Jennifer R Brum §

J Cesar Ignacio-Espinoza

Simon Roux

Guilhem Doulcier

Silvia G Acinas

Adriana Alberti

Samuel Chaffron

Corinne Cruaud

Colomban De Vargas

Josep M Gasol

Gabriel Gorsky

Ann C Gregory

Lionel Guidi

Pascal Hingamp

Daniele Iudicone

Fabrice Not

Hiroyuki Ogata

Stephane Pesant

Bonnie T Poulos

Sarah M Schwenck

Sabrina Speich

Celine Dimier

Stefanie Kandels- Lewis

Marc Picheral

Sarah Searson

Tara Oceans

Chris Bowler

Shinichi Sunagawa

Patrick Wincker

Eric Karsenti
email: karsenti@embl.de

Matthew B Sullivan

Weissenbach

Patterns and ecological drivers of ocean viral communities

Viruses influence ecosystems by modulating microbial population size, diversity, metabolic outputs, and gene flow. Here we use quantitative double-stranded DNA (dsDNA) viral-fraction metagenomes (viromes) and whole viral community morphological datasets from 43 Tara Oceans expedition samples to assess viral community patterns and structure in the upper ocean. Protein cluster cataloging defined pelagic upper-ocean viral community pan and core gene sets and suggested this sequence space is well-sampled. Analyses of viral protein clusters, populations, and morphology revealed biogeographic patterns whereby viral communities were passively transported on oceanic currents and locally structured by environmental conditions that impact host community structure. Together these investigations establish a global ocean dsDNA viromic dataset with analyses supporting the seed-bank hypothesis to explain how oceanic viral communities maintain high local diversity.

One Sentence Summary: Global patterns that emerge from the Tara Oceans Virome dataset support a seed-bank structure underlying observed biogeography in ocean viral communities.

Main Text: Ocean microbes produce half of the oxygen we breathe [START_REF] Field | Primary production of the biosphere: Integrating terrestrial and oceanic components[END_REF] and drive much of the substrate and redox transformations that fuel Earth's ecosystems [START_REF] Falkowski | The microbial engines that drive Earth's biogeochemical cycles[END_REF]. However, they do so in a constantly evolving network of chemical, physical and biotic constraintsinteractions which are only beginning to be explored. Marine viruses are presumably key players in these interactions [START_REF] Breitbart | Marine viruses: Truth or dare[END_REF][START_REF] Suttle | Marine viruses -major players in the global ecosystem[END_REF] as they affect microbial populations through lysis, reprogramming of host metabolism, and horizontal gene transfer. Here we strive to develop an overview of ocean viral community patterns and ecological drivers.

The Tara Oceans expedition provided a platform for sampling ocean biota from viruses to fish larvae with comprehensive environmental context [START_REF] Karsenti | A holistic approach to marine eco-systems biology[END_REF]. Prior virus-focused work from this expedition has helped optimize the dsDNA viromic sample-to-sequence workflow [START_REF] Solonenko | Sequencing platform and library preparation choices impact viral metagenomes[END_REF], evaluate ecological drivers of viral community structure as inferred from morphology [START_REF] Brum | Global morphological analysis of marine viruses shows minimal regional variation and dominance of non-tailed viruses[END_REF], and map ecological patterns in the large dsDNA nucleo-cytoplasmic viruses using marker genes [START_REF] Hingamp | Exploring nucleo-cytoplasmic large DNA viruses in Tara Oceans microbial metagenomes[END_REF]. Here we explore global patterns and structure of ocean viral communities using 43 samples from 26 stations in the Tara Oceans expedition (Supplementary File S1) to establish dsDNA viromes from viral-fraction (<0.22 µm) concentrates and quantitative whole viral community morphological datasets from unfiltered seawater. Viruses lack shared genes that can be used for investigation of community patterns. Therefore, we used three levels of information to study such patterns: (i) protein clusters (PCs, 9) as a means to organize virome sequence space commonly dominated by unknown sequences (63-93%, 10), (ii) populations, using established metrics for viral contig recruitment [START_REF] Deng | Viral tagging reveals discrete populations in Synechococcus viral genome sequence space[END_REF], and (iii) morphology, using quantitative transmission electron microscopy (qTEM, 7).

The Tara Oceans Viromes (TOV) dataset

The 43 Tara Oceans Viromes (TOV) dataset is comprised of 2.16 billion ~101-bp paired-end Illumina reads (Supplementary File 1), largely representing epipelagic ocean viral communities (only 1 of 43 viromes are from mesopelagic waters, Environment Ontology feature ENVO:00000213) from the surface (ENVO:00002042) and deep chlorophyll maximum (DCM; ENVO:01000326) throughout seven oceans and seas (Supplementary File S1). The TOV dataset offers deeper sampling of surface ocean viral communities, but under-represents the deep ocean relative to the Pacific Ocean Viromes dataset (POV, 10) which includes 16 viromes from aphotic zone waters. In all viromes, sampling and processing affects what viruses are represented [START_REF] Solonenko | Sequencing platform and library preparation choices impact viral metagenomes[END_REF][START_REF] Duhaime | Ocean viruses: Rigorously evaluating the metagenomic sample-to-sequence pipeline[END_REF][START_REF] Duhaime | Towards quantitative metagenomics of wild viruses and other ultra-low concentration DNA samples: a rigorous assessment and optimization of the linker amplification method[END_REF][START_REF] Hurwitz | Evaluation of methods to concentrate and purify ocean virus communities through comparative, replicated metagenomics[END_REF]. We filtered TOV seawater samples through 0.22 µm pore-sized filters and then concentrated viruses in the filtrate using iron chloride flocculation [START_REF] John | A simple and efficient method for concentration of ocean viruses by chemical flocculation[END_REF]. These steps would have removed most cells, but also excluded any viruses larger than 0.22 µm. We then purified the resulting TOV viral concentrates using DNase treatment, which is as effective as density gradients for purifying ocean viral concentrates [START_REF] Hurwitz | Evaluation of methods to concentrate and purify ocean virus communities through comparative, replicated metagenomics[END_REF]. This DNAse-only step is unlikely to impact viral representation in the viromes, but reduces non-viral DNA contamination. Finally, we extracted DNA from the samples and prepared sequence libraries using linker amplification [START_REF] Duhaime | Towards quantitative metagenomics of wild viruses and other ultra-low concentration DNA samples: a rigorous assessment and optimization of the linker amplification method[END_REF]. These steps preserve quantitative representation of dsDNA viruses in the resulting viromes [START_REF] Duhaime | Ocean viruses: Rigorously evaluating the metagenomic sample-to-sequence pipeline[END_REF][START_REF] Duhaime | Towards quantitative metagenomics of wild viruses and other ultra-low concentration DNA samples: a rigorous assessment and optimization of the linker amplification method[END_REF], but the ligation step excludes RNA viruses, and is biased against single-stranded DNA (ssDNA) viruses [START_REF] Duhaime | Ocean viruses: Rigorously evaluating the metagenomic sample-to-sequence pipeline[END_REF].

We additionally applied qTEM [START_REF] Brum | Global morphological analysis of marine viruses shows minimal regional variation and dominance of non-tailed viruses[END_REF] to paired whole seawater samples to evaluate patterns in whole viral communities. This method simultaneously considers ssDNA, dsDNA, and RNA viruses, though without knowledge of their relative abundances since particle morphology does not identify nucleic acid type. In the oceans, total virus abundance estimates based on TEM analyses, which include all viral particles, are similar to estimates based on fluorescent staining, which inefficiently stains ssDNA and RNA viruses [START_REF] Steward | Are we missing half of the viruses in the ocean?[END_REF][START_REF] Holmfeldt | Cultivated single stranded DNA phages that infect marine Bacteroidetes prove difficult to detect with DNA binding stains[END_REF][START_REF] Tomaru | Flow cytometric detection and enumeration of DNA and RNA viruses infecting marine eukaryotic microalgae[END_REF][START_REF] Brussaard | Flow cytometric detection of viruses[END_REF][START_REF] Bettarel | A comparison of methods for counting viruses in aquatic systems[END_REF][START_REF] Hennes | Direct counts of viruses in natural waters and laboratory cultures by epifluorescence microscopy[END_REF][START_REF] Weinbauer | Comparison of epifluorescence and transmission electron microscopy for counting viruses in natural marine waters[END_REF][START_REF] Noble | Use of SYBR Green I for rapid epifluorescence counts of marine viruses and bacteria[END_REF][START_REF] Marie | Enumeration of marine viruses in culture and natural samples by flow cytometry[END_REF]. This suggests that most ocean viruses are dsDNA viruses. However, one study quantifying nucleic acids at a single marine location suggests RNA viruses may constitute as much as half of the viral community there [START_REF] Steward | Are we missing half of the viruses in the ocean?[END_REF].

