# Advancing environmental monitoring across the water continuum combining biomarker analysis in multiple sentinel species: A case study in the Seine-Normandie Basin (France)

Sylvain Slaby, Alain Geffard, Cédric Fisson, Matthieu Bonnevalle-Normand, Anne-Sophie Allonier-Fernandes, Rachid Amara, Anne Bado-Nilles, Isabelle Bonnard, Marc Bonnard, Mayélé Burlion-Giorgi, et al.

## ► To cite this version:

Sylvain Slaby, Alain Geffard, Cédric Fisson, Matthieu Bonnevalle-Normand, Anne-Sophie Allonier-Fernandes, et al. "Advancing environmental monitoring across the water continuum combining biomarker analysis in multiple sentinel species: A case study in the Seine-Normandie Basin (France)." Journal of Environmental Management, 2024, 358, pp.120784. 10.1016/j.jenvman.2024.120784 hal-04542022

HAL Id: hal-04542022

https://normandie-univ.hal.science/hal-04542022

Submitted on 27 May 2024

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Research article

<figure><img src="image_1.png" /></figure>

# Advancing environmental monitoring across the water continuum combining biomarker analysis in multiple sentinel species: A case study in the Seine-Normandie Basin (France)

Sylvain Slaby<sup>a</sup>, Alain Geffard<sup>b</sup>, Cédric Fisson<sup>c</sup>, Matthieu Bonnevalle-Normand<sup>a</sup>, Anne-Sophie Allonier-Fernandes<sup>d</sup>, Rachid Amara<sup>e</sup>, Anne Bado-Nilles<sup>f</sup>, Isabelle Bonnard<sup>b</sup>, Marc Bonnard<sup>b</sup>, Mayélé Burlion-Giorgi<sup>a</sup>, Amélie Cant<sup>f,1</sup>, Audrey Catteau<sup>b</sup>, Arnaud Chaumot<sup>g</sup>, Katherine Costil<sup>h</sup>, Romain Coulaud<sup>a</sup>, Laurence Delahaut<sup>b</sup>, Mamadou Diop<sup>e</sup>, Aurélie Duflot<sup>a</sup>, Olivier Geffard<sup>g</sup>, Emmanuel Jestin<sup>d</sup>, Frank Le Foll<sup>a</sup>, Antoine Le Guernic<sup>b,2</sup>, Christelle Lopes<sup>i</sup>, Mélissa Palos-Ladeiro<sup>b</sup>, Quentin Peignot<sup>b,3,4</sup>, Agnès Poret<sup>a</sup>, Antoine Serpentini<sup>h</sup>, Gauthier Tremolet<sup>a</sup>, Cyril Turiès<sup>f</sup>, Benoît Xuereb<sup>a,*</sup>

## ARTICLE INFO

Handling Editor: Lixiao Zhang

**Keywords:**
Biomonitoring
Water quality
Caging
WFD

## ABSTRACT

Nowadays, biomarkers are recognized as valuable tools to complement chemical and ecological assessments in biomonitoring programs. They provide insights into the effects of contaminant exposures on individuals and establish connections between environmental pressure and biological response at higher levels. In the last decade, strong improvements in the design of experimental protocols and the result interpretation facilitated the use of biomarker across wide geographical areas, including aquatic continua. Notably, the statistical establishment of reference values and thresholds enabled the discrimination of contamination effects in environmental conditions, allowed interspecies comparisons, and eliminated the need of a reference site.

E-mail addresses: sylvain.slaby@univ-lehavre.fr (S. Slaby), alain.geffard@univ-reims.fr (A. Geffard), cfisson@seine-aval.fr (C. Fisson), matthieunormand5@gmail.com (M. Bonnevalle-Normand), allonier-fernandes.annesophie@aesn.fr (A.-S. Allonier-Fernandes), rachid.amara@univ-littoral.fr (R. Amara), anne.Bado-Nilles@ineris.fr (A. Bado-Nilles), isabelle.bonnard@univ-reims.fr (I. Bonnard), marc.bonnard@univ-reims.fr (M. Bonnard), mayele.burlion-giorgi@univ-lehavre.fr (M. Burlion-Giorgi), amelie.cant@ifremer.fr (A. Cant), audrey.catteau@univ-reims.fr (A. Catteau), arnaud.chaumot@inrae.fr (A. Chaumot), katherine.costil@unicaen.fr (K. Costil), romain.coulaud@univ-lehavre.fr (R. Coulaud), laurence.delahaut@univ-reims.fr (L. Delahaut), mamedou82@yahoo.fr (M. Diop), duflota@univ-lehavre.fr (A. Duflot), olivier.geffard@inrae.fr (O. Geffard), jestin.emmanuel@aesn.fr (E. Jestin), frank.le-foll@univ-lehavre.fr (F. Le Foll), aleguernic@uco.fr (A. Le Guernic), christelle.lopes@univ-lyon1.fr (C. Lopes), melissa.palos@univ-reims.fr (M. Palos-Ladeiro), quentin.peignot@etu.univ-lehavre.fr (Q. Peignot), agnes.poret@univ-lehavre.fr (A. Poret), antoine.serpentini@unicaen.fr (A. Serpentini), gauthier.tremolet@univ-lehavre.fr (G. Tremolet), cyril.turies@ineris.fr (C. Turiès), benoit.xuereb@univ-lehavre.fr (B. Xuereb).

<footnote><sup>a</sup> Normandie Univ, UNILEHAVRE, FR CNRS 3730 SCALE, UMR-I 02 Environmental Stress and Aquatic Biomonitoring (SEBIO), F-76600, Le Havre, France</footnote>

<footnote><sup>b</sup> Université de Reims Champagne-Ardenne (URCA), UMR-I 02 SEBIO, UFR Sciences Exactes et Naturelles, Campus Moulin de la Housse, BP 1039, 51687, Reims, France</footnote>

<footnote><sup>c</sup> GIP Seine-Aval, Hangar C - Espace des Marégraphes, CS 41174, 76176, Rouen, Cedex 1, France</footnote>

<footnote><sup>d</sup> Agence de l'eau Seine-Normandie, 12 rue de l'Industrie CS 80148 92416 Courbevoie Cedex, France</footnote>

<footnote><sup>e</sup> Université Littoral Côte d'Opale, Université Lille, CNRS, IRD, UMR 8187, LOG, Laboratoire d'Océanologie et de Géosciences, Wimereux, France</footnote>

<footnote><sup>f</sup> Institut National de l'Environnement Industriel et des Risques (INERIS), UMR-I 02 SEBIO, Parc Technologique Alata, BP 2, 60550, Verneuil-en-Halatte, France</footnote>

<footnote><sup>g</sup> INRAE, RiverLy, Ecotoxicology Laboratory, 5 Avenue de la Doua, CS20244, 69625, Villeurbanne Cedex, France</footnote>

<footnote><sup>h</sup> Biologie des Organismes et Ecosystèmes Aquatiques (BOREA) Université de Caen Normandie UNICAEN, Sorbonne Université, MNHN, UPMC Univ Paris 06, UA, CNRS 8067, IRD 207, Esplanade de la paix, Caen F-14032, France</footnote>

<footnote><sup>i</sup> Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Evolutive UMR5558, 69622, Villeurbanne, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote n="1">¹ Ifremer, Littoral Unit, Environmental Resources Laboratory (Normandie), avenue du Général de Gaulle, F-14520, Port-en-Bessin, France.</footnote>

<footnote n="2">² Laboratoire BIOSSE, Université Catholique de L'Ouest (UCO), 3 Place André Leroy, 49,100, Angers, France.</footnote>

<footnote n="3">³ Normandie Univ, UNILEHAVRE, FR CNRS 3730 SCALE, UMR-I 02 Environmental Stress and Aquatic Biomonitoring (SEBIO), F-76600 Le Havre, France.</footnote>

<footnote n="4">⁴ Institut des Sciences de la Mer, Québec-Océan, Université du Québec à Rimouski, 310 Allée des Ursulines, Rimouski G5L 3A1, Canada.</footnote>
Research article

<figure><img src="image_2.png" /></figure>

# Advancing environmental monitoring across the water continuum combining biomarker analysis in multiple sentinel species: A case study in the Seine-Normandie Basin (France)

Sylvain Slaby<sup>a</sup>, Alain Geffard<sup>b</sup>, Cédric Fisson<sup>c</sup>, Matthieu Bonnevalle-Normand<sup>a</sup>, Anne-Sophie Allonier-Fernandes<sup>d</sup>, Rachid Amara<sup>e</sup>, Anne Bado-Nilles<sup>f</sup>, Isabelle Bonnard<sup>b</sup>, Marc Bonnard<sup>b</sup>, Mayélé Burlion-Giorgi<sup>a</sup>, Amélie Cant<sup>f,1</sup>, Audrey Catteau<sup>b</sup>, Arnaud Chaumot<sup>g</sup>, Katherine Costil<sup>h</sup>, Romain Coulaud<sup>a</sup>, Laurence Delahaut<sup>b</sup>, Mamadou Diop<sup>e</sup>, Aurélie Duflot<sup>a</sup>, Olivier Geffard<sup>g</sup>, Emmanuel Jestin<sup>d</sup>, Frank Le Foll<sup>a</sup>, Antoine Le Guernic<sup>b,2</sup>, Christelle Lopes<sup>i</sup>, Mélissa Palos-Ladeiro<sup>b</sup>, Quentin Peignot<sup>b,3,4</sup>, Agnès Poret<sup>a</sup>, Antoine Serpentini<sup>h</sup>, Gauthier Tremolet<sup>a</sup>, Cyril Turiès<sup>f</sup>, Benoît Xuereb<sup>a,*</sup>

## ARTICLE INFO

Handling Editor: Lixiao Zhang

**Keywords:**
Biomonitoring
Water quality
Caging
WFD

## ABSTRACT

Nowadays, biomarkers are recognized as valuable tools to complement chemical and ecological assessments in biomonitoring programs. They provide insights into the effects of contaminant exposures on individuals and establish connections between environmental pressure and biological response at higher levels. In the last decade, strong improvements in the design of experimental protocols and the result interpretation facilitated the use of biomarker across wide geographical areas, including aquatic continua. Notably, the statistical establishment of reference values and thresholds enabled the discrimination of contamination effects in environmental conditions, allowed interspecies comparisons, and eliminated the need of a reference site.

E-mail addresses: sylvain.slaby@univ-lehavre.fr (S. Slaby), alain.geffard@univ-reims.fr (A. Geffard), cfisson@seine-aval.fr (C. Fisson), matthieunormand5@gmail.com (M. Bonnevalle-Normand), allonier-fernandes.annesophie@aesn.fr (A.-S. Allonier-Fernandes), rachid.amara@univ-littoral.fr (R. Amara), anne.Bado-Nilles@ineris.fr (A. Bado-Nilles), isabelle.bonnard@univ-reims.fr (I. Bonnard), marc.bonnard@univ-reims.fr (M. Bonnard), mayele.burlion-giorgi@univ-lehavre.fr (M. Burlion-Giorgi), amelie.cant@ifremer.fr (A. Cant), audrey.catteau@univ-reims.fr (A. Catteau), arnaud.chaumot@inrae.fr (A. Chaumot), katherine.costil@unicaen.fr (K. Costil), romain.coulaud@univ-lehavre.fr (R. Coulaud), laurence.delahaut@univ-reims.fr (L. Delahaut), mamedou82@yahoo.fr (M. Diop), duflota@univ-lehavre.fr (A. Duflot), olivier.geffard@inrae.fr (O. Geffard), jestin.emmanuel@aesn.fr (E. Jestin), frank.le-foll@univ-lehavre.fr (F. Le Foll), aleguernic@uco.fr (A. Le Guernic), christelle.lopes@univ-lyon1.fr (C. Lopes), melissa.palos@univ-reims.fr (M. Palos-Ladeiro), quentin.peignot@etu.univ-lehavre.fr (Q. Peignot), agnes.poret@univ-lehavre.fr (A. Poret), antoine.serpentini@unicaen.fr (A. Serpentini), gauthier.tremolet@univ-lehavre.fr (G. Tremolet), cyril.turies@ineris.fr (C. Turiès), benoit.xuereb@univ-lehavre.fr (B. Xuereb).

<footnote><sup>a</sup> Normandie Univ, UNILEHAVRE, FR CNRS 3730 SCALE, UMR-I 02 Environmental Stress and Aquatic Biomonitoring (SEBIO), F-76600, Le Havre, France</footnote>

<footnote><sup>b</sup> Université de Reims Champagne-Ardenne (URCA), UMR-I 02 SEBIO, UFR Sciences Exactes et Naturelles, Campus Moulin de la Housse, BP 1039, 51687, Reims, France</footnote>

<footnote><sup>c</sup> GIP Seine-Aval, Hangar C - Espace des Marégraphes, CS 41174, 76176, Rouen, Cedex 1, France</footnote>

<footnote><sup>d</sup> Agence de l'eau Seine-Normandie, 12 rue de l'Industrie CS 80148 92416 Courbevoie Cedex, France</footnote>

<footnote><sup>e</sup> Université Littoral Côte d'Opale, Université Lille, CNRS, IRD, UMR 8187, LOG, Laboratoire d'Océanologie et de Géosciences, Wimereux, France</footnote>

<footnote><sup>f</sup> Institut National de l'Environnement Industriel et des Risques (INERIS), UMR-I 02 SEBIO, Parc Technologique Alata, BP 2, 60550, Verneuil-en-Halatte, France</footnote>

<footnote><sup>g</sup> INRAE, RiverLy, Ecotoxicology Laboratory, 5 Avenue de la Doua, CS20244, 69625, Villeurbanne Cedex, France</footnote>

<footnote><sup>h</sup> Biologie des Organismes et Ecosystèmes Aquatiques (BOREA) Université de Caen Normandie UNICAEN, Sorbonne Université, MNHN, UPMC Univ Paris 06, UA, CNRS 8067, IRD 207, Esplanade de la paix, Caen F-14032, France</footnote>

<footnote><sup>i</sup> Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Evolutive UMR5558, 69622, Villeurbanne, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote n="1">¹ Ifremer, Littoral Unit, Environmental Resources Laboratory (Normandie), avenue du Général de Gaulle, F-14520, Port-en-Bessin, France.</footnote>

<footnote n="2">² Laboratoire BIOSSE, Université Catholique de L'Ouest (UCO), 3 Place André Leroy, 49,100, Angers, France.</footnote>

<footnote n="3">³ Normandie Univ, UNILEHAVRE, FR CNRS 3730 SCALE, UMR-I 02 Environmental Stress and Aquatic Biomonitoring (SEBIO), F-76600 Le Havre, France.</footnote>

<footnote n="4">⁴ Institut des Sciences de la Mer, Québec-Océan, Université du Québec à Rimouski, 310 Allée des Ursulines, Rimouski G5L 3A1, Canada.</footnote>
The aim of this work was to study freshwater-estuarine-coastal water continua by applying biomarker measurements in multi-species caged organisms. During two campaigns, eight sentinel species, encompassing fish, mollusks, and crustaceans, were deployed to cover 25 sites from rivers to the sea. As much as possible, a common methodology was employed for biomarker measurements (DNA damage and phagocytosis efficiency) and data interpretation based on guidelines established using reference values and induction/inhibition thresholds (establishment of three effect levels).

The methodology was successfully implemented and allowed us to assess the environmental quality. Employing multiple species per site enhances confidence in observed trends. The results highlight the feasibility of integrating biomarker-based environmental monitoring programs across a continuum scale. Biomarker results align with Water Framework Directive indicators in cases of poor site quality. Additionally, when discrepancies arise between chemical and ecological statuses, biomarker findings offer a comprehensive perspective to elucidate the disparities. Presented as a pilot project, this work contributes to gain insights into current biomonitoring needs, providing new questions and perspectives.

# 1. Introduction

Since 2000, the European Union (EU) has implemented the Water Framework Directive (WFD, Directive, 2000/60/EC) to safeguard water bodies including lakes, rivers, transitional waters, coastal waters, and groundwater. It has the advantage of serving as a single legal basis for all EU countries. However, the methods currently involved in its context are encountering certain limitations in the assessment of the aquatic environment as outlined by Milinkovitch et al. (2019). Regarding the chemical aspect, the list of priority substances is limited to 45 compounds, and the analyses do not provide insights into biological impacts or interactions between compounds (Crane et al., 2007; Kuzmanović et al., 2016). On the other hand, the ecological status is based on parameters which does not constitute early indicators of environmental quality degradation. Moreover, biodiversity indices are so integrative that they are usually not specific to the impacts of contaminant exposure (Wernersson et al., 2015), even if some studies succeed in defining parameters at high biological levels that are mainly modulated by the effects of chemical substances. For instance, a relationship has been established between the macroinvertebrate assemblages and the occurrence and bioavailability of metals in rivers (Rico-Sánchez et al., 2022). Meyer et al. (2022) also identified trait syndromes correlated with the presence of specific contaminant mixtures within diatom, macroinvertebrate, and fish communities.

Effect-based tools (EBT) offer several advantages for improving the environmental quality assessment of aquatic systems (Wernersson et al., 2015). Among them, biomarkers are defined as observed or measured changes at (sub)individual levels (e.g., DNA degradation, protein modulations), including the behavior, caused by exposure to pollutants (van der Oost et al., 2003). They are proposed as complementary tools to chemical and ecological analyses providing insights into the effects of bioavailable contaminants and consider the cocktail effects of contaminant mixtures (Lam, 2009; Wernersson et al., 2015). The results obtained can be used to better understand the relationship between environmental pressures and high-level biological impacts. As evidence, they are already implemented in the Marine Strategy Framework Directive (MSFD, 2008/56/EC). Among the various descriptors used to determine the quality status of a site and to assess the impact of human activities, this directive focuses on whether exposures to substances have adverse effects on the health of species.

One of the greatest challenges associated with the utilization of biomarkers is to clearly define their applicable conditions and interpretation, and to produce outputs which can be effectively used by water resource managers (Milinkovitch et al., 2019). A response modulation is a misinterpretation when it results from confounding factor effects (Forbes et al., 2006; Hanson, 2011). Since the 2000s, substantial efforts have been made to improve the interpretation of biomarker modulations, including a deeper understanding of their natural variability, in response to environmental and biotic confounding factors. Technically, employing controlled organisms through caging methods (i.e., active

biomonitoring) has been recognized as a suitable approach for monitoring the quality of aquatic environmental matrix (Baird et al., 2007). One of the major benefits of active biomonitoring is the reduction of the intrinsic variability of biomarkers by using standardized organisms with a same life history, eliminating the variations potentially linked to sex, age/size classes or local phenotypic characteristics of populations (Bourgeault et al., 2010; Catteau et al., 2019, 2022; Crane et al., 2007; Gouveia et al., 2017; Le Guernic et al., 2016; Oikari, 2006; Slaby et al., 2023). Moreover, it allows for research in species-poor sites and avoids sampling pressures on local populations. Moreover, another strong lever for improving the *in situ* application and interpretation of biomarkers had been the establishment of reference data set and the associated threshold values taking into account the variability induced by environmental parameters (i.e., temperature, water hardness; Barbarin et al., 2022; Barrick et al., 2016; Catteau et al., 2023; Charron et al., 2014; Coulaud et al., 2011; Erraud et al., 2018a; Lacaze et al., 2011; Leprêtre et al., 2022a; Xuereb et al., 2009). These benchmarks improve the impact assessment of contaminants by discriminating the outcome of chemical stress from the background noise and then reducing the occurrence of false negatives or false positives, and enable independence from reference sites.

Several studies have reported the application of biomarkers in caged organisms for monitoring aquatic environments in different water bodies, including rivers (Bonnail et al., 2019; Catteau et al., 2021; Lunardelli et al., 2018), lentic water bodies (Brodeur et al., 2021; Georgieva et al., 2022; Slaby et al., 2023), estuarine environments (Borcier et al., 2019), and marine ecosystems (de los Ríos et al., 2012; Maisano et al., 2017; Tsangaris et al., 2010). However, to the best of our knowledge, the choice of study sites is constrained by the species-specific characteristics and no attempts have been made to establish a consistent methodology across different species that would allow for the assessment of environments with significant differences in physicochemical properties such as an aquatic continuum. In fact, at the scale of a hydrographic watershed, water flow encompasses ecosystems with large variations in physicochemical properties (e.g., salinity, conductivity, hydrodynamics, turbidity). For instance, a complete network includes freshwater, transitional water, and coastal water parts, as well as lotic and lentic systems. These environments are different but form a continuum that must be fully covered in environmental monitoring programs. As a consequence, the deployment of active biomonitoring programs could take advantage of the use of multiple species in which the same biomarkers can be studied and consistently interpreted from small headwater streams to marine ecosystems. In this context, a collaborative project involving French research laboratories and a field management agency introduced an innovative strategy to address the challenges of studying continuous aquatic environments ("SASHIMI" project, funded by the Office Français de la Biodiversité, Vincennes, France, https://www.seine-aval.fr/projet/sashimi/). It proposed several model species (i.e., *Gasterosteus aculeatus* Linnaeus, 1758, *Platichthys flesus* Linnaeus, 1758, *Dreissena polymorpha* Pallas, 1771, *Mytilopsis*
The aim of this work was to study freshwater-estuarine-coastal water continua by applying biomarker measurements in multi-species caged organisms. During two campaigns, eight sentinel species, encompassing fish, mollusks, and crustaceans, were deployed to cover 25 sites from rivers to the sea. As much as possible, a common methodology was employed for biomarker measurements (DNA damage and phagocytosis efficiency) and data interpretation based on guidelines established using reference values and induction/inhibition thresholds (establishment of three effect levels).

The methodology was successfully implemented and allowed us to assess the environmental quality. Employing multiple species per site enhances confidence in observed trends. The results highlight the feasibility of integrating biomarker-based environmental monitoring programs across a continuum scale. Biomarker results align with Water Framework Directive indicators in cases of poor site quality. Additionally, when discrepancies arise between chemical and ecological statuses, biomarker findings offer a comprehensive perspective to elucidate the disparities. Presented as a pilot project, this work contributes to gain insights into current biomonitoring needs, providing new questions and perspectives.

