Baptiste Barreau
email: baptiste.barreau@bnpparibas.com

Laurent Carlier
email: laurent.carlier@bnpparibas.com

HISTORY-AUGMENTED COLLABORATIVE FILTERING FOR FINANCIAL RECOMMENDATIONS A PREPRINT

Keywords: matrix factorization, collaborative filtering, context-aware, time, neural networks

In many businesses, and particularly in finance, the behavior of a client might drastically change over time. It is consequently crucial for recommender systems used in such environments to be able to adapt to these changes. In this study, we propose a novel collaborative filtering algorithm that captures the temporal context of a user-item interaction through the users' and items' recent interaction histories to provide dynamic recommendations. The algorithm, designed with issues specific to the financial world in mind, uses a custom neural network architecture that tackles the non-stationarity of users' and items' behaviors. The performance and properties of the algorithm are monitored in a series of experiments on a G10 bond request for quotation proprietary database from BNP Paribas Corporate and Institutional Banking.

Introduction

In a financial market, liquidity is provided by market makers, whose role is to constantly offer both buy (bid) and sell (ask) prices. Market makers benefit from the difference between the two, called the bid-ask spread. Corporate and institutional banks such as BNP Paribas CIB play the role of market makers in financial markets across many asset classes and their derivatives. When a client wants to trade a financial product, she either requests prices on an electronic platform where many different market makers operate in a process called a request for quotation (RFQ), or contact a salesperson of a bank. Reciprocally, salespeople can also directly contact clients and suggest relevant trade ideas to them, e.g., financial products held by the bank and on which it might offer a better price than its competitors. Proactive salespeople, which are particularly important for the bank, help manage financial inventories to minimize the risk to which the bank is exposed, and serve better their clients when correctly anticipating their needs. Providing salespeople with an RFQ recommender system would support their proactivity by allowing them to navigate the complexity of the markets more easily. Our goal is to design a financial recommender system that suits the particularities of the financial world to assist salespeople in their daily tasks. RFQ recommendation is an implicit feedback problem, as we do not explicitly observe clients' opinions about the products they request. Implicit feedback is a classic recommender system setup already addressed by the research community, e.g., in [START_REF] Hu | Collaborative filtering for implicit feedback datasets[END_REF]. The financial environment, however, brings about specific issues that require attention. To that end, the algorithm we introduce here has three main aims:

• To incorporate time. In a classic e-commerce environment, leaving aside popularity and seasonal effects, recommendations provided at a given date may remain relevant for a couple of months, since users' shopping tastes do not markedly evolve with time. In the financial world, a user is interested in a financial product at a given date not only because of the product's intrinsic characteristics but also because of its current market conditions. Time is consequently crucial for RFQ prediction and should be taken into account in a manner that allows for future predictions.

• To obtain dynamic embeddings. Being able to capture how clients (resp. financial products) relate to each other and how this relationship evolves with time is of great interest for business, as it allows getting a deeper understanding of the market. One of our goals is consequently to keep the global architecture of matrix factorization algorithms, where the recommendation score of a (client, product) couple is given by the scalar product of their latent representations.

• To symmetrize users and items. Classic recommender systems focus on the user-side. However, the product-side is also of interest in the context of a corporate bank. To control her risk, a market maker needs to control her positions, i.e., make sure she does not hold a given position for too long. Consequently, coming up with a relevant client for a given asset is equally important, and the symmetry of clients and assets will be integrated here in both our algorithms and evaluation strategies.

We tackle these goals with a context-aware recommender system that only uses client-product interactions as its signal source. In this work, the context is dynamic, and is inferred at a given time from the previous interactions of clients and products. The terms clients and users (resp. financial products/assets and items) will be used interchangeably to match the vocabulary used in recommender systems literature.

Related work

The work presented in this article is a context-aware and time-dependent recommender system, which are both active areas of research [START_REF] Adomavicius | Context-aware recommender systems[END_REF][START_REF] Shi | Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges[END_REF]. Notably, in [START_REF] Hariri | Context adaptation in interactive recommender systems[END_REF], the recommendations of a bandit algorithm are dynamically adapted to contextual changes. In [START_REF] Hosseinzadeh Aghdam | Adapting recommendations to contextual changes using hierarchical hidden Markov models[END_REF], the recommendations of a hierarchical hidden Markov model are contextualized based on the users' feedback sequences. Down-weighing past samples in memory-based collaborative filtering algorithms helps better match temporal dynamics [START_REF] Ding | Time weight collaborative filtering[END_REF]. The latent factors of a matrix factorization can also directly include temporal dynamics [START_REF] Koren | Collaborative filtering with temporal dynamics[END_REF]. Temporal probabilistic matrix factorization [START_REF] Zhang | Latent factor transition for dynamic collaborative filtering[END_REF] introduces dynamics of users' latent factors with a timeinvariant transition matrix that can be computed using Bayesian approaches, thereby extending probabilistic matrix factorization [START_REF] Mnih | Probabilistic matrix factorization[END_REF]. It is also possible to enhance latent factor models with Kalman filters [START_REF] Sarkar | A latent space approach to dynamic embedding of co-occurrence data[END_REF] or recurrent neural networks [START_REF] Chao-Yuan | Recurrent recommender networks[END_REF] to capture the dynamics of rating vectors.

Tensor factorization models handle time by considering the user-item co-occurrence matrix as a three-dimensional tensor where the additional dimension corresponds to time [START_REF] Xiong | Temporal collaborative filtering with bayesian probabilistic tensor factorization[END_REF][START_REF] Wu | Neural Tensor Factorization[END_REF], and where temporal dynamics can be accounted for using recurrent neural networks [START_REF] Wu | Neural Tensor Factorization[END_REF] -these approaches, however, do not allow for future predictions. Using historical data to introduce dynamics in a neural network recommender system was done in [START_REF] Covington | Deep neural networks for YouTube recommendations[END_REF], where users are assimilated to their items' histories. The Caser algorithm [START_REF] Tang | Personalized top-n sequential recommendation via convolutional sequence embedding[END_REF] uses convolutional filters to embed histories of previous items and provide dynamic recommendations to users.

The algorithm introduced in this work is to some extent reminiscent of graph neural networks [START_REF] Hamilton | Inductive representation learning on large graphs[END_REF] and their adaptation to recommendation [START_REF] Wang | Neural graph collaborative filtering[END_REF]. Using time to enhance recommendations with random walks on bipartite graphs was explored in [START_REF] Xiang | Temporal recommendation on graphs via long-and short-term preference fusion[END_REF]. How graph neural networks behave with dynamic bipartite graphs is to the best of our knowledge yet to be discovered and could lead to an extension of this work. A first attempt at financial products recommendation was made in [START_REF] Wright | Machine learning and corporate bond trading[END_REF] with the particular example of corporate bonds.

Methodology

We introduce a neural network architecture that aims at producing recommendations through dynamic embeddings of users and items, that we call History-augmented Collaborative Filtering (HCF).

Let U be the set of all users and I the set of all items. Let us note

x u ∈ R d for u ∈ U , x i ∈ R d for i ∈ I the static d-dimensional
embeddings of all the users and items we consider. Let t ∈ [[0 ; ∞]] be a discrete time-step, taken as days in this work. For a given u, at a given t, we define h t u as the items' history of u, i.e., the set of items found to be of interest to u in the past -we respectively define h t i as item i users' history. We use here as histories the last n events that happened strictly before t to either user u or item i. If at a given t we observe 0 ≤ n < n previous events, histories are only formed of those n events. Users are consequently on a same event scale -high-activity users' histories may span a couple of days, whereas low-activity ones may span a couple of months (resp. for items). Histories formed of items/users of interest in a past window of fixed size were also tried, but led to inferior performance. Figure 1.a shows the global architecture of the HCF network. It is composed of two symmetric blocks -a user block and an item block. At a given time t, the user block of HCF produces dynamic user embeddings x t u using as inputs static embeddings of users x u and their corresponding histories' embeddings h t u , defined as

h t u = 1 |h t u | i∈h t u x i . (1)
If h t u is empty, we use h t u = 0. Respectively, the item block produces dynamic item embeddings x t i , and the score of a (u,i) couple at a given t is given by the scalar product of their dynamic embeddings. In each block, dynamic embeddings are computed using a network of one-dimensional convolution filters of kernel size 1 along the embedding dimension axis, considering user and history embeddings as channels (see Fig. 1.b). Convolutions were chosen because we empirically found that all architectures performing computations involving both x u,l and h t u,l with l = l ∈ [[1; d]] the l-th component of the embedding systematically led to poorer performance than a linear componentwise combination of x u and h t u . The chosen convolutions can be seen as a variation of the linear component-wise combination with shared parameters across all components, and allow for network depth.

