Forest simulation models in Switzerland: main developments and challenges

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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
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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

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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)

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Forest modelling approaches and trends
Integrated
Landscape
ForClim (gap m.)
Treeline dynamics/ land use
Plant hydraulic model
DisCForM
TreeMig
WoodPam
Cost/benefit protection forest m.
ForClim improved
MASSIMOimproved
Mountland
MEPHYSTO
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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

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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

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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

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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)

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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

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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

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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.

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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)
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TREEMIG a spatio-temporal forest model (Lischke
et al. 2006, www.wsl.ch/projects/TreeMig/treemig.h
tml
Seed dispersal
Seed production
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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?
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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)
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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).
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Gap model ForClim (Bugmann, 1996)

• Concept of individualistic, cyclical succession
  on small patches(H. Gleason)

• Quantitative description of tree population
  dynamicsgap models(D. Botkin, H. Shugart)

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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)
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Improved landscape model MEPHYSTO Merging
empirical, ecophysiological and spatio-temporal
population dynamics forest models

• 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