Christelle Pierkot
email: christelle.pierkot@lirmm.fr

Using metadata to improve spatial dataset quality during updates

Introduction

Nowadays, several different actors are involved in acquiring, distributing and updating spatial data. Such a situation naturally leads to a multiplication of heterogeneous data of different types, formats, abstraction levels and qualities. Blind integration of this data can undermine the consistency of the database and degrade the dataset's quality.

Within this broad issue, we limit our scope to the updating of geographic data by different actors spread out over a communications network -with some data liable to change while the actor is disconnected from the network. Data is replicated on different sites and there is no server holding centralized information. Moreover, updating is conducted simultaneously by the different actors, sometimes in disconnected mode. The evolutions are therefore not necessarily relevant to a particular user, sometimes contain errors, can be conflicting and consistency issues can arise during their integration into different datasets.

In this context, our objective is to propose solutions to allow coherent integration, as far as possible automatically, of spatial-data updates in a multi-master and asynchronous optimistic replication environment.

To achieve this, we propose a global integration strategy, based on standardized metadata, to provide solutions to improve the quality of a dataset during updates by multi-source evolutions. We will have to, on the one hand, deal with the relevance of the proposed evolutions and, on the other, ensure dataset consistency commensurate with the final user's requirements. This strategy depends on the prior establishment of a spatial data infrastructure in which a communications network has been defined, the users and their different roles are known, and updates can be exchanged using a common strategy [START_REF] Pierkot | Using metadata to help the integration of several multi-sources set of updates[END_REF]. In this infrastructure, a metadata model that allows formal relationships between data, actors and the evolutions can be set up. This paper is structured as follows: We start by defining consistency and quality in spatial databases. Then, we present the different parts of the metadata model which we have defined in the infrastructure and we cover in some detail the quality metadata relating to the evolutions. Subsequently, we present the integration strategy and, in particular, the modules for verifying the relevance and consistency. Then, we analyze the results obtained for the consistency-checking process. Finally, we conclude and provide some perspectives for this work.

Quality and consistency of spatial databases

In the domain of geographic information, the concept of consistency is tightly linked to the dataset quality and can be impacted by numerous sources of errors. In the context of updating, it is the capacity to satisfy a set of criteria during the integration and/or the propagation of new data and evolutions. [START_REF] David | Qualité d'une base de données géographique : concepts et terminologie[END_REF]] distinguish between two types of spatial-data quality:

• Internal quality is the set of properties and characteristics of a product or service which confers on it the ability to satisfy the specifications of the content of this product or service. It is measured by the difference between the data which should have been produced and the data which has actually been produced. It is linked to specifications (and, in particular, to errors that can be committed during data production) and is evaluated in terms of the producer. • External quality is defined as the suitability of the specifications to the user's requirements. It is measured by the difference between data wished for by the user and the data actually produced. It is linked to the users' needs and thus varies from one user to the next. Quality evaluation thus comes down to verifying the conformity between data of the database and the data considered correct (from the producer's or user's point of view).

[ Bel-Hadj-Ali, 2001] emphasizes that geographic-data quality is so complex that it is impossible to use a single global measurement; one has to rely on several components for determining it. Consequently, several criteria have been defined to be able to define a spatial dataset's internal quality such as the lineage, the geometric accuracy, the semantic accuracy, completeness, actuality, logical consistency and semantic consistency [Moellering, 1987], [START_REF] Clarke | Elements of Spatial Data Quality, chapter Lineage[END_REF], [Drummond, 1995], [START_REF] Goodchild | Development and test of an error model for categorical data[END_REF], [Bicking, 1994], [START_REF] Brassel | Elements of Spatial Data Quality, chapter Completeness[END_REF], [Guptill, 1995], [Salgé, 1995]. All these criteria have been widely tested and are nowadays used in several standardization works [CEN, 1998], [START_REF] Fgdc | Content Standard for Digital Geospatial Metadata[END_REF], [ISO19115, 2003]. They form a basis for evaluating a geographic dataset's quality.

Data relevance is a concept that we can link to the concept of fitness for use and, in particular, to the external quality [START_REF] Dassonville | [END_REF]. In these last few years, a lot of research work has been done for an improved taking into account of the external quality [START_REF] Bruin | Assessing fitness for use: the expected value of spatial data sets[END_REF], [START_REF] Rev | Revision of the uncertain geographic information[END_REF]] [Vasseur, 2004], [Devillers, 2004], [START_REF] Devillers | Qualité de l'information géographique[END_REF]. Two broad approaches have been proposed, one is based on the evaluation of the risk incurred by using unsuitable data [START_REF] Agumya | Fitness for use: Reducing the impact of geographic information uncertainty[END_REF]], [START_REF] Bruin | Assessing fitness for use: the expected value of spatial data sets[END_REF], the other on the use of metadata to analyze the similarity between data produced and users' needs [Frank, 1998], [Hunter, 2001], [Devillers, 2004], [START_REF] Rev | Revision of the uncertain geographic information[END_REF], [Vasseur, 2004]. These methods differ in the way they are developed but both lead to the appraisal of the data quality with respect to the use that it will be put to. They thus allow better targeted interpretations and less risky decision making.

Evaluating the quality of a geographic dataset is not a task to be undertaken lightly; a database of poor quality can lead to numerous errors that can imperil data consistency. Furthermore, different work on geographic-data quality ( [START_REF] David | Qualité d'une base de données géographique : concepts et terminologie[END_REF], [START_REF] Vasseur | Evaluation de la qualité externe de l'information géographique : une approche ontologique[END_REF], [START_REF] Devillers | Qualité de l'information géographique[END_REF], [Harding, 2005]) has repeatedly shown the necessity of considering the user's point of view so that the data is fit for his use. This is all the more true in a context of decision making or where the data will serve as basis for the actions of various actors.

In our context, a user could integrate several sets of evolutions originating from multiple sources. This implies that the updates are not necessarily all relevant and that these updates could be in conflict or lead to inconsistencies with his data. For proper integration of these updates, their quality has to be evaluated taking the user's needs into account. Nevertheless, to the best of our knowledge, no study has been conducted on the quality of evolutions; all research into updates has always assumed that the evolutions are relevant to the user. Our study thus has to answer this question -which has so far remained unasked in the literature: How to evaluate the quality of updates so as to retain only those that are consistent and relevant to the user?

Metadata model

One solution to evaluate data quality is to use metadata. In fact, from the numerous pieces of information that we can find in metadata, there are those that relate to the resource's quality. However, the quality metadata generally indicates the products' internal quality. In fact, metadata has been developed from the producer's point of view and contains little information for the user to judge the uses it can be put to [Bucher, 2002]. This, in particular, poses the problem of the relevance of data for the final user, especially as far as fitness for use is concerned.

Yet, in our study, metadata has to, on the one hand, filter out irrelevant evolutions and, on the other, ensure the consistency of the dataset, all the while taking the user's expectations into account. We thus have to consider both types of quality and have to add criteria to metadata to be able to define the suitability of the evolutions to the user's requirements.

It also seems relevant to us to define the quality at different granularities, i.e., not only for the entire evolution set but also for the evolutions themselves. In fact, quality evaluation at the level of the entire set allows us to quickly filter out the collections which are irrelevant to the user, whereas quality evaluation at the level of the evolutions helps in reconciling two conflicting evolutions.

ISO 19115: Metadata for data

To supply shared and consistent knowledge of data between different communities, we have to standardize the metadata [Luzet, 1998], [START_REF] Gunther | Managing Mutimedia Data : Using Metadata to Integrate and Apply Digital Data[END_REF], [START_REF] Spéry | Vers une structuration des métadonnées[END_REF]]. Standards for describing metadata have been developed and provide a common base to the users [CEN, 1998], [START_REF] Fgdc | Content Standard for Digital Geospatial Metadata[END_REF], [ISO19115, 2003]. The use of normalized metadata in the infrastructure thus promotes interoperability between different actors and systems.

The standard that draws the most attention nowadays is [ISO19115, 2003]. The ISO 19115 standard defines a large set of metadata elements so that it can be used by several different types of users. However, a community of a given set of geographic-data users normally uses only a part of the different metadata elements defined in the standard and, in spite of the wide variety of elements, often needs to add elements not specified in the standard. ISO 19115 allows this thanks to the possibility of defining community profiles. A profile thus allows use of the standard restricted to a subset of mandatory elements and also extended by the addition of missing sections, entities and elements. ISO 19115 has been designed to provide information on the use and exchange of datasets. The evolutions sets and basic evolutions (creation, deletion or modification) are not included in the standard. It is therefore not currently possible to provide metadata relating to a set of evolutions that we would want to provide to a user who already possesses the reference dataset. Consequently, metadata elements have to be added to take these new requirements into account. We thus propose to extend ISO 19115 to encompass evolutions. Towards this end, we have created a metadata profile which we call MUMSDI (Metadata for Updating a Military Spatial Data Infrastructure). It is specifically designed to manage the evolutions of military data1 .

