The seine sampling program extant at MNPS has sampled finfish 

 typical of the shore zone along the northeast Atlantic coast, and provided 

 both a long term and seasonal description of the fluctuations that these 

 assemblages experience. Additionally, potentially impacted species have 

 been represented in these collections. The next step in this investigation 

 was to determine our ability to detect changes in the shore-zone finfish 

 assemblages using the accumulated data, and to distinguish plant induced 

 changes from natural changes in the fish populations. 



Hypotheses generation and statistical testing provide the most 

 quantitative way to detect differences or changes. Standard normal 

 theory tests assume, among other things, that the observations come from 

 a probability distrbution that is normal, that the variances are homogen- 

 eous, and that the observations are independent. A knowledge of the 

 data base characteristics, then is desirable for the correct application 

 and interpretation of various tests and results. After determining that 

 there were no changes in methodology to contribute undesirable variance 

 to the data (see Table 1) , the characteristics of the underlying proba- 

 bility distribution were described. 



The probability distributions of total catch and catches of silversides, 

 killifishes, sand lance, menhaden and sticklebacks were characterized by 

 first examining the frequency distributions of which Fig. 3 is an example. 

 The high proportion of zeros result in highly skewed, non-normal distri- 

 btuions. Such distributions typically have variances that are related 

 to the means. The variances may be stabilized by subjecting the data to 

 a log transformation prior to analyses. Alternatively these distributions 

 may approximate a negative binomial distribution (Poole 1974), and can 

 be normalized with another appropriate transformation (ln(CPUE+k/2) . 



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