# Exploring microbial diversity in relation to hydrological signatures and anthropic pressures in the Beauce Aquifer, France

Jennifer Hellal, Hugues Thouin, Catherine Joulian, Stéphanie Pinson, Mickael Charron, Nicolas Devau

► To cite this version:

Jennifer Hellal, Hugues Thouin, Catherine Joulian, Stéphanie Pinson, Mickael Charron, et al. Exploring microbial diversity in relation to hydrological signatures and anthropic pressures in the Beauce Aquifer, France. Science of the Total Environment, 2025, 977, pp.179402. 10.1016/j.scitotenv.2025.179402. hal-05036960

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# Exploring microbial diversity in relation to hydrological signatures and anthropic pressures in the Beauce Aquifer, France

Jennifer Harris*, Hugues Thouin, Catherine Joulian, Stéphanie Pinson, Mickael Charron, Nicolas Devau

BRGM, F-45060 Orléans, France

## HIGHLIGHTS

* Bacterial diversity was found to be higher in unconfined aquifers.

* Hydrogeochemistry was linked to baseline bacterial diversity in the Beauce aquifer.

* Baseline biodiversity can help to identify aquifer areas under anthropic pressure.

## GRAPHICAL ABSTRACT

<figure><img src="image_4.png" /></figure>

## ARTICLE INFO

Editor: Abasiofiok Mark Ibekwe

**Keywords:**

Groundwater

Bacterial diversity

Bioindication

Anthropic pressures

Biogeochemistry

Microbial ecotoxicology

## ABSTRACT

Groundwater is one of the largest reservoirs of liquid fresh water on earth. Aquifers are complex environments where water quality can be affected by geological origins, geochemistry and biological activity. These environments are under pressure due to climate change and pollution. Strongly involved in the biogeochemistry of groundwater ecosystems, microorganisms can also contribute to reducing pollution and potentially be used as indicators of water quality. However, the diversity and role of microorganisms in groundwater remains largely unknown. The present work aimed to characterize bacterial diversity in 60 wells over 8000 km² in the Beauce Aquifer, France, determine the baseline diversity expected in relation to the hydro geochemical signatures and identify variations that could be due to anthropogenic pressures. We analysed amplicon sequence variants (ASVs) of the 16S rRNA gene as well as the abundance of functional genes associated to arsenic and nitrate (*aioA*, *arsB*, *narG*), organic carbon contents and the geochemical composition. Bacterial diversity and bacterial co-occurrences were found to change according to the hydrogeochemical signatures identified in the aquifer. A lower diversity was measured in confined aquifers, where low oxygen and reducing conditions occurred with high levels of dissolved iron and manganese. This could indicate a higher vulnerability in the face of pressure. Finally, a baseline bacterial diversity composition was found to be linked to each hydrogeochemical signature and several wells that differed from the expected variations of this diversity were identified.

<footnote>* Corresponding author.
E-mail address: j.harris@brgm.fr (J. Harris).</footnote>
# 1. Introduction

Aquifers contain around 95 % of all the liquid freshwater on earth (United Nations World Water Assessment Programme, 2015; Zektser and Everett, 2004). Aquifer ecosystems provide services that are of immense societal and economic value (drinking water supply, nutrient recycling, mitigation of floods and droughts, support to dependent surface ecosystems) (Griebler and Avramov, 2015; Hérivaux and Grémont, 2019; Fillinger et al., 2023). They are also one of the largest habitats for microorganisms, containing an estimated 25 % of the total prokaryotic biomass (Flemming and Wuertz, 2019).

Groundwater is characterized by the absence of light and a low supply of easily assimilated organic matter. These characteristics make it an energy-poor environment, which distinguishes it considerably from surface ecosystems. These conditions have led some authors to consider them as “extreme” environments (Danielopol et al., 2000). They are home to heterotrophic microorganisms adapted to nutrient-poor, oligotrophic conditions (Ghiorse and Wilson, 1988; Madsen and Ghiorse, 1993) as well as litho-autotrophic organisms that fix CO₂ and oxidize inorganic electron donors (Griebler and Lueders, 2009). Past assessments of groundwater microbial diversity using culture based approaches revealed α- and β-Proteobacteria, Actinobacteria, Firmi-cutes, Bacteriodetes, and Bacilli (Griebler and Lueders, 2009). Although molecular approaches have subsequently deepened this knowledge, they find the same dominant phyla as the culture and isolation ap-proaches (Sonthiphand et al., 2019; Vargas-García et al., 2023; Beyer et al., 2015). As a result, no endemic population has been identified for these underground environments compared to other surface ecosystems, as is the case for fauna (Marmonier et al., 1993). The widespread use of metagenomics is, however, changing our knowledge of the biodiversity of these environments; for example, Anantharaman et al. (2016) applied Shotgun sequencing in 15 groundwater samples and revealed 47 new, hitherto unknown phyla that could contain yet unknown biotechno-logical potentials or support specific biogeochemical reactions. Bougon et al. (2012) also found that 6 of the 27 16S rRNA gene sequence phy-lotypes in an aquifer in Brittany, France, were unknown even at the class or phylum level.

Numerous factors can influence biodiversity in groundwater, these are summarized by Griebler and Lueders (2009). Briefly, in addition to depth, mineralogy, geology and water physico-chemistry and water flow, other phenomena such as evolution, biogeography, habitat size and spatial heterogeneity, temporal variability, not to mention all forms of disturbance to which these environments may be subjected, whether anthropogenic (contamination) or natural (erosion, flooding...), can also contribute to the shaping of microbial communities. The role of some of these factors is tending to be validated by studies using meta-genomic approaches. This is the case, for example, of the study by Hubalek et al. (2016), which looked at the dynamics of microbial communities in three aquifers over a 6-year period and showed that community structure and dynamics are strongly linked to their degree of isolation from the surface, on the one hand, and the availability of nu-trients and energy, on the other. Hug et al. (2015) suggest that sets of organisms are found systematically together geographically due to a “shared” adaptation to environmental conditions in aquifer sediments near the Colorado River, while Fillinger et al. (2019a) highlighted that local environmental conditions were the main drivers of microbial community composition when they compared 4 aquifers along at 700 km transect in Germany.

Many studies have focused on specific groundwater-related issues, such as chemically or pathogenically polluted contexts, the impact of saline intrusion or the potential for site (bio)remediation (Goldscheider et al., 2006) and in several recent studies, links between groundwater geochemistry and bacterial diversity have been shown in different sit-uations, i.e. volcanic (Amalfitano et al., 2014), under the influence of slag heaps (Bärenstrauch et al., 2022), in deep karst environments (Hershey et al., 2018), under impacts of NO₃/NH₄ contamination

(Morrissy et al., 2022), in springs (Pedron et al., 2022) and shallow aquifers (Ji et al., 2022) or linked to hydrogeological conditions (Bougon et al., 2012) or confinement (Smith et al., 2012). However, few studies have looked at microbial diversity at larger scales and none have been carried out in France. In New Zealand, following initial work carried out on a regional scale and showing differences in diversity ac-cording to groundwater chemistry (Van Bekkum et al., 2006), studies were carried out at a national scale in 2013 and 2014 and again in 2018. The first two studies looked at the relationships between microbial biodiversity, water hydrochemistry and geochemical parameters (Sirisena et al., 2013; Sirisena et al., 2014), while in 2018, Sirisena et al. went further using a metabarcoding approach (Sirisena et al., 2018). Another study identified was conducted on a smaller scale with eight sites and 54 aquifers, spread along the west coast of southern Australia and presenting different levels of contamination, to assess the impact of anthropogenic activity on aquifer biodiversity (Smith et al., 2018). Data have also been acquired on the biodiversity of prokaryotes and eu-karyotes in groundwater from 4 catchments and 25 sites in eastern Australia (Korbel et al., 2017). In 2018, the biodiversity of 41 waters from 13 drinking water systems, 6 using surface water and 7 ground-water, was characterized by metabarcoding in Belgium (Van Assche et al., 2018). More recently, two studies have been carried out, one in China on 733 newly drilled wells (Zhong et al., 2023) and one on 35 sampling points over a 10,000 km² region in Quebec (Groult et al., 2022). Obtaining a broader understanding of the structure and function of microbial communities inhabiting groundwater systems is particu-larly important given the increasing need for managing reserves of potable water (Smith et al., 2012) and improving sustainable ground-water management and ecosystem service maintenance (Gregory et al., 2014; Larned, 2012; Griebler and Avramov, 2015; Amalfitano et al., 2014; Smith et al., 2012).

