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Slide 1 - Forest simulation models in Switzerland: main developments and challenges WG1 COST ACTION FP0603: Forest models for research and decision support in sustainable forest management 1st Workshop and Management Committee Meeting. Institute of Silviculture, BOKU. 8-9 of May 2008 Vienna, Austria
Slide 2 - Forest simulation models in Switzerland: main developments and challenges WG1 COST ACTION FP0603: Forest models for research and decision support in sustainable forest management 1st Workshop and Management Committee Meeting. Institute of Silviculture, BOKU. 8-9 of May 2008 Vienna, Austria Main features of Swiss forests (BAFU, Steckbrief Schweizer Wald, 2nd Swiss National Forest Inventory, 1999) Forest cover (total, share): 1.2 Mio ha, 30% Timber growing stock: 418 Mio m3 annual growth: 7.4 Mio m3/year cuts: 5.7 Mio. m3/year Main species: Norway spruce (48%), Beech (17%), Fir (15%), Larch (5%), Pine (3.5%) Main non-wood products and services: Protection against rockfall and avalanches CO2 sequestration Biodiversity the herb layer and of the landscape Recreation and scenic beauty (walking, nordic ski) (typical image of wooded pastures in Swiss mountains) In wooded pastures: forage, milk, meat Regulation of water-household
Slide 3 - Forest simulation models in Switzerland: main developments and challenges WG1 COST ACTION FP0603: Forest models for research and decision support in sustainable forest management 1st Workshop and Management Committee Meeting. Institute of Silviculture, BOKU. 8-9 of May 2008 Vienna, Austria Main features of Swiss forests (BAFU, Steckbrief Schweizer Wald, 2nd Swiss National Forest Inventory, 1999) Forest cover (total, share): 1.2 Mio ha, 30% Timber growing stock: 418 Mio m3 annual growth: 7.4 Mio m3/year cuts: 5.7 Mio. m3/year Main species: Norway spruce (48%), Beech (17%), Fir (15%), Larch (5%), Pine (3.5%) Main non-wood products and services: Protection against rockfall and avalanches CO2 sequestration Biodiversity the herb layer and of the landscape Recreation and scenic beauty (walking, nordic ski) (typical image of wooded pastures in Swiss mountains) In wooded pastures: forage, milk, meat Regulation of water-household Main features of Swiss forests Main risks… … against which the forests protect avalanche formation rockfall …to forests Reduced protection function due to too old stands Climate change Inadequate species in already dry regions due to climate change Increased pest abundance (e.g. bark beetle) Changes in landscape structure due to segregation of land use and climate change: separation of closed forests and open grasslands, increased aggregation of land cover, decline of dominant species (Norway spruce and larch) Management and silvicultural characteristics: Small clear cuts Single tree felling (Plenterwirtschaft)
Slide 4 - Forest simulation models in Switzerland: main developments and challenges WG1 COST ACTION FP0603: Forest models for research and decision support in sustainable forest management 1st Workshop and Management Committee Meeting. Institute of Silviculture, BOKU. 8-9 of May 2008 Vienna, Austria Main features of Swiss forests (BAFU, Steckbrief Schweizer Wald, 2nd Swiss National Forest Inventory, 1999) Forest cover (total, share): 1.2 Mio ha, 30% Timber growing stock: 418 Mio m3 annual growth: 7.4 Mio m3/year cuts: 5.7 Mio. m3/year Main species: Norway spruce (48%), Beech (17%), Fir (15%), Larch (5%), Pine (3.5%) Main non-wood products and services: Protection against rockfall and avalanches CO2 sequestration Biodiversity the herb layer and of the landscape Recreation and scenic beauty (walking, nordic ski) (typical image of wooded pastures in Swiss mountains) In wooded pastures: forage, milk, meat Regulation of water-household Main features of Swiss forests Main risks… … against which the forests protect avalanche formation rockfall …to forests Reduced protection function due to too old stands Climate change Inadequate species in already dry regions due to climate change Increased pest abundance (e.g. bark beetle) Changes in landscape structure due to segregation of land use and climate change: separation of closed forests and open grasslands, increased aggregation of land cover, decline of dominant species (Norway spruce and larch) Management and silvicultural characteristics: Small clear cuts Single tree felling (Plenterwirtschaft) Static PNV Selected tree species Treeline Mountland Ecophys. BIOME-BGC, CLM, LPJ-(GUESS) SEIB DGVM Plant hydraulic model Landscape TreeMig WoodPam MEPHYSTO LandClim ForLand MASSIMO improved Growth/yield MASSIMO EFISCEN Integrated Cost/benefit protection forest m. ForClim (gap m.) DisCForM Forece (gap.m.) Population dynamics Markov chain m. Forest modelling approaches and trends ForClim improved Treeline dynamics/ land use
Slide 5 - Forest simulation models in Switzerland: main developments and challenges WG1 COST ACTION FP0603: Forest models for research and decision support in sustainable forest management 1st Workshop and Management Committee Meeting. Institute of Silviculture, BOKU. 8-9 of May 2008 Vienna, Austria Main features of Swiss forests (BAFU, Steckbrief Schweizer Wald, 2nd Swiss National Forest Inventory, 1999) Forest cover (total, share): 1.2 Mio ha, 30% Timber growing stock: 418 Mio m3 annual growth: 7.4 Mio m3/year cuts: 5.7 Mio. m3/year Main species: Norway spruce (48%), Beech (17%), Fir (15%), Larch (5%), Pine (3.5%) Main non-wood products and services: Protection against rockfall and avalanches CO2 sequestration Biodiversity the herb layer and of the landscape Recreation and scenic beauty (walking, nordic ski) (typical image of wooded pastures in Swiss mountains) In wooded pastures: forage, milk, meat Regulation of water-household Main features of Swiss forests Main risks… … against which the forests protect avalanche formation rockfall …to forests Reduced protection function due to too old stands Climate change Inadequate species in already dry regions due to climate change Increased pest abundance (e.g. bark beetle) Changes in landscape structure due to segregation of land use and climate change: separation of closed forests and open grasslands, increased aggregation of land cover, decline of dominant species (Norway spruce and larch) Management and silvicultural characteristics: Small clear cuts Single tree felling (Plenterwirtschaft) Static PNV Selected tree species Treeline Mountland Ecophys. BIOME-BGC, CLM, LPJ-(GUESS) SEIB DGVM Plant hydraulic model Landscape TreeMig WoodPam MEPHYSTO LandClim ForLand MASSIMO improved Growth/yield MASSIMO EFISCEN Integrated Cost/benefit protection forest m. ForClim (gap m.) DisCForM Forece (gap.m.) Population dynamics Markov chain m. Forest modelling approaches and trends ForClim improved Treeline dynamics/ land use Forest modelling approaches and trends Empirical models Approaches Several static models for distribution of Potential natural vegetation Tree species Timberline position (Gehrig-Fasel, 2005) Application of EFISCEN MASSIMO (Kaufmann, 2001) Individual based, stochastic growth model NFI derived Markov-Chain models Recent research is concentrating in: Recalibration of MASSIMO with latest NFI data (2004-2007)  Growth function, harvesting probabilities, regeneration, mortality Trends in modelling Impact of climate change in MASSIMO on growth function, tree species composition and mortality Long-term harvesting potential (30-100 years)
Slide 6 - Forest simulation models in Switzerland: main developments and challenges WG1 COST ACTION FP0603: Forest models for research and decision support in sustainable forest management 1st Workshop and Management Committee Meeting. Institute of Silviculture, BOKU. 8-9 of May 2008 Vienna, Austria Main features of Swiss forests (BAFU, Steckbrief Schweizer Wald, 2nd Swiss National Forest Inventory, 1999) Forest cover (total, share): 1.2 Mio ha, 30% Timber growing stock: 418 Mio m3 annual growth: 7.4 Mio m3/year cuts: 5.7 Mio. m3/year Main species: Norway spruce (48%), Beech (17%), Fir (15%), Larch (5%), Pine (3.5%) Main non-wood products and services: Protection against rockfall and avalanches CO2 sequestration Biodiversity the herb layer and of the landscape Recreation and scenic beauty (walking, nordic ski) (typical image of wooded pastures in Swiss mountains) In wooded pastures: forage, milk, meat Regulation of water-household Main features of Swiss forests Main risks… … against which the forests protect avalanche formation rockfall …to forests Reduced protection function due to too old stands Climate change Inadequate species in already dry regions due to climate change Increased pest abundance (e.g. bark beetle) Changes in landscape structure due to segregation of land use and climate change: separation of closed forests and open grasslands, increased aggregation of land cover, decline of dominant species (Norway spruce and larch) Management and silvicultural characteristics: Small clear cuts Single tree felling (Plenterwirtschaft) Static PNV Selected tree species Treeline Mountland Ecophys. BIOME-BGC, CLM, LPJ-(GUESS) SEIB DGVM Plant hydraulic model Landscape TreeMig WoodPam MEPHYSTO LandClim ForLand MASSIMO improved Growth/yield MASSIMO EFISCEN Integrated Cost/benefit protection forest m. ForClim (gap m.) DisCForM Forece (gap.m.) Population dynamics Markov chain m. Forest modelling approaches and trends ForClim improved Treeline dynamics/ land use Forest modelling approaches and trends Empirical models Approaches Several static models for distribution of Potential natural vegetation Tree species Timberline position (Gehrig-Fasel, 2005) Application of EFISCEN MASSIMO (Kaufmann, 2001) Individual based, stochastic growth model NFI derived Markov-Chain models Recent research is concentrating in: Recalibration of MASSIMO with latest NFI data (2004-2007)  Growth function, harvesting probabilities, regeneration, mortality Trends in modelling Impact of climate change in MASSIMO on growth function, tree species composition and mortality Long-term harvesting potential (30-100 years) Mechanistic models Approaches Population dynamical models Gap, distribution based models Ecophysiological models Plant water household model Applications of biogeochemical models and DGVMSs BIOME-BGC, LPJ, CLM Various landscape models Integrated models With disturbances Cost/Benefit Starting: with socioeconomy Trends in modelling Integrated models Merging of approaches Forest modelling approaches and trends
Slide 7 - Forest simulation models in Switzerland: main developments and challenges WG1 COST ACTION FP0603: Forest models for research and decision support in sustainable forest management 1st Workshop and Management Committee Meeting. Institute of Silviculture, BOKU. 8-9 of May 2008 Vienna, Austria Main features of Swiss forests (BAFU, Steckbrief Schweizer Wald, 2nd Swiss National Forest Inventory, 1999) Forest cover (total, share): 1.2 Mio ha, 30% Timber growing stock: 418 Mio m3 annual growth: 7.4 Mio m3/year cuts: 5.7 Mio. m3/year Main species: Norway spruce (48%), Beech (17%), Fir (15%), Larch (5%), Pine (3.5%) Main non-wood products and services: Protection against rockfall and avalanches CO2 sequestration Biodiversity the herb layer and of the landscape Recreation and scenic beauty (walking, nordic ski) (typical image of wooded pastures in Swiss mountains) In wooded pastures: forage, milk, meat Regulation of water-household Main features of Swiss forests Main risks… … against which the forests protect avalanche formation rockfall …to forests Reduced protection function due to too old stands Climate change Inadequate species in already dry regions due to climate change Increased pest abundance (e.g. bark beetle) Changes in landscape structure due to segregation of land use and climate change: separation of closed forests and open grasslands, increased aggregation of land cover, decline of dominant species (Norway spruce and larch) Management and silvicultural characteristics: Small clear cuts Single tree felling (Plenterwirtschaft) Static PNV Selected tree species Treeline Mountland Ecophys. BIOME-BGC, CLM, LPJ-(GUESS) SEIB DGVM Plant hydraulic model Landscape TreeMig WoodPam MEPHYSTO LandClim ForLand MASSIMO improved Growth/yield MASSIMO EFISCEN Integrated Cost/benefit protection forest m. ForClim (gap m.) DisCForM Forece (gap.m.) Population dynamics Markov chain m. Forest modelling approaches and trends ForClim improved Treeline dynamics/ land use Forest modelling approaches and trends Empirical models Approaches Several static models for distribution of Potential natural vegetation Tree species Timberline position (Gehrig-Fasel, 2005) Application of EFISCEN MASSIMO (Kaufmann, 2001) Individual based, stochastic growth model NFI derived Markov-Chain models Recent research is concentrating in: Recalibration of MASSIMO with latest NFI data (2004-2007)  Growth function, harvesting probabilities, regeneration, mortality Trends in modelling Impact of climate change in MASSIMO on growth function, tree species composition and mortality Long-term harvesting potential (30-100 years) Mechanistic models Approaches Population dynamical models Gap, distribution based models Ecophysiological models Plant water household model Applications of biogeochemical models and DGVMSs BIOME-BGC, LPJ, CLM Various landscape models Integrated models With disturbances Cost/Benefit Starting: with socioeconomy Trends in modelling Integrated models Merging of approaches Forest modelling approaches and trends Modelling non-timber products and services Static models, ForClim, TreeMig Species distributions after climate change Species suitabilities WoodPaM Forage production available for livestock Diversity indexes at patch and landscape scales Landscape aggregation index Planned models within MOUNTLAND Diversity indexes at patch and landscape scales Landscape aggregation index
