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

View publications on Google Scholar

Grants

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

NC Soybean Producers Association, Inc.(2/01/21 - 1/31/22)

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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: 1/31/22

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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Creating ShellBase: The Southeast Shellfish Water Quality Database

National Oceanic & Atmospheric Administration (NOAA)(1/15/21 - 5/31/21)

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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: 5/31/21

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.

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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 (USDA) - National Institute of Food and Agriculture(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 (USDA) - National Institute of Food and Agriculture

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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Harmful Algal Blooms in the Indian River Lagoon-North

US Environmental Protection Agency (EPA)(12/02/19 - 9/15/20)

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Harmful Algal Blooms in the Indian River Lagoon-North

Sponsor: US Environmental Protection Agency (EPA)

Start Date: 12/02/19

End Date: 9/15/20

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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NRI: INT: Towards the Development of a Customizable Fleet of Autonomous Co-Robots for Advancing Aquaculture Production

National Science Foundation (NSF)(11/01/20 - 10/31/24)

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NRI: INT: Towards the Development of a Customizable Fleet of Autonomous Co-Robots for Advancing Aquaculture Production

Sponsor: National Science Foundation (NSF)

Start Date: 11/01/20

End Date: 10/31/24

Abstract

Aquaculture, the rearing and harvesting of organisms in water environments, is a rapidly expanding industry that now produces more seafood than all wild caught fisheries worldwide. This inevitable growth must be steered towards sustainable production practices, which requires intensive monitoring in areas that are difficult and potentially dangerous to access. The vision of this project is to improve the efficiency and sustainability of near-shore aquaculture production through integrating a flexible, customizable, multi-task vehicle fleet, consisting primarily of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs), with a biologically-relevant framework for accelerated prototyping. This project will use oyster production along the Eastern US shoreline as a case study and testbed.

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Unifying biological and environmental data streams to monitor emerging lepidopteran resistance to genetically engineered crops

US Dept. of Agriculture (USDA) - National Institute of Food and Agriculture(9/01/20 - 8/31/24)

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Unifying biological and environmental data streams to monitor emerging lepidopteran resistance to genetically engineered crops

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

Start Date: 9/01/20

End Date: 8/31/24

Abstract

Accurate monitoring for changes in pest susceptibility to insecticidal toxins expressed in genetically engineered agronomic crops is currently an ineffective process limited by both scale and scope of deployment. Although long-term scientific and social change will be necessary to minimize pest resistance evolution, understanding near-term shifts in susceptibility through novel monitoring will also be essential to enable more effective resistance management strategies. To address this limitation on resistance monitoring, we propose to develop and deploy real-time pheromone-based sensor platforms to indicate patterns of lepidopteran pest activity in landscapes. We will use cotton bollworm (Helicoverpa zea Boddie) as a case study to develop and refine automated monitoring tools designed to detect shifts in pest susceptibility.

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Characterizing Water Quality Status, Trends and Potential Watershed Management Opportunities from NPDES Stormwater Data

NC Clean Water Management Trust Fund(9/16/20 - 5/31/22)

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Characterizing Water Quality Status, Trends and Potential Watershed Management Opportunities from NPDES Stormwater Data

Sponsor: NC Clean Water Management Trust Fund

Start Date: 9/16/20

End Date: 5/31/22

Abstract

The proposed research will utilize geospatial and statistical analyses combined with water quality modeling to evaluate a large water quality database for streams located within the City of Charlotte and Mecklenburg County. The data will be provided by Charlotte-Mecklenburg Stormwater Services (CMSWS) including 15 years of monthly grab samples, data from automated ISCO samplers, data collected at YSI monitoring stations and annual macroinvertebrate data. The overall goal of the proposed project is to identify future monitoring, management and planning decisions that North Carolina municipalities can employ to better protect and restore urban stream water quality. To achieve this goal, extensive statistical analyses and watershed modeling will be conducted. In addition, contemporary watershed models and watershed pollutant load estimation tools will be employed to individual sub-watersheds of specific stream sampling locations in order to identify which models and tools best predict water quality of Charlotte’s highly urbanized streams.

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Gulf Research Program Early-Career Research Fellowship

National Academies(9/01/20 - 8/31/22)

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Gulf Research Program Early-Career Research Fellowship

Sponsor: National Academies

Start Date: 9/01/20

End Date: 8/31/22

Abstract

The Gulf Research Program’s Early-Career Research Fellowship supports emerging scientific leaders as they take risks on research ideas not yet tested, pursue unique collaborations, and build a network of colleagues who share their interest in improving offshore energy system safety and the well-being of coastal communities and ecosystems.

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Improving Crop Productivity and Value Through Heterogeneous Data Integration, Analytics, and Decision Support Platforms

Game-Changing Research Incentive Program for Plant Sciences (GRIP4PSI)(2/17/20 - 6/30/21)

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Improving Crop Productivity and Value Through Heterogeneous Data Integration, Analytics, and Decision Support Platforms

Sponsor: Game-Changing Research Incentive Program for Plant Sciences (GRIP4PSI)

Start Date: 2/17/20

End Date: 6/30/21

Abstract

Inconsistent quality and aesthetics in agricultural crops can result in increased consumer and producer food waste, reduced industry resiliency and decreased farmers’ and growers’ profit, poor consumer satisfaction, and inefficiencies across the supply chain. Although there are opportunities to characterize and quantify sources of phenotypic variability across the agricultural supply chain - from cultural practices of growers and producers to storage and handling by distributors - the data available to allow for assessment of horticultural quality drivers are disparate and disconnected. The absence of data integration platforms that link heterogeneous datasets across the supply chain precludes the development of strategies and solutions to constrain variability in produce quality. This project’s central hypothesis is that multi-dimensional produce data can be securely integrated and used to optimize management practices in the field while simultaneously adding value across the entire food supply chain. We propose to develop multi-modal sensing platform along with a trust-based, data management, integration, and analytics framework for systematic organization and dynamic abstraction of heterogeneous data across the supply chain of agricultural crops. The projects short term goals are to (1) engage growers to refine research and extension priorities; (2) develop a first-of-its-kind modular imaging system that responds to grower needs by analyzing existing and novel multi-dimensional data; (3) establish the cyberinfrastructure, including analytics and blockchain, to make meaningful inference of the acquired data as related to management practices while ensuring data security; (4) deploy the sensing system at NCSU’s Horticultural Crops Research Station in Clinton, NC and on a large-scale system at a major commercial farm and distribution facility, and (5) extend findings to producers and regulators through NC Cooperative Extension. The proposed sensing and cyberinfrastructure platforms will be crop-agnostic and our findings will be transferable to other horticultural crops produced in NC and beyond.

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