The network is trained using the bayesian personalized ranking (BPR) loss [START_REF] Rendle | BPR: Bayesian personalized ranking from implicit feedback[END_REF], a surrogate of the ROC AUC score [START_REF] Manning | Introduction to information retrieval[END_REF]. It is defined in [START_REF] Rendle | BPR: Bayesian personalized ranking from implicit feedback[END_REF] as

L BP R = - (u,i,j)∈D ln σ(x uij ) , (2)
where D = {(u, i, j)|i ∈ I + u ∧ j ∈ I\I + u } with I + u the subset of items that were of interest for user u in the considered dataset, and x uij = x ui -x uj , with x ui the score of the (u, i) couple. σ is the sigmoid function, defined as σ(x) = 1/(1 + e -x ). The underlying idea is to rank items of interest for a given u higher than items of no interest for that u, and D corresponds to the set of all such possible pairs for all users appearing in the considered dataset. As D grows exponentially with the number of users and items considered, it is usual to approximate L BP R with negative sampling [START_REF] Mikolov | Distributed representations of words and phrases and their compositionality[END_REF].

In our proposed methodology, scores become time-dependent. Data samples are therefore not seen as couples, but as triplets (t, u, i). To enforce user-item symmetry in the sampling strategy, we define

D t u = (t, u, i, j)|i ∈ I t,+ u ∧ j ∈ I\h t u ( 3
)
D t i = (t, u, v, i)|u ∈ U t,+ i ∧ v ∈ U \h t i (4)
with I t,+ u the subset of items that had a positive interaction with user u at time t, and U t,+ i the subset of users that had a positive interaction with item i at time t. For a given positive triplet (t, u, i), we sample either a corresponding negative one in D t u or D t i with equal probability. Note that considering samples as triplets adds a sampling direction, as a couple that was active at a time t may no longer be active at other times t + t or t -t", t , t" > 0. Such sampling strategies will be more extensively studied in further iterations of this work.

Experiments

We conduct a series of experiments to understand the behavior of the proposed HCF algorithm on a proprietary database of RFQs on governmental bonds from the G10 countries. This database, which describes every day the RFQs performed by the clients of the bank on G10 bonds, accounts for hundreds of clients and thousands of bonds and ranges from 08/01/2018 to 09/30/2019. On average, we observe tens of thousands of user-item interactions every month.

We examine here the performance of our proposal in comparison to benchmark algorithms in two experiments. Benchmark algorithms, chosen for their respect of the aims outlined in Section 1, are a historical baseline and two matrix factorization algorithms trained using a confidence-weighted euclidean distance [START_REF] Hu | Collaborative filtering for implicit feedback datasets[END_REF] and the BPR objective [START_REF] Rendle | BPR: Bayesian personalized ranking from implicit feedback[END_REF], that we respectively call MF -implicit and MF -BPR. MF -implicit is trained with gradient descent, and we adopt for MF -BPR the symmetrical sampling strategy outlined in Section 3. The historical baseline scores a (user, item) couple with their observed number of interactions during the considered training period.

In this section, the performance of our models is evaluated using mean average precision (mAP) [START_REF] Manning | Introduction to information retrieval[END_REF], defined as

mAP = 1/|Q| * q∈Q AP(q) , ( 5
)
where Q is the set of queries to the recommender system, and AP(q) is the average precision score of a given query q. To monitor the performance of our algorithms on both the user-and item-sides, we define two sets of queries over which averaging:

• User-side queries. Queries correspond to the recommendation list formulated every day for all users;

• Item-side queries. Queries correspond to the recommendation list formulated every day for all items.

These two query sets lead to user-and item-side mAPs that we summarize with a harmonic mean in a symmetrized mAP score used to monitor all the following experiments, as

mAP sym = 2 * mAP u * mAP i mAP u + mAP i . (6)
The user-and item-sides equally contribute to the symmetrized score since both sides equally matter in our financial context, as seen in Section 1. The mAP scoring perimeter corresponds to the Cartesian product of all the users and items observed in the training period1 .

Evolution of forward performance with training window size

This experiment aims at showing how benchmark algorithms and our HCF proposal behave with regards to stationarity issues. We already advocated the importance of time in the financial setup -taking the user side, clients' behavior is non-stationary, owing to the non-stationarity of the financial markets themselves but also externalities such as punctual needs for liquidity, regulatory requirements, . . . In machine learning, this translates into the fact that the utility of past data decreases with time. However, machine learning and more particularly deep learning algorithms work best when provided with large datasets [START_REF] Lecun | Deep learning[END_REF]: there is an apparent trade-off between non-stationarity and the need for more training data. Our goal in this experiment is to show that introducing time in the algorithm helps to reduce this trade-off.

To prove this, we examine the evolution of forward performance as the training window size grows. We split the G10 bonds RFQ dataset into three contiguous parts -a train part that ranges from up to 08/01/2018 to 07/31/2019 (up to one year), a validation part from 08/01/2019 to 08/30/2019 (one month) and a test part from 09/01/2019 to 09/30/2019 (one month). Validation is kept temporally separated from the training period to avoid signal leakages [START_REF] Lopez | Advances in financial machine learning[END_REF], and is taken forward to match business needs. For all the considered algorithms, we train an instance of each on many training window sizes ranging from a week to a year using carefully hand-tuned hyperparameters and early stopping, monitoring validation symmetrized mAP.

We see in Fig. 2 that all benchmark algorithms present a bell-shaped curve. They attain a peak after which their performance only degrades as we feed these models more data, corroborating the non-stationarity vs. amount of data trade-off. On the contrary, HCF only gets better with training data size. Notably, HCF 12m which obtained best validation performance used n = 20 and blocks with two hidden layers and ReLU activations. To show that these bell-shaped curves are not an artifact of the hyperparameters chosen in the previous experiment, we conduct a systematic hyperparameter search for multiple training window sizes using a combination of a hundred trials of random search [START_REF] Bergstra | Random search for hyper-parameter optimization[END_REF] and hand-tuning. The optimal sets of hyperparameters for each considered window size are then used as in the previous experiment to obtain graphs of their validation mAP scores against the training window size. Results are shown in Figure 3. MF -BPR 6m and 12m optimal hyperparameters happened to coincide, and their results are consequently shown on the same graph. We see here that for both MF algorithms, hyperparameters optimized for many different window sizes seem to agree on optimal window size, respectively around one month and two months, with slight variations around these peaks. Consequently, bell shapes are inherent to these algorithms, which proves their non-stationarity vs. data size trade-off.

To obtain test performances that are not penalized by the discontinuity of the training and test windows, we retrain all these algorithms with the best hyperparameters and window size found for the validation period on dates directly preceding the test period, using the same number of training epochs as before. The performances of all the considered algorithms are reported in Table 1.

It appears that our HCF algorithm, augmented with the temporal context, obtains better performances on both validation and test periods than the static benchmark algorithms. Consequently, introducing temporal dynamics is essential in the financial setup that we consider. It follows from the previous experiment that our benchmark algorithms cannot make proper use of large amounts of past data and have to use short training window sizes to obtain good performances compared to historical models. Moreover, the results of these algorithms are less stable in time than HCF results. Fig. 4 visualizes the results reported on Table 1 on a daily basis for all the considered algorithms. We see a downward performance trend for all the benchmark algorithms -the further away from the training period, the lower the daily symmetrized mAP. On the contrary, HCF has stable results over the whole test period: introducing temporal context through user and item histories hinders the non-stationarity effects on forward performances.

The results from Section 4.1 and the observed downward performance trend consequently suggest that benchmark models need frequent retraining to remain relevant regarding future interests. A simple way to improve their results is to retrain these models on a daily basis with a constant, sliding window size w -predictions for each day of the testing period are made using a model trained on the w previous days. Each model uses here the number of epochs and hyperparameters determined as best on the validation period in the previous experiment. Results of these sliding models are shown in Table 2, where HCF results corresponds to the previous ones. We see that both MF -implicit and MF -BPR significantly improved their results compared to their static versions from Table 1, but are still below the results of HCF trained on 12 months. Consequently, our HCF proposal is inherently more stable than our benchmark algorithms and captures time in a more efficient manner than their daily retrained versions.

