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Global picophytoplankton niche partitioning predicts overall positive response to ocean warming

Nature Geoscience volume 13, pages 116–120 (2020)Cite this article

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Abstract

Ocean phytoplankton biomass is predicted to decline in Earth system models, due in large part to an expansion of nutrient-deplete ocean regions. However, the representation of ecosystems in these models is simplified and based on only a few functional types. As a result, they fail to capture the high diversity known to exist within and across phytoplankton communities. Here we present an assessment of the global biogeography of the very abundant but little studied picoeukaryotic phytoplankton by analysing a global abundance dataset with a neural-network-derived quantitative niche model. Combining this niche model with previous assessments of the distribution of Prochlorococcus and Synechococcus, we find that different cell sizes among picophytoplankton lineages are clearly partitioned into latitudinal niches. In addition, picophytoplankton biomass increases along a temperature gradient in low-latitude regions. We infer that future warmer ocean conditions can lead to elevated phytoplankton biomass in regions that are already dominated by picophytoplankton. Finally, we demonstrate that elevated upper-ocean nutrient recycling and lower nutrient requirements of phytoplankton have the potential to support increasing low-latitude phytoplankton biomass with future warming.

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Fig. 1: Global distribution picoeukaryotic phytoplankton abundance.
Fig. 2: Picoeukaryotic phytoplankton observations and niche model predictions as a function of environmental variation.
Fig. 3: Niche partitioning among picoeukaryotic phytoplankton, Synechococcus and Prochlorococcus.
Fig. 4: Projected impact of climate change on total picophytoplankton carbon biomass.
Fig. 5: Evaluation of ecosystem regulation mechanisms on phytoplankton biomass.

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Data availability

All observations and phytoplankton model data are available at BCO-DMO (https://www.bco-dmo.org/project/764270). The biogeochemical model data are available here (https://zenodo.org/record/3543774).

Code availability

The biogeochemical model code is available at https://doi.org/10.5281/zenodo.3543779.

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Acknowledgements

We thank the many contributing researchers for the oceanographic data and K. Mackey and J. Martiny at UCI for helpful comments. Financial support for this work was provided by the National Science Foundation (OCE-1046297 and OCE-1848576 to A.C.M.), CONICET, UBACYT (20020170100620BA), Agencia Nacional de Promoción Científica y Tecnológica (PICT-2017-3020 to P.F.) and US Department of Energy Office of Biological and Environmental Research (DE-SC0012550 to F.W.P.).

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Authors and Affiliations

  1. Department of Earth System Science, University of California, Irvine, CA, USA

    Pedro Flombaum, Wei-Lei Wang, Francois W. Primeau & Adam C. Martiny

  2. Centro de Investigaciones del Mar y la Atmósfera, CONICET, Buenos Aires, Argentina

    Pedro Flombaum

  3. Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Ecología, Genética y Evolución, Buenos Aires, Argentina

    Pedro Flombaum

  4. Department of Ecology and Evolutionary Biology, University of California, Irvine, CA, USA

    Adam C. Martiny

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  1. Pedro Flombaum
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Contributions

P.F. and A.C.M. designed the study, P.F., W.-L.W. and F.W.P. did the analysis and A.C.M. wrote the paper.

Corresponding author

Correspondence to Adam C. Martiny.

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Peer review information Primary handling editors: Xujia Jiang; Heike Langenberg.

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Extended data

Extended Data Fig. 1

Conceptual model for linking ecotype diversity, environmental, and biotic factors to the fundamental and realized niches of phytoplankton lineages.

Extended Data Fig. 2

Global distribution of sampling sites for the 13,771 observations used in this study.

Extended Data Fig. 3 Scatter density for observed versus predicted picoeukaryotic phytoplankton cell abundance.

Cell abundances were predicted based on ancillary environmental information associated with each observation. The dashed line represents the 1:1 relationship.

Extended Data Fig. 4 Predicted seasonal distributions of picoeukaryotic phytoplankton at the surface.

Mean quarterly surface picoeukaryotic phytoplankton abundance for (a) January to March, (b) April to June, (c) July to September, and (d) October to December.

Extended Data Fig. 5 Predicted picoeukaryotic phytoplankton cell abundance and number of observations for the combination of temperature and (a) nitrate, and (b) photosynthetic active radiation (PAR).

The predicted abundance represents the mean quantitative niche model output based on 100 trained neural networks at constant (a) PAR (3.2 E m−2 d−1) and (b) nitrate (3.2 µM). Circle size represents the number of observations on a gridded combination of environmental variables.

Extended Data Fig. 6 Distribution of total picophytoplankton carbon biomass and relative contribution of each lineage at the ocean surface.

Proportional contribution to total picophytoplankton carbon biomass by (a) picoeukaryotic phytoplankton, (b) Synechococcus and (c) Prochlorococcus. (d) Total picophytoplankton carbon biomass. (e) Proportional contribution of picophytoplankton to total phytoplankton carbon biomass. Total picophytoplankton carbon biomass was estimated as the sum of picoeukaryotes, Synechococcus and Prochlorococcus cellular abundance weighted by their cellular carbon biomass. Total phytoplankton biomass was predicted by the GFDL ES2M model.

Extended Data Fig. 7 Projected impact of climate change on total picophytoplankton carbon biomass.

Proportional area in the 30˚N-30˚S band accounted for by (a) total picophytoplankton biomass concentration for the historic and RCP8.5 CMIP5 scenarios, and (b) changes in biomass between RCP8.5 and historic CMIP5. (c) Percentage of change in surface total picophytoplankton carbon biomass estimated for the end of 21st and 20th centuries based on temperature and nitrate concentration simulated under the RCP8.5 and historic CMIP5 scenarios.

Extended Data Fig. 8 Design of a simple model describing the relationship between nutrient cycling and standing stock of phytoplankton biomass.

Fluxes are identified by arrows and stocks by boxes. N and Nd represent nutrient concentration at the euphotic and deep layer respectively, and P represents phytoplankton biomass. k represents phytoplankton remineralization rate, u the phytoplankton nutrient uptake rate, s the phytoplankton sinking rate, and q the vertical mixing rate.

Extended Data Fig. 9 Sensitivity of global picophytoplankton biomass to changes in remineralization rates estimated with an ocean biogeochemical model.

b is the exponential decay of particulate organic matter in the water column following a power law Martin curve that represents nutrient trapping, and κd is the remineralization rate of DOP to DIP. Global biomass for the control used values in Table S2.

Extended Data Fig. 10 Availability of observations to inform future environmental conditions.

Number of observations and percent of ocean volume for the RCP8.5 in a combination of temperature and nitrate.

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Supplementary Tables 1 and 2.

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Flombaum, P., Wang, WL., Primeau, F.W. et al. Global picophytoplankton niche partitioning predicts overall positive response to ocean warming. Nat. Geosci. 13, 116–120 (2020). https://doi.org/10.1038/s41561-019-0524-2

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