projections des impacts du changement climatique sur les fore ̂ ts : quelles stratégies...
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Projections des impacts du changement climatique sur les
forets : quelles strategiesd'adaptation face a des
incertitudes considerables ?
Paul Leadley
Laboratoire ESE
Universite Paris-Sud
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Sources of uncertainty in projections
1. Développement Socio-économique
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 EcosystémiquesEx : provisioning,
regulating, sustaining and cutural services
5. Réponses:AtténuationAdaptation
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Model projections of climate change impacts
on French forests
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# Laboratoire Site PI
1 ESE, UMR Univ. Paris-Sud / CNRS / ENGREF Orsay Paul LEADLEY
2 FGEP, INRA Clermont-Ferrand
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 NOBLET-DUCOUDRE
10 UMR & USM CNRS / MNHN / Conservatoire Botanique National du Bassin Parisien
Paris Nathalie MACHON
ANR QDiv: Quantification des effets des changements globaux sur la diversite vegetale
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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 Dufrene1, Emmanuel Gritti3, Wilfried Thuiller5,
Nicolas Viovy6 and Paul Leadley1
1 Laboratoire d’Écophysiologie Végétale, UMR d’Écologie, Systématique et Évolution CNRS 8079, Université Paris-
Sud XI 91405 Orsay Cedex France2 UMR 1137, INRA UHP, Forest Ecology and Ecophysiology, Phytoecology Team, route de la Forêt-d’Amance,
54280 Champenoux, France3 Centre d’Ecologie Fonctionnelle et Evolutive, Equipe BIOFLUX, CNRS, 1919 route de Mende, 34293 Montpellier
cedex4 CNRS Meteo-France – Toulouse, France5 Laboratoire d’ Écologie Alpine, UMR CNRS 5553 , Université´ J. Fourier, BP 53, 38041 Grenoble Cedex 9 France6 Laboratoire des Sciences du Climat et de l’Environnement, CEA/ CNRS, Saclay, France.
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Treating UNCERTAINTY in modeling climate impacts on forests: an example from the ANR QDiv project
High resolution
(8 km) climate
scenarios
Collaboration with
CERFACS
(L. Terray)
High resolution (8 km) maps
of current tree
distributions
Collaboration IFN
(C. Cluzeau)
High resolution
maps of key soil
properties
Furnished by INRA Orleans
A broad range of
models of tree
response to climate change
Niche Based
BIOMOD
NANCY-NBM
STASH
Phenology-based
PHENOFIT
DGVM
Orchidée (PFT)
IBIS (PFT)
LPJ-Guess (Species)
Mechanistic Tree growth
CASTANEA
E.g., Simulated mean August
temperatures in 2098
E.g., current distribution of
Fagus sylvatica (Common beach)
10 35
Assessment of climatic
risk for:
Quercus robur
Quercus petraea
Fagus sylvatica
Pinus sylvestris
Quercus ilex
+ Plant functional
groups
A broad range of modelling concepts: 7 Models
Mechanistic approaches
« Niche - Based » Models or « Bioclimate envelope » Models
ORCHIDEE (Krinner et al, 2005. N. Viovy CEA)
PHENOFIT(Chuine and Beaubien 2001. I. Chuine, CEFE Montepellier)
CASTANEA(E. Dufrêne et al, 2005. C. François and A. Cheaib, ESE Orsay)
Correlative approaches
Nancy NBM (V. Badeau, INRA Nancy) BIOMOD (W. Thuiller, 2003. W. Thuiller, Grenoble) STASH (Sykes et al, 1996. E. Gritti, CEFE Montpellier)
« Phenology – Based » Model
Tree C balance and Growth
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
CO2
0
100
200
300
400
500
600
700
800
1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Year
CO
2 (p
pm
)
Time Slice11971- 2000
Time Slice 2 2046 - 2065
Time Slice 3 2079-2098
~ 8989 pixels in France: Spatial resolution 8Km x 8Km (L. Terray, CERFACS, Meteo France)
0
200
400
600
800
TS1
TS2
TS3
346
544668
Ave
rag
e C
O2
020406080
