# Annual bacterial community cycle in a seasonally ice-covered river reflects environmental and climatic conditions

Perrine Cruaud, Adrien Vigneron, Marie-stéphanie Fradette, Caetano Dorea, Alexander Culley, Manuel Rodriguez, Steve Charette

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## ► To cite this version:

Perrine Cruaud, Adrien Vigneron, Marie-stéphanie Fradette, Caetano Dorea, Alexander Culley, et al. Annual bacterial community cycle in a seasonally ice-covered river reflects environmental and climatic conditions. Limnology and Oceanography, 2019, Linking Metagenomics to Aquatic Microbial Ecology and Biogeochemical Cycles, 65 (S1), 10.1002/lno.11130. hal-05120402

HAL Id: hal-05120402

https://hal.science/hal-05120402v1

Submitted on 19 Jun 2025

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Distributed under a Creative Commons Attribution 4.0 International License
LIMNOLOGY
and
OCEANOGRAPHY

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Linnol. Oceanogr. 65, 2020, S21-S37

© 2019 The Authors. *Linnology and Oceanography* published by Wiley Periodicals, Inc. on behalf of Association for the Sciences of Limnology and Oceanography.

doi: 10.1002/lino.11130

# Annual bacterial community cycle in a seasonally ice-covered river reflects environmental and climatic conditions

Perrine Cruaud<sup>D, 1,2,3*</sup> Adrien Vigneron,<sup>1,4,5</sup> Marie-Stéphanie Fradette,<sup>1,2,3</sup> Caetano C. Dorea,<sup>6</sup> Alexander I. Culley,<sup>1,2,7</sup> Manuel J. Rodriguez,<sup>3,8</sup> Steve J. Charette<sup>1,2,9</sup>

## Abstract

Bacteria are key players in biogeochemical cycles and control water quality in freshwater ecosystems. Nevertheless, little is known about the identity and ecology of riverine bacteria, especially during ice-covered periods that are often mistakenly perceived as periods with negligible biological activities. Here, we analyzed in detail the effects of environmental and climatic conditions on freshwater bacterial community structure and diversity over a 2-yr sampling campaign, targeting a seasonally ice-covered river of the Quebec City (Canada) area, the Saint-Charles River. Quantitative polymerase chain reaction and 16S ribosomal RNA gene high-throughput sequencing demonstrated a strong seasonal cycle of the bacterial community composition with rapid successions of bacterial lineages, reflecting the harsh climatic condition of the region. During the summer, the bacterial community was dominated by typical freshwater microorganisms such as *Limnohabitans*, *Sporichthyaceae* hgcl/acl clade, and *Pseudarcicella*. In contrast, the results suggest that during the cold season, the low water temperatures, combined with other prevailing conditions such as reduced light availability and minimal particulate inputs from the catchment, created various environmental niches for potential methanotrophic *Gammaproteobacteria*, such as *Crenothrix* and *Methylobacter*, other *Betaproteobacteria*, such as *Candidatus* Methylopumilus, *Candidatus* Nitrotoga, and *Rhodoferax*, as well as *Verrucomicrobia* and *Parcubacteria*. The presence of these taxa in the winter suggests active carbon, iron, and nitrogen cycling under ice, whereas summer lineages are dormant or in a phase of reduced activity. These results increase our understanding of bacterial dynamic and potential metabolic processes occurring in seasonally ice-covered inland waters, providing evidence that winter can be an especially important period for freshwater ecological processes.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Additional Supporting Information may be found in the online version of this article.

**Special Issue:** Linking Metagenomics to Aquatic Microbial Ecology and Biogeochemical Cycles. Edited by: Hans-Peter Grossart, Ramon Massana, Katherine McMahon and David A. Walsh.

Many lakes and rivers are located in the northern hemisphere, in high-latitude or high-altitude regions, where air temperatures remain under 0°C for a part of the year, resulting in frequent and persistent snow and ice cover on land and surface water (Weyhenmeyer et al. 2011). Consequently, about half of the world's lakes (~ 50 million lakes; Verpoorter et al., 2014; Hampton et al. 2017) and probably as many rivers are seasonally covered by ice. Under this condition, surface waters are characterized by low temperature and restricted light penetration when snow accumulates, limiting photosynthetic activities. Limited light penetration also reduces organic

<footnote n="1"><sup>1</sup>Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, Québec, Canada</footnote>

<footnote n="2"><sup>2</sup>Département de Biochimie, de Microbiologie et de Bio-Informatique, Faculté des Sciences et de Génie, Université Laval, Québec City, Québec, Canada</footnote>

<footnote n="3"><sup>3</sup>Centre de Recherche en Aménagement et Développement (CRAD), Université Laval, Québec City, Québec, Canada</footnote>

<footnote n="4"><sup>4</sup>Centre d'Études Nordiques (CEN), Université Laval, Québec City, Québec, Canada</footnote>

<footnote n="5"><sup>5</sup>Département de Biologie, Université Laval, Québec City, Québec, Canada</footnote>

<footnote n="6"><sup>6</sup>Department of Civil Engineering, University of Victoria, Victoria, British Columbia, Canada</footnote>

<footnote n="7"><sup>7</sup>Groupe de Recherche en Ecologie Buccale (GREB), Faculté de Médecine Dentaire, Université Laval, Québec City, Québec, Canada</footnote>

<footnote n="8"><sup>8</sup>École Supérieure d'Aménagement du Territoire et de Développement Régional (ESAD), Université Laval, Québec City, Québec, Canada</footnote>

<footnote n="9"><sup>9</sup>Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec, City, Québec, Canada</footnote>

<footnote>*Correspondence: ferrine.cruaud@gmail.com</footnote>
matter photodegradation, altering the quality and biodegrad-
ability of the organic carbon available to microorganisms
(Ghylin et al. 2014; Cory and Kling 2018). Ice cover also
reduces gas exchange with the atmosphere and particulate
inputs from the surrounding landscape (Catalan 1992;
Kankaala et al. 2006; Bertilsson et al. 2013; Hampton et al.
2017; Denfeld et al. 2018). Furthermore, with the decrease in
temperature and light availability, many algae and plants, in
the water, as well as in the ground, enter into dormancy
(Perry 1971; Lacoul and Freedman 2006) with major conse-
quences for biogeochemical cycles and nutrient availability.

