CyberSEES: Type 2: SEA-MASCOT: Spatio-temporal Extremes and Associations : Marine Adaptation and Survivorship under Changes in extreme Ocean Temperatures

该提案开发了新的计算和统计模型，以解决海洋生态学中的一个科学问题：海洋生物如何在极端气候压力下适应和生存，特别是在海洋温度上升及其极端情况下？要解决这一海洋生态问题，需要预测尺度在1米到几米的极端气候温度变量；然而，目前的全球气候模型只能得出100公里范围内的可信数据。解决这些科学问题的关键是开发用于从非线性和相对非平稳系统中发现关联和预测模型的计算模型，其中依赖结构在空间和时间上可能是复杂的。在这个项目中，我们提出了新的统计依赖度量，以捕获极端和时空应用中常见的非线性依赖和非平稳特性。特别是，我们研究了基于满足公平性属性的copulas的依赖度量（统计学中的一个新概念，描述了对转换不变性的度量），并开发了利用这种依赖度量进行特征选择的计算模型，以识别高维气候和海洋生态数据上的相关变量并去除冗余变量。然后，我们开发新的预测模型，利用稀疏模型的进步，贝叶斯非参数，以及气候和海洋生态的物理和科学知识。所有关于特征选择和预测的新型计算方法都将能够在更精细的分辨率下发现与海洋生态生存相关的气候极端事件的关联和预测。除了通过更好的海洋生态预测模型对社会产生更广泛的影响外，我们还利用我们的多学科团队提供跨学科教育，并鼓励女性和少数民族学生参与我们的研究项目，从而对教育产生更广泛的影响。

This proposal develops novel computational and statistical models to address a science question in marine ecology: How will marine organisms adapt and survive under extreme climate stressors, in particular, rising ocean temperatures and their extremes? Addressing this marine ecology question requires prediction of extreme climate temperature variables at scales of one to a few meters; whereas, current global climate models only yield credible insights at 100 kilometers. Key to addressing these science questions is to develop computational models for discovering associations and predictive models from nonlinear and relatively non-stationary systems, where the dependence structures can be complex in space and time. In this project, we propose novel statistical dependence measures that capture nonlinear dependencies and non-stationary properties common in extremes and spatio-temporal applications. In particular, we investigate dependence measures based on copulas that satisfy the equitability property (a new concept in statistics describing measures that are invariant to transformations) and develop computational models that utilize this dependence measure to perform feature selection to identify relevant variables and remove redundant ones on high-dimensional climate and marine ecology data. We then develop novel prediction models, leveraging on advances in sparse models, Bayesian nonparametrics, and knowledge of the physics and science of climate and marine ecology. All the novel computational methods on feature selection and prediction will enable the discovery of associations and prediction of climate extremes at finer resolutions relevant for marine ecology survivorship prediction. Besides broader impact to society through better marine ecology prediction models, we also provide broader impact to education by leveraging our multi-disciplinary team in offering cross-discipline education and encouraging mentoring of women and minority students into our research program.