Global Trends in Marine Plankton Diversity across Kingdoms of Life

35 pages, 18 figures, 1 table, supplementary information https://doi.org/10.1016/j.cell.2019.10.008.-- Raw reads of Tara Oceans are deposited at the European Nucleotide Archive (ENA). In particular, newly released 18S rRNA gene metabarcoding reads are available under the number ENA: PRJEB9737. ENA references for the metagenomics reads corresponding to the size fraction < 0.22 μm (for prokaryotic viruses) analyzed in this study are included in Gregory et al. (2019); see their Table S3. ENA references for the metagenomics reads corresponding to the size fraction 0.22-1.6/3 μm (for prokaryotes and giruses) correspond to Salazar et al. (2019) (see https://zenodo.org/record/3473199). Imaging datasets from the nets are available through the collaborative web application and repository EcoTaxa (Picheral et al., 2017) under the address https://ecotaxa.obs-vlfr.fr/prj/412 for regent data, within the 3 projects https://ecotaxa.obs-vlfr.fr/prj/397, https://ecotaxa.obs-vlfr.fr/prj/398, https://ecotaxa.obs-vlfr.fr/prj/395 for bongo data, and within the 2 projects https://ecotaxa.obs-vlfr.fr/prj/377 and https://ecotaxa.obs-vlfr.fr/prj/378 for WP2 data. A table with Shannon values and multiple samples identifiers, plus a table with flow cytometry data split in six groups are available (https://doi.org/10.17632/p9r9wttjkm.1). Contextual data from the Tara Oceans expedition, including those that are newly released from the Arctic Ocean, are available at https://doi.org/10.1594/PANGAEA.875582The ocean is home to myriad small planktonic organisms that underpin the functioning of marine ecosystems. However, their spatial patterns of diversity and the underlying drivers remain poorly known, precluding projections of their responses to global changes. Here we investigate the latitudinal gradients and global predictors of plankton diversity across archaea, bacteria, eukaryotes, and major virus clades using both molecular and imaging data from Tara Oceans. We show a decline of diversity for most planktonic groups toward the poles, mainly driven by decreasing ocean temperatures. Projections into the future suggest that severe warming of the surface ocean by the end of the 21st century could lead to tropicalization of the diversity of most planktonic groups in temperate and polar regions. These changes may have multiple consequences for marine ecosystem functioning and services and are expected to be particularly significant in key areas for carbon sequestration, fisheries, and marine conservationTara Oceans (which includes both the Tara Oceans and Tara Oceans Polar Circle expeditions) would not exist without the leadership of the Tara Ocean Foundation and the continuous support of 23 institutes (https://oceans.taraexpeditions.org/). 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), the European Molecular Biology Laboratory (EMBL), Genoscope/CEA, the French Ministry of Research, and the French Government “Investissements d’Avenir” programs OCEANOMICS (ANR-11-BTBR-0008), FRANCE GENOMIQUE (ANR-10-INBS-09-08), MEMO LIFE (ANR-10-LABX-54), the PSL∗ Research University (ANR-11-IDEX-0001-02), as well as EMBRC-France (ANR-10-INBS-02). Funding for the collection and processing of the Tara Oceans data set was provided by NASA Ocean Biology and Biogeochemistry Program under grants NNX11AQ14G, NNX09AU43G, NNX13AE58G, and NNX15AC08G (to the University of Maine); the Canada Excellence research chair on remote sensing of Canada’s new Arctic frontier; and the Canada Foundation for Innovation. We also thank agnès b. and Etienne Bourgois, the Prince Albert II de Monaco Foundation, the Veolia Foundation, Region Bretagne, Lorient Agglomeration, Serge Ferrari, Worldcourier, and KAUST for support and commitment. The global sampling effort was enabled by countless scientists and crew who sampled aboard the Tara from 2009–2013, and we thank MERCATOR-CORIOLIS and ACRI-ST for providing daily satellite data during the expeditions. We are also grateful to the countries who graciously granted sampling permission. We thank Stephanie Henson for providing ocean carbon export data and are also grateful to the other researchers who kindly made their data available. We thank Juan J. Pierella-Karlusich for advice regarding single-copy genes. C.d.V. and N.H. thank the Roscoff Bioinformatics platform ABiMS (http://abims.sb-roscoff.fr) for providing computational resources. C.B. acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Program (grant agreement 835067) as well as the Radcliffe Institute of Advanced Study at Harvard University for a scholar’s fellowship during the 2016-2017 academic year. M.B.S. thanks the Gordon and Betty Moore Foundation (award 3790) and the National Science Foundation (awards OCE#1536989 and OCE#1829831) as well as the Ohio Supercomputer for computational support. S.G.A. thanks the Spanish Ministry of Economy and Competitiveness (CTM2017-87736-R), and J.M.G. is grateful for project RT2018-101025-B-100. F.L. thanks the Institut Universitaire de France (IUF) as well as the EMBRC platform PIQv for image analysis. M.C.B., D.S., and J.R. received financial support from the French Facility for Global Environment (FFEM) as part of the “Ocean Plankton, Climate and Development” project. M.C.B. also received financial support from the Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES 99999.000487/2016-03)Peer Reviewe

