The past, present and future of the Great Barrier ReefThe past, present and: integrating sedimentology and ecology via modelling and machine learning.

大堡礁的过去、现在和未来过去、现在和：通过建模和机器学习整合沉积学和生态学。

• 批准号: 2683401
• 金额: --
• 依托单位: University of York
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2683401
• 关键词: past; present; future; Great; Barrier

Coral reefs are the marine biodiversity hotspots, occupying 0.1% of the world's ocean surface but hosting 38% of marine species. Reefs around the world are currently being destroyed by a number of factors. How will they react to predicted climate and anthropogenic change? We can use geological and biological knowledge, such as rock cores and changes in ecology through time, to predict the future. This project will create a numerical model of reef dynamics and incorporate both sedimentological and biological data using state-of-the-art technology. In turn, this will enable more accurate predictions of the impacts of future climate and anthropogenic change on reef dynamics. The model will be based on the current prototype of the Carst model.

珊瑚礁是海洋生物多样性的热点，占世界海洋面积的0.1%，但拥有38%的海洋物种。目前，世界各地的珊瑚礁正受到多种因素的破坏。他们将如何应对预测的气候和人为变化？我们可以利用地质学和生物学知识，如岩石核心和生态学随时间的变化，来预测未来。该项目将建立一个珊瑚礁动态的数字模型，并利用最新技术将沉积学和生物学数据结合起来。反过来，这将能够更准确地预测未来气候和人为变化对珊瑚礁动态的影响。该模型将以目前的Carst模型原型为基础。