Quantifying the Impact of Multiple Stressors on Microbial Communities in Dianshan Lake Sediments Using Random Forest Analysis

ZhiyiYang1Emailyzy0625@icloud.com

YinyanRuan1

BokunZhang1

XinyangHuang1

FeipengLi1Emaillifeipeng@usst.edu.cnEmail772087567@qq.com

LingchenMao1✉Emailmao.lingchen@usst.edu.cnEmail289374438@qq.comEmail232311949@st.usst.edu.cn

1School of Environment and ArchitectureUniversity of Shanghai for Science and Technology200093ShanghaiChina

Zhiyi Yang, Yinyan Ruan, Bokun Zhang, Xinyang Huang, Feipeng Li, Lingchen Mao*

Z. Yang, Y. Ruan, B. Zhang, X. Huang, F. Li, L. Mao(∗)

School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai, 200093, China

E-mail: mao.lingchen@usst.edu.cn

Z. Yang

E-mail: yzy0625@icloud.com

Y. Ruan

E-mail: 289374438@qq.com

B. Zhang

E-mail: 772087567@qq.com

X. Huang

Email: 232311949@st.usst.edu.cn

F. Li

E-mail: lifeipeng@usst.edu.cn

Abstract

Alluvial plain lakes, characterized by low hydrodynamic activity and fine sediments, host microorganisms whose diversity and community structure are strongly shaped by the presence of nutrients and pollutants. However, quantifying the complex impacts of these multiple stresses is challenging. This study focused on Dianshan Lake, a human-impacted plain lake in Shanghai, China, to quantify the contributions of sediment properties, pollution levels, and nutrients on microorganisms, using classical statistical methods and Random Forest (RF) analysis. The RF model showed good fit, with R² values ranging from 0.75 to 0.90. Results indicated that nutrients, particularly organic carbon and NH₄⁺-N, were the main factors determining microbial diversity in sediments, contributing 46.6%. However, sediment redox conditions were the most influential single factor. In lightly to moderately polluted freshwater lakes, benthic microorganisms displayed common dominance, but the contributions of influencing factors varied. Proteobacteria and Chloroflexi, the dominant phyla, were significantly impacted by pollutants, with contributions exceeding 50%. PAHs primarily suppressed genera within Proteobacteria, while Anaerolineaceae in Chloroflexi exhibited strong tolerance to Cd. The dominant species in Dianshan Lake sediments are primarily influenced by NO₃⁻-N, far exceeding the impact of various forms of phosphorus. This also highlights the issue of nitrate-driven eutrophication in the region. This study demonstrates that RF analysis effectively identifies key controlling factors in lightly to moderately polluted sedimentary environments, providing valuable insights into the ecological processes and a scientific foundation for ecological risk management in similar aquatic environments.

Keywords:

benthic microorganisms

environmental stressors

potential toxic elements

nutrients

1. Introduction

Lake sediments and their resident microorganisms are integral components of aquatic ecosystems, playing a crucial role in the biogeochemical cycling of key elements(Rathour et al. 2020). The physicochemical properties of sediments—such as pH, particle size distribution, redox conditions, and habitat characteristics—are important drivers of microbial diversity and community composition(Manirakiza et al. 2022). In freshwater lakes, sediments are known to accumulate a wide range of contaminants, including toxic metals, polychlorinated biphenyls (PCBs), and excess nutrients. Microbial communities in these environments are subject to various anthropogenic stressors(Allan, 2004). When combined with natural environmental stressors, these human-induced factors can substantially alter microbial community structure and function, with cascading effects on ecosystem health and resilience(Chen et al. 2024). Consequently, recent studies have increasingly focused on identifying the key factors that most significantly influence microbial diversity in sediment ecosystems.

The physicochemical properties of sediments are key determinants of microbial survival and community structure. In aquatic environments, dissolved oxygen plays a critical role in determining the types and distribution of microorganisms. Typically, aerobic microorganisms thrive in environments with redox potentials ranging from + 300 to + 400 mV, while anaerobic microorganisms prefer conditions with redox potentials below + 100 mV(Hunting and Kampfraath 2013). Additionally, pH can have a direct impact on the relative abundance of certain bacterial taxa, influencing the overall microbial community structure(Lauber et al. 2009). In sediments, pH values between 5.5 and 9.0 generally support the growth of most bacteria and help maintain intracellular pH within a stable range of 7.4 to 7.8(Padan et al. 2005). However, conditions with pH > 7.8 may impose stricter limits on bacterial survival and adaptability(Tripathi et al. 2018). Xiong et al.(2012) observed an inverse relationship between sediment pH and microbial α-diversity in sediments from 15 lakes in the Tibetan Plateau, although the response of different bacterial groups to pH changes varied. For example, an increase in pH reduced the abundance of δ-Proteobacteria, while α-Proteobacteria exhibited the opposite response. Liao et al.(2021) found significant differences in bacterial and archaeal diversity with sediment depth, concluding that changes in sediment properties were the primary drivers of these variations.

In addition to physicochemical factors, the availability of nutrients in the pore water of sediments—such as organic carbon, nitrogen, phosphorus and sulfate—has a profound influence on microbial growth and community composition(Zhang et al. 2015a). Fan et al.(2019) identified total nitrogen content as a key factor driving bacterial community structure in sediments from Chaohu Lake, China. Prasath et al.(2021) highlighted the role of polyphosphate bacteria in the phosphorus cycle, noting that different forms of phosphorus, particularly Fe/Al-P, could influence the relative abundance of polyphosphate bacteria. Furthermore, nutrient enrichment, particularly in eutrophic conditions, often leads to reduced microbial diversity and a shift toward more homogeneous bacterial and fungal communities(Yu et al. 2021). This is attributed to environmental filtering and a reduction in habitat diversity, which leads to biological homogenization(Geng et al. 2022). Sheng et al.(2011) found that increasing nitrogen and phosphorus concentrations in Taihu Lake (China) sediments decreased bacterial diversity while increasing microbial biomass. Excessive organic carbon also poses a threat to microbial populations, as it can lead to the overconsumption of dissolved oxygen and favor the proliferation of anaerobic microorganisms, such as sulfate-reducing bacteria, while inhibiting the growth of aerobic organisms like nitrifying bacteria(Jeppesen et al. 2005). On the other hand, some microorganisms can utilize sulfate to produce reduced sulfur compounds, which can promote microbial growth and reproduction(Zhang et al. 2024e). This is part of a larger set of sulfur cycle-related processes that shape microbial community structure in lake sediments, as sulfur-oxidizing bacteria can alter pH and redox conditions, further influencing community composition(Hu et al. 2018).

