic network in the sub-lakes sediment, with its larger network size, more modules, and lower average degree, may have greater resilience to environmental perturbations than that in the corresponding water columns.
Keystone taxa in co-occurrence networks play a fundamental role in maintaining the structure and function of microbial communities [62], and the removal of these taxa could disrupt modules and networks [44]. Here, among the genera to which keystone OTUs belonged, Geobacter was present in both the water and sediment of the sub-lakes, and Bradyrhizobium had the most keystone OTUs (7/11) in the sediment, suggesting that Geobacter and Bradyrhizobium played key roles in nitrogen fixation in the sub-lakes of Poyang Lake. Similarly, keystone taxa belonging to Geobacter and Bradyrhizobium have also been frequently found in diazotrophic co-occurrence networks in paddy fields and maize rhizosphere soils in China [63,64,65].

4.3. Deterministic Processes Dominate the Community Assembly of Diazotrophs in Both the Water and Sediment of the Sub-Lakes

A key challenge in microbial ecology is to understand the mechanisms of community assembly [66,67,68]. Niche theory and neutral theory, respectively, suggest that the microbial community assembly is driven by deterministic processes (e.g., environmental filtering and interspecies interactions) and stochastic processes (e.g., species birth, death, and dispersal) [68]. Our results revealed that the assembly of the diazotrophic community in both the water and sediment was dominated by deterministic processes, which was in agreement with the reports on Poyang Lake sediments [69], diverse crop soils [63,70,71], and Tibetan Plateau desert soils [72]. Many studies have shown that environmental factors influence the diazotrophic community in water and sediment in freshwater ecosystems [21,22,31,69]. Furthermore, the relative role of deterministic processes was stronger for sediment (mean MST 0.16) than water (mean MST 0.36) in the present study, which was similar to the reports on Lake Taihu and Bahe River (China) [48]. A possible explanation for this could be related to the difference in physical properties between water and sediment [48]. Water, a fluidic habitat, could facilitate the dispersal of planktonic organisms and thus weaken the environmental selective effects [73], whereas sediment, a non-fluidic habitat, could impose long-term and stable selective effects on benthic organisms [48].
In this study, we further clarified the environmental factors associated with the community composition of diazotrophs in the water and sediment of the sub-lakes. For the water, TP was found to be significantly related to the community structure of diazotrophs at the genus level. Similarly, Wang et al. [21] have found that the composition of the diazotrophic community in Lake Fuxian water is significantly correlated to TP. For the sediment, TN, organic matter, and the N:P molar ratio were found to be significantly associated with the composition of the diazotrophic community at the phylum level. These environmental impact factors have been reported to play an important role in the variation of diazotrophic community structure in freshwater lake sediment and in the Gurbantunggut Desert (China) [22,69,74].

5. Conclusions

Proteobacteria and Spirochaetes were the main diazotrophic phyla in the water of the sub-lakes, whereas Proteobacteria was the dominant diazotrophic phylum in the sediment of sub-lakes. There were significant differences in the composition of diazotrophic communities between the water and sediment of the sub-lakes. The sediment had a greater diversity of diazotrophs than the water. The structure of the diazotrophic co-occurrence network in the sediment was more complex than in the corresponding water. Deterministic processes dominate the community assembly of diazotrophs in both the water and sediment of the sub-lakes, and the relative role of deterministic processes was stronger for sediment than water. TP was associated with the variation in water diazotrophic community structure at the genus level. The variation in the composition of the sediment diazotrophic community at the phylum level was related to TN, organic matter, and the molar ratio of N:P. Our study is the first to simultaneously focus on diazotrophic communities in the water and sediment in natural freshwater ecosystems and to investigate the diazotrophs in the sub-lakes, which are of significant ecological value worldwide.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w16162277/s1. Table S1: The diazotrophic abundance in the water and sediment of the sub-lakes; Figure S1: Rarefaction curves based on (a) OTUs and (b) Shannon index for all diazotrophic communities. W, water; S, sediment; BS, Baishahu Lake; CH, Changhu Lake; CC, Changhuchi Lake; DC, Dachahu Lake; DH, Donghu Lake; NS, Beishenhu Lake; SN, Sanniwan Lake; ZB, Zhanbeihu Lake; ZC, Zhonghuchi Lake; Figure S2: Neighbor-joining phylogenetic tree of the representative nifH OTUs (which accounted for >0.2% of total sequences) in the water samples; Figure S3. Neighbor-joining phylogenetic tree of the representative nifH OTUs (which accounted for >0.2% of total sequences) in the sediment samples; Figure S4: Relative abundance of the classes found in the water and sediment diazotrophic communities. W, water; S, sediment; BS, Baishahu Lake; CC, Changhuchi Lake; CH, Changhu Lake; DC, Dachahu Lake; DH, Donghu Lake; NS, Beishenhu Lake; SN, Sanniwan Lake; ZB, Zhanbeihu Lake; ZC, Zhonghuchi Lake.

