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Article

Effects of Vegetation Cover Varying along the Hydrological Gradient on Microbial Community and N-Cycling Gene Abundance in a Plateau Lake Littoral Zone

by
Jing Yuan
1,2,†,
Jing Cao
1,2,†,
Wanxue Liao
1,2,
Feng Zhu
1,2,3,
Zeying Hou
1,2 and
Zhaosheng Chu
1,2,*
1
National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
2
State Environmental Protection Key Laboratory for Lake Pollution Control, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
3
College of Water Sciences, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Processes 2024, 12(6), 1276; https://doi.org/10.3390/pr12061276
Submission received: 28 May 2024 / Revised: 13 June 2024 / Accepted: 19 June 2024 / Published: 20 June 2024
(This article belongs to the Special Issue State-of-the-Art Wastewater Treatment Techniques)
Browse Figures
Versions Notes

Abstract

:
The lake littoral zone is periodically exposed to water due to water level fluctuations, driving the succession and distribution of littoral vegetation covers, which complexly affect nutrient biogeochemical transformation. However, the combined effects of water level fluctuations and other environmental factors on microbial characteristics and functions at the regional scale remain unclear. In this study, typical vegetation cover types along various water levels were chosen to investigate the effects of water level and vegetation cover on the microbial community and functional genes in the Lake Erhai littoral zone. The results showed that water level fluctuations influenced oxygen and nitrogen compound contents due to oxic–anoxic alternations and intensive material exchange. Meanwhile, vegetation cover affected the organic matter and total nitrogen content through plant residues and root exudation supplying exogenous carbon and nitrogen. Along the hydrological gradient, the high microbial diversity and abundant microbes related to nitrogen cycling were observed in interface sediments. It was attributed to the alternating aerobic–anaerobic environments, which filtered adaptable dominant phyla and genera. The abundances of amoA AOA, nirS, and amx were higher than those of the other genes and were strongly related to flooding days and water content. In conclusion, water level fluctuations and vegetation type jointly affect microbial community structure and nitrogen-related functional genes.

1. Introduction

In recent decades, the rapid growth of agriculture and increased travel around lakesides have significantly increased nutrient runoff into lakes, particularly nitrogen and phosphorus, causing freshwater eutrophication [1,2]. The lake littoral zone, a transitional junction between terrestrial and aquatic ecosystems, acts as a crucial barrier filtering nutrient-rich runoff before it enters the lake [3]. Due to the increased nutrient input and material exchange, this transitional zone has been reported as a hotspot for nitrogen cycling, playing a vital role in controlling lake eutrophication [4]. Microorganisms are the key mediators and drivers of biogeochemical processes in lake littoral zones, responsible for nitrogen decomposition and mineralization [5,6]. Understanding their composition and function in response to the microenvironment in this ecotone is essential for advancing research in this area.
The lake littoral zone is periodically exposed to water due to water level fluctuations, which are critical in shaping the hydrological characteristics of the ecotone [7]. The dynamic hydrologic connectivity resulting from these fluctuations causes the periodic drying and rewetting of ecotone soil and sediment, creating highly heterogeneous conditions with oxic–anoxic alternations [8]. This environmental variation, coupled with changes in the microbial community, affects the degree of the biogeochemical transformation of various nitrogen compounds in the lake littoral zone [9]. A comparative study of the nitrogen-cycling microbial community in the littoral and limnetic sediments of Lake Chaohu in China concluded that microbial abundance and diversity were higher in the littoral zone, identifying it as a nitrogen-cycling hotspot [1]. Previous studies have investigated the coupling of nitrification and denitrification in the riparian zone by examining the specific distribution of microbial communities and functional genes, attributing microbial composition characteristics to water level fluctuations [5,6,10]. However, there is still no consensus on the specific microbial features that change with water level fluctuations and their exact impact. Therefore, more research is needed to understand the complex influence of water level fluctuations and other environmental factors on microbial characteristics and functions at a regional scale.
Periodic inundations in the lake littoral zone drive the succession and distribution of littoral vegetation, creating a unique vegetation landscape [11]. Different vegetation types, as direct participants in the soil environment, impact soil physicochemical properties and further affect nitrogen-cycling microorganisms in the soil and sediment. The rhizosphere of vegetation, crucial for nitrogen cycling, interacts with microorganisms through litter inputs and root exudates, affecting soil aggregate stability, water-holding capacity, and carbon sequestration [7]. Previous studies on the riparian zones of reservoirs have shown differing results, one study based on the Three Gorges Reservoir [6] and another based on the Miyun Reservoir [12]. The unique periodic fluctuations with water level variation from 145 m to 175 m in riparian zones of the Three Gorges Reservoir resulted in water level being the main factor in microbial composition and nitrogen-related gene abundance, while plant types were critical in the riparian zones of the Miyun Reservoir with 6 cm water level variation in two sampling seasons. These differences may be attributed to significant altitude variations (over 30 m) between the riparian zones of the two reservoirs, indicating that the impact of rapid and robust fluctuations in the ecotone is not yet fully understood. Therefore, a more comprehensive study of the complex environment in the lake littoral zone, including upstream and downstream areas with a more refined division, is needed.
Lake Erhai is the second largest plateau freshwater lake in China. Rapid urbanization in the lakeside area over the past few decades has increased the pollution load into the lake [13]. Due to the narrow width of the catchment area and the short surface-inflow path, the purification effectiveness of the lake littoral zone is crucial for controlling eutrophication. Since the construction of hydroelectric power plants in the 1980s, the water level of Lake Erhai has been artificially regulated. The water level is low in the summer (May to June) and high in the autumn (September to November). Over the past two decades, interannual and seasonal water level variations have been 1.0 and 1.2 m, respectively [14]. The long-term artificial regulation of water levels has significantly affected the littoral hydrological regime and environmental conditions. However, there is limited knowledge about the influence of nitrogen removal in the lake littoral zone, particularly concerning the microbial community and N-cycling functional genes.
This study aimed to investigate the impact of water level and vegetation cover on the microbial community and functional genes in the littoral zone of Lake Erhai. The research focused on nine different types of vegetation covers across three vertical spatial zones: the landward zone, the interface zone, and the waterward zone. We examined soil and sediment microenvironment properties, microbial communities, and N-cycling functional genes to (1) characterize the physicochemical properties of soil and sediment under various vegetation covers along the hydrological gradient; (2) explore the response of microbial community structure and N-cycling functional genes to water level fluctuations and vegetation types; and (3) reveal the relationships between water level fluctuations, vegetation types, soil/sediment properties, and microbial compositions.