Conclusion

We introduce a novel HCF algorithm, a time-aware recommender system that uses user and item histories to capture the dynamics of the user-item interactions and that provides dynamic recommendations. In the context of financial G10 bonds RFQ recommendations, we show that for classic matrix factorization algorithms, a trade-off exists between the non-stationarity of users' and items' behaviors and the size of the training datasets. This trade-off is overcome with history-augmented embeddings. Moreover, these embeddings outperform sliding versions of classic matrix factorization algorithms and prove to be more stable predictors of the future interests of the users and items. Further research on the subject will include a more thorough investigation of alternative histories' embeddings formulations and timeaware sampling strategies. Finally, the HCF algorithm could be applied beyond the financial world to tasks where temporal dynamics drive users' behaviors, e.g., music and movie recommendation, where the current mood of a user highly influences her next decisions.

Figure 1 :

 1 Figure 1: a. Global architecture of the HCF network, composed of two symmetric user and item blocks. b. Illustration of the application of a one-dimensional convolution filter of kernel size 1 along the embedding dimension axis of our inputs x u and h t u for a user u ∈ U , where α, β, γ are the parameters of the convolution filter and l ∈ [[1; d]]. The same computation holds for items i ∈ I respectively.

Figure 2 :

 2 Figure 2: Evolution of validation symmetrized mAP with training window size. Whereas benchmark algorithms seem to have an optimal training window size, our HCF proposal keeps improving with the training window size.

Figure 3 :

 3 Figure 3: Evolution of validation symmetrized mAP with training window size. Left: Optimal MF -implicit for the 1m, 3m, 6m and 12m windows. A consensus arises around the 1m window. Right: Optimal MF -BPR for the 1m, 2m, 3m, 6m and 12m windows. A consensus arises around the 2m-3m windows, slightly favouring the 2m window.

Figure 4 :

 4 Figure 4: Daily evolution of symmetrized mAP during the test period. Light shades correspond to the true daily symmetrized mAP values, and dark ones to exponentially weighted averages (α = 0.2) of these values.

Table 1 :

 1 Window size study -symmetrized mAP scores, in percentage.

	Algorithm	Window Valid mAP Test mAP
	Historical	3m	18.51	16.86
	MF -implicit	1m	19.94	19.24
	MF -BPR	2m	21.89	20.05
	HCF	12m	24.43	25.02
	4.2 Evolution of forward performance with time

Table 2 :

 2 Sliding study -symmetrized mAP scores, expressed in percentage.

	Algorithm	Window Test mAP
	Historical (sliding)	3m	20.32
	MF -implicit (sliding)	1m	24.27
	MF -BPR (sliding)	2m	24.46
	HCF	12m	25.02

This scoring perimeter proved to be the fairest with regard to all considered models, as a model is consequently scored only on what it can score and nothing else. Fixing the perimeter for all algorithms to the Cartesian product of all the users and items observed in the maximal training window and attributing the lowest possible score to otherwise unscorable couples only lowers the mAP scores of candidate algorithms that cannot obtain satisfactory results on such window sizes.

Acknowledgements

This work has been conducted under the French CIFRE Ph.D. program, in collaboration between the MICS Laboratory at CentraleSupélec and BNP Paribas CIB Global Markets. We thank Dan Sfedj and Camille Garcin for their work on early versions of the HCF algorithm, and Damien Challet, Sarah Lemler and Julien Dinh for helpful discussions and feedback on drafts of this work.


Ioanna Siokou-Frangou

Micheline Bianchi

Urania Christaki

E D Christou

Antionia Giannakourou

O Gotsis

L Ignatiades

P Pagou

P Pitta

K Pagou

S Psarra

E Souvermezoglou

F Van Wambeke

V Zervakis

Carbon flow in the planktonic food web

Keywords: Plankton, Food web, Carbon flow, Oligotrophy, E. Mediterranean

come

Introduction

Carbon flux in the ocean depends mainly on the magnitude of primary production and the biochemical processes within the photic zone, as well as on the complexity of the pelagic food web, i.e. the relative abundance or biomass of its components and interactions between them. Thus, whatever the productivity level, studies in the photic zone based on simultaneous estimates of the biomass and production of phytoplankton, bacteria, heterotrophic nano-and microplankton and mesozooplankton together are essential for the assessment of the carbon flux [START_REF] Nielsen | Structure of plankton communities in the Dogger Bank area (North Sea) during a stratified situation[END_REF][START_REF] Nielsen | Plankton community structure and carbon cycling on the western coast of Greenland during and after the sedimentation of a diatom bloom[END_REF][START_REF] Richardson | Spatial heterogeneity in the structure of the planktonic food web in the North Sea[END_REF][START_REF] Bradford-Grieve | Pelagic ecosystem structure and functioning in the Subtropical Front region east of New Zealand in austral winter and spring 1993[END_REF]. In oligotrophic areas, the food web is dominated by minute producers and consumers (e.g., [START_REF] Caron | Picoplankton and nanoplankton and their trophic coupling in surface waters of the Sargasso Sea south of Bermuda[END_REF] and most of the carbon flow is through microbial communities [START_REF] Azam | The ecological role of water-column microbes in the sea[END_REF][START_REF] Sherr | Role of microbes in pelagic food webs: a revised concept[END_REF][START_REF] Roman | Spatial and temporal changes in the partitioning of organic carbon in the planktonic community of the Sargasso sea off Bermuda[END_REF]. In the Mediterranean Sea, an oligotrophic area, organic carbon and nutrients are remineralized and recycled efficiently within a complex microbial food web with little energy transfer to the higher trophic levels [START_REF] Van Wambeke | Carbon fluxes from the microbial food web to mesozooplankton. An approach in the surface layer of a pelagic area (NW Mediterranean Sea)[END_REF][START_REF] Turley | Relationship between primary producers and the bacteria in an oligotrophic sea-the Mediterranean and biochemical implications[END_REF]. Within the Mediterranean Sea, there exists a west-east gradient [START_REF] Krom | Phosphorus limitation of primary productivity in the eastern Mediterranean Sea[END_REF][START_REF] Dolan | Planktonic ciliates in the Mediterranean Sea: longitudinal trends[END_REF][START_REF] Turley | Relationship between primary producers and the bacteria in an oligotrophic sea-the Mediterranean and biochemical implications[END_REF]: the Eastern Mediterranean has been identified as one of the most oligotrophic areas of the world [START_REF] Azov | Seasonal patterns of phytoplankton productivity and abundance in nearshore oligotrophic waters of the Levant Basin (Mediterranean)[END_REF][START_REF] Krom | Nutrient distribution during an annual cycle across a warm-core eddy from the E. Mediterranean Sea[END_REF][START_REF] Zohary | Experimental study of microbial Plimitation in the eastern Mediterranean[END_REF][START_REF] Souvermezoglou | Distribution and seasonal variability of nutrients and dissolved oxygen in the northeastern Ionian Sea[END_REF][START_REF] Mazzocchi | Mesozooplankton distribution from Sicily to Cyprus (Eastern Mediterranean): I. General aspects[END_REF].

The Aegean Sea is a distinct sub-system of the Eastern Mediterranean Sea due to its geographical position between the Black Sea and the other seas of the eastern basin (Ionian and Levantine Seas). The bottom topography of the region is characterised by an alternation of plateaux and deep troughs: the Cyclades plateau separates the North from the South Aegean basins, allowing water exchange above 400-m depth. Below 400 m, the deep basins of the North Aegean are filled with very dense water of local origin [START_REF] Zervakis | The role of the North Aegean in triggering the recent Eastern Mediterranean climatic changes[END_REF] and the deep basin of the South Aegean with Cretan Deep Water [START_REF] Theocharis | A synthesis of the circulation and hydrography of the South Aegean Sea and the Straits of the Cretan Arc[END_REF]. The general circulation within the Aegean Sea is cyclonic: highly saline (S > 38.8 psu) water of Levantine origin, dominant in the South Aegean, travels northwards along the western coast of Turkey [START_REF] Theocharis | A synthesis of the circulation and hydrography of the South Aegean Sea and the Straits of the Cretan Arc[END_REF]. A surface layer of very light, brackish (S f 30 psu) water is formed in the Northeast Aegean by the inflow of modified Black Sea Water (BSW) through the Dardanelles straits; this water mass affects the uppermost (20 -30 m) layer of the North Aegean Sea [START_REF] Theocharis | Dense water formation over the Samothraki and Limnos plateaux in the North Aegean Sea (Eastern Mediterranean Sea)[END_REF] and is modified moving westward and southward [START_REF] Stergiou | The Hellenic seas: physics, chemistry, biology and fisheries[END_REF] by lateral and diapycnal mixing with the waters of Levantine origin. The latter, being diluted to salinities about 38 psu by overlying BSW in the North Aegean, flows southwards along the eastern coast of the Hellenic peninsula, eventually entering the South Aegean and the Cretan Sea. In that region, the water column is largely homogeneous; however, a slightly less saline layer of relatively nutrient-rich Transition Mediterranean Water occupies intermediate depths (200 -300 m), separating Cretan Intermediate Water and Cretan Deep Water layers [START_REF] Theocharis | A synthesis of the circulation and hydrography of the South Aegean Sea and the Straits of the Cretan Arc[END_REF].

