Background

In aquatic habitats, microbial communities in sediments play a crucial role in regulating nutrient cycle and energy flow ranging from small ponds to the global ocean. Many of them are highly structured with depth and exhibit a nonuniform turnover, which coincides with geochemical transition zones (GTZs) [1]. Microbial communities exhibit changes that reflect the oxic–anoxic transition zone (OATZ) [2], the nitrate–ammonium transition zone (NATZ) [3], the nitrate/nitrite-methane transition zone (NMTZ) [4, 5], and the most well-known sulfate–methane transition zone (SMTZ) [6]. Characterizing the burial depth of GTZs is important for estimating greenhouse gas emissions as well as enhancing the understanding of subfloor biogeochemical cycles [7].

A consensus is that these stratifications and orders are primarily governed by redox chemistry. Resembling the Winogradsky column [8], energy availability drives the hierarchy of electron acceptors [9,10,11]. However, the diverse patterns of sediment stratification suggest the existence of other potential drivers beyond redox chemistry. In marine systems, redox cascades can be disrupted by hydrodynamic disturbance [12] or benthic bioturbation [13], or vary with water depth [14], eutrophication, and sedimentation rates [7, 11]. In comparison, few studies have clearly discussed these effects on redox zonation in freshwater sediments. Differences between the two systems make it challenging to apply marine-derived models to freshwater sediments [15]. Freshwater systems typically have lower dissolved sulfate levels, where sulfur cycling is considered relatively minor [16]; whether the SMTZ well established in marine systems is prevalent in freshwater sediments and whether it would be replaced by NMTZ remain to be explored. Another difference is the degree of human impact: the higher wetted perimeter of lakes and closer ties with human activities make it a nontrivial factor in sha** freshwater sediment structure.

Dam construction, surging in the mid-twentieth century, is considered one of the most far-reaching anthropogenic modifications of aquatic ecosystems [17, 18]. Unlike episodic sedimentation caused by floods or extreme weather, these management strategies could induce hidden inherited changes known as legacy effects. It may bring priority effects on microbial community assemblages and generate alternative successional trajectories during burial [19,20,21]. Coupled with enhanced fertilizer and wastewater inputs, it may also permanently alter sediment biogeochemical properties by accelerating N/P retention [22]. This shift is likely to result in nitrate becoming the primary electron acceptor for anaerobic methane oxidation (AOM). However, previous studies on the damming impact on lake microbial ecosystems have mostly overlooked the vertical response of sediment microbiomes, limiting our understanding of stratified changes and their relationship with redox cascades.

Understanding community assembly processes and stratification formation requires depicting community stability patterns. Steep transitions in sediments closely fit the ecotone model, wherein species richness often tends to peak and the local communities are highly dynamic and unstable over time [23, 24]. Community stability can be reflected in both the multi-timepoint dissimilarity and the holistic flexibility of microbes to adapt to different environments. The former can be directly obtained by temporal investigations; community dynamic processes being rhythmic or chaotic, deterministic or stochastic, largely depend on the observational time scale. The latter can be further manifested as cell chemotaxis and niche breadth. Chemotaxis, by which cells sense chemical gradients and move directionally with preference, reflects spatiotemporal heterogeneity of energy supply; those living in stable, homogenous environments harbor fewer methyl-accepting chemotaxis proteins (MCPs) and response regulatory proteins [25, 26]. On the other hand, in a niche-based community, higher metabolic flexibility indicates broader niche breadth, as variations in resource availability select flexible habitat generalists rather than niche-restricted specialists [27]. To date, the link between stratification and stability in sediment microbiomes remains poorly established.