It remains unknown what the relative contribution of these viral types is to the whole viral community, but our analyses suggest small dsDNA viruses likely dominate as follows. The viromes capture the <0.22 µm dsDNA viruses of bacteria and archaea that are thought to dominate marine viral communities, whereas qTEM analysis includes all viruses regardless of size, nucleic acid type, or host [START_REF] Brum | Global morphological analysis of marine viruses shows minimal regional variation and dominance of non-tailed viruses[END_REF]. In these whole seawater samples used for qTEM, we found that viral capsid diameters ranged from 26 to 129 nm, with the per-sample average capsid diameter constrained at 46-66 nm (Fig. 1). We detected no viral particles larger than 0.22 µm among 100 randomly counted particles from each of 41 qTEM samples. These findings are similar to those from a subset of these Tara Oceans stations (14 of the 26 stations; 7), and indicate that size fractionation using 0.22 µm filtration to prepare viromes did not substantially bias the TOV dataset.

TOV Protein Clusters for Comparison of Local and Global Genetic Richness and Diversity

Across the 43 viromes, a total of 1,075,763 PCs were observed, with samples beyond the 20 th virome adding few PCs (Fig. 2A). When combining TOV with 16 photic-zone viromes from the POV dataset [START_REF] Hurwitz | The Pacific Ocean Virome (POV): A marine viral metagenomic dataset and associated protein clusters for quantitative viral ecology[END_REF], the number of PCs increased to 1,323,921, but again approached a plateau (Fig. 2B). These results suggest that, while impossible to sample completely, the sequence space corresponding to dsDNA viruses from the epipelagic ocean is now relatively well sampled. This contrasts results from marine microbial metagenomic surveys using older sequencing technologies [START_REF] Yooseph | The Sorcerer II global ocean sampling expedition: expanding the universe of protein families[END_REF], but is consistent with those from this expedition [START_REF] Sunagawa | Structure and function of the global ocean microbiome[END_REF], as well as findings from viral sequence datasets which suggest a limited range of functional diversity derived from bacterial and archaeal viral isolates [START_REF] Kristensen | Orthologous gene clusters and taxon signature genes for viruses of prokaryotes[END_REF] and the POV dataset [START_REF] Ignacio-Espinoza | The global virome: not as big as we thought?[END_REF].

PCs were next used to establish the core genes shared across the TOV dataset (Fig. 2A).

Broadly, there were 220, 710 and 424 core PCs shared across all surface and DCM viromes, surface viromes only, and DCM viromes only, respectively. The number of core PCs in the upper-ocean TOV samples (220 PCs) was thus less than the number of photic-zone core PCs in POV (565 PCs; 28), likely because the POV dataset includes only the Pacific Ocean while TOV includes samples from seven oceans and seas. However, the number of core PCs in the upperocean TOV samples exceeded the total number of core PCs observed in POV (180 PCs; 28), likely because of deep-ocean representation in POV (half of the samples in POV are from the aphotic zone). Consistent with the latter, the addition of the sole deep-ocean TOV sample, TARA_70_MESO, decreased the number of core PCs shared by all TOV samples from 220 to 65, which suggests that deep-ocean viral genetic repertoires are different from those in the upper oceans. Indeed, niche-differentiation has been observed in viromes sampled across these oceanic zones in the POV dataset [START_REF] Hurwitz | Depth-stratified functional and taxonomic niche specialization in the 'core' and 'flexible' Pacific Ocean Virome[END_REF], and similar findings were observed in the microbial metagenomic counterparts from the Tara Oceans Expedition [START_REF] Sunagawa | Structure and function of the global ocean microbiome[END_REF]. Thus viral communities from the deep ocean remain poorly explored and appear to hold different gene sets from those in the epipelagic oceans.

Beyond core and pan metagenomic analyses, PCs also provide a metric for viral community diversity comparisons (Fig. 3A; Supplementary File S1) from which three trends emerge in the TOV dataset. First, high-latitude viromes (82_DCM and 85_DCM) were least diverse (Shannon's H' of 8.93 and 9.22 nats), consistent with patterns in marine macroorganisms [START_REF] Tittensor | Global patterns and predictors of marine biodiversity across taxa[END_REF] and epipelagic ocean bacteria [START_REF] Sunagawa | Structure and function of the global ocean microbiome[END_REF][START_REF] Sul | Marine bacteria exhibit a bipolar distribution[END_REF]. Second, the remaining viromes had similar diversity (Shannon's H' between 9.47 and 10.55 nats) and evenness (Pielou's J from 0.85 to 0.91) indicating low dominance of any particular PCs [START_REF] Jarvis | A global perspective of the richness and evenness of traditional cropvariety diversity maintained by farming communities[END_REF]. Third, local diversity was relatively similar to global diversity (local:global ratios of H' from 0.73 to 0.87), suggesting high dispersal of viral genes [START_REF] Hubbell | The Unified Neutral Theory of Biodiversity and Biogeography[END_REF] across the sampled ocean viral communities.

TOV Viral Populations for Assessing Global Viral Community Structure

We next estimated abundances of the 5,476 dominant viral populations in TOV, which represented up to 14.5% of aligned reads in a sample and were defined by applying empiricallyderived recruitment cut-offs from naturally-occurring T4-like cyanophages [START_REF] Deng | Viral tagging reveals discrete populations in Synechococcus viral genome sequence space[END_REF] to highconfidence contigs from bacterial and archaeal viruses (see Methods). Assigning viral populations based on virome data remains challenging [START_REF] Deng | Viral tagging reveals discrete populations in Synechococcus viral genome sequence space[END_REF][START_REF] Brum | Rising to the challenge: accelerated pace of discovery transforms marine virology[END_REF], but here assembly of large contigs (up to 100 kb) aided our ability to accomplish not only analyses at the gene-level using PCs, but also the genome-level using viral populations. Viral populations were rarely endemic to one station (15%), and instead were commonly observed across >4 stations (47%), and up to 24 of the 26 stations (Fig. 4 and Fig. 5A). Exceptional samples include those from the Benguela upwelling region (TARA_67_SUR) and high-latitude samples from the Antarctic Circumpolar and Falklands currents (TARA_82_DCM and TARA_85_DCM, respectively). These samples were also divergent when assessing microbial communities (TARA_82_DCM and TARA_85_DCM displayed lower microbial genetic richness; [START_REF] Sunagawa | Structure and function of the global ocean microbiome[END_REF]) and eukaryotic communities (TARA_67_SUR had specific and unique eukaryotic communities in all size fractions; 34).

While many viral populations were broadly distributed, they were much more abundant at the original location (origin inferred from longest contig assembled; see Methods) compared to alternate stations (Fig. 5B). Thus most populations were relatively widespread, but with variable sample-to-sample abundances. As was observed with PCs, diversity and evenness estimates based on viral populations were similar across all samples except for high-latitude samples (TARA_82_DCM and TARA_85_DCM) and one sample in the Red Sea (TARA_32_DCM) that displayed lower diversity (Fig. 3B; Supplementary File S1). Finally, local diversity was relatively similar to global diversity (local:global ratios of H' from 0.23 to 0.86, average 0.74, Supplementary File S1), reflecting the high dispersal of viruses as highlighted by PC analysis.

Only 39 of the 5,476 populations we identified could be affiliated to cultured viruses, reflecting the dearth of reference viral genomes in databases. These cultured viruses include those infecting the abundant and widespread hosts SAR11, SAR116, Roseobacter, Prochlorococcus and Synechococcus (Fig. 6). The most abundant and widespread viral populations observed in TOV lack cultured representatives (Fig. 6), which suggests that most upper ocean viruses remain to be characterized even though viruses from known dominant microbial hosts [START_REF] Kang | Genome of a SAR116 bacteriophage shows the prevalence of this phage type in the oceans[END_REF][START_REF] Labrie | Genomes of marine cyanopodoviruses reveal multiple origins of diversity[END_REF][START_REF] Sullivan | Genomic analysis of oceanic cyanobacterial myoviruses compared with T4-like myoviruses from diverse hosts and environments[END_REF][START_REF] Zhao | Abundant SAR11 viruses in the ocean[END_REF][START_REF] Rohwer | The complete genomic sequence of the marine phage Roseaphage SIO1 shares homology with nonmarine phages[END_REF] have been cultured. Methods independent of cultivation, including viral tagging [START_REF] Deng | Viral tagging reveals discrete populations in Synechococcus viral genome sequence space[END_REF] and mining of microbial genomic datasets [START_REF] Roux | Ecology and evolution of viruses infecting uncultivated SUP05 bacteria as revealed by single-cell-and meta-genomics[END_REF][START_REF] Mizuno | Expanding the marine virosphere using metagenomics[END_REF], show promise to expand the number of available viral reference genomes [START_REF] Brum | Rising to the challenge: accelerated pace of discovery transforms marine virology[END_REF].

Drivers of Global Viral Community Composition and Distribution

We next leveraged this global dataset to evaluate ecological drivers (including environmental variables, sample location, and microbial abundances; Supplementary File S1) of viral community structure using all three data typesmorphology, populations, and PCs. These metrics revealed increasing resolution, respectively, and showed that viral community structure was influenced by region and/or environmental conditions (Table 1). We conducted the analysis of ecological drivers using all samples in this study as well as a sample subset that omitted samples with exceptional environmental conditions and divergent viral communities observed using PC and population analyses (see above; TARA_67_SUR, TARA_82_DCM, TARA_85_DCM, and TARA_70_MESO). Within the sample subset, oceanic viral communities varied significantly with Longhurst province, biome, latitude, temperature, oxygen concentration, and microbial concentrations (including total bacteria, Synechococcus, and Prochlorococcus). Viral communities were not structured by depth (surface vs DCM) except when considering PCs, likely reflecting minimal variation between samples in the epipelagic zone compared to that of globally-sourced samples, and higher resolution provided by PCs.

