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Identifying Algal Indicators for Streamflow Duration Assessment Methods
Citation:
Fritz, K., R. Kashuba, G. Pond, J. Christensen, AND S. DeCelles. Identifying Algal Indicators for Streamflow Duration Assessment Methods. 2022 Joint Aquatic Sciences Meeting, Grand Rapids, MI, May 14 - 20, 2022.
Impact/Purpose:
The presence of surface flow is a fundamental basis for stream classification used in the management of water resources. However, because direct measurement of flow duration is too resource intensive there is a need for rapid approaches for accurately characterize flow duration class. Streamflow duration assessment methods (SDAMs) are rapid, indicator-based tools for classifying streamflow duration at the reach scale (40 - 200 m). This study explores the utility of algae (presence, density, and biovolume) at different taxonomic levels to accurate predict whether or not the associated stream reach has year-round flow (perennial), dries seasonally during the year (intermittent), or flows only in response to precipitation (ephemeral). Findings from this study support the use of algae as indicators for SDAMs developed for small forested streams and the degree of detail needed to optimize classification accuracy. More information on SDAMs and how they are being developed by USEPA's Office of Water and Office of Research and Development can be found here: https://www.epa.gov/streamflow-duration-assessment
Description:
Streamflow duration assessment methods (SDAMs) are rapid tools for reach-scale streamflow duration classification. SDAMs use single-visit indicator data as surrogates because direct measurement of long-term flow duration is too resource intensive. Algae (diatoms and soft algae) are potentially strong indicators of streamflow duration because of geographical ubiquity and taxonomic diversity across moisture gradients. Algal data (presence/absence, density, and biovolume) at species- and genus-levels were analyzed from 508 samples across 22 ephemeral, 37 intermittent, and 51 perennial reaches distributed along 31 forested headwater streams within 4 ecoregions. Random forest models for species- and genus-level datasets had classification accuracy ranging from 70.3% to 82.1%, with higher accuracy for density than presence/absence and lowest for biovolume datasets. Species-level models had 3.6% to 6.9% higher accuracy for density and presence/absence datasets than genus-level models. Balancing bootstrap selection of training data for flow class or ecoregion did not decrease overall classification accuracy substantially but balancing for flow class improved classification of ephemeral reaches by up to 23.9%. Algal cover index (categorical visual-tactile assessment) scores were lower at ephemeral than at intermittent and perennial reaches, making it a top predictor (median minimum mean depth = 2.4 to 3.0). Except for the red alga, Audouinella, the most important indicator taxa for predicting streamflow duration class were diatoms. Our findings support the use of epilithic algal cover as an SDAM indicator for forested headwater streams. However, until taxonomic apps are available for field identifications, laboratory diatom identifications are needed to best use algal data for streamflow duration classification.
