Restructuring of plankton genomic biogeography in the surface ocean under climate change

Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science https://doi.org/10.1126/science.281.5374.237 (1998).

Guidi, L. et al. Plankton networks driving carbon export in the oligotrophic ocean. Nature https://doi.org/10.1038/nature16942 (2016).

Henson, S. A., Sanders, R. & Madsen, E. Global patterns in efficiency of particulate organic carbon export and transfer to the deep ocean. Glob. Biogeochem. Cycles https://doi.org/10.1029/2011GB004099 (2012).

Azam, F. et al. The ecological role of water-column microbes in the sea. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps010257 (1983).

Saab, M. A. Day-to-day variation in phytoplankton assemblages during spring blooming in a fixed station along the Lebanese coastline. J. Plankton Res. https://doi.org/10.1093/plankt/14.8.1099 (1992).

Djurhuus, A. et al. Environmental DNA reveals seasonal shifts and potential interactions in a marine community. Nat. Commun. https://doi.org/10.1038/s41467-019-14105-1 (2020).

Kavanaugh, M. T. et al. Seascapes as a new vernacular for pelagic ocean monitoring, management and conservation. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsw086 (2016).

Longhurst, A. R. Ecological Geography of the Sea (Elsevier, 2007).

Fay, A. R. & McKinley, G. A. Global open-ocean biomes: mean and temporal variability. Earth Syst. Sci. Data https://doi.org/10.5194/essd-6-273-2014 (2014).

Reygondeau, G. et al. Dynamic biogeochemical provinces in the global ocean. Glob. Biogeochem. Cycles https://doi.org/10.1002/gbc.20089 (2013).

Richter, D. J. et al. Genomic evidence for global ocean plankton biogeography shaped by large-scale current systems. Preprint at bioRxiv https://doi.org/10.1101/867739 (2020).

Dutkiewicz, S. et al. Dimensions of marine phytoplankton diversity. Biogeosciences https://doi.org/10.5194/bg-17-609-2020 (2020).

Hellweger, F. L., Van Sebille, E. & Fredrick, N. D. Biogeographic patterns in ocean microbes emerge in a neutral agent-based model. Science https://doi.org/10.1126/science.1254421 (2014).

Laso-Jadart, R. et al. Investigating population-scale allelic differential expression in wild populations of Oithona similis (Cyclopoida, Claus, 1866). Ecol. Evol. https://doi.org/10.1002/ece3.6588 (2020).

Delmont, T. O. et al. Single-amino acid variants reveal evolutionary processes that shape the biogeography of a global SAR11 subclade. eLife https://doi.org/10.7554/eLife.46497 (2019).

Carradec, Q. et al. A global ocean atlas of eukaryotic genes. Nat. Commun. https://doi.org/10.1038/s41467-017-02342-1 (2018).

Salazar, G. et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell https://doi.org/10.1016/j.cell.2019.10.014 (2019).

Alberti, A. et al. Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition. Sci. Data https://doi.org/10.1038/sdata.2017.93 (2017).

Pesant, S. et al. Open science resources for the discovery and analysis of Tara Oceans data. Sci. Data https://doi.org/10.1038/sdata.2015.23 (2015).

Karsenti, E. et al. A holistic approach to marine eco-systems biology. PLoS Biol. https://doi.org/10.1371/journal.pbio.1001177 (2011).

Duarte, C. M. Seafaring in the 21st century: the Malaspina 2010 circumnavigation expedition. Limnol. Oceanogr. Bull. https://doi.org/10.1002/lob.10008 (2015).

Barton, A. D., Irwin, A. J., Finkel, Z. V. & Stock, C. A. Anthropogenic climate change drives shift and shuffle in North Atlantic phytoplankton communities. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1519080113 (2016).

Benedetti, F., Guilhaumon, F., Adloff, F. & Ayata, S. D. Investigating uncertainties in zooplankton composition shifts under climate change scenarios in the Mediterranean Sea. Ecography https://doi.org/10.1111/ecog.02434 (2018).

Beaugrand, G. et al. Prediction of unprecedented biological shifts in the global ocean. Nat. Clim. Change 9, 237–243 (2019).

Article 

Google Scholar 

Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science https://doi.org/10.1126/science.1239352 (2013).

Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences https://doi.org/10.5194/bg-10-6225-2013 (2013).

Thomas, M. K., Kremer, C. T., Klausmeier, C. A. & Litchman, E. A global pattern of thermal adaptation in marine phytoplankton. Science https://doi.org/10.1126/science.1224836 (2012).

Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell https://doi.org/10.1016/j.cell.2019.10.008 (2019).

Busseni, G. et al. Large scale patterns of marine diatom richness: drivers and trends in a changing ocean. Glob. Ecol. Biogeogr. https://doi.org/10.1111/geb.13161 (2020).

Hutchinson, G. E. Concluding remarks. Cold Spring Harb. Symp. Quant. Biol. 22, 415–427 (1957).

Article 

Google Scholar 

Delmont, T. O. et al. Functional repertoire convergence of distantly related eukaryotic plankton lineages revealed by genome-resolved metagenomics. Preprint at bioRxiv https://doi.org/10.1101/2020.10.15.341214 (2020).

Delmont, T. O. et al. Heterotrophic bacterial diazotrophs are more abundant than their cyanobacterial counterparts in metagenomes covering most of the sunlit ocean. ISME J. https://doi.org/10.1038/s41396-021-01135-1 (2021).

Boyer, et al. World Ocean Database 2013, NOAA Atlas NESDIS 72 (National Oceanic and Atmospheric Administration, 2013); https://doi.org/10.7289/V5NZ85MT

Sunagawa, S. et al. Tara Oceans: towards global ocean ecosystems biology. Nat. Rev. Microbiol. https://doi.org/10.1038/s41579-020-0364-5 (2020).

Moon, K. R. et al. Visualizing structure and transitions in high-dimensional biological data. Nat. Biotechnol. https://doi.org/10.1038/s41587-019-0336-3 (2019).

van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic Change https://doi.org/10.1007/s10584-011-0148-z (2011).

Polovina, J. J., Dunne, J. P., Woodworth, P. A. & Howell, E. A. Projected expansion of the subtropical biome and contraction of the temperate and equatorial upwelling biomes in the North Pacific under global warming. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsq198 (2011).

Flombaum, P., Wang, W. L., Primeau, F. W. & Martiny, A. C. Global picophytoplankton niche partitioning predicts overall positive response to ocean warming. Nat. Geosci. https://doi.org/10.1038/s41561-019-0524-2 (2020).

Richardson, A. J. In hot water: zooplankton and climate change. ICES J. Mar. Sci. 65, 279–295 (2008).

Article 

Google Scholar 

Wrightson, L. & Tagliabue, A. Quantifying the impact of climate change on marine diazotrophy: insights from Earth system models. Front. Mar. Sci. 7, 635 (2020).

Article 

Google Scholar 

Zehr, J. P. & Capone, D. G. Changing perspectives in marine nitrogen fixation. Science 368, eaay9514 (2020).

CAS 
Article 

Google Scholar 

Luo, Y.-W. et al. Database of diazotrophs in global ocean: abundance, biomass and nitrogen fixation rates. Earth Syst. Sci. Data 4, 47–73 (2012).

Article 

Google Scholar 

Eppley, R. W. & Peterson, B. J. Particulate organic matter flux and planktonic new production in the deep ocean. Nature 282, 677–680 (1979).

Article 

Google Scholar 

Laws, E. A., Falkowski, P. G., Smith, W. O., Ducklow, H. & McCarthy, J. J. Temperature effects on export production in the open ocean. Glob. Biogeochem. Cycles 14, 1231–1246 (2000).

CAS 
Article 

Google Scholar 

Agrawal, R. & Srikant, R. in Proceedings of the 20th International Conference on Very Large Data Bases (eds Bocca, J. B. et al.) 487–499 (Morgan Kaufmann, 1994).

Laufkötter, C. et al. Projected decreases in future marine export production: the role of the carbon flux through the upper ocean ecosystem. Biogeosciences 13, 4023–4047 (2016).

Article 

Google Scholar 

Iudicone, D. Some may like it hot. Nat. Geosci. https://doi.org/10.1038/s41561-020-0535-z (2020).

Gorsky, G. et al. Expanding Tara Oceans protocols for underway, ecosystemic sampling of the ocean–atmosphere interface during Tara Pacific expedition (2016–2018). Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00750 (2019).

Istace, B. et al. de novo assembly and population genomic survey of natural yeast isolates with the Oxford Nanopore MinION sequencer. Gigascience https://doi.org/10.1093/gigascience/giw018 (2017).

Grand, M. M. et al. Developing autonomous observing systems for micronutrient trace metals. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00035 (2019).

Becker, R. A., Wilks, A. R., Brownrigg, R., Minka, T. P. & Deckmyn, A. maps: Draw geographical maps. R version 3.5.0 https://cran.r-project.org/web/packages/maps/index.html (2021).

