EAGER: LINKING MICROBIAL COMMUNITY STRUCTURE AND FUNCTION FOR IMPROVED UNDERSTANDING OF ECOSYSTEM PROCESSES

渴望：将微生物群落结构和功能联系起来，以增进对生态系统过程的理解

• 批准号: 2026815
• 负责人: Kusum Naithani
• 金额: $ 15万
• 依托单位: University of Arkansas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026815&HistoricalAwards=false
• 关键词: EAGER; LINKING; MICROBIAL; COMMUNITY; STRUCTURE

A fundamental challenge of microbial ecology is to discover unifying links between microbial communities and ecosystem processes that hold across biomes. This EAGER project will develop a procedure to analyze soil microbial data collected by the National Ecological Observatory Network (NEON), which has the advantage of generating high-quality, comparable data through standardized and quality-controlled collection and processing methods at field sites across the continent. The high-risk, high-payoff aspect of this project will involve development and testing of a 'DNA barcoding' technique for characterizing soil microbiomes in the field. Improved understanding of the links between microbial community structure and ecosystem processes will enable scientists to better predict how environmental changes impact biogeochemical cycles and stewardship of natural resources. This project will include training opportunities at the undergraduate and graduate levels. This EAGER project integrates soil microbiology with ecosystem ecology to improve understanding of ecosystem processes, and will chart new ground using a new method, based on a MinION genomics platform, for characterizing soil microbes in the field. Specific research objectives are to (1) develop a data analysis pipeline for the NEON soil microbiota data; (2) identify unifying links between microbial structure (species richness, abundance, diversity, composition) and ecosystem processes (soil respiration and nitrogen mineralization) through analyses of publicly available NEON data products; and (3) collect and analyze soil microbial data in the field using MinION based DNA barcoding technique at NEON domain sites (D10 – CPER, D10 – RMNP, D10 – STER, D13 – NIWO) near Boulder, CO to develop and benchmark [using next generation sequencing and Phospholipid Fatty Acid (PLFA) profiling] soil analysis methods by coordinating sampling with NEON scientists. The results will reveal consistencies across biomes in microbial community structure and ecosystem processes. The methods and pipeline developed for these studies will facilitate improvements in ecosystem models that are key to reducing uncertainty in predictions of carbon fluxes and stocks, nitrification/denitrification, carbon decomposition, and overall land management. The studies will include training of graduate students, development of a course on using large data sets, and use of tutorials to engage undergraduates in using and analyzing NEON data.. The data collected at the NEON sites will be shared through GitHub and repositories like DRYAD & PANGAEA. Results from this work will be shared with scientific community via presentations and workshops at scientific meetings, an "R" library, and peer-reviewed publications.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.

微生物生态学的一个基本挑战是发现微生物群落与跨生物群落的生态系统过程之间的统一联系。这个EAGER项目将制定一个程序，分析由国家生态观测网（氖）收集的土壤微生物数据，该网络的优势是通过标准化和质量控制的收集和处理方法，在整个非洲大陆的实地收集和处理高质量的可比数据。该项目的高风险、高回报方面将涉及开发和测试用于表征田间土壤微生物组的“DNA条形码”技术。对微生物群落结构和生态系统过程之间联系的进一步了解将使科学家能够更好地预测环境变化如何影响生物地球化学循环和自然资源的管理。该项目将包括本科生和研究生一级的培训机会。EAGER项目将土壤微生物学与生态系统生态学相结合，以提高对生态系统过程的理解，并将使用基于MinION基因组学平台的新方法绘制新的地面，以表征该领域的土壤微生物。具体的研究目标是（1）为氖土壤微生物群数据开发一个数据分析管道;（2）确定微生物结构之间的统一联系（物种丰富度、丰度、多样性、组成）和生态系统过程（土壤呼吸和氮矿化）;（3）利用基于MinION的DNA条形码技术在氖域位点收集和分析田间土壤微生物数据（D10 - CPER、D10 - RMNP、D10 - STER、D13 - NIWO），通过与氖科学家协调采样，[使用下一代测序和磷脂脂肪酸（PLFA）分析]开发和基准土壤分析方法。研究结果将揭示微生物群落结构和生态系统过程中跨生物群落的差异。为这些研究开发的方法和管道将促进生态系统模型的改进，这对于减少碳通量和储量、硝化/反硝化、碳分解和整体土地管理预测的不确定性至关重要。这些研究将包括培训研究生，开发使用大型数据集的课程，以及使用教程让本科生使用和分析氖数据。在氖网站收集的数据将通过GitHub和DRYAD PANGAEA等存储库共享。这项工作的成果将通过在科学会议上的演讲和研讨会、“R”图书馆和同行评审的出版物与科学界分享。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

People, infrastructure, and data: A pathway to an inclusive and diverse ecological network of networks

人员、基础设施和数据：通往包容性和多元化的网络生态网络的途径

• DOI: 10.1002/ecs2.4262
• 发表时间: 2022
• 期刊: Ecosphere
• 影响因子: 2.7
• 作者: SanClements, Michael D.;Record, Sydne;Rose, Kevin C.;Donnelly, Alison;Chong, Steven S.;Duffy, Katharyn;Hallmark, Alesia;Heffernan, James B.;Liu, Jianguo;Mitchell, Jessica J.
• 通讯作者: Mitchell, Jessica J.

