@article{chazal_carr_haines_leight_nelson_2024, title={Assessing the Utility of Shellfish Sanitation Monitoring Data for Long-Term Estuarine Water Quality Analysis}, url={https://doi.org/10.22541/essoar.170561621.10518854/v1}, DOI={10.22541/essoar.170561621.10518854/v1}, abstractNote={Regular testing of coastal waters for fecal coliform bacteria by shellfish sanitation programs could provide data to fill large gaps in existing coastal water quality monitoring, but research is needed to understand the opportunities and limitations of using these data for inference of long-term trends. In this study, we analyzed spatiotemporal trends from multidecadal fecal coliform concentration observations collected by a shellfish sanitation program, and assessed the feasibility of using these monitoring data to infer long-term water quality dynamics. We evaluated trends in fecal coliform concentrations for a 20-year period (1999-2021) using data collected from spatially fixed sampling sites (n = 466) in North Carolina (USA). Findings indicated that shellfish sanitation data can be used for long-term water quality inference under relatively stationary management conditions, and that salinity trends can be used to measure the extent of management-driven bias in fecal coliform observations collected in a particular area. 1. INTRODUCTIONHealthy estuarine environments are critical for maintaining ecological stability, coastal economies, and human health standards. In order to maintain and even improve these habitats, metrics of current and past conditions must be evaluated to inform proper management. Water quality measurements can be used to indicate overall estuarine health and can aid in understanding increasing coastal threats such as rising sea levels, increased salinities, and urbanization. Long-term water quality analysis is key for developing target thresholds for future management action as well as assessing the efficacy of past management measures (Cloern et al., 2016). The value of historical observations in advancing understanding of estuarine water quality has been demonstrated by multi-decadal studies of several systems, including the San Francisco Bay area (Beck et al., 2018; Cloern et al., 2016), May River, South Carolina (Souedan et al., 2021), Texas's coastline (Bugica et al., 2020), and the Chesapeake Bay area (Zhang et al., 2018; Harding et al., 2019). Most notably, long-term water quality monitoring in the Chesapeake Bay has led to the identification of climatic and anthropogenic drivers for certain water quality parameters and subsequent evaluation of the effectiveness of past management and restoration efforts (Kemp et al., 2005; Leight et al., 2011; Zhang et al., 2018; Harding et al., 2019).Datasets used for prior longitudinal water quality studies are commonly a product of governmental agencies developing localized programs, like the Chesapeake Bay Program (Chesapeake Bay Monitoring Program, 2022), in response to increasing population and significant degradation of vital estuarine ecosystems. While national and regional efforts have attempted to provide unbiased, sustained monitoring, these programs currently lack the spatial extent needed to capture coastwide water quality trends. The National Estuarine Research Reserve System (NERRS) is one of the few organizations with dedicated coastal water quality monitoring stations, which are included as part of the NERRS System Wide Monitoring Program (SWMP) that maintains 355 coastal water quality monitoring stations across 29 designated coastal reserves along the USA coastline (National Estuarine Research Reserve System, 2022). Compared to the over 13,500 freshwater monitoring stations maintained by the United States Geological Survey (USGS, 2022), the relatively small number of water quality monitoring stations across coastal and estuarine waters (NOAA Tides & Currents, 2022; US EPA, 2022) are likely not representative of the variations in environmental conditions that we observe across the tens of thousands of miles of shoreline along the United States.Because of the limited number of unbiased monitoring programs, the ability to use water quality data from regulatory operations presents a potentially valuable resource for assessing long-term estuarine conditions. Regulatory programs differ from monitoring programs by collecting water quality samples to meet regulatory requirements and inform short-term decision-making. For example, in North Carolina (NC), there are four NERRS SWMP monitoring stations and eight coastal stations with water quality data available through the USGS (South Atlantic Water Science Center, North Carolina Office, 2022) and fifty stations from the NC Ambient Monitoring System (Water Quality Portal, 2021), but the NC Division of Marine Fisheries (NCDMF) shellfish sanitation program maintains 1,924 water quality monitoring stations. In fact, state shellfish sanitation programs across the USA collect an abundance of water quality observations, and often have for decades. Shellfish mariculture is highly dependent on water quality monitoring due to the direct influence that ambient conditions have on the safety of shellfish meat consumption. The U.S. Food and Drug Administration's National Shellfish Sanitation Program (NSSP) was developed in 1925 to maintain public safety and human health standards in relation to the consumption of shellfish grown in potentially polluted waters (NSSP, 2019). The implementation of the NSSP has resulted in systematic sampling of water quality for day-to-day fisheries regulation, specifically for Fecal Indicator Bacteria (FIB), a group of bacteria that are commonly used as a proxy measure for harmful pathogen loads in the waterway that could potentially be incorporated into shellfish meat through filter feeding. Thus, fecal coliforms (FC), a type of FIB, and other environmental factors that contribute to FC load and water quality, are regularly measured in shellfish growing waters due to the food safety implications. As a product of this regular testing, fisheries operations have accumulated decades of data with the potential to provide insights on historical trends with wide spatial extents, potentially filling gaps in long-term water quality monitoring capacity.However, because of the limited resources and industry specific priorities, regulatory data can maintain underlying biases as a result of the sampling methodology used to collect the water quality sample. Often, the collection of a sample can be motivated by day-to-day operational decisions, such as weather, the availability of field technicians, and ease of collection. These operational decisions lead to non-random sampling that provides observations that are not always representative of the system's true dynamics. Engaging regulatory personnel to understand their fisheries management and sampling decisions is necessary to properly analyze the observations collected by shellfish sanitation programs.For example, the NSSP permits states to employ one of two sampling strategies when collecting regulatory water quality data in shellfish growing waters: adverse pollution condition sampling and systematic random sampling. The adverse pollution condition sampling strategy describes sampling in periods when known contamination events (commonly due to point-source pollution events or rainfall events) have degraded the water quality, and data collected under these conditions capture peak contamination. States must collect "a minimum of five samples… annually under adverse pollution conditions from each sample station in the growing area" (NSSP, 2019) to meet NSSP sampling requirements. In contrast, the systematic random sampling strategy describes the collection of data across "a statistically representative cross section of all meteorological, hydrographic, and/or other pollution events" (NSSP, 2019), resulting in the data collection under varied environment and climactic conditions. For state programs that use systematic random sampling, the NSSP requires samples be collected at least 6 times throughout the year (NSSP, 2019). As a result of the requirements for the conditions under which the two systems of sampling can take place, the resulting data may be biased and impact their utility for use in long-term water quality assessments. With our growing reliance on aquaculture and the expanding value of shellfish production driving the development of fisheries management infrastructure (Azra et al., 2021), long-term datasets available through shellfish sanitation programs will become increasingly valuable. Realizing the potential of regulatory datasets to inform long-term water quality trends is a vital next step for assessing the health of our coastal ecosystems, but research is needed to determine the utility of these data for water quality analyses.The goal of this study was to utilize shellfish management data to infer long-term spatiotemporal trends in water quality parameters, including FC and salinity, while accounting for variation in routine sampling conditions and environmental landscapes. Study objectives included (1) analyzing spatiotemporal trends from multidecadal fecal coliform concentration observations collected by a shellfish sanitation program, (2) identifying possible management and environmental drivers of fecal coliform trends, and (3) assessing the feasibility of using these monitoring data to infer long-term water quality dynamics. We focused on North Carolina's shellfish waters as a representative study system due to the availability of public, digitized multidecadal data, and the region's rapidly growing population, wide variety of land use characteristics along the coast, presence of the second largest estuarine system in the contiguous USA, and growing shellfish industry. Ultimately, this study demonstrates the application of shellfish management data for long-term water quality trend analysis in estuaries, informs future resource management strategies, and reveals new insights into the functioning of coastal systems.}, author={Chazal, Natalie and Carr, Megan and Haines, Andrew and Leight, Andrew K. and Nelson, Natalie}, year={2024}, month={Jan} }
 @article{chazal_carr_haines_leight_nelson_2024, title={Assessing the utility of shellfish sanitation monitoring data for long-term estuarine water quality analysis}, volume={203}, ISSN={["1879-3363"]}, DOI={10.1016/j.marpolbul.2024.116465}, abstractNote={Regular testing of coastal waters for fecal coliform bacteria by shellfish sanitation programs could provide data to fill large gaps in existing coastal water quality monitoring, but research is needed to understand the opportunities and limitations of using these data for inference of long-term trends. In this study, we analyzed spatiotemporal trends from multidecadal fecal coliform concentration observations collected by a shellfish sanitation program, and assessed the feasibility of using these monitoring data to infer long-term water quality dynamics. We evaluated trends in fecal coliform concentrations for a 20-year period (1999-2021) using data collected from spatially fixed sampling sites (n = 466) in North Carolina (USA). Findings indicated that shellfish sanitation data can be used for long-term water quality inference under relatively stationary management conditions, and that salinity trends can be used to investigate management-driven bias in fecal coliform observations collected in a particular area.}, journal={MARINE POLLUTION BULLETIN}, author={Chazal, Natalie and Carr, Megan and Haines, Andrew and Leight, Andrew K. and Nelson, Natalie G.}, year={2024}, month={Jun} }
 @article{montefiore_kaplan_phlips_milbrandt_arias_morrison_nelson_2024, title={Downstream Nutrient Concentrations Depend on Watershed Inputs More Than Reservoir Releases in a Highly Engineered Watershed}, volume={60}, ISSN={["1944-7973"]}, url={https://doi.org/10.1029/2023WR035590}, DOI={10.1029/2023WR035590}, abstractNote={Abstract}, number={3}, journal={WATER RESOURCES RESEARCH}, author={Montefiore, L. R. and Kaplan, D. and Phlips, E. J. and Milbrandt, E. C. and Arias, M. E. and Morrison, E. and Nelson, N. G.}, year={2024}, month={Mar} }
 @article{carr_gold_harris_anarde_hino_sauers_da silva_gamewell_nelson_2024, title={Fecal Bacteria Contamination of Floodwaters and a Coastal Waterway From Tidally-Driven Stormwater Network Inundation}, volume={8}, ISSN={["2471-1403"]}, url={https://doi.org/10.1029/2024GH001020}, DOI={10.1029/2024GH001020}, abstractNote={Abstract Inundation of coastal stormwater networks by tides is widespread due to sea‐level rise (SLR). The water quality risks posed by tidal water rising up through stormwater infrastructure (pipes and catch basins), out onto roadways, and back out to receiving water bodies is poorly understood but may be substantial given that stormwater networks are a known source of fecal contamination. In this study, we (a) documented temporal variation in concentrations of Enterococcus spp. (ENT), the fecal indicator bacteria standard for marine waters, in a coastal waterway over a 2‐month period and more intensively during two perigean spring tide periods, (b) measured ENT concentrations in roadway floodwaters during tidal floods, and (c) explained variation in ENT concentrations as a function of tidal inundation, antecedent rainfall, and stormwater infrastructure using a pipe network inundation model and robust linear mixed effect models. We find that ENT concentrations in the receiving waterway vary as a function of tidal stage and antecedent rainfall, but also site‐specific characteristics of the stormwater network that drains to the waterway. Tidal variables significantly explain measured ENT variance in the waterway, however, runoff drove higher ENT concentrations in the receiving waterway. Samples of floodwaters on roadways during both perigean spring tide events were limited, but all samples exceeded the threshold for safe public use of recreational waters. These results indicate that inundation of stormwater networks by tides could pose public health hazards in receiving water bodies and on roadways, which will likely be exacerbated in the future due to continued SLR.}, number={4}, journal={GEOHEALTH}, author={Carr, M. M. and Gold, A. C. and Harris, A. and Anarde, K. and Hino, M. and Sauers, N. and Da Silva, G. and Gamewell, C. and Nelson, N. G.}, year={2024}, month={Apr} }
 @article{carr_gold_harris_anarde_hino_sauers_silva_gamewell_nelson_2024, title={Fecal bacteria contamination of floodwaters and a coastal waterway from tidally-driven stormwater network inundation}, url={https://doi.org/10.22541/essoar.171829378.87699420/v1}, DOI={10.22541/essoar.171829378.87699420/v1}, abstractNote={Daily observations of Enterococcus spp.concentrations in a coastal waterway were similar during and outside perigean spring tides.• Tidal inundation of stormwater networks occurred daily, but rainfall runoff produced the greatest bacterial contamination in the waterway.• High Enterococcus spp.concentrations were observed in roadway floodwaters and the receiving waterway during perigean spring tides.}, author={Carr, Megan M. and Gold, Adam C. and Harris, Angela and Anarde, Katherine and Hino, Miyuki and Sauers, Nora and Silva, Gabe Da and Gamewell, Catherine and Nelson, Natalie G.}, year={2024}, month={Jun} }
 @article{carr_gold_harris_anarde_hino_sauers_silva_gamewell_nelson_2024, title={Fecal bacteria contamination of floodwaters and a coastal waterway from tidally-driven stormwater network inundation}, url={https://doi.org/10.22541/essoar.170688995.57378457/v1}, DOI={10.22541/essoar.170688995.57378457/v1}, abstractNote={Daily observations of Enterococcus spp.concentrations in a coastal waterway were similar during and outside perigean spring tides.• Tidal inundation of stormwater networks occurred daily, but rainfall runoff produced the greatest bacterial contamination in the waterway.• High Enterococcus spp.concentrations were observed in roadway floodwaters and the receiving waterway during perigean spring tides.}, author={Carr, Megan M. and Gold, Adam and Harris, Angela and Anarde, Katherine and Hino, Miyuki and Sauers, Nora and Silva, Gabriel Da and Gamewell, Catherine and Nelson, Natalie G.}, year={2024}, month={Feb} }
 @article{carbajal-carrasco_jones_williams_nelson_2024, title={In-season Sweetpotato Yield Forecasting using Multitemporal Remote Sensing Environmental Observations and Machine Learning}, url={https://doi.org/10.22541/essoar.171415899.94920111/v1}, DOI={10.22541/essoar.171415899.94920111/v1}, abstractNote={Data-driven modeling approaches for crop yield prediction have exponentially increased in the last decade due to the greater availability of spatial data from various sensors.Yet, most yield modeling has focused on major commodities, leaving lesser-cultivated horticultural crops like sweetpotato relatively undertooled, though these crops considerably contribute to the global economy and food supply.The U.S. is the primary exporter of sweetpotato (271 K tonnes), with 21% of U.S.-grown sweetpotatoes being exported.Early yield forecasting at the county scale offers crucial insights for growers, packers, wholesalers, and associated industries, enabling them to anticipate variations in yield to make informed decisions.While roots and tubers have demonstrated a relationship between yields and above-ground plant characteristics, it remains uncertain whether forecasting models that utilize remotely sensed data, including vegetation indices, are suitable for sweetpotato.We developed county-scale in-season sweetpotato yield forecast models using machine learning (ML) algorithms and multitemporal remote sensing environmental data.Four of the most commonly used ML algorithms for predicting crop yield -Random Forest Regression (RFR), Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB) -were applied using stationary (topography and soil characteristics), and temporal (weather, NDVI, and Growing Degree Days) variables as potential predictors.Six predictor sets were tested to identify key predictor variables, optimal aggregation time (16 or 32 days composite) of the temporal variables, and how early in the growing season the models can reliably predict end-of-season yields.U.S. Annual CropScape land cover layers were used to identify sweetpotato fields, over which temporal variables were aggregated, and sweetpotato yields were tabulated from the USDA Agricultural Survey from 2008 to 2022.The Boruta method was used for feature selection across each predictor set before training the ML models.RFR outperformed other ML algorithms and the RFR models' evaluation metrics were the most consistent across the six predictor sets.The RFR model that incorporated early and mid season temporal variables as 16-day composites was selected and proposed for future sweetpotato yield forecasting due to its performance (R 2 = 0.44, RMSE = 3.53 tonnes.ha - ), as well as ability to predict early enough 1 in the season to provide actionable information.In the final model, several stationary variables (elevation, nitrogen, cec, soc, and clay content) were the most predictive of sweetpotato yield.After these stationary variables, NDVI and precipitation from the time around storage root initiation and bulking (July), and minimum temperature around planting (June) followed in importance.}, author={Carbajal-Carrasco, Mariella and Jones, Daniela and Williams, Cranos and Nelson, Natalie}, year={2024}, month={Apr} }
 @article{chazal_carr_leight_saia_nelson_2024, title={Short-term forecasting of fecal coliforms in shellfish growing waters}, volume={200}, ISSN={["1879-3363"]}, DOI={10.1016/j.marpolbul.2024.116053}, abstractNote={This study sought to develop models for predicting near-term (1-3 day) fecal contamination events in coastal shellfish growing waters. Using Random Forest regression, we (1) developed fecal coliform (FC) concentration models for shellfish growing areas using watershed characteristics and antecedent hydrologic and meteorologic observations as predictors, (2) tested the change in model performance associated when forecasted, as opposed to measured, rainfall variables were used as predictors, and (3) evaluated model predictor importance in relation to shellfish sanitation management criteria. Models were trained to 10 years of coastal FC measurements (n = 1285) for 5 major shellfish management areas along the Florida (USA) coast. Model performance varied between the 5 management areas with R2 ranging from 0.36 to 0.72. Antecedent precipitation variables were among the most important predictors in the day-of forecast models in all management areas. When forecasted rainfall was included in the models, wind components became increasingly important.}, journal={MARINE POLLUTION BULLETIN}, author={Chazal, Natalie and Carr, Megan and Leight, Andrew K. and Saia, Sheila M. and Nelson, Natalie G.}, year={2024}, month={Mar} }
