Juan Pablo

Astudillo León

Anthony Busson
email: anthony.busson@univ-lyon1.fr

Towards a Distributed Congestion Control mechanism for Smart Grid Neighborhood Area Networks

Keywords: CCS Concepts:, Networks → Traffic engineering algorithms, Network performance evaluation, Network measurement, Smart Grid, Neighborhood Area Networks, Congestion Control ACM Reference

The need for significant improvements in the management and efficient use of electrical energy has led to the evolution from the traditional electrical infrastructures towards modern Smart Grid networks. Taking into account the critical importance of this type of networks, multiple research groups focus their work on issues related to the generation, transport and consumption of electrical energy. One of the key research points is the data communication network associated with the electricity transport infrastructure, and specifically the network that interconnects the devices in consumers' homes, the so-called Neighborhood Area Networks (NANs). In this paper, a new distributed congestion control mechanism is proposed, implemented and evaluated for NANs. Besides, different priorities have been considered for the traffic flows transmitted by different applications. The main goal is to provide with the needed Quality of Service (QoS) to all traffic flows, especially in high traffic load situations. The proposal is evaluated in the context of a wireless ad hoc network made up by a set of smart meter devices, using the Ad hoc On-Demand Distance Vector (AODV) routing protocol and the IEEE 802.11ac physical layer standard. The application of the proposed congestion control mechanism, together with the necessary modifications made to the AODV protocol, lead to performance improvements in terms of packet delivery ratio, network throughput and transit time, fairness between different traffic sources and QoS provision.

INTRODUCTION

Electricity is an essential resource widely used in our daily activities. Taking into account the importance of this resource, research groups have focused their efforts to improve the management and operation of the traditional electricity distribution networks. To this end, in Smart Grids a data communication network has been incorporated into the electric infrastructure. The objective is to provide new services to both the supplying companies and to their costumers [START_REF] Shaukat | A survey on consumers empowerment, communication technologies, and renewable generation penetration within Smart Grid[END_REF]. In a society where new technologies are growing fast, a big amount of new services can be offered by the Smart Grid data communication network. Generally speaking, most Smart Grid applications have strong security and reliability requirements. Therefore, keeping the desired Quality of Service (QoS) for both classical and emerged applications is of paramount importance when planning and operating this type of networks [START_REF] Gungor | A Survey on Smart Grid Potential Applications and Communication Requirements[END_REF].

The Smart Grid data communication network is divided into three different sub-networks: Home Area Network (HAN), Neighborhood Area Network (NAN) and Wide Area Network (WAN) [START_REF] Meng | Smart grid neighborhood area networks: A survey[END_REF]. On the one hand, the HAN interconnects the smart meters (SM) and other possible appliances present inside the homes. Some standards for HANs are IEEE 802. 15.4 (Low-Rate Wireless Personal Area Networks, LR-WPAN) or the traditional IEEE 802.11 (Wireless Local Area Networks, WLAN). In turn, HANs are interconnected through the NANs. Here, selectable technologies are the well-known Power Line Communication (PLC), or wireless standards such as IEEE 802.15.4g, IEEE 802.11s and also WLAN. Finally, WAN allows the information exchange between the different NANs and the control center. A wired backbone or a wireless technology such as Wireless Metropolitan Area Networks (WirelessMAN, IEEE 802. [START_REF] Tsado | Performance analysis of variable Smart Grid traffic over ad hoc Wireless Mesh Networks[END_REF]) can be considered.

The goal of this paper is to present and evaluate a distributed congestion control mechanism that allows improving the performance offered by the NANs, when the selected technologies are IEEE 802.11 Wireless Ad Hoc Network (WANET) and IEEE 802.11ac physical layer standard. Our solution is conceptually simple as well as easily tunable and implementable. In addition, it is independent of the routing protocol, mac and physical layers. The proposed mechanism combines several algorithms that allow differentiating the quality of service offered to each traffic based on its criticality, while providing a fair service to all nodes in the network. Note that our proposed solution can be viewed as a middleware candidate for Smart Grids that will perform over the network infrastructure with the aim of ensuring a fair sharing of the resources between nodes and classes as well as high levels for resource utilization and packet delivery ratio.

The rest of the paper is organized as follows. Section 2 discusses related work. The proposed solution is presented in Section 3, and its performance is evaluated in Section 4. Finally, Section 5 concludes the paper.

RELATED WORK

Several researchers have focused their efforts with the aim of improving the performance offered by the smart grid neighborhood area networks. For instance, authors in [START_REF] Tsado | Performance analysis of variable Smart Grid traffic over ad hoc Wireless Mesh Networks[END_REF] present a performance evaluation and comparison of Optimized Link State Protocol (OLSR) [START_REF] Clausen | Optimized Link State Routing Protocol (OLSR)[END_REF] and HWMP (IEEE 802.11s) [START_REF]IEEE Standard for Information technology Telecommunications, information exchange between systems Local, metropolitan area networks Specific requirements Part 11: Wireless LAN Medium Access Control (MAC), and Physical Layer (PHY) Specifications[END_REF] routing protocols, where a classification of the main AMI (Advanced Metering Infrastructure) application traffics is taking into account. Later, in [START_REF] Tsado | Improving the Reliability of Optimised Link State Routing in a Smart Grid Neighbour Area Network based Wireless Mesh Network Using Multiple Metrics[END_REF], the same authors propose an enhancement of the OLSR protocol by using the combination of different basic metrics. They use Relevant Link Metric Types (RLMTs) for each application, together with the AHP (Analytical Hierarchy Process) algorithm in order to select the best path. Based on IEEE 802.11s, a multigate communication network is proposed in [START_REF] Gharavi | Multigate Communication Network for Smart Grid[END_REF] for improving the network performance for Smart Grids. They present a multi gateway, multichannel and a real-time traffic scheduling. On the other hand, to ensure the needed QoS of different Smart Grid applications, in [START_REF] Deng | A reliable QoS-aware routing scheme for neighbor area network in smart grid[END_REF] a modification to the HWMP protocol is presented (HWMP-NQ). They also present a modification of the airtime link metric by considering the packet size and the transmission rate. Finally, they present the benefits of a multi-gateway backup routing, and a modification of the path error mechanism to reduce the routing overhead.

Given that most data traffic is transmitted upstream from the smart meters to the data concentrator, authors in [START_REF] Jung | Improving IEEE 802.11s Wireless Mesh Networks for Reliable Routing in the Smart Grid Infrastructure[END_REF] present modifications to the airtime link metric calculation and to the path selection mechanism, in order to provide greater relevance to the upstream transmissions. Besides, authors address the need for congestion control when the network size is increased. Later, some of the authors of [START_REF] Jung | Improving IEEE 802.11s Wireless Mesh Networks for Reliable Routing in the Smart Grid Infrastructure[END_REF] analyze in [START_REF] Kim | Improving the reliability of IEEE 802.11s based wireless mesh networks for smart grid systems[END_REF] the reliability and the weaknesses of the Manuscript submitted to ACM HWMP protocol when it is integrated to Smart Grid networks. Authors propose some solutions based again on some modifications to the airtime link metric and in the possibility of routes reservation. Besides, they present a novel delay-tolerant traffic management method in order to provide a better quality of service to some applications.

A different modification of the HWMP protocol was presented in [START_REF] Pablo | A Joint Multi-Path and Multi-Channel Protocol for Traffic Routing in Smart Grid Neighborhood Area Networks[END_REF], to allow an efficient selection of paths among multiple possibilities, depending on the service quality needs of the different traffic flows. Also, the mechanism is complemented with the assignment of different frequency channels to each available path. The main goal is to transmit priority traffics through the best paths, and using also the less congested channel. Besides, different channels are used for data and control traffic. Later, in [START_REF] Pablo | Emergency aware congestion control for smart grid neighborhood area networks[END_REF], a multi-channel allocation scheme together with a congestion control mechanism was presented and evaluated, considering the different service quality needs of the different applications. The congestion control mechanism takes into account if the network is in an emergency state caused by intrinsic or extrinsic reasons.

Different congestion control functions are proposed for each traffic, which provide a higher transmission probability to the traffic with higher QoS needs, based on the network congestion level and on the emergency state.

In this work, a different solution to improve the network performance for NANs is proposed, implemented and evaluated. Instead of considering IEEE 802.11s mesh networks, the basic IEEE 802.11 adhoc networks and the recent IEEE 802.11ac physical layer standard have been chosen. On the other hand, unlike the strategy followed in [START_REF] Pablo | A Joint Multi-Path and Multi-Channel Protocol for Traffic Routing in Smart Grid Neighborhood Area Networks[END_REF] and [START_REF] Pablo | Emergency aware congestion control for smart grid neighborhood area networks[END_REF] where relay nodes are in charge of discarding packets in network congestion situations, in this paper it is up to the source nodes to reduce their packet generation rate. Thus, depending on the sensed degree of network congestion, source nodes may increase or decrease the rate at which they generate their packets. This way, we avoid unnecessary transmissions of packets that will be likely discarded later on their way to their destination. To this end, new signaling messages must be transmitted from relay nodes to source nodes. Besides, the proposed mechanism is also designed to provide a fair distribution of the available network resources between all the source nodes, avoiding a higher utilization by the nodes with higher packet generation rate or simply favorably located. Finally, our solution is independent of the routing protocol used.

PROPOSED SOLUTION

Before fully detailing our proposed mechanism for congestion control, we discuss some of its properties. The algorithms that compose the proposed mechanism are executed individually in every node (be it a source, relay or both). The amount of signaling traffic due to our mechanism is typically relatively small compared to the network traffic intensity, and overall the total traffic load is expected to be significantly reduced since our solution throttles the source nodes. In addition, the proposed algorithms do not significantly load the CPU of the nodes, which is advisable if we take into account that in many cases these devices are built on a large scale and at low cost, and so they could have limited resources.

