Ronan L Hébert

Matthieu Angeli

Eric Doehne

Sandra Dochez

RECORDING OF THE THERMAL EVOLUTION OF LIMESTONES UNDERGOING EXPERIMENTAL ACCELERATED AGEING TESTS

Keywords: heptahydrate, mirabilite, sodium sulfates, accelerated ageing test

Sodium sulfates are widely regarded as the most destructive salts for porous stone, concrete and brick. Thenardite (Na2SO4), mirabilite (Na2SO4, 10H2O) and heptahydrate (Na2SO4, 7H2O) are the common phases that occur under surface conditions. The heptahydrate phase has been largely neglected in most modern work about salt weathering. However, several recent publications suggest that it could play an important role in geochemical and planetary processes. Therefore its role in the Na 2 SO 4 -H 2 O system should be clarified.

We present here results of accelerated ageing experiments performed on samples of a micritic limestone under two different ambient temperatures: 20°C (i.e. below the upper limit of metastability of the heptahydrate), and 30°C (i.e. above this limit and also below the upper limit of stability of mirabilite). Thermocouples were placed into the samples in order to follow the evolution of the temperature of the stones during the weathering tests. This method allows the recording of exo-and endothermic reactions that are linked to physicochemical processes, such as crystallization, wetting, evaporative cooling, etc.

The experiments show significant differences in the pattern and degree of damage. In the experiment at 20°C, the loss of material is noticeable, and occurs as crumbling and scaling. In the experiment at 30°C, we observe just efflorescence with no visible loss of material. The recorded thermal evolution is also different for the two experiments, which provides some clues as to the different physicochemical processes occurring in each situation, depending on the ambient temperature.

Introduction

Sodium sulphates are widely regarded as the most destructive salts for porous stones, and other building materials such as concrete or brick [START_REF] Goudie | Salt Weathering Hazards[END_REF]. It is important to fully understand their crystallization process in porous networks and, in the future, to find ways to prevent or limit their damage to porous materials.

Anhydrous sodium sulphates (i.e. Na2SO4) occur in several polymorphic forms labeled I-V (Kracek 1929;[START_REF] Eysel | Crystal chemistry of the system Na2SO4-K2SO4-K2CrO4-Na2CrO4 and of the glaserite phase[END_REF]Rasmussen et al. 1996[START_REF] Rodriguez-Navarro | How does sodium sulfate crystallize ? Implications for the decay and testing of building materials[END_REF]. Phases I, II and IV are high temperature polymorphs (I >270°C and II >225°C). Phase IV is poorly known (e.g. Rasmussen et al. 1996;[START_REF] Rodriguez-Navarro | How does sodium sulfate crystallize ? Implications for the decay and testing of building materials[END_REF] but it is considered to be metastable with a small temperature range of existence (210-220°C). Phase V, known as thenardite (Thn), is the stable phase at low temperature (i.e. close to ordinary temperature), low relative humidity and is the naturally occurring mineral. Phase III is metastable and is frequently observed during evaporation of a sodium sulphate solution [START_REF] Amirthalingam | Topotaxic phase change in Na2SO4[END_REF]. Note that there is no indication that phases I, II and IV occur as metastable phases close to ambient temperatures [START_REF] Steiger | Crystallization of sodium sulfate phases in porous materials: The phase diagram Na2SO4-H2O and the generation of stress[END_REF].

Sodium sulphates can also occur as hydrates: the decahydrate (Mirabilite: Na2SO4.10H2O; Mir), which is stable below 32.4°C (Figure 1) and the heptahydrate (unnamed, Na2SO4.7H2O; SS7), which is metastable below 24°C (Figure 1). These limited temperatures of stability (for Mir) and metastability (for SS7) correspond to peritectic temperatures where the hydrates undergo an incongruent melting to Thn. These temperatures are higher when peritectic reactions produce anhydrous form III (34.06°C for mirabilite, 28.25°C for heptahydrate; [START_REF] Steiger | Crystallization of sodium sulfate phases in porous materials: The phase diagram Na2SO4-H2O and the generation of stress[END_REF]. Although it is known since the middle of the 19 th century [START_REF] Loewel | Observations sur la sursaturation des dissolutions salines[END_REF], the heptahydrate has been largely neglected in almost all modern works about salt weathering. Heptahydrate has been recently the subject of abundant publication (e.g. [START_REF] Genkinger | Crystallization of sodium sulphate: supersaturation and metastable phases[END_REF]Hamilton et al. 2008;Hamilton and Hall 2008;[START_REF] Steiger | Crystallization of sodium sulfate phases in porous materials: The phase diagram Na2SO4-H2O and the generation of stress[END_REF]Espinosa Marzal and Scherer 2008;[START_REF] Derluyn | Sodium sulfate heptahydrate I: The growth of single crystals[END_REF][START_REF] Saidov | Nucleation of sodium heptahydrate on mineral substrates studied by nuclear magnetic resonance[END_REF] suggesting that it very likely plays an important role into geochemical and planetary processes, and therefore it should be reconsidered in the Na2SO4-H2O system.

Anhydrous Na2SO4 (III and V) can form directly upon evaporation of a sodium sulfate solution above 32.4 °C (Amirthalingen et al. 1977) but also by dehydration of mirabilite [START_REF] Rodriguez-Navarro | How does sodium sulfate crystallize ? Implications for the decay and testing of building materials[END_REF]. Hydrated Na2SO4 can form upon evaporation of a sodium sulfate solution below 32.4°C, as well as upon cooling of the same solution. The relationship between the heptahydrate and the decahydrate is not straightforward. There are several studies that show that mirabilite rarely forms directly from supersaturated sodium sulfate solutions (in the absence of seed crystals), and that the first solid to form is the metastable heptahydrate when cooling or evaporating a supersaturated sulfate sodium solution (e.g. [START_REF] Loewel | Observations sur la sursaturation des dissolutions salines[END_REF][START_REF] Hartley | The Spontaneous Crystallization of Sodium Sulfate Solutions[END_REF][START_REF] Genkinger | Crystallization of sodium sulphate: supersaturation and metastable phases[END_REF][START_REF] Rijniers | Experimental evidence of crystallization pressure inside porous media[END_REF]Espinosa Marzal and Scherer 2008;[START_REF] Pel | The formation of meta-stable sodium sulfate heptahydrate in porous materials as studied by NMR[END_REF][START_REF] Derluyn | Sodium sulfate heptahydrate I: The growth of single crystals[END_REF][START_REF] Saidov | Nucleation of sodium heptahydrate on mineral substrates studied by nuclear magnetic resonance[END_REF]. The destructive effect of sodium sulfates in porous media is attributed to Mir (rather than Thn) especially when forming from highly supersaturated solution originating from the rapid dissolution of Na2SO4 [START_REF] Chatterji | Efflorescence and breakdown of building materials[END_REF][START_REF] Flatt | Salt damage in porous materials: how high supersaturations are generated[END_REF][START_REF] Tsui | Crystallization damage by sodium sulfate[END_REF][START_REF] Steiger | Crystallization of sodium sulfate phases in porous materials: The phase diagram Na2SO4-H2O and the generation of stress[END_REF]. SS7, according to [START_REF] Rijniers | Experimental evidence of crystallization pressure inside porous media[END_REF] is considered to be harmless.

Several studies (e.g. [START_REF] Rodriguez-Navarro | Salt weathering: influence of evaporation rate, supersaturation and crystallization pattern[END_REF][START_REF] Flatt | Salt damage in porous materials: how high supersaturations are generated[END_REF][START_REF] Scherer | Stress from crystallization of salt[END_REF][START_REF] Angeli | Influence of temperature and salt concentration on the salt weathering of sedimentary stones with sodium sulphate[END_REF] have shown that temperature plays a major role on salt weathering of sedimentary rocks. For this reason, we present in this study some results of accelerated ageing experiments performed on samples of a detritic limestone under two different ambient temperatures: 20°C, which is below the upper limit of metastability of SS7, and 30°C, which is above this limit and also below the upper limit of stability of Mir. The goal of this experimental study is to evaluate the possible role of SS7 in the damage of porous material by sodium sulfate crystallization. The originality of this study is that we placed thermocouples into the samples in order to follow the evolution of the temperature of the stones during the accelerated ageing tests. We discuss the temperature curves for each phase of the cycle with the aim of understanding the processes that occur and that are responsible for the damage observed in the two experiments.

Material and methods

Material

Cubic samples (7 cm/side) of a lutetian-aged limestone from the Paris Basin commercially known as "Roche fine" have been used in this study. This rock has been chosen for several reasons: it is relatively homogenous from one sample to another, it has been widely used for construction in Paris (historic buildings and monuments), it is one of the stones required for use in restoration and construction in the protected areas of the "Ile de France" region, and it is also the rock that has been used in previous experiments from which the current study originated (e.g. [START_REF] Angeli | Influence of temperature and salt concentration on the salt weathering of sedimentary stones with sodium sulphate[END_REF]. Roche fine is a fine-grained detritic limestone made of calcite (90%) and quartz (10%) with a very low tensile strength (1.5 MPa), a high porosity (37, 2%) with two pore entry sizes (mainly around 20 µm, and another one between 0.03 to 5 µm). Complete hydromechanic properties are available in [START_REF] Angeli | Salt crystallization in pores: quantification and estimation of damage[END_REF].

We used four samples per experiment. All samples undergo several cycles of washing by immersion in demineralized water at room temperature for 6 hours and drying at 105°C until constant weight, in order to remove any possible natural occurrence of soluble salts in the stone. A single thermocouple is placed in each cube at the center of the sample. The drill hole used to insert the thermocouple is filled up with a mixture of lime and limestone powder resulting from the drilling of the sample.

The thermocouples (type K) are connected to a thermal acquisition station (Agilent 34970A) with 22 recording channels. The acquisition station is connected to a personal computer using ® Agilent BenchLink Data Logger software for programming the thermal station and recording data. According to the manufacturer, the accuracy of measurement is ± 0.5°C.