Author Contributions

Conceptualization, Q.W.; formal analysis, Q.W., Z.Z., L.L., Y.Q., Y.J. and W.Z.; investigation, Z.Z., J.L., Q.W. and F.W.; data curation, Q.W., Z.Z., Y.C. and J.L.; writing—original draft preparation, Q.W., Z.Z. and L.L.; writing—review and editing, Q.W. and Z.Z.; funding acquisition, Q.W., Y.C. and Z.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Science and Technology Research Project of Jiangxi Provincial Education Department, China (GJJ211935); the Jiangxi High-level Innovative Leading Talent Project (jxsq2019101020); the Open Research Fund for Key Laboratory of Water Resources and Environment of Poyang Lake, Jiangxi Province (2022SKSH05); and the Key Project of Innovation and Entrepreneurship Training Programme for College Students of Nanchang Institute of Technology (202111319004).

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Wang, X.L.; Xu, L.G.; Wan, R.G.; Chen, Y.W. Seasonal variations of soil microbial biomass within two typical wetland areas along the vegetation gradient of Poyang Lake, China. Catena 2016, 137, 483–493. [Google Scholar] [CrossRef]
  2. Hui, F.; Bing, X.U.; Huang, H.; Qian, Y.U.; Gong, P. Modeling spatial-temporal change of Poyang Lake using multi-temporal Landsat imagery. Int. J. Remote Sens. 2008, 29, 5767–5784. [Google Scholar] [CrossRef]
  3. Zhao, J.K.; Li, J.F.; Yan, H.; Zheng, L.; Dai, Z.J. Analysis on the water exchange between the main stream of the Yangtze River and the Poyang Lake. Procedia Environ. Sci. 2011, 10, 2256–2264. [Google Scholar] [CrossRef]
  4. Feng, L.; Hu, C.M.; Chen, X.L.; Cai, X.B.; Tian, L.Q.; Gan, W.X. Assessment of inundation changes of Poyang Lake using MODIS observations between 2000 and 2010. Remote Sens. Environ. 2012, 121, 80–92. [Google Scholar] [CrossRef]
  5. Xu, G.H.; Qin, Z.W. Flood estimation methods for Poyang Lake area. J. Lake Sci. 1998, 10, 31–36. [Google Scholar]
  6. Hu, B.J.; Hu, X.R.; Nie, X.; Zhang, X.K.; Wu, N.C.; Hong, Y.J.; Qin, H.M. Seasonal and inter-annual community structure characteristics of zooplankton driven by water environment factors in a sub-lake of Lake Poyang, China. Peerj 2019, 7, e7590. [Google Scholar] [CrossRef] [PubMed]
  7. Hu, Z.P.; Zhang, Z.F.; Liu, Y.Z.; Ji, W.T.; Ge, G. The functionand significanceof the Shallow-Lakesin the Poyang Lake wetland ecosystem. Jiangxi Hydraul. Sci. Technol. 2015, 41, 317–323. [Google Scholar]
  8. Hu, Z.P.; Ge, G.; Liu, C.L. Response of wintering migratory birds to hydrological processes in Poyang Lake. J. Nat. Resour. 2014, 29, 1770–1779. [Google Scholar]
  9. Qi, S.H.; Liu, Y.; Yu, X.B.; Liao, F.Q. Effect of “Lake enclosed in autumn” on the hanitat of winter bird in Poyang Lake. Resour. Environ. Yangtze Basin 2011, 20, 18–21. [Google Scholar]
  10. Zhang, B.; Wang, J.H. Preliminary opinions on fishery natural resources, protection and utilization in Poyang Lake. Freshw. Fish. 1982, 3, 1–5. [Google Scholar]
  11. Capone, D.G.; Burns, J.A.; Montoya, J.P.; Subramaniam, A.; Mahaffey, C.; Gunderson, T.; Michaels, A.F.; Carpenter, E.J. Nitrogen fixation by Trichodesmium spp.: An important source of new nitrogen to the tropical and subtropical North Atlantic Ocean. Glob. Biogeochem. Cycles 2005, 19, GB2024. [Google Scholar] [CrossRef]
  12. Dong, Y.R.; Sanford, R.A.; Connor, L.; Chee-Sanford, J.; Wimmer, B.T.; Iranmanesh, A.; Shi, L.; Krapac, I.G.; Locke, R.A.; Shao, H.B. Differential structure functional gene response to geochemistry associated with the suspended attached shallow aquifer microbiomes from the Illinois Basin, I.L. Water Res. 2021, 202, 117431. [Google Scholar] [CrossRef] [PubMed]
  13. Kapili, B.J.; Barnett, S.E.; Buckley, D.H.; Dekas, A.E. Evidence for phylogenetically and catabolically diverse active diazotrophs in deep-sea sediment. Isme J. 2020, 14, 971–983. [Google Scholar] [CrossRef] [PubMed]
  14. Hallstrom, S.; Benavides, M.; Salamon, E.R.; Evans, C.W.; Potts, L.J.; Granger, J.; Tobias, C.R.; Moisander, P.H.; Riemann, L. Pelagic N2 fixation dominated by sediment diazotrophic communities in a shallow temperate estuary. Limnol. Oceanogr. 2022, 67, 364–378. [Google Scholar] [CrossRef]
  15. Hou, L.J.; Wang, R.; Yin, G.Y.; Liu, M.; Zheng, Y.L. Nitrogen fixation in the intertidal sediments of the Yangtze estuary: Occurrence and environmental implications. J. Geophys. Res.-Biogeosci. 2018, 123, 936–944. [Google Scholar] [CrossRef]
