Sediment bacterial assemblages inside and outside macrophyte beds in Yunnan Plateau lakes with contrasting nutrient levels and water depths

Sediment bacterial assemblages inside and outside macrophyte beds in Yunnan Plateau lakes with contrasting nutrient levels and water depths | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sediment bacterial assemblages inside and outside macrophyte beds in Yunnan Plateau lakes with contrasting nutrient levels and water depths Jing Zhou, Yuying Che, Ying Liu, Yexin Yu, Long Zhou, Yan Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6075353/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Eutrophication followed by deterioration of water quality and loss of aquatic plants is a global problem in aquatic ecosystems. Sediment microbial communities are often used as indicators of environmental changes due to their sensitive responses. However, the joint effects of nutrient levels and aquatic plants on the structure and function of bacterial assemblages remain unclear. In this study, four Yunnan Plateau lakes with contrasting nutrient levels were sampled in November 2021. Sediment samples were collected from areas with and without aquatic plants to explore the potential interactive effects of nutrients and plants. We found (1) that Lake Qilu had a significantly higher richness index than the other three lakes. Lake Lugu had the highest evenness variable among the four lakes. (2) PCoA showed strong clustering of bacterial communities according to nutrients, aquatic plants, and water depth. (3) A significant nutrient effect was also observed on bacterial function, as suggested by the increasing proportion of the five metabolic functions, including translation, ribosomal structure, and biogenesis, with increasing nutrient concentrations. (4) Nutrients explained 10.4% of the variation in bacterial communities, followed by plants (1.4%) and water depth (0.8%). Our findings suggest that eutrophication increased bacterial richness and affected key microbial taxa and functions. plateau lakes lake eutrophication water depth aquatic plant bacterial community Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Microbes, important components in lakes (Kim et al., 2016 ), are at the hub of biogeochemical cycles (Newton et al., 2011 ) and function as indicators when assessing aquatic ecosystem health (López-López and Sedeño-Díaz, 2015 ). Sediment bacterial communities are sensitive to environmental factors such as trophic status (Llirós et al., 2014 ), aquatic plant coverage (Zhang et al., 2022 ), algal blooms (Su et al., 2017 ), and water depth (Probst et al., 2018 ). To understand the relationship between bacterial communities and lake environments, it is particularly important to consider how these factors combine to influence the bacterial communities of the sediments. Eutrophication can affect the structure of microbial communities in lake sediments (Han et al., 2020 ). With increasing nutrient concentrations, many adverse environmental effects may occur, including accelerated growth of algae and loss of aquatic macrophytes due to deteriorated water quality (Jeppesen et al., 1998 ; Khan and Mohammad, 2014 ; Moss, 1990 ). Microbial communities are affected by organic matter (Bergauer et al., 2018 ), nitrogen (Zhou et al., 2022 ), several forms of phosphorus (LeBrun et al., 2018 ), chlorophyll a (Chl- a ) (Eronen-Rasimus et al., 2017 ), and other nutrients, which are all important for the microbial growth and metabolism. The abundance and biomass of various microbial components may increase with eutrophication in aquatic ecosystems. For example, the bacterial biomass increased fourfold and the ciliate and heterotrophic nanoflagellate biomasses 28- and 32-fold, respectively, as Chl-a concentrations increased from 5 to 20 µg L − 1 in a series of Mediterranean shallow lakes (Conty and Bécares, 2012 ). The hydrostatic pressure caused by the water column is a common factor that has considerable impact the surface sediment structure (Wang et al., 2022 ), and it also influences the microbial community. Once the total gas partial pressure exceeds the local hydrostatic pressure, free gas in the sediment pore will be released into the water column. Meanwhile, microorganisms, which are sensitive to gas and other changes in environmental factors in the sediment (Zhou et al., 2022 ), will respond correspondingly. Moreover, sediment microorganisms have developed survival strategies and grow slowly under anaerobic conditions, and their biomass, activity, and diversity decrease (Mattsson et al., 2015 ). Therefore, water depth is another important factor affecting the microbial community in the sediment as the hydrostatic pressure rivalries with depth and risk of anaerobic conditions in the sediment typically increases with water depth (Johnson and Page, 2011 ). Aquatic plants have important effects in lake ecosystems, and they provide oxygen and appropriate environmental conditions for epiphytic microbial communities (Zhang et al., 2022 ). A previous study by Wu et al. ( 2021 ) reported that submersed macrophytes decreased the diversity of most N-cycling bacterial assemblages, including nitrifying, denitrifying, and DNRA (dissimilatory nitrate reduction to ammonium) bacteria, while increasing their abundance. Our recent study showed that shining pondweed ( Potamogeton lucens ) can decrease bacterial alpha diversity and 16S rRNA gene copies in the sediment and increase bacterial alpha diversity in the water (Zhang et al., 2022 ). Therefore, the presence or absence of aquatic plants may be an important factor affecting the microbial community in the sediment. Little is known about the combined effects of nutrients, water depth, and aquatic plants on bacterial communities and potential ecological functions in lake sediments. The main aim of this study was to investigate the composition of and differences in bacterial communities in lake sediments under different nutrient regimes, water depths, and aquatic plants presence. Four plateau lakes with distinct regional characteristics were selected: Lakes Lugu, Chenghai, Erhai, and Qilu. We hypothesized that the composition and predicted function of sediment bacterial communities in lakes would differ among nutrient regimes and aquatic plant presence driven both by direct and indirect forces. 2 Materials and methods 2.1 Study area The study lakes (Lake Lugu, Lake Chenghai, Lake Erhai, and Lake Qilu) are all located on the Yunnan Plateau in Southwest China. Lake Lugu (27°39′ – 27°45′ N, 100°44′ – 100°50′ E and 2690 m a.s.l.) is a typical alpine lake with an average depth of 40.3 m, a maximum depth of 93.5 m, and a surface area of 48.45 km 2 (Chang et al., 2018 ; Ndayishimiye et al., 2020 ) (Fig. 1 ). The main aquatic plants in Lake Lugu are Potamogeton wrightii Morong, Ottelia acuminata , Charophyceae, Hydrilla verticillata , Myriophyllum spicatum L., P. lucens L., P. pectinatus L., Ceratophyllum demersum L., and Utricularia aurea Lour. Lake Chenghai (26°27′ – 26°38′ N, 100°38′ – 100°41′ E, and 1500 m a.s.l.) is a plateau lake with an average depth of 25.7 m (Liu et al., 2015 ); it is a typical terminal plateau lake that has become increasingly susceptible to eutrophication due to diminished water exchange under the external pressure of basin development (Zou et al., 2014 ). Samples from Chenghai waters did not have aquatic plant beds. Lake Erhai (25°36′ – 25°58′ N, 100°05′ – 100°17′ E) is one of the largest fault lakes in China, with an average depth of 10.8 m (Wang et al., 2020 ; Yang et al., 2017 ), and it plays a significant role for the local socioeconomic development, including drinking water sources, irrigation, fisheries, and tourism (Ni and Wang, 2015 ). The main aquatic plants in the lake are P. wrightii Morong , C. demersum L., M. spicatum L. , P. maackianus , V. natans , and P. pectinatus L. Lake Qilu is a large (36.9 km 2 ), shallow (Z Max = 6.8 m) lake with hard, fresh, and productive water (Mg = 3.2 meq L − 1 , Ca = 1.3 meq L − 1 , conductivity = 380 µS cm − 1 , and Secchi < 0.4 m) (Brenner et al., 1991 ). Lake Qilu (24°08′ – 24°13′ N, 102°43′ – 102°49′ E) is a semiclosed alpine lake with an average depth of approximately 4.0 m (Liu et al., 2010 ; Yang et al., 2020 ) and the main aquatic plants are M . spicatum L. and M . aquaticum . 2.2 Sampling and measurements The survey was conducted in November 2021 (see the sampling sites in Fig. 1 ). Water and sediment were sampled from both inside and outside of the submersed macrophytes beds if present in the lake. Water samples were collected from three-layer of the water column (0.5 m, 1.0 m, and 3 m, respectively below the water surface) with a 5 L polymethyl methacrylate water sampler, then mixed up. The samples from areas with and without aquatic plants are defined as 2 types (no aquatic plant /aqutic plant, respectively). The surface layer (0–20 cm) of sediment was collected with a 1/16 m 2 Peterson grab. Six sediment samples were taken in triplicate at different water depths in Lakes Chenghai, Erhai, and Lugu and seven and four sediment samples in Lake Qilu and Lake Lugu, respectively (Table S1 ). Two levels of water depth were used: 0–10 m (shallow) and 10–30 m (middle). Well-mixed water and sediment samples were transferred to sterile bottles and bags, respectively, and immediately transported to the laboratory on ice. Water transparency (Z SD ) was determined by using a Secchi disc in situ (Lee et al., 2015 ). The chlorophyll a (Chl- a ) concentration in the water was determined by 90% acetone extraction (spectrophotometer method) (Jiang et al., 2020 ). The total nitrogen (TN) concentration in the water was determined using semimicro Kjeldahl after digestion by H 2 SO 4 and HClO 4 ; the total phosphorus (TP) concentration in the water was determined by boiling with H 2 SO 4 and HClO 4 (molybdenum antimony resistance). TN, TP, and organic matter (OM) in the sediment were determined according to Strickland and Sollins ( 1987 ). Inorganic P fractions of the sediment were determined by 0.5 mol L − 1 NaHCO 3 -soluble P (Ca 2 -P), 0.5 mol L − 1 NH 4 Ac-soluble P (Ca 8 -P), 0.5 mol L − 1 NH 4 F-soluble P (Al-P), 0.1 mol L − 1 NaOH-Na 2 CO 3 -soluble P (Fe-P), occluded P extracted with 0.3 mol L − 1 sodium citrate + 1 g Na 2 S 2 O 4 + 0.5 mol L − 1 NaOH (O-P), and 0.25 mol L − 1 H 2 SO 4 -soluble P (Ca 10 -P). P in the extracts was determined by the molybdenum-blue method (Murphy and Riley, 1962 ). Sediment total DNA was extracted by a Fast DNA SPIN Kit (MP Biomedicals, OH, USA). The bacterial 16S rRNA gene V4 and V5 regions were amplified with primers 515-F/907-R (Wu et al., 2018 ). The PCR process was predenatured at 94 ℃ for 5 min, followed by 35 cycles of 94℃ for 30 s, 55℃ for 35 s, and 72℃ for 30 s, with a final extension at 72℃ for 10 min. The amplified PCR products were sequenced on the Illumina 2 × 250 platform at Beijing Fix-gene Co., Ltd. (Beijing, China). Slip window quality was cut for the two-end raw sequence datasets using fastp, and the QC paired-end clean reads were obtained by removing the primers with cutadapt software. Usearch-fastq mergepairs (V10, http://www.drive5.com/usearch/ ) were used to filter the unqualified tags to obtain raw tags. Raw tags data were trimmed using fastp to produce clean tags (Chen et al., 2018 ). Each representative OTU sequence was aligned against the SILVA database using usearch-sintax to obtain species annotation information (Quast et al., 2013 ). To minimize the number of OTUs retained by sequencing errors, we removed species with fewer than five sequences in three samples from each group and all samples with less than 20 total sequences to obtain OTU tables (Zhou and Fong, 2021 ). 