Tomasz Daktera

Emmanuel Bourgeois

Pierre Schmitt

Thierry Jeanmaire

Laure Delva

Grégoire Priol

Design of deep supported excavations: comparison between real behavior and predictions based on the subgrade coefficient method Conception de fouilles profondes : comparaison entre comportement réel et prédictions basées sur la méthode du coefficient de réaction

Keywords: diaphragm wall, field testing & monitoring, temporary works, subgrade reaction coefficient

The stations of the line 15 of the Grand Paris Express include diaphragm walls 50 m deep with excavation depths over 30 m. Their behavior at "Les Ardoines" metro station has been carefully monitored during the excavation process by inclinometers in diaphragm walls, strain gauges on steel props and topographic monitoring. A complete analysis of the monitoring data has been performed, especially for the steel propping system where the earth-induced and temperature-induced loads have been separated. The aim of this work is to improve the methodology of determination of the parameters of the subgrade reaction coefficient. Comparison with finite element simulations will also provide an opportunity to improve consistency between the parameters used in both methods.

RÉSUMÉ :

Les gares de la ligne 15 du Grand Paris Express comportent des parois moulées de plus de 50 m de profondeur et plus de 30 m de profondeur excavée. Leur comportement pendant les étapes d'excavation à la station « Les Ardoines » a fait l'objet d'une instrumentation détaillée : mesures inclinométriques pour suivre les déformations des parois moulées, jauges de déformation sur les butons métalliques, mesures topographiques classiques. Une analyse complète des données des instrumentations a été faite, en particulier pour les butons métalliques où les efforts induits par la poussée des terres et par la température ont été séparés. Le but de ce travail est d'améliorer la méthodologie de détermination des paramètres de la méthode du coefficient de réaction. La comparaison des mesures avec des modélisations par éléments finis permettra également d'améliorer la cohérence entre les paramètres utilisés dans les deux méthodes.

ECSMGE-2019 -Proceedings

2 IGS

INTRODUCTION

The Grand Paris Express is the largest infrastructure project currently under construction in Europe. This megaproject consists of over 200 kilometers of automatic metro lines (mostly tunnels built with TBMs) and 68 stations. This paper focuses on the station of Les Ardoines, on the South section of line 15, which should be put into service in 2025. The TBM will bore through the station after a complete excavation. Figure 1 shows the excavation works in July 2018.

General structure's information

The station is enclosed within a 112 m long and 31.2 m wide rectangle made of diaphragm walls. It is divided into two "pits", called Western and Eastern pits, separated by a diaphragm wall. This article focuses on the Western pit construction only. During the excavation process, the wall is supported by three levels of steel props. An additional prop level holds the intermediate diaphragm wall that stands in between both pits. A cross-section of the Western Pit and a top view of the diaphragm walls are shown on Figure 2 and Figure 3. After the construction of the diaphragm walls of both pits, the Eastern Pit has been excavated to 26.15NGF (cantilever). Then, the construction stages for the Western pit were as follows: 1. Cantilever excavation to 28.4NGF; 2. Prop installation at 29NGF; 3. Excavation to 18.5NGF; 4. Prop installation at 19.1NGF and at 20.5NGF; 5. Excavation to 11.4NGF; 6. Prop installation at 12NGF; 7. excavation to 4.65NGF.

Geotechnical parameters

The geotechnical study for this project consisted of laboratory tests (classification tests, triaxial drained and undrained tests and other) as well as in-situ pressuremeter tests (PMT). For the backanalysis, an objective study of all geotechnical data available was performed. Such a study allows to understand the reliability of the parameters and their potential heterogeneity and to obtain a representative profile for the analyzed cross-section. The chosen soil parameters are shown in Table 1 where EM and a are Ménard's modulus and rheological coefficient.

Instrumentation of Les Ardoines metro station -Western Pit

The monitoring program of the site is composed of the following items: 5 inclinometer tubes within the diaphragm walls, 24 thermally matched vibrating wire strain gauges, installed on 8 steel props on three different levels (n°4, 9, 15) and 12 convergence measurements at three levels over four cross-sections on the diaphragm walls, to measure the horizontal displacements. The monitored elements are shown on Figure 1-3.

STRAIN GAUGES ON STEEL PROPS ANALYSIS

The position of the strain gauges must be taken into account in the analysis. On the instrumented circular props, three strain gauges were placed at the uppermost point and at 120 degrees on each side. The mean value of the three strain gauges gives the mean normal stress in the section. The strain gauges were installed 5 prop diameters away from the diaphragm wall in order to avoid the connection's influence [START_REF] Twine | Temporary propping of deep excavationsguidance on design[END_REF].

3.1 The temperature-induced force 3.1.1 Temperature effects -generalities Because of thermal expansion, a change in temperature affects the normal force in the prop (unless the ends of the prop are free to move, which is not the case). For a fully restrained prop (where no increase in length is possible), the load difference due to temperature variation is:

∆P temp = β • ∆T • E • A (1)
where β (K -1 ), E (MPa) and A (m 2 ) are the thermal expansion coefficient, Young's modulus and cross-sectional area of the prop and ∆T (°K) is the difference between the current and installation temperature. A prop placed between two diaphragm walls is not fully restrained, thus the prop load variation due to temperature changes is lower than the value given by eq. (1).

Separation of earth-induced and temperature-induced loads in props

To separate temperature-induced and earth-induced loads, the method described by [START_REF] Boone | Braced excavations: temperature, elastic modulus and strut loads[END_REF] has been adopted. It is illustrated for prop B1-9. Figure 4a) shows the variations of the mean force in the instrumented prop section with time.

The red curve is the temperature variation. Figure 4b) shows a plot of all the measurement points (one measurement every hour) of the incremental temperature vs incremental load in order to understand the dependence between the temperature and the force increase. The colors on both Figure 4a) and b) match. One can distinguish the weekends with an almost perfectly linear elastic behavior (stage 3 orange and 5 dark red) from the workweeks where the earth-induced load increases (stage 2 dark green, 4 gray and 6 yellow) due to excavations. The slope of ∆T/∆Pdenoted by m, allows to quantify the dependency between the variables. For B1-9, m = 45kN/°C. For a fully restrained prop: m restrained = 93.8 kN/°C. The earth-induced loads can be estimated by:

P earth = P measured -m • ∆T (2)
For B1-9 the earth-induced load due to the excavation below the first prop level is considered to be 1800 kN. The same analysis has been performed for the other props. The separated loads are shown on Figure 5. 2 shows the earth induced forces in all 8 struts. The two inclined props (n°4) at the corners of the pit show much lower mean normal force measurements than the straight props. This is interesting as the theoretical stiffness difference between the straight and inclined props does not justify such discrepancies, nor does the distance of the prop to the pits corner (12m) nor arching effects, as most of the applied pressure on the wall consists of water. No explanation of the much lower measured forces is given at this point, but more instrumented inclined props will be analyzed on other deep excavation pits.

INCLINOMETER READINGS ANALYSIS

Five 45m long inclinometer tubes have been installed within the diaphragm walls, with their toes embedded 5m below the diaphragm wall mechanical toe. Hydraulic toe has also been modeled in both analyses (FEM and SGRM). Initial inclinometer readings were taken before the 27m deep pumping test.

Subgrade reaction coefficient analysis

The French code on retaining walls (AFNOR 2009), presents two main formulas for the subgrade reaction coefficient kh determination:

𝑘 ℎ = 2.1 • ( 𝐸 𝑀 𝛼 ) 4 3 ⁄ /(𝐸𝐼) 1 3 ⁄ (3)
derived from the more general formula:

𝑘 ℎ = 𝐾 • ( 𝐸 𝑀 𝛼 ) /𝐴 (4)
where EI (kNm 2 ) is wall's flexural stiffness, A (m) is a parameter called "interaction depth" and K is considered as 3.6. The diaphragm wall Young's modulus considered in the analysis is 27GPa. Formula (3), also called Schmitt's formula, has shown its validity on regular excavations in France and in the Parisian Region and thus has been used for the excavation stages of the pit [START_REF] Schmitt | Méthode empirique d'évaluation du coefficient de réaction du sol vis-à-vis des ouvrages de soutènement souples[END_REF][START_REF] Serrai | Analysis of measured deflections of a diaphragm wall in Colombes using finite element calculations. 16 th ICSMGE[END_REF]. According to AFNOR 2009, for the pumping stage formula (4) should be used. For our calculation, the interaction depth A has been considered as the height between the external GWL and the interface between MC and SB layers. Schmitt's formula was used below 4.2NGF.

FEM analysis

Two FEM simulations have been performed: one with Mohr-Coulomb (Mohr-C) and the other with the Hardening Soil Model (HSM). For the Mohr-C Soil Model, the moduli were [START_REF] Serrai | Analysis of measured deflections of a diaphragm wall in Colombes using finite element calculations. 16 th ICSMGE[END_REF]:

𝐸 = 4 • 𝐸 𝑀 𝛼 ⁄ (5)
For the HSM the moduli were:

𝐸 50 = 𝐸 𝑜𝑒𝑑 = 𝐸 𝑢𝑟 3 ⁄ = 2 • 𝐸 𝑀 𝛼 (6)
The exponent m is 0 for all soil layers as the PMT's showed little moduli increase with depth.

Analysis of pumping test stage

The pumping stage consists of dewatering the pit prior to any excavation. The analysis of this stage is independent of the excavation stages calculations. Four inclinometer curves for this stage are shown on Figure 6a), assuming for comparison purpose that the toe of the inclinometer is fixed. All curves are almost vertical from the bottom to the interface between the MC and SB layers and then leaning forward to a displacement of about 4mm. P56 and P53 inclinometers show inexplicable curvatures above 28NGF and are considered as erroneous measurements.

(a) (b) Figure 6. Inclinometer readings for the pumping test with SGRM and FEM (Mohr-C and HSM models)

Calculation results are shown on Figure 6b). With formula (3), the interaction depth is conditioned by the wall's stiffness EI and shows much lower displacements (continuous black curve) than measured. With formula (4), the interaction depth is 27m above the pumping level, to model a translation behavior of the wall. The dotted black curve shows larger displacements, together with a behavior in which the top of the wall seems to be supported. This is not observed on the measured curves. FEM results are represented with green and red curves for Mohr-C and HSM respectively.

