Andreas Herzig
email: herzig@irit.fr

Ivan Varzinczak

Cohesion, coupling and the meta-theory of actions

In this work we recast some design principles commonly used in software engineering and adapt them to the design and analysis of domain descriptions in reasoning about actions. We show how the informal requirements of cohesion and coupling can be turned into consistency tests of several different arrangements of modules. This gives us new criteria for domain description evaluation and clarifies the link between software and knowledge engineering in what concerns the meta-theory of actions.

Introduction

Among the principles of the object-oriented paradigm are the following:

1. Work with modules (or components, functions, etc.).

Minimize interactions between such modules.

3. Organize the modules into well-defined layers to help minimize interactions. The goal is to have components of one layer using only components from immediate neighbors, wherever possible.

4. Anticipate what kind of extensions or modifications might be made in the future, and support this at design time so that one can extend the system with minimal disruption later.

There seems to be an agreement that such principles for object-oriented programming or design are the same as for knowledge representation. To witness, the design of domain descriptions in reasoning about actions has much more in common with software engineering than one might think: in the same way as for software projects, one can talk about consistency, evolution and correctness of domain descriptions.

All the principles above can be applied to the design of domain descriptions, too. We argue that a good domain description should be one whose consistency check and maintenance complexities are minimized, so that any further modification is localized, with a bounded scope.

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Supported by a fellowship from the government of the FEDER-ATIVE REPUBLIC OF BRAZIL. Grant: CAPES BEX 1389/01-7.

With this in mind, one can see the specification of domain descriptions as a task similar to project development in software engineering: Item 4 above is what has been called elaboration tolerance [McCarthy, 1988]. In this way a representation is elaboration tolerant to the extent that the effort required to add new information (a new action or effect) to the representation is proportional to the complexity of that information [Shanahan, 1997]. Items 1, 2 and 3 reflect the concept of modularity, which means that different modules have no elements in common. Such a notion of modularity is going to lead us along the present work. This paper is an elaboration of the results we have presented in [Herzig and Varzinczak, 2004]. Here we pursue the following plan: in Section 2 we recall some important concepts from software engineering; after discussing the ontology of dynamic domains (Section 3) we apply the concepts of Section 2 to the design of domain descriptions (Sections 4 and 5) making a step towards formal criteria for domain description evaluation. In Section 6 we present the main results that follow from our approach, and before concluding we address related work found in the literature on this subject.

Some principles of software engineering

One of the first steps in software development is that of abstraction. Abstraction consists mainly in rendering lowerlevel details invisible to upper levels in order to facilitate the understanding and design of complex systems. As an example, a specification of a data or knowledge base query does not need to take into account the algorithmic process that will be carried out in order to answer the query.

In parallel to abstraction, one of the most important guidelines in project design is that of modularity: dividing the software into modules, based on their functionality or on the similarity of the information they handle. This means that instead of having a "jack of all trades" program, it is preferable to split it up into specialized subprograms. For instance, a program made of a module for querying a database and a module for checking its integrity is more modular than a single module that does these two tasks at the same time.

Among the major benefits of modular systems are reusability, scalability and better management of complexity.

There is more than one way to split up a program. One of the most used techniques is that of forcing functional in-dependence of its modules. One ensures functional independence in a project by defining modules with only one purpose and "aversion" to excessive interaction with other modules [Pressman, 1992].

Among the criteria commonly used for evaluating functional independence of modules (and thus how modular a piece of software is) are the informal notions of cohesion and coupling.

Cohesion is how closely related pieces of a single component are to each other. A module is cohesive when at the high level of abstraction it does only a single task. The more a module is focused on a precise goal the more it is cohesive.

A highly cohesive module will be simpler to understand, having to do only a single task, while a lowly cohesive module, performing so many tasks, will be difficult to understand.

It is difficult to reuse a task-overloaded module, while a highly cohesive module is simpler to reuse and to extend.

Coupling is the interdependency between a method and the environment (other methods, objects and classes). Low coupling means to keep dependencies (communication, information sharing) between components at a minimum.

A design that has low coupling is more amenable to change, since it reduces the probability of changes cascading and affecting a larger part of the system.

Unanimously in object-oriented development, the best way to design a software is to have low coupling and high cohesion. We sum this up in two informal design principles: P1. Maximal cohesion: Every module should be conceived in such a way that it is maximally cohesive.

P2. Minimal coupling: All modules should be conceived in such a way that they minimize coupling.

Natural modules in domain descriptions

Like in object-oriented programming, in describing a domain different entities should be separated in different modules. Moreover, each module should be conceived in such a way that it has no direct access to the contents of the others. In reasoning about actions, accessing a module means using it to perform reasoning tasks like prediction, postdiction, planning and others. This amounts to using its logical formulas in inferences. In this section we establish the ontology of domain descriptions and present the way we arrange in different modules the axioms commonly used to describe them.

Every domain description contains a representation of action effects. We call effect laws formulas relating an action to its effects. Statements of conditions under which an action cannot be executed are called inexecutability laws. Executability laws in turn stipulate the context where an action is guaranteed to be executable. Finally, state constraints are formulas that do not mention actions and express constraints that must hold in every possible state. These are our four ingredients that we introduce more formally in the sequel.

If we think of a domain description as a software application, we can imagine its organization in an object-oriented view and attempt to have a kind of class diagram for it. This is illustrated by Figure 1, where we can see the relationship among the different types of entities.

A domain description consists of a description of effects of actions, their non-effects, executabilities, inexecutabilities and also state constraints that do not depend on any particular action.

domain description Among the effects of actions, we can distinguish direct effects and indirect effects (ramifications).

¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¥ ¥ ¥ ¥ ¥ ¦ ¦ ¦ ¦ ¦ § § § § § § § § § §
Non-effects of actions are related with the frame problem [START_REF] Mccarthy | [END_REF], and indirect effects with the ramification problem [Finger, 1987]. In this work we abstract from these problems and assume we have a consequence relation powerful enough to derive the intended conclusions. We suppose given a 'doped' consequence relation © , which encapsulates some traditional approach in the literature (e.g., [Schubert, 1990;Lin, 1995;[START_REF] Mccain | [END_REF]), with which all intended frame axioms and indirect effects can be derived, and we use it henceforth. As examples we have loaded © loaded do wait ! (i.e., waiting does not change the status of loaded) and

" walking # %$ & walking ' )( alive & 0 alive do shoot 1 ! 2 © 0 walking do shoot 1# %$ 3
Hence shooting has the indirect effect that the victim will no longer be walking. We use small letters to denote variables, and capital letters to denote constant symbols. Free variables are supposed to be universally quantified.

To sum it up, our main concern here will be with direct effects (henceforth effects), inexecutabilities, executabilities and state constraints. We introduce this in what follows.

Effect laws Logical frameworks for reasoning about actions contain expressions linking actions and their effects. We suppose that such effects might be conditional, and thus get a third component of such axioms. An effect law for action 4 is of the form Poss 54 6 1 )( 7 98 @ A( CB D do 54 6 ! 3 where 8 @ is a simple state formula about situation , and B D do 54 6 1 ! is a simple state formula about situation do 54 6 1 .

A simple state formula about a situation term E contains no Poss-predicate and no situation terms other than E [Lin, 1995].

An example of effect law is Poss shoot )( 7 loaded A( 0 alive do shoot 1 3 ! F saying that whenever shoot is executable and the gun is loaded then after shooting the turkey is dead. Another one is Poss tease 1 G( walking do tease 3 : the result of teasing is that the turkey starts walking.

Inexecutability laws

The design of domain descriptions must also provide a way to express qualifications of actions, i.e., conditions under which an action cannot be executed at all. An inexecutability law for action 4 is of the form 8 @ ' H( 0 Poss 54 6 1 where 8 @ ' is a simple state formula about .

For example, 0 HasGun I( 0 Poss shoot states that shoot cannot be executed if the agent has no gun.

State constraints (alias domain constraints)

Frameworks allowing for indirect effects make use of formulas that link invariant propositions about the world. Such formulas characterize the set of possible states. A state constraint is a simple state formula about the situation term that is consistent. An example is walking )( alive ' , saying that if a turkey is walking, then it must be alive [Thielscher, 1995].

Executability laws With only state constraints and effect laws one cannot guarantee that action shoot is executable if the agent has a gun. An executability law for action 4 is of the form 8 @ ' P( Poss 54 6 , where 8 @ is a simple state formula about . For instance HasGun @( Poss shoot 1 says that shooting can be executed whenever the agent has a gun, and Poss tease 1 that the turkey can always be teased.

Whereas all the extant approaches in the literature that allow for indirect effects of actions contain state constraints and effect laws, the status of executability laws is less consensual: some authors [Schubert, 1990;Doherty et al., 1996;[START_REF] Mccain | [END_REF]Thielscher, 1995] more or less tacitly consider that executability laws should not be made explicit but rather inferred by the reasoning mechanism. Others [Lin, 1995;[START_REF] Zhang | [END_REF] have executability laws as first class objects one can reason about.

We nevertheless would like to point out that maximizing executability, as usually done in the literature, is not always intuitive: suppose we know that if we have the ignition key, the tank is full, Q RQ RQ , and the battery tension is beyond 10V, then the car (necessarily) will start. Suppose we also know that if the tension is below 8V, then the car will not start. What should we conclude in situations where we know that the tension is 9V? Maximizing executabilities makes us infer that it will start, but such reasoning is not what we want if we would like to be sure that all possible executions lead to the goal.

