# Anthropogenic sources and transfer of trace metals from the environment to brown trout (Salmo trutta) in mountainous rivers

Laurine Gouthier, Lisa Jacquin, Bénédicte Lalot, Jules Giraud, Maxime Descat, Céline Jézéquel, Séverine Jean, Sophia Hansson

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## To cite this version:

Laurine Gouthier, Lisa Jacquin, Bénédicte Lalot, Jules Giraud, Maxime Descat, et al.. Anthropogenic sources and transfer of trace metals from the environment to brown trout (Salmo trutta) in mountainous rivers. Science of the Total Environment, 2025, 975, pp.179289. 10.1016/j.scitotenv.2025.179289. hal-05350308

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Submitted on 14 Nov 2025

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(Author version)

# Anthropogenic sources and transfer of trace metals from the environment to brown trout (*Salmo trutta*) in mountainous rivers

Laurine Gouthier<fnref n="1" />*, Lisa Jacquin<fnref n="1" />,<fnref n="2" />, Bénédicte Lalot<fnref n="1" />, Jules Giraud<fnref n="1" />, Maxime Descat<fnref n="1" />, Céline Jézéquel<fnref n="1" />, Séverine Jean<fnref n="1" />, Sophia V. Hansson<fnref n="1" />

* Corresponding author. Present e-mail address: laurine.gouthier@univ-tlse3.fr

## Abstract

Mountainous ecosystems are particularly sensitive to global change and are affected by both historical and current anthropogenic activities. For instance, past and contemporary mining and agriculture are responsible for increasing metal contamination in riverine systems. This study investigated the sources and transfer routes of copper (Cu), zinc (Zn), arsenic (As) and lead (Pb) across soil, biofilm, sediment and fish (*Salmo trutta*) along a gradient of human activity (mining, agriculture, urbanization) in the French Pyrenees. Drawing on a comprehensive data set obtained from X-Ray Fluorescence probe and TQ-ICP-MS analysis, in combination with a statistical Structural Equation Modelling approach, we found that historical mining activities, although no longer active, were the primarily contamination source of Zn and Pb contamination in the soil, as well as Pb contamination in the biofilm, whereas contemporary agricultural activities were the main source of Cu, As and Pb contamination in the biofilm. This underscores the long-lasting impacts of human activities on riverine pollution. Furthermore, we showed that several environmental parameters could modulate metal content in fish. For instance, finer substrate particle size and lower oxygen levels increased Cu and Zn uptake in fish tissue. This study provides new insights into the complex dynamics of trace metals in riverine ecosystems and emphasize the importance of using large-scale, multi-compartment and multi-metal approaches to assess metal contamination of aquatic ecosystems.

## Keywords

*Anthropogenic activities, multi-compartment, metal partitioning, biofilm, sediment, fish.*

<footnote n="1">¹Centre de Recherche sur la Biodiversité et l'Environnement (CRBE), UMR 5300, Université de Toulouse, CNRS, IRD, Toulouse INP, Toulouse, France</footnote>

<footnote n="2">²Institut Universitaire de France (IUF), Paris, France</footnote>
1. Introduction

Trace elements, including metals, occur naturally in the environment, generally as a result of rock erosion or volcanic activity (Dias and Edwards, 2003; Ali et al., 2019). However, anthropogenic activities such as mining, discharge of urban and industrial wastewater, smelting and runoff from agriculture considerably increase metal concentrations in the environment, sometimes leading to chronic ecosystem contamination with potential deleterious effects on biodiversity (Järup, 2003; Rodríguez Martín et al., 2014, 2015). Some metals are essential trace elements for organisms, such as iron (Fe), copper (Cu) or zinc (Zn), but can turn toxic to animals and humans in larger amounts (Javed and Usmani, 2019). Arsenic (As) can be considered as an essential nutrient for certain organisms such as mammals and birds (Uthus, 1992), however, no such evidence has been found for fish species, in which As is considered more as a toxic element (Kumari et al., 2017). Other metals, such as cadmium (Cd), mercury (Hg) and lead (Pb), are inherently highly poisonous for organisms, with adverse effects on health (Järup, 2003; Jaishankar et al., 2014). Due to their long-time persistence and potential harmful effects on organisms and ecosystems, trace metals have drawn increasing attention worldwide, but their origin, fate and effects on wildlife health remain unclear, especially in the context of multiple global changes (Ali and Khan, 2019; Ali et al., 2019; Kumar et al., 2019).

Metal contamination in riverine ecosystems is of particular concern (Gilbert, 2010), as rivers play an important role in conserving water, providing “goods and services”. Indeed, rivers provide drinking water and food for humans and wildlife, while also supplying and managing water resources for various ecosystems, which is particularly the case for high altitude rivers, often acting as water reservoirs (Vörösmarty et al., 2010; Cosgrove and Loucks, 2015; Ali and Khan, 2018). However, high altitude soils and rivers are more prone to precipitation of atmospheric contaminants and tend to be shallow and thus susceptible to erosion; these are all natural sources that can increase the remobilisation of natural and anthropogenic trace metals in the environment (Weathers et al., 2000; Stankwitz et al., 2012; Le Roux et al., 2016). As a result, mountainous regions are particularly sensitive to global change (Whitehead et al., 2009; Le Roux et al., 2016). Added to this, mountainous regions are rapidly changing due to both historic and current anthropogenic activities, such as mining, pastoralism and land use
changes that can result in increased contamination levels upstream (Hansson et al., 2017; Martín-Moreno et al., 2018). In lowlands, the discharge of urban and industrial wastewaters, as well as the runoff from agricultural areas, are also potential sources of metal contamination in downstream riverine ecosystems (Mokarram et al., 2020; Xie et al., 2022). However, anthropogenic sources and the transfer routes of trace metals from the environment to aquatic organisms are still unclear. Investigating the potential sources and transfer routes of trace metals in aquatic ecosystems is therefore a key issue to assess environmental risk linked to global changes and human activities. Most studies often focus on the metal content of a single matrix (soil, biofilm, sediment or wildlife) (Lin and Chen, 1998; Yang et al., 2020; Hou et al., 2023), or investigate the content of a single metal in different matrices (Hansson et al., 2019a). There is thus an urgent need to simultaneously study the partitioning of concentrations of several metals in different environmental and biotic compartments (e.g., biofilm, fish) in mountainous and lowland rivers across space to improve our knowledge of the complex contamination patterns of aquatic ecosystem.

In mountainous ecosystems, mining activities and smelting are the main sources of metal contamination of the surrounding soil through acid mine drainage and atmospheric deposition of dust emissions from mine tailings (Akcil and Koldas, 2006; Viadero Jr et al., 2020; Bailey et al., 2021). Due to leaching and surface runoff, these trace metals then move to rivers (Yang et al., 2013), reaching organic rich material such as biofilm (Ancion et al., 2010). Biofilm is a mixture of bacteria, algae and fine detrital material (Farag et al., 1998), and is an important part of the diet of several aquatic organisms. It can thus play a pivotal role in the transfer of trace metals from the soil, water, algae and ultimately to animal in aquatic ecosystems (Farag et al., 2007; Ancion et al., 2010; Wen et al., 2024). However, more than 90% of metal contamination entering into water bodies will be ultimately immobilized in bottom sediments through adsorption to suspended particulate matter, precipitation, coagulation or flocculation processes (Calmano et al., 1988; Lin and Chen, 1998; Miranda et al., 2022). Interestingly, trace metals accumulated in sediments can be remobilized back into surrounding water when environmental conditions fluctuate (e.g., pH, temperature, dissolved oxygen, redox potential) (Salomons et al., 1987; Yang et al., 2020; Liang et al., 2023). Sediment could also be disturbed during flooding or construction/breaching of dams (Shim et al., 2015), which can lead to secondary pollution with adverse
effects on aquatic organisms (Weber et al., 2013). Fish, in particular, can bioaccumulate metals, presenting a substantial risk to wildlife health and to human consumption (Castro-González and Méndez-Armenta, 2008; Javed and Usmani, 2019). Consequently, beyond their total concentration, the ecotoxicological impact of metal contamination in aquatic environments is closely associated with their partitioning between environmental- (soil, biofilm, sediment) and wildlife- (fish) components. It is therefore crucial to i) determine the partitioning and transfer of trace metals across the environmental compartments, and ii) identify the potential anthropogenic sources and underlying ecological factors driving metal pathways from environment to fish, to improve ecological safety in mountainous aquatic ecosystems.