The microbial community is more likely to be influenced by a combination of factors, rather than any single variable in isolation. For example, while the proportion of silt and clay in sediments can significantly affect microbial diversity, this influence is often indirect(Marschner et al. 2003). First, sediments with different particle sizes can adsorb various substances, such as organic carbon and enzymes, which are vital for microbial metabolism and growth(Chen et al. 2022). The concentration of these substances is often closely linked to sediment particle size(Zhang et al. 2007). However, excessive organic carbon can alter sediment properties, such as particle size and porosity, disrupting microbial habitats and diminishing their potential for survival(Burdige 2007). Second, physical factors such as particle size and porosity also influence microbial community structure by affecting sediment permeability and the release of gases like methane(Lu et al. 2022). Fine-grained sediments can provide protective refuges for microorganisms, shielding them from predation by other organisms(Sessitsch et al. 2001). In contrast, sandy sediments are more susceptible to hydrodynamic forces, which can negatively impact microbial survival(Scheidweiler et al. 2021). Juvigny-Khenafou et al.(2021) highlighted that particle size and flow velocity play a more significant role in determining benthic bacterial diversity than nutrient levels alone.

As anthropogenic factors, toxic pollutants typically exert a significant inhibitory effect on microbial growth(Yan et al. 2024). Numerous studies have reported a negative correlation between microbial biomass or diversity and pollutant concentrations, including heavy metals(Jiang et al. 2016), PAHs(Gao et al. 2024), PCBs(Wallberg and Andersson 1999). Pan et al.(2022) reported that heavy metal contamination in a river in South China had a greater impact on microbial community variation than nutrient levels or pH. However, the relationship between pollutants and microbial diversity is not always linear. Microbial community structure is often dominated by species that are resistant to or tolerant of pollutants(Pan et al. 2022). Wang et al.(2023a) suggested that low concentrations of Sb and As (< 80 mg/kg) can even stimulate bacterial growth. Studies have indicated that certain pollutant-resistant microorganisms become more abundant in heavily contaminated sediments, while others are inhibited(Rahman 2020). For example, Proteobacteria, such as Pseudomonas syringae, carry copper resistance genes(Mellano and Cooksey 1988), while some bacteria, like Alicyclobacillus, Brevibacterium, and Lactobacillus, possess the ability to degrade PAHs(Li et al. 2019). Ramsay et al.(2000) observed that PCB contamination promoted the growth of aerobic heterotrophic bacteria and alkane-degrading bacteria in mangrove sediments. Under certain pollutant concentrations, microbial diversity can increase due to a reduction in competitive exclusion among dominant species, allowing less dominant species to thrive and contribute to overall community diversity(Wang et al. 2022b).

Finally, in urban freshwater environments, habitat factors often exert a stronger influence on microbial community structure than pollution levels. This is especially true in cases where there is a strong distance-decay effect in sediment communities(Abdullah Al et al. 2022). Microbial communities are shaped by both abiotic factors, such as sediment characteristics, and biotic interactions, including competitive and cooperative relationships between microorganisms(Freilich et al. 2011). Studies have shown that microorganisms form non-random associative networks, which means that abiotic factors alone cannot fully explain changes in community structure. This phenomenon has been observed in various environments, including soil(Barberán et al. 2012), ocean photic zones(Heidari et al. 2023), and freshwater ecosystems(Battin et al. 2016).

Given the complexity of factors influencing sediment microorganisms, this study uses a typical multifunctional water body as a case study and tested the Random Forest (RF) algorithm to quantify the effects of various environmental drivers on microbial diversity and community structure in sediments under multiple pressures. We hypothesize that in lightly to moderated-polluted sedimentary environments, the controlling factors for microbial diversity and dominant species, differ greatly, with the contributions of sediment properties, nutrients, and contaminants being distinct. The case study focuses on Dianshan Lake, the largest lake in Shanghai, a major metropolitan area in China. Dianshan Lake serves multiple functions, including as a drinking water source, as well as for shipping, tourism, and fishing. Various environmental factors directly influence the safety of drinking water for residents in the surrounding areas. While much of the existing research on Dianshan Lake addresses issues such as eutrophication, organic pollution, and heavy metal contamination, there is limited focus on the microbial communities in sediments and the factors that affect them. Machine learning techniques have already been applied in studies investigating the ecological impacts of environmental factors(Heidari et al. 2023; Grekousis et al. 2022; Zhang et al. 2024d; Sa’adi et al. 2024), with the RF algorithm being particularly advantageous in some of these studies(Zhang et al. 2024c; Ramos Collin et al. 2024; Cardiology et al. 2023). In this research, high-throughput sequencing technology was used to assess differences in microbial community structure and diversity in surface sediments from various regions of the lake. By employing RF analysis and correlating microbial community structure with sediment properties, nutrient concentrations, and levels of contaminants, this study aims to determine the impact of environmental factors on microbial communities. The findings will provide theoretical insights to support water quality management and environmental protection in Dianshan Lake, and potentially other similar freshwater system.

2. Materials and Methods

2.1 Site description and sediment sampling

Dianshan Lake is located in the suburban area of Shanghai, adjacent to Jiangsu Province (30°40′-31°53′N, 120°52′~122°12′E). It is the largest natural freshwater lake in Shanghai, covering approximately 63 km², with an average depth of 2.1 m. Situated on an alluvial plain, the lake is part of the extensive river network downstream of the Yangtze River, connecting to 59 rivers. The inflowing rivers are predominantly located on the west and north sides of the lake, while the outflows are mainly on the east side, with Lanlu Port accounting for 71% of the total outflow. The maximum vertical current speed ranges between 0.02 and 0.05 m/s(Ming-Wei et al. 2011), thus minimizing the impact of flow rate on the spatial distribution of sediment microbes.

Following field investigations and surveys, a total of 16 sampling points was selected in November 2022 (Fig. 1). Surface and subsurface sediment samples were collected using a sediment core sampler. After allowing the samples to settle, non-decomposed litter was removed, and the overlying water was discarded. The sediment samples were then sealed in sterilized polyethylene bags, labeled with corresponding sampling point numbers, placed in a thermal insulation box with ice packs, and immediately transported to the laboratory for further analysis.

Fig. 1

Sampling sites and land-use in the study area.

2.2 Sediment property analysis

All samples were air-dried and passed through a 2.0 mm nylon sieve for storage and subsequent physico-chemical analysis. Sediment pH was determined using a pH meter with a combined glass electrode (Mettler Toledo, PE 20) in a suspension of sediment and deionized water at a ratio of 1g: 2.5 mL(Yan et al. 2018). The particle size distribution was analysed using a laser diffraction particle analyser (Baite, BT-9300Z), with particle classification based on Wentworth’s Sedimentary Scale(Wentworth 1922). The dried samples were carefully ground to particles smaller than 0.15 mm to measure the total organic carbon (TOC) content, using a total organic carbon analyzer (TOCVCPN, Shimadzu, Japan)(Muñoz et al. 2015).

