Franck Luthon
email: franck.luthon@univ-pau.fr

Benoît Larroque

Remote Laboratory for Game-Based Distance Learning in Electronics

The advances in communication networks and web technologies, in conjunction with the improved connectivity of test and measurement devices make it possible to implement e-learning applications that encompass the whole learning process. In electrical engineering, it means not only lecture, tutorial, quiz and simulation, but also practical labwork for training with real-world devices that are controlled remotely. To make e-labs attractive, they should be easily implemented and accessed on the web by a client. This keypoint raises technical issues that are discussed in this paper. Nonetheless pedagogical issues are equally important. Here, to foster student motivation, a game-like scenario embedded in a learning management system is proposed.

INTRODUCTION

This paper presents LaboREM, a remote laboratory implemented for e-learning in electronics. Bayonne Technological University Institute in Anglet, France, promotes the introduction of distance learning for the education of students in electrical and mechanical engineering, either to learn teleworking or for lifelong learning. As part of the academic curriculum for the bachelor of science degree in industrial engineering, the course of analog electronics deals with operational amplifiers (OAs), active filters and oscillators. The learning objectives are to implement basic electronic functions with OAs, and to use measurement devices for the characterization of circuits. The practical labwork assigned to students can be done remotely, instead of classical hands-on sessions lasting 3 to 4 hours each. Here, we focus on 1st year undergraduate students (teenagers of 18-19 years old). For that target audience who often lacks personal motivation in studying, both game-based learning and adaptive learning are good solutions in order to enhance students' motivation and autonomy.

The lab platform is based on the design and control of virtual instruments (VIs) for the management of remote experimentation through the web, together with the use of a Learning Management System (LMS) for the management of students and for the collaborative work supervision. The client-server architecture that allows remote control of instruments in realtime, with the management of both the stack of experiments and the list of users, is a classical solution.The originality of the proposed work is twofold: (i) a feel-ing of the lab via the use of a video camera and a robotic arm for placement of components, plus the optional use of computer vision to address user-specific requests to see the equipment in detail (zoom-in for displaying instruments or device under test DUT); this is intended to mimic both the user eye and the user hand; (ii) a game-based scenario (treasure hunt) that is implemented as learning approach, with an attempt to mimic the behaviour of students when they work close together (collaborative work through use of chat, forum) and with a learning itinerary adapted to each student (difficulty level depending on the evaluated knowledge and motivation).

The paper is organized as follows. In section 2, the motivations of the work are given with respect to the state of the art. Section 3 shows the technical choices for software architecture and hardware setup of the lab, while section 4 describes the game-like pedagogical scenario implemented via an LMS.

MOTIVATIONS AND STATE OF THE ART

Practice is known to be an essential part of learning in engineering education, as long as clear learning objectives are assigned (ABET criteria). The pros and cons of the three types of labs (hands-on, virtual and remote) are wellknown. Virtual labs are based on simulations only (mathematical models), whereas remote labs use real-world instruments in a physical lab (Garcia-Zubia & Alves 2011). Virtual and remote labs belong to the generic group of e-labs, made possible thanks to the development of Internet. They are the modern counterpart of traditional hands-on labs. Since access to real-world labs is restricted due to high number of students, cost of equipment, supply and maintenance needs, e-labs are great alternatives. The complementarity of activities is also well known: a mixed pedagogy including hands-on, simulations and remote control is preferable, which is called blended learning. A specific argument in favor of real remote labs (compared to virtual labs) is the fact that nowadays in industry, more and more engineering activities take place remotely, for example the maintenance of systems or control of processes through a computer interface located in a distant supervision room (SCADA: supervisory control and data acquisition). Hence, there is a strong need to educate students to this new way of working.

However, the bottlenecks of remote labs lie in the interaction with instruments and the interaction between people. Smart software is needed in order to: (i) design an open (flexible enough) architecture for distant control of hardware devices (technical need) and (ii) develop tools and interfaces to enable and track the cognitive behavior of the user in a distant instrumentation application (human need). When distance activities are developed, motivational features are very important for the self-regulation of learning [START_REF] Mendez | Implementing motivational features in reactive blended learning: Application to an introductory control engineering course[END_REF]. To get real-time knowledge of the state of work of a student and for automatic assessment procedures, the use of an LMS is mandatory. Some authors already addressed the coupling of remote labs with LMS [START_REF] Orduña | Generic integration of remote laboratories in learning and content management systems through federation protocols[END_REF], or the coupling of virtual labs with games, but the present work is one of the first attempts to mix three views together: remote lab, LMS and game. The lab is piloted by a web-based client application (accessed via a browser). Thanks to the LMS, the collaborative dimension is added to the remote labwork, together with the supervision of students activity. Moreover, interaction tools for better immersion and motivation are proposed: a Top 10 score is displayed in real-time to motivate students by a challenge, and a robotic arm plays the role of the student hand for choosing and placing components to build the DUT. This solution is an alternative to the virtual wiring of a relay switching matrix implemented in [START_REF] Tawfik | Virtual instrument systems in reality (VISIR) for remote wiring and measurement of electronic circuits on breadboard[END_REF].

ARCHITECTURE OF LABOREM

Software Architecture

The software should enable a completely connectible application and easy installation by the client, run in real-time, communicate easily with hardware devices and with clients, enable extensibility (to add new experiments), implement collaborative work. The choice of network technology should therefore be compliant with various issues: accessibility, reusability, portability, security, scalability, interoperability, traceability. The importance of universality versus software power discussed in (Garcia-Zubia et al.

2009) leads to the conclusion that a service-oriented architecture (SOA) should be adopted. In [START_REF] Guimaraes | Design and implementation issues for modern remote laboratories[END_REF], a formal reference model for weblabs described in UML language identifies the major functions that a remote lab with SOA must support in order to yield the proper quality of service. Four main entities interact: (i) the client that uses the lab, (ii) the lab that operates the hardware resources for experiments, (iii) the LMS that organizes the learning activity, and (iv) the corporate webserver that gives access to the platform. LaboREM is compliant with this model (Fig. 1). Based on the use of two servers, it is designed to make the LMS communicate with the remote lab. The LMS is the middleware including databases for management of users (using lightweight directory access protocol LDAP), central authentication service (CAS), administration, learning tools, content packaging, evaluation, communication tools for collaborative work. The remote lab is the hardware part that includes instruments (with software drivers). The labwork is accessed through the use of the LMS, by calling, at specific steps in the learning sequence, the proper URL address corresponding to the remote VI to pilot. The e-lab availability service is taken in charge by the labserver itself (management of the queue of users to give access to experiments). The e-lab control service is for piloting the remote devices (instruments, robot). Compared to other existing solutions like NetLab [START_REF] Nedic | Remote laboratory Net-Lab for effective teaching of 1st year engineering students[END_REF] or iLab shared architecture [START_REF] Harward | The iLab Shared Architecture: A web services infrastructure to build communities of internet accessible laboratories[END_REF], the main differences are as follows: the collaborative tools are provided by the LMS, not by the remote lab. A single labserver is in use, and most of the services are taken in charge by the LMS (authentication, trace recording, data storage), apart from the actual task of interaction with hardware.

Hardware architecture

In addition to the corporate webserver, the lab application server located inside the industrial engineering department laboratory is another dedicated server for connection to the lab equipment (Fig. 2). A typical hardware setup consists of instruments connected through GPIB (waveform generator, multimeter, oscilloscope), plus other equipment connected through USB, RS232, PCI or wireless (data acquisition board, electronic protoboard, switching matrix with relays, on-off button, robotic arm, camera, automatic background lighting).

Robotic Arm for Component Placement: La-boREM gives the opportunity to build the circuit using an old fashioned robotic arm (Fig. 3). The DUTs are circuits consisting of OAs, resistors and capacitors only (no inductance). Thus, a simple solution for easily placing components is to equip them with magnets, so that the right place and good connection contact are achieved, without needing high spatial precision or big mechanical effort. Fig. 3 shows the two-step action: first, grasp one component from the bank of components; then, move and drop it down on the receiver board connected to the breadboard where OAs are already in place. In addition to the robot-built Note that in case of failure of the robot placement, the client can switch to precabled filters instead, that do not require the robot use.

Server-Side Application

The block-diagram of the lab application is shown in Fig. 4. The application is made of four main VIs Visualization gives a feedback on the remote lab. The client can observe this VI. It includes three functions: (i) VideoFeedback is for video capture and image processing in real-time. A template matching algorithm, applied on color and shape information in each image captured at video-rate, enables the user to focus selectively and zoom on each of the devices. (ii) WaitingList displays the waiting queue of users that are requesting the control on instruments. (iii) TOP10 displays the ranking in a "hall of fame" (best answers to measurement requests).

Simulator is directly accessible via the LMS. It is the main VI of the virtual lab (as opposed to the remote lab) that offers numerical simulations of the various DUTs. It gives students the opportunity to make comparisons between simulation results and actual measures. Since it does not communicate with any instrument, there is no waiting queue to manage: many users can run simulations in the same time.

Virtual Manager is in charge of the lab management. It ensures 3 main functions. (i) Storage: it stores data of each experiment (username, timer, experiment selection, Top 10 answers); (ii) Scheduling: it manages the waiting queue of connected clients. It uses a specific tool (Remote Panel Connection Manager) for the management of remote front panel connections. There is no sophisticated scheduling mechanism nor concurrency problem, since a user is allowed 5 minutes only, and a first-in first-out solution is adopted (no time slot reservation). (iii) Initialization and Security: it reinitializes the system (interface and robot) to its default state, each time an experiment ends, or if another user takes the control of the lab.

