Bay Morphology Regulates Local Hydrodynamics and Drives the Structural Differentiation of Microbial Communities in Small Ponds of the littoral zone

Bay Morphology Regulates Local Hydrodynamics and Drives the Structural Differentiation of Microbial Communities in Small Ponds of the littoral zone | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 17 February 2026 V1 Latest version Share on Bay Morphology Regulates Local Hydrodynamics and Drives the Structural Differentiation of Microbial Communities in Small Ponds of the littoral zone Authors : juan yang 0009-0000-2650-7916 , Ziye Liu , Songlin Ye , Qiyao Li , Nan Shao , Yaning Xu , Nan Li , Mingping Gou , Lu Tan , and Shuoran Liu [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177133333.36205530/v1 131 views 57 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Fungi play crucial ecological roles in freshwater ecosystems through participating in material cycling, organic matter decomposition, and water purification. Their community structure is jointly regulated by hydrological connectivity, spatial barriers, seasonal variations, and specific hydrodynamic processes. This study focused on small ponds along the southwest littoral zone of Erhai Lake, Yunnan Province. A total of 50 sampling sites were established, and samples were collected in October 2022 and April 2023. Combined with water environmental physicochemical indicators and high-throughput sequencing data, the response mechanisms of microbial community structure to changes in the water environment were analyzed. The results showed that both the water environment and microbial community structure exhibited significant spatiotemporal heterogeneity, and environmental factors had strong explanatory power for microbial community structure. Site 31, adjacent to a large backwater bay, was controlled by unique local hydrodynamics. Processes such as water retention, wind-driven mixing and sediment resuspension collectively enhanced small-scale environmental heterogeneity, forming an isolation effect similar to an ecological island, which resulted in significant differentiation of microbial community structure between the north and south sides of this site. In addition, similar community differentiation trends were observed in multiple small bays distributed in the littoral zone. This study reveals the composition and distribution of microbial communities in small ponds of the Erhai Lake littoral zone and their comprehensive responses to multi-level ecological processes, highlighting the key role of local hydrodynamic processes in shaping environmental factors and microbial spatial patterns. Bay Morphology Regulates Local Hydrodynamics and Drives the Structural Differentiation of Microbial Communities in Small Ponds of the littoral zone Juan Yang 1 , Ziye Liu 1 , Songlin Ye 1 , Qiyao Li 1 , Nan Shao 1 , Yaning Xu 1 , Nan Li 1 , Mingping Gou 1 , Lu Tan 3,† , Shuoran Liu 1,2,4,* 1 College of Agriculture and Biological Science, Dali University, Dali 671003, Yunnan, P.R. China 2 Co-Innovation Center for Cangshan Mountain and Erhai Lake Integrated Protection and Green Development of Yunnan Province, Dali 671003, Yunnan, P.R. China 3 Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, Hubei, P.R. China 4 Dali University Eryuan Wetland Research Institute, 671203, Yunnan, P.R. China * Corresponding authors (primary) † Co-corresponding author E-mail address: [email protected] (S. Liu); [email protected] (L. Tan) ABSTRACT Fungi play crucial ecological roles in freshwater ecosystems through participating in material cycling, organic matter decomposition, and water purification. Their community structure is jointly regulated by hydrological connectivity, spatial barriers, seasonal variations, and specific hydrodynamic processes. This study focused on small ponds along the southwest littoral zone of Erhai Lake, Yunnan Province. A total of 50 sampling sites were established, and samples were collected in October 2022 and April 2023. Combined with water environmental physicochemical indicators and high-throughput sequencing data, the response mechanisms of microbial community structure to changes in the water environment were analyzed. The results showed that both the water environment and microbial community structure exhibited significant spatiotemporal heterogeneity, and environmental factors had strong explanatory power for microbial community structure. Site 31, adjacent to a large backwater bay, was controlled by unique local hydrodynamics. Processes such as water retention, wind-driven mixing and sediment resuspension collectively enhanced small-scale environmental heterogeneity, forming an isolation effect similar to an ecological island, which resulted in significant differentiation of microbial community structure between the north and south sides of this site. In addition, similar community differentiation trends were observed in multiple small bays distributed in the littoral zone. This study reveals the composition and distribution of microbial communities in small ponds of the Erhai Lake littoral zone and their comprehensive responses to multi-level ecological processes, highlighting the key role of local hydrodynamic processes in shaping environmental factors and microbial spatial patterns. Keywords: Shallow aquatic ecosystems, Seasonal dynamics, Environmental factors, Backwater bay, Microbial community structur 1. Introduction As an important component of freshwater ecosystems, small ponds in the littoral zone are characterized by small size and wide distribution. Their island-like ecological features make them unique ecological units, exhibiting high environmental heterogeneity at the spatial scale (MacArthur and Wilson, 2001). These small water bodies not only exist relatively independently but also maintain close hydrological connections with the surrounding lake, enabling species migration and material exchange through connectivity (Huang et al., 2023). Small ponds adjacent to the littoral are often regarded as lake shore ecological buffers, improving the water quality of the main lake at physical and biochemical levels (Deng et al., 2024), and providing diverse habitats for aquatic organisms, which helps maintain and enhance local biodiversity (Scheffer et al., 2006). With both the autonomy of independent ecological units and the interactivity of hydrological connectivity processes, small ponds in the littoral zone are ideal research carriers for exploring the spatiotemporal response mechanisms between environmental factors and microbial communities. The characteristics and functions of microbial communities in lakes change with the spatiotemporal variations of environmental conditions (Allgaier and Grossart, 2006a; Schweitzer‐Natan et al., 2022), and the interactions between the aquatic environment and aquatic organisms have attracted increasing attention. As a key component of aquatic ecosystems, microorganisms are comprehensively driven by multiple ecological factors, among which environmental heterogeneity and hydrodynamic conditions are the core drivers shaping their community structure. Meanwhile, microorganisms in turn affect the aquatic environment through their metabolic activities, forming an important link of bidirectional feedback in lake ecosystems. Fungi regulate nutrient cycling by decomposing organic matter, thereby influencing the distribution and transformation of carbon, nitrogen and phosphorus in water (Grossart et al., 2019). They can also alter the composition and bioavailability of dissolved organic carbon (DOC) through metabolic activities, regulating the dynamics of microbial communities and primary producers (Jobard et al., 2010). In addition, the presence and decomposition functions of fungi in organic particles and sediments (Wurzbacher et al., 2010) can enhance sediment stability and affect water transparency and suspended solid concentrations. Even