Next subsections describe in detail the system and the designed algorithms that build the congestion control mechanism. In order to have a quick reference for all the variables used in the algorithms, Table 1 provides their definition and description. In our proposal, applications are grouped into four different classes depending on their relevance. As mentioned, one of the characteristics of the congestion control mechanism is that it will favor the transmission of packets belonging to high classes, but at the same time providing sufficient transmission opportunities to low categories. Said differently, absolute priority is not given to high categories over low ones. In this way, it is intended to offer all categories their minimum service quality requirements in terms of throughput and network transit time. Note that this behavior is different from the one presented by the mechanisms that assign absolute priority to some traffic classes over others, such as the EDCA medium access control. Absolute prioritization is typically convenient when priority traffic represents a small fraction of the total, which is not necessarily true here. Besides, EDCA is not a network congestion control mechanism, and thus buffers can indiscriminately overflow in the absence of a mechanism that explicitly deals with congestion, as our proposed solution does.

Systems under study

Distributed congestion control mechanism

To build the congestion control mechanism, three different algorithms, together with new signaling messages, have been designed, implemented and evaluated. For the sake of clarity, a first and simple scenario (Figure 2) will be considered to Manuscript submitted to ACM detail the algorithms. This scenario will also be used in next section, together with the more realistic one (Figure 1), to carry out the performance evaluation of the proposal.

In the first scenario, source nodes (S1, S2 and S3) transmit different NAN applications to the destination node (D).

Relay nodes (R1 and R2) are in charge of the retransmission of the data packets. It must be kept in mind that relay nodes play a central role in our solution. The objective is to avoid saturation in the relay nodes by adjusting the current application rates from the source nodes. To ensure traffic differentiation to the NAN applications with different QoS needs, different algorithm parameters can be tuned.

3.2.1

Relay nodes algorithm to detect network congestions. The distributed congestion mechanism is based on the value of the current channel utilization factor. This parameter is measured by each device n and it is denoted as ρ n . It represents the proportion of time where the channel is sensed as busy. It is evaluated as the average value over a period of time A.

Algorithm 1 is performed independently on all relay nodes. This algorithm has two main tasks. On the one hand, each node n updates its current value for the utilization factor ρ n and estimates the amount of traffic that passes through them for each class and each source node. We use y k j to denote the measured traffic rate of class k originated at source j. On the other hand, Algorithm 1 is also in charge of notifying the source nodes so that they regulate their packet generation rates whenever a network congestion is detected. In practice, when the current node becomes saturated 3.2.2 Relay nodes algorithm to determine the constrained packet generation rates for source nodes. Algorithm 2 is also implemented on the relay nodes. It computes the packet generation rates that will be notified to the sources depending on the different network congestion situations. This algorithm may be called to increase (with proportion I ) or decrease (with proportion D) the total transmission rate from the sources. More precisely, the amount of bandwidth that will be allocated is F • y k j (with F = I or F = D) and where y k j represents the overall transmission rate measured by the relay node. The transmission rate that will be assigned to a flow from source j with priority k is initially set to 0 (z k j,n = 0). In a first time (lines 4 -7), these transmission rates are increased in a while loop with increments equal to δ k . For a given flow, it stops increasing when a proportion α k of the current rate is reached (z k j,n < t k j ). Both thresholds α k and δ k depend on the flows priority k with typically

α 1 > α 2 > • • • > α K and δ 1 > δ 2 > • • • > δ K
. This first step aims to provide a fair proportion (α . ) to each flow according to their current throughput and priority. In a second step (lines Manuscript submitted to ACM 8 -11), if the threshold on the overall transmission rate has not been reached ( z k j,n < F • y k j ), the resources left is shared among the class and sources with the same principle, i.e. increasing with increment δ k .

Algorithm 2: Run at each relay node n to determine the constrained packet generation rates for source nodes.

Input: Parameters y k j , F = I or D (percentage increase or decrease of the source rates) Output: z k j, n 1 δ k : defines the increments for each class 2 t k j = α k • y k j : calculate the thresholds for each source and class

3 z k j,n = 0 4 while j, k z k j,n < F • j,k y k j do 5 for (j, k ) in {1, ,S } × {1, ,K } do 6 if z k j,n < t k j and j,k z k j, n < F • y k j then 7 z k j,n = z k j,n + δ k 8 while j, k z k j,n < F • j,k y k j do 9 for (j, k ) in {1, ,S } × {1, , K } do 10 if j,k z k j,n < F • y k j then 11 z k j,n = z k j,n + δ k 3.2.
3 Source nodes algorithm to implement the newly determined packet generation rates. Algorithm 3 collects the different z k j,n values every A seconds, from the different relay nodes, and selects the minimum in order to apply the transmission rates demanded by the most stringent (typically the most saturated) relay node. In some cases, the application demands may be lower than the amount allowed by the relay nodes. Should it be the case, the new transmission rate would simply be the application one.

Algorithm 3: Run at each source node j to implement the newly determined packet generation rate.

NUMERICAL RESULTS

We now describe our experimental setup to evaluate the performance of our proposed solution. The network performance parameters of interest are the following:

Manuscript submitted to ACM

• Channel utilization factor, measured by each node, refers to the occupation of the radio channel averaged over a period of time of 1 second. • Packet Delivery Ratio (PDR) defines the ratio of successfully received packets.

• Network attained throughput represents the number of bytes successfully transmitted per second. Note that its value is typically observed together with the PDR.

• Network transit time is the time needed by packets to reach their destination through the relay nodes.

• Jain's index is a common metric to assess the level of fairness between N entities. Its value ranges from 1/N (worst case) to 1 (best case). In our case, it is maximum when all sources receive the same allocation.

Simulation details

We implemented our solution in C++ within the ns-3 [START_REF]Ns-3 a Discrete-Event Network Simulator[END_REF] simulator. The wireless communications between nodes are operated using the recent amendment 802.11ac of the IEEE 802.11 standard on a single 20MHz channel with a MCS (Modulation Coding Scheme) set to 0 so that transmissions occur at a physical rate of 6.5 Mbps. Note that all nodes can store in their buffer up to 100 packets.

Source nodes deliver four different classes of traffic, typically corresponding to applications with different traffic

properties and QoS requirements. The different classes are presented in Table 2. Without loss of generality and for the sake of clarity, we let the lengths of packets be constant (200 Bytes) and the packet generation rate at source nodes be equal to 100 packets per second (specifically, the time between two successive packet generations is exponentially distributed with a mean of 10 ms). Note that class priorities are ranked in an ascending manner so that class 1 is viewed as the most critical. Nonetheless, let us recall that in our context, we consider that the traffic from all class matters. It follows that our goal is not to entirely guarantee the best possible performance for class 1 traffic at the expense of the other classes but rather to be able to favor class 1 while keeping the communications from lower classes alive. Note that all communications are performed using the transport protocol UDP.

Paths between source and destination nodes are found using the AODV [START_REF] Perkins | Ad hoc On-Demand Distance Vector (AODV) Routing[END_REF] routing protocol wherein the weight of links derives from the expected number of frames transmitted to send a packet (ETX). It is important to mention that the ns-3 simulator includes by default the AODV routing protocol and the hop count metric. In addition, the ETX metric implemented within the AODV by [START_REF] Nenad | Implementation of ETX Metric within the AODV Protocol in the NS-3 Simulator[END_REF] was used in our experiments. Finally, as it was mentioned in previous sections, our solution works independently to the routing protocol. Therefore, other routing protocols can be used without problem.

All simulations are run for a period of 400 sec so as to ensure the discovery of the steady-state regime, where for each scenario, different simulation runs were performed. Each run was configured with different random seeds in order to obtain the network performance parameters explained before. Table 3 details the main simulation parameters used, and Table 4 presents the values used for our distributed solution.

Scenario A: Tree topology

In our first scenario, we consider a tree topology made of six nodes with three sources, two relays and one destination as depicted previously in Figure 2. As discussed above, each source node (S1, S2 and S3) delivers traffic from four different classes. In any case, packets are first sent to node R1, then forwarded to node node R2 before ultimately reaching node D. To ensure the network topologies shown in Figures 1 and2, 80 meters was chosen as the distance between nodes located on the sides. On the other hand, 113 meters was chosen as the distance between nodes located on the opposite side. Therefore, with these selected distances and the default propagation model values, each node only establishes Manuscript submitted to ACM

Parameter Value K 4 A 1 sec D 75 % I 5 % [α 1 , α 2 , α 3 , α 4 ] [0.9, 0.8, 0.7, 0.5] [δ 1 , δ 2 , δ 3 , δ 4 ] [1, 0.6, 0.4, 0.2]
link with its neighbors. Note that with a packet generation rate at each source node of 400 pkt/sec (viz 0.64 Mbps), the combined workload of S1, S2 and S3 amounts to 1.92 Mbps and will undoubtedly overflow node R1 whose transmission rate of 6.5 Mbps is reduced by half due to DCF overheads and shared among the 4 nodes neighboring R1.

Manuscript submitted to ACM

We now compare the performance attained by the NAN applications with and without our proposed solution.

To begin with, we look at the channel utilization factor at the relay node R1. Channel utilization here refers to the occupation factor of the radio channel averaged over a period of time of 1 second. Figure 3 shows the corresponding results. The blue curve (labelled not controlled) represents the case where our solution does not operate and sources are free to generate packets at their initial rate, namely 100 pkt/sec for each class. The channel utilization factor nears 0.81 (meaning that R1 perceives the radio channel as busy about 80% of the time). In practice, the radio channel can be viewed as fully saturated since the 20% left corresponds to unavoidable delays such as those incurred by the contention window. Unlike the blue curve, the green curve corresponding to the case wherein our solution is applied (labelled controlled). The ρ factor oscillates between 0.6 and 0.8 with a mean value of 0.71. This behavior indicates that, with our solution, short saturations may occur but they are rapidly vanishing. We now study the time evolution of the packet generation rates at the source node S1. Figure 4 represents the corresponding results for each of the four classes. As expected, in the absence of our solution, the packet generation rate holds its initial value of 100 pkt/sec for any class. On the other hand, with the help of our solution, we can observe that the classes exhibit different patterns. For instance, the boxplot for class 1 indicates that the generation rate may vary from less than 95 up to 100 pkt/sec while its median value is 100 pkt/sec. Looking at the less critical classes, we note that the median value for their packet generation rate tends to be significantly smaller but yet far from 0 though. Manuscript submitted to ACM In Figure 5, we study the packet delivery ratio (PDR) attained for each class with and without our solution. Because the radio channel tends to be saturated in the absence of our solution (see Figure 3), the corresponding PDR only peaks at 55%. When our solution is applied, network sources are regulated so that congestions do not last thereby preventing packet losses. It follows that the the PDR attains 100%. Manuscript submitted to ACM Figure 6 shows the throughout attained by the different applications depending on the class they belong to. Note that here the attained throughput refers to the number of bytes that were successfully transmitted from the sources to the destination node. Not surprisingly, we notice that in the absence of our solution, all classes exhibit the same pattern with a constant throughput equal to 0.28 Mbps. However, when using our solution, each class obtains a level of throughput according to its degree of criticality. Note that the attained throughputs in Figure 6 can be derived as the product of the packet generation rates shown in Figure 4 and the PDR from Figure 5 (converting packets into Mbits). Overall, this first scenario illustrates the usefulness of our solution. In the next section, we consider a second scenario where the number of nodes (sources and relays) is much larger.