Figure 7 presents corrected SGRM and FEM curves in order to obtain a better agreement with the inclinometric measurements. Green and red curves are Mohr-C and HSM calculations respectively, with E = 6 E M α ⁄ and E 50 = 4 E M α ⁄ for the MC and CG soil layers. For the black curves, a kh increasing with depth was considered to model the dragging of the top layers by the deeper layers which is probably the reason why SGRM conventional calculations in Figure 6 For both continuous and dashed curves, the interaction depths per layers have been determined as the distance between the layer in question and the lowered water level (3.7NGF). K coefficients of 1.8 and 1.2 have been used respectively. These values account for the fact that the pumping is the primary loading of the soil layers. kh coefficient in SGRM is defined in a unique way, without distinction between unloading and reloading and thus it is legitimate to change its value for different load paths. For the layers below the pumping level, formula (3) has been used. The SGRM curves show a much closer fit to the measured ones with variable interaction depths. The FEM models even with artificially higher moduli below the MC and SB interface do not properly model the lower parts of the curves where the measurements show almost no rotation and different curvatures.

Analysis of the excavations stages

The water level within the excavation has been maintained at 3.7NGF during all excavation stages. The pumping test inclinometer results have been taken as reference for the analysis of the subsequent excavation stages. Formulas (3), ( 5) and ( 6) were chosen for the SGRM (as per AFNOR 2009), Mohr-C and HSM models. The inclinometric curves for the first excavation were not very smooth. SGRM, Mohr-C and HSM models show similar curvatures for both stages shown on Figure 8. Top displacements are 5mm, 2.5mm and 4mm respectively for the 28.4NGF stage. For the excavation stage to 18.5NGF, displacements were slightly overestimated by SGRM and HSM models by less than 2mm. Mohr-C maximum displacement is very close to the measured maximum at about 22.5NGF depth.

The displacement curves of the SGRM and Mohr-C models are not as smooth as the measurements or the HSM between 5 and 15NGF. For the 11.4NGF excavation (see Figure 9), all models show correct curvatures below 5NGF. SGRM's maximum displacement of 14mm is between the maximum of the measured inclinometer 56 and 39 curves. Mohr-C underestimates the maximum displacement by 2mm. HSM shows a good fit for absolute displacements and curvatures below the peak. For the excavation stages at 18.5NGF and 11.4NGF, all models overestimate displacements but give similar curvatures above level 25NGF.

For the final excavation at 4.65NGF, all curves are very similar between 7NGF and the wall toe.

The SGRM gives a maximum displacement of 17 mm, between both values measured by the inclinometers. HSM's maximum displacement is just below the measured inc39 -16mm. By The measured prop force in B2-09 is 15% greater than in B2-15 which is probably due to the load taken by the inclined prop B2-16 just above B2-15. SGRM and FEM calculations show that the prop at 20.5NGF supports about 20% of the force of B2-15, which is close to the measured 15%.

Concerning the 3 rd prop level, all models overestimate the prop forces, where both monitored straight props show the same value of 650kN/m. HSM and Mohr-C models overestimate the measurements by about 33% -SGRM by 57%. A sum of all three prop levels in comparison to props n°15 measurements gives an overestimation of 42% for HSM, of 31% for Mohr-C and of 27% for SGRM for the final excavation stage.

CONCLUSIONS

Thanks to accurate measurements a complete analysis of the inclinometers, prop forces and convergence measurements has been performed. The convergence measurements match the inclinometric displacements at all 3 levels. Inclinometric measurements for the pumping test and all excavation phases were compared with SGRM and FEM calculations.

For the pumping test, a contrast of rigidity can be noticed at the MC and SB soil layers interface. In order to model this rigid behavior below the SB layer, FEM moduli for Mohr-C and HSM were increased (as per §4.3). For SGRM, the subgrade reaction coefficient kh was determined by adjusting the interaction depth. SGRM gives a better fit than the FEM to the inclinometric measurements.

With regards to the excavation stages, for the FEM models the usual 𝐸 𝑀 𝛼 ⁄ factors were chosen (4 for Mohr-C and 2 for HSM models). For the SGRM calculations Schmitt's formula for kh without any modifications was used. Mohr-C underestimates wall displacements for all construction stages. HSM and SGRM curves show a very good fit for all construction stages below level 25NGF. Above this level HSM and SGRM models slightly overestimate the displacements. The maximum displacements match the inclinometric measurements and fit between both measured curves. Concerning the prop forces, they were firstly measured by strain gauges then separated into earth and temperature induced forces. Then the earth-induced component was compared to SGRM and FEM models. For the first prop level, the force is overestimated by FEM models and shows a better fit with the SGRM. For the 2 nd and 3 rd prop level, all models overestimate the prop forces and show a similar behavior, with an increase in prop force as excavation proceeds.

SGRM shows the closest calculated forces in comparison to the analyzed section (props n°15). The back-analysis of Les Ardoines Western pit, which is a 30m deep excavation with a 26m ground water head, shows that: the Mohr-C (with 𝐸 = 4 • 𝐸 𝑀 𝛼 ⁄ ) slightly underestimates the displacements but shows correct prop forces, both the HSM (with the usual 𝐸 𝑀 𝛼 ⁄ factors) as well as the SGRM models (with unmodified Schmitt's formula) are suitable for both wall displacements and prop forces determination. The authors would like to add that SGRM or FEM methods are only mathematical modeling tools that try to represent reality. Although there can be some value in comparing various numerical models, real practical conclusions can only be drawn by comparing modeling tools with quality in-situ structure's measurements.

Figure 1 .

 1 Figure 1. Les Ardoines metro station during the excavation stage -25m of excavation 2 LES ARDOINES METRO STATION

Figure 2 .

 2 Figure 2. Les Ardoines Western Pit cross section between inclinometers incP39 and incP56

Figure 4

 4 Figure 4.a) Force measured by the strain gauges for B1-9 b) Incremental load change to the incremental temperature change for B1-9

Figure 7 .

 7 Figure 7. Inclinometer readings for the pumping test with SGRM and FEM corrected models

Figures

  Figures 8, 9 and 10 compare inclinometric curves and calculations.

Figure 8 .

 8 Figure 8. Measurements and calculations for 28.40 NGF and 18.50NGF excavations

  -C underestimates the maximum displacement (13mm). All curves have similar shapes above 25NGF.

Figure 9 .

 9 Figure 9. Curves for 11.40NGF excavation

Figure 11 .

 11 Figure 11. Measured forces for B1-15 and B1-9 compared to SGRM, Mohr-C and HSM

Figure 13 .

 13 Figure 13. Forces in props at 12NGF

Table 1 .

 1 Geotechnical parameters for Les Ardoines

	Strain gauges

Mean force at excavation (kN/m) Level Prop 18.5NGF 11.4NGF 3.7NGF

	29	B1-9	320	360	370
	NGF	B1-15	290	300	240
	19.1 NGF	B2-9 B2-15 B2-4	---	1160 1460 570	1430 1200 690
	12 NGF	B3-9 B3-15 B3-4	---	---	650 650 360

Table 2 .

 2 Mean earth-induced forces per prop

ACKNOWLEDGEMENTS

The authors thank Horizon's (Soletanche Bachy and Bouygues TP Joint-Venture) monitoring department and Sixense Soldata for every help concerning the measurements provided.


Julien Joliat

Sylvain Picaud

Antoine Patt

Gas trapping at the ice surface and in clathrate hydrates under astrophysical conditions, as studied by GCMC simulations

Gas trapping at the ice surface and in clathrate hydrates under astrophysical conditions, as studied by GCMC simulations

J. Joliat The prevalence of water ice in the Solar System and interstellar medium has been evidenced, with examples found on icy moons such as Enceladus, Europa, Titan, and Triton, as well as on interstellar grains. These ices may be able to trap organic molecules, as it has been theoretically inferred on Enceladus [START_REF] Joliat | Adsorption of organic compounds at the surface of Enceladus' ice grains. A grand canonical Monte Carlo simulation study[END_REF].

Natural clathrate hydrates may also be present in these environments where they are suspected to play a role in the evolution of planetary atmospheres and/or oceanic compositions. Moreover, due to the unique environmental conditions present in extraterrestrial environments, clathrate hydrates not observed on Earth may be stabilized there.

Because of the difficulty of reproducing these conditions in laboratory experiments, Monte Carlo molecular simulations in the grand canonical ensemble (GCMC) have been used to study, at the molecular scale, the surface adsorption of organic compounds on ice [START_REF] Picaud | Molecular-scale simulations of organic compounds on ice: application to atmospheric and interstellar sciences[END_REF][START_REF] Joliat | Adsorption of C2-C5 alcohols on ice: A grand canonical Monte Carlo simulation study[END_REF] and to characterize the trapping of gas mixture in clathrate hydrates [START_REF] Petuya | Molecular selectivity of CO-N2 mixed hydrates: Raman spectroscopy and GCMC studies[END_REF][START_REF] Patt | Molecular Selectivity of CH4-C2H6 Mixed Hydrates: A GCMC Study[END_REF]. These simulations have been used primarily in astrophysical contexts, where experimental data are limited, to provide useful information to the planetary and astrophysical communities.

  1 , A. Patt 2 , S. Picaud1

	1 Institut UTINAM, UMR 6213 CNRS / Université Bourgogne Franche-Comté, Besançon, France 2 Centro de Fisica de Materiales, CSIC -UPV/EHU, San Sebastian, Spain


est destinée au dépôt

Molecular-scale studies of organic aerosols : from the formation of aerosol precursors to the characterization of the aerosol interactions with surrounding water molecules.

Bastien Radola, Delphine Vardanega-Bonneton, Sylvain Picaud.

Institut UTINAM, UMR 6213 CNRS / Université Bourgogne Franche-Comté, Besançon, France

Atmospheric aerosols play a central role on current atmospheric research, because aerosol particles have a direct effect on climate not only by scattering and absorbing solar radiation but also by scattering, absorbing, and emitting thermal radiation. They also have an indirect effect by acting as condensation nuclei for water, thus impacting on cloudiness. Organic matter represents an important fraction of the fine aerosol mass with relative abundance depending on, e.g., location, time, and meteorological conditions.

Given the complexity of aerosol chemistry, modeling studies based on numerical simulations at the molecular level is an important way to get insights on the corresponding systems, complementary to numerous observations and laboratory characterizations.

In our group, we use various theoretical methods to achieve a better characterization of the aerosol behavior, at the molecular scale.

