Geology topics

Jennifer L. Graham

Publications and source records attributed to Jennifer L. Graham.

At least 19 recordsLinked to original sources

USGS2026

Field sampling guidelines for developing and verifying satellite remote sensing chlorophyll a concentration and fluorescence models in inland waters

Harmful algal blooms are increasing in frequency in inland waters across the United States, resulting in a need to monitor phytoplankton bloom events to track ecosystem health and productivity. Remote sensing of chlorophyll a values offers a cost-effective and powerful method for early detection and characterization of bloom events and serves as an overall indicator of water quality and trophic state, with regular, repeated sampling of landscape-wide, high spatial resolution measurements. Field measurements are necessary for developing and verifying chlorophyll a retrieval models. For model verification, chlorophyll a concentration or fluorescence and light attenuation measurements are needed; for model development, turbidity and colored dissolved organic matter concentration measurements are additionally needed; and for model development and verification, radiometric measurements, taxonomic identification of phytoplankton, inherent optical properties, and cyanotoxin concentration are further measurements that can provide context. This report outlines detailed methods and priority considerations for collecting high-quality field data in inland waters (defined as rivers, lakes, reservoirs, estuaries, streams, and wetlands). The described methods include best practices for collecting and preparing discretely collected water samples and for calibration, maintenance, and quality assurance and quality control of field sensors. Whereas the priorities will vary between applications, some general guidelines are to collect field samples (1) as close in time to a satellite overpass as possible, (2) from representative areas of the waterbody to capture the range of spatial variability, and (3) near the surface to match remote sensing reflectance data.

Techniques and Methods↗

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

Using quantitative polymerase chain reaction to assess phytoplankton and indicate eutrophication in freshwater rivers: A multiyear nationwide study across the United States

Phytoplankton are essential primary producers in fresh surface water that are critical to the health of ecosystems. However, phytoplankton overgrowth due to eutrophication threatens ecological, economic, and public health. Therefore, assessing phytoplankton is fundamental for understanding the productivity, health, and trophic status of freshwater ecosystems. Light microscopy and chlorophyll a assessment are common approaches for studying phytoplankton. They are easy to use, cost-effective, and reliable but have significant limitations. Microscopy has a low throughput and is time-consuming and labor-intensive. Chlorophyll a assessment does not reveal phytoplankton community composition and structure. For comparison, quantitative polymerase chain reaction (qPCR) is widely applied in quantifying microorganisms, offering multiple advantages, including high throughput, sensitivity, accuracy, and robustness. However, a research gap remains regarding the feasibility of using qPCR to assess phytoplankton and indicate trophic status of freshwater bodies. We conducted a nationwide, multiyear study in the United States to compare the performance of qPCR, microscopy, and chlorophyll a assessment in assessing phytoplankton and trophic statuses of multiple freshwater rivers. From early summer to late fall in 2017, 2018, and 2019, we assessed phytoplankton, chlorophyll a , pheophytin a , and the overall Trophic Level Index ( TLI Overall ) at the sampling sites in 12 large freshwater rivers in three regions (western, midcontinent, and eastern) across the United States. The seasonal summed abundance of four major phytoplankton taxa [Bacillariophyta (diatoms), Cyanobacteria (blue-green algae), Chlorophyta (green algae), and Dinoflagellates (Dinophyta)] ranged from 6.88 log 10 (GCN·L –1 ) (the Connecticut River, 2017) to 9.29 log 10 (GCN·L –1 ) (the Kansas River, 2019) (GCN: gene or genome copy number). qPCR- and microscopy-based phytoplankton abundance of eight phytoplankton taxa had a significant positive allometric or log-linear correlation (adjusted R 2 = 0.836, p -value < 0.001, n = 815). In addition, qPCR-based phytoplankton abundance had positive allometric or log-linear correlations with chlorophyll a (adjusted R 2 = 0.5437, p -value < 0.001, n = 164), pheophytin a (adjusted R 2 = 0.3378, p -value < 0.001, n = 164), and TLI Overall (adjusted R 2 = 0.4789, p -value < 0.001, n = 164). Therefore, qPCR is a promising alternative to microscopy and chlorophyll a for studying phytoplankton and trophic status in freshwater rivers. Moreover, phytoplankton abundance had limited temporal variation within each sampling season and over the three sampling seasons in 2017, 2018, and 2019 but showed clear spatial variation. The midcontinent sites had significantly higher phytoplankton abundance, chlorophyll a concentrations, pheophytin a concentrations, and TLI Overall values than those in the eastern and western rivers, reflecting the higher trophic statuses of the midcontinent rivers. This work also provides the thresholds of qPCR-based phytoplankton abundance for delineating trophic statuses in freshwater rivers. Overall, this work demonstrates that qPCR is a promising tool for studying phytoplankton and characterizing the trophic status of freshwater rivers.