Harnessing the NEON data revolution to advance open environmental science with a diverse and data‐capable community

• DOI: 10.1002/ecs2.3833
• 发表时间: 2021-12
• 期刊: Ecosphere
• 影响因子: 2.7
• 作者: R. C. Nagy;J. Balch;E. Bissell;M. Cattau;N. Glenn;B. Halpern;N. Ilangakoon;Brian Johnson;Maxwell B. Joseph;S. Marconi;Catherine O’Riordan;J. Sanovia;T. Swetnam;W. Travis;L. wasser;Elizabeth Woolner;P. Zarnetske;M. Abdulrahim;J. Adler;G. Barnes;K. Bartowitz;Rachael E. Blake;Sara P. Bombaci;J. Brun;Jacob D. Buchanan;K. Chadwick;Melissa S. Chapman;Steven S. Chong;Y. A. Chung;Jessica R. Corman;Jannelle Couret;E. Crispo;T. Doak;Alison Donnelly;Katharyn A. Duffy;K. Dunning;S. Durán;J. Edmonds;D. Fairbanks;A. Felton;C. Florian;D. Gann;M. Gebhardt;N. Gill;W. Gram;Jessica S. Guo;Brian J. Harvey;K. Hayes;Matthew R. Helmus;R. Hensley;K. Hondula;T. Huang;Wiley J. Hundertmark;Virginia Iglesias;P. Jacinthe;L. Jansen;Marta A. Jarzyna;T. M. Johnson;Katherine D. Jones;Megan A. Jones;M. G. Just;Y. Kaddoura;Aurora K. Kagawa‐Vivani;A. Kaushik;A. B. Keller;Katelyn B. S. King;J. Kitzes;M. J. Koontz;P. Kouba;Wai-Yin Kwan;J. M. LaMontagne;E. LaRue;Daijiang Li;Bonan Li;Yang Lin;D. Liptzin;William Long;Adam L. Mahood;S. Malloy;S. Malone;J. McGlinchy;C. Meier;B. Melbourne;Nathan P. Mietkiewicz;J. Morisette;Moussa Moustapha;C. Muscarella;J. Musinsky;R. Muthukrishnan;K. Naithani;M. Neely;Kari E. A. Norman;S. Parker;Mariana Perez Rocha;L. Petri;Colette A. Ramey;S. Record;M. Rossi;M. SanClements;Victoria M. Scholl;A. Schweiger;B. Seyednasrollah;D. Sihi;Kathleen R. Smith;Eric R Sokol;S. Spaulding;Anna I. Spiers;Lise A. St. Denis;Anika P. Staccone;Kaitlin Stack Whitney;D. Stanitski;E. Stricker;Thilina D. Surasinghe;Sarah K. Thomsen;P. M. Vasek;Liao Xiaolu;Di Yang;Rong Yu;Kelsey M. Yule;K. Zhu

Building communities of teaching practice and data‐driven open education resources with NEON faculty mentoring networks

通过 NEON 教师指导网络构建教学实践和数据驱动的开放教育资源社区

• DOI: 10.1002/ecs2.4210
• 发表时间: 2022
• 期刊: Ecosphere
• 影响因子: 2.7
• 作者: Naithani, Kusum;Jones, Megan;Grayson, Kristine L.
• 通讯作者: Grayson, Kristine L.

From DNA sequences to microbial ecology: Wrangling NEON soil microbe data with the neonMicrobe R package

从 DNA 序列到微生物生态学：使用 neonMicrobe R 软件包整理 NEON 土壤微生物数据

• DOI: 10.1002/ecs2.3842
• 发表时间: 2021
• 期刊: Ecosphere
• 影响因子: 2.7
• 作者: Qin, Clara;Bartelme, Ryan;Chung, Y. Anny;Fairbanks, Dawson;Lin, Yang;Liptzin, Daniel;Muscarella, Chance;Naithani, Kusum;Peay, Kabir;Pellitier, Peter
• 通讯作者: Pellitier, Peter