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Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010
Projections des impacts du
changement climatique sur les
forêts : quelles stratégies
d'adaptation face à des
incertitudes considérables ?
Paul Leadley
Laboratoire ESE
Université Paris-Sud
Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010
Sources of uncertainty in projections
1. Développement
Socio-économique
5. Réponses:
Atténuation
Adaptation
2. Déterminants de
la biodiversité
Ex : climat, usage des
sols, gestion des
ressources génétiques,
etc.
3. Etat de la
biodiversité
Ex : diversité génétique,
diversité des especes,
communautés, paysages
4. Services
Ecosystémiques
Ex : provisioning,
regulating, sustaining and
cutural services
Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010
Model projections of
climate change impacts
on French forests
Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010
ANR QDiv: Quantification des effets des changements
globaux sur la diversité végétale
#
Laboratoire
Site
PI
1
ESE, UMR Univ. Paris-Sud / CNRS / ENGREF
Orsay
Paul LEADLEY
2
FGEP, INRA
ClermontFerrand
Jean-François
SOUSSANA
3
ISEM, UMR Université Montpellier II / CNRS
Montpellier
Christine DELIRE
4
CEFE, UMR CNRS / Univ. Montpellier I,II,III /
CIRAD / ENSAM
Montpellier
Isabelle CHUINE
5
BIOGECO, UMR CNRS / Univ. Bordeaux 1
Bordeaux
Antoine KREMER
6
Arboretum National des Barres, ENGREF
Nogent
Stephanie BRACHET
7
LECA, UMR CNRS /
Université Joseph Fourier
Grenoble
Sandra LAVOREL
8
Ecologie et Ecophys. Forestières, UMR INRA /
Université Nancy
Nancy
Jean-Luc DUPOUEY
9
LSCE, UMR CEA / CNRS
Gif-sur-Yvette
Nathalie DE NOBLETDUCOUDRE
10
UMR & USM CNRS / MNHN / Conservatoire
Botanique National du Bassin Parisien
Paris
Nathalie MACHON
Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010
Projecting potential shifts in tree ranges in
response to climate change: an
intermodel comparison approach to
qualifying and quantifying uncertainty
Alissar Cheaib1, Christophe François1, Vincent Badeau2, Isabelle Chuine3,
Christine Delire4, Eric Dufrêne1, Emmanuel Gritti3, Wilfried Thuiller5,
Nicolas Viovy6 and Paul Leadley1
1 Laboratoire
d’Écophysiologie Végétale, UMR d’Écologie, Systématique et Évolution CNRS 8079, Université ParisSud XI 91405 Orsay Cedex France