In this study, we ask about the role of anthropogenic activities in forming biogeochemical stratification in freshwater lake sediments. We attempted to identify the legacy effect of dam construction with emphasis on (i) microbial taxonomic and functional stratification, (ii) the coupling effect with classic redox cascade, (iii) community assembly processes, and (iv) community stability. We undertook this study with a 1-year sampling strategy in Lake Chaohu, a major freshwater lake in China (Fig. 1). Chaohu Dam was constructed in 1962 which triggered eutrophication in the 1970s [28] (see Text S1 for details). We hypothesized (i) damming would disrupt the regular redox order, leading to rapid stratification through changes in energy inputs, sedimentation properties, and community assembly processes; (ii) the NMTZ would replace the SMTZ with N-dependent AOM as the key factor controlling upward methane flux, and (iii) significant energy differences would occur at the rapid stratification zone and enhance local community fluctuations. To test these hypotheses, we identified microbial taxonomic and functional stratifications with combination of shotgun metagenomics and amplicon sequencing approaches and proposed a potential past–present coupling mechanism for stratification by elucidating the processes of sedimentation and microbial community dynamics.

Fig. 1
figure 1

Sampling site and strategy. A Location of the sampling site and map of the Chaohu Basin, China. B Present physicochemical parameters of each sediment interval were determined in situ on cores without destroying the connectivity between layers. C Schematic plot of the spatiotemporal sampling strategy

Full size image

Methods

Study site and sampling procedures

Consistent with previous chronological studies of Chaohu sediments [28, 29], the sampling site was set in the central part of western Chaohu (31° 37′ 23″ N, 117° 22′ 21″ E, Fig. 1A), where the lake is more eutrophic and away from the nearshore hydraulic disturbance zone. We sampled bimonthly from August 2014 to July 2015, which provided seasonal snapshots for the sediment profile (Fig. 1B–C). On June 2 2019, we performed additional sampling for gaseous methane estimation. Sediment cores were collected using a gravity corer outfitted with a clear polycarbonate tube (inner diameter of 8.2 cm, length of 60 cm). Considering the destructive nature of sediment sampling, we controlled the spatial sampling-site bias within a 100 m-distance for seasonal samples. An excision of a 1-cm-thick margin section was applied for each 5-cm-interval subsample to avoid sidewall contamination and minimize margin effects. Samples were debris-trimmed and zoobenthos-removed and then transferred into a clean sealed sample bag and briefly homogenized by manual manipulation. These homogenized samples were frozen in liquid nitrogen and stored at − 80 °C for subsequent analysis.

Environmental parameters

The physicochemical properties of the sediments were measured immediately after lifting sediment cores off the water surface to maintain the integrity of the core structure (Fig. 1B). In situ sediment temperature was obtained using an electric thermometer. Volumetric water content (\({\mathrm{Moi}}_{(v)}\)) and conductivity (Cond) were measured via a portable soil moisture and EC sensor (TR-6D, Shunkeda, Bei**g, China). Redox potential (ORP) and pH were measured using Unisense redox (metal tip, standard hydrogen potential) and pH microelectrodes (Unisense, Aarhus, Denmark). In the laboratory, total organic carbon (TOC) was determined using an organic carbon analyzer (Multi N/C 2100 TOC, Analytik AG, Jena, Germany) after pretreating samples with 1 mol−1 HCl to remove inorganic carbon. Sediment grain size was analyzed by a laser diffraction particle size analyzer (LS 13320, Beckman Coulter, USA, measuring range: 0.017 ~ 2000 μm). We calculated the median diameter (D50) and mean grain size (MGS) to represent the particle size characteristics. We identified the texture class of the sediments according to the soil textural triangle [30].

