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Fishery Bulletin 104(2) 



the coast of Georgia. CA arranges stations and spe- 

 cies along gradients (Greenacre, 1984), creating a low- 

 dimensional map (an ordination). Points that occur 

 in close proximity can be considered to have similar 

 species composition and abundance. Points that occur 

 on the same dimension define gradients in the data. 

 The eigenvalues, which are a relative measure of the 

 amount of variance explained by each CA dimension 

 (ter Braak and Smilauer, 2002), were used to determine 

 the number of dimensions that best described the data. 

 CA on untransformed CPUE data was used to define 

 assemblages for the cross-shelf, inshore, and offshore 

 data sets in relation to season. 



Canonical correspondence analysis, which incorpo- 

 rates environmental variables by aligning species and 

 station data along environmental gradients, was used to 

 explore the relationship between juvenile assemblages 

 and the environment. The species-environment correla- 

 tion is a measure of the strength of the relation between 



the species data and the environmental data for each 

 CCA dimension (ter Braak and Smilauer, 2002). The 

 product of the species-environment correlation and the 

 eigenvalue can be used to describe the variance in the 

 data. CCA on untransformed CPUE data and standard- 

 ized environmental data was used to explore the rela- 

 tion between the assemblages and environment for the 

 cross-shelf, inshore, and offshore data sets in relation 

 to season. Environmental data were standardized to a 

 mean of zero and a standard deviation of one. 



Results 



Habitat characterization 



The environmental variables showed the typical seasonal 

 and depth patterns across the shelf (Fig. 2). During 

 the spring, bottom water temperature was near 20°C 