  16. Wang, G.Q.; Xia, X.H.; Liu, S.D.; Wang, J.F.; Zhang, S.B. Low diffusive nitrogen loss of urban inland waters with high nitrogen loading. Sci. Total Environ. 2021, 789, 148023. [Google Scholar] [CrossRef]
  17. Zhang, X.; Gu, Q.; Long, X.E.; Li, Z.L.; Liu, D.X.; Ye, D.H.; He, C.Q.; Liu, X.Y.; Väänänen, K.; Chen, X.P. Anthropogenic activities drive the microbial community and its function in urban river sediment. J. Soils Sediments 2016, 16, 716–725. [Google Scholar] [CrossRef]
  18. Moutinho, F.H.M.; Marafao, G.A.; Calijuri, M.D.; Moreira, M.Z.; Marcarelli, A.M.; Cunha, D.G.F. Environmental factors and thresholds for nitrogen fixation by phytoplankton in tropical reservoirs. Int. Rev. Hydrobiol. 2021, 106, 5–17. [Google Scholar] [CrossRef]
  19. Wang, L.N.; Yu, Z.; Yang, J.; Zhou, J. Diazotrophic bacterial community variability in a subtropical deep reservoir is correlated with seasonal changes in nitrogen. Environ. Sci. Pollut. Res. 2015, 22, 19695–19705. [Google Scholar] [CrossRef]
  20. Tian, L.Q.; Yan, Z.S.; Wang, C.H.; Xu, S.Q.; Jiang, H.L. Habitat heterogeneity induces regional differences in sediment nitrogen fixation in eutrophic freshwater lake. Sci. Total Environ. 2021, 772, 145594. [Google Scholar] [CrossRef]
  21. Wang, L.N.; Xing, P.; Li, H.B.; Zhou, L.J.; Wu, Q.L.L. Distinct intra-lake heterogeneity of diazotrophs in a deep oligotrophic mountain lake. Microb. Ecol. 2020, 79, 840–852. [Google Scholar] [CrossRef] [PubMed]
  22. Wu, Q.; Wang, F.; Chen, Y.W.; Zou, W.X.; Zhu, Z.G. Diazotrophic community in the sediments of Poyang Lake in response to water level fluctuations. Front. Microbiol. 2024, 15, 1324313. [Google Scholar] [CrossRef] [PubMed]
  23. Scott, J.T.; Grantz, E.M. N2 fixation exceeds internal nitrogen loading as a phytoplankton nutrient source in perpetually nitrogen-limited reservoirs. Freshw. Sci. 2013, 32, 849–861. [Google Scholar] [CrossRef]
  24. Xu, Y.; Wang, H.M.; Xiang, X.; Wang, R.C.; Tian, W. Vertical variation of nitrogen fixers and ammonia oxidizers along a sediment profile in the dajiuhu peatland, central China. J. Earth Sci. 2019, 30, 397–406. [Google Scholar] [CrossRef]
  25. Zehr, J.P.; Church, M.J.; Moisander, P.H. Diversity, distribution and biogeochemical significance of nitrogen-fixing microorganisms in anoxic and suboxic ocean environments. In Past and Present Water Column Anoxia; Neretin, L.N., Ed.; Springer: Dordrecht, The Netherlands, 2006; pp. 337–369. [Google Scholar]
  26. Geisler, E.; Rahav, E.; Bar-Zeev, E. Contribution of heterotrophic diazotrophs to N2 fixation in a eutrophic river: Free-living vs. aggregate-associated. Front. Microbiol. 2022, 13, 779820. [Google Scholar] [CrossRef] [PubMed]
  27. Tian, L.Q.; Jiang, H.L.; Bai, L.L.; Wang, C.L.; Xu, S.Q. Biological nitrogen fixation in sediments of a cyanobacterial bloom-occurring bay in one eutrophic shallow lake: Occurrence and related environmental factors. J. Geophys. Res.-Biogeosci. 2021, 126, e2021JG006342. [Google Scholar] [CrossRef]
  28. Fernandez, L.; Peura, S.; Eiler, A.; Linz, A.M.; McMahon, K.D.; Bertilsson, S. Diazotroph genomes and their seasonal dynamics in a stratified humic bog lake. Front. Microbiol. 2020, 11, 1500. [Google Scholar] [CrossRef] [PubMed]
  29. Ren, Z.; Qu, X.D.; Peng, W.Q.; Yu, Y.; Zhang, M. Functional properties of bacterial communities in water and sediment of the eutrophic river-lake system of Poyang Lake, China. Peerj 2019, 7, e7318. [Google Scholar] [CrossRef] [PubMed]
  30. Yao, X.L.; Zhang, L.; Zhang, Y.L.; Zhang, B.; Zhao, Z.H.; Zhang, Y.B.; Li, M.; Jiang, X.Y. Nitrogen fixation occurring in sediments: Contribution to the nitrogen budget of lake Taihu, China. J. Geophys. Res.-Biogeosci. 2018, 123, 2661–2674. [Google Scholar] [CrossRef]
  31. Li, B.; Wang, L.; Li, H.B.; Xue, J.Y.; Luo, W.L.; Xing, P.; Wu, Q.L. Phosphorus-driven regime shift from heterotrophic to autotrophic diazotrophs in a deep alpine lake. Water Res. 2024, 248, 120848. [Google Scholar] [CrossRef]
  32. Liu, X.; Li, Y.A.; Liu, B.G.; Qian, K.M.; Chen, Y.W.; Gao, J.F. Cyanobacteria in the complex river-connected Poyang Lake: Horizontal distribution and transport. Hydrobiologia 2016, 768, 95–110. [Google Scholar] [CrossRef]
  33. Wang, L.Q.; Liang, T. Distribution characteristics of phosphorus in the sediments and overlying water of Poyang Lake. PLoS ONE 2015, 10, e0125859. [Google Scholar] [CrossRef] [PubMed]