Client-Side Communication Interface

CGI scripts: A first solution is low-level programming with http protocol: to use CGI scripts (common gateway interface) to transmit information (Put, Get and Post instructions). It implies upload and download of data (input parameters and output measurements). The deployment on client-side is a desktop application: executable installed and running on the client computer [START_REF] Steriu | Open architecture for signal processing lab distance learning[END_REF]. The main drawback is that it only works in batch mode (asynchronous communication), so that real-time interaction is not possible Another drawback is that the desktop application must be updated by the client each time the lab-server application undergoes a software upgrade.

Remote Front Panels: A 2nd solution is to use remote front panels (RFP) available with NI-LabVIEW software. Starting from an existing VI including drivers to communicate locally with instruments, the web publishing tool, with remote control option activated, allows to create a HTML file embedding the front panel object code, so that a VI is visible and controllable from Internet in real-time. As soon as the lab application server (LabVIEW webserver) is started, the HTML file is accessible over the Internet through its specific URL address. Therefore it is both easy for a client to use the lab and for a tutor to add a new lab activity: there is very little programming overhead needed to publish new VIs on the web. The front panel seen by the remote client is exactly the same as the one seen within the local application: the interface is rich and convivial (Fig. 5). The main drawback is that RFP client-server data communication uses a proprietary non-http protocol. It might by detected and stopped by some firewalls, anti-viruses or proxies. A second drawback is that the client has to download and install the shareware LabVIEW RunTime Engine (LVRTE) which is a plugin of size 40Mbytes. Fortunately, since the latest release of LabVIEW 2014, most browsers support this plugin: not only Internet Explorer, but also Chrome, Firefox, Netscape, Safari or Opera. A final point worth mentioning is that the VI is executed on the server-side. Clients who are connected to the remote front panel, and waiting to get the control over the VI, can observe what the current client who has the control is doing. Note that this can be a good point for student collaboration. Webservices: A third solution is the use of webservices [START_REF] Dutta | November). A web-service and interface for remote electronic device characterization[END_REF]). The advantage is to conform to standard communication protocol (fully compatible with any network and web browser configuration, no restriction due to firewall or antiviruses, mobile device compatibility). The client interface may be thin. A specific URL address with command parameters generates dataflows in real-time, containing output data described in XML, TEXT or JSON format. This solution may even be deployed starting from a LabVIEW project (that provides a tool to build Restful Webservices and a WebUI Builder). This alternative yields a remote interface to LabVIEW that avoids the use of the non-http RFP protocol. Its main drawback is that the remote graphical interface requires dedicated programming. Adding a new lab activity is not straightforward since it requires extra coding first.

We have tested the three solutions for LaboREM, and we advocate the 3rd solution using webservices. The prospective use of HTML5, merging JavaScript and XML, is the best technical choice. Among others, it allows to address the flexibility and ubiquitous use of the application on mobile devices (smart phone, tablet) which is desirable for gaming purpose. Nevertheless, as the 2nd solution based on RFPs does not require extra-coding and provides a richer interface, we present here the results based on RFP: Fig. 5 shows a typical interface seen by a client (remote student) when asking for plotting Bode curves to characterize the transfer function of active filters. The user builds his own circuit by piloting the robotic arm, makes measurements in real-time, exploits graphs thanks to cursors and markers, optionally takes part in the Top 10, has a visual feedback of the lab through a camera, and gets information about the waiting list of users with a time-counter, so that he knows exactly when he will be served. In the meanwhile, he can switch to other activities proposed in the pedagogical scenario. Motivation of students practicing e-learning may not only be modeled and measured, but also fostered, with the proper use of an LMS. In addition, the use of games may contribute in enhancing motivation. Games are characterized by 6 key-dimensions: fantasy, clear rules and goals, sensory stimuli, challenge, mystery and control. They are taken into account as follows: mystery (unknown filter to uncover), sensory stimuli (visual feedback and robotic motion), challenge (Top 10), control (choice of DUT), rules (limited time and number of trials), goals (clear learning objectives with three difficulty levels to choose from), fantasy (freedom to conceive one's own circuit). The student has the choice between various filters: some of them are known (SallenKey, Wien etc.) or can be built by the student himself; others are unknown and should be identified during the lab session. Depending on the filters chosen for study, and on the exactness of answers, the student accumulates more or less points during his "travel" and may take place in the "hall of fame". If he discovers what the unknown filter is, he has reached the final objective of the "quest". If he fails, he may choose to backtrack and try again (limited repetitions). All the scenario is managed by the LMS. The game-like scenario conceived to boost student motivation [START_REF] Luthon | LaboRem: open lab for remote work[END_REF]) is based on 4 concepts as in e-games: levels, lives, points, and time. Four counters are used for evaluating student's activity: a timer, a counter for highest level achieved (difficulty level), a counter for number of lives used (number of trials), a counter for scoring (marks obtained to online quizzes and final exam). This allows to automatically estimate a participation index and a performance index which are taken into account as in [START_REF] Mendez | Implementing motivational features in reactive blended learning: Application to an introductory control engineering course[END_REF] for driving motivation in auto-regulated distance e-learning. A polylinear learning path is implemented in the LMS as a "treasure hunt", that is adapted to the competence level and motivation of the student. Depending on the level, a flag (red, orange or green) is generated to orientate the student towards the best path and with the best workload according to the student's profile (Fig. 6). A typical scenario consists of: (i) videopresentation of the lab; (ii) test on prerequisites; (iii) lesson (online documents); (iv) formative test; (v) remote labworks with three levels of difficulty; (vi) comparison with simulator (virtual lab); (vii) final summative test with upload of work report; (viii) satisfaction survey. Fig. 7 is a snapshot of the screen seen by a client when asking for the remote lab within the LMS: he can download a preformatted text-file for writing the report to answer the questions (left button), click on "Travaux Pratiques" (middle button) to run real experiment, or run the simulator (right button). The traffic light indicates the best difficulty level to choose from, depending on previous answers to quizzes. The student can also watch a tutorial that explains the use of the remote lab by playing a video1 (bottom movie).

Student Motivation

In 2011, a group of 29 students was tested. At first, an a priori survey (Pre-Inquiry) about their motivation2 showed that their main motivation was intrinsic acquisition of knowlegde (mean score of about 3.8/5), long before their position with respect to others (mean score of about 2/5). To enhance their external motivation, a Top 10 list was added in 2013, that gives in real-time the best score achieved in measurement activity ("hall of fame"). Moreover, the robotic arm was also added. This proved to increase extrinsic motivation by more than 20%. Tab.1 gives the average values for intrinsic and extrinsic motivation measured from pre-and post-inquiries3 (before and after the lab activity) in 2011, 2012and 2013. It also shows (in bold) . It also shows (in bold) the increments between 2012 and 2013. Worth to mention is the fact that the students that most participated in the Top 10 were not necessarily the "best" of our students (in terms of academic marks). Indeed, competitiveness increased their motivation.

DISCUSSION AND FUTURE WORK

If motivation increase is proven, we do not claim yet better learning outcomes, compared to hands-on. The LaboREM platform is one supplementary tool to help students in their studies, especially in an autonomous and student-centered context, as recommended in Europe by the Bologna process. Moreover, this tool is exhibited each year during the "open doors day" of the university and proves to be very attractive for visitors which are teenagers coming from high schools. It contributes to increase the number of students willing to undergo a STEM curriculum (science, technology, engineering, mathematics). The perspective of this work is manifold. A multi-server solution is of high interest to give access to experiments in various remote labs (cf. iLab shared architecture). We intend to make LaboREM fully compliant with SCORM standard for complete integration of the weblab into the LMS. As regards image processing, face expression analysis may help to know the state of motivation of a student. Giving control of the camera to the client is attractive for closer collaboration with a colleague or tutor. In addition to the wide-angle camera that supervises the whole scene, a minidrone equipped with a micro-camera can be used to fly over the lab at user's request. Other labwork to be added is to pilot photovoltaic panels on the roof of the university buildings.

Figure 1 .

 1 Figure 1. Network and software architecture.

Figure 2 .

 2 Figure 2. Hardware architecture: INSTR means instruments; DAQ means data acquisition.

Figure 3 .

 3 Figure 3. Robot in action for (a) choosing and (b) placing a component equipped with magnets (c).

Figure 4 .

 4 Figure 4. Application block-diagram: blocks in gray correspond to functions that are added to get a remote lab, starting from a hands-on lab (blocks in white).listed below, that are designed and controlled with NI-LabVIEW software.LaboratoryWorks is the main VI of the remote lab. It enables to select the type of experiment (GPIB-Bode plots or DAQ-Signal spectrum analyses) and type of filter. It allows to control the instruments and the robotic arm to build the DUT, to make measurements, to recover data graphs and to participate to Top 10 ranking. The client has full control over this VI.Visualization gives a feedback on the remote lab. The client can observe this VI. It includes three functions: (i) VideoFeedback is for video capture and image processing in real-time. A template matching algorithm, applied on color and shape information in each image captured at video-rate, enables the user to focus selectively and zoom on each of the devices. (ii) WaitingList displays the waiting queue of users that are requesting the control on instruments. (iii) TOP10 displays the ranking in a "hall of fame" (best answers to measurement requests).Simulator is directly accessible via the LMS. It is the main VI of the virtual lab (as opposed to the remote lab) that offers numerical simulations of the various DUTs. It gives students the opportunity to make comparisons between simulation results and actual measures. Since it does not communicate with any instrument, there is no waiting queue to manage: many users can run simulations in the same time.Virtual Manager is in charge of the lab management. It ensures 3 main functions. (i) Storage: it stores data of each experiment (username, timer, experiment selection, Top 10 answers); (ii) Scheduling: it manages the waiting queue of connected clients. It uses a

Figure 5 .

 5 Figure 5. Client rich interface showing a remote front panel: left) learning steps of the pedagogical scenario; center) experimental results (Bode plots of a high-pass filter); right) video feedback of lab and instruments, Top 10 list and queuing list of users.

Figure 6 .

