For State agencies and other water-resources managers, determining which waterbodies to allocate limited funds for protection and restoration while also maximizing cost benefit is challenging. This data release contains trophic state designations determined from secchi depth, and concentrations of chlorophyll a and microcystin at 232 lakes and reservoirs having a surface area of greater than 0.1 square kilometer in watersheds that drain to the Atlantic and eastern Gulf of Mexico coasts of the United States and in watersheds within the Tennessee River Basin. Estimates of nutrient loading (nitrogen and phosphorus, Hoos and others, 2013; Moorman and others, 2014) and flushing rates were combined with waterbody morphometry (Hollister and Milstead, 2010; Hollister and others, 2011; U.S. Environmental Protection Agency, 2018) to predict summer-season Secchi depth and concentrations of chlorophyll a and microcystin. Waterbodies were categorized by type — natural lakes, headwater reservoirs, and downstream reservoirs — and were assessed independently. Recursive partitioning and the model-based boosting routine were implemented in a R script, which is provided in this data release. The script was used to create four-node regression trees that group waterbodies into five endpoints along individual low-to-high gradients of Secchi depth, chlorophyll a concentration, and microcystin concentration, according to shared nutrient loading, flushing rate, and morphometric characteristics. Trophic state designations were assigned on the basis of the average values within each of the five endpoints. These regression trees can be used to place all waterbodies within the study area greater than or equal to 0.1 square kilometer into one of the different Secchi depth, chlorophyll a, or microcystin endpoints. Results of this study will aid water-resource managers in prioritizing lake and reservoir protection and restoration efforts based on the susceptibility of these waterbodies to eutrophication related to nutrient loading, flushing rate, and morphometric characteristics. References: Hollister J.W., and Milsted W.B., 2010, Using GIS to estimate lake volume from limited data: Reservoir Management, vol. 26, pp. 194-199. Hollister J.W., Milstead W.B., Urrutia M.A., 2011, Predicting maximum lake depth from surrounding topography: PloS ONE, vol. 6, article no. 25764, https://doi.org/10.1371/journal.pone.0025764 Hoos, A.B., Moore, R.B., Garcia, A.M., Noe, G.B., Terziotti, S.E., Johnston, C.M., and Dennis, R.L., 2013, Simulating stream transport of nutrients in the eastern United States, 2002, using a spatially-referenced regression model and 1:100,000 scale hydrography: U.S. Geological Survey Scientific Investigations Report 2013-5102, 33p. Moorman, M.C., Hoos, A.B., Bricker, S.B., Garcia, A.M., and Ator, S.W., 2014, Nutrient load summaries for major lakes and estuaries of the eastern United States, 2002: U.S. Geological Survey Data Series 820, 94p. U.S. Environmental Protection Agency, 2018, Data from the national aquatic resource surveys: U.S. Environmental Protection Agency database, accessed February 8, 2018 at https://edg.epa.gov/clipship/. [Data downloaded for AL, FL, GA, MS, NC, SC, TN, VA, CT, DE, MA, ML, ME, NY, NH, NJ, PA, and RI] U.S. Geological Survey, 2017, Forecasting toxic cyanobacterial blooms throughout the southeastern U.S., accessed February 13, 2017 at http://wilsonlab.com/bloom_network/. [U.S. Geological Survey project homepage on Wilsonlab at Auburn University website]

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.

This U.S. Geological Survey (USGS) Data Release provides phytoplankton data for samples collected from eleven large river sites throughout the United States, from June through September 2017. All data are reported as raw calculated values and are not rounded to USGS significant figures. The dataset includes all routine and quality assurance/quality control samples collected as part of a National Water Quality Assessment Project pilot study to describe the potential for cyanobacteria and cyanotoxin occurrence in the Nation's large rivers. Phytoplankton were identified to the lowest possible taxonomic level and abundances (density reported as natural units) are reported.