Methodology

The samples are subjected to 24 hours accelerated ageing cycles modified from the EN 12370 standard test. The cycles consist of three stages. (i) 2 hours imbibition at "ambient" temperature; the 4 samples are placed in a cooled incubator (® LMS 303NP) where the temperature is fixed at 20°C or 30°C according to the experiment. 2 samples soak in approximately 0.5 mm height of a sodium sulfate solution of 5.5 weight % Na2SO4; the two other samples are used for control and comparison. For one of these samples, the sodium sulfate solution is substituted with demineralized water, and for the other one there is no imbibition solution at all. Each sample is placed into an individual box that is supplied with solution by a system of drop by drop, and with an overflow in order to keep the level of solution constant. The solutions are stored at ambient temperature in the incubator. -(ii) 16 hours of drying. All the samples are placed in a drying oven (®memmert UE600) at 105°C. -(iii) 6 hours of cooling; the samples are replaced into the cooling incubator with fixed temperature so that they return to their initial "ambient" temperature, i.e. respectively 20°C and 30°C . The samples are weighed and photographed after each stage. Seven full cycles have been performed during which the temperature was recorded every 3 minutes.

Results

Rock decay vs. cycle

Figure 2 shows the different samples at the different stages of the 7 cycles. The reference samples do not show any damage after 7 cycles. The weight monitoring (Figure 3) confirms that their weight remains constant through the full experiment independently of the ambient temperature, suggesting that the limestone is inert with respect to demineralized water.

Samples subject to Na2SO4 solution at 20°C show intense damage starting with efflorescence (from cycle 1), then contour scaling (from cycle 3), crumbling, and desquamation leading to an important change of shape. The distribution of the damage is heterogeneous. An important loss of material is observed at the bottom of the samples from cycle 4. It seems related to a granular decohesion as suggested by the presence of loose material at the end of the imbibition stage (starting from cycle 3). Vertical faces are more affected by crumbling and the top of the samples by cracking and desquamation. In contrast, samples in exp 30 do not present any major damage but just efflorescence that starts to occur as soon as cycle 1 and increases with continuing cycles. The monitoring of sample weight during cycles (Figure 3a) shows consistent results with the shape evolution described above. All the samples undergo a similar weight increase during the two first cycles, but the evolution differs from cycle 3. Indeed, samples in exp 30 show a continuous and progressive weight gain, while samples in exp 20 show a smaller weight increase during the third cycle, before undergoing weight decrease from cycle 4. All these results are consistent with previous research [START_REF] Price | The use of the sodium sulphate crystallisation test for determining the weathering resistance of untreated stone[END_REF][START_REF] Angeli | Salt crystallization in pores: quantification and estimation of damage[END_REF][START_REF] Angeli | Influence of temperature and salt concentration on the salt weathering of sedimentary stones with sodium sulphate[END_REF].

Temperature evolution of the control samples

The temperature curves for the "dry" reference samples are shown on Figure 4. The curves are very regular. They show in particular a constant and high heating rate of the samples during drying until reaching a plateau at 105°C. Then a progressive and constant decrease of temperature is observed during cooling until reaching the ambient temperature where a second plateau is observed. This indicates that the studied limestone presents an inert behavior with respect to temperature. Temperature curves are consistent with the model of heat diffusion through the rock. The temperature curves of reference samples undergoing imbibition by demineralized water (Figure 5) are also very regular and similar whatever the ambient temperature. During wetting, the temperature of the sample decreases ( -8 °C in exp 20, and -6°C in exp 30). This is due to the evaporative cooling. The drying period is characterized by a variation of the heating rate. First, we observe a rapid increase of temperature until a sort of plateau at ~59°C, then a deceleration of the temperature increase up to an inflection point at ~85°C where it becomes more rapid again (but not as much as the beginning of drying) until reaching the final plateau at 105°C. The variation of the heating rate indicates the occurrence of two endothermic reactions, the first, requiring more energy than the second. The first, leading to the ~59°C plateau, would correspond to the departure of water on the surface as well as the water trapped in the 20 µm size entry pores, the second (inflection point at 85°C) would correspond to the departure of the water trapped within the small entry size pores. Cooling is characterized by a rapid, progressive and continuous return to ambient temperature.

Temperature evolution of the samples subject to salt weathering

Before discussing the different stages of the cycles for each experiment, it is worth noting that the first cycle is always very different from the following cycles that are more repetitive, as if the first cycle is a transitory phase (Figure 6). The difference between cycle 1 and following cycles the absence of salt in the porous network of the stone at the beginning of the imbibition. Therefore, cycle one will not be discussed in this study, in particular the drying stage on which we cannot rely.

Evolution of the temperature during imbibition

The first imbibition is characterized by a significant temperature decrease of several degrees (~ -5.5°C in exp 20, and in exp 30) that lasts all along this stage. From cycle 2, the imbibition is characterized first by a temperature rise immediately followed by a fall of temperature. The temperature rise matches with the hydration of the system, which leads to the dissolution of the salts (Thn?) formed during former drying period. Depending on the salinity and temperature, the rehydration of the system can also correspond with the formation of hydrates. This interpretation is consistent with the lack of temperature increase during the first imbibition as there is no salt in the sample at the beginning of the experiment. Temperature decrease is due to evaporative cooling as in the first imbibition.

It is worth noting that the temperature rise is more important in exp 20 than that in exp 30 (more Mir crystallized). In a similar way, the temperature decrease due to evaporative cooling increases progressively with cycles in exp 20 whilst it decreases in exp 30. Then several chemical phenomena occur during this part of the cycle, which correspond to the major (if not single) phase of damage.

Evolution of the temperature during drying

The drying behavior is very different during the first cycle than during the subsequent cycles. In exp 20, the drying period can be divided into 4 stages: (i) rapid heating, similar to what happens on the reference samples; (ii) slower heating period; (iii) rapid heating again, same velocity as in first stage; and (iv) temperature step at 105°C (plateau). The differences in the drying period all occur during the lower velocity heating. Comparing this velocity to the dry sample velocity shows that an endothermic reaction occurs at this moment, and that this reaction draws its energy from the heat of the oven. There are several features to be noted: -the drying velocity decreases progressively from the 2nd cycle to the 5th cycle from the 5th cycle to the end, the temperature remains constant for a period and then starts to increase again the duration of the step at 105°C increases at every cycle It appears that this endothermic reaction requires more and more energy at every cycle, showing that there are more and more reactants (salts) at every cycle. Since the salt content in the sample increases at every cycle, we can assume that the hydrate (Mir?) production during the imbibition (damaging) stage increases accordingly. Thus, there is a larger amount of hydrate to dehydrate.

From this point, the "evaporation duration" is defined as the time necessary to reach 105°C. It is approximately the amount of time needed to remove all the water in the sample. The evaporation duration increases until the appearance of decay. This suggests that the efflorescence partially blocks the pores at the surface, thus reducing the evaporative surface. Once the decay starts at cycle 4, the evaporation duration is progressively reduced. This increase in the drying velocity shows that the decay of the surface creates cracks and/or reduces the sealing efficiency of the salt crust.

The drying stage in exp 30 is much simpler. The initial drying velocity is similar to the reference sample until water starts to evaporate. Then, the evaporation velocity starts to decrease, which corresponds to the evaporation of water in the sample and the precipitation of Thn. The remaining bulk (stone + Thn) is then heated to 105°C. Apparently very little of the damaging hydrated phase occurred during imbibition and no damage has been observed in the sample after 7 cycles. Mir is a mineral that grows easily but nucleates very seldomly. A possible explanation for the lack of Mir crystallization in exp 30 could be that since Mir is difficult to nucleate [START_REF] Hartley | The Spontaneous Crystallization of Sodium Sulfate Solutions[END_REF], it could need an intermediate heptahydrate phase nucleation to be initiated and later grow from the heptahydrate nucleus as saturation increases by evaporation or cooling (but temperature is over the limit of metastability of SS7 in this precise case). Another explanation (according to the phase diagram) is that until supersaturation is reached, no Mir can precipitate, and once it has, only a small amount of Mir crystallizes.

Evolution of the temperature during cooling

The temperature evolution during cooling is more straightforward. It requires about 2 hours for each sample to return to its ambient temperature from the peak of temperature achieved at the end of the drying stage (Figure 6). This cooling is fast (~50°C/hour) at the beginning, and slows down when approaching ambient temperature.

We can consider that all the samples have returned to their ambient T after 2 hours and therefore, they do not undergo further temperature variation until the beginning of next cycle.

Conclusions

After 7 complete cycles of 24 hours, the samples at 20°C and 30°C show different damage patterns as well as different temperature curves. It illustrates the difficulty of completely removing the water from the rock under certain conditions--as illustrated by the impossibility of reaching 105°C at the end of the drying period for the experiment at 30°C. This suggests that the crystallization of salts can fill the pore entries, trapping sodium sulfate solutions or more likely hydrated phases. It requires either more energy or a longer period of drying to release water from the samples. This result raises questions about the accuracy and the relevance of the standard stone durability

The evaporative cooling phenomenon shows that imbibition is a favorable stage for the precipitation of sodium sulfate hydrates, especially if the concentration of the solution is close to supersaturation. Evaporation during this stage can trigger nucleation, and the cooling favors crystal growth.

The temperature profiles of this experiment do not enable the positive identification of two different hydrates. Supersaturation is achieved sooner at 20 than 30°C, which drives more hydrates to precipitate during imbibition. In addition, as complete dehydration of the sample is not reached in exp 30, there is less (or even no) Thn to transform during imbibition. Therefore, the current results do not allow an estimate of the role of SS7 in the weathering of rocks by sodium sulfates.

Finally, this study shows that recording temperature cycles can provide a simple and inexpensive method that enables monitoring exo/endothermic physico-chemical processes such as phase crystallization. It can be useful to identify mineral precipitation, sequences of crystallization and the important stages (wetting, evaporation, cooling) that are involved in rock damage.

Figure 1 .

 1 Figure 1. Phase diagram of the Na2SO4-H2O system. Stars represent the concentration and Temperature for the two experiments.

Figure 2 .

 2 Figure 2. Macroscopic evolution of the samples during the accelerated ageing cycles

Figure 3 .

 3 Figure 3. Weight evolution of the samples in the dry state at 20°C (A) and 30°C (B)

Figure 4 .

 4 Figure 4. Temperature evolution of the "dry" reference during the 7 cycles (A), and for a single cycle of 24 hours (B). The Temperature profile is in exp20.

Figure 5 .

