Oceans are brimming with life invisible to our eyes, a myriad of species of bacteria, viruses and other microscopic organisms essential for the health of the planet. These ‘marine plankton’ are unable to swim against currents and should therefore be constantly on the move, yet previous studies have suggested that distinct species of plankton may in fact inhabit different oceanic regions. However, proving this theory has been challenging; collecting plankton is logistically difficult, and it is often impossible to distinguish between species simply by examining them under a microscope. However, within the last decade, a research schooner called Tara has travelled the globe to gather thousands of plankton samples. At the same time, advances in genomics have made it possible to identify species based only on fragments of their DNA sequence.

To understand the hidden geography of plankton communities in Earth’s oceans, Richter et al. pored over DNA from the Tara Oceans expedition. This revealed that, despite being unable to resist the flow of water, various planktonic species which live close to the surface manage to occupy distinct, stable provinces shaped by currents. Different sizes of plankton are distributed in different sized provinces, with the smallest organisms tending to inhabit the smallest areas. Comparing DNA similarities and speeds of currents at the ocean surface revealed how these might stretch and mix plankton communities.

Plankton play a critical role in the health of the ocean and the chemical cycles of planet Earth. These results could allow deeper investigation by marine modellers, ecologists, and evolutionary biologists. Meanwhile, work is already underway to investigate how climate change might impact this hidden geography.

Plankton communities are constantly on the move, transported by ocean currents. Transport involves both advection and mixing. While being advected by currents, plankton can be influenced by multiple processes, both physicochemical (fluxes of heat, light,and nutrients Moore et al., 2013) and biological (species interactions, life cycles, behavior, and acclimation/adaptation [Armbrust, 2009; Flynn et al., 2015]), which act across various spatial and temporal scales. In turn, plankton impact seawater physicochemistry while they are being advected (Moore et al., 2013). The community composition and biogeochemical properties of a water mass at a given site are also partially dependent on its history of mixing with neighboring water masses during transport. These intertwined processes occurring along transport by currents form the pelagic seascape (Pittman, 2017; Figure 1—figure supplement 1a). Due to logistical and analytical constraints, previous studies on plankton distribution have tended to be geographically or taxonomically restricted (Hanson et al., 2012; Martiny et al., 2006; McGowan and Walker, 1979; Reygondeau and Dunn, 2019; Roux et al., 2016), to focus on individual factors such as nutrient or light availability (Longhurst, 2006; Tagliabue et al., 2017), or have investigated the influence of transport on specific nutrients (Letscher et al., 2016) or types of planktonic organisms (Hellweger et al., 2014; Villarino et al., 2018; Wilkins et al., 2013). We set out to test for the first time at genomic resolution the hypotheses that a global-scale plankton biogeography exists and that it is closely linked to transport via large-scale ocean currents. To do this, we integrated metagenomic data from epipelagic samples collected during the Tara Oceans expedition (Karsenti et al., 2011) with in situ and satellite environmental metadata and large-scale ocean circulation simulations. Our sampling largely focused on open ocean sites located in the main gyres, but also included other areas with distinct oceanographic features, such as coastal upwelling zones and lagoons (Figure 1—figure supplement 1b). We chose to study biogeographic patterns along large-scale currents in the principal oceanic gyres, with counterpoints to other oceanographic features in which the influence of ocean transport by the main currents is likely to be relatively weaker, such as upwellings. Our analyses focus on the sunlit (epipelagic) layer of the ocean (subsurface and deep chlorophyll maximum [DCM] samples); at lower depths (the mesopelagic and below), the relationship between plankton community composition and ocean transport may be different than at the surface. The use of DNA as a primary proxy for global plankton diversity has several important advantages over classical morphology-based analyses, notably because methods can be standardized and applied across the entire range of plankton sizes, from viruses through prokaryotes and protists to animals.

DNA sequence data was obtained from samples collected at 113 worldwide stations during the Tara Oceans expedition. Each plankton community sample was sequenced for up to six operational size fractions: one virus-enriched (0–0.22 μm; Roux et al., 2016), one prokaryote-enriched (either 0.22–1.6 or 0.22–3 μm; Sunagawa et al., 2015), and four eukaryote-enriched (0.8–5 μm, 5–20 μm, 20–180 μm, and 180–2000 μm; de Vargas et al., 2015; Figure 1—figure supplement 1b). These size fractions are operational in that each contains the organisms captured between two physical filters of a given size (either filters or nets, depending on size fraction [Pesant et al., 2015]). We estimated the average percentage of metagenomic sequence reads in samples from the prokaryote-enriched 0.22–1.6/3 µm size fractions that were of eukaryotic origin to be 12%, and the average percentage of reads in eukaryote-enriched size fractions that were of prokaryote origin as follows: 0.8–5 μm: 39%, 5–20 μm: 23%, 20–180 μm: 3%, 180–2000 μm: 5% (see Materials and methods). The Tara Oceans project produced a total of 24.2 terabases of metagenomic sequence reads (Supplementary Table 1). To account for uneven sequencing depth among samples, we analyzed a subset of 11.9 terabases, after testing that this subset accurately represented the complete data set (see Materials and methods). We also analyzed operational taxonomic units (OTUs, representing groups of genetically related organisms), consisting of previously published viral populations (Brum et al., 2015) previously derived bacterial 16S miTAGs (Sunagawa et al., 2015), and 738 million 18S V9 ribosomal DNA marker sequences in the eukaryote-enriched size fractions, enlarging a previously described Tara Oceans data set (de Vargas et al., 2015). We used metagenomic data and OTUs independently to compute pairwise comparisons of plankton community dissimilarity (as proxies for β-diversity). Metagenomic dissimilarity highlighted, at species and subspecies resolution, differences in the genomic identity of organisms between stations. Our metagenomic sampling resulted in pairwise metagenomic dissimilarities that likely represent an overestimate of β-diversity (Appendix 1). However, we applied an identical procedure to compute metagenomic dissimilarity for all size fractions (correlations among fractions ranged Spearman’s ρ 0.6–0.9, p≤10⁻⁴, Figure 1—figure supplement 2). The more thoroughly sampled OTU dissimilarity, in contrast, incorporated more numerous rare taxa within the plankton, but at genus or higher-level taxonomic resolution (de Vargas et al., 2015). Metagenomic and OTU dissimilarities were correlated for all size fractions (Spearman’s ρ 0.53–0.97, p≤10⁻⁴, Figure 1—figure supplement 2), indicating that both proxies, although characterized by different sampling levels and taxonomic resolution, provided coherent and complementary estimates of β-diversity (Appendix 1). We performed subsequent analyses using both measures, which produced consistent results. The taxonomic composition of these Tara Oceans samples, not discussed here, is instead presented in a parallel analysis of the spatial dynamics of planktonic eukaryotes, based on the same environmental data and large-scale ocean circulation simulations (Sommeria-Klein et al., 2021).

We focus on analyses of metagenomic dissimilarity here, with accompanying results for OTU dissimilarity presented in Supplementary Figures, and validation by comparison to abundance differences among metagenome-assembled genomes (MAGs; Delmont et al., 2022a) and to more traditional imaging data presented independently below.

Globally, we observed substantial metagenomic dissimilarities (average pairwise dissimilarity >80%) between sampled stations (including adjacent sites) across all size fractions (Figure 1—figure supplement 3a, Appendix 1). The resulting portrait is of a heterogeneous oceanic ecosystem at all scales separating Tara Oceans sampling sites (even those separated by only a few kilometers), dominated by a small number of abundant and cosmopolitan taxa, with a much larger number of less abundant taxa found at fewer sampling sites (Figure 1—figure supplement 3b-e), corroborating other studies (de Vargas et al., 2015).

Overlying this heterogeneity, we found robust evidence for the existence of large-scale biogeographical patterns within all plankton size classes using two complementary analyses of dissimilarity among samples (Figure 1a, Figure 1—figure supplement 4a-f, Figure 1—figure supplement 5, Appendix 2). First, we grouped metagenomic samples within each size fraction into ‘genomic provinces’ via hierarchical clustering (Figure 1—figure supplement 6). Second, we derived colors for each sample based on a principal coordinates analysis (PCoA-RGB; see Materials and methods) in order to visualize transitions in community composition within and between genomic provinces. Genomic provinces were mostly composed of geographically clustered stations (consistent with previous studies documenting patterns in plankton biogeography [Hanson et al., 2012; Martiny et al., 2006; McGowan and Walker, 1979; Roux et al., 2016; Figure 1a, Figure 1—figure supplement 4a-f]). Although the large majority of our samples were located in oceanic gyres, samples located in physically distant zones but with shared environmental conditions, such as oceanic upwellings, also grouped together (e.g. genomic province B6 in the bacterial-enriched size fraction). Genomic provinces of smaller plankton (viruses, bacteria, and eukaryotes <20 µm), with some exceptions (e.g. genomic province B5), tended to be limited to a single ocean basin and to approximately correspond to Longhurst biogeochemical provinces (BGCPs; Longhurst, 2006; Figure 1—figure supplement 4a-d; Appendix 3). In contrast, provinces of larger plankton (micro- and mesoplankton, >20 µm) spanned multiple basins (Figure 1—figure supplement 4e-f, Appendix 4).

Figure 1 with 7 supplements see all

Download asset Open asset

These large-scale biogeographical patterns derived from metagenomes were linked to environmental parameters including nutrients and temperature. Seawater surface temperature was significantly different among genomic provinces for all plankton size classes (Kruskal-Wallis test, p<10⁻⁵), corroborating previous results for prokaryotes (Sunagawa et al., 2015), whereas other environmental conditions were significantly different only with respect to specific size classes (Figure 1—figure supplement 7). The geography of combined nutrient and temperature variations resembled the biogeography of smaller plankton size classes (Figure 1a–b, Figure 1—figure supplement 4a-d,h), whereas temperature alone more closely matched the distribution of larger plankton (Figure 1—figure supplement 4e,f,i), potentially reflecting different ecological constraints.

