News & events
News
A new study shows that we can use existing conservation data to predict which currently unthreatened species could become threatened and take proactive action to prevent their decline before it is too late.
Superb fairy-wrens are more likely to take risks to help members of their close social circle, according to a new study.
Dr Tobias Hayashi studied a delicate little native orchid with a cool and clever adaptation to attract pollinators.
While most spiders are creatures of solitude, a study involving researchers from ANU has found some species have become more gregarious.
Helen Osmond has watched the ins and outs of one superb fairy-wren population for three decades.
Events
Plants synthesize an amazing diversity of volatile organic compounds (VOCs) that facilitate interactions with their environment, ranging from attracting pollinators and seed dispersers to protecting themselves from pathogens, parasites, and herbivores.
This seminar is presented by Dr Emily Stringer and Dr Jarrod Sopniewski, Postdoctoral Research Fellows from the Centre for Conservation Ecology and Genomics at the University of Canberra.
Many flowering plants have evolved diverse strategies to communicate with and attract animal pollinators. We have discovered exciting new evidence for the role of anthocyanins and terpenes in the evolution of the highly diverse terrestrial orchid subtribe Caladeniinae (Diurideae).
Using case studies from my research on black-cockatoos and a formal partnership with Bush Heritage Australia, I will discuss how bioacoustics’ focus on machine learning and analysis over the last decade has now landed us in a place to use the technology in applied conservation settings.
Dietary shifts—particularly the inclusion of animal resources—were pivotal in human evolution, yet direct evidence of meat consumption in early hominins remains limited and debated.
By addressing key modeling challenges in mass spectrometry and tissue image analysis, this research advances the scalability, precision, and applicability of deep learning in clinical genomics, computational pathology, and personalized medicine.