 5 Figure 5. Temperature evolution of the reference samples undergoing imbibition by demineralized water during the 7 cycles (A), and for a single cycle of 24 hours (B).

Figure 6 .

 6 Figure 6. Temperature evolution of samples undergoing accelerated ageing cycles with sodium sulfates at 20°C (A) and 30°C (B).


Ramiro Logares
email: ramiro.logares@gmail.com

Stéphane Audic

David Bass

Lucie Bittner

Christophe Boutte

Richard Christen

Jean-Michel Claverie

Johan Decelle

John R Dolan

Micah Dunthorn

Bente Edvardsen

Angélique Gobet

Wiebe H C F Kooistra

Frédéric Mahé

Fabrice Not

Hiroyuki Ogata

Jan Pawlowski

Massimo C Pernice

Sarah Romac

Kamran Shalchian-Tabrizi

Nathalie Simon

Thorsten Stoeck

Sébastien Santini

Raffaele Siano

Patrick Wincker

Adriana Zingone

Thomas A Richards

Colomban De Vargas

Ramon Massana

Patterns of Rare and Abundant Marine Microbial Eukaryotes

Keywords: microbial eukaryotes, community ecology, marine, abundance distribution, next-generation sequencing

Regular proportions of abundant and rare microeukaryotes

INTRODUCTION

Microbes are the dominant form of life in the oceans, playing fundamental roles in ecosystem functioning and biogeochemical processes at local and global scales [START_REF] Falkowski | The microbial engines that drive Earth's biogeochemical cycles[END_REF][START_REF] Delong | The microbial ocean from genomes to biomes[END_REF][START_REF] Massana | Eukaryotic picoplankton in surface oceans[END_REF][START_REF] Caron | Marine Protistan Diversity[END_REF].

However, limited knowledge of their diversity and community structure across space and time [START_REF] Logares | Does the global microbiota consist of a few cosmopolitan species?[END_REF][START_REF] Martiny | Microbial biogeography: putting microorganisms on the map[END_REF] hinders our understanding of the links between microbial life and ecosystem functioning [START_REF] Arrigo | Marine microorganisms and global nutrient cycles[END_REF]. During the last decade, technological progress in molecular ecology and environmental sequencing has substantially boosted our understanding of marine microbes, unveiling notable patterns of abundant and rare sub-communities [START_REF] Caron | Marine Protistan Diversity[END_REF][START_REF] Pedrós-Alió | Marine microbial diversity: can it be determined?[END_REF][START_REF] Pedrós-Alió | The rare bacterial biosphere[END_REF], reminiscent of patterns observed in classical plant and animal ecology [START_REF] Magurran | Biological Diversity: Frontiers in measurements and assessment[END_REF]. The recently discovered large amount of rare taxa in microbial communities is now referred as the "Rare Biosphere" [START_REF] Sogin | Microbial diversity in the deep sea and the underexplored "rare biosphere[END_REF], whose exploration is made feasible today by means of High-Throughput Sequencing (HTS) technologies [START_REF] Logares | Environmental microbiology through the lens of high-throughput DNA sequencing: Synopsis of current platforms and bioinformatics approaches[END_REF].

Abundant and rare microbial sub-communities may have fundamentally different characteristics and ecological roles. For example, rare marine microbes are hypothesized to include ecologically redundant taxa that could increase in abundance following environmental perturbation or change, and maintain continuous ecosystem functioning [START_REF] Caron | Hypotheses on the role of the protistan rare biosphere in a changing world[END_REF]. Locally rare taxa can also act as seeds for seasonal succession or sporadic blooms. Conversely, the drastic decrease in abundance or even extinction of a globally abundant oceanic microbe with no ecologically comparable counterpart in the rare biosphere could have significant and unpredictable effects on the global ecosystem.

Most of the studies to date that have differentiated between abundant and rare marine microbial sub-communities concern bacteria. Abundant bacteria contribute mostly to biomass, carbon flow and nutrient cycling, while the generally large numbers of rare bacteria contribute predominantly to species richness [START_REF] Pedrós-Alió | The rare bacterial biosphere[END_REF]. Different strategies are observed among rare bacteria, such as dormant or inactive taxa that grow exponentially when the right conditions are met [START_REF] Pedrós-Alió | Marine microbial diversity: can it be determined?[END_REF][START_REF] Jones | Dormancy contributes to the maintenance of microbial diversity[END_REF][START_REF] Campbell | Activity of abundant and rare bacteria in a coastal ocean[END_REF][START_REF] Sjostedt | Recruitment of members from the rare biosphere of marine bacterioplankton communities after an environmental disturbance[END_REF] or taxa that seem to remain members of the rare biosphere [START_REF] Campbell | Activity of abundant and rare bacteria in a coastal ocean[END_REF][START_REF] Galand | Ecology of the rare microbial biosphere of the Arctic Ocean[END_REF] even when they have high relative metabolic activity. Rare bacteria may perform crucial ecosystem functions [START_REF] Pester | A 'rare biosphere' microorganism contributes to sulfate reduction in a peatland[END_REF] and some of them can be metabolically more active than abundant taxa in the same community [START_REF] Jones | Dormancy contributes to the maintenance of microbial diversity[END_REF][START_REF] Campbell | Activity of abundant and rare bacteria in a coastal ocean[END_REF]. Both abundant and rare bacteria can present similar biogeographic patterns [START_REF] Galand | Ecology of the rare microbial biosphere of the Arctic Ocean[END_REF][START_REF] Logares | Biogeography of bacterial communities exposed to progressive long-term environmental change[END_REF] indicating similar community assembly mechanisms.

Compared to bacteria, we know even less about abundant and rare marine microbial eukaryote (protists) sub-communities and, overall, marine protists remain as one of the least explored features of natural biodiversity [START_REF] Caron | Protists are microbes too: a perspective[END_REF]. Recent studies using 454 pyrosequencing [START_REF] Logares | Environmental microbiology through the lens of high-throughput DNA sequencing: Synopsis of current platforms and bioinformatics approaches[END_REF] recovered few dominant protist taxa and a large number of rare ones from specific marine and freshwater communities [START_REF] Stoeck | Massively parallel tag sequencing reveals the complexity of anaerobic marine protistan communities[END_REF][START_REF] Cheung | Composition and genetic diversity of picoeukaryotes in subtropical coastal waters as revealed by 454 pyrosequencing[END_REF][START_REF] Nolte | Contrasting seasonal niche separation between rare and abundant taxa conceals the extent of protist diversity[END_REF][START_REF] Stoeck | Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water[END_REF][START_REF] Mangot | Short-term dynamics of diversity patterns: evidence of continual reassembly within lacustrine small eukaryotes[END_REF]. Little information is available regarding protist metabolic activity. A recent study in freshwater lakes comparing rRNA/rDNA ratios (a proxy of microbial activity) suggested that, in contrast to bacteria, dormancy does not play an important role in planktonic protist communities [START_REF] Jones | Dormancy contributes to the maintenance of microbial diversity[END_REF].

Here we explore fundamental patterns of rare and abundant marine-planktonic protistan sub-communities occurring along the European coastline, from the North Sea (Norway) to the Black Sea (Bulgaria) (Figure 1A). Using Illumina [START_REF] Logares | Environmental microbiology through the lens of high-throughput DNA sequencing: Synopsis of current platforms and bioinformatics approaches[END_REF] and, to a more limited extent, 454 HTS platforms, we generated a large dataset of both 18S rRNA and rDNA tags based on total RNA and DNA extracts from three organismal size-fractions, the pico-(0.8-3μm), nano-(3-20μm) and micro/meso-(20-2,000μm) plankton [START_REF] Massana | Eukaryotic picoplankton in surface oceans[END_REF][START_REF] Sieburth | Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationships to plankton size fractions[END_REF].

The wide geographical and organismal scales of this dataset, combined with ultra-deep sequencing, allowed us to address the following main questions: Are the relative proportions of abundant and rare protist sub-communities fluctuating across space and time? What structural and biogeographic patterns present these sub-communities? Do locally abundant taxa tend to be regionally abundant? And, are there specific phylogenetic and activity patterns associated to abundant and/or rare marine protistan 130 sub-communities? We found that abundant and rare assemblages present contrasting structuring patterns and phylogenetic characteristics, despite a remarkable consistency in their relative proportions across individual samples. Furthermore, rare subcommunities included a large number of predominantly active lineages that presented biogeography. 135

RESULTS

General patterns of richness and evenness

Unless stated otherwise, our results derive from the Illumina V9 18S rRNA tag dataset clustered into 95% similarity Operational Taxonomic Units (OTUs; Table S1) and prepared using RNA extracts. The 95% threshold was selected for all downstream analyses in order to minimize any inflation of diversity estimates [START_REF] Kunin | Wrinkles in the rare biosphere: pyrosequencing errors can lead to artificial inflation of diversity estimates[END_REF] caused by remaining tags (if any) with misincorporated nucleotides. In a local community, we defined OTUs as "abundant" when they reached relative abundances above 1% of the tags and "rare" when their abundances were below 0.01%, following other studies in bacteria [START_REF] Pedrós-Alió | The rare bacterial biosphere[END_REF][START_REF] Galand | Ecology of the rare microbial biosphere of the Arctic Ocean[END_REF] and protists [START_REF] Mangot | Short-term dynamics of diversity patterns: evidence of continual reassembly within lacustrine small eukaryotes[END_REF]. In the regional community (combination of local communities), the thresholds for abundant or rare were >0.1% and <0.001% respectively. In addition, we tested a 97% OTU clustering threshold and comparable patterns regarding proportions of locally abundant and rare sub-communities were obtained (Table S2).

In total, ~72% of all OTUs were found only in a single size fraction, being either the pico-(0.8-3μm), nano- (3-20μm) or micro/meso-(20-2,000μm) plankton (Figure S1). Similarly, ~75% of the rare and ~62% of the abundant OTUs in the regional community were restricted to a single organismal size fraction. This indicates that our sea-water filtering protocol used to separate total plankton communities into three distinct organismal size fractions was effective.