Although the Aegean Sea has been characterised as an overall oligotrophic environment, it exhibits significant horizontal variability: nutrient concentrations, plankton and benthos abundance as well as fish catch densities have been found higher in the North -Northwest Aegean Sea than in the South -Southeast Aegean Sea [START_REF] Stergiou | The Hellenic seas: physics, chemistry, biology and fisheries[END_REF]. Furthermore, the Black Sea surface outflow in the NE Aegean Sea has been found to be enriched in dissolved organic carbon and dissolved organic nitrogen [START_REF] Polat | Chemical exchange between the Mediterranean and the Black Sea via the Turkish straits. Dynamics of Mediterranean Straits and Channels[END_REF]. The South Aegean Sea has been recently characterised as one of the most oligotrophic areas of the Mediterranean Sea [START_REF] Ignatiades | The productive and optical status of the oligotrophic waters of the Southern Aegean Sea (Cretan Sea), Eastern Mediterranean[END_REF][START_REF] Gotsis-Skretas | Seasonal, horizontal and vertical variability in primary production and standing stocks of phytoplankton and zooplankton in the Cretan Sea and the Straits of the Cretan Arc (March 1994 -January 1995)[END_REF][START_REF] Van Wambeke | Heterotrophic bacterial production in the Cretan Sea (NE Mediterranean)[END_REF][START_REF] Psarra | Primary productiv-ity in the oligotrophic Cretan Sea (NE Mediterranean): seasonal and interannual variability[END_REF].

Taking into account the above-mentioned differences within the Aegean Sea, we have examined the hypothesis whether or not they reflect to differences in pelagic food web structure and the carbon flow towards the high levels (fish). The goal of the present study, conducted within the EU project MATER, was to assess the organic carbon partitioning between autotrophic and heterotrophic plankters of different size and to investigate the carbon flow in the photic zone among the different areas of the Aegean Sea.

For this purpose, we have concurrently measured the biomass of autotrophs and heterotrophs, the production of autotrophs, bacteria and copepods, the grazing of heterotrophic nanoflagellates on bacteria and the grazing of copepods on phytoplankton. Furthermore, autotrophic biomass and production were measured by size fractions, since the size of primer producers is of great importance in the characterisation and the functioning of the food web [START_REF] Legendre | Plankton and nutrient dynamics in marine waters[END_REF][START_REF] Fasham | Modelling the relative contributions of autotrophs and heterotrophs to carbon flow at a Lagrangian JGOFS station in the Northeast Atlantic: the importance of DOC[END_REF][START_REF] Nielsen | Plankton community structure and carbon cycling on the western coast of Greenland during the stratified summer situation: I. Hydrography, phytoplankton and bacterioplankton[END_REF].

Methodology

Study area and sampling

The strategy was to sample different degrees of oligotrophy (North Aegean versus South Aegean Sea) in two contrasting seasons: in March and September when the water column is well mixed and stratified, respectively. Sampling was performed on board the R/ V AEGAEO from 28 March to 2 April 1997 and from 18 to 23 September 1997 at seven stations (N1 to N7) in the North Aegean Sea. In the South Aegean Sea samples were collected at four stations (S1, S2, S6 and S7) from 6 to 9 March 1997 and at five stations (S1, S2, S3, S6 and S7) from 9 to 15 September 1997 (Fig. 1).

Hydrographic measurements

Hydrographic measurements (temperature, pressure, conductivity) were made using a SeaBird Electronics SBE 9/11 + CTD-General Oceanics Rosette assembly with 10-l Niskin bottles. Water samples for inorganic nutrients, phytoplankton biomass and production, bacteria biomass and production, heterotrophic nanoflagellates (HNAN) and ciliates were collected at 2-, 10-, 20-, 30-, 40-, 50-, 75-and 100m depths.

Nutrients

Samples for the determination of nutrients were collected in 100-ml polyethylene bottles and kept continuously under deep freeze ( À 20 jC) until analysis in the laboratory by a ALPKEM Flow Solution III, autoanalyser. Phosphate was measured on board, by a Perkin Elmer Lambda 2S UV/VIS Spectrometer. The methods described by [START_REF] Murphy | A modified solution method for determination of phosphate in natural waters[END_REF] for phosphate, [START_REF] Mullin | The colorimetric determination of silicate with special reference to sea and natural waters[END_REF] for silicate, [START_REF] Shinn | A colorimetric method for the determination of nitrite[END_REF] and [START_REF] Strickland | A practical handbook of sea water analysis[END_REF] for nitrite and nitrate were employed. The precision is estimated at F 0.02 AM for phosphate and F 0.1 AM for nitrate and silicate.

Phytoplankton biomass and production measurements

Water samples (2 l) were size-fractionated by separate filtration on 3.0-, 1.2-and 0.2-Am polycarbonate Millipore filters and chlorophyll-a was determined using a TURNER 00-AU-10 fluorometer [START_REF] Holm-Hansen | Fluorometric determination of chlorophyll[END_REF]. Chlorophyll-a concentrations were converted to carbon biomass using the conversion factors applied by [START_REF] Malone | Transient variations in phytoplankton productivity at the JGOFS Bermuda time series station[END_REF] for the Sargasso Sea (oligotrophic area).

Photosynthetic productivity was measured according to the in situ 14 C method of [START_REF] Steemann-Nielsen | The use of radioactive carbon 14 C for measuring organic production in the sea[END_REF]. The samples were dispensed in 250-ml transparent polycarbonate bottles (three light and one dark for each depth) and each one was inoculated with 5 ACi 14 C -NaHCO 3 and incubated at the collection depths for 2 h. The incubated samples were then size-fractionated using the same protocol as in chlorophyll-a measurements. Filters were placed in scintillation vials, acidified with 0.5 N HCL and counted in a liquid scintillation counter. Daily rates were calculated from hourly rates multiplied by the 12 h of daylight period during sampling. Euphotic zone (0 -100 m) integrated phytoplankton biomass and primary production rates were calculated by the trapezoid rule.

Samples for the identification and enumeration of larger phytoplankton cells (>5 Am) from selected sampling depths (2, 10, 20, 50, 75, 100 m) were preserved in alkaline Lugol solution. Cell abundance were determined under the inverted microscope (Utermohl, 1958) on 100 ml settled volumes.

Bacteria, nanoflagellates and microzooplankton biomass and production

Heterotrophic bacteria and heterotrophic nanoflagellates (HNAN) were counted by epifluorescence microscopy as described in [START_REF] Christaki | Nanoflagellates (mixotrophs, heterotrophs and autotrophs) in the oligotrophic Eastern Mediterranean: standing stocks, bacterivory and relationships with bacterial production[END_REF]. During the March 1997 cruise, heterotrophic nanoflagellates (HNAN) were sampled only in the S. Aegean Sea. Bacterial abundance data were converted into biomass using a conversion factor of 20 fg C cell À 1 [START_REF] Lee | Relationships between biovolume and biomass of naturally derived marine bacterioplankton[END_REF]. Biovolume-carbon conversion for flagellates was 220 fg C Am À 3 [START_REF] Børsheim | Cell volume to cell carbon conversion factors for a bacterivorous Monas sp. enriched from seawater[END_REF]. Bacterial production (BP) was estimated by the 3 H-leucine method [START_REF] Kirchman | Leucine incorporation as a measure of biomass production by heterotrophic bacteria[END_REF]; for more details see [START_REF] Van Wambeke | Heterotrophic bacterial production in the Cretan Sea (NE Mediterranean)[END_REF][START_REF] Christaki | Nanoflagellates (mixotrophs, heterotrophs and autotrophs) in the oligotrophic Eastern Mediterranean: standing stocks, bacterivory and relationships with bacterial production[END_REF]. Bacterial grazing by HNAN was measured based on ingestion of fluorescent-labelled minicells [START_REF] Pace | Planktonic community structure determines the fate of bacterial production in a temperate lake[END_REF][START_REF] Christaki | Nanoflagellates (mixotrophs, heterotrophs and autotrophs) in the oligotrophic Eastern Mediterranean: standing stocks, bacterivory and relationships with bacterial production[END_REF]. HNAN production was calculated as biomass  growth rate (equal to 0.17 day À 1 , [START_REF] Kuosa | Bacteria and heterotrophic flagellates in the pelagic carbon cycles in the northern Baltic Sea[END_REF]. The carbon demand of HNAN was calculated assuming a growth yield of 33% [START_REF] Hansen | Zooplankton grazing and growth: scaling within the 2 -2000 Am body size range[END_REF].

