Computational Models

Agent Based Models in Plant Science

Agent-based modelling models systems as an interaction between different units, or agents. An example would be something like SimCity. In a review in Annals of Botany Zhang and DeAngelis review Agent Based Modelling in the context of Functional-Structural Plant Models. When modelling a single plant, the agents are building blocks or metamers, which interact to build the plant.

“ABM at the population and community levels aims at predicting plant population or community dynamics by modelling multiple individual plants (agents) that interact with their environment and each other,” write Zhang and DeAngelis. “Each agent has a set of state variables, which can include age, size, condition and spatial location, as well as adaptations, which can include both physiological traits and behavioural traits. This is critical because plant traits play a crucial role in plant ecology via determining the success or failure of species in a given environment. These traits govern an individual’s growth, reproduction, dispersal, allocation of nutrients and energy, and mortality in relation to environmental factors.”

Image: Canva.

“Traits may vary among the individuals due to genetic variation but may also change through time due to ontogeny and plasticity. ABMs differ from differential equation (DE) population and matrix model (MM) size–structure models, in which a top-down description is imposed on populations through population-level parameters (i.e. birth and death rates at the population level). ABMs are bottom up-models such that population-level behaviours emerge from the interactions that autonomous individuals have with each other and their environment. The number of individual-level attributes that an individual can have in an ABM is virtually unlimited, in contrast to DE or MM models of populations, in which it is awkward to include more than a few attributes.”

Zhang and DeAngelis provide examples of applications for agent-based models from plant intercropping, plant invasions, weed control and effects of climate change, among other examples.

“Altogether, plant ABMs cover a huge range of styles, levels of detail and applications, so there is no easy way to sort these out along a small number of axes. However, all ABMs can be viewed in terms of two spatial scales, their scale of spatial resolution and the scale of spatial extent.” write Zhang and De Angelis.

Zhang and DeAngelis highlight future directions for Agent Based Modelling. Increasing availability of of data at the plant level, as well as increased computational power could provide opportunities at the very small scale, by integrating microbes as well as expanding to better model changes at the landscape level.“It is also crucial to integrate biodiversity and the ecosystem in plant ecology,” conclude Zhang and DeAngelis. “As stated in Grimm et al. (2017), next-generation scientists should include the individual-based approach in their toolkit and focus on addressing real systems by developing theory for individual behaviours just detailed enough to reproduce and explain patterns observed at the system level. If some or all of these trends can be accelerated, the next decade in plant ABM will be very exciting.”

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