In rarefaction analyses considering all reads (5.69 x 10 6 ) and samples from the regional community, richness (based on 95% similarity OTUs) approached saturation at ~9,000 OTUs (Figure S2). OTU-richness also approached saturation in most local communities (between 800 -3,000 OTUs; Figure S2A). The highest richness was observed in the nano-plankton (3-20μm; 4,786 OTUs after normalization), and the lowest in the micro/meso-plankton (20-2000μm; 2,941 OTUs; Table 1). Evenness was low in the regional community, within different size fractions, and in all studied local communities (Figures S3A,B & S4), with the majority of OTUs being rare and only a few abundant. In the regional community, considering both pooled and separate size fractions, abundant taxa were <3.5% of the total OTUs while rare taxa were >63.4% of the OTUs (Table 1). When considering pooled normalized size fractions, the percentage of total reads falling into rare OTUs in the regional community was 1.1% (20,000 reads), while the percentage of total reads falling into abundant OTUs was 80.7%

(1,448,079 reads).

Overall, a total of 20 out of 23 analyzed samples fitted the log-normal model [START_REF] Magurran | Biological Diversity: Frontiers in measurements and assessment[END_REF][START_REF] Magurran | Measuring Biological Diversity[END_REF] of Species Abundance Distribution (SAD) according to the Akaike's Information Criterion [START_REF] Akaike | A new look at the statistical model identification[END_REF] (Figure S4). Assuming a log-normal distribution, we fitted our regional community data to the truncated Preston log-normal model [START_REF] Magurran | Biological Diversity: Frontiers in measurements and assessment[END_REF][START_REF] Preston | The commonness, and rarity, of species[END_REF] (Figure S5A) and estimated that we recovered 64-67% of the OTUs in the European coastal region (Figure S5B). Therefore, even though our deep Illumina sequencing approach recovered the majority of OTUs from our sample-set, extra sampling effort is needed to recover the total richness of the studied area.

Community structure across space, time, and organismal size-fractions

The proportions of locally rare (<0.01%) and abundant (>1%) OTUs (by our definition) were relatively constant across communities (Figure 1B, Table S1), with ranges of 66.2 -76.6% for rare OTUs and 0.9 -2.7% for abundant ones (Figure 1B, Table S1). Reads corresponding to locally abundant OTUs represented on average 70.1% (SD=9.5) of the dataset, while reads corresponding to rare OTUs represented on average 1.9% (SD=0.7) (Figure 1B). β-diversity, as described by Bray-Curtis dissimilarities between samples, within rare and abundant regional communities (i.e. pooled rare or abundant sub-communities) showed a moderate but significant correlation when considering normalized OTUs from all size fractions together (Mantel test r (abundant|rare) =0.73; p<0.001) as well as within the pico-plankton (Mantel r (abundant | rare, 0.8-3) =0.69; p<0.05). The correlation was weaker, but still significant, in the nano-and micro/meso-plankton (Mantel r (abundant | rare, 3-20) =0.44; r (abundant | rare, 20-2000) =0.46; p<0.05). These correlations indicate that some abundant and rare taxa share similar biogeography. Yet, the abundant regional community was structured mostly by size fraction, as the OTU composition of abundant micro/mesoplankton was more similar among samples of this fraction than to any sample of the pico-and nano-plankton (Figure 2A). In contrast, the rare regional community was mostly structured by sampling site, with samples from different organismal size fractions but from the same site being normally more similar in OTU composition when compared to samples from other sites (Figure 2A). Network analyses provided further insight into these patterns by showing that within the abundant regional community, the smaller size fractions (pico-and nano-plankton) shared more OTUs between them than with the larger size fraction (micro/meso-plankton; Figure 2B). In contrast, the rare regional community network showed that several OTUs were unique to single samples, and that the few shared OTUs tended to be shared between samples of the same site (Figure 2B).

Further exploration across samples of OTUs that were locally abundant in at least one sample (total 175 OTUs) showed that none of them presented abundances >1% in all samples. In analyses of individual size fractions, most OTUs with abundances >1% were abundant at a single site/sample (Figure 3A-C), being often rare or of intermediate abundance elsewhere. Only one OTU within the fraction 3-20μm displayed abundances >1% in all samples (Figure 3B).

Phylogenetic patterning of abundant versus rare regional communities

The constructed phylogeny contained 11 reference OTUs that exclusively represented regionally abundant OTUs, 107 reference OTUs that represented both regionally abundant and rare OTUs, and 1,225 reference OTUs that exclusively represented regionally rare OTUs (Figure 4). While the majority of the regionally abundant OTUs had relatively close evolutionary relatives among the rare, the majority of the regionally rare OTUs had no close evolutionary relatives among the abundant. BLASTing Illumina representative reads from abundant and rare OTUs against each other supported this pattern. About 90% of the abundant OTUs (n=154) produced significant BLAST hits against the rare (that is, hits with coverage >97% and identity >70%), while only about 31% of the rare OTUs (n=5,329) produced significant hits against the abundant. Faith's Phylogenetic Diversity (PD) measure [START_REF] Faith | Conservation evaluation and phylogenetic diversity[END_REF] was higher in the rare regional community when compared with the abundant at a similar sampling depth (Figure 4B). Both the Mean Phylogenetic Distance (MPD) and the Mean Nearest Taxon Distance (MNTD) [START_REF] Webb | Phylogenies and Community Ecology[END_REF] indicated that regionally abundant OTUs included in the phylogeny (n=118) clustered together at a higher frequency than what was expected by chance (Figure 4C). Such a pattern is expected to occur when the environment selects related taxa that share favorable traits [START_REF] Webb | Phylogenies and Community Ecology[END_REF][START_REF] Cavender-Bares | The merging of community ecology and phylogenetic biology[END_REF]. Conversely, the MPD and MNTD among regionally rare OTUs (n=1,332) did not present deviations from a random distribution (Figure 4C).

Activity versus abundance

In order to check to what extent the community and phylogenetic patterns described above are due to the use of RNA, and not DNA tags, and thus be partially explained by cellular activity, we analyzed 15 samples for which both DNA-and RNA-based tags (V4 18S, 454 tags) were obtained. The relative abundance of OTUs in the regional community that were present in both the DNA and RNA datasets showed on average a nearly 1:1 relationship (Figure 5A). Both the DNA and RNA recovered a number of OTUs as consistently rare or abundant in the regional community (Figure 5A). However, some OTUs were rare in the regional community according to the DNA dataset but showed intermediate abundances within the RNA dataset and vice versa.

Approximately 25 OTUs were disproportionately underrepresented by RNA tags (Figure 5B, grey area), suggesting low-activity or large numbers of rDNA copies in the genome. On the contrary, ~20 OTUs were disproportionately overrepresented by RNA tags, which may result from high ribosomal activity (Figure 5B, yellow area).

DISCUSSION

Marine microbial eukaryotes constitute arguably the most poorly characterized biodiversity component in the biosphere [START_REF] Caron | Protists are microbes too: a perspective[END_REF]. Here we provide new insights into the structural and phylogenetic organization of their communities using the first ultra-deep sequencing dataset of 18S rRNA tags extracted from surface pico-, nano-and micro/meso-plankton collected at six marine-coastal locations across Europe. Illumina sequencing of >150 million V9 rRNA amplicons followed by highly-stringent sequence quality filtering allowed us to approach richness saturation (OTUs 95%) in both the entire regional community as well as in most local communities, therefore allowing the exploration of the rare protist biosphere. The highest richness was observed in the proximity of the smallest cell(body)-sizes (in the nano-plankton (3-20μm)), thus resembling patterns observed in animals by early ecologists [START_REF] Hutchinson | A theoretical ecological model of size distributions among species of animals[END_REF][START_REF] Rosenzweig | Species Diversity in Space and Time[END_REF]. We estimate a recovery of ~64% of the total number of OTUs in the entire region, therefore more samples are required to cover the total diversity of European coastal waters. As observed in aquatic prokaryotes [START_REF] Pedrós-Alió | The rare bacterial biosphere[END_REF], most of the recovered OTUs (>63.0%) belonged to the rare biosphere; since we used RNA as template, we can attest that these OTUs represent living, ribosomically active cells.

Despite the strong spatio-temporal variability characterizing marine coastal waters and the different β-diversity among sites, the proportion of locally rare and abundant taxa was remarkably constant across all sampled communities. This pattern suggests community self-organization arising from local species interactions, with the observed regular proportions representing stable community configurations [START_REF] Zimmermann | An experimentallyderived map of community assembly space[END_REF]. Given this striking consistency observed in our data, we hypothesize that in other marine planktonic communities >70% of protist OTUs are rare as well. Yet, it should be considered that rarity was analyzed according to one pre-existing definition; future studies should explore multiple definitions in order determine which one is the most meaningful [START_REF] Magurran | Biological Diversity: Frontiers in measurements and assessment[END_REF].

Both the abundant and rare regional communities demonstrated contrasting patterns regarding their general structure. The abundant regional community was predominantly structured by organismal size fraction, while the rare regional community was structured mostly according to geographic site. On the one hand, size fraction structuring reflects the fact that, excluding protists with complex cell cycles, ontogenic processes, or cell shapes and colony forms markedly distinct from a sphere, most taxa have rather constant cell sizes. On the other hand, site-associated clustering indicates that the differences between communities from different sites are larger than the differences between size fractions within the same site. Such groupings of the rare pico-, nano-and micro/meso-plankton were generated by only a few OTUs that were present in only one site and shared between the large and smaller size-fractions. These OTUs could represent low abundance protists with life cycles that involve different cell sizes, different lifestyles (host associated or not), as well as issues related to a nonoptimal size fractionation during filtering. Site-associated clustering can be promoted by historical contingencies occurring in different communities, such as local random extinctions or stochastic immigration events [START_REF] Ricklefs | Community diversity: relative roles of local and regional processes[END_REF], which are expected to have a larger impact in rare sub-communities, making them generally more distinct between each other than abundant counterparts.

Even though abundant and rare regional communities presented a markedly different general structure, we found a moderate but significant correlation in their βdiversity that points to similar biogeography for some rare and abundant taxa. This suggests that similar structuring processes can affect both abundant and rare subcommunities, and that the rare protist biosphere is not a random collection of taxa.

Comparable results have been reported for marine and lacustrine prokaryotes [START_REF] Galand | Ecology of the rare microbial biosphere of the Arctic Ocean[END_REF][START_REF] Logares | Biogeography of bacterial communities exposed to progressive long-term environmental change[END_REF][START_REF] Gobet | Diversity and dynamics of rare and of resident bacterial populations in coastal sands[END_REF].