MUMSDI: Metadata for evolutions

Quality metadata in the MUMSDI profile

Quality metadata defined by ISO 19115 are used to describe the data quality from the producer's point of view, which is not sufficient for our context. We therefore propose to add quality elements to take the user's point of view into account. We also restrict some elements of the standard which are not useful to us. Figure 1 gives an overview of quality information in our MUMSDI profile. The first modification we have made concerns the cardinality of the dataQualityInfo role which we have restricted because we think that to evaluate the evolutions correctly, quality information has to be made mandatory. In the same way, we have modified the cardinalities of the lineage roles and the report of the dataQualityInfo section to make them also mandatory in the profile. We have also modified the scope of the quality elements (attribute level of the DQ_Scope class). This information is provided in the standard in a list (MD_ScopeCode) which contains some elements that allow the description of the level to which the information will apply. We have modified this list in the MUMSDI profile to take into account only those elements that relate to the quality information of the evolutions. We have also deleted a certain number of attributes that we did not consider useful in an updating context. Our reason for doing so derives from the observation that the main difficulty in the use of metadata comes from the very large number of metadata elements to fill in. We therefore think that fewer the unnecessary elements in the MUMSID profile, easier will be their entry and better their interpretation.

Figure 1: Quality information in the MUMSDI profile

All the information on the quality is available thanks to a set of quality measurements accessible via the DQElement class of which only the name (attribute nameOfMeasure) and the result (attribute result) of quality measurements are specified in the MUMSDI profile (see figure 2). We have deactivated the sub-classes of DQElement that are not relevant to our context (shown with white backgrounds in the figure). We have then added a sub-class MU_Usability to be able to judge the evolutions' ability to satisfy the usage the user may put them to (shown with orange backgrounds in figure 4). This element indicates the degree of the evolutions' conformity with the usage that a given user type may put them to. This class has two attributes to indicate the type of user concerned by the quality measurement (attribute finalUserRole) and the site where the user is located (attribute finalUserLocation). Finally, the expression of the result of the quality in MUMSDI is shown in figure 3. The results available in the standard (DQ_QuantitativeResult and DQ_ConformanceResult) can be easily supplied by a producer but with difficulty, or not at all, by the other users. In fact, some users do not even have the technical means to evaluate the evolutions accurately and have to judge by themselves the quality of the updates they have made. We therefore think that the quantitative results are not sufficient to describe the information on the evolutions' quality and we have thus added qualitative elements to be able to judge the evolutions' quality in a more flexible manner. To this end, a class MU_QualitativeResult class has been created. It allows us, on the one hand, to quickly see whether non-spatial information attached to the basic evolutions has been correctly documented (attribute documentation) and, on the other, to see what are the types of errors that the updates may contain (attribute errorType of type MUerror).

Metadata for actors

Infrastructure users do not all have the same requirements as far as evolutions are concerned. These requirements depend on several factors such as the user's role, the site where he is located, etc. Indeed, a certain number of consistency constraints linked to the dataset used and to the technical methods available have to be defined for each actor. In fact, an infrastructure actor has a dataset that is, admittedly, derived from a unique reference set, but which may have been transformed so that it can be used by the user's system. This entire information (needs and constraints) constitutes the actors' metadata. The needs are not fixed and can change over time. On the other hand, the constraints are imposed at the beginning and cannot be changed.

We have specified several criteria defining the entirety of user needs, such as the maximum spatial extent, the minimum occurrence date, the different thematic layers and the type of evolutions required. Also specified are the minimum geometric and semantic accuracies, as well as the reliability. The list of consistency constraints has, on its part, been defined by spatial constraints (geometric accuracy, minimum resolution, etc.), semantic constraints (quantitative and qualitative accuracy) and context constraints (type of sources allowed, occurrence date and maximum extent of the dataset, etc.).

Integration strategy for multi-source updates

General approach

Figure 4 shows the general approach for the evolution integration strategy. The integration strategy consists of three stages and applies to the dataset of an infrastructure user for whom evolution sets originating from multiple sources are destined. The execution of the three stages of the integration strategy leads to the updating of the user's dataset.

Figure 4: Strategy of integrating updates into a user dataset

The first stage consists of the evaluation of the relevance of the evolutions. Here, we filter out those evolutions that are not useful to the user. To do this, we use metadata associated with evolutions and metadata associated with the users' needs. At the end of this stage, the evolution set proposed for the integration contains only those evolutions that are suitable for the usage the user wants to put them to. The second stage concerns the checking of the consistency. Its aim is to detect and process any possible conflicts that can arise from the fact that the evolution sets originate from multiple sources. This stage which executes in a two-step process consists, firstly, of consistency verification followed by a phase to reconcile conflicting data. We use here the structure of the evolutions and geometric processing to detect possible conflicts. We then use metadata associated with the evolutions, with the data and with the user's needs to define the reconciliation methods. At the end of this stage, we obtain a set of relevant and non-conflicting evolutions that we can integrate into the user's dataset. The third and final stage of the strategy is closely linked to the second and permits the integration of the evolutions already processed into the user's dataset.

Relevance of the evolutions

The sources used in the infrastructure originate from the same reference dataset but have evolved (updates and transformations) depending on the specific requirements of the users and the technical methods they have at their disposal. In addition, the evolutions could have been entered in different ways, under different conditions and at different locations. They are thus heterogeneous, of varying qualities and do not necessarily relate to the same geographical coverage zone. For all these reasons, an evolution set originating from an infrastructure actor does not, all by itself, fulfil the final user's needs. In fact, for a given particular requirement, an evolution set usually contains more information than really needed by the user. Finally, an infrastructure actor has to retrieve several evolution sets originating from different sources to completely satisfy his requirements but has to exclude evolutions which are definitely not relevant to the usage he wants to put them to. The goal of a consistency checking process is therefore to filter out, from all the evolution sets proposed, the evolutions that are not relevant to the user and which would needlessly risk deteriorating his dataset's external quality. The solution we advance to do this filtering is based on the metadata associated with evolutions and to the users' needs. The idea is to proceed by analysis and comparison of metadata to determine if the evolutions are relevant or not to the user. To do this, we use the work by [START_REF] Jeansoulin | Model-based semantics for ontologies of geographic information[END_REF] and [Vasseur, 2004] as a basis. They evaluated a dataset's external quality as a function of the use it could be done by a user community. We provide here the general method of doing so without going into each module's details.

Three stages are necessary to evaluate the external quality:

• Creation of problem and product ontologies in a common reference base [START_REF] Jeansoulin | Model-based semantics for ontologies of geographic information[END_REF] • Definition of expected and internal quality matrices [Vasseur, 2004] • Evaluation of suitability to needs using utility calculations [Vasseur, 2004] Product and problem ontologies provide a real world view from the points of view, respectively, of data producers and of the final user, by formalizing the data characteristics and the user needs. In our study, the product ontology is specified by the evolutions proposed for integration and the problem ontology by the user's needs. The metadata associated with evolutions and to user needs is used to define these ontologies.

From the problem and product ontologies, matrices of internal quality and expected quality are created in a common reference base.

In our study, the matrix of internal quality corresponds to the evolutions' quality and the matrix of expected quality to the user needs.

Since the metadata associated with evolutions and with actors is normalized, they can be used without problems in the different matrices.

The utility calculation then evaluates the data in terms of user expectations and therefore provides an estimate of the data adequacy to different needs. In our study, the utility calculation informs us if the evolutions' quality is suitable for the user's needs. If not, the evolution is excluded from the evolution set.

Consistency checking

We restrict ourselves to studying the data consistency and not that of models or schemas which are assumed handled within the infrastructure.

Different consistency levels are required depending on the needs and roles of the actors in the infrastructure. In fact, heavy producers who have to provide reference information and prepare future datasets have access to system resources for updating and sharing their data. These actors' objective is therefore to retrieve quality information to be able to obtain a reliable and accurate dataset. This means that care has to be taken to limit as far as possible the inconsistencies that can be produced while integrating evolutions into their datasets. The consistency level should therefore be high because we focus on the quality and consistency of data rather than the quantity of information. Users, on the other hand, use data for purposes of decision making. Their primary objective is to rapidly react and take initiatives as soon as required. The goal is here to retrieve a maximum amount of information as fulfilling a specific need irrespective of its quality. We therefore accept that inconsistencies will arise when evolutions are integrated into the dataset. The consistency level desired can therefore be considered weak because we prefer the data quantity over data quality and consistency.

Light producers have to supply evolutions to the users and also transmit upstream the information retrieved to the heavy producers.

The light producers have simplified system resources which nevertheless allow them to update their data and to then share it with the other actors. The goal for these actors is to retrieve a maximum amount of information and to transmit to the other actors (heavy producers and the users), based on their needs, while preserving a certain quality level. We try to limit but accept some inconsistencies during the integration of evolutions into the dataset. The consistency level desired here is intermediate between the other two cases.

We therefore propose a consistency checking protocol which, on the one hand, allows the detection of conflicts that could lead to inconsistencies in the dataset during integration and, on the other, offers reconciliation routines for conflicting evolutions as a function of the desired consistency level. We handle concurrency by dividing the conflicts into 3 distinct types (update conflicts, topological conflicts and conflicts of creation).

We then apply methods to detect the conflicts (semantic and geometric matching, tests on the spatial relationships between the objects).

The reconciliation protocol starts when one or more conflicts are detected during the stage of concurrency checking. This process's goal is to offer the most suitable solution for resolving the conflict(s). The result depends on the desired consistency level and the desired balance between quality and quantity.

