# Sub-community-level network analysis reveals distinct microbial dynamics in a lake littoral sediment

Vincent Tardy, Emilie Lyautey, Victor Frossard

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Vincent Tardy, Emilie Lyautey, Victor Frossard. Sub-community-level network analysis reveals distinct microbial dynamics in a lake littoral sediment. BMC Microbiology, 2025, 25 (1), pp.804. 10.1186/s12866-025-04512-4 . hal-05525672

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# Sub-community-level network analysis reveals distinct microbial dynamics in a lake littoral sediment

Vincent Tardy<sup>1,2,3*</sup>, Emilie Lyautey<sup>1,2</sup> and Victor Frossard<sup>1,2</sup>

**Abstract**

**Background** Lake littoral sediments are dynamic environments supporting diverse ecological functions, yet the temporal dynamics of their microbial communities remain understudied. This study investigated the temporal changes in microbial communities within the surficial sediments of Lake Bourget, with a high temporal resolution (2.3 ± 2 days over five months). We hypothesized that microbial temporal dynamics are shaped by different ecological processes (deterministic vs. stochastic), depending on the organizational scales considered. To test this, we examined patterns at both the whole-community and sub-community (module) scales, combining β-diversity metrics with co-occurrence network analysis.

**Results** At the community scale, temporal changes in microbial composition were relatively constrained. Null model analysis indicated that stochastic processes, such as ecological drift and homogenizing dispersal, predominated, while deterministic processes from environmental variables played a minimal role. This was supported by variance partitioning, which showed that environmental parameters explained only 17% of the temporal variation in community structure. By reducing the community into smaller co-varying groups of taxa (modules) using network-based clustering, we identified six microbial modules, each with distinct temporal patterns. The two largest modules showed contrasting responses to environmental variables, highlighting distinct ecological niches succeeding over time. At the module scale, environmental variables explained a larger fraction of temporal variation (up to 51% for Module M3), indicating that deterministic processes, particularly environmental filtering, played a more dominant role than at the whole-community scale.

**Conclusion** Our study demonstrates that considering multiple organizational scales within microbial communities provides deeper insights into the ecological processes driving microbial temporal dynamics. Finer-scale resolution via network modules reveals patterns and drivers that may be masked at the broader community level, allowing for a better understanding of the balance between stochastic and deterministic forces in sediment microbiomes.

**Keywords** Co-occurrence network, Temporal dynamic, Ecological processes, Microbial communities

<footnote>*Correspondence:
Vincent Tardy
vpm.tardy@gmail.com
¹Université Savoie Mont Blanc, INRAE, CARRTEL, Thonon-Les-Bains
74200, France</footnote>

<footnote n="2">²Pôle R&D ‘ECLA’, Thonon-Les-Bains, France</footnote>

<footnote n="3">³Université Savoie Mont Blanc, UMR 042 CARRTEL, Domaine Universitaire
Bât 8A, Le Bourget-du-Lac 73376, France</footnote>
## Background

Characterizing the temporal dynamics of microbiomes is a major challenge in microbial ecology [1]. Microbial communities harbor vast species and metabolic diversity and have short generation times, making it essential to investigate their temporal patterns in order to predict ecosystems trajectories under global change. Advances in high-throughput sequencing technologies have greatly facilitated time series studies across diverse environments, including aquatic [2–4] and terrestrial [5, 6] ecosystems as well as human the gut [7]. These studies consistently show that microbial community composition shift over time, driven by both biotic and abiotic factors specific to each ecosystem.

From conceptual and experimental framework, temporal dynamics of microbial communities result of the combination between deterministic and stochastic processes. Deterministic processes involve non-random mechanisms—such as environmental filtering or biotic interactions (e.g., competition, predation)—that consistently favor certain taxa under specific conditions. In contrast, stochastic processes include random events—such as ecological drift, random colonization, or extinction—that influence community composition regardless of environmental conditions [8]. The intensity and prevalence of these processes over time can vary based on habitat specificity [1, 9, 10], microbial taxa [11, 12], the initial size of communities [13] and the time scale considered [14, 15]. For example, the recent study of Stadler and colleagues [9] demonstrated that the strength of microbial selection processes within a large boreal aquatic network varied depending on the hydrological conditions of the habitat. In another work, Ayarza and Erijman [16] found that in lab-scale bioreactors with activated sludge flocs, stochastic processes became more frequent as species richness increased within the microbial community.

Despite these insights, a clear consensus on the balance between these processes remains elusive. The relative contribution of stochastic and deterministic drivers appears to vary depending on habitat type and community richness. These apparently contrasting results also point to a deeper conceptual issue: stochasticity may either reflect true random processes (e.g., drift, dispersal limitation) or emerge from the masking of deterministic signals in highly diverse communities, where taxa respond idiosyncratically to environmental changes. As result, the microbial dynamics in natural systems may not be governed by a single dominant process, but rather by different processes operating at different levels of organization.

In time series studies, the temporal dynamics of microbial communities are often characterized using β-diversity indices to decipher temporal shifts at the whole community scale [17]. Weighted network

analysis offers a complementary and powerful approach to analyze DNA-based time series by identifying groups of species that are strongly interconnected over time. This method enables a hierarchical decomposition of the community into smaller, ecologically meaningful clusters of co-occurring taxa, referred to as “module”, getting closer to ecological niche concept [16]. This “downscaling” involves detecting modules through network-based clustering, thereby revealing fine-scale temporal variations that might be obscured when analyzing the entire community. For instance, Aylward et al. [18] applied module analysis from weighted networks to monitor daily microbial transcripts from planktonic communities across two different oceanic provinces, revealing fine-scale temporal shifts. Similarly, Lemonnier et al. [19] employed this method to evaluate seasonal influences on free-living bacterioplankton communities, identifying distinct patterns associated with ecological strategies of bacterial species, such as oligotrophs and copiotrophs.