Underlying the β-diversity patterns at the regional level, locally abundant (>1%) OTUs within the pico-, nano-, and micro/meso-plankton showed marked variations in relative abundance between samples. Most locally abundant OTUs were abundant in only one sample, having intermediate or low (<0.01%) abundances in the others. This pattern, besides reflecting strong fluctuations in protistan abundance across heterogeneous coastal locations or seasonality in the same site [START_REF] Nolte | Contrasting seasonal niche separation between rare and abundant taxa conceals the extent of protist diversity[END_REF][START_REF] Kim | Monthly to interannual variability of microbial eukaryote assemblages at four depths in the eastern North Pacific[END_REF], points to a general decoupling between local and regional abundances, as most OTUs that are abundant in only one location will not be regionally abundant.

Last but not least, the rare protistan biosphere presented a distinctive phylogenetic composition, with a significant proportion of rare OTUs phylogenetically unrelated to abundant ones. In particular, several clades contained exclusively rare OTUs that were relatively distantly related in phylogenetic terms to the nearest abundant taxon. Although we cannot exclude that some taxa from these exclusively rare clades may be abundant in other locations/seasons, the overall pattern suggests that permanent or semi-permanent rarity (achieved e.g. through a low cell division rate) may be an evolutionary trait of some marine protist groups. Avoidance of competition, predation and parasitism are potential advantages of a low-abundance life [START_REF] Pedrós-Alió | Marine microbial diversity: can it be determined?[END_REF], which could evolve through negative frequency-dependent selection [40]. On the contrary, rare OTUs in the regional community that were phylogenetically closely related to abundant ones could represent intra-genomic variation or erroneous variants of abundant OTUs generated during PCR or sequencing [START_REF] Huse | Ironing out the wrinkles in the rare biosphere through improved OTU clustering[END_REF][START_REF] Quince | Removing noise from pyrosequenced amplicons[END_REF], although we minimized this bias by working with a relatively loose definition of OTUs at 95% similarity threshold [START_REF] Kunin | Wrinkles in the rare biosphere: pyrosequencing errors can lead to artificial inflation of diversity estimates[END_REF].

The structuring of the abundant regional community seems to have been influenced by environmental selection of evolutionary related taxa presenting favorable traits, as abundant taxa were phylogenetically more closely related than expected by chance [START_REF] Webb | Phylogenies and Community Ecology[END_REF][START_REF] Cavender-Bares | The merging of community ecology and phylogenetic biology[END_REF]. Our comparison of rRNA-versus rDNA-derived OTUs indicated that both types of markers are broadly positively correlated, supporting the hypothesis that low metabolic activity or dormancy is not common among planktonic microbial eukaryotes [START_REF] Jones | Dormancy contributes to the maintenance of microbial diversity[END_REF][START_REF] Massana | Eukaryotic versus prokaryotic marine picoplankton ecology[END_REF].

Thus, metabolically active taxa likely prevail in the protistan rare planktonic biosphere.

In addition, rRNA/rDNA comparisons suggested that a disproportionately high activity is unusual in planktonic protists. Altogether, this contrasts markedly with planktonic bacteria, where dormancy appears to be more prevalent [START_REF] Pedrós-Alió | The rare bacterial biosphere[END_REF][START_REF] Jones | Dormancy contributes to the maintenance of microbial diversity[END_REF] and where, for some taxa, activity can increase as abundance decreases [START_REF] Campbell | Activity of abundant and rare bacteria in a coastal ocean[END_REF].

CONCLUSION

Overall, our results indicate that marine-planktonic protist communities are composed of, predominantly active, abundant and rare sub-communities with contrasting structuring patterns and phylogenetic characteristics, which nevertheless display striking consistency in their local relative proportions, even in dissimilar coastal waters. Further analyses of protist community structuring in contrasting oceanic biomes will provide a wider test of the patterns we found in European coastal waters, contributing altogether to a better understanding of the community organization mechanisms in microbial eukaryotes and their links to local and global ecosystem functioning.

EXPERIMENTAL PROCEDURES

Sampling and Illumina / 454 sequencing Surface (<5m depth) seawater samples were collected in six European coastal offshore sites: Blanes (Mediterranean), Gijon (Bay of Biscay), Naples (Mediterranean), Oslo (North Sea / Skagerrak), Roscoff (English Channel), and Varna (Black Sea) (Figure 1A, Table S3). Pico-(0.8-3µm) and nano-(3-20µm) plankton samples were collected using Niskin bottles. A total of 15 to 40 liters of water were pre-filtered through a 20µm sieve and then sequentially filtered through a polycarbonate membrane of 3 and 0.8µm.

Micro/meso (20-2000µm) plankton samples were collected and concentrated using a 20µm-porosity plankton net during 20 min, then pre-filtered through a 2,000µm sieve and afterwards filtered through a 20µm polycarbonate membrane. Total RNA and DNA were extracted simultaneously from the three membranes. For Illumina GAIIx sequencing, hypervariable V9 18S tags were PCR amplified from cDNA obtained from RNA template, while V4 18S tags were PCR amplified from both DNA and RNA (cDNA) templates for 454-Titanium sequencing.

Sequence analysis for Illumina reads

A total of 23 samples were selected for downstream analyses (Table S1). For the forward reads (hereafter "reads"), about 15 Gigabases of raw sequence data (100bp reads) were produced (Table S1). Reads (minimum 90bp) were quality-checked using a sliding 10bp-window and each window had to have a phred-quality average >34 to pass the control. The number of clean reads after quality control is shown in Table S1.

Quality-checked reads were analyzed in QIIME v1.4 (Quantitative Insight Into Microbial Ecology; [START_REF] Caporaso | QIIME allows analysis of high-throughput community sequencing data[END_REF]). Reads were clustered into OTUs using UCLUST v1.2.22 [START_REF] Edgar | Search and clustering orders of magnitude faster than BLAST[END_REF] with a 95% similarity threshold. Chimeras were detected using ChimeraSlayer [START_REF] Haas | Chimeric 16S rRNA sequence formation and detection in Sanger and 454pyrosequenced PCR amplicons[END_REF] with a reference database derived from PR2 [START_REF] Guillou | The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy[END_REF]. Taxonomy assignment was done by BLASTing [START_REF] Altschul | Basic local alignment search tool[END_REF] the most abundant (representative) sequence of each OTU against different reference databases, and unwanted OTUs (e.g. Metazoa and prokaryotes) were removed. The final curated Illumina RNA dataset included 5,696,049 reads. Illumina sequences are publicly available at MG-RAST (http://metagenomics.anl.gov/, accessions: 4549916.3 -4549968.3).

Sequence Analysis for 454 reads

We analyzed 15 samples for which both DNA and RNA V4 18S tags were sequenced (Table S4); these samples were also present in the Illumina dataset (Table S1). All 454 reads between 200-500bp were run through QIIME v1.4. Reads were checked for quality using a sliding window of 50bp (Phred average >25 in each window) and truncated to the last good window. Sequences were denoised using DeNoiser (v 0.851; [START_REF] Reeder | Rapidly denoising pyrosequencing amplicon reads by exploiting rank-abundance distributions[END_REF]) as implemented in QIIME v1.4 and then clustered into OTUs using UCLUST v1.2.22 with a 99% similarity threshold. Chimera detection and taxonomy assignment were done using the same approaches as with the Illumina reads. In the final V4 curated dataset, RNA included 233,085 reads and DNA 221,898 reads (total 454,983 reads). 454 sequences are publicly available at MG-RAST (http://metagenomics.anl.gov/, accessions: 4549916.3 -4549968.3).

Final OTU tables

Single singletons as well as OTUs present in a single sample were removed from both Illumina V9 and 454 V4 OTU tables. For both datasets, we randomly subsampled OTU tables to the number of reads present in the sample with the lowest amount of reads. This value was 78,000 reads per sample for Illumina and 3,000 reads per sample for 454.

Ad hoc definitions of rare and abundant OTUs

OTUs were classified into abundant or rare in relation to their local and regional relative abundances. Locally abundant OTUs were defined as those with relative abundances >1%, and locally rare OTUs as those with abundances <0.01% following studies in prokaryotes [START_REF] Pedrós-Alió | The rare bacterial biosphere[END_REF][START_REF] Galand | Ecology of the rare microbial biosphere of the Arctic Ocean[END_REF] and protists [START_REF] Mangot | Short-term dynamics of diversity patterns: evidence of continual reassembly within lacustrine small eukaryotes[END_REF]. Regional relative abundances for specific OTUs were calculated as the average of local relative abundances for such OTU across all samples, including zero values. The thresholds for defining abundant and rare at the regional level were arbitrarily defined as the local thresholds divided by a factor of 10.

OTUs abundant in the regional community had a mean relative abundance >0.1%, while regionally rare OTUs had a mean relative abundance <0.001%.

Diversity analyses

Most analyses were run in R [START_REF]R: A language and environment for statistical computing[END_REF] environment using the packages Vegan [START_REF] Oksanen | vegan: Community Ecology Package[END_REF] and Picante [START_REF] Kembel | Picante: R tools for integrating phylogenies and ecology[END_REF]. Rarefactions and Species (OTUs) Accumulation Curves were calculated in Vegan. OTU networks were constructed in QIIME based on the subsampled OTU table and graphically edited in Cytoscape [START_REF] Shannon | Cytoscape: a software environment for integrated models of biomolecular interaction networks[END_REF] using the layout "Edge-Weighted Spring Embedded" with eweights.