# 1. Introduction

Since 2000, the European Union (EU) has implemented the Water Framework Directive (WFD, Directive, 2000/60/EC) to safeguard water bodies including lakes, rivers, transitional waters, coastal waters, and groundwater. It has the advantage of serving as a single legal basis for all EU countries. However, the methods currently involved in its context are encountering certain limitations in the assessment of the aquatic environment as outlined by Milinkovitch et al. (2019). Regarding the chemical aspect, the list of priority substances is limited to 45 compounds, and the analyses do not provide insights into biological impacts or interactions between compounds (Crane et al., 2007; Kuzmanović et al., 2016). On the other hand, the ecological status is based on parameters which does not constitute early indicators of environmental quality degradation. Moreover, biodiversity indices are so integrative that they are usually not specific to the impacts of contaminant exposure (Wernersson et al., 2015), even if some studies succeed in defining parameters at high biological levels that are mainly modulated by the effects of chemical substances. For instance, a relationship has been established between the macroinvertebrate assemblages and the occurrence and bioavailability of metals in rivers (Rico-Sánchez et al., 2022). Meyer et al. (2022) also identified trait syndromes correlated with the presence of specific contaminant mixtures within diatom, macroinvertebrate, and fish communities.

Effect-based tools (EBT) offer several advantages for improving the environmental quality assessment of aquatic systems (Wernersson et al., 2015). Among them, biomarkers are defined as observed or measured changes at (sub)individual levels (e.g., DNA degradation, protein modulations), including the behavior, caused by exposure to pollutants (van der Oost et al., 2003). They are proposed as complementary tools to chemical and ecological analyses providing insights into the effects of bioavailable contaminants and consider the cocktail effects of contaminant mixtures (Lam, 2009; Wernersson et al., 2015). The results obtained can be used to better understand the relationship between environmental pressures and high-level biological impacts. As evidence, they are already implemented in the Marine Strategy Framework Directive (MSFD, 2008/56/EC). Among the various descriptors used to determine the quality status of a site and to assess the impact of human activities, this directive focuses on whether exposures to substances have adverse effects on the health of species.

One of the greatest challenges associated with the utilization of biomarkers is to clearly define their applicable conditions and interpretation, and to produce outputs which can be effectively used by water resource managers (Milinkovitch et al., 2019). A response modulation is a misinterpretation when it results from confounding factor effects (Forbes et al., 2006; Hanson, 2011). Since the 2000s, substantial efforts have been made to improve the interpretation of biomarker modulations, including a deeper understanding of their natural variability, in response to environmental and biotic confounding factors. Technically, employing controlled organisms through caging methods (i.e., active

biomonitoring) has been recognized as a suitable approach for monitoring the quality of aquatic environmental matrix (Baird et al., 2007). One of the major benefits of active biomonitoring is the reduction of the intrinsic variability of biomarkers by using standardized organisms with a same life history, eliminating the variations potentially linked to sex, age/size classes or local phenotypic characteristics of populations (Bourgeault et al., 2010; Catteau et al., 2019, 2022; Crane et al., 2007; Gouveia et al., 2017; Le Guernic et al., 2016; Oikari, 2006; Slaby et al., 2023). Moreover, it allows for research in species-poor sites and avoids sampling pressures on local populations. Moreover, another strong lever for improving the *in situ* application and interpretation of biomarkers had been the establishment of reference data set and the associated threshold values taking into account the variability induced by environmental parameters (i.e., temperature, water hardness; Barbarin et al., 2022; Barrick et al., 2016; Catteau et al., 2023; Charron et al., 2014; Coulaud et al., 2011; Erraud et al., 2018a; Lacaze et al., 2011; Leprêtre et al., 2022a; Xuereb et al., 2009). These benchmarks improve the impact assessment of contaminants by discriminating the outcome of chemical stress from the background noise and then reducing the occurrence of false negatives or false positives, and enable independence from reference sites.

Several studies have reported the application of biomarkers in caged organisms for monitoring aquatic environments in different water bodies, including rivers (Bonnail et al., 2019; Catteau et al., 2021; Lunardelli et al., 2018), lentic water bodies (Brodeur et al., 2021; Georgieva et al., 2022; Slaby et al., 2023), estuarine environments (Borcier et al., 2019), and marine ecosystems (de los Ríos et al., 2012; Maisano et al., 2017; Tsangaris et al., 2010). However, to the best of our knowledge, the choice of study sites is constrained by the species-specific characteristics and no attempts have been made to establish a consistent methodology across different species that would allow for the assessment of environments with significant differences in physicochemical properties such as an aquatic continuum. In fact, at the scale of a hydrographic watershed, water flow encompasses ecosystems with large variations in physicochemical properties (e.g., salinity, conductivity, hydrodynamics, turbidity). For instance, a complete network includes freshwater, transitional water, and coastal water parts, as well as lotic and lentic systems. These environments are different but form a continuum that must be fully covered in environmental monitoring programs. As a consequence, the deployment of active biomonitoring programs could take advantage of the use of multiple species in which the same biomarkers can be studied and consistently interpreted from small headwater streams to marine ecosystems. In this context, a collaborative project involving French research laboratories and a field management agency introduced an innovative strategy to address the challenges of studying continuous aquatic environments ("SASHIMI" project, funded by the Office Français de la Biodiversité, Vincennes, France, https://www.seine-aval.fr/projet/sashimi/). It proposed several model species (i.e., *Gasterosteus aculeatus* Linnaeus, 1758, *Platichthys flesus* Linnaeus, 1758, *Dreissena polymorpha* Pallas, 1771, *Mytilopsis*
leucophaeata Conrad, 1831, *Mytilus edulis* Linnaeus, 1758, *Gammarus fossarum* Koch, 1836, *Palaemon longirostris* Milne Edwards, 1837, and *Palaemon serratus* Pennant, 1777) to study various water body types along with biomarkers of immunotoxicity (phagocytosis efficiency) and genotoxicity (DNA damages). The results defined specific caging conditions for each species and a methodology of measure and interpretation of biomarkers based on the threshold values mobilization in order to permit interspecies comparisons along watershed continuum.

The purpose of the present study was to test a deployment of this methodology on different continua at a regional scale. This work was conducted as a part of the project "BIOSURVEILLANCE" funded by the Seine-Normandie Water Agency (Courbevoie, France, https://www.seine-aval.fr/projet/biosurveillance/). A total of 25 sites was studied in Seine-Normandie Basin, including three aquatic continua from the river to the sea corresponding to small and large streams spanning, as well as one coastal continuum (from west Cotentin to the Seine Bay). Different species of fish, mollusks, and crustaceans were involved in order to cover different hydrosystems. These species occupy different trophic levels and allow us to consider interspecies variations in sensitivity to contamination. The monitoring of DNA damage levels and the phagocytosis efficiency was conducted the species collection during two caging campaigns over consecutive years (in autumn). The results were interpreted using the determined reference and threshold values for the two biomarkers across the different species. This approach allowed for the comparison of responses not only between species but also across various study sites along the continuum. Furthermore, the outputs of the present study were confronted to conventional chemical and ecological status determined according to the WFD at the same location in order to assess the consistency of the biomarker approach and highlight situations in which biomarkers can provide additional insights into the evaluation process.

## 2. Materials and methods

### 2.1. Site description

Twenty-five sites located in north-western France were investigated to cover various continua from freshwater to marine water (Figs. 1 and 2). These sites were chosen because they were already monitored as part of national water quality monitoring networks supervised by the Seine Normandie Water Agency, and the French Research Institute for Exploitation of the Sea, IFREMER (Table A1). Briefly, three distinct continua can be defined: (i) the Seine River continuum with sites distributed along (SE1, SE6-7), upstream of the confluence with important tributaries (SE2-5, SE8), in the estuary of the Seine River (SE9-10), and in the English Channel near the coast (SE11-12), (ii) the Orne River continuum with sites located in the freshwater (OR1-2) and the transitional (OR3-4) parts, as well as along the littoral (OR5) of the Orne River, (iii) the Vire River continuum with sites located in the freshwater part (VI1), the transitional zone (VI2), and along the littoral (VI3) of the Vire River. Also, sites located along the Cotentin Peninsula littoral were studied (CO1-5). Detailed information regarding their precise locations is given in Supplementary Information (Tables A1). Latest chemical and ecological status established under the WFD (2022) are presented in Table 1 for all sites when available.

### 2.2. Caging experiments

To encompass all ecosystems across the three continua, eight species from three different taxa were selected (Fig. 2). The list included two teleost fish, *G. aculeatus* and *P. flesus*, three bivalve mollusks, *D. polymorpha*, *M. leucophaeata*, and *M. edulis*, and three crustaceans *G. fossarum*, *P. longirostris*, and *P. serratus*. Fig. 2 illustrates the deployment locations for each species. Except at SE10 and VI2, at least two species from distinct taxonomic groups were deployed at each site.

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Distribution of the sampling sites across the Seine (SE1-12), Orne (OR1-5), Vire (VI1-5), and Cotentin Peninsula littoral continua (CO1-5). Black dots: Sampling sites, Right corner: The Seine-Normandie Basin.</figcaption></figure>
leucophaeata Conrad, 1831, *Mytilus edulis* Linnaeus, 1758, *Gammarus fossarum* Koch, 1836, *Palaemon longirostris* Milne Edwards, 1837, and *Palaemon serratus* Pennant, 1777) to study various water body types along with biomarkers of immunotoxicity (phagocytosis efficiency) and genotoxicity (DNA damages). The results defined specific caging conditions for each species and a methodology of measure and interpretation of biomarkers based on the threshold values mobilization in order to permit interspecies comparisons along watershed continuum.

The purpose of the present study was to test a deployment of this methodology on different continua at a regional scale. This work was conducted as a part of the project "BIOSURVEILLANCE" funded by the Seine-Normandie Water Agency (Courbevoie, France, https://www.seine-aval.fr/projet/biosurveillance/). A total of 25 sites was studied in Seine-Normandie Basin, including three aquatic continua from the river to the sea corresponding to small and large streams spanning, as well as one coastal continuum (from west Cotentin to the Seine Bay). Different species of fish, mollusks, and crustaceans were involved in order to cover different hydrosystems. These species occupy different trophic levels and allow us to consider interspecies variations in sensitivity to contamination. The monitoring of DNA damage levels and the phagocytosis efficiency was conducted the species collection during two caging campaigns over consecutive years (in autumn). The results were interpreted using the determined reference and threshold values for the two biomarkers across the different species. This approach allowed for the comparison of responses not only between species but also across various study sites along the continuum. Furthermore, the outputs of the present study were confronted to conventional chemical and ecological status determined according to the WFD at the same location in order to assess the consistency of the biomarker approach and highlight situations in which biomarkers can provide additional insights into the evaluation process.

## 2. Materials and methods

### 2.1. Site description

Twenty-five sites located in north-western France were investigated to cover various continua from freshwater to marine water (Figs. 1 and 2). These sites were chosen because they were already monitored as part of national water quality monitoring networks supervised by the Seine Normandie Water Agency, and the French Research Institute for Exploitation of the Sea, IFREMER (Table A1). Briefly, three distinct continua can be defined: (i) the Seine River continuum with sites distributed along (SE1, SE6-7), upstream of the confluence with important tributaries (SE2-5, SE8), in the estuary of the Seine River (SE9-10), and in the English Channel near the coast (SE11-12), (ii) the Orne River continuum with sites located in the freshwater (OR1-2) and the transitional (OR3-4) parts, as well as along the littoral (OR5) of the Orne River, (iii) the Vire River continuum with sites located in the freshwater part (VI1), the transitional zone (VI2), and along the littoral (VI3) of the Vire River. Also, sites located along the Cotentin Peninsula littoral were studied (CO1-5). Detailed information regarding their precise locations is given in Supplementary Information (Tables A1). Latest chemical and ecological status established under the WFD (2022) are presented in Table 1 for all sites when available.

### 2.2. Caging experiments

To encompass all ecosystems across the three continua, eight species from three different taxa were selected (Fig. 2). The list included two teleost fish, *G. aculeatus* and *P. flesus*, three bivalve mollusks, *D. polymorpha*, *M. leucophaeata*, and *M. edulis*, and three crustaceans *G. fossarum*, *P. longirostris*, and *P. serratus*. Fig. 2 illustrates the deployment locations for each species. Except at SE10 and VI2, at least two species from distinct taxonomic groups were deployed at each site.

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Distribution of the sampling sites across the Seine (SE1-12), Orne (OR1-5), Vire (VI1-5), and Cotentin Peninsula littoral continua (CO1-5). Black dots: Sampling sites, Right corner: The Seine-Normandie Basin.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Species distribution by study sites classified according to water salinity. SE1-12: Seine river continuum, OR1-5: Orne river continuum, VI1-5: Vire river continuum, CO1-5: Contentin Peninsula littoral continua.</figcaption></figure>

<table><caption>**Table 1**

Chemical and ecological assessments in the context of the WFD (2022) of the study sites.</caption><thead><tr><th rowspan="2">EU Waterbody ID</th><th rowspan="2">Site ID</th><th colspan="2">Chemical status</th><th colspan="2">Ecological status</th></tr><tr><th>Status with uPBT</th><th>Reason for failure to achieve good status</th><th>Status</th><th>Reason for failure to achieve good status</th></tr></thead><tbody><tr><td>FRHR34-F2150600</td><td>SE1</td><td>Good</td><td>None</td><td>Moderate</td><td>Multi-metric invertebrate index (i2m2)</td></tr><tr><td>FRHR70A</td><td>SE2</td><td>Failing to achieve good*</td><td>Benzo[a]pyrene, Perfluorooctanesulfonate</td><td>Good</td><td>None</td></tr><tr><td>FRHR211</td><td>SE3</td><td>Failing to achieve good*</td><td>Benzo[a]pyrene, Mercure, Perfluorooctanesulfonate</td><td>Moderate</td><td>Biological Diatom Index (IBD)</td></tr><tr><td>FRHR216C</td><td>SE4</td><td>Failing to achieve good*</td><td>Benzo[a]pyrene, Perfluorooctanesulfonate</td><td>Good</td><td>None</td></tr><tr><td>FRHR137</td><td>SE5</td><td>Failing to achieve good</td><td>Benzo[a]pyrene, Mercure, Perfluorooctanesulfonate</td><td>Good</td><td>None</td></tr><tr><td>FRHR73A</td><td>SE6</td><td>Failing to achieve good*</td><td>Benzo[a]pyrene, Perfluorooctanesulfonate</td><td>Moderate</td><td>Macrophyte index (IBRM)</td></tr><tr><td>FRHR230A</td><td>SE7</td><td>Failing to achieve good</td><td>Benzo[a]pyrene, Benzo[g,h,i]perylene, Fluoranthene, Perfluorooctanesulfonate, C10-C13 chloroalkanes</td><td>Moderate</td><td>Biological Diatom Index (IBD)</td></tr><tr><td>FRHR261</td><td>SE8</td><td>Good</td><td>None</td><td>Good</td><td>None</td></tr><tr><td>FRHT01</td><td>SE9</td><td>Failing to achieve good</td><td>4-Nonylphenol, Aclonifen, Bis(2-Ethylhexyl) Phthalate, Dichlorvos, Heptachlor, Lindane, PAH, Perfluorooctanesulfonate, Tributyltin, Trichlorobenzene</td><td>Bad</td><td>Fish</td></tr><tr><td>FRHT03</td><td>SE10<br>SE11</td><td>Failing to achieve good*</td><td>Heptachlor, PAH, PCB (52/101/118/138/153), Tributyltin</td><td>Moderate</td><td>Fish, Invertebrate</td></tr><tr><td>FRHC16</td><td>SE12</td><td>Failing to achieve good*</td><td>PCB (52/101/118/138), Tributyltin</td><td>Good</td><td>None</td></tr><tr><td>FRHR361</td><td>OR1<br>OR2</td><td>Failing to achieve good*</td><td>Benzo[a]pyrene</td><td>Moderate</td><td>Biological Diatom Index (IBD)</td></tr><tr><td>FRHT04</td><td>OR3<br>OR4</td><td>NA</td><td>NA</td><td>Moderate</td><td>Intertidal macroalgae</td></tr><tr><td>FRHC14</td><td>OR5</td><td>Failing to achieve good*</td><td>PCB (101/118/138)</td><td>Good</td><td>None</td></tr><tr><td>FRHR317</td><td>VI1</td><td>Failing to achieve good</td><td>Benzo[a]pyrene, Fluoranthene</td><td>Moderate</td><td>Fish</td></tr><tr><td>FRHT06</td><td>VI2<br>VI3</td><td>Failing to achieve good*</td><td>PCB (118)</td><td>Moderate</td><td>Biological Diatom Index (IBD)</td></tr><tr><td>FRHC09</td><td>CO1</td><td>Failing to achieve good*</td><td>PCB (118)</td><td>Moderate</td><td>Angiosperms, Intertidal macroalgae</td></tr><tr><td>FRHC07</td><td>CO2</td><td>Good (2019)¹</td><td>NA</td><td>Good</td><td>None</td></tr><tr><td>FRHC61</td><td>CO3</td><td>Failing to achieve good*</td><td>PCB (118), Tributyltin</td><td>Good</td><td>None</td></tr><tr><td>FRHC03</td><td>CO4</td><td>Good</td><td>None</td><td>Moderate</td><td>Angiosperma</td></tr><tr><td>FRHC01</td><td>CO5</td><td>Good</td><td>None</td><td>Good</td><td>None</td></tr></tbody></table>

uPBT: ubiquitous, Persistent, Bioaccumulative and Toxic.

*Good chemical status if uPBT are not considered.

1Data from the 2019 report.
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Species distribution by study sites classified according to water salinity. SE1-12: Seine river continuum, OR1-5: Orne river continuum, VI1-5: Vire river continuum, CO1-5: Contentin Peninsula littoral continua.</figcaption></figure>

<table><caption>**Table 1**

Chemical and ecological assessments in the context of the WFD (2022) of the study sites.</caption><thead><tr><th rowspan="2">EU Waterbody ID</th><th rowspan="2">Site ID</th><th colspan="2">Chemical status</th><th colspan="2">Ecological status</th></tr><tr><th>Status with uPBT</th><th>Reason for failure to achieve good status</th><th>Status</th><th>Reason for failure to achieve good status</th></tr></thead><tbody><tr><td>FRHR34-F2150600</td><td>SE1</td><td>Good</td><td>None</td><td>Moderate</td><td>Multi-metric invertebrate index (i2m2)</td></tr><tr><td>FRHR70A</td><td>SE2</td><td>Failing to achieve good*</td><td>Benzo[a]pyrene, Perfluorooctanesulfonate</td><td>Good</td><td>None</td></tr><tr><td>FRHR211</td><td>SE3</td><td>Failing to achieve good*</td><td>Benzo[a]pyrene, Mercure, Perfluorooctanesulfonate</td><td>Moderate</td><td>Biological Diatom Index (IBD)</td></tr><tr><td>FRHR216C</td><td>SE4</td><td>Failing to achieve good*</td><td>Benzo[a]pyrene, Perfluorooctanesulfonate</td><td>Good</td><td>None</td></tr><tr><td>FRHR137</td><td>SE5</td><td>Failing to achieve good</td><td>Benzo[a]pyrene, Mercure, Perfluorooctanesulfonate</td><td>Good</td><td>None</td></tr><tr><td>FRHR73A</td><td>SE6</td><td>Failing to achieve good*</td><td>Benzo[a]pyrene, Perfluorooctanesulfonate</td><td>Moderate</td><td>Macrophyte index (IBRM)</td></tr><tr><td>FRHR230A</td><td>SE7</td><td>Failing to achieve good</td><td>Benzo[a]pyrene, Benzo[g,h,i]perylene, Fluoranthene, Perfluorooctanesulfonate, C10-C13 chloroalkanes</td><td>Moderate</td><td>Biological Diatom Index (IBD)</td></tr><tr><td>FRHR261</td><td>SE8</td><td>Good</td><td>None</td><td>Good</td><td>None</td></tr><tr><td>FRHT01</td><td>SE9</td><td>Failing to achieve good</td><td>4-Nonylphenol, Aclonifen, Bis(2-Ethylhexyl) Phthalate, Dichlorvos, Heptachlor, Lindane, PAH, Perfluorooctanesulfonate, Tributyltin, Trichlorobenzene</td><td>Bad</td><td>Fish</td></tr><tr><td>FRHT03</td><td>SE10<br>SE11</td><td>Failing to achieve good*</td><td>Heptachlor, PAH, PCB (52/101/118/138/153), Tributyltin</td><td>Moderate</td><td>Fish, Invertebrate</td></tr><tr><td>FRHC16</td><td>SE12</td><td>Failing to achieve good*</td><td>PCB (52/101/118/138), Tributyltin</td><td>Good</td><td>None</td></tr><tr><td>FRHR361</td><td>OR1<br>OR2</td><td>Failing to achieve good*</td><td>Benzo[a]pyrene</td><td>Moderate</td><td>Biological Diatom Index (IBD)</td></tr><tr><td>FRHT04</td><td>OR3<br>OR4</td><td>NA</td><td>NA</td><td>Moderate</td><td>Intertidal macroalgae</td></tr><tr><td>FRHC14</td><td>OR5</td><td>Failing to achieve good*</td><td>PCB (101/118/138)</td><td>Good</td><td>None</td></tr><tr><td>FRHR317</td><td>VI1</td><td>Failing to achieve good</td><td>Benzo[a]pyrene, Fluoranthene</td><td>Moderate</td><td>Fish</td></tr><tr><td>FRHT06</td><td>VI2<br>VI3</td><td>Failing to achieve good*</td><td>PCB (118)</td><td>Moderate</td><td>Biological Diatom Index (IBD)</td></tr><tr><td>FRHC09</td><td>CO1</td><td>Failing to achieve good*</td><td>PCB (118)</td><td>Moderate</td><td>Angiosperms, Intertidal macroalgae</td></tr><tr><td>FRHC07</td><td>CO2</td><td>Good (2019)¹</td><td>NA</td><td>Good</td><td>None</td></tr><tr><td>FRHC61</td><td>CO3</td><td>Failing to achieve good*</td><td>PCB (118), Tributyltin</td><td>Good</td><td>None</td></tr><tr><td>FRHC03</td><td>CO4</td><td>Good</td><td>None</td><td>Moderate</td><td>Angiosperma</td></tr><tr><td>FRHC01</td><td>CO5</td><td>Good</td><td>None</td><td>Good</td><td>None</td></tr></tbody></table>

uPBT: ubiquitous, Persistent, Bioaccumulative and Toxic.

