Identifying algal indicators for streamflow duration assessment methods in forested headwater streams

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 resource intensive. Algae (diatoms and soft-bodied algae) are potentially strong indicators of streamflow-duration class (SDC) because of geographical ubiquity and biodiversity across moisture gradients. Algal cover and algal assemblage 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 in the contiguous United States. Random forest models using species- and genus-level datasets to predict SDCs had classification accuracy ranging from 69.5% - 88.4%, with accuracy being highest for density, intermediate for presence/absence, lowest for biovolume data. Species-level models had 0.6% - 5.9% higher accuracy for density and presence/absence datasets than genus-level models. Based on their median ranks of minimum node depth among random forest models, season (wet vs dry) was a more important factor than habitat sampled (erosional vs depositional) when distinguishing ephemeral, intermittent and perennial but habitat was more important when distinguishing ephemeral from non-ephemeral reaches. Algal cover index (categorical visual-tactile assessment) was the most important indicator of SDC. Except for the red alga, Audouinella, the identified indicator taxa were diatoms. Our findings support epilithic algal cover as an SDAM indicator for forested headwater streams. Until taxonomic tools are available for field identifications, algal cover index and abundance of soft-bodied algae and live diatoms may be candidate indicators for regional SDAMs.