The Predominant Role of Stochastic Processes in Bacterial Community Assembly across Varied Hydrological Connectivity of Watershed Surface Water

The Predominant Role of Stochastic Processes in Bacterial Community Assembly across Varied Hydrological Connectivity of Watershed Surface Water | 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 Search Search ADVANCED SEARCH SCROLL Hydrological Processes This is a preprint and has not been peer reviewed. Data may be preliminary. 8 April 2025 V1 Latest version Share on The Predominant Role of Stochastic Processes in Bacterial Community Assembly across Varied Hydrological Connectivity of Watershed Surface Water Authors : X. D. Hu , Y. W. Deng , S. X. Yu , L. Wang , J. Q. Huang , W. Y. Huang , H. B. Xiao [email protected] , J. Wang , and Zhihua Shi Authors Info & Affiliations https://doi.org/10.22541/au.174410500.08947423/v1 Published Hydrological Processes Version of record Peer review timeline 384 views 178 downloads Contents Abstract INTRODUCTION 2. MATERIALS AND METHODS 3. RESULTS 5. CONCLUSIONS Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Microbial community assembly processes are closely related to the composition, structure, and distribution of microbes. The changes in environmental conditions and species dispersal capacity induced by hydrological connectivity may significantly impact the microbial community assembly process in surface water, but the mechanisms remain unclear. To reveal how hydrological connectivity affects microbial community assembly processes, surface water samples were collected from the study watershed during periods of low, intermediate, and high hydrological connectivity. An integrated 16S amplicon sequencing technology and phylogenetic null model approach were used to identify the assembly processes of the bacterial communities. The results showed an inverse relationship between hydrological connectivity and environmental heterogeneity, with the highest environmental heterogeneity observed at low connectivity level. Bacterial alpha diversity under high hydrological connectivity gradient significantly exceeded those under low and intermediate hydrological connectivity. Beta diversity exhibited a trend toward biotic homogenization as hydrological connectivity increased. The co-occurrence network of bacterial communities under low hydrological connectivity were characterized by robust clustering and intricate interactions, whereas those under intermediate hydrological connectivity tended to form more straightforward network. Furthermore, stochastic processes play a crucial role in bacterial community assembly, accounting for approximately 80% of the observed patterns. This was substantiated by piecewise structural equation modelling, which showed that environmental factors and biotic interactions exerted minimal influence on the bacterial community assembly. As hydrologic connectivity increases, the assembly process shaping bacterial community appears more stochasticity. Moreover, the contributions of drift and heterogeneous selection in assembly processes was found to increase with hydrological connectivity, while the impact of dispersal limitation and homogenous selection diminished. These insights provide a deeper understanding of the ecological mechanisms that govern microbial distribution pattern and succession in watershed surface water. INTRODUCTION Microbial community assembly refers to the processes regulating the composition, structure, and dynamics of microbial populations (Logares et al., 2018; Nemergut et al., 2013; Stegen et al., 2013a). The assembly processes of microbial community can be explained using a combination of ecological niche theory and neutral theory (Stegen et al., 2012). Traditional ecological niche theory states that, due to habitat preferences and fitness of microorganisms, microbial communities are mainly determined by deterministic processes, including biotic (e.g., competition, mutualism, and predation) and abiotic (e.g., pH, salinity, and oxygen) factors (Lima-Mendez et al., 2015). Conversely, neutral theory assumes that microbial community assembly is generated by stochastic processes, such as birth, death, immigration, probabilistic dispersal, and speciation (Bahram et al., 2016; Hubbell, 2001). Vellend (2010) grouped numerous ecological processes affecting community assembly into four fundamental groups differing in determinism and stochasticity, namely selection, dispersal, drift, and speciation. Stegen et al. (2013b) quantified the relative importance of these processes by developing a statistical framework based on a phylogenetic null model. This framework has enabled an increased understanding of the mechanisms driving microbial communities and their responses to environmental variations at a process level (Yang et al., 2023). In the past decade, the ecological mechanisms of microbial community assembly have been widely investigated across various systems (Li and Hu, 2021; Liu et al., 2015; Liu et al., 2024; Meyerhof et al., 2016). Most studies indicated that bacterial community structure was largely regulated by deterministic processes in aquatic systems (Liu, et al., 2024; Wu et al., 2022). Namely, bacterial community assembly have commonly been determined by environmental factors (e.g., pH, salinity, heavy metal and altitude) and biotic interactions (Lozupone and Knight, 2007; Lu