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

Area of Science:

• Ecology
• Hydrology
• Phycology

Background:

• Streamflow duration assessment methods (SDAMs) are crucial for classifying streamflow at the reach scale.
• Direct measurement of long-term streamflow is resource-intensive, necessitating the use of single-visit indicator data.
• Algae, due to their ubiquity and diversity across moisture gradients, are potential indicators for streamflow-duration classes (SDCs).

Purpose of the Study:

• To evaluate the effectiveness of algal cover and assemblage data in predicting SDCs.
• To compare the accuracy of species-level versus genus-level algal data in SDAMs.
• To identify key algal indicators for streamflow duration classification in forested headwater streams.

Main Methods:

• Analysis of 508 algal samples (cover, assemblage data) from 22 ephemeral, 37 intermittent, and 51 perennial reaches.
• Utilized random forest models with species- and genus-level algal datasets to predict SDCs.
• Assessed the importance of seasonal and habitat factors in distinguishing streamflow classes.

Main Results:

• Random forest models achieved classification accuracy ranging from 69.5% to 88.4%.
• Density data yielded higher accuracy than presence/absence or biovolume data.
• Algal cover index was the most significant predictor of SDC, with season and habitat also playing roles.

Conclusions:

• Epilithic algal cover is a reliable indicator for SDAMs in forested headwater streams.
• Algal cover index and abundance of soft-bodied algae and live diatoms are candidate indicators for regional SDAMs.
• Further development of taxonomic tools for field identification would enhance algal-based SDAMs.