  34. Jin, X.C.; Tu, Q.Y. The Standard Methods for Observation and Analysis in Lake Eutrophication, 2nd ed.; Chinese Environmental Science Press: Beijing, China, 1990. (In Chinese) [Google Scholar]
  35. Kjeldahl, J. Neue methode zur bestimmung des stickstoffs in organischen Körpern. Z. Für Anal. Chem. 1883, 22, 366–382. [Google Scholar] [CrossRef]
  36. Zehr, J.P.; McReynolds, L.A. Use of degenerate oligonucleotides for amplification of the nifH gene from the marine cyanobacterium Trichodesmium thiebautii. Appl. Environ. Microbiol. 1989, 55, 2522–2526. [Google Scholar] [CrossRef]
  37. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef] [PubMed]
  38. Yuan, Y.; Wang, S.Y.; Liu, Y.; Li, B.K.; Wang, B.; Peng, Y.Z. Long-term effect of pH on short-chain fatty acids accumulation and microbial community in sludge fermentation systems. Bioresour. Technol. 2015, 197, 56–63. [Google Scholar] [CrossRef] [PubMed]
  39. Edgar, R.C.; Haas, B.J.; Clemente, J.C.; Quince, C.; Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 2011, 27, 2194–2200. [Google Scholar] [CrossRef] [PubMed]
  40. Edgar, R.C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 2013, 10, 996. [Google Scholar] [CrossRef] [PubMed]
  41. Wu, X.T.; Sun, Y.; Deng, L.T.; Meng, Q.X.; Jiang, X.; Bello, A.; Sheng, S.Y.; Han, Y.; Zhu, H.F.; Xu, X.H. Insight to key diazotrophic community during composting of dairy manure with biochar and its role in nitrogen transformation. Waste Manag. 2020, 105, 190–197. [Google Scholar] [CrossRef]
  42. Deng, Y.; Jiang, Y.H.; Yang, Y.F.; He, Z.L.; Luo, F.; Zhou, J.Z. Molecular ecological network analyses. BMC Bioinform. 2012, 13, 113. [Google Scholar] [CrossRef]
  43. Bastian, M.; Heymann, S.; Jacomy, M. Gephi: An open source software for exploring and manipulating networks. In Proceedings of the Third International Conference on Weblogs and Social Media, San Jose, CA, USA, 17–20 May 2009; Volume 3, pp. 361–362. [Google Scholar]
  44. Guimerà, R.; Amaral, L.A.N. Functional cartography of complex metabolic networks. Nature 2005, 433, 895–900. [Google Scholar] [CrossRef]
  45. Chen, W.D.; Ren, K.X.; Isabwe, A.; Chen, H.H.; Liu, M.; Yang, J. Stochastic processes shape microeukaryotic community assembly in a subtropical river across wet and dry seasons. Microbiome 2019, 7, 138. [Google Scholar]
  46. Mo, Y.Y.; Peng, F.; Gao, X.F.; Xiao, P.; Logares, R.; Jeppesen, E.; Ren, K.X.; Xue, Y.Y.; Yang, J. Low shifts in salinity determined assembly processes and network stability of microeukaryotic plankton communities in a subtropical urban reservoir. Microbiome 2021, 9, 128. [Google Scholar] [CrossRef] [PubMed]
  47. Sun, X.; Pei, J.; Zhao, L.; Ahmad, B.; Huang, L.F. Fighting climate change: Soil bacteria communities and topography play a role in plant colonization of desert areas. Environ. Microbiol. 2021, 23, 6876–6894. [Google Scholar] [CrossRef] [PubMed]
  48. Wang, L.X.; Han, M.Z.; Li, X.; Ginawi, A.; Ning, K.; Yan, Y.J. Niche and neutrality work differently in microbial communities in fluidic and non-fluidic ecosystems. Microb. Ecol. 2020, 79, 527–538. [Google Scholar] [CrossRef]
  49. Yu, Z.; Zhou, J.; Yang, J.; Yu, X.Q.; Liu, L.M. Vertical distribution of diazotrophic bacterial community associated with temperature and oxygen gradients in a subtropical reservoir. Hydrobiologia 2014, 741, 69–77. [Google Scholar] [CrossRef]
  50. Liu, X.H.; Li, P.; Wang, H.L.; Han, L.L.; Yang, K.; Wang, Y.H.; Jiang, Z.; Cui, L.; Kao, S.J. Nitrogen fixation and diazotroph diversity in groundwater systems. Isme J. 2023, 17, 2023–2034. [Google Scholar] [CrossRef] [PubMed]
  51. Li, X.M.; Meng, Z.H.; Chen, K.; Hu, F.F.; Liu, L.; Zhu, T.B.; Yang, D.G. Comparing diversity patterns and processes of microbial community assembly in water column and sediment in Lake Wuchang, China. Peerj 2023, 11, e14592. [Google Scholar] [CrossRef]
  52. Ren, Z.; Qu, X.D.; Peng, W.Q.; Yu, Y.; Zhang, M. Nutrients drive the structures of bacterial communities in sediments and surface waters in the river-lake system of Poyang Lake. Water 2019, 11, 930. [Google Scholar] [CrossRef]
  53. Sun, Y.; Li, X.; Liu, J.J.; Yao, Q.; Jin, J.; Liu, X.B.; Wang, G.H. Comparative analysis of bacterial community compositions between sediment and water in different types of wetlands of northeast China. J. Soils Sediments 2019, 19, 3083–3097. [Google Scholar] [CrossRef]