et al., 2022). However, the stochastic processes appear to be stronger in surface water systems subject to hydrologic fluctuations, whereas environmental filters seem to not act as significant selective forces on species sorting (Chen et al., 2019). High discharge causes low environmental heterogeneity, which reduces diversification in bacterial survival conditions and species-environment matching in lotic habitats, thereby facilitating stochasticity in bacterial community assembly (Rillig et al., 2015). Conversely, a relatively low discharge condition may lead to contrasting patterns in bacterial communities due to species sorting (Liu et al., 2015; Zhao et al., 2022). Therefore, elucidating the influences of hydrological processes on bacterial community assembly is crucial for understanding ecological processes shaping the composition of microbial communities in watershed surface water. Hydrological connectivity reflects the ability of matter, energy, and organisms to be transported through heterogeneous landscapes in watershed surface water (Bracken et al., 2013). Variations of hydrological connectivity induced by flow accessibility between landscape compartments can directly influence the dispersal of microorganisms (Neill et al., 2019). Specifically, increased hydrological connectivity coincides with connection of surface water bodies and the creation of temporary aquatic habitats in low-lying areas, which homogenizes microbial communities. In contrast, the period of low hydrological connectivity is characterized by disconnection of temporary aquatic environments, leading to the emergence of distinct microbial communities within isolated water bodies (Tan et al., 2019). Furthermore, hydrological connectivity also affects the migration processes of solutes and environmental conditions of microbial habitats, thereby directly altering environmental heterogeneity in watershed surface water (Li et al., 2021; Xiao et al., 2024). For example, Li et al. (2019) used a hydrodynamic model (MIKE 21) to simulate hydrological connectivity of seasonal lakes, and they found an enhanced spatial similarity in eight water quality parameters as hydrological connectivity increases. Changes in microbial community assembly processes are primarily attributed to comprehensive effects of environmental heterogeneity and microbial dispersal, which are strongly affected by watershed hydrological connectivity (Huber et al., 2020; Neill et al., 2019). Nevertheless, the effect of hydrological connectivity on bacterial community assembly processes remains inadequately understood. To fill this gap, we collected surface water samples from 34 sites across low, intermediate, and high hydrological connectivity in the Jiazhu River watershed. By using 16S amplicon sequencing technology and phylogenetic null model, we investigated the spatiotemporal dynamics of bacterial community and inferred the influencing mechanisms of hydrological connectivity on bacterial community assembly in watershed surface water. The objectives of the present study were to: (1) investigate the diversity and co-occurrence patterns of surface water bacterial community across different hydrological connectivity; (2) clarify the impacts of hydrological connectivity on bacterial community assembly processes; (3) reveal the key factors influencing bacterial community assembly under different hydrological connectivity. The results of the present study can improve understanding of the diversity, spatiotemporal distribution, and succession of microbial communities in surface water and can act as a basis for the conservation and restoration of aquatic ecosystem. 2. MATERIALS AND METHODS The study was conducted in the Jiazhu River watershed (115°03’01” E–115°04’22” E, 26°44’12” N–26°45’28” N), located in Taihe Couty, Jiangxi Province, China (Fig. 1). The Jiazhu River is a secondary tributary of the Ganjiang River and has a length of 20.98 km. The area of the Jiazhu River watershed is 121.61 km 2 , with elevation ranging between 49 m and 263 m. The climate is subtropical monsoon, with average annual temperature and precipitation of extends from March to June, accounting for ~70% of annual precipitation, with this strong seasonality leading to highly variable hydrological fluctuations and seasonal lakes in the watershed. The watershed terrain decreases from south to north, with an average water surface slope of 5‰. Red loam in the main soil type in the watershed, classified by the United States Department of Agriculture (USDA) soil taxonomy standard as oxisols. The main land-use types are woodland, cropland, grassland, and water bodies. Zonal vegetation in the watershed can be classified as meso-subtropical broad-leaved evergreen forests, with main tree species including Pinus elliottii Engelm , Cunninghamia lanceolatea , Pinus massoniana Lamb , and Schima crenata Korthals . Insert : Fig. 1 The present study established 34 sampling sites in the Jiazhu River watershed (Fig. 1). These sampling sites were categorized into four representative environmental types based on morphological features (Huber et al., 2020): (1) main and large secondary channels (MC); (2) minor secondary channels (SC); (3) connected lakes (CL), and; (4) isolated lakes and swamps (IL). Water samples were collected at all sampling sites in October 2022, March 2023, and June 2023. Sampling was conducted after