2.3 Statistical analysis Chao1, Simpson, Shannon, and evenness diversity indices were calculated using usearch-alpha-div (V10, http://www.drive5.com/usearch/ ) based on the OTU abundance table. Additionally, also based on the OTU abundance table, we performed a PCoA analysis using the “prcomp” package in R software to visualize the differences between the samples of each group. At the generic level, these systematic magnetic trees of x-rich OTUs were inferred with the neighborhood-joining method in MEGAv.6.1 and displayed using iTOL (Interactive Tree of Life, https://itol.embl.de/ ) and mean relative abundance data (Ivica and Peer, 2016 ). Bacterial community functional analysis was performed by PICRUSt ( https://picrust.github.io/picrust/ ), based on the OTU abundance table (Langille et al., 2013 ). Then, the cluster of the orthologous group (COG) database was compared them to obtain the COG family information corresponding to the OTUs, and the abundance of each functional category was calculated to analyze the pathway differences between multiple groups. Differences in water and sediment properties and bacterial community composition at the generic level in the samples from the four plateau lakes were determined using nonparametric tests applying IBM SPSS Statistics 21 (Zhou et al., 2022 ). The effects of nutrients, presence of aquatic plants, and water depth on water and sediment properties was compared using generalized linear models (GLMs) applying IBM SPSS Statistics 21 (Eshima et al., 2011). Three-way permutational multivariate of variance (PERMANOVA) was used to analyze the individual and interactive effects of the three factors on beta diversity with the “adonis” function in R (Zhou and Fong, 2021 ). At the OTU level, variation partitioning analysis (VPA) was performed with R software to quantify the contribution of each environmental factor, Lake, Plants and Depth to variation in the microbial community distribution (Zhou and Fong, 2021 ). Detrended correspondence analysis (DCA) was used to explain the relative effects of lake nutrients (properties of sediment) on the microbial community (at OTU level) using CANOCO 5.0 (Zhou and Fong, 2021 ). In DCA data analysis, the “lengths of gradient” values were shorter than 3; thus, the RDA was best choice. The obtained sequences were submitted to the NCBI Sequence Read Archive (SRA) under accession number PRJNA860051. 3 Results 3.1 Water and sediment characteristics of the four plateau lakes The properties of the water and sediment of the four plateau lakes are shown in Tables 1 and S2. TN, TP, and Chl- a were significantly higher, while Z SD was significantly lower in Lake Qilu than in the other three lakes. TN, TP, and Chl- a were significantly lower, while Z SD was significantly higher in Lake Lugu than in the other three lakes. Generally, Lake Lugu was oligotrophic, Lakes Erhai and Chenghai were mesotrophic, and Lake Qilu was eutrophic (Table 1 ). For sediment samples, TN, TP, OM, and four forms of phosphorus (Ca 2 -P, Ca 8 -P, Al-P, and O-P in sediment) differed significantly among the four lakes (Table 1 ). TN was lowest in Lake Chenghai and highest in Lake Qilu. Ca 2 -P was significantly lower in Lake Lugu than in the other three lakes. However, TN, Ca 2 -P, Al-P, O-P, and OM were significantly higher in Lake Qilu than in the other three lakes. Table 1 Water and sediment characteristics (mean ± standard error) in the four study lakes Water and sediment properties Lugu Chenghai Erhai Qilu Water samples Chl-a(µg/L) 0.6 ± 0.1 a 6.1 ± 4.4 b 27.6 ± 3.5 c 85.0 ± 18.0 d Z SD (m) 6.5 ± 3.5 c 2.4 ± 0.6 b 2.1 ± 0. 6 b 0. 7 ± 0.2 a TN-Water (µg/L) 0.09 ± 0.01 a 0.5 ± 0.05 b 0.7 ± 0.16 c 4.7 ± 1.7 d TP-Water (µg/L) 0.005 ± 0.0001 a 0.03 ± 0.004 b 0.03 ± 0.018 b 0.14 ± 0.01 c Sediment samples TN-Sediment (mg/kg) 2754 ± 563 b 1984 ± 693 a 4225 ± 1555 c 7213 ± 1209 d TP-Sediment (mg/kg) 1123 ± 1001 a 832 ± 221 a 1078 ± 140 b 1488 ± 399 c OM (g/kg) 44.8 ± 12.7 b 32.9 ± 14.1 a 64.6 ± 22.7 c 152.4 ± 42.2 d Ca 2 -P (mg/kg) 18.1 ± 9.3 a 22.4 ± 6.5 a 22.9 ± 4.5 a 33.0 ± 6.7 b Ca 8 -P (mg/kg) 28.2 ± 24.3 a 30.2 ± 14.4 b 15.5 ± 2.3 a 41.7 ± 18.9 c Al-P (mg/kg) 19.2 ± 7.6 a 26.8 ± 7.4 b 14.6 ± 2.6 a 36.4 ± 19. 9 b Fe-P (mg/kg) 77.0 ± 75.3 b 34.5 ± 36.8 a 58.4 ± 20.2 ab 208.9 ± 97.5 c O-P (mg/kg) 189.6 ± 84.1 b 120.0 ± 39.8 a 175.1 ± 29.7 b 301.5 ± 17.9 b Ca 10 -P (mg/kg) 711 ± 104 a 543 ± 219 ab 563 ± 125 c 726 ± 266 d Note: The different letters indicate significant differences at P < 0.05 level. TN, total nitrogen concentration in lake water; TP, total phosphorus concentration in lake water; Chl-a, phytoplankton chlorophyll a concentration; and Z SD , Secchi depth. OM, organic matter content in sediment, Ca 2 -P: NaHCO 3 soluble phosphorus, Ca 8 -P: NH 4 Ac soluble phosphorus, Al-P: soluble aluminum phosphate, Fe-P: NaOH-Na 2 CO 3 soluble phosphorus, O-P: closed storage phosphorus, and Ca 10 -P: H 2 SO 4 soluble phosphorus. Significant effects of nutrients were observed on all the water and sediment variables ( P < 0.05, Table 2 ). A significant impact of aquatic plants was observed on TP-Sediment and on Ca 2 -P in the sediment. A significant water depth effect was observed on TP-Water, TP-Sediment, Ca 2 -P-Sediment, Ca 8 -P-Sediment, and Ca 10 -P-Sediment. We also noted a significant interactive effects of nutrient concentrations with presence of aquatic plants on TP-Sediment, Ca 10 -P-Sediment, and Fe-P-Sediment. No interactive effects of aquatic plants × water depth and nutrients × aquatic plants × water depth on any nutrient variables. Table 2 The effects of nutrients, presence of aquatic plants, and water depth on water and sediment properties. Water and sediment properties Nutrients Plant Depth Nutrients×Plant Nutrients×Depth Plant×Depth Nutrients×Plant×Depth Water properties TN-Water 0.001 0.042 0.520 0.005 NA NA NA TP-Water 0.001 0.051 0.003 0.006 0.04 NA NA Chl-a 0.001 0.342 0.520 NA NA NA NA Z SD 0.003 0.074 0.005 0.083 0.004 NA NA Sediment properties TN-Sediment 0.001 0.059 0.032 0.065 0.001 0.051 0.08 TP-Sediment 0.001 0.002 0.001 0.019 0.04 NA NA OM 0.001 0.131 0.341 0.231 0.232 NA NA Ca 2 -P 0.001 0.018 0.01 0.026 0.061 NA NA Ca 8 -P 0.001 0.073 0.01 0.075 0.071 NA NA Al-P 0.001 0.082 0.784 NA NA NA NA Fe-P 0.001 0.061 0.023 0.024 0.012 NA NA O-P 0.001 0.075 0.016 0.001 NA NA NA Ca 10 -P 0.001 0.29 0.001 0.001 0.081 NA NA Note: See Table 1 for explanation of variables; NA, not available. 3.2 Sediment microbial assemblages of the four studied lakes Proteobacteria, followed by Chloroflexi, Firmicutes, Nitrospirae, and Bacteroidetes, were the top five dominant phyla and accounted for 80.9–91.6% of the bacterial sequences obtained from the sediment samples (Fig. 2 A). The relative abundance of Proteobacteria was relatively lower (45.3%, Table S3) in the oligotrophic lake (Lake Lugu) than in the two mesotrophic lakes Chenghai and Erhai. The composition of Proteobacteria was lowest in the eutrophic lake (Lake Qilu). With respect to the generic composition (Fig. 2 B and Table S4), the proportions of eight genera ( Psychrobacter , Carnobacterium , Brochothrix , Geobacter , Nitrospira , Exiguobacterium , Syntrophobacter , and Methanoregula ) were highest in the oligotrophic lake (Lake Lugu), followed by the mesotrophic lakes (Lakes Chenghai and Erhai) and the eutrophic lake (Lake Qilu). In contrast, the percentages of five genera ( Candidatus , Anaerolinea , Leptolinea , Sporacetigenium , and Bacillus ) were lowest in the oligotrophic lake (Lake Lugu), elevated in the mesotrophic lakes, and highest in the eutrophic lake. In addition, the percentages of 18 genera increased at first but then decreased with increasing nutrient concentrations, with the lowest concentration in Lake Lugu, a higher concentration in Lakes Erhai and Chenghai, and a decrease in Lake Qilu (Fig. 2 B and Table S4). Differences in P values found in the three-way PERMANOVA analysis of bacterial microbiota at the OTU level between the three factors (nutrients, aquatic plants, and water depth) are shown in Table 3 . We observed significant effects, both individual (nutrient, aquatic plants, and water depth) and interactive (nutrient × aquatic plants and nutrient × water depth), on bacterial communities. Table 3 Three-way permutational multivariate analysis of variance (PERMANOVA) exploring the effects of nutrients, presence of aquatic plants, and water depth based on bacterial Bray-Curtis distance and functional analysis. Factors Bacterial communities Predicted functional profiles F significance F significance Nutrients 27.9432 0.001 15.7826 0.001 Plant 6.9849 0.001 2.5797 0.098 Depth 4.3284 0.001 2.2449 0.083 Nutrients×Plant 2.9935 0.002 0.6319 0.605 Nutrients×Depth 3.7862 0.001 2.6374 0.056 Plant×Depth 0.0112 0.876 0.0113 0.976 Nutrients×Plant×Depth 0.0001 0.923 0.0002 0.983 3.3 Predicted functional profiles Based on the COG database, we obtained 25 functional clusters for all samples at level 2 (Table S5). The most abundant function was [J] translation, ribosomal structure, and biogenesis, and its proportion ranged from 9.6–12.1%. P values for three-way PERMANOVA of predicted functional profiles at the L2 level between the three factors (nutrient, aquatic plants, and water depth) are shown in Table 3 . We only observed a significant lake effect on bacterial functions. In total, 10 functions differed greatly among the four lakes (Fig. 3 ). The proportions of [J] translation, ribosomal structure, and biogenesis; [H] coenzyme transport and metabolism; [O] posttranslational modification, protein turnover, and chaperones; [T] signal transduction mechanisms; and [P] inorganic ion transport and metabolism increased with increasing nutrient concentrations. 