Sediment interstitial space feature and methane accumulation

In sediments, methane (CH4) supersaturation occurs and forms bubbles. Previous studies have shown that CH4 acts as the main component of sediment gas space [31,32,33]. Based on this, we used the gas space as a proxy for the CH4 accumulation degree (ebullitive CH4 flux). The gas space volume percent (\({\mathrm{VP}}_{(a)}\)) was calculated based on the measured volumetric and mass water contents at each specific depth, which is rigorously physics-oriented [32]. In brief, the total interstitial space volume percent (TIS) consists of the gas space volume percent \({\mathrm{VP}}_{(a)}\) and the volumetric water content \({\mathrm{Moi}}_{(v)}\):

$$\mathrm{TIS}={\mathrm{Moi}}_{(v)}+{\mathrm{VP}}_{(a)}$$
(1)

The physical meaning of volumetric water content is as follows:

$${\mathrm{Moi}}_{(v)}=\frac{{V}_{(w)}}{{V}_{(T)}}=\frac{{V}_{(w)}}{{V}_{(s)}+{V}_{(w)}+{V}_{(a)}}$$
(2)

where \({V}_{(w)}\) is the volume of the pore water, \({V}_{(s)}\) is the volume of the solid particles, \({V}_{(a)}\) is the volume of the gas space, and \({V}_{(T)}\) represents the total volume of the sediment layer sample.

Measured through the drying method in lab, the mass water content \({\mathrm{Moi}}_{(m)}\) is calculated as:

$${\mathrm{Moi}}_{(m)}=\frac{{M}_{(w)}}{{M}_{(T)}}=\frac{{M}_{(w)}}{{M}_{(s)}+{M}_{(w)}}$$
(3)

where \({M}_{(w)}\) is the mass of the sediment water, \({M}_{(s)}\) is the mass of solids, and \({M}_{(T)}\) is the total mass of the sediment layer sample.

As described in our previous study [32], the layered gas space volume percent \({\mathrm{VP}}_{(a)}\) was calculated as follows:

$${\mathrm{VP}}_{(a)}=1-\frac{{\mathrm{Moi}}_{(v)}\cdot\rho_{(w)}}{{\mathrm{Moi}}_{(m)}\cdot\rho_{(w\&s)}}$$
(4)

where \({\rho }_{(w)}\) is the pore water density measured by the weighing method and \({\rho }_{(w\&s)}\) represents for the density of mixed sediment measured by the submerged method.

Data collection on chronology and eutrophication history

Sediment dating data were updated by interpolation and extension based on the age-depth model established in [29, 34] (site C4), which was calculated by 210Pbex dating for sedimentation rate and 137Cs activity analysis for absolute age using the constant rate of supply (CRS) model [28, 35]. Coincidentally, the 1963 bomb test peak exactly labeled the depth of the 1962 damming event. In the subsequent analysis, we coded the establishment of the Chaohu Dam as a dummy variable (i.e., 1 or 0). Data on the past epilimnetic total phosphorus (TP) concentrations estimated by diatom-inferred TP (DI-TP) collected from [28] were used to quantify the long-term trend of the eutrophication degree of Chaohu Lake. The DI-TP was calculated using sedimentary fossil diatom assemblages with a DI-TP transfer function model developed from a set of 45 lakes in the middle and lower reaches of the Yangtze River [36]. Furthermore, available data on the percentage of diatoms estimated by sedimentary pigments (PDESP) from [34] were used for comparison and verification.

Nucleic acid extraction, sequencing, and bioinformatic analysis

Nucleic acid extraction

DNA extraction from sediment samples was performed using the PowerSoil® DNA Isolation Kit (MoBio Laboratories Inc., Carlsbad, USA) following the manufacturer’s Alternative PowerSoil Protocol for RNA and DNA from Low Biomass Soil. The DNA quality was assessed by ratios of 260 nm/280 nm and 260 nm/230 nm using a NanoDrop spectrophotometer (ND-2000, Thermo Scientific, USA). The DNA extraction protocol applied to further amplicon and metagenomic sequencing.