2.3 Analysis of contaminants in sediments

The concentrations of nine potential toxic element (PTE) elements (Cd, Co, Cr, Cu, Mo, Ni, Pb, Sb, and Zn) were measured in 0.1 g of finely ground samples (< 75 µm). The samples were digested using a mixture of 1 mL H₂O₂ (30%), 1 mL HF (40%), and 3 mL HNO₃ (65%) in sealed Teflon vessels within a microwave digestion system (PreeKem, TOPEX). For samples with high total organic carbon (TOC) content, an additional 1 mL of HClO₄ was included. The digests were then heated on a hot plate (~ 150°C) to drive away the HClO₄ before being diluted to 50 mL in a volumetric flask with Milli-Q water and 5% HNO₃(Mao et al. 2022). Element concentrations were analysed using inductively coupled plasma mass spectrometry (ICP-MS, PerkinElmer, NexIon 300X).

Quality assurance and quality control (QA/QC) procedures for PTE concentration analysis included the use of reagent blanks and standard reference materials (SRM 1646a, National Institute of Standards and Technology). To prevent contamination, all acids used in the experiment were of Guaranteed Reagent grade. Containers were soaked overnight in 10% (v/v) diluted HNO₃ and rinsed thoroughly with diluted HNO₃ and Milli-Q water before use. Replicates were analysed every eight samples, and the relative standard deviation (RSD) among replicates was consistently below 10.8%. The recovery rates for selected PTEs ranged between 80% and 120%.

2.4 Analysis of nitrogen and phosphorous in sediments

Total nitrogen was (TN) determined on dried and homogenized sediments using an automatic Kjeldahl apparatus according to the method described in China’s Soil testing Part 24: Determination of total nitrogen in soil, Automatic nitrogen determinator method (NY/T 1121.24–2012)(Ministry of Agriculture 2012). Exchangeable NH₄⁺-N and NO₃⁻-N were determined according to the extraction method described in Fan et al.(2018). The concentrations were measured by a spectrophotometer. Total phosphorus (TP) was determined by solubilizing all phosphorus in the sediment using NaOH at high temperatures (400°C and 640°C), with concentrations measured spectrophotometrically at 700 nm, the method described in China’s Soil - Determination of Total Phosphorus by alkali fusion–Mo - Sb Anti spectrophotometric method(HJ 632–2011)(Ministry of Ecology 2011). Phosphorus fractionation, including organic phosphorus (OP), apatite (AP) and non-apatite inorganic phosphorus (NAIP), was measured according to the Standards, Measurements and Testing programme (SMT) protocol(Ruban et al. 1999). The recovery of P fractionation was in the range of 95 ~ 103%.

2.5 DNA extraction, PCR amplification, and sequencing

Microbial DNA was extracted from sediment samples using the FastDNA® Spin Kit for Soil, following the manufacturer's protocol with careful attention to sterility throughout the process. The purified DNA was dissolved in 200 µL of TE buffer (pH 8.0) and immediately placed in a thermally insulated container with ice packs for transport to Shanghai Meiji Biomedical Technology Co., Ltd. (Meiji Biotech) for 16S rDNA gene sequencing. PCR amplification targeted the V3-V4 hypervariable region of the 16S rDNA gene using universal bacterial primers 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAY-3'). High-throughput sequencing was then conducted on the Illumina MiSeq PE300 platform at Meiji Biotech. The resulting sequencing data were aligned with a reference database to generate an Operational Taxonomic Unit (OTU) classification table, providing a detailed profile of the microbial community structure.

2.6 Statistical analysis and GIS mapping

This study selected the Sobs, Heip, Shannon, and Simpson indices to analyze the richness, evenness, and diversity of microbial communities at the OTU level in sediment, while the Coverage index was used to assess the accuracy of sequencing results. Beta diversity analysis focused on comparing the diversity between different habitats or microbial communities to explore the differences in community structure composition between samples(Li et al. 2022). Partial Least Squares Discriminant Analysis (PLS-DA) was employed to classify the sampling sites according to microbial diversity(de Paulo et al. 2024). The potential relationship between environmental factors was investigated using Spearman correlation analysis. Co-occurrence network of Microbial Communities was analysed using the MAJORBIO Bioinformatics Cloud Platform (https://cloud.majorbio.com/), the P-value is lower than 0.05. Constructing a co-occurrence network using the Fruchterman Reingold layout algorithm with Gephi 0.9.2 software. The topological characteristics of the co-occurrence networks were analysed, including the positive and negative correlation numbers, as well as the numbers of edges and nodes. Area digitization and mapping were performed using ArcGIS 10.6.

The Random Forest (RF) model, proposed by Breiman in 2001, is a machine learning algorithm based on decision trees(Breiman 2001). RF uses bootstrap resampling to extract and generate subsets of the training sample from the original training dataset. It then builds multiple decision trees based on these training subsets and forms a random forest, with classification or regression model outcomes determined by the voting scores of the decision trees(Breiman, 2001). In this study, a RF model was constructed based on 2112 data points, including the physicochemical properties, nutrients and pollution factors of Dianshan Lake sediments, microbial diversity (Shannon index), and the abundance of the top five dominant species at the phylum level. Environmental factors from 16 sampling points were treated as independent variables, while Shannon diversity and species abundance indices were treated as dependent variables. The RF simulation was performed using the custom software PyCharm, and the importance of pollution factors on microbial diversity and abundance was assessed. The variables were ranked according to their importance in the model, and the accuracy of model was evaluated using the correlation coefficient (R²) and the root mean square error (RMSE) values. A flowchart of the machine learning analysis process is provided in Figure S1 and text S1 of the supplementary material.

3. Results

3.1 Environmental Factors in Dianshan Lake Sediment

3.1.1 Sediment characterization

The physicochemical characteristics of sediment samples from Dianshan Lake are summarized in Table 1(a). The sediment composition predominantly comprised silt and clay, with a relatively low sand fraction. The coefficients of variation (CV) for grain size distributions indicate that silt, which constitutes the majority, exhibited minimal variation, suggesting an even grain size distribution across the study area. The average pH was 7.73, with a range of 7.33 to 8.07, reflecting a weakly alkaline environment. The CV of pH was 2.9%, indicating minimal variation. Lower pH values were observed near inflow channels, while higher values were concentrated in the northeastern part of the lake and around discharge areas such as Lanlu Port and the Dupu River. The average redox potential (Eh) was + 258 mV, varying between + 219 and + 285 mV, indicating a weakly oxidizing environment for surface sediments. As a typical shallow lake with lower clay content, the organic matter in these silty sediments was less than that found in clay-rich sediments, making them more susceptible to influences from overlying water, which may hinder the metabolic activities of anaerobic bacteria. This shallowness contributes to elevated sediment Eh values, with a CV of 6.7%. As illustrated in Figure S2, higher Eh values were concentrated near inflow channels, while lower values are predominantly found in the northeastern region of the lake.

