Citation:

Riato, L., R. Hill, A. Herlihy, D. Peck, P. Kaufmann, J. Stoddard, AND S. Paulsen. Genus-level, trait-based multimetric diatom indices for assessing the ecological condition of river and stream across the conterminous United States. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, 141:109131, (2022). https://doi.org/10.1016/j.ecolind.2022.109131

Impact/Purpose:

For large-scale and long-term assessment programs that require multiple taxonomic laboratories to process samples, such as the United States Environmental Protection Agency’s (U.S. EPA’s) National Rivers and Streams Assessment (NRSA), discrepancies in species-level identification or nomenclature can preclude the use of diatom data in regional and national assessments. To address this problem, ORISE post-doctoral associate Luisa Riato, and Ryan Hill, Alan Herlihy, David Peck, Philip Kaufmann, John Stoddard and Steven Paulsen of EPA-ORD-PESD, developed diatom multimetric indices (MMIs) using genus-level diatom taxonomy and trait-based autecological information, effectively circumventing the problem of taxonomic inconsistencies in the National River and Stream Assessment diatom datasets from 2008-2009 and 2013-2014. The MMIs are designed to assess river and stream ecological condition in three large ecoregions across the conterminous United States. In contrast to traditional species-level approaches, trait-based approaches can use genus-level data, which is simpler and less-expensive to obtain, and can eliminate discrepancies in species-level identification or nomenclature that can reduce confidence and defensibility of the datasets. The authors applied their trait-based MMI to NRSA data collected within three large ecoregions within the U.S. - the East, Plains and West. The MMI for the East had the greatest ability to discriminate reference from disturbed sites, followed by MMIs for the Plains and West, respectively. MMI performance was comparable to that observed in existing NRSA fish and macroinvertebrate MMIs. The authors show that trait-based diatom indices constructed on genus-level taxonomy offer a suitable alternative when reliable species-level data are not available. The approach developed in this study requires less labor, and could allow the inclusion of diatoms in state or regional programs with limited time and financial resources. The authors also demonstrate that trait-based MMI’s can be effective for large-scale assessments, and may enable programs such as NRSA to assess historical trends in freshwater condition by revisiting older diatom datasets, where inconsistencies in species-level identification, and recent advances in standardization of identification may have rendered historical datasets unreliable and inconsistent with current standards.

Description:

Taxonomic inconsistency in species-level identifications has constrained use of diatoms as biological indicators in aquatic assessments. This study addressed this problem by developing diatom multimetric indices (MMIs) of ecological condition using genus-level taxonomy and trait-based autecological information. The MMIs were designed to assess river and stream chemical, physical and biological condition in three large ecoregions across the conterminous United States. Trait-based approaches have the advantage of using genus-level data, which require less effort and expense to acquire than traditional species-based approaches and eliminating the persistent taxonomic bias introduced over vast geographic scales. For large-scale assessment programs that require multiple taxonomic laboratories to process samples, such as the United States Environmental Protection Agency’s (U.S. EPA’s) National Rivers and Streams Assessment (NRSA), the trait approach can eliminate discrepancies in species-level identification or nomenclature that can render diatom data difficult to interpret. We applied our trait-based MMI to NRSA data collected within three large ecoregions within the U.S. - the East, Plains, and West. The MMI for the East had the greatest ability to discriminate least-disturbed from most-disturbed sites, followed by MMIs for the Plains and West, respectively. The performance of the MMIs was comparable to that observed in existing NRSA fish and macroinvertebrate MMIs. Our research shows that trait-based diatom indices constructed on genus-level taxonomy can be effective for large-scale assessments, and may also allow programs such as NRSA to assess trends in freshwater condition retrospectively, by revisiting older diatom datasets. Moreover, our genus-based approach may allow the inclusion of diatoms in state assessment programs with limited resources if data with genus-level taxonomic resolution can meet state decision-making needs.

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