3.4 Bacterial alpha and beta diversity The sequencing results (high-quality reads and good coverage) and alpha diversity variables (Chao1, Simpson, Shannon, and evenness) of the sediment microbial communities in our four studied plateau lakes are shown in Table S6. Compared to the other three lakes, Lake Chenghai had significantly lower Chao1 (Fig. 4 A) and Shannon (Fig. 4 C) variables and lower evenness variables (Fig. 4 D). Lake Qilu had a significantly higher Chao1 (Fig. 4 A) index than the other three lakes. Lake Lugu had the highest evenness variable among the four lakes (Fig. 4 D). For beta diversity, PCoA analyses at OTU level revealed strong clustering of bacterial communities on the basis of nutrients (Fig. 5 A), aquatic plants (Fig. 5 B), and water depth (Fig. 5 C). The first two principal coordinates accounted for 23.8% (Axis 1) and 8.0% (Axis 2) of the variation. PC2 generally divided the samples into two groups based on the trophic state of the lakes [oligotrophic: green ellipse (Lake Lugu), mesotrophic: orange ellipse (Lake Erhai) and blue ellipse (Lake Chenghai), and eutrophic: red ellipse (Lake Qilu)] (Fig. 5 A). Neither Axis 1 nor Axis 2 could divide the samples with or without aquatic plants (Fig. 5 B). Based upon different water depths, PC2 also divided the samples into three groups, and the deep water-depth samples (green ellipse, Fig. 5 C) were obviously separated from the other two groups. The middle (red ellipse, Fig. 5 C) and shallow water depths (blue ellipse, Fig. 5 C) were not completely separated but were still distinguishable to some extent. 3.5 Relative contribution of factors In the VPA (Fig. S1 A), the three studied factors (nutrients, aquatic plants, and water depth) explained 15.8% of the total observed variation in the bacterial communities, with nutrients constituting the largest proportion (10.4%), followed by aquatic plants (1.4%) and water depth (0.8%). Interactions between pairs had a limited contribution to the variation in bacterial communities, with nutrient × aquatic plants showing the largest value (1.3%). Based on the RDA results, nine sediment parameters explained 73.3% of the total variation in the bacterial communities (Table S7). The concentration of TN in the sediment was the most important factor (explaining 38.0%) affecting the bacterial communities, followed by Ca 10 -P, Al-P, Ca 8 -P, O-P, OM, Fe-P, and Ca 2 -P in the sediment (Fig. S1 B, Table S7). 4 Discussion 4.1 Significant differences in taxa among the four lakes Proteobacteria are involved in autotrophy coupled to sulfur, methane, and hydrogen oxidation, sulfate reduction; and denitrification (Zhou et al., 2020 ). We found that the proportion of Proteobacteria were lowest in eutrophic lake, indicating that higher nutrient concentrations reduced the population of these microbes. Previous studies have shown that Proteobacteria are essential in bioremediation processes (Shahi et al., 2016 ), and our results indicate a lower capacity for microbial bioremediation in eutrophic lakes. Species of Thiobacillus occupy extreme ecological niches and cling to sulfur/iron-rich environments with heavy metal interactions (Amaresan et al., 2020 ). We found that microorganisms of this genus were less important in Lake Qilu, indicating lower removal capacity of harmful pollutants such as sulfur and heavy metals from microorganisms under eutrophic conditions. Another genus that decreased with eutrophication was Spirochaeta , which also showed higher abundance in the presence than in the absence of aquatic plants. This may be because Spirochaeta species can secrete glycoside hydrolases that are highly affinitive for cellulose and also useful for plant biomass degradation (Angelov et al., 2016 ). A rich cellulose content in the water with aquatic plants may have promoted the high number of Spirochaeta species. Two taxa were more dominant in eutrophic lakes. One is Anaerolineae , which exhibit versatile metabolic abilities in carbohydrate fermentation and is regarded as a typical fermentative component in the bacteria community (Xia et al., 2016 ). The population of this genus increased in importance with increasing nutrient concentrations, likely reflecting that eutrophic lakes have higher concentrations of carbohydrates and the potential to produce more hydrogen, as hydrogen is produced during fermentation of soluble sugar (Narihiro et al., 2012 ). The other one is Leptolinea , which is known as a dominant genus of sulfate reduction (Zhang et al., 2017 ), likely indicating a lower redox potential than in mesoeutrophic lakes (Jones and Ingle, 2005 ). 4.2 Effects of nutrients, aquatic plants and water depth on microbial communities Studies have shown that eutrophication is a driver of the microbial community structure in lake sediments and affects the microbial community richness (Han et al., 2020 ). We found higher bacteria richness in the eutrophic lake, which corresponds with results obtained by Kiersztyn et al. ( 2019 ), who showed that bacterial communities in the highly eutrophicated lakes not only had higher richness but also more diverse taxonomical structure than in the meso-eutrophic lakes. We found higher evenness in the oligotrophic state than at eutrophic conditions. For β-diversity, nutrients were the only significant variable; Lake Chenghai, Lake Erhai, and Lake Qilu were relatively similar, whereas there were significant differences between these three lakes and Lake Lugu. A potential explanation is that trophic state permanently alters the chemical composition of insoluble organic macromolecules and that major groups of bacteria are primary degraders of these macromolecules. OM concentration was a key factor shaping bacterial communities based on RDA results. Therefore, the increased sedimentary input of easily degradable OM in eutrophic lakes appears to be an important driver behind the observed patterns in microbial community structure at the different trophic states. Water depth and the presence or absence of aquatic plants did not have marked effects. In our study, the bacterial community composition (PCoA and three-way PERMANOVA) was more strongly influenced by lake nutrients than by water depth and aquatic plants presence/absence. The bacterial communities at the different sampling sites within the same lakes tended to be similar, probably because of sediment redistribution. At both the phylum and the generic levels, more bacterial taxa showed significant inter-lake differences compared to the effects of water depth and the presence of aquatic plants in a given lake. Our VPA analyses revealed that the effect of aquatic plants (1.4%) was lower than that of nutrients (10.4%), but the important role of aquatic plants was still reflected in the results. Aquatic plants not only alter nutrient and sediment characteristics but also affect the diversity and composition of bacterial communities (Bai et al., 2020 ). Bacteria dissolve inorganic phosphate by releasing organic acid anions (Mora et al., 2017 ). Our study confirmed that five of them, Ca 10 -P Ca 8 -P, O-P, Al-P, and Ca 2 -P, were significantly related to bacterial community structure as demonstrated also by Zhang and Liang ( 1992 ). 4.3 Predicted functional profiles Prokaryotic ribosomal synthesis is a complex, multistep process that requires coordinated synthesis, cleavage, posttranscriptional modification, and folding of ribosomal RNA as well as translation, posttranslational modification, folding, and binding of approximately 50 ribosomal proteins (Davi and Williamso, 2017 ). We found that essential functional categories for growth maintenance, such as translation, ribosomal structure and biogenesis, coenzyme transport, and metabolism (Osman et al., 2017 ) were more abundant in the eutrophic lake, where cyanobacteria are often dominant (Tilzer, 1987 ) and may play an important role in providing heterotrophic bacteria with newly fixed nitrogen and reduced carbon compounds (Osman et al., 2017 ). Several related proteins involved in protein folding and belonging to the category ‘posttranslational modifications, protein turnover, and chaperones’ were also more abundant in the eutrophic Lake Qilu. The signal transduction system is the major system of bacteria for sensing environmental stresses and transducing the information inside the cells (Eguchi and Utsumi, 2005 ). 5 Conclusions We investigated sediment bacterial communities in four plateau freshwater lakes in Southwest China to obtain a comprehensive view of how they responded to nutrients, aquatic plants, and water depth factors. The four lakes were oligotrophic (Lake Lugu), mesotrophic (Lakes Chenghai and Erhai), and eutrophic (Lake Qilu). Of the three studied variables, nutrients had the largest effect, while bacterial community richness in the sediments of the lakes varied significantly. Compared with the other three lakes, Lake Qilu had the highest richness, while the richness and diversity of Lake Chenghai were low, and Lake Lugu had the highest evenness of the four lakes. We observed strong clustering of bacterial communities relative to nutrients, aquatic plants, and water depth. Our study indicates that eutrophication promotes various functions such as translation, ribosomal structure and biogenesis as well as coenzyme transport and metabolism. Declarations Ethics approval and consent to participate This study did not involve human or animal subjects, and therefore, ethical approval was not required. All data were collected from natural lake sediment and water samples in compliance with relevant environmental research guidelines. Consent for publication All authors have consented to the publication of this manuscript. The manuscript does not contain any personal data. Availability of data and material The datasets generated and analyzed during this study are available in the NCBI Sequence Read Archive (SRA) under the accession number PRJNA860051. Additional data related to this study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding: This research was supported by the Yunnan Provincial Department of Science and Technology (202401AS070119; 202103AC100001) and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB31000000). Author Contributions: Conceptualization, Y.L., J.Z. and H.W.; methodology, J.Z. H., W., FY., S., and H.W.; software, L.Z; validation, Y.L., FY., S and K.Y; formal analysis, Y.C; investigation, J.Z. and H.W.; resources, Y.L.; data curation, Y.L.; writing—original draft preparation, Y.L.; writing—review and editing, J.Z; visualization, Y.C; supervision, Y.C.; project administration, J.Z.