2. Materials and Methods

2.1. Study Area and Sampling Process

Lake Erhai is a large and deep fault lake in the Yunnan Province, located on the Yunnan–Guizhou Plateau of southwest China (Figure 1a). It has an area of 252 km2 and a maximum depth of 21 m. [14]. The lake experiences a low-latitude subtropical monsoon climate, with a latitude ranging from 25°36′ N to 25°38′ N and a water level ranging from 1966.0 m to 1964.3 m. The annual mean temperature of the basin is 15 °C, and the temperature of the lake water varies from 10 °C to 20 °C. [13]. In 2022, the water level varied by 0.55 m, ranging from 1965.5 m to 1964.9 m.
Ten typical sampling areas were selected surrounding Lake Erhai based on the hydrographic and topographic features of the lake, as shown in Figure 1b. Each sampling area was divided into three vertical zones based on the average annual flooding days to study the hydrological impacts of water level changes (Figure 1c). These zones were as follows: (1) landward zones (rarely flooded, i.e., no flooding in a year); (2) interface zones (intermittently flooded, i.e., 133–270 flooding days in a year); (3) waterward zones (always flooded, i.e., about 365 flooding days in a year). In each zone, the main vegetation cover was recorded and classified into nine types across the three spatial zones. In the landward zones, the three vegetation types were forest (LF), grassland (LG), and inter-forest grassland (LFG). In the interface zones, the three vegetation types were wet grassland (IG), inter-forest grassland (IFG), and emergent plants (IE). In the waterward zones, the three vegetation types were emergent plants (WE), submerged plants (WS), and no plants (WN). At each sampling site, triplicated samples were randomly collected from 1 m × 1 m quadrats. Additional details about each sampling site can be found in Table S1, and the typical photographs are shown in Figure S1.
The samples were collected in August 2022, at a time when nitrogen removal activity in the lakeshore was high [15]. Surface soil and sediment samples (0–10 cm) were collected in the landward and interface zones using a stainless steel corer, and in the waterward zones using a mud bucket. After removing stones and plant residues, each soil/sediment sample was thoroughly mixed and divided into two sub-samples. One sub-sample was stored at −20 °C for the physicochemical property test, while the other was stored at −60 °C for Illumina-MiSeq sequencing and functional gene testing.

2.2. Analysis of Soil and Sediment Physicochemical Properties

The contents of the total nitrogen (TN), total carbon (TC), ammonia (NH4+-N), nitrate (NO3−-N), nitrite (NO2−-N), soi/sediment organic matter (SOM), pH, and water content (WC) of the soil and sediment were tested as the following methods. Total nitrogen (TN) and total carbon (TC) were measured by an element analyzer (Vario EL, Elementar Analysensysteme, Hanau, Germany). The contents of NH4+-N, NO3−-N, and NO2−-N were determined by extraction with 2 M KCl followed by the indophenol blue colorimetry, copperized cadmium reduction, and naphthalene ethylenediamine methods, respectively [16]. The SOM content was determined by the oxidation of potassium dichromate (K2Cr2O7), followed by titration with iron (II) sulfate (FeSO4) [17]. The pH was measured by a pH meter (solid/water = 2.5:1). The WCs of the soil and sediment were tested by drying the fresh samples at 105 °C for 24 h [18].

2.3. DNA Extraction, Quantitative Real-Time PCR (qPCR), and Sequencing

Microbial DNA was extracted by a Soil Microbe Microprep Kit (ZYMO RESEARCHE R2040, Orange, CA, USA) from 0.5 g fresh soil or sediment samples. Quantitative real-time PCR (qPCR) was conducted to target the nitrogen-related functional genes, i.e., amoA AOA and amoA AOB for the ammonia oxidizers, narG and napA for the nitrate reducers, nirS, nirK, and nosZ for the denitrifiers, and 16S rRNA for ANAMMOX bacteria (amx), and the primers are listed in Table S2. All the qPCRs were conducted using an instrument (BIO-RAD CFX96, Hercules, CA, USA) with AceQ qPCR SYBR Green Master Mix (Jizhenbio JZ121-02, Shanghai, China). The qPCR reactions were conducted in triplicate with mixtures containing 7.5 μL of SYBR Green Mix (2×), 0.7 μL of primers (each 10 μM), 1 μL of gNDA, and 15 μL ddH2O. The thermal cycling was conducted by following steps: 95 °C for 5 min, 40 cycles of 95 °C for 10 s, and 60 °C for 30 s. In this study, the qPCR amplification efficiencies were from 93.96% to 104.77%, with the R2 of calibration curves ≥99%. The Illumina-MiSeq sequencing was conducted by steps described in a previous study by Yuan et al. [3].

2.4. Statistical Analyses

Origin 2021 was employed to calculate Spearman rank correlation coefficients, conduct principal component analysis (PCA) with Bray–Curtis distances, and characterize bar graphs and heatmaps. SPSS 23.0 was used to perform the ANOVA and t-test to assess the difference [19]. Canoco 5.0 was used to perform the constrained ordination of redundancy analysis (RDA). Cytoscape 3.9.0 was employed to visualize the network analysis [20]. Mantel test analysis was conducted using the vegan 2.4.3 packages in R 3.6.1 [21].

