Abstract
Principal component regression analysis was used to investigate the relationships between the abundance of the earthworm Eisenia rosea and soil characteristics at two Ontario locations. To this end we summarized our environmental data matrix with principal component analysis and then used the first several principal components in a multiple regression analysis. This two-step procedