# Biodiversity-dependent invasiveness of naive river epilithic biofilms by anthropogenic antibiotic resistance at the interface between the human, animal and environmental spheres

Frédérique Changey, Elisa Catão Caldeira Pires, Manon Grange, Mai-Lan Tran, Sabine Delannoy, Marie-Noëlle Pons, François Guerold, Jean-Baptiste Colon, Anne Poszwa, Christophe Merlin

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Frédérique Changey, Elisa Catão Caldeira Pires, Manon Grange, Mai-Lan Tran, Sabine Delannoy, et al.. Biodiversity-dependent invasiveness of naive river epilithic biofilms by anthropogenic antibiotic resistance at the interface between the human, animal and environmental spheres. Science of the Total Environment, 2026, 1012, pp.181237. 10.1016/j.scitotenv.2025.181237. hal-05483660

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Submitted on 19 Feb 2026

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# Biodiversity-dependent invasiveness of naive river epilithic biofilms by anthropogenic antibiotic resistance at the interface between the human, animal and environmental spheres

Frédérique Changey<sup>a,g</sup>, Elisa Caldeira Pires Catão<sup>a,b</sup>, Manon Grange<sup>c</sup>, Mai-Lan Tran<sup>d</sup>, Sabine Delannoy<sup>d</sup>, Marie-Noëlle Pons<sup>e,g</sup>, François Guerold<sup>f,g</sup>, Jean-Baptiste Colon<sup>c</sup>, Anne Poszwa<sup>c,g</sup>, Christophe Merlin<sup>a,g,*</sup>

## HIGHLIGHTS

* Even low anthropic activity elicit massive river invasions by antibiotic resistance.

* In headwater streams, one third of the resistance genes are of anthropogenic origin.

* Invasion of river biofilms by antibiotic resistance was promoted by low biodiversity.

## GRAPHICAL ABSTRACT

<figure><img src="image_5.png" /></figure>

## ARTICLE INFO

**Keywords:**

Antibiotic resistance dissemination

Environment

Microbial community invasion

Microbial diversity

Anthropogenization

## ABSTRACT

With more than 1 million deaths attributed each year, antibiotic resistance has become a major societal issue. The emergence and dissemination of antibiotic resistance in bacteria rests on two pillars, the enrichment of resistant variants upon selection and the contagion of the resistant bacteria and their resistance genes within and across the human, animal and environmental spheres. Although poorly described, this contagion process necessarily implies the persistence of invading resistant bacteria from one microbiome to another. In this study, we carefully selected a series of headwater streams located in the Vosges Mountains (North-eastern, France), with a clear pristine-like upstream part and well identified primary exposure to modest anthropic activities, to explore invasion processes while avoiding multiple pollution effect. Using high-throughput qPCR for 45

<footnote><sup>a</sup> Université de Lorraine, CNRS, LCPME, F-54000, Nancy, France</footnote>

<footnote><sup>b</sup> Université de Toulon, MAPIEM, Toulon, France</footnote>

<footnote><sup>c</sup> Université de Lorraine, CNRS, LIEC, F-54000, Nancy, France</footnote>

<footnote><sup>d</sup> IdentyPath Genomics Platform, Food Safety Laboratory, Anses, 94706, Maisons-Alfort, France</footnote>

<footnote><sup>e</sup> Université de Lorraine, CNRS, LRGP, F-54000, Nancy, France</footnote>

<footnote><sup>f</sup> Université de Lorraine, CNRS, LIEC, F-57000, Metz, France</footnote>

<footnote><sup>g</sup> LTSER France, Zone Atelier du Bassin de la Moselle, Nancy, France</footnote>

<footnote>* Corresponding author at: Université de Lorraine, CNRS, LCPME, F-54000, Nancy, France.
E-mail address: christophe.merlin@univ-lorraine.fr (C. Merlin).</footnote>
resistance genes and mobile genetic elements we showed that one third of the markers were already widespread, while another third massively invaded the river epilithic biofilm communities at primary exposure to anthropic activities, with the concomitant entry of fecal pollution. We used 16S rRNA gene metabarcoding to explore the structure of the bacterial biofilm communities along river continuums and showed that the extent of the invasion process was inversely correlated with the level of biodiversity, but positively correlated with the magnitude of propagule pressure.

# 1. Introduction

Bacterial resistance to antibiotics is a major societal issue widely recognized by the highest UN authorities (2016 UN resolution A_RES_71_3-EN). In 2019, it was estimated that antibiotic resistance was directly responsible for 1.27 million deaths worldwide (Antimicrobial Resistance Collaborators, 2022), and this number is predicted to reach 2 million by 2050 (Antimicrobial Resistance Collaborators, 2024). While cardiovascular diseases and cancer remain leading causes of death, with approximately 20 and 10 million deaths per year respectively, antibiotic resistance now exhibits an annual death toll comparable to that of diabetes (2 million), alcohol consumption (2.6 million), or even road accidents (1.2 million), and already surpasses that of HIV (0.7 million) (WHO health topics, 2025). The increasing occurrence of antibiotic-resistant bacteria (ARB) is intimately linked to our use and misuse of antibiotics that select and promote the multiplication of resistant variants that pre-exist within globally susceptible microbial communities (Andersson and Hughes, 2014). However, if antibiotic consumption is a key element of the problem, recent epidemiological reports suggested that the “contagion” of antibiotic resistance is just as important (Collignon et al., 2018). Actually, such “contagion” refers to two co-acting mechanisms, the contamination of naive bacterial communities by ARB, but also the transfer of antibiotic resistance genes (ARGs) from the contaminating ARB to these naive bacteria, which in turn become vectors of antibiotic resistance. In both cases, controlling the spread of antibiotic resistance will require well understanding the fate of the contaminating ARB and their ARGs when entering a naïve community.