The reconciliation protocol we propose is original in its use of available metadata to offer a result commensurate with the actor's expectations.

In fact, the metadata included with the evolutions and data provides information such as on its quality or on its origin. This information allows the process to compare the items with the actor's expectations (constraints or needs).

The reconciliation process consists of several stages:

• First, a comparison of metadata associated with the items in conflict is made with the actors' metadata and a calculation for measuring the quality of each of the items' characteristics (geometry, attributes, reliability, etc.) is made. This part is based on the work done on the utility calculation by [START_REF] Grum | How to select the best dataset for a task?[END_REF] and [START_REF] Frank | Procedure to select the best dataset for a task[END_REF]]. • Then, we calculate a measurement of overall quality for each item in conflict to be able to obtain a result that is a function of the final user's expectations and of the desired consistency level. • Finally, a comparison of the overall quality measurements of the items in conflict is made and the item found to be the most relevant is selected for future integration. This part can be executed automatically, semi-automatically or interactively.

To be as close as possible to our study's contextual reality and to estimate as best as possible the reconciliation results, we have simulated the following two cases: in the first, the reference actor has requirements that can change over time. In the second case, the reference actor is a producer whose requirements were fixed and cannot change. Proceeding in this manner, we can illustrate the reconciliation process as a function of each actor's own constraints and as a function of the consistency level inherent to their roles within the infrastructure.

The first stage in the reconciliation process consists of retrieving the information contained in the metadata, then of calculating a quality measurement for each item of information. The quality measurement is calculated as a function of the reference actor's needs and of the desired consistency level.

For each characteristic, the process calculates a quality measurement, and then normalizes this measurement to obtain a result between -1 and 1. Closer the quality measurement is to -1, better is the external quality.

The table below shows the result of the normalized quality measurement calculation that we have obtained for a few characteristics attached to the evolutions (geometric accuracy, semantic accuracy, reliability, etc.). The process then calculates the overall quality measurement for each conflicting data. For this simulation, we have considered two types of reference actors: a heavy producer and a light producer. The heavy producer's consistency level is higher than that of the light producer. In addition, the heavy producer prefers obtaining reliable data with a geometric accuracy close to his requirements rather than voluminous data which is of suspect quality. The weight allocated to the geometric accuracy and to the reliability will be higher than to the completeness. On the other hand, the light producer prefers to obtain recent information rapidly irrespective of its quality. The weight allocated to completeness and to actuality will be higher than that to reliability or geometric and semantic accuracies. Finally, the process calculates the weighted mean to obtain an overall quality measurement for each of the conflicting data. With the values assumed for our simulation, we obtain the following results:

Characteristic

Heavy Producer Light Producer Evolution -0.2246 -0.3118 Data -0.437 -0.17

Tab 2 : Overall qualities measurements

The analysis of results shows that we obtain overall quality measurements which differ depending on the actor's role in the infrastructure and therefore depending on the expected consistency level. In fact, we see in particular that the evolution will be better for the light producer whereas the data will more suitable for the heavy producer. The process will therefore have a different preference for conflicting data depending on the actor who will finally use the data after integration. This result proves that it is possible to develop a reconciliation process which uses information present in metadata to offer a choice between conflicting data by considering, on the one hand, the needs and constraints of the actor -based on his role in the infrastructure -and, on the other, the desired consistency level for the different datasets which will be used.

Conclusions and perspectives

The solution we have proposed in this paper is to process evolutions originating from multiple sources relies on a spatial data infrastructure and an integration strategy for evolutions using normalized metadata. The main benefits of this method are:

• The formalization of a metadata model for managing quality evolutions, normalized and thus favouring interoperability.

• The taking of user needs into account to evaluate the suitability of the evolutions for the use they will be put to.

• The elimination of conflicts thanks to a reconciliation protocol using the information contained in the metadata.

• The integration of evolutions that are relevant and of those that will not lead to inconsistencies.

• At the end, we obtain an updated dataset whose quality has been preserved.

The improvements we could bring to this work relate to several aspects of the integration strategy. We believe that automatization of the filling in of metadata during the data-entry of evolutions could lead to an optimal number of information items usable by the reconciliation process. We could study the work of [Libourel, 2003] and, especially, the thesis of [Barde, 2005] which rely on a RDBMS to control the entry of certain metadata elements.

The development of an interface to manage the interactivity also seems to us to be worth considering for improving the integration strategy. We could, for example, draw inspiration from the work of [Devillers, 2004] and use quality indicators. This would help the actor in making the best choice when the reconciliation of conflicting data cannot be done automatically. Finally, there are many general perspectives to this work since so many possibilities remain to be explored regarding the quality during the updating of distributed geographic databases. In fact, substantial work remains to be done on the fulfilling of user requirements; it would allow us to improve our results regarding the filtering of irrelevant evolutions and the reconciliation of conflicting data.

Figure 2 :

 2 Figure 2: Quality elements in the MUMSDI profile

Figure 3 :

 3 Figure 3: Expression of the quality result in the MUMSDI profile

normalized quality measurement for characteristics attached to the evolution

		Actual value	Desired Value Normalized quality measurement
	Geometric accuracy 150m < x < 200m	≤ 500m	-0.43
	Semantic accuracy	50%	≥ 50%	0
	Reliability	50%	≥ 40%	-0.1
	Completeness	42%	≥ 10%	-0.32
	Actuality	15/03/08	≥ 01/02/08	-0.4
	Tab 1 :

Our study's specific context is that of a military mission wherein the actors are spread out over different sites, use spatial data and exchange update information. MUMSDI was thus specified for the military community.

Comparison of metadata and computation of quality measurements

The first action in the reconciliation process is therefore to compare metadata. A comparison is only possible if the two metadata are standardized and if correspondences are explicitly established. Metadata sets that we have specified in the infrastructure are in formats that are very close because they have been specified conforming to the requirements of the ISO 19115 standard. We can thus establish correspondences between the different items of each metadata set.

In addition, we classify the metadata items according to the type of information they carry. We distinguish five principal characteristics for measuring the quality of a resource with respect to a user's expectations: geometric characteristics (accuracy and resolution), semantic characteristics (quantitative accuracy and qualitative accuracy), lineage characteristics (sources and processes of the construction of resources), reliability characteristics (error type and the trust accorded to the actors) and other, more general, characteristics such as timeliness, completeness and extent of the set containing the resources. We then calculate the quality measurements for each characteristic taken individually. We calculate the quality measurement of each characteristic using a distance calculation which measures the difference between the quality that the actor desires and the actual quality of the resource. This calculation is possible for the majority of characteristics because quality values are numeric values or percentages. For other types of values, we have to create a metric associated with the items.

Computation of the measurement of overall quality

At the end of this second stage, we wish to obtain a measurement of the overall quality which corresponds to the quality measurement of the resource in conflict with respect to the expectations of the user who will use it. This measurement depends on the desired consistency level and will, therefore, differ from actor to actor (even if every item's quality measurements are identical). This stage is divided into three phases:

• First, we normalize the quality measurements of each characteristic obtained from the previous stage so that all the measurements are in the same unit and are, thus, comparable. The normalization should lead to values between -1 and 1. • Then, we allocate a weight to each of these individual quality measurements so as to take into account the consistency level and expectations of the user who will use the data. The value of the weight depends on the desired consistency level and the actor's requirements. It is thus contextual in nature. • Finally, we aggregate the quality measurements of each characteristic to obtain an overall measurement. To do this, we take the mean value. This provides us with an overall idea of the resource's external quality with respect to the user's needs and to the expected consistency level.

Selection of the relevant item

The third and final stage of the reconciliation process allows an item to be chosen from conflicting ones. The final selection can take one of three forms:

• The evolution corresponds best to the actor's expectations; it is selected.

• The data corresponds best to the actor's expectations; it is retained and the evolution is deleted.

• Neither of the two resources, considered globally, entirely satisfies the actor's expectations but some characteristics taken separately may be relevant. In such a case, a new evolution can be built with these characteristics. This happens when one part of the evolution and one part of the other resource provide a better solution if they are combined rather than if either is taken individually.

This third stage can be executed automatically, semi-automatically or interactively depending on the results obtained. It is always preferable to automate as far as possible the reconciliation protocol so that the results obtained may be harmonized. This is possible when there is sufficient information (in quantity and of quality) available with the evolutions and the data for the quality measurements to be calculated accurately. However, when a new evolution is proposed by aggregation of the relevant characteristics of two resources, it is sometimes difficult to choose between several solutions, all of which may appear acceptable. In such cases, the protocol proposes a list of choices to the user for his selection. As a last recourse, when no satisfying solution has been found, the process defers to the user's choice of a solution he deems best. No doubt this situation has to be avoided as far as possible because the actors, even if they are handling a dataset with identical constraints, have different viewpoints of the real world and their analyses of the same situation can lead to diverging interpretations, which is obviously not desirable.

Implementation and evaluation

The simulation context we have selected is that of an actor with a dataset derived from a reference set but which he has changed to suit his environment and his requirements. Evolutions originating from other infrastructure actors are proposed to our reference actor for possible integration. We have focused on the consistency checking part of the integration strategy. In particular, we have wanted to show that the case is convincing for the use of metadata for the reconciliation of conflicting data.

