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.

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.

Algal toxins are a growing concern worldwide. Rapid assessment test strips are a newer technology and their accuracy in detecting toxins in different lakes with different phytoplankton and toxins present is unknown. This data release is supported by our testing of toxin test strips. This research took place in Voyageurs National Park in northern Minnesota. The research will indicate whether these test strips are accurate for this system, and hopefully lay the foundation for a cost-effective harmful algal blooms (HABs) monitoring and communication tool for Voyageurs National Park and other parks. The utility of the test strips in this system may lead to broader applications, for instance in other inland systems like the nearby Lake of the Woods and Isle Royale National Park or other northern temperate lakes.

Algal toxins are a growing concern worldwide. Rapid assessment test strips are a newer technology and their accuracy in detecting toxins in different lakes with different phytoplankton and toxins present is unknown. This data release is supported by our testing of toxin test strips. This research took place in Voyageurs National Park in northern Minnesota. The research will indicate whether these test strips are accurate for this system, and hopefully lay the foundation for a cost-effective harmful algal blooms (HABs) monitoring and communication tool for Voyageurs National Park and other parks. The utility of the test strips in this system may lead to broader applications, for instance in other inland systems like the nearby Lake of the Woods and Isle Royale National Park or other northern temperate lakes.

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.

Data release including concentrations of cyanotoxins and phytoplankton community composition data for water samples collected from the Lake Mattamuskeet National Wildlife Refuge in North Carolina during 2015.

Data release including concentrations of cyanotoxins and phytoplankton community composition data for water samples collected from the Lake Mattamuskeet National Wildlife Refuge in North Carolina during 2015.

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.

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.