It seems a matter of debate whether one can always do without executabilities. We think that in several domains one wants to explicitly state under which conditions a given action is guaranteed to be executable, such as that a robot should never get stuck and should always be able to execute a move action. In any case, allowing for executability laws gives us more flexibility and expressive power.

Domain descriptions

Given the four types of entities defined above, we arrange them in the following way: for a given action 4 , S UT IV is the set of its effect laws, W &X `Y ba `V is the set of its inexecutability laws, and S ca dY eV is the set of its executability laws. f %g h ig denotes the set of all state constraints of a given domain. Thus, S cT V , S Ua dY V and W RX `Y ba V , for each action 4 , and f %g h ig are the natural modules we consider here in designing a domain description.

For parsimony's sake, we define S cT qp qr sS cT V , W &X tY ba up r vW &X `Y ba wV , and S ca dY Dp xr vS ca dY yV . We suppose all these sets are consistent.

A domain description is a tuple of the form S cT !W RX `Y ba !S ca dY 6 f dg h ig .

Once the information contained in a module is not mixed with others', it can be expected that undesirable side effects due to further modifications are less likely to propagate to other parts of the domain description. The same thing can be obtained for the consistency check if beyond of being separated the modules are designed in such a way that their interaction is minimized. This is what we address in this section.

As we have seen, in software engineering functional independence is evaluated by means of two criteria: cohesion, a criterion for evaluating the relative functional strength of a module, and coupling, an assessment of relative interdependence among different modules. Both these notions are quite informal, even in software engineering, and cannot be measured in an objective way.

Here we explore these concepts when applied to domain descriptions and show how the informal requirements of software engineering can be turned into tests of consistency of several different arrangements of modules.

Cohesion

Normally cohesion comes with modularization, and its evaluation depends mainly on the entities that one takes into account when describing a domain.

In talking about sets of logical formulas we take cohesion as how simple or well-defined a logical module is, considering the different types of formulas that can be derived from it. We thus refine our first design principle: P1'. The less types of laws a given module entails alone, the more cohesive it is. As an example consider the following module:

0 HasGun ' )( 0 Poss shoot 1 F HasGun ' A( Poss shoot 1
From such a set alone one can derive both 0 HasGun ' D( 0 Poss shoot 1 and HasGun A( Poss shoot 1 , which are formulas of two different kinds. In this case we say that such a set is a lowly cohesive module, for alone it functions to derive executabilities and inexecutabilities. A better approach would be to decompose such a module into the following ones:

W &X tY a shoot p 0 HasGun A( 0 Poss shoot S ca dY shoot p

HasGun ' H( Poss shoot 1 Total cohesion is not always easy to achieve. Suppose, for instance, a hypothetical situation in which we reason about the effects of drinking a cup of coffee:

S UT drink p

Poss drink 1 )( sugar )( happy do drink 3 F Poss drink 1 )( salt )( 0 happy do drink 1 3

Then, S cT drink entails sugar ' F salt 3 H( 0 Poss drink 1 . This means that from S cT drink alone we do not get only effect laws but also inexecutability laws. Therefore S cT drink is not as cohesive as one might have expected.

One step towards augmenting cohesion of a module of effect laws can be by completely specifying the preconditions of effects of actions. For example, the weaker effect laws S cT drink p Poss drink )( sugar d 0 salt ' ! A( happy do drink ! F Poss drink )( salt ' % 0 sugar ' ! A( 0 happy do drink 3 guarantee a higher cohesion of module S cT drink in comparison to that of S UT drink .

By the definition of f dg h eg , it is easy to see that from the state constraints we can derive formulas of any type, so f %g h ig is by nature a lowly cohesive module.

We are thus interested in refining even more our principle of high cohesion P1' by the following ones: P1'-1. If W &X tY ba d© e8 @ , then f g© e8 @ . P1'-2. If W &X `Y ba d© e8 @ ' x( Poss 54 6 1 , then f g© e8 @ x( Poss 54 6 . P1'-3. If S ca dY © e8 @ , then f I© e8 @ ' . P1'-4. If S ca dY h© e8 @ v( 0 Poss 4 6 1 , then f g© e8 @ i( 0 Poss 4 j . P1'-5. If S Ua dY h© Poss 4 j c( k 98 @ c( lB D do 54 6 1 3 ! , then f g© Poss 4 6 1 A( 7 98 @ A( CB D do 54 6 ! 3 . P1'-6. If S cT m© e8 @ , then f g© e8 @ . P1'-7. If S cT m© e8 @ n( Poss 54 6 1 , then f I© e8 @ ' o( Poss 54 6 . P1'-8. If S UT p© e8 @ ' x( 0 Poss 54 6 1 , then f g© e8 @ x( 0 Poss 4 j .

All these principles say is that a formula of a given type entailed by a module of a different type must be a theorem of the logic.

Coupling

As we have seen, coupling evaluates how much a module is tied to or dependent upon other modules. We take as coupling of two or more sets of different types of action laws how much interaction among them is needed to derive a formula of a given type. Interaction here means sharing logical formulas. Now we refine our second design principle: P2'. The less new consequences two or several modules have, the less coupled they are.

(The new consequences of modules q sr and q ut are those consequences of q r wv q t that are not consequences neither of q pr nor of q ut alone.) For instance, consider the domain description wr :

S cT r p

Poss tease 1 )( walking do tease Observe that to derive the domain constraint walking ' }( 0 dead one only needs f %g h ig r , i.e., no other module is required for that. On the other hand, to conclude dead }( 0 Poss tease one needs both f %g h ig ~r and W RX `Y ba tr . Totally decoupled descriptions are not common in applications of real interest. For the example above, it seems to be impossible to diminish the interaction between f dg h eg ~r and W &X tY ba r without abandoning the concept of state constraints.

On the other hand, if S Ua dY yr in our example contained Poss tease , things would be different: in this case, with W &X tY ba `r one would be able to infer the state constraint alive , but such a law cannot be derived from f %g h ig {r alone. A higher degree of interaction between this set and the others is necessary in order to do that. In such a case one would say that there is a high coupling among r 's modules.

The principle of minimal coupling P2' can be refined in two more specific design principles: P2'-1. No implicit inexecutability laws:

if © 8 @ ' H( 0 Poss 54 6 1 , then

W &X tY ba f dg h ig © e8 @ ' )( 0 Poss 54 6 1 P2'-2.
No implicit state constraints: if © 8 @ ' , then f %g h ig gp z8 @ ' .

P2'-2 is a useful feature of descriptions: beyond being a reasonable principle of design that helps avoiding mistakes, it clearly restricts the search space, and thus makes reasoning easier. To witness, if satisfies P2'-2, then its consistency amounts to that of f %g h ig : Theorem 5.1 If has no implicit state constraints, then n© e iff f dg h ig gp x gQ

No implicit inexecutability laws

Consider the following domain description t :

S cT t Up

Poss tease 1 )( walking do tease 3 F Poss shoot )( loaded A( 0 alive do shoot 3 3 W RX `Y ba wt Up S Ua dY bt cp f i )f %g h ig t cp walking ' )( alive F From Poss tease H( walking do tease 1 ! it follows with f dg h ig t that Poss tease H( alive do tease 1 3 , i.e., in every situation, after teasing the turkey is alive:

S cT t { f dg h eg t © Poss tease 1 )( alive do tease 1 3
Now as Pt © 0 alive ( 0 alive do tease 1 ! , the status of fluent alive is not modified by the tease action, and we have S UT t Ff %g h ig t © x Poss 5E 4 y b 1 d 0 alive ' 3 )( alive do 5E 4 y b 1 ! & 0 alive do E 4 i 3 3 . From this it follows Pt © 0 alive )( 0 Poss tease , i.e., the turkey cannot be teased if it is dead. But W RX `Y ba t f dg h ig t ¨© 0 alive H( 0 Poss tease , hence Principle P2'-1 is violated. The formula 0 alive ' H( 0 Poss tease 1 is an example of what we call an implicit inexecutability law.

In the literature, such laws are also known as implicit qualifications [START_REF] Ginsberg | [END_REF], and it has been argued that it is a positive feature of reasoning about actions frameworks to leave them implicit and provide mechanisms for inferring them [Lin, 1995;Thielscher, 1995]. The other way round, one might argue as well that implicit qualifications indicate that the domain has not been described in an adequate manner: inexecutability laws have a form simpler than that of effect laws, and it might be reasonably expected that it is easier to exhaustively describe them. (Note that nevertheless this is not related to the qualification problem, which basically says that it is difficult to state all the executability laws of a domain.) Thus, all the inexecutabilities should be explicitly stated, and this is what Principle P2'-1 says.

No implicit state constraints

Executability laws increase expressive power, but might conflict with inexecutability laws. For instance, let be such that S UT p S UT t , W &X tY a p 0 alive )( 0 Poss tease F ,

S ca dY p

Poss tease 1 , and f %g h ig p f dg h ig t . (Note that Principle P2'-1 is satisfied.) We have the unintuitive W &X tY ba d ~ 1S Ua dY b @© alive : the turkey is immortal! This is an implicit state constraint because alive does not follow from f dg h ig alone: P2'-2 is violated.

The existence of implicit state constraints may thus indicate too strong executability laws: in our example, one wrongly assumed that tease is always executable. It may also indicate that the inexecutability laws are too strong, or that the state constraints are too weak.