two consecutive days of no rainfall, thereby minimizing the impact of rainfall on water levels and microorganism. During sampling, a 1.5-L water sample was collected at a depth of 0.5 m and filtered through a 0.22-μm nitrocellulose membrane filter. A sub-sample was sealed in a 50-mL sterilized polypropylene tube and stored in dry ice (–80 ℃) for subsequent DNA extraction. The residual water samples were refrigerated at –20 ℃ and transported to the laboratory for physical and chemical analysis. In-situ measurement of water temperature, pH, specific conductivity (SPC), and total dissolved solids (TDS) was conducted using a multi-parameter water quality probe YSI (ProQuatro, USA). A portable doppler flow meter (Anion, AN-PDF01) was used to measure water level at sampling sites. The latitude and longitude of each sampling site were also recorded. Hydrological connectivity is generally positively correlated with river water level (Pekel et al., 2016). Therefore, the channel water level of each sampling site was used to qualitatively characterize the hydrological connectivity gradient for each sampling period (Fig. 2). The average water levels in October 2022, March 2023, and June 2023 were 20.0 cm, 37.8 cm, and 46.4 cm, respectively. Since the median water levels for the same periods increased from 12.4 cm to 48.7 cm, levels of hydrological connectivity in October 2022, March 2023, and June 2023 were considered to be low, intermediate, and high, respectively. Insert : Fig. 2 Physicochemical properties of water samples were analyzed according to the standard methods (Jin and Tu, 1990). Turbidity of water samples was measured using the spectrophotometry method; total nitrogen (TN) was determined through the alkaline potassium persulfate UV spectrophotometric method; total phosphorus (TP) was quantified using the ammonium molybdate spectrophotometric method; chemical oxygen demand (COD Mn ) was determined using the potassium permanganate method; dissolved organic carbon (DOC) was determined by the total organic carbon analyzer (Elementar vario TOC, Germany) method; nitrate nitrogen (NO 3 − -N) and ammonium nitrogen (NH 4 + -N) were measured by the AA3 continuous flow injection analysis instrument; heavy metal concentrations were determined by inductively coupled plasma optical emission spectrometry (ICP-OES, Agilent 5110). Environmental heterogeneity (Ed) was used to characterize the dissimilarity between sites in each hydrological connectivity gradient (Ranjard et al., 2013). First, a Euclidean distance matrix (SciPy, Python) for each hydrological connectivity gradient was calculated based on nine habitat characteristics (pH, SPC, turbidity, DOC, COD Mn , NO 3 − -N, NH 4 + -N, TP, and Ba). Dissimilarity between sites (Ed) was then calculated as: \begin{equation} \begin{matrix}Ed=\left(1-\frac{\text{Euc}}{\text{Euc}_{\max}}\right)+0.001\ \#\text{(1)}\\ \end{matrix}\nonumber \\ \end{equation} where Euc is the Euclidean distance between two sites and Euc max is the maximum Euclidean distance in the overall matrix. The value 0.001 was added to substitute for the case of zero similarity between sites. The mean Ed ( \(\overline{\text{Ed}}\) ) of each matrix was used to represent environmental heterogeneity. In addition, the coefficients of variation (CV%) of abiotic variables were calculated under various hydrological connectivity gradients. The total DNA from water sample was extracted using the E.Z.N.A. Kit (Omega Bio-tek, Inc., USA) according to the manufacturer’s instructions. The V3-V4 hypervariable region of the bacterial 16S rRNA gene was amplified with the universal primers 338F (5’-ACTCCTACGGGAGGCAGCAG-3’) and 806R (5’-GGACTACNNGGGTATCTAAT-3’). The PCR products were purified using a Agencourt AmPure XP Kit (Beckman Coulter, Inc., USA) according to the manufacturer’s recommendations, and sequencing was performed on an Illumina Miseq (Illumina, Inc., USA) platform at Beijing Allwegene Technology Co., Ltd. Image analysis, base calling and error estimation were performed at run termination using Illumina Analysis Pipeline Version 2.6 (Illumina, Inc., USA). Qualified sequences were clustered into operational taxonomic units (OTUs) at a similarity threshold of 97% using the Uparse algorithm of the Vsearch (ver. 2.7.1) software (Fig. S1) . Alpha diversity indices of bacterial communities were evaluated using the Chao 1 and Shannon indices in the QIIME software (ver. 1.8.0). The Chao 1 index reflects community richness, whereas the Shannon index considers community diversity and uniformity. The Bray–Curtis distances were quantified using the “vegan” package in R (ver. 4.3.1). The Bray-Curtis matrices of bacterial community dissimilarity for different hydrological connectivity gradients were computed in QIIME. Non-metric multidimensional scaling (NMDS) analysis was performed to determine UniFrac distance of the bacterial community among sampling sites. Network analysis was conducted using the Molecular Ecological Network Analysis Pipeline (MENAP, http://ieg2.ou.edu/MENA) to evaluate the co-occurrence patterns of the bacterial community among the three hydrological connectivity gradients, with the results visualized in Gephi 0.10. OTUs present in over 50% of the sampling sites were considered as nodes. An approach based on Random Matrix Theory (RMT) was used to automatically generated the appropriate similarity threshold