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# Study of chemical contamination pressures in allis shad (Alosa alosa) spawning grounds in the Gironde-Garonne-Dordogne catchment

Benjamin Bellier

► To cite this version:

Benjamin Bellier. Study of chemical contamination pressures in allis shad (Alosa alosa) spawning grounds in the Gironde-Garonne-Dordogne catchment. Environmental Sciences. 2023. hal-04623271

HAL Id: hal-04623271

https://hal.inrae.fr/hal-04623271

Submitted on 25 Jun 2024

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# Study of chemical contamination pressures in allis shad (Alosa alosa) spawning grounds in the Gironde-Garonne-Dordogne catchment

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

1000µm

Benjamin Bellier

benjaminbellier@yahoo.com

Internship supervisors: Sarah Bancel¹,², Bertrand Villeneuve¹ and Dimitri Kalenitchenko³

1. INRAE EABX (Freema team)

2. Université de Bordeaux

3. La Rochelle Université

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

RÉPUBLIQUE
FRANÇAISE

Liberté
Égalité
Fraternité

eau

GRAND SUD-OUEST

AGENCE DE L'EAU ADOUR-GARONNE

INRAE

La Rochelle
Université
# Study of chemical contamination pressures in allis shad (Alosa alosa) spawning grounds in the Gironde-Garonne-Dordogne catchment

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

1000µm

Benjamin Bellier

[benjaminbellier@yahoo.com](mailto:benjaminbellier@yahoo.com)

Internship supervisors: Sarah Bancel¹,², Bertrand Villeneuve¹ and Dimitri Kalenitchenko³

1. INRAE EABX (Freema team)

2. Université de Bordeaux

3. La Rochelle Université

<figure><img src="image_3.png" /></figure>

RÉPUBLIQUE
FRANÇAISE

Liberté
Égalité
Fraternité

eau

GRAND SUD-OUEST

AGENCE DE L'EAU ADOUR-GARONNE

INRAE

La Rochelle
Université
« There's nothing more satisfying than working hard at something you're passionate about. »

Yukimura Sanada (2007)
« There's nothing more satisfying than working hard at something you're passionate about. »

Yukimura Sanada (2007)
i. Acknowledgements

I'd like to thank many people who assisted me in completing this work.

Firstly, thanks to Eric Rochard for leading and supporting the project. Following, thanks to my supervisors, Sarah, for her insightful and relevant remarks and for the days spent together in the field at Golfech, and Bertrand, for his professionalism and attentiveness at work.

Next, I would like to express my gratitude to the FREEMA team and, more generally, to the EABX unit in Cestas-Gazinet. Thanks to Marion and Mathilde for their friendliness and the cohabitation in the office. I would also like to thank Carl, Marie, Chloé (and Switch), Violette, Thibault, Manu, Jérémy, Baptiste, Margaux, Seb, Estelle, Mario, Melvin, Stéphane and all the other team members.

Thanks to everyone who has contributed in any way to my fulfillment in this double degree between La Rochelle and Costa Rica (Coralie, Nico, Kenza, Louis, Alexandra, Aurélien, Monica, Jorge, Gregorio, Chepe, all the teaching staff, etc.).

Then, I would like to thank my *lato sensu* family. Thank you to my parents (Poulpe and Moule) and my sisters (from Australia and Vendée) for their support since my return to France. Thanks to my life partner, Coralie, for making me laugh every day and for being there to support me. Thanks to her parents and brother for all the relaxing moments. Thanks to my friends for helping me unwind when I needed it, especially Edgar and Ju for the board game nights.

Furthermore, thanks to all the people who reviewed my work: Sarah, Bertrand, Coralie, Maud, Margaux, Flavie, etc.

Finally yet importantly, thanks to the people who helped me when I had questions. Thanks to Mr Payet, Ms Beaugelin-Seiller and Ms Sarkis for reassuring me and guiding me in the use of the (ms)PAF method. Thanks to Mr Geffard, Mr Cachot and Mr Rochard (one more time) for their attentiveness and pertinent advice.
i. Acknowledgements

I'd like to thank many people who assisted me in completing this work.

Firstly, thanks to Eric Rochard for leading and supporting the project. Following, thanks to my supervisors, Sarah, for her insightful and relevant remarks and for the days spent together in the field at Golfech, and Bertrand, for his professionalism and attentiveness at work.

Next, I would like to express my gratitude to the FREEMA team and, more generally, to the EABX unit in Cestas-Gazinet. Thanks to Marion and Mathilde for their friendliness and the cohabitation in the office. I would also like to thank Carl, Marie, Chloé (and Switch), Violette, Thibault, Manu, Jérémy, Baptiste, Margaux, Seb, Estelle, Mario, Melvin, Stéphane and all the other team members.

Thanks to everyone who has contributed in any way to my fulfillment in this double degree between La Rochelle and Costa Rica (Coralie, Nico, Kenza, Louis, Alexandra, Aurélien, Monica, Jorge, Gregorio, Chepe, all the teaching staff, etc.).

Then, I would like to thank my *lato sensu* family. Thank you to my parents (Poulpe and Moule) and my sisters (from Australia and Vendée) for their support since my return to France. Thanks to my life partner, Coralie, for making me laugh every day and for being there to support me. Thanks to her parents and brother for all the relaxing moments. Thanks to my friends for helping me unwind when I needed it, especially Edgar and Ju for the board game nights.

Furthermore, thanks to all the people who reviewed my work: Sarah, Bertrand, Coralie, Maud, Margaux, Flavie, etc.

Finally yet importantly, thanks to the people who helped me when I had questions. Thanks to Mr Payet, Ms Beaugelin-Seiller and Ms Sarkis for reassuring me and guiding me in the use of the (ms)PAF method. Thanks to Mr Geffard, Mr Cachot and Mr Rochard (one more time) for their attentiveness and pertinent advice.
## ii. Glossary

The numbers correspond to the pages where the abbreviation appears for the first time.

* Ci = Concentration of a chemical compound, 15

* EC10 = Effect Concentration on 10% of individuals, 14

* EC50 = Effect Concentration on 50% of individuals, 14

* ECx = Effect Concentration on x% of individuals, 14

* GGD = Gironde-Garonne-Dordogne, 4

* HC50 = Hazardous Concentration on 50% of individuals, 15

* HC50F = Hazardous Concentration on 50% of individuals for the fish taxon, 18

* HC50SE = Hazardous Concentration on 50% of individuals for each life stage of fish taxon, 18

* HCPs = Hygiene and Care Products, 10

* LOD = Limits Of Detection, 13

* LOEC = Lowest Observed Effect Concentration, 14

* LOQ = Limits Of Quantification, 13

* msPAF = Potentially Affected Fraction of species by chemical mixtures, 15

* msPAF<sub>F-AM</sub> = average annual msPAF representing the fish taxon, 18

* msPAF<sub>F-T</sub> = periodic average of msPAF representing the fish taxon, 18

* msPAF<sub>SE-T</sub> = periodic average of msPAF representing each life stage of the fish taxon, 19

* NOEC = No Observed Effect Concentration, 14

* OIPs = Other Industrial Pollutants, 10

* PAF = Potentially Affected Fraction of species, 13

* PAFi = Potentially Affected Fraction of species by one chemical compound, 15

* PAH = Polycyclic Aromatic Hydrocarbon, 6

* PBDE = PolyBrominated Diphenyl Ether, 6

* PCB = PolyChlorinated Biphenyl, 6

* PFASs = Per- and polyFluoroAlkyl Substances, 10

* SIE = "Systèmes d'Informations sur l'Eau", 8

* SIEAG = "Système d'Informations sur l'Eau" Adour-Garonne, 8

* SSD = Species Sensitivity Distribution, 13

* Th<sub>5%</sub> = Threshold of 5% potentially affected fraction of species, 15

* WFD = Water Framework Directive, 7
## ii. Glossary

The numbers correspond to the pages where the abbreviation appears for the first time.

* Ci = Concentration of a chemical compound, 15

* EC10 = Effect Concentration on 10% of individuals, 14

* EC50 = Effect Concentration on 50% of individuals, 14

* ECx = Effect Concentration on x% of individuals, 14

* GGD = Gironde-Garonne-Dordogne, 4

* HC50 = Hazardous Concentration on 50% of individuals, 15

* HC50F = Hazardous Concentration on 50% of individuals for the fish taxon, 18

* HC50SE = Hazardous Concentration on 50% of individuals for each life stage of fish taxon, 18

* HCPs = Hygiene and Care Products, 10

* LOD = Limits Of Detection, 13

* LOEC = Lowest Observed Effect Concentration, 14

* LOQ = Limits Of Quantification, 13

* msPAF = Potentially Affected Fraction of species by chemical mixtures, 15

* msPAF<sub>F-AM</sub> = average annual msPAF representing the fish taxon, 18

* msPAF<sub>F-T</sub> = periodic average of msPAF representing the fish taxon, 18

* msPAF<sub>SE-T</sub> = periodic average of msPAF representing each life stage of the fish taxon, 19

* NOEC = No Observed Effect Concentration, 14

* OIPs = Other Industrial Pollutants, 10

* PAF = Potentially Affected Fraction of species, 13

* PAFi = Potentially Affected Fraction of species by one chemical compound, 15

* PAH = Polycyclic Aromatic Hydrocarbon, 6

* PBDE = PolyBrominated Diphenyl Ether, 6

* PCB = PolyChlorinated Biphenyl, 6

* PFASs = Per- and polyFluoroAlkyl Substances, 10

* SIE = "Systèmes d'Informations sur l'Eau", 8

* SIEAG = "Système d'Informations sur l'Eau" Adour-Garonne, 8

* SSD = Species Sensitivity Distribution, 13

* Th<sub>5%</sub> = Threshold of 5% potentially affected fraction of species, 15

* WFD = Water Framework Directive, 7
iii. List of Figures and Tables

A. *Figures*

**Figure 1.** Simplified diagram of the allis shad life cycle with the approximate period of each phase (inspired by Gaillagot and Carry, 2014).

**Figure 2.** Map of the GGD catchment, where the main current allis shad spawning grounds are located between ~Bergerac and ~Tuilières (Dordogne) and ~Agen and ~Golfech (Garonne).

**Figure 3.** Number of estimated spawners in Garonne (Golfech) and Dordogne (Tuilières) spawning grounds between 1999 and 2021 (data from MIGADO, n.d.).

**Figure 4.** Map of the land use (Corin Land Cover in 2018, data from Union européenne, 2018) for the study area with studied stations in the current main allis shad spawning grounds of the Garonne and Dordogne. Station abbreviations are shown in **Table I**. Be = Bergerac and Ag = Agen.

**Figure 5.** Schematic representation for enhanced comprehension of the origin of environmental water data; concepts of raw water, aqueous phase of water, particulate phase of water and sediment-water partition coefficient.

**Figure 6.** Principles of limits of detection (LOD) and quantification (LOQ) and extrapolation problems associated with data where the actual environmental concentration (red) is below the laboratory-specific LOQ (grey and blue) for the same substance.

**Figure 7.** Classical concentration-response curve with differences between endpoints in ecotoxicology (inspired by Ghillebaert-Giuliani and Ghillebaert, 2017). The red lines represent the uncertainty around NOECs and LOECs. An example would be to study the effect (on reproduction, behavior, mortality, etc.) of glyphosate-containing agrochemical on adult zebrafish (*Danio rerio*).

**Figure 8.** Example of SSD concept substance-specific (inspired by Posthuma and de Zwart, 2006). The square (□) represent compound-specific toxicity data for a set of species belonging to different taxa (in black EC50s for fish taxon). The dashed blue line represent the estimation of the percentage of species potentially affected by the environmental concentration of a substance "i" (Ci). The dashed gray line represents the hazardous concentration of a substance "i" affecting 50% of species (HC50i). The red line represents the assumption of linearity below the HC50.

**Figure 9.** Summary diagram of the method used to obtain (ms)PAF values.

**Figure 10.** Absence and presence of environmental data for each category in Garonne and Dordogne spawning grounds. Blue rectangles represent the presence of quantified or unquantified data, grey rectangles represent the absence of quantified or unquantified data, and grey lines represent the absence of quantified data.

**Figure 11.** Number of toxicity tests (fish/freshwater) used to calculate HC50s by life stage of fish taxa and by effects tested (species with fewer than 20 tests are not represented here).

**Figure 12.** Percentage of (A) stage lifes of fish and (B) toxicity parameters used to calculate HC50s for each category of chemicals (the number corresponds to the total number of toxicity tests carried out on freshwater fish).

**Figure 13.** Spatio-temporal trends (Garonne-Dordogne and 2006-2021) in msPAF<sub>F-AM</sub> when considering quantified and unquantified environmental data (black) or only quantified environmental data (gray). The values shown at the top represent msPAF<sub>F-T</sub> values. The red line indicate the Th<sub>5%</sub>. As a reminder, msPAF<sub>F-AM</sub> and msPAF<sub>F-T</sub> represent, respectively, the average anual and average periodic msPAF, specific to the fish taxon in each spawning ground.

**Figure 14.** Values of msPAF<sub>SE-T</sub> (in %) with (A) quantified + unquantified environmental data and (B) only quantified environmental data for each lifestage and each effect in the Dordogne and Garonne
iii. List of Figures and Tables

A. Figures

**Figure 1.** Simplified diagram of the allis shad life cycle with the approximate period of each phase (inspired by Gaillagot and Carry, 2014).

**Figure 2.** Map of the GGD catchment, where the main current allis shad spawning grounds are located between ~Bergerac and ~Tuilières (Dordogne) and ~Agen and ~Golfech (Garonne).

**Figure 3.** Number of estimated spawners in Garonne (Golfech) and Dordogne (Tuilières) spawning grounds between 1999 and 2021 (data from MIGADO, n.d.).

**Figure 4.** Map of the land use (Corin Land Cover in 2018, data from Union européenne, 2018) for the study area with studied stations in the current main allis shad spawning grounds of the Garonne and Dordogne. Station abbreviations are shown in **Table I**. Be = Bergerac and Ag = Agen.

**Figure 5.** Schematic representation for enhanced comprehension of the origin of environmental water data; concepts of raw water, aqueous phase of water, particulate phase of water and sediment-water partition coefficient.

**Figure 6.** Principles of limits of detection (LOD) and quantification (LOQ) and extrapolation problems associated with data where the actual environmental concentration (red) is below the laboratory-specific LOQ (grey and blue) for the same substance.

**Figure 7.** Classical concentration-response curve with differences between endpoints in ecotoxicology (inspired by Ghillebaert-Giuliani and Ghillebaert, 2017). The red lines represent the uncertainty around NOECs and LOECs. An example would be to study the effect (on reproduction, behavior, mortality, etc.) of glyphosate-containing agrochemical on adult zebrafish (*Danio rerio*).

**Figure 8.** Example of SSD concept substance-specific (inspired by Posthuma and de Zwart, 2006). The square (□) represent compound-specific toxicity data for a set of species belonging to different taxa (in black EC50s for fish taxon). The dashed blue line represent the estimation of the percentage of species potentially affected by the environmental concentration of a substance "i" (Ci). The dashed gray line represents the hazardous concentration of a substance "i" affecting 50% of species (HC50i). The red line represents the assumption of linearity below the HC50.

**Figure 9.** Summary diagram of the method used to obtain (ms)PAF values.

**Figure 10.** Absence and presence of environmental data for each category in Garonne and Dordogne spawning grounds. Blue rectangles represent the presence of quantified or unquantified data, grey rectangles represent the absence of quantified or unquantified data, and grey lines represent the absence of quantified data.

**Figure 11.** Number of toxicity tests (fish/freshwater) used to calculate HC50s by life stage of fish taxa and by effects tested (species with fewer than 20 tests are not represented here).

**Figure 12.** Percentage of (A) stage lifes of fish and (B) toxicity parameters used to calculate HC50s for each category of chemicals (the number corresponds to the total number of toxicity tests carried out on freshwater fish).

**Figure 13.** Spatio-temporal trends (Garonne-Dordogne and 2006-2021) in msPAF<sub>F-AM</sub> when considering quantified and unquantified environmental data (black) or only quantified environmental data (gray). The values shown at the top represent msPAF<sub>F-T</sub> values. The red line indicate the Th<sub>5%</sub>. As a reminder, msPAF<sub>F-AM</sub> and msPAF<sub>F-T</sub> represent, respectively, the average annual and average periodic msPAF, specific to the fish taxon in each spawning ground.

**Figure 14.** Values of msPAF<sub>SE-T</sub> (in %) with (A) quantified + unquantified environmental data and (B) only quantified environmental data for each lifestage and each effect in the Dordogne and Garonne
spawning grounds (\* : error standard > 15%; \*\* : error standard and periodic average > 15%). The values of msPAF<sub>SE-T</sub> and their standard deviation are shown. The red line indicate the Th<sub>5%</sub>. As a reminder, msPAF<sub>SE-T</sub> represents the periodic average msPAF specific to each life stage of the fish taxon for each effect tested in each spawning ground.

**Figure 15.** Trends of msPAF<sub>SE-AM</sub> using quantified + unquantified environmental data (in %) for the sublethal effects (development, physiology and behavior), for early stages (embryo and larva) and for each category of chemicals in Dodrogne and Garonne spawning grounds (\* : averages exceeding the graph box; their values are shown next to the \*). The red line indicate the Th<sub>5%</sub>. As a reminder, msPAF<sub>SE-AM</sub> represents the average annual msPAF specific to each life stage of the fish taxon for each effect tested and category studied in each spawning ground.

**Figure 16.** Trends of msPAF<sub>SE-AM</sub> using quantified + unquantified environmental data (in %) for the lethal effects (mortality), for early stages (embryo and larva) and for each category of chemicals in Dodrogne and Garonne spawning grounds (\* : averages exceeding the graph box; their values are shown next to the \*). The red line indicate the Th<sub>5%</sub>. As a reminder, msPAF<sub>SE-AM</sub> represents the average annual msPAF specific to each life stage of the fish taxon for each effect tested and category studied in each spawning ground.

**Figure 17.** Probability of survival of allis shad embryos exposed to Garonne water (red) and tap water (yellow) for batch n°1 (A) and batch n°2 (B) (data from Bancel, in prep.).

B. Tables

**Table I.** Summary table of data used per station and river (data from SIEAG, 2023). The code used in the study, the period covered by the data, the coordinates (in Lambert 93), the data total before (Raw data) and after (Selected data) standardization, selection and sorting are indicated for each station. See **Figure 4** for station locations. For final data, a cross is indicated when stations have data for the entire study period (2006-2021).

**Table II.** Summary table of chemical product categories with the color code used for the study.

**Table III.** Summaries of the meanings of all (eco)toxicity indicators mentioned in the report (see Vaughan and Greenslade, 1998 and Posthuma *et al.*, 2002 for definitions).

**Table IV.** Values of PAFi above the Th<sub>5%</sub> (representative of a potential ecological risk) for each effect, life stage, year and river studied when using unquantified and quantified environmental data. Substances above the Th<sub>5%</sub> using only quantified environmental data are shown in bold. In dark gray, values about embryos and in light gray, larvae. As a reminder, PAFi represents the average annual individual PAF specific to each chemical compound and each life stage of the fish taxon for each effect tested in each spawning ground.
spawning grounds (\* : error standard > 15%; \*\* : error standard and periodic average > 15%). The values of msPAF<sub>SE-T</sub> and their standard deviation are shown. The red line indicate the Th<sub>5%</sub>. As a reminder, msPAF<sub>SE-T</sub> represents the periodic average msPAF specific to each life stage of the fish taxon for each effect tested in each spawning ground.

**Figure 15.** Trends of msPAF<sub>SE-AM</sub> using quantified + unquantified environmental data (in %) for the sublethal effects (development, physiology and behavior), for early stages (embryo and larva) and for each category of chemicals in Dodrogne and Garonne spawning grounds (\* : averages exceeding the graph box; their values are shown next to the \*). The red line indicate the Th<sub>5%</sub>. As a reminder, msPAF<sub>SE-AM</sub> represents the average annual msPAF specific to each life stage of the fish taxon for each effect tested and category studied in each spawning ground.

**Figure 16.** Trends of msPAF<sub>SE-AM</sub> using quantified + unquantified environmental data (in %) for the lethal effects (mortality), for early stages (embryo and larva) and for each category of chemicals in Dodrogne and Garonne spawning grounds (\* : averages exceeding the graph box; their values are shown next to the \*). The red line indicate the Th<sub>5%</sub>. As a reminder, msPAF<sub>SE-AM</sub> represents the average annual msPAF specific to each life stage of the fish taxon for each effect tested and category studied in each spawning ground.

**Figure 17.** Probability of survival of allis shad embryos exposed to Garonne water (red) and tap water (yellow) for batch n°1 (A) and batch n°2 (B) (data from Bancel, in prep.).

B. Tables

**Table I.** Summary table of data used per station and river (data from SIEAG, 2023). The code used in the study, the period covered by the data, the coordinates (in Lambert 93), the data total before (Raw data) and after (Selected data) standardization, selection and sorting are indicated for each station. See **Figure 4** for station locations. For final data, a cross is indicated when stations have data for the entire study period (2006-2021).