Results for a dependence based solution to the frame problem

Given an axiomatic theory of actions with a solution to the frame and the ramification problems, we are interested in knowing whether domain descriptions encoded in it satisfy or not our set of design principles. Here we chose to use the modal framework of G [START_REF] Castilho | [END_REF], which has been shown to support Reiter's solution to the frame problem [Demolombe et al., 2003] and also proposes an assessment of the ramification problem. Let ' { 6 3 be a translation of a domain description in H into the Situation Calculus. Dependences 4 are translated into predicates 9¡ A 54 6 1 , meaning that action 4 may cause literal to be true. The extension of dep is then circumscribed (cf. Schubert's explanation closure assumption).

As examples, ~ ¢¡ A shoot 0 walking means that shoot may cause walking to be false, and the absence of 9¡ A tease alive induces the frame axiom 0 alive A( 0 alive do tease 1 3 .

Theorem 6.1 If P£ e¤ `¥ w¦ is a domain description in H , then { § 6 3 ¨ © 5 P£ e¤ `¥ ¦ satisfies Principles P1'-1-P1'-7.

Even in G , however, it is possible to derive inexecutabilities from S UT (see the example in Section 4), which violates Principle P1'-8. Establishing maximal cohesion of S cT in this case involves weakening of preconditions of action effects. Anyway, conceiving an algorithm to accomplish this task is not difficult (due to space limitations we omit its presentation here).

Checking whether a domain description satisfies Principle P2'-2 can be made with little adaptation of the material on the subject present in the literature [START_REF] Zhang | [END_REF][START_REF][END_REF]Herzig and Varzinczak, 2004]. We do not deepen into further details here, and just present the main results that we obtain when considering descriptions that satisfy the design principles that have been proposed (due to space limits no proof is given). Theorem 6.2 Let be the translation into Situation Calculus of a domain description in G . If has no implicit state constraints, then n© Poss 54 6 1 ( '8 @ ' ( B D do 4 j 3 3 iff S cT V i !W &X tY ba wV b f dg h ig © Poss 54 6 1 )( 7 98 @ A( CB D do 4 j 3 3 . This means that under P2'-2 one has modularity inside S UT , too: when deducing the effects of action 4 we need not consider the action laws for the other actions. Versions for executability and inexecutability can be stated as well.

Theorem 6.3 There exist descriptions not satisfying P2'-2 such that © Poss 4 j ( '8 @ ( B D do 4 j 3 3 and S cT V i !W &X tY ba wV b f dg h ig ¨© Poss 54 6 1 )( 7 98 @ A( CB D do 4 j 3 3 . For example, just take as before:

© Poss shoot ( ª 0 alive ' g( alive do shoot 1 ! 3 , however S cT shoot !W &X tY ba w shoot Ff %g h ig ¨© Poss shoot 1 «( 0 alive A( alive do shoot ! 3 .

7 Related work [START_REF] Pirri | [END_REF] have investigated the metatheory of the Situation Calculus. In a spirit similar to ours, they use executability laws and effect laws. Contrarily to us, their executability laws are equivalences and are thus at the same time inexecutability laws. There are no state constraints, i.e., f dg h ig ¬p f . For this setting they give a syntactical condition on effect laws forcing them not to interact with executability laws, which precludes implicit state constraints. Basically, the condition says that when there are effect laws Poss 54 6 1 u( 98 @ u( 8 @ do 54 6 ! 3 and Poss 4 j u( '8 ' }( ®8 do 4 6 1 3 ! , then 8 @ and 8 are inconsistent (which essentially amounts to having in their domain descriptions a kind of "implicit state constraint schema" of the form 0 98 @ w 8 3 ).

This then allows them to show that such descriptions are always consistent. Moreover they thus simplify the entailment problem for this calculus, and show for several problems such as consistency or regression that only some of the modules of a domain description are necessary. Amir [2000] focuses on design and maintenance of action descriptions applying concepts of the object-oriented paradigm in the Situation Calculus. In that work, guidelines for a partitioned representation of a given description are presented, with which the inference task can also be optimized, as it is restricted to the part of the domain description that is really relevant to a given query. This is observed specially when different agents are involved: the design of an agent's description can be done with no regard to others', and after the integration of multiple agents, queries about an agent's beliefs do not take into account the belief state of other agents.

In that work, executabilities are as in [START_REF] Pirri | [END_REF] and the same condition on effect laws is assumed, which syntactically avoids implicit state constraints.

Despite of using many of the object-oriented paradigm tools and techniques, no mention is made to the concepts of cohesion and coupling. In the approach presented in [Amir, 2000], even if modules are highly cohesive, they are not lowly coupled, due to the dependence between objects in the reasoning process defined there. We do not investigate this further here, but conjecture that this could be done there by, during the reasoning process, avoiding passing to a module a formula of a type different from those it contains.

The present work generalizes and extends Pirri and Reiter's result to the case where f %g h ig ¯p zf and both [START_REF] Pirri | [END_REF]Amir, 2000] where the syntactical restriction on effect laws is not made. This gives us more expressive power, as we can reason about inexecutabilities, and a better modularity in the sense that we do not combine formulas that are conceptually different (viz. executabilities and inexecutabilities).

Conclusion

We have established a link between knowledge engineering and software engineering showing that many of the concepts and techniques developed for the latter are useful in the design and maintenance of domain descriptions. In particular, with the concepts of cohesion and coupling we get better criteria for domain description evaluation.

Our central hypothesis is that the different types of axioms should be neatly separated and only interfere in one sense: state constraints together with action laws may have consequences that do not follow from the action laws alone. The other way round, action laws should not allow to infer new state constraints, effect laws should not allow to infer inexecutability laws, etc.

At first glance, because of f dg h ig 's interaction with other modules, it could be said that domain descriptions described in our way do not completely minimize coupling. However, given the intrinsic nature of f %g h ig , observe that we cannot do otherwise: in the same way it is not possible to write completely decoupled methods (or the program will not work!), we cannot have totally decoupled domain descriptions (unless, of course, we constrain ourselves to domains without ramifications like in [START_REF] Pirri | [END_REF]).

It could be argued that unintuitive consequences in domain descriptions are mainly due to badly written axioms and not to the lack of modularity. True enough, but what we have presented here is the case that making a domain description modular gives us a tool to detect at least some of such problems and correct it. (But note that we do not claim to correct badly written axioms automatically and once for all). Besides this, having separate entities in the ontology and controlling their interaction help us to localize where the problems are, which can be crucial for real world applications.

  Poss tease 1S Ua dY yr xp zf f dg h ig {r xp walking )( alive ' & dead ' )| 0 alive


Kahina Djaoudi
email: kdjaoudi@email.arizona.edu

Javier Angel Tesan

Amine Boukra

Lucas Guesnay

Aurélie Portas

Raphaëlle Barry-Martinet

Bernard Angeletti

Stéphane Mounier

Véronique Lenoble

Jean-François Briand
email: briand@univ-tln.fr

Seawater copper content controls biofilm bioaccumulation and microbial community on microplastics

Keywords: Microplastics, bioconcentration, copper, microbial community composition, biofilm. 1

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

Introduction

Over the last few decades, plastic debris of the millimetre size range (<5 mm), identified as microplastics (MPs) [START_REF] Derraik | The pollution of the marine environment by plastic debris: a review[END_REF][START_REF] Andrady | Microplastics in the marine environment[END_REF], Law, 2017), have become of an increasing concern due to their prevalence within the water column (114 studies performed in the last 20 years; [START_REF] Aretoulaki | Α systematic meta-review analysis of review papers in the marine plastic pollution literature[END_REF], impact on ecosystems [START_REF] Huang | Microplastics and associated contaminants in the aquatic environment: A review on their ecotoxicological effects, trophic transfer, and potential impacts to human health[END_REF]Khalid et al., 2020) and their potential to sorb chemical contaminants [START_REF] Holmes | Adsorption of trace metals to plastic resin pellets in the marine environment[END_REF][START_REF] Turner | Adsorption of trace metals by microplastic pellets in fresh water[END_REF][START_REF] Brennecke | Microplastics as vector for heavy metal contamination from the marine environment[END_REF][START_REF] Acosta-Coley | Trace elements in microplastics in Cartagena: a hotspot for plastic pollution at the Caribbean[END_REF]. Hydrophobic organic pollutants have been highlighted as being incorporated into MPs and/or adsorbed onto their surface [START_REF] Fossi | Are baleen whales exposed to the threat of microplastics? A case study of the Mediterranean fin whale (Balaenoptera physalus)[END_REF][START_REF] Antunes | Resin pellets from beaches of the Portuguese coast and adsorbed persistent organic pollutants[END_REF]. In contrast, sorption of trace metals (TMs) on MPs as well as their incorporation into these artificial particles have been shown as limited [START_REF] Klöckner | The diverse metal composition of plastic items and its implications. Science of The Total Environment[END_REF][START_REF] Zou | Adsorption of three bivalent metals by four chemical distinct microplastics[END_REF].

Nevertheless, these reported studies on TMs sorption/incorporation were performed on pristine MPs, therefore on polymer matrices devoid of any biological colonization. However, MPs are clearly suitable for biofilm formation as they were shown to host a diverse microbial community, including heterotrophs, autotrophs, predators and symbionts, referred to as 'a plastisphere' [START_REF] Zettler | Life in the "plastisphere": microbial communities on plastic marine debris[END_REF][START_REF] Amaral-Zettler | Ecology of the plastisphere[END_REF]. Because biofilms are typically built up by a complex matrix of extracellular polymeric compounds, it is very likely that its formation on MPs could increase and modify the exchange surface of these synthetic particles within the water column, which would potentially influence the interaction with TMs.

