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This article is contribution number 120 of Tara Oceans.-- 15 pages, 4 figures, supplementary materials https://www.science.org/doi/suppl/10.1126/sciadv.abg1921/suppl_file/sciadv.abg1921_SM.pdf.-- Data and materials availability: Data described here are available at the EBI under the project identifiers PRJEB402 and PRJEB7988 and at PANGAEA (96). All data (raw abundance matrices and interactome graphML files) needed to evaluate the conclusions of the paper are available in the Supplementary Materials. A web server for exploring and searching the global ocean interactome is available at https://saas.ls2n.fr/Tara-Oceans-interactome/, Marine plankton form complex communities of interacting organisms at the base of the food web, which sustain oceanic biogeochemical cycles and help regulate climate. Although global surveys are starting to reveal ecological drivers underlying planktonic community structure and predicted climate change responses, it is unclear how community-scale species interactions will be affected by climate change. Here, we leveraged Tara Oceans sampling to infer a global ocean cross-domain plankton co-occurrence network—the community interactome—and used niche modeling to assess its vulnerabilities to environmental change. Globally, this revealed a plankton interactome self-organized latitudinally into marine biomes (Trades, Westerlies, Polar) and more connected poleward. Integrated niche modeling revealed biome-specific community interactome responses to environmental change and forecasted the most affected lineages for each community. These results provide baseline approaches to assess community structure and organismal interactions under climate scenarios while identifying plausible plankton bioindicators for ocean monitoring of climate change, We further thank the commitment of the following sponsors: CNRS (in particular Groupement de Recherche GDR3280 and the Research Federation for the study of Global Ocean Systems Ecology and Evolution, FR2022/Tara Oceans-GOSEE), European Molecular Biology Laboratory (EMBL), Genoscope/CEA, the French Ministry of Research, the French Government “Investissements d’Avenir” programmes OCEANOMICS (ANR-11-BTBR-0008), FRANCE GENOMIQUE (ANR-10-INBS-09-08), MEMO LIFE (ANR-10-LABX-54), PSL* Research University (ANR-11-IDEX-0001-02), ETH and the Helmut Horten Foundation, MEXT/JSPS/KAKENHI (projects 16H06429, 16K21723, 16H06437, and 18H02279), the Spanish Ministry of Economy and Competitiveness (project MAGGY-CTM2017-87736-R), ERC Advanced Award Diatomic (grant agreement 835067 to CB), the CNRS MITI through the interdisciplinary program Modélisation du Vivant (GOBITMAP grant to SC), and the H2020 European Commission project AtlantECO (award number 862923). […]. E.D. is supported by the RFI ATLANSTIC2020 grant (PROBIOSTIC grant to DE). M.Bu. received financial support from the French Facility for Global Environment (FFEM) as part of the “Ocean Plankton, Climate and Development” project. P.C.J. was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP (PhD grant 2017/26786-1). H.S. is supported by a Brazilian Research Council (CNPq) productivity grant (process 309514/2017-7) and FAPESP (grant 2014/14139-3). […] Additional funding from the Natural Sciences and Engineering Council (NSERC) Canada Discovery program is gratefully acknowledged., With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

ASLO 2021 Aquatic Sciences Meeting, Aquatic Sciences for a Sustainable Future: Nurturing Cooperation, 22–27 June 2021, Understanding how microbiomes are spatio-temporally structured and how their structure affects ecosystem function are central questions in aquatic microbial ecology that so far have been partially answered. A particular challenge is to attain a mechanistic understanding of microbial community assembly. Recent works have investigated how ecological processes (i.e. selection, drift, and dispersal) shape microbial communities over spatio-temporal scales in distinct aquatic ecosystems. However, much less is known about the connections between the processes that structure communities, the microbial assemblages that are formed, and the ultimate effects of structuring mechanisms on ecosystem function across variable spatial (from micrometers to thousands of kilometers) and temporal scales (e.g. hourly, diel, seasonal, inter-annually and over decades). Microbial features, such as minute organismal size, high dispersal, potential dormancy, high reproductive rates, huge population sizes and potentially high adaptability to environmental change need to be considered when investigating the links between ecological processes, microbial community composition, and indicators of overall ecosystem function (e.g. primary production, organic matter decomposition, nutrient cycling and gas emissions). A major goal is to comprehend how the composition of microbiomes will be affected by changes in abiotic and biotic drivers induced by global change, and how these changes will affect main ecosystem processes. We welcome abstracts investigating the structure of aquatic microbiomes, the underlying processes shaping them, and the potential effects in ecosystem function across diverse spatio-temporal scales in freshwater or marine ecosystems

Additional file 17: Table S7. The 36 Malaspina stations (out of 120) featuring significant (p < 0.05) Local Contributions to Beta Diversity (LCBD) in prokaryotes and/or picoeukaryotes.