3. Results

3.1. Physicochemical Characteristics

Various environmental factors in the soil and sediment were measured to investigate their physicochemical characteristics under various water levels and vegetation types, including the contents of total nitrogen (TN), ammonia (NH4+-N), nitrate (NO3−-N), nitrite (NO2−-N), soi/sediment organic matter (SOM), the ratio of TC to TN (C/N), pH, and water content (WC), as summarized in Table 1. For the distribution of nitrogen, the NH4+-N and NO3−-N contents in the interface sediments were the lowest, while the NO2−-N and TN were similar to those in the landward soils and waterward sediments, respectively. The SOM contents followed the order of waterward < interface < landward, and the C/N ratios in landward soils and interface sediments were similar, but lower than those in the waterward sediments. Regarding the effects of vegetation type, the TN and SOM contents in the landward soils and interface sediments followed a similar order: grassland (LG and IG) > inter-forest grassland (LFG and IFG) > forest and emergent plants (LF and IE). In the landward soils, the TN and SOM contents were significantly different between grassland (LG) and other plant types (LF and LFG) (p < 0.05). In the waterward sediments, the NO3−-N contents varied significantly between the sites with or without plants (p < 0.05), but there was little difference between various vegetation types (p > 0.05). Additionally, the NH4+-N and NO3−-N contents in the waterward sediments without plants (WN) were the highest, and the NO2−-N contents in the inter-forest grassland soils in landward (LFG) were the highest. The water contents in the landward soils (0.32 on average) were lower than in the interface and waterward sediments (both 0.45 on average), and varied significantly between up and down the ordinary water level (p < 0.05), but showed little variation along the water depth (p > 0.05). The water content under various vegetation covers in the interface sediments showed little variation (p > 0.05), while it varied significantly in the landward soils and waterward sediments (p < 0.05). The pH of the soils and sediments ranged from 6.16 to 7.42, indicating a slightly acidic state, with little spatial difference across the sampling sites (p > 0.05).
The principal component analysis (PCA) (Figure 2a) showed a clear separation between the landward and waterward sites, with the samples in the interface sites falling in the transition area from landward to waterward. The sample correlation distance heatmap results (Figure 2b) further indicated that the inter-forest grassland (IFG) and emergent plant (IE) in the interface area have more similar environmental characteristics to the vegetation belt in the waterward area (WS and WE), while the terrestrial inter-forest grassland (LFG) and forest land (LF) have more similar environmental characteristics. Furthermore, the areas without plants (WN) differed significantly from those with vegetation cover.

3.2. Microbial Community Composition and Diversity

3.2.1. Richness and Diversity of Microbial Communities

The sequences and microbial α-diversity of microbial communities are compared in Table S3 and Table S4, respectively. The filtered 16S rRNA sequences ranged from 29,074 to 170,008 per sample (Table S3). To ensure sequencing uniformity, all the reads were denoised into 5456 OTUs, ranging from 1182 to 2306 per sample at 97% identity. In summary, the highest number of OTUs were found in the interface zones, while the lowest were in the land. The microorganisms were clustered according to different plant types from land to waterward zones, as indicated in the results of PLS-DA (Figure S2).
The α-diversity indices (Sobs, ACE, Chao 1, Shannon, and Simpson) were used to describe the richness and diversity of the microbial communities in the soil and sediment samples. The Sobs index indicates the observed richness, while ACE and Chao 1 indicate the statistical richness. The Shannon and Simpson indices represent the statistical diversity [3]. The results of α-diversity indices are presented in Table S4. The highest richness and diversity of microbial communities were observed in the interface, while the lowest was in the land. Specifically, the diversity of IE was the highest, while the richness of IFG was the highest. The diversity and richness of no plants (WN) were both the lowest among all samples.

3.2.2. Microbial Community Structure in Various Soil and Sediment Samples

The sequences obtained from the soil and sediment samples were categorized into 75 phyla, 225 classes, 528 orders, 909 families, and 1888 genera. The microbial composition at the phylum level in different sites is shown in Figure 3a. The findings suggested that the microbial community structures in the soil/sediments were similar, but there were variations in the dominant phyla in the areas with different types of plant cover in terms of their relative abundance. Proteobacteria (with a relative abundance of 18.17–25.45%), Actinobacteriota (9.53–32.88%), Chloroflexi (13.39–21.54%), Acidobacteriota (8.03–14.16%), Firmicutes (5.00–12.29%), and Desulfobacterota (0.85–7.48%) together accounted for over 85% of the relative abundance. Proteobacteria was the most abundant phylum in the interface and waterward sediments. The relative abundance of Acidobacteriota decreased from the land to waterward zones, while the relative abundance of Bacteroidota and Desulfobacterota increased.
The microbial structures at the genus level in the various soil and sediment samples are illustrated in Figure 3b. Across all the sites, the dominant genera belonged to Chloroflexi, Acidobacteriota, Actinobacteriota, and Firmicutes, which are the dominant phyla and are associated with carbon and nitrogen cycling. The relative abundances of nitrogen-related functional genera were calculated to identify the typical genera in each site, as shown in Figure S3. Nitrospira slightly decreased (from 2.86% to 2.19%) while Thiobacillus increased (from 3.84% to 4.84%) from the land to water. Nitrospira is related to nitrification, and Thiobacillus is related to facultative autotrophic denitrification [3]. The relative abundances of Dechloromonas in the land soil and water sediment were about 8–10 times higher than those in the interface sediment. The relative abundances of Bacillus were the highest (average 1.59%) in the interface sediment, especially in the IFG (average 1.83%) and IG (average 1.97%) zones.
The NMDS analysis based on Bray–Curtis distances was performed to display the variations in the microbial composition at the genus level across the different soil and sediment samples, as depicted in Figure 3c. The ADONIS method was employed to compare differences between the groups, and the results indicated that the variation between the groups was greater than within the groups (R2 = 0.472, p < 0.001). The sampling sites were clearly grouped based on spatial factors, with the interface samples showing similarity to both the landward and waterward samples. The samples from the areas without plant growth (WN) formed a distinct cluster. Additionally, the heat map of the top 50 genera (Figure S4) further supported the spatial distribution of the genera across the land, interface, and waterward sites.

3.3. Relationship of Microbial Community to the Various Water Levels and Vegetation Types

The mantel correlation analysis was used to identify differences in the microbial communities (at the OUT level) and environmental variables (NH4+-N, NO3−-N, NO2−-N, TN, SOM, C/N, pH, WC, and flooding days) under various water levels and vegetation covers as shown in Figure 4. As shown in Figure 4a, all the microbial communities under different water levels were found to be related to SOM and TN (r > 0.22, p < 0.05). Additionally, the microbial communities in the land soil were also correlated to WC (r = 0.28, p < 0.05), while those in the interface sediment were related to C/N and flooding days (r > 0.25, p < 0.05), and those in the waterward sediment were associated with WC and pH (r > 0.16, p < 0.05).
As shown in Figure 4b, the dominant environmental factors in various plant types affecting the microbial communities varied. The microbial communities in the forest soil (LF and IF) were highly related to SOM and TN (r > 0.94, p < 0.05), while those in the grassland soil (LG and IG) were correlated with NH4+-N (r = 0.39, p < 0.05). The microbial communities in the emergent plant zone sediment (IE and WE) were significantly influenced by NO2−-N, TN, and SOM (0.25 < r < 0.48, p < 0.01), and those in the submerged plant zone sediment (WS) were significantly influenced by C/N and WC (0.22 < r < 0.24, p < 0.01). The microbial communities in the no plant zone sediment were significantly related to NO2−-N and NO3−-N (0.34 < r < 0.47, p < 0.01).

3.4. Abundances of Nitrogen-Related Functional Genes

To further analyze the microbial communities involved in the nitrogen cycle, the abundances of nitrogen-related functional genes were tested and compared, as shown in Figure 5. Nitrogen-related functional genes were observed in all the soil/sediment samples studied, although their abundances varied significantly among the different sites.
The genes amoA AOA and amoA AOB are related to the ammonia oxidation process, and the abundance and activity of amoA AOA and amoA AOB are reflected in the gene transcript abundances [6]. The abundance of amoA AOA was 2–3 orders of magnitude higher than that of amoA AOB, as indicated in Figure 5a,b. In the landward soil, the gene abundances of amoA AOA and amoA AOB were remarkably higher compared to the interface and waterward sediments. The abundances of the two genes showed opposite trends in the sample sites. The abundance of amoA AOA in LFG (3.50 × 108 copies g−1) was significantly higher than in LG (0.60 × 108 copies g−1) (p < 0.05), but there was no significant difference among the other sites.
Additionally, genes such as narG, napA, nirS, nirK, and nosZ encode enzymes involved in the reduction reaction of the denitrification process, and the abundance results are shown in Figure 5c–g, respectively. The abundances of nirK and nosZ were significantly lower in the land soil and interface sediment (p < 0.05), while there was no significant spatial difference among the sites from land to water in the abundances of narG, napA, and nirS. In the land soil, the order of narG, napA, nirS, and nirK gene abundances was LG > LFG > LF, with the gene abundances in LF significantly lower than those in LG and LFG. A similar trend was observed in the interface sediment samples as well. In the water sediment, the abundances of narG and nirS in WN were significantly higher than those in WS and WE (p < 0.05).
The abundances of the ANAMMOX bacteria (amx) were also investigated to illustrate the anaerobic ammonia oxidation process in the soil/sediment. The amx abundances were at least two orders of magnitude higher than those of the other genes (Figure 5h). The amx abundances varied little from land to water, but significantly in the different plant types in the landward and interface zones (p < 0.05). Specifically, the amx abundances in the grassland (LG and IG) and the inter-forest grassland (LFG and IFG) were significantly higher than those in the other plant types (LF and IE) (p < 0.05).

3.5. Relationships between Environmental Variables, Microbial Communities, and Functional Genes

To elucidate the response of the microbial community and functional genes to environmental factors under various water levels and plant covers, RDA was performed to identify correlations between the environmental variables (NH4+-N, NO3−-N, NO2−-N, TN, TC, SOM, C/N, pH, and WC) and the microbial community and functional genes (Figure 6). The results indicated that SOM (18.5%, F = 15.4, p < 0.01) and WC (16.4%, F = 10.8, p < 0.01) were the primary environmental factors influencing the microbial community at the phylum level (Figure 6a), followed by NO2−-N (4.1%, F = 3.8, p < 0.01) and NO3−-N (4.1%, F = 3.6, p < 0.05). For the functional genes as shown in Figure 6b, SOM (36.5%, F = 31.6, p < 0.01) and TC (13.8%, F = 15.0, p < 0.01) were the two dominant environmental factors. Spearman’s correlation coefficients (Figure S5) further revealed that flooding duration differentiated the land soil, interface sediment, and waterward sediment, with most phyla and functional genes showing significant correlations with flooding days. Specific correlations were identified between the environmental factors and microbial communities and functional genes. Actinobacteriota was negatively related to WC and C/N but positively related to NO2−-N (p < 0.05), while Chloroflexi displayed the opposite trend. Additionally, phyla such as Desulfobacterota, Bacteroidota, Verrucomicrobiota, and Cyanobacteria were negatively correlated with SOM and TN (p < 0.05), whereas Desulfobacterota, Nitrospirota, and Nitrospinota were positively influenced by WC and C/N (p < 0.05). Moreover, nitrogen cycling-related functional genes were positively correlated with SOM, TC, and TN but negatively related to C/N (p < 0.05). The amoA AOA and nosZ genes were negatively correlated with WC (p < 0.05), while napA was positively correlated. The gene narG showed positive correlations with NH4+-N and NO3−-N (p < 0.05), whereas napA was positively influenced by NO2−-N (p < 0.05). Additionally, the amoA AOB and nirK were more correlated with pH.
The network analysis demonstrated the interaction effects among the environmental variables, dominant phylum, microbial α-diversity, and functional genes (Figure 6c). The edge numbers represent the factors significantly positively or negatively correlated to each node. Flooding days and water level were the primary influencing factors, followed by the environmental variables such as SOM, TN, TC, WC, and NO3−-N. These findings were consistent with the Mantel test results between the environmental variables and microbial communities (Figure S6). The functional genes, particularly amx and nirK, were highly influenced by the environmental factors. The dominant phyla Desulfobacterota and Bacteroidota were the most sensitive to the environmental variation.