Therefore, TMs load on MPs should be looked at whilst considering the role of biofilm in this accumulation. So far, to the best of our knowledge, only few studies involving MPs, biofilm and metals were conducted, thus revealing a higher metal sorption when comparing MPs as pristine versus colonized by biofilm [START_REF] Guan | Microplastics as an emerging anthropogenic vector of trace metals in freshwater: Significance of biofilms and comparison with natural substrates[END_REF]. Recently, metal accumulation has been shown to correlate with the amount of biofilm [START_REF] Richard | Biofilm facilitates metal accumulation onto microplastics in estuarine waters[END_REF].

Although evidence emerged supporting the role of biofilm in enhancing the adsorption and stabilization of trace metal on MPs [START_REF] Wang | Biofilm alters tetracycline and copper adsorption behaviors onto polyethylene microplastics[END_REF], the magnitude of their bioconcentration on MPs as well as the feedback influence on its microbial community composition is still poorly known.

Toulon bay is a coastal marine ecosystem in the NW Mediterranean Sea (South of France), representative of an area coping with anthropogenic metallic pollutants mainly attributed to an important anthropization and extensive harbours activities. Concentrations of TMs in Toulon-bay have been reported as being of the same order of magnitude or even higher than strongly impacted aquatic ecosystems [START_REF] Pringault | Interactions between Zn and bacteria in marine tropical coastal sediments[END_REF][START_REF] Misson | Chemical multi-contamination drives benthic prokaryotic diversity in the anthropized Toulon Bay[END_REF], with a decreasing gradient from the most anthropized parts to the open waters of the Mediterranean Sea. Along this gradient, bacterioplankton composition is shaped, including shifting the autotrophic community from picoeukaryote dominated community in the most contaminated parts to cyanobacteria (more representative of the open ocean) in less impacted areas [START_REF] Coclet | Trace metal contamination as a toxic and structuring factor impacting ultraphytoplankton communities in a multicontaminated Mediterranean coastal area[END_REF][START_REF] Coclet | Changes in bacterioplankton communities resulting from direct and indirect interactions with trace metal gradients in an urbanized marine coastal area[END_REF][START_REF] Coclet | Impacts of copper and lead exposure on prokaryotic communities from contaminated contrasted coastal seawaters: the influence of previous metal exposure[END_REF]. Alongside TMs pollution, an important amount of MPs can be found in the surface water of Toulon bay, with an average of 32 800 MPs/km-2 (Tesan- [START_REF] Onrubia | Quantification of Microplastics in North-Western Mediterranean Harbors: Seasonality and Biofilm-Related Metallic Contaminants[END_REF]2021). Potential bioaccumulation of TMs on MPs may also influence microbial biofilm diversity. Recently, biofilm formation on plastics panels did not appear to be mainly driven by seawater TMs content [START_REF] Coclet | Trace metal contamination impacts predicted functions more than structure of marine prokaryotic biofilm communities in an anthropized coastal area[END_REF], Catao et al., 2021a), despite strong evidence of the influence of Cu on other biofilm communities (Catao et al., 2019(Catao et al., , 2021b)). However, in the Toulon-bay as well as in other coastal areas, a better understanding of the interactions of MPs-TMs is critical as MPs are most likely to be ingested by marine organisms and potentially transferred to high trophic levels though the trophic network (Andray, 2011).

In this study, incubation experiments with natural seawater were conducted to investigate the relationship between an increasing range of seawater Cu content and biofilm formation MPs, its composition including both prokaryotes and eukaryotes, and its role in Cu bioaccumulation.

Methods

Seawater sampling and bioassay experiments

In this study, two sets of incubation experiments were carried out with surface seawater sampled into 10-L LDPE carboys at two locations in the bay of Toulon,Pt12 (43.11 °N;5.93°E) and Pt41p (43.26°N; 6.58°E) (Fig. 1). Sampled seawater was then filtered in situ, through 90 µm mesh plankton net and homogeneously distributed once in the laboratory, into 1 L ultraclean PFA bottles, under a laminar flow hood. Experimental sampling and incubation bottles were previously washed three times with Milli-Q water (18.2 MΩ, Millipore), then with 10% (v/v) nitric acid (HNO3, Analytical Grade, Fisher), rinsed three times with Milli-Q water then filled with Milli-Q water acidified with 0.1% HNO3 Trace Metal Grade (Fluka). For all experiments, PFA bottles were chosen so as to prevent any metallic contamination.

The sampling locations were chosen based on the long-time monitoring of trace metals performed in the area, highlighting contrasted Cu concentrations in the considered locations [START_REF] Tessier | Study of the spatial and historical distribution of sediment inorganic contamination in the Toulon bay (France)[END_REF], Layglon et al., 2020;[START_REF] Coclet | Trace metal contamination as a toxic and structuring factor impacting ultraphytoplankton communities in a multicontaminated Mediterranean coastal area[END_REF][START_REF] Coclet | Changes in bacterioplankton communities resulting from direct and indirect interactions with trace metal gradients in an urbanized marine coastal area[END_REF][START_REF] Coclet | Impacts of copper and lead exposure on prokaryotic communities from contaminated contrasted coastal seawaters: the influence of previous metal exposure[END_REF]. Pt12 sampling station is located in the enclosed part of the bay of Toulon, near the main harbour, therefore submitted to important metallic inputs from the anthropogenic activities (Fig. 1). In contrast, Pt41p sampling station is located in the large bay of Toulon, with trace metal concentrations similar to the encountered values in the Mediterranean Sea. In both sampling stations, subsamples were taken for in situ measurements of Cu and exhibited concentration of 30 nM for Pt12 and 6 nM and Pt41p. The MPs pellets used in this study were obtained by flash freezing of polymeric polyethylene microbeads (CRT102,50, Carat) in liquid nitrogen and their subsequent grinding, using an ultra-centrifugal mill (ZM200, Retsch). Obtained grinded pellets were further sieved from 500 to 100 µm and were thoroughly rinsed with Milli-Q water. MPs were then dried (1 h, 60°C) and pellets within a size range between 200 and 300 µm were further used.

The two sets of experiments were run in triplicates (1-L each).

Seawater from Pt12 or Pt41p was incubated with a single density of MPs (10 mg L -1 ) and an increasing gradient of Cu concentrations. Two ranges of Cu concentrations were applied whether the seawater was sampled in Pt12 or Pt41p as the initial natural Cu concentrations differed: 30 and 6 nM in Pt12 and Pt41p, respectively. Thereby, Cu was added to reach final concentrations of 60, 100, 200 and 400 nM in the seawater sampled at Pt12 (hereafter, small bay experiment) and of 20, 30 and 60 nM in the seawater sampled at Pt41p (hereafter, large bay experiment). For each sampling station, a triplicate without any Cu addition (reflecting in situ concentration), was run in parallel. Likewise, two bottles of 1-L of sterile (filtered at 0.2 µm, precleaned acetate filters) seawater amended with 10 mg L -1 MPs and a Cu concentration of 200 nM and 60 nM for seawater of Pt12 and Pt41p, respectively, were also run as controls. All experimental bottles were gently mixed, using PFA stirring bars and were incubated over 13 days at 18°C, in a phytotron (Mlr 351 Sanyo) with a day night cycle of 12:12 h.

Subsampling over the incubation period

Incubated seawater was subsampled at T0 (1 h after MPs and Cu amendment), an intermediate time (T7 or 8), and at Tfinal (after 13 days of incubation period) for bacterioplankton cell abundance and only at Tfinal for Cu. Subsamples for Cu (10 mL) were filtered through 0.2 µm precleaned acetate syringe filters (HCl, trace metal grade), acidified with 0.5% HCl (trace metal grade) and stored at 4°C until analysis. Subsamples for heterotrophic and autotrophic cell abundance determination were fixed with 0.5% glutaraldehyde (final concentration), incubated in the dark for 15 min at ambient temperature and then transferred into -80°C freezer, until analysis. Unfiltered seawater (10 mL) was subsampled at the same time scale for both heterotrophic and autotrophic bacterioplankton cell abundance. These samples were fixed with 0.5% glutaraldehyde (final concentration), incubated in the dark for 15 min at ambient temperature and then transferred into -80°C freezer, until analysis.

At the end of the incubation period, for all experiments, each incubation bottle was filtered through 0.2 µm polycarbonate filters (HCl washed, trace metal grade) to recover MPs. Half of the recovered MPs was used for biofilm diversity determination, through DNA extraction while the other half was suspended in 10 mL NaOH (0.1 M) and mixed up for 24 h, for biofilm extraction. A few randomly chosen pellets were also fixed for biofilm visualization using confocal laser scanning microscopy (CLSM).

DNA extraction, amplification, sequencing, and data processing

DNA from MPs was extracted using the DNeasy PowerBiofilm Kit (Qiagen, Courtaboeuf, France) according to the manufacturer's instructions.

For all samples, the V4-V5 region of the 16S rDNA gene was targeted and amplified using 515F-Y/926R primers [START_REF] Parada | Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples[END_REF] for prokaryotes identification whereas the 18S rDNA gene was targeted and amplified using V7 primers [START_REF] Debroas | Overview of freshwater microbial eukaryotes diversity: a first analysis of publicly available metabarcoding data[END_REF] for eukaryotes identification. Amplicons were sent to Eurofins (Konstanz, Germany) for Illumina MiSeq 2 × 300 pb paired-end sequencing.

