71 



The second principal component was significantly correlated 

 with with the morphometric variables mean depth, shoreline length 

 and lake surface area. The third principal component was 

 significantly correlated with pH, specific conductance and percent- 

 volume infestation. A coefficient of determination indicates that the 

 third principal component would explain 16.0% of the variance in 

 percent-volume infestation, which is not sufficiently robust for 

 predictive purposes. None of the remaining 60 correlation 

 coefficients between principal components 4-8 and the 

 environmental variables was statistically significant. 



Principal components analysis of percentage data was repeated 

 while TSI(AVG) and pH were partialled out. The first principal 

 component, which accounted for 11.8% of the variance in diatom taxa 

 still showed significant correlations with TSI(AVG) and pH (Table 7). 

 The second principal component explained 9.5% of the variance in 

 the diatom assemblages and was significantly correlated with mean 

 depth. The third principal component explained 8.6% of the variance 

 in the diatom assemblages and was not correlated with any of the 

 macrophyte or environmental variables considered. The fourth 

 principal component explained 7.5% of the variance, and was 

 negatively correlated with floating-leaved biomass (r = -0.442, p = 

 0.040, n = 22). A model based on this principal component would 

 explain approximately 19.5% of the variance in floating-leaved 

 biomass. None of the correlation coefficients between principal 

 components 5-8 and the environmental variables was significant. 



Principal components analysis was performed on sedimentary 

 diatom concentrations for the 47 diatom taxonomic groups. The first 