Ciliates were counted under a fluorescenceinverted microscope [START_REF] Pitta | Planktonic ciliates in the oligotrophic Eastern Mediterranean: vertical, spatial distribution and mixotrophy[END_REF]. Biovolume-carbon conversion for ciliates was 0.14 pg C Am À 3 [START_REF] Putt | An experimentally determined carbon volume ratio for marine oligotrichous ciliates from estuarine and coastal waters[END_REF]) Their production was calculated as biomass  growth rate (0.55 day À 1 ) and the carbon demand was calculated assuming a growth yield of 33% [START_REF] Hansen | Zooplankton grazing and growth: scaling within the 2 -2000 Am body size range[END_REF].

Integrated values from surface to 100 m for bacteria, HNAN and ciliates biomass and production rates were calculated by the trapezoid rule also.

Mesozooplankton biomass, copepod production and grazing

Mesozooplankton samples were collected at 0 -50 and 50 -100 m layers by vertical haul of a WP-2 net (200 Am) during daytime. Biomass was estimated from dry weight values, which were converted into carbon units assuming a 40% carbon content [START_REF] Gorsky | C and N composition of some northwestern Mediterranean zooplankton and micronekton species[END_REF]. Samples were collected at 0 -100 m by the above net during daytime for the estimation of copepod production and in midday and midnight for grazing. Egg production rates of the dominant calanoid copepods were determined by incubation of fertilised females. For the final estimation of copepod production, lengths (copepods and eggs) were converted to body carbon [START_REF] Hopcroft | Production of tropical copepods in Kingston Harbour, Jamaica: the importance of small species[END_REF] and production was estimated from biomass and weight-specific egg production rates, by assuming that those rates are representative for juvenile specific growth rates [START_REF] Berggreen | Food size spectra, ingestion and growth of copepod Acartia tonsa: implications for the determination of copepod production[END_REF]. The carbon demand of the copepod community was estimated from production rates by assuming one-third gross growth efficiency [START_REF] Kiorboe | Bioenergetics of the planktonic copepod Acartia tonsa: relation between feeding, egg production and composition of specific dynamic action[END_REF][START_REF] Peterson | Rates of egg production by the copepod Calanus marshallae in the laboratory and in the sea off Oregon, USA[END_REF]. Copepod grazing on autotrophs was estimated from gut fluorescence measurements made according to the method described by [START_REF] Mackas | Fluorescence analysis of zooplankton gut contents and an investigation of diel feeding patterns[END_REF], as modified by [START_REF] Morales | Estimates of ingestion in copepod assemblages: gut fluorescence in relation to body size[END_REF]. The gut evacuation rate was calculated according to [START_REF] Dam | The effect of temperature on the gut clearance rate constant of planktonic copepods[END_REF].

Differences among areas and seasons, regarding biomass and production data, were tested by a oneway ANOVA (area or season) and differences were considered as significant at p < 0.05. The Tukey test was employed for comparison among areas or seasons.

Results

Hydrology

Fig. 2 sums up the water-column structure in the regions of the Northeast, North and South Aegean during the cruises of March and September 1997. As expected, the South Aegean (station S1) exhibited very small salinity differences from the surface down to 300 m. The summer stratification was attributed entirely to the shallow seasonal thermocline. The surface mixed layer is about 30 m thick in September, characterised by high temperature and salinity due to increased evaporation, while it extended down to 130 m in March. Thus, we expect that there was no significant upwelling of nutrient-rich TMW waters into the euphotic zone in March. In contrast to the South Aegean, the Northeast Aegean (station N4) is highly stratified, characterised by the presence of a 20-m-thick surface layer of modified BSW. The depths below 100 m are occupied by LIW, while a mixture of LIW and BSW occupied the layer between 20 and 100 m. Finally, the North Aegean station (station N2) was characterised by the dispersion of the BSW signal downwards through vertical diffusion, and thus the dilution of the LIW layer to lower salinities down to a depth of 200 m in the winter and 100 m in the summer, a period of higher stratification.

Thus, in terms of the structure of the water column, three distinct areas have been identified in the Aegean Sea during the study period. The upper 300-m part of the water column has a two-layer nature in the Northeast Aegean (NEA: stations N4 and N5), with a layer of water from the Black Sea overlying water of Levantine origin. The South Aegean (SA: stations S1, S2, S3, S6, S7) was almost homogeneously occupied by the latter water mass. The North Aegean (NA: stations N1, N2, N3, N6, and N7) had a transitional character between the two cases, as the twolayer nature of the Northeast Aegean was eroded due to vertical diffusion processes.

Nutrients

The nutrient (nitrates and phosphates) concentrations in the surface layer were similar among NEA, NA and SA (Fig. 3 for nitrates). In the layer above 200 m, nitrates varied between 0.05 and 1.6 AM in NEA and NA, and 0.05 and 2.5 AM in SA. The range of phosphates was 0.02 -0.08 and 0.02-0.06 AM for the NEA -NA and SA, respectively. The seasonal variability was low in the whole Aegean Sea. The small increase of nutrients in the NEA and NA during March is related to winter mixing. In SA, the core of the ''nutrient rich -oxygen poor'' Transitional Mediterranean Water (TMW) lied between 200 and 300 m and the layer above 150 m was found poor in nutrients during March 1997, since the vertical mixing did not reach the nutrient-rich intermediate layer (200 -300 m) and could not contribute to the productivity, supplying nutrients in the surface layer. The nutrient-poor surface layer was found shallower in September (100 m).

Distribution of plankton biomass

Generally, the planktonic biomass was found higher in NEA when compared to NA and SA in both seasons (Table 1), but differences were statistically significant only in March ( p = 0.011). Although val-ues were higher in March than in September in all studied areas, differences among seasons were not significant.

Autotrophs

During March 1997 the integrated to 100-m autotrophic biomass was 2568 mg C m À 2 in NEA, 1621 mg C m À 2 in NA and 1488 mg C m À 2 in SA, but differences among areas were not significant. Smallsize cells ( < 3 Am) dominated the autotroph population (80% of biomass in NEA, 81% in NA and 57% in SA, Table 1). In NEA and NA, the majority of nanophytoplankton cells were < 10 Am (in terms of abundance), whereas in SA most of them were larger than 10 Am. Furthermore, in the latter area Coccoli- thophorids and Diatoms constituted the 46% and 40% (in terms of abundance), respectively, of the large size nanophytoplankton and microphytoplankton, whereas the contribution of Coccolithophorids was higher in NA (65%) and in NEA (53%) than in SA. It is important to stress that differences among areas regarding biomass were more important in the upper 0-50 m layer (Fig. 4) and the size distribution of autotrophic biomass was more or less similar between the upper (0-50 m) and the lower (50 -100 m) layers.

In September 1997, the biomass in the 0 -100 m column was found higher in NEA (1607 mg C m À 2 ) than in NA (1072 mg C m À 2 ) and in SA (645 mg C m À 2 ), but the difference was significant only between NEA and SA ( p = 0.002). During this period a clear difference between the upper (0-50 m) and lower (50 -100 m) layers was detected only in SA, with most of the biomass found in the lower layer (Fig. 4). The contribution of the smaller cells was very important in all areas (72 -82%) (Table 1), whereas most of the larger cells (in terms of abundance) were >10 Am in all areas. The size distribution of biomass did not vary between layers in NEA and NA, whereas in SA the contribution of the larger cells was found more important in the lower layer (Fig. 4).

The autotroph biomass in the 0-100 m column was lower in September than in March in all areas, but the seasonal difference was significant only for NA and SA ( p = 0.03 and p = 0.0003, respectively).

Heterotrophs

In March, the biomass of heterotrophs was higher in NEA (2421 mg C m À 2 ) than in NA (1847 mg C m À 2 ) and SA (1886 mg C m À 2 , Table 1). Bacteria constituted the largest fraction of the heterotrophic carbon and their biomass did not vary significantly among areas. Although ciliate biomass was very low in all areas, it was significantly higher in SA than in NA and NEA ( p = 0.0008). In contrast, mesozooplankton biomass decreased significantly from NEA to NA and even more to SA ( p = 0.007). In terms of abundance copepods constituted the bulk of mesozooplankton (93% in NEA, 81% in NA and 74% in SA) and the presence of appendicularians was quite important in SA (10%) and in NA (6%). It is worth mentioning that if we consider the integrated values in the 0 -50 and 50 -100 m layers separately, total heterotrophic biomass was higher in the upper than the lower layer (Fig. 5). Within the upper layer, there was gradual decrease of heterotrophic biomass from H/A = ratio of heterotrophs over autotrophs, mH = microheterotrophs (bacteria + heterotrophic nanoflagellates + ciliates). (%): Percentage of the biomass of each group compared to total heterotrophs or total autotrophs. In March there was no sampling for nanoflagellates in the North Aegean Sea.

