Robin P M Gauff
email: gauff.robin@yahoo.de

Dominique Davoult

Stéphane Greff

Olivier Bohner

Jérôme Coudret

Stéphanie Jacquet

Stéphane Loisel

Simon Rondeau

Laure Sevin

Emmanuel Wafo

Christophe Lejeusne

Pollution gradient leads to local adaptation and small-scale spatial variability of communities and functions in an urban marine environment

Keywords: local adaptation, fouling, pollution, metabolomics, respiration, marinas local adaptation, fouling, pollution, metabolomics, respiration, marinas

Urbanization of coastal habitats, of which harbors and marinas are the paragon, has led to various ecological paradigms about their functioning. Harbor infrastructures offer new hard substrata that are colonized by a wide variety of organisms (biofouling) including many introduced species. These structures also modify hydrodynamism and contaminant dispersal, leading to strong disturbance gradients within them. Differences in sessile community structure have previously been correlated to these gradients at small spatial scale (< 100 m). Local adaptation might be involved to explain such results, but as correlation is not causation, the present study aims to understand the causal link between the environmental gradients and community structure through a reciprocal transplant experiment among three sites of a marina (inner, middle, entrance). Our results highlighted strong small-scale spatial variations of contaminants (trace metals, PCB, pesticides, and PAH) in sediments and animal samples which have been causally linked to changes in community composition after transplant. But historical contingency and colonization succession also play an important role. Our results provided strong evidence for local adaptation since community structure, respiration, and pollutant uptake in Bugula neritina, as well as the metabolomes of B. neritina and Ciona intestinalis were impacted by the transplant with a disadvantage for individuals transplanted from the entrance to the inner location. The here observed results may thus indicate that the disturbance gradient in marinas might constitute a staple for selecting pollutant-resistant species and populations, causing local adaptation. This highlights the importance of conducting further studies into small scale local adaptation.

Graphical abstract.

Introduction

With increasing anthropic pressure on ecosystems, understanding how communities and species respond and adapt to global change remains a major challenge. Anthropic disturbances are often intense and may overcome resistance and resilience of the local communities (Type II and III disturbances; [START_REF] Sutherland | The Fouling Community at Beaufort, North Carolina: A Study in Stability[END_REF]. In natural habitats, the resilience of communities (i.e., the "elasticity" of a community and its capacity to return to a stable state after disturbance) has been relatively well studied. However, resistance (i.e., the capacity of communities to resist disturbance-induced changes) remains poorly studied although it is the major driver of community structure in urban habitats where disturbances do rarely relax and tend to even increase in pressure (see 'press disturbance'; [START_REF] Nimmo | Vive la résistance: Reviving resistance for 21st century conservation[END_REF]. As adaptation may be a major driver of resistance [START_REF] Nimmo | Vive la résistance: Reviving resistance for 21st century conservation[END_REF], understanding how eco-evolutionary feedbacks drive species adaptation in habitats subjected to anthropic disturbance seems crucial [START_REF] Alberti | Eco-evolutionary dynamics in an urbanizing planet[END_REF]. Harbors and marinas constitute excellent study models to dive into these paradigms, as they combine multiple facets of anthropogenic disturbances [START_REF] Bulleri | Is it time for urban ecology to include the marine realm ?[END_REF][START_REF] Bulleri | The introduction of coastal infrastructure as a driver of change in marine environments[END_REF]. Artificial structures are extremely numerous and diverse in marine urban areas where they constitute a major substratum for benthic communities [START_REF] Mineur | Changing coasts : Marine Aliens and Artificial Structures[END_REF]. Their properties are however different from natural substratum, resulting in different communities and diversity [START_REF] Glasby | Nonindigenous biota on artifcial structures: could habitat creation facilitate biological invasions?[END_REF][START_REF] Oricchio | Distinct community dynamics at two artificial habitats in a recreational marina[END_REF][START_REF] Chan | An overview of recent research in marine biological invasions[END_REF]. In harbors, some artificial structures like breakwaters and jetties are intended to modify/reduce hydrodynamism which may impact connectivity and local biodiversity [START_REF] Floerl | Boat harbour design can exacerbate hull fouling[END_REF][START_REF] Fauvelot | Lower genetic diversity in the limpet Patella caerulea on urban coastal structures compared to natural rocky habitats[END_REF]. This reduction of water exchange with the open sea can also lead to an increased risk of eutrophication [START_REF] Lee | Eutrophication dynamics of Tolo harbour, Hong Kong[END_REF], higher temperature fluctuations [START_REF] Menniti | The Importance of Offshore Exchange for Water Temperatures in Norwalk Harbor[END_REF], and can favor the accumulation of pollutants like Persistent Organic Pollutants (POPs) or Metallic Trace Elements (MTEs) [START_REF] Owen | Heavy metal accumulation and anthropogenic impacts on Tolo Harbour, Hong Kong[END_REF][START_REF] Tolun | The pollution status and the toxicity of surface sediments in Izmit Bay (Marmara Sea), Turkey[END_REF][START_REF] Schiff | Extent and magnitude of copper contamination in marinas of the San Diego region, California, USA[END_REF][START_REF] Mohammed | Polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) in harbor sediments from Sea Lots, Port-of-Spain, Trinidad and Tobago[END_REF][START_REF] Aly | Metal contamination in water, sediment and biota from a semi-enclosed coastal area[END_REF]). These disturbances may thus affect the communities in harbors, leading to reduced biodiversity and increasing the presence of opportunistic species [START_REF] Dijkstra | The role of colonial ascidians in altering biodiversity in marine fouling communities[END_REF][START_REF] Saloni | Impacts of multiple stressors during the establishment of fouling assemblages[END_REF][START_REF] Kinsella | Separate and combined effects of copper and freshwater on the biodiversity and functioning of fouling assemblages[END_REF]. The innermost parts of a marina may be the most affected by this phenomenon due to even lower water mixing [START_REF] Schiff | Extent and magnitude of copper contamination in marinas of the San Diego region, California, USA[END_REF]). Hence, as these disturbances may increase in intensity and frequency towards the inner parts of marinas, they might result in a disturbance gradient and constitute a selective filter for organisms, with gradient magnitude and orientation potentially modulated by harbor morphology [START_REF] Floerl | Boat harbour design can exacerbate hull fouling[END_REF].

Harbors are a major area of species introduction as they are at the end points of main introduction vectors through maritime traffic [START_REF] Mineur | Macroalgal introductions by hull fouling on recreational vessels: Seaweeds and sailors[END_REF][START_REF] Sylvester | Hull fouling as an invasion vector: Can simple models explain a complex problem?[END_REF][START_REF] Ferrario | Role of commercial harbours and recreational marinas in the spread of non-indigenous fouling species[END_REF].

Introduced species, if they become invasive, sometimes completely restructure ecosystems, often leading to the loss of biodiversity and ecosystem services [START_REF] Pejchar | Invasive species, ecosystem services and human well-being[END_REF]Johnston et al. 2015b;[START_REF] Walsh | Invasive species triggers a massive loss of ecosystem services through a trophic cascade[END_REF]. Together with the cost of mitigating invasive species, this loss of services can cause severe economic impacts, ranging from hundreds of millions to tens of billions dollars, depending on the country [START_REF] Lovell | The economic impacts of aquatic invasive species: A review of the literature[END_REF][START_REF] Olson | The economics of terrestrial invasive species: A review of the literature[END_REF][START_REF] Jardine | Estimating the cost of invasive species control[END_REF]. Introduced species seem to be favored in disturbance regimes [START_REF] Hobbs | Disturbance, Diversity, and Invasion: Implications for Conservation[END_REF][START_REF] Altman | Effects of small-scale disturbance on invasion success in marine communities[END_REF][START_REF] Bulleri | Linking disturbance and resistance to invasion via changes in biodiversity: A conceptual model and an experimental test on rocky reefs[END_REF] as they are often described as opportunistic and more resistant to high and drastic fluctuations. They have for instance been shown to be more resistant to extreme temperature events [START_REF] Zerebecki | Protocol for the derivation of canadian sediment quality guidelines for the protection of aquatic life[END_REF][START_REF] Kelley | The role thermal physiology plays in species invasion[END_REF][START_REF] Marie | Transcriptomic response to thermal and salinity stress in introduced and native sympatric Palaemon caridean shrimps[END_REF]Kenworthy et al. 2018a) which are more common in harbors due to the reduced water volume and exchange with the open sea [START_REF] Menniti | The Importance of Offshore Exchange for Water Temperatures in Norwalk Harbor[END_REF]. Copper, used as active component of many antifouling coatings of ships, demonstrated its detrimental effect on the native communities [START_REF] Piola | Pollution reduces native diversity and increases invader dominance in marine hard-substrate communities[END_REF][START_REF] Piola | The influence of antifouling practices on marine invasions[END_REF][START_REF] Osborne | Copper pollution enhances the competitive advantage of invasive ascidians[END_REF], and with other pollutants has been shown to favor many introduced species in harbors due to their higher resistance to them (Piola and Johnston 2006a;[START_REF] Dafforn | Differential effects of tributyltin and copper antifoulants on recruitment of non-indigenous species[END_REF][START_REF] Osborne | Copper pollution enhances the competitive advantage of invasive ascidians[END_REF]. The strong selective pressure exerted by copper can even lead to differential resistance among populations of the same introduced species (Piola and Johnston 2006b).

Previous studies have observed local variation of fouling (native and introduced) communities between different parts of harbors/marinas, which has been linked to pollution gradients [START_REF] Je | Changes in benthic communities along a presumed pollution gradient in Vancouver Harbour[END_REF][START_REF] Ryu | The impact of heavy metal pollution gradients in sediments on benthic macrofauna at population and community levels[END_REF]Kenworthy et al. 2018b). This may indicate that the selective pressure exerted by disturbances in marinas may operate at small spatial scales, structuring the community and potentially causing local adaptation at the species level. Such studies remain however putative regarding the causal link between disturbance and community composition as they show correlative results. As correlations between phenotypes and environmental conditions may be caused by phenotypic plasticity, manipulative experiments are required to expose the link between concurrent natural selection and observed variability [START_REF] Kawecki | Conceptual issues in local adaptation[END_REF][START_REF] Thorpe | Common Garden and Natural Selection Experiments Support Ecotypic Differentiation in the Dominican Anole (Anolis oculatus) Phenotypic geographic variation within a species can be due to natural selection for current ecological conditions Study System[END_REF]. To expose the causal link between local adaptation and specific environmental factors, reciprocal transplant experiments should be conducted with one species or with entire communities [START_REF] Kawecki | Conceptual issues in local adaptation[END_REF][START_REF] Angert | The Evolution of Species ' Distributions : Reciprocal Transplants across the Elevation Ranges of Mimulus cardinalis and M . lewisii[END_REF][START_REF] Chang | Quantifying the role of colonization history and biotic interactions in shaping communities -a community transplant approach[END_REF][START_REF] Sork | Genomic Studies of Local Adaptation in Natural Plant Populations[END_REF]. It is then possible to reveal the presence of local adaptation, if fitness differences exist between 'locals' originating from a studied location and 'foreign' which were transplanted to this location ('locals vs foreign' criterion; [START_REF] Kawecki | Conceptual issues in local adaptation[END_REF][START_REF] Martin | In a nutshell, a reciprocal transplant experiment reveals local adaptation and fitness trade-offs in response to urban evolution in an acorn-dwelling ant[END_REF]. Additionally, the 'home vs away' criterion can be satisfied if individuals at 'home' have higher fitness than their transplanted counterparts ('away'). This last criterion is however not a strict requirement to conclude on local adaptation [START_REF] Kawecki | Conceptual issues in local adaptation[END_REF].