for network construction. A cutoff of 0.85 was used to construct the network edges. In addition, the network topological properties were computed in MENAP, including the total number of nodes and edges, density, modularity, connectedness, and average clustering coefficient. Network density represents the average connectivity of a node in the network, with values ranging between 0 and 1. Values close to 1 are expected with increasing connectivity of the network. A high degree of modularity (> 0.4) suggests a modular structure of networks in each hydrological connectivity gradient has a modular structure. The clustering coefficient characterizes how nodes are embedded in their neighborhood and the degree to which they tend to cluster together. Low values of the clustering coefficient represent less interconnected association networks. Connectedness indicates the presence of information flow paths between nodes representing accessibility between nodes in a network. The present study applied the Stegen’s null model-based method to quantify the relative contributions of deterministic and stochastic processes to the structure of the bacterial community (Stegen et al., 2013b). This approach quantified various ecological processes considering homogeneous selection, heterogeneous selection, homogenizing dispersal, dispersal limitation, and ecological drift. Prior to the above analysis, a Mantel correlogram analysis was conducted between OTU niche and OTU phylogenetic distances in the “microeco” R package to assess the phylogenetic signal (Fig. S2). This analysis was used to demonstrate that habitat preferences of closely related taxa are more similar than those of distantly related taxa. First, the beta nearest taxon index (βNTI) was used to assess the relative contributions of deterministic and stochastic processes to bacterial community assembly in each hydrological connectivity gradient. βNTI is a measure of community diversity and considers both the phylogenetic relationships between species and randomization of replacement of individuals and dispersion in the algorithm. This index is defined as the standard deviation between the observed beta-mean-nearest taxon distance (βMNTD, the pairwise phylogenetic turnover between bacterial communities) and the mean of the null βMNTD values. More specifically, |βNTI| > 2 indicates that the community assembly is governed primarily by deterministic processes, which can be divided into homogeneous selection (βNTI 2). The Bray-Curtis-based Raup-Crick (RC bray ), representing the deviation between the observed Bray–Curtis and the null distribution, was then used to quantify the stochastic processes. |RC bray | > 0.95 indicates the dominance of either homogenizing dispersal (RC bray 0.95), while |RC bray | < 0.95 indicates significant departures from the degree of turnover and the stand-along influence of drift. All the analyses were performed in R v4.3.1 using the “iCAMP” package. One-way repeated measures analysis of variance (ANOVA) was conducted to identify differences in environmental properties and bacterial species diversity among various hydrological connectivity gradients and environmental types. Differences in the Bray-Curtis dissimilarity among the three hydrological connectivity gradients were compared using the Kruskal-Wallis method, with significance assumed at p < 0.05, and visualized with a boxplot. Non-metric multi-dimensional scaling (NMDS) analysis based on Bray–Curtis dissimilarity and PERMANOVA with 999 permutations were performed using the adonis function in the R Vegan package to evaluate the complexity of the community composition and compare differences among the three hydrological connectivity gradients. Piecewise structural equation modelling (piecewise SEM) was used to explore direct and indirect relationships among all variables envisaged in the model. βNTI was used as the dependent variable, whereas environmental and biotic variables computed using Euclidean distance were considered independent variables. The environmental variables included physio-chemical properties of water. The distance matrix of biotic variables was calculated based on the taxa associations in each water sampling site. A piecewise SEM analysis was performed for each hydrological connectivity gradient. Finally, Fisher’s C statistic was used to assess the goodness of fit of the modelling results by directed separation (Shipley, 2009; Shipley, 2013). A p-value of the Fisher’s C exceeds 0.05 indicated a reasonable structure of the constructed model and that all valid paths were identified. The present study conducted piecewise SEM using the “piecewise SEM” package in R (ver. 4.3.1). 