Ten years of individual-based modelling in ecology: what have we learned and what could we learn in the future?
- ISSN: 03043800
- DOI: 10.1016/S0304-3800(98)00188-4
Each modeller who builds and analyses an individual-based model learns of course a great deal, but what has ecology as a whole learned from the individual-based models published during the last decade? Answering this question proves extremely difficult as there is no common motivation behind individual-based models. The distinction is introduced between 'pragmatic' motivation, which uses the individual-based approach as a tool without any reference to the theoretical issues which have emerged from the classical state variable approach and 'paradigmatic' motivation, which explicitly refers to theoretical ecology. A mini-review of 50 individual-based animal population models shows that the majority are driven by pragmatic motivation. Most models are very complex and special techniques to cope with this complexity during their analysis are only occasionally applied. It is suggested that in order to orient individual-based modelling more towards general theoretical issues, we need increased explicit reference to theoretical ecology and an advanced strategy for building and analysing individual-based models. To this end, a heuristic list of rules is presented which may help us to advance the practice of individual-based modelling and to learn more general lessons from individual-based modelling in the future than we have during the last decade. The main ideas behind these rules are as follows: (1) Individual-based models usually make more realistic assumptions than state variable models, but it should not be forgotten that the aim of individual-based modelling is not 'realism' but modelling. (2) The individual-based approach is a bottom-up approach which starts with the 'parts' (i.e. individuals) of a system (i.e. population) and then tries to understand how the system's properties emerge from the interaction among these parts. However, bottom-up approaches alone will never lead to theories at the systems level. State variable or top-down approaches are needed to provide an appropriate integrated view, i.e. the relevant questions at the population level. (C) 1999 Elsevier Science B.V. All rights reserved.