in polluted water bodies, fungi exhibit the ability to adsorb, transform, and deposit heavy metals and organic pollutants, holding potential application value in pollution control and water quality remediation (Mustapha and Halimoon, 2015). This bidirectional feedback mechanism indicates that microbial communities are not only responders to changes in the aquatic environment but also remodelers of the aquatic environment through their own metabolism and ecological functions. Seasonal variation is recognized as one of the most direct and persistent external factors driving environmental fluctuations in small water bodies within the littoral zone. By regulating key environmental factors such as water temperature, hydrological processes, and nutrient supply, it exerts a continuous influence on aquatic ecological processes. Microbial communities are highly sensitive to seasonal changes, and the temporal dynamics of their community structure and diversity can directly reflect the coupling relationship between seasonal environmental fluctuations and ecological responses (Kritzberg and Bååth, 2022). Seasonally driven environmental changes largely shape the composition, structure, and diversity of aquatic microbial communities and play a crucial regulatory role (Allgaier and Grossart, 2006b). This pattern has also been verified in different lake systems: a study on Hulun Lake found that the structure of aquatic fungal communities was significantly regulated by seasonally related environmental factors (Shang et al., 2022), while in water bodies on the Qinghai-Tibet Plateau, temperature was identified as one of the key environmental factors affecting changes in fungal community structure (Tian et al., 2017). In addition, a study on shallow sediments in lake areas indicates that variations in environmental heterogeneity, such as temperature and organic matter content, have significantly reshaped the fungal community structure (Yi and Yin, 2024). The distribution of microbial communities is not determined solely by seasonal variation. The connectivity between ponds can also regulate the material exchange and energy flow among water bodies (Heino et al., 2014), leading to relatively consistent environmental characteristics across different water bodies within the same catchment (Taylor et al., 2025). These environmental changes largely shape the distribution pattern and diversity of microorganisms (Crump et al., 2012; Leibold et al., 2004; Lindström and Langenheder, 2011). High connectivity can enhance the consistency of local species composition, thereby reducing community differentiation (Chase and Ryberg, 2004). However, in small water bodies of the littoral zone, such connectivity is often constrained by various ecological dispersal barriers. By limiting the effective connectivity between water bodies, these dispersal barriers further weaken the cross-habitat dispersal capacity of microorganisms, thereby strengthening the environmental filtering effect and exacerbating the spatial differentiation of communities (Tian et al., 2018). In the watershed along the James River in southeastern Virginia, USA, hydrological isolation has enhanced the environmental filtering effect, exerting a significant impact on the spatial distribution of fungal communities (French et al., 2024). In addition, lakes are usually located at the edges of river networks, and their relative isolation enhances dispersal limitation (Huang et al., 2023). Therefore, the assembly process of microbial communities in the littoral zone is jointly regulated by both connectivity and dispersal barriers. Most importantly, the spatial patterns shaped by the combined effects of environmental heterogeneity and dispersal limitation are often further amplified in specific hydrodynamically distinct zones of lakes—particularly in areas with marked hydrodynamic retention characteristics, such as backwater bays, concave coves, wind-sheltered zones, and tributary inlets. These zones typically exhibit reduced flow velocity, prolonged water residence time, and restricted hydrodynamic exchange, which influence material accumulation and biological processes (Li et al., 2020; Strayer and Findlay, 2010). Among these, backwater bays are common geomorphic features in water bodies like lakes (Herwig et al., 2004), formed by the combined effects of shoreline morphology, water flow direction, or tributary inflows, and function as critical ecological heterogeneity islands. Microbial community structure in water bodies is driven by multiple factors, including hydrodynamic changes induced by wind direction and speed (Chen et al., 2022) and nutrient inputs from anthropogenic activities, ultimately leading to unique community structures and functional characteristics. First, wind direction is a key hydrodynamic driver in shallow lakes (Kopasakis et al., 2016). Studies of Lake Taihu demonstrated that wind speed and direction significantly affect water mixing and nutrient resuspension, thereby regulating the relationship between biotic community structure, habitat nutrient distribution, and hydrodynamics. This indirectly indicates that hydrodynamic disturbance alters the spatial distribution of nutrients: nutrients mix more uniformly in high-disturbance zones, while localized accumulation may occur in low-disturbance zones (Wang et al., 2015; Xiao et al., 2024). Second, backwater bays are also vulnerable to anthropogenic disturbances such as agricultural runoff, urban drainage, and tourism activities (Jian et al., 2025). These activities introduce nutrients and pollutants, further altering the physicochemical properties of the water and microbial community structure. A systematic study of urbanized estuarine ecosystems showed that microbial community structure is closely correlated with water nutrient concentrations and pollutant levels, with this correlation being particularly pronounced in highly urbanized water areas (Kaestli et al., 2017). The littoral zone is an ecotone between land and lake; its hydrodynamic characteristics not only directly affect the local water environment and microbial communities but also indirectly influence the small ponds and their water environments in the surrounding area through hydrological exchange processes (Mieczan et al., 2015). In summary, as a key hydrodynamic unit in the littoral zone, backwater bays play a central role in environmental factor regulation and microbial structure shaping. Investigating their local-scale ecological effects helps to comprehensively understand the multi-scale response mechanisms of littoral zone microorganisms. In this study, we investigated the microbial community structure and its environmental driving factors in small ponds within the littoral zone of Erhai Lake, to explore the spatiotemporal variation characteristics of microbial community structure and evaluate the effects of ecological factors (e.g., hydrological connectivity, ecological barriers and hydrodynamic conditions) on microbial diversity and community structure. We hypothesized that: (1) there are significant differences in microbial community structure among different small ponds, and these variations are regulated by seasons; (2) ponds with lower hydrological connectivity and stronger dispersal barriers exhibit more significant differences in microbial community structure; (3) the hydrological dynamics in the backwater bays of the lake may indirectly affect the composition and distribution of microorganisms in adjacent small ponds by altering local environmental conditions. This study aims to improve our understanding of the role of small ponds in lake ecosystems and provide theoretical support for the ecological protection, restoration, and management of the Erhai Lake littoral zone. 2. Study Area and Methods 2.1. Overview of the Study Area and Sampling Sites Setup This study was conducted in the littoral zone of Erhai