*Good chemical status if uPBT are not considered.

1Data from the 2019 report.
Life cycle stage used, organism size, and exposure duration for each species are presented in Table 2. All details regarding caging conditions are provided in Table A2 of Supplementary Information. This includes information such as sex ratio (e.g., controlled or not), population origin, optimal caging period and duration, density, cage specifications, immersion depth, salinity, and hydrodynamics. The use *M. leucophaeata* and *D. polymorpha* were done with caution as both are naturalized species. The first record of *M. leucophaeata* in France was in the canal from Caen to the Channel (north-western France) in 1898 (Chemin, 1911). Regarding *D. polymorpha*, it was recorded for the first time in the Seine river in 1855 (Prié, 2023). Consequently, these species were only caged in sites where they were already present in the associated catchment.

The caging experiments were conducted during two successive years, in Autumn (2020); 2021. Test dates for each site and species are available in Supplementary Information (Table A3). This timing ensured a natural food supply and avoided periods of unfavorable climatic and hydrodynamic conditions such as heatwaves, droughts, and heavy flooding. Additionally, it fell outside the breeding period of the *G. aculeatus*, *D. polymorpha*, *M. edulis*, and *M. leucophaeata*. This last consideration holds less significance for other species, given that only juvenile *P. flesus* were used, and that crustacean model reproduction is distributed throughout the year and their sexual phenotype can be easily calibrated before the exposure.

## 2.3. Biomarker measurements

Phagocytosis is a conserved immune defense mechanism in the living (Yutin et al., 2009). The phagocytosis efficiency, defined as the proportion of cells that have internalized three or more fluorescent beads, was determined using flow cytometer analyses. The methodology was adapted from Marchand et al. (2017), Barjhoux et al. (2020), Diop et al. (2022), and Gendre et al. (2023). Genotoxic impacts were assessed by measuring the DNA integrity. It was conducted using the alkaline comet assay which enables the detection of DNA double-strand breaks, single-strand breaks, and alkali-labile sites (Singh et al., 1988). The results were expressed as the proportion of DNA located in the tail (% tail DNA), which is an indicator of DNA damage. The comet assay procedure was homogenized across the species of interest based on the methodologies described by Lacaze et al. (2011), Erraud et al. (2018b), Borcier et al. (2019), and Cant et al. (2022). Tables 3 and 4 describe the key steps involved in both procedures for each species.

## 2.4. Animal welfare

All experiments were conducted in accordance with the European directive 2010/63/EU. For *G. aculeatus*, experimental protocols were conducted at INERIS (registration number E60-769-02), were submitted and reviewed by a French nationally recognized ethical committee (CREMEAPS, registration number 96) and were approved by the French ethics committee in animal experimentation (APAFIS project number #20,760). For *P. flesus*, the study was conducted in accordance with European Commission recommendation 2007/526/EC, on revised guidelines for the accommodation and care of animals used for experimental and other scientific purposes. Both species were anesthetized using tricaine mesylate at a concentration (100 mg.L⁻¹ for *G. aculeatus* and 10 mg.L⁻¹ for *P. flesus*), and then euthanized by cervical dislocation.

## 2.5. Application of threshold values for the interpretation of biomarkers

A reference value, defined as the baseline response under “normal” conditions (including individual variability) along with induction and inhibition thresholds were established for all combinations of biomarkers and species (Table 2). These values enable the comparison of sites using different model species and eliminate the need for defining a reference site. Their determination depended on the size of the dataset and the available knowledge about biomarker variability.

For all species, only the induction of DNA damage was interpreted for the assessment of a toxicological impact and the environmental quality. It differed for the phagocytosis marker for which induction and/or inhibition of the response were considered. The interpretation of an impact on organism immunity was influenced by the dataset available for each species, leading us to consider only the inhibition of responses for *D. polymorpha* and *M. leucophaeata* and the induction of responses for *M. edulis*, due to the high dispersion of the measures and/or elevated baseline levels.

For *D. polymorpha*, *M. edulis*, and *P. serratus* the statistical method of evaluation of the reference values and associated thresholds is adapted from Besse et al. (2013) and was depicted in Leprêtre et al. (2022) and Catteau et al. (2023). Concerning *P. longirostris*, the phagocytosis efficiency lacked a sufficient dataset to establish reference values and thresholds. Nevertheless, those determined for the DNA damage biomarker in *P. serratus* were applied to *P. longirostris*, as the transferability was previously demonstrated (Erraud et al., 2019). For *G. fossarum*, the reference value, the induction threshold, and inhibition threshold were established as the median, the upper, and the lower

<table><caption>**Table 2**
Caging conditions and interpretation guidelines.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Caging conditions</th><th colspan="3">Reference value [Inhibition threshold, Induction threshold]</th></tr><tr><th>Stage</th><th>Size</th><th>Exposure duration</th><th>Phagocytosis efficiency (%)</th><th>DNA damage (% of tail DNA)</th><th>Interpretation grid (x = % of individuals exceeding the threshold)</th></tr></thead><tbody><tr><td><strong>Dreissena polymorpha</strong></td><td>Adult</td><td>22-25 mm</td><td>21 days</td><td>56 [41, NA]</td><td>4.2 [NA, 8.7]</td><td>- x ≤ 25%: No effect<br/>-25% &lt; x ≤ 75%: Effect</td></tr><tr><td><strong>Mytilus edulis</strong></td><td>Adult</td><td>40-50 mm</td><td>15 days</td><td>17.5 [NA, 37]</td><td>2.4 [NA, 6.3]</td><td>- x &gt; 75%: Strong effect</td></tr><tr><td><strong>Mytilopsis leucophaeata</strong></td><td>Adult</td><td>&gt;15 mm</td><td>28 days</td><td>22.4 [15, NA]</td><td>9.2 [NA, 12.3]</td><td></td></tr><tr><td><strong>Gammarus fossarum</strong></td><td>Adult</td><td>8-10 mm</td><td>7 days</td><td>31.4 [17.5, 58.2]</td><td>10.1 [NA, 18.5]</td><td></td></tr><tr><td><strong>Palaemon longirostris</strong></td><td>Adult</td><td>30-40 mm</td><td>15 days</td><td>NA</td><td>4.7 [NA, 11.4]<sup>*</sup></td><td></td></tr><tr><td><strong>Palaemon serratus</strong></td><td>Adult</td><td>55-65 mm</td><td>15 days</td><td>18.4 [8.4, 30.4]</td><td>4.7 [NA, 11.4]</td><td></td></tr><tr><td><strong>Gasterosteus aculeatus</strong></td><td>Adult</td><td>35-45 mm</td><td>21 days</td><td>Model size</td><td>7.61 [NA, 17.6]</td><td>- x ≤ 5%: No effect<br/>-5% &lt; x ≤ 20%: Effect<br/>- x &gt; 20%: Strong effect</td></tr><tr><td><strong>Platichthys flesus</strong></td><td>Juvenile</td><td>80-90 mm</td><td>15 days</td><td>38.7 [35.9, 40.5]</td><td>32.3 [NA, 33.5]</td><td>- x ≤ 40%: No effect<br/>-40% &lt; x ≤ 80%: Effect<br/>- x &gt; 80%: Strong effect</td></tr></tbody></table>

<footnote>* Reference value and thresholds obtained with *P. serratus*.</footnote>
Life cycle stage used, organism size, and exposure duration for each species are presented in Table 2. All details regarding caging conditions are provided in Table A2 of Supplementary Information. This includes information such as sex ratio (e.g., controlled or not), population origin, optimal caging period and duration, density, cage specifications, immersion depth, salinity, and hydrodynamics. The use *M. leucophaeata* and *D. polymorpha* were done with caution as both are naturalized species. The first record of *M. leucophaeata* in France was in the canal from Caen to the Channel (north-western France) in 1898 (Chemin, 1911). Regarding *D. polymorpha*, it was recorded for the first time in the Seine river in 1855 (Prié, 2023). Consequently, these species were only caged in sites where they were already present in the associated catchment.

The caging experiments were conducted during two successive years, in Autumn (2020); 2021. Test dates for each site and species are available in Supplementary Information (Table A3). This timing ensured a natural food supply and avoided periods of unfavorable climatic and hydrodynamic conditions such as heatwaves, droughts, and heavy flooding. Additionally, it fell outside the breeding period of the *G. aculeatus*, *D. polymorpha*, *M. edulis*, and *M. leucophaeata*. This last consideration holds less significance for other species, given that only juvenile *P. flesus* were used, and that crustacean model reproduction is distributed throughout the year and their sexual phenotype can be easily calibrated before the exposure.

## 2.3. Biomarker measurements

Phagocytosis is a conserved immune defense mechanism in the living (Yutin et al., 2009). The phagocytosis efficiency, defined as the proportion of cells that have internalized three or more fluorescent beads, was determined using flow cytometer analyses. The methodology was adapted from Marchand et al. (2017), Barjhoux et al. (2020), Diop et al. (2022), and Gendre et al. (2023). Genotoxic impacts were assessed by measuring the DNA integrity. It was conducted using the alkaline comet assay which enables the detection of DNA double-strand breaks, single-strand breaks, and alkali-labile sites (Singh et al., 1988). The results were expressed as the proportion of DNA located in the tail (% tail DNA), which is an indicator of DNA damage. The comet assay procedure was homogenized across the species of interest based on the methodologies described by Lacaze et al. (2011), Erraud et al. (2018b), Borcier et al. (2019), and Cant et al. (2022). Tables 3 and 4 describe the key steps involved in both procedures for each species.

## 2.4. Animal welfare

All experiments were conducted in accordance with the European directive 2010/63/EU. For *G. aculeatus*, experimental protocols were conducted at INERIS (registration number E60-769-02), were submitted and reviewed by a French nationally recognized ethical committee (CREMEAPS, registration number 96) and were approved by the French ethics committee in animal experimentation (APAFIS project number #20,760). For *P. flesus*, the study was conducted in accordance with European Commission recommendation 2007/526/EC, on revised guidelines for the accommodation and care of animals used for experimental and other scientific purposes. Both species were anesthetized using tricaine mesylate at a concentration (100 mg.L⁻¹ for *G. aculeatus* and 10 mg.L⁻¹ for *P. flesus*), and then euthanized by cervical dislocation.

## 2.5. Application of threshold values for the interpretation of biomarkers

A reference value, defined as the baseline response under “normal” conditions (including individual variability) along with induction and inhibition thresholds were established for all combinations of biomarkers and species (Table 2). These values enable the comparison of sites using different model species and eliminate the need for defining a reference site. Their determination depended on the size of the dataset and the available knowledge about biomarker variability.

For all species, only the induction of DNA damage was interpreted for the assessment of a toxicological impact and the environmental quality. It differed for the phagocytosis marker for which induction and/or inhibition of the response were considered. The interpretation of an impact on organism immunity was influenced by the dataset available for each species, leading us to consider only the inhibition of responses for *D. polymorpha* and *M. leucophaeata* and the induction of responses for *M. edulis*, due to the high dispersion of the measures and/or elevated baseline levels.

For *D. polymorpha*, *M. edulis*, and *P. serratus* the statistical method of evaluation of the reference values and associated thresholds is adapted from Besse et al. (2013) and was depicted in Leprêtre et al. (2022) and Catteau et al. (2023). Concerning *P. longirostris*, the phagocytosis efficiency lacked a sufficient dataset to establish reference values and thresholds. Nevertheless, those determined for the DNA damage biomarker in *P. serratus* were applied to *P. longirostris*, as the transferability was previously demonstrated (Erraud et al., 2019). For *G. fossarum*, the reference value, the induction threshold, and inhibition threshold were established as the median, the upper, and the lower

<table><caption>**Table 2**
Caging conditions and interpretation guidelines.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Caging conditions</th><th colspan="3">Reference value [Inhibition threshold, Induction threshold]</th></tr><tr><th>Stage</th><th>Size</th><th>Exposure duration</th><th>Phagocytosis efficiency (%)</th><th>DNA damage (% of tail DNA)</th><th>Interpretation grid (x = % of individuals exceeding the threshold)</th></tr></thead><tbody><tr><td><i>Dreissena polymorpha</i></td><td>Adult</td><td>22-25 mm</td><td>21 days</td><td>56 [41, NA]</td><td>4.2 [NA, 8.7]</td><td>- x ≤ 25%: No effect<br/>-25% &lt; x ≤ 75%: Effect</td></tr><tr><td><i>Mytilus edulis</i></td><td>Adult</td><td>40-50 mm</td><td>15 days</td><td>17.5 [NA, 37]</td><td>2.4 [NA, 6.3]</td><td>- x &gt; 75%: Strong effect</td></tr><tr><td><i>Mytilopsis leucophaeata</i></td><td>Adult</td><td>&gt;15 mm</td><td>28 days</td><td>22.4 [15, NA]</td><td>9.2 [NA, 12.3]</td><td></td></tr><tr><td><i>Gammarus fossarum</i></td><td>Adult</td><td>8-10 mm</td><td>7 days</td><td>31.4 [17.5, 58.2]</td><td>10.1 [NA, 18.5]</td><td></td></tr><tr><td><i>Palaemon longirostris</i></td><td>Adult</td><td>30-40 mm</td><td>15 days</td><td>NA</td><td>4.7 [NA, 11.4]<sup>*</sup></td><td></td></tr><tr><td><i>Palaemon serratus</i></td><td>Adult</td><td>55-65 mm</td><td>15 days</td><td>18.4 [8.4, 30.4]</td><td>4.7 [NA, 11.4]</td><td></td></tr><tr><td><i>Gasterosteus aculeatus</i></td><td>Adult</td><td>35-45 mm</td><td>21 days</td><td>Model size</td><td>7.61 [NA, 17.6]</td><td>- x ≤ 5%: No effect<br/>-5% &lt; x ≤ 20%: Effect<br/>- x &gt; 20%: Strong effect</td></tr><tr><td><i>Platichthys flesus</i></td><td>Juvenile</td><td>80-90 mm</td><td>15 days</td><td>38.7 [35.9, 40.5]</td><td>32.3 [NA, 33.5]</td><td>- x ≤ 40%: No effect<br/>-40% &lt; x ≤ 80%: Effect<br/>- x &gt; 80%: Strong effect</td></tr></tbody></table>

<footnote>* Reference value and thresholds obtained with *P. serratus*.</footnote>
<table><caption>**Table 3**
Key steps of the phagocytosis efficiency measurement.</caption><thead><tr><th></th><th><em>D. polymorpha</em></th><th><em>M. leucophaeata</em></th><th><em>M. edulis</em></th><th><em>G. fossarum</em></th><th><em>P. serratus</em></th><th><em>G. aculeatus</em></th><th><em>P. flesus</em></th></tr></thead><tbody><tr><td><strong>Cell sampling</strong></td><td>Hemocyte, Post. adductor muscle<br>7-10 ind.</td><td>Hemocyte, Post. adductor muscle<br>9-10 ind.</td><td>Hemocyte, Post. adductor muscle<br>9-20 ind.</td><td>Hemocyte, Dorsal puncture<br>4 pools of 5 ind.</td><td>Hemocyte, Cardiac puncture<br>5-13 ind.<br>PI stain<br>Flow cytometry</td><td>Leukocyte, Spleen<br>25-30 ind.</td><td>Leukocyte, Spleen<br>5-14 ind.</td></tr><tr><td><strong>Numeration and quality assessment</strong></td><td>Hoescht 33,342 and PI stains</td><td>Microscopy</td><td>Hoescht 33,342 and PI stains</td><td>Hoescht 33,342 stain<br>Fluo. microscopy</td><td>Flow cytometry</td><td>Microscopy</td><td>Microscopy</td></tr><tr><td><strong>Exposure conditions</strong></td><td>Fluo. microscopy<br>10<sup>5</sup> cells/well<br>96-well microplate<br>Beads Ø: 2 µm<br>Cell:bead = 1:50<br>4 h, 16 °C, dark</td><td>10<sup>5</sup> cells/250 µL<br>Microtube<br>Beads Ø: 1 µm<br>Cell:bead = 1:100<br>1 h, 17 °C, dark</td><td>Flow cytometry<br>10<sup>5</sup> cells/well<br>96-well microplate<br>Beads Ø: 2 µm<br>Cell:bead = 1:50<br>2 h, 15 °C, dark</td><td>Bead adjustment<br>96-well microplate<br>Beads Ø: 2 µm<br>Cell:bead = 1:200<br>4 h, 16 °C, dark</td><td>8 × 10<sup>4</sup> cells/well<br>96-well microplate<br>Beads Ø: 1 µm<br>Cell:bead = 1:250<br>2 h, 15 °C, dark<br>Double labelling: PI, beads</td><td>8 × 10<sup>4</sup> cells/well<br>96-well microplate<br>Beads Ø: 1 µm<br>Cell:bead = 1:250<br>2 h, 15 °C, dark<br>Single labelling: Beads</td><td>2 × 10<sup>5</sup> cells/well<br>96-well microplate<br>Beads Ø: 1 µm<br>Cell:bead = 1:150<br>1 h, RT, dark<br>Single labelling: Beads</td></tr><tr><td><strong>Flow cytometry analysis</strong></td><td>Triple labelling: Hoescht, PI, beads</td><td>Double labelling: Hoescht, beads</td><td>Double labelling: PI, beads</td><td>Double labelling: Propidium iodide, beads</td><td></td><td></td><td></td></tr></tbody></table>

Ind.: Individuals, PI: Propidium iodide, Fluo.: Fluorescent, Beads: Yellow-green fluorescent polystyrene microspheres.

<table><caption>**Table 4**
Key steps of the comet assay for the DNA damage measurement.</caption><thead><tr><th></th><th><em>D. polymorpha</em></th><th><em>G. fossarum</em></th><th><em>P. longirostris</em></th><th><em>G. aculeatus</em></th></tr></thead><tbody><tr><td><strong>Cell sampling</strong></td><td>Hemocyte, Post. adductor muscle<br>8-15 ind.</td><td>Spermatozoa, Testis<br>4-5 pools, 5 ind.</td><td>Spermatozoa, Spermatophore<br>8-10 ind.</td><td>Erythrocyte, Blood<sup>1</sup><br>5-30 individuals</td></tr><tr><td><strong>Cell density and viability</strong></td><td>6 × 10<sup>5-6</sup> cells/mL<br>Survival rate threshold: ≥85%</td><td></td><td></td><td></td></tr><tr><td><strong>Inclusion</strong></td><td>0.5% low-melting point agarose-type VII in PBS (10 mM pH 7.4, 37 °C) on microscope slide</td><td></td><td></td><td></td></tr><tr><td><strong>Lysis</strong></td><td>2.5 M NaCl, 100 mM Na<sub>2</sub>EDTA, 10 mM Tris, 1% Triton X-100, 10% DMSO, pH 10<br>1 h</td><td>18 h</td><td>1 h</td><td></td></tr><tr><td><strong>Denaturation</strong></td><td>300 mM NaOH, 1 mM Na<sub>2</sub>EDTA, pH > 13, 30 min</td><td></td><td></td><td></td></tr><tr><td><strong>Electrophoresis</strong></td><td>300 mA, 20 V, 24 min, Standard Comet assay tank (20 slides)</td><td></td><td></td><td></td></tr><tr><td><strong>Neutralization</strong></td><td>0.4 mM Tris, pH > 7.5, 2 × 10<sup>6</sup> min</td><td></td><td></td><td></td></tr><tr><td><strong>Dehydration</strong></td><td>100% EtOH, 5 min</td><td></td><td></td><td></td></tr><tr><td><strong>Coloration</strong></td><td>SYBR green (1X), 5 min</td><td></td><td></td><td></td></tr><tr><td><strong>Quantification</strong></td><td>Image acquisition: Fluo. Microscopy<br>Scoring: 100 nucleoids/gel (Comet assay IV software, Perceptive instrument, Suffolk, UK)</td><td></td><td></td><td></td></tr></tbody></table>

quartiles, respectively, of distributions derived from results obtained with 4 pools of 5 individuals each from 20 contrasted study sites for phagocytosis efficiency and 10 pools of 3 individuals from a control condition for DNA damage. For *G. aculeatus*, a linear model was previously developed to obtain reference values and thresholds for the phagocytosis efficiency according to the organism size and sex (Marchand et al., 2019). Finally, the reference value for *P. flesus* was determined as the mean of the results obtained from caged organisms in control conditions at three distinct periods, and the thresholds were set as 95% confidence interval.

Table 2 provides an overview of the interpretation guidelines established for all biomarker-species pairs. These guidelines were defined according to statistical approaches and expert statements, taking into consideration variations in methods and species. Their purpose is to ensure clarity for a non-specialist audience and to be used by stakeholders. Three distinct levels were established: “no effect”, “effect”, and “strong effect” based on the number of individuals outside the thresholds. The numbers of individuals used to define levels are provided in Tables A.4-A11. To determine the environmental quality of a study site, the precautionary approach known as the “one out–all out” strategy, similar to the approach in the WFD, was employed. In the present work, this implies that the worst level determined by any biomarker-species pairs defined the overall quality assessment of the site.