Scenario B: Smart grid scenario

Our second scenario deals with a set of 16 nodes where all nodes but the destination (Data concentrator) are both sources and relays. Nodes are arranged, grid-like, into 4 rows and 4 columns as illustrated previously by Figure 1.

Clearly, given the workload delivered by each node, the network will sustain congestion unless some form of control is made on the source packet generation rates.

First, we study the packet delivery ratio attained by nodes for each of their class. Figure 7 indicates that in the absence of our solution roughly 18% packets will ultimately reaching their destination. Said differently, most of them will be lost on their way towards the data concentrator. When using our solution, Figure 7 shows that virtually all generated packets successfully reach the destination node.

Manuscript submitted to ACM To have a better understanding on the network performance, we look at the attained throughput. Figure 8 shows that, without our solution, each class manages to get an attained throughput of about 0.47 Mbps. On the other hand, when our solution is applied, the attained throughput strongly varies with the class the traffic originates from. Nonetheless, all classes manage to get an access, be it small, to the network communication resources.

Manuscript submitted to ACM So far, the combination of Figures 7 and8 has shown that the implementation of our solution enables all applications to experience a near 100% packet delivery ratio and to differentiate their attained throughput based on their assumed criticality while maintaining a high level of the network resource utilization. We now investigate if nodes experience different levels of attained throughput based on their location. For instance, one can expect that nodes further away from the data concentrator undergo poorer performance than nodes in the vicinity of it. To do that, we compute a measure of fairness, namely the Jain's index, which somehow reflects the variability in the attained throughput by nodes. Figure 9 represents the corresponding results. While the Jain's index attains a value of 0.3 in the absence of our solution, its value ranges from 0.8 to 0.95 with the help of our solution. These results indicate that the 15 source nodes and their different classes of traffic of the smart grid topology are much more fair in their sharing of the network resources when our solution is applied.

Manuscript submitted to ACM We now investigate the network transit time experienced by packets sent from the nodes. Figure 10 represents the network transit time found for each traffic class with and without our solution. We observe that without our solution the network transit times tend to vary between 443 to 4307 ms. On the other hand, using our solution enables to decrease these transit times to a range of 1 to 14 ms. In practice, our proposed solution throttles the sources at lower paces so that buffers at the relay nodes are less occupied and queueing delays significantly reduced.

Manuscript submitted to ACM Finally, we study the overhead due to the additional signaling traffic caused by our solution. While the signaling traffic represents between 3 and 4% of the total network load, this traffic enables to significantly decrease the packet generation rate at the source nodes and therefore has a positive impact on the total network load.

CONCLUSIONS AND FUTURE WORK

In this work, we propose and evaluate the performance of a distributed congestion control mechanism for the Smart Grid Neighborhood Area Networks. The scenario considered is built by intelligent devices interconnected with each other through an IEEE 802.11 ad hoc network that uses the IEEE 802.11ac standard as the physical layer. The proposed solution offers traffic differentiation (depending on the application criticality) and fair distribution of network resources between all the network nodes.

In order to assess the network performance, two scenarios have been tested with the help of a discrete-event simulator.

Numerical results show the improvements obtained from our congestion control mechanism in terms of packet delivery ratio, network throughput, transit time as well as fairness between different traffic sources.

As future lines of work, mathematical formulation will be made in order to forecast how the throughput will be distributed between different classes. Besides, different scenarios will be implemented. That is, the network performance of our solution will be evaluated, when the grid size is increased from 16 to 36 nodes. On the other hand, the traffic from the control center towards downstream to the the smart meters, will be implemented and evaluated with our solution.

Finally, the different variables will be adjusted in order to provide the minimum QoS requirements for all classes.

Manuscript submitted to ACM

Figure 1

 1 Figure 1 shows the main NAN scenario used to evaluate our distributed solution. This NAN scenario is made up of a set of smart devices arranged in a grid topology, where data packets travel from the devices to the data concentrator and vice versa. Home users (smart meter devices and other utilities present at the HANs) transmit data to the Smart Grid control center through the data concentrator. Examples of data are meter reading data, home energy and Electric Manuscript submitted to ACM

Fig. 1 .

 1 Fig. 1. Smart Grid Neighborhood Area Networks scenario

Fig. 2 .Algorithm 1 : 2 update value of ρ n and y k j 3 if 5 notify the sources with new values of z k j,n 6 else if ρ n ≤ C n then 7 relax

 2123567 Fig. 2. Tree topology scenario

1 while true do 2 initialize all z k j,n = ∞ 3 collect values z k j, n from the relay nodes 4 6 if y k j < s k j and x k j ≥ y k j then 7 increase y k j to its new value s k j 8 if y k j > s k j then 9 decrease y k j to its new value s k j 10 wait A seconds

 234678910 compute s k j = min(z k j,n ) 5for k in {1, . . . , K } do

Fig. 3 .

 3 Fig. 3. Scenario A -Evolution of the channel utilization factor at R1 node.

Fig. 4 .

 4 Fig. 4. Scenario 1 -Packet generation rate at node S1 for each class.

Fig. 5 .

 5 Fig. 5. Scenario A -Packet delivery ratio at node D for each class.

Fig. 6 .

 6 Fig. 6. Scenario A Attained throughput at node D for each class.

Fig. 7 .

 7 Fig. 7. Scenario B -Packet delivery ratio at node D for each class.

Fig. 8 .

 8 Fig. 8. Scenario B -Attained throughput at node D for each class.

Fig. 9 .

 9 Fig. 9. Scenario B -Fairness on the attained throughputs.

Fig. 10 .

 10 Fig. 10. Scenario B -Network transit time.

Table 2 .

 2 NAN applications transmitted over the Smart Grid.

	Class	Applications	Packet lengths	Packet gener-
			(Bytes)	ation rate (per
				sec)
	1	Demand Response,	200	100
		Outage Management	Deterministic	Exponential
	2	Video surveillance,	200	100
		Overhead Transmission	Deterministic	Exponential
		Line Monitoring,
		Substation Automation
		systems (SASs)
	3	Home Energy	200	100
		Managament (HEM),	Deterministic	Exponential
		Electric Vehicles (EVs)
		Charging
	4	Meter Data	200	100
		Management	Deterministic	Exponential

Table 3 .

 3 Main simulation parameters.

	Description	Value
	Network simulator.	ns-3.28
	Simulation time	400 s
	Tranport layer	UDP
	Random number generator MRG32k3a
	Routing protocol	AODV
	Routing metric	ETX
	Queue size	100 packets
	Wireless physical layer	802.11ac
	Modulation coding scheme 0
	Short guard interval	1
	Frame aggregation factor	0
	Channel width	20 MHz

Table 4 .

 4 Distributed congestion control parameters.

ACKNOWLEDGMENTS

This work was supported by the Spanish Research Council under projects INRISCO (TEC2014-54335-C4-1-R) and MAGOS (TEC2017-84197-C4-3-R), and Juan Pablo Astudillo León is the recipient of a full scholarship from the Secretaria Nacional de Educación Superior, Ciencia y Tecnología.


Yan Liu

Pavla Debeljak

Mathieu Rembauville

Stéphane Blain

Ingrid Obernosterer
email: ingrid.obernosterer@obs-banyuls.fr

Diatoms shape the biogeography of heterotrophic prokaryotes in early spring in the Southern Ocean

Keywords:

The interplay among microorganisms profoundly impacts biogeochemical cycles in the ocean.

Culture based work has illustrated the diversity of diatom-prokaryote interactions, but the question of whether these associations can affect the spatial distribution of microbial communities is open. Here, we investigated the relationship between assemblages of diatoms and of heterotrophic prokaryotes in surface waters of the Indian sector of the Southern Ocean in early spring. The community composition of diatoms and that of total and active prokaryotes were different among the major ocean zones investigated. We found significant relationships between compositional changes of diatoms and of prokaryotes. By contrast, spatial changes in the prokaryotic community composition were not related to geographic distance and to environmental parameters when the effect of diatoms was accounted for.

Diatoms explained 30% of the variance in both the total and active prokaryotic community composition in early spring in the Southern Ocean. Using co-occurrence analyses we identified a large number of highly significant correlations between abundant diatom species and prokaryotic taxa. Our results show that key diatom species of the Southern Ocean are each associated with a distinct prokaryotic community, suggesting that diatom assemblages contribute to shaping the habitat type for heterotrophic prokaryotes.

Originality-Significance Statement

The observation that diatoms shape the biogeography of heterotrophic microbes in the Southern Ocean rather than geographic distance and environmental conditions represents the originality of the present study. Key diatom species were each associated with specific prokaryotic taxa across a large spatial scale, illustrating their potential role in shaping microbial diversity in this part of the ocean.