To occur, local adaptation requires a strong selective filter [START_REF] Kawecki | Conceptual issues in local adaptation[END_REF], such as anthropogenic disturbances. If selective pressure is strong enough to overcome the homogenizing effect of dispersion, local adaptation at smaller scales than the dispersal neighborhood of the considered species may occur (microgeographic adaptation; [START_REF] Richardson | Microgeographic adaptation and the spatial scale of evolution[END_REF]. While spatial differentiation of phenotypes may result from other processes such as spatial sorting [START_REF] Shine | An evolutionary process that assembles phenotypes through space rather than through time[END_REF], selection within one generation [START_REF] Carlson | Eco-evolutionary dynamics in Pacific salmon[END_REF], or preferential settlement according to phenotype [START_REF] Bierne | Habitat preference and the marine-speciation paradox[END_REF], local adaptation seems to be the most frequently described. Using transplant experiments, some studies have been able to show local adaptation of various species, whether marine or terrestrial, to urban habitats, to pollution, and for an introduced species, to environmental conditions in its invaded areas [START_REF] Roesijadi | Behavior of trace metals in Mytiius edulis during a reciprocal transplant field experiment[END_REF][START_REF] Colautti | Contemporary evolution during invasion : evidence for differentiation , natural selection , and local adaptation[END_REF][START_REF] Martin | In a nutshell, a reciprocal transplant experiment reveals local adaptation and fitness trade-offs in response to urban evolution in an acorn-dwelling ant[END_REF]. Kenworthy et al. (2018b) correlated a significant variation in contaminants, such as copper, zinc, and hydrocarbons in marina sediments, with heterogeneity in fouling communities from pillars at small spatial scale (< 100 m). To show the causal effect of the spatial variation of contaminants on community composition and its effect on local adaptation, we conducted a reciprocal transplant experiment in the very same marina. We chose to combine the classical tools of community analysis with a functional approach (community respiration, pollutant uptake and metabolomics) to investigate the response of the fauna to this environmental variation as there may be a direct interaction between respiration or metabolite variations with the environment [START_REF] Macel | Metabolomics: The chemistry between ecology and genetics[END_REF][START_REF] Jones | Metabolomics and its use in ecology[END_REF]Johnston et al. 2015a;Kenworthy et al. 2018a). As metabolomics constitutes a non-targeted analysis of molecules, it can be used to study non-model organisms in great detail [START_REF] Macel | Metabolomics: The chemistry between ecology and genetics[END_REF][START_REF] Jones | Metabolomics and its use in ecology[END_REF]). It has already been used to attempt to explain the success of certain introduced species in terrestrial and marine habitats [START_REF] Nylund | Metabolomic Assessment of Induced and Activated Chemical Defence in the Invasive Red Alga Gracilaria vermiculophylla[END_REF][START_REF] Greff | Chemogeography of the red macroalgae Asparagopsis: metabolomics, bioactivity, and relation to invasiveness[END_REF][START_REF] Utermann | Comparative microbiome and metabolome analyses of the marine tunicate ciona intestinalis from native and invaded habitats[END_REF][START_REF] Skubel | Metabolomic differences between invasive alien plants from native and invaded habitats[END_REF]) and to observe how heavy metal pollution may impact different organisms [START_REF] Booth | Metabolomics and its application to studying metal toxicity[END_REF][START_REF] Kwon | Characterizing the effect of heavy metal contamination on marine mussels using metabolomics[END_REF][START_REF] Ji | Toxicological evaluation of two pedigrees of clam Ruditapes philippinarum as bioindicators of heavy metal contaminants using metabolomics[END_REF].

Our aim was to identify an anthropogenic disturbance gradient, illustrated by pollution levels and temperature differences, with higher values at the inner locations of the studied marina. We hypothesized that community structure would correlate with this gradient and that transplanted communities would resemble the communities of the location they were transplanted to, exposing a causal link between environmental conditions and community structure. Furthermore, we expected to verify the 'locals vs foreign' criterion of local adaptation on the respiration and metabolome of the studied models. Understanding if local variations of environmental conditions at small scales could lead to locally distinct communities and local adaptation may be crucial for understanding the processes of contemporary evolution of introduced species [START_REF] Colautti | Contemporary evolution during invasion : evidence for differentiation , natural selection , and local adaptation[END_REF]; and in consequence might be crucial for understanding the process of invasion itself.

Materials and methods

Study site

The Marina du Château (48°22'43.4"N; 4°29'22.1"W) in Brest, France was chosen as study site. This recreational marina, nested within the highly anthropized urban area of Brest, is integrated in a 250 ha harbor including a commercial (> 2.10 6 t.yr -1 merchants, fishing and petroleum activities) and a military (2 nd biggest French arsenal) part, which occupies the major part of the city coastline. The marina is characterized by numerous artificial substrata, colonized by fouling communities including several introduced species (Kenworthy et al. 2018b). We focused this study on floating pontoons as they are one of the most frequent substrata in the marina. In accordance with this previous study, the same three locations (inner, middle and entrance; spaced < 100 m) were studied.

Reciprocal transplant experiment

At each of the 3 locations, 20 black polyethylene panels (Correx, 0.2 m x 0.2 m, 3 mm thick) were deployed in April 2019 for a reciprocal transplant experiment. The panels were sufficiently spaced to be considered as independent samples. After two months in the field (60 days, June), panels were gathered and photographed. At each location, 5 panels were sampled for immediate species identification. The remaining panels were randomly assigned to either be transplanted to one of the two other locations (10 panels, 5 per location), or stayed in place as control (5 panels per location).

Consequently, over a total of 15 panels present at each location, 10 were originating from the other two locations after transplant. Each panel could thus be sorted in further analysis according to a treatment depending on its origin (i.e., where recruitment started before transplant) and its destination (i.e., where it was transplanted to). Henceforth, we use the following code for treatment identification: 'I' Inner, 'M' Middle, 'E' Entrance; '>' indicates the direction of transplant (e.g., I>E corresponds to panels from the inner location transplanted to the entrance E); C control (ex. IC is the inner control, i.e., a treatment where panels stayed at the inner location during the whole experiment).

After two additional months (70 days, September 2019) the whole set of panels was measured in situ for community respiration, then photographed, and fauna samples (individuals of key species) were immediately fixed in dry ice for contaminant and metabolomic assessment. The panels with the community were then transported in sea-water containers and stored in aquaria facilities (max. 1 week) for taxonomic determination on life communities.

Environmental assessment

At each location, water temperature was recorded with 1-hour interval all over the 4-month long experiment at 1 m depth (panels depth) using a HOBO® (Onset®) TidbiT v2 Water Temperature Data Logger. Here the daily average was used for further analysis. Hydrodynamism was measured with 5min interval using a HOBO® (Onset®) Pendant G Data Logger until the battery went flat (73 days).

Hydrodynamism was approximated by the daily total position change based on differences in acceleration between successive 5-min intervals which was calculated as

∑([a ⃗ x (t) -a ⃗ x (t -1)] + [a ⃗ y (t) -a ⃗ y (t -1)] + [a ⃗ z (t) -a ⃗ 𝑧 (t -1)]) 𝑡0 𝑡𝑛
where t is the measurement at a given time; n the number of measurements during a day, 𝑎 the acceleration in g (9.81 m.s -2 ) in the x, y, or z axis. As the logger data consist in time series, requiring specific statistical tests, two linear mixed effects (LME) model fit by restricted maximum likelihood (REML) were used (explaining temperature or hydrodynamism by location and using date as random factor) with t-test with Satterthwaite's method from the 'lme4 ' and 'lmerTest' package in R (version 3.1-3, Zeileis and Hothorn 2002;version 1.1-26;[START_REF] Bates | Fitting Linear Mixed-Effects Models Using lme4[END_REF].

At the end of the experiment, at each location, surface sediments were sampled by divers for quantification of Metallic Trace Elements (MTEs, 5 replicates of 400 g each) and for Persistent Organic Pollutants (POPs, 3 replicates of 400 g each). Several colonies of the bryozoan Bugula neritina (Linnaeus, 1758), which was present at all locations all along the experiment, were sampled from each panel (5 replicates per treatment, sometimes pooled colonies) and frozen in situ in dry ice before storage in the lab at -80°C. Large colonies were favored as they were more likely to have recruited before transplant. Due to the abundance of this species, the sampling of colonies was assumed not to influence the community structure.

Sediment and animal MTEs samples were dissolved in a mixture of three-acids (HCl, HNO3, HF; all Suprapur grade). The solutions were then analyzed for Metallic Trace Elements (MTE) using a Sector Field Inductively Coupled Plasma Mass Spectrometry (SF-ICP-MS; ELEMENT XR, ThermoElement) as in [START_REF] Jacquet | Characterization of the submarine disposal of a Bayer effluent (Gardanne alumina plant, southern France): I. Size distribution, chemical composition and settling rate of particles forming at the outfall[END_REF]. The instrument was calibrated using standard solutions, and we used an external calibration method with Indium as an internal standard. The efficiency of the dissolution procedure and the quality of the SF-ICP-MS measurements were controlled by the determination of element concentrations on Certified Reference Materials (MESS-4, SLRS-5, SLEW-3 and CASS-5). Concentrations and the list of targeted MTEs is reported in Tab. 1.

The POPs analytical method for Polychlorinated biphenyls (PCBs) and pesticides in sediments has been described in [START_REF] Wafo | Polychlorinated biphenyls and DDT residues distribution in sediments of Cortiou (Marseille, France)[END_REF]. The protocol for B. neritina samples followed the same procedure. Our analysis focused on 33 individual PCB congeners which include PCB contamination indicators targeted by the International Council for the Exploration of the Sea (ICES) and congeners with high environmental prevalence [START_REF] Webster | Determination of polychlorinated biphenyls (PCBs) in sediment and biota[END_REF]. A total of 17 pesticides including lindane, DDT, aldrin were measured (list in Sup. Tab. 1). Polycyclic Aromatic Hydrocarbons (PAH) determination followed established protocols [START_REF] Sarrazin | Determination of polycyclic aromatic hydrocarbons (PAHs) in marine, brackish, and river sediments by HPLC, following ultrasonic extraction[END_REF][START_REF] Ratier | Characterization of atmospheric emission sources in lichen from metal and organic contaminant patterns[END_REF][START_REF] Dron | Contaminant signatures and stable isotope values qualify European conger (Conger conger) as a pertinent bioindicator to identify marine contaminant sources and pathways[END_REF] and is also available in detail in the supplementary material of [START_REF] Gauff | Alien vs. predator: influence of environmental variability and predation on the survival of ascidian recruits of a native and alien species[END_REF]. All 16 PAH congeners defined by the US Environmental Protection Agency (USEPA) priority list (US EPA 2014) were targeted (list in Tab. 2) and each was identified taking into account the retention time and the chromatogram mass spectrum in full scan mode.

For consistency, all contaminants were tested for differences among locations using a Kruskal-Wallis test in R (version 4.0.3; R Core

Community taxonomic assessment

Photography analysis of panels was used to check for community homogeneity within each location before transplant. This allows to exclude experimental artifacts resulting from initial differences between control and transplanted panels as well as panels sampled before transplant. The outermost centimeter of the panels was excluded from our analysis to avoid an edge effect. All photos were analyzed by a single observer using a 144-point stratified random-point overlay. This number of points (> 0.4 points.cm -2 ) provides a good tradeoff between efficiency and precision, providing a 0.95 confidence interval for species contributing at least to 5% cover (Taormina et al. 2020). Similarity of panels within each location before transplant, was checked using a PERMANOVA with the R package 'vegan' (version 2.4-6;[START_REF] Oksanen | Vegan: community ecology package[END_REF]. Confirming this homogeneity allowed to entirely focus on the analysis of panels sampled before transplant (5 per location) and at the end of the experiment (15 per location) for which higher data quality is expected due to higher taxonomic resolution.

