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. Here we assessed the global structure of plankton geography and its relation to the biological, chemical and physical context of the ocean (the 'seascape') by analyzing metagenomes of plankton communities sampled across oceans during the Tara Oceans expedition, in light of environmental data and ocean current transport. Using a consistent approach across organismal sizes that provides unprecedented resolution to measure changes in genomic composition between communities, we report a pan-ocean, size-dependent plankton biogeography overlying regional heterogeneity. We found robust evidence for a basin-scale impact of transport by ocean currents on plankton biogeography, and on a characteristic timescale of community dynamics going beyond simple seasonality or life history transitions of plankton.Supplementary Table 1. List of Tara 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). [This Table is identical in version 2.]Supplementary Table 2. Table of environmental parameters for each sample. [This Table is identical in version 2.]Supplementary Table 3. Matrix of metagenomic dissimilarity for the 0-0.22 μm size fraction. [This Table is identical in version 2.]Supplementary Table 4. Matrix of metagenomic dissimilarity for the 0.22-1.6/3 μm size fraction. [This Table is identical in version 2.]Supplementary Table 5. Matrix of metagenomic dissimilarity for the 0.8-5 μm size fraction. [This Table is identical in version 2.]Supplementary Table 6. Matrix of metagenomic dissimilarity for the 5-20 μm size fraction. [This Table is identical in version 2.]Supplementary Table 7. Matrix of metagenomic dissimilarity for the 20-180 μm size fraction. [This Table is identical in version 2.]Supplementary Table 8. Matrix of metagenomic dissimilarity for the 180-2000 μm size fraction. [This Table is identical in version 2.]Supplementary Table 9. Matrix of OTU dissimilarity for the 0-0.22 μm size fraction. [This Table is identical in version 2.]Supplementary Table 10. Matrix of OTU dissimilarity for the 0.22-1.6/3 μm size fraction. [This Table is identical in version 2.]Supplementary Table 11. Matrix of OTU dissimilarity for the 0.8-5 μm size fraction. [This Table is identical in version 2.]Supplementary Table 12. Matrix of OTU dissimilarity for the 5-20 μm size fraction. [This Table is identical in version 2.]Supplementary Table 13. Matrix of OTU dissimilarity for the 20-180 μm size fraction. [This Table is identical in version 2.]Supplementary Table 14. Matrix of OTU dissimilarity for the 180-2000 μm size fraction. [This Table is identical in version 2.]Supplementary Table 15. Matrix of minimum travel time, in years. [This Table is identical in version 2.]Supplementary Table 16. Matrix of minimum geographic distance (without traversing land), in kilometers. [This Table is identical in version 2.]Supplementary Table 17. Matrix of imaging-based dissimilarity. [This Table is identical in version 2.]Supplementary Table 18. Matrix of metagenome-assembled genome (MAG)-based dissimilarity for the 20-180 μm size fraction. [The filename of this Table was modified from version 2. The contents of the Table are identical.]Supplementary Table 19. The cophenetic correlation coefficient for different methods of clustering metagenomic dissimilarity. [This Table is identical in version 2.]Supplementary Table 20. Baker's Gamma index comparing clustering results within size fractions. [This Table is identical in version 2.]Supplementary Table 21. Rand Index for K-means and spectral clustering, and multivariate ANOVA calculated by the adonis function. [This Table is identical in version 2.]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 > sign) contains a unique identifier derived from GenBank accession number, followed by the taxonomic path associated to the reference barcode. [This Dataset is identical in version 2.]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). [This Dataset is identical in version 2.]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). [This Dataset is identical in version 2.]Dataset 4. Relative abundances of metagenome-assembled genomes (MAGs) in metagenomic samples from the 20-180 μm size fraction. [This Dataset is new in version 3.]

