Pascaline J Ory

Hans J Hartmann

Florence J Jude

Christine Dupuy

Yolanda Del Amo

Philippe Catala

Françoise Mornet

Valérie Huet

Jan J Benoit

Dorothée Vincent

Yolanda Del Amo

Benoit Jan

Benoit Sautour

Hélène Montanié

Pelagic food web patterns: do they modulate virus and nanoflagellate effects on picoplankton during the phytoplankton spring bloom?

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The long held paradigm that primary production is transferred through a herbivorous chain to small and large phytovores neglects the importance of most microorganisms. Yet bacteria, which are present at up to 10 6 cells.ml -1 , and viruses, whose concentration can reach 10 8 viral particles.ml -1 , dominate in the oceans [START_REF] Borsheim | Enumeration and biomass estimation of planktonic bacteria and viruses by transmission electron microscopy[END_REF][START_REF] Hennes | Direct counts of viruses in natural waters and laboratory cultures by epifluorescence microscopy[END_REF][START_REF] Li | Annual average abundance of heterotrophic bacteria and Synechococcus in surface ocean waters[END_REF]. Bacteria were primarily considered as remineralizers providing inorganic nutrients for primary producers. Later, the microbial loop concept [START_REF] Azam | The ecological role of water-column microbes in the sea[END_REF] highlighted the role of bacteria as recyclers of DOM originating either from primary production or from losses at different trophic levels. Thus, the role of bacteria in the microbial loop evolved from that of a matter sink to a trophic link towards bacterivorous organisms. Moreover, bacterial community dynamics are not only controlled by resources i.e. bottom-up control, such as the availability of inorganic nutrients and the quality of DOM [START_REF] Kirchman | Carbon versus iron limitation of bacterial growth in the California upwelling regime[END_REF], but also by predation i.e. top-down control, examples being the mortality induced by protists such as nanoflagellates and ciliates and by viral lysis. [START_REF] Sanders | Relationships between bacteria and heterotrophic nanoplankton in marine and fresh waters: an inter-ecosystem comparison[END_REF][START_REF] Fuhrman | Marine viruses and their biogeochemical and ecological effects[END_REF]. The selectivity of protist predation based on size, motility, shape and cell surface properties of prey [START_REF] Gonzalez | Size-selective grazing on bacteria by natural assemblages of estuarine flagellates and ciliates[END_REF][START_REF] Monger | Feeding selection of heterotrophic marine nanoflagellates based on the surface hydrophobicity of their picoplankton prey[END_REF] can potentially impact on the morphological characteristics of bacteria and shape their community composition (BCC) [START_REF] Hahn | Grazing of protozoa and its effect on populations of aquatic bacteria[END_REF][START_REF] Posch | Size selectivity feeding in Cyclidium glaucoma (Ciliophora, Scuticociliatida) and its effects on bacterial community structure: a study from a continuous cultivation system[END_REF]. On an ecosystem level, protist predation may exert a stronger control in oligotrophic waters than in eutrophic waters [START_REF] Gasol | Regulation of bacterial assemblages in oligotrophic plankton systems: results from experimental and empirical approaches[END_REF].

Since the 1990's, viruses have been considered as an additional factor in the mortality of bacteria [START_REF] Fuhrman | Marine viruses and their biogeochemical and ecological effects[END_REF], other micro-organisms such as microalgae [START_REF] Cottrell | Dynamics of a lytic virus infecting the photosynthetic marine picoflagellate Micromonas pusilla[END_REF] and unpigmented flagellates [START_REF] Garza | Large double-stranded DNA viruses which cause the lysis of a marine heterotrophic nanoflagellate (Bodo sp.) occur in natural marine viral communities[END_REF][START_REF] Massana | Crash of a population of the marine heterotrophic flagellate Cafeteria roenbergensis by viral infection[END_REF]. Virus-mediated bacterial mortality could account for 48-84% of bacterial production in oligotrophic areas and 59% in eutrophic areas [START_REF] Boras | Annual changes of bacterial mortality due to viruses and protists in an oligotrophic coastal environment (NW Mediterranean)[END_REF]. Regarding the biogeochemical aspects of the ecosystem, viral lysis shunts carbon flow [START_REF] Fuhrman | Marine viruses and their biogeochemical and ecological effects[END_REF]: 6 to 26% of photosynthetically fixed carbon flows back to DOM, becoming available for microbial uptake and consequently modifying the competitive balance of phytoplankton and bacteria for limiting nutrients [START_REF] Wilhelm | Viruses and nutrient cycles in the sea[END_REF]. Moreover, viral infection could potentially maintain the species richness of bacteria, as described in the "kill the winner" concept [START_REF] Thingstad | Theoretical models for the control of bacterial growth rate, abundance, diversity and carbon demand[END_REF]. The density dependence of viral infection could limit dominant and competitive bacteria and give an advantage to less competitive ones [START_REF] Bouvier | Key role of selective viral-induced mortality in determining marine bacterial community composition[END_REF][START_REF] Weinbauer | Synergistic and antagonistic effects of viral lysis and protistan grazing on bacterial biomass, production and diversity[END_REF][START_REF] Auguet | Potential effect of freshwater virus on the structure and activity of bacterial communities in the Marennes-Oléron Bay (France)[END_REF], although this idea is still controversial [START_REF] Winter | Impact of virioplankton on archaeal and bacterial community richness as assessed in seawater batch cultures[END_REF]. In addition, host specificity could concern strains instead of species because of the phenotype plasticity of the viral permissivity-resistance characters [START_REF] Schwalbach | Viral effects on bacterial community composition in marine plankton microcosms[END_REF][START_REF] Fuhrman | Microbial community structure and its functional implications[END_REF].

Even if virioplankton is mainly composed of bacteriophages [START_REF] Fuhrman | Marine viruses and their biogeochemical and ecological effects[END_REF][START_REF] Suttle | Viruses in the sea[END_REF], a link with phytoplankton or chlorophyll a concentrations, suggesting phytophage pathogenicity [START_REF] Wommack | Virioplankton: Viruses in aquatic ecosystems[END_REF], has already been underlined in some long term surveys where there were high densities of algal cells [START_REF] Maranger | Viral and bacterial dynamics in Arctic sea ice during the spring algal bloom near Resolute, N.W.T., Canada[END_REF] as well as in offshore stations [START_REF] Boehme | Viruses, bacterioplankton, and phytoplankton in the southeastern Gulf of mexico: distribution and contribution to oceanic DNA pools[END_REF][START_REF] Guixa-Boixereu | Distribution of viruses and their potential effect on bacterioplankton in an oligotrophic marine system[END_REF] or in coastal regions at the deep chlorophyll a maximum [START_REF] Baudoux | Estimates of virus-vs. grazing induced mortality of picophytoplankton in the North Sea during summer[END_REF]. Organisms less than 3µm in size may be responsible of large part of primary production, in particular in oligotrophic areas [START_REF] Platt | Photosynthesis of picoplankton in the oligotrophic ocean[END_REF]. Autotrophic as well as heterotrophic pico-and nanoplankton are too small to be efficiently consumed by metazoans [START_REF] Sherr | Role of microbes in pelagic food webs: A revised concept[END_REF]; they are mainly consumed by flagellates and ciliates [START_REF] Sherr | Bacterivory and herbivory: Key roles of phagotrophic protists in pelagic food web[END_REF] and can significantly contribute to their diet [START_REF] Hirose | Growth and grazing mortality rates of Prochlorococcus, Synechococcus and eukaryotic picophytoplankton in the bay of the Uwa Sea, Japan[END_REF]. However, the grazing yield from phytoplankton is lower than from bacteria. Furthermore, predation on protists indirectly affects bacterial grazing, enhancing viral impact [START_REF] Boras | Annual changes of bacterial mortality due to viruses and protists in an oligotrophic coastal environment (NW Mediterranean)[END_REF]. Thus, as the preferential prey of copepods [START_REF] Nielsen | Regulation of zooplankton biomass and production in a temperate, coastal system. 2. Ciliates[END_REF], ciliates can be considered as a trophic link between picoand nano-plankton and mesozooplankton (superior trophic levels) [START_REF] Calbet | The ciliate-copepod link in marine ecosystems[END_REF].

To assess the importance of microbiological plankton compartments in matter flow, [START_REF] Legendre | Plankton and nutrient dynamics in marine waters[END_REF] described 4 major trophic concepts each incorporating different dominant carbon pathways: (1) The herbivorous or classical food web, which is based on large-phytoplankton (>5µm) primary production leading to large zooplankton organisms. (2) The microbial food web, in which primary production is mainly due to small phytoplankton and comprises pathways involving heterotrophic bacteria and protozoa. (3) The microbial loop, a nearly closed system where carbon flows are limited to heterotrophic bacteria and unpigmented protists; DOM released by the grazers becomes substrate for bacteria. (4) Finally, the multivorous food web, a transitional trophic system comprising diverse heterotrophic carbon pathways in which both large and small phytoplankters are significant primary producers.