These long-held perceptions have led to the assumption that
the cold season in shallow freshwater ecosystems is a “dormant
season” in contrast to the warm “growing season”. However,
recent research has shown that the winter can be a season of
important biological activity in freshwater ecosystems
(Campbell et al. 2005; Kankaala et al. 2006; Bertilsson et al.
2013; Hampton et al. 2017; Tran et al. 2018). Indeed, many
biological processes persist throughout the winter while others
initiate and develop at low temperatures. As ice cover results in
a lower availability of organic substrates from terrestrial input
or phytoplankton, other energy and carbon sources, not
restricted by ice cover, might be used by other organisms.
Organic matter degradation, scavenging of decaying photosyn-
thetic biomass and chemolithotrophic pathways have been
identified in under-ice freshwater ecosystems, indicating that
under-ice microbial communities may also contribute to carbon
and energy cycling (Sundh et al. 2005; Kankaala et al. 2006;
Tran et al. 2018). Higher nitrogen and nitrate concentrations
have been also frequently observed in freshwater ecosystems
during the cold season (Suberkropp et al. 1976; Hampton et al.
2017), indicating changes in nitrogen consumers and producers
as well as associated processes at low temperatures. These stud-
ies have demonstrated the importance of the cold season in
freshwater nutrient cycling. Furthermore, a feedback loop
between seasons has been observed for abiotic and biotic vari-
ables, with summer conditions influencing winter ecology and
vice versa (Hampton et al. 2017).

Although the number of studies investigating winter ecol-
ogy in lakes is increasing (e.g., Sundh et al. 2005; Kankaala
et al. 2006; Bižić-Ionescu et al. 2014; Hampton et al. 2017;
Tran et al. 2018), other inland waters, such as rivers and
streams, remain poorly investigated. However, riverine eco-
systems should not be considered as simple passive pipes
but as biophysical reactors that could transform the geochemi-
cal compounds they transport and thus must be considered as
important contributors to global biogeochemical cycles (Cole
et al. 2007; Tranvik et al. 2009; Proia et al. 2016). It is also
clear that in order to understand the impact of rivers on
biogeochemical cycles, we must have a better understanding
of how pronounced seasonal variations affect these ecosystem-
s including the associated aquatic microbial communities and
processes. Nevertheless, year-round investigations of ecological

and biogeochemical traits of riverine microorganisms, including
microbial community dynamics, microbial interactions with
other organisms, and biogeochemical processes, factors that
together strongly influence water quality, remain scarce. Fur-
thermore, a better understanding of the ecological and biogeo-
chemical processes that occur during the winter period in
riverine environments is both important for the present days
and also for predicting how ecosystems will respond to future
climate change.

Here, we investigated bacterial communities inhabiting a
seasonally ice-covered river in order to (1) determine the
annual dynamics of the bacterial community composition
under pronounced seasonal gradients, (2) identify the main
components of the bacterial communities during the warm
and cold seasons, and (3) better understand the influence of
environmental parameters (e.g., temperature, ice cover, rain-
fall events, organic carbon, and nutrient availability) on the
bacterial community structure and hence water quality.
Finally, in the light of the results, we discuss the ecological
strategies potentially employed by microbial lineages for sur-
vival and growth in river ecosystems. The study was carried
out in the Saint-Charles River, the main source of drinking
water in the city of Quebec, Canada. Microbial community
analysis of samples collected over 21 months and covering
two summer and two winter seasons has allowed us to assess
microbial community cycle of the river. In combination with
data on physicochemical parameters measured throughout
the sampling period, this study provided a unique opportu-
nity to better understand microbial ecology and geochemical
processes of seasonally ice-covered rivers.

Materials and methods

Sampling sites and methods

The Saint-Charles River springs from the Saint-Charles Lake (Quebec, Canada). The Saint-Charles River watershed is primarily characterized by forest and residential areas (APEL, 2014), and the river flow is mainly controlled using flood gates associated with the Saint-Charles Lake dam. Water samples from the river were collected weekly or bimonthly at the raw water intake of the Loretteville Drinking Water Treatment Plant (DWTP, Quebec City, Canada), located along the Saint-Charles River, 11 km downstream of the Saint-Charles Lake, from May 2016 to June 2017 with additional samples collected on 31 January 2018 and 12 February 2018. Water was collected in three different sterile and nucleic acid-free 4-liter Cubitainers (replicates) and then transported at 4°C to the laboratory for processing within 2 h.

For DNA analyses, approximately 400 mL of water was first
passed through a 20-µm mesh and then filtered through a 3-µm
pore size, 47-mm diameter polycarbonate membrane filter
(Whatman, GE Healthcare; *large-pore-size filter*) in line with a
0.22-µm Sterivex unit (MF-Millipore Membrane; *small-pore-size*
filter) using a peristaltic pump as described in Crevecoeur et al. (2015). Filtration was carried out in duplicate with two sets of filters per Cubitainer. Sequential filtration through both large- and small-pore-size filters was performed to separate particle-associated bacteria or large cells from small and free-living bacteria. Negative controls for the filtration step were carried out by filtering 400 mL of sterile water through both large- and small-pore-size filters. Thus, eight negative controls (four large- and four small-pore-size filters) were achieved at four different dates between the beginning and the end of the sampling campaign. Filters of environmental samples and negative controls were stored at -80°C until further analyses.

## DNA extraction

For each sampling date, nucleic acids were extracted from two 3-µm-pore-size filters and two 0.22-µm Sterivex units. Before DNA extraction, 3-µm filters and 0.22-µm filters removed from their casing were cut into small pieces with a sterile scalpel as described in Cruaud et al. (2017b). DNA extraction was subsequently performed with the AllPrep DNA/RNA mini kit (QIAGEN), as described in Cruaud et al. (2017b). Negative controls for the DNA extraction step were also carried out by achieving all steps of nucleic acid extraction without any sample. In total, eight negative controls of DNA extraction were achieved in parallel with DNA extractions from samples. DNA extracts were stored at -20°C until processed.