The French Pyrenean mountains and their rivers are an ideal system to study the variations in metal contamination on a broad time and spatial scale due to past mining activity (Hansson et al., 2017, 2019a; Le Roux et al., 2020). Since the 18th century, hundreds of mine records exist, mainly polymetallic mines extracting aluminium (Al), sulphur (S), Fe, Cu, Zn, As, silver (Ag), tungsten (W) and Pb (De Dietrich, 1786; Castera, 1935; Jorré, 1936; Chevalier, 1954; Laforêt et al., 1983). Nowadays, most of the mines have been closed since the end of the last century, but there are debates about the reopening of some mines due to an increased demand and financial interest (Balan, 2018, 2021). In addition, the Pyrenees watersheds are also subject to other increasing anthropogenic pressures such as agricultural-, industrial- and urban-activities (Figure 1).

In this study, we aimed at comparing the concentrations of four frequently found metals (i.e., Cu, Zn, As and Pb) in samples of soil, biofilm, sediment and fish along gradients of human activity (mainly mining, agriculture, urbanization). First, we aimed at assessing the transfer factor between these three environmental compartments for each metal to understand their partitioning in the natural environment. Second, we aimed at determining the potential anthropogenic sources (mining, agriculture, urbanization) and ecological factors (hydromorphology and water physicochemistry) modulating metal content in these different environmental compartments (soil, biofilm, sediment) as well as in fish to better understand the pathways of trace metals in streams. We focused specifically on brown trout (*Salmo trutta*) as a model fish species due to its widespread use as a biological indicator for monitoring water quality and metal contamination levels (e.g., bioaccumulation, sensitivity to metal content) in
aquatic ecosystems (Meland et al., 2010; Monna et al., 2011; Camizuli et al., 2014; Hansson et al., 2019a), as well as its significant cultural, economic and conservation importance. Specifically, we asked the following questions i) how are trace metal partitioned and transferred in the environment, from the soil to the river? And ii) does metal content in soil, biofilm, sediment and fish depend mainly on human activities, such as past mining and current agriculture and urbanization or could other ecological factors (hydromorphology or physicochemistry of the watercourse) be involved? The novelty of this study lies in the exploration of these hypotheses within a large-scale, multi-compartment and multi-metal framework, using a Structural Equation Modelling (SEM) approach. This methodological framework provides new insights into the dynamics of trace metals across diverse ecological contexts.

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## 2. Material and Methods

### 2.1 Sampling design

To study the distribution of trace metal across different biological matrices, a total of 42 sites were selected along a metal contamination gradient in the French Pyrenees (Figure 1, Table S1) (Haute-Pyrénées, Haute-Garonne and Ariège departments in southwest of France). We estimated an *a priori* metal contamination gradient based on i) past mining activity, identified through grey literature and historic records of human activities in the study area (Table S2) (Castera, 1935; Jorré, 1936; Chevalier, 1954; Laforêt et al., 1983; BRGM, 1994; Dubois, 1999), and ii) artificialized (urban) and agricultural land use, based on the CORINE Land Cover dataset (CLC 2012 – Copernicus Land Monitoring Service, European Environment Agency), to ensure that we would have sites and samples covering a full gradient of contamination, ranging from highly impacted locations to more distant and less affected sites. For each sampling site, a wide range of environmental variables were measured or extracted from available datasets to assess their influence on metal content in environmental and biotic matrices. We thus estimated the anthropogenic activities of the watershed by extracting past mining, agricultural and artificialized (urban) areas as well as wastewater treatment plants and dams (see details in section 2.2.1). We also measured hydromorphological (river flow and substrate size) and physicochemical variables (conductivity, nitrate, nitrite, phosphate, oxygen, pH and temperature) of the watercourse (see details in
sections 2.2.2 and 2.2.3). We collected samples of soil, biofilm, sediment and an average of 15 fish at each sampling site to measure trace metal concentration in these different biological matrices (see details in section 2.3). We took particular care to minimize temporal inconsistencies. All metal concentration data in soil, sediment, biofilm and fish were collected within the shortest possible time frame (within 3 months from June to August of the same year, see details in section 2.3), to ensure comparability. However, for physicochemical parameters, we opted for a slightly broader temporal window (within 5 months from June to October of the same year, see details in section 2.2.3) to capture natural variability and obtain a representative average of site conditions.

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## 2.2 Environmental factors

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### 2.2.1 Anthropogenic activities

The effects of anthropogenic activities on metal levels depends on the source (agriculture, industry, mining, natural emissions) and the type of transport (aquatic or atmospheric) of metals (Romic and Romic, 2003; Pascaud et al., 2015; Yang et al., 2020; Xiao et al., 2021). We extracted spatial data from available datasets to investigate the potential influence of anthropogenic activities (i.e., mining, artificialized and agricultural areas, Wastewater Treatment Plants – WWTP and dams) on metal content in different environmental and biotic matrices.

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To estimate the potential impact of mining activity, we used the distance to closest mines. Indeed, the amount of trace metal decreases with distance from the mine depending on conditions (Hansson et al., 2017, 2019b, 2020). Although atmospheric transfer of mining metal dust is possible, (Steinnes et al., 1997; Søndergaard et al., 2013), contaminants transferred by water tend to have a greater impact on metal contamination levels in riverine systems (Bindler et al., 2012). Therefore, we used the distance by the river network from mines to our sampling sites. The upstream sub-basin area of each site was delineated by applying a flow accumulation algorithm to the BD ALTI 25 m (BD ALTI V2.0, IGN-F, 2017) using the *ArcHydro* Tools plugin on ArcGIS (v.10.8). The number of mines present upstream was evaluated for the whole upstream sub-basin and at three spatial extents with buffers having radii of 2, 5 and 10 km intersected with the sub-basin extent for each sampling site (Figure S1). The average distance through the river network between the sampling site and all mines localised in the

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upstream sub-basin and, finally, the distance by the river network to the closest upstream mine were estimated. For further analysis, we finally retained the number of mines within a 10 km buffer area upstream of each sampling sites as a proxy for the mining activity, as it was the most relevant based on previous studies showing a reduction in metal concentration above 10 km downstream of the pollution source (Fuller and Harvey, 2000; Hansson et al., 2019b; Yang et al., 2020).

To assess agricultural and urban activity, we estimated the land use using the CORINE Land Cover dataset for 2012 (CLC 2012 – Copernicus Land Monitoring Service, European Environment Agency), because land use impacts water quality through nutrient input and chemical pollution (Tong and Chen, 2002). We measured the percentage of agricultural land (arable land, permanent crops and grasslands) as a proxy of agricultural activity for the three upstream buffer areas (2, 5 and 10 km) for each sampling site using QGIS (QGIS Development Team, 2008, v 3.12) (Figure S1). We measured the percentage of artificialized land (urban zones, industrial or commercial zones, communication networks and landfill sites) as a proxy of urban and industrial activities for the three upstream buffer areas (2, 5 and 10 km, Figure S1). We finally retained the percentage of agricultural and artificialized land in a 5 km upstream buffer area for each sampling sites as a proxy for the land use index, as it was the most relevant in accordance with the work of Fiedler (2021).

