83 



Sum of Floating and Submerged Biomass 



Because submerged macrophyte biomass could not be separated 

 from floating-leaved biomass in predictive models, an attempt was 

 made to combine these macrophyte variables in a single predictive 

 model. Sixteen diatom taxonomic groups (Appendix 3.11) were 

 selected that appeared to show response to both of these macrophyte 

 variables. The stepwise multiple regression procedure was applied 

 to these groups and it produced the following best model: 



FLOAT-SUB = 13.292 - 0.384(S-ACH) - 1.159(S-AULA) - 



23.126(EUNPEC) + 0.921(S-FRUSRH) - 0.912(S- 

 NAVS) + 7.115(S-STAU) +2.492(EUNVAN) 3.6 



R2 = 0.592, p = 0.042, n = 22 



in which FLOAT-SUB = sum of floating-leaved and submerged 



macrophyte biomass in kg wet mass m"2. Acronyms for the 



taxonomic groups are defined in Appendices 1 and 2. The adjusted 



R2 for this model was 0.389. 



Emergent Biomass 



The stepwise multiple regression procedure was performed on 

 percentage data of 17 diatom taxonomic groups (Appendix 3.12) to 

 predict emergent biomass. Cp values of all models with significant R2 

 values were much less than the p values, which indicated that these 

 models were subject to substantial random error. 



Results of Canonical Correspondence Analysis 

 The canonical correspondence analysis option of CANOCO (ter 

 Braak 1987) produced eigenvectors (Table 9) ordinating the 47 