Plankton biogeographical patterns suggested a particular role for large-scale surface transport (a core component of the seascape) in the emergence of spatial patterns of plankton community composition (as previously proposed [Clayton et al., 2013]), as many genomic provinces were spatially consistent with ocean basin-scale circulation patterns (such as western boundary currents or major subtropical gyres [Talley et al., 2011; Figure 1a, Figure 1—figure supplement 4a-f]). To investigate whether plankton dynamics are related to ocean current timescales, we analyzed community metagenomic composition differences between sampled stations in light of the corresponding transit time, which has previously been suggested as the relevant factor for studying dispersal mechanisms (Wilkins et al., 2013). We inferred the characteristic timescale of main transport paths between stations from trajectories computed with the physically well-constrained MITgcm ocean model (see Materials and methods), which takes into account directionalities (Watson et al., 2010) and meso- to large-scale circulation, potential dispersal barriers, and mixing effects (Goetze et al., 2017; Mousing et al., 2016). For this we used the minimum travel time (Jönsson and Watson, 2016; T_min) between pairs of Tara stations. These trajectories corresponded to the dominant paths that transport the majority of water volume and its contents (e.g. heat, nutrients, and plankton; Figure 1c). For all plankton size classes, community composition differences between stations were significantly correlated to travel time (Figure 2—figure supplement 1).

Because the relationships between metagenomic dissimilarities and T_min are complex (Figure 2—figure supplement 1), global correlations do not necessarily accurately summarize the relationship between communities and currents. To provide more detail on the relationship, we examined cumulative correlation, namely, correlations between community dissimilarity and T_min computed for an increasing range of T_min, which can directly reveal the time window during which plankton dynamics are strongly correlated to ocean current timescales. Cumulative correlation values were maximal for pairs of stations separated by T_min <~1.5 years for all size classes, with correlation values (Spearman’s ρ 0.45–0.71 depending on size class, p≤10^–4; Figure 2a, Figure 3—figure supplement 1) far exceeding those based on previous studies of morphological and/or metabarcode data (Villarino et al., 2018) or considering geographic distance rather than travel time (Louca et al., 2016). These high correlations between metagenomic dissimilarity and T_min for travel times up to 1.5 years, which correspond well with the average time to travel across a basin or gyre (Lumpkin and Johnson, 2013), hence reveal measurable plankton community dynamics on time scales far longer than typical plankton growth rates or life cycles. In contrast, no such unimodal pattern was found for correlations between metagenomic dissimilarity and geographic distance (without traversing land; Figure 3—figure supplement 1f).

Figure 2 with 2 supplements see all

Download asset Open asset

We compared our analyses of metagenomic data to those based on more traditional zooplankton imaging data collected for the same Tara Oceans samples. β-diversity calculated from zooplankton imaging was correlated with metagenomic dissimilarity (Spearman’s ρ between 0.32 and 0.60; Figure 1—figure supplement 2), indicating that the two data sources provide concordant measurements of variation in plankton community composition. However, correlations with ocean transport time were far weaker for zooplankton imaging data than for metagenomic data from all organismal size fractions (Figure 3—figure supplement 1). We interpret this as being a result of the expected significantly lower resolution in imaging data as compared to metagenomic data (a similar difference of resolution in OTU data versus metagenomic data is discussed in Appendix 1). Finally, we also confirmed our metagenome sequence read comparison-based results by comparing them to β-diversity among sampling sites using a collection of MAGs, which are likely to represent the most abundant genomes, from the 20–180 µm size fraction (the size fraction in which the largest proportion of metagenomic reads were mapped to MAGs, 18.4%; Delmont et al., 2022a). Metagenomic and MAG β-diversity were highly correlated (Spearman’s ρ 0.94) and consequently they displayed similar biogeographical patterns (Figure 1—figure supplement 4e,g).

Up to ~1.5 years of travel time, the timescale of large-scale transport is therefore the appropriate framework for studying differences in plankton genomic community composition (Figure 2b). The fact that simulated transport times and metagenomic dissimilarity were correlated despite a 3-year pan-season sampling campaign, which could be considered to weaken our inference, suggests instead that a large-scale impact of the seascape promotes the existence of a biogeographical structure at a large spatial scale that is resilient to seasonal or other smaller spatiotemporal variations (across all size fractions, genomic provinces consist of stations sampled over an average of 4.7±2.8 different months and 2.7±1.2 different seasons, adjusted for hemisphere). Consistent with our results, seasonal variations have previously been shown to have minor effects on the boundary positions of BGCPs based on satellite data, but not enough to affect the overall pattern of ocean regionalization (Reygondeau et al., 2013).

Differences in environmental conditions for pairs of stations also covaried (although less strongly) with transit time for T_min <~1.5 years (Figure 3). This indicates that changes in environmental conditions and plankton community composition are concurrent along large-scale oceanic current systems. In our data, beyond ~1.5 years of transport, correlations of T_min with metagenomic dissimilarity decreased (Figures 2a and 3, Figure 3—figure supplement 1a-e), meaning the signature of transport in the timescale of large-scale diversity changes weakened and travel time therefore becomes a less appropriate context to study β-diversity. A similar trend was observed for the correlation between T_min and nutrient concentrations, whereas temperature, the gradients of which are mostly dictated by Earth-scale processes that are unaffected by plankton communities, remained well correlated for longer transit times (Figure 3).

Figure 3 with 1 supplement see all

Download asset Open asset

Together, these analyses suggest the existence in the seascape of biogeochemical continua stretched by currents on the basin scale with predictable, interlinked changes in environmental conditions and plankton community composition (Appendix 5). It has previously been posited that transport could generate continuous transitions between niches based on physical processes (Lévy et al., 2014), but it was not anticipated that plankton dynamics would be governed on the time and length scales of main ocean currents. Moreover, beyond ~1.5 years, the correlation of metagenomic dissimilarity with differences in temperature increased while that with differences in nutrients decreased (Figure 3, Figure 3—figure supplement 1a-e), although both of these correlations with metagenomic dissimilarity remained strong on these time scales. This might be related to distant Tara Oceans stations experiencing similar oceanographic phenomena (notably temperature), e.g., upwelling zones (stations 67, 92, and 135; Figure 1—figure supplement 1), producing generally similar environmental conditions.

We present the following hypothesis as a potential mechanism for the partitioning of the global ocean into genomic provinces. The relatively large separation (in terms of transport time and season) among sampling stations allowed us to detect the large-scale effects of ocean circulation, which are superimposed on smaller-scale effects such as local patchiness and seasonality (as previously observed Longhurst, 2006; Reygondeau et al., 2013). Within ocean basins, as the intertwined dynamics of plankton and chemistry continuously occur along transport, smooth variations have emerged due to the periodic recirculation of within-basin currents. This leads to stable, continuous patterns of changes in community structure and nutrient concentrations, and also explains how temporally stable genomic provinces can exist in the face of ocean currents. Among ocean basins, depending on the sensitivity to the environment of a plankton community, higher heterogeneity in environmental conditions across different circulation patterns can disrupt the equilibrium of seascape processes within a given continuum, leading to a global delimitation into distinguishable ecological continua among different large-scale current systems, resulting in the genomic provinces that we detected.

The existence of a size-class-dependent (smaller or larger than 20 µm) structure of plankton geography indicates that the continua that we observe vary among size fractions because of different reactions of organisms within the seascape (i.e. the interplay among organismal biology, nutrients, and local environmental conditions), in agreement with a parallel survey based on taxonomic groups (Sommeria-Klein et al., 2021). In the case of the North Atlantic current system (including the Mediterranean Sea), a simple exponential fit of metagenomic dissimilarity along T_min for T_min <~1.5 years (Figure 2c) revealed that the smaller size classes (<20 µm) had a shorter metagenomic turnover time (ca. 1 year) than larger plankton (ca. 2 years; Figure 2—figure supplement 2, Appendix 6). At global geographical scales, the genomic provinces of small size classes, which are enriched in phytoplankton (de Vargas et al., 2015; Frémont et al., 2022; Sommeria-Klein et al., 2021; Sunagawa et al., 2015), corresponded in our data with differences in environmental parameters such as nutrient levels (Figure 1b, Figure 1—figure supplement 7) that are often constrained by regional oceanographic processes (Sarmiento and Gruber, 2006). On the other hand, genomic provinces of larger plankton, enriched in heterotrophic and symbiotic organisms (de Vargas et al., 2015; Frémont et al., 2022; Sommeria-Klein et al., 2021), were less coupled with geochemical parameters and were more related to global scale gradients and circulation patterns, notably major latitudinal temperature zones (Martin et al., 2021) or the separation between Atlantic and Indo-Pacific large-scale surface circulations (Figure 1—figure supplement 4e,f,i). These divergent effects were also evident in comparisons of metagenomic dissimilarity with variations in either nutrient concentrations or temperature (Figure 3—figure supplement 1b). For smaller plankton, correlations with differences in nutrient concentrations were strongest, at T_min up to ~1.5 years, while for larger plankton, correlations were strongest with temperature variations, for T_min beyond ~1.5 years. Larger plankton are dominated by eukaryotes, often multicellular, with much longer life cycles, potentially leading to slower community turnover. Organisms with long life cycles, on the order of several months or years, can be transported through basins spanning multiple biogeochemical niches in which they may encounter strong environmental variability; this trend was also detected in a taxonomy-based analysis accounting for differences in both body size and ecology among groups (Sommeria-Klein et al., 2021). As observed here, their biogeography is less affected by nutrient limitation and rather depends on large-scale temperature gradients among basins. This dependence may be linked to the known correlation between body size and organismal metabolic rate (Ikeda, 1985). Conversely, variants within populations of organisms with short life cycles have the capacity to increase their relative abundance within restricted ecological niches to which they are adapted. This difference, detectable at genomic resolution, may not be picked up in analyses performed using biological traits with less resolution. These results indicate a significant size-based decoupling within planktonic food webs. For example, large size predators will encounter different prey when transiting through the genomic provinces of small sized organisms (see Appendix 4).

