This file includes the following data for 25 stream sites in the Little Miami River watershed: diatom operational taxonomic units with their numbers and relative abundances of rbcL gene sequence reads, watershed land cover, and nutrient concentration and conductivity data. This dataset is associated with the following publication: Yuan, L., N. Smucker, C. Nietch, and E. Pilgrim. Quantifying spatial and temporal relationships between diatoms and nutrients in streams strengthens evidence of nutrient effects from monitoring data. Freshwater Science. The Society for Freshwater Science, Springfield, IL, 41(1): 100-112, (2022).

LMR watershed temporal DNA metabarcoding 2016 study. This dataset is associated with the following publication: Smucker, N., E. Pilgrim, H. Wu, C. Nietch, J. Darling, M. Molina, B. Johnson, and L. Yuan. Characterizing temporal variability in streams supports nutrient indicator development using diatom and bacterial DNA metabarcoding. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 831: 154960, (2022).

DNA barcoding gene sequences and files associated with their analysis

Raw data associated with EPA 2018-2019 National Rivers and Streams Assessment Diatom Diversity and Phosphorus Study. This dataset is associated with the following publication: Yuan, L., R. Mitchell, A. Pollard, C. Nietch, E. Pilgrim, and N. Smucker. Understanding the effects of phosphorus on diatom richness in rivers and streams using taxon–environment relationships. FRESHWATER BIOLOGY. Blackwell Publishing, Malden, MA, USA, 68(3): 473-486, (2023).

Raw data associated with EPA 2018-2019 National Rivers and Streams Assessment Diatom Diversity and Phosphorus Study. This dataset is associated with the following publication: Yuan, L., R. Mitchell, A. Pollard, C. Nietch, E. Pilgrim, and N. Smucker. Understanding the effects of phosphorus on diatom richness in rivers and streams using taxon–environment relationships. FRESHWATER BIOLOGY. Blackwell Publishing, Malden, MA, USA, 68(3): 473-486, (2023).

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]

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]

This dataset was developed in partnership with the Tennessee Department of Environmental Conservation to determine the susceptibility of selected Tennessee reservoirs to eutrophication and potential harmful algal blooms. A R script, based on recursive partitioning and the model-based boosting routine, was used to generate regression trees that grouped Tennessee reservoirs into five endpoints along individual low-to-high gradients of Secchi depth and chlorophyll a concentrations (Green, et al, 2021; Heal and Green, 2021). Input data for these reservoirs were obtained from SPAtially Referenced Regression On Watershed (SPARROW) attributes models that estimate total phosphorus and total nitrogen loads in Tennessee water bodies (Roland II, 2019). References: Green, W. Reed, Hoos, Anne B., Wilson, Alan E., and Heal, Elizabeth N., 2021, Development of a screening tool to examine lake and reservoir susceptibility to eutrophication in selected watersheds of the eastern and southeastern United States: U.S. Geological Survey Scientific Investigations Report, https://doi.org/... Heal, E.N., and Green, W.R., 2021, Nutrient loading, flushing rate, and lake morphometry data used to identify trophic states in selected watersheds of the eastern and southeastern United States: U.S. Geological Survey data release, https://doi.org/10.5066/P9K7EOH0. Roland II, V.L., 2019, Data used in the creation of total phosphorus and total nitrogen SPARROW models for the state of Tennessee (ver. 1.1, February 2020): U.S. Geological Survey data release, https://doi.org/10.5066/P96RWGU0.

This dataset was developed in partnership with the Tennessee Department of Environmental Conservation to determine the susceptibility of selected Tennessee reservoirs to eutrophication and potential harmful algal blooms. A R script, based on recursive partitioning and the model-based boosting routine, was used to generate regression trees that grouped Tennessee reservoirs into five endpoints along individual low-to-high gradients of Secchi depth and chlorophyll a concentrations (Green, et al, 2021; Heal and Green, 2021). Input data for these reservoirs were obtained from SPAtially Referenced Regression On Watershed (SPARROW) attributes models that estimate total phosphorus and total nitrogen loads in Tennessee water bodies (Roland II, 2019). References: Green, W. Reed, Hoos, Anne B., Wilson, Alan E., and Heal, Elizabeth N., 2021, Development of a screening tool to examine lake and reservoir susceptibility to eutrophication in selected watersheds of the eastern and southeastern United States: U.S. Geological Survey Scientific Investigations Report, https://doi.org/... Heal, E.N., and Green, W.R., 2021, Nutrient loading, flushing rate, and lake morphometry data used to identify trophic states in selected watersheds of the eastern and southeastern United States: U.S. Geological Survey data release, https://doi.org/10.5066/P9K7EOH0. Roland II, V.L., 2019, Data used in the creation of total phosphorus and total nitrogen SPARROW models for the state of Tennessee (ver. 1.1, February 2020): U.S. Geological Survey data release, https://doi.org/10.5066/P96RWGU0.

This data release consists of the data used to develop SPAtially Referenced Regression On Watershed(SPARROW) attributes models for estimating loads of total phosphorus and total nitrogen in Tennessee streams. These data support the publication containing the Tennessee SPARROW models results (Hoos and others, 2019) and include model input used in the South Atlantic-Gulf Drainages and Tennessee River Basin (SAGT) nutrient SPARROW models (Hoos and McMahon, 2009; Garcia and others, 2011) as well as model input for river basins in Tennessee not included in the domain of the published SAGT SPARROW models. Also included in this data release are model coefficients, the software required to execute the Tennessee SPARROW models, and the model output for all streams in Tennessee. Each data set is listed in this data release with an accompanying file containing metadata for the dataset. For additional details, please refer to the README.txt file.