Thus, DFT calculations have been devoted to model the formation of aerosol precursors via, e.g., the interactions between a small number of sulfuric acid and small organic molecules [START_REF] Radola | DFT study of the formation of atmospheric aerosol precursors from the interaction between sulfuric acid and benzenedicarboxylic acid molecules[END_REF][START_REF] Radola | Formation of atmospheric molecular clusters from organic waste products and sulfuric acid molecules. A DFT study[END_REF]. In addition, classical molecular dynamics simulations considering much larger systems (i.e., made of hundreds of organic molecules) have been undertaken to model the evolution with temperature of organic aerosol nanoparticles interacting with different amounts of water molecules [START_REF] Radola | Analysis of Mixed Formic and Acetic Acid Aggregates Interacting with Water. A Molecular Dynamics Simulation Study[END_REF][START_REF] Radola | Molecular Dynamics Simulations of the Interaction between Water Molecules and Aggregates of Acetic or Propionic Acid Molecules[END_REF][START_REF] Vardanega | Water and formic acid aggregates: A molecular dynamics study[END_REF].

Bibliography

  Molecular-scale studies of organic aerosols : from the formation of aerosol precursors to the characterization of the aerosol interactions with surrounding water molecules. Sylvain Picaud, Delphine Vardanega-Bonneton, Bastien Radola To cite this version: Sylvain Picaud, Delphine Vardanega-Bonneton, Bastien Radola. Molecular-scale studies of organic aerosols : from the formation of aerosol precursors to the characterization of the aerosol interactions with surrounding water molecules.. Goldschmidt 2023 Conference, European Association of Geochemistry and the Geochemical Society, Jul 2023, Lyon, France. 10.7185/gold2023.16798. hal-04384130


Maurine Hammel
email: maurine.hammel@umontpellier.fr

Fanny Touchard

V Burioli

Laure Paradis

Frédérique Cerqueira

E Chailler

I Bernard

H Cochet

A Simon

Frédéric Thomas

Marine transmissible cancer navigates urbanised waters, threatening spillover

Keywords: Transmissible cancer, Bivalve Transmissible Neoplasia, epidemiology, biofouling, spillover, Mytilus mussels

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Transmissible cancers are caused by malignant cell lineages that have acquired the ability to infect new hosts through the transmission of living cancer cells. Fourteen transmissible cancer lineages have been described to date: one in dogs (canine transmissible venereal tumour, CTVT [START_REF] Murgia | Clonal Origin and Evolution of a Transmissible Cancer[END_REF][START_REF] Rebbeck | Origins and evolution of a transmissible cancer[END_REF]), two in Tasmanian devils (devil facial tumour, DFT1 and DFT2 [START_REF] Pearse | Allograft theory: Transmission of devil facial-tumour disease[END_REF][START_REF] Pye | A second transmissible cancer in Tasmanian devils[END_REF]), and eleven in different marine bivalve species (bivalve transmissible neoplasia, BTNs [START_REF] Garcia-Souto | Mitochondrial genome sequencing of marine leukaemias reveals cancer contagion between clam species in the Seas of Southern Europe[END_REF][START_REF] Metzger | Horizontal transmission of clonal cancer cells causes leukemia in soft-shell clams[END_REF][START_REF] Metzger | Widespread transmission of independent cancer lineages within multiple bivalve species[END_REF][START_REF] Michnowska | Horizontal transmission of disseminated neoplasia in the widespread clam Macoma balthica from the Southern Baltic Sea[END_REF][START_REF] Yonemitsu | A single clonal lineage of transmissible cancer identified in two marine mussel species in South America and Europe[END_REF][START_REF] Yonemitsu | Multiple lineages of transmissible neoplasia in the basket cockle (Clinocardium nuttallii) with repeated horizontal transfer of mitochondrial DNA[END_REF]). While direct contact is necessary for CTVT and DFTs transmission, via coitus for the former and biting for the latter, the transmission of BTNs between shellfish is assumed to occur through the seawater column [START_REF] Burioli | Traits of a mussel transmissible cancer are reminiscent of a parasitic life style[END_REF][START_REF] Giersch | Survival and Detection of Bivalve Transmissible Neoplasia from the Soft-Shell Clam Mya arenaria (MarBTN) in Seawater[END_REF]. Mytilus spp. mussels are affected by two BTN lineages known as MtrBTN1 and MtrBTN2 that originate from two different M. trossulus founder hosts. MtrBTN2 is distributed worldwide and has crossed the species barrier several times to infect four Mytilus species: M. trossulus in East Asia and Northern Europe [START_REF] Skazina | Two lineages of bivalve transmissible neoplasia affect the blue mussel Mytilus trossulus Gould in the subarctic Sea of Okhotsk[END_REF][START_REF] Skazina | Genetic features of bivalve transmissible neoplasia in blue mussels from the Kola Bay (Barents Sea) suggest a recent trans-Arctic migration of the cancer lineages[END_REF][START_REF] Skazina | First description of a widespread Mytilus trossulus-derived bivalve transmissible cancer lineage in M. trossulus itself[END_REF], M. chilensis in South America [START_REF] Yonemitsu | A single clonal lineage of transmissible cancer identified in two marine mussel species in South America and Europe[END_REF], and M. edulis and M. galloprovincialis in Europe [START_REF] Yonemitsu | A single clonal lineage of transmissible cancer identified in two marine mussel species in South America and Europe[END_REF][START_REF] Hammel | Prevalence and polymorphism of a mussel transmissible cancer in Europe[END_REF]. Cancerous process in mussels was firstly described fifty years ago as disseminated neoplasia (DN, [START_REF] Farley | Sarcomatoid proliferative disease in a wild population of blue mussels (mytilus edulis)[END_REF]) but the demonstration that some DNs are transmissible is recent [START_REF] Metzger | Horizontal transmission of clonal cancer cells causes leukemia in soft-shell clams[END_REF][START_REF] Metzger | Widespread transmission of independent cancer lineages within multiple bivalve species[END_REF]. As non-transmissible neoplasia also occurs in mussels [START_REF] Yonemitsu | A single clonal lineage of transmissible cancer identified in two marine mussel species in South America and Europe[END_REF][START_REF] Hammel | Prevalence and polymorphism of a mussel transmissible cancer in Europe[END_REF], a genetic diagnosis is required to ensure the identification of MtrBTNs. The oldest known genetically validated MtrBTN2 was sampled in 2009 [START_REF] Yonemitsu | A single clonal lineage of transmissible cancer identified in two marine mussel species in South America and Europe[END_REF]. Genetic data suggest that it could be at least one or two orders of magnitude more ancient, although dating is difficult due to an apparent acceleration of the mitochondrial clock [START_REF] Skazina | First description of a widespread Mytilus trossulus-derived bivalve transmissible cancer lineage in M. trossulus itself[END_REF][START_REF] Hammel | Prevalence and polymorphism of a mussel transmissible cancer in Europe[END_REF].

The global distribution of the MtrBTN2 lineage remains enigmatic [START_REF] Yonemitsu | A single clonal lineage of transmissible cancer identified in two marine mussel species in South America and Europe[END_REF][START_REF] Skazina | Genetic features of bivalve transmissible neoplasia in blue mussels from the Kola Bay (Barents Sea) suggest a recent trans-Arctic migration of the cancer lineages[END_REF]. The other known transmissible cancer with such a worldwide distribution is CTVT in dogs. CTVT emerged 4,000 to 8,000 years ago, probably in Eastern Asia, and has expanded rapidly worldwide over the past 500 years, probably facilitated by the development of maritime transportation [START_REF] Baez-Ortega | Somatic evolution and global expansion of an ancient transmissible cancer lineage[END_REF]. Transmission of MtrBTN2 within mussel populations occurs presumably through filter-feeding (discussed in [START_REF] Burioli | Traits of a mussel transmissible cancer are reminiscent of a parasitic life style[END_REF][START_REF] Caza | Hemocytes released in seawater act as Trojan horses for spreading of bacterial infections in mussels[END_REF]). MtrBTN2 cells can survive for several days in seawater [START_REF] Burioli | Traits of a mussel transmissible cancer are reminiscent of a parasitic life style[END_REF], which greatly increases their chances of infecting new hosts. However, it is unlikely that transport of cancer cells by marine currents alone explains such a global distribution. Indeed, the dilution of cancer cells in the marine environment may strongly limit the success of transmission over long distances.

Shipping traffic has been proposed as the most likely explanation for the global distribution of MtrBTN2 -i.e., the transport of disease-carrying mussels on ship hulls [START_REF] Yonemitsu | A single clonal lineage of transmissible cancer identified in two marine mussel species in South America and Europe[END_REF][START_REF] Skazina | Genetic features of bivalve transmissible neoplasia in blue mussels from the Kola Bay (Barents Sea) suggest a recent trans-Arctic migration of the cancer lineages[END_REF][START_REF] Skazina | First description of a widespread Mytilus trossulus-derived bivalve transmissible cancer lineage in M. trossulus itself[END_REF]. To our knowledge, these claims have remained speculative and no formal evidence of the effect of shipping traffic and port habitat on the epidemiology of BTN has been provided to date.