Abundance (mean integrated to 100m) of bacteria (cells  10 8 l À 1 ), heterotrophic nanoflagellates (cells  10 6 l À 1 ), ciliates (cells l À 1 ), mesozooplankton (ind m À 3 ). NEA = Northeast Aegean, NA = North Aegean, SA = South Aegean.

NEA to NA and SA, due to mesozooplankton biomass decrease. In the lower layer heterotrophic biomass was slightly higher in SA than in NA and NEA and interestingly mesozooplankton biomass decreased from NEA to SA accompanied by a parallel increase of bacteria.

In September 1997, heterotrophic biomass was found increasing from NEA (1907 mg C m À 2 ) to NA (2126 mg C m À 2 ) and SA (2537 mg C m À 2 ) due to the contribution of bacteria, heterotrophic nanoflagellates (HNAN) and ciliates (Table 1). Bacterial contribution was high in all areas, representing 59 - 69% of the total heterotrophs. The contribution of HNAN was lower than that of bacteria and even more than that of ciliates (1-3% in all areas). Interestingly, ciliate biomass was significantly higher in SA than in NEA and NA ( p = 0.01). The biomass of mesozooplankton decreased significantly from NEA towards SA ( p = 0.02), as well as its contribution to total heterotrophic biomass. On considering the distribution of heterotrophic biomass in the 0-50 and 50-100 m layers, the upper layers showed no differences, but the lower layer exhibited an increase from NEA and NA to SA due to bacteria and HNAN contribution (Fig. 5). During this period, cladocerans were abundant (especially Penilia avirostris) in NEA (12% of total mesozooplankton abundance) and NA (19%); in the same areas the presence of appendicularians was important (20% and 7%, respectively, in terms of abundance).

During September, the heterotrophic biomass was lower than March in NEA, but higher in SA. Bacterial biomass was not different to that found in March in the whole region, whereas mesozooplankton was lower in September than in March, but seasonal difference was significant only in NA ( p = 0.008).

Carbon partitioning

The partitioning of carbon among the different plankton compartments was not a broad-based pyramid where autotrophs' biomass overwhelms that of heterotrophs, in all areas and in both seasons (Table 1, Fig. 6). During March the ratio of total heterotrophs/ autotrophs was 0.9 in NEA, 1.1 in NA and 1.3 in SA, due to the importance of microheterotrophs (Bacteria + HNAN + Ciliates), especially in SA. The contribution of autotrophs in the plankton biomass was lower in September and the heterotrophs/autotrophs ratio was 1.2 in NEA, 2 in NA and 3.9 in SA. These values imply a gradual decreasing contribution of autotrophs and a parallel increasing contribution of heterotrophs from NEA to SA, which was due to microheterotrophs.

Planktonic production and carbon flow

In March 1997, primary production was found decreasing significantly ( p = 0.0018) from NEA (2339 mg C m À 2 day À 1 ) to NA (1406 mg C m À 2 day À 1 ) and SA (574 mg C m À 2 day À 1 ) (Table 2). Almost 77% and 73% of primary production was due to cells < 3 Am in NEA and NA, respectively, whereas in SA this contribution was lower (60%). In NEA, primary production was higher in the upper (0 -50 m) layer than in the lower layer (50 -100 m) and this difference was small in NA and even smaller in SA. Bacterial production was higher in NEA than in the other areas but differences were not significant. This production was very low as compared to PP as it is clear from the ratio BP/PP: 0.03 -0.13. Copepod production was found significantly higher in the NEA than in NA and SA.

No difference was detected among areas regarding primary production in September and the contribu-tion of the smaller cells was found decreasing from NEA to SA. In NEA and NA primary production was slightly higher in the upper than in the lower layer, but there was no difference among layers in SA. Low values of bacterial production were detected during this period in all areas (58 -71 mg C m À 2 day À 1 ). The BP/PP ratio values were higher during this period (0.24 -0.32) due to the important Fig. 7. Carbon budgets in March 1997. Numbers inside boxes show biomasses (mg C m À 2 ); numbers inside ellipses show carbon demands (mg C m À 2 day À 1 ); numbers below the white arrows show productions (mg C m À 2 day À 1 ) and above black arrows show consumption (mg C m À 2 day À 1 ). decrease of primary production. A decrease of copepod production was found from NEA (58 mg C m À 2 day À 1 ) to NA (15 mg C m À 2 day À 1 ) and SA (6 mg C m À 2 day À 1 ). Primary, bacterial and copepod productions were lower in September than in March, but seasonal difference was significant only for primary production.

Carbon stocks and flux within autotrophs (cells < 3 Am and cells>3 Am), bacteria, heterotrophic nanoflagellates, ciliates and copepods were established for the investigated areas and seasons. Values presented in the figures are average values for all stations of each sampling area. (Figs. 7 and8). Standing stocks (and rates) are presented as mg C m À 2 (day À 1 ).

During March 1997, the carbon budget suggests a balance between prey and predators in all areas, except in SA, where bacterial production could not satisfy the carbon demand of HNAN. However, this demand could be met also by the primary production of cells < 3 Am. Copepods have been found to consume a greater proportion of the larger cell production in SA (14%) than in NEA (6.2%) and NA (1%).

Similarly, in September there is a balanced carbon budget in all areas, with two exceptions: in NEA the carbon demand of copepods could be roughly balanced by the production of the larger autotrophs together with that of ciliates. In SA, the bacterial production together with the smaller cell primary production is not enough for the carbon demand of HNAN. In this region, copepods consumed 35% of the larger cell production. Overall there was a lower availability of food to all trophic levels in September than in March.

In both seasons there was a greater availability of prey to the microheterotroph carbon demand in NEA than in SA; in contrast, there was a lower availability of prey to copepod carbon demand in NEA than in SA. It is important to stress that the measured HNAN bacterivory matched more or less the bacte- rial production in all studied areas [START_REF] Christaki | Nanoflagellates (mixotrophs, heterotrophs and autotrophs) in the oligotrophic Eastern Mediterranean: standing stocks, bacterivory and relationships with bacterial production[END_REF].

Discussion

4.1. Trophic state of the study area and carbon partitioning within the food web Our findings suggest that the Aegean Sea is an area of low nutrients, plankton biomass and production, as the rest of the eastern Mediterranean Sea [START_REF] Berman | Optical transparency, chlorophyll and primary productivity in the Eastern Mediterranean near the Israeli coast[END_REF][START_REF] Krom | Nutrient distribution during an annual cycle across a warm-core eddy from the E. Mediterranean Sea[END_REF][START_REF] Robarts | Bacterial abundance, biomass, and production in relation to phytoplankton biomass in the Levantine Basin of the southeastern Mediterranean Sea[END_REF][START_REF] Mazzocchi | Mesozooplankton distribution from Sicily to Cyprus (Eastern Mediterranean): I. General aspects[END_REF]. Besides the confirmation of the oligotrophic character of the area, in terms of both biomass and production, they also revealed another interesting feature: the existence of a North -South gradient within this oligotrophic region.

The concentration of nutrients in the surface layer of NEA and NA is of the same low level as in SA. According to [START_REF] Polat | Chemical exchange between the Mediterranean and the Black Sea via the Turkish straits. Dynamics of Mediterranean Straits and Channels[END_REF], the annual mean concentration of dissolved inorganic nitrogen (DIN) in the surface water outflowing from Bosphorus is 1.81 AM and that of dissolved inorganic phosphorus (DIP) is 0.11 AM. However, in the surface water outflowing from Dardanelles, the annual mean concentrations of DIN and DIP are low (0.48 and 0.09 AM, respectively) and these values are very close to those detected during the present study. They also found that the Black Sea surface outflow reaches the Aegean basin with large dissolved organic nitrogen (DON) and dissolved organic carbon (DOC) values, due to their low decay rates, but with a net decrease in the mean nitrate value due to consumption in the Marmara basin. The significantly higher total organic carbon (TOC) values, observed in the low salinity upper layer of NEA relatively to those of SA [START_REF] Sempere | Total organic carbon dynamics in the Aegean Sea[END_REF], confirm the above findings.