生物地理学研究传统上集中于易于观察的生物体，然而，近年来技术的进步使得对微生物大规模分布的分析成为可能，这些微生物的生物地理学模式长期以来一直存在争议。在本研究中，我们通过分析Tara Oceans远洋考察期间在全球范围内采集的浮游生物群落的宏基因组，结合环境数据和海洋环流传输信息，评估了浮游生物地理学的全球结构及其与海洋的生物、化学和物理环境的关联（即‘海洋景观’）。采用一致的跨生物体大小的分析方法，为测量群落之间基因组组成的变迁提供了前所未有的分辨率，我们报告了全球范围内的、与区域异质性叠加的、依赖于尺寸的浮游生物地理学。我们发现了海洋环流对浮游生物地理学影响的坚实基础，以及超越简单季节性或浮游生物生命周期转变的群落动态特征的典型时间尺度。补充表1：使用宏条形码（18S V9）方法与宏基因组方法对Tara Oceans样本进行测序的列表，包括存入DDBJ/ENA/GenBank短读档案（SRA）的测序读数的标识符。[此表与版本2相同。]补充表2：每个样本的环境参数表。[此表与版本2相同。]补充表3：0-0.22 μm尺寸分数的宏基因组差异矩阵。[此表与版本2相同。]补充表4：0.22-1.6/3 μm尺寸分数的宏基因组差异矩阵。[此表与版本2相同。]补充表5：0.8-5 μm尺寸分数的宏基因组差异矩阵。[此表与版本2相同。]补充表6：5-20 μm尺寸分数的宏基因组差异矩阵。[此表与版本2相同。]补充表7：20-180 μm尺寸分数的宏基因组差异矩阵。[此表与版本2相同。]补充表8：180-2000 μm尺寸分数的宏基因组差异矩阵。[此表与版本2相同。]补充表9：0-0.22 μm尺寸分数的OTU差异矩阵。[此表与版本2相同。]补充表10：0.22-1.6/3 μm尺寸分数的OTU差异矩阵。[此表与版本2相同。]补充表11：0.8-5 μm尺寸分数的OTU差异矩阵。[此表与版本2相同。]补充表12：5-20 μm尺寸分数的OTU差异矩阵。[此表与版本2相同。]补充表13：20-180 μm尺寸分数的OTU差异矩阵。[此表与版本2相同。]补充表14：180-2000 μm尺寸分数的OTU差异矩阵。[此表与版本2相同。]补充表15：最小旅行时间矩阵，以年为单位。[此表与版本2相同。]补充表16：最小地理距离矩阵（不穿越陆地），以公里为单位。[此表与版本2相同。]补充表17：基于成像的相似性矩阵。[此表与版本2相同。]补充表18：20-180 μm尺寸分数的基于宏基因组组装基因组（MAG）的相似性矩阵。[此表的文件名已从版本2中修改。表的其余内容与版本2相同。]补充表19：不同聚类方法的共分位数相关系数。[此表与版本2相同。]补充表20：不同尺寸分数内聚类结果的Baker's伽马指数。[此表与版本2相同。]补充表21：K-means和光谱聚类的Rand指数，以及通过adonis函数计算的多变量方差分析。[此表与版本2相同。]数据集1：用于执行宏条形码分类的参考数据库（FASTA格式）。每个参考V9 rDNA条形码的标题行（带有>符号）包含从GenBank访问号派生的唯一标识符，后跟与参考条形码相关的分类路径。[此数据集与版本2相同。]数据集2：V9 rDNA在宏条形码水平上的丰度。md5sum = 唯一标识符；totab = 所有样本中的总丰度；cid = 属于OTU的条形码的标识符（参见数据集3）；pid = 与数据集1中条形码的最佳百分比相似度；refs = 数据集1中最佳匹配条形码的标识符；lineage = 数据集1中最佳匹配的最佳分类谱系；taxogroup = 数据集1中最佳匹配的高级分类群；sequence = V9 rDNA序列；TV9_XXX = 样本的条形码丰度（参见补充表1中的样本标识符）。[此数据集与版本2相同。]数据集3：V9 rDNA在OTU（操作分类单元）水平上的丰度。cid = OTU的标识符；md5sum = OTU中最丰富条形码的唯一标识符；pid、refs、lineage、taxogroup、sequence = 如数据集2中定义；rtotab = OTU中最丰富条形码的总丰度；ctotab = OTU中所有条形码的总丰度；TV9_XXX = OTU中所有条形码的样本丰度（参见补充表1中的样本标识符）。[此数据集与版本2相同。]数据集4：20-180 μm尺寸分数的宏基因组样本中宏基因组组装基因组（MAG）的相对丰度。[此数据集在版本3中为新数据。]