Selecting Environmental variables
|Top Previous Next|
The choice of environmental variables determines the outcome of CCA. For an exploratory analysis, include all the variables that you think are important determinants of the community. If there are other variables that you do not believe to be important but are easy to measure, then these should also be included during an exploratory analysis. You can always subsequently remove superfluous variables that add little to your insight or are difficult to interpret. Remember, if you are testing a hypothesis about the influence of selected variables on a community then the post-hoc removal of variables until you get an interesting result is not the way to proceed.
The number of environmental variables can range from 1 to more than the number of samples. If only 1 environmental variable is used then there is only one canonical axis and it is not possible to produce a 2-dimensional graph. You can, if desired, produce a 2-dimensional image in which the second axis is the first residual axis. If you have at least as many environmental variables as you have samples then your ordination is no longer constrained, and a simple correspondence analysis would result. When using ECOM, the number of environmental variables must be less than the number of samples.
Since the statistical significance of a CCA analysis is determined by a randomization test, there is no need to transform data to fulfill statistical assumptions. However, transformations can be used to dampen the influence of outliers. The choice of transformation impacts the location of sample scores, species scores, and environmental scores. A dampening transformation (e.g. square root) tend to make samples and species more evenly spread out. Only rarely will transformation of environmental variables change the overall interpretation of an ordination.
See also selected references for self-education.
Roberts, D. W. 1986. Ordination on the basis of fuzzy set theory. Vegetatio 66:123-31.