# 3. Results & discussion

## 3.1. Methodological readiness

First, the results highlighted that biomonitoring using biomarkers measured in caged species at the continuum scale is technically feasible. In fact, only 7 of the 131 (5.3%) cages deployed during the experimental campaigns were lost due to either siltation, water level drop, or degradation by other site users (Table 5). This represents a low percentage and did not affect the overall implementation of all the monitoring program. The survival rates of organisms were also satisfactory for all species during the two campaigns (Table 5, *D. polymorpha*: 93.8 ± 5.4%, *M. leucophaeata*: 89.8 ± 16.9%, *M. edulis*: 90.9 ± 8%, *G. fossarum*: 84.3 ± 6.6%, *P. longirostris*: 78.4 ± 7.7%, *P. serratus*: 59.8 ± 11.5%, *G. aculeatus*: 94 ± 9.3%, and *P. flesus*: 84.7 ± 16.3%, mean ± SD). The lower survival rates observed for *P. longirostris* and *P. serratus* could be attributed to cannibalistic behavior within the cage, despite the inclusion of refuges such as polyvinyl chloride modules and grid substrates. In the future, modifications of caging conditions for these species could be implemented by individualizing the organisms. A very low survival rate of 38% was observed for *M. leucophaeata* placed at OR4 in 2021 thus, biomarker measurements from this specific condition were excluded from the subsequent analysis. In contrast, all other cages containing this species exhibited notably higher rates (≥85.1%).

<footnote n="1">¹ Cryopreserved and rapidly defrosted (37 °C, 18 s) prior to the analysis, Ind.: Individuals, Fluo.: Fluorescent.</footnote>
<table><caption>**Table 3**
Key steps of the phagocytosis efficiency measurement.</caption><thead><tr><th></th><th><i>D. polymorpha</i></th><th><i>M. leucophaeata</i></th><th><i>M. edulis</i></th><th><i>G. fossarum</i></th><th><i>P. serratus</i></th><th><i>G. aculeatus</i></th><th><i>P. flesus</i></th></tr></thead><tbody><tr><td><strong>Cell sampling</strong></td><td>Hemocyte, Post. adductor muscle 7-10 ind.</td><td>Hemocyte, Post. adductor muscle 9-10 ind.</td><td>Hemocyte, Post. adductor muscle 9-20 ind.</td><td>Hemocyte, Dorsal puncture 4 pools of 5 ind.</td><td>Hemocyte, Cardiac puncture 5-13 ind. PI stain Flow cytometry</td><td>Leukocyte, Spleen 25-30 ind.</td><td>Leukocyte, Spleen 5-14 ind.</td></tr><tr><td><strong>Numeration and quality assessment</strong></td><td>Hoescht 33,342 and PI stains</td><td>Microscopy</td><td>Hoescht 33,342 and PI stains</td><td>Hoescht 33,342 stain Fluo. microscopy</td><td></td><td>Microscopy</td><td>Microscopy</td></tr><tr><td><strong>Exposure conditions</strong></td><td>Fluo. microscopy 10<sup>5</sup> cells/well 96-well microplate Beads Ø: 2 µm Cell:bead = 1:50 4 h, 16 °C, dark</td><td>10<sup>5</sup> cells/250 µL Microtube Beads Ø: 1 µm Cell:bead = 1:100 1 h, 17 °C, dark</td><td>Flow cytometry 10<sup>5</sup> cells/well 96-well microplate Beads Ø: 2 µm Cell:bead = 1:50 2 h, 15 °C, dark</td><td>Bead adjustment 96-well microplate Beads Ø: 2 µm Cell:bead = 1:200 4 h, 16 °C, dark</td><td>8 × 10<sup>4</sup> cells/well 96-well microplate Beads Ø: 1 µm Cell:bead = 1:250 2 h, 15 °C, dark Double labelling: PI, beads</td><td>8 × 10<sup>4</sup> cells/well 96-well microplate Beads Ø: 1 µm Cell:bead = 1:250 2 h, 15 °C, dark Single labelling: Beads</td><td>2 × 10<sup>5</sup> cells/well 96-well microplate Beads Ø: 1 µm Cell:bead = 1:150 1 h, RT, dark Single labelling: Beads</td></tr><tr><td><strong>Flow cytometry analysis</strong></td><td>Triple labelling: Hoescht, PI, beads</td><td>Double labelling: Hoescht, beads</td><td>Double labelling: PI, beads</td><td>Double labelling: Propidium iodide, beads</td><td></td><td></td><td></td></tr></tbody></table>

Ind.: Individuals, PI: Propidium iodide, Fluo.: Fluorescent, Beads: Yellow-green fluorescent polystyrene microspheres.

<table><caption>**Table 4**
Key steps of the comet assay for the DNA damage measurement.</caption><thead><tr><th></th><th><i>D. polymorpha</i><br><i>M. leucophaeata</i><br><i>M. edulis</i></th><th><i>G. fossarum</i></th><th><i>P. longirostris</i><br><i>P. serratus</i></th><th><i>G. aculeatus</i><br><i>P. flesus</i></th></tr></thead><tbody><tr><td><strong>Cell sampling</strong></td><td>Hemocyte, Post. adductor muscle 8-15 ind.</td><td>Spermatozoa, Testis 4-5 pools, 5 ind.</td><td>Spermatozoa, Spermatophore 8-10 ind.</td><td>Erythrocyte, Blood<sup>1</sup> 5-30 individuals</td></tr><tr><td><strong>Cell density and viability</strong></td><td>6 × 10<sup>5-6</sup> cells/mL<br>Survival rate threshold: ≥85%</td><td></td><td></td><td></td></tr><tr><td><strong>Inclusion</strong></td><td>0.5% low-melting point agarose-type VII in PBS (10 mM pH 7.4, 37 °C) on microscope slide</td><td></td><td></td><td></td></tr><tr><td><strong>Lysis</strong></td><td>2.5 M NaCl, 100 mM Na<sub>2</sub>EDTA, 10 mM Tris, 1% Triton X-100, 10% DMSO, pH 10 1 h</td><td>18 h</td><td>1 h</td><td></td></tr><tr><td><strong>Denaturation</strong></td><td>300 mM NaOH, 1 mM Na<sub>2</sub>EDTA, pH > 13, 30 min</td><td></td><td></td><td></td></tr><tr><td><strong>Electrophoresis</strong></td><td>300 mA, 20 V, 24 min, Standard Comet assay tank (20 slides)</td><td></td><td></td><td></td></tr><tr><td><strong>Neutralization</strong></td><td>0.4 mM Tris, pH > 7.5, 2 × 10<sup>6</sup> min</td><td></td><td></td><td></td></tr><tr><td><strong>Dehydration</strong></td><td>100% EtOH, 5 min</td><td></td><td></td><td></td></tr><tr><td><strong>Coloration</strong></td><td>SYBR green (1X), 5 min</td><td></td><td></td><td></td></tr><tr><td><strong>Quantification</strong></td><td>Image acquisition: Fluo. Microscopy<br>Scoring: 100 nucleoids/gel (Comet assay IV software, Perceptive instrument, Suffolk, UK)</td><td></td><td></td><td></td></tr></tbody></table>

quartiles, respectively, of distributions derived from results obtained with 4 pools of 5 individuals each from 20 contrasted study sites for phagocytosis efficiency and 10 pools of 3 individuals from a control condition for DNA damage. For *G. aculeatus*, a linear model was previously developed to obtain reference values and thresholds for the phagocytosis efficiency according to the organism size and sex (Marchand et al., 2019). Finally, the reference value for *P. flesus* was determined as the mean of the results obtained from caged organisms in control conditions at three distinct periods, and the thresholds were set as 95% confidence interval.

Table 2 provides an overview of the interpretation guidelines established for all biomarker-species pairs. These guidelines were defined according to statistical approaches and expert statements, taking into consideration variations in methods and species. Their purpose is to ensure clarity for a non-specialist audience and to be used by stakeholders. Three distinct levels were established: “no effect”, “effect”, and “strong effect” based on the number of individuals outside the thresholds. The numbers of individuals used to define levels are provided in Tables A.4-A11. To determine the environmental quality of a study site, the precautionary approach known as the “one out–all out” strategy, similar to the approach in the WFD, was employed. In the present work, this implies that the worst level determined by any biomarker-species pairs defined the overall quality assessment of the site.

# 3. Results & discussion

## 3.1. Methodological readiness

First, the results highlighted that biomonitoring using biomarkers measured in caged species at the continuum scale is technically feasible. In fact, only 7 of the 131 (5.3%) cages deployed during the experimental campaigns were lost due to either siltation, water level drop, or degradation by other site users (Table 5). This represents a low percentage and did not affect the overall implementation of all the monitoring program. The survival rates of organisms were also satisfactory for all species during the two campaigns (Table 5, *D. polymorpha*: 93.8 ± 5.4%, *M. leucophaeata*: 89.8 ± 16.9%, *M. edulis*: 90.9 ± 8%, *G. fossarum*: 84.3 ± 6.6%, *P. longirostris*: 78.4 ± 7.7%, *P. serratus*: 59.8 ± 11.5%, *G. aculeatus*: 94 ± 9.3%, and *P. flesus*: 84.7 ± 16.3%, mean ± SD). The lower survival rates observed for *P. longirostris* and *P. serratus* could be attributed to cannibalistic behavior within the cage, despite the inclusion of refuges such as polyvinyl chloride modules and grid substrates. In the future, modifications of caging conditions for these species could be implemented by individualizing the organisms. A very low survival rate of 38% was observed for *M. leucophaeata* placed at OR4 in 2021 thus, biomarker measurements from this specific condition were excluded from the subsequent analysis. In contrast, all other cages containing this species exhibited notably higher rates (≥85.1%).

<footnote n="1">¹ Cryopreserved and rapidly defrosted (37 °C, 18 s) prior to the analysis, Ind.: Individuals, Fluo.: Fluorescent.</footnote>
<table><caption>**Table 5**
Survival rates (%) and cage recoveries per site during both campaign years.</caption><thead><tr><th rowspan="2">ID</th><th colspan="2">D. polymorpha</th><th colspan="2">M. leucophaeata</th><th colspan="2">M. edulis</th><th colspan="2">G. fossarum</th><th colspan="2">P. longirostris</th><th colspan="2">P. serratus</th><th colspan="2">G. aculeatus</th><th colspan="2">P. flesus</th></tr><tr><th>2020</th><th>2021</th><th>2020</th><th>2021</th><th>2020</th><th>2021</th><th>2020</th><th>2021</th><th>2020</th><th>2021</th><th>2020</th><th>2021</th><th>2020</th><th>2021</th><th>2020</th><th>2021</th></tr></thead><tbody><tr><td>SE1</td><td>98.3</td><td>91</td><td>-</td><td>-</td><td>-</td><td>-</td><td>68.6</td><td>80</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SE2</td><td>99.2</td><td>88.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>76.4</td><td>87.8</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SE3</td><td>99.2</td><td>83.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>83.6</td><td>89.4</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SE4</td><td>99.2</td><td>Loss</td><td>-</td><td>-</td><td>-</td><td>-</td><td>88.6</td><td>82.2</td><td>-</td><td>-</td><td>-</td><td>-</td><td>96</td><td>93</td><td>-</td><td>-</td></tr><tr><td>SE5</td><td>Loss</td><td>89</td><td>-</td><td>-</td><td>-</td><td>-</td><td>87.1</td><td>92.8</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Loss</td><td>Loss</td><td>-</td><td>-</td></tr><tr><td>SE6</td><td>99.2</td><td>91</td><td>-</td><td>-</td><td>-</td><td>-</td><td>85.7</td><td>85.6</td><td>-</td><td>-</td><td>-</td><td>-</td><td>100</td><td>100</td><td>-</td><td>-</td></tr><tr><td>SE7</td><td>97.5</td><td>91.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>85.7</td><td>86.7</td><td>-</td><td>-</td><td>-</td><td>-</td><td>100</td><td>100</td><td>-</td><td>-</td></tr><tr><td>SE8</td><td>99.2</td><td>92</td><td>-</td><td>-</td><td>-</td><td>-</td><td>82.1</td><td>91.1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SE9</td><td>96.7</td><td>82</td><td>-</td><td>-</td><td>-</td><td>-</td><td>81.4</td><td>76.7</td><td>Loss</td><td>75.6</td><td>-</td><td>-</td><td>73</td><td>90</td><td>93.3</td><td>93.3</td></tr><tr><td>SE10</td><td>-</td><td>-</td><td>-</td><td>-</td><td>95</td><td>82.5</td><td>-</td><td>-</td><td>Loss</td><td>Loss</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SE11</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>85</td><td>77.5</td><td>-</td><td>-</td><td>-</td><td>46</td><td>42</td><td>-</td><td>-</td><td>93.3</td><td>53.3</td></tr><tr><td>SE12</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>56</td><td>64</td><td>-</td><td>-</td><td>100</td><td>66.7</td></tr><tr><td>OR1</td><td>98.3</td><td>92</td><td>97.7</td><td>93.5</td><td>-</td><td>-</td><td>82.9</td><td>90.6</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>OR2</td><td>99.2</td><td>87.5</td><td>95.5</td><td>93.5</td><td>-</td><td>-</td><td>80</td><td>91.1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>OR3</td><td>-</td><td>-</td><td>96.7</td><td>89.2</td><td>-</td><td>-</td><td>-</td><td>-</td><td>72.5</td><td>87.2</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>OR4</td><td>-</td><td>-</td><td>85.1</td><td>38</td><td>82.5</td><td>75</td><td>-</td><td>-</td><td>-</td><td>-</td><td>58</td><td>62</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>OR5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>92.5</td><td>87</td><td>-</td><td>-</td><td>-</td><td>-</td><td>50</td><td>52</td><td>-</td><td>-</td><td>100</td><td>66.7</td></tr><tr><td>VI1</td><td>95</td><td>94</td><td>100</td><td>91.9</td><td>-</td><td>-</td><td>71.4</td><td>95.6</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>VI2</td><td>-</td><td>-</td><td>100</td><td>97</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>VI3</td><td>-</td><td>-</td><td>-</td><td>-</td><td>100</td><td>82.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>60</td><td>54</td><td>-</td><td>-</td><td>93.3</td><td>86.7</td></tr><tr><td>CO1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>97.5</td><td>100</td><td>-</td><td>-</td><td>-</td><td>-</td><td>39</td><td>83</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>CO2</td><td>-</td><td>-</td><td>-</td><td>-</td><td>95</td><td>97.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>77</td><td>66</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>CO3</td><td>-</td><td>-</td><td>-</td><td>-</td><td>95</td><td>89.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>73</td><td>60</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>CO4</td><td>-</td><td>-</td><td>-</td><td>-</td><td>95</td><td>100</td><td>-</td><td>-</td><td>-</td><td>-</td><td>63</td><td>74</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>CO5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>97.4</td><td>-</td><td>-</td><td>-</td><td>-</td><td>53</td><td>64</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>

## 3.2. Biomarker interpretation

### 3.2.1. Geographical pattern

Overall, even if the sites were not selected to represent a gradient of anthropogenic pressures, an improvement of the environmental quality was observed in downstream sites across all continua and in the littoral areas (Table 6 & A.4-A.11). This improvement is particularly noticeable in taxa deployed all along the continua (i.e., mollusks and crustaceans) whether in 2020 or in 2021. It could be attributed to a potential dilution effect, suggesting lower-contamination levels. In the case of the Seine River, this observation is confirmed by other studies. Dendievel et al.

(2022, 2020) showed that the sediment contamination with PCB and metals increases with the strong influence of anthropic activities occurring in Paris and Rouen, but then decreases towards the estuary. In contrast, this trend was not confirmed in the sites where *P. flesus* was caged, as a “strong effect” level was principally identified in the respective sites (discussed in Section 3.2.3).

### 3.2.2. The benefit of studying two biomarkers

The use of different biomarkers enables the integration of various modes of action of contaminants, thereby enhancing the assessment of environmental quality (Catteau et al., 2022; Slaby et al., 2023; Yancheva

<caption>**Table 6**</caption>

Quality status determined using the biomarker approach (phagocytosis efficiency or DNA damage) according to species in 2020 and 2021. Sites are presented from upstream to downstream for the Seine, Orne, and Vire continua.

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

PE: Phagocytosis efficiency, DD: DNA damage, NA: Not analyzed.
<table><caption>**Table 5**
Survival rates (%) and cage recoveries per site during both campaign years.</caption><thead><tr><th rowspan="2">ID</th><th colspan="2">D. polymorpha</th><th colspan="2">M. leucophaeata</th><th colspan="2">M. edulis</th><th colspan="2">G. fossarum</th><th colspan="2">P. longirostris</th><th colspan="2">P. serratus</th><th colspan="2">G. aculeatus</th><th colspan="2">P. flesus</th></tr><tr><th>2020</th><th>2021</th><th>2020</th><th>2021</th><th>2020</th><th>2021</th><th>2020</th><th>2021</th><th>2020</th><th>2021</th><th>2020</th><th>2021</th><th>2020</th><th>2021</th><th>2020</th><th>2021</th></tr></thead><tbody><tr><td>SE1</td><td>98.3</td><td>91</td><td>-</td><td>-</td><td>-</td><td>-</td><td>68.6</td><td>80</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SE2</td><td>99.2</td><td>88.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>76.4</td><td>87.8</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SE3</td><td>99.2</td><td>83.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>83.6</td><td>89.4</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SE4</td><td>99.2</td><td>Loss</td><td>-</td><td>-</td><td>-</td><td>-</td><td>88.6</td><td>82.2</td><td>-</td><td>-</td><td>-</td><td>-</td><td>96</td><td>93</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SE5</td><td>Loss</td><td>89</td><td>-</td><td>-</td><td>-</td><td>-</td><td>87.1</td><td>92.8</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Loss</td><td>Loss</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SE6</td><td>99.2</td><td>91</td><td>-</td><td>-</td><td>-</td><td>-</td><td>85.7</td><td>85.6</td><td>-</td><td>-</td><td>-</td><td>-</td><td>100</td><td>100</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SE7</td><td>97.5</td><td>91.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>85.7</td><td>86.7</td><td>-</td><td>-</td><td>-</td><td>-</td><td>100</td><td>100</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SE8</td><td>99.2</td><td>92</td><td>-</td><td>-</td><td>-</td><td>-</td><td>82.1</td><td>91.1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SE9</td><td>96.7</td><td>82</td><td>-</td><td>-</td><td>-</td><td>-</td><td>81.4</td><td>76.7</td><td>Loss</td><td>75.6</td><td>-</td><td>-</td><td>73</td><td>90</td><td>93.3</td><td>93.3</td><td>-</td></tr><tr><td>SE10</td><td>-</td><td>-</td><td>-</td><td>-</td><td>95</td><td>82.5</td><td>-</td><td>-</td><td>Loss</td><td>Loss</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SE11</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>85</td><td>77.5</td><td>-</td><td>-</td><td>-</td><td>46</td><td>42</td><td>-</td><td>-</td><td>93.3</td><td>53.3</td><td>-</td></tr><tr><td>SE12</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>56</td><td>64</td><td>-</td><td>-</td><td>100</td><td>66.7</td><td>-</td></tr><tr><td>OR1</td><td>98.3</td><td>92</td><td>97.7</td><td>93.5</td><td>-</td><td>-</td><td>82.9</td><td>90.6</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>OR2</td><td>99.2</td><td>87.5</td><td>95.5</td><td>93.5</td><td>-</td><td>-</td><td>80</td><td>91.1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>OR3</td><td>-</td><td>-</td><td>96.7</td><td>89.2</td><td>-</td><td>-</td><td>-</td><td>-</td><td>72.5</td><td>87.2</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>OR4</td><td>-</td><td>-</td><td>85.1</td><td>38</td><td>82.5</td><td>75</td><td>-</td><td>-</td><td>-</td><td>-</td><td>58</td><td>62</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>OR5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>92.5</td><td>87</td><td>-</td><td>-</td><td>-</td><td>-</td><td>50</td><td>52</td><td>-</td><td>-</td><td>100</td><td>66.7</td><td>-</td></tr><tr><td>VI1</td><td>95</td><td>94</td><td>100</td><td>91.9</td><td>-</td><td>-</td><td>71.4</td><td>95.6</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>VI2</td><td>-</td><td>-</td><td>100</td><td>97</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>VI3</td><td>-</td><td>-</td><td>-</td><td>-</td><td>100</td><td>82.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>60</td><td>54</td><td>-</td><td>-</td><td>93.3</td><td>86.7</td><td>-</td></tr><tr><td>CO1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>97.5</td><td>100</td><td>-</td><td>-</td><td>-</td><td>-</td><td>39</td><td>83</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>CO2</td><td>-</td><td>-</td><td>-</td><td>-</td><td>95</td><td>97.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>77</td><td>66</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>CO3</td><td>-</td><td>-</td><td>-</td><td>-</td><td>95</td><td>89.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>73</td><td>60</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>CO4</td><td>-</td><td>-</td><td>-</td><td>-</td><td>95</td><td>100</td><td>-</td><td>-</td><td>-</td><td>-</td><td>63</td><td>74</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>CO5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>97.4</td><td>-</td><td>-</td><td>-</td><td>-</td><td>53</td><td>64</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>

## 3.2. Biomarker interpretation

### 3.2.1. Geographical pattern

Overall, even if the sites were not selected to represent a gradient of anthropogenic pressures, an improvement of the environmental quality was observed in downstream sites across all continua and in the littoral areas (Table 6 & A.4-A.11). This improvement is particularly noticeable in taxa deployed all along the continua (i.e., mollusks and crustaceans) whether in 2020 or in 2021. It could be attributed to a potential dilution effect, suggesting lower-contamination levels. In the case of the Seine River, this observation is confirmed by other studies. Dendievel et al.

(2022, 2020) showed that the sediment contamination with PCB and metals increases with the strong influence of anthropic activities occurring in Paris and Rouen, but then decreases towards the estuary. In contrast, this trend was not confirmed in the sites where *P. flesus* was caged, as a “strong effect” level was principally identified in the respective sites (discussed in Section 3.2.3).

