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
The Antarctic Ozone Hole
One of the largest phenomena in global biogeochemistry
One of the biggest surprises
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
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
Scale:
Extrapolation from point measurements to larger scales
Drivers that operate at different scalesCross-scale linkages
Species heterogeneity:
Eriophorum vaginatum
Vaccinium vitus-idea
Betula nana
Salix pulchra
Hylocomium splendens
Sphagnum spp.
Net Primary Production?
N mineralization?
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)
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
Key processes occur at different scales and they interact across scales.
⇒ Inter-scale
Case studies: Arctic climate system:
integrated experimental approach
Decompositionmodeling
Arctic Climate System
ACIA Overview reportCambridge University Press, 2004
(c) Arctic Climate Impact Assessment
Shrub tundraTussock tundra
Changes in the Arctic affect the planet: linked feedback loops
Atmosphere
Vegetation
AlbedoEnergy balanceC-balance
Ayiyak River: Increasing shrubs
Image: Sturm et al. 2001. Nature
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
Environment changes stimulate birch growth
Fertilized Warming
Warming & fertilization
Bret-Harte et al. 2002 J. Ecol. 90: 251–267
Cross-section of a Betula stem:effect of fertilization
Bret-Harte et al. 2002 J. Ecol. 90: 251–267
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?
What regulates decomposition and N supply rates in tundra soils?
Evaluate bioavailable pools of soil C
Long-term incubation1 year, 20° C
Time
Res
pira
tion
rate
From recalcitrant pool
From active pool
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
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
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
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”
SOM Conclusions:Tussock tundra (sedges and mosses)
Large inputs of simple ligno-cellulose:
● Bioavailable C is plentiful.● N is immobilized.
∴ N limits microbes
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
SOM Conclusions: shrub – nutrient feedback
Wood ↑N availability ↑
Shrubs ↑
Labile C ↓
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
+
Changes in the Arctic affect the planet: linked feedback loops
Atmosphere
Vegetation
AlbedoEnergy balanceC-balance
Soil
C-inputsNutrient supply
Mediterranean and arid systems
Dominated by pulse rain events
What is the role of pulse events in biogeochemistry?
How do we model them?
Source: http://www.nrel.colostate.edu/projects/century/
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!
SOM models & pulse dynamics
Can 1st order models handle pulse-dominated ecosystems?
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
SOM models & pulse dynamics
Can we do better than 1st order models in pulse-dominated ecosystems?
LIGHT
DOC
HEAVY
CO2
MICROBES
First Order Model
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))
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
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
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
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
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)
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.
Overall conclusions“Surprises” are often the result of inter-scale phenomena.
Explaining and anticipating such surprises requires inter-scale approaches:Experiments and modeling.