Individual-based approaches in ecology provide a new approach to spatially
explicit modelling. They are paralleled by the emergence of agent-based modelling
in the field of artificial intelligence (AI) that is manifest in object-based
approaches in a number of geographical disciplines, from hydrology to sociology.
An individual-based approach to the simulation of organisms in a spatial
context allows for a greater understanding of how individual-level behaviour and
interactions result in population-level phenomena at the landscape-scale. Such
models are inherently flexible and adaptable to other species or systems, and
the model can be parameterised from biological behavioural information widely
available in the literature.
This research constructs, analyses and experiments with an individual-based
model of aphid (Rhopalosiphum padi) population dynamics in agricultural landscapes
during the autumn and winter. The model combines deterministic equations
governing the development of the aphids with stochastic, behavioural rules.
Several stages of model assessment validate the model: assessment at the conceptual,
developmental and operational stages. The need for a solution for the
model to cope with large population sizes led to experimentation with both mathematical
and computational solutions to this problem. It was found that parallel
computing to distribute the simulation across a 30-node Beowulf cluster was the
most effective at increasing model efficiency whilst preserving model behaviour.
Key scenarios are presented, that show the power of this approach in predicting
potential impacts of agricultural landscape change, including the effects of crop
management, marginal habitat configuration and pesticide regime. This research
clearly demonstrates the potential of spatially explicit individual-based modelling
to predict scenarios that may advise policy decision-makers as a landscape management
tool