Using the SANDRE (Service d'Administration Nationale des Données et Référentiels sur l'Eau) database, we also included the cumulative number of the Wastewater Treatment Plants (WWTP) in a 5 km upstream buffer area for each sampled site into our analyses as they can potentially be a source of pollution (Carey and Migliaccio, 2009). Finally, the presence of dams was evaluated through the total cumulative height of all dams and weirs present in a 5 km upstream buffer area for each sampled site using the ROE dataset (Référentiel national des Obstacles à l'Écoulement de l'ONEMA) (Shim et al., 2015).

### 2.2.2 Hydromorphological variables

River flow can have a significant influence on the accumulation of trace metals in sediments and biofilm (Poot et al., 2007). We therefore considered the mean flow of the river for each sampling sites using the RHT dataset (Réseau Hydrographique Théorique, Pella et al., 2012).
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Substrate size in watercourse may also influence the accumulation of trace metals in the sediment or biofilm (Conly et al., 2007). We thus characterised the type of substrate during field sampling as mud, sand, gravel, pebble, cobble, boulder, rock or bedrock and a score from 1 to 8 was assigned to each substrate type to define a size (the lower the value, the finer the substrate).

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### 2.2.3 Physicochemical variables

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It has been shown that environmental physicochemical factors such as conductivity, oxygen concentration, pH, temperature or nutrient levels can affect the physical properties of metals to bound to dissolved organic matter in water, hence altering metals bioavailability (Canli and Canli, 2015; De Paiva Magalhães et al., 2015; Yang et al., 2020; Liang et al., 2023). It is also known that brown trout is highly sensitive to conductivity, water eutrophication (nitrate, nitrite, phosphate content), oxygen concentration, pH and temperature (Elliott, 1994). We therefore recorded, at each site, conductivity (µcm/cm²), O₂ concentration (mg L⁻¹) and pH with a Multi 3620 IDS WTW® Multiparameter probe by three snapshot measures in June, in July or August and in October. We also recorded the mean water temperature using HOBO® loggers placed into the river between June and October 2022. The loggers provided one measurement every 4 hours, so we averaged all measurements between June and October 2022 as a mean water temperature during the sampling period. Other temperature measurements were initially calculated (maximum, minimum, variability) but they were highly correlated or brought no additional information and were thus not incorporated in final models.

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In addition, we quantified nitrate (NO₃⁻), nitrite (NO₂⁻) and phosphate (PO₄³⁻) concentrations reflecting water eutrophication at each sampling site, largely due to agriculture and urban activities (Carpenter et al., 1998). We sampled approximatively 50 mL of filtered water through a 1.2 µm cellulose nitrate Sartorius® filter (Ø 50 mm), using a Buerkle™ Vampire Sampler (model 5327-1000) and Sartorius® filter holders. Water nitrate, nitrite and phosphate analyses were conducted at the CRBE laboratory (PAPC platform) according to the ISO 15681-2 and ISO 13395 norms, respectively. As the nitrite values were extremely low, they were added to the nitrate values for each site to give a single value.

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## 2.3 Metal concentrations

### 2.3.1 Trace metals in the environment

Three replicate samples of soil, biofilm and sediment were collected at each site in June 2022. Approximately 250-300 g of soil were extracted from the soil A-horizon in small 10x10x10 cm pits along the riverbanks using a pre-cleaned garden shovel. Biofilm was extracted from 3 to 5 comparable stones and suspended in 50 mL Milli-Q water (see Hansson et al., 2019a for details). Finally, approximately 200 g of sediment was 'cored' (corresponding to the sampling area of the soil and biofilm collection) by placing a decontaminated 150 mL plastic vial into the streambed and using a plastic spatula collecting the top layer of sediments (2–3 cm). All the samples were stored in dark and cool conditions during transport to the laboratory after which they were placed in a at -20°C for conservation before further laboratory analyses.

Soil samples were dried during five days at 38°C using a Memmert UM600 drying-oven, and biofilm and sediment samples were freeze dried during five days at -54°C and 0.05 mbar using an Alpha 1-4 LDPlus® freeze dryer. Samples were then homogenized using a QIAGEN TissueLyser II grinder. Analyses of Cu, Zn, As and Pb concentrations (ppm) in soil, biofilm and sediment samples were performed using an X-Ray Fluorescence (XRF) probe running in soil mode (Thermo Scientific™ Nilton XL3t), which provide immediate, on-site results. These metals were selected because of their relatively common abundance in/near (historical) mine sites and their relatively frequent inclusion in previous studies focusing on metal contamination on freshwater species (e.g., Linde et al., 2004; Brumbaugh et al., 2005; Foata et al., 2009; Monna et al., 2011; Hansson et al., 2019a, 2020). For quality assurance, we also included certified reference materials (CRMs, i.e., BCR-142-R Sandy soil, LKSD-3 Lake sediment, IAEA-413 Algae, IAEA-SL-1 Lake sediment and MESS-3 Marine sediment) and replicates in analyses (see Table S3 for details and % recovery). Environmental samples were collected prior to fish sampling to ensure a high variability in contamination levels within our sampling design, which was made possible by the rapid analysis provided by the XRF probe.

### 2.3.2 Trace metals in fish
In total, 617 juvenile brown trout were sampled on 40 sites (with on average 15 fish per site, Table S1), through electrofishing (EFKO FEG 1500) from July to August 2022. We targeted juvenile trout (mean = 82 mm, range = 40-158 mm) corresponding mainly to young-of-year (0+) to limit cohort effects. Fish were euthanized in accordance with local and European guidelines and regulations (authorizations 65-2022-04-29-00001, 31-2022-06-14 and 09-2022-06-02) with an overdose of Benzocaine (120 mg L⁻¹) and were dissected in the field to collect tissue samples for a separate study, i.e., a part of the kidney, liver and muscle were removed whereas the remaining carcass was collected and used to measure trace metal concentration. Fish bodies were then stored on ice and in dark conditions during transport and then placed in a freezer at -20°C.

Prior to wet-chemistry preparations and analysis, all fish carcass were weighed and freeze dried during 48 hours at -54°C and 0.05 mbar using an Alpha 1-4 LDPlus® freeze dryer. Dried samples were then weighed again to obtain the water content (in %) per individual and homogenized using a QIAGEN TissueLyser II grinder. Approximately 150 mg of dried samples were added to acid cleaned (2% HNO₃) and pre-weighed 50 mL digitubes. In each tube, 3 mL of concentrated ultra-pure HNO₃ (Fisher Chemical, nitric acid HNO₃, Optima Grade, 67-69%) was added for sample digestion. Tubes were then sealed with airtight caps and placed on a hotplate at 90°C overnight (~12 hours). After digestion, the solution was transferred to a 15 mL Falcon tube in which Milli-Q H₂O was added up to the 14 mL mark to get a Mother Solution (MS). Prior to the TQ-ICP-MS analysis, the MS were then further diluted to 1.5:8.5 MS:Milli-Q H₂O in clean 15 mL Falcon tubes. All sample preparations were performed under clean laboratory conditions (Clean room class 10000, ISO 7) using acid cleaned lab ware. Analysis of Cu, Zn, As and Pb concentrations (ppm) in fish carcasses were performed using a Triple Quadrupole Inductively Coupled Plasma Mass Spectrometer (Thermoscientific, iCap TQ-ICP-MS) at the ICP-MS platform of the Observatoire Midi-Pyrenees, Toulouse, France. To ensure analytical quality and accuracy, CRMs (i.e., DORM-4 Fish protein, DOLT-5 Dogfish liver, NIST-1515 Apple leaves and IAEA-336 Lichen), blanks and replicates were included with every 10 samples (Table S3).