To the best of our knowledge, no studies have been conducted in France on the microbial composition in groundwater, except some seminal works (Bougon et al., 2012; Ben Maamar et al., 2015; Battaglia-Brunet et al., 2022; Bärenstrauch et al., 2022), essentially conducted on small territories and/or exhibiting only one environmental driver that impacts microbial communities. For instance, Battaglia-Brunet et al. (2022) specifically addressed the issue of arsenic and selenium avail-ability in the southern, confined part of the Beauce Aquifer around Orléans, whereas Bougon et al. (2012) investigated temporal and spatial changes of microbial communities in a small granitic aquifer. Bärenstrauch et al. (2022) focused on the impact of steel slag leachates on the microbial community of the underlying aquifer. None of them have been conducted at large-scale. There are several large aquifers in France, among them the Beauce aquifer is one of major interest due to the geological, hydrogeological, hydrogeochemical and anthropogenic context. This aquifer is one of France's biggest aquifers with a surface area of nearly 10,000 km² (Verley, 2020). It is of particular interest due to its size and its extensive use for irrigation and drinking water. It covers contrasting hydrogeological conditions (confined, unconfined) and can be under the pressure of different anthropogenic activities such as agriculture and industry. In such a context, one can envision that two main types of drivers, the hydrogeochemical and hydrogeological context encountered in the Beauce aquifer and the magnitude of the pressures related to the anthropogenic contaminants, can impact mi-crobial diversity and function (Fig. 1). Interactions between the two types of drivers must also be questioned to correctly delineate spatially distinct hydrogeochemical facies in the Beauce aquifer. In return, changes in microbial diversity can strongly alter microbially mediated biogeochemical cycles in the aquifer as well as the microbial mediated redox reactions supporting natural attenuation of anthropogenic con-taminants (Hellał et al., 2023). As highlighted by Deboe et al. (2024), defining core microbiomes in environmental compartments is one of the five priorities to integrate microbiomes into risk assessments. Indeed, associated to other approaches such as the use of deuterium (²H) and oxygen (¹⁸O) isotopes that can highlight groundwater contamination
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Triangle of the interactions between groundwater, microbial communities and anthropic pressures.</figcaption></figure>

(Zhan et al., 2024; Sappa et al., 2023), changes in microbial community structure or presence of indicator species could act as early warning signs of contamination. Furthermore, identifying links between the impacts of anthropic pressures on microbial communities and the implications and risks for environmental functioning is one of the key challenges faced by microbial ecotoxicology (Hellal et al., 2023).

Based on these observations, although there is an increasing amount of data being acquired on the microbial diversity of groundwater, there are still knowledge gaps that need to be addressed to define new management tools for groundwater resources. The present study aims to initiate this in France with the first large-scale assessment of microbial communities. In particular, the following questions were raised; (i) what is the microbial diversity in this aquifer?; (ii) Can a microbiological signature be attributed to a hydrogeochemical facies? (ii) Is it possible to highlight disturbances by microbiological profiles or the presence of microbiological markers? First, these were addressed through the identification of hydrogeochemical facies in the Beauce aquifer and highlighting of the geochemical abnormalities. Then, microbial diversity was characterized through the sequencing of the gene coding for 16S rRNA using Illumina, and functional gene abundances were measured using quantitative PCR (qPCR) targeting the genes involved in arsenic oxidation and reduction (*aoxB* and *arsA*) as well as nitrate reduction (*narG*). From this data, diversity indexes were calculated, co-occurrence networks were established and statistical approaches including LefSe (Linear discriminant analysis size effect), analysis were used to identify taxa characterizing differences between the geochemical conditions. Finally, microbial and geochemical data were combined to identify expected core diversity, natural variation and situations suggesting disruptive conditions.

## 2. Materials and methods

### 2.1. Aquifer context

The Beauce aquifer extends across several departments in the Centre region of France and corresponds to an area where lacustrine limestone, deposited from the Middle Eocene (43 million years ago) to the beginning of the Miocene (25 million years ago), outcrop. Covering a surface area of 7500 km², the Beauce's lacustrine limestone includes two main layers: the Pithiviers limestone and the Étampes limestone, which are separated by the Gâtinais molasse, a crucial layer for protecting the underlying Étampes limestone from contamination induced by human activities on the surface. Pithiviers limestone outcrops in the north and west of the Beauce region and Étampes limestone outcrops towards the east edge of the region. The Beauce limestone is covered by Sologne sand and clay formations (Mio-Pliocene) in the north-east and south of the Loire River. This formation is characterized by detrital sedimentation that immediately follows the lacustrine sedimentation of the Beauce

limestone. The fluvial to sub-fluvial nature of the sand deposits was suggested by crossbedding, the presence of lenticular formations as well as the morphology of quartz grains (Debrand-Passad et al., 1998).

The Beauce aquifer is the main groundwater resource for these areas and supplies drinking water to over one million inhabitants in the southern part of the Paris region. The Beauce limestone aquifer is a multi-layered aquifer system, primarily recharged by effective precipitation from October to April. Due to the complex stratigraphy and the inconsistent presence of impermeable layers, it is challenging to classify the aquifer as either confined or unconfined in the northeast sector. However, it is likely that the main system is confined under the Miocene detrital deposits of the Orléans Forest and the Sologne geological formations and unconfined north of the Loire River. A map describing the main lithological facies is provided in the Supplementary material (SD-1.1).

The Beauce aquifer is part of a complex aquifer system, drained by two surface basins: the Seine basin in the northeast and the Loire basin in the south-west. The hydraulic crest separating the two sub-basins starts in the northeast confined zone of the aquifer beneath the Orléans forest and crosses up to the north of a hydraulic head plateau in the northern part of the aquifer. The hydraulic head gradient in the plateau is very low (0.01 %), suggesting high transmissivity as expected in fractured limestone. The location of this crest evolves between high water and low water regimes during the year. Aquifer thickness varies from 10 m to 200 m. The stream network is heterogeneous. In the central part, rainwater infiltration towards the aquifer unit is dominant, leading to the existence of few rivers. A denser river network develops over the remainder of the domain. Transmissivity in the Beauce aquifer is heterogeneous. Limestone is more fractured in the north-west part than in the north-east, hence transmissivity is higher in the former.

In the part of the aquifer located in the Seine basin, the existing piezometric maps illustrate the presence of a hydraulic head ranging from 110 mNGF to 55 mNGF (Verley et al., 2003; see SD-1.2). The Juine and Essone rivers drain this part of the Beauce aquifer. Hydraulic conductivity is low due to denser limestone (less fractures/fissures), leading to higher hydraulic gradient (0.1 %) than in the central plateau. The part of the Beauce aquifer located in the Loire basin is also mainly drained by the Loire River and its tributaries. Due to the high discharge of the Beauce aquifer in the Loire River, it is assumed that the northern part and the southern part of the aquifer are two distinct hydrogeological basins. In the northern part, the hydraulic head ranges from 110 mNGF to 80 mNGF (Verley et al., 2003). A hydraulic head gradient occurs from north-east to south-west. Several discharge areas of the aquifer in the Loire River are drained by several Loire tributaries (Conie, Mauves), distinguishing several water flow pathways in this part of the Beauce aquifer. In the part of the Beauce aquifer located south of the Loire River (Calligee, 2004; see SD-1.3), the piezometric head map reveals a hydraulic gradient from the south-east to the south-west of this part of the Beauce aquifer (from approximately 110 mNGF to 70 mNGF). The main discharge area of this southern part of the Beauce aquifer is the Loire River in the vicinity of the city of Blois. In the most southern part of the Beauce aquifer, the discharge occurs in the Cher River. Despite the Sologne sand and clay formations confining the Beauce aquifer from the surface, some tributaries (Cosson and Beuvron rivers) of the Loire seem to surprisingly impact groundwater flow. Four sub-basins are distinguished in this southern part of the Beauce aquifer.

