Dataset: Prochlorococcus, Synechococcus and picoeukaryotic phytoplankton abundance climatology in the global ocean from quantitative niche models.

Name: Prochlorococcus, Synechococcus and picoeukaryotic phytoplankton abundance climatology in the global ocean from quantitative niche models.
Published: 2020-05-11
Keywords: oceans

This dataset has not been validatedFinal no updates expectedDOI: 10.26008/1912/bco-dmo.811147.1Version 1 (2020-05-11)Dataset Type:model results

Principal Investigator: Adam Martiny (University of California-Irvine)

Co-Principal Investigator: Pedro Flombaum (Universidad de Buenos Aires)

Student, Contact: Natalia Visintini (Universidad de Buenos Aires)

BCO-DMO Data Manager: Mathew Biddle (Woods Hole Oceanographic Institution)

Project: Convergence: RAISE: Linking the adaptive dynamics of plankton with emergent global ocean biogeochemistry (Ocean Stoichiometry)

Abstract

Prochlorococcus, Synechococcus and picoeukaryotic phytoplankton estimated mean cell abundance (cells/ml) in 1-degree grids for 25 layers from 0m to 200 m depth. Cell abundance was estimated with quantitative niche models for each lineage (Flombaum et al., 2013; Flombaum et al., 2020), inputs from the monthly mean of temperature and nitrate from the World Ocean Atlas, and PAR from MODIS-Aqua Level-3 Mapped Photosynthetically Available Radiation Data Version 2018.

Cite Dataset

Spatial Extent: N:89.50 E:179.50 S:-77.50 W:-179.50

123

Views

58

Downloads

These numbers come from web analytics and reflect real user activity on the site. They reliably show dataset usage and are mostly free of bot traffic.

Prochlorococcus, Synechococcus and picoeukaryotic phytoplankton estimated mean cell abundance (cells/ml) in 1-degree grids for 25 layers from 0m to 200 m depth. Cell abundance was estimated with quantitative niche models for each lineage (Flombaum et al., 2013; Flombaum et al., 2020), inputs from the monthly mean of temperature and nitrate from the World Ocean Atlas (Boyer et al. 2013), and PAR from MODIS-Aqua Level-3 Mapped Photosynthetically Available Radiation Data Version 2018 (NASA Ocean Biology Processing Group, 2017).

For annual climatology, the annual mean represents the average of the 12 months for each grid cell.

Related Datasets

IsDerivedFrom

Dataset: https://doi.org/10.7289/V5NZ85MT

Boyer, T. P., Antonov, J. I., Baranova, O. K., Garcia, H. E., Johnson, D. R., Mishonov, A. V., O'Brien, T. D., Seidov, D., Smolyar, I. (Igor), Zweng, M. M., Paver, C. R., Locarnini, R. A., Reagan, J. R., Coleman, C., & Grodsky, A. World ocean database 2013. U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, National Oceanographic Data Center, Ocean Climate Laboratory. https://doi.org/10.7289/V5NZ85MT

IsDerivedFrom

Dataset: https://doi.org/10.5067/aqua/modis/l3m/par/2018

NASA Ocean Biology Processing Group. (2017). MODIS-Aqua Level 3 Mapped Photosynthetically Available Radiation Data Version R2018.0 [Data set]. NASA Ocean Biology DAAC. https://doi.org/10.5067/AQUA/MODIS/L3M/PAR/2018

Related Publications

Results

Visintini, N., Martiny, A. C., & Flombaum, P. (2021). Prochlorococcus , Synechococcus , and picoeukaryotic phytoplankton abundances in the global ocean. Limnology and Oceanography Letters, 6(4), 207–215. doi:10.1002/lol2.10188

Related Research

Flombaum, P., Gallegos, J. L., Gordillo, R. A., Rincon, J., Zabala, L. L., Jiao, N., … Martiny, A. C. (2013). Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus. Proceedings of the National Academy of Sciences, 110(24), 9824–9829. doi:10.1073/pnas.1307701110

Related Research

Flombaum, P., Wang, W.-L., Primeau, F. W., & Martiny, A. C. (2020). Global picophytoplankton niche partitioning predicts overall positive response to ocean warming. Nature Geoscience, 13(2), 116–120. doi:10.1038/s41561-019-0524-2

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Dataset: Prochlorococcus, Synechococcus and picoeukaryotic phytoplankton abundance climatology in the global ocean from quantitative niche models.