### 3.2.2. The benefit of studying two biomarkers

The use of different biomarkers enables the integration of various modes of action of contaminants, thereby enhancing the assessment of environmental quality (Catteau et al., 2022; Slaby et al., 2023; Yancheva

<caption>**Table 6**</caption>

Quality status determined using the biomarker approach (phagocytosis efficiency or DNA damage) according to species in 2020 and 2021. Sites are presented from upstream to downstream for the Seine, Orne, and Vire continua.

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

PE: Phagocytosis efficiency, DD: DNA damage, NA: Not analyzed.
et al., 2022). In this work, a degraded status determined by one biomarker was often associated with a degraded status by the second biomarker. In fact, the determined quality level was consistent between both biomarker analyses for a single species in 75.1 ± 20.2% of sites (“no effect” vs. “effect” and “strong effect”). Nevertheless, interspecies variations were observed. Both biomarker analyses derived either an “effect” or “strong effect” levels in 78.8 ± 8.6% of sites for *D. polymorpha*, 51.7 ± 22.4% of sites for *G. fossarum*, 56.7 ± 16.5% of sites for *M. leucophaeata*, 87.5 ± 8.8% of sites for *G. aculeatus*, and 87.5 ± 8.8% of sites for *P. flesus* (across the two campaign periods). For *P. serratus* and *M. edulis*, it never happened (0 ± 0% of sites) because, as shown in Table 6, the majority of the concerned sites were classified with a “no effect” level by both biomarker analyses in 2020 and 2021 (74.6 ± 2.2% and 81.9 ± 4.9%, respectively).

When the effect levels were different between both biomarker ana-
lyses in a species, DNA damage analyses led to a worst evaluation in
66.7% of sites. As approximately one-third of environmental contami-
nants are classified as genotoxic (Claxton et al., 1998), this result could
be expected. Nevertheless, it does not suggest that DNA damage is more
suitable to detect environmental contamination as in several cases the
phagocytosis efficiency analyses led to an “effect” or a “strong effect”
level while the genotoxicity biomarker reported a “no effect” level.

3.2.3. *Implementation of several species per site*

Considering multiple species when using biomarkers to assess envi-
ronmental quality is of major importance. As demonstrated by Catteau
et al. (2022), including various species in biomarker approaches
enhanced the accuracy of pollution assessments. In the present study,
variations were observed between species at a specific site during a
single caging period (Table 6 and A4-A.11). Indeed, the species involved
in this study had distinct physiological and behavioral characteristics,
leading to differences in bioavailability and sensitivity to contaminants.
Notably, they do not occupy the same level in the food chain. Thus, the
responses obtained with the different species can either corroborate the
findings or provide additional information about the contamination.

Herein, the assigned quality levels differed between species for
52.4% and 68.4% of sites for the phagocytosis efficiency measurements
and for 63.2% and 54.5% of sites for the DNA damage measurements in
2020 and 2021, respectively. However, as the available knowledge
regarding biomarker modulations and the type of dataset used to
determine reference values and thresholds were not consistent across all
species, the comparison between species caged at one site during the
same period should be done with caution. In this study, certain species
require the acquisition of more extensive datasets to enhance the
interpretation grid in the present study. Herein, it seemed more appro-
priate to compare the changes in environmental quality among species
over geographical or temporal scales. By doing so, the use of multiple
species enabled us to identify trends (Table 6). For example, improve-
ments in the gradation using the biomarker of genotoxicity were high-
lighted between 2020 and 2021 for all concerned species at SE12. The
same observation can be made at SE9 except for *D. polymorpha* for which
a “strong effect” level was determined in 2020 and 2021. This difference
can be attributed to the specific characteristics of *D. polymorpha* (sessile
and filter-feeding organisms) compared to *G. fossarum*, *P. longirostris*,
*G. aculeatus*, and *P. flesus*.

As previously mentioned in Section 3.2.1, results from mollusk and
crustacean species corroborate the improvement in environmental
quality in the water column at downstream and littoral sites. Interest-
ingly, this observation is contrasted by results obtained with *P. flesus*,
potentially providing valuable insights to refine the assessment of
environmental quality. This difference can be explained by the distinct
ecological traits of *P. flesus*, which need to be caged in another envi-
ronmental compartment—directly in contact with the sediment (Borcier
et al., 2019; Diop et al., 2022), unlike *M. edulis* and *P. serratus*, which
need to be caged in the water column (Erraud et al., 2018a; Hagger et al.,
2008). Moreover, these species do not occupy the same trophic level. In

consequence, they may not be exposed to the same bioavailable fraction
of contaminants.

In continental and transitional water sites (SE1-9, OR1-4, VI1-2), a
degradation of the environmental quality, as determined by the immu-
notoxic biomarker, was generally observed across all concerned species
between 2020 and 2021 (M. edulis, P. serratus, and P. flesus not consid-
ered as they were deployed in only one transitional water site each,
Table 6). It is particularly noteworthy that for D. polymorpha, all sites
with an “no effect” level in 2020 were downgraded in 2021. In the other
sites, we did not determine a modification in the environmental quality
between 2020 and 2021, except at SE7, which experienced an
improvement. However, the opposite was determined for SE7 using
G. aculeatus. The same observation can be made for M. leucophaeata.
With that species, 60% of sites had a worse quality in 2021 compared to
2020, and 40% remained at the same level (“effect” or “strong effect”).
Interestingly, this degradation trend assessed by the immunotoxic
biomarker can be mitigated by the evaluation using G. fossarum. In fact,
even though 42.9% of sites were downgraded between 2020 and 2021,
we observed an improvement in environmental quality also in 42.9% of
sites (including SE1 and OR1 for which a degradation was determined
using both mollusk species). It confirms the usefulness of deploying
different species, which may not respond to the contamination exposure
in the same manner.

3.2.4. *Comparison with WFD assessment*

The biomarker analysis outcomes were compared to the chemical
and ecological assessments determined under the Water Framework
Directive (WFD). This comparison aimed to determine whether our
findings aligned with regulatory environmental assessments or if they
provided complementary information.

As shown in Table 7, the overall analysis in 2020 of the biomarker
results indicated a "no effect" level at CO sites and an "effect" level at OR4
and VI2. In 2021, a "no effect" level was observed at CO1 and CO3-5,
while an "effect" level was determined at OR4 and CO2. For all other
sites, a "strong effect" level was consistently observed in both 2020 and
2021, accounting for 70.8% of sites in 2020 and 75% in 2021.

First, a strong coherence between the WFD statuses and the
biomarker outcomes was observed. Among the sites which was aligned
fort both the WFD chemical and ecological status, the biomarker ana-
lyses performed in 2020 and 2021 confirmed the status in 78.6% of cases
(i.e., 11 sites for a total of 14: SE3, SE6, SE7, SE9, SE11, OR1, OR2, VI1,
VI2, VI3, and CO5, Table 7). These sites did not meet the criteria for
good status, with the exception of CO5. Regarding SE8, both chemical
and ecological assessments indicated good environmental status, which
contrast with the results of the biomarker analysis in 2020 and 2021.
This site was located within the WFD surface water body FRHR261 (Eure
River at its confluence with the Seine River), the latter was selected as
the reference station. However, it is worth noting that the exact location
of the WFD sampling was notably upstream of SE8 (data not shown),
which may also be subject to tidal influences. In consequence, SE8 could
be integrated to the WFD surface water body FRHT01 (upper Seine es-
tuary as SE9). In this case, the results of the biomarker evaluation were
more consistent with the WFD outcomes (fail to achieve a good status).
At CO1, the biomarker-based methodology indicated a "no effect" level,
which was in line with the majority of CO sites (2020 and 2021).
However, these assessments did not align with WFD approaches. In
terms of the chemical aspect, CO1 did not achieve a good status due to
the presence of compounds classified as ubiquitous, persistent, bio-
accumulative, and toxic (uPBT), as indicated in Table 1: PCB 118. The
exposure to this compound among present contaminants or their
bioavailable fraction was not associated with the genotoxic and immu-
notoxic responses under studied, which were not specific to this
particular contaminant. It is possible that the substances did not affect
the biological responses investigated in this work or that they were not
bioavailable for the concerned species. The use of additional biomarkers
to encompass a wide range of biological functions could be
et al., 2022). In this work, a degraded status determined by one biomarker was often associated with a degraded status by the second biomarker. In fact, the determined quality level was consistent between both biomarker analyses for a single species in 75.1 ± 20.2% of sites (“no effect” vs. “effect” and “strong effect”). Nevertheless, interspecies variations were observed. Both biomarker analyses derived either an “effect” or “strong effect” levels in 78.8 ± 8.6% of sites for *D. polymorpha*, 51.7 ± 22.4% of sites for *G. fossarum*, 56.7 ± 16.5% of sites for *M. leucophaeata*, 87.5 ± 8.8% of sites for *G. aculeatus*, and 87.5 ± 8.8% of sites for *P. flesus* (across the two campaign periods). For *P. serratus* and *M. edulis*, it never happened (0 ± 0% of sites) because, as shown in Table 6, the majority of the concerned sites were classified with a “no effect” level by both biomarker analyses in 2020 and 2021 (74.6 ± 2.2% and 81.9 ± 4.9%, respectively).

When the effect levels were different between both biomarker ana-
lyses in a species, DNA damage analyses led to a worst evaluation in
66.7% of sites. As approximately one-third of environmental contami-
nants are classified as genotoxic (Claxton et al., 1998), this result could
be expected. Nevertheless, it does not suggest that DNA damage is more
suitable to detect environmental contamination as in several cases the
phagocytosis efficiency analyses led to an “effect” or a “strong effect”
level while the genotoxicity biomarker reported a “no effect” level.

3.2.3. *Implementation of several species per site*

Considering multiple species when using biomarkers to assess envi-
ronmental quality is of major importance. As demonstrated by Catteau
et al. (2022), including various species in biomarker approaches
enhanced the accuracy of pollution assessments. In the present study,
variations were observed between species at a specific site during a
single caging period (Table 6 and A4-A.11). Indeed, the species involved
in this study had distinct physiological and behavioral characteristics,
leading to differences in bioavailability and sensitivity to contaminants.
Notably, they do not occupy the same level in the food chain. Thus, the
responses obtained with the different species can either corroborate the
findings or provide additional information about the contamination.

Herein, the assigned quality levels differed between species for
52.4% and 68.4% of sites for the phagocytosis efficiency measurements
and for 63.2% and 54.5% of sites for the DNA damage measurements in
2020 and 2021, respectively. However, as the available knowledge
regarding biomarker modulations and the type of dataset used to
determine reference values and thresholds were not consistent across all
species, the comparison between species caged at one site during the
same period should be done with caution. In this study, certain species
require the acquisition of more extensive datasets to enhance the
interpretation grid in the present study. Herein, it seemed more appro-
priate to compare the changes in environmental quality among species
over geographical or temporal scales. By doing so, the use of multiple
species enabled us to identify trends (Table 6). For example, improve-
ments in the gradation using the biomarker of genotoxicity were high-
lighted between 2020 and 2021 for all concerned species at SE12. The
same observation can be made at SE9 except for *D. polymorpha* for which
a “strong effect” level was determined in 2020 and 2021. This difference
can be attributed to the specific characteristics of *D. polymorpha* (sessile
and filter-feeding organisms) compared to *G. fossarum*, *P. longirostris*,
*G. aculeatus*, and *P. flesus*.

As previously mentioned in Section 3.2.1, results from mollusk and
crustacean species corroborate the improvement in environmental
quality in the water column at downstream and littoral sites. Interest-
ingly, this observation is contrasted by results obtained with *P. flesus*,
potentially providing valuable insights to refine the assessment of
environmental quality. This difference can be explained by the distinct
ecological traits of *P. flesus*, which need to be caged in another envi-
ronmental compartment—directly in contact with the sediment (Borcier
et al., 2019; Diop et al., 2022), unlike *M. edulis* and *P. serratus*, which
need to be caged in the water column (Erraud et al., 2018a; Hagger et al.,
2008). Moreover, these species do not occupy the same trophic level. In

consequence, they may not be exposed to the same bioavailable fraction
of contaminants.

In continental and transitional water sites (SE1-9, OR1-4, VI1-2), a
degradation of the environmental quality, as determined by the immu-
notoxic biomarker, was generally observed across all concerned species
between 2020 and 2021 (M. edulis, P. serratus, and P. flesus not consid-
ered as they were deployed in only one transitional water site each,
Table 6). It is particularly noteworthy that for D. polymorpha, all sites
with an “no effect” level in 2020 were downgraded in 2021. In the other
sites, we did not determine a modification in the environmental quality
between 2020 and 2021, except at SE7, which experienced an
improvement. However, the opposite was determined for SE7 using
G. aculeatus. The same observation can be made for M. leucophaeata.
With that species, 60% of sites had a worse quality in 2021 compared to
2020, and 40% remained at the same level (“effect” or “strong effect”).
Interestingly, this degradation trend assessed by the immunotoxic
biomarker can be mitigated by the evaluation using G. fossarum. In fact,
even though 42.9% of sites were downgraded between 2020 and 2021,
we observed an improvement in environmental quality also in 42.9% of
sites (including SE1 and OR1 for which a degradation was determined
using both mollusk species). It confirms the usefulness of deploying
different species, which may not respond to the contamination exposure
in the same manner.

3.2.4. *Comparison with WFD assessment*

The biomarker analysis outcomes were compared to the chemical
and ecological assessments determined under the Water Framework
Directive (WFD). This comparison aimed to determine whether our
findings aligned with regulatory environmental assessments or if they
provided complementary information.

As shown in Table 7, the overall analysis in 2020 of the biomarker
results indicated a "no effect" level at CO sites and an "effect" level at OR4
and VI2. In 2021, a "no effect" level was observed at CO1 and CO3-5,
while an "effect" level was determined at OR4 and CO2. For all other
sites, a "strong effect" level was consistently observed in both 2020 and
2021, accounting for 70.8% of sites in 2020 and 75% in 2021.

First, a strong coherence between the WFD statuses and the
biomarker outcomes was observed. Among the sites which was aligned
fort both the WFD chemical and ecological status, the biomarker ana-
lyses performed in 2020 and 2021 confirmed the status in 78.6% of cases
(i.e., 11 sites for a total of 14: SE3, SE6, SE7, SE9, SE11, OR1, OR2, VI1,
VI2, VI3, and CO5, Table 7). These sites did not meet the criteria for
good status, with the exception of CO5. Regarding SE8, both chemical
and ecological assessments indicated good environmental status, which
contrast with the results of the biomarker analysis in 2020 and 2021.
This site was located within the WFD surface water body FRHR261 (Eure
River at its confluence with the Seine River), the latter was selected as
the reference station. However, it is worth noting that the exact location
of the WFD sampling was notably upstream of SE8 (data not shown),
which may also be subject to tidal influences. In consequence, SE8 could
be integrated to the WFD surface water body FRHT01 (upper Seine es-
tuary as SE9). In this case, the results of the biomarker evaluation were
more consistent with the WFD outcomes (fail to achieve a good status).
At CO1, the biomarker-based methodology indicated a "no effect" level,
which was in line with the majority of CO sites (2020 and 2021).
However, these assessments did not align with WFD approaches. In
terms of the chemical aspect, CO1 did not achieve a good status due to
the presence of compounds classified as ubiquitous, persistent, bio-
accumulative, and toxic (uPBT), as indicated in Table 1: PCB 118. The
exposure to this compound among present contaminants or their
bioavailable fraction was not associated with the genotoxic and immu-
notoxic responses under studied, which were not specific to this
particular contaminant. It is possible that the substances did not affect
the biological responses investigated in this work or that they were not
bioavailable for the concerned species. The use of additional biomarkers
to encompass a wide range of biological functions could be
<caption>Table 7</caption>

Quality status defined according to the biomarker analyses applying the “one out-all out” principle and WFD chemical and ecological status (2022) associated to each study site.

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

PE: Phagocytosis efficiency, DD: DNA damages, NA: Not analyzed.

recommended, especially since a moderate ecological status was deter-
mined for CO1. Worldwide, the inclusion of uPBT compounds impor-
tantly contributes to the failure to achieve a good status (Zacharias et al.,
2020). In our study, they were detected in 55% of the WFD surface water
bodies. The use of biomarkers in this context offers an alternative to
evaluations using contamination thresholds and addresses their draw-
backs by assessing the toxicity of bioavailable compounds present in the
environment. For CO2, the WFD assessments determined good status in
contrary to the biomarker results only in 2021. The shift from a “no
effect” (2020) to an “effect” level (2021) using our methodology can be
attributed to the fact that biomarkers act as early warning systems
compared to the assessment carried at larger biological scales, such as
communities or populations (Wernersson et al., 2015). It was also worth
considering the possibility of an adaptation by local populations. How-
ever, a more insightful study would be required to draw such
conclusions.

Then, it appeared that the chemical and ecological statuses provided
contradictory quality assessments in 8 sites (i.e., SE1, SE2, SE4, SE5,
SE12, OR5, CO3, and CO4, Table 7). In such cases, biomarker analyses

provided new information, offering potential explanations for the dis-
crepancies. At SE1, while the chemical status was good, the opposite
result was obtained with the ecological and biomarker analyses. This can
be explained by the fact that biological tools provide a more integrative
analysis, considering factors such as cocktail effects and contaminant
bioavailability, compared to the conventional chemical assessment
based on a priority list of compounds. At CO4, a similar disparity be-
tween the chemical and ecological statuses was observed, however,
biomarker results indicated a “no effect” level suggesting a potential
influence of other stresses besides chemical contaminants on the envi-
ronment. Concerning SE2, SE4, SE5, SE12, and OR5, the ecological
status was good, contrary to the results of chemical and biomarker an-
alyses. As previously mentioned for CO2, biomarkers could act as early
warning systems or maybe there is adaptation of local populations. At
last, CO3 exhibited good status with the biomarker and ecological an-
alyses but a degraded one with the chemical evaluation. Similarly to
CO1, a hypothesis that needs verification is that the compounds known
to be present (uPBT: PCB 118 and tributyltin, Table 1) might not be
bioavailable for the caged species or may not induce effects on the
<caption>Table 7</caption>

Quality status defined according to the biomarker analyses applying the “one out-all out” principle and WFD chemical and ecological status (2022) associated to each study site.

<figure><img src="image_6.png" /></figure>

PE: Phagocytosis efficiency, DD: DNA damages, NA: Not analyzed.

recommended, especially since a moderate ecological status was deter-
mined for CO1. Worldwide, the inclusion of uPBT compounds impor-
tantly contributes to the failure to achieve a good status (Zacharias et al.,
2020). In our study, they were detected in 55% of the WFD surface water
bodies. The use of biomarkers in this context offers an alternative to
evaluations using contamination thresholds and addresses their draw-
backs by assessing the toxicity of bioavailable compounds present in the
environment. For CO2, the WFD assessments determined good status in
contrary to the biomarker results only in 2021. The shift from a “no
effect” (2020) to an “effect” level (2021) using our methodology can be
attributed to the fact that biomarkers act as early warning systems
compared to the assessment carried at larger biological scales, such as
communities or populations (Wernersson et al., 2015). It was also worth
considering the possibility of an adaptation by local populations. How-
ever, a more insightful study would be required to draw such
conclusions.

Then, it appeared that the chemical and ecological statuses provided
contradictory quality assessments in 8 sites (i.e., SE1, SE2, SE4, SE5,
SE12, OR5, CO3, and CO4, Table 7). In such cases, biomarker analyses

provided new information, offering potential explanations for the dis-
crepancies. At SE1, while the chemical status was good, the opposite
result was obtained with the ecological and biomarker analyses. This can
be explained by the fact that biological tools provide a more integrative
analysis, considering factors such as cocktail effects and contaminant
bioavailability, compared to the conventional chemical assessment
based on a priority list of compounds. At CO4, a similar disparity be-
tween the chemical and ecological statuses was observed, however,
biomarker results indicated a “no effect” level suggesting a potential
influence of other stresses besides chemical contaminants on the envi-
ronment. Concerning SE2, SE4, SE5, SE12, and OR5, the ecological
status was good, contrary to the results of chemical and biomarker an-
alyses. As previously mentioned for CO2, biomarkers could act as early
warning systems or maybe there is adaptation of local populations. At
last, CO3 exhibited good status with the biomarker and ecological an-
alyses but a degraded one with the chemical evaluation. Similarly to
CO1, a hypothesis that needs verification is that the compounds known
to be present (uPBT: PCB 118 and tributyltin, Table 1) might not be
bioavailable for the caged species or may not induce effects on the
analyzed parameters.

## 4. What improvement levers for the use of biomarkers in the study of the continuum?

This study has demonstrated the feasibility of defining environmental quality using biomarkers. It has proved that this methodology can be applied on a large geographic scale, suggesting its potential incorporation into regulatory monitoring programs. One critical factor for the success of this continuum study project, based on biomarker analysis, was the establishment of reference values and thresholds. This paper does not aim to propose a universal method to define them, as it was based on existing statistical methodologies designed by experts of the various model species. Here we showed the strong interest to obtain these values and to use it in a biomonitoring context. Reference values and thresholds permitted us to consider the natural variability of biomarkers and the influence of confounding factors. However, their accuracy depends on the quality and quantity available data. In this study, certain species, such as *G. fossarum*, *D. polymorpha* or *G. aculeatus*, benefited from a strong hindsight that directly influenced the accuracy of the site grading. We strongly encourage the study of biomarker functioning under realistic conditions (i.e., *in situ*) to generate data that can enhance or establish rigorous interpretation grids.

One limitation of this work was to study only two biomarkers. There is a real interest to include more biomarkers into the program to encompass a broader range of contaminant modes of action. However, one of the difficulties remains the availability of consensual approaches that would enable interspecific comparisons and thus to cover an entire continuum. In this regard, high-throughput omics-based technologies appear to offer good candidates offering the possibility to study different modes of action related to physiological functions through a unique analysis and using a common method across different species (Bahamonde et al., 2016; Espeyte et al., 2023; Gouveia et al., 2019; Leprétre et al., 2023). The development of novel biomarkers using these technologies is a topical focus. For instance, analyzing the screening of *G. fossarum* proteomes, coupled with data from field exposure, enabled the identification of protein modulations associated with environmental pressures (Leprétre et al., 2022b). In order to propose tools for use in biomonitoring, new methodologies are being developed and proposed with the aim to reduce the number of samplings, as well as the quantity and cost of analyses. In a recent article, Faugere et al. (2023) introduced a multi-omics assessment that allows the screening of proteins, metabolites, and lipids and successfully applied it to a unique *G. fossarum* sample. This kind of approach is of significant interest and provide valuable perspectives in the context of biomarker studies involving multiple species.

