How to build and use individual-based models (IBM) as hypothesis testing tools

No abstracts are to be cited without prior reference to the author.

Individual-based models (IBMs) allow us to bridge the gap between the level at which environmental impacts occur (individuals) and the level at which observations are made (populations). Here, we present a conceptually and programmatically simple approach for building a stochastic stage-based IBM that can be used to statistically test hypotheses. A unique aspect of our model is a “Fitness” variable that characterizes individual variability, and may be prescribed a priori (e.g. genetic), or solved for dynamically based on individual history, environment, condition, etc. We demonstrate the power of our model to test hypotheses concerning environmental effects on copepod physiology through simulation of the seasonal dynamics of C. finmarchicus in the northwest Atlantic. We also discuss how this IBM structure can be adapted for other applications.