European mussels are affected by MtrBTN2, with a much higher prevalence in M. edulis (of the order of 1/100) than in M. galloprovincialis (of the order of 1/1000) [START_REF] Hammel | Prevalence and polymorphism of a mussel transmissible cancer in Europe[END_REF]. These two host species are parapatric in Europe and can coexist in contact zones where hybridisation is taking place [START_REF] Skibinski | Aspects of the population genetics of Mytilus (Mytilidae; Mollusca) in the British Isles[END_REF][START_REF] Coustau | Genetic characterization of the hybridization between Mytilus edulis and M. galloprovincialis on the Atlantic coast of France[END_REF][START_REF] Simon | How do species barriers decay? Concordance and local introgression in mosaic hybrid zones of mussels[END_REF]. Associations between genetic backgrounds and environmental variables indicate that the environment partly influences the structure and maintenance of the hybrid zones: in the study area, M. edulis genotypes are more frequent in sheltered habitats under freshwater influence, while M. galloprovincialis genotypes are more frequent in habitats exposed to wave action and ocean salinities [START_REF] Bierne | Habitat preference and the marine-speciation paradox[END_REF][START_REF] Bierne | Can habitat specialisation maintain a mosaic hybrid zone in marine bivalves?[END_REF]. In this context, the distribution and propagation of MtrBTN2 is likely to be influenced by host genetic background and population composition [START_REF] Hammel | Prevalence and polymorphism of a mussel transmissible cancer in Europe[END_REF], as well as by other environmental factors such as population density, reduced hydrodynamics, and stressful environmental factors (e.g., heat, salinity or pollution stresses). MtrBTN2 global distribution indicates its resilience across a broad range of temperature with varying seasonal patterns [START_REF] Skazina | Genetic features of bivalve transmissible neoplasia in blue mussels from the Kola Bay (Barents Sea) suggest a recent trans-Arctic migration of the cancer lineages[END_REF], reflecting host species distribution range. Prior to evidence of horizontal transmission, pollutants have been suggested as a potential cause of DN; however, the association with disease prevalence remained inconclusive [START_REF] Lowe | Cytology and Quantitative Cytochemistry of a Proliferative Atypical Hemocytic Condition in Mytilus edulis (Bivalvia, Mollusca)[END_REF][START_REF] Landsberg | Neoplasia and biotoxins in bivalves: Is there a connection?[END_REF][START_REF] Green | Neoplasia in Mytilus edulis L. from United Kingdom waters[END_REF], and as we now know that some neoplasias are transmissible, it is no longer the initial carcinogenesis but the transmission that needs to be explained. Seasonal influence on DN prevalence was observed at a local scale (higher during winter in M. trossulus, [START_REF] Mix | Haemic neoplasms of bay mussels, Mytilus edulis L., from Oregon: occurrence, prevalence, seasonality and histopathological progression[END_REF][START_REF] Rasmussen | Occurrence, prevalence and seasonality of neoplasia in the marine mussel Mytilus edulis from three sites in Denmark[END_REF], and M. edulis, [START_REF] Charles | Etude des pathogènes, des conditions physiologiques et pathologiques impliqués dans les mortalités anormales de moules (Mytilus sp[END_REF]), whereas no significant influence of seasonality was noted at the European scale [START_REF] Bramwell | A review of the potential effects of climate change on disseminated neoplasia with an emphasis on efficient detection in marine bivalve populations[END_REF]. Unfortunately, in the absence of genetic diagnostics, we cannot know how many of these DNs were indeed BTNs (i.e. transmissible) or not, and rigorous evaluation of MtrBTN2 prevalence seasonality is still lacking. In brief, little is known about the ecology of MtrBTN2 in relation to its host and the possible environmental factors influencing the epidemiology of this transmissible cancer.

In the present study, we investigated the occurrence of MtrBTN2 in a survey area in Southern Brittany (France) which has contrasting natural and anthropogenic habitats and a mosaic distribution of M. edulis and M. galloprovincialis. We sampled 40 natural populations as well as 9 mussel farms, 7 floating buoys, and 20 ports. To detect MtrBTN2-infected mussels, we used digital PCR and real-time PCR, amplifying one nuclear and two mitochondrial genes with published and newly designed primers targeting specific variants. We found a low prevalence of MtrBTN2 (23/1516), equally affecting the two coexisting Mytilus species. Interestingly, most affected sites (7/9) were ports, suggesting that ports could be favourable habitats for MtrBTN2 transmission and/or that maritime transport could play a role as a vector for the spread of the disease. This highlights the possible role of ports as epidemiological hubs for MtrBTN2 propagation.

Material and methods

Sampling

We collected 1516 Mytilus spp. mussels from 76 sites of the French Atlantic coast, from the Bay of Quiberon to Pornic, between January and March 2020. Approximately 20 mussels were sampled at each site (Table S1), allowing us to have a large number of closely located sites. We collected hemolymph from adductor muscle (with a 1 ml syringe, 26G needle) and a piece of mantle; both samples were fixed with 96% ethanol as described in [START_REF] Hammel | Prevalence and polymorphism of a mussel transmissible cancer in Europe[END_REF]. DNA extraction was performed with the NucleoMag 96 Tissue kit (Macherey-Nagel) using a Kingfisher Flex extraction robot (serial number 711-920, ThermoFisher Scientific). We followed the kit protocol with a modified lysis time of 1 hour at 56°C and modified the volumes of some reagents: beads were diluted twice in water, 200 µl of MB3 and MB4, 300 µl of MB5 and 100 µl of MB6. DNA concentration (ng/μl) was quantified using the Nanodrop 8000 spectrophotometer (ThermoFisher Scientific). DNA from hemolymph was used for MtrBTN2 detection and DNA from mantle was used for mussel genotyping (Figure S1).

MtrBTN2 detection

As the prevalence of MtrBTN2 is expected to be low [START_REF] Hammel | Prevalence and polymorphism of a mussel transmissible cancer in Europe[END_REF][START_REF] Burioli | Implementation of various approaches to study the prevalence, incidence and progression of disseminated neoplasia in mussel stocks[END_REF] and the number of samples is high (n = 1516), we chose to pre-screen by pooling 12 samples (pooled screening step; Figure S1) and then demultiplexing positive pools to specifically target cancerous samples (simplex screening step; Figure S1). Prior to pooling, DNA concentrations were adjusted to 10 ng/µl to obtain an unbiased representation of each sample in the pool. This allowed us to considerably reduce the cost and time of detection, but we acknowledge that this protocol could result in some very early stages of the disease being missed. To partly circumvent this problem, MtrBTN2 detection in pools was performed by digital PCR (ddPCR) targeting one nuclear (Elongation Factor, EF) and one mitochondrial (Control region, described in [START_REF] Yonemitsu | A single clonal lineage of transmissible cancer identified in two marine mussel species in South America and Europe[END_REF]) marker (Table S2). ddPCR is a sensitive method, based on the Taqman method which requires two primers and one probe, and which directly estimates the copy number of the targeted sequence. In addition, the use of a mitochondrial marker should provide a more sensitive detection as the mitochondrial genome has more copies than the nuclear genome. ddPCR primers design and analysis was subcontracted to the company IAGE using the QIAcuity™ system (QIAGEN). Positive pools were then demultiplexed (simplex screening step; Figure S1) and MtrBTN2 detection was performed by real-time PCR targeting one nuclear (Elongation Factor, EF1α-i3, described in [START_REF] Burioli | Traits of a mussel transmissible cancer are reminiscent of a parasitic life style[END_REF]) and one mitochondrial (cytochrome c oxidase I, mtCOI-sub) marker (Table S2). To design the mtCOIsub primers, we used sequences of M. edulis, M. galloprovincialis, M. trossulus, and MtrBTN2 available from the National Center for Biotechnology Information (NCBI). Please note that mtCOI-sub primers were initially designed to amplify a polymorphism that discriminates between two MtrBTN2 sub-lineages "a" and "b" [START_REF] Hammel | Prevalence and polymorphism of a mussel transmissible cancer in Europe[END_REF]. However, attempts to identify sublineages based on melting temperature (Tm) did not yield conclusive results, most likely due to additional polymorphism. Amplification of EF1α-i3 and mtCOI-sub markers was performed using a three-step cycling protocol (95°C for 10 s, 58°C for 20 s, 72°C for 25 s) for 40 cycles. We carried out real-time PCRs using the SensiFAST TM SYBR No-ROX Kit (Bioline) and the LightCycler 480 Real-Time PCR System (Roche Diagnostics). We also confirmed the positive samples diagnosed by real-time PCR using ddPCR (simplex screening step; Figure S1). The specificity of both methods and markers was tested using various control samples of M. edulis, M. galloprovincialis, M. trossulus, and MtrBTN2 samples of early, moderate, or late stages (based on cytological observations, following [START_REF] Burioli | Implementation of various approaches to study the prevalence, incidence and progression of disseminated neoplasia in mussel stocks[END_REF], Tableau S3). To test the sensitivity, we used positive controls with MtrBTN2 DNA diluted 10-fold (x10) and 100-fold (x100) in M. edulis DNA.

Real-time PCR results were analysed using the LightCycler480 software. Two parameters were extracted: threshold cycle (CT), obtained by Absolute Quantification analysis using the Second Derivative Maximum method, and Tm, obtained by Tm calling analysis. Positive samples were defined as those with a CT of less than 35 cycles and a Tm between 77.46 to 78.71 (based on Tm values of the positive control samples; see result, Figure S2). ddPCR results were analysed with the QuantaSoft™ Analysis Pro software (Bio-Rad), which provides the copy number for each sample. Samples were considered positive when there were more than two positive droplets.

Mussel genotyping

We used 10 biallelic SNPs known to discriminate between M. edulis and M. galloprovincialis mussel species (Table S4). These markers were identified as being ancestry-informative (fixed for alternative alleles in M. edulis and M. galloprovincialis) in the [START_REF] Fraïsse | Local interspecies introgression is the main cause of extreme levels of intraspecific differentiation in mussels[END_REF] dataset and were subsequently confirmed as near diagnostic by analysis of larger datasets [START_REF] Simon | How do species barriers decay? Concordance and local introgression in mosaic hybrid zones of mussels[END_REF][START_REF] Simon | Replicated anthropogenic hybridisations reveal parallel patterns of admixture in marine mussels[END_REF]. Genotyping was performed using the Kompetitive Allele Specific PCR (KASP) method [START_REF] Semagn | Single nucleotide polymorphism genotyping using Kompetitive Allele Specific PCR (KASP): Overview of the technology and its application in crop improvement[END_REF]. As we wanted to know the mean ancestry of each sample (G ancestry) and reduce the cost of genotyping, we developed a multiSNPs marker by multiplexing the 10 SNPs. Rapidly, 1µL of assay mix (KASP-TF V4.0 2X Master Mix, 1X target concentration; primers, 1µM target concentration; HyClone™ HyPure water) and 0.5 µL of DNA at 10 ng/µL were mixed in qPCR 384-well plates using Labcyte Echo525 (BECKMAN COULTER). KASP analysis was performed on the LightCycler 480 Instrument (Roche Diagnostics) with the following thermal cycling conditions: initialisation 15 min at 95°C, first amplification 20 s at 94°C and 1 min at 61°C to 55°C with steps of 0.6°C for 10 cycles, second amplification 20 s 94°C and 1 min at 55°C for 29 cycles, read 1 min at 37°C and 1 s at 37°C. KASP results are a combination of two fluorescence values, one for allele X, and another for allele Y. Here, we oriented all SNPs so that allele X corresponds to M. edulis and allele Y to M. galloprovincialis. Following [START_REF] Cuenca | Assignment of SNP allelic configuration in polyploids using competitive allele-specific PCR: application to citrus triploid progeny[END_REF], we transformed the data to obtain a single measure of the relative fluorescence of the two alleles, using the following formula: ݕ , ൌ ܻ , ሺܺ , ܻ , ሻ ⁄

; where X i,j and Y i,j are the fluorescence values of allele X and allele Y, respectively for, i th SNP of the j th sample. To scale y i,j value from 0 when M. edulis allele fluorescence dominates at the 10 SNPs to 1 when M. galloprovincialis allele fluorescence dominates, we used the following formula: ݕԢ , ൌ ሺݕ , െ min ሺݕ ሻሻ ሺmaxሺݕ ሻ െ min ሺݕ ሻሻ ⁄

; where y i,j is the relative fluorescence value for the i th SNP of the j th sample. In the results section and figures, y' i,j corresponds to "G ancestry".