This U.S. Geological Survey (USGS) Data Release provides phytoplankton data for samples collected from eleven large river sites throughout the United States, from June through September 2017. All data are reported as raw calculated values and are not rounded to USGS significant figures. The dataset includes all routine and quality assurance/quality control samples collected as part of a National Water Quality Assessment Project pilot study to describe the potential for cyanobacteria and cyanotoxin occurrence in the Nation's large rivers. Phytoplankton were identified to the lowest possible taxonomic level and abundances (density reported as natural units) are reported.

This U.S. Geological Survey (USGS) Data Release provides phytoplankton data for samples collected from eleven large river sites throughout the United States, from June through October 2018. All data are reported as raw calculated values and are not rounded to USGS significant figures. The dataset includes all routine and quality assurance/quality control samples collected as part of a National Water Quality Assessment Project pilot study to describe cyanobacteria and cyanotoxin occurrence in the Nation's large rivers. Phytoplankton were identified to the lowest possible taxonomic level and abundance (reported as both natural units and cells) is reported.

This U.S. Geological Survey (USGS) Data Release provides phytoplankton data for samples collected from eleven large river sites throughout the United States, from June through October 2018. All data are reported as raw calculated values and are not rounded to USGS significant figures. The dataset includes all routine and quality assurance/quality control samples collected as part of a National Water Quality Assessment Project pilot study to describe cyanobacteria and cyanotoxin occurrence in the Nation's large rivers. Phytoplankton were identified to the lowest possible taxonomic level and abundance (reported as both natural units and cells) is reported.

This U.S. Geological Survey (USGS) Data Release provides phytoplankton data collected from the Kansas River and tributaries, during July 2012 through February 2017. All data are reported as raw calculated values and are not rounded to USGS significant figures. This data release was produced in compliance with the open data requirements as a way to make scientific products associated with USGS research efforts and publications available to the public. The dataset includes all routine and quality assurance/quality control samples collected at two locations along the Kansas River (USGS station numbers 06887500, 06892350) and three tributary sites (USGS station numbers 06857100, 06887000, 06890900). Phytoplankton were identified to the lowest possible taxonomic level and abundance (density) and biovolume are reported.

This dataset contains algal identification and enumeration data for phytoplankton samples collected by the U.S. Geological Survey (USGS) between July 2019 and November 2019 at seven reservoirs across the United States. Reservoirs sampled included Lake Koshkonong, Wisconsin, Pelican Lake, Minnesota, Lake Ida, Minnesota, Pomme de Terre, Minnesota, Lake Emily, Minnesota, Milford Lake, Kansas, and Jordan Lake, North Carolina. The samples were analyzed at the Caribbean - Florida Water Science Center (CFWSC) Phycology Laboratory using morphology-based microscopy methods. This data is part of a larger multi-agency project between the U.S. Environmental Protection Agency, the National Aeronautics and Space Administration, the National Oceanic and Atmospheric Administration, and USGS called the Cyanobacteria Assessment Network (CyAN). The goal of the CyAN project is to develop a satellite-based, early warning system to detect harmful algal blooms (HABs) in freshwater systems.

This dataset contains algal identification and enumeration data for phytoplankton samples collected by the U.S. Geological Survey (USGS) between July 2019 and November 2019 at seven reservoirs across the United States. Reservoirs sampled included Lake Koshkonong, Wisconsin, Pelican Lake, Minnesota, Lake Ida, Minnesota, Pomme de Terre, Minnesota, Lake Emily, Minnesota, Milford Lake, Kansas, and Jordan Lake, North Carolina. The samples were analyzed at the Caribbean - Florida Water Science Center (CFWSC) Phycology Laboratory using morphology-based microscopy methods. This data is part of a larger multi-agency project between the U.S. Environmental Protection Agency, the National Aeronautics and Space Administration, the National Oceanic and Atmospheric Administration, and USGS called the Cyanobacteria Assessment Network (CyAN). The goal of the CyAN project is to develop a satellite-based, early warning system to detect harmful algal blooms (HABs) in freshwater systems.