Data from NLA 2012 was used to assess biovolume results for cyanobacteria (phytoplankton) in relation to both landscape and in lake factors. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.

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.

Data for analysis for modeling probability of measuring microcystin toxin, cyanobacteria cell abundance, and chlorophyll a concentration in ~2,200 lakes based on cyanobacteria summer bloom magnitude measured by satellite imagery. This dataset is associated with the following publication: Handler, A., J. Compton, R. Hill, S. Leibowitz, and B. Schaeffer. Identifying lakes at risk of toxic cyanobacterial blooms using satellite imagery and field surveys across the United States. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 869: 161784, (2023).

Data for analysis for modeling probability of measuring microcystin toxin, cyanobacteria cell abundance, and chlorophyll a concentration in ~2,200 lakes based on cyanobacteria summer bloom magnitude measured by satellite imagery. This dataset is associated with the following publication: Handler, A., J. Compton, R. Hill, S. Leibowitz, and B. Schaeffer. Identifying lakes at risk of toxic cyanobacterial blooms using satellite imagery and field surveys across the United States. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 869: 161784, (2023).

In 2015-2018, the U.S. Geological Survey (USGS) in cooperation with the U.S. Environmental Protection Agency Great Lakes Restoration Initiative investigated the biodegradation of microcystins in source waters and sand filters from drinking-water plants in the Western Lake Erie Basin. Four source waters and three sand filtrate samples were collected from the intakes and sand filters of Lake Erie drinking-water plants and transported to the USGS Ohio Water Microbiology Laboratory, where investigators set up microcosms to enrich for and identify indigenous bacteria capable of degrading microcystins. Quality control samples were set up in the microcosms to check analyses and included positive controls, negative controls, and replicates. Microcystin biodegradation was quantified by the disappearance of the toxin as compared to control cultures in microcosm and microplate experiments, and by the presence of a gene within microcystin-degrading bacteria that encodes for an enzyme involved in the initial steps of biodegradation. Bacteria were isolated from microcosms enriched with microcystin-LR (MC-LR) and MC-LR concentrations were measured over time by ELISA (table 1). Isolates were selected from the microcosm experiments for further growth testing in microplate experiments with various enrichment media and MC-LR over 96 hours (table 2). Biofilm formation potential for the isolates were also measured and data is shown in table 3. Isolate absorbances of ten potential microcystin degraders were incubated in a microplate with MC-LR as the sole carbon source (table 4) and concentrations of MC-LR in microplate wells were measured over time (table 5).

In 2015-2018, the U.S. Geological Survey (USGS) in cooperation with the U.S. Environmental Protection Agency Great Lakes Restoration Initiative investigated the biodegradation of microcystins in source waters and sand filters from drinking-water plants in the Western Lake Erie Basin. Four source waters and three sand filtrate samples were collected from the intakes and sand filters of Lake Erie drinking-water plants and transported to the USGS Ohio Water Microbiology Laboratory, where investigators set up microcosms to enrich for and identify indigenous bacteria capable of degrading microcystins. Quality control samples were set up in the microcosms to check analyses and included positive controls, negative controls, and replicates. Microcystin biodegradation was quantified by the disappearance of the toxin as compared to control cultures in microcosm and microplate experiments, and by the presence of a gene within microcystin-degrading bacteria that encodes for an enzyme involved in the initial steps of biodegradation. Bacteria were isolated from microcosms enriched with microcystin-LR (MC-LR) and MC-LR concentrations were measured over time by ELISA (table 1). Isolates were selected from the microcosm experiments for further growth testing in microplate experiments with various enrichment media and MC-LR over 96 hours (table 2). Biofilm formation potential for the isolates were also measured and data is shown in table 3. Isolate absorbances of ten potential microcystin degraders were incubated in a microplate with MC-LR as the sole carbon source (table 4) and concentrations of MC-LR in microplate wells were measured over time (table 5).