This dataset has not been validatedFinal no updates expectedDOI: 10.26008/1912/bco-dmo.811147.1Version 1 (2020-05-11)Dataset Type:model results

Principal Investigator: Adam Martiny (University of California-Irvine)

Co-Principal Investigator: Pedro Flombaum (Universidad de Buenos Aires)

Student, Contact: Natalia Visintini (Universidad de Buenos Aires)

BCO-DMO Data Manager: Mathew Biddle (Woods Hole Oceanographic Institution)

Project: Convergence: RAISE: Linking the adaptive dynamics of plankton with emergent global ocean biogeochemistry (Ocean Stoichiometry)

Abstract

Cite Dataset

Spatial Extent: N:89.50 E:179.50 S:-77.50 W:-179.50

123

Views

58

Downloads

These numbers come from web analytics and reflect real user activity on the site. They reliably show dataset usage and are mostly free of bot traffic.

Prochlorococcus, Synechococcus and picoeukaryotic phytoplankton estimated mean cell abundance (cells/ml) in 1-degree grids for 25 layers from 0m to 200 m depth. Cell abundance was estimated with quantitative niche models for each lineage (Flombaum et al., 2013; Flombaum et al., 2020), inputs from the monthly mean of temperature and nitrate from the World Ocean Atlas (Boyer et al. 2013), and PAR from MODIS-Aqua Level-3 Mapped Photosynthetically Available Radiation Data Version 2018 (NASA Ocean Biology Processing Group, 2017).

For annual climatology, the annual mean represents the average of the 12 months for each grid cell.

Related Datasets

IsDerivedFrom

Dataset: https://doi.org/10.7289/V5NZ85MT

IsDerivedFrom

Dataset: https://doi.org/10.5067/aqua/modis/l3m/par/2018

Related Publications

Results

Related Research

Dataset: Prochlorococcus, Synechococcus and picoeukaryotic phytoplankton abundance climatology in the global ocean from quantitative niche models.

Principal Investigator: Adam Martiny (University of California-Irvine)

Co-Principal Investigator: Pedro Flombaum (Universidad de Buenos Aires)

Student, Contact: Natalia Visintini (Universidad de Buenos Aires)

BCO-DMO Data Manager: Mathew Biddle (Woods Hole Oceanographic Institution)

Project: Convergence: RAISE: Linking the adaptive dynamics of plankton with emergent global ocean biogeochemistry (Ocean Stoichiometry)

Abstract

123

58

Files

Related Datasets

Related Publications

Dataset: Prochlorococcus, Synechococcus and picoeukaryotic phytoplankton abundance climatology in the global ocean from quantitative niche models.

Principal Investigator: Adam Martiny (University of California-Irvine)

Co-Principal Investigator: Pedro Flombaum (Universidad de Buenos Aires)

Student, Contact: Natalia Visintini (Universidad de Buenos Aires)

BCO-DMO Data Manager: Mathew Biddle (Woods Hole Oceanographic Institution)

Project: Convergence: RAISE: Linking the adaptive dynamics of plankton with emergent global ocean biogeochemistry (Ocean Stoichiometry)

Abstract

123

58

Files

Related Datasets

Related Publications

Dataset: Prochlorococcus, Synechococcus and picoeukaryotic phytoplankton abundance climatology in the global ocean from quantitative niche models.

Principal Investigator: Adam Martiny (University of California-Irvine)

Co-Principal Investigator: Pedro Flombaum (Universidad de Buenos Aires)

Student, Contact: Natalia Visintini (Universidad de Buenos Aires)

BCO-DMO Data Manager: Mathew Biddle (Woods Hole Oceanographic Institution)

Project: Convergence: RAISE: Linking the adaptive dynamics of plankton with emergent global ocean biogeochemistry (Ocean Stoichiometry)

Abstract

Metadata

123

58

Files

Related Datasets

Related Publications

Dataset: Prochlorococcus, Synechococcus and picoeukaryotic phytoplankton abundance climatology in the global ocean from quantitative niche models.

Principal Investigator: Adam Martiny (University of California-Irvine)

Co-Principal Investigator: Pedro Flombaum (Universidad de Buenos Aires)

Student, Contact: Natalia Visintini (Universidad de Buenos Aires)

BCO-DMO Data Manager: Mathew Biddle (Woods Hole Oceanographic Institution)

Project: Convergence: RAISE: Linking the adaptive dynamics of plankton with emergent global ocean biogeochemistry (Ocean Stoichiometry)

Abstract

Metadata

123

58

Files

Related Datasets

Related Publications