 6 Figure 6. Learning map as a treasure hunt with various steps implemented as activities in the LMS: lessons (documentation and help), quizzes (self evaluation and final test), remote labworks.

Figure 7 .

 7 Figure 7. LMS interface seen by the client when requesting access to remote lab experiment.

Table 1 .

 1 Student motivation (on a 5-point Likert scale)

	year (# students)		Intrinsic	Extrinsic
	Pre-Inquiry	mean increment mean increment
	2011 (29 stud.)	3.73		2.06
	2012 (22 stud.)	3.89		1.95
	2013 (25 stud.)	4.18	+0.29	2.42	+0.47
	Post-Inquiry	mean increment mean increment
	2012 (22 stud.)	3.1		2.55
	2013 (25 stud.)	3.5	+0.4	3.1	+0.55

https://www.youtube.com/watch?feature=player_ embedded&v=m7pyIr2ub54

Sample statements for measuring motivation are, among others: "It is important for me to learn"; "It is important for me to look like being proficient"; "I am motivated by this remote learning activity"; "I am motivated by the final mark I will obtain for this labwork".

3 Pre-and Post-inquiry values are not directly comparable since they are based on two different types of questionnaire; moreover, in 2011, there was no Post-inquiry available.


Marine Blanchet

Olivier Pringault

Marc Bouvy

Philippe Catala

Louise Oriol

Jocelyne Caparros

Eva Ortega-Retuerta

Laurent Intertaglia

Nyree West

Martin Agis

Patrice Got

Fabien Joux
email: joux@obs-banyuls.fr

Changes in bacterial community metabolism and composition during the degradation of dissolved organic matter from the jellyfish Aurelia aurita in a Mediterranean coastal lagoon

Keywords: Aurelia aurita, jellyfish, organic matter, heterotrophic bacteria, biodegradation, bacterial growth efficiency, bacterial diversity

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

Bacteria are key organisms in carbon cycling in aquatic ecosystems, acting as a sink (mineralization of dissolved organic carbon to CO 2 ) or as a link (production of biomass that can be transferred through the microbial food web) [START_REF] Cotner | Small players, large role: microbial influence on biogeochemical processes in pelagic aquatic ecosystems[END_REF]. The quality and the quantity of organic matter greatly influence bacterial metabolism and community structure [START_REF] Azam | Microbial structuring of marine ecosystems[END_REF]. The major autochthonous source of organic matter in the marine ecosystem comes from the phytoplankton, and a substantial part (10 to 50%) of primary production is channeled through bacteria [START_REF] Cole | Bacterial production in fresh and saltwater ecosystems: a crosssystem overview[END_REF]. A number of field and experimental studies have indicated that DOM released during phytoplanktonic blooms was associated to important changes in the microbial community diversity and metabolic properties (e.g. [START_REF] Mccarren | Microbial community transcriptomes reveal microbes and metabolic pathways associated with dissolved organic matter turnover in the sea[END_REF][START_REF] Sarmento | Use of phytoplankton derived dissolved organic carbon by bacterioplankton large phylogenetic groups[END_REF]. Besides phytoplankton, DOM can be provided by jellyfish (used here to refer to medusa of the phylum Cnidarian and to members of the phylum Ctenophora) that can attain enormous biomasses in marine waters when the conditions are favorable (e.g. high nutrient concentrations) [START_REF] Pitt | Influence of jellyfish blooms on carbon, nitrogen and phosphorus cycling and plankton production[END_REF][START_REF] Purcell | Jellyfish and Ctenophore blooms coincide with human proliferations and environmental perturbations[END_REF].

The sudden appearance and disappearance of massive jellyfish blooms is one of the distinct features of this group [START_REF] Condon | Questioning the rise of gelatinous[END_REF]. Top-down trophic control of jellyfish populations is low and jellyfish have been suggested as being a trophic "dead end" [START_REF] Arai | Predation on pelagic coelenterates: a review[END_REF].

Nonetheless, bacteria can recycle jellyfish biomass rapidly by different ways. Firstly, jellyfish release dissolved organic matter (DOM) by excretion and mucus production [START_REF] Pitt | Influence of jellyfish blooms on carbon, nitrogen and phosphorus cycling and plankton production[END_REF].

Secondly, during massive die-out events, jellyfish biomass sinks to the seafloor because dead animals have greater density than live animals [START_REF] Yamamoto | Transportation of organic matter to the sea floor by carrion falls of the giant jellyfish Nemopilema nomurai in the Sea of Japan[END_REF]). The release rates of total organic carbon by dead jellyfish is significantly higher than that of living jellyfish (Pitt et al. 2009). Depending of the water depth, decomposition of dead jellyfish can occur in the water column or at the sediment surface. Rates of decomposition of both excretion and dead jellyfish biomass result in a large input of nutrients in the marine environment [START_REF] Pitt | Influence of jellyfish blooms on carbon, nitrogen and phosphorus cycling and plankton production[END_REF][START_REF] West | Influence of decomposing jellyfish on the sediment oxygen demand and nutrient dynamics[END_REF][START_REF] Tinta | Degradation of the Adriatic medusa Aurelia sp. by ambient bacteria[END_REF]. Recent studies also reported a change in the bacterial structure in response to the use of organic matter from jellyfish for different coastal waters [START_REF] Tinta | Degradation of the Adriatic medusa Aurelia sp. by ambient bacteria[END_REF][START_REF] Condon | Jellyfish blooms result in a major microbial respiratory sink of carbon in marine systems[END_REF][START_REF] Tinta | Jellyfish modulate bacterial dynamic and community structure[END_REF][START_REF] Dinasquet | Functional and compositional succession of bacterioplankton in response to a gradient in bioavailable dissolved organic carbon[END_REF].

Coastal lagoons are semi-enclosed systems occupying approximately 13% of the world's coastline with both marine and fluvial components [START_REF] Wit | Biodiversity of coastal lagoon ecosystems and their vulnerability to global change[END_REF]. The Mediterranean coast is bordered by a series of coastal lagoons with varying sizes, and some of them are associated with important economic activities (fisheries, aquaculture, tourism, recreation activities) (De [START_REF] Wit | Biodiversity of coastal lagoon ecosystems and their vulnerability to global change[END_REF]. Jellyfish blooms have been reported in these coastal lagoons and in particular were attributed to the scyphomedusa Aurelia aurita [START_REF] Lo | Population succession and feeding of scyphomedusae, Aurelia aurita, in a eutrophic tropical lagoon in Taiwan[END_REF]Lo et al. 2008;Bonnet et al. 2013). A. aurita is widespread in coastal and shelf sea environments around the world, predominantly inhabiting highly eutrophic waters where maximum abundance can reach 300 individuals.m -3 [START_REF] Lo | Population succession and feeding of scyphomedusae, Aurelia aurita, in a eutrophic tropical lagoon in Taiwan[END_REF]. Due to the shallow depth of coastal lagoons, dead jellyfish can accumulate rapidly down to the sediment leading to hypoxic and anoxic conditions in these environments [START_REF] West | Influence of decomposing jellyfish on the sediment oxygen demand and nutrient dynamics[END_REF]).

In the present study, we investigated the response of a Mediterranean coastal lagoon bacterial community to the addition of fresh jellyfish biomass (as DOM) of A. aurita using a microcosm approach. Our objective was to follow the changes in nutrients, bacterial activities and bacterial community structure during the total degradation of DOM from A. aurita in order to determine the resilience of the bacterial community exposed to the disturbance caused by a jellyfish bloom. For our purposes, we measured simultaneously bacterial production and respiration to determine the bacterial growth efficiency. We also combined the use of 454-tag pyrosequencing and DNA fingerprinting based on 16S rRNA genes and culture approach to characterize the bacterial diversity in response to the DOM addition from the jellyfish. To complete previous studies on this topic, we employed longer incubation period (22 days) with four sampling points allowing relevant analyses on the succession of taxa concurrent to the degradation of jellyfish DOM. Moreover, by using an incubation, long enough to reach the complete mineralization of DOC from the jellyfish, it was possible to determine and then compare the resilience of bacterial functions and diversity after jellyfish DOM addition.

Material and methods

Preparation of dissolved organic matter from A. aurita

A. aurita jellyfish used in this study were produced in aquarium using Artemia salina nauplii as food (Lautan Production, Mèze, France). The jellyfish were less than 4 months old with a diameter around 8 cm. Twelve organisms were crushed with a blender. The homogenate was prefiltered onto 10-µm mesh (Nytex) and then filtered with a peristaltic pump onto 0.2-µm capsule filter (Polycap TC, Whatman) (previously rinsed with 10% HCl and washed thoroughly with Milli-Q water). The filtrate was recovered in a pre-combusted (450°C, 6 h) glassware bottle before being dispensed in the microcosms (see below).

Preparation of the microcosms and sampling

Water samples were collected in October 2010 from the shoreline of a Mediterranean coastal lagoon France,[42°36'21'' N,2°53'49'' E]). The Bages-Sigean Lagoon covers 38 km 2 with a mean depth of 1.3 m and a maximal depth of 3 m. The catchment area covers greater than 456 km 2 and is drained by three rivers flowing into the lagoon. Water samples (salinity 33‰) were filtered sequentially by gravity onto 250, 100, 50 and 25 µm mesh (Nytex) and then onto 1-µm filter capsule (Polycap TC, Whatman) to remove fine particles.

Six 10-L polycarbonate carboys (Nalgene) were filled with 8-L of the filtrated lagoon water.

Three carboys received the DOM from A. aurita (300 ml) and the three other were used as controls. All the carboys were incubated in the dark at in situ temperature (18°C) under magnetic agitation. The caps of the carboys were maintained open to avoid any oxygen limitation. The microcosms were sampled 30 min after DOM addition (T0) and over 22 days (T22). At the last sampling, more than 60% of the initial volume was present in the microcosms.