Pairwise comparisons between all samples in each dataset.

<p>Boxplot of median, range and interquartile range of (A) Bray-Curtis dissimilarity, (B) weighted UniFrac distance and (C) unweighted UniFrac distance.</p

Constrained Analysis of Principal Coordinates (CAP).

<p>CAP was performed using the average weighted UniFrac distances and four measured operational parameters: temperature, mixed liquor suspended solids (MLSS), sludge volume index (SVI) and influent biochemical oxygen demand (BOD). (A) V1–V3 region; (B) V4 region.</p

Distribution of bacterial phyla and classes of <i>Proteobacteria</i> according to the 16S rRNA gene region.

<p>Data of each 16S rRNA region correspond to the average of 12 duplicate monthly samples. Sequences were classified against RDP database at a confidence threshold of 80%. Phyla with average percentage of abundances lower than 1% were included in “other Phyla” (<i>Spirochaetes</i>, <i>Armatimonadetes</i>, <i>Epsilonproteobacteria</i>, SR1, <i>Deinococcus-Thermus, Synergistetes</i>, <i>Fusobacteria</i>, <i>Verrucomicrobia</i>, <i>Gemmatimonadetes</i>, TM7 and <i>Planctomycetes</i>).</p

Moving-window analysis.

<p>Symbols indicate the mean of (A) Bray-Curtis dissimilarities or (B) weighted UniFrac distances between consecutive sampling points within the V1–V3 (▵) and the V4 (○) datasets. Error bars represent SEM.</p

Impact of primer choice on the similarity decay.

<p>(A, B) Bray-Curtis dissimilarities, (C, D) weighted UniFrac, and (E, F) unweighted UniFrac distances, were converted to similarities and fitted to a log-linear model. Symbols represent each of the pairwise comparisons according to V1–V3 region (▵) and to V4 region (○). Technical replicates are represented with the same symbols, but different filling (white and gray). Linear regressions were calculated independently for each replicate and plotted with continuous and dashed lines. Slopes derived from V1–V3 and V4 data sets were not significantly different.</p

Impact of primer choice on bacterial turnover.

<p>The rate of species replacement (<i>w</i>) was calculated on the basis of (A) the classified sequences and (B) OTUs with a cutoff of 97% similarity. Symbols represent the average values for each time point according to V1–V3 region (▵) and to V4 region (○). Error bars in the log-log space represent SEM of log values.</p

Nonmetric multidimensional scaling based on classified sequences at the genus level.

<p>Symbols represent each of the time points corresponding to V1–V3 region (▵) and to V4 region (○). Technical replicates are represented with the same symbols, but different filling (white and gray). The adjoining numbers identify the samples. Stress  =  0.13.</p

Quantification of <i>Thiothrix</i> sp. in activated sludge.

<p>(A) Representative images of fluorescence in situ hybridization of activated sludge at times 1 to 12. Two images of the same microscopic field are shown for each time point. Right panels: cells binding to <i>Thiothrix-specific</i> Cy3-labeled G123T probe. Left panels: corresponding views of DAPI stained cells. Photomicrographs were acquired in a CSLM at a magnification of 600X. Scale bar  =  50 µm, applies to all panels. (B) Biovolume fraction of <i>Thiothrix</i> relative to total bacteria determined by FISH (⧫). Relative abundances of <i>Thiothrix</i> sp determined by amplicon sequencing using the V1–V3 region (▵) and the V4 region (○).</p