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

60 Chapter 3
gBFlag s =
0 U(0,1) < gBrP s = (kBrow s –1) · kBrPr
30
1 else
(3.4)
where U(0,1) is a random number with uniform distribution in the range [0…1], kBrow s
is the species-specific browsing tolerance, and kBrPr is browsing pressure on a nominal
scale between 0 (no browsing) and 10 (heavy browsing). The sapling mortality (gBrP s )
thus increases linearly with increasing browsing intensity, and the maximum mortality
ranges from 0 to 66.7% depending on the parameter kBrow s (Fig. 3.5 right). The
current quantification of this mortality is entirely speculative because a quantitative basis
could not be found in the literature (e.g. Näscher 1979, Eiberle & Nigg 1986, Liss 1988,
Albrecht 1989). However, latest research (Rechsteiner 1993, Kräuchi 1994) may allow
for a more reliable formulation of this environmental filter controlling sapling establishment.
Moreover, the browsing pressure parameter kBrPr could be replaced by an input
variable uBrPr, thus providing the link to models of game population dynamics (e.g.
Schröder 1976, Buchli 1979).
Degree-days
Sapling establishment is assumed to be impossible when the annual sum of degree-days
does not conform to the degree-day requirements of the tree species, which are defined by
the parameters kDDMin s and kDDMax s (Shugart 1984):
gDFlag s =
1 kDDMin s < uDD < kDDMax s
0 else
(3.5)
Immigration
The last factor that modifies sapling establishment rates is introduced to simulate simple
immigration scenarios of the tree species:
gIFlag s =
1 kImmYr s > t
0 else
(3.6)
where kImmYr s is a parameter denoting the first simulation year where the species may
establish, and t is the current simulation time. The choice of kImmYr s depends on the
hypothesis to be tested, such as a specific immigration scenario or the complete exclusion
of a given species from a simulation experiment.