3.1.2 Nutrient levels

The concentrations of nitrogen and phosphorus species in the surface sediments of Dianshan Lake are detailed in Table 1(a). The distribution of these elements was notably uneven, with CV exceeding 20%. The concentrations of NH₄⁺-N and NO₃⁻-N ranged from 35.47 to 95.37 mg/kg and 3.58 to 13.27 mg/kg, respectively, with higher concentrations located near inflow channels and a general decrease observed downstream. Overall, IP concentrations surpassed those of OP, with NAIP constituting approximately 69% of total inorganic phosphorus content. Elevated concentrations of phosphorus forms were primarily found in proximity to inflow channels, notably Qian Deng Pu and Da Zhu Ku. Based on Table 1a, it can be concluded that while Dianshan Lake shows signs of eutrophication, its severity is relatively lower compared to other alluvial lakes worldwide.

Table 1
Summary of the a) sediment properties and b) concentration of contaminants in Dianshan Lake sediments, along with data from other lakes. (a)
Location	pH	Eh (mV)	Sand (%)	Silt (%)	Clay (%)	TOC (%)	NO₃⁻-N (mg/kg)	NH₄⁺-N (mg/kg)	TN (mg/kg)	OP (mg/kg)	NAIP (mg/kg)	AIP (mg/kg)	TP (mg/kg)	Reference
Dianshan Lake China	7.73 (2.9%)^*	258 (6.7%)	3.3 (108%)	81.2% (3.5%)	15.6 (19.8%)	1.3 (20.3%)	7.18 (34.4%)	68.65 (26.1%)	483.29 (22.9%)	278.23 (26.1%)	209.45 (26.4%)	94.57 (27.8%)	669.89 (27.3%)	This study
Taihu Lake (China)	7.1	145.93				2.35	6.45	82.53	1315.50	172.557			878.169	^{[63, 64]}
Chaohu Lake (China)	6.3							29.52	532	70.59		12.28	358	^[65]
Lake Kirkkojärvi (Finland)	7.5					11	434.6	58.6	1383.3				73.3	^[66]
Lake Bützow (Germany)			15	35.7	17.7	11.8		1.886	13000	170	169	78	1520	^[67]

^*numbers in brackets are the coefficient of variance.

(b)

Location	Cd (mg/kg)	Co (mg/kg)	Cr (mg/kg)	Cu (mg/kg)	Mo (mg/kg)	Ni (mg/kg)	Pb (mg/kg)	Sb (mg/kg)	Zn (mg/kg)	∑16PAHs (ng/kg)	Reference
Dianshan Lake (China)	0.18 (34.2%)	13.72 (14.5%)	62.93 (11.9%)	49.54 (36.4%)	0.59 (23.7%)	46.59 (18.3%)	26.51 (15.8%)	2.32 (21.0%)	119.28 (24.7%)	36.32 (6.06%)	This study
Taihu Lake (China)	0.52		80.13	33.81		39.26	32.27		102.99	102.0	^[63]
Chaohu Lake (China)	0.77		67.1	33.0		28.7	54.1	1.45	306.0	334.67	^{[68, 69]}
Lake Erie (America)	1.5	14.0	51.8	47.1	1.7	53.4	43.4		156.3	420.0	^{[70, 71]}
Lake Srebarna (Bulgaria)	0.6		117	52.6		47.7	183		130	2556	^[72]
Lake Nicaragua(Nicaragua)	0.15		39	130			7.0		89	640	^[73]

3.1.3 Level and Distribution of Contaminants in Surface Sediment

Table 1b presents the concentrations of 9 PTEs (Cd, Pb, Sb, Cr, Ni, Cu, Zn, Co, and Mo) in the surface sediments of Dianshan Lake. Only Cr and Co exhibited concentrations below the background values established for the Taihu lake plain(Yin et al. 2011), while concentrations of Cd, Pb, Sb, Ni, Cu, Zn, and Mo were significantly elevated, being 2.20, 1.11, 3.01, 1.32, 1.83, 1.73, and 1.37 times their respective background values. This suggests a pronounced anthropogenic influence on these elements, particularly for Cd and Sb. The spatial distribution of these 9 PTEs demonstrates a consistent pattern, with higher concentrations observed near inflow channels and a gradual decline downstream. Notably, elevated levels of Cu and Mo were concentrated in the northern region near inflow river of Qian Deng Pu, while Cd, Pb, and Co were enriched in inflow areas on the western side of the lake. Furthermore, the distributions of Sb, Cr, Ni, and Zn displayed similarities, with high concentrations found in both the northern and western inflow regions, including sites such as Baishi Ji, Jishui Port, and Da Zhu Ku. As shown in Fig. 1, the northern region is primarily an industrially dense area, while the western region is a mixed area of residential and agricultural zones. A comparison of the PTE contents in the sediments of other lakes, as shown in Table 1(b), reveals that, except for slightly elevated levels of Cu, Ni, and Sb, the concentrations of all other PTEs are lower than those in the other lakes.

In comparison to other aquatic environments, the total concentration of 16 polycyclic aromatic hydrocarbons (Σ16PAHs) in the surface sediments of Dianshan Lake ranged from 7.54 to 128.94 ng/g, with an average concentration of 36.32 ng/g (Table 1b). All 16 PAHs were detected, with detection rates and concentration ranges provided in Table S1. Notably, PAHs such as Phe, Flt, Pyr, BaA, Chr, BbF, BkF, BaP, and InP were consistently present, each exhibiting a 100% detection rate. Among the 16 PAHs, Flt, Pyr, and BaP exhibited the highest average concentrations at 5.47, 5.30, and 4.93 ng/g, respectively. The highest concentrations of PAHs were primarily located near the inflow channels in the northern region, with additional accumulation observed in the southwestern part of the lake (Fig. 2). As it has been described above the northern region is mainly industrial area. Based on the data comparison in Table 1(b), the PAHs content in Dianshan Lake is significantly lower than that in the sediments of other lakes, indicating that the PAHs pollution in Dianshan Lake is not severe.

Fig. 2

Distribution of 16ΣPAHs in Dianshan Lake sediment

3.2 Microbial Community in Dianshan Lake Sediment

3.2.1 α and β biodiversity

The α-diversity analysis across the study area revealed that the Coverage indices for all sampling sites > 0.96, approaching 1.0, indicating sufficient sequencing depth and reliable results. The Sobs, Heip, Shannon, and Simpson indices varied from 2334 to 2829, 0.169 to 0.245, 6.005 to 6.541, and 0.00474 to 0.01079, respectively, with mean values of 2493, 0.209, 6.251, and 0.00666 (Supplementary Material, Figure S3). Compared to other freshwater lake sediments, the Shannon diversity index for the microbial community in Dianshan Lake was higher than that of Nanhai Lake(Zhao et al. 2017), Hengshui Lake(Yang et al. 2023), and Chaohu Lake(Yang et al. 2020), but lower than that of Baiyangdian Lake(Yu et al. 2021), Taihu Lake(Xue et al. 2018), Datong Lake(Chao et al. 2021) in China, and Lonar Lake(Paul et al. 2016) in India.