## DNA amplification and sequencing

The library preparation approach involved two polymerase chain reaction (PCR) steps with different primer pairs, providing an opportunity to build on existing primers and PCR conditions, as described in Cruaud et al. (2017a). During the first PCR step, ribosomal RNA (rRNA) genes were amplified (from two 3-µm filters and two 0.22-µm filters per sampling date) using 16S rRNA bacterial primers (Bakt341F and Bakt805R [Herlemann et al. 2011]). Each reaction contained 5 µL of Green GoTaq Reaction Buffer (Promega), 0.5 µL of deoxyribo-Nucleotide TriPhosphate (dNTP) mix (40 mmol.L⁻¹ total), 0.05 µL of each primer (100 µmol.L⁻¹), 0.125 µL of GoTaq DNA polymerase (Promega), and 1 µL of DNA template. The final volume was adjusted to 25 µL with sterile water. PCR conditions were 95°C for 2 min; 30 cycles that included 30 s at 95°C, 30 s at 58°C, and 45 s at 72°C; and a final extension at 72°C for 10 min. All amplifications were completed on an Eppendorf Mastercycler pro PCR System (Eppendorf). Four negative controls for the first PCR amplification step were carried out using sterile water as DNA template. The amplicons were visualized on 1% agarose gels stained with ethidium bromide, and absence of contamination was checked for all the 20 negative controls (no band on the agarose gels). Nonethe-less, negative controls were kept for the next steps.

Illumina MiSeq adaptors and barcodes were added during the second PCR step. Each reaction contained 5 µL of Green

GoTaq Reaction Buffer (Promega), 0.5 µL of dNTP mix (40 mmol.L⁻¹ total), 1 µL of each primer (10 µmol.L⁻¹), 0.125 µL of GoTaq DNA polymerase (Promega), and 1 µL of corresponding PCR1 product. The final volume was adjusted to 25 µL with sterile water. PCR conditions were 95°C for 2 min; 10 cycles of 95°C for 40 s, 55°C for 45 s, and 72°C for 60 s; and a final extension at 72°C for 10 min. Four negative controls for the second PCR amplification step were also carried out using sterile water instead of PCR1 product.

PCR amplifications were then pooled and purified on an agarose gel using the QIAquick Gel Extraction Kit (QIAGEN). The library was then quantified using the QuBit DNA HS Assay Kit (Life Technologies) and a QuBit 3.0 Fluorometer (Life Technologies) following the manufacturer's instructions and then paired-end sequenced on an Illumina MiSeq sequencer using a V3 MiSeq sequencing kit (2 × 300 bp) at the Institut de Biologie Intégrative et des Systèmes (IBIS) sequencing platform (Université Laval, Quebec, Canada). Image analysis, base calling, and data quality assessment were performed on the MiSeq instrument. The raw sequencing data have been submitted to the National Center for Biotechnology Information database under BioProject accession number PRJNA486319.

Negative controls carried out at each step (water filtration, DNA extraction, and PCR amplifications) were sequenced as samples even if no contamination was revealed by visualization on 1% agarose gels stained with ethidium bromide. In addition, we added unused index combination in the sample sheet of the Illumina sequencing run to measure the magnitude of tag switching events (mistagging) (Kircher et al. 2011; Esling et al. 2015).

## Sequence analyses

Quality checks were performed on raw sequence data with FastQC v.0.11.5. Overlapping paired-end reads were reassembled using FLASH v2.2.00 (Magoč and Salzberg 2011) with default settings and extended maximum overlap length (350 bp). CUTADAPT v1.12 (Martin 2011) was used to remove primers and to filter out sequences shorter than 350 bp. Sorted sequences were dereplicated, clustered into Operational Taxonomic Unit (OTUs) (97% similarity), and putative chimeric sequences and singletons were removed using VSEARCH v.2.3.4 (Rognes et al. 2016). Finally, taxonomic assignments of the reads were performed using the *Mothur* version of the “Bayesian” classifier (Schloss et al. 2009), on the SILVA database (release 128; https://www.arb-silva.de/) and on the freshwater 16S rRNA gene sequence database curated by the McMahon-Bertilsson research groups (updated February 2016; https://github.com/mcmahonuw/FWMFG; Newton et al. 2011). Analysis scripts, documentation, and further details are available at https://github.com/CruaudPe/MiSeq_Multigenique. Taxonomic affiliations given in the text and figures were obtained with the SILVA database. In addition, taxonomic affiliations based on the Newton et al. (2011) database were also indicated in brackets for lineages of interest.
The few sequences obtained for the negative controls were considered as potential contamination and were filtered from OTU tables, as follows. For each OTU, the maximum number of sequences detected in negative controls were subtracted from the sequence number obtained for each sample. If the number of sequences in samples was lower than those of the negative controls, sequence number was set to 0 for these samples. OTUs affiliated with *Archaea* or *Eukaryota* were also removed, and results were normalized to keep each sample with the same number of sequences (14,548 sequences).

## Quantitative real-time PCR

The bacterial abundance at 19 sampling dates were estimated by quantitative PCR (qPCR; two replicates of large-pore-size filter and two replicates of small-pore-size filter for 19 sampling dates). Amplifications were performed with a Step One Plus instrument (Life technologies), according to the manufacturer's instructions, in a final volume of 25 µL using Brilliant III Ultra-Fast QPCR Master Mix (Agilent Technologies), 1 ng of crude DNA template, and 700 nmol.L⁻¹ of 16S rRNA bacterial primers (BACT1369F and PROK1492R [Suzuki et al. 2000]; annealing temperature of 60°C). Standard curves were obtained in triplicate with 10-fold serial dilution (10²–10⁶ copies · µL⁻¹) DNA from *Methylomonas methanica* (DSM25384). The efficiency of the qPCR was close to 100%, and the R² of standard curves were close to 0.99. qPCR results were expressed in copy number per milliliter of filtered water.