3. RESULTS 3.1. Relationship between Environmental Heterogeneity and Hydrological Connectivity pH, TP, and DOC exhibited significant inverse relationships with hydrologic connectivity. Under low and intermediate hydrological connectivity gradient, levels of pH, turbidity, DOC, COD Mn , and NH 4 + -N in isolated lakes and swamps were significantly higher than those in channels. Whereas the average NO 3 − -N and Ba in lakes and swamps were lower than those in channels (Fig. 3, Table S1). Under lower hydrological connectivity gradients, physicochemical parameters exhibited larger ranges of variation. For example, the variation of TP under low hydrological connectivity gradient exceeded those under intermediate and high hydrological connectivity gradient by factors of 2.0 and 3.6, respectively. Insert : Fig. 3 The mean environmental heterogeneity (\(\overline{\text{Ed}}\)) in the low, intermediate, and high connectivity gradient were 0.307, 0.304, and 0.225, respectively (Fig. 4). The results of environmental heterogeneity exhibited a remarkable decreasing trend along hydrological connectivity gradient. In addition, the coefficient of variation (CV%) for all environmental variables ranged from 0.04 to 1.55. Environmental variables generally showed lower coefficients of variation under high hydrological connectivity gradient, and the variation coefficients of SPC, TDS, DOC, COD Mn , and Ba were decreased with the increase of hydrological connectivity. Insert : Fig. 4 3.2. Variations in Bacterial Community over Different Hydrological Connectivity A total of 13113 OTUs within bacterial domain were obtained from all samples analyzed. The average values of Chao 1 index obtained under low, intermediate, and high hydrological connectivity gradient were 2348.7, 1997.3, and 2046.9, respectively (Fig. 5a). The mean values of Shannon index for low, intermediate, and high connectivity gradient were 6.7, 6.3, and 7.1, respectively (Fig. 5b). Furthermore, Chao 1 index in channels significantly exceeded those in lakes and swamps ( p < 0.05). Shannon index under high connectivity gradient significantly exceeded those under low and intermediate hydrological connectivity gradient ( p < 0.05). Insert : Fig. 5 The main taxonomic groups of bacterial communities in water samples were Proteobacteria (19.1%–41.1%), Actinobacteria (13.9%–39.3%), Bacteroidota (2.3%–43.6%), Cyanobacteria (1.2%–27.0%), and Verrucomicrobiota (0.4%–9.8%) (Fig. 6a). The relative abundances of Proteobacteria and Bacteroidota showed an initial increase, followed by a decrease with increasing hydrological connectivity, whereas those of Actinobacteria, Cyanobacteria, and Verrucomicrobiota showed opposite trends. The relative abundances of Actinobacteria in channels and Proteobacteria in isolated lakes and swamps increased with increasing hydrological connectivity. The main genera in the bacterial community (Fig. 6b) were Flavobacterium (10.9%), HgcI_clade (5.2%), Polynucleobacter (3.1%), Arthrobacter (4.8%), and Pseudomonas (4.9%). The relative abundance of HgcI_clade increased with increasing hydrological connectivity, and the maximum relative abundances of Flavobacterium, Polynucleobacter, Arthrobacter, and Pseudomonas occurred under intermediate hydrological connectivity. Insert : Fig. 6 The present study applied non-metric multidimensional scaling analysis (NMDS) based on Bray-Curtis dissimilarity to compare the dissimilarities in bacterial community structure among the three hydrological connectivity gradients (Fig. 7b). The Results of NMDS ordination (Stress = 0.1859) indicated significant differences in bacterial communities among the three hydrological connectivity gradients (PERMANOVE, R 2 = 0.21, p = 0.001). The variability in composition of the bacterial community under low hydrological connectivity exceeded those under intermediate and high hydrological connectivity. Furthermore, the mean values of Bray-Curtis dissimilarities, indicating heterogeneity in the bacterial community, were 0.77, 0.67, and 0.56 under low, intermediate, and high hydrological connectivity gradient, respectively (Fig. 7a). Bacterial community heterogeneity showed a significant inverse relationship with the hydrological connectivity gradient ( p < 0.05). Insert : Fig. 7 3.3. Co-occurrence Networks of the Bacterial Community under Different Hydrological Gradients Node numbers of co-occurrence networks showed a positive relationship with the hydrological connectivity gradient, and were 419, 434, and 667 under low, intermediate, and high connectivity gradient, respectively (Fig. 8, Table 1). The number of edges in the co-occurrence networks were 1112, 759, and 2412 under low, intermediate, and high connectivity gradient, respectively. Network modularity under all levels of hydrological connectivity exceeded 0.4, with the maximum occurred under low hydrological connectivity gradient. Network connectedness showed an increasing trend with increasing hydrological connectivity. The maximum network average clustering coefficient and density were observed under low hydrological connectivity gradient, whereas their minimum occurred under intermediate hydrological connectivity gradient. An interesting result of this analysis was the low prevalence of negative (antagonistic co-occurrence patterns) relationships in the co-occurrence networks. Insert : Fig. 8 Insert : Table 1 3.4. Bacterial Community Assembly Processes under Different Hydrological Connectivity Gradients The results of Mantel correlation analysis indicated that phylogenetic signals were detected at shorter phylogenetic distances, consistent with the premise of the null model (Fig. S2). Absolute values of βNTI in all hydrological connectivity gradients were mostly below 2, indicating the important role of stochastic processes (Fig. 9a). Taxonomic turnover (RC bray ) under all hydrological connectivity gradients mainly concentrated over approximated 0.95, indicating the dominant role of dispersal limitation in stochastic processes (Fig. 9b). The results of the null model found that dispersal limitation to be the most