Introduction

Marine autotrophic and heterotrophic microbes are tightly coupled on spatial and temporal scales. The production and remineralization of dissolved organic matter (DOM) is a basic interaction that mediates the fluxes of large quantities of carbon and energy between these components of the microbial community (Ducklow, 2006). Roughly half of recent primary production is taken up by heterotrophic microbes, resulting in a respiration that is several-fold higher than the annual increase in atmospheric carbon dioxide (CO 2 ) of 4 Gt (Ciais et al., 2014). Diatoms are ubiquitous phototrophic microbes known to produce large quantities of DOM [START_REF] Biddanda | Major contribution from mesopelagic plankton to heterotrophic metabolism in the upper ocean[END_REF][START_REF] Myklestad | Dissolved organic carbon from phytoplankton[END_REF]. This group is abundant at high latitudes and dominates phytoplankton blooms in the nutrient-rich, cold surface waters of the Southern Ocean [START_REF] Smetacek | The role of grazing in structuring Southern Ocean pelagic ecosystems and biogeochemical cycles[END_REF][START_REF] Quéguiner | Iron fertilization and the structure of planktonic communities in high nutrient regions of the Southern Ocean[END_REF][START_REF] Assmy | Thick-shelled, grazer-protected diatoms decouple ocean carbon and silicon cycles in the iron-limited Antarctic Circumpolar Current[END_REF][START_REF] Malviya | Insights into global diatom distribution and diversity in the world's ocean[END_REF], thereby influencing the dynamics of heterotrophic microbes.

Phytoplankton blooms markedly affect the prokaryotic community composition (see reviews by [START_REF] Buchan | Master recyclers: features and functions of bacteria associated with phytoplankton blooms[END_REF][START_REF] Bunse | Marine bacterioplankton seasonal succession dynamics[END_REF]. The successional shifts were linked to the temporal modifications in the quantity and quality of the phytoplankton-derived DOM providing new niches for either opportunistic prokaryotic taxa or those adapted in the preferential utilization of the compounds released [START_REF] Rinta-Kanto | Bacterial community transcription patterns during a marine phytoplankton bloom: Phytoplankton bloom metatranscriptome[END_REF][START_REF] Sarmento | Use of phytoplankton-derived dissolved organic carbon by different types of bacterioplankton: Use of phytoplankton-derived DOC by bacterioplankton[END_REF][START_REF] Teeling | Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom[END_REF][START_REF] Li | Metaproteomics reveals major microbial players and their metabolic activities during the blooming period of a marine dinoflagellate Prorocentrum donghaiense: Microbial metaproteome during a dinoflagellate bloom[END_REF]. Using the exudate of a common diatom species as growth substrate in an experimental approach and in situ observations allowed to establish a link between substrate preferences of several prokaryotic taxa and their presence during spring phytoplankton blooms in the Southern Ocean [START_REF] Landa | Shifts in bacterial community composition associated with increased carbon cycling in a mosaic of phytoplankton blooms[END_REF]. The reported co-occurrence patterns between eukaryotic and prokaryotic taxa extend the potential importance of biotic interactions to larger temporal and spatial scales [START_REF] Gilbert | Defining seasonal marine microbial community dynamics[END_REF][START_REF] Lima-Mendez | Determinants of community structure in the global plankton interactome[END_REF][START_REF] Milici | Co-occurrence Analysis of Microbial Taxa in the Atlantic Ocean Reveals High Connectivity in the Free-Living Bacterioplankton[END_REF][START_REF] Needham | Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom[END_REF][START_REF] Zhou | Distribution Patterns of Microbial Community Structure Along a 7000-Mile Latitudinal Transect from the Mediterranean Sea Across the Atlantic Ocean to the Brazilian Coastal Sea[END_REF]. These networks could in part be driven by the exchange of a suite of metabolites, discovered in experimental studies using model organisms.

The interplay between autotrophic and heterotrophic microbes could be governed by mechanisms that are species-specific [START_REF] Amin | Interactions between diatoms and bacteria[END_REF]. The production of compounds of the vitamin B group, such as B1 by Marinomonas sp. SBI22 [START_REF] Paerl | Use of plankton-derived vitamin B1 precursors, especially thiazole-related precursor, by key marine picoeukaryotic phytoplankton[END_REF], B2 [START_REF] Johnson | Evidence for quorum sensing and differential metabolite production by a marine bacterium in response to DMSP[END_REF] and B12 by Ruegeria pomeroyi DSS-3 [START_REF] Durham | Cryptic carbon and sulfur cycling between surface ocean plankton[END_REF] was shown to support the growth of the phototrophs Ostreococcus lucimarinus and Thalassiosira pseudonana, respectively. The exchange of nitrogen and carbon governs certain associations between the nitrogen fixing cyanobacterium UCYN-A and the unicellular algae Braarudosphaera bigelowii [START_REF] Thompson | Unicellular cyanobacterium symbiotic with a single-celled eukaryotic alga[END_REF][START_REF] Zehr | How single cells work together[END_REF]. The production of siderophores by Marinobacter sp. DG879 that help acquire iron could promote interactions with phytoplankton, such as the dinoflagellate Scrippsiella trochoidea [START_REF] Amin | Photolysis of iron-siderophore chelates promotes bacterial-algal mutualism[END_REF].

Diatom cell division was recently shown to be regulated by the excretion of hormones released by prokaryotes based on the co-cultured Pseudo-nitzschia multiseries with Sulfitobacter sp. SA11 and Thalassiosira pseudonana with Croceibacter atlanticus (Amin et al., 2015;[START_REF] Van Tol | Ubiquitous marine bacterium inhibits diatom cell division[END_REF]. These findings in combination with the observed cooccurrence of microbial taxa [START_REF] Gilbert | Defining seasonal marine microbial community dynamics[END_REF][START_REF] Lima-Mendez | Determinants of community structure in the global plankton interactome[END_REF][START_REF] Milici | Co-occurrence Analysis of Microbial Taxa in the Atlantic Ocean Reveals High Connectivity in the Free-Living Bacterioplankton[END_REF][START_REF] Needham | Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom[END_REF][START_REF] Zhou | Distribution Patterns of Microbial Community Structure Along a 7000-Mile Latitudinal Transect from the Mediterranean Sea Across the Atlantic Ocean to the Brazilian Coastal Sea[END_REF] raise the question of whether interactions have the potential to drive biogeographic patterns of microbial communities in the aquatic environment.

The objective of the present study was to investigate the association between diatom assemblages and the prokaryotic community composition on a spatial scale. We specifically asked the question of whether diatom community composition can explain biogeographic patterns of prokaryotes. Both the total and potentially active prokaryotic community was considered with the aim to identify differences in their respective associations with diatoms.

We explored this question in surface waters of the Indian sector of the Southern Ocean in early spring, when diatoms are the major contributor to phytoplankton biomass. We concurrently tested for the possible effects of geographic distance and environmental parameters, key factors identified in previous biogeographic studies of prokaryotic communities (see review by [START_REF] Hanson | Beyond biogeographic patterns: processes shaping the microbial landscape[END_REF].

Results

Environmental context

Our study was conducted over a large spatial distance (≈ 3000 km) in the Indian sector of the Southern Ocean, covering four oceanic zones, the subtrophical zone (STZ), the subantarctic zone (SAZ), the polar front zone (PFZ) and the antarctic zone (AAZ) (Table 1; Fig. 1). Gradients in environmental parameters, determined in seawater collected at 10 m, were particularly pronounced between the STZ and the SAZ, while overall minor variations within surface waters of the PFZ and AAZ were detectable (see Table S1 for dissolved oxygen and salinity). Temperature decreased from 20.09 °C in the STZ to -1.12 °C at the southernmost station SI. Concentrations of DOC dropped from roughly 70 µM in the subtropical zone to 50 µM within and south of the SAZ. An opposite trend was observed for the major inorganic nutrients nitrate and nitrite, phosphate and silicic acid. Abundances of heterotrophic prokaryotes decreased by a factor of 3 from the STZ to the southernmost stations O11 and SI. The coastal site BDT (1 nautical mile from shore, 50 m overall depth) within a bay of Kerguelen Island was characterized by higher DOC concentrations and lower Synechococcus abundances, and much lower diatom abundances as compared to the stations located within the PFZ. Concentrations of chlorophyll a had no pronounced N to S gradient and highest concentrations were detectable above the Kerguelen plateau (stations A3-1 and A3-2), paralleled by the highest diatom abundances. Diatoms dominated in terms of abundance over dinoflagellates at all sampling sites except at station O22 in the STZ. Diatom carbon biomass accounted for 2-14% of total phytoplankton biomass in subtropical waters, while its contribution was on average 59% for all other stations [START_REF] Rembauville | Plankton assemblage estimated with BGC-Argo Floats in the Southern Ocean: Implications for seasonal successions and particle export[END_REF].

Diatom community composition

The composition of the diatom community, determined by microscopic observations, was significantly different among the 4 ocean zones (ANOSIM, R = 0.78, P < 0.01) based on Bray-Curtis dissimilarity. Samples originating from the SAZ (O25), the PFZ (O12 and TNS6) and the AAZ (A3, O11, KER, FS and SI) formed one main cluster, and samples originating from the STZ (O22 and O24) formed an independent branch, including the coastal site BDT (Fig. 2). Within the main group, separate clusters for the samples from the SAZ, the PFZ, station A3, located above the Kerguelen plateau, and the stations south of Kerguelen Island were detectable. Major diatoms in the STZ were small centric spp. (19-67% of total diatom abundance), corresponding to an assemblage of diatoms < 25 µm that could not be determined to the species level, and Pseudo-nitzschia spp. (8-59%), while in the SAZ Chaetoceros radicans (51%) and Chaetoceros socialis (18%) dominated. In the PFZ Thalassionema nitzschioides (~32%), small centric spp. (9-38%), Fragilariopsis kerguelensis (7-22%) and Eucampia antarctica (2-16%) were the main contributors to the diatom community (Fig. 2). These diatoms contributed also substantially to the community at station A3. However, the dominant species at this site was Chaetoceros curvisetus, accounting for 53% of total diatom abundance, followed by Thalassionema nitzschioides (22-28%). In surface waters south of Kerguelen Island (O11, KER, FS and SI), Fragilariopsis kerguelensis contributed between 15 and 37% of total diatom abundance. Pseudo-nitzschia spp. (2-31%), small centric spp. (3-21%), Chaetoceros curvisetus (3-18%), and Thalassionema nitzschioides (2-10%) accounted for overall lower and more variable fractions of diatom abundance at these sites.

Composition of total and active prokaryotic communities

From the 12 stations, each sampled in biological triplicates, we obtained 36 subsamples for the total (DNA) and 36 subsamples for the active (RNA) prokaryotic community. From the total of 72 subsamples, we obtained 1326 operational taxonomic units (OTUs) defined at 97% similarity based on the 16S rRNA gene amplicon sequencing targeted on the V4 -V5 region.

