Author(s)

B.M Ruck, W.J. Walley, T.B. Reynoldson & K.E. Day

Abstract

A neural network predictor of benthic community structure in the Canadian waters of the Laurentian Great Lakes B.M Ruck," W.J. Walley,* T.B. Reynoldson/ K.E. Day* " Department of Civil Engineering, Aston University, UK * National Water Research Institute, Burlington, Ontario, Canada ABSTRACT A method of predicting benthic community structure from environmental variables using artificial neural networks is described. The input variables represent geophysical, limnological and sedimentological characteristics of sites in Canadian waters of the Laurentian Great Lakes. A single output from the network predicts the number of individuals of a given taxon to be found in a 5.5cm by 10cm deep core sample of lake sediment taken at the site in question. Networks have been trained for four taxa; namely Oligochaeta, Porifera, Chironomidae and Pelecypoda. Three input vector sets were compared: the 28 dimension raw data set, a subset of 9 variables and a 7 dimension eigenvecto

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