16S rRNA gene amplicon sequencing

PCR amplification was performed with the primer pair 515F (5′-GTGYCAGCMGCCGCGGTAA-3′) and 926R (5′-CCGYCAATTYMTTTRAGTTT-3′) targeting the SSU V4-V5 region, which targets both bacterial and archaeal domains [37, 38]. High-throughput sequencing was performed using the Illumina HiSeq2500 platform (2 × 250 paired ends, Illumina, San Diego, USA) at Biomarker Technologies Corporation, Bei**g, China. We prepared two sample treatments before amplicon sequence processing: one kept individual samples separate (i.e., the 57 sediment subsamples), and the other combined seasonal samples by pooling raw sequencing data of temporal samples of the same depth, which consisted of eleven depth-related samples. The merged samples were set to identify general vertical patterns without seasonal variation (see Fig. 1 and Table S1). Details of PCR amplification and amplicon sequence processing are available in Text S2. Overall, the average effective sequence number was 73668 ± 619 (mean ± SD), CV = 0.84% for seasonal individual samples, and 390759 ± 56565, CV = 14% for seasonal merged samples. To minimize the bias of sequencing depth, each sample set’s OTU table was rarefied to the lowest number among samples as a standard number of sequences.

Metagenomic sequencing and assembly

We further performed metagenomic sequencing for the upper nine sediment layer samples (0 ~ 45 cm) to obtain a reliable functional profile. The quality and quantity of the extracted DNA were examined using a Qubit dsDNA HS Assay Kit on a Qubit 3.0 Fluorometer (Life Technologies, Carlsbad, CA, USA) and electrophoresis on a 1% agarose gel, respectively. Paired-end libraries (insert size, ~ 350 bp) were prepared using a VAHTS Universal Plus DNA Library Prep Kit for Illumina (Vazyme Biotech). The library was sequenced on an Illumina NovaSeq 6000 platform (Biomarker Technologies Co., Ltd., Bei**g, China) using the PE150 mode. Adaptor removal and low-quality sequence filtering were conducted in Trimmomatic v0.33. In detail, reads with a quality score < 20 over a sliding window size of 50 bp or with a sequence length < 100 bp were filtered. The average number of clean reads we obtained was 21.3 M (~ 6.5 G clean data base) per sample (Table S2). The high-quality short reads were de novo assembled using MEGAHIT which makes use of succinct de Bruijn graphs [39]. Assembly quality was assessed using QUAST v2.3 [40]. Contigs with lengths greater than 300 bp were selected as the final assembly result for further gene annotation. The contig number, contig length statistics, and mapped ratio are listed in Table S3.

Gene annotation and functional prediction

The open reading frames (ORFs) from each assembled contig were predicted using MetaGeneMark v3.26 [41] with default parameters. The prediction statistics are available in Table S4. All predicted genes with a 95% sequence identity (90% coverage) were clustered using MMseqs2 [42] to remove sequence redundancy. Functional annotations of representative sequences of nonredundant gene catalogs were performed against the NCBI nonredundant protein database (NR) and the Kyoto Encyclopedia of Genes and Genomes database (KEGG) with an e-value cutoff of 1e−5 using DIAMOND [43]; a manually curated integrative database NCycDB was also used for metagenomic profiling of nitrogen cycling genes [44] (Table S5-S11). As complementary to shotgun metagenomics, the 16S rRNA gene sequences provided extra functional prediction via PICRUSt [45] and the FAPROTAX database (Functional Annotation of Prokaryotic Taxa) [46]; the former was used to estimate the total gene content relevant to S/N/CH4 metabolism based on available sequenced genomes, while the latter was used to estimate the potential of fermentation based on experimental evidence (Table S12).

