Using CAPSIS to assess the genetic impacts of sylviculture

Transcription

1 Using CAPSIS to assess the genetic impacts of sylviculture R&D project funded by RMT AFORCE & CG Vaucluse INRA (Avignon, Montpellier, Orléans) ONF (RD&I, 84) CNPF-IDF PNR Luberon & CG84 SF-CDC François Lefèvre, INRA URFM, Avignon

2 Climate change : not just a change of state but a state of change IPCC 2013 forestry horizon multiple uncertainties : global scenarios & local impacts extreme events complexe interactions impact of adaptive measures

3 Short-term : more constraint, less uncertainty => adaptation Long-term : less constraint, more uncertainty => preserve options IPCC 2013 forestry horizon Anticipate risks and opportunities, consider uncertainties for each decision, identify short-term and long-term benefits and risks anticipate and manage possible trade-offs between short- vs long-term

4 Rationale of this project 1) Genetic diversity is a lever for adaptation in the context of change and uncertainties

5 Pinus radiata : extended climatic use range after breeding and selection Native range & use range Mean annual precipitation Tmean coldest month Tmean hottest month California (5 pops) N-Z (Southland) N-Z (Kaingaroa) Chile (Valdivia) South Afr. (Cap) Aust. (Bathurst) Aust. (Tumut) China (Aba,Sichuan) Yan et al (2006) For Ecol Manag

6 Introduction of Cedrus atlantica in France French provenances perform better in the provenance tests Height Diameter

7 Introduction of Picea abies in Norway Rapid genetic changes in phenology in 1 generation after transplantation % budset Date % budset Skrøppa et al 2010 Tree Genet Genomes Date

8 but everything is not possible, species' niches still have limits and there are empty niches, there are also constraints that limit adaptation : 1. genetic constraints 2. developmental constraints 3. lack of genetic diversity 4. demographic stochasticity 5. random genetic drift 6. low mortality 7. asymetric gene flow (e.g. niche limit) Futuyma 2010 Evolution ; Kuparinen et al 2010 For Ecol Manag

9 Rationale of this project 1) Genetic diversity is a lever for adaptation in the context of change and uncertainties 2) Forests generally harbor a large genetic diversity that contribute to their evolvability, i.e. genetic flexibility

10 Trees have more genetic diversity than other organisms Hamrick et al 1992 New Forests trees annual plants nb species mean nb pop. mean nb loci total diversity (HeT) within pop diversity (Hs) differentiation (FST) > > < Some trees have much less genetic diversity than others (Pinus pinea, Pinus resinosa ) Current genetic diversity is determined by : - phylogeny - ancient processes - current processes

11 A large genetic diversity within stands, even for functional traits 19 traits 59 tree species Phenology traits, 27 European conifers range 16 sp. fragmented 11 sp. continuous mean He mean FST mean QST Alberto et al 2013 Global Change Biol

12 Rationale of this project 1) Genetic diversity is a lever for adaptation in the context of change and uncertainties 2) Forests generally harbor a large genetic diversity that contribute to their evolvability, i.e. genetic flexibility 3) Evolvability is variable, locally driven by neutral processes and selection on which practices may have a significant impact

13 Impact of density on mating success in Pinus sylvestris Pichot et al, 2006

14 Impact of spatial arrangement on SGS of the regeneration Sagnard, 2001

15 Combination of genetic processes during introduction their combination may contribute to the performance Lefèvre et al, 2004 ; Karam, 2014

16 Evolution-oriented forest management A process-based approach to assess the impact of practices on FGR drivers forestry practice induced changes mechanical or chemical treatments impacted selection and genetic drift parameters environment (biotic / abiotic) local density environment (competition) systematic thinning spatial structure mating system (s, Nep) pruning allocation to reproduction var. reproductive success (V) selective thinning phenotypic selection P², A², Ne ( A², F) i Lefèvre et al 2014 Ann For Sci

17 Rationale of this project 1) Genetic diversity is a lever for adaptation in the context of change and uncertainties 2) Forests generally harbor a large genetic diversity that contribute to their evolvability, i.e. genetic flexibility 3) Evolvability is variable, locally driven by neutral processes and selection on which practices may have a significant impact 4) Simulations are required to test innovative forest management practices, e.g. combining natural regeneration and plantation systems

18 Evolution-oriented forest management Genetic impacts of management practices on the dynamics of adaptation (100 or 200 years)? Plantation after clear-cut (static /dynamic) Genetic enrichment plantation Environmental engineering Systematic thinnings (intensity, regime) Selective thinnings (criteria, intensity) Prunning forestry horizon

19 Objectives of the project Develop a simulation tool to compare management options in different contexts, and implement in 2 case studies Integrate genetic diversity and processes in simulation tools currently used by the R&D to assess the genetic impacts of sylviculture on the current population and the next generations of trees User friendly interface to parameterize simulations and analyse output indicators of genetic impacts Simple demo-genetic model to assess genetic impacts, not functional, sanitary or economic impacts

20 4) qu est-ce qu un modèle démo-génétique Indicators of genetic impacts (CAPSIS or R?) Demo-genetic processes (CAPSIS) Initial genetic diversity & structure (quantinemo, Metagene)

21 Management options and contexts 1) Forest dynamics with regeneration and disturbance regime 2) Practices related to the introduction of new genetic material Impacts of the design of introduction of new genetic material on the local and the introduced gene pools Impacts of the design of introduction on the neighboring plot Rational management of genetic mixtures 3) Practices related to stand management Impacts of adaptation-oriented sylviculture practices in even-aged and non even-aged stands Valorize environmental heterogeneity to foster genetic adaptation Reserve of long lasting old trees New practices of evolution-oriented forest management

22 Demo-genetic parameters and indicators 1) Demo-genetic parameters and processes Genetic architecture of growth and survival performance with trade-off vigour x resistance to disturbance Inbreeding depression Individual and temporal variation of reproduction Pollen and seed flow 2) Indicators of genetic impact (at least 2 generations) Demography (survival, growth, reproduction), mean and variance Sensitivity to disturbance, mean and variance Genetic diversity (various parameters), on QTL and global Evolvability (various parameters) Inbreeding, mean and variance Spatial genetic structure...

23

24

25 Evolution-oriented forest management Innovative practices Lefèvre et al 2014 Ann For Sci

26 Evolution-oriented forest management Innovative practices Lefèvre et al 2014 Ann For Sci

27 Some references Savolainen O, Kärkkäinen K (1992) Effect of forest management on gene pools. New Forests 6: Lefèvre F (2004) Human impacts on forest genetic resources in the temperate zone: an updated review. Forest Ecology and Management, 197: Dreyfus et al (2005) Couplage de modèles de flux de gènes et de modèles de dynamique forestière. Les Actes du BRG, 5: Oddou-Muratorio et al (2005) Comment les pratiques forestières influent-elles sur la diversité génétique des arbres forestiers? RdVT ONF, hs n 1:3-6, Pichot et al (2006) Déterminants et conséquences de la qualité génétique des graines et semis lors de la phase initiale de régénération naturelle des peuplements forestiers. Les Actes du BRG, 6: Valadon A (2009). Effets des interventions sylvicoles sur la diversité génétique des arbres forestiers, analyse bibliographique. Les Dossiers Forestiers de l ONF, N 21, 157p Lefèvre et al (2014) Considering evolutionary processes in adaptive forestry. Annals of Forest Science, 71: Lefèvre et al (2015) Les processus biologiques de réponse des arbres et forêts au changement climatique : adaptation et plasticité phénotypique. Innovations Agronomiques, 47:63-79.