This dataset includes algal and invertebrate metrics used to assess water quality conditions in agricultural streams of the Midwest. Data was collected in wadable streams from 2003 to 2008. Data includes two benthic algal metrics (percent eutrophic taxa and observed taxa/expected taxa) and two benthic invertebrate metrics (EPT taxa and observed taxa/expected taxa).

We used 14 years (10-01-1999 to 09-30-2014) of biological data (benthic macroinvertebrate and stream fish community data and complementary biological metrics) that was collected from Alabama streams confined to the Mobile River basin and other Gulf Coast drainages in conjunction with land use data and process-based model hydrological (i.e., Precipitation-Runoff Modeling System; PRMS), and water quality (i.e., Spatially Referenced Regression On Watershed Attributes, SPARROW) outputs to explore the effects of land use-driven high and low flow conditions on resource limited taxa abundances and three biological metrics across two landscapes. A landscape consisted of all level III ecoregions above or below the geological feature referred to as the fall line across Alabama. We created two taxa-specific datasets for each landscape by connecting taxa-specific biological samples and the corresponding biological metrics to NHDPlus COMIDs and then used this spatial reference to relate these data to PRMS stream segments. This process enabled us to compile hydrologic metrics, long-term estimates of urban and agricultural land use, and water quality gradients for each biological sample. Biological datasets were compiled from samples collected by two Alabama state agencies: the Alabama Department of Environmental Management (ADEM) and the Geological Survey of Alabama (GSA). ADEM collected all benthic macroinvertebrate samples, while GSA collected all stream fish samples. All ADEM's benthic macroinvertebrate samples included raw community data, along with biological condition gradient (BCG) and Ephemeroptera, Plecoptera, and Trichoptera scores. GSA's stream fish samples included the raw community data and fish index of biological integrity scores. For all biological samples, NHDPlus COMIDs, and PRMS segments we also integrated the following attributes into each of our four datasets; for each biological sample we included its collection date, site ID, and geographic coordinates (decimal degrees); for each COMID, we included its cumulative drainage area (square kilometers) and slope (percentage) and identified the segment’s relevant level III ecoregion; and for each PRMS segment we included its cumulative drainage area (square kilometers). For each of the four datasets, we used PRMS predicted daily streamflow data to calculate 171 biologically relevant hydrologic metrics for each PRMS stream segment and used SPARROW long-term annual, COMID-specific estimates of total nitrogen, total phosphorus, and suspended sediment to generate standardized water quality gradients by incorporating these variables into principal component analyses. We then used annual land cover datasets (2001, 2004, 2005, 2006, 2008, 2011, 2012, 2013, and 2014) to calculate long-term averages of the percentages of urban and agricultural land use associated with each PRMS stream segment, and then estimates were used to identify high and low flow metrics that were only significantly correlated with either land use type. We then integrated the standardized water quality gradients, subsets of hydrologic metrics, and taxa-specific community data into community models to identify resource-limited taxa that were responsive to land use- driven flow conditions. Finally, we used these resource-limited taxa, the three biological metrics, standardized water quality gradients and subsets of hydrologic metrics to evaluate the impact of land use-driven flow conditions on aquatic communities native to Alabama streams. References: Olden, J. D., & Poff, N. L. (2003). Redundancy and the choice of hydrologic indices for characterizing streamflow regimes. River research and applications, 19(2), 101-121. LaFontaine, J.H., Hay, L.E., and Farmer, W.H., 2019, Model Input and Output for Hydrologic Simulations of the Southeastern United States for Historical and Future Conditions: U.S. Geological Survey data release, https://doi.org/10.5066/F74X56PH. Roland, V.L., II, and Hoos, A.B., 2020, SPARROW model

We used 14 years (10-01-1999 to 09-30-2014) of biological data (benthic macroinvertebrate and stream fish community data and complementary biological metrics) that was collected from Alabama streams confined to the Mobile River basin and other Gulf Coast drainages in conjunction with land use data and process-based model hydrological (i.e., Precipitation-Runoff Modeling System; PRMS), and water quality (i.e., Spatially Referenced Regression On Watershed Attributes, SPARROW) outputs to explore the effects of land use-driven high and low flow conditions on resource limited taxa abundances and three biological metrics across two landscapes. A landscape consisted of all level III ecoregions above or below the geological feature referred to as the fall line across Alabama. We created two taxa-specific datasets for each landscape by connecting taxa-specific biological samples and the corresponding biological metrics to NHDPlus COMIDs and then used this spatial reference to relate these data to PRMS stream segments. This process enabled us to compile hydrologic metrics, long-term estimates of urban and agricultural land use, and water quality gradients for each biological sample. Biological datasets were compiled from samples collected by two Alabama state agencies: the Alabama Department of Environmental Management (ADEM) and the Geological Survey of Alabama (GSA). ADEM collected all benthic macroinvertebrate samples, while GSA collected all stream fish samples. All ADEM's benthic macroinvertebrate samples included raw community data, along with biological condition gradient (BCG) and Ephemeroptera, Plecoptera, and Trichoptera scores. GSA's stream fish samples included the raw community data and fish index of biological integrity scores. For all biological samples, NHDPlus COMIDs, and PRMS segments we also integrated the following attributes into each of our four datasets; for each biological sample we included its collection date, site ID, and geographic coordinates (decimal degrees); for each COMID, we included its cumulative drainage area (square kilometers) and slope (percentage) and identified the segment’s relevant level III ecoregion; and for each PRMS segment we included its cumulative drainage area (square kilometers). For each of the four datasets, we used PRMS predicted daily streamflow data to calculate 171 biologically relevant hydrologic metrics for each PRMS stream segment and used SPARROW long-term annual, COMID-specific estimates of total nitrogen, total phosphorus, and suspended sediment to generate standardized water quality gradients by incorporating these variables into principal component analyses. We then used annual land cover datasets (2001, 2004, 2005, 2006, 2008, 2011, 2012, 2013, and 2014) to calculate long-term averages of the percentages of urban and agricultural land use associated with each PRMS stream segment, and then estimates were used to identify high and low flow metrics that were only significantly correlated with either land use type. We then integrated the standardized water quality gradients, subsets of hydrologic metrics, and taxa-specific community data into community models to identify resource-limited taxa that were responsive to land use- driven flow conditions. Finally, we used these resource-limited taxa, the three biological metrics, standardized water quality gradients and subsets of hydrologic metrics to evaluate the impact of land use-driven flow conditions on aquatic communities native to Alabama streams. References: Olden, J. D., & Poff, N. L. (2003). Redundancy and the choice of hydrologic indices for characterizing streamflow regimes. River research and applications, 19(2), 101-121. LaFontaine, J.H., Hay, L.E., and Farmer, W.H., 2019, Model Input and Output for Hydrologic Simulations of the Southeastern United States for Historical and Future Conditions: U.S. Geological Survey data release, https://doi.org/10.5066/F74X56PH. Roland, V.L., II, and Hoos, A.B., 2020, SPARROW model

