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Paul Leadley Laboratoire ESE Université Paris-Sud

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1 Paul Leadley Laboratoire ESE Université Paris-Sud
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

2 Sources of uncertainty in projections
Laboratoire d’Ecologie, Systématique et Evolution ACC – Paris, Sept 2010 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 Ecosystémiques Ex : provisioning, regulating, sustaining and cutural services 5. Réponses: Atténuation Adaptation

3 Model projections of climate change impacts on French forests
Laboratoire d’Ecologie, Systématique et Evolution ACC – Paris, Sept 2010 Model projections of climate change impacts on French forests

4 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 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 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 • Representing - CNRS, INRA, CEA, ENGREF, Universities, the National Natural History Museum, National Botanical Conservatory in Paris • Isabelle Chuine now team leader for CEFE Montpellier • Occasionally low % of time of seniors, but comprised of teams that have always had a strong focus on climate or CO2 effects on vegetation (Exception of Arboretum and Bot. Conserv). Highly complementary to other ongoing research in these labs. Strong implication of teams in terms of junior scientists, post-docs, graduate students and technicians.

5 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 Paris-Sud XI Orsay Cedex France 2 UMR 1137, INRA UHP, Forest Ecology and Ecophysiology, Phytoecology Team, route de la Forêt-d’Amance, Champenoux, France 3 Centre d’Ecologie Fonctionnelle et Evolutive, Equipe BIOFLUX, CNRS, 1919 route de Mende, Montpellier cedex 4 CNRS Meteo-France – Toulouse, France 5 Laboratoire d’ Écologie Alpine, UMR CNRS 5553 , Université´ J. Fourier, BP 53, Grenoble Cedex 9 France 6 Laboratoire des Sciences du Climat et de l’Environnement, CEA/ CNRS, Saclay, France.

6 Treating UNCERTAINTY in modeling climate impacts on forests: an example from the ANR QDiv project
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 High resolution (8 km) climate scenarios Collaboration with CERFACS (L. Terray) High resolution (8 km) maps of current tree distributions Collaboration IFN (C. Cluzeau) Assessment of climatic risk for: Quercus robur Quercus petraea Fagus sylvatica Pinus sylvestris Quercus ilex + Plant functional groups High resolution maps of key soil properties Furnished by INRA Orléans • I will use 5 examples to illustrate how we will address our key questions and how we will make quantitative comparisons within QDiv. • Example: Key question: what does "committed to extinction" mean? • Generate maps of risk for forests and key tree species in France 10 35 E.g., Simulated mean August temperatures in 2098 E.g., current distribution of Fagus sylvatica (Common beach)

7 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, W. Thuiller, Grenoble) STASH (Sykes et al, E. Gritti, CEFE Montpellier) Mechanistic approaches PHENOFIT (Chuine and Beaubien I. Chuine, CEFE Montepellier) « Phenology – Based » Model CASTANEA (E. Dufrêne et al, C. François and A. Cheaib, ESE Orsay) Tree C balance and Growth Dynamic Global Vegetation Models (DGVMs) ORCHIDEE (Krinner et al, N. Viovy CEA)  IBIS (Kucharik et al, C. Delire Meteo France)  LPJ (Stich et al, E. Gritti, CEFE Montpellier)

8 Climate scenario (regionalized Arpège) : The A1B Story line
~ 8989 pixels in France: Spatial resolution 8Km x 8Km (L. Terray, CERFACS, Meteo France) CO2 TS3 TS2 668 Average CO2 TS1 544 346 Time Slice1 Time Slice 2 Time Slice 3 2050  2.85°C mean temperature increase 2050  200 mm/year decrease in precipitation

9 Fagus sylvatica European beech Hêtre commun
Laboratoire d’Ecologie, Systématique et Evolution ACC – Paris, Sept 2010 Fagus sylvatica European beech Hêtre commun

