Lionel Bertaux
email: lbertaux@laas.fr

Akram Hakiri
email: akram.hakiri@laas.fr

Samir Medjiah
email: medjiah@laas.fr

Pascal Berthou
email: berthou@laas.fr

Slim Abdellatif

A DDS/SDN Based Communication System for Efficient Support of Dynamic Distributed Real-Time Applications

Keywords: Data Distribution Service, Software Defined Networking, Openflow, Quality of Service, Distributed Simulation

Many distributed real-time applications have dynamic requirements regarding communication delay and bandwidth. The Data Distribution Service (DDS) middleware is a key enabling technology used to support such applications. Indeed, the publish/subscribe distribution model of DDS with the ability to assign dynamic QoS (Quality of Service) parameters to DDS distribution services is able to take into account changes in the exchanged data flows and in the required QoS. This variability is taken into account at the middleware level to adjust some of DDS QoS mechanisms but is rarely propagated to the network layer to provide dynamic network communication services that fit the varying DDS distribution service needs. Usually, an over provisioned network is used, leading to network resource wastage. This paper addresses this issue and proposes a communication architecture that combines DDS with a new emerging class of communication networks named Software Defined Networks (SDN) to support efficiently dynamic distributed applications. SDN bring flexibility to the network and enable the provision of on-demand dynamic network communication services.

I. INTRODUCTION

Distributed real-time applications are dynamic in nature, i.e. the data flows that are exchanged and their Quality of Service (QoS) requirements vary over time. Moreover, some applications operate in changing environments with heterogeneous mobile nodes involved that communicate via wireless communication links. Air traffic management, power grid control, network games, live simulation, and distributed interactive simulation fall into this category. For instance, if we consider the case of distributed interactive simulation for vehicle driver training with many networked driving simulators that evolve in a shared virtual world, each simulator needs to get some state information (position, speed, etc.) from nearby simulators. The closer the simulator, the more stringent the required delay to get information is. Movements of driving simulators (in the virtual world) introduce dynamicity in the data flows that are delivered/consumed by each simulator and on the QoS requirements related to the delivery of these data flows.

The dynamic nature of these applications is one of the major challenges for the underlying communication system and in particular for the communication network that adds other challenges such as scalability and high QoS support. Indeed, if network resource utilization is a concern, the network must be flexible enough to be reprogrammed in accordance with any change in the application. Current approaches neither address the dynamicity of applications nor care about resource utilization. They are either based on static and overprovisioned dedicated networks or overlay networks (that are difficult to set up on demand and costly), or require from the application to adapt to network's performance. The main reason is that current networks are rather static because of their complexity, the specificities of each network device, etc. An emerging class of communication networks named Software Defined Networks (SDN) have the ability to build flexible networks whose behavior can be programmed and reprogrammed on demand and in a fine grained manner. They are a promising answer to the efficient support of dynamic distributed real-time applications from a network resource utilization perspective

The main goal of this work is to propose a communication system that combines the OMG Data Distribution Service (DDS) middleware with Software Defined Networks (SDN) to efficiently support dynamic real-time applications distributed on non-dedicated network infrastructures. The rationale behind using DDS and SDN is the following. DDS has been devised (and successfully used) for high performance distributed real-time systems. It offers many QoS parameters that can be used to express the requirements on the data distribution services needed by the application. Most are dynamic and can be changed during run-time, enabling DDS to capture dynamically application QoS requirements. The Publish/Subscribe model of DDS captures the second facet of the dynamicity of the application and allows changes in the data flows that are exchanged by the application's components.

To enforce this QoS, end-host and network resources control is mandatory. For the former, since DDS resides on end-hosts, it defines a set of mechanisms that implement QoS aware control and access to host resources. For the latter, interfaces exposed by the network are needed to manage its resources according to applications needs. This is exactly the opportunity brought by SDN.

This paper is organized as follow. In section II, DDS and SDN concepts are briefly exposed. Then, section III gives a brief state of the art concerning the concept of Application Driven Networking. Section IV details some relevant case studies. Section V explains our proposed architecture. Finally, section VI concludes this paper.

II. BACKGROUND

A. Data Distribution System (DDS)

The OMG DDS specification defines a standard architecture for data exchanges in pub/sub systems. DDS provides a global data store in which publishers and subscribers respectively write and read data. DDS provides a flexible and modular structure by decoupling: (1) location, via anonymous publish-subscribe, (2) redundancy, by allowing any numbers of readers and writers, (3) time, by providing asynchronous, time-independent data distribution, (4) message flow, by providing message-based data-centric connection management, and (5) platform, by supporting a platformindependent model that can be mapped to different platformspecific models, such as C++ running on VxWorks or Java running on Real-Time Linux.

The DDS architecture consists of two layers. The Data-Centric Publish Subscribe (DCPS) layer provides efficient, scalable, predictable, and resource-aware data distribution. The Data Local Reconstruction Layer (DLRL) provides an object-oriented facade atop the DCPS so that applications can access object fields rather than raw data and defines navigable associations between objects.

Figure 1 depicts the relation between DDS entities; domains, domain participants, topics, publishers, data writers, subscribers, and data readers. DCPS layer supports a global data store (DDS domain) where publishers write and subscribers read data, respectively. A DDS domain represents a virtual global data-space; information provided in the domain are accessible by the applications registered to that domain.

Figure 1. A View of the Data Distribution Service

DDS is topic-based, allowing strongly-typed data dissemination. A DDS topic describes the type and structure of the data, data readers and data writers can respectively subscribe and publish to specific topics. Publishers manage one or more data writers and subscribers manage one or more data readers. Publishers and subscribers can aggregate data from multiple data writers and readers for efficient transmission of data across a network. Publishers and subscribers discover each other automatically and match whenever they have compatible topics and QoS. DDS can use multiple topic samples called instances that are differentiated by their associated unique key. Topics can be configured with a wide range of DDS QoS capabilities imposed by peers. A DDS QoS policy can be viewed as a function that returns the state of the communication since it enables changing some aspects of the message exchange, such as connection and error handling.

Moreover, topic samples are exchanged between peers within the global data space according to a contract established in the discovery phase. The discovery process is managed by the Real-Time Publish-Subscribe (RTPS) protocol. During the discovery process each domain participant maintains a local database about all the active DataWriters and DataReaders that are in the same domain. Additionally, the discovery mechanism known as Simple Discovery Protocol (SDP) includes two phases: (1) Simple Participant Discovery which is performed by the Simple Participant Discovery Protocol (SPDP), where remote domain participants learn about each other by sending participant declaration messages (also known as participant DATA submessages) and (2) Endpoint Discovery which is performed by the Simple Endpoint Discovery Protocol (SEDP), in which DataWriters and DataReaders exchange information (i.e., such as QoS, data types, etc.) to match each other by sending publication/subscription declarations in DATA messages that we will refer to as publication DATAs and subscription DATAs.

B. SDN concepts and OpenFlow

Recently, Software-Defined Networking (SDN) [START_REF] Kim | Improving network management with software defined networking[END_REF] has emerged as a new approach for network programmability and management, where the centralized control plane logic is decoupled from the forwarding plane as shown in Figure 2. Network programmability is a software approach to dynamically control, manage, configure or even use a network. Amongst others, it covers the existence of open interfaces for device configuration, the capability to design virtual overlay networks or the opportunity to easily deploy network functionalities. In an SDN, centralized control uses the network-wide view of the network to ease management and configuration of data-path for network services such as resource allocation or virtual network deployment. Such networking concept can help migrate legacy networks to vendor-free platform adaptable to each user needs that could be seen as a Network as a Service (NaaS).

In Software-Defined Networks (SDNs), control plane is logically separated from data plane. SDN architectures define a new entity that centralizes control intelligence of one or more network elements (basically switches). A protocol is then defined to communicate between control plane and data plane. Examples of such protocols are OpenFlow [START_REF]OpenFlow Switch Specificationversion 1.4.0[END_REF] or ForCES [START_REF] Doria | Forwarding and Control Element Separation (ForCES) Protocol Specification[END_REF].

In an OpenFlow-based SDN architecture, the control entity is called a controller. The controller offers northbound interfaces to network applications and southbound interfaces to communicate with data plane. OpenFlow [START_REF]OpenFlow Switch Specificationversion 1.4.0[END_REF] is one of the possible southbound protocols. OpenFlow behavior is simple but it can allow complex configurations: the hardware processing pipeline from legacy switches is replaced by a software pipeline based on flow tables. These flow tables are composed by simple rules to process packets, forward them to another table and finally send them to an output queue or port. Flow tables can be built in a proactive or in a reactive way.

With such functionalities, OpenFlow can be used to implement Quality of Service policies in a switch by dynamically managing flow differentiation. For example, a DiffServ-like architecture can be easily deployed in a switch with several queues per output port and differentiation can be applied based on various criteria (destination address, sender address, port numbers, etc.). An important point is that OpenFlow is not in charge of configuring queues attached to output ports of a switch. To this end, OpenFlow Configuration (OF-Config) protocol [START_REF]Open Networking Foundation, OF-CONFIG 1.2 -OpenFlow Management and Configuration Protocol[END_REF] has been defined based on NETCONF and manages the "OpenFlow context": establishing links between switches and controller, configure output port in switches, etc. Since version 1.3 and the introduction of a meter table, OpenFlow is able to monitor flow rates and perform actions if a predefined limit is exceeded: drop packets or mark the DSCP field. The drop policy is efficient but very aggressive and can lead to the introduction of data bursts for TCP flows [START_REF] Mohan | Performance study of TCP flows with QoS-supported OpenFlow in data center networks[END_REF]. Globally, OpenFlow QoS capabilities are considered incomplete to implement an effective QoS architecture and the protocol needs some external features to be fully functional [START_REF] Sonkoly | On QoS Support to Ofelia and OpenFlow[END_REF] (e.g. configuration of outgoing queues in switches).

However, full network configuration is possible by dynamically installing flow processing rules in switches with OpenFlow and configuring outgoing queues with another application. These kind of configurations can be managed by an application on the northbound interface of a controller, the latter being performed with OF-Config or a legacy protocol like SNMP. Another approach to implement QoS with OpenFlow is to perform flow splitting and to assign specific routes to given flows. Coupled with an optimization algorithm, such mechanisms can improve the quality of the communication in the case of video streaming [START_REF] Egilmez | An Optimization Framework for QoS-Enabled Adaptive Video Streaming Over OpenFlow Networks[END_REF]. Virtual networks (virtual switches and virtual links) can also be deployed by "slicing" the OpenFlow network. An intermediate controller acts as a "proxy" for the real control plane located above him and provides each controller with a virtual network (e.g. FlowVisor [START_REF] Sherwood | Can the production network be the testbed?[END_REF] and more recently OpenVirteX [START_REF] Al-Shabibi | OpenVirteX: A Network Hypervisor[END_REF]).

