Wissam Mazouz

Redundancy Analysis (RDA) and Canonical Correspondence Analysis (CCA) are multivariate statistical methods commonly used in ecology to examine the relationships between biological communities (e.g., species presence and abundance) and environmental factors (such as pH, temperature, pollution, soil moisture, etc.). The main difference between them lies in the assumption about how species respond to environmental gradients. RDA is a linear method suitable when species responses to environmental changes are assumed to be linear. It is based on principal component analysis (PCA) but incorporates environmental variables to explain variance in biological data. RDA is typically used when data are standardized and the relationships between species and environmental factors are simpler. In contrast, CCA is a nonlinear method that assumes unimodal (bell-shaped) species responses to environmental gradients, meaning each species has an optimum condition where it performs best. CCA is especially useful when analyzing more complex ecosystems with diverse species responding to subtle environmental changes. In practice, the choice between RDA and CCA depends on the nature of the data. It is recommended to perform Detrended Correspondence Analysis (DCA) first to determine the gradient length—if it is less than 3 standard deviations, RDA is appropriate; if longer, CCA is preferred. Both methods are extremely useful for visualizing ecological relationships and understanding complex interactions between species and their environment.

Dear Dragan Ugrinov ,

Thank you for the detailed explanation of Redundancy Analysis (RDA) and Canonical Correspondence Analysis (CCA). Your insights into the assumptions and applications of these methods are very valuable.

I am currently working on an ecological study requiring the use of these analyses, and I would like to kindly ask if you would be able to assist us in conducting the RDA and CCA for our dataset. Your expertise would be greatly appreciated in ensuring the robustness and accuracy of the analysis and interpretation.

Looking forward to your positive response.

Best regards

Wissam Mazouz

Dear ,

Thank you for reaching out and for your trust. I’ll be happy to assist with the RDA and CCA analyses. Please ask your colleague to specify in what way support is needed – whether it’s data preparation, conducting the analyses, interpreting the results, or something else – so that we can coordinate more concretely.

Best regards, Dragan Ugrinov