MMI meeting, March 2013 Mick Follows

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

2. Observed seasonal variation of phytoplankton at Georges Bank Phytoplankton, B J F M A M J J A S O N D month G. Riley, J. Marine Res. 6, 54-73 (1946)

3. 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)

4. Parameterization of growth Riley (1946) Monod (1942)

5. Riley’s mechanistic model theoretical curve observed Phytoplankton, B J F M A M J J A S O N D growth respiration grazing

6. Extending Riley’s model Phytoplankton • Monod and Droop kinetics • NPZ-type models • e.g. Steele (1958) g P μ Kr N Z Nutrient Zooplankton

7. Multiple resources, diverse populations • Functional group models – multiple phytoplankton types • e.g. Chai et al (2002), Moore et al (2002) P P Z N D N1 N2

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

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

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

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

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

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

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

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

16. Why do diazotrophs grow so slowly? • Why do nitrogen fixers grow slowly?

17. 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

18. Must be backwards compatible 1940s 1960s 1970s 2000s Monod/ Droop/CaperonShuter, 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)

19. Model of AzotobacterVinelandii • Nitrogen fixing soil bacteria • Conserve internal fluxes of mass, electrons and energy • McCarty (1965), Vallino et al (1996) … • Biophysical model of substrate and O2 uptake • Pasciak and Gavis (1974), Staal et al (2003), … • Demand intra-cellular O2 ~ 0 O2 CO2 Molecular diffusion O2 CO2 “biomass” C5H7O2N pyruvate NH4+ sucrose N2 Keisuke Inomura

20. Laboratory data: continuous culture Kuhle and Oetze (1988) Model (Keisuke Inomura) [O2] Low yields in oxygenated medium Slow growth rates if substrate limited

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

22. 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