III. RELATED WORK

Prior middleware solutions for network QoS management focus on how to add network QoS services for CORBA-based communication [START_REF] Dasarathy | Adaptive network QoS in layer-3/layer-2 networks as a middleware service for mission-critical applications[END_REF]. A large-scale event notification infrastructure for topic-based pub/sub applications has been suggested for peer-to-peer routing overlaid on the Internet [START_REF] Rowtron | SCRIBE: The design of a large-scale event notification infrastructure[END_REF]. Those approaches can be deployed only in a singledomain network, where one administrative domain manages the whole network. Extending network QoS solutions to the Internet can result in traffic specified at each end-system being dropped by the transit network infrastructure of other domains [START_REF] Balasubramanian | Netqope: A model-driven network qos provisioning engine for distributed real-time and embedded systems[END_REF]. For example, authors in [START_REF] Zhang | A user-centric network communication broker for multimedia collaborative computing[END_REF] have presented a network communication broker to enable per-class QoS for multimedia collaborative applications. Even if this network broker enhanced the QoS allocation by differentiating the traffic processing at the network edges, it supports neither mobility service management nor scalability since it adds complicated interfaces to both applications and middleware for the QoS notification.

Our prior work [START_REF] Hakiri | Supporting end-to-end quality of service properties in OMG data distribution service publish/subscribe middleware over wide area networks[END_REF] on Velox used an MPLS tunneling mechanism to propagate DDS QoS over an overlay model. Velox statically created a logical tunnel between remote DDS participants to conduct QoS negotiation and resource reservations. Any time communication failed, the user had to reconfigure the network manually. Moreover, application requirements could not change during runtime communication between participants. To overcome these limitations, we introduced the NetQSIP framework [START_REF] Hakiri | Supporting SIP-based End-to-End Data Distribution Service QoS in WANs[END_REF] to enhance prior research on QoS management at the network layer by integrating QoS along two key dimensions: (1) the horizontal direction, i.e., between different adjacent layers in the application, middleware, and network, and (2) the vertical direction, i.e., within a particular layer. In particular, our NetQSIP framework maps application flow requirements into the DDS layer to allow end-to-end QoS provisioning. Hence, NetQSIP provides dynamic QoS management and simplifies resource allocation by using SIP to automate end-to-end QoS provisioning.

However, both Velox and NetQSIP were integrated on legacy network devices so their deployment depends on the internal capabilities of each network fabric, which the internals differ from vendor to vendor, with no open software platform to experiment new ideas. These devices have their control and forwarding logic parts both integrated in monolithic, closed, and mainframe-like boxes. Consequently, only a small amount of external interfaces are standardized (e.g., packet forwarding) but all of their internal flexibility is hidden.

Likewise, the work presented by [START_REF] Jose | RELOAD extension for data discovery and transfer in data-centric publish-subscribe environments[END_REF] outlines an extension to the IETF REsource Location And Discovery (RELOAD) protocol [START_REF] Jennings | REsource LOcation And Discovery (RELOAD) Base Protocol, IETF[END_REF] for content discovery and DDS messages transfer over large-scale legacy networks. Although this paper addressed the discovery scalability issue of DDS, it centers on a structured P2P overlay architecture in legacy WANs, which is different from our work described in this paper. The work described in this paper is based on SDN programmable networks, where DDS is used atop of the SDN northbound interface to control network devices and program QoS in flexible and scalable way.

IV.

CASE STUDY

This section intends to give an example of what could be the benefits of using Software Defined Networking for Distributed Real-time applications. Requirements for this class of applications are stringent and some of them will be explored when using SDN concepts. However, real-time communication could not be provided without dedicated network architecture. To make clear the discussion, we propose to take an example of a multi-site company network given in Figure 3. A Virtual Private Network between multiple offices of the same company is considered: the headquarters (A) are connected to the other offices (B) and (C) with a Virtual Ethernet Service. Then, business applications and services are running as if they were deployed over a classical LAN.

Figure 3. Multi-site network architecture

The network is then essentially composed of switches, few routers, and middle-box appliances. This kind of network architecture is simple and easy to maintain. Links between sites have to be provisioned according to the distributed applications needs, but also to the generic communication (telephony, network appliances, etc.).

However, the lack of traffic isolation and the difficulty to deploy complex quality of service policies in the network are the counter part of the simplicity. It is then not possible to ensure the communication requirements of real-time applications and especially if requirements are stringent and variable. Overprovisioning is then the only solution, but at a high cost.

A. Software Defined Networking benefits

Using SDN concepts could knock down the technological barriers that slowdown the dissemination of real-time distributed applications. The main idea in this paper is to combine objects and communication descriptions provided when using the DDS middleware, especially related to Quality of Service, and SDN concepts as a flexible solution to configure the network with dedicated and flexible QoS solutions.

The benefits are multiple and this article proposes to explore them following three use cases.

First, generally speaking and without any consideration on using a middleware, the management of a distributed network across several entities is complex. Providing an acceptable level of service to distributed real-time applications, but also offering enough bandwidth to network appliance is a complex tradeoff that have to be implemented with a fine-tuned QoS policy. The problem is hardened when considering the evolution of the requirements over time: the policy has to be adapted frequently and consistently. It mainly consists in collecting the communication requirements from the applications, defining the new policy, and scripting the control of network switches with SNMP for instance. This could be a hard task, as the consistency of the configuration must be ensured on all devices and at the same time. With a unique control point (the controller) OpenFlow provides a robust solution to this task. Application requirements could be collected within the controller thanks to the centralized description of the application (DDS configuration file), an optimal policy could be computed and deployed thanks to the OpenFlow protocol.

The next use case, extends the previous one related to the configuration, and addresses the automatic network slicing. When several applications are coexisting within the same network, it is sometime relevant to segregate traffics from each application. Several VPN are then configured to ensure that bandwidth resources from one application would not be consumed by another application. OpenFlow provides solutions for network isolation, called slicing that simplifies this operation. The switches could monitor network resource consumption and forwarding decisions could be taken on the go by the controller.

Finally, the last use case takes benefit from DDS smart capabilities for data diffusion. Commonly, broadcasting is massively used over a LAN to maintain the consistence of the distributed application. However, considering a virtual Ethernet service over private leased line it could be inefficient and costly when resources are scarce. DDS offers a filtering mechanism that only propagates data to the concerned subscribers instead of broadcasting. However, this mechanism is useless over a classical Ethernet network as there is no way to filter data. It is possible to implement a dynamic filtering mechanism with OpenFlow that reduce the traffic between interconnected switches at its minimal value.

B. Discussion

The combination of DDS middleware capabilities with the OpenFlow SDN concepts is promising. However, this is not possible without an appropriate architecture that defines the role and the place of the different entities. The next section is devoted to the architecture definition.

V. PROPOSED ARCHITECTURE

This section presents the details of the network architecture required to deploy enhanced QoS mechanisms for DDS over SDN-enabled networks.

A. Targeted QoS parameters

The network quality of service can be described through multiple parameters related to different aspects of the data transmission such as:

-Transmission errors (bit error rate, packet error rate, packet loss, FEC level, etc.) -Transmission delay (end-to-end delay, jitter, queuing delay, first packet arrival, etc.) -Transmission throughput (bandwidth, packet success, etc.)

Through DDS, applications can specify their QoS requirements by the mean of various QoS parameters. Indeed, DDS allows the specification of the following QoS policies: In the DDS middleware, these QoS parameters are handled in different ways and at different layers of the communication stack. For example the "History" parameter related to "Volatility" is handled at the highest layer (sub-application layer). Whereas, other parameters such as "Latency" or "Transport priority" can be handled at the network layer or below.

On the other side, QoS management is implemented within OpenFlow. QoS management is handled via 3 techniques: (a) per-port queue management, (b) per-flow priority, and (c) queue and flow statistics. The QoS management in OpenFlow has been progressively introduced through the consecutive versions. OpenFlow v1.0 brought the specification of the minimum rate for a port queue, while OpenFlow v1.2 brought the specification of the maximum rate for a port queue and the specification of priorities among flows. Finally, OpenFlow v1.3 brought Meter tables for statistics purposes.

In order to enhance DDS applications running on top of SDN-enabled networks, it is important to translate all the DDS QoS parameters into L2/L3 policies that can be implemented in the network.

B. QoS parameters capture

Before translating the DDS QoS parameters into L2/L3 policies, it is important to capture all the relevant parameters in the most seamless way for the running applications. To this end, we can envision two different approaches, both requiring no modification in high level application:

(1) Middleware-independent QoS parameters capture: QoS parameters can be captured using the DDS monitoring service. With the monitoring service, the SDN controller subscribes to a discovery DDS topic and thus, takes part of the DDS domain. It then notifies QoS parameters of any running DDS-based application. This approach is transparent to the application. However, it requires the controller to be part of the DDS domain and thus, it must run the DDS middleware. A more seamless way would be to capture the QoS parameters specification by monitoring the traffic among the DDS entities within the network. This is very challenging and may be less efficient since it involves the reverse engineering of the DDS protocols (signaling, data, etc.).

(2) Middleware-dependent QoS parameters capture: In this approach, the DDS middleware should be augmented with a new SDN module. As illustrated in Figure 4, this module will communicates with the Network Application which is running on top of the SDN controller in order to provide it with all the relevant QoS information to optimize and/or reconfigure the underlying L2/L3 network topology. Furthermore, a network managed via OpenFlow can be "sliced" to create virtual networks. It is thus possible to deploy virtual switches and links with a topology adapted to current state and active DDS flows: point-to-point, mesh, tree, etc.

