Modeling Marine Microbial Communities
and Biogeochemical Cycles
Mick Follows, Stephanie Dutkiewicz, Andrew Barton,
Fanny Monteiro, Jason Bragg, Sallie Chisholm, Scott
Grant, Oliver Jahn, Chris Hill
Outline
• Marine microbes and carbon cycle
– Production and fate of organic carbon
• Modeling marine phytoplankton
– “Self-assembling” model communities
– Simulations and interpretations of Prochlorococcus
biogeography
• Traits and trade-offs at cellular scale
– Parameterizations of microbial physiology
Chl-a estimated from remote ocean color
observations (MODIS)
Chlorophyll a estimated from space
Biologically driven downwards transfer of
nutrient elements (C, N, P, Fe, ...)
Photosynthesis &
production of sinking
organic particles
Respiration and remineralization by
zooplankton & heterotrophic
microbes
• Sinking flux of particulate
organic carbon through
water column
Martin et al (1987)
Chl-a estimated from remote ocean color
observations (MODIS)
Chlorophyll a estimated from space
Vast functional and taxonomic diversity
Community composition
influences
– export
– DMS production
– calcification
– nitrogen fixation
– ...
Atlantic Meridional
Transect (AMT):
Aiken et al. (2000)
- diatoms
- coccolithophorids
– pico-cyanobacteria
- Prochlorococcus
- Synechococcus
Fine-scale diversity:
ecotypes of Prochlorococcus
Johnson et al.
(2006)
• Micron-size cyanobacteria
• (Cultured strains) cannot utilize nitrate
• 6 genetically identified ecotypes
• Corresponding physiological differences
Global ocean carbon cycle and climate
models would like to represent...
• Phototrophs
– Who is living where and why?
– What is their impact on export and nutrient cycles?
• Sinking/ballasting, elemental composition, nitrogen fixation, DMS
production, calcification...
• Heterotrophic microbes
– Who is living where and why?
– Remineralization of organic nutrients/carbon storage
• Other issues:
– Chemo-autotrophy
– Prevalance of mixotrophy in surface ocean
genome
Physiological information:
Mapping metabolic pathways
Ecological information:
Mapping environmental
distributions of cell types and
biogeochemical function
dP
= P−K −gZ 
dt
rate of
change
growth respiration grazing
Phytoplankton abundance
Modeling Marine Phytoplankton:
Gordon Riley (J. Marine Res. 1946)
J F M A M J J
A S O N D
Modeling Marine Phytoplankton:
Riley (1946)
dP
= P−K −gZ 
dt
rate of
change
growth respiration grazing
Large scale ocean models:
Monod kinetics, fixed elemental ratios
N1
N2
 = o f  I  f T  min[
,
]
N 1 K N1 N 2 K N2
Representing diverse phenotypes
• Presence/absence of function
• Values of parameters which set rates or
efficiencies for each organism type
dP 1
=P 1 1− K 1− g 1 Z 
dt
dP 2
= P 2 2 −K 2− g 2 Z 
dt
Organization of diverse microbial
communities
physical and
chemical
environment
genetics and
physiology
fitness
and
selection
ecosystem structure
and function
“Everything is everywhere but the environment selects” – Baas Becking (1934)
Organization of diverse microbial
communities
physical and
chemical
environment
genetics and
physiology
fitness
and
selection
dispersal and
adaptation
ecosystem structure
and function
Feedback on
environment
Governing principle for ocean model
environment
physiology
Fitness &
selection
Many 10s of initialized
phytoplankton types.
Stochastically assigned
physiological characteristics
2 grazers
Follows et al. (2007)
Dutkiewicz et al. (2009)
Monteiro et al. (2010)
Barton et al (2010)
Global 3d ocean circulation
model
N, P, Fe, Si cycles
ecosystem structure
and function
Physiological characteristics
High light
Low light
small
large
Si NO3 NO2 NH4 N2 fix
Stochastically
assigned
Simple, size-based
trade-offs
Global simulation: chlorophyll
• ECCO2 configuration of MIT ocean circulation model
• 18km resolution globally
• 78 initialized phytoplankton types
Prognostic Equations
Nutrients:
DN i
=−∑ [ μ j γ Tj γ Ij γ j P j Rij ] +S i
Dt
j
Phytoplankton:
s
DP j
w
Pj
j
T I
mP
=μ j γ j γ j γ j P j Rij − P j −k j P j−∑ g jk
Z k,i=1
g
Dt
Δz
P j +k jk
k
Zooplankton:
DZ ki
Dt
=Z ki ∑ g jk
j
Pj
P j +k gjk
Rij −k mZ
k Z ki
+ DOM, POM …
Nutrient limitation
Temperature sensitivity
Light sensitivity
γ j =min
[
Γ ij N 1
N 1 +k 1jN1
,
Γ ij N 2
N 2 +k N2
2j
, .. .
γ Tj =C1 exp  C 2 ΔT −C 3 ΔT β 
I
γ j =1−exp −C 4 I  
]
ΔT=T −T opt
3/ 2
exp −C 5 I  
Final biomass of 78 initialized “genotypes”
Global biomass
Diversity
rank of global abundance
• Global biomass dominated by ~20 phytoplankton types
• Many types at very low abundance
Biogeography: “functional groups”
Biomes and biodiversity
• Emergent
Longhurst-like
provinces
• “Speciesrichness”
– Meridional
gradient
– “hot spots”
Barton et al (2010),
Follows and Dutkiewicz (2011)
Prochlorococcus ecotypes on AMT 13
Emergent analogs of
Prochlorococcus
have appropriate
•
Habitat
•
Abundance rank
•
Physiological
specialisms
Observed Prochlorococcus
ecotypes - AMT13
(Johnson et al., 2006)
Follows et al. (2007), Dutkiewicz et al (2009), Monteiro et al (2010)
Model-ecotypes
log(cells ml-1)
Optimum T and I for growth
Io p t
To p t
Physiological characteristics of emergent
Prochlorococcus analogs
Cold adapted analogs of
Prochlorococcus enabled
but not viable
•
Light and temperature
sorting of analogs
consistent with observed
ecotypes
•
Consistent clustering in
parameter space over
ensemble of 10
integrations
•
“MIT9312”
“MIT9313”
“NATL2”
“MED4”
Organization in trait space
Initialized organism types
final organism types
Looking forwards...
• Constraining traits and trade-offs
– Sensible constraints on possible trait combinations
– Energy and elemental allocation at individual scale
– allometry
• Adaptive models
Physiological parameterizations
1940s
Monod
Fixed elemental ratios
1960s
Droop
internal stores
1980s
Macromolecular
composition/energetics
2000s ===>
Metabolic networks
Detailed trait models vs
computational cost
• Major computational cost advection-diffusion of
state variables
• Monod, fixed elemental ratios = 1 state variable
per cell type
Elemental and energetic constraints
on the individual
• Heterotrophy and autotrophy
• Explicit energetic constraints
• Dynamic elemental composition of
biomass
• Additional allometric (biophysical)
constraints
• O(10) state variables per cell type –
computationally tractable
• Basic framework to “plug-in” more
detailed metabolic networks, protein
allocation, trace metals...
c.f. Algal models of Geider et al (1997), Shuter (1979), Klausmeier et al., (2005), Pahlow and
Oschlies (2009), Kooijman (2001 – DEB). Numerous bioenergetic models of heterotrophic
bacteria.
Simple case: heterotrophic respiration
and growth rate in continuous culture ...
q
Oliver Jahn, Jason Bragg
D
Summary
• Key goals for global ocean biogeochemical models: Dynamic
representation of
– Community and response of primary producers
– Community and response of heterotrophs
• Stochastic models with “self-assembling communities
– Potentially useful tool
– Self-tuning
– Dynamic climate response
• Descriptions of cell physiology extremely crude
– Resolving macromolecular composition brings elemental and
energy constraints at cellular scale
Respiration and growth rate
Resolving macromolecular composition,
elemental and energy flow
• Explicit energy constraints
• Macromolecular elemental
composition prescribed
• Whole cell elemental
composition dynamic
• Numerous models of
similar nature published in
bio-engineering literature