### 2.3.3 Toxic Unit of metal contamination
We computed a contamination gradient using a Toxic Unit (TU) for graphical representation (Figure 1).

For each sampling site, a TU was calculated for the targeted metals (Cu, Zn, As and Pb) depending on their concentration and toxicity (i.e., lethal threshold concentration) (Sprague, 1970) as follows:

$$TU (Toxic Unit)_{site} = \log \sum_{i=1}^{n} \frac{Ci}{LC_{50}}, \text{ with } i \text{ corresponding to the targeted metals; } Ci \text{ corresponding to}$$

the mean concentration of a metal measured in the bodies of all fish from the same sampling site; LC₅₀ corresponding to the lethal threshold concentration for 50% of the exposed fish in standardized toxicity bioassay, with the model species varying depending on the available toxicity data from the literature:

*Pimephales promelas* fish for Cu and As and *Onchorhynchus mykiss* fish for Zn and Pb (LC₅₀ = 134.5 µg L⁻¹, 70 µg L⁻¹, 99000 µg L⁻¹ and 110 µg L⁻¹ for Cu, Zn, As and Pb, respectively, Hodson et al., 1980; USEPA, 2008; INERIS, 2015).

## 2.4 Statistical analyses

### 2.4.1 Metal transfer across environmental matrices

We assessed the distribution and transfer of trace metals among the three sampled environmental compartments (soil, biofilm, sediment) by calculating the transfer factor (TF) of each metal as follows:

$$TF = \frac{C_n}{C_{n-1}}, \text{ where } C_n \text{ represents the metal concentration in the receiving compartment and } C_{n-1} \text{ is the}$$

metal concentration in the source compartment, based on previous studies (Jolly et al., 2013; Ali and Khan, 2019; Dar et al., 2019). To define the source and receiving compartments, we postulate – based on literature – that metals could be transferred from the soil to the sediment, from the soil to the biofilm, from the biofilm to the sediment, and inversely, from the sediment to the biofilm (Salomons et al., 1987; Calmano et al., 1988; Ancion et al., 2010; Miranda et al., 2022). Based on this assumption, we calculated the TF for each metal using the median concentration of each compartment. A TF > 1 indicates a higher metal concentration in the receiving compartment compared to the supposed source compartment, suggesting a transfer of metals. Conversely, a TF < 1 implies that the receiving compartment has a lower metal concentration than the supposed source compartment, suggesting limited transfer from the source matrix.

### 2.4.2 Influence of environmental factors on trace metals in soil, biofilm and sediment
We characterized the influence of environmental factors (anthropogenic activities, hydromorphological and physicochemical variables) on metal concentrations in all environmental compartments (soil, biofilm, sediment) by conducting a redundancy analysis (RDA) using the *rda* function from the R package *vegan* (v. 2.6-6.1, (Borcard et al., 2011) (see supplementary information file for further details).

### 2.4.3 *Underlying environmental factors explaining trace metals in environment and fish*

We conducted a Structural Equation Modelling (SEM) approach to complete the RDA analyses and testing several networks of relationships between environmental and biological factors. Indeed, SEM analyses can simultaneously test the relationships between environmental factors (anthropogenic activities, hydromorphological and physicochemical variables) and metal content in environmental (soil, biofilm, sediment) and wildlife (fish) matrices, while considering the interdependence of metal content among matrices. SEM is a type of path analysis designed to examine complex networks of causal relationships among multiple predictors and response variables by testing different explanatory scenarios (Grace, 2006; Fan et al., 2016).

To perform these SEM analyses, we first identified the most relevant models explaining metal concentrations (Cu, Zn, As and Pb) in each compartment by building full models for each metal independently. Initial full models for metals in soil included mining activity, agricultural and artificialized areas as predictors. For metal content in biofilm, sediment and fish, we included all known anthropogenic activities (mining, agricultural and artificialized areas, WWTP and dams), hydromorphological variables (river flow and substrate size) and physicochemical variables (conductivity, nitrate & nitrite, phosphate, oxygen, pH and temperature). Please note that each site is considered an independent unit, representative of its own specific environmental conditions. Additionally, for metal content in fish, we also included i) metal content in biofilm and sediment as fixed covariates to account for their potential impact on fish metal concentrations, and ii) the identity of the sampling site as a random effect to address the non-independence of samples from the same population. All metal concentrations were log-transformed and environmental factors were scaled. For metal content in soil, biofilm, sediment, a linear regression model (LM) was fitted for each metal using the *lm* function from the R package *stats* (v. 3.6.2). For metal content in fish, a generalized linear mixed model (GLMM)
with a Gaussian distribution associated with an identity link function was fitted – except for Pb, where
a gamma error distribution with a log link function was fitted – using the *Template Model Builder (TMB)*
in the R package *glmmTMB* (v1.1.8) (Brooks et al., 2017). The variance inflation factor (VIF) was
calculated (*check_collinearity* function in R package *performance* v0.10.8) (Lüdecke et al., 2021). VIF
values for each predictor in each model were below the threshold of 4, indicating low collinearity among
explanatory variables and reliable estimated model coefficients (James et al., 2013; Marcoulides and
Raykov, 2019). We checked the goodness of fit of all models using the *plotResiduals* function from the
R package *DHARMa* (v0.4.6) (Hartig, 2018). We finally selected the best models based on the lowest
corrected Akaike Information Criterion (AICc), using the *dredge* function from the R package *MuMln*
(v1.47.5) (Barton, 2019).

We then built SEMs based on the previously selected optimal models (Table S4) using the R
package *piecewiseSEM* (v2.3.0), which supports models like *glmmTMB* (Lefcheck, 2016). We included
(i) networks between environmental matrices for each metal (based on previously measured TF) and (ii)
correlations between metals in each matrix to account for all pathways within the SEM, reducing
parameter estimation bias and improving model reliability. Because of different sample sizes, we
conducted two separate SEM analyses on each metal: one SEM analysis including metal concentrations
in soil, biofilm and sediment (N=42 environmental samples) and one SEM model focusing on fish
(N=617 fish samples). We then graphically combined the two final SEM in a single graphical
representation for each metal for the sake of clarity.

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319 **3. Results**

320 **3.1 Metal concentrations in environment and fish**

The relative abundance of metal concentrations in soil was in the order of Zn>Pb>Cu>As and ranged
from 4.79-340.87 mg kg⁻¹, 58.91-33 083.14 mg kg⁻¹, 9.21-391.89 mg kg⁻¹ and 17.19-9 875.91 mg kg⁻¹
for Cu, Zn, As and Pb, respectively. In the biofilm, the relative abundance of metal concentrations was
in the order of Zn>Cu>Pb>As and ranged from 0.001-261.19 mg kg⁻¹, 31.06-2 669.91 mg kg⁻¹, 2.45-
289.93 mg kg⁻¹ and 3.36-405.68 mg kg⁻¹ for Cu, Zn, As and Pb, respectively. In the sediment, metal
concentrations were ordered as follows: Zn>Pb>Cu>As, ranging from 0.001-63.49 mg kg⁻¹, 34.65-831.90 mg kg⁻¹, 3.98-43.46 mg kg⁻¹ and 10.43-193.81 mg kg⁻¹ for Cu, Zn, As and Pb, respectively.

Finally, the relative abundance of metal concentrations found in fish bodies was in the order of Zn>Cu>As>Pb, ranging from 1.98-6.03 mg kg⁻¹, 98.07-273.50 mg kg⁻¹, 0.07-2.16 mg kg⁻¹ and 0.03-3.90 mg kg⁻¹ for Cu, Zn, As and Pb, respectively (Table S5).

Based on the median concentration across all sites, the partitioning of Cu, Zn and As concentration was biofilm>soil>sediment, whereas for the Pb concentration, it was soil>biofilm>sediment (Figure 2, Table S5). Friedman tests followed by Dunn's post-hoc tests with Bonferroni correction were performed. Analyses revealed that Cu was significantly more concentrated in the biofilm than in the sediment (p = 0.04), but there were no significant differences between the biofilm and the soil, and between the soil and the sediment (p = 1.00, p = 0.13, respectively). Zn and As concentrations followed the same pattern; Zn was significantly more concentrated in the biofilm than in the sediments (p < 0.001), while there were no significant differences between the biofilm and the soil, and between the soil and the sediment (p = 0.11, p = 0.16, respectively); and As was significantly more concentrated in the biofilm than in the sediment (p = 0.03), while there were no significant differences between the biofilm and the soil, and between the soil and the sediment (p = 0.58, p = 0.59, respectively).

However, analyses revealed that Pb was significantly more concentrated in the soil than in the biofilm (p < 0.001) and in the sediment (p = 0.001), while there were no significant differences between the biofilm and the sediment (p = 1.00).

## 3.2 Metal transfer across environmental matrices

According to the TF values, the main route of transfer for Cu, Zn and As was from the sediment to the biofilm, with TF values of 1.71, 2.63 and 1.56, respectively. The second major transfer pathway for these trace metals was from the soil to the biofilm, with TF values of 1.23, 1.72 and 1.36 for Cu, Zn and As, respectively (Figure 2). In contrast, the TF values from the soil to the sediment and from the biofilm to the sediment was < 1 for these three metals, suggesting limited transfer from the supposed source compartment. Pb displayed a slightly different pattern, with a main route of transfer from the sediment
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to the biofilm (TF = 1.02), while the transfer of Pb from the soil to the biofilm and to the sediment, and
from the biofilm to the sediment was limited (TF < 1) (Figure 2).

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## 3.3 Underlying environmental factors explaining trace metals in environment and fish

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SEM analyses were consistent with RDA analyses and revealed statistically significant relationships between some environmental factors and metal concentrations in soil, biofilm and sediment. SEM analyses also highlighted that nearly all metal concentrations were highly correlated across different matrices (Table S6). This pattern, which holds true across all compartments (soil, biofilm, sediment and fish), suggests that a compartment contaminated by one metal is likely contaminated by the others as well. Furthermore, SEM analyses revealed the complex interactions between soil, biofilm, sediment and fish for each metal. For instance, Cu in biofilm was linked to Cu in sediment (Figure 3, Table S7), demonstrating how metal concentrations are interconnected across compartments. Consequently, after accounting for these networks (which could not be captured through RDA analysis), some relationships between environmental factors and metal concentrations showed slight variations compared to the RDA results. This highlights the importance of considering these intricate networks to gain a more comprehensive understanding of metal transfer dynamics across different matrices.

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### 3.3.1 Copper

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Cu content in the soil was not linked to mining, agricultural, or urban activities (Figure 3).

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Cu concentrations in the biofilm significantly increased near agricultural areas (standardized estimate = 0.31, p = 0.02), but decreased in more fragmented rivers with more dams (std estimate = -0.32, p = 0.02) and higher water conductivity (std estimate = -0.59, p = 0.004). Additionally, SEM analyses revealed that Cu content in the biofilm was significantly and positively linked with Cu content in the sediment (std estimate = 0.59, p = 0.002) (Figure 3, Table S7).

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Cu concentrations in the sediment were significantly and negatively impacted by artificialized areas (std estimate = -0.26, p = 0.03) and nitrate & nitrite content in water (std estimate = -0.55, p < 0.001). Furthermore, SEM analysis suggested that Cu content in the sediment was significantly and positively shaped by Cu content in the soil (std estimate = 0.35, p = 0.003) (Figure 3, Table S7).
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SEM analyses revealed that fish exhibit higher amount of Cu in rivers near agricultural areas (std estimate = 0.59, p < 0.001) with more phosphates (std estimate = 0.41, p < 0.001). However, fish exhibit lower Cu content in rivers near artificialized areas (std estimate = -0.18, p = 0.04) and close to WWTP (std estimate = -0.23, p = 0.005). Additionally, fish had higher Cu content in their bodies in rivers with finer substrates (std estimate = -0.22, p = 0.003) and with low levels of nitrate & nitrite, oxygen and temperature (std estimate = -0.41, p < 0.001; std estimate = -0.42, p < 0.001; std estimate = -0.46, p < 0.001, respectively). No link was found between Cu content in the environment and in fish (Figure 3, Table S7).

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### 3.3.2 Zinc

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SEM analysis showed that Zn concentrations in the soil were mainly shaped by mining activity (std estimate = 0.37, p = 0.02) (Figure 3, Table S7).

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In the biofilm, Zn concentrations decreased in more fragmented rivers with more dams (std estimate = -0.20, p = 0.02) and with higher nitrate & nitrite levels in water (std estimate = -0.36, p = 0.02). However, Zn concentrations in the biofilm increased with higher phosphate levels in water (std estimate = 0.37, p = 0.004). Moreover, Zn concentrations in the biofilm were significantly and positively linked to the amount of Zn in the sediment (std estimate = 0.65, p < 0.001) (Figure 3, Table S7).

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No environmental variable had an impact on the concentrations of Zn in the sediment, but they were linked to the Zn concentrations in the soil (std estimate = 0.61, p < 0.001) (Figure 3, Table S7).

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Fish exhibited higher concentrations of Zn in rivers that were more fragmented with dams (std estimate = 0.19, p = 0.02), in higher pH water (std estimate = 0.21, p = 0.01) and in rivers characterized by finer substrates (std estimate = -0.33, p < 0.001) and lower oxygen levels (std estimate = -0.35, p < 0.001). No link was found between Zn concentrations in the environment and in fish (Figure 3, Table S7).

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### 3.3.3 Arsenic

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As content in the soil was not linked to mining, agricultural, or urban activities (Figure 3, Table S7).

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SEM analysis revealed that As concentrations in the biofilm were higher in rivers near agricultural lands (std estimate = 0.28, p = 0.001) and in low water conductivity (std estimate = -0.52, p = 0.001).

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< 0.001). Additionally, As concentrations in the biofilm were positively linked to the As content in both soil (std estimate = 0.38, p = 0.002) and sediment (std estimate = 0.32, p = 0.004) (Figure 3, Table S7).

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In the sediment, the amount of As decreased in rivers near artificialized lands (std estimate = -0.22, p = 0.05), but it was positively linked to the level of As in the soil (std estimate = 0.69, p < 0.001) (Figure 3, Table S7).

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Fish exhibited higher As content in rivers near past mining activities (std estimate = 0.30, p = 0.01), but they exhibited lower As content with higher conductivity and phosphate levels in water (std estimate = -0.39, p = 0.01; std estimate = -0.39, p = 0.02, respectively). In addition, As content in fish was positively linked to the concentrations of As in sediment (std estimate = 0.26, p = 0.02) (Figure 3, Table S7).

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### 3.3.4 Lead

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SEM analysis showed that Pb concentrations in the soil were mainly shaped by mining activity (std estimate = 0.33, p = 0.03) (Figure 3, Table S7).

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Analysis revealed that Pb content in biofilm was also shaped by mining activity (std estimate = 0.20, p = 0.03) as well as agricultural areas (std estimate = 0.29, p = 0.002) and low water conductivity (std estimate = -0.31, p = 0.002). Furthermore, Pb content in the biofilm was positively linked to the Pb content in the soil (std estimate = 0.31, p = 0.01) and in the sediment (std estimate = 0.42, p < 0.001) (Figure 3, Table S7).

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No environmental variable had an impact on the concentrations of Pb in the sediment, but they were linked to the Pb concentrations in the soil (std estimate = 0.6032, p < 0.001) (Figure 3, Table S7).

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Fish displayed higher Pb content in their bodies when the river flow was slower (unstandardized estimate = -0.15, p = 0.003) and in higher pH water (unstandardized estimate = 0.16, p = 0.001). Additionally, Pb content in fish was positively linked to the Pb content in both biofilm (unstandardized estimate = 0.34, p < 0.001) and sediment (unstandardized estimate = 0.33, p = 0.001) (Figure 3, Table S7).

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## 4. Discussion
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In this study, we conducted an extensive field study to document anthropogenic sources and transfer of trace metal contamination in riverine ecosystems exposed to contrasted human activities. First, we assessed the route of transfer between soil, biofilm and sediment. Our findings highlight a main transfer from the sediment to biofilm for Cu, Zn, As and Pb. Second, we identified the main anthropogenic sources that drive trace metals content in the environment and fish, depending on the metal considered.

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## 4.1 Anthropogenic sources and transfer routes of copper

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In 36 out of our 42 studied sites, soil concentrations of Cu exceeded the ranges commonly observed elsewhere in France (i.e., 22 to 341 mg kg⁻¹ in our soils vs 2 to 20 mg kg⁻¹ in the ASPITET program; INRA - Baize 1994), but only one sites exceed the national guideline AFNOR norms of 100 mg kg⁻¹ (NFU 44-041 - JORF 1998) (i.e., LEZSen, with Cu concentration of 341 ± 151 mg kg⁻¹). Overall, this suggests that the Cu contamination levels of the French Pyrenean rivers studied here are moderate except in one area.

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Our results indicate that anthropogenic activities did not explain Cu levels in the soil. This is likely because our study sites were not directly influenced by vineyards or orchards using Cu sulphates ending up in the soil in other French regions (Borkow and Gabbay, 2009; Panagos et al., 2018). However, we did observe a significant effect of agriculture on Cu concentrations in the biofilm, which aligns with previous studies (Schorer and Eisele, 1997; Wen et al., 2022). This could be explained by the application of Cu-containing pesticides and fertilizers in most agricultural practices (Masakazu and Ryo, 2013; Ballabio et al., 2018). Cu may have entered watercourses through leaching and/or surface runoff from agricultural activities, traveling over long distances and accumulating in the biofilm (Farag et al., 2007).

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Our SEM models highlighted a potential Cu transfer beginning from the soil, which is then deposits into the sediment and ultimately accumulates in the biofilm. However, we acknowledge that other sources or processes may also contribute to the Cu transfer. The biofilm exhibited the highest Cu concentrations compared to both soil and sediment, suggesting that it plays a key role in Cu retention in these riverine systems, which is consistent with previous studies (Montana, USA, Farag et al., 2007; Auckland, NZ, Ancion et al., 2013). One possible transfer route could involve i) leaching and/or surface runoff of Cu from the soil (Yang et al., 2013), leading to its mobilisation in the water column and

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subsequent adsorption onto sediment and ii) remobilization (via resolubilisation) of metals bound to sediment particulates (i.e., colloids, iron oxides) in the water column during for instance flooding, modifying water chemistry (Salomons et al., 1987; Shim et al., 2015; Yang et al., 2020; Liang et al., 2023). Colloids are particles of 0.001 to 0.45 µm that play an important role in the transport of trace metals in the water column (Kimball et al., 1995; Schemel et al., 2000) and are then trapped by biofilms on rock surfaces (Farag et al., 2007). Algae and microbes present in biofilms absorb and retain metals bound colloids for extended periods, thus acting as a reservoir, while metals associated with the iron oxides (or detrital organic matter in the case of Cu) present in sediments are rapidly released (Calmano et al., 1988). This could explain the observed difference in Cu concentrations between sediment and biofilm. In line with this, we also found that Cu concentrations in the biofilm were lower in rivers fragmented by dams (for example, JOBEnc and OURSar which exhibited cumulative heigh of dams of 23.5 and 16 meters, respectively). It is likely that dams act as barriers during floods, thereby limiting the remobilisation of particulate matters in the water column and their subsequent transfer to the biofilm. In addition, we found a negative effect of artificialized areas on Cu concentrations in the sediment, but our sampling design did not include larger urban agglomerations, which are often significant sources of metal pollution. The absence of such highly urbanized areas in our dataset may partly explain the observed relationship, as the influence of artificialized land cover on metal contamination could differ in more heavily industrialized regions.

Surprisingly, we found no direct link between Cu concentrations in the sediment, soil, or biofilm and those in fish, although our results show higher Cu levels in fish from rivers close to agricultural areas. One explanation could be that the Cu contamination in fish may be more strongly linked to the water column than to direct exposure through sediments or biofilm (Abdel-Baki et al., 2011). As with the biofilm, Cu contamination in fish could result from the use of Cu-containing pesticides and fertilizers in agricultural areas (Masakazu and Ryo, 2013; Ballabio et al., 2018), with Cu entering watercourses via leaching and surface runoff, traveling over long distances, and becoming bioavailable in the water column (leading to a subsequent transfer to both biofilm and fish) (De Paiva Magalhães et al., 2015). Based on previous studies, Cu uptake in fish is often associated with branchial absorption rather than ingestion of contaminated sediments or biofilm (Abdel-Baki et al., 2011). Although not directly assessed
in our study, this pathway could explain the observed accumulation patterns. This hypothesis is further supported by the significant effect of substrate size and oxygen levels on fish Cu concentrations. Finer particle substrates like mud (silt/clay) tend to accumulate more organic matter due to their high surface area and low hydrodynamic energy (Yang et al., 2020). Organic matter can enhance the resolubilisation of metals into the water column, thereby contributing to the bioavailability of metals in water (Mountouris et al., 2002; De Paiva Magalhães et al., 2015). This process could make Cu more accessible to fish and more likely to be absorbed through the gills, contributing to higher Cu levels in fish. Furthermore, lower oxygen levels increased Cu concentrations in fish, which is consistent with Cu contamination through the water column rather than directly from sediments or biofilm. Indeed, fish in more hypoxic conditions ventilate more frequently and intensely, which could increase trace metal uptake from the water through the gills (Fitzgerald et al., 2019). Given these observations, future studies should investigate the potential role of dissolved Cu and Cu bound to colloids in the water column, to better understand the pathways of Cu uptake and contamination in aquatic environments.

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## 4.2 Anthropogenic sources and transfer routes of zinc

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The mean soil concentrations of Zn observed in 28 out of our 42 study sites exceeded the common range observed in France (i.e., 103 to 33,000 mg kg⁻¹ in our sites vs 10 to 100 mg kg⁻¹ in the ASPITET program; INRA - Baize 1994) and six sites exceeded the AFNOR threshold (NFU 44-041 - JORF 1998) established at 300 mg kg⁻¹, notably LEZSen site with Zn concentrations 100 times higher (mean concentration of 33,000 ± 12,000 mg kg⁻¹), which can likely cause toxicity to wildlife in this area of the Pyrenees.

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Our study show that mining activity is the main source of Zn concentrations in Pyrenean soil, which is consistent with previous studies (Tembo et al., 2006; Tepanosyan et al., 2018; Wang et al., 2022). Our statistical models identified some similarities between Zn and Cu transfers. Specifically, Zn seem to be transferred from the soil to the sediment, likely through processes such as leaching or surface runoff of Zn into the water column and ultimately in sediment through adsorption (Lin and Chen, 1998; Yang et al., 2013; Miranda et al., 2022). From there, Zn seem to be further transferred from the sediment to the biofilm, probably through resolubilisation mechanisms of metals bound to sediment particulates
in the water column followed by a subsequent absorption into the biofilm. Similarly to Cu, the biofilm
exhibited a higher concentration of Zn compared to sediment, which is consistent with previous studies
(Farag et al., 2007; Ancion et al., 2013). This supports our hypothesis of trapping Zn bound to particulate
matter originating from the sediment by the biofilm, increasing the concentrations of Zn found in the
biofilm. In line with this assumption, we found that dams decreased Zn levels in biofilm (similarly to
Cu), likely because dams limit floods and the remobilisation of particulate matters in the water and a
subsequent transfer of Zn to the biofilm. This result reinforced the role of biofilm as the main
compartment for trace metal accumulation in aquatic ecosystems.

