Citation:

Riato, L., Ryan A Hill, A. Herlihy, Dave Peck, Phil Kaufmann, J. Stoddard, AND Steve Paulsen. Genus-level, trait-based multimetric diatom indices for assessing the ecological condition of rivers and streams across the conterminous United States. Society for Freshwater Science, Brisbane, AUSTRALIA, June 03 - 07, 2023.

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 also demonstrate that trait-based MMI’s can be effective for large-scale assessments, and may allow 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. 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.

Description:

Taxonomic inconsistency in species-level identifications has constrained use of diatoms as biological indicators in aquatic assessments. We 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 across the conterminous United States. Trait-based approaches have the advantage of using both species-level and genus-level data, which is simpler and less costly to obtain than traditional species-based approaches and eliminate the persistent taxonomic biases introduced over vast geographic extents. For large-extent 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 hinder diatom data interpretation. We developed trait-based MMIs using genus-level NRSA diatom data for each of the three large ecoregions across the U.S. - the East, Plains, and West. All three MMIs performed well in discriminating least-disturbed from most-disturbed sites. The MMI for the East had the greatest discrimination ability, followed by MMIs for the Plains and West, respectively. The discrimination ability 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 allow programs such as NRSA to create assessments based on past diatom datasets. Moreover, our genus-based approach facilitates including diatoms into other assessment programs that have limited monitoring resources. Disclaimer: The views expressed in this presentation are those of the authors and do not necessarily represent the views or policies of the U.S. EPA.

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