Geology topics

Rebecca M. Gorney

Publications and source records attributed to Rebecca M. Gorney.

11 recordsLinked to original sources

USGS2026

Opportunities and challenges in using Solid Phase Adsorption Toxin Tracking (SPATT) samplers for monitoring cyanotoxins in freshwater and estuarine environments

Cyanobacterial toxins (cyanotoxins) represent a substantial threat to drinking water supplies and safe recreational uses of freshwater resources in watersheds worldwide. Monitoring cyanotoxins can be difficult because toxin events are variable in both space and time, are not always persistent, can be moved easily by wind and currents, and may be degraded biotically or abiotically. Thus, monitoring programs that collect discrete samples on a monthly or even bimonthly interval can miss key events and underestimate cyanotoxin risk or if they capture a high-concentration event, can give a false impression that cyanotoxins are a widespread health hazard. The use of Solid Phase Adsorption Toxin Tracking (SPATT) samplers helps address this issue by providing a time-weighted average estimate of dissolved cyanotoxin occurrence and relative concentrations. SPATT samplers have been used as a complement to traditional monitoring programs and can help elucidate cyanotoxin dynamics. SPATT samplers have been used by six U.S. Geological Survey (USGS) Water Science Centers (New York, California, Oregon, Upper Midwest, New Jersey, and Lower Mississippi-Gulf) to monitor various cyanotoxins in waterbodies such as streams, rivers, lakes, waterfalls, estuaries, and drinking-water intakes. Despite their use across the USGS, there is little guidance available to ensure consistent approaches and data quality across the Bureau. This report summarizes best practices for SPATT deployment and analysis, synthesizes data and describes lessons learned from USGS studies, identifies priority knowledge gaps, and offers considerations for future targeted experiments to help improve data collection and interpretation.

Scientific Investigations Report↗

USGS2026

Occurrence of cyanobacteria and associated cyanotoxins in the Raritan Basin Water Supply Complex, New Jersey, August 2020 to August 2021

Harmful algal blooms, particularly cyanobacteria harmful algal blooms (cyanoHABs), have emerged as a substantial global concern because of their detrimental effects on water quality and aquatic ecosystem health. CyanoHABs can produce cyanotoxins, which pose serious health risks to humans and wildlife, such as liver failure and respiratory distress. This is particularly concerning for water bodies that serve as drinking-water sources. Recent trends indicate an increase in the frequency and intensity of cyanoHABs globally. This study focuses on the Raritan Basin Water Supply Complex in New Jersey, where extensive monitoring was conducted from August 2020 to August 2021 to assess the presence of cyanobacteria and associated cyanotoxins. The research utilized a combination of discrete water-quality sampling, continuous monitoring, and solid phase adsorption toxin tracking (SPATT) to capture the dynamics of cyanotoxin occurrence and potential transport. Findings revealed a widespread presence of cyanobacteria and potential for cyanotoxin production, although actual cyanotoxin concentrations remained below drinking water and recreational thresholds. The study, conducted by the U.S. Geological Survey (USGS) in collaboration with the New Jersey Water Supply Authority (NJWSA) and the New Jersey Department of Environmental Protection (NJDEP), highlighted the limitations of traditional sampling methods and emphasized that continuous monitoring can support better understanding of how cyanoHAB conditions change over time and in different places. Genetic testing included quantitative polymerase chain reaction (qPCR) analyses, which demonstrated higher sensitivity, or increased findings of cyanobacteria compared to microscopy, indicating the potential for use in early warning systems. This research underscores that integrating various detection methods and hydrological data can enhance understanding of cyanotoxin dynamics in river systems.