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript. Acknowledgements This research was supported by the Yunnan Provincial Department of Science and Technology (202401AS070119; 202103AC100001) and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB31000000). 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A. Phyla (with relative abundance \u0026gt;1%) B. Genera were significantly different in the four lakes at the generic level.\u003c/p\u003e","description":"","filename":"Fig.2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6075353/v1/17c9abd8c499a3b570cd99ef.jpg"},{"id":81036747,"identity":"9274a992-61e6-462d-87ef-6ceb83992211","added_by":"auto","created_at":"2025-04-21 12:30:16","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":566890,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional analysis of the sediment bacterial community in four study lakes\u003c/p\u003e","description":"","filename":"Fig.3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6075353/v1/a39136fdff69b72e2b70be94.jpg"},{"id":81037106,"identity":"721ce15a-29f2-4659-bf12-f694fd031bcf","added_by":"auto","created_at":"2025-04-21 12:38:16","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1462143,"visible":true,"origin":"","legend":"\u003cp\u003eBacterial alpha diversity indices in the four study lakes.\u003c/p\u003e","description":"","filename":"Fig.4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6075353/v1/37a25db673a674ad913e1df3.jpg"},{"id":81037107,"identity":"1c9d75da-74b2-44d1-8e7b-9f1ee6cfbe0a","added_by":"auto","created_at":"2025-04-21 12:38:16","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":768995,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal coordinate analyses of the sediment bacterial communities at OTU level.\u003c/p\u003e","description":"","filename":"Fig.5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6075353/v1/c21e12f1f6bb658e9a141058.jpg"},{"id":81038812,"identity":"19ac3480-e6bc-40bc-91fa-1aa52512c7f1","added_by":"auto","created_at":"2025-04-21 13:02:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5395206,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6075353/v1/3db1acf3-15ef-4814-90ed-8393e4b33c80.pdf"},{"id":81036746,"identity":"ff9f39d8-9c22-4cdb-b7be-259b19070a85","added_by":"auto","created_at":"2025-04-21 12:30:16","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":37469,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1S7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6075353/v1/aea7f4de4871e4ee4c450a79.xlsx"},{"id":81037115,"identity":"9233c754-d775-469e-ad7e-13154166ab04","added_by":"auto","created_at":"2025-04-21 12:38:16","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2308452,"visible":true,"origin":"","legend":"","description":"","filename":"FIG.S1.tif","url":"https://assets-eu.researchsquare.com/files/rs-6075353/v1/5e2140f1236665eb1d8475d8.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sediment bacterial assemblages inside and outside macrophyte beds in Yunnan Plateau lakes with contrasting nutrient levels and water depths","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eMicrobes, important components in lakes (Kim et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), are at the hub of biogeochemical cycles (Newton et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and function as indicators when assessing aquatic ecosystem health (L\u0026oacute;pez-L\u0026oacute;pez and Sede\u0026ntilde;o-D\u0026iacute;az, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Sediment bacterial communities are sensitive to environmental factors such as trophic status (Llir\u0026oacute;s et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), aquatic plant coverage (Zhang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), algal blooms (Su et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and water depth (Probst et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To understand the relationship between bacterial communities and lake environments, it is particularly important to consider how these factors combine to influence the bacterial communities of the sediments.\u003c/p\u003e \u003cp\u003eEutrophication can affect the structure of microbial communities in lake sediments (Han et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). With increasing nutrient concentrations, many adverse environmental effects may occur, including accelerated growth of algae and loss of aquatic macrophytes due to deteriorated water quality (Jeppesen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Khan and Mohammad, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Moss, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Microbial communities are affected by organic matter (Bergauer et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), nitrogen (Zhou et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), several forms of phosphorus (LeBrun et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), chlorophyll \u003cem\u003ea\u003c/em\u003e (Chl-\u003cem\u003ea\u003c/em\u003e) (Eronen-Rasimus et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and other nutrients, which are all important for the microbial growth and metabolism. The abundance and biomass of various microbial components may increase with eutrophication in aquatic ecosystems. For example, the bacterial biomass increased fourfold and the ciliate and heterotrophic nanoflagellate biomasses 28- and 32-fold, respectively, as Chl-a concentrations increased from 5 to 20 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in a series of Mediterranean shallow lakes (Conty and B\u0026eacute;cares, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe hydrostatic pressure caused by the water column is a common factor that has considerable impact the surface sediment structure (Wang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and it also influences the microbial community. Once the total gas partial pressure exceeds the local hydrostatic pressure, free gas in the sediment pore will be released into the water column. Meanwhile, microorganisms, which are sensitive to gas and other changes in environmental factors in the sediment (Zhou et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), will respond correspondingly. Moreover, sediment microorganisms have developed survival strategies and grow slowly under anaerobic conditions, and their biomass, activity, and diversity decrease (Mattsson et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Therefore, water depth is another important factor affecting the microbial community in the sediment as the hydrostatic pressure rivalries with depth and risk of anaerobic conditions in the sediment typically increases with water depth (Johnson and Page, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAquatic plants have important effects in lake ecosystems, and they provide oxygen and appropriate environmental conditions for epiphytic microbial communities (Zhang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A previous study by Wu et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported that submersed macrophytes decreased the diversity of most N-cycling bacterial assemblages, including nitrifying, denitrifying, and DNRA (dissimilatory nitrate reduction to ammonium) bacteria, while increasing their abundance. Our recent study showed that shining pondweed (\u003cem\u003ePotamogeton lucens\u003c/em\u003e) can decrease bacterial alpha diversity and 16S rRNA gene copies in the sediment and increase bacterial alpha diversity in the water (Zhang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, the presence or absence of aquatic plants may be an important factor affecting the microbial community in the sediment. Little is known about the combined effects of nutrients, water depth, and aquatic plants on bacterial communities and potential ecological functions in lake sediments.\u003c/p\u003e \u003cp\u003eThe main aim of this study was to investigate the composition of and differences in bacterial communities in lake sediments under different nutrient regimes, water depths, and aquatic plants presence. Four plateau lakes with distinct regional characteristics were selected: Lakes Lugu, Chenghai, Erhai, and Qilu. We hypothesized that the composition and predicted function of sediment bacterial communities in lakes would differ among nutrient regimes and aquatic plant presence driven both by direct and indirect forces.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe study lakes (Lake Lugu, Lake Chenghai, Lake Erhai, and Lake Qilu) are all located on the Yunnan Plateau in Southwest China. Lake Lugu (27\u0026deg;39\u0026prime; \u0026ndash; 27\u0026deg;45\u0026prime; N, 100\u0026deg;44\u0026prime; \u0026ndash; 100\u0026deg;50\u0026prime; E and 2690 m a.s.l.) is a typical alpine lake with an average depth of 40.3 m, a maximum depth of 93.5 m, and a surface area of 48.45 km\u003csup\u003e2\u003c/sup\u003e (Chang et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ndayishimiye et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The main aquatic plants in Lake Lugu are \u003cem\u003ePotamogeton wrightii\u003c/em\u003e Morong, \u003cem\u003eOttelia acuminata\u003c/em\u003e, Charophyceae, \u003cem\u003eHydrilla verticillata\u003c/em\u003e, \u003cem\u003eMyriophyllum spicatum\u003c/em\u003e L., \u003cem\u003eP. lucens\u003c/em\u003e L., \u003cem\u003eP. pectinatus\u003c/em\u003e L., \u003cem\u003eCeratophyllum demersum\u003c/em\u003e L., and \u003cem\u003eUtricularia aurea\u003c/em\u003e Lour. Lake Chenghai (26\u0026deg;27\u0026prime; \u0026ndash; 26\u0026deg;38\u0026prime; N, 100\u0026deg;38\u0026prime; \u0026ndash; 100\u0026deg;41\u0026prime; E, and 1500 m a.s.l.) is a plateau lake with an average depth of 25.7 m (Liu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e); it is a typical terminal plateau lake that has become increasingly susceptible to eutrophication due to diminished water exchange under the external pressure of basin development (Zou et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Samples from Chenghai waters did not have aquatic plant beds. Lake Erhai (25\u0026deg;36\u0026prime; \u0026ndash; 25\u0026deg;58\u0026prime; N, 100\u0026deg;05\u0026prime; \u0026ndash; 100\u0026deg;17\u0026prime; E) is one of the largest fault lakes in China, with an average depth of 10.8 m (Wang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and it plays a significant role for the local socioeconomic development, including drinking water sources, irrigation, fisheries, and tourism (Ni and Wang, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The main aquatic plants in the lake are \u003cem\u003eP. wrightii Morong\u003c/em\u003e, \u003cem\u003eC. demersum\u003c/em\u003e L., \u003cem\u003eM. spicatum L.