Slide 8 - Forest simulation models in Switzerland: main developments and challenges WG1 COST ACTION FP0603: Forest models for research and decision support in sustainable forest management 1st Workshop and Management Committee Meeting. Institute of Silviculture, BOKU. 8-9 of May 2008 Vienna, Austria Main features of Swiss forests (BAFU, Steckbrief Schweizer Wald, 2nd Swiss National Forest Inventory, 1999) Forest cover (total, share): 1.2 Mio ha, 30% Timber growing stock: 418 Mio m3 annual growth: 7.4 Mio m3/year cuts: 5.7 Mio. m3/year Main species: Norway spruce (48%), Beech (17%), Fir (15%), Larch (5%), Pine (3.5%) Main non-wood products and services: Protection against rockfall and avalanches CO2 sequestration Biodiversity the herb layer and of the landscape Recreation and scenic beauty (walking, nordic ski) (typical image of wooded pastures in Swiss mountains) In wooded pastures: forage, milk, meat Regulation of water-household Main features of Swiss forests Main risks… … against which the forests protect avalanche formation rockfall …to forests Reduced protection function due to too old stands Climate change Inadequate species in already dry regions due to climate change Increased pest abundance (e.g. bark beetle) Changes in landscape structure due to segregation of land use and climate change: separation of closed forests and open grasslands, increased aggregation of land cover, decline of dominant species (Norway spruce and larch) Management and silvicultural characteristics: Small clear cuts Single tree felling (Plenterwirtschaft) Static PNV Selected tree species Treeline Mountland Ecophys. BIOME-BGC, CLM, LPJ-(GUESS) SEIB DGVM Plant hydraulic model Landscape TreeMig WoodPam MEPHYSTO LandClim ForLand MASSIMO improved Growth/yield MASSIMO EFISCEN Integrated Cost/benefit protection forest m. ForClim (gap m.) DisCForM Forece (gap.m.) Population dynamics Markov chain m. Forest modelling approaches and trends ForClim improved Treeline dynamics/ land use Forest modelling approaches and trends Empirical models Approaches Several static models for distribution of Potential natural vegetation Tree species Timberline position (Gehrig-Fasel, 2005) Application of EFISCEN MASSIMO (Kaufmann, 2001) Individual based, stochastic growth model NFI derived Markov-Chain models Recent research is concentrating in: Recalibration of MASSIMO with latest NFI data (2004-2007)  Growth function, harvesting probabilities, regeneration, mortality Trends in modelling Impact of climate change in MASSIMO on growth function, tree species composition and mortality Long-term harvesting potential (30-100 years) Mechanistic models Approaches Population dynamical models Gap, distribution based models Ecophysiological models Plant water household model Applications of biogeochemical models and DGVMSs BIOME-BGC, LPJ, CLM Various landscape models Integrated models With disturbances Cost/Benefit Starting: with socioeconomy Trends in modelling Integrated models Merging of approaches Forest modelling approaches and trends Modelling non-timber products and services Static models, ForClim, TreeMig Species distributions after climate change Species suitabilities WoodPaM Forage production available for livestock Diversity indexes at patch and landscape scales Landscape aggregation index Planned models within MOUNTLAND Diversity indexes at patch and landscape scales Landscape aggregation index Models for predicting risk of hazards Protection forest model LANDCLIM Fire-forest dyn. interaction Mountland model (Davos), starting Interaction between forest dynamics and avalanche (risk)
Slide 9 - Forest simulation models in Switzerland: main developments and challenges WG1 COST ACTION FP0603: Forest models for research and decision support in sustainable forest management 1st Workshop and Management Committee Meeting. Institute of Silviculture, BOKU. 8-9 of May 2008 Vienna, Austria Main features of Swiss forests (BAFU, Steckbrief Schweizer Wald, 2nd Swiss National Forest Inventory, 1999) Forest cover (total, share): 1.2 Mio ha, 30% Timber growing stock: 418 Mio m3 annual growth: 7.4 Mio m3/year cuts: 5.7 Mio. m3/year Main species: Norway spruce (48%), Beech (17%), Fir (15%), Larch (5%), Pine (3.5%) Main non-wood products and services: Protection against rockfall and avalanches CO2 sequestration Biodiversity the herb layer and of the landscape Recreation and scenic beauty (walking, nordic ski) (typical image of wooded pastures in Swiss mountains) In wooded pastures: forage, milk, meat Regulation of water-household Main features of Swiss forests Main risks… … against which the forests protect avalanche formation rockfall …to forests Reduced protection function due to too old stands Climate change Inadequate species in already dry regions due to climate change Increased pest abundance (e.g. bark beetle) Changes in landscape structure due to segregation of land use and climate change: separation of closed forests and open grasslands, increased aggregation of land cover, decline of dominant species (Norway spruce and larch) Management and silvicultural characteristics: Small clear cuts Single tree felling (Plenterwirtschaft) Static PNV Selected tree species Treeline Mountland Ecophys. BIOME-BGC, CLM, LPJ-(GUESS) SEIB DGVM Plant hydraulic model Landscape TreeMig WoodPam MEPHYSTO LandClim ForLand MASSIMO improved Growth/yield MASSIMO EFISCEN Integrated Cost/benefit protection forest m. ForClim (gap m.) DisCForM Forece (gap.m.) Population dynamics Markov chain m. Forest modelling approaches and trends ForClim improved Treeline dynamics/ land use Forest modelling approaches and trends Empirical models Approaches Several static models for distribution of Potential natural vegetation Tree species Timberline position (Gehrig-Fasel, 2005) Application of EFISCEN MASSIMO (Kaufmann, 2001) Individual based, stochastic growth model NFI derived Markov-Chain models Recent research is concentrating in: Recalibration of MASSIMO with latest NFI data (2004-2007)  Growth function, harvesting probabilities, regeneration, mortality Trends in modelling Impact of climate change in MASSIMO on growth function, tree species composition and mortality Long-term harvesting potential (30-100 years) Mechanistic models Approaches Population dynamical models Gap, distribution based models Ecophysiological models Plant water household model Applications of biogeochemical models and DGVMSs BIOME-BGC, LPJ, CLM Various landscape models Integrated models With disturbances Cost/Benefit Starting: with socioeconomy Trends in modelling Integrated models Merging of approaches Forest modelling approaches and trends Modelling non-timber products and services Static models, ForClim, TreeMig Species distributions after climate change Species suitabilities WoodPaM Forage production available for livestock Diversity indexes at patch and landscape scales Landscape aggregation index Planned models within MOUNTLAND Diversity indexes at patch and landscape scales Landscape aggregation index Models for predicting risk of hazards Protection forest model LANDCLIM Fire-forest dyn. interaction Mountland model (Davos), starting Interaction between forest dynamics and avalanche (risk) Simulators and information systems List existing forest simulators or decision support systems Stand level simulators Forest level decision support systems Include name and reference or web page Process based simulators TreeMig (Lischke et al. ForClim MASSIMO WOODPAM See specific slides
Slide 10 - Forest simulation models in Switzerland: main developments and challenges WG1 COST ACTION FP0603: Forest models for research and decision support in sustainable forest management 1st Workshop and Management Committee Meeting. Institute of Silviculture, BOKU. 8-9 of May 2008 Vienna, Austria Main features of Swiss forests (BAFU, Steckbrief Schweizer Wald, 2nd Swiss National Forest Inventory, 1999) Forest cover (total, share): 1.2 Mio ha, 30% Timber growing stock: 418 Mio m3 annual growth: 7.4 Mio m3/year cuts: 5.7 Mio. m3/year Main species: Norway spruce (48%), Beech (17%), Fir (15%), Larch (5%), Pine (3.5%) Main non-wood products and services: Protection against rockfall and avalanches CO2 sequestration Biodiversity the herb layer and of the landscape Recreation and scenic beauty (walking, nordic ski) (typical image of wooded pastures in Swiss mountains) In wooded pastures: forage, milk, meat Regulation of water-household Main features of Swiss forests Main risks… … against which the forests protect avalanche formation rockfall …to forests Reduced protection function due to too old stands Climate change Inadequate species in already dry regions due to climate change Increased pest abundance (e.g. bark beetle) Changes in landscape structure due to segregation of land use and climate change: separation of closed forests and open grasslands, increased aggregation of land cover, decline of dominant species (Norway spruce and larch) Management and silvicultural characteristics: Small clear cuts Single tree felling (Plenterwirtschaft) Static PNV Selected tree species Treeline Mountland Ecophys. BIOME-BGC, CLM, LPJ-(GUESS) SEIB DGVM Plant hydraulic model Landscape TreeMig WoodPam MEPHYSTO LandClim ForLand MASSIMO improved Growth/yield MASSIMO EFISCEN Integrated Cost/benefit protection forest m. ForClim (gap m.) DisCForM Forece (gap.m.) Population dynamics Markov chain m. Forest modelling approaches and trends ForClim improved Treeline dynamics/ land use Forest modelling approaches and trends Empirical models Approaches Several static models for distribution of Potential natural vegetation Tree species Timberline position (Gehrig-Fasel, 2005) Application of EFISCEN MASSIMO (Kaufmann, 2001) Individual based, stochastic growth model NFI derived Markov-Chain models Recent research is concentrating in: Recalibration of MASSIMO with latest NFI data (2004-2007)  Growth function, harvesting probabilities, regeneration, mortality Trends in modelling Impact of climate change in MASSIMO on growth function, tree species composition and mortality Long-term harvesting potential (30-100 years) Mechanistic models Approaches Population dynamical models Gap, distribution based models Ecophysiological models Plant water household model Applications of biogeochemical models and DGVMSs BIOME-BGC, LPJ, CLM Various landscape models Integrated models With disturbances Cost/Benefit Starting: with socioeconomy Trends in modelling Integrated models Merging of approaches Forest modelling approaches and trends Modelling non-timber products and services Static models, ForClim, TreeMig Species distributions after climate change Species suitabilities WoodPaM Forage production available for livestock Diversity indexes at patch and landscape scales Landscape aggregation index Planned models within MOUNTLAND Diversity indexes at patch and landscape scales Landscape aggregation index Models for predicting risk of hazards Protection forest model LANDCLIM Fire-forest dyn. interaction Mountland model (Davos), starting Interaction between forest dynamics and avalanche (risk) Simulators and information systems List existing forest simulators or decision support systems Stand level simulators Forest level decision support systems Include name and reference or web page Process based simulators TreeMig (Lischke et al. ForClim MASSIMO WOODPAM See specific slides Future challenges Describe the main challenges modelers and modelling face in your country so that can respond effectively to management or scientific questions/problems in your country Management issues: Prediction of tree species composition and stand structure in forested areas under various scenarios of management (including silvopastoral management) and climate change (warming, episodic events) Scientific issues: Heterogeneity due to topography Shifting mosaics in natural and silvopastoral systems (grazing ecology and forest dynamics) Consequences of the hierarchical organization of ecosystems