New Jersey↗

USGS2026

Fifty years of riverine harmful algal bloom modeling: A global synthesis of approaches, challenges, and opportunities

This systematic literature review critically examines 162 articles on harmful algal bloom (HAB) modeling in riverine systems to uncover persistent gaps, redefine critical challenges, and propose trackable opportunities to advance future modeling efforts. Articles largely focused on site-specific applications (93%) across more than 80 rivers worldwide. Most modeled systems were large, eutrophic rivers with flow modifications or obstructions. Geographic clustering of modeled systems was pronounced, with South Korea accounting for 26% of articles, followed by Europe (25%), United States (21%), and China (12%). Modeling approaches were led by process-based models (59%), though use of data-driven models (37%) increased over time, reflecting advances in computing and monitoring technology. Modeling endpoints varied widely across the articles with many focused on gross measures of algal abundance and fewer representing more refined endpoints like algal toxins or community composition. Furthermore, inconsistent units and taxonomic resolution hindered comparability between models. Datasets used for model development and calibration typically spanned 5 years, with weekly to monthly sampling at 1–10 sites, though durations and site counts were positively skewed. Quantitative metrics of model skill were often absent and included a diverse set of metrics when reported. Across all models, nutrients, light availability, streamflow, algal physiological processes, and water temperature emerged as key predictors, though algal processes were rarely incorporated in data-driven models. Scenario analyses primarily were conducted with process-based models and addressed flow management, whereas forecasting applications were less common and typically used data-driven models. After almost 50 years of riverine HAB modeling, persistent challenges include underrepresentation of benthic habitats, neglect of side-channel and backwater influences, insufficient documentation of river features, and weak linkages between modeled endpoints and potential harms. Addressing these gaps through reporting of contextual information, models from other aquatic settings, benchmark datasets, and community-driven tools could advance riverine HAB modeling towards increased transferability and ultimately operational forecasts.

Water Research↗

USGS2025

Bacterial community diversity and potential eco-physiological roles in toxigenic blooms composed of Microcystis, Aphanizomenon or Planktothrix

Cyanobacterial toxicity, cyanotoxins, and their impact on aquatic ecosystems and human health are well documented. In comparison, less is known about bloom-associated bacterial communities. Co-occurring bacteria can influence bloom development, physiology and collapse, and may also provide a niche for pathogenic bacteria. Existing research focuses on the cyanosphere of Microcystis -dominated blooms, despite the increasing prevalence of filamentous genera ( Aphanizomenon and Planktothrix ). This pilot study aimed to broaden our understanding of the bacterial consortia attached to morphologically distinct cyanobacteria (coccoid and filamentous) dominating phytoplankton communities and to explore their potential roles in amplifying the impacts of cyanobacterial blooms. We investigated four shallow freshwater bodies across three continents and two climate zones: an urban pond in the USA, a dammed reservoir and a natural lake in Poland, and an urban water body in Singapore. Amplicon sequencing (16S rRNA gene) was used to characterize bacterial communities, while shotgun metagenomics identified nitrogen- and phosphorus-cycling genes to infer potential eco-physiological functions. Cyanobacteria dominated bacterioplankton assemblages at all sites (>35.6%), with bloom composition influencing toxigenic profiles. A mixed bloom of Microcystis , Snowella , and Aphanizomenon had the broadest range of cyanotoxin synthetase genes ( mcy E, cyr J, ana F and sxt A). Microcystis blooms correlated with increased Roseomonas , while Planktothrix co-occurred with Flavobacterium – both bacteria likely contribute to nutrient-cycling within blooms and represent potential opportunistic pathogens for aquatic organisms and humans. The Microcystis cyanosphere exhibited the highest number of significant positive correlations with bacteria (19 relations), compared to Planktothrix and Aphanizomenon (11 and 2 relations, respectively). Non-diazotrophic blooms of Microcystis and Planktothrix showed greater abundances of nitrogen – ( ure B, gln A, nar B, and nar HZ) and phosphorus-cycling genes ( pho BHPR and ppk 1), indicating a strong dependence on associated bacteria for nutrient acquisition compared to diazotrophic Aphanizomenon . These findings suggest that Aphanizomenon -dominated blooms may be sustained by simpler microbiomes. Our results provide preliminary evidence of cyanosphere heterogeneity potentially shaped by the dominance or coexistence of three morphologically and eco-physiologically distinct genera of cyanobacteria. A comprehensive knowledge of the taxonomy and functional roles of bloom-associated microbiomes is therefore essential to understand bloom activity, evaluate the environmental threat, and develop effective strategies for prevention and mitigation.

