Comparative biosecurity informatics to anticipate invasive species threats

比较生物安全信息学以预测入侵物种威胁

• 批准号: DP210102904
• 负责人: Dr Matthew McGee
• 金额: $ 24.08万
• 依托单位: Monash University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2022
• 资助国家: 澳大利亚
• 起止时间: 2022-06-22 至 2025-06-21
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP210102904
• 关键词: Comparative; biosecurity; informatics; anticipate; invasive

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.

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并造成伤害之前识别它们。该项目旨在利用机器学习的最新技术，结合数千个物种的遗传、生态和功能数据集，克服这一挑战。该项目预计将产生一个新的框架，使我们能够以公正的方式识别和排名危险的入侵物种，帮助保护澳大利亚独特的生物群落。预期成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巨大的经济效益。