The spatial distribution of the Sobs, Heip, and Shannon indices exhibited consistent patterns, with higher values predominantly located near the outflow river channels and the western inflow areas. These results suggest that these regions are characterized by elevated microbial richness, evenness, and diversity. Conversely, lower values were observed in the central and eastern parts of the lake, as well as around the Qiandengpu River inflow in the northern region, indicating lower diversity in these areas.

Partial Least Squares Discriminant Analysis (PLS-DA) of the spatial relationships between sampling points further delineated the study area into four distinct zones (Fig. 3). Zone A, comprising sites S1–S7, was primarily located in the southern and southwestern parts of the lake, a region characterized by two navigational channels and surrounded by villages and agricultural land. Zone B encompassed the central lake region, including sites S8, S9, and S11–S13, as well as S15. Zone C, represented by sites S15 and S16, was situated in the northern part of the lake, receiving inflows primarily from Qiandengpu River. Zone D, which included sites S10 and S14, was located in the western part of the lake, near tourist attractions and the outflow river channels.

Fig. 3

PLS-DA analysis at different sample OUT levels.

3.2.2 Micro-organism community structure on OUT level

A total of 5717 OTUs were identified from 16 surface sediment samples. Based on database comparison, these OTUs were classified into 60 phyla, 174 classes, 367 orders, 534 families, 828 genera, and 810 species within the bacterial domain. Figure 4a presents a Venn diagram of species from the 16 sampling sites at the OTU level, allowing for a comparative analysis of the shared and unique OTUs across different sampling sites. The diagram shows that while there are variations in the number of unique OTUs across the sites, the differences are generally small. Sampling site S3 had the fewest unique OTUs, with only 22, whereas site S16 had the most unique OTUs, indicating a higher presence of site-specific microbial species.

Figure 4b illustrates the microbial community composition at the phylum level for species with a relative abundance ≥ 1% in the sediment of Dianshan Lake. While the types of microbial species were consistent across the sampling sites, their relative abundances varied significantly. Specifically, the surface sediments at each site were primarily dominated by Chloroflexi (14.91–35.21%), Proteobacteria (17.13–31.07%), Acidobacteriota (4.78–21.64%), Actinobacteriota (6.41–11.87%), Desulfobacterota (3.28–7.87%), Nitrospirota (2.90–8.58%), Firmicutes (2.15–7.72%), and Bacteroidota (2.13–4.90%).

At the genus level, a large proportion of the microbial genera had a relative abundance of less than 1%, with these low-abundance genera accounting for more than 35% of the total abundance (Figure S4). This indicates a high genus-level richness in the sediments of Dianshan Lake, consistent with the results of the α-diversity analysis. The two most dominant genera in the sediments were from the family Anaerolineaceae, with relative abundances ranging from 3.60–24.77%, and from the class Thermodesulfovibrionia, with relative abundances ranging from 2.20–7.01%. Although the microbial genera were similar across the sampling sites, the relative abundance of each genus varied across the sites.

Fig. 4

a) Venn diagram of sediment species at each site based on OTU levels; b) Relative abundance of sediment microorganisms at the phylum level across sampling sites

3.2.3 Co-occurrence network of Microbial Communities

The study analysed the co-occurrence network of the top 50 dominant microorganisms at the genus level, along with their key topological properties, to elucidate the complex interactions among microorganisms and reveal the relationships between key species in the environment (Figure. 5 and Table S2). In the microbial network of Dianshan Lake sediment, a total of 369 edges were identified, with 199 positive connections, accounting for 53.9%. This indicates a predominance of positive interactions between species, suggesting that these microorganisms may exhibit strong functional synergy, likely through close cooperation in metabolic pathways or environmental adaptation(Faust and Raes 2012). Generally, a higher proportion of positive correlations may imply a less stable network(Yang et al. 2024;Pang et al. 2023). Conversely, significant negative correlations suggest that these microorganisms are engaged in intense competition for resources, such as living space and nutrients, which could lead to mutual exclusion in their abundance or distribution(Fuhrman and Steele 2008). Additionally, the network average degree (14.76) and average clustering coefficient (0.612) are relatively high, indicating a large and complex network structure. The size of the nodes is directly proportional to their degree of connectivity, meaning that nodes with higher connectivity are more extensively linked to other nodes(Rong et al. 2021). As shown in the figure, uncultured_f__Anaerolineaceae (Chloroflexi) exhibits the highest number of connections with other microorganisms, followed by unclassified_o__Vicinamibacterales (Acidobacteriota), unclassified_f__SC-I-84 (Proteobacteria), unclassified_c__MB-A2-108 (Actinobacteriota), and Sva0081_sediment_group (Desulfobacterota), all of which also demonstrate significant interactions with other microorganisms. This highlights their role as key species within the network. Notably, unclassified_c__Thermodesulfovibrionia (Nitrospirota), although exhibiting many connections with other species, is not among the most abundant species in the network.

Fig. 5

Co-occurrence networks of microbial community at the top 50 genera in sediments. The size of each node is pro-portional to the connectivity degree. The colours of the nodes represent different phyla. Red and green edges represent positive and negative correlations, respectively.

3.3 Relationship between Environmental Factors and Microbial Communities

3.3.1 Correlation between Environmental Factors and Microbial Communities

To better understand the relationships between microbial communities in surface sediments of Dianshan Lake and individual environmental factors, Spearman correlation analyses were conducted. These analyses assessed the relative abundances of the top 10 dominant phyla (Fig. 6a) and the top 20 dominant genera (Fig. 6b) against sediment physicochemical properties, concentrations of pollutants, and nutrient contents. At the phylum level, negative correlations with environmental factors were more frequently observed. Total nitrogen (TN) and NO₃⁻-N emerged as key factors, influencing the largest number of dominant phyla. Specifically, Bacteroidota, Desulfobacterota, and Sva0485 were significantly negatively correlated with TN (p < 0.05). Among the phyla, Bacteroidota and Acidobacteriota were the most affected by individual environmental factors, although their response patterns were entirely opposite. In terms of pollutant influence, PTEs affected half of the dominant phyla. Notably, Cd was significantly positively correlated with Chloroflexi (p < 0.05), while Sb, Cu, Zn, and Co were significantly negatively correlated with other dominant phyla. Additionally, PAHs exhibited a significant positive correlation with Acidobacteriota (p < 0.05) and a significant negative correlation with Bacteroidota (p < 0.05).