Environmental data

Source of environmental data

The 76 sampling sites were selected to cover four different habitats (ports, natural beds, farms and floating buoys) presenting contrasted environments and genetic backgrounds (M. edulis, M.

galloprovincialis and hybrids). The description of environmental data considered in this study is reported in Table S5 and all site information is available in Table S1. Note that natural beds are intertidal sites, while the other habitats are subtidal. During sampling, we collected two habitat discrete descriptive variables: population density (1: isolated, 2: patchy beds, 3: continuous beds)

and wave action (1: exposed, 2: sheltered). We used the E.U. Copernicus Marine Service Information (doi: 10.48670/moi-00027) to access temperature (°C), salinity (PSU 1e-3) and surface current velocity (m.s-1) data for each site (monthly recorded from 2021, resolution of 0.028°x0.028°). The velocity variable was formed by combining its Northward (N) and Eastward (E) components, as follow: ܧ√ ଶ ܰ ଶ . We also used EMODnet Human Activities (source:

EMSA Route Density Map) to retrieve the maritime traffic density of fishing, passenger, and recreational boats in the study area (seasonal record from 2018, 2019 and 2020, resolution 1x1 km).

Preparation of environmental data

Among the 76 sampled sites, 13 fell outside the coverage scope of the Copernicus model (i.e situated beyond the model's pixel boundaries). Consequently, for these sites (see Table S1), we were unable to extract temperature, salinity, or velocity data. Certain sites recorded a velocity of 0 for all month (Table S1), raising concerns about the model resolution for this variable. To validate the accuracy of Copernicus data, we computed the mean values of neighbouring pixels (MNP, within a 0.05° buffer zone) and compared them to the pixel values at the respective sites of interest (SP, referred to as the 'central' pixel, Table S6). We note that, due to the proximity of our sampled sites to the coastline, the number of neighbouring pixels varied within a certain range (Table S6). Notably, there was no correlation between these variations and the absolute difference between SP and MNP values (Figure S3C). The comparison between SP and MNP values was carried out for all sites, encompassing temperature, salinity, and surface current velocity, and the results are presented in Figure S3. We observed a robust positive correlation for temperature and salinity variables, providing confidence in the reliability of the extracted data for our small-scale analysis. However, as expected, the inconclusive correlation for surface current velocity led us to exclude this environmental variable from the analyses.

In the statistical analysis (see section 2.5) we used the annual average of salinity and boat density (maritime traffic). For temperature, we used the annual variance as we identified strong seasonal variations in temperature that would be misinterpreted if only the annual average temperature were used (Figure S4).

Statistical analysis: relationship between MtrBTN2 prevalence, host genotype and environmental variables

To ascertain whether cancer prevalence varied among the four habitats sampled (ports, natural beds, buoys and farms), we performed Fisher's exact test on the contingency table (fisher.test() function of the stats package v4.1.3 [START_REF] Core | R: A language and environment for statistical computing[END_REF]). Subsequently, we aimed to determine whether specific environmental variables could provide a more comprehensive explanation of MtrBTN2

prevalence across these habitats. To do so, we pursued the following steps: an exploratory analysis with a Principal Component Analysis (PCA), an examination of data dependencies (Spearman correlation test and Moran's I test), and lastly, a regression model adapted to our dataset (spatial logistic regression model). All statistical analyses were performed in R v4.1.3 [START_REF] Core | R: A language and environment for statistical computing[END_REF].

Principal component analysis

As an exploratory analysis, we firstly performed a PCA to investigate the relationship between the quantitative environmental variables. Missing values were estimated using imputePCA() (missMDA package v1.18 [START_REF] Josse | missMDA: A Package for Handling Missing Values in Multivariate Data Analysis[END_REF]), prior to performing the PCA using PCA() (FactoMineR package v2.6 [START_REF] Lê | FactoMineR: An R Package for Multivariate Analysis[END_REF]).

Spearman correlation test

To test for correlation between environmental variables, we performed a non-parametric test of Spearman using rcorr() function from Hmisc package [START_REF] Harrell | _Hmisc: Harrell Miscellaneous_. R package version[END_REF], that automatically excludes missing data.

Moran I test for spatial autocorrelation

Given the proximity of the sampled sites, which constitutes a fine spatial matrix, we used the Moran's I statistic to test for spatial autocorrelation among the environmental variables. After converting longitude and latitude data into UTM coordinates (spTransform() function of rgdal package v1.6-7 [START_REF] Bivand | rgdal: Bindings for the 'Geospatial' Data Abstraction Library[END_REF]), this analysis was performed using the moran.test() function (spdep package version 1.2-8 [START_REF] Bivand | R Packages for Analyzing Spatial Data: A Comparative Case Study with Areal Data[END_REF]) with the k-nearest neighbour classification.

Spatial logistic regression model

The association of MtrBTN2 prevalence and environmental variables was tested at the site level using a multivariate spatial logistic regression (fitme() function, spaMM package v4.3.0 [START_REF] Rousset | Testing environmental and genetic effects in the presence of spatial autocorrelation[END_REF]).

Spatial autocorrelation was accounted for by including a Matern covariance function as a random effect (i.e. Matern(1 | Longitude +Latitude)). MtrBTN2 prevalence was represented as a binary matrix (N, N site -N) with N and N site indicating site-specific MtrBTN2 case counts and total number of sampled mussels, respectively. The following explanatory variables were included into the full model: temperature variance, salinity mean, population abundance, wave action, average M. galloprovincialis ancestry of the mussel host population (G ancestry), maritime traffic (as described above, section 2.4). We also included a binary variable "port" into the model, where 1 denotes port habitat and 0 represents other habitats. Model suitability was evaluated using the residual diagnostics tool implemented in the "DHARMa" package [START_REF] Hartig | _DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models[END_REF]. To assess the significance of fixed effects (environmental variables), we performed Bartlett-

1 0
corrected likelihood ratio tests (LR tests) by comparing the full model with a model that excluded the variable of interest (fixedLRT() function of spaMM package v4.3.0 [START_REF] Thomas | Applying ecological and evolutionary theory to cancer: a long and winding road[END_REF]). To account for bias associated with rare events and collinearity of environmental variables (see results), LR tests were performed using 999 bootstraps.

Results

Validation of the screening method

In order to detect MtrBTN2 in thousands of individuals, we developed dedicated ddPCR and qPCR screening tools. The design of real-time PCR and ddPCR primers and probes allowed us to amplify nuclear and mitochondrial markers specific to M. trossulus mussels and MtrBTN2

(Figure S5, TableS7). However, for the mtCOI-sub locus, the melting temperature (Tm) range of MtrBTN2 was different from M. trossulus controls and allowed us to specifically detect MtrBTN2 based on Tm values (Figure S2). With ddPCR, all MtrBTN2 control samples were detected, regardless of marker or dilution, except for one MtrBTN2 sample diluted at x100 which was negative for the EF locus (Figure S5). Copy numbers appeared higher for the mtCR marker, showing a higher sensitivity for this mitochondrial marker. The same pattern was observed for EF1α-i3 and mtCOI-sub in real-time PCR, with a higher sensitivity of the mitochondrial marker based on the lower CT values observed (Figure S5). However, two MtrBTN2 samples diluted at x100 were negative for mtCOI-sub and positive for EF1α-i3. Even though the mitochondrial marker appears to be more sensitive in both methods, we chose to screen for MtrBTN2 using both markers in order to have comparisons and additional validation.

MtrBTN2 screening reveals a low prevalence

We amplified nuclear and mitochondrial markers using ddPCR (EF, mtCR) and real-time PCR (EF1α-i3, mtCOI-sub) to detect the presence of MtrBTN2 in the 1516 mussels sampled from the 76 sites of the study area (Figure S1). All results of the pooled and simplex screening are presented in TableS8.

The pooled screening step using ddPCR revealed 25/127 positive pools: 9/127 with EF, 14/127 with mtCR, and 2/127 were positive for both markers (Figure S5). As the 14 mtCR-positive pools showed low copy numbers (median = 7.59), we assume that EF did not amplify because of a detection threshold. Although the 9 EF-positive pools were not expected, they were included

1 1
for further investigation by simplex screening (see below). For pools positive for both markers, the EF copy numbers appear relatively low compared to mtCR copy numbers. This is due to the high mitochondrial fluorescence masking the nuclear fluorescence and underestimating EF copy numbers. To eliminate this bias in the simplex ddPCR step (Figure S1), we amplified the two markers separately. In order to maximise cancer detection in the simplex screening step, we chose to keep the pools positive for at least one gene, corresponding to 25 positive pools of 12 samples (n = 300).

We performed the simplex screening step using real-time PCR and then confirmed positive samples with ddPCR (Figure S1). In total, 44/300 samples were positive for at least one real-time PCR marker: 16 with EF1α-i3 only, 23 with mtCOI-sub only, and 5 with both markers (Figure S5, Table S9). Pooled and simplex screening results are mostly consistent, as 20 of the 25 positive pools had at least one individual detected by real-time PCR amplification (Table S10).

The 5 pools without positive samples had low ddPCR copy numbers (less than 3.37 for EF and less than 6.65 for mtCR). These false positives were to be expected as we intentionally applied low threshold values in the pooled screening step. Two samples from mtCR-positive pools were found to be positive for both markers at the simplex level, which was expected as the mitochondrial marker is likely to be more sensitive than the nuclear marker. On the contrary, we did not expect to observe samples positive for EF1α-i3/EF and negative for mtCOI-sub/mtCR.