It is now recognized that ARB and ARGs do not confine within a given ecosystem, but are also disseminating across the human, animal and environmental spheres (Larsson and Flach, 2022). The dissemination journey starts with the enrichment of resistant variants from the human or animal host microbiota during antibiotic therapies (Cantón and Morosini, 2011). Once excreted, ARB can reach the environmental compartment because human and animal waste treatment systems, when they exist, are not designed to retain them specifically (Rizzo et al., 2013; Cacacea et al., 2019; Marano et al., 2020; Zhou et al., 2018; Vaz-Moreira et al., 2014; Gerba, 2009; Gerba and Pepper, 2019). Most often, the released ARB are unlikely to survive or multiply as they are not adapted to environmental conditions. However, even if short, their environmental persistence allows the transfer of their ARGs to autochthonous bacteria using mechanisms involving mobile genetic elements (MGEs) (Bellanger et al., 2014; Gillings et al., 2017; Partridge et al., 2018). Once transferred into environmentally adapted bacteria, ARGs may persist longer and form environmental reservoirs, with a risk of spreading back to humans or animals. Some observations have shown that the size of this reservoir, and therefore the risk of spreading ARGs, directly depends on the level of persistence of the contaminating ARB within the indigenous communities (Bellanger et al., 2014; Gillings et al., 2017; Jones et al., 2017). Altogether, the dissemination of ARGs resembles an amplification loop successively alternating selection (antibiotic therapy) and spread (contamination and gene transfer) processes, with the environment acting as a transmission belt.

Whether it involves the transfer of ARGs or the establishment of ARB in natural communities, the dissemination of antibiotic resistances supposedly implies phenomena known as “biological invasions”. According to Mallon et al. (2015a), microbial invasion is a process defined by a succession of stages: (i) introduction, (ii) establishment, (iii) growth

and spread, and finally (iv) impact on native communities. For the invasion process to succeed, the invader must overcome the biotic resistance of the invaded niche, a step considered to be more difficult when the invaded community diversity is high because it reduces the trophic opportunities of the invaders (Mallon et al., 2015a; Mallon et al., 2015b; Yang et al., 2017; Li et al., 2019; Li et al., 2022; Vila et al., 2019). Consequently, an increase in local resources through anthropogenic pollution could promote invasive processes by providing new trophic opportunities. Considering that anthropogenic/animal fecal pollutions are both sources of ARB and a contribution to new nutritive resources, the contact points between the animal/human and environmental spheres appear particularly vulnerable with respect with the spread of antibiotic resistance. To date, most obvious ARB entry points such as agricultural soil manuring or the discharge of urban effluents into surface waters have attracted a great attention (Rizzo et al., 2013; Liu et al., 2025). However, the multiplicity of the ARB/ARG pollution sources in time and space in these already widely anthropized environments prevents a clear understanding of the dissemination pathways, as well as the proper discrimination between the local anthropogenic practices/sources and the pervasiveness of ARG in the environment (Larsson et al., 2018). On the other hand, scientific literature relating the way single sources of pollution affect pristine environments is relatively scarce, thus missing the extent of the initial invasion events at first-exposure points (Scott et al., 2020).

Since they interconnect many habitats, from pristine to anthropogenized, river environments, are recognized as key players in the dissemination of antibiotic resistance (Manaia et al., 2016). Aquatic environments are peculiar in the sense that they combine (i) circulating microbiota, which are rather transient but mobile and thus act as vectors of ARB/ARG dissemination, but also (ii) sedentary microbiota such as biofilms that can act as microbial reservoirs, including reservoirs for ARB/ARGs when invaded (Balcázar et al., 2015; Flores-Vargas et al., 2021). As far as ARB/ARGs are concerned, the fluxes between a pollution source, the transient river microbiota and the sedentary microbiota are not fully elucidated, thus preventing a full understanding of environmental reservoir formation. The massive interest for heavy pollution sources such as urban wastewater treatment plants and their effluents, in locations where contaminants combine with upstream pollution (Albini et al., 2023), has occulted the impact that a single anthropic activity could have on its surrounding environment. Ideally, understanding the spread of antibiotic resistance and the formation of reservoir in the sedentary microbiota requires studying the effect of single and perfectly identifiable source of contamination at first exposure points in low-anthropogenic-impact settings.

This work focused on the invasion of epilithic biofilms by ARGs in river ecosystems exposed to a first anthropic pressure. We first carefully selected three small remote forested headwater streams, free of any anthropic activity (except forestry), with clearly identifiable downstream first exposure points to low-anthropogenic activities. Nowadays, the Card database list over 5000 ARGs determinants (Alcock et al., 2023), and ARG prevalences that are not always contrasted between human, animal and environmental spheres (Zhang et al., 2021). Rather than arbitrarily choosing a set of relevant ARG indicators to monitor, with the risk of selecting some with singular behaviors, we use of high throughput qPCR (HT-qPCR) for monitoring 45 ARGs/MGEs determinants at once, thus limiting the risk of selecting a poorly represented indicator. Metabarcoding of 16S rRNA genes was then used to
characterize the microbial diversity of epilithic biofilms along the river continuums. Alpha and beta diversity analyses were finally used to decipher the relationship between community structure and the pervasiveness of ARGs following the first exposure of aquatic environmental spheres to anthropic activities.

## 2. Materials and methods

### 2.1. Sampling zones characteristics

Three rivers (Fig. 1) - La Hutte (LH), Rouge-Rupt (RR) and Cellet (C) - located in the Vosges mountains (North-eastern, France), were selected among a set of 16 headwater streams whose physico-chemical quality has been monitored for 20 years as part of the Vosges Acidification

<figure><img src="image_6.png" /><figcaption>Fig. 1. (A) Location, (B) land cover, and (C) sampling site coordinates for the Cellet (C), Rouge-Rupt (RR), and La Hutte (LH) watersheds. Black lines represent topographic watershed boundaries. Scales are given in meters, and the compass roses indicate the north direction. Maps were generated using QGIS software v.3.32.3 "Lima" with the RGF93 / Lambert-93 projection. The eight sampling sites along the three selected streams are represented by red dots (u: Upstream; m: Mid-site; d: Downstream).</figcaption></figure>
Observatory of the "Zone Atelier du bassin de la Moselle" (DEIMS-SDR, 2025). The three streams drain forested catchments laying on magmatic acid bedrocks granite type.