Slide 11 - Forest simulation models in Switzerland: main developments and challenges WG1 COST ACTION FP0603: Forest models for research and decision support in sustainable forest management 1st Workshop and Management Committee Meeting. Institute of Silviculture, BOKU. 8-9 of May 2008 Vienna, Austria Main features of Swiss forests (BAFU, Steckbrief Schweizer Wald, 2nd Swiss National Forest Inventory, 1999) Forest cover (total, share): 1.2 Mio ha, 30% Timber growing stock: 418 Mio m3 annual growth: 7.4 Mio m3/year cuts: 5.7 Mio. m3/year Main species: Norway spruce (48%), Beech (17%), Fir (15%), Larch (5%), Pine (3.5%) Main non-wood products and services: Protection against rockfall and avalanches CO2 sequestration Biodiversity the herb layer and of the landscape Recreation and scenic beauty (walking, nordic ski) (typical image of wooded pastures in Swiss mountains) In wooded pastures: forage, milk, meat Regulation of water-household Main features of Swiss forests Main risks… … against which the forests protect avalanche formation rockfall …to forests Reduced protection function due to too old stands Climate change Inadequate species in already dry regions due to climate change Increased pest abundance (e.g. bark beetle) Changes in landscape structure due to segregation of land use and climate change: separation of closed forests and open grasslands, increased aggregation of land cover, decline of dominant species (Norway spruce and larch) Management and silvicultural characteristics: Small clear cuts Single tree felling (Plenterwirtschaft) Static PNV Selected tree species Treeline Mountland Ecophys. BIOME-BGC, CLM, LPJ-(GUESS) SEIB DGVM Plant hydraulic model Landscape TreeMig WoodPam MEPHYSTO LandClim ForLand MASSIMO improved Growth/yield MASSIMO EFISCEN Integrated Cost/benefit protection forest m. ForClim (gap m.) DisCForM Forece (gap.m.) Population dynamics Markov chain m. Forest modelling approaches and trends ForClim improved Treeline dynamics/ land use Forest modelling approaches and trends Empirical models Approaches Several static models for distribution of Potential natural vegetation Tree species Timberline position (Gehrig-Fasel, 2005) Application of EFISCEN MASSIMO (Kaufmann, 2001) Individual based, stochastic growth model NFI derived Markov-Chain models Recent research is concentrating in: Recalibration of MASSIMO with latest NFI data (2004-2007)  Growth function, harvesting probabilities, regeneration, mortality Trends in modelling Impact of climate change in MASSIMO on growth function, tree species composition and mortality Long-term harvesting potential (30-100 years) Mechanistic models Approaches Population dynamical models Gap, distribution based models Ecophysiological models Plant water household model Applications of biogeochemical models and DGVMSs BIOME-BGC, LPJ, CLM Various landscape models Integrated models With disturbances Cost/Benefit Starting: with socioeconomy Trends in modelling Integrated models Merging of approaches Forest modelling approaches and trends Modelling non-timber products and services Static models, ForClim, TreeMig Species distributions after climate change Species suitabilities WoodPaM Forage production available for livestock Diversity indexes at patch and landscape scales Landscape aggregation index Planned models within MOUNTLAND Diversity indexes at patch and landscape scales Landscape aggregation index Models for predicting risk of hazards Protection forest model LANDCLIM Fire-forest dyn. interaction Mountland model (Davos), starting Interaction between forest dynamics and avalanche (risk) Simulators and information systems List existing forest simulators or decision support systems Stand level simulators Forest level decision support systems Include name and reference or web page Process based simulators TreeMig (Lischke et al. ForClim MASSIMO WOODPAM See specific slides Future challenges Describe the main challenges modelers and modelling face in your country so that can respond effectively to management or scientific questions/problems in your country Management issues: Prediction of tree species composition and stand structure in forested areas under various scenarios of management (including silvopastoral management) and climate change (warming, episodic events) Scientific issues: Heterogeneity due to topography Shifting mosaics in natural and silvopastoral systems (grazing ecology and forest dynamics) Consequences of the hierarchical organization of ecosystems Innovative references Bugmann, H.K.M., 1996. A simplified forest model to study species composition along climate gradients. Ecology, 77: 2055-2074. Gillet F. (in press). Modelling vegetation dynamics in heterogeneous pasture-woodland landscapes. Ecological Modelling. Kaufmann, E., 2001. Prognosis and management scenarios. In: P. Brassel and H. Lischke (Editors), Swiss National Forest Inventory: Methods and Models of the Second Assessment. Swiss Federal Research Institute WSL, Birmensdorf, pp. 336. Lischke, H., Löffler, T.J. and Fischlin, A., 1998. Aggregation of individual trees and patches in forest succession models - Capturing variability with height structured random dispersions. Theor. Popul. Biol., 54: 213-226. Lischke, H., Zimmermann, N.E., Bolliger, J., Rickebusch, S. and Löffler, T.J., 2006. TreeMig: A forest-landscape model for simulating spatio-temporal patterns from stand to landscape scale. Ecol. Model., 199: 409-420. Rickebusch, S., Lischke, H., Bugmann, H., Guisan, A. and Zimmermann, N.E., 2007. Understanding the low-temperature limitations to forest growth through calibration of a forest dynamics model with tree-ring data. For. Ecol. Manage., 246: 251-263. Schumacher, S., Bugmann, H. and Mladenoff, D.J., 2004. Improving the formulation of tree growth and succession in a spatially explicit landscape model. Ecol. Model., 180: 175-194. Zweifel, R., Zimmermann, L. and Newbery, D.M., 2005. Modeling tree water deficit from microclimate: an approach to quantifying drought stress. Tree Physiol., 25: 147-156.
Slide 12 - Forest simulation models in Switzerland: main developments and challenges WG1 COST ACTION FP0603: Forest models for research and decision support in sustainable forest management 1st Workshop and Management Committee Meeting. Institute of Silviculture, BOKU. 8-9 of May 2008 Vienna, Austria Main features of Swiss forests (BAFU, Steckbrief Schweizer Wald, 2nd Swiss National Forest Inventory, 1999) Forest cover (total, share): 1.2 Mio ha, 30% Timber growing stock: 418 Mio m3 annual growth: 7.4 Mio m3/year cuts: 5.7 Mio. m3/year Main species: Norway spruce (48%), Beech (17%), Fir (15%), Larch (5%), Pine (3.5%) Main non-wood products and services: Protection against rockfall and avalanches CO2 sequestration Biodiversity the herb layer and of the landscape Recreation and scenic beauty (walking, nordic ski) (typical image of wooded pastures in Swiss mountains) In wooded pastures: forage, milk, meat Regulation of water-household Main features of Swiss forests Main risks… … against which the forests protect avalanche formation rockfall …to forests Reduced protection function due to too old stands Climate change Inadequate species in already dry regions due to climate change Increased pest abundance (e.g. bark beetle) Changes in landscape structure due to segregation of land use and climate change: separation of closed forests and open grasslands, increased aggregation of land cover, decline of dominant species (Norway spruce and larch) Management and silvicultural characteristics: Small clear cuts Single tree felling (Plenterwirtschaft) Static PNV Selected tree species Treeline Mountland Ecophys. BIOME-BGC, CLM, LPJ-(GUESS) SEIB DGVM Plant hydraulic model Landscape TreeMig WoodPam MEPHYSTO LandClim ForLand MASSIMO improved Growth/yield MASSIMO EFISCEN Integrated Cost/benefit protection forest m. ForClim (gap m.) DisCForM Forece (gap.m.) Population dynamics Markov chain m. Forest modelling approaches and trends ForClim improved Treeline dynamics/ land use Forest modelling approaches and trends Empirical models Approaches Several static models for distribution of Potential natural vegetation Tree species Timberline position (Gehrig-Fasel, 2005) Application of EFISCEN MASSIMO (Kaufmann, 2001) Individual based, stochastic growth model NFI derived Markov-Chain models Recent research is concentrating in: Recalibration of MASSIMO with latest NFI data (2004-2007)  Growth function, harvesting probabilities, regeneration, mortality Trends in modelling Impact of climate change in MASSIMO on growth function, tree species composition and mortality Long-term harvesting potential (30-100 years) Mechanistic models Approaches Population dynamical models Gap, distribution based models Ecophysiological models Plant water household model Applications of biogeochemical models and DGVMSs BIOME-BGC, LPJ, CLM Various landscape models Integrated models With disturbances Cost/Benefit Starting: with socioeconomy Trends in modelling Integrated models Merging of approaches Forest modelling approaches and trends Modelling non-timber products and services Static models, ForClim, TreeMig Species distributions after climate change Species suitabilities WoodPaM Forage production available for livestock Diversity indexes at patch and landscape scales Landscape aggregation index Planned models within MOUNTLAND Diversity indexes at patch and landscape scales Landscape aggregation index Models for predicting risk of hazards Protection forest model LANDCLIM Fire-forest dyn. interaction Mountland model (Davos), starting Interaction between forest dynamics and avalanche (risk) Simulators and information systems List existing forest simulators or decision support systems Stand level simulators Forest level decision support systems Include name and reference or web page Process based simulators TreeMig (Lischke et al. ForClim MASSIMO WOODPAM See specific slides Future challenges Describe the main challenges modelers and modelling face in your country so that can respond effectively to management or scientific questions/problems in your country Management issues: Prediction of tree species composition and stand structure in forested areas under various scenarios of management (including silvopastoral management) and climate change (warming, episodic events) Scientific issues: Heterogeneity due to topography Shifting mosaics in natural and silvopastoral systems (grazing ecology and forest dynamics) Consequences of the hierarchical organization of ecosystems Innovative references Bugmann, H.K.M., 1996. A simplified forest model to study species composition along climate gradients. Ecology, 77: 2055-2074. Gillet F. (in press). Modelling vegetation dynamics in heterogeneous pasture-woodland landscapes. Ecological Modelling. Kaufmann, E., 2001. Prognosis and management scenarios. In: P. Brassel and H. Lischke (Editors), Swiss National Forest Inventory: Methods and Models of the Second Assessment. Swiss Federal Research Institute WSL, Birmensdorf, pp. 336. Lischke, H., Löffler, T.J. and Fischlin, A., 1998. Aggregation of individual trees and patches in forest succession models - Capturing variability with height structured random dispersions. Theor. Popul. Biol., 54: 213-226. Lischke, H., Zimmermann, N.E., Bolliger, J., Rickebusch, S. and Löffler, T.J., 2006. TreeMig: A forest-landscape model for simulating spatio-temporal patterns from stand to landscape scale. Ecol. Model., 199: 409-420. Rickebusch, S., Lischke, H., Bugmann, H., Guisan, A. and Zimmermann, N.E., 2007. Understanding the low-temperature limitations to forest growth through calibration of a forest dynamics model with tree-ring data. For. Ecol. Manage., 246: 251-263. Schumacher, S., Bugmann, H. and Mladenoff, D.J., 2004. Improving the formulation of tree growth and succession in a spatially explicit landscape model. Ecol. Model., 180: 175-194. Zweifel, R., Zimmermann, L. and Newbery, D.M., 2005. Modeling tree water deficit from microclimate: an approach to quantifying drought stress. Tree Physiol., 25: 147-156. MASSIMO (Kaufmann 2001): (Management Scenario-Simulation Model) Model type Empirical, stochastic & dynamic, individual-based, distance independent model 4 Modules: Growth, mortality, harvesting, and regeneration Calibration data NFI 1 (1983-85) & 2 (1993-95) Evaluation Growth-function (non-linear regression function estimating individual basal-area increment) Validation data Forest Inventory Liechtenstein Thürig et al. (2005) Accuracy: -5.44%