  54. Perkins, T.L.; Clements, K.; Baas, J.H.; Jago, C.F.; Jones, D.L.; Malham, S.K.; McDonald, J.E. Sediment composition influences spatial variation in the abundance of human pathogen indicator bacteria within an estuarine environment. PLoS ONE 2014, 9, e112951. [Google Scholar] [CrossRef] [PubMed]
  55. Dong, X.Y.; Zhang, C.W.; Peng, Y.Y.; Zhang, H.X.; Shi, L.D.; Wei, G.S.; Hubert, C.R.J.; Wang, Y.; Greening, C. Phylogenetically and catabolically diverse diazotrophs reside in deep-sea cold seep sediments. Nat. Commun. 2022, 13, 4885. [Google Scholar] [CrossRef] [PubMed]
  56. Luo, Z.W.; Zhong, Q.P.; Han, X.G.; Hu, R.W.; Liu, X.Y.; Xu, W.J.; Wu, Y.J.; Huang, W.M.; Zhou, Z.Y.; Zhuang, W.; et al. Depth-dependent variability of biological nitrogen fixation and diazotrophic communities in mangrove sediments. Microbiome 2021, 9, 212. [Google Scholar] [CrossRef] [PubMed]
  57. Weber, S.C.; Carpenter, E.J.; Coles, V.J.; Yager, P.L.; Goes, J.; Montoya, J.P. Amazon River influence on nitrogen fixation and export production in the western tropical North Atlantic. Limnol. Oceanogr. 2017, 62, 618–631. [Google Scholar] [CrossRef]
  58. Chen, W.D.; Wen, D.H. Archaeal and bacterial communities assembly and co-occurrence networks in subtropical mangrove sediments under Spartina alterniflora invasion. Environ. Microbiome 2021, 16, 10. [Google Scholar] [CrossRef] [PubMed]
  59. Li, J.B.; Li, C.N.; Kou, Y.P.; Yao, M.J.; He, Z.L.; Li, X.Z. Distinct mechanisms shape soil bacterial and fungal co-occurrence networks in a mountain ecosystem. Fems Microbiol. Ecol. 2020, 96, fiaa030. [Google Scholar] [CrossRef] [PubMed]
  60. Yang, C.Y.; Wang, Q.; Simon, P.N.; Liu, J.Y.; Liu, L.C.; Dai, X.Z.; Zhang, X.H.; Kuang, J.L.; Igarashi, Y.; Pan, X.J.; et al. Distinct network interactions in particle-associated and free-living bacterial communities during a Microcystis aeruginosa bloom in a plateau lake. Front. Microbiol. 2017, 8, 1202. [Google Scholar] [CrossRef]
  61. Zhou, J.Z.; Deng, Y.; Luo, F.; He, Z.L.; Tu, Q.C.; Zhi, X.Y. Functional molecular ecological networks. Mbio 2010, 1, e00169-10. [Google Scholar] [CrossRef] [PubMed]
  62. Xue, Y.Y.; Chen, H.H.; Yang, J.R.; Liu, M.; Huang, B.Q.; Yang, J. Distinct patterns and processes of abundant and rare eukaryotic plankton communities following a reservoir cyanobacterial bloom. Isme J. 2018, 12, 2263–2277. [Google Scholar] [CrossRef]
  63. Cao, J.L.; Liu, Z.Y.; Zhao, H.Y.; Lai, F.Y.; Han, Y.; Lin, X.G. Effects of copper oxide nanoparticles on soil diazotrophic communities in maize rhizosphere. J. Soils Sediments 2023, 23, 1760–1774. [Google Scholar] [CrossRef]
  64. Dai, X.L.; Song, D.L.; Guo, Q.K.; Zhou, W.; Liu, G.R.; Ma, R.P.; Liang, G.Q.; He, P.; Sun, G.; Yuan, F.S.; et al. Predicting the influence of fertilization regimes on potential N fixation through their effect on free-living diazotrophic community structure in double rice cropping systems. Soil Biol. Biochem. 2021, 156, 108220. [Google Scholar] [CrossRef]
  65. Wang, H.H.; Li, X.; Li, X.Y.; Li, F.L.; Su, Z.C.; Zhang, H.W. Community composition and co-occurrence patterns of diazotrophs along a soil profile in paddy fields of three soil types in China. Microb. Ecol. 2021, 82, 961–970. [Google Scholar] [CrossRef] [PubMed]
  66. Luan, L.; Liang, C.; Chen, L.J.; Wang, H.T.; Xu, Q.S.; Jiang, Y.J.; Sun, B. Coupling bacterial community assembly to microbial metabolism across soil profiles. Msystems 2020, 5, e00298-20. [Google Scholar] [CrossRef] [PubMed]
  67. Morrison-Whittle, P.; Goddard, M.R. Quantifying the relative roles of selective and neutral processes in defining eukaryotic microbial communities. Isme J. 2015, 9, 2003–2011. [Google Scholar] [CrossRef] [PubMed]
  68. Zhou, J.Z.; Ning, D.L. Stochastic community assembly: Does it matter in microbial ecology? Microbiol. Mol. Biol. Rev. 2017, 81, e00002-17. [Google Scholar] [CrossRef] [PubMed]
  69. Tian, L.Q.; Deng, Y.; Jiang, H.L. Comparing the benthic nitrogenase activity and diazotrophic community assembly of three large river-connected freshwater lakes in eastern China. J. Environ. Sci. 2025, 150, 134–148. [Google Scholar] [CrossRef]
  70. Feng, M.M.; Adams, J.M.; Fan, K.K.; Shi, Y.; Sun, R.B.; Wang, D.Z.; Guo, X.S.; Chu, H.Y. Long-term fertilization influences community assembly processes of soil diazotrophs. Soil Biol. Biochem. 2018, 126, 151–158. [Google Scholar] [CrossRef]