2 UMR 1137, INRA UHP, Forest Ecology and Ecophysiology, Phytoecology Team, route de la Forêt-d’Amance,
54280 Champenoux, France
3 Centre d’Ecologie Fonctionnelle et Evolutive, Equipe BIOFLUX, CNRS, 1919 route de Mende, 34293 Montpellier
cedex
4 CNRS Meteo-France – Toulouse, France
5 Laboratoire d’ Écologie Alpine, UMR CNRS 5553 , Université´ J. Fourier, BP 53, 38041 Grenoble Cedex 9 France
6 Laboratoire des Sciences du Climat et de l’Environnement, CEA/ CNRS, Saclay, France.
Treating UNCERTAINTY in modeling climate impacts on forests:
an example from the ANR QDiv project
High
resolution
(8 km)
climate
scenarios
High
resolution
(8 km) maps
of current
tree
distributions
Collaboration
with
CERFACS
Collaboration
IFN
(L. Terray)
(C. Cluzeau)
High
resolution
maps of key
soil
properties
Furnished by
INRA Orléans
10
A broad
range of
models of
tree
response to
climate
change
Assessment
of climatic
risk for:
Niche Based
BIOMOD
NANCY-NBM
STASH
Quercus
petraea
Phenology-based
PHENOFIT
DGVM
Orchidée (PFT)
IBIS (PFT)
LPJ-Guess
(Species)
Quercus
robur
Fagus
sylvatica
Pinus
sylvestris
Quercus ilex
35
E.g., Simulated
mean August
temperatures in
2098
E.g., current
distribution of
Fagus sylvatica
(Common beach)
Mechanistic Tree
growth
CASTANEA
+ Plant
functional
groups
A broad range of modelling concepts: 7 Models
Correlative approaches
« Niche - Based » Models or « Bioclimate envelope » Models
 Nancy NBM (V. Badeau, INRA Nancy)
 BIOMOD (W. Thuiller, 2003. W. Thuiller, Grenoble)
 STASH (Sykes et al, 1996. E. Gritti, CEFE Montpellier)
Mechanistic approaches
« Phenology – Based » Model
 PHENOFIT
Tree C balance and Growth
 CASTANEA
(Chuine and Beaubien 2001. I. Chuine, CEFE Montepellier)
(E. Dufrêne et al, 2005. C. François and A. Cheaib, ESE Orsay)
 ORCHIDEE
(Krinner et al, 2005. N. Viovy CEA)
Dynamic Global Vegetation
Models (DGVMs)
 IBIS
(Kucharik et al, 2000. C. Delire Meteo France)
 LPJ
(Stich et al, 2003. E. Gritti, CEFE Montpellier)
Climate scenario (regionalized Arpège) : The A1B Story line
~ 8989 pixels in France: Spatial resolution 8Km x 8Km (L. Terray, CERFACS, Meteo France)
800
CO2
CO2
700
Average CO2
CO2 (ppm)
600
500
400
300
TS3
800
TS2
668
600
TS1
400
544
346
200
0
200
Time Slice1
1971- 2000
100
0
1970
1980
1990
Time Slice 2
2046 - 2065
2000
2010
2020
2030
2040
2050
Time Slice 3
2079-2098
2060
2070
2080
2090
2100
100
80
60
40
20
0
T°C
Precipitations
(mm)
Year
0
2
4
6
8
10 12
Month
2050
 200 mm/year decrease in precipitation
25
20
15
10
5
0
0
2
4
6
8
10 12
Month
2050
 2.85°C mean temperature
increase
Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010
Fagus sylvatica
European beech
Hêtre commun
Current distribution simulations and model evaluation
Fagus sylvatica
Models Evaluation: True Skill Statistic (TSS) method (Allouche et al, 2006)
TSS = Sensitivity + Specificity - 1
Sensitivity = True presence / (True presence + false absence)
Proportion of observed presences that are predicted as such: quantifies omission errors
Specificity = True absence / (True absence + false presence)
Proportion of observed absences that are predicted as such: quantifies commission errors
TSS = 0.73
BIOMOD
TSS = 0.16
TSS = 0.66
Nancy NBM
TSS = 0.18
STASH
TSS = 0.48
TSS = 0.33
Current distribution
(IFN)
0: No forest
1: Absence
2: Presence
PHENOFIT
CASTANEA
LPJ
Fagus sylvatica
NE
Vosges
NW
2050
Projections of
distribution
-0.174
-0.40
Alsace
-0.453
100 %
48 %
-0.55
58 %
84 %
V Saône
53 %
70 %
Y axis
(Sum 2050 - Sum Current)
Sum Current
Brittany
-0.479
Jura
99%
-0.553
SW
-0.038
82 %
16 %
Alps
58 %
-0.70
0.073
Average response
75 %
Center
Pyrenees
-0.426
0: No forest
1: Absence
2: Presence
-0.16
Fagus sylvatica
2050
Projections of
distribution
NW
-0
-0.453
Y axis
(Sum 2050 - Sum Current)
Sum Current
48 %
53 %
Brittany
0: No forest
1: Absence
2: Presence
Fagus sylvatica
NE
Vosges
NW
2050
Projections of
distribution
-0.174
-0.40
Alsace
-0.453
100 %
48 %
-0.55
58 %
84 %
V Saône
53 %
70 %
Y axis
(Sum 2050 - Sum Current)
Sum Current
Brittany
-0.479
Jura
99%
-0.553
SW
-0.038
82 %
16 %
Alps
58 %
-0.70
0.073
Average response
75 %
Center
Pyrenees
-0.426
0: No forest
1: Absence
2: Presence
-0.16
Fagus sylvatica
Examples
Tests of mechanisms
1
Niche models show a
strong negative response
to warming, this
response is weaker or
even reversed in
mechanistic models
(TS2-TS1)/TS1
BIOMOD
NE
PHENOFIT
Jura
Current
Temp
2050
Temp
2050 Current
Temp Temp
PHENOFIT
CASTANEA
Alsace
2
Mechanistic models
are very responsive
to reductions in
precipitation
(TS2-TS1)/TS1
V Saône
2050
Current
Rainfall Rainfall
2050
Rainfall
LPJ
(TS2-TS1)/TS1
Rising CO2 offsets
negative climate
change impacts in
mechanistic models
(not accounted for in
niche models)
(TS2-TS1)/TS1
CASTANEA
3
Current
Rainfall
Alps
NE
2050
CO2
2050
CO2
Current
CO2
Current
CO2
NE
Alps
Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010
Beech: Take home messages
• Bioclimatic-envelope models do a remarkably good job of
simulating current distributions, in some cases with as few as three
climate parameters.