A large number of OTUs (1217) were shared among the DNA and RNA data sets, with 1320 and 1323 OTUs obtained from the DNA and RNA samples, respectively. Unshared OTUs were not abundant in either the DNA or the RNA data set. nMDS ordination analysis showed that prokaryotic communities were significantly different among ocean zones (ANOSIM, R = 0.69, P < 0.01) (Fig. 3). The three biological replicates analysed from each station closely clustered together, illustrating the high replicability among samples for both DNA and RNA (Fig. S1). For the subsequent illustrations and analyses, the biological triplicates were pooled separately for the DNA and RNA data sets and the mean relative abundance was calculated for each OTU.

According to OTU abundances, sample assemblages were dominated by Alphaproteobacteria (DNA 42% of the sequences of all samples pooled, RNA 38%), Bacteroidetes (DNA 28%, RNA 34%), Gammaproteobacteria (DNA 18%, RNA 16%) and Cyanobacteria (DNA 9%, RNA 8%), with SAR11, Flavobacteria, Thiomicrospirales and Synechococcales as major subtaxa respectively (Fig. S2). Deltaproteobacteria and Archaea were not abundant in surface waters (each < 0.6% of sequences pooled from all sites). Dominant OTUs (i.e. ≥ 1% of the sequences in at least one sample) represented similar proportions of the total (DNA) and active (RNA) communities at a given site (Fig. S2). For clarity, the following brief description is focused on DNA data. Within Alphaproteobacteria, SAR11 was the most abundant taxon, with 2 distinct OTUs belonging to SAR11 Ia being abundant (2-16% of the sequences at a given site) either in the STZ or in the AAZ. Sphingomonadales were present in almost all samples. Within Gammaproteobacteria, one OTU belonging to Thiomicrospirales had high relative abundances in the SAZ, PFZ (except for BDT) and AAZ (3-9%), and groups such as SAR86, Pseudomonadales and Alteromonadales revealed abundances < 2%. Within Flavobacteria, 2 OTUs belonging to the NS2b marine group were abundant in the SAZ, PFZ (except for BDT) and AAZ (3-12%), with highest relative abundances at station SI. Marine group NS9 was present with relatively high abundance at station A3-2 (~ 4%). SAR324 marine group belonging to Deltaproteobacteria represented 1-2% of the reads at the AAZ stations, except for A3-2. Cyanobacteria had high abundances in the STZ and at station BDT, represented by Synechococcus and Prochlorococcus. Archaeal Thaumarchaeota varied between 0.5-1% at stations KER, FS, A3-1 and A3-2.

Linking prokaryotic and diatom community composition

To explore the question of whether prokaryotic community composition was linked to the diatom assemblages, we calculated the dissimilarity for each pair of samples of both prokaryotic and diatom communities. Based on the Mantel test, we observed significant relationships between changes in the diatom assemblages and in the total (DNA) (Fig. 4; R = 0.55; P < 0.01) and active (RNA) (Fig. S3; R = 0.55; P < 0.01) prokaryotic communities for the Southern Ocean samples (SAZ, PFZ and AAZ; excluding BDT). This illustrates that the dissimilarity in the composition of each the total and active prokaryotic community increases with an increasing dissimilarity of the diatom assemblages. Significant relationships were also obtained for the entire data set, including the two stations in the STZ and station BDT (Fig. S4; R = 0.78, P < 0.01). We then tested the potential influence of geographic distance and environmental parameters. Geographic distance was not correlated to the changes in prokaryotic community composition (Mantel test, P > 0.05), as shown by the lack of any significant distance decay relationship (Fig. S5). To determine the potential influence of environmental parameters, we performed a canonical correspondence analysis. For the Southern Ocean, temperature explained in part the separate clustering of the SAZ sample (Fig. S6; ANOVA, P < 0.05). For the entire data set, temperature, salinity and DOC explained the separate clustering of the samples from the STZ (Fig. S6; ANOVA, P < 0.05). We then applied the partial Mantel test to consider concurrently the influence of diatoms and geographic distance, and diatoms and environmental parameters on prokaryotic community composition (Table 2). Diatoms explained significantly the variance of the prokaryotic community composition, whereas geographic distance or environmental variables alone could not when the effect of diatoms on prokaryotic communities was controlled for (Table 2; overall P < 0.05). In addition, within the AAZ, the relationship between prokaryotic communities and geographic distance was not significant with and without controlling for the effect of diatoms (P > 0.05). Diatom community composition explained 30% of the variance in both the total and active Southern Ocean prokaryotic community composition. Including the two stations in the STZ and BDT, the respective variances explained 70% and 50% (Table S2). It was interesting to note that in contrast to prokaryotes, diatom community composition could be explained by the combined environmental parameters and in particular, salinity, dissolved oxygen, nitrate and nitrite (Table S2).

The potential relationships between dominant diatom species and prokaryotic OTUs were further investigated using co-occurrence analysis (Fig. 5). Diatom species and prokaryotic OTUs with ≥ 1% relative abundance in at least one sample were considered. The correlation patterns were similar for total and active prokaryotic communities. The cluster analysis revealed several groups of diatoms, each associated with distinct prokaryotic assemblages.

For the description of these groups we focus on a few representative diatom species.

Fragilariopsis kerguelensis, Thalassionema nitzschoides and Chaetoceros curvisetus were numerically abundant at several Southern Ocean sites (Fig. 2), and revealed strong positive correlations (r ≥ 0.6) with distinct prokaryotic OTUs. Fragilariopsis kerguelensis was strongly positively correlated with several Flavobacteria OTUs belonging to groups NS5, NS9 and NS10. Positive correlations were also found with alphaproteobacterial OTUs belonging to the SAR11 I and SAR116 clades, and to Rhodobacterales, further with gammaproteobacterial OTUs belonging to Pseudomonadales, OM182 and SAR86, and to one OTU of the deltaprotebacterial SAR324 group. Thalassionema nitzschoides was positively correlated with Flavobacteria OTUs belonging to the NS4, NS7, NS9, Polaribacter and Ulvibacter groups, several Rhodobacterales OTUs as well as SAR11 Ib. Chaetoceros curvisetus, a diatom that accounted for 53% of the diatom abundance at station A3 above the plateau revealed strong correlations with Flavobacteria OTUs exclusively belonging to the NS9 group, one OTU belonging to the gammaproteobacterial Thiomicrospirales, and one OTU belonging to Thaumarchaeota. For comparison, Chaetoceros radicans and Chaetoceros socialis, dominant diatom species in the SAZ, revealed strong correlations to a distinct set of OTUs belonging to Flavobacteria groups NS2b, NS10 and NS4, to one OTU belonging each to Rhodobacterales, SAR11, SAR86 and Thiomicrospirales. Small centric spp. and Pseudonitzschia spp., diatoms with overall higher relative abundances in subtropical waters were again associated with distinct OTUs belonging to all major groups. In addition, several strong (r ≥ 0.6) positive correlations between Cyanobacterial OTUs and diatoms abundant in the STZ and at BDT were detectable.

Discussion

Contemporary environmental selection has widely been identified as one central mechanism for shaping the spatial distribution of heterotrophic microbes in the ocean (reviewed in [START_REF] Hanson | Beyond biogeographic patterns: processes shaping the microbial landscape[END_REF]. Dispersal and drift are additional factors that were identified in biogeographic studies [START_REF] Lindström | Local and regional factors influencing bacterial community assembly: Bacterial community assembly[END_REF][START_REF] Wilkins | Advection shapes Southern Ocean microbial assemblages independent of distance and environment effects[END_REF]. By contrast, the potential influence of the community composition of microorganisms that can interact with heterotrophic microbes has only recently been taken into consideration in this context [START_REF] Lima-Mendez | Determinants of community structure in the global plankton interactome[END_REF][START_REF] Milici | Co-occurrence Analysis of Microbial Taxa in the Atlantic Ocean Reveals High Connectivity in the Free-Living Bacterioplankton[END_REF][START_REF] Zhou | Distribution Patterns of Microbial Community Structure Along a 7000-Mile Latitudinal Transect from the Mediterranean Sea Across the Atlantic Ocean to the Brazilian Coastal Sea[END_REF]. The present study reveals that the spatial pattern in the community structure of heterotrophic microbes in surface waters is tightly associated to changes in the diatom assemblages, rather than to geographic distance and environmental conditions. Our survey covers the major oceanographic zones of the Southern Ocean where diatoms are dominant components of the autotrophic community and our results illustrate their potential role in shaping microbial diversity in surface waters of this part of the ocean.

The key diatom species that we identified, based on their high relative abundances at several sites, could be indicative for two broad ecological niches in the Southern Ocean.

Fragilariopsis kerguelensis, a chain forming, heavily silicified pennate diatom with overall low growth rates is typically associated with high-nutrient-low-chlorophyll (HNLC) systems [START_REF] Smetacek | The role of grazing in structuring Southern Ocean pelagic ecosystems and biogeochemical cycles[END_REF][START_REF] Quéguiner | Iron fertilization and the structure of planktonic communities in high nutrient regions of the Southern Ocean[END_REF]. By contrast, small diatoms (<25 µm), such as Chaetoceros spp., Thalassionema nitzschioides and Thalassiosira spp. are more lightly silicified and known to develop rapidly under favourable conditions, such as in response to iron supply by natural fertilization in the region east of Kerguelen Island [START_REF] Blain | Effect of natural iron fertilization on carbon sequestration in the Southern Ocean[END_REF][START_REF] Lasbleiz | Composition of diatom communities and their contribution to plankton biomass in the naturally iron-fertilized region of Kerguelen in the Southern Ocean[END_REF] or to mesoscale artificial iron additions [START_REF] Assmy | Thick-shelled, grazer-protected diatoms decouple ocean carbon and silicon cycles in the iron-limited Antarctic Circumpolar Current[END_REF].

