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

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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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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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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North Carolina Volunteer Wetland Monitoring Pilot Program

US Environmental Protection Agency (EPA)(10/01/19 - 9/01/21)

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North Carolina Volunteer Wetland Monitoring Pilot Program

Sponsor: US Environmental Protection Agency (EPA)

Start Date: 10/01/19

End Date: 9/01/21

Abstract

Wetland monitoring in North Carolina (NC) has a 14-year history conducted by NC Department of Environment and Natural Resources (DENR) for the first 10 years and North Carolina State University (NCSU) for the last 4 years. We will use the outcomes and lessons learned from these monitoring efforts to develop and implement a pilot, volunteer wetland monitoring program administered by NCSU with support from the Carolina Wetlands Association and RTI. This pilot program will establish the procedures and methods for a sustainable, citizen-based wetland monitoring program that can be expanded in future years. In Task 1, we will determine the methods used to monitor the hydrology, water quality, soil chemistry, vegetation and biota based on the outcomes of the September 2019 meeting of wetland experts from WPDG No. CD-00D25014. In Tasks 2 and 3, we will develop a project QAPP and SOPs for monitoring activities and provide training to volunteers including representatives from local and state agencies. In Task 4, we will establish 10-12 monitoring stations within undisturbed, protected wetland sites recognized as Wetland Treasures of the Carolina. Task 5 will develop a data portal for volunteers to share data, provide tools to visualize results, and develop applications to enhance the volunteer experience. In Task 6, we will develop and populate a database compatible with NC DEQ’s data structure and submit the data to NC DEQ’s for use in their wetland and other water programs. In Task 7, we will disseminate information and results through a quarterly newsletter, website, reporting to the EPA, and annual meetings, where we will also provide training to prospective volunteers. The outputs include a pilot volunteer wetland monitoring and outreach program, training materials and SOPs, data portal and supporting website, and various education materials (factsheets, newsletters, webinars). The data from these sites will determine the condition of different wetland types and demonstrate/quantify services provided by different wetland types. By becoming a sustainable volunteer citizen-based wetland monitoring program, this program will launch a state-wide wetland monitoring network and establish baseline metrics for wetland restoration, and protection.

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Development of a Tool to Forecast Closure of Shellfish Aquaculture Harvest Areas

NCSU Sea Grant Program(2/01/20 - 1/31/21)

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Development of a Tool to Forecast Closure of Shellfish Aquaculture Harvest Areas

Sponsor: NCSU Sea Grant Program

Start Date: 2/01/20

End Date: 1/31/21

Abstract

North Carolina’s shellfish aquaculture industry is in an exciting growth period thanks to support from state agencies, legislators, and members of coastal communities who are eager to pursue new economic opportunities. The ability to culture high-value seafood products with minimal environmental impact has also galvanized interest in shellfish aquaculture at the national level, as policymakers are keen on reducing the United States’ growing $16B domestic seafood deficit without harming our nation’s coastal ecosystems. Despite burgeoning national and local interest in shellfish aquaculture, the industry is currently small relative to other food production sectors, and consequently attracts little private investment from companies that develop tools and services to optimize and support shellfish farming operations. To accelerate the expansion of the shellfish aquaculture industry, decision-support tools that disseminate actionable information to growers to help them reduce risk and maximize harvest are needed. For example, NC shellfish growers are currently in need of a means to forecast the likelihood that aquaculture harvest areas will be temporarily closed due to excess rainfall and runoff. Per FDA’s National Shellfish Sanitation Program guidelines, state regulators must establish criteria that trigger temporary closures of shellfish aquaculture harvest areas when they may be at-risk of bacteriological contamination. In practice, closure criteria are developed as depths of rainfall in a 24-hour period that could result in bacteria being transported from land to coastal waters where shellfish are produced. Growers are currently notified of rainfall-driven closures through announcements posted on the NC Division of Marine Fisheries (DMF) website after a rainfall event has occurred; they receive no prior warning, leaving no time for mitigating action. Armed with the knowledge of near-term closure risk, growers could decide to either harvest early or move their product to another aquaculture harvest area. Work proposed here will develop a forecast closure app that predicts the probability of near-term harvest area closures as a function of weather forecasts provided by the National Weather Service (NWS), and issues alerts to subscribers (i.e., shellfish growers). Prior to app development, stakeholders will be engaged to identify their priorities and desired attributes for a decision-support closure forecasting tool. The back-end algorithm of the forecasting tool and app will be created by NC State University developers in Year 1, and beta-tested by 10-15 growers in Year 2. Stakeholders will again be engaged after having used the closure forecasting tool to provide feedback and ideas for improvement. Insight provided by growers will be utilized to improve the tool. Along with the development of the closure forecasting tool, long-term records of bacteriological water quality observations curated by the NC Division of Marine Fisheries (DMF) will be analyzed to identify opportunities for improving existing rainfall-based closure thresholds. Specifically, statistical and machine learning methods will be employed to determine whether including additional factors (e.g., watershed characteristics, multi-day rainfall) in closure thresholds could improve the efficiency of shellfish aquaculture harvest area management. Findings from this data mining analysis will be transferred directly to DMF regulators, providing new insight to inform regulatory improvements to better protect the quality of cultured shellfish and health of NC’s coastal systems.