 @article{neville_emanuel_nelson_bernhardt_ardon_2024, title={Standard metrics for characterizing episodic salinization in freshwater systems}, volume={6}, ISSN={["1541-5856"]}, DOI={10.1002/lom3.10629}, abstractNote={Abstract Salinization threatens freshwater resources and freshwater‐dependent wetlands in coastal areas worldwide. Many research efforts focus on gradual or chronic salinization, but the phenomenon is also episodic in nature, particularly in small streams and artificial waterways. In surface waters, salinization events may coincide with storms, droughts, wind tides, and other episodic events. A lack of standardized quantitative methods and metrics for describing and discussing episodic salinization hinders cross‐disciplinary efforts by scientists and others to analyze, discuss, and make recommendations concerning these events. Here, we present a set of metrics that use statistics which describe flow characteristics in rivers and streams as a template for empirically describing and characterizing salinization events. We developed a set of metrics to quantify the duration, magnitude, and other characteristics of episodic salinization, and we apply the metrics to extensive time‐series data from a field site in coastal North Carolina. We then demonstrate the utility of these metrics by coupling them with ancillary data to perform an unsupervised classification that groups individual salinization events by their primary meteorological driver. We provide simple and flexible code needed to compute metrics in any environment experiencing salinization events in hopes that it will facilitate more standardized approaches to the quantification and study of widespread freshwater salinization.}, journal={LIMNOLOGY AND OCEANOGRAPHY-METHODS}, author={Neville, J. A. and Emanuel, R. E. and Nelson, N. G. and Bernhardt, E. S. and Ardon, M.}, year={2024}, month={Jun} }
 @article{phlips_badylak_mathews_milbrandt_montefiore_morrison_nelson_stelling_2023, title={Algal blooms in a river-dominated estuary and nearshore region of Florida, USA: the influence of regulated discharges from water control structures on hydrologic and nutrient conditions}, volume={2}, ISSN={["1573-5117"]}, DOI={10.1007/s10750-022-05135-w}, abstractNote={Abstract}, journal={HYDROBIOLOGIA}, author={Phlips, E. J. and Badylak, S. and Mathews, A. L. and Milbrandt, E. C. and Montefiore, L. R. and Morrison, E. S. and Nelson, N. and Stelling, B.}, year={2023}, month={Feb} }
 @article{fidan_gray_doll_nelson_2023, title={Machine learning approach for modeling daily pluvial flood dynamics in agricultural landscapes}, volume={167}, ISSN={["1873-6726"]}, url={https://doi.org/10.1016/j.envsoft.2023.105758}, DOI={10.1016/j.envsoft.2023.105758}, abstractNote={Despite rural, agricultural landscapes being exposed to pluvial flooding, prior predictive flood modeling research has largely focused on urban areas. To improve and extend pluvial flood modeling approaches for use in agricultural regions, we built a machine learning model framework that uses remotely sensed imagery from Planet Labs, gridded rainfall data, and open-access geospatial landscape characteristics to produce a pluvial flood timeline. A Random Forest model was trained and daily flood timeline was generated for Hurricane Matthew (2016) at a 10-m resolution. The results show the model predicts pluvial flooding well, with overall accuracy of 0.97 and F1 score of 0.69. Further evaluation of model outputs highlighted that corn and soybean crops were most impacted by the pluvial flooding. The model may be used to identify agricultural areas susceptible to pluvial flooding, crops that may be potentially impacted, and characteristics of areas that experience pluvial flooding.}, journal={ENVIRONMENTAL MODELLING & SOFTWARE}, author={Fidan, Emine and Gray, Josh and Doll, Barbara and Nelson, Natalie G.}, year={2023}, month={Sep} }
 @article{shehata_gentine_nelson_sayde_2023, title={Optimization of the number and locations of the calibration stations needed to monitor soil moisture using distributed temperature sensing systems: A proof-of-concept study}, volume={620}, ISSN={["1879-2707"]}, DOI={10.1016/j.jhydrol.2023.129449}, abstractNote={The single-probe heat-pulse (SPHP) technique combined with the Fiber-optic Distributed Temperature Sensing (DTS) technology can offer novel high-resolution measurements of soil moisture (θ) over spatial scales ranging from several centimeters to several kilometers. However, the key limitation of this method is in obtaining the calibration relationship between θ and soil thermal conductivity (λ) across a specific field. In a previous study, a new methodology using a Gaussian processes model was presented to account for the spatial variability in the λ-θ relationship. The model aggregated θ measurements from soil moisture sensors scattered over the SPHP transect with the corresponding DTS λ measurements at their locations. In this study, a novel methodology is tested to optimize the number and locations of soil moisture sensors required to account for the spatial variability of the λ-θ relationship to achieve higher accuracy from the SPHP technique. The proposed methodology utilizes hierarchical clustering to analyze the information contained in the spatial structure of the SPHP measurements as the soil dries from a nearly-saturated condition. The proposed methodology was tested using data from a field in Oklahoma. Monte-Carlo simulation was performed to validate the performance of the proposed methodology. The predictions obtained from the proposed methodology resulted in θ measurements accuracy comparable to those obtained from the 10% best Monte-Carlo iterations of randomly assigned soil moisture locations. This study demonstrates that the proposed methodology is more efficient than the traditional practice of randomly spreading calibration soil moisture sensors along the SPHP transect.}, journal={JOURNAL OF HYDROLOGY}, author={Shehata, Mahmoud and Gentine, Pierre and Nelson, Natalie and Sayde, Chadi}, year={2023}, month={May} }
 @article{fidan_emanuel_reich_harris_nelson_2023, title={Patterns and drivers of nutrient trends in flood-impacted surface waters: Insights from Bayesian modeling approaches}, url={https://doi.org/10.5194/egusphere-egu23-6890}, DOI={10.5194/egusphere-egu23-6890}, abstractNote={Extreme events, including regional floods caused by hurricanes, have the potential to mobilize and transport nutrients across the landscape, creating public and environmental health concerns. Several studies have characterized the contaminants in floodwaters, but few studies offer insights into which watershed characteristics explain flood water quality signatures. To address lack of understanding on flood water quality descriptors, we aimed to explain floodwater nutrient concentrations as a function of different environmental variables. Specifically, we quantified nitrogen and phosphorus concentrations in floodwaters across the Atlantic Coastal Plain of North Carolina (USA) after Hurricane Florence, a major tropical storm that delivered up to 700 mm of rainfall to the region during September 2018. We also constructed a multivariate, spatial Bayesian model to explain nutrient responses as a function of different hydroclimatic factors, land use classifications, and nearby pollution point sources. Nutrient samples were collected at 51 different sites at four different time points spanning a year after Hurricane Florence impact: during major flood conditions and after floodwaters had receded. Samples were assessed for total Kjeldahl nitrogen, total ammonia nitrogen, nitrate and nitrites, total phosphorus, and orthophosphate. Results from this analysis show that nutrient concentrations were very low in floodwaters, with the exception of several sites that exhibited excessively high total Kjeldahl nitrogen, total phosphorus, and orthophosphate concentrations. Furthermore, modeling results indicate that swine production facilities (concentrated animal feeding operations; CAFOs), wastewater treatment plant (WWTP) proximity, and precipitation variables were important in explaining nutrient concentrations in floodwaters. This research suggests that swine CAFOs and WWTPs were likely sources of nutrient exports associated with Hurricane Florence, with rainfall amount being a primary driver. }, author={Fidan, Emine and Emanuel, Ryan and Reich, Brian and Harris, Angela and Nelson, Natalie}, year={2023}, month={May} }
 @article{reynolds_schaeffer_guertault_nelson_2023, title={Satellite and in situ cyanobacteria monitoring: Understanding the impact of monitoring frequency on management decisions}, url={http://dx.doi.org/10.1016/j.jhydrol.2023.129278}, DOI={10.1016/j.jhydrol.2023.129278}, abstractNote={Cyanobacterial harmful algal blooms (cyanoHABs) in reservoirs can be transported to downstream waters via scheduled discharges. Transport dynamics are difficult to capture in traditional cyanoHAB monitoring, which can be spatially disparate and temporally discontinuous. The introduction of satellite remote sensing for cyanoHAB monitoring provides opportunities to detect where cyanoHABs occur in relation to reservoir release locations, like canal inlets. The study objectives were to assess (1) differences in reservoir cyanoHAB frequencies as determined by in situ and remotely sensed data and (2) the feasibility of using satellite imagery to identify conditions associated with release-driven cyanoHAB export. As a representative case, Lake Okeechobee and the St. Lucie Estuary (Florida, USA), which receives controlled releases from Lake Okeechobee, were examined. Both systems are impacted by cyanoHABs, and the St. Lucie Estuary experienced states of emergency for extreme cyanoHABs in 2016 and 2018. Using the European Space Agency's Sentinel-3 OLCI imagery processed with the Cyanobacteria Index (CIcyano), cyanoHAB frequencies across Lake Okeechobee from May 2016-April 2021 were compared to frequencies from in situ data. Strong agreement was observed in frequency rankings between the in situ and remotely sensed data in capturing intra-annual variability in bloom frequencies across Lake Okeechobee (Kendall's tau = 0.85, p-value = 0.0002), whereas no alignment was observed when evaluating inter-annual variation (Kendall's tau = 0, p-value = 1). Further, remotely sensed observations revealed that cyanoHABs were highly frequent near the inlet to the canal connecting Lake Okeechobee to the St. Lucie Estuary in state-of-emergency years, a pattern not evident from in situ data alone. This study demonstrates how remote sensing can complement traditional cyanoHAB monitoring to inform reservoir release decision making.}, journal={Journal of Hydrology}, author={Reynolds, Natalie and Schaeffer, Blake A. and Guertault, Lucie and Nelson, Natalie G.}, year={2023}, month={Apr} }
 @article{martinez_ward_collins_nelson_2023, title={TESTING THE AGREEMENT BETWEEN A TRADITIONAL AND UAV-BASED METHOD FOR QUANTIFYING SKIPS IN SUBOPTIMAL COTTON STANDS}, volume={66}, ISSN={["2769-3287"]}, DOI={10.13031/ja.14760}, abstractNote={
						Highlights
					}, number={1}, journal={JOURNAL OF THE ASABE}, author={Martinez, Enrique E. Pena and Ward, Jason K. and Collins, Guy and Nelson, Natalie}, year={2023}, pages={149–153} }