  71. Wang, J.L.; Li, Q.K.; Shen, C.C.; Yang, F.T.; Wang, J.C.; Ge, Y. Significant dose effects of fertilizers on soil diazotrophic diversity, community composition, and assembly processes in a long-term paddy field fertilization experiment. Land Degrad. Dev. 2021, 32, 420–429. [Google Scholar] [CrossRef]
  72. Ding, X.W.; Liu, K.H.; Yan, Q.Y.; Liu, X.Y.; Chen, N.; Wang, G.L.; He, S. Sugar and organic acid availability modulate soil diazotroph community assembly and species co-occurrence patterns on the Tibetan Plateau. Appl. Microbiol. Biotechnol. 2021, 105, 8545–8560. [Google Scholar] [CrossRef]
  73. Qin, M.Y.; Xu, H.M.; Zhao, D.Y.; Zeng, J.; Wu, Q.L.L. Aquaculture drives distinct patterns of planktonic and sedimentary bacterial communities: Insights into co-occurrence pattern and assembly processes. Environ. Microbiol. 2022, 24, 4079–4093. [Google Scholar] [CrossRef]
  74. Rong, X.Y.; Zhou, X.B.; Li, X.Z.; Yao, M.J.; Lu, Y.X.; Xu, P.; Yin, B.F.; Li, Y.G.; Aanderud, Z.T.; Zhang, Y.M. Biocrust diazotrophs and bacteria rather than fungi are sensitive to chronic low N deposition. Environ. Microbiol. 2022, 24, 5450–5466. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Map of the nine sub-lakes sampling locations.
Figure 1. Map of the nine sub-lakes sampling locations.
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Figure 2. Comparison of alpha diversity indices between water and sediment diazotrophic communities in the sub-lakes. (a) OTU richness; (b) Chao richness estimator; (c) Shannon index. * p < 0.05, ** p < 0.01.
Figure 2. Comparison of alpha diversity indices between water and sediment diazotrophic communities in the sub-lakes. (a) OTU richness; (b) Chao richness estimator; (c) Shannon index. * p < 0.05, ** p < 0.01.
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Figure 3. Relative abundance of the phyla found in the water and sediment diazotrophic communities. W, water; S, sediment; BS, Baishahu Lake; CC, Changhuchi Lake; CH, Changhu Lake; DC, Dachahu Lake; DH, Donghu Lake; NS, Beishenhu Lake; SN, Sanniwan Lake; ZB, Zhanbeihu Lake; ZC, Zhonghuchi Lake.
Figure 3. Relative abundance of the phyla found in the water and sediment diazotrophic communities. W, water; S, sediment; BS, Baishahu Lake; CC, Changhuchi Lake; CH, Changhu Lake; DC, Dachahu Lake; DH, Donghu Lake; NS, Beishenhu Lake; SN, Sanniwan Lake; ZB, Zhanbeihu Lake; ZC, Zhonghuchi Lake.
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Figure 4. Comparison of the relative abundance of (a) Alphaproteobacteria, (b) Betaproteobacteria, and (c) Deltaproteobacteria between water and sediment diazotrophic communities in the sub-lakes. * p < 0.05, ** p < 0.01.
Figure 4. Comparison of the relative abundance of (a) Alphaproteobacteria, (b) Betaproteobacteria, and (c) Deltaproteobacteria between water and sediment diazotrophic communities in the sub-lakes. * p < 0.05, ** p < 0.01.
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Figure 5. Z-score normalized heatmap of the eight dominant diazotrophic genera (average relative abundance > 5% in the water or sediment). W, water; S, sediment; BS, Baishahu Lake; CC, Changhuchi Lake; CH, Changhu Lake; DC, Dachahu Lake; DH, Donghu Lake; NS, Beishenhu Lake; SN, Sanniwan Lake; ZB, Zhanbeihu Lake; ZC, Zhonghuchi Lake. * p < 0.05, ** p < 0.01.
Figure 5. Z-score normalized heatmap of the eight dominant diazotrophic genera (average relative abundance > 5% in the water or sediment). W, water; S, sediment; BS, Baishahu Lake; CC, Changhuchi Lake; CH, Changhu Lake; DC, Dachahu Lake; DH, Donghu Lake; NS, Beishenhu Lake; SN, Sanniwan Lake; ZB, Zhanbeihu Lake; ZC, Zhonghuchi Lake. * p < 0.05, ** p < 0.01.
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Figure 6. PCoA ordination plot of the water and sediment diazotrophic OTUs from the nine sub-lakes. S, sediment; W, water.
Figure 6. PCoA ordination plot of the water and sediment diazotrophic OTUs from the nine sub-lakes. S, sediment; W, water.
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Figure 7. Co-occurrence networks of the (a) water and (b) sediment diazotrophic communities at the OTU level. Red and green edges, respectively, represent positive and negative correlations.
Figure 7. Co-occurrence networks of the (a) water and (b) sediment diazotrophic communities at the OTU level. Red and green edges, respectively, represent positive and negative correlations.