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Spatiotemporal Reconstruction of Historical Swine CAFO Spread in North Carolina and Connections to Water Quality

NCSU Sea Grant Program(3/01/19 - 2/20/20)

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Spatiotemporal Reconstruction of Historical Swine CAFO Spread in North Carolina and Connections to Water Quality

Sponsor: NCSU Sea Grant Program

Start Date: 3/01/19

End Date: 2/20/20

Abstract

In North Carolina, swine CAFOs produce over 17 million tons of manure per year. Most swine CAFOs use one or several open air lagoons to store waste, which is later distributed over sprayfields. Presently, there are an estimated 3,379 lagoons in North Carolina, most of which are clustered in the Coastal Plain. These lagoons were largely constructed prior to 1997, at which time a moratorium was placed on the construction or expansion of hog farms and lagoons. Although the post-1997 locations of swine waste lagoons are known, the spatiotemporal growth of the swine industry prior to the moratorium remains undocumented. The proposed project will achieve the following two objectives: (1) Reconstruct the historical geospatial growth of swine CAFO waste lagoons from 1984 to 2012 within the Coastal Plain of North Carolina using satellite imagery. Waste lagoons are readily identifiable through analysis of satellite imagery and are co-located with swine CAFOs. (2) Analyze connections, or lack thereof, between historical water quality trends and swine waste lagoon densities. Data obtained by achieving Objective 1 will be used in spatial regressions applied to assess relationships between swine CAFO densities and water quality.

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FACT: Developing Data-to-Decision Pipelines for Agroecosystem Management through High-performance Computing and Big Data Analytics

US Dept. of Agriculture (USDA) - National Institute of Food and Agriculture(8/01/19 - 7/31/23)

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FACT: Developing Data-to-Decision Pipelines for Agroecosystem Management through High-performance Computing and Big Data Analytics

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

Start Date: 8/01/19

End Date: 7/31/23

Abstract

The modern availability of novel data analytics and cost-effective high-performance computing creates unique opportunities to tap into the wellspring of potential offered by big data for creating decision-making tools that inform sustainable agroecosystem management. When coupled with climate, land use, and policy-related data streams through analytics, long-term monitoring data can be applied to develop data-driven decision-support tools designed for land and water resource managers, but foundational research is needed to develop such data-rich decision-support platforms. As a case study, this research will develop a data-to-decision pipeline for nearshore water quality management in support of shellfish agroecosystem protection. Shellfish growing areas are regularly screened for coliform bacteria to inform on-the-fly decision-making by regulators who are evaluating the sanitation of cultured shellfish, which has led to the accrual of a vast record of spatiotemporal bacterial observations. These national-scale data remain poorly explored and underutilized due to challenges associated with analyzing big, multi-scale data, but could be mined to develop critically-needed decision-support platforms.

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Building a Smarter Pheromone Trap Network: Development of a Real-time, Infrared Sensor for Corn Earworm Pheromone Traps

NC Soybean Producers Association, Inc.(2/01/19 - 1/31/21)

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Building a Smarter Pheromone Trap Network: Development of a Real-time, Infrared Sensor for Corn Earworm Pheromone Traps

Sponsor: NC Soybean Producers Association, Inc.

Start Date: 2/01/19

End Date: 1/31/21

Abstract

Corn earworm (Helicoverpa zea Boddie) has been the target of black light and pheromone trapping networks across North Carolina for decades. Information generated by this network has been communicated to soybean growers through traditional extension meetings and digital resources (e.g., blogs, twitter, websites). Although the information provides an indication of adult corn earworm activity, the time lag between moth counting and online data availability limits growers ability to time scouting activities, determine economic thresholds, and apply insecticides. Automation of earworm specific trapping networks can address this need by eliminating the observer via seamless data integration into web and phone app interfaces. Moreover, automated traps require less effort to maintain than conventional black light systems that catch many different moth species. As a result, the number of traps could be expanded to monitor moth activity in areas that are not served by the NCSU Earworm Trap Network.

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RAPID:Collaborative Research:Assessing Chemical and Microbiological Contamination in Environmental Waters in Eastern North Carolina After Hurricane Florence

National Science Foundation (NSF)(12/01/18 - 5/31/20)

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RAPID:Collaborative Research:Assessing Chemical and Microbiological Contamination in Environmental Waters in Eastern North Carolina After Hurricane Florence

Sponsor: National Science Foundation (NSF)

Start Date: 12/01/18

End Date: 5/31/20

Abstract

The flooding caused by Hurricane Florence, which charted a path through southeast NC, provides an immediate and unprecedented opportunity to study the potential dispersal of contaminants into the environment by floodwaters. Contaminants include pathogens and nutrients associated with waste products of confined animal feeding operations (CAFOs) and slaughterhouses, heavy metals associated with coal ash, and emerging contaminants related to industrial facilities and wastewater treatment operations. In particular, the NC counties most affected by Florence house thousands of swine operations that treat the waste of millions of animals in open lagoons and sprayfields. Thousands of additional poultry operations collect dry waste and transport it to nearby farms for land application. Waters from Hurricane Florence are currently inundating these lands, mobilizing agricultural wastes and emerging contaminants. This work is prudent, given that extreme flooding events are likely to become more common with the growing threats of climate change.

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