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A plant consists of components such as leaves, roots, stem, flowers and buds. Over time the form of the plant changes; new parts grow, existing parts change their shape or size and old parts fall off or are removed. From this perspective, plants can be seen as dynamic structures of connected parts. Intimately related to this changing of the external form and size of the plant are processes happening inside the plant. From this perspective, plants can be seen as biochemical machines; using the energy of the sun to assimilate carbon and converting this carbon into the material of which they are made; obtaining and distributing other nutrients and water from the soil. Plants are also continually responding to and affecting their environment and other organisms. Plant growth thus involves the interaction between this structural development, these physiological processes, the physical environment that surrounds them, and the animals, other plants, fungi and bacteria with which they interact. This four-way interaction results in plant behaviour that is variable, adaptable, complex and difficult to fully understand and predict. Simulation modelling provides a tool that can help understand and predict the behaviour of complex systems, such as plants and plant ecosystems, by synthesising available knowledge and hypotheses. But how should a plant be modelled? Traditionally the dynamics of populations of plants or the growth of individual plants was simulated with differential equations that integrated processes across time and space. Increases in computational power allowed these models to be extended to include much more detailed and realistic representation of the spatial and temporal variability of the plant's environment, such as variation in moisture levels through a soil profile, light through a canopy, and rainfall through a season. Computational power also opened up the possibility of individual-based modelling (IBM) approaches, which can account for the variability between individual plants by representing each plant separately, together with the interactions between them. The functional-structural plant model (FSPM) approach goes further still by separately representing the individual components of the plant, such as leaves, internodes, flowers or even cells, together with their interactions. These approaches open up the possibility of modelling at greater and greater levels of detail and realism, but does modelling at greater levels of detail and realism necessarily help a model achieve its aim of helping understand and predict the behaviour of a plant or a plant ecosystem? In this paper, I present a range of examples of FSPMs and IBMs involving plants and their ecosystems. I show how these relatively new computational approaches can be used to address a range of purposes across a spectrum of spatial and temporal scales, from the movement of molecules of plant hormones in individual plant cells during a fraction of a second, to the migration of plants across landscapes under changing climates during hundreds of years. I point out the advantages of FSPM/IBM approaches in each of these examples, while also arguing that choices about the amount of detail to be included in a FSPM/IBM plant model should always be made with the objective of achieving the purpose of the modelling study as effectively but efficiently as possible.

Function, form, environment and ecological interactions: what’s the right way to model a plant?