EAR-PF: Integrating scale-dependency of geomicrobial controls on soil C dynamics in predictive models

EAR-PF：在预测模型中整合地质微生物控制对土壤碳动态的尺度依赖性

• 批准号: 2204571
• 负责人: Katherine Shek
• 金额: $ 18万
• 依托单位: Shek, Katherine L
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2204571&HistoricalAwards=false
• 关键词: EAR; PF; Integrating; scale; dependency

Dr. Katherine Shek has been awarded an NSF EAR Postdoctoral Fellowship to carry out research and education plans at the University of New Hampshire under the mentorship of Dr. Adam Wymore. This study will improve our understanding of soil carbon dynamics under global change through identifying patterns in microbial composition and function. Current models aiming to predict soil carbon dynamics do not effectively represent the small-scale processes controlling emergent large-scale processes such as CO2 gas flux and organic carbon storage, because the small-scale processes are performed by complex microbial communities that can change across environmental gradients. Dr. Shek will address this discrepancy through a synthesis of large datasets available through ecological and Earth system monitoring networks describing geomicrobiome structure and emergent functions related to the carbon cycle. From this data synthesis, cross-scale patterns in geomicrobial processes will be integrated to improve the predictive power of carbon dynamics in Earth Systems Models. Better understanding of cross-scale interactions between geomicrobiome variability and carbon cycle processes will connect several research programs and disciplines addressing urgent challenges under climate change. For instance, characterizing general patterns linking geomicrobial processes with carbon cycling under different environmental contexts could inform policy makers, land managers and scientists aiming to predict the consequences of climate variability and change on carbon dynamics. Dr. Shek’s research and professional development plan includes application of machine learning techniques and training in biogeochemistry and data science, while presenting findings and communicating science with diverse audiences including both nonscientists and fellow researchers.Interactions between ecosystem structure and climate are difficult to characterize across environmental gradients due to the scale-dependency of ecological processes governing emergent biogeochemical outcomes, especially those acting on the micro scale. Earth’s largest terrestrial carbon pool is in soils, where diverse consortia of microorganisms control the turnover and stability of organic carbon through micro scale differences in metabolic activity, directly influencing Earth’s climate. Despite geomicrobial controls determining shifts in soil organic carbon distribution and stability, Earth Systems Models (ESMs) aiming to predict soil carbon dynamics under global change do not adequately represent these fine scale processes. Building an understanding of cross-scale interactions among factors controlling macro scale differences in soil carbon dynamics requires process-based modeling of geomicrobial functional profiles and how they change across environmental gradients. This project will leverage the robust microbial metagenomic and environmental datasets available through programs such as NEON, LTER and CZnet to build process-based microbial models that will improve our ability to predict soil carbon under global change. Dr. Shek will use machine learning algorithms to distill complex cross-scale geomicrobial and environmental patterns to characterize geomicrobiome-soil carbon dynamics across environmental gradients, and then integrate these patterns to improve soil carbon projections in process-based ESMs. This fellowship will enable Dr. Shek to synthesize an open-source, cross-network database linking biogeochemical measurements with metagenomic data for fellow Earth system and ecological researchers to utilize in future studies. This research contributes broadly to society’s general ability to project the stability and behavior of Earth’s ecosystems under global change by directly improving ESMs.This project is jointly funded by the Critical-Zone Collaborative Network Program and the Established Program to Stimulate Competitive Research (EPSCoR).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.

Katherine Shek 博士获得了 NSF EAR 博士后奖学金，在 Adam Wymore 博士的指导下在新罕布什尔大学开展研究和教育计划。这项研究将通过识别微生物组成和功能的模式，增进我们对全球变化下土壤碳动态的理解。当前旨在预测土壤碳动态的模型不能有效地代表控制新兴大规模过程（例如二氧化碳气体通量和有机碳储存）的小规模过程，因为小规模过程是由复杂的微生物群落执行的，这些微生物群落可以随着环境梯度的变化而变化。石博士将通过综合生态和地球系统监测网络提供的大型数据集来解决这一差异，这些数据描述了与碳循环相关的地球微生物组结构和新兴功能。通过这种数据合成，地球微生物过程的跨尺度模式将被整合，以提高地球系统模型中碳动力学的预测能力。更好地理解地球微生物组变异性和碳循环过程之间的跨尺度相互作用将连接多个研究项目和学科，以应对气候变化下的紧迫挑战。例如，描述不同环境背景下地球微生物过程与碳循环之间联系的一般模式可以为政策制定者、土地管理者和科学家提供信息，旨在预测气候变化和碳动态变化的后果。 Shek 博士的研究和专业发展计划包括机器学习技术的应用以及生物地球化学和数据科学方面的培训，同时向包括非科学家和研究人员在内的不同受众展示研究结果和交流科学。由于控制新兴生物地球化学结果的生态过程具有尺度依赖性，特别是在微观尺度上发挥作用的生态过程，生态系统结构和气候之间的相互作用很难在环境梯度上进行表征。地球最大的陆地碳库位于土壤中，其中不同的微生物群通过代谢活动的微观差异控制有机碳的周转和稳定性，直接影响地球的气候。尽管地球微生物控制决定了土壤有机碳分布和稳定性的变化，但旨在预测全球变化下土壤碳动态的地球系统模型（ESM）并不能充分代表这些精细尺度过程。要了解控制土壤碳动态宏观尺度差异的因素之间的跨尺度相互作用，需要对地球微生物功能概况及其如何随环境梯度变化进行基于过程的建模。该项目将利用 NEON、LTER 和 CZnet 等项目提供的强大微生物宏基因组和环境数据集来构建基于过程的微生物模型，从而提高我们预测全球变化下土壤碳的能力。 Shek 博士将使用机器学习算法提炼复杂的跨尺度地球微生物和环境模式，以表征跨环境梯度的地球微生物组-土壤碳动态，然后整合这些模式以改进基于过程的 ESM 中的土壤碳预测。这项奖学金将使石博士能够合成一个开源的跨网络数据库，将生物地球化学测量结果与宏基因组数据联系起来，供地球系统和生态研究人员在未来的研究中使用。这项研究通过直接改进 ESM，对社会在全球变化下预测地球生态系统的稳定性和行为的一般能力做出了广泛贡献。该项目由关键区域协作网络计划和刺激竞争性研究既定计划 (EPSCoR) 共同资助。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。