Slide 13 - Forest simulation models in Switzerland: main developments and challenges WG1 COST ACTION FP0603: Forest models for research and decision support in sustainable forest management 1st Workshop and Management Committee Meeting. Institute of Silviculture, BOKU. 8-9 of May 2008 Vienna, Austria Main features of Swiss forests (BAFU, Steckbrief Schweizer Wald, 2nd Swiss National Forest Inventory, 1999) Forest cover (total, share): 1.2 Mio ha, 30% Timber growing stock: 418 Mio m3 annual growth: 7.4 Mio m3/year cuts: 5.7 Mio. m3/year Main species: Norway spruce (48%), Beech (17%), Fir (15%), Larch (5%), Pine (3.5%) Main non-wood products and services: Protection against rockfall and avalanches CO2 sequestration Biodiversity the herb layer and of the landscape Recreation and scenic beauty (walking, nordic ski) (typical image of wooded pastures in Swiss mountains) In wooded pastures: forage, milk, meat Regulation of water-household Main features of Swiss forests Main risks… … against which the forests protect avalanche formation rockfall …to forests Reduced protection function due to too old stands Climate change Inadequate species in already dry regions due to climate change Increased pest abundance (e.g. bark beetle) Changes in landscape structure due to segregation of land use and climate change: separation of closed forests and open grasslands, increased aggregation of land cover, decline of dominant species (Norway spruce and larch) Management and silvicultural characteristics: Small clear cuts Single tree felling (Plenterwirtschaft) Static PNV Selected tree species Treeline Mountland Ecophys. BIOME-BGC, CLM, LPJ-(GUESS) SEIB DGVM Plant hydraulic model Landscape TreeMig WoodPam MEPHYSTO LandClim ForLand MASSIMO improved Growth/yield MASSIMO EFISCEN Integrated Cost/benefit protection forest m. ForClim (gap m.) DisCForM Forece (gap.m.) Population dynamics Markov chain m. Forest modelling approaches and trends ForClim improved Treeline dynamics/ land use Forest modelling approaches and trends Empirical models Approaches Several static models for distribution of Potential natural vegetation Tree species Timberline position (Gehrig-Fasel, 2005) Application of EFISCEN MASSIMO (Kaufmann, 2001) Individual based, stochastic growth model NFI derived Markov-Chain models Recent research is concentrating in: Recalibration of MASSIMO with latest NFI data (2004-2007)  Growth function, harvesting probabilities, regeneration, mortality Trends in modelling Impact of climate change in MASSIMO on growth function, tree species composition and mortality Long-term harvesting potential (30-100 years) Mechanistic models Approaches Population dynamical models Gap, distribution based models Ecophysiological models Plant water household model Applications of biogeochemical models and DGVMSs BIOME-BGC, LPJ, CLM Various landscape models Integrated models With disturbances Cost/Benefit Starting: with socioeconomy Trends in modelling Integrated models Merging of approaches Forest modelling approaches and trends Modelling non-timber products and services Static models, ForClim, TreeMig Species distributions after climate change Species suitabilities WoodPaM Forage production available for livestock Diversity indexes at patch and landscape scales Landscape aggregation index Planned models within MOUNTLAND Diversity indexes at patch and landscape scales Landscape aggregation index Models for predicting risk of hazards Protection forest model LANDCLIM Fire-forest dyn. interaction Mountland model (Davos), starting Interaction between forest dynamics and avalanche (risk) Simulators and information systems List existing forest simulators or decision support systems Stand level simulators Forest level decision support systems Include name and reference or web page Process based simulators TreeMig (Lischke et al. ForClim MASSIMO WOODPAM See specific slides Future challenges Describe the main challenges modelers and modelling face in your country so that can respond effectively to management or scientific questions/problems in your country Management issues: Prediction of tree species composition and stand structure in forested areas under various scenarios of management (including silvopastoral management) and climate change (warming, episodic events) Scientific issues: Heterogeneity due to topography Shifting mosaics in natural and silvopastoral systems (grazing ecology and forest dynamics) Consequences of the hierarchical organization of ecosystems Innovative references Bugmann, H.K.M., 1996. A simplified forest model to study species composition along climate gradients. Ecology, 77: 2055-2074. Gillet F. (in press). Modelling vegetation dynamics in heterogeneous pasture-woodland landscapes. Ecological Modelling. Kaufmann, E., 2001. Prognosis and management scenarios. In: P. Brassel and H. Lischke (Editors), Swiss National Forest Inventory: Methods and Models of the Second Assessment. Swiss Federal Research Institute WSL, Birmensdorf, pp. 336. Lischke, H., Löffler, T.J. and Fischlin, A., 1998. Aggregation of individual trees and patches in forest succession models - Capturing variability with height structured random dispersions. Theor. Popul. Biol., 54: 213-226. Lischke, H., Zimmermann, N.E., Bolliger, J., Rickebusch, S. and Löffler, T.J., 2006. TreeMig: A forest-landscape model for simulating spatio-temporal patterns from stand to landscape scale. Ecol. Model., 199: 409-420. Rickebusch, S., Lischke, H., Bugmann, H., Guisan, A. and Zimmermann, N.E., 2007. Understanding the low-temperature limitations to forest growth through calibration of a forest dynamics model with tree-ring data. For. Ecol. Manage., 246: 251-263. Schumacher, S., Bugmann, H. and Mladenoff, D.J., 2004. Improving the formulation of tree growth and succession in a spatially explicit landscape model. Ecol. Model., 180: 175-194. Zweifel, R., Zimmermann, L. and Newbery, D.M., 2005. Modeling tree water deficit from microclimate: an approach to quantifying drought stress. Tree Physiol., 25: 147-156. MASSIMO (Kaufmann 2001): (Management Scenario-Simulation Model) Model type Empirical, stochastic & dynamic, individual-based, distance independent model 4 Modules: Growth, mortality, harvesting, and regeneration Calibration data NFI 1 (1983-85) & 2 (1993-95) Evaluation Growth-function (non-linear regression function estimating individual basal-area increment) Validation data Forest Inventory Liechtenstein Thürig et al. (2005) Accuracy: -5.44% TREEMIG: a spatio-temporal forest model (Lischke et al. 2006, www.wsl.ch/projects/TreeMig/treemig.html Implemented in space Seed production Seed dispersal Density regulation
Slide 14 - Forest simulation models in Switzerland: main developments and challenges WG1 COST ACTION FP0603: Forest models for research and decision support in sustainable forest management 1st Workshop and Management Committee Meeting. Institute of Silviculture, BOKU. 8-9 of May 2008 Vienna, Austria Main features of Swiss forests (BAFU, Steckbrief Schweizer Wald, 2nd Swiss National Forest Inventory, 1999) Forest cover (total, share): 1.2 Mio ha, 30% Timber growing stock: 418 Mio m3 annual growth: 7.4 Mio m3/year cuts: 5.7 Mio. m3/year Main species: Norway spruce (48%), Beech (17%), Fir (15%), Larch (5%), Pine (3.5%) Main non-wood products and services: Protection against rockfall and avalanches CO2 sequestration Biodiversity the herb layer and of the landscape Recreation and scenic beauty (walking, nordic ski) (typical image of wooded pastures in Swiss mountains) In wooded pastures: forage, milk, meat Regulation of water-household Main features of Swiss forests Main risks… … against which the forests protect avalanche formation rockfall …to forests Reduced protection function due to too old stands Climate change Inadequate species in already dry regions due to climate change Increased pest abundance (e.g. bark beetle) Changes in landscape structure due to segregation of land use and climate change: separation of closed forests and open grasslands, increased aggregation of land cover, decline of dominant species (Norway spruce and larch) Management and silvicultural characteristics: Small clear cuts Single tree felling (Plenterwirtschaft) Static PNV Selected tree species Treeline Mountland Ecophys. BIOME-BGC, CLM, LPJ-(GUESS) SEIB DGVM Plant hydraulic model Landscape TreeMig WoodPam MEPHYSTO LandClim ForLand MASSIMO improved Growth/yield MASSIMO EFISCEN Integrated Cost/benefit protection forest m. ForClim (gap m.) DisCForM Forece (gap.m.) Population dynamics Markov chain m. Forest modelling approaches and trends ForClim improved Treeline dynamics/ land use Forest modelling approaches and trends Empirical models Approaches Several static models for distribution of Potential natural vegetation Tree species Timberline position (Gehrig-Fasel, 2005) Application of EFISCEN MASSIMO (Kaufmann, 2001) Individual based, stochastic growth model NFI derived Markov-Chain models Recent research is concentrating in: Recalibration of MASSIMO with latest NFI data (2004-2007)  Growth function, harvesting probabilities, regeneration, mortality Trends in modelling Impact of climate change in MASSIMO on growth function, tree species composition and mortality Long-term harvesting potential (30-100 years) Mechanistic models Approaches Population dynamical models Gap, distribution based models Ecophysiological models Plant water household model Applications of biogeochemical models and DGVMSs BIOME-BGC, LPJ, CLM Various landscape models Integrated models With disturbances Cost/Benefit Starting: with socioeconomy Trends in modelling Integrated models Merging of approaches Forest modelling approaches and trends Modelling non-timber products and services Static models, ForClim, TreeMig Species distributions after climate change Species suitabilities WoodPaM Forage production available for livestock Diversity indexes at patch and landscape scales Landscape aggregation index Planned models within MOUNTLAND Diversity indexes at patch and landscape scales Landscape aggregation index Models for predicting risk of hazards Protection forest model LANDCLIM Fire-forest dyn. interaction Mountland model (Davos), starting Interaction between forest dynamics and avalanche (risk) Simulators and information systems List existing forest simulators or decision support systems Stand level simulators Forest level decision support systems Include name and reference or web page Process based simulators TreeMig (Lischke et al. ForClim MASSIMO WOODPAM See specific slides Future challenges Describe the main challenges modelers and modelling face in your country so that can respond effectively to management or scientific questions/problems in your country Management issues: Prediction of tree species