We have considered the situation where some data and an evolution have been declared as conflicting and have to be processed. Several metadata elements describing the quality of the evolutions and of the data (geometric accuracy, completeness, reliability, actuality date, etc.) are supplied with the data and the evolution.


domain: chim sde

text:
Jean-Alix Barrat
email: barrat@univ-brest.fr.

Germain Bayon

Robert S Carney

Laurent Chauvaud

Rare Earth Elements as new biogeochemical proxies in deep-sea mussels By

Keywords: rare earth elements, bivalve, shell chemistry, hydrothermal system, cold seep, methanotrophy

We report on the abundances of REE in a comprehensive suite of shells of deep-sea chemosynthetic mussels from hydrothermal vents and cold seeps. Except for mussel shells from oceanic hydrothermal sites that often show extremely pronounced Eu anomalies ((Eu/Sm)sn=2-200), and abundances for this element that can occasionally exceed 200 ng/g, REE concentrations are usually low and typically between 10 -4 and 10 -3 times the shale reference.

In addition to exhibiting commonly high Eu anomalies, mussel shells from hydrothermal vents are depleted in light-REE and heavy-REE compared to reference shales (e.g., Pr/Sm)sn<1, (Tb/Yb)sn=1-6). These features are inherited from hydrothermal fluids.

Mussel shells from cold seeps have very different REE concentrations, which also reflect the compositions of the waters they filter: their (Eu/Sm)sn ratios are much lower (<2) and are much less heavy-REE fractionated ((Tb/Yb)sn =0.5-2.7). Furthermore, the REE distributions show a clear dichotomy between thiotrophic and methanotrophic mussels. The latter show marked enrichments in light-REE and even sometimes La enrichments much larger than those classically observed in deep-sea waters, leading to huge positive La anomalies (La/La*>10).

These light-REE enrichments are likely related to REE-dependent methanol dehydrogenase enzymes used by the symbionts that these mussels host in their gills. These data show that REE chemistry is a promising tool to study chemosynthetic faunas living near hydrothermal vents or cold seeps. Furthermore, REE chemistry, coupled with stable isotopes, should reveal the footprint of aerobic methanotrophy in carbonates formed in cold seeps, but also potentially in ancient sediments.

1/ Introduction

Over the last fifty years, rare earth elements (REEs) have become one of the most studied groups of elements by geochemists, finding applications in all fields, from the petrogenesis of rocks, terrestrial or not, to the origin of the first condensed solids in the solar system, through rock dating and isotope tracing (e.g., [START_REF] Henderson | General geochemical properties and abundances of the rare earth elements[END_REF]. In marine geochemistry, REEs and their isotopes have been also extensively studied since they allow, among other things, the characterization of the different water masses composing the oceans and their circulation (e.g., [START_REF] Elderfield | The oceanic chemistry of the rare-earth elements[END_REF]. For decades, it has been assumed that these elements do not participate in any biochemical cycle and therefore that their distributions at the Earth's surface are not impacted by life. Nature is certainly more complex. Notable biological effects of the REEs have been observed in plants, and REEs are even used as fertilizers [START_REF] Tommasi | Review of rare earth elements as fertilizers and feed additives: a knowledge gap analysis[END_REF]. Furthermore, some species can accumulate large amounts of these elements, as exemplified by the fern Dicranopteris linearis [START_REF] Ozaki | Determination of Lanthanides and other trace elements in ferns by instrumental neutron activation analysis[END_REF]. More recently, it was established that light REEs are vital for aerobic methanotrophic and methylotrophic bacteria that play an essential role in global carbon cycling (e.g., [START_REF] Pol | Rare earth metals are essential for methanotrophic life in volcanic mud pots[END_REF][START_REF] Semrau | Metals and Methanotrophy[END_REF][START_REF] Cotruvo | The chemistry of lanthanides in biology: recent discoveries, emerging principles, and technological applications[END_REF]. Methanotrophic bacteria first convert methane to methanol, which is subsequently degraded into formaldehyde using Ca-dependent (MxaF type) or REE-dependent (XoxF type) methanol dehydrogenase enzymes [START_REF] Skovran | XoxF is required for expression of methanol dehydrogenase in Methylobacterium extorquens AM1[END_REF]. [START_REF] Ramachandran | Investigation of XoxF methanol dehydrogenases reveals new methylotrophic bacteria in pelagic marine and freshwater ecosystems[END_REF] and [START_REF] Taubert | XoxF encoding an alternative methanol dehydrogenase is widespread in coastal marine environments[END_REF] have shown that XoxF type enzymes are more frequently used by marine bacteria. These recent findings clearly indicate the ability of micro-organisms to fractionate and redistribute REE, emphasizing the importance of life in the REE cycling in Earth's surface environments. For instance, there is growing evidence that bacterial activity and chemosynthetic ecosystems can influence the REEs distribution in seawater, at least locally. During the blowout of the Deepwater Horizon well (Gulf of Mexico), the depletion of light REE in a submerged plume of methane-rich water was explained by an uptake of these elements by methanotrophic bacteria [START_REF] Shiller | Light rare earth element depletion during Deepwater Horizon blowout methanotrophy[END_REF]. More recently, light REE depletions were observed in seawater from the Sargasso Sea, at depths between approximately 200 and 500 m, ascribed to the activity of methanotrophic or methylotrophic microorganisms [START_REF] Meyer | Selective uptake of rare earth elements in marine systems as an indicator of and control on aerobic bacterial methanotrophy[END_REF]. Therefore, methanotrophic bacteria, at least those using enzymes containing REEs, display distinctive REE distributions, as do animals living in symbiosis with them.

Within this scope, deep-sea fauna associated with black smokers and cold seeps are ideal targets to evaluate the distinctive chemical signatures developed by organisms. In these environments, reduced sulfur compounds, methane and/or hydrogen fuel primary productivity sustained by the symbiotic bacteria that form here the basis of the trophic web [START_REF] Cavanaugh | Prokaryotic cells in the hydrothermal vent tube worm Riftia pachyptila Jones: possible chemoautotrophic symbionts[END_REF][START_REF] Cavanaugh | Symbiosis of methylotrophic bacteria and deep-sea mussels[END_REF][START_REF] Felbeck | Chemoautotrophic potential of the hydrothermal vent tube worm, Riftia pachyptila Jones (Vestimentifera)[END_REF][START_REF] Childress | A methanotrophic marine molluscan (Bivalvia, Mytilidae) symbiosis: mussels fueled by gas[END_REF][START_REF] Petersen | Hydrogen is an energy source for hydrothermal vent symbioses[END_REF], which are vital for many thiotrophic or methanotrophic animals. Recently, two investigations of chemosynthetic fauna have provided direct support for an active consumption of light-REE associated with aerobic microbial oxidation of methane at cold seeps. Bayon et al. (2020a) determined trace element abundances along a one-meter-long chitin tube of a siboglinid worm (Escarpia southwardae) collected at the Regab pockmark (Congo margin), identifying distinctive light REE enrichments that were attributed to methanotrophic activity in the branchial plume region of the worm. Additionally, similar light-REE enrichments were found in the gills of methanotrophic mussels collected at seeps from the South China Sea, but not in thiotrophic clams or mussels from the same sites [START_REF] Wang | Lanthanum anomalies as fingerprints of methanotrophy[END_REF]. Because gills are the organs hosting the methanotrophic symbionts in mussels, [START_REF] Wang | Lanthanum anomalies as fingerprints of methanotrophy[END_REF] proposed that these light-REE enrichments were related to aerobic methanotrophy. In that latter study, other soft tissues and the shell of studied methanotrophic mussels, while displaying lower light-REE abundances, also show higher positive La anomalies relative to seawater or pore water.

Rare earth element data for deep-sea shells are still sparse. To date, mussels from only three hydrothermal vent fields [START_REF] Bau | Rare earth elements in mussel shells of the Mytilidae family as tracers for hidden and fossil high-temperature hydrothermal systems[END_REF] and two cold seeps [START_REF] Wang | Lanthanum anomalies as fingerprints of methanotrophy[END_REF] have been investigated. Here we present a comprehensive review for the distribution of REE in mussel shells from deep-sea chemosynthetic environments, which combine previously published literature data [START_REF] Bau | Rare earth elements in mussel shells of the Mytilidae family as tracers for hidden and fossil high-temperature hydrothermal systems[END_REF][START_REF] Wang | Lanthanum anomalies as fingerprints of methanotrophy[END_REF] and new results obtained on a suite of deep-sea mussels from both submarine methane seeps and hydrothermal vents (Fig. 1). The aim of this study was to further investigate the factors influencing the REE distribution in marine mollusks and assess the degree to which REE abundances in shells can be used to monitor the activity of methanotrophic symbiotic bacteria.

2/ Sampling

Forty-five mussel shells belonging to ten different species were analyzed here, of which nine reside in the genus Bathymodiolus. Application of DNA analyses to newly discovered species has led to the assignment of the intensively studied Bathymodiolus childressi to the genus Gigantidas [START_REF] Xu | A new species of deep-sea mussel (Bivalvia: Mytilidae; Gigantidas) from the South China Sea: Morphology, phylogenetic position, and gill-associated microbes[END_REF]. Twenty-five shell samples, belonging to nine species of Bathymodiolus, were sourced from the Scripps Institution of Oceanography Benthic Invertebrate Collection (SIO-BIC) in La Jolla (California, USA). Our sampling includes both thiotrophic and methanotrophic species. Descriptive data for each sample (site, depth, species, museum catalog number, etc.) are given in Supplementary Table 1.

