Preparing bilbies for feral weather: developing an early warning system for conservation management

Preparing bilbies for feral weather: developing an early warning system for conservation management

Advisor/s

Martijn van de Pol (JCU), Jane McDonald (Queensland Dept. Env. & Science, Threatened Species Research and Monitoring), Chris Dickman (Sydney University), Katherine Moseby (UNSW)

College or Research Centre

College of Science & Engineering

Summary of Project

Greater bilbies, though once abundant in Australia, are a threatened species. In Queensland a wild population of bilbies persists in the Channel Country in the far west of the state. This population is separated from the rest of the Australian population and is thus of high conservation value. Arid environments, where bilbies live, are characterized by boom-bust cycles driven by rare rainfall events. Heavy rainfall results in a vegetation-boom, causing plant-eating mammal numbers to increase, in turn causing feral predators like cats to boom as well. Initially, cats feed mainly on common mammals like long-haired rats, but once rat numbers drop, they switch to bilbies. Rain-driven cat booms have over the past decades reduced the bilby population to undetectable levels several times, and therefore feral cat management programs have been started. However, effective cat management and bilby conservation in such remote areas requires timely planning of resources well in advance. What is urgently needed is an early warning system that can reliably predict when rat and cat booms and subsequent prey-switch events will likely occur. Decade-long census time series are available on various species from arid Australia to determine over what time-frame cat and rat (and other boom-bust mammals) numbers will increase after rainfall and how we can best manage their peaks. They await to be analysed and translated into practical tools for conservation management.

Key Words

Ecology; Statistics; Mathematical modelling; Conservation

Would suit an applicant who

is looking for an Honours, MSc by Research, PhD project or postdoctoral fellowship application.

Requirements:

• Experience with statistical modelling of time-series data / regression analysis
• An affinity with wildlife conservation
• People with a background in population dynamical / mathematical / ecological modelling can also develop the project in that direction

Updated: 15 Aug 2022