68 



12 clusters of diatom taxa. TSI(AVG), pH and specific conductance 

 had 3 significant correlations each with diatom clusters. Shoreline 

 development and lake surface area were correlated with 2 clusters 

 each, and mean depth was correlated with one cluster. Of 60 

 correlation coefficients related to the macrophyte variables, only two 

 were statistically significant. Seven of the 9 taxa in one cluster that 

 was correlated with floating-leaved biomass were the same taxa 

 used in the stepwise multiple regression procedure described above 

 for unpartialled effects of TSI(AVG) and pH. Another cluster 

 consisted of Fragilaria crotonensis, a euplanktonic taxon occurring in 

 lakes high in water-column nutrients, that had an apparently 

 spurious correlation with floating-leaved biomass. 



Results of Principal Components Analyses 

 Principal components analysis seems to be an appropriate 

 indirect ordination method to apply because species distributions 

 were observed to be linear or curvilinear, though not modal, over the 

 range of percent-area coverage and percent-volume infestation. 

 Correlation coefficients were examined between the first 8 principal 

 components based on percentage data for 47 diatom taxonomic 

 groups and environmental variables. Eigenvalues, which are equal to 

 the variances of the components, indicate that the first 3 principal 

 components account for 15.9%, 8.5% and 7.2% of the variance in 

 diatom assemblages, respectively. The first principal component was 

 found to be highly correlated with TSI(AVG), pH and specific 

 conductance (Table 7), indicating that these environmental variables 

 were responsible for most of the variance in the diatom assemblages. 