At the genus level, all environmental factors, except for TOC, Pb, and Mo, were significantly correlated with the relative abundances of microbial taxa. Overall, nitrogen species had the most pronounced effect on sediment microbial communities, influencing 8 dominant genera, most of which displayed significant positive correlations. Vicinamibacterales and Luteitalea were strongly correlated with NH₄⁺⁻N, with R² of 0.78 and 0.75, respectively, while MB-A2-108 was strongly correlated with NO₃⁻-N (R² = 0.77). Particle size distribution was significantly associated with five genera. Regarding pollutant effects, PAHs and multiple PTEs demonstrated significant correlations with microbial genera, but these effects varied between taxa. Phosphorus primarily impacted unclassified_o__1–20, a genus within Steroidobacteraceae, and unclassified_o__SJA-15, with all three genera showing significant negative correlations with phosphorus levels.

Fig. 6

Spearman correlation between environmental factors and sediment microbials on phylum level a) and generic level b).

3.3.2 Random Forest

A random forest algorithm was applied to predict the Shannon diversity index. The model yielded a R² of 0.90 and a RMSE of 0.043, indicating robust predictive performance for this α-diversity index in sediments. According to the ranking of factor contributions provided by the model (Fig. 7a), sediment nutrient levels had the greatest influence on diversity, accounting for 46.6% of the total effect, primarily driven by N. Physicochemical properties of the sediment were the second most influential factors, with Eh showing the highest contribution among them. Contaminants, predominantly metals, contributed 25.5% to the total variation.

A similar simulation was conducted for the top five dominant bacterial phyla, including Proteobacteria, Actinobacteriota, Chloroflexi, Acidobacteriota, and Desulfobacterota, with results presented in Fig. 7b ~ f. The ML model demonstrated good predictive accuracy for these phyla. Except for Chloroflexi (R² = 0.75), the other phyla exhibited correlations ranging from 0.81 to 0.88.

Significant differences in factor contributions were observed among the bacterial phyla. Pollutants had the greatest influence on Proteobacteria and Chloroflexi, contributing over 50% to the variation. Among contaminants, Cd and PAHs were identified as the most impactful on the abundance of all phyla. Nutrient factors ranked second in importance, with NO₃⁻-N exerting significant effects on all phyla, particularly on Actinobacteriota, where the model assigned an importance value exceeding 0.25, substantially higher than other factors. In contrast, the sediment properties made the smallest overall contribution to the five dominant microbial phyla in Dianshan Lake, with considerable variability in their effects. For example, Eh and silt ranked second in their impact on Acidobacteriota and Desulfobacterota, respectively, but had minimal influence on Proteobacteria. These findings align with correlation analysis results.

(a)	(b)
(c)	(d)
(e)	(f)

Figure 7. Contribution of factors on Shannon index (a), the amoundance of Proteobacteria (b), Actinobacteriota (c), Chloroflexi (d), Acidobacteriota (e) and Desulfobacterota (f) calculated by random forest.

4. Discussion

This spatial segregation of diversity metrics suggests distinct environmental gradients and anthropogenic influences across different lake zones, contributing to the observed heterogeneity in microbial community structure. These findings underscore the complex interactions between microbial communities and environmental gradients in lake sediments, highlighting the sensitivity of microbial populations to nutrient and pollutant dynamics.

4.1 Effects of Sediment Properties on Microbial Community

The physicochemical properties of sediments in Dianshan Lake exert a significant influence on the diversity and community structure of the microorganisms they host., As a key indicator of the sediment environment, pH reflects the influence of various environmental factors on bacterial community composition(Shang et al. 2020). These effects are mediated through pH, which also affects bacterial enzyme production, thereby regulating bacterial growth, metabolism, and reproduction(Rousk et al. 2010). In this study, sediment pH ranged from neutral to weakly alkaline, and no significant effect was observed on the dominant species at the phylum level. However, a significant negative correlation was found with the genera unclassified_o__Vicinamibacterales and Luteitalea, suggesting that these genera are better suited to grow under neutral pH conditions(Sait et al. 2006). Similarly, redox potential (Eh) serves as a comprehensive indicator of sediment nutrient status, pollutant levels, and biological activity(Husson 2013). It is one of the most significant parameters associated with microbial functional gene structure(Zhang et al. 2015b), and is closely linked to denitrification rates and overall bacterial respiration(Hunting and van der Geest 2011). In this study, Random Forest (RF) analysis revealed that Eh accounted for the largest contribution to the Shannon diversity index. In line with these findings, Lam et al.(2018) reported that greater biodiversity in sediments was associated with more negative redox potentials, which provide greater available energy for microbial metabolism. At the genus level, Eh exhibited a significant positive correlation with SBR1031, a heterotrophic microorganism, indicating that dissolved oxygen in sediment pore water promotes the growth of this species(Hu et al. 2023).

Sediment particle size influences microbial distribution indirectly, by affecting nutrient availability on particle surfaces and facilitating gas and ion exchange within pore spaces, rather than having a direct impact on microbial communities(Lin et al. 2023). Similar to many other lakes in floodplain environments, the sediment in Dianshan Lake is predominantly composed of silt, with minimal variation in particle size distribution. As a result, the direct impact of particle size on microbial distribution is not consistent(Lin et al. 2023).

4.2 Effect of Nutrients on Microbial Community

The concentrations of essential nutrients, such as carbon (C), nitrogen (N), and phosphorus (P), in sediments play a crucial role in shaping microbial diversity and community structure(Yao et al. 2022). In the sediments of Dianshan Lake, C and N emerged as the most significant factors influencing microbial diversity. Conversely, the impact of phosphorus on microbial communities in this region was relatively less pronounced. In fact, some studies suggest that high phosphorus concentrations can even act as a limiting factor for microbial growth. For example, Sheng et al.(2011) and Yu et al.(2021), demonstrated that increased phosphorus levels in lake sediments can constrain microbial community structure and reduce bacterial diversity.

Organic carbon is a critical nutrient for many chemotrophic microorganisms. The low TOC content in the northern sediments of Dianshan Lake appears to be a key factor contributing to the lower microbial diversity in this area. In terms of community structure, TOC was found to have a significant impact primarily on the dominant phylum Proteobacteria, with little effect on other predominant microbial groups. Notably, Proteobacteria tends to thrive in environments with higher organic matter content, whereas other groups, such as Chloroflexi and Acidobacteriota, seem less influenced by TOC levels(Qi et al. 2024).