Book chapter↗

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

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↗

USGS2024

Comparison of imaging flow cytometry and microscopy for freshwater algal bloom detection

Imaging flow cytometry (IFC) is an emerging tool that allows for rapid identification and enumeration of phytoplankton in freshwater systems. However, few studies have assessed the effects of preservation on IFC results or compared live IFC and microscopy results in freshwater systems. Understanding the effects of preservation and differences between IFC and microscopy will improve interpretation of these data and inform strategies to use IFC-based approaches in freshwater systems. Our study objectives were to compare IFC and phase contrast with epifluorescence microscopy as techniques for phytoplankton identification and enumeration, and the effects of sample preservation with an emphasis on taxa forming harmful cyanobacterial blooms (HCBs). During June through October 2020, samples were collected from 2 lakes in the Finger Lakes region of New York. Live and preserved samples were analyzed by laboratory-based IFC, and preserved samples were analyzed by microscopy. The IFC approach captured community dynamics while detecting potential cyanobacterial bloom-forming taxa earlier and at lower abundances than microscopy. Laboratory-based IFC allowed for an intermediate level of taxonomic information when compared to microscopy, gross techniques, such as extracted chlorophyll a or fluorescence sensors, and field-based operation of IFC approaches. The laboratory-based application of IFC in this study allowed receipt of results in 5 d or less, a substantial improvement over microscopy, which can be time-consuming to conduct. However, the laboratory-based IFC approach had some limitations. Imaging flow cytometry-estimated biovolume may be less accurate than microscopy for some taxa because of the algorithms used to calculate biovolume, particularly for chrysophytes and coccoid cyanobacteria. Colonial dissociation during preservation appeared to affect detection of Microcystis by IFC less than for other fragile bloom-forming taxa like chrysophytes. Our study results advance understanding of how IFC may translate to field-based approaches for early harmful algal bloom indicators in freshwater.

Lake and Reservoir Management↗

USGS2024

Observing systems, modeling, and forecasting

Predicting harmful algal blooms (HABs) requires integrating physical, chemical, and biological data collected from observing networks and then assimilating these data into models, which are used to generate forecasts. In 2005, the Harmful Algal Research and Response: A National Environmental Science Strategy 2005-2015 (HARRNESS, 2005) made recommendations on how to improve HAB modeling and forecasting over the next decade. Key HARRNESS recommendations related to sensing, networking, and modeling HABs included: ● Support the development and validation of new and improved technologies for remote cell and toxin detection, and for modeling and forecasting, ● Improve coordination of monitoring/ and modeling efforts, both at national and regional levels, ● Improve the use of networking technologies for monitoring and modeling efforts, ● Conduct sustained time series measurements of the biotic, chemical, and physical environments impacted by HABs, ● Develop food web models on the ecosystem fate and effects of toxins, ● Develop and improve species-specific models that link to physical-biological models. Here we review HAB observing, modeling, and forecasting advances and technologies and recommend research and management priorities for the next decade and beyond. Our report encompasses sensing technologies, sensor networking and data management, models and forecasts, and the paths to operationalize forecasts. Continued improvements of deployable sensors are foundational to improving early warning indicators, models, and forecasts, which are only as good as the underlying data. Sensing technology has advanced considerably in the last decade; for example, more capable fluorometric pigment sensors can track changes in bloom biomass in real-time. Additionally, automated imaging/classifying systems to identify and quantify key harmful algal (HA) taxa are being routinely deployed. However, deployable toxin sensors are available for only some HAB toxins and continue to be identified as a critical need by researchers and managers. As more and improved sensors and technologies become available, the data quality associated with each sensor needs to be assessed. Data quality encompasses the reliability, accuracy, and uncertainty associated with sensor-generated data. These data need to be of known quality so that researchers, managers, and end-users can reliably determine if the information is appropriate for their intended applications. Many of the data quality recommendations from HARRNESS (2005) are still relevant and have been reiterated within the management community. Understanding and documenting data quality, and when applicable, standardizing best practices for sensor use, continue to be recommended.

