Playing scales in global biogeochemistry: linking … scales in global biogeochemistry: linking from...
Transcript of Playing scales in global biogeochemistry: linking … scales in global biogeochemistry: linking from...
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Playing scales in global biogeochemistry: linking from the bacterium to the biome
Josh SchimelUniv. California Santa Barbara
Mike Weintraub, UCSB
Jason Neff, Univ. Colorado Boulder
Corey Lawrence, Univ. Colorado Boulder
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The Antarctic Ozone Hole
One of the largest phenomena in global biogeochemistry
One of the biggest surprises
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Producing those ice particles requires unique weather patterns
ICE
HCl + ClONO2
HNO3
Cl2 2 Cl•
UV
Caused by chemistry on ice-particle surfaces
Coupling of micro- and meso-scale phenomena produces a global phenomenon
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Scale:
Extrapolation from point measurements to larger scales
Regional flux = rate x area
But, this is a model
All models are limited by their assumptions
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Scale:
Extrapolation from point measurements to larger scales
Drivers that operate at different scalesCross-scale linkages
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Species heterogeneity:
Eriophorum vaginatum
Vaccinium vitus-idea
Betula nana
Salix pulchra
Hylocomium splendens
Sphagnum spp.
Net Primary Production?
N mineralization?
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Micro-scale heterogeneity: Denitrification
Whole soil core:
Mass: 96 g
Total denitrification rate: 5100 ng N d-1
Single leaf fragment:
Mass: 0.08 g0.08% of mass
Total denitrification rate:4430 ng N d-1
85% of total denitrification
Parkin (1987)
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Micro-scale heterogeneity: Nitrogen turnover
Case A: Extremely N limitedArctic tundra
Relatively N richmicrosite
Relatively N poormicrosite
protein
protein
amino acids
amino acids
microbes
microbes
Schimel & Bennett 2004
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Key processes occur at different scales and they interact across scales.
⇒ Inter-scale
Case studies: Arctic climate system:
integrated experimental approach
Decompositionmodeling
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Arctic Climate System
ACIA Overview reportCambridge University Press, 2004
(c) Arctic Climate Impact Assessment
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Shrub tundraTussock tundra
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Changes in the Arctic affect the planet: linked feedback loops
Atmosphere
Vegetation
AlbedoEnergy balanceC-balance
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Ayiyak River: Increasing shrubs
Image: Sturm et al. 2001. Nature
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Changes in the Arctic affect the planet: Carbon cycle
Increased nutrient
availability
Plantgrowth
++
-Negativefeedback
loop
Increased nutrient
availability
Plantgrowth
++
-Negativefeedback
loop
CO2
Warming
Accelerated decomposition
+
++
Positivefeedback
loop
CO2
Warming
Accelerated decomposition
+
++
Positivefeedback
loop
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Environment changes stimulate birch growth
Fertilized Warming
Warming & fertilization
Bret-Harte et al. 2002 J. Ecol. 90: 251–267
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Cross-section of a Betula stem:effect of fertilization
Bret-Harte et al. 2002 J. Ecol. 90: 251–267
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Changes in the Arctic affect the planet: linked feedback loops
Atmosphere
Vegetation
AlbedoEnergy balanceC-balance
Soil
C-inputsNutrient supply
How do the belowground feedbacks function?
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What regulates decomposition and N supply rates in tundra soils?
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Evaluate bioavailable pools of soil C
Long-term incubation1 year, 20° C
Time
Res
pira
tion
rate
From recalcitrant pool
From active pool
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Evaluate bioavailable pools of soil C
Long-term incubation
Chemical fraction analysis
Sample
CH Cl2 2
H O2
NaClO /Acetate2
H SO2 4
Fats, oils, waxes
Solubles
“Lignin”
α-cellulose Hemicellulose
Chemical Fractionation Approach
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Long term soil carbon mineralization
C MINERALIZATION RATES at 20o C
Days0 100 200 300
ug C
/ g
Soi
l C /
day
0
500
1000
1500
2000 Shrub Tussock Tundra - Tussocks Tussock Tundra - InterTussock Wet Meadow 0-5 CUMULATIVE C
MINERALIZED: (mg C / g SOIL C)
355228297251
Large bioavailable poolsConstant respiration rates, except for shrub
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Long term soil nitrogen mineralization
LONG TERM N MINERALIZATION RATES
Days0 50 100 150 200 250 300 350
ug N
/ g
Soi
l N /
day
0
250
500
750
1000
1250
1500Shrub Tussock InterTussock
Shrub mineralizes N immediatelyTussock never mineralizes N
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Changes in chemical fractions
SHRUB TUNDRA
01020304050
WET MEADOW 0-5cm
% C
OM
POS
ITIO
N
01020304050
InitialFinal
TUSSOCK TUNDRATUSSOCKS
01020304050
TUSSOCK TUNDRA INTERTUSSOCK
% C
OM
PO
SITI
ON
01020304050
FATS, OILS,
& WAXES
TOTAL
SOLUBLES
ALPHA-
CELLULOSE
HEMI-
CELLULOSELIGNIN
SOIL ORGANIC MATTER FRACTIONS BEFORE AND AFTERONE YEAR INCUBATON AT 20o C
FATS, OILS,
& WAXES
TOTAL
SOLUBLES
ALPHA-
CELLULOSEHEM
I-
CELLULOSELIGNIN
InitialFinal
Little change in pools over incubationMaterial is already “old litter”
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SOM Conclusions:Tussock tundra (sedges and mosses)
Large inputs of simple ligno-cellulose:
● Bioavailable C is plentiful.● N is immobilized.