### 2.2. Sampling strategy

Sixty sampling points were identified in the Beauce Aquifer around the city of Orléans (Fig. 2). They were selected based on several criteria including the nature of the aquifer (unconfined or confined), whether they belong to either the Pithiviers or Étampes limestone, their accessibility, monitoring history (drinking water wells were privileged) and their geographical position to obtain a representative network of points. All sampling points are registered in the BSS-Eau database (http:
<figure><img src="image_6.png" /><figcaption>Fig. 2. Map of sampling points.</figcaption></figure>

//infoterre.brgm.fr) and the historical monitoring data is available in ADES (French national portal on groundwater data https://ades.eaufrance.fr/). A list of the sampling points with their reference number is provided in the Supplementary data (SD-1.4).

Sampling was carried out between the 4th May and the 4th June 2021 by sampling water directly from the pumping device before remediation treatment prior to producing drinking water (i.e., before chloration, iron and manganese precipitation, removal of arsenic or selenium is carried out). At each sampling point, we made sure that the pumps had been activated previously to guaranty that the sampling is representative of the surrounding groundwater without suffering any drawbacks due to water stagnation in the borehole.

## 2.3. Characterisation of the microbial community

### 2.3.1. DNA extraction and quantification

At each point, ten litres of water were filtered on Sterivex® filters (ø 0.22 µm membrane Express, Merck Millipore, France) to recover microbial biomass. Filters were conserved at -20 °C until DNA extraction using the DNeasy PowerWater Sterivex Kit (Qiagen, France) following manufacturer's instructions. Extracted total DNA was quantified using the Quantifluor dsDNA sample kit and the Quantus fluorimeter according to the manufacturer's instructions (Quantus, Promega, USA) and stored at -20 °C.

### 2.3.2. qPCR

Quantitative PCR (qPCR) was carried out to estimate bacterial abundance by targeting the gene coding 16S rRNA and abundances of functional groups by targeting genes linked to arsenic reduction (*arsB*), arsenic oxidation (*aioA*) and nitrate reduction (*narG*). qPCR was performed using a CFX Connect™ Real-Time PCR Detection System (Bio-Rad, France) with primers and programs listed in SD-2. qPCR reactions were performed on 2 µL of template DNA (concentrations of total DNA ranging from <0.01 ng·µl⁻¹ to 3.8 ng·µl⁻¹) in a master mix of 20 µL containing sterile, nuclease-, RNA- and DNA-free water, 1x SSO Advanced Supermix (Bio-Rad, France), 0.3 to 0.5 µM of reverse and forward primers (final concentration in SD-2), 100 ng of T4GP32 (for *aioA* and *narG* genes). Sterile, nuclease-, RNA- and DNA-free water was added instead of DNA in no template controls (NTC). A calibration curve

was obtained from serial dilutions of a known quantity of linearized plasmids each containing known copy numbers of 16S rRNA, *arsB*, *aioA* or *narG* gene fragments. All samples, controls and standards were analysed in duplicate. Limits of quantification (LOQ) were 1.0·10³ gene copies·µl⁻¹ for the 16S rRNA gene and *arsB* gene and 1.0·10² gene copies·µl⁻¹ for *aioA* and *narG* genes. Raw data were analysed with the CFX Manager 3.1 (Bio-Rad, France) and the results were expressed as gene copies per L of water.

### 2.3.3. Illumina sequencing

Bacterial and archaeal diversity were determined by 16S rRNA gene metabarcoding. Amplicon libraries construction and Fastq generation were performed by INRAe Transfert Methys (France). In brief, PCR using the barcoded, universal primer set (515WF/918WR) and the AccuStart II PCR ToughMix kit, followed by cleaning (HighPrep PCR beads, Mokascience), amplified a portion of the 16S rRNA gene (V4-V5 region). Pooled triplicates were submitted for sequencing on Illumina MiSeq instrument at GeT-PlaGe (Auzeville, France). Fastq sequences were processed on the Galaxy platform using the FROGS pipeline (Escudié et al., 2018) based on the Galaxy analysis platform (Afgan et al., 2016). Sequences were demultiplexed, dereplicated, oligonucleotides were removed from sequences, and paired reads were merged with VSEARCH. Sequences were removed from the data set if they exhibited ambiguous bases or did not match expectations in amplicon size. Remaining sequences were clustered into amplicon sequence variants (ASV) based on the iterative SWARM algorithm with an aggregation distance of 1, then chimeras and ASV representing <0.005 % of all sequences were removed. Bacterial double affiliation was performed on the remaining sequences by blasting ASVs against SILVA 138.1 database (Quast et al., 2013). ASV with affiliation <100 % at the phylum level were removed from the data set. ASV at lower taxonomic ranks than the phylum level was considered as “unidentified” below the RDP bootstrap value of <0.70. A normalization by random sampling was applied to obtain the same number of sequences per sample (2716 sequences – rarefaction curves in SD-3). Finally, a second clustering was applied to group ASV with at least 97 % similarity and 99 % sequence identity. The cleaning of the sequencing results and the normalization of the number of sequences of the samples resulted in an identification of 890 ASVs from 165,676 sequences. The ASV abundance table was analysed by Phyloseq R
implemented in FROGS. Alpha diversity indexes were determined; Observed (species richness, number of ASV per sample), Chao1 (number of estimated ASV), Shannon (specific diversity considering ASV abundance number, the higher it is the higher is the diversity) and InvSimpson (species richness considering evenness, a low value indicates lower diversity with a few dominating ASV).

## 2.4. Physico-chemical water characterisation

Water temperature, electrical conductivity (standardized to 25 °C), pH and redox potential (Eh) were measured on site using IDS multi-parameter probes (WTW, Germany) according to recommendations of the French standard FD X31-615.

Groundwater samples were filtered for major and trace elements (ø 0.45 µm, Millipore, France) then acidified with HNO₃ (1 %), except for the NO₃ measurements. For organic carbon analysis, raw samples were taken and then acidified with H₂SO₄ (1 %). Concentrations of NO₃, NH₄ and PO₄ were measured by spectrophotometry, inorganic anions (Cl, F, NO₃ and SO₄) were quantified by ionic chromatography (NF EN ISO 10304-1), dissolved compounds (Ca, K, Fe, Mg, Na, Si, Al, Mn, As and Se) were measured by ICP-AES (NF EN ISO 11885 and NF EN ISO 17294-2), total organic carbon (TOC) and dissolved organic carbon (DOC) were measured by flash pyrolysis (NF EN 1484). Detection limits are provided in SD-4.

Concerning pollutants related to anthropogenic activities (pesticides, organochlorides), a list of 538 organic pollutants was defined and requested in the ADES database (SD-5). For each sampling point, the number and name of pollutants detected and quantified were listed. Then, for each organic pollutant with, at least, three measurements above the limit of quantification (LOQ), the following information/ metrics were calculated: number of measurements available, date of measurements as well as mean, median, standard deviation, maximum and minimum values. Furthermore, a metric similar to the one used in the Water Directive Framework to assess quality status of groundwater was used. The metric used is the sum of all mean values calculated for each quantified organic pollutant. Instead of using one measurement period, we used the mean value of the time series measurements.

## 2.5. Statistical analysis

All statistical analyses were carried out using R-Stats (R Core Team, 2024).

### 2.5.1. Hydrogeochemical data

Analysis of the hydrogeochemical data was carried out to identify hydrogeochemical facies in the Beauce aquifer as such data is not available despite previous research on the hydrogeochemical context of the Beauce aquifer. For this purpose, compositional data analysis (CoDa) approach associated to multivariate statistical analysis (MSA) and box-plot analysis was developed.

Prior to any analyses, data pre-treatment was conducted to build a data set compatible to be used in compositional data analysis. First, concentrations were converted from mg·l⁻¹ or µg·l⁻¹ to mmol·c⁻¹·l⁻¹. Note that ionic balance errors cannot be calculated precisely as HCO₃ measurements are not available. Any variable with a large proportion of samples below the Limit of Quantification (LOQ) (>33 % of samples below the LOQ) were discarded. The discarded variables included PO₄, NH₄ and NO₂. The raw dataset was divided into 3 subsets: i) a first subset of physico-chemical parameters (pH, Eh, conductivity, temperature, O₂ saturation), ii) a second subset of 8 major chemical compounds (Ca, Mg, Na, K, Cl, SO₄, NO₃ and Si) which concentrations are higher than 1 mg·l⁻¹ in most of the samples and a third subset of 4 chemical compounds with concentrations lower than 1 mg·L⁻¹ (F, Fe, Al, Mn, As and Se).

