Releasing the predictive power of animal movement models: Multiscale inference to account for complex cognition

MVLS/EPSRC PhD fully funded positions are available within the College of Medical, Veterinary and Life Sciences, University of Glasgow. Our aim is to deliver high quality, collaborative research and training for our PhD students within the Biosciences. The West of Scotland is a key region in the UK for academic excellence, industrial support and investment.

Areas of research include (but are not restricted to) mathematical biology, bioenergy, synthetic biology, nanobiology and research into healthcare devices. A list of available projects, abstracts and supervisors can be found HERE. We strongly recommend that students examine the list of projects available, and contact potential supervisors to discuss the projects before applying.

Visit programme website for more information: https://www.gla.ac.uk/colleges/mvls/graduateschool/mvlsepsrcstudentships/

Supervisors:

Prof Jason Matthiopoulos

Prof Janine B Illian

Dr Grant Hopcraft

PhD Project Summary:

Understanding the drivers behind animal movement is key in making predictions which accurately inform conservation efforts and minimize the impact of human-wildlife conflict. 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 sophisticated mathematical framework. This framework is currently lacking, limiting our ability to predict the movement of animals in the wild over the extended timescales that are biologically relevant and necessary for conservation planning.

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