Nutrients influenced viral community structure when considering the whole dataset, but were much less explanatory when the few high-nutrient samples were removed, except for the influence of phosphate concentration on viral populations. Thus nutrient concentrations may influence viral community structure, but testing this hypothesis would require analysis of samples across a more continuous nutrient gradient.

Global-scale analyses of oceanic macro-( 29) and micro-organisms [START_REF] Sul | Marine bacteria exhibit a bipolar distribution[END_REF] have been conducted, including a concurrent Tara Oceans study showing that temperature and oxygen influence microbial community structure [START_REF] Sunagawa | Structure and function of the global ocean microbiome[END_REF]. Environmental conditions have also been shown to affect global viral community morphological traits [START_REF] Brum | Global morphological analysis of marine viruses shows minimal regional variation and dominance of non-tailed viruses[END_REF]. Our TOV study is consistent with these earlier findings in that viral communities are influenced by temperature and oxygen concentration, but not chlorophyll concentration (Table 1). Biogeographic structuring of TOV viral communities based on the significant influence of latitude and Longhurst provinces is also consistent with the conclusion that geographic region influences community structure in Pacific Ocean viruses [START_REF] Hurwitz | Modeling ecological drivers in marine viral communities using comparative metagenomics and network analyses[END_REF]. While only PC analysis showed depth-based divergence, this likely reflects poor (n=1) deep sample representation in the TOV dataset as discussed above. Prior POV viral investigation and concurrent Tara Oceans microbial analysis, both of which have better deepwater representation, show stronger depth patterns whereby photic and aphotic zone communities diverge [START_REF] Sunagawa | Structure and function of the global ocean microbiome[END_REF][START_REF] Hurwitz | Depth-stratified functional and taxonomic niche specialization in the 'core' and 'flexible' Pacific Ocean Virome[END_REF][START_REF] Hurwitz | Modeling ecological drivers in marine viral communities using comparative metagenomics and network analyses[END_REF]. Thus our results suggest biogeography of upper-ocean viral communities is structured by environmental conditions.

Since viruses require host organisms to replicate, viral community structure follows from environmental conditions shaping the host community, as observed in paired Tara Oceans microbial samples [START_REF] Sunagawa | Structure and function of the global ocean microbiome[END_REF], which would then indirectly affect viral community composition.

However, global distribution of viruses can also be directly influenced by environmental conditions, such as salinity, that affect their ability to infect their hosts [START_REF] Kukkaro | Virus-host interactions in environments with a wide range of ionic strengths[END_REF]. Additionally, the variable decay rates of cultivated viruses and whole viral communities (44) could also influence their distribution as viruses with lower inherent decay rates will persist for longer in the environment, and environments with more favorable conditions (such as fewer extracellular enzymes) will also contribute to increased viral persistence. Until methods to link viruses to their host cells in natural communities mature to the point of investigating this issue at larger scales (emerging possible methods reviewed by [START_REF] Brum | Rising to the challenge: accelerated pace of discovery transforms marine virology[END_REF][START_REF] Dang | Emerging methods to study bacteriophage infection at the singlecell level[END_REF], analyses such as ours remain the only means to assess ecological drivers of viral community structure.

To further investigate how ocean viral communities are distributed throughout the oceans, we compared population abundances between neighboring samples to assess the net direction and magnitude of population exchange (Fig. 7, see Methods). These genomic signals revealed that population exchange between dsDNA viral communities was largely directed along major oceanic current systems [START_REF] Talley | Descriptive Physical Oceanography: An Introduction[END_REF]. For example, the Agulhas current and subsequent ring formation [START_REF] Olson | Rings of the Agulhas current[END_REF] connects viral communities between the Indian and Atlantic Oceans, as also observed in planktonic communities from the Tara Oceans expedition [START_REF] Villar | Dispersal and remodeling of plankton communities by Agulhas rings[END_REF], while increased connection between the high-latitude stations (TARA_82 and TARA_85) reflects their common origin at the divergence of the Falklands and Antarctic Circumpolar currents. Further, current strength [START_REF] Talley | Descriptive Physical Oceanography: An Introduction[END_REF] was generally related to the magnitude of inter-sample population exchange, as higher and lower exchange was observed, respectively, in stronger currents such as the Agulhas current, and within the open ocean gyres or between land-restricted basins such as the Mediterranean and Red Seas. These findings suggest that the intensity of water mass movement, in addition to environmental conditions, may explain the degree to which viral populations cluster globally (Fig. 4). Beyond such current-driven biogeographic evidence, vertical viral transport from surface to DCM samples was also observed (Fig. 4). This is consistent with POV observations wherein deep-sea viromes include a modest influx of genetic material derived from surfaceocean viruses that are presumably transported on sinking particles [START_REF] Hurwitz | Depth-stratified functional and taxonomic niche specialization in the 'core' and 'flexible' Pacific Ocean Virome[END_REF]. Exceptions include areas such as the Arabian Sea upwelling region, where increased mixing and upwelling likely exceed sinking within the upper ocean.

Our TOV results enabled evaluation of a hypothesis describing the structure of viral communities in the environment. Gene-marker-based studies targeting subsets of ocean viruses previously found high local and low global diversity [START_REF] Breitbart | Here a virus, there a virus, everywhere the same virus?[END_REF], a pattern also recently observed genome-wide in natural cyanophage populations [START_REF] Deng | Viral tagging reveals discrete populations in Synechococcus viral genome sequence space[END_REF]. To explain this, a seed-bank viral community structure has been invoked whereby high local genetic diversity can exist by drawing variation from a common and relatively limited global gene pool [START_REF] Breitbart | Here a virus, there a virus, everywhere the same virus?[END_REF]. Our results support this hypothesis regarding viral community structure. Ecological driver analyses suggests that such local 'seed' communities are influenced by environmental conditions, which directly impact their microbial hosts and then indirectly restructure viral communities. These seed communities then form the 'bank' in neighboring samples, presumably when passively transported by ocean currents as shown here through the population-level analyses of net viral movement between samples. This systematically-sampled, global dataset suggests large-and small-scale processes play roles in structuring viral communities and offers empirical grounding for the seed-bank hypothesis with regards to viral community distribution and structure.

Conclusions

Our large-scale dataset provides a picture of global upper-ocean viral communities in which we assessed patterns using multiple parameters including morphology, populations and PCs. Our data provide advanced and complementary views on viral community structure including nonmarker-gene-based diversity estimates and broad application of population-based viral ecology.

We affirm the seed-bank model for viruses, hypothesized nearly a decade ago [START_REF] Breitbart | Here a virus, there a virus, everywhere the same virus?[END_REF], which explains how high local viral diversity can be consistent with limited global diversity [START_REF] Deng | Viral tagging reveals discrete populations in Synechococcus viral genome sequence space[END_REF][START_REF] Ignacio-Espinoza | The global virome: not as big as we thought?[END_REF].

The mechanism underlying this seed-bank population structure appears to be a local production of viruses under small-scale environmental constraints and passive dispersal with oceanic currents. Improving sequencing, assembly and experimental methods are transforming the investigation of viruses in nature [START_REF] Brum | Rising to the challenge: accelerated pace of discovery transforms marine virology[END_REF][START_REF] Dang | Emerging methods to study bacteriophage infection at the singlecell level[END_REF], and pave the way towards assessment of viral community structure and analysis of virus-host co-occurrence networks (50) without requiring marker genes [START_REF] Needham | Short-term observations of marine bacterial and viral communities: patterns, connections and resilience[END_REF][START_REF] Chow | Top-down controls on bacterial community structure: microbial network analysis of bacteria, T4-like viruses and protists[END_REF]. Such experimental and analytical progress, coupled to sampling opportunities from the Tara Oceans expedition, are advancing viral ecology towards the quantitative science needed to model the nano-(viruses) and micro-(microbes) scale entities driving Earth's ecosystems.

Materials and Methods

Sample Collection

Forty-three samples were collected between November 2, 2009, and May 13, 2011, at 26 locations throughout the world's oceans (Supplementary File S1) through the Tara Oceans Expedition [START_REF] Karsenti | A holistic approach to marine eco-systems biology[END_REF]. These included samples from a range of depths (surface, deep chlorophyll maximum, and one mesopelagic sample) located in 7 oceans and seas, 4 different biomes and 11

Longhurst oceanographic provinces (Supplementary File S1). Longhurst provinces and biomes are defined based on Longhurst [START_REF] Longhurst | Ecological Geography of the Sea[END_REF] and environmental features are defined based on Environment Ontology (http://environmentontology.org/). Sampling strategy and methodology for the Tara Oceans Expedition is fully described by Pesant et al. [START_REF] Pesant | Tara Oceans Data: A sampling strategy and methodology for the study of marine plankton in their environmental context[END_REF].

