Tracking the past to predict the future 

Supervisor: Prof Andrea Manica

The natural world faces a climate crisis, with species having to deal with major changes in their environment. Crucially, our ability to predict how species respond to such changes is limited. Past climate fluctuations over glacial cycles provide a natural experiment to investigate the processes that shape species range changes and adaption. In this project, we will combine population genetics, species distribution models and climate reconstructions to calibrate models that can reconstruct how animals responded to past major climatic changes, and then use these models to make predictions for the future and inform possible mitigating strategies. 

Type of work 

The student will use climate informed population genetic models, a framework to quantitively combine ecological, genetic and climatic information, to study responses of species to past climate change. The work is computer based, and requires mastering a number of disciplines. The student should be proficient in R programming, and will join a group of students and postdocs who work together in developing tools to ask ecological and evolutionary questions. They will participate in weekly hackathons, facilitating their up-skilling in coding (in R, and potentially other languages). 

Importance of the area of research concerned

With anthropogentic climate change threatening a large portion of life on earth, the ability to predict how species will respond to such changes is essential if we want to mitigate the impact of this crisis. 

References 

Pierpaolo Maisano Delser, Mario Krapp, Robert Beyer, Eppie R Jones, Eleanor F Miller, Anahit Hovhannisyan, Michelle Parker, Veronika Siska, Maria Teresa Vizzari, Elizabeth J. Pearmain, Ivan Imaz-Rosshandler, Michela Leonardi, Gian Luigi Somma, Jason Hodgson, Eirlys Tysall, Zhe Xue, Lara Cassidy, Daniel G Bradley, Anders Eriksson, Andrea Manica, Climate and mountains shaped humans ancestral genetic lineages, bioRxiv (2021), https://doi.org/10.1101/2021.07.13.452067