Forest simulation models in France : main developments and challenges WG1 J-D Bontemps, C Meredieu

1. COST ACTION FP0603: Forest models for research and decision support in sustainable forest management COUNTRY REPORTING TEMPLATE Forest simulation models in France : main developments and challenges WG1 J-D Bontemps, C Meredieu 1st Workshop and Management Committee Meeting.Institute of Silviculture, BOKU.8-9 of May 2008Vienna, Austria

2. Main features of French forests • Forest cover (total/share): 15.7 million ha, 28.6% of the territory (1,2) • Growing stock, annual growth and cuts: 2.4 billion m3, 103.1 million m3/yr, fellings around 61 million m3 /yr (1998-2002) including windfall volumes, felling rate ~ 60% (1) • Main species: (by decreasing total growing stock) - sessile oak/Quercus petraea (Liebl.), pedunculate oak/Quercus robur (L.), common beech/Fagus sylvatica (L.), norway spruce/Picea abies (Karst.), maritime pine/Pinus pinaster (Ait.), silver fir/Abies alba (Mill.), Scots pine/Pinus sylvestris (L.) (1) • Main non-wood products and services: protection forests for erosion/landscapes (1/5 of total area mainly in mountain regions : French Alps & Pyrennées), hunting activities (7-35% of income), recreation, water quality (unquantified), biodiversity (1% of total area) • Main risks: storm damage (regular dense & high stands), fires (Mediterranean areas, between 10000 and 70000 ha/yr) • Management and silvicultural characteristics : • even-aged multi-purpose but production dominated forestry in plains (naturally regenerated hardwoods, planted softwoods), hardwoods : rotation age ~ 130 - 170 yr - high-dimension quality-wood targeted / softwoods : rotation age ~ 40-80 yr, medium-dimension/quality wood targeted, short production cycles • coniferous plantations as protection or production forests or pure/mixed coniferous-dominated close-to-nature forestry in mountain areas • French Guyana ? (1) IFN (2006). The French Forest, 141p. (2) according to the FAO definition

3. Forest modelling approaches and trends Empirical models Main types of models developed (by order of recent importance) Distance-dependent tree (DDT) models Distance-independent tree (DIT) models Stand with DIT downscaling models Trends in modelling DDT model development (and gap model) for mixed and/or irregular stands Hybrid models : explicit process incorporation in DDT models - light resource and competition - reproduction, regeneration & mortality Coupling between empirical models & quality/risk/economy/visualization modules Recent research is concentrating on Understanding of mixed-stands dynamics Knowledge integration (connexions of models to : 1- upstream (resource/climate/nutrition), 2- downstream (quality/risk/economy) environments

4. Forest modelling approaches and trends Mechanistic models Which exist ? - Castanea (Dufrêne et al, 2005, Ecological Modelling) for several species in pure-regular stands - GRAECO (Porté, 2001 ; Bosc et al., 2005 only French papers) for Martime pine in pure-regular stands - Hybrid models : SAMSARA (DDT, Courbaud et al, 2003) + light resource Main features - ecosystem forest models with explicit connexion to the environment light, water, temperature - simulation of NPP - ongoing research on allocation processes

5. Modelling non-timber products and services • Prediction of Dead Wood as an indicator of biodiversity Brin et al., 2008 FEM (in press) For Maritime pine stands, connecting with thinnings and clear cut • Impact of species substitution on Carbon storage Vallet et al., 2008 FEM (in press) Substitution of a slow-growing hardwood species (Quercus petraea) by a fastgrowing conifer plantation (Pinus nigra subsp. laricio)