2.1/ Hydrothermal vents

We analyzed three shells from three different sites in the Pacific Ocean for this study, which complement the dataset obtained by [START_REF] Bau | Rare earth elements in mussel shells of the Mytilidae family as tracers for hidden and fossil high-temperature hydrothermal systems[END_REF] on thiotrophic mussels from the Mid-Atlantic Ridge. Two mussels (Bathymodiolus thermophilus) originate from the East Pacific Rise (SIO-BIC M16462) and the Rose Garden vent field (SIO-BIC M8223) located east of the Galápagos Islands, which was among the first submarine hydrothermal sites discovered (e.g., [START_REF] Corliss | Oases of life in the cold abyss[END_REF]. The third shell (Bathymodiolus septemdierum; SIO-BIC M15353) comes from the Nifonea vent field [START_REF] Schmidt | Boiling vapour-type fluids from the Nifonea vent field (New Hebrides Back-Arc, Vanuatu, SW Pacific): geochemistry of an early-stage, post-eruptive hydrothermal system[END_REF], located within the caldera of a large shield-type volcano of the Vate Trough, a young extensional rift in the New Hebrides back-arc (Vanuatu, SW Pacific).

2.2/ Cold seeps

We analyzed samples from seven seeps located in three different regions: the Gulf of Mexico, the western margin of Costa Rica, and Melanesia.

The Gulf of Mexico seeps (Brine Pool, Bush Hill, and West Florida Escarpment) have been extensively studied both geologically and biologically [START_REF] Paull | Biological communities at the Florida Escarpment resemble hydrothermal vent taxa[END_REF][START_REF] Paull | Stable isotope evidence for chemosynthesis in an abyssal seep community[END_REF][START_REF] Martens | Biogenic methane in the Florida Escarpment brine seeps[END_REF][START_REF] Chanton | Sulfur isotope and porewater geochemistry of Florida Escarpment seep sediments[END_REF], and were the basis for seminal observations on chemoautrophic life, including the demonstration of methane-based symbiosis between animals and intracellular bacteria [START_REF] Childress | A methanotrophic marine molluscan (Bivalvia, Mytilidae) symbiosis: mussels fueled by gas[END_REF]. The mussels of these sites are particularly well characterized, and the reader is referred to the numerous papers concerning in particular their symbionts or their isotopic compositions allowing analysis of their diet (e.g., [START_REF] Duperron | Diversity, relative abundance and metabolic potential of bacterial endosymbionts in three Bathymodiolus mussel species from cold seeps in the Gulf of Mexico[END_REF][START_REF] Macavoy | Stable isotope variation among the mussel Bathymodiolus childressi and associated heterotrophic fauna at four cold-seep communities in the Gulf of Mexico[END_REF][START_REF] Riekenberg | Shell carbon isotope indicators of metabolic activity in the deepsea mussel Bathymodiolus childressi[END_REF][START_REF] Feng | The stable isotope fingerprint of chemosymbiosis in the shell organic matrix of seep-dwelling bivalves[END_REF].

Twenty Gigantidas childressi shells from Brine Pool and Bush Hill, and one Bathymodiolus heckerae shell from West Florida Escarpment (SIO-BIC M8018), were analyzed. Gigantidas childressi is associated with only one type of methanotrophic symbionts, and consumes primarily methane. The metabolism of Bathylomodius heckerae, predominantly methanotrophic, is however potentially much more complex as its gills harbor a greater diversity of symbionts, including one methanotrophic, one methylotrophic, and two thiotrophic polytypes [START_REF] Duperron | Diversity, relative abundance and metabolic potential of bacterial endosymbionts in three Bathymodiolus mussel species from cold seeps in the Gulf of Mexico[END_REF]. However, δ 13 C values of these mussels confirm that seep hydrocarbons represent the dominant carbon source in their soft tissues [START_REF] Cavanaugh | Symbiosis of methylotrophic bacteria and deep-sea mussels[END_REF][START_REF] Paull | Old carbon in living organisms and young CaCO3 cements from abyssal brine seeps[END_REF][START_REF] Duperron | Diversity, relative abundance and metabolic potential of bacterial endosymbionts in three Bathymodiolus mussel species from cold seeps in the Gulf of Mexico[END_REF].

Cold seeps at the continental margin offshore Costa Rica harbor a varied fauna [START_REF] Sahling | Fluid seepage at the continental margin offshore Costa Rica and southern Nicaragua[END_REF][START_REF] Levin | A hydrothermal seep on the Costa Rica margin: middle ground in a continuum of reducing ecosystems[END_REF][START_REF] Levin | Biodiversity on the rocks: macrofauna inhabiting authigenic carbonate at Costa Rica methane seeps[END_REF], including mytilid species whose distribution shows some stratification with water depth [START_REF] Mccowin | Spanning the depths or depth-restricted: three new species of Bathymodiolus (Bivalvia, Mytilidae) and a new record for the hydrothermal vent Bathymodiolus thermophiles at methane seeps along the Costa Rica margin[END_REF]: Bathymodiolus billschneideri (~1400-1900 m), B. earlougheri (~1000-1900 m), and B. nancyschneiderae (1000-1100 m). Furthermore, Bathymodiolus thermophilus, a thiotrophic mussel typically encountered at hydrothermal vents along the Galápagos Rift Zone and the East Pacific Rise (e.g., [START_REF] Kenk | A new mussel (Bivalvia, Mytilidae) from hydrothermal vents, in the Galapagos Rift zone[END_REF], has been also identified in this area [START_REF] Mccowin | Spanning the depths or depth-restricted: three new species of Bathymodiolus (Bivalvia, Mytilidae) and a new record for the hydrothermal vent Bathymodiolus thermophiles at methane seeps along the Costa Rica margin[END_REF].

These four species are all thiotrophic [START_REF] Levin | A hydrothermal seep on the Costa Rica margin: middle ground in a continuum of reducing ecosystems[END_REF][START_REF] Brzechffa | Contrasting influences on bacterial symbiont specificity by co-occurring deep-sea mussels and tubeworms[END_REF]. A total of twenty shells from three seeps (Jaco Scar, Mound 12, and Parrita Seep) were analyzed (SIO-BIC specimens listed in Table S-1 but to the best of our knowledge, the nature of their symbionts and their isotopic compositions are unknown. The abundance of methane at these sites [START_REF] Herzig | Petrology, gold mineralisation and biological communities at shallow submarine volcanoes of the New Ireland fore-arc (Papua-New Guinea): preliminary results of R/V Sonne cruise SO-133[END_REF], as well as the similarities of these shells to Gigantidas childressi from Gulf of Mexico, strongly suggest that they are methanotrophic [START_REF] Von Cosei | Bathymodioline mussels of the Bathymodiolus (s.l.) childressi clade from methane seeps near Edison Seamount, New Ireland, Papua New Guinea[END_REF].

3/ Analytical procedures

Periostracum was removed from the shells by scraping using a clean steel blade. The determination of the REE abundances follows the procedure designed by [START_REF] Barrat | A new chemical separation procedure for the determination of rare earth elements and yttrium abundances in carbonates by ICP-MS[END_REF].

About 150-200 mg of shell were precisely weighted, spiked with Tm, and dissolved with HNO3. REE and Y were separated using ion-exchange chromatographic columns loaded with about 1 ml DGA resin. The measurements were performed on a Thermo Scientific ELEMENT XR™ spectrometer located at the "Pôle Spectrométrie Ocean", Institut Universitaire Européen de la Mer (IUEM), Plouzané (France). The reader is referred to previous papers where our analytical methodology for carbonate samples [START_REF] Barrat | A new chemical separation procedure for the determination of rare earth elements and yttrium abundances in carbonates by ICP-MS[END_REF], calibration, isobaric interference corrections and calculation of concentrations based on Tm addition are extensively described, and in which reference values for many international rock standards are reported (e.g., [START_REF] Barrat | Determination of rare earth elements in sixteen silicate reference samples by ICP-MS after Tm addition and ion exchange separation[END_REF][START_REF] Barrat | Geochemistry of CI chondrites: Major and trace elements, and Cu and Zn isotopes[END_REF][START_REF] Barrat | Evidence from Tm anomalies for non-CI refractory lithophile element proportions in terrestrial planets and achondrites[END_REF][START_REF] Charles | Trace element determinations in Fe-Mn oxides by high resolution ICP-MS after Tm addition[END_REF].

