National Water-Quality Assessment (NAWQA) Project
Assess the status and trends of aquatic ecological conditions (invertebrates, fish, algae and habitat) in rivers and wadeable streams.
Relate ecological conditions to chemical stressors (such as nutrients and pesticides), physical disturbances (such as habitat and hydrologic alterations) in the context of different environmental settings and land uses.
Enhance understanding of factors that influence the biological integrity of streams and how these stream ecosystems may respond to diverse natural and human factors.
Develop key ecological indicators of aquatic health.
Marina G. Potapova, Donald F. Charles, Karin C. Ponader, and Diane M. Winter
Patrick Center for Environmental Research, The Academy of Natural Sciences 1900 Benjamin Franklin Parkway, Philadelphia, Pennsylvania 19103-1195, U.S.A.
ABSTRACT. This study compares two approaches for constructing diatom-based indices for monitoring river eutrophication. The first approach is based on weighted averaging of species indicator values with the underlying assumption that species have symmetrical unimodal distributions along the nutrient gradient, and their distributions are sufficiently described by a single indicator value per species. The second approach uses multiple indicator values for individual taxa and is based on the possibility that species have complex asymmetrical response curves. Multiple indicator values represent relative probabilities that a species would be found within certain ranges of nutrient concentration. We used 155 benthic diatom samples collected from rivers in the Northern Piedmont ecoregion (Northeastern U.S.A.) to construct two datasets: one used for developing models and indices, and another for testing them. To characterize the shape of species response curves we analyzed changes in the relative abundance of 118 diatom taxa common in this dataset along the total phosphorus (TP) gradient by fitting parametric and non-parametric regression models. We found that only 34 diatoms had symmetrical unimodal response to TP. Among several indices that use a single indicator value for each species, the best was the weighted averaging partial least square (WA-PLS) inference model. The correlation coefficient between observed and inferred TP in the test dataset was 0.67. The best index that employed multiple indicator values for each species had approximately the same predictive power as the WA-PLS based index, but in addition, this index provided a sample-specific measure of uncertainty for the TP estimation.