In this study, we provide genomic evidence for an organism size-dependent global-scale open ocean plankton biogeography shaped by currents. Using analysis of standardized metagenomic data together with environmental and physical data, we reveal that, in a background of significant local patchiness, the integration of seascape physical, chemical, and biological processes over time and space produces a quasi-stationary biological partitioning of the oceans that supersedes short-term variability and seasonal cycles, ultimately generating global biogeographical patterns. Although the strong cross-coupling among our metagenomic, environmental, and physical data prevents a systematic disentangling of their various influences, we hypothesize that transport by ocean currents acts essentially as a conveyor on which interacting environmental and biological effects are layered. In this hypothesis, direct effects of currents (such as turbulent diffusivity) on plankton composition are secondary, and instead environmental and biological effects occurring during transport result in the emergence of a global plankton biogeography in the surface ocean. Future studies both on smaller spatiotemporal scales or specific oceanographic features (e.g. coastal regions) and on the global-scale constraints and influences on the seascape itself could lead to a more detailed understanding of plankton dynamics. The ocean is a three-dimensional system in which the primary axis of variation is depth. Our metagenomic data and simulations were limited to the sunlit layer of the ocean and therefore capture one part of seascape dynamics. At greater ocean depths (i.e. in the mesopelagic and below), the relationship between ocean transport and plankton community composition may differ from the one we describe at the surface. In addition, an understanding of plankton biogeography is a key component of future studies on the function of the genes they express; analyses that synergize both characterizations will refine the definition and ecological interpretation of plankton communities within genomic provinces. Overall, our work shows that studies of the dynamics of plankton communities must consider the critical influence of ocean currents in stretching, on the scale of basins, the distribution of both planktonic organisms and the physicochemical nature of the water mass in which they reside. We also demonstrate that the combination of ocean circulation modeling with the use of metagenomic DNA as a tracer of plankton communities provides a resolution above the minimum necessary for assessing the role of transport in community turnover over time and space. The open ocean planktonic ecosystem is fundamentally different in many ways from other major planetary ecosystems, and this study provides a basis to understand and potentially predict the structuring of the ocean ecosystem in a scenario of rapid environmental and current system changes (Beaugrand et al., 2002; Caesar et al., 2018; Frémont et al., 2022).

The authors 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 in whose waters the Tara Oceans expedition sampled. Metagenomic and metabarcoding sequencing reads have been deposited at the European Nucleotide Archive under accession numbers provided in Supplementary Table 1. Contextual metadata of Tara Oceans stations are available in Supplementary Table 2. Metagenomic dissimilarity, OTU community dissimilarity, imaging community dissimilarity, simulated travel times, geographic distances and MAG dissimilarity are provided in Supplementary Tables 3-18. All Supplementary Tables, in addition to Datasets 1-4 (tables of 18S V9 barcodes and OTUs, the V9 reference database and MAG abundances) are available on FigShare at the following URL: https://doi.org/10.6084/m9.figshare.11303177. Images and their taxonomic assignations are accessible within Ecotaxa (https://ecotaxa.obs-vlfr.fr/prj/377).

1. 1. Richter D
   2. Watteaux R
   3. Vannier T
   4. Leconte J
   5. Frémont P
   6. Reygondeau G
   7. Maillet N
   8. Henry N
   9. Benoit G
   10. Ophélie DS
   11. delmont t
   12. Fernàndez-Guerra A
   13. Suweis S
   14. Narci R
   15. Berney C
   16. Eveillard D
   17. Gavory F
   18. Guidi L
   19. Labadie K
   20. Mahieu E
   21. Poulain J
   22. Romac S
   23. Roux S
   24. Dimier C
   25. Kandels S
   26. Picheral M
   27. Searson S
   28. Coordinators TO
   29. Pesant S
   30. Aury JM
   31. Brum JR
   32. Lemaitre C
   33. Pelletier E
   34. Bork P
   35. Sunagawa S
   36. Lombard F
   37. Karp-Boss L
   38. Bowler C
   39. Sullivan MB
   40. Karsenti E
   41. Mariadassou M
   42. Probert I
   43. Peterlongo P
   44. Wincker P
   45. Vargas Cd
   46. d'Alcalà MR
   47. Iudicone D
   48. Jaillon O

   (2021) figshare

   Data from: Genomic evidence for global ocean plankton biogeography shaped by large-scale current systems.

   https://doi.org/10.6084/m9.figshare.11303177

1. 1. Tara Oceans Consortium

   (2016) NCBI Sequence Read Archive

   ID PRJEB16766. sequence data corresponding to the V9 loop of the 18S rDNA in 850 size-fractionnated plankton communities sampled at 123 location including samples from the mesopelagic zone.

   https://www.ncbi.nlm.nih.gov/sra/?term=PRJEB16766
2. 1. Tara Oceans Consortium

   (2013) NCBI Sequence Read Archive

   ID PRJEB4352. Shotgun Sequencing of Tara Oceans DNA samples corresponding to size fractions for protist.

   https://www.ncbi.nlm.nih.gov/sra/?term=PRJEB4352
3. 1. Tara Oceans Consortium

   (2013) NCBI Sequence Read Archive

   ID PRJEB1787. Shotgun Sequencing of Tara Oceans DNA samples corresponding to size fractions for prokaryotes.

   https://www.ncbi.nlm.nih.gov/sra/?term=PRJEB1787
4. 1. Tara Oceans Consortium

   (2013) NCBI Sequence Read Archive

   ID PRJEB4419. Shotgun Sequencing of Tara Oceans DNA samples corresponding to size fractions for viruses.

   https://www.ncbi.nlm.nih.gov/sra/?term=PRJEB4419

1. 1. Alberti A
   2. Poulain J
   3. Engelen S
   4. Labadie K
   5. Romac S
   6. Ferrera I
   7. Albini G
   8. Aury JM
   9. Belser C
   10. Bertrand A
   11. Cruaud C
   12. Da Silva C
   13. Dossat C
   14. Gavory F
   15. Gas S
   16. Guy J
   17. Haquelle M
   18. Jacoby E
   19. Jaillon O
   20. Lemainque A
   21. Pelletier E
   22. Samson G
   23. Wessner M
   24. Acinas SG
   25. Royo-Llonch M
   26. Cornejo-Castillo FM
   27. Logares R
   28. Fernández-Gómez B
   29. Bowler C
   30. Cochrane G
   31. Amid C
   32. Hoopen PT
   33. De Vargas C
   34. Grimsley N
   35. Desgranges E
   36. Kandels-Lewis S
   37. Ogata H
   38. Poulton N
   39. Sieracki ME
   40. Stepanauskas R
   41. Sullivan MB
   42. Brum JR
   43. Duhaime MB
   44. Poulos BT
   45. Hurwitz BL
   46. Pesant S
   47. Karsenti E
   48. Wincker P
   49. Genoscope Technical Team
   50. Tara Oceans Consortium Coordinators

   (2017) Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition

   Scientific Data 4:170093.

   https://doi.org/10.1038/sdata.2017.93

   • PubMed
   • Google Scholar
2. 1. Armbrust EV

   (2009) The life of diatoms in the world’s oceans

   Nature 459:185–192.

   https://doi.org/10.1038/nature08057

   • PubMed
   • Google Scholar
3. 1. Baker FB
   2. Hubert LJ

   (1975) Measuring the power of hierarchical cluster analysis

   Journal of the American Statistical Association 70:31–38.

   https://doi.org/10.1080/01621459.1975.10480256

   • Google Scholar
4. 1. Beaugrand G
   2. Reid PC
   3. Ibañez F
   4. Lindley JA
   5. Edwards M

   (2002) Reorganization of North Atlantic marine copepod biodiversity and climate

   Science 296:1692–1694.

   https://doi.org/10.1126/science.1071329

   • PubMed
   • Google Scholar
5. Software

   1. Becker RA
   2. Wilks AR
   3. Brownrigg R
   4. Minka TP
   5. Deckmyn A

   (2018) Draw Geographical Maps, version 3.4.0

   R Package Maps.

   https://cran.r-project.org/web/packages/maps/maps.pdf
6. 1. Benoit G
   2. Peterlongo P
   3. Mariadassou M
   4. Drezen E
   5. Schbath S
   6. Lavenier D
   7. Lemaitre C

   (2016) Multiple comparative metagenomics using multiset k -mer counting

   PeerJ Computer Science 2:e94.

   https://doi.org/10.7717/peerj-cs.94

   • Google Scholar
7. 1. Bostock M
   2. Ogievetsky V
   3. Heer J

   (2011) D

   IEEE Transactions on Visualization and Computer Graphics 17:2301–2309.

   https://doi.org/10.1109/TVCG.2011.185

   • PubMed
   • Google Scholar
8. 1. Brum JR
   2. Ignacio-Espinoza JC
   3. Roux S
   4. Doulcier G
   5. Acinas SG
   6. Alberti A
   7. Chaffron S
   8. Cruaud C
   9. de Vargas C
   10. Gasol JM
   11. Gorsky G
   12. Gregory AC
   13. Guidi L
   14. Hingamp P
   15. Iudicone D
   16. Not F
   17. Ogata H
   18. Pesant S
   19. Poulos BT
   20. Schwenck SM
   21. Speich S
   22. Dimier C
   23. Kandels-Lewis S
   24. Picheral M
   25. Searson S
   26. Bork P
   27. Bowler C
   28. Sunagawa S
   29. Wincker P
   30. Karsenti E
   31. Sullivan MB
   32. Tara Oceans Coordinators

   (2015) Ocean plankton: Patterns and ecological drivers of ocean viral communities

   Science 348:1261498.

   https://doi.org/10.1126/science.1261498

   • PubMed
   • Google Scholar
9. 1. Caesar L
   2. Rahmstorf S
   3. Robinson A
   4. Feulner G
   5. Saba V

   (2018) Observed fingerprint of a weakening Atlantic Ocean overturning circulation

   Nature 556:191–196.

