Algae as indicators of stress in great rivers: Phytoplankton versus periphyton

Algal assemblages are useful indicators for environmental monitoring of rivers because they integrate the effects of anthropogenic stressors. Algal communities are usually present before and after disturbances, and they are highly sensitive to important water quality parameters such as nutrients. Taxonomic composition of algal communities has long been used to track human impacts on aquatic ecosystems, and application of algal indicators to rivers is increasing. In this study, periphyton and phytoplankton samples were collected and analyzed from 393 locations in three mid-continent (US) great rivers: the Upper Mississippi, Missouri and Ohio rivers. Algae assemblages were quantified by multiple metrics including biovolume (based on algal shape formulae and cell measurements), relative biovolume, cell density, relative cell density, entity density (based on numbers of colonies, filaments or free-living cells), and relative entity density. Relationships between algal metrics and both water quality (e.g., nutrients, ionic properties, physicochemical parameters) and landscape-scale stressor data (e.g., agriculture, urban development, impoundment, pollution point-sources) were examined using multivariate analyses. Not surprisingly, algal metrics were closely related to water quality. Metrics had lower, but still significant, relationships to landscape stressors which would indirectly influence algal assemblages. Phytoplankton cell density was the best indicator of water quality. We suspect that weaker relationships between periphyton and water quality were because water grab samples did not reflect the prevailing conditions to which the periphyton had been exposed. Phytoplankton also had a slightly stronger relationship to landscape-scale stressor data than did periphyton. Biovolume metrics were the best periphytic indicators of water quality and stressors. Absolute algal metrics, especially cell density, consistently had stronger relationships to water quality and stressors than relative (percentage-based) metrics. These findings have significant implications on the way algal data are collected and analyzed in river monitoring programs.