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Philippe CHOLERPhilippe CHOLERPlant EcologistPlant EcologistUniversity of Grenoble. FRANCEUniversity of Grenoble. FRANCE
Marie-Curie Fellows Marie-Curie Fellows (from 15/01/2008 - to 15/01/2010)(from 15/01/2008 - to 15/01/2010)
including two years as aincluding two years as a
Visiting ScientistVisiting ScientistCSIRO Marine and Atmospheric ResearchCSIRO Marine and Atmospheric ResearchBlack Mountain LaboratoriesBlack Mountain LaboratoriesCanberra, ACTCanberra, ACT
CASOAR Project:
Plant Functional Diversity and Land-Surface Biogeochemical Modelling
CASOAR
Plant Functional Ecology (for dummies)Plant Functional Ecology (for dummies)
Species A
Small plant
Species B
Tall plant
Plants are getting smaller
under drier climates(size as a
response trait)
Tall and Small Plants do not affect
the Atmosphere in the same way
(size as an effect trait)
CASOAR
1.0 1.5 2.0 2.5 3.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
-0.4-0.2
0.0 0.2
0.4 0.6
0.8
SLA (mm2 g-1) [log scale]
Nm
ass
(%
) [lo
g s
cale
]
Le
af L
on
ge
vity
(m
o)
[log
sca
le]
Fundamental trade-offs among leaf Fundamental trade-offs among leaf traitstraits
NN massmass
(%) [
log scale]
SLASLA (mm-2 g-1) [ log scale]
Lea
f lo
ng
evit
yL
eaf
lon
gev
ity
(mo
nth
s) [
log
sca
le]
NOT everything is possible (at least for leaves)NOT everything is possible (at least for leaves)
"glopnet" leaf economics datasetWright I.J. et al. (2004) Nature
CASOAR
Basic questionsBasic questions
• What are (if existing) the fundamental trade-offs among Community
Aggregated Traits ?
• How are Community Aggregated Traits spatially distributed ?
insights from mechanistic (plant physiology...) and empirical approaches
n
1i
ii tpTCApi Relative Abundance (Cover) of species i in the community
ti Mean Trait Value of species i
Scaling-up from leaves to canopy
Community Aggregated Trait = Functional Property of Canopy
CASOAR
A case studyA case study
0 100 200 300 km
Adelaide
Albury
Geelong Melbourne
New castle
Sydney
Wollongong1
2
3
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1415
16171819
2021
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23 2425
2627
28
293031
3233
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434445
46
4748
49
50
200 500 1000 2000
20
50
10
02
00
50
01
00
02
00
0
Rainfall (mm yr-1) (log scale)
So
il P
(m
g k
g -
1)
(lo
g s
cale
)
1
2
3
4
5
6
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8
9
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303132
33
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49
50
Rainfall gradient
Pho
sph
orus
gra
dien
t
Which are the best predictors of SLACA values ?(Envir. variables or Seasonal Canopy Reflectances)
CASOAR
Use of Remotely Sensed DataUse of Remotely Sensed Data
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<--TIME-->
Sampling sites
Evergreen Broad Leaf ForestsOpen Shrublands
Closed Shrublands Savanna
MODIS data : MOD13A1 - level 3(VI products) - Collection 5250m Composite 16 days. (2000-2005)
Global Land Cover Class Averaged (3km around measured site)
Principal Component Analysis on time series correlation matrix(Data reduction process)
Retrieval of n independent Principal Components
CASOAR
Measured vs. Predicted SLAMeasured vs. Predicted SLACACA
3.6 3.8 4.0 4.2 4.4
3.6
3.8
4.0
4.2
4.4
SLA measured [log scale]
SLA
fitt
ed [lo
g s
cale
]
1
2
34
5
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9
10
1112141516
17
1819 20
21
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2426
28
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30 313233
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4950
3.6 3.8 4.0 4.2 4.4
3.6
3.8
4.0
4.2
4.4
SLA measured [log scale]
SLA
fitt
ed [lo
g s
cale
]
1
2
34 56
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29
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3.6 3.8 4.0 4.2 4.4
3.6
3.8
4.0
4.2
4.4
SLA measured [log scale]
SLA
fitt
ed [lo
g s
cale
]
1
2
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MODEL 1.Rainfall
PhosphorusElevation
MODEL 2.
MODIS time series Principal Components
MODEL 3.
All explanatory variables
Explanatory Variables
ModelPerformance
(n=43)
r2 = 0.55
P (Elevation = 0)= 0.002
P (Slope = 1)= 0.0062
RMSE = 0.12
r2 = 0.64
P (Elevation = 0)= 0.012
P (Slope = 1)= 0.022
RMSE = 0.107
r2 = 0.77
P (Elevation = 0)= 0.07
P (Slope = 1)= 0.09
RMSE = 0.085(around 1 unit SLA)
CASOAR
ConclusionConclusion
Community 1()
Plant Physiology
(understqnding trade-offs)
Plant Functional Traits
Community 2()
De
nsi
ty
Trait space
RemoteSensing
Spatial Distribution Models of CA Traits
Ground Measurements
Aim: Land-Surface Biogeochemical Modelsmore firmly grounded in ecological knowledge
Thank you
