19:I[42426,["/_next/static/immutable/chunks/1xctgzqre-wsy.js","/_next/static/immutable/chunks/06_-wz6pdtp2b.js","/_next/static/immutable/chunks/30e52g49hg40p.js","/_next/static/immutable/chunks/02yq6tul2z-oh.js","/_next/static/immutable/chunks/10bx_ec2vj7n_.js","/_next/static/immutable/chunks/33bfn03fo8xnt.js","/_next/static/immutable/chunks/0tfjf78scu61b.js","/_next/static/immutable/chunks/31xnvej4n1bi9.js"],"default"] 1a:T52f,

We estimated Prochlorococcus, Synechococcus and picoeukaryotic phytoplankton global cell abundance (cell) using quantitative niche models (Flombaum et al 2013, 2020). Inputs for the niche models were temperature and nitrate from Earth System Models, and light from Ocean Color (oceancolor.gsfc.nasa.gov) which identical across simulations. We estimated Prochlorococcus and Synechococcus using two methodologies, parametric niche model, and artificial neural networks (ANN). For picoeukaryotic phytoplankton we used only the ANN. The parametric model used only temperature and photosynthetic active radiation as inputs, and the ANN also included nitrate concentration. Mean values were estimated for the parametric model from a 10.000 iteration bootstrap on a yearly basis, and for the ANN as 100 iterations for each unique geographic and depth location.

Data columns containing mean global cell abundance are labeled after lineage, niche model, scenario, and Earth System Model.
Units: cells.
Lineages: Prochlorococcus, Synechococcus, and picoeukaryotic phytoplankton.
Niche model: Parametric; ANN.
Scenarios: Historic, RCP2.6, RCP4.5, RCP8.5.
Earth System Models: GFDL-ESM2G, HadGEM2-ES, IPSL-CM5A-MR, MPI-ESM-LR, NorESM1-ME (Taylor et al 2012).

1b:Taa2,NSF Award Abstract: Due to their sheer abundance and high activity, microorganisms have the potential to greatly influence how ecosystems are affected by changes in their environment. However, descriptions of microbial physiology and diversity are local and highly complex and thus rarely considered in Earth System Models. Thus, the researchers focus on a convergence research framework that can qualitatively and quantitatively integrate eco-evolutionary changes in microorganisms with global biogeochemistry. Here, the investigators will develop an approach that integrates the knowledge and tools of biologists, mathematicians, engineers, and geoscientists to understand the link between the ocean nutrient and carbon cycles. The integration of data and knowledge from diverse fields will provide a robust, biologically rich, and computationally efficient prediction for the variation in plankton resource requirements and the biogeochemical implications, addressing a fundamental challenge in ocean science. In addition, the project can serve as a road map for many other research groups facing a similar lack of convergence between biology and geoscience. Traditionally, the cellular elemental ratios of Carbon, Nitrogen, and Phosphorus (C:N:P) of marine communities have been considered static at Redfield proportions but recent studies have demonstrated strong latitudinal variation. Such regional variation may have large - but poorly constrained - implications for marine biodiversity, biogeochemical functioning, and atmospheric carbon dioxide levels. As such, variations in ocean community C:N:P may represent an important biological feedback. Here, the investigators propose a convergence research framework integrating cellular and ecological processes controlling microbial resource allocations with an Earth System model. The approach combines culture experiments and omics measurements to provide a molecular understanding of cellular resource allocations. Using a mathematical framework of increasing complexity describing communicating, moving demes, the team will quantify the extent to which local mixing, environmental heterogeneity and evolution lead to systematic deviations in plankton resource allocations and C:N:P. Optimization tools from engineering science will be used to facilitate the quantitative integration of models and observations across a range of scales and complexity levels. Finally, global ocean modeling will enable understanding of how plankton resource use impacts Earth System processes. By integrating data and knowledge across fields, scales and complexity, the investigators will develop a robust link between variation in plankton C:N:P and global biogeochemical cycles.6:["$","$L19",null,{"dataset":{"_id":"793690","description":"

Prochlorococcus, Synechococcus, and picoeukaryotic phytoplankton yearly mean global abundance for four CMIP5 climate scenarios using an ensemble of five circulation models from 1900 to 2100.

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BCO-DMO Processing:
\n- renamed columns to conform with BCO-DMO naming conventions;
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year, reported as YYYY, e.g. 1995

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Mean global cell abundance; labeled after lineage, niche model, scenario, and Earth System Model: Prochlorococcus Parametric Historic GFDL-ESM2G