Can machine learning be used to accurately identify wildlife in remote camera trap images? (with WildCount, National Parks and Wildlife Service, NSW Government)
This project will work closely with WildCount, a large-scale wildlife monitoring program run by the National Parks and Wildlife Service, NSW Government and the School of Life and Environmental Sciences, University of Sydney. It will test the feasibility of using machine learning algorithms for identifying species in camera trap images.
DigiFarm: incorporating biodiversity into farming decisions (with the Sydney Institute of Agriculture)
We aim to develop a digitally enabled network which will monitor native flora and fauna to inform sustainable agricultural practices. A unique combination of methods will be used: We will test new methods in camera trapping and acoustic recorders (birds and bats) in quantifying on-farm biodiversity and develop spatial models to identify biodiversity hotspots.
Simpson Desert Insights: designing Citizen Science programs for identifying wildlife in remote camera trap images (with Australian Museum)
This project will work closely with DigiVol at the Australian Museum, and the School of Life and Environmental Sciences, University of Sydney. It will determine the level of uncertainty in using Citizen Scientists to identify species in remote camera trap images.
Identifying species of high conservation value for restoring ecosystem function after disturbance.
This project aims to determine how ecosystem function changes after a disturbance (e.g. wildfire) event and partition each source of change from disturbance—species loss, gain and change in resident species dynamics—to ecosystem function. We aim to discover the mechanisms of how disturbance changes ecosystem function in order to identify species of high conservation value or act as a threatening process.