Can microbial functional traits predict the response and resilience of

decomposition to global change?

Steve AllisonUC Irvine

Ecology and Evolutionary BiologyEarth System Science

allisons@uci.edu

Project goals

• Determine how microbial taxa respond to reduced precipitation and increased N

• Determine the distribution of enzyme genes among taxa

• Predict enzyme function and litter decomp based on first two goals

• Test if microbial communities are resilient to environmental change

Project design

Binoculation

A

A

N

N

A Ambient

N Nitrogen enriched

Ambient

Nitrogen enrichedA

A

N

N

A

A

P

P

Precip reduced

P Precip reduced

Nitrogenexperiment

Precipexperiment

Feb June

2011

Dec Feb June

2012

Dec Feb

2013 composition samples

additional samples

A

Mic. comm. origin

Plot Litter origin

Dec

Allison lab responsibilities

• Litter mass remaining• Fungal and bacterial counts

• Microscopy (fungi), flow cytometer (bacteria)

• Extracellular enzyme activities• Litterbag and plot-level

• Litter chemistry• nIR, C/N analysis

• Decomposition model

Litter mass remaining: Drought• Microbes from reduced water leave more mass

remaining (6-12 months)• Less mass loss in reduced water plots (6 months)

Microbe Origin (P=0.013)

Pe

rce

nt

Ma

ss

Re

ma

inin

g

60

70

80

90

100

X R

Plot Effect (P=0.005)

Pe

rce

nt

Ma

ss

Re

ma

inin

g

60

70

80

90

100

X R

Litter mass remaining: N addition• Significant plot by litter interactions that differ at 6

vs. 12 monthsPlot By Litter Interaction (P=0.008)

Pe

rce

nt

Ma

ss

Re

ma

inin

g

60

70

80

90

100

XX XN NX NN

Plot Effect Litter Origin

Plot By Litter Interaction (P=0.034)

Pe

rce

nt

Ma

ss

Re

ma

inin

g

60

70

80

90

100

XX XN NX NN

Plot Effect Litter Origin

Fungal counts: Drought• More fungi in reduced water plots (3-6 months)• Significant and contradictory microbial origin effects

Plot Effect (P=0.032)

Fu

ng

i/mg

Lit

ter

0

2

4

6

8

10

X R

Bacterial counts: Drought• Strong negative effects of reduced water; microbial

origin effect disappears by 6 monthsPlot Effect (P=0.000)

Ba

cte

ria

/g L

itte

r x

10

^9

0.0

0.5

1.0

1.5

2.0

X R

Litter Origin (P=0.000)

Ba

cte

ria

/g L

itte

r x

10

^9

0.0

0.5

1.0

1.5

2.0

X R

Bacterial counts: N addition• Positive effect of N in litter origin at 6 months

Litter Origin (P=0.000)

Ba

cte

ria

/g L

itte

r x

10

^9

0.0

0.5

1.0

1.5

2.0

X N

Enzymes: Drought• Higher activities of all hydrolytic enzymes except LAP

Plot Effect (P=0.000)

Ce

llob

ioh

yd

rola

se

0

2

4

6

8

10

X R

Plot Effect (P=0.000)

Le

uc

ine

am

ino

pe

pti

da

se

0.0

0.5

1.0

1.5

2.0

2.5

X R

Enzymes: N addition• Higher LAP in fertilized litter; other effects are weak

Litter Origin (P=0.000)L

eu

cin

e a

min

op

ep

tid

as

e

0.0

0.5

1.0

1.5

2.0

2.5

X N

Initial litter chemistry• Similar for litter from control and added N plots• Litter from reduced water plots has more lignin,

protein, labile compounds; less cellulose and hemicellulose

• Some differences are maintained after 3 months:Litter Origin (P=0.000)

Lig

nin

0

2

4

6

8

10

12

14

X R

Litter Origin (P=0.000)

Su

ga

rs

0

1

2

3

4

5

X R

Litter chemistry: Drought• 3-6 months: relatively more labile constituents

remaining in reduced water plotsPlot Effect (P=0.016)

Lig

nin

0

2

4

6

8

10

12

14

X R

Plot Effect (P=0.000)

Cru

de

pro

tein

0

2

4

6

8

X R

Litter chemistry: N addition• Greater lignin loss in litter from N plots (6 months)