D. Network ressources allocation

In our design, we chose to build an application located on top of the controller NorthBound interface, since it is more compliant with SDN standards than deploying an extension to a controller (see Figure 5). Globally, this application is responsible for translating DDS QoS constraints into forwarding rules that can be implemented in network switches. It is composed by 4 logical blocks: a DDS information handler, a network monitor, a planner and a command executor. Interactions and behavior of theses logical blocks are summarized in Figure 6. The DDS handler is connected to DDS domain and uses RTPS to collect data on DDS flows (required bandwidth, delay tolerance, data generation model ...). The network monitor element uses the SDN controller NorthBound API to gather information on the underlying network (topology, capacity ...) and measurements on active flows (rate, counter ...). Based on monitoring data from previous elements, the planner computes the optimal set of network resources that is needed to support DDS flows with their required QoS. Optimization may induce splitting pointto-multi-point flows at RP-points (Rendezvous point), adapting DDS publishing rates to subscriber needs by shaping network flow rate or load balancing flows over different paths while preserving delay requirements. The last element is in charge of applying the network configuration built by the planner in the real world either via the controller NB-API for OpenFlow related rules or via OF-Config for switch configuration (Ex: configuring queues attached to outgoing ports). In order to check the technical feasibility of our solution, we considered of the OpenFlow controller Floodlight [START_REF]Floodlight Controller[END_REF]. Based on the REST communication architecture, the NB-API of Floodlight allows an application to capture information from the network and to set up various rules in the data plane (see Table 2). These requests are specific to the use of OpenFlow and take into account the possibilities of this South Bound protocol. They can help designing high level functionalities needed by our proposition:

 Build network topology based on known switches and links with accurate information such as port speed (i.e. path bandwidth),  Monitor network events by periodically checking available links and switches,  Monitor network flows by periodically checking traffic counters,  Reserve resources on a path between subscriber and publisher by adding a static flow,  Establish a dedicated network for data flows of specific topics by adding a virtual network. These functionalities are executed by the Network Monitor and the Executor modules of our Network Services Allocator (Figure 5) to achieve their respective goals: obtain status and statistics from the network and control the data-forwarding elements of the network. In the near future, software defined components will be a predominant part in communication networks thanks to the growing use of virtualization (resources or network). The design presented in this paper shows how SDN computer networks can be used to provide dynamic customized network services to dynamic DDS applications with efficient network resource utilization. The feasibility of this solution has been demonstrated through the description of its components along with their functionalities and how they can be implemented.

Figure 2 .

 2 Figure 2. Overview of the software-defined networking (SDN)

Figure 4 .

 4 Figure 4. DDS QoS parameters capture to L2/L3 flows orchestration

Figure 5 .

 5 Figure 5. Network application in charge of configuration.

Figure 6 .

 6 Figure 6. Network services allocation algorithm.

Table 1 . QoS Parameters in DDS

 1

	Volatility	-Durability
		-History
		-Reader data lifecycle
		-Writer data lifecycle
		-Lifespan
	Infrastructure	-Entity factory
		-Resource limits
	Delivery	-Reliability
		-Time based filter
		-Deadline
		-Content filters
	User QoS	-User data
		-Topic data
		-Group data
	Presentation	-Partition
		-Presentation
		-Destination order
	Redundancy	-Ownership
		-Ownership strength
		-Liveliness
	Transport	-Latency budget
		-Transport priority

Table 2 . Examples from the Floodlight Northbound-API

 2

	Description	REST	Possible Options
		Method
	List traffic counters	GET	Per controller / per switch
	List all switches	GET
	List all links	GET	Internal / External domain
	Add a static flow	POST	Arguments and output
	Add virtual network	PUT	ID and possible Gateway
		VI. CONCLUSION

ACKNOWLEDGEMENTS

This work was partially funded by the French National Research Agency (ANR) and the French Defense Agency (DGA) under the project ANR DGA ADN (ANR-13-ASTR-0024)


Hélène Agogué

Dominique Lamy

Phillip R Neal

Mitchell L Sogin

Gerhard J Herndl
email: gerhard.herndl@univie.ac.at

Water mass-specificity of bacterial communities in the North Atlantic revealed by massively parallel sequencing

Keywords: bacterial diversity, massively parallel tag sequencing, North Atlantic Ocean, deep water masses, bacterial biogeography Antarctic Intermediate Water, DOC, dissolved organic carbon, DON, dissolved organic nitrogen, AOU, apparent oxygen utilization, HNA, high nucleic acid, MDS, non-metric multidimensional scaling, CCA, canonical correspondence analysis

Bacterial assemblages from subsurface (100 m depth), meso-(200-1000 m depth) and bathypelagic (below 1000 m depth) zones at 10 stations along a North Atlantic Ocean transect from 60°N to 5°S were characterized using massively parallel pyrotag sequencing of the V6 region of the 16S rRNA gene (V6 pyrotags). In a dataset of more than 830,000 pyrotags we identified 10,780 OTUs of which 52% were singletons. The singletons accounted for less than 2% of the OTU abundance, while the 100 and 1,000 most abundant OTUs represented 80% and 96%, respectively, of all recovered OTUs. Non-metric Multi-Dimensional Scaling and Canonical Correspondence Analysis of all the OTUs excluding the singletons revealed a clear clustering of the bacterial communities according to the water masses. More than 80% of the 1,000 most abundant OTUs corresponded to Proteobacteria of which 55% were Alphaproteobacteria, mostly composed of the SAR11 cluster. Gammaproteobacteria increased with depth and included a relatively large number of OTUs belonging to Alteromonadales and Oceanospirillales. The bathypelagic zone showed higher taxonomic evenness than the overlying waters, albeit bacterial diversity was remarkably variable. Both abundant and low-abundance OTUs were responsible for the distinct bacterial communities characterizing the major deepwater masses. Taken together, our results reveal that deep-water masses act as bio-oceanographic islands for bacterioplankton leading to water mass-specific bacterial communities in the deep waters of the Atlantic.

INTRODUCTION

Prokaryotes represent an important component of the marine plankton, comprising up to 70% and 75% of the total biomass in surface [START_REF] Fuhrman | Dominance of bacterial biomass in the Sargasso Sea and its ecological implications[END_REF]) and deep [START_REF] Aristegui | Microbial oceanography of the dark ocean's pelagic realm[END_REF]) waters, respectively. They serve a fundamental role in mediating a wide range of biogeochemical cycles [START_REF] Azam | The Ecological Role of Water-Column Microbes in the Sea[END_REF][START_REF] Karl | Nutrient dynamics in the deep blue sea[END_REF]. The introduction of molecular tools has substantially increased our knowledge about marine microbial community structure and has shown that the vast majority of environmental microbes represents novel taxa that have yet to be cultivated [START_REF] Handelsman | Metagenomics: Application of Genomics to Uncultured Microorganisms[END_REF][START_REF] Olsen | Microbial Ecology and Evolution: A Ribosomal RNA Approach[END_REF]). High-throughput sequencing methods and pyrosequencing allow for efficient deep molecular sampling efforts of the microbial populations [START_REF] Gilbert | The seasonal structure of microbial communities in the Western English Channel[END_REF][START_REF] Huber | Microbial population structures in the deep marine biosphere[END_REF][START_REF] Huse | Exploring Microbial Diversity and Taxonomy Using SSU rRNA Hypervariable Tag Sequencing[END_REF][START_REF] Sogin | Microbial diversity in the deep sea and the underexplored "rare biosphere[END_REF]) and sidestep the need to clone individual DNA molecules [START_REF] Margulies | Genome sequencing in microfabricated high-density picolitre reactors[END_REF]. Furthermore, data from these massively parallel sequencing approaches provide an exhaustive description of the taxonomic affiliation of microbial community. This information forms the basis for estimating both the richness and evenness of the microbial populations present in the environment which are, in turn, essential to refine our knowledge on the biogeography of marine microbes (Galand et al. 2009b;[START_REF] Galand | Hydrography shapes bacterial biogeography of the deep Arctic Ocean[END_REF][START_REF] Martiny | Microbial biogeography: putting microorganisms on the map[END_REF][START_REF] Pommier | Biogeographic analysis of ribosomal RNA clusters from marine bacterioplankton[END_REF] and to relate microbial diversity and ecosystem properties [START_REF] Andersson | Pyrosequencing reveals contrasting seasonal dynamics of taxa within Baltic Sea bacterioplankton communities[END_REF].

By applying a high-throughput pyrosequencing strategy, [START_REF] Sogin | Microbial diversity in the deep sea and the underexplored "rare biosphere[END_REF] found a remarkably high bacterial diversity in the deep-water masses of the North Atlantic and in diffuse flow hydrothermal vents. The study highlighted the existence of thousands of low-abundance populations, coined the "rare biosphere", which accounted for most of the observed phylogenetic bacterial diversity [START_REF] Sogin | Microbial diversity in the deep sea and the underexplored "rare biosphere[END_REF]. The rare phylotypes are assumed to be recruited by immigration and to have extremely low loss rates from grazing and viral lysis [START_REF] Pedros-Alio | Marine microbial diversity: can it be determined?[END_REF]. Conventional molecular techniques fail to detect microbial phylotypes that make up the long tail of taxon rank-distribution curves because dominant populations (comprising > 1% of the total community) mask the detection of the highly diverse, low-abundance organisms. The massively parallel pyrotag sequencing approach, however, allows for deep sequencing that can capture information about these low-abundance populations (Galand et al. 2009a;[START_REF] Palacios | Contrasting microbial community assembly hypotheses: a reconciling tale from the Rio Tinto[END_REF][START_REF] Sogin | Microbial diversity in the deep sea and the underexplored "rare biosphere[END_REF].

The spatial variability of microbial diversity across habitats has not been investigated extensively [START_REF] Galand | Hydrography shapes bacterial biogeography of the deep Arctic Ocean[END_REF][START_REF] Pommier | Global patterns of diversity and community structure in marine bacterioplankton[END_REF]. The availability of resources, selective loss factors (grazing and viral lysis), and physical parameters such as temperature and salinity can influence microbial population structure. This is in accordance with the deterministic theory "everything is everywhere, but, the environment selects" (Baas [START_REF] Becking | Geobiologie of inleiding tot de milieukunde[END_REF] which has been debated extensively recently (de Wit & Bouvier 2006;O'Malley 2008). In contrast, stochastic neutral models of biodiversity and biogeography (Hubbel 2001;[START_REF] Sloan | Quantifying the roles of immigration and chance in shaping prokaryote community structure[END_REF]) postulate that immigration, dispersal rates, size of the habitat (i.e., taxa-area relationships) (Woodcock et al. 2007) and ecological invariance among microbial phylotypes shape microbial community structure.