4. Discussion

4.1. Relationships between Environmental Variables, Water Levels, and Plant Covers

The fluctuating water level is a primary hydrologic characteristic of the lake littoral zone, directly affecting the physicochemical properties of the soil and sediments [4]. This study demonstrated that water level fluctuations significantly influenced WC and the oxidized and reduced nitrogen compounds (p < 0.05), aligning with previous research [6]. Furthermore, this study further examined the effects of vegetation type in various water level zones, given that the rhizosphere microenvironment is a crucial site for N-cycling in the lake littoral zone. Vegetation cover displays a vertical landscape pattern that varies along the hydrological gradient.
The interface zones exhibited transitional characteristics between land and water, combining the physicochemical properties of both zones (Table 1, Figure 2a). Intensive material exchange in these zones, driven by water level fluctuations, affects nitrogen compounds, resulting in lower NH4+-N and NO3−-N contents compared to the landward and waterward zones [4]. In the landward zones, where flooding is rare, vegetation residues not carried away by surface water act as exogenous organic substances, significantly influencing differences among various plant types. In the landward grass zones (LG), high biomass and debris that leach C and N resulted in higher TN and SOM contents compared to the other plant types. In contrast, the evergreen coniferous forest (dominated by Taxodium distichum and Metasequoia glyptostroboides) in the landward forest zones (LF) has lower TN and SOM due to reduced plant biomass input [12]. Similarly, biomass differences among various plant types in interface grass zones (IG) lead to higher TN and SOM contents than in other zones. In the waterward zones, vegetation types were not the primary factors influencing the sediment properties. Previous studies have reported that nitrogen and carbon deposition occur under consistent inundation without nutrient limitation in most flooding zones [22]. The NH4+-N and NO3−-N contents were higher in the non-vegetated waterward zones (WN) compared to the vegetated zones, as anaerobic conditions in the non-vegetated sediment (WN) promote NH4+-N accumulation, and NO3−-N accumulated without plant consumption.
Flooding results in similar environmental properties between the interface emergent plant zones and inter-forest grassland (IE/IFG) and waterward emergent and submerged plant zones (WE/WS) (Figure 2b). Conversely, similar vegetation configurations in the landscape resulted in more similar environmental characteristics in the landward forest (LF) and landward inter-forest grassland (LFG) (Figure 2b). The varied physicochemical properties across different zones indicated that water level fluctuations and vegetation cover jointly affect the soil and sediment microenvironment.

4.2. Effects of Water Level Fluctuations and Vegetation Cover on the Microbial Diversity and Composition

Water level fluctuations significantly affected the α-diversity of the microbial communities across various zones, with the highest microbial diversity found in the interface sediments and the lowest in land soil (Table S4). This trend differs from a previous study where bacterial community α-diversity increased from water sediments (altitude 145 m) to landward soil (altitude 175 m) in the riparian zone of the Three Gorges Reservoir [6]. The large water level variation (up to 30 m) in the reservoir caused cyclic inundation and drying in the soil and sediments, leading to spatial variation in soil moisture and resulting in different microbial diversity patterns compared to this study [5]. Research on nitrogen cycling in the terrestrial–aquatic continuum suggested that interactions between the oxic and suboxic environments occur on small spatial (centimeters or less) and temporal (<1 d) scales but are substantially separated over larger spatial (tens of meters to kilometers) and temporal (weeks to years) scales [23]. In this study, the oxic–anoxic interface sediments, arising from a 0.51 m water level variation in the interface zones, led to high microbial diversity. Vegetation types had little effect on the microbial diversity, but significant differences were observed between the zones with and without plants. The rhizosphere of vegetation, especially the aerenchyma and shallow rooting of amphibious plants, promotes an oxidative state by providing more air channels and rhizosphere respiration, contributing to a complex microenvironment underwater for the microorganisms in the interface zones [24,25].
The microbial composition at the phylum and genus levels further supported the observed diversity trends (Figure 3a,b). Besides Proteobacteria, the most abundant phylum in the interface sediments, dominant phyla in the landward and waterward zones, such as Actinobacteriota, Bacteroidota, and Desulfobacterota, were also prevalent in the interface zones. These phyla are closely associated with the degradation of complex organic matter and denitrification processes in ecosystems [3,20,26]. Additionally, rare phyla, such as Myxococcota and Gemmatimonadota, were also abundant in the interface sediments, related to dissimilatory nitrate reduction to ammonium (DNRA) and nitrate reduction to nitrite [27,28]. The microbial composition at the genus level confirmed the environmental filtering effect on the microorganisms noted in previous studies, indicating that the species resistant to unfavorable conditions were selected by the frequent environmental variations in the interface zones [5,6]. The dominant genus Bacillus, which is strictly aerobic or facultatively anaerobic, is well adapted to the alternating aerobic–anaerobic conditions in the interface sediments. The NMDS results supported the direct response of the microbial spatial distribution to water level fluctuations (Figure 3c).
In addition, the Mantel test results showed that water level fluctuations indirectly affected the spatial distribution of the microbial communities by influencing the nitrogen and carbon content in soil and sediments (Figure 4a). The RDA results and Spearman’s correlation coefficients between the dominant phyla and environmental factors further illustrate the specific response of the microbial composition to the environmental variables (Figure 6a,c). SOM and TN were the key environmental factors in the waterward zones due to the high abundance of Bacteroidota and Desulfobacterota, which were common in activated sludge in wastewater treatment systems. Bacteroidota is typically dominant in denitrification systems, while Desulfobacterota is often involved in DNRA under anaerobic conditions [29,30]. WC in the landward zones was lower than in the other zones and was a vital factor influencing the microbial community structures. Additionally, the negative relationship between the dominant phylum Actinobacteriota and WC explained its high abundance in landward soils. The direct influence of vegetation types on the microbial spatial distribution was minimal, but the dominant environmental factors affecting the microbial communities varied among plant types. According to the Mantel test results, vegetation type mainly indirectly affects the microbial community structure by influencing soil physicochemical properties (Figure 4b). The high TN and SOM in the grassland (LG/IG) provide a relative supplement for N-cycling in soil and sediments, and NH4+-N was the limiting factor for the microbial communities. Myxococcota (dominant genera such as Haliangium) was abundant in the grassland, which is associated with the degradation of organic carbon and nitrogen [31].