However, all but one of the individuals from the EF-positive pools were positive only for the EF1α-i3 marker (Table S10). The absence of mitochondrial genes in these samples does not seem to be related to a detection threshold issue, especially as ddPCR and real-time PCR target two different genes (mtCR and mtCOI-sub). We suspect that some host alleles that were present at low frequencies in the 'dock mussels' populations (the hybrid lineage found in the port of Saint-Nazaire [START_REF] Simon | Replicated anthropogenic hybridisations reveal parallel patterns of admixture in marine mussels[END_REF]) may sometimes be amplified with EF1α-i3. This could be explained by shared polymorphism between host species rather than the presence of MtrBTN2. Therefore, the 16 samples positive only for EF1α-i3 were conservatively considered as false positives (note they were mostly sampled in the port of Saint-Nazaire, which does not affect our main result).

For the remaining 28 samples, we performed ddPCR with both markers to confirm the real-time mitochondrial PCR diagnosis (Figure S1, TableS9). For 23/28 samples, mtCR ddPCR confirmed the mtCOI-sub real-time PCR diagnosis. For 10/23 samples positive only for mtCOI-sub, ddPCR revealed few EF copies. Finally, we considered all samples positive for mitochondrial markers with both methods to be cancerous samples. We found 23/1516 positive samples from 9/76 sites in which the number of cases varied from 1 to 8 (Figure 1). The highest proportion of cancerous mussels was found at site P20 (Croisic Pontoon), and the next highest in the vicinity of this site (P19, P21, and P23; Figure 1). To determine whether host genetic background or population composition influences cancer prevalence and distribution, mussels were genotyped using 10 SNPs known to be diagnostic between M. edulis and M. galloprovincialis (the two species coexisting in the studied area).

To reduce the cost of genotyping, we developed a multiplex assay, mixing 10 SNPs that directly give the average ancestry of the sample (G ancestry, Figure S1). This approach was validated by comparing the multiplex fluorescence and average simplex fluorescence of SNPs from a set of samples (Figure S6a). We genotyped each individual from the 9 sites affected by MtrBTN2 (180 mussels) and estimated the average ancestry of each site by genotyping pools of individuals (76 pools). The positive correlation between the average individual fluorescence of a site and the fluorescence of the corresponding pool validated our approach (Figure S6b). Mussels with G ancestry values below 0.3 were assigned to M. edulis, those with values above 0.59 to M.

galloprovincialis, and those in between were assigned as hybrids (Figure 2, FigureS7). Of the mussels affected by MtrBTN2, 15/23 were assigned to M. edulis, 7/23 to M. galloprovincialis and 1/23 was a hybrid (Table S11). Considering the sites affected by MtrBTN2, MtrBTN2

prevalence did not appear to be significantly different between species with 15/105 (14%) in M. edulis, 7/60 (11%) in M. galloprovincialis and 1/15 (6%) in hybrids (Fisher's exact test, p-value = 0.83). Most M. edulis mussels affected by MtrBTN2 were found in populations with a majority of M. edulis (P44, P23, P20), only two were found in populations with some hybrids and M.

galloprovincialis mussels (P25, P71) and only one in a population with a majority of M.

galloprovincialis (P11, Figure 2, Figure S7). Interestingly, all M. galloprovincialis mussels affected by MtrBTN2 were found in M. galloprovincialis populations (P19, P21, Figure 2, FigureS7), which are close to the site with the highest prevalence (Croisic pontoon, Figure 1).

Finally, the hybrid individual affected by MtrBTN2 was found in a population where the two species coexist (P22, Figure 2, FigureS7).

MtrBTN2 infected mussels are mostly found in ports

After the MtrBTN2 detection step, which revealed 23/1516 individuals infected with this transmissible cancer, we investigated associations with habitat types. Cancerous samples were found in 7 ports (16/23 infected mussels) and 2 natural beds (7/23) near the Croisic pontoon (P20, Figure 1). MtrBTN2 was not detected in the 180 individuals from the 9 mussel farms or in the 140 individuals from the 7 floating buoys. Mussels affected by MtrBTN2 were significantly more frequent in ports than in other habitats (Fisher's exact test, p-value = 0.0001, Table S12). In addition, the sampled ports are not significantly spatially autocorrelated (Moran I test, p-value = 0.25, Table S13).

Exploring the effect of some environmental variables, none other than the port habitat explains MtrBTN2 prevalence after accounting for spatial autocorrelation

Given that MtrBTN2 was mostly found in ports, we aimed to gain a deeper understanding of whether environmental variables (including population ancestry) could explain this pattern.

Univariate correlation between MtrBTN2 prevalence and all environmental variables across habitats are presented in Figure S8. The two best correlations are found with population density and maritime traffic, but we also have strong multicollinearity among variables that needs to be accounted for (see Table S14). The PCA reveals an association between environmental variables and habitat types rather than the presence of cancer itself (Figure 3). Indeed, anthropogenic habitats (ports, floating buoys and farms) with high population density and sheltered from wave action are well separated from natural beds on the second axis of the PCA. Interestingly, maritime traffic (i.e., annual mean of passenger, fishing, and recreational boat density) explains a direction of variation towards human-altered habitats, but also towards sites affected by MtrBTN2. MtrBTN2 seems to be found in sites with buffered temperature (low annual variance) and higher mean annual salinity, environmental variables that are associated with ports in our study. Water temperature indeed tends to be buffered in ports, being warmer in winter and cooler in summer (Figure S4). However, these variables are difficult to interpret as they also explain well the inertia of axis 1 of the PCA which differentiates the sites located near estuaries (La Loire or La Vilaine) from the others. To investigate more rigorously whether some environmental variables could explain prevalence of MtrBTN2, we used a spatial logistic regression model. As MtrBTN2 was mostly found in ports (see section 3.4), we included an environmental variable "port" (1 for port habitat, 0 for others) in the regression model analysis. To test for spatial structure, we conducted Moran's I tests on all environmental variables. The results indicate significant spatial autocorrelation in most environmental variables (Table S13), which was expected as we selected this area due to its mosaic of contrasted environments. However, "population density" and "port" variables were found randomly distributed in space (Table S13).

To overcome spatial autocorrelation bias we performed a logistic regression model that corrects for spatial effect (spaMM package, [START_REF] Rousset | Testing environmental and genetic effects in the presence of spatial autocorrelation[END_REF]) and assessed environmental variables significance via bootstrapped Likelihood Ratio (LR) tests. Bootstrapped LRTs are expected to handle the problem of variables multicollinearity. MtrBTN2 prevalence is significantly explained by the port habitat variable (LR test: χ2 = 7.21, df = 1, p-value = 0.0072; Table S15), and none of the other environmental variables significantly explain MtrBTN2 prevalence after accounting for spatial effects (Table S15). Despite the residuals following the distributional assumptions of the model (Figure S9) and the significance of the 'Port' variable (also supported by the Fisher's exact test, see 3.4), we acknowledge that quantifying the magnitude of this trend remains challenging and requires further investigations.

Discussion

Our results show that MtrBTN2 is present at low prevalence along the French Atlantic coast (23/1516 = 1.5%). Strikingly, it is mostly found in ports and is equally distributed in M. edulis and M. galloprovincialis. Our analysis shows that ports are likely epidemiological hubs for MtrBTN2 propagation. However, the precise environmental factors influencing MtrBTN2

epidemiology have yet to be fully characterised. In this context, we discuss various environmental variables and assess their relevance to the port environment and marine parasite epidemiology.

Most MtrBTN2-infected mussels were found in ports and a prevalence hotspot was found around the Croisic pontoon. Given the low prevalence of MtrBTN2 previously described [START_REF] Hammel | Prevalence and polymorphism of a mussel transmissible cancer in Europe[END_REF][START_REF] Burioli | Implementation of various approaches to study the prevalence, incidence and progression of disseminated neoplasia in mussel stocks[END_REF] and confirmed in this study, we acknowledge that our sample sizes per site may be too small (n = 20)

to assess MtrBTN2 prevalence with confidence at a site level. However, this was not the scale of resolution we were interested in. Instead, our overall sampling design, capturing replications of habitat features, was chosen to evaluate the effect of habitats and genetic composition of host populations. This allowed us to reveal similar MtrBTN2 prevalence in both species of the mosaic hybrid zone, and a higher prevalence in ports.

The highest prevalence of MtrBTN2 in ports could be explained by two non-exclusive hypotheses. Firstly, ports could offer a favourable environment as it is conducive to disease development and propagation. Indeed, ports are often polluted, confined and permanently immersed habitats (without tidal constraints) with high density of mussels. It is important to note here that our objective is not to explain the emergence of a transmissible cancer, i.e. the initial carcinogenesis in the founder host, but its maintenance and spread. The emergence probably took place long before the industrial period and, despite studying a cancer, the mutagenic effect of pollutants does not have to be put forward to explain our results, but rather their effect on host resistance to infection by transmissible cancer cells. Secondly, there is increased connectivity between ports due to maritime traffic and ports are connected to other sites (other ports or wild sites) through biofouling from vessels.

Ports are often confined areas with longer particle residence times which could increase the contact time between parasites and hosts [START_REF] Morrisey | In-water cleaning of vessels: biosecurity and chemical contamination risks[END_REF][START_REF] Gadd | In-water cleaning of vessels: biosecurity and chemical contamination risks[END_REF], thereby increasing the probability of transmission and favouring the persistence of a passively dispersing parasite. However, the possible hotspot on Croisic Pontoon, which is affected by strong tidal currents, provides evidence against this hypothesis given that it is not a confined area.

Host population density is an important epidemiological parameter, as high density is known to favour parasite persistence in host populations [START_REF] Bommarito | Effects of first intermediate host density, host size and salinity on trematode infections in mussels of the south-western Baltic Sea[END_REF][START_REF] Hopkins | Systematic review of modelling assumptions and empirical evidence: Does parasite transmission increase nonlinearly with host density?[END_REF]. Our results suggest that ports are prone to harbouring high population density (Figure 3). However, we found no evidence of MtrBTN2 cases in mussel farms or floating buoys despite high mussel population density in these sites as well. Although population density could favour MtrBTN2 persistence in an affected population, this parameter alone cannot explain the higher prevalence of MtrBTN2 in ports.

Port-based shipping activities are major contributors to water pollution, including issues such as oil spills, plastic contamination, wastewater discharge, and the use of antifouling paint [START_REF] Široka | A novel approach for assessing the ports' environmental impacts in real time -The IoT based port environmental index[END_REF][START_REF] Roberts | Ports in a Storm: Port-City Environmental Challenges and Solutions[END_REF].

