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.