Controls over ecosystem functioning across spatial scales as derived from studies in drylands José...
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Controls over ecosystem functioning across spatial scales as derived from
studies in drylands
José M. Grünzweig Hebrew University of Jerusalem, Rehovot, Israel
in colloboration withMarcelo Sternberg, Tel Aviv University, Israel
Katja Tielbörger, University of Tübingen, Germany
ClimMani & INTERFACE Workshop, Scaling climate change experiments across space and time: Challenges of informing large-scale models with small-scale experiments, Mikulov, Czech Republic, June 2013
Climate anomalies, Europe, summer 2003
Ciais et al. 2005 Nature
Climate extremes in systems not adapted to those extremes
Global extent of drylands
Levant (SE Mediterranean)
Research in regions adapted to heat and/or drought
Climate
The physical properties of the Levant (SE Mediterranean)
Shrubland as spatially heterogeneous, mosaic-type ecosystem composed of different microsites
The physical properties of the Levant (SE Mediterranean)
Biomes/ecosystem types
Outline of questions
• Can we predict ecosystem functioning across a precipitation range with common biological and abiotic drivers?
• What processes control carbon pools and fluxes when it gets drier?
• Do climate-change experiments reveal tipping points in ecosystem structure and functioning?
• What can we learn from climate-extremes studies in drylands?
• Where are we going from here?
Rainfall manipulations along an aridity gradient
Aridity gradient – GLOWA Jordan River project
– South facing slopesshallow soil (Terra Rossa to lithosol) onsimilar bedrock
– Similar seasonal temperture range
– Eight-fold difference in mean annual rainfall
– Large difference in rainfall variability
Mediterranean: 540 mm, CV 30%
Semiarid: 300 mm, CV 37%
~ 2
45 k
m
Arid: 90 mm, CV 51%
Mesic Mediterranean: 780 mm, CV 22%
Biomes/ecosystem types
300 mm
Semiarid550 mm
Mediterranean
-30% +30%
Experimental rainfall manipulations
Control
Rainfall manipulations
Rainfall manipulations along an aridity gradient
Observed Rs (μmol m-2 s-1)
Pred
icte
d R s (
μmol
m-2
s-1
)
r2 = 0.48
Talmon et al. 2011 GCB
Soil respiration at the herbaceous microsite during the growing season?
Prediction:Rs = soil respiration; Ts = soil temperature; θ = soil moisture
Site- and treatment- specific equations better fits
Overall inter-site and inter-treatment controls?
Can we predict ecosystem functioning across a precipitation range with common biological and abiotic drivers?
two sites, three climate-change treatments
Observed Rs (μmol m-2 s-1)
Pred
icte
d R s (
μmol
m-2
s-1
)
r2 = 0.48
Ts , θ
Observed Rs (μmol m-2 s-1)
r2 = 0.73
Ts , θ , cover
Pred
icte
d R s (
μmol
m-2
s-1
)
Vegetation cover is a factor that explains part of the inter-site and inter-treatment variation in soil respiration climate change modeling
Addition of vegetation cover as a driver of soil respiration
Prediction of ecosystem functioning with common biological and abiotic drivers?
Talmon et al. 2011 GCB
0 100 200 300 400 500 600 700 8000.0
0.5
1.0
1.5
2.0
2.5
3.0
Mean annual precipitation (mm)
Soi
l org
anic
C s
tock
(kg
m-2)
What processes control carbon pools and fluxes when it gets drier?
Soil organic carbon stocks at the herbaceous microsite along the aridity gradient
0 100 200 300 400 500 600 700 8000
10
20
30
40
y=2.75x0.402
R2=0.968
An
nu
al li
tter
dec
om
po
siti
on
(%
)
Mean annual precipitation (mm)
2002-2005
0 100 200 300 400 500 600 700 8000
50
100
150
200
250
300
350
2003-2011
Ab
ove
gro
un
d N
PP
(g
m-2 y
r-1)
Mean annual precipitation (mm)
y=0.0153x1.492
R2=0.998
0 100 200 300 400 500 600 700 8000.0
0.5
1.0
1.5
2.0
2.5
3.0
Mean annual precipitation (mm)
Soi
l org
anic
C s
tock
(kg
m-2)
What processes control carbon pools and fluxes when it gets drier?