Accordingly, the question arises: do the interactions between microbial compartments (in particular between viruses, picoplankton and nanoflagellates) vary depending on the trophic pathway? So far, despite the clear importance of heterotrophic microorganisms in pelagic food webs, descriptions of trophic models have mostly been based on primary producer size, water column stratification and nutrients [START_REF] Rivkin | Vertical flux of biogenic carbon in the ocean: Is there food web control?[END_REF][START_REF] Marquis | Inverse analysis of the planktonic food web dynamics related to phytoplankton bloom development on the continental shelf of the Bay of Biscay, French coast[END_REF]. Thus, to answer this question, two sites along the French Atlantic coast that are distinctive in terms of their river inputs and hydrology (shape, mixing regime and water residence time) were chosen as study case areas to describe the temporal succession of trophic pathways. The temporal dynamics of abiotic parameters and microbiological compartments were assessed over 7 months in Arcachon Bay in 2005 and over one year in Marennes Oléron Bay in 2006. Located North-Southward along the coast of the Bay of Biscay, Arcachon and Marennes-Oléron Bays are both semi-enclosed and macrotidal lagoons, largely composed of intertidal mudflats [START_REF] Blanchet | Use of biotic indices in semi-enclosed coastal ecosystems and transitional waters habitats-Implications for the implementation of the European Water Framework Directive[END_REF][START_REF] Allard | A synthesis of morphological evolutions and Holocene stratigraphy of a wave-dominated estuary: The Arcachon lagoon, SW France[END_REF]. Like many estuaries and bays, the 2 basins are highly productive, featuring a large-phytoplankton spring bloom [START_REF] Struski | Preliminary estimate of primary production by phytoplankton in Marennes-Oléron Bay, France[END_REF][START_REF] Glé | Variability of nutrients and phytoplankton primary production in a shallow macrotidal coastal ecosystem (Arcachon Bay, France)[END_REF]. In this context, does the large-phytoplankton spring bloom entail similar trophic pathways in each bay and thus similar interactions between the above-mentioned microbial compartments? To answer this question, we first defined the trophic succession for each bay, then located the spring phytoplankton bloom within this succession, and finally in vitro experiments were performed during this period to analyse the quantitative and qualitative impact of nanoflagellate grazing and viral lysis on picoplankton.

Results

IN SITU SURVEYS

Environmental context

The evolution of the abiotic parameters in the 2 bays followed a typical temperate-coastal pattern featuring peak temperatures and salinities in the summer together with low nutrient levels during the period of low runoff. On average, Arcachon Bay (Arc) had fewer nutrients than Marennes (M-O) (Table S1 andS2). Arc waters were spatially heterogeneous, with significant differences of salinity, temperature and most nutrients between the 2 stations (ANOVA, H 0 no difference between stations is rejected, p<0.05). By contrast, M-O was homogeneous over 4 of the 5 stations (ANOVA p>0.05).

The Charente station differed from the four others, with higher nutrient levels and lower salinity (ANOVA with Tukey multiple comparisons, p>0.05) (Table S1).

Zonation and seasonality of microbial distribution

The bacteria/virus relationship (r²≥0.6) in Arc was stronger than in M-O. However, the response of bacteria to increasing viruses was weaker in Arc (log/log regression slopes ≤ 0.6) than in M-O (slopes ≥ 0.84, Table 1). In Arc, bacterial (BA) and viral (VA) abundances in Comprian were significantly higher than in B13 (ANOVA, p < 0.0002, Table 1). In M-O, the BA/VA relationship was equal in 4 of the 5 stations (Slope comparison, ANCOVA, p<0.05, n=96), but the relationship was insignificant at Charente station (p=0.09) (Table 1). Nevertheless, no significant differences in the annual abundance of viruses and bacteria were found between the 5 stations (ANOVA, p<0.05).

Consequently, the global monthly and annual means (BA: 4.11±0.34x10 6 cells.ml -1 and VA: 3.38±0.34x10 7 particles.ml -1 ) are representative for the whole basin. The VBR (virus to bacteria ratio) was higher in M-O (annual mean= 11.16 ± 5.39) compared to Arc (4.06 ± 1.17 and 4.89 ± 1.96 at B13 and Comprian,respectively). In M-O, VBR tended to be higher from November to April (>10) and lower during summer (<10), while no trend was visible in Arc (data not shown).

The results of PCA and HAC were analysed to compare the spatial and temporal organization of the 2 bays (Fig. 1). Only samples from January to August were taken into account. Nutrients (23%) and viruses (7.5%) contributed positively to axis 1 while Cryptophyceae (10%), salinity (5%), diatoms (5%) and Synechococcus (5%) contributed negatively. Small phytoeukaryotes (16%), temperature (14%), bacteria (12.5%) and ciliates (12.5%) contributed positively to axis 2. The 2 axes accounted for 52.67% of the total variance among the samples. Along the first axis, Arc was distinguished from M-O by low nutrients (Table S1), a high abundance of autotrophs [Diatoms (mean: 3.52±4.17x10 5 cell.l -1 vs. 0.65±1.45x10 5 cell.l -1 ), Cyanobacteria (mean: 1.77±2.83x10 4 vs.

3.9±2.9x10 3 cell.ml -1 ), Cryptophyceae (6.46±5.17x10 4 vs. 13±28cell.l -1 )], as well as low VA (5.71±5.23x10 6 vs. 3.37±1.21x10 7 particles.ml -1 ). Seasonal clusters were found along the second axis for both basins ("winter": January -April and "summer": May -August), principally due to higher summer temperatures combined with a higher abundance of microorganism in Arc (bacteria, ciliates and pico-nanophytoplankton). Furthermore, a spatial subdivision appeared in Arc along axis 1, distinguishing the inner (Comprian) from the outer (B13) sampling station. By contrast, the M-O stations were homogeneous, although the Charente station points were always shifted to the right because of a lower salinity and a higher level of nutrients. Moreover, the Charente station was also distinguished by large amounts of suspended matter (Table S1).

Based on the results of this multivariate assessment, the basin dynamics were further distinguished using the inner station of Arc (Comprian) and averages of the five M-O stations (because of their spatial homogeneity) using multiple regressions. VA and BA dynamics can be related to the more complex relationships in M-O compared to Arc (Table 2). The opposite was observed for heterotrophic nanoflagellates. There are several important details: (i) in winter, BA was positively related to viruses in both bays and to HNF in M-O but negatively in Arc. In summer, only the link with viruses remained along with supplementary links with nutrients (ammonium, nitrite and phosphate); (ii) there was a negative relationship between viruses and picophytoplankton all year long in both bays as well as with HNF in Arc during the summer; viruses were positively linked to chla all year round in M-O but not in Arc. (iii) HNF were negatively related to ciliates during winter (Table 2).

Temporal succession of trophic pathways

Herbivorous food webs were identified for 20% of the sampling period in Arc (February and August) and for 27% of the period in M-O (May and July-August, Fig. 2A,B). Multivorous webs were present for 21% of the period in Arc (May and the second half of August) and for 25% of the period in M-O (3 weeks in February-March and 2 months between mid-October and mid-December). The microbial food web represented about half of the sampling period in both bays, yet they were not synchronized, except during March-April (Fig. 2). Finally, the microbial loop was absent in Arc, while it was present for 5% of the period in M-O (two weeks at the end of March and one in September). During these two periods, bacteria grew 2.0x and 2.4x, viruses 2.0x and 2.0x, nanoflagellates 6.7x and 1.5x and ciliates 0 and 20x, respectively (Fig. 2B, "Microbial loop").

BA and VA evolved in parallel during all the trophic periods in Arc. However, the synchronisation was less obvious in M-O, particularly during the herbivorous and multivorous phases when VA did not decrease as much (-1.6x) as BA (-2.1x).

In Arc, the small phytoplankton (annual mean = 3.4±1.5x10 4 cells.ml -1 ) were dominated by picoplankton, with 3 major growth periods (early April, early May accompanied by nanoplankton, and from mid-June to end of July, also accompanied by nanoplankton). Large-phytoplankton, with an annual mean of 4.73±4.16x10 5 cells.l -1 , dominated at the beginning of the year, with mainly dinophyceae and cryptophyceae species; this was followed later by two diatom peaks (mid-May and late August). Three periods of growth of small phytoplankton were observed in M-O (annual mean = 1.98±1.71x10 4 cell.ml -1 ) that were mostly due to picoplankton (end of April, 4.9x10 4 cells.ml -1 , early June, 6.8x10 4 cells.ml -1 and early July, 4x10 4 cells.ml -1 ). Large-phytoplankton (annual mean=7.05±14.6x10 4 cells.l -1 ) developed at the end of March (mainly diatoms), early May (mainly diatoms) and early July (diatoms and dinoflagellates). Moreover, picophytoplankton and large phytoplankton accounted for the viral dynamics during the herbivorous and multivorous periods in M-O (simple regression, p=0.01 and p=0.006, respectively).

Nanoflagellate numbers increased systematically during the microbial phases in both Arc and M-O at the beginning of the herbivorous and multivorous periods, just before the large-phytoplankton blooms. Small cells dominated in the latter (annual mean: 88.2±6.9% <3µm cells, Fig. 2). In Arc, the pigmented/unpigmented ratio of nanoflagellates was higher (mean=11.6 vs. 3.9 in M-O).

Ciliate dynamics followed those of bacteria in M-O except in mid-February (Fig. 2B).

Heterotrophic species dominated (annual mean of Strombidium spp plus Strobilidium spp: 55% and 48% of the total abundance, respectively). The only exception was during the spring phytoplankton bloom when autotrophic and potentially autotrophic taxa (Myrionecta rubra, Tontonia spp, Laboea sp) represented 60% and 80% of the total abundance. In Arc, ciliate dynamics followed those of nanoflagellates, increasing during the microbial food web periods.

Finally, beyond the fact that the changes in the trophic pathways in the two bays are not synchronized, the major difference was the contrasting organization at the beginning of the year, which was herbivorous in Arc and microbial in M-O. Moreover, during the large-phytoplankton spring blooms, M-O featured a herbivorous web pathway while Arc was in a multivorous phase.

IN VITRO EXPERIMENTS

Experiments

Protists

In Arc, the abundance of picophytoeukaryote (picoeuk) was equivalent in all treatments at the beginning of the experiment (1.8x10 3 cells.ml -1 ). In the absence of predators, virus enrichment had a positive effect from 18h to 38h, during which picoeuk abundance increased 18-fold (Fig. 3A). But in the presence of flagellates, picoeuk abundance dropped 4.5-fold. In M-O water, the abundance of picoeuk was not significantly different between treatments despite an initial difference due to the filtration step.

Pigmented nanoflagellates were few and varied little in both sites, (means: 6.4x10 4 cells.l -1 in Arc and 9.6x10 4 cells.l -1 in M-O), making up <1% and <10% of the total nanoflagellates, respectively.

The nanoflagellates started growing at 18h (Fig. 3E). Their growth rates were 2-to 4-fold higher in Arc than in M-O (in 3µ-Tr and 20µ-Tr respectively). Moreover, in both bays, the nanoflagellates in the latter were significantly higher than in 3µ-Tr (2way-ANOVA p<0.01) while the small cells (<3µm) dominated and grew more rapidly than the large ones. During the 48h incubations, the small/large flagellate ratio increased from 5 to 100 in Arc and from 1.5 to 20 in M-O.

Bacterial abundance and growth rate

During the different treatments, bacterial abundance after 48h increased 20-35 fold in Arc (stationary phase) and 20-60 fold in M-O (logarithmic phase) (Fig. 3B). In Arc, the net bacterial abundance increase (NBI) was always significantly greater in virus enriched treatments (p=0.005).

In M-O, the viral enrichment enhanced BA only in the 0.8µm treatments (p<0.05).

The maximum specific bacterial growth rate (µmax) showed significant differences between treatments: µmax was significantly higher in both bays with virus-enriched treatments compared to the corresponding controls (2way ANOVA, p=0.02) (cf Fig. 3B black versus clear symbols). The growth stimulation was 9-14% in Arc and 12-33% in M-O. However, the presence of flagellates did not significantly modify µmax in either of the bays (20µm and 3µm treatments versus 0.8µm treatments; Fig. 3B)

In Arc, during the exponential phase, the growth rate of population 6 of HNA cells, which were the most abundant, was stimulated in enriched-0.8µ-Tr (+44%) compared to the control, while populations 3, 4 and 5 were inhibited. The presence of flagellates (3µ and 20µ treatments) masked this particularity. In M-O, the growth rate of population 3 in the presence of flagellates was significantly higher than those of the other populations (data not shown).

Viral abundance and production

The initial viral abundance was multiplied 2-3-fold in enriched-treatments in both Arc and M-O (Fig. 3C). In Arc, some viruses were produced during the first 12 hours, except in 20µ-Tr. A second, stronger production phase was observed between 24 and 38h in all treatments, persisting till the end of the experiment in 3µ-Tr. The presence of flagellates significantly decreased the total viral production rate (-50% in 3µ-Tr and -75% in 20µ-Tr) from 0.04 h -1 in 0.8µ-Tr to 0.02 h -1 in 3µ-Tr and to 0.01 h -1 in 20µ-Tr (2way-ANOVA, p=0.0002). Moreover, viral enrichment decreased the viral production rate notably in presence of small predators (-47.5%, p=0.009) and in their absence, yet not significantly (-23%; p=0.1). In M-O, VA evolved differently with the various treatments: viruses were produced between 12 and 18h only in the presence of flagellates (3µ-and 20µtreatments). There was a second production phase from 38h in all treatments except in the enriched-0.8µ-Tr and 20µ-Tr. The net viral production over 48h was negative (-31%, -72%, -5.3%, -70% and -65% in 0.8µ-Tr, enriched-0.8µ-Tr, 3µ-Tr, 20µ-Tr and enriched-20µ-Tr respectively) except in enriched-3µ-Tr (+5.8%). In contrast to Arc, a combination of the presence of flagellates and virus enrichment increased 2.5-fold the total viral production rate (from 0.01 h -1 in 0.8µ-Tr to 0.025 h -1 in enriched-3µ-Tr). The addition of viruses alone had the opposite effect (2way-ANOVA, interaction, p=0.006), inducing a significant 6.5-fold decrease (from 0.01 h -1 in 0.8µ-Tr to 0.0016 h -1 in enriched-0.8µ-Tr).

Virus mediated bacteriolysis

In Arc, we calculated that 70±15% of the bacterial stock was lysed per day (0.8µ-Tr). In the presence of flagellates, this impact decreased significantly to 33.7±1.9% (3µ-Tr) and 39±11% (20µ-Tr) (ANOVA: p=0.01). By contrast, virus mediated lysis was less severe in M-O (13.6±1.7% of bacterial stock lysed per day without any predators). Small flagellates had no effect (9.4±4.6% in 3µ-Tr, p>0.05). However, in the presence of all the flagellates (20µ-Tr), the percentage of lysed bacteria significantly decreased to 2.6±0.3% (ANOVA, p=0.009).

Bacterial enzymatic activity and community composition

In Arc, the change in specific maximum velocity of potential aminopeptidase activity (specific Vmax) was similar in all treatments, with a peak at 24h. Specific activity dropped at 38h except in 3µ-Tr, where a strong activity was maintained until the end of the experiment (Fig. 3D). There was a negative relationship between picoeuk abundance and bacterial proteolytic activity, which was significant only in the presence of flagellates (Simple regression: r²=92% p<0.0001 n=15 in 3µ-Tr, r²=29% p=0.022, n=15 in enriched-3µ-Tr). The specific Vmax peaked also at 24h in M-O, except in the enriched-3µ-Tr at 12h. In both bays, peaks of activity occurred during the exponential growth phase of bacteria (Fig. 3B,D). Moreover, a decline in bacterial activity coincided with an increase in viral abundance in both bays: in M-O, there was a negative relationship between VA and specific Vmax in the 0.8µ and 3µ treatments (log VA=4.11-0.63 x logVmax, r²=0.15, n=60, p=0.002). This relationship was still significant (r²=0.13, p=0.007) when 20µ treatments were taken into account.

At the beginning of the incubations, the Eubacteria were less FISH-targeted in Arc (53% of DAPI stained bacteria in 0.8µ-Tr and 35% in 3µ-Tr) than in M-O (62% in 0.8µ-Tr and 81% in 3µ-Tr). In Arc, the dominance of γ-proteobacteria (Gamma) was further increased in the presence of small nanoflagellates (15% versus 8%), while αand β-proteobacteria (Alpha and Beta) were present in relatively similar proportions (2-2.5%) (Fig. 4). In M-O, Gamma were more numerous in the 0.8µ treatments (8.9%) than in the 3µ treatments (3%), while among the other groups, only Beta in 0.8µ-Tr showed up significantly (2.6%). Over 48h, the proportion of Eubacteria increased in both bays (78-85% in Arc, 83-90% of total cells in M-O), with a dominance of Gamma. However, while the proportion of Gamma at 48h was higher in Arc in the presence of flagellates (70±5.4%) than in their absence (58±3%), independent of viral enrichment, Gamma dominated in M-O (≈80%) with no difference between treatments (Fig. 4). Furthermore, at 48h in Arc, a positive effect of viruses was observed on Beta and Alpha in the presence of flagellates (+38% in enriched-3µ-Tr compared to the control 0.8µ-Tr), counterbalancing the negative impact of predators (-65% in 3µ-Tr). However, earlier on, at 12h, Alpha cells were sensitive to virus inputs (-70% and -75% in enriched-0.8µ-Tr and enriched-3µ-Tr respectively, compared to the corresponding control). In M-O, among the rare groups, Alpha increased notably in virus-enriched treatments without predators at 48h (+50% in enriched-0.8µ-Tr).

