92 



determined mostly by trophic-state, pH, and specific conductance 

 rather than by macrophyte presence was thus the result of the scale 

 of analysis as discussed by Duarte and Kalff (1990). The dominant 

 effects of trophic state and pH may have overridden differences in 

 community structure that resulted from the influence of 

 macrophytes. In addition, most lakes in the survey were shallow 

 (mean depth < 3.0 m), and periphytic diatom communities may be 

 less specific about substrate types than previous qualitative studies 

 (e.g. Round 1956) have suggested. 



Future studies might minimize error variance in macrophyte 

 predictive models by focusing on a calibration set of lakes that 

 covers a narrower range of trophic state and pH. This approach 

 would sacrifice generality but improve the precision of prediction for 

 lakes with a limited range of macrophyte standing crop. Duarte and 

 Kalff warn, however, that "There is no reason to expect that patterns 

 found at any one scale are transferable to other scales" (Duarte and 

 Kalff 1990, p. 362). A problem of scale arises when any predictive 

 model derived from a set of limnologically diverse lakes is applied 

 historically to a single lake that has remained comparatively constant 

 in character over time. Confidence intervals are inappropriately 

 large for historical predictions because fewer factors affect the error 

 variance within a single basin than within a calibration data set. 



Ne gative Relationship Between Chi, a and Macrophytes 



Lower water-column total P values may occur in lakes with high 

 macrophyte standing crop for the following possible reasons invoked 

 by Canfield et al. (1984) to explain macrophytic influence on Chi a: 