Jaccard, P. Distribution comparée de la flore alpine dans quelques régions des Alpes occidentales et orientales. Bull. Murith. 31, 81–92 (1902).

Google Scholar 

Watson, R. A. A database of global marine commercial, small-scale, illegal and unreported fisheries catch 1950–2014. Sci. Data https://doi.org/10.1038/sdata.2017.39 (2017).

Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 11 (Flanders Marine Institute, 2019); https://doi.org/10.14284/386

Aumont, O., Ethé, C., Tagliabue, A., Bopp, L. & Gehlen, M. PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies. Geosci. Model Dev. https://doi.org/10.5194/gmd-8-2465-2015 (2015).

Bibby, T. S. & Moore, C. M. Silicate:nitrate ratios of upwelled waters control the phytoplankton community sustained by mesoscale eddies in sub-tropical North Atlantic and Pacific. Biogeosciences https://doi.org/10.5194/bg-8-657-2011 (2011).

Brun, P., Kiørboe, T., Licandro, P. & Payne, M. R. The predictive skill of species distribution models for plankton in a changing climate. Glob. Change Biol. https://doi.org/10.1111/gcb.13274 (2016).

Redfield, A. C. in James Johnstone Memorial Volume (ed. Daniel, R. J.) 176–192 (Liverpool Univ. Press, 1934).

Michelangeli, P. A., Vrac, M. & Loukos, H. Probabilistic downscaling approaches: application to wind cumulative distribution functions. Geophys. Res. Lett. https://doi.org/10.1029/2009GL038401 (2009).

Ridgeway, G. gbm: Generalized boosted regression models. R version 1.6–3.1 https://cran.r-project.org/web/packages/gbm/gbm.pdf (2010).

Breiman, L. & Cutler, A. randomForest: Breiman and Cutler’s random forests for classification and regression. R package 4.1.0 https://www.stat.berkeley.edu/~breiman/RandomForests/ (2012).

Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S 4th edn (Springer, 2002).

Wood, S. N. Stable and efficient multiple smoothing parameter estimation for generalized additive models. J. Am. Stat. Assoc. https://doi.org/10.1198/016214504000000980 (2004).

Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Lett. https://doi.org/10.1016/j.patrec.2005.10.010 (2006).

Biecek, P. DALEX: explainers for complex predictive models. J. Mach. Learn. Res. 19, 1–5 (2018).

Google Scholar 

Jones, M. C. & Cheung, W. W. L. Multi-model ensemble projections of climate change effects on global marine biodiversity. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsu172 (2015).

Vallejos, C. A. Exploring a world of a thousand dimensions. Nat. Biotechnol. https://doi.org/10.1038/s41587-019-0330-9 (2019).

Kaufman, L. and Rousseeuw, P.J. in Statistical Data Analysis Based on the L1 Norm and Related Methods (ed. Dodge, Y.) 405–416 (North-Holland, 1987).

Rousseeuw, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. https://doi.org/10.1016/0377-0427(87)90125-7 (1987).

Orsi, A. H., Whitworth, T. & Nowlin, W. D. On the meridional extent and fronts of the Antarctic Circumpolar Current. Deep Sea Res. Part I https://doi.org/10.1016/0967-0637(95)00021-W (1995).

Hubert, L. & Arabie, P. Comparing partitions. J. Classif. https://doi.org/10.1007/BF01908075 (1985).

Somerfield, P. J. Identification of the Bray–Curtis similarity index: comment on Yoshioka (2008). Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps07841 (2008).

Bloom, S. Similarity indices in community studies: potential pitfalls. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps005125 (1981).

Welch, B. L. The generalisation of student’s problems when several different population variances are involved. Biometrika 34, 28–35 (1947).

CAS 

Google Scholar 

Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).

Google Scholar 

Mann, H. B. & Whitney, D. R. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18, 50–60 (1947).

Article 

Google Scholar 

Sthle, L. & Wold, S. Analysis of variance (ANOVA). Chemom. Intell. Lab. Syst. 6, 259–272 (1989).

Article 

Google Scholar 

Bozdogan, H. Model selection and Akaike’s Information Criterion (AIC): the general theory and its analytical extensions. Psychometrika 52, 345–370 (1987).

Article 

Google Scholar 

Frémont, P. et al. Biogeographies of genomic provinces from ‘Restructuring of plankton genomic biogeography in the surface ocean under climate change’. figshare. https://figshare.com/articles/dataset/Biogeographies_genomic_provinces/19071620 (2022).