The isometric log-ratio transformation was used to convert the dataset into the algebraic-geometric structure (Aitchison geometry)

used in compositional data analysis (more details are given in SD-6.1). A simplified version of the isometric log ratio was used, called pivot log ratio that prevents making hypothesis on the link between chemical compounds by summing all pairs of log-ratio. After data transformation, multivariate statistical analysis was performed. Principal component analysis (PCA) was applied on log-transformed variables following the CoDa approaches. The variables used are concentrations of the major chemical compounds plus concentrations in Fe and Mn. PCA was applied to explore the variance of the dataset by reducing the number of dimensions. The results were interpreted using biplot analysis summarizing information from samples and variables. Note that the interpretation of covariance biplots has been adapted for compositional data. Then, an unsupervised hierarchical cluster analysis (HCA) was performed to reveal the similarities/dissimilarities between sampling points in terms of hydrogeochemical properties. Agglomerative HCA Euclidean distance and Ward's linkage method were used on the cl-transformed data issued from PCA. The HCA results visualized as a dendrogram were then analysed using a bootstrap method to determine a posteriori the number of clusters minimizing the sum of squares within each cluster and maximizing the sum of squares between the clusters.

To illustrate in more detail the changes in concentration for some chemical compounds (Eh, Fe, Mn and Se), results were expressed according to the clusters identified with the MSA according to a boxplot. Significant changes in variance between groups were analysed using a Kruskal-Wallis test as data did not respect hypotheses related to parametric tests. A post-hoc test (Conover and Iman) was then performed to detect significant changes in variance between each pair-group.

To assess the representativeness of the hydrogeochemical data acquired during the field campaign, a framework based on the comparison between the measurements done during the field campaign and past measurements done on the same sampling points was elaborated. First, a request was conducted in the ADES database to collect the previous measurements of the aforementioned physico-chemical variables done in the sixty sampling points. For each sampling point, mean values were calculated for each physicochemical parameter with, at least, three measurements above LOQ available. PCA was then performed on the dataset after transforming the data using the same isometric log transformation as used on the field campaign dataset. Results were projected in a biplot. This biplot was compared to the one built based on the field campaign dataset.

### 2.5.2. Diversity data

Analysis of diversity data was carried out using a principal coordinates analysis (PCoA, see SD-6.2). This unconstrained ordination technique allows the objects studied to be projected along the projection axes created to maximize the conservation of the distance between the objects as calculated from the original data set. It is therefore necessary to first calculate a similarity/dissimilarity matrix resolved using a singular value decomposition method. The similarity/dissimilarity matrix was constructed considering the phylogenetic distance between the identified species and their abundance using the method called "weighted Unifrac" (Lozupone and Knight, 2005). The data was clustered according to the identified hydrogeochemical facies.

A LEfSe (Linear discriminant analysis effect size (Segata et al., 2011)), analysis was used to identify taxa characterizing differences between several biological conditions. It focuses on both statistical significance and biological relevance and identifies discriminating features that are statistically different between the identified hydrogeochemical groups. The non-parametric Kruskal-Wallis (KW) sum-rank factorial test was used to detect features showing significant differential abundances with respect to the groups of interest. Finally, LEfSe uses linear discriminant analysis to estimate the size effect of each differential abundance feature and classify the feature accordingly.

Co-occurrence networks were established on sampling points in the Beauce aquifer. They highlight the relationships between microorganisms according to the hydrogeochemical conditions in which they
develop. Prior to network analysis, water sampled community data from the 3 hydrogeochemical signatures were filtered by selecting ASVs representing at least 0.05 % of sequences and removing all taxa that weren't present in at least three sampling points. From the filtered data, taxon co-occurrence based on Spearman rank correlations, and network topological parameters were calculated separately for each hydro-geochemical signature cluster using the R microeco package.

# 3. Results

## 3.1. Hydrogeochemical facies in the Beauce aquifer

Fig. 3 illustrates that sampling points are distributed according to their hydrogeochemical facies based on major chemical elements (minor HCO₃) plus two redox chemical elements (Fe and Mn). Eigenvalues defined for each principal component were high showing that a major part of the variance associated to the dataset was captured (>75 %). The first principal axis is the main descriptor of the dataset variance, the main physico-chemical variables related to this axis are Mn, and in a lesser extent Mg, Na, K, Cl and NO₃. The variance explained by the second axis is lower. The main variables associated are Fe, Ca and SO₄. Comparison between field measurements and historical data illustrates stability over time for the physico-chemical parameters (see SD-7).

The HCA analysis revealed that the sampling points could be divided into three clusters based on their physico-chemical properties. These clusters correspond to hydrogeochemical facies. They are related to the different geological and hydrological contexts encountered in the Beauce aquifer. This relationship between clusters and hydrogeological

contexts is illustrated in SD-8.1 and 8.2. The first cluster (Cl1) is composed of 18 points mainly situated in the unconfined Pithiviers limestone part of the aquifer. Only Tigy, Tours-en-Sologne and Chapelon-les-3-Ormes are geographically distant, the first is situated in the Loire alluvions, the second south of the Loire at the limit with the Sologne formation and the third is in the Étampes limestone part of the aquifer. This first cluster is here after named "unconfined limestone" and is characterized by oxic waters containing nitrate. The second cluster (Cl2) is composed of 19 sampling points all located in the most confined areas south and northeast of the Loire River under the Sologne sands and clay formations. This second cluster, here after named "confined limestone" is characterized by low or negative redox conditions that can be anoxic and are rich in dissolved iron and manganese but poor in NO₃. The third cluster (Cl3) is composed of 23 sampling points that are all located in transition zones of the sands and clays of Sologne. This cluster is here after named "Transition zone" where hydro-geochemical parameters highlight the mixing zones between waters from the unconfined and confined parts of the aquifer. These points still contain high amounts of dissolved iron or manganese but in smaller quantities than Cluster 2. Redox fluctuates between anoxic and oxic conditions and nitrate is occasionally found.

Fig. 4 illustrates the main characteristics of the 3 clusters and their variability, whereas SD-9 provides the detailed results of Fe, Mn, SO₄²⁻ and NO₃ for each sampling point according to the cluster. Dissolved Fe concentrations ranged between 0 and 0.5 mg·l⁻¹ in the unconfined limestone, 0 and 0.4 mg·l⁻¹ in the confined zone and between 0 and 0.5 mg·l⁻¹ in the transition zone whereas dissolved Mn ranged between 0.1 and 34 mg·l⁻¹, 0.8 and 333 mg·l⁻¹, 0.1 and 244 mg·l⁻¹ in the three

<figure><img src="image_7.png" /><figcaption>Fig. 3. Biplot illustrating the results provided by the PCA analysis performed on a dataset of several physico-chemical variables. A CoDa transformation was used prior analysis. Samples are clustered in three groups: cluster 1 (red), cluster 2 (green) and cluster 3 (green). These clusters are associated with hydrogeochemical context: unconfined limestone, confined limestone (centre) and confined limestone (transition zone).</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Fig. 4. Variability of Mn, Fe, NO₃ and Eh in the three hydrogeochemical clusters.</figcaption></figure>

clusters, respectively. Sulfate concentrations were generally lower than 18 mg·l⁻¹ which is far lower than the maximum authorised concentration for drinking water in France (250 mg·l⁻¹). However, several sampling points contained higher concentrations, up to 68 mg·l⁻¹ at Neuville-Sous-Bois (Cl3).

## 3.2. Towards the identification of geochemical abnormalities

### 3.2.1. Organic carbon

The average total organic carbon (TOC) concentrations for each cluster were 1.29 ± 0.62 mg·l⁻¹, 1.58 ± 0.45 mg·l⁻¹ and 1.07 ± 0.36 mg·l⁻¹ in Cluster 1, Cluster 2 and Cluster 3, respectively. Detailed concentrations for each sampling point in each cluster are presented in SD-10. Values found at Saint-Péravy-la-Colombe and Vitry-aux-Loges appeared particularly high (>3 mg·l⁻¹) and low concentrations (< 0.5 mg·l⁻¹) were observed at Bacon, Terminiers, Chilleurs-aux-Bois, Seris, Fay-aux-Loges, Boigny-sur-Bionne and Chécy.

