S=0.80. Since station and time of year factors provided significant 

 contributions to the over all variances of the species considered, the 

 ability to detect a 20% change in the annual geometric means at a =0.05,3 

 =0.90 ora =0.05, g =0.70 may be reasonably precise. 



In addition to the assumptions of normality and homogeneous variances, 

 data used in normal theory tests should meet the assumption of independence. 

 The result of serial dependence in the data over time is to affect 

 probability statements in an unpredictable way (Glass et al. 1975). 

 Time-series analysis actually makes use of these internal autocorrelations 

 and has been proposed as a powerful tool for statistical testing in 

 environmental monitoring (Saila et al. 1980). The initial application 

 of this analytical method used the autocorrelation structure to forecast 

 fish catches a year in advance. However, techniques exist for removing 

 the autocorrelations and then testing for differences in the remaining 

 values. Additionally, tests can be constructed to determine if an 

 intervention effect, like the start-up of a power plant, is detectable 

 in the series. This approach, then, would assist in meeting both the 

 second and the third objectives of the trawling program, that is to be 

 able to distinguish natural changes in finfish population levels or 

 changes in community composition from those which are power plant induced. 

 Because 50 serial data points are needed to adequately identify a time 

 series model (Glass et al. 1975), and results are best if at least two 

 observations contribute to each data point (Saila, pers. comm.), it is 

 recommended that any changes in the trawling program be considered in 

 terms of the potential for using the extant historical data base in time 

 series analysis. The data collected since October 1977 fulfill these 

 requirements. Triplicate observations contributed to each of 26 data 



35 



