39 



the same genus. Because of the large number of rare taxa present, 

 this preserved most of the diatom information in each sample (mean 

 = 96.6%) while substantially reducing the number of species. 



Hierarchical cluster analysis was performed on the 47 taxonomic 

 groups using the SAS VARCLUS procedure (SAS Inst., Inc. 1985). I 

 applied this procedure to the percentages, concentrations and 

 accumulation rates of the taxonomic groups, and repeated the 

 procedures after partialling out the effects of TSI(AVG) and pH. Tree 

 diagrams of the hierarchical clusters from each analysis were 

 constructed using the SAS TREE procedure (SAS Inst., Inc. 1985). 

 Scores for diatom clusters were obtained for each lake in the survey 

 using standardized scoring coefficients from the cluster analyses and 

 the SAS SCORE procedure (SAS Inst., Inc. 1985). I then correlated the 

 scores for each cluster with macrophyte, chemical and morphometric 

 variables using the SAS CORR procedure to determine which 

 variables most influenced each cluster of taxa. 



Principal components analyses of the percentages, concentrations 

 and accumulation rates of the 47 diatom taxonomic groups were 

 performed using the SAS PRINCOMP procedure (SAS Inst., Inc. 1985). 

 I repeated the PRINCOMP procedure for each of the three models 

 while partialling out the effects of TSI(AVG) and pH. The 

 standardized principal component scores of the first 8 principal 

 components in each test were calculated for the diatom assemblages 

 in the survey lakes using the SAS SCORE procedure. I then correlated 

 the principal component scores with macrophyte, water chemistry 

 and morphometric variables to identify the environmental variables 

 influencing each principal component. 