Sequences were analysed following the FROGS process [START_REF] Escudié | FROGS: find, rapidly, OTUs with galaxy solution[END_REF] in a Galaxy environment following current guidelines. Clustering relies on Swarm which does not agglomerate sequences based on the typical 97% threshold but relies instead on both the number of differences and the likely series of accumulation of those differences [START_REF] Mahé | Swarm: robust and fast clustering method for amplicon-based studies[END_REF]. Chimera detection relies on VSEARCH with de novo UCHIME method [START_REF] Edgar | UCHIME improves sensitivity and speed of chimera detection[END_REF][START_REF] Rognes | VSEARCH: a versatile open source tool for metagenomics[END_REF]. The innovative crosssample validation step from FROGS was used to confirm the chimeric status on all samples. Rare OTUs representing less than 0.005% of all sequences were removed. Affiliation by blastn+ was performed using the SILVA v132 database for prokaryotes and the Protist Ribosomal 2 (PR2) database [START_REF] Guillou | The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small subunit rRNA sequences with curated taxonomy[END_REF] for eukaryotes. The datasets for this study can be found in NCBI SRA database, Bioproject PRJNA735079.

Confocal laser scanning microscopy (CLSM) and Scanning

Electron microscopy (SEM)

MPs were washed in artificial seawater and subsequently fixed with 3.8% formaldehyde solution. To visualize cells, staining was performed with 250µl of Syto 9 or Syto 61 (1µM-ThermoFisher) in 24-well plates for 30 min, in the dark and at room temperature. In addition, exopolymeric substances (EPS) characterizing biofilm development were also visualized using FITC (proteins), concanavalin A (ConA, sugar) and calcofluor white (CFW, sugar).

After 2 washes in phosphate buffered saline solution, microplastics were mounted on microscope slices with one drop of Prolong Diamond antifade (ThermoFisher) and incubated for 24 hours at room temperature. Confocal images were performed with 20X/0.75NA and 63xoil/1.4NA objectives, on a Zeiss Confocal LSM 510 Meta (excitation: 488 nm -emission: BP 505-530 nm).

Copper extraction and analysis

NaOH mixtures were filtered (0.2 µm) and the retained MPs were dried and then weighed. A volume of 5 mL of the filtered NaOH mixture was taken and completed to 10 mL with filtered Milli-Q water (18.2 MΩ, Millipore). Then, the solution (10 mL) was UV-irradiated (Metrohm) for 24 h in presence of H2O2, to ensure a complete digestion of the extracted biofilm.

The solution was finally analysed for Cu concentration.

Cu analyses were performed by cell-inductively coupled plasma mass spectrometry (ICP-MS; Perkin Elmer NexIon 300X), in a direct injection mode. The international geostandards SLRS-3 (Riverine water reference material) was used to check the validity and reproducibility of the analysis.

To avoid interference with seawater salt, incubation bottle samples were diluted 25 times in nitric acid, while extracted biofilm samples were diluted only 5 times. With obtained Cu concentrations in the incubation bottle (T=13 days) and in the extracted biofilm, the partition coefficient (Kd) was determined as follow:

Kd (L/Kg) = ([Cu] Biofilm ⁄MPs mass) ⁄ [Cu] final
Where Cu biofilm and Cu final are Cu concentrations in the extracted biofilm solution and in incubated water after 13 days of incubation time, respectively. MPs mass is the dry weight of MPs.

Flow cytometry analyses

Cell abundance counting was performed using a FACSCalibur (BD Biosciences) flow cytometer (PRECYM flow cytometry platform, http://precym.mio.univ-amu.fr/). Instrument specific beads were used to calibrate the cytometer. For heterotrophic bacteria, frozen samples were thawed at room temperature and subsamples were stained with 1:10 diluted SYBR Green II (2 µL, Molecular Probes) and incubated for 15 min in the dark, in order to stain nucleic acids. Each cell was then characterized by 3 optical parameters: light diffusion side scatter (SSC); green fluorescence (515-545 nm; SYBERgreen), and red fluorescence (670 LP; chlorophyll-a).

Group-specific phytoplankton were distinguished based on plots of forward scatter vs. orange fluorescence (phycoerythrin containing Synechococcus like), and forward scatter vs. red fluorescence (eukaryotes). Size classes of eukaryotic phytoplankton were further distinguished based on forward scatter (pico, nano and large eukaryotes). Flow cytometry data were acquired and counted with the Cell Quest Pro software.

Statistical analysis

Cu concentrations in the incubation bottles as well as in the extracted biofilm, Kd and alpha diversity indexes were compared using a one-way analysis of variance (ANOVA) and Tuckey posthoc tests (p-value > 5%).

Alpha diversity indexes (Chao 1, Shannon), clustering using Weighted Unifrac distances (Prokaryotes) and Unweighted Unifrac distances (Eukaryotes) followed by Principal Coordinates Analysis (PCoA) and PERMANOVA were performed using R Cran version (3.5.0.4) with "phyloseq", "vegan" and "ggplot" program packages. The approach taking only into account the presence or absence of eukaryotic taxa, in addition to phylogenetic distances, was considered appropriate to account for possible biases associated with the limited quantification capacity of eukaryotic metabarcoding [START_REF] Kelly | Phylogenetic trees do not reliably predict feature diversity[END_REF]. SIMPER analyses were performed to precise the contributors to the dissimilarity between pairs of samples (PRIMER ® 6).

Interactions among prokaryotic and eukaryotic OTUs were identified using CoNeT (Co-occurrence Network) plugin 1.1.1.beta [START_REF] Faust | Microbial co-occurrence relationships in the human microbiome[END_REF]. Data were processed using Pearson and Spearman correlation, with Bray-Curtis and Kullback-Leiber dissimilarity distances and using a threshold of 150 and 400 edges for Pt12 and Pt41p, respectively. Edge specific p-values were computed according to Brown's method, in the permutation step. In the final step, the Benjamini-Hochberg multiple testing correction was applied (p < 0.05).

Results

Influence of Cu seawater content on pico-and nanoplankton cell abundance within the incubation experiments

One hour after Cu amendments (T0), picophytoplankton composition in seawater from Pt12 (Fig. S1-a) was dominated by picoeukaryotes (between 6000 ± 427 and 11000 ± 364 cells mL -1 ), followed by

Prochlorococcus-like (between 508 ± 12 and 1630 ± 158 cells mL -1 ) and -like (between 45 ± 4 and 603 ± 40), in all considered treatments. Following the increasing gradient of Cu (from 30 to 400 nM), the initial cell abundance of each group significantly decreased (ANOVA; In both investigated sites, principal component analysis (PCA) on cell abundance of the different identified groups showed that picoplankton composition in mesocosms was more influenced by the incubation experiment period rather than Cu seawater content (Fig. S2).

Synechococcus

Biofilms visualization on MPs

The visualization of microbial cells using CLSM (Fig. 2, Fig. S3)

showed that microbial colonization settled on MPs in incubation experiments conducted at both Pt12 and Pt41p whereas MPs incubated with 0.2 µm sterile filtered seawater (control treatments) did not show any microbial colonization. In addition, using specific markers, exopolysaccharide (EPS) were also detected within the biofilm formed on MPs, thus showing enriched regions in proteins (FITC) or sugar (concanavalin A and calcofluor white) (Fig. 2).

Effect of an increasing gradient of Cu on biofilm and its bioaccumulation on MPs

In the water from Pt12 (small bay of Toulon), following an increasing gradient of Cu concentrations (between 30 and 400 nM, Fig. S4), the amount of bioaccumulated Cu per dry weight of MPs exhibited a significant increase (ANOVA; F=23.3; df=4; p<0.01), with values ranging between 18±7 and 119±30 nmol/g MPs (Fig. 3). Conversely, although not significantly different due to the variability within triplicates (ANOVA;

F=3.1, df=4, p=0.07), Kd seemed to exhibit a decrease of its average value, from 970 ± 437 to 380 ± 83 L kg -1 for treatments with 30 and 400 nM of Cu, respectively. In the sterile filtered treatment, both bioaccumulated Cu (ANOVA; F=31.9; df=4; p<0.01) and Kd (ANOVA; F=7.7; df=4; p<0.01)

were low (Fig. 3).

In the water from Pt41p (large bay of Toulon), the amount of bioaccumulated Cu per dry weight MPs was similar between treatments (ANOVA; F=0. 8; df=4; p=0.53), ranging between 0.52±0.06 nmol/g MPs and 1.06±.06 nmol/g MPs (Fig. 3). Similarly to the previous case, Kd exhibited an decrease with the increasing gradient of Cu (ANOVA; F=5.9; df=4; p=0.02), ranging between 12 ± 10 Kg L -1 and 66 ± 15 Kg L -1 .

Alpha diversity of microbial biofilm on MPs

The Illumina Miseq run produced 1984472 and 2340978 paired-end reads, leading to 1543558 and 2018175 merged sequences for 16SrDNA and 18SrDNA gene sequencing respectively. 1030 and 512 OTUs could be generated after clustering for prokaryotes and eukaryotes respectively.

Archaea were excluded from the prokaryotic set of reads (<0.5%). Some samples could not be amplified due to low DNA extracts (Pt41p_C2r1 and r3, C3r1 and r2, C4r3 for 16S rDNA and Pt41p_C5r3 for 18S rDNA).

Sequence normalization by a random resampling to the sample with the fewest sequences was performed (n=13345 and 24308 for prokaryotes and eukaryotes respectively), leading to the exclusion of few samples (Pt12_C5r3 for 16S rDNA and Pt41p_C1r2 and C3r2 for 18S rDNA).

All alpha diversity indexes (Fig. 4) displayed significant differences among biofilms developed on MPs, within seawater from the two sites (Pt12 and Pt41p) and for both prokaryotes and eukaryotes. Richness and diversity were always much higher at Pt12 (small bay) than at Pt41p (large bay).