Lake, located in Dali Bai Autonomous Prefecture, Yunnan Province, China. This area is characterized by a dense distribution of small ponds, which exhibit excellent representativeness and research value. The microbial community structure within these ponds is jointly regulated by multiple ecological factors, making them ideal subjects for investigating ecological processes and mechanisms. A tourism route named the Ecological Corridor runs through the entire sampling area. A total of 50 sampling sites were established on the southwest shore of Erhai Lake. These sites were small ponds with strong spatial continuity and concentrated distribution, located on both sides of the Ecological Corridor (the left side adjacent to buildings and the right side close to the lake) ( Figure 1 ). Among them, Site 1 was located at Caicun Wharf. Sites 1-22 were distributed southward along the littoral zone of Erhai Lake, while Sites 23-50 were distributed northward. The sampling sites also included three regions with hydrologically connected water bodies ( Figure 2 ). Sampling was carried out in autumn (October 2022) and spring (April 2023). All 50 sites were sampled in autumn, but 22 sites could not be re-sampled in spring due to seasonal drying. A total of 78 samples were collected from the two sampling campaigns. 2.2. Sample Collection In each pond, based on the shape of the pond (including irregular shapes), two sampling points with the longest distance difference at the edge of the pond (such as the diagonal vertices of a rectangular pond) and two adjacent sampling points with the shortest distance difference inside or at the edge of the pond were selected. This method aims to cover as many heterogeneous aquatic environments in the small pond as possible (Figure 3) . Thoroughly mix the water samples from each point, collect a total of 500 ml of the mixed sample, add concentrated sulfuric acid on-site to adjust its pH to less than 2, store it at 4℃ in the dark to prevent light from affecting the microorganisms in the water sample, and then transport it back to the laboratory for determination of hydrochemical indices. 2.2.1. Determination of Environmental Factors Field in-situ aquatic environment indicators were measured using a portable multi-parameter water quality analyzer (YSI Professional Plus, USA). The aquatic environment indicators included conductivity (Cond), total dissolved solids (TDS), dissolved oxygen (DO), pH value (pH), water temperature (WT), oxidation-reduction potential (ORP) and salinity (Sal). Laboratory water chemical indicators were determined as follows: total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (CODMn), Nitrate nitrogen (NO 3 - -N+ NO 2 - -N), Ammonia Nitrogen (NH 3 -N), Phosphate (PO 4 3- ), Silicate (Si), Dissolved Organic Carbon (DOC), Nitrate Nitrogen (NO 3 - -N) and Nitrite Nitrogen (NO 2 - -N). All these indices were determined using an automatic flow analyzer (Skalar SAN++, Netherlands). The content of Chlorophyll a (Chla) was determined by an ultraviolet spectrophotometer (UV-5500) (Gundersen et al., 2022; Yates et al., 2022). 2.2.2. Determination of Microbial Diversity and Chlorophyll a Two 100 ml water samples were taken from each sampling site, filtered through a glass fiber filter membrane with a pore size of 1.2 μm, and stored in a -20°C refrigerator. The filtered filter membranes were divided into two groups for treatment:(1) One group of filter membranes was sent to Shanghai Majorbio Bio-pharm Technology Co., Ltd. for high-throughput sequencing of the microbial 16S rDNA (Zhang et al., 2018). (2) The other one was used to detect the chlorophyll a content by spectrophotometry, and the chlorophyll a concentration was calculated using a correction formula (Huang and Cong, 2006). 2.3. Data Processing and Mapping The longitude and latitude data of sampling sites were organized into a table, imported into ArcMap 10.8 under the WGS84 coordinate system, with visualization parameters set, and combined with the base map of Erhai Lake’s riparian zone to intuitively present the spatial distribution pattern of sampling sites. The process for obtaining microbial community OTU data includes the following steps: first, quality control and assembly of the raw sequencing data are performed to remove low-quality sequences and merge them into complete fragments; then further optimization is conducted on the data to filter out potential noise sequences; next, OTU clustering is carried out on the high-quality sequences to generate operational taxonomic units; finally, the representative sequences and abundance table of each OTU are produced for subsequent diversity and community structure analyses. After collating the latitude and longitude data of the sampling sites into a table, the data were imported into ArcMap 10.8 in the WGS84 coordinate system, with visualization parameters configured. Combined with the base map of the Erhai Lake riparian zone, the spatial distribution pattern of the sampling sites was intuitively presented. The cloud tools provided by Majorbio Bioinformatics Technology Co., Ltd. were used to generate microbial clustering heatmaps and bar charts for species difference tests; cluster analysis was performed using a beta diversity distance matrix, and a dendrogram structure was effectively constructed based on the UPGMA algorithm. SPSS Statistics Version 26 was employed to conduct descriptive statistics (including maximum, minimum and Mean±SD) and Z-score standardization (formula: Z=(X-μ)/σ ) for the environment factor data preprocessed in Excel. All subsequent analyses were performed using R language 4.2.3. The α-diversity of microbial communities was assessed using the Chao1 richness index, calculated with the vegan package in R. To reduce the influence of extreme values and make the data more suitable for subsequent statistical tests, the Chao1 values were log(x+1) transformed. Differences in Chao1 richness among groups were evaluated for overall significance using the non-parametric Kruskal–Wallis test, followed by pairwise comparisons with the Wilcoxon rank-sum test. Data visualization was performed using ggplot2 and ggpubr to generate boxplots with significance levels indicated (Schloss, 2024). After importing the standardized environmental factor data, Redundancy Analysis (RDA) was conducted via the rda function in the vegan package, with customized visualization implemented using the ggplot2 package. Principal Component Analysis (PCA) based on Euclidean distance was performed to analyze the driving forces of environmental variables among sampling sites. R programme (version 4.2.3) with the rstatix package was employed for Kolmogorov-Smirnov (K-S) and Shapiro-Wilk tests to check the normality of the data. If the data were normally distributed, independent-samples t-tests were applied; if not, non-parametric tests (Kruskal-Wallis test) were used to analyze differences between datasets. Levene’s test for homogeneity of variance was performed, showing significant differences in distribution characteristics between different factors across different groups. Specifically, for variables that meet normality and homogeneity of variance, one-way analysis of variance (ANOVA) was used; for variables that meet normality but not homogeneity of variance, Welch ANOVA was applied; and for non-normal variables, non-parametric tests (Kruskal-Wallis test) were used. To eliminate the interference of extreme values on the analysis results, this study used the box plot method to identify and eliminate outliers in the temperature (WT) and dissolved oxygen (DO) data. Specifically, based on the Interquartile Range (IQR), values lower than the first quartile (Q1) minus 1.5 times IQR or higher than the third quartile (Q3) plus 1.5 times IQR were defined as outliers (Karim et al., 2025).In actual processing, 1 outlier was identified and eliminated from the temperature data (accounting for 1.3% of the total temperature records), and 5 outliers were identified and eliminated from the dissolved oxygen data (accounting for 6.5% of the total dissolved oxygen records). The above methods were used to complete the description and significance analysis of environmental factors. The correlation between environmental factors and biological communities was first calculated by Detrended Correspondence Analysis (DCA), used to calculate the gradient length (axis length) of community species composition. If the axis length is <4, linear models Redundancy Analysis (RDA) are chosen; if the axis length is ≥4, non-linear models Canonical Correspondence Analysis (CCA) are chosen. For the comparisons between spring and autumn, as well as between connected and non-connected groups, the length of the first axis was 3.6367 (close to 4), so CCA was selected; for the groups on both sides of the ecological corridor, the length of the first axis was 3.3068 (< 4), thus RDA was adopted to analyze the quantitative relationship between environmental factors and community structure based on a linear model. During the preparation of this manuscript, we used ChatGPT to generate the preliminary figures for Figure 2 , Figure 3 and Figure 7 (d), and independently added additional content. We have reviewed and edited the figures as necessary and take full responsibility for the published work. 3. Results and Analysis 3.1. Characteristics of Environmental Factors 3.1.1. Characteristics of Environmental Factors in Different Seasons Of the 50 small ponds sampled in autumn, 4 had improperly preserved water samples, and complete environmental indicators were ultimately determined for 46 samples. Statistical results of environmental factors in spring and autumn are shown in Table 1 . Significant seasonal differences were observed in DO, pH, ORP and Chla (p = 0.000 for the first three factors, p = 0.003 for Chla). The average water temperature of autumn samples (21.54℃) was higher than that of spring, and the average concentration of PO 4 3- was also higher in autumn, indicating a higher degree of eutrophication in the water during this period. In the difference analysis of environmental factors with Sample 31 as the boundary dividing the study area into northern and southern groups, the results showed that NO 2 - -N, Si and CODMn exhibited significant differences (p = 0.022, 0.035, 0.026), while Sal, WT, TDS and pH displayed extremely significant differences (p = 0.000 for the first three factors, p = 0.010 for pH). Figure 4a shows the PCA results of environmental factors at the sampling sites in spring and autumn, with the first axis (PC1) and the second axis (PC2) together explaining 53.1% of the variation in environmental factors. In the biplot, autumn samples are mainly distributed in the positive direction of PC1, associated with factors including TP, PO 4 3- , DOC, CODMn, NH 3 -N and Chla, indicating that these nutrient and organic matter indicators are the dominant variables for autumn samples. In contrast, spring samples are relatively concentrated in the plot and show a strong correlation with WT, Cond and NO 3 - -N+ NO 2 - -N, reflecting a more stable physical environment of the water body in spring. 3.1.2. Influence of Connectivity and Ecological Dispersal Barriers on Environmental Factors in Autumn and Spring To explore the effects of connectivity relationships on the aquatic environment, the small ponds in the study area were divided into two groups, connected (Y) and disconnected (N), based on their hydrological connectivity status. Figure 4b presents the PCA results of environmental factors for the two groups of sampling sites across different seasons, with the first axis (PC1) and the second axis (PC2) together explaining 52.3% of the variation in environmental factors. Non-connected group samples are widely distributed along PC1, with some strongly driven by variables such as TP, PO 4 3- , CODMn, DOC and NH 3 -N, indicating higher environmental heterogeneity. In contrast, connected group samples are more concentrated overall, reflecting smaller differences among their environmental variables. Regarding variable distribution, connected group samples show relatively weak correlations with environmental factors, while non-connected group samples exhibit stronger positive correlations with multiple nutrients (TP, PO 4 3- and NH 3 -N) and organic matter (DOC, CODMn). Figure 4c displays the PCA biplot of aquatic environmental factors at sampling sites on both sides of the ecological corridor in spring. The first axis (PC1) and the second axis (PC2) together explain 56.0% of the variation in environmental factors, which can well reflect the differences in the aquatic environment between sampling sites on both sides of the ecological corridor in spring. Samples on both sides of the ecological corridor in spring show a certain separation trend in the PCA plot: samples on the left are strongly correlated with environmental factors such as ORP, NO 2 - -N, DOC and Si, while samples on the right are more closely associated with factors including Sal, TDS, Cond, TN, WT and NO 3 - -N. 3.2. Spatiotemporal Characteristics of Microbial Diversity and Community Structure 3.2.1. Results of Microbial Sequencing The species statistics of high-throughput sequencing were as follows: Domain: 1, Kingdom: 1, Phylum: 15, Class: 60, Order: 160, Family: 410, Genus: 1017, Species: 1928. The results of this study will be analyzed at the Operational Taxonomic Unit (OTU) resolution. After screening, the identifiable species statistics were as follows: Family: 294, Genus: 697, OTU: 1028. 3.2.2. Characteristics of Microbial species richness To explore the effects of factors such as seasonality, hydrological connectivity between water bodies, and ecological dispersal barriers on the richness of microbial communities, boxplots were generated based on the Chao1 index ( Figure 5 ). The Chao1 index ranged from 90.67 to 432.91 across autumn and spring samples, 73.00 to 432.91 between connected and non-connected groups, 285.69 to 605.67 between left and right sides of the ecological corridor. The Chao1 index of autumn samples was significantly higher than that of spring samples, with a statistically significant difference between the two groups (p = 0.0001586, ***). In addition, no significant differences were observed between connected and non-connected groups (p = 0.077, ns), nor between the right and left sides of the ecological corridor (p = 0.118, ns). 3.3. Microbial Community Structure Characteristics and Environmental Driving Factors 3.3.1. Effects of Seasonal Variation on Microbial Communities To investigate the relationship between biotic community structure and environmental factors across different seasons, canonical correspondence analysis (CCA) was performed on the microbial communities and environmental factors of the two seasons (A: autumn, S: spring), with the results presented in Figure 6a . The ordination results showed that CCA1 and CCA2 together explained 13.7% of the total variation, and the microbial communities of autumn and spring barely overlapped in the CCA ordination plot. In general, DO, pH, TDS, Cond, ORP, Si and WT were the key factors significantly affecting microbial community structure: Cond, DO and pH had a greater impact on the spring microbial community composition, while TDS, Si and WT exerted a stronger influence on the autumn community. These results indicate significant seasonal differences in microbial community structure, which are jointly driven by multiple physicochemical factors, among which DO, ORP and nutrients are the key environmental variables. 