Evolutionary impacts of Tasmanian devil population restoration


Tasmanian devils are in decline due to devil facial tumour disease (DFTD), so the Save the Tasmanian Devil Program has been using translocations to help restore wild populations and prevent inbreeding. This project will combine molecular genetic data and sophisticated computational simulations to project the long-term effects of supplementation for improving the status of wild populations, and potential impacts on the spread of DFTD. The results will provide immediate data to maximize the success of devil recovery and inform the conservation of many other species for which current threats cannot be fully removed.


Dr Catherine Grueber

Research Location

School of Life and Environmental Sciences

Program Type



This project will bring together two important tools for understanding and projecting the impacts of conservation management on the genetic profile of threatened populations: molecular genotyping, and computational simulations. By comparing genetic data before and after translocations, we can examine how populations have changed over short timeframes (a few generations). Feeding this data into simulation models allows us to project these outcomes long into the future, by simulating “evolutionary time”. Testing our assumptions and updating the simulations with innovative molecular and field data will give us robust insights into what might happen to devils, and DFTD, in coming years. The results will help show how to maximise the species’ chance of survival in the long term. This project will suit a motivated student looking to further their skills in molecular genetics (lab work), computational modelling, evolutionary biology, statistics and academic writing. We work very closely with our conservation partners in the Save the Tasmanian Devil Program, so the successful applicant will also have the opportunity to gain knowledge and experience working with the conservation industry.

Additional Information

A degree in genetics/genomics, evolutionary biology, or a related field is required.
Additional Supervisor Dr Carolyn Hogg.

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conservation, disease, Evolution, Genetics, genomics, population genetics, Simulation modelling.

Opportunity ID

The opportunity ID for this research opportunity is: 2830

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