composition and stand structure in forested areas under various scenarios of management (including silvopastoral management) and climate change (warming, episodic events) Scientific issues: Heterogeneity due to topography Shifting mosaics in natural and silvopastoral systems (grazing ecology and forest dynamics) Consequences of the hierarchical organization of ecosystems Innovative references Bugmann, H.K.M., 1996. A simplified forest model to study species composition along climate gradients. Ecology, 77: 2055-2074. Gillet F. (in press). Modelling vegetation dynamics in heterogeneous pasture-woodland landscapes. Ecological Modelling. Kaufmann, E., 2001. Prognosis and management scenarios. In: P. Brassel and H. Lischke (Editors), Swiss National Forest Inventory: Methods and Models of the Second Assessment. Swiss Federal Research Institute WSL, Birmensdorf, pp. 336. Lischke, H., Löffler, T.J. and Fischlin, A., 1998. Aggregation of individual trees and patches in forest succession models - Capturing variability with height structured random dispersions. Theor. Popul. Biol., 54: 213-226. Lischke, H., Zimmermann, N.E., Bolliger, J., Rickebusch, S. and Löffler, T.J., 2006. TreeMig: A forest-landscape model for simulating spatio-temporal patterns from stand to landscape scale. Ecol. Model., 199: 409-420. Rickebusch, S., Lischke, H., Bugmann, H., Guisan, A. and Zimmermann, N.E., 2007. Understanding the low-temperature limitations to forest growth through calibration of a forest dynamics model with tree-ring data. For. Ecol. Manage., 246: 251-263. Schumacher, S., Bugmann, H. and Mladenoff, D.J., 2004. Improving the formulation of tree growth and succession in a spatially explicit landscape model. Ecol. Model., 180: 175-194. Zweifel, R., Zimmermann, L. and Newbery, D.M., 2005. Modeling tree water deficit from microclimate: an approach to quantifying drought stress. Tree Physiol., 25: 147-156. MASSIMO (Kaufmann 2001): (Management Scenario-Simulation Model) Model type Empirical, stochastic & dynamic, individual-based, distance independent model 4 Modules: Growth, mortality, harvesting, and regeneration Calibration data NFI 1 (1983-85) & 2 (1993-95) Evaluation Growth-function (non-linear regression function estimating individual basal-area increment) Validation data Forest Inventory Liechtenstein Thürig et al. (2005) Accuracy: -5.44% TREEMIG: a spatio-temporal forest model (Lischke et al. 2006, www.wsl.ch/projects/TreeMig/treemig.html Implemented in space Seed production Seed dispersal Density regulation WOODPAM: (Gillet, in press) Vegetation dynamics in pasture-woodland landscapes under climate change- towards a modeling tool for active adaptive management of silvopastoral systems Goal To develop a decision tool for active adaptive management of silvopastoral systems Spatially explicit dynamic mosaic model suitable to simulate various scenarios of climate change and land use Geographic area and scale Jura, Alps Extent: local landscapes (up to several km2) Grain: 625 m2 (25 m x 25 m square cells) Modeling approach 3 hierarchical levels (cell, management unit, landscape) and 6 submodels (wood, herb, cattle, soil, management, climate) Coupling of population, community and ecosystem processes Focus on vegetation-cattle interactions under climate and management constraints Warming? Storms? Fires?
Slide 15 - Forest simulation models in Switzerland: main developments and challenges WG1 COST ACTION FP0603: Forest models for research and decision support in sustainable forest management 1st Workshop and Management Committee Meeting. Institute of Silviculture, BOKU. 8-9 of May 2008 Vienna, Austria Main features of Swiss forests (BAFU, Steckbrief Schweizer Wald, 2nd Swiss National Forest Inventory, 1999) Forest cover (total, share): 1.2 Mio ha, 30% Timber growing stock: 418 Mio m3 annual growth: 7.4 Mio m3/year cuts: 5.7 Mio. m3/year Main species: Norway spruce (48%), Beech (17%), Fir (15%), Larch (5%), Pine (3.5%) Main non-wood products and services: Protection against rockfall and avalanches CO2 sequestration Biodiversity the herb layer and of the landscape Recreation and scenic beauty (walking, nordic ski) (typical image of wooded pastures in Swiss mountains) In wooded pastures: forage, milk, meat Regulation of water-household Main features of Swiss forests Main risks… … against which the forests protect avalanche formation rockfall …to forests Reduced protection function due to too old stands Climate change Inadequate species in already dry regions due to climate change Increased pest abundance (e.g. bark beetle) Changes in landscape structure due to segregation of land use and climate change: separation of closed forests and open grasslands, increased aggregation of land cover, decline of dominant species (Norway spruce and larch) Management and silvicultural characteristics: Small clear cuts Single tree felling (Plenterwirtschaft) Static PNV Selected tree species Treeline Mountland Ecophys. BIOME-BGC, CLM, LPJ-(GUESS) SEIB DGVM Plant hydraulic model Landscape TreeMig WoodPam MEPHYSTO LandClim ForLand MASSIMO improved Growth/yield MASSIMO EFISCEN Integrated Cost/benefit protection forest m. ForClim (gap m.) DisCForM Forece (gap.m.) Population dynamics Markov chain m. Forest modelling approaches and trends ForClim improved Treeline dynamics/ land use Forest modelling approaches and trends Empirical models Approaches Several static models for distribution of Potential natural vegetation Tree species Timberline position (Gehrig-Fasel, 2005) Application of EFISCEN MASSIMO (Kaufmann, 2001) Individual based, stochastic growth model NFI derived Markov-Chain models Recent research is concentrating in: Recalibration of MASSIMO with latest NFI data (2004-2007)  Growth function, harvesting probabilities, regeneration, mortality Trends in modelling Impact of climate change in MASSIMO on growth function, tree species composition and mortality Long-term harvesting potential (30-100 years) Mechanistic models Approaches Population dynamical models Gap, distribution based models Ecophysiological models Plant water household model Applications of biogeochemical models and DGVMSs BIOME-BGC, LPJ, CLM Various landscape models Integrated models With disturbances Cost/Benefit Starting: with socioeconomy Trends in modelling Integrated models Merging of approaches Forest modelling approaches and trends Modelling non-timber products and services Static models, ForClim, TreeMig Species distributions after climate change Species suitabilities WoodPaM Forage production available for livestock Diversity indexes at patch and landscape scales Landscape aggregation index Planned models within MOUNTLAND Diversity indexes at patch and landscape scales Landscape aggregation index Models for predicting risk of hazards Protection forest model LANDCLIM Fire-forest dyn. interaction Mountland model (Davos), starting Interaction between forest dynamics and avalanche (risk) Simulators and information systems List existing forest simulators or decision support systems Stand level simulators Forest level decision support systems Include name and reference or web page Process based simulators TreeMig (Lischke et al. ForClim MASSIMO WOODPAM See specific slides Future challenges Describe the main challenges modelers and modelling face in your country so that can respond effectively to management or scientific questions/problems in your country Management issues: Prediction of tree species composition and stand structure in forested areas under various scenarios of management (including silvopastoral management) and climate change (warming, episodic events) Scientific issues: Heterogeneity due to topography Shifting mosaics in natural and silvopastoral systems (grazing ecology and forest dynamics) Consequences of the hierarchical organization of ecosystems Innovative references Bugmann, H.K.M., 1996. A simplified forest model to study species composition along climate gradients. Ecology, 77: 2055-2074. Gillet F. (in press). Modelling vegetation dynamics in heterogeneous pasture-woodland landscapes. Ecological Modelling. Kaufmann, E., 2001. Prognosis and management scenarios. In: P. Brassel and H. Lischke (Editors), Swiss National Forest Inventory: Methods and Models of the Second Assessment. Swiss Federal Research Institute WSL, Birmensdorf, pp. 336. Lischke, H., Löffler, T.J. and Fischlin, A., 1998. Aggregation of individual trees and patches in forest succession models - Capturing variability with height structured random dispersions. Theor. Popul. Biol., 54: 213-226. Lischke, H., Zimmermann, N.E., Bolliger, J., Rickebusch, S. and Löffler, T.J., 2006. TreeMig: A forest-landscape model for simulating spatio-temporal patterns from stand to landscape scale. Ecol. Model., 199: 409-420. Rickebusch, S., Lischke, H., Bugmann, H., Guisan, A. and Zimmermann, N.E., 2007. Understanding the low-temperature limitations to forest growth through calibration of a forest dynamics model with tree-ring data. For. Ecol. Manage., 246: 251-263. Schumacher, S., Bugmann, H. and Mladenoff, D.J., 2004. Improving the formulation of tree growth and succession in a spatially explicit landscape model. Ecol. Model., 180: 175-194. Zweifel, R., Zimmermann, L. and Newbery, D.M., 2005. Modeling tree water deficit from microclimate: an approach to quantifying drought stress. Tree Physiol., 25: 147-156. MASSIMO (Kaufmann 2001): (Management Scenario-Simulation Model) Model type Empirical, stochastic & dynamic, individual-based, distance independent model 4 Modules: Growth, mortality, harvesting, and regeneration Calibration data NFI 1 (1983-85) & 2 (1993-95) Evaluation Growth-function (non-linear regression function estimating individual basal-area increment) Validation data Forest Inventory Liechtenstein Thürig et al. (2005) Accuracy: -5.44% TREEMIG: a spatio-temporal forest model (Lischke et al. 2006, www.wsl.ch/projects/TreeMig/treemig.html Implemented in space Seed production Seed dispersal Density regulation WOODPAM: (Gillet, in press) Vegetation dynamics in pasture-woodland landscapes under climate change- towards a modeling tool for active adaptive management of silvopastoral systems Goal To develop a decision tool for active adaptive management of silvopastoral systems Spatially explicit dynamic mosaic model suitable to simulate various scenarios of climate change and land use Geographic area and scale Jura, Alps Extent: local landscapes (up to several km2) Grain: 625 m2 (25 m x 25 m square cells) Modeling approach 3 hierarchical levels (cell, management unit, landscape) and 6 submodels (wood, herb, cattle, soil, management, climate) Coupling of population, community and ecosystem processes Focus on vegetation-cattle interactions under climate and management constraints Warming? Storms? Fires? Goal: Build upon a climate-sensitive forest succession model to Increase local precision, thus bridging the gap between forest growth (local precision) and forest succession (wide range of applicability) models Approach: Systematic model evaluation against empirical data (yield trials etc.) and systematic model-model comparisons Model improvements (growth, regeneration) Model applications to study climate change impacts on protection forests in the Alps & other European mountain ranges Geographic area and scale: Alps, other European mountain ranges (TBD) Stand scale assessments Left: Simulated (filled bars) vs. measured (semi-transparent bars) stand structure at the site Niederhünigen after 54 simulation years. Right: Simulated equilibrium species basal area for the Swiss sites Grande Dixence (cold), Adelboden (cool-wet) and the eastern German site Schwerin (dry and warm) ForClim Improvement : Bridging the gap between forest growth and forest succession models