\u003c/em\u003e, \u003cem\u003eP. maackianus\u003c/em\u003e, \u003cem\u003eV. natans\u003c/em\u003e, and \u003cem\u003eP. pectinatus\u003c/em\u003e L. Lake Qilu is a large (36.9 km\u003csup\u003e2\u003c/sup\u003e), shallow (Z\u003csub\u003eMax\u003c/sub\u003e = 6.8 m) lake with hard, fresh, and productive water (Mg\u0026thinsp;=\u0026thinsp;3.2 meq L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, Ca\u0026thinsp;=\u0026thinsp;1.3 meq L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, conductivity\u0026thinsp;=\u0026thinsp;380 \u0026micro;S cm \u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and Secchi\u0026thinsp;\u0026lt;\u0026thinsp;0.4 m) (Brenner et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Lake Qilu (24\u0026deg;08\u0026prime; \u0026ndash; 24\u0026deg;13\u0026prime; N, 102\u0026deg;43\u0026prime; \u0026ndash; 102\u0026deg;49\u0026prime; E) is a semiclosed alpine lake with an average depth of approximately 4.0 m (Liu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and the main aquatic plants are \u003cem\u003eM\u003c/em\u003e. \u003cem\u003espicatum\u003c/em\u003e L. and \u003cem\u003eM\u003c/em\u003e. \u003cem\u003eaquaticum\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sampling and measurements\u003c/h2\u003e \u003cp\u003eThe survey was conducted in November 2021 (see the sampling sites in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Water and sediment were sampled from both inside and outside of the submersed macrophytes beds if present in the lake. Water samples were collected from three-layer of the water column (0.5 m, 1.0 m, and 3 m, respectively below the water surface) with a 5 L polymethyl methacrylate water sampler, then mixed up. The samples from areas with and without aquatic plants are defined as 2 types (no aquatic plant /aqutic plant, respectively). The surface layer (0\u0026ndash;20 cm) of sediment was collected with a 1/16 m\u003csup\u003e2\u003c/sup\u003e Peterson grab. Six sediment samples were taken in triplicate at different water depths in Lakes Chenghai, Erhai, and Lugu and seven and four sediment samples in Lake Qilu and Lake Lugu, respectively (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Two levels of water depth were used: 0\u0026ndash;10 m (shallow) and 10\u0026ndash;30 m (middle). Well-mixed water and sediment samples were transferred to sterile bottles and bags, respectively, and immediately transported to the laboratory on ice.\u003c/p\u003e \u003cp\u003eWater transparency (Z\u003csub\u003eSD\u003c/sub\u003e) was determined by using a Secchi disc in situ (Lee et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The chlorophyll \u003cem\u003ea\u003c/em\u003e (Chl-\u003cem\u003ea\u003c/em\u003e) concentration in the water was determined by 90% acetone extraction (spectrophotometer method) (Jiang et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The total nitrogen (TN) concentration in the water was determined using semimicro Kjeldahl after digestion by H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e and HClO\u003csub\u003e4\u003c/sub\u003e; the total phosphorus (TP) concentration in the water was determined by boiling with H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e and HClO\u003csub\u003e4\u003c/sub\u003e (molybdenum antimony resistance). TN, TP, and organic matter (OM) in the sediment were determined according to Strickland and Sollins (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Inorganic P fractions of the sediment were determined by 0.5 mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e NaHCO\u003csub\u003e3\u003c/sub\u003e-soluble P (Ca\u003csub\u003e2\u003c/sub\u003e-P), 0.5 mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e NH\u003csub\u003e4\u003c/sub\u003eAc-soluble P (Ca\u003csub\u003e8\u003c/sub\u003e-P), 0.5 mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e NH\u003csub\u003e4\u003c/sub\u003eF-soluble P (Al-P), 0.1 mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e NaOH-Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e-soluble P (Fe-P), occluded P extracted with 0.3 mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e sodium citrate\u0026thinsp;+\u0026thinsp;1 g Na\u003csub\u003e2\u003c/sub\u003eS\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.5 mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e NaOH (O-P), and 0.25 mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e-soluble P (Ca\u003csub\u003e10\u003c/sub\u003e-P). P in the extracts was determined by the molybdenum-blue method (Murphy and Riley, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1962\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSediment total DNA was extracted by a Fast DNA SPIN Kit (MP Biomedicals, OH, USA). The bacterial 16S rRNA gene V4 and V5 regions were amplified with primers 515-F/907-R (Wu et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The PCR process was predenatured at 94 ℃ for 5 min, followed by 35 cycles of 94℃ for 30 s, 55℃ for 35 s, and 72℃ for 30 s, with a final extension at 72℃ for 10 min. The amplified PCR products were sequenced on the Illumina 2 \u0026times; 250 platform at Beijing Fix-gene Co., Ltd. (Beijing, China).\u003c/p\u003e \u003cp\u003eSlip window quality was cut for the two-end raw sequence datasets using fastp, and the QC paired-end clean reads were obtained by removing the primers with cutadapt software. Usearch-fastq mergepairs (V10, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.drive5.com/usearch/\u003c/span\u003e\u003cspan address=\"http://www.drive5.com/usearch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used to filter the unqualified tags to obtain raw tags. Raw tags data were trimmed using fastp to produce clean tags (Chen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Each representative OTU sequence was aligned against the SILVA database using usearch-sintax to obtain species annotation information (Quast et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). To minimize the number of OTUs retained by sequencing errors, we removed species with fewer than five sequences in three samples from each group and all samples with less than 20 total sequences to obtain OTU tables (Zhou and Fong, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e \u003cp\u003eChao1, Simpson, Shannon, and evenness diversity indices were calculated using usearch-alpha-div (V10, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.drive5.com/usearch/\u003c/span\u003e\u003cspan address=\"http://www.drive5.com/usearch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) based on the OTU abundance table. Additionally, also based on the OTU abundance table, we performed a PCoA analysis using the \u0026ldquo;prcomp\u0026rdquo; package in R software to visualize the differences between the samples of each group. At the generic level, these systematic magnetic trees of x-rich OTUs were inferred with the neighborhood-joining method in MEGAv.6.1 and displayed using iTOL (Interactive Tree of Life, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://itol.embl.de/\u003c/span\u003e\u003cspan address=\"https://itol.embl.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and mean relative abundance data (Ivica and Peer, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBacterial community functional analysis was performed by PICRUSt (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://picrust.github.io/picrust/\u003c/span\u003e\u003cspan address=\"https://picrust.github.io/picrust/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), based on the OTU abundance table (Langille et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Then, the cluster of the orthologous group (COG) database was compared them to obtain the COG family information corresponding to the OTUs, and the abundance of each functional category was calculated to analyze the pathway differences between multiple groups.\u003c/p\u003e \u003cp\u003eDifferences in water and sediment properties and bacterial community composition at the generic level in the samples from the four plateau lakes were determined using nonparametric tests applying IBM SPSS Statistics 21 (Zhou et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The effects of nutrients, presence of aquatic plants, and water depth on water and sediment properties was compared using generalized linear models (GLMs) applying IBM SPSS Statistics 21 (Eshima et al., 2011). Three-way permutational multivariate of variance (PERMANOVA) was used to analyze the individual and interactive effects of the three factors on beta diversity with the \u0026ldquo;adonis\u0026rdquo; function in R (Zhou and Fong, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). At the OTU level, variation partitioning analysis (VPA) was performed with R software to quantify the contribution of each environmental factor, Lake, Plants and Depth to variation in the microbial community distribution (Zhou and Fong, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Detrended correspondence analysis (DCA) was used to explain the relative effects of lake nutrients (properties of sediment) on the microbial community (at OTU level) using CANOCO 5.0 (Zhou and Fong, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In DCA data analysis, the \u0026ldquo;lengths of gradient\u0026rdquo; values were shorter than 3; thus, the RDA was best choice. The obtained sequences were submitted to the NCBI Sequence Read Archive (SRA) under accession number PRJNA860051.