 @article{montefiore_nelson_staudinger_terando_2023, title={Vulnerability of Estuarine Systems in the Contiguous United States to Water Quality Change Under Future Climate and Land-Use}, volume={11}, ISSN={["2328-4277"]}, url={https://doi.org/10.1029/2022EF002884}, DOI={10.1029/2022EF002884}, abstractNote={Abstract}, number={3}, journal={EARTHS FUTURE}, author={Montefiore, L. R. and Nelson, N. G. and Staudinger, M. D. and Terando, A.}, year={2023}, month={Mar} }
 @article{montefiore_nelson_2022, title={Can a simple water quality model effectively estimate runoff-driven nutrient loads to estuarine systems? A national-scale comparison of STEPLgrid and SPARROW}, volume={150}, ISSN={["1873-6726"]}, url={https://doi.org/10.1016/j.envsoft.2022.105344}, DOI={10.1016/j.envsoft.2022.105344}, abstractNote={This study investigated whether a simple model could scale across watersheds and effectively predict runoff-driven nutrient loading as compared to a model with more complex process representation. A lumped model, the Spreadsheet Tool for Estimating Pollutant Load (STEPL), was adapted to use gridded data (STEPLgrid) and applied to 112 coastal watersheds across the Atlantic, Gulf, and Pacific coasts of the contiguous United States (U.S.) to estimate annual runoff-driven total nitrogen (TN) and total phosphorus (TP) loads. STEPLgrid outputs were compared to those of the SPAtially Referenced Regression on Watershed Attributes (SPARROW) model. Relative to SPARROW, STEPLgrid produced comparable estimates of TN and TP loads for most watersheds studied and its predicted loads were more similar to SPARROW for TN than TP. STEPLgrid was particularly effective at rank-ordering watersheds by TN and TP loads as compared to SPARROW, indicating that STEPLgrid was useful for relative comparisons across diverse watersheds. • STEPL was adapted to allow for its application with gridded data at large spatial scales (STEPLgrid). • STEPLgrid and SPARROW estimates of total N and P loads were compared for 112 coastal watersheds. • STEPLgrid estimates of total P loads were more similar to those of SPARROW than total N. • STEPLgrid was particularly effective at rank-ordering watersheds by nutrient load. • Simple models can be useful for comparing nutrient load scenarios across multiple basins.}, journal={ENVIRONMENTAL MODELLING & SOFTWARE}, publisher={Elsevier BV}, author={Montefiore, Lise R. and Nelson, Natalie G.}, year={2022}, month={Apr} }
 @article{shehata_gentine_nelson_sayde_2022, title={Characterizing soil water content variability across spatial scales from optimized high-resolution distributed temperature sensing technique}, volume={612}, ISSN={["1879-2707"]}, DOI={10.1016/j.jhydrol.2022.128195}, abstractNote={Fiber-optic Distributed Temperature Sensing, when combined with the Single-probe Heat-pulse technique can measure soil moisture (θ) across spatial scales. The key limitation of this system is in obtaining the relationship between soil thermal conductivity (λ) and θ for a specific field. Using the Department of Energy Atmospheric Radiation Measurement (ARM) site, this study tested a new methodology to account for the spatial variability in the λ-θ relationship using a Gaussian processes model. The resulting accurate θ measurements (RMSE = 0.03 m3m−3) were used to characterize the spatial variability of θ across scales and to develop an empirical equation that can correct for the changes in the θ spatial variability observed at different spatial resolutions. In addition, the number of required samples to accurately characterize θ and its variability over scales ranging from 5 m and 350 m were estimated. These findings provide key information to scale soil moisture from centimeters to hundreds of meters for process understanding.}, journal={JOURNAL OF HYDROLOGY}, author={Shehata, Mahmoud and Gentine, Pierre and Nelson, Natalie and Sayde, Chadi}, year={2022}, month={Sep} }
 @article{nelson_cothran_ramage_carr_skiles_porter_2022, title={Implementing FAIR data management practices in shellfish sanitation}, volume={26}, ISSN={["2352-5134"]}, DOI={10.1016/j.aqrep.2022.101324}, abstractNote={In the United States (U.S.), state agencies in charge of mariculture regulation are mandated under the U.S. Food and Drug Administration’s (FDA) National Shellfish Sanitation Program (NSSP) to monitor fecal indicator bacteria (FIB) concentrations, commonly of fecal coliforms, to determine the safety of coastal waters for supporting harvestable shellfish for human consumption. Many states have monitored bacteriological water quality for decades, creating impressive long-term records with the potential to advance foundational understanding of coastal systems and contribute to other complementary monitoring efforts. However, state shellfish sanitation programs differ in how they collect, manage, and share bacteriological monitoring data, resulting in their data typically being available in disparate state-level repositories with non-standardized database structures. Here, we outline three key recommendations as to how shellfish sanitation programs could implement practices to make their data more Findable, Accessible, Interoperable, and Reusable (FAIR), in turn creating new opportunities for the full potential of the data to be realized. We also offer sample materials of a standardized database, ShellBase, to provide an example of how diverse shellfish sanitation data may be integrated with a common data structure.}, journal={AQUACULTURE REPORTS}, author={Nelson, Natalie G. and Cothran, Jeremy and Ramage, Dan and Carr, Megan and Skiles, Keith and Porter, Dwayne E.}, year={2022}, month={Oct} }
 @article{basu_van meter_byrnes_van cappellen_brouwer_jacobsen_jarsjo_rudolph_cunha_nelson_et al._2022, title={Managing nitrogen legacies to accelerate water quality improvement}, volume={15}, ISSN={["1752-0908"]}, DOI={10.1038/s41561-021-00889-9}, abstractNote={Increasing incidences of eutrophication and groundwater quality impairment from agricultural nitrogen pollution are threatening humans and ecosystem health. Minimal improvements in water quality have been achieved despite billions of dollars invested in conservation measures worldwide. Such apparent failures can be attributed in part to legacy nitrogen that has accumulated over decades of agricultural intensification and that can lead to time lags in water quality improvement. Here, we identify the key knowledge gaps related to landscape nitrogen legacies and propose approaches to manage and improve water quality, given the presence of these legacies. Agricultural nitrogen legacies are delaying improvements to water quality. Comprehensive management strategies that address legacy issues are needed to ensure better environmental outcomes.}, number={2}, journal={NATURE GEOSCIENCE}, author={Basu, Nandita B. and Van Meter, Kimberly J. and Byrnes, Danyka K. and Van Cappellen, Philippe and Brouwer, Roy and Jacobsen, Brian H. and Jarsjo, Jerker and Rudolph, David L. and Cunha, Maria C. and Nelson, Natalie and et al.}, year={2022}, month={Feb}, pages={97–105} }
 @article{montefiore_nelson_dean_sharara_2022, title={Reconstructing the historical expansion of industrial swine production from Landsat imagery}, volume={12}, ISSN={["2045-2322"]}, DOI={10.1038/s41598-022-05789-5}, abstractNote={Abstract}, number={1}, journal={SCIENTIFIC REPORTS}, author={Montefiore, Lise R. and Nelson, Natalie G. and Dean, Amanda and Sharara, Mahmoud}, year={2022}, month={Feb} }
 @article{saia_nelson_young_parham_vandegrift_2022, title={Ten Simple Rules for Researchers Who Want to Develop Web Apps}, volume={1}, url={https://doi.org/10.31223/X57P6R}, DOI={10.31223/X57P6R}, abstractNote={Growing interest in data-driven, decision-support tools across the life sciences and physical sciences has motivated development of web applications, also known as web apps. Web apps can help disseminate research findings and present research outputs in ways that are more accessible and meaningful to the general public--from individuals, to governments, to companies. Specifically, web apps enable exploration of scenario testing and policy analysis (i.e., to answer “what if?”) as well as co-evolution of scientific and public knowledge. However, the majority of researchers developing web apps receive little formal training or technical guidance on how to develop and evaluate the effectiveness of their web-based decision support tools. Take some of us for example. We (Saia and Nelson) are agricultural and environmental engineers with little experience in web app development, but we are interested in creating web apps to support sustainable aquaculture production in the Southeast. We had user (i.e., shellfish growers) interest, a goal in mind (i.e., develop a new forecast product and decision-support tool for shellfish aquaculturalists), and received funding to support this work. Yet, we experienced several unexpected hurdles from the start of our project that ended up being fairly common hiccups to the seasoned web app developers among us (Young, Parham). As a result, we share the following Ten Simple Rules, which highlight take home messages, including lessons learned and practical tips, of our experience as burgeoning web app developers. We hope researchers interested in developing web apps draw insights from our (in)experience as they set out on their decision support tool development journey.}, publisher={California Digital Library (CDL)}, author={Saia, Sheila and Nelson, Natalie and Young, Sierra and Parham, Stanton and Vandegrift, Micah}, year={2022}, month={Jan} }
 @article{saia_nelson_young_parham_vandegrift_2022, title={Ten simple rules for researchers who want to develop web apps}, volume={18}, ISSN={["1553-7358"]}, url={https://doi.org/10.1371/journal.pcbi.1009663}, DOI={10.1371/journal.pcbi.1009663}, abstractNote={Growing interest in data-driven, decision-support tools across the life sciences and physical sciences has motivated development of web applications, also known as web apps. Web apps can help disseminate research findings and present research outputs in ways that are more accessible and meaningful to the general public--from individuals, to governments, to companies. Specifically, web apps enable exploration of scenario testing and policy analysis (i.e., to answer “what if?”) as well as co-evolution of scientific and public knowledge. However, the majority of researchers developing web apps receive little formal training or technical guidance on how to develop and evaluate the effectiveness of their web-based decision support tools. Take some of us for example. We (Saia and Nelson) are agricultural and environmental engineers with little experience in web app development, but we are interested in creating web apps to support sustainable aquaculture production in the Southeast. We had user (i.e., shellfish growers) interest, a goal in mind (i.e., develop a new forecast product and decision-support tool for shellfish aquaculturalists), and received funding to support this work. Yet, we experienced several unexpected hurdles from the start of our project that ended up being fairly common hiccups to the seasoned web app developers among us (Young, Parham). As a result, we share the following Ten Simple Rules, which highlight take home messages, including lessons learned and practical tips, of our experience as burgeoning web app developers. We hope researchers interested in developing web apps draw insights from our (in)experience as they set out on their decision support tool development journey.}, number={1}, journal={PLOS COMPUTATIONAL BIOLOGY}, author={Saia, Sheila M. and Nelson, Natalie G. and Young, Sierra N. and Parham, Stanton and Vandegrift, Micah}, editor={Markel, ScottEditor}, year={2022}, month={Jan} }
