THE REFERENCE CONDITION - A COMPARISON OF MULTIMETRIC AND MULTIVARIATE APPROACHES TO ASSESS WATER-QUALITY IMPAIRMENT USING BENTHIC MACROINVERTEBRATES

Traditional methods of establishing control sites in field-oriented bi omonitoring studies of water quality are limited. The reference-condit ion approach offers a powerful alternative because sites serve as repl icates rather than the multiple collections within sites that are the replicates in traditional designs using inferential statistics. With t he reference-condition approach, an array of reference sites character ises the biological condition of a region; a test site is then compare d to an appropriate subset of the reference sites, or to all the refer ence sites with probability weightings. This paper compares the proced ures for establishing reference conditions, and assesses the strengths and deficiencies of multimetric (as used in the USA) and multivariate methods (as used in the UK, Canada, and Australia) for establishing w ater-quality status. A data set of environmental measurements and macr oinvertebrate collections from the Eraser River, British Columbia, was used in the comparison. Precision and accuracy of the 2 multivariate methods tested (AUStralian RIVer Assessment Scheme: AusRivAS, BEnthic Assessment of SedimenT: BEAST) were consistently higher than for the m ultimetric assessment. Classification by ecoregion, stream order, and biotic group yielded precisions of 100% for the AusRivAS, 80-100% for the BEAST, and 40-80% for multimetrics; and accuracies of 100%, 100%, and 38-88%, respectively. Multimetrics are attractive because they pro duce a single score that is comparable to a target value and they incl ude ecological information. However, not all information collected is used, metrics are often redundant in a combination index, errors can b e compounded, and it is difficult to acquire current procedures. Multi variate methods are attractive because they require no prior assumptio ns either in creating groups out of reference sites or in comparing te st sites with reference groups. However, potential users may be discou raged by the complexity of initial model construction. The complementa ry emphases in the multivariate methods examined (presence/absence in AusRivAS cf. abundance in BEAST) lead us to recommend that they be use d together, and in conjunction with, multimetric studies.