Distinct water masses characterize the hydrodynamic conditions of the ocean, most notably the major water masses driving the thermohaline ocean circulation [START_REF] Tomczak | Regional Oceanography: An Introduction[END_REF]. Non-sinking free-living prokaryotes inhabiting oceanic deep waters might be trapped in these distinct water masses leading overall to water mass-specific prokaryotic community composition and activity [START_REF] Agogué | Major gradients in putatively nitrifying and non-nitrifying Archaea in the deep North Atlantic[END_REF]Galand et al. 2009b;[START_REF] Galand | Hydrography shapes bacterial biogeography of the deep Arctic Ocean[END_REF]Varela et al. 2008a;Varela et al. 2008b). Hence, dispersal and immigration of free-living microbes in the ocean might be more limited than generally assumed. These physical boundary conditions of oceanic water masses might constrain the applicability of some of the fundamental theories and models on the biogeographic distribution of microbes in the ocean.

This study aimed at describing the composition of bacterial assemblages in the North Atlantic Ocean throughout the water column by using massively parallel pyrotag sequencing to resolve the spatial distribution of bacterial richness and evenness in different water masses. We hypothesized that the bacterial communities exhibit a biogeography according to the water masses. Hence, we expected that bacterial communities collected several 1000 km apart from each other but originating from the same water mass are more similar than bacterial communities collected less than a few hundred meters apart but from different water masses. We used data from the hypervariable V6 region of the bacterial 16S rRNA gene from 45 samples collected from the main deep water masses along a 8000 km north-to-south transect in North Atlantic Ocean ranging from 60°N to 5°S.

MATERIAL AND METHODS

Study site and sampling.

Sampling was conducted during the cruises TRANSAT-1 (September 2002) and-2 (May 2003) and ARCHIMEDES-2 (November/December 2006) on board R/V Pelagia following the North Atlantic Deep Water (NADW) from 60°N to 5°S in the eastern basin of the Atlantic Ocean (Fig. 1). Ten stations were occupied, and samples were taken from 6 to 10 sampling depths at each station from 100m to 4500m depth. In total, 45 samples were collected for massively parallel pyrotag sequencing (Table S1).

The water masses along the eastern North Atlantic section were identified by their distinct potential temperature and salinity characteristics (Table S2, Fig. S1) (van Aken 2000a, b). At 4000-5000m depth, the Lower Deep Water (LDW) has low salinity (34.9) and temperature (1.9 -2.6°C) (Table S2, Fig. S1). The NEADW (North East Atlantic Deep Water) is characterized by a temperature between 2.5 and 4.1°C and a higher salinity than LDW (Table S2, Fig. S1). The core of NEADW was identifiable throughout the transect at around 2750m depth.

Two types of mesopelagic waters were found in the (sub)equatorial region: the transitional and South Atlantic Central Water (tCW/SACW) exhibiting the same temperature and salinity characteristics, and the Antarctic Intermediate Water (AAIW) characterized by lower salinity and temperature than tCW/SACW (Table S2, Fig. S1). In the northernmost region of the transect, the Northern Intermediate Water (NIW) was found at 500m depth, showing the same temperature but higher salinity than AAIW. Labrador Sea Water (LSW) was clearly identifiable at depths between 1200-2100m throughout the transect (Table S2, Fig. S1). Samples from the distinct water masses were collected with 10-L NOEX (no oxygen exchanges) bottles mounted in a CTD (conductivity, temperature, depth) frame. Samples were collected from: (i) subsurface waters (lower euphotic layer, 100-150m layer), (ii) mesopelagic waters including tCW and SACW (250 -500m layer), AAIW (750 -900m layer) and NIW (500m), and (iii) bathypelagic waters including LSW (1200 -2100m layer), NEADW (1750 -4000m layer) and LDW (4000 -5000m layer) (Table S1).

DNA extraction, pyrosequencing and identification of the bacterial phylotypes

DNA extraction. During the TRANSAT cruises, 1 L of seawater from each depth was filtered onto a 0.2 µm polycarbonate filter (Millipore) and the filters were subsequently stored at -80°C until further processing in the lab. During the ARCHIMEDES-2 cruise, 10 L of seawater from each depth was filtered through a 0.22 µm Sterivex filter GP unit (Millipore). Lysis buffer (40 mM EDTA, 50 mM Tris-HCl, 0.75 M sucrose) was then added into the Sterivex (1.8 mL) and the filters were subsequently stored at -80°C until analysis. Extraction of total DNA was performed using an UltraClean soil DNA and Mega soil DNA isolation kit (Mobio) for TRANSAT-1 & -2 and ARCHIMEDES-2 samples, respectively. PCR amplicon library construction and pyrosequencing. The hypervariable V6 region of the 16S rRNA of bacteria was amplified from TRANSAT samples using primers 967F, 5'gcctccctcgcgccatcag-CAACGCGAAGAACCTTACC-3' and 1046R, 5'-gccttgccagcccgctcag-CGACAGCCATGCANCACCT-3' and pyrosequenced on a Roche Genome Sequencer 20 under conditions described in [START_REF] Sogin | Microbial diversity in the deep sea and the underexplored "rare biosphere[END_REF]. A cocktail of five fused primers at the 5' end of the V6 region (E. coli positions 967-985) and four primers at the 3' end (E. coli positions 1046-1028) that capture the full diversity of rRNA sequences represented in molecular databases [START_REF] Huber | Microbial population structures in the deep marine biosphere[END_REF][START_REF] Sogin | Microbial diversity in the deep sea and the underexplored "rare biosphere[END_REF]) amplified ARCHIMEDES-2 environmental DNA samples for pyrosequencing on a Roche Genome GS FLX system. For both the TRANSAT and ARCHIMEDES-2 we prepared amplicon libraries from at least three independent PCR cocktails to minimize the impact of potential early-round PCR errors. To minimize effects of sequencing errors, we employed a quality trimming procedure to remove low quality pyrotags and to eliminate sequences with multiple undetermined residues or mismatches to the PCR primers at the beginning of a read [START_REF] Huse | Accuracy and quality of massively parallel DNA pyrosequencing[END_REF].

Clustering and assignment of the OTUs: identification of bacterial phylotypes.

The clustering of V6 pyrotags into Operational Taxonomic Units (OTUs) was done with the new single-linkage preclustering (SLP) algorithm to smooth sequencing errors and reduce noise, followed by primary pairwise, average linkage clustering (PW-AL) described in [START_REF] Huse | Ironing out the wrinkles in the rare biosphere through improved OTU clustering[END_REF]. The advantage of this new method is that it corrects for sequencing errors and minimizes the propagation of OTUs with sequencing effort. This method provides a comparable reduction in spurious OTUs as a previously published algorithm (e.g. PyroNoise, Qunice et al.

2009

), but requires less computational expense [START_REF] Huse | Ironing out the wrinkles in the rare biosphere through improved OTU clustering[END_REF], see also the Discussion part on pyrosequencing errors). OTUs were created using clustering thresholds of 3% corresponding to 97% similarity.

We assigned taxonomic identifiers to OTUs by using the rRNA indexing algorithm Global Assignment of Sequence Taxonomy (GAST) [START_REF] Sogin | Microbial diversity in the deep sea and the underexplored "rare biosphere[END_REF], which compares OTUs to known rRNA genes that have already been placed in a phylogenetic framework of more than 1,000,000 nearly full-length rRNA reference sequences (RefSSU) based on the SILVA database (Pruesse et al. 2007)

Diversity estimation and statistical analysis.

Diversity indices. The non-parametric ACE and the Chao1 richness index were calculated with the CatchAll software program [START_REF] Bunge | CatchAll: Parametric and nonparametric estimation of species richness and population size[END_REF].

The Gini index of evenness [START_REF] Wittebolle | Initial community evenness favours functionality under selective stress[END_REF]) was calculated on the relative abundance of all OTUs except the singletons using the ineq function in the ineq package of the software package R for subsurface, meso-and bathypelagic samples. The higher the Gini index is, the more unevenly distributed are the OTUs.

Cluster analysis. Non-metric multidimensional scaling (MDS) (Kruskal 1964a, b) was used to determine the similarity between samples. This data-reduction method shows the differences (or similarities) between samples by reducing the comparisons between samples from a multidimensional space to fewer dimensions, preferably 2 or 3. Differences between samples were calculated based on the relative abundance of (i) all the OTUs, (ii) all the OTUs except the singletons and (iii) the 1,000 most abundant OTUs. MDS analysis was also applied to (i) all the pyrotags except the singletons, (ii) the abundant pyrotags (frequency > 1% within a sample) and

(iii) the rare pyrotags (frequency < 0.01% within a sample). The similarities are presented in a multidimensional space by plotting more similar samples closer together (Kruskal 1964a, b).

Analysis of similarity (ANOSIM) was used to verify the significance of the MDS clustering by testing the hypothesis that bacterial communities from the same cluster were more similar to each other than to communities in different clusters. A Bray-Curtis similarity matrix computed from the relative abundance of all OTUs except the singletons was used to generate one-way ANOSIM statistics with 999 permutations.

SIMPER analysis. Similarity percentage (SIMPER) [START_REF] Clarke | Change in marine communities: an approach to statistical analysis and interpretation[END_REF] was used to determine which individual sequence contributed most to the dissimilarity between water masses (Dataset S1). The SIMPER analysis was also used to determine the percentage of similarity (i)

between each station and the northernmost station of the transect (station 27, Transat-2 cruise) (Fig. 1, Table S1) within specific water layers and (ii) between water layers, based on the relative abundance of all OTUs except the singletons. The data of the NEADW from [START_REF] Sogin | Microbial diversity in the deep sea and the underexplored "rare biosphere[END_REF] were included in these analyses to allow full comparison of all the stations occupied from 60°N to 5°S, i.e., a stretch of 8000 km.

Mantel analysis. The Mantel test was used to analyze the phylogenetic composition of the 1,000 most abundant OTUs and the singletons among all the samples. Mantel analysis was also used to determine the relationships between (i) bacterial assemblage structure, (ii) environmental factors and (iii) bacteria-related parameters. The three similarity matrices (Euclidian distance, n = 45) included the following variables: (i) the relative abundance of all OTUs except the singletons, (ii) temperature, salinity, concentration of inorganic nutrients (nitrite, nitrate, ammonia, silicate and phosphate), dissolved organic carbon (DOC) and nitrogen (DON), apparent oxygen utilization (AOU), latitude, longitude and depth and (iii) bacterial-related variables comprising bacterial abundance and production, percentage of high nucleic acid cells (HNA), potential respiration (measured as activity of the electron transport system), alpha-and beta-glucosidase, leucine aminopeptidase and alkaline phosphatase activity and its enzyme kinetics.