Both correlation analysis and RF show that N was found to be a primary factor driving microbial diversity and community composition in Dianshan Lake sediments, consistent with similar findings from studies of sediments in several lakes in Nanjing, China(Zhao et al. 2012). These results suggest that NO₃^⁻-N and NO₂^⁻-N, as key nitrogen sources, likely promote nitrogen cycling and energy metabolism in microorganisms facilitating their growth and proliferation(Wang et al. 2022a). Zhang et al.(Zhang et al. 2024a) suggested that in freshwater lake sediments, microorganisms were found to be much more involved in N cycling than that in the C or S cycles. Our study found that NH₄⁺-N played a more substantial role than NO₃⁻-N in influencing microbial biodiversity. This is consistent with the fact that NH₄⁺-N is the predominant form of inorganic nitrogen in Dianshan Lake sediments, with much higher concentrations than NO₃⁻-N. This finding aligns with previous studies in similar environments(Zheng et al. 2015;Yoo et al. 1999; Virdis et al. 2011). On the other hand, NO₃⁻-N was particularly influential at the phylum level (except for Acidobacteriota), with Actinobacteriota showing a marked response to NO₃⁻-N concentration. Supporting this, Chen et al.(2025) and Zhang et al.(2024b) found that in an aerobic sludge reactor, the addition of NO₃⁻-N significantly increased the abundance of Actinobacteriota, which are known to harbor denitrification genes. Recent research has also identified denitrification-related enzymes in bacteria of the Chloroflexi phylum, particularly within Anaerolineaceae(Feng et al. 2023; Qian et al. 2023; Wu et al. 2024a). Further, studies on coastal wetland sediments by Lin and Lin(2022) highlighted NH₄⁺-N as a key factor affecting the relative abundance of Acidobacteriota, which also aliens with our result.

However, other studies have reported contrasting results. For instance, Wang et al.(2022a) observed a negative correlation between the abundance of the Sva0485 phylum and NO₃⁻-N content in sediments from four estuaries near Yantai, China. Similarly, Shao et al.(2023) and Lin et al.(2022) found that the abundance of Acidobacteriota and Bacteroidetes responded differently to changes in sediment pollution levels, with opposing trends observed. These discrepancies highlight the complexity of microbial community dynamics and underscore the challenge of interpreting single-factor correlation analyses, as multiple interacting factors often influence microbial composition.

4.3 Effect of Contaminants on Microbial Community

The dominant phyla in the surface sediments of Dianshan Lake are largely consistent with those found in other major freshwater lakes both domestically and internationally(Zhao et al. 2019; Chao et al. 2023; Yang et al. 2020; Chao et al. 2021; Paul et al. 2016; Borsetto et al. 2021), suggesting a universal microbial community composition. Among these, Chloroflexi is typically the most abundant phylum. This group consists of bacteria capable of utilizing light energy for autotrophy and is highly adaptable to a range of environmental conditions. Most Chloroflexi bacteria are also able to degrade organic pollutants and exhibit strong resistance to PTEs(Zhao et al. 2019), which gives them a distinct competitive advantage in environments that are heavily influenced by human activities.

RF analysis revealed that the overall impact of PAHs on microbial diversity was not significant, probably because of their relatively low concentration in Dianshan Lake sediment. However, PAHs did affect the abundance of specific phyla and genera. Among these, PAHs contributed most significantly to Proteobacteria, particularly by negatively affecting the genus Steroidobacteraceae, which is the most abundant genus within this phylum. Acidobacteriota showed a significant positive correlation with PAHs, making PAHs the pollutant that contributes the most to the impact on this phylum. Su et al.(2017) reported that Acidobacteriota can use PAHs as a carbon source for growth and reproduction, with its potential for degradation increasing in proportion to PAH concentration. In Dianshan Lake sediments, the primary effects of PAHs were observed in the genera subgroup_17 and Luteitalea in Acidobacteriota. However, these genera accounted for only 1.78% and 1.90% of the total microbial abundance, respectively. As a result, PAHs had a less significant impact on the overall Acidobacteriota phylum than on Proteobacteria in the RF analysis.

PTEs such as Zn, Co, and Cd significantly impacted microbial diversity in the sediments of Dianshan Lake, but their contribution was far lower than that of nutrients and Eh, indicating that sediment pollution is not severe from an ecological perspective. In terms of community structure, the abundance of most dominant phyla was influenced by PTEs, with Cd showing the most significant effect on Chloroflex, Actinobacteriota, and Desulfobacterota, ranking first in its contribution among all pollutants. Antimony, Cu, Zn, and Co were all significantly negatively correlated with the abundance of dominant microbial phyla, suggesting that PTEs exert a generally suppressive effect on microbial communities. Guo et al.(2019) observed similar trends in a study on the impact of illegal PTEs discharge on bacterial communities in freshwater lake sediments, where Cu and Zn caused a significant decline in the relative abundance of Proteobacteria and Bacteroidota. Typically, the toxic effects of PTEs on microorganisms result from interactions between metal ions and certain ligands (such as thiol groups in enzymes), which inhibit the activity of enzymes essential for many biological and physiological functions(Olaniran et al. 2013). On the other hand, several genera exhibited positive correlations with PTE concentrations, such as Anaerolineaceae (Chloroflexi), and Vicinamibacterales (Acidobacteriota) and Luteitalea (Acidobacteriota), all of which were positively correlated with Cd concentrations. Wang et al.(2023a) observed similar results in sediment samples from the downstream of a mining area in the Zijiang River. One reason for this is that microorganisms can tolerate PTE pollution at concentrations below certain thresholds(Giller et al. 2009). Lee et al.(2024) found that after 80 days of a pot experiment with added Cu and Pb in soil, Luteitalea was able to tolerate the contaminated environment.

Furthermore, the combined influence of multiple environmental factors and the interrelationships between microorganisms may also affect microbial tolerance to PTEs, making the correlation between pollutant concentrations and the abundance of specific species more complex. For example, studies on the effects of PTEs on Gaiella (Actinobacteriota) have shown inconsistent results. In the studies by Gao et al.(2021) and Wu et al.(2024b), Gaiella exhibited significantly higher abundance in heavily polluted soils compared to controls. However, Guo et al.(2023) argued that this microorganism would be suppressed by long-term PTE pollution, which aligns with the findings of this study. As shown in Fig. 4, Gaiella may be more likely affected by amensalism and competition with the three dominant genera Anaerolineaceae, Vicinamibacterales, and Luteitalea(Fan et al. 2020), all of which exhibit stronger tolerance to Cd. Specifically, Anaerolineaceae may release Cd from the degradation of organic matter in the sediments, and the presence of methane-metabolizing microbes may promote Cd precipitation(Meng et al. 2019). Since the distribution of TOC and PTE pollution in Dianshan Lake sediments are similar (e.g., higher in inflow areas such as Qiandengpu), the organic matter in the sediments provides energy for Anaerolineaceae, allowing it to better adapt to environments with higher PTE concentrations(Wang et al. 2023b; Zárate et al. 2021). Moreover, Vicinamibacterales may resist PTE toxicity by forming symbiotic relationships with other microorganisms and can even tolerate highly polluted environments such as mining areas(Chun et al. 2021).