4.3. Effects of Water Level Fluctuations and Vegetation Cover on the Nitrogen-Related Functional Genes

The transcript abundances of nitrogen-related functional genes indicated the activity of nitrification and denitrification processes. The ammonia oxidation process in soil and sediments is driven by ammonia-oxidizing archaea (amoA AOA) and ammonia-oxidizing bacteria (amoA AOB), which occupy distinct niches and are influenced by N-substrate levels [4] and environmental factors such as WC and pH [10]. In this study, amoA AOA dominated the ammonia oxidation process at all the sites, with abundances 2–3 orders of magnitude higher than those of amoA AOB (Figure 5). This result is consistent with several studies on anaerobic ammonium oxidation in terrestrial–aquatic interfaces, indicating that amoA AOA is more tolerant of high moisture and hypoxic environments [12,18]. Additionally, the NH4+-N affinity of amoA AOA resulted in its high abundance in the land soil, where the NH4+-N contents were higher than in the interface sediments [4,6]. The narG, nirS, and nirK gene copies were higher than the other denitrification genes at all the sites, with nirS gene abundances surpassing those of nirK, aligning with studies on denitrifying microbial communities in coastal wetlands [10]. Core nirS-type aerobic denitrifying genera, such as Pseudomonas and Thiobacillus [32], were also the dominant functional genera in soil and sediments, with abundance trends consistent with nirS gene copies (Figure S3). Furthermore, amx gene copies were abundant at most sampling sites. Although the high abundance of amx may overestimate the actual abundance of the ANAMMOX bacteria, as the abundances were measured for all the anaerobic ammonia oxidation process-related bacteria instead of specific functional genes, the results still reflect the relative abundance trends across the various sites [10].
The RDA results (Figure 6b) and Spearman rank correlation coefficients (Figure S5) further elucidated the impacts of water level fluctuations and plant types on nitrogen-related microbes. Nearly all the functional genes were associated with flooding days, which represent the spatial position of the sampling sites along elevation. These flooding days were closely linked to WC (Figure 6c and Figure S5), directly influencing oxygen diffusion to the microbes and thereby regulating the nitrification and denitrification processes [6]. As crucial carbon and nitrogen substrates for the nitrogen-cycling process, SOM, TN, and TC were strongly and positively correlated with most functional genes. Consequently, a stronger correlation was observed in the landward grassland (LG) due to the abundant biomass input from residues. Water level fluctuations in the interface zones led to frequent material exchange and minimal variations in soil and sediment pH (average values varying from 6.83 to 6.95). Although pH is a vital environmental factor for pH-sensitive microbes, no significant relationship between the pH and microbial communities was observed in this study. It may be attributed to the minor changes in the pH values of soils and sediments, which ranged from 6.16 to 7.42, with little spatial difference across the sampling sites (p > 0.05), not to mention the effect of vegetation covers (Table 1). The similar pH environment was insufficient to be an environmental factor in grouping the nitrogen-related microbes.

4.4. Mechanism of Water Level Fluctuation and Vegetation Type Effecting on the Microbial Communities

Based on the above-mentioned results, Figure 7 illustrates the potential mechanisms driving the effects of water level fluctuations and vegetation types on microbial communities. Water level fluctuations frequently result in a typical drought–rewetting soil and sediment environment, directly filtering adaptive microorganisms. Furthermore, hydrological fluctuations in the lake littoral zone regulate the physicochemical properties of soil and sediment, indirectly influencing microbial composition mainly through WC, nitrogen substrate levels, and pH values. It is noteworthy that WC is closely related to oxygen content in soil and sediments, making water level fluctuations a key determinant of environmental oxygen content. This multifaceted impact of water level fluctuations contributes to the composition of dominant microbial genera and the abundance of functional genes in the lake littoral zone. Vegetation, a vital participant in the nitrogen cycle of the lake littoral zone, directly affects microbial composition and activity through root exudation [27]. The physicochemical factors vary in vegetation-covered soil and sediments, and litterfall from lake littoral vegetation provides exogenous organic substances, further enriching microbial species and functional genera abundances. The vegetation types in this study represent the lakeshore landscape with plant species combinations, exhibiting a vertical landscape patch and pattern along the hydrological gradient [7]. In conclusion, water level fluctuations and vegetation types jointly affect microbial community structure and nitrogen-related functional genes. This study proposes a simplified topological relationship network to highlight key influencing factors and core pathways affecting microbial community structure and functional gene abundances in the lake littoral zone, providing a possible scientific explanation for the impact of water level fluctuations on nitrogen removal capacity in littoral zones.

