50
P.A. Whigham . G.ß. Fogel
allowed ecologists to collect data at new resolutions and with higher accuracy and
frequency. This improved data collection offers the possibility for the use of
inductive modelling techniques to develop new theories and models of ecological
systems at many sc ales and complexities. This data can also be used to verify
theoretical models of ecology and to give insight to the complex hierarchical
structures inherent in ecosystems.
This chapter outlines some of the basic approaches to ecological modelling and
shows that evolutionary computation techniques have been successful in a number
of different ecological areas. The diverse range of techniques supported by
evolutionary algorithms is shown to be appropriate for the extension of current
approaches and the development of new techniques for understanding ecological
systems, and is a major area of the emerging discipline of ecological informatics.
4.2
Ecological Modelling
Ecology is a discipline that covers many scales of description, both spatial and
temporal, and is fundamentally concerned with the interactions between
organisms and their environment (Gillman and Hails 1997). Haeckel (1866) first
defined the concept of ecology in terms of an understanding of components (biotic
and abiotic), and how they can be viewed, described and modelled as a system.
An ecological model must be able to describe the changes in a system based on
generalities of how a system is functioning, the selected components that make up
the system, and within certain temporal and spatial resolutions. Ideally these
models allow either a prediction in terms of future states of the system, or elicit
greater understanding of how the system functions and the driving forces and
interactions of the system. Ecology mayaiso be defined as the study of the
processes that influence the distribution and abundance of organisms, their
interactions and the transformations of energy within a system.
4.2.1
The Challenges of Ecological Modelling
Ecological modelling is achallenging discipline due to the inherent complexity,
nonlinearity, and spatiotemporal dynamics of the multivariate system being
described. For example, computational models of individual interactions in
ecological systems can be very complex (Judson 1994). These models are
typically characterized by many parameters that describe how each individual
behaves. As a result, the system as a whole produces complex, often unpredictable
behavior. Alternative models of individuals produce a mathematical equation that
relates several biotic and abiotic independent quantities to a particular dependent
P.A. Whigham . G.ß. Fogel
allowed ecologists to collect data at new resolutions and with higher accuracy and
frequency. This improved data collection offers the possibility for the use of
inductive modelling techniques to develop new theories and models of ecological
systems at many sc ales and complexities. This data can also be used to verify
theoretical models of ecology and to give insight to the complex hierarchical
structures inherent in ecosystems.
This chapter outlines some of the basic approaches to ecological modelling and
shows that evolutionary computation techniques have been successful in a number
of different ecological areas. The diverse range of techniques supported by
evolutionary algorithms is shown to be appropriate for the extension of current
approaches and the development of new techniques for understanding ecological
systems, and is a major area of the emerging discipline of ecological informatics.
4.2
Ecological Modelling
Ecology is a discipline that covers many scales of description, both spatial and
temporal, and is fundamentally concerned with the interactions between
organisms and their environment (Gillman and Hails 1997). Haeckel (1866) first
defined the concept of ecology in terms of an understanding of components (biotic
and abiotic), and how they can be viewed, described and modelled as a system.
An ecological model must be able to describe the changes in a system based on
generalities of how a system is functioning, the selected components that make up
the system, and within certain temporal and spatial resolutions. Ideally these
models allow either a prediction in terms of future states of the system, or elicit
greater understanding of how the system functions and the driving forces and
interactions of the system. Ecology mayaiso be defined as the study of the
processes that influence the distribution and abundance of organisms, their
interactions and the transformations of energy within a system.
4.2.1
The Challenges of Ecological Modelling
Ecological modelling is achallenging discipline due to the inherent complexity,
nonlinearity, and spatiotemporal dynamics of the multivariate system being
described. For example, computational models of individual interactions in
ecological systems can be very complex (Judson 1994). These models are
typically characterized by many parameters that describe how each individual
behaves. As a result, the system as a whole produces complex, often unpredictable
behavior. Alternative models of individuals produce a mathematical equation that
relates several biotic and abiotic independent quantities to a particular dependent