Postdoctoral Fellowship: OPP-PRF: Leveraging Community Structure Data and Machine Learning Techniques to Improve Microbial Functional Diversity in an Arctic Ocean Ecosystem Model

博士后奖学金：OPP-PRF：利用群落结构数据和机器学习技术改善北冰洋生态系统模型中的微生物功能多样性

• 批准号: 2317681
• 负责人: Emelia Chamberlain
• 金额: $ 34.32万
• 依托单位: Woods Hole Oceanographic Institution
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-15 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2317681&HistoricalAwards=false
• 关键词: Postdoctoral; Fellowship; OPP; PRF; Leveraging

The Arctic Ocean is undergoing rapid environmental change, with cascading effects on nearly all aspects of the polar marine ecosystem, including the abundance and community composition of marine microbes, microscopic organisms (i.e., bacteria and archaea) that play critical roles in the cycling of elements within the ice-ocean system. Numerical modeling is a critical method for testing ecological mechanisms and predicting the impacts of change. However, current approaches are typically not well resolved for bacterial diversity or activity rates, a source of uncertainty for future projections of critical marine ecosystem functions such as biological carbon drawdown. Through statistical exploration of sequence-based observations of the current Arctic microbial community, this research aims to transform our understanding of cellular environmental responses into a scale relevant for ecosystem processes and improve numerical modeling of microbial community structure and functional diversity. This project funds one post-doctoral scholar and supports undergraduate student training to build capacity in computational Arctic research. Leveraging publicly archived genomic, biogeochemical, and environmental time-series data from the central Arctic Ocean, this project seeks to update a microbial oriented, one-dimensional biogeochemical model to assess the variable contributions of specific members of the polar bacterial community in modeled ecological processes. Applying machine learning modeling techniques, upper ocean community structure data will be segmented into distinct bacterial ecotypes. Predicted metabolic information and co-located biogeochemical rate measurements will be used to identify critical physiological and functional differences among community types. These results will inform modifications to the bacterial state variable(s) used within the numerical modeling framework. A series of modeling experiments will then be used to compare model skill between the machine-learning integrated and base model frameworks and explore possible improvements to the fidelity of Arctic climate change predictions. Adapted source codes of the produced model will be made accessible through open-source archiving as a resource for the Arctic science community.This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences.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.

北极海洋正在发生迅速的环境变化，对极地海洋生态系统的几乎所有方面的层叠作用，包括海洋微生物的丰度和社区组成，微观生物（即细菌和古细菌）在冰山系统内部元素中起关键作用。数值建模是测试生态机制并预测变化影响的关键方法。但是，对于细菌多样性或活动率，当前的方法通常无法很好地解决，这是关键海洋生态系统功能（例如生物碳缩减）未来预测的不确定性来源。通过对当前北极微生物群落的基于序列的观察的统计探索，该研究旨在将我们对细胞环境反应的理解转变为与生态系统过程相关的规模，并改善微生物社区结构和功能多样性的数值建模。该项目为一名博士后学者提供资金，并支持本科生培训，以增强计算北极研究的能力。该项目利用中央北极海洋的公开归档基因组，生物地球化学和环境时间序列数据，旨在更新一个以微生物为导向的，一维生物地球化学模型来评估模型生态过程中极地细菌社区特定成员的可变贡献。应用机器学习建模技术，上海洋社区结构数据将分为不同的细菌生态型。预测的代谢信息和共同确定的生物地球化学率测量将用于确定社区类型之间的关键生理和功能差异。这些结果将为数值建模框架中使用的细菌状态变量的修改提供信息。然后，一系列建模实验将用于比较机器学习集成和基本模型框架之间的模型技能，并探索可能改善北极气候变化预测的忠诚度。将通过开源归档作为北极科学界的资源来访问该模型的改编源代码。该项目由地球科学局共同资助，以支持地球科学中的AI/ML进步。该奖项奖均反映了NSF的法定任务，并通过评估了Infection cr Crifia and Intellitia and Foundation and Foundation and Foundation and Foundation and Foundation and Foundation and Intfactial and Intfactial and Intfactial的支持。