3.3.2. Effects of Hydrological Connectivity and Dispersal Barriers on Microbial Communities The results for different fungal species in the connected and disconnected groups are shown in Figure 6b. In the connected group, Rachicladosporium sp., Sporobolomyces phaffii, Dioszegia sp. and Paraconiothyrium brasiliense were significantly increased (P<0.05). Epicoccum sorghinum , Spegazzinia sp., Sporidiobolus pararoseus , Curvularia hominis and Periconia sp. showed significance at P < 0.01, indicating that the abundances of these species were significantly increased under connected conditions. In contrast, in the non-connected group, Sarocladium summerbellii and Vishniacozyma carnescens were significant at P<0.05, while Sampaiozyma sp., Cladosporium delicatulatum, Cadophora luteo-olivacea and Cladosporium halotolerans reached significance at P < 0.01. These results demonstrate significant differences, suggesting higher abundances of these species in non-connected environments. Hydrological connectivity shapes distinctly different fungal community structures by altering water physicochemical properties or microbial dispersal processes. Grouping samples by their different hydrological connectivity statuses, we revealed the effects of hydrological connectivity on environmental factors and microbial community structure through CCA ( Figure 6c ). The first and second CCA axes collectively explain 13.93% of the community variation (CCA1: 8.67%, CCA2: 5.26%). Samples show a certain grouping trend in the ordination plot: factors such as DO, pH, Cond, TDS and Sal are negatively correlated with CCA1, which mainly drive the distribution of connected group samples. In contrast, factors including TP, PO 4 ³⁻ and ORP are positively correlated with CCA1, influencing the community characteristics of the non-connected group. RDA was conducted to explore the relationship between environmental factors and microbial communities on both sides of the ecological corridor in spring and autumn ( Figure 6d ). To eliminate the influence of hydrological connectivity on community structure, interconnected sampling sites were excluded from the analysis. The first and second axes of the RDA explained 41.12% and 9.75% of the community variation, respectively, with a total explanatory rate of 50.87%. Samples showed a certain grouping trend in spatial distribution: some left-side samples (e.g. L46, L42, L40, L39) were concentrated in the upper right quadrant of the plot, while right-side samples (e.g. R22, R22, R18) were mainly distributed in the lower left quadrant, indicating that the ecological corridor partially affected the structural composition of microbial communities. Nutrients and physicochemical parameters such as NO 2 - , Cond, pH, DO, ORP and WT were significantly correlated with changes in community structure, among which pH, DO and Cond exerted a stronger influence on the community composition of left-side samples. 3.3.3. Effects of Hydrological Dynamics in Backwater Bays on Microbial Communities Figure 7a , 7b and 7c shows the hierarchical clustering trees at the OTU level. Among these, Figure 7a was grouped according to different seasons, revealing the clustering relationships and compositional differences of microbial communities between spring and autumn. The dendrogram illustrates the similarity among different samples, with different colors marking samples from different seasons (red for spring and blue for autumn). It was found that in samples north of Sample 31, Filobasidium sp . and Rhodotorula babjevae had higher abundances in both spring and autumn. In contrast, species distribution was more uniform in samples south of Sample 31: Cladosporium delicatulatum and Naganishia sp . were more abundant in spring, while Cystobasidium slooffiae, Dokmaia monthadangi and Cryptococcus unigultulatus had higher abundances in autumn. Field investigations revealed that the obvious north-south distribution discontinuity of microbial community structure at sampling site No. 31 was mainly attributed to the abrupt hydrological environment transition in this area. Sampling site No. 31 is adjacent to the backwater bay zone in the riparian zone of Erhai Lake ( Figure 7d ), and its hydrodynamic characteristics are significantly different from those of the surrounding areas. The circulation direction shown in this figure refers to the research results of (Ma et al., 2021) on the flow direction in this region To further explore whether this pattern is consistent across the same seasons, we divided the sampling sites into northern and southern groups using site No. 31 as the boundary, with separate analyses conducted for spring and autumn (corresponding to Figure 7b and Figure 7c ; where N denotes north and S denotes south). The results showed that the aforementioned pattern remained valid in both seasons. 4. Discussion 4.1. Responses of the Environment, Microbial Community, and Their Interactions to Seasonal Changes This study revealed that both the water environment and microbial community of small ponds in the littoral zone exhibit high sensitivity to seasonal changes, indicating that seasonal climate and hydrological conditions collectively shape community diversity and structural patterns by influencing nutrient dynamics, allochthonous microbial inputs, and resource availability. Differential analysis showed that PO 4 3- , TN and TP in the water displayed significant seasonal dynamics in autumn, confirming that time is a core driver regulating the evolution of the littoral zone’s water environment. The average temperature in autumn (21.54℃) was higher than that in spring (20.50℃). Elevated temperatures can promote algal growth, and the increase in algal biomass impairs the water’s self-purification capacity, leading to varying degrees of water pollution (Wu et al., 2007). Rainfall in the study area is mainly concentrated in autumn, and the concentrations of water quality indicators such as NH 3 -N, TN and TP increased significantly during this season (Wang et al., 2023). The diversity and structure of the microbial community showed seasonal variations consistent with the water environment, with species diversity in autumn being significantly higher than that in spring (p = 0.0001586, ***). Temperature differences between seasons can also affect microbial community structure (Cruaud et al., 2019; Fang et al., 2023). 4.2. Water Connectivity and Ecological Barriers Regulate the Environmental Status and Microbial Community of Small Ponds Water connectivity dominates water dilution capacity and material exchange intensity, thereby further influencing the environmental status of small ponds. Connected ponds exhibit more stable hydrological conditions and lower concentrations of nutrients (TN, TP). In contrast, isolated ponds tend to form closed systems; affected by endogenous inputs and evaporative concentration effects, they have higher levels of Cond and TP. This environmental heterogeneity may provide distinct habitats for microbial communities. Connectivity can increase allochthonous nutrient inputs, promote nitrogen and phosphorus cycling as well as water exchange (Wolf et al., 2013), reduce nutrient accumulation and water quality degradation, and enhance ecosystem stability. Although the difference in Chao1 richness index between connected and isolated ponds was not statistically significant (p = 0.077), the microbial richness in the connected group was relatively higher, indicating that hydrological connectivity can facilitate microbial dispersal and community reconstruction. In highly connected environments, intensified nitrogen pollution and habitat disturbance indirectly reduce microbial diversity and lead to community homogenization (Chen et al., 2025). Results from differential tests and CCA analysis showed that water connectivity is involved in regulating microbial community patterns. It promotes community merging, enhances competitiveness and coordinated succession, accelerates responses to disturbances or nutrient inputs, and thereby influences community assembly pathways (Pan et al., 2022). In contrast, ecological barriers (e.g., physical structures