MMI meeting, March 2013Mick Follows

How do ocean ecosystem models work?Applications and links to ‘omics-based observationsPhysiological sub-models

Observed seasonal variation of phytoplankton at Georges Bank

G. Riley, J. Marine Res. 6, 54-73 (1946)

Phyt

opla

nkto

n, B

J F M A M J J A S O N D month

Riley’s mechanistic model

Rate of growth respiration grazingchange

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

Phyt

opla

nkto

n, B theoretical curve

observed

Extending Riley’s modelMonod and Droop kineticsNPZ-type models

e.g. Steele (1958)

N

P

Z

μKr

gPhytoplankton

Nutrient Zooplankton

Multiple resources, diverse populations

P

N

P

Z

D

N1 N2

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 diversityData 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 diazotrophynot dependent on fixed nitrogenhigh iron quotaslow maximum growth rate

Ocean model Fanny Monteiro

InterpretationResource 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 modelsFor biogeochemical modeling purposes we would like:

Flexible and prognostic elemental ratiosMechanistic understanding/parameterizations of population growth ratesRelatively few state variables for computational tractability

1940s 1960s 1970s 2000sMonod/ Droop/Caperon Shuter, McCarty MetabolicRedfield Internal stores Macro-molecular reconstruction, FBA

Flexible elemental ratiosFew state variablesGeneralized framework for heterotrophs/phototrophs

fixed elementalRatios, 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 O2 uptake• Pasciak and Gavis

(1974), Staal et al (2003), …

• Demand intra-cellular O2 ~ 0

Keisuke Inomura

pyruvate

“biomass”

sucroseNH4+

O2

CO2

O2

CO2

N2

C5H7O2N

Moleculardiffusion

Laboratory data: continuous cultureKuhle and Oetze (1988) Model (Keisuke Inomura)

[O2]

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 fluxese.g. Varma and Palsson (1994)

predicts yield as function of substrate