Environmental Engineering Reference
In-Depth Information
Fig. 8.8 Plot of relative abundance for 1,000 iterations for the three species:
Phalaris
,
Hordeum
,
Bituminaria
starting from same abundance (33%). Disturbance occurring on iteration 500 affects
20% of the landscape [from Matsinos and Troumbis (2002)]
8.6 Outlook and Applicability
In a unique way, Cellular Automata combine conceptual simplicity, the potential to
expand simple interactions to complex structures, and an enormous range of
application fields for quite demanding problems - with the potential to capture
surprising self-organizing effects. This makes it worthwhile and desirable for any
ecological modeller to familiarize with this approach.
It is possible to run CAs without much effort in pre-defined modelling environ-
ments, each of which specializes in a particular field of rule types. Yet, it is equally
easy to escape the restrictions that customized software frequently have, and develop
a unique CA according to one's specific applications, with the additional power to
modify it to specific situations or explorations, e.g. by time-dependent or situation
specific variations of the neighbourhood or through self-modifying rule systems.
Other ecological modelling applications, and especially those that require a spatially
structured input in order to provide an environment with particular statistical features,
can be easily generated with a CA and used as a grid input. Clearly, Cellular
Automata can contribute not only to strengthen ecological theory, but also for the
development of predictive tools for ecology and conservation. In the process, one
may reveal that modelling itself can be fun as well.