 @article{saia_nelson_huseth_grieger_reich_2022, title={Transitioning Machine Learning from Theory to Practice in Natural Resources Management}, volume={1}, url={https://doi.org/10.31223/X5D01H}, DOI={10.31223/X5D01H}, abstractNote={Advances in sensing and computation have accelerated at unprecedented rates and scales, in turn creating new opportunities for natural resources managers to improve adaptive and predictive management practices by coupling large environmental datasets with machine learning (ML). Yet, to date, ML models often remain inaccessible to managers working outside of academic research. To identify challenges preventing natural resources managers from putting ML into practice more broadly, we convened a group of 23 stakeholders (i.e., applied researchers and practitioners) who model and analyze data collected from environmental and agricultural systems. Workshop participants shared many barriers regarding their perceptions of, and experiences with, ML modeling. These barriers emphasized three main areas of concern: ML model transparency, availability of educational resources, and the role of process-based understanding in ML model development. Informed by workshop participant input, we offer recommendations on how the ecological modelling community can overcome key barriers preventing ML model use in natural resources management and advance the profession towards data-driven decision-making.}, publisher={California Digital Library (CDL)}, author={Saia, Sheila and Nelson, Natalie and Huseth, Anders and Grieger, Khara and Reich, Brian}, year={2022}, month={Jan} }
 @article{messer_moore_nelson_ahiablame_bean_boles_cook_hall_mcmaine_schlea_2021, title={CONSTRUCTED WETLANDS FOR WATER QUALITY IMPROVEMENT: A SYNTHESIS ON NUTRIENT REDUCTION FROM AGRICULTURAL EFFLUENTS}, volume={64}, ISSN={["2151-0040"]}, DOI={10.13031/trans.13976}, abstractNote={Abstract.}, number={2}, journal={TRANSACTIONS OF THE ASABE}, author={Messer, T. L. and Moore, T. L. and Nelson, N. and Ahiablame, L. and Bean, E. Z. and Boles, C. and Cook, S. L. and Hall, S. G. and McMaine, J. and Schlea, D.}, year={2021}, pages={625–639} }
 @article{haque_lobaton_nelson_yencho_pecota_mierop_kudenov_boyette_williams_2021, title={Computer vision approach to characterize size and shape phenotypes of horticultural crops using high-throughput imagery}, volume={182}, ISSN={0168-1699}, url={http://dx.doi.org/10.1016/j.compag.2021.106011}, DOI={10.1016/j.compag.2021.106011}, abstractNote={For many horticultural crops, variation in quality (e.g., shape and size) contributes significantly to the crop’s market value. Metrics characterizing less subjective harvest quantities (e.g., yield and total biomass) are routinely monitored. In contrast, metrics quantifying more subjective crop quality characteristics such as ideal size and shape remain difficult to characterize objectively at the production-scale due to the lack of modular technologies for high-throughput sensing and computation. Several horticultural crops are sent to packing facilities after having been harvested, where they are sorted into boxes and containers using high-throughput scanners. These scanners capture images of each fruit or vegetable being sorted and packed, but the images are typically used solely for sorting purposes and promptly discarded. With further analysis, these images could offer unparalleled insight on how crop quality metrics vary at the industrial production-scale and provide further insight into how these characteristics translate to overall market value. At present, methods for extracting and quantifying quality characteristics of crops using images generated by existing industrial infrastructure have not been developed. Furthermore, prior studies that investigated horticultural crop quality metrics, specifically of size and shape, used a limited number of samples, did not incorporate deformed or non-marketable samples, and did not use images captured from high-throughput systems. In this work, using sweetpotato (SP) as a use case, we introduce a computer vision algorithm for quantifying shape and size characteristics in a high-throughput manner. This approach generates 3D model of SPs from two 2D images captured by an industrial sorter 90 degrees apart and extracts 3D shape features in a few hundred milliseconds. We applied the 3D reconstruction and feature extraction method to thousands of image samples to demonstrate how variations in shape features across SP cultivars can be quantified. We created a SP shape dataset containing SP images, extracted shape features, and qualitative shape types (U.S. No. 1 or Cull). We used this dataset to develop a neural network-based shape classifier that was able to predict Cull vs. U.S. No. 1 SPs with 84.59% accuracy. In addition, using univariate Chi-squared tests and random forest, we identified the most important features for determining qualitative shape type (U.S. No. 1 or Cull) of the SPs. Our study serves as a key step towards enabling big data analytics for industrial SP agriculture. The methodological framework is readily transferable to other horticultural crops, particularly those that are sorted using commercial imaging equipment.}, journal={Computers and Electronics in Agriculture}, publisher={Elsevier BV}, author={Haque, Samiul and Lobaton, Edgar and Nelson, Natalie and Yencho, G. Craig and Pecota, Kenneth V. and Mierop, Russell and Kudenov, Michael W. and Boyette, Mike and Williams, Cranos M.}, year={2021}, month={Mar}, pages={106011} }
 @article{phlips_badylak_nelson_hall_jacoby_lasi_lockwood_miller_2021, title={Cyclical Patterns and a Regime Shift in the Character of Phytoplankton Blooms in a Restricted Sub-Tropical Lagoon, Indian River Lagoon, Florida, United States}, volume={8}, ISSN={["2296-7745"]}, DOI={10.3389/fmars.2021.730934}, abstractNote={This paper examines the character of phytoplankton blooms in a restricted sub-tropical lagoon along the Atlantic coast of central Florida. The results of the 23-year study (1997–2020) provide evidence for multiple types of variability in bloom activity, including cyclical patterns, stochastic events, and most prominently a regime shift in composition and intensity. Cyclical patterns (e.g., El Niño/La Niña periods) and stochastic events (e.g., tropical storms) influenced rainfall levels, which in turn impacted nutrient concentrations in the water column and the timing and intensity of blooms. In 2011, a major change occurred in the character of blooms, with a dramatic increase in peak biomass levels of blooms and the appearance of new dominant taxa, including the brown tide speciesAureoumbra lagunensisand other nanoplanktonic species. Results of quantitative analyses reveal system behavior indicative of a regime shift. The shift coincided with widespread losses of seagrass community and reduced drift algae biomass. A combination of exceptionally low water temperatures in the winters of 2009/2010 and 2010/2011, hypersaline conditions associated with drought conditions, and high light attenuation caused by blooms appear to have contributed to the widespread and protracted decline in seagrass and drift macroalgal communities in the lagoon, leading to shifts in distribution of internal and external nutrient sources toward phytoplankton.}, journal={FRONTIERS IN MARINE SCIENCE}, author={Phlips, Edward J. and Badylak, Susan and Nelson, Natalie G. and Hall, Lauren M. and Jacoby, Charles A. and Lasi, Margaret A. and Lockwood, Jean C. and Miller, Janice D.}, year={2021}, month={Sep} }
 @article{wells_gilmore_nelson_mittelstet_bohlke_2021, title={Determination of vadose zone and saturated zone nitrate lag times using long-term groundwater monitoring data and statistical machine learning}, volume={25}, ISSN={["1607-7938"]}, url={https://doi.org/10.5194/hess-25-811-2021}, DOI={10.5194/hess-25-811-2021}, abstractNote={Abstract. In this study, we explored the use of statistical machine learning and long-term groundwater nitrate monitoring data to estimate vadose zone and saturated zone lag times in an irrigated alluvial agricultural setting. Unlike most previous statistical machine learning studies that sought to predict groundwater nitrate concentrations within aquifers, the focus of this study was to leverage available groundwater nitrate concentrations and other environmental variables to determine mean regional vertical velocities (transport rates) of water and solutes in the vadose zone and saturated zone (3.50 and 3.75 m yr−1, respectively). The statistical machine learning results are consistent with two primary recharge processes in this western Nebraska aquifer, namely (1) diffuse recharge from irrigation and precipitation across the landscape and (2) focused recharge from leaking irrigation conveyance canals. The vadose zone mean velocity yielded a mean recharge rate (0.46 m yr−1) consistent with previous estimates from groundwater age dating in shallow wells (0.38 m yr−1). The
saturated zone mean velocity yielded a recharge rate (1.31 m yr−1) that was more consistent with focused recharge from leaky irrigation canals, as indicated by previous results of groundwater age dating in intermediate-depth wells (1.22 m yr−1). Collectively, the statistical machine learning model results are consistent with previous observations of relatively high water fluxes and short transit times for water and nitrate in the primarily oxic aquifer. Partial dependence plots from the model indicate a sharp threshold in which high groundwater nitrate concentrations are mostly associated with total travel times of 7 years or less, possibly reflecting some combination of recent management practices and a tendency for nitrate concentrations to be higher in diffuse infiltration recharge than in canal leakage water. Limitations to the machine learning approach include the non-uniqueness of different transport rate combinations when comparing model performance and highlight the need to corroborate statistical model results with a robust conceptual model and complementary information such as groundwater age.
