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 stratégiesd'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 5. Réponses: Atténuation Adaptation 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

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 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 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 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 10 35 E.g., Simulated mean August temperatures in 2098 E.g., current distribution of Fagus sylvatica (Common beach)

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 • PHENOFIT (Chuine and Beaubien 2001. I. Chuine, CEFE Montepellier) « Phenology – Based » Model • CASTANEA (E. Dufrêne et al, 2005. C. François and A. Cheaib, ESE Orsay) Tree C balance and Growth • 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) CO2 TS3 TS2 668 Average CO2 TS1 544 346 Time Slice1 1971- 2000 Time Slice 2 2046 - 2065 Time Slice 3 2079-2098 2050  2.85°C mean temperature increase 2050  200 mm/yeardecrease in precipitation

Laboratoire d’Ecologie, Systématique et Evolution ACC – Paris, Sept 2010 Fagus sylvaticaEuropean beechHêtre 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 TSS = 0.66 TSS = 0.18 BIOMOD Nancy NBM STASH TSS = 0.16 TSS = 0.48 TSS = 0.33 Current distribution (IFN) PHENOFIT CASTANEA LPJ

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 2050 - 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

Fagus sylvatica Examples Tests of mechanisms BIOMOD PHENOFIT NE 1 Jura Niche models show a strong negative response to warming, this response is weaker or even reversed in mechanistic models Current Temp (TS2-TS1)/TS1 2050 Temp 2050 Temp Current Temp PHENOFIT CASTANEA Alsace V Saône 2 2050 Rainfall Current Rainfall Current Rainfall Mechanistic models are very responsive to reductions in precipitation (TS2-TS1)/TS1 2050 Rainfall CASTANEA Alps NE 2050 CO2 (TS2-TS1)/TS1 3 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

Laboratoire d’Ecologie, Systématique et Evolution ACC – Paris, Sept 2010 • 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 Beech: Take home messages

Laboratoire d’Ecologie, Systématique et Evolution ACC – Paris, Sept 2010 Pinus sylvestrisScots pinePin sylvestre

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

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 % Jura Y axis -0.943 -0.098 72 % (Sum TS2-SumTS1) SumTS1 SW 68 % Alps 16 % -0.082 -0.932 Center Average models 38 % Pyrenees -0.555 -0.247 0: No forest 1: Absence 2: Presence

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 LPJ All models are relatively insensitive to reductions in precipitation PHENOFIT Vosges NE (TS2-TS1)/TS1 Current Rainfall 2050 Rainfall LPJ NE Rising CO2 attenuates climate change impacts in mechanistic models 3 Vosges (TS2-TS1)/TS1 2050 CO2 Current CO2

Laboratoire d’Ecologie, Systématique et Evolution ACC – Paris, Sept 2010 • 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. Scots Pine: Take home messages

Laboratoire d’Ecologie, Systématique et Evolution ACC – Paris, Sept 2010 Quercus ilexholm oakchêne vert

Quercus ilex 2050 2050 2050 0: No forest 1: Absence 2: Presence BIOMOD NBM Nancy STASH 2050 2050 2050 LPJ ORCHIDEE 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 evapo-transpiration 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 • 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 Conclusions

15-16 Sept, Paris

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

Quercus robur NE Vosges NW -0.254 0.009 -0.276 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

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 CO2 TS1 (TS2-TS1)/TS1 TS2 Climate CO2 TS1

TeBS NE Vosges NW -0.165 -0.073 -0.245 100 % Alsace 85 % 98 % 88 % 0: No forest 1: Absence 2: Presence 80 % -0.177 98 % V Saône Brittany -0.267 Y axis 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

Temperate Broadleaf Summergreen BIOMOD Alps NE 1 T°C of TS1 T°C TS1 (TS2-TS1)/TS1 T°C TS1 TS2 Climate 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

Paul Leadley Laboratoire ESE Université Paris-Sud