**Table II.** Summary table of chemical product categories with the color code used for the study.

**Table III.** Summaries of the meanings of all (eco)toxicity indicators mentioned in the report (see Vaughan and Greenslade, 1998 and Posthuma *et al.*, 2002 for definitions).

**Table IV.** Values of PAFi above the Th<sub>5%</sub> (representative of a potential ecological risk) for each effect, life stage, year and river studied when using unquantified and quantified environmental data. Substances above the Th<sub>5%</sub> using only quantified environmental data are shown in bold. In dark gray, values about embryos and in light gray, larvae. As a reminder, PAFi represents the average annual individual PAF specific to each chemical compound and each life stage of the fish taxon for each effect tested in each spawning ground.
iv. Dataset

The raw files, code and figures/tables can be found in the pcloud link below:

<figure><img src="image_3.png" /></figure>

https://e.pcloud.link/publink/show?code=kZz4X2ZeeTVwtba0i4LUK3Q5qAm9HDAE2Ey
iv. Dataset

The raw files, code and figures/tables can be found in the pcloud link below:

→ https://e.pcloud.link/publink/show?code=kZz4X2ZeeTVwtba0i4LUK3Q5qAm9HDAE2Ey
# 1. Presentation of the host structure

Ecology is a scientific discipline that studies interactions between organisms and their environment. Taking into account that our actions have a significant effect on both terrestrial and marine ecosystems, this discipline is essential to understand, evaluate and characterize the consequences of our actions. In this context, I joined the "Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement" (INRAE) in the Cestas-Gazinet center to do my Master 2 internship. INRAE is a national research institute working on three different main areas: agriculture, food and environment. More generally, it covers subjects such as the protection of biodiversity and the fight against global warming. Within INRAE, I was part of the "Écosystèmes Aquatiques et Changements Globaux" (EABX) unit and more particularly the "Fonctionnement et Restauration des Ecosystèmes Estuariens et des populations de Migrateurs Amphihalins" (FREEMA) team that focuses, as its name suggests, on diadromous migratory fish populations and estuarine ecosystems.

My supervisors for this internship were Sarah Bancel, realizing her thesis about "*ex-situ*" experiments to evaluate and understand the response of early fish stages to environmental conditions" and Bertrand Villeneuve, ecologist and coordinator of the FREEMA team. They brought me their expertise and experience throughout this internship. Sarah was able to teach me a lot about ecotoxicology and Bertrand about concepts related to water management. These two complementary profiles gave me a lot to learn during this internship.

This internship aimed to provide knowledge on the dynamics of the presence of contaminants present in the water at the level of allis shad spawning grounds in Gironde-Garonne-Dordogne catchment. In this way, I was able to put into practice the theoretical knowledge acquired during my academic studies through bibliographical research, database management, field manipulation, data analysis, etc. This work gave me the opportunity to see how research and management were really correlated in the world of work. I was able to contribute to uniting these two scientific axes and to see just how essential multidisciplinarity is in a context of ecological crisis.
# 1. Presentation of the host structure

Ecology is a scientific discipline that studies interactions between organisms and their environment. Taking into account that our actions have a significant effect on both terrestrial and marine ecosystems, this discipline is essential to understand, evaluate and characterize the consequences of our actions. In this context, I joined the "Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement" (INRAE) in the Cestas-Gazinet center to do my Master 2 internship. INRAE is a national research institute working on three different main areas: agriculture, food and environment. More generally, it covers subjects such as the protection of biodiversity and the fight against global warming. Within INRAE, I was part of the "Écosystèmes Aquatiques et Changements Globaux" (EABX) unit and more particularly the "Fonctionnement et Restauration des Ecosystèmes Estuariens et des populations de Migrateurs Amphihalins" (FREEMA) team that focuses, as its name suggests, on diadromous migratory fish populations and estuarine ecosystems.

My supervisors for this internship were Sarah Bancel, realizing her thesis about "*ex-situ*" experiments to evaluate and understand the response of early fish stages to environmental conditions" and Bertrand Villeneuve, ecologist and coordinator of the FREEMA team. They brought me their expertise and experience throughout this internship. Sarah was able to teach me a lot about ecotoxicology and Bertrand about concepts related to water management. These two complementary profiles gave me a lot to learn during this internship.

This internship aimed to provide knowledge on the dynamics of the presence of contaminants present in the water at the level of allis shad spawning grounds in Gironde-Garonne-Dordogne catchment. In this way, I was able to put into practice the theoretical knowledge acquired during my academic studies through bibliographical research, database management, field manipulation, data analysis, etc. This work gave me the opportunity to see how research and management were really correlated in the world of work. I was able to contribute to uniting these two scientific axes and to see just how essential multidisciplinarity is in a context of ecological crisis.
## 2. Introduction

### A. *The allis shad, Alosa alosa*

The allis shad (*Alosa alosa*) is mostly a diadromous fish (i.e.; migrating between marine and freshwater environments), anadromous (i.e.; going up the rivers to spawn) and semelparous (i.e.; reproducing only once in their lifetime) species of the *Clupeidae* family (*Mota and Antunes, 2012; Rougier et al., 2012*). As a result, this species evolves in several salinity ranges during its life (Lochet, 2006). The marine phase (Fig. 1) lasts from three to seven years and ends with the return to rivers (April to May) of sexually mature adults (Mennesson-Boisneau, 1990; Baglinière et al., 2003). The freshwater phase (Fig. 1) lasts just a few months (April to October) with a breeding period estimated from May to July (Mennesson-Boisneau, 1990; Baglinière et al., 2003). This reproduction is nocturnal and characterized by a particular noise corresponding to the impact of the caudal fins of organisms against the surface in a circular movement (Boisneau et al., 1990). The whirlpool of water caused by this behavior leads to the expulsion of the genital products, followed by fertilization (Boisneau et al., 1990). Fertilized eggs are released into the water column, settling preferably on a pebble substrate where they are better embedded (Belaud et al., 2001). They hatch five at six days after fertilization, the time required for embryonic development at 20°C (Blaya et al., 2022). Larvae are then guided downstream in slower-moving water, using first the superficial zones of the water column then a significant part of the water column for twenty-three days onwards (Jatteau et al., 2002; Véron et al., 2003). Following larval development, juveniles begin their migration to the marine environment (August to October) spending around thirteen days in estuarine environment (Taverny et al., 2000; Lochet, 2006; Dambrine, 2017). Much remains unknown about the ecology of these early stages (Baumann, 2021).

<figure><img src="image_4.png" /><figcaption>**Figure 1.** Simplified diagram of the allis shad life cycle with the approximate period of each phase (inspired by Gaillagot and Carry, 2014).</figcaption></figure>
## 2. Introduction

### A. *The allis shad, Alosa alosa*

The allis shad (*Alosa alosa*) is mostly a diadromous fish (i.e.; migrating between marine and freshwater environments), anadromous (i.e.; going up the rivers to spawn) and semelparous (i.e.; reproducing only once in their lifetime) species of the *Clupeidae* family (*Mota and Antunes, 2012; Rougier et al., 2012*). As a result, this species evolves in several salinity ranges during its life (Lochet, 2006). The marine phase (Fig. 1) lasts from three to seven years and ends with the return to rivers (April to May) of sexually mature adults (Mennesson-Boisneau, 1990; Baglinière et al., 2003). The freshwater phase (Fig. 1) lasts just a few months (April to October) with a breeding period estimated from May to July (Mennesson-Boisneau, 1990; Baglinière et al., 2003). This reproduction is nocturnal and characterized by a particular noise corresponding to the impact of the caudal fins of organisms against the surface in a circular movement (Boisneau et al., 1990). The whirlpool of water caused by this behavior leads to the expulsion of the genital products, followed by fertilization (Boisneau et al., 1990). Fertilized eggs are released into the water column, settling preferably on a pebble substrate where they are better embedded (Belaud et al., 2001). They hatch five at six days after fertilization, the time required for embryonic development at 20°C (Blaya et al., 2022). Larvae are then guided downstream in slower-moving water, using first the superficial zones of the water column then a significant part of the water column for twenty-three days onwards (Jatteau et al., 2002; Véron et al., 2003). Following larval development, juveniles begin their migration to the marine environment (August to October) spending around thirteen days in estuarine environment (Taverny et al., 2000; Lochet, 2006; Dambrine, 2017). Much remains unknown about the ecology of these early stages (Baumann, 2021).

<figure><img src="image_4.png" /><figcaption>**Figure 1.** Simplified diagram of the allis shad life cycle with the approximate period of each phase (inspired by Gaillagot and Carry, 2014).</figcaption></figure>
B. Population crash of allis shad in the Gironde-Garonne-Dordogne catchment

In the past, allis shad had a distribution along the Atlantic coast from Norway to Morocco and in France the Gironde-Garonne-Dordogne (GGD, Fig. 2) catchment had the most important population (Martin-Vandembulcke, 1999; Baglinière and Elie, 2000). This heritage species was once considered a socio-economic asset, particularly in fisheries where landings could exceed 1 000 metric tons in the GGD catchment in the 1980's (Baglinière et al., 2003). However, in the early 2000's, a crash of the allis shad population has been mentioned at the European scale (Limburg and Waldman, 2009). Especially in the GGD catchment, a decrease in the estimated number of juveniles and potential spawners has been observed (Rougier et al., 2012). Indeed, potential spawners at Golfech (Garonne) and Tuilières (Dordogne) have fallen drastically (Fig. 3). The species was subjected to a fishing moratorium in the GGD catchment in 2008 in order to reduce the impact of fisheries but no sign of recovery has been demonstrated (Bouyssonnie and Filloux, 2021). As a result, this species is now on the "IUCN Red List" as "critically endangered" at the French level (UICN Comité français et al., 2019).

Rougier et al. (2012) discussed theories on the collapse of allis shad population: overfishing, dam construction, degradation of water quality and deterioration of spawning grounds contributing to reproductive failure or early-stages mortality. As previously mentioned, despite the introduction of the moratorium the level of spawner stock is still low. Consequently, fishing does not appear to be the main pressure on the population. In the past, spawning grounds of allis shad in the Garonne-Gironde-Dordogne catchment have shifted further downstream (Acolas et al., 2006; Bardonnet and Jatteau, 2008). Currently, migratory barriers, particularly dams, don't seem to be main pressure on the population, as the main barriers are located relatively far upstream on the Garonne and Dordogne rivers (Paumier et al., 2019; Maire et al., 2023) (Fig. 2). Global change has recently been included in these hypotheses and several studies have been carried out to assess its potential impact on the GGD population (Baumann, 2021). However, it does not appear to impact the range of the GGD catchment population (Lassalle et al., 2009) and don't seem to have any short-term impact on early stages of allis shad (Jatteau et al., 2017). Nevertheless, to date, there is a lack of conclusive evidence regarding the theories related to trophic resources and water quality, especially concerning the early life stages (Baumann, 2021). Early life stages of fish are considered as the critical phase of development due to their high sensitivity to environmental stress (McKim, 1977; Von Westernhagen, 1988; Hutchinson et al., 1998). Therefore, a number of recent studies have focused on these life stages in allis shad in order to close the knowledge gap with adult stage. In particular, these studies have investigated the thermal preferences (Paumier et al., 2019), thermal tolerances (Jatteau et al., 2017), ecological preferenda and pressures (Baumann, 2021) of early stages. Also, the study from Blaya et al. (2022) investigated embryonic development and reported a potential link between water contamination and embryonic survival.
B. Population crash of allis shad in the Gironde-Garonne-Dordogne catchment

In the past, allis shad had a distribution along the Atlantic coast from Norway to Morocco and in France the Gironde-Garonne-Dordogne (GGD, Fig. 2) catchment had the most important population (Martin-Vandembulcke, 1999; Baglinière and Elie, 2000). This heritage species was once considered a socio-economic asset, particularly in fisheries where landings could exceed 1 000 metric tons in the GGD catchment in the 1980's (Baglinière et al., 2003). However, in the early 2000's, a crash of the allis shad population has been mentioned at the European scale (Limburg and Waldman, 2009). Especially in the GGD catchment, a decrease in the estimated number of juveniles and potential spawners has been observed (Rougier et al., 2012). Indeed, potential spawners at Golfech (Garonne) and Tuilières (Dordogne) have fallen drastically (Fig. 3). The species was subjected to a fishing moratorium in the GGD catchment in 2008 in order to reduce the impact of fisheries but no sign of recovery has been demonstrated (Bouyssonnie and Filloux, 2021). As a result, this species is now on the "IUCN Red List" as "critically endangered" at the French level (UICN Comité français et al., 2019).

Rougier et al. (2012) discussed theories on the collapse of allis shad population: overfishing, dam construction, degradation of water quality and deterioration of spawning grounds contributing to reproductive failure or early-stages mortality. As previously mentioned, despite the introduction of the moratorium the level of spawner stock is still low. Consequently, fishing does not appear to be the main pressure on the population. In the past, spawning grounds of allis shad in the Garonne-Gironde-Dordogne catchment have shifted further downstream (Acolas et al., 2006; Bardonnet and Jatteau, 2008). Currently, migratory barriers, particularly dams, don't seem to be main pressure on the population, as the main barriers are located relatively far upstream on the Garonne and Dordogne rivers (Paumier et al., 2019; Maire et al., 2023) (Fig. 2). Global change has recently been included in these hypotheses and several studies have been carried out to assess its potential impact on the GGD population (Baumann, 2021). However, it does not appear to impact the range of the GGD catchment population (Lassalle et al., 2009) and don't seem to have any short-term impact on early stages of allis shad (Jatteau et al., 2017). Nevertheless, to date, there is a lack of conclusive evidence regarding the theories related to trophic resources and water quality, especially concerning the early life stages (Baumann, 2021). Early life stages of fish are considered as the critical phase of development due to their high sensitivity to environmental stress (McKim, 1977; Von Westernhagen, 1988; Hutchinson et al., 1998). Therefore, a number of recent studies have focused on these life stages in allis shad in order to close the knowledge gap with adult stage. In particular, these studies have investigated the thermal preferences (Paumier et al., 2019), thermal tolerances (Jatteau et al., 2017), ecological preferenda and pressures (Baumann, 2021) of early stages. Also, the study from Blaya et al. (2022) investigated embryonic development and reported a potential link between water contamination and embryonic survival.
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Map of the GGD catchment, where the main current allis shad spawning grounds are located between ~Bergerac and ~Tuilières (Dordogne) and ~Agen and ~Golfech (Garonne).</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 3.** Number of estimated spawners in Garonne (Golfech) and Dordogne (Tuilières) spawning grounds between 1999 and 2021 (data from MIGADO, n.d.).</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Map of the GGD catchment, where the main current allis shad spawning grounds are located between ~Bergerac and ~Tuilières (Dordogne) and ~Agen and ~Golfech (Garonne).</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 3.** Number of estimated spawners in Garonne (Golfech) and Dordogne (Tuilières) spawning grounds between 1999 and 2021 (data from MIGADO, n.d.).</figcaption></figure>
### C. The historical pollution of GGD catchment

Despite the fact that GGD catchment was considered as an ecological reference in the past (Lucas et al., 2021), numerous studies have demonstrated significant and diverse water pollution in the catchment (e.g. Budzinski et al., 1997; Blanc et al., 2006), which can impact the allis shad population. Historically, smelting and mining activities at the Decazeville site (near the Lot river, see Fig. 2) have generated metal pollution: lead, zinc, selenium and in particular cadmium which has been measured in high concentrations (Grousset et al., 1999; Blanc et al., 2006). The cadmium has been measured in the liver and kidneys of estuarine fish such as the European eel (Anguilla anguilla) and thinlip mullet (Chelon ramada) (Durrieu et al., 2005). The most recent presence of chromium and vanadium has been associated with the activities of electrolysis and tanneries near the Dordogne and Tarn rivers (Grousset et al., 1999). Other metals found in this catchment include arsenic in the Garonne, Dordogne and Isle rivers (Masson et al., 2007) and silver in the Gironde estuary (Lanceleur et al., 2011b). The silver contribution could be linked to various factors such as wastewater (Lanceleur et al., 2011a). The study by Masson et al. (2006) reports that the presence of certain metals (zinc, copper, etc.) is also linked to intensive agriculture in the GGD catchment.

Furthermore, in the catchment, the study by Acolas et al. (2020) demonstrated a close link between the transcription of certain genes useful for reproduction in European sturgeon (Acipenser sturio) and levels of organochlorine pesticide contamination. In addition, other pollutants of industrial sources have been found in this catchment. A study found high concentrations of polychlorinated biphenyls (PCBs) in European eel (Anguilla anguilla) muscle and European flounder (Platichthys flesus) liver in the Gironde estuary (Bodin et al., 2014). Another study by Tapie et al. (2011) in the Gironde estuary revealed the presence of PCBs and polybrominated diphenyl ethers in European eels (Anguilla anguilla). One more study carried out in the Gironde estuary revealed the presence of polycyclic aromatic hydrocarbons (PAHs) in sediments of this estuary (Budzinski et al., 1997). A recent study highlighted microplastic pollution in the Garonne and its tributaries, with a particular risk near urban areas and during the warm season (De Carvalho et al., 2021). Microplastics have been found in European sturgeon (Acipenser sturio) in the GGD catchment (Samson et al., in prep.).

However, even if the presence of all these compounds in the GGD catchment is documented, we know that the presence of these compounds does not allow us to draw conclusions in terms of toxicity and therefore potential effects (Faggiano, 2013; Geffard et al., 2019). Furthermore, to our knowledge no study has looked specifically at contamination in allis shad spawning grounds in this catchment. For this reason, comparison of environmental concentrations of compounds present in these spawning grounds with toxicity values is essential to deduce the historical and current impact of this contamination (Posthuma et al., 2002; Geffard et al., 2010).

### D. Problematic and objectives

In this context, our study aims to address the following issue: what is the potential toxic risk associated with chemical contamination of allis shad spawning grounds in the GGD catchment for the allis shad?
### C. The historical pollution of GGD catchment

Despite the fact that GGD catchment was considered as an ecological reference in the past (Lucas et al., 2021), numerous studies have demonstrated significant and diverse water pollution in the catchment (e.g. Budzinski et al., 1997; Blanc et al., 2006), which can impact the allis shad population. Historically, smelting and mining activities at the Decazeville site (near the Lot river, see Fig. 2) have generated metal pollution: lead, zinc, selenium and in particular cadmium which has been measured in high concentrations (Grousset et al., 1999; Blanc et al., 2006). The cadmium has been measured in the liver and kidneys of estuarine fish such as the European eel (Anguilla anguilla) and thinlip mullet (Chelon ramada) (Durrieu et al., 2005). The most recent presence of chromium and vanadium has been associated with the activities of electrolysis and tanneries near the Dordogne and Tarn rivers (Grousset et al., 1999). Other metals found in this catchment include arsenic in the Garonne, Dordogne and Isle rivers (Masson et al., 2007) and silver in the Gironde estuary (Lanceleur et al., 2011b). The silver contribution could be linked to various factors such as wastewater (Lanceleur et al., 2011a). The study by Masson et al. (2006) reports that the presence of certain metals (zinc, copper, etc.) is also linked to intensive agriculture in the GGD catchment.

Furthermore, in the catchment, the study by Acolas et al. (2020) demonstrated a close link between the transcription of certain genes useful for reproduction in European sturgeon (Acipenser sturio) and levels of organochlorine pesticide contamination. In addition, other pollutants of industrial sources have been found in this catchment. A study found high concentrations of polychlorinated biphenyls (PCBs) in European eel (Anguilla anguilla) muscle and European flounder (Platichthys flesus) liver in the Gironde estuary (Bodin et al., 2014). Another study by Tapie et al. (2011) in the Gironde estuary revealed the presence of PCBs and polybrominated diphenyl ethers in European eels (Anguilla anguilla). One more study carried out in the Gironde estuary revealed the presence of polycyclic aromatic hydrocarbons (PAHs) in sediments of this estuary (Budzinski et al., 1997). A recent study highlighted microplastic pollution in the Garonne and its tributaries, with a particular risk near urban areas and during the warm season (De Carvalho et al., 2021). Microplastics have been found in European sturgeon (Acipenser sturio) in the GGD catchment (Samson et al., in prep.).

However, even if the presence of all these compounds in the GGD catchment is documented, we know that the presence of these compounds does not allow us to draw conclusions in terms of toxicity and therefore potential effects (Faggiano, 2013; Geffard et al., 2019). Furthermore, to our knowledge no study has looked specifically at contamination in allis shad spawning grounds in this catchment. For this reason, comparison of environmental concentrations of compounds present in these spawning grounds with toxicity values is essential to deduce the historical and current impact of this contamination (Posthuma et al., 2002; Geffard et al., 2010).

