Monitoring the community structure and metabolic activities of cyanobacterial blooms in Upper Klamath Lake, Oregon, is critical to lake management because these blooms degrade water quality and produce toxic microcystins that are harmful to humans, domestic animals, and wildlife. Genetic tools, such as DNA fingerprinting by terminal restriction fragment length polymorphism (T-RFLP) analysis, high-throughput DNA sequencing (HTS), and real-time, quantitative polymerase chain reaction (qPCR) provide more sensitive and rapid assessments of bloom ecology than traditional techniques. The objectives of this study were (1) to characterize the microbial community at one site in Upper Klamath Lake and determine changes in the cyanobacterial community through time using T-RFLP and HTS in comparison with traditional light microscopy; (2) to determine relative abundances and changes in abundance over time of toxigenic Microcystis using qPCR; and (3) to determine relative abundances and changes in abundance over time of Aphanizomenon, Microcystis, and total cyanobacteria using qPCR. T-RFLP analysis of total cyanobacteria showed a dominance of only one or two distinct genotypes in samples from 2013, but results of HTS in 2013 and 2014 showed more variations in the bloom cycle that fit with the previous understanding of bloom dynamics in Upper Klamath Lake and indicated that potentially toxigenic Microcystis was more prevalent in 2014 than in years prior. The qPCR-estimated copy numbers of all target genes were higher in 2014 than in 2013, when microcystin concentrations also were higher. Total Microcystis density was shown with qPCR to be a better predictor of late-season increases in microcystin concentrations than the relative proportions of potentially toxigenic cells. In addition, qPCR targeting Aphanizomenon at one site in Upper Klamath Lake indicated a moderate bloom of this species (corresponding to chlorophyll a concentrations between approximately 75 and 200 micrograms per liter) from mid-June to mid-August, 2014. After August 18, the Aphanizomenon bloom was overtaken by Microcystis late in the season as microcystin concentrations peaked. Overall, results of this study showed how DNA-based, genetic methods may provide rapid and sensitive diagnoses for the presence of toxigenic cyanobacteria and that they are useful for general monitoring or ecological studies and identification of cyanobacterial community members in complex aquatic habitats. These same methods can also be used to simultaneously address spatial (horizontal and vertical) and temporal variation under different conditions. Additionally, with some modifications, the same techniques can be applied to different sample types, including water, sediment, and tissue.

With increasing concerns about freshwater cyanobacteria blooms, there is a need to identify which waterbodies are at risk for developing these blooms, especially those that produce cyanotoxins. To address this concern, we developed spatial statistical models using the US National Lakes Assessment, a survey with over 3,000 spring and summer observations of cyanobacteria abundance and microcystin concentration in lakes across the conterminous US. We combined these observations with other nationally available data to model which lake and watershed factors best explain the presence of harmful cyanobacterial blooms. We then used these models to estimate the cyanobacteria abundance and probability of microcystin detection in 124,500 lakes across the CONUS. This dataset includes the compiled data used to generate the models and the dataset used to generate prediction for a much larger population of lakes. The data package includes two tabular data files, two tabular metadata files, and one methods document.

This data release provides phytoplankton environmental DNA data and cyanotoxin gene qPCR data from an algal bloom at Ash River Boat Docks on Kabetogama Lake, Voyageurs National Park, Minnesota, USA (USGS site ID 482603092511801) collected in 2021 and analyzed by the commercial laboratory Jonah Ventures. We sampled cyanobacteria over a 24-hour period at Ash River Boat Docks in Kabetogama Lake, along with photosynthetically active radiation, to assess the relation between sunlight and these cyanobacteria. Sixteen environmental samples and three quality assurance replicates were collected from two sites, one at the end of the dock and one at the shoreline, between September 9, 2021 and September 10, 2021.

Multiple linear regression models were developed using data collected in 2016 and 2017 from three recurring bloom sites in Kabetogama Lake in northern Minnesota. These models were developed to predict concentrations of cyanotoxins (anatoxin-a, microcystin, and saxitoxin) that occur within the blooms. Virtual Beach software (version 3.0.6) was used to develop four models: two cyanotoxin mixture (MIX) models and two microcystin (MC) models. Models include those using readily available environmental variables (for example, wind speed and specific conductance) and those using additional comprehensive variables (based on laboratory analyses). Many of the independent variables were averages over a certain time period prior to a sample date, whereas other independent variables were lagged between 4 and 8 days. Funding for this work was provided by the U.S Geological Survey – National Park Service Partnership and the U.S. Geological Survey Environmental Health Program (Toxic Substance Hydrology and Contaminant Biology). The resulting model equations and final datasets are included in this data release while an associated child item model archive includes all the files needed to run and develop these VB models.

Multiple linear regression models were developed using data collected in 2016 and 2017 from three recurring bloom sites in Kabetogama Lake in northern Minnesota. These models were developed to predict concentrations of cyanotoxins (anatoxin-a, microcystin, and saxitoxin) that occur within the blooms. Virtual Beach software (version 3.0.6) was used to develop four models: two cyanotoxin mixture (MIX) models and two microcystin (MC) models. Models include those using readily available environmental variables (for example, wind speed and specific conductance) and those using additional comprehensive variables (based on laboratory analyses). Many of the independent variables were averages over a certain time period prior to a sample date, whereas other independent variables were lagged between 4 and 8 days. Funding for this work was provided by the U.S Geological Survey – National Park Service Partnership and the U.S. Geological Survey Environmental Health Program (Toxic Substance Hydrology and Contaminant Biology). The resulting model equations and final datasets are included in this data release while an associated child item model archive includes all the files needed to run and develop these VB models.

