Forest simulation models in Switzerland: main developments and challenges WG1

1. COST ACTION FP0603: Forest models for research and decision support in sustainable forest management Forest simulation models in Switzerland: main developments and challenges WG1 1st Workshop and Management Committee Meeting.Institute of Silviculture, BOKU.8-9 of May 2008Vienna, Austria

2. 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

3. 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)

4. Forest modelling approaches and trends Population dynamics Static Growth/yield Ecophys. Markov chain m. PNV Selected tree species Treeline Forece (gap.m.) BIOME-BGC, CLM, EFISCEN LPJ-(GUESS) SEIB DGVM ForLand LandClim MASSIMO Integrated Landscape ForClim (gap m.) Treeline dynamics/ land use Plant hydraulic model DisCForM TreeMig WoodPam Cost/benefit protection forest m. ForClim improved MASSIMOimproved MEPHYSTO Mountland

5. 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)

6. Forest modelling approaches and trends 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

7. 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

8. 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)

9. 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

10. 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.

11. 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 Accuracy: -5.44% Thürig et al. (2005)

12. Local forest dynamics Implemented in space - Density regulation TREEMIG: a spatio-temporal forest model (Lischke et al. 2006, www.wsl.ch/projects/TreeMig/treemig.html Seed dispersal Seed production

13. 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 Storms?Fires? Warming?

14. ForClim Improvement :Bridging the gap between forest growth and forest succession models 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)

15. Protection forest model Risk reduction = benefits Management Costs 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).

16. Concept of individualistic, cyclical succession on small patches(H. Gleason) Gap model ForClim(Bugmann, 1996) • Quantitative description of tree population dynamics:“gap“ models(D. Botkin, H. Shugart)

17. N 1km Landscape model LandClim(Schumacher et al, 2004) DIVERSITY • Spatially explicit(grid cells, ca. 30x30 m) • Dynamic • Modeling of succession • Dynamics of cohorts of trees: establishment, growth, mortality • based on biomass and treenumber per cohort • Modeling of ‘disturbances’ • Fire • Windthrow • Management • Modeled processes sensitive to climate Schumacher et al. (2004, 2006)

18. 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