Environmental Parameters

Temperature, salinity, and oxygen data were collected from each station using a CTD (Sea-Bird Electronics, Bellevue, WA, USA; SBE 911plus with Searam recorder) and dissolved oxygen sensor (Sea-Bird Electronics; SBE 43). Nutrient concentrations were determined using segmented flow analysis [START_REF] Aminot | Practical Guidelines for the Analysis of Seawater[END_REF] and included nitrite, phosphate, nitrite plus nitrate, and silica. Nutrient concentrations below the detection limit (0.02 µmol kg -1 ) are reported as 0.02 µmol kg - 1 . Chlorophyll concentrations were measured using HPLC [START_REF] Ras | Spatial variability of phytoplankton pigment distributions in the Subtropical South Pacific Ocean: comparison between in situ and predicted data[END_REF][START_REF] Van Heukelem | Computer-assisted high-performance liquid chromatography method development with applications to the isolation and analysis of phytoplankton pigments[END_REF]. These environmental parameters are available in Pangaea (www.pangaea.de) using the accession numbers in Supplementary File S1.

Microbial Abundances

Flow-cytometry was used to determine the concentration of Synechococcus, Prochlorococcus, total bacteria, low-DNA bacteria, high-DNA bacteria, and the percent of bacteria with high DNA in each sample [START_REF] Gasol | Using flow cytometry for counting natural planktonic bacteria and understanding the structure of planktonic bacterial communities[END_REF].

Morphological Analysis of Viral Communities

Quantitative transmission electron microscopy (qTEM) was used to evaluate the capsid diameter distributions of viral communities as previously described [START_REF] Brum | Global morphological analysis of marine viruses shows minimal regional variation and dominance of non-tailed viruses[END_REF]. Briefly, preserved unfiltered samples (EM-grade glutaraldehyde; Sigma-Aldrich, St. Louis, MO, USA; 2% final concentration) were flash-frozen and stored at -80ºC until analysis. Viruses were deposited onto TEM grids using an air-driven ultracentrifuge (Airfuge CLS, Beckman Coulter, Brea, CA, USA), followed by positive staining of the deposited material with 2% uranyl acetate (Ted Pella, Redding, CA, USA). Samples were then examined using a transmission electron microscope (Philips CM12, FEI, Hilsboro, OR, USA) with 100 kV accelerating voltage. Micrographs of 100 viruses were collected per sample using a Macrofire Monochrome CCD camera (Optronics, Goleta, CA, USA) and analyzed using ImageJ software (US National Institutes of Health, Bethesda, MD, USA; 59) to measure the capsid diameter. A subset [START_REF] Hennes | Direct counts of viruses in natural waters and laboratory cultures by epifluorescence microscopy[END_REF] of the 41 samples presented here had previously been analyzed in a different study [START_REF] Brum | Global morphological analysis of marine viruses shows minimal regional variation and dominance of non-tailed viruses[END_REF].

Virome Construction

For each sample, 20 L of seawater were 0.22 µm-filtered and viruses were concentrated from the filtrate using iron chloride flocculation [START_REF] John | A simple and efficient method for concentration of ocean viruses by chemical flocculation[END_REF] followed by storage at 4ºC. After resuspension in ascorbic-EDTA buffer (0.1 M EDTA, 0.2 M Mg, 0.2 M ascorbic acid, pH 6.0), viral particles were concentrated using Amicon Ultra 100 kDa centrifugal devices (Millipore), treated with DNase I (100U/mL) followed by the addition of 0.1 M EDTA and 0.1 M EGTA to halt enzyme activity, and extracted as previously described [START_REF] Hurwitz | Evaluation of methods to concentrate and purify ocean virus communities through comparative, replicated metagenomics[END_REF]. Briefly, viral particle suspensions were treated with Wizard PCR Preps DNA Purification Resin (Promega, WI, USA) at a ratio of 0.5 mL sample to 1 mL resin, and eluted with TE buffer (10 mM Tris, pH 7.5, 1 mM EDTA) using Wizard Minicolumns. Extracted DNA was Covaris-sheared and size selected to 160-180 bp, followed by amplification and ligation per the standard Illumina protocol.

Sequencing was done on a HiSeq 2000 system at the Genoscope facilities (Paris, France).

Quality Control of Reads and Assembly

Individual reads of 43 metagenomes were quality controlled using a combination of trimming and filtering as previously described [START_REF] Schloissnig | Genomic variation landscape of the human gut microbiome[END_REF]. Briefly, bases were trimmed at the 5' end if the number of base calls for any base (A, T, G, C) diverged by more than two standard deviations from the average across all cycles. Conversely, bases were trimmed at the 3' end of reads if the quality score was <20. Finally, reads that were shorter than 95 bp or reads with a median quality score <20 were removed from further analyses. Assembly of reads was done using SOAPdenovo [START_REF] Luo | SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler[END_REF] where insert and k-mer size are calculated at run-time and are specific to each virome as implemented in the MOCAT pipeline [START_REF] Kultima | MOCAT: A metagenomics assembly and gene prediction toolkit[END_REF]. On average, 34.2% of the virome reads were included in the assembled contigs (min: 21.08%, max: 48.52%). Virome reads were deposited in the European Nucleotide Archive (http://www.ebi.ac.uk/ena/) under accession numbers reported in Supplementary File S1.

Protein Clustering

Open Reading Frames (ORFs) were predicted from all quality-controlled contigs using Prodigal [START_REF] Hyatt | Prodigal: prokaryotic gene recognition and translation initiation site identification[END_REF] with default settings. Predicted ORFs were clustered based on sequence similarity as described previously [START_REF] Yooseph | The Sorcerer II global ocean sampling expedition: expanding the universe of protein families[END_REF][START_REF] Hurwitz | The Pacific Ocean Virome (POV): A marine viral metagenomic dataset and associated protein clusters for quantitative viral ecology[END_REF]. Briefly, ORFs were initially mapped to existing clusters (POV, GOS and phage genomes), using cd-hit-2d ('-g 1 -n 4 -d 0 -T 24 -M 45000'; 60% percent identity and 80% coverage). Then the remaining, unmapped ORFs were self-clustered, using cdhit with the same options as above. Only protein clusters (PCs) with more than two ORFs were considered bona fide and were used for subsequent analyses. To develop read counts per PC for statistical analyses, reads were mapped back to predicted ORFs in the contigs dataset using Mosaik with the following settings: "-a all -m all -hs 15 -minp 0.95 -mmp 0.05 -mhp 100 -act 20" (version 1.1.0021; http://bioinformatics.bc.edu/marthlab/Mosaik). Read counts to PCs were normalized by sequencing depth of each virome. Shannon diversity (H') was calculated from PC read counts using only PCs with more than two predicted ORFs. Observed richness is reported as the total number of reads in each PC. Pielou's evenness (J) was calculated as the ratio of H' / H max , where H max = ln N, and N = total number of observed PCs in a sample.

Analysis of Viral Populations

Considering the size of the entire dataset (3,821,756 assembled contigs), we decided to focus the analysis of viral populations using contigs most likely originating from bacterial or archaeal viruses. For this, we mined only the 22,912 contigs with more than 10 predicted genes (corresponding to an average of 6.41% of the assembled reads per sample, min: 1.29%, max: 14.52%), as the origin of contigs with only a few predicted genes can be spurious. First, we removed 6,706 contigs suspected of having originated from cellular genomes [START_REF] Roux | Assessment of viral community functional potential from viral metagenomes may be hampered by contamination with cellular sequences[END_REF], whether due to free genomic DNA contamination or viral-encapsidation of cellular DNA (for example, in gene transfer agents or generalized transducing phages). These suspect cellular contigs were those containing no typical viral genes (such as virion-related genes including major capsid proteins and large subunits of the terminase) and displaying as many 'characterized genes' (such as genes with a significant similarity to a PFAM domain through Hmmsearch, 65) as a typical cellular genome, whereas phage genomes are typically enriched in 'uncharacterized genes' [START_REF] Roux | Ecology and evolution of viruses infecting uncultivated SUP05 bacteria as revealed by single-cell-and meta-genomics[END_REF].

We also removed all contigs posited to originate from eukaryotic viruses. These were contigs that contained at least three predicted proteins with best BLAST hits to a eukaryotic virus, and more than half of the affiliated proteins were not associated to bacteriophages or archaeal viruses. Not surprisingly, given that eukaryotes are outnumbered by bacteria and archaea in the marine environment, this step removed only 142 contigs associated with eukaryotic viruses.

From the remaining 16,124 contigs most likely to have originated from bacterial or archaeal viruses, the population study only used those longer than 10kb in sizea total of 6,322 contigs, which corresponded to an average of 4.04% of the assembled reads per sample, min: 0.98%, max: 9.97%). These 6,322 contigs were then clustered into populations if they shared more than 80% of their genes at >95% nucleotide identity; a threshold derived from naturally-occurring T4-like cyanophages [START_REF] Deng | Viral tagging reveals discrete populations in Synechococcus viral genome sequence space[END_REF]. This resulted in 5,476 'populations' from the 6,322 contigs, where as many as 12 contigs (average 1.15 contigs) were included per population. For each population, the longest contig was chosen as the 'seed' sequence.