CCA analysis.

A canonical correspondence analysis (CCA) was used to investigate the variations in the relative abundance of the 1,000 most abundant OTUs under the constraint of our set of environmental variables. We assumed a unimodal response of OTUs to environmental variations.

Generally, nonlinear models are required for analysis of ecological data collected over a large range of habitats [START_REF] Braak | Canonical correspondence analysis and related multivariate methods in aquatic ecology[END_REF]. When a linear response is assumed, the percentage of explained variation is lower (redundancy analysis not shown). The null hypothesis that the bacterial assemblage is independent of the environmental parameters was tested using constrained ordination with a Monte Carlo permutation test (499 permutations). The parameters were selected to obtain significant canonical axes (p < 0.05) and to maximize the percentage of variance explained. The cluster, the SIMPER and the Mantel analyses were performed with PRIMER 6.1.7 (Primer-E, Ltd) and XLSTAT Pro ( 2006) software. The CCA analysis was performed with the Canoco version 4.5 software (Ter [START_REF] Braak | CANOCO -an extension of DECORANA to analyze species-environment relationships[END_REF].

Variability of the 100 most abundant OTUs per water mass

The deviation (in %) from the mean relative abundance in all the samples was calculated for the 100 most abundant OTUs for the different water masses. This deviation was calculated for each water mass and for each of the 100 most abundant OTUs as follows:

deviation (%) =         100 T WM X X -100
where WM X is the mean relative abundance of a OTU in a specific water mass and T X is the mean relative abundance of this OTU in all the samples.

RESULTS

The sequencing effort yielded on average 18,111 ± 14,869 reads per sample, and ranged from 2,083 to 62,100 pyrotags among the 45 samples (Table 1). For all samples combined, we identified a total of 49,517 unique pyrotag sequences. The pyrotag length averaged 62.51 ± 3.47 bp, varying from 51 to 165 bp. On average, the unique V6 pyrotags (i.e., present in only one out of the 45 samples analyzed at an abundance >1) accounted for 14 ± 3% of the total number of pyrotags in each sample. On average each sample had 2,249 ± 1,529 unique bacterial sequences representing nearly 835 ± 421 OTUs (Operational Taxonomic Units) at the 3% difference level.

The non-parametric Chao1 and ACE estimate predicted an average number per sample of 1,416 ± 787 and 1,733 ± 1,220 OTUs, respectively. The rarefaction curves (Fig. S2) indicate that, despite obtaining on average more than 18,000 pyrotags and 10,700 OTUs identified as Bacteria, our sampling of bacterial richness was not complete.

To examine another aspect of diversity, the Gini's index of evenness was calculated for each OTU except the singletons (Fig. S3). Most of the OTUs were highly unevenly distributed (Gini > 0.5) comprising both abundant and rare OTUs. The OTUs from subsurface samples exhibited a higher evenness than the OTUs from meso-and bathypelagic samples, indicating that OTUs from subsurface were more equally distributed among samples than OTUs from deeper layers.

The 100 and 1,000 most abundant OTUs represented on average 80 ± 3% and 96 ± 1%, respectively, of the total OTU abundance in the individual samples with no significant difference among the different depth layers or water masses (Fig. 2). Significant differences between depth layers were only observed in the relative contribution of the 5 most abundant OTUs (Kruskal-Wallis test, H = 7.01, p = 0.03; Fig. 2) representing between 37% and 47% of total OTU abundance. The 5 most abundant OTUs and the 6 -100 (34-39%) most abundant OTUs contributed roughly equally to the total OTU abundance (Fig. 2). The rank-frequency distribution of the 1,000 most abundant OTUs indicates that only a few OTUs are very abundant with a long tail of low-abundance OTUs (inset, Fig. 2). The rank-frequency distribution of meso-and bathypelagic communities exhibited a steeper slope (slope of 0.58 ± 0.01 and 0.57 ± 0.01, respectively) than that of subsurface (slope of 0.32 ± 0.01) OTUs (slope comparison, Student-t test, p < 0.001 for all comparisons). The rank-frequency distributions of OTUs of meso-and bathypelagic waters were not significantly different (Student-t test, p > 0.05).

Singleton OTUs (i.e., present in only one sample at an absolute abundance of 1) comprised 5,567 OTUs out of the 10,780 distinct OTUs in our analysis. They represented half (52%) of the total number of OTUs. However, they accounted for less than 2% of the total OTUs abundance (Fig. 2) and, they were equally distributed among the water layers.

Non-metric Multidimensional Scaling (MDS) based on the relative abundance of all

OTUs except singletons was used to discriminate bacterial community composition in the different water masses. Cluster analysis showed that bacterial community composition clustered according to the water masses (Fig. 3). The samples separated into one cluster containing bacterial communities of the subsurface zone, one cluster of the mesopelagic waters (AAIW and water masses (Fig. 3, Table S1). The ANOSIM test showed that the differences between the water mass clusters were significant (p < 0.05) except for AAIW and tCW/SACW (p = 0.062) and for LSW and NIW (p = 0.1; Table S3). For this latter pairwise comparison, the R-value was still high (R = 1), and the insignificant difference might be due to the low number of samples for each water mass (n = 3 for LSW and n = 2 for NIW) allowing only 10 permutations (Table S3).

A MDS of the 1,000 most abundant OTUs versus all OTUs excluding the singletons produced the same clustering (data not shown). Including singletons in the MDS analysis, however, resulted in a lack of water mass-specificity of bacterial communities (data not shown). SIMPER analysis indicated that the differences in bacterial community composition between water masses are explained by the combination of abundant and rare OTUs (Dataset S1 in Suppl. Information).

The clustering of all pyrotags except the singletons was comparable to the clustering of all OTUs except the singletons (Fig S4a). The clustering of the rare sequences (frequency < 0.01% of total pyrotag abundance within a sample, Fig S4c) was similar to the clustering of the abundant sequences (frequency > 1% within a sample, Fig. S4b), albeit with a generally lower percentage of similarity. The matrices of the abundant and rare pyrotags were significantly related (Mantel test, r = 0.68, p < 0.001).

The similarity of the bacterial communities of the bathy-and mesopelagic waters decreased rapidly from the northernmost station at 60°N (station 27, Transat-2 cruise) to ≈ 50°N

and remained fairly constant thereafter towards the equator (Fig. 4). Besides this pronounced latitudinal trend in similarity in the northern part of the North Atlantic, a pronounced stratification of the bacterial communities was detected (Fig. S5). In the northern part of the North Atlantic, the meso-and bathypelagic bacterial communities were more similar (47.4%) than in the southern part of the North Atlantic (37.7%) indicating an increasing stratification of bacterial communities with decreasing latitude (Fig. S5).

The relationship between bacterial assemblage structure (based on the relative abundance of all OTUs), environmental factors and the bacterial activity parameters was assessed by correlating the three distance matrices with a Mantel test. The bacterial assemblage structure correlated with the environmental factors (r = 0.57, p = 0.0001) but not with bacterial activity parameters (r = 0.06, p = 0.072). The potential link of environmental factors with bacterial community structure was analyzed by the ordination technique of canonical correspondence analysis (CCA). The CCA indicated that samples clearly clustered according to the water masses (Fig. 5a). Depth and latitude emerged as highly significant explanatory variables, separating samples along the first and the second axes, respectively (Fig. 5a). When depth and latitude were removed from the analysis (Fig. 5b), O 2 concentration, temperature and DOC clearly separated subsurface and bathypelagic samples along the first axis. DON concentration and salinity appeared to be key factors for determining subsurface bacterial assemblage structure. When CCA was applied to bathypelagic samples (Fig. 5c), depth and temperature appeared to be significant explanatory variables, separating bathypelagic samples along the first axis, while DOC and O 2 concentrations separated samples along the second axis. The combination of depth, temperature, salinity, DOC and O 2 concentrations explained 67% of the total variance in the relative abundance of the 1,000 most abundant OTUs in bathypelagic samples. When potential density was used in the analysis instead of depth, the distribution of the bathypelagic samples in the CCA analysis was similar. When CCA was applied to mesopelagic samples (Fig. 5d), the O 2 concentration and AOU (apparent oxygen utilization) separated samples along the first axis, while depth and DOC concentration separated samples along the second axis. The combination of these parameters explained about 55% of the total variance in the relative abundance of the 1,000 most abundant OTUs in mesopelagic samples.

Phylogenetic affiliation of North Atlantic deep-water bacterial communities.

Among the 1,000 most abundant OTUs, Proteobacteria were, overall, the most abundant phylum (Fig. 6a), representing 84 ± 8% of the 1,000 most abundant OTUs. The bathypelagic zone exhibited the highest proportion of unassigned bacteria and a higher evenness than the overlying waters. Deferribacteres and, to a lesser extent, Verrucomicrobia increased in relative abundance with depth, and contributed 2.5% and 3.9%, respectively, to the bacterial abundance in the bathypelagic zone. Chloroflexi contributed to total bacterial abundance 5-fold more in bathypelagic zone (0.25%) than in subsurface (0.05%). In contrast, Cyanobacteria decreased with depth from 1.7% of total bacterial abundance in the subsurface layer to 0.2% in the bathypelagic zone (Fig. 6a).

Within Proteobacteria, Alphaproteobacteria, mostly composed of the SAR11 cluster (data not shown), was the most abundant class and accounted for 47% and 68% of the 1,000 most abundant proteobacterial OTUs in the bathypelagic and subsurface waters, respectively (Fig. 6b). As at the phylum level, the evenness increased with depth at the class level, and was higher in the bathypelagic zone than in the subsurface and mesopelagic zones. Gamma-, Deltaand Betaproteobacteria increased in relative abundance with depth, and represented 25%, 5% and 5%, respectively, of Proteobacteria in the bathypelagic waters.

Within Gammaproteobacteria, unassigned Gammaproteobacteria comprised the major fraction amounting to 83% of the total Gammaproteobacteria in the bathypelagic and to 91% in the subsurface waters (Fig. 6c). Also at the order level, evenness increased with depth and some groups were found to be specific to a specific water layer. For example, higher proportions of Alteromonadales (7%) and, to a lesser extent, Oceanospirillales (4%), Enterobacteriales (2%)

and Pseudomonadales (1.6%) were found in the bathypelagic than in the subsurface zone (Fig. 6c). In contrast, Chromatiales were more restricted to subsurface (4%) than deep layers (1%).