                    }, number={2}, journal={HYDROLOGY AND EARTH SYSTEM SCIENCES}, publisher={Copernicus GmbH}, author={Wells, Martin J. and Gilmore, Troy E. and Nelson, Natalie and Mittelstet, Aaron and Bohlke, John K.}, year={2021}, month={Feb}, pages={811–829} }
 @article{alonso_nelson_yurek_kaplan_olabarrieta_frederick_2021, title={Estimating the Influence of Oyster Reef Chains on Freshwater Detention at the Estuary Scale Using Landsat-8 Imagery}, volume={5}, ISSN={["1559-2731"]}, url={https://doi.org/10.1007/s12237-021-00959-6}, DOI={10.1007/s12237-021-00959-6}, journal={ESTUARIES AND COASTS}, publisher={Springer Science and Business Media LLC}, author={Alonso, Alice and Nelson, Natalie G. and Yurek, Simeon and Kaplan, David and Olabarrieta, Maitane and Frederick, Peter}, year={2021}, month={May} }
 @article{milbrandt_martignette_thompson_bartleson_phlips_badylak_nelson_2021, title={Geospatial distribution of hypoxia associated with a Karenia brevis bloom}, volume={259}, ISSN={["1096-0015"]}, url={http://dx.doi.org/10.1016/j.ecss.2021.107446}, DOI={10.1016/j.ecss.2021.107446}, abstractNote={In 2018, the presence of bottom water hypoxia along the SW Florida coast was investigated during a bloom of the toxic dinoflagellate Karenia brevis . The bloom was first detected in November 2017. Monitoring of oxygen levels and bloom densities was carried out in 2018 and 2019 using sampling grids. Vertical profiles indicated a pycnocline at 3–4 m where warmer, lower salinity water was at the surface, while the deeper hypoxic layer was colder with higher salinity. There were significantly higher abundances of K. brevis in the surface water compared to the hypoxic bottom water in September 2018. At two fixed sites, dissolved oxygen was measured continuously showing hypoxic conditions during that month. Geospatial analysis of vertical profile data yielded an estimate that the hypoxic layer covered an area of at least 655 km 2 . The possible influences of red tides on hypoxic conditions along the coast of the eastern Gulf of Mexico are discussed within the context of the 2018 K. brevis bloom event. Hypoxia occurring in parallel to a red tide bloom is more likely to occur with warmer ocean temperatures and increased fluxes of nutrients and fresh water to the Gulf of Mexico after hurricanes. • Hypoxia co-occurred with a red tide bloom ( Karenia brevis ) in shallow waters of the eastern Gulf of Mexico in 2018. • Vertical stratification of the water column led to the development of the hypoxic layer that was estimated to be 655 km 2 . • The toxic dinoflagellate Karenia brevis was the dominant taxon in the red tide. • A large die-off of fish, turtles, and other marine life during the red tide was widely reported in the region of hypoxia.}, journal={ESTUARINE COASTAL AND SHELF SCIENCE}, publisher={Elsevier BV}, author={Milbrandt, Eric C. and Martignette, A. J. and Thompson, M. A. and Bartleson, R. D. and Phlips, E. J. and Badylak, S. and Nelson, N. G.}, year={2021}, month={Sep} }
 @article{nelson_cuchiara_hendren_jones_marshall_2021, title={Hazardous Spills at Retired Fertilizer Manufacturing Plants Will Continue to Occur in the Absence of Scientific Innovation and Regulatory Enforcement}, volume={55}, ISSN={["1520-5851"]}, url={https://doi.org/10.1021/acs.est.1c05311}, DOI={10.1021/acs.est.1c05311}, abstractNote={ADVERTISEMENT RETURN TO ISSUEViewpointNEXTHazardous Spills at Retired Fertilizer Manufacturing Plants Will Continue to Occur in the Absence of Scientific Innovation and Regulatory EnforcementNatalie G. Nelson*Natalie G. NelsonBiological and Agricultural Engineering, North Carolina State University, Raleigh 27695, North Carolina, United StatesCenter for Geospatial Analytics, North Carolina State University, Raleigh 27695, North Carolina, United States*Phone: 919-515-6741; email: [email protected]More by Natalie G. NelsonView Biographyhttps://orcid.org/0000-0002-3258-7622, Maude L. CuchiaraMaude L. CuchiaraMaterials Science and Engineering, North Carolina State University, Raleigh 27695, North Carolina, United StatesMore by Maude L. Cuchiarahttps://orcid.org/0000-0001-8493-6620, Christine Ogilvie HendrenChristine Ogilvie HendrenResearch Institute for Environment, Energy and Economics, Appalachian State University, Boone 28608-2067, North Carolina, United StatesGeological and Environmental Science, Appalachian State University, Boone 28608-2067, North Carolina, United StatesMore by Christine Ogilvie Hendrenhttps://orcid.org/0000-0002-9546-6545, Jacob L. JonesJacob L. JonesMaterials Science and Engineering, North Carolina State University, Raleigh 27695, North Carolina, United StatesMore by Jacob L. Joneshttps://orcid.org/0000-0002-9182-0957, and Anna-Maria MarshallAnna-Maria MarshallSociology, University of Illinois Urbana−Champaign, Urbana 61801, United StatesMore by Anna-Maria MarshallCite this: Environ. Sci. Technol. 2021, 55, 24, 16267–16269Publication Date (Web):November 29, 2021Publication History Received6 August 2021Published online29 November 2021Published inissue 21 December 2021https://pubs.acs.org/doi/10.1021/acs.est.1c05311https://doi.org/10.1021/acs.est.1c05311article-commentaryACS PublicationsCopyright © 2021 The Authors. Published by American Chemical Society. This publication is licensed under CC-BY 4.0. License Summary*You are free to share (copy and redistribute) this article in any medium or format and to adapt (remix, transform, and build upon) the material for any purpose, even commercially within the parameters below:Creative Commons (CC): This is a Creative Commons license.Attribution (BY): Credit must be given to the creator.View full license*DisclaimerThis summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials. This publication is Open Access under the license indicated. Learn MoreArticle Views2392Altmetric-Citations4LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (2 MB) Get e-AlertscloseSUBJECTS:Byproducts,Manufacturing,Phosphorus,Separation science,Wastewater Get e-Alerts}, number={24}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, publisher={American Chemical Society (ACS)}, author={Nelson, Natalie G. and Cuchiara, Maude L. and Hendren, Christine Ogilvie and Jones, Jacob L. and Marshall, Anna-Maria}, year={2021}, month={Dec}, pages={16267–16269} }
 @article{harris_fidan_nelson_emanuel_jass_kathariou_niedermeyer_sharara_reyes_riveros-iregui_et al._2021, title={Microbial Contamination in Environmental Waters of Rural and Agriculturally-Dominated Landscapes Following Hurricane Florence}, volume={1}, ISSN={["2690-0637"]}, url={https://doi.org/10.1021/acsestwater.1c00103}, DOI={10.1021/acsestwater.1c00103}, abstractNote={Hurricane Florence brought unprecedented rainfall and flooding to Eastern North Carolina in 2018. Extensive flooding had the potential to mobilize microbial contaminants from a variety of sources. Our study evaluated microbial contaminants in surface waters at 40 sites across Eastern North Carolina 1 week after the hurricane made landfall (Phase 1) and one month later (Phase 2). High concentrations of Escherichia coli were detected in flowing channel and floodwater samples across both phases; however, channel samples during Phase 2 had higher concentrations of E. coli compared to Phase 1. Human- and swine-associated fecal markers were detected in 26% and 9% of samples, respectively, with no trends related to phase of sampling. Arcobacter butzleri was previously shown to be recovered from most (73%) samples, and detection of this pathogen was not associated with any source-associated fecal marker. Detection of Listeria spp. was associated with the swine-associated fecal marker. These results suggest that improved swine and human feces management should be explored to prevent microbial contamination in surface water, especially in regions where extreme rainfall may increase due to climate change. Sampling at higher frequency surrounding rainfall events would provide more detailed characterization of the risks posed by floodwater at different time scales and under different antecedent conditions.}, number={9}, journal={ACS ES&T WATER}, publisher={American Chemical Society (ACS)}, author={Harris, Angela R. and Fidan, Emine N. and Nelson, Natalie G. and Emanuel, Ryan E. and Jass, Theo and Kathariou, Sophia and Niedermeyer, Jeffrey and Sharara, Mahmoud and Reyes, Francis Lajara, III and Riveros-Iregui, Diego A. and et al.}, year={2021}, month={Sep}, pages={2012–2019} }
 @article{donatich_doll_page_nelson_2020, title={Can the Stream Quantification Tool (SQT) Protocol Predict the Biotic Condition of Streams in the Southeast Piedmont (USA)?}, volume={12}, url={https://doi.org/10.3390/w12051485}, DOI={10.3390/w12051485}, abstractNote={In some states, the Stream Quantification Tool (SQT) has been adopted to quantify functional change of stream mitigation efforts. However, the ability of the SQT protocol to predict biological function and uphold the premise of the Stream Functions Pyramid (Pyramid) remains untested. Macroinvertebrate community metrics in 34 headwater streams in Piedmont, North Carolina (NC, USA) were related to NC SQT protocol (version 3.0) factors and other variables relevant to ecological function. Three statistical models, including stepwise, lasso, and ridge regression were used to predict the NC Biotic Index (NCBI) and Ephemeroptera, Plecoptera, and Trichoptera (EPT) richness using two datasets: 21 SQT variables and the SQT variables plus 13 additional watershed, hydraulic, geomorphic, and physicochemical variables. Cross-validation revealed that stepwise and ridge outperformed lasso, and that the SQT variables can reasonably predict biology metrics (R2 0.53–0.64). Additional variables improved prediction (R2 0.70–0.88), suggesting that the SQT protocol is lacking metrics important to macroinvertebrates. Results moderately support the Pyramid: highly predictive ridge models included metrics from all levels, while highly predictive stepwise models included metrics from higher levels, and not watershed hydrology. Reach-scale metrics were more important than watershed hydrology, providing encouragement for projects limited by watershed condition.}, number={5}, journal={Water}, author={Donatich, Sara and Doll, Barbara and Page, Jonathan and Nelson, Natalie}, year={2020}, month={May} }
 @article{wells_gilmore_nelson_mittelstet_böhlke_2020, title={Determination of vadose and saturated-zone nitrate lag times using long-term groundwater monitoring data and statistical machine learning}, volume={5}, url={https://doi.org/10.5194/hess-2020-169}, DOI={10.5194/hess-2020-169}, abstractNote={Abstract. In this study, we explored the use of statistical machine learning and long-term groundwater nitrate monitoring data to estimate vadose-zone and saturated-zone lag times in an irrigated alluvial agricultural setting. Unlike most previous statistical machine learning studies that sought to predict groundwater nitrate concentrations within aquifers, the focus of this study was to leverage available groundwater nitrate concentrations and other environmental variable data to determine mean vertical velocities (transport rates) of water and solutes in the vadose zone and saturated zone (3.50 m/year and 3.75 m/year, respectively). Although a saturated-zone velocity that is greater than a vadose-zone velocity would be counterintuitive in most aquifer settings, the statistical machine learning results are consistent with two contrasting primary recharge processes in this aquifer: (1) diffuse recharge from irrigation and precipitation across the landscape, and (2) focused recharge from leaking irrigation conveyance canals. The vadose-zone mean velocity yielded a mean recharge rate (0.46 m/year) consistent with previous estimates from groundwater age-dating in shallow wells (0.38 m/year). The saturated zone mean velocity yielded a recharge rate (1.31 m/year) that was more consistent with focused recharge from leaky irrigation canals, as indicated by previous results of groundwater age-dating in intermediate-depth wells (1.22 m/year). Collectively, the statistical machine-learning model results are consistent with previous observations of relatively high-water fluxes and short transit times for water and nitrate in the aquifer. Partial dependence plots from the model indicate a sharp threshold where high groundwater nitrate concentrations are mostly associated with total travel times of seven years or less, possibly reflecting some combination of recent management practices and a tendency for nitrate concentrations to be higher in diffuse infiltration recharge than in canal leakage water. Limitations to the machine learning approach include potential non-uniqueness when comparing model performance for different transport rate combinations and highlight the need to corroborate statistical model results with a robust conceptual model and complementary information such as groundwater age.
