Skógræktarritið - 15.05.2001, Qupperneq 151

Table I. Examples of some of the important processes to be included in the
HIBECO mountain birch forest ecosystem model. Suggestive links to their respec-
tive characteristic scales (response times and spatial correlation lengths) are also
given. The spatial and temporal aspects of a given factor may be correlated (not
shown). Preliminary definitions of scale: Fine spatial scale, approx. 500-2000 m2;
Coarse spatial scale, approx. 100-200 km and beyond; fine temporal scale: approx.
I day; coarse temporal scale: approx. one season or longer.
Birch growth
Space:
Fine scale processes: Local exposition, altitude, soil quality, local
climate conditions
Coarse scale nrocesse.s: Global warming effects, provenance differ-
ences and regional adaptation (e.g., frost tolerance of buds)
Time:
Fine sc.ale processes: Episodic influence from frost and herbivory
Coarse scale processes: Dieback due to repeated defoliation from
insect attack over consecutive years (threshold effect); herbivory
inhibitory response (delayed density dependence) on expense of
maximum growth rate, successional stage effects.
Herbivory
Space:
Fine scale nroc.esses: Sheep as a fine-scale forager (stand edges vs.
interior parts: Soffia Arnthorsdottir, personal comm.)
Coarse scale orocesses: Sheep as a coarse-scale forager due to site
fidelity (home range): For example, problem with re-growth on
clear-cuttings if domestic animals turn the clear-cutting into inten-
sively utilized grazing patches (faciiitating grass on expense of
herbs and new birch tree stems sprouting from tree stumps); long-
distance dispersal of moths (correlation length functions for bal-
looning and female movements must be clarified for the model)
Time:
Fine-scale orocesses: Local insect outbreaks limited to old-age
smaller stands and single trees
Coarse-sc.ale nrocesses: Development of ungulate site fidelity to
(and cultural transmission over generations) a local network of pre-
ferred grazing patches.
Direct anthropogenic and socioeconomic influence
Space:
Fine-sc.ale nrocesses: From mixed-age stands to more even-aged
stand structure due to small clear-cuttings (loggings: firewood and
forestry),
Coarse scale orocesses: Distance to roads and villages (firewood
and forestry: probability of a tree being logged at old age, and pro-
bability of birch forest patch or stand mosaic); macroeconomic
conditions for forestry; management plans with influence from offi-
cial regulations and land use arrangements.
Time:
Fine scale orocesses: Episodic trampling effects on young birch
shoots and seedlings at fine spatial scales
Coarse scale orocesses: Shifting regulation policies for a region.
"scale 2” special in this case?
May the answer be revealed by
exploring the relationship
between spatio-temporal abun-
dance pattern generated by the
model under variable parameter
settings for intrinsic growth rate,
dispersal length and dispersal
rate? These aspects among oth-
ers need to be explored during
the HIBECO modeling work, as
an important validation of the
birch-insect interactions.
Table 1 gives some additional
examples of scale-related chal-
lenges for the model develop-
ment. Scaling complexities
emerge as a result of making the
model sufficiently realistic (in
accordance with definition above)
by making it spatially explicit.
Conclusion
Spatially explicit models in gen-
eral may often give unexpected
results in comparison with their
spatially implicit counterparts,
and their greater level of realism
make them a better starting
point for validation against real
systems like the mountain birch
forest ecosystem.
On the other hand a spatially
explicit model puts huge demand
on data, which also (at least for
some parts) need to have specific
spatially explicit details (e.g.,
insect outbreak series with spatial
details, not just a local time series
with a mean abundance variable).
It is important to remember the
old proverb for any modeler:
"garbage in - garbage out". Thus,
a model’s level of refinement and
complexity must always be cali-
brated and balanced in accor-
dance with the quality and quan-
tity of the available data. This is a
prerequisite for model validation
and production of scenario simu-
lations with sufficient level of
confidence. The actual HIBECO
model development will be per-
formed with this balance in mind.
SKÓGRÆKTARRITIÐ 2001 l.tbl
149