Nutrients, dissolved organic carbon, nitrogen and phosphorus

Samples (60 ml) for nitrate (NO 3 -), nitrite (NO 2 -) and phosphate (PO 4 3-) were stored at -20°C and analyzed within 1 month of collection by colorimetry using a nutrient autoanalyzer (SEAL Analytical AA3HR) [START_REF] Aminot | Dosage automatique des nutriments dans les eaux marines[END_REF]. Samples (100 ml in duplicate) for ammonium (NH 4 + ) were analyzed immediately according to [START_REF] Holmes | A simple and precise method for measuring ammonium in marine and freshwater ecosystems[END_REF] with a fluorometer (Jasco). Samples (20 ml in duplicate) for dissolved organic carbon (DOC) were filtered through two pre-combusted (450°C, 6 h) 25-mm GF/F filters; the filtrate was transferred into precombusted glass tubes, poisoned with 85% H 3 PO 4 (final pH=2), closed with Teflon lined screw caps and stored in the dark at room temperature until analysis. DOC was analyzed using the high temperature catalytic oxidation (HTCO) technique [START_REF] Cauwet | HTCO method for dissolved organic carbon analysis in seawater: influence of catalyst on blank estimation[END_REF]) using a Shimadzu TOC-V analyzer. Prior to analyses and between each set of samples, an international certified reference sample for DOC concentration was analyzed to check the calibration of the analyzer and its stability over time.

Samples for dissolved organic nitrogen (DON) and phosphorus (DOP) were filtered through 2 pre-combusted (450°C, 6 h) 25-mm GF/F filters (Whatman). Samples were collected directly in Teflon bottles and immediately frozen (-20°C) until analysis. DON and DOP were simultaneously determined by the wet oxidation procedure (Pujo-Pay and Raimbault 1994).

DON (±0.1 µM) and DOP (±0.02 µM) concentrations were determined by sample oxidation (30 min, 120°C) corrected for NH 4 + , NO 3 -+NO 2 -and PO 4 3-concentrations, respectively.

CDOM fluorescence

The fluorescence properties of colored dissolved organic matter (CDOM) were determined on samples filtered through 2 pre-combusted (450°C, 6 h) 25-mm GF/F filters (Whatman).

Fluorescence was measured on a Perkin Elmer LS55 spectrofluorometer using a 1-cm quartz cuvette. Different excitation/emission couples were used to determine protein-like (275/340 nm) and humic-like (320/420 nm) compounds. Fluorescence intensity values were calibrated using the Raman scatter peak of Milli-Q water [START_REF] Lawaetz | Fluorescence intensity calibration using the Raman scatter peak of water[END_REF].

Virus count

Samples (2 ml) were fixed with 0.02 µm filtered formaldehyde (0.5% final concentration) and stored at -80°C after flash freezing in liquid nitrogen. Samples were filtered onto 0.02-µm filters (Anodisc, 25 mm diameter; Whatman) and virus-like particles (VLP) were stained on the filters using SYBR-Green I and enumerated under a Zeiss Axiophot microscope equipped for epifluorescence microscopy as previously described [START_REF] Noble | Use of SYBR Green I for rapid epifluorescence counts of marine viruses and bacteria[END_REF]. At least 400 VLP were counted per filter in several randomly selected microscopic fields.

Heterotrophic prokaryotes abundance, production and respiration

Heterotrophic prokaryotic (including Bacteria and Archaea) abundance (HPA) was determined by flow cytometry. Duplicate 3 ml samples in cryovials were preserved with 0.2 µm filtered formalin (2% final concentration). The samples were gently mixed and left in the dark at room temperature for 10 min before quick-freezing in liquid nitrogen and storing at -80°C. The samples were later thawed at room temperature, stained with SYBR Green I (final concentration 0.025% (v/v) of the commercial solution; Molecular Probes Inc., OR) for at least 15 min at 20°C in the dark and analyzed on a flow cytometer (FACScan, Becton Dickinson, San Jose, CA) equipped with a 488 nm, 15 mW argon laser. HP cells were detected on a plot of green fluorescence (515-545 nm) versus right angle light scatter (SSC), using the green fluorescence as threshold parameter. Fluorescent beads (1.0 µm; Polysciences Inc., Warrington, PA) were added to each sample analyzed to normalize SSC and green fluorescence. HP growth rate (µ, d -1 ) was calculated from the following equation µ = (Ln HPT 2 -Ln HPT 1 ) / (T 2 -T 1 ). Generation time (g, h -1 ) was determined as g = (Ln(2) x 24) / µ.

Heterotrophic prokaryotic production (HPP) was measured by 3 H-thymidine incorporation applying the centrifugation method [START_REF] Smith | A simple, economical method for measuring bacterial protein synthesis rates in seawater using 3 H-leucine[END_REF]. Samples (1 ml in triplicate) were incubated in the dark at 18°C for 1 h with 20 nM [ 3 H]-thymidine (specific activity 83.2 Ci mmole -1 , Perkin Elmer) in 2 ml microtubes. Incorporations were terminated by the addition of trichloroacetic acid (TCA) to a final concentration of 5%. One killed control was prepared for each assay by the addition of TCA 15 min before the addition of 3 H-thymidine. Samples were stored for at least 1 h at 4°C and then centrifuged for 15 min at 12,000 g. The precipitate was rinsed twice with 5% TCA. The precipitates were resuspended in 1.0 ml of liquid scintillation cocktail (FilterCount, Perkin Elmer) and radioactivity determined by liquid scintillation counter (LS 5000CE Beckman). Thymidine incorporation rates were converted into carbon production using the conversion factors of 2.10 18 cells produced by mole of thymidine incorporated and 20 fg C by cell [START_REF] Ducklow | Oceanic bacterial production[END_REF].

Heterotrophic prokaryotic respiration (HPR) was measured at each time point using an oxygen microelectrode [START_REF] Briand | The use of oxygen microprobes to measure bacterial respiration for determining bacterioplankton growth efficiency[END_REF]). The microprobes (Unisense, Denmark) are designed with an exterior guard cathode, which results in extremely low oxygen consumption by the electrodes (4.7-47 × 10 -7 µmol O 2 h -1 ). Probes have a response time shorter than 1 s and a precision of 0.05%. The HPR was measured over 4 h to 24 h in duplicate samples for each microcosm, placed in microchambers (2 ml) and immerged in a water bath with controlled temperature (18°C). A specific measurement of the dissolved O 2 concentration was carried out with a minimum of 4 times during incubation in microchambers. HPR was deduced from the linear regression established on these points of measurement. HPR were expressed in mgC m -3 d -1 using a respiratory quotient of 1 (del Giorgio and Cole 1998). We assume here that most of the respiration measured came from heterotrophic prokaryotes because the water was filtered on 1-µm at the start of the experiment. However, we cannot exclude the growth of protozoa then after, leading to an overestimation of HPR. Bacterial growth efficiency (BGE, %) was calculated from the following equation BGE= HPP/(HPP+HPR).

Bacterial DNA extraction

Samples (500 ml) at T0, T2, T9 and T22 were filtered onto a 0.2 µm Sterivex filter (Durapore, Millipore) and stored at -80°C. For analysis, 840 ml of alkaline lysis buffer (50 mM Tris hydrochloride pH 8.3, 40 mM EDTA and 0.75 M sucrose) was added in the Sterivex. Cell lysis was accomplished by an initial incubation for 45 min at 37°C after adding 50 ml of freshly prepared lysozyme solution (20 mg ml -1 ), and a second incubation at 55°C for 1 h after adding 100 ml of 10% sodium dodecyl sulfate and 10 ml of proteinase K (20 mg ml -1 ).

Six hundred ml of lysate was treated with 10 µl of a 100 mg ml -1 RNase A solution (Qiagen) before DNA extraction with the All Prep DNA/RNA mini (Qiagen) according to the manufacturer's instructions.

Analysis of bacterial community structure by capillary electrophoresis SSCP-based single strand conformational polymorphism (CE-SSCP) analysis

CE-SSCP fingerprinting was performed (i) to follow the changes in total bacterial community structure and (ii) to check the reproducibility between replicate microcosms. DNA was used as a template for PCR amplification of the variable V3 region of the 16S rDNA (Escherichia coli positions 329-533), as previously described [START_REF] Ghiglione | Spatial and temporal scales of variation in bacterioplankton community structure in the NW Mediterranean Sea[END_REF]. CE-SSCP was performed using the ABI 310 Genetic Analyzer (Applied Biosystems), equipped with a capillary tube (47 cm Í 50 mm) filled with a polymer mix composed of 5.6% GeneScan polymer (Applied Biosystems), 10% glycerol and 1Í buffer with EDTA (Applied Biosystems). The similarity of the CE-SSCP profiles was assessed using the software SAFUM [START_REF] Zemb | SAFUM: statistical analysis of SSCP fingerprints using PCA projections, dendrograms and diversity estimators[END_REF], which normalized the total area of the profiles and mobilities between different runs using an internal standard. SAFUM renders a profile of fluorescence intensity as a function of retention time per sample, thus taking into account the presence and intensity of each individual signal. Ordination of Bray-Curtis similarities among normalized sample profiles was calculated using PRIMER 5 software (PRIMER-E, Ltd., UK).

Analysis of bacterial diversity and community structure by pyrosequencing

Bacterial tag-encoded FLX amplicon pyrosequencing (bTEFAP) was performed using the universal bacterial primers guidelines targeting the V1 to V3 hypervariable regions of the bacterial 16S rRNA gene: 27Fmod (5′-AGRGTTTGATCMTGGCTCAG-3′) and 519r (5′-GWATTACCGCGGCKGCTG-3) as described previously [START_REF] Dowd | Evaluation of the bacterial diversity in the feces of cattle using 16S rDNA bacterial tag-encoded FLX amplicon pyrosequencing (bTEFAP)[END_REF]. Initial generation of the sequencing library was accomplished by a one-step PCR with a total of 30 cycles using the HotStarTaq Plus Master Mix Kit (Qiagen, Valencia, CA) and amplicons originating and extending from the 27Fmod primer. Tag-encoded FLX amplicon pyrosequencing analyses were completed using the Roche 454 FLX instrument with Titanium reagents, and procedures were performed at MR DNA (Shallowater, TX, USA) following manufacturer's guidelines.