such as roads and dams) indirectly affect microbial community structure by restricting material exchange between water bodies and altering local water environmental characteristics. In this study, there were significant statistical differences in indicators such as pH and Cond between the two sides of the ecological corridor, indicating that ecological barriers can change the physicochemical stability of local water bodies. However, the difference in Chao1 richness index between sampling points on both sides of the corridor was not statistically significant (p = 0.118), suggesting that physical barriers did not directly impair microbial dispersal capacity. Microbes possess strong dispersal ability and ecological plasticity, and can cross physical barriers through various pathways such as rainwater runoff (Williamson et al., 2014), groundwater exchange, animal vectors, or wind dispersal, enabling continuous population communication and spread. Therefore, in this study, ecological barriers mainly functioned to regulate the physicochemical conditions of water bodies rather than directly blocking microbial dispersal; the spatial variation of microbial communities was still primarily driven by the environmental heterogeneity of the water bodies themselves, rather than the existence of physical barriers. 4.3. Small-Scale Community Differentiation Driven by Local Hydrodynamic Heterogeneity Taking sampling site 31 (a small pond adjacent to the backwater bay in the Erhai Lake littoral zone) as the boundary, extremely significant differences were observed in indicators such as Sal, WT, TDS and pH between its northern and southern sides. Meanwhile, the microbial community structure also showed obvious differentiation, reflecting the strong shaping effect of local microhabitat conditions on the environment and microbial communities. The community composition of sampling sites south of site 31 was relatively stable without significant dominant species: Cladorsporium delicatulum and Naganishia sp. were more abundant in spring, while Cystobasidium slooffiae, Dokmaia monthadangi and Cryptococcus unigultulatus had higher abundances in autumn. In contrast, Filobasidium sp. and Rhodotorula babjevae dominated the spring and autumn samples of northern sites. The formation mechanisms of these differences will be elaborated in the following sections from the perspectives of hydrodynamic characteristics, wind effects, and potential human activities. The unique hydrodynamic pattern of the backwater bay exerted a significant impact on the local water environment and microbial community structure, and indirectly affected adjacent small ponds through hydrological exchange processes. Its curved shoreline and restricted flow channels significantly reduce water velocity after entering the bay, easily forming local stagnant water or reverse backflow. This impairs the renewal capacity of local and surrounding water bodies, leading to the accumulation of pollutants and nutrients within small scales that are difficult to disperse. Additionally, the fungal community composition is closely related to water physicochemical indicators (such as nitrogen, phosphorus, and dissolved oxygen) (Siriarchawatana et al., 2024). Oxygen deficiency in littoral sediments can drive changes in rare and moderately abundant microbial communities (Sinkko et al., 2019), and various trophic functions of fungi are also affected by water quality conditions (Chen et al., 2018). Functional prediction studies have shown that Filobasidium sp. in the Basidiomycota is a saprotrophic taxon in the fungal community, with strong organic matter degradation capabilities and tolerance to oxygen concentration fluctuations. It has a survival advantage in organic matter-rich environments, thus often becoming a dominant group in polluted or eutrophic waters (Zheng et al., 2021). As a cosmopolitan fungal species, Cladorsporium delicatulum can also rapidly reproduce to form a dominant community under eutrophic and low-disturbance conditions (Bensch et al., 2012). On this basis, wind effects further amplified the inherent hydrodynamic differences in the backwater bay area. The hydrodynamics of shallow lakes are highly wind-driven, which can enhance vertical water mixing (Cao et al., 2006), alter thermal stratification and nutrient resuspension processes, thereby forming multiple small-scale ecological islands and further strengthening the spatial differentiation of microbial communities (Ferrari et al., 2015). Moreover, the hydrodynamic characteristics of the backwater bay indirectly affect the water environment and microbial community of the adjacent small pond (site 31) through hydrological exchange. In addition, potential anthropogenic disturbances in this area have exacerbated the formation of this spatial heterogeneity. Due to its geographical location and flow characteristics, the region around site 31 often serves as a convergence point for agricultural runoff and emissions from tourism activities, leading to the local enrichment of nutrients such as nitrogen and phosphorus, which significantly affects the functional structure of microbial communities. In addition to the large backwater bay where site 31 is located, the studied littoral zone also distributes multiple smaller bays. Comprehensive analysis combining CCA, RDA and community clustering results revealed that in some small bays, sampling sites on the northern and southern sides belong to different major branches or distinctly different sub-branches on the systematic clustering tree, showing similar community differentiation characteristics to large backwater bays. This indicates that the ecological boundary effect is not unique to large bays; small bays can also form relatively independent microenvironments under specific topographic and hydrodynamic conditions. Spatial separation caused by local hydrodynamic structures can affect microbial community composition at a smaller scale. In summary, the special hydrodynamic environment formed by backwater bays and the accompanying nutrient accumulation process break the spatial continuity of water bodies in the littoral zone, thereby leading to obvious differentiation of microbial community structures in small ponds on the northern and southern sides of the bay. This suggests that local hydrodynamic units such as backwater bays are not only important areas for water material retention but also key nodes driving the spatial heterogeneity of microbial communities, which have important indicative significance for watershed ecological management and water quality monitoring. 5. Conclusions The results showed that seasonal change, as a core driving factor, significantly regulated water physicochemical parameters and community structure differentiation. Specifically, the DO concentration in water was significantly lower in autumn than in spring (P<0.01), while the concentrations of nutrients, water temperature, and other indicators were relatively higher, and seasonal changes significantly affected community structure. Hydrological connectivity altered water physicochemical conditions or microbial dispersal processes, resulting in lower nutrient concentrations in highly connected water bodies. Additionally, the abundance of specific species was higher under disconnected conditions, shaping distinctly different community structures. Ecological barriers restricted the dispersal of water nutrients and microorganisms. As an important local hydrodynamic unit in the littoral zone, backwater bays could reshape the microbial community structures on their northern and southern sides by regulating water exchange intensity and nutrient distribution. Beyond the large backwater bay where sampling site 31 is located, similar community differentiation phenomena were also observed on both sides of multiple small bays in the study area. This study deepens the understanding of the