Fragilariopsis kerguelensis was associated to several OTUs of the SAR11 Ia and SAR116 clades and OTUs belonging to OM182 and SAR86, groups that were previously characterized as oligotrophs to be adapted to conditions of low resource supply [START_REF] Sowell | Transport functions dominate the SAR11 metaproteome at low-nutrient extremes in the Sargasso Sea[END_REF][START_REF] Yooseph | Genomic and functional adaptation in surface ocean planktonic prokaryotes[END_REF][START_REF] Swan | Prevalent genome streamlining and latitudinal divergence of planktonic bacteria in the surface ocean[END_REF]. Noticeably strong correlations were also found for deltaproteobacterial SAR324 OTUs. Members of this group are chemolithotrophic [START_REF] Swan | Potential for chemolithoautotrophy among ubiquitous bacteria lineages in the Dark Ocean[END_REF][START_REF] Sheik | Metabolic flexibility of enigmatic SAR324 revealed through metagenomics and metatranscriptomics: Disentangling the ecophysiological role of SAR324[END_REF] and thus likely better adapted to oligotrophic conditions. By contrast, Thalassionema nitzschoides and Chaetoceros curvisetus had strong correlations with OTUs that were previously identified as rapid responders to phytoplankton blooms, as for example the flavobacterial OTUs Polaribacter, Ulvibacter and OTUs from the NS7 group [START_REF] Buchan | Master recyclers: features and functions of bacteria associated with phytoplankton blooms[END_REF]. Among these, Polaribacter has frequently been observed in temperate [START_REF] Teeling | Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom[END_REF][START_REF] Klindworth | Diversity and activity of marine bacterioplankton during a diatom bloom in the North Sea assessed by total RNA and pyrotag sequencing[END_REF][START_REF] Needham | Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom[END_REF] and polar regions under bloom conditions [START_REF] Williams | The role of planktonic Flavobacteria in processing algal organic matter in coastal East Antarctica revealed using metagenomics and metaproteomics: Metaproteomics of marine Antarctic Flavobacteria[END_REF]Delmont, 2014;[START_REF] Luria | Seasonal succession of free-living bacterial communities in coastal waters of the Western Antarctic Peninsula[END_REF]Dadaglio et al., 2018) and in response to labile organic matter supply (Dinasquet et al., 2017;[START_REF] Luria | Seasonal shifts in bacterial community responses to phytoplankton-derived dissolved organic matter in the Western Antarctic Peninsula[END_REF][START_REF] Tada | Distinct bacterial community and diversity shifts after phytoplankton-derived dissolved organic matter addition in a coastal environment[END_REF]. Similarly, Rhodobacterales had a higher number of strong positive correlations with Thalassionema nitzschoides and Chaetoceros curvisetus than with Fragilariopsis kerguelensis. The rapid response of the above mentioned OTUs to phytoplankton blooms has been attributed to their uptake capacities of specific phytoplankton-derived substrates, using functional profiling based on metatranscriptomics [START_REF] Teeling | Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom[END_REF][START_REF] Rinta-Kanto | Bacterial community transcription patterns during a marine phytoplankton bloom: Phytoplankton bloom metatranscriptome[END_REF][START_REF] Beier | The transcriptional response of prokaryotes to phytoplankton-derived dissolved organic matter in seawater: The response of prokaryotes to DOM in seawater[END_REF] and metaproteomics [START_REF] Li | Metaproteomics reveals major microbial players and their metabolic activities during the blooming period of a marine dinoflagellate Prorocentrum donghaiense: Microbial metaproteome during a dinoflagellate bloom[END_REF].

We expected the composition of total and active prokaryotes to be different, but the spatial distribution and the co-occurrence patterns revealed to be highly similar between these communities. The use of RNA-based approaches to distinguish the active microbial community members from inactive or dormant ones is based on the relationship between cellular content of ribosomal RNA (rRNA) and metabolic activity [START_REF] Kerkhof | Comparison of Nucleic Acid Hybridization and Fluorometry for Measurement of the Relationship between RNA/DNA Ratio and Growth Rate in a Marine Bacterium[END_REF][START_REF] Poulsen | Use of rRNA fluorescence in situ hybridization for measuring the activity of single cells in young and established biofilms[END_REF]. Several limitations were pointed out [START_REF] Blazewicz | Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses[END_REF], including differences in the relationship between rRNA content and growth rate among taxa, and the possibly high rRNA content of dormant and inactive cells [START_REF] Flärdh | Ribosomes exist in large excess over the apparent demand for protein synthesis during carbon starvation in marine Vibrio sp. strain CCUG 15956[END_REF][START_REF] Fegatella | Implications of rRNA Operon Copy Number and Ribosome Content in the Marine Oligotrophic Ultramicrobacterium Sphingomonas sp. Strain RB2256[END_REF]. Previous investigations in the study region have shown that bulk prokaryotic production and growth rates in early spring are substantially lower as compared to midsummer, in particular at bloom sites [START_REF] Christaki | Microbial food web dynamics during spring phytoplankton blooms in the naturally iron-fertilized Kerguelen area (Southern Ocean)[END_REF]. In contrast to the present study, total and active prokaryotic communities were different from each other in summer, both within the phytoplankton bloom above the Kerguelen plateau (Station A3) and in HNLC waters [START_REF] West | Major differences of bacterial diversity and activity inside and outside of a natural iron-fertilized phytoplankton bloom in the Southern Ocean[END_REF]. These observations suggest that while prokaryotes are stimulated by phytoplankton growth in early spring, our approach does not allow to differentiate the active community members from those potentially inactive or dormant [START_REF] Lennon | Microbial seed banks: the ecological and evolutionary implications of dormancy[END_REF].

Our observation that key diatom species are each associated to several distinct prokaryotic taxa raises the question on the underlying mechanisms. While the exact processes are not tractable with the present data set, we provide a possible scenario. In contrast to heterotrophic prokaryotes, the spatial distribution of diatoms was significantly correlated to some environmental parameters. Because our study took place in early spring when the effect of phytoplankton activity on inorganic nutrient drawdown is minor, this result suggests that environmental parameters are drivers of diatom assemblages, and that diatoms may be setting the pace for the observed spatial changes in prokaryotic community composition. Previous field studies and satellite observations have indicated that the onset of spring phytoplankton blooms occurring in the region off Kerguelen Island can differ by several weeks [START_REF] Sallée | Characterization of distinct bloom phenology regimes in the Southern Ocean[END_REF]. Considering each site independently, the varying contributions of the most abundant diatom species could represent different stages of the spring phytoplankton development at a given location with potential consequences on the prokaryotic community composition. The qualitative and quantitative differences in the DOM produced by the various diatoms are likely to present an important selection process [START_REF] Landa | Shifts in bacterial community composition associated with increased carbon cycling in a mosaic of phytoplankton blooms[END_REF][START_REF] Sarmento | Bacterioplankton niche partitioning in the use of phytoplankton-derived dissolved organic carbon: quantity is more important than quality[END_REF].

The pronounced association between assemblages of diatoms and heterotrophic microbes observed in the present study could be driven by two particular features of the Southern Ocean that are the growth-limiting concentrations of bioavailable iron and organic carbon in surface waters. Iron availability controls autotrophic and heterotrophic metabolism, but the lack of bioavailable organic carbon represents an additional constraint for heterotrophic microbes (Church et al., 2000;[START_REF] Obernosterer | Fe and C co-limitation of heterotrophic bacteria in the naturally fertilized region off the Kerguelen Islands[END_REF]. In early spring, autotrophic plankton outcompete heterotrophs for the access to iron [START_REF] Fourquez | Microbial iron uptake in the naturally fertilized waters in the vicinity of the Kerguelen Islands: phytoplankton-bacteria interactions[END_REF].

Thus, diatom-derived organic matter is key in driving both heterotrophic microbial activity and diversity in this ocean region [START_REF] Arrieta | Response of bacterioplankton to iron fertilization in the Southern Ocean[END_REF][START_REF] Obernosterer | Rapid bacterial mineralization of organic carbon produced during a phytoplankton bloom induced by natural iron fertilization in the Southern Ocean[END_REF][START_REF] Landa | Shifts in bacterial community composition associated with increased carbon cycling in a mosaic of phytoplankton blooms[END_REF][START_REF] Luria | Seasonal succession of free-living bacterial communities in coastal waters of the Western Antarctic Peninsula[END_REF]. The spatial distribution of the diatom assemblages as observed in the present study in early spring likely reflects their respective ecological niches driven by the light-nutrient regime, including iron availability. Our results let us conclude that diatom assemblages shape the habitat type for heterotrophic microbes and thereby directly influence their community composition.

Experimental procedures

Study area and sampling strategy

The present study was performed during the Southern Ocean and Climate (SOCLIM) cruise (DOI:10.17600/16003300) aboard the R/V Marion Dufresne (October 08 to November 1 2016). The 12 stations sampled for the present study were located in four oceanographic zones, the subtropical zone (STZ), the subantarctic zone (SAZ), the polar front zone (PFZ), and the antarctic zone (AAZ) (Fig. 1; Table 1). In the AAZ, station A3, located above the Kerguelen plateau, was visited twice during the sampling period (stations A3-1 and A3-2).

Station BDT (Baie de la Table ) is a coastal site (50m overall depth) within a bay of Kerguelen Island. All environmental data and the samples for the diatom community composition were collected at 10m with 12L Niskin bottles mounted on a rosette equipped with a conductivitytemperature-depth (CTD, Seabird SBE9+) sensor. Seawater samples for microbial diversity analyses were taken from the underway seawater supply system that collected water at 5m depth.

