Education

Research Description

Dr. Nelson’s research takes a data-intensive, management-focused, and interdisciplinary approach to the study of complex biological system dynamics. Her specific research interests include: using data analytics and integrated models to inform agriculture, aquaculture, water, and coastal resources management; estimating estuarine system futures as a function of local and global change; identifying risks to food security in coastal basins; linking data mining and mechanistic modeling approaches; and investigating spatial and temporal dynamics of socio-environmental systems across settings and scales.

Publications

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Grants

Indian River Lagoon Microalgae and Harmful Algal Bloom Monitoring

US Environmental Protection Agency (EPA)(11/30/-1 - 9/15/22)

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Indian River Lagoon Microalgae and Harmful Algal Bloom Monitoring

Sponsor: US Environmental Protection Agency (EPA)

Start Date: 11/30/-1

End Date: 9/15/22

Abstract

Harmful algal blooms (HABs) are major threats to the health and stability of the Indian River Lagoon (IRL), and directly or indirectly impact the integrity of many key components of the ecosystem, including seagrasses, drift algae, benthic invertebrates (e.g. clams and oysters), fish and marine mammals. In addition, HABs can negatively impact commercially and recreationally important human uses of the IRL and can directly affect human health via the production of toxins. In this study, a network of ten strategically placed sites that span the IRL will be sampled twice per month and analyzed for phytoplankton composition, abundance, biovolume and biomass, including the identification of HAB species. In the short-term, observations of major phytoplankton blooms and the dominant taxa in the blooms, will be reported to IRL NEP and other key agencies. The data produced will be used in quantitative modeling applications to define environmental drivers for blooms, and how they vary by region of the IRL and HAB species. Additional medium/long term goals are to generate model outputs that can forecast HAB development and identify optimal targets for management efforts to control blooms.

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Coupling Lake, Watershed, and Estuarine Models to Better Understand the Role of Engineered Freshwater Discharges in Driving the Severity, Location, and Timing of Harmful Algal Blooms

Engineer Research and Development Center (ERDC)(11/30/-1 - 8/28/23)

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Coupling Lake, Watershed, and Estuarine Models to Better Understand the Role of Engineered Freshwater Discharges in Driving the Severity, Location, and Timing of Harmful Algal Blooms

Sponsor: Engineer Research and Development Center (ERDC)

Start Date: 11/30/-1

End Date: 8/28/23

Abstract

Multi-objective management of Lake Okeechobee (Lake O) has been a “wicked problem” for the Army Corps of Engineers for nearly 100 years (Vedwan et al. 2008). The need to maintain in-lake storage capacity for flood protection while also meeting water quality requirements for water flowing south to the Everglades has led to frequent, deleterious discharges to the Caloosahatchee and St. Lucie estuaries (Doering and Chamberlain 1999, Rumbold and Doering 2020). Depending on discharge timing, Lake O can contribute large nutrient and cyanobacteria loads to the coast that have been associated with both freshwater and marine harmful algal blooms (HABs) along both coasts, and a recent report by the University of Florida Water Institute (Graham et al., 2020) recommended that Lake O management efforts explicitly account for the effects of phytoplankton and nutrient export on coastal estuaries. At the same time, surrounding watersheds contribute substantial nutrient loads (Bailey et al. 2009, Julian and Osborne 2018, Rumbold and Doering 2020), and offshore conditions drive patterns of flow and mixing that affect HAB initiation and persistence (Phlips et al. 2020), complicating attribution of increasingly frequent and intense HABs solely on Lake O. In short, while Lake O discharges have been associated with the occurrence of nearshore HABs (Medina et al. 2020, Phlips et al. 2020), the specific connections among managed discharges, watershed-derived nutrient loads, phytoplankton dynamics, and coastal hydrodynamics have not been adequately resolved. In this context, our overarching goal is to develop data-driven guidance for Lake Okeechobee releases and Caloosahatchee River watershed management based on an improved understanding of the interactive effects of engineered discharges, watershed flow and nutrient deliveries, phytoplankton community dynamics, and river/near-shore hydrodynamics. For example, discharge strategies that consider the quantity and quality of water releases in the context of expected watershed loads and Caloosahatchee River/Estuary hydrodynamics could be used to target discharges during less ecologically vulnerable time periods (i.e., when nutrient/cyanobacteria loads were low or would be rapidly exported and diluted offshore based on regional hydrodynamic forecasts). Currently, Lake O releases are often “pulsed” in the dry season, which results in small volumes being discharged on a regular basis. This approach was adopted to mitigate the occurrence of hypersaline conditions in the estuary, but it may have the unintended consequence of providing persistent nutrient loads to the estuary. Because the pulsing schema only requires low-discharge flows, the estuary does not readily “flush” out when pulsed releases occur, providing greater opportunity for phytoplankton, including bloom-forming cyanobacteria, in the estuary to assimilate nutrients transported via releases; reduced residence times may allow these loads to quickly leave the estuary under certain nearshore hydrodynamic conditions.