Pollution is a major environmental stressor that could affect host physiology and increase susceptibility to parasites, particularly by altering its immune response (reviewed in [START_REF] Destoumieux-Garzón | The one health concept: 10 years old and a long road ahead[END_REF]). As filter-feeders, species of bivalve molluscs are particularly concerned [START_REF] Canesi | Evidence for immunomodulation and apoptotic processes induced by cationic polystyrene nanoparticles in the hemocytes of the marine bivalve Mytilus[END_REF][START_REF] Girón-Pérez | Relationships between innate immunity in bivalve molluscs and environmental pollution[END_REF][START_REF]Immunotoxicological effects of environmental contaminants on marine bivalves[END_REF]. However, in our study, most of the affected individuals (15/23) were found in three open sea sites known to be healthy and protected ecosystems with abundant resources (Croisic Pontoon, Natural beds 19 and 21 part of Natura 2000 area; Figure 1), suggesting that pollution alone cannot explain the higher prevalence of MtrBTN2 cancers in ports. Conversely, it can be hypothesised that a healthy, less stressful environment may increase the host permissiveness and allow MtrBTN2 (and possibly other BTNs) to persist at a higher detectable prevalence. Host nutrition can affect disease outcome by driving host immunity and parasite resource availability [START_REF] Chandra | Nutrition, immunity and infection: From basic knowledge of dietary manipulation of immune responses to practical application of ameliorating suffering and improving survival[END_REF][START_REF] Tseng | The Relationship between Parasite Fitness and Host Condition in an Insect -Virus System[END_REF][START_REF] Pike | On the diverse and opposing effects of nutrition on pathogen virulence[END_REF]. In invertebrates, increased host nutrition can lead to increased parasite load [START_REF] Cressler | Disentangling the interaction among host resources, the immune system and pathogens[END_REF] and improved immunity against parasites [START_REF] Tseng | The Relationship between Parasite Fitness and Host Condition in an Insect -Virus System[END_REF][START_REF] Pike | On the diverse and opposing effects of nutrition on pathogen virulence[END_REF]. This balance could apply to mussel-MtrBTN2 interaction, and a healthy environment with abundant resources could increase host carrying capacity and/or slow down disease progression. A polluted environment could also affect the survival of MtrBTN2 cells in seawater and reduce their chances of infecting a new host. However, the impact of pollution on mussel hosts and cancer cells needs to be further investigated to clearly establish its influence on the transmission and persistence of such transmissible cancers.

Connectivity through maritime traffic appears to be the main common feature between affected ports, although this explanatory variable does not withstand spatial logistic regression analysis any better than others. Vessels could facilitate MtrBTN2 propagation through transport of affected mussels fixed on the hull. Maritime traffic has been identified as a vector of marine mollusk pathogens (for review see [START_REF] Georgiades | The Role of Vessel Biofouling in the Translocation of Marine Pathogens: Management Considerations and Challenges[END_REF]), microorganisms [START_REF] Ruiz | Global spread of microorganisms by ships[END_REF] and non-indigenous species [START_REF] Massé | An Overview of Marine Non-Indigenous Species Found in Three Contrasting Biogeographic Metropolitan French Regions: Insights on Distribution, Origins and Pathways of Introduction[END_REF][START_REF] Molnar | Assessing the global threat of invasive species to marine biodiversity[END_REF], through ballast water and/or hull biofouling. As such, ports are considered hotspots of marine non-indigenous species introductions [START_REF] Massé | An Overview of Marine Non-Indigenous Species Found in Three Contrasting Biogeographic Metropolitan French Regions: Insights on Distribution, Origins and Pathways of Introduction[END_REF][START_REF] Bishop | Unheralded arrivals: non-native sessile invertebrates in marinas on the English coast[END_REF][START_REF] Couton | Water eDNA metabarcoding is effective in detecting non-native species in marinas, but detection errors still hinder its use for passive monitoring[END_REF] and of marine parasites [START_REF] Pagenkopp Lohan | International shipping as a potent vector for spreading marine parasites[END_REF]. Mytilus mussels are commonly reported as principal taxa composing hull biofouling (fixed adult mussels; [START_REF] Murray | Recreational boating: a large unregulated vector transporting marine invasive species[END_REF][START_REF] Salta | Marine fouling organisms and their use in antifouling bioassays[END_REF][START_REF] Davidson | The role of containerships as transfer mechanisms of marine biofouling species[END_REF]). If a port is affected by a local outbreak, it could become a source of contamination to other ports through biofouling of vessels, while natural beds will probably remain isolated by density troughs that could halt the propagation (Figure 4). It is also conceivable that prevalence hotspots exist at other unsampled locations along the coast, either due to environmental factors

that have yet to be characterised, or simply due to stochastic local outbreaks. These local outbreaks can be the source of contamination to the anthropogenic connectivity network providing the disease reaches a port. While this does not disqualify the factors discussed abovee.g., pollution, population density and confinement-as drivers of contamination within ports, our study suggests that maritime routes between ports could be anthropogenic gateways to MtrBTN2 propagation. Furthermore, despite a high density of maritime traffic around the floating buoys sampled, these sites remained free of MtrBTN2. Long mooring times in ports increase the probability of hull surface colonisation [START_REF] Davidson | The role of containerships as transfer mechanisms of marine biofouling species[END_REF][START_REF] Bouda | Risk Analysis of Invasive Species Introduction in the Port of Arzew, by Calculation of Biofouling Surface on Ships' Hulls[END_REF][START_REF] Sylvester | Hull fouling as an invasion vector: can simple models explain a complex problem?[END_REF] as well as the risk of pathogen transmission [START_REF] Georgiades | The Role of Vessel Biofouling in the Translocation of Marine Pathogens: Management Considerations and Challenges[END_REF].

This suggests that, in addition to cruising, ships may have to dock for the spread of MtrBTN2 to be effective, either through transmission of cancer cells or detachment of infected mussels (Figure 4). This could provide an explanation as to why maritime traffic is not a sufficient explanatory variable for MtrBTN2 prevalence. Further investigations would be necessary to ascertain the requisite propagule density (comprising both numerical and time-scale components)

for effective transmission, as well as to evaluate possible disruption of byssus quality in affected hosts. Blue and orange mussels correspond to M. edulis and M. galloprovincialis, respectively.

Diagram created with BioRender.

A possible hotspot of MtrBTN2 prevalence was found in the area around the Croisic pontoon.

Even though this possible hotspot will need to be confirmed, this site could represent a source for other ports and surrounding natural beds. Indeed, this pontoon is frequently used by maritime shuttles, as well as by recreational and fishing boats, and is connected by maritime traffic to many ports in the studied area. In particular, this pontoon is the departure site of boats travelling to Hoedic (site P44 in Figure 1), an isolated island for which the presence of cancer is most likely explained by anthropogenic transport given the distance from the mainland. MtrBTN2 is also found in foreshores near Croisic. This reveals a possible spillover from ports to adjacent wild populations (Figure 4). Maritime traffic has been identified as a vector for non-indigenous species from commercial ports to natural populations via marinas [START_REF] Floerl | The importance of transport hubs in stepping-stone invasions[END_REF][START_REF] Guzinski | Population genomics of the introduced and cultivated Pacific kelp Undaria pinnatifida: Marinas-not farms-drive regional connectivity and establishment in natural rocky reefs[END_REF]. In our study, when sites outside standard enclosed ports (e.g., enclosed marinas) were sampled, MtrBTN2 was never detected, suggesting that contagion often remains confined to the port area. Spillover may therefore depend on the degree of confinement of a port.

Mytilus galloprovincialis was thought to be more resistant to MtrBTN2 because of lower prevalence in this species [START_REF] Hammel | Prevalence and polymorphism of a mussel transmissible cancer in Europe[END_REF], and population patches of M. galloprovincialis could also have acted as barriers to disease propagation between patches of M. edulis. However, our results suggest that M. galloprovincialis population patches close to M. edulis populations may also show enhanced prevalence. This does not contradict the idea that M. galloprovincialis population patches could impose a slightly higher resistance to MtrBTN2 propagation. Under the hypothesis that natural propagation is hindered by density troughs between population patches, or by population patches of a more resistant species, ports and ship traffic would create anthropogenic gateways that favour connections between patches of host populations (Figure 4). This hypothesis could also suggest that BTNs spread naturally and progressively from one host to another by close contact rather than drifting with the currents, making enhanced R0 by anthropogenic dispersal a threat to host populations.

Our results represent a significant advance in our understanding of MtrBTN2 epidemiology, both by proposing ports as epidemiological hubs and by suggesting that the natural propagation of this transmissible cancer could be naturally halted by density troughs in the distribution of host mussel populations. Detection of MtrBTN2 presence on boat biofouling would provide more direct evidence of its role as a vector. It would also be interesting to investigate whether prevalence hotspots are more commonly located in areas with particular environmental conditions, or whether they are stochastic local outbreaks. Sampling vessel biofouling in international ports linking known MtrBTN2 sites (East and North Europe, North West and South Pacific) and nearby marinas, along with genetic comparison of cancer cells, would provide a better understanding of the global and local transmission routes of this transmissible cancer since its emergence. While MtrBTN2 is not associated with extensive mortality events [START_REF] Charles | Etude des pathogènes, des conditions physiologiques et pathologiques impliqués dans les mortalités anormales de moules (Mytilus sp[END_REF], other pathogens could be translocated and affect wild and farmed mussel populations. Various measures are employed to mitigate biofouling, including antifouling painting, dry docking, inwater cleaning with capture, and ballast water filtration (as discussed in [START_REF] Georgiades | The Role of Vessel Biofouling in the Translocation of Marine Pathogens: Management Considerations and Challenges[END_REF]). While biofouling management for international vessel exchanges is established [START_REF]International Convention for the Control and Management of Ships' Ballast Water and Sediments[END_REF][START_REF]Guidelines for the Control and Management of Ships' Biofouling to Minimize the Transfer of Invasive Aquatic Species[END_REF][START_REF] Mcclay | Vessel biofouling prevention and management options report[END_REF], regulations for biofouling control in local recreational boats remain to be improved [START_REF] Murray | Recreational boating: a large unregulated vector transporting marine invasive species[END_REF][START_REF]Biofouling Management for Recreational Boating: Recommendations to Prevent the Introduction and Spread of Invasive Aquatic Species[END_REF]. This is a significant concern, as the involvement of recreational boats in the spread of invasive species and possible associated pathogens is increasingly documented [START_REF] Briski | Role of domestic shipping in the introduction or secondary spread of nonindigenous species: biological invasions within the Laurentian Great Lakes[END_REF][START_REF] Ulman | Alien species spreading via biofouling on recreational vessels in the Mediterranean Sea[END_REF][START_REF] Ashton | Recreational boats routinely transfer organisms and promote marine bioinvasions[END_REF]. Prioritising the survey of vessels that stay in port for long periods [START_REF] Lim | Managing the risk of non-indigenous marine species transfer in Singapore using a study of vessel movement[END_REF] or identifying nodes of busy maritime route [START_REF] Floerl | The importance of transport hubs in stepping-stone invasions[END_REF] could contribute considerably to local and international biofouling management.