## Environmental parameters

Standard physicochemical (ultraviolet [UV] absorbance, alkalinity, total nitrogen, total organic carbon [TOC], dissolved organic carbon [DOC], conductivity, apparent color, $NO_3^-$/$NO_2^-$, pH, total phosphorus, and turbidity) and microbiological (total coliforms and *Escherichia coli*) water quality parameters were measured in the certified water quality laboratory of Quebec City following standard procedures (APHA, AWWA, and WEF 2012). The water temperature and information on the opening of the floodgates were provided by the Loretteville Water Treatment Plant operators. Air temperatures, precipitations, and snow cover were obtained from the neighboring weather stations (Municipality of Quebec and Environment Canada, National Climate Archive). Environmental variables were standardized for zero mean and univariance (function *decostand* and method *standardize*) using the software environment R (v. 3.4.4).

## Statistical analyses

All statistical analyses (Shannon diversity indices, Bray-Curtis indexes, Unweighted Pair Group Method with Arithmetic Mean (UPGMA), Non-Metric Multidimensional Scaling (NMDS), Non-Parametric Multivariate ANalysis Of VARIance (NP-MANOVA), Student t-tests, and Pearson's correlation tables) were conducted using the software environment R (v. 3.4.4) and the RStudio toolkit (v. 1.0.143) implemented with *Vegan* (Oksanen

et al. 2007), *stringr* (Wickham 2015), *dendextend* (Galili 2015), *gplots* (Warnes et al. 2015), *plothrix* (Lemon 2006), and *corrplot* (Wei et al. 2017) packages.

As microbial communities from duplicate samples strongly clustered together in UPGMA dendrogram analyses (Supporting Information Fig. S1), an average relative proportion of each OTU was therefore calculated for each sampling date for the subsequent analyses.

## Results and discussion

### Seasonal cycle

Water from the Saint-Charles River was collected and analyzed in detail from 35 time points between May 2016 and February 2018, and sequential filtration using large- and small-pore-size filters (3 and 0.22 µm, respectively) was performed to separate particle-associated bacteria or large cells from free-living bacteria.

Based on 16S rRNA gene sequencing, differences in bacterial community compositions were detected between water samples collected during the *warm season* (mid-May/early October, water temperature >10°C, hereafter “summer”) and those collected during the *cold season* (January/mid-May, water temperature <10°C, hereafter “winter”; UPGMA dendrograms and NMDS ordination plots with Bray-Curtis similarity indices and NP-MANOVA, *p* < 0.001; Fig. 1; Supporting Information Figs. S1, S2). The bacterial community structure associated with samples collected between October and December did not show clear clustering with either group but appeared to be intermediate between the two groups (Fig. 1; Supporting Information Figs. S1, S2), suggesting that bacteria sampled from those months represent transition stages between the warm and cold season communities. Bray-Curtis dissimilarity indices (ranging from 0, the two samples have the same bacterial composition, to 1, the two samples do not share any species) calculated between samples from the different seasons indicate that bacterial community composition was more dissimilar for the large-pore-size filters than for the small-pore-size filters (0.72 and 0.64, respectively, *t*-test, *p* < 0.0001). This suggests that the community composition of particle-associated bacteria or large cells might be more affected by the seasonal changes than the free-living bacteria.

We also observed differences in bacterial abundances and alpha-diversity indices calculated between summer and winter samples. However, these results showed the opposite pattern than beta-diversity indices considering the two sets of filters (Bray-Curtis indices, see above). Indeed, the contrast between summer and winter samples in terms of bacterial abundances, OTU richness, and evenness was marked for the small-pore-size filters, whereas these variables were stable and nonstatistically different for the large-pore-size filters. Bacterial abundance decreased during winter on the small-pore-size filters (from $8.10 \times 10^5$ to $2.60 \times 10^5$ 16S rRNA genes · mL⁻¹, *t*-test, *p* < 0.0001; Supporting Information Fig. S3). In contrast, for the
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** NMDS ordination plot (center) derived from the Bray–Curtis similarity measure between all samples. Seasons are indicated in yellow and blue for summer and winter, respectively, and size fraction in circles and diamonds for the large-pore–size and the small-pore–size filters, respectively. Color intensity of the circle and diamond borders represents the progress of the seasons. Light color borders represent the beginning and the end of the seasons, whereas dark color borders represent the middle of the seasons. Pie charts representing the average distribution of the main phyla or classes (for *Proteobacteria*) detected in the samples collected during the *warm* (left) and the *cold* (right) season as defined by UPGMA dendrograms, for the large (top)- and the small (bottom)-pore–size filters. Asterisks on pie charts indicate significant (**, *p* < 0.01) or very significant (***, *p* < 0.001) differences in average relative proportions between the *warm* and the *cold* seasons. Pictures of the Saint-Charles River were taken at the water intake of the Loretteville DWTP during the *warm* and the *cold* season, respectively.</figcaption></figure>

same filters, species richness (number of OTUs: 5088 in winter and 2838 in summer, *t*-test, *p* < 0.0001; Supporting Information Fig. S4A) and species diversity (Shannon indices: 6.11 in winter and 4.83 in summer, *t*-test, *p* < 0.0001; Supporting Information Fig. S4B) increased during winter. This increase of richness and evenness on the small-pore–size filters during winter might indicate a larger diversity in winter season or could result of decreasing proportions of predominant summer taxa that allows the detection of additional OTUs during winter.

Ordination analyses of the bacterial community composition with environmental vectors fitting suggest that water temperature and covarying factors such as air temperature,

snow cover, and nitrogen concentrations were among the main drivers of the microbial community detected throughout the year (*envfit*, goodness of fit statistic *R*² > 0.85, *p* < 0.001; Supporting Information Fig. S2). Therefore, the clear clustering of samples during the *warm* and *cold* seasons probably reflect the strong influence of weather conditions on microbial community structure and function. The climate in this Canadian region is characterized by strong temperature fluctuations between summer and winter with average daytime air temperatures of 23°C in July and –10°C in January (Environment Canada, National Climate Archive). Furthermore, in the region studied, relatively large quantities of snow accumulate during winter (300 cm · yr⁻¹
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Heatmap display summarizing the variations in the environmental, physicochemical, and microbiological parameters. The color shades represent the values of parameters with darker colors representing the high values and lighter colors representing low values. Each variable has been standardized for zero mean and univariance.</figcaption></figure>

on average, Environment Canada, National Climate Archive), and rivers and lakes are often covered with ice and snow, reducing atmospheric inputs and light penetration in water compared to open-water conditions observed during summer (Figs. 1–2; Supporting Information Figs. S5, S6). Furthermore, relatively frequent rainfall events with variable durations and intensities occur during the warm season (between 800 and 1000 mm · yr⁻¹, depending on the year), leaching soils and providing additional inputs from the catchment.