Report↗

USGS2024

Interdisciplinary science approach for harmful algal blooms (HABs) and algal toxins—A strategic science vision for the U.S. Geological Survey

Executive Summary Algal blooms in water, soils, dusts, and the environment have captured national attention because of concerns associated with exposure to algal toxins for humans and animals. Algal blooms naturally occur in all surface-water types and are important primary producers for aquatic ecosystems. However, excessive algae growth can be associated with many harmful effects ranging from aesthetic to toxicity concerns, so this excessive growth is commonly called a harmful algal bloom (HAB). Ecological imbalances that can lead to excessive algal growth, such as increased nutrient availability to waterbodies from natural and anthropogenic sources, are well documented in scientific literature. On the other hand, fundamental scientific understandings of environmental causes and controls leading to algal toxin production, environmental exposures, and adverse health outcomes for humans and animals could benefit from more attention by U.S. Geological Survey (USGS) scientists. Understanding when, why, and how the toxin is produced by individual algal cells or communities and why the toxin is released to the surrounding waterbody requires fundamental research to determine a toxin’s role, whether it provides competitive advantage or if other potential reasons exist for toxin production and release, such as secretions from otherwise benign biological processes. This research will require groundbreaking scientific discovery about underlying biologic and abiotic (non-living) processes commonly complicated by local variation in land use, microbial species composition, and ecosystem structure of the surrounding watershed. Although underlying processes by which HABs form may be similar from one waterbody to another, individual waterbodies may be controlled by local factors for HAB development and toxin production that are unique to the watershed. Consequently, many fundamental science gaps exist that prevent informed mitigation and prevention of toxic HAB events. There are also gaps in understanding local conditions that control algal growth unique to specific watersheds. Addressing these science gaps is needed to inform evidence-based decisions that protect human and animal health and that reduce recreational and socioeconomic losses.

Circular↗

USGS2024

Role of edaphic, hydrologic, and land cover variables in determining dissolved organic carbon in Missouri (USA) reservoirs and streams

In Missouri, distinct geophysical gradients influence statewide patterns in water quality. Here, we quantify the spatiotemporal variability of dissolved organic carbon (DOC) in reservoirs and streams and the edaphic, hydrologic, and land cover variables that account for cross-system variation. Datasets included statewide inventories collected over decades and studies with greater temporal resolution ( n = >6350 DOC measurements). Among reservoirs, the smallest DOC concentration was measured in a spring-fed system within a forested watershed, and the largest was where agricultural biosolids were applied to the land (range 1.0–15.9 mg/L, overall mean 5.8 mg/L). Reservoir values increased from the southern forested Highlands (mean 4.7 mg/L) to the northern agricultural Plains (mean 7.0 mg/L). Stream DOC was similar to reservoir values (overall mean in streams 6.3 mg/L; Highlands mean 4.0 mg/L; Plains mean 6.6 mg/L), despite differences in study design and collection period. Reservoir DOC increased in spring, indicative of allochthonous loading, with small autochthonous additions during a broad summer peak. Temporal variability in DOC was low relative to macronutrients and chlorophyll in both reservoirs and streams, indicating DOC may be a sensitive and readily detected indicator of temporal change in these systems. In regression analyses, watershed features accounted for more than 60% of overall cross-system variability in DOC in both reservoirs and streams. Driver-response relations, however, differed between regions. This analysis extends our understanding of environmental influences on surface water chemistry in Missouri and indicates DOC is nearly as predictable as macronutrients using landscape-level features.