formation mechanisms of microbial communities in small ponds of the littoral zone and provides a scientific basis for the ecological protection and management of small water bodies in the Erhai Lake littoral zone. Acknowledgements This research was funded by the Yunnan provincial “Xing Dian Talent Support Program” [XDYCQNRC-2022-0040]; the National Natural Science Foundation Program of P.R. China [31760126, 31960255]; and the Development Funding of Dali University [FZ2025ZX011]. We also thank Shanghai Majorbio Bio-pharm Technology Co., Ltd. for providing technical assistance with high-throughput sequencing and data processing. Author contributions Conceptualisation: S.R.L. Developing methods: S.R.L., Z.Y.L., S.L.Y., Q.Y.L. Conducting the research: J.Y., Z.Y.L., S.L.Y., Q.Y.L., N.S., Y.N.X., N.L., M.P.G.Data analysis, Data interpretation, Preparation of figures and tables: J.Y.Writing:J.Y., Z.Y.L., S.L.Y., Q.Y.L., N.S., Y.N.X., N.L., M.P.G., S.R.L., L.T. Conflict of interest We declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data Availability Statement All the required data are uploaded as supplementary material. References Allgaier, M., and Grossart, H. P. (2006). Seasonal dynamics and phylogenetic diversity of free-living and particle-associated bacterial communities in four lakes in northeastern Germany. Aquatic Microbial Ecology , 45(2), 115–128. doi:10.3354/ame045115Bensch, K., Braun, U., Groenewald, J. Z., and Crous, P. W. (2012). The genus Cladosporium. Studies in Mycology , 72, 1-401. doi:10.3114/sim0003Cao, H., Kong, F., Luo, L., Shi, X., Yang, Z., Zhang, X., and Tao, Y. (2006). Effects of Wind and Wind-Induced Waves on Vertical Phytoplankton Distribution and Surface Blooms of Microcystis aeruginosa in Lake Taihu. Journal of Freshwater Ecology , 21, 231-238. doi:10.1080/02705060.2006.9664991Chase, J. M., and Ryberg, W. A. (2004). Connectivity, scale‐dependence, and the productivity–diversity relationship. Ecology Letters , 7, 676-683. doi:10.1111/j.1461-0248.2004.00622.xChen, H., Zhang, L., Zheng, Z., Gao, Y., and Zhao, Y. (2025). Hydrological connectivity shape the nitrogen pollution sources and microbial community structure in a river-lake connected system. Frontiers in Microbiology , 16, doi:10.3389/fmicb.2025.1563578Chen, Y. J., Leung, P. M., Cook, P. L. M., Wong, W. W., Hutchinson, T., Eate, V., . . . Greening, C. (2022). Hydrodynamic disturbance controls microbial community assembly and biogeochemical processes in coastal sediments. The ISME Journal , 16, 750-763. doi:10.1038/s41396-021-01111-9Chen, Z., Yuan, J., Sun, F., Zhang, F., Chen, Y., Ding, C., . . . Yao, L. (2018). Planktonic fungal community structures and their relationship to water quality in the Danjiangkou Reservoir, China. Scientific Reports , 8, 10596. doi:10.1038/s41598-018-28903-yCruaud, P., Vigneron, A., Fradette, M. S., Dorea, C. C., Culley, A. I., Rodriguez, M. J., and Charette, S. J. (2019). Annual bacterial community cycle in a seasonally ice‐covered river reflects environmental and climatic conditions. Limnology and Oceanography , 65, s21-s37. doi:10.1002/lno.11130Crump, B. C., Amaral-Zettler, L. A., and Kling, G. W. (2012). Microbial diversity in arctic freshwaters is structured by inoculation of microbes from soils. The ISME Journal , 6, 1629-1639. doi:10.1038/ismej.2012.9Deng, Z., Chen, Y., Jiang, F., Zhang, Y., Xie, Z., Zhang, Y., and Zhao, L. (2024). Spatio-temporal study on coupling coordination between urbanization and eco-resilience in the Erhai Lake Basin. All Earth , 36(1), 1-20. doi:10.1080/27669645.2024.2317053Fang, W., Fan, T., Wang, S., Yu, X., Lu, A., Wang, X., . . . Zhang, L. (2023). Seasonal changes driving shifts in microbial community assembly and species coexistence in an urban river. Science of The Total Environment , 905, doi:10.1016/j.scitotenv.2023.167027Ferrari, B. C., Bissett, A., Snape, I., van Dorst, J., Palmer, A. S., Ji, M., . . . Brown, M. V. (2015). Geological connectivity drives microbial community structure and connectivity in polar, terrestrial ecosystems. Environmental Microbiology , 18(6), 1834-1849. doi:10.1111/1462-2920.13034French, L. C., Jusino, M. A., Chambers, R. M., and Skelton, J. (2024). Community structure and functional diversity of aquatic fungi are correlated with water quality: Insights from multi‐marker analysis of environmental DNA in a coastal watershed. Environmental DNA , 6(3), doi:10.1002/edn3.576Grossart, H. P., Silke, V. d. W., Kagami, M., Wurzbacher, C., and Cunliffe, M. (2019). Fungi in aquatic ecosystems. Nature Reviews Microbiology , 17(6), 339–354. doi:10.1038/s41579-019-0175-8Gundersen, K., Møgster, J. S., Lien, V. S., Ershova, E., Lunde, L. F., Arnesen, H., and Olsen, A.-K. (2022). Thirty Years of Nutrient Biogeochemistry in the Barents Sea and the adjoining Arctic Ocean, 1990–2019. Scientific Data , 9, 649. doi:10.1038/s41597-022-01781-wHeino, J., Melo, A. S., Siqueira, T., Soininen, J., Valanko, S., and Bini, L. M. (2014). Metacommunity organisation, spatial extent and dispersal in aquatic systems: patterns, processes and prospects. Freshwater Biology , 60, 845-869. doi:10.1111/fwb.12533Herwig, B. R., Soluk, D. A., Dettmers, J. M., and Wahl, D. H. (2004). Trophic structure and energy flow in backwater lakes of two large floodplain rivers assessed using stable isotopes. Transactions of the American Fisheries Society , 61(1), 130-140. doi:10.1139/f03-164Huang, T., and Cong, H. (2006). A New Method for Determination of Chlorophylls in Freshwater Algae. Environmental Monitoring and Assessment , 129, 1-7. doi:10.1007/s10661-006-9419-yHuang, Z., Pan, B., Soininen, J., Hou, Y., and Liu, X. (2023). Seasonal variation of phytoplankton community assembly processes in Tibetan Plateau floodplain. Frontiers in Microbiology , 14, doi:10.3389/fmicb.2023.1122838Jian, C., Wang, X., Liu, T., Zhang, Y., Wang, L., Chen, H., . . . He, Y. (2025). Nutrient accumulation and eutrophication restricted CO2 emissions in the two backwater bays in the Three Gorges Reservoir (TGR), China. Journal of Environmental Sciences , 160, 656–669. doi:10.1016/j.jes.2025.04.043Jobard, M. n., Rasconi, S., and Sime-Ngando, T. l. (2010). Diversity and functions of microscopic fungi: a missing component in pelagic food webs. Aquatic Sciences , 72, 255-268. doi:10.1007/s00027-010-0133-zKaestli, M., Skillington, A., Kennedy, K., Majid, M., Williams, D., McGuinness, K., . . . Gibb, K. (2017). Spatial and Temporal Microbial Patterns in a Tropical Macrotidal Estuary Subject to Urbanization. Frontiers in Microbiology , 8, doi:10.3389/fmicb.2017.01313Karim, M. R., Syeed, M. M. M., Rahman, A., Ayaz Rabbani, K., Fatema, K., Khan, R. H., . . . Uddin, M. F. (2025). A Comprehensive Dataset of Surface Water Quality Spanning 1940-2023 for Empirical and ML Adopted Research. Scientific Data , 12, 391. doi:10.1038/s41597-025-04715-4Kopasakis, K., Laspidou, C., Spiliotopoulos, M., Kofinas, D., and Mellios, N. (2016). Numerical Investigation of Wind Driven Circulation and Horizontal Dispersion in the Surface Layer of a Re-flooded Shallow Lake. Environmental Processes , 3, 693-710. doi:10.1007/s40710-016-0165-zKritzberg, E., and Bååth, E. (2022). Seasonal variation in temperature sensitivity of bacterial growth in a temperate soil and lake. FEMS Microbiology Ecology , 98(10), 1-9. doi:10.1093/femsec/fiac111Leibold, M. A., Holyoak, M., Mouquet, N., Amarasekare, P., Chase, J. M., Hoopes, M. F., . . . Gonzalez, A. (2004). The metacommunity concept: a framework for multi‐scale community ecology. Ecology Letters , 7, 601-613. doi:10.1111/j.1461-0248.2004.00608.xLi, X., Liu, B., Wang, Y., Yang, Y., Liang, R., Peng, F., . . . Li, K. (2020). Hydrodynamic and environmental characteristics of a tributary bay influenced by backwater jacking and intrusions from a main reservoir. Hydrology and Earth System Sciences , 24, 5057–5076. doi:10.5194/hess-24-5057-2020Lindström, E. S., and Langenheder, S. (2011). Local