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
J
F M A M J
J A
S O N D
month
G. Riley, J. Marine Res. 6, 54-73 (1946)
Riley’s mechanistic model
Rate of
change
growth
respiration
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)
grazing
Riley (1946)
nnu. Rev. Microbiol. 1949.3:371-394. Downloaded from arjournals.annualreviews.org
by California Institute of Technology on 09/17/07. For personal use only.
Annu. Rev. Mic
by Califo
Parameterization of growth
Monod (1942)
Riley’s mechanistic model
theoretical curve
observed
J
growth
respiration
F M A M J
grazing
J A
S O N D
Extending Riley’s model
Monod and Droop
kinetics
NPZ-type models
e.g. Steele (1958)
Phytoplankton
P
μ
N
Nutrient
g
Kr
Zooplankton
Z
Multiple resources, diverse populations
Functional group
models – multiple
phytoplankton types
P
P
e.g. Chai et al (2002),
Moore et al (2002)
Z
N
D
N1 N2
MOVIE – removed for compactness
Remotely
sensed
chlorophyll
NASA
MODIS
Comparison of remotely sensed and simluated
surface ocean chlorophyll
Ocean model
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)
o, Mick Follows, Stephanie Dutkiewicz, Sallie
Trade-offs define biogeography
Massachusetts Institute of Technology
Trade-offs for diazotrophy
Contact: mick@ocean.mit.edu
not dependent on fixed nitrogen
high iron quota
Model slow
Solutions:
Emergent
maximum growth
rate
Total phytoplankton biomass
(μmol P l-1)
Diazotroph Analogs
Total diazotroph biomass
(μmol P l-1)
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
Must be backwards compatible
1940s
Monod/
Redfield
fixed elemental
Ratios, 1 state
variable
1960s
Droop/Caperon
Internal stores
1970s
Shuter, McCarty
Macro-molecular
2000s
Metabolic
reconstruction, FBA
Flexible elemental ratios
Few state variables
Generalized framework for
heterotrophs/phototrophs
Prognostic elemental ratios
(Ecological Stoichiometry)
Model of Azotobacter
Vinelandii
Molecular
diffusion
O2
CO2
O2
CO2
“biomass”
C5H7O2N
• 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
pyruvate
NH4+
sucrose
N2
Keisuke Inomura
Laboratory data: continuous culture
Kuhle 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
fluxes
e.g. Varma and Palsson (1994)
predicts yield as function of
substrate