How CanoDraw fits into your data analytical work

Analysis of multivariate data often involves not only the response variables, but also the explanatory variables. Canoco recognizes at least three kinds of explanatory variables, depending on the role assigned to them in particular analysis: environmental variables, covariables, and supplementary variables. Ecologists often ask several different questions about the same datasets, and the individual questions are reflected in separate Canoco projects.
CanoDraw supports this project-oriented approach to data analysis, so it is also centered around projects, stored in files using a CDW extension in their names. These projects correspond one-to-one with Canoco projects. Unlike the Canoco project files (which have the CON extension in their names), CanoDraw project files contain not only the information about analysis properties, but also the original analyzed data, ordination results produced by Canoco, as well as any additional project properties defined by the user in the CanoDraw program (groups of objects, objects classifications, etc.)

You create a new CanoDraw project by specifying the name of Canoco project file. CanoDraw reads the project file, finds and retrieves the results of Canoco analysis (in a file with SOL name extension), and also retrieves the original data files used in the analysis, if available.

While the separation of work with particular datasets into projects provides useful decomposition of complex questions into individual analyses, this separation hides the sometimes-sought connection between them. You may need, for example, to compare results of direct and indirect gradient analysis on the same datasets to see, in what extents the total variability in species composition data corresponds to the variability explainable with the measured environmental factors. CanoDraw supports various methods of such a comparison, with its ability to export and import individual variables, ordination analysis results, or even classifications of samples, species, or explanatory variables in individual projects.