### 3.2.2. Trace elements

The average concentrations of As and Se are presented in Table 1 and detailed concentrations for each sampling point according to their hydro-geochemical cluster are presented in SD-11. Se was mainly encountered in the unconfined limestone area with values up to 14 µg·l⁻¹ (Chilleurs-aux-Bois) and 18 µg·l⁻¹ (Engeville). None was detected in the confined zones and then again in the transition zone in two points, Chevilly and Saint-Lyé where concentrations of 16 and 13 µg·l⁻¹ were measured, respectively. As on the other hand was essentially found in

the confined limestone and the transition sampling points, with concentrations frequently higher than 10 µg·l⁻¹.

### 3.2.3. Pesticides

SD-12.1 and SD-12.2 show results of the metrics calculated for the pesticide measurements available for each sampling point from the ADES database. SD-12.1 shows a map with the number of pesticides detected at each sampling point and SD 12.2 gives a map of the sampling points with the average pesticide concentrations in µg·l⁻¹. Several points stand out with concentrations in the higher range between 0.07 and 3 µg·l⁻¹; Merouvilliers, Chilleurs-aux-Bois, La-Chapelle-Onzerain, Bacon, Huisseau-sur-Mauves, Chécy, Ardon, Mareau-aux-Bois and Crouy-sur-Cosson. Although the total pesticide concentration found was low, Rouvry and Tigy stand out in the second map (SD-12.2) as having >10 different pesticides detected. The main pesticides detected here were atrazine and its transformation products.

## 3.3. Characterisation of microbial communities in the hydrogeochemical clusters

### 3.3.1. Bacterial biomass and functional gene abundances

Fig. 5 presents the results for molecular biomass (concentration of microbial DNA), and the Log gene copy numbers for 16S rRNA (bacterial abundance), *aioA* (arsenic oxidation), *arsB* (arsenic reduction) and *narG* (nitrate reduction) in each hydro-geochemical cluster. The average quantity of microbial DNA was 3.9 ng DNA·l⁻¹ water, most of them being below 10 ng DNA·l⁻¹. Only four samples contained more DNA, up to 30 ng DNA·l⁻¹, and were mainly in the transition zone (Cluster 3): Olivet, Cours-Cheverny and Châteauneuf-sur-Loire, except for Eole-en-Beauce which is in Cluster 1. Abundance of the gene coding 16S rRNA, a proxy for estimating bacterial abundance, varied between 10⁴ and 10⁷ gene copies per litre of water whereas estimations of the numbers of gene copies for a selection of functional genes involved in As and N cycling strongly fluctuated between sites without any direct mathematical correlation with the presence of As or nitrate (data not shown).

**Table 1**

Average concentrations and standard deviation of As and Se in the three hydro-geochemical clusters.

<table><thead><tr><th></th><th>As (µg·l<sup>-1</sup>)</th><th>Se (µg·l<sup>-1</sup>)</th></tr></thead><tbody><tr><td>Unconfined limestone</td><td>2.8 +/- 4.3</td><td>3.2 +/- 5.05</td></tr><tr><td>Confined limestone</td><td>5.8 +/- 7.1</td><td>0.1 +/- 0.05</td></tr><tr><td>Transition zone</td><td>8.3 +/- 8.1</td><td>1.2 +/- 3.5</td></tr></tbody></table>
<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Molecular biomass (DNA) and Log gene copy numbers (*aioA*, *arsB*, *narG* and 16S rRNA) for each hydro-geochemical cluster. Data are ordered according to an increasing Mn/NO₃ ratio.</figcaption></figure>

### 3.3.2. Diversity indexes

Alpha diversity indexes were calculated based on the Illumina sequencing data. Shannon index and the number of ASVs are presented in Fig. 6 and the detailed results for each sampling point according to the clusters are presented in SD-13. Although there was a high variability in each cluster, results showed an overall gradient from the unconfined

limestone aquifer to the most confined zones with a higher diversity is the less confined zones of the aquifer.

### 3.3.3. Diversity of bacteria and archaea

#### 3.3.3.1. Overall diversity and dominant phyla.
The diversity of ASVs is first represented by their affiliation and relative proportion (% of sequences) to a phylum for each sampling point and hydrogeochemical cluster (SD-14.1). The results were then statistically analysed (Fig. 7) in a manner like that used for geochemical parameters, to identify similar biodiversity profiles and points that deviate from these profiles for each hydrogeochemical signature. Finally, a focus on the dominant and recurrent ASVs in each cluster was carried out.

The 890 identified ASVs belonged to 51 phyla (SD-14.1). Among the main ones, Proteobacteria were found in all samples and globally dominated groundwater diversity, followed by Verrucomicrobiota. The third most abundant phylum found in all the samples was Nitrospiota, which, like Verrucomicrobiota, was present in all the samples except for Tours-de-Sologne and which was particularly abundant in Yvoy-le-Marron where it represented 76 % of the sequences linked to a single ASV. Apart from Yvoy-le-Marron where the ASV was not identified beyond the phylum level, the three known classes of Nitrospiota were found (Leptospirillia, Nitrospiria and Thermodesulfovibrionia). The fourth most abundant phylum and, like Proteobacteria, present in all the samples, was Bacteroidota. Other phyla whose ASVs represented high percentages of sequences were Nanoarchaeota and Firmicutes. Nanoarchaeota were found in most of the samples, in particular La-Chapelle-Saint-Mesmin where this phylum represented 18 % of the sequences. Firmicutes, on the other hand, were not systematically present and were abundant in 3 samples, Batilly-en-Gâtinais (42 % of sequences), Menestreau-en-Villette (30 % of sequences) and Nouan-le-Fuselier (79 % of sequences).

The statistical analysis of the structuring of the diversity profiles (PCoA) regarding the 3 hydrogeochemical groups (Fig. 7) provides further information on this diversity. First, a greater dispersion of the diversity profiles between the sample points of cluster 1 (unconfined limestone) compared to the two other clusters of the confined part of the aquifer was observed. This variability could be an indicator of sub-profiles potentially linked to a hydrogeochemical gradient that had not been statistically highlighted during the clustering carried out on the chemical parameters. Second, we found that several points of each hydrogeochemical signature differed from their group or even resem-bled another cluster.

#### 3.3.3.2. Towards defining a microbial diversity baseline.
Based on the geochemistry and diversity analyses for each water sample, the next aim was to identify the microbiological composition that can be expected for a given hydrogeochemical signature, fluctuations that could be expected due to the natural gradient and connectivity of the aquifer and finally,

<figure><img src="image_10.png" /><figcaption>**Fig. 6.** Boxplot of the number of ASV and Shannon index in the three clusters.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>Fig. 7. PCoA analysis of microbial diversity in the three hydro-geochemical clusters.</figcaption></figure>

differences in diversity that could be due to a perturbation.

3.3.3.2.1. Cluster 1 unconfined limestone. In cluster 1, from the point of view of hydrogeochemical signatures, 4 samples stood out from the group: Séris, Huisseau-sur-Mauves, Meung-sur-Loire and Chapelle-Saint-Mesmin. From the point of view of the microbiological profiles of the points of cluster 1 on the other hand, only Meung-sur-Loire couples a second deviation from the group. Looking more closely at the diversity in this well (SD-14.2 and SD-14.3), it appears to be similar to the profiles of cluster 2, dominated by ASVs from the Gallionellaceae and Hydrogenophilaceae families.