The relative abundance of each population was computed by mapping all qualitycontrolled reads to the set of 5,476 non-redundant populations (considering only mapping quality scores greater than 1) with Bowtie 2 [START_REF] Langmead | Fast gapped-read alignment with Bowtie 2[END_REF]. For each sample-sequence pair, if more than 75% of the reference sequence was covered by virome reads, the relative abundance was computed as the number of base pairs recruited to the contig normalized to the total number of base pairs available in the virome and the contig length. Shannon diversity index (H') and Pielou's evenness (J) were calculated as done for PCs using the relative abundance of viral populations.

The sample containing the seed sequence (the longest contig in a population) was also considered the best estimate of that population's origin. We reasoned this was because the longest contig in a population would derive most often from the sample with the highest coverage (a metric for population abundance) and likely corresponded to the location with the greatest viral abundance for this population. This assumption was supported by the results showing that populations were most abundant in their original samples (Fig. 4, Fig. 5B). Even though some individual cases could diverge from this rule, we expected to correctly identify most of these original locations, hence getting an accurate global signal.

The seed sequence was also used to assess taxonomic affiliation of the viral population.

Cases where >50% of the genes were affiliated to a specific reference genome from RefSeq (based on a BLASTp comparison with thresholds of 50 for bit score and 10 -5 for e-value) with an identity percentage of at least 75% (at the protein sequence level) were considered as confident affiliations to the corresponding reference virus.

Finally, estimations of net viral population movement between samples were made based on the relative abundance of populations in one sample compared to that of its neighboring samples (Fig. 4). For each neighboring sample pair, the average relative abundance of populations originating from sample A in sample B was compared with the relative abundance of populations originating from sample B in sample A. The origin of each population was defined as the sample in which the longest contig of the population was assembled. The magnitude of these differences was carried through the analysis to estimate the level of transport between each pair of samples (depicted as line width in Fig. 7) and the difference between these values was used to estimate the directionality of the transfer. For example, if sample B contains many populations from sample A, but very few populations from sample B are detected in sample A, we calculate that the net movement is from sample A to sample B. Again, while the sampling of some populations may not be strong, the net movement was calculated as the average of all shared populations between neighboring sample pairs, which corresponded to 105 different populations on average (ranging from 2 to 412).

Statistical Ordination of Samples

Viral community composition based on capsid diameter distributions (from qTEM; using 7-nm histogram bin sizes), population abundances, and normalized PC read counts (using only protein clusters with more than 20 representatives) were compared using non-metric multidimensional scaling (NMDS) performed using the 'metaMDS' function (default parameters) of the vegan package [START_REF] Oksanen | vegan: Community Ecology Package, R package version 2[END_REF] in R version 2.15.2 [START_REF] Core | R: A language and environment for statistical computing[END_REF]. The influence of metadata on sample ordination was evaluated using the functions 'envfit' for factor variables including depth category, Longhurst province, and biome, and 'ordisurf' for all linear variables, in the vegan package [START_REF] Oksanen | vegan: Community Ecology Package, R package version 2[END_REF][START_REF] Wood | Fast stable restricted maximum likelihood estimation of semiparametric generalized linear models[END_REF]. Several samples had exceptional environmental conditions (TARA_67_SUR, TARA_70_MESO, TARA_82_DCM, and TARA_85_DCM), thus all statistical ordination analyses were conducted with and without these samples (referred to as the 'sample subset') to evaluate their influence. Overall, 1,323,921 PCs were identified in all viromes combined. Fig. 3. Alpha diversity measurements in TOV dataset. A) Shannon's richness H' and Pielou's evenness J calculated from protein clusters counts for each sample and a pool of all samples, normalized to 5 million reads. B) Shannon's richness H' and Pielou's evenness J calculated from relative abundances of viral populations for each sample and a pool of all samples, with subsamples of 100,000 reads. Outliers corresponding to values outside of the average value plus or minus two standard deviations are colored in green and red, respectively. Values calculated from the pool of all samples are colored in blue. Longhurst provinces are indicated below samples using the same abbreviations as in Fig. 1.

Fig. 4. Relative abundance of viral populations in TOV by sample.

This heatmap displays the relative abundance of each population (sorted according to its original sample; y-axis) in each sample (x-axis). Relative abundance of one population in a sample is based on recruitment of reads to the population reference contig, and only considered if more than 75% of the reference contig is covered. Longhurst provinces are indicated below samples (using the same abbreviations as in Fig. 1) and outlined in black on the heatmap. Boxplots are constructed as in Fig. 1.

Fig. 6. Taxonomic affiliation of TOV viral populations sorted by distribution and average

abundance. A population was considered as similar to a known virus when less than half of its reference contig genes were uncharacterized, and all characterized genes had taxonomic affiliations to the same reference genome. As in Fig. 4, the relative abundance (y-axis) is computed for each sample as the number of bp mapped to a contig per kb of contig per Mb of metagenome sequenced. Here, the relative abundance of a population is defined as the average abundance of its reference contig across all samples. This heatmap displays the relative abundance of each population (sorted according to its original sample; y-axis) in each sample (x-axis). Relative abundance of one population in a sample is based on recruitment of reads to the population reference contig, and only considered if more than 75% of the reference contig is covered. Longhurst provinces are indicated below samples (using the same abbreviations as in Fig. 1) and outlined in black on the heatmap. A population was considered as similar to a known virus when less than half of its reference contig genes were uncharacterized, and all characterized genes had taxonomic affiliations to the same reference genome. As in Fig. 4, the relative abundance (y-axis) is computed for each sample as the number of bp mapped to a contig per kb of contig per Mb of metagenome sequenced. Here, the relative abundance of a population is defined as the average abundance of its reference contig across all samples. and Methods). For each sample pair, the average relative population abundances in one sample originating from a neighboring sample were calculated and compared (for example, relative abundance of populations from sample A found in sample B are compared with relative abundance of populations from sample B found in sample A). The sign of the relative abundance difference between neighboring samples was used to estimate the movement direction (arrowhead), and the absolute value of the difference was interpreted as reflecting the movement magnitude (line width). Stations are labeled with station number. 'Down' and 'up' refer to net vertical movement of viral populations between the surface and DCM samples at the same station.

Table 1.

Relationships between viral community structure (based on viral morphology, populations, and PCs) and metadata using NMDS analysis of all samples and the sample subset (all samples except for TARA_67_SRF, TARA_70_MESO, TARA_82_DCM, and TARA_85_DCM due to exceptional environmental conditions at these locations). Significant relationships are italicized and in bold.

Fig. 1 .

 1 Fig. 1. Distribution of viral capsid diameters in each sample (n = 100 viruses per sample). Data are not available for samples TARA_18_DCM and TARA_70_MESO. Boxplots are constructed with the upper and lower lines corresponding to the 25th and 75th percentiles, while outliers are displayed as points. Longhurst provinces are indicated below samples (MEDI, Mediterranean Sea; REDS, Red Sea; ARAB, NW Arabian Upwelling; MONS, Indian Monsoon Gyres; ISSG, Indian S. Subtropical Gyre; EAFR, E. Africa Coastal; BENG, Benguela Current Coastal; SATL, S. Atlantic Gyre; FKLD, SW Atlantic Shelves; APLR, Austral Polar; PNEC, N. Pacific Equatorial Countercurrent).

Fig. 2 .

 2 Fig. 2. Protein cluster (PC) richness in core and pan viromes from the TOV and POV datasets. A) Accumulation curves of core and pan PCs in the TOV dataset. Vertical axis shows the number of shared (core virome) and total (pan virome) PCs when n viromes are compared (n = 1 to 43; from 3 to 41 only 1000 combinations are shown). Lines: i) total number of PCs (1,075,763 PCs), ii) core surface virome (710 PCs), iii) core DCM virome (424 PCs), iv) core surface and DCM virome (220 PCs), v) all samples (including the deep-ocean sample TARA_70_MESO; 65 PCs). B) Core and pan PCs in all TOV and photic-zone POV samples combined. Vertical axis shows the number of shared (core virome) and total (pan-virome) PCs when n viromes are compared (n = 1 to 57; from 3 to 57 only 1,000 combinations are shown).

Fig. 5 .

 5 Fig. 5. Relative abundance of viral populations in TOV by station. A) Evaluation of viral population distribution showing the number of stations (y-axis) in which each population (sorted by their original station, x-axis) is distributed. Populations are grouped by station, merging surface and DCM samples from the same station. B) Relative abundance of populations at the original stations where the contigs were assembled compared to their abundance at other stations.

Fig. 7 .

 7 Fig. 7. Net movement of viral populations throughout the oceans. Calculations are based on reciprocal comparison of viral population abundances between neighboring samples (see Fig.3and Methods). For each sample pair, the average relative population abundances in one sample originating from a neighboring sample were calculated and compared (for example, relative abundance of populations from sample A found in sample B are compared with relative abundance of populations from sample B found in sample A). The sign of the relative abundance difference between neighboring samples was used to estimate the movement direction (arrowhead), and the absolute value of the difference was interpreted as reflecting the movement magnitude (line width). Stations are labeled with station number. 'Down' and 'up' refer to net vertical movement of viral populations between the surface and DCM samples at the same station.

Fig. 1 .