Although nutrients showed similar values all over the studied area, there is a gradual differentiation of the planktonic community from the Dardanelles strait (Northeast Aegean) to the Cretan Arc straits (South Aegean). Autotrophic biomass decreased from NEA to NA and SA in both seasons as well as primary production during March, suggesting an increasing trend of oligotrophy from the area influenced by the Black Sea Water (NEA) towards the deep basin of the South Aegean Sea. This inverse correlation between nutrients and autotrophs (low nutrient concentrations -high biomass), regarding the North Aegean, might be characterised as ''an anomalous interrelationship'' that has been recorded previously in the Gulf of California [START_REF] Hernandez-Becerril | Vertical distribution of phytoplankton in the Central and Northern part of the Gulf of California (June 1982)[END_REF]. However, nitrate and ammonium uptake experiments performed in the framework of the project have shown elevated new production values during March 1997: NO 3 uptake rates about 10 times higher at NEA than NA or SA (Psarra, unpublished data). These features suggest an input of new nutrients in the euphotic zone from the Black Sea, which enrich the area, but are very rapidly assimilated and thus significantly reduced.

Similar spatial pattern (decrease from NEA to NA and SA) was observed for mesozooplankton biomass and copepod production, suggesting the existence of the oligotrophy gradient within the Aegean Sea even in the higher trophic levels. Since this differentiation was detected mainly in the 0-50 m layer, it can be attributed to the influence of the BSW, which occupies the upper 20 m of the water column.

Bacterial biomass was more or less horizontally homogeneous in March 1997, but in September it showed, together with that of HNAN, an increase from NEA to NA and SA. Ciliates exhibited a similar pattern from NEA to SA in both seasons. Overall the ratios Heterotrophs/Autotrophs and Microheterotrophs/Autotrophs were high and increasing from NEA to SA. They were also higher in September compared to March. The H/A values found in the SA were close to those found in the Sargasso Sea (and with a similar seasonal variability), where the microbial loop has been found dominant [START_REF] Roman | Spatial and temporal changes in the partitioning of organic carbon in the planktonic community of the Sargasso sea off Bermuda[END_REF]. These findings imply that microheterotrophs play a very important role in the whole Aegean Sea and this role seems to be more significant in SA than in NA and NEA, whereas on a temporal scale, it is more important during the stratification period (September) than during the mixing period (March).

In the Aegean Sea, most of the autotrophic carbon biomass (almost 80%) and primary production (75%) was due to small-sized cells ( < 3 Am), except in the SA during March. It appears as though this smaller cell autotrophic biomass can support an equal (during March) or greater (during September) heterotrophic biomass, which becomes more important from NEA to SA. It is assumed that more photosynthetic DOC would flow to the microbial loop if nano-and picophytoplankton dominate the phytoplankton community [START_REF] Huang | Size-fractionated primary productivity and the phytoplankton -bacteria relationship in the Taiwan strait[END_REF].

Trophic pathways and carbon flow

The bacterial to primary production (BP/PP) ratio was found to be lower in NEA and NA than in SA in March (0.05 and 0.03 versus 0.13), whereas in September this ratio was generally higher (0.23 -0.32). In the Levantine Basin in September, [START_REF] Robarts | Bacterial abundance, biomass, and production in relation to phytoplankton biomass in the Levantine Basin of the southeastern Mediterranean Sea[END_REF] reported BP/PP ratios of 0.55. [START_REF] Turley | Relationship between primary producers and the bacteria in an oligotrophic sea-the Mediterranean and biochemical implications[END_REF] found that bacterial production comprised 34% of the integrated primary production in the eastern Mediterranean Sea and 21% in the western Mediterranean Sea. They have hypothesised that a greater proportion of the primary production may flow to bacteria in the eastern Mediterranean Sea than in the western Mediterranean Sea and consequently a smaller amount of production is left for the higher trophic levels in the former area than in the latter. Based on the above hypothesis, we could assume that the observed lower BP/PP ratio in NEA and NA than in SA during March suggests that the same difference seems to exist in the Aegean Sea at a N -S direction; thus a higher proportion of primary production is left for the higher trophic levels in NEA than in SA.

However, it is also worth recalling that the increase of this ratio from March to September could reflect temporal variations, as more phytoplankton-derived material is available for bacteria during post-bloom situations; it also reflects the lag between peaks of primary and bacterial production, as it was described in a temporal survey in western Mediterranean [START_REF] Van Wambeke | Small-scale variability in the coupling/uncoupling of bacteria, phytoplankton and organic carbon fluxes along the con-tinental margin of the Gulf of Lions, Northwestern Mediterranean Sea[END_REF] and the eastern basin [START_REF] Van Wambeke | Heterotrophic bacterial production in the Cretan Sea (NE Mediterranean)[END_REF]. A similar seasonal variability of the BP/PP as that observed in the Aegean Sea, has been detected in the Sargasso Sea [START_REF] Roman | Spatial and temporal changes in the partitioning of organic carbon in the planktonic community of the Sargasso sea off Bermuda[END_REF].

Clearly, many different mechanisms may account for the BP/PP ratio variability. Local temporal changes can be caused spatial short-scale variability as, for example, frontal structures [START_REF] Ferna `ndez | Bacterial biomass, heterotrophic production and utilisation of dissolved organic matter photosynthetically produced in the Almeria -Oran front[END_REF][START_REF] Yoro | Cross-slope variations of organic carbon and bacteria in the Gulf of Lions in relation to water dynamics (northwestern Mediterranean)[END_REF] or allochthonous detritus or DOC advection in coastal waters [START_REF] Pedros-Alio | Bacterioplankton and phytoplankton biomass and production during summer stratification in the northwestern Mediterranean Sea[END_REF][START_REF] Van Wambeke | Small-scale variability in the coupling/uncoupling of bacteria, phytoplankton and organic carbon fluxes along the con-tinental margin of the Gulf of Lions, Northwestern Mediterranean Sea[END_REF]; such phenomena may contribute to measuring a greater variability of this ratio in Mediterranean [START_REF] Conan | Relationship between phytoplankton efficiency and the proportion of bacterial production to primary production in the Mediterranean Sea[END_REF].

The whole study area was dominated by smallsized ( < 3 Am) primary producers, which are not grazed by copepods but are rather consumed by nanoand microheterotrophs; therefore, most of the fixed carbon flows through the microbial food web [START_REF] Legendre | Plankton and nutrient dynamics in marine waters[END_REF]. However, the contribution of the small autotrophs was found decreasing from NEA towards SA, accompanied by a parallel increase of microheterotrophs, suggesting a differentiation in the trophic pathways and the carbon flow between these areas.

The very low grazing pressure of copepods on primary production during March in NA and NEA when compared to SA, suggests that copepods could not efficiently consume microphytoplankton in the former areas. This could be due to the small size ( < 10 Am) of most microphytoplankters found in NEA and NA, since copepods feed better on >10-Am particles [START_REF] Fortier | Export of biogenic carbon to fish and to deep ocean: the role of large planktonic microphages[END_REF][START_REF] Nielsen | Plankton community structure and carbon cycling on the western coast of Greenland during and after the sedimentation of a diatom bloom[END_REF]. The above findings related to the very low ciliate biomass in NEA and NA, confirms the hypothesis of the strong top -down control of the ciliate population in these regions [START_REF] Pitta | Planktonic ciliates in the oligotrophic Eastern Mediterranean: vertical, spatial distribution and mixotrophy[END_REF]; low concentrations of ciliates may be due to copepod predation [START_REF] Nielsen | Effects of a storm on the structure of the pelagic food web with special emphasis on planktonic ciliates[END_REF][START_REF] Bradford-Grieve | Pelagic ecosystem structure and functioning in the Subtropical Front region east of New Zealand in austral winter and spring 1993[END_REF][START_REF] Levinsen | Plankton community structure and carbon cycling on the western coast of Greenland during the stratified summer situation: II. Heterotrophic dinoflagellates and ciliates[END_REF]. In contrast, in SA the observed higher biomass of ciliates could be attributed to a weaker grazing control by mesozooplankters. The differences detected in mesozooplankton and ciliate community composition within the Aegean Sea (Siokou-Frangou et al., manuscript in preparation;[START_REF] Pitta | Planktonic ciliates in the oligotrophic Eastern Mediterranean: vertical, spatial distribution and mixotrophy[END_REF], probably may account for the different among areas grazing pressure on ciliates, due to prey selectivity.

According to the classification of the trophic pathways given by [START_REF] Legendre | Plankton and nutrient dynamics in marine waters[END_REF], a microbial food web (as a more open system than the microbial loop which also includes autotrophic picoand nanoplankton) seems to have been developed during March 1997 in NEA and NA. During the stratification period (September) the same pathways were important, but no information is available for the grazing pressure of copepods on phytoplankton in the above regions. Interestingly this pressure was found significant in SA during March and much more in September. Therefore, in the latter area the food web could be classified as multivorous (where herbivorous and microbial grazing modes both have significant roles).