Frontiers in Microbiology↗

USGS2025

From sample to sonde to Sentinel-2: Insights from a multi-scale chlorophyll-a monitoring effort in the Hudson River, New York

Monitoring cyanobacteria and other nuisance phytoplankton in the Hudson River is of great interest given its societal and ecological importance. Satellite remote sensing provides a cost-effective method to monitor chlorophyll- a (chl-a), a common proxy for algal biomass; however, the dynamic nature of rivers complicates approaches traditionally applied to lakes and oceans. During 2021–2023, we collected discrete samples for laboratory measurement of chl-a and measured in situ chl-a fluorescence during a series of longitudinal boat surveys along a 220-km reach of the lower Hudson River. Surveys were timed to coincide with Sentinel-2 satellite overpasses. We first investigated relations between laboratory-measured chl-a concentration and field-measured chl-a fluorescence, observing a weak correlation ( r 2 = 0.25) that improved substantially after splitting data by day (mean r 2 = 0.53). Separately, to estimate chl-a fluorescence using satellite data, we developed a series of random forest models leveraging the rich fluorescence dataset collected. We tested three model types: individual day models, leave-one-out models trained on all days except a holdout test day, and a single pooled model trained on all days. Generally, individual day models exhibited lowest error (mean of mean absolute error [MAE] = 0.16 relative fluorescence units [RFU]), followed by the single pooled model (MAE = 0.22 RFU). Daily holdout models showed highest error (mean MAE = 0.40 RFU); this approach was intended to represent model performance on a day unseen in the training set, providing a more conservative estimate of performance than the more traditional pooled approach. Findings from both analyses emphasize the importance of considering temporal variability when modeling riverine systems.

New Jersey, New York↗

USGS2025

Evaluation of passive samplers for cyanotoxin detection by immunoassay and chromatographic-mass spectrometry

Harmful algal blooms, particularly cyanobacterial harmful algal blooms, threaten aquatic ecosystems, drinking water supplies, and recreational resources. In 2019, the U.S. Geological Survey, in collaboration with the New York State Department of Environmental Conservation, deployed solid phase adsorption toxin tracking (SPATT) samplers in Seneca Lake, Owasco Lake, and Skaneateles Lake to monitor the cyanotoxins microcystins, cylindrospermopsins, anatoxins, and saxitoxins. SPATT samplers can passively adsorb dissolved cyanotoxins over time, providing time-integrated data capable of detecting low concentrations of cyanotoxins that traditional discrete sampling may miss. SPATT samples were analyzed using enzyme-linked immunosorbent assay (ELISA), liquid chromatography with mass spectrometry (LC–MS), and with tandem mass spectrometry (LC–MS/MS). The effects of ELISA-required preservative on measurements by mass spectrometry methods were also evaluated. SPATT samplers consistently detected microcystins and anatoxins more frequently than concurrent discrete sampling. ELISA results often showed higher cyanotoxin concentrations than LC–MS/MS, likely due to interference from dissolved organic matter and the ability of ELISA to detect a broader range of congeners. The addition of preservative influenced results for some analytes, particularly microcystins, which showed higher concentrations in preserved samples. Limitations in ELISA methods for cylindrospermopsins and saxitoxins were identified, potentially related to cross-reactivity, low sensitivity, or other matrix interferences. This study demonstrates the utility of SPATT samplers in capturing cyanotoxin variability, especially in environments with low cyanotoxin levels or ephemeral blooms. Further research could help improve the reliability of ELISA and other analytical methods in freshwater ecosystems.