Community statistics

We performed principal coordinates analyses (PCoA) based on binary Jaccard (presence–absence) and Bray–Curtis (abundance-weighted) dissimilarities to depict both taxonomic and abundance-based variations in community composition. Permutational multivariate analysis of variance (PERMANOVA) was applied using the R package vegan to test the hypothesis that depth dominates the distribution of prokaryotes. The seasonal fluctuation of microbial community at each depth was measured as the multiple-timepoint Bray–Curtis dissimilarity using the function beta.multi.abund in the R package betapart [47, 48]. Unweighted pair-group method with arithmetic means (UPGMA) clustering based on Bray–Curtis distance was performed to identify sediment layering properties. The layering significance was tested by analysis of similarities (ANOSIM). To visualize the microbial community transition patterns, we conducted an OTU-sample bipartite network analysis in Cytoscape with an edge-weighted spring-embedded layout using a seasonally merged OTU abundance matrix. Before that, a base-10 logarithmic conversion was applied, and the OTUs with log10-transformed abundance less than 1 were filtered. To map the effect of environmental factors in energy metabolism, we computed Pearson’s correlations within environmental data and Mantel correlations between community features and environmental data based on merged samples using the R package ggcor [49]. We also performed detrended correspondence analysis (DCA) and redundancy analysis (RDA) in vegan to visualize and test the specific influences of each environmental factor on abundant microbial taxa.

Random-forest identification of damming-sensitive taxa

We applied the random forest (RF) algorithm to help identify the damming-sensitive phyla (DSPs) and classes (DSCs), the key components that distinguish between the two sediment layers divided by the damming line mapped in the sediment profile. We first performed a supervised classification, i.e., given the classification strategy, the 57 sediment samples were divided into the pre-damming group and the post-damming group; 54 observed phyla (or 134 observed classes) were viewed as characteristic variables for classification. We used the mean decrease Gini (MDG) to evaluate variable importance. The higher the MDG is, the more critical the corresponding feature. Specifically, we constructed 100 RF replicates (each with 1000 trees for DSP or 2000 trees for DSC) using the R package randomForest [50] and pooled them using the function combine to obtain a robust importance ranking. Then, we computed the optimal breakpoints based on linear regression models via the function breakpoints implemented in the R package strucchange [51] to identify the DSP/DSCs that respond most strongly to the legacy effect. Classification accuracy was assessed using the out-of-bag (OOB) error rate. In addition, we also performed unsupervised RF clustering without prior classification information to verify the plausibility of the damming-line classification strategy.

Quantifying microbial community assembly processes

To evaluate community assembly processes, we first performed variation partition analysis (VPA) using the function varpart in vegan [52]. This approach estimates the contribution of measured deterministic factors to the metacommunity assembly process and the importance of past events in altering the present environment. We classified all environmental variables into what we term “present parameters,” “sedimentary features,” and “historical parameters” (see details in Text S3). In each group, a stepwise model selection algorithm was applied to streamline the variable subset. These explanatory data frames were Hellinger transformed. We interpreted the unexplained fraction as stochastic composition.

Because there may be important unmeasured variables that influence community assembly, we used null model analysis based on phylogenetic information to infer underlying ecological processes. MNTD (mean nearest taxon distance) and βMNTD (i.e., between-assemblage analogs of MNTD) were calculated using mntd and comdistnt in the R package picante v1.8 [53]. Measuring standard deviations of observed MNTD/βMNTD from mean MNTD/βMNTD in the null model in which taxa are randomized (999 randomizations) across the tips of phylogenetic trees, NTI (nearest taxon index), and βNTI were calculated using picante and R code [54] to quantify the deviation from pure stochastic ecological processes governing local community structure and dynamics. The fraction of pairwise comparisons with significant βNTI values (|βNTI|> 2) indicates the influence of selection, while nonsignificant results (|βNTI|< 2) indicate stochastic processes [55]. To evaluate differences in the processes of community assembly across depths, we computed the pairwise comparisons of βNTI values between adjacent layers within each sediment column section. We then partitioned stochastic processes into dispersal limitation (i.e., low rates of dispersal leading to dissimilar community structure), homogenizing dispersal (i.e., high rates of dispersal leading to community homogenization), and ecological drift (i.e., undominated processes of birth, depth, and reproduction) by calculating the Bray–Curtis-based Raup–Crick metric (RCbray) by comparing empirically observed Bray–Curtis (BCobs) to simulated Bray–Curtis (BCnull) under 9999 randomizations [54]. As such, the relative contributions of each ecological process in the assembly of communities under different categories (full system including all samples and the two-layer system clustered by UPGMA) were quantified based on the following rules: the fraction of all pairwise comparisons with |βNTI|> 2 was taken as an estimate for the influence of selection, |βNTI|< 2 and RCbray > 0.95 as dispersal limitation, |βNTI|< 2 and RCbray <  − 0.95 as homogenizing dispersal, and |βNTI|< 2 with |RCbray|< 0.95 as ecological drift (the undominated fraction). The selection process was further divided into homogeneous (βNΤI <  − 2) and heterogeneous (βNΤI > 2) types.

