Forecasting coral reef recovery with new data-driven dispersal models

使用新的数据驱动的扩散模型预测珊瑚礁恢复

• 批准号: FT170100274
• 负责人: Prof Michael Bode
• 金额: $ 53.21万
• 依托单位: Queensland University of Technology
• 依托单位国家: 澳大利亚
• 项目类别: ARC Future Fellowships
• 财政年份: 2017
• 资助国家: 澳大利亚
• 起止时间: 2017-07-03 至 2022-07-01
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/FT170100274
• 关键词: Forecasting; coral; reef; recovery; new

This project aims to combine innovative mathematical methods and new genetic data to accurately predict the larval dispersal patterns of reef fish and corals. Larval dispersal is central to the ecology of coral reefs, and has vital implications for conservation. Most marine organisms spend their early life dispersing in the ocean, but our understanding of where these tiny larvae go is limited by sparse data and unvalidated models. Applied to extensive case-studies from Australia and across the western Pacific Ocean, these methods will be used to forecast and understand the recovery of fish and coral populations following severe disturbances. This will provide benefits such as enabling us to prioritise conservation actions in the aftermath of severe disturbances, including the catastrophic 2016 mass coral bleaching on the Great Barrier Reef.

本项目旨在结合创新的数学方法和新的遗传数据，准确预测珊瑚礁鱼类和珊瑚的幼虫扩散模式。幼虫的扩散是珊瑚礁生态的核心，对保护具有重要意义。大多数海洋生物的早期生活都分散在海洋中，但我们对这些微小幼虫去向的理解受到稀疏数据和未经验证的模型的限制。应用于澳大利亚和整个西太平洋的广泛案例研究，这些方法将用于预测和了解严重干扰后鱼类和珊瑚种群的恢复。这将带来好处，例如使我们能够在严重干扰之后优先采取保护行动，包括2016年大堡礁灾难性的大规模珊瑚白化。