New York↗

USGS2025

Estimating indicators of cyanobacterial harmful algal blooms in New York State

Cyanobacteria harmful algal blooms (cyanoHABs) are a global concern for aquatic ecosystem and human health. Limited funding for monitoring programs and inconsistent determination of cyanoHAB occurrence present challenges for identifying commonly effective variables for characterizing cyanoHABs and the development of generalized models. We compiled a combination of water quality measurements, lake morphology, climatology, remote sensing data, and observations of cyanoHAB occurrence across New York State and used this dataset to develop two sets of predictive models. The first model predicted chlorophyll a , a common indicator of algal biomass, and assessed the importance of variables for modeled predictions. The most important variables were then used in a second set of models to classify cyanoHAB occurrence. The irradiance attenuation coefficient ( K d ), which was estimated from Secchi depth measurements, and total phosphorus were the two most important variables for predicting chlorophyll a . The second model examined several variables for their ability to classify cyanoHAB occurrence. Predicted cyanoHAB occurrence based on thresholds of chlorophyll a , K d , or total nitrogen all had moderate agreement and were able to correctly classify approximately 70% of observed cyanoHABs. Our analysis indicated that multiple data types were important for predicting chlorophyll a statewide and that simple widely available water quality parameters could classify cyanoHABs occurrence with reasonable accuracy. Identifying variables that can be monitored with increased frequency and decreased latency to detect cyanoHAB occurrence will better inform water managers and provide valuable additional data for further refining predictive models of the likelihood of cyanoHABs occurrence.

New York↗

USGS2025

A systematic literature review of forecasting and predictive models of harmful algal blooms in flowing waters

Occurrences of harmful algal blooms (HABs) in rivers challenge the belief that rivers are not susceptible to HABs because of their short residence times and fluctuating hydrology. Here we present a systematic literature review of predictive and forecasting models for HABs in flowing waters, including rivers, flowing in-stream reservoirs (e.g., run-of-river reservoirs and lock-and-dam systems) and tidal or estuarine systems with riverine processes. The review aimed to understand current and historical modeling approaches for predicting and forecasting river HABs, without restricting to specific taxa, such as cyanobacteria, or modeling endpoints. The review included 162 articles published over nearly 50 years, covering more than 80 rivers worldwide. Eutrophic, non-wadable rivers with in-stream obstruction were commonly modeled, though diverse environmental characteristics were reported. Most articles used algal biomass or chlorophyll as modeling endpoints, with a quarter using novel or unique endpoints. Algal toxins motivated model development in 23% of the articles, however just 5% used algal toxins as an endpoint. Only 6% of the articles modeled benthic HABs; the rest focused on pelagic HABs. There was no standard model used for modeling river HABs. Process-based models were more common (59%) than data-driven approaches (37%), with model formulations ranging from simple to complex, which contrasts with a lake-focused literature review of HAB models that found data-driven models were more common. Models in river settings shared similar input variables as those previously identified for lakes, such as water temperature, nutrients, and light availability. However, streamflow and other transport metrics took prominence in river models compared to lake models. Algal cell physiology (such as growth, predation, and motility) was routinely included as input data or as mathematical formulations in process-based models and these processes were frequently identified as an important predictor by the articles’ authors. Conversely, data-driven models rarely included these processes, instead using predictors related to environmental conditions, such as nutrients, water quality, water temperature, and streamflow. These important proxy predictors have apparent success with modeling overall algal biomass (irrespective of taxa) whereas other factors, such as those related to algal physiology and other biological processes, are likely responsible for more subtle shifts in community composition. These differences highlight the influence of data availability, especially for processes that are difficult, time-consuming, or expensive to measure, on model development and model outcomes, raising questions about the selection of modeling inputs and endpoints. Challenges to advancing river HAB modeling include the lack of site-specific model inputs representing key processes (e.g., photosynthetic parameters and predation rates), overlooked riverine environments like the benthos and side/back-channel areas, lack of information on environmental settings, and poorly reported model performance metrics. This review emphasizes opportunities for advancing river HAB modeling by learning from well-honed estuarine models, supporting current forecasting and operationalization efforts, and developing common datasets for river HAB model development and evaluation.