Missouri↗

USGS2024

Evaluation of metrics and thresholds for use in national-scale river harmful algal bloom assessments

The spatiotemporal distribution of harmful algal blooms (HABs) in rivers remains poorly understood, and there is an urgent need to develop a consistent set of metrics to better document HAB occurrences and forecast future events. Using data from seven sites in the Illinois River Basin, we computed metrics focused on HAB conditions related to excess algal growth and hypoxia. Daily mean chlorophyll and dissolved oxygen (DO) concentrations, gross primary productivity (GPP), and net ecosystem productivity (NEP) rates, focused on water quality status, identifying the timing of the transition from a clear-water to an algal dominated state. Early warning indicators (EWIs), the first-order autoregressive process (Ar1) and standard deviation (SD) of chlorophyll concentrations, focused on future events, forecasting blooms. Metrics were compared to either literature-derived or statistical-based thresholds and were normalized by total number of daily samples for an exceedance rate. Exceedances of a daily mean chlorophyll concentration averaged 50 % across all sites using a 10 µg L −1 threshold but increasing the threshold to 50 μg L −1 reduced the average exceedance rate to 5 %. The average exceedance rate for GPP (∼8 g O 2 m 2 d −1 threshold) was 15 %, similar to the daily amplitude DO concentration (∼3 mg L −1 threshold), but the average for NEP (0 g O 2 m 2 d −1 threshold) was higher, at 28 %. The number of days with at least 1 continuous DO concentration below the threshold of 5, 3, or 2 mg L −1 , had basin wide exceedance rates of 9 %, 3 %, and 2 %, respectively. Thresholds for EWIs, Ar1 and SD, were exceeded at 5 of the 7 sites with high chlorophyll concentrations and GPP rates. The correlation between proxies for algal biomass (chlorophyll concentration) and productivity (GPP) was strongest for sites in the middle region of the basin, with R 2 values between 0.54 and 0.74. Although, cyanotoxin concentrations are the most commonly used metrics by states to define an inland water HAB, there is a paucity of publicly available data. The wider availability of chlorophyll and oxygen data combined with the results from this study suggest that biomass and productivity state and event-based metrics may be a promising way to assess and predict the vulnerability of rivers to some of the deleterious effects of HABs at broad spatial scales.

Illinois, Indiana, Wisconsin↗

USGS2024

Assessing the relationship between cyanobacteria blooms and respiratory-related hospital visits: Green Bay, Wisconsin 2017–2019

Potential acute and chronic human health effects associated with exposure to cyanobacteria and cyanotoxins, including respiratory symptoms, are an understudied public health concern. We examined the relationship between estimated cyanobacteria biomass and the frequency of respiratory-related hospital visits for residents living near Green Bay, Lake Michigan, Wisconsin during 2017–2019. Remote sensing data from the Cyanobacteria Assessment Network was used to approximate cyanobacteria exposure through creation of a metric for cyanobacteria chlorophyll-a (Chl BS ). We obtained counts of hospital visits for asthma, wheezing, and allergic rhinitis from the Wisconsin Hospital Association for ZIP codes within a 3-mile radius of Green Bay. We analyzed weekly counts of hospital visits versus cyanobacteria, which was modelled as a continuous measure (Chl BS ) or categorized according to World Health Organization's (WHO) alert levels using Poisson generalized linear models. Our data included 2743 individual hospital visits and 114 weeks of satellite derived cyanobacteria biomass indicator data. Peak values of Chl BS were observed between the months of June and October. Using the WHO alert levels, 60% of weeks were categorized as no risk, 19% as Vigilance Level, 15% as Alert Level 1, and 6% as Alert Level 2. In Poisson regression models adjusted for temperature, dewpoint, season, and year, there was no association between Chl BS and hospital visits (rate ratio [RR] [95% Confidence Interval (CI)] = 0.98 [0.77, 1.24]). There was also no consistent association between WHO alert level and hospital visits when adjusting for covariates (Vigilance Level: RR [95% CI] 0.88 [0.74, 1.05], Alert Level 1: 0.82 [0.67, 0.99], Alert Level 2: 0.98 [0.77, 1.24], compared to the reference no risk category). Our methodology and model provide a template for future studies that assess the association between cyanobacterial blooms and respiratory health.