5. Conclusions

In this study, the effects of water level and vegetation cover on the microbial community and functional genes were investigated by comparing and analyzing the environmental factors, microbial diversity, community composition, and functional genes under nine typical vegetation cover types in three vertical spatial zones (i.e., the landward zone, the interface zone, and the waterward zone) in the littoral zone of Lake Erhai. The results showed that water level fluctuations significantly and directly affected water content (WC) and oxidized and reduced nitrogen compounds due to the oxic–anoxic alternations and intensive material exchange conditions in the interface zone. The vegetation cover influenced the organic matter and total nitrogen content, as plant residues and root exudation supplied exogenous carbon and nitrogen. The microbial diversity in the interface sediments was the highest due to the alternating aerobic–anaerobic environments to which the dominant phyla and genera were more adaptable. The abundances of amoA AOA, nirS, and amx were higher than those of the other genes and were strongly related to flooding days and water content. In conclusion, water level fluctuation and vegetation type jointly affect the microbial community structure and nitrogen-related functional genes. This study provides insight into the impact of water level fluctuations in lakes on the nitrogen removal capacity of littoral zones.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/pr12061276/s1. Figure S1. The typical photographs of the sampling sites in the lakeshore. Figure S2. PLS-DA of microorganism on OUT level of the various soil/sediments of lakeshore. Figure S3. The Relative abundances of nitrogen-related functional genera in various samples of lakeshore. Figure S4. Heatmap of the top 50 genera in various soil/sediments of lakeshore. Figure S5. Heatmap of Spearman rank correlation coefficients between environmental variables and microbial communities and functional genes. Figure S6. Relationship between environmental variables, microbial community, and functional genes. Table S1. Basic information for various sites of lakeshore. Table S2. Primers of target genes used in the qPCR analysis [6,7,10,33,34,35,36,37]. Table S3. Sequencing statistics for various soil/sediment samples of lakeshore. Table S4. Microbial diversity indices for various soil/sediment samples of lakeshore.

Author Contributions

Conceptualization, J.Y. and J.C.; methodology, J.Y. and J.C.; investigation, W.L., F.Z. and Z.H.; software, J.Y. and W.L.; formal analysis, J.C. and W.L.; writing—original draft preparation, J.Y. and J.C.; writing—review and editing, J.Y. and Z.C.; visualization, J.Y.; supervision, Z.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Project (No. 2021YFC3201003).

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 authors.

