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

了解微生物群落（微生物组），河流健康和水质之间的联系依赖于对常年流动的河流的研究。然而，全球一半以上的河流英里并不是连续流动的。这些间歇性的溪流遍布整个国家--从西部的沙漠到东部的森林。尽管它们无处不在，但对间歇性流动的河流的研究受到缺乏的阻碍：1）旨在测量流动性的物理基础设施，以及2）跨越水生和陆生生态学的科学培训。水生间歇性对溪流微生物组的影响（AIMS）项目将通过创建一个仪器站点网络来解决第一个障碍，该网络旨在生成“大数据”，以量化水流间歇性，溪流微生物组和水质。AIMS将面临第二个障碍，利用其网络提供合作科学和跨学科方法的培训，以研究间歇性河流，并通过新的数据科学计划提供环境“大数据”工具的劳动力培训，该计划将重点关注微生物组测序，环境传感器和地理信息系统（GIS）生成的数据。该基础设施和培训将支持一个由18名研究人员组成的团队，其中包括跨越五个EPSCoR司法管辖区（AL，ID，KS，MS，OK）的9名早期职业科学家。为了建立团队科学的能力，将采用队列模型招募11名研究生和2名博士后，该模型将提供科学交流，包容性指导，数据管理和协作方面的跨司法管辖区培训。学生将通过AIMS本科项目（AIMS UP）接受培训，该项目将从哈斯克尔印第安民族大学、亚拉巴马A M和肖肖尼-班诺克暑期青年项目等区域合作伙伴中招募参与者。我们的总体目标是建立能够整合大数据源的研究基础设施和培训，以解决间歇性和常年性河流之间的关键联系的水质问题。我们对河流的科学理解来自常年流动的系统;然而，世界上一半以上的河流和河流只是间歇性流动-预计随着气候变化，这一比例将增加。这些研究较少的间歇性渠道形成了陆地和水生生态系统之间的联系，是影响下游水质的潜在重要控制点。此外，水文学，生物地球化学过程和微生物群落（以下简称微生物群落）如何相互作用，以影响水质可能是不同的间歇性流相比，常年流。水生间歇性对河流微生物组的影响（AIMS）项目将填补这一知识空白，以预测间歇性河流如何影响下游水质，这需要量化微生物组和水文如何相互作用，以控制生物地球化学循环和水质。AIMS将整合三个地区的水文、微生物组和生物地球化学数据集，以测试物理驱动因素（例如，气候，水文）与生物驱动因素（例如，微生物，生物地球化学）来控制间歇性河流的水质。我们建立科学能力和劳动力发展的解决方案是：1）创建一个仪器站点网络，以量化和预测间歇性如何控制下游水质，2）教育和培训来自不同背景的科学家，以合作科学和跨学科方法研究间歇性河流，以及3）提供环境“大数据”工具的劳动力培训，包括微生物组测序，水文和水质环境传感器，以及地理信息系统（GIS），通过一个新的计划在斜坡数据科学。该项目将在五个EPSCoR管辖区（AL，ID，KS，MS，OK）支持18名教师（50%的早期职业研究人员; ECR），并将在协作环境中雇用和培训一名项目经理，两名博士后研究人员和11名研究生。ECR将受益于支持，指导和网络计划，而职业中期和后期的教师将获得专注于数据科学，新技能和新合作者的新技能。AIMS本科生项目（AIMS UP）每年夏天将从区域合作伙伴，如哈斯克尔印第安民族大学、亚拉巴马A M和肖肖尼-班诺克暑期青年项目中招收两名学生。总体目标是建立能够整合数据流的研究基础设施和培训，以解决水质及其与间歇性和常年河流生态系统之间关键联系的微生物组的联系。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。

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.