Wisconsin↗

USGS2024

qPCR-based phytoplankton abundance and chlorophyll a: A multi-year study in twelve large freshwater rivers across the United States

Phytoplankton overgrowth, which characterizes the eutrophication or trophic status of surface water bodies, threatens ecosystems and public health. Quantitative polymerase chain reaction (qPCR) is promising for assessing the abundance and community composition of phytoplankton. However, applications of qPCR to indicate eutrophication and trophic status, especially in lotic systems, have yet to be comprehensively evaluated. For the first time, this study correlates qPCR-based phytoplankton abundance with chlorophyll a (the most widely used indicator of eutrophication and trophic status) in multiple freshwater rivers. From early summer to late fall in 2017, 2018, and 2019, we evaluated phytoplankton, chlorophyll a , pheophytin a , and the Trophic Level Index (TLI) in twelve large freshwater rivers in three regions (western, midcontinent, and eastern) in the United States. Chlorophyll a concentration had positive allometric correlations with qPCR-based phytoplankton abundance (adjusted R 2 = 0.5437, p -value < 0.001), pheophytin a concentration (adjusted R 2 = 0.3378, p -value <0.001), and TLI (adjusted R 2 = 0.4789, p -value < 0.001). Thus, a greater phytoplankton abundance suggests a higher trophic status. This work also presents the numerical values of qPCR-based phytoplankton abundance defining the boundaries among trophic statuses (e.g., oligotrophic, mesotrophic, and eutrophic) of freshwater rivers. The sampling sites in the midcontinent rivers were more eutrophic because they had significantly higher chlorophyll a concentrations, pheophytin a concentrations, and TLI values than the sites in the western and eastern rivers. The higher phytoplankton abundance at the midcontinent sites confirmed their higher trophic status. By linking qPCR-based phytoplankton abundance to chlorophyll a , this study demonstrates that qPCR is a promising avenue to investigate the population dynamics of phytoplankton and the trophic status (or eutrophication) of freshwater rivers.

Continental united States↗

USGS2024

River control points for algal productivity revealed by transport analysis

Measurement of planktonic chlorophyll- a —a proxy for algal biomass—in rivers may represent local production or algae transported from upstream, confounding understanding of algal bloom development in flowing waters. We modeled 3 years of chlorophyll- a transport through a 394-km portion of the Illinois River and found that although algal biomass is longitudinally widespread, most net production occurs at river control points in the upper reaches (up to 3.7 Mg chlorophyll- a y −1 km −1 ). Up to 69% of the algal biomass in the upper river was a result of within-reach production, with the remainder recruited from headwaters and tributaries. High chlorophyll- a measured farther downstream was largely because of transport from source-area control points, with substantial net losses of algal biomass occurring in the lower river. Modeling the often-overlooked river transport component is necessary to characterize where, when, and why planktonic algae grow and predict how far and fast they move downstream.

Illinois↗

USGS2024

Advancing the understanding of Cyano-HABs in New York State

Most harmful algal blooms (HABs) in freshwater lakes, rivers, and streams are caused by cyanobacteria (Cyano-HABs). Under certain environmental conditions, dense concentrations (blooms) can form, which have the potential to cause aesthetic, ecological, and economic damage to afflicted waterbodies. Most importantly, Cyano-HABs can cause physical harm to people, pets, and livestock through the production of cyanotoxins.

New York↗