Discovery Early Career Researcher Award - Grant ID: DE180101558
Funder
Australian Research Council
Funding Amount
$365,058.00
Summary
The biodiversity consequences of evolutionary innovation. This project aims to increase knowledge of how evolutionary innovations affect biodiversity. This project will focus on a classic example of evolutionary innovation, the specialized throat jaws found in many fish groups, including damselfishes, wrasses, and cichlids. These unique jaws may explain why these fish groups contain so many species and are such successful invasive species in Australia and elsewhere. This project proposes an inte ....The biodiversity consequences of evolutionary innovation. This project aims to increase knowledge of how evolutionary innovations affect biodiversity. This project will focus on a classic example of evolutionary innovation, the specialized throat jaws found in many fish groups, including damselfishes, wrasses, and cichlids. These unique jaws may explain why these fish groups contain so many species and are such successful invasive species in Australia and elsewhere. This project proposes an integrative combination of methods and functional experiments to reveal the biodiversity consequences of evolutionary innovation. It will also enhance Australian biosecurity through the production of new ways to assess invasion risk from aquaculture and aquarium trade species.Read moreRead less
Comparative biosecurity informatics to anticipate invasive species threats. Invasive species cause billions in economic damages to Australia, but we do not have effective means to identify dangerous species before they arrive and cause harm. This project aims to overcome this challenge using the latest techniques in machine learning combined with genetic, ecological, and functional datasets for thousands of species. This project expects to generate a novel framework that allows us to identify an ....Comparative biosecurity informatics to anticipate invasive species threats. Invasive species cause billions in economic damages to Australia, but we do not have effective means to identify dangerous species before they arrive and cause harm. This project aims to overcome this challenge using the latest techniques in machine learning combined with genetic, ecological, and functional datasets for thousands of species. This project expects to generate a novel framework that allows us to identify and rank dangerous invasive species in an unbiased way, helping to safeguard Australia's unique biological community. Expected outcomes include improved methods for detecting ecologically and functionally similar species, providing substantial economic efficiency benefits to Australian biosecurity.Read moreRead less