10 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 TSS = 0.66 TSS = 0.18 BIOMOD Nancy NBM STASH TSS = 0.16 TSS = 0.33 TSS = 0.48 Current distribution (IFN) PHENOFIT CASTANEA LPJ

11 Projections of distribution
Fagus sylvatica 2050 Projections of distribution NE Vosges NW -0.174 -0.40 Alsace -0.453 100 % 48 % -0.55 58 % 84 % V Saône 53 % 70 % Y axis Brittany -0.479 (Sum Sum Current) Sum Current Jura 99% -0.553 SW 82 % -0.038 16 % Alps 58 % -0.70 0.073 Average response 75 % Center Pyrenees -0.426 0: No forest 1: Absence 2: Presence -0.16

12 Projections of distribution
Fagus sylvatica 2050 Projections of distribution 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 % 70 % 99% 53 % -0.553 -0.70 16 % 75 % -0.16 82 % -0.426 Y axis (Sum Sum Current) Sum Current 0: No forest 1: Absence 2: Presence

13 Projections of distribution
Fagus sylvatica 2050 Projections of distribution NE Vosges NW -0.174 -0.40 Alsace -0.453 100 % 48 % -0.55 58 % 84 % V Saône 53 % 70 % Y axis Brittany -0.479 (Sum Sum Current) Sum Current Jura 99% -0.553 SW 82 % -0.038 16 % Alps 58 % -0.70 0.073 Average response 75 % Center Pyrenees -0.426 0: No forest 1: Absence 2: Presence -0.16

14 Fagus sylvatica Tests of mechanisms Examples
BIOMOD PHENOFIT NE 1 Niche models show a strong negative response to warming, this response is weaker or even reversed in mechanistic models Jura (TS2-TS1)/TS1 Current Temp 2050 Temp 2050 Temp Current Temp PHENOFIT CASTANEA V Saône Alsace 2 2050 Rainfall Current Rainfall Mechanistic models are very responsive to reductions in precipitation Current Rainfall (TS2-TS1)/TS1 2050 Rainfall CASTANEA Alps NE 2050 CO2 3 (TS2-TS1)/TS1 Rising CO2 offsets negative climate change impacts in mechanistic models (not accounted for in niche models) 2050 CO2 Current CO2 Current CO2 LPJ NE Alps (TS2-TS1)/TS1

15 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

16 Pinus sylvestris Scots pine Pin sylvestre
Laboratoire d’Ecologie, Systématique et Evolution ACC – Paris, Sept 2010 Pinus sylvestris Scots pine Pin sylvestre

17 Current distribution simulations and model evaluation
Pinus sylvestris TSS = 0.48 TSS = 0.30 TSS = 0.25 BIOMOD Nancy NBM STASH TSS = 0.26 TSS = 0.26 PHENOFIT LPJ Current distribution (IFN) TSS = 0.03 TSS = 0.07 Needleleaf Evergreen ORCHIDEE IBIS

18 Pinus sylvestris Projections for 2050
NE Vosges NW -0.310 -0.678 -0.922 82 % Alsace 41 % 46 % 55 % -0.661 V saône 33 % 35 % Brittany -0.708 49 % Y axis Jura -0.943 -0.098 72 % (Sum TS2-SumTS1) SumTS1 SW 68 % Alps 16 % -0.082 -0.932 Average models Center 38 % Pyrenees -0.555 -0.247 0: No forest 1: Absence 2: Presence

19 Pinus sylvestris Tests of mechanisms
BIOMOD Vosges NE T°C TS1 Current Temp (TS2-TS1)/TS1 1 All models are highly sensitve to warming 2050 Temp PHENOFIT Vosges NE (TS2-TS1)/TS1 2 All models are relatively insensitive to reductions in precipitation LPJ PHENOFIT Vosges NE (TS2-TS1)/TS1 2050 Rainfall Current Rainfall LPJ 3 Rising CO2 attenuates climate change impacts in mechanistic models NE Vosges (TS2-TS1)/TS1 2050 CO2 Current CO2

20 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.

