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.]

传统生物地理学研究长期聚焦于易观测的大型生物，但近年来的技术进步使得研究者得以解析微型生物的全球分布格局——这类生物的生物地理分布模式长期以来一直存在广泛争议。本研究依托塔拉海洋科考（Tara Oceans expedition）期间在全球各海域采集的浮游生物群落宏基因组数据，结合环境数据与洋流输运机制，系统剖析了浮游生物地理分布的全球结构，及其与海洋生物、化学与物理环境（即“海洋景观（seascape）”）的关联。研究采用统一的分析框架覆盖不同体型级别的生物，以前所未有的分辨率量化了群落间的基因组组成差异，结果显示泛海洋尺度下存在体型依赖型浮游生物生物地理格局，且该格局叠加了区域异质性。本研究提供了确凿证据，证明洋流输运对洋盆尺度的浮游生物生物地理格局具有显著调控作用，其驱动的群落动态存在特征性时间尺度，远超单纯的季节波动或浮游生物的生活史转变。

补充表1. 采用宏条形码（metabarcoding，18S V9）与宏基因组方法完成测序的塔拉海洋科考样本列表，包含提交至DDBJ/ENA/GenBank短读长档案库（Short Read Archives, SRA）的测序读数标识符。【本表格第2版内容完全一致】
补充表2. 各样本的环境参数表。【本表格第2版内容完全一致】
补充表3. 0-0.22 μm粒径组分的宏基因组相异度矩阵。【本表格第2版内容完全一致】
补充表4. 0.22-1.6/3 μm粒径组分的宏基因组相异度矩阵。【本表格第2版内容完全一致】
补充表5. 0.8-5 μm粒径组分的宏基因组相异度矩阵。【本表格第2版内容完全一致】
补充表6. 5-20 μm粒径组分的宏基因组相异度矩阵。【本表格第2版内容完全一致】
补充表7. 20-180 μm粒径组分的宏基因组相异度矩阵。【本表格第2版内容完全一致】
补充表8. 180-2000 μm粒径组分的宏基因组相异度矩阵。【本表格第2版内容完全一致】
补充表9. 0-0.22 μm粒径组分的操作分类单元（operational taxonomic unit, OTU）相异度矩阵。【本表格第2版内容完全一致】
补充表10. 0.22-1.6/3 μm粒径组分的OTU相异度矩阵。【本表格第2版内容完全一致】
补充表11. 0.8-5 μm粒径组分的OTU相异度矩阵。【本表格第2版内容完全一致】
补充表12. 5-20 μm粒径组分的OTU相异度矩阵。【本表格第2版内容完全一致】
补充表13. 20-180 μm粒径组分的OTU相异度矩阵。【本表格第2版内容完全一致】
补充表14. 180-2000 μm粒径组分的OTU相异度矩阵。【本表格第2版内容完全一致】
补充表15. 以年为单位的最小传播时间矩阵。【本表格第2版内容完全一致】
补充表16. 不跨陆地的最小地理距离矩阵，单位为千米。【本表格第2版内容完全一致】
补充表17. 基于成像技术的相异度矩阵。【本表格第2版内容完全一致】
补充表18. 20-180 μm粒径组分的宏基因组组装基因组（Metagenome-Assembled Genome, MAG）相异度矩阵。【本表格文件名相较于第2版已修改，表格内容完全一致】
补充表19. 不同宏基因组相异度聚类方法的共祖相关系数。【本表格第2版内容完全一致】
补充表20. 用于比较各粒径组分内聚类结果的贝克伽马指数（Baker's Gamma index）。【本表格第2版内容完全一致】
补充表21. K均值与谱聚类的兰德指数（Rand Index），以及通过adonis函数计算的多变量方差分析结果。【本表格第2版内容完全一致】

数据集1. 用于对宏条形码（metabarcodes）进行分类学注释的参考数据库（FASTA格式）。每条参考V9 rDNA条形码的标题行（以>符号开头）包含由GenBank登录号衍生的唯一标识符，其后跟随与该参考条形码关联的分类学路径。【本数据集第2版内容完全一致】
数据集2. 宏条形码水平的V9 rDNA丰度表。包含以下字段：md5sum为唯一标识符；totab为所有样本中的总丰度；cid为该条形码所属的OTU标识符（详见数据集3）；pid为与数据集1中条形码的最佳匹配百分比同一性；refs为数据集1中最佳匹配条形码的标识符；lineage为数据集1中最佳匹配的分类学谱系；taxogroup为数据集1中最佳匹配的高级分类学分组；sequence为V9 rDNA序列；TV9_XXX为各样本的条形码丰度（样本标识符详见补充表1）。【本数据集第2版内容完全一致】
数据集3. OTU水平的V9 rDNA丰度表。包含以下字段：cid为OTU标识符；md5sum为该OTU中丰度最高的条形码的唯一标识符；pid、refs、lineage、taxogroup、sequence的定义与数据集2中一致；rtotab为该OTU中丰度最高条形码的总丰度；ctotab为该OTU中所有条形码的总丰度；TV9_XXX为该OTU中所有条形码的各样本丰度（样本标识符详见补充表1）。【本数据集第2版内容完全一致】
数据集4. 20-180 μm粒径组分宏基因组样本中宏基因组组装基因组（MAG）的相对丰度。【本数据集为第3版新增内容】