From 2017-2019, the Upper Midwest Environmental Sciences Center (UMESC) analyzed microcystin concentrations in samples collected from three different studies. The first study was on the movement and distribution of invasive carp (Bighead Carp, Silver Carp, Grass Carp) in the upper Mississippi River between lock and dam 16 and lock and dam 19. Samples were collected from May through October of 2017 and 2018 from backwaters, impounded areas and main channel areas in this reach of the Mississippi River. The second study was a nutrient and metal amendment study performed on natural phytoplankton communities from Lake Erie and Lake Michigan. This was a laboratory study where natural phytoplankton communities were incubated for 72 hours with amendments of ammonium, phosphate and metals (iron, zinc, molybdenum, nickel and manganese). After 72 hours, communities were sampled for microcystin concentration (among other metrics not reported here). The third study was a nutrient diffusing substrate study, where periphyton were grown on suspended substrates that leached nutrients or metals. After two weeks of deployment periphyton was collected from the substrates, diluted in purified water and then analyzed for microcystin concentration. Microcystin concentrations for all experiments were estimated using enzyme-linked immunosorbent assay (ELISA) test kits. We used a Bayesian method to calibrate the absorbance data from the kit and report here on both the microcystin concentrations of the samples analyzed, but also report the raw absorbance data from both samples and calibration standards so that others could recreate the microcystin analysis using other methods if they so choose.

From 2017-2019, the Upper Midwest Environmental Sciences Center (UMESC) analyzed microcystin concentrations in samples collected from three different studies. The first study was on the movement and distribution of invasive carp (Bighead Carp, Silver Carp, Grass Carp) in the upper Mississippi River between lock and dam 16 and lock and dam 19. Samples were collected from May through October of 2017 and 2018 from backwaters, impounded areas and main channel areas in this reach of the Mississippi River. The second study was a nutrient and metal amendment study performed on natural phytoplankton communities from Lake Erie and Lake Michigan. This was a laboratory study where natural phytoplankton communities were incubated for 72 hours with amendments of ammonium, phosphate and metals (iron, zinc, molybdenum, nickel and manganese). After 72 hours, communities were sampled for microcystin concentration (among other metrics not reported here). The third study was a nutrient diffusing substrate study, where periphyton were grown on suspended substrates that leached nutrients or metals. After two weeks of deployment periphyton was collected from the substrates, diluted in purified water and then analyzed for microcystin concentration. Microcystin concentrations for all experiments were estimated using enzyme-linked immunosorbent assay (ELISA) test kits. We used a Bayesian method to calibrate the absorbance data from the kit and report here on both the microcystin concentrations of the samples analyzed, but also report the raw absorbance data from both samples and calibration standards so that others could recreate the microcystin analysis using other methods if they so choose.

Site-specific multiple linear regression models were developed for eight sites in Ohio—six in the Western Lake Erie Basin and two in northeast Ohio on inland reservoirs--to quickly predict action-level exceedances for a cyanotoxin, microcystin, in recreational and drinking waters used by the public. Real-time models include easily- or continuously-measured factors that do not require that a sample be collected. Real-time models are presented in two categories: (1) six models with continuous monitor data, and (2) three models with on-site measurements. Real-time models commonly included variables such as phycocyanin, pH, specific conductance, and streamflow or gage height. Many of the real-time factors were averages over time periods antecedent to the time the microcystin sample was collected, including water-quality data compiled from continuous monitors. Comprehensive models use a combination of discrete sample-based measurements and real-time factors. Comprehensive models were useful at some sites with lagged variables (< 2 weeks) for cyanobacterial toxin genes, dissolved nutrients, and (or) N to P ratios. Comprehensive models are presented in three categories: (1) three models with continuous monitor data and lagged comprehensive variables, (2) five models with no continuous monitor data and lagged comprehensive variables, and (3) one model with continuous monitor data and same-day comprehensive variables. Funding for this work was provided by the Ohio Water Development Authority and the U.S. Geological Survey Cooperative Water Program.

Site-specific multiple linear regression models were developed for eight sites in Ohio—six in the Western Lake Erie Basin and two in northeast Ohio on inland reservoirs--to quickly predict action-level exceedances for a cyanotoxin, microcystin, in recreational and drinking waters used by the public. Real-time models include easily- or continuously-measured factors that do not require that a sample be collected. Real-time models are presented in two categories: (1) six models with continuous monitor data, and (2) three models with on-site measurements. Real-time models commonly included variables such as phycocyanin, pH, specific conductance, and streamflow or gage height. Many of the real-time factors were averages over time periods antecedent to the time the microcystin sample was collected, including water-quality data compiled from continuous monitors. Comprehensive models use a combination of discrete sample-based measurements and real-time factors. Comprehensive models were useful at some sites with lagged variables (< 2 weeks) for cyanobacterial toxin genes, dissolved nutrients, and (or) N to P ratios. Comprehensive models are presented in three categories: (1) three models with continuous monitor data and lagged comprehensive variables, (2) five models with no continuous monitor data and lagged comprehensive variables, and (3) one model with continuous monitor data and same-day comprehensive variables. Funding for this work was provided by the Ohio Water Development Authority and the U.S. Geological Survey Cooperative Water Program.