important process in bacterial community assembly, regardless of hydrological connectivity, with relative importance of 70%, 61%, and 56% under low, intermediate, and high hydrological connectivity gradient, respectively (Fig. 9c). The relative importance of dispersal limitation showed an inverse relationship with hydrological connectivity, while that of drift showed an opposite pattern, ranging from 9% to 23% with increasing hydrological connectivity. Moreover, deterministic processes also played a prevalent role across all hydrological connectivity gradients, with an average relative importance of about 21%. The relative contribution of heterogeneous selection increased from 5% to 15% with increasing hydrological connectivity, whereas that of homogeneous selection decreased from 14% to 6%. Insert : Fig. 9 The results of piecewise structural equation modelling (piece SEM) indicated direct or indirect effects of environmental factors on bacterial community assembly by changing biotic associations. Under low hydrological connectivity gradient (Fig. 10a), phylogenetic turnover (βNTI) was positively affected by pH (λ = 0.18, p < 0.01) and TN (λ = 0.44, p < 0.001), whereas it was negatively influenced by NH 4 + -N (λ = −0.46, p < 0.001). Under intermediate hydrological connectivity gradient, turbidity (λ = 0.29, p < 0.001), DOC (λ = −0.17, p < 0.01), NH 4 + -N (λ = −0.19, p < 0.001), and Ba (λ = 0.19, p < 0.001) significantly impacted βNTI (Fig. 10b). Turbidity, DOC, Ba, and NO 3 − -N indirectly affected bacterial community assembly by altering biotic associations. Furthermore, under high hydrological connectivity gradient (Fig. 10c), NO 3 − -N (λ = −0.14, p < 0.01), COD Mn (λ = −0.14, p < 0.01), and biotic associations (λ = −0.15, p < 0.001) had significantly negative effects on phylogenetic turnover, whereas turbidity (λ = 0.12, p < 0.001) and TP (λ = 0.51, p < 0.001) showed positive effects on βNTI. Turbidity, pH, DOC, COD Mn , TP, and NO 3 − -N directly affected biotic associations and indirectly affected bacterial community assembly. However, the maximum variance in βNTI explained by environmental factors and biotic associations was only 11%, thereby confirming that bacterial community assembly in watershed surface water was dominated by stochastic processes. Insert : Fig. 10 4. DISCUSSION 4.1. Differences in Bacterial Community among Different Hydrological Connectivity The present study showed that the greatest diversity of bacterial communities in surface water occurred under high hydrological connectivity (Fig. 5b). This result can be attributed to the influx of microorganisms from surrounding soil into surface water during high hydrological connectivity, resulting in bacterial communities with higher species diversity (Luo et al., 2020). The observation in the present study is consistent with previous studies (Chen et al., 2019). In terms of different environmental types, both of bacterial community richness and diversity indices in channels exceeded those in the lakes and swamps (Fig. 5). This phenomenon is possibly attributable to the long-distance water flow in channels, which accelerates recruitment of dispersed and diverse propagules into the bacterial community (Yang et al., 2023). Moreover, an inverse relationship was observed between bacterial community dissimilarity and hydrological connectivity (Fig. 7). The observation may be related to the changes in environmental heterogeneity and microbial dispersal induced by hydrological connectivity. (Amoros and Bornette, 2002; Murray et al., 2019). Specifically, high hydrological connectivity often was accompanied by low environmental heterogeneity in watershed surface water, and the dissimilarity among bacterial communities similarly decreased with decreasing environmental heterogeneity (Amoros and Bornette, 2002). Furthermore, during high hydrological connectivity, a significant increase in flow rate facilitates microbial dispersal, leading to homogenization of the bacterial community (Murray et al., 2019). The analysis of co-occurrence patterns in bacterial communities was further conducted to understand their structural complexity among different hydrological connectivity (Fig. 8). In these patterns, network modules can be interpreted as overlapping niches in which bacterial communities are more densely associated to each other (Faust and Raes, 2012). Our results showed that the highest modularity in the co-occurrence network was observed under low hydrological connectivity (Table 1). One possible explanation is that high environmental heterogeneity provides various ecological niches for microbial habitats during low hydrological connectivity, which occurs the segregation of ecological niches for a wide range of microorganisms, thereby increasing modularity in the co-occurrence network (Olesen et al., 2007). We also found that both of clustering coefficient and density in the co-occurrence network were the highest under low hydrological connectivity gradient, which implied a co-occurrence pattern characterized by strong clustering and complex interactions. Furthermore, connectedness in network topology increased with increasing hydrological connectivity (Table 1). This suggested that hydrologic connectivity promotes linkages between bacterial communities, while the community stability may be less poor (Liao et al., 2021). 