Developing a nested, multiscale framework for modelling animal movement processes from long-term tracking data

Understanding the drivers behind animal movement is key in making predictions which accurately inform conservation efforts.

However, animals with high levels of cognition make movement decisions at multiple spatial and temporal scales; creating an overall 'movement system' which can only be represented with a complex mathematical framework.

This framework is currently lacking, limiting our ability to predict the movement of animals in the wild over the extended timescales that multi-scale processes can be expected to occur.

This project aims to develop the framework for modelling and statistically estimating the dependence between cognitive processes that drive animal movement across a hierarchy of spatial and temporal scales.

In particular, it will account for uncertainty in the observation process of recording consecutive location data within a complex spatiotemporal point process model.

Quantifying such fundamental cognitive processes as animal memory, volition, and communication, at biologically appropriate scales, will undoubtedly lead to better predictions for the dynamics of real movement processes.