By contrast, nanoflagellates restrained their growth (-49.6% in 3µ-Tr compared to the 0.8µ-Tr control), and even more so after viral enrichment (-76.2% in enriched-3µ-Tr) (Fig. 4). Conversely, the combined effect of nanoflagellates and viruses resulted in an increase of Beta cells at 12h (enriched-3µ-Tr) while the presence of flagellates alone reduced Beta growth (3µ-Tr versus 0.8µ-Tr). At 48h, this pattern was reversed: Beta cells increased in the presence of flagellates (+112% compared to the control) while they decreased with additional viral enrichment (-50%).

Discussion

For each site, the survey concerned only one year without taking into account the potential interannual variability of microbial dynamics in the pattern of trophic succession [START_REF] Guarini | Benthic contribution to pelagic microalgal communities in two semi-enclosed, European-type littoral ecosystems (Marennes-Oléron Bay and Aiguillon Bay, France)[END_REF][START_REF] Glé | Typology of environmental conditions at the onset of winter phytoplankton blooms in a shallow macrotidal coastal ecosystem, Arcachon Bay (France)[END_REF]. Unfortunately, due to logistic restricitions, the comparison of the bays had to be carried out for two adjacent years, and the bias of inter-annual variability cannot be assessed.

After in situ surveys of one year, 48h in vitro incubation experiments were performed with different water categories treated by differential filtration, corresponding to a top-down manipulation of microbial compartments and consequently the removal of predators. Because of the complexity of the microbial food web, with parameters such as predation/competition links and the size overlap of predators and competitors, the size-fractionation method may reduce or eliminate some elements of normal trophic webs. Currently employed methods, including artificially composed trophic webs, necessarily simplify natural systems. We consider that, of those currently employed, the filtration method is one which involves the least disturbance of the webs [START_REF] Chen | Trophic interactions within the microbial food web in the South China Sea revealed by size-fractionation method[END_REF].

Temporal trophic succession

Several previous studies have analysed seasonal production patterns in Arcachon Bay (Arc) [START_REF] Robert | Hydrobiological parameters during an annual cycle in the Arcachon Basin[END_REF][START_REF] Glé | Variability of nutrients and phytoplankton primary production in a shallow macrotidal coastal ecosystem (Arcachon Bay, France)[END_REF] and Marennes Bay (M-O) [START_REF] Guarini | Dynamics of spatial patterns of microphytobenthic biomass: inferences from a geostatistical analysis of two comprehensive surveys in Marennes-Oléron Bay (France)[END_REF][START_REF] Struski | Preliminary estimate of primary production by phytoplankton in Marennes-Oléron Bay, France[END_REF]. In Arc, river runoff and high levels of tidal exchange create a gradient of nutrients along the basin, except for phosphate [START_REF] Glé | Variability of nutrients and phytoplankton primary production in a shallow macrotidal coastal ecosystem (Arcachon Bay, France)[END_REF]. Seasonally, as in our study, Arc is early nutrient limited with a depletion of nitrate from May to the autumn and of phosphate during spring. In M-O, nutrient availability was higher and limitations were only encountered episodically during certain years [START_REF] Soletchnik | Hydrobiology of the Marennes-Oléron Bay. Seasonal indices and analysis of trends from 1978 to 1995[END_REF][START_REF] Struski | Modélisation des flux de matières dans la baie de Marennes-Oléron: Couplage de l'hydrodynamisme, de la production primaire et de la consommation par les huîtres[END_REF]. However, during our 2006 in situ survey, no limitations were found. Based on our multivariate analysis, Arc is spatially heterogeneous while M-O is spatially homogeneous, despite the influence of the Charente River. Moreover, as already illustrated by [START_REF] Soletchnik | Relationships between oyster mortality patterns and environmental data from monitoring databases along the coasts of France[END_REF], seasonal variations, characterized by changes in salinity, temperature and chla are higher in M-O than in Arc.

Our trophic organization framework was based on microbiological and environmental dynamics, as proposed by [START_REF] Legendre | Plankton and nutrient dynamics in marine waters[END_REF]. [START_REF] Mousseau | Assessing the trophic pathways that dominate planktonic food webs: an approach based on simple ecological ratios[END_REF] illustrated the periodicity of trophic organization using ratios of biomasses, production and uptake rates. For the present study, the entire spectrum of abundance from virioplankton to mesozooplankton were used in a similar manner to [START_REF] Rodriguez | Temporal variability of viruses, bacteria, phytoplankton and zooplankton in the western English Channel off Plymouth[END_REF]. We used microbial abundance instead of biomass since the contact rates between virus/host cells or prey/predator determine viral infections and grazing rates. Our empirical trophic-period definitions were validated by a factorial discriminant analysis (FDA) in which the discriminant variable was the defined trophic model (Fig. S1). The clusters remained separated even in the absence of the abundance data used for characterizing the trophic organization (Lambda Wilks test, p<0.05).

The microbial food web dominated in the two bays for 54% and 43% of the seven months of the surveys in Arc and M-O, respectively; although this consisted of short periods of domination in M-O and only 2 long ones in Arc. This domination was longer than what has been found in other coastal sites: on the Canadian East coast and in Plymouth Channel, the microbial food web dominated for less than 20% of the year and only during summer [START_REF] Rodriguez | Temporal variability of viruses, bacteria, phytoplankton and zooplankton in the western English Channel off Plymouth[END_REF][START_REF] Mousseau | Assessing the trophic pathways that dominate planktonic food webs: an approach based on simple ecological ratios[END_REF]. A herbivorous food web dominated in Canada for 50% of the year while it represented only 15% (2 short phytoplankton blooms) in Plymouth Channel. In the latter, there was a domination of the transition model i.e. the multivorous food web. In our study sites, the herbivorous web persisted over longer periods in M-O (43% of the first seven months) while the transitional multivorous web dominated in Arc (22% over the same period). The trophic systems of Arc, a largely closed lagoon, appear to be more stable and less subject to trophic variations than the more open M-O (Fig. 2; see also Fig. 4 in [START_REF] Soletchnik | Relationships between oyster mortality patterns and environmental data from monitoring databases along the coasts of France[END_REF]. In the latter, there was a succession of microbial and herbivorous models without a systematic passage through the transitional multivorous model. Moreover, the microbial food web was easier to distinguish from the other webs than in Arc (Fig. S1). Consequently, the dominance of the microbial food web attests the importance of microbial compartments in both ecosystems.

Another major difference between the bays is the contrasting trophic organization during winter: a herbivorous food web in Arc and a microbial food web in M-O. The presence of winter blooms has already been described in the Bay of Biscay [START_REF] Labry | Initiation of winter phytoplankton blooms within the Gironde plume waters in the Bay of Biscay[END_REF][START_REF] Marquis | Inverse analysis of the planktonic food web dynamics related to phytoplankton bloom development on the continental shelf of the Bay of Biscay, French coast[END_REF] and suggested for the two basins through analyses of long-term data series [START_REF] Soletchnik | Relationships between oyster mortality patterns and environmental data from monitoring databases along the coasts of France[END_REF]. These phytoplankton blooms are influenced neither by temperature nor by nitrogen or phosphorus availability but are probably due to silicate input combined with a potential for salinity stratification or light availability.

The spring phytoplankton bloom occurred at the same period in both bays (April-May) yet it was greater in Arc, where it consisted of large phytoplankton forms such as diatoms and picoplankton. However, estimations of pelagic primary production illustrate a higher annual productivity in M-O of 185 gC.m -2 .yr -1 compared with 103 gC.m -2 .yr -1 in Arc [START_REF] Struski | Preliminary estimate of primary production by phytoplankton in Marennes-Oléron Bay, France[END_REF][START_REF] Glé | Variability of nutrients and phytoplankton primary production in a shallow macrotidal coastal ecosystem (Arcachon Bay, France)[END_REF].