The inclusion of several biomarkers in the analyses raises the question of how integrate their responses. The “one out-all out” strategy was implemented in the WFD to ensure a conservative and standardized environmental assessment. This precautionary principle has faced criticism for potentially underestimating the number of sites with a good status (Moe et al., 2015; Prato et al., 2014). In the present study, we have chosen to apply it, and, even with only two biomarkers, it also tended to downgrade sites. When the phagocytosis efficiency and the DNA damage responses were interpreted together, all sites failed to achieve good status, with the exception of CO1-5 sites in 2020 and CO1 and CO3-5 in 2021 (Table 7). The development and validation of the methodology will inevitably involve an increase in the number of biomarkers analyzed to cover a wider range of effects, to provide a more accurate assessment, and potentially a comprehensive health index for the exposed organisms. In this context, the issue caused by the “one out-all out” strategy becomes more critical, as the likelihood of downgrading an environment would be positively correlated with the number of parameters studied. With the growing number of biomarkers in the methodology, the question of integration of results should also be considered. In this context, the Integrated Biomarker Responses version

2 (IBRv2) was developed (Sanchez et al., 2013) and recently modified in order to determine a score from the biomarker responses using reference and threshold values instead of a reference site (IBR-T, Cattseau et al., 2023; Leptrétre et al., 2022a). This approach considers significant variations in several biomarker responses and is intended to be a robust method in the biomonitoring field. Future challenges will involve the ability to assign different weights to biomarkers according to their respective physiological and ecological relevance (i.e., their ability to predict effects for populations or communities).

## 5. Conclusion

The use of biomarkers in water quality assessment has been shown to be relevant for defining a quality status, prioritizing sites for remediation actions, and evaluating the improvements linked to regulatory decisions. However, employing biomarkers for studying various types of environment along continua poses challenges. The present study proposed and, above all, implemented a consistent methodology across species and sites including the characterization of the technical specificities, analysis, and interpretation of results. The methodology was successfully applied during two campaign periods, along different continua. It allowed us to characterize an environmental status based on biological effects for each site, that can be easily useable by stakeholders and other water protection actors. The results confirmed the trends of the chemical and ecological assessment of WFD, when the two statuses were similar. Additionally, they provided additional insights when the WFD statuses contradicted each other.

However, this work remains a pilot test which should serve as a basis for future studies. The challenge now is to repeat this type of deployment in order to improve hindsight. Indeed, there is a need to generate large datasets in order to (1) establish robust reference values and thresholds (whatever the species or biomarker considered), and (2) better define the links and complementarity between biomarker outputs and chemical and ecological indices. In this context, collaboration with stakeholders and water protection actors is necessary to establish scopes and strategies (e.g., regulatory framework or otherwise, large-scale surveys, or targeted impact studies) for the optimal use of biomarkers approaches in monitoring programs.

### CRediT authorship contribution statement

**Sylvain Slaby:** Writing – review & editing, Writing – original draft, Visualization, Formal analysis. **Alain Geffard:** Writing – review & editing, Writing – original draft, Visualization, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization. **Cédric Fisson:** Writing – review & editing, Writing – original draft, Visualization, Supervision, Resources, Project administration, Methodology, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Matthieu Bonnevalle-Normand:** Visualization, Formal analysis, Data curation. **Anne-Sophie Allonier-Fernandes:** Project administration. **Rachid Amara:** Resources, Methodology, Investigation. **Anne Bado-Nilles:** Writing – review & editing, Resources, Methodology, Investigation. **Isabelle Bonnard:** Investigation. **Marc Bonnard:** Writing – review & editing, Resources, Methodology, Investigation. **Mayélé Burlion-Giorgi:** Investigation. **Amélie Cant:** Writing – review & editing, Investigation, Formal analysis. **Audrey Cattseau:** Writing – review & editing, Investigation, Formal analysis. **Arnaud Chaumot:** Writing – review & editing, Resources, Methodology. **Katherine Costil:** Writing – review & editing, Resources, Methodology, Investigation, Formal analysis. **Romain Coulaud:** Writing – review & editing, Resources, Methodology, Investigation, Formal analysis. **Laurence Delahaut:** Investigation. **Mamadou Diop:** Investigation, Formal analysis. **Aurélie Duflot:** Investigation. **Olivier Geffard:** Writing – review & editing, Visualization, Resources, Methodology. **Emmanuel Jestin:** Project administration. **Frank Le Foll:** Resources, Methodology. **Antoine Le Guernic:** Investigation,
analyzed parameters.

## 4. What improvement levers for the use of biomarkers in the study of the continuum?

This study has demonstrated the feasibility of defining environmental quality using biomarkers. It has proved that this methodology can be applied on a large geographic scale, suggesting its potential incorporation into regulatory monitoring programs. One critical factor for the success of this continuum study project, based on biomarker analysis, was the establishment of reference values and thresholds. This paper does not aim to propose a universal method to define them, as it was based on existing statistical methodologies designed by experts of the various model species. Here we showed the strong interest to obtain these values and to use it in a biomonitoring context. Reference values and thresholds permitted us to consider the natural variability of biomarkers and the influence of confounding factors. However, their accuracy depends on the quality and quantity available data. In this study, certain species, such as *G. fossarum*, *D. polymorpha* or *G. aculeatus*, benefited from a strong hindsight that directly influenced the accuracy of the site grading. We strongly encourage the study of biomarker functioning under realistic conditions (i.e., *in situ*) to generate data that can enhance or establish rigorous interpretation grids.

One limitation of this work was to study only two biomarkers. There is a real interest to include more biomarkers into the program to encompass a broader range of contaminant modes of action. However, one of the difficulties remains the availability of consensual approaches that would enable interspecific comparisons and thus to cover an entire continuum. In this regard, high-throughput omics-based technologies appear to offer good candidates offering the possibility to study different modes of action related to physiological functions through a unique analysis and using a common method across different species (Bahamonde et al., 2016; Espeyte et al., 2023; Gouveia et al., 2019; Leprétre et al., 2023). The development of novel biomarkers using these technologies is a topical focus. For instance, analyzing the screening of *G. fossarum* proteomes, coupled with data from field exposure, enabled the identification of protein modulations associated with environmental pressures (Leprétre et al., 2022b). In order to propose tools for use in biomonitoring, new methodologies are being developed and proposed with the aim to reduce the number of samplings, as well as the quantity and cost of analyses. In a recent article, Faugere et al. (2023) introduced a multi-omics assessment that allows the screening of proteins, metabolites, and lipids and successfully applied it to a unique *G. fossarum* sample. This kind of approach is of significant interest and provide valuable perspectives in the context of biomarker studies involving multiple species.

The inclusion of several biomarkers in the analyses raises the question of how integrate their responses. The “one out-all out” strategy was implemented in the WFD to ensure a conservative and standardized environmental assessment. This precautionary principle has faced criticism for potentially underestimating the number of sites with a good status (Moe et al., 2015; Prato et al., 2014). In the present study, we have chosen to apply it, and, even with only two biomarkers, it also tended to downgrade sites. When the phagocytosis efficiency and the DNA damage responses were interpreted together, all sites failed to achieve good status, with the exception of CO1-5 sites in 2020 and CO1 and CO3-5 in 2021 (Table 7). The development and validation of the methodology will inevitably involve an increase in the number of biomarkers analyzed to cover a wider range of effects, to provide a more accurate assessment, and potentially a comprehensive health index for the exposed organisms. In this context, the issue caused by the “one out-all out” strategy becomes more critical, as the likelihood of downgrading an environment would be positively correlated with the number of parameters studied. With the growing number of biomarkers in the methodology, the question of integration of results should also be considered. In this context, the Integrated Biomarker Responses version

2 (IBRv2) was developed (Sanchez et al., 2013) and recently modified in order to determine a score from the biomarker responses using reference and threshold values instead of a reference site (IBR-T, Cattseau et al., 2023; Leptrétre et al., 2022a). This approach considers significant variations in several biomarker responses and is intended to be a robust method in the biomonitoring field. Future challenges will involve the ability to assign different weights to biomarkers according to their respective physiological and ecological relevance (i.e., their ability to predict effects for populations or communities).

## 5. Conclusion

The use of biomarkers in water quality assessment has been shown to be relevant for defining a quality status, prioritizing sites for remediation actions, and evaluating the improvements linked to regulatory decisions. However, employing biomarkers for studying various types of environment along continua poses challenges. The present study proposed and, above all, implemented a consistent methodology across species and sites including the characterization of the technical specificities, analysis, and interpretation of results. The methodology was successfully applied during two campaign periods, along different continua. It allowed us to characterize an environmental status based on biological effects for each site, that can be easily useable by stakeholders and other water protection actors. The results confirmed the trends of the chemical and ecological assessment of WFD, when the two statuses were similar. Additionally, they provided additional insights when the WFD statuses contradicted each other.

However, this work remains a pilot test which should serve as a basis for future studies. The challenge now is to repeat this type of deployment in order to improve hindsight. Indeed, there is a need to generate large datasets in order to (1) establish robust reference values and thresholds (whatever the species or biomarker considered), and (2) better define the links and complementarity between biomarker outputs and chemical and ecological indices. In this context, collaboration with stakeholders and water protection actors is necessary to establish scopes and strategies (e.g., regulatory framework or otherwise, large-scale surveys, or targeted impact studies) for the optimal use of biomarkers approaches in monitoring programs.

### CRediT authorship contribution statement

**Sylvain Slaby:** Writing – review & editing, Writing – original draft, Visualization, Formal analysis. **Alain Geffard:** Writing – review & editing, Writing – original draft, Visualization, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization. **Cédric Fisson:** Writing – review & editing, Writing – original draft, Visualization, Supervision, Resources, Project administration, Methodology, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Matthieu Bonnevalle-Normand:** Visualization, Formal analysis, Data curation. **Anne-Sophie Allonier-Fernandes:** Project administration. **Rachid Amara:** Resources, Methodology, Investigation. **Anne Bado-Nilles:** Writing – review & editing, Resources, Methodology, Investigation. **Isabelle Bonnard:** Investigation. **Marc Bonnard:** Writing – review & editing, Resources, Methodology, Investigation. **Mayélé Burlion-Giorgi:** Investigation. **Amélie Cant:** Writing – review & editing, Investigation, Formal analysis. **Audrey Cattseau:** Writing – review & editing, Investigation, Formal analysis. **Arnaud Chaumot:** Writing – review & editing, Resources, Methodology. **Katherine Costil:** Writing – review & editing, Resources, Methodology, Investigation, Formal analysis. **Romain Coulaud:** Writing – review & editing, Resources, Methodology, Investigation, Formal analysis. **Laurence Delahaut:** Investigation. **Mamadou Diop:** Investigation, Formal analysis. **Aurélie Duflot:** Investigation. **Olivier Geffard:** Writing – review & editing, Visualization, Resources, Methodology. **Emmanuel Jestin:** Project administration. **Frank Le Foll:** Resources, Methodology. **Antoine Le Guernic:** Investigation,
Formal analysis. **Christelle Lopes:** Writing – review & editing, Resources, Methodology. **Mélissa Palos-Ladeiro:** Writing – review & editing, Resources, Methodology, Investigation. **Quentin Peignot:** Investigation. **Agnès Poret:** Investigation. **Antoine Serpentini:** Writing – review & editing, Resources, Methodology, Investigation, Formal analysis. **Gauthier Tremolet:** Investigation. **Cyril Turiès:** Investigation. **Benoît Xuereb:** Writing – review & editing, Writing – original draft, Visualization, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

## 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.

## Data availability

Data will be made available on request.

## Acknowledgements

This study was funded by the *Agence de l'Eau Seine-Normandie* within the framework of the project BIOSURVEILLANCE. This project is a contribution to the Seine Aval research program (www.seine-aval.fr), which belongs to the Zone Atelier Seine, part of the international Long-Term Socio-Ecological Research (LTSER) network.

The project team would like to thank:

(1) Olivier Perceval (French Office for Biodiversity) for his expertise in environmental management, and Michel Auffret (UMR 6539 LEMAR) for his scientific expertise on marine bivalve immunity,

(2) The Departmental Directorates for territories and Sea (DDTM) of the Calvados and Manche departments, HAROPA PORT, the harbour master's office of Antifer, the harbour master's office of Ouistreham, the sailing club of Merille-Franceville and the oyster farmers of Saint-Vaast-La-Hougue (GAEC de Tatihu), for their authorizations to the deployments of caging systems on the different maritime domains,

(3) "Voies navigables de France" and the regional directorates for environment, development and housing (DREAL) of the 02, 14, 50, 60, 76, 77, 78, 89 departments, for their authorizations to the deployments of caging systems on continental stations, and

(4) The "syndicat mixte Synergie Mer et Littoral" (SMEL) of Blainville-sur-mer (in particular Olivier Basuyaux), the *Prelev'mar* company, the DYNeco-PHYSD Ifremer laboratory and the Luc-sur-Mer marine station (BOREA laboratory) for helping us to install caging systems at the coastal stations.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jenvman.2024.120784.

## References

Bahamonde, P.A., Feswick, A., Isaacs, M.A., Munkittrick, K.R., Martyniuk, C.J., 2016. Defining the role of omics in assessing ecosystem health: perspectives from the Canadian environmental monitoring program. Environ. Toxicol. Chem. 35, 20-35. https://doi.org/10.1002/etc.3218.

Baird, D.J., Brown, S.S., Lagadic, L., Liess, M., Maltby, L., Moreira-Santos, M., Schulz, R., Scott, G.I., 2007. In situ-based effects measures: determining the ecological relevance of measured responses. Integr Environ Assess Manag 3, 259-267. https://doi.org/10.1897/IEAM-2006-031.1.

Barbarin, M., Muttin, F., Thomas, H., 2022. First study on the determination of baseline biomarkers in Mimachlamys varia for an intra-port environmental biomonitoring in French Atlantic coastline (La Rochelle). Mar. Pollut. Bull. 182, 113979 https://doi.org/10.1016/j.marpolbul.2022.113979.

Barjhoux, I., Rioult, D., Geffard, A., Palos Ladeiro, M., 2020. A new protocol for the simultaneous flow cytometric analysis of cytotoxicity and immunotoxicity on zebra mussel (Dreissena polymorpha) hemocytes. Fish Shellfish Immunol. 98, 224-235. https://doi.org/10.1016/j.fsi.2019.12.092.

Barrick, A., Châtel, A., Marion, J.-M., Perrein-Ettajani, H., Bruneau, M., Mouneyrac, C., 2016. A novel methodology for the determination of biomarker baseline levels in the marine polychaete Hediste diversicolor. Mar. Pollut. Bull. 108, 275-280. https://doi.org/10.1016/j.marpolbul.2016.04.056.

Besse, J.-P., Coquery, M., Lopes, C., Chaumot, A., Budzinski, H., Labadie, P., Geffard, O., 2013. Caged Gammarus fossarum (Crustacea) as a robust tool for the characterization of bioavailable contamination levels in continental waters: towards the determination of threshold values. Water Res. 47, 650-660. https://doi.org/10.1016/j.watres.2012.10.024.

Bonnail, E., Riba, I., de Seabra, A.A., DelValls, T.A., 2019. Sediment quality assessment in the Guadalquivir River (SW, Spain) using caged Asian clams: a biomarker field approach. Sci. Total Environ. 650, 1996-2003. https://doi.org/10.1016/j.scitotenv.2018.09.346.

Borcier, E., Artigaud, S., Gaillard, J.-C., Armengaud, J., Charrier, G., Couteau, J., Receveur, J., Ouddane, B., Diop, M., Amara, R., Laroche, J., Pichereau, V., 2019. Coupling caging and proteomics on the European flounder (Platichthys flesus) to assess the estuarine water quality at micro scale. Sci. Total Environ. 695, 133760 https://doi.org/10.1016/j.scitotenv.2019.133760.

Bourgeault, A., Gourlay-Francé, C., Vincent-Hubert, F., Palais, F., Geffard, A., Biagianti-Risbourg, S., Pain-Devin, S., Tusseau-Vuillemin, M.-H., 2010. Lessons from a transplantation of zebra mussels into a small urban river: an integrated ecotoxicological assessment. Environ. Toxicol. 25, 468-478. https://doi.org/10.1002/tox.20591.

Brodeur, J.C., Poletta, G.L., Simoniello, M.F., Carriquiriborde, P., Cristos, D.S., Pautasso, N., Paravani, E., Poliserpi, M.B., d'Andrea, M.F., Gonzalez, P.V., Aca, V.L., Curto, A.E., 2021. The problem with implementing fish farms in agricultural regions: a trial in a pampean pond highlights potential risks to both human and fish health. Chemosphere 262, 128408. https://doi.org/10.1016/j.chemosphere.2020.128408.

Cant, A., Bonnard, M., Porcher, J.-M., Prygiel, J., Catteau, A., Delahaut, L., Palluel, O., Turiès, C., Geffard, A., Bado-Nilles, A., 2022. Integration of genotoxic biomarkers in environmental biomonitoring analysis using a multi-biomarker approach in three-spined stickleback (Gasterosteus aculeatus Linnaeus, 1758). Toxics 10. https://doi.org/10.3390/toxics10030101.

Catteau, A., Bado-Nilles, A., Beaudouin, R., Tebby, C., Joachim, S., Palluel, O., Turiès, C., Chrétien, N., Nott, K., Ronkart, S., Geffard, A., Porcher, J.-M., 2021. Water quality of the Meuse watershed: assessment using a multi-biomarker approach with caged three-spined stickleback (Gasterosteus aculeatus L.). Ecotoxicol. Environ. Saf. 208, 111407 https://doi.org/10.1016/j.ecoenv.2020.111407.

Catteau, A., Le Guernic, A., Marchand, A., Hani, Y.M.I., Palluel, O., Turiès, C., Bado-Nilles, A., Dedourge-Geffard, O., Geffard, A., Porcher, J.-M., 2019. Impact of confinement and food access restriction on the three-spined stickleback (Gasterosteus aculeatus, L.) during caging: a multi-biomarker approach. Fish Physiol. Biochem. 45, 1261-1276. https://doi.org/10.1007/s10695-019-00670-1.

Catteau, A., Le Guernic, A., Palos Ladeiro, M., Dedourge-Geffard, O., Bonnard, M., Bonnard, I., Delahaut, L., Bado-Nilles, A., Porcher, J.-M., Lopes, C., Geffard, O., Geffard, A., 2023. Integrative biomarker response - threshold (IBR-T): refinement of IBRv2 to consider the reference and threshold values of biomarkers. J. Environ. Manag. 341, 118049 https://doi.org/10.1016/j.jenvman.2023.118049.

Catteau, A., Porcher, J.-M., Bado-Nilles, A., Bonnard, I., Bonnard, M., Chaumot, A., David, E., Dedourge-Geffard, O., Delahaut, L., Delorme, N., François, A., Garnero, L., Lopes, C., Nott, K., Noury, P., Palluel, O., Palos-Ladeiro, M., Quéau, H., Ronkart, S., Sessey-Alaoui, K., Turiès, C., Tychon, B., Geffard, O., Geffard, A., 2022. Interest of a multispecies approach in active biomonitoring: application in the Meuse watershed. Sci. Total Environ. 808, 152148 https://doi.org/10.1016/j.scitotenv.2021.152148.

Charron, L., Geffard, O., Chaumot, A., Coulaud, R., Jaffal, A., Gaillet, V., Dedourge-Geffard, O., Geffard, A., 2014. Influence of molting and starvation on digestive enzyme activities and energy storage in Gammarus fossarum. PLoS One 9, 1-9. https://doi.org/10.1371/journal.pone.0096393.

Chemin, E., 1911. Sur la Congeria cochleata Kickx du Canal de Caen à la Mer. Bull. Soc. Linn. Normandie 6, 183-206.

Claxton, L.D., Houk, V.S., Hughes, T.J., 1998. Genotoxicity of industrial wastes and effluents. Mutat. Res. Rev. Mutat. Res. 410, 237-243. https://doi.org/10.1016/S1383-5742(98)00008-8.

Coulaud, R., Geffard, O., Xuereb, B., Lacaze, E., Quéau, H., Garric, J., Charles, S., Chaumot, A., 2011. In situ feeding assay with Gammarus fossarum (Crustacea): modelling the influence of confounding factors to improve water quality biomonitoring. Water Res. 45, 6417-6429. https://doi.org/10.1016/j.watres.2011.09.035.

Crane, M., Burton, G.A., Culp, J.M., Greenberg, M.S., Munkittrick, K.R., Ribeiro, R., Salazar, M.H., St-Jean, S.D., 2007. Review of aquatic in situ approaches for stressor and effect diagnosis. Integr Environ Assess Manag 3, 234-245. https://doi.org/10.1897/IEAM-2006-027.1.

de los Ríos, A., Juanes, J.A., Ortiz-Zarragoitia, M., López de Alda, M., Barceló, D., Cajaraville, M.P., 2012. Assessment of the effects of a marine urban outfall discharge on caged mussels using chemical and biomarker analysis. Mar. Pollut. Bull. 64, 563-573. https://doi.org/10.1016/j.marpolbul.2011.12.018.

Dendievell, A.-M., Grosbois, C., Ayrault, S., Evrard, O., Coynel, A., Debret, M., Gardes, T., Euzen, C., Schmitt, L., Chabaux, F., Winiarski, T., Van Der Perk, M., Mourier, B., 2022. Key factors influencing metal concentrations in sediments along Western European Rivers: a long-term monitoring study (1945-2020). Sci. Total Environ. 805, 149778 https://doi.org/10.1016/j.scitotenv.2021.149778.