• Bioclimatic-envelope models project nearly complete loss of
favorable climate in the plains of France by 2050 for this A1b
climate scenario and “average” soils. This appears to be driven
largely by high sensitivity to climate warming.
• Mechanistic models project small or moderate losses of favorable
climate in the plains and increased range in mountains. Most
models project increased productivity in the Northern plains and
mountains (not shown). Rising CO2 plays a key role in counteracting
negative effects of climate change.
• Recent observations and experiments tend to side with
mechanistic models, but “hidden” or long-term effects (e.g.,
competition, disease, regeneration) might explain current and future
distributions as simulated by bioclimatic models
Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010
Pinus sylvestris
Scots pine
Pin sylvestre
Current distribution simulations and model evaluation
Pinus sylvestris
TSS = 0.48
BIOMOD
TSS = 0.26
Current distribution
(IFN)
PHENOFIT
TSS = 0.03
TSS = 0.30
Nancy NBM
TSS = 0.25
STASH
TSS = 0.26
LPJ
TSS = 0.07
0: No forest
1: Absence
2: Presence
Needleleaf Evergreen
ORCHIDEE
IBIS
Pinus sylvestris
NE
Vosges
NW
Projections for
2050
-0.310
-0.678
-0.922
82 %
41 %
Alsace
46 %
55 %
33 %
35 %
-0.661
V saône
-0.708
Brittany
Jura
49 %
Y axis
-0.943
(Sum TS2-SumTS1)
SumTS1
-0.098
72 %
SW
16 %
-0.082
-0.932
Average models
38 %
Pyrenees
0: No forest
1: Absence
2: Presence
Alps
68 %
-0.247
Center
-0.555
Pinus sylvestris
1
All models are
highly sensitve to
warming
BIOMOD
Vosges
(TS2-TS1)/TS1
Tests of mechanisms
T°C TS1
Current
Temp
2050
Temp
Vosges
NE
(TS2-TS1)/TS1
PHENOFIT
NE
3
Rising CO2
attenuates climate
change impacts in
mechanistic models
LPJ
Vosges
(TS2-TS1)/TS1
All models are
relatively
insensitive to
reductions in
precipitation
LPJ
(TS2-TS1)/TS1
2
PHENOFIT
NE
2050 Current
Rainfall Rainfall
NE
Vosges
2050
CO2 Current
CO2
Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010
Scots Pine: Take home messages
• Current distributions are difficult to simulate, in part
due to use of Scots pine outside its natural range
• All models project substantial loss of favorable climate
in the plains of France by 2050 for this A1b climate
scenario and “average” soils. This is driven by high
sensitivity to climate warming in all models.
Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010
Quercus ilex
holm oak
chêne vert
Quercus ilex
2050
2050
0: No forest
1: Absence
2: Presence
BIOMOD
NBM Nancy
2050
LPJ
2050
STASH
2050
ORCHIDEE
2050
IBIS
Evergreen Broadleaf
Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010
Forest plant diversity
Identifier des modifications en cours de l’aire de distribution des
espèces au travers des relevés de l’Inventaire forestier national (IFN). –
J-L Dupouey, V Badeau (EEF, INRA Nancy)
Arpège B2 Climate
simulation +
Statistical
distrubution model
Current
Statistical model based on IFN
and AURELHY climate data
2100
Projected distrubution
Evolution de la composante méditerranéenne de la
végétation forestière entre 1990 et 2100 selon le scénario
climatique Arpège B2 de Météo-France.
Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010
Adaptation:
What to do in the face of
uncertain impacts?
Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010
Adaptation of French forests to climate change
An ONF/INRA manual of
management techniques to
limit climate change
impacts on forests
Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010
Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010
Examples of adaptive management
strategies
• Reinforce “natural” processes to
increase resilience
• Reduce exposure to climate change
• Plant species or genotypes, including
introduced species, that are more
tolerant of projected changes in climate
Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010
Reinforce “natural” processes to
increase resilience
• Increase the use of
mixed species stands
• Maintain or increase
genetic diversity, e.g.,
through natural
regeneration rather than
the planting of clones
• Respect knowledge of
tree ecology (e.g., soils,
climate)
• Avoid soil compaction
during forestry activities
• Reduce evapotranspiration through
management of leaf area
Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010
Reduce exposure to climate change
Old growth
Douglas fir
Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010
Reduce exposure to climate change
• Reduce rotation times. Shift
to fast growing trees (esp.
conifers) or to “coppice”
plantations (if 2nd generation
biofuels take off, GM trees
are permitted).
Douglas fir (Pseudotsuga sp.)
plantation
Coppice poplar plantation
Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010
Plant species or genotypes that are more tolerant of “predicted”
changes in climate
• Introduce new drought and
heat tolerant species and
genotypes, i.e., introduced
species and possibly GM trees.
• Use transplants exploiting the
natural differences in genotypes
across species range
Use provenance trials and other information
to identify ‘pre-adapted’ genotypes, e.g.,
lodgepole pine in W. Canada (O’Neill et al.
2007, Wang et al. 2010)
Eucalyptus plantation
Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010
Plant species or genotypes, including introduced species, that
are more tolerant of projected changes in climate
• Introduce drought and heat
tolerant species, possibly
including GM trees.
• Use transplants exploiting
the natural differences in
genotypes across species
range
Use provenance trials and other information
to identify ‘pre-adapted’ genotypes, e.g.,
lodgepole pine in W. Canada (O’Neill et al.
2007, Wang et al. 2010)
Eucalyptus plantation
Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010
Conclusions
• There is a tremendous need to improve
biodiversity scenarios and their use in
management and political decision
making
• Regardless of the advances in scenarios
we will face difficult choices for forest
management in the face of very large
uncertainties
15-16 Sept, Paris
The way forward
Programme Phare: Modélisation et scénarios de la biodiversité
‘Humboldt’ project
Quercus robur
Vosges
NE
NW
-0.254
-0.276
0.009
72 %
80 %
78 %
0: No forest
1: Absence
2: Presence
-0.322
95 %
V Saône
80 %
Y axis
Alsace
85 %
-0.406
Brittany
Jura
75 %
(Sum TS2-SumTS1)
SumTS1
-0.218
0.128
72 %
SW
Alps
15 %
78 %
-0.439
0.039
Average models
78 %
Pyrenees
-0.206
Center
-0.421
Quercus robur
Hypothesis: Tests
Examples
1
T°C of TS1
TS2 Climate
NE
PHENOFIT
Jura
(TS2-TS1)/TS1
BIOMOD
T°C TS1
T°C TS1
(TS2-TS1)/TS1
LPJ
LPJ
Jura
NE
T°C TS1
T°C TS1
Rainfall of TS1
CO2 TS1
3
TS2 Climate
LPJ
(TS2-TS1)/TS1
TS2 Climate
(TS2-TS1)/TS1
BIOMOD
2
NE
PHENOFIT
V Saône
Rainfall TS1
Rainfall TS1
Alps
NE
CO2 TS1
CO2 TS1
TeBS
NE
NW
Vosges
-0.165
-0.245
-0.073
100 %
Alsace
85 %
0: No forest
1: Absence
2: Presence
98 %
88 %
-0.177
80 %
98 %
Y axis
V Saône
-0.267
Brittany
99 %
Jura
-0.234
(Sum TS2-SumTS1)
SumTS1
95 %
SW
-0.483
0.013
61 %
Alps
86 %
-0.022
Average models
79 %
Center
Pyrenees
-0.343
-0.190
Temperate Broadleaf Summergreen
1
T°C of TS1
(TS2-TS1)/TS1
BIOMOD
Alps
NE
T°C TS1
T°C TS1
TS2 Climate
(TS2-TS1)/TS1
ORCHIDEE
TS2 Climate
CO2 TS1
3
TS2 Climate
ORCHIDEE
(TS2-TS1)/TS1
2
ORCHIDEE
(TS2-TS1)/TS1
Rainfall of TS1
Alps
NE
T°C TS1
T°C TS1
Alps
NE
Rainfall TS1
Rainfall TS1
Alps
CO2 TS1
NE
CO2 TS1