∴ N limits microbes
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SOM Conclusions:Shrub tundraLarge inputs of wood & small inputs of foliage:
● Lots of total C, but bioavailable C is limited.● C cycle dominated by turnover of labile pool.● N is mineralized.
∴ C limits microbes
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SOM Conclusions: shrub – nutrient feedback
Wood ↑N availability ↑
Shrubs ↑
Labile C ↓
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Changes in the Arctic affect the planet: Carbon cycle
Increased nutrient
availability
Plantgrowth
++
-Negativefeedback
loop
Increased nutrient
availability
Plantgrowth
++
-Negativefeedback
loop
CO2
Warming
Accelerated decomposition
+
++
Positivefeedback
loop
CO2
Warming
Accelerated decomposition
+
++
Positivefeedback
loop
+
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Changes in the Arctic affect the planet: linked feedback loops
Atmosphere
Vegetation
AlbedoEnergy balanceC-balance
Soil
C-inputsNutrient supply
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Mediterranean and arid systems
Dominated by pulse rain events
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What is the role of pulse events in biogeochemistry?
How do we model them?
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Source: http://www.nrel.colostate.edu/projects/century/
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CENTURY Model Structure:
Soil Organic Matter
CO2
dC/dt = k * Ck ⇒1st order rate constantC ⇒ Size of C pool
Assumption:Decomposer pools are constant ⇒ microbes, extra-cellular enzymes
Unlikely in a pulse-dominated environment!
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SOM models & pulse dynamics
Can 1st order models handle pulse-dominated ecosystems?
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California chaparral: DayCent Results
Net N mineralization (g m-2 month-1)Meas. Model Diff.
Early spring 0.21 0.35 67%Late spring 0.87 0.56 36%Summer 0.02 0.14 600%
1st orderfails
1st orderfails
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SOM models & pulse dynamics
Can we do better than 1st order models in pulse-dominated ecosystems?
LIGHT
DOC
HEAVY
CO2
MICROBES
First Order Model
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Mechanism-based model: exoenzymes
Bio-Available
DOC
LIGHT
DOC
HEAVY
CO2
MICROBES
ENZYMES
Exoenzyme Catalyzed
Decomposition is enzyme catalyzed:
= Kd * DOC * (Enz/(Ke+Enz))
Uptake is Michaelis-Menton :
= Kup * Mic * (BAD/(Kb+BAD))
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Laboratory Rewetting Experiment
• Chaparral soils incubated with regular drying/rewetting cycles (4 week cycle).
• CO2 efflux measured on a daily interval
*Miller and Schimel, In Press
0.00
0.01
0.02
0.03
0.04
0.05
0.06
10 30 50 70 90 110
Experiment Day
CO
2 Effl
ux (g
C /
m2 )
Measured
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Results: First Order Model
0.00
0.01
0.02
0.03
0.04
0.05
0.06
10 30 50 70 90 110
Experiment Day
CO
2 Effl
ux (g
C /
m2 )
MeasuredFO Model
R2 = 0.60 % CO2 Flux = 117.6
Magnitude: marginal
Timing: marginal
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Results: First Order Model
R2 = 0.85 % CO2 Flux = 64.3
0.00
0.01
0.02
0.03
0.04
0.05
0.06
10 30 50 70 90 110
Experiment Day
CO
2 Effl
ux (g
C /
m2 )
MeasuredFO Model
Magnitude: poor
Timing: good
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Results: Exoenzyme Catalyzed
R2 = 0.84 % CO2 Flux = 96.9
0.00
0.01
0.02
0.03
0.04
0.05
0.06
10 30 50 70 90 110
Experiment Day
CO
2 Effl
ux (g
C /
m2 )
MeasuredEC Model
Magnitude: excellent
Timing: excellent
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Model sensitivity:Models are most sensitive to microbial parameters
First Order ModelTiming: Microbial turnover rateMagnitude: DOC Turnover Rates (Kd)
Respiration Efficiency (Re)
Enzyme Catalyzed ModelTiming: Enzyme turnover rateMagnitude: Maximum uptake rate (Kup)
Microbial Turnover (Km)
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Modeling conclusionsTraditional SOM models are “single-scale,” and they do poorly at capturing pulse events.
Even a simple “interscale model,” that incorporates microbial mechanisms captured pulse events.
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Overall conclusions“Surprises” are often the result of inter-scale phenomena.
Explaining and anticipating such surprises requires inter-scale approaches:Experiments and modeling.