Slide 16 - Forest simulation models in Switzerland: main developments and challenges WG1 COST ACTION FP0603: Forest models for research and decision support in sustainable forest management 1st Workshop and Management Committee Meeting. Institute of Silviculture, BOKU. 8-9 of May 2008 Vienna, Austria Main features of Swiss forests (BAFU, Steckbrief Schweizer Wald, 2nd Swiss National Forest Inventory, 1999) Forest cover (total, share): 1.2 Mio ha, 30% Timber growing stock: 418 Mio m3 annual growth: 7.4 Mio m3/year cuts: 5.7 Mio. m3/year Main species: Norway spruce (48%), Beech (17%), Fir (15%), Larch (5%), Pine (3.5%) Main non-wood products and services: Protection against rockfall and avalanches CO2 sequestration Biodiversity the herb layer and of the landscape Recreation and scenic beauty (walking, nordic ski) (typical image of wooded pastures in Swiss mountains) In wooded pastures: forage, milk, meat Regulation of water-household Main features of Swiss forests Main risks… … against which the forests protect avalanche formation rockfall …to forests Reduced protection function due to too old stands Climate change Inadequate species in already dry regions due to climate change Increased pest abundance (e.g. bark beetle) Changes in landscape structure due to segregation of land use and climate change: separation of closed forests and open grasslands, increased aggregation of land cover, decline of dominant species (Norway spruce and larch) Management and silvicultural characteristics: Small clear cuts Single tree felling (Plenterwirtschaft) Static PNV Selected tree species Treeline Mountland Ecophys. BIOME-BGC, CLM, LPJ-(GUESS) SEIB DGVM Plant hydraulic model Landscape TreeMig WoodPam MEPHYSTO LandClim ForLand MASSIMO improved Growth/yield MASSIMO EFISCEN Integrated Cost/benefit protection forest m. ForClim (gap m.) DisCForM Forece (gap.m.) Population dynamics Markov chain m. Forest modelling approaches and trends ForClim improved Treeline dynamics/ land use Forest modelling approaches and trends Empirical models Approaches Several static models for distribution of Potential natural vegetation Tree species Timberline position (Gehrig-Fasel, 2005) Application of EFISCEN MASSIMO (Kaufmann, 2001) Individual based, stochastic growth model NFI derived Markov-Chain models Recent research is concentrating in: Recalibration of MASSIMO with latest NFI data (2004-2007)  Growth function, harvesting probabilities, regeneration, mortality Trends in modelling Impact of climate change in MASSIMO on growth function, tree species composition and mortality Long-term harvesting potential (30-100 years) Mechanistic models Approaches Population dynamical models Gap, distribution based models Ecophysiological models Plant water household model Applications of biogeochemical models and DGVMSs BIOME-BGC, LPJ, CLM Various landscape models Integrated models With disturbances Cost/Benefit Starting: with socioeconomy Trends in modelling Integrated models Merging of approaches Forest modelling approaches and trends Modelling non-timber products and services Static models, ForClim, TreeMig Species distributions after climate change Species suitabilities WoodPaM Forage production available for livestock Diversity indexes at patch and landscape scales Landscape aggregation index Planned models within MOUNTLAND Diversity indexes at patch and landscape scales Landscape aggregation index Models for predicting risk of hazards Protection forest model LANDCLIM Fire-forest dyn. interaction Mountland model (Davos), starting Interaction between forest dynamics and avalanche (risk) Simulators and information systems List existing forest simulators or decision support systems Stand level simulators Forest level decision support systems Include name and reference or web page Process based simulators TreeMig (Lischke et al. ForClim MASSIMO WOODPAM See specific slides Future challenges Describe the main challenges modelers and modelling face in your country so that can respond effectively to management or scientific questions/problems in your country Management issues: Prediction of tree species composition and stand structure in forested areas under various scenarios of management (including silvopastoral management) and climate change (warming, episodic events) Scientific issues: Heterogeneity due to topography Shifting mosaics in natural and silvopastoral systems (grazing ecology and forest dynamics) Consequences of the hierarchical organization of ecosystems Innovative references Bugmann, H.K.M., 1996. A simplified forest model to study species composition along climate gradients. Ecology, 77: 2055-2074. Gillet F. (in press). Modelling vegetation dynamics in heterogeneous pasture-woodland landscapes. Ecological Modelling. Kaufmann, E., 2001. Prognosis and management scenarios. In: P. Brassel and H. Lischke (Editors), Swiss National Forest Inventory: Methods and Models of the Second Assessment. Swiss Federal Research Institute WSL, Birmensdorf, pp. 336. Lischke, H., Löffler, T.J. and Fischlin, A., 1998. Aggregation of individual trees and patches in forest succession models - Capturing variability with height structured random dispersions. Theor. Popul. Biol., 54: 213-226. Lischke, H., Zimmermann, N.E., Bolliger, J., Rickebusch, S. and Löffler, T.J., 2006. TreeMig: A forest-landscape model for simulating spatio-temporal patterns from stand to landscape scale. Ecol. Model., 199: 409-420. Rickebusch, S., Lischke, H., Bugmann, H., Guisan, A. and Zimmermann, N.E., 2007. Understanding the low-temperature limitations to forest growth through calibration of a forest dynamics model with tree-ring data. For. Ecol. Manage., 246: 251-263. Schumacher, S., Bugmann, H. and Mladenoff, D.J., 2004. Improving the formulation of tree growth and succession in a spatially explicit landscape model. Ecol. Model., 180: 175-194. Zweifel, R., Zimmermann, L. and Newbery, D.M., 2005. Modeling tree water deficit from microclimate: an approach to quantifying drought stress. Tree Physiol., 25: 147-156. MASSIMO (Kaufmann 2001): (Management Scenario-Simulation Model) Model type Empirical, stochastic & dynamic, individual-based, distance independent model 4 Modules: Growth, mortality, harvesting, and regeneration Calibration data NFI 1 (1983-85) & 2 (1993-95) Evaluation Growth-function (non-linear regression function estimating individual basal-area increment) Validation data Forest Inventory Liechtenstein Thürig et al. (2005) Accuracy: -5.44% TREEMIG: a spatio-temporal forest model (Lischke et al. 2006, www.wsl.ch/projects/TreeMig/treemig.html Implemented in space Seed production Seed dispersal Density regulation WOODPAM: (Gillet, in press) Vegetation dynamics in pasture-woodland landscapes under climate change- towards a modeling tool for active adaptive management of silvopastoral systems Goal To develop a decision tool for active adaptive management of silvopastoral systems Spatially explicit dynamic mosaic model suitable to simulate various scenarios of climate change and land use Geographic area and scale Jura, Alps Extent: local landscapes (up to several km2) Grain: 625 m2 (25 m x 25 m square cells) Modeling approach 3 hierarchical levels (cell, management unit, landscape) and 6 submodels (wood, herb, cattle, soil, management, climate) Coupling of population, community and ecosystem processes Focus on vegetation-cattle interactions under climate and management constraints Warming? Storms? Fires? Goal: Build upon a climate-sensitive forest succession model to Increase local precision, thus bridging the gap between forest growth (local precision) and forest succession (wide range of applicability) models Approach: Systematic model evaluation against empirical data (yield trials etc.) and systematic model-model comparisons Model improvements (growth, regeneration) Model applications to study climate change impacts on protection forests in the Alps & other European mountain ranges Geographic area and scale: Alps, other European mountain ranges (TBD) Stand scale assessments Left: Simulated (filled bars) vs. measured (semi-transparent bars) stand structure at the site Niederhünigen after 54 simulation years. Right: Simulated equilibrium species basal area for the Swiss sites Grande Dixence (cold), Adelboden (cool-wet) and the eastern German site Schwerin (dry and warm) ForClim Improvement : Bridging the gap between forest growth and forest succession models Protection forest model The protection forest model combines a markov chain approach for simulating forest dynamics with risk assess-ment and cost-benefit analysis. integrates ecological, technical and economic aspects of protection forest management. can be used to comparatively evaluate the long-term effects of management strategies (e.g., thinning, planting, salvage harvesting, construction of defensive structures). Management Costs Risk reduction = benefits
Slide 17 - Forest simulation models in Switzerland: main developments and challenges WG1 COST ACTION FP0603: Forest models for research and decision support in sustainable forest management 1st Workshop and Management Committee Meeting. Institute of Silviculture, BOKU. 8-9 of May 2008 Vienna, Austria Main features of Swiss forests (BAFU, Steckbrief Schweizer Wald, 2nd Swiss National Forest Inventory, 1999) Forest cover (total, share): 1.2 Mio ha, 30% Timber growing stock: 418 Mio m3 annual growth: 7.4 Mio m3/year cuts: 5.7 Mio. m3/year Main species: Norway spruce (48%), Beech (17%), Fir (15%), Larch (5%), Pine (3.5%) Main non-wood products and services: Protection against rockfall and avalanches CO2 sequestration Biodiversity the herb layer and of the landscape Recreation and scenic beauty (walking, nordic ski) (typical image of wooded pastures in Swiss mountains) In wooded pastures: forage, milk, meat Regulation of water-household Main features of Swiss forests Main risks… … against which the forests protect avalanche formation rockfall …to forests Reduced protection function due to too old stands Climate change Inadequate species in already dry regions due to climate change Increased pest abundance (e.g. bark beetle) Changes in landscape structure due to segregation of land use and climate change: separation of closed forests and open grasslands, increased aggregation of land cover, decline of dominant species (Norway spruce and larch) Management and silvicultural characteristics: Small clear cuts Single tree felling (Plenterwirtschaft) Static PNV Selected tree species Treeline Mountland Ecophys. BIOME-BGC, CLM, LPJ-(GUESS) SEIB DGVM Plant hydraulic model Landscape TreeMig WoodPam MEPHYSTO LandClim ForLand MASSIMO improved Growth/yield MASSIMO EFISCEN Integrated Cost/benefit protection forest m. ForClim (gap m.) DisCForM Forece (gap.m.) Population dynamics Markov chain m. Forest modelling approaches and trends ForClim improved Treeline dynamics/ land use Forest modelling approaches and trends Empirical models Approaches Several static models for distribution of Potential natural vegetation Tree species Timberline position (Gehrig-Fasel, 2005) Application of EFISCEN MASSIMO (Kaufmann, 2001) Individual based, stochastic growth model NFI derived Markov-Chain models Recent research is concentrating in: Recalibration of MASSIMO with latest NFI data (2004-2007)  Growth function, harvesting probabilities, regeneration, mortality Trends in modelling Impact of climate change in MASSIMO on growth function, tree species composition and mortality Long-term harvesting potential (30-100 years) Mechanistic models Approaches Population dynamical models Gap, distribution based models Ecophysiological models Plant water household model Applications of biogeochemical models and DGVMSs BIOME-BGC, LPJ, CLM Various landscape models Integrated models With disturbances Cost/Benefit Starting: with socioeconomy Trends in modelling Integrated models Merging of approaches Forest modelling approaches and trends Modelling non-timber products and services Static models, ForClim, TreeMig Species distributions after climate change Species suitabilities WoodPaM Forage production available for livestock Diversity indexes at patch and landscape scales Landscape aggregation index Planned models within MOUNTLAND Diversity indexes at patch and landscape scales Landscape aggregation index Models for predicting risk of hazards Protection forest model LANDCLIM Fire-forest dyn. interaction Mountland model (Davos), starting Interaction between forest dynamics and avalanche (risk) Simulators and information systems List existing forest simulators or decision support systems Stand level simulators Forest level decision support systems Include name and reference or web page Process based simulators TreeMig (Lischke et al. ForClim MASSIMO WOODPAM See specific slides Future challenges Describe the main