In 1991, the U.S. Geological Survey (USGS) began a study of more than 50 major river basins across the Nation as part of the National Water-Quality Assessment (NAWQA) project of the National Water Quality Program. One of the major goals of the NAWQA project is to determine how water quality and ecological conditions change over time. To support that goal, long-term consistent and comparable ecological monitoring has been conducted on streams and rivers throughout the Nation. Fish, invertebrate, and algae data collected as part of the NAWQA program were retrieved from the USGS Aquatic Bioassessment database for use in trend analysis. Ultimately, these data will provide insight into how natural features and human activities have contributed to changes in ecological condition over time in the Nation’s streams and rivers. This dataset is a model archive containing all input files and R source code to reproduce the results of the ecological trend analysis described in the associated U.S. Geological Survey Scientific Investigations Report. Associated output files are also included in the archive. Data preparation for input to the model is fully described in the above mentioned report.

In 1991, the U.S. Geological Survey (USGS) began a study of more than 50 major river basins across the Nation as part of the National Water-Quality Assessment (NAWQA) project of the National Water Quality Program. One of the major goals of the NAWQA project is to determine how water quality and ecological conditions change over time. To support that goal, long-term consistent and comparable ecological monitoring has been conducted on streams and rivers throughout the Nation. Fish, invertebrate, and algae data collected as part of the NAWQA program were retrieved from the USGS Aquatic Bioassessment database for use in trend analysis. Ultimately, these data will provide insight into how natural features and human activities have contributed to changes in ecological condition over time in the Nation’s streams and rivers. This USGS data release contains all of the input and output files necessary to reproduce the results of the ecological trend analysis described in the associated U.S. Geological Survey Scientific Investigations Report. Data preparation for input to the model is also fully described in the above mentioned report.

In 1991, the U.S. Geological Survey (USGS) began a study of more than 50 major river basins across the Nation as part of the National Water-Quality Assessment (NAWQA) project of the National Water Quality Program. One of the major goals of the NAWQA project is to determine how water quality and ecological conditions change over time. To support that goal, long-term consistent and comparable ecological monitoring has been conducted on streams and rivers throughout the Nation. Fish, invertebrate, and algae data collected as part of the NAWQA program were retrieved from the USGS Aquatic Bioassessment database for use in trend analysis. Ultimately, these data will provide insight into how natural features and human activities have contributed to changes in ecological condition over time in the Nation’s streams and rivers. This USGS data release contains all of the input and output files necessary to reproduce the results of the ecological trend analysis described in the associated U.S. Geological Survey Scientific Investigations Report. Data preparation for input to the model is also fully described in the above mentioned report.

This data release includes metrics from the Regional Stream Quality Assessment (RSQA) from the Southeast Region for habitat stressors related to water-quality and habitat substrate. The goals of RSQA are to characterize multiple water-quality factors that are stressors to aquatic life ‐ contaminants, nutrients, sediment, and streamflow alteration – and to develop a better understanding of the relation of these stressors to ecological conditions in streams throughout the region. In order to characterize water-quality variables and stream-habitat measurements as an aggregation of multiple measurements over a sampling period, and in support of ecological stressor modelling, metrics (summary statistics or indices) were computed from individual results by site using consistent methods over a consistent time frame. Water-quality metrics are based on discrete samples as well as long-term deployed passive samplers.

This data release includes metrics from the Regional Stream Quality Assessment (RSQA) from the Southeast Region for habitat stressors related to water-quality and habitat substrate. The goals of RSQA are to characterize multiple water-quality factors that are stressors to aquatic life ‐ contaminants, nutrients, sediment, and streamflow alteration – and to develop a better understanding of the relation of these stressors to ecological conditions in streams throughout the region. In order to characterize water-quality variables and stream-habitat measurements as an aggregation of multiple measurements over a sampling period, and in support of ecological stressor modelling, metrics (summary statistics or indices) were computed from individual results by site using consistent methods over a consistent time frame. Water-quality metrics are based on discrete samples as well as long-term deployed passive samplers.