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·谢克（Katherine Shek）博士在亚当·威莫尔（Adam Wymore）博士的心态下被授予NSF耳朵博士后奖学金，以在新罕布什尔大学（University of New Hampshire）进行研究和教育计划。这项研究将通过识别微生物组成和功能的模式来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并不能有效地代表控制紧急大规模过程的小规模过程，例如CO2气通量和有机碳储存，因为小规模的过程是由复杂的微生物群落执行的，这些过程可以在环境梯度之间改变。 Shek博士将通过生态和地球系统监测网络可用的大型数据集来解决这种差异，这些数据集可描述与碳循环相关的地质生物组结构和新兴功能。从这些数据综合中，将集成地震过程中的跨尺度模式，以提高地球系统模型中碳动态的预测能力。更好地了解地球生物组变异性与碳循环过程之间的跨尺度相互作用将连接几个研究计划，并将纪律解决，以应对气候变化下的紧急挑战。例如，表征将地貌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，旨在预测气候变异性和变化对碳动态的后果。 Shek博士的研究和专业发展计划包括在生物地球化学和数据科学中应用机器学习技术和培训，同时呈现发现和与潜水员的沟通科学，包括非认识主义者和研究人员。生态系统结构和气候之间的相互作用很难跨环境梯度表征，这是由于生态学过程中尤其是这些型号量表，尤其是对这些型号的范围，尤其是这些型号量表，这些量表尤其是对这些型号的行为。地球最大的陆生碳池在土壤中，微生物的不同财团控制着有机碳通过微观碳的周转率和稳定性，这直接影响了地球气候。尽管地球动物控制决定了土壤有机碳分布和稳定性的变化，但地球系统模型（ESMS）旨在预测全球变化下的土壤碳动态，并不能充分代表这些精细的规模过程。在控制土壤碳动态的宏观量表差异的因素之间建立对跨尺度相互作用的理解，需要基于过程的地貌功能曲线建模及其在环境梯度之间的变化。该项目将利用诸如NEON，LTER和CZNET等程序可用的强大微生物元基因组和环境数据集来构建基于过程的微生物模型，以提高我们在全球变化下预测土壤碳的能力。 Shek博士将使用机器学习算法来提炼复杂的跨尺度地貌和环境模式来表征环境梯度跨环境梯度的地球生物组 - 土壤碳动力学，然后整合了这些模式以改善基于过程的ESM中的土壤碳项目。该奖学金将使Shek博士能够合成一个开源的跨网络数据库，该数据库将生物地球化学测量与元基因组数据联系起来，用于地球系统和生态学研究人员在未来的研究中使用。这项研究通过直接改善ESM来投射地球生态系统的稳定性和行为的总体能力，通过直接改善ESM。该项目由Critical-Zone合作网络计划和既定的竞争性研究（EPSCOR）共同资助，以反映NSF的法规宣传和范围的范围，这对范围的范围进行了评估。