A web-based tool for assessing the condition of benthic diatom assemblages in streams and rivers of the conterminous United States

• Published in: Ecological indicators Vol. 135; pp. 1 - 13
• Main Authors: Carlisle, Daren M., Spaulding, Sarah A., Tyree, Meredith A., Schulte, Nicholas O., Lee, Sylvia S., Mitchell, Richard M., Pollard, Amina A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.02.2022; Elsevier
• Subjects: Biological assessment; Diatom; Water quality; Web application; Diatom; Biological assessment; Web application; Water quality
• ISSN: 1470-160X; 1872-7034
• DOI: 10.1016/j.ecolind.2021.108513

•A data-driven approach produced multi-metric indices (MMIs) with high performance.•Most metrics in the best MMIs required statistical control for natural variability.•Individual metrics were associated with measured stressors in independent datasets.•A web-based tool was developed for others to access and apply the MMIs. 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. After ranking the best-performing MMIs, we selected the final MMIs by evaluating stress-response relations in independent regional datasets of diatom samples paired with measures of several water-quality stressors, including herbicides and streamflow flashiness. Each regional MMI performed well at calibration sites and represented diverse aspects of the structure and function of diatom communities. Most metrics included in the best MMIs were modeled to account for natural variation including climate, topography, soil characteristics, lithology, and groundwater influence on streamflow. MMI performance improved with higher numbers of component metrics, but this effect diminished beyond six metrics. Component metrics of MMIs were associated with a broad suite of measured stressors in every region, including salinity, nutrients, herbicides, and streamflow flashiness. We provide a web-based software application that allows users in the conterminous United States to apply our MMIs to their own datasets and compare MMI scores from their sites to a broader regional context.