Comparative biosecurity informatics to anticipate invasive species threats

入侵物种给澳大利亚造成了数十亿美元的经济损失，但我们没有有效的手段在危险物种到来之前识别它们并造成伤害。该项目旨在利用机器学习中的最新技术，结合数千个物种的遗传、生态和功能数据集来克服这一挑战。该项目预计将产生一个新的框架，允许我们以公正的方式识别危险的入侵物种并对其进行排序，帮助保护澳大利亚独特的生物群落。预期结果包括改进检测生态和功能相似物种的方法，为澳大利亚的生物安全提供可观的经济效益。

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.