In this study, we hypothesized that microbial dynamics are structured by different ecological processes (deterministic vs. stochastic) depending on the organizational scales considered—from the whole-community level, where stochasticity may dominate due to high diversity and spatial heterogeneity, to sub-community modules, where environmental filtering might emerge more clearly. To test this, we investigated the temporal dynamics of bacterial and archaeal communities (hereafter referred to as the microbial community) in littoral surficial sediments of a large peri-alpine lake over five consecutive months. In lake ecosystems, littoral sediments are subjected to seasonal environmental fluctuation and variable inputs from the watershed (e.g. nutrients, organic and mineral particles [20]). These areas can also be impacted by wind-induced hydrodynamic events (i.e. waves) which act as disturbances by resuspending surface layers and homogenizing the microbial habitat [21, 22]. At each time point, sediment microbial communities were characterized using high-throughput sequencing of the 16S rRNA gene, and environmental parameters—including a wave energy index derived from fetch-based modeling—were measured to quantify hydrodynamic disturbance. Temporal patterns were analyzed at both the whole-community and sub-community levels using two complementary statistical approaches.

Following the framework of Stegen et al. [23], we interpreted temporal changes in terms of three main ecological processes: selection, homogenizing dispersal and ecological drift. We expected that stochastic meteorological events would lead to temporal shifts among these processes. For instance, homogenizing dispersal was expected to prevail during wave energy periods, causing brief peaks in community turnover followed by a phase of reduced temporal variability due to
community homogenization. Conversely, under more
stable environmental conditions (i.e. low wave energy),
environmental selection was anticipated to structure
communities through changes aligned with local envi-
ronmental gradients. Ecological drift was inferred when
observed communities were statistically indistinguish-
able from random expectations – that is, when commu-
nity variation over time did not significantly differ from
a null model approach simulating stochastic fluctuations.

To capture microbial dynamics across different orga-
nizational levels, we applied two complementary
approaches. Temporal changes in whole-community
were assessed using phylogenetic beta-diversity metric,
while sub-community patterns were investigated using
a weighted co-occurrence network analysis. The latter
allowed us to identify modules of co-varying OTUs and
to relate their temporal profile to environmental drivers
through module eigengene analysis. This dual approach
provided a comprehensive understanding of the temporal
dynamics shaping sediment microbial communities.

Methods

Site and sample collection

Lake Bourget (France) is a deep peri-alpine lake (45° 43′ 46.842" N, 5° 52′ 10.484" E, maximum depth of 147 m, length = 18 km, Additional file 1, Fig. S1a-b). Its littoral areas are mainly located in the Northern and Southern parts of the lake. The dominant wind regime is oriented from North to South (Additional file 1, Fig. S1b-c). The study site was selected in the Southern littoral area because of its high wind exposure, which is likely to induce wind-driven hydrodynamics (Fig. 1b). The sampling site (45° 39′ 8.821" N, 5° 52′ 24.888" E) consisted of a 10 × 10 square area (100m²) at 2.5 m depth. Due to this hydrodynamic exposure, the substrate was composed of sandy sediment (d50 = 210 ± 5 µm) with a low organic matter content (loss on ignition, LOI = 1.8%) [24]. From July 17th to December 10th 2015, 44 sampling events were conducted at an average interval of 2 ± 2 days. At each date, three replicate samples of surficial sediment (top 2 cm) were randomly collected within the sampling

<figure><img src="image_3.png" /><figcaption>**Fig. 1** Environmental variables over time. Temporal dynamics of (a) water temperature, (b) inferred wave energy, (c) porewater nitrate and total phosphorus concentrations, and bulk sediment C:N ratio. Gray dots on graphs represent sampling dates over the studied period</figcaption></figure>
area. Samples were collected by scuba diving using 50-ml tubes dragged horizontally over ~50 cm. They were kept at 4 °C during transport to the laboratory and stored at -20 °C within 6 h prior to subsequent analyses.

## Environmental characteristics monitoring

Water temperature and oxygen saturation were monitored hourly using a miniDOT logger installed 10 cm above the sediment surface. Water oxygen saturation remained stable around 100% over the experimental period (Additional file 1, Fig. S2) and was not selected in further analyses.

Sediment porewater was collected by centrifuging 50 mL of sediment at 6000×g for 15 min immediately after sampling. Total phosphorus and nitrate were quantified using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES, NF EN ISO 11885) and Ionic Chromatography (NF EN ISO 10304-1) respectively.

Bulk sediment organic matter content (OM, mg g⁻¹) was estimated by loss on ignition following [25] and total organic carbon content (TOC, mg g⁻¹) was inferred using the following Eq. (1) from [26] and [27].

$$TOC = \frac{OM}{1.7} \qquad (1)$$

Total nitrogen was quantified using a Flash 2000 Elemental Analyzer. TOC/TN ratios (hereafter C/N ratio) were then computed to qualify the organic matter available in the sediment.

