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.

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.

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.

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.