estimation of bacterial growth efficiency by a coupled ... · estimation of bacterial growth...
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Estimation of bacterial growth efficiency by a coupled experimental-modelling approach
Influence of substrate regime
Brest 20-22 April 2009DEB Symposium
M. Eichinger, S.A.L.M. Kooijman, R. Sempéré, D. Lefèvre, G. Grégori, B. Charrière and J.C. Poggiale
The oceanic carbon cycle
Air-sea interface
nutrients
CO2
phytoplankton zooplankton
heterotrophicflagellates
ciliatesbacteriaDOC
Brest 20-22 April 2009DEB Symposium
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The oceanic carbon cycle
Air-sea interface
nutrients
CO2
phytoplankton zooplankton
heterotrophicflagellates
ciliatesbacteriaDOC
Brest 20-22 April 2009DEB Symposium
Dissolved organic carbon(DOC)
2nd stock of bioreactive carbon in the oceans (Williams & Druffel 1987)
3 pools (Kirchman et al. 1993):
• Labile DOC (L-DOC)• Semi-labile DOC (SL-DOC)• Refractory DOC (R-DOC)
Heterotrophic bacteria
Major DOC consumers (Pomeroy 1974)
DOC producers (Kaiser and Benner 2008)
≈ 50% of total respiration in ocean interior (del Giorgio & Duarte 2002)
Central component of the microbial loop (Legendre & Rassoulzadegan 1995)
The oceanic carbon cycle
Brest 20-22 April 2009DEB Symposium
Dissolved organic carbon(DOC)
2nd stock of bioreactive carbon in the oceans (Williams & Druffel 1987)
3 pools (Kirchman et al. 1993):
• Labile DOC (L-DOC)• Semi-labile DOC (SL-DOC)• Refractory DOC (R-DOC)
?
biomasssubstrate
BGE∆=∆
L-DOCBGE
(1-BGE)
CO2
biomassgrowth
respirationHeterotrophic bacteria
Major DOC consumers (Pomeroy 1974)
DOC producers (Kaiser and Benner 2008)
≈ 50% of total respiration in ocean interior (del Giorgio & Duarte 2002)
Central component of the microbial loop (Legendre & Rassoulzadegan 1995)
BGE = bacterial growth efficiency
quantifies carbon fluxes through bacterioplankton
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The oceanic carbon cycle
Brest 20-22 April 2009DEB Symposium
Dissolved organic carbon(DOC)
2nd stock of bioreactive carbon in the oceans (Williams & Druffel 1987)
3 pools (Kirchman et al. 1993):
• Labile DOC (L-DOC)• Semi-labile DOC (SL-DOC)• Refractory DOC (R-DOC)
?Heterotrophic bacteria
Major DOC consumers (Pomeroy 1974)
DOC producers (Kaiser and Benner 2008)
≈ 50% of total respiration in ocean interior (del Giorgio & Duarte 2002)
Central component of the microbial loop (Legendre & Rassoulzadegan 1995)
BGE = bacterial growth efficiency
quantifies carbon fluxes through bacterioplankton
BGE varies greatly according to numerous factors:
• chemical nature (Cherrier et al. 1996)
• molecular weight (Amon and Benner 1996)
• elemental ratios (Goldman et al. 1987)
• distance from seashore (La Ferla et al. 2005)
• season (Reinthaler & Herndl 2005)
• temperature (Rivkin & Legendre 2001)
• UV exposure (Abboudi et al. 2007)
biomasssubstrate
BGE∆=∆
BUT considers as constant in biogeochemical models!!
Objectives
Brest 20-22 April 2009DEB Symposium
Influence of DOC availability on BGE estimationConsequences for ecosystem modelling
• DOC degradation experiments: one load vs pulsed load
• construction of a DEB model
• comparison with empirical models
• BGE estimation
• impact for biogeochemical modelling
4
Objectives
Brest 20-22 April 2009DEB Symposium
Influence of DOC availability on BGE estimationConsequences for ecosystem modelling
• DOC degradation experiments: one load vs pulsed load
• construction of a DEB model
• comparison with empirical models
• BGE estimation
• impact for biogeochemical modelling
DOC degradation experiments
Experiment BBatch
Experiment PPulsed
Mimic natural DOC dynamicTypical experiment
Same experimental conditionsSame total amount of substrate
Nutrients in excess
unique L-DOC source:Pyruvic acid
unique bacterial strain:Alteromonas infernus
Effects of substrate regime on DOC and bacterial dynamics?