Similarly to Cu, Zn in fish was not explained by Zn concentrations in sediment or biofilm,
suggesting that fish Zn contamination could be rather transferred through the water column and fish
gills, and influenced by particle size and oxygen levels, although this hypothesis remains to be formally
tested.

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4.3 Anthropogenic sources and transfer routes of arsenic

In 16 out of our 42 studied sites, soil concentrations of As exceeded the ranges commonly observed
elsewhere in France (i.e., 27 to 392 mg kg⁻¹ vs 1 to 25 mg kg⁻¹ from the ASPITET program; INRA -
Baize 1994), with notably the site of LEZSen showing a concentration 15 times higher (mean
concentration of 392 ± 107 mg kg⁻¹), which can likely cause toxicity to wildlife in this area of the
Pyrenees.

We could not identify a clear anthropogenic source of As contamination in the soil, suggesting
that atmospheric deposition may be the primary source, as observed in previous studies (Doušová et al.,
2007). Consequently, local human activities such as mining, agriculture and industry seem to have a less
pronounced effect on soil As contamination since atmospheric deposition can occur over long distances
(Steinnes et al., 1997). Our SEM models identified the most likely As transfer pathway, from the soil
into the sediment and then into the biofilm. We also highlighted a potential transfer from soil to biofilm,
but we acknowledge that other sources or processes not investigated here may also contribute to As
transfer in these environmental compartments. Our results suggest that As can enter watercourses
through leaching and/or surface runoff, contributing to its accumulation either in the biofilm or in the
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sediment. Surrounding agriculture increased As concentrations in the biofilm, likely due to the use of As-containing pesticides and fertilizers (Smith et al., 1998), leaching into the river, where biofilm absorbs and retains As (Ancion et al., 2010; Yang et al., 2013). Similarly to Cu, we found a negative effect of artificialized areas on As concentrations in the sediment, likely due to the lack of large urban agglomerations in our sampling design, which are more prone to significant sources of metal pollution.

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Interestingly, our SEM models identified the sediment as the main source of As contamination for fish. This contamination could occur from dietary intake through the consumption of benthic invertebrates, as demonstrated in a previous study (Farag et al., 1998). Although this variable was not taken into account in our study, it should be relevant to investigate this hypothesis in future research. Indeed, benthic invertebrates are an important trophic source for young brown trout and they can bioaccumulate trace metals, transferring it to fish through the trophic web. It is also likely that fish ingest trace metals of the sediment during feeding. Further analyses focusing on the diet of fish and the level of contamination of macroinvertebrates are needed to test this hypothesis. Interestingly, we found a significant direct effect of historical mining activities on As concentrations in fish, yet this effect was not observed through the soil, as initially expected. One possible explanation is that As from past mining operations may bind tightly to soil particles, reducing its bioavailability in terrestrial environments, and thus may primarily be bioavailable through the water and sediments, leading to higher bioavailability for aquatic organisms (Naidu et al., 2006). This result emphasizes the role of historical mining activities in contributing to As contamination for aquatic organisms through water and sediment pathways rather than through the soil. Disturbances such as sediment resuspension during floods or extreme weather events are problematic because they can remobilize As-bound sediments, increasing the bioavailability of As in aquatic ecosystems and thereby enhancing the risk of contamination to fish (Salomons et al., 1987; Shim et al., 2015; Yang et al., 2020; Liang et al., 2023). Consequently, our study suggest that fish could be exposed to As through multiple pathways, including absorption from the water, sediments and through their diet, leading to bioaccumulation and potentially biomagnification within the food chain (Ali and Khan, 2019; Córdoba-Tovar et al., 2022). These findings highlight the importance of considering both sediment dynamics and historical land use practices, particularly mining, when assessing the contamination risks to aquatic fauna.

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## 4.4 Anthropogenic sources and transfer routes of lead

Pb contamination was generally moderate but almost half of the sites (18 out of 42 sites) exceeded the common ranges of Pb concentrations observed in soils in France (51 to 9,000 mg kg⁻¹ vs 9 to 50 mg kg⁻¹ from the ASPITET program; INRA - Baize 1994). Nine sites also exceeded the AFNOR threshold (NFU 44-041 - JORF 1998) set at 100 mg kg⁻¹, especially LEZSen site, which exhibited Pb concentration almost 100 times higher (mean concentration of 9,000 ± 2,800 mg kg⁻¹), which can likely cause toxicity to wildlife in this area of the Pyrenees.

We found that the surrounding past mining activities were the main source of Pb contamination in the soil, which is consistent with previous study in the French Pyrenees (Hansson et al., 2019a). This result show that Pb contamination is still of concern for environment, even centuries after the closing of mines, raising concerns for future mining activities in this region. Our SEM models identified the most likely Pb transfer pathway, from the soil into the sediment and then into the biofilm. We also highlighted a potential direct transfer from soil to biofilm. This suggest that Pb can enter watercourses through leaching and/or surface runoff, contributing to its accumulation either in the biofilm or in the sediment. Our results show that both mining and agricultural activities also directly increase Pb content in biofilm.

Furthermore, our study highlights a negative impact of water conductivity on the biofilm ability to accumulate Pb, which it was also the case for Cu and As. Increased conductivity indicates a higher concentration of dissolved ions (like Na, K, Ca and Mg) in the water. These ions can compete with metal ions for binding sites on biofilm components (Laderriere et al., 2021). When the concentration of competing ions is higher, biofilms would therefore tend to bind more to these ions instead of metal ions, resulting in lower concentrations of Cu, As and Pb in the biofilm. In addition, high conductivity can impact the microbial community within the biofilm, potentially altering its composition and health (Vatansever and Turetgen, 2018). If the biofilm community is stressed or altered due to high ionic strength, its ability to absorb trace metals may be reduced. This underlines the importance to consider physicochemical factor such as conductivity when assessing the biofilm ability to accumulate metals in aquatic environments. By accounting for these interactions, we can gain a more nuanced understanding of how water chemistry influences metal bioavailability and the overall health of the ecosystem.
Our SEM models highlighted that Pb concentrations in fish mainly came from the biofilm and the sediment. This result is consistent with previous studies (Monna et al., 2011; Wen et al., 2024), and is likely due to dietary intake and ingestion of particulate matter. Indeed, it is possible that fish consume invertebrates from the biofilm and sediments, leading to the accumulation of Pb in their tissues through biomagnification process (Farag et al., 1998; Wen et al., 2024). Although not directly assessed in our study, this pathway could explain the observed accumulation patterns. Adding to this, fish might inadvertently ingest metal-bound particles from biofilm or sediment while feeding, which could contribute to their Pb burden. Further research should focus on the diet of fish and the level of contamination of macroinvertebrates to test this hypothesis.

Interestingly, we also found significant effects of river flow on decreasing Pb concentrations in fish, possibly due to dilution effects. This suggests that hydrological conditions may play a crucial role in moderating exposure. This ties together the role of dietary intake and environmental conditions, emphasizing the importance of considering various factors when evaluating metal contamination in aquatic organisms.