The wave energy flux (Kw m⁻¹) at sampling site was computed using the R package waver according to Eq. (2) following [28] and [29]. Hourly measurements of wind speed at 10 m (U₁₀, m s⁻¹) and associated directions over the studied time period were retrieved from the meteorological station nearby the sampling site (Météo-France, Aix-les-Bains, see Appendix 1D for the wind rose). Bathymetric data from [30] were used to get water depth and support the computation of fetch distances (i.e. distances on the lake over which wind blows, m).

$$Wave energy flux = \frac{\rho \times g^2}{64\pi} \times H^2 T \qquad (2)$$

where ρ is the density of water (998 kg m³), g is the acceleration of gravity (9.81 m s²).

The height (H, m) and period (T, s) of waves were obtained according to Eqs. (3) and (4) respectively.

$$H = \frac{\left( 0.0413 \times \left( \frac{g \times F}{U_f^2} \right)^{\frac{1}{2}} \right) \times U_f^2}{g} \qquad (3)$$

$$T = \frac{\left( 0.651 \times \left( \frac{g \times F}{U_f^2} \right)^{\frac{1}{3}} \right) \times U_f}{g} \qquad (4)$$

where F is the fetch distance and Uf is the friction velocity (m s⁻¹) derived from wind speed following Eq. (5).

$$U_f = \sqrt{0.001 \times (1.1 + 0.035U_{10}) \times U_{10}^2} \qquad (5)$$

The maximal wave period (Tmax) was constrained by the local water depth (d, m) following Eq. (6).

$$T_{max} = 9.78 \times \sqrt{\frac{d}{g}} \qquad (6)$$

Every hour, the fetch distance (F) at any local spatial point was determined by identifying the minimal difference between the actual wind direction and 16 bearings (directions) at regular 22.5° increments. Once the dominant bearing was identified, the average fetch distance for 9 sub-bearings spread at 2.5° angles (from -10° to 10°) around the dominant bearing were computed to provide a robust estimate of the fetch distance. This procedure was repeated for any spatial points present in a 100 m-radius buffer around the sampling site and averaged to get robust estimates for wave energy flux at the study site.

## DNA extraction and sequencing

Total sediment DNA was extracted from 0.5 g of wet sediment using a NucleoSpin Soil Kit (Macherey-Nagel EURL) following the manufacturer's instructions, and using SL1 lysis buffer and additive Enhancer SX buffer. The extracted DNA was quantified fluorometrically after staining with QuantiFluor dsDNA Dye (QuantiFluor dsDNA System, Promega) using a Plate Chameleon™ fluorometer (Hidex; excitation: 485 nm, emission: 590 nm).

PCR amplification for high throughput 16S rRNA sequencing was carried out with the universal primer pair 515F (5'-GTGYCAGCMGCCGCGGTA-3') and 909R (5'-CCCCGYCAATTCMTTTRAGT-3') targeting the V4-V5 hypervariable region of the 16S rRNA gene [31]. Indexes were integrated to both primers following the dual-indexing procedure described by [32]. Triplicate PCR amplifications for each sample were realized with a total amount of ~5 ng of DNA per reaction. Ampli-con products were quantified using Picogreen assay (Life Technologies, Carlsbad, USA) and pooled equimolarly. The final pool was purified with CleanPCR beads (CleanNA). Sequencing was realized by Fasteris (Geneva, Switzerland) on an Illumina HiSeq with 2×300 bp. The analysis yielded 12 Gb of sequences with average error rate of 1.945%, and average Q30 of 79%.
## Bioinformatic analysis

Adapters were removed using trimmomatic [33] and barcode sorted using a Fasteris internal script (Perl script). Sequences were then processed using the FROGS (Find Rapidly OTUs with Galaxy Solution) Galaxy-supported pipeline [34]. Paired-ends reads were joined using FLASH [35] and quality check was performed using FastQC. Sequences with primers having no mismatch were kept. They were then filtered by size (between 350 and 420 bp) and those containing N bases were discarded. The 16S rRNA gene sequences were then denoised and clustered using the Swarm method [36] with a 3 bases maximum difference, deletion of clusters having less than 0.005% abundance and cluster occurrence in minimum 2 samples of the total library. Chimeras were removed using uchime of vsearch package [37]. Affiliation was done using the Silva SSU database 138 [38] through BLAST [39] with allowed multiple affiliation and manual curation. Chloroplast and mitochondrial sequences were searched and removed from the dataset. In total, illumina sequencing yielded 5,591,163 high-quality 16S rRNA sequences and described 1083 OTUs. Sequencing data was then rarefied to the smallest sample in order to remove bias comparison of communities' similarity indices between sampling dates [40], accounting 20,491 quality sequences per sample. All analyses were done on the Galaxy instance of the INRA MIGALE bioinformatics platform (http://migale.jouy.inra.fr).

## Statistical analysis

### Environmental parameters

Temperature and inferred wave energy were averaged over two days before the 44-sampling date to account for prior environmental conditions that could constraint microbial communities. For nutrients, porewater nitrate and phosphorus concentrations as well as bulk sediment C/N ratios at sampling times were used.

### Characterizing the dynamic of microbial community structure

The temporal dynamic of the microbial community structure was characterized using weighted Unifrac distance [41] and visualized through a Non-Metric Multidimensional Scaling (NMDS) ordination. Weighted Unifrac was chosen because it accounts for both phylogenetic relatedness and relative abundances of OTUs, thereby capturing community turnover while minimizing the disproportionate effect of sequencing noise associated with very rare taxa. An analysis of similarity (ANOSIM) was performed to test whether microbial community structure significantly differed across sampling dates.