At the two studied dates (before transplant and 2 months after transplant), the sampled panels were analyzed in the laboratory for taxonomic identification on living organisms. A 144-point grid was superimposed on the panels (stratified point design) and every individual of each species was counted at each point. This allowed to account for epibionts as well as the different strata of the community and to give more detail than a photographic (2-D) analysis. Species were identified to the lowest possible taxonomic level [START_REF] Hayward | British ascophoran bryozoans[END_REF][START_REF] Hayward | Cyclostome Bryozoans (Synopses of the British Fauna 34)[END_REF], 1995, 1998;[START_REF] Brunetti | Ascidiacea of the European waters[END_REF]. All community data were explored with the R package 'vegan' (version 2.4-6; Oksanen et al.

2018

) and analyzed according to recommended workflows applicable for this kind of data [START_REF] Borcard | Numerical Ecology R[END_REF]. The multi-strata community matrix was transformed into a Bray-Curtis dissimilarity matrix.

First, we analyzed the panels sampled before transplant. Upon confirming homogeneity of group dispersions with the 'betadisper' function, a PERMANOVA (10 4 permutations) was conducted, followed by a pairwise PERMANOVA from the 'pairwisAadonis' R package (version 0.3; Martinez Arbizu 2019) including a Benjamini-Hochberg correction [START_REF] Benjamini | Controlling The False Discovery Rate -A Practical And Powerful Approach To Multiple Testing[END_REF]. For all sampled panels, the mean species richness, Shannon diversity index and Pielou's evenness were calculated and the mean cover of the three most abundant species was assessed for each treatment. A PERMANOVA (10 4 permutations) testing for the effect of origin and destination of panels as well as their interaction was conducted for panels at the end of the experiment. For the factor 'Origin' as well as the interaction of Origin and Destination (i.e., Treatment), homogeneity of group dispersions was compromised.

However PERMANOVA shows high robustness in balanced designs such as here [START_REF] Anderson | PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing?[END_REF]. A pairwise PERMANOVA (10 4 permutations) including a Benjamini-Hochberg correction was conducted to test for differences of communities between treatments. The results were visualized using a Principal Coordinates Analysis (PCoA). The 'envfit' function was used to overlay species vectors, which were significantly correlated to the ordination (with p < 0.05 and R² > 0.2). To identify the indicator species in our experiment, we used a multipattern analysis from the 'indicspecies' R package (version 1.7.9; [START_REF] Cáceres | A framework for estimating niche metrics using the resemblance between qualitative resources[END_REF]. Parameters were set to 10 4 permutations while testing for individual treatments, allowing combinations of treatments within the same origin or within the same destination (ex.: origin inner = IC + I>M + I>E).

Respiration measures

Community respiration was measured in situ for all panels at the end of the experiment. Panels were detached from the pontoons and individually hooked in 23.3 L transparent, hermetically sealed methacrylate chambers. A magnetic stirrer was used to ensure water circulation within the chamber.

Chambers were placed under the pontoons for similar light conditions as during the experiment. Three empty control chambers (blank) were incubated at each location to control for potential photosynthesis or respiration caused by microbial and plankton activity within the water. Dissolved oxygen concentration was measured extracting 200 mL of water from a valve on the chamber using a syringe and with an oxygen probe with temperature sensor (Hach-Lange LDO101). Oxygen concentration was measured right after sealing off the chamber and at the end of incubation (between 14 and 23 min). The measured ΔO2 (in mg O2.L -1 ) was then rated according to the volume of the chamber, the incubation time, and the ash free dry mass (AFDM) to obtain the respiration in mg O2.h -

1

.gAFDM -1 . The AFDM was determined after the community analyses in the laboratory were finished. The whole community was dried for one week at 60°C for each panel. After a first mass measure, it was burned at 520°C for 6 h to allow to calculate the AFDM. For respiration, two aberrant values were excluded from analysis. Respiration measurements were compared between treatments via an ANOVA followed by a Tukey HSD test.

Metabolome assessment

Three species occurring at the three locations in the marina were sampled for metabolomic analyses:

two large colonies of the bryozoan Bugula neritina per panel and one individual per panel of the two solitary ascidians Ciona intestinalis (Linnaeus, 1767) and Ascidiella aspersa (Müller, 1776). For B.

neritina and C. intestinalis, only four samples could be constituted for the E>I. Additionally, the IC and M>I treatment are missing entirely for C. intestinalis due to the absence of individuals for these conditions. After sampling on the field, samples were immediately frozen in dry ice and then stored at -80°C. Samples were analyzed according to different mass spectrometry approaches. The detailed extraction, injection protocols and Mass Spectrometry (MS) modalities (non-fragmenting modes LCMS 1+ , LCMS 1-; Fragmenting modes LCMS 2+ , LCMS 2-; and GCMS) can be consulted in the supplementary material. For each species, all features from the LCMS 1+ , the LCMS 1-as well as the GCMS were combined into one matrix and treated with 'Metaboanalyst' (version 5.0; [START_REF] Xia | MetaboAnalyst: A web server for metabolomic data analysis and interpretation[END_REF]). The imported matrix (with less than 5000 features) was log transformed and auto scaled. We conducted a Partial Least Squares Discriminant Analysis (PLS-DA) with class order assuming a gradient from the inner to the entrance location, providing the best discrimination among treatments [START_REF] Xia | MetaboAnalyst: A web server for metabolomic data analysis and interpretation[END_REF][START_REF] Kuhlisch | Metabolomics in chemical ecology[END_REF]. In parallel a PPLS-DA driven permutation Model Validation Analysis (MVA)

as well as a pairwise MVA test using the 'RVAideMemoire' package in R (version 0.9-79; Hervé 2021) were performed to test for statistical differences between treatments (10 4 permutations, 6 components). After revealing significant differences between treatments for B. neritina, the 100 first Variable Important in Projection (VIP) scores were selected and tentatively annotated with the help of the LCMS 2 (fragmentation) data. The annotated VIP were gathered in a table including an expression heatmap ranging from the highest to the lowest ion intensity in LCMS 1 for each treatment. Mean intensity was used when a feature was detected as a VIP in LCMS 1+ and LCMS 1-. Annotated metabolites were sorted following their heatmap similarity in the dendrogram, while allowing compromise for keeping chemical families together for clarity. Molecules within a family were sorted by numbers of carbons, oxygens, and unsaturations. For C. intestinalis a more streamlined approach was used for simplification as some treatments were missing. Using a molecular network generated on Global Natural Products Social Molecular Networking (GNPS; version 1.3.15;[START_REF] Wang | Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking[END_REF]) and visualized on 'cytoscape' (version 3.7.2), we localized clusters in LCMS 2+ and LCMS 2-corresponding to glycerophospholipids and only highlighted VIP within these clusters.

Results

Environmental characterization

Significantly higher hydrodynamism indicating higher water exchanges with external mass waters was noted at the entrance location (LME; t = 3.61; p < 0.001; Sup. Fig. 1). Temperature did also significantly vary between locations (LME; t = 5.33; p < 0.05; Sup. Fig. 1). However, these differences (< 0.1°C) are well below temperatures that are expected to have an impact on fauna [START_REF] Brunetti | Combined Effects of Temperature and Salinity on Colonies of Botryllus schlosseri and Botrylloides leachi (Ascidiacea) from the Venetian Lagoon[END_REF][START_REF] Hitoshi | Growth of the Bryozoan Bugula neritina in the Sea at Various Water Temperatures[END_REF][START_REF] Qiu | Combined effects of salinity and temperature on juvenile survival, growth and maturation in the polychaete[END_REF][START_REF] Sorte | Geographic variation in temperature tolerance as an indicator of potential population responses to climate change[END_REF][START_REF] Lord | Impact of seawater temperature on growth and recruitment of invasive fouling species at the global scale[END_REF].

Almost all MTEs were distributed along a decreasing concentration gradient from the inner location to the entrance, although differences were not always supported by statistics. MTEs excepted Cu and As showed significant differences between the three locations, with systematically higher concentrations from individuals sampled at the inner location (Kruskal-Wallis test; χ² > 6.76; p < 0.05; Tab. 1). tPCB and eight pesticides did vary with distinct distribution profiles (Kruskal-Wallis test; χ² > 7.2; p < 0.05; Tab. 2; Sup. Tab. 1). None of the PAHs significantly varied in

B. neritina (Tab. 2).
The normalized concentrations of all combined contaminants varied significantly between treatments for B. neritina with a significant effect of 'origin' (two-way ANOVA; p < 0.001; F = 7.23), 'destination'

(two-way ANOVA; p < 0.001; F = 77.86) and their interaction (i.e. treatment; two-way ANOVA; p < 0.001; F = 9.70). The Tukey HSD test revealed that contaminant concentrations for the inner control IC had significantly higher values than the two other controls (p < 0.01; Fig. 1). Higher contaminant concentrations in B. neritina samples transplanted to the inner location were systematically observed, compared to samples present at the entrance location at the end of the experiment, regardless of their origin (p < 0.001; Fig. 1). It was only possible to observe an effect of origin for samples transplanted to the inner location: E>I individuals had significantly higher normalized contaminant concentrations than M>I and IC (p < 0.01; Fig. 1).

The PCA biplot revealed differences of contaminant compositions of B. neritina between the treatments (Sup. Fig. 2). The PC1 axis (30.2%) represents general pollution levels with MTE, PCBs, Pesticides (group 2, see Sup.

Community

The photography-based community analysis did not show any difference among panels within each location before transplant (PERMANOVA; p > 0.57; R² = 0.001), thus excluding experimental artifacts due to panel selection. Panels sampled before transplant were significantly different according to their origin: IC vs MC (pairwise PERMANOVA; p = 0.009; R² = 0.55), MC vs EC (pairwise PERMANOVA; p = 0.009; R² = 0.87), IC vs EC (pairwise PERMANOVA; p = 0.009; R² = 0.89). The PCoA (Fig. 2a) clearly discriminated EC from IC and MC along the first PCoA axis (76.6% of variability) while the second PCoA axis discriminated the two latter (13.2% of variability). The cover of several species varied between treatments before transplant. C. intestinalis, A. aspersa and B. neritina had the highest cover at the entrance location, while W. subatra had a lower cover at this location and higher at the inner location (Dunn Test; p < 0.05; Sup. Fig. 3). Bare space was the highest at the inner location compared to the entrance (Dunn Test; p = 0.002). The total cover of introduced species was higher at the entrance location than at the inner location (Dunn Test; p = 0.012).

Two months after transplant, the effect of 'origin' and 'destination' of panels as well as their interaction was tested and showed a significant effect of all three (PERMANOVA; p < 0.001; R² > 0.22). The PCoA explained a total of 79.3% of the variability (Fig. 2b), with the first axis (56.6%) associated with the destination effect (inner locations on the right side). Most species correlated positively along this axis (p < 0.05; R² > 0. Subsequent pairwise PERMANOVA for panels after the 2-months transplant revealed significant differences between almost all treatments (p < 0.05; R² > 0.23). Mean species richness (S), Shannon index (H') and Pielou's evenness (J') varied among sites (Sup. Tab. 2). The most abundant species (cover > 30%) were Watersipora subatra (Ortmann, 1890), B. neritina and C. intestinalis. Their cover was highly variable depending on the treatment (Sup. Tab. 2 and Sup. Fig. 3). Diversity indices for each treatment varied between treatments, ranging from 14.6 to 18.4 for mean species richness, from 2.0 to 2.9 for Shannon index and from 0.52 to 0.70 for Pielou's evenness. The only exception were E>M panels with very low diversity (S = 11; H' = 0.6; J' = 0.19; Sup. Tab. 2). W. subatra had a higher cover at the inner location compared to the entrance (Dunn Test; p = 0.01). Further details on the cover of each can be seen in Sup. Fig. 3. The multipattern analysis identified several species associated to the three locations before transplant as well as multiple species according to specific origins (origin effect) or to specific destinations (destination effect; Tab. 3). Over both periods, between 40% and 45% of these indicator species were NIS. While NIS were associated to every location before transplant, no NIS was associated to panels having the entrance as origin or destination after transplant.