   https://doi.org/10.1038/s41586-018-0006-5

   • PubMed
   • Google Scholar
10. 1. Carradec Q
    2. Pelletier E
    3. Da Silva C
    4. Alberti A
    5. Seeleuthner Y
    6. Blanc-Mathieu R
    7. Lima-Mendez G
    8. Rocha F
    9. Tirichine L
    10. Labadie K
    11. Kirilovsky A
    12. Bertrand A
    13. Engelen S
    14. Madoui M-A
    15. Méheust R
    16. Poulain J
    17. Romac S
    18. Richter DJ
    19. Yoshikawa G
    20. Dimier C
    21. Kandels-Lewis S
    22. Picheral M
    23. Searson S
    24. Tara Oceans Coordinators
    25. Jaillon O
    26. Aury J-M
    27. Karsenti E
    28. Sullivan MB
    29. Sunagawa S
    30. Bork P
    31. Not F
    32. Hingamp P
    33. Raes J
    34. Guidi L
    35. Ogata H
    36. de Vargas C
    37. Iudicone D
    38. Bowler C
    39. Wincker P

    (2018) A global ocean atlas of eukaryotic genes

    Nature Communications 9:373.

    https://doi.org/10.1038/s41467-017-02342-1

    • PubMed
    • Google Scholar
11. 1. Clayton S
    2. Dutkiewicz S
    3. Jahn O
    4. Follows MJ

    (2013) Dispersal, eddies, and the diversity of marine phytoplankton

    Limnology and Oceanography 3:182–197.

    https://doi.org/10.1215/21573689-2373515

    • Google Scholar
12. 1. Clayton S
    2. Dutkiewicz S
    3. Jahn O
    4. Hill C
    5. Heimbach P
    6. Follows MJ

    (2016) Biogeochemical versus ecological consequences of modeled ocean

    Biogeophysics 10:1–20.

    https://doi.org/10.5194/bg-2016-337

    • Google Scholar
13. Software

    1. Dahl E
    2. Neer E
    3. Karstens L

    (2022) microshades: A custom color palette for improving data visualization

    Microshades.

    https://karstenslab.github.io/microshades
14. 1. de Vargas C
    2. Audic S
    3. Henry N
    4. Decelle J
    5. Mahé F
    6. Logares R
    7. Lara E
    8. Berney C
    9. Le Bescot N
    10. Probert I
    11. Carmichael M
    12. Poulain J
    13. Romac S
    14. Colin S
    15. Aury JM
    16. Bittner L
    17. Chaffron S
    18. Dunthorn M
    19. Engelen S
    20. Flegontova O
    21. Guidi L
    22. Horák A
    23. Jaillon O
    24. Lima-Mendez G
    25. Lukeš J
    26. Malviya S
    27. Morard R
    28. Mulot M
    29. Scalco E
    30. Siano R
    31. Vincent F
    32. Zingone A
    33. Dimier C
    34. Picheral M
    35. Searson S
    36. Kandels-Lewis S
    37. Acinas SG
    38. Bork P
    39. Bowler C
    40. Gorsky G
    41. Grimsley N
    42. Hingamp P
    43. Iudicone D
    44. Not F
    45. Ogata H
    46. Pesant S
    47. Raes J
    48. Sieracki ME
    49. Speich S
    50. Stemmann L
    51. Sunagawa S
    52. Weissenbach J
    53. Wincker P
    54. Karsenti E
    55. Tara Oceans Coordinators

    (2015) Ocean plankton - Eukaryotic plankton diversity in the sunlit ocean

    Science 348:1261605.

    https://doi.org/10.1126/science.1261605

    • PubMed
    • Google Scholar
15. 1. Delmont TO
    2. Gaia M
    3. Hinsinger DD
    4. Frémont P
    5. Vanni C
    6. Fernandez-Guerra A
    7. Eren AM
    8. Kourlaiev A
    9. d’Agata L
    10. Clayssen Q
    11. Villar E
    12. Labadie K
    13. Cruaud C
    14. Poulain J
    15. Da Silva C
    16. Wessner M
    17. Noel B
    18. Aury JM
    19. de Vargas C
    20. Bowler C
    21. Karsenti E
    22. Pelletier E
    23. Wincker P
    24. Jaillon O
    25. Sunagawa S
    26. Acinas SG
    27. Bork P
    28. Karsenti E
    29. Bowler C
    30. Sardet C
    31. Stemmann L
    32. de Vargas C
    33. Wincker P
    34. Lescot M
    35. Babin M
    36. Gorsky G
    37. Grimsley N
    38. Guidi L
    39. Hingamp P
    40. Jaillon O
    41. Kandels S
    42. Iudicone D
    43. Ogata H
    44. Pesant S
    45. Sullivan MB
    46. Not F
    47. Lee KB
    48. Boss E
    49. Cochrane G
    50. Follows M
    51. Poulton N
    52. Raes J
    53. Sieracki M
    54. Speich S

    (2022a) Functional repertoire convergence of distantly related eukaryotic plankton lineages abundant in the sunlit ocean

    Cell Genomics 2:100123.

    https://doi.org/10.1016/j.xgen.2022.100123

    • Google Scholar
16. 1. Delmont TO
    2. Pierella Karlusich JJ
    3. Veseli I
    4. Fuessel J
    5. Eren AM
    6. Foster RA
    7. Bowler C
    8. Wincker P
    9. Pelletier E

    (2022b) Heterotrophic bacterial diazotrophs are more abundant than their cyanobacterial counterparts in metagenomes covering most of the sunlit ocean

    The ISME Journal 16:927–936.

    https://doi.org/10.1038/s41396-021-01135-1

    • PubMed
    • Google Scholar
17. 1. Dornelas M
    2. Gotelli NJ
    3. McGill B
    4. Shimadzu H
    5. Moyes F
    6. Sievers C
    7. Magurran AE

    (2014) Assemblage time series reveal biodiversity change but not systematic loss

    Science 344:296–299.

    https://doi.org/10.1126/science.1248484

    • PubMed
    • Google Scholar
18. 1. Eppley RW

    (1972)

    Temperature and phytoplankton growth in the sea

    Fish Bull 70:1063–1085.

    • Google Scholar
19. 1. Flynn KJ
    2. St John M
    3. Raven JA
    4. Skibinski DOF
    5. Allen JI
    6. Mitra A
    7. Hofmann EE

    (2015) Acclimation, adaptation, traits and trade-offs in plankton functional type models: reconciling terminology for biology and modelling

    Journal of Plankton Research 37:683–691.

    https://doi.org/10.1093/plankt/fbv036

    • Google Scholar
20. Book

    1. Fofonoff NP.

    (1981)

    The Gulf Stream In

    In: Warren BA, Wunsch C, editors. Evolution of Physical Oceanography: Scientific Surveys in Honor of Henry Stommel. Cambridge, MA: MIT Press. pp. 112–139.

    • Google Scholar
21. 1. Franklin B

    (1786) A letter from dr. Benjamin franklin, to mr. Alphonsus le roy, member of several academies, at paris: Containing sundry maritime observations

    Transactions of the American Philosophical Society 2:294.

    https://doi.org/10.2307/1005200

    • Google Scholar
22. 1. Frémont P
    2. Gehlen M
    3. Vrac M
    4. Leconte J
    5. Delmont TO
    6. Wincker P
    7. Iudicone D
    8. Jaillon O

    (2022) Restructuring of plankton genomic biogeography in the surface ocean under climate change

    Nature Climate Change 12:393–401.

    https://doi.org/10.1038/s41558-022-01314-8

    • Google Scholar
23. 1. Galili T

    (2015) dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering

    Bioinformatics 31:3718–3720.

    https://doi.org/10.1093/bioinformatics/btv428

    • PubMed
    • Google Scholar
24. Software

    1. Gerritsen H

    (2014) Data Visualization on Maps, version 1.5.1

    R Package Mapplots.

    https://cran.r-project.org/web/packages/mapplots/mapplots.pdf
25. 1. Goetze E
    2. Hüdepohl PT
    3. Chang C
    4. Van Woudenberg L
    5. Iacchei M
    6. Peijnenburg KTCA

    (2017) Ecological dispersal barrier across the equatorial Atlantic in a migratory planktonic copepod

    Progress in Oceanography 158:203–212.

    https://doi.org/10.1016/j.pocean.2016.07.001

    • Google Scholar
26. 1. Gorsky G
    2. Ohman MD
    3. Picheral M
    4. Gasparini S
    5. Stemmann L
    6. Romagnan JB
    7. Cawood A
    8. Pesant S
    9. Garcia-Comas C
    10. Prejger F

    (2010) Digital zooplankton image analysis using the ZooScan integrated system

    Journal of Plankton Research 32:285–303.

    https://doi.org/10.1093/plankt/fbp124

    • Google Scholar
27. 1. Hanson CA
    2. Fuhrman JA
    3. Horner-Devine MC
    4. Martiny JBH

    (2012) Beyond biogeographic patterns: processes shaping the microbial landscape

    Nature Reviews. Microbiology 10:497–506.

    https://doi.org/10.1038/nrmicro2795

    • PubMed
    • Google Scholar
28. 1. Hellweger FL
    2. van Sebille E
    3. Fredrick ND

    (2014) Biogeographic patterns in ocean microbes emerge in a neutral agent-based model

    Science 345:1346–1349.

    https://doi.org/10.1126/science.1254421

    • PubMed
    • Google Scholar
29. 1. Ikeda T

    (1985) Metabolic rates of epipelagic marine zooplankton as a function of body mass and temperature

    Marine Biology 85:1–11.

    https://doi.org/10.1007/BF00396409

    • Google Scholar
30. 1. Jönsson BF
    2. Watson JR

    (2016) The timescales of global surface-ocean connectivity

    Nature Communications 7:11239.

    https://doi.org/10.1038/ncomms11239

    • PubMed
    • Google Scholar
31. 1. Karsenti E
    2. Acinas SG
    3. Bork P
    4. Bowler C
    5. De Vargas C
    6. Raes J
    7. Sullivan M
    8. Arendt D
    9. Benzoni F
    10. Claverie JM
    11. Follows M
    12. Gorsky G
    13. Hingamp P
    14. Iudicone D
    15. Jaillon O
    16. Kandels-Lewis S
    17. Krzic U
    18. Not F
    19. Ogata H
    20. Pesant S
    21. Reynaud EG
    22. Sardet C
    23. Sieracki ME
    24. Speich S
    25. Velayoudon D
    26. Weissenbach J
    27. Wincker P
    28. Tara Oceans Consortium

    (2011) A holistic approach to marine eco-systems biology

    PLOS Biology 9:e1001177.

    https://doi.org/10.1371/journal.pbio.1001177

    • PubMed
    • Google Scholar
32. 1. Kreft H
    2. Jetz W

    (2010) A framework for delineating biogeographical regions based on species distributions

    Journal of Biogeography 37:2029–2053.

    https://doi.org/10.1111/j.1365-2699.2010.02375.x

    • Google Scholar
33. Book

    1. Legendre P
    2. Legendre L.

    (2012)

    Numerical Ecology

    Elsevier.