BioRxiv↗

USGS2024

An evaluation of cyanobacterial occurrence and bloom development in Adirondack lakes

Cyanobacterial harmful algal blooms (cyanoHABs) have occurred in many low nutrient (oligotrophic) lakes in the northeastern United States. The Adirondack Park in New York is a large, mountainous region with many low nutrient lakes. There is a gap in understanding regarding whether cyanoHAB reporting data are truly reflective of the susceptibility of lakes to develop bloom conditions. We evaluated lakes with and without documented cyanoHABs for cyanotoxin synthetase gene quantification, phytoplankton community composition, and akinete abundance to identify conditions associated with the observation of cyanoHABs. We analyzed: (1) contributions of cyanobacteria to the overall phytoplankton community; (2) differences in cyanobacterial communities and the presence of cyanotoxin synthetase genes; and (3) lake physical and geomorphological attributes as drivers of differences in cyanobacteria occurrence. Two sample types (water and sediment) were collected from two sample locations (nearshore and open water) in five lakes in 2021. We found cyanobacteria in all lakes and sample locations. Phytoplankton biovolume and cyanotoxin synthetase genes differed among lakes and by cyanoHAB history. Samples from lakes with documented blooms were associated with marginally higher total phosphorus. Non-metric multidimensional scaling was used to identify which environmental factors influenced community structure. Our study demonstrates the importance of multifaceted approaches to detect cyanobacteria that may only be apparent during ephemeral bloom events and the similarities among lakes with and without a history of bloom reports. This work contributes to a better understanding of cyanoHAB occurrence in Adirondack lakes, and conditions that may cause low nutrient lakes to be susceptible to cyanoHABs.

New York↗

USGS2023

The "H," "A," and "B" of a HAB: A definitional framework

The use of the phrase “harmful algal bloom” and the acronym HAB originated in the marine science world, and referred to blooms also known as red tides, which can kill fish and sea life. The organisms that make up marine HABs generally do not thrive in lakes. In freshwater, HABs are most often associated with blooms of toxin-producing cyanobacteria. The term HAB started to be used broadly in the early 2000s to encompass both marine and freshwater phenomena. Beyond just lakes, cyanobacterial blooms occur in reservoirs, impoundments, streams, rivers, estuaries, or brackish water all over the world (Meriluoto 2017). In addition to cyanobacteria, other freshwater algal groups can accumulate and lead to detrimental impacts on humans, animals, the environment, and the economy.

Lakeline↗

USGS2022

A structured decision-making framework for managing cyanobacterial harmful algal blooms in New York State parks

Cyanobacteria are increasingly a global water-quality concern because of the potential for these organisms to develop into potentially harmful blooms that affect ecological, economic, and public health. Cyanobacterial harmful algal blooms (CyanoHABs) can lead to a decrease in water quality and affect many of the recreational and ecological benefits of parks that include lakes. The New York State Office of Parks, Recreation and Historic Preservation (OPRHP) is a State agency within the New York State Executive Department charged with the operation of State parks and historic sites. Many New York State parks include lakes or other freshwater bodies, which can be susceptible to CyanoHABs. The OPRHP faces difficult decisions regarding prevention of and response to CyanoHABs. The U.S. Geological Survey partnered with the OPRHP and the New York State Department of Environmental Conservation to develop a structured decision-making template for managing CyanoHABs in OPRHP parks. Two parks, Moreau Lake State Park and Rockland Lake State Park, served as case studies to motivate and test the template. This report describes how the principles of structured decision making can be used to navigate the challenges associated with managing CyanoHABs in OPRHP parks. Management objectives and strategies for CyanoHABs in parks are described, strategies to evaluate consequences and manage tradeoffs are discussed, and potential challenges to the implementation of preferred alternatives are considered. General guidance is provided so the OPRHP can undertake the structured decision-making process for CyanoHABs in any of its parks. In addition, this report represents the first effort to create a strategy for applying decision analysis tools to the complex natural resource challenge of CyanoHAB mitigation and management. The case studies and template are intended to serve as an example that natural resource managers faced with CyanoHABs challenges can use to inform their decision-making processes.

New York↗