The sampling, storage and analyses of common environmental parameters were done according to previously described protocols. The major inorganic nutrients nitrite (NO 2 -), nitrate (NO 3 -), phosphate (PO 4 3-) and silicic acid (Si(OH) 4 ) were determined using continuous flow analysis with a Skalar instrument [START_REF] Aminot | Dosage automatique des nutriments dans les eaux marines: méthodes en flux continu[END_REF][START_REF] Blain | Distributions and stoichiometry of dissolved nitrogen and phosphorus in the iron fertilized region near Kerguelen (Southern Ocean)[END_REF]. Dissolved organic carbon (DOC) concentrations were determined by high temperature oxydation on a Shimadzu TOC-VCP analyser [START_REF] Benner | A critical evaluation of the analytical blank associated with DOC measurements by high-temperature catalytic oxidation[END_REF][START_REF] Obernosterer | Fe and C co-limitation of heterotrophic bacteria in the naturally fertilized region off the Kerguelen Islands[END_REF]. Concentrations of chlorophyll a (Chl a) were determined by High Performance Liquid Chromatography (HPLC)

analyses [START_REF] Uitz | A phytoplankton class-specific primary production model applied to the Kerguelen Islands region (Southern Ocean)[END_REF]. The abundance of heterotrophic and autotrophic prokaryotes, and pico-and nanoeukaryotes was done by flow cytometric analyses on a BD FACS canto [START_REF] Marie | Flow cytometry analysis of marine picoplankton[END_REF][START_REF] Obernosterer | Rapid bacterial mineralization of organic carbon produced during a phytoplankton bloom induced by natural iron fertilization in the Southern Ocean[END_REF]. For the identification and enumeration of the diatom and dinoflagellate assemblages, 100 mL of seawater were fixed with Lugol solution (1 % final concentration) and stored in the dark at 4°C for 2 months. Observations were done in an Utermöhl counting chamber (24h, dark) using an inverted microscope with phase contrast (Olympus IX70) with x400 magnification as described in [START_REF] Rembauville | Plankton assemblage estimated with BGC-Argo Floats in the Southern Ocean: Implications for seasonal successions and particle export[END_REF].

For the analyses of the prokaryotic community composition, three 6L biological replicates were collected at each site, filtered on 60 µm nylon screens, and then through 0.8

µm polycarbonate filters (47-mm diameter, Nuclepore, Whatman, Sigma Aldrich, St Louis, MO, USA). Cells were then concentrated on 0.2 µm Sterivex filter units (Sterivex, Millipore, EMD, Billerica, MA, USA) using a peristaltic pumping system (Masterflex L/S Easy-Load II).

The Sterivex filter units were kept at -80 °C until extraction.

DNA and RNA extraction and sequencing preparation

DNA and RNA were simultaneously extracted from one Sterivex filter unit using the AllPrep DNA /RNA kit (Qiagen, Hiden, Germany) with the following modifications. Filter units were thawed and closed with a sterile pipette tip end at the outflow. Lysis buffer was added (40 mM EDTA, 50 mM Tris, 0.75 M sucrose) and 3 freeze and thaw cycles were performed using liquid nitrogen and a water bath at 65 °C. Lysozyme solution (0.2 mg ml -1 final concentration)

was added and filter units were placed on a rotary mixer at 37°C for 45 minutes. Proteinase K (0.2 mg ml -1 final concentration) and SDS (1% final concentration) were added and filter units were incubated at 55 °C with gentle agitation every 10 minutes for 1 hour. To protect the RNA, 10 µl of β-mercaptoethanol was added to 1 ml of RLT plus buffer provided by the kit. To each filter unit, 1550 µl RLT plus-β-mercaptoethanol was added and inverted to mix.

The lysate was recovered by using a sterile 5 ml syringe and loaded in three additions onto the DNA columns by centrifuging at 10,000 g for 30 seconds. DNA and RNA purification were performed following manufacturer's guidelines (Qiagen, Germany) in which RNA was treated with DNase to avoid DNA contamination after the first wash step.

RNA was converted to cDNA using SuperScript III Reverse Transcriptase according to the manufacturer's instructions. Prior to reverse transcription, quality of RNA samples was examined by PCR test with general primer sets 341F (5' -CCTACGGGNGGCWGCAG) and 805R (5'-GACTACHVGGGTATCTAATCC) for the prokaryotic 16S rRNA gene, followed was used as a standard for subsequent analyses and considered as a DNA sample for all treatments.

Data analysis

All samples from the same sequencing run have been demultiplexed by Fasteris SA and barcodes have been trimmed off. A total number of 4 150 902 sequences was obtained.

Processing of sequences were performed using the Usearch pipeline [START_REF] Edgar | UPARSE: highly accurate OTU sequences from microbial amplicon reads[END_REF]. Pairedend reads were merged using fastq-mergepairs. The positions of primers were checked using search_oligodb and fastx_subsample were used to subset a portion of all sequences to check (e.g. 5000 sequences). Primers were trimmed and merged reads in range of length from 336 to 486 were kept using fastq-filter based on maximum expected error [START_REF] Needham | Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom[END_REF]. 2 000 219 sequences were kept after quality filtering. Data were denoised and chimera were identified and removed using unoise3 with a minsize 10 according to mock community DNA [START_REF] Edgar | UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing[END_REF]. During this step, singletons were discarded. Sequences were clustered using usearch_global and defined as OTUs at a 97% sequence similarity level. OTUs were assigned using assign_taxonomy.py against SILVA release 132 database [START_REF] Quast | The SILVA ribosomal RNA gene database project: improved data processing and webbased tools[END_REF].

Sequences assigned to chloroplast were removed prior to subsequent analyses.

Statistical analyses

All statistical analyses were performed using R 3.4.2 version. The OTU and taxa tables were combined into one object using phyloseq R package.

Data were Hellinger transformed prior to the analyses based on Bray-Curtis dissimilarity [START_REF] Legendre | Ecologically meaningful transformations for ordination of species data[END_REF]. Bray-Curtis dissimilarity matrices were generated via vegdist() function. Non-metric dimensional scaling (NMDS) ordinations was generated based on Bray-Curtis dissimilarity using monoMDS() function in the package Vegan [START_REF] Oksanen | vegan: community ecology package[END_REF]. Analysis of similarity (ANOSIM) was performed to test significant differences between sampling zones in microbial communities with R. Dendrograms were performed using hclust() with method "average" in the Vegan package.

Mantel and partial Mantel tests were performed in Vegan using mantel() and mantel.partial() based on the Pearson correlation method. The linear model was applied for the relationship between prokaryotic communities versus diatom communities in R. Geographical coordinates were transformed using the Haversine formula [START_REF] Sinnott | Virtues of the Haversine[END_REF]. Prior to correlation analysis, environmental variables were z-score transformed. The amount of variance in prokaryotic community composition explained by diatoms were estimated as the square of the correlation coefficient (Rho 2 ) based on partial Mantel test.

Sequences alignment was carried out using MAFFT algorithm web services by defaults [START_REF] Katoh | MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization[END_REF]. The phylogenetic tree was constructed using PhyML 3.0 online programs based on maximum likelihood method and 100 bootstraps with HKY85 substitution model [START_REF] Guindon | A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood[END_REF]. The tree was visualized with SeaView version 4.7 and saved as rooted tree. Heat maps were generated using heatmap3 package and rows were reordered corresponding to phylogenetic tree. The correlation heatmaps were generated using spls() and cim() in the mixOmics package with log-transformed data (Chun and Keleş, 2010;[START_REF] Rohart | mixOmics: An R package for 'omics feature selection and multiple data integration[END_REF].

Canonical correspondance analysis were performed using cca() in Vegan package with zscore transformed data. The significance of environmental parameters was tested with an analysis of variance (ANOVA) using Vegan package in R. The values shown in the tables are Rho values. Each value represents the correlation between two matrices while controlling for the effect of the third matrix. ** P < 0.01, * P < 0.05 S1.

O 2 5 A A Z A 3 -1 A A Z A 3 -2 A A Z S I A A Z K E R A A Z F S A A Z O 1 1 P F Z O 1 2 P F Z T N S 6 P F Z B D T S T Z O 2 2 S T Z O 2 4
Relative abundance (%)

  by the examination of amplification products on 1% agarose electrophoresis. Based on the results of the PCR test, DNase was performed once more on the RNA extracts of those samples with contaminating DNA left, to completely remove the residual DNA. DNA and cDNA was amplified and pooled as described in[START_REF] Parada | Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples: Primers for marine microbiome studies[END_REF] with a modification to the PCR amplification step. Briefly, the V4 -V5 region of the 16S rRNA gene from DNA and cDNA samples was amplified with the primer sets 515F-Y (5' -GTGYCAGCMGCCGCGGTAA) and 926R (5' -CCGYCAATTYMTTTRAGTTT).Triplicate 10 µl reaction mixtures contained 2 µg DNA, 5 µl KAPA2G Fast HotStart ReadyMix, 0.2 µM forward primer and 0.2 µM reverse primer. Cycling reaction started with a 3min heating step at 95 °C followed by 22 cycles of 95 °C for 45 s, 50 °C for 45 s, 68 °C for 90 s, and a final extension of 68 °C for 5 min. The presence of amplification products was confirmed by 1 % agarose electrophoresis and triplicate reactions were pooled. Each sample were added with unique paired barcodes. The Master 25 µl mixtures contained 1 µl PCR product, 12.5 µl KAPA2G Fast HotStart ReadyMix (Kapa Biosystems, USA), 0.5 µl barcode 1 and 0.5 µl barcode 2. The cycling program included a 30 s initial denaturation at 98°C followed by 8 cycles of 98 °C for 10 s, 60 °C for 20 s, 72 °C for 30 s, and a final extension of 72 °C for 2 min. 3 µl PCR product were used to check for amplification on 1% agarose electrophoresis. The remaining 22 µl barcoded amplicon product was cleaned to remove unwanted dNTPs and primers by Exonuclease I and Shrimp Alkaline Phosphatase at 37 °C, 30 min for treatment and 85 °C, 15 min to inactivate. The concentration of double-stranded DNA was quantified by PicoGreen fluorescence assay (Life Technologies). After calculating the PCR product concentration of each samples, they were pooled at equal concentrations manually. The pooled PCR amplicons were concentrated using Wizard SV gel and PCR clean-up system (Promega, USA) according to the manufacturer's protocol. 16S rRNA gene amplicons were sequenced with Illumina MiSeq 2 x 300 bp chemistry on one flow-cell at Fasteris SA sequencing service (Switzerland). Mock community DNA (LGC standards, UK)

  dimension reduction and variable selection. J Roy Stat Society: Series B (Stat Met) 72: 3-25. Church, M.J., Hutchins, D.A. and Ducklow, H.W. (2000) Limitation of bacterial growth by dissolved organic matter and iron in the Southern Ocean. Appl Environ Microb 66: 455-466. Ciais, P., C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, et al. (2014) Carbon and Other Biogeochemical Cycles. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, et al. (eds). Cambridge: Cambridge University Press, pp. 465-570. Dadaglio, L., Dinasquet, J., Obernosterer, I., and Joux, F. (2018) Differential responses of bacteria to diatom-derived dissolved organic matter in the Arctic Ocean. Aquat Microb Ecol 82: 59-72. Delmont, T.O., Hammar, K.M., Ducklow, H.W., Yager, P.L., and Post, A.F. (2014) Phaeocystis antarctica blooms strongly influence bacterial community structures in the Amundsen Sea polynya. Front Microbiol 5: 646. Dinasquet, J., Richert, I., Logares, R., Yager, P., Bertilsson, S., and Riemann, L. (2017) Mixing of water masses caused by a drifting iceberg affects bacterial activity, community composition and substrate utilization capability in the Southern Ocean: Iceberg influence on bacterioplankton. Environ Microbiol 19: 2453-2467.Ducklow, H.W., Fraser, W.,Karl, D.M., Quetin, L.B., Ross, R.M., Smith, R.C., et al. (2006) Water-column processes in the West Antarctic Peninsula and the Ross Sea:

Fig. 1 .