Tab. 3: Indicator species associated to treatments identified by multipattern analysis before and 2-months after transplant. Association statistic (stat.) of a species with a group and p value of the multipattern analysis as well as Non-indigenous species (NIS) are indicated. Groups are labelled according to their origin or destination, composed by the three treatments within the respective origin or destination (ex: Origin inner = Inner Control, Inner>Middle and Inner>Entrance; where > represents the direction of transplant).

Group

Community respiration

Community respiration varied considerably among treatments with panels having the inner location as destination (including IC) showing 3 to 5 times higher respiration rates than any other treatments (Fig. 3). Respiration was significantly different between IC (17.66 mg O2.h -1 .gAFDM -1 ) and the two other controls (MC: 3.39 mg O2.h -1 .gAFDM -1 ; EC: 4.83 mg O2.h -1 .gAFDM -1 ; Tukey HSD; p < 0.001). M>I and E>I had each higher respiration rates than treatments in other destinations (Tukey HSD; p < 0.001). However, E>I respiration was even higher than the IC (E>I: 21.40 mg O2.h -1 .gAFDM -1 ; Tukey HSD; p = 0.033).

Metabolome

The merged data matrix groups 597 features (LCMS 1+ : 303, LCMS 1-: 282 and GCMS: 12) after metabolome analysis for B. neritina, 522 features (LCMS 1+ : 181, LCMS 1-: 344 and GCMS: 7) for C.

intestinalis and 428 features (LCMS 1+ : 165, LCMS 1-: 171 and GCMS: 92) for A. aspersa. The PLS-DA of the detected metabolome (LCMS 1+ , LCMS 1-and GCMS) of the three studied species was established (Fig. 4; Sup. Fig. 4). For B. neritina and C. intestinalis the treatments are well separated on the Component 1 axis, grouped by destination with the inner origin at the left side and the entrance origin on the right for each destination group. Component 2 reflects the variability within each treatment.

The PPLS-DA driven permutation Model Validation Analysis (MVA) revealed significant differences among locations after transplant in the metabolomes of B. neritina (CER 'Classification Error Rate' = 0.53; p < 0.001; Fig. 4a) and of C. intestinalis (CER = 0.44; p < 0.001; Fig. 4c), but not of A. aspersa (CER = 0.82; p = 0.095; Sup. Fig. 4). The pairwise MVA test showed a strong effect on the metabolome of B. neritina with differences according to the treatments (Fig. 4b). The IC metabolome was significantly different from the two other controls (MVA; p < 0.05). Several times, transplant resulted in significantly different metabolomes compared to origin controls (destination effect) but also to destination controls (origin effect). For C. intestinalis, it was not possible to recover samples from the IC and M>I panels due to low biomass. Only the metabolome of E>I panels was significantly different from all other treatments, except from M>I and MC (p < 0.047).

For B. neritina the 100 highest Variable of Importance in Projection (VIP) scores linked to the PLS-DA were selected to putatively identify the metabolites linked to metabolome differentiation according to treatments. Almost two third (59 %) of the VIPs were either sulfated or phosphated compounds.

Using LCMS 2 spectra to annotate metabolites, 46 of the 100 VIPs could be connected to a specific chemical family (Tab. 4), including 16 glycerophosphoethanolamines (35%) and 13 sulfoethanolamines (28%, a yet undescribed lipid class). While sulfoethanolamine VIPs had varying expression profiles, associated to the inner or the entrance destinations, glycerophosphoethanolamine VIPs were more prevalent in the MC and treatments including the entrance as a destination. Among other families, we were also able to annotate some oxylipins, amines and purines. For C. intestinalis only some glycerophospholipids were highlighted due to the absence of IC (Sup. Tab. 3). However, their pattern was similar to glycerophosphoethanolamines in B. neritina.

Discussion

Previous studies have observed local variations of fouling communities between different parts of harbors/marinas, which have been linked to pollution gradients (Rondeau et al. in press;[START_REF] Je | Changes in benthic communities along a presumed pollution gradient in Vancouver Harbour[END_REF][START_REF] Ryu | The impact of heavy metal pollution gradients in sediments on benthic macrofauna at population and community levels[END_REF]Kenworthy et al. 2018b). The associated mechanistic process might be linked to a MTEs) gradient from the entrance to the inner of the marina and assumed that community structure would correlate with this gradient as previously observed. We also hypothesized that transplanted communities would be driven according to their new environmental conditions, thus revealing a causal link between the environment and community structure. We also hypothesized that such filter would be acting at different biological organization levels, potentially resulting in local adaptation which would be detectable in the community respiration, pollutant accumulation, and species metabolomes.

4. Among the different locations studied within the marina, contaminants did, as expected, vary between the three locations spaced by less than 100 m. We observed significant differences in benzo[g,h,i]perylene, fluorene and total PCBs concentrations among locations, with maximal values at the inner location, but not organized as a gradient. MTEs in sediments, on the other hand, had maximal concentrations in the innermost part of the marina and diminished towards the entrance. This trend was particularly marked for Cu, Pb and Zn, two of which have been previously reported in 2016 (Kenworthy et al. 2018b). Such lower contaminant concentrations at the entrance might be linked to the higher hydrodynamism and higher depth that we noted at this location (Sup. Fig. 1), as stronger water mixing might dilute pollutants [START_REF] Schiff | Extent and magnitude of copper contamination in marinas of the San Diego region, California, USA[END_REF].

Contaminant concentrations in the filter feeder Bugula neritina approximatively followed the same trends as in sediments with, however, lower concentrations in general but high heterogeneity between

samples. An exception lays in pesticides which were equivalently concentrated or even higher (aldrin, diazinon and chlordanes) in B. neritina samples than in the sediments. As previously reported for various benthic species [START_REF] Reynoldson | Interactions between sediment contaminants and benthic organisms[END_REF], the filter-feeding B. neritina might have bioaccumulated these contaminants following sediment resuspensions due to meteorological or hydrodynamic events.

Interestingly, Cu presented low concentrations and no significant differences between locations in B.

neritina samples while it had a strong spatial variability and a higher pollution levels than most MTEs in sediments (moderate, according to [START_REF] Guerra-García | Assessing environmental pollution levels in marinas[END_REF]. Due to its high toxicity, it is a major driver of harbor fouling communities and of the prevalence of non-indigenous species [START_REF] Dafforn | Differential effects of tributyltin and copper antifoulants on recruitment of non-indigenous species[END_REF][START_REF] Canning-Clode | The Effects of Copper Pollution on Fouling Assemblage Diversity : A Tropical-Temperate Comparison[END_REF]Kenworthy et al. 2018b;[START_REF] Osborne | Copper pollution enhances the competitive advantage of invasive ascidians[END_REF]. Bugula neritina is particularly resistant to Cu, even compared to other introduced bryozoans like Watersipora spp.

commonly present in harbors (Piola and Johnston 2006a). Detoxification mechanisms may thus explain why despite high Cu concentrations in sediments, B. neritina had overall low concentrations.

Community analyses

Previous studies have shown that pollution gradients may structure communities by inducing local differences among them (Rondeau et al. in press;[START_REF] Je | Changes in benthic communities along a presumed pollution gradient in Vancouver Harbour[END_REF][START_REF] Ryu | The impact of heavy metal pollution gradients in sediments on benthic macrofauna at population and community levels[END_REF]Kenworthy et al. 2018b). In the same marina as the present study, Kenworthy et al. (2018b) could correlate the high but varying levels of some sediments contaminants (Cu, Zn, and hydrocarbons) at small spatial scales (< 100 m) with heterogeneity in fouling communities of vertical pillars. While increasing the number of tested contaminants, the present study showed similar results with significant differences maintained throughout the whole duration of the experiment among communities from control panels (Fig. 2). Such similar spatial patterns maintained over time might be caused ecological selection [START_REF] Vellend | Conceptual synthesis in community ecology[END_REF]. However, only an experimental approach such as reciprocal transplant may establish a causal relationship between the environmental conditions, community structure and biological functions [START_REF] Osman | The control of the development of a marine benthic community by predation on recruits[END_REF][START_REF] Cifuentes | Does primary colonization or community structure determine the succession of fouling communities?[END_REF][START_REF] Chang | Quantifying the role of colonization history and biotic interactions in shaping communities -a community transplant approach[END_REF].

It was possible to observe a strong effect of the transplant on community structures. The global PERMANOVA revealed a strong effect of origin, destination, and their interaction (Treatment). The destination effect directly results from the transplant experiment as it reveals how location may select the presence and/or abundance of certain taxa, driving similarities between communities at the inner location. Here we confirmed differences in community composition between locations as previously observed [START_REF] Je | Changes in benthic communities along a presumed pollution gradient in Vancouver Harbour[END_REF][START_REF] Ryu | The impact of heavy metal pollution gradients in sediments on benthic macrofauna at population and community levels[END_REF]Kenworthy et al. 2018b), but we also demonstrated a causal relationship between location and community composition through the transplant experiment.

Predation or recruitment dynamics may contribute to the observed differences among communities.

However previous studies in the same marina have shown that despite spatially variable predation and temporally variable recruitment dynamics, the impact of location remained constant (Rondeau et al. in press;[START_REF] Gauff | Alien vs. predator: influence of environmental variability and predation on the survival of ascidian recruits of a native and alien species[END_REF]. Pollution and specifically Cu pollution has been thoroughly shown to highly influence fouling communities [START_REF] Piola | Pollution reduces native diversity and increases invader dominance in marine hard-substrate communities[END_REF][START_REF] Canning-Clode | The Effects of Copper Pollution on Fouling Assemblage Diversity : A Tropical-Temperate Comparison[END_REF][START_REF] Kinsella | Separate and combined effects of copper and freshwater on the biodiversity and functioning of fouling assemblages[END_REF]. As pollution is highly variable among locations, this might indicate that the selective pressure exerted by pollution in the inner part of the harbor may be major driver of community composition.

On the other side, population dynamics of C. intestinalis might explain the effect of panel origin. Before transplant, C. intestinalis was completely covering panels from the entrance (Sup. Fig. 3 and Sup. Fig. 5), while at the end of the experiment, EC and E>I panels had a comparatively lower cover. Conversely E>M panels were completely covered by very large individuals that were most likely the same individuals noted before transplant (Sup. Fig. 5, middle). It is unlikely that this observation is linked to the effect of predators on C. intestinalis as benthic predators have reduced access to the suspended panels and as pelagic predators exert the strongest top-down effect at the middle of the marina [START_REF] Gauff | Alien vs. predator: influence of environmental variability and predation on the survival of ascidian recruits of a native and alien species[END_REF], where C. intestinalis were abundant at the end of the experiment. Indeed, this species has a rather short life cycle in the present harbor, recruiting twice a year (spring and late summer) and living 2-4 months before reproducing and dying [START_REF] Bouchemousse | Do settlement dynamics influence competitive interactions between an alien tunicate and its native congener?[END_REF]. The here observed recruitment and die off of C. intestinalis is similar to previous observations in the same marina (Rondeau et al. in press). Lethal and sublethal effects for this species were reported for some environmental factors such as heavy metal pollution [START_REF] Bellas | Toxicity of Hg, Cu, Cd, and Cr on early developmental stages of Ciona intestinalis (Chordata, Ascidiacea) with potential application in marine water quality assessment[END_REF][START_REF] Bellas | Sublethal Effects of Trace Metals (Cd, Cr, Cu, Hg) on Embryogenesis and Larval Settlement of the Ascidian Ciona intestinalis[END_REF]. Considering the contaminant levels recorded in the present harbor, the entrance may have been the most favorable location for this species' high recruitment in the early phases of our experiment, then for completing its reproduction cycle and subsequent mass mortality (hence its decline at the end of the experiment).

At the inner location, lethal effects of contaminants may explain the low presence of C. intestinalis in inner controls and the sharp decline in E>I treatments. Individuals on E>M panels however, may have encountered sublethal effects of pollution delaying their reproductive cycle and explaining the high cover by large individuals at the end of the experiment. This illustrates the origin effect observed in the PERMANOVA. Niche preemption as priority effect may highly influence the historical contingency of a community [START_REF] Fukami | Historical Contingency in Community Assembly: Integrating Niches, Species Pools, and Priority Effects[END_REF]. Previous transplant studies on fouling communities have already demonstrated the importance of primary colonization on the subsequent community composition in harbors [START_REF] Cifuentes | Does primary colonization or community structure determine the succession of fouling communities?[END_REF][START_REF] Chang | Quantifying the role of colonization history and biotic interactions in shaping communities -a community transplant approach[END_REF]. Stochastic colonization may also have a high structuring role [START_REF] Chase | Disentangling the importance of ecological niches from stochastic processes across scales[END_REF]. Ciona intestinalis is a highly competitive species with a tendency to monopolize substratum and reduce diversity [START_REF] Blum | The nonnative solitary ascidian Ciona intestinalis (L.) depresses species richness[END_REF]). Considering all this, the monopolization of space by C. intestinalis at the entrance before transplant may explain why communities originating from this location strongly differentiate from all others even if this species dies off if transplanted to the inner location.

The indicator species analysis revealed that species may be associated to certain origins or to certain destinations, which reflects the coexistence of those effects as observed at the community level. The indicator species associated to specific destinations revealed the causal effect between environment and community structure. Cryptosula pallasiana and Austrominius modestus seem both to be species associated with the inner location, as they were indicator species of this location before transplant and are indicator species of the inner destination (Tab. 3). In general, the inner part of the marina seems to be more frequently correlated or associated with encrusting species and NIS (see Fig. 2 and Tab. 3).

The indicator species associated to specific origins reveal that historical contingency plays an important role in the community structure and species present on panels. Interestingly, none of the indicator species for any location before transplant is an indicator species for the same origin after transplant (Tab. 3). Some of the indicator species associated to origin effects (ex. Mytilus edulis or Asterocarpa humilis) were not detected at all before transplant. Two very notable indicator species, B. neritina and W. subatra (both introduced bryozoans), were associated to the middle origin. These were also the panels where their percent cover was often the highest compared to panels from the same destination and the panels for which introduced species cover was the highest (Sup. Fig. 3). This might indicate that once these species gain a foothold on the substratum, they persist even if disturbance levels diminish, which may pose problems for their management in fluctuating environments.

Local adaptation is revealed at various organization levels and functions

A population is locally adapted if local selective pressures result in differentiated phenotypes among populations of the same species [START_REF] Williams | Adaptation and natural selection; a critique of some current evolutionary thought[END_REF][START_REF] Kawecki | Conceptual issues in local adaptation[END_REF][START_REF] Blanquart | A practical guide to measuring local adaptation[END_REF]. In its most common definition, local adaptation requires genotypic differences underlying these adaptive phenotypes, excluding adaptive plasticity [START_REF] Kawecki | Conceptual issues in local adaptation[END_REF]). Yet, transgenerational plasticity plays an important role in adaptation [START_REF] Mousseau | The adaptive significance of maternal effects[END_REF][START_REF] Ghalambor | Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments[END_REF]) and in recent literature transgenerational adaptive plasticity through epigenetics is increasingly included in studies focusing on local adaptation and adaptation in general (Richards et al. 2010a;[START_REF] Platt | Genome-wide signature of local adaptation linked to variable CpG methylation in oak populations[END_REF][START_REF] Moler | Population Epigenomics: Advancing Understanding of Phenotypic Plasticity[END_REF][START_REF] Sork | Genomic Studies of Local Adaptation in Natural Plant Populations[END_REF]). DNA-methylation can often be observed in association with environmental gradients [START_REF] Dubin | DNA methylation in Arabidopsis has a genetic basis and shows evidence of local adaptation[END_REF][START_REF] Platt | Genome-wide signature of local adaptation linked to variable CpG methylation in oak populations[END_REF][START_REF] Clark | Life in the intertidal: Cellular responses, methylation and epigenetics[END_REF]) and may contribute to local adaptation if it affects adaptive traits (Richards et al. 2010b;[START_REF] Moler | Population Epigenomics: Advancing Understanding of Phenotypic Plasticity[END_REF]. As DNA-methylation might increase genetic mutation on the considered gene [START_REF] Klironomos | How epigenetic mutations can affect genetic evolution: Model and mechanism[END_REF], locally adaptive phenotypes might even become genetically fixed [START_REF] Ghalambor | Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments[END_REF]). Considering the spatial and temporal scales relevant for our study we included transgenerational plasticity our definition of local adaptation. Here, we investigated several biological functions to see if the 'local vs foreign' criterion (fitness differences among transplanted and destination control individuals) for local adaptation was respected [START_REF] Kawecki | Conceptual issues in local adaptation[END_REF]. For this, we chose to focus on metabolic metrics to discuss the potential fitness of organisms.

Increased stress due to disturbance may engage energetically costly regulatory mechanisms like osmoregulation or detoxification [START_REF] Roast | Respiratory responses of the estuarine mysid Neomysis integer ( Peracarida : Mysidacea ) in relation to a variable environment[END_REF][START_REF] Pook | The metabolic and fitness costs associated with metal resistance in Nereis diversicolor[END_REF][START_REF] Lenz | Non-native marine invertebrates are more tolerant towards environmental stress than taxonomically related native species: Results from a globally replicated study[END_REF], which may in turn increase respiration (Kenworthy et al. 2018b). Here we did observe higher respiration rates in IC, M>I and E>I communities, which could indicate that the inner location may constitute a high stress environment for them. Interestingly, community respiration from the E>I treatment is even higher than the IC respiration, which would indicate an even higher stress level for these communities. This would be consistent with a 'foreign' disadvantage of these communities compared to IC (locals) and be a potential indicator that at least some species might be locally adapted to the environmental conditions. Respiration was 3-5 times higher than what has been reported in the literature [START_REF] Bakke | Effects of toluene on the survival, respiration, and adenylate system of a marine isopod[END_REF][START_REF] Lenz | Non-native marine invertebrates are more tolerant towards environmental stress than taxonomically related native species: Results from a globally replicated study[END_REF]Kenworthy et al. 2018a;[START_REF] Oh | Effects of polluted and non-polluted suspended sediments on the oxygen consumption rate of olive flounder, Paralichthys olivaceus[END_REF]. Bacterial respiration might thus contribute to the elevated values at the inner location as it may account for between 30% and 60% of community respiration [START_REF] Smith | Respiration of a Sublittoral Community[END_REF][START_REF] Martínez-García | Experimental assessment of marine bacterial respiration[END_REF].

Contamination in animal tissues may be an important measure to be accounted for, as it may impact fitness through reproduction and survival (Piola and Johnston 2006a;[START_REF] Reynoldson | Interactions between sediment contaminants and benthic organisms[END_REF][START_REF] Ruiz | Environmental quality of mussel farms in the Vigo estuary: Pollution by PAHs, origin and effects on reproduction[END_REF]. In a recent meta-analysis encompassing over 300 species, pollution has been demonstrated to be the anthropic disturbance inducing the strongest phenotypic changes, potentially leading to rapid evolution [START_REF] Sanderson | The pace of modern life, revisited[END_REF]. Here, the contaminant composition of B. neritina (see Sup. Fig. 2, Tab. 1, 2 and Sup. Tab 1) is mainly driven by MTE and PAH. Consequently, the EC (control at the least contaminated location) had a significantly lower normalized concentration than the two other controls (Fig. 1), but the transplant had a strong effect on B. neritina normalized contaminant concentrations: whatever their origin, individuals transplanted towards the entrance showed the lowest concentrations, while individuals transplanted to the inner location had systematically higher concentrations (Fig. 1).

Interestingly, it is also possible to observe a differential accumulation of contaminants among individuals transplanted to the inner (most contaminated) location, with E>I individuals having a significantly higher concentration than IC and M>I individuals (Fig. 1). This might be an indication for local adaptation at very small scale (< 100 m) for this species, where individuals from less contaminated locations (entrance) may have a lower detoxification activity (or higher contaminant uptake) compared to their counterpart in contaminated locations (inner). There are many molecular and cellular mechanisms used for detoxification which can be explained by genetic adaptation and differences in expression regulation of detoxification pathways [START_REF] Rainbow | The significance of trace metal concentrations in marine invertebrates. A need for laboratory investigation of accumulation strategies[END_REF][START_REF] Janssens | Molecular mechanisms of heavy metal tolerance and evolution in invertebrates[END_REF]). This species has been shown to have differential resistance to Cu among populations (Piola and Johnston 2006b). Maternal effects seem to have a particularly strong effect on B. neritina facing spatial variability of environmental factors [START_REF] Marshall | Transgenerational plasticity in the sea: Context-dependent maternal effects across the life history[END_REF][START_REF] Burgess | Temperature-induced maternal effects and environmental predictability[END_REF]. Local adaptation through epigenetics and maternal effects might explain the differential accumulation/detoxification of this species in our case and need to be further explored.

Environmental conditions and pollution levels may be an important driver of the metabolome of a species [START_REF] García-Sevillano | Environmental metabolomics: Biological markers for metal toxicity[END_REF][START_REF] Xue | Towards mass spectrometry-based chemical exposome: Current approaches, challenges, and future directions[END_REF]. The metabolomes of B. neritina and C. intestinalis were significantly influenced by location and transplant. For B. neritina, it was possible to observe differences in the metabolome among locations (IC distinct from other controls). Furthermore, a clear pattern linked to the transplant could be observed (distinct metabolomes among panels exchanged between the inner and entrance locations with their respective controls). For C. intestinalis, a clear difference between metabolomes of EC and E>I individuals has been identified, but due to the absence of IC metabolomes (not enough individuals for sampling), no clear patterns could be highlighted for this species. Destination effects on the metabolome of C. intestinalis and B. neritina showed that both species were able to modify their metabolism to accommodate for new environmental conditions.

Changes in the metabolome might be a direct response to variable contamination levels among locations. For B. neritina, this destination effect however coincides with an origin effect since the E>I metabolome was not only distinct from EC, but also from IC. This effect is even more visible for metabolomes originating from the middle, which were different from MC and from both their destination controls (EC and IC). These results are in accordance with the normalized pollutant concentration in B. neritina, whose entrance individuals transplanted to the inner location seem to present a lower detoxification activity (or higher pollutant uptake) compared to the inner control. Since these activities are dependent on metabolic pathways [START_REF] Rainbow | The significance of trace metal concentrations in marine invertebrates. A need for laboratory investigation of accumulation strategies[END_REF][START_REF] Janssens | Molecular mechanisms of heavy metal tolerance and evolution in invertebrates[END_REF], they should also manifest in the metabolome and at least partly influence it. Indeed, such a tendency may be also reported from the expression profiles of some of B. neritina's metabolites (Tab. 4). Glycerophospho-ethanolamines (GlyPE), more specifically Lyso-phospho-ethanolamines (Lyso-PE), have a very distinct expression pattern with high expression in MC, M>E and EC. Expressions of this chemical family varied according to their transplant destination (I>E > I>M > IC and EC > E>M > E>I). Interestingly, an origin effect is also visible for Lyso-PE, since the concentration of E>I < IC < EC. This would further indicate that local adaptation of this species may be present at this small spatial scale. Data from literature suggest a potential link between GlyPE expression and reproductive activity. They have been shown to vary with the reproductive cycle of the sponge Oscarella tuberculata (Schmidt, 1868), being highly expressed during embryogenesis and early development [START_REF] Ivanisevic | Lysophospholipids in the Mediterranean Sponge Oscarella tuberculata: Seasonal Variability and Putative Biological Role[END_REF]. Moreover, lysophospholipids also play a role in mammalian reproduction [START_REF] Birgbauer | Visions & Reflections (Minireview) New developments in the biological functions of lysophospholipids[END_REF]. Phospholipids in general appear to be impacted by various types of pollutions in vertebrates and invertebrates [START_REF] Al-Salhi | The xenometabolome and novel contaminant markers in fish exposed to a wastewater treatment works effluent[END_REF][START_REF] García-Sevillano | Environmental metabolomics: Biological markers for metal toxicity[END_REF][START_REF] Salihovic | The metabolic fingerprint of p,p′-DDE and HCB exposure in humans[END_REF][START_REF] Wang | A metabolomics strategy to assess the combined toxicity of polycyclic aromatic hydrocarbons (PAHs) and short-chain chlorinated paraffins[END_REF]). Lyso-PE levels have an almost exact opposite distribution than pollution levels in B. neritina indicating a negative effect of pollution on Lyso-PE levels. It is possible to assume that the inner, more polluted location is metabolically at higher cost for B. neritina due to higher energy cost of detoxification, leaving less resources to allocate to reproduction.

Although it is difficult to make clear statements upon the metabolome of C. intestinalis, the analysis of its metabolome allowed to identify several VIP as glycerophosphocholines (GlyPC) and GlyPE. They had a similar distribution pattern to B. neritina's ones. However, GlyPC and GlyPE levels in C. intestinalis had a slightly lower expression at the EC compared to I>E, M>E and compared to MC (Sup. Tab. 3).

Their expression profile would be compatible with the low survival of C. intestinalis when transplanted from the entrance to the inner location and the delay in reproductive activity due to less favorable conditions when transplanted to the middle (Sup. Fig. 3, Sup. Fig. 5). This suggests that glycerophospholipids may be excellent markers of environmental stress linked to pollution across multiple animal classes, since they seem to be linked to a tradeoff between reproduction and detoxification. Subsequent studies could use targeted metabolomics to assess levels of these molecules in several study species.

Here we show strong indications for local adaptation, since the 'local vs foreign' criterion is satisfied for the community respiration, contaminant accumulation and the metabolome of B. neritina. Our results do however not satisfy the 'home vs away' (fitness differences among transplanted and origin control individuals) criterion since I>E individuals had lower pollutant concentrations and higher Lyso-PE levels than IC. This is due to differences in the intrinsic habitat quality, which is why the 'foreign vs local' criterion is the foremost one to satisfy to conclude on local adaptation [START_REF] Kawecki | Conceptual issues in local adaptation[END_REF].

While the 'locals vs foreign' criterion may be an indicator for local adaptation, it is here, however, not entirely possible to exclude within-generation plasticity (i.e., acclimation) and common garden experiments may be used to disentangle [START_REF] Kawecki | Conceptual issues in local adaptation[END_REF][START_REF] Thorpe | Common Garden and Natural Selection Experiments Support Ecotypic Differentiation in the Dominican Anole (Anolis oculatus) Phenotypic geographic variation within a species can be due to natural selection for current ecological conditions Study System[END_REF][START_REF] De Villemereuil | Common garden experiments in the genomic era: new perspectives and opportunities[END_REF][START_REF] Moler | Population Epigenomics: Advancing Understanding of Phenotypic Plasticity[END_REF]. However, due to the short generation time of our studied communities (months) and the duration of our experiment, and due to the strong effect of pollution on individual fitness and survival, it is likely that acclimation after transplant should occur quickly. The remaining 'origin effect' may thus mostly be linked to more anchored phenomena. Strong selective forces are primordial in generating local adaptation and contemporary evolution for introduced species [START_REF] Kawecki | Conceptual issues in local adaptation[END_REF][START_REF] Colautti | Contemporary evolution during invasion : evidence for differentiation , natural selection , and local adaptation[END_REF]. Pollutants constitute a strong selective filter that can act on at the community level [START_REF] Je | Changes in benthic communities along a presumed pollution gradient in Vancouver Harbour[END_REF][START_REF] Canning-Clode | The Effects of Copper Pollution on Fouling Assemblage Diversity : A Tropical-Temperate Comparison[END_REF][START_REF] Ryu | The impact of heavy metal pollution gradients in sediments on benthic macrofauna at population and community levels[END_REF]), but also on the species which might develop hereditary resistances (Piola and Johnston 2006b;[START_REF] Marshall | Transgenerational plasticity in the sea: Context-dependent maternal effects across the life history[END_REF][START_REF] Mckenzie | Heritable pollution tolerance in a marine invader[END_REF]. While for the E>I treatment the 'foreign' disadvantage is striking, it is much more subtle for the I>E treatment. However, if we use the detoxification activity as example, transplant from the inner to the entrance should lead to a fitness disadvantage compared to EC 'locals'. Detoxification depends on costly metabolic pathways since these require trade-offs between metabolism and other activities [START_REF] Handy | Metabolic trade-off between locomotion and detoxification for maintenance of blood chemistry and growth parameters by rainbow trout (Oncorhynchus mykiss) during chronic dietary exposure to copper[END_REF][START_REF] Pook | The metabolic and fitness costs associated with metal resistance in Nereis diversicolor[END_REF]. High detoxification activity in less polluted environments could thus impair fitness and cause a competitive disadvantage (wasted energy) even if we were not able to measure it. This may explain why glycerophospholipids, which we presume as fitness markers, are lower in the I>E treatment compared to EC for B. neritina, which would satisfy the 'foreign vs local' criterion in both directions. Many introduced species seem to be exclusive to anthropic structures and do not 'escape' to natural habitats [START_REF] Glasby | Differences between subtidal epibiota on pier pilings and rocky reefs at Marinas in Sydney, Australia[END_REF][START_REF] Marins | Non indigenous ascidians in port and natural environments in a tropical Brazilian bay[END_REF][START_REF] Simkanin | Anthropogenic structures and the infiltration of natural benthos by invasive ascidians[END_REF], which could at least partially be explained by such disadvantage.

Conclusion

The present results highlight strong small-scale spatial variation of contaminants in sediments and animal samples. The community composition did not only vary in space in accordance to pollution as it has already previously been observed (Kenworthy et al. 2018b), but it was causally linked to environmental conditions as shown by the changes in community composition after transplant.

However, this destination effect is concomitantly associated with an effect of origin location indicating that historical contingency and colonization succession may still play an equally crucial role. Our results provide strong evidence for local adaptation at a very small spatial scale (< 100 m) since community respiration, pollutant content of B. neritina and the metabolome of two species were impacted. We expose a clear disadvantage for the 'foreign' condition for individuals transplanted from the entrance to the inner, more polluted, location in community respiration, pollutant accumulation and metabolome. As the considered distances are lower than the potential dispersal distances of the studied species [START_REF] Keough | Dispersal and Population Variation in the Bryozoan Bugula Neritina[END_REF][START_REF] Svane | Spawning and Dispersal in Ciona intestinalis (L.)[END_REF][START_REF] Marshall | Variation in the dispersal potential of non-feeding invertebrate larvae: The desperate larva hypothesis and larval size[END_REF][START_REF] Shanks | Propagule Dispersal Distance and the Size and Spacing of Marine Reserves[END_REF][START_REF] Collin | Optimizing early detection of nonindigenous species : Estimating the scale of dispersal of a nascent population of the invasive tunicate Ciona intestinalis ( L .)[END_REF], the local adaptation observed here may even constitute a case of microgeographic adaptation [START_REF] Richardson | Microgeographic adaptation and the spatial scale of evolution[END_REF]). Dispersion and preferential settlement observed in fouling species [START_REF] Mckenzie | A widespread contaminant enhances invasion success of a marine invader[END_REF][START_REF] Mcdonald | Vessel generator noise as a settlement cue for marine biofouling species[END_REF]) might also accentuate local differences if preferential settlement happens according to the individual phenotype [START_REF] Bierne | Habitat preference and the marine-speciation paradox[END_REF]. While it might be possible that the studied species retained certain plastic traits from their original location, time after transplant was long enough for transplanted communities to acclimate to their new environment (one must consider that fouling communities are rather short-lived, mostly months). We consider that the observed differences are thus likely profoundly anchored in the local populations and may be regarded as local adaptation. The here observed results may thus indicate that the disturbance gradient in marinas might constitute a staple for selecting pollutant-resistant species and populations and cause local adaptation, which may explain why many NIS remain exclusive to anthropic structures and marinas. However, in natural and urbanized habitats surrounding marinas, pollution may occur in pulses due to wash off and water mixing events. Such pulse-pollution has already been shown to have drastic impacts on settled communities (Johnston andKeough 2000, 2002;Johnston et al. 2002). This highlights the importance of conducting further studies into small scale local adaptation, using reciprocal transplant and common garden experiments to determine if the selective pressure in marinas might play an important role in restricting many introduced species to marinas and similarly polluted environments; or if it confers an advantage in areas with variable pollution levels.

of invasive species success. PLoS One 6. doi:10.1371/journal.pone.0014806 1253 A. Fig. 1: Temperature curve (A) and boxplot of hydrodynamism (B) at the three locations. Red: Inner; Green: Middle; Blue: Entrance. For Temperature, the curves overlap although significant differences between pontoons were detected (LME, t = 5.33; p < 0.05). On a biological perspective, the differences were however negligible (< 0.1°C). Daily position change in g (9.81 m.s -2 ) calculated from the difference between tridimensional vectors at Tn and Tn-1, revealed a significant higher hydrodynamism at the entrance compared to the inner location (LME; t = 3.61; p < 0.001).

A. Tab. 2: Mean diversity indices (Species richness: S; Shannon index: H'; Pielou's evenness: J') and percent cover of the most abundant species for each treatment before transplant and 2 months after transplant. Treatments are labelled according to their origin followed (>) by their destination upon transplant (I: inner, M: middle, E: entrance; C: controls i.e. identical Origin and Destination). Cover can exceed 100% if species overlap (See methods).

Treat.

Mean LCMS profiles were automatically recalibrated using internal calibration in 'Bruker Compass DataAnalysis' (version 4.3) assuring a m/z precision of 2-5 ppm on the mass range. The generated netCDF data were processed with the R package 'XCMS' (version 3.12; [START_REF] Smith | XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification[END_REF]. The first step consisted in peak picking for the detection of different features, followed by a retention time correction (obiwarp method), a grouping of features in a fixed time frame (5 s) and peak filling to integrate portions where peaks were initially absent. The report and data matrix were then generated in Microsoft Excel format. Ions were normalized in Microsoft Excel following an established protocol [START_REF] Van Der Kloet | Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping[END_REF]. The normalized matrix was subjected to three consecutive filtering in-house steps on R. These filtering methods consist in filtering ions originating from the blanks based on signal/noise ratio (S/N = 10), filtering ions for which intensities were highly variable in the pooled sample (> 0.2), filtering ions that were auto-correlated (> 0.8) to discard redundancy.

  Fig.2 legend) and HAP being correlated to the axis, and Phenantrene and pesticides (group 1, see Sup. Fig.2legend) being anticorrelated. Individuals transplanted from other locations to the inner, most notably E>I, were correlated to PC1 indicating high levels of contaminants in their tissues. Individuals which had the entrance as their destination were anticorrelated with MTE, PCBs, pesticides (group 2) and HAP and correlated with pesticides (group 1) and Phenantrene. On the PC2 (21%) MTE, PCBs and Pestistices distinguish from HAP pollutants forming two large vector groups.Most individuals were however in the center of both PC, indicating intermediate levels for all contaminant groups.

