Benthic diatom assemblages are indicative of water quality, but have yet to be widely adopted in biological assessments in the United States due to several limitations. To overcome some of these limitations, we aggregated bioassessment data from USGS and EPA programs. We developed three 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 used a data-driven approach 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 many anthropogenic stressors. 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 for more than six metrics. Component metrics of MMIs were associated with a broad suite of measured stressors in every region, including salinity, nutrients, herbicides, and streamflow modification.