In addition to the regular upstream "river survey sites", five sampling sites were defined along the river continuums based on land use, two for both La Hutte and the Rouge-Rupt Rivers, and one for the Cellet River, respectively (Fig. 1, Fig. S1, Fig. S2, Fig. S3). Except logging and some tourism, there is no current anthropogenic activity upstream of the upper sampling sites LH<sub>u</sub>, RR<sub>u</sub>, C<sub>u</sub> that were then defined as "pristine-like". For La Hutte River and the Rouge-Rupt River, a second sampling site was defined as the site of primary exposure to anthropogenic activities (pasture, housing, hamlet), and for each river continuum, a last sampling site was selected further downstream.

Land cover and land use (Fig. 1) were determined using a large-scale vectorial database covering the whole France: the BD TOPO® 2022 from French National Institute for Geographic and Forestry Information (IGN). Residential areas, economic activity zones, collective infrastructure, and transportation networks were classified as artificialized areas. Regarding natural and semi-natural areas, coniferous, deciduous, and mixed forest areas follow the classification established by the IGN. The "diverse vegetation" category includes clear-cut areas, plantations, and other pre-forest vegetation formations. All grass-covered agricultural areas, fallow lands, semi-natural meadows, and mountain grasslands have been classified as "grasslands". The postal address database (IGN, inventory for 2022-2024 period) was used to estimate the number of households and thus the population in the three watersheds. They are 69 addresses in La Hutte watershed, 57 in Le Cellet, and 573 in Rouge-Rupt. Households' density per hectare for the three watersheds are shown in Fig. S4.

La Hutte River watershed (~ 870 ha) is composed of Paleozoic calcalkaline granite and quaternary sandy moraines. Upstream the LH_u sampling site, the surface is mainly covered by mixed forest (deciduous and conifer trees). Coniferous stands are only present in the immediate vicinity of the stream. Upstream the mid-sampling site LH_m, the bottom of the valley opens up with pastures and becomes urbanized, houses appear all along both sides of the stream. The LH_M sampling site is located in the center of La Hutte hamlet. Between the LH_m site and the downstream sampling site LH_d, the bottom of the valley widens and is essentially occupied by pastures, with scattered farms and dwellings, while the slopes and summits remain forested.

The Rouge-Rupt watershed (~ 2300 ha) is composed of Paleozoic granite and quaternary sandy moraines at the bottom of the valley, along the stream. Upstream the RR_u sampling sites, mixed coniferous and deciduous forests are as well represented as pure coniferous stands. Between the RR_u site and the mid site RR_m, after a large area covered by coniferous forest, the valley widens and the forests give way to open areas of pastures and the first scattered houses. Downstream of the RR_m site, the Rouge-Rupt River flows in the Xoulces River while the bottom of the valley becomes urbanized until the downstream sampling site RR_d located in the town of Cornimont (over 3000 inhabitants).

The Cellet watershed (~ 2685 ha) is mainly composed of Paleozoic granite riche in quartz. On the ridges, coarse sandstone rich in quartz outcrops. At the bottom of the valley, the stream passes through quaternary sandy moraines. The watershed upstream site C_u is only covered by pure coniferous stands. Between C_u and C_d (596 m), after a large area covered by coniferous forest, the stream runs alongside a very large quarry exploiting quaternary glacial deposits on the left bank, while meadows and scattered houses occupy the right bank.

The La Bresse meteorological station (latitude 48.00767 N, longitude 6.9415 E), located between the three studied watersheds, was used to estimate local average monthly temperature and precipitation during 2021, period of the study (Fig. S5). The annual rainfall was 2100 mm, and the mean annual temperature was around 9 °C.

## 2.2. River water chemistry

Since 2002, every four weeks exactly, waters are collected at the upstream sampling sites (C_u, LH_u and RR_u on Fig. 1). Thirteen sampling campaigns were carried out in 2021. Each water sample was analyzed for major cations concentrations (calcium, magnesium, potassium and sodium) by atomic absorption spectrometry (flame) or inductively coupled plasma atomic emission spectroscopy. Dissolved aluminum concentrations were measured by atomic absorption spectrometry (graphite furnace) or inductively coupled plasma atomic emission spectroscopy. Anions contents (nitrates, sulfates, chlorides) were analyzed by ionic chromatography. Measurement of total conductivity were conducted at 25 °C with a conductimeter, while pH and Acid neutralizing capacity (ANC) of waters were determined respectively using a pH meter and by Gran's titration. Dissolved organic carbon, dissolved inorganic carbon, and dissolved total nitrogen were analyzed with a Shimadzu TOC VCHS combined with a TNM-1 module.

## 2.3. Epilithic biofilms and river water sampling for microbial characterization

Each river was subject to two sampling campaigns in 2021 to collect epilithic biofilms and water, one during the spring and the second during the fall: 21/04/2021 and 27/09/2021 for La Hutte River, 19/05/2021 and 27/09/2021 for the Cellet River, 31/05/2021 and 21/09/2021 for the Rouge-Rupt River, respectively. Epilithic biofilms were scraped off from immersed rocks with a sterilized toothbrush from approximately 300 cm² of non-light exposed rock surfaces to avoid photosynthetic organism over-representation. For each sampling site, three composite biofilm samples were obtained by scrapping three rocks each, while 1 L of river water was sampled in parallel. Both, water samples and epilithic biofilms were collected in the middle of the streams, otherwise considered as diverse-flow rivers, thus well homogenized. Both waters and biofilms were kept on ice until reaching the laboratory. Biofilm cell suspensions were then centrifuged at 4000 ×g for 15 min, supernatants discarded, and pellets was stored at -20 °C until nucleic acid extraction. A fraction of the water samples was immediately frozen upon arrival and until DNA extraction. The other fraction was kept at 4 °C and processed within 24 h to enumerate viable heterotrophic bacteria and coliforms.

