showed no detectable longitudinal pattern to water quality in the stream, based on either fathead minnow growth or Ceriodaphnia reproduction in 7-day tests (Boston et al., 1993). Water from km 13.8 of East Fork Poplar Creek, however, appeared to be consistently better than water from the other sites: It was never the worst for either species and was the best for one or the other of the two species in six of the eight test periods.

The ANOVA, contingency-table, and concordance-pattern methods can be used to reveal biologically based water-quality differences among sites. A more powerful and predictive framework for the analysis of ambient toxicity test outcomes can be established by linking responses of the test organisms specifically to chemical measurements of water quality. Various statistical methods are available for this purpose. Examples of two such methods—principal components analysis followed by multiple regression analysis, and logistic regression—are summarized below.

Principal components analysis (PCA) and multiple regression analysis were used to inspect relationships between ambient toxicity test outcomes and chemical variables for 180 site and test-period combinations (15 sites each tested 12 times) in receiving streams near Oak Ridge National Laboratory (Stewart et al., 1990). Ceriodaphnia and fathead minnow larvae were tested concurrently in each test period. Chemical water-quality parameters measured for each site-date