 1 Fig. 1. Distribution of viral capsid diameters in each sample (n = 100 viruses per sample). Data are not available for samples TARA_18_DCM and TARA_70_MESO. Boxplots are constructed with the upper and lower lines corresponding to the 25th and 75th percentiles, while outliers are displayed as points. Longhurst provinces are indicated below samples (MEDI, Mediterranean Sea; REDS, Red Sea; ARAB, NW Arabian Upwelling; MONS, Indian Monsoon Gyres; ISSG, Indian S. Subtropical Gyre; EAFR, E. Africa Coastal; BENG, Benguela Current Coastal; SATL, S. Atlantic Gyre; FKLD, SW Atlantic Shelves; APLR, Austral Polar; PNEC, N. Pacific Equatorial Countercurrent).

Fig. 2 .

 2 Fig. 2. Protein cluster (PC) richness in core and pan viromes from the TOV and POV datasets. A) Accumulation curves of core and pan PCs in the TOV dataset. Vertical axis shows the number of shared (core virome) and total (pan virome) PCs when n viromes are compared (n = 1 to 43; from 3 to 41 only 1000 combinations are shown). Lines: i) total number of PCs (1,075,763 PCs), ii) core surface virome (710 PCs), iii) core DCM virome (424 PCs), iv) core surface and DCM virome (220), v) all samples (including the deep-ocean sample TARA_70_MESO; 65 PCs). B) Core and pan PCs in all TOV and photic-zone POV samples combined. Vertical axis shows the number of shared (core virome) and total (pan-virome) PCs when n viromes are compared (n = 1 to 57; from 3 to 57 only 1,000 combinations are shown). Overall, 1,323,921 PCs were identified in all viromes combined.

Fig. 3 .

 3 Fig.3. Alpha diversity measurements in TOV dataset. A) Shannon's richness H' and Pielou's evenness J calculated from protein clusters counts for each sample and a pool of all samples, normalized to 5 million reads. B) Shannon's richness H' and Pielou's evenness J calculated from relative abundances of viral populations for each sample and a pool of all samples, with subsamples of 100,000 reads. Outliers corresponding to values outside of the average value plus or minus two standard deviations are colored in green and red, respectively. Values calculated from the pool of all samples are colored in blue. Longhurst provinces are indicated below samples using the same abbreviations as in Fig.1.

Fig. 4 .

 4 Fig. 4. Relative abundance of viral populations in TOV by sample.This heatmap displays the relative abundance of each population (sorted according to its original sample; y-axis) in each sample (x-axis). Relative abundance of one population in a sample is based on recruitment of reads to the population reference contig, and only considered if more than 75% of the reference contig is covered. Longhurst provinces are indicated below samples (using the same abbreviations as in Fig.1) and outlined in black on the heatmap.

Fig. 5 .

 5 Fig. 5. Relative abundance of viral populations in TOV by station. A) Evaluation of viral population distribution showing the number of stations (y-axis) in which each population (sorted by their original station, x-axis) is distributed. Populations are grouped by station, merging surface and DCM samples from the same station. B) Relative abundance of populations at the original stations where the contigs were assembled compared to their abundance at other stations. Boxplots are constructed as in Fig. 1.

Fig. 6 .

 6 Fig. 6. Taxonomic affiliation of TOV viral populations sorted by distribution and average abundance.A population was considered as similar to a known virus when less than half of its reference contig genes were uncharacterized, and all characterized genes had taxonomic affiliations to the same reference genome. As in Fig.4, the relative abundance (y-axis) is computed for each sample as the number of bp mapped to a contig per kb of contig per Mb of metagenome sequenced. Here, the relative abundance of a population is defined as the average abundance of its reference contig across all samples.

Fig. 7 .

 7 Fig. 7. Net movement of viral populations throughout the oceans. Calculations are based on reciprocal comparison of viral population abundances between neighboring samples (see Fig.3and Methods). For each sample pair, the average relative population abundances in one sample originating from a neighboring sample were calculated and compared (for example, relative abundance of populations from sample A found in sample B are compared with relative abundance of populations from sample B found in sample A). The sign of the relative abundance difference between neighboring samples was used to estimate the movement direction (arrowhead), and the absolute value of the difference was interpreted as reflecting the movement magnitude (line width). Stations are labeled with station number. 'Down' and 'up' refer to net

3

 3 Fig. 7. Net movement of viral populations throughout the oceans. Calculations are based on reciprocal comparison of viral population abundances between neighboring samples (see Fig.3and Methods). For each sample pair, the average relative population abundances in one sample originating from a neighboring sample were calculated and compared (for example, relative abundance of populations from sample A found in sample B are compared with relative abundance of populations from sample B found in sample A). The sign of the relative abundance difference between neighboring samples was used to estimate the movement direction (arrowhead), and the absolute value of the difference was interpreted as reflecting the movement magnitude (line width). Stations are labeled with station number. 'Down' and 'up' refer to net

Acknowledgements. We thank Jesse Czekanski-Moir for advice on statistics and Laurent Coppola for assistance with validating nutrient data. We thank the commitment of the following people and sponsors: CNRS (in particular Groupement de Recherche GDR3280), European Molecular Biology Laboratory (EMBL), Genoscope/CEA, VIB, Stazione Zoologica Anton Dohrn, UNIMIB, Fund for Scientific Research -Flanders, Rega Institute, KU Leuven, The French Ministry of Research, the French Government 'Investissements d'Avenir' programmes

Tara Oceans Coordinators

Silvia G. Acinas 1 , Peer Bork 2,3 , Emmanuel Boss 4 , Chris Bowler 5 , Colomban de Vargas 6,7 , Michael Follows 8 , Gabriel Gorsky 9,31 , Nigel Grimsley 10,11 , Pascal Hingamp 12 , Daniele Iudicone 13 , Olivier Jaillon 14,15,16 , Stefanie Kandels-Lewis 2,17 , Lee Karp-Boss 18 , Eric Karsenti 5,17 , Uros Krzic 19 , Fabrice Not 6,7 , Hiroyuki Ogata 20 , Stephane Pesant 21,22 , Jeroen Raes 23,24,25 , Emmanuel G. Reynaud 26 , Christian Sardet 27,28 , Mike Sieracki 29, † , Sabrina Speich 30, ‡ , Lars Stemmann 9,31 , Matthew B. Sullivan 32 , Shinichi Sunagawa 2 , Didier Velayoudon 33 , Jean

Bourgois, the Veolia Environment Foundation, Region Bretagne, Lorient Agglomeration, World Courier, Illumina, the EDF Foundation, FRB, the Prince Albert II de Monaco Foundation, the Tara schooner and its captains and crew. We thank MERCATOR-CORIOLIS and ACRI-ST for providing daily satellite data during the expedition. We are also grateful to the French Ministry of Foreign Affairs for supporting the expedition and to the countries who graciously granted sampling permissions. Tara Oceans would not exist without continuous support from 23 institutes (http://oceans.taraexpeditions.org). We also acknowledge excellent assistance from the European Bioinformatics Institute (EBI), in particular Guy Cochrane and Petra ten Hoopen, as well as the EMBL Advanced Light Microscopy Facility (ALMF), in particular Rainer Pepperkok. The authors further declare that all data reported herein are fully and freely available from the date of publication, with no restrictions, and that all of the samples, analyses, publications, and ownership of data are free from legal entanglement or restriction of any sort by the various nations whose waters the Tara Oceans expedition sampled in. Data described herein is available at EBI (project identifiers PRJEB402 and PRJEB7988) and Pangaea (see Supplementary Table S1), and the data release policy regarding future public release of Tara Oceans data is described in Pesant et al [START_REF] Pesant | Tara Oceans Data: A sampling strategy and methodology for the study of marine plankton in their environmental context[END_REF]. We also acknowledge the EMBL Advanced Light Microscopy Facility (ALMF), and in particular Rainer Pepperkok. This research is funded in part by the Gordon and Betty Moore Foundation through grants GBMF2631 and GBMF3790 to MBS. We also acknowledge support from UA high-performance computing; the foundation for


Chloé Serre-Combe

Nicolas Meyer

Thomas Opitz

Gwladys Toulemonde

Modeling moderate and extreme urban rainfall at high spatio-temporal resolution

come   L'archive ouverte pluridisciplinaire

Univariate modeling of the rainfall distribution

Let Y denote the rainfall measurement at a given site.

Generalized Pareto Distribution (GPD) [START_REF] Coles | An introduction to statistical modeling of extreme values[END_REF] for rainfall excesses above a high threshold u

Y -u | Y > u ∼ H ξ , where H ξ y σ = 1 -1 + ξ y σ -1/ξ + si ξ ̸ = 0 , 1 -e -y σ si ξ = 0 ,
where a + = max(a, 0), σ > 0, ξ ∈ R and y > 0.

Extended Generalized Pareto Distribution (EGPD) [START_REF] Naveau | Modeling jointly low, moderate, and heavy rainfall intensities without a threshold selection[END_REF] for the entire distribution

The cumulative distribution function (cdf) of the EGPD is given by

F Y (y) = G H ξ y σ ,
with G(x) = x κ , κ > 0.