Grazing and viriolysis control

Our results demonstrate that primary producers (small and large phytoplankton cells) and autotrophy-based mechanisms (cf. PCA results) are important in Arcachon Bay (Arc). However, Marennes Bay (M-O) is characterized by a higher potential for primary production, suggesting an uncoupling between phytoplankton abundance and productivity. The difference in phytoplankton abundance could be typically explained by (1) pelagic and benthic metazoan grazing, which is relatively low in M-O [START_REF] Sautour | Importance of microzooplanktonic crustaceans in the coastal food chain: Bay of Marennes-Oléron, France[END_REF] compared to Arc; (2) flushing out, which is relatively higher in M-O because it is quite an open system; (3) settling [START_REF] Sautour | Distribution of zooplankton populations in Marennes-Oléron Bay (France), structure and grazing impact of copepod communities[END_REF];

(4) light limitation [START_REF] Pomeroy | temperature regulation of bacterial activity during the spring bloom in newfoundland coastal waters[END_REF][START_REF] Sautour | Distribution of zooplankton populations in Marennes-Oléron Bay (France), structure and grazing impact of copepod communities[END_REF] and trophic status (mesoeutrophic in M-O vs. mesotrophic in Arc).

In addition, we show that in M-O, phytoplankton could also be controlled by viruses; this is corroborated by the simultaneous virus-bacteria uncoupling (Fig. 2). Viruses have been shown to modify the taxonomic diversity of their host communities, i.e. prokaryotes [START_REF] Thingstad | Theoretical models for the control of bacterial growth rate, abundance, diversity and carbon demand[END_REF], and protists [START_REF] Suttle | Marine viruses-major players in the global ecosystem[END_REF] restraining the rapidly growing and succeeding species. Moreover, these successions of active phage-host systems would maintain a relatively constant baseline of host standing stocks [START_REF] Wommack | Virioplankton: Viruses in aquatic ecosystems[END_REF]. In addition, in M-O, virus infection could maintain the phytoplankton standing stock baseline at a low level. Thus, viral production may rely on autotroph lysis as well as bacterial lysogeny events. Moreover, the in vitro experiments show that, in the presence of protist predators, the potential for viral lysis of the bacteria standing stock was lower in M-O (2.6%) than in Arc (39%) during the phytoplankton bloom. Indeed, viral control of phytoplankton was less clear in Arc and the link between bacteria and viruses was strong. Without nanoflagellates, the release of large numbers of free viruses was in line with an increase in bacterial mortality of up to 70% of the bacterial standing stock, as already suggested by the contrasting dynamics of nanoflagellates and viruses during the in situ survey (Fig. 2; Table 2). In contrast, the dynamics of nanoflagellates and viruses were similar in M-O, and during the spring bloom, virusmediated bacterial mortality did not change significantly (9.4-13.6% with and without small nanoflagellates, respectively). Thus, the two trophic models that prevailed during the phytoplankton spring bloom are also reflected in the interactions between the microbial compartments. The impact of nanoflagellates and viruses on picoplankton differs in a multivorous web (Arc) compared to a herbivorous web (M-O).

The in vitro experiments carried out in Arc showed that the strong potential for viral bacteriolysis (viral shunt: [START_REF] Wilhelm | Viruses and nutrient cycles in the sea[END_REF] contributes to the development of picophytoeukaryotes (picoeuk) due to an increased supply of organic and inorganic nutrients. The presence of nanoflagellates, previously described as predators of picoeuk [START_REF] Sherr | Bacterivory and herbivory: Key roles of phagotrophic protists in pelagic food web[END_REF], appears to counteract the indirect positive effect of viruses on the latter. Thus, the contrasting effect of viruses and nanoflagellates on picoeuk can be defined as antagonist top-down control.

While the herbivorous web dominated in M-O, the antagonist impact could not be observed during in vitro experiments because of the very low abundance of picoeuk in the sampling water. The bacterioplankton were the only picoplankton compartment affected by viral enrichment in M-O: through the viral shunt, the absence of picoeuk reduces the competition for nutrients and thus increases the positive effect of viral enrichment on the growth of non-lysed bacteria.

In our study, nanoflagellate treatment in both bays did not affect bacterial abundance or growth, in contrast to published data [START_REF] Zhang | Viruses and flagellates sustain apparent richness and reduce biomass accumulation of bacterioplankton in coastal marine waters[END_REF]. However, this does not necessarily mean that predation by nanoflagellates had no effect. Indeed, flagellate grazing on picoeuk in Arc potentially released particulate organic matter, provoking the observed increase of bacterial proteolysis.

Furthermore, with size-selective predation, the loss of bacteria through grazing is compensated by the growth of predation-resistant cells, suggesting that nanoflagellates can affect the phenotypic structure of bacterioplankton [START_REF] Gonzalez | Size-selective grazing on bacteria by natural assemblages of estuarine flagellates and ciliates[END_REF][START_REF] Monger | Prey-size dependency of grazing by free-living marine flagellates[END_REF][START_REF] Simek | Morphological and Compositional Shifts in an Experimental Bacterial Community Influenced by Protists with Contrasting Feeding Modes[END_REF][START_REF] Posch | Predator-induced changes of bacterial size-structure and productivity studied on an experimental microbial community[END_REF]. Moreover, considering the BCC data, the morphological diversity of bacteria and the mean biovolumes of bacterial sub-populations, there should be a preferential grazing of αproteobacteria (small coccoid-shaped cells) by nanoflagellates compared to β-proteobacteria (large bacillus-shaped cells). Beta dominate in freshwater but can also be found in coastal waters, without a defined marine or riverine origin [START_REF] Nold | Patterns and governing forces in aquatic microbial communities[END_REF]. Indeed, they are present in both bays in proportions equivalent to more specifically marine groups like Alpha during spring phytoplankton blooms. More specifically, the hypothesis that Alpha are more vulnerable to grazing is confirmed in M-O. Moreover, Beta are not as opportunistic as Gamma but nevertheless are metabolically very reactive: the sensitivity of Alpha to grazing and to viral infection provides nutrients for Beta, which are resistant, and this is highlighted by an increase of bacterial proteolysis. However, Beta were as vulnerable to grazing as Alpha in Arc. A different clonal composition of Beta could explain the variable response to predation pressure between the two bays [START_REF] Brümmer | Diversity and seasonal variability of beta-proteobacteria in biofilms of polluted rivers: analysis by temperature gradient gel electrophoresis and cloning[END_REF][START_REF] Schwalbach | Viral effects on bacterial community composition in marine plankton microcosms[END_REF].

The variable responses of bacterial groups to viral lysis in the present study confirm previous results

concerning BCC [START_REF] Bouvier | Key role of selective viral-induced mortality in determining marine bacterial community composition[END_REF] and bacterial metabolism [START_REF] Middelboe | Effects of viruses on nutrients turnover and growth efficiency of non-infected marine bacterioplankton[END_REF]. Consequently, viral production can be modified according to the trophic period. During the multivorous phase of the phytoplankton spring bloom in Arc, viral production was synergistically depleted by the presence of flagellates and viral enrichment. The predation of picoeuk and bacteria decreases the number of host cells and could lead to a general drop of lytic or lysogenic viral production. Moreover, the acquisition of viral resistance, as observed with Alpha bacteria in virusenriched incubation, would accentuate the negative impact on viral production. During the herbivorous phase of the phytoplankton spring bloom in M-O, the response in terms of viral production was the opposite. In the presence of flagellates, after virus enrichment, the development of predation-resistant and virus-sensitive Beta, at the expense of Alpha, led to a positive synergistic action of predators and viruses on viral production. This stimulation in the presence of flagellates has already been described (in contrast to the inhibition found in Arcachon) and explained by changes in BCC under grazing pressure [START_REF] Simek | Changes in bacterial community composition and dynamics and viral mortality rates associated with enhanced flagellate grazing in mesoeutrophic reservoir[END_REF][START_REF] Weinbauer | Comparing the effects of resource enrichment and grazing on viral production in a meso-eutrophic reservoir[END_REF]Weinbauer et al., 2007;[START_REF] Pradeep Ram | Functional responses of prokaryotes and viruses to grazer effects and nutrient additions in freshwater microcosms[END_REF]. Interestingly, our results also suggest that this stimulation occurs during the herbivorous period.

Conclusion

The temporal structure of trophic networks has been described in two bays on the French Atlantic coast, providing major new elements for future studies, in particular for comparing microbiological relationships during similar trophic periods.

Considering that trophic periods were defined in the same way for the two bays, the results of picoplankton and viral production can be extrapolated in relation to how multivorous and herbivorous trophic models work in general. Thus, during a multivorous period, viral lysis together with flagellate grazing may reduce viral production and act antagonistically toward picoeukaryotes.

During a herbivorous period, flagellates and virus could both stimulate viral production. These contrasting responses sustain the debate about potential shifts in viral production due to grazing: positive or undetectable responses have already been found [START_REF] Hornak | Effects of decreased resource availability, protozoan grazing and viral impact on the structure of bacterioplankton assemblage in a canyon-shaped reservoir[END_REF][START_REF] Weinbauer | Synergistic and antagonistic effects of viral lysis and protistan grazing on bacterial biomass, production and diversity[END_REF]. For the first time, a negative effect of flagellate predation and viral lysis on viral production has been described and linked to a trophic model, the multivorous food web.