 1 Fig. 1. Map of the study region of the Southern Ocean and Climate (SOCLIM) cruise (October 2016) with stations sampled for the present study indicated by white dots. Surface chlorophyll a is derived from ocean color satellite images (COPERNICUS-GLOBCOLOR, October 2016, resolution 25km). Dotted and full lines indicate ocean zones and fronts, respectively. STZ-Subtropical Zone, STF-Subtropical Front, SAZ-Subantarctic Zone, SAF-Subantarctic Front, PFZ-Polar Front Zone, PF-Polar Front, AAZ-Antarctic Zone.

Fig. 3 .

 3 Fig. 3. Non-metric multidimensional scaling (NMDS) of total (DNA) and active (RNA) prokaryotic communities based on Bray-Curtis dissimilarity. Biological replicates of total and active prokaryotic communities are shown for each station. Samples from the 4 ocean zones were significantly different (2D stress = 0.08; ANOSIM, R = 0.69, P < 0.01). STZ-Subtropical Zone, SAZ-Subantarctic Zone, PFZ-Polar Front Zone, AAZ-Antarctic Zone.

Fig. 4 .

 4 Fig. 4. Relationship between changes in the community composition of diatoms and of total prokaryotes for the Southern Ocean stations (SAZ, PFZ and AAZ excluding station BDT). Each point represents the Bray-Curtis dissimilarity between pairs of samples. The correlation coefficient was determined by the Mantel test. Dashed line denotes linear regression.

Fig. 5 .

 5 Fig. 5. Heatmap of correlations between dominant diatom species and prokaryotic OTUs from total communities based on Pearson correlations. Diatom species and prokaryotic OTUs with ≥ 1% relative abundance in at least one sample are shown. Colour key: Red and blue colours represent positive and negative correlations, respectively. a.t = ambiguous taxa

∑N

  = nitrate + nitrite; DOC = dissolved organic carbon; Chl a = chlorophyll a; BA = bacterial abundance; Synecho = Synechococcus; Pico-Nano = Pico-Nanophytoplankton; Dinoflag = Dinoflagellates; n.a = data not available; b.d. = below detection. Salinity and concentrations of dissolved oxygen are provided in Table

Table Legends Table 1 .

 Legends1 Brief description of the study sites. All values are from 10m depth.

Table 2 .

 2 Partial Mantel test for prokaryotic and diatom community composition and

	geographic distance (a) and combined environmental parameters (b) for Southern Ocean
	samples.

Table 1 .

 1 Brief description of the study sites. All values are from 10m depth.

	Station	Date	Lat/ Long	Temp (°C)	∑N (µM)	PO 4 (µM) 3-	Si(OH) 4 (µM)	DOC (µM)	Chl a (µg L -1 )	BA (x10 8 L -1 )	Synecho (x10 4 L -1 )	PicoNano (x 10 6 L -1 )	Diatoms (x 10 4 L -1 )	Dinoflag (x 10 4 L -1 )
	Subtropical Zone
	O22	08-Oct-16	29.00°S 58.93°E	20.09	0.04	0.04	1.75	76	0.06	5.51	170.0	1.13	0.24	1.96
	O24	10-Oct-16	38.00°S 63.65°E	14.60	2.91	0.21	2.37	62	0.29	7.64	3160.0	12.07	14.75	3.55
	Subantarctic Zone
	O25	12-Oct-16	45.00°S 67.77°E	7.61	19.06	1.25	2.03	51	0.42	5.05	902.0	9.65	63.20	1.55
	Polar Front Zone
	O12	25-Oct-16	47.00°S 72.22°E	4.60	25.42	1.68	7.72	50	0.39	6.02	134.0	4.79	14.52	3.44
	TNS6	15-Oct-16	48.78°S 72.28°E	2.92	26.51	1.93	13.93	52	1.02	3.94	57.4	3.86	64.40	11.40
	BDT	17-Oct-16	49.50°S 69.21°E	2.94	26.30	1.81	14.29	68	n.a	4.46	47.7	6.74	1.51	0.19
	Antarctic Zone
	A3-1	18-Oct-16	50.63°S 72.06°E	2.19	28.21	1.85	19.48	52	1.44	3.66	19.5	2.88	150.70	5.40
	A3-2	24-Oct-16	50.63°S 72.06°E	2.06	28.11	1.84	19.85	51	1.64	4.88	17.6	1.59	138.30	4.50
	KER	18-Oct-16	50.68°S 68.38°E	2.38	28.23	1.97	18.41	51	0.32	2.89	15.6	5.37	13.52	3.88
	FS	19-Oct-16	52.50°S 67.00°E	2.00	29.36	1.96	21.90	52	0.28	2.81	9.4	5.23	10.07	2.15
	O11	20-Oct-16	56.50°S 63.00°E	1.00	29.90	2.01	30.95	46	0.27	2.30	3.4	4.00	13.72	1.62
	SI	21-Oct-16	58.50°S 61.50˚E	-1.12 29.27	1.87	35.89	48	0.22	1.88	b.d.	2.23	13.36	3.10

Table 1 .

 1 Brief description of the study sites. All values are from 10m depth. DOC = dissolved organic carbon; Chl a = chlorophyll a; BA = bacterial abundance; Synecho = Synechococcus; Pico-Nano = Pico-Nanophytoplankton; Dinoflag = Dinoflagellates; n.a = data not available; b.d. = below detection. Salinity and concentrations of dissolved oxygen are provided in TableS1.

	Station	Date	Lat/ Long	Temp (°C)	∑N (µM)	PO 4 (µM) 3-	Si(OH) 4 (µM)	DOC (µM)	Chl a (µg L -1 )	BA (x10 8 L -1 )	Synecho (x10 4 L -1 )	PicoNano (x 10 6 L -1 )	Diatoms (x 10 4 L -1 )	Dinoflag (x 10 4 L -1 )
	Subtropical Zone
	O22	08-Oct-16	29.00°S 58.93°E	20.09	0.04	0.04	1.75	76	0.06	5.51	170.0	1.13	0.24	1.96
	O24	10-Oct-16	38.00°S 63.65°E	14.60	2.91	0.21	2.37	62	0.29	7.64	3160.0	12.07	14.75	3.55
	Subantarctic Zone
	O25	12-Oct-16	45.00°S 67.77°E	7.61	19.06	1.25	2.03	51	0.42	5.05	902.0	9.65	63.20	1.55
	Polar Front Zone
	O12	25-Oct-16	47.00°S 72.22°E	4.60	25.42	1.68	7.72	50	0.39	6.02	134.0	4.79	14.52	3.44
	TNS6	15-Oct-16	48.78°S 72.28°E	2.92	26.51	1.93	13.93	52	1.02	3.94	57.4	3.86	64.40	11.40
	BDT	17-Oct-16	49.50°S 69.21°E	2.94	26.30	1.81	14.29	68	n.a	4.46	47.7	6.74	1.51	0.19
	Antarctic Zone
	A3-1	18-Oct-16	50.63°S 72.06°E	2.19	28.21	1.85	19.48	52	1.44	3.66	19.5	2.88	150.70	5.40
	A3-2	24-Oct-16	50.63°S 72.06°E	2.06	28.11	1.84	19.85	51	1.64	4.88	17.6	1.59	138.30	4.50
	KER	18-Oct-16	50.68°S 68.38°E	2.38	28.23	1.97	18.41	51	0.32	2.89	15.6	5.37	13.52	3.88
	FS	19-Oct-16	52.50°S 67.00°E	2.00	29.36	1.96	21.90	52	0.28	2.81	9.4	5.23	10.07	2.15
	O11	20-Oct-16	56.50°S 63.00°E	1.00	29.90	2.01	30.95	46	0.27	2.30	3.4	4.00	13.72	1.62
	SI	21-Oct-16	58.50°S 61.50˚E	-1.12 29.27	1.87	35.89	48	0.22	1.88	b.d.	2.23	13.36	3.10

Acknowledgments

We thank the captain and the crew of the R/V Marion Dufresne for their support aboard. We thank Olivier Crispi for sampling and analyses of inorganic nutrients, Jocelyne Caparros for the analyses of dissolved organic carbon and Philippe Catala for flow cytometry analyses. Chlorophyll a concentrations were determined by HPLC analyses at the SAPIGH analytical platform at IMEV, Villefranche-sur-Mer, by Josefine Ras and Celine Dimier. The detailed comments from two anonymous reviewers helped improve previous versions of the manuscript. The project SOCLIM (Southern Ocean and Climate) is supported by the Climate Initiative of the BNP Paribas foundation, the French Polar Institute (Institut Polaire Emile Victor), and the French program LEFE-CYBER of the CNRS-INSU. This work is part of the PhD thesis of Y.L. supported by the China Scholarship Council (CSC; NO. 201606330072).

Accession numbers

Demultiplexed sequence files are available in NCBI under accession number PRJNA494099.

Conflict of interest

The authors declare no conflict of interest.

Supporting information

Additional Supporting Information may be found in the online version of this article: Table S1. Additional environmental variables of the study sites.

Table S2. Partial Mantel test for prokaryotic and diatom community composition and geographic distance (a) and environmental parameters (b) for the entire dataset and Southern Ocean samples.