Conclusion

Our study reveals that the prevalence of MtrBTN2 along the South Brittany French coast is low, but that the most affected sites are ports (marinas, commercial ports, and docks). This result suggests that ports are epidemiological hubs and that maritime routes are anthropogenic gateways for the propagation of MtrBTN2. Investigations into the presence of other BTNs in boat biofouling would provide more direct evidence of its role as vector, although mussels are the most common species of bivalve mollusc in biofouling. The spread of marine (micro)parasites and pathogens through vessel biofouling is a legitimate concern, and results such as ours -i.e., on a smaller scale than translocations identified in the literature on marine bioinvasion or by [START_REF] Yonemitsu | A single clonal lineage of transmissible cancer identified in two marine mussel species in South America and Europe[END_REF]-highlight the critical need for politicy regulation to limit the effects of biofouling, both on ship hulls and port docks, in addition to ballast water control.

Figure 1 :

 1 Figure 1: Location of sampling sites. Zoomed-in sites correspond to sites where mussels are affected by MtrBTN2. Pie graphs and numbers in italics correspond to the proportion of cancerous samples in each site. Coloured points represent the type of site, as indicated in the figure's legend. Map origin: Ersi gray (light).

3. 3 .

 3 M. edulis and M. galloprovincialis are equally affected by MtrBTN2

Figure 2 :

 2 Figure 2: Individual and population ancestry. G ancestry of healthy (empty grey circle) and MtrBTN2 affected (dark red) hosts is shown for the 9 sites with cancer (bold labels). Population average ancestry for all sampled sites is represented by black crosses. Blue, white and orange rectangles correspond to the G ancestry range of M. edulis, hybrids and M. galloprovincialis respectively.

Figure 3 :

 3 Figure 3: Principal Component Analysis on environmental variables. Axis 1 mainly captures the variance associated with annual temperature variance and annual salinity mean and Axis 2 mainly captures the variance associated with wave action, population density and maritime traffic.

Figure 4 :

 4 Figure 4: Diagram illustrating the discussion on the propagation of MtrBTN2 across Mytilus sp. populations. Arrows' widths correspond to the probability of MtrBTN2 propagation.

Figure S1 :

 S1 Figure S1: Diagram of screening and genotyping experimental steps.

Figure S2 :

 S2 Figure S2: TM threshold determination for EF1α-i3 (left) and mtCOI-sub (right) real-time PCR markers. Grey rectangles correspond to TM threshold based on MtrBTN2 positive control samples (77.46<TM<78.71). Sample type colour and diagnostic based on TM values are indicated in the legend. M. edulis and M. galloprovincialis correspond to negative controls, M. trossulus are M. trossulus controls, and MtrBTN2 samples are positive controls (ND: cancer stage not defined, initial: early stage, moderate: moderate stage, heavy: late stage, see TableS3)

Figure S3 :

 S3 Figure S3: Evaluation of Copernicus model resolution for salinity, temperature and velocity data. Correlation between the value of the site pixel (SP) and the mean value of neighbouring pixels (MNP) for each variable. (A) Correlation including all months of year 2021 and (B) each month separately. No correlation observed between the absolute difference of SP and MNP values and the number of neighbouring pixels (C).

Figure S4 :

 S4 Figure S4: Sampled sites temperature, recorded monthly in 2021. Points correspond to a temperature measurement, solid lines refer to MtrBTN2-free sites (grey) and MtrBTN2 affected sites (darkred).

Figure S5 :

 S5 Figure S5: Real-time PCR and digital PCR results. (A) and (C) represent ddPCR results of EF and mtCR, and (B) and (D) represent real-time PCR results of EF1α-i3 and mtCOI-sub, respectively. Dotted lines correspond to the negative thresholds, 0 for no DNA copy detected by ddPCR and 40 for no amplification in real-time PCR. Solid lines in each graph refer to the same sample diluted to different concentrations (pure, x10, x100). M. edulis and M. galloprovincialis correspond to negative controls, M. trossulus are M. trossulus controls, and MtrBTN2 samples are positive controls (ND: cancer stage not defined, Initial: early stage, moderate: moderate stage, heavy: late stage, see TableS2). Screening results using both method and markers are shown for Pool and Simplex. Sample type colours and diagnostic status are indicated in the figure legend.

Figure S6 :

 S6 Figure S6: Genotyping method validation. (A) Positive correlation between multiSNPs G ancestry and the mean of the 10 single SNPs from a subset of Individual and Pool samples. (B) Positive correlation between population DNA pool G ancestry and the mean individual G ancestry of individuals from the same population.

Figure S7 :

 S7 Figure S7: Population and individual ancestry composition across the sampled area. Zoomed-in sites correspond to sites with mussels affected by MtrBTN2. Barplots represent the estimated ancestry of individuals based on the 10SNPs multiplex tool. MtrBTN2 individuals are indicated by stars under each barplot. Coloured points represent the population average ancestry, as indicated in the figure's legend. Map origin: Ersi gray (light).

Figure S8 :

 S8 Figure S8: Univariate correlations between the prevalence of MtrBTN2 and environmental variables. (A) MtrBTN2 prevalence among habitats. (B) Correlations for the six environmental variables studied. Coloured points correspond to the prevalence of MtrBTN2 as indicated in the legend.

Table S2 : Primer and probe sequences used in real-time PCR and digital PCR. Table S3: Control samples used to validate real-time PCR and ddPCR screening tools.

 S2 Allsamples are from our laboratory sample collection. M. edulis and M. galloprovincialis correspond to negative controls, M. trossulus are M. trossulus controls, and MtrBTN2 samples are positive controls. MtrBTN2 stages were defined by cytology of the hemolymph, according to the following classification: early (<15% cancer cells), moderate (15-75% cancer cells), late (>75% cancer cells), ND when cancer stage was not defined.

Table S4 : Bi-allelic nuclear SNPs used for the multiSNPs genotyping. TableS5: Source and description of environmental variables collected for each habitat (ports, natural beds, farms, buoys). TableS6: Data for the evaluation of Copernicus model resolution.

 S4 Sources of environmental variables are provided in TableS5. Site: site name; N_pixels: number of neighbouring pixels within a buffer zone of 0.05°; SP: value of the site pixel; MNP: mean value of the neighbouring pixels; SP_MNP: absolute difference between SP and MNP values; .s, .t and .v refer to salinity, temperature and velocity values, respectively. Velocity correspond to the combination of squared Northward and Eastward velocity values. Missing data (blank) correspond to sites outside of Copernicus model pixels (n = 13).

Table S7 : Control sample results validate real-time PCR and ddPCR screening tools.

 S7 EF_CopyNb: copy number for EF ddPCR marker; mtCR_CopyNb: copy number for mtCR ddPCR marker; CT: threshold cycle, TM: melting temperature for EF1ߙ-i3 and mtCOI-sub realtime PCR markers. NP: not performed.TableS8: Pooled and simplex screening results. Pool_CopyNb_EF: copy number for EF ddPCR marker (pooled screening); Pool_CopyNb_mtCR: copy number for mtCR ddPCR marker (pooled screening); CT: threshold cycle, TM: melting temperature for EF1ߙ-i3 and mtCOI-sub real-time PCR markers; Simplex_CopyNb_EF: copy number for EF ddPCR marker (simplex screening); Simplex_CopyNb_mtCR: copy number for mtCR ddPCR marker (simplex screening). NP: not performed.TableS9: Simplex screening results for positive samples. CT: threshold cycle, TM: melting temperature for EF1ߙ-i3 and mtCOI-sub real-time PCR markers; PCR_result: marker name positive in real-time PCR; EF_CopyNb: copy number for EF ddPCR marker; mtCR_CopyNb: copy number for mtCR ddPCR marker; ddPCR_result: marker name positive in ddPCR; MtrBTN2: final diagnostic status. NP: not performed.

Table S10 : Correspondence between pool and simplex screening

 S10

. Pool_CopyNb_EF : copy number for EF ddPCR marker (pooled screening); Pool_CopyNb_mtCR : copy number for mtCR ddPCR marker (pooled screening); Pool_ddPCR_result: marker name positive in ddPCR (pool); Nb_ind_positive: number of individuals in the pool found positive for one or two marker in simplex screening; Simplex_PCR_result: marker name of the positive individual in real-time PCR; Simplex_ddPCR_result: marker name of the positive individual in ddPCR, negative if the sample did not amplify. NP: not performed.

Table S11 :

 S11 MultiSNPs genotyping results. G ancestry: fluorescence value; Ancestry: ancestry defined based on G ancestry values.

Table S12 : Contingency table used for the Fisher exact test.Table S13 :

 S12S13 Moran I test on environmental variables. Results were obtained using moran.test() function in R.

Table S14 : Correlation coefficient and Spearman correlation test results for each environmental data pairs.

 S14

Acknowledgement

We are grateful to the IAGE company for carrying out the ddPCR assay of this study. We thank the GENSEQ and QPCR HAUT-DEBIT platforms for access to equipments and their expertise.

Credit is owed to Olivier Gimenez, François Rousset and an anonymous reviewer for their valuable advice on statistical analysis. Finally, we thank Cécile Perrin for her valuable reading of the manuscript.

Fundings

This work was supported by Montpellier Université d'Excellence (BLUECANCER project) and Agence Nationale de la Recherche (TRANSCAN project, ANR-18-CE35-0009 and DockEvol project, ANR-23-CE02-0020-01). This study falls within the framework of the "Laboratoires d'Excellence (LABEX)" CEMEB (10-LABX-0004) and Tulip (ANR-10-LABX-41). FT was supported by the EVOSEXCAN project (ANR-23-CE13-0007), the MAVA Foundation and the HOFFMANN Family.

Data accessibility

All data used in this study are available in the supplementary material.

Conflict of interest declaration

We declare we have no competing interests.