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Water and sediment characteristics of the four plateau lakes\u003c/h2\u003e \u003cp\u003eThe properties of the water and sediment of the four plateau lakes are shown in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and S2. TN, TP, and Chl-\u003cem\u003ea\u003c/em\u003e were significantly higher, while Z\u003csub\u003eSD\u003c/sub\u003e was significantly lower in Lake Qilu than in the other three lakes. TN, TP, and Chl-\u003cem\u003ea\u003c/em\u003e were significantly lower, while Z\u003csub\u003eSD\u003c/sub\u003e was significantly higher in Lake Lugu than in the other three lakes. Generally, Lake Lugu was oligotrophic, Lakes Erhai and Chenghai were mesotrophic, and Lake Qilu was eutrophic (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor sediment samples, TN, TP, OM, and four forms of phosphorus (Ca\u003csub\u003e2\u003c/sub\u003e-P, Ca\u003csub\u003e8\u003c/sub\u003e-P, Al-P, and O-P in sediment) differed significantly among the four lakes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). TN was lowest in Lake Chenghai and highest in Lake Qilu. Ca\u003csub\u003e2\u003c/sub\u003e-P was significantly lower in Lake Lugu than in the other three lakes. However, TN, Ca\u003csub\u003e2\u003c/sub\u003e-P, Al-P, O-P, and OM were significantly higher in Lake Qilu than in the other three lakes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWater and sediment characteristics (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error) in the four study lakes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWater and sediment properties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLugu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChenghai\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eErhai\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQilu\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWater samples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChl-a(\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.0\u0026thinsp;\u0026plusmn;\u0026thinsp;18.0\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZ\u003csub\u003eSD\u003c/sub\u003e(m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0. 6\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0. 7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTN-Water (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTP-Water (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.018\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eSediment samples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTN-Sediment (mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2754\u0026thinsp;\u0026plusmn;\u0026thinsp;563\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1984\u0026thinsp;\u0026plusmn;\u0026thinsp;693\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4225\u0026thinsp;\u0026plusmn;\u0026thinsp;1555\u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7213\u0026thinsp;\u0026plusmn;\u0026thinsp;1209\u003csub\u003ed\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTP-Sediment (mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1123\u0026thinsp;\u0026plusmn;\u0026thinsp;1001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e832\u0026thinsp;\u0026plusmn;\u0026thinsp;221\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1078\u0026thinsp;\u0026plusmn;\u0026thinsp;140\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1488\u0026thinsp;\u0026plusmn;\u0026thinsp;399\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOM (g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.9\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.6\u0026thinsp;\u0026plusmn;\u0026thinsp;22.7\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e152.4\u0026thinsp;\u0026plusmn;\u0026thinsp;42.2\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCa\u003csub\u003e2\u003c/sub\u003e-P (mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCa\u003csub\u003e8\u003c/sub\u003e-P (mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.2\u0026thinsp;\u0026plusmn;\u0026thinsp;24.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.7\u0026thinsp;\u0026plusmn;\u0026thinsp;18.9\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl-P (mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.4\u0026thinsp;\u0026plusmn;\u0026thinsp;19. 9\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFe-P (mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.0\u0026thinsp;\u0026plusmn;\u0026thinsp;75.3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.5\u0026thinsp;\u0026plusmn;\u0026thinsp;36.8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.4\u0026thinsp;\u0026plusmn;\u0026thinsp;20.2\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e208.9\u0026thinsp;\u0026plusmn;\u0026thinsp;97.5\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO-P (mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189.6\u0026thinsp;\u0026plusmn;\u0026thinsp;84.1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120.0\u0026thinsp;\u0026plusmn;\u0026thinsp;39.8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e175.1\u0026thinsp;\u0026plusmn;\u0026thinsp;29.7\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e301.5\u0026thinsp;\u0026plusmn;\u0026thinsp;17.9\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCa\u003csub\u003e10\u003c/sub\u003e-P (mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e711\u0026thinsp;\u0026plusmn;\u0026thinsp;104\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e543\u0026thinsp;\u0026plusmn;\u0026thinsp;219\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e563\u0026thinsp;\u0026plusmn;\u0026thinsp;125\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e726\u0026thinsp;\u0026plusmn;\u0026thinsp;266\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: The different letters indicate significant differences at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level. TN, total nitrogen concentration in lake water; TP, total phosphorus concentration in lake water; Chl-a, phytoplankton chlorophyll \u003cem\u003ea\u003c/em\u003e concentration; and Z\u003csub\u003eSD\u003c/sub\u003e, Secchi depth. OM, organic matter content in sediment, Ca\u003csub\u003e2\u003c/sub\u003e-P: NaHCO\u003csub\u003e3\u003c/sub\u003e soluble phosphorus, Ca\u003csub\u003e8\u003c/sub\u003e-P: NH\u003csub\u003e4\u003c/sub\u003eAc soluble phosphorus, Al-P: soluble aluminum phosphate, Fe-P: NaOH-Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e soluble phosphorus, O-P: closed storage phosphorus, and Ca\u003csub\u003e10\u003c/sub\u003e-P: H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e soluble phosphorus.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSignificant effects of nutrients were observed on all the water and sediment variables (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A significant impact of aquatic plants was observed on TP-Sediment and on Ca\u003csub\u003e2\u003c/sub\u003e-P in the sediment. A significant water depth effect was observed on TP-Water, TP-Sediment, Ca\u003csub\u003e2\u003c/sub\u003e-P-Sediment, Ca\u003csub\u003e8\u003c/sub\u003e-P-Sediment, and Ca\u003csub\u003e10\u003c/sub\u003e-P-Sediment. We also noted a significant interactive effects of nutrient concentrations with presence of aquatic plants on TP-Sediment, Ca\u003csub\u003e10\u003c/sub\u003e-P-Sediment, and Fe-P-Sediment. No interactive effects of aquatic plants \u0026times; water depth and nutrients \u0026times; aquatic plants \u0026times; water depth on any nutrient variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe effects of nutrients, presence of aquatic plants, and water depth on water and sediment properties.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWater and sediment properties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNutrients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDepth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNutrients\u0026times;Plant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNutrients\u0026times;Depth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePlant\u0026times;Depth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNutrients\u0026times;Plant\u0026times;Depth\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWater properties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTN-Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTP-Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChl-a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZ\u003csub\u003eSD\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eSediment properties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTN-Sediment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTP-Sediment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCa\u003csub\u003e2\u003c/sub\u003e-P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCa\u003csub\u003e8\u003c/sub\u003e-P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl-P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFe-P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO-P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCa\u003csub\u003e10\u003c/sub\u003e-P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for explanation of variables; NA, not available.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Sediment microbial assemblages of the four studied lakes\u003c/h2\u003e \u003cp\u003eProteobacteria, followed by Chloroflexi, Firmicutes, Nitrospirae, and Bacteroidetes, were the top five dominant phyla and accounted for 80.9\u0026ndash;91.6% of the bacterial sequences obtained from the sediment samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The relative abundance of Proteobacteria was relatively lower (45.3%, Table S3) in the oligotrophic lake (Lake Lugu) than in the two mesotrophic lakes Chenghai and Erhai. The composition of Proteobacteria was lowest in the eutrophic lake (Lake Qilu).