III: Medium: Collaborative Research: Bayesian Modeling and Inference for Quantifying Terrestrial Ecosystem Functions

III：媒介：协作研究：量化陆地生态系统功能的贝叶斯建模和推理

• 批准号: 1563950
• 负责人: Arindam Banerjee
• 金额: $ 72.4万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1563950&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Bayesian

While the past decade has seen considerable advances in predicting missing entries in data matrices, existing approaches have demonstrated sobering performance and several limitations in an important scientific problem: characterizing plant traits, such as plant height, seed mass, leaf area, and leaf nitrogen, over space and time. Detailed global maps of plant traits will enable accurate quantification of terrestrial ecosystem functions, such as agricultural and forest productivity, and regulation of atmospheric CO2 levels. This project uses the largest and most comprehensive plant trait database on the planet (TRY, www.try-db.org) to develop a detailed characterization of plant functional traits and trait diversity at relatively fine scales across most of the terrestrial land surface. In doing so, the project produces the first detailed uncertainty quantified maps of plant traits across all of earth's major land ecosystems as well as their future projections. The project trains a new generation of interdisciplinary scientists who can cross the traditional boundaries between computer science, spatial statistics, and the Earth sciences.The research in the project makes substantial advances on Bayesian probabilistic models for matrix gap filling or matrix completion, as well as spatiotemporal gap filling with emphasis on continuous fields. In particular, the project develops probabilistic matrix completion models which can incorporate domain specific hierarchies, such as plant taxonomic or phylogenetic trees, as well as spatial variations across different environmental regimes. The project also develops methods for gap filling in continuous fields based on spatiotemporal process models along with highly scalable inference methods based on dynamic nearest-neighbor Gaussian processes. The models and methods are expected to have impact beyond the scope of quantifying ecosystem functions.

尽管过去十年在预测数据矩阵缺失的条目方面取得了很大进步，但现有的方法表明了一个重要的科学问题中的性能和几个局限性：表征植物特征，例如植物高度，种子质量，叶片面积，叶子氮和叶子氮，超时和时间。详细的植物特征的全球地图将能够准确地量化陆地生态系统功能，例如农业和森林生产力，以及大气二氧化碳水平的调节。该项目使用地球上最大，最全面的植物特征数据库（尝试，www.try-db.org），在相对细大的地面上，在相对细大的地面上开发了植物功能性状和性状多样性的详细表征。在此过程中，该项目生成了第一个详细的不确定性量化植物特征的地图，构成了地球所有主要土地生态系统及其未来预测。该项目训练了新一代的跨学科科学家，他们可以在计算机科学，空间统计和地球科学之间跨越传统的界限。该项目的研究在贝叶斯概率模型上完成了矩阵间隙填充或矩阵完成的概率模型，并在空间上填充了矩阵差距填充或矩阵的完成，并强调了连续的领域。特别是，该项目开发了概率矩阵完成模型，这些模型可以结合域特定的层次结构，例如植物分类或系统发育树，以及不同环境方案的空间变化。该项目还开发了基于时空过程模型连续场中填充差距填充的方法，以及基于动态近邻居高斯过程的高度可扩展的推理方法。预期模型和方法将超出量化生态系统功能的范围。