RII Track-2 FEC: Aquatic Intermittency Effects on Microbiomes in Streams (AIMS)

RII Track-2 FEC：水生间歇性对溪流中微生物组的影响 (AIMS)

基本信息

批准号：
2019603
负责人：
Amy Burgin
金额：
$ 599.89万
依托单位：
University of Kansas Center for Research Inc
依托单位国家：
美国
项目类别：
Cooperative Agreement
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2019603&HistoricalAwards=false
关键词：
RII Track FEC Aquatic Intermittency

项目摘要

Understanding of links among microbial communities (microbiomes), stream health, and water quality relies on studies of perennially flowing streams. However, more than half of global stream-miles do not flow continuously. These intermittent streams occur across the entire country--from western deserts to eastern forests. Despite their ubiquity, research on intermittently flowing streams is impeded by a lack of: 1) physical infrastructure designed to measure intermittency, and 2) scientific training that straddles aquatic and terrestrial ecology. The Aquatic Intermittency effects on Microbiomes in Streams (AIMS) project will address the first obstacle by creating a network of instrumented sites designed to generate “Big Data” to quantify flow intermittency, stream microbiomes, and water quality. AIMS will confront the second obstacle by using its network to provide training in collaborative science and interdisciplinary methods to study intermittent streams, and by providing workforce training in environmental "Big Data" tools through a new On Ramps to Data Science program, which will focus on data generated by microbiome sequencing, environmental sensors, and Geographic Information Systems (GIS). This infrastructure and training will support a team of 18 investigators, including nine early career scientists spanning five EPSCoR jurisdictions (AL, ID, KS, MS, OK). To build capacity in team science, 11 graduate students and two postdoctoral associates will be recruited using a cohort model that will provide cross-jurisdictional training in scientific communication, inclusive mentoring, data management and collaboration. Students will be trained through AIMS Undergraduate Program (AIMS UP), which will recruit participants from regional partners, such as Haskell Indian Nations University, Alabama A&M, and the Shoshone-Bannock Summer Youth Program. Our overarching objective is to create research infrastructure and training capable of integrating big data sources needed to address water quality at the critical nexus between intermittent and perennial streams.Our scientific understanding of streams derives from perennially flowing systems; yet, over half of the world’s streams and rivers only flow intermittently -- a fraction that is projected to increase with climate change. These less-studied intermittent channels form the nexus between terrestrial and aquatic ecosystems and are a potentially important control point for influencing downstream water quality. Furthermore, how hydrology, biogeochemical processes and microbial communities (microbiomes hereafter) interact to affect water quality is likely distinct in intermittent streams compared to perennial streams. The Aquatic Intermittency effects on Microbiomes in Streams (AIMS) project will fill this knowledge gap in order to predict how intermittent streams influence downstream water quality, which requires quantifying how microbiomes and hydrology interact to control biogeochemical cycling and water quality. AIMS will integrate datasets on hydrology, microbiomes, and biogeochemistry in three regions to test the overarching hypothesis that physical drivers (e.g., climate, hydrology) interact with biological drivers (e.g., microbes, biogeochemistry) to control water quality in intermittent streams. Our solution to build scientific capacity and workforce development is to: 1) create a network of instrumented sites to quantify and predict how intermittency controls downstream water quality, 2) educate and train scientists from diverse backgrounds in collaborative science and interdisciplinary methods to study intermittent streams, and 3) provide workforce training in environmental “big data” tools including microbiome sequencing, environmental sensors for hydrology and water quality, and Geographic Information Systems (GIS) through a new program On Ramps to Data Science. The project will support 18 faculty members (50% early-career researchers; ECR) in five EPSCoR jurisdictions (AL, ID, KS, MS, OK), and will hire and train one project manager, two postdoctoral researchers, and 11 graduate students in a collaborative environment. ECRs will benefit from support, mentoring and networking programs, while mid- and late-career faculty will gain new skills focused on Data Science, new skills and new collaborators. The AIMS Undergraduate Program (AIMS UP) will recruit two students per summer from regional partners, such as Haskell Indian Nations University, Alabama A&M, and the Shoshone-Bannock Summer Youth Program. The overarching objective is to create research infrastructure and training capable of integrating data streams needed to address water quality and its links to microbiomes at the critical nexus between intermittent and perennial stream ecosystems.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.