To quantify the magnitude of temporal changes in community composition, we calculated weighted Unifrac distances between replicates (n = 3) across successive

sampling dates, which reflects the degree of compositional turnover overtime. For each sampling date, mean intra-replicate distances were also quantified and compared with inter-timepoint distances using a Wilcoxon rank-sum test. In addition, the relationship between inter-timepoint Unifrac distances and temporal lag (days between sampling dates) was examined using linear regression.

Following the framework proposed by Stegen et al. [22], we used a null model approach to infer the dominant ecological processes (deterministic vs. stochastic) driving these changes. The null model was built by calculating 1000 Inter Sample Distance (ISD) values between random pairs of samples drawn from the entire series, providing an expected distribution of ISD under the assumption of random community variation. Observed ISD (ISD<sub>obs</sub>) values were then compared to the null distribution (ISD<sub>null</sub>) to identify the dominant process at each time step. If ISD<sub>obs</sub> > 90th percentile of ISD<sub>null</sub>, community turnover was greater than expected by chance, suggesting that environmental filtering (deterministic selection) was the dominant process. If ISD<sub>obs</sub> < 10th percentile of ISD<sub>null</sub>, community turnover was lower than expected by chance, indicating homogenizing dispersal, where physical mixing (e.g., sediment resuspension) may have led to similar communities across time. Finally, if ISD<sub>obs</sub> fell between the 10th and 90th percentiles of ISD<sub>null</sub>, community change was not significantly different from random expectations, and ecological drift (random demographic fluctuations) was inferred to be the prevailing process.

To explore the influence of environmental factors on these temporal dynamics, we conducted a distance-based redundancy analysis (dbRDA) [42], using the best-fitting model selected by forward selection. The significance of the overall model and individual axes was tested using permutation-based ANOVA.

### Weighted co-occurrence network analysis

Microbial co-occurrence network was built from pruned matrix containing OTUs with relative abundance more than 0.01% of the total number sequences. The resulting matrix contained 962 OTUs occurring at least in 25 times in 20% of sediment samples analyzed and accounted for 98.9% of the rarefied sequences dataset. Network modules (i.e., groups of strongly co-varying OTUs) were identified using the "WGCNA" R package [43] based a Pearson correlation adjacency matrix, with a soft thresholding power of 14. To enhance network robustness, the adjacency matrix was converted into a Topological Overlap Matrix (TOM), and OTUs were hierarchically clustered into modules using a dynamic tree cut algorithm. Different minimum module sizes (10, 20, and 30 OTUs) were tested, and a threshold of 30 OTUs was retained
as it allowed modules to be clearly separated at a 70% similarity distance, while avoiding the merging of clusters sharing high similarity (>70%). For each module, we computed a single representative abundance profile — hereafter referred to as the “module profile” — by calculating the first principal component (eigengene) of the standardized abundance table of all OTUs belonging to that module, as implemented in the moduleEigengenes() function of WGCNA [44]. This module profile summarizes the main temporal trend of variation shared by the OTUs within the module. Hub OTUs (i.e., the most central taxa within each module) were identified using the chooseTopHubInEachModule() function from the WGCNA package, which selects the node with the highest intramodular connectivity.

To assess how microbial network modules responded to environmental variation, Pearson correlations were computed between modules profiles and environmental variables. In addition, Pearson correlations were also calculated between individual OTU abundances and environmental parameters. To estimate the proportion of variance explained by environmental factors, variance partitioning analysis was performed. Significant environmental drivers were selected through a stepwise regression procedure that maximized the adjusted r2 and minimized the Akaike Information Criterion (AIC) [45]. Statistical significance was assessed by 1000 permutations of the reduced model.

Significant Pearson correlations between all pairs of 962 OTUs (p<0.05, post Benjamini-Hochberg correction [46]) with R>0.6 or R<-0.6 were retained for network visualization and calculation of topological parameters using Gephi software (version 0.10.0).

All statistical analyses were performed using the free R software (version 3.5.3). Data and R codes are available at the Zenodo repository https://doi.org/10.5281/zenodo.14615243.

# Results

## Environmental context

Water temperature decreased over the sampling period from 27 °C in July to 10 °C in December (Fig. 1a) with an average daily variation of 1.5 °C. The inferred wave energy revealed several brief and well-defined events occurred in September, October and late November, each lasting only a few days and standing out from the background variability. (Fig. 1b). Nitrate and phosphate measured in sediment porewater fluctuated through the sampling period (Fig. 1c) and ranged from 0.003 to 0.88 mg l⁻¹ and from 0.03 to 0.2 mg l⁻¹, respectively. Higher nitrate concentrations were observed in November and December period. The organic matter content of the sediment ranged from 1.05% to 2.54%, with higher values observed in November (Additional file 1, Fig. S2). The C/N ratio of the

sediment organic matter decreased over sampling time period ranging on average from 20 in July and August to 14 in November and December.