### D. Problematic and objectives

In this context, our study aims to address the following issue: what is the potential toxic risk associated with chemical contamination of allis shad spawning grounds in the GGD catchment for the allis shad?
To address this issue, we defined three objectives: (1) Examine the spatio-temporal variation of this potential toxic risk within these spawning grounds for the fish taxon; (2) Estimate the potential toxic risk associated with chemical contamination of the various life stages of the fish taxon species; and (3) Investigate, for early stages, the contribution of chemical compounds, individually or in mixture, to the estimation of this potential toxic risk.

To address these objectives, the types of chemical compounds quantified at least once since 2006 in the main spawning grounds of the allis shad within the GGD catchment was characterized and identified. To estimate the ecological impact of these compounds, a relationship between the environmental concentrations (annual averages) of these compounds and toxicity data was established to determine their potential toxic effects on organisms. The toxicity data were obtained from toxicity tests conducted on a species-contaminant pair (Ramade, 2007). However, no toxicity data were available for allis shad. Thus, we focused on toxicity data specific to the fish taxon obtained in freshwater environments, assuming that toxic effects were similar between the allis shad and the average species within the fish taxon.

Based on this, an indicator of potential toxicity (called Potentially Affected Fraction of species) specific to fish taxon and to different life stages of this taxon (embryo, larva, juvenile and adult) was calculated. Thanks to this indicator, the toxic risk within the allis shad spawning grounds of GGD catchment has been quantified for this taxon. First, the spatio-temporal variation in the values of this indicator representative of the fish taxon was visualized. Then, given that sensitivity to chemical compounds varies depending on fish life stages (McKim, 1977), the toxic risk was estimated for each life stage of this taxon. Lastly, as they are considered the most sensitive (Von Westernhagen, 1988; Hutchinson et al., 1998), the contribution of chemical compounds, individually or as a mixture, was studied for the early life stages of this taxon.

# 3. Materials & Methods

## A. Environmental data: context & use (summarized in Tab. III and Fig. 9).

### a. Context: Water Framework Directive to « Systèmes d'Informations sur l'Eau »

At the end of the 21st century, preserving water quality became a major concern and has been emphasized in various studies (e.g., Norton et al., 1992; Reckhow, 1994; U.S. Environmental Protection Agency, 1998). To address this issue at the European scale, the Water Framework Directive (WFD) was introduced in 2000 (European Commission, 2000, see Ministère de la Transition écologique et solidaire (2019) for its objectives and functioning).

The WFD aims to harmonize regulations regarding water quality throughout Europe and imposes rigorous monitoring of the chemical and ecological status of water bodies. It aims to achieve a good ecological and chemical status of surface waters and groundwater.

Ecological status is divided into five classes: very good, good, moderate, poor and bad, depending on biological, hydromorphological, and physico-chemical factors. Thus, each water body is
To address this issue, we defined three objectives: (1) Examine the spatio-temporal variation of this potential toxic risk within these spawning grounds for the fish taxon; (2) Estimate the potential toxic risk associated with chemical contamination of the various life stages of the fish taxon species; and (3) Investigate, for early stages, the contribution of chemical compounds, individually or in mixture, to the estimation of this potential toxic risk.

To address these objectives, the types of chemical compounds quantified at least once since 2006 in the main spawning grounds of the allis shad within the GGD catchment was characterized and identified. To estimate the ecological impact of these compounds, a relationship between the environmental concentrations (annual averages) of these compounds and toxicity data was established to determine their potential toxic effects on organisms. The toxicity data were obtained from toxicity tests conducted on a species-contaminant pair (Ramade, 2007). However, no toxicity data were available for allis shad. Thus, we focused on toxicity data specific to the fish taxon obtained in freshwater environments, assuming that toxic effects were similar between the allis shad and the average species within the fish taxon.

Based on this, an indicator of potential toxicity (called Potentially Affected Fraction of species) specific to fish taxon and to different life stages of this taxon (embryo, larva, juvenile and adult) was calculated. Thanks to this indicator, the toxic risk within the allis shad spawning grounds of GGD catchment has been quantified for this taxon. First, the spatio-temporal variation in the values of this indicator representative of the fish taxon was visualized. Then, given that sensitivity to chemical compounds varies depending on fish life stages (McKim, 1977), the toxic risk was estimated for each life stage of this taxon. Lastly, as they are considered the most sensitive (Von Westernhagen, 1988; Hutchinson et al., 1998), the contribution of chemical compounds, individually or as a mixture, was studied for the early life stages of this taxon.

## 3. Materials & Methods

### A. Environmental data: context & use (summarized in Tab. III and Fig. 9).

#### a. Context: Water Framework Directive to « Systèmes d'Informations sur l'Eau »

At the end of the 21st century, preserving water quality became a major concern and has been emphasized in various studies (e.g., Norton et al., 1992; Reckhow, 1994; U.S. Environmental Protection Agency, 1998). To address this issue at the European scale, the Water Framework Directive (WFD) was introduced in 2000 (European Commission, 2000, see Ministère de la Transition écologique et solidaire (2019) for its objectives and functioning).

The WFD aims to harmonize regulations regarding water quality throughout Europe and imposes rigorous monitoring of the chemical and ecological status of water bodies. It aims to achieve a good ecological and chemical status of surface waters and groundwater.

Ecological status is divided into five classes: very good, good, moderate, poor and bad, depending on biological, hydromorphological, and physico-chemical factors. Thus, each water body is
compared to a "reference" water body whose state is not (or minimally) influenced by human activities. For example, the fish species diversity in the Garonne or Dordogne rivers could be compared to that of a "reference" river, free from human activities. If any of the elements used to determine the ecological quality of a water body is classified as being in poor status, then the water body is classified as being in poor status. The chemical status, on the other hand, consists of two classes: poor or good, depending on the environmental concentration of 45 priority substances, of which 21 are considered "priority and hazardous". These substances are characterized by a long lifespan in the environment and toxicity at low concentrations (Posthuma et al., 2020). To ensure a good chemical status, environmental quality standards have been established, representing limit values not to be exceeded for these substances (Posthuma et al., 2019).

If both ecological and chemical statuses are classified as being in good status, then the water body is also considered to be in good status.

To achieve good ecological and chemical statuses, the WFD requires that the member states of the European Union develop management plans within the river catchments. In this context, various management measures have been implemented within the river catchments, including a state of play assessment, a monitoring program, a management plan and a program of measures (Petit and Michon, 2015). As part of these monitoring programs, "Water Information Systems" (in french, "Systèmes d'Informations sur l'Eau", called SIE) have been created with the objective of facilitating the collection, management and dissemination of public information related to water in the European Union member states (SIEAG, 2023). Within the data shared by the SIE of each river catchment, one can find environmental concentrations of a whole range of pollutants associated with a specific location and date, and monitored by different data collection devices. These pollutants are measured several times per year.

b. Use of « Systèmes d'Informations sur l'Eau » Adour-Garonne

Within the SIE of the Adour-Garonne catchment (SIEAG, https://adour-garonne.eaufrance.fr/), the relevant data about chemicals were present in the "physicochimie.csv" files for the 21 selected stations used in the study (Fig. 4 and Tab. I). These stations were located within the current main spawning grounds (referred to as "spawning grounds" in the report) of allis shad in the Gironde-Garonne-Dordogne catchment (i.e. between Bergerac-Tuillières on the Dordogne, n = 10 and between Agen-Golfech on the Garonne, n = 11). A total of 221 739 raw data was available.

The environmental data were extracted from a Structured Query Language (SQL) database and then processed in RStudio (Ver. 4.2.2) for further data treatment. A significant effort was made to standardize, select, and sort the data. For example, physico-chemical (e.g., pH) and biological (e.g. gammarids) data were removed to obtain a data set representing only chemical presence in the environment (water, sediment and suspended matter). The data covered the period from 2000 to 2021, but only years from 2006 were retained to ensure data homogeneity. Data before 2006 (~1.25% of the total data) consisted almost exclusively of metals and agrochemicals.
compared to a "reference" water body whose state is not (or minimally) influenced by human activities. For example, the fish species diversity in the Garonne or Dordogne rivers could be compared to that of a "reference" river, free from human activities. If any of the elements used to determine the ecological quality of a water body is classified as being in poor status, then the water body is classified as being in poor status. The chemical status, on the other hand, consists of two classes: poor or good, depending on the environmental concentration of 45 priority substances, of which 21 are considered "priority and hazardous". These substances are characterized by a long lifespan in the environment and toxicity at low concentrations (Posthuma et al., 2020). To ensure a good chemical status, environmental quality standards have been established, representing limit values not to be exceeded for these substances (Posthuma et al., 2019).

If both ecological and chemical statuses are classified as being in good status, then the water body is also considered to be in good status.

To achieve good ecological and chemical statuses, the WFD requires that the member states of the European Union develop management plans within the river catchments. In this context, various management measures have been implemented within the river catchments, including a state of play assessment, a monitoring program, a management plan and a program of measures (Petit and Michon, 2015). As part of these monitoring programs, "Water Information Systems" (in french, "Systèmes d'Informations sur l'Eau", called SIE) have been created with the objective of facilitating the collection, management and dissemination of public information related to water in the European Union member states (SIEAG, 2023). Within the data shared by the SIE of each river catchment, one can find environmental concentrations of a whole range of pollutants associated with a specific location and date, and monitored by different data collection devices. These pollutants are measured several times per year.

b. Use of « Systèmes d'Informations sur l'Eau » Adour-Garonne

Within the SIE of the Adour-Garonne catchment (SIEAG, https://adour-garonne.eaufrance.fr/), the relevant data about chemicals were present in the "physicochimie.csv" files for the 21 selected stations used in the study (Fig. 4 and Tab. I). These stations were located within the current main spawning grounds (referred to as "spawning grounds" in the report) of allis shad in the Gironde-Garonne-Dordogne catchment (i.e. between Bergerac-Tuillières on the Dordogne, n = 10 and between Agen-Golfech on the Garonne, n = 11). A total of 221 739 raw data was available.

The environmental data were extracted from a Structured Query Language (SQL) database and then processed in RStudio (Ver. 4.2.2) for further data treatment. A significant effort was made to standardize, select, and sort the data. For example, physico-chemical (e.g., pH) and biological (e.g. gammarids) data were removed to obtain a data set representing only chemical presence in the environment (water, sediment and suspended matter). The data covered the period from 2000 to 2021, but only years from 2006 were retained to ensure data homogeneity. Data before 2006 (~1.25% of the total data) consisted almost exclusively of metals and agrochemicals.
Finally, selected data comprised a total of 174 789 measures distributed across 539 chemical compounds, sampled either in water, sediments or suspended matter and distributed over 21 stations (Tab. I).

<figure><img src="image_7.png" /><figcaption>**Figure 4.** Map of the land use (Corin Land Cover in 2018, data from Union européenne, 2018) for the study area with studied stations in the current main allis shad spawning grounds of the Garonne and Dordogne. Station abbreviations are shown in **Table I**. Be = Bergerac and Ag = Agen.</figcaption></figure>

<table><caption>**Table I.** Summary table of data used per station and river (data from SIEAG, 2023). The code used in the study, the period covered by the data, the coordinates (in Lambert 93), the data total before (Raw data) and after (Selected data) standardization, selection and sorting are indicated for each station. See **Figure 4** for station locations. For final data, a cross is indicated when stations have data for the entire study period (2006-2021).</caption><thead><tr><th>Stations<br>(upstream to downstream)</th><th>ID<sub>SIEAG</sub></th><th>Code<sub>study</sub></th><th>X</th><th>Y</th><th>Raw<br>data</th><th>Selected<br>data</th><th>Entire<br>period</th></tr></thead><tbody><tr><td colspan="8"><strong>Garonne (11 stations)</strong></td></tr><tr><td>Saint-Nicolas de la Grave</td><td>05118950</td><td>SNDG</td><td>541608</td><td>6331383</td><td>31 959</td><td>29 102</td><td>X</td></tr><tr><td>Saint-Loup</td><td>05118880</td><td>SL</td><td>529701</td><td>6334780</td><td>570</td><td>35</td><td></td></tr><tr><td>Golfech</td><td>05117110</td><td>G</td><td>528485</td><td>6336920</td><td>2 130</td><td>979</td><td></td></tr><tr><td>Lamagistère</td><td>05117000</td><td>Lm</td><td>525386</td><td>6338460</td><td>39 659</td><td>34 696</td><td>X</td></tr><tr><td>Lafox</td><td>05116530</td><td>Lf</td><td>516621</td><td>6342490</td><td>1 355</td><td>71</td><td></td></tr><tr><td>Boe</td><td>05115940</td><td>Bo</td><td>513805</td><td>6343040</td><td>8</td><td>7</td><td></td></tr><tr><td>Upstream Agen</td><td>05113000</td><td>UA</td><td>509659</td><td>6343380</td><td>12 388</td><td>10 335</td><td></td></tr><tr><td>Downstream Agen</td><td>05112000</td><td>DA</td><td>508685</td><td>6348360</td><td>2 709</td><td>77</td><td></td></tr><tr><td>Colayrac Saint-Cirq</td><td>05111900</td><td>CSC</td><td>503168</td><td>6349210</td><td>1 737</td><td>129</td><td></td></tr><tr><td>Port Sainte-Marie</td><td>05111500</td><td>PSM</td><td>491863</td><td>6353390</td><td>10 871</td><td>6 230</td><td>X</td></tr><tr><td>Upstream Lot</td><td>05104000</td><td>UL</td><td>486212</td><td>6358050</td><td>47 957</td><td>39 866</td><td>X</td></tr><tr><td colspan="8"><strong>Dordogne (10 stations)</strong></td></tr><tr><td>Tremolat</td><td>05048210</td><td>T</td><td>528055</td><td>6421613</td><td>14 623</td><td>10 292</td><td>X</td></tr><tr><td>Lalinde</td><td>05047710</td><td>Ld</td><td>514731</td><td>6418510</td><td>1 175</td><td>42</td><td></td></tr><tr><td>Creysse</td><td>05061138</td><td>C</td><td>507709</td><td>6420140</td><td>51</td><td>41</td><td></td></tr><tr><td>Cours de Pile</td><td>05047600</td><td>CdP</td><td>504691</td><td>6419120</td><td>33 490</td><td>29 305</td><td>X</td></tr><tr><td>Bergerac</td><td>05047510</td><td>Be</td><td>501173</td><td>6419700</td><td>7 427</td><td>6 803</td><td></td></tr><tr><td>Downstream Bergerac</td><td>05047100</td><td>DBe</td><td>498288</td><td>6419130</td><td>51</td><td>41</td><td></td></tr><tr><td>Downstream Bergerac (D4)</td><td>05047000</td><td>DBe4</td><td>489203</td><td>6419170</td><td>11 469</td><td>6 511</td><td>X</td></tr><tr><td>Downstream Gardonne</td><td>05046900</td><td>DG</td><td>487753</td><td>6419990</td><td>51</td><td>41</td><td></td></tr><tr><td>Fleix</td><td>05046740</td><td>F</td><td>482317</td><td>6421660</td><td>2 008</td><td>145</td><td></td></tr><tr><td>Saint-Antoine de Breuilh</td><td>05046400</td><td>SAB</td><td>475291</td><td>6418250</td><td>51</td><td>41</td><td></td></tr></tbody></table>
Finally, selected data comprised a total of 174 789 measures distributed across 539 chemical compounds, sampled either in water, sediments or suspended matter and distributed over 21 stations (Tab. I).

<figure><img src="image_7.png" /><figcaption>**Figure 4.** Map of the land use (Corin Land Cover in 2018, data from Union européenne, 2018) for the study area with studied stations in the current main allis shad spawning grounds of the Garonne and Dordogne. Station abbreviations are shown in **Table I**. Be = Bergerac and Ag = Agen.</figcaption></figure>

<table><caption>**Table I.** Summary table of data used per station and river (data from SIEAG, 2023). The code used in the study, the period covered by the data, the coordinates (in Lambert 93), the data total before (Raw data) and after (Selected data) standardization, selection and sorting are indicated for each station. See **Figure 4** for station locations. For final data, a cross is indicated when stations have data for the entire study period (2006-2021).</caption><thead><tr><th>Stations<br>(upstream to downstream)</th><th>ID<sub>SIEAG</sub></th><th>Code<sub>study</sub></th><th>X</th><th>Y</th><th>Raw<br>data</th><th>Selected<br>data</th><th>Entire<br>period</th></tr></thead><tbody><tr><td colspan="8"><strong>Garonne (11 stations)</strong></td></tr><tr><td>Saint-Nicolas de la Grave</td><td>05118950</td><td>SNDG</td><td>541608</td><td>6331383</td><td>31 959</td><td>29 102</td><td>X</td></tr><tr><td>Saint-Loup</td><td>05118880</td><td>SL</td><td>529701</td><td>6334780</td><td>570</td><td>35</td><td></td></tr><tr><td>Golfech</td><td>05117110</td><td>G</td><td>528485</td><td>6336920</td><td>2 130</td><td>979</td><td></td></tr><tr><td>Lamagistère</td><td>05117000</td><td>Lm</td><td>525386</td><td>6338460</td><td>39 659</td><td>34 696</td><td>X</td></tr><tr><td>Lafox</td><td>05116530</td><td>Lf</td><td>516621</td><td>6342490</td><td>1 355</td><td>71</td><td></td></tr><tr><td>Boe</td><td>05115940</td><td>Bo</td><td>513805</td><td>6343040</td><td>8</td><td>7</td><td></td></tr><tr><td>Upstream Agen</td><td>05113000</td><td>UA</td><td>509659</td><td>6343380</td><td>12 388</td><td>10 335</td><td></td></tr><tr><td>Downstream Agen</td><td>05112000</td><td>DA</td><td>508685</td><td>6348360</td><td>2 709</td><td>77</td><td></td></tr><tr><td>Colayrac Saint-Cirq</td><td>05111900</td><td>CSC</td><td>503168</td><td>6349210</td><td>1 737</td><td>129</td><td></td></tr><tr><td>Port Sainte-Marie</td><td>05111500</td><td>PSM</td><td>491863</td><td>6353390</td><td>10 871</td><td>6 230</td><td>X</td></tr><tr><td>Upstream Lot</td><td>05104000</td><td>UL</td><td>486212</td><td>6358050</td><td>47 957</td><td>39 866</td><td>X</td></tr><tr><td colspan="8"><strong>Dordogne (10 stations)</strong></td></tr><tr><td>Tremolat</td><td>05048210</td><td>T</td><td>528055</td><td>6421613</td><td>14 623</td><td>10 292</td><td>X</td></tr><tr><td>Lalinde</td><td>05047710</td><td>Ld</td><td>514731</td><td>6418510</td><td>1 175</td><td>42</td><td></td></tr><tr><td>Creysse</td><td>05061138</td><td>C</td><td>507709</td><td>6420140</td><td>51</td><td>41</td><td></td></tr><tr><td>Cours de Pile</td><td>05047600</td><td>CdP</td><td>504691</td><td>6419120</td><td>33 490</td><td>29 305</td><td>X</td></tr><tr><td>Bergerac</td><td>05047510</td><td>Be</td><td>501173</td><td>6419700</td><td>7 427</td><td>6 803</td><td></td></tr><tr><td>Downstream Bergerac</td><td>05047100</td><td>DBe</td><td>498288</td><td>6419130</td><td>51</td><td>41</td><td></td></tr><tr><td>Downstream Bergerac (D4)</td><td>05047000</td><td>DBe4</td><td>489203</td><td>6419170</td><td>11 469</td><td>6 511</td><td>X</td></tr><tr><td>Downstream Gardonne</td><td>05046900</td><td>DG</td><td>487753</td><td>6419990</td><td>51</td><td>41</td><td></td></tr><tr><td>Fleix</td><td>05046740</td><td>F</td><td>482317</td><td>6421660</td><td>2 008</td><td>145</td><td></td></tr><tr><td>Saint-Antoine de Breuilh</td><td>05046400</td><td>SAB</td><td>475291</td><td>6418250</td><td>51</td><td>41</td><td></td></tr></tbody></table>
The chemical compounds were classified into seven major distinct classes (Table II), inspired by the review by Margot et al. (2015). The agrochemicals consisted of herbicides, fungicides, insecticides and other biocides (rodenticides, acaricides, molluscicides, nematicides, etc.). According to the periodic table of elements (see Stewart, 2007), the metals included certain alkaline earth metals (e.g., calcium, beryllium), transition metals (e.g., copper, silver, zinc), post-transition metals (e.g., aluminum, lead), actinides (e.g., uranium), metalloids (e.g., arsenic, antimony) and some other non-metals (e.g., selenium). The hygiene and care products (HCPs) category comprised pharmaceuticals (e.g., ibuprofen, paracetamol, metformin), cosmetics and hygiene products (e.g., musk xylene, butylparaben, octocrylene) and hormones (e.g., estrone, norethindrone). Among the remaining industrial compounds, the PCBs, PAHs, and per- and polyfluoroalkyl substances (PFASs) were isolated due to their significant persistence and toxicity (O'Connor et al., 2022). Lastly, the other industrial pollutants (OIPs) category included various compounds used for different industrial purposes, such as flame retardants (e.g., polybrominated diphenyl ethers), plasticizers (e.g., phthalates, bisphenol A), solvents (e.g., benzene, xylene) or other applications when industrial use was multiple (e.g., chloralkanes, tributyltin). Industrial degradation products (e.g., dioxins) have also been added to this category.