and regional factors influencing bacterial community assembly. Environmental Microbiology Reports , 4(1), 1-9. doi:10.1111/j.1758-2229.2011.00257.xMa, W., Zhou, Y., Su, J., Jiang, R., and Hu, X. (2021). Study on the hydrodynamic characteristics and transportation and diffusion of pollutants in Erhai Lake. Journal of China Institute of Water Resources and Hydropower Research , 19(3), doi:10.5846 /stxb201905171019 (in Chinese)MacArthur, R. H. & Wilson, E. O. (2001). The theory of island biogeography (1st ed.). Princeton, NJ, USA: Princeton University Press.Mieczan, T., Adamczuk, M., Tarkowska-Kukuryk, M., and Nawrot, D. (2015). Effect of water chemistry on zooplanktonic and microbial communities across freshwater ecotones in different macrophyte-dominated shallow lakes. Journal of Limnology , 75(2), 262-274. doi:10.4081/jlimnol.2015.1338Mustapha, M. U., and Halimoon, N. (2015). Microorganisms and Biosorption of Heavy Metals in the Environment: A Review Paper. Journal of Microbial & Biochemical Technology , 7(5), 253-256. doi:10.4172/1948-5948.1000219Pan, B., Liu, X., Chen, Q., Sun, H., Zhao, X., and Huang, Z. (2022). Hydrological connectivity promotes coalescence of bacterial communities in a floodplain. Frontiers in Microbiology , 13, doi:10.3389/fmicb.2022.971437Scheffer, M., Van Geest, G. J., Zimmer, K., Jeppesen, E., Søndergaard, M., Butler, M. G., and De Meester, A. L. (2006). Small habitat size and isolation can promote species richness: second-order effects on biodiversity in shallow lakes and ponds. Oikos , 112(1), doi:10.1111/j.0030-1299.2006.14145.xSchloss, P. D. (2024). Waste not, want not: revisiting the analysis that called into question the practice of rarefaction. mSphere , 9(1), 1-22. doi:10.1128/msphere.00355-23Schweitzer‐Natan, O., Ofek‐Lalzar, M., Sher, D., and Sukenik, A. (2022). The microbial community spatially varies during a Microcystis bloom event in Lake Kinneret. Freshwater Biology , 68, 349-363. doi:10.1111/fwb.14030Shang, Y., Wu, X., Wang, X., Dou, H., Wei, Q., Ma, S., . . . Zhang, H. (2022). Environmental factors and stochasticity affect the fungal community structures in the water and sediments of Hulun Lake, China. Ecology and Evolution , 12(22), e9510. doi:10.1002/ece3.9510Sinkko, H., Hepolehto, I., Lyra, C., Rinta-Kanto, J. M., Villnäs, A., Norkko, J., . . . Timonen, S. (2019). Increasing oxygen deficiency changes rare and moderately abundant bacterial communities in coastal soft sediments. Scientific Reports , 9, 16341. doi:10.1038/s41598-019-51432-1Siriarchawatana, P., Harnpicharnchai, P., Phithakrotchanakoon, C., Kitikhun, S., Mayteeworakoon, S., Chunhametha, S., . . . Ingsriswang, S. (2024). Fungal communities as dual indicators of river biodiversity and water quality assessment. Water Research , 253, 121252. doi:10.1016/j.watres.2024.121252Strayer, D. L., and Findlay, S. E. G. (2010). Ecology of freshwater shore zones. Aquatic Sciences , 72(2), 127–163. doi:10.1007/s00027-010-0128-9Taylor, P., Carvalho, L., Chapman, D., Law, A., Miller, C., Scott, M., . . . Willby, N. (2025). Understanding the hydrological and landscape connectivity of lakes. Landscape Ecology , 40, 140. doi:10.1007/s10980-025-02153-6Tian, J., Wu, B., Liu, X., Chen, H., Jiang, N., and Kang, X. (2017). Patterns and drivers of fungal diversity along an altitudinal gradient on Mount Gongga, China. J Soils Sediments , 17, 2856–2865. doi:10.1007/s11368-017-1701-9Tian, J., Zhu, D., Wang, J., Wu, B., Hussain, M., and Liu, X. (2018). Environmental factors driving fungal distribution in freshwater lake sediments across the Headwater Region of the Yellow River, China. Scientific Reports , 8, 3768. doi:10.1038/s41598-018-21995-6Wang, H., Liu, X., Wang, Y., Zhang, S., Zhang, G., Han, Y., . . . Liu, L. (2023). Spatial and temporal dynamics of microbial community composition and factors influencing the surface water and sediments of urban rivers. Journal of Environmental Sciences , 124, 187-197. doi:10.1016/j.jes.2021.10.016Wang, J., Pang, Y., Li, Y., Huang, Y., and Luo, J. (2015). Experimental study of wind-induced sediment suspension and nutrient release in Meiliang Bay of Lake Taihu, China. Environmental Science and Pollution Research , doi:10.1007/s11356-015-4247-7Williamson, K. E., Harris, J. V., Green, J. C., Rahman, F., and Chambers, R. M. (2014). Stormwater runoff drives viral community composition changes in inland freshwaters. Frontiers in Microbiology , 14(5), 105. doi:10.3389/fmicb.2014.00105Wolf, K. L., Noe, G. B., and Ahn, C. (2013). Hydrologic Connectivity to Streams Increases Nitrogen and Phosphorus Inputs and Cycling in Soils of Created and Natural Floodplain Wetlands. Journal of Environmental Quality , 42, 1245-1255. doi:10.2134/jeq2012.0466Wu, X., Xi, W., Ye, W., and Yang, H. (2007). Bacterial community composition of a shallow hypertrophic freshwater lake in China, revealed by 16S rRNA gene sequences. FEMS Microbiology Ecology , 61, 85-96. doi:10.1111/j.1574-6941.2007.00326.xWurzbacher, C. M., Bärlocher, F., and Grossart, H. P. (2010). Fungi in lake ecosystems. Aquatic Microbial Ecology , 59, 125-149. doi:10.3354/ame01385Xiao, P., Wu, Y., Zuo, J., Grossart, H.-P., Sun, R., Li, G., . . . Li, R. (2024). Differential microbiome features in lake–river systems of Taihu basin in response to water flow disturbance. Frontiers in Microbiology , 15, doi:10.3389/fmicb.2024.1479158Yates, C. A., Johnes, P. J., Brailsford, F. L., Evans, C. D., Evershed, R. P., Glanville, H. C., . . . Owen, A. T. (2022). Determining patterns in the composition of dissolved organic matter in fresh waters according to land use and management. Biogeochemistry , 164(1), 143-162. doi:10.1007/s10533-022-00964-2Yi, Y., and Yin, S. (2024). Seasonal Variations of Sediment Fungal Community of a Shallow Lake in North China. Microorganisms , 12(11), 2127. doi:10.3390/microorganisms12112127Zhang, H., Wang, Y., Chen, S., Zhao, Z., Feng, J., Zhang, Z., . . . Jia, J. (2018). Water Bacterial and Fungal Community Compositions Associated with Urban Lakes, Xi’an, China. International Journal of Environmental Research and Public Health , 15, doi:10.3390/ijerph15030469Zheng, B., Wang, X., Li, Y., Chen, Y., Li, B., Li, Y., and Chen, Z. (2021). Community Structure，Function，and Influencing Factors of Planktonic Fungi in the Danjiangkou Ｒeservoir. 42(1)(doi:org/10.13227/j.hjkx.202006192（in Chinese） Supplementary Material File (figures.docx) Download 17.50 MB File (table.docx) Download 18.25 KB Information & Authors Information Version history V1 Version 1 17 February 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords community ecology freshwater microbial sequencing Authors Affiliations juan yang 0009-0000-2650-7916 Dali University View all articles by this author Ziye Liu Dali University View all articles by this author Songlin Ye Dali University View all articles by this author Qiyao Li Dali University View all articles by this author Nan Shao Dali University View all articles by this author Yaning Xu Dali University View all articles by this author Nan Li Dali University View all articles by this author Mingping Gou Dali University View all articles by this author Lu Tan Institute of Hydrobiology Chinese Academy of Sciences View all articles by this author Shuoran Liu [email protected] Dali University View all articles by this author Metrics & Citations Metrics Article Usage 131 views 57 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation juan yang, Ziye Liu, Songlin Ye, et al. Bay Morphology Regulates Local Hydrodynamics and Drives the Structural Differentiation of Microbial Communities in Small Ponds of the littoral zone. Authorea . 17 February 2026. DOI: https://doi.org/10.22541/au.177133333.36205530/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.177133333.36205530/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fe2d9e85e59300f',t:'MTc3OTE5MTA4Mg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();