## Temporal dynamic and drivers at the community-scale

The NMDS ordination from weighted Unifrac distances revealed temporal shifts in bacterial community structure (confirmed by ANOSIM: R=0.5, p=0.001; Fig. 2a) although substantial variability among replicates from the same sampling date was also observed. Quantification of intra- versus inter-timepoint dissimilarity confirmed that inter-timepoint UniFrac distances were significantly higher than intra-replicate distances (Wilcoxon test, p=0.003; Fig. S3a), demonstrating that temporal turnover exceeded replicate-level variability. Moreover, inter-timepoint UniFrac distances increased significantly with temporal lag (R²=0.18, p<0.001; Fig. S3b), indicating progressive divergence of microbial communities over time.

dbRDA analysis on weighted Unifrac distances with our environmental constraints explained 20% of microbial community variation over time (Fisher test, P<0.01) (Fig. 2b). According to variance partitioning, temperature was the primary driver of the microbial variability accounting for 12.4% of the total variance. It was followed by the inferred wave energy (3%) and C/N ratio (2%).

To further investigate temporal dynamics of microbial community, we used the mean weighted Unifrac distance values among triplicates between successive sampling times compared to those generated from our null model, (Fig. 2c) to identify possible ecological processes involved in the microbial community dynamics. The ISD values fluctuated between 0.04 to 0.10, with none exceeding the 90th percentile of the null model associated to selection process. ISD values falling below the 10th percentile of the null model, indicative of homogenizing processes, were primarily observed in July and October. The remaining ISD values fell within the range encompassing 80% of the null model's values and were attributed to ecological drift.

## Microbial co-occurrence network and module identification

The microbial co-occurrence network built using all replicates over the 44 sampling sessions comprised 962 nodes (OTUs) linked by 4711 edges (Fig. 3a and Additional file 1, Fig. S4 for taxonomic information). The modularity, which reflects the tendency of the network to subdivide into subcommunities (modules) that are more highly connected among themselves than with the rest of the network, was 0.61.

The weighted network analysis based on TOM similarity matrix identified 6 modules (Fig. 3a) defining cluster of OTUs with similar co-variation patterns across
<figure><img src="image_4.png" /><figcaption>**Fig. 2** Temporal dynamic and drivers of microbial genetic structure. Ordination plots of (a) Non-Metric Multidimensional Scaling (NMDS) and (b) Distance-based Redundancy Analysis (dbRDA) fitted with significant environmental variables, both based on the weighted pairwise Unifrac distances for sediment microbial communities collected at each sampling date in triplicates. The inset indicates the variance explained by each variable following variance partitioning. (c) Inter-Sample Distance (ISD) representing the magnitude of change of the microbial community between two sampling dates based on the weighted Unifrac distance. Blue and white dots suggest predominance of "Homogenizing" and "Ecological drift" processes in microbial dynamic, respectively. Horizontal dashed gray lines refer to ISD quantiles generated by the null model using 10³ random distance permutations. Vertical gray bars highlight the mean wave energy (J m⁻¹) characterizing the inferred hydrodynamic events that occurred during sampling period</figcaption></figure>

the entire time series. Modules were characterized by dif-
ferent sizes and microbial composition (Fig. 3b, 3c and
Additional file 1, fig. S5). Examination of the correlation
structure within modules showed that all intramodular
associations were positive, indicating that modules reflect
groups of OTUs with synchronous temporal dynamics.
The two major modules (M2 and M3) involved 255 and
222 OTUs accounting for 25% and 36% of the total ampli-
fied microbial sequences, respectively. M2 was domi-
nated by Bacteroidetes and Betaproteobacteria affiliated
OTUs (23% and 20% of module sequences) while M3 was
dominated by Gammaproteobacteria affiliated OTUs
(42% of module sequences for 34 OTUs). The modules

M4, M5 and M6 had similar richness and sizes as they
comprised from 133 to 148 OTUs and accounted respec-
tively for 13%, 11% and 12% of the total number of reads
but differed in their OTU compositions. Specifically,
M4 had the most even distribution of microbial classes
with co-dominance of Gammaproteobacteria and Delta-
proteobacteria affiliated OTUs (25% and 20% of module
sequences, respectively) followed by Chlorobi, Betapro-
teobacteria, and Bacteroidetes. M5 was mainly composed
of Deltaproteobacteria representing 43% of the module
sequences while M6 had a co-dominance of Cyanobac-
teria and Betaproteobacteria (23% and 19% of module
sequences, respectively). Finally, the minor module (M1)
<figure><img src="image_5.png" /><figcaption>**Fig. 3** Sediment microbial network and modules characteristics. a Sediment microbial network constructed from all sediment samples across the full time series. Nodes (circles) represent OTU, colored according to their module (i.e., cluster of highly interconnected OTU). Node size is proportional to node degree. Edges represent significant and strong positive (gray curves) and negative (orange curves) correlations between OTUs (Pearson R ≥ 0.6, P < 0.01). b Modules sizes, with the number of OTUs indicated above each bar. c Taxonomic composition of each module. Dot colors represent bacterial phyla. Phyla representing less than 1% of the total sequence relative abundance are groups as "Minor phyla". The Proteobacteria phylum is displayed at the class level</figcaption></figure>

accounted for only 1.7% of the total amplified microbial sequences and was dominated by Bacteroidetes and Verrucomicrobia (24% and 16% of module sequences, respectively).