<table><caption>**Table II.** Summary table of chemical product categories with the color code used for the study.</caption><thead><tr><th>Chemical categories</th><th>Abbreviations</th><th>Constitution</th><th>Color code</th></tr></thead><tbody><tr><td>Agrochemicals</td><td>-</td><td>Herbicides, Fungicides, Insecticides and Other biocides</td><td></td></tr><tr><td>Metals</td><td>-</td><td>Metals</td><td></td></tr><tr><td>Hygiene and Care Products</td><td>HCPs</td><td>Pharmaceuticals, Cosmetics and hygiene products and Hormones</td><td></td></tr><tr><td>PAHs</td><td>PAHs</td><td>PAHs</td><td></td></tr><tr><td>PCBs</td><td>PCBs</td><td>PCBs</td><td></td></tr><tr><td>PFASs</td><td>PFASs</td><td>PFASs</td><td></td></tr><tr><td>Other Industrial Pollutants</td><td>OIPs</td><td>Flame retardants, Plasticizers, Solvents, Degradation products and Others</td><td></td></tr></tbody></table>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

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

<figure><img src="image_14.png" /></figure>

For these chemical compounds, only the concentrations in water were of interest for the study. In the SIEAG database, the data available for water consisted of two different fractions: raw water and the aqueous phase of water. The environmental concentrations of chemical compounds present in raw water corresponded to the concentrations found in water as it is in the environment. This fraction is schematically composed of the addition of the environmental concentrations of chemical compounds from both the aqueous phase of water and the particulate phase of water (Fig. 5). The environmental concentrations of chemical compounds present in the aqueous phase of water were
The chemical compounds were classified into seven major distinct classes (Table II), inspired by the review by Margot et al. (2015). The agrochemicals consisted of herbicides, fungicides, insecticides and other biocides (rodenticides, acaricides, molluscicides, nematicides, etc.). According to the periodic table of elements (see Stewart, 2007), the metals included certain alkaline earth metals (e.g., calcium, beryllium), transition metals (e.g., copper, silver, zinc), post-transition metals (e.g., aluminum, lead), actinides (e.g., uranium), metalloids (e.g., arsenic, antimony) and some other non-metals (e.g., selenium). The hygiene and care products (HCPs) category comprised pharmaceuticals (e.g., ibuprofen, paracetamol, metformin), cosmetics and hygiene products (e.g., musk xylene, butylparaben, octocrylene) and hormones (e.g., estrone, norethindrone). Among the remaining industrial compounds, the PCBs, PAHs, and per- and polyfluoroalkyl substances (PFASs) were isolated due to their significant persistence and toxicity (O'Connor et al., 2022). Lastly, the other industrial pollutants (OIPs) category included various compounds used for different industrial purposes, such as flame retardants (e.g., polybrominated diphenyl ethers), plasticizers (e.g., phthalates, bisphenol A), solvents (e.g., benzene, xylene) or other applications when industrial use was multiple (e.g., chloralkanes, tributyltin). Industrial degradation products (e.g., dioxins) have also been added to this category.

<table><caption>**Table II.** Summary table of chemical product categories with the color code used for the study.</caption><thead><tr><th>Chemical categories</th><th>Abbreviations</th><th>Constitution</th><th>Color code</th></tr></thead><tbody><tr><td>Agrochemicals</td><td>-</td><td>Herbicides, Fungicides, Insecticides and Other biocides</td><td></td></tr><tr><td>Metals</td><td>-</td><td>Metals</td><td></td></tr><tr><td>Hygiene and Care Products</td><td>HCPs</td><td>Pharmaceuticals, Cosmetics and hygiene products and Hormones</td><td></td></tr><tr><td>PAHs</td><td>PAHs</td><td>PAHs</td><td></td></tr><tr><td>PCBs</td><td>PCBs</td><td>PCBs</td><td></td></tr><tr><td>PFASs</td><td>PFASs</td><td>PFASs</td><td></td></tr><tr><td>Other Industrial Pollutants</td><td>OIPs</td><td>Flame retardants, Plasticizers, Solvents, Degradation products and Others</td><td></td></tr></tbody></table>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

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

<figure><img src="image_14.png" /></figure>

For these chemical compounds, only the concentrations in water were of interest for the study. In the SIEAG database, the data available for water consisted of two different fractions: raw water and the aqueous phase of water. The environmental concentrations of chemical compounds present in raw water corresponded to the concentrations found in water as it is in the environment. This fraction is schematically composed of the addition of the environmental concentrations of chemical compounds from both the aqueous phase of water and the particulate phase of water (Fig. 5). The environmental concentrations of chemical compounds present in the aqueous phase of water were
obtained from the concentrations of the water fraction obtained after removing the particulate phase through filtration and centrifugation. Therefore, the data from this fraction were underestimated since the environmental concentrations of the particulate phase of water were not available in the database. Metals mostly represented this fraction (~99.7%). Moreover, for metals, data from this fraction represented ~80% of the data sampled in water. Thus, it is important to keep in mind that most of the environmental concentrations of metals have been underestimated. However, the data concerning metals in this fraction (i.e., dissolved in the water) represent the bioavailable fraction for organisms (Billon et al., 2010). Thus, even if underestimated, the metal concentrations were environmentally relevant. Furthermore, data related to aluminum were removed from the analysis due to the disproportionate magnitude of quantified environmental concentrations. Some values exceeded 1 mg/L, whereas studies have shown that dissolved aluminum concentrations are generally below 0.1 mg/L (CEPA, 2010). Thus, the unrealistic aluminum concentrations could be attributed to errors during data entry, among others.

Furthermore, some lipophilic chemical compounds (i.e., having an affinity for fatty supports such as sediments) can be found in the water due to resuspension phenomena (Bromilow et al., 2003). Moreover, since embryos of allis shad use these habitats, they may come into contact with these compounds, hence the relevance to add this data to the analysis (see Baglinière et al., 2003). Thus, to obtain potential environmental concentrations in the water for lipophilic compounds, a sediment-water partition coefficient (K) was calculated using the same method as Sarkis (2018). This method involved calculating a Ki for each year, each station, and each quantified chemical compound "i" in both water and sediments. Ki (in L/kg) represents the quotient of the average data concentration in sediment (called meanS in mg/kg) divided by the average concentration in the water (called meanW in mg/L):

$$ (1) \quad Ki = \frac{meanS}{meanW} $$

Next, by taking the average of all Ki values, we obtained a Kmean. Finally, using this Kmean (in L/kg), the concentrations in the water (called [water], in mg/L) for both quantified and unquantified values of lipophilic compounds was extrapolated, as follows:

$$ (2) \quad [water] = \frac{[sediment]}{Kmean} \quad ; \quad [sediment] \text{ is the actual sediment concentration (in mg/kg)} $$

Thus, an initially quantified or unquantified value in the sediment ended up being a quantified or unquantified value in the water after extrapolation (Fig. 5, black arrow). This extrapolation added data: 1% (HCPs), 3% (agrochemicals), 6% (PFASs), 8% (OIPs), 15% (metals), 17% (PAHs), and 48% (PCBs). Therefore, the environmental concentrations observed in the water were classified into three fractions (Fig. 5): raw water (~80.5%), aqueous phase of water (~12.5%) and values of raw water extrapolated from the sediments (~7%).
obtained from the concentrations of the water fraction obtained after removing the particulate phase through filtration and centrifugation. Therefore, the data from this fraction were underestimated since the environmental concentrations of the particulate phase of water were not available in the database. Metals mostly represented this fraction (~99.7%). Moreover, for metals, data from this fraction represented ~80% of the data sampled in water. Thus, it is important to keep in mind that most of the environmental concentrations of metals have been underestimated. However, the data concerning metals in this fraction (i.e., dissolved in the water) represent the bioavailable fraction for organisms (Billon et al., 2010). Thus, even if underestimated, the metal concentrations were environmentally relevant. Furthermore, data related to aluminum were removed from the analysis due to the disproportionate magnitude of quantified environmental concentrations. Some values exceeded 1 mg/L, whereas studies have shown that dissolved aluminum concentrations are generally below 0.1 mg/L (CEPA, 2010). Thus, the unrealistic aluminum concentrations could be attributed to errors during data entry, among others.

Furthermore, some lipophilic chemical compounds (i.e., having an affinity for fatty supports such as sediments) can be found in the water due to resuspension phenomena (Bromilow et al., 2003). Moreover, since embryos of allis shad use these habitats, they may come into contact with these compounds, hence the relevance to add this data to the analysis (see Baglinière et al., 2003). Thus, to obtain potential environmental concentrations in the water for lipophilic compounds, a sediment-water partition coefficient (K) was calculated using the same method as Sarkis (2018). This method involved calculating a Ki for each year, each station, and each quantified chemical compound "i" in both water and sediments. Ki (in L/kg) represents the quotient of the average data concentration in sediment (called meanS in mg/kg) divided by the average concentration in the water (called meanW in mg/L):

$$ (1) \quad Ki = \frac{meanS}{meanW} $$

Next, by taking the average of all Ki values, we obtained a Kmean. Finally, using this Kmean (in L/kg), the concentrations in the water (called [water], in mg/L) for both quantified and unquantified values of lipophilic compounds was extrapolated, as follows:

$$ (2) \quad [water] = \frac{[sediment]}{Kmean} \quad ; \quad [sediment] \text{ is the actual sediment concentration (in mg/kg)} $$

Thus, an initially quantified or unquantified value in the sediment ended up being a quantified or unquantified value in the water after extrapolation (Fig. 5, black arrow). This extrapolation added data: 1% (HCPs), 3% (agrochemicals), 6% (PFASs), 8% (OIPs), 15% (metals), 17% (PAHs), and 48% (PCBs). Therefore, the environmental concentrations observed in the water were classified into three fractions (Fig. 5): raw water (~80.5%), aqueous phase of water (~12.5%) and values of raw water extrapolated from the sediments (~7%).
<figure><img src="image_15.png" /><figcaption>**Figure 5.** Schematic representation for enhanced comprehension of the origin of environmental water data; concepts of raw water, aqueous phase of water, particulate phase of water and sediment-water partition coefficient.</figcaption></figure>

Next, only the chemical compounds that were quantified (i.e., those exceeding the limit of quantification) at least once in the water between 2006 and 2021 and in at least one of the 21 studied stations, were retained for the study. For these chemical compounds, both the quantified concentrations (i.e., exceeding the limit of quantification) and the concentrations below the limit of quantification but above the limit of detection were preserved. In other words, only the concentrations of chemicals detected, but not necessarily quantified, were considered (see the **Figure 6** to understand the concepts of limits of quantification and detection). Thus, 83 808 environmental data (representing 245 substances) were used, with 11 159 quantified data and 72 649 unquantified data. In the rest of the report, these data were used either using quantified data only (n = 11 159) or using both quantified and unquantified data (n = 83 808). Each year, there were between 1.29 and 3.69 times more substances when considering both quantified and unquantified data compared to quantified data only. Concentrations below the limit of quantification, they were extrapolated to half of the limit of quantification instead of using the limit of quantification or a value of 0, considered too extreme scenarios (**Fig. 6**, red lines) (EFSA et al., 2018). However, it is important to note that extrapolating environmental concentrations of unquantified chemical compounds to half of their limit of quantification could lead to an overestimation or underestimation of these concentrations, without certainty. Furthermore, it is important to consider that limits of quantification can vary within a station, from station to station and from year to year, due to different measurement methods used by laboratories to quantify the environmental concentration of a sample (**Fig. 6**, blue and gray).

Finally, average annual concentrations were calculated for each station and then for the spawning grounds of the Garonne and Dordogne rivers. Average annual concentrations for each spawning grounds are presented in **supplementary material #1** and **supplementary material #2** when environmental data are quantified and unquantified and when environmental data are quantified only, respectively. These two sets of environmental data (quantified + unquantified or only quantified) were used as the basis for comparing the environmental data with the toxicity data, in order to take into account the bias associated with extrapolation to half the limit of quantification.
<figure><img src="image_15.png" /><figcaption>**Figure 5.** Schematic representation for enhanced comprehension of the origin of environmental water data; concepts of raw water, aqueous phase of water, particulate phase of water and sediment-water partition coefficient.</figcaption></figure>

Next, only the chemical compounds that were quantified (i.e., those exceeding the limit of quantification) at least once in the water between 2006 and 2021 and in at least one of the 21 studied stations, were retained for the study. For these chemical compounds, both the quantified concentrations (i.e., exceeding the limit of quantification) and the concentrations below the limit of quantification but above the limit of detection were preserved. In other words, only the concentrations of chemicals detected, but not necessarily quantified, were considered (see the **Figure 6** to understand the concepts of limits of quantification and detection). Thus, 83 808 environmental data (representing 245 substances) were used, with 11 159 quantified data and 72 649 unquantified data. In the rest of the report, these data were used either using quantified data only (n = 11 159) or using both quantified and unquantified data (n = 83 808). Each year, there were between 1.29 and 3.69 times more substances when considering both quantified and unquantified data compared to quantified data only. Concentrations below the limit of quantification, they were extrapolated to half of the limit of quantification instead of using the limit of quantification or a value of 0, considered too extreme scenarios (**Fig. 6**, red lines) (EFSA et al., 2018). However, it is important to note that extrapolating environmental concentrations of unquantified chemical compounds to half of their limit of quantification could lead to an overestimation or underestimation of these concentrations, without certainty. Furthermore, it is important to consider that limits of quantification can vary within a station, from station to station and from year to year, due to different measurement methods used by laboratories to quantify the environmental concentration of a sample (**Fig. 6**, blue and gray).

Finally, average annual concentrations were calculated for each station and then for the spawning grounds of the Garonne and Dordogne rivers. Average annual concentrations for each spawning grounds are presented in **supplementary material #1** and **supplementary material #2** when environmental data are quantified and unquantified and when environmental data are quantified only, respectively. These two sets of environmental data (quantified + unquantified or only quantified) were used as the basis for comparing the environmental data with the toxicity data, in order to take into account the bias associated with extrapolation to half the limit of quantification.
<figure><img src="image_16.png" /><figcaption>**Figure 6.** Principles of limits of detection (LOD) and quantification (LOQ) and extrapolation problems associated with data where the actual environmental concentration (red) is below the laboratory-specific LOQ (grey and blue) for the same substance.</figcaption></figure>

B. Toxicological data: (ms)PAF method and our case study (summarized in **Tab. III** and **Fig. 9**)

a. Indicators used in the (ms)PAF method

To investigate the potential impact of chemical compounds present in allis shad spawning grounds in the GGD catchment, we employed a methodological approach called "Potentially Affected Fraction of species" (PAF). This method has been used as an indicator of potential toxicity in various previous studies (e.g. Klepper et al., 1998; Beaumelle et al., 2017; Rämö et al., 2018). It allows estimating the toxic pressure on the ecosystem, defined as the fraction of species (in %) that could potentially be affected by one or more substances actually present in the environment (Labouze, 2000; Carafa et al., 2011). It is based on the concept of "Species Sensitivity Distribution" (SSD) (Fox et al., 2021), defined as all models representing the variation in sensitivity of a species to a specific source of damage such as chemical exposure (EFSA, 2023). This concept is widely used in ecological risk assessment studies (Posthuma et al., 2002), which assess the probability that exposure to an environmental stressor (e.g., chemicals exposure) will result in an environmental impact (EPA, 2023). The SSDs are constructed based on the general assumption that the differences in species sensitivities identified during toxicity tests (usually conducted in the laboratory) follow a statistical distribution, which allows prediction of the PAF in the field (Posthuma et al., 2002; de Zwart and Posthuma, 2005). However, this approach is highly dependent on the quantity and quality of the toxicity data that are used to construct the model (Wheeler et al., 2002).

These toxicity data are derived from toxicity tests, as explained in detail in the book by Ramade (2007). These tests are conducted to determine the impact of a chemical compound on a target species or a particular life stage of that target species. This is done by testing several individuals of the species. They are usually divided into two groups: a "control" group (exposed to a concentration of the test chemical equal to zero - not exposed) and a "test" group (exposed to a concentration of the test chemical not equal to zero - exposed). This "test" group is divided in different sub-groups, each exposed to a different concentration of the test chemical, in order to have an increasing gradient of concentrations tested. After a specified period, the responses of individuals from both
<figure><img src="image_16.png" /><figcaption>**Figure 6.** Principles of limits of detection (LOD) and quantification (LOQ) and extrapolation problems associated with data where the actual environmental concentration (red) is below the laboratory-specific LOQ (grey and blue) for the same substance.</figcaption></figure>

B. Toxicological data: (ms)PAF method and our case study (summarized in **Tab. III** and **Fig. 9**)

a. Indicators used in the (ms)PAF method

To investigate the potential impact of chemical compounds present in allis shad spawning grounds in the GGD catchment, we employed a methodological approach called "Potentially Affected Fraction of species" (PAF). This method has been used as an indicator of potential toxicity in various previous studies (e.g. Klepper et al., 1998; Beaumelle et al., 2017; Rämö et al., 2018). It allows estimating the toxic pressure on the ecosystem, defined as the fraction of species (in %) that could potentially be affected by one or more substances actually present in the environment (Labouze, 2000; Carafa et al., 2011). It is based on the concept of "Species Sensitivity Distribution" (SSD) (Fox et al., 2021), defined as all models representing the variation in sensitivity of a species to a specific source of damage such as chemical exposure (EFSA, 2023). This concept is widely used in ecological risk assessment studies (Posthuma et al., 2002), which assess the probability that exposure to an environmental stressor (e.g., chemicals exposure) will result in an environmental impact (EPA, 2023). The SSDs are constructed based on the general assumption that the differences in species sensitivities identified during toxicity tests (usually conducted in the laboratory) follow a statistical distribution, which allows prediction of the PAF in the field (Posthuma et al., 2002; de Zwart and Posthuma, 2005). However, this approach is highly dependent on the quantity and quality of the toxicity data that are used to construct the model (Wheeler et al., 2002).

These toxicity data are derived from toxicity tests, as explained in detail in the book by Ramade (2007). These tests are conducted to determine the impact of a chemical compound on a target species or a particular life stage of that target species. This is done by testing several individuals of the species. They are usually divided into two groups: a "control" group (exposed to a concentration of the test chemical equal to zero - not exposed) and a "test" group (exposed to a concentration of the test chemical not equal to zero - exposed). This "test" group is divided in different sub-groups, each exposed to a different concentration of the test chemical, in order to have an increasing gradient of concentrations tested. After a specified period, the responses of individuals from both
groups exposed to the chemical compound are compared to quantify lethal toxicity (mortality effects) or sublethal toxicity (effects on reproduction, development, physiology, etc.). Essentially, this quantification allows expressing the degree of the effect related to the presence of the tested chemical on the tested species compared to a scenario where the chemical compound is absent.

However, there are different ways of expressing toxicity: ECx (Effect Concentration of x% of individuals), NOEC (No Observed Effect Concentration), LOEC (Low Observed Effect Concentration), the definitions of which are summarized in the **Table III** at the end of **Materials & Methods, Part D**. These data are called "endpoints" (Viau and Tardif, 2003). The ECx values are used to define a lethal or sublethal effect for x% of the organisms exposed to a chemical compound compared to organisms that are not exposed to this chemical compound. NOECs and LOECs are used to define the highest concentration showing no effect and the lowest concentration showing the presence of an effect, respectively, for organisms exposed to a chemical in comparison with non-exposed organisms. The **Figure 7** represents graphically the differences between these endpoints.

Then, SSDs can be constructed using this data. In this study, the selection of toxicity data focused on ECx with x equal to 50 (i.e., EC50) for several reasons. Firstly, EC50 data are considered the most reliable for constructing an SSD (Haye et al., 2007). Secondly, they constitute the largest group of ECx in terms of available data quantity (Aurisano et al., 2019). Thirdly, they are more precise than other ECx values such as EC10 (Radix et al., 2000). Fourthly, the use of NOECs and LOECs is highly controversial in ecotoxicology (e.g. Laskowski, 1995; Warne and van Dam, 2008; Green et al., 2013). Indeed, they only consider two levels of effects; no observed effect for NOECs and observed effect for LOECs, which can lead to a loss of important information about the sensitivity of tested organisms to different levels of environmental stress (Figure 7, red lines) (Landis and Chapman, 2011; Iwasaki et al., 2015).

<figure><img src="image_17.png" /><figcaption>**Figure 7.** Classical concentration-response curve with differences between endpoints in ecotoxicology (inspired by Ghillebaert-Giuliani and Ghillebaert, 2017). The red lines represent the uncertainty around NOECs and LOECs. An example would be to study the effect (on reproduction, behavior, mortality, etc.) of glyphosate-containing agrochemical on adult zebrafish (*Danio rerio*).</figcaption></figure>
groups exposed to the chemical compound are compared to quantify lethal toxicity (mortality effects) or sublethal toxicity (effects on reproduction, development, physiology, etc.). Essentially, this quantification allows expressing the degree of the effect related to the presence of the tested chemical on the tested species compared to a scenario where the chemical compound is absent.