Additional file 20: Figure S11. Same as Figure S10, Additional file 19 but using OTUs-ASVs. Solid and open squares indicate significant and nonsignificant (using p=0.05) correlations respectively between environmental similarity (in terms of temperature and fluorescence) and phylogenetic relatedness. Correlations that are significantly positive indicate that the phylogenetic distance between OTUs-ASVs increases as environmental similarity decreases for the phylogenetic range being analysed. Phylogenetic distances were measured as abundance-weighted β-Mean Nearest Taxon Distances (βMNTD).

Additional file 9: Figure S6. Species association networks for the tropical and subtropical surface-ocean microbiota as inferred from the Malaspina dataset. Left-hand side: Association networks of picoeukaryotes and prokaryotes considering positive (red) and negative (blue) correlations in panels A) [Eukaryotic Network (+-e)] and B) [Prokaryotic Network (+-e)], and only positive correlations in C) [Eukaryotic Network (+e)] and D) [Prokaryotic Network (+e)]. On the right-hand side, we present an alternative visualization of the network as well as the following network characteristics: number of nodes (n), number of edges with positive correlation (+e) and negative correlation (-e), average degree (avg. d), average path length (avg. l), global transitivity (t), number of modules with at least 3 nodes (m) and the number of nodes in each of those modules (sizes: n). The smaller network visualization on the right-hand side groups the nodes according to the modules. The colors of nodes in Left- and Right-hand side networks indicate the modules to which they belong (NB: colors in panels A, B, C & D are independent of each other).

Additional file 6: Supplementary Methods.

Additional file 13: Figure S7. Percentage of OTUs-99% significantly associated to different environmental variables (MIC > 0.4) [left] and their corresponding contribution to total sequence abundance (i.e. percentage of reads) [right] in the Malaspina dataset. NB: Temperature, Oxygen, Conductivity and Salinity are correlated. OTUs can be associated to more than one variable.

Additional file 11: Table S4. Summary of significant OTUs-99% associations using MIC for the Malaspina dataset.

Additional file 11: Table S4. Summary of significant OTUs-99% associations using MIC for the Malaspina dataset.

Additional file 16: Figure S9. Stations (total 36) from the Malaspina dataset featuring a comparatively large contribution to the overall β-diversity (LCBD = Local Contributions to Beta Diversity [38]; p

Additional file 14: Table S6. Significant MIC associations (MIC > 0.5) between OTUs and environmental parameters in the TARA Oceans dataset.

Additional file 12: Table S5. Summary of significant OTUs-99% associations for the TARA Oceans dataset based on MIC.

Additional file 6: Supplementary Methods.

Background The ocean microbiota modulates global biogeochemical cycles and changes in its configuration may have large-scale consequences. Yet, the underlying ecological mechanisms structuring it are unclear. Here, we investigate how fundamental ecological mechanisms (selection, dispersal and ecological drift) shape the smallest members of the tropical and subtropical surface-ocean microbiota: prokaryotes and minute eukaryotes (picoeukaryotes). Furthermore, we investigate the agents exerting abiotic selection on this assemblage as well as the spatial patterns emerging from the action of ecological mechanisms. To explore this, we analysed the composition of surface-ocean prokaryotic and picoeukaryotic communities using DNA-sequence data (16S- and 18S-rRNA genes) collected during the circumglobal expeditions Malaspina-2010 and TARA-Oceans. Results We found that the two main components of the tropical and subtropical surface-ocean microbiota, prokaryotes and picoeukaryotes, appear to be structured by different ecological mechanisms. Picoeukaryotic communities were predominantly structured by dispersal-limitation, while prokaryotic counterparts appeared to be shaped by the combined action of dispersal-limitation, selection and drift. Temperature-driven selection appeared as a major factor, out of a few selected factors, influencing species co-occurrence networks in prokaryotes but not in picoeukaryotes, indicating that association patterns may contribute to understand ocean microbiota structure and response to selection. Other measured abiotic variables seemed to have limited selective effects on community structure in the tropical and subtropical ocean. Picoeukaryotes displayed a higher spatial differentiation between communities and a higher distance decay when compared to prokaryotes, consistent with a scenario of higher dispersal limitation in the former after considering environmental heterogeneity. Lastly, random dynamics or drift seemed to have a more important role in structuring prokaryotic communities than picoeukaryotic counterparts. Conclusions The differential action of ecological mechanisms seems to cause contrasting biogeography, in the tropical and subtropical ocean, among the smallest surface plankton, prokaryotes and picoeukaryotes. This suggests that the idiosyncrasy of the main constituents of the ocean microbiota should be considered in order to understand its current and future configuration, which is especially relevant in a context of global change, where the reaction of surface ocean plankton to temperature increase is still unclear., Microbiome, 8 (1), ISSN:2049-2618

Additional file 10: Table S3. Summary of association networks from the Malaspina dataset based on SparCC.