Sequences were processed and analyzed using the Mothur software version 1.33 [START_REF] Schloss | Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities[END_REF]) with default settings excluding sequences <200bp. Sequences were denoised using the Mothur implementation of PyroNoise and SeqNoise. Chimeras were removed using Chimera Slayer [START_REF] Haas | Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons[END_REF]. The resulting clean sequences were clustered using operational taxonomic units (OTUs) at a 97% sequence identity level using the UCLUST algorithm [START_REF] Edgar | Search and clustering orders of magnitude faster than BLAST[END_REF]) and a representative sequence from each OTU was classified using the Ribosomal Database Project (RDP) classifier [START_REF] Wang | Naive bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy[END_REF]) using the SILVA training set. Taxonomic identification of the sequence reads (tags) followed the approach by [START_REF] Sogin | Microbial diversity in the deep sea and the underexplored "rare biosphere[END_REF] and [START_REF] Huse | Ironing out the wrinkles in the rare biosphere through improved OTU clustering[END_REF]. All samples were clustered into operational taxonomic unit (OTU) at a distance of 0.03 [START_REF] Ghiglione | Pronounced summer to winter differences and higher wintertime richness in coastal Antarctic marine bacterioplankton[END_REF]. All OTU and diversity analyses were performed on the randomly re-sampled datasets using Mothur.

Culturable bacterial counts, isolation and 16S rRNA gene sequencing

Culturable heterotrophic aerobic marine bacteria were enumerated during the experiment at T0, T2, T9 and T22 by plating 100 µl of diluted (in sterile seawater) or undiluted samples on marine agar 2216 (MA, Difco, Detroit, Mich.). Cycloheximide (100 mg/L) was added to the media to inhibit fungal growth. All culture were incubated at 25°C in the dark during two weeks before counting the colony forming units (CFU).

The identification of the most representative culturable strains was performed for one of the replicate for each condition after two days of incubation. The colonies were categorized using morphologic characteristics. All the different morphotypes (colony morphology) were picked for two successive sub-culturing steps on MA to ensure purification. One colony of each isolate was then grown in marine broth media (MB, Difco, Detroit, Mich.) during 48h at 25°C under agitation (100 rpm). Each culture was cryopreserved in 5% dimethylsulfoxide or 35% glycerol at -80°C. For genomic DNA extraction, 2 ml of each liquid culture were spun down (10,000 x g, 3 min). DNA extraction, PCR and sequencing were done as previously described [START_REF] Fagervold | Pleionea mediterranea gen. nov., sp. nov., a gammaproteobacterium isolated from coastal seawater[END_REF]. Partial 16S rRNA gene sequences were trimmed manually, double checked and dereplicated using the package Staden-GAP4 [START_REF] Staden | Managing sequencing projects in the GAP4 environment. Introduction to bioinformatics. A theoretical and practical approach[END_REF]. For bacterial strain identification, each FASTA file was uploaded in Ez Taxon-e [START_REF] Kim | Introducing EzTaxon: a prokaryotic 16S rRNA Gene sequence database with phylotypes that represent uncultured species[END_REF]) and compared with the cultured bacterial strain database using BLAST (Basic Local Alignment Search Tool). Sequences were deposited in Genbank (NCBI) under the following numbers:

MOLA851-858 and MOLA865-880 from enriched microcosms and MOLA859-864 and MOLA881-887 from control microcosms.

Statistical analyses

Statistical analysis of the effects of treatment on chemical and biological parameters was performed using a one-way analysis of variance (ANOVA) and post-hoc Tukey tests with repeated measures (i.e. microcosms) and assuming homoscedasticity and normality of the data. Statistical significance was set at p = 0.05 and analysis was computed using XLSTAT 2014.2 software (Addinsoft).

Bacterial community structures, either as number and area of the peaks in the CE-SSCP profiles or presence and abundance of OTU in the pyrosequencing data, were compared using ordination of Bray-Curtis similarities and used to build dendrograms by the unweighted-pair group method with arithmetic averages (UPGMA). A similarity profile test (SIMPROF, PRIMER 6) was performed on a null hypothesis that a specific sub-cluster can be recreated by permuting the entry species and samples. The significant branch (SIMPROF, p <0.05) was used as a prerequisite for defining bacterial clusters.

The extent of the correlation of bacterial diversity analyzed by pyrosequencing for both conditions with the chemical parameters and the viral abundance was assessed by canonical correspondence analysis (CCA). CCA was performed with MVSP v3.12d software (Kovach Computing Service, Anglesey Wales). Relative abundances of OTUs were transformed with arcsin (× 0.5 ) to normalize the distribution of the data as suggested by [START_REF] Legendre | Numerical ecology, 2nd English edition[END_REF].

Results

Degradation kinetics of jellyfish dissolved organic matter

The water in the control microcosms (i.e. lagoon water without any addition) was characterized by a high concentration of dissolved organic carbon (DOC, 573 µM), nitrogen (DON, 50 µM) and phosphorus (DOP, 0.7 µM), underlying the eutrophic status of this ecosystem (Fig. 1a,b,c). Addition of dissolved organic matter (DOM) from A. aurita provided 252 µM DOC, 85 µM DON and 3 µM DOP, increasing significantly (ANOVA, p<0.05) their initial concentrations by 1.4, 2.5 and 5.8 times, respectively, compared to the control microcosms. The C:N ratio of the DOM was significantly (ANOVA, p<0.05) lower in the enriched microcosms (6:1) compared to the control microcosms (12:1) due to the high protein content of the jellyfish.

The DOC from the jellyfish was consumed at a high rate during the first four days of incubation (57 µM d -1 ) and then at a lower rate (5.6 µM d -1 ) (Fig. 1a). In contrast, the DOC in the control microcosms was consumed at a constant and low rate (4 µM d -1 ) during the entire incubation. At the end of experiment, the same concentration of DOC (500 µM) was noted in the control and the enriched microcosms (ANOVA, p>0.05). The DON and the DOP concentrations were measured less frequently in the microcosms (Fig. 1b,c). The DON concentration decreased by 67 µM during the nine first days and then remained almost constant in the enriched microcosms. In contrast the DOP concentration increased during the first two days and then decreased during the rest of the incubation. For both DON and DOP, we did not observe significant changes in the control microcosms (ANOVA, p>0.05). At the end of the experiment, 86% of the jellyfish-derived DON and 57% of the jellyfish-derived DOP were degraded. The C:N ratio of the DOM in the control was close to the enriched microcosms by the end of the experiment (10:1 and 9:1, respectively).

The initial concentrations of NO 3 +NO 2 (5.5 µM), NH 4 (6 µM) and PO 4 (0.5 µM) in the control microcosms were low and remained stable during the experiment time (Fig. 1d,e,f).

The addition of jellyfish biomass led to an enrichment factor by 1.7, 1.0, and 11 for NO 3 +NO 2 , NH 4 and PO 4 , respectively. NO 3 +NO 2 concentrations remained stable in enriched microcosms, with the exception of an increase (+ 4 µM) at the end of the experiment (Fig. 1d). This increase was only due to changes in NO 2 concentration (data not shown). We observed a continuous increase of NH 4 up to 100 µM at T9, followed by a plateau (Fig. 1e).

PO 4 concentrations (Fig. 1f) evolved in an opposite way to the DOP concentrations in the enriched microcosms: after a decrease during the first two days, the concentration increased during the rest of the incubation mainly between the days 2 and 9.

Protein-like and humic-like components of DOM were characterized by their fluorescence properties (Fig. 2). Protein-like components measured in the control microcosms remained constant during the experiment (Fig. 2a). In contrast, the protein-like components in the enriched microcosms showed a rapid decrease during the first nine days until reaching the value measured in the control microcosms at the end of the experiment. The concentration of humic-like components (Fig. 2b) was identical in both conditions, remaining almost constant during the experiment. However, we noticed a slight but significantly (ANOVA, p<0.05) higher concentration of humic-like components in enriched conditions compared to the controls after 15 and 22 days.

Effect of jellyfish DOM addition on viral abundance, bacterial abundance and metabolism

Virus abundance remained almost constant during the entire period of the experiment in both controls and enriched microcosms (Fig. S1). A small but significant difference was observed between the two conditions at T2 and T4, with higher virus abundance in microcosms enriched with DOM from A. aurita (ANOVA, p<0.05).

During the three first days, we measured a rapid bacterial growth without lag time after the DOM addition from A. aurita (Fig. 3a). The bacterial growth rates calculated on the basis of bacterial abundance between T0 and T3 were 1.44 d -1 and 0.48 d -1 in the enriched and the control conditions, respectively. A sharp decrease in bacterial abundance occurred at T4 in the enriched microcosms, leading to a value identical to the control condition. Then, the bacterial abundance remained almost constant for the rest of the experiment.

A rapid increase in bacterial production (BP) and bacterial respiration (BR) was also observed in the enriched condition during the first days (Fig. 3b,c). After 3 days, both activities slowly decreased at a constant and similar rate during the rest of the experiment. After 22 days, the BP and BR were not significantly different between the control and the enriched microcosms (ANOVA, p>0.05).

Bacterial growth efficiency (BGE) calculated on the basis of bacterial production and respiration was quite low at the start of the experiment (<1%) (Fig. 3d). During the first 9 days, the BGE increased up to 17% in the microcosms enriched with DOM from A. aurita.

The BGE measured in the control microcosms remained lower than the enriched microcosms until day 15, when both conditions presented the same mean value (13%).