However, there are different ways of expressing toxicity: ECx (Effect Concentration of x% of individuals), NOEC (No Observed Effect Concentration), LOEC (Low Observed Effect Concentration), the definitions of which are summarized in the **Table III** at the end of **Materials & Methods, Part D**. These data are called "endpoints" (Viau and Tardif, 2003). The ECx values are used to define a lethal or sublethal effect for x% of the organisms exposed to a chemical compound compared to organisms that are not exposed to this chemical compound. NOECs and LOECs are used to define the highest concentration showing no effect and the lowest concentration showing the presence of an effect, respectively, for organisms exposed to a chemical in comparison with non-exposed organisms. The **Figure 7** represents graphically the differences between these endpoints.

Then, SSDs can be constructed using this data. In this study, the selection of toxicity data focused on ECx with x equal to 50 (i.e., EC50) for several reasons. Firstly, EC50 data are considered the most reliable for constructing an SSD (Haye et al., 2007). Secondly, they constitute the largest group of ECx in terms of available data quantity (Aurisano et al., 2019). Thirdly, they are more precise than other ECx values such as EC10 (Radix et al., 2000). Fourthly, the use of NOECs and LOECs is highly controversial in ecotoxicology (e.g. Laskowski, 1995; Warne and van Dam, 2008; Green et al., 2013). Indeed, they only consider two levels of effects; no observed effect for NOECs and observed effect for LOECs, which can lead to a loss of important information about the sensitivity of tested organisms to different levels of environmental stress (Figure 7, red lines) (Landis and Chapman, 2011; Iwasaki et al., 2015).

<figure><img src="image_17.png" /><figcaption>**Figure 7.** Classical concentration-response curve with differences between endpoints in ecotoxicology (inspired by Ghillebaert-Giuliani and Ghillebaert, 2017). The red lines represent the uncertainty around NOECs and LOECs. An example would be to study the effect (on reproduction, behavior, mortality, etc.) of glyphosate-containing agrochemical on adult zebrafish (*Danio rerio*).</figcaption></figure>
Thus, from these EC50s, it is possible in many steps to estimate the concentration required to obtain 50% of potentially affected species among the total species studied (i.e. HC50, grey dotted line in **Figure 8**) specifically for each chemical compound (Payet, 2004; Plouffe et al., 2016; Sarkis, 2018). The first step is to consider the geometric mean of the EC50s for each species-substance pair. Next, we consider the geometric mean of these EC50s for each taxon-substance pair. Finally, the geometric mean of the EC50s representative of each taxon is calculated to obtain a HC50 specific to each substance and representative of the ecosystem. Then, this HC50 can be compared with an environmental concentration (average or maximum) of a chemical compound to estimate the PAF (dotted blue line in **Figure 8**). To sum up, by associating a relevant environmental concentration of a chemical compound with toxicity data, this method attempts to come as close as possible to an estimation of the toxic risk (in fine, ecological risk) potentially generated in reality by this compound.

Initially, this method was used to study the effects of individual chemical compounds (Klepper and van de Meent, 1997). However, aquatic organisms are constantly exposed to a large number of chemical compounds from various environmental and anthropogenic sources (Sumpter and Jobling, 1995; Brack et al., 2016; Escher et al., 2020). Therefore, since the early 2000s, due to the need to assess the impact of mixtures of potentially toxic substances, the method has been extended to mixtures (Goedkoop and Spriensma, 2000; de Zwart and Posthuma, 2005). Thus, the approach used in our study allows estimating, in addition to the fraction of species potentially affected by a single chemical compound (PAFi), the fraction affected by a mixture of substances (msPAF). When using the msPAF indicator, two additive models should be considered: the addition of concentrations and the addition of responses depending on the similarity of the toxic mode of action of the chemical compounds (Faggiano, 2013). In our case, we used a simplification of the msPAF indicator (see Pennington et al., 2004), which avoids discriminating the chemical compounds studied based on their toxic mode of action. The method, as applied, is based on the linearity theory of the SSD below the HC50 (**Figure 8**, red line). Considering this method, the (ms)PAF indicators (PAFi and msPAF) can be calculated using the following equations:

$$ (3) \text{ For a unique substance: } \text{PAFi} = \left[ \text{Ci} \frac{0.5}{\text{HC50i}} \right] \times 100 $$

$$ (4) \text{ For a mixture: } \text{msPAF} = \left[ 0.5 \sum_i \frac{\text{Ci}}{\text{HC50i}} \right] \times 100 $$

The abbreviation Ci refers to the concentration (here, annual average) of a chemical compound.

This method has been widely used in ecological risk assessment studies (eg., Beaumelle et al., 2017; Rämö et al., 2018), where exceeding the threshold of 5% of PAF (PAFi or msPAF > 5%) characterises a potential ecological risk (Rico et al., 2022). This threshold (called Th5%, in this report) is a convention and is not based on scientific fact (Posthuma et al., 2002). Moreover, this threshold is much debated, given that 5% of species would not be considered (van Straalen, 2002). In summary, the (ms)PAF method can be used to estimate whether the presence of chemical compounds in an environment can, individually or in a mixture, give rise to an ecological risk (i.e. a potential effect on more than 5% of species of the community tested).
Thus, from these EC50s, it is possible in many steps to estimate the concentration required to obtain 50% of potentially affected species among the total species studied (i.e. HC50, grey dotted line in **Figure 8**) specifically for each chemical compound (Payet, 2004; Plouffe et al., 2016; Sarkis, 2018). The first step is to consider the geometric mean of the EC50s for each species-substance pair. Next, we consider the geometric mean of these EC50s for each taxon-substance pair. Finally, the geometric mean of the EC50s representative of each taxon is calculated to obtain a HC50 specific to each substance and representative of the ecosystem. Then, this HC50 can be compared with an environmental concentration (average or maximum) of a chemical compound to estimate the PAF (dotted blue line in **Figure 8**). To sum up, by associating a relevant environmental concentration of a chemical compound with toxicity data, this method attempts to come as close as possible to an estimation of the toxic risk (in fine, ecological risk) potentially generated in reality by this compound.

Initially, this method was used to study the effects of individual chemical compounds (Klepper and van de Meent, 1997). However, aquatic organisms are constantly exposed to a large number of chemical compounds from various environmental and anthropogenic sources (Sumpter and Jobling, 1995; Brack et al., 2016; Escher et al., 2020). Therefore, since the early 2000s, due to the need to assess the impact of mixtures of potentially toxic substances, the method has been extended to mixtures (Goedkoop and Spriensma, 2000; de Zwart and Posthuma, 2005). Thus, the approach used in our study allows estimating, in addition to the fraction of species potentially affected by a single chemical compound (PAFi), the fraction affected by a mixture of substances (msPAF). When using the msPAF indicator, two additive models should be considered: the addition of concentrations and the addition of responses depending on the similarity of the toxic mode of action of the chemical compounds (Faggiano, 2013). In our case, we used a simplification of the msPAF indicator (see Pennington et al., 2004), which avoids discriminating the chemical compounds studied based on their toxic mode of action. The method, as applied, is based on the linearity theory of the SSD below the HC50 (**Figure 8**, red line). Considering this method, the (ms)PAF indicators (PAFi and msPAF) can be calculated using the following equations:

$$ (3) \text{ For a unique substance: } \text{PAFi} = \left[ \text{Ci} \frac{0.5}{\text{HC50i}} \right] \times 100 $$

$$ (4) \text{ For a mixture: } \text{msPAF} = \left[ 0.5 \sum_i \frac{\text{Ci}}{\text{HC50i}} \right] \times 100 $$

The abbreviation Ci refers to the concentration (here, annual average) of a chemical compound.

This method has been widely used in ecological risk assessment studies (eg., Beaumelle et al., 2017; Rämö et al., 2018), where exceeding the threshold of 5% of PAF (PAFi or msPAF > 5%) characterises a potential ecological risk (Rico et al., 2022). This threshold (called Th5%, in this report) is a convention and is not based on scientific fact (Posthuma et al., 2002). Moreover, this threshold is much debated, given that 5% of species would not be considered (van Straalen, 2002). In summary, the (ms)PAF method can be used to estimate whether the presence of chemical compounds in an environment can, individually or in a mixture, give rise to an ecological risk (i.e. a potential effect on more than 5% of species of the community tested).
<figure><img src="image_18.png" /><figcaption>**Figure 8.** Example of SSD concept substance-specific (inspired by Posthuma and de Zwart, 2006). The square (□) represent compound-specific toxicity data for a set of species belonging to different taxa (in black EC50s for fish taxon). The dashed blue line represent the estimation of the percentage of species potentially affected by the environmental concentration of a substance “i” (Ci). The dashed gray line represents the hazardous concentration of a substance “i” affecting 50% of species (HC50i). The red line represents the assumption of linearity below the HC50.</figcaption></figure>

b. Adaptation of the (ms)PAF method to the case of the allis shad

Initially, we wanted to use toxicity data obtained on species of the *Alosa* genus or migratory fish species present in the GGD catchment. However, after a preliminary literature review, no toxicity data were available for the *Alosa alosa* species, and only limited toxicity data were available for species in the same genus (e.g., Zhang et al., 2015; Du et al., 2022). We therefore extended our search to any species belonging to the fish taxon with toxicity data (in our case EC50s) obtained in freshwater. Consequently, we assumed that the sensitivity of the allis shad was similar to the sensitivity of the average species of the fish taxon for each chemical compound studied.

By associating this toxicity data with the annual averages of measured environmental concentrations in the main spawning grounds of the allis shad in the GGD catchment, we were able to estimate the PAF for fish taxon in these areas. These spawning grounds are also where the early life stages of the allis shad (embryo and larvae) develop, considered as critical life stages particularly sensitive to environmental pressures (McKim, 1977; Von Westernhagen, 1988; Hutchinson et al., 1998). Therefore, it was interesting to estimate the potential toxic risk that early life stages may face, since they are poorly studied and have recently received special attention in the context of studies on the decline of the allis shad population in the GGD catchment (e.g., Baumann, 2021; Blaya et al., 2022; Bancel, in prep.).
<figure><img src="image_18.png" /><figcaption>**Figure 8.** Example of SSD concept substance-specific (inspired by Posthuma and de Zwart, 2006). The square (□) represent compound-specific toxicity data for a set of species belonging to different taxa (in black EC50s for fish taxon). The dashed blue line represent the estimation of the percentage of species potentially affected by the environmental concentration of a substance “i” (Ci). The dashed gray line represents the hazardous concentration of a substance “i” affecting 50% of species (HC50i). The red line represents the assumption of linearity below the HC50.</figcaption></figure>

b. Adaptation of the (ms)PAF method to the case of the allis shad

Initially, we wanted to use toxicity data obtained on species of the *Alosa* genus or migratory fish species present in the GGD catchment. However, after a preliminary literature review, no toxicity data were available for the *Alosa alosa* species, and only limited toxicity data were available for species in the same genus (e.g., Zhang et al., 2015; Du et al., 2022). We therefore extended our search to any species belonging to the fish taxon with toxicity data (in our case EC50s) obtained in freshwater. Consequently, we assumed that the sensitivity of the allis shad was similar to the sensitivity of the average species of the fish taxon for each chemical compound studied.

By associating this toxicity data with the annual averages of measured environmental concentrations in the main spawning grounds of the allis shad in the GGD catchment, we were able to estimate the PAF for fish taxon in these areas. These spawning grounds are also where the early life stages of the allis shad (embryo and larvae) develop, considered as critical life stages particularly sensitive to environmental pressures (McKim, 1977; Von Westernhagen, 1988; Hutchinson et al., 1998). Therefore, it was interesting to estimate the potential toxic risk that early life stages may face, since they are poorly studied and have recently received special attention in the context of studies on the decline of the allis shad population in the GGD catchment (e.g., Baumann, 2021; Blaya et al., 2022; Bancel, in prep.).
We therefore adapted the method presented above by adding the life stage of the fish species considered and the effect tested to the analysis.

### c. Use of ECOTOX Knowledgebase

Our study involved significant research and selection of ecotoxicological databases. We initially used the 'Aquatic Indicator Impact DAtabase' to gather relevant data (Aiida, Version 3.0, https://aiida.tools4env.com/home). However, due to the lack of precision on the parameters of toxicity tests (stage life and effects) in Aiida, we decided to switch to another database that provided more information. Finally, we chose to use the "ECOTOX Knowledgebase" (Version 5.0, www.epa.gov/ecotox) from the U.S. Environmental Protection Agency (EPA), which is the largest database on the environmental toxicity of chemical substances in aquatic and terrestrial environments (Olker et al., 2022).

For each chemical included in our analysis, we extracted EC50s and NOECs from toxicity tests conducted in freshwater on different life stages of fish species and different toxicity effects. Although the use of NOECs is controversial (see **Materials and Methods, Part B.a.**), we decided to include them in the analysis. However, since EC50s are primarily used for lethal effects, while NOECs are more commonly used for sublethal effects. Thus, we deemed it appropriate to transform NOECs into EC50s to ensure a more balanced dataset between lethal and sublethal data. Out of 9 683 toxicity data obtained in total, we had 2 293 lethal EC50s, 212 sublethal EC50s, 573 lethal NOECs, and 6 605 sublethal NOECs.

Since, in reality, individuals are exposed to contaminants over a long period of time, the transformation of selected toxicity data into chronic EC50s (i.e. EC50s for a prolonged exposition of a chemical compound) was performed. We classified this data as acute or chronic based on the exposure duration: for fish, values equal to or greater than 7 days were considered chronic, while values less than 7 days were considered acute, following the classification of Payet (2014) and Aurisano et al. (2019). Out of 9 683 toxicity data obtained in total, we had 2 281 acute EC50s, 224 chronic EC50s, 3 436 acute NOECs and 3 742 chronic NOECs. To convert acute values to chronic values, we used extrapolation factors calculated in the study by Aurisano et al. (2019) specifically for the fish taxon. We divided acute NOECs were divided by 3.14 to become chronic NOECs. Then, we multiplied these chronic NOECs by 3.41 to be transformed into chronic EC50s. Finally, we divided acute EC50s by 1.71 to become chronic EC50s. By applying these transformations, all toxicity data were now considered as chronic EC50s.

In order to approximate the effects experienced by the different life stages of allis shad, we also recorded the life stage and species of the tested organisms, as well as the tested effect during the toxicity test for each toxicity data. We therefore had to classify the life stages of fish into four categories: embryo, larva, juvenile and adult (supplementary material #3) and the tested effects in four categories: development, physiology, behavior and mortality (supplementary material #4). Reproductive effects were not included in the study due to limited data availability. Thus,
We therefore adapted the method presented above by adding the life stage of the fish species considered and the effect tested to the analysis.

### c. Use of ECOTOX Knowledgebase

Our study involved significant research and selection of ecotoxicological databases. We initially used the 'Aquatic Indicator Impact DAtabase' to gather relevant data (Aiida, Version 3.0, https://aiida.tools4env.com/home). However, due to the lack of precision on the parameters of toxicity tests (stage life and effects) in Aiida, we decided to switch to another database that provided more information. Finally, we chose to use the "ECOTOX Knowledgebase" (Version 5.0, www.epa.gov/ecotox) from the U.S. Environmental Protection Agency (EPA), which is the largest database on the environmental toxicity of chemical substances in aquatic and terrestrial environments (Olker et al., 2022).

For each chemical included in our analysis, we extracted EC50s and NOECs from toxicity tests conducted in freshwater on different life stages of fish species and different toxicity effects. Although the use of NOECs is controversial (see **Materials and Methods, Part B.a.**), we decided to include them in the analysis. However, since EC50s are primarily used for lethal effects, while NOECs are more commonly used for sublethal effects. Thus, we deemed it appropriate to transform NOECs into EC50s to ensure a more balanced dataset between lethal and sublethal data. Out of 9 683 toxicity data obtained in total, we had 2 293 lethal EC50s, 212 sublethal EC50s, 573 lethal NOECs, and 6 605 sublethal NOECs.

Since, in reality, individuals are exposed to contaminants over a long period of time, the transformation of selected toxicity data into chronic EC50s (i.e. EC50s for a prolonged exposition of a chemical compound) was performed. We classified this data as acute or chronic based on the exposure duration: for fish, values equal to or greater than 7 days were considered chronic, while values less than 7 days were considered acute, following the classification of Payet (2014) and Aurisano et al. (2019). Out of 9 683 toxicity data obtained in total, we had 2 281 acute EC50s, 224 chronic EC50s, 3 436 acute NOECs and 3 742 chronic NOECs. To convert acute values to chronic values, we used extrapolation factors calculated in the study by Aurisano et al. (2019) specifically for the fish taxon. We divided acute NOECs were divided by 3.14 to become chronic NOECs. Then, we multiplied these chronic NOECs by 3.41 to be transformed into chronic EC50s. Finally, we divided acute EC50s by 1.71 to become chronic EC50s. By applying these transformations, all toxicity data were now considered as chronic EC50s.

In order to approximate the effects experienced by the different life stages of allis shad, we also recorded the life stage and species of the tested organisms, as well as the tested effect during the toxicity test for each toxicity data. We therefore had to classify the life stages of fish into four categories: embryo, larva, juvenile and adult (supplementary material #3) and the tested effects in four categories: development, physiology, behavior and mortality (supplementary material #4). Reproductive effects were not included in the study due to limited data availability. Thus,
approximately 42.6% of the EC50s were related to early life stages of fish, with 3 029 values for embryos and 1 092 values for larvae. The remaining data corresponded to tests conducted on juveniles (3 175 data representing ~32.8% of EC50s) and adults (2 387 data representing ~24.7% of EC50s). Additionally, ~29.6%, ~16.2%, ~4.9% and ~49.3% of the toxicity tests showed an effect on mortality, development, behavior, and physiology, respectively.

Therefore, out of the 245 chemical compounds that were quantified in the spawning grounds of the allis shad during the study period, 133 (~54%) had toxicity data available for at least one effect and life stage of a fish species. These data have enabled us to study, in addition to the potential risks to the fish taxon in the GGD catchment, the various effects on the different fish life stages, thus providing valuable information on the potential risks to allis shad at different stages of development.

Firstly, we calculated a fish taxon-specific HC50 for each chemical compound independent of life stage and effect (HC50<sub>F</sub>, see **supplementary materials #5**). Then, for each effect and life stage of the fish taxon, we calculated an HC50 for each chemical compound (HC50<sub>SE</sub>, see **supplementary materials #6**). Some HC50 values were calculated with limited toxicity data (i.e., few different species and tests). In total, 100 chemical compounds had an HC50<sub>SE</sub> associated with the embryonic stage, 73 with the larval stage, 93 with the juvenile stage, and 68 with the adult stage. Additionally, there were 125 compounds with an HC50<sub>SE</sub> for lethal effects and 111 for sublethal effects.

In the end, there were 4 959 toxicity data available for agrochemicals, 2 749 for OIPs, 723 for metals, 669 for PFASs, 420 for HCPs, 160 for PAHs and 3 for PCBs.

### C. PAFi & msPAF calculations (summarized in Tab. III and Fig. 9)

Once all the necessary data was collected, we proceeded with the calculation of (ms)PAF, as presented in the equations of the **Materials and Methods, Part B.a.** Firstly, we based the PAFi calculations on HC50<sub>SE</sub>.

Secondly, we used HC50<sub>F</sub> and environmental data (quantified and unquantified or only quantified) to calculate annual average of msPAF per station and category. Then, by averaging these values for each spawning ground (Garonne and Dordogne), we obtained the annual average of msPAF per category in the Garonne and Dordogne spawning grounds. Summing up these category-specific annual averages of msPAF, we obtained, for each spawning grounds, the annual average of msPAF with no distinction between categories (msPAF<sub>F-AM</sub>). Meaning these msPAF<sub>F-AM</sub>, we obtained a average of msPAF for the period (msPAF<sub>F-T</sub>) for each spawning grounds.

Thirdly, we used HC50<sub>SE</sub> and environmental data (quantified and unquantified or only quantified) to calculate annual average of msPAF by station, effect, life stage and category. Then, by averaging these values for each spawning ground (Garonne and Dordogne), we obtained the annual average of msPAF by category, year, effect and life stage (msPAF<sub>SE-AM</sub>). By averaging these msPAF<sub>SE-AM</sub>, we were able to obtain a average of msPAF for the period for each spawning grounds, category, effect and life stage. Summing these period-average values, we obtained an average of msPAF for the period, each spawning grounds, effect and life stage (msPAF<sub>SE-T</sub>).
approximately 42.6% of the EC50s were related to early life stages of fish, with 3 029 values for embryos and 1 092 values for larvae. The remaining data corresponded to tests conducted on juveniles (3 175 data representing ~32.8% of EC50s) and adults (2 387 data representing ~24.7% of EC50s). Additionally, ~29.6%, ~16.2%, ~4.9% and ~49.3% of the toxicity tests showed an effect on mortality, development, behavior, and physiology, respectively.

