can microbial functional traits predict the response and resilience of decomposition to global...
TRANSCRIPT
Can microbial functional traits predict the response and resilience of
decomposition to global change?
Steve AllisonUC Irvine
Ecology and Evolutionary BiologyEarth System Science
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