As the Saint-Charles River is mainly fed by the Saint-Charles Lake (49.8% in summer and 49.0% in winter, in average; Boulé et al. 2010) and by two rivers (Yellow and Nelson Rivers, 50.2% in summer and 51% in winter; Boulé et al. 2010) for the part located upstream of the water intake, the shifts observed at the water intake could originate from changes not only in the Saint-Charles River itself but also in the lake and/or in the two tributaries.

## Bacterial community composition during summer

During summer, bacterial communities were largely dominated by members of the *Betaproteobacteria*, *Actinobacteria*, and

*Bacteroidetes*, which when combined, represented 64% and 79% of the total community collected using the large- and the small-pore-size filters, respectively (Fig. 1). These results are consistent with previous studies on temperate and cold freshwater ecosystems (Glöckner et al. 2000; Zwart et al. 2002; Allgaier and Grossart 2006; Crump et al. 2009; Newton et al. 2011; Portillo et al. 2012), indicating that the Saint-Charles River microbial system is comparable to other river ecosystems in the temperate latitudes of the northern hemisphere.

*Sporichthyaceae* hgCI clade (acI), *Limnohabitans* (betI-A/Lhab), *Pseudarcicella* (bacIII-A), *Candidatus* Rhodoluna (Luna1-A), and *Dinghuibacter* (bacI) represented the major lineages identified in the summer bacterial communities (with combined totals of up to 28% and 38.3% of the reads on the large- and the small-pore-size filters respectively; Figs. 3–4). Relative proportions of these lineages were significantly different between the two seasons (Mann–Whitney test, *p* < 0.0001) except for the *Sporichthyaceae* hgCI clade on the small-pore-size filters. *Limnohabitans* (6.8% and 13.4% of the sequences on average in large and small fractions, respectively, during summer) and *Sporichtyaceae* hgCI clade (2.9% and 4.3% on average) are common freshwater
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Heatmap display of the relative proportion of main genera (subset 5%) for the large (blue)- and the small (green)-pore-size filters. The color intensity represents relative proportion of each genus with dark colors representing high relative proportions and light colors representing low relative proportions. Colored diamonds next to the genus names indicate the phylum or the class (Proteobacteria) of each taxa. Uncl., unclassified.</figcaption></figure>

microorganisms (Warnecke et al. 2005; Newton et al. 2011). They are known to be mainly aerobic with optimal growth temperatures corresponding to summertime water temperatures (18°C on average for our sampling dates; Fig. 2; Supporting Information Figs. S5, S6) and grow on the degradation of particulate organic matter (Kämpfer et al. 2011, 2012; Newton et al. 2011; Kasalický et al. 2013; Lv et al. 2016). By contrast, members of the *Dinghuibacter* lineage (1.6% and 0.4% on average) were previously isolated from forest soil (Lv et al. 2016), suggesting a potential input of the surrounding forested areas to

the riverine bacterial community. Furthermore, the relative proportions of *Sporichthyaceae* hgCI clade, *Candidatus* Rhodoluna (2.9% and 4.2% on average), and the pink-pigmented *Pseudarcicella* (3.5% and 5.2% on average) during summer could also be explained by their potential phototrophic capabilities, allowing them to outcompete nonphototrophic microorganisms during open water conditions (Warnecke et al. 2005; Sharma et al. 2009; Ghylin et al. 2014).

The impact of recent rainfall events was evident on the water physicochemical features. Indeed UV absorbance, apparent color,
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Double-stacked barplot representing the variation in relative proportions of five taxa, showing higher proportions during the summer than during the winter. For each double-stacked barplot, the barplot above the line represents relative proportions detected on the large-pore-size filters and barplot below the line represents relative proportions detected on the small-pore-size filters. *Betaproteobacteria* are represented in blue, *Actinobacteria* in pink, and *Bacteroidetes* in green.</figcaption></figure>

TOC, as well as phosphorus concentrations increased for the corresponding sampling dates (Pearson, $r^2$ = 0.67, 0.62, 0.56, and 0.55, respectively, for rainfall events 1 day prior to sampling; Fig. 2; Supporting Information Fig. S7B), as previously observed (Prathumratana et al. 2008; Delpla et al. 2009; Leight and Hood 2018). However, relative proportions of dominant bacterial lineages (Figs. 3–4; Supporting Information Fig. S8) as well as the bacterial abundance pattern (Supporting Information Fig. S3) remained unchanged after rain events, suggesting that the microbial community is relatively resistant to moderate perturbations. Nonetheless, an increased number of OTUs was detected when rainfall events characterized by relatively high volumes of precipitations (>10 mm) occurred 1 or 2 days before sampling (t-test, $p$ < 0.05 for both pore size filters, Pearson, $r^2$ > 0.7 with rainfall events 1 day prior to sampling; Fig. 5; Supporting Information Fig. S7B). Thus, 161 and 136 taxa were only detected at these sampling dates (31 May, 07 and 14 June, 19 and 26 July, and 24 October 2016; Fig. 5). This suggests that precipitation events might increase OTU richness in the river. These additional OTUs were mainly affiliated with soil microbial lineages, such as *Amniobacterium* (Jin et al. 2013; Li et al. 2018), *Litorilinea* (Loganathachetti et al. 2016), *Cohnella* (Yoon et al. 2007; García-Fraile et al. 2008; Kim et al. 2010), and *Flavisolibacter* (Joo et al. 2015; Lee et al. 2016), suggesting that the relatively high volume of precipitations could then potentially carry soil bacteria into the river. Other rain-associated OTUs were related to members of

the fecal microbiome such as *Coprococcus* (Holdeman and Moore 1974; Kohl et al. 2018) and *Sporobacter* (Grech-Mora et al. 1996; O'Donnell et al. 2017; Fig. 5). This is consistent with the detection of *E. coli* and other coliforms by culture-based methods in the same samples (Fig. 2; Supporting Information Figs. S6, S7B) and suggests that major rain events can also temporarily enrich the river microbial community with potential human pathogens and fecal contaminants from the neighboring habitations and human activities (domestic septic tank systems, recreational activities, etc.). Consequently, these results support the fact that rainfalls could be associated with individual outbreaks of water-borne disease caused by fecal-oral pathogens (Rose et al. 2000; Curriero et al. 2001; Charron et al. 2004; Leight and Hood 2018). This is of particular significance considering the changes in physicochemical water quality also observed during rainfall events that may require an adaptive strategy with regard to drinking water treatment process operations.