Therefore, out of the 245 chemical compounds that were quantified in the spawning grounds of the allis shad during the study period, 133 (~54%) had toxicity data available for at least one effect and life stage of a fish species. These data have enabled us to study, in addition to the potential risks to the fish taxon in the GGD catchment, the various effects on the different fish life stages, thus providing valuable information on the potential risks to allis shad at different stages of development.

Firstly, we calculated a fish taxon-specific HC50 for each chemical compound independent of life stage and effect (HC50<sub>F</sub>, see **supplementary materials #5**). Then, for each effect and life stage of the fish taxon, we calculated an HC50 for each chemical compound (HC50<sub>SE</sub>, see **supplementary materials #6**). Some HC50 values were calculated with limited toxicity data (i.e., few different species and tests). In total, 100 chemical compounds had an HC50<sub>SE</sub> associated with the embryonic stage, 73 with the larval stage, 93 with the juvenile stage, and 68 with the adult stage. Additionally, there were 125 compounds with an HC50<sub>SE</sub> for lethal effects and 111 for sublethal effects.

In the end, there were 4 959 toxicity data available for agrochemicals, 2 749 for OIPs, 723 for metals, 669 for PFASs, 420 for HCPs, 160 for PAHs and 3 for PCBs.

### C. PAFi & msPAF calculations (summarized in Tab. III and Fig. 9)

Once all the necessary data was collected, we proceeded with the calculation of (ms)PAF, as presented in the equations of the **Materials and Methods, Part B.a**. Firstly, we based the PAFi calculations on HC50<sub>SE</sub>.

Secondly, we used HC50<sub>F</sub> and environmental data (quantified and unquantified or only quantified) to calculate annual average of msPAF per station and category. Then, by averaging these values for each spawning ground (Garonne and Dordogne), we obtained the annual average of msPAF per category in the Garonne and Dordogne spawning grounds. Summing up these category-specific annual averages of msPAF, we obtained, for each spawning grounds, the annual average of msPAF with no distinction between categories (msPAF<sub>F-AM</sub>). Meaning these msPAF<sub>F-AM</sub>, we obtained a average of msPAF for the period (msPAF<sub>F-T</sub>) for each spawning grounds.

Thirdly, we used HC50<sub>SE</sub> and environmental data (quantified and unquantified or only quantified) to calculate annual average of msPAF by station, effect, life stage and category. Then, by averaging these values for each spawning ground (Garonne and Dordogne), we obtained the annual average of msPAF by category, year, effect and life stage (msPAF<sub>SE-AM</sub>). By averaging these msPAF<sub>SE-AM</sub>, we were able to obtain a average of msPAF for the period for each spawning grounds, category, effect and life stage. Summing these period-average values, we obtained an average of msPAF for the period, each spawning grounds, effect and life stage (msPAF<sub>SE-T</sub>).
## D. Summary of the materials and methods

The complete method is summarized in **Figure 9** and the definitions of all the indicators (used and not used) are presented in the **Table III**. As a reminder, if the results indicated that more than 5% of the species were potentially affected, this signaled a potentially significant toxic pressure on the studied fish community, and therefore, a potential ecological risk (Carafa et al., 2011; Rico et al., 2022).

<table><caption>**Table III.** Summaries of the meanings of all (eco)toxicity indicators mentioned in the report (see Vaughan and Greenslade, 1998 and Posthuma et al., 2002 for definitions).</caption><thead><tr><th>Endpoints</th><th>Definitions</th></tr></thead><tbody><tr><td colspan="2"><strong>Environmental data useful for calculating (ms)PAF indicators</strong></td></tr><tr><td>Ci</td><td>Environmental concentration of a chemical called "i" (annual average in this study, whether quantified + unquantified data or only quantified data)</td></tr><tr><td colspan="2"><strong>Toxicological data useful for estimating HC50s</strong></td></tr><tr><td>ECx</td><td>Effect concentration on x% of individuals</td></tr><tr><td>NOEC</td><td>No observed effect concentration</td></tr><tr><td>LOEC</td><td>Lowest observed effect concentration</td></tr><tr><td colspan="2"><strong>Toxicological data useful for calculating (ms)PAF indicators</strong></td></tr><tr><td>HC50</td><td>Hazardous concentration on 50% of individuals</td></tr><tr><td>HC50F</td><td>Hazardous concentration on 50% of individuals specific to fish taxon</td></tr><tr><td>HC50SE</td><td>Hazardous concentration on 50% of individuals specific to each life stage of fish taxon for each effect</td></tr><tr><td colspan="2"><strong>(ms)PAF indicators</strong></td></tr><tr><td>PAFi</td><td>Potentially Affected Fraction of species for a substance "i" → Using Ci and HC50SE</td></tr><tr><td>msPAF</td><td>Potentially Affected Fraction of species for a chemicals mixture "ms"</td></tr><tr><td>msPAF<sub>F-AM</sub></td><td>Annual average of msPAF for the fish taxon (without discriminating between categories) in each spawning grounds → Using Ci and HC50F</td></tr><tr><td>msPAF<sub>F-T</sub></td><td>Periodic average of msPAF for the fish taxon (without discriminating between categories) in each spawning grounds → Using Ci and HC50F</td></tr><tr><td>msPAF<sub>SE-AM</sub></td><td>Annual average of msPAF specific at each life stages of fish taxon for each effect and category in each spawning grounds → Using Ci and HC50SE</td></tr><tr><td>msPAF<sub>SE-T</sub></td><td>Periodic average of msPAF specific at each life stages of fish taxon for each effect (without discriminating between categories) in each spawning grounds → Using Ci and HC50SE</td></tr><tr><td colspan="2"><strong>Reference threshold not to be exceeded by (ms)PAF indicators</strong></td></tr><tr><td>Th<sub>5%</sub></td><td>Threshold above which a potential toxic risk / significant toxic pressure / potential ecological risk is generated by the presence of one or more chemical compounds in the environment, resulting in the potential loss of more than 5% of the species in the community studied.</td></tr></tbody></table>
## D. Summary of the materials and methods

The complete method is summarized in **Figure 9** and the definitions of all the indicators (used and not used) are presented in the **Table III**. As a reminder, if the results indicated that more than 5% of the species were potentially affected, this signaled a potentially significant toxic pressure on the studied fish community, and therefore, a potential ecological risk (Carafa et al., 2011; Rico et al., 2022).

<table><caption>**Table III.** Summaries of the meanings of all (eco)toxicity indicators mentioned in the report (see Vaughan and Greenslade, 1998 and Posthuma et al., 2002 for definitions).</caption><thead><tr><th>Endpoints</th><th>Definitions</th></tr></thead><tbody><tr><td colspan="2"><strong>Environmental data useful for calculating (ms)PAF indicators</strong></td></tr><tr><td>Ci</td><td>Environmental concentration of a chemical called "i" (annual average in this study, whether quantified + unquantified data or only quantified data)</td></tr><tr><td colspan="2"><strong>Toxicological data useful for estimating HC50s</strong></td></tr><tr><td>ECx</td><td>Effect concentration on x% of individuals</td></tr><tr><td>NOEC</td><td>No observed effect concentration</td></tr><tr><td>LOEC</td><td>Lowest observed effect concentration</td></tr><tr><td colspan="2"><strong>Toxicological data useful for calculating (ms)PAF indicators</strong></td></tr><tr><td>HC50</td><td>Hazardous concentration on 50% of individuals</td></tr><tr><td>HC50F</td><td>Hazardous concentration on 50% of individuals specific to fish taxon</td></tr><tr><td>HC50SE</td><td>Hazardous concentration on 50% of individuals specific to each life stage of fish taxon for each effect</td></tr><tr><td colspan="2"><strong>(ms)PAF indicators</strong></td></tr><tr><td>PAFi</td><td>Potentially Affected Fraction of species for a substance "i" → Using Ci and HC50SE</td></tr><tr><td>msPAF</td><td>Potentially Affected Fraction of species for a chemicals mixture "ms"</td></tr><tr><td>msPAF<sub>F-AM</sub></td><td>Annual average of msPAF for the fish taxon (without discriminating between categories) in each spawning grounds → Using Ci and HC50F</td></tr><tr><td>msPAF<sub>F-T</sub></td><td>Periodic average of msPAF for the fish taxon (without discriminating between categories) in each spawning grounds → Using Ci and HC50F</td></tr><tr><td>msPAF<sub>SE-AM</sub></td><td>Annual average of msPAF specific at each life stages of fish taxon for each effect and category in each spawning grounds → Using Ci and HC50SE</td></tr><tr><td>msPAF<sub>SE-T</sub></td><td>Periodic average of msPAF specific at each life stages of fish taxon for each effect (without discriminating between categories) in each spawning grounds → Using Ci and HC50SE</td></tr><tr><td colspan="2"><strong>Reference threshold not to be exceeded by (ms)PAF indicators</strong></td></tr><tr><td>Th<sub>5%</sub></td><td>Threshold above which a potential toxic risk / significant toxic pressure / potential ecological risk is generated by the presence of one or more chemical compounds in the environment, resulting in the potential loss of more than 5% of the species in the community studied.</td></tr></tbody></table>
<figure><img src="image_19.png" /></figure>

**1/ SPATIO-TEMPORAL DATA SELECTION**

- STATIONS: spawning grounds GGD catchment (n = 21)

- PERIOD: 2006-2021

**2/ ENVIRONMENTAL DATA TRANSFORMATION**

- SEDIMENT VALUES: sediment-water partition coefficient (Sarkis, 2018)

- ENV. CONCENTRATION < LIMIT OF QUANTIFICATION: limit of quantification / 2

**3/ CHEMICAL SELECTION:** at least 1 env. concentration > limit of quantification

**4/ CALCULATION:** annual average by station (called Ci)

- Quantified + unquantified

- Quantified only

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

**5/ TOXICITY DATA SELECTION:** EC50s and NOECs (fish & fresh water)

- Stage lifes: embryo, larva, juvenile, adult

- Effects: development, physiology, behavior, mortality

**6/ TOXICITY DATA TRANSFORMATION:** to obtain only EC50s (Aurisano et al., 2019)

- NOECs acute to NOECs chronic: dividing by 3.14

- NOECs chronic to EC50s chronic: multiplying by 3.41

- EC50s acute to EC50s chronic: dividing by 1.71

**6/ HC50 CALCULATIONS:** geometric mean of EC50s

- HC50<sub>F</sub>: representative of the fish taxon

- HC50<sub>SE</sub>: representative of each life stage of fish taxon for each effect

<figure><img src="image_22.png" /></figure>

**7/ PAFi CALCULATIONS** (using HC50<sub>SE</sub>)

**8/ msPAF<sub>F</sub> CALCULATIONS** (using HC50<sub>F</sub>)

- msPAF<sub>F-AM</sub>: annual average of msPAF representative of fish taxon

- msPAF<sub>F-T</sub>: periodic average of msPAF representative of fish taxon

**9/ msPAF<sub>SE</sub> CALCULATIONS** (using HC50<sub>SE</sub>)

- msPAF<sub>SE-AM</sub>: annual average of msPAF representative of each life stage of fish taxon for each effect and category

- msPAF<sub>SE-T</sub>: periodic average of msPAF representative of each life stage of fish taxon for each effect

Using Ci

- either **quantified** +
  **unquantified**

- or **quantified only**

**10/ POTENTIAL TOXIC (i.e., ECOLOGICAL) RISK = EXCEEDING THRESHOLD OF 5% POTENTIALLY AFFECTED FRACTION OF SPECIES (Th<sub>5%</sub>)**

<caption>**Figure 9.** Summary diagram of the method used to obtain (ms)PAF values.</caption>
<figure><img src="image_19.png" /></figure>

## 1/ SPATIO-TEMPORAL DATA SELECTION

- STATIONS: spawning grounds GGD catchment (n = 21)

- PERIOD: 2006-2021

## 2/ ENVIRONMENTAL DATA TRANSFORMATION

- SEDIMENT VALUES: sediment-water partition coefficient (Sarkis, 2018)

- ENV. CONCENTRATION < LIMIT OF QUANTIFICATION: limit of quantification / 2

## 3/ CHEMICAL SELECTION: at least 1 env. concentration > limit of quantification

## 4/ CALCULATION: annual average by station (called Ci)

- Quantified + unquantified

- Quantified only

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

## 5/ TOXICITY DATA SELECTION: EC50s and NOECs (fish & fresh water)

- Stage lifes: embryo, larva, juvenile, adult

- Effects: development, physiology, behavior, mortality

## 6/ TOXICITY DATA TRANSFORMATION: to obtain only EC50s (Aurisano et al., 2019)

- NOECs acute to NOECs chronic: dividing by 3.14

- NOECs chronic to EC50s chronic: multiplying by 3.41

- EC50s acute to EC50s chronic: dividing by 1.71

## 6/ HC50 CALCULATIONS: geometric mean of EC50s

- HC50<sub>F</sub>: representative of the fish taxon

- HC50<sub>SE</sub>: representative of each life stage of fish taxon for each effect

<figure><img src="image_22.png" /></figure>

## 7/ PAFi CALCULATIONS (using HC50<sub>SE</sub>)

## 8/ msPAF<sub>F</sub> CALCULATIONS (using HC50<sub>F</sub>)

- msPAF<sub>F-AM</sub>: annual average of msPAF representative of fish taxon

- msPAF<sub>F-T</sub>: periodic average of msPAF representative of fish taxon

## 9/ msPAF<sub>SE</sub> CALCULATIONS (using HC50<sub>SE</sub>)

- msPAF<sub>SE-AM</sub>: annual average of msPAF representative of each life stage of fish taxon for each effect and category

- msPAF<sub>SE-T</sub>: periodic average of msPAF representative of each life stage of fish taxon for each effect

Using Ci

- either quantified + unquantified

- or quantified only

## 10/ POTENTIAL TOXIC (i.e., ECOLOGICAL) RISK = EXCEEDING THRESHOLD OF 5% POTENTIALLY AFFECTED FRACTION OF SPECIES (Th<sub>5%</sub>)

<caption>**Figure 9.** Summary diagram of the method used to obtain (ms)PAF values.</caption>
# 4. Results

## A. Summary of data used for msPAF calculations

### a. Presence and absence of quantified and unquantified environmental data

The availability of environmental data varies from one category to another and more data has been available since 2016 (Fig. 10). Agrochemicals and metals presented both quantified and unquantified data for each year studied in both spawning grounds (Garonne and Dordogne). In contrast, HCPs had data (unquantified and quantified) available in the water of both spawning grounds only from 2016, indicating a recent interest in monitoring these chemical compounds. With regard to PCBs, the years 2008, 2010 and 2011 showed no data in both spawning grounds. The other years had unquantified data and data were quantified only in 2009, 2012, 2013, 2016 and 2018 in the Garonne and only in 2012, 2014 to 2018 and 2020 in the Dordogne. For PAHs, no data was available in 2008 in the Garonne and 2006, 2008, 2010 and 2011 in the Dordogne. The other years of the study period had both quantified and unquantified data available. The PFASs presented no data from 2006 to 2009 in the Garonne and from 2006 to 2008, in 2010, 2011 and from 2013 to 2015 in the Dordogne. All other years had unquantified data and quantified data only in 2011 and 2016 in the Garonne and only in 2016 and 2018 in the Dordogne. Finally, the OIPs presented unquantified data from 2006 to 2021 in the Garonne and from 2007 to 2021 in the Dordogne. Quantified data were available from 2007 to 2021 in the Garonne and from 2008 to 2021 in the Dordogne. With regard to the presence of quantified and unquantified data, we observed few differences between the Garonne and Dordogne spawning grounds for agrochemicals, metals, OIPs and HCPs. On the other hand, there were visible differences for PCBs, PAHs and PFASs. The total number of substances quantified with at least one quantified value in the water of both spawning grounds over the period studied suggests comparable chemical contamination between the two spawning grounds studied for the chemical compounds monitored by SIEAG.

<figure><img src="image_23.png" /><figcaption>**Figure 10.** Absence and presence of environmental data for each category in Garonne and Dordogne spawning grounds. Blue rectangles represent the presence of quantified or unquantified data, grey rectangles represent the absence of quantified or unquantified data, and grey lines represent the absence of quantified data.</figcaption></figure>
# 4. Results

## A. Summary of data used for msPAF calculations

### a. Presence and absence of quantified and unquantified environmental data

The availability of environmental data varies from one category to another and more data has been available since 2016 (Fig. 10). Agrochemicals and metals presented both quantified and unquantified data for each year studied in both spawning grounds (Garonne and Dordogne). In contrast, HCPs had data (unquantified and quantified) available in the water of both spawning grounds only from 2016, indicating a recent interest in monitoring these chemical compounds. With regard to PCBs, the years 2008, 2010 and 2011 showed no data in both spawning grounds. The other years had unquantified data and data were quantified only in 2009, 2012, 2013, 2016 and 2018 in the Garonne and only in 2012, 2014 to 2018 and 2020 in the Dordogne. For PAHs, no data was available in 2008 in the Garonne and 2006, 2008, 2010 and 2011 in the Dordogne. The other years of the study period had both quantified and unquantified data available. The PFASs presented no data from 2006 to 2009 in the Garonne and from 2006 to 2008, in 2010, 2011 and from 2013 to 2015 in the Dordogne. All other years had unquantified data and quantified data only in 2011 and 2016 in the Garonne and only in 2016 and 2018 in the Dordogne. Finally, the OIPs presented unquantified data from 2006 to 2021 in the Garonne and from 2007 to 2021 in the Dordogne. Quantified data were available from 2007 to 2021 in the Garonne and from 2008 to 2021 in the Dordogne. With regard to the presence of quantified and unquantified data, we observed few differences between the Garonne and Dordogne spawning grounds for agrochemicals, metals, OIPs and HCPs. On the other hand, there were visible differences for PCBs, PAHs and PFASs. The total number of substances quantified with at least one quantified value in the water of both spawning grounds over the period studied suggests comparable chemical contamination between the two spawning grounds studied for the chemical compounds monitored by SIEAG.

<figure><img src="image_23.png" /><figcaption>**Figure 10.** Absence and presence of environmental data for each category in Garonne and Dordogne spawning grounds. Blue rectangles represent the presence of quantified or unquantified data, grey rectangles represent the absence of quantified or unquantified data, and grey lines represent the absence of quantified data.</figcaption></figure>
b. Toxicity data: life stages and effects

Toxicity data are not uniform for each life stage of fish taxon in terms of the diversity of species used and effect tested in toxicity tests (Fig. 11). In total, 135 species provided toxicity data, 39 of which had more than 20 toxicity data. *Danio rerio* had over 2000 toxicity tests, followed by *Oryzias latipes*, *Oncorhynchus mykiss* and *Pimephales promelas*, each with over 500. The juvenile stage was the life stage with the greatest diversity of species with toxicity data (85 species). Next came the larval stage (59 species), the adult stage (51 species) and, with less diversity, the embryonic stage (24 species).

For the embryonic stage, most toxicity data came from tests on *Danio rerio* (mainly for effects on physiology) and, to a lesser extent, on *Oryzias latipes* (mainly for effects on physiology), *Oncorhynchus mykiss* (mainly for effects on mortality), *Pimephales promelas* (mainly for effects on mortality and development) and *Salmo salar* (mainly for effects on physiology). Concerning the larval stage, most of the toxicity data available concerned the species *Pimephales promelas* (mainly for effects on mortality), *Oncorhynchus mykiss* (mainly for effects on mortality), *Oryzias latipes* (mainly for effects on physiology and mortality) and *Danio rerio* (mainly for effects on physiology). Toxicity data for the embryonic and larval stages came mainly from the same species (*Oncorhynchus mykiss*, *Oryzias latipes* and *Danio rerio*), although the diversity of species presenting toxicity data for the larval stage was greater. As for the juvenile stage, toxicity data came from a wide variety of species, most notably *Cyprinus carpio* (mainly for effects on physiology), *Prochilodus lineatus* (mainly for effects on physiology), *Labeo rohita* (mainly for effects on physiology), *Oncorhynchus mykiss* (mainly for effects on physiology and mortality) and *Pimephales promelas* (mainly for effects on mortality). Finally, for the adult stage, most of the toxicity data came from *Gobiocypris rarus* (mainly for effects on physiology), *Carassius auratus* (mainly for effects on physiology), *Danio rerio* (mainly for effects on physiology), *Pimephales promelas* (mainly for effects on physiology) and *Oryzias latipes* (mainly for effects on physiology).

The species that provided the most data for all life stages were: *Danio rerio*, *Pimephales promelas* and *Oryzias latipes*. It should be noted that of the species with the most toxicity data, only embryos and adults of the species *Danio rerio* provided toxicity data for behavioral effects.

The categories of chemical compounds are not studied in the same way in toxicity tests, in terms of the life stages studied and the type of effects tested (Fig. 12).