    • Google Scholar
34. 1. Letscher RT
    2. Primeau F
    3. Moore JK

    (2016) Nutrient budgets in the subtropical ocean gyres dominated by lateral transport

    Nature Geoscience 9:815–819.

    https://doi.org/10.1038/ngeo2812

    • Google Scholar
35. 1. Lévy M
    2. Jahn O
    3. Dutkiewicz S
    4. Follows MJ

    (2014) Phytoplankton diversity and community structure affected by oceanic dispersal and mesoscale turbulence

    Limnology and Oceanography 4:67–84.

    https://doi.org/10.1215/21573689-2768549

    • Google Scholar
36. Book

    1. Longhurst A.

    (2006)

    Ecological Geography of the Sea (2nd ed)

    London: Academic Press.

    • Google Scholar
37. Software

    1. Losch M

    (2022) MITgcm, version swh:1:rev:fe1862e69b370b599f9e14fcd7b7f1174abf21c9

    GitHub.

    https://github.com/MITgcm/MITgcm
38. 1. Louca S
    2. Parfrey LW
    3. Doebeli M

    (2016) Decoupling function and taxonomy in the global ocean microbiome

    Science 353:1272–1277.

    https://doi.org/10.1126/science.aaf4507

    • PubMed
    • Google Scholar
39. 1. Lumpkin R
    2. Johnson GC

    (2013) Global ocean surface velocities from drifters: Mean, variance, El Niño-Southern Oscillation response, and seasonal cycle

    Journal of Geophysical Research 118:2992–3006.

    https://doi.org/10.1002/jgrc.20210

    • Google Scholar
40. 1. Madoui MA
    2. Poulain J
    3. Sugier K
    4. Wessner M
    5. Noel B
    6. Berline L
    7. Labadie K
    8. Cornils A
    9. Blanco-Bercial L
    10. Stemmann L
    11. Jamet JL
    12. Wincker P

    (2017) New insights into global biogeography, population structure and natural selection from the genome of the epipelagic copepod Oithona

    Molecular Ecology 26:4467–4482.

    https://doi.org/10.1111/mec.14214

    • PubMed
    • Google Scholar
41. Software

    1. Maechler M
    2. Rousseeuw PJ
    3. Struyf A
    4. Hubert M
    5. Hornik K

    (2015) Cluster Analysis Basics and Extensions, version 2.1.3

    R Package Cluster.

    https://cran.r-project.org/web/packages/cluster/cluster.pdf
42. 1. Martin K
    2. Schmidt K
    3. Toseland A
    4. Boulton CA
    5. Barry K
    6. Beszteri B
    7. Brussaard CPD
    8. Clum A
    9. Daum CG
    10. Eloe-Fadrosh E
    11. Fong A
    12. Foster B
    13. Foster B
    14. Ginzburg M
    15. Huntemann M
    16. Ivanova NN
    17. Kyrpides NC
    18. Lindquist E
    19. Mukherjee S
    20. Palaniappan K
    21. Reddy TBK
    22. Rizkallah MR
    23. Roux S
    24. Timmermans K
    25. Tringe SG
    26. van de Poll WH
    27. Varghese N
    28. Valentin KU
    29. Lenton TM
    30. Grigoriev IV
    31. Leggett RM
    32. Moulton V
    33. Mock T

    (2021) The biogeographic differentiation of algal microbiomes in the upper ocean from pole to pole

    Nature Communications 12:5483.

    https://doi.org/10.1038/s41467-021-25646-9

    • PubMed
    • Google Scholar
43. 1. Martiny JBH
    2. Bohannan BJM
    3. Brown JH
    4. Colwell RK
    5. Fuhrman JA
    6. Green JL
    7. Horner-Devine MC
    8. Kane M
    9. Krumins JA
    10. Kuske CR
    11. Morin PJ
    12. Naeem S
    13. Ovreås L
    14. Reysenbach A-L
    15. Smith VH
    16. Staley JT

    (2006) Microbial biogeography: putting microorganisms on the MAP

    Nature Reviews Microbiology 4:102–112.

    https://doi.org/10.1038/nrmicro1341

    • PubMed
    • Google Scholar
44. 1. McGowan JA
    2. Walker PW

    (1979) Structure in the copepod community of the north pacific central gyre

    Ecological Monographs 49:195–226.

    https://doi.org/10.2307/1942513

    • Google Scholar
45. Software

    1. McIlroy D
    2. Brownrigg R
    3. Minka TP
    4. Bivand R

    (2015) Map Projections, version 1.2.8

    R Package Mapproj.

    https://cran.r-project.org/web/packages/mapproj/mapproj.pdf
46. 1. Menemenlis D
    2. Campin J
    3. Heimbach P
    4. Hill C
    5. Lee T
    6. Nguyen A
    7. Schodlok M
    8. Zhang H

    (2008)

    ECCO2: High resolution global ocean and sea ice data synthesis

    Mercat Ocean Q Newsl 31:13–21.

    • Google Scholar
47. 1. Moore CM
    2. Mills MM
    3. Arrigo KR
    4. Berman-Frank I
    5. Bopp L
    6. Boyd PW
    7. Galbraith ED
    8. Geider RJ
    9. Guieu C
    10. Jaccard SL
    11. Jickells TD
    12. La Roche J
    13. Lenton TM
    14. Mahowald NM
    15. Marañón E
    16. Marinov I
    17. Moore JK
    18. Nakatsuka T
    19. Oschlies A
    20. Saito MA
    21. Thingstad TF
    22. Tsuda A
    23. Ulloa O

    (2013) Processes and patterns of oceanic nutrient limitation

    Nature Geoscience 6:701–710.

    https://doi.org/10.1038/ngeo1765

    • Google Scholar
48. 1. Morel A
    2. Huot Y
    3. Gentili B
    4. Werdell PJ
    5. Hooker SB
    6. Franz BA

    (2007) Examining the consistency of products derived from various ocean color sensors in open ocean (Case 1) waters in the perspective of a multi-sensor approach

    Remote Sensing of Environment 111:69–88.

    https://doi.org/10.1016/j.rse.2007.03.012

    • Google Scholar
49. 1. Mousing EA
    2. Richardson K
    3. Bendtsen J
    4. Cetinić I
    5. Perry MJ
    6. Cornell W

    (2016) Evidence of small‐scale spatial structuring of phytoplankton alpha‐ and beta‐diversity in the open ocean

    Journal of Ecology 104:1682–1695.

    https://doi.org/10.1111/1365-2745.12634

    • Google Scholar
50. Software

    1. Oksanen J
    2. Blanchet FG
    3. Friendly M
    4. Kindt R
    5. Legendre P
    6. McGlinn D
    7. Minchin PR
    8. O’Hara RB
    9. Simpson GL
    10. Solymos P
    11. Stevens MHH
    12. Szoecs E
    13. Wagner H

    (2019) R Package Vegan: Community Ecology Package

    Package ‘Vegan.’.

    https://cran.r-project.org/web/packages/vegan/vegan.pdf
51. 1. Oliver MJ
    2. Irwin AJ

    (2008) Objective global ocean biogeographic provinces

    Geophysical Research Letters 35:GL034238.

    https://doi.org/10.1029/2008GL034238

    • Google Scholar
52. 1. Pesant S
    2. Not F
    3. Picheral M
    4. Kandels-Lewis S
    5. Le Bescot N
    6. Gorsky G
    7. Iudicone D
    8. Karsenti E
    9. Speich S
    10. Troublé R
    11. Dimier C
    12. Searson S
    13. Acinas SG
    14. Bork P
    15. Boss E
    16. Bowler C
    17. De Vargas C
    18. Follows M
    19. Gorsky G
    20. Grimsley N
    21. Hingamp P
    22. Iudicone D
    23. Jaillon O
    24. Kandels-Lewis S
    25. Karp-Boss L
    26. Karsenti E
    27. Krzic U
    28. Not F
    29. Ogata H
    30. Pesant S
    31. Raes J
    32. Reynaud EG
    33. Sardet C
    34. Sieracki M
    35. Speich S
    36. Stemmann L
    37. Sullivan MB
    38. Sunagawa S
    39. Velayoudon D
    40. Weissenbach J
    41. Wincker P

    (2015) Open science resources for the discovery and analysis of Tara Oceans data

    Scientific Data 2:150023.

    https://doi.org/10.1038/sdata.2015.23

    • PubMed
    • Google Scholar
53. Website

    1. Picheral M
    2. Colin S
    3. Irisson JO

    (2017) EcoTaxa, a tool for the taxonomic classification of images

    Accessed June 2, 2020.

    http://ecotaxa.obs-vlfr.fr
54. 1. Piganeau G
    2. Eyre-Walker A
    3. Jancek S
    4. Grimsley N
    5. Moreau H

    (2011) How and why DNA barcodes underestimate the diversity of microbial eukaryotes

    PLOS ONE 6:e16342.

    https://doi.org/10.1371/journal.pone.0016342

    • PubMed
    • Google Scholar
55. Book

    1. Pittman SJ

    (editors) (2017)

    Seascape Ecology

    Wiley-Blackwell.

    • Google Scholar
56. Book

    1. R Core Team T

    (2017)

    R: A Language and Environment for Statistical Computing

    Vienna, Austria: R Foundation for Statistical Computing.