This data release provides phytoplankton and fish environmental DNA (eDNA) and cyanotoxin biosynthesis gene abundance data from an algal bloom at Ash River Boat Docks on Kabetogama Lake (USGS site ID 482603092511801) collected between September 12 and September 13, 2023. Algal bloom samples were collected every three hours over a 24-hour period at two sites, one at the end of a dock and one on the adjacent shoreline. Two quality assurance replicates were collected. Additionally, nine algal bloom samples were collected from an isolated chamber located near the shoreline sampling location in an effort to reduce variability caused by potential wave action in the lake. Phytoplankton and fish assemblage composition was determined through eDNA sequencing techniques using the same filtered algal bloom samples. Phytoplankton eDNA yielded sequences from cyanobacteria and eukaryotic algae, while the fish eDNA yielded sequences from mammals and birds in addition to sequences from fish. Cyanotoxin biosynthesis genes for microcystin (mcyE gene), anatoxin (anaC gene), saxitoxin (sxtA gene), and cylindrospermopsin (cyn gene) were quantified.

The National Lakes Assessment (NLA) is a statistical survey of the condition of our nation's lakes, ponds, and reservoirs. It is designed to provide information on the extent of lakes that support healthy biological condition and recreation, estimate how widespread major stressors are that impact lake quality, and provide insight into whether lake quality is improving or getting worse. This dataset is an archived (zipped) file comprised of chemical, physical and biological files used in developing the NLA 2022 report. Sampling was conducted in the summer of 2022 at approximately 1000 sites in the conterminous U.S. Sites were selected using a statistical survey (probabilistic) design. The files include water chemistry, profile data, benthic macroinvertebrates, physical habitat, landscape metrics, secchi depth, tropic status, zooplankton, etc. Users are encouraged to visit the NARS data webpage for updates to data files and data from other surveys. https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys Citation for the NLA 2022 archived data: U.S. Environmental Protection Agency. 2024. National Lakes Assessment: The fourth collaborative survey of lakes in the United States. EPA 841-R-24-006. U.S. Environmental Protection Agency, Office of Water and Office of Research and Development. https://nationallakesassessment.epa.gov/webreport EPA encourages users who are publishing subsets of the data (say as part of a journal article publication) to include the above citation. EPA also encourages users of the data to include the following acknowledgement: “The National Lakes Assessment 2022 data were a result of the collective efforts of dedicated field crews, laboratory staff, data management and quality control staff, analysts and many others from EPA, states, tribes, federal agencies, universities, and other organizations. Please contact nars-hq@epa.gov with any questions.”. Citation information for this dataset can be found in Data.gov's References section.

From August 2018 to October 2019, the U.S. Geological Survey collected high-resolution spatial water quality from a total of five shoreline synoptic surveys conducted around the perimeters of Owasco Lake, Seneca Lake, and Skaneateles Lake within the Finger Lakes Region. Water-quality data were collected just below water surface utilizing YSI EXO2 multiparameter sondes and portable nitrate sensors paired with real-time GPS data collection as part of an Advanced HABs Monitoring Program in the Finger Lakes Region. In October 2019, water-quality data collection was paired with discrete phytoplankton grab samples on Owasco Lake and Seneca Lake. Phytoplankton grab samples were collected just below water surface with a peristaltic pump at twelve unique locations on each of the two lakes.

From August 2018 to October 2019, the U.S. Geological Survey collected high-resolution spatial water quality from a total of five shoreline synoptic surveys conducted around the perimeters of Owasco Lake, Seneca Lake, and Skaneateles Lake within the Finger Lakes Region. Water-quality data were collected just below water surface utilizing YSI EXO2 multiparameter sondes and portable nitrate sensors paired with real-time GPS data collection as part of an Advanced HABs Monitoring Program in the Finger Lakes Region. In October 2019, water-quality data collection was paired with discrete phytoplankton grab samples on Owasco Lake and Seneca Lake. Phytoplankton grab samples were collected just below water surface with a peristaltic pump at twelve unique locations on each of the two lakes.

From August 2018 to October 2019, the U.S. Geological Survey collected high-resolution spatial water quality from a total of five shoreline synoptic surveys conducted around the perimeters of Owasco Lake, Seneca Lake, and Skaneateles Lake within the Finger Lakes Region. Water-quality data were collected just below water surface utilizing YSI EXO2 multiparameter sondes and portable nitrate sensors paired with real-time GPS data collection as part of an Advanced HABs Monitoring Program in the Finger Lakes Region. In October 2019, water-quality data collection was paired with discrete phytoplankton grab samples on Owasco Lake and Seneca Lake. Phytoplankton grab samples were collected just below water surface with a peristaltic pump at twelve unique locations on each of the two lakes.