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

历史上的黑人学院和大学本科项目（HBCU-UP）通过研究启动奖为历史上的黑人学院和大学的初级和中期职业教师提供支持，他们正在建立新的研究项目或重新指导和重建现有的研究项目。期望该奖项有助于进一步提高教师的研究能力和效率，并改善本国机构的研究和教学。该奖项授予迈尔斯学院，支持教师和本科生研究地表水中调节粪便指示细菌（FIB）模式的潜在来源和环境因素。具体而言，本提案旨在结合创新方法，监测地表水的粪便污染输入，并研究将微生物源跟踪（MST）中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。项目总体目标是预测贝类养殖水域中FIB超过总最大日负荷（TMDL）的发生情况。为了实现这一目标，本项目旨在将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。本项目产生的工具和数据将：(i)为阿拉巴马州沿海流域的水质知识和FIB来源的基线数据做出贡献；（ii）验证替代指标方法的使用，如MST跟踪和扩增子测序，用于现有流域模型的水质监测；（iii）确定可扩展的流域建模框架，有利于MST数据预测不同环境条件下特定来源的FIB。该项目的成果将改进FIB（一种有价值的水质指数）的预测模式。该项目的实施将扩大PI和机构的STEM教育和研究能力，增加在校学生的校园研究培训机会，并与美国地质调查局下密西西比湾水科学中心建立伙伴关系。该奖项反映了美国国家科学基金会的法定使命，并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，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.