Measured REE concentrations were normalized to the Post Archean Australian Shale (PAAS; [START_REF] Pourmand | A novel extraction chromatography and MC-ICP-MS technique for rapid analysis of REE, Sc and Y: Revising CI-chondrite and Post-Archean Australian Shale (PAAS) abundances[END_REF], adjusted to standard results obtained in our laboratory [START_REF] Barrat | A new chemical separation procedure for the determination of rare earth elements and yttrium abundances in carbonates by ICP-MS[END_REF]. The La, Ce, and Gd anomalies were calculated using the La/La*, Ce/Ce*, Gd/Gd* ratios, where X* is the extrapolated concentration for a smooth PAAS-normalized REE pattern and Xsn is the concentration of element X normalized to PAAS: Lasn* = Prsn 3 /Ndsn 2 , Cesn* = Prsn 2 /Ndsn, Gdsn* = Tbsn 2 /Dysn. Replicate measurements obtained for two carbonate standards (CAL-S and BEAN) during the analytical sessions are given in Supplementary Table 1, together with reference values from the literature. The precision (RSD) on measured abundances and elemental ratios was generally better than 3 %. Note that there is no analytical bias between the REE concentrations presented here and those obtained by [START_REF] Wang | Lanthanum anomalies as fingerprints of methanotrophy[END_REF], which were obtained using the same procedure in our laboratory; or those from [START_REF] Bau | Rare earth elements in mussel shells of the Mytilidae family as tracers for hidden and fossil high-temperature hydrothermal systems[END_REF] obtained at Jacobs University (Bremen), which are also of very high quality. However, Y concentrations obtained in Bremen on different standards (e.g., [START_REF] Bau | Rare earth elements in mussel shells of the Mytilidae family as tracers for hidden and fossil high-temperature hydrothermal systems[END_REF][START_REF] Bau | Discriminating between different genetic types of marine ferro-manganese crusts and nodules based on rare earth elements and yttrium[END_REF][START_REF] Bau | The European Shale: an improved data set for normalization of rare earth elements and yttrium concentrations in environmental and biological samples from Europe[END_REF] were systematically lower than those obtained in Brest (e.g., [START_REF] Barrat | Geochemistry of CI chondrites: Major and trace elements, and Cu and Zn isotopes[END_REF], 2020[START_REF] Charles | Trace element determinations in Fe-Mn oxides by high resolution ICP-MS after Tm addition[END_REF]. This calibration bias was corrected here by multiplying all Y concentrations obtained by [START_REF] Bau | Rare earth elements in mussel shells of the Mytilidae family as tracers for hidden and fossil high-temperature hydrothermal systems[END_REF] by 1.10.

4/ Results

Sixty-six shell analyses were obtained here (supplementary Table 1), greatly increasing the number of available data for deep-sea mussels. These shells are very poor in REEs. Only rarely did concentrations of an element exceed 100 or 200 ng/g (e.g. Eu for some samples from hydrothermal vents). PAAS-normalized REY patterns from the available data set [START_REF] Bau | Rare earth elements in mussel shells of the Mytilidae family as tracers for hidden and fossil high-temperature hydrothermal systems[END_REF]Wang et al., 2020 and this work) are given in Figures 2345and provide an overview of the diversity of compositions of these shells.

4.1/ Hydrothermal vent mussels

As shown by [START_REF] Bau | Rare earth elements in mussel shells of the Mytilidae family as tracers for hidden and fossil high-temperature hydrothermal systems[END_REF], the shells of hydrothermal vent mussels have REY patterns showing large positive Eu anomalies, inherited from hydrothermal fluids (Fig. 2). In this regard, the shells analyzed here are no exception. The East Pacific shells show identical patterns (Fig. 2b), except for Eu, with the EPR sample having an Eu-anomaly similar to those from Logatchev (Fig. 2a) and the Rose Garden sample similar to those from Golden Valley (Fig. 2c) obtained by [START_REF] Bau | Rare earth elements in mussel shells of the Mytilidae family as tracers for hidden and fossil high-temperature hydrothermal systems[END_REF]. Both samples are also marked by positive Y and negative Ce anomalies that are unquestionably the fingerprint of seawater. Two samples of the same shell from Nifonea were analyzed and the results are almost identical, suggesting a homogeneity of the latter (Fig. 2e). The patterns obtained show positive Eu anomalies lower than for the two other samples from Pacific, as well as negative anomalies in Ce and positive in Y quite pronounced. These patterns are similar to those of Lilliput (Fig. 2b). The less pronounced Eu anomalies here could be explained by the composition of hydrothermal fluids, whose Eu anomalies are much lower than those of typical oceanic hydrothermal fluids [START_REF] Schmidt | Boiling vapour-type fluids from the Nifonea vent field (New Hebrides Back-Arc, Vanuatu, SW Pacific): geochemistry of an early-stage, post-eruptive hydrothermal system[END_REF], but also by a more important contribution of ambient seawater than for the other sites.

4.2/ Cold seep mussels

4.2.1/ Thiotrophic mussels

Only three thiotrophic mussel shells from the China Sea (Site F, Fig. 1) had been analyzed before this study [START_REF] Wang | Lanthanum anomalies as fingerprints of methanotrophy[END_REF]. Here we studied a much larger series of such mussels collected from three sites located in western Costa Rica. Four species of mussels were analyzed. For these three sites, whatever the species considered, the REY-patterns are quite similar (Fig. 3), and comparable in abundance and shape to those of Bathymodiolus aduloides collected in the China Sea (Fig. 4 b). Unlike shells from hydrothermal sites, these shells display low (Eu/Sm)sn ratios (=1.21-1.92). The shape of their REY patterns resembles those of marine carbonates (e.g., [START_REF] Northdurft | Rare earth element geochemistry of Late Devonian reefal carbonates, Canning Basin, Western Australia: confirmation of a seawater REE proxy in ancient limestones Geochim[END_REF]. The patterns are characterized by marked negative Ce anomalies (Ce/Ce*= 0.34-0.77 for Costa Rica samples, and 0.77-0.81 for China Sea samples) and positive Y anomalies (Y/Ho= 51-80 for Costa Rica samples, and 50-64 for China Sea samples). They also display positive anomalies in La (La/La*=1.9-3.0) in the range of seawater values, and apparent positive anomalies in Gd (Gd/Gd*=1.1-1.37).

4.2.1/ Methanotrophic mussels

The two Bathymodiolus shells from the Edison Seamount display lower REE than the Gigantidas from the China Sea [START_REF] Wang | Lanthanum anomalies as fingerprints of methanotrophy[END_REF], but their patterns show quite similar characteristics (Fig. 4). Two fragments per shell were analyzed, and the results are very consistent, suggesting that both shells do not show marked heterogeneities for REE and Y. As expected, they show positive anomalies in Y (Y/Ho=59.8-69.5) and negative in Ce (Ce/Ce*=0.39-0.46), but also striking La anomalies (La/La*= 7.9-11.9). [START_REF] Wang | Lanthanum anomalies as fingerprints of methanotrophy[END_REF] previously showed that the shells of methanotrophic mussels from the China Sea display exceptionally high positive anomalies in La (La/La*=2.5-5.1). Those measured in Edison Seamount shells are even higher, and to the best of our knowledge, represent the most pronounced La anomalies ever measured to date in natural environments not affected by emerging micropollutants. In addition, these shells also show apparent anomalies in Gd (Gd/Gd*=1.26-1.77).

Different REY patterns were obtained for mussels from the Gulf of Mexico (Fig. 5):

-The patterns of Gigantidas shells from Brine Pool and Bush Hill show distinctive light REE enrichments, moderate negative Ce anomalies, variable Gd anomalies (Gd/Gd*=0.88-1.53), as well as usual seawater-like positive Y anomalies (Y/Ho=43.8-72.5).

Their La anomalies (La/La*=1.73-2.70) are not as high as those discussed above, being comparable to typical marine values. Mussels from these two sites display similar REE concentrations, showing that the brines at Brine Pool do not affect the REE composition of shells. Note that the mussels at this site do not live in direct contact with the brines but within at ~10-cm-thick layer of seawater immediately overlying the brine pool or the surficial sediment at the pool edge. However, a sample from Brine Pool (BP7a) stands out from other shells by displaying higher heavy REE contents. We analyzed a second fragment from the same shell (BP7b), and obtained results that are closer to the rest of studied samples, suggesting that the first sub-sample may have been contaminated by impurities. Alternatively, the range of shell composition could be larger than suggested by the other samples.

-Two fragments of the same Bathymodiolus heckerae shell from the West Florida Escarpment were analyzed, with identical results (Fig. 5c). The corresponding patterns are different from those of other methanotrophic mussels. They do not show notable enrichments in light REE like those obtained from the Brine Pool or Bush Hill Gigantidas, nor large positive La anomalies like the methanotrophic mussels from China Sea or Melanesia. Their La/La* (=2.35-2.40) are in the range of seawater values. They are similar to those of thiotrophic mussels (e.g., those from Costa Rican seeps, Fig. 3), with similar negative Ce and positive Y anomalies (Ce/Ce*=0.39-0.41) and positive Y (Y/Ho=55.2-56.3).

