1b: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"] 1c:Tdbb,

As described in Lomas et al. 2014, a model that considers interactions between ecology and evolution is used with the aim of replicating the cruise data contained in this project’s database. The model consists of an ecological model that combines the classic Droop model for cell growth with a mechanistic representation of phytoplankton’s ability to regulate the number of proteins used for nutrient uptake, positively correlated with maximum uptake rate. Size is chosen as adaptive trait, as size is linked to all the key traits in this trait-based representation.

For each species to be replicated, a specific allometry for half-saturation constant and maximum growth rate is chosen. Thus, each species (or, more generally, ecotype or functional group) is characterized by fixed parameters for these two allometries (see model database). Different chemostat conditions (i.e. dilution rates) are used to simulate different locations.

For each location, an initial ecotype reproduces, and mutations are allowed. As time goes by, more and more mutants co-exist temporarily, with continuous alternation in dominance and extinctions, and ecological responses (plasticity, i.e. changes in the number of uptake proteins) and adaptive responses (i.e. mutations) occurring simultaneously. After a transient, the population finds a strain that dominates over any other possible strain, or in other words, the system reaches the evolutionary stable strategy for size. When that happens, the simulation stops, and the uptake-related observables of that strain are selected as potential representative of that location; a series of replicates per location is necessary, due to the stochastic nature of the adaptive process (see ecolutionary-simulation entries in the model database). Use uptake rate for each location and phosphate availability in the database to replicate Fig.4 in main text of Lomas et al 2014.

For the kinetic data (and Fig.S5 in the mentioned paper), a representative size is chosen per species that is to be replicated. Then, different dilution rates allow for increases in stationary nutrient concentrations.

Taxon	Allometry	a	b	Units
Prochlorococcus	K=a*size^b	3.98E+00	3.00E-01	nmol/l
	mu_max=a*size^b	7.50E-01	-3.00E-01	1/d
Synechococcus	K=a*size^b	3.98E+00	3.00E-01	nmol/l
	mu_max=a*size^b	3.00E+00	-3.00E-01	1/d
PicoEukaryotes	K=a*size^b	2.00E+00	5.60E-01	nmol/l
	mu_max=a*size^b	1.50E+00	-2.00E-01	1/d
NanoEukaryotes	K=a*size^b	2.00E+00	5.60E-01	nmol/l
	mu_max=a*size^b	8.00E+00	-2.00E-01	1/d

1d:Tf78,One of the fundamental patterns of ocean biogeochemistry is the Redfield ratio, linking the stoichiometry of surface plankton with the chemistry of the deep ocean. There is no obvious mechanism for the globally consistent C:N:P ratio of 106:16:1 (Redfield ratio), especially as there is substantial elemental variation among plankton communities in different ocean regions. Thus, knowing how biodiversity regulates the elemental composition of the ocean is important for understanding the ocean and climate as a whole -- now and in the future. The conceptual hypotheses for this study are as follows: 1. The C:N:P ratio of a cell is constrained by its broad taxonomic group, which determines, for example, whether it has an outer shell, its size, functional metabolism, membrane lipid composition. 2. Within a taxon, there is high genetic diversity. Some of this genetic diversity is potentially laterally transferred, or can be lost within taxa, and confers various functional abilities (organic phosphate assimilation, nitrate assimilation, photoheterotrophy, etc.). Functional diversity provides the cell with further flexibility, such as the ability to respond to varying nutrient supply rates/ratios, and affects a cell's C:N:P ratio within the range specified by the taxon. 3. Given these taxonomic and genetic constraints, a cell is physiologically plastic and modifies how it allocates cellular resources in response to nutrient supply rates/ratios in the environment. 4. The microbial diversity (taxonomic, genetic, and functional) of the surface ocean varies over time and space, driven by many factors in addition to nutrients. The sum of this mixture composes the ecosystem C:N:P, the ratio that Redfield described. Based on this framework, the CoPIs will make field observations of taxon-specific stoichiometry and growth rates, genomic analyses, and conduct laboratory chemostat experiments to improve understanding of how ocean taxonomic, genetic, and functional biodiversity control the stoichiometry of the surface ocean plankton. Their analyses of these data would lead to a mechanistic understanding of variations in the Redfield ratio, both spatially and temporally. This study will greatly expand knowledge of the genomic diversity among ocean microbes and how this diversity affects biogeochemistry. The stoichiometry of the ocean's microbes is a parameter that nearly every chemical or biological oceanographer uses, from converting measurements made in one element to another, to estimating regional and global nitrogen budgets. The research also has important implications for the global carbon budget and any changes that might result from climate change. To understand mechanistically temporal and spatial variability of the plankton C:N:P ratio, biodiversity must be studied not only at the traditional taxonomic level, but at the genetic and functional levels which dictate organism response to their environment. Data will be integrated into a combined ocean ecological, evolutionary, and biogeochemical model, with flexible stoichiometry, including cellular biochemical allocations. Seeding a coupled physical-biological model of the oceans with multiple competing genotypes enables the exploration of ecological and evolutionary patterns of resource acquisition and C:N:P ratios. Developing a more mechanistic examination of the course of ecology and evolution, in which laboratory and field data define tradeoffs between different growth and nutrient acquisition strategies, would estabblish the framework of adaptive dynamics for determining "evolutionarily convergence". Finally, model outcomes will be evaluated against field data. The field work planned for this project includes several cruises: BV46 (September/October 2011), BV48 (September 2012), a June 2013 cruise from Bermuda to the Labrador Sea, and a cruise from Hawaii to Tahiti (May 2014). Additionally, samples will be be acquired during cruises of opportunity.6:["$","$L1b",null,{"dataset":{"_id":"540481","description":"

The model using this data was discussed in Lomas et al. 2014 PNAS.

Related datasets:
\n- P-uptake - kinetics
\n- P-uptake - taxon specific
\n- P-uptake - bulk
\n- P-uptake - biogeochemistry

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In order to obtain one representative per replicate of the evolutionary simulations, an average over replicates was measured, for each of the variables depicted in the table above and associated data file.

BCO-DMO Processing Notes:
\n- added conventional header with dataset name, PI name, version date
\n- renamed parameters to BCO-DMO standard
\n- replaced spaces and other special characters with underscore
\n- revised the data to include cell sizes for the kinetic simulation; removed cell size column from table in the metadata. This replaces data from 2014-11-17.

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