challenges modelers and modelling face in your country so that can respond effectively to management or scientific questions/problems in your country Management issues: Prediction of tree species composition and stand structure in forested areas under various scenarios of management (including silvopastoral management) and climate change (warming, episodic events) Scientific issues: Heterogeneity due to topography Shifting mosaics in natural and silvopastoral systems (grazing ecology and forest dynamics) Consequences of the hierarchical organization of ecosystems Innovative references Bugmann, H.K.M., 1996. A simplified forest model to study species composition along climate gradients. Ecology, 77: 2055-2074. Gillet F. (in press). Modelling vegetation dynamics in heterogeneous pasture-woodland landscapes. Ecological Modelling. Kaufmann, E., 2001. Prognosis and management scenarios. In: P. Brassel and H. Lischke (Editors), Swiss National Forest Inventory: Methods and Models of the Second Assessment. Swiss Federal Research Institute WSL, Birmensdorf, pp. 336. Lischke, H., Löffler, T.J. and Fischlin, A., 1998. Aggregation of individual trees and patches in forest succession models - Capturing variability with height structured random dispersions. Theor. Popul. Biol., 54: 213-226. Lischke, H., Zimmermann, N.E., Bolliger, J., Rickebusch, S. and Löffler, T.J., 2006. TreeMig: A forest-landscape model for simulating spatio-temporal patterns from stand to landscape scale. Ecol. Model., 199: 409-420. Rickebusch, S., Lischke, H., Bugmann, H., Guisan, A. and Zimmermann, N.E., 2007. Understanding the low-temperature limitations to forest growth through calibration of a forest dynamics model with tree-ring data. For. Ecol. Manage., 246: 251-263. Schumacher, S., Bugmann, H. and Mladenoff, D.J., 2004. Improving the formulation of tree growth and succession in a spatially explicit landscape model. Ecol. Model., 180: 175-194. Zweifel, R., Zimmermann, L. and Newbery, D.M., 2005. Modeling tree water deficit from microclimate: an approach to quantifying drought stress. Tree Physiol., 25: 147-156. MASSIMO (Kaufmann 2001): (Management Scenario-Simulation Model) Model type Empirical, stochastic & dynamic, individual-based, distance independent model 4 Modules: Growth, mortality, harvesting, and regeneration Calibration data NFI 1 (1983-85) & 2 (1993-95) Evaluation Growth-function (non-linear regression function estimating individual basal-area increment) Validation data Forest Inventory Liechtenstein Thürig et al. (2005) Accuracy: -5.44% TREEMIG: a spatio-temporal forest model (Lischke et al. 2006, www.wsl.ch/projects/TreeMig/treemig.html Implemented in space Seed production Seed dispersal Density regulation WOODPAM: (Gillet, in press) Vegetation dynamics in pasture-woodland landscapes under climate change- towards a modeling tool for active adaptive management of silvopastoral systems Goal To develop a decision tool for active adaptive management of silvopastoral systems Spatially explicit dynamic mosaic model suitable to simulate various scenarios of climate change and land use Geographic area and scale Jura, Alps Extent: local landscapes (up to several km2) Grain: 625 m2 (25 m x 25 m square cells) Modeling approach 3 hierarchical levels (cell, management unit, landscape) and 6 submodels (wood, herb, cattle, soil, management, climate) Coupling of population, community and ecosystem processes Focus on vegetation-cattle interactions under climate and management constraints Warming? Storms? Fires? Goal: Build upon a climate-sensitive forest succession model to Increase local precision, thus bridging the gap between forest growth (local precision) and forest succession (wide range of applicability) models Approach: Systematic model evaluation against empirical data (yield trials etc.) and systematic model-model comparisons Model improvements (growth, regeneration) Model applications to study climate change impacts on protection forests in the Alps & other European mountain ranges Geographic area and scale: Alps, other European mountain ranges (TBD) Stand scale assessments Left: Simulated (filled bars) vs. measured (semi-transparent bars) stand structure at the site Niederhünigen after 54 simulation years. Right: Simulated equilibrium species basal area for the Swiss sites Grande Dixence (cold), Adelboden (cool-wet) and the eastern German site Schwerin (dry and warm) ForClim Improvement : Bridging the gap between forest growth and forest succession models Protection forest model The protection forest model combines a markov chain approach for simulating forest dynamics with risk assess-ment and cost-benefit analysis. integrates ecological, technical and economic aspects of protection forest management. can be used to comparatively evaluate the long-term effects of management strategies (e.g., thinning, planting, salvage harvesting, construction of defensive structures). Management Costs Risk reduction = benefits Gap model ForClim (Bugmann, 1996) Concept of individualistic, cyclical succession on small patches (H. Gleason) Quantitative description of tree population dynamics: “gap“ models (D. Botkin, H. Shugart)
Slide 18 - Forest simulation models in Switzerland: main developments and challenges WG1 COST ACTION FP0603: Forest models for research and decision support in sustainable forest management 1st Workshop and Management Committee Meeting. Institute of Silviculture, BOKU. 8-9 of May 2008 Vienna, Austria Main features of Swiss forests (BAFU, Steckbrief Schweizer Wald, 2nd Swiss National Forest Inventory, 1999) Forest cover (total, share): 1.2 Mio ha, 30% Timber growing stock: 418 Mio m3 annual growth: 7.4 Mio m3/year cuts: 5.7 Mio. m3/year Main species: Norway spruce (48%), Beech (17%), Fir (15%), Larch (5%), Pine (3.5%) Main non-wood products and services: Protection against rockfall and avalanches CO2 sequestration Biodiversity the herb layer and of the landscape Recreation and scenic beauty (walking, nordic ski) (typical image of wooded pastures in Swiss mountains) In wooded pastures: forage, milk, meat Regulation of water-household Main features of Swiss forests Main risks… … against which the forests protect avalanche formation rockfall …to forests Reduced protection function due to too old stands Climate change Inadequate species in already dry regions due to climate change Increased pest abundance (e.g. bark beetle) Changes in landscape structure due to segregation of land use and climate change: separation of closed forests and open grasslands, increased aggregation of land cover, decline of dominant species (Norway spruce and larch) Management and silvicultural characteristics: Small clear cuts Single tree felling (Plenterwirtschaft) Static PNV Selected tree species Treeline Mountland Ecophys. BIOME-BGC, CLM, LPJ-(GUESS) SEIB DGVM Plant hydraulic model Landscape TreeMig WoodPam MEPHYSTO LandClim ForLand MASSIMO improved Growth/yield MASSIMO EFISCEN Integrated Cost/benefit protection forest m. ForClim (gap m.) DisCForM Forece (gap.m.) Population dynamics Markov chain m. Forest modelling approaches and trends ForClim improved Treeline dynamics/ land use Forest modelling approaches and trends Empirical models Approaches Several static models for distribution of Potential natural vegetation Tree species Timberline position (Gehrig-Fasel, 2005) Application of EFISCEN MASSIMO (Kaufmann, 2001) Individual based, stochastic growth model NFI derived Markov-Chain models Recent research is concentrating in: Recalibration of MASSIMO with latest NFI data (2004-2007)  Growth function, harvesting probabilities, regeneration, mortality Trends in modelling Impact of climate change in MASSIMO on growth function, tree species composition and mortality Long-term harvesting potential (30-100 years) Mechanistic models Approaches Population dynamical models Gap, distribution based models Ecophysiological models Plant water household model Applications of biogeochemical models and DGVMSs BIOME-BGC, LPJ, CLM Various landscape models Integrated models With disturbances Cost/Benefit Starting: with socioeconomy Trends in modelling Integrated models Merging of approaches Forest modelling approaches and trends Modelling non-timber products and services Static models, ForClim, TreeMig Species distributions after climate change Species suitabilities WoodPaM Forage production available for livestock Diversity indexes at patch and landscape scales Landscape aggregation index Planned models within MOUNTLAND Diversity indexes at patch and landscape scales Landscape aggregation index Models for predicting risk of hazards Protection forest model LANDCLIM Fire-forest dyn. interaction Mountland model (Davos), starting Interaction between forest dynamics and avalanche (risk) Simulators and information systems List existing forest simulators or decision support systems Stand level simulators Forest level decision support systems Include name and reference or web page Process based simulators TreeMig (Lischke et al. ForClim MASSIMO WOODPAM See specific slides Future challenges Describe the main challenges modelers and modelling face in your country so that can respond effectively to management or scientific questions/problems in your country Management issues: Prediction of tree species composition and stand structure in forested areas under various scenarios of management (including silvopastoral management) and climate change (warming, episodic events) Scientific issues: Heterogeneity due to topography Shifting mosaics in natural and silvopastoral systems (grazing ecology and forest dynamics) Consequences of the hierarchical organization of ecosystems Innovative references Bugmann, H.K.M., 1996. A simplified forest model to study species composition along climate gradients. Ecology, 77: 2055-2074. Gillet F. (in press). Modelling vegetation dynamics in heterogeneous pasture-woodland landscapes. Ecological Modelling. Kaufmann, E., 2001. Prognosis and management scenarios. In: P. Brassel and H. Lischke (Editors), Swiss National Forest Inventory: Methods and Models of the Second Assessment. Swiss Federal Research Institute WSL, Birmensdorf, pp. 336. Lischke, H., Löffler, T.J. and Fischlin, A., 1998. Aggregation of individual trees and patches in forest succession models - Capturing variability with height structured random dispersions. Theor. Popul. Biol., 54: 213-226. Lischke, H., Zimmermann, N.E., Bolliger, J., Rickebusch, S. and Löffler, T.J., 2006. TreeMig: A forest-landscape model for simulating spatio-temporal patterns from stand to landscape scale. Ecol. Model., 199: 409-420. Rickebusch, S., Lischke, H., Bugmann, H., Guisan, A. and Zimmermann, N.E., 2007. Understanding the low-temperature limitations to forest growth through calibration of a forest dynamics model with tree-ring data. For. Ecol. Manage., 246: 251-263. Schumacher, S., Bugmann, H. and Mladenoff, D.J., 2004. Improving the formulation of tree growth and succession in a spatially explicit landscape model. Ecol. Model., 180: 175-194. Zweifel, R., Zimmermann, L. and Newbery, D.M., 2005. Modeling tree water deficit from microclimate: an approach to quantifying drought stress. Tree Physiol., 25: 147-156. MASSIMO (Kaufmann 2001): (Management Scenario-Simulation Model) Model type Empirical, stochastic & dynamic, individual-based, distance independent model 4 Modules: Growth, mortality, harvesting, and regeneration Calibration data NFI 1 (1983-85) & 2 (1993-95) Evaluation Growth-function (non-linear regression function estimating individual basal-area increment) Validation data Forest Inventory Liechtenstein Thürig et al. (2005) Accuracy: -5.44% TREEMIG: a spatio-temporal forest model (Lischke et al. 2006, www.wsl.ch/projects/TreeMig/treemig.html Implemented in space Seed production Seed dispersal Density regulation WOODPAM: (Gillet, in press) Vegetation dynamics in pasture-woodland landscapes under climate change- towards a modeling tool for active adaptive management of silvopastoral systems Goal To develop a decision tool for active adaptive management of silvopastoral systems Spatially explicit dynamic mosaic model suitable to simulate various scenarios of climate change and land use Geographic area and scale Jura, Alps Extent: local landscapes (up to several km2) Grain: 625 