Dendievell, A.-M., Mourier, B., Coynel, A., Evrard, O., Labadie, P., Ayrault, S., Debret, M., Koltalo, F., Copard, Y., Faivre, Q., Gardes, T., Vauclin, S., Budzinski, H., Grosbois, C., Winiarski, T., Desmet, M., 2020. Spatio-temporal assessment of the polychlorinated
**Formal analysis.** **Christelle Lopes:** Writing – review & editing, Resources, Methodology. **Mélissa Palos-Ladeiro:** Writing – review & editing, Resources, Methodology, Investigation. **Quentin Peignot:** Investigation. **Agnès Poret:** Investigation. **Antoine Serpentini:** Writing – review & editing, Resources, Methodology, Investigation, Formal analysis. **Gauthier Tremolet:** Investigation. **Cyril Turiès:** Investigation. **Benoît Xuereb:** Writing – review & editing, Writing – original draft, Visualization, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

## 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.

## Data availability

Data will be made available on request.

## Acknowledgements

This study was funded by the *Agence de l'Eau Seine-Normandie* within the framework of the project BIOSURVEILLANCE. This project is a contribution to the Seine Aval research program (www.seine-aval.fr), which belongs to the Zone Atelier Seine, part of the international Long-Term Socio-Ecological Research (LTSER) network.

The project team would like to thank:

(1) Olivier Perceval (French Office for Biodiversity) for his expertise in environmental management, and Michel Auffret (UMR 6539 LEMAR) for his scientific expertise on marine bivalve immunity,

(2) The Departmental Directorates for territories and Sea (DDTM) of the Calvados and Manche departments, HAROPA PORT, the harbour master's office of Antifer, the harbour master's office of Ouistreham, the sailing club of Merille-Franceville and the oyster farmers of Saint-Vaast-La-Hougue (GAEC de Tatihu), for their authorizations to the deployments of caging systems on the different maritime domains,

(3) "Voies navigables de France" and the regional directorates for environment, development and housing (DREAL) of the 02, 14, 50, 60, 76, 77, 78, 89 departments, for their authorizations to the deployments of caging systems on continental stations, and

(4) The "syndicat mixte Synergie Mer et Littoral" (SMEL) of Blainville-sur-mer (in particular Olivier Basuyaux), the *Prelev'mar* company, the DYNeco-PHYSD Ifremer laboratory and the Luc-sur-Mer marine station (BOREA laboratory) for helping us to install caging systems at the coastal stations.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jenvman.2024.120784.

## References

Bahamonde, P.A., Feswick, A., Isaacs, M.A., Munkittrick, K.R., Martyniuk, C.J., 2016. Defining the role of omics in assessing ecosystem health: perspectives from the Canadian environmental monitoring program. Environ. Toxicol. Chem. 35, 20–35. https://doi.org/10.1002/etc.3218.

Baird, D.J., Brown, S.S., Lagadic, L., Liess, M., Maltby, L., Moreira-Santos, M., Schulz, R., Scott, G.I., 2007. In situ-based effects measures: determining the ecological relevance of measured responses. Integr Environ Assess Manag 3, 259–267. https://doi.org/10.1897/IEAM-2006-031.1.

Barbarin, M., Muttin, F., Thomas, H., 2022. First study on the determination of baseline biomarkers in Mimachlamys varia for an intra-port environmental biomonitoring in French Atlantic coastline (La Rochelle). Mar. Pollut. Bull. 182, 113979 https://doi.org/10.1016/j.marpolbul.2022.113979.

Barjhoux, I., Rioult, D., Geffard, A., Palos Ladeiro, M., 2020. A new protocol for the simultaneous flow cytometric analysis of cytotoxicity and immunotoxicity on zebra mussel (Dreissena polymorpha) hemocytes. Fish Shellfish Immunol. 98, 224–235. https://doi.org/10.1016/j.fsi.2019.12.092.

Barrick, A., Châtel, A., Marion, J.-M., Perrein-Ettajani, H., Bruneau, M., Mouneyrac, C., 2016. A novel methodology for the determination of biomarker baseline levels in the marine polychaete Hediste diversicolor. Mar. Pollut. Bull. 108, 275–280. https://doi.org/10.1016/j.marpolbul.2016.04.056.

Besse, J.-P., Coquery, M., Lopes, C., Chaumot, A., Budzinski, H., Labadie, P., Geffard, O., 2013. Caged Gammarus fossarum (Crustacea) as a robust tool for the characterization of bioavailable contamination levels in continental waters: towards the determination of threshold values. Water Res. 47, 650–660. https://doi.org/10.1016/j.watres.2012.10.024.

Bonnail, E., Riba, I., de Seabra, A.A., DelValls, T.A., 2019. Sediment quality assessment in the Guadalquivir River (SW, Spain) using caged Asian clams: a biomarker field approach. Sci. Total Environ. 650, 1996–2003. https://doi.org/10.1016/j.scitotenv.2018.09.346.

Borcier, E., Artigaud, S., Gaillard, J.-C., Armengaud, J., Charrier, G., Couteau, J., Receveur, J., Ouddane, B., Diop, M., Amara, R., Laroche, J., Pichereau, V., 2019. Coupling caging and proteomics on the European flounder (Platichthys flesus) to assess the estuarine water quality at micro scale. Sci. Total Environ. 695, 133760 https://doi.org/10.1016/j.scitotenv.2019.133760.

Bourgeault, A., Gourlay-Francé, C., Vincent-Hubert, F., Palais, F., Geffard, A., Biagianti-Risbourg, S., Pain-Devin, S., Tusseau-Vuillemin, M.-H., 2010. Lessons from a transplantation of zebra mussels into a small urban river: an integrated ecotoxicological assessment. Environ. Toxicol. 25, 468–478. https://doi.org/10.1002/tox.20591.

Brodeur, J.C., Poletta, G.L., Simoniello, M.F., Carriquiriborde, P., Cristos, D.S., Pautasso, N., Paravani, E., Poliserpi, M.B., d'Andrea, M.F., Gonzalez, P.V., Aca, V.L., Curto, A.E., 2021. The problem with implementing fish farms in agricultural regions: a trial in a pampean pond highlights potential risks to both human and fish health. Chemosphere 262, 128408. https://doi.org/10.1016/j.chemosphere.2020.128408.

Cant, A., Bonnard, M., Porcher, J.-M., Prygiel, J., Catteau, A., Delahaut, L., Palluel, O., Turiès, C., Geffard, A., Bado-Nilles, A., 2022. Integration of genotoxic biomarkers in environmental biomonitoring analysis using a multi-biomarker approach in three-spined stickleback (Gasterosteus aculeatus Linnaeus, 1758). Toxics 10. https://doi.org/10.3390/toxics10030101.

Catteau, A., Bado-Nilles, A., Beaudouin, R., Tebby, C., Joachim, S., Palluel, O., Turiès, C., Chrétien, N., Nott, K., Ronkart, S., Geffard, A., Porcher, J.-M., 2021. Water quality of the Meuse watershed: assessment using a multi-biomarker approach with caged three-spined stickleback (Gasterosteus aculeatus L.). Ecotoxicol. Environ. Saf. 208, 111407 https://doi.org/10.1016/j.ecoenv.2020.111407.

Catteau, A., Le Guernic, A., Marchand, A., Hani, Y.M.I., Palluel, O., Turiès, C., Bado-Nilles, A., Dedourge-Geffard, O., Geffard, A., Porcher, J.-M., 2019. Impact of confinement and food access restriction on the three-spined stickleback (Gasterosteus aculeatus, L.) during caging: a multi-biomarker approach. Fish Physiol. Biochem. 45, 1261–1276. https://doi.org/10.1007/s10695-019-00670-1.

Catteau, A., Le Guernic, A., Palos Ladeiro, M., Dedourge-Geffard, O., Bonnard, M., Bonnard, I., Delahaut, L., Bado-Nilles, A., Porcher, J.-M., Lopes, C., Geffard, O., Geffard, A., 2023. Integrative biomarker response - threshold (IBR-T): refinement of IBRv2 to consider the reference and threshold values of biomarkers. J. Environ. Manag. 341, 118049 https://doi.org/10.1016/j.jenvman.2023.118049.

Catteau, A., Porcher, J.-M., Bado-Nilles, A., Bonnard, I., Bonnard, M., Chaumot, A., David, E., Dedourge-Geffard, O., Delahaut, L., Delorme, N., François, A., Garnero, L., Lopes, C., Nott, K., Noury, P., Palluel, O., Palos-Ladeiro, M., Quéau, H., Ronkart, S., Sessey-Alaoui, K., Turiès, C., Tychon, B., Geffard, O., Geffard, A., 2022. Interest of a multispecies approach in active biomonitoring: application in the Meuse watershed. Sci. Total Environ. 808, 152148 https://doi.org/10.1016/j.scitotenv.2021.152148.

Charron, L., Geffard, O., Chaumot, A., Coulaud, R., Jaffal, A., Gaillet, V., Dedourge-Geffard, O., Geffard, A., 2014. Influence of molting and starvation on digestive enzyme activities and energy storage in Gammarus fossarum. PLoS One 9, 1–9. https://doi.org/10.1371/journal.pone.0096393.

Chemin, E., 1911. Sur la Congeria cochleata Kickx du Canal de Caen à la Mer. Bull. Soc. Linn. Normandie 6, 183–206.

Claxton, L.D., Houk, V.S., Hughes, T.J., 1998. Genotoxicity of industrial wastes and effluents. Mutat. Res. Rev. Mutat. Res. 410, 237–243. https://doi.org/10.1016/S1383-5742(98)00008-8.

Coulaud, R., Geffard, O., Xuereb, B., Lacaze, E., Quéau, H., Garric, J., Charles, S., Chaumot, A., 2011. In situ feeding assay with Gammarus fossarum (Crustacea): modelling the influence of confounding factors to improve water quality biomonitoring. Water Res. 45, 6417–6429. https://doi.org/10.1016/j.watres.2011.09.035.

Crane, M., Burton, G.A., Culp, J.M., Greenberg, M.S., Munkittrick, K.R., Ribeiro, R., Salazar, M.H., St-Jean, S.D., 2007. Review of aquatic in situ approaches for stressor and effect diagnosis. Integr Environ Assess Manag 3, 234–245. https://doi.org/10.1897/IEAM-2006-027.1.

de los Ríos, A., Juanes, J.A., Ortiz-Zarragoitia, M., López de Alda, M., Barceló, D., Cajaraville, M.P., 2012. Assessment of the effects of a marine urban outfall discharge on caged mussels using chemical and biomarker analysis. Mar. Pollut. Bull. 64, 563–573. https://doi.org/10.1016/j.marpolbul.2011.12.018.

Dendieviel, A.-M., Grosbois, C., Ayrault, S., Evrard, O., Coynel, A., Debret, M., Gardes, T., Euzen, C., Schmitt, L., Chabaux, F., Winiarski, T., Van Der Perk, M., Mourier, B., 2022. Key factors influencing metal concentrations in sediments along Western European Rivers: a long-term monitoring study (1945–2020). Sci. Total Environ. 805, 149778 https://doi.org/10.1016/j.scitotenv.2021.149778.

Dendieviel, A.-M., Mourier, B., Coynel, A., Evrard, O., Labadie, P., Ayrault, S., Debret, M., Koltalo, F., Copard, Y., Faivre, Q., Gardes, T., Vauclin, S., Budzinski, H., Grosbois, C., Winiarski, T., Desmet, M., 2020. Spatio-temporal assessment of the polychlorinated
biphenyl (PCB) sediment contamination in four major French river corridors (1945–2018). Earth Syst. Sci. Data 12, 1153–1170. https://doi.org/10.5194/essd-12-1153-2020.

Diop, M., Couteau, J., Bado-Nilles, A., Tavernier, E., Ouddane, B., Denis, J., Duong, G., Gevaert, F., Monchy, S., Laroche, J., Amara, R., 2022. Bioaccumulation of trace metal elements and biomarker responses in caged juvenile flounder at a polluted site: effects of fish density and time exposure. Mar. Pollut. Bull. 185, 114289 https://doi.org/10.1016/j.marpolbul.2022.114289.

Erraud, A., Bonnard, M., Chaumot, A., Geffard, O., Duflot, A., Forget-Leray, J., Le Foll, F., Geffard, A., Xuereb, B., 2018a. Use of sperm DNA integrity as a marker for exposure to contamination in Palaemon serratus (Pennant 1777): intrinsic variability, baseline level and in situ deployment. Water Res. 132, 124–134. https://doi.org/10.1016/j.watres.2017.12.057.

Erraud, A., Bonnard, M., Duflot, A., Geffard, A., Danger, J.-M., Forget-Leray, J., Xuereb, B., 2018b. Assessment of sperm quality in palaemonid prawns using Comet assay: methodological optimization. Environ. Sci. Pollut. Control Ser. 25, 11226–11237. https://doi.org/10.1007/s11356-017-8754-6.

Erraud, A., Bonnard, M., Geffard, O., Chaumot, A., Duflot, A., Geffard, A., Forget-Leray, J., Xuereb, B., 2019. Assessment of sperm DNA integrity within the Palaemonon longirostris (H. Milne-Edwards, 1837) population of the Seine estuary. Environ. Pollut. 245, 485–493. https://doi.org/10.1016/j.envpol.2018.11.006.

Espeyre, A., Degli Esposti, D., Leprétre, M., Detante, H., Sugier, K., Geffard, O., Chaumot, A., 2023. Protéomique ciblée pour la quantification multiplexée de biomarqueurs, perspectives pour la surveillance environnementale: Application d'une stratégie généralisable entre espèces pour l'identification de protéines d'intérêt en biosurveillance.

Faugere, J., Brunet, T.A., Clément, Y., Espeyre, A., Geffard, O., Lemoine, J., Chaumot, A., Degli-Esposti, D., Aycirix, S., Salvador, A., 2023. Development of a multi-omics extraction method for ecotoxicology: investigation of the reproductive cycle of Gammarus fossarum. Talanta 253, 123806. https://doi.org/10.1016/j.talanta.2022.123806.

Forbes, V.E.,PMqvist, A., Bach, L., 2006. The use and misuse of biomarkers in ecotoxicology. Environ. Toxicol. Chem. 25, 272–280. https://doi.org/10.1897/05-257R.1.

Gendre, H., Ben Cheikh, Y., Le Foll, F., Geffard, A., Palos Ladeiro, M., 2023. Comparative immune responses of blue mussel and zebra mussel haemocytes to simultaneous chemical and bacterial exposure. Fish Shellfish Immunol. 135, 108654 https://doi.org/10.1016/j.fsi.2023.108654.

Georgieva, E., Antal, L., Stoyanova, S., Armaudova, D., Velcheva, I., Iliev, I., Vasileva, T., Bivolarski, V., Mitkovska, V., Chassovnikarova, T., Todorova, B., Uzochukwu, I.E., Nyeste, K., Yancheva, V., 2022. Biomarkers for pollution in caged mussels from three reservoirs in Bulgaria: a pilot study. Heliyon 8, e09069. https://doi.org/10.1016/j.heliyon.2022.e09069.

Gouveia, D., Almunia, C., Cogne, Y., Pible, O., Degli-Esposti, D., Salvador, A., Cristobal, S., Sheehan, D., Chaumot, A., Geffard, O., Armengaud, J., 2019. Ecotoxicoproteomics: a decade of progress in our understanding of anthropogenic impact on the environment. J Proteomics 198, 66–77. https://doi.org/10.1016/j.jprot.2018.12.001.

Gouveia, D., Chaumot, A., Charnot, A., Almunia, C., François, A., Navarro, L., Armengaud, J., Salvador, A., Geffard, O., 2017. Ecotoxico-Proteomics for aquatic environmental monitoring: first application of a new proteomics-based multi biomarker assay using caged amphipods. Environ. Sci. Technol. 51, 13417–13426. https://doi.org/10.1021/acs.esb.7b03736.

Hagger, J.A., Jones, M.B., Lowe, D., Leonard, D.R.P., Owen, R., Galloway, T.S., 2008. Application of biomarkers for improving risk assessments of chemicals under the Water Framework Directive: a case study. Mar. Pollut. Bull. 56, 1111–1118. https://doi.org/10.1016/j.marpolbul.2008.03.040.

Hanson, N., 2011. Using biological data from field studies with multiple reference sites as a basis for environmental management: the risks for false positives and false negatives. J. Environ. Manag. 92, 610–619. https://doi.org/10.1016/j.jenvman.2010.09.026.

Kuzmanović, M., López-Doval, J.C., De Castro-Català, N., Guasch, H., Petrović, M., Munoz, I., Ginebreda, A., Barceló, D., 2016. Ecotoxicological risk assessment of chemical pollution in four Iberian river basins and its relationship with the aquatic macroinvertebrate community status. Sci. Total Environ. 540, 324–333. https://doi.org/10.1016/j.scitotenv.2015.06.112.

Lacaze, E., Devaux, A., Jubeaux, G., Mons, R., Gardette, M., Bony, S., Garric, J., Geffard, O., 2011. DNA damage in Gammarus fossarum sperm as a biomarker of genotoxic pressure: intrinsic variability and reference level. Sci. Total Environ. 409, 3230–3236. https://doi.org/10.1016/j.scitotenv.2011.05.012.

Lam, P.K.S., 2009. Use of biomarkers in environmental monitoring. Ocean Coast Manag. 52, 348–354. https://doi.org/10.1016/j.ocecoaman.2009.04.010.

Le Guernic, A., Sanchez, W., Palluel, O., Bado-Nilles, A., Turies, C., Chadili, E., Cavalié, I., Adam-Guillermin, C., Porcher, J.-M., Geffard, A., Betouille, S., Gagnaire, B., 2016. In situ experiments to assess effects of constraints linked to caging on ecotoxicity biomarkers of the three-spined stickleback (Gasterosteus aculeatus L.). Fish Physiol. Biochem. 42, 643–657. https://doi.org/10.1007/s10695-015-0166-2.

Leprétre, M., Degli Esposti, D., Sugier, K., Espeyre, A., Gaillard, J.-C., Delorme, N., Duflot, A., Bonnard, I., Coulaud, R., Boulange-Lecomte, C., Xuereb, B., Palos Ladeiro, M., Geffard, A., Geffard, O., Armengaud, J., Chaumot, A., 2023. Organ-oriented proteogenomics functional atlas of three aquatic invertebrate sentinel species. Sci. Data 10, 643. https://doi.org/10.1038/s41597-023-02545-w.

Leprétre, M., Geffard, A., Palos Ladeiro, M., Dedourge-Geffard, O., David, E., Delahaut, L., Bonnard, I., Barjhoux, I., Nicolaï, M., Noury, P., Espeyre, A., Chaumot, A., Degli-Esposti, D., Geffard, O., Lopes, C., 2022a. Determination of biomarkers threshold values and illustration of their use for the diagnostic in large-scale freshwater biomonitoring surveys. Environ. Sci. Eur. 34, 115. https://doi.org/10.1186/s12302-022-00692-2.

Leprétre, M., Geffard, O., Espeyre, A., Faugere, J., Aycirix, S., Salvador, A., Delorme, N., Chaumot, A., Degli-Esposti, D., 2022b. Multiple reaction monitoring mass spectrometry for the discovery of environmentally modulated proteins in an aquatic invertebrate sentinel species, Gammarus fossarum. Environ. Pollut. 315, 120393 https://doi.org/10.1016/j.envpol.2022.120393.

Lunardelli, B., Cabral, M.T., Vieira, C.E.D., Oliveira, L.F., Risso, W.E., Meletti, P.C., Martinez, C.B.R., 2018. Chromium accumulation and biomarker responses in the neotropical fish Prochilodus lineatus caged in a river under the influence of tannery activities. Ecotoxicol. Environ. Saf. 153, 188–194. https://doi.org/10.1016/j.ecoenv.2018.02.023.

Maisano, M., Cappello, T., Natalotto, A., Vitale, V., Parrino, V., Giannetto, A., Oliva, S., Mancini, G., Cappello, S., Mauceri, A., Fasulo, S., 2017. Effects of petrochemical contamination on caged marine mussels using a multi-biomarker approach: histological changes, neurotoxicity and hypoxic stress. Mar. Environ. Res. 128, 114–123. https://doi.org/10.1016/j.marenvres.2016.03.008.

Marchand, A., Porcher, J.-M., Turies, C., Chadili, E., Palluel, O., Baudoin, P., Betoulle, S., Bado-Nilles, A., 2017. Evaluation of chlorpyrifos effects, alone and combined with lipopolysaccharide stress, on DNA integrity and immune responses of the three-spined stickleback, Gasterosteus aculeatus. Ecotoxicol. Environ. Saf. 145, 333–339. https://doi.org/10.1016/j.ecoenv.2017.07.025.

Marchand, A., Tebby, C., Beaudouin, R., Hani, Y.M.I., Porcher, J.-M., Turies, C., Bado-Nilles, A., 2019. Modelling the effect of season, sex, and body size on the three-spined stickleback, Gasterosteus aculeatus, cellular innate immunomarkers: a proposition of laboratory reference ranges. Sci. Total Environ. 648, 337–349. https://doi.org/10.1016/j.scitotenv.2018.07.381.

Meyer, A., Alric, B., Dézerald, O., Billoir, E., Coulaud, R., Larras, F., Mondy, C.P., Ussepolio-Polatera, P., 2022. Linking micropollutants to trait syndromes across freshwater diatom, macroinvertebrate, and fish assemblages. Water (Basel) 14. https://doi.org/10.3390/w14081184.

Milinkovitch, T., Geffard, O., Geffard, A., Mouneyrac, C., Chaumot, A., Xuereb, B., Fisson, C., Minier, C., Auffret, M., Perceval, O., Egea, E., Sanchez, W., 2019. Biomarkers as tools for monitoring within the Water Framework Directive context: concept, opinions and advancement of expertise. Environ. Sci. Pollut. Control Ser. 26, 32759–32763. https://doi.org/10.1007/s11356-019-06434-x.