CNRS Polytech

EGPD fit with κ = 0.56, σ = 0.26 and ξ = 0.51

Spatio-temporal dependence modeling [4]

Let X = {X(s, t) , (s, t) ∈ S × [0, ∞)} be a strictly stationary isotropic Brown-Resnick process. For a spatial lag v ≥ 0 and a temporal lag h ≥ 0, the extremogram of X is given by

χ(v, h) = 2 1 -ϕ 1 2 δ(v, h)
with ϕ the standard normal distribution function and δ a stationary and isotropic variogram.

Assumption of additive separability:

δ(v,h) 2 = θ 1 v α 1 + θ 2 h α 2 , 0 < α 1 , α 2 ≤ 2 , θ 1 , θ 2 > 0

Spatial extremogram

For all spatial lags v = k × ∆v, k = 1, 2, . . ., we define

N (v) = (s i , s j ) ∥s i -s j ∥ ∈ ]v -∆v, v]
For ∆v = 100 meters For fixed t and for any (s i , s j ) ∈ N (v)

χ (t)
ij,q (v, 0) = P X(s i , t) > q, X(s j , t) > q P (X(s i , t) > q) Estimator:

χ (t) q (v, 0) = 1 |N (v)| i,j | (s i ,s j )∈N (v) 1 { X(s i ,t)>q ,X(s j ,t)>q } 1 |S| |S| i=1 1 {X(s i ,t)>q}
with q a high quantile (99.8%).

WLSE

Empirical variogram

Spatial

δ(v, 0) = 2 θ 1 v α 1 Temporal δ(0, h) = 2 θ 2 h α 2 Spatio-temporal χ(v, h) = 2 1 -ϕ 1 2 δ(v, h) Spatial η (χ(v, 0)) = log θ 1 + α 1 log v , v > 0 := c 1 + α 1 x v Temporal η (χ(0, h)) = log θ 2 + α 2 log h , h > 0 := c 2 + α 2 x h Transformation: η(χ) = 2 log ϕ -1 1 -1 2 χ
Weighted Least Squares Estimation (WLSE)

c i α i = argmin c i ,α i x w x (η ( χ) -(c i + α i x)) 2

Temporal extremogram

For a site s and for any t χ (s)

t,q (0, h) = P (X(s, t) > q, X(s, t + h) > q) P (X(s, t) > q)

Estimator:

χ (s) q (0, h) = 1 T -h T -h k=1 1 {X(s,t k )
>q ,X(s,t k +h)>q} 1 T T k=1 1 {X(s,t k )>q} with q a high quantile (99.8%) and t k ∈ {t 1 , . . . , t T }.

Data [ 1 ]

 1 S = {17 rain gauges} ▶ Period: [2019, 2022] ▶ High temporal resolution: Every minute 5-minute aggregation ▶ High spatial resolution: Interdistance ∈ [77, 1531] meters

Estimate Std. Error c 1

 1 -3.465 * * * 0.605 α 1 0.242 * 0.093 * p-value< 0.05; * * * p-value< 0.001

  With ∆h = 5 minutes WLSE Estimate Std. Error c 2 -1.252 * * * 0.023 α 2 0.702 * * * 0.012

Author portfolio website

EVA Conference 2023


Chloé Serre-Combe

Nicolas Meyer

Thomas Opitz

Gwladys Toulemonde

Modélisation statistique de précipitations urbaines à fine échelle spatio-temporelle

▶ Situation géographique :

Bassin versant du Verdanson, affluent du Lez, situé en zone urbaine

Generalized Pareto Distribution

H ξ y σ = 1 + ξ y σ -1/ξ + si ξ ̸ = 0 , e -y σ si ξ = 0 ,

Generalized Pareto Distribution

Extended GPD 1

H ξ y σ = 1 + ξ y σ -1/ξ + si ξ ̸ = 0 , e -y σ si ξ = 0 , où a + = max(a, 0), ξ ∈ R, σ > 0 et y > 0 ▶ Modélise les pluies extrêmes ▶ Dépend d'un choix de seuil F Y (y) = G H ξ y σ , avec G(x) = x κ , κ > 0 ▶ Modélise

Extrémogramme spatio-temporel d'un processus de Brown-Resnick

Soient h ≥ 0 un lag spatial et τ ≥ 0 un lag temporel. On a

χ(h, τ ) = 2 1 -ϕ 1 2 δ(h, τ )
avec ϕ la f.d.r. d'une loi normale centrée-réduite et δ le variogramme associé.

2 = θ 1 h α1 + θ 2 τ α2 , 0 < α1, α2 ≤ 2 , θ1, θ2 > 0 Spatio-temporel χ(h, τ ) = 2 1 -ϕ 1 2 δ(h, τ ) Spatial η (χ(h, 0)) = log θ1 + α1 log h , h > 0 Temporel η (χ(0, τ )) = log θ2 + α2 log τ , τ > 0 Transformation : η(χ) = 2 log ϕ -1 1 -1 2 χ = θ 1 h α1 + θ 2 τ α2 , 0 < α1, α2 ≤ 2 , θ1, θ2 > 0 Spatio-temporel χ(h, τ ) = 2 1 -ϕ 1 2 δ(h, τ ) Spatial η (χ(h, 0)) := c1 + α1x h , h > 0 Temporel η (χ(0, τ )) := c2 + α2xτ , τ > 0 Transformation : η(χ) = 2 log ϕ -1 1 -1 2 χ
Modèle linéaire pondéré [START_REF] Buhl | Semiparametric estimation for isotropic max-stable space-time processes[END_REF] Extrémogramme spatial

Pour tout temps t et pour tout (si, sj) ∈ N(h),

χ (t)
ij,q (h, 0) = P Xs i ,t > q, Xs j ,t > q P (Xs i ,t > q)

Estimateur :

χ (t) q (h, 0) = 1 |N(h)| i,j | (s i ,s j )∈N(h) 1 X s i ,t >q ,X s j ,t >q 1 |S| |S| i=1 1 {Xs i ,t >q}
avec q un quantile assez grand.

Modèle linéaire pondéré spatial Estimation des paramètres

c1 α1 = argmin c 1 ,α 1 h w h (η ( χ(h, 0)) -(c1 + α1x h )) 2

Résultats

▶

  Fine échelle temporelle : À la minute avec agrégation à 5 minutes ▶ Fine échelle spatiale : Inter-distance entre 77 et 1531 mètres

▶

  où a + = max(a, 0), ξ ∈ R, σ > 0 et y > 0 Modélise les pluies extrêmes ▶ Dépend d'un choix de seuil

  ( χ(0, τ )) -(c2 + α2xτ )) 0) = 2 θ 1 h α 1 Temporel δ(0, τ ) = 2 θ 2 τ α 2 ▶ déplacement horizontal des masses d'air ▶ vecteur de vélocité V ▶ direction fixéeModélisation de la dépendanceDans le référentiel Lagrangien, on aδL(h, τ ) = δ(h -τ V, τ ) avec h ∈ R 2 , τ ∈ R et V ∈ R 2 2 Inspiration : Fritsche 1999Cycle hydrologique 2

  = {X s,t , (s, t) ∈ S × [0, ∞)} un processus max-stable de Brown-Resnick, strictement stationnaire et isotrope[START_REF] Buhl | Semiparametric estimation for isotropic max-stable space-time processes[END_REF]).

les pluies hautes et modérées ▶ Évite le choix d'un seuil 1 Naveau et al. 2016

Cadre : X

Finaud-Guyot et al.

σ = 1, ξ = 0.5

( κ = 0.56, σ = 0.26 et ξ = 0.51)

* * p-value<0.01; * * * p-value<0.001

Mesure de dépendance extrémale

Soient U ∼ U(0, 1) et V ∼ U(0, 1). On définit

Considération de rayons autour de chaque site

Chaque lag h représente un rayon. L'ensemble des paires de même lag spatial h est q (0, τ ) = P (Xs,t > q, Xs,t+τ > q) P (Xs,t > q)

Estimateur :

avec q un quantile assez grand et t k ∈ {t1, . . . , tT}.

Perspectives

▶ Structure des décalages spatiaux ▶ Cas de non séparabilité avec variogrammes plus complexes ▶ Structure anisotrope et advection ▶ Modélisation multi-échelle


Aurélie Leclercq-Vandelannoitte

François De Corbière

Christophe Elie-Dit-Cosaque

Chères lectrices et chers lecteurs,

Alors que nos sociétés, nos façons de vivre, le monde du travail et les organi-sations ont radicalement changé depuis maintenant deux ans, nous souhaitons vous présenter dans cet éditorial quelques éléments de continuité et de changement au sein de la revue SIM .