\u003c/p\u003e \u003cp\u003eWith respect to the generic composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and Table S4), the proportions of eight genera (\u003cem\u003ePsychrobacter\u003c/em\u003e, \u003cem\u003eCarnobacterium\u003c/em\u003e, \u003cem\u003eBrochothrix\u003c/em\u003e, \u003cem\u003eGeobacter\u003c/em\u003e, \u003cem\u003eNitrospira\u003c/em\u003e, \u003cem\u003eExiguobacterium\u003c/em\u003e, \u003cem\u003eSyntrophobacter\u003c/em\u003e, and \u003cem\u003eMethanoregula\u003c/em\u003e) were highest in the oligotrophic lake (Lake Lugu), followed by the mesotrophic lakes (Lakes Chenghai and Erhai) and the eutrophic lake (Lake Qilu). In contrast, the percentages of five genera (\u003cem\u003eCandidatus\u003c/em\u003e, \u003cem\u003eAnaerolinea\u003c/em\u003e, \u003cem\u003eLeptolinea\u003c/em\u003e, \u003cem\u003eSporacetigenium\u003c/em\u003e, and \u003cem\u003eBacillus\u003c/em\u003e) were lowest in the oligotrophic lake (Lake Lugu), elevated in the mesotrophic lakes, and highest in the eutrophic lake. In addition, the percentages of 18 genera increased at first but then decreased with increasing nutrient concentrations, with the lowest concentration in Lake Lugu, a higher concentration in Lakes Erhai and Chenghai, and a decrease in Lake Qilu (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and Table S4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDifferences in \u003cem\u003eP\u003c/em\u003e values found in the three-way PERMANOVA analysis of bacterial microbiota at the OTU level between the three factors (nutrients, aquatic plants, and water depth) are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. We observed significant effects, both individual (nutrient, aquatic plants, and water depth) and interactive (nutrient \u0026times; aquatic plants and nutrient \u0026times; water depth), on bacterial communities.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThree-way permutational multivariate analysis of variance (PERMANOVA) exploring the effects of nutrients, presence of aquatic plants, and water depth based on bacterial Bray-Curtis distance and functional analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBacterial communities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePredicted functional profiles\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003esignificance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003esignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutrients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.9432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.7826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.9849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.5797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.3284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.2449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutrients\u0026times;Plant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.9935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutrients\u0026times;Depth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.7862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.6374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant\u0026times;Depth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutrients\u0026times;Plant\u0026times;Depth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Predicted functional profiles\u003c/h2\u003e \u003cp\u003eBased on the COG database, we obtained 25 functional clusters for all samples at level 2 (Table S5). The most abundant function was [J] translation, ribosomal structure, and biogenesis, and its proportion ranged from 9.6\u0026ndash;12.1%. \u003cem\u003eP\u003c/em\u003e values for three-way PERMANOVA of predicted functional profiles at the L2 level between the three factors (nutrient, aquatic plants, and water depth) are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. We only observed a significant lake effect on bacterial functions. In total, 10 functions differed greatly among the four lakes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The proportions of [J] translation, ribosomal structure, and biogenesis; [H] coenzyme transport and metabolism; [O] posttranslational modification, protein turnover, and chaperones; [T] signal transduction mechanisms; and [P] inorganic ion transport and metabolism increased with increasing nutrient concentrations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Bacterial alpha and beta diversity\u003c/h2\u003e \u003cp\u003eThe sequencing results (high-quality reads and good coverage) and alpha diversity variables (Chao1, Simpson, Shannon, and evenness) of the sediment microbial communities in our four studied plateau lakes are shown in Table S6. Compared to the other three lakes, Lake Chenghai had significantly lower Chao1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) and Shannon (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) variables and lower evenness variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Lake Qilu had a significantly higher Chao1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) index than the other three lakes. Lake Lugu had the highest evenness variable among the four lakes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor beta diversity, PCoA analyses at OTU level revealed strong clustering of bacterial communities on the basis of nutrients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), aquatic plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), and water depth (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The first two principal coordinates accounted for 23.8% (Axis 1) and 8.0% (Axis 2) of the variation. PC2 generally divided the samples into two groups based on the trophic state of the lakes [oligotrophic: green ellipse (Lake Lugu), mesotrophic: orange ellipse (Lake Erhai) and blue ellipse (Lake Chenghai), and eutrophic: red ellipse (Lake Qilu)] (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Neither Axis 1 nor Axis 2 could divide the samples with or without aquatic plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Based upon different water depths, PC2 also divided the samples into three groups, and the deep water-depth samples (green ellipse, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) were obviously separated from the other two groups. The middle (red ellipse, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) and shallow water depths (blue ellipse, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) were not completely separated but were still distinguishable to some extent.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Relative contribution of factors\u003c/h2\u003e \u003cp\u003eIn the VPA (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA), the three studied factors (nutrients, aquatic plants, and water depth) explained 15.8% of the total observed variation in the bacterial communities, with nutrients constituting the largest proportion (10.4%), followed by aquatic plants (1.4%) and water depth (0.8%). Interactions between pairs had a limited contribution to the variation in bacterial communities, with nutrient \u0026times; aquatic plants showing the largest value (1.3%).\u003c/p\u003e \u003cp\u003eBased on the RDA results, nine sediment parameters explained 73.3% of the total variation in the bacterial communities (Table S7). The concentration of TN in the sediment was the most important factor (explaining 38.0%) affecting the bacterial communities, followed by Ca\u003csub\u003e10\u003c/sub\u003e-P, Al-P, Ca\u003csub\u003e8\u003c/sub\u003e-P, O-P, OM, Fe-P, and Ca\u003csub\u003e2\u003c/sub\u003e-P in the sediment (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB, Table S7).\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Significant differences in taxa among the four lakes\u003c/h2\u003e \u003cp\u003eProteobacteria are involved in autotrophy coupled to sulfur, methane, and hydrogen oxidation, sulfate reduction; and denitrification (Zhou et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We found that the proportion of Proteobacteria were lowest in eutrophic lake, indicating that higher nutrient concentrations reduced the population of these microbes. Previous studies have shown that Proteobacteria are essential in bioremediation processes (Shahi et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and our results indicate a lower capacity for microbial bioremediation in eutrophic lakes. Species of \u003cem\u003eThiobacillus\u003c/em\u003e occupy extreme ecological niches and cling to sulfur/iron-rich environments with heavy metal interactions (Amaresan et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We found that microorganisms of this genus were less important in Lake Qilu, indicating lower removal capacity of harmful pollutants such as sulfur and heavy metals from microorganisms under eutrophic conditions. Another genus that decreased with eutrophication was \u003cem\u003eSpirochaeta\u003c/em\u003e, which also showed higher abundance in the presence than in the absence of aquatic plants. This may be because \u003cem\u003eSpirochaeta\u003c/em\u003e species can secrete glycoside hydrolases that are highly affinitive for cellulose and also useful for plant biomass degradation (Angelov et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A rich cellulose content in the water with aquatic plants may have promoted the high number of \u003cem\u003eSpirochaeta\u003c/em\u003e species.\u003c/p\u003e \u003cp\u003eTwo taxa were more dominant in eutrophic lakes. One is \u003cem\u003eAnaerolineae\u003c/em\u003e, which exhibit versatile metabolic abilities in carbohydrate fermentation and is regarded as a typical fermentative component in the bacteria community (Xia et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The population of this genus increased in importance with increasing nutrient concentrations, likely reflecting that eutrophic lakes have higher concentrations of carbohydrates and the potential to produce more hydrogen, as hydrogen is produced during fermentation of soluble sugar (Narihiro et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The other one is \u003cem\u003eLeptolinea\u003c/em\u003e, which is known as a dominant genus of sulfate reduction (Zhang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), likely indicating a lower redox potential than in mesoeutrophic lakes (Jones and Ingle, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Effects of nutrients, aquatic plants and water depth on microbial communities\u003c/h2\u003e \u003cp\u003eStudies have shown that eutrophication is a driver of the microbial community structure in lake sediments and affects the microbial community richness (Han et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We found higher bacteria richness in the eutrophic lake, which corresponds with results obtained by Kiersztyn et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who showed that bacterial communities in the highly eutrophicated lakes not only had higher richness but also more diverse taxonomical structure than in the meso-eutrophic lakes. We found higher evenness in the oligotrophic state than at eutrophic conditions.