## Temporal dynamics and environmental drivers at the microbial module scale

Each of the 6 identified modules (M1 to M6) displayed distinct temporal dynamics, as illustrated by their module profile (Fig. 4a). M1 showed no clear tendency of temporal variation and was characterized by a pronounced peak in early September (mean eigengene score = 0.20 ± 0.27). M2 increased steadily from mid-July to a maximum in mid-August (0.25 ± 0.004), before gradually declining toward December (−0.06 ± 0.04). M3 showed low activity from July to September (e.g., eigengene score < 0), followed by a marked increase in October (0.15 ± 0.04), a plateau phase through mid-November, and a subsequent decline in late November. M4 and M5 exhibited similar bimodal patterns: both peaked in mid-July (M4 = 0.17 ± 0.06; M5 = 0.28 ± 0.14), declined

afterward, and showed a secondary increase in November–December. M6 followed a distinct dynamic, remaining stable and low until mid-September, then peaking sharply (maximum = 0.19 ± 0.008 in September), before returning to lower values in October. A second rise was observed from early November to early December.

Multiple regression model with environmental parameters allowed to explain the dynamics of microbial modules (Fig. 4b and 4c). Temperature was the best driver, significantly explaining the variation in five module profiles. Temperature showed a positive correlation with the profiles of M2 and M5, while it was negatively correlated with M3, M4, and M6 (Fig. 4b). The quality of sediment organic matter, as indicated by the C/N ratio, accounted for 3%, 4%, and 6% of the variation in the profiles of M1, M2, and M3, respectively. Wave energy was identified as the third key driver, explaining 3%, and 13% of the temporal dynamics of modules M3 and M6. It was positively associated with M3 and negatively with M6. Nitrate concentration explained variations in the M3 module profile,
<figure><img src="image_6.png" /><figcaption>**Fig. 4** Module dynamic and relationships with environmental variables. a Temporal variation of each module profile (i.e., eigengene), defined as the first principal component of standardized relative abundances of OTUs within each module. Values represent the mean ± standard deviation across replicates (n = 3) at each sampling date. The Y-axis reflects standardized, unitless eigengene scores summarizing the dominant abundance trend within each module. Dashed lines indicate the overall temporal mean of each module profile. b Pearson correlation coefficients between module profiles and environmental variables (diamonds: significant at P < 0.01; squares: not significant). Boxplots show the distribution of Pearson correlations between individual OTUs within each module and the same environmental variables. c Variance partitioning results showing the relative contribution of environmental variables in explaining the temporal variation of each module profile</figcaption></figure>

whereas total phosphorus concentration did not emerge as a significant driver for any of module profiles.

Interestingly, contrasting patterns in response to environmental parameters were observed between the two dominant modules, M2 and M3 (Fig. 4b). Notably, when significant correlations were detected, module profiles generally exhibited stronger associations (positive or negative) than the overall OTU response (Fig. 4b).

## Discussion

This study aimed at tracking the temporal dynamics of the microbial (bacterial and archaeal) community in the littoral surficial sediment of a large peri-Alpine Lake

(Lake Bourget). We specifically focused on fine time scale of investigations to align with time scale (i.e., days) over which microbial community can respond to environmental changes [20, 47]. Temporal pattern of sediment microbial community was scrutinized at two different organizational scales: the community and sub-community scales (modules). We evaluated how microbial dynamics unfold over time at varying levels of organization and determined the ecological processes (deterministic versus stochastic) underpinning these dynamics.
Deciphering temporal pattern at community scales and
underlying ecological processes

Sediment microbial community showed temporal
changes; however, this pattern was not strongly pro-
nounced, as microbial compositions from different dates
tended to overlap. This result is consistent with previ-
ous studies showing that microbial communities in sedi-
ments exhibit greater stability compared to those in the
water column, likely due to the more stable conditions
in the sediment environment [48, 49]. Additionally, sedi-
ments act as repository environment, storing DNA from
both active and inactive microbial communities, includ-
ing dead or dormant species, as well as microbial DNA
transferred from the water column by sedimentation
[50]. In times-series studies based on DNA analysis this
accumulation can mitigate the detection of true temporal
dynamics within sediment microbial communities.

Interestingly, high intra-replicate variability was
observed over time within the sediment sampling area.
Similar findings have been reported in various aquatic
environments [51, 52], suggesting strong sediment het-
erogeneity at small spatial scales. In littoral lake zones,
this fine-scale spatial variability may be influenced by
localized differences in nutrient and organic matter com-
position, particle size distribution, and the presence of
other organisms [53]. Sediment heterogeneity in the sam-
pling area may also contribute to dampening the tempo-
ral patterns observed at the community scale. Although
intra-replicate variability was substantial, it remained
lower than temporal turnover across the sampling period.
This pattern is consistent with ecological observations in
other systems, where the majority of variance is often
expressed at the smallest examined scales [54], but in our
case temporal dynamics still emerged as the dominant
structuring factor.

Temperature acted as the dominant environmental
driver of the microbial temporal pattern while wave-
induce hydrodynamic as well as C/N ratio had a signifi-
cant yet secondary influences. Ranking temperature as
major microbial driver is due to the strong thermal gra-
dient covered by our study during the autumn–winter
period (27 °C to 10 °C), hence acting as press perturba-
tion for sediment microbial communities over time. This
result is in line with the clear seasonal successions com-
monly observed for microbial communities in open water
[55–57]. C/N ratio is also a common driver constraining
microbial communities inhabiting lake sediment [58, 59].
In our case, C:N ratio tended to decreased over time and
was associated to temperature drop suggesting a link
between temperature and resource quality. One possi-
ble explanation is the sedimentation of decaying littoral
macrophytes and/or phytoplanktonic communities along
water column during autumnal-winter period, enriching
surficial sediment with low C:N organic material [60].

