Natalie Nelson

The Science and Technologies for Phosphorus Sustainability (STEPS) Center is a convergence research hub for addressing the fundamental challenges associated with phosphorus sustainability. The vision of STEPS is to develop new scientific and technological solutions to regulating, recovering and reusing phosphorus that can readily be adopted by society through fundamental research conducted by a broad, highly interdisciplinary team. Key outcomes include new atomic-level knowledge of phosphorus interactions with engineered and natural materials, new understanding of phosphorus mobility at industrial, farm, and landscape scales, and prioritization of best management practices and strategies drawn from diverse stakeholder perspectives. Ultimately, STEPS will provide new scientific understanding, enabling new technologies, and transformative improvements in phosphorus sustainability.

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.

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

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).

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.

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.

This project will address the food animal production industry’s need for professionals and extension specialists who possess the skills needed to thrive in today’s data-rich world. The long-term goal of this project is to meet increasing meat demands across the global market through sustainable intensification, thereby also broadening export opportunities for American meat producers. To meet this goal and address a critical shortage of personnel who are data analytics-aware, the food animal production workforce needs to be modernized with professionals and extension specialists who possess data literacy and holistic problem-solving skills. We will create a 10-week summer program dedicated to supplying the workforce with students trained in these three proficiency areas. This summer program, titled the Pigs, Poultry, the Planet, and data-driven Problem Solving (P4) Summer Fellowship Program, will prepare undergraduate students for contemporary careers in food animal production, and specifically target the swine and poultry production industries.

Coastal ecosystems are uniquely vulnerable to changes in freshwater quantity and quality. With a warming climate, changes in freshwater discharge into estuaries will interact with rising sea levels. Coastal natural resource managers need guidance on the potential impacts and vulnerabilities to better manage the risks to species and habitat. This project will provide guidance and recommendations for resource managers on the development of ecological flow targets that protect coastal ecosystems and wildlife while accounting for changes in freshwater availability and sea level rise.

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.

The Caloosahatchee River and Estuary periodically experience harmful algal blooms of marine (Karenia brevis) and freshwater (Microcystis spp.) species. A limited number of observations on species-level phytoplankton community dynamics are available for this system. We propose to develop the Harmful Algal Bloom Observatory Network for the Caloosahatchee Estuary, through which we will measure phytoplankton community composition across the estuary's fresh-marine gradient. Observational data generated through the project will be paired with hydrologic and water chemistry measurements and analyzed through statistical modeling.