\u003c/p\u003e \u003cp\u003eFor β-diversity, nutrients were the only significant variable; Lake Chenghai, Lake Erhai, and Lake Qilu were relatively similar, whereas there were significant differences between these three lakes and Lake Lugu. A potential explanation is that trophic state permanently alters the chemical composition of insoluble organic macromolecules and that major groups of bacteria are primary degraders of these macromolecules. OM concentration was a key factor shaping bacterial communities based on RDA results. Therefore, the increased sedimentary input of easily degradable OM in eutrophic lakes appears to be an important driver behind the observed patterns in microbial community structure at the different trophic states. Water depth and the presence or absence of aquatic plants did not have marked effects.\u003c/p\u003e \u003cp\u003eIn our study, the bacterial community composition (PCoA and three-way PERMANOVA) was more strongly influenced by lake nutrients than by water depth and aquatic plants presence/absence. The bacterial communities at the different sampling sites within the same lakes tended to be similar, probably because of sediment redistribution. At both the phylum and the generic levels, more bacterial taxa showed significant inter-lake differences compared to the effects of water depth and the presence of aquatic plants in a given lake. Our VPA analyses revealed that the effect of aquatic plants (1.4%) was lower than that of nutrients (10.4%), but the important role of aquatic plants was still reflected in the results. Aquatic plants not only alter nutrient and sediment characteristics but also affect the diversity and composition of bacterial communities (Bai et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Bacteria dissolve inorganic phosphate by releasing organic acid anions (Mora et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Our study confirmed that five of them, Ca\u003csub\u003e10\u003c/sub\u003e-P Ca\u003csub\u003e8\u003c/sub\u003e-P, O-P, Al-P, and Ca\u003csub\u003e2\u003c/sub\u003e-P, were significantly related to bacterial community structure as demonstrated also by Zhang and Liang (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1992\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Predicted functional profiles\u003c/h2\u003e \u003cp\u003eProkaryotic ribosomal synthesis is a complex, multistep process that requires coordinated synthesis, cleavage, posttranscriptional modification, and folding of ribosomal RNA as well as translation, posttranslational modification, folding, and binding of approximately 50 ribosomal proteins (Davi and Williamso, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). We found that essential functional categories for growth maintenance, such as translation, ribosomal structure and biogenesis, coenzyme transport, and metabolism (Osman et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) were more abundant in the eutrophic lake, where cyanobacteria are often dominant (Tilzer, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1987\u003c/span\u003e) and may play an important role in providing heterotrophic bacteria with newly fixed nitrogen and reduced carbon compounds (Osman et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Several related proteins involved in protein folding and belonging to the category \u0026lsquo;posttranslational modifications, protein turnover, and chaperones\u0026rsquo; were also more abundant in the eutrophic Lake Qilu. The signal transduction system is the major system of bacteria for sensing environmental stresses and transducing the information inside the cells (Eguchi and Utsumi, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eWe investigated sediment bacterial communities in four plateau freshwater lakes in Southwest China to obtain a comprehensive view of how they responded to nutrients, aquatic plants, and water depth factors. The four lakes were oligotrophic (Lake Lugu), mesotrophic (Lakes Chenghai and Erhai), and eutrophic (Lake Qilu). Of the three studied variables, nutrients had the largest effect, while bacterial community richness in the sediments of the lakes varied significantly. Compared with the other three lakes, Lake Qilu had the highest richness, while the richness and diversity of Lake Chenghai were low, and Lake Lugu had the highest evenness of the four lakes. We observed strong clustering of bacterial communities relative to nutrients, aquatic plants, and water depth. Our study indicates that eutrophication promotes various functions such as translation, ribosomal structure and biogenesis as well as coenzyme transport and metabolism.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThis study did not involve human or animal subjects, and therefore, ethical approval was not required. All data were collected from natural lake sediment and water samples in compliance with relevant environmental research guidelines.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eAll authors have consented to the publication of this manuscript. The manuscript does not contain any personal data.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe datasets generated and analyzed during this study are available in the NCBI Sequence Read Archive (SRA) under the accession number PRJNA860051. Additional data related to this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research was supported by the Yunnan Provincial Department of Science and Technology (202401AS070119; 202103AC100001) and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB31000000).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Conceptualization, Y.L., J.Z. and H.W.; methodology, \u0026nbsp;J.Z. H., W., FY., S.,\u0026nbsp;and H.W.; software, L.Z; validation, Y.L., FY., S\u0026nbsp;and K.Y; formal analysis, Y.C; investigation, J.Z. and H.W.; resources, Y.L.; data curation, Y.L.; writing\u0026mdash;original draft preparation, Y.L.; writing\u0026mdash;review and editing, J.Z; visualization, Y.C; supervision, Y.C.; project administration, J.Z.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThis research was supported by the Yunnan Provincial Department of Science and Technology (202401AS070119; 202103AC100001) and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB31000000). We thank all colleagues and technical staff who contributed to this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmaresan, N., Kumar, M. 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Hydrol. 514, 1-14.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Environment](https://www.springer.com/44274/)","snPcode":"44274","submissionUrl":"https://submission.nature.com/new-submission/44274/3","title":"Discover Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"plateau lakes, lake eutrophication, water depth, aquatic plant, bacterial community","lastPublishedDoi":"10.21203/rs.3.rs-6075353/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6075353/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEutrophication followed by deterioration of water quality and loss of aquatic plants is a global problem in aquatic ecosystems. Sediment microbial communities are often used as indicators of environmental changes due to their sensitive responses. However, the joint effects of nutrient levels and aquatic plants on the structure and function of bacterial assemblages remain unclear. In this study, four Yunnan Plateau lakes with contrasting nutrient levels were sampled in November 2021. Sediment samples were collected from areas with and without aquatic plants to explore the potential interactive effects of nutrients and plants. We found (1) that Lake Qilu had a significantly higher richness index than the other three lakes. Lake Lugu had the highest evenness variable among the four lakes. (2) PCoA showed strong clustering of bacterial communities according to nutrients, aquatic plants, and water depth. (3) A significant nutrient effect was also observed on bacterial function, as suggested by the increasing proportion of the five metabolic functions, including translation, ribosomal structure, and biogenesis, with increasing nutrient concentrations. (4) Nutrients explained 10.4% of the variation in bacterial communities, followed by plants (1.4%) and water depth (0.8%). Our findings suggest that eutrophication increased bacterial richness and affected key microbial taxa and functions.\u003c/p\u003e","manuscriptTitle":"Sediment bacterial assemblages inside and outside macrophyte beds in Yunnan Plateau lakes with contrasting nutrient levels and water depths","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 12:30:11","doi":"10.21203/rs.3.rs-6075353/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-17T20:11:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-23T17:25:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-21T04:14:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246911247846864709107022283113528621978","date":"2025-05-13T12:34:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92392890907500878226063132916821548797","date":"2025-05-13T07:43:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-07T00:21:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208851667380270475690494780354796571546","date":"2025-05-01T13:56:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-08T12:15:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172162089612038867237086782880988736061","date":"2025-04-04T05:17:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-31T14:07:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-26T04:31:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-26T04:29:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Environment","date":"2025-02-21T01:56:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Environment](https://www.springer.com/44274/)","snPcode":"44274","submissionUrl":"https://submission.nature.com/new-submission/44274/3","title":"Discover Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d86ff2e4-832a-49a1-91ab-1f4a95c79a30","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-15T11:53:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-21 12:30:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6075353","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6075353","identity":"rs-6075353","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}