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