Fig. 1 :

 1 Fig. 1: Boxplot of normalized concentrations of all contaminants in the bryozoan Bugula neritina according to their treatment. Treatments are labelled according to their origin followed (>) by their destination 2 months after transplant (I: inner, M: middle, E: entrance; C: controls i.e. identical Origin and Destination) and indicated according to their destination (red = inner; green = middle; blue = entrance). Significant pairwise differences (Tukey's honest significance test of a 2-way Anova) are indicated by black brackets. *: p < 0.05; **: p < 0.01; ***: p < 0.001; no bracket: non-significant. All bottom brackets are ***. (n = 5)

Fig. 2 :

 2 Fig. 2: Principal Coordinate Analysis (PCoA) biplot of the community structure before transplant (a) and 2 months after transplant (b). Treatments are labelled according to their origin followed (>) by their destination after transplant (I: inner, M: middle, E: entrance; C: controls i.e. identical Origin and Destination) and indicated in the biplot according to their destination (red = inner; green = middle; blue = entrance) and origin ( circle= inner, square = middle, diamond = entrance). Vector overlay indicates species with a positive correlation (p < 0.05; R² > 0.2) with groups. Non-Indigenous species are indicated by an asterisk. Species list: Ascidiella aspersa, Austrominius modestus*, Botryllus schlosseri, Bugula neritina*, Bugulina flabellata, Bugulina fulva, Ciona intestinalis, Cellepora pumicosa, Clavelina lepadiformis, Corella eumyota*, Cradoscrupocellaria ellisii, Cryptosula pallasiana*, Diplosoma listerianum, Electra pilosa, Tricellaria inopinata*

Fig. 3 :

 3 Fig. 3: Boxplot of the community respiration (mg O2.h -1 .g(AFDM) -1 ) for all treatments. Treatments are labelled according to their origin followed (>) by their destination after transplant (I: inner, M: middle, E: entrance; C: controls i.e. identical Origin and Destination) and indicated according to their destination (red = inner; green = middle; blue = entrance). Significant differences (Tukey HSD; p < 0.05) indicated by letter groups (n = 5).

Fig. 4 :Tab. 4 :

 44 Fig. 4: a) Partial Least Squares Discriminant Analysis (PLS-DA) of the metabolomes of Bugula neritina with b) its associated Statistical metrics for the pairwise comparisons and c) PLS-DA of the metabolome of Ciona intestinalis. Ellipses indicate confidence intervals of 0.95 for each treatment. Treatments are labelled according to their origin followed (>) by their destination after transplant (I: inner, M: middle, E: entrance; C: controls i.e. identical Origin and Destination) and indicated in the PLS-DA according to their destination (red = inner; green = middle; blue = entrance) and origin (circle= inner, square = middle, diamond = entrance). Full line arrow: pairwise MVA with p < 0.05. Dashed line arrow: non-significant. For both species one sample is missing in E>I while the treatments IC and M>I are completely absent for C. intestinalis. CER = Classification Error Rate with p-value after permanova test (permutations 10 4 ).

  selective pressure exerted by such disturbances inducing locally distinct communities as well as local adaptation. To expose the causal link between local adaptation and environmental factors, reciprocal transplant experiments represent one of the best ways, though their use remains scarce in marine ecology due to technical difficulties to run such experiments (Kawecki and Ebert 2004; Angert and Schemske 2005; Chang and Marshall 2016; Sork 2018). Based on previous results in the very same marina few years before (Kenworthy et al. 2018b), we hypothesized a disturbance (here POPs and

A. Tab. 3 :

 3 Variable Importance in Projection (VIP) scores associated to the PLS-DA of Ciona intestinalis. Molecules within a family are sorted with increasing number of carbons, number of oxygens, and number of unsaturations. Treatments are labelled in the heatmap according to their origin (top) followed by their destination (below) 2 months after transplant (I: inner, M: middle, E: entrance; C: controls i.e. identical Origin and Destination). Heatmap legend on top of heatmap.Three species occurring at all three locations in the harbor were sampled for metabolomic analyses on every settlement plate: two sufficiently large colonies of the bryozoan Bugula neritina and one individual of the two solitary ascidians Ciona intestinalis and Ascidiella aspersa were sampled. They were immediately frozen in dry ice in the marina, and then stored at -80°C at the laboratory. The samples were then all freeze-dried and mechanically ground to dust. The metabolome of the three species was extracted out of 40 mg of dry powder with 3 times 1 mL MeOH/DCM (CH2Cl2; 1:1) and ultrasonication during 2 min at room temperature. The supernatants were filtered with PTFE syringe filters (13 mm, 0.22 µm, Restek®) and transferred to vials containing silica powder (40 mg, C-18 Polygoprep® 60-50, Macherey-Nagel®). The resulting volume of 3 mL of extract was dried, first under the hood (for DCM evaporation) and secondly under vacuum (SpeedVac®, SPD111V, Thermo Scientific® Savant). The dried extract fixed on silica was fractioned by SPE on C-18 cartridges (Strata® C18-E 55 µm, 70 Å, 500 mg / 6 mL, Phenomenex®). The cartridges were flushed two times with 2 x 5 mL MeOH and 2 x 5 mL DCM, intensively dried under vacuum and conditioned with 2 x 5 mL H2O. The extracts were transferred onto the cartridges, rinsed with 3 x 5 mL H2O to remove salts and eluted with 5 mL MeOH and 5 mL DCM. The MeOH fractions were dried under vacuum, while the DCM fractions were dried under the hood, and stored in a freezer. Samples were resuspended in 500 µL MeOH for the polar fractions and in 500 µL DCM for non-polar fractions before analysis in LCMS and GCMS, respectively. For each species and fractions, a pool sample was created by mixing 30 µL of each sample. Three blank samples, that underwent the entirety of the extraction and fractioning process, were made for each species and fraction. DCM fractions were analyzed with a gas chromatograph (7890B GC System -7693 autosampler, Agilent Technologies®) coupled to a mass selective detector (5977A MSD, Agilent Technologies®). A volume of 1 µL was injected in splitless mode at 250 °C. Metabolites were separated on a HP-5MS 5% Phenyl-Methyl Siloxane column (30 m × 0.25 mm, 0.25 µm, Agilent Technologies®) using helium as carrier gas. The temperature was fixed at 40°C for 5 min and progressively raised from 40 °C to 300 °C (10 °C.min¯¹). The fixed flow rate was set to 1 mL.min -1 all along the run. A solution of C8-C20 and C21-C40 alkanes (Fluka Analytical) was injected for the determination of compound retention indexes.GC-MSAgilent data was exported into CDF format using MSD Chemstation (F.01.001903, Agilent Technologies®) and processed in R using the 'eRah' package (version 1.1.0; Domingo-Almenara et al. 2016) following these different steps : preprocessing, peak deconvolution [min. peak width = 5, min. peak height = 500, noise threshold = 100, avoid processing m/z = c (73:75,147:149,207:208)], peak alignment (min. spectra correlation = 0.90, max. time distance = 20, m/z range = 40:500) and missing compound recovery (compound detected in > 2 samples). CGMS compound annotation was performed by comparison of mass spectra with those of the NIST 2014 database and Kovàts' index (van Den Dool and Dec. Kratz, 1963).The injections in LCMS were performed with an UHPLC instrument (Dionex Ultimate 3000 equipped with RS Pump, autosampler, thermostated column and UV diode array, Thermo Scientific®) coupled to a Quadrupole Time of Flight spectrometer (QqToF) equipped with an ESI source (Impact II, Bruker Daltonics®). UHPLC separation was conducted on an Acclaim TM RSLC 120 C18 column (150 mm x 2.1 mm, 2.2 µm, ThermoScientific®) at 40 °C with an elution rate of 0.5 mL.min -1 . We used water (LC/MS grade, Carlo Erba®) with 0.1% formic acid (A) and acetonitrile (LC/MS grade, Carlo Erba®) with 0.1% formic acid (B) as chromatographic solvents. The chromatography cycled through 1) a mix of 5:95 of A:B during 2 min, 2) a linear increase up to 100% B during 8 min followed by 100% B during 4 min, 3) then a return to initial conditions (5:95, A:B) for 3 min for a total runtime of 17 min per sample. An injection of 2 µL in LCMS 1+ and LCMS 2+ (positive mode) and of 0.2 µL in LCMS 1-and LCMS 2-(negative mode) was performed for B. neritina. For the other species, the injection volume was of 1 µL in LCMS 1+ and LCMS 2+ and of 0.2 µL in LCMS 1-and LCMS 2-. LCMS parameters were set as follows: nebulizer gas, N2 at 3.5 bars; dry gas, N2 at 12 L.min -1 , capillary temperature at 200°C and voltage at 3500 V. A full scan from 50 to 1200 mass units at 2 Hz was performed for both modes. The spectrometer was calibrated with a formate:acetate solution in basic media (Bruker) and before each sample for mass calibration. The Pooled sample of each species was injected at regular intervals (every 9 samples) to correct for possible mass spectrometer drift during the sequence. Blank samples were injected to filter for ions linked to the solvent and the extraction process.

  Team 2020) followed by a Dunn test from the 'FSA' package (version 0.8.32; Ogle et al. 2021), as conditions for the application of parametric tests were not always satisfied.

	Reference values by the Canadian sediment quality guidelines (CCME 1999), by the US Environmental
	Protection Agency (US EPA 2005), and by the French national monitoring network of water and
	sediment qualities in maritime harbors (REPOM 2013) were added. For MTEs, an additional sediment
	quality category (SQC) by Guerra-García et al. (2021) was reported. In order to integrate all
	contaminants into one metric, normalized contaminant concentrations in B. neritina were used:
	𝐶 𝑖(𝑛𝑜𝑟𝑚) =	(𝐶 𝑖 -𝐶 𝑚𝑖𝑛 ) ⁄ (𝐶 𝑚𝑎𝑥 -𝐶 𝑚𝑖𝑛 )
	where: Ci(norm) is the i th normalized value; Ci is the i th value; Cmin and Cmax are the minimum and maximum
	values of contaminant concentrations. The normalized data was plotted in a boxplot for each
	transplant and control treatment with 'ggplot2' in R (version 3.3.3; Wickham 2016). A two-level ANOVA
	was used to test for the effect of Origin and Destination on the normalized concentration of all
	contaminants in B. neritina, as well as for the effect of their interaction. A Tukey's honest significance
	test (Tukey HSD; Tukey 1949) was conducted to test for differences of the normalized concentration
	between treatments. To visualize the importance of individual contaminants in B. neritina samples, we
	additionally conducted a Principal Component Analysis (PCA; 45 variables) with auto-scaled variables
	using the R package 'FactoMineR' (version 1.34; Lê et al. 2008). PCAs are a highly powerful technique
	for analyzing multinormal quantitative 'environmental' data (here contaminants in tissue samples;
	Borcard et al. 2018)

  Mean values (mg.kg -1 = ppm) with standard deviation of the Metallic Trace Element (MTE) concentrations in the sediments of the three locations (n = 5) sampled at the end of the experiment and in the bryozoan Bugula neritina (control individuals; n = 5). Stars indicate significant differences between locations (Kruskal-Wallis test; *: p < 0.05; **: p < 0.01). Sediment quality guidelines provided by the Canadian Council of Ministers of the Environment (CCME 1999; ISQG: Interim Sediment Quality Guideline; PEL: Probable Effect Level); the United Stated Environmental Protection Agency (US EPA 2005, Tab. 11; T20: probability of 20% of toxicity among samples; T50: probability of 50% of toxicity among samples); the Réseau de surveillance des ports maritimes (REPOM 2013, Tab. 4; N1 level of contamination requiring further assessment; N2 level of contamination with probable negative impact); and Sediment Quality category (SQC) by Guerra-García et al. (2021), are indicated. Bold font for reference values: threshold exceeded at least at one location. Mean values (µg.kg -1 = ppb) with standard deviation of Polycyclic Aromatic Hydrocarbons (PAH) and Total 352 Polychlorinated Biphenyl (tPCB) concentrations in the sediments (n = 3) and in the bryozoan Bugula neritina 353 control individuals (n = 5) of the three locations. Stars indicate significant differences between locations (Kruskal-354 Wallis test; *: p < 0.05). Sediment quality guidelines provided by the Canadian Council of Ministers of the

