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Arrhenius Kinetics Arrhenius Kinetics

Arrhenius Kinetics - PowerPoint Presentation

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Arrhenius Kinetics - PPT Presentation

Reaction Velocity A e Ea RT where A preexponential factor or y intercept Ea activation energy of the substrate R universal gas constant T temperature o K Boone et al 2003 Nature 396570572 ID: 476405

microbial soil substrate temperature soil microbial temperature substrate reaction energy respiration cue efficiency global nature 2010 warming climate supply

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Slide1
Slide2
Slide3

Arrhenius Kinetics

Reaction Velocity = A e

-

Ea/RT where,

A = pre-exponential factor, or y-interceptEa = activation energy of the substrateR = universal gas constantT = temperature, oKSlide4

Boone et al (2003) Nature 396:570-572.

Figure 1:

Season time course of soil respiration.

Figure 2.

Relationship between soil temperature and rate of soil respiration. The different experimental treatments reflect modifications to the rate of substrate supply.Slide5

In the above energy diagram for the reaction

A

B we have the following features:

1.Overall, the reaction is energetically favorable. In other words, the product, B, is at a lower energy level than the reactant, A. Energetically, the reaction will proceed with a net release of energy (i.e. goes downhill energetically as it goes from A

B)

2. However

, for the reaction to proceed, there is an activation energy barrier that molecule A will have to overcome.Molecules of A will have to acquire enough energy to overcome Ea in order for the reaction to proceed. This energy will come from the kinetic energy associated with molecular collisions

EnergySlide6

Craine

et al (2010) Nature Geoscience 3:854-857

Widespread coupling between the rate and temperature sensitivity of organic matter

decay

High Ea decomposes slowly

Low

Ea

decomposes rapidly

R20 = microbial respiration rate @ 20 oCSlide7

Issue of substrate supply

Low to highSlide8

What processes affect heterotrophic respiration?

activation energy of the substrate (e.g.,

Craine

et al. 2010) soil temperature & moisture (e.g., Lloyd & Taylor 1994

substrate supply

(e.g., Davidson and

Janssens

2006) O2 concentration (e.g., Skopp

et al. 1990)

C-use efficiency (e.g., Allison et al. 2010)

sorption – desorption dynamics (e.g.,

Hinsinger

2001)

substrate

supply

O

2

concentration

max. rate

of reaction

double

Michaelis-MentonfunctionSlide9

What processes affect heterotrophic respiration?

activation energy of the substrate

(e.g.,

Craine et al. 2010)

soil temperature

& moisture

(e.g., Lloyd & Taylor 1994

substrate supply (e.g., Davidson and Janssens 2006) O2 concentration (e.g.,

Skopp

et al. 1990)

C-use efficiency (e.g., Allison et al. 2010)

sorption – desorption dynamics (e.g.,

Hinsinger

2001)

substrate

supply

O

2

concentration

max. rate

of reaction

doubleMichaelis-Menton

function

Arrhenius

function

reaction rate increases with TSlide10

A simple test: predicting exoenzyme activity [Davidson et al.

2011

]

known substrate concentrations

constant temperature during incubation

36.5

o

C

27.5

oC4.5

o

C

12.7

o

C

23.7

o

C

aaaaa

50

40

30

20

10

0

reaction velocity

(

μmol

hr

-1

)

0.04

0.03

0.02

0.01

0

reaction velocity

(

μmol

hr

-1

)

substrate concentration [

Sx

]

0 20000 40000 60000 80000 100000 120000

0 20000 40000 60000 80000 100000

substrate concentration [

Sx

]Slide11

A complex test: predicting heterotrophic respiration in a trenching expt.

at the Harvard Forest [Davidson et

al. 2011]

substrate concentration @ reaction site

O

2

concentration @ reaction site

Sx

total

=soil C content

p

= solubility fraction

D

liq

=

difussivity

in water

Θ

= soil moisture

a

=air filled

porosity

BD=bulk density

PD=particle density

D

gas

=diffusivity of O

2

in airSlide12

A complex test: predicting heterotrophic respiration in a trenching expt.

at the Harvard Forest [Davidson et al.

2011

]

observations

model

model

w

/seasonality

by allowing variation

in

α

Sx

of

Vmax

Sx

=

α

Sx

X

e

-EaSx/RTSlide13
Slide14
Slide15

Wieder

et al. 2013 Nature

Climate

Change 3:909-912 Global soil carbon projections are improved by modelling microbial processesdoi:10.1038/nclimate1951

Observations, global total = 1,259 Pg C. b, CLM4cn, global total = 691 Pg C (spatial correlation with observations (r) = 0.55, model-weighted root mean square error (r.m.s.e) = 7.1 kg C m−2). c, DAYCENT, global total = 939 Pg C (r = 0.53, r.m.s.e = 7.6). d, The CLM microbial model, global total = 1,310 

Pg

 C (r = 0.71,

r.m.s.e

 = 5.3).Slide16

Tarnocai

et al. 2009 Global Biogeochemical Cycles 0-30cm

191

Total=3224x1015gC

Circumarctic permafrost region 0-100cm 496 ~32% of global total 0-300cm 1024Slide17

Microbial C-use Efficiency:

an emerging topic in terrestrial biogeochemistry

Figure Source:

Schimel

and Weintraub (2003) Soil Biology and BiochemistrySlide18

Microbial C-use Efficiency:

an emerging topic in terrestrial biogeochemistry

Melillo

et al (2003) Science 13:2173-2176 Soil Warming and Carbon-Cycle Feedbacks to the Climate SystemSlide19

Figure 1.

Soil

samples were collected from control plots at two soil warming studies at the Harvard Forest LTER site, amended with one of four substrates (glucose, glutamic acid, oxalic acid or phenol) and incubated at 5, 15 or 25 °C. Error bars represent one standard error.

direct uptake,

not temperature sensitivehigh efficiency

direct uptake

not temperature sensitive

low efficiency

indirect uptake via extracellular decomposition

temperature sensitiveEfficiency decreases with increasing temperature& molecular complexity [

Ea

]

30% decrease

60% decrease

Frey et al. (2013) Nature Climate Change 3:395-398

The temperature response of soil microbial efficiency and its feedback to

climateSlide20

Frey et al. (2013) Nature Climate Change 3:395-398

The temperature response of soil microbial efficiency and its feedback to

climate

Two years following there is little change in the microbial CUE of phenol in warmed compared to control plots

18 years following experimental warming, phenol CUE “acclimates” in treatment relative to control plots* shifts in microbial physiology

* shifts in microbial community compositionSlide21

Allison et al (2010) Nature Geoscience 3:336 – 340

Soil

-carbon response to warming dependent on microbial

physiology

Model simulates temperature sensitivity of microbial [growth, CUE] and exoenzyme activitySlide22

CUE

w/T when respiration more sensitive to T than biomass production

Soil studies suggest CUE declines by at least 0.016 oC-1

Model Simulation [+5 o

C

]

Warming + varying CUE

CUE declines 0.31 to 0.23Warming + constant CUE CUE remains at 0.31Warming + acclimation thermal acclimation of microbial respiration

[

evolutionary adaptation, community

shifts

and

physiological

changes]

simulated by reducing T

sensitivity

of CUEAllison et al (2010) Nature Geoscience 3:336 – 340Soil-carbon response to warming dependent on microbial physiology

M o d e l D y n a m i c s microbial enzyme prod./ respiration biomass activity SOC small transient

 

large transient  

30%

long term - substrate limitation via SOC depletion

intermediate

15%

transientSlide23