Considering the influence of Cu seawater content on prokaryotes, only the highest concentration (400 nM) decreased the Chao 1 at Pt12 and both Chao 1 and Shannon index at Pt41p, when compared to natural seawater.

No significant difference could be noticed for eukaryotes regardless of the index.

Beta-diversity and composition of microbial biofilms on MPs

For both prokaryotic and eukaryotic communities, the first clustering factor was definitely the site (Fig. 5). In addition, in each incubation experiment, a clear shift of natural communities was observed on the y-axis following the increasing gradient of Cu, except for eukaryotes from Pt41p for which no dissimilarity could be noticed (Fig. 5; panel B). S1).

Taxa the most involved in the dissimilarity between mesocosms filled with natural seawater at Pt41p (D1) and with 60 nM of copper (D4) were four OTUs affiliated to Flavobacteriaceae. Aureicoccus and Winogradskyella showed higher relative abundance in D1 whereas Pseudofulvibacter and

Croceibacter in D4 (Table S1). In addition, an unknown Rhodobacteraceae and two Sphongibacteraceae, Spongibacter and Erythrobacter, highly contributed to the dissimilarity between D1 and D4 and were more abundant in D4 too. Biofilm eukaryotic communities were mostly composed of microeukaryotes (Fig. 6B andS6). Oithona, a planktonic copepod (crustaceans), ubiquitous and abundant in oceans, which was probably entrapped in biofilms when filtered, was the only multicellular organism appeared to be also sensitive to copper, with percent decreasing from 30 and 16, respectively, to 0% at the highest copper concentration at Pt41p.

Finally, Fungi accounted for less than 2% (except one sample at Pt41p), without clear variation depending on the site or copper concentration, as well as Ciliophora (from 0 to 11%).

SIMPER analyses showed that the eukaryotic taxa was the most involved in the dissimilarity between mesocosms with C1 and C4, where OTUs mostly affiliated to Bacillariophyta but with different taxa abundances in the two conditions (Table S1,7). C4 was used rather than C5 because the latter appeared less representative of natural ecosystems. Two OTUs of Nitzschia, one Amphiprora and an additional but unaffiliated Raphidpennate contributed to the dissimilarity between conditions and were abundant in C4 whereas Pseudo-Nitzschia and an additional but unaffiliated Networks allowing to identify the co-occurrence among prokaryotic and eukaryotic taxa on MPs were constructed using CoNet for each of the two experiments (Fig. 8, Table S2). For Pt12, the main cluster of the network displayed 79 nodes (OTUs) associated with 95 degrees or edges.

Ten small clusters with only two to three nodes completed the network.

Most of the edges were positive (62%). The ten nodes with the highest

4.

Discussion

In this study, for the sake of viability of encountered microbial communities, Cu was added in a range between 30 and 400 nM and between 6 and 60 nM when seawater was sampled from Pt12 (small bay of Toulon) and from Pt41p (large bay of Toulon), respectively, as Cu concentrations were shown to exhibit a decreasing gradient from the most enclosed part of the bay to the open waters [START_REF] Coclet | Trace metal contamination as a toxic and structuring factor impacting ultraphytoplankton communities in a multicontaminated Mediterranean coastal area[END_REF][START_REF] Coclet | Trace metal contamination impacts predicted functions more than structure of marine prokaryotic biofilm communities in an anthropized coastal area[END_REF][START_REF] Layglon | Longterm monitoring emphasizes impacts of the dredging on dissolved Cu and Pb contamination along with ultraplankton distribution and structure in Toulon Bay (NW Mediterranean Sea, France)[END_REF]. In agreement with in-situ observations [START_REF] Coclet | Trace metal contamination as a toxic and structuring factor impacting ultraphytoplankton communities in a multicontaminated Mediterranean coastal area[END_REF][START_REF] Coclet | Changes in bacterioplankton communities resulting from direct and indirect interactions with trace metal gradients in an urbanized marine coastal area[END_REF][START_REF] Delpy | Pico-and nanophytoplankton dynamics in two coupled but contrasting coastal bays in the NW Mediterranean Sea (France)[END_REF]

Copper bioaccumulation on MPs is due to biofilm

In this work, abiotic alterations of MPs in link with light exposure and mixing (see method section) may have promoted adsorption of Cu onto their surface at the time scale of the incubation period. Yet, the low observed values in 0.2 µm sterile filtered seawater treatments in both incubation experiments highly suggested the role of biofilm as a striking link within MPs-Cu interplay. Trace metals (TMs) bioaccumulation onto MPs through biofilm formation is still poorly resolved, especially in seawater. As a matter of fact, so far, most studies which reported MPs-TMs interaction in marine and/or freshwater systems focused on pristine microplastics, devoid of any biological colonization, but also on the influence of aging, resulting from physical alterations and/or photooxidation, on these pellets in promoting TMs adsorption [START_REF] Turner | Adsorption of trace metals by microplastic pellets in fresh water[END_REF][START_REF] Brennecke | Microplastics as vector for heavy metal contamination from the marine environment[END_REF][START_REF] Mao | Aging mechanism of microplastics with UV irradiation and its effects on the adsorption of heavy metals[END_REF]. Nevertheless, there is increasing evidence that investigating the interaction between TMs and pristine MPs can be in fact misleading.

Recently, reported studies have shown that biofilm developments on these synthetic particles eased metals accumulation, including Cu [START_REF] Richard | Biofilm facilitates metal accumulation onto microplastics in estuarine waters[END_REF][START_REF] Guan | Microplastics as an emerging anthropogenic vector of trace metals in freshwater: Significance of biofilms and comparison with natural substrates[END_REF], Wang et al., 2020).

In this study, biofilm formation around MPs was proven in both studied sites. In addition, dissimilar alpha and beta diversity was observed in biofilms depending on the sites, but also Cu seawater content. This differences in biofilm formations between sites were reflected in Cu bioaccumulation, thus exhibiting quantitatively higher values in seawater from Pt12 (up to 119±30 nmol/g MPs) than Pt41p (up to 47±32 nmol/g MP),

with a clearer increasing bioaccumulation of Cu following seawater Cu contents in Pt12. Higher alpha-diversity combined to higher DNA extracts (data not shown) suggested that greater biofilm biomass occurred on MPs at Pt12, in agreement with higher Cu bioaccumulation. Biofilm accumulation of metals have been essentially highlighted in freshwater systems [START_REF] Ancion | Three common metal contaminants of urban runoff (Zn, Cu & Pb) accumulate in freshwater biofilm and modify embedded bacterial communities[END_REF][START_REF] Tien | Patterns of metal accumulation by natural river biofilms during their growth and seasonal succession[END_REF], with retention time up to 14 days before a subsequent release into the surrounding waters [START_REF] Ancion | Three common metal contaminants of urban runoff (Zn, Cu & Pb) accumulate in freshwater biofilm and modify embedded bacterial communities[END_REF]. Within the biofilm, the sorptive properties of EPS have been shown to effectively bind large amounts of metals (Decho, 2000 and references therein). In this study, EPS among others, may have promoted Cu bioaccumulation. In fact, in the water, Cu speciation is dominated by organic complexes (Wong et al., 2018 and reference herein), especially in areas where dissolved organic matter (DOM) is found at high concentrations. TMs cations complexation by organic ligand or organic matter occurred through several complexation acidic sites [START_REF] Luo | Sequestering uranium and technetium through co-precipitation with aluminum in a contaminated acidic environment[END_REF], defined by their concentration and complexing constant. Such parameters are closely related to the surrounding DOM. For example, the complexing affinities towards Cu of a natural DOM considered, from a river end-member to seawater end-member, was revealed to change according to the location along an estuary: from a concentrated one-type of complexing site with a modelled concentration and a high constant to a 2-types of complexing sites with lower concentrations but higher constants [START_REF] Louis | Characterisation and modelling of marine dissolved organic matter interactions with major and trace cations[END_REF]. Similarly, organic ligands produced in the biofilm matrix, EPS for example, will differ from one incubation experiment to another, as richness and diversity indices differ among biofilms. The concentrations and complexing constants of each produced biofilm could not be determined, the dedicated experiments needing an important quantity of organic material [START_REF] Louis | Characterisation and modelling of marine dissolved organic matter interactions with major and trace cations[END_REF], which is impossible when dealing with the very thin biofilm layer around MPs. Yet, it is well known that a biofilm will produce organic ligands whose nature and quantity depends on the biofilm composition [START_REF] Fang | Biosorption mechanisms of Cu (II) by extracellular polymeric substances from Bacillus subtilis[END_REF][START_REF] Mahapatra | Application of bacterial extracellular polymeric substances for detoxification of heavy metals from contaminated environment: A mini-review[END_REF]. It was already demonstrated that the biofilm cultivated on polyethylene molecularly differs from a biofilm developing on cobblestones, with a weaker interaction between EPS and Cu in the polyethylene biofilm [START_REF] Wang | Biofilm alters tetracycline and copper adsorption behaviors onto polyethylene microplastics[END_REF]. It is therefore obvious that the biofilm developing in each of the tested conditions (and especially seawater) of this study will have its own composition and hence organic ligands properties. This is reflected in copper accumulation.