Acknowledgments

We thank the National Cryosphere Desert Data Center for providing the SRTM DEM data set of China. (http://www.ncdc.ac.cn, accessed on 13 June 2024). We thank Shenglin Yang for drawing the picture of the sampling sites on the lakeshore.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Schematic illustration of the sampling site’s location and distribution. (a) The location of Lake Erhai in China. (b) The location of the sampling area around Lake Erhai. (c) The vertical distribution of the sampling sites on the lakeshore. The photographs show the typical sampling sites in various zones with certain plant types.
Figure 1. Schematic illustration of the sampling site’s location and distribution. (a) The location of Lake Erhai in China. (b) The location of the sampling area around Lake Erhai. (c) The vertical distribution of the sampling sites on the lakeshore. The photographs show the typical sampling sites in various zones with certain plant types.
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Figure 2. Physicochemical characteristics of the sampling sites showing the vegetation type and space variation. (a) The PCA of the soil/sediment physicochemical parameters between the sampling sites, with the 95% confidence intervals represented by colored ellipses. (b) The heatmap of the soil/sediment physicochemical parameters.
Figure 2. Physicochemical characteristics of the sampling sites showing the vegetation type and space variation. (a) The PCA of the soil/sediment physicochemical parameters between the sampling sites, with the 95% confidence intervals represented by colored ellipses. (b) The heatmap of the soil/sediment physicochemical parameters.
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Figure 3. Microbial compositions of the various soil/sediment samples. The relative abundances of the microorganisms (a) at the phylum level and (b) at the genus level. (c) The NMDS analysis of the microbial community at the genus level from the various sample sites, with the 95% confidence intervals represented by colored ellipses.
Figure 3. Microbial compositions of the various soil/sediment samples. The relative abundances of the microorganisms (a) at the phylum level and (b) at the genus level. (c) The NMDS analysis of the microbial community at the genus level from the various sample sites, with the 95% confidence intervals represented by colored ellipses.
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Figure 4. Relationship between the environmental variables and microbial communities. Mantel test for correlation between the environmental variables and microbial communities in (a) various water levels and (b) vegetation covers. A single, double, and triple asterisk indicate 0.05 < p ≤ 0.01, 0.01 < p ≤ 0.001, and p < 0.001, respectively.
Figure 4. Relationship between the environmental variables and microbial communities. Mantel test for correlation between the environmental variables and microbial communities in (a) various water levels and (b) vegetation covers. A single, double, and triple asterisk indicate 0.05 < p ≤ 0.01, 0.01 < p ≤ 0.001, and p < 0.001, respectively.
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Figure 5. Variations in the abundance of functional genes in the soil/sediment samples. (a) amoA AOA; (b) amoA AOB; (c) narG; (d) napA; (e) nirS; (f) nirK; (g) nosZ; (h) amx. The dots represent the data values for each sample. Different letters indicate significant differences between the samples in different groups (p < 0.05) and the single asterisk (*) indicates that between the different groups (p < 0.05).
Figure 5. Variations in the abundance of functional genes in the soil/sediment samples. (a) amoA AOA; (b) amoA AOB; (c) narG; (d) napA; (e) nirS; (f) nirK; (g) nosZ; (h) amx. The dots represent the data values for each sample. Different letters indicate significant differences between the samples in different groups (p < 0.05) and the single asterisk (*) indicates that between the different groups (p < 0.05).
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Figure 6. Interaction effects of the various factors. The results of RDA between the environmental variables and (a) microbial communities and (b) functional genes. (c) The network analysis of the various factors. The nodes are colored based on 4 factor types, which are the dominant phylum, the microbial α-diversity, the functional gene, and the environmental variables. The edges represent the Spearman rank correlation coefficients (p < 0.01). The red links represent positive correlations, and the blue links represent negative correlations.
Figure 6. Interaction effects of the various factors. The results of RDA between the environmental variables and (a) microbial communities and (b) functional genes. (c) The network analysis of the various factors. The nodes are colored based on 4 factor types, which are the dominant phylum, the microbial α-diversity, the functional gene, and the environmental variables. The edges represent the Spearman rank correlation coefficients (p < 0.01). The red links represent positive correlations, and the blue links represent negative correlations.
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Figure 7. The topological analysis for the main effect mechanism of water level fluctuation and vegetation type on the microbial community.
Figure 7. The topological analysis for the main effect mechanism of water level fluctuation and vegetation type on the microbial community.
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Table 1. Physicochemical characteristics of soil and sediment in different sampling sites.
Table 1. Physicochemical characteristics of soil and sediment in different sampling sites.
SamplesNH4+-N
(mg kg−1)		NO3−-N
(mg kg−1)		NO2−-N
(mg kg−1)		TN
(mg g−1)		SOM
(mg g−1)		C/NpHWC
LF80.54 ± 43.0 ab12.41 ± 3.8 b25.83 ± 10.4 a1.32 ± 0.5 bc21.06 ± 6.7 bc16.33 ± 7.8 a6.62 ± 0.3 a0.25 ± 0.1 b
LFG42.70 ± 25.5 b7.14 ± 3.8 b34.57 ± 34.61 a2.72 ± 0.8 abc50.24 ± 11.7 bc12.91 ± 1.4 a6.89 ± 0.5 a0.34 ± 0.1 b
LG80.46 ± 45.3 ab28.69 ± 26.4 ab21.87 ± 7.5 a7.01 ± 3.8 a128.50 ± 71.1 a13.72 ± 2.4 a7.03 ± 0.2 a0.35 ± 0.2 b
IE57.81 ± 19.2 ab8.38 ± 7.2 b16.95 ± 5.3 a1.37 ± 1.0 bc26.22 ± 18.0 bc14.63 ± 3.7 a6.95 ± 0.4 a0.43 ± 0.0 ab
IFG76.39 ± 23.5 ab13.77 ± 10.5 b16.80 ± 3.4 a2.26 ± 0.9 abc44.79 ± 20.5 bc13.52 ± 1.3 a6.89 ± 0.4 a0.40 ± 0.1 b
IG39.90 ± 10.4 b14.05 ± 18.4 b15.46 ± 2.8 a5.12 ± 2.1 ab88.66 ± 39.5 ab14.02 ± 2.7 a6.83 ± 0.2 a0.51 ± 0.2 ab
WE53.69 ± 30.6 b12.22 ± 8.8 b16.73 ± 5.0 a1.85 ± 1.1 bc32.39 ± 18.60 bc17.65 ± 6.8 a6.85 ± 0.3 a0.43 ± 0.1 b
WS69.18 ± 47.0 ab16.88 ± 21.1 b16.05 ± 2.3 a1.10 ± 0.7 c18.35 ± 11.2 c18.96 ± 7.6 a6.65 ± 0.3 a0.36 ± 0.1 b
WN116.97 ± 61.6 a73.50 ± 80.9 a22.74 ± 16.9 a4.64 ± 4.6 ab71.04 ± 69.5 ab17.59 ± 6.8 a6.70 ± 0.3 a0.62 ± 0.2 a
Landward soil (L)65.96 ± 39.5 a15.39 ± 16.5 a27.97 ± 21.7 a3.61 ± 3.1 a65.34 ± 58.3 a14.21 ± 4.4 a6.85 ± 0.4 a0.32 ± 0.1 b
Interface sediment (I)58.03 ± 22.9 a12.07 ± 12.0 a16.41 ± 3.7 b2.92 ± 2.1 a53.22 ± 37.1 a14.06 ± 2.5 a6.89 ± 0.3 a0.45 ± 0.1 a
Waterward sediment (W)74.07 ± 50.4 a27.48 ± 45.7 a16.05 ± 9.0 b2.13 ± 2.6 a34.68 ± 39.0 a18.20 ± 6.90 a6.73 ± 0.3 a0.44 ± 0.2 a
Note: The first letter in the sample classification is short for landward (L), interface (I), and waterward (W), meaning the spatial classification. The second and third letters refer to the vegetation types. F, forest; FG, inter-forest grassland; G, grassland; E, emergent plants zones; S, submerged plants zones; N, no plant zones. Different letters in the columns indicate significant differences between different sites (p < 0.05).
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MDPI and ACS Style

Yuan, J.; Cao, J.; Liao, W.; Zhu, F.; Hou, Z.; Chu, Z. Effects of Vegetation Cover Varying along the Hydrological Gradient on Microbial Community and N-Cycling Gene Abundance in a Plateau Lake Littoral Zone. Processes 2024, 12, 1276. https://doi.org/10.3390/pr12061276

AMA Style

Yuan J, Cao J, Liao W, Zhu F, Hou Z, Chu Z. Effects of Vegetation Cover Varying along the Hydrological Gradient on Microbial Community and N-Cycling Gene Abundance in a Plateau Lake Littoral Zone. Processes. 2024; 12(6):1276. https://doi.org/10.3390/pr12061276

Chicago/Turabian Style

Yuan, Jing, Jing Cao, Wanxue Liao, Feng Zhu, Zeying Hou, and Zhaosheng Chu. 2024. "Effects of Vegetation Cover Varying along the Hydrological Gradient on Microbial Community and N-Cycling Gene Abundance in a Plateau Lake Littoral Zone" Processes 12, no. 6: 1276. https://doi.org/10.3390/pr12061276

APA Style

Yuan, J., Cao, J., Liao, W., Zhu, F., Hou, Z., & Chu, Z. (2024). Effects of Vegetation Cover Varying along the Hydrological Gradient on Microbial Community and N-Cycling Gene Abundance in a Plateau Lake Littoral Zone. Processes, 12(6), 1276. https://doi.org/10.3390/pr12061276

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