Biological guidelines for freshwater sediment based on BEnthic Assessment of SedimenT (the BEAST) using a multivariate approach for predicting biological state
Article first published online: 28 JUL 2006
Australian Journal of Ecology
Volume 20, Issue 1, pages 198–219, March 1995
How to Cite
REYNOLDSON, T. B., BAILEY, R. C., DAY, K. E. and NORRIS, R. H. (1995), Biological guidelines for freshwater sediment based on BEnthic Assessment of SedimenT (the BEAST) using a multivariate approach for predicting biological state. Australian Journal of Ecology, 20: 198–219. doi: 10.1111/j.1442-9993.1995.tb00532.x
- Issue published online: 28 JUL 2006
- Article first published online: 28 JUL 2006
- multivariate analysis;
- water quality
Abstract This paper describes the first results for an alternative approach to the development of sediment quality criteria in the nearshore areas of the Laurentian Great Lakes. The approach is derived from methods developed in the United Kingdom for establishing predictive relationships between macroinvertebrate fauna and the physico-chemistry of riverine environments. The technique involves a multivariate statistical approach using (i) data on the structure of benthic invertebrate communities, (ii) functional responses (survival, growth and reproduction) in four sediment toxicity tests (bioassays) with benthic invertebrates; and (iii) selected environmental variables at 96 reference (‘clean’) sites in the nearshore areas of all five Great Lakes (Lakes Superior, Huron, Erie, Ontario and Michigan). Two pattern recognition techniques (using the computer software package PATN) are employed in the analysis: cluster analysis and ordination. The ordination vector scores from the original axes of the pattern analysis are correlated (using CORR in SAS) with environmental variables which are anticipated to be least affected by anthropogenic activities (e. g. alkalinity, depth, silt, sodium etc.). Multiple discriminant analysis (MDA) is used to relate the site groupings from the pattern analysis to the environmental variables and to generate a model that can be used to predict community assemblages and functional responses at new sites with unknown but potential contamination. The predicted community assemblages and functional responses are then compared with the actual benthic communities and responses at a site, and the need for remedial action is determined.
The predictive capability of the discriminant model was confirmed by performing several validation runs on subsets of the data. An example of the use of the model for sediment in Collingwood Bay (an area of concern designated by the IJC in Georgian Bay, Lake Huron) is presented and the technique is shown to be more precise in determining the need for remediation than the currently used provincial sediment quality criteria based on Screening Level Concentration (SLC) and laboratory toxicity tests. The ultimate goal of the study is the development of a method to determine the need for, and the success of, remedial action and to predict what benthic communities should look like at a site if it were clean and what responses of organisms in sediment toxicity tests constitute an acceptable end-point.