4.2. Responses of Bacterial Community Assembly to Hydrological Connectivity In the present study, stochastic processes were dominant in shaping bacterial community assembly across all hydrological connectivity gradients (Fig. 9), suggesting that the role of biotic ecological succession (e.g., stochastic births, deaths, and immigration, etc.) in shaping bacterial communities may exceed that of abiotic factors (such as physical and chemical factors) (Read et al., 2015). Several past studies have also identified stochastic processes to have prominent roles (Chen et al., 2019). Among stochastic processes, relative importance of dispersal limitation exceeded 55% across all hydrological connectivity gradients (Fig. 9). This indicated that dispersal limitation appeared to dominate microbial community assembly, although its effect decreased with increasing hydrological connectivity. Our results showed that environmental factors and biotic interactions partially influence the microbial community assembly (Fig. 9). Consequently, we further applied the piecewise structural equation model to identify the direct and indirect impacts of environmental and biotic factors on microbial community structure (Fig. 10). These results suggested the impacts of local environmental parameters on resident communities changed with hydrological connectivity. However, piece SEM was not sufficient to effectively explain the effects of environmental and biotic factors on bacterial community assembly, which indirectly confirmed that stochastic processes have the largest impacts on bacterial community assembly. Similarly, previous studies also found a large of unexplained variations of microbial community (Caruso et al., 2011; Li et al., 2022). This is possibly attributable to the effects of environmental filters being counteracted by higher dispersal capacity of species under hydrological fluctuation conditions. Furthermore, large proportions of noise are often evident in snapshot sampling in extremely dynamic systems (Huber et al., 2020). This noise potentially masks the dominant ecological patterns, thereby increasing the difficulty in understanding the effects of environmental conditions on bacterial community assembly. Finally, we summarized the characteristics of the bacterial assembly processes in three hydrological connectivity gradients based on the phylogenetic null model (Fig. 11). Overall, stochastic processes, especially dispersal limitation, was dominant in shaping bacterial community assembly across three hydrological connectivity gradients, which may determine composition and diversity in bacterial community. Furthermore, under low hydrological connectivity, homogeneous selection played a secondary role in bacterial community assembly. The observation was consistent with the co-occurrence network results that microbial community co-occurrence patterns under low hydrological connectivity were characterized by strong clustering and complex interactions, possibly attributable to coalescence of the bacterial community (Gao et al., 2021). The relative importance of heterogeneous and homogeneous selections in regulating microbial community assembly had similar weight under intermediate hydrological connectivity. This indicated that both of these selection forces had similar impacts on the homogenization and segregation of local microbial communities. Under high hydrological connectivity, dispersal limitation and drift were the main processes regulating bacterial community assembly, indicating a lower dissimilarity between bacterial communities under a higher hydrological connectivity gradient. Future studies could aim to investigate bacterial community under a wider range of hydrological conditions to reveal the theoretical threshold in various hydrological connectivity, and clarify the relative contributions of stochastic and selective processes to microbial community assembly change. Insert : Fig. 11 5. CONCLUSIONS The present study aimed to identify the effect of hydrological connectivity on bacterial community assembly in watershed surface water. The highest bacterial species diversity was observed under high hydrological connectivity gradient. The dissimilarity in taxonomic structure of bacterial community decreased with increasing hydrologic connectivity. Under low hydrological connectivity gradient, bacterial communities were characterized by a strong clustering and complex co-occurrence network, whereas they showed a simpler network under intermediate hydrological connectivity gradient. Furthermore, the present study proposed a conceptual model of the relationship between bacterial community assembly and hydrological connectivity. The effects of stochastic processes, particularly dispersal limitation, on bacterial community assembly exceeded those of deterministic processes. In addition, increased hydrological connectivity resulted in an increase in the relative importance of drift and heterogeneous selection and a reduction in those of homogeneous selection and dispersal limitation in regulating bacterial community assembly. The results of piecewise SEM further confirmed that environmental factors and biotic interactions played a minor role in shaping the bacterial community. ACKNOWLEDGEMENTS This work was supported by the National Natural Science Foundation of China (U22A20611), National Key Research and Development Program of China (SQ2024YFD1700126), Fundamental Research Funds for the Central Universities (2662023ZHQD001), Key Research Project of Water Conservancy in Hubei Province (HBSLKY202337), and Double Thousand Plan of Jiangxi Province (JXSQ2023102244). We appreciate the support of the Qianyanzhou Experimental Station for our field experiments. DATA AVAILABILITY The data that support the findings of this study are available from the corresponding author upon reasonable request. REFERENCES Amoros, C., Bornette, G., 2002. Connectivity and biocomplexity in waterbodies of river floodplains. Freshwater Biol. 47, 517-539. Bahram, M., Kohout, P., Anslan, S., Harend, H., Abarenkov, K., Tedersoo, L., 2016. Stochastic distribution of small soil eukaryotes resulting from high dispersal and drift in a local environment. ISME J. 10(4), 885-896. 