CAREER: Integrating a Microbial Data System with an Earth System Model for Evaluating Microbial Biogeochemistry

职业：将微生物数据系统与地球系统模型集成以评估微生物生物地球化学

• 批准号: 2145130
• 负责人: Xiaofeng Xu
• 金额: $ 64.12万
• 依托单位: San Diego State University Foundation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2145130&HistoricalAwards=false
• 关键词: CAREER; Integrating; Microbial; Data; System

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Climate warming has become a key threat to society, and it is projected to become even worse by the end of the twenty-first century. In the U.S., each 1°C of climate warming can lead to roughly 1.2% loss in gross domestic product. Increases in atmospheric concentrations of carbon dioxide (CO2) and methane (CH4) have contributed to more than 90% of climate warming since the Industrial Revolution. Microbes account for more than 65% of global CO2 production, approximately 85% of CH4 production, and 60% of CH4 consumption in soils. However, the ability to predict production and consumption of these greenhouse gases in terrestrial ecosystems is constrained by the poor representation of soil microbes in Earth system computer models, and the lack of comprehensive soil microbial data for model development and validation. To address these limitations, this NSF CAREER project will develop a community dataset of microbial variables and improve a highly-used Earth system model to better represent microbial physiology and community structure in response to changing climate. The unprecedented microbial data system will be integrated with a microbial-explicit Earth system model to better understand how soil microbes respond and feedback to climate change. Next-generation scientists will be trained with state-of-art quantitative skills including meta-analysis, ecological modeling, and machine learning.A team of trainees (high school students, undergraduate students, graduate students, and a postdoc) will be trained to: (A) develop a microbial data system focusing on soil carbon cycling and CH4 processes; (B) improve an extant land surface model (CLM-Microbe) embedded in the Community Earth System Model to explicitly represent microbial physiology and community structure for CO2 and CH4 processes and to further parameterize and validate the model at a site-level; (C) apply the improved model to quantify microbial roles in CO2 and CH4 fluxes at various spatial and temporal scales; and (D) develop a hierarchical mentoring system to recruit students and provide them with training to successfully merge mathematics and ecology through research activities on data-model integration. Four summer tutorial workshops will be organized to broaden participation of undergraduate students from underrepresented groups in research through the San Diego State University Maximizing Access to Research Careers program, promoting recruitment and retention in STEM fields.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.

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。气候变暖已成为社会的主要威胁，预计到21世纪末将变得更加严重。在美国，气候变暖每升高1°C，就会导致国内生产总值损失约1.2%。自工业革命以来，大气中二氧化碳（CO2）和甲烷（CH 4）浓度的增加导致了90%以上的气候变暖。微生物占全球CO2产量的65%以上，约占土壤CH 4产量的85%，占土壤CH 4消耗量的60%。然而，预测陆地生态系统中这些温室气体的生产和消费的能力受到地球系统计算机模型中土壤微生物代表性差以及缺乏用于模型开发和验证的全面土壤微生物数据的限制。为了解决这些限制，NSF CAREER项目将开发微生物变量的社区数据集，并改进高度使用的地球系统模型，以更好地代表微生物生理学和社区结构，以应对气候变化。这一前所未有的微生物数据系统将与微生物明确的地球系统模型相结合，以更好地了解土壤微生物如何对气候变化做出反应和反馈。下一代科学家将接受最先进的定量技能培训，包括元分析、生态建模和机器学习。（高中生、本科生、研究生和博士后）将接受以下培训：（A）开发一个侧重于土壤碳循环和CH 4过程的微生物数据系统;（B）改进现有陆面模型（CLM-Microbe）嵌入社区地球系统模型，以明确表示CO2和CH 4过程的微生物生理学和社区结构，并在地点一级进一步参数化和验证模型;（C）应用改进的模型，在不同的空间和时间尺度上量化微生物在CO2和CH 4通量中的作用;（D）开发一个分层指导系统，以招募学生并为他们提供培训，通过数据模型整合的研究活动成功地将数学和生态学结合起来。将组织四个夏季辅导研讨会，通过圣地亚哥州立大学最大化研究职业机会计划，扩大来自代表性不足群体的本科生参与研究，促进STEM领域的招聘和保留。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。

项目成果

Macroecology Differentiation Between Bacteria and Fungi in Topsoil Across the United States

• DOI: 10.1029/2023gb007706
• 发表时间: 2023-10
• 期刊: Global Biogeochemical Cycles
• 影响因子: 5.2
• 作者: Liyuan He;N. Viovy;Xiaofeng Xu

Upscaling Methane Flux From Plot Level to Eddy Covariance Tower Domains in Five Alaskan Tundra Ecosystems

• DOI: 10.3389/fenvs.2022.939238
• 发表时间: 2022-07
• 作者: Yihui Wang;F. Yuan;K. Arndt;Jianzhao Liu;Liyuan He;Yunjiang Zuo;D. Zona;D. Lipson;W. Oechel;D. Ricciuto;S. Wullschleger;P. Thornton;Xiaofeng Xu

Microbial mechanisms for methane source-to-sink transition after wetland conversion to cropland

湿地转化为农田后甲烷从源到汇转变的微生物机制

• DOI: 10.1016/j.geoderma.2022.116229
• 发表时间: 2023
• 期刊: Geoderma
• 影响因子: 6.1
• 作者: Wang, Nannan;Zhu, Xinhao;Zuo, Yunjiang;Liu, Jianzhao;Yuan, Fenghui;Guo, Ziyu;Zhang, Lihua;Sun, Ying;Gong, Chao;Guo, Dufa
• 通讯作者: Guo, Dufa

Mapping turnover of dissolved organic carbon in global topsoil

绘制全球表土中溶解有机碳的周转率

• DOI: 10.1016/j.scitotenv.2023.167621
• 发表时间: 2024
• 期刊: Science of The Total Environment
• 影响因子: 9.8
• 作者: Guo, Ziyu;Wang, Yihui;Liu, Jianzhao;He, Liyuan;Zhu, Xinhao;Zuo, Yunjiang;Wang, Nannan;Yuan, Fenghui;Sun, Ying;Zhang, Lihua
• 通讯作者: Zhang, Lihua

Wetland conversion to cropland alters the microbes along soil profiles and over seasons

• DOI: 10.1016/j.catena.2022.106282
• 发表时间: 2022-07
• 期刊: CATENA
• 影响因子: 6.2
• 作者: Xinhao Zhu;Fenghui Yuan;Liyuan He;Ziyu Guo;Nannan Wang;Yunjiang Zuo;Jianzhao Liu;Kexin Li;Yihui Wang;Ying Sun;L. Zhang;C. Song;Yanyu Song;Chao Gong;Yowhan Son;D. Guo;Xiaofeng Xu