Experimental translocations to understand and combat eastern quoll declines. The project aims to understand the causes of observed declines of the eastern quoll in Tasmania, and develop tools to safeguard this species in their last wild stronghold. The project will test the innovative approach of undertaking a series of experimental translocations at an early stage of a population decline. This approach will provide reliable information on the causes of observed declines, while simultaneously te ....Experimental translocations to understand and combat eastern quoll declines. The project aims to understand the causes of observed declines of the eastern quoll in Tasmania, and develop tools to safeguard this species in their last wild stronghold. The project will test the innovative approach of undertaking a series of experimental translocations at an early stage of a population decline. This approach will provide reliable information on the causes of observed declines, while simultaneously testing the effectiveness of translocations of captive-bred animals as a management tool for the species. It will also develop evidence-based protocols for undertaking captive-bred translocations, to improve the outcomes of eastern quoll recovery efforts as well as promoting early intervention for other declining species. Read moreRead less
Digitally-Integrated Smart Sensing of Diverse Airborne Grass Pollen Sources. Grass pollen is the main outdoor allergen source globally, triggering hayfever and asthma in up to 500 million people. With over 10,000 species, the influence of grass type, location and climate on pollen in the air is not yet known. This is a key issue since subtropical and temperate grasses differ in response to environmental factors. The project aims to use artificial intelligence on digital camera images to learn to ....Digitally-Integrated Smart Sensing of Diverse Airborne Grass Pollen Sources. Grass pollen is the main outdoor allergen source globally, triggering hayfever and asthma in up to 500 million people. With over 10,000 species, the influence of grass type, location and climate on pollen in the air is not yet known. This is a key issue since subtropical and temperate grasses differ in response to environmental factors. The project aims to use artificial intelligence on digital camera images to learn to see local grass flowers and integrate this with air sensors trained to detect grass pollen types. The expected outcomes are new capacities to track airborne grass pollen types. These outcomes can transform how pollen can be monitored to reduce the burden of allergies, and provide evidence of changing airborne pollen loads.
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