## 2.4. Enumeration of culturable bacteria

Culturable heterotrophic bacteria and coliforms from river waters were enumerated on R2A agar and mFC agar, respectively. Briefly, freshly sampled waters were serially diluted in sterile distilled water of which 100 mL were filtered through 47 mm diameter 0.22 µm pore size nitrocellulose sterile membrane filters (Millipore®). Three technical replicates were obtained for each dilution, site and media. Total and thermotolerant coliforms were enumerated after culture on mFC agar (Difco™) supplemented with rosolic acid (Difco™) for 24 h at 37 °C and 44 °C, respectively. Total heterotrophic bacteria were enumerated on R2A agar (Difco™) at 20 °C after 72 h to 96 h.

## 2.5. DNA extraction

Total community DNAs from biofilms (500 mg) were extracted according to the manufacturer recommendations (QIAGEN, Hilden, Germany) using DNeasy® PowerSoil® Pro Kit. For all sampling campaigns and sites, DNA extractions were carried out in triplicate from three sets of biofilms collected independently. DNA concentrations were measured using a QuBit™ 4 Fluorometer (Invitrogen™, Waltham, USA), while DNA purity was assessed spectrophotometrically by measuring both 260/280 nm and 260/230 nm ratios with a NanoDrop™ spectrophotometer (Thermo Scientific, Waltham, USA). DNA was stored at -20 °C until used.
## 2.6. High-throughput qPCR

All DNA samples were submitted to a pre-amplification step prior to HT-qPCR (Michelet et al., 2014). For DNA pre-amplification, the TaqMan PreAmp Master Mix (Standard BioTools, USA) was used according to the manufacturer's instructions and as described in Delannoy et al. (2022). The Biomark™ real-time PCR system (Standard BioTools, USA) was used for high-throughput microfluidic real-time PCR amplification using 48.48 dynamic arrays (Standard BioTools) as described in Delannoy et al. (2022). PCR cycling started with 2 min at 50 °C and 10 min at 95 °C, followed by 40 cycles of 2-step amplification of 15 s at 95 °C, and 1 min at 60 °C. One negative water control was included per dynamic array, as well as pools of positive controls for each assay. A 16S rRNA gene assay was included as loading/inhibition control. The samples were screened for the presence of a set of antimicrobial resistance genes (ARGs) and mobile genetic elements (MGEs) mainly carried by Gram-negative bacteria. All primers and probes targeting the ARGs and MGEs sequences were previously described by Delannoy et al. (2023). The list of markers quantified by HT-qPCR and their relevance in human health are provided in Table S1.

## 2.7. Amplicon sequencing for 16 rDNA metabarcoding

DNA extracts were sent to the Illumina MiSeq platform from the Institute of Clinical Molecular Biology (IKMB, Kiel, Germany) for metabarcoding analysis. Sequencing was performed of the V3-V4 hypervariable regions of the bacterial 16S rRNA gene, PCR-amplified beforehand using a primer pair authorizing a dual-barcoding approach according to Kozich et al. (2013). Sequence data were deposited in the NCBI Sequence Read Archive (http://www.ncbi.nlm.nih.gov/bioproject/1391041) with the reference SUB15743738.

## 2.8. Bioinformatic analysis

DNA sequence data were imported into the FROGS pipeline (Find Rapidly OTU with Galaxy Solution) implemented on a Galaxy instance (v2.3.0) (http://sigenae-workbench.toulouse.inra.fr/galaxy/) (Escudié et al., 2018). After a quality control, 16S rRNA gene paired-end sequences were merged into contigs with VSEARCH (Rognes et al., 2016). Sequences were dereplicated before being clustered using SWARM algorithm (v2.1.5) (Mahé et al., 2022) with a first denoising step using an aggregation distance equal to 1 and a second one equal to 3. Chimeras were removed using VSEARCH (Rognes et al., 2016). Filters were applied to remove clusters that were not present in at least three samples or with an abundance below a 0.005 % threshold (Bokulich et al., 2013). The taxonomic assignation of each OTU was performed using the BLAST tools (Camacho et al., 2009) against the database SILVA 132 16S (Pruesse et al., 2007). Phyloseq (1.26.1) R package was used to analyze community composition, to normalize and to generate α-diversity indexes (richness) after a rarefaction curve using the transform counts method (McMurdie and Holmes, 2013).

## 2.9. Statistical analysis

All statistical analyses were carried out using R software version 3.3.1 (R Development Core Team, http://www.R-project.org). First α-diversity indexes were compared to summarize the structure of microbial communities with respect to its richness (number of Operational Taxonomic Unit (OTU)) and evenness (Pielou Index) with the "phyloseq" package (1.26.1) (McMurdie and Holmes, 2013). After normality and homoscedasticity verifications (Shapiro and Bartlett tests, respectively), ANOVA (significance was declared at *P* < 0.05) were performed on richness and evenness indexes and Tukey HSD tests were used for pairwise comparisons using the "agricolae" package. The β-diversity, to assess the degree of community differentiation, was estimated using the relative abundance matrices and multivariate analyses. The

dissimilarities (Bray-Curtis distances) among treatments were transformed to a distance matrix for a non-metric multidimensional scaling (NMDS) using the "phyloseq" package. After testing the multivariate homogeneity of group dispersions, permutational analysis of variance (PERMANOVA) ("Adonis" function on "vegan" 3.0.2 R package) was performed to test for significant differences in sample clustering patterns across sampling points (9999 permutations). Plots were drawn using "ggplot2" (3.1.1). Heatmaps for metabarcoding and HT-qPCR datasets were plotted with R package "ampvis2" (2.5.1) after relative abundance calculation (Andersen et al., 2018).