Litter Origin (P=0.000)L

ign

in

0

2

4

6

8

10

12

14

X N

Data summary• Reduced water effects generally stronger than N

effects• Direct effects of plot on decomposition generally

stronger than indirect effects on plants and microbes• Reduced water favors fungi over bacteria, slows

decomposition, and allows enzymes and labile substrates to accumulate

Project goal: model integration• Incorporate disturbance responses and gene

distributions into a model• Predict response of litter decomposition to

treatments• Validate model with reciprocal transplant results

Approaches to modeling decompositionExponential decay (Olson 1963)

Schimel and Weintraub (2003)

Moorhead and Sinsabaugh (2006)“Guild decomposition model”(functional groups)

What is a “trait-based” model?

• Organisms are represented explicitly (biomass, physiology, etc.)

• Each taxon possesses a specific set of trait values• Trait values can be randomly chosen and/or

empirically derived• Community composition

is an emergent property

www.brooklyn.cuny.edu

Represented traits

• Extracellular enzymes and uptake proteins:• Gene presence/absence• Vmax, Km• Specificity• Production and maintenance costs

• Carbon use efficiency• Cellular stoichiometry• Dispersal distance www-news.uchicago.edu

Model structure

Example question and application

• Under what conditions are generalist versus specialist strategies favored?• Generalist = broad range of enzymes produced

Specialist Generalist

Model set-up

• 100 taxa, 100 x 100 grid• Taxa may possess 0 to 20 enzymes• 12 chemical substrates (approximates fresh litter)

• Each degraded by at least 1 enzyme

1 … 20

1 0 1 0

… 0 0 0

100 1 0 0

1 … 12

1 0 2.5 0

… 0 0 1.2

20 1.7 0 0

Enzymes

Ta

xa

En

zym

es

Substrates

Vmaxvalues

Model set-up

• Equivalent uptake across taxa• Could also implement uptake matrices

1 … 20

1 0 1 0

… 0 0 0

100 1 0 0

1 … 14

1 0 2.5 0

… 0 0 1.2

20 1.7 0 0

Transporters

Ta

xa

Tra

ns

po

rte

rs

Monomers

Vmaxvalues

Model experiments

• Simulate leaf litter decomposition (no inputs)• Test effect of tradeoffs in enzyme traits• Increase litter N or lignin• Model validation with Hawaiian litter

Model results

• Taxa vary in density over time (succession)

log10(days)

Mic

rob

ial d

en

sity

[ lo

g 10(m

g c

m3

)]

0.0 0.5 1.0 1.5 2.0 2.5

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

Model results

• Should be selection to link uptake with enzymes

Number of enzyme genes

Ma

xim

um

de

nsi

ty [

log 1

0(m

g c

m3

)]

0 5 10 15 20

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

Number of enzyme genes

Ma

xim

um

de

nsi

ty [

log 1

0(m

g c

m3

)]

0 5 10 15 20

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

Enzymes and uptake correlated No correlation

Model results

• Species interactions are present but vary by taxon and model conditions

Maximum density [log10(mg cm 3)]

Ave

rag

e c

orr

ela

tion

-2.0 -1.5 -1.0 -0.5 0.0 0.5

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

Model validation

• Fits are better for decomposition than enzymes

0.0 0.1 0.2 0.3 0.4 0.5 0.6

0

2

4

6

8

10

12

Model CBH activity

Em

pir

ica

l CB

H a

ctiv

ity

0.0 0.1 0.2 0.3 0.4 0.5 0.6

0

2

4

6

8

10

12 UnfertilizedFertilizedR2 = 0.35

P < 0.001

R2 = 0.81P < 0.001Slope = 1.7±0.2

0 2 4 6 8 10

0

2

4

6

8

10

Model k-value (1/yr)

Em

pir

ica

l k-v

alu

e (

1/y

r)

0 2 4 6 8 10

0

2

4

6

8

10 UnfertilizedFertilized

outliers

Model summary

• Enzyme genes and uptake proteins should be correlated

• Species interactions may be important• Empirical and genomic data can tell us about

tradeoffs, trait correlations, and trait distributions

Thank you!

NSF ATB, DOE BER, audience