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Cluster analysis of diatom assemblages from a set of lakes, for 

 instance, would result in clusters of diatom species that demonstrate 

 a similar response to the environmental variables responsible for 

 between-lake variance in the assemblages. 



Indirect ordination techniques, which include reciprocal 

 averaging, principal components analysis and detrended 

 correspondence analysis, reduce the number of variables (e.g. diatom 

 taxa) by combining the variables into a series of linear combinations 

 of the original variables called ordination axes. With indirect 

 ordination methods, ordination axes are just particular combinations 

 of variables that are uncorrelated and appear in the order that best 

 explains the variance within the data set. The relationship between 

 indirect ordination axes and environmental variables that influence 

 the data set can then be determined by correlating axes with 

 environmental variables. Other ordination axes, however, may be 

 more suitable for establishing the relationship between the taxa and 

 specific environmental variables influencing diatom assemblages. 



Canonical correspondence analysis (CCA), included in canonical 

 community ordination (CANOCO), is a direct ordination technique in 

 which variables are combined into ordination axes that are 

 constrained by specific environmental variables (ter Braak 1987). 

 Ordination axes are independent and uncorrelated, and are created in 

 order of their variance explained by the environmental variables. 

 CANOCO, therefore, effectively inserts a regression model into the 

 ordination model. When a single environmental variable is specified 

 in the CCA procedure, CANOCO can be used to obtain eigenvectors to 



