In multivariate analysis, canonical correspondence analysis (CCA) is an ordination technique that determines axes from the response data as a unimodal combination of measured predictors.
CCA extends Correspondence Analysis (CA) with regression, in order to incorporate predictor variables.
CCA was developed in 1986 by Cajo ter Braak [1] and implemented in the program CANOCO, an extension of DECORANA.
[2] To date, CCA is one of the most popular multivariate methods in ecology, despite the availability of contemporary alternatives.
Also, the data are categorical and that the independent variables are consistent within the sample site and error-free.