## 2.10. Calculation of the APP index

The APP index (ARG number normalized to the propagule pressure) was calculated to account for the influence of propagule pressure when assessing the sensitivity of river biofilms to ARG/MGE invasion. The number of ARGs detected (i.e., ARGs with HT-qPCR values different from the undetectable threshold) was subsequently normalized to the propagule pressure, which was approximated by the concentration of fecal coliforms in the water. The relationship between antibiotic resistance introduction and bacterial community richness and evenness was evaluated using linear regression analysis.

# 3. Results and discussion

## 3.1. Identification of contrasted rivers with a gradient of anthropic activities

When this work was initiated, a careful attention was given to the selection of small rivers with a clear absence of any anthropic activities (no farming, no housing, no industries) in the catchment head in order to avoid multiple sources of contaminating ARGs when analyzing downstream pollution entries. With this respect the Vosges Mountains (North-eastern France) offer numerous opportunities to study small rivers with remote catchment heads, where first exposure to ARG pollution can be anticipated based on local land use. Three of them were selected among 16 rivers based on their contrasted geochemistry, which was expected to favor differences in microbial biodiversity, a parameter supposedly important in the invasion process.

### 3.1.1. Physicochemical characterization of the selected river continuums

Contrasted geochemistry for the three selected rivers was expected based on previous studies (DEIMS-SDR, 2025). This was however confirmed with a new series of chemical data obtained from waters collected at the upstream "river survey sites" compiled from 13 campaigns conducted throughout year 2021 (Table S2), and for which PCA illustrates a clear discrimination, explaining more than 67 % of the variability, between the 3 streams (Fig. 2).

La Hutte River was the stream with the highest conductivity and pH, close to neutrality (between 6.5 and 7), linked to the highest calcium (Ca) and magnesium (Mg) concentrations and the lowest aluminum concentrations (Table S2). The Cellet River was the most acidic stream (pH between 4.7 and 5.4) with low Ca and Mg concentrations. These chemical characteristics probably result from the fact that (i) the stream collects surface water draining very acidic rocks (very rich in quartz) and (ii) the upstream watershed is exclusively occupied by more "acidifying" coniferous forests. Finally, with intermediate chemical characteristics in the upstream part of its watershed, the Rouge-Rupt River has rather acidic waters, but with a higher pH in summer (between 5.1 and 6.4). It differs from the other two streams by its very low nitrate concentrations throughout the year.

Similar chemical analyses were carried out on waters collected from the eight sampling sites of the three studied rivers (Fig. 1) during the two 2021 biofilm sampling campaigns, and PCA confirmed that (i) the three rivers kept diverging in chemistry, and (ii) there were very little differences between the three sampling sites for a same river (upstream,
<figure><img src="image_7.png" /><figcaption>**Fig. 2.** Contrasted chemical characteristics of the Rouge-Rupt, Cellet and La Hutte rivers. River differentiations are revealed by Principal Component Analysis (PCA) based on chemical characteristics (pH, conductivity, ANC, NO3, SO4, Ca, Mg, Al at the upstream river survey sites (Cu, LHu, RRu) throughout 2021 (n=13). The dotted ellipse covers 80% of the points in each group, while the colored ellipse covers 50%.</figcaption></figure>

midpoint, downstream) with respect to the physicochemical parameters considered (Fig. S6).

### 3.1.2. Land use and typologies of the selected rivers

The land use and local characteristics of the three rivers selected were determined using the BD TOPO® 2022 database enabling the delineation of the different plant cover along the continuum as well as the areas subjected to anthropogenic activities. As shown in Fig. 1, the watersheds are mostly covered by forests with an increasing artificialization of the land along the continuum. From this cartographic exploration, eight sampling sites were selected for the three rivers. For each river, the upstream sampling points did not appear to receive any noticeable anthropic influence (pristine-like) while the mid- and downstream ones are surrounded by increasing anthropogenic activities. It is worth noting that for the three rivers, the sampling sites following the pristine-like areas are exposed to moderate anthropic activities in a rural context: scattered housing, small hamlets, pastures, and farms (Fig. S1, Fig. S2, Fig. S3, Fig. S4). As such, the sampling sites in the river continuums that immediately follows the pristine-like ones were defined as sites of primary exposure where human, animal and environmental spheres interconnect microbiologically.

### 3.2. Spatial distribution of ARGs/MGEs in river epilithic biofilms

A set of 29 ARGs and 16 MGEs were selected for their high prevalence or critical importance for human medicine, based on as previous work (Delannoy et al., 2022). The 29 ARGs are distributed across the four health risk categories proposed by Zhang et al. (2021), from the lowest risk (Rank IV), with widely spread ARGs in the human and the environmental spheres, to elevated risk (Rank I), with MGE associated ARGs hosted by ESKAPE pathogens (Table S1). The 16 MGEs were previously described as mobilizing numerous ARGs, including those selected, and potentially covering all risk categories (Table S1). Some of the selected ARGs/MGEs have also been shown to be associated with the sheep and cow microbiomes (Delannoy et al., 2023), or as exhibiting an increasing prevalence in bacterial isolates closer to a fecal contamination source (Delannoy et al., 2022).

The invasiveness of 45 ARGs/MGEs was explored by HT-qPCR in river epilithic biofilms collected along the defined gradients of anthropization in the three selected rivers. Whatever the campaign and river, the results obtained showed that the ARGs/MGEs quantified distributed into 3 groups according to their occurrence (Fig. 3). Roughly, one third of the tested ARGs/MGEs was sporadically present at a very low prevalence or even absent in all epilithic biofilms from pristine-like to anthropogenized areas (e.g. *bla*<sub>KPC2/3</sub>, *bla*<sub>NDM1/2</sub>, IncAC plasmids). Another third was detected everywhere, thus referring to ARGs/MGEs that are already widespread in the environmental sphere (e.g. *strA*, *sul1*, *intI1*). Finally, a last third of ARGs/MGEs was absent, or poorly represented, in the pristine-like areas and massively invaded the epilithic biofilms as the rivers met the first anthropogenic activities (hamlets, pastures, cities) (e.g. *aac(6)-Ib*, *tetA*, *IncI-I1* plasmids).