5/ Discussion

5.1/ REE characteristics of hydrothermal versus cold seep mussels

Notable differences are apparent between the REE concentrations of mussel shells from hydrothermal vents and those from cold seeps. [START_REF] Bau | Rare earth elements in mussel shells of the Mytilidae family as tracers for hidden and fossil high-temperature hydrothermal systems[END_REF] showed that mussel shells living in the vicinity of Atlantic hydrothermal vents exhibit characteristic REY patterns, with large positive Eu anomalies [(Eu/Sm)sn up to 5, and >2 in most cases], which are inherited from hydrothermal fluids. The analyses obtained here confirm this observation, showing that all shells from submarine hydrothermal vents display positive Eu anomalies. In contrast, significant positive Eu anomalies are totally absent in shells from cold seeps (Fig. 6), which exhibit (Eu/Sm)sn ratios with little variability (between 1.1 to 1.9). Furthermore, 'hydrothermal' shells show much more variable and generally much higher REE concentrations than those from cold seeps, as illustrated by the histograms of Sm concentrations (Fig. 7). Their patterns also sometimes show enrichments in middle REE compared to heavy REE, which can be quantified with (Tb/Yb)sn ratios frequently exceeding 2 (Fig. 8). These mid-REE enrichments, combined with presence of significant positive Eu anomalies, reflect the contribution of hydrothermal fluids with REY distributions very different from seawater abundances [START_REF] Bau | Comparing yttrium and rare earths in hydrothermal fluids from the Mid-Atlantic Ridge: implications for Y and REE behaviour during near-vent mixing and for the Y/Ho ratio of Proterozoic seawater[END_REF][START_REF] Douville | Yttrium and the rare earth elements in fluids from various deep-sea hydrothermal systems[END_REF][START_REF] Schmidt | Geochemistry of hydrothermal fluids from the ultramafic-hosted Logatchev hydrothermal field, 15_N on the Mid-Atlantic Ridge: temporal and spatial investigation[END_REF], 2017;[START_REF] Craddock | Rare earth element abundances in hydrothermal fluids from the Manus Basin, Papua New Guinea: indicators of sub-seafloor hydrothermal processes in back-arc basins[END_REF] ; [START_REF] Cole | Rare earth elements as indicators of hydrothermal processes within the East Scotia subduction zone system[END_REF], with no evidence for any presumed biological effect.

5.2/ Differences between thiotrophic and methanotrophic mussels at cold seeps

At cold seeps, the REE distributions in mussel shells appear, at least in part, to reflect some characteristics of the surrounding water that they filter (Fig. 8, Fig. 9). Thiotrophic mussels have REE ratios falling in the range of seawater and pore water values. Some samples, however, have slightly higher (Tb/Sm)sn ratios than pore waters, but we do not consider this discrepancy to be very significant since the fluid composition at the sites where our shells originate is not known. As for the mussels from hydrothermal sites, there is no evidence for measurable imprint of any biochemical process involving REE for these animals.

The situation is quite different for methanotrophic mussels. While their heavy-REE ratios are similar to those for seawater or pore waters (Fig. 8), light-REE abundances depart substantially from both seawater and pore water values (Fig. 9). They are, in most cases, much more enriched in light-REE than waters, but also more than oil that can be present in some seeps like those of the Gulf of Mexico [START_REF] Smrzka | Oil seepage and carbonate formation: A case study from the southern Gulf of Mexico[END_REF]. As mentioned above, [START_REF] Wang | Lanthanum anomalies as fingerprints of methanotrophy[END_REF] showed that the shells of methanotrophic mussels at methane seeps from the South China Sea display high La anomalies relative to nearby thiotrophic mussel species, clams and seawater. The decoupling of La from other trivalent REE at cold seeps has been interpreted as reflecting the enzymatic activity of associated methanotrophic symbionts, hence indicating aerobic methanotrophy. The new data obtained in this study provide further support to this hypothesis. First, the thiotrophic mussel shells are not particularly enriched in light-REE, and do not show positive La anomalies exceeding usual seawater values, confirming that the enzymatic activity of their symbionts has no notable effect on these elements. On the other hand, the REY patterns obtained for methanotrophic mussel shells are much more variable than those associated with thiotrophic species. Light REE provide a clear-cut separation between most thiotrophic and methanotrophic mussels (Figs. 8 and9). Except for one sample that will be discussed later, methanotrophic mussels show light-REE enrichments (illustrated by (Pr/Sm)sn or (Pr/Nd)sn ratios, Figs. 8 and9) commonly accompanied by even more pronounced La enrichments (illustrated by the ratios (La/Nd)sn and La/La*, Fig. 9). The range of measured light-REE enrichments or La anomalies cannot be explained by local or environmental factors because it far exceeds the range of values known for sediments and marine waters (e.g., Fig. 8 and9). In agreement with [START_REF] Wang | Lanthanum anomalies as fingerprints of methanotrophy[END_REF], these enrichments are most likely related to the enzymatic activity of methanotrophic symbionts hosted by the gills of these mussels. It is logical to link them to the degradation of methane to methanol and then to formaldehyde, since REE-dependent (XoxF type) methanol dehydrogenase enzymes are used by these organisms (e.g., [START_REF] Skovran | XoxF is required for expression of methanol dehydrogenase in Methylobacterium extorquens AM1[END_REF][START_REF] Semrau | Metals and Methanotrophy[END_REF][START_REF] Cotruvo | The chemistry of lanthanides in biology: recent discoveries, emerging principles, and technological applications[END_REF]. The large variability of REY patterns obtained for methanotrophic shells suggests that the use of light-REE by these organisms differs from one species to another. For example, some animals generate very large La anomalies in their shells (e.g., Gigantidas platifrons from China Sea or Bathymodiolus from Edison Seamount), whereas others display only light REE enrichments, with La anomalies similar to those in seawater (e.g., Gigantidas childressi from Gulf of Mexico). These differences, which are extremely marked, necessarily reflect different needs in light-REE for the enzymatic activity of these animals. It should be noted, however, that the transfer of these elements from the gills (which are the seat of symbiont activity) to the mantle (which synthesizes the shell) has never been considered until now, and that REE fractionation during this transfer cannot be ignored.

One sample, a Bathymodiolus heckerae shell from the West Florida Escarpment (Gulf of Mexico), differs from all other methanotrophic mussel shells analyzed to date (Wang et al., 2020 and this study) in that it lacks significant light REE enrichment, and has an anomaly in La within the range of conventional marine values. This sample even shows REE distributions very similar to those of thiotrophic mussel shells (Fig. 5c). Note that these particular REE signatures associated with Bathymodiolus heckerae cannot be related to analytical artefacts because two separate shell fragments were analyzed, both returning similar results (Supplementary Table 1). Clearly, the symbionts of this mussel did not require light-REE to degrade methane. It is logical to assume that they do not use REE-dependent (XoxF type) methanol dehydrogenase enzymes, but rather Ca-dependent (MxaF type) methanol dehydrogenase enzymes. However, only one shell of this species was available for our study.

It would be appropriate at this stage to analyze other samples from this site, and obviously to identify the enzymes used by the mussel symbionts of this species.

5.3/ Gd anomalies in mussels from cold seeps

An unexpected result of our study concerns the presence of marked positive Gd anomalies in many cold seep shells (Fig. 10), whereas those from hydrothermal vents show a much narrower range of Gd/Gd* values.

Gd anomalies are a common feature of REE sorption in the presence of strong solution complexation, for example with carbonate (dominant in the ocean) or with organic ligands (e.g., [START_REF] Quinn | Sorption of yttrium and rare earth elements by amorphous ferric hydroxide: Influence of solution complexation with carbonate[END_REF][START_REF] Christenson | Stability of YREE complexes with the trihydroxamate siderophore desferrioxamine B at seawater ionic strength[END_REF]. Gd/Gd* ratios on the order of 1.2 are typically encountered in the marine environment. The cold seep mussels investigated here typically display Gd/Gd* ratios exceeding 1.2. We are confident that these anomalies do not correspond to analytical artifacts resulting from biased correction of interferences produced by Ce and Pr oxides. Our procedure effectively corrects for these interferences (e.g., [START_REF] Charles | Trace element determinations in Fe-Mn oxides by high resolution ICP-MS after Tm addition[END_REF], and the results obtained on standards during the course of the study demonstrate the quality of our analyses (Supplementary Table 1). Moreover, these anomalies were confirmed through multiple measurements of different fragments of the same shells, and cannot possibly relate to inappropriate normalization values, because similar Gd/Gd* values are calculated by normalizing abundances to other shale references or to chondrites (e.g., [START_REF] Barrat | Geochemistry of CI chondrites: Major and trace elements, and Cu and Zn isotopes[END_REF]Bau et al., 2018, not shown). We are also confident that measured Gd excesses in this study were not caused by an artefact related to the calculation of Gd anomalies. Here, Gd anomalies have been calculated by extrapolating Gd* using Tb and Dy concentrations, but similar results are obtained when Gd* is interpolated between Sm and Tb (not shown).

