Predicting Species’ Responses to Climate Change Using Spread Models, Data Inference & Climate Velocities

Based at the Centre for Ecology & Hydrology (CEH), Wallingford.

Climate change is arguably the greatest threat that biodiversity will be facing over the next decades. While the threats to certain individual species are well characterized, there is a remarkable paucity of realistic assessments of how a wide range of species might respond to climate change, or of which types of species will be most affected. Those assessments that have been done have the drawback that they rarely take account of the fact that that individuals disperse. The realistic spatial dynamics of populations must be integral to analyzing how species may respond to a changing climate.
This PhD project will address the questions: 1) What proportions of species in different taxonomic groups will be able to track climate change?; 2) Which life history traits are related to this ability to track climate?; and 3) Can we therefore predict which types of species are most at risk from a changing climate? We will use and develop new approaches to combining spatial population models with data on demography and dispersal to project shifts in distribution across a wide range of species.
The project will use the “climate velocity” concept, i.e. the rate at which a climate isocline moves across the Earth’s surface at any particular location. It can be thought of as the rate at which a species will need to shift its range to stay in its optimal climate space. We have been modelling the rate at which different types of species can spread, given their dispersal abilities and population growth rates, and comparing these with climate velocities as an assessment of their ability to track a shifting climate.
The student will be working with a thriving group of ecological modellers at CEH and Reading and will benefit also from interacting with the Biological Records Centre and Disease Modelling group at CEH and the Process Modelling group and Phylogenetics group at Reading. The student will make use of the supervisory group by spending two weeks at Aberdeen learning the Bayesian approach developed for trait-space modelling and two weeks at Sheffield to learn about use and analysis of the COMADRE and COMPADRE databases.

More details are available on the project description at http://www.met.reading.ac.uk/nercdtp/home/available/desc/entry2017/SC201728.pdf