Predicting the macroinvertebrate faunas of rivers by multiple regression of biological and environmental differences

1. The prediction of macroinvertebrate community composition in flowing waters from environmental data has enabled pollution assessments that take account of natural variability. Polluted sites are identified by discrepancies between the observed fauna and the fauna expected at an unpolluted site on the same type of river. 2. The usual method of prediction involves a sequence of (a) classification of unpolluted reference sites by cluster analysis of macroinvertebrate community data (b) multiple discriminant analysis to relate site clusters to environmental variables, and (c) use of site clusters, discriminant functions and environmental data to estimate the probability of collection of each macroinvertebrate taxon at sites that are to be assessed (test sites). 3. This paper describes an alternative method that does not require classification and predicts abundance rather than probability of occurrence. The main steps are (a) multiple regression of biological differences between pairs of reference sites on differences in physical variables (b) use of the multiple regression relationship to predict the biological similarity of a test site to each reference site, and (c) estimation of the expected fauna at the test site as a weighted mean of the faunas at the reference sites. The predicted similarities of the test site to each reference site are used to derive the weightings. 4. The method is illustrated using macroinvertebrate and environmental data collected in the upper Murrumbidgee River catchment as part of Australia's Monitoring River Health initiative. In comparison with a classification-based analysis of these data, macroinvertebrate indices generated by the new method showed a greater distinction between human-disturbed and undisturbed test sites, and a similar or higher degree of correlation with physical and chemical indicators of human disturbance.