relevant abiotic variables and combinations of variables that were significant at a probability level of 

 a < 0.1 5. This probability was deemed sufficient to guard against fitting more parameters than can be 

 reliably estimated, given the sample size. The model that minimized the mean square error and 

 maximized the R-square was selected as best describing observed variablity in abundance and numbers 

 of species. 



Analyses of covariance were then conducted to test for annual differences in abundance and species 

 using significant explanatory variables as covariates. Results of these analyses and pair-wise t-tests on 

 adjusted means (least square means) were used to identify significant (a < 0.05) long-term and annual 

 differences species abundance and number. 



Although some abiotic factors were not independent of each other, all variables were initially 

 included in the stepwise regression analyses, because our objective was to identify and remove the natural 

 temporal variability and thus improve our ability to detect power plant related changes. Future analyses 

 of data collected during 3-unit operation will be directed to identifying the extent and effect of individual 

 abiotic factors in structuring local infaunal communities. 



Biological Index Value 



The Biological Index Value (BIV) of McCloskey (1970), an index of dominance, calculated using 

 annual totals of the 10 most abundant taxa at each station collected from 1980-1985. Species were 

 ranked according to their total abundance in each sampling year and the ranks summed for all years. 

 To calculate the BIV, the sum for each taxon was expressed as a percentage of a theoretical maximum 

 sum that would occur if a species ranked first in all sampling years. For example, the BIV would be 

 equal to 100"''o and the theoretical maximum equal to 60 when a species ranks first in abundance in 

 each of six years and a total of 10 species are collected. 



10 