Analysis of the taxonomic composition of the singletons revealed that they were as diverse as the abundant OTUs (Fig. 7). Most of the singletons belonged to the Proteobacteria (61%) but 11% were unassigned (Fig. 7a). Within Proteobacteria, Alpha-(38%) and Gammaproteobacteria (29%) dominated and 16% of the gammaproteobacterial OTUs remained unassigned (Fig. 7b). Overall, the phylogenetic composition of the singleton community was similar among all three water layers and similar to the bacterial assemblage structure of the 1,000

most abundant OTUs at the phylum (Mantel test, r = 0.56, p = 0.001) and the class level within the Proteobacteria (Mantel test, r = 0.33, p = 0.001).

The deviation (in %) from the mean relative abundance in all the samples of the 100 most abundant OTUs (accounting for 79 ± 8% of the OTUs abundance over all the water masses) was calculated for each water mass (Fig. 8). Lower abundance OTUs exhibit higher water mass specificity than the more abundant OTUs. OTUs specific for LSW included members of the SAR324 clade (belonging to the Deltaproteobacteria; OTU #3, Fig. 8 and Table S4) which accounted for 7% of the 100 most abundant OTUs, members of the SAR406 clade (belonging to S4). Members of the Chloroflexi-type SAR202 cluster (OTUs #39, 69) were generally specific for deep waters (NEADW and LSW). A large number of OTUs specific for mesopelagic waters were affiliated to unassigned Alphaproteobacteria (OTUs #12,53,71,83 and 90) and one OTU to the Verrucomicrobiales phylum (OTU #20) (Fig. 8 and Table S4). Surface-specific OTUs were mainly members of unassigned Alphaproteobacteria (OTUs #18,32,38,48,61,70) and Gammaproteobacteria (OTUs # 50, 67, 85) and one OTU from the Actinobacteria (OTU #99).

Members of the SAR11 clade (OTUs #1, 2, and 96) exhibited a ubiquitous distribution, albeit they were relatively less abundant in deep-water masses (NEADW, core NEADW and LSW)

than in subsurface and mesopelagic waters (250 -500m and AAIW) (Fig. 8 and Table S4). The SAR11 clade contributed about 37% to the relative abundance of the 100 most abundant OTUs.

DISCUSSION

The pyrosequencing approach allowed a deep sampling of bacterial communities in North Atlantic waters. The analyses of 45 samples, originating from 10 stations along a 8,000 km north-to-south transect provide an exhaustive description of the distribution of bacterial communities in the major deep-water masses of the North Atlantic.

We found water mass-specific clustering of bacterial communities throughout the major North Atlantic water masses. These differences in the phylogenetic composition of the bacterial community between water masses are not only due to the most abundant OTUs, but the less abundant OTUs including the rare ones (but not the singletons) are also responsible for the water mass-specificity of the bacterial community composition. This implies that the less abundant bacterial populations exhibit biogeographical traits while the most abundant ones are more ubiquitously distributed. Previous studies suggested that the rare biosphere should exhibit a high rate of dispersal [START_REF] Fenchel | The ubiquity of small species: Patterns of local and global diversity[END_REF][START_REF] Finlay | Global dispersal of free-living microbial eucaryote species[END_REF]) and might act as a "seed-bank" of microorganisms which might become abundant in case of environmental changes [START_REF] Palacios | Contrasting microbial community assembly hypotheses: a reconciling tale from the Rio Tinto[END_REF][START_REF] Pedros-Alio | Marine microbial diversity: can it be determined?[END_REF]. [START_REF] Kirchman | The structure of bacterial communities in the western Arctic Ocean as revealed by pyrosequencing of 16S rRNA genes[END_REF], however, argued against the "seed-bank" hypothesis as rare phylotypes of the winter bacterial community remained rare also in the summer community in the Arctic Ocean. The distribution patterns observed for rare OTUs in the North Atlantic Ocean indicate that the rare bacterial biosphere contributes to the water mass-specificity of bacterial communities. Hence, the rare biosphere is most likely not maintained by stochastic immigration but appears to be a water mass property as also shown for the Arctic Ocean [START_REF] Galand | Hydrography shapes bacterial biogeography of the deep Arctic Ocean[END_REF]. A ubiquitous distribution of members of the rare bacterial biosphere, as previously suggested (Pedros-Alio 2006), was not found in the deep-waters of the Atlantic challenging the idea that "everything is everywhere" (Baas [START_REF] Becking | Geobiologie of inleiding tot de milieukunde[END_REF].

The singletons, i.e., the OTUs present only once in all the samples, are included in the rare bacterial biosphere [START_REF] Sogin | Microbial diversity in the deep sea and the underexplored "rare biosphere[END_REF]. Before speculating on the potential ecological role of the rare bacterial biosphere and its origin, we need to address the question whether the presence of singletons generated by the pyrosequencing approach are fact or artifact. Random sequencing errors and miscalled bases could potentially be an explanation for the high number of singleton sequences [START_REF] Gomez-Alvarez | Systematic artifacts in metagenomes from complex microbial communities[END_REF][START_REF] Quince | Accurate determination of microbial diversity from 454 pyrosequencing data[END_REF][START_REF] Reeder | The 'rare biosphere': a reality check[END_REF] and could inflate actual diversity estimates [START_REF] Kunin | Wrinkles in the rare biosphere: pyrosequencing errors lead to artificial inflation of diversity estimates[END_REF]. Increased sampling effort leads to a greater probability that OTUs from a given template will include variant sequences and therefore inflate the number of observed singletons. However, the frequency of variant sequences will be a function of the relative abundance of a unique starting template and the number of generated OTUs for the entire community. Sequences that correspond to high frequency templates are more likely to produce variants. A systematic and stringent trimming procedure was used in this study to remove low quality sequences and reads that did not represent the targeted region. This procedure results in a per-base error rate of 0.2-0.4% [START_REF] Huse | Accuracy and quality of massively parallel DNA pyrosequencing[END_REF]. Moreover, the newly developed clustering method applied in this study removes spurious OTUs produced by pyrosequencing errors and minimizes the inflation of OTUs with sequencing effort [START_REF] Huse | Ironing out the wrinkles in the rare biosphere through improved OTU clustering[END_REF]. In our study, the singletons represent only 2% of the total OTU abundance in each depth zone of the North Atlantic. No clear clustering according to water masses was obtained when the singletons were included in the MDS analysis, while the bacterial community structure using all the OTUs except the singletons exhibits a water-mass specific clustering. As the singletons have the same phylogenetic composition as the 1,000 most abundant OTUs, some of the singletons might be artifacts resulting from noise in high-throughput pyrosequencing of the most abundant OTUs [START_REF] Reeder | The 'rare biosphere': a reality check[END_REF].

Biogeography of bacterial communities in the thermohaline ocean circulation of the North

Atlantic

Distance, latitudinal gradients and area have been shown to influence bacterial community composition in lakes and the ocean [START_REF] Fuhrman | A latitudinal diversity gradient in planktonic marine bacteria[END_REF][START_REF] Hewson | Remarkable heterogeneity in meso-and bathypelagic bacterioplankton assemblage composition[END_REF][START_REF] Reche | Does ecosystem size determine aquatic bacterial richness?[END_REF]. Recent phylogenetic surveys revealed a pronounced stratification among specific groups of planktonic Bacteria [START_REF] Delong | Microbial community genomics in the ocean[END_REF][START_REF] Galand | Hydrography shapes bacterial biogeography of the deep Arctic Ocean[END_REF][START_REF] Hewson | Remarkable heterogeneity in meso-and bathypelagic bacterioplankton assemblage composition[END_REF][START_REF] Kirchman | The structure of bacterial communities in the western Arctic Ocean as revealed by pyrosequencing of 16S rRNA genes[END_REF][START_REF] Lopez-Garcia | Metagenomics of the Deep Mediterranean, a Warm Bathypelagic Habitat[END_REF][START_REF] Suzuki | Phylogenetic screening of ribosomal RNA genecontaining clones in bacterial artificial chromosome (BAC) libraries from different depths in Monterey Bay[END_REF][START_REF] Treusch | Seasonality and vertical structure of microbial communities in an ocean gyre[END_REF]) and Archaea [START_REF] Agogué | Major gradients in putatively nitrifying and non-nitrifying Archaea in the deep North Atlantic[END_REF]Galand et al. 2009b;[START_REF] Garcia-Martinez | Microdiversity in uncultured marine prokaryotes: the SAR11 cluster and the marine Archaea of Group I[END_REF]. In our study, the similarity in bacterial community composition decreases rapidly in the meso-and bathypelagic waters from 60°N to 50°N and remains fairly stable thereafter. Moreover, the similarity in bacterial community composition between meso-and bathypelagic waters was higher in the northern than in the southern part of the transect. This reflects the large scale deepwater formation in the northern North Atlantic leading to a more uniform deep-water bacterial community in the generally younger deep waters in the northern North Atlantic also exhibiting smaller differences in physico-chemical parameters between meso-and bathypelagic waters than further south towards the (sub)tropical Atlantic. Further south, distance was influencing bacterial community composition much less than water mass identity in the (sub)tropical Atlantic Ocean.

Analysis of the affiliation of the 100 most abundant OTUs revealed obvious water massspecific signatures of bacterial community structure. Due to their distinct temperature and salinity characteristics, water masses might act as presumed dispersal barriers for bacterioplankton and hence, might limit immigration of bacterial phylotypes (Aristegui et al. In contrast to our current perception of the deep ocean as a rather low diversity environment, bacterial community richness and evenness in the bathypelagic waters of the North Atlantic are as high and as variable as subsurface and mesopelagic waters. This observation is consistent with previous reports on bacterial diversity of the North Pacific and Atlantic [START_REF] Hewson | Remarkable heterogeneity in meso-and bathypelagic bacterioplankton assemblage composition[END_REF]) and in the eastern Mediterranean Sea [START_REF] Moeseneder | Terminal-restriction fragment length polymorphism (T-RFLP) screening of a marine archaeal clone library to determine the different phylotypes[END_REF]) using fingerprinting techniques. This reported heterogeneity in deep bacterial assemblages contrasts the very slow assemblage growth rates estimated to be 0.061 ± 0.008 d -1 in the deep Pacific [START_REF] Aristegui | Microbial oceanography of the dark ocean's pelagic realm[END_REF]) and deep-water movement (1.5 cm s -1 ) (van Aken 2007). The heterogeneity in deep-water bacterial community composition has been interpreted to result from episodic input of organic matter from surface waters and patchiness [START_REF] Hewson | Remarkable heterogeneity in meso-and bathypelagic bacterioplankton assemblage composition[END_REF]). However, it appears that microbial life in the dark ocean is likely more dependent on slowly sinking or buoyant, laterally advected suspended particles than hitherto assumed [START_REF] Bochdansky | Role of macroscopic particles in deep-sea oxygen sonsumption[END_REF]) which might generate the water mass-specific biogeochemical conditions leading ultimately to water massspecific bacterial assemblages (Baltar et al. 2009).