The non-negligible implications of wave-induced
hydrodynamic as independent from temperature and
C:N ratio clearly signing its rather independent effect
from seasonal variation due to the stochastic occurrences
of meteorological events. Wave-induced hydrodynamic is
expected to enhance homogenization processes, thereby
disrupting organization of microbial niches. In addition,
wave-induced hydrodynamics contribute at supplying
nutrient and organic matter to sediments supporting pos-
sible temporal variations at local scale [22, 61, 62]. Over-
all, the three environmental parameter outputs from our
model explained a restricted fraction of microbial tempo-
ral pattern (only 17.6% of variance explained) suggesting
that other abiotic and/or biotic variables not measured in
this study should have been taken into account.

In line with the sparse variance explained by environ-
mental drivers, selection did not appear at any time as
an ecological process shaping microbial dynamic during
sampling time period (ISD never exceeded 90% quan-
tile of null model). Although dbRDA showed that tem-
perature explained a modest but significant fraction
(~12%) of the total variance variance across the whole
time series, this apparent discrepancy highlights a scale
dependence: while stochastic processes dominate short-
term transitions between sampling dates, deterministic
selection associated with the broader seasonal tempera-
ture gradient becomes detectable at longer temporal
scales. In addition, microbial temporal dynamic did not
reveal clear peaks of community composition change
(ISD values) following meteorological events, as was ini-
tially expected. Thus, our hypothesis that wave-induced
hydrodynamic would provoke extensive microbial rear-
rangements, followed by stable meteorological condi-
tions associated with periods of low microbial turnover,
was not supported. However, 45% of ISD values were
lower than the 10% quantiles of our null model, indicat-
ing episodes of homogenizing dispersal. These occurred
at several occasions outside meteorological events, such
as in late July and more occasionally from mid-September
to mid-November. This suggests that periods of homoge-
nizing dispersal may also be driven by other factors, such
as biological perturbations or hydrodynamic processes
at larger scales (e.g. internal seiches [63]). The studied
area shelters benthivorous fish (e.g. carps, breams) as
well as diving birds (e.g. coots), that can actively resus-
pend surficial sediment over several centimeters depth
and participate in the homogenizing dispersal process
of microbial community. Complementarily, the remain-
ing 55% of ISD values fell within the 10%–90% quantiles
of the null model, suggesting that community drift was a
dominant ecological process shaping sediment microbial
community. Taken together, these results indicate that
short-term sediment microbial dynamics were primarily
driven by stochastic processes (drift and homogenizing
dispersal), whereas deterministic selection associated with seasonal gradients (e.g., temperature decrease from 27 °C to 10 °C) became apparent over longer temporal scales.

## Sub-communities analysis revealed distinct temporal pattern

Module detection applied in co-occurrence network enable to explore the temporal dynamics of sediment microbial community at a finer organizational scale. The resulting microbial network exhibited high modularity index (0.61) indicating a strong tendency for the network to partition into distinct modules [64]. In network theory, modules represent groups of species that occupy similar ecological niches [16] often reflecting habitat heterogeneity and niche diversification within the ecosystem [65]. In this study, six distinct modules were identified, each differing in size (relative sequence abundance), diversity (number of OTUs per module), and taxonomic composition, suggesting the coexistence of distinct microbial niches over the examined period. This finding aligns with the observed high intra-replicate variability at the community level, underlining the considerable heterogeneity of sediment within the sampling site.

As expected, analyzing microbial sub-communities through network-derived modules revealed temporal patterns that differed from those observed at the broader community scale. We identified six distinct temporal patterns, indicating a succession of module variation over time. Temporal dynamic of microbial modules appeared to be linked to changes in environmental conditions during the sampling period, which spanned from late summer to early winter. Variance partitioning analysis on module eigengenes revealed similar drivers than community scale, but the overall explained variation was more significant. For instance, temperature was identified as the most significant driver of module temporal patterns, accounting for up to 45.8% of the variation in the two largest modules (M2 and M3), as compared to the 12% obtained at the community level. This suggests that deterministic processes, such as selection by environmental filters, play a more dominant role in the microbial dynamics at the module scale than at the community scale, where stochastic events might have a greater influence. Although statistical effects of grouping cannot be entirely excluded, the modular approach highlights subsets of taxa that respond coherently to environmental drivers, thereby revealing ecological signals that remain hidden at the broader community scale. While we recognize the potential influence of compositionality and temporal autocorrelation, our objective was not to infer direct ecological interactions but rather to detect groups of OTUs with similar temporal trajectories. In this respect, our use of WGCNA with Pearson correlations

follows previous studies that successfully applied this framework to microbial temporal dynamics [18, 19, 66].

