Canonical correspondence analysis (CCA) is a multivariate method for analyzing species-environment relationships. It extends weighted averaging ordination, which arranges species along environmental variables. CCA constructs linear combinations of environmental variables that maximize species distribution separation, with eigenvalues measuring this separation. CCA is also a correspondence analysis technique, but with ordination axes constrained to environmental variable combinations. It visualizes community variation patterns and species distributions along environmental variables. CCA can detect species-environment relations and answer community ecology questions directly, unlike indirect gradient analysis. Direct gradient analysis relates species presence/abundance to environmental variables using same-sample data. Simple methods include plotting species abundance against environmental variables or drawing isopleths in two-variable spaces. However, multiple regression is not popular in vegetation science due to data issues, non-linear relationships, and high environmental variable correlations. CCA addresses these by visualizing community variation and species-environment relationships. CCA was derived as an approximation to Gaussian canonical ordination and relates to correspondence analysis. It is based on weighted averaging, calculating species scores as weighted averages of environmental variable values. CCA provides a multivariate direct gradient analysis, visualizing species-environment relationships effectively.Canonical correspondence analysis (CCA) is a multivariate method for analyzing species-environment relationships. It extends weighted averaging ordination, which arranges species along environmental variables. CCA constructs linear combinations of environmental variables that maximize species distribution separation, with eigenvalues measuring this separation. CCA is also a correspondence analysis technique, but with ordination axes constrained to environmental variable combinations. It visualizes community variation patterns and species distributions along environmental variables. CCA can detect species-environment relations and answer community ecology questions directly, unlike indirect gradient analysis. Direct gradient analysis relates species presence/abundance to environmental variables using same-sample data. Simple methods include plotting species abundance against environmental variables or drawing isopleths in two-variable spaces. However, multiple regression is not popular in vegetation science due to data issues, non-linear relationships, and high environmental variable correlations. CCA addresses these by visualizing community variation and species-environment relationships. CCA was derived as an approximation to Gaussian canonical ordination and relates to correspondence analysis. It is based on weighted averaging, calculating species scores as weighted averages of environmental variable values. CCA provides a multivariate direct gradient analysis, visualizing species-environment relationships effectively.