Mapping of Illumina reads to reference Sanger sequences and phylogeny construction

Representative reads of regionally abundant or rare OTUs were mapped separately using BLASTn to a custom V9 18S rDNA Sanger reference database based on the PR2 [START_REF] Guillou | The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy[END_REF]. Using an e-value <1x10 -6 with a percentage of identity >90%, all abundant (n=154), and 95% of the rare (n=5,329) OTUs were assigned to reference taxa. The chosen parameters allowed for different OTUs to be mapped to the same Sanger reference taxa, and for this reason, the final phylogeny had less taxa than the sum of abundant and rare OTUs. For phylogeny construction, we extracted the full-length 18S sequence corresponding to all reference V9. Sequences were aligned using Mothur against the aligned SILVA 108 database (eukaryotes only). A Maximum Likelihood reference tree (8,311 sequences) was inferred using RAxML HPC-MPI (v7.2.8; [START_REF] Stamatakis | RAxML-VI-HPC: Maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models[END_REF]) under the model GTR+CAT/G+I and checked against other phylogenies of marine protists [START_REF] Pernice | General patterns of diversity in major marine microeukaryote lineages[END_REF] for consistency. The tree was pruned using the R package APE (Analyses of Phylogenetics and Evolution; [START_REF] Paradis | APE: Analyses of Phylogenetics and Evolution in R language[END_REF]) to keep only those reference taxa that were hit by abundant or rare OTUs. The final pruned tree, including 1,343 Sanger sequences, was used to calculate the Mean Phylogenetic Distance (MPD) and Mean Nearest Taxon Distance (MNTD) [START_REF] Webb | Phylogenies and Community Ecology[END_REF] with Picante. Phylogenetic diversity [START_REF] Faith | Conservation evaluation and phylogenetic diversity[END_REF] was computed using Picante.

See more details on Experimental Procedures in Supplemental Information. (normalized dataset) for both the rare and abundant regional communities. Branch colors indicate groups of samples originating from the same geographic site. Note that two large clusters are present within the abundant regional community, separating the pico-& nano-from the micro/meso-plankton.

(B) Networks representing abundant and rare regional communities. Larger nodes (circles) represent samples, while smaller nodes represent OTUs that may connect (that is, may be present in) different samples through edges (lines). The most relevant structuring features for both the abundant and rare regional communities were mapped onto the networks with colors. These were organismal size-fraction (abundant) and geographic origin (rare). Note that in (B) & (C), sequences that represented both abundant and rare OTUs ("shared") were considered within both the abundant and rare datasets. See also Table S4. 1, Table S3). Pico-(0.8-3µm) and nano- (3-20µm) plankton samples were collected using Niskin bottles. A total of 15 to 40 liters of water were pre-filtered through a 20µm sieve and then sequentially filtered through a polycarbonate membrane of 3 and 0.8 µm. Meso/micro-(20-2000µm) plankton samples were collected and concentrated using a 20µm-porosity plankton net during 20 min, then pre-filtered through a 2,000µm sieve and afterwards filtered through a 20µm polycarbonate membrane. Filtration time did not surpass 30 minutes to avoid RNA degradation. Filters were flash-frozen and stored at -80° C. Total DNA and RNA were extracted simultaneously from the same filter using the NucleoSpin® RNA L kit (Macherey-Nagel) and quantified using a Nanodrop ND-1000 Spectrophotometer. To remove DNA from RNA extracts, we used the TurboDNA kit (Ambion). A total of 100ng of extracted RNA were immediately reverse transcribed to DNA using the RT Superscript III random primers kit (Invitrogen).

Amplification and Illumina-sequencing of the V9 18S rRNA

The hypervariable V9 18S region was amplified with the eukaryotic primers 1389f (5'-TTGTACACACCGCCC-3') and 1510r (5'-CCTTCYGCAGGTTCACCTAC-3') [S3].

The V9 was chosen as it normally has between 90-140bp in microeukaryotes [S3], and therefore most of it can be covered by 100bp Illumina GAIIx reads. Amplifications were conducted with Phusion® High-Fidelity DNA Polymerase (Finnzymes). The PCR mixture (25µL final volume) contained about 5ng of cDNA template with 0.35µM final concentration of each primer, 3% of DMSO and 2X of GC buffer Phusion Master Mix (Finnzymes). Amplifications were done following the PCR program: initial denaturation step at 98°C for 30 sec, followed by 25 cycles of 10 sec at 98°C, 30 sec at 57°C, 30 sec at 72°C, and a final elongation step at 72°C for 10 minutes. Each sample was amplified in triplicates to get enough concentration of amplicons and reduce potential biases. Products of the reactions were run on 1.5% agarose gels to check for successful amplification within the expected sequence length. Amplicons were then pooled and purified using the NucleoSpin® Extract II kit (Macherey-Nagel). Ligation of amplicons with Illumina adapters and library construction were performed according to Illumina instructions. Paired-end 100bp sequencing was performed using a Genome Analyzer IIx (GAIIx) system (Illumina) located at Genoscope (http://www.genoscope.cns.fr/spip/, France).

Amplification and 454-sequencing of the V4 18S rDNA/rRNA

The universal primers TAReuk454FWD1 (5'-CCAGCASCYGCGGTAATTCC-3') and

TAReukREV3 (5'-ACTTTCGTTCTTGATYRA-3') were used to amplify the hypervariable V4 region (~380 bp) of the eukaryotic 18S rDNA/rRNA [S4]. The primers were adapted for 454 using the manufacturers specifications for Lib-L unidirectional sequencing, and had the configuration A-adapter-Tag (7 or 8bp)-forward primer, and B-adapter-reverse primer. PCR reactions were performed in 25μl, and consisted in 1X MasterMix Phusion® High-Fidelity DNA Polymerase (Finnzymes),

Figure 1 .

 1 Figure 1. Communities displayed regularity in the proportions of locally abundant

Figure 2 .

 2 Figure 2. Contrasting structuring patterns in abundant and rare regional

Figure 3 .

 3 Figure 3. Locally abundant OTUs (>1%) tended to be abundant in a single sample

Figure 4 .

 4 Figure 4. Phylogenetic patterns in the abundant and rare regional communities

Figure 5 .

 5 Figure 5. Abundance of OTUs in the regional community according to rDNA and

5 RNA average relative abundance (%) DNA average relative abundance (%) 1:1 line Linear Regression (R 2 =0.76, p<0.01) 1 5 10 50 100 500 Rank (DNA-based) Average Relative Abundance (%) DNA RNA A B

	A										RARE	B
	Rare					Abundant					ABUNDANT
	0.5
							Abundant
	0.05
	0.005
	0.0005							Rare
	0.001	0.01			0.1	1	10
						Abundant (DNA)		Rare (DNA)
			H	i g h	e r	a c t i v i t y
			*
	C	Lower activity
			# OTUs	MPDobserved	MPDnull	MPDz	p	MNTDobserved	MNTDnull	MNTDz	p
	Regionally Abundant OTUs	118			0.94	1.25	-4.34 < 0.001	0.22	0.39	-4.62 < 0.001
	Regionally Rare OTUs		1332			1.24	1.24	-0.1	0.386	0.19	0.19	-0.76	0.160

Table 1 .

 1 General description of both complete as well as normalized V9 18S rRNA Illumina datasets

		Combined size fractions	0.8-3 (μm)	3-20 (μm)	20-2000 (μm)
	Number of clean reads	5696049 a / 1794000 b	2279669 a / 624000 b	2298280 a / 624000 b	1118100 a / 546000 b
	Samples	23 / 23	8 / 8	8 / 8	7 / 7
	Geographic sites	6 / 6	6 / 6	6 / 6	5 / 5
	All OTUs c	9007 / 7035	6597 / 4412	7157 / 4786	3491 / 2941
	Abundant OTUs d	155 (1.7%) / 154 (2.2%)	153 (2.3%) / 153 (3.5%)	143 (1.9%) / 144 (3.0%)	95 (2.7%) / 95 (3.2%)
	Rare OTUs e	7333 (81%) / 5329 (75.7%) 5145 (77.9%) / 2981 (67.5%)	5614 (78.4%) / 3242 (67.7%)	2432 (69.6%) / 1865 (63.4%)
	a All data; variable number of reads per sample
	b Normalized data; 78000 reads per sample in all samples
	c All OTUs included in the dataset
	d OTUs abundant in the regional community; average relative abundances >0.1%
	e OTUs rare in the regional community; average relative abundances <0.001%
	See also Figures S1, S2, S3 & S5

Table S1 (related to Figure 1 & Experimental Procedures).

 S1 General description of the community sequencing approach (using Illumina V9 18S rRNA reads; 90-100 bp) and the obtained OTUs (95 % clustering) High-Quality reads remaining after a strict quality control: quality-checked reads with phred-average(10bp window) > 34 plus removal of chimeras, Bacteria, Archaea and Metazoa. c Normalized OTUs using 78,000 reads in each sample. * Samples that were sequenced also with 454 (rDNA/rRNA)

	Sample #	Site (Country)	Year	Size	# Raw	# Clean	% Clean	# OTUs	Locally	Locally rare	% Locally	% Locally
				fraction (μm)	reads a	reads b	reads	(95%) c	abundant	OTUs	abundant	rare
									OTUs (>1 %) c	(<0.01%) c	OTUs c	OTUs c
	1*	Blanes (Spain)	2010	0.8-3	8089694	302912	3.7	1437	17	1082	1.2	75.3
	2	Gijon (Spain)	2010	0.8-3	7554382	358386	4.7	1023	20	686	2.0	67.1
	3	Naples (Italy)	2009	0.8-3	8431092	385651	4.6	1261	20	839	1.6	66.5
	4	Naples (Italy)	2010	0.8-3	5858636	410095	7.0	1146	20	851	1.7	74.3
	5*	Oslo (Norway)	2009	0.8-3	6009959	281955	4.7	1441	24	984	1.7	68.3
	6*	Oslo (Norway)	2010	0.8-3	7154407	212433	3.0	701	19	475	2.7	67.8
	7*	Roscoff (France)	2010	0.8-3	3544916	188820	5.3	857	22	604	2.6	70.5
	8*	Varna (Bulgaria)	2010	0.8-3	8768061	139417	1.6	819	18	557	2.2	68.0
	9*	Blanes (Spain)	2010	3-20	7120547	363060	5.1	1773	22	1359	1.2	76.6
	10	Gijon (Spain)	2010	3-20	8425151	555166	6.6	1222	18	809	1.5	66.2
	11	Naples (Italy)	2009	3-20	6023114	285961	4.7	1189	11	871	0.9	73.3
	12*	Naples (Italy)	2010	3-20	5443795	146688	2.7	1230	20	911	1.6	74.1
	13*	Oslo (Norway)	2009	3-20	6512071	385031	5.9	1215	18	830	1.5	68.3
	14*	Oslo (Norway)	2010	3-20	5340317	199465	3.7	895	22	643	2.5	71.8
	15	Roscoff (France)	2010	3-20	4432905	165498	3.7	1068	18	773	1.7	72.4
	16*	Varna (Bulgaria)	2010	3-20	6060973	197411	3.3	894	18	616	2.0	68.9
	17	Blanes (Spain)	2010	20-2000	9492667	112054	1.2	1138	17	838	1.5	73.6
	18*	Naples (Italy)	2009	20-2000	7761710	148199	1.9	955	20	692	2.1	72.5
	19*	Naples (Italy)	2010	20-2000	10087160	244797	2.4	492	8	358	1.6	72.8
	20	Oslo (Norway)	2009	20-2000	4774982	106151	2.2	879	21	649	2.4	73.8
	21*	Oslo (Norway)	2010	20-2000	6657899	79368	1.2	598	13	398	2.2	66.6
	22*	Roscoff (France)	2010	20-2000	3196190	78643	2.5	803	20	596	2.5	74.2
	23*	Varna (Bulgaria)	2010	20-2000	6770080	348888	5.2	468	8	350	1.7	74.8
		Sum			153510708	5696049	N/A				N/A	N/A
		Average			6674378.6	247654.3	3.7				1.8	71.2
		Standard deviation			1787256.2	125008.1	1.7				0.5	3.3
	a Only the forward direction has been used.

b

Table S2 (related to Figure 1 & Results).