Moe, S.J., Lyche Solheim, A., Soszka, H., Golub, M., Hutorowicz, A., Kolada, A., Picińska-Faltynowicz, J., Bialokoz, W., 2015. Integrated assessment of ecological status and misclassification of lakes: the role of uncertainty and index combination rules. Ecol Indic 48, 605–615. https://doi.org/10.1016/j.ecolind.2014.08.018.

Oikari, A., 2006. Caging techniques for field exposures of fish to chemical contaminants. Aquat. Toxicol. 78, 370–381. https://doi.org/10.1016/j.aquatox.2006.03.010.

Prato, S., La Valle, P., De Luca, E., Lattanzi, L., Migliore, G., Morgana, J.G., Munari, C., Nicoletti, L., Izzo, G., Mistri, M., 2014. The "one-out, all-out" principle entails the risk of imposing unnecessary restoration costs: a study case in two Mediterranean coastal lakes. Mar. Pollut. Bull. 80, 30–40. https://doi.org/10.1016/j.marpolbul.2014.01.054.

Prié, V., 2023. How was France invaded? 170 years of colonisation of metropolitan France by freshwater mussels. Hydrobiologia. https://doi.org/10.1007/s10750-023-05274-8.

Rico-Sánchez, A.E., Rodríguez-Romero, A.J., Sedeño-Díaz, J.E., López-López, E., Sundermann, A., 2022. Aquatic macroinvertebrate assemblages in rivers influenced by mining activities. Sci. Rep. 12, 3209. https://doi.org/10.1038/s41598-022-06869-2.

Sanchez, W., Burgeot, T., Porcher, J.-M., 2013. A novel "Integrated Biomarker Response" calculation based on reference deviation concept. Environ. Sci. Pollut. Control Ser. 20, 2721–2725. https://doi.org/10.1007/s11356-012-1359-1.

Singh, N.P., McCoy, M.T., Tice, R.R., Schneider, E.L., 1988. A simple technique for quantitation of low levels of DNA damage in individual cells. Exp. Cell Res. 175, 184–191. https://doi.org/10.1016/0014-4827(88)90265-0.

Slaby, S., Catteau, A., Le Cor, F., Cant, A., Dufour, V., Iurétig, A., Turiés, C., Palluel, O., Bado-Nilles, A., Bonnard, M., Cardoso, O., Dauchy, X., Porcher, J.-M., Banas, D., 2023. Chemical occurrence of pesticides and transformation products in two small lentic waterbodies at the head of agricultural watersheds and biological responses in caged Gasterosteus aculeatus. Sci. Total Environ. 904, 166326 https://doi.org/10.1016/j.scitotenv.2023.166326.

Tsangaris, C., Kormas, K., Strogyloudi, E., Hatzianestis, I., Neofitou, C., Andral, B., Galgani, F., 2010. Multiple biomarkers of pollution effects in caged mussels on the Greek coastline. Comp. Biochem. Physiol. C Toxicol. Pharmacol. 151, 369–378. https://doi.org/10.1016/j.cbpc.2009.12.009.

van der Oost, R., Beyer, J., Vermeulen, N.P.E., 2003. Fish bioaccumulation and biomarkers in environmental risk assessment: a review. Environ. Toxicol. Pharmacol. 13, 57–149. https://doi.org/10.1016/S1382-6689(02)00126-6.

Wernersson, A.-S., Carere, M., Maggi, C., Tusil, P., Soldan, P., James, A., Sanchez, W., Dulio, V., Broeg, K., Reifferscheid, G., Buchinger, S., Maas, H., Van Der Grinten, E., O'Toole, S., Ausili, A., Manfra, L., Marziali, L., Polesello, S., Lacchetti, I., Mancini, L., Lilja, K., Linderoth, M., Lundeberg, T., Fjällborg, B., Porsbring, T., Larsson, D.G.J., Bengtsson-Palme, J., Förlin, Kienle, C., Kunz, P., Vermeirssen, E., Werner, I., Robinson, C.D., Lyons, B., Katsiadaki, I., Whalley, C., den Haan, K., Messiaen, M., Clayton, H., Lettieri, T., Carvalho, R.N., Gawlik, B.M., Hollert, H., Di Paolo, C., Brack, W., Kammann, U., Kase, R., 2015. The European technical report on aquatic effect-based monitoring tools under the water framework directive. Environ. Sci. Eur. 27, 7. https://doi.org/10.1186/s12302-015-0039-4.

Xuereb, B., Chaumot, A., Mons, R., Garric, J., Geffard, O., 2009. Acetylcholinesterase activity in Gammarus fossarum (Crustacea Amphipoda): intrinsic variability,
biphenyl (PCB) sediment contamination in four major French river corridors (1945–2018). Earth Syst. Sci. Data 12, 1153–1170. https://doi.org/10.5194/essd-12-1153-2020.

Diop, M., Couteau, J., Bado-Nilles, A., Tavernier, E., Ouddane, B., Denis, J., Duong, G., Gevaert, F., Monchy, S., Laroche, J., Amara, R., 2022. Bioaccumulation of trace metal elements and biomarker responses in caged juvenile flounder at a polluted site: effects of fish density and time exposure. Mar. Pollut. Bull. 185, 114289 https://doi.org/10.1016/j.marpolbul.2022.114289.

Erraud, A., Bonnard, M., Chaumot, A., Geffard, O., Duflot, A., Forget-Leray, J., Le Foll, F., Geffard, A., Xuereb, B., 2018a. Use of sperm DNA integrity as a marker for exposure to contamination in Palaemon serratus (Pennant 1777): intrinsic variability, baseline level and in situ deployment. Water Res. 132, 124–134. https://doi.org/10.1016/j.watres.2017.12.057.

Erraud, A., Bonnard, M., Duflot, A., Geffard, A., Danger, J.-M., Forget-Leray, J., Xuereb, B., 2018b. Assessment of sperm quality in palaemonid prawns using Comet assay: methodological optimization. Environ. Sci. Pollut. Control Ser. 25, 11226–11237. https://doi.org/10.1007/s11356-017-8754-6.

Erraud, A., Bonnard, M., Geffard, O., Chaumot, A., Duflot, A., Geffard, A., Forget-Leray, J., Xuereb, B., 2019. Assessment of sperm DNA integrity within the Palaemon longirostris (H. Milne-Edwards, 1837) population of the Seine estuary. Environ. Pollut. 245, 485–493. https://doi.org/10.1016/j.envpol.2018.11.006.

Espeyte, A., Degli Esposti, D., Leprêtre, M., Detante, H., Sugier, K., Geffard, O., Chaumot, A., 2023. Protéomique ciblée pour la quantification multiplexée de biomarqueurs, perspectives pour la surveillance environnementale: Application d'une stratégie généralisable entre espèces pour l'identification de protéines d'intérêt en biosurveillance.

Faugere, J., Brunet, T.A., Clément, Y., Espeyte, A., Geffard, O., Lemoine, J., Chaumot, A., Degli-Esposti, D., Aycirix, S., Salvador, A., 2023. Development of a multi-omics extraction method for ecotoxicology: investigation of the reproductive cycle of Gammarus fossarum. Talanta 253, 123806. https://doi.org/10.1016/j.talanta.2022.123806.

Forbes, V.E.,PMqvist, A., Bach, L., 2006. The use and misuse of biomarkers in ecotoxicology. Environ. Toxicol. Chem. 25, 272–280. https://doi.org/10.1897/05-257R.1.

Gendre, H., Ben Cheikh, Y., Le Foll, F., Geffard, A., Palos Ladeiro, M., 2023. Comparative immune responses of blue mussel and zebra mussel haemocytes to simultaneous chemical and bacterial exposure. Fish Shellfish Immunol. 135, 108654 https://doi.org/10.1016/j.fsi.2023.108654.

Georgieva, E., Antal, L., Stoyanova, S., Armaudova, D., Velcheva, I., Iliev, I., Vasileva, T., Bivolarski, V., Mitkovska, V., Chassovnikarova, T., Todorova, B., Uzochukwu, I.E., Nyeste, K., Yancheva, V., 2022. Biomarkers for pollution in caged mussels from three reservoirs in Bulgaria: a pilot study. Heliyon 8, e09069. https://doi.org/10.1016/j.heliyon.2022.e09069.

Gouveia, D., Almunia, C., Cogne, Y., Pible, O., Degli-Esposti, D., Salvador, A., Cristobal, S., Sheehan, D., Chaumot, A., Geffard, O., Armengaud, J., 2019. Ecotoxicoproteomics: a decade of progress in our understanding of anthropogenic impact on the environment. J Proteomics 198, 66–77. https://doi.org/10.1016/j.jprot.2018.12.001.

Gouveia, D., Chaumot, A., Charnot, A., Almunia, C., François, A., Navarro, L., Armengaud, J., Salvador, A., Geffard, O., 2017. Ecotoxico-Proteomics for aquatic environmental monitoring: first application of a new proteomics-based multi biomarker assay using caged amphipods. Environ. Sci. Technol. 51, 13417–13426. https://doi.org/10.1021/acs.esb.7b03736.

Hagger, J.A., Jones, M.B., Lowe, D., Leonard, D.R.P., Owen, R., Galloway, T.S., 2008. Application of biomarkers for improving risk assessments of chemicals under the Water Framework Directive: a case study. Mar. Pollut. Bull. 56, 1111–1118. https://doi.org/10.1016/j.marpolbul.2008.03.040.

Hanson, N., 2011. Using biological data from field studies with multiple reference sites as a basis for environmental management: the risks for false positives and false negatives. J. Environ. Manag. 92, 610–619. https://doi.org/10.1016/j.jenvman.2010.09.026.

Kuzmanović, M., López-Doval, J.C., De Castro-Català, N., Guasch, H., Petrović, M., Munoz, I., Ginebreda, A., Barceló, D., 2016. Ecotoxicological risk assessment of chemical pollution in four Iberian river basins and its relationship with the aquatic macroinvertebrate community status. Sci. Total Environ. 540, 324–333. https://doi.org/10.1016/j.scitotenv.2015.06.112.

Lacaze, E., Devaux, A., Jubeaux, G., Mons, R., Gardette, M., Bony, S., Garric, J., Geffard, O., 2011. DNA damage in Gammarus fossarum sperm as a biomarker of genotoxic pressure: intrinsic variability and reference level. Sci. Total Environ. 409, 3230–3236. https://doi.org/10.1016/j.scitotenv.2011.05.012.

Lam, P.K.S., 2009. Use of biomarkers in environmental monitoring. Ocean Coast Manag. 52, 348–354. https://doi.org/10.1016/j.ocecoaman.2009.04.010.

Le Guernic, A., Sanchez, W., Palluel, O., Bado-Nilles, A., Turies, C., Chadili, E., Cavalié, I., Adam-Guillermin, C., Porcher, J.-M., Geffard, A., Betouille, S., Gagnaire, B., 2016. In situ experiments to assess effects of constraints linked to caging on ecotoxicity biomarkers of the three-spined stickleback (Gasterosteus aculeatus L.). Fish Physiol. Biochem. 42, 643–657. https://doi.org/10.1007/s10695-015-0166-2.

Leprêtre, M., Degli Esposti, D., Sugier, K., Espeyte, A., Gaillard, J.-C., Delorme, N., Duflot, A., Bonnard, I., Coulaud, R., Boulange-Lecomte, C., Xuereb, B., Palos Ladeiro, M., Geffard, A., Geffard, O., Armengaud, J., Chaumot, A., 2023. Organ-oriented proteogenomics functional atlas of three aquatic invertebrate sentinel species. Sci. Data 10, 643. https://doi.org/10.1038/s41597-023-02545-w.

Leprêtre, M., Geffard, A., Palos Ladeiro, M., Dedourge-Geffard, O., David, E., Delahaut, L., Bonnard, I., Barjhoux, I., Nicolaï, M., Noury, P., Espeyte, A., Chaumot, A., Degli-Esposti, D., Geffard, O., Lopes, C., 2022a. Determination of biomarkers threshold values and illustration of their use for the diagnostic in large-

scale freshwater biomonitoring surveys. Environ. Sci. Eur. 34, 115. https://doi.org/10.1186/s12302-022-00692-2.

Leprêtre, M., Geffard, O., Espeyte, A., Faugere, J., Aycirix, S., Salvador, A., Delorme, N., Chaumot, A., Degli-Esposti, D., 2022b. Multiple reaction monitoring mass spectrometry for the discovery of environmentally modulated proteins in an aquatic invertebrate sentinel species, Gammarus fossarum. Environ. Pollut. 315, 120393 https://doi.org/10.1016/j.envpol.2022.120393.

Lunardelli, B., Cabral, M.T., Vieira, C.E.D., Oliveira, L.F., Risso, W.E., Meletti, P.C., Martinez, C.B.R., 2018. Chromium accumulation and biomarker responses in the neotropical fish Prochilodus lineatus caged in a river under the influence of tannery activities. Ecotoxicol. Environ. Saf. 153, 188–194. https://doi.org/10.1016/j.ecoenv.2018.02.023.

Maisano, M., Cappello, T., Natalotto, A., Vitale, V., Parrino, V., Giannetto, A., Oliva, S., Mancini, G., Cappello, S., Mauceri, A., Fasulo, S., 2017. Effects of petrochemical contamination on caged marine mussels using a multi-biomarker approach: histological changes, neurotoxicity and hypoxic stress. Mar. Environ. Res. 128, 114–123. https://doi.org/10.1016/j.marenvres.2016.03.008.

Marchand, A., Porcher, J.-M., Turies, C., Chadili, E., Palluel, O., Baudoin, P., Betoulle, S., Bado-Nilles, A., 2017. Evaluation of chlorpyrifos effects, alone and combined with lipopolysaccharide stress, on DNA integrity and immune responses of the three-spined stickleback, Gasterosteus aculeatus. Ecotoxicol. Environ. Saf. 145, 333–339. https://doi.org/10.1016/j.ecoenv.2017.07.025.

Marchand, A., Tebby, C., Beaudouin, R., Hani, Y.M.I., Porcher, J.-M., Turies, C., Bado-Nilles, A., 2019. Modelling the effect of season, sex, and body size on the three-spined stickleback, Gasterosteus aculeatus, cellular innate immunomarkers: a proposition of laboratory reference ranges. Sci. Total Environ. 648, 337–349. https://doi.org/10.1016/j.scitotenv.2018.07.381.

Meyer, A., Alric, B., Dézerald, O., Billoir, E., Coulaud, R., Larras, F., Mondy, C.P., Ussepolio-Polatera, P., 2022. Linking micropollutants to trait syndromes across freshwater diatom, macroinvertebrate, and fish assemblages. Water (Basel) 14. https://doi.org/10.3390/w14081184.

Milinkovitch, T., Geffard, O., Geffard, A., Mouneyrac, C., Chaumot, A., Xuereb, B., Fisson, C., Minier, C., Auffret, M., Perceval, O., Egea, E., Sanchez, W., 2019. Biomarkers as tools for monitoring within the Water Framework Directive context: concept, opinions and advancement of expertise. Environ. Sci. Pollut. Control Ser. 26, 32759–32763. https://doi.org/10.1007/s11356-019-06434-x.

Moe, S.J., Lyche Solheim, A., Soszka, H., Golub, M., Hutorowicz, A., Kolada, A., Picińska-Faltynowicz, J., Bialokoz, W., 2015. Integrated assessment of ecological status and misclassification of lakes: the role of uncertainty and index combination rules. Ecol Indic 48, 605–615. https://doi.org/10.1016/j.ecolind.2014.08.018.

Oikari, A., 2006. Caging techniques for field exposures of fish to chemical contaminants. Aquat. Toxicol. 78, 370–381. https://doi.org/10.1016/j.aquatox.2006.03.010.

Prato, S., La Valle, P., De Luca, E., Lattanzi, L., Migliore, G., Morgana, J.G., Munari, C., Nicoletti, L., Izzo, G., Mistri, M., 2014. The "one-out, all-out" principle entails the risk of imposing unnecessary restoration costs: a study case in two Mediterranean coastal lakes. Mar. Pollut. Bull. 80, 30–40. https://doi.org/10.1016/j.marpolbul.2014.01.054.

Prié, V., 2023. How was France invaded? 170 years of colonisation of metropolitan France by freshwater mussels. Hydrobiologia. https://doi.org/10.1007/s10750-023-05274-8.

Rico-Sánchez, A.E., Rodríguez-Romero, A.J., Sedeño-Díaz, J.E., López-López, E., Sundermann, A., 2022. Aquatic macroinvertebrate assemblages in rivers influenced by mining activities. Sci. Rep. 12, 3209. https://doi.org/10.1038/s41598-022-06869-2.

Sanchez, W., Burgeot, T., Porcher, J.-M., 2013. A novel "Integrated Biomarker Response" calculation based on reference deviation concept. Environ. Sci. Pollut. Control Ser. 20, 2721–2725. https://doi.org/10.1007/s11356-012-1359-1.

Singh, N.P., McCoy, M.T., Tice, R.R., Schneider, E.L., 1988. A simple technique for quantitation of low levels of DNA damage in individual cells. Exp. Cell Res. 175, 184–191. https://doi.org/10.1016/0014-4827(88)90265-0.

Slaby, S., Catteau, A., Le Cor, F., Cant, A., Dufour, V., Iurétig, A., Turiés, C., Palluel, O., Bado-Nilles, A., Bonnard, M., Cardoso, O., Dauchy, X., Porcher, J.-M., Banas, D., 2023. Chemical occurrence of pesticides and transformation products in two small lentic waterbodies at the head of agricultural watersheds and biological responses in caged Gasterosteus aculeatus. Sci. Total Environ. 904, 166326 https://doi.org/10.1016/j.scitotenv.2023.166326.

Tsangaris, C., Kormas, K., Strogyloudi, E., Hatzianestis, I., Neofitou, C., Andral, B., Galgani, F., 2010. Multiple biomarkers of pollution effects in caged mussels on the Greek coastline. Comp. Biochem. Physiol. C Toxicol. Pharmacol. 151, 369–378. https://doi.org/10.1016/j.cbpc.2009.12.009.

van der Oost, R., Beyer, J., Vermeulen, N.P.E., 2003. Fish bioaccumulation and biomarkers in environmental risk assessment: a review. Environ. Toxicol. Pharmacol. 13, 57–149. https://doi.org/10.1016/S1382-6689(02)00126-6.

Wernersson, A.-S., Carere, M., Maggi, C., Tusil, P., Soldan, P., James, A., Sanchez, W., Dulio, V., Broeg, K., Reifferscheid, G., Buchinger, S., Maas, H., Van Der Grinten, E., O'Toole, S., Ausili, A., Manfra, L., Marziali, L., Polesello, S., Laccetti, I., Mancini, L., Lilja, K., Linderoth, M., Lundeberg, T., Fjällborg, B., Porsbring, T., Larsson, D.G.J., Bengtsson-Palme, J., Förlin, Kienle, C., Kunz, P., Vermeirssen, E., Werner, I., Robinson, C.D., Lyons, B., Katsiadaki, I., Whalley, C., den Haan, K., Messiaen, M., Clayton, H., Lettieri, T., Carvalho, R.N., Gawlik, B.M., Hollert, H., Di Paolo, C., Brack, W., Kammann, U., Kase, R., 2015. The European technical report on aquatic effect-based monitoring tools under the water framework directive. Environ. Sci. Eur. 27, 7. https://doi.org/10.1186/s12302-015-0039-4.

Xuereb, B., Chaumot, A., Mons, R., Garric, J., Geffard, O., 2009. Acetylcholinesterase activity in Gammarus fossarum (Crustacea Amphipoda): intrinsic variability,
reference levels, and a reliable tool for field surveys. Aquat. Toxicol. 93, 225-233.
https://doi.org/10.1016/j.aquatox.2009.05.006.

Yancheva, V., Georgieva, E., Velcheva, I., Iliev, I., Stoyanova, S., Vasileva, T., Bivolarski, V., Todorova-Bambaldokova, D., Zulkipli, N., Antal, L., Nyeste, K., 2022. Assessment of the exposure of two pesticides on common carp (Cyprinus carpio Linnaeus, 1758): are the prolonged biomarker responses adaptive or destructive?

Comp. Biochem. Physiol. C Toxicol. Pharmacol. 261, 109446 https://doi.org/
10.1016/j.cbpc.2022.109446.

Yutin, N., Wolf, M.Y., Wolf, Y.I., Koonin, E.V., 2009. The origins of phagocytosis and
eukaryogenesis. Biol. Direct 4, 9. https://doi.org/10.1186/1745-6150-4-9.

Zacharias, I., Liakou, P., Biliani, I., 2020. A review of the status of surface European waters twenty years after WFD introduction. Environmental Processes 7, 1023-1039. https://doi.org/10.1007/s40710-020-00458-z.
reference levels, and a reliable tool for field surveys. Aquat. Toxicol. 93, 225–233.
https://doi.org/10.1016/j.aquatox.2009.05.006.

Yancheva, V., Georgieva, E., Velcheva, I., Iliev, I., Stoyanova, S., Vasileva, T., Bivolarski, V., Todorova-Bambaldokova, D., Zulkipli, N., Antal, L., Nyeste, K., 2022. Assessment of the exposure of two pesticides on common carp (Cyprinus carpio Linnaeus, 1758): are the prolonged biomarker responses adaptive or destructive?

Comp. Biochem. Physiol. C Toxicol. Pharmacol. 261, 109446 https://doi.org/
10.1016/j.cbpc.2022.109446.

Yutin, N., Wolf, M.Y., Wolf, Y.I., Koonin, E.V., 2009. The origins of phagocytosis and
eukaryogenesis. Biol. Direct 4, 9. https://doi.org/10.1186/1745-6150-4-9.

Zacharias, I., Liakou, P., Biliani, I., 2020. A review of the status of surface European waters twenty years after WFD introduction. Environmental Processes 7, 1023–1039. https://doi.org/10.1007/s40710-020-00458-z.