    • Google Scholar
57. 1. Rattray A
    2. Andrello M
    3. Asnaghi V
    4. Bevilacqua S
    5. Bulleri F
    6. Cebrian E
    7. Chiantore M
    8. Claudet J
    9. Deudero S
    10. Evans J
    11. Fraschetti S
    12. Guarnieri G
    13. Mangialajo L
    14. Schembri PJ
    15. Terlizzi A
    16. Benedetti-Cecchi L

    (2016) Geographic distance, water circulation and environmental conditions shape the biodiversity of Mediterranean rocky coasts

    Marine Ecology Progress Series 553:1–11.

    https://doi.org/10.3354/meps11783

    • Google Scholar
58. 1. Reygondeau G
    2. Maury O
    3. Beaugrand G
    4. Fromentin JM
    5. Fonteneau A
    6. Cury P

    (2012) Biogeography of tuna and billfish communities

    Journal of Biogeography 39:114–129.

    https://doi.org/10.1111/j.1365-2699.2011.02582.x

    • Google Scholar
59. 1. Reygondeau G
    2. Longhurst A
    3. Martinez E
    4. Beaugrand G
    5. Antoine D
    6. Maury O

    (2013) Dynamic biogeochemical provinces in the global ocean

    Global Biogeochemical Cycles 27:1046–1058.

    https://doi.org/10.1002/gbc.20089

    • Google Scholar
60. Book

    1. Reygondeau G
    2. Dunn D

    (2019) Pelagic BiogeographyEncyclopedia of Ocean Sciences

    Elsevier.

    https://doi.org/10.1016/B978-0-12-409548-9.11633-1

    • Google Scholar
61. 1. Ridgway KR
    2. Dunn JR
    3. Wilkin JL

    (2002) Ocean interpolation by four-dimensional weighted least squares—application to the waters around australasia

    Journal of Atmospheric and Oceanic Technology 19:1357–1375.

    https://doi.org/10.1175/1520-0426(2002)019<1357:OIBFDW>2.0.CO;2

    • Google Scholar
62. 1. Rousseeuw PJ

    (1987) Silhouettes: A graphical aid to the interpretation and validation of cluster analysis

    Journal of Computational and Applied Mathematics 20:53–65.

    https://doi.org/10.1016/0377-0427(87)90125-7

    • Google Scholar
63. 1. Roux S
    2. Brum JR
    3. Dutilh BE
    4. Sunagawa S
    5. Duhaime MB
    6. Loy A
    7. Poulos BT
    8. Solonenko N
    9. Lara E
    10. Poulain J
    11. Pesant S
    12. Kandels-Lewis S
    13. Dimier C
    14. Picheral M
    15. Searson S
    16. Cruaud C
    17. Alberti A
    18. Duarte CM
    19. Gasol JM
    20. Vaqué D
    21. Tara Oceans Coordinators
    22. Bork P
    23. Acinas SG
    24. Wincker P
    25. Sullivan MB

    (2016) Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses

    Nature 537:689–693.

    https://doi.org/10.1038/nature19366

    • PubMed
    • Google Scholar
64. Book

    1. Sarmiento JL
    2. Gruber N

    (2006) Ocean Biogeochemical Dynamics

    Princeton, New Jersey: Princeton University Press.

    https://doi.org/10.1515/9781400849079

    • Google Scholar
65. 1. Seeleuthner Y
    2. Mondy S
    3. Lombard V
    4. Carradec Q
    5. Pelletier E
    6. Wessner M
    7. Leconte J
    8. Mangot J-F
    9. Poulain J
    10. Labadie K
    11. Logares R
    12. Sunagawa S
    13. de Berardinis V
    14. Salanoubat M
    15. Dimier C
    16. Kandels-Lewis S
    17. Picheral M
    18. Searson S
    19. Pesant S
    20. Poulton N
    21. Stepanauskas R
    22. Bork P
    23. Bowler C
    24. Hingamp P
    25. Sullivan MB
    26. Iudicone D
    27. Massana R
    28. Aury J-M
    29. Henrissat B
    30. Karsenti E
    31. Jaillon O
    32. Sieracki M
    33. de Vargas C
    34. Wincker P
    35. Tara Oceans Coordinators

    (2018) Single-cell genomics of multiple uncultured stramenopiles reveals underestimated functional diversity across oceans

    Nature Communications 9:310.

    https://doi.org/10.1038/s41467-017-02235-3

    • PubMed
    • Google Scholar
66. Book

    1. Sneath PHA
    2. Sokal RR.

    (1973)

    Numerical taxonomy. The principles and practice of numerical classification

    San Francisco: W.H. Freeman and Company.

    • Google Scholar
67. 1. Sokal RR
    2. Rohlf FJ

    (1962) The comparison of dendrograms by objective methods

    TAXON 11:33–40.

    https://doi.org/10.2307/1217208

    • Google Scholar
68. 1. Sommeria-Klein G
    2. Watteaux R
    3. Ibarbalz FM
    4. Pierella Karlusich JJ
    5. Iudicone D
    6. Bowler C
    7. Morlon H

    (2021) Global drivers of eukaryotic plankton biogeography in the sunlit ocean

    Science 374:594–599.

    https://doi.org/10.1126/science.abb3717

    • PubMed
    • Google Scholar
69. 1. Speich S
    2. Blanke B
    3. Cai W

    (2007) Atlantic meridional overturning circulation and the Southern Hemisphere supergyre

    Geophysical Research Letters 34:n.

    https://doi.org/10.1029/2007GL031583

    • Google Scholar
70. 1. Sunagawa S
    2. Coelho LP
    3. Chaffron S
    4. Kultima JR
    5. Labadie K
    6. Salazar G
    7. Djahanschiri B
    8. Zeller G
    9. Mende DR
    10. Alberti A
    11. Cornejo-Castillo FM
    12. Costea PI
    13. Cruaud C
    14. d’Ovidio F
    15. Engelen S
    16. Ferrera I
    17. Gasol JM
    18. Guidi L
    19. Hildebrand F
    20. Kokoszka F
    21. Lepoivre C
    22. Lima-Mendez G
    23. Poulain J
    24. Poulos BT
    25. Royo-Llonch M
    26. Sarmento H
    27. Vieira-Silva S
    28. Dimier C
    29. Picheral M
    30. Searson S
    31. Kandels-Lewis S
    32. Bowler C
    33. de Vargas C
    34. Gorsky G
    35. Grimsley N
    36. Hingamp P
    37. Iudicone D
    38. Jaillon O
    39. Not F
    40. Ogata H
    41. Pesant S
    42. Speich S
    43. Stemmann L
    44. Sullivan MB
    45. Weissenbach J
    46. Wincker P
    47. Karsenti E
    48. Raes J
    49. Acinas SG
    50. Bork P
    51. Tara Oceans coordinators

    (2015) Ocean plankton: Structure and function of the global ocean microbiome

    Science 348:1261359.

    https://doi.org/10.1126/science.1261359

    • PubMed
    • Google Scholar
71. 1. Suzuki R
    2. Shimodaira H

    (2006) Pvclust: an R package for assessing the uncertainty in hierarchical clustering

    Bioinformatics 22:1540–1542.

    https://doi.org/10.1093/bioinformatics/btl117

    • PubMed
    • Google Scholar
72. 1. Tagliabue A
    2. Bowie AR
    3. Boyd PW
    4. Buck KN
    5. Johnson KS
    6. Saito MA

    (2017) The integral role of iron in ocean biogeochemistry

    Nature 543:51–59.

    https://doi.org/10.1038/nature21058

    • PubMed
    • Google Scholar
73. Book

    1. Talley LD
    2. Pickard GL
    3. Emery WJ
    4. Swift JH.

    (2011)

    Descriptive Physical Oceanography: An Introduction (6th ed)

    Boston: Elsevier.

    • Google Scholar
74. Software

    1. Terada Y
    2. Luxburg U

    (2016) R Package Loe: Local Ordinal Embedding

    Loe.

    https://cran.r-project.org/web/packages/loe/index.html
75. 1. Vannier T
    2. Leconte J
    3. Seeleuthner Y
    4. Mondy S
    5. Pelletier E
    6. Aury J-M
    7. de Vargas C
    8. Sieracki M
    9. Iudicone D
    10. Vaulot D
    11. Wincker P
    12. Jaillon O

    (2016) Survey of the green picoalga Bathycoccus genomes in the global ocean

    Scientific Reports 6:37900.

    https://doi.org/10.1038/srep37900

    • PubMed
    • Google Scholar
76. 1. Villarino E
    2. Watson JR
    3. Jönsson B
    4. Gasol JM
    5. Salazar G
    6. Acinas SG
    7. Estrada M
    8. Massana R
    9. Logares R
    10. Giner CR
    11. Pernice MC
    12. Olivar MP
    13. Citores L
    14. Corell J
    15. Rodríguez-Ezpeleta N
    16. Acuña JL
    17. Molina-Ramírez A
    18. González-Gordillo JI
    19. Cózar A
    20. Martí E
    21. Cuesta JA
    22. Agustí S
    23. Fraile-Nuez E
    24. Duarte CM
    25. Irigoien X
    26. Chust G

    (2018) Large-scale ocean connectivity and planktonic body size

    Nature Communications 9:142.

    https://doi.org/10.1038/s41467-017-02535-8

    • PubMed
    • Google Scholar
77. Software

    1. Warnes GR
    2. Bolker B
    3. Bonebakker L
    4. Gentleman R
    5. Huber W
    6. Liaw A
    7. Lumley T
    8. Maechler M
    9. Magnusson A
    10. Moeller S
    11. Schwartz M
    12. Venables B

    (2015) Various R Programming Tools for Plotting Data, version 3.1.3

    R Package Gplots.

    https://cran.r-project.org/web/packages/gplots/gplots.pdf
78. 1. Watson JR
    2. Mitarai S
    3. Siegel DA
    4. Caselle JE
    5. Dong C
    6. McWilliams JC

    (2010) Realized and potential larval connectivity in the Southern California Bight

    Marine Ecology Progress Series 401:31–48.

