Research Initiation Award: Integrated Approach Toward Examining Fecal Indicator Bacteria Trends in a Coastal Watershed

通过研究启动奖的历史黑人学院和大学本科课程(HBCU-UP)为历史黑人学院和大学的初级和职业生涯中期教师提供支持，这些教师正在建立新的研究项目或重新定向和重建现有的研究项目。预计该奖项有助于进一步提高教师的研究能力和有效性，并改善所在机构的研究和教学。这项授予迈尔斯学院的奖项支持教师和本科生研究调节地表水中排泄物指示菌(FIB)模式的潜在来源和环境因素。具体地说，这项建议旨在结合创新方法，监测地表水的粪便污染输入，并研究将微生物来源追踪(MST)的粪便标记物整合到当前和基于机器学习的流域模型中的有效性，以预测地表水中FIB的趋势。总体项目目标是预测贝类养殖水域中FIB超过总最大日负荷(TMDL)的情况。为了实现这一目标，该项目旨在将MST数据、16S rRNA序列和环境参数整合到现有和下一代流域模型中。该项目产生的工具和数据将：(I)促进水质知识和关于阿拉巴马州沿海流域固定生物量来源的基线数据；(Ii)验证替代指标方法的使用，如MST追踪和扩增序列测序，用于现有流域模型中的水质监测；以及(Iii)确定有利于MST数据在不同环境条件下预测特定来源固定生物量的可扩展流域建模框架(S)。该项目的结果将改进对FIB模式的预测，FIB是一种有价值的水质指数。该项目的实施将扩大PI和机构的STEM教育和研究能力，增加在校学生的在校研究培训机会，并与美国地质调查局密西西比河下游海湾水科学中心建立合作伙伴关系。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The Historically Black Colleges and Universities Undergraduate Program (HBCU-UP) through Research Initiation Awards provide support for junior and mid-career faculty at Historically Black Colleges and Universities who are building new research programs or redirecting and rebuilding existing research programs. It is expected that the award helps to further the faculty member's research capability and effectiveness and improve research and teaching at the home institution. This award to Miles College supports faculty and undergraduate research experiences examining potential sources and environmental factors that regulate fecal indicator bacteria (FIB) patterns in surface water. Specifically, using a combination of innovative approaches, this proposal aims to monitor fecal pollution inputs to surface waters and study the efficacy of integrating fecal markers from Microbial Source Tracking (MST) into current and machine learning-based watershed models, to predict trends of FIB in surface water.The overall project goal is to predict the occurrence of FIB exceeding total maximum daily load (TMDL) in shellfish growing waters. Toward this goal, this project aims to integrate MST data, 16S rRNA sequences, and environmental parameters into existing and next generation watershed models. Tools and data generated from this project will: (i) contribute to water quality knowledge and baseline data on sources of FIB in a coastal Alabama watershed, (ii) validate use of alternative indicator methods, such as MST tracking and amplicon sequencing, for water quality monitoring in existing watershed models, and (iii) identify scalable watershed modelling framework(s) conducive for MST data to predict source-specific FIB in different environmental conditions. Outcomes from this project will provide improvements in predicting patterns of FIB, a valued water quality index. Implementation of this project would extend the STEM education and research capacity of the PI and institution, increase on-campus research training opportunities for enrolled students, and cultivate a partnership with the United States Geological Survey Lower Mississippi-Gulf Water Science Center.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.