Individual-based models

Individual-based models (IBMs, also known as agent-based models) are mechanistic models in which demographic population trends emerge from processes at the individual level. IBMs are used instead of more aggregated approaches whenever one or more of the following aspects are deemed too relevant to be ignored: intraspecific trait variation, local interactions, adaptive behaviour, and response to spatially and temporally heterogeneous environments, which often results in nonlinear feedbacks. IBMs offer a high degree of flexibility and therefore vary widely in structure and resolution, depending on the research question, system under investigation, and available data. Data used to parameterise an IBM can be divided into two categories: species and environmental data. Unlike other model types, qualitative empirical knowledge can be taken into account via probabilistic rules. IBM flexibility is often associated with higher number of parameters and hence higher uncertainty; therefore sensitivity analysis and validation are extremely important tools for analysing these models. The chapter presents three examples: a vole–mustelid model used to understand the mechanisms underlying population cycles in rodents; a wild boar–virus model to study persistence of wildlife diseases in heterogeneous landscapes; and a wild tobacco-moth caterpillar model to study emergence of delayed chemical plant defence against insect herbivores. These examples demonstrate the ability of IBMs to decipher mechanisms driving observed phenomena at the population level and their role in planning applied conservation measures. IBMs typically require more data and effort than other model types, but rewards in terms of structural realism, understanding, and decision support are high.