Once the metacommunity of the full system was proven deterministically assembled, the Levin’s niche theory [56] was applied to estimate each local community’s emergent niche property. Mean niche breadth \(\overline{{B }_{i}}\) of a local community at a specific depth was calculated as follows:

$$\left\{\begin{array}{c}\overline{B_i}=\sum\nolimits_{j=1}^SQ_{ij}\cdot B_j\\B_j=1/\sum\nolimits_{i=1}^NP_{ij}^2\end{array}\right.$$
(5)

where \({B}_{j}\) is the Levin’s measure of the niche breadth of OTU \(j\) across the metacommunity, \({Q}_{ij}\) represents the relative abundance of OTU \(j\) in the local community \(i\), \({P}_{ij}\) is the percentage of the OTU \(j\) in environment \(i\) to the total abundance of OTU \(j\) across the metacommunity, \(S\) is the total number of OTUs, and N is the total number of local communities.

Interpreting emergent stochasticity via path modeling

We quantitively constructed the causality between historical damming and emergent stochasticity using partial least squares path modeling (PLS-PM) in the R package plspm [57]. Our modeling assumptions are (i) temporal environmental variations and spatial energy difference would be responsible for local community fluctuation and emergent stochasticity of community assemblage; (ii) historical damming and eutrophication would largely explain the spatial energy difference and strengthen the polarization of the oxic, nutrient-rich surface and the anoxic, barren deep sediments; (iii) compared to those with progressive changes, such layering property would narrow the metacommunity’s mean niche breadth (MNB) by shortening the spatial scale of community turnover, but the local MNB in the rapid transition zone would be larger where the generalists with high metabolic plasticity are selected; the larger the MNB, the more emergent stochasticity can be locally observed.

In measurement models, we set the legacy effect and environmental fluctuation as the two exogenous variables mutually independent. The latent variable “legacy effect” was constructed by Damming, \((1-PDESP)\), and DI-TP in a formative way; similarly, the variable “environmental fluctuation” was formed by coefficients of variation of seasonal moisture (\({\mathrm{CV}}_{\mathrm{moi}}\)) and temperature (\({\mathrm{CV}}_{\mathrm{temp}}\)) in each sediment layer (Fig. S11). Differently, “Energy difference” was set as an endogenous latent variable measured in a reflective way by chemotaxis and the difference between neighboring layers of both redox potential and TOC. “Community fluctuaton,” “mean niche breadth,” and “emergent stochasticity” were set as endogenous manifest variables measured by multiple-timepoint dissimilarity, MNB, and \((10-{\mathrm{\beta NTI}}_{\mathrm{merged}})\), respectively.

Results

Sediment dating, limnological information, and gaseous methane sequestration

Sediment cores with the effective depth of 55 cm had a maximum age of ~ 306 years (Fig. 2 and Fig. S1). The age-depth model placed the 137Cs peak, a synchronous sign of the Chaohu Dam construction, at a depth of 21.4 cm within the fifth layer. The average deposition rate in the upper 21 cm (\({\overline{r} }_{\mathrm{upper}}=0.41\mathrm{ cm}/\mathrm{year}\)) rose over twofold compared to the pre-damming condition (\({\overline{r} }_{\mathrm{lower}}=0.13\mathrm{ cm}/\mathrm{year}\)). Diatom stratigraphic data showed a marked transition since damming with a succession of diatom-cyanobacteria in 1966 [

Availability of data and materials

The data generated in the current study are publicly available. The amplicon sequence files have been deposited in the NCBI Sequence Read Archive database (SRA) under accession number SRP154610 (BioProject PRJNA482178). The metagenomic sequence files are available at the NCBI SRA as part of BioProject PRJNA838605.

