Geology topics

Michael D.W. Stouder

Publications and source records attributed to Michael D.W. Stouder.

6 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↗

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

Spatiotemporal variability of algal biomass and nitrate in Owasco and Seneca Lakes in the Finger Lakes Region, New York, in 2019

Cyanobacterial harmful algal blooms (CyanoHABs) have become increasingly common, threatening the security of water resources globally. The U.S. Geological Survey conducted high-resolution nearshore mapping surveys using boat-mounted multiparameter sondes and nitrate sensors during the summer and fall of 2019 on Owasco Lake and Seneca Lake, two lakes with documented CyanoHABs in the Finger Lakes region of New York State. Discrete sensor measurements and water-quality samples were collected at fixed points along survey routes and continuous data were generated at open-water monitoring platforms. This investigation examined whether water-quality information from nearshore surveys was representative of open-water conditions and if nearshore surveys could be used to identify areas with localized nearshore CyanoHABs and potential sources of nutrients not captured by tributary sampling. In addition to comparisons across methods, nearshore concentrations of nitrate and chlorophyll were evaluated relative to tributary outlets, cyanobacterial abundance and biovolume at discrete locations, and the locations of near-surface CyanoHABs that were designated as “confirmed with high toxins” by the New York State Department of Environmental Conservation. Nitrate and chlorophyll concentrations were comparable across methods for each lake, although concentration ranges were typically higher for nearshore mapping datasets than for nearshore discrete datasets. Nearshore surveys indicated areas of nitrate enrichment that varied temporally in both lakes. Orthophosphate was not routinely detected. Across methods, median chlorophyll concentrations were higher for the summer survey than for the fall survey in Owasco Lake. Nearshore chlorophyll concentrations varied more temporally in Owasco Lake than in Seneca Lake. Phytoplankton and cyanobacterial abundance and biovolume were about five times higher in Owasco Lake than in Seneca Lake. Cyanobacteria dominated the phytoplankton community in most samples, and Microcystis comprised the bulk of the cyanobacterial biovolume. The most abundant potential cyanotoxin-producing (specifically microcystins) genera were Microcystis , Synechococcus , Aphanocapsa , and Pseudanabaena . The cyanobacterial community composition was comparable between open-water monitoring platforms and nearshore samples. Microcystins were detected in seven survey samples from Owasco Lake, in one survey sample from Seneca Lake, and in one sample each from the open-water monitoring platforms on Owasco and Seneca Lakes that were collected about 7 days before the fall surveys. Microcystin detections were not consistently associated with high cyanobacterial cell counts or cyanotoxin-producing genera. Results from nearshore surveys were comparable to open-water monitoring platforms and discrete nearshore observations in the absence of nearshore or open-water CyanoHABs in these systems during the study. Patterns of nearshore concentrations of nitrate and chlorophyll from nearshore surveys may aid in the identification of areas with localized nitrate loading and shifts in phytoplankton abundance and community composition.

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↗

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↗

USGS2024

Evaluation of sensors for continuous monitoring of harmful algal blooms in the Finger Lakes region, New York, 2019 and 2020

In response to the increasing frequency of cyanobacterial harmful algal blooms (CyanoHABs) in the Finger Lakes region of New York State, a pilot study by the U.S. Geological Survey, in collaboration with the New York State Department of Environmental Conservation, was conducted to enhance CyanoHAB monitoring and understanding. High-frequency sensors were deployed on open water monitoring-station platforms at Seneca Lake in 2019–20, at Owasco Lake in 2019–20, and at Skaneateles Lake in 2019. One of the goals of this study was to evaluate the ability of in-place sensors to make representative measurements of dissolved organic matter, nutrients, and algal pigments (as indicators of phytoplankton biomass) while collecting routine field parameters (water temperature, specific conductance, pH, dissolved oxygen, turbidity, weather, and light) to provide additional information about environmental conditions. Despite challenges like power issues and sensor fouling, the sensors performed well overall. However, correlation analyses between sensor readings and laboratory measurements revealed variable performance. Results indicate the relation between the fluorescent dissolved organic matter sensor and laboratory-measured dissolved organic carbon was weak at all study lakes. The nitrate sensors can be sensitive to ambient temperature and have a substantial power requirement, and the relation between sensor- and laboratory-measured nitrate values differed among lakes. The orthophosphate sensors, which were complex and prone to data loss, yielded results that were difficult to interpret because orthophosphate detections are rare in the study lakes. The multichannel fluorometer was also complex to use and required several unique procedures for its operation. Chlorophyll measurements from the fluorometers correlated moderately well with laboratory-measured chlorophyll- a , although relations with total phytoplankton biovolume were weaker. Relations between phycocyanin concentration measurements from the dual-channel fluorometers and cyanobacterial biovolume were not significant; however, the cyanobacterial biovolume correlation was moderately strong with chlorophyll contribution from cyanobacteria measurements from the multichannel fluorometer. Of all collected parameters, water temperature was among the strongest correlated with chlorophyll- a , total phytoplankton biovolume, and cyanobacterial biovolume. Stepwise regression analysis was used to identify the best parameters for modeling variance in laboratory measures of phytoplankton biomass. This analysis included factors such as chlorophyll fluorescence, pH, water temperature, and others, which varied by lake. Overall, the models had limited explanatory power for chlorophyll- a and other biovolumes, possibly due to the absence of CyanoHABs at the open-water monitoring locations. Multivariate models did not outperform simple fluorescence-based models. Notably, turbidity was a more significant indicator of cyanobacterial biovolume variability than phycocyanin from dual-channel fluorometers. The study concludes that while single and multivariate models based on sensor data are useful, they did not explain any more variance than fluorescence-based models. Broader data collection, including more CyanoHAB events, is necessary to refine these models. Integrating machine learning could leverage large, complex datasets to improve CyanoHAB predictions, thereby enhancing the management and understanding of these blooms.

New York↗