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.

生态系统可能会在没有任何预警的情况下发生根本性的变化，在陆地、河流、湖泊和海洋中，从非洲大草原到加利福尼亚海带森林，这些生态系统的“政权转变”已经发生了巨大的变化。需要新的生物信息学和网络基础设施来预测这些制度转变，并确定此类变化的驱动因素，以便制定政策和技术。可以开发一些方法来帮助避免它们（如果需要的话）。当前预测政权转变的方法表现不佳：生态系统动力学的理论模型过于抽象，无法提供有用的操作预测，或者数据驱动的方法存在过度拟合，无法准确预测。在这个项目中，将开发一种预测生态系统状况变化的新方法，这种新方法称为“模型支持的机器学习”，它结合了科学。对生态动力学的理解（即理论模型）具有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，以便模型的输出有用且可操作。模型支持的机器学习将针对三个生态系统案例进行开发。研究并针对其他最先进的方法进行测试，以预测生态系统状况的变化，这将涉及为每个案例研究使用现有的和开发新的生态系统动力学数学模型，以及收集用于训练机器学习模型的经验数据。目标是显着改进预测生态系统状况变化的现有方法。 生态系统案例研究包括：1) 在珊瑚和藻类为主的状态之间切换的热带珊瑚生态系统；2) 出现有害藻华的淡水湖泊；3) 受到影响的红树林生态系统。模型支持的机器学习作为生态系统管理者使用的新生物信息工具的潜力不仅在于其预测能力，还在于其提供的清晰可解释性，这将最大限度地发挥其效用。重要的是，模型支持的机器学习有潜力通过减少机器学习驱动的预测的数据需求来促进公平科学，为数据匮乏的系统中的利益相关者提供一个有用的操作工具，以促进用户参与。该项目中开发的支持模型的机器学习方法将作为 R 和 Julia 编码包/库（利益相关者社区使用的两种常见编码语言）进行操作，这些包中的数值方法将与利益相关者共同设计，以确保未来。预计生态系统体制将发生转变该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。