    https://doi.org/10.3354/meps08376

    • Google Scholar
79. Software

    1. Wei T
    2. Simko V

    (2016) Visualization of a Correlation Matrix, version 0.92

    R Package Corrplot.

    https://CRAN.R-project.org/package=corrplot
80. 1. Wilkins D
    2. van Sebille E
    3. Rintoul SR
    4. Lauro FM
    5. Cavicchioli R

    (2013) Advection shapes Southern Ocean microbial assemblages independent of distance and environment effects

    Nature Communications 4:2457.

    https://doi.org/10.1038/ncomms3457

    • PubMed
    • Google Scholar
81. 1. Worden AZ
    2. Lee JH
    3. Mock T
    4. Rouzé P
    5. Simmons MP
    6. Aerts AL
    7. Allen AE
    8. Cuvelier ML
    9. Derelle E
    10. Everett MV
    11. Foulon E
    12. Grimwood J
    13. Gundlach H
    14. Henrissat B
    15. Napoli C
    16. McDonald SM
    17. Parker MS
    18. Rombauts S
    19. Salamov A
    20. Von Dassow P
    21. Badger JH
    22. Coutinho PM
    23. Demir E
    24. Dubchak I
    25. Gentemann C
    26. Eikrem W
    27. Gready JE
    28. John U
    29. Lanier W
    30. Lindquist EA
    31. Lucas S
    32. Mayer KFX
    33. Moreau H
    34. Not F
    35. Otillar R
    36. Panaud O
    37. Pangilinan J
    38. Paulsen I
    39. Piegu B
    40. Poliakov A
    41. Robbens S
    42. Schmutz J
    43. Toulza E
    44. Wyss T
    45. Zelensky A
    46. Zhou K
    47. Armbrust EV
    48. Bhattacharya D
    49. Goodenough UW
    50. Van de Peer Y
    51. Grigoriev IV

    (2009) Green evolution and dynamic adaptations revealed by genomes of the marine picoeukaryotes Micromonas

    Science 324:268–272.

    https://doi.org/10.1126/science.1167222

    • PubMed
    • Google Scholar
82. 1. Wu S
    2. Xiong J
    3. Yu Y
    4. Fontaneto D

    (2015) Taxonomic resolutions based on 18s rrna genes: A case study of subclass copepoda

    PLOS ONE 10:e0131498.

    https://doi.org/10.1371/journal.pone.0131498

    • Google Scholar

Author details

1. Daniel J Richter

   1. Sorbonne Université, CNRS, Station Biologique de Roscoff, UMR7144, ECOMAP, Roscoff, France
   2. Institut de Biologia Evolutiva (CSIC‐Universitat Pompeu Fabra), Passeig Marítim de la Barceloneta, Barcelona, Spain

   Contribution

   Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review and editing, Investigation, Software

   Contributed equally with

   Romain Watteaux and Thomas Vannier

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9238-5571

2. Romain Watteaux

   1. Stazione Zoologica Anton Dohrn, Villa Comunale, Naples, Italy
   2. CEA, DAM, DIF, F‐91297, Arpajon Cedex, France

   Contribution

   Conceptualization, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review and editing, Data curation, Investigation, Software

   Contributed equally with

   Daniel J Richter and Thomas Vannier

   Competing interests

   No competing interests declared

3. Thomas Vannier

   1. Génomique Métabolique, Genoscope, Institut de Biologie François Jacob, CEA, CNRS, Université Evry, Université Paris‐Saclay, Evry, France
   2. Research Federation for the study of Global Ocean systems ecology and evolution, FR2O22/Tara GOSEE, Paris, France
   3. Aix Marseille Univ., Université de Toulon, CNRS, IRD, MIO UM, Marseille, France

   Contribution

   Conceptualization, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review and editing, Data curation, Investigation, Software

   Contributed equally with

   Daniel J Richter and Romain Watteaux

   Competing interests

   No competing interests declared

4. Jade Leconte

   1. Génomique Métabolique, Genoscope, Institut de Biologie François Jacob, CEA, CNRS, Université Evry, Université Paris‐Saclay, Evry, France
   2. Research Federation for the study of Global Ocean systems ecology and evolution, FR2O22/Tara GOSEE, Paris, France

   Contribution

   Formal analysis, Visualization, Writing – review and editing, Investigation

   Competing interests

   No competing interests declared

5. Paul Frémont

   1. Génomique Métabolique, Genoscope, Institut de Biologie François Jacob, CEA, CNRS, Université Evry, Université Paris‐Saclay, Evry, France
   2. Research Federation for the study of Global Ocean systems ecology and evolution, FR2O22/Tara GOSEE, Paris, France

   Contribution

   Formal analysis, Visualization, Writing – review and editing, Investigation, Software

   Competing interests

   No competing interests declared

6. Gabriel Reygondeau

   1. Changing Ocean Research Unit, Institute for the Oceans and Fisheries, University of British Columbia. Aquatic Ecosystems Research Lab, Vancouver, Canada
   2. Ecology and Evolutionary Biology, Yale University, New Haven, CT, United States

   Contribution

   Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

7. Nicolas Maillet

   Institut pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, Paris, France

   Contribution

   Formal analysis, Visualization

   Competing interests

   No competing interests declared

8. Nicolas Henry

   1. Sorbonne Université, CNRS, Station Biologique de Roscoff, UMR7144, ECOMAP, Roscoff, France
   2. Research Federation for the study of Global Ocean systems ecology and evolution, FR2O22/Tara GOSEE, Paris, France

   Contribution

   Formal analysis, Visualization

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7702-1382

9. Gaëtan Benoit

   Univ Rennes, CNRS, Inria, IRISA-UMR 6074, Rennes, France

   Contribution

   Methodology, Investigation

   Competing interests

   No competing interests declared

10. Ophélie Da Silva

    1. Research Federation for the study of Global Ocean systems ecology and evolution, FR2O22/Tara GOSEE, Paris, France
    2. Sorbonne Universités, CNRS, Laboratoire d’Oceanographie de Villefranche, LOV, Villefranche‐sur‐Mer, France

    Contribution

    Formal analysis, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

11. Tom O Delmont

    1. Génomique Métabolique, Genoscope, Institut de Biologie François Jacob, CEA, CNRS, Université Evry, Université Paris‐Saclay, Evry, France
    2. Research Federation for the study of Global Ocean systems ecology and evolution, FR2O22/Tara GOSEE, Paris, France

    Contribution

    Investigation, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

12. Antonio Fernàndez-Guerra

    1. Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
    2. MARUM, Center for Marine Environmental Sciences, University of Bremen, Bremen, Germany
    3. Max Planck Institute for Marine Microbiology, Bremen, Germany

    Contribution

    Formal analysis, Visualization, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8679-490X

13. Samir Suweis

    Dipartimento di Fisica e Astronomia ‘G. Galilei’ & CNISM, INFN, Università di Padova, Padova, Italy

    Contribution

    Formal analysis, Visualization, Writing – review and editing

    Competing interests

    No competing interests declared

14. Romain Narci

    MaIAGE, INRAE, Université Paris‐Saclay, Jouy‐en‐Josas, France

    Contribution

    Funding acquisition, Writing – review and editing

    Competing interests

    No competing interests declared

15. Cédric Berney

    1. Sorbonne Université, CNRS, Station Biologique de Roscoff, UMR7144, ECOMAP, Roscoff, France
    2. Research Federation for the study of Global Ocean systems ecology and evolution, FR2O22/Tara GOSEE, Paris, France

    Contribution

    Formal analysis

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8689-9907

16. Damien Eveillard

    1. Research Federation for the study of Global Ocean systems ecology and evolution, FR2O22/Tara GOSEE, Paris, France
    2. Nantes Université, Ecole Centrale Nantes, CNRS, LS2N, Nantes, France

    Contribution

    Formal analysis, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8162-7360

17. Frederick Gavory

    Génomique Métabolique, Genoscope, Institut de Biologie François Jacob, CEA, CNRS, Université Evry, Université Paris‐Saclay, Evry, France

    Contribution

    Investigation

    Competing interests

    No competing interests declared

18. Lionel Guidi

    1. Research Federation for the study of Global Ocean systems ecology and evolution, FR2O22/Tara GOSEE, Paris, France
    2. Sorbonne Universités, CNRS, Laboratoire d’Oceanographie de Villefranche, LOV, Villefranche‐sur‐Mer, France

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-6669-5744

19. Karine Labadie

    Genoscope, Institut de biologie François‐Jacob, Commissariat à l'Energie Atomique (CEA), Université Paris‐Saclay, Evry, France

    Contribution

    Investigation, Methodology, Resources

    Competing interests

    No competing interests declared

20. Eric Mahieu

    Genoscope, Institut de biologie François‐Jacob, Commissariat à l'Energie Atomique (CEA), Université Paris‐Saclay, Evry, France

    Contribution

    Investigation

    Competing interests

    No competing interests declared

21. Julie Poulain

    1. Génomique Métabolique, Genoscope, Institut de Biologie François Jacob, CEA, CNRS, Université Evry, Université Paris‐Saclay, Evry, France
    2. Research Federation for the study of Global Ocean systems ecology and evolution, FR2O22/Tara GOSEE, Paris, France

    Contribution

    Data curation, Investigation, Project administration, Resources

    Competing interests

    No competing interests declared

22. Sarah Romac

    1. Sorbonne Université, CNRS, Station Biologique de Roscoff, UMR7144, ECOMAP, Roscoff, France
    2. Research Federation for the study of Global Ocean systems ecology and evolution, FR2O22/Tara GOSEE, Paris, France

    Contribution

    Resources

    Competing interests

    No competing interests declared

23. Simon Roux

    Department of Microbiology, The Ohio State University, Columbus, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

24. Céline Dimier

    1. Sorbonne Université, CNRS, Station Biologique de Roscoff, UMR7144, ECOMAP, Roscoff, France
    2. Institut de Biologie de l’Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France

    Contribution

    Resources

    Competing interests

    No competing interests declared

25. Stefanie Kandels

    1. Structural and Computational Biology, European Molecular Biology Laboratory, Heidelberg, Germany
    2. Directors’ Research European Molecular Biology Laboratory, Heidelberg, Germany