	Tab. 2: 355
	356	Environment (CCME 1999; ISQG: Interim Sediment Quality Guideline; PEL: Probable Effect Level); the United
	357	Stated Environmental Protection Agency (US EPA 2005, Tab. 11; T20: probability of 20% of toxicity among
	358	samples; T50: probability of 50% of toxicity among samples); and the Réseau de surveillance des ports maritimes
	(REPOM 2013, Tab. 5 and 6; N1 level of contamination requiring further assessment; N2 level of contamination Metallic Trace Elements Location (mean ± SD) CCME with probable negative impact) are indicated. Bold font for reference values: threshold exceeded at least at one K-W test Inner Middle Entrance ISQG PEL T20 US EPA T50 location. (mg.kg¯¹) 27 Al Sediment 29207 ± 3825 26647 ± 9596 23125 ± 12905 ns B. neritina 416 ± 142 246 ± 85 123 ± 36 ** Location mean ± SD K-W CCME US EPA REPOM PAH (µg.kg¯¹) 359 360 361 test Inner Middle Entrance ISQG PEL T20 T50 N1 N2	REPOM N1 N2	342 SQC 343
	75 As 59 Co Acenaphthene 52 Cr Acenaphthylene 63 Cu Anthracene 56 Fe Benzo[a]anthracene 55 Benzo[a]pyrene Mn 60 Benzo[b]fluoranthene Ni 208 Pb Benzo[g,h,i]perylene 47 Ti Benzo[k]fluoranthene 51 V Chrysene 66 Zn Dibenzo[a,h]anthracene Fluoranthene	Sediment B. neritina Sediment Sediment B. neritina 1.97 ± 1.2 14.4 ± 3.2 3.46 ± 0.9 5.78 ± 0.6 39 ± 8.5 34.1 ± 2 2.09 ± 0.1 0.88 ± 0.1 11.3 ± 2.9 3.51 ± 0.8 5.5 ± 1.2 33.6 ± 1 B. neritina 0.2 ± 0.1 0.18 ± 0.1 Sediment 57.4 ± 7.3 50.7 ± 11 Sediment 50.6 ± 17 65.7 ± 8.3 60.6 ± 22 B. neritina 2.81 ± 0.7 2.26 ± 0.1 1.21 ± 0.2 B. neritina 0.96 ± 0.1 0.64 ± 0.2 Sediment 83.9 ± 14 45 ± 10 Sediment 58.6 ± 7.9 52.1 ± 12 44.6 ± 14 B. neritina 5.3 ± 0.2 5 ± 0.1 1.87 ± 0.1 B. neritina 3.08 ± 3.5 1.1 ± 1.2 Sediment 13569 ± 1427 12639 ± 2980 Sediment 379 ± 23 343 ± 15 354 ± 1.7 B. neritina 8.43 ± 0.5 2.46 ± 0.1 6.1 ± 0.8 B. neritina 521 ± 105 408 ± 138 Sediment 150 ± 22 154 ± 29 Sediment 324 ± 69 425 ± 109 407 ± 43 B. neritina 9.93 ± 0.7 8.9 ± 0.3 7.4 ± 0.8 B. neritina 22.6 ± 5.1 20.2 ± 10 Sediment 18.1 ± 1.8 16.6 ± 3.8 Sediment 700 ± 67 651 ± 141 880 ± 232 B. neritina 23.5 ± 2.3 26.8 ± 3.8 21.4 ± 1.2 B. neritina 0.72 ± 0.2 0.54 ± 0.2 Sediment 85 ± 12 48.2 ± 14 B. neritina 5.3 ± 2.3 2.71 ± 0.5 Sediment 407 ± 79 212 ± 51 283 ± 7.9 B. neritina 10 ± 0.3 7.95 ± 0.3 10 ± 0.1 Sediment 2283 ± 291 2285 ± 506 B. neritina 6.15 ± 4 3.44 ± 1.5 Sediment 328 ± 72 413 ± 48 537 ± 78 B. neritina 26.5 ± 4.1 13.4 ± 0.2 13.1 ± 0.2 Sediment 89.8 ± 11 75.6 ± 19 B. neritina 2.84 ± 1.4 2.53 ± 0.7 Sediment 943 ± 185 1051 ± 61 1192 ± 173 B. neritina 21.5 ± 1.3 24.8 ± 2 15.7 ± 1 Sediment 236 ± 89 178 ± 77 B. neritina 58.5 ± 16 53.5 ± 13 Sediment 83.6 ± 19 69.9 ± 20 76.9 ± 19 B. neritina 4.73 ± 0.5 3.94 ± 0.1 2.85 ± 0.3 Sediment 1532 ± 430 1049 ± 71 1829 ± 268 B. neritina 43.4 ± 1.6 33.9 ± 2.6 15.5 ± 0.1	ns ns ns ns ns ns ns ns ns ns ns ns * ns ns ns ns ns ns ns ns ns	8.77 ± 5.1 2.56 ± 0.1 4.72 ± 2.7 6.71 88.9 19 120 ns ns ns 0.07 ± 0.03 * 43.6 ± 25 ns 5.87 128 14 140 0.29 ± 0.1 ** 32.8 ± 19 ** 46.9 245 34 290 4.61 ± 5.7 ns 10643 ± 6037 74.8 693 61 470 260 7.24 41.6 7.4 15 52.3 160 49 40 18.7 108 32 85 ns 172 ± 56 * 134 ± 74 ns 88.8 763 69 520 430 8.1 ± 3.3 * 14.3 ± 8.2 ns 15 130 1110 400 0.27 ± 0.1 ** 45.8 ± 25 * 30.2 112 30 1.56 ± 0.4 67 500 1700 ** 1923 ± 1094 ns 1.53 ± 0.7 70 540 200 * 64.6 ± 37 ns 1.23 ± 0.3 108 846 82 650 380 * 111 ± 60 * 124 271 94 31.9 ± 4.2 6.22 135 19 110 60 * 113 1494 120 1030 600	344 345 346 347 348 349 350 351 Good Good Good 45 90 Moderate 25 50 90 180 37 74 Good 100 200 Moderate 240 276 552 20 140 94 47 94 Good
		Fluorene	Sediment B. neritina 3.42 ± 0.1 725 ± 19	567 ± 43 2.58 ± 0.2 1.28 ± 0.1 642 ± 56	* ns	21.2 144 19 110	20
	Indeno[1,2,3-cd]pyrene	Sediment B. neritina 14 ± 1.4 701 ± 106	713 ± 302 843 ± 313 11.6 ± 0.4 12.5 ± 0.9	ns ns	68 490 1700
		Naphthalene	Sediment B. neritina 17 ± 2.7 232 ± 32		239 ± 37 12.5 ± 0.3 7.23 ± 0.3 344 ± 103	ns ns	34.6 391 30 220 160
		Phenanthrene	Sediment B. neritina 12.6 ± 0.5 1629 ± 363 1154 ± 66 1996 ± 239 44.9 ± 3.9 26.3 ± 3	ns ns	86.7 544 68 460 240
		Pyrene	Sediment B. neritina 10.8 ± 0.7 545 ± 7.8	510 ± 23 9.58 ± 0.6 5.66 ± 0.6 556 ± 17	ns ns	153 1398 120 930 500
	PCB (µg.kg¯¹)	Inner	Location mean ± SD Middle Entrance	K-W test	CCME ISQG PEL T20 T50 N1 US EPA	REPOM N2
		tPCB	Sediment B. neritina 360 ± 13 603 ± 5,9	506 ± 12 216 ± 10	544 ± 2,5 155 ± 7.1	* 21.5 189 35 370 500 1000
	362

Only Cu, Pb and Zn showed significantly different concentrations between locations (Kruskal-Wallis tests; χ² > 8.07; p < 0.05; Tab. 1), with Cu and Zn significantly higher at the inner location (Dunn Test; Z > 2.83; p.adj < 0.05). When applicable, most MTE concentrations are barely above the Canadian sediment quality guideline (CCME) and correspond to moderate to good Sediment Quality Categories for Cu and Pb according to

[START_REF] Guerra-García | Assessing environmental pollution levels in marinas[END_REF] Tab. 1)

. For Cu this constitutes an increase compared to 2011 and 2012, when concentrations were below REPOM category N1 (45 mg.kg¯¹; REPOM 2013), while they were above in the present study.

Seven pesticides, two PAHs, as well as total PCB (tPCB), also showed significant differences in concentration in sediments between locations (Kruskal-Wallis test; χ² > 6.47; p < 0.05; Sup. Tab. 1; Tab.

2). All tested PAHs exceed CCME guidelines and US-EPA concentrations for which 20% of sediments may be toxic to model amphipods

(Tab. 2;[START_REF] Ccme | Protocol for the derivation of canadian sediment quality guidelines for the protection of aquatic life[END_REF] US EPA 2005)

. REPOM classification for PAHs was constant between 2012 and 2019 (REPOM 2013), and a slight improvement of the contamination in Benzo[a]pyrene decreasing below N1 can be noted. The PAHs chrysene, fluoranthene, fluorene and phenanthrene largely exceeded values at which adverse effects on fauna may be encountered

(Tab. 3;[START_REF] Ccme | Protocol for the derivation of canadian sediment quality guidelines for the protection of aquatic life[END_REF] US EPA 2005)

. Total PCBs (tPCB) and two pesticides (Lindane and pp'-DDD) were also more concentrated than recommended (Sup. Tab. 1; Tab. 2; CCME 1999). Tab. 1: * For control (i. e. not transplanted) individuals of B. neritina sampled at the end of the experiment, all

  2) are encrusting species including NIS like Cryptosulla pallasiana (Moll, 1803) and Watersipora subatra. The second axis (22.7% of variability) reflects an origin effect (all panels originating from the entrance are on the top side). E>M treatment were very distinct from all other treatments in relation with C. intestinalis and Diplosoma listerianum (Milne Edwards, 1841).

  1 Contamination in sediments and animals While no official common European guidelines for contamination in marine sediments do exist, we could compare our results with two North American and two European guidelines. The harbor According to the Sediment Quality Category (SQC) established from southern Spanish harbors (Guerra-García et al. 2021), MTEs appear at good or moderate (Zn and Cu) levels. This is in agreement with the French national monitoring network REPOM, for which the studied marina was sampled in 2011 and

	2012 (and had levels below the lowest concentration category N1 for most contaminants; REPOM
	2013). However, our present values and those recorded in 2016 by Kenworthy et al. (2018b), indicate
	increasing Cu concentrations, exceeding the REPOM N1 (45 mg.kg -1 ) category at various locations.
	sediments largely exceeded the various thresholds for some PAHs (chrysene, fluorene, fluoranthene
	and phenanthrene) as well as for total PCBs (33 congeners). This may constitute an important stress
	for local species, as the encountered concentrations would exceed a 50% probability of toxicity on

model amphipods exposed to the sediment (US EPA 2005). MTEs pollution presents higher global values than recommended by CCME and US EPA guidelines, especially in the inner parts (Tab. 1).

Acknowledgements

We would like to warmly thank Ports de Plaisance Brest and the Bureau du port de la Marina du Château for providing access to the marina pontoons for experimentations. Special thanks to Aurélie Dufour (MIO -UM 110) for participating in the analysis of MTE contaminants in sediment samples. Special thanks also to Centre de Ressources Biologiques Marines (CRBM) and the Roscoff Aquarium Services (RAS), specifically Gaëtan Schires and Sébastien Henry for providing access to aquaria facilities and caring for the aquaria during species identification. Metabolomics analyses were conducted at Chemical Ecology and Metabolomics facilities at IMBE (funded by ANR, French National Research Agency, INEE the Institute of Ecology and Environment of the CNRS, the Total Foundation and the French Sud PACA regional council). We also want to acknowledge Jean-Charles Martin (C2VN BIOMET platform, Marseille) for creating in house metabolome filtering scripts for R. Funding for this project has been provided through a PhD grant from the Sorbonne Université -Museum National d'Histoire Naturelle (Ecole Doctorale 227). Financial support was also provided by the program 'InPor' of the INEE-CNRS' PEPS EcoMob (PIs: C. Lejeusne and D. Davoult). The authors declare that no known

competing financial interests or personal relationships that could have appeared to influence the work reported here exist. All applicable ethical guidelines were followed for our experimentations.

Supplementary material

Pollution gradient leads to local adaptation and small-scale spatial variability of communities and functions in an urban marine environment

Robin P. M. Gauff