 S2 Comparison between 95% and 97% OTU clustering thresholds for the Illumina dataset. Number of OTUs and proportions of locally abundant or rare taxa are indicated

		Size fraction	Total OTUs	Total OTUs	#OTUs >1%	#OTUs >1%	%OTUs >1%	%OTUs >1%	#OTUs <0.01%	#OTUs <0.01%	%OTUs <0.01%	%OTUs <0.01%
	Site	(μm)	(95%) 1	(97%) 1	(95%) 2	(97%) 2	(95%) 3	(97%) 3	(95%) 4	(97%) 4	(95%) 5	(97%) 5
	Blanes	0.8-3	2502	3410	17	18	0.68	0.53	2166	2999	86.57	87.95
	Gijon	0.8-3	1766	2555	20	20	1.13	0.78	1443	2181	81.71	85.36
	Naples 2009	0.8-3	2160	2924	19	18	0.88	0.62	1754	2469	81.20	84.44
	Naples 2010	0.8-3	1995	2925	19	18	0.95	0.62	1701	2569	85.26	87.83
	Oslo 2009	0.8-3	2260	3364	24	21	1.06	0.62	1813	2834	80.22	84.24
	Oslo 2010	0.8-3	1030	1416	18	15	1.75	1.06	816	1161	79.22	81.99
	Roscoff	0.8-3	1231	1670	21	24	1.71	1.44	983	1390	79.85	83.23
	Varna	0.8-3	1018	1480	17	18	1.67	1.22	748	1171	73.48	79.12
	Blanes	3-20	3138	4167	22	20	0.70	0.48	2742	3674	87.38	88.17
	Gijon	3-20	2384	3433	19	20	0.80	0.58	1973	2963	82.76	86.31
	Naples 2009	3-20	1930	2629	11	14	0.57	0.53	1627	2265	84.30	86.15
	Naples 2010	3-20	1572	2310	20	17	1.27	0.74	1260	1918	80.15	83.03
	Oslo 2009	3-20	2079	3057	18	19	0.87	0.62	1706	2610	82.06	85.38
	Oslo 2010	3-20	1249	1747	22	24	1.76	1.37	1006	1437	80.54	82.26
	Roscoff	3-20	1407	1976	19	16	1.35	0.81	1117	1632	79.39	82.59
	Varna	3-20	1228	1810	19	22	1.55	1.22	968	1507	78.83	83.26
	Blanes	20-2000	1313	1722	16	17	1.22	0.99	1026	1315	78.14	76.36
	Naples 2009	20-2000	1189	1616	19	18	1.60	1.11	940	1325	79.06	81.99
	Naples 2010	20-2000	756	1090	9	8	1.19	0.73	623	930	82.41	85.32
	Oslo 2009	20-2000	989	1356	21	18	2.12	1.33	768	1090	77.65	80.38
	Oslo 2010	20-2000	603	876	12	13	1.99	1.48	403	631	66.83	72.03
	Roscoff	20-2000	805	1082	20	23	2.48	2.13	597	827	74.16	76.43
	Varna	20-2000	842	1507	9	8	1.07	0.53	734	1353	87.17	89.78
	Average						1.32	0.94			80.36	83.20
	Standard Dev.						0.50	0.42			4.66	4.23
	1 Total number of OTUs
	2 Number of locally abundant OTUs
	3 Percentage of locally abundant OTUs
	4 Number of locally rare OTUs
	5 Percentage of locally rare OTUs

Table S3 (related to Figure 1 & Experimental Procedures).

 S3 Description of the sampling sites; all samples were taken from surface waters Nutrients were measured according to Murphy and Riley [S1] and Grasshoff et al. [S2] in a Bran+Luebbe Autoanalyzer 3 at the University of Oslo. Note that Naples and Oslo have been sampled twice. N/A= not applicable, ND= no data.

	(<5m)
	Description	Latitude , Longitude	Distance	Max.	Sampling	Temp.	Salinity	DCM	ChlA b	Nitrate	Phosphate	Tot-P
			to coast (KM)	depth a (m)	date	surface ( C)	surface	(m)	(µg/l)	Surf./DCM c (µg/l)	Surf./DCM (µg/l)	Surf./DCM d (µg/l)
	Blanes	41° 40' N, 2° 48' E	1.0	20	2/2010	12.5	37.5	N/A	1.0	2/2	7/6	13/13
	Gijon	43° 40' N; 5° 35' W	12.0	110	9/2010	20.2	35.7	40	7.0	2/26	3/4	10/12
	Naples	40° 48' N, 14° 15' E	4.0	75	10/2009	22.8	37.7	23	1.4	16/0	1/1	22/16
		40° 48' N, 14° 15' E	4.0	76	5/2010	19.2	37.2	35	1.2	<2/<2	4/3	14/8
	Oslo	59° 16' N, 10° 43' E	1.5	100	09/2009	15.0	25.0	8	3.2	9/1	4/3	22/21
		59° 16' N, 10° 43' E	1.5	100	06/2010	15.0	22.0	9	1.9	<2/<2	3/2	12/11
	Roscoff	48° 46' N, 3° 57' W	5.0	60	4/2010	9.9	34.9	ND	0.5	87/80	12/12	29/17
	Varna	43°10' N, 28° 50' E	40.0	400	5/2010	21.5	16.0	40	8.0	2/2	4/3	11/11

a Maximum depth of the water column. b Maximum Chlorophyll A concentration in the water column measured with fluorometry (fluorometer attached to a CTD). c Values for Surface and DCM samples given in the format "Surface/DCM". d Total Phosphorus.

Table S4 (related to Figure 5 & Experimental Procedures).

 S4 Set of samples where both the rDNA and rRNA were sequenced using 454 (V4 18S) Total number of 454 reads after quality control and denoising (DeNoiser). A total of 454,983 reads were used for both RNA and DNA.Surface (<5m) seawater samples were collected through the BioMarKs consortium (http://www.BioMarKs.org/) in six European coastal stations: offshore Blanes (Mediterranean, Spain), Gijon (Bay of Biscay, Spain), Naples (Mediterranean, Italy), Oslo (North Sea / Skagerrak, Norway), Roscoff (English Channel, France), and Varna (Black Sea, Bulgaria) (see Figure

	Sample # Site (Country) Year 1 Blanes (Spain) 2010 2 Oslo (Norway) 2009 3 Oslo (Norway) 2010 4 Roscoff (France) 2010 5 Varna (Bulgaria) 2010 6 Blanes (Spain) 2010 7 Naples (Italy) 2010 8 Oslo (Norway) 2009 9 Oslo (Norway) 2010 10 Varna (Bulgaria) 2010 11 Naples (Italy) 2009 12 Naples (Italy) 2010 13 Oslo (Norway) 2010 14 Roscoff (France) 2010 15 Varna (Bulgaria) 2010 Total 1 SUPPLEMENTAL EXPERIMENTAL PROCEDURES Size fraction (μm) 0.8-3 0.8-3 0.8-3 0.8-3 0.8-3 3-20 3-20 3-20 3-20 3-20 20-2000 20-2000 20-2000 20-2000 20-2000 Sampling and RNA/DNA extraction	# Clean reads RNA 1 # Clean reads DNA 1 13679 8210 22061 14419 31172 27197 7055 12639 7938 7691 19951 6268 12257 8474 26883 26009 7921 34514 24868 22444 8750 22655 16058 3158 11747 5487 3498 6553 19247 16180 233085 221898

ACKNOWLEDGEMENTS

We thank the BioMarKs (Biodiversity of Marine euKaryotes) consortium, which was funded by the EU ERA-Net program BiodivERsA (2008-6530). Extra-Financial support was provided by the Marie Curie IEF (PIEF-GA-2009-235365) and Juan de la Cierva programmes (JCI-2010-06594) to R.L. and FLAME (CGL2010-16304, MICINN, Spain) to R.M. The Barcelona Supercomputing Center (BSC) provided access to the MareNostrum Supercomputer (grants BCV-2011-2-0003/3-0005, 2012-1-0006/2-0002 to R.L. and R.M). We thank N. Le Bescot for assistance with Figure 1 and to the two reviewers that helped to improve this work.

Conclusions:

We propose that marine-planktonic microeukaryote assemblages incorporate dynamic and metabolically active abundant and rare sub-communities with contrasting structuring patterns but fairly regular proportions across space and time. 75

Inventory of Supplemental Information SUPPLEMENTAL DATA

Supplemental Figures

Figure S1: Distribution of all OTUs as well as regionally abundant and rare OTUs across the different organismal size fractions. Related to Figure 1, Table 1 & Experimental Procedures.

Figure S2:

Rarefaction curves pointing to richness saturation. Related to Figure 1 & Table 1.

Figure S3:

Regional Species (OTUs) Abundance Distributions (SADs) with different SAD models fitted, as well as regional SADs for the different organismal size fractions. Related to Table 1.

Figure S5:

Regional community data fitted to the Preston log-normal model and the associated calculation of the "veiled" OTUs. Related to Table 1.