Changes in culturable heterotrophic bacteria counts and diversity

At the start of the experiment, culturable bacteria represented only a small part of the total bacteria (0.25%) (Fig. 4). After two days of incubation, culturable bacteria accounted for 65% of total bacteria in enriched microcosms, compared to 3% in the control microcosms. The bacterial growth rate calculated on the basis of culturable bacteria counts during this period was 4.8 d -1 in the enriched condition compared to 1.92 d -1 in the controls. After 9 days, there was a significant difference in the culturable bacteria fraction between the two conditions (2% and 12%, for control and enriched microcosms, respectively) (ANOVA, p<0.05). At the end of the experiment, the culturable bacteria fraction accounted for the same percentage (~10%) in both control and enriched microcosms.

Most of the culturable strains isolated after 2 days from an enriched microcosm, belonged to the Pseudoalteromonas and Vibrio genera (class of Gammaproteobacteria) (Table 1). After 22 days, a clear shift in culturable diversity was observed in this microcosm, with the dominance of Flavobacteriaceae (phylum of Bacteroidetes) and Rhodobacteraceae (class of Alphaproteobacteria). Even if the number of bacterial strains isolated from the control microcosm was less important than for enriched microcosm (6 versus 18), the diversity seemed to be more stable during the experiment with a majority of Rhodobacteraceae.

Changes in bacterial community structure induced by Aurelia-derived DOM

A similarity dendrogram based on CE-SSCP data showed a clear separation of the bacterial community diversity into four distinct clusters (Fig. 5a). With the exception of the microcosm C2 which presented outliers at T0 and T2, cluster I included the community profiles from To analyze in more detail the composition of the bacterial community, pyrosequencing was performed on one replicate microcosm for each condition at different sampling times (Fig. 5a in bold). A total of 76,027 partial 16S rRNA gene sequences remained after quality controls, yielding on average 9,503 reads per sample (5,080 -11,600). The number of sequences was normalized to 5,080 per sample (i.e., the lowest number of sequences obtained for a sample).

The total of unique OTUs was 2,800 in the whole dataset (at 97% similarity). The rarefaction curves for all samples did not reach a plateau (Fig. S1). Hence, our sequencing effort did not cover completely the bacterial diversity. However, the shape of the rarefaction curves was similar for all samples allowing the comparison of these samples. The taxonomic richness based on the Chao1 index was slightly lower in the enriched microcosm at T0 and T2 compared to the control microcosm, and higher at T9 and T22 (Table 2). The diversity based on the inverse Simpson index (1/ λ) ranged from 3.6 to 35.2. The diversity was higher in the enriched condition at T0 compared to the control condition then lower at T2 and T9, and similar at T22. Both conditions showed a transient decrease in diversity at T9 (Table 2).

A dendrogram based on Bray-Curtis similarities using the total number of OTUs was similar to that observed with the DNA fingerprint approach (Fig. 5b). The samples at T0 (with or without the addition of DOM from A. aurita) grouped more closely with the samples from the control microcosms at T2, T9 and T22. No significant difference (SIMPROF test, p >0.05)

was observed between the samples in the control microcosms at T0 and T9. In contrast, a rapid shift in diversity was observed in the enriched microcosm at T2 (85% dissimilarity).

After T9, the diversity tended to stabilize in this microcosm and no significant difference was observed in the bacterial structure at T22 (SIMPROF test, p >0.05).

The community composition at the phylum level and proteobacterial subclasses varied between treatments and with time (Fig. 6). The changes in abundances described below refer to relative abundances of the sequences and do not refer to absolute abundances of the different bacterial groups. At T0, the enriched microcosm was characterized by a higher proportion of Actinobacteria (49% versus 19% in the control microcosm) and by a lower proportion of Alphaproteobacteria (16% versus 49% in the control microcosm). The control microcosm showed continuous changes over the experiment. At the end of the experiment, Alphaproteobacteria remained the most abundant taxon (67%), whereas the Actinobacteria and Bacteroidetes tended to decrease (1.4% and 12%, respectively). Gammaproteobacteria showed a transient increase at T9 (24%) to reach the initial percentage at the end of the experiment (8%). The bacterial community in the enriched microcosm was characterized by a high proportion of Gammaproteobacteria (65%) at T2, followed by a dominance of Bacteroidetes (77%) at T9. Finally, at T22, the Bacteroidetes decreased (46%) in favor of Alphaproteobacteria (36%) and Plantomycetes (5%).

Figure 7 shows the taxonomy information for the most abundant OTUs (i.e., present at a percentage higher than 1% in a specific sample). These 71 OTUs represented only 2.5% of the total number of OTUs, however their cumulative abundances represented between 52%

and 88% of all the OTUs present in a specific sample. The majority of the Alphaproteobacteria sequences in the control microcosm were assigned to SAR11 throughout the experiment. Actinobacteria present in both the control and the enriched microcosms at T0 belonged to Microbacteriaceae. The bloom of Gammaproteobacteria at T2 was affiliated to Pseudoalteromonas and Vibrio species, and the Bacteroidetes at T9 were mainly composed of

Discussion

Processing of organic matter from the jellyfish and consequences for the system

If we estimate an average wet weight of 13 g per jellyfish [START_REF] Lo | Population succession and feeding of scyphomedusae, Aurelia aurita, in a eutrophic tropical lagoon in Taiwan[END_REF] and apply the relationship between the wet weight and the organic carbon content determined by [START_REF] Schneider | Chemische zusammensetzung und biomasseparameter der ohrenqualle Aurelia aurita[END_REF] for A. aurita, the enrichment in DOM corresponded to the addition of the total biomass from one jellyfish in 11 L (i.e., 91 individuals m -3 ). This value can be considered as high but not unrealistic [START_REF] Lo | Population succession and feeding of scyphomedusae, Aurelia aurita, in a eutrophic tropical lagoon in Taiwan[END_REF]. After 22 days of incubation, the same DOC concentration was observed in the enriched and control microcosms suggesting that the bacterial community has degraded the entire DOC from A. aurita. This result highlights the high bioavailability of the DOM from A. aurita jellyfish as already reported by [START_REF] Tinta | Jellyfish modulate bacterial dynamic and community structure[END_REF][START_REF] Purcell | Jellyfish and Ctenophore blooms coincide with human proliferations and environmental perturbations[END_REF]. The excess of organic and inorganic nitrogen and phosphorus in the enriched microcosms at the end of the experiment suggests that the decrease of bacterial activities were mainly due to limitation by bioavailable carbon. In these conditions, the excess of DON and DOP after degradation of jellyfish DOM can persist in the system until a new pulse of labile organic carbon. The release of nutrients during the decomposition of organic matter from the jellyfish observed in this study and others (e.g., [START_REF] West | Influence of decomposing jellyfish on the sediment oxygen demand and nutrient dynamics[END_REF][START_REF] Tinta | Degradation of the Adriatic medusa Aurelia sp. by ambient bacteria[END_REF]2012) might stimulate primary production. However, high concentrations of ammonium, associated to unionized ammonia (NH 3 ), can be also inhibitory or toxic for some phytoplankton, amphipods, crustacean and fish species (Ferreti and Calesso 2011;[START_REF] Collos | Acclimation and toxicity of high ammonium concentrations to unicellular algae[END_REF].

The fact that only 14% DOC was degraded over 22 days in the control microcosm indicates that the bulk of DOC in coastal lagoons is refractory. The priming effect is a process well demonstrated in the case of soils that enhances the microbial decomposition of preexisting refractory organic matter upon addition of labile organic matter [START_REF] Kuzyakov | Review of mechanisms and quantification of priming effects[END_REF].

During the decline of the bloom, jellyfish biomass can constitute a large reservoir of labile organic matter at the disposal of bacteria that can enhance the priming effect. Nevertheless, there was no evidence that DOM from the jellyfish helped for biodegradation of refractory DOM from the coastal lagoon (i.e., the final DOC and humic-like components concentrations in the enriched treatment were similar and higher, respectively, compared to the control treatment), suggesting that the priming effect did not occur under these experimental conditions. Recent studies on the priming effect in aquatic ecosystems gave contrasting results underlying the need for further studies on this process [START_REF] Fonte | The combination of different carbon sources enhances bacterial growth efficiency in aquatic ecosystems[END_REF][START_REF] Bengtsson | No evidence of aquatic priming effects in hyporheic zone microcosms[END_REF][START_REF] Guenet | Fast mineralization of land-born C in inland waters: first experimental evidences of aquatic priming effect[END_REF]).

In our study, DOM from A. aurita increased the BGE relative to the control condition after 4 days of incubation. In contrast, [START_REF] Condon | Jellyfish blooms result in a major microbial respiratory sink of carbon in marine systems[END_REF] observed that the rapid increase of bacterial metabolism in response to the addition of DOM released by a jellyfish was accompanied by a significant decline of BGE by 10% to 15% compared to the control treatment. [START_REF] Dinasquet | Functional and compositional succession of bacterioplankton in response to a gradient in bioavailable dissolved organic carbon[END_REF] observed a similar BGE (30-40%) for bacterial communities exposed or not to DOM from the ctenophore Mnemiopsis leidyi. These contrasting observations may result from differences in experimental setup and bacterial communities composition in these studies. Even if the BGE was stimulated with the presence of DOM from Aurelia in our study, its value remained relatively low (<20%) indicating that organic carbon was mainly shunted toward bacterial respiration rather than creation of new biomass. According to [START_REF] Condon | Jellyfish blooms result in a major microbial respiratory sink of carbon in marine systems[END_REF], the low value of BGE induced by labile DOM from jellyfish could be explained by a inadequate organic C:N and C:P stoichiometry resulting in nongrowth energy dissipation (i.e., overflow metabolism). It must be underlined that for all these studies, the true value of the BGE remains questionable due to uncertainties in the different conversion factors used for its calculation, including the conversion factor for converting 3 H-thymidine uptake into bacterial carbon production [START_REF] Kirschner | Variability and predictability of the empirical conversion factor for converting 3 H-thymidine uptake into bacterial carbon production for a eutrophic lake[END_REF]) and the respiratory quotient that may change with substrate quality [START_REF] Berggren | Magnitude and regulation of bacterioplankton respiratory quotient across freshwater environmental gradients[END_REF].