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Acknowledgements

We thank Prof. Jianjun Wang for the comments and suggestions. We thank Yaofei Xu, Zhipeng Wei, Chenxiao Liu, and Heng Wei for the sampling assistance. We thank Hesen Wang, Siyan Li, Jie Xu, and Jie Lin for the partial experimental assistance.

Funding

This research was supported by the National Natural Science Foundation of China (No. 42077221), Fundamental Research Funds for the Central Universities (B200203044), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX20_0458).

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Authors and Affiliations

  1. The National Key Laboratory of Water Disaster Prevention, Hohai University, Nan**g, 210024, China

    **aotian Zhou, **ang Lu & Aidong Ruan

  2. College of Hydrology and Water Resources, Hohai University, Nan**g, 210024, China

    **aotian Zhou, **ang Lu & Aidong Ruan

  3. Department of Biology, Indiana University, Bloomington, IN, 47405, USA

    Jay T. Lennon

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Contributions

AR supervised and financed the project. XZ and AR conceptualized the research idea and collected the samples. XZ and XL performed the experiments and measurements. XZ analyzed the data and wrote the initial draft. XL assisted in analyzing the geophysical data. JTL guided partial data analyses and revised the manuscript. All authors read and approved the final manuscript.

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Correspondence to Aidong Ruan.

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Supplementary Information

Additional file 1:

Text S1. Introduction to the Chaohu Lake and damming history. Text S2. PCR amplification and 16S amplicon sequence processing. Text S3. Classification of environmental factors. Text S4. Prokaryotic motility and chemotaxis. Table S1. Sample summary and naming scheme. Table S2. Summary of clean data statistics of metagenomic sequencing. Table S3. Assessment of metagenomic assembly. Table S4. Statistics of gene prediction based on assembled contigs. Table S5. List of genes involved in the nitrate reduction pathway. Table S6. List of genes involved in the (complete) nitrification pathway. Table S7. List of genes involved in the anammox pathway. Table S8. List of genes involved in the sulfate reduction pathway. Table S9. List of genes involved in the methanogenesis pathway. Table S10. List of genes involved in the methane oxidation pathway. Table S11. Relative abundance of chemotaxis-related genes obtained by metagenomic data. Table S13. Assessment of the “legacy → emergent stochasticity” PLS path model. Fig. S1. Age-depth model of the sediment profile Fig. S2. Principal coordinates analysis (PCoA) based on binary Jaccardand Bray-Curtis dissimilarities  Fig. S3. Error rate distributions in typical random forests for identification of damming-sensitive taxa. Fig. S4. Computation of the optimal breakpoints based on linear regression models for identification of damming-sensitive taxa. Fig. S5. The scaling coordinates of the proximity matrix from unsupervised random forest clustering. Fig. S6. Redundancy analysis (RDA) Fig. S7. Sigmoidal curve fitting for the abundance pattern of fermentation across depth. Fig. S8. Relationship between nearest taxon index (NTI) and depth using both individual and pooling sample sets. Fig. S9. Evidence from the phylogenetic-info-based null model that shows the deterministic assembly of the sediment microbial metacommunity. Fig. S10. A pattern of mean Levin’s niche breadth index of local communities at different depth layers.  Fig. S11. Coefficients of variation (CVs) of moisture, TOC, and temperature over time at each sediment layer.

Additional file 2:

Table S12. Fermentation-related functional annotation of prokaryotic taxa based on FAPROTAX.

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Zhou, X., Lennon, J.T., Lu, X. et al. Anthropogenic activities mediate stratification and stability of microbial communities in freshwater sediments. Microbiome 11, 191 (2023). https://doi.org/10.1186/s40168-023-01612-z

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