Carbon loss
Carbon addition
Changes in plant strategies with increasing aridity
Mean annual precipitation (mm)
Unweighted community mean
2.0
3.0
4.0
Log (
specific
leaf
are
a)
aab
b b
p<0.001 10
30
50
Leaf
Nitro
gen (
mg
g1) a
ab
cbc
p<0.001
4.5
5.5
6.5
Log (
leaf
dry
matt
er
conte
nt)
ns
-1.0
0.0
1.0
Log (
tensile
str
ength
)ns
B L M Y
12
34
Log (
pla
nt
heig
ht)
a
ab
b
b
p<0.05
B L M Y
-3-1
13
Log (
leaf
siz
e) ns
Unweighted community average of species leaf traits
Site
300 78055090300 78055090
Mean annual precipitation (mm)Sternberg & Lebrija
What processes control carbon pools and fluxes when it gets drier?
Alternative drivers of litter decay in the dry season
What processes control carbon pools and fluxes when it gets drier?
0 30 40 50 60 70 80 90 1000
10
20
30
40 Shrub Intershrub Lab
Mois
ture
conte
nt (%
)
Relative humidity (%)
y = 3.10 + 0.414 e0.0451x
r2 = 0.998
Dirks et al. 2010 GCB
Dew Water vapor < saturation
Solar radiation
To what extent is soil respiration along the aridity gradient directly controlled by changes in climatic variables and indirectly controlled by shifts in shrub cover?
0 300 600 9000
300
600
900
ecosystem
shrub microsite
An
nu
al s
oil
resp
irat
ion
(g
C m
-2 y
r-1)
Mean annual precipitation (mm)
herbaceous microsite
What processes control carbon pools and fluxes when it gets drier?
0 200 400 600 8000
200
400
600
800
1000
Ann
ual s
oil r
espi
ratio
n (g
C m
-2 y
r-1)
Mean annual precipitation (mm)
0 200 400 600 8000
200
400
600
800
1000
-12%-15%
-20% -21%
-60%
Ann
ual s
oil r
espi
ratio
n (g
C m
-2 y
r-1)
Mean annual precipitation (mm)
assuming no change in shrub cover assuming change in shrub cover
-64%
13% 35% 55% 75%Shrub cover
-15%-12%
-21%-20%
-60%-64%
What processes control carbon pools and fluxes when it gets drier?
Kigel & Konsens
0 100 200 300 400 500 600 700 8000
50
100
150
200
250
300
350
2003-2011
Ab
ove
gro
un
d N
PP
(g
m-2 y
r-1)
Mean annual precipitation (mm)
y=0.0153x1.492
R2=0.998
0 100 200 300 400 500 600 700 8000
50
100
150
200
250
300
350
Control Dry tmt. Wet tmt.
2003-2011
Ab
ove
gro
un
d N
PP
(g
m-2 y
r-1)
Mean annual precipitation (mm)
y=0.0153x1.492
R2=0.998
Do climate-change experiments reveal tipping points in ecosystem structure and functioning?
Do climate-change experiments reveal tipping points in ecosystem structure and functioning?
Sternberg & Navon
0 100 200 300 400 500 600 700 8000
10
20
30
40
y=2.75x0.402
R2=0.968
An
nu
al li
tter
dec
om
po
siti
on
(%
)
Mean annual precipitation (mm)
2002-2005
0 100 200 300 400 500 600 700 8000
10
20
30
40
50
y=2.75x0.402
R2=0.968
An
nu
al li
tter
dec
om
po
siti
on
(%
)
Mean annual precipitation (mm)
2002-2005
Control Dry tmt. Wet tmt.