Proteins which were identified as the major constituent of jellyfish are characterized by a RQ of 0.8. By lowering the value of RQ for jellyfish condition to 0.8, BGE was increased by 21% in average (range: 19.5%-24.4%). However, this modification did not change the general shape of the curve and the comparison with the control condition (data not shown). Moreover, the calculation of BGE is complicated because HPP and HPR were measured at different time scales: while the time of incubation of HPP was short (i.e., 1h), HPR required sometimes 24h of incubation. According to del Giorgio et al. (2011), this methodological aspect in activities measurements can introduce a bias for the BGE determination.

Dynamics of bacterial community structure during the degradation of DOM from A. aurita

Interpretation of diversity changes during incubation in microcosms is always questionable due to a potential bottle effect [START_REF] Massana | Changes in marine bacterioplankton phylogenetic composition during incubations designed to measure biogeochemically significant parameters[END_REF]). The bottle effect may have occurred in our experiment, as observed by the changes in the culturable bacteria fraction in the control microcosms (0.2% at T0 and 10% at T22). However, diversity as measured by pyrosequencing and DNA fingerprinting indicated that the bacterial community structure in the control microcosms did not change as drastically as in the enriched microcosms. In addition, HPP and HPR did not show significant changes over time (ANOVA test, p>0.05) in the control incubation. Both observations suggest that even if bottle effect cannot be ruled out, consequences on diversity and functions were minor compared to the changes caused by the increase of DOM. The differences in the bacterial community structure at T0 revealed by pyrosequencing between the control and enriched microcosms may be explained by the introduction of bacteria in the microcosms with the DOM from Aurelia, even if the DOM was previously filtered on 0.2 µm. The high percentage of Actinobacteria found in these microcosms might be facilitated by the very small size of these bacteria allowing them to escape the filtration [START_REF] Ghai | Metagenomics uncovers a new group of low GC and ultra-small marine Actinobacteria[END_REF].

The pyrosequencing and the DNA fingerprint data demonstrate the rapid and profound effect of DOM addition from Aurelia on the bacterial diversity changes that persisted until the end of the experiment. Our results confirm the major role played by Gammaproteobacteria during the first steps of DOM degradation from jellyfish as observed in other studies [START_REF] Condon | Jellyfish blooms result in a major microbial respiratory sink of carbon in marine systems[END_REF][START_REF] Tinta | Jellyfish modulate bacterial dynamic and community structure[END_REF][START_REF] Dinasquet | Functional and compositional succession of bacterioplankton in response to a gradient in bioavailable dissolved organic carbon[END_REF]. Surprisingly, the Pseudoalteromonas and Vibrio species blooming in the enriched microcosms in the first days were not found in the ~5,000 sequences identified for each sample at the start of the experiment, suggesting that they belong to the rare biosphere (i.e., less than 0.1%) (Pedrós-Alió 2012). If we hypothesize that these species represent less than 0.02% (1 sequence on 5,000) of the 2.10 6 bacteria/ml present at the start of the experiment, the theoretical bacterial growth rate for these bacteria during the three first days would be 4.32 d -1 (generation time = 3.8 h) to reach the concentration of 9.10 7 bacteria/ml measured in the enriched microcosms after 3 days. This bacterial growth rate is not unrealistic and is comparable to the value calculated on the basis of the culturable count. [START_REF] Tinta | Jellyfish modulate bacterial dynamic and community structure[END_REF] also observed that culturable bacteria isolated during the degradation of organic matter coming from different jellyfish in Adriatic Sea belonged to Gammaproteobacteria with a dominance of strains affiliated to Vibrionaceae and Pseudoalteromonadaceae. Vibrio and Pseudoalteromonas species can rapidly outcompete other bacterial species in a context of high concentration of organic matter due to greater metabolic versatility and the presence of multiple copies of rRNA genes [START_REF] Williams | Physiology of marine oligotrophic ultramicrobacteria[END_REF]. Changes in bacterial composition may not result only from competition for organic matter, but also from sensitivity of bacterial species for antagonistic compounds. For instance, [START_REF] Titelman | Turnover of dead jellyfish: stimulation and retardation of microbial activity[END_REF] reported that extracts from the scyphomedusa Periphylla periphylla can inhibit some bacterial species, including Actinobacteria. An antimicrobial peptide, aurelin, exhibiting activity against Gram-positive and Gram-negative bacteria has been also isolated from A. aurita [START_REF] Ovchinnikova | Aurelin, a novel antimicrobial peptide from jellyfish Aurelia aurita with structural features of defensins and channel-blocking toxins[END_REF]. Overall, the diversity index as revealed by pyrosequencing (Table 2) remained unchanged in enriched microcosms at T2. In contrast, [START_REF] Tinta | Jellyfish modulate bacterial dynamic and community structure[END_REF] observed a reduction in the diversity of the bacterial community during the biodegradation of A. aurita. This difference could be explained by the lower number of 16S rRNA clone library sequences analysed in the study of Tinta el al. (2012).

Different reasons can be evoked to explain the sudden decrease in bacterial concentration in the enriched microcosms between T3 and T4 (94% less bacteria in one day). Viral infection can induce rapid changes in bacterial abundance in aquatic environments [START_REF] Berdjeb | Effect of grazers and viruses on bacterial community structure and production in two contrasting trophic lakes[END_REF]). However, lytic infection is associated with the release of viruses in the environment.

We did not observe significant changes in viral abundance during this period, suggesting that viruses were certainly not the main factor to explain the loss of bacteria observed. Protozoan grazing can be also a cause of bacterial abundance regulation. Most of the heterotrophic nanoflagellates (HNFs) were excluded at the start of the experiment by filtering the water on 1 µm but we cannot exclude the possibility that some smaller HNFs passed through the filter and then proliferated during the experiment. Unfortunately, we did not measure HNF abundance in our experiment to confirm this hypothesis. Even if the role of HNFs cannot be excluded, the bacterial loss rate measured during this period (3.4 10 6 bacteria/ml.h) seems particularly high (i.e., two orders of magnitude higher than grazing rates values reported in the sea [START_REF] Jürgens | Protistan grazing on marine bacterioplankton[END_REF]); thus grazing by HNF was certainly not the only explanation for the decreasing of bacterial cell numbers. An additional and not exclusive hypothesis, concerns the autolysis of bacteria after cessation of growth.

After the use of the most bioavailable DOM by Gammaproteobacteria, the bacterial community shifted towards a dominance of Bacteroidetes at T9. These bacteria are specialized in degrading polymeric organic matter compounds, including proteins [START_REF] Fernández-Gómez | Ecology of marine Bacteroidetes: a comparative genomics approach[END_REF]. Interestingly, high proportion of Bacteroidetes have been reported in a

Norwegian fjord where persistent high jellyfish biomass is observed [START_REF] Riemann | Links between jellyfish and microbes in a jellyfish dominated fjord[END_REF] and in association with the ctenophore Mnemiopsis leidy (water, tissue and gut) [START_REF] Dinasquet | Stimulated bacterioplankton growth and selection for certain bacterial taxa in the vicinity of the ctenophore Mnemiopsis leidyi[END_REF]). In our experiment, structural components of bacterial cells including membranes and peptidoglycan were certainly released in the microcosms during the bacterial decay of Gammaproteobacteria whatever the reason (protozoan grazing, viral infection or autolysis) [START_REF] Nagata | Microbial degradation of peptidoglycan in seawater[END_REF]. Bacteroidetes have the capacities to recycle efficiently this material [START_REF] Pinhassi | Coupling between bacterioplankton species composition, population dynamics, and organic matter degradation[END_REF][START_REF] Cottrell | Natural assemblages of marine proteobacteria and members of the Cytophaga-Flavobacter cluster consuming low-and high molecular-weight dissolved organic matter[END_REF]. Finally, we cannot exclude also the possibility that Bacteroidetes were favoured due to a negative selection by bacterivorous protists or viruses [START_REF] Berdjeb | Effect of grazers and viruses on bacterial community structure and production in two contrasting trophic lakes[END_REF]. At the end of the experiment, there was an emergence of Planctomycetes (5% at T22). In aerobic environments, members of Planctomycetes, including the genera Planctomyces and Rhodopirellula identified in our study, have been reported to degrade complex organic matter into simpler compounds [START_REF] Pizzetti | Temporal variability of coastal Planctomycetes clades at Kabeltonne Station, North Sea[END_REF].

Resilience of bacterial communities

Massive jellyfish blooms can be considered as a disturbance for the aquatic ecosystem.

