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

生态系统可以在没有任何征兆的情况下发生根本、突然的变化。在陆地上，在我们的河流、湖泊和海洋中，有许多这样的例子。从非洲大草原到加利福尼亚海带森林，这些被称为生态系统“政权转移”的生态系统对提供关键的生态系统服务（如食物和收入）产生了巨大影响。需要新的生物信息学和网络基础设施来预测这些制度的转变，并确定这些变化的驱动因素，以便制定政策和技术来帮助避免这些变化（如果需要的话）。目前预测制度变化的方法表现不佳：要么是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，要么是数据驱动的方法存在过拟合问题，无法准确预测新情况的出现（即，那些在模型训练的历史数据中没有看到的情况）。在这个项目中，将开发一种预测生态系统制度变化的新方法。这种新方法被称为模型支持机器学习，它将对生态动力学的科学理解（即理论模型）与机器学习的预测能力相结合。这种新方法将与生态系统利益相关者共同开发，以便模型的输出是有用的和可操作的。模型支持机器学习将为三个生态系统案例研究开发，并针对预测生态系统制度变化的其他最先进方法进行测试。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，以及收集经验数据以训练机器学习模型。目标是显著改进现有的预测生态系统变化的方法。生态系统案例研究包括：1)在珊瑚和藻类主导状态之间转换的热带珊瑚生态系统；2)出现有害藻华的淡水湖；3)红树林生态系统遭受多重压力。作为生态系统管理者使用的一种新的生物信息学工具，模型支持机器学习的潜力不仅在于它的预测能力，还在于它提供的清晰的可解释性，这将使其作为一种操作工具的效用最大化。重要的是，模型支持机器学习有可能通过减少机器学习驱动的预测的数据需求来促进公平的科学，为数据贫乏系统中的利益相关者提供一个有用的操作工具，否则将不可用。为了促进用户参与，本项目开发的模型支持机器学习方法将作为R和Julia编码包/库进行操作，这是利益相关者社区使用的两种常用编码语言。这些方案中的数值方法将与利益相关者共同设计，以确保预测和管理未来生态系统制度的变化。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.