Benthic diatom assemblages are known to be indicative of water quality but have yet to be widely adopted in biological assessments in the United States due to several limitations. Our goal was to address some of these limitations by developing regional multi-metric indices (MMIs) that are robust to inter-laboratory taxonomic inconsistency, adjusted for natural covariates, and sensitive to a wide range of anthropogenic stressors. We aggregated bioassessment data from two national-scale federal programs and used a data-driven analysis in which all-possible combinations of 2-7 metrics were compared for three measures of performance. The datasets in this release support the Carlisle, et al. 2022 report cited herein. The article provides full details of data aggregation, model development, and application.

ABSTRACT Diatom data have been collected in large-scale biological assessments in the United States, such as the U.S. Environmental Protection Agency’s National Rivers and Streams Assessment (NRSA). However, the effectiveness of diatoms as indicators may suffer if inconsistent taxon identifications across different analysts obscure the relationships between assemblage composition and environmental variables. To reduce these inconsistencies, we harmonized the 2008-2009 NRSA data from nine analysts by updating names to current synonyms and by statistically identifying taxa with high analyst signal (taxa with more variation in relative abundance explained by the analyst factor, relative to environmental variables). We then screened a subset of samples with QA/QC data and combined taxa with mismatching identifications by the primary and secondary analysts. When these combined “slash groups” did not reduce analyst signal, we elevated taxa to the genus level or omitted taxa in difficult species complexes. We examined the variability explained by analyst in the original and revised datasets. Further, we examined how revising the datasets to reduce analyst signal can reduce inconsistency, thereby uncovering the variation in assemblage composition explained by total phosphorus (TP), an environmental variable of high priority for water managers. To produce a revised dataset with the greatest taxonomic consistency, we ultimately made 124 slash groups, omitted 7 taxa in the small naviculoid (e.g., Sellaphora atomoides) species complex, and elevated Nitzschia, Diploneis, and Tryblionella taxa to the genus level. Relative to the original dataset, the revised dataset had more overlap among samples grouped by analyst in ordination space, less variation explained by the analyst factor, and more than double the variation in assemblage composition explained by TP. Elevating all taxa to the genus level did not eliminate analyst signal completely, and analyst remained the most important predictor for the genera Sellaphora, Mayamaea, and Psammodictyon, indicating that these taxa present the greatest obstacle to consistent identification in this dataset. Although our process did not completely remove the analyst signal, this work clarifies the extent of the problem and provides a method to minimize analyst signal. Resolution of these taxonomic issues makes large datasets such as the NRSA more suitable for the development of diatom-based water quality indicators. This dataset is associated with the following publication: Lee, S., I. Bishop, S. Spaulding, R. Mitchell, and L. Yuan. Taxonomic harmonization may reveal a stronger association between diatom assemblages and total phosphorus in large datasets.. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, USA, 102: 166-174, (2019). NOTE: This dataset has been removed from public access due to revocation. Please refer inquiries regarding this dataset to the listed contact person.

ABSTRACT Diatom data have been collected in large-scale biological assessments in the United States, such as the U.S. Environmental Protection Agency’s National Rivers and Streams Assessment (NRSA). However, the effectiveness of diatoms as indicators may suffer if inconsistent taxon identifications across different analysts obscure the relationships between assemblage composition and environmental variables. To reduce these inconsistencies, we harmonized the 2008-2009 NRSA data from nine analysts by updating names to current synonyms and by statistically identifying taxa with high analyst signal (taxa with more variation in relative abundance explained by the analyst factor, relative to environmental variables). We then screened a subset of samples with QA/QC data and combined taxa with mismatching identifications by the primary and secondary analysts. When these combined “slash groups” did not reduce analyst signal, we elevated taxa to the genus level or omitted taxa in difficult species complexes. We examined the variability explained by analyst in the original and revised datasets. Further, we examined how revising the datasets to reduce analyst signal can reduce inconsistency, thereby uncovering the variation in assemblage composition explained by total phosphorus (TP), an environmental variable of high priority for water managers. To produce a revised dataset with the greatest taxonomic consistency, we ultimately made 124 slash groups, omitted 7 taxa in the small naviculoid (e.g., Sellaphora atomoides) species complex, and elevated Nitzschia, Diploneis, and Tryblionella taxa to the genus level. Relative to the original dataset, the revised dataset had more overlap among samples grouped by analyst in ordination space, less variation explained by the analyst factor, and more than double the variation in assemblage composition explained by TP. Elevating all taxa to the genus level did not eliminate analyst signal completely, and analyst remained the most important predictor for the genera Sellaphora, Mayamaea, and Psammodictyon, indicating that these taxa present the greatest obstacle to consistent identification in this dataset. Although our process did not completely remove the analyst signal, this work clarifies the extent of the problem and provides a method to minimize analyst signal. Resolution of these taxonomic issues makes large datasets such as the NRSA more suitable for the development of diatom-based water quality indicators. This dataset is associated with the following publication: Lee, S., I. Bishop, S. Spaulding, R. Mitchell, and L. Yuan. Taxonomic harmonization may reveal a stronger association between diatom assemblages and total phosphorus in large datasets.. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, USA, 102: 166-174, (2019). NOTE: This dataset has been removed from public access due to revocation. Please refer inquiries regarding this dataset to the listed contact person.

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 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.

Detecting trends in biological attributes is central to many stream monitoring programs; however, understanding how natural variability in environmental factors affects trend results is not well understood. We evaluated the influence of antecedent streamflow and sample timing (covariates) on trend estimates for fish, invertebrate, and diatom taxa richness and biological condition from 2002 to 2012 at 51 sites distributed across the conterminous United States. This data release contains all of the input and output files necessary to reproduce the results presented and discussed in the associated journal article.

Detecting trends in biological attributes is central to many stream monitoring programs; however, understanding how natural variability in environmental factors affects trend results is not well understood. We evaluated the influence of antecedent streamflow and sample timing (covariates) on trend estimates for fish, invertebrate, and diatom taxa richness and biological condition from 2002 to 2012 at 51 sites distributed across the conterminous United States. This data release contains all of the input and output files necessary to reproduce the results presented and discussed in the associated journal article.

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 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.