100
0 2 4 6 8 10 12
Prec
ipita
tions
(m
m)
Month Month
05
10152025
0 2 4 6 8 10 12T
°C
2050 2.85°C mean temperature increase
2050 200 mm/year decrease in precipitation
CO2
Fagus sylvaticaEuropean beechHêtre commun
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Current distribution simulations and model evaluation
Fagus sylvatica
Current distribution (IFN)
0: No forest1: Absence 2: Presence
BIOMOD Nancy NBM STASH
TSS = 0.73 TSS = 0.66 TSS = 0.18
PHENOFIT CASTANEA LPJ
TSS = 0.16 TSS = 0.33 TSS = 0.48
TSS = Sensitivity + Specificity - 1Sensitivity = True presence / (True presence + false absence)
Proportion of observed presences that are predicted as such: quantifies omission errors
Models Evaluation: True Skill Statistic (TSS) method (Allouche et al, 2006)
Specificity = True absence / (True absence + false presence)Proportion of observed absences that are predicted as such: quantifies commission errors
0: No forest 1: Absence2: Presence
V Saône
Alsace
Vosges
Alps
Jura
NE
NW
SW
Pyrenees
Center
Brittany
Average response
48 %
-0.453
-0.40
84 %
-0.174
-0.55
-0.479
-0.038
0.073
58 %
100 %
58 %
70 %
99%
53 %
-0.553
-0.70
16 %
75 %
-0.16
82 %
-0.426
Fagus sylvatica
2050Projections of
distribution
Y axis(Sum 2050 - Sum Current)
Sum Current
NW
Brittany
48 %
-0.453
-0.40
53 %
-0.553
0: No forest 1: Absence2: Presence
Fagus sylvatica
2050Projections of
distribution
Y axis(Sum 2050 - Sum Current)
Sum Current
V Saône
Alsace
Vosges
Alps
Jura
NE
NW
SW
Pyrenees
Center
Brittany
Average response
48 %
-0.453
-0.40
84 %
-0.174
-0.55
-0.479
-0.038
0.073
58 %
100 %
58 %
70 %
99%
53 %
-0.553
-0.70
16 %
75 %
-0.16
82 %
-0.4260: No forest 1: Absence2: Presence
Fagus sylvatica
2050Projections of
distribution
Y axis(Sum 2050 - Sum Current)
Sum Current
Fagus sylvatica
Tests of mechanisms
1
2
3
(TS
2-T
S1)
/TS
1
Alps NECASTANEA
LPJCurrent
CO2
(TS
2-T
S1)
/TS
1 AlpsNE
PHENOFIT
(TS
2-T
S1)
/TS
1 CurrentRainfall
ExamplesBIOMOD NE
(TS
2-T
S1)
/TS
1 Jura
CurrentTemp
PHENOFIT
AlsaceCASTANEA
V Saône
CurrentTemp
2050Temp
2050Temp
2050Rainfall
Niche models show a strong negative response
to warming, this response is weaker or
even reversed in mechanistic models
Mechanistic models are very responsive
to reductions in precipitation
Rising CO2 offsets negative climate
change impacts in mechanistic models(not accounted for in
niche models)
2050Rainfall
CurrentRainfall
2050CO2
2050CO2
CurrentCO2
• 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
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Beech: Take home messages
Pinus sylvestrisScots pine
Pin sylvestre
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Current distribution simulations and model evaluation
Pinus sylvestris
Current distribution (IFN)
0: No forest1: Absence 2: Presence
BIOMOD Nancy NBM
TSS = 0.48 TSS = 0.25
STASH
PHENOFIT LPJ
TSS = 0.30
ORCHIDEE IBIS
TSS = 0.26 TSS = 0.26
TSS = 0.07TSS = 0.03
Needleleaf Evergreen
0: No forest 1: Absence2: Presence
Alps
Jura
V saône
Vosges
Alsace
NE
NW
Brittany
SW
Pyrenees
Center
68 %
-0.082
Average models
-0.661
46 %
-0.943 -0.098
33 %
49 %
72 %
-0.555
-0.678
55 %
41 %
-0.922
38 %
-0.247
-0.932
16 %
35 %
-0.708
-0.310
82 %
Pinus sylvestris
Projections for 2050
Y axis
(Sum TS2-SumTS1)SumTS1
Pinus sylvestris
1
2
3LPJ
BIOMOD
(TS
2-T
S1)
/TS
1
Vosges
(TS
2-T
S1)
/TS
1
PHENOFIT Vosges
NE
T°C TS1
NE
NEPHENOFITLPJ
(TS
2-T
S1)
/TS
1
Vosges
Vosges
(TS
2-T
S1)
/TS
1
NE
All models are highly sensitve to
warming
All models are relatively
insensitive to reductions in precipitation
Rising CO2 attenuates climate change impacts in
mechanistic models
Tests of mechanisms
CurrentTemp
2050Temp
CurrentRainfall
2050Rainfall
CurrentCO2
2050CO2
• 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.