## Bacterial community composition during the winter

The winter season was associated with low water and air temperatures (from 10°C to 0.5°C and from 5°C to -27°C, respectively), the presence of an ice cover, and lower organic carbon concentrations (winter vs. summer: 3.61 vs. 4.78 and 2.93 vs. 4.63 mg C · L⁻¹ for TOC and DOC, respectively, $p$ < 0.01) but higher nitrogen content (0.64 vs. 0.41 and 0.41 vs. 0.22 mg N · L⁻¹ for total nitrogen and nitrates/nitrites,
respectively, *p* < 0.001; Fig. 2; Supporting Information Fig. S6). Bacterial abundances measured by qPCR was lower during winter (from 8.10 × 10⁵ to 2.60 × 10⁵ 16S rRNA genes · mL⁻¹ on average for the small-pore–size filters; Supporting Information Fig. S3), and the relative proportions of some lineages decreased substantially, such as the *Limnohabitans* lineage (from 6.8% to 0.9% and from 13.4% to 3.4% on the large- and the small-pore–size filters, respectively, *t*-test, *p* < 0.0001) and more broadly for the entire *Actinobacteria* phylum (from 18% to 3.6% and from 28% to 12%, *t*-test, *p* < 0.001; Figs. 1, 3–4). This shift is potentially due to suboptimal growth temperatures (Newton et al. 2011; Kämpfer et al. 2012; Kasalický et al. 2013), light limitation (Sharma et al. 2009; Newton et al. 2011), or a lower availability of nutrients used by these communities, as discussed above. Therefore, these results support previous investigations of under-ice lakes and highlight the potential negative effect of the observed environmental features on some bacterial populations (Bertilsson et al. 2013).

In contrast, the relative proportion of other lineages increased during winter, suggesting that winter conditions might create suitable conditions for other organisms. Thus, the relative proportions of *Gammaproteobacteria* were, for example, 5.4 and 5.0 greater between winter and summer for the large- and the small-pore-size filters, respectively (from 3.3% to 18% and from 1.2% to 6%, respectively, *t*-test, *p* < 0.0001; Fig. 1). Interestingly, as winter progressed, the microbial community composition gradually changed (Fig. 6), highlighting a potential succession of ecological niches during the cold season. Indeed, different lineages have been detected early in the winter season, during the middle of this season, or later in the season, as described in the following paragraphs.

Early winter (october/november) lineages included *Candidatus* Methylopumilus, *Chloroflexi* SL56, *Verrucomicrobia* OPB35, and *Bacteroidetes* envOPS17 taxa (Fig. 6). *Candidatus* Methylopumilus (betIV-A/LD28) is a known psychrotolerant methylo-trophic bacteria benefiting from degradation products of plant decay (Salcher et al. 2015; Balmonte et al. 2016), whereas a cel-lulolytic activity has been previously detected in *Chloroflexi*, *Verrucomicrobia*, and *Bacteroidetes* species (Schellenberger et al. 2010; Herlemann et al. 2013; Xia et al. 2016). Early in the win-ter, the water temperatures gradually decrease to 0°C while the lake and river remain ice free (Supporting Information Figs. S5, S6). Therefore, these lineages might be triggered at the begin-ning of the winter by the considerable input of organic matter from the decline of riverine plants/algae and the leaves from trees adjacent to the river (Perry 1971; Boylen and Sheldon 1976; Lacoul and Freedman 2006). The relative proportion of sequences affiliated to *Betaproteobacteria* lineages other than those detected during summer (e.g., *Rhodoferax* [betI-B/Rhodo], *Sphaerotilus*, and other *Comamonadaceae*) also increased during winter on the large-pore-size filters, resulting in a general increase of the entire *Betaproteobacteria* phylum on these filters (23% to 35%, *t*-test, *p* < 0.0001; Fig. 6). The relative proportion of sequences affiliated to *Parcubacteria* (formerly candidate

division OD1) also increased on the small-pore–size filters dur-ing winter (2.4% to 9%, *t*-test, *p* < 0.0001; Fig. 1). The relative proportion of these lineages remained constant throughout this season (Fig. 6), indicating favorable growth conditions for these microorganisms and a psychrotolerant or psychrophilic life-style. Furthermore, *Sphaerotilus* and *Rhodoferax*, as well as some other members of the *Comamonadaceae*, are known to be involved in the iron cycle (Finneran et al. 2003; Gridneva et al. 2011; Schmidt et al. 2014), a metal that exists in substantial concentrations in the Saint-Charles River (frequently measured higher than 0.3 mg.L⁻¹ compared to 0.12 mg.L⁻¹ in average in river water of North America; Boulé et al. 2010; Livingstone 1963). This raises the possibility that metabolisms related to iron cycle could play an important role in the river, especially during the winter, resulting in additional pathways to particu-late organic matter mineralization (Konhauser et al. 2011; Chen and Jiang 2016). In addition, these early winter lineages might also benefit from the changes occurring in the upstream lake. Indeed, nutrients isolated in the hypolymnion during the sum-mer season are redistributed throughout the water column dur-ing the fall turnover in the Saint-Charles Lake (Wetzel 2001; Boulé et al. 2010). This mixing could potentially modify the microbial community composition through nutrients or bacte-ria intake.