Six other samples stood out from cluster 1 due to their microbiological composition, these are:

- Eole-en-Beauce, dominated by an ASV of the Acidithiobacillaceae family (70 % relative abundance), an ASV also found at Rouvray-Saint-Denis and Terminiers but at lower rates (13 and 18 % relative abundance, respectively);

- Saint-Péravy-la-Colombe, dominated by an ASV of the Sphingomonaceae family (71 % relative abundance), also found at Terminiers and Neuville-sur-Essonne but at lower rates (12 and 17 % relative abundance, respectively);

- Engeville, whose profile, due to its location and its composition, dominated by the genus *Thiotrix* (47 % relative abundance) with the presence of ASVs from the Gallionellaceae and Hydrogenophilaceae families, was similar to profiles found in clusters 2 and 3;

- Neuville-sur-Essonne does not have a profile dominated by a particular ASV but a profile which differs from those found in cluster 1, or even in the other clusters, but which is like certain profiles of cluster 3 such as Saint-Cyr-en-Val and Chambord;

- Tours-en-Sologne on the other hand seems to present a unique profile dominated by ASVs of the Oxalobacteraceae family (20 % relative abundance), and of the *Flavobacterium* and *Brevundimonas* genera (10 % relative abundance) which are not or very little found in other samples;

- Batilly-en-Gâtinais was also considered to be outside a typical cluster since it presented a strong dominance (40 % relative abundance) of an ASV affiliated with the genus *Lactococcus*, just like Nouan le Fuselier (cluster 2), dominated at 75 % by this same ASV.

Thus, some samples differed from the common profile of cluster 1 due to their singularity and others due to their similarity to profiles of other clusters. To identify a "typical profile" of cluster 1, these samples which are biologically and/or chemically different were excluded from the analysis. Thus, in samples with a hydrogeochemical signature as defined for cluster 1, unconfined limestone, the dominant taxa that can be expected to be found are presented in Fig. 8.

3.3.3.2.2. Cluster 2 confined limestone. Four samples in the confined limestone aquifer stood out from the hydrogeochemical signature and four from the biological signature. Two locations stood out both from a hydrogeochemical and microbial diversity aspect, Izy and Montrieux-en-Sologne. Izy was closer to the biological and chemical profiles of cluster 1 whereas Montrieux-en-Sologne was closer to Cl1 geochemically but for the biodiversity profile, it was closer to samples from cluster 3 such as Boigny-sur-Bionne or Mareau-aux-Près. The two other samples which differed by their hydrogeochemical signature are Sennely which was close to cluster 3 and Menestreau-en-Villette which had a separate signature. Those which differed only by their diversity profile are Nouan-le-Fuselier, dominated by an ASV affiliated to the genus *Lactococcus* (75 % relative abundance) and Yvoy-le-Marron, dominated by an
<figure><img src="image_12.png" /><figcaption>Fig. 8. Most abundant ASVs in each hydrogeochemical signature.</figcaption></figure>

ASV affiliated, only at the phylum level, to Nitrospirota (class 4–29-1) (71 % relative abundance). As for cluster 1, to identify a “typical profile” of diversity in cluster 2, the samples which were biologically and/or chemically different were excluded from the analysis. Thus, in samples associated to cluster 2, confined limestone, the dominant taxa that are expected are presented in Fig. 8.

3.3.3.2.3. Cluster 3 confined transition zone. Cluster 3 is at the interface between the other two hydrogeochemical signatures and harbours the highest number of sampling points and the highest dispersion

around the barycentre of the CoDa analysis (Fig. 3). From the point of view of the diversity profiles however (Fig. 7), only 4 samples deviate significantly from a central profile. Nevoy, Chevilly and Chambord are close to the biological signature of cluster 1, while Boigny-sur-Bionne resembles that of the Montrieux-en-Sologne site of cluster 2, both having distinct signatures. It therefore appears that despite a greater hydrogeochemical disparity of the samples associated with this cluster, the biological variability is lower. As before, the samples showing a strong divergence were excluded from the definition of a biological

<figure><img src="image_13.png" /><figcaption>Fig. 9. LEfSe (Linear discriminant analysis Effect Size) analysis identifying taxonomic differences between clusters. Histogram of LDA (Linear Discriminant Analysis) scores calculated for the first 30 significant taxa (α = 0.05). Cladogram representing the taxonomic distribution of the 30 significant bacterial taxa.</figcaption></figure>
signature specific to cluster 3, as were the sites of Saint-Cyr-en-Val and Fontaine-en-Sologne which are the most different from a hydrogeochemical point of view and resemble cluster 2. The dominant taxa that are expected to be found in cluster 3, transition zone, are also presented in Fig. 8.

### 3.3.4. Differential abundance

Linear discriminant analysis (LEfSe) identified bacterial groups that were significantly overrepresented in the three clusters. This analysis completes the previous identification of taxa associated to a hydrogeochemical signature and made it possible to identify 65 taxa with a significant differential abundance between the three clusters. Fig. 9 represents the 30 most significant taxa. The phyla Methylomirabilia and Myxococcota, and the genera KCM-B-112 (belonging to the Acidithiobacillaceae family), *Pedobacter* and *Leptospirillum* are overrepresented in the free limestone cluster. In cluster 2, the phylum Desulfobacterota and the genus MSBL7 (belonging to the family Desulfurivibrionaceae) as well as the order Burkholderiales and the families Gallionellaceae and Rhodocyclaceae have a greater differential abundance. In the cluster of limestone under cover of the fringe, Gammaproteobacteria, Hydrogenophilacea, Thermodesulfovibrionia and Anaerolineae are overrepresented.

### 3.3.5. Co-occurrence networks

After data filtration and co-occurrence analysis (Spearman, $\rho > 0.7$, $P < 0.01$), we obtained 242 nodes and 1892 links in cluster 1, 43 nodes and 34 links in cluster 2 and 127 nodes and 389 links in cluster 3 (SD-15). In unconfined limestone (SD 15-1), the phyla with the greatest number of connections were Verrucomicrobiota and Nanoarchaeota. Indeed, the ASVs of these two phyla structure the co-occurrence network as there are >350 links between these two phyla. Among the 10 ASVs having the most links with other ASVs, 8 belonged to the genus *Candidatus Omnitrophus* (Verrucomicrobiota). The other two belonged to the phylum Methylomirabilota, one belongs to the order Rokubacteriales and the other to the genus MIZ17 (*Methylomirabilaceae*).

In the confined limestone (SD 15-2), there were very few co-occurrence relationships between ASVs. The three largest modules (set of interlinked nodes) included 8, 7 and 5 ASVs, respectively. The first was composed of ASVs of the phyla Methylomirabilota, Crenarchaeota, Myxococcota, Nitrospirota, DTB120, Verrucomicrobiota and Nanoarchaeota, the second was composed of ASVs affiliated with Proteobacteria, Elusimicrobiota and Verrucomicrobiota (including the genera *Reyranella* and *Opitutus*), finally the last was composed of 5 ASVs affiliated with *Candidatus* Omnitrophus. Two ASVs, among the most abundant in cluster 2 were also linked; the ASV affiliated to the genus MSBL7 (*Desulfurivibrionaceae*) and an ASV from the Anaerolineaceae family.

In the transition zone (SD 15-3), the phyla with the greatest number of connections were Verrucomicrobiota and Nanoarchaeota (as observed in the unconfined limestone). There were nearly 80 links between these two phyla. Among the 10 ASVs having the most links with other ASVs, 4 belonged to the genus *Candidatus* Omnitrophus (Verrucomicrobiota), 4 belonged to the order Woesearchaeales (Nanoarchaeota) and the last two were affiliated to the genera *Luteolibacter* (Verrucomicrobiota) and *Sulfurifustis* (Proteobacteria). The *Thiothrix*-affiliated ASV, among the most abundant ASVs in cluster 3, was present along with two other Gammaproteobacteria from the families *Areniellaceae* and *Sedimenticolaceae*.

## 4. Discussion and conclusions

By acquiring a first dataset of the microbial diversity in the Beauce aquifer, this work sought to explore the microbial diversity in this aquifer, then question its link to the groundwater hydrogeochemical signatures and finally discuss whether it was possible to highlight disturbances using microbiological profiles or the presence of microbiological markers. As discussed below, these objectives were partially

reached but further research is needed to elucidate the complexity of these still poorly known environments.

### 4.1. Hydrogeochemical data identified three clusters

Quantitative and qualitative features of the Beauce aquifer have been previously collected (Flipo et al., 2012; Verley, 2020; Abbas et al., 2022). However, identification of the hydrogeochemical facies at the scale of the Beauce aquifer is still lacking. Statistical treatments were applied to calculate the hydrogeochemical signature of the waters. This signature results from certain properties of the aquifer such as flow rates, transport properties, as well as the nature of the mineralogical assemblage of the geological material constituting the aquifer's surroundings. In turn, these features lead to water-rock interactions, at the origin of groundwater chemistry that can differ in nature or intensity between these formations. Consequently, it is possible to identify different hydrogeochemical signatures for different aquifer formations (Glynn and Plummer, 2005). It is also possible to characterize the variability of the hydrogeochemical signature within a formation, thus making it possible to identify sectors with a possibly different functioning.