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## 4.5 Overview

This study reveals distinct pathways of metal contamination among soil, sediment, biofilm and ultimately fish in riverine environments. Results of the SEM models, which identified the most likely transfer pathway and sources, suggest that Cu, Zn, As and Pb contamination typically moved from soil to sediment and then to biofilm. We also highlighted a potential direct transfer pathway from the soil to the biofilm for As and Pb. These different patterns could be explained by differences in the affinity of metals to bind to suspended matter and consequently the way to be adsorbed in sediment (Calmano et al., 1988) or trapped by biofilm (Farag et al., 2007).

Taken together, our results indicate that the partitioning of Cu, Zn and As concentration was (from greatest to least): biofilm>soil>sediment; while for Pb concentration the partitioning was soil>biofilm>sediment. Historical mining activities, although no longer active, were the primarily contamination source of Zn and Pb contamination in the soil, as well as Pb contamination in the biofilm, whereas contemporary agricultural activities were the main source of Cu, As and Pb contamination in
the biofilm. This pattern can be attributed to the rapid metal accumulation capacity of the biofilm compared to soil or sediment. Biofilms, composed of microbes and algae, provide a high reactive surface area facilitating swift metal adsorption from water. As a result, both historical and contemporary contamination sources influence metal accumulation in biofilms. Historical mining activities, despite being inactive for over a century, continue to release metals into the environment through the gradual leaching of contaminated soil and sediment, leading to Pb accumulation in biofilms. At the same time, agricultural activities act as an additional, ongoing source of Cu, As, and Pb contamination, likely due to the use of agrochemicals and surface runoff. While metals from historical mining primarily accumulate in soil over long timescales, their transfer to biofilms occurs through continued erosion, resuspension, and waterborne transport of metal-rich particles. This dual influence highlights the role of the biofilm as both an immediate and long-term indicator of metal contamination dynamics in aquatic environments.

Furthermore, this study highlighted that As and Pb concentrations in fish mainly originated from the sediment (for As) or from both the sediment and biofilm (for Pb), potentially suggesting a dietary exposure of As and Pb to fish through invertebrates living on these substrates, although not directly investigated in our study. This finding suggests that the biofilm and sediment likely play a pivotal role in absorbing and releasing metals from the ecosystems, and is likely the entry door of metals in the food chain (Farag et al., 1998, 2007; Ancion et al., 2013; Wen et al., 2024). Therefore, future studies investigating the pathways of contamination from the environment to wildlife should take into account invertebrates as an intermediate vector between the environment and top predators in food chains. Stable isotope analyses of carbon (¹³C) and nitrogen (¹⁵N) as well as metals (Cu, Zn, As and Pb) would be valuable for providing deeper insights into these contamination pathways (Monna et al., 2011; Hansson et al., 2017, 2019a) thereby enhancing our understanding of metal dynamics in ecosystems.

Moreover, substrate particle size influences Cu and Zn concentrations in fish, likely by modulating the levels of organic matter (Yang et al., 2020), which can alter metal bioavailability in the water (Mountouris et al., 2002; De Paiva Magalhães et al., 2015), and promote metal uptake for fish through their gills. Future studies should therefore focus on metal concentrations in the water and fish gills, as well as considering the specific chemical forms of metals (either dissolved or not) (Butler et al.,
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2008). This approach will provide deeper insights into the bioavailability and dynamics of metal contamination in aquatic ecosystems.

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Interestingly, results show that water physicochemistry (notably temperature, pH, nitrate & nitrite or phosphate) affect how metal content in fish likely because of biological effects on accumulation and/or detoxification processes within the fish. Indeed, several physiological traits such as metabolic rate, energy reserves, feeding rate and antioxidant/immune status could modulate the ability of fish to uptake and eliminate metals from their bodies (bearing in mind that feeding habits and age also matter), thus affecting their final metal concentration in their tissues (Zeitoun and Mehana, 2014; Javed and Usmani, 2019; Banday et al., 2020). Future research should therefore explore the impacts of these metals on fish health by using health biomarkers to decipher the ecotoxicological consequences of metal contamination in riverine environments.

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## 5 Conclusion

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This study highlights the complexity of metal contamination pathways in the French Pyrenees and, more broadly, in riverine ecosystems involving both mountain and lowland rivers. It emphasizes the need to consider both historical and contemporary human sources of pollution. Our results outline that both current agricultural-and past mining- activities significantly influence the distribution of metals across soil, sediment and biofilm, highlighting the long-lasting impacts of human activities on riverine pollution. Ultimately, this contamination reaches fish. This survey also reveals that biofilm play a major role in absorbing and releasing metals in riverine ecosystems. Along with sediment, it is a key factor in transferring metal contamination from the environment to fish. Understanding the dynamics of metal contamination across compartments and space is crucial for developing effective management and remediation strategies for freshwater ecosystems affected by historical and contemporary metallic pollution.

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## Acknowledgements

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The authors wish to thank Juliette Khalifa, Raphael Wilkinson, Jérôme Prunier, Olivier Brisset, Henar Margenat-Hervas, Margarita Granada, Laetitia Leroy and Linda Chouache for their assistance in the field. Special thanks to Jessica Côte for help with technical field preparation, Lucy Bouly for her support
687 with lipid reserve analyses, Yves Cuenot for all his technical assistance with laboratory analyses, Hugo
688 Gilbert for help with XRF analyses, Quentin Petitjean for assistance with statistical analyses, Flavien
689 Garcia for support with immune cell analyses and Camille Duquenoy and Aurélie Marquet (both at the
690 ICP-MS platform at the Observatoire Midi-Pyrenees, Toulouse, France) for assistance with the ICP-MS
691 analysis. We also thank the Fédération de Pêche de l'Ariège (L. Garmendia and A Lautraite), de la Haute-
692 Garonne et des Hautes-Pyrénées, the Associations Agréées de Pêche et de Protection des Milieux
693 Aquatiques (AAPPMA) and the Office Français de la Biodiversité (OFB) for help and support at
694 different stages of the study.

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696 Author contributions

697 SVH, LJ and LG designed the study; LG and BL conducted the field sampling; LG, BL prepared all the
698 samples in laboratory (i.e., preparation of soil, biofilm and sediment for XRF probe, preparation of fish
699 for ICP-MS analyses); JG performed digestions and ICP-MS analysis; SVH processed the ICP-MS
700 results; LG, MD and CJ acquired data from the geographic information system (i.e., anthropogenic
701 activities, hydromorphological variables); LG performed statistical analyses and graphical work with
702 inputs from SVH and LC; LG, SVH, LJ and SJ participated in the interpretation of results; LG drafted
703 the manuscript under the guidance of SVH and LJ; SVH, LJ, SJ and CJ revised manuscript.

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**Declaration of interests.** The authors declare no competing financial interest nor any other conflict of interest.

**Ethical approval.** The authors declare that collection and sacrifice of all fish included in this study was
performed in accordance to local guidelines and regulations, i.e., authorized by Direction
Départementale des Territoires (DDT) de l'Ariège, la Haute-Garonne and les Hautes-Pyrénées
(authorizations N°65-2022-04-29-00001, 31-2022-06-14, 09-2022-06-02), with inputs from either the
Fédération de Pêche de l'Ariège, la Haute-Garonne and les Hautes-Pyrénées or the Association Agréée
de Pêche et de Protection des Milieux Aquatiques (AAPPMA) and the service départemental de l'Office
Français de la Biodiversité (OFB).

**Funding sources.** This project was supported by the Université Fédérale Midi-Pyrénées and the Région
Occitanie (projects POLLPAT-pilot and POLLPAT) to SVH, the ANR JCJC (MULTIPAT ANR-21-
CE34-0018-01) to LJ and the ANR ATCAF (ANR-21-CE34-0001) to SVH. Some study sites are part of
the ZA PYGAR and/or OHM Vicdessos. LJ is supported by the Institut Universitaire de France. CRBE
is part of the Laboratoire d'Excellence Labex TULIP.
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