Individual-based models

Name: Individual-Based Models, also known as Agent-Based Models. Also known in the early days as Behaviour-Based Models.

Key references: (Grimm and Railsback, 2005, Railsback and Grimm, 2012)

Key examples: Paccala (1996) modelled hardwood forests in North America to understand the dynamics of transition oak. MORPH has been applied to address management problems of wading birds in many coastal environments around Europe (Stillman, 2008). ALMaSS is a comprehensive model of large landscapes used to predict, e.g., the consequences of building highways or changing farming practice, e.g. by applying novel agricultural chemicals.

Description: IBMs take all that is known about the behavioural and physiological ecology of a studied species and formalises this knowledge into a system of algorithms that specify what an individual will do in each environmental situation that it may encounter. Individuals may be distinguished by age, size or sex, for example as juveniles/adults, males/females. Each may have its own energy budget, such that it forages when its energy reserves are depleted, and allocates ingested energy between maintenance, growth and/or reproduction according to defined rules. The lives of individuals are simulated, usually in a detailed map of an environment, to discover where they are, and how many there are, at specified times in the future.

Prerequisites: Programming skills. A good knowledge of the behavioural ecology of the species and knowledge of the environment it lives in.

Strengths: Can predict where animals will be in mapped environments at specified times in the future. Useful for many management purposes, e.g. predicting the effects on animal species of conservation measures in nature reserves, of highways or windfarms, or agricultural chemicals.

Weaknesses: High level of complexity. Many model parameters. Implementation and use may not be user friendly. IBMs are often difficult to evaluate because we lack agreed methods of evaluation.

Other uses: To provide a mechanistic explanation of results or patterns derived from statistical correlations. To teach programming.

Resources: Existing general IBMs: Morph, ALMaSS. Software languages: Netlogo, C, C++, C#, Java, R, Python, etc.

Validation: Pattern oriented modelling. Methods of Bayesian inference confronting model outputs with data: Approximate Bayesian Computation (ABC); data assimilation; particle filtering. Model complexity may be addressed by removing model components.

Similar methods: cellular automata; stochastic game theory.


References:

GRIMM, V. & RAILSBACK, S. F. 2005. Individual-Based Modeling and Ecology, Princeton, NJ, Princeton University Press.

RAILSBACK, S. F. & GRIMM, V. 2012. Agent-Based and Individual-Based Modeling:
A Practical Introduction, Princeton, Princeton University Press.

STILLMAN, R. A. 2008. MORPH – An individual-based model to predict the effect of environmental change on foraging animal populations. Ecological Modelling, 216, 265-276.

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