At the middle of the winter season, we observed a transi-tion in the river microbial community once the ice cover started to form and the ground became frozen (end of novem-ber; Fig. 6). The relative proportion of methane oxidizing *Gammaproteobacteria* related to *Crenothrix* (*gamI*), *Methylobacter* (*gamI*), and other Methylococcales increased, representing together up to 20.3% of the sequences on the large-pore–size filter (23 February; Figs. 3, 6). This result is consistent with the detection of such methanotrophs in other freshwater ecosystems (Zaiss et al. 1982; Sundh et al. 2005; Oswald et al. 2017; Samad and Bertilsson 2017) and previous incubation studies with these microorganisms that indicated growth at cold tem-teratures (Sundh et al. 2005; Ricão Canelhas et al. 2016). Moreover, these lineages were detected in higher proportions on the large-pore–size filters than on the small-pore–size filters (up to 14.89% and 11.84% on the large-pore–size filters for *Crenothrix* and *Methylobacter*, respectively, vs. up to 1.04% and 3.94% on the small-pore–size filters). This result suggests a size greater than 3 µm for these lineages, potentially because they are filamentous in shape, as previously observed for *Crenothrix polyspora* (Cohn 1870; Oswald et al. 2017) or through the for-mation of floc. The detection of these dominant lineages on the large-pore–size filters could explain the steadiness of Shan-non indices calculated for these filters between the two sea-sons (5.8 and 5.7, for winter and summer, respectively; Supporting Information Fig. S4B). Indeed, these dominant lin-eages detected on the large-pore–size filters could replace the dominant lineages detected during summer in the calculation of these diversity indices. With snow and ice accumulation on land and in surface water, labile organic substrates from the
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Double-stacked barplots representing the variation in relative proportions of 30 taxa observed in higher proportions during the winter than during the summer. For each double-stacked barplot, the barplot above the line represents relative proportions detected on the large-pore-size filters and barplot below the line represents relative proportions detected on the small-pore-size filters. The different bacterial taxa are clustered according to their relative proportion and their respective variation along the cold season. Unc., unclassified.</figcaption></figure>

catchment and photosynthetic aquatic organisms are likely to decline rapidly, potentially degraded by the early winter community. Methane might have been produced by sedimentary methanogens that used end-products of the organic matter degradation (volatile fatty acids, hydrogen, and CO₂) produced by early winter microorganisms (e.g., *Chloroflexi*, *Verrucomicrobia*, and *Bacteroidetes*). Ice cover, by limiting gas exchanges with the atmosphere, could potentially lead to methane accumulation in the river and/or the lake, thus allowing development of methanotrophic lineages and justifying the time lag observed between the onset of winter and the detection of these lineages in our dataset. Together, these results suggest that in ice-covered conditions, when the organic matter inputs from the surrounding catchment and the photodegradation of the organic matter are limited, the

methane cycle could represent a significant process in this river ecosystem, supporting recent results on ice-covered lakes (Denfeld et al. 2018), and that these organisms have a competitive advantage under winter conditions.

Later in the winter season (February), relative proportions of *Candidatus* Nitrotoga and Unc. *Nitrosomonadaceae* also increased, attaining 4% of the bacterial community (23 February 2017; Fig. 6). Relative proportions of these putative ammonium and nitrate oxidizers in the water column (Suzuki et al. 1974; Alawi et al. 2007; Prosser et al. 2014) were positively correlated with total nitrogen and nitrate/nitrite concentrations (Pearson, *r*² = 0.59 and 0.76 for *Candidatus* Nitrotoga, respectively), suggesting that they could play a major role in the winter nitrogen cycling. In the Saint-Charles River, the highest total nitrogen and nitrate/nitrite concentrations were measured during the
winter, reaching 0.88 and 0.58 mg N·L⁻¹, respectively, on 23 February 2017 (Pearson, r² > 0.81 with snow cover and r² > -0.56 with air and water temperatures; Fig. 2; Supporting Information Fig. S6), as previously observed in numerous freshwater ecosystems (Hampton et al. 2017). This higher nitrogen content can likely be attributed to the mineralization processes associated with the degradation of early winter organic matter inputs coupled with the decline of nitrogen assimilating plants and algae. In addition, the presence of two municipal wastewater treatment plants upstream of the Saint-Charles Lake and numerous domestic septic tank systems on the shores of the lake, the river and the related tributaries, could potentially participate to the higher nitrogen concentrations measured in winter. Low temperatures during the winter could lead to reduced efficiency of nitrification during wastewater treatment and therefore the discharge of ammonia into the lake and river, also observed in higher concentrations at the sampling location during the winters of 2017 and 2018 (up to 124 µg N·L⁻¹ during summer compared to an average of 13.6 µg N·L⁻¹ during winter; Ville de Québec, pers. comm.). These results suggest that in addition to the carbon cycle, the bacterial nitrogen cycle in river ecosystem is also potentially activated during the winter.

Finally, the highest relative proportions of the genus *Polaromonas* (betl), frequently observed in glacial environments (Darcy et al. 2011), were detected at the end of the winter (Fig. 6). While sequences affiliated with this lineage were detected throughout the winter, its relative proportion showed an unusual pattern compared with the other bacterial taxa, reaching the highest percentages on 04 May 2017 on the large- and the small-pore-size filters (10.4% and 10.6% of the sequences, respectively; Fig. 6). Highest proportions of *Polaromonas* coincided with the spring breakup, corresponding to an increase of air and water temperatures that led to the thaw of ice and snow and the opening of the floodgates at the upstream dam (Fig. 2; Supporting Information Figs. S5, S6; Pearson, r² = 0.97 and 0.88 with river flow and opening of the floodgates, respectively, on the small-pore-size filters). This suggests that some of the microorganisms such as *Polaromonas* might be dumped in the river by snow and ice melting.