m2 (25 m x 25 m square cells) Modeling approach 3 hierarchical levels (cell, management unit, landscape) and 6 submodels (wood, herb, cattle, soil, management, climate) Coupling of population, community and ecosystem processes Focus on vegetation-cattle interactions under climate and management constraints Warming? Storms? Fires? Goal: Build upon a climate-sensitive forest succession model to Increase local precision, thus bridging the gap between forest growth (local precision) and forest succession (wide range of applicability) models Approach: Systematic model evaluation against empirical data (yield trials etc.) and systematic model-model comparisons Model improvements (growth, regeneration) Model applications to study climate change impacts on protection forests in the Alps & other European mountain ranges Geographic area and scale: Alps, other European mountain ranges (TBD) Stand scale assessments Left: Simulated (filled bars) vs. measured (semi-transparent bars) stand structure at the site Niederhünigen after 54 simulation years. Right: Simulated equilibrium species basal area for the Swiss sites Grande Dixence (cold), Adelboden (cool-wet) and the eastern German site Schwerin (dry and warm) ForClim Improvement : Bridging the gap between forest growth and forest succession models Protection forest model The protection forest model combines a markov chain approach for simulating forest dynamics with risk assess-ment and cost-benefit analysis. integrates ecological, technical and economic aspects of protection forest management. can be used to comparatively evaluate the long-term effects of management strategies (e.g., thinning, planting, salvage harvesting, construction of defensive structures). Management Costs Risk reduction = benefits Gap model ForClim (Bugmann, 1996) Concept of individualistic, cyclical succession on small patches (H. Gleason) Quantitative description of tree population dynamics: “gap“ models (D. Botkin, H. Shugart) Landscape model LandClim (Schumacher et al, 2004) Spatially explicit (grid cells, ca. 30x30 m) Dynamic Modeling of succession Dynamics of cohorts of trees: establishment, growth, mortality based on biomass and tree number per cohort Modeling of ‘disturbances’ Fire Windthrow Management Modeled processes sensitive to climate 1km Schumacher et al. (2004, 2006) DIVERSITY
Slide 19 - Forest simulation models in Switzerland: main developments and challenges WG1 COST ACTION FP0603: Forest models for research and decision support in sustainable forest management 1st Workshop and Management Committee Meeting. Institute of Silviculture, BOKU. 8-9 of May 2008 Vienna, Austria Main features of Swiss forests (BAFU, Steckbrief Schweizer Wald, 2nd Swiss National Forest Inventory, 1999) Forest cover (total, share): 1.2 Mio ha, 30% Timber growing stock: 418 Mio m3 annual growth: 7.4 Mio m3/year cuts: 5.7 Mio. m3/year Main species: Norway spruce (48%), Beech (17%), Fir (15%), Larch (5%), Pine (3.5%) Main non-wood products and services: Protection against rockfall and avalanches CO2 sequestration Biodiversity the herb layer and of the landscape Recreation and scenic beauty (walking, nordic ski) (typical image of wooded pastures in Swiss mountains) In wooded pastures: forage, milk, meat Regulation of water-household Main features of Swiss forests Main risks… … against which the forests protect avalanche formation rockfall …to forests Reduced protection function due to too old stands Climate change Inadequate species in already dry regions due to climate change Increased pest abundance (e.g. bark beetle) Changes in landscape structure due to segregation of land use and climate change: separation of closed forests and open grasslands, increased aggregation of land cover, decline of dominant species (Norway spruce and larch) Management and silvicultural characteristics: Small clear cuts Single tree felling (Plenterwirtschaft) Static PNV Selected tree species Treeline Mountland Ecophys. BIOME-BGC, CLM, LPJ-(GUESS) SEIB DGVM Plant hydraulic model Landscape TreeMig WoodPam MEPHYSTO LandClim ForLand MASSIMO improved Growth/yield MASSIMO EFISCEN Integrated Cost/benefit protection forest m. ForClim (gap m.) DisCForM Forece (gap.m.) Population dynamics Markov chain m. Forest modelling approaches and trends ForClim improved Treeline dynamics/ land use Forest modelling approaches and trends Empirical models Approaches Several static models for distribution of Potential natural vegetation Tree species Timberline position (Gehrig-Fasel, 2005) Application of EFISCEN MASSIMO (Kaufmann, 2001) Individual based, stochastic growth model NFI derived Markov-Chain models Recent research is concentrating in: Recalibration of MASSIMO with latest NFI data (2004-2007)  Growth function, harvesting probabilities, regeneration, mortality Trends in modelling Impact of climate change in MASSIMO on growth function, tree species composition and mortality Long-term harvesting potential (30-100 years) Mechanistic models Approaches Population dynamical models Gap, distribution based models Ecophysiological models Plant water household model Applications of biogeochemical models and DGVMSs BIOME-BGC, LPJ, CLM Various landscape models Integrated models With disturbances Cost/Benefit Starting: with socioeconomy Trends in modelling Integrated models Merging of approaches Forest modelling approaches and trends Modelling non-timber products and services Static models, ForClim, TreeMig Species distributions after climate change Species suitabilities WoodPaM Forage production available for livestock Diversity indexes at patch and landscape scales Landscape aggregation index Planned models within MOUNTLAND Diversity indexes at patch and landscape scales Landscape aggregation index Models for predicting risk of hazards Protection forest model LANDCLIM Fire-forest dyn. interaction Mountland model (Davos), starting Interaction between forest dynamics and avalanche (risk) Simulators and information systems List existing forest simulators or decision support systems Stand level simulators Forest level decision support systems Include name and reference or web page Process based simulators TreeMig (Lischke et al. ForClim MASSIMO WOODPAM See specific slides Future challenges Describe the main challenges modelers and modelling face in your country so that can respond effectively to management or scientific questions/problems in your country Management issues: Prediction of tree species composition and stand structure in forested areas under various scenarios of management (including silvopastoral management) and climate change (warming, episodic events) Scientific issues: Heterogeneity due to topography Shifting mosaics in natural and silvopastoral systems (grazing ecology and forest dynamics) Consequences of the hierarchical organization of ecosystems Innovative references Bugmann, H.K.M., 1996. A simplified forest model to study species composition along climate gradients. Ecology, 77: 2055-2074. Gillet F. (in press). Modelling vegetation dynamics in heterogeneous pasture-woodland landscapes. Ecological Modelling. Kaufmann, E., 2001. Prognosis and management scenarios. In: P. Brassel and H. Lischke (Editors), Swiss National Forest Inventory: Methods and Models of the Second Assessment. Swiss Federal Research Institute WSL, Birmensdorf, pp. 336. Lischke, H., Löffler, T.J. and Fischlin, A., 1998. Aggregation of individual trees and patches in forest succession models - Capturing variability with height structured random dispersions. Theor. Popul. Biol., 54: 213-226. Lischke, H., Zimmermann, N.E., Bolliger, J., Rickebusch, S. and Löffler, T.J., 2006. TreeMig: A forest-landscape model for simulating spatio-temporal patterns from stand to landscape scale. Ecol. Model., 199: 409-420. Rickebusch, S., Lischke, H., Bugmann, H., Guisan, A. and Zimmermann, N.E., 2007. Understanding the low-temperature limitations to forest growth through calibration of a forest dynamics model with tree-ring data. For. Ecol. Manage., 246: 251-263. Schumacher, S., Bugmann, H. and Mladenoff, D.J., 2004. Improving the formulation of tree growth and succession in a spatially explicit landscape model. Ecol. Model., 180: 175-194. Zweifel, R., Zimmermann, L. and Newbery, D.M., 2005. Modeling tree water deficit from microclimate: an approach to quantifying drought stress. Tree Physiol., 25: 147-156. MASSIMO (Kaufmann 2001): (Management Scenario-Simulation Model) Model type Empirical, stochastic & dynamic, individual-based, distance independent model 4 Modules: Growth, mortality, harvesting, and regeneration Calibration data NFI 1 (1983-85) & 2 (1993-95) Evaluation Growth-function (non-linear regression function estimating individual basal-area increment) Validation data Forest Inventory Liechtenstein Thürig et al. (2005) Accuracy: -5.44% TREEMIG: a spatio-temporal forest model (Lischke et al. 2006, www.wsl.ch/projects/TreeMig/treemig.html Implemented in space Seed production Seed dispersal Density regulation WOODPAM: (Gillet, in press) Vegetation dynamics in pasture-woodland landscapes under climate change- towards a modeling tool for active adaptive management of silvopastoral systems Goal To develop a decision tool for active adaptive management of silvopastoral systems Spatially explicit dynamic mosaic model suitable to simulate various scenarios of climate change and land use Geographic area and scale Jura, Alps Extent: local landscapes (up to several km2) Grain: 625 m2 (25 m x 25 m square cells) Modeling approach 3 hierarchical levels (cell, management unit, landscape) and 6 submodels (wood, herb, cattle, soil, management, climate) Coupling of population, community and ecosystem processes Focus on vegetation-cattle interactions under climate and management constraints Warming? Storms? Fires? Goal: Build upon a climate-sensitive forest succession model to Increase local precision, thus bridging the gap between forest growth (local precision) and forest succession (wide range of applicability) models Approach: Systematic model evaluation against empirical data (yield trials etc.) and systematic model-model comparisons Model improvements (growth, regeneration) Model applications to study climate change impacts on protection forests in the Alps & other European mountain ranges Geographic area and scale: Alps, other European mountain ranges (TBD) Stand scale assessments Left: Simulated (filled bars) vs. measured (semi-transparent bars) stand structure at the site Niederhünigen after 54 simulation years. Right: Simulated equilibrium species basal area for the Swiss sites Grande Dixence (cold), Adelboden (cool-wet) and the eastern German site Schwerin (dry and warm) ForClim Improvement : Bridging the gap between forest growth and forest succession models Protection forest model The protection forest model combines a markov chain approach for simulating forest dynamics with risk assess-ment and cost-benefit analysis. integrates ecological, technical and economic aspects of protection forest management. can be used to comparatively evaluate the long-term effects of management strategies (e.g., thinning, planting, salvage harvesting, construction of defensive structures). Management Costs Risk reduction = benefits Gap model ForClim (Bugmann, 1996) Concept of individualistic, cyclical succession on small patches (H. Gleason) Quantitative description of tree population dynamics: “gap“ models (D. Botkin, H. Shugart) Landscape model LandClim (Schumacher et al, 2004) Spatially explicit (grid cells, ca. 30x30 m) Dynamic Modeling of succession Dynamics of cohorts of trees: establishment, growth, mortality based on biomass and tree number per cohort Modeling of ‘disturbances’ Fire Windthrow Management Modeled processes sensitive to climate 1km Schumacher et al. (2004, 2006) DIVERSITY Goal: Spatial forest model stand-size grain to be applied on large areas for assessment of, e.g., climate change or management effects on forest functions Model approach: Combination of large scale ecophysiological, forest growth, tree species migration models Dynamic, spatio-temporal, process based Focus on natural processes Management included via scenarios Improved landscape model MEPHYSTO: Merging empirical, ecophysiological and spatio-temporal population dynamics forest models