Aquatic ecological surveys are valuable to understanding the interaction between the biotic and abiotic components in rivers and streams. However, large-scale assessments of the water chemistry, geomorphology, and ecological community are usually not feasible due to limited resources. Beginning in 2013, the Regional Stream Quality Assessment Project of the US Geological Survey’s National Water Quality Program, began sampling 89-120 streams in each of 5 regions across the conterminous United States—the Midwest (2013), Southeast (2014), Pacific Northwest (2015), Northeast (2016), and California (2017). Sampling included water and streambed sediment chemistry, stage and temperature (Journey and others, 2015). The abiotic data is available from the National Water Information System (nwis.waterdata.usgs.gov). Geospatial data for the Southeastern U.S. study sites are available from Qi and others (2017). Ecological data collected included benthic algae, macroinvertebrates, and fish communities, in addition to in-stream habitat and geomorphology measurements for each reach. Ecological and habitat data for the Southeastern United States are summarized in this data release.

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

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.

Montgomery County, Maryland Department of Environmental Protection has collected datasets to assess the health of streams since the early 1990s. Datasets include geomorphic stream cross-sectional surveys, fish and benthic macroinvertebrate counts and taxa abundance, and water chemistry data collected at the time of benthic and fish sampling (dissolved oxygen, pH, specific conductance, air temperature, and water temperature). Data span years 1992 to 2020 at five watersheds within the Clarksburg study area. Watersheds include a forested reference site (Soper), an urban site with centralized stormwater management (Crystal Rock), and three treatment watersheds (TR104, TR109, and Cabin Branch) within the Clarksburg Special Protection Area that transitioned from agriculture to suburban development with distributed stormwater management. These data were used to assess the impacts of distributed stormwater management on stream ecosystem function. All datasets were collected by Montgomery County, Maryland Department of Environmental Protection.

Montgomery County, Maryland Department of Environmental Protection has collected datasets to assess the health of streams since the early 1990s. Datasets include geomorphic stream cross-sectional surveys, fish and benthic macroinvertebrate counts and taxa abundance, and water chemistry data collected at the time of benthic and fish sampling (dissolved oxygen, pH, specific conductance, air temperature, and water temperature). Data span years 1992 to 2020 at five watersheds within the Clarksburg study area. Watersheds include a forested reference site (Soper), an urban site with centralized stormwater management (Crystal Rock), and three treatment watersheds (TR104, TR109, and Cabin Branch) within the Clarksburg Special Protection Area that transitioned from agriculture to suburban development with distributed stormwater management. These data were used to assess the impacts of distributed stormwater management on stream ecosystem function. All datasets were collected by Montgomery County, Maryland Department of Environmental Protection.

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 diatom 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 diatom 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 diatom 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.