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Towards Real-Time Fecal Indicator Bacteria Monitoring in Coastal Waters

US Army - Corps of Engineers(3/04/22 - 3/04/25)

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Towards Real-Time Fecal Indicator Bacteria Monitoring in Coastal Waters

Sponsor: US Army - Corps of Engineers

Start Date: 3/04/22

End Date: 3/04/25

Abstract

The ability to continuously monitor fecal bacteria concentrations in nearshore waters through field sensing has the potential to transform the way in which bacteria-driven public health risks are anticipated, mitigated, and managed by allowing for near real-time detection and the creation of high-quality datasets from which forecast models can be developed. Advances in freshwater monitoring reveal that fecal contamination can be predicted using data collected via high-frequency water quality sondes, but additional research is needed to extend these frameworks to coastal waters. We propose to observe water quality conditions every 15 minutes in Bald Head Creek, North Carolina, a tributary of the Cape Fear River, using a multiparameter sonde (YSI EXO2). The sonde will include sensors to monitor conductivity, temperature, dissolved oxygen, pH, turbidity, total algae (phycocyanin, phycoerythrin, and chlorophyll), fluorescent dissolved organic matter, tryptophan-like fluorescence, and water depth. In addition to observing water quality variables, we will analyze creek water samples for fecal indicator bacteria, antibiotic resistant bacteria, amino acids, and particulate and dissolved organic carbon isotopic signatures across four intensive field campaigns. Data collected via this project will be used to develop an innovative observation and machine learning modeling framework for predicting fecal contamination at high frequencies. Insights gained through the project will be shared with local and federal partners (e.g., Village of Bald Head Island, NC Coastal Federation, FDA Division of Seafood Science).

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Developing a Pilot Data Science Extension Program to Advance Data-Driven Soybean Production

United Soybean Board(10/01/21 - 9/30/23)

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Developing a Pilot Data Science Extension Program to Advance Data-Driven Soybean Production

Sponsor: United Soybean Board

Start Date: 10/01/21

End Date: 9/30/23

Abstract

We propose to develop a pilot Data Science Extension program at North Carolina State University, with a primary focus on data-driven soybean production. In the near-term, we will leverage our networks to interview stakeholder groups and identify grower interests and needs in data services and training, and use this information to prepare a vision and series of recommendations for Extension programs across the country regarding data science Extension programming. Additionally, we will develop a series of data science Extension products -- including tutorials, workshop materials, social media releases -- tailored for soybean growers. These products will be advertised through the Soybean Research Information Network to ensure national visibility and access. In the long-term, we will pursue additional funds to establish additional data-driven soybean research and Extension projects that serve the priorities identified through interviews held during this project, and advance the field and practice of data science Extension. USB funding is specifically sought to support personnel who will interview growers, summarize findings in a white paper, and prepare data-focused soybean Extension products.

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Leveraging Real-Time Insect Traps and Data Analytics to Improve Corn Earworm Risk Prediction

NC Soybean Producers Association, Inc.(2/01/21 - 1/31/23)

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Leveraging Real-Time Insect Traps and Data Analytics to Improve Corn Earworm Risk Prediction

Sponsor: NC Soybean Producers Association, Inc.

Start Date: 2/01/21

End Date: 1/31/23

Abstract

Corn earworm (Helicoverpa zea Boddie) has been the target of black light and pheromone trapping networks across North Carolina for decades. Analysis of this historical data has shown that high numbers of corn earworm are positively related to the abundance of soybean in the surrounding landscape (Dorman and Huseth in prep). However, we do not know how to leverage this new knowledge into accurate risk predictions for soybean growers. In 2019, the NCSPA funded the development of a real-time pheromone trap targeting corn earworm. Following a period of development and small scale testing, we deployed 20 traps in soybean fields across 5 NC counties. First, we tested the trap durability and identified several improvements that will be needed to move this trap toward commercialization (power usage, weatherizing). Second, we documented a remarkable amount of corn earworm abundance variation in space and time. Here, we propose to refine our trap design and develop predictive data analytics using the near real-time data. Results of this work will provide the foundation for grower accessible corn earworm risk prediction tools.

