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Individual-based modelling and ecology

C. Stadtländer

Published 2012 in Journal of Biological Dynamics

ABSTRACT

The general idea behind developing models is to get a better understanding of phenomena, processes, features, and conditions of a particular system. In mathematical modelling, the investigator attempts to describe a system with the help of mathematical formulas and computations. When these abstract descriptions are applied to biological phenomena (e.g. ecological phenomena), scientists can gain a deeper understanding of how the components and dynamics of an ecosystem change over time as observed under certain conditions. An example would be the distribution of an animal population in a changing environment during a given time period. Mathematical modelling in ecology not only describes phenomena by using a set of equations on a set of biological data, it actually allows us to simulate conditions and make predictions about the future of a system. These simulations are typically conducted with the help of computers or a network of computers. Many consider modelling an art form because it requires from the investigator not only a solid knowledge of the mathematical language, but also an ability to carefully observe and meticulously describe the biological phenomena under study. This indicates that mathematical modelling in ecology is truly interdisciplinary. Individual-based modelling (IBM) is a special kind of modelling. More specifically, this type of modelling investigates larger ecological system consequences of individual-level processes, such as the interaction of individual animals in a given ecosystem. Grimm and Railsback define an IBM as follows: ‘A model of a system of individuals and their environment, in which system behaviour arises from traits of the individuals and characteristics of the environment’. The authors emphasize that these models ‘do not include system-level models that consider individual variation, nor do they include models of a single individual’. The IBM approach is in strong contrast to other (classical) mathematical modelling methods, where parameters of a higher-level organization (e.g. a population) are summed up and averaged, and then changes in these averaged parameters are investigated in order to understand the whole organization. IBM is thus a unique and distinctively different approach to studying ecology. As one would expect, the literature is full of terms similar to or related in some way to IBM. These include, for example, individual-oriented modelling (the individual level is recognized, but without attempting to trace the systems’ properties back to the behaviour of the individual; i.e. this method is still adhering to the classical modelling approach), discrete-event simulations (the general category that includes IBM), agent-based modelling (IBM in fields other than ecology), entity-based modelling (often used synonymously with agent-based modelling), and patternoriented modelling (an approach of using real patterns for designing, testing, and parameterizing

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