5. Conclusions

This study analyzes the environmental factors in Dianshan Lake and reveals that its sediments possess the typical physicochemical characteristics of alluvial plain lakes, with their distribution significantly influenced by inflow and outflow rivers. The majority of sampling sites exhibit high variability observed in the concentration of N and P species, as well as Cd, Cu, Mo, Sb, Zn, and PAHs. These variations are primarily driven by inflow rivers and surrounding land use, particularly industrial and agricultural activities. The average Shannon Index of microorganisms in Dianshan Lake sediments was 6.251, placing the α-diversity at a medium level compared to other typical plain lakes. The dominant species in the sediments, mainly Chloroflexi and Proteobacteria, are consistent with those found in other lakes. The co-occurrence network’s relatively high average degree indicates a large and complex microbial population structure at the genus level, with Anaerolineaceae being the most abundant (ranging from 3.60–24.77%).

Correlation and Random Forest (RF) analysis identified significant relationships between key environmental factors and microorganisms, showing a clear regional pattern. Nutrients, particularly C and N sources, contributed 46.6% to microbial diversity. Redox conditions emerged as the most significant single factor, as they determine the types of microorganisms involved in different respiratory pathways. However, the impact of environmental factors on dominant species varied significantly. PAHs had the greatest impact on Proteobacteria, mainly inhibiting the genus Steroidobacteraceae. RF analysis also showed that Cd was the most significant pollutant affecting Chloroflexi, Actinobacteriota, and Desulfobacterota. Furthermore, genera such as Anaerolineaceae (Chloroflexi), Vicinamibacterales (Acidobacteriota), and Luteitalea (Acidobacteriota) exhibited a significant positive correlation with specific PTEs, including Cd, indicating their tolerance to contamination. These interactions reflect the relationships among microorganisms in Dianshan Lake sediments, particularly influenced by amensalism and competition.

Overall, this study demonstrates that the RF algorithm can effectively quantify the contribution of environmental factors to microbial diversity and structure in sedimentary environments. It also identifies the key controlling factors in lightly to moderately polluted sedimentary environments, providing a scientific basis for ecological risk management in similar aquatic systems.

Funding Declaration

This work was supported by the National Natural Science Foundation of China (41601229 and 51679140).

Author Contribution

Zhiyi Yang: Conceptualization, Writing-Original Draft, Formal Analysis, Methodology, Software; Yinyan Ruan: Methodology, Visualization; Bokun Zhang: Methodology, Investigation, Validation;Xinyang Huang: Investigation, Data Curation;Feipeng Li: Resources, Project Administration; Lingchen Mao: Supervision, Writing-Review & Editing, Resources, Data Curation

Data available statement

The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information files. Should any raw data files be needed in another format they are available from the corresponding author upon reasonable request.

Conflict of Interest

The authors declare that they have no conflict of interest.

Electronic Supplementary Material

Below is the link to the electronic supplementary material

Supplementary Material 1

Data Availability

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Figure 1. Sampling sites and land-use in the study area.

Figure 2. Distribution of 16ΣPAHs in Dianshan Lake sediment

Figure 3. PLS-DA analysis at different sample OUT levels.

Figure 4. a) Venn diagram of sediment species at each site based on OTU levels; b) Relative abundance of sediment microorganisms at the phylum level across sampling sites

Figure 5. Co-occurrence networks of microbial community at the top 50 genera in sediments. The size of each node is pro-portional to the connectivity degree. The colours of the nodes represent different phyla. Red and green edges represent positive and negative correlations, respectively.

Figure 6. Spearman correlation between environmental factors and sediment microbials on phylum level a) and generic level b).

(a)	(b)
(c)	(d)
(e)	(f)

Fig. 7

Contribution of factors on Shannon index (a), the amoundance of Proteobacteria (b), Actinobacteriota (c), Chloroflexi (d), Acidobacteriota (e) and Desulfobacterota (f) calculated by random forest.

Table 1. Summary of the a) sediment properties and b) concentration of contaminants in Dianshan Lake sediments, along with data from other lakes (a)

Location	pH	Eh (mV)	Sand (%)	Silt (%)	Clay (%)	TOC (%)	NO₃⁻-N (mg/kg)	NH₄⁺-N (mg/kg)	TN (mg/kg)	OP (mg/kg)	NAIP (mg/kg)	AIP (mg/kg)	TP (mg/kg)	Reference
Dianshan Lake China	7.73 (2.9%)^*	258 (6.7%)	3.3 (108%)	81.2% (3.5%)	15.6 (19.8%)	1.3 (20.3%)	7.18 (34.4%)	68.65 (26.1%)	483.29 (22.9%)	278.23 (26.1%)	209.45 (26.4%)	94.57 (27.8%)	669.89 (27.3%)	This study
Taihu Lake (China)	7.1	145.93				2.35	6.45	82.53	1315.50	172.557			878.169	(Yin et al. 2011; Li et al. 2020)
Chaohu Lake (China)	6.3							29.52	532	70.59		12.28	358	(Pan et al. 2007)
Lake Kirkkojärvi (Finland)	7.5					11	434.6	58.6	1383.3				73.3	(Holmroos et al. 2012)
Lake Bützow (Germany)			15	35.7	17.7	11.8		1.886	13000	170	169	78	1520	(Selig 2003)

(b)

Location	Cd (mg/kg)	Co (mg/kg)	Cr (mg/kg)	Cu (mg/kg)	Mo (mg/kg)	Ni (mg/kg)	Pb (mg/kg)	Sb (mg/kg)	Zn (mg/kg)	∑16PAHs (ng/kg)	Reference
Dianshan Lake (China)	0.18 (34.2%)	13.72 (14.5%)	62.93 (11.9%)	49.54 (36.4%)	0.59 (23.7%)	46.59 (18.3%)	26.51 (15.8%)	2.32 (21.0%)	119.28 (24.7%)	36.32 (6.06%)	This study
Taihu Lake (China)	0.52		80.13	33.81		39.26	32.27		102.99	102.0	(Yin et al. 2011)
Chaohu Lake (China)	0.77		67.1	33.0		28.7	54.1	1.45	306.0	334.67	(Liu et al. 2014;Tian et al. 2024)
Lake Erie (America)	1.5	14.0	51.8	47.1	1.7	53.4	43.4		156.3	420.0	(Yuan 2017; Debruyn et al. 2009)
Lake Srebarna (Bulgaria)	0.6		117	52.6		47.7	183		130	2556	(Ricking and Terytze 1999)
Lake Nicaragua(Nicaragua)	0.15		39	130			7.0		89	640	(Scheibye et al. 2014)

^*numbers in brackets are the coefficient of variance.

Yes