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Abbreviations: MC: main and large secondary channels, SC: minor secondary channels, CL: connected lakes, IL: isolated lakes and swamps. Different capital letters indicate significant differences among hydrologic connectivity gradients and different lowercase letters indicate significant differences among environmental types ( p < 0.05). Fig. 4 Environmental heterogeneity (\(\overline{\text{Ed}}\), gray bars) under different hydrological connectivity gradients and the coefficients of variation (CV%) of selected environmental variables. Fig. 5 Bacterial community alpha diversity among various environmental types under different hydrological connectivity gradients. (a) Chao 1 richness index; (b) Shannon diversity index. Abbreviations: MC: main and large secondary channels, SC: minor secondary channels, CL: connected lakes, IL: isolated lakes and swamps. Fig. 6 Relative abundances of bacterial taxonomic groups under different hydrological connectivity gradients at phylum (a) and genus level (b). Abbreviations: MC: main and large secondary channels, SC: minor secondary channels, CL: connected lakes, IL: isolated lakes and swamps. Fig. 7 (a) Comparison of the Bray-Curtis dissimilarity for all pairs of communities; (b) Non-metric multidimensional scaling (NMDS) analysis of the bacterial community based on the Bray-Curtis distance under three hydrological connectivity gradients. Abbreviations: MC: main and large secondary channels, SC: minor secondary channels, CL: connected lakes, IL: isolated lakes and swamps. Fig. 8 Co-occurrence network analysis of (a) low connectivity gradient, (b) intermediate connectivity gradient, and (c) high connectivity gradient. The nodes and edges are colored based on the modularity, and the size of each node is proportional to the degree of OTUs. Fig. 9 ( a) Distribution of phylogenetic turnover (βNTI), the vertical dashed lines mark the positions of -2 and 2; (b) Taxonomic turnover (RC bray ), the vertical dashed lines mark the positions of -0.95 and 0.95; (c) The percentages of the five assembly processes. Fig. 10 Piecewise structural equation model (piecewise SEM) showing the direct and indirect effects of environmental variables and biotic associations on phylogenetic turnover under (a) low connectivity gradient, (b) intermediate connectivity gradient, and (c) high connectivity gradient. Numbers above the arrows indicate path coefficients, the thicker arrow has a stronger correlation. The R 2 values associated with the response variables indicate the proportion of variation as explained by relationships with other variables. *, ** and *** indicate significant levels of p < 0.05, p < 0.01, and p < 0.001, respectively. Fig. 11 Conceptual model of the effects of hydrological connectivity on bacterial assembly processes: dispersal limitation, drift, homogeneous selection, and heterogeneous selection. Supplementary Material File (tables.docx) Download 14.88 KB Information & Authors Information Version history V1 Version 1 08 April 2025 Peer review timeline Published Hydrological Processes Version of Record 18 Sep 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Hydrological Processes Keywords environmental heterogeneity hydrological connectivity microbial community assembly phylogenetic null model stochastic processes Authors Affiliations X. D. Hu Huazhong Agricultural University College of Resources and Environment View all articles by this author Y. W. Deng Huazhong Agricultural University College of Resources and Environment View all articles by this author S. X. Yu Huazhong Agricultural University College of Resources and Environment View all articles by this author L. Wang Huazhong Agricultural University College of Resources and Environment View all articles by this author J. Q. Huang Changjiang River Scientific Research Institute View all articles by this author W. Y. Huang Huazhong Agricultural University College of Resources and Environment View all articles by this author H. B. Xiao [email protected] Huazhong Agricultural University College of Resources and Environment View all articles by this author J. Wang Huazhong Agricultural University College of Resources and Environment View all articles by this author Zhihua Shi Huazhong Agricultural University College of Resources and Environment View all articles by this author Metrics & Citations Metrics Article Usage 384 views 178 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation X. D. Hu, Y. W. Deng, S. X. Yu, et al. 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