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

Biogeographical studies have traditionally focused on readily visible organisms, but recent technological advances are enabling analyses of the large-scale distribution of microscopic organisms, whose biogeographical patterns have long been debated. The most prominent global biogeography of marine plankton was derived by Longhurst based on parameters principally associated with photosynthetic plankton. Localized studies of selected plankton taxa or specific organismal sizes have mapped community structure and begun to assess the roles of environment and ocean current transport in shaping these patterns. Here we assess global plankton biogeography and its relation to the biological, chemical and physical context of the ocean (the ‘seascape’) by analyzing 24 terabases of metagenomic sequence data and 739 million metabarcodes from the Tara Oceans expedition in light of environmental data and simulated ocean current transport. In addition to significant local heterogeneity, viral, prokaryotic and eukaryotic plankton communities all display near steady-state, large-scale, size-dependent biogeographical patterns. Correlation analyses between plankton transport time and metagenomic or environmental dissimilarity reveal the existence of basin-scale biological and environmental continua emerging within the main current systems. Across oceans, there is a measurable, continuous change within communities and environmental factors up to an average of 1.5 years of travel time. Modulation of plankton communities during transport varies with organismal size, such that the distribution of smaller plankton best matches Longhurst biogeochemical provinces, whereas larger plankton group into larger provinces. Together these findings provide an integrated framework to interpret plankton community organization in its physico-chemical context, paving the way to a better understanding of oceanic ecosystem functioning in a changing global environment.<br>Supplementary Table 1. List of <i>Tara</i> Oceans samples sequenced with a metabarcoding (18S V9) approach and with a metagenomic approach, including identifiers for sequencing reads deposited in the DDBJ/ENA/GenBank Short Read Archives (SRA).<br><br>Supplementary Table 2. Table of environmental parameters for each sample.<br>Supplementary Table 3. Matrix of metagenomic dissimilarity for the 0-0.22 μm size fraction.<br>Supplementary Table 4. Matrix of metagenomic dissimilarity for the 0.22-1.6/3 μm size fraction.<br>Supplementary Table 5. Matrix of metagenomic dissimilarity for the 0.8-5 μm size fraction.<br>Supplementary Table 6. Matrix of metagenomic dissimilarity for the 5-20 μm size fraction.<br>Supplementary Table 7. Matrix of metagenomic dissimilarity for the 20-180 μm size fraction.<br>Supplementary Table 8. Matrix of metagenomic dissimilarity for the 180-2000 μm size fraction.<br>Supplementary Table 9. Matrix of OTU dissimilarity for the 0-0.22 μm size fraction.<br>Supplementary Table 10. Matrix of OTU dissimilarity for the 0.22-1.6/3 μm size fraction.<br>Supplementary Table 11. Matrix of OTU dissimilarity for the 0.8-5 μm size fraction.<br>Supplementary Table 12. Matrix of OTU dissimilarity for the 5-20 μm size fraction.<br>Supplementary Table 13. Matrix of OTU dissimilarity for the 20-180 μm size fraction.<br>Supplementary Table 14. Matrix of OTU dissimilarity for the 180-2000 μm size fraction.<br>Supplementary Table 15. Matrix of minimum travel time, in years.<br>Supplementary Table 16. Matrix of minimum geographic distance (without traversing land), in kilometers.<br>Supplementary Table 17. The cophenetic correlation coefficient for different methods of clustering metagenomic dissimilarity.<br>Supplementary Table 18. Baker's Gamma index comparing clustering results within size fractions.<br>Supplementary Table 19. Rand Index for K-means and spectral clustering, and multivariate ANOVA calculated by the adonis function.<br>Dataset 1. Reference database (in FASTA format) used to perform taxonomic assignment of metabarcodes. The header line of each reference V9 rDNA barcode (with a &gt; sign) contains a unique identifier derived from GenBank accession number, followed by the taxonomic path associated to the reference barcode.<br>Dataset 2. V9 rDNA abundance at the metabarcode level. md5sum = unique identifier; totab = total abundance across all samples; cid = identifier of the OTU to which the barcode belongs (see Dataset 3); pid = best percentage identity to a barcode in Dataset 1; refs = identifier(s) of the best matching barcode(s) in Dataset 1; lineage = taxononmic lineage of the best match in Dataset 1; taxogroup = high-level taxonomic grouping of the best match in Dataset 1; sequence = V9 rDNA sequence; TV9_XXX = barcode abundance by sample (see Supplementary Table 1 for sample identifiers).<br>Dataset 3. V9 rDNA abundance at the OTU (operational taxonomic unit) level. cid = identifier of the OTU; md5sum = unique identifier of the most abundant barcode in the OTU; pid, refs, lineage, taxogroup, sequence = defined as in Dataset 2; rtotab = total abundance of the most abundant barcode in the OTU; ctotab = total abundance of all barcodes in the OTU; TV9_XXX = abundance by sample of all barcodes in the OTU (see Supplementary Table 1 for sample identifiers).<br>