On one hand, we can see that PCBs only present toxicity data on the larva stage. PAHs, on the other hand, mainly present toxicity data associated with the juvenile, larva and embryonic stages, with few data on the adult stage. Metals present mainly data for juveniles and, to a lesser extent, also in the other three life stages. Agrochemicals, HCPs and OIPs have a lot of toxicity data associated with adults, juveniles and embryos, and few toxicity data associated with larvae. Finally, PFASs had a lot of toxicity data associated with the adult and embryonic stages and few toxicity data on the juvenile and larval stages.
b. Toxicity data: life stages and effects

Toxicity data are not uniform for each life stage of fish taxon in terms of the diversity of species used and effect tested in toxicity tests (Fig. 11). In total, 135 species provided toxicity data, 39 of which had more than 20 toxicity data. *Danio rerio* had over 2000 toxicity tests, followed by *Oryzias latipes*, *Oncorhynchus mykiss* and *Pimephales promelas*, each with over 500. The juvenile stage was the life stage with the greatest diversity of species with toxicity data (85 species). Next came the larval stage (59 species), the adult stage (51 species) and, with less diversity, the embryonic stage (24 species).

For the embryonic stage, most toxicity data came from tests on *Danio rerio* (mainly for effects on physiology) and, to a lesser extent, on *Oryzias latipes* (mainly for effects on physiology), *Oncorhynchus mykiss* (mainly for effects on mortality), *Pimephales promelas* (mainly for effects on mortality and development) and *Salmo salar* (mainly for effects on physiology). Concerning the larval stage, most of the toxicity data available concerned the species *Pimephales promelas* (mainly for effects on mortality), *Oncorhynchus mykiss* (mainly for effects on mortality), *Oryzias latipes* (mainly for effects on physiology and mortality) and *Danio rerio* (mainly for effects on physiology). Toxicity data for the embryonic and larval stages came mainly from the same species (*Oncorhynchus mykiss*, *Oryzias latipes* and *Danio rerio*), although the diversity of species presenting toxicity data for the larval stage was greater. As for the juvenile stage, toxicity data came from a wide variety of species, most notably *Cyprinus carpio* (mainly for effects on physiology), *Prochilodus lineatus* (mainly for effects on physiology), *Labeo rohita* (mainly for effects on physiology), *Oncorhynchus mykiss* (mainly for effects on physiology and mortality) and *Pimephales promelas* (mainly for effects on mortality). Finally, for the adult stage, most of the toxicity data came from *Gobiocypris rarus* (mainly for effects on physiology), *Carassius auratus* (mainly for effects on physiology), *Danio rerio* (mainly for effects on physiology), *Pimephales promelas* (mainly for effects on physiology) and *Oryzias latipes* (mainly for effects on physiology).

The species that provided the most data for all life stages were: *Danio rerio*, *Pimephales promelas* and *Oryzias latipes*. It should be noted that of the species with the most toxicity data, only embryos and adults of the species *Danio rerio* provided toxicity data for behavioral effects.

The categories of chemical compounds are not studied in the same way in toxicity tests, in terms of the life stages studied and the type of effects tested (Fig. 12).

On one hand, we can see that PCBs only present toxicity data on the larva stage. PAHs, on the other hand, mainly present toxicity data associated with the juvenile, larva and embryonic stages, with few data on the adult stage. Metals present mainly data for juveniles and, to a lesser extent, also in the other three life stages. Agrochemicals, HCPs and OIPs have a lot of toxicity data associated with adults, juveniles and embryos, and few toxicity data associated with larvae. Finally, PFASs had a lot of toxicity data associated with the adult and embryonic stages and few toxicity data on the juvenile and larval stages.
PCBs mainly present toxicity data only related to mortality of the organisms tested. Agrochemicals, and OIPs, for their part, present a majority of toxicity data associated with mortality and physiology effects, and few toxicity data on development and behavior. HCPs and PFASs presented a lot of toxicity data associated with development and physiology, and few toxicity data associated with mortality and behavioral effects. Finally, PAHs and metals presented a lot of toxicity data associated with mortality effects and few on development, physiology and behavior.

<figure><img src="image_24.png" /><figcaption>**Figure 11.** Number of toxicity tests (fish/freshwater) used to calculate HC50s by life stage of fish taxa and by effects tested (species with fewer than 20 tests are not represented here).</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>**Figure 12.** Percentage of (A) stage lifes of fish and (B) toxicity parameters used to calculate HC50s for each category of chemicals (the number corresponds to the total number of toxicity tests carried out on freshwater fish).</figcaption></figure>
PCBs mainly present toxicity data only related to mortality of the organisms tested. Agrochemicals, and OIPs, for their part, present a majority of toxicity data associated with mortality and physiology effects, and few toxicity data on development and behavior. HCPs and PFASs presented a lot of toxicity data associated with development and physiology, and few toxicity data associated with mortality and behavioral effects. Finally, PAHs and metals presented a lot of toxicity data associated with mortality effects and few on development, physiology and behavior.

<figure><img src="image_24.png" /><figcaption>**Figure 11.** Number of toxicity tests (fish/freshwater) used to calculate HC50s by life stage of fish taxa and by effects tested (species with fewer than 20 tests are not represented here).</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>**Figure 12.** Percentage of (A) stage lifes of fish and (B) toxicity parameters used to calculate HC50s for each category of chemicals (the number corresponds to the total number of toxicity tests carried out on freshwater fish).</figcaption></figure>
## B. Contributions of quantified and unquantified data to msPAF results

The values of msPAF<sub>F-AM</sub> differ in space and time, and the values of msPAF<sub>F-T</sub> differ in space (Fig. 13). In both cases, the use of environmental data (quantified and unquantified or only quantified) has an impact on the values obtained.

In the Garonne spawning grounds, the msPAF<sub>F-T</sub> were above the Th<sub>5%</sub>, regardless of the environmental data used. Using only quantified environmental data, the msPAF<sub>F-T</sub> was 8.06%. Using both quantified and unquantified environmental data, the msPAF<sub>F-T</sub> was 11.02%. In the Dordogne spawning grounds, msPAF<sub>F-T</sub> values were above the Th<sub>5%</sub> only when using both quantified and unquantified environmental data (5.92%). In the other case, the value was slightly below the Th<sub>5%</sub> (4.14%).

Looking at the msPAF<sub>F-AM</sub>, there are few visible differences considering only quantified environmental values or quantified and unquantified environmental values. The only significant differences are seen in 2007 and 2019 in the Dordogne, and in 2019, 2020 and 2021 in the Garonne, with higher values using both quantified and unquantified environmental data. In the Garonne spawning grounds, msPAF<sub>F-AM</sub> were above the Th<sub>5%</sub>, regardless of the environmental data used. In the Dordogne spawning grounds, this Th<sub>5%</sub> was exceeded only from 2006 to 2008, in 2010, in 2011 and in 2019 using both quantified and unquantified data, and only from 2006 to 2008, in 2010, in 2011, in 2013 and in 2015 using only quantified data. However, all standard deviations exceeded the Th<sub>5%</sub>, which means that when considering individual stations, values may exceed this threshold.

Considering only quantified environmental data, visually higher msPAF<sub>F-AM</sub> were recorded in the Dordogne spawning grounds in 2006, 2008, 2010 and 2011, and in the Garonne spawning grounds in 2006, 2007, 2008, 2010 and 2011.

<figure><img src="image_26.png" /></figure>

**Figure 13.** Spatio-temporal trends (Garonne-Dordogne and 2006-2021) in msPAF<sub>F-AM</sub> when considering quantified and unquantified environmental data (black) or only quantified environmental data (gray). The values shown at the top represent msPAF<sub>F-T</sub> values. The red line indicate the Th<sub>5%</sub>. As a reminder, msPAF<sub>F-AM</sub> and msPAF<sub>F-T</sub> represent, respectively, the average annual and average periodic msPAF, specific to the fish taxon in each spawning ground.
## B. Contributions of quantified and unquantified data to msPAF results

The values of msPAF<sub>F-AM</sub> differ in space and time, and the values of msPAF<sub>F-T</sub> differ in space (Fig. 13). In both cases, the use of environmental data (quantified and unquantified or only quantified) has an impact on the values obtained.

In the Garonne spawning grounds, the msPAF<sub>F-T</sub> were above the Th<sub>5%</sub>, regardless of the environmental data used. Using only quantified environmental data, the msPAF<sub>F-T</sub> was 8.06%. Using both quantified and unquantified environmental data, the msPAF<sub>F-T</sub> was 11.02%. In the Dordogne spawning grounds, msPAF<sub>F-T</sub> values were above the Th<sub>5%</sub> only when using both quantified and unquantified environmental data (5.92%). In the other case, the value was slightly below the Th<sub>5%</sub> (4.14%).

Looking at the msPAF<sub>F-AM</sub>, there are few visible differences considering only quantified environmental values or quantified and unquantified environmental values. The only significant differences are seen in 2007 and 2019 in the Dordogne, and in 2019, 2020 and 2021 in the Garonne, with higher values using both quantified and unquantified environmental data. In the Garonne spawning grounds, msPAF<sub>F-AM</sub> were above the Th<sub>5%</sub>, regardless of the environmental data used. In the Dordogne spawning grounds, this Th<sub>5%</sub> was exceeded only from 2006 to 2008, in 2010, in 2011 and in 2019 using both quantified and unquantified data, and only from 2006 to 2008, in 2010, in 2011, in 2013 and in 2015 using only quantified data. However, all standard deviations exceeded the Th<sub>5%</sub>, which means that when considering individual stations, values may exceed this threshold.

Considering only quantified environmental data, visually higher msPAF<sub>F-AM</sub> were recorded in the Dordogne spawning grounds in 2006, 2008, 2010 and 2011, and in the Garonne spawning grounds in 2006, 2007, 2008, 2010 and 2011.

<figure><img src="image_26.png" /></figure>

**Figure 13.** Spatio-temporal trends (Garonne-Dordogne and 2006-2021) in msPAF<sub>F-AM</sub> when considering quantified and unquantified environmental data (black) or only quantified environmental data (gray). The values shown at the top represent msPAF<sub>F-T</sub> values. The red line indicate the Th<sub>5%</sub>. As a reminder, msPAF<sub>F-AM</sub> and msPAF<sub>F-T</sub> represent, respectively, the average annual and average periodic msPAF, specific to the fish taxon in each spawning ground.
### C. Differences in potential toxicity for different life stages of fish in the GGD catchment

Using msPAF<sub>SE-T</sub>, we can see that the use of environmental data (quantified and unquantified or only quantified) has a strong impact on the values obtained (Fig. 14).

The top 4 boxes in **Figure 14** show the msPAF<sub>SE-T</sub> value when using both quantified and unquantified environmental data. The msPAF<sub>SE-T</sub> showed a potential ecological risk (> Th<sub>5%</sub>) related to effects on larval development (9.42% in the Dordogne and 8.68% in the Garonne), larval physiology (5.26% in the Dordogne and 9.74% in the Garonne), embryonic physiology (5.92% in the Dordogne and 10.02% in the Garonne), juvenile physiology (13.89% in the Dordogne and 24.98% in the Garonne), adult development (17.99% in the Dordogne) and adult physiology (6.26% in the Garonne). Where msPAF<sub>SE-T</sub> did not exceed the Th<sub>5%</sub>, standard deviations above this threshold were still observed. This shows that on some of the studied stations, significant msPAF<sub>SE-T</sub> was observed. This was the case for effects on embryonic mortality, embryonic development, embryonic behavior, larval mortality, larval behavior and adult behavior in Dordogne spawning grounds, and for effects on embryonic development, embryonic behavior, larval behavior and adult development in Garonne spawning grounds.

The bottom 4 boxes in **Figure 14** show the msPAF<sub>SE-T</sub> when using only quantified environmental data. The msPAF<sub>SE-T</sub> showed a potential ecological risk (> 5%) related to effects on larval physiology (7.31% in the Dordogne and 12.95% in the Garonne), larval development (11.43% in the Dordogne and 9.94% in the Garonne) and adult physiology (5.82% in the Garonne). Although below the Th<sub>5%</sub>, msPAF<sub>SE-T</sub> were quite high for effects on embryo physiology (3.81% in the Dordogne and 4.37% in the Garonne) and for effects on adult physiology (3.57% in the Dordogne). As in the previous case, when msPAF<sub>SE-T</sub> did not exceed the Th<sub>5%</sub>, standard deviations above this threshold were still observed. This was the case for effects on embryonic mortality, embryonic physiology, juvenile physiology, adult behavior and adult physiology in Dordogne spawning grounds, and for effects on embryonic physiology in Garonne spawning grounds.

The larval stage was considered the most at-risk life stage at quantified environmental concentrations (msPAF<sub>SE-T</sub> max. of 12.95% associated with the effect on physiology in the Garonne and msPAF<sub>SE-T</sub> max. of 11.43% associated with the effect on development in the Dordogne). The juvenile stage represented the least at-risk stage at quantified environmental concentrations, with msPAF<sub>SE-T</sub> reaching a maximum of 1.32% in the Garonne. The embryonic stage was the only life stage of fish taxon for which a value of msPAF<sub>SE-T</sub> associated with a mortality effect exceeded 1%, and this in both spawning grounds.

At quantified environmental values, only the msPAF<sub>SE-T</sub> associated with effects on larval development was higher in the Dordogne than in the Garonne. In all other cases, msPAF<sub>SE-T</sub> were similar into the two rivers, or higher in the Garonne.
### C. Differences in potential toxicity for different life stages of fish in the GGD catchment

Using msPAF<sub>SE-T</sub>, we can see that the use of environmental data (quantified and unquantified or only quantified) has a strong impact on the values obtained (Fig. 14).

The top 4 boxes in **Figure 14** show the msPAF<sub>SE-T</sub> value when using both quantified and unquantified environmental data. The msPAF<sub>SE-T</sub> showed a potential ecological risk (> Th<sub>5%</sub>) related to effects on larval development (9.42% in the Dordogne and 8.68% in the Garonne), larval physiology (5.26% in the Dordogne and 9.74% in the Garonne), embryonic physiology (5.92% in the Dordogne and 10.02% in the Garonne), juvenile physiology (13.89% in the Dordogne and 24.98% in the Garonne), adult development (17.99% in the Dordogne) and adult physiology (6.26% in the Garonne). Where msPAF<sub>SE-T</sub> did not exceed the Th<sub>5%</sub>, standard deviations above this threshold were still observed. This shows that on some of the studied stations, significant msPAF<sub>SE-T</sub> was observed. This was the case for effects on embryonic mortality, embryonic development, embryonic behavior, larval mortality, larval behavior and adult behavior in Dordogne spawning grounds, and for effects on embryonic development, embryonic behavior, larval behavior and adult development in Garonne spawning grounds.

The bottom 4 boxes in **Figure 14** show the msPAF<sub>SE-T</sub> when using only quantified environmental data. The msPAF<sub>SE-T</sub> showed a potential ecological risk (> 5%) related to effects on larval physiology (7.31% in the Dordogne and 12.95% in the Garonne), larval development (11.43% in the Dordogne and 9.94% in the Garonne) and adult physiology (5.82% in the Garonne). Although below the Th<sub>5%</sub>, msPAF<sub>SE-T</sub> were quite high for effects on embryo physiology (3.81% in the Dordogne and 4.37% in the Garonne) and for effects on adult physiology (3.57% in the Dordogne). As in the previous case, when msPAF<sub>SE-T</sub> did not exceed the Th<sub>5%</sub>, standard deviations above this threshold were still observed. This was the case for effects on embryonic mortality, embryonic physiology, juvenile physiology, adult behavior and adult physiology in Dordogne spawning grounds, and for effects on embryonic physiology in Garonne spawning grounds.

The larval stage was considered the most at-risk life stage at quantified environmental concentrations (msPAF<sub>SE-T</sub> max. of 12.95% associated with the effect on physiology in the Garonne and msPAF<sub>SE-T</sub> max. of 11.43% associated with the effect on development in the Dordogne). The juvenile stage represented the least at-risk stage at quantified environmental concentrations, with msPAF<sub>SE-T</sub> reaching a maximum of 1.32% in the Garonne. The embryonic stage was the only life stage of fish taxon for which a value of msPAF<sub>SE-T</sub> associated with a mortality effect exceeded 1%, and this in both spawning grounds.

At quantified environmental values, only the msPAF<sub>SE-T</sub> associated with effects on larval development was higher in the Dordogne than in the Garonne. In all other cases, msPAF<sub>SE-T</sub> were similar into the two rivers, or higher in the Garonne.
<figure><img src="image_27.png" /><figcaption>**Figure 14.** Values of msPAF<sub>SE-T</sub> (in %) with (A) quantified + unquantified environmental data and (B) only quantified environmental data for each life stage and each effect in the Dordogne and Garonne spawning grounds (*: error standard > 15%; **: error standard and periodic average > 15%). The values of msPAF<sub>SE-T</sub> and their standard deviation are shown. The red line indicate the Th<sub>5%</sub>. As a reminder, msPAF<sub>SE-T</sub> represents the periodic average msPAF specific to each life stage of the fish taxon for each effect tested in each spawning ground.</figcaption></figure>

### D. Contribution of categories to (sub)lethal effects in early life stages of fish

For each category, the contribution to msPAF<sub>SE-AM</sub> differs according to the life stage studied, the sublethal effect tested, the spawning ground and the time (Fig. 15). For effects on larval development and larval physiology, the contribution of metals (■) is visually significant and constant over the entire study period in both spawning grounds. This contribution is moreover constant at a level exceeding the Th<sub>5%</sub>. For effects on embryonic development, metals contribute more variably, but still significantly, exceeding the Th<sub>5%</sub>, in particular in Garonne spawning grounds. This category of chemical compounds appears to make a small contribution to msPAF<sub>SE-AM</sub> for effects on embryo
<figure><img src="image_27.png" /><figcaption>**Figure 14.** Values of msPAF<sub>SE-T</sub> (in %) with (A) quantified + unquantified environmental data and (B) only quantified environmental data for each life stage and each effect in the Dordogne and Garonne spawning grounds (*: error standard > 15%; **: error standard and periodic average > 15%). The values of msPAF<sub>SE-T</sub> and their standard deviation are shown. The red line indicate the Th<sub>5%</sub>. As a reminder, msPAF<sub>SE-T</sub> represents the periodic average msPAF specific to each life stage of the fish taxon for each effect tested in each spawning ground.</figcaption></figure>

### D. Contribution of categories to (sub)lethal effects in early life stages of fish

For each category, the contribution to msPAF<sub>SE-AM</sub> differs according to the life stage studied, the sublethal effect tested, the spawning ground and the time (Fig. 15). For effects on larval development and larval physiology, the contribution of metals (■) is visually significant and constant over the entire study period in both spawning grounds. This contribution is moreover constant at a level exceeding the Th<sub>5%</sub>. For effects on embryonic development, metals contribute more variably, but still significantly, exceeding the Th<sub>5%</sub>, in particular in Garonne spawning grounds. This category of chemical compounds appears to make a small contribution to msPAF<sub>SE-AM</sub> for effects on embryo
physiology, embryo behavior and larval behavior in both spawning grounds. For all the effects tested, a constant and low contribution from agrochemicals (■) was observed. In terms of effects on embryo physiology and larval physiology, these chemical compounds may exceed the $Th_{5\%}$ in both spawning grounds. Since they have been monitored (i.e. 2016), HCPs (■) have contributed strongly to $msPAF_{SE-AM}$ for all sublethal effects tested for embryos and for the behavioral effect for larvae at a level exceeding the $Th_{5\%}$. PCBs (■) and PFASs (□) make almost no contribution to $msPAF_{SE-AM}$. PAHs (■) made a significant contribution to $msPAF_{SE-AM}$ in the Dordogne in 2007, for effects on embryo development and embryo physiology. As for OIPs (■), a low and constant contribution was observed in both spawning grounds. In addition, in Dordogne spawning grounds, this category made a significant contribution to very strong and sudden $msPAF_{SE-AM}$ at a level exceeding the $Th_{5\%}$, for all sublethal effects tested and all life stages except for effects on larval behavior, where no extreme values were recorded.

<figure><img src="image_28.png" /><figcaption>**Figure 15.** Trends of $msPAF_{SE-AM}$ using quantified + unquantified environmental data (in %) for the sublethal effects (development, physiology and behavior), for early stages (embryo and larva) and for each category of chemicals in Dordogne and Garonne spawning grounds (': averages exceeding the graph box; their values are shown next to the '). The red line indicate the $Th_{5\%}$. As a reminder, $msPAF_{SE-AM}$ represents the average annual $msPAF$ specific to each life stage of the fish taxon for each effect tested and category studied in each spawning ground.</figcaption></figure>
physiology, embryo behavior and larval behavior in both spawning grounds. For all the effects tested, a constant and low contribution from agrochemicals (■) was observed. In terms of effects on embryo physiology and larval physiology, these chemical compounds may exceed the $Th_{5\%}$ in both spawning grounds. Since they have been monitored (i.e. 2016), HCPs (■) have contributed strongly to $msPAF_{SE-AM}$ for all sublethal effects tested for embryos and for the behavioral effect for larvae at a level exceeding the $Th_{5\%}$. PCBs (■) and PFASs (□) make almost no contribution to $msPAF_{SE-AM}$. PAHs (■) made a significant contribution to $msPAF_{SE-AM}$ in the Dordogne in 2007, for effects on embryo development and embryo physiology. As for OIPs (■), a low and constant contribution was observed in both spawning grounds. In addition, in Dordogne spawning grounds, this category made a significant contribution to very strong and sudden $msPAF_{SE-AM}$ at a level exceeding the $Th_{5\%}$, for all sublethal effects tested and all life stages except for effects on larval behavior, where no extreme values were recorded.