Statistical analysis identified three clusters with relatively distinct hydrogeochemical signatures. A first cluster (Cl1) found mainly in the unconfined Pithiviers limestone, was characterized by well-oxygenated waters containing nitrate. A second cluster (Cl2) located in the central zone of confined limestone aquifer under the Sologne formation, was characterized by low or even negative redox conditions with the highest dissolved iron and manganese contents and no nitrate. A third cluster (Cl3), located in transition areas of the Sologne sands and clays, had an intermediate signature. Iron and manganese were still present but in lower quantities than for cluster 2. Conditions varied between anoxic or oxic and nitrate was found in several samples. The boundaries between these hydrogeochemical signatures were also related to the water flow gradient in the aquifer (see SD-8.1 and 8.2). For instance, the north-eastern part of the confined aquifer was divided between Cl2 and Cl3. This division can be related to the piezometric crest separating the Loire and Seine basins. The sampling sites belonging to Cl2 (Sury, Quiers, Izy, Ingrannes, Vitry and Nibelle) are in the Seine basin while the others belong to Cl3. Another example is provided by the sampling points from Olivet, Saint-Cyr-en-Val, Ardon and Mézières-lez-Cléry, which are situated in a local piezometric head plateau with higher values than their surroundings, implying a distinct hydrogeochemical facies. Thus, although they were classified as belonging to Cl3, they are in the confined part of the Beauce aquifer. According to the biplot projection, several sampling points stray from the barycentre of their identified cluster. These outliers can arise as the hydrodynamic properties in the Beauce aquifer, especially in the transition zone between unconfined and confined parts, are transient, implying changes in water mixing in the aquifer as well as rock-water interactions. This can lead to some sampling points from Cluster 3 resembling either Cluster 1 or Cluster 2 samples. In addition, some local variabilities in the geological and hydrogeological context can explain that some sampling points belonging to the same cluster, expressed some variability of their hydrogeochemical facies. However, it is at this stage also possible that some samples differ due to an anthropogenic disturbance.

### 4.2. Towards identifying pressures of natural or anthropic origin

Pressures exerted to the watershed can also influence the hydrogeochemical facies. Contaminants of anthropogenic origin (pharmaceutical products, emerging contaminants, etc.) are thus likely to participate to the hydrogeochemical facies of groundwater as well as geogenic elements.

A particularity of the Beauce aquifer is the presence of arsenic (As) and selenium (Se) as discussed by Battaglia-Brunet et al. (2022). As is a metalloid, whose presence in groundwater due to the geology is a threat
to many sources of drinking water across the planet. As can be found in concentrations higher than the authorised drinking water concentration which in France is 10 µg·l⁻¹. Here, as the sampling was carried out before water treatment was applied and four sampling points contained As concentrations above 10 µg·l⁻¹; Marcilly-en-Gault, Yvoy-le-Marron, Boigny-sur-Bionne and Fontaine-en-Sologne. These four samples are all located in the confined zone beneath the Sologne formation. The occurrence of As in the confined aquifer of Sologne appears to find its origins in the microbial reduction of As-rich Fe oxides contained in the detrital sediments of Sologne formations covering the limestone aquifer (Battaglia-Brunet et al., 2022). Although As speciation was not analysed in this work, due to the presence of reducing conditions, it is probable that As³⁺, the most toxic and mobile form of As can be found as was observed in previous work (Battaglia-Brunet et al., 2022).

Unlike As, high concentrations of Se are observed in unconfined areas of the aquifer. Several studies show that microbial reduction of Se (VI) and Se(IV) ions to insoluble elemental Se(0) can be inhibited by high concentrations of nitrate (Bailey et al., 2012; Cary et al., 2021). In confined conditions, Se is most probably immobilised on carrier phases, either organic matter (Li et al., 2017) or pyrites (Bailey et al., 2012). Behaviour of Se is particularly driven by nitrate, sulfur fate and redox sequence (Cary et al., 2021).

Sulfate concentrations were generally lower than 18 mg·l⁻¹ which is far lower than the maximum authorised concentration for drinking water in France (250 mg·l⁻¹). However, several sampling points contained higher concentrations, up to 68 mg·l⁻¹ at Neuville-Sous-bois (Cl3). The reducing conditions found in cluster 2 can favour sulfate reduction which would explain why, as for nitrate, they are not found in these waters.

Concerning pesticides, sampling points with the highest mean concentrations (see SD-12.2) and the largest number of pesticides detected are in the unconfined part of the Beauce aquifer. In consequence, there is an overlapping between some hydrogeological and hydrogeochemical contexts and presence of anthropogenic contamination. The same trend was found for nitrate contamination. This overlapping is not surprising as direct recharge from surface to groundwater occurs in unconfined conditions. Moreover, the main anthropogenic activities using synthetic fertilizers and/or pesticides are in the northern part of the surface basins above the Beauce aquifer. One should note that some sampling points expressed higher intensities of contamination related either to pesticides or nitrates while they are in the confined part of the Beauce aquifer, and no land use related to contamination emission can be identified. For some of them (Neuville-au-Bois, Saint-Léyé, Chevilly, Quiers, Chanteau), the number of pesticides quantified is higher than in the other sampling points belonging to Cl2 and Cl3. This result could be explained by the hydrodynamic conditions in the Beauce aquifer. Pesticides detected in these sampling points have migrated from the northern part of the Beauce aquifer, which is unconfined and where human activities relying on the use of pesticides exist, due to groundwater flow paths (see SD-8.1). The microbial diversity does not seem to differ significantly from these sampling points compared to others belonging to the same cluster group. This requires further investigations as the role of pesticide exposure on changes in microbial diversity is well illustrated for soil (Jeyaseelan et al., 2024) but to a lesser extent in groundwater (Michel et al., 2021). These results are in line with the fact that the microbial diversity of the Checy, Mareau, Ardon, Crouy and Tigy sampling points does not differ significantly for these sampling points compared to others belonging to the same cluster group. These results could be explained by some uncertainties in the assessment of pesticide contamination as historical data are used rather than direct measurement during the field campaign. In addition, the difficulty to assess the response of microbial diversity in response to contamination by xenobiotics needs to consider the duration of exposure, identified as a main driver for microbial diversity, at least in soils (Labouyrie et al., 2023).

### 4.3. Level of aquifer confinement affected the microbial diversity structure

Within the three hydrogeochemical clusters, a gradient of alpha diversity was found, from the unconfined limestone (cluster 1) to the transition zone (cluster 3) and then the confined limestone (cluster 2). The number of ASVs and the diversity indices (Shannon and Simpson) were higher in the unconfined part of the aquifer, whereas in the confined aquifer microbial diversity was lower. This higher diversity in the unconfined zone could be due to its greater exposure to surface variations and water exchanges, whereas the more confined waters are on the one hand more selective due to the absence of oxygen but also less exposed and subject to changes in hydrogeochemical conditions. Smith et al. (2018) studied 54 groundwater microbial communities in South Australia. They reported a low richness in urban and rural confined aquifers, isolated in nature and characterized by slow groundwater movement and elevated groundwater age distributions, by opposition to high richness in urban unconfined aquifers characterized by a dynamic system due to recharge events. A higher diversity due to land-use compared to pristine groundwater of similar systems was also observed by Stein et al. (2010), while Barba et al. (2019) suggested that an increase in biodiversity in impacted systems such as during managed aquifer recharge was in adequation with the IDH hypothesis (Intermedi-ate Disturbance Hypothesis) where small perturbations increase resource availability and competition-colonization trade-offs.

Co-occurrence networks also showed differences in community interactions between the hydrogeochemical clusters. Indeed, the microbial community was very structured in the unconfined limestone (Cl1) whereas the interactions between ASVs were reduced in the transition zone (Cl3) and almost non-existent in the confined limestone (Cl2). Despite their large relative abundance, Proteobacteria-affiliated ASVs showed very few co-occurrence links with other ASVs. This result seems to show that Proteobacteria have very few structuring effects on the community and that they do not need the presence of other bacteria to develop. However, Verrucomicrobiota, particularly *Candidatus* Omni-trophus, most often associated with Nanoarchaeota, seem to play a very important role in the bacterial community of water. Co-occurrence analysis of microbial communities in aquifers under pressure have previously highlighted keystone taxa which could play significant roles in biogeochemical cycling (Aibirga et al., 2022) and suggested their relevance for understanding the ecosystem functioning of groundwater ecosystems (Sha et al., 2023).