了解微生物群落之间的联系（微生物组），流健康和水质依赖于多年生的研究，超过一半的全球流膜不会连续流动。东部森林的沙漠。创建旨在产生不可思议，流微生物组和水质的仪器站点，将面临第二个障碍，即通过使用网络来提供协作和跨学科方法，以通过对数据科学计划进行间歇性的流媒体。关注微生物组测序和地理C信息系统（GIS）。研究生和两名后阶层将使用同类模型进行招募，该模型在科学沟通，数据和协作方面进行了跨司法训练。能够整合大数据来源的培训需要在多年生和多年生的关键联系中进行水质。我们对多年来流动系统流的科学理解；陆生和水生生态系统与存在的是潜在的重要控制点，这是影响水质的潜在重要控制点。流中的微生物（AIMS）项目将知道差距，以便看到下游水质的流，微生物组和水文学如何相互作用，以控制生物技术循环和水质。（例如，气候，水文学）与生物驱动因素（例如，微生物。尝试）相互作用，以控制间歇流中的水质。间歇性控制下游水质的eRSE背景，以合作的纪律方法来研究间歇性流的“大数据”工具，包括微生物组测序，环境学和水质，地理信息系统（GIS），以及通过对数据CIENTE的坡道上的新程序支持18位EPSCOR司法管辖区（AL，KS，MS，OK）的18位教职员工（50％的早期研究人员； ECR），将雇用和培训一名项目经理，两名博士后研究人员和S的11名研究生将从S中受益支持，指导和网络计划，而后期职业教师将获得专注于数据科学，新技能和合作者的新技能。 - 班诺克夏季青年计划。使用基金会的智力优点和更广泛的影响评估标准进行评估。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Drivers of spatiotemporal patterns of surface water inputs in a catchment at the rain-snow transition zone of the water-limited western United States

美国西部水资源有限的雨雪过渡区流域地表水输入时空模式的驱动因素

DOI：
10.1016/j.jhydrol.2022.128699
发表时间：
2023
期刊：
Journal of Hydrology
影响因子：
6.4
作者：
Hale, K.;Kiewiet, L.;Trujillo, E.;Krohe, C.;Hedrick, A.;Marks, D.;Kormos, P.;Havens, S.;McNamara, J.;Link, T.
通讯作者：
Link, T.

The Drying Regimes of Non‐Perennial Rivers and Streams

非常年河流和溪流的干涸状况

DOI：
10.1029/2021gl093298
发表时间：
2021
期刊：
Geophysical Research Letters
影响因子：
5.2
作者：
Price, Adam N.;Jones, C. Nathan;Hammond, John C.;Zimmer, Margaret A.;Zipper, Samuel C.
通讯作者：
Zipper, Samuel C.

Assessing placement bias of the global river gauge network

DOI：
10.1038/s41893-022-00873-0
发表时间：
2022-04-25
期刊：
NATURE SUSTAINABILITY
影响因子：
27.6
作者：
Krabbenhoft, Corey A.;Allen, George H.;Olden, Julian D.
通讯作者：
Olden, Julian D.

Causes, Responses, and Implications of Anthropogenic versus Natural Flow Intermittence in River Networks

河网中人为与自然流量间歇的原因、响应和影响

DOI：
10.1093/biosci/biac098
发表时间：
2022
期刊：
BioScience
影响因子：
10.1
作者：
Datry, Thibault;Truchy, Amélie;Olden, Julian D;Busch, Michelle H;Stubbington, Rachel;Dodds, Walter K;Zipper, Sam;Yu, Songyan;Messager, Mathis L;Tonkin, Jonathan D
通讯作者：
Tonkin, Jonathan D

Pervasive changes in stream intermittency across the United States