Positive Gd anomalies have been measured in mollusk shells living in waters polluted by Gd-based contrast agents (GBCA) used for medical imaging by magnetic resonance (e.g., [START_REF] Merschel | Rare earth elements in the aragonitic shell of freshwater mussel Corbicula fluminea and the bioavailability of anthropogenic lanthanum, samarium and gadolinium in river water[END_REF][START_REF] Ponnurangam | Mussel shells of Mytilus edulis as bioarchives of the distribution of rare earth elements and yttrium in seawater and the potential impact of pH and temperature on their partitioning behavior[END_REF][START_REF] Akagi | Sources of rare earth elements in shells and soft tissues of bivalves from Tokyo Bay[END_REF][START_REF] Goff | Compound-specific recording of gadolinium pollution in coastal waters by great scallops[END_REF]. Even if this pollution is now common in coastal waters worldwide, it is highly unlikely that anthropogenic Gd pollution affects the deep sea at which our samples were collected. We tentatively propose that these Gd anomalies are the result of either biological fractionation or that they reflect the composition of the water filtered by the mussels. Since Gd anomalies are present in the shells of both thiotrophic and methanotrophic animals, they are probably not the result of any specific enzymatic activity related to nutritional symbiosis. It is possible that cold seep mussels fractionate Gd for an undiscovered use in other biological functions. One may wonder why these anomalies are not ubiquitous features of bivalve shells, including in thiotrophic species encountered at hydrothermal vents, where mussel shells lack the marked Gd anomalies. As an alternative, Gd anomalies could be inherited from water filtered by mussels. Recently, [START_REF] Möller | REY patterns and their natural anomalies in waters and brines: the correlation of Gd and Y anomalies[END_REF] reported high Gd/Gd* ratios in natural, uncontaminated, spring waters and hypersaline waters (e.g., Dead Sea). Gd/Gd* ratios close to 1.7 or higher are unknown in seawaters and oceanic hydrothermal fluids. What about methane-rich fluids from cold seeps? There are limited REE data for methane-rich fluids at cold seeps (e.g., [START_REF] Himmler | Rare earth element geochemistry in cold-seep pore waters of Hydrate Ridge, northeast Pacific Ocean[END_REF][START_REF] Lemaitre | Trace element behaviour at cold seeps and the potential export of dissolved iron to ocean[END_REF]. However, some of their characteristics can be inferred from the composition of the carbonates they deposit (e.g., [START_REF] Smrzrka | Trace elements in methane-seep carbonates: potentials, limitations, and perspectives[END_REF]Bayon et al., 2020a). To date, it has not been convincingly shown that cold seeps carbonates can exhibit such anomalies, making it more than hypothetical that they are present in cold seeps fluids. The origin and significance of the Gd anomalies observed in cold seeps mussel shells is not yet understood.

6/ Conclusions and perspectives

In this study, we compared REE abundances in deep-water mussel shells from various hydrothermal and cold seep systems worldwide, confirming that animals living at hydrothermal sites display compositional shell characteristics of surrounding fluids such as positive Eu anomalies [START_REF] Bau | Rare earth elements in mussel shells of the Mytilidae family as tracers for hidden and fossil high-temperature hydrothermal systems[END_REF]. The REE distribution in bivalve shells at submarine methane seeps provides a window into the enzymatic activity of associated symbionts. Unlike thiotrophic mussels, methanotrophic species whose symbionts use light-REE to degrade methane, show characteristic light-REE enrichments in their shells. Our results confirm that only methanotrophic mussels display distinctive positive La anomalies, but selective La enrichments are not systematic to all species analyzed. The degree of light REE enrichment in shells probably depends on the distribution pattern of hosted symbionts and associated enzymatic activities, suggesting regional variability. For instance, only shells from western Pacific sites displayed strong positive La anomalies in this study. At this stage, it is unclear whether this characteristic relates to the presence of specific symbiont lineages. Future investigations of other mussel species from additional sites combined with symbiont characterization will be required to address this issue. In future studies, REE concentrations in abyssal mussel shells could be used to distinguish methanotrophic from thiotrophic species, hence serving as tools for identifying their general enzymatic pathway. This novel approach could be also applied to the sedimentary record and the investigation of fossil shells in ancient seep deposits, providing of course that diagenesis or any other post-depositional processes have not modified their REE abundances. Overall, these findings open up important multidisciplinary perspectives in biology, Earth and environmental sciences, requiring further improvements in our understanding of the REE biogeochemistry and inventory in organisms, and their role in enzymatic activities and metabolic pathways. Additional work will be also needed to assess the impact of life on REE cycling in the ocean, something that has been largely overlooked until now. Our new data reinforce the idea that methanotrophic organisms, at least those using REE-based enzymes, can leave a distinctive chemical fingerprint in rocks, in particular in methane-derived carbonates at cold seeps. Some of these authigenic carbonates contain organic-rich aragonitic cements exhibiting high light-REE enrichments [START_REF] Zwicker | Rare earth elements as tracers for microbial activity and early diagenesis: A new perspective from carbonate cements of ancient methane-seep deposits[END_REF]. So far, these cements have been interpreted as fossil biofilms [START_REF] Hagemann | Biomarkers from individual carbonate phases of an Oligocene cold-seep deposit[END_REF], but it is very tempting to see here the imprint of aerobic methanotrophic activity. Finally, we draw the attention of the large geochemical community studying ancient marine sedimentary formations of Precambrian age, to the interest of carrying out detailed investigations coupling REE to other conventional biogeochemical proxies such as stable isotopes in order to detect the emergence of aerobic methanotrophy on Earth. for REE and Y abundances [START_REF] Bau | Rare earth elements in mussel shells of the Mytilidae family as tracers for hidden and fossil high-temperature hydrothermal systems[END_REF][START_REF] Wang | Lanthanum anomalies as fingerprints of methanotrophy[END_REF] this study). et al., 2010, Wang et al., 2020, andthis study). When two fragments of the same shell were analyzed, we used their average here. Bau et al., 2010, Wang et al., 2020, andthis study). Fields for seawater (SW), pore waters (PW) and vent fluids (VF) are drawn from a database of results of the literature (see Bayon et al. (2020b) for the references for seawaters and pore waters, and Bau andDulski (1999, Douville et al. (1999), [START_REF] Schmidt | Geochemistry of hydrothermal fluids from the ultramafic-hosted Logatchev hydrothermal field, 15_N on the Mid-Atlantic Ridge: temporal and spatial investigation[END_REF][START_REF] Schmidt | Boiling vapour-type fluids from the Nifonea vent field (New Hebrides Back-Arc, Vanuatu, SW Pacific): geochemistry of an early-stage, post-eruptive hydrothermal system[END_REF] Bau et al., 2010, Wang et al., 2020, andthis study).

Fields for seawater (SW), pore waters (PW) and vent fluids (VF) are drawn from a database of results of the literature (see figure 8 for the references). Oils from seeps from the southern Gulf of Mexico are shown for comparison [START_REF] Smrzka | Oil seepage and carbonate formation: A case study from the southern Gulf of Mexico[END_REF].

29

Figure 10. Histograms of Gd/Gd* ratios in mussels shells from hydrothermal vents and cold seeps (data from Bau et al., 2010, Wang et al., 2020, andthis study). When two fragments of the same shell were analyzed, we used their average here.

  ). Two mussel shells (Bathymodiolus edisonensis, SIO-BIC M18361; and B. anteumbonatus, SIO-BIC M15360) from a cold seep in the vicinity of Edison Seamount (New Ireland Basin, Melanesia, Herzig et al., 1998) complement our sampling of methane seep mollusks. Mussels from this location have been described by von Cosei and Janssen (2008),

Figure 1 .

 1 Figure 1. World map and location of the hydrothermal vents and cold seeps where mussels have been analyzed

Figure 3 .

 3 Figure 3. PAAS-normalized REY patterns for mussel shells from vents located west of Costa Rica. Here, all the mussels are thiotrophic (T).

Figure 4 .

 4 Figure 4. PAAS-normalized REY patterns for mussel shells from cold seeps located in the China Sea (site F and Haima, Wang et al., 2020), and from Edison Seamount (this work). Bathymodiolus aduloides mussels are thiotrophic (T). The others are methanotrophic (M).

Figure 5 .

 5 Figure 5. PAAS-normalized REY patterns for mussel shells from the Gulf of Mexico (M: methanotrophic; T: thiotrophic).

Figure 6 .

 6 Figure 6. Eu vs. Sm plot for mussel shells from hydrothermal vents and cold seeps (data from Bau et al., 2010, Wang et al., 2020, and this study).

Figure 7 .

 7 Figure 7. Histograms of Sm abundances in mussel shells from hydrothermal vents and cold seeps (data from Bau

Figure 8 .

 8 Figure 8. (Tb/Yb)sn vs. (Pr/Sm)sn plot for mussels shells (data fromBau et al., 2010, Wang et al., 2020, and this

  , Craddock et al. (2010), and Cole et al. (2014) for vent fluids).

Figure 9 .

 9 Figure 9. (La/Nd)sn vs. (Pr/Nd)sn plot for mussels (data from Bau et al., 2010, Wang et al., 2020, and this study).

Acknowledgements.

This project was founded by "Laboratoire d'Excellence" LabexMER and co-funded by grants from the French Government. Shell fragments from museum specimens at SIO-BIC were provided by Charlotte Seid and Greg Rouse. Specimens from Costa Rica were collected under permits SINAC-CUS-PI-R-035-2017 and SINAC-CUSBSE-PI-R-032-2018, issued by the Ministerio de Ambiente y Energía (MINAE), Sistema Nacional de Areas de Conservación (SINAC). Collection of the Costa Rica specimens was funded by the United States National Science Foundation, grant # OCE-1634172 to Greg Rouse and Lisa Levin. We thank the captain and crew of the R/V Atlantis, the pilots and technicians of the HOV Alvin, chief scientist Erik Cordes, and the scientists of cruises AT37-13 and AT42-03 for assistance with specimen collection in Costa Rica. Gigantidas childressi samples were collected by R.S. Carney and made available through grant GM-09-01-08 from the US Bureau of Ocean Energy Management to R.S. Carney and B. Fry of Louisiana State University. We thank Balz Kamber for the editorial handling, Johan Schijf and Dong Feng for their constructive reviews.