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Scots Pine: Take home messages
Quercus ilexholm oak
chêne vert
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Quercus ilex
0: No forest 1: Absence2: Presence BIOMOD
2050 2050
NBM Nancy STASH
2050
LPJ
2050 2050
ORCHIDEE IBIS
2050
Evergreen Broadleaf
Forest plant diversity
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Identifier des modifications en cours de l’aire de distribution des espèces au travers des releves de l’Inventaire forestier national (IFN). –
J-L Dupouey, V Badeau (EEF, INRA Nancy)
Evolution de la composante mediterraneenne de la vegetation forestière entre 1990 et 2100 selon le scenario
climatique Arpège B2 de Meteo-France.
CurrentStatistical model based on IFN
and AURELHY climate data
2100Projected distrubution
Arpège B2 Climate simulation +
Statistical distrubution model
Adaptation:What to do in the face of
uncertain impacts?
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Adaptation of French forests to climate change
An ONF/INRA manual of management techniques to limit climate change impacts on forests
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• 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
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Examples of adaptive management strategies
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 evapo-transpiration through management of leaf area
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Reduce exposure to climate changeLa
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Old growth Douglas fir
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).
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Douglas fir (Pseudotsuga sp.) plantation Coppice poplar plantation
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
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Eucalyptus plantationUse 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)
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
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Eucalyptus plantationUse 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)
• 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
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Conclusions
15-16 Sept, Paris
The way forward
Programme Phare: Modélisation et scénarios de la biodiversité
‘Humboldt’ project
0: No forest 1: Absence2: Presence
Alps
Jura
V Saône
Alsace
VosgesNE
NW
Brittany
SW
PyreneesCenter
15 %
0.039
75 %
0.128
85 %
-0.406
-0.32278 %
0.009
72 %
-0.254
95 %
80 %
-0.276
-0.218
80 %
78 %
78 %
-0.439
-0.206 -0.421
72 %
Average models
Quercus robur
Y axis
(Sum TS2-SumTS1)SumTS1
Quercus robur
Hypothesis: Tests
TS2 Climate
T°C of TS1
TS2 Climate
1
2Rainfall of TS1
TS2 Climate
3CO2 TS1
ExamplesBIOMOD
NE
(TS
2-T
S1)
/TS
1
PHENOFIT
T°C TS1
V SaôneNE
Rainfall TS1
PHENOFIT
Rainfall TS1
(TS
2-T
S1)
/TS
1BIOMOD
CO2 TS1
AlpsLPJ
(TS
2-T
S1)
/TS
1
NE
CO2 TS1
Quercus robur
Jura
T°C TS1
LPJ Jura
T°C TS1
LPJ
(TS
2-T
S1)
/TS
1 NE
T°C TS1
0: No forest 1: Absence2: Presence
Alps
Jura
V Saône
Alsace
VosgesNENW
SW
Brittany
Pyrenees
Center
85 %
-0.245
98 %
-0.165
100 %
-0.073
88 %-0.177
98 %
-0.267
0.013
99 %
61 %
-0.022
95 %
-0.343
-0.190
86 %
79 %
-0.483
-0.234
80 %
Average models
TeBS
Y axis
(Sum TS2-SumTS1)SumTS1
Temperate Broadleaf Summergreen
TS2 Climate
T°C of TS1
TS2 Climate
1
2Rainfall of TS1
TS2 Climate
3CO2 TS1
BIOMOD
ORCHIDEE
CO2 TS1
Alps
(TS
2-T
S1)
/TS
1
NE
CO2 TS1
ORCHIDEE(T
S2-
TS
1)/T
S1
Alps NE
Rainfall TS1
Rainfall TS1
(TS
2-T
S1)
/TS
1
ORCHIDEEAlps
T°C TS1
NE
T°C TS1
(TS
2-T
S1)
/TS
1
Alps NE
T°C TS1
T°C TS1
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