Vertical distribution of bacterial phylotypes

As found in previous studies on deep-sea bacterial diversity [START_REF] Delong | Community genomics among stratified microbial assemblages in the ocean's interior[END_REF][START_REF] Fuhrman | Widespread archaea and novel bacteria from the deep sea as shown by 16S rRNA gene sequences[END_REF][START_REF] Lopez-Garcia | Diversity of free-living prokaryotes from a deep-sea site at the Antarctic Polar Front[END_REF][START_REF] Pham | Phylogenetic analyses of ribosomal DNA-containing bacterioplankton genome fragments from a 4000 m vertical profile in the North Pacific Subtropical Gyre[END_REF], Proteobacteria, mostly from the alpha subdivision, dominate the bacterial community. Members of the alphaproteobacterial clade SAR11, ubiquitously present in the ocean [START_REF] Delong | Community genomics among stratified microbial assemblages in the ocean's interior[END_REF][START_REF] Field | Diversity and depth-specific distribution of SAR11 cluster rRNA genes from marine planktonic bacteria[END_REF][START_REF] Giovannoni | Genetic Diversity in Sargasso Sea Bacterioplankton[END_REF][START_REF] Morris | SAR11 clade dominates ocean surface bacterioplankton communities[END_REF], decreased in relative abundance with depth.

The relative abundance of Gammaproteobacteria, however, increased with depth as reported previously for the North Atlantic [START_REF] Lauro | Prokaryotic lifestyles in deep sea habitats[END_REF][START_REF] Sogin | Microbial diversity in the deep sea and the underexplored "rare biosphere[END_REF] et al. 1997;[START_REF] Lauro | The Unique 16S rRNA Genes of Piezophiles Reflect both Phylogeny and Adaptation[END_REF]). We also found members of the Oceanospirillales more abundant in the bathypelagic North Atlantic waters than in the subsurface and mesopelagic layers. Members of this order are also known to be symbionts of the deep-sea worm Osedax sp. [START_REF] Goffredi | Genetic Diversity and Potential Function of Microbial Symbionts Associated with Newly Discovered Species of Osedax Polychaete Worms[END_REF][START_REF] Rouse | Spawning and development in Osedax boneworms (Siboglinidae, Annelida)[END_REF].

Betaproteobacteria also increased in relative abundance with depth. OTUs affiliated to this class are closely related to endosymbionts of bivalves found at hydrothermal vents [START_REF] Kimura | Endosymbiotic microflora of the vestimentiferan tubeworm (Lamellibrachia sp.) from a bathyal cold seep[END_REF]. Their relatively high abundance in the free-living mode in bathypelagic waters, et al. 2006;[START_REF] Lopez-Garcia | Diversity of free-living prokaryotes from a deep-sea site at the Antarctic Polar Front[END_REF][START_REF] Pham | Phylogenetic analyses of ribosomal DNA-containing bacterioplankton genome fragments from a 4000 m vertical profile in the North Pacific Subtropical Gyre[END_REF][START_REF] Zaballos | Comparison of prokaryotic diversity at offshore oceanic locations reveals a different microbiota in the Mediterranean Sea[END_REF]). The SAR406 cluster [START_REF] Gordon | Detection of stratified microbial populations related to Chlorobium and Fibrobacter species in the Atlantic and Pacific Oceans[END_REF] previously detected in various oceanic provinces [START_REF] Gallagher | Spatio-temporal variability and diversity of water column prokaryotic communities in the eastern North Atlantic[END_REF][START_REF] Pham | Phylogenetic analyses of ribosomal DNA-containing bacterioplankton genome fragments from a 4000 m vertical profile in the North Pacific Subtropical Gyre[END_REF]) is another abundant clade of the deep Atlantic ocean, specific to the LSW. Chloroflexi-type SAR202 cluster, described as highly abundant in the bathypelagic subtropical North Atlantic [START_REF] Morris | Prevalence of the Chloroflexi-Related SAR202 Bacterioplankton Cluster throughout the Mesopelagic Zone and Deep Ocean[END_REF]Varela et al. 2008a;[START_REF] Wright | A novel delta-subdivision proteobacterial lineage from the lower ocean surface layer[END_REF]) was found to be a specific member of the NEADW.

Concluding remarks

By applying a high-throughput pyrosequencing strategy, we achieved a deep coverage of the bacterial populations in the deep-water masses of the North Atlantic. The bacterial assemblages clearly clustered according to the distinct water masses. The low abundance OTUs were as important for the observed water mass-specificity of bacterial community composition as the more abundant OTUs. Density differences separate water masses and their inhabiting bacterial communities, generating bio-oceanographic islands with specific signatures of bacterial community composition such as shown most pronouncedly for the deep waters. The deep bacterial assemblages exhibit a higher richness and evenness than bacterial assemblages at the base of the euphotic layer suggesting that the bathypelagic waters might offer a more heterogeneous environment for microbial life than hitherto assumed. S1. Characteristics of the samples are indicated in Table S1. The final solution was based on 25 iterations with a final stress of 0.12. Tables Table 1. Sequencing information and diversity estimates for the 45 samples of the North Atlantic Ocean obtained by pyrosequencing.

  tCW/SACW) and two deep-water clusters (NEADW and LDW) at 45% of similarity. Two clusters of deep LSW and mesopelagic NIW bacterial communities were identified at the 55% similarity level. The bacterial communities of bathypelagic waters were less similar to each other than samples from subsurface and intermediate waters (Fig.3). Bacterial communities from the same water mass but separated by thousands of kilometers (S6 from St.A2-11 and S41 from St. A2-45 belonging to subsurface; S12 from St. A2-19 and S40 from St. A2-45 belonging to tCW/SACW; S42 from St. A2-50 and S17 from St. A2-25 belonging to NEADW) were more similar to each other than communities separated by only a few hundred meters at individual sites (S14 and S21 from St. A2-25; S7 and S10 from St. A2-19) but originating from different

Fibrobacteres,

  OTUs #30, 40 and 60) which showed the greatest variability observed among the 100 most abundant OTUs, members of the Gammaproteobacteria (OTUs #77, 89), Gemmatimonadetes (OTU #73) and Actinobacteria (OTU #86). OTUs specific for the NEADW included members of the Alphaproteobacteria (OTUs #11, 16, 24, 65 and 87), the Betaproteobacteria (OTU #23), the Gammaproteobacteria (OTUs #35, 41, 52, 66, 74 and 100), the Deltaproteobacteria (OTUs #75, 81, 91), the Fibrobacteres (members of the SAR406 clade, OTUs #25 and 98) and the Verrucomicrobiales (OTU #46) (Fig.8and Table

  2009) from adjacent water masses. In the bathypelagic realm, depth and temperature were the main factors determining the composition of bacterial communities and discriminating samples from different water masses. Salinity, temperature and consequently density act as potential oceanographic barriers separating water masses and their inhabiting bacterial communities[START_REF] Pommier | Global patterns of diversity and community structure in marine bacterioplankton[END_REF]) generating bio-oceanographic islands with specific signatures of bacterial community composition such as shown most pronouncedly for North East Atlantic Deep Water and Labrador Sea Water. Besides water mass identity and latitude, the specific environmental properties of the water masses, such as the concentrations of DOC and O 2 , and AOU determine the bacterial community composition in the North Atlantic Ocean.

  particularly in the North East Atlantic Deep Water (NEADW) might indicate their spreading from hydrothermal vents and/or seafloor as the NEADW flows mainly along the eastern slope of the mid-Atlantic ridge. Free-living Betaproteobacteria are common in freshwaters[START_REF] Kirchman | Biogeography of major bacterial groups in the Delaware Estuary[END_REF] but not abundant in the bathypelagic realm. However, metagenomic analysis from deep-sea methane vents have reported Betaproteobacteria-related phylotypes[START_REF] Pernthaler | Diverse syntrophic partnerships from deep-sea methane vents revealed by direct cell capture and metagenomics[END_REF], suggesting a potential niche for these organisms within methane-rich deep marine environments. In the North Pacific Ocean,[START_REF] Brown | Microbial community structure in the North Pacific ocean[END_REF] recently reported an increasing relative abundance of Betaproteobacteria V9 pyrotags at 800m and 4400m depth. The bathypelagic bacterial community of the North Atlantic was also enriched in Deltaproteobacteria, as previously shown for the deep Arctic Ocean as well[START_REF] Galand | Hydrography shapes bacterial biogeography of the deep Arctic Ocean[END_REF]) and for Station ALOHA in the North Pacific Subtropical Gyre[START_REF] Delong | Community genomics among stratified microbial assemblages in the ocean's interior[END_REF].The comparison of the phylogenetic composition of the 1,000 most abundant OTUs reveals an increase of OTUs richness and evenness with depth. Thus, the bacterial community of the North Atlantic is more diverse in the bathypelagic realm than that at the base of the euphotic layer. The composition of the 100 most abundant OTUs indicates distinct water mass-specific OTUs, especially in deep waters. In meso-and bathypelagic waters, the Deltaproteobacteria were dominated by the SAR324 cluster[START_REF] Wright | A novel delta-subdivision proteobacterial lineage from the lower ocean surface layer[END_REF], a typical deep-water clade (DeLong

Figure captions Figure 1 .

 captions1 Figure captions Figure 1. Location of the stations where samples for pyrosequencing were taken during the

Figure 2 .

 2 Figure 2. Depth distribution of the 5, 6 -100 and 101 -1,000 most abundant OTUs and the

Figure 3 .

 3 Figure 3. Non-metric multidimensional analysis based on relative abundance of all OTUs except

Figure 4 .

 4 Figure 4. Similarity in relative abundance of all OTUs except the singletons (in %, calculated

Figure 5 .

 5 Figure 5. Canonical correspondence analysis of the relative abundance of the 1,000 most

Figure 6 .

 6 Figure 6. Relative abundance and affiliation of the 1000 most abundant bacterial OTUs of each

Figure 7 .