For the Rouge-Rupt River, the increasing prevalence of ARGs/MGEs in biofilms along the river continuum was rather modest at the mid-site and appeared massive in the downstream sampling site at Cornimont City with the occurrence of new ARGs/MGEs emerging. It is worth noting that the mid-site on the Rouge-Rupt continuum, is located in an area hosting just a few scattered housings, thus presenting a relatively low level of anthropogenic activities (Fig. S2, Fig. S4). Conversely, in the case of La Hutte River, the prevalence of ARGs/MGEs increased massively in the mid-zone (hamlet) and stabilized in the downstream zone (pastures). Compared to the Rouge-Rupt River, the mid-zone of La Hutte River consists of an approximately 50 housings hamlet exhibiting a stronger anthropogenic activity (Fig. S3, Fig. S4). Finally, the Cellet River contrasted with the two other rivers by a lower watershed covered with scattered houses (Fig. S1, Fig. S4), thus with diffuse but widespread anthropogenic activities occurring between the two sampling sites. Here again the prevalence of ARGs/MGEs in biofilms increased massively at the downstream site, reaching a level comparable to those observed for the Rouge-Rupt and La Hutte downstream sites. These observations tend to demonstrate that (i) even modest, anthropogenic activities effectively imprint biofilm communities in rivers, and (ii) the invasion by ARGs/MGEs seemed to be all the more marked as the anthropogenic activity is developed. This latter observation was confirmed by multivariate analyses carried out on ARG/MGE abundance data obtained from the first
<figure><img src="image_8.png" /><figcaption>**Fig. 3.** Occurrence of ARGs/MGEs in river biofilms. Biofilms were sampled along the continuum of the three selected rivers (LH: La Hutte; RR: Rouge-Rupt; C: Cellet) at eight sampling sites (u: Upstream; m: Mid-site; d: Downstream), during the Spring campaigns (April to May 2021) and the Fall campaigns (September 2021). The colors of the heatmap are scaled from the highest (red) to the lowest (blue) values of ARGs/MGEs relative abundances (data normalized to 16S rRNA gene). Numbers correspond to the relative abundance of ARGs/MGEs calculated as ΔCts from HT-qPCR.</figcaption></figure>

campaign showing that the distribution of ARGs/MGEs grouped the biofilms samples according to the level of anthropogenic activities (Fig. S7). Finally, when considering health risk, it is tempting to hypothesize that ARGs highly detected in pristine environments would rather belong to the widespread category (rank IV according to Zhang et al., 2021), while ARGs appearing concomitantly with the fecal pollution would rather belong to higher risk categories typically enriched in the human sphere. This is sporadically the case, as for the pristine associated *strA* gene that belongs to rank IV, but more generally speaking, such assumption would need further sequence-based exploration as different sequence variants for a same ARG can belong to different risk ranks (Zhang et al., 2021).

### 3.3. Evaluation of local anthropic pressures in rivers

The distribution of ARGs/MGEs in epilithic biofilms reported above indicates that some of them result from gene invasions occurring upon primary exposures to anthropogenic activities. This necessarily implies the entry of pollution into the river ecosystems. To assess the local anthropogenic pressures, fecal coliforms were enumerated in waters collected at each sampling site during the two biofilm sampling

campaigns. The results reported in Fig. 4 indicate a gradient of fecal coliforms from the upstream to the downstream sampling sites on the Rouge-Rupt River and the Cellet River. Conversely, a higher abundance of fecal coliforms was found on the mid-zone of La Hutte River, which was also characterized by a higher prevalence in ARGs/MGEs compared to the downstream point. The relative stability of the heterotroph concentrations in water indicates that the variation in coliforms results from fecal pollution rather than a global change in bacterial load. Altogether, the abundance of coliforms appeared to follow the same spatial trends as the distribution of ARGs/MGEs in epilithic biofilms. This shows that in river biofilms the level of ARG/MGE invasion clearly follows the level of anthropogenic activities, which matches the level of pollution in the water. The origin of the fecal pollution in the three studied rivers, whether human or animal, remains to be elucidated using classical fecal source-tracking approaches with specific qPCR designs.

### 3.4. Permissiveness of biofilm microbial communities to ARG invasion

If an increase of ARG/MGE prevalence was observed along the continuum of the three rivers, alongside a gradient of anthropic activities, it was also noticed that each river displayed some specificities in
<figure><img src="image_9.png" /><figcaption>**Fig. 4.** Fecal pollution in river waters. Fecal coliforms (A) and heterotrophic bacteria (B) measured in waters collected in the three rivers (LH: La Hutte; RR: Rouge-Rupt; C: Cellet), at the different sampling sites (u: upstream; m: mid-site; d: downstream). The error bars are the standard errors of the mean. The letters above the bars indicate significant differences (*P* < 0.05, ANOVA and Tukey's post-hoc test.) between water samples. BDL: below detection limit.</figcaption></figure>

terms of ARG/MGE occurrence and prevalence (Fig. 3, Fig. S7). Considering (i) the theoretical relationship between biological invasion and biodiversity (Mallon et al., 2015a; Mallon et al., 2015b), (ii) the importance of the local propagule pressure and thus the local anthropogenic activities (Vila et al., 2019), and finally (iii) the different river chemical characteristics that supposedly support different biofilm communities, we hypothesized that the different levels of ARG/MGE invasion may result from different levels of biofilm biodiversity. For this reason, the biodiversity of the collected biofilms was further explored and tentatively correlated to the magnitude of ARG/MGE invasion.