Talmon et al. 2011 GCB
Do climate-change experiments reveal tipping points in ecosystem structure and functioning?
Rainfall manipulations had no significant effect on species diversity at the semiarid site (same for the Mediterranean site)
Kigel et al., unpublished
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 20110.8
1
1.2
1.4
1.6
1.8
2
Species diversity
Dry Control Irrig
Sh
ann
on
in
dex
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 20112
3
4
5
Species diversity
Dry Control Irrig
Sim
pso
n r
ecip
roca
l in
dex
Year
Year*** Treatment NS
T x Y*
Year*** Treatment NS
T x Y*
Do climate-change experiments reveal tipping points in ecosystem structure and functioning?
Spe
cies
ric
hnes
s
Spe
cies
eve
nnes
s (J
’)
Mediter.
arid
mesic Mediter.
semiarid
2001 2002 2003 2004 2005 2006 2007 2008 20090
10
20
30
40
50Station *** Year *** S x Y ***
2001 2002 2003 2004 2005 2006 2007 2008 20090
0.5
1
1.5
2
2.5
3
3.5
4Station *** Year *** S x Y **
2001 2002 2003 2004 2005 2006 2007 2008 20090
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Station *** Year *** SxY **
Spe
cies
div
ersi
ty (
H’)
Do climate-change experiments reveal tipping points in ecosystem structure and functioning?
Species diversity along the aridity gradient
What can we learn from climate-extremes studies in drylands?
Rs = soil respiration; Ts = soil temperature; θ = soil moisture; PPFD = photon flux density
Moist season:Dry season:
R s
Grünzweig et al. 2009 JGR
R s
Testing extreme conditions: irrigation during the hot summer
Rs = soil respiration; Ts = soil temperature; θ = soil water content; PPFD = photon flux density
PredictionsIrrigation:Control:
Grünzweig et al. 2009 JGR
Final ConferenceLimassol, Cyprus, 2011 Katja Geissler, Martin Köchy, Florian Jeltsch, Dan Malkinson
Scaling of output: the model Wadiscape
topographic and
Spatial information
GIS
slopes (DEM)
MAP
(characterization of region by mean
annual precipitation )
1.0 km
geographic variation
•Experiments (GLOWA)•Surveys (GLOWA)•Literature•Experts (GLOWA)
global circulation
models
pattern (GLOWA)
Modeling (semi-) natural vegetation
Final ConferenceLimassol, Cyprus, 2011 Katja Geissler, Martin Köchy, Florian Jeltsch
Modeling (semi-) natural vegetation
(Over)grazed vegetation is highly vulnerable to climate change
climate changeclimate change, moderate grazing
climate change, current grazing
Summary
Vegetation cover is a driver of soil respiration together with climatic drivers.
The relative distribution of vegetation types has a small impact on ecosystem-scale soil respiration under a drier climate; the decrease in soil respiration is mainly driven by the decline in biological activity.
The decrease in soil organic carbon storage with increased aridity is related to greatly reduced productivity and less drastically reduced decomposition; alternative drivers start to become important under drier conditions.
Climate change studies might reveal tipping points in species richness.
Where are we going from here?
• Climate-change studies in systems adapted to drought provide better understanding of ecosystem functioning under more realistic conditions• Climate-extremes studies, even unrealistic ones, can teach us about processes and potentially about thresholds and tipping points
Funding sourcesInternational Arid Lands Consortium(IALC) Climate Change and Impact Research: the Mediterranean Environment (CIRCE) (EU FP6)German Federal Ministry of Education and Research (BMBF)Israeli Ministry of Science and Technology (MOST)
CollaboratorsHebrew University Tel Aviv University Haifa UniversityJaime Kigel Yael Navon Dan MalkinsonYiftach Talmon Edwin LebrijaIrit KonsensRita Dumbur
Acknowledgements
Thank you for your attention!