Experimental procedures

Study site and sampling strategy of in situ surveys

The study was carried out in two sites along the Bay of Biscay on the French Atlantic coast, Arcachon Bay and Marennes Oleron Bay, which are 150 km apart. Both are shallow, soft-bottom bays that are under strong tidal influence (Fig. S2). Arcachon Bay (44°40N, 1°10W) is a 180 km² triangular shaped coastal lagoon that communicates with the Atlantic through a broad channel to the south (Fig. S2). The Leyre River is the major source of freshwater for the lagoon (100 000 m 3 .y -1 ; [START_REF] Glé | Typology of environmental conditions at the onset of winter phytoplankton blooms in a shallow macrotidal coastal ecosystem, Arcachon Bay (France)[END_REF] which has a meso-to macrotidal regime with amplitudes from 1.1m to 4.9m. It is about 25 m deep at the entrance but the inside channels are shallow (5-10 m) , while about 70% of the bay is covered by intertidal flats [START_REF] Blanchet | Use of biotic indices in semi-enclosed coastal ecosystems and transitional waters habitats-Implications for the implementation of the European Water Framework Directive[END_REF]. The water residence time is more than 3 weeks in the South East part of the bay. Marennes Oleron Bay to the North (46°00N, 1°10W) is similar in size (175 km²) and depth (mean=8.6m; [START_REF] Stanisière | Caractérisation des composantes hydrodynamiques d'un système côtier semi-fermé: Le Bassin de Marennes-Oléron[END_REF], and intertidal mudflats cover about 60% of its surface (Fig. S2). Freshwater arrives mainly from the Charente River, with 3x10 9 m 3 .y -1 , or 90% of the total freshwater input (DDE-Charente Maritime/HYDRO-MEDD/DE).

The bay is characterised by a macrotidal system, with amplitudes from 2 to 6m. It communicates with the ocean through two passes, the Pertuis de Maumusson to the South and the Pertuis d'Antioche to the North, with a relatively short residence time of 11 days. Tidal exchanges are greater through the northern pass, resulting in a north-south circulation of oceanic (Bay of Biscay) water [START_REF] Struski | Modélisation des flux de matières dans la baie de Marennes-Oléron: Couplage de l'hydrodynamisme, de la production primaire et de la consommation par les huîtres[END_REF].

Sampling was carried out twice weekly, from January to August 2005, at 2 stations in Arcachon Bay: B13, near the mouth of the Bay, which is under oceanic influence, and Comprian, in the inner basin, which is affected by the continental runoff. Five stations were surveyed in Marennes Oleron Bay on a bi-monthly basis (January to December 2006). The stations were spread out over the entire basin: one was close to the southern pass (A= Auger), two were above mudflats close to the continent (Br=Brouage) and island (O= Oleron) coasts, one was close to the northern pass (B=Boyard) and one near the mouth of the Charente River (C=Charente).

Water samples were collected in both bays near the surface (<1m) using Niskin bottles. Subsamples were fixed on board. The rest were kept in 8L polypropylene dark bottles for laboratory analyses (transfer time <3h). Physical parameters (temperature, salinity, pH) were recorded on board with multiparameter probes (Seabird SBE25 in Arcachon and YSI 6600EDS-M in Marennes).

Batch culture design

Experimental water was collected at high tide at the surface from the central Comprian station in Arcachon Bay on 17 May 2006 and from the northern "E" station in Marennes Bay (Fig. S2) on 24 April 2007. The experimental protocols were based on a slightly modified version of the method of [START_REF] Auguet | Potential effect of freshwater virus on the structure and activity of bacterial communities in the Marennes-Oléron Bay (France)[END_REF]. In the lab, three water categories were obtained by sequential filtration from 75L of seawater: (1) <20µm, filtered through nylon membranes, (2) <3µm, filtered through Versapor acrylic polymer membranes (Gelman Sciences Inc.) and (3) <0.8µm, filtered through cellulose acetate membranes (Sartorius). Viral particles were then concentrated from 40L of <0.2water, as a high molecular weight concentrate (HMWC) using a 30kDa polysulfone cartridge (Sartorius). Viral-free water was kept for use as dilution water. Six different treatments were prepared with the three water categories (Table 3), each diluted ten fold with the dilution water and enriched (x2) or not with viruses (HMWC).

Each of the six treatments were triplicated in 2.4L Nalgene polycarbonate bottles filled with 2200 ml and incubated under in situ conditions of light and temperature in circulating seawater tanks.

Samples for bacterial, viral, phytoplankton and flagellate counts were collected at 0, 6, 12, 18, 24, 38 and 48h. Samples for bacterial activity and diversity were collected every 12h. Samples for ciliate abundance were collected at the beginning and the end of the experiment (<20µm treatment).

Physical and chemical parameters

Seston was assessed by filtering 200 to 500 ml of water according to turbidity and processed according to [START_REF] Auguet | Dynamic of virioplankton abundance and its environmental control in the Charente estuary (France)[END_REF]. Samples for dissolved inorganic nutrients (Silicate (Si), Phosphate (PO 4 ), Urea, Ammonium (NH 4 ), Nitrite (NO 2 ) and Nitrate (NO 3 )) were filtered (20ml, Whatman GF/F) into glass flasks and stored at -20°C for analyses with an autoanalyser (Bran and Luebbe, AA3 for Arcachon and Skalar for Marennes (Strickland and Parsons, 1972)).

Biological parameters

Chlorophyll a (chla) and pheopigment were analysed by filtering 200 ml of water through Whatman GF/F filters (25 mm). Filters were stored for less than a month at -20°C before extraction (acetone 90% overnight at 4°C and shaken). Samples were analysed by the fluorimetry method (Turner, TD 700) according to [START_REF] Yentsch | A method for the determination of phytoplankton chlorophyll and pheophytin by fluorescence[END_REF]. Subsamples (3ml) for viral and bacterial counts were fixed with 0.02-µm filtered formaldehyde (2% final concentration) and stored for less than a week at 4°C. Samples were enumerated by epifluorescence microscopy after staining for 30 min with Sybr Green I [START_REF] Noble | Use of SYBR Green I for rapid epifluorescence counts of marine viruses and bacteria[END_REF].

Bacteria and viruses were counted in at least 15 fields chosen randomly under blue excitation (Zeiss Axioskop 1000x). Using flow cytometry (FACSCalibur, Bekton Dickinson) and Sybr-GreenI staining, bacteria cells were distinguished between High Nucleic Acid (HNA) and Low Nucleic Acid (LNA) cells. Furthermore, for batch culture samples, HNA cells were clustered in 4 subgroups (populations 3, 4, 5 and 6) according to decreasing biovolume (2, 0.8, 0.3 and 0.15 µm 3 , respectively) and fluorescence. Small phytoplankton cells (<10µm; picoeukaryotes, nanoeukaryotes and Synechococcus) were analysed using flow cytometry according to [START_REF] Joux | Distribution of picophytoplankton and bacterioplankton along a river plume gradient in the Mediterranean Sea[END_REF].

Microphytoplankton (>10µm) and ciliate subsamples were fixed and stained in alkaline lugol (final concentration, 1%). 10-20 ml subsamples were decanted in Utermöhl settling chambers before counting and sizing by inverted microscopy (white light, Leica DMIRB, 400x -630x). For ciliate samples, the suspended matter of a 1L subsample was settled for 6h at in situ temperature before siphoning off the top 850ml and fixing a 150ml aliquot of the siphoned water. Nanoflagellates (60ml) were fixed with paraformaldehyde (final concentration 1%) and stored at 4°C for less than a week. Cells filtered onto 0.8µm black polycarbonate membranes (Nucleopore) were stained with DAPI [START_REF] Porter | The use of DAPI for identifying and counting aquatic microflora[END_REF] and counted under ultraviolet excitation (Zeiss Axioskop 1000x) with a distinction between unpigmented (HNF) and pigmented (ANF) nanoflagellates based on the absence of chlorophyll fluorescence under green excitation. At least 100 nanoflagellates were counted for each slide.

Mesozooplankton were collected 1m below the surface in Arcachon Bay using a standard 200µm WP-2 net towed slowly against the current. The catch was fixed in 5% seawater/formalin and counted with a binocular loupe.

Bacterial growth, viral production and bacterial mortality

The maximum specific growth rate (h -1 ) of bacteria was calculated from log transformed abundance data during the exponential growth phase in each bottle. Total viral production rate (h -1 ) was calculated by adding the net increase of logarithmic viral abundance during each peak divided by the incubation time. The bacterial mortality (cell.ml -1 .h -1 ) induced by viral lysis was determined in unenriched treatments from the viral production (sum of the net increase of viral abundance divided by the duration of the experiment, particles.ml -1 .h -1 ) divided by an assumed burst size of 50. The percentage of bacterial cells lysed (d -1 ) was equivalent to the bacterial mortality rate (cell.ml -1 .d -1 ) divided by the total bacterial abundance.

