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

Dataset: phytoplankton climatologyProchlorococcus, 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. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/811147NSF Division of Ocean Sciences (NSF OCE) OCE-1848576, Agencia Nacional de Promoción Científica y Tecnológica () PICT-2017-3020, Universidad de Buenos Aires () UBACyT 20020170100620B

Concentrations and ratios of particulate organic carbon, nitrogen, and phosphorus in the global ocean

Knowledge of concentrations and elemental ratios of suspended particles are important for understanding many biogeochemical processes in the ocean. These include patterns of phytoplankton nutrient limitation as well as linkages between the cycles of carbon and nitrogen or phosphorus. To further enable studies of ocean biogeochemistry, we here present a global dataset consisting of 100,605 total measurements of particulate organic carbon, nitrogen, or phosphorus analyzed as part of 70 cruises or time-series. The data are globally distributed and represent all major ocean regions as well as different depths in the water column. The global median C:P, N:P, and C:N ratios are 163, 22, and 6.6, respectively, but the data also includes extensive variation between samples from different regions. Thus, this compilation will hopefully assist in a wide range of future studies of ocean elemental ratios

Global cell abundance of picoeukaryotic phytoplankton, predicted by neural network models using average temperatures and nitrate from the World Ocean Atlas 2005

Dataset: Global picoeukaryotic phytoplankton distributionGlobal cell abundance of picoeukaryotic phytoplankton, predicted by our neural network models using average temperatures and nitrate from the World Ocean Atlas 2005 (1°x1° resolution), and 8 d average PAR and K490 values derived from satellite data (SeaWiFS 0.083°x0.083°) and obtained as an output cells/ml for each set of conditions in a 1°x1° resolution. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/783537NSF Division of Ocean Sciences (NSF OCE) OCE-1848576, Agencia Nacional de Promoción Científica y Tecnológica () PICT-2017-3020, Universidad de Buenos Aires () UBACyT 20020170100620B

Prochlorococcus, Synechococcus, and picoeukaryotic phytoplankton abundances in the global ocean

Marine picophytoplankton is the most abundant photosynthetic group on Earth; however, it is still underrepresented in dynamic ecosystem models. Major constraints for understanding its role in the ecosystem at a global scale are sparse data and lack of a baseline description of its distribution. Here, we present three datasets to assess the global abundance of the principal groups of picophytoplankton, Prochlorococcus, Synechococcus, and picoeukaryotic phytoplankton: (1) a compilation of 109,045 field observations with ancillary environmental data, (2) a global monthly climatology of 1° grids from 0 to 200 m, and (3) four climate scenarios projections, from the Coupled Model Intercomparison Project 5, spanning years 1901 to 2100. Together this set of observational and modeled data can improve our understanding of the role of picophytoplankton in the global ecosystem.Fil: Visintini Adomaitis, Natalia Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Martiny, Adam Camilo. University of California at Irvine; Estados UnidosFil: Flombaum, Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentin

Biomass historic CMIP5 data - mean picophytoplankton surface biomass estimated for climate models under the Historical scenario

Dataset: Biomass historic CMIP5 dataMean picophytoplankton surface biomass (mg/m3) estimated for the climate models (CanESM2, CESM1 BGC, GFDL ESM2G, HadGEM2 ES, IPSL CM5A MR, MIROC ESM, MPI, and NorESM1 ME) under the Historical scenario. Light fields were identical across simulations. Picophytoplankton biomass results from the sum of the biomass estimated for the Prochlorococcus, Synechococcus, and picoeukaryotic phytoplankton. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/783516NSF Division of Ocean Sciences (NSF OCE) OCE-1848576, Agencia Nacional de Promoción Científica y Tecnológica () PICT-2017-3020, Universidad de Buenos Aires () UBACyT 20020170100620B

Biomass rcp85 CMIP5 data - mean picophytoplankton surface biomass estimated for the climate models under the Representative Concentration Pathway 8.5

Dataset: Biomass rcp85 CMIP5 dataMean picophytoplankton surface biomass (mg/m3) estimated for the climate models (CanESM2, CESM1 BGC, GFDL ESM2G, HadGEM2 ES, IPSL CM5A MR, MIROC ESM, MPI, and NorESM1 ME) under the Representative Concentration Pathway 8.5 (RCP8.5) – equivalent to a radiative forcing of 8.5 W m-2 in 2100) scenario. Light fields were identical across simulations. Picophytoplankton biomass results from the sum of the biomass estimated for the Prochlorococcus, Synechococcus, and picoeukaryotic phytoplankton. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/783527NSF Division of Ocean Sciences (NSF OCE) OCE-1848576, Agencia Nacional de Promoción Científica y Tecnológica () PICT-2017-3020, Universidad de Buenos Aires () UBACyT 20020170100620B

Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change

We acknowledge funding from the US DOE Genomic Science Program, BER, Office of Science project DE-SC0016410. We thank Bin Wang for discussion and inputs on trait-based modelling.Peer reviewedPublisher PD

Cellulolytic potential under environmental changes in microbial communities from grassland litter

In many ecosystems, global changes are likely to profoundly affect microorganisms. In Southern California, changes in precipitation and nitrogen deposition may influence the composition and functional potential of microbial communities and their resulting ability to degrade plant material. To test whether such environmental changes impact the distribution of functional groups involved in leaf litter degradation, we determined how the genomic diversity of microbial communities in a semi-arid grassland ecosystem changed under reduced precipitation or increased N deposition. We monitored communities seasonally over a period of 2 years to place environmental change responses into the context of natural variation. Fungal and bacterial communities displayed strong seasonal patterns, Fungi being mostly detected during the dry season whereas Bacteria were common during wet periods. Most putative cellulose degraders were associated with 33 bacterial genera and predicted to constitute 18% of the microbial community. Precipitation reduction reduced bacterial abundance and cellulolytic potential whereas nitrogen addition did not affect the cellulolytic potential of the microbial community. Finally, we detected a strong correlation between the frequencies of genera of putative cellulose degraders and cellulase genes. Thus, microbial taxonomic composition was predictive of cellulolytic potential. This work provides a framework for how environmental changes affect microorganisms responsible for plant litter deconstruction

Differential Response of Bacterial Microdiversity to Simulated Global Change

ACKNOWLEDGMENTS UC Irvine and the LRGCE are located on the ancestral homelands of the Indigenous Kizh and Acjachemen nations. We thank Alejandra Rodriguez Verdugo, Katrine Whiteson, Kendra Walters, Cynthia Rodriguez, Kristin Barbour, Alberto Barron Sandoval, Joanna Wang, Joia Kai Capocchi, Pauline Uyen Phuong Nguyen, Khanh Thuy Huynh, and Clara Barnosky for their input on analyses and previous drafts and for laboratory help. This work was supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research grants DE-SC0016410 and DE-SC0020382.Peer reviewedPublisher PD