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Figure 8. (a) Zi (within-module connectivity)-Pi (among-module connectivity) plot showing the distribution of the water and sediment diazotrophic OTUs based on their topological roles. Peripherals have Zi < 2.5 and Pi < 0.62, module hubs have Zi > 2.5 and Pi < 0.62, and connectors have Zi < 2.5 and Pi > 0.62. Module hubs and connectors are regarded as keystone taxa. (b) Number and taxonomic assignment of keystone OTUs.
Figure 8. (a) Zi (within-module connectivity)-Pi (among-module connectivity) plot showing the distribution of the water and sediment diazotrophic OTUs based on their topological roles. Peripherals have Zi < 2.5 and Pi < 0.62, module hubs have Zi > 2.5 and Pi < 0.62, and connectors have Zi < 2.5 and Pi > 0.62. Module hubs and connectors are regarded as keystone taxa. (b) Number and taxonomic assignment of keystone OTUs.
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Water 16 02277 g009
Figure 9. Neutral community model of diazotrophic OTUs in (a) water and (b) sediment. OTUs that occur more or less frequently than predicted by the model are represented by blue or red dots. The dashed blue line shows the 95% confidence interval around the model prediction (the solid blue line). The value of R2 indicates the fit to the neutral model, and Nm represents the metacommunity size times immigration.
Figure 9. Neutral community model of diazotrophic OTUs in (a) water and (b) sediment. OTUs that occur more or less frequently than predicted by the model are represented by blue or red dots. The dashed blue line shows the 95% confidence interval around the model prediction (the solid blue line). The value of R2 indicates the fit to the neutral model, and Nm represents the metacommunity size times immigration.
Water 16 02277 g009
Water 16 02277 g010
Figure 10. Modified stochasticity ratio (MST) of the water and sediment diazotrophic communities. Solid and hollow blocks represent MST values for paired samples and mean MST values, respectively.
Figure 10. Modified stochasticity ratio (MST) of the water and sediment diazotrophic communities. Solid and hollow blocks represent MST values for paired samples and mean MST values, respectively.
Water 16 02277 g010
Table 1. Topological properties of the co-occurrence network of the water and sediment diazotrophic communities.
Table 1. Topological properties of the co-occurrence network of the water and sediment diazotrophic communities.
Network PropertiesWaterSediment
No. of nodes158266
No. of edges219342
No. of positive edges106168
No. of negative edges113174
Average clustering coefficient (avgCC)0.1080.118
Average degree (avgK)2.7722.571
Modularity (no. of modules)0.753 (23)0.783 (41)
Average path distance (GD)7.4265.966
Table 2. C-score analysis of the water and sediment diazotrophic communities.
Table 2. C-score analysis of the water and sediment diazotrophic communities.
HabitatObserved C-ScoreSimulated C-ScorepSES
Water1.6011.588<0.0013.391
Sediment1.6121.6060.0153.175
Note: C-score, checkerboard score; SES, standardized effect size.
Table 3. The relationships between the environmental factors and the water diazotrophic communities at the phylum and genus level using the Mantel test.
Table 3. The relationships between the environmental factors and the water diazotrophic communities at the phylum and genus level using the Mantel test.
Environmental FactorsPhylum LevelGenus Level
rprp
WT−0.070.529−0.240.886
pH−0.240.925−0.030.542
DO−0.150.7940.130.200
EC0.010.4140.040.374
TN−0.080.5700.030.422
NH4-N0.210.166−0.040.559
PO4-P0.100.317−0.010.494
TP0.050.3460.410.014 *
CODMn−0.120.7170.0030.452
Note: WT, water temperature; DO, dissolved oxygen; EC, electrical conductivity; TN, total nitrogen; NH4-N, ammonia nitrogen; PO4-P, orthophosphate phosphorous; TP, total phosphorous; CODMn, chemical oxygen demand; * p < 0.05.
Table 4. The relationships between the environmental factors and the sediment diazotrophic communities at the phylum and genus level using the Mantel test.
Table 4. The relationships between the environmental factors and the sediment diazotrophic communities at the phylum and genus level using the Mantel test.
Environmental FactorsPhylum LevelGenus Level
rprp
pH0.410.0590.340.082
EC0.190.0670.230.068
TN0.450.036 *0.250.149
TP−0.270.978−0.240.952
OM0.420.039 *0.370.066
N:P0.550.016 *0.410.053
Note: EC, electrical conductivity; TN, total nitrogen; TP, total phosphorous; OM, organic matter; N:P, molar ratio of N:P; * p < 0.05.
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MDPI and ACS Style