Supplemental Tables

Table S1: General description of the community sequencing approach (using Illumina V9 18S rRNA) and the obtained OTUs (95 % clustering). Related to Figure 1 and Experimental Procedures.

Table S2: Comparison between 95% and 97% OTU clustering thresholds for the Illumina dataset. Related to Figure 1 and Results.

Table S3: Description of the sampling sites. Related to Figure 1 and Experimental Procedures.

Table S4: Description of samples where both the rDNA and rRNA were sequenced using 454. Related to Figure 5 and Experimental Procedures.

SUPPLEMENTAL EXPERIMENTAL PROCEDURES

Supplemental information on sampling, molecular methods and data analyses.

SUPPLEMENTAL REFERENCES

Includes all references mentioned in the Supplemental Information section.

SUPPLEMENTAL DATA

Supplemental Figures and Legends

Figure S1 (related to Figure 1, Table 1 & Experimental Procedures).

Venn diagrams indicating the distribution of OTUs (normalized Illumina dataset) into the pico (0.8-3µm), nano (3-20µm), and micro/meso (20-2000µm) organismal sizefractions. The distribution is shown for all OTUs in the regional community (7035 OTUs), as well as for regionally abundant (154) and rare (5329) OTUs.

Figure S2 (related to Figure 1 & Table 1).

(A) Rarefaction curves using all Illumina tags for the combined set of samples (regional community; upper panel) as well as for the individual samples (local communities; lower panel). Note the different scales in y and x axes. (B) Species (OTUs) accumulation curves based on the progressive addition of samples calculated using "Ugland" and "Random" methods.

Figure S3 (related to Table 1).

(A) Species (OTUs) Abundance Distribution (SAD) for all pooled non-normalized samples (entire regional community) indicating the four fitted models (Null, Preemption, Lognormal & Zipf). The model with the best fit was the Lognormal according to the Akaike's Information Criterion. (B) SADs for all normalized samples separated by organismal size fractions (sizefractionated regional community).

Figure S5 (related to Table 1).

(A) Fit of the entire dataset (regional community) to the Preston log-normal model using two approximations: Quasi-Poisson fit to octaves and Maximized likelihood to log 2 abundances.

(B) Estimation of the "veiled" OTUs (i.e. OTUs not sampled) using both approximations presented in (A).

Supplemental Tables

DNA/cDNA to each PCR reaction. PCR reactions consisted of an initial denaturation step at 98°C during 30 sec, followed by 10 cycles of 10 sec at 98°C, 30 sec at 53°C and 30 sec at 72°C, and afterwards by 15 cycles of 10 sec at 98°C, 30 sec at 48°C and 30 sec at 72°C, including a final elongation step at 72°C for 10 minutes. Amplicons were checked in 1.5% agarose gels for successful amplification within the expected length.

Triplicate amplicon reactions were pooled and purified using NucleoSpin® Extract II (Macherey-Nagel). Purified amplicons were eluted in 30µl of elution buffer and quantified using Quant-it dsDNA Picogreen kit (Invitrogen). The final total amount of pooled amplicons for 454 tag-sequencing was approximately 1µg. Amplicon sequencing was carried out on a 454 GS FLX Titanium system (454 Life Sciences, USA) installed at Genoscope (http://www.genoscope.cns.fr/spip/, France).

Sequence analysis for Illumina reads

A total of 23 samples sequenced with Illumina GAIIx and based on RNA (cDNA) template were used (Table S1). We used RNA as this molecule is much less stable than DNA in extracellular conditions. Therefore, most RNA recovered from the environment should have originated from living cells (ensuring that nucleic acids originate from living cells is particularly relevant when investigating the rare biosphere). Furthermore, there are studies indicating that RNA provides a better representation of community composition than DNA in microbial eukaryotes (less biases due to uneven rDNA copy number between taxa) [S5].

Despite using paired-end reads (2x100bp), we opted for using the forward reads only, as some merged (assembled) reads presented reduced quality; similar strategies have been used in other works [S6]. Illumina GAIIx fastq files (Phred +64) were translated into different fasta and phred-quality files. The number of raw (i.e. not quality checked) reads obtained per sample is indicated in Table S1. Reads were qualitychecked using a sliding window (window length = 10bp) and each window had to have a phred-quality average >34 to pass the control (see [S7]). Quality-checked reads had a minimum length of 90bp with a maximum homopolymer size = 10bp; no ambiguous bases were allowed. This highly stringent cleaning protocol insured only reads of the highest quality were retained for downstream analysis. All quality checks were run in Mothur v1.2X [S8]. The number of clean reads after quality control is shown in Table S1. Quality-checked reads were analyzed in QIIME v1.4 [S9]. Reads were clustered into OTUs using UCLUST v1.2.22 with a 95% similarity threshold and the parameters --max_accepts =20 and --max_rejects =500. This conservative clustering threshold was selected in order to reduce any artifactual increase in richness produced by sequencing errors that could remain in our dataset (see [S10]). Chimeras were detected using ChimeraSlayer [S11] with a reference database derived from PR2 [S12], and subsequently removed. One representative sequence per OTU (the most abundant) was selected. OTU representative sequences were assigned taxonomy by BLASTing them (blastn; [S13]) against the databases SILVA v108 (BLAST threshold e-value=e -6 ) as well as a PR2-derived database [S12] (BLAST threshold e-value=e -100 ). Both the SILVA v108 and the PR2-derived reference databases were pre-clustered at 97% similarity to reduce the chances of ambiguous classifications (hereafter referred as SILVA v108 (97%) & PR2 (97%) ). The initial BLAST against SILVA v108 (97%) served to detect and remove unwanted taxa (e.g. Bacteria, Archaea and Metazoa). Further OTU removal was done using the classifications obtained with the PR2 (97%) . The final curated Illumina RNA dataset included 5,696,049 reads.

Sequence Analysis for 454 reads

A total of 15 samples, where both DNA and RNA (cDNA) were sequenced using 454 (V4 18S), were used in downstream analyses (Table S4). Note that these samples were all included in the main Illumina dataset (Table S1). All 454 reads were run through QIIME v1.4. Only reads between 200-500bp were used. Reads were checked for quality using a sliding window of 50bp (Phred average >25 in each window), truncated to the last good window and subsequently checked again for minimum length. Reads that passed the quality control were denoised using DeNoiser (v 0.851; [S14]) as implemented in QIIME v1.4. Subsequently, reads were clustered into OTUs using UCLUST v1.2.22 with a 99% threshold of sequence similarity (compared to Illumina reads, a higher clustering threshold was used since this region is longer, less hypervariable and furthermore, this dataset was denoised). UCLUST was run with the parameters --max_accepts =20 and --max_rejects =500. One representative read per OTU (the most abundant) was selected. Chimeras were detected using ChimeraSlayer with a reference database derived from PR2 [S12], and subsequently removed.

Representative reads were assigned taxonomy by BLASTing them against the databases SILVA v108 (97%) (BLAST threshold e-value=e -6 ), the custom PR2 (97%) (BLAST threshold e-value=e -100 ), as well as the MP database (BLAST threshold e-value=e -100 ).

MP is an in-house 18S V4 database that contains only marine microeukaryotes, with improved taxonomy for specific groups [S15]. The MP database was also pre-clustered at 97% to prevent ambiguous classifications. Thus, each database provided different degrees of taxonomic resolution. After taxonomy assignment, Metazoan sequences were removed (Bacteria and Archaea were not present). In the final dataset, after all unwanted sequences were removed, RNA included 233,085 reads and DNA 221,898 reads (total 454,983 reads).

Construction of OTU tables

Single singletons as well as OTUs present in only one sample were removed from the global dataset (i.e. the dataset including samples that were not considered in our work, such as deeper planktonic or sediment samples). After the exclusion of samples that were not considered in our analyses, single singletons were present again in both the Illumina and 454 datasets, but these were included in downstream analyses as they were already validated by other reads in unused samples.

Diversity and network analyses

Most analyses were run in the R [S16] environment. Venn diagrams were calculated using the R-package Limma [S17]. Rarefactions as well as Species (OTUs)

Accumulation Curves (SAC) were calculated using the R-package Vegan [S18]. SACs were estimated using the methods "Ugland" and "Random". Ugland's method finds the expected SAC using the method proposed by Ugland et al. [S19]. The "Random" method calculates the mean SAC from random permutations of the data [S20]. Rankabundance (or Species Abundance Distribution) plots were produced in R and different models were fitted using the "radfit" function in Vegan. The fitted models were "Null"

(brokenstick), "Preemption" (niche preemption model [geometric series] or Motomura model), "Lognormal" (log-normal model), "Zipf" (Zipf model) and "Mandelbrot" (Zipf-Mandelbrot model) (see [S21]); the "Mandelbrot" model could not be fitted to all datasets. In addition, we fitted our data to the Preston log-normal model [S22] using the functions "prestonfit" (fit estimation using second degree log-polynomial with Poisson error) and "prestondistr" (fit estimation using direct maximization of log-likelihood) in Vegan (see Vegan user manual for more details). OTU networks were constructed in QIIME using the subsampled OTU table and graphically edited in Cytoscape [S23] using the layout "Edge-Weighted Spring Embedded" with eweights. Networks were filtered using eweight values (eweight thresholds: "Abundant"=500, "Rare"=3, "All"=200).

Mapping of short Illumina reads to longer reference sequences and phylogeny construction

Regionally abundant or rare OTUs (one representative read per OTU) were mapped using BLASTn to a V9 18S rDNA Sanger reference database based on the custom PR2 (97%) database. We did not BLAST OTUs against the custom PR2 database containing areas other than V9 as this produced poorer results. To construct the reference phylogeny, we used the full-length 18S sequences corresponding to the entire custom V9 PR2 (97%) database. Only sequences >1,500bp were used for phylogeny construction, leaving a dataset of 8,311 reference sequences. Sequences were aligned using Mothur against the aligned SILVA 108 (eukaryotes only). A Maximum Likelihood tree (FULL-TREE) was inferred using RAxML HPC-MPI (v7. Taxon Distance (MNTD) [S27] were calculated using the R-package Picante [S28] and compared to a null model that shuffled taxa-labels 1,000 times across all taxa included in the phylogeny-based distance matrix (see [S29]).