The diversity of the epilithic biofilms was analyzed based on 16S rRNA gene metabarcoding using the same DNA extracts as those used to assess the distribution of ARGs/MGEs by HT-qPCR. After assigning the sequence reads to their respective samples an average of 12,356 sequence reads were obtained per sample. Two different measures of microbial community diversity, richness and evenness, were calculated to shed light on different aspects of microbial alpha-diversity. Depending on the river and the position on the continuum, the richness ranged from 400 to 800 OTUs with common trends across the two campaigns

(Fig. 5A). All rivers exhibited an increasing number of OTUs from upstream to downstream in the watershed but different levels of diversity, with the species richness being significantly lower in the Cellet River and higher in La Hutte River (*p* < 0.005, ANOVA). Similarly these OTUs are not evenly distributed I the same phylum depending on the river and the position of the rivers as shown in the bar plots of microbial composition (Fig. 5 insets). Considering bacterial phyla closely associated to fecal pollution, it can be noted that the Bacteroidota phylum in biofilms follows the dynamics of the fecal contamination in water (as approximated by fecal coliform counts), thus suggesting the establishment of fecal bacteria in biofilm.

Multivariate analyses were used to compare the different biofilm communities between themselves, within the same rivers and across the three selected rivers for the two sampling campaigns. The beta diversity analysis (based on the Bray–Curtis distance) revealed clear clustering between positions and rivers (Fig. 5B, Fig. 5C) that fit what was observed in terms of ARG/MGE clustering (Fig. S7). Significant effects of the rivers, the position in the watershed, and the interaction between rivers and sampling sites were statistically confirmed (PERMANOVA; *p* <
<figure><img src="image_10.png" /><figcaption>**Fig. 5.** Microbial diversity of epilithic biofilms. (A) Alpha diversity presented as a measure of species richness determined from the observed features (ASVs) for biofilms at the different sampling sites (U: upstream; M: mid-site; D: downstream). The letters above the box plots indicate significant differences between the samples (ANOVA, p < 0.05). (B and C) Beta diversity analyzed by nonmetric multidimensional scaling (NMDS) of biofilm microbial communities among different rivers and position for the spring 2021 and fall 2021 campaigns, respectively. The insets represent the distribution of the main bacterial classes (%).</figcaption></figure>
0.001). In agreement with what was observed in terms of ARGs/MGEs prevalence, the levels of biodiversity of biofilm communities from the Rouge-Rupt River upstream and mid-points (i.e., with low/absent anthropogenic activities) clustered together and clearly separated from the biodiversity level of the downstream point (i.e. the urbanized area). Similarly, the level of biodiversity of biofilms at the upstream point of La Hutte River (pristine-like) was distinct from those of the mid- and downstream points (i.e. hamletsand downstream pastures), which themselves clustered together. Taken together, these observations support the idea that there could be (i) a certain porosity between the mobile water compartment and the static biofilm compartment, and (ii) a causal relationship between community invasion by ARGs/MGEs, the level of pollution in the water (coliforms), and the levels of biodiversity of the biofilm communities. This was further confirmed with a Procruste analysis comparing the matrix of the microbial community structures and that of ARGS/MGEs, indicating a significant similarity (data not shown). With this respect, it should be noted that it has recently been demonstrated that an alteration in river biofilm biodiversity can lead to an increased persistence of bacterial invaders (Bagra et al., 2023), and that the frequency of plasmid transfer in natural bacterial communities increases with the persistence of an invading donor (Bellanger et al., 2014).

To gain further insight into the relationship between microbial diversity and the pervasiveness of environmental ARGs/MGEs, a series of correlation tests were attempted. First, the microbial diversity obtained for the different biofilm communities (richness and Pielou index for evenness) was tentatively correlated with the sum of the corresponding relative abundances of ARGs/MGEs obtained by HT-qPCR using Pearson's correlation tests. At this stage, no correlation could be detected. However, this first test missed to consider the effect of the propagule pressure, which is an important factor in the invasion process (Jones et al., 2017). To overcome variations in propagule pressure within the watersheds, the relative abundances of ARGs/MGEs were further normalized to the propagule pressure (here understood as a confounding factor), which was approximated using the abundance of fecal coliforms in water. This new index, referred to as APP (ARG normalized to the Propagule Pressure), was then correlated with the diversity indices calculated previously (i.e. richness and evenness...) using Pearson's correlations. This second series of tests, non-ambiguously showed that there were significant inverse correlations (p = 0.039 and 0.00092) between the APP index and both the richness and the evenness of the corresponding communities (ANOVA, p = 0.027 and 0.00092, respectively) (Fig. 6). In other words, this tends to show that communities with higher biodiversity are less prone to ARG/MGE invasion, as proposed by Bagra et al. (2023) from a microcosm study while simulating the effect of a biofilm biodiversity alteration. This intricate relationship between

biodiversity and invasion also reminds what was previously observed in soil by Klümper et al. (2024), where biodiversity negatively correlated the relative abundance of ARGs. In same study, this relationship could not be observed for river epilithic biofilms, but the author did not consider the propagule pressure in their analyses. On the other hand, the Vosges mountains, concerned by this work, were affected by acid rains for decades in the past, leading to an alteration of the river water chemistry, which in turn altered the river ecosystems with visible effects on biodiversity and microbial activities (Clivot et al., 2013; Guerold et al., 2000). At this stage, an exacerbated river sensitivity to biological invasion cannot be ruled out and may well illustrate typical consequences of ecosystem behavior when under stress, as recently simulated by Bagra et al. (2023).

## 4. Conclusion

By combining (i) a careful selection of river ecosystems at the interface between pristine-like environments and sites of primary exposure to anthropogenic activities, and (ii) the quantification of a relatively large panel of ARGs/MGEs by HT-qPCR, we could readily demonstrate the significant invasion of ARGs/MGEs in natural river biofilms. This level of invasion was rather surprising given that it was observed in three independent rivers in remote rural areas, where anthropogenic activities are relatively low. This finding demonstrates that even when modest, anthropogenic activities notably imprint local ecosystems, thus contributing significantly to the pervasiveness of ARGs/MGEs in the environment. Additionally, the level of ARGs/MGEs invasion could be correlated to the level of biodiversity when considering the propagule pressure, thus raising the question of the invasiveness of natural communities when their biodiversity is altered. For now, this study can be considered as proof of-of-concept based on carefully chosen sites and rivers. Next, a more detailed analysis of biofilms collected all along the river continuums, combined with fecal source-tracking of the corresponding river water, will soon allow (i) defining the origin of the pollution, whether human or animal, and (ii) how a first exposure to anthropogenic activity locally alters the structure of endogenous microbial communities.