Tout d'abord les crises actuelles ont mis en évidence le rôle fondamental joué par le numérique dans nos sociétés [START_REF] Ågerfalk | Information systems in the age of pandemics: COVID-19 and beyond[END_REF][START_REF] Dugoin-Clément | Ukraine : la guerre se joue également dans le cyberespace . The Conversation[END_REF] . D'une part, dans le contexte de pandémie de COVID 19, les technologies de l'information, réseaux, outils collaboratifs, ont rendu possible la continuité de nos activités à distance, notamment dans des situations extrêmes, de crise [START_REF] De Corbière | Confé-rences à distance, oui, mais… Systèmes d[END_REF][START_REF] Godé | Les technologies émergentes en contexte extrême : de l'adaptation à l'anticipation ? Systèmes d[END_REF] . Ils nous ont permis de renouveler nos façons de travailler, d'échanger, de collaborer et de vivre. Les organisations sont désormais amenées à revoir leurs modes de fonctionnement, leurs pratiques, leurs modes de management et leurs espaces de travail. D'autre part, alors que la guerre en Ukraine fait rage, sur la « ligne de front » et « en ligne », sur le champ de bataille, mais aussi sur le champ numérique [START_REF] Dugoin-Clément | Ukraine : la guerre se joue également dans le cyberespace . The Conversation[END_REF], par écrans interposés et à hauteur de smartphones [START_REF] Filipetti | Tribune : « La terrible horreur de la guerre à hauteur de smartphone[END_REF], à grand renfort de cyberattaques, de désinfor-mation numérique et de guerre d'images sur les réseaux sociaux, force est de constater le poids encore croissant du numérique dans nos sociétés et, dans ces contextes, la diversité des formes et des rôles des technologies informatisées.

Désormais, le monde est numérique . Nous n'interagissons pas avec la technologie de l'information. Elle n'est plus un « medium » de communication. Les technologies et sys-tèmes d'information ne représentent ni ne reflètent une réalité qui leur préexisterait . Le numérique crée tout en la façonnant notre réalité (Baskerville et al., 2021) . Nous vivons à travers la technologie, en elle, comme elle vit en nous [START_REF] Serres | Petite poucette[END_REF] . Ce « renversement ontologique » [START_REF] Baskerville | Digital first: The ontological reversal and new challenges for information systems research[END_REF], cette nouvelle « semiosis numérique » (de Vaujany et Mitev, 2017) nous met au défi de réfléchir au rôle et aux impacts du numérique dans la société, à revoir nos théories et nos concepts, tout en nous amenant à réfléchir au rôle de la recherche en systèmes d'information dans ce monde digital [START_REF] Ågerfalk | Information systems in the age of pandemics: COVID-19 and beyond[END_REF][START_REF] De Vaujany | Apocalypse managériale[END_REF] .

La ligne directrice de la nouvelle équipe éditoriale est de mettre en lumière toute l'importance et tous les enjeux (managériaux, sociaux, organisationnels, techno-logiques, éthiques) du numérique, des systèmes et technologies de l'information et de la communication, dans une société dont les contextes et évolutions contraignent les acteurs sociaux et les organisations à en prendre toute la mesure… mais aussi toutes les responsabilités .

Ainsi, dans la continuité des discussions menées dans la revue jusqu'alors, nous souhaitons contribuer à la compréhension des grandes problématiques relatives au numérique, aux systèmes et technologies d'information . Ce faisant, nous souhaitons ouvrir la revue à de nouvelles perspectives, et l'inscrire notamment dans des débats de fond sur l'évolution de la société en lien avec le numérique, de façon pluridisciplinaire . Nous souhaitons par ailleurs diversifier les contributions publiées dans SIM [START_REF] De Corbière | Pour des contributions variées dans la revue SIM[END_REF], tout en main-tenant la rigueur et la qualité des étapes de publication des articles, à travers un pro-cessus d'évaluation constructif et humain . La poursuite de la valorisation de la revue sur le plan national notamment auprès de la communauté AIM et sur le plan interna-tional demeure une priorité .

Les articles publiés dans ce numéro (détaillés ci-dessous) témoignent de l'éclectisme, mais aussi, et surtout, de la richesse et de l'acuité des thèmes développés dans la revue . La question de la transformation numérique des activités (notamment des activités d'enseignement), développée dans le premier article, de Marie-Laure Weber, Florence Rodhain et Pierre Loup, se pose de façon inévitable aujourd'hui. De même, plus que jamais, les organisations ont dû, et doivent faire preuve d'agilité afin de faire face à toutes sortes d'évolutions, comme le relèvent avec justesse Philippe Cohard et Karim Messeghem dans le deuxième article .

Enfin, l'adoption de nouveaux systèmes d'information et technologies soulève la question essentielle de leur légitimation, qui peut s'effectuer à travers diverses stra-tégies, comme nous l'expliquent Blandine Chapel, Roxana Ologeanu-Taddei, Anne-Sophie Cases et Maurice Hayot dans le troisième article .

NOUVELLES MODALITÉS DU RENOUVELLEMENT DU COMITÉ SCIENTIFIQUE

Dans ce contexte, la nouvelle équipe des rédacteurs de la revue SIM a souhaité revoir les modalités de fonctionnement et de renouvellement de son comité scienti-fique, afin d'y apporter un souffle nouveau . Le comité scientifique de la revue SIM est composé de 17 membres. Parmi ceux-ci, trois ont une responsabilité spécifique et forment l'équipe des rédacteurs : le rédacteur en chef et ses deux adjointes ou adjoints .

Sous la responsabilité du rédacteur en chef, l'équipe des rédacteurs pilote le pro-cessus de révision des articles soumis et est garante de l'approche scientifique des articles acceptés dans le champ thématique de sa ligne éditoriale . Le rédacteur en chef prépare ensuite les numéros complets et gère la relation avec l'éditeur pour la diffusion des contenus publiés .

Sous le pilotage de l'équipe des rédacteurs, le comité scientifique a pour rôle et missions de :

 Définir l'orientation stratégique de la revue, l'évolution de la politique éditoriale, la stratégie de diffusion de la revue .  Valider les thématiques des propositions de numéros spéciaux .  Prendre une décision sur des articles en révision pour lesquels l'équipe des rédac-teurs sollicite un avis.  Participer à des projets ou groupes de travail sur des sujets spécifiques et ponctuels .

  Le conseil scientifique (CS) de la revue SIM lors de sa réunion en novembre 2021 a décidé de clarifier et de définir les modalités de son renouvellement. Il a été décidé d'instaurer des mandats pour tous les membres du comité scientifique, d'une durée de 4 ans, renouvelables. L'objectif est ainsi de clarifier la durée de l'engagement des membres actuels et des postulants . Un appel à candidatures pour les sièges à pourvoir sera publié via les réseaux sociaux et le site Internet de l'AIM à l'automne chaque année . Lors de la dernière réunion annuelle du comité scientifique (novembre/décembre), les candidatures seront discutées et un vote anonyme sera organisé pour départager les candidatures. La prise de fonction des nouveaux membres du CS sera effective en début d'année civile suivante .Concernant le poste de rédactrice en chef ou de rédacteur en chef, le mandat sera aussi de 4 ans, que la personne retenue soit déjà membre du CS ou non. Enfin, pour ses adjointes ou adjoints, la mandature sera de 2 ans, renouvelable.Cet éditorial est l'occasion, au nom de l'ensemble de la communauté, de remer-cier les membres du comité qui viennent de quitter leurs fonctions : Cécile Godé, Liette Lapointe, Rolande Marciniak, Chantal Morley, Nathalie Mitev, Jessie Pallud, François-Xavier de Vaujany, Isabelle Walsh, Isabelle Wattiau . Et de souhaiter la bien-venue à celles et ceux qui l'ont rejoint : Lise Arena, Samuel Fosso-Wamba, Aurélie Leclercq-Vandelannoitte, Amandine Pascal, Nicolas Prat, Stéphanie Missonier, Jean-Loup Richet, Matthieu Templier, Claudio Vitari. Ils mobilisent la théorie des capacités dynamiques pour extraire les dimensions de l'agilité organisationnelle testées quantitativement suite au développement d'une échelle de mesure de ces dimensions. Cette approche permet d'affiner leur modèle de l'agilité organisationnelle et ses dimensions ainsi que de construire un indice d'évaluation de l'agité organisationnelle.Dans le dernier article de ce numéro, Blandine Chapel, Roxana Ologeanu-Taddei, Anne-Sophie Cases et Maurice Hayot s'intéressent à la construction de la légitimité d'un projet de conception d'une application mobile de e-santé . L'articulation de trois stra-tégies de légitimationpragmatiques, morales et cognitives -est analysée à travers une étude de cas. Celle-ci montre l'importance de la légitimité du projet et de celle d'une technologie, cette dernière étant discutée à travers le concept de déficit de légitimation.

	Bonne lecture !
	ARTICLES PUBLIÉS DANS CE NUMÉRO
	Trois articles, aux accents montpelliérains, sont publiés dans ce numéro et
	revêtent une acuité particulière dans le contexte actuel.
	Le premier article analyse le rôle de l'intelligence professionnelle dans la
	transfor-mation numérique de l'activité d'enseigne-ment dans le contexte
	de l'enseignement supérieur et la recherche . Par la mobilisation de la théorie
	de l'activité et la Q method, Marie-Laure Weber, Florence Rodhain et
	Pierre Loup présentent différentes trajectoires de transformation et cinq
	profils d'activité. Avec l'introduction de la réalité virtuelle dans l'activité
	d'enseignement, l'intelligence professionnelle devient un levier
	d'adaptation et de conceptualisation de la transformation de l'activité .

Philippe Cohard et Karim Messeghem proposent dans leur article de considérer l'agilité comme une orientation organisationnelle.