## What is happening to microorganisms during their "off-season"

The relative proportions of bacterial communities shifted again when the water temperature exceeded 10°C with the predominant lineages affiliated with the *Actinobacteria* taxa and *Limnohabitans* (from 7.2°C on 04 May 2017 to 12.8°C on 17 May 2017; Supporting Information Figs. S5, S6). This change did not coincide with the spring breakup that occurred in mid-April (air temperatures >0°C; Supporting Information Fig. S5). However, the lake was totally ice free in early May, and the spring turnover of the lake probably occurred between these two sampling dates. Therefore, water mixing in the lake might also contribute to the microbial community shift observed in spring in the river. This shift in the microbial

community composition took place within 13 days (between 04 and 17 May 2017), highlighting the rapid resilience and resurgence of the summer freshwater bacterial community. Additional samples collected in the winter of 2018 had a similar community composition to the communities from the winter of 2017, indicating that the annual cycle of the Saint-Charles River bacterial community might be stable and recurrent. Although the relative proportion of the major OTUs (OTUs detected in proportions >1% [subset 1%] or >5% [subset 5%] for at least one sampling date) fluctuated between summer and winter, most of these OTUs were detected throughout the sampling period (Supporting Information Fig. S8). Together, these results indicate that most of the microorganisms remained somehow present in the river throughout the annual cycle, presumably ready to respond to environmental stimulations. Survival of individual cells is a prerequisite for persistence and recovery of these communities (Shade et al. 2012), and different potential strategies were potentially detected in our samples, as described below.

The persistence of some microorganisms throughout the year, such as *Flavobacterium* (bacII-A), which presented a similar proportion between the two seasons (5.5% ± 2.7% and 5.3% ± 2.4% of the sequences for the large- and the small-pore-size filters, respectively; Fig. 3), might be explained by physiological plasticity or the ability to use different carbon and energy sources, as previously proposed (Eiler 2006; Shade et al. 2012).

The switch in relative proportions between the two seasons in both size fractions, as observed in our study for *Candidatus Rhodoluna, Dinghuibacter, Crenothrix, Methylobacter*, and *Polaromonas*, and their rapid response to environmental changes (Figs. 3–4, 6) suggest that these microorganisms might adopt a strategy of reduced metabolic activity during their "off-season" (Stevenson 1977; Fuhrman et al. 2006; Jones and Lennon 2010). Thus, we hypothesize that some of these microorganisms could enter in diapause or dormancy, maintaining a relatively low abundance but capable of resisting stressors such as nonoptimal temperature or low nutrient availability and to respond rapidly to favorable environmental conditions (Sussman and Douthit 1973; Roszak and Colwell 1987).

Finally, members of the *Sporichthyaceae* hgcI clade, detected in significantly higher proportions during the summer, almost disappeared from the large size fraction during the winter but remained relatively unchanged on the small-pore-size filters (6.8% vs. 4.0% for the winter vs. the summer; Fig. 4). Although we cannot exclude variability in shape or size within the same taxa members, this result could indicate that these organisms undergo a seasonal change of morphology, perhaps provoked by changing environmental conditions. It has been previously observed that this lineage can change its shape (from cocci to filamentous form) in response to microeukaryote predation pressure (Güde 1979; Lampert 1987; Jürgens and Stolpe 1995; Hahn et al. 1999; Newton et al. 2011), which may contribute to its remarkable persistence in freshwater lakes (Allgaier and Grossart 2006; Newton et al. 2011; Rösel
and Grossart 2012). Therefore, our differential detection of *Sporichthyaceae* members might result from a reduced grazing activity under ice or from other environmental triggers such as temperature, available carbon, or sunlight. This result raises the possibility that the change of shape strategy, illustrated by the *Sporichthyaceae* members, might be an efficient strategy in the freshwater bacterial communities to cope with changing top down pressure, unfavorable environmental conditions, and rapid environmental fluctuations.

## Conclusion

In this study, major seasonal shifts of bacterial communities in the Saint-Charles River were observed, reflecting the dramatic seasonal changes that occur in the northeastern region of North America. Our results indicate the presence of a complex annual bacterial cycle, shaped by temperature fluctuations and other covariant parameters, in seasonally ice-covered inland water ecosystems. During summer, the bacterial community was typical of many freshwater ecosystems, where *Betaproteobacteria*, *Actinobacteria*, and *Bacteroidetes* lineages were dominant. Rainfall events during this season led to an increase in rare OTUs potentially originating from the watershed, with potential consequences on water quality. When water temperatures dropped under 10°C, relative proportions of taxa mainly detected during summer declined, potentially due to suboptimal temperatures, light limitation, or shifts in nutrient availability, liberating ecological niches for cold adapted microorganisms. During this period, bacteria potentially involved in organic matter degradation followed by those potentially involved in methane and nitrogen cycling appeared to flourish, indicating that the degradation of decaying plants and algae growing during summer might sustain a major fraction of the winter bacterial community. Furthermore, our results suggest that bacterial lineages might employ various ecological strategies, such as metabolic plasticity and variable morphologies to survive their “off-season”. Together, these results demonstrate the need to study the full annual cycles of microbial communities in rivers, to better understand the cascading effects between seasons and the impact of the riverine bacterial community on the annual biogeochemical processes and nutrient budgets. Finally, water temperature and its impact on various environmental parameters, such as the duration of ice and snow cover, appeared to be among the major drivers of the riverine bacterial community composition suggesting that seasonally ice-covered river ecosystems may be particularly vulnerable to the effects of global warming.

## Author Contribution Statement

P.C., C.C.D., A.I.C., M.J.R., and S.J.C. designed the study. P.C. and M.S.F. collected the samples. P.C. performed laboratory work, sequencing data analyses, and statistical analyses. P.C., A.V., and S.J.C. analyzed and interpreted the results.

P.C. and A.V. wrote the manuscript with a revision done by S.J.C., M.J.R., C.C.D., and A.I.C.

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## Acknowledgments

We thank all the AD team, especially Dimitri Kalenitchenko, and all members of the Lab Charette for very helpful scientific discussions. We thank Alex Bernatchez for sampling assistance. We would also like to thank the Ville de Québec and operators of the Loretteville Water Treatment Plant, with a special thanks to Louis Collin and François Proulx and the anonymous reviewers for their very useful comments on this manuscript. This research project was supported by grants from Natural Sciences and Engineering Research Council of Canada (NSERC), the Drinking Water Chair of Laval University (CREPUL), and Sentinel North. Steve J. Charette is a research scholar from the Fonds de Recherche du Québec en Santé.

## Conflict of Interest

None declared

Submitted 18 August 2018

Revised 22 November 2018

Accepted 08 January 2019

Associate editor: Anssi Vähätalo