Bacterial activity

Leucine aminopeptidase activity was measured in duplicate at T0, 12, 24, 36 and T48h by using L-Leucine-7-amino-4-methylcoumarin hydrochloride (Leu-MCA, Sigma) as a model protein substrate, according to [START_REF] Hoppe | Use of fluorogenic model substrates for extracellular enzyme activity (EEA) of bacteria[END_REF]. The enzyme Vmax was determined by adding substrate to the sample, giving a final saturating concentration of 1000µM. Incubations were performed in the dark and at in situ temperature for around 5h, then stopped using Sodium-dodecyl sulfate (1% final concentration)

The release of 7-amino-4-methylcoumarin dye (MCA, Sigma) was measured with a spectrofluorometer (Kontron, model SFM 25) at 380nm excitation and 440nm emission. Solutions of MCA (20 to 2000nM) were used as a standard for calibration. The potential enzymatic activity per cell was obtained by dividing Vmax by the corresponding bacterial abundance (specific Vmax, fM.cell -1 .h -1 ).

Bacterial diversity

Bacterial community composition was determined with fluorescent in situ hybridization using eight Cy-3 labelled probes (MWG-Biotech): a mix of Eub 338 I,II and III for Eubacteria, Pla5a for Planctomycetes, CF319a for Cytophaga-Flavobacterium cluster, Alf1b for α-proteobacteria, Bet43a for β-proteobacteria, Gam42a for γ-proteobacteria, Arch915 for archaea and a non specific probe as a control [START_REF] Amann | Phylogenetic identification and in situ detection of individual microbial cells without cultivation[END_REF][START_REF] Bouvier | Key role of selective viral-induced mortality in determining marine bacterial community composition[END_REF].

Samples were fixed in paraformaldehyde (2% final) and stored for at least 12h at 4°C before freezing at -20°C. Bacteria were filtered onto 0.2µm polycarbonate membranes (Whatman), rinsed with 0.2µm-filtered milli-Q water, dried and stored at -20°C. Pieces of membrane were hybridized with 9µl of hybridization solution (Cy3-labelled probe, 2.5ng.µl -1 ) for 4h30 at 46°C and washed at 48°C for 15 mn. The membrane was then rinsed for a few seconds successively in 0.2µm-filtered Milli-Q water then 80% ethanol and finally air dried. Bacterial cells were counter-stained with DAPI mounting solution (0.25µg.µl -1 in (Citifluor (Biovalley)/Vectashield (Ablys, SA, Paris): 4/1).

At least 10 fields were counted on an epifluorescence microscope (Axioskop, Zeiss) using Cy3 (blue) and DAPI (UV) filters.

Identification of trophic dynamics

Trophic periods were defined empirically using the 4 conceptual trophic flux models of [START_REF] Legendre | Plankton and nutrient dynamics in marine waters[END_REF], primarily from the abundances of phytoplankton and bacteria but also taking into account the dynamics of viruses, HNF and ciliates (Table 4).

The herbivorous food web is characterized by blooms of large (>10µm) phytoplankton cells. During this period there is in general an increase in the number of ciliates. The microbial food web is defined by an increase of small autotrophs (phytoplankton <10µm) and heterotrophs (bacteria).

Their potential predators (HNF and ciliates) may increase in number. The microbial loop, a closed system maintained by nutrient regeneration, is characterized by high abundance of exclusively heterotrophic micro-organisms (bacteria, HNF and ciliates). Finally, the multivorous food web is a transitional model where all trophic pathways are actively contributing to the dynamics of the ecosystem.

The trophic organization identified for each bay was then formalized using multivariate factorial discriminant analysis (FDA). FDA classifies the samples using predefined criteria (i.e. the trophic model) to discriminate samples according to independent variables (all abiotic and biotic parameters). Prior to the FDA analysis, data were normalized and tested for heteroskedasticity (Levene test, Ho: equal variances between criteria). The purpose was to test whether samples are classified as predicted by the empirical trophic model and to assess the relative importance of the independent variables in the classification. The significance of clustering is tested by a Wilks Lambda test (low Lambda values corresponds to significant (p<0.05) clustering).

Statistical analysis

To compare field data between the 2 bays, the Arcachon results ( 3 (min-8.70x10 5 /max-1.01x10 7 )

3.95x10 6

(min-1.01x10 6 /max-1.30x10 6 ) 4.60x10 6

(min-1.16x10 6 /max-1.17x10 7 )

3.70x10 6

(min-1.05x10 6 /max-9.03x10 6 ) 4.28x10 6

(min-7.10x10 5 /max-1.8x10 7 )

3.18x10 7

(min-5.24x10 6 /max-5.97x10 7 )

3.36x10 7

(min-1.82x10 7 /max-5.59x10 7 )

3.96x10 7

(min-2.01x10 7 /max-6.36x10 7 )

3.07x10 7

(min-1.82x10 7 /max-5.07x10 7 )

3.35x10 7

(min-1.66x10 FDA generated from microbiological, physical and chemical data (log transformed) and using an empirical trophic model as a criterion variable (■, ▲, ♦): (1) with all the parameters and (2) excluding the principal factors used for the definition of trophic groups (large and small phytoplankton and bacteria abundance). Samples are clustered according to the trophic pattern. The microbial loop class was combined with the microbial food web class because few samples representing the former were found in Marennes and it was absent in Arcachon. Independent variables: salinity, water temperature, silicate, phosphate, ammonium, nitrate, nitrite, organic seston, pheopigment and chlorophyll a concentrations, abundance of viruses, bacteria, autotrophic nanoflagellates, heterotrophic nanoflagellates, ciliates, picoplankton, nanoplankton, Synechococcus, diatoms and Dinophyceae, biomass of ciliates and bacteria, copepods. Structuring variables are annotated along each axis: S%: salinity, ANF: Autotrophic nanoflagellates, Ciliate biom: Biomass of ciliates, Chla: Chlorophyll a concentration, Picopk: Picoplankton abundance, Nanopk: Nanoplankton abundance, Si: Silicate, NO 2 : Nitrite, NO 3 : Nitrate, NH 4 : Ammonium, PO 4 : Phosphate, Seston org.: Organic seston, Pheo: Pheopigment concentration, T°w: water temperature.

  were performed with water from Arcachon-Comprian (2006; T°in situ= 18.6°C; S= 32 PSU) and the Marennes-Station E (2007; T°in situ= 15°C; S= 32.5 PSU) collected at morning high tide. During incubation, the nighttime/daytime water temperature varied from 19.5°C to 23.3°C in Arc and from 18°C to 22°C in M-O, respectively.

  -day intervals) were regrouped by sliding means to obtain 15-day time steps, as in Marennes Bay. Statistical analyses were performed with log transformed data using XlStat (Addinsoft; version 7.5.2: one way-or two way-ANOVA, simple regression, Principal Component Analysis (PCA) combined with Hierarchical Ascendant Classification (HAC), Factorial Discriminant Analysis (FDA)) and Minitab (version 15: stepwise multiple regressions and Levene test for Heteroskedasticity).

  data in Arcachon (January to September 2005; ▲ B13, ■ Comprian, with sampling date) and Marennes-Oléron Bay (2006; • with sampling stations and date). Arrows parallel to each axis: structuring variables. Ellipses: clusters based on hierarchical ascendant classification analysis (HAC) performed with coordinates of the station plots (straight and dotted lines: first and second discriminant in HAC, respectively).

Fig. 2 .

 2 Fig. 2. Temporal dynamics of bacteria (▲), viruses (■), nanoflagellates (□), ciliates (x) abundance and chlorophyll a (∆) concentration in the 2 study sites, Arcachon in 2005 (A: Comprian station) and Marennes in 2006 (B: Mean of the 5 stations). The trophic periods are represented by different background patterns: Herbivorous food web; Multivorous food web; Microbial food web; Microbial loop. Arrows indicate the in vitro experiment periods.

Table and figure legends:Table 1 :

 1 Annual means and simple linear regressions between bacterioplankton (BA) and virioplankton (VA) at the sampling stations of Marennes OléronBay (2006) and Arcachon Bay (2005). Linear regressions were performed with log transformed data.

		Stations	BA (cells.ml -1 )	VA (particles.ml -1 )	Equation	r²	p	n
	Marennes	Auger	4.04x10 6
		Boyard
		Charente
		Oléron
		Brouage

Table 2 .