### CRediT authorship contribution statement

**Frédérique Changey:** Writing – review & editing, Writing – original draft, Visualization, Validation, Investigation, Formal analysis. **Elisa Caldeira Pires Catão:** Writing – review & editing, Validation, Methodology, Investigation, Formal analysis. **Manon Grange:** Writing – review & editing, Visualization, Investigation. **Mai-Lan Tran:** Writing – review & editing, Investigation. **Sabine Delannoy:** Writing – review &

<figure><img src="image_11.png" /><figcaption>**Fig. 6.** Correlation between the alpha-diversity indices (richness and evenness) and APP index. The APP index corresponds to the sum of the different ARG/MGE relative abundances for one sample then normalized to the local propagule pressure (approximated to the level of fecal coliforms in water).</figcaption></figure>
editing, Investigation. Marie-Noëlle Pons: Writing – review & editing, Investigation, Formal analysis. François Guerold: Writing – review & editing, Conceptualization. Jean-Baptiste Colon: Writing – review & editing, Visualization, Investigation. Anne Poszwa: Writing – review & editing, Validation, Supervision, Funding acquisition, Formal analysis, Conceptualization. Christophe Merlin: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Project administration, Methodology, Funding acquisition, Formal analysis, Conceptualization.

## Ethics approval

This study did not need ethical approval.

## Funding

This work was supported by the LTSER-France / Zone Atelier Moselle network (AntibioDiv project); the French Agency for Food, Environmental and Occupational Health & Safety (Project "AntibioInv," PNREST Anses, EST-23-008); and by the ANTIVERSA project (BioDivERsa2018-A-452) here funded by the French Agence Nationale de la Recherche under grant number ANR-19-EBI3-0005-04. The 2021 chemical data in the survey sampling sites of the three watersheds were acquired within the framework of the "Observatory of the acidification of the Vosges rivers", supported by an OTELO funding (Lorraine University, France).

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

We would like to thank the staff of the IKMB microbiome laboratory for processing the samples, IKMB sequencing facilities for excellent technical support and the Deutsche Forschungsgemeinschaft (DFG) Cluster of Excellence "Precision Medicine in Chronic Inflammation" (PMI, EXC2167) for infrastructural support. The authors would like to thank our colleagues from the ONF French forestry office (UT "Haute Moselotte") who have helped us collect water from the Rouge-Rupt every four weeks since 2002. Water analysis at the upstream survey site was partly done with resources from the "Pôle de compétences en chimie analytique environnementale" of ANATELO platform, LIEC laboratory, UMR 7360 CNRS – Université de Lorraine and from the SAMPLE platform, LRGP laboratory, UMR 7274 CNRS – Université de Lorraine. The authors specifically thank V. Concalves, M. Beuret and S. Pontvianne for their contribution, and N. Hamiaux for fruitful discussions.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2025.181237.

## Data availability

Data will be made available on request.

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Update

Science of the Total Environment

Volume 1017, Issue , 20 February 2026, Page

DOI: https://doi.org/10.1016/j.scitotenv.2026.181513
<figure><img src="image_12.png" /></figure>

Corrigendum

Corrigendum to “Biodiversity-dependent invasiveness of naive river epilithic biofilms by anthropogenic antibiotic resistance at the interface between the human, animal and environmental spheres” [Sci. Total Environ. Volume 1012 (2026) 181237]

<figure><img src="image_13.png" /></figure>

Frédérique Changey<sup>a,g</sup>, Elisa Caldeira Pires Catão<sup>a,b</sup>, Manon Grange<sup>c</sup>, Mai-Lan Tran<sup>d</sup>, Sabine Delannoy<sup>d</sup>, Marie-Noëlle Pons<sup>e,g</sup>, François Guerold<sup>f,g</sup>, Jean-Baptiste Colon<sup>c</sup>, Anne Poszwa<sup>c,g</sup>, Christophe Merlin<sup>a,g,*</sup>

The authors regret that the published version of the above article contained a mistake in the annotation of Fig. 3 for which the labels “La Hutte River” and “Rouge-Rupt River”, inscribed at the bottom of the

figure were accidentally swapped. We would like to clarify that this correction does not affect any of the data, results or discussion presented in the original article. The authors would like to apologise for any

<footnote><sup>a</sup> Université de Lorraine, CNRS, LCPME, F-54000, Nancy, France</footnote>

<footnote><sup>b</sup> Université de Toulon, MAPIEM, Toulon, France</footnote>

<footnote><sup>c</sup> Université de Lorraine, CNRS, LIEC, F-54000, Nancy, France</footnote>

<footnote><sup>d</sup> IdentyPath Genomics Platform, Food Safety Laboratory, Anses, 94706, Maisons-Alfort, France</footnote>

<footnote><sup>e</sup> Université de Lorraine, CNRS, LRGP, F-54000, Nancy, France</footnote>

<footnote><sup>f</sup> Université de Lorraine, CNRS, LIEC, F-57000, Metz, France</footnote>

<footnote><sup>g</sup> LTSER France, Zone Atelier du Bassin de la Moselle, Nancy, France</footnote>

<footnote>DOI of original article: https://doi.org/10.1016/j.scitotenv.2025.181237.</footnote>

<footnote>* Corresponding author at: Université de Lorraine, CNRS, LCPME, F-54000, Nancy, France.
E-mail address: christophe.merlin@univ-lorraine.fr (C. Merlin).</footnote>
<figure><img src="image_14.png" /></figure>

inconvenience caused.

A corrected version of Fig. 3 can be found below.