 7 Figure 7. Relative abundance and affiliation of the singletons of each zone of the Atlantic Ocean

Figure 8 .

 8 Figure 8. Deviation (in %) from the mean relative abundance of the 100 most abundant OTUs

  Figure 1.

  Figure 2.

  Figure 3.

  Figure 4.

  2

Accession numbers and data availability.

  . GAST methodology is freely available through the VAMPS (Visualisation

	and	Analysis	of	Microbial	Population	Structure)	website
	(http://vamps.mbl.edu/resources/faq.php#gasting). The V6 reference database (V6RefDB: high-
	quality, full-length 16S rRNA sequences) is publically available at: http://vamps.mbl.edu.
		The OTU sequences and supporting data have been submitted to the NCBI Short Read
	Archive (http://www.ncbi.nlm.nih.gov/Traces/home/). Run IDs are SRR029056-SRR029102
	inclusive. One run corresponds to one sample. The sequences obtained from each run are
	downloadable from the SRA (sequence read archive) web site of NCBI. In addition, the VAMPS
	site http://vamps.mbl.edu provides individual and edited sequences and analytical functions for
	interrogating the data.

Table S1 .

 S1 Description of the 45 samples from the North Atlantic Ocean selected for bacterial pyrosequencing. LDW: Lower Deep Water, NEADW: North East Atlantic Deep Water, AAIW: Antarctic Intermediate Water, tCW/SACW: transitional/South Atlantic Central Water, NIW: Northern Intermediate Water, LSW: Labrador Sea Water).

		600
		500
		400
	% deviation	100 200 300
		-100	Sample ID		Cruise			Station	Longitude °E Latitude °N Depth (m) Water mass
			53R	3	4	5 Transat-1	6	7	15	8	9 -29.13 10	11	12 58.3	13	14 1400	15	16 LSW	17	18	19	20
			55R			Transat-1			15		-29.13			58.3		500		NIW
			112R			Transat-1			27		-25			50.4		4000		LDW
			115R			Transat-1			27		-25			50.4		500		NIW
			137			Transat-2			27		-38.52			60.9		1700		LSW
			138			Transat-2			27		-38.52			60.9		700		LSW
			S1			Archimedes-2		11		-20.21			11.98		4000		LDW
			S2			Archimedes-2		11		-20.21			11.98		2750		NEADW
			S3			Archimedes-2		11		-20.21			11.98		900		AAIW
			S4			Archimedes-2		11		-20.21			11.98		500		tCW
			S5			Archimedes-2		11		-20.21			11.98		250		tCW
			S6			Archimedes-2		11		-20.21			11.98		100		Subsurface
		21	22 S7	23	24	25 Archimedes-2 26	27 19	28	29 -15.19	30	31	32 4.9	33	34 4500	35	36 LDW	37	38	39	40
			S8			Archimedes-2		19		-15.19			4.9		4000		LDW
			S9			Archimedes-2		19		-15.19			4.9		2750		NEADW
			S10			Archimedes-2		19		-15.19			4.9		900		AAIW
			S11			Archimedes-2		19		-15.19			4.9		500		SACW
			S12			Archimedes-2		19		-15.19			4.9		250		SACW
			S13			Archimedes-2		19		-15.19			4.9		100		Subsurface
			S14			Archimedes-2		25		-13.24			0.81		4500		LDW
			S15			Archimedes-2		25		-13.24			0.81		3500		NEADW
			S16			Archimedes-2		25		-13.24			0.81		3000		NEADW
			S17			Archimedes-2		25		-13.24			0.81		2000		NEADW
			S18			Archimedes-2		25		-13.24			0.81		1800		NEADW
			S19			Archimedes-2		25		-13.24			0.81		900		AAIW
			S20			Archimedes-2		25		-13.24			0.81		750		AAIW
			S21			Archimedes-2		25		-13.24			0.81		500		SACW
			S23			Archimedes-2		25		-13.24			0.81		100		Subsurface
			S24			Archimedes-2		30		-13.24			0.81		2500		NEADW
			S26			Archimedes-2		30		-15.91			-4.09		750		AAIW
			S27			Archimedes-2		30		-15.91			-4.09		500		tCW
			S29			Archimedes-2		30		-15.91			-4.09		100		Subsurface
			S30			Archimedes-2		37		-22.05			7.78		2500		NEADW
			S31			Archimedes-2		37		-22.05			7.78		1800		NEADW
			S32			Archimedes-2		37		-22.05			7.78		750		AAIW
			S33			Archimedes-2		37		-22.05			7.78		500		tCW
			S36			Archimedes-2		45		-26.0			16.83		2500		NEADW
			S38			Archimedes-2		45		-26.0			16.83		900		AAIW
			S39			Archimedes-2		45		-26.0			16.83		500		tCW
			S40			Archimedes-2		45		-26.0			16.83		250		tCW
			S41			Archimedes-2		45		-26.0			16.83		100		Subsurface
			S42			Archimedes-2		50		-23.11			21.09		2500		NEADW
			S43			Archimedes-2		50		-23.11			21.09		1800		NEADW
			S44			Archimedes-2		50		-23.11			21.09		900		AAIW
			S47			Archimedes-2		50		-23.11			21.09		100		Subsurface

Acknowledgements

We acknowledge the help of H.M. van Aken for the characterization of the water masses, J. M. Arrieta for collecting and extracting the samples of the TRANSAT-1 cruise, M. Brink and D. de Corte for extracting the samples of ARCHIMEDES-2 cruise, F. Baltar for the dataset on ectoenzymatic activity, D.M Welch and S.M. Huse for the dataset on OTUs sequences and T. Reinthaler for providing the parameters datasets of Transat and Archimedes cruises. The captain and crew of RV Pelagia provided excellent service at sea. -Author contribution. G.J.H. and M.S. sampling scheme and sample analyses, H.A., D.L. and P.N. analyzed the data, G.J.H., P.N. and M.S. contributed the reagents/materials/analysis tools, H.A., D.L. and G.J.H wrote the paper. -Funding. This research was supported by two Marie Curie Fellowships of the European Community to D.L. and H.A., by the Alfred P. Sloan Foundation's ICoMM field project, the W. M. Keck Foundation and a subcontract from the Woods Hole Center for Oceans and Human Health from the National Institutes of Health and the National Science Foundation (NIH_NIEHS 1 P50 ES012742-01 and NSF_OCE 0430724-J. Stegeman PI to M.L.S.). Shiptime was provided through grants of the Earth and Life Science Division of the Dutch Science Foundation (ALW-NWO) (TRANSAT and ARCHIMEDES projects) to G.J.H. -Competing interests. The authors have declared that no competing interests exist.

Supplementary methods.

Environmental parameters

Inorganic nutrients -The methods for determining inorganic nutrient concentrations followed Joint Global Ocean Fluxes Study recommendations [START_REF] Gordon | A suggested protocol for continuous flow automated analysis of seawater nutrients (phosphate, nitrate, nitrite and silicic acid) in the WOCE Hydrographic Program and the Joint Global Ocean Fluxes Study[END_REF]. The concentrations of dissolved inorganic nutrients (NH 4 + , NO 3 -, NO 2 -, PO 4 3-) were determined in a TRAACS autoanalyzer immediately after collecting the samples and gentle filtration through 0.2 µm filters (Acrodisc, Gelman Science). NH 4 + was detected with the indophenol blue method (pH 10.5) at 630 nm. NO 2 -was detected after diazotation with sulfanilamide and N-(1-naphtyl)-ethylene diammonium dichloride as the reddishpurple dye complex at 540 nm. NO 3 -was reduced in a copper cadmium coil to nitrite (with imidazole as a buffer) and then measured as nitrite.

Inorganic PO 4 3-was determined via the molybdenum blue complex at 880 nm.

Dissolved organic carbon (DOC) -Samples for DOC were filtered through rinsed 0.2 µm polycarbonate filters and sealed in precombusted (450°C for 4 h) glass ampoules after adding 50 µL of 40% phosphoric acid. The samples were stored frozen at -20°C until analysis back in the lab. DOC concentrations of duplicate samples were determined using a Shimadzu TOC-5000 analyzer. Three-point standard curves, prepared with potassium hydrogen phthalate (Nacalai Tesque, Kyoto, Japan), were used to calculate DOC concentrations. The instrument's performance and the validity of the calibration were determined using reference material of the Hansell CRM program (44-46 µmol L -1 for the reference samples; n = 3 and 1-2 µmol L -1 for low carbon water; n = 3). The average analytical precision of the instrument was < 3%.

Dissolved organic nitrogen (DON) -Total dissolved nitrogen (TDN) was analyzed with a TRAACS 800 continuous-flow analysis system following the persulfate oxidation method. DON concentrations were calculated by subtracting the sum of the inorganic nitrogen species from TDN concentrations. The recovery of DON was estimated on a mixture of 10 different organic compounds containing known concentrations of N [START_REF] Kramer | Elemental composition of dissolved organic matter and bacterioplankton production in the Faroe-Shetland Channel (North Atlantic)[END_REF]. For this reference material, the recovery efficiency of organic nitrogen was 92% of the calculated organic nitrogen concentration.

Bacterial-related parameters

Prokaryotic abundance determined by flow cytometry -Prokaryotic plankton collected from the different depth layers of the water column were enumerated using flow cytometry. Samples (2 ml) were fixed with 1% paraformaldehyde (final concentration), shock-frozen in liquid nitrogen for 5 min and stored at -80°C [START_REF] Kamiya | Effects of fixation and storage on flow cytometric analysis of marine bacteria[END_REF]. Picoplankton cells were stained with SYBR- Leu assuming no internal isotope dilution [START_REF] Kirchman | Estimating converting factors for thymidine and leucine methods for measuring bacterial production[END_REF].

Activity of the ETS -Electron transport system (ETS) activity was measured following the modifications of the tetrazolium reduction technique as described earlier [START_REF] Aristegui | The relationship between community respiration and ETS activity in the ocean[END_REF]. Some minor modifications of the method were made to increase its sensitivity (Baltar et al. 2009). phosphate was measured to estimate potential activity rates of alpha-, beta-glucosidase, aminopeptidase and alkaline phosphatase, respectively (Baltar et al. 2009;[START_REF] Hoppe | Significance of exoenzymatic activities in the ecology of brackish water: measurements by means of methylumbelliferyl-subtrates[END_REF].

Measurements of prokaryotic extracellular enzymatic activity

The environmental data used in statistical analysis can be found at:

http://icomm.mbl.edu/microbis.