6. Models for predicting risk of hazards Models for predicting damages (non-exhaustive list) are developed as generic modules that can be coupled to existing G&Y empirical models (mechanistic models currently excluded) : - Wind Damage - “Biomechanics” : tree biomechanics for predicting wind damage in a forest stand (Ancelin & al, 2004, Forest Ecology and Management) - Mechanical resistance with static winching tests (Cucchi & al, 2004 ; 2005, Forest Ecology and Management) - Rock fall Risk - “RockforNET” : quantifying downstream rockfall risk in protection forests (Stoffel & al, 2005, 2006, Forest Ecology and Management) - Fire Risk - “Fire Paradox” : wildland fire management by the wise-use of fire and post fire dynamics (http://www-capsis.cirad.fr/models  Fire paradox)

7. Simulators and information systems • Most French Growth&Yield models are - or in the process of being - implemented on the CAPSIS plateform on a free-license simulator (see http://www-capsis.cirad.fr/ and next slide) • Therefore, they are available for use by forest managers, and suppose exchanges between users and modellers. Rather used for building realistic sivlvicultural guidelines than locally optimizing a resource • No process-based model is of current use for decision support • Illustrations for empirical models : • “Fagacees”, “PNN”, “Sylvestris”, “PP3” are models for regular stand and were used a for drawing silvicutural guides from the French Forest Service (Jarret, ONF, 2004 ; Sardin, ONF, 2005...) • “Mountain” & “Samsara” (DDT models) have been at use for Alps silvicultural guide • “Eucalypt” encloses a GIS-connexion for simulating extended resource in an area • Maritime Pine models are able to use FNI plots to provide ressource evaluation in a regional simulator “Sylvogène”

8. Research highlight CAPSIS Project : http://www-capsis.cirad.fr/ Development of an integrative simulation plateform aimed at : - integrating forest production and dynamics models with consideration for ergonomy and tool interactivity - developing generic simulation tools useable for all related modelling approaches (virtual thinning, graphs...) - favouring connexions between others tools (data bases, GIS, others software) - intended for forest modellers, managers and educational purposes  More than 60 ongoing projects

9. Future challenges - Knowledge integration in CAPSIS plateform (ergonomy, universality) - Connexion of models to wood quality/genetic improvement/risk/economy modeling extensions - Connexion of empirical models to environment in the context of climate change : either statistical or process-based - Landscape simulator with GIS-connexion to test the effect of spatial effects : edges, recruitment, harvested areas… - Regional/ National simulator to provide resource information - Structure-function modeling linking architecture and functioning : quantitative/qualitative assessment of resource - For process-based models : NPP allocation to tree/stand compartments, spatial upscaling using satellite data for calibration

10. Innovative references On coupling with risk modules : - Ancelin, Courbaud, Fourcaud (2004). Development of an individual tree-based mechanical model to predict wind damage within forest stands, For Ecol Manage, 203:101-121. On connecting empirical models to environment (coupling with G&Y simulators) : - Seynave, Gégout, Hervé et al (2005). Picea abies site index prediction by environmental factors and understorey vegetation : a two-scale approach based on survey databases. Can J For Res, 35:1669-1678 On dynamics of heterogeneous stands (Guyana tropical forest) : • Picard, Bar-Hen, Franc (2001). Modelling forest dynamics with a combined matrix/individual-based model. For Sci, 48:643-652 On coupling gene fluxes and forest dynamic model : - Dreyfus Ph. et al. (2005). Couplage de modèles de flux de gènes et de modèles de dynamique forestière. Un dialogue pour la diversité génétique - Actes du 5ème colloque national BRG, Lyon, On hybrid modeling : - Courbaud, Coligny de, Cordonnier (2003). Simulating radiation distribution in a heterogenous Norway spruce forest on a slope. Ann For Sci, 116:1-18. - Courbaud, Coligny de, Goreaud (submitted). An individual model of competition for light allows to simulate coherently the development patterns of dense monospecific forest stands, Can J For Res. On structure-function modeling : - Yan, Kang, De Reffye, Dingkuhn (2004). A Dynamic, Architectural Plant Model Simulating Resource-dependent Growth. Ann Bot, 93:591 - 602.