Additional file 14: Table S6. Significant MIC associations (MIC > 0.5) between OTUs and environmental parameters in the TARA Oceans dataset.

Additional file 3: Table S1. Regionally abundant or rare prokaryotic and picoeukaryotic OTUs-99% from the Malaspina dataset.

Additional file 5: Table S2. OTUs-99% displaying Cosmopolitan, Intermediate and Restricted distributions in the Malaspina dataset.

Additional file 12: Table S5. Summary of significant OTUs-99% associations for the TARA Oceans dataset based on MIC.

Additional file 8: Figure S5. Percentage of variance in Picoeukaryotic and Prokaryotic community composition (ADONIS R2) explained by water temperature and fluorescence when using different β-diversity metrics. Figure based on the Malaspina Meta-57 dataset.

Additional file 7: Figure S4. The 57 Malaspina stations for which 17 environmental parameters were available (Meta-57 dataset).

Additional file 2: Figure S2. Bray Curtis and gUniFrac distances between picoeukaryotes and prokaryotes from the Malaspina dataset. Regression (blue) and 0:1 (red) lines are indicated.

Additional file 8: Figure S5. Percentage of variance in Picoeukaryotic and Prokaryotic community composition (ADONIS R2) explained by water temperature and fluorescence when using different β-diversity metrics. Figure based on the Malaspina Meta-57 dataset.

Background: The ocean microbiota modulates global biogeochemical cycles and changes in its configuration may have large-scale consequences. Yet, the underlying ecological mechanisms structuring it are unclear. Here, we investigate how fundamental ecological mechanisms (selection, dispersal and ecological drift) shape the smallest members of the tropical and subtropical surface-ocean microbiota: prokaryotes and minute eukaryotes (picoeukaryotes). Furthermore, we investigate the agents exerting abiotic selection on this assemblage as well as the spatial patterns emerging from the action of ecological mechanisms. To explore this, we analysed the composition of surface-ocean prokaryotic and picoeukaryotic communities using DNA-sequence data (16S- and 18S-rRNA genes) collected during the circumglobal expeditions Malaspina-2010 and TARA-Oceans. Results: We found that the two main components of the tropical and subtropical surface-ocean microbiota, prokaryotes and picoeukaryotes, appear to be structured by different ecological mechanisms. Picoeukaryotic communities were predominantly structured by dispersal-limitation, while prokaryotic counterparts appeared to be shaped by the combined action of dispersal-limitation, selection and drift. Temperature-driven selection appeared as a major factor, out of a few selected factors, influencing species co-occurrence networks in prokaryotes but not in picoeukaryotes, indicating that association patterns may contribute to understand ocean microbiota structure and response to selection. Other measured abiotic variables seemed to have limited selective effects on community structure in the tropical and subtropical ocean. Picoeukaryotes displayed a higher spatial differentiation between communities and a higher distance decay when compared to prokaryotes, consistent with a scenario of higher dispersal limitation in the former after considering environmental heterogeneity. Lastly, random dynamics or drift seemed to have a more important role in structuring prokaryotic communities than picoeukaryotic counterparts. Conclusions: The differential action of ecological mechanisms seems to cause contrasting biogeography, in the tropical and subtropical ocean, among the smallest surface plankton, prokaryotes and picoeukaryotes. This suggests that the idiosyncrasy of the main constituents of the ocean microbiota should be considered in order to understand its current and future configuration, which is especially relevant in a context of global change, where the reaction of surface ocean plankton to temperature increase is still unclear.

Additional file 2: Figure S2. Bray Curtis and gUniFrac distances between picoeukaryotes and prokaryotes from the Malaspina dataset. Regression (blue) and 0:1 (red) lines are indicated.

Additional file 10: Table S3. Summary of association networks from the Malaspina dataset based on SparCC.

Additional file 3: Table S1. Regionally abundant or rare prokaryotic and picoeukaryotic OTUs-99% from the Malaspina dataset.