On the Ecology of Mountainous Forests in a Changing Climate: A ...

Discussion 171
Finally, the analysis of the formulation of climate-dependent factors in forest gap models
revealed that many conventional models implicitly assume a constant climate, and that
model behaviour is sensitive to relaxing these assumptions (cf. Bugmann & Fischlin
1994, Fischlin et al. 1994). Other researchers came to similar findings using a different
approach, i.e. by combining conventional forest gap models with detailed biophysical or
physiological submodels for calculating the influence of climatic parameters (Martin
1990, 1992, Bonan & van Cleve 1992, Friend et al. 1993). However, the fact that a
model is sensitive to the formulation of a factor is a necessary, but not a sufficient condition
to show that a detailed submodel is required to calculate that factor. The present
study suggests that at least in some instances simple yet realistic parametrizations of
abiotic factors can be developed, and that they improve the reliability of a model considerably.
Thus, the call for detailed biophysical or physiological submodels forming part of
forest gap models appears not conclusive yet (cf. Bonan 1993).
7.2 Structure and behaviour of FORCLIM
The construction of FORCLIM as a forest gap model composed of three submodels (E –
abiotic environment, P – plant population dynamics, and S – soil organic matter turnover)
provided the flexibility to evaluate the behaviour of each submodel and any desirable
combination of the submodels. This constitutes a distinct advantage over conventional
forest gap models, where the complete model is the single scope of simulation studies.
These analyses revealed that FORCLIM-P on its own does not provide realistic species
compositions under conditions of strong environmental stress, e.g. when approaching
the alpine and the dry timberline, suggesting that FORCLIM-E is of paramount importance
for simulating forest dynamics under these conditions. The forest gap model FORSKA-2
(Prentice et al. 1993) does not incorporate the effects of a stochastic environment, although
the model was designed for boreal and broadleaf forests of Scandinavia, where
precipitation sums often are small and drought stress is large. Thus it would be interesting
to investigate if the above findings are restricted to forests in the European Alps, or
whether they apply also to other areas.
The influence of FORCLIM-P on the amount of litter and humus simulated by FORCLIM-
S is small. This may partly be due to the fact that the quality of a large fraction of the litter
produced by FORCLIM-P does not vary with the species producing it, i.e. twig, wood,
and root litter, which constitute up to 90% of the total litter production. Thus, a more detailed
modelling of litter production would be desirable; unfortunately, the data base for