    Contribution

    Project administration

    Competing interests

    No competing interests declared

26. Marc Picheral

    1. Research Federation for the study of Global Ocean systems ecology and evolution, FR2O22/Tara GOSEE, Paris, France
    2. Sorbonne Universités, CNRS, Laboratoire d’Oceanographie de Villefranche, LOV, Villefranche‐sur‐Mer, France

    Contribution

    Data curation, Investigation, Resources

    Competing interests

    No competing interests declared

27. Sarah Searson

    1. Research Federation for the study of Global Ocean systems ecology and evolution, FR2O22/Tara GOSEE, Paris, France
    2. Sorbonne Universités, CNRS, Laboratoire d’Oceanographie de Villefranche, LOV, Villefranche‐sur‐Mer, France

    Contribution

    Resources

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4721-0027

28. Tara Oceans Coordinators

    Contribution

    Funding acquisition, Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

    1. Silvia G Acinas, Department of Marine Biology and Oceanography, Institut de Ciències del Mar (ICM), CSIC, Barcelona, Spain
    2. Peer Bork, Structural and Computational Biology, European Molecular Biology Laboratory, Heidelberg, Germany
    3. Emmanuel Boss, School of Marine Sciences, University of Maine, Orono, United States
    4. Chris Bowler, Research Federation for the study of Global Ocean systems ecology and evolution, FR2022/Tara GOSEE, Paris, France
    5. Guy Cochrane, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL‐EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
    6. Colomban de Vargas, Research Federation for the study of Global Ocean systems ecology and evolution, FR2022/Tara GOSEE, Paris, France
    7. Gabriel Gorsky, Research Federation for the study of Global Ocean systems ecology and evolution, FR2022/Tara GOSEE, Paris, France
    8. Nigel Grimsley, CNRS, UMR 7232, BIOM, Avenue Pierre Fabre, Banyuls‐sur‐Mer, France
    9. Lionel Guidi, Research Federation for the study of Global Ocean systems ecology and evolution, FR2022/Tara GOSEE, Paris, France
    10. Pascal Hingamp, Aix Marseille Univ., Université de Toulon, CNRS, IRD, MIO UM 110, 13288, Marseille, France
    11. Daniele Iudicone, Stazione Zoologica Anton Dohrn, Villa Comunale, Naples, Italy
    12. Olivier Jaillon, Research Federation for the study of Global Ocean systems ecology and evolution, FR2022/Tara GOSEE, Paris, France
    13. Stefanie Kandels, Structural and Computational Biology, European Molecular Biology Laboratory, Heidelberg, Germany
    14. Lee Karp-Boss, School of Marine Sciences, University of Maine, Orono, United States
    15. Eric Karsenti, Research Federation for the study of Global Ocean systems ecology and evolution, FR2022/Tara GOSEE, Paris, France
    16. Fabrice Not, Research Federation for the study of Global Ocean systems ecology and evolution, FR2022/Tara GOSEE, Paris, France
    17. Hiroyuki Ogata, Institute for Chemical Research, Kyoto University, Gokasho, Kyoto, Japan
    18. Stéphane Pesant, MARUM, Center for Marine Environmental Sciences, University of Bremen, Bremen, Germany
    19. Jeroen Raes, Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium
    20. Christian Sardet, Research Federation for the study of Global Ocean systems ecology and evolution, FR2022/Tara GOSEE, Paris, France
    21. Mike Sieracki, National Science Foundation, Arlington, United States
    22. Sabrina Speich, Laboratoire de Physique des Océans, UBO‐IUEM, Place Copernic, Plouzané, France
    23. Lars Stemmann, Research Federation for the study of Global Ocean systems ecology and evolution, FR2022/Tara GOSEE, Paris, France
    24. Matthew B Sullivan, Department of Microbiology, The Ohio State University, Columbus, United States
    25. Shinichi Sunagawa, Structural and Computational Biology, European Molecular Biology Laboratory, Heidelberg, Germany
    26. Patrick Wincker, Research Federation for the study of Global Ocean systems ecology and evolution, FR2022/Tara GOSEE, Paris, France

29. Stéphane Pesant

    1. MARUM, Center for Marine Environmental Sciences, University of Bremen, Bremen, Germany
    2. PANGAEA, Data Publisher for Earth and Environmental Science, University of Bremen, Bremen, Germany

    Contribution

    Data curation

    Competing interests

    No competing interests declared

30. Jean-Marc Aury

    Génomique Métabolique, Genoscope, Institut de Biologie François Jacob, CEA, CNRS, Université Evry, Université Paris‐Saclay, Evry, France

    Contribution

    Data curation, Investigation, Software

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1718-3010

31. Jennifer R Brum

    1. Department of Microbiology, The Ohio State University, Columbus, United States
    2. Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, United States

    Contribution

    Resources

    Competing interests

    No competing interests declared

32. Claire Lemaitre

    Univ Rennes, CNRS, Inria, IRISA-UMR 6074, Rennes, France

    Contribution

    Methodology, Investigation

    Competing interests

    No competing interests declared

33. Eric Pelletier

    1. Génomique Métabolique, Genoscope, Institut de Biologie François Jacob, CEA, CNRS, Université Evry, Université Paris‐Saclay, Evry, France
    2. Research Federation for the study of Global Ocean systems ecology and evolution, FR2O22/Tara GOSEE, Paris, France

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

34. Peer Bork

    1. Structural and Computational Biology, European Molecular Biology Laboratory, Heidelberg, Germany
    2. Yonsei Frontier Lab, Yonsei University, Seoul, Republic of Korea
    3. Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

35. Shinichi Sunagawa

    1. Structural and Computational Biology, European Molecular Biology Laboratory, Heidelberg, Germany
    2. Institute of Microbiology, Department of Biology, ETH Zurich, Vladimir‐Prelog‐Weg, Zurich, Switzerland

    Contribution

    Formal analysis, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3065-0314

36. Fabien Lombard

    1. Research Federation for the study of Global Ocean systems ecology and evolution, FR2O22/Tara GOSEE, Paris, France
    2. Sorbonne Universités, CNRS, Laboratoire d’Oceanographie de Villefranche, LOV, Villefranche‐sur‐Mer, France
    3. Institut Universitaire de France (IUF), Paris, France

    Contribution

    Data curation, Formal analysis, Investigation, Writing – review and editing, Resources

    Competing interests

    No competing interests declared

37. Lee Karp-Boss

    School of Marine Sciences, University of Maine, Orono, United States

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

38. Chris Bowler

    1. Research Federation for the study of Global Ocean systems ecology and evolution, FR2O22/Tara GOSEE, Paris, France
    2. Institut de Biologie de l’Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France
    3. Institut de Biologie de l’Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3835-6187

39. Matthew B Sullivan

    1. Department of Microbiology, The Ohio State University, Columbus, United States
    2. EMERGE Biology Integration Institute, The Ohio State University, Columbus, United States
    3. Center of Microbiome Science, The Ohio State University, Columbus, United States
    4. Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, United States

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

40. Eric Karsenti

    1. Research Federation for the study of Global Ocean systems ecology and evolution, FR2O22/Tara GOSEE, Paris, France
    2. Institut de Biologie de l’Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France
    3. Directors’ Research European Molecular Biology Laboratory, Heidelberg, Germany

    Contribution

    Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

41. Mahendra Mariadassou

    MaIAGE, INRAE, Université Paris‐Saclay, Jouy‐en‐Josas, France

    Contribution

    Funding acquisition, Writing – review and editing

    Competing interests

    No competing interests declared

42. Ian Probert

    1. Sorbonne Université, CNRS, Station Biologique de Roscoff, UMR7144, ECOMAP, Roscoff, France
    2. Research Federation for the study of Global Ocean systems ecology and evolution, FR2O22/Tara GOSEE, Paris, France

    Contribution

    Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-1643-1759

43. Pierre Peterlongo

    Univ Rennes, CNRS, Inria, IRISA-UMR 6074, Rennes, France

    Contribution

    Methodology, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

44. Patrick Wincker

    1. Génomique Métabolique, Genoscope, Institut de Biologie François Jacob, CEA, CNRS, Université Evry, Université Paris‐Saclay, Evry, France
    2. Research Federation for the study of Global Ocean systems ecology and evolution, FR2O22/Tara GOSEE, Paris, France

    Contribution

    Supervision, Writing – review and editing, Funding acquisition

    Competing interests

    No competing interests declared

45. Colomban de Vargas

    1. Sorbonne Université, CNRS, Station Biologique de Roscoff, UMR7144, ECOMAP, Roscoff, France
    2. Research Federation for the study of Global Ocean systems ecology and evolution, FR2O22/Tara GOSEE, Paris, France

    Contribution

    Conceptualization, Software, Writing – original draft, Writing – review and editing

    For correspondence

    vargas@sb-roscoff.fr

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-6476-6019

46. Maurizio Ribera d'Alcalà

    Stazione Zoologica Anton Dohrn, Villa Comunale, Naples, Italy

    Contribution

    Conceptualization, Formal analysis, Supervision, Writing – original draft, Writing – review and editing

    For correspondence

    maurizio@szn.it

    Competing interests

    No competing interests declared

47. Daniele Iudicone

    Stazione Zoologica Anton Dohrn, Villa Comunale, Naples, Italy

    Contribution

    Conceptualization, Formal analysis, Methodology, Supervision, Writing – original draft, Writing – review and editing, Funding acquisition, Investigation

    For correspondence

    iudicone@szn.it

    Competing interests

    No competing interests declared

48. Olivier Jaillon

    1. Génomique Métabolique, Genoscope, Institut de Biologie François Jacob, CEA, CNRS, Université Evry, Université Paris‐Saclay, Evry, France
    2. Research Federation for the study of Global Ocean systems ecology and evolution, FR2O22/Tara GOSEE, Paris, France

    Contribution

    Conceptualization, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review and editing, Funding acquisition, Investigation, Supervision

    For correspondence

    ojaillon@genoscope.cns.fr

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7237-9596

• 3,524

  views

• 668

  downloads

• 52

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.