The observed gradient between cluster1→cluster3→cluster2 for both community structure and overall diversity could indicate a greater fragility of the more confined part of the aquifer to disturbances, such as climatic events with intensified groundwater fluctuations and an increased oxygen supply, which would modify the hydrogeochemical and biological balance.

### 4.4. Microbial diversity patterns could be linked to the hydrogeochemical clusters

From the point of view of the microbial diversity of bacteria and archaea, overall the dominant phylum found in the Beauce aquifer waters was Proteobacteria, as has been found in other groundwater diversity studies (Smith et al., 2012; Ben Maamar et al., 2015; Morrissy et al., 2022; Sonthiphand et al., 2019; Zhong et al., 2023). It was followed by Verrucomicrobiota, a phylum composed of mostly free bacteria (not forming biofilms) and found in abundance in aquatic environments but also in soils in temperate environments (Bandopadhyay and Shade, 2024). Metagenomic analyses suggest their important role in polysaccharide degradation (Fuerst, 2019). Nitro-spirota was the third most abundant phylum that was found. This phylum includes nitrifying bacteria, which oxidize nitrite to nitrate or even Comammox (COMplete AMMonia OXidation) which oxidize ammonium to nitrate (Al-Ajeel et al., 2022). Bacteriodota was also an abundant phylum and found in all waters like Proteobacteria.
Nanoarcheota, Firmicutes, Crenarcheota and Desulfobacterota were also abundant. Thus the dominant phyla that were found are mainly involved in an array of natural biogeochemical cycles (i.e. anammox, methane oxidation nitrification, sulfate reduction). However, phyla like Desulfobacterota could be more related to the anoxic conditions found in many of the sampled wells. In our study, ASVs from the Desulfobacterota phylum, particularly those affiliated to MSBL7 genus, were predominantly detected in the confined aquifer. Bacteria from this taxonomic group are known to perform sulfur reduction under anaerobic conditions (Magnuson et al., 2023; Deja-Sikora et al., 2019). Thermodesulfovibrionia ASVs overrepresented in Cl3, was capable of hydrogenotrophic sulfate reduction (Rogers et al., 2023).

At the level of the dominant ASVs of each hydrogeochemical signature, the three major ASVs of Cl2 and Cl3 are the same, namely an ASV from the Gallionellaceae family, an ASV from the Hydrogenophylaceae family and an ASV affiliated to the genus *Thiothrix*. These ASVs could be strongly linked to the presence of dissolved iron which they oxidize in the presence of oxygen, suggesting zones of anaerobic/aerobic interaction. In Cl1 there is also an ASV from the Gallionellaceae family, but the dominant genus is *Candidatus Omnitrophus* whose functional capacities are varied and have been mainly deduced from genome analyses. They include CO₂ fixation, hydrogen oxidation, sulfide and nitrate reduction (Williams et al., 2021). This genus was found in great abundance in an aquifer below slag heaps in the Loire region in France (Bärenstrauch et al., 2022) and the authors suggest future research on the remediation capabilities of this little known genus.

### 4.5. In search of biomarkers of an anthropic disturbance

Beyond the analysis of hydrogeochemical signatures related to bacterial biodiversity profiles, several approaches were used to identify chemical disturbances and their demonstration by microbiological community assessment. First, molecular biomass and bacterial abundance were used since an increase in biomass has previously been proposed as an indicator of a disturbance because it can be associated with an influx of carbon in these environments which are generally deprived of it (Fillinger et al., 2019b). In several of the sampling points we found values above the baseline of 5 ng·l⁻¹ of DNA up to 30 ng·l⁻¹ in the communes of Eole-en-Beauce in cluster 1, then Olivet, Cours-Cheverny and Châteauneuf-sur-Loire in Cluster 3. However, these high values do not seem to be directly related to an increase in TOC in the aquifer waters (SD-10). In recharged aquifers, an increase in DOC was associated to an increase of Bacteroidetes which were suggested as an indicator for water management (Voisin et al., 2020), although this was not the case here, possibly because DOC/TOC remained low. Other suggestions have been made to link the presence of a phyla to groundwater contamination, for example, Morrissy et al. (2022) found that Planctomycetes were linked to the presence of NH₄⁺ contamination.

The second bioindication approach used is quantitative PCR (qPCR) measuring the abundance of genes involved in nitrate reduction (*narG*) and oxidation (*aioA*) and reduction (*arsB*) of inorganic arsenic. Although in this region As is of natural occurrence, its presence in groundwater could be modulated by microbial activities (Battaglia-Brunet et al., 2022). These measurements showed strong disparities between the sampling points without there being an established mathematical relationship between the abundance of genes and the presence or absence of nitrate or arsenic. In connection with the biotransformation of metals, the specific search for genera known to be involved in these bio-transformations could be another approach, with the difficulty all the same of the level of identification of ASVs as mentioned previously. Likewise, the search for other specific genera such as *Dehalococcoides*, involved in the dehalorespiration of chlorinated organic solvents, could be an indicator of the presence of this type of pollutant. Dehalococcoidia class were overrepresented in unconfined limestone formations, particularly in the samples of Chapelon-les-Ormes, Chilleurs-aux-Bois, Cheville, Nevoy, Trainou, Tigy or Rouvray. This distribution may indicate

both anthropogenic pressure and a greater aquifer vulnerability in this area, although we lack chemical data to support this hypothesis. KCM-B-112 is an uncultured bacterial genus from the Acidithiobacillaceae family, which includes bacteria involved in sulfur oxidation. This genus was particularly abundant in the northwestern part of the unconfined area (Éole-en-Beauce, Mérouvilliers, Rouvray-Saint-Denis, Terminiers). It has previously been detected in urban deposits as well as in hydrocarbon-contaminated soils with neutral to slightly alkaline pH (Marti et al., 2017; Mafiana et al., 2021) and appears to be associated with siloxane degradation (Pascual et al., 2021). Given the current literature, the presence of this genus suggests the possibility of anthropogenic pressure in these areas.

### 4.6. Conclusion

In conclusion, this study provides a first set of metagenomic data that allows us to explore the spatial distribution of the microbial diversity in the Beauce aquifer in France, which was previously unexplored. They revealed Proteobacteria as the overall dominant phylum as commonly found in aquifers. When studied through extensive exploration with existing hydrogeochemical data, they enabled the determination of a specific microbial diversity signature for each of the three distinct hydrogeochemical signatures established according to the redox conditions of the waters. This is a first step towards the identification of microbial markers that could indicate a perturbation changing the biological and chemical equilibrium of the aquifer. Hydrogeochemical signatures were also linked with the diversity richness and its level of structuration obtained from co-occurrence networks. Both were lower in the confined parts of the aquifer where low oxygen and reducing conditions prevail, compared to the mixed transition zones then the oxic unconfined zones. This finding questions on the less diverse communities which may be more sensitive to any disturbances of aquifer quality linked to climate change or anthropogenic pressure but remains to be elucidated. Combining hydrogeochemical and microbial community analysis is a promising approach for developing a better understanding of aquifer functioning and highlights new considerations for sustainable groundwater management.

### CRediT authorship contribution statement

**Jennifer Harris:** Writing – review & editing, Writing – original draft, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization. **Hugues Thouin:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Conceptualization. **Catherine Joulian:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Conceptualization. **Stéphanie Pinson:** Methodology, Investigation. **Mickael Charron:** Investigation. **Nicolas Devau:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Conceptualization.

### Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Jennifer Harris reports financial support was provided by Bureau for Geological and Mining Research. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Data availability

Data will be made available on request.
## Acknowledgements

Thanks to Fabienne Poirier for administrative support, and to Marc Cramon, Sylvain Stephant, Setareh Rad, Fabienne Battaglia-Brunet and Caroline Michel for their help with the field work.

This project received financing from the BRGM in the framework of the BRGM GESTEAU programme coordinated by Dominique Darmendrail.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2025.179402.

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