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Integrated Decision Support and Management Tools for Adaptive Public Health Practices: An Early Advisement and Reporting System for Recreational and Shellfish Harvesting Waters of the Southeast

Southeast Coastal Ocean Observing Regional Association - SECOORA(7/01/21 - 6/30/22)

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Integrated Decision Support and Management Tools for Adaptive Public Health Practices: An Early Advisement and Reporting System for Recreational and Shellfish Harvesting Waters of the Southeast

Sponsor: Southeast Coastal Ocean Observing Regional Association - SECOORA

Start Date: 7/01/21

End Date: 6/30/22

Abstract

The overarching goal of the project is to protect public health among those who use coastal resources in the Southeast. Specific objectives include: (1) Enhance and expand the scope of the existing decision support tool How's the Beach that couples rainfall, water temperature, wind, tide, and salinity data with direct measures of Enterococcus concentrations to provide daily nowcasts of bacteria concentrations for identified swimming beaches and recreational waters in NC, SC, GA, and FL; (2) Integrate How's the Beach with ShellCast to support nowcasting and forecasting of shellfish waters in the Carolinas.

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Creating ShellBase: The Southeast Shellfish Water Quality Database

National Oceanic & Atmospheric Administration (NOAA)(1/15/21 - 1/31/22)

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Creating ShellBase: The Southeast Shellfish Water Quality Database

Sponsor: National Oceanic & Atmospheric Administration (NOAA)

Start Date: 1/15/21

End Date: 1/31/22

Abstract

We propose to develop ShellBase, a water quality database for shellfish waters in the Southeast. ShellBase will synthesize bacteriological monitoring data from NC, SC, GA, and FL using standardized database and metadata structures, in turn creating a comprehensive and accessible spatiotemporal record of the Southeast’s shellfish waters. Integrating currently disconnected monitoring data will allow for new insights on bacterial water quality trends and drivers to be generated through the use of data mining techniques. Insights gained through analysis of data archived in ShellBase will enhance the ability of coastal regulators to manage shellfish aquaculture harvest areas and coastal water quality, offer new scientific insights on nearshore water quality dynamics in the Southeast’s coastal ocean, and enhance researchers’ abilities to develop effective predictive models.

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Indian River Lagoon Microalgae and Harmful Algal Bloom Monitoring

US Environmental Protection Agency (EPA)(10/14/20 - 9/15/21)

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Indian River Lagoon Microalgae and Harmful Algal Bloom Monitoring

Sponsor: US Environmental Protection Agency (EPA)

Start Date: 10/14/20

End Date: 9/15/21

Abstract

Harmful algal blooms (HABs) are major threats to the health and stability of the Indian River Lagoon (IRL), and directly or indirectly impact the integrity of many key components of the ecosystem, including seagrasses, drift algae, benthic invertebrates (e.g. clams and oysters), fish and marine mammals. In addition, HABs can negatively impact commercially and recreationally important human uses of the IRL and can directly affect human health via the production of toxins. In this study, a network of ten strategically placed sites that span the IRL will be sampled twice per month and analyzed for phytoplankton composition, abundance, biovolume and biomass, including the identification of HAB species. In the short-term, observations of major phytoplankton blooms and the dominant taxa in the blooms, will be reported to IRL NEP and other key agencies. The data produced will be used in quantitative modeling applications to define environmental drivers for blooms, and how they vary by region of the IRL and HAB species. Additional medium/long term goals are to generate model outputs that can forecast HAB development and identify optimal targets for management efforts to control blooms.

Close

CAREER: Characterizing the Unseen Water Quality Consequences of Sunny-Day Floods in Nearshore Waters

National Science Foundation (NSF)(8/01/21 - 7/31/26)

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CAREER: Characterizing the Unseen Water Quality Consequences of Sunny-Day Floods in Nearshore Waters

Sponsor: National Science Foundation (NSF)

Start Date: 8/01/21

End Date: 7/31/26

Abstract

My long-term research goal is to be a leading engineering scientist in the study of nearshore water quality dynamics across diverse human, ecological, and policy landscapes. My immediate research goal is to develop fundamental understanding on the ways tidal floods and stormwater runoff interact to influence the rates and magnitudes of fecal bacteria loading in nearshore waters in order to develop predictive models that inform the design of sustainable coastal and stormwater infrastructure. My long-term educational career goal is to equip students of diverse backgrounds with the training and computational skills needed to harness the power of data science to advance environmental sustainability in their communities.

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Cultivating A Resilient Workforce By Integrating A Culturally Competent Community Of Scholarship & Data Science in Food & Agricultural Research

US Dept. of Agriculture - National Institute of Food and Agriculture (USDA NIFA)(1/15/21 - 1/14/26)

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Cultivating A Resilient Workforce By Integrating A Culturally Competent Community Of Scholarship & Data Science in Food & Agricultural Research

Sponsor: US Dept. of Agriculture - National Institute of Food and Agriculture (USDA NIFA)

Start Date: 1/15/21

End Date: 1/14/26

Abstract

A Pipeline of a Resilient Workforce that integrates Advanced Analytics to the Agriculture, Food and Energy Supply Chain

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