                        }, publisher={Copernicus GmbH}, author={Wells, Martin J. and Gilmore, Troy E. and Nelson, Natalie and Mittelstet, Aaron and Böhlke, John Karl}, year={2020}, month={May} }
 @article{doll_kurki-fox_page_nelson_johnson_2020, title={Flood Flow Frequency Analysis to Estimate Potential Floodplain Nitrogen Treatment during Overbank Flow Events in Urban Stream Restoration Projects}, volume={12}, url={https://doi.org/10.3390/w12061568}, DOI={10.3390/w12061568}, abstractNote={Stream restoration for mitigation purposes has grown rapidly since the 1980s. As the science advances, some organizations (Chesapeake Bay Program, North Carolina Department of Environmental Quality) have approved or are considering providing nutrient credits for stream restoration projects. Nutrient treatment on floodplains during overbank events is one of the least understood processes that have been considered as part of the Chesapeake Bay Program’s Stream Restoration Nutrient Crediting program. This study analyzed ten years of streamflow and water quality data from five stations in the Piedmont of North Carolina to evaluate proposed procedures for estimating nitrogen removal on the floodplain during overbank flow events. The volume of floodplain flow, the volume of floodplain flow potentially treated, and the nitrogen load retained on the floodplain were calculated for each overbank event, and a sensitivity analysis was completed. On average, 9% to 15% of the total annual streamflow volume accessed the floodplain. The percentage of the average annual volume of streamflow potentially treated ranged from 1.0% to 5.1%. Annually, this equates to 0.2% to 1.0% of the total N load retained/removed on the floodplain following restoration. The relatively low nitrogen retention/removal rates were due to a majority of floodplain flow occurring during a few large events each year that exceeded the treatment capacity of the floodplain. On an annual basis, 90% of total floodplain flow occurred during half of all overbank events and 50% of total floodplain flow occurred during two to three events each year. Findings suggest that evaluating only overbank events may lead to undervaluing stream restoration because treatment is limited by hydrologic controls that restrict floodplain retention time. Treatment is further governed by floodplain and channel size.}, number={6}, journal={Water}, publisher={MDPI AG}, author={Doll, Barbara A. and Kurki-Fox, J. Jack and Page, Jonathan L. and Nelson, Natalie G. and Johnson, Jeffrey P.}, year={2020}, month={May}, pages={1568} }
 @article{phlips_badylak_nelson_havens_2020, title={Hurricanes, El Niño and harmful algal blooms in two sub-tropical Florida estuaries: Direct and indirect impacts}, volume={10}, url={https://doi.org/10.1038/s41598-020-58771-4}, DOI={10.1038/s41598-020-58771-4}, abstractNote={Abstract}, number={1}, journal={Scientific Reports}, author={Phlips, Edward J. and Badylak, Susan and Nelson, Natalie G. and Havens, Karl E.}, year={2020}, month={Feb} }
 @article{nelson_munoz-carpena_phlips_2020, title={Parameter uncertainty drives important incongruities between simulated chlorophyll-a and phytoplankton functional group dynamics in a mechanistic management model}, volume={129}, ISSN={["1873-6726"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85083178178&partnerID=MN8TOARS}, DOI={10.1016/j.envsoft.2020.104708}, abstractNote={Mechanistic phytoplankton functional group (PFG) models are used to develop water quality targets designed to mitigate cyanobacteria blooms, but it remains unclear whether PFG models adequately simulate cyanobacteria dynamics as most are evaluated against observations of chlorophyll-a instead of PFG biomass. To address this challenge, we analyzed an application of CE-QUAL-ICM, a 3D mechanistic PFG model used by water managers and modelers. Global Sensitivity Analysis was employed to assess the sensitivity of modeled chlorophyll-a, cyanobacteria biomass, and eukaryotic phytoplankton biomass to 42 uncertain input factors in CE-QUAL-ICM's PFG growth and loss functions. Results revealed that parameterization of CE-QUAL-ICM captured bloom variation but underpredicted bloom peaks, and simulated chlorophyll-a with greater skill than PFG biomass. Additionally, when run across realistic ranges of PFG parameter values, model outputs were highly sensitive to chlorophyll-to-carbon ratios and phosphorus uptake parameters, indicating that these factors should be the focus of targeted parameterization efforts.}, journal={ENVIRONMENTAL MODELLING & SOFTWARE}, author={Nelson, Natalie G. and Munoz-Carpena, Rafael and Phlips, Edward}, year={2020}, month={Jul} }
 @article{niedermeyer_miller_yee_harris_emanuel_jass_nelson_kathariou_2020, title={Search for Campylobacter reveals high prevalence and pronounced genetic diversity of Arcobacter butzleri in floodwater samples associated with Hurricane Florence, North Carolina, USA}, volume={86}, url={http://dx.doi.org/10.1128/aem.01118-20}, DOI={10.1128/aem.01118-20}, abstractNote={
            Climate change and associated extreme weather events can have massive impacts on the prevalence of microbial pathogens in floodwaters. However, limited data are available on foodborne zoonotic pathogens such as
            Campylobacter
            or
            Arcobacter
            in hurricane-associated floodwaters in rural regions with intensive animal production. With a high density of intensive animal production as well as pronounced vulnerability to hurricanes, eastern North Carolina presents unique opportunities in this regard. Our findings revealed widespread incidence of the emerging zoonotic pathogen
            Arcobacter butzleri
            in floodwaters from Hurricane Florence. We encountered high and largely unexplored diversity while also noting the potential for regionally abundant and persistent clones. We noted pronounced partitioning of the floodwater genotypes into two source-associated clades. The data will contribute to elucidating the poorly understood ecology of this emerging pathogen and highlight the importance of surveillance of floodwaters associated with hurricanes and other extreme weather events for
            Arcobacter
            and other zoonotic pathogens.
          }, number={20}, journal={Applied and Environmental Microbiology}, publisher={American Society for Microbiology}, author={Niedermeyer, Jeffrey A. and Miller, William G. and Yee, Emma and Harris, Angela and Emanuel, Ryan E. and Jass, Theo and Nelson, Natalie and Kathariou, Sophia}, editor={Elkins, Christopher A.Editor}, year={2020}, month={Aug}, pages={1–14} }
 @article{wells_gilmore_nelson_mittelstet_böhlke_2020, title={Supplementary material to "Determination of vadose and saturated-zone nitrate lag times using long-term groundwater monitoring data and statistical machine learning"}, volume={5}, url={https://doi.org/10.5194/hess-2020-169-supplement}, DOI={10.5194/hess-2020-169-supplement}, abstractNote={Dynamic predictors}, publisher={Copernicus GmbH}, author={Wells, Martin J. and Gilmore, Troy E. and Nelson, Natalie and Mittelstet, Aaron and Böhlke, John Karl}, year={2020}, month={May} }
 @article{saia_nelson_huseth_grieger_reich_2020, title={Transitioning Machine Learning from Theory to Practice in Natural Resources Management}, volume={435}, ISSN={0304-3800}, url={http://dx.doi.org/10.1016/j.ecolmodel.2020.109257}, DOI={10.1016/j.ecolmodel.2020.109257}, journal={Ecological Modelling}, publisher={Elsevier BV}, author={Saia, S.M. and Nelson, N. and Huseth, A.S. and Grieger, K and Reich, B.J.}, year={2020}, month={Nov}, pages={109257} }
 @article{jones_nelson_smith_2019, title={Featured Collection Introduction: The Emerging Science of Aquatic System Connectivity I}, volume={55}, ISSN={["1752-1688"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85063958321&partnerID=MN8TOARS}, DOI={10.1111/1752-1688.12739}, abstractNote={Abstract}, number={2}, journal={JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION}, author={Jones, C. Nathan and Nelson, Natalie G. and Smith, Lora L.}, year={2019}, month={Apr}, pages={287–293} }
 @article{smith_jones_nelson_2019, title={Featured Collection Introduction: The Emerging Science of Aquatic System Connectivity II}, volume={55}, ISSN={["1752-1688"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85066619383&partnerID=MN8TOARS}, DOI={10.1111/1752-1688.12760}, abstractNote={The science of aquatic systems connectivity has developed rapidly over the past 20 years (Jones, Nelson, et al. 2019). Research spans the different forms and functions of connectivity (hydrologic, biogeochemical, and biological) at vastly different spatial scales and hydrologic settings (Larsen et al. 2012; Bracken et al. 2013; Harvey and Gooseff 2015; U.S. Environmental Protection Agency 2015; Cohen et al. 2016; Covino 2017; Fritz et al. 2018; Wohl et al. 2019). This collection, The Emerging Science of Aquatic Systems Connectivity II, includes eight papers focused on aquatic system connectivity and follows the featured collection The Emerging Science of Aquatic Systems Connectivity I (Jones, Nelson, et al. 2019). The papers in both collections are products of the 2017 American Water Resources (AWRA) Specialty Conference, Connecting the Dots: The Emerging Science of Aquatic System Connectivity, which took place in May 2017 in Snowbird, Utah. The conference consisted of 33 technical sessions with over 140 presenters and with this most recent collection produced a total of 19 papers. As with Collection I, these papers increase our understanding of the functions of aquatic systems and include novel modeling approaches to characterizing connectivity and contribute to management and restoration of these functions. Three papers in this issue used modeling approaches to examine the hydrologic role of wetlands at the landscape scale. Ameli and Creed (2019) modeled effects of wetland location relative to stream networks on flows during floods and droughts in the Nose Creek watershed of the Prairie Pothole Region. Their model combined historical, existing, and drained wetlands in the watershed to estimate the hydrological functions of wetlands located at different distances from the main stream network. They found wetlands close to the main stream network play a disproportionately important role in attenuating peakflow, but wetland location is less important for regulating baseflow. These findings can help managers prioritize wetland restoration efforts for flood or drought risk mitigation. Green et al. (2019) modeled the runoff storage potential of drained upland depressions on the Des Moines Lobe of Iowa using hydrologically enforced Light Detection and Ranging (LIDAR)-derived Digital Elevation Models and a unique geoprocessing algorithm to determine storage capacities. In contrast to Ameli and Creed (2019), Green et al. (2019) determined the drained upland depressions in this region have insufficient storage capacity to significantly alter regional and local flood events. Jones, Ameli, et al. (2019) review the current capabilities of hydrologic models and their ability to simulate hydrologic connectivity of non-floodplain wetlands. They present four distinct case studies that employ process-based models which vary in complexity, spatial representation of hydrologic processes, and fidelity (i.e., the models ability to faithfully}, number={3}, journal={JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION}, author={Smith, Lora L. and Jones, C. Nathan and Nelson, Natalie G.}, year={2019}, month={Jun}, pages={526–528} }