Stability is the general capacity of a community to return to equilibrium after perturbation, and includes components of resistance, recovery and resilience [START_REF] Pimm | The complexity and stability of ecosystems[END_REF]. Resistance is a community's ability to remain unchanged when challenged with disturbance. Recovery is a community's ability to return to its pre-disturbance composition or function, and resilience is the rate at which this return occurs [START_REF] Shade | Resistance, resilience and recovery: aquatic bacterial dynamics after water column disturbance[END_REF]. These different ecological concepts can be applied for a bacterial community exposed suddenly to the organic matter from jellyfish. Our results and others [START_REF] Condon | Jellyfish blooms result in a major microbial respiratory sink of carbon in marine systems[END_REF][START_REF] Tinta | Jellyfish modulate bacterial dynamic and community structure[END_REF][START_REF] Dinasquet | Functional and compositional succession of bacterioplankton in response to a gradient in bioavailable dissolved organic carbon[END_REF] showed that the resistance of bacteria is low, with rapid changes occurring in functions and structure when organic matter from a jellyfish is added (exudate or dead biomass). The determination of the recovery and the resilience needs experimental approaches sufficiently long to analyze these concepts. Most of the different studies previously performed on jellyfish biodegradation have used short-term incubations (<9 days) and ended before the complete degradation of organic carbon (dissolved or particulate) from the jellyfish (e.g., [START_REF] Condon | Jellyfish blooms result in a major microbial respiratory sink of carbon in marine systems[END_REF][START_REF] Tinta | Jellyfish modulate bacterial dynamic and community structure[END_REF]). In our study, it was possible to follow the complete degradation of DOC during the 22 days of incubation. Over this incubation we observed the recovery of the bacterial activities (production, respiration) but not for the bacterial structure (as measured by the Bray-Curtis distance), suggesting that resilience was higher for functions than for diversity. Consequently, jellyfish blooms can induce durable changes in chemistry (release of NH 4 , DON and DOP) but also in the bacterial community structure of coastal lagoons. Future investigations on jellyfish blooms might be conducted in larger experimental systems (i.e., mesocosms) to measure at longer time scale these effects on different trophic levels by including both pelagic and benthic systems. This more realistic approach will also permit to explore the role of particle-attached bacteria in the jellyfish degradation, which was not considered in this study. Only OTUs with a contribution higher than 1% in the sample were used. Multiple OTUs with the same taxonomic assignment were numbered sequentially. The contribution of these OTUs to the total OTUs present in each sample is indicated at the bottom. S1.

  control and enriched microcosms at T0, and cluster II grouped together the control microcosms at T2, T9 and T22. The later time points for the enriched microcosm clustered apart from the two clusters into two subclusters comprised of time point T2 (III) and T9, T22 respectively (IV). The bacterial community structure observed in the control microcosms remained closer to the initial bacterial structure than to the enriched microcosms. With the exception of microcosm C2, the replicate microcosms grouped together for a specific condition and time, indicating that the diversity changes observed between conditions or over the time are robust.

Flavobacteriaceae.

  At the end of the experiment, the Alphaproteobacteria were dominated by Roseobacter clade bacteria (RCB) in the enriched microcosms.In order to determine what factors were potentially controlling bacterial community composition, we applied a canonical correspondence analysis (CCA) to the data for both conditions (Fig.8) using the distribution of the dominant OTUs (relative abundance >1%) determined by pyrosequencing. The variance explained by the two first axes represented almost 50 % (26.6% and 21.9% for axis 1 and 2, respectively). The first group comprising all the samples from the control condition and the sample at T0 from the enriched condition was related to none of chemical parameters or viral abundance. The sample at T2 from the enriched condition was isolated from the other clusters and dominated by the OTUs 0008 and 0014 (Pseudoalteromonas) and by the OTUs 0017 and 0027 (Vibrio). This group was positively structured by DOM and CDOM protein-like components. The third group including the samples at T9 and T22 from the enriched condition was dominated by the OTUs 0002, 0011 and 0021 belonging to the Flavobacteriaceae family and OTU0010 belonging to the Roseobacter genus. This cluster was positively related to inorganic nutrients, CDOM humiclike components and viral abundance.

Fig. 1 .

 1 Fig. 1. Dissolved organic carbon (DOC, a), dissolved organic nitrogen (DON, b), dissolved organic phosphorus (DOP, c), nitrates plus nitrites (NO 3 +NO 2 , d), ammonium (NH 4 , e) and phosphate (PO 4 , f) concentrations during the incubations. Each point represents three replicates (mean ± standard deviation). The initial enrichment factor (EF) between enriched and control microcosms is indicated for each parameter.

Fig. 2 .

 2 Fig. 2. CDOM fluorescence of protein-like (a) and humic-like substances (b) concentrations during the incubations (ru= Raman Units). Each point represents three replicates (mean ± standard deviation).

Fig. 3 .

 3 Fig. 3. Bacterial abundance (a), production (b), respiration (c) and growth efficiency (d) during the incubations. Each point represents three replicates (mean ± standard deviation).

Fig. 4 .

 4 Fig. 4. Percentages of Colony Forming Units (CFU) to total direct counts (flow cytometry) during the incubations. Each point represents three replicates (mean ± standard deviation).

Fig. 5 .

 5 Fig. 5. Dendrograms of similarity based on (a) DNA CE-SSCP fingerprints and (b) OTUs table from the 16S rDNA 454-tag sequences for control microcosms (C1, C2, C3) and enriched microcosms with DOM from A. aurita (A1, A2, A3) after 0, 2, 9 and 22 days of incubation (T0, T2, T9, T22). Clustering is on the basis of a distance matrix computed using the Bray-Curtis index of similarity. The dendrogram was inferred with the unweighted pairgroup average algorithm (UPGMA). a: The clusters were numbered from I to IV. Samples selected for 454 pyrosequencing are indicated in bold. b: Red branches do not differ significantly (SIMPROF test, p >0.05).

Fig. 6 .

 6 Fig. 6. Cumulative bar charts comparing the main relative phyla and Proteobacteria class abundances for control microcosms (A) and microcosms with DOM from A. aurita (B).

Fig. 7 .

 7 Fig. 7. Heatmap displaying the relative abundances of specific OTUs across the samples.

Fig. 8 .

 8 Fig. 8. Canonical correspondence analysis (CCA) of bacterial community structure (OTUs presented on Fig. 7) and chemical factors and viral abundance. The percent of explained variation is shown in brackets. The inflation factors for the analysis are given in TableS1.

  Figure 1

  Figure 4

  Fig. S2. Rarefaction curves of observed operational taxonomic units (OTU) based on 16S rRNA sequences retrieved from the different samples.

Table 1 .

 1 List of 16S rRNA sequences of bacterial colonies isolated in the control and enriched microcosms after 2 and 22 days of incubation with high similarity (>97%) to sequences previously reported.

Treatment,

time incubation Isolate Closest relative strain in EZ biocloud %similarity Family Isolation source

	Tidal flat of the Yellow Sea	Coastal water during a massive green algae bloom	Tidal flat sediment	Arctic Ocean	Estuary	Coastal water during a massive green algae bloom	Seawater	Thallus of the brown alga Fucus evanescens	Pufferfish	Cultured oysters	Gut microflora of sea urchin	Mediterranean oysters	Southern Ocean	Seawater	Estuary	Green macroalga	Sea urchin	Tidal flat sediment	Larval cultures of scallop	Star fish	Marine electroactive biofilm	Marine electroactive biofilm	Sea water	Estuarine sediment
	Flavobacteriaceae	Rhodobacteraceae	Rhodobacteraceae	Rhodobacteraceae	Alteromonadaceae	Rhodobacteraceae	Pseudoalteromonadaceae	Pseudoalteromonadaceae	Pseudoalteromonadaceae	Vibrionaceae	Vibrionaceae	Vibrionaceae	Flavobacteriaceae	Pseudoalteromonadaceae	Alteromonadaceae	Flavobacteriaceae	Flavobacteriaceae	Rhodobacteraceae	Rhodobacteraceae	Rhodobacteraceae	Rhodobacteraceae	Rhodobacteraceae	Rhodobacteraceae	Sphingomonadaceae
	100.00	100.00	99.75	98.31	97.78	97.40	100.00	100.00	99.53	99.77	99.76	99.75	99.28	100.00	98.35	99.53	98.48	99.75	99.27	98.50	97.88	97.57	97.12	97.38
	Muricauda lutimaris	Lentibacter algarum	Celeribacter neptunius	Phaeobacter arcticus	Alteromonas tagae	Lentibacter algarum	Pseudoalteromonas undina	Pseudoalteromonas issachenkonii	Pseudoalteromonas tetraodonis	Vibrio gigantis	Vibrio hemicentroti	Vibrio lentus	Olleya marilimosa	Pseudoalteromonas undina	Alteromonas tagae	Arenibacter palladensis	Arenibacter echinorum	Celeribacter baekdonensis	Phaeobacter gallaeciensis	Sulfitobacter dubius	Ruegeria scottomollicae	Phaeobacter caeruleus	Shimia biformata	Novosphingobium pentaromativorans
	MOLA864	MOLA860 (3 isolates)	MOLA922	MOLA888	MOLA890	MOLA881	MOLA855 (5 isolates)	MOLA917	MOLA857 (2 isolates)	MOLA853	MOLA852	MOLA850 (2 isolates)	MOLA858	MOLA874 (3 isolates)	MOLA892	MOLA869 (4 isolates)	MOLA870	MOLA880	MOLA889	MOLA879	MOLA877	MOLA878	MOLA871 (2 isolates)	MOLA867
	Control, 2 days		Control, 22 days				DOM Aurelia, 2 days							DOM Aurelia, 22 days

Table 2 .

 2 Number of OTUs, microbial richness and diversity estimates based on randomly picked OTUs normalized to 5080 sequences per sample to accommodate for the sample with the lowest number of sequences.

			Control				+$DOM$Aurelia
		0	2	9	22	0	2	9	22
	OTU$number	557	960	343	774	484	569	579	878
	Richness$(Chao1)	1034	1164	782	1309	991	986	1219	1426
	Diversity$(Inverse$Simpson)		35.2	8.7	22.6	14.8	13.9	3.6	21.1

Acknowledgements

This study was supported by the CNRS-EC2CO program through the GELPROC project (2010)(2011)(2012): "Interactions between gelatinous plankton and dynamics of prokaryotic communities". We wish to thank P. Roman for the jellyfish maintenance. We are grateful to the BIO2MAR platform (http://bio2mar.obs-banyuls.fr) for providing technical support and access to instrumentation. We thank the two anonymous reviewers for their comments that greatly improved the manuscript.

Supplementary information