Brest 20-22 April 2009DEB Symposium
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DOC degradation experiments
measurements
Experiment BBatch
Experiment PPulsed
Same experimental conditionsSame total amount of substrate
Nutrients in excess
BGE
models
processes
Mimic natural DOC dynamicTypical experiment
unique L-DOC source:Pyruvic acid
unique bacterial strain:Alteromonas infernus
Effects of substrate regime on DOC and bacterial dynamics?
Brest 20-22 April 2009DEB Symposium
DOC degradation experiments
DOC = dissolved organic carbonPOC = particulate organic carbon (bacterial biomass)
Brest 20-22 April 2009DEB Symposium
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DOC degradation experiments
[DOC] substrate (L-DOC) consumption[POC] bacterial growth
[DOC] unlabile DOC production[POC] or bacterial reduction
growth
reduction
1 model
Brest 20-22 April 2009DEB Symposium
Objectives
Brest 20-22 April 2009DEB Symposium
Influence of DOC availability on BGE estimationConsequences for ecosystem modelling
• DOC degradation experiments: one load vs pulsed load
• construction of a DEB model
• comparison with empirical models
• BGE estimation
• impact for biogeochemical modelling
7
Construction of a DEB model
• 3 state variablesL = L-DOCME = reserve massMV = structural body mass
• 3 processes
Brest 20-22 April 2009DEB Symposium
Case 1: growth
MEassimilation
maintenance
growthMV
L
Construction of a DEB model
• 3 state variablesL = L-DOCME = reserve massMV = structural body mass
• 3 processes
Kooijman 2000Tolla et al. 2007
Brest 20-22 April 2009DEB Symposium
Case 1: growth
MEassimilation
maintenance
growthMV
L
Case 2: reduction
MEassimilation
maintenance
growth
L MV
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Construction of a DEB model
productionR-DOC
Case 2: reduction
Case 1: growth
ME
Lassimilation
maintenance
maintenance
• 4 state variablesL = L-DOCR = unlabile-DOCME = reserve massMV = structural body mass
• 4 processes
V EPOC M M
DOC L R
= += +
Eichinger et al., accepted
Brest 20-22 April 2009DEB Symposium
growthMV
MEassimilation
maintenance
growth
L MV
2 new parameters:
MVj
RVy
maintenance from structure
yield coefficient from structure to unlabile-DOC
Objectives
Brest 20-22 April 2009DEB Symposium
Influence of DOC availability on BGE estimationConsequences for ecosystem modelling
• DOC degradation experiments: one load vs pulsed load
• construction of a DEB model
• comparison with empirical models
• BGE estimation
• impact for biogeochemical modelling
9
Comparison with empirical models
Brest 20-22 April 2009DEB Symposium
MonodMarr-PirtDEB
reserve2 maintenances (ME + MV)R-DOC production
1 maintenance (MV)R-DOC production
Processes
Special cases of the DEB modelwith particular parameter values
Comparison with empirical models
Brest 20-22 April 2009DEB Symposium
MonodMarr-PirtDEB
Pulse
Batch
reserve2 maintenances (ME + MV)R-DOC production
1 maintenance (MV)R-DOC production
Processes
10
Comparison with empirical models
Brest 20-22 April 2009DEB Symposium
MonodMarr-PirtDEB
Pulse
Batch
reserve2 maintenances (ME + MV)R-DOC production
1 maintenance (MV)R-DOC production
Processes
Comparison with empirical models
Brest 20-22 April 2009DEB Symposium
+ original data+ original data – R-DOC
MonodMarr-PirtDEB
Pulse
Batch
reserve2 maintenances (ME + MV)R-DOC production
1 maintenance (MV)R-DOC production
Processes
11
Objectives
Brest 20-22 April 2009DEB Symposium
Influence of DOC availability on BGE estimationConsequences for ecosystem modelling
• DOC degradation experiments: one load vs pulsed load
• construction of a DEB model
• comparison with empirical models
• BGE estimation
• impact for biogeochemical modelling
BGE estimation
Brest 20-22 April 2009DEB Symposium
biomasssubstrate
BGE∆=∆
MonodMarr-PirtDEB
VN
dMBGE
dL=