 2 Stepwise linear regression of bacteria, virus, flagellate and ciliate abundance according to environmental parameters in Arcachon and Marennes bays for the entire year and according to the season. The seasons were defined based on the PCA results. Descriptors: BA: Bacterial abundance, VA: Viral abundance, HNF: Heterotrophic nanoflagellate abundance, ANF: Autotrophic nanoflagellate abundance, HNA: Abundance of high nucleic acid bacteria, Cil: Ciliate abundance, Pico: Picoplankton abundance, Nano: Nanoplankton abundance, Syn: Synechococcus abundance, Dino: Dinophyceae abundance, Diat: Diatom abundance, Cop: Copepods abundance T°w: Water temperature, S%: Salinity, Crypto: Cryptophyceae abundance, Seston Org: Organic seston concentration, NH 4 : Ammonium, PO 4 : Phosphate, NO 2 : Nitrite, Chla: Chlorophyll a concentrations, Pheo: Pheopigment concentrations. Logarithmic transformations were applied to all data. The coefficient of determination (adjusted r²) was corrected according to the number of variables used in the regression. Copepods were analysed only at Arcachon and taken into account in stepwise regression analysis, but their inclusion does not interfere with regression results except for HNF in winter. Bold parameters are discussed in the text.

		log BA= 1.5logVA-4.7	0.60	<0.0001	20
		log BA= 1.45logVA-4.39	0.46	0.001	20
		log BA= 0.84logVA+0.23	0.18	0.092	17
		log BA= 1.74logVA-6.51	0.45	0.001	20
	7 /max-6.08x10 7 )	log BA= 1.6logVA-5.5	0.51	0.001	19

Table S1 .

 S1 Mean, minimum and maximum values of the abiotic and chlorophyll a parameters, with dates, at Marennes Bay (4 inner stations: A, B, O, Br and one riverine station: C). C was analysed separately because of the significant differences compared to the four other stations (H 0 rejected in a Tukey multiple comparison test). The seasons were defined based on the clustering of stations using PCA. Winter season: January to 5 April and November-December 2006. The remainder of the year was considered as the summer season. Runoff data were supplied by DDE-Charente Maritime/HYDRO-MEDD/DE for the Charente River.

	Marennes		Winter			Summer
		Mean	Min/date	Max/date	Mean	Min/date	Max/date
	Water temperature (°C)
	A,B,O,Br	9.19	5.1/1Mar.	14.5/28 Nov.	18.74	13.3/2 May	24.7/26 Jul.
	C	8.59	4.8/7 Feb.	12.9/28 Nov.	19.67	13.6/2 May	24.8/26 Jul.
	Salinity (PSU)
	A,B,O,Br	31.96	28.3/4 Apr.	34.3/7 Nov.	33.86	31/25 Apr.	35.3/24 Aug.
	C	20.61	10.4/1 Mar.	29.8/24 Jan.	28.2	12.7/25 Apr.	33.9/1 Aug.
	Nitrate (µM)
	A,B,O,Br	42.40	12.09/12 Dec.	97.98/28 Nov.	15.12	0.53/1 Aug.	78.17/25 Apr.
	C	90.76	46.95/7 Nov.	192.9/12 Dec.	47.46	6.65/1 Aug.	167.5/25 Apr.
	Ammonium (µM)
	A,B,O,Br	7.16	0.01/12 Dec.	68.62/28 Nov.	2.43	0.35/25 Apr.	18.85/24 Aug.
	C	13.07	1.15/18 Jan.	68.83/12 Dec.	2.66	0.41/14 Jun.	7.4/25 Apr.
	Phosphate (µM)
	A,B,O,Br	0.97	0.5/5 Apr.	3.04/1 Mar.	0.48	0.01/2 May	1.22/18 Sep.
	C	1.25	0.77/12 Dec.	1.61/28 Nov.	1.70	0.39/4 Jul.	3.8/2 May
	Silicate (µM)
	A,B,O,Br	27.74	11.35/18 Jan.	94.28/12 Dec.	13.97	4.19/2 May	56.43/26 Jul.
	C	39.14	27.03/24 Jan.	58.09/28 Nov.	31.97	18.3/2 May	60.45/18 Sep.
	Chl a (µg.l -1 )
	A,B,O,Br	1.18	0.34/7 Feb.	8.31/1 Mar.	3.8	1.03/18 Sep.	16.78/2 May
	C	3.58	0.43/24 Jan.	15.53/7 Nov.	4.34	1.35/30 Oct.	13.84/2 May
	Total seston (mg.l -1 )
	A,B,O,Br	44.16	9.54/24 Jan.	163.34/7 Nov.	16.24	4.8/5 Jun.	134.6/24 Aug.
	C	1637.9	18.31/7 Feb.	8820.5/1 Mar.	57.92	12.38/5 Jun.	263.5/24 Aug.
	Charente runoff (m 3 .s -1 )	104.19	24.50/16 Nov.	380/10 Mar.	29.46	9.83/8 Sep.	71.2/25 Apr.

Table S2 .

 S2 Mean, minimum and maximum values of the abiotic and chlorophyll a parameters with date occurrence at Arcachon Bay (B13 and Comprian stations). The seasons were defined based on the results of PCA clustering temporally the sampling stations; winter season from January to the end of April 2005. The rest of the year was considered as the summer season. Runoff data were supplied by DIREN-Aquitaine/HYDRO-MEDD/DE for Leyre River

	Arcachon		Winter			Summer
		Mean	Min/date	Max/date	Mean	Min/date	Max/date
	Water temperature (°C)
	B13	10.30	7.4/5Mar.	14.8/29 Apr.	18.04	14.27/14 May	21.07/23 Jul.
	Comprian	10.01	5.4/5 Mar.	16.42/29 Apr.	20.72	16.85/14 May	22.92/16 Jul.
	Salinity (PSU)
	B13	34.26	24.02/29 Apr.	34.55/9 Apr.	34.49	32.3/16 Jul.	35.4/2 Jul.
	Comprian	31.06	30.15/10 Feb.	32/9 Apr.	33.5	31.4/6 May.	35/30 Aug.
	Nitrate (µM)
	B13	5	0.77/29 Apr.	11.21/19 Feb.	0.27	0.19/23 Jul.	0.48/6 Ma.
	Comprian	8.19	2/2 Apr.	15.51/31 Jan.	0.93	0.26/16 Jul.	4.99/6 May
	Ammonium (µM)
	B13	0.93	0.12/29 Apr.	4.71/31 Jan.	0.15	0.03/14 May	0.59/30 Aug.
	Comprian	1.98	0.59/12 Mar.	6.46/31 Jan.	1.15	0.42/16 Jul.	2.38/30 Aug.
	Phosphate (µM)
	B13	0.76	0.37/29 Apr.	2.34/26 Mar.	0.27	0.18/30 Aug.	0.36/6 May
	Comprian	0.59	0.28/10 Feb.	1.8/26 Mar.	0.4	0.27/9 Jul.	0.82/13 Aug.
	Silicate (µM)
	B13	4.65	0.82/9 Apr.	7.10/23 Jan.	3.6	1.31/20 Aug.	11.11/25 Jun.
	Comprian	13.96	6.59/2 Apr.	21.93/31 Jan.	10.69	5.75/30 Aug.	17.78/25 Jun.
	Chl a (µg.l -1 )
	B13	0.95	0.44/12 Mar.	1.61/2 Apr.	0.77	0/30 Aug.	1.55/28 May
	Comprian	1.09	0.6/5 Mar.	1.9/2 Apr.	1.37	0.72/18 Jun.	2.32/4 Jun.
	Total seston (mg.l -1 )
	B13	34.64	5.59/2 Apr.	216.2/23 Jan.	0.765	0.470/6 May	1.15/18 Jun.
	Comprian	33.74	6.44/19 Mar.	208.7/23 Jan.	0.920	0.56/30 Aug.	1.36/9 Jul.
	Leyre runoff (m3.s -1 )	13.46	9.69/26 Mar.	22.8/27 Apr.	6.44	4.02/25 Aug.	16.5/1 May

Acknowledgements

This study was supported by the French Program PNEC-Littoral Atlantique. P.O.'s work was supported by a PhD fellowship from the CNRS and the Région Poitou-Charente. The authors thank the pilots of IFREMER-La Tremblade for the sampling logistics in Marennes Bay and Martine Bréret and Pascal Lebleu for their helpful technical assistance. We also thank two anonymous reviewers for their constructive criticism.

 4. Definitions of the characteristics of the four trophic models defined by [START_REF] Legendre | Plankton and nutrient dynamics in marine waters[END_REF] and according to [START_REF] Rodriguez | Temporal variability of viruses, bacteria, phytoplankton and zooplankton in the western English Channel off Plymouth[END_REF] and [START_REF] Mousseau | Assessing the trophic pathways that dominate planktonic food webs: an approach based on simple ecological ratios[END_REF]. Arrows describe the abundance dynamics of the biological compartments (increasing, decreasing or fluctuating). ** Major parameters taken into account for the definition of trophic periods; * secondary parameters. HNF: unpigmented nanoflagellates.