Wu, Q.; Zhu, Z.; Liu, L.; Qin, Y.; Jiang, Y.; Liu, J.; Zou, W.; Wang, F.; Chen, Y. Distinct Diazotrophic Communities in Water and Sediment of the Sub-Lakes in Poyang Lake, China. Water 2024, 16, 2277. https://doi.org/10.3390/w16162277

AMA Style

Wu Q, Zhu Z, Liu L, Qin Y, Jiang Y, Liu J, Zou W, Wang F, Chen Y. Distinct Diazotrophic Communities in Water and Sediment of the Sub-Lakes in Poyang Lake, China. Water. 2024; 16(16):2277. https://doi.org/10.3390/w16162277

Chicago/Turabian Style

Wu, Qiang, Zhigang Zhu, Longlingfeng Liu, Yin Qin, Yufang Jiang, Jinfu Liu, Wenxiang Zou, Fei Wang, and Yuwei Chen. 2024. "Distinct Diazotrophic Communities in Water and Sediment of the Sub-Lakes in Poyang Lake, China" Water 16, no. 16: 2277. https://doi.org/10.3390/w16162277

APA Style

Wu, Q., Zhu, Z., Liu, L., Qin, Y., Jiang, Y., Liu, J., Zou, W., Wang, F., & Chen, Y. (2024). Distinct Diazotrophic Communities in Water and Sediment of the Sub-Lakes in Poyang Lake, China. Water, 16(16), 2277. https://doi.org/10.3390/w16162277

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