Collaborative Research: MODEL ENABLED MACHINE LEARNING (MnML) FOR PREDICTING ECOSYSTEM REGIME SHIFTS

合作研究：用于预测生态系统制度转变的模型机器学习 (MnML)

• 批准号: 2233982
• 负责人: James Watson
• 金额: $ 75.77万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-15 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2233982&HistoricalAwards=false
• 关键词: Collaborative; Research; MODEL; ENABLED; MACHINE

Ecosystems can change radically, suddenly and without warning. There are numerous examples of this on land, in our rivers, lakes and oceans. From African savannahs to Californian kelp forests, these ecosystem “regime shifts'' as they are called, have had large impacts on the provision of key ecosystem services, such as food and income. There is a need for new bioinformatics and cyberinfrastructure that can predict these regime-shifts, and for identifying the drivers of such changes so that policies and technologies can be developed to help avoid them (should that be desired). Current methods for anticipating regime shifts perform poorly: either theoretical models of ecosystem dynamics are too abstract to provide useful operational forecasts, or data-driven approaches suffer from overfitting and cannot accurately forecast the emergence of novel conditions (i.e., those not seen in historical data on which models are trained). In this project, a new approach for forecasting ecosystem regime shifts will be developed. This new approach is called Model Enabled Machine Learning and it combines scientific understanding of ecological dynamics (i.e., theoretical models) with the predictive power of machine learning. This new approach will be co-developed with ecosystem stakeholders, so that the outputs of the models are useful and actionable.Model Enabled Machine Learning will be developed for three ecosystem case-studies and tested against other state-of-the-art approaches for predicting ecosystem regime shifts. This will involve using existing and developing new mathematical models of ecosystem dynamics for each case-study, as well as collecting empirical data for training the machine learning models. The goal is to significantly improve upon existing methods for predicting ecosystem regime shifts. The ecosystem case-studies include: 1) Tropical coral ecosystems that switch between coral- and algal-dominated states; 2) Freshwater lakes that exhibit harmful algal blooms; 3) Mangrove ecosystems that suffer from multiple stressors. The potential of Model Enabled Machine Learning as a new bioinformatic tool used by ecosystem managers lies not just in its predictive skill, but also in the clear interpretability it provides, which will maximize its utility as an operational tool. Importantly, Model Enabled Machine Learning has the potential to promote equitable science by reducing the data requirements of machine learning driven predictions, giving stakeholders in data-poor systems a useful operational tool that would otherwise be unavailable. To facilitate user engagement, the Model Enabled Machine Learning methods developed in this project will be operationalized as R and Julia coding packages/libraries, two common coding languages used by the stakeholder communities. Numerical methods in these packages will be co-designed with stakeholders to ensure that future ecosystems regime shifts are anticipated and managed.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

生态系统可以突然，没有警告来根本变化。在我们的河流，湖泊和海洋中，有许多例子。从非洲萨ack虫到加利福尼亚海带森林，这些生态系统被称为“政权转变”，对提供关键的生态系统服务（例如食品和收入）产生了很大的影响。需要新的生物信息学和网络基础结构，以预测这些政权转移，并确定这种变化的驱动因素，以便可以制定政策和技术来帮助避免这种变化（如果需要的话）。当前的预测政权转移的方法效果较差：生态系统动力学的理论模型要么太抽象了，无法提供有用的操作森林，要么数据驱动的方法过度适应，并且无法准确预测新型条件的出现（即，在历史数据中未见的是哪些模型受过训练的模型）。在这个项目中，将开发一种预测生态系统制度转移的新方法。这种新方法称为模型启用机器学习，它结合了对生态动态（即理论模型）的科学理解与机器学习的预测能力。这种新方法将与生态系统利益相关者共同开发，以便模型的产出有用且可操作。启用模型的机器学习将针对三个生态系统案例研究开发，并针对预测生态系统机制的其他最新方法进行了测试。这将涉及使用现有的和开发新的生态系统动力学数学模型，并收集用于培训机器学习模型的经验数据。目的是显着改善现有的预测生态系统制度转移的方法。生态系统案例研究包括：1）在珊瑚和藻类主导状态之间切换的热带珊瑚生态系统； 2）暴露有害藻类血液的淡水湖泊； 3）遭受多种压力源的红树林生态系统。模型使机器学习作为一种新的生物信息学工具的潜力不仅在于其预测技能，而且还在于它提供的清晰可解释性，这将最大程度地将其作为操作工具最大化。重要的是，启用模型的机器学习有可能通过减少机器学习驱动器预测的数据要求来促进公平科学，从而使数据贫困系统中的利益相关者成为一种有用的操作工具，否则该工具将是不可用的。为了促进用户参与，该项目开发的模型启用机器学习方法将以R和Julia编码包/库，这是利益相关者社区使用的两种常见的编码语言。这些软件包中的数值方法将与利益相关者共同设计，以确保预期和管理未来的生态系统政权转移。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响标准来评估通过评估来诚实地支持支持。