Besides copepods, appendicularians contributed significantly in the mesozooplankton community in March (mainly in SA), as well as in September (mainly in NEA and NA). Appendicularians can feed on small particles ( < 15 Am) [START_REF] Alldredge | The impact of appendicularian grazing on natural food concentrations in situ[END_REF] and present high grazing rates [START_REF] Hopcroft | Zooplankton growth rates: extraordinary production by the larvacean Oikopleura dioica in tropical waters[END_REF]. The cladoceran P. avirostris, which was found dominant among mesozooplankton in NEA and NA during September, appears to be adapted for suspension feeding on particles as small as 2 Am [START_REF] Turner | The marine clado-ceran Penilia avirostris and the ''microbial loop'' of pelagic food webs[END_REF]. Thus, although the carbon demand and the production of appendicularians and cladocerans have not been estimated, one can hypothesise that during September in NEA and NA, a non-negligible portion of heterotrophic and autotrophic picoplankton and nanoplankton production was be harvested by mesozooplankton. This indicates that during the period of low primary production (September) more efficient pathways were developed within the food web in NEA and NA. The reduction of mass transfer through intermediate populations of microbial predators is of considerable bioenergetic significance, since these mesozooplankters can mediate the microbial biomass directly to fish [START_REF] Deibel | Retention efficiency of sub-micrometer particles by the pharyngeal filter of the pelagic tunicate Oikopleura vanhoeffeni[END_REF][START_REF] Fortier | Export of biogenic carbon to fish and to deep ocean: the role of large planktonic microphages[END_REF]. Moreover, appendicularians and cladocerans present a high population growth rate under favourable conditions [START_REF] Gorsky | Aspects de la biologie de l'appendiculaire Oikopleura dioica Fol 1872 (Chordata, Tunicata)[END_REF][START_REF] Egloff | Reproductive biology of marine cladocerans[END_REF] and they could contribute to a high carbon turnover in NEA and in NA. This assumption together with the observed differences in the mesozooplankton biomass and copepod production among areas suggest that there is a better transfer of energy towards mesozooplankters in NEA than in NA and SA. Therefore, one could assume that this difference in the degree of energy transfer will be reflected in the higher trophic levels: fish [START_REF] Stergiou | The Hellenic seas: physics, chemistry, biology and fisheries[END_REF] and benthos [START_REF] Lykousis | Major outputs of the recent multi-disciplinary biogeochemical researches in the Aegean Sea[END_REF]. The lower grazing pressure of copepods and greater availability of the smaller autotrophs to microheterotroph carbon demand in NEA than in SA suggest that there will be a greater export of primary production from the euphotic zone in NEA than in SA. [START_REF] Lykousis | Major outputs of the recent multi-disciplinary biogeochemical researches in the Aegean Sea[END_REF] have found higher fluxes of labile POC in the Northern part of the Aegean Sea than in the Southern.

Conclusions

The results of this study confirm our hypothesis that there is a spatial differentiation within the Aegean Sea regarding the pelagic food web structure and the carbon flow within it. This differentiation both in terms of biomass and production, was expressed along a gradient of oligotrophy from the Northeast Aegean Sea (NEA), which is influenced by the BSW, towards the South Aegean Sea (SA). The microbial food web seems to have been developed in NEA and NA, whereas a multivorous food web was found in SA. Most of the carbon is fixed by small-sized cells, but the energy transfer is variable along the oligotrophy gradient. In NEA, a large part of the fixed carbon is channelled through the microbial food web towards copepods during both seasons, while during the stratification period it seems that a portion of the microbial biomass is removed by cladocerans and appendicularians, suggesting a more efficient energy transfer. In contrast, the energy transfer towards copepods in SA is low in both seasons, while an intermediate flow seems to exist in NA.

Fig. 1 .

 1 Fig. 1. Sampling stations.

Fig. 2 .

 2 Fig. 2. Salinity (upper panel) and potential temperature (lower panel) profiles down to 100 m, for stations characteristic of the Northeast (N4), North (N2) and South Aegean (S1) for March 1997 (left) and September 1997 (right) are presented.

Fig. 3 .

 3 Fig. 3. Nitrate (AM) distribution in the sampling areas in March and September 1997.

Fig. 4 .

 4 Fig. 4. Biomass distribution of autotrophs in the 0 -50 and 50 -100 m layers during March and September 1997.

Fig. 5 .

 5 Fig. 5. Biomass distribution of heterotrophs in the 0 -50 and 50 -100 m layers during March and September 1997 (BACT = bacteria, HNAN = heterotrophic nanoflagellates, CIL = ciliates, MESO = mesozooplankton).

Fig. 6 .

 6 Fig. 6. Carbon partitioning among auto-and heterotrophs in March and September 1997. Auto: autotrophic biomass; BACT: heterotrophic bacteria; HNAN: heterotrophic nanoflagellates; CIL: ciliates; MESO: mesozooplankton.

Fig. 8 .

 8 Fig. 8. Carbon budgets in September 1997.

Table 1

 1 Biomass (mg C m À 2 integrated to 100 m) of autotrophs, bacteria (BACT), heterotrophic nanoflagellates (HNAN), ciliates (CIL), and mesozooplankton (MESO)

		Area
		NEA		NA		SA
		Biomass	Abundance	Biomass	Abundance	Biomass	Abundance
	March
	Autotrophs
	0.2 -3.0 Am	2052 F 64 (80%)		1320 F 384 (81%)		856 F 311 (57%)
	>3.0 Am	516 F 142 (20%)		301 F 90 (19%)		632 F 61 (43%)
	Total autotrophs	2568 F 78		1621 F 426		1488 F 364
	Heterotrophs
	BACT	1403 F 142	8.33 F 3.93	1406 F 327	7.52 F 3.38	1423 F 43 (75%)	8.21 F 1.88
	HNAN					192 F 54 (10%)	0.34 F 0.14
	CIL	31 F 11	327 F 82	17 F 8	163 F 101	84 F 20 (4%)	417 F 111
	MESO	987 F 696	2761 F 2120	424 F 165	1056 F 789	187 F 43 (10%)	390 F 88
	Total heterotrophs	2421 F 564		1847 F 364		1886 F 94
	Total plankton	4989 F 643		3468 F 377		3374 F 423
	H/A	0.9		1.1		1.3
	mH/A	0.6		0.9		1.1
	September
	Autotrophs
	0.2 -3.0 Am	1326 F 539 (82%)		817 F 196 (76%)		465 F 64 (72%)
	>3.0 Am	281 F 76 (18%)		255 F 33 (24%)		180 F 33 (28%)
	Total autotrophs	1607 F 616		1072 F 227		645 F 81
	Heterotrophs
	Bact	1251 F 107 (66%)	6.67 F 1.82	1471 F 273 (69%)	7.14 F 3.01	1505 F 432 (59%)	6.88 F 2.57
	HNAN	301 F 31 (16%)	1.43 F 0.92	418 F 90 (20%)	0.96 F 0.37	812 F 376 (32%)	1.14 F 0.4
	CIL	18 F 1 (1%)	184 F 1	3 1 F 8 (1%)	155 F 54	65 F 24 (3%)	152 F 21
	MESO	338 F 120 (18%)	2099 F 654	207 F 38 (10%)	1594 F 411	156 F 44 (6%)	706 F 272
	Total heterotrophs	1908 F 195		2127 F 225		2538 F 607
	Total plankton	3515 F 811		3199 F 219		3183 F 577
	H/A	1.2		2		3.9
	mH/A	1		1.8		3.7

Table 2

 2 Primary, bacterial and copepod production in the study areas

		PP	BP	Copepod production	PP < 3 Am	BP/PP
		(mg C m À 2 day À 1 )	(mg C m À 2 day À 1 )	(mg C m À 2 day À 1 )	(% of total PP)
	March
	NEA	2339 F 905	110 F 77	41 F 31	76.66	0.05
	NA	1406 F 362	48 F 31	5 F 2	72.54	0.03
	SA	574 F 176	75 F 9	5 F 3	59.76	0.13
	September
	NEA	221 F 13	71	58 F 45	73.73	0.32
	NA	253 F 70	60 F 11	15 F 2	67.19	0.24
	SA	218 F 63	58 F 13	6 F 4	58.26	0.27

Acknowledgements

This work is part of the MTP-II-MATER project funded by the EU under the MAST III/MTP-II contract MASTIII-CT096-0051. The authors wish to thank the Captain and crew of R/V AEGAEO for their important contribution to the fieldwork and sampling. Two anonymous reviewers are also acknowledged for their comments and helpful suggestions.