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.

凯瑟琳石博士已被授予美国国家科学基金会博士后奖学金，在新罕布什尔州大学进行研究和教育计划的指导下博士。这项研究将通过确定微生物组成和功能的模式，提高我们对全球变化下土壤碳动态的理解。目前的模型旨在预测土壤碳动态不能有效地代表小尺度过程控制紧急大尺度过程，如CO2气体通量和有机碳储量，因为小尺度过程是由复杂的微生物群落，可以跨环境梯度变化。Shek博士将通过生态和地球系统监测网络提供的大型数据集的综合来解决这一差异，这些数据集描述了与碳循环相关的地质微生物群结构和紧急功能。通过这种数据合成，将整合地质微生物过程中的跨尺度模式，以提高地球系统模型中碳动力学的预测能力。更好地了解地质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几个研究项目和学科，以应对气候变化下的紧迫挑战。例如，描述不同环境背景下将地质微生物过程与碳循环联系起来的一般模式，可以为决策者、土地管理者和科学家提供信息，以预测气候变异和变化对碳动态的影响。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并进行科学交流。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，这是由于控制紧急地球化学结果的生态过程的尺度依赖性，尤其是那些作用于微观尺度的。土壤是地球上最大的陆地碳库，其中各种微生物通过代谢活动的微尺度差异控制有机碳的周转和稳定性，直接影响地球的气候。尽管地质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细尺度过程。建立一个跨尺度的相互作用的因素控制土壤碳动态的宏观尺度差异的理解，需要基于过程的建模地质微生物功能配置文件，以及它们如何在环境梯度变化。该项目将利用氖、LTER和CZnet等项目提供的强大的微生物宏基因组和环境数据集，建立基于过程的微生物模型，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来提取复杂的跨尺度地质微生物和环境模式，以表征跨环境梯度的地质微生物-土壤碳动态，然后整合这些模式，以改善基于过程的ESM中的土壤碳预测。这项研究金将使Shek博士能够合成一个将地球化学测量与宏基因组数据联系起来的开源跨网络数据库，供地球系统和生态研究人员在未来的研究中使用。该研究通过直接改善ESM，为社会预测全球变化下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。该项目由关键区域合作网络计划和刺激竞争研究的既定计划（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评估被认为值得支持。影响审查标准。