Among these, the two largest microbial modules (M2 and M3) illustrate the strength of this approach. They exhibited contrasting associations with key environmental variables (temperature and C:N ratio), highlighting their distinct and potentially complementary ecological niches, succeeding over time. This pattern underlines that even within the same sediment community, different groups of taxa can follow divergent ecological trajectories in response to environmental change. The OTUs within these two modules both belong to the β-Proteobacteria class, but are affiliated with distinct genera, with different ecological characteristics. The most connected OTU (i.e., the top hub based on intramodular connectivity) in M2 was associated with the genus *Comamonas*. Members of this genus are aerobic and known for their diverse metabolic abilities, including the degradation of aromatic compounds, nitrate reduction, and denitrification [67]. This high metabolic versatility makes *Comamonas* a key player in oligotrophic sediments, where it can adapt to various nutrient conditions [68]. The most connected OTU in M3 was affiliated with *Dechloromonas*, a genus commonly found in lake sediments. *Dechloromonas* species are anaerobic and versatile, recognized for their ability to participate in anaerobic processes such as denitrification and the degradation of xenobiotic and recalcitrant organic compounds [69]. This genus can utilize nitrate and other electron acceptors under anoxic conditions, making it well-suited to lake sediments where environmental conditions often fluctuate between oxic and anoxic states.

The succession of these two central OTUs, along with their co-varying OTUs, may be influenced by the enrichment of surficial sediments with low C:N organic material during the autumn-winter period. Although oxygen was not measured in the surficial sediment, this organic enrichment can create a low-oxygen environment, favoring the establishment of *Dechloromonas* and other associated species over bacterial communities that interact with *Comamonas*. A similar seasonal pattern was observed by Yu et al., [70], who reported high abundances of *Dechloromonas* during autumn and winter in the sediments of the Zhangjiayan reservoir, linked to a decrease in dissolved oxygen. Consistent with these findings, Xue et al., [71] demonstrated that the addition of plant residues to sediment microcosms significantly increased the abundance of *Dechloromonas* within microbial communities. Interestingly, the succession observed between modules M2 and M3 might also reflect shifts in bacterial life-history strategies, with temperature changes differentially favoring slow- versus fast-growing taxa [72, 73]. Although our study did not directly infer growth strategies from 16S rRNA operon copy numbers, further
investigations are needed to directly link taxonomic turnover with functional traits such as growth strategies.

As observed at the community scale, the press perturbation characterized by the seasonal decrease in temperature had a stronger influence on the littoral microbial community than pulse perturbations expressed through short-term fluctuations of wave energy. At the module scale, wave-induced hydrodynamic activity accounted for a significant 13% of the variation in the M6 module profile, where it negatively impacted the abundance of its constituent OTUs. The central OTU in this module was affiliated with the Desulfobacteraceae family, which belongs to the group of Sulfate-Reducing Bacteria (SRB). Members of this family are strictly anaerobic and are involved in the breakdown of complex organic materials, contributing to the degradation of detrital substances in lake sediments [74]. The observed decline in the M6 module profile in response to wave-induced hydrodynamics could be attributed to the resuspension of sediment particles, leading to reoxygenation of the microbial habitat and a temporary decrease in the population of strict anaerobes.

## Conclusion

Applying ecological concepts to microbial communities remains challenging, and there is still no consensus on the relative contributions of stochastic versus deterministic processes in shaping microbial dynamics within ecosystems. Microbiomes are characterized by immense diversity, making it difficult to accurately assess species dynamics and their driving mechanisms at the community-scale. Our high-frequency survey in the littoral zone of a large peri-alpine lake suggests that random processes, such as drift and homogenizing dispersal, predominantly govern microbial dynamics in surficial sediments. However, when we downscaled from the community level to the module level, deterministic drivers, particularly environmental selection, appeared to play a more significant role. The presence of microbial modules that exhibit specific responses to environmental factors indicates the coexistence and succession of distinct microbial niches in littoral sediments over the study period-patterns that were not evident at the community scale. These findings suggest that considering multiple organizational scales within microbiomes can provide deeper insights into the balance between deterministic and stochastic processes driving microbial dynamics, revealing ecological patterns that may be overlooked when only the community scale is examined.

## Abbreviations

ANOSIM Analysis of similarity

dbRDA Distance-based redundancy analysis

DNA Deoxyribonucleic acid

ISD Inter sample distance

NMDS Non-metric multidimensional scaling

RNA Ribonucleic acid

OUT Operational taxonomic unit

PCR Polymerase chain reaction

TOC Total organic carbon

TOM Topological overlap matrix

TN Total Nitrogen

## Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12866-025-04512-4.

Supplementary Material 1. Supplementary Figures: The additional file contains four supplementary Figures presenting the study site, additional environmental variables measured over time, and taxonomic information on the microbial network and its modules

## Acknowledgements

The authors thank Dr. Emmanuel Naffrechoux for quantifying nutrient in sediment porewater, Dr. Damien Bouffard for early discussion on littoral hydrodynamics and field support and Annie Millery for the analyses of sediment composition.

## Authors' contributions

EL and VF design the study. EL and VF collect sediment samples and acquire chemical and microbiological data. VT analyzed and interpret data. VT and VF draft the work and EL revised the work. All authors read and approved the final manuscript.

## Funding

This study was financially supported through two grants obtained from Université Savoie Mont Blanc (AAP HyMic) and INRAE (research project leaded by Dr. Bernard Montuelle).

## Data availability

The raw 16S rRNA sequence dataset has been deposited in the European Bioinformatics Institute (EBI) database (Short Read Archive) under the project accession number PRJEB83682. The dataset analyzed, along with the R scripts used in this study, is available in the Zenodo repository at: https://doi.org/10.5281/zenodo.14615243.

## Declarations

### Ethics approval and consent to participate

Not applicable.

### Consent for publication

Not applicable.

### Competing interests

The authors declare no competing interests.

Received: 13 June 2025 / Accepted: 27 October 2025

Published online: 29 December 2025

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