 @article{nelson_montefiore_anthony_merriman_kuster_fox_2019, title={Undergraduate Perceptions of Climate Education Exposure in Natural Resources Management}, volume={62}, ISSN={2151-0040}, url={http://dx.doi.org/10.13031/trans.13361}, DOI={10.13031/trans.13361}, abstractNote={Abstract. To meet rising demands for climate-literate workers, undergraduate courses and curricula will require updates so that students are afforded opportunities to engage in climate science education. Previous research on undergraduate climate education has primarily focused on evaluating whether students have grounding in essential climate science principles, but these studies fail to capture the degree to which students feel they are exposed to climate education in their undergraduate programs and courses. In this study, we characterize recent trends in undergraduates’ perceived exposure to climate education across the U.S. by analyzing responses to a national survey of graduate students who attended undergraduate institutions in the U.S. (n = 423). Survey respondents scored the levels of exposure that they received to a variety of climatological topics during their undergraduate studies, which ranged from applied (e.g., earth observations, numerical modeling) to interdisciplinary (e.g., agricultural climatology, hydroclimatology) and specialized (e.g., boundary-layer climatology). Our results reveal that those who received bachelor’s degrees from programs related to human dimensions of natural resources management (e.g., geography, resource economics) generally felt that their undergraduate curricula provided them with exposure to climate education, whereas those who graduated from programs in engineering and the agricultural and life sciences largely reported a lack of climate coverage in their undergraduate studies. Students of all disciplinary backgrounds indicated that they received poor exposure to numerical modeling of historical and future climatic conditions. Findings from this study underline key areas in which curricular or course improvements are needed to ensure that future decision-makers are confident in their practical use of climate science. Keywords: Climate change, Climate science, Natural resources management, Postsecondary education, Undergraduate education, United States.}, number={3}, journal={Transactions of the ASABE}, publisher={American Society of Agricultural and Biological Engineers (ASABE)}, author={Nelson, Natalie G. and Montefiore, Lise and Anthony, Cord and Merriman, Laura and Kuster, Emma and Fox, Garey A.}, year={2019}, pages={831–839} }
 @article{messer_douglas-mankin_nelson_etheridge_2019, title={Wetland Ecosystem Resilience: Protecting and Restoring Valuable Ecosystems}, volume={62}, url={http://dx.doi.org/10.13031/trans.13578}, DOI={10.13031/trans.13578}, number={6}, journal={Transactions of the ASABE}, author={Messer, T.L. and Douglas-Mankin, K.R. and Nelson, N.G. and Etheridge, J.R.}, year={2019}, pages={1541–1543} }
 @article{rong_padron_hagerty_nelson_chi_keyhani_katz_datta_gomes_mclamore_2018, title={Post hoc support vector machine learning for impedimetric biosensors based on weak protein-ligand interactions}, volume={143}, ISSN={["1364-5528"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85046295035&partnerID=MN8TOARS}, DOI={10.1039/c8an00065d}, abstractNote={We develop a simple, open source machine learning algorithm for analyzing impedimetric biosensor data using a mobile phone.}, number={9}, journal={ANALYST}, author={Rong, Y. and Padron, A. V. and Hagerty, K. J. and Nelson, N. and Chi, S. and Keyhani, N. O. and Katz, J. and Datta, S. P. A. and Gomes, C. and McLamore, E. S.}, year={2018}, month={May}, pages={2066–2075} }
 @article{nelson_munoz-carpena_phlips_kaplan_sucsy_hendrickson_2018, title={Revealing Biotic and Abiotic Controls of Harmful Algal Blooms in a Shallow Subtropical Lake through Statistical Machine Learning}, volume={52}, ISSN={["1520-5851"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85044223867&partnerID=MN8TOARS}, DOI={10.1021/acs.est.7b05884}, abstractNote={Harmful algal blooms are a growing human and environmental health hazard globally. Eco-physiological diversity of the cyanobacteria genera that make up these blooms creates challenges for water managers tasked with controlling the intensity and frequency of blooms, particularly of harmful taxa (e.g., toxin producers, N2 fixers). Compounding these challenges is the ongoing debate over the efficacy of nutrient management strategies (phosphorus-only versus nitrogen and phosphorus), which increases decision-making uncertainty. To improve our understanding of how different cyanobacteria respond to nutrient levels and other biophysical factors, we analyzed a unique 17 year data set comprising monthly observations of cyanobacteria genera and zooplankton abundances, water quality, and flow in a bloom-impacted, subtropical, flow-through lake in Florida (United States). Using the Random Forests machine learning algorithm, an ensemble modeling approach, we characterized and quantified relationships among environmental conditions and five dominant cyanobacteria genera. Results highlighted nonlinear relationships and critical thresholds between cyanobacteria genera and environmental covariates, the potential for hydrology and temperature to limit the efficacy of cyanobacteria bloom management actions, and the importance of a dual nutrient management strategy for reducing bloom risk in the long term.}, number={6}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, author={Nelson, Natalie G. and Munoz-Carpena, Rafael and Phlips, Edward J. and Kaplan, David and Sucsy, Peter and Hendrickson, John}, year={2018}, month={Mar}, pages={3527–3535} }
 @article{a novel quantile method reveals spatiotemporal shifts in phytoplankton biomass descriptors between bloom and non-bloom conditions in a subtropical estuary_2017, volume={567}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85020414792&partnerID=MN8TOARS}, DOI={10.3354/meps12054}, abstractNote={MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 567:57-78 (2017) - DOI: https://doi.org/10.3354/meps12054 A novel quantile method reveals spatiotemporal shifts in phytoplankton biomass descriptors between bloom and non-bloom conditions in a subtropical estuary Natalie G. Nelson1, Rafael Muñoz-Carpena1,*, Edward J. Phlips2 1Hydrology & Water Quality, Agricultural & Biological Engineering, University of Florida, Gainesville, Florida 32611, USA 2Fisheries & Aquatic Sciences, School of Forest Resources & Conservation, University of Florida, Gainesville, Florida 32653, USA *Corresponding author: carpena@ufl.edu ABSTRACT: Estuarine environments support dynamic phytoplankton blooms, especially in low-latitude regions, where the effects of local drivers dominate. Identifying key bloom drivers from entangled ecological and anthropogenic influences is particularly challenging in stressed systems where several disturbances interact. Additionally, processes controlling bloom and non-bloom phytoplankton biomass dynamics can differ spatially, further confounding characterization of disturbance regimes that create bloom-favorable conditions. This study aims to explore the question of whether the shift from non-bloom to bloom conditions is matched by a shift in the relative importance of water quality drivers. Florida Bay (USA), a shallow subtropical inner shelf lagoon, was chosen as the study site due to its unique bloom dynamics and low-latitude location, as well as for the availability of long-term (16 yr) water quality data consisting of monthly measurements from 28 locations across the 2200 km2 bay. At each of the locations, we applied a novel threshold-based quantile regression analysis to chlorophyll a data to define bloom conditions, separate data from non-bloom conditions, and evaluate phytoplankton biomass dynamics of each of the 2 states. The final suite of explanatory covariates revealed spatial trends and differences in the relative importance of water quality descriptors of phytoplankton between the 2 conditions. The effects of turbidity and salinity on phytoplankton biomass became pronounced during blooms, whereas non-bloom conditions were primarily explained by autoregressive phytoplankton biomass trends and nutrient dynamics. The proposed analytical approach is not limited to any particular aquatic system type, and can be used to produce practical spatiotemporal information to guide management, restoration, and conservation efforts. KEY WORDS: Quantile regression · Time series modeling · Spatial patterns · Cyanobacteria · Florida Bay Full text in pdf format Supplement 1Supplement 2(.zip) PreviousNextCite this article as: Nelson NG, Muñoz-Carpena R, Phlips EJ (2017) A novel quantile method reveals spatiotemporal shifts in phytoplankton biomass descriptors between bloom and non-bloom conditions in a subtropical estuary. Mar Ecol Prog Ser 567:57-78. https://doi.org/10.3354/meps12054 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 567. Online publication date: March 13, 2017 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2017 Inter-Research.}, journal={Marine Ecology Progress Series}, year={2017}, month={Mar}, pages={57–78} }
 @article{nelson_muñoz-carpena_neale_tzortziou_megonigal_2017, title={Temporal variability in the importance of hydrologic, biotic, and climatic descriptors of dissolved oxygen dynamics in a shallow tidal-marsh creek}, volume={53}, ISSN={0043-1397}, url={http://dx.doi.org/10.1002/2016WR020196}, DOI={10.1002/2016WR020196}, abstractNote={Abstract}, number={8}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Nelson, Natalie G. and Muñoz-Carpena, Rafael and Neale, Patrick J. and Tzortziou, Maria and Megonigal, J. Patrick}, year={2017}, month={Aug}, pages={7103–7120} }
 @article{smith_nelson_oommen_hartjes_folmes_terzic_nelson_2012, title={Apoptotic susceptibility to DNA damage of pluripotent stem cells facilitates pharmacologic purging of teratoma risk}, volume={1}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84876510977&partnerID=MN8TOARS}, DOI={10.5966/sctm.2012-0066}, abstractNote={Abstract}, number={10}, journal={Stem Cells Translational Medicine}, author={Smith, A.J. and Nelson, N.G. and Oommen, S. and Hartjes, K.A. and Folmes, C.D. and Terzic, A. and Nelson, T.J.}, year={2012}, pages={709–718} }