Detecting the effect of climate change on animals: how mathematics can help optimise data-gathering efforts

Are you a mathematics/statistics graduate, or finalist, with an interest in animal conservation and a desire to use your knowledge to help understand and mitigate against climate change?

Climate change is having a dramatic effect on a wide range of animals, by altering the environment in which they move and live. However, animal behaviour is complex and data is difficult and expensive to obtain. Consequently, detecting the effects of changing environments on an animal movement and space use can be challenging. Often, the myriad competing environmental effects often drown out the signal of climate changes in the limited data available. This can lead to a tendency to gather as much data as possible to ensure a signal. However, this is expensive and can have a negative impact on animal welfare. So it is important to understand exactly how much, and what sort of data, is required to understand how environmental change is affecting an animal population?

The aim of this PhD project is to provide rigorous analysis of this question, in relation to the effects of environmental changes on animal movement and space use. The approach will be mathematical, drawing from sampling theory (a branch of statistics), and applying these tools to widely-used classes of models for assessing the effects of habitat on animal movement and space use. The student will then validate these analytic predictions on a wide range of data, initially on seabirds (working with co-supervisor, Samantha Patrick), then extending beyond to other marine systems and perhaps also terrestrial systems, depending on data availability and the student’s interests.

This project is suited to a graduate (or prospective graduate) of any discipline within the mathematical sciences. The successful candidate would have a desire to collaborate across disciplines, an interest in animal ecology (and nature more broadly), but need not have any formal biological/ecological training.

We encourage informal enquiries prior to application. If you are interested, please contact the lead supervisor, Dr. Jonathan Potts.

Science Graduate School
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