An alternative classification method for northern Wisconsin lakes

Abstract

Wisconsin has nearly 15 000 lakes with great variation in limnology, morphometry, and origin. Classification of lakes into groups is a continuing goal. This study examines two alternative approaches to lake classification, one common and the other somewhat novel. Both approaches used lake morphometry and limnological variables and were compared for ability to form groups and assign lakes to groups with a high probability of correct classification. The first approach used nonhierarchical cluster analysis to form lake groups and discriminant analysis to put lakes into these groups. The second approach formed lake groups by iterative dichotomous splitting of the sampling space into smaller and smaller subspaces. Each binary split was done using nonhierarchical cluster analyses on a subset of the original variables. This iterative splitting resulted in a hierarchical classification tree with reduced dimensionality in comparison with the original data set. At each branch, multiple logistic regression was used to place lakes into nodes of the tree. Validation of both approaches was performed with a resubstitution analysis of the model building data set as well as a separate validation data set. The decision tree method yielded significantly lower rates of misclassification and was more easily interpreted than the discriminant analysis approach.