MMI meeting, March 2013 Mick Follows How do ocean ecosystem models work? Applications and links to ‘omics-based observations Physiological sub-models

Observed seasonal variation of phytoplankton at Georges Bank G. Riley, J. Marine Res. 6, (1946) J F M A M J J A S O N D month

Riley’s mechanistic model Rate of growth respiration grazing change B = phytoplankton biomass (mol C m -3 ) Z = zooplankton biomass (mol C m -3 ) μ = growth rate (s -1 ) K = respiration rate (s -1 ) g = grazing rate (s -1 (mol C m -3 ) -1 )

Parameterization of growth Riley (1946) Monod (1942)

Riley’s mechanistic model growth respiration grazing J F M A M J J A S O N D theoretical curve observed

Extending Riley’s model Monod and Droop kinetics NPZ-type models e.g. Steele (1958) N P Z μ KrKr g Phytoplankton NutrientZooplankton

Multiple resources, diverse populations P N P Z D N1N1 N2N2 Functional group models – multiple phytoplankton types e.g. Chai et al (2002), Moore et al (2002)

Remotely sensed chlorophyll NASA MODIS Ocean model MOVIE – removed for compactness Comparison of remotely sensed and simluated surface ocean chlorophyll

Phytoplankton diversity predicted by ocean model Ocean model resolving O(100) phytoplankton types

Measures of diversity Data Fuhrman et al (2008), model Barton et al (2010) Fuhrman et al (2008)

Genomic mapping of ecotypes with known physiologies Prochloroccocus Data Johnson et al (2006); model Follows et al (2007)

Mapping of abundance of specific functional types Data Church et al (2008), model Monteiro et al (2010)

Mapping of abundance of specific functional types Data from Luo et al (2012)

Trade-offs define biogeography Trade-offs for diazotrophy not dependent on fixed nitrogen high iron quota slow maximum growth rate Ocean model Fanny Monteiro

Interpretation Resource ratio perspective (Tilman, 1982) Relative rates of delivery of N, P, Fe define range of diazotrophs (Ward et al, 2013; submitted)

Why do diazotrophs grow so slowly? Why do nitrogen fixers grow slowly?

Physiological models For biogeochemical modeling purposes we would like: Flexible and prognostic elemental ratios Mechanistic understanding/parameterizations of population growth rates Relatively few state variables for computational tractability

1940s 1960s 1970s 2000s Monod/ Droop/Caperon Shuter, McCarty Metabolic Redfield Internal stores Macro-molecular reconstruction, FBA Flexible elemental ratios Few state variables Generalized framework for heterotrophs/phototrophs fixed elemental Ratios, 1 state variable Prognostic elemental ratios (Ecological Stoichiometry) Must be backwards compatible

Model of Azotobacter Vinelandii Nitrogen fixing soil bacteria Conserve internal fluxes of mass, electrons and energy McCarty (1965), Vallino et al (1996) … Biophysical model of substrate and O 2 uptake Pasciak and Gavis (1974), Staal et al (2003), … Demand intra-cellular O 2 ~ 0 Keisuke Inomura pyruvate “biomass” sucrose NH 4 + O2O2 CO 2 O2O2 N2N2 C5H7O2NC5H7O2N Molecular diffusion

Laboratory data: continuous culture Kuhle and Oetze (1988) Model (Keisuke Inomura) [O 2 ] Low yields in oxygenated medium Slow growth rates if substrate limited

Genome-scale metabolic reconstructions and Flux Balance Analysis e.g. Palsson, Systems Biology, (2006)

Genome-scale models: Flux Balance Analysis Reconstruction of significant fraction of metabolic pathways (e.g. Palsson, 2006) Explicit model of equilibrium fluxes e.g. Varma and Palsson (1994) predicts yield as function of substrate