21 Quercus ilex holm oak chêne vert
Laboratoire d’Ecologie, Systématique et Evolution ACC – Paris, Sept 2010 Quercus ilex holm oak chêne vert

22 Quercus ilex 2050 2050 2050 2050 2050 2050 Evergreen Broadleaf
0: No forest 1: Absence 2: Presence BIOMOD NBM Nancy STASH 2050 2050 2050 LPJ ORCHIDEE IBIS Evergreen Broadleaf

23 Forest plant diversity
Laboratoire d’Ecologie, Systématique et Evolution ACC – Paris, Sept 2010 Forest plant diversity

24 Arpège B2 Climate simulation + Statistical distrubution model
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.

25 Adaptation: What to do in the face of uncertain impacts?
Laboratoire d’Ecologie, Systématique et Evolution ACC – Paris, Sept 2010 Adaptation: What to do in the face of uncertain impacts?

26 Adaptation of French forests to climate change
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

27 Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010

28 Examples of adaptive management strategies
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

29 Reinforce “natural” processes to increase resilience
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 evapo-transpiration through management of leaf area

30 Laboratoire d’Ecologie, Systématique et Evolution
ACC – Paris, Sept 2010 Reduce exposure to climate change Old growth Douglas fir

31 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

32 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

33 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

34 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

35 15-16 Sept, Paris

36 Programme Phare: Modélisation et scénarios de la biodiversité
The way forward Programme Phare: Modélisation et scénarios de la biodiversité ‘Humboldt’ project

37

38 Quercus robur Y axis NE Vosges NW -0.254 0.009 -0.276 Alsace -0.322
72 % Alsace 80 % 78 % 0: No forest 1: Absence 2: Presence 95 % -0.322 V Saône 80 % 85 % Y axis -0.406 Brittany Jura 75 % (Sum TS2-SumTS1) SumTS1 -0.218 72 % 0.128 SW Alps 15 % 78 % -0.439 0.039 Average models 78 % Center Pyrenees -0.206 -0.421

39 Quercus robur Quercus robur Hypothesis: Tests Examples
BIOMOD PHENOFIT 1 NE Jura T°C of TS1 (TS2-TS1)/TS1 TS2 Climate T°C TS1 T°C TS1 LPJ LPJ Jura NE T°C TS1 T°C TS1 (TS2-TS1)/TS1 Rainfall of TS1 2 BIOMOD PHENOFIT NE V Saône TS2 Climate (TS2-TS1)/TS1 Rainfall TS1 Rainfall TS1 CO2 TS1 LPJ 3 Alps NE TS2 Climate (TS2-TS1)/TS1 CO2 TS1 CO2 TS1

40 TeBS Y axis NE Vosges NW -0.165 -0.073 -0.245 Alsace -0.177 V Saône
100 % Alsace 85 % 98 % 88 % 0: No forest 1: Absence 2: Presence 80 % -0.177 98 % V Saône Y axis Brittany -0.267 99 % Jura -0.234 (Sum TS2-SumTS1) SumTS1 95 % 0.013 SW 61 % Alps 86 % -0.483 -0.022 Average models 79 % Center Pyrenees -0.343 -0.190

41 Temperate Broadleaf Summergreen
BIOMOD Alps NE 1 T°C of TS1 T°C TS1 (TS2-TS1)/TS1 TS2 Climate T°C TS1 ORCHIDEE NE Alps T°C TS1 (TS2-TS1)/TS1 T°C TS1 Rainfall of TS1 2 ORCHIDEE Alps NE Rainfall TS1 TS2 Climate (TS2-TS1)/TS1 Rainfall TS1 CO2 TS1 ORCHIDEE 3 Alps NE (TS2-TS1)/TS1 TS2 Climate CO2 TS1 CO2 TS1


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