<figure><img src="image_28.png" /><figcaption>**Figure 15.** Trends of $msPAF_{SE-AM}$ using quantified + unquantified environmental data (in %) for the sublethal effects (development, physiology and behavior), for early stages (embryo and larva) and for each category of chemicals in Dordogne and Garonne spawning grounds (': averages exceeding the graph box; their values are shown next to the'). The red line indicate the $Th_{5\%}$. As a reminder, $msPAF_{SE-AM}$ represents the average annual $msPAF$ specific to each life stage of the fish taxon for each effect tested and category studied in each spawning ground.</figcaption></figure>
In the case of a mortality effect, for each category, the contribution to msPAF<sub>SE-AM</sub> differs according to the life stage studied, the spawning ground and the time (**Fig. 16**). Metals (■) made a significant and constant contribution throughout the period, particularly in embryos, where in some years a potential ecological risk was observed (msPAF<sub>SE-AM</sub> > 5%). Two significant values were recorded, for this category, with ~33% recorded in the Dordogne in 2009 and close to ~15% in the Garonne in 2009. The contribution of agrochemicals (■) to msPAF<sub>SE-AM</sub> was low in both spawning grounds for embryos and larvae. HCPs (■), PCBs (■) and PFASs (●) contributed very little to the msPAF<sub>SE-AM</sub>. PAHs (■) made a significant contribution in 2007 in the Dordogne, particularly for larvae. The contribution of OIPs (■) to msPAF<sub>SE-AM</sub> was significant in both spawning grounds, particularly for larvae. For this category a value more than 55% was recorded in the Dordogne in 2007 and values above Th<sub>5%</sub> were observed in the Garonne in 2009 and 2015. Thus, for a lethal effect, metals seem to affect embryos and OIPs seem to affect larvae.

<figure><img src="image_29.png" /><figcaption>**Figure 16.** Trends of msPAF<sub>SE-AM</sub> using quantified + unquantified environmental data (in %) for the lethal effects (mortality), for early stages (embryo and larva) and for each category of chemicals in Dodrogne and Garonne spawning grounds (\* : averages exceeding the graph box; their values are shown next to the \*). The red line indicate the Th<sub>5%</sub>. As a reminder, msPAF<sub>SE-AM</sub> represents the average annual msPAF specific to each life stage of the fish taxon for each effect tested and category studied in each spawning ground.</figcaption></figure>

### E. Individual contributions from chemical compounds exceeding the Th<sub>5%</sub>

Some chemical compounds may contribute individually to toxic risk (PAFi > Th5%), but these and their number differ according to the effect tested, the year, the life stage studied and the spawning ground (**Tab. IV**).

#### a. Effects on the development of early life stages

Estrone (HCPs) had a significant PAFi associated with embryos between 2019 and 2021 in the Garonne (41%) and in 2019 in the Dordogne (33%). Among metals, calcium had a significant PAFi from 2006 to 2021 associated with larvae in both spawning grounds (from 10 to 18% in the Dordogne and
In the case of a mortality effect, for each category, the contribution to msPAF<sub>SE-AM</sub> differs according to the life stage studied, the spawning ground and the time (Fig. 16). Metals (■) made a significant and constant contribution throughout the period, particularly in embryos, where in some years a potential ecological risk was observed (msPAF<sub>SE-AM</sub> > 5%). Two significant values were recorded, for this category, with ~33% recorded in the Dordogne in 2009 and close to ~15% in the Garonne in 2009. The contribution of agrochemicals (■) to msPAF<sub>SE-AM</sub> was low in both spawning grounds for embryos and larvae. HCPs (■), PCBs (■) and PFASs (●) contributed very little to the msPAF<sub>SE-AM</sub>. PAHs (■) made a significant contribution in 2007 in the Dordogne, particularly for larvae. The contribution of OIPs (■) to msPAF<sub>SE-AM</sub> was significant in both spawning grounds, particularly for larvae. For this category a value more than 55% was recorded in the Dordogne in 2007 and values above Th<sub>5%</sub> were observed in the Garonne in 2009 and 2015. Thus, for a lethal effect, metals seem to affect embryos and OIPs seem to affect larvae.

<figure><img src="image_29.png" /><figcaption>**Figure 16.** Trends of msPAF<sub>SE-AM</sub> using quantified + unquantified environmental data (in %) for the lethal effects (mortality), for early stages (embryo and larva) and for each category of chemicals in Dodrogne and Garonne spawning grounds (\* : averages exceeding the graph box; their values are shown next to the \*). The red line indicate the Th<sub>5%</sub>. As a reminder, msPAF<sub>SE-AM</sub> represents the average annual msPAF specific to each life stage of the fish taxon for each effect tested and category studied in each spawning ground.</figcaption></figure>

### E. Individual contributions from chemical compounds exceeding the Th<sub>5%</sub>

Some chemical compounds may contribute individually to toxic risk (PAFi > Th5%), but these and their number differ according to the effect tested, the year, the life stage studied and the spawning ground (Tab. IV).

#### a. Effects on the development of early life stages

Estrone (HCPs) had a significant PAFi associated with embryos between 2019 and 2021 in the Garonne (41%) and in 2019 in the Dordogne (33%). Among metals, calcium had a significant PAFi from 2006 to 2021 associated with larvae in both spawning grounds (from 10 to 18% in the Dordogne and
from 22 to 29% in the Garonne). Copper also had a significant PAFi in 2009 (9%) and 2012 (6%) in the Dordogne, as well as in 2009 (5%), 2010 (14%), 2012 (7%) and 2015 (9%) in the Garonne, for embryos. Zinc had a significant PAFi in 2012 (6%) in the Garonne for larvae. In 2007, tributylstannane (9%, for embryos in the Dordogne), dioctyl phthalate (57%, for larvae in the Dordogne) and benzolalpyrene (6%, for embryos in the Dordogne) had a significant PAFi. In 2012, bisphenol A, had a significant PAFi for embryos (9%) and an extreme PAFi for larvae (798%), in the Dordogne. Bisphenol A in the Dordogne, zinc in the Garonne, copper and calcium in both spawning grounds contributed when using only quantified environmental data were used.

### b. Effects on the behavior of early life stages

Estrone had a significant PAFi for embryos and larvae between 2019 and 2021 (37%) in the Garonne and in 2019 (30%) in the Dordogne. In the Dordogne, 4-tert-octyl phenol (25%, in 2007) and bisphenol A (7%, in 2012) also had a significant PAFi for embryos. For this effect, only bisphenol A in the Dordogne contributed above the $Th_{5\%}$ when only quantified environmental data were used.

### c. Effects on the physiology of early life stages

Simazine had significant PAFi associated with embryos in 2007 (8%) in the Dordogne, as well as in 2010 (8%) and 2011 (7%) for embryos in the Garonne. Cypermethrin had significant PAFi associated with larvae in the Dordogne in 2018 and 2019 (6%) and in the Garonne in 2016 to 2021 (6%). Metformin (7 to 17%) and carbamazepine (12 to 98%) had significant PAFi for embryos from 2016 to 2021 in both spawning grounds. Estrone showed significant PAFi for embryos from 2019 to 2021 in the Garonne (135%) and from 2016 to 2021 in the Dordogne (5 to 109%). Among metals, calcium had a significant PAFi from 2006 to 2021 associated with larvae in both spawning grounds (from 10 to 18% in the Dordogne and from 22 to 29% in the Garonne). Copper had significant PAFi associated with larvae in the Garonne from 2009 to 2021 (7 to 29%) and in the Dordogne in 2009, in 2010, in 2012, from 2015 to 2018 and in 2021 (5 to 19%). Dioctyl phthalate (10% for embryos and 14% for larvae in the Dordogne), benzolalpyrene (15% for embryos in the Dordogne) and fluoranthene (8% for embryos in the Dordogne) had a high PAFi in 2007. Phenol, 4-nonyl-, branched had a significant PAFi in 2007 (18% in the Dordogne) and 2019 (6% in the Garonne) for embryos. Simazine and phenol, 4-nonyl-, branched in the Garonne, cypermethrin in the Dordogne, and metformin, carbamazepine, calcium and copper in both spawning grounds had significant PAFi, with values above the $Th_{5\%}$ using quantified environmental data.

### d. Effects on the mortality of early life stages

In 2009, lead was associated with a high PAFi with embryos in the Garonne (10%) and in the Dordogne (29%). The 4-tert-octylphenol had a high PAFi for larvae in the Dordogne in 2007 (48%). The bisphenol A had a high PAFi for embryos in the Dordogne in 2012 (7%). Dioctyl phthalate had a high PAFi for larvae in the Dordogne larvae in 2007 (6%). Bisphenol A and 4-tert-octyl phenol in the Dordogne and lead in both spawning grounds exceeded the $Th_{5\%}$ using quantified environmental data.
from 22 to 29% in the Garonne). Copper also had a significant PAFi in 2009 (9%) and 2012 (6%) in the Dordogne, as well as in 2009 (5%), 2010 (14%), 2012 (7%) and 2015 (9%) in the Garonne, for embryos. Zinc had a significant PAFi in 2012 (6%) in the Garonne for larvae. In 2007, tributylstannane (9%, for embryos in the Dordogne), dioctyl phthalate (57%, for larvae in the Dordogne) and benzolalpyrene (6%, for embryos in the Dordogne) had a significant PAFi. In 2012, bisphenol A, had a significant PAFi for embryos (9%) and an extreme PAFi for larvae (798%), in the Dordogne. Bisphenol A in the Dordogne, zinc in the Garonne, copper and calcium in both spawning grounds contributed when using only quantified environmental data were used.

### b. Effects on the behavior of early life stages

Estrone had a significant PAFi for embryos and larvae between 2019 and 2021 (37%) in the Garonne and in 2019 (30%) in the Dordogne. In the Dordogne, 4-tert-octyl phenol (25%, in 2007) and bisphenol A (7%, in 2012) also had a significant PAFi for embryos. For this effect, only bisphenol A in the Dordogne contributed above the $Th_{5\%}$ when only quantified environmental data were used.

### c. Effects on the physiology of early life stages

Simazine had significant PAFi associated with embryos in 2007 (8%) in the Dordogne, as well as in 2010 (8%) and 2011 (7%) for embryos in the Garonne. Cypermethrin had significant PAFi associated with larvae in the Dordogne in 2018 and 2019 (6%) and in the Garonne in 2016 to 2021 (6%). Metformin (7 to 17%) and carbamazepine (12 to 98%) had significant PAFi for embryos from 2016 to 2021 in both spawning grounds. Estrone showed significant PAFi for embryos from 2019 to 2021 in the Garonne (135%) and from 2016 to 2021 in the Dordogne (5 to 109%). Among metals, calcium had a significant PAFi from 2006 to 2021 associated with larvae in both spawning grounds (from 10 to 18% in the Dordogne and from 22 to 29% in the Garonne). Copper had significant PAFi associated with larvae in the Garonne from 2009 to 2021 (7 to 29%) and in the Dordogne in 2009, in 2010, in 2012, from 2015 to 2018 and in 2021 (5 to 19%). Dioctyl phthalate (10% for embryos and 14% for larvae in the Dordogne), benzolalpyrene (15% for embryos in the Dordogne) and fluoranthene (8% for embryos in the Dordogne) had a high PAFi in 2007. Phenol, 4-nonyl-, branched had a significant PAFi in 2007 (18% in the Dordogne) and 2019 (6% in the Garonne) for embryos. Simazine and phenol, 4-nonyl-, branched in the Garonne, cypermethrin in the Dordogne, and metformin, carbamazepine, calcium and copper in both spawning grounds had significant PAFi, with values above the $Th_{5\%}$ using quantified environmental data.

### d. Effects on the mortality of early life stages

In 2009, lead was associated with a high PAFi with embryos in the Garonne (10%) and in the Dordogne (29%). The 4-tert-octylphenol had a high PAFi for larvae in the Dordogne in 2007 (48%). The bisphenol A had a high PAFi for embryos in the Dordogne in 2012 (7%). Dioctyl phthalate had a high PAFi for larvae in the Dordogne larvae in 2007 (6%). Bisphenol A and 4-tert-octyl phenol in the Dordogne and lead in both spawning grounds exceeded the $Th_{5\%}$ using quantified environmental data.
<table><caption>**Table IV.** Values of PAFi above the Th5% (representative of a potential ecological risk) for each effect, life stage, year and river studied when using unquantified and quantified environmental data. Substances above the Th5% using only quantified environmental data are shown in bold. In dark gray, values about embryos and in light gray, larvae. As a reminder, PAFi represents the average annual individual PAF specific to each chemical compound and each life stage of the fish taxon for each effect tested in each spawning ground.</caption><thead><tr><th>Chemicals (Categories)</th><th>Rivers</th><th>Life stages</th><th>2006</th><th>2007</th><th>2008</th><th>2009</th><th>2010</th><th>2011</th><th>2012</th><th>2013</th><th>2014</th><th>2015</th><th>2016</th><th>2017</th><th>2018</th><th>2019</th><th>2020</th><th>2021</th></tr></thead><tbody><tr><td colspan="20"><strong>Development</strong></td></tr><tr><td>Estrone (HCPs)</td><td>D</td><td>embryon</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><td>33</td><td></td><td></td></tr><tr><td>Estrone (HCPs)</td><td>G</td><td>embryon</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><td>41</td><td>41</td><td>41</td></tr><tr><td>Calcium (Metals)</td><td>D</td><td>larva</td><td>18</td><td>13</td><td>14</td><td>14</td><td>13</td><td>16</td><td>15</td><td>13</td><td>11</td><td>12</td><td>14</td><td>14</td><td>12</td><td>14</td><td>15</td><td>10</td></tr><tr><td>Calcium (Metals)</td><td>G</td><td>larva</td><td>23</td><td>29</td><td>27</td><td>27</td><td>23</td><td>23</td><td>22</td><td>27</td><td>27</td><td>26</td><td>25</td><td>24</td><td>25</td><td>23</td><td>23</td><td>28</td></tr><tr><td>Copper (Metals)</td><td>D</td><td>embryon</td><td></td><td></td><td></td><td></td><td>9</td><td></td><td></td><td>6</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Copper (Metals)</td><td>G</td><td>embryon</td><td></td><td></td><td></td><td></td><td>5</td><td>14</td><td></td><td>7</td><td></td><td></td><td>9</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Zinc (Metals)</td><td>G</td><td>larva</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>6</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Tributylstannane (OIPs)</td><td>D</td><td>embryon</td><td></td><td>9</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><td></td></tr><tr><td>Bisphenol A (OIPs)</td><td>D</td><td>larva</td><td></td><td></td><td></td><td>7</td><td></td><td></td><td></td><td>798</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Bisphenol A (OIPs)</td><td>D</td><td>embryon</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>9</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Dioctyl phthalate (OIPs)</td><td>D</td><td>larva</td><td></td><td>57</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><td></td></tr><tr><td>Benzolalpyrene (PAHs)</td><td>D</td><td>embryon</td><td></td><td>6</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><td></td></tr><tr><td colspan="20"><strong>Behavior</strong></td></tr><tr><td>Estrone (HCPs)</td><td>D</td><td>embryon</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><td>30</td><td></td><td></td></tr><tr><td>Estrone (HCPs)</td><td>D</td><td>larva</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><td>30</td><td></td><td></td></tr><tr><td>Estrone (HCPs)</td><td>G</td><td>embryon</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><td>37</td><td>37</td><td>37</td></tr><tr><td>Estrone (HCPs)</td><td>G</td><td>larva</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><td>37</td><td>37</td><td>37</td></tr><tr><td>4-tert-Octylphenol (OIPs)</td><td>D</td><td>embryon</td><td></td><td></td><td>25</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>Bisphenol A (OIPs)</td><td>D</td><td>embryon</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>7</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td colspan="20"><strong>Physiology</strong></td></tr><tr><td>Simazine (Agroch.)</td><td>D</td><td>embryon</td><td></td><td></td><td>8</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>Simazine (Agroch.)</td><td>G</td><td>embryon</td><td></td><td></td><td></td><td></td><td>8</td><td>7</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Cypermethrin (Agroch.)</td><td>D</td><td>larva</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>6</td><td>6</td><td>6</td><td>6</td></tr><tr><td>Cypermethrin (Agroch.)</td><td>G</td><td>larva</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>6</td><td>6</td><td>6</td><td>6</td><td>6</td></tr><tr><td>Metformin (HCPs)</td><td>D</td><td>embryon</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>16</td><td>13</td><td>12</td><td>9</td><td>17</td><td>7</td></tr><tr><td>Metformin (HCPs)</td><td>G</td><td>embryon</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>12</td><td>14</td><td>8</td><td>12</td><td>12</td><td>12</td></tr><tr><td>Carbamazepine (HCPs)</td><td>D</td><td>embryon</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>30</td><td>33</td><td>31</td><td>31</td><td>28</td><td>12</td></tr><tr><td>Carbamazepine (HCPs)</td><td>G</td><td>embryon</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>46</td><td>58</td><td>48</td><td>98</td><td>64</td><td>46</td></tr><tr><td>Estrone (HCPs)</td><td>D</td><td>embryon</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>9</td><td>5</td><td>5</td><td>109</td><td>5</td><td>5</td></tr><tr><td>Estrone (HCPs)</td><td>G</td><td>embryon</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><td></td><td>135</td><td>135</td><td>135</td></tr><tr><td>Calcium (Metals)</td><td>D</td><td>larva</td><td>18</td><td>13</td><td>14</td><td>14</td><td>13</td><td>16</td><td>15</td><td>13</td><td>11</td><td>12</td><td>14</td><td>14</td><td>12</td><td>14</td><td>15</td><td>10</td></tr><tr><td>Calcium (Metals)</td><td>G</td><td>larva</td><td>23</td><td>29</td><td>27</td><td>27</td><td>23</td><td>23</td><td>22</td><td>27</td><td>27</td><td>26</td><td>25</td><td>24</td><td>25</td><td>23</td><td>23</td><td>28</td></tr><tr><td>Copper (Metals)</td><td>D</td><td>larva</td><td></td><td></td><td></td><td>19</td><td>5</td><td></td><td>12</td><td></td><td></td><td>7</td><td>7</td><td>6</td><td>5</td><td></td><td></td><td>5</td></tr><tr><td>Copper (Metals)</td><td>G</td><td>larva</td><td></td><td></td><td></td><td>11</td><td>29</td><td>8</td><td>14</td><td>9</td><td>10</td><td>19</td><td>6</td><td>6</td><td>7</td><td>7</td><td>10</td><td>7</td></tr><tr><td>Phenol, 4-nonyl-, branched (OIPs)</td><td>D</td><td>embryon</td><td></td><td>18</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><td></td></tr><tr><td>Phenol, 4-nonyl-, branched (OIPs)</td><td>G</td><td>embryon</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><td>6</td><td></td><td></td></tr><tr><td>Dioctyl phthalate (OIPs)</td><td>D</td><td>embryon</td><td></td><td>10</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><td></td></tr><tr><td>Dioctyl phthalate (OIPs)</td><td>D</td><td>larva</td><td></td><td>14</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><td></td></tr><tr><td>Benzolalpyrene (PAHs)</td><td>D</td><td>embryon</td><td></td><td>15</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><td></td></tr><tr><td>Fluoranthene (PAHs)</td><td>D</td><td>embryon</td><td></td><td>8</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><td></td></tr><tr><td colspan="20"><strong>Mortality</strong></td></tr><tr><td>Lead (Metals)</td><td>D</td><td>embryon</td><td></td><td></td><td></td><td></td><td>29</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>Lead (Metals)</td><td>G</td><td>embryon</td><td></td><td></td><td></td><td></td><td>10</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>4-tert-Octylphenol (OIPs)</td><td>D</td><td>larva</td><td></td><td></td><td>48</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>Bisphenol A (OIPs)</td><td>D</td><td>embryon</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>7</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Dioctyl phthalate (OIPs)</td><td>D</td><td>larva</td><td></td><td></td><td>6</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></tbody></table>

## 5. Discussion

### A. Summary of main results

Using only quantified environmental data, we have seen that there was a toxic risk for the fish taxon (msPAF<sub>F-AM</sub> > Th<sub>5%</sub>) in the Garonne spawning grounds for each year of the study period. In the Dordogne, this risk was only present for part of the period (2006, 2007, 2008, 2010, 2011, 2013 and 2015). Furthermore, at quantified environmental concentrations, msPAF<sub>F-T</sub> values were twice as high in the Garonne spawning grounds (~8% > Th<sub>5%</sub>) as in the Dordogne spawning grounds (~4% < Th<sub>5%</sub>). There was also a high degree of variability within spawning grounds each year (high standard