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PhD in Mathematics & Statistics: Multiscale statistical inference for understanding collective animal movement

Project Description

This PhD project will address the topic of multiscale statistical inference for understanding collective animal movement. The statistical analysis of animal movement is a rapidly developing area of science, fuelled in large part by the explosion in availability of movement data. To gain useful insight from these data new mathematical techniques are required and the aim of the project is to develop these techniques by applying computational statistics and machine learning to the analysis of collective animal movement.

The appointed student will develop multiscale non-parametric inference techniques using simulation models, statistical emulation, and Bayesian optimisation to fit models of individual and collective movement simultaneously. The methods will then be used to investigate the dynamics of two empirical study systems.

The first empirical study system will be free roaming sheep in Argentinean Patagonia and the student will have access to existing movement data from this system. The dataset describes the movement, behaviour, body condition, and reproductive success of an entire herd of free-ranging sheep tracked with GPS devices and accelerometers in Northern Patagonia. The student will examine how macroscale quantities such as the group centroid or degree of alignment are caused by the behaviour of individuals according to their own physiological needs.

The second empirical study will focus on the collective dynamics of wildebeest herds in the Serengeti National Park, Tanzania. Using data from an existing study that deploys small drones to collect video footage of wildebeest, the student will develop models of collective and individual behaviour. These models will consider the escape response of herds to threat stimuli and the transitions between behavioural states (foraging, resting, moving) and herd geometries.

The project provides an excellent opportunity for a student with strong mathematical and computing skills who is interested in cross-disciplinary research with applications of their quantitative skills in ecology.

Funding Notes

Funding is available to cover tuition fees for UK/EU applicants for 3.5 years, as well as paying a stipend at the Research Council rate (£14,999 for Session 2019-20).

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