Individuals within a population differ in attributes such as size, sex, spatial location, behaviour and age. Structured population models allow us to understand the effects of within population trait variation on demographic and population level processes. Data-driven modelling approaches allow parameters and functional relationships of a model to be statistically estimated from actual individual observations. This help our models reflect reality.
Integral projections models (IPM’s) are a data-driven structured population modelling approach, in which individuals are described by continuous traits, rather than being classified into arbitrary categories. They can be used to obtain quantitative and predictive inferences about a wide range of population types. They can describe secies with a very broad range of life histories, ranging from plants with dormant seed banks, to animals with size dependent growth, survival and reproduction.
I will demonstrate how IPM’s can be constructed in order to make full use of precious experimental and field observation. I will show current work that is being undertaken to advance IPM’s and I will outline future directions for the application of these models in cancer research.