Designing landscapes that are robust to climate change

A PhD position is available to develop new mathematical and computational methods that will support the design of new nature networks. Your mathematical models will account for population dynamics and dispersal, ensuring that the best ecological information is used when designing ‘connected landscapes’ that will allow species to shift their geographic distributions in response to climate change.

Global warming and habitat loss present significant challenges because a species needs to be able to reach new (climatically suitable) locations before its current geographical range becomes inhospitable. Therefore, the limited amount of habitat that can be preserved or restored must be arranged as networks that are sufficiently well connected to allow species’ ranges to shift over time, and extensive enough that they can move long distances. But, what is the best design for these networks? Unfortunately, existing computer models that incorporate realistic details of a species’ ecology are far too slow to design habitat networks, and currently available tools make over-simplistic assumptions.

You will build on recent advances in mathematical population dynamics to develop new and efficient methods for accurately predicting the spread of populations through habitat networks, and you will test the robustness of the model’s predictions using empirically observed range expansions for butterfly species in the UK.

This project will suit a graduate of a quantitative discipline (e.g. mathematics, physics, computer science) who wishes to apply their skills to environmental change biology, or a biologist with strong mathematical and/or computational skills. You will be part of the NERC ACCE PhD programme, which will provide both support and training, and based at the University of Liverpool. You will be supervised by a multi-disciplinary team comprising Stephen Cornell (Mathematical Ecologist), Jenny Hodgson (Conservation Biologist), and Chris Thomas (Biodiversity Researcher).