Supporting Material
Appendix S1: Details of models used.
LPJ-GUESS
LPJ-GUESS is a generalized, process-based model of vegetation dynamics and
biogeochemistry that includes a representation of tree population dynamics based on simulating
age cohorts of tree species (Gerten et al., 2004, Sitch et al., 2003, Smith et al., 2001). The
model aims to capture physiological processes (e.g. photosynthesis, plant respiration, and
microbial decomposition) and associated fluxes of carbon and water between soil layers,
vegetation, and the atmosphere, which are simulated on a daily time step. Growth and vegetation
dynamics are updated annually by allocating net primary production (NPP) to leaves, sapwood,
and fine roots, and by simulating sapwood-to-heartwood conversion, litterfall, fine root turnover,
establishment, and mortality (Sitch et al., 2003). The model is driven by daily temperature,
precipitation, and percentage sunshine hours, and atmospheric CO2 concentration. We used the
species-specific tree parameterization developed by Hickler et al. (2012) that includes the main
European tree, shrub and grass species. The different species are characterized by specific
establishment, growth, mortality and metabolic rates, and bioclimatic limits determining their
distribution range (Sitch et al., 2003, Smith et al., 2001). The LPJ model framework has been
successfully used to simulate vegetation cover and carbon and water fluxes at a number of
European locations (Hickler et al., 2012, Rammig et al., 2010), and has previously been applied
to central European alpine sites (Manusch et al., 2012, Portner et al., 2010, Wolf et al., 2012).
To account for the stochastic nature of tree establishment and mortality we simulated 200
replicates of each vegetation patch, with projected vegetation at each patch taken as the mean of
these replicates. Forest management was approximated according to site- specific management
regimes (see main text). During forest management tree boles were removed while all leaves
and roots, and 5% of trees’ sapwood and heartwood, was added to the litter pool.
ForClim
ForClim (v3.0) is a climate-sensitive forest “gap” model, developed to simulate forest
stand dynamics over a wide range of environmental conditions (Bugmann & Cramer, 1998,
Bugmann, 1996). ForClim simulates the establishment, growth and mortality of trees on
multiple forest patches to derive stand properties at a spatial extent of ca. 15-20 ha by averaging
the properties simulated at the patch scale. Tree establishment and growth are regulated by
growing degree-days, light availability, and soil moisture and nitrogen status. The model is
driven by mean monthly temperatures and the monthly precipitation sums (Bugmann &
Solomon, 2000). Tree growth (i.e. DBH increment) is modelled using an empirical growth
equation (cf. Moore, 1989) that incorporates species-specific constraints on maximum growth
rate and maximum tree height (Rasche et al., 2012). From diameter at breast height, allometric
equations are used to estimate other tree characteristics as well as total aboveground biomass
(Bugmann, 1994). The model is currently parameterized for 31 European tree species, and has
been widely tested for the representation of forest dynamics in the northern temperate zone of
three continents (Bugmann, 2001, Bugmann & Cramer, 1998, Bugmann & Solomon, 2000,
Didion et al., 2011, Rasche et al., 2012, Shao et al., 2001). Within ForClim a wide range of
silvicultural treatments can be simulated (Rasche et al., 2011).
LandClim
LandClim is a spatially explicit forest landscape model that incorporates competitiondriven forest dynamics, climate effects, and forest disturbances to simulate forest dynamics on a
landscape scale (Elkin et al., 2012, Schumacher & Bugmann, 2006, Schumacher et al., 2004,
Schumacher et al., 2006). LandClim simulates forest growth in 625 m2 cells using simplified
versions of tree recruitment, growth and competition derived from ForClim. Forest growth is
determined by climatic parameters (monthly temperature and precipitation), soil properties and
topography, and the spatially explicit processes of seed dispersal, landscape disturbances such as
fire and windthrow, and forest management that link individual cells.. Forest succession
processes within each cell are simulated on a yearly time step, while landscape-level processes
are simulated on a decadal time step. The model has been used in the Central Alps, North
American Rocky Mountains, and Mediterranean forests, to simulate current forest (Schumacher
et al. 2006) as well as paleo-ecological (Colombaroli et al., 2010, Henne et al., 2011) and future
forest dynamics (Schumacher & Bugmann, 2006, Temperli et al., 2012). The simulation of
forest management is spatially explicit such that dynamic interactions between management and
landscape heterogeneity are captured (Temperli et al., 2012).
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