Biofilm community structure is primarily controlled by the sampling site

Sampling site clearly appeared as the primary driver for biofilm community shaping. Even though Cu seawater contents were significantly different at the two sites, Cu alone may not explain differences in biofilm microbial community. In fact, a recently conducted study at these two sites, as well as at three additional ones in the Toulon bay, has concluded that the Cu gradient failed to explain the clustering of prokaryotic community structure of mature biofilms [START_REF] Coclet | Trace metal contamination impacts predicted functions more than structure of marine prokaryotic biofilm communities in an anthropized coastal area[END_REF]. In this same study, interestingly, most of the reported environmental biofilm community drivers, such as temperature , pH, dissolved oxygen, and nutrients , were measured and did not show any significant difference among sites, suggesting that additional parameters like DOM, hydrodynamics or topdown control, among others, could have played a role in shaping biofilm microbial community (Catao et al., 2019;[START_REF] Mazoyer | Impact of wind-driven circulation on contaminant dispersion in a semi-enclosed bay[END_REF][START_REF] Ferguson | The ecological impacts of multiple environmental stressors on coastal biofilm bacteria[END_REF]. In other studies, performed on biofilms formed on large artificial polymer surfaces, environmental parameters also clearly drive biofilm communities which was reported in the litterature too (e.g. Oberbeckman et al., 2016;2018;[START_REF] Lawes | Elevated nutrients change bacterial community composition and connectivity: high throughput sequencing of young marine biofilms[END_REF][START_REF] Briand | Spatiotemporal variations of marine biofilm communities colonizing artificial substrata including antifouling coatings in contrasted French coastal environments[END_REF]Catao et al., 2021).

When samples within the plastisphere failed to be related to environmental factors characterizing the sampling site (e.g. [START_REF] Bryant | Diversity and activity of communities inhabiting plastic debris in the North Pacific Gyre[END_REF][START_REF] Dussud | Evidence of niche partitioning among bacteria living on plastics, organic particles and surrounding seawaters[END_REF], the buoyant character of plastic promoting the influence of multiple environmental conditions may provide an explanation.

In addition, while supplying seawater from Pt41p and Pt12 with the same concentration of Cu (60 nM), biofilm communities on MPs did not converge neither for prokaryotes nor eukaryotes, thus confirming that Cu did not primarily drive biofilm microbial communities. However, the wellknown toxicity influence of Cu on phytoplankton can indirectly influence biofilm, through an exerted control on cell abundance in one hand but also via alteration of DOM nature in the other hand. Finally, we assume that the two clusters of biofilm communities observed in mesocosms of the two sites were also the result of different initial planktonic communities. As a matter of fact, biofilm taxa come from the rare planktonic community, particularly copiotrophs, which make their determination within the bacterioplankton difficult, using classical Illumina approach [START_REF] Zhang | Marine biofilms constitute a bank of hidden microbial diversity and functional potential[END_REF], Catao et al., 2021[START_REF] Coclet | Trace metal contamination impacts predicted functions more than structure of marine prokaryotic biofilm communities in an anthropized coastal area[END_REF].

Communities on MPs included typical biofilm taxa. Discrepancies for taxa affiliation between this study and the previous biofilm study on polymer surface in the bay of Toulon [START_REF] Briand | Spatiotemporal variations of marine biofilm communities colonizing artificial substrata including antifouling coatings in contrasted French coastal environments[END_REF] should probably be associated with the considered surface size of plastic (large polymers versus MPs), the type of conducted experiments (mesocosm versus field study), the immersion time (13 versus 30 days) but perhaps the interannual variability too. Cryomorphaceae taxa enriched in Pt12 biofilms seem more predominant in coastal ecosystems, with enhanced organic loads (Bowman, 2020), and also enriched in the marine plastisphere [START_REF] Oberbeckmann | Microbes on a bottle: substrate, season and geography influence community composition of microbes colonizing marine plastic debris[END_REF]. The co-occurrence network analysis highlighted the central role of several Rhodobacteraceae in interactions with eukaryotes. In addition to the low richness and diversity observed in biofilms from Pt41p, taxa reputed as pioneer ones, like Alteromonas [START_REF] Dang | Bacterial primary colonization and early succession on surfaces in marine waters as determined by amplified rRNA gene restriction analysis and sequence analysis of 16S rRNA genes[END_REF][START_REF] Lee | Bacterial communities in the initial stage of marine biofilm formation on artificial surfaces[END_REF][START_REF] Pinto | The composition of bacterial communities associated with plastic biofilms differs between different polymers and stages of biofilm succession[END_REF] or Hyphomonadaceae [START_REF] Dang | Microbial surface colonization and biofilm development in marine environments[END_REF], could be identified, which strengthen our hypothesis of weak biofilms on MPs with seawater from Pt41p.

Considering the poorly studied eukaryotes on MPs (Kettner et al.2019) showing a high diversity of genera among the raphid pennate (Amphiprora, Cylindrotheca, Navicula, Nitzchia, Pseudo-Nitzschia, Surirella). Likewise, the co-occurrence network underlined the significance of diatoms in interactions with prokaryotes and primarily with Rhodobacteraceae. Despite often identified on scanning electron microscopy (e.g. [START_REF] Zettler | Life in the "plastisphere": microbial communities on plastic marine debris[END_REF][START_REF] Kirstein | Mature biofilm communities on synthetic polymers in seawater-Specific or general?[END_REF], diatom communities were scarcely studied on MPs compared to prokaryotes. Diatoms in biofilms were studied on large artificial surfaces, all using microscopic approaches, mainly in tropical areas, and with the aim to describe their diversity depending on the substrate type including antifouling coatings [START_REF] Patil | Biofilm diatom community structure: influence of temporal and substratum variability[END_REF][START_REF] Mitbavkar | Seasonal variations in the fouling diatom community structure from a monsoon influenced tropical estuary[END_REF][START_REF] Molino | The biology of biofouling diatoms and their role in the development of microbial slimes[END_REF][START_REF] Zargiel | Static vs dynamic settlement and adhesion of diatoms to ship hull coatings[END_REF][START_REF] Briand | Spatiotemporal variations of marine biofilm communities colonizing artificial substrata including antifouling coatings in contrasted French coastal environments[END_REF][START_REF] Muthukrishnan | Diatom communities on commercial biocidal fouling control coatings after one year of immersion in the marine environment[END_REF]. This major microphytobenthic taxa was conversely highly investigated in freshwater ecosystems, mostly in the context of bioindication (e.g. Sabater et al., 2017[START_REF] Rivera | Metabarcoding of lake benthic diatoms: from structure assemblages to ecological assessment[END_REF][START_REF] Polst | Hydrodynamics alter the tolerance of autotrophic biofilm communities toward herbicides[END_REF].

Pt41p exhibited the highest number of families with a lower importance of diatoms in the natural seawater condition. The presence of vagile predators of bacteria like unicellular flagellates Bicoecea or amoeboids-like Filosa

Imbricateae and Thecofilosea could be explained. However, diatoms are mainly represented by polar centric mediophyceae including Chaetoceros, Dinophyceae including Pentapharsodinium or picoplanktonic phototrophs in the Prymnesiophyceae, which are more surprising to be found in biofilms.

Specifically considering the centric diatoms, some authors tend to consider them as members of the plastisphere, but considering their preferential planktonic lifestyle and taken in consideration that specific lifestyle occurred for other microorganisms as bacteria (e.g. [START_REF] Wright | Marine plastic debris: A new surface for microbial colonization[END_REF], we may hypothesized that such polar centric diatoms may belong to the rare biofilm community. Consequently, a contamination issue with planktonic taxa from the mesocosms seawater during the filtration of the MPs should not be excluded. Moreover, the high number of polar centric mediophyceae, which increased with Cu addition in biofilms on MPs especially in Pt41p, could be also linked to the relative increase of nanophytoplankton observed in the mesocosm seawater (Figure S1-2).

Cu also controlled biofilm community on MPs

As mentioned above, Cu was not identified as the main driver of biofilm community on plastic surface in a previous experiment with natural seawater Cu concentrations in Toulon bay [START_REF] Coclet | Trace metal contamination impacts predicted functions more than structure of marine prokaryotic biofilm communities in an anthropized coastal area[END_REF]. Alphadiversity appeared not affected in this study as well except at the highest Cu concentration (400 nM), which though is not environmentally realistic as such high concentrations are not naturally observed. Conversely, Cu clearly influenced the beta-diversity of biofilm communities for each site, except for eukaryotes with seawater from Pt41p. It may look contradictory because substrates were immersed in waters from the same sites, and conventional plastics (polycarbonate and polyethylene in these two cases) are now reputed to exhibit similar biofilm communities [START_REF] Kirstein | Mature biofilm communities on synthetic polymers in seawater-Specific or general?[END_REF][START_REF] Wright | Marine plastic debris: A new surface for microbial colonization[END_REF]. However, the size of the substrates was significantly different with 5x5 cm coupons in the previous experiment [START_REF] Briand | Spatiotemporal variations of marine biofilm communities colonizing artificial substrata including antifouling coatings in contrasted French coastal environments[END_REF] whereas MPs were used here. Hence, thicker biofilms have probably grown on MPs [START_REF] Fazey | Biofouling on buoyant marine plastics: An experimental study into the effect of size on surface longevity[END_REF], allowing higher interactions between copper and biofilm cells, consequently with a higher selection within the biofilm communities compared to thick and mature biofilms observed on large substrates. The substrate size effect was still not fully studied but CLSM pictures confirmed the presence of thin biofilms on MPs.

Biofilms on MPs formed with the seawater from Pt12 exhibited higher richness, diversity and Cu bioaccumulation than at Pt41p, which suggested that a higher biomass, including EPS. While some EPS were clearly visualized with CLSM, their more in-depth study and characterization may provide a better understanding of interactions with metals. As two dissimilar biofilm communities occurred with seawater from the two sites, the assumption of dissimilar produced EPS can be made. However, the higher resulting Cu bioaccumulation in Pt12 compared to Pt41p could also relate to a quantitatively higher EPS production rather than the production of qualitatively different EPS. Even reputedly complex to study, EPS would be a key component for further insight regarding the interaction between biofilm and metal bioaccumulation [START_REF] Flemming | Biofilms: an emergent form of bacterial life[END_REF]. The relative contribution of bacteria and diatoms to this production will be another important key point to address as well.

The Rhodobacteraceae genus Tateyamaria which was observed as one of the most enriched taxa at Pt12 following Cu content increase here, was already enriched in the most contaminated site in the previous study in the Toulon bay [START_REF] Coclet | Trace metal contamination impacts predicted functions more than structure of marine prokaryotic biofilm communities in an anthropized coastal area[END_REF]. In addition, the network analysis highlighted the co-occurrence of this genus with several diatoms (Amphora, Berkeleya, Navicula, Nitzchia, Pteroncola), especially in high Cu concentration. Nevertheless, it remains difficult to conclude on simple coselection, direct or indirect interactions. Tateyamaria was also reported as a biomarker on copper-based antifouling paints, with a specific correlation with the abundance of the copper resistance gene CueO (Catao et al., 2021). Similarly, the relative abundance of Erythrobacter (Sphingomonadaceae) increased in biofilms from Pt41p with the increasing copper content, which is consistent with its enrichment in biofilm community on copper-based antifouling coatings (Catao et al., 2019(Catao et al., , 2021)).

As already reported for copper resistance in the context of antifouling paints, the selection was mainly observed at the genus or species level rather than family. As a matter of fact, especially considering two of the main families, Rhodobacteraceae and Flavobacteriaceae, it was already reported that both sensitive and tolerant to copper taxa were identified in the same biofilm communities (Catao et al., 2019(Catao et al., , 2021)).

Among Bacillariophyta, Cylindrotheca closterium or Pseudo-Nitzschia appeared to be sensitive to Cu whereas Nitzschia or Surirella, but also Amphora or Grammatophora to a lesser extent, were enriched in high Cu concentrations. Similarly, araphid pennates, without taxonomic affiliation at the genus level, were enriched with Cu addition, both at Pt12 and Pt41p.

The selection of resistant taxa together with the decrease of sensitive ones indicated that, similarly as for freshwaters, community structure and/or specific taxa could be used for bioindication [START_REF] Tlili | Pollution-induced community tolerance (PICT): towards an ecologically relevant risk assessment of chemicals in aquatic systems[END_REF][START_REF] Vasselon | Assessing ecological status with diatoms DNA metabarcoding: Scaling-up on a WFD monitoring network (Mayotte island, France)[END_REF]. Indeed, Bacillaryophyta could be used to find out if MPs move through contaminated or non-contaminated sea waters, which could give information on MPs -followed current.

Conclusion

This study revealed a tight interaction among MPs, biofilm and Cu concentration in seawater of the bay of Toulon (NW Mediterranean Sea).

Indeed, Cu bioaccumulation on MPs was clearly linked to biofilm.

Furthermore, the seawater Cu-content controlled biofilm bioaccumulation on MPs but was also involved in biofilm microbial communities shaping.

Figure 1 :

 1 Figure 1: Sampling stations in the small (Pt12) and large (Pt41p) bay of Toulon.

  Fig. S1-a), Synechococcus-like disappeared while both picoeukaryotes and Prochlorococcus-like drastically decreased (ANOVA; p<0.01), particularly in treatments with the highest levels of Cu. Initial nanophytoplankton cell abundance (for each experiment, bars on the left, Fig. S1-c) in Pt12 exhibited a decrease with the increase in Cu gradient. After 13 days of incubation, nanophytoplankton cells abundance significantly increased (ANOVA; p<0.01) compared to T0, in treatments having received 30 and 60 nM of Cu, remained constant with amendments of 100 and 200 nM of Cu (ANOVA; p>0.05) and decreased in the treatment with 400 nM of Cu (ANOVA; p<0.01). Regarding heterotrophic bacteria (Fig. S1-e), initial cell abundance was not influenced by the increasing Cu gradient. On the contrary, after 13 days of incubation time, cell abundance significantly decreased in each treatment (ANOVA; p<0.01). Nevertheless, following the increase in Cu concentration, an increase in final cell abundance (T=13 days) was observed, except in the treatment with 400 nM (ANOVA; p<0.01) (Fig. S1).

Figure 2 :

 2 Figure 2: Confocal Laser Scanning Microscopy of microplastics. Attached microbial cells were visualized using the SYTO 61 as well as differentiated interference contrast microscopy (DIC). In addition, exopolymeric substances (EPS) characterizing biofilm development were visualized using FITC (proteins), concanavalin A (ConA, sugar) and calcofluor white (CFW, sugar).

Figure 3 .

 3 Figure 3. Bioaccumulated Cu on microplastics at the scale of the incubation period and its associated repartition coefficient (Kd) at Pt12 (small bay) and at Pt41p (large bay). C refers to treatments with sterile filtered seawater. Note that the scale of y axes of both bioaccumulated Cu and Kd are different at Pt12 compared to Pt41p.

Figure 4 :

 4 Figure 4: Alpha diversity indexes for experiments at both sites Pt12 and Pt41p: Chao1 index (A) and Shanon index (B) for prokaryotes and Chao1 index for eukaryotes (C).

Figure 5 :

 5 Figure 5: Principal Coordinates Analysis (PCoA) of biofilm prokaryotes (A) and eukaryotes (B) from microplastics, for incubation experiments conducted in the seawater from the Pt12 (circles) and Pt41p (triangles).Clustering was studied using Weighted Unifrac distances for prokaryotes and Unweighted Unifrac distances for eukaryotes.

Figure 6 :

 6 Figure 6: MPs biofilm community composition in the experiments with the seawater from the two sites (Pt12 and Pt41p) at the family level for prokaryotes (A) and the Class level for eukaryotes (B).

  Raphid pennate were more abundant in C1. The copepod Oithona, the flagellate Bicosoeca, the ciliate Holosticha and a Choanoflagellatea were the other contributors to the dissimilarity between C1 and C4. The most involved taxa in the dissimilarity between D1 and D4, and enriched in D4, were mostly OTUs affiliated to Bacillaryophyceae (Polar centric Mediophyceae, araphid and raphid pennates), whereas taxa abundant in D1 were heterotroph microeukaryotes (Filosa imbricatea, Sordaryomycetes, centroheliozoa or MAST-3J).

Figure 7 :

 7 Figure 7: Visualization of diatoms within the biofilm developed onto MPs at Pt12 incubation experiment, using scanning electron microscopy.

  number of edges corresponded to five Rhodobacteraceae (Candidatus Rhodobacter lobularis, Pelagimonas, Roseobacter, Tateyamaria and an unknown genus) followed by five Bacillariophyta, four of them being araphid pennates including Grammatophora undulata. For Pt41p, the main cluster of the network displayed 45 nodes (OTUs) associated with 79 edges. Twelve small clusters with only two to four nodes completed the network. Most of the edges were positive (72%). The ten nodes with the highest number of edges corresponded to three bacterial and seven eukaryotic OTUs showing the highest diversity. Bacteria belonged to the Flavobacteriaceae (Winogradskyella), Stappiaceae (unknown genus) and Pirellulaceae (Blastopirellula) when Eukaryotes were Bacillariophyta (Thalassiosira profunda and an unknown polar centric Mediophyceae) but also three Cercozoa (one unknown genus among Filosa Theocofilosa and another one among Filosa Imbricata, and a mixotrophic amoeboid-like microalgae Minorisa), a Prymnesiophyceae (Phaeocystis) and a Ciliophora (unknown genus among Strombidae).

Figure 8 :

 8 Figure 8: Interactions among prokaryotic and eukaryotic OTUs were identified using CoNeT (Co-occurrence Network) in the two experiments with seawater from the sites Pt12 and Pt41p. Blue circles stand for bacterial OTUs whereas green OTUs stand for eukaryotes. Relative abundances in each sample are indicated within circles. Samples are colored depending on the Cu content. Positive edges are in green whereas negative ones are in red.

  , distinct picophytoplankton composition was observed in each incubation experiment, thus exhibiting picoeukaryotes as a dominant phytoplankton group in seawater form Pt12 (small bay of Toulon) and cyanobacteria (Synechococcus and Prochlorococcus groups) in seawater from Pt41p (large bay of Toulon). At the time scale of the incubation experiment, all phytoplankton groups as well as heterotrophic bacteria decreased overall following the increasing gradient of Cu, thus supporting the role of Cu in shaping planktonic microbial community composition. Considering this context, this study aimed to demonstrate, using controlled conditions with natural seawater, that: (1) Cu concentrations in seawater controlled its bioaccumulation onto MPs, with this bioaccumulation strictly linked to biofilm formation and (2) Cu bioaccumulation shaped the microbial community within the biofilm formed on MPs.

  , most of them were microeukaryotes, as multicellular one's development should be limited by the size of MPs and the incubation time. Unfortunately, the taxonomic affiliation of the 18SrDNA gene (Whatever the data bank PR2 or Silva, data not shown) allowed the identification of only a few of OTUs to the genus level in biofilms on MPs (41% for PR2). Diatoms (Bacillariophyta), especially pennates, clearly dominated the most anthropized site (Pt12)

Acknowledgement

This study was conducted as part of the Interreg Marittimo SPLASH! (Stop alle plastiche in H2O!) research project. The authors would like to thank the Regional Flow Cytometry Platform for Microbiology (PRECYM).