−
V
VN V
dLL M
dtdM
BGE L Mdt
α
α
= −
=
V VMN
dM jBGE
dL Lα= −
−
V
VN V VM V
RV VM V
dL L MdtdM BGE L M j M
dtdR y j Mdt
α
α
= −
= −
=
( )E EV V EL EM
EV EV
d M y M y j
dL y y Lα+
= −−
V
EEL V EM V
E E EM VEV V
E EV V
E E EM VVV
E EV V
dL L MdtdM y L M j M
dtk M j M
y MM y M
k M j MdM Mdt M y M
α
α
= −
= −
−− +−= +
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BGE estimation
Brest 20-22 April 2009DEB Symposium
biomasssubstrate
BGE∆=∆
MonodMarr-PirtDEB
VN
dMBGE
dL=
−
V
VN V
dLL M
dtdM
BGE L Mdt
α
α
= −
=
V VMN
dM jBGE
dL Lα= −
−
V
VN V VM V
RV VM V
dL L MdtdM BGE L M j M
dtdR y j Mdt
α
α
= −
= −
=
( )E EV V EL EM
EV EV
d M y M y j
dL y y Lα+
= −−
V
EEL V EM V
E E EM VEV V
E EV V
E E EM VVV
E EV V
dL L MdtdM y L M j M
dtk M j M
y MM y M
k M j MdM Mdt M y M
α
α
= −
= −
−− +−= +
BGE estimation
Brest 20-22 April 2009DEB Symposium
MonodMarr-PirtDEB
VN
dMBGE
dL=
−
V
VN V
dLL M
dtdM
BGE L Mdt
α
α
= −
=
V VMN
dM jBGE
dL Lα= −
−
V
VN V VM V
RV VM V
dL L MdtdM BGE L M j M
dtdR y j Mdt
α
α
= −
= −
=
( )E EV V EL EM
EV EV
d M y M y j
dL y y Lα+
= −−
V
EEL V EM V
E E EM VEV V
E EV V
E E EM VVV
E EV V
dL L MdtdM y L M j M
dtk M j M
y MM y M
k M j MdM Mdt M y M
α
α
= −
= −
−− +−= +
biomasssubstrate
BGE∆=∆
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Brest 20-22 April 2009DEB Symposium
BGE estimation
0.340.380.23Pulse
0.210.200.14Batch
DEBMarr-PirtMonodExperiment
BGE for model including maintenance are higher than without maintenance
Brest 20-22 April 2009DEB Symposium
BGE estimation
BGE for model including maintenance are higher than without maintenance
BGE estimated from the Marr-Pirt and DEB models are equivalent
0.340.380.23Pulse
0.210.200.14Batch
DEBMarr-PirtMonodExperiment
14
Brest 20-22 April 2009DEB Symposium
BGE estimation
BGE for model including maintenance are higher than without maintenance
BGE estimated from the Marr-Pirt and DEB models are equivalent
BGE estimated experimentally and from the Monod model are equivalent
0.340.380.230.27Pulse
0.210.200.140.14Batch
DEBMarr-PirtMonodExperimentalExperiment
Brest 20-22 April 2009DEB Symposium
BGE estimation
BGE for model including maintenance are higher than without maintenance
BGE estimated from the Marr-Pirt and DEB models are equivalent
BGE estimated experimentally and from the Monod model are equivalent
Bacterial growth on pulse load is more efficient
0.340.380.230.27Pulse
0.210.200.140.14Batch
DEBMarr-PirtMonodExperimentalExperiment
15
Objectives
Brest 20-22 April 2009DEB Symposium
Influence of DOC availability on BGE estimationConsequences for ecosystem modelling
• DOC degradation experiments: one load vs pulsed load
• construction of a DEB model
• comparison with empirical models
• BGE estimation
• impact for biogeochemical modelling
Brest 20-22 April 2009DEB Symposium
Impact for biogeochemical modelling
CO2
phytoplankton zooplankton
heterotrophicflagellates
ciliates
nutrients
Air-sea interface
bacteriaDOC
• Bacterial DOC production
new DEB bacterial modelonly 2 more parameters
16
Brest 20-22 April 2009DEB Symposium
Impact for biogeochemical modelling
• BGE (pulse) > BGE (batch)
link to the trophic chain
CO2
phytoplankton zooplankton
heterotrophicflagellates
ciliates
nutrients
Air-sea interface
bacteriaDOC
• Bacterial DOC production
• BGE (pulse) > BGE (batch)
incorporation of a better DOC dynamic → higher BGE
Brest 20-22 April 2009DEB Symposium
Impact for biogeochemical modelling
• BGE (maintenance) > BGE (without maintenance)
maintenance was experimentally demonstrated
under-estimation of the BGE with the typical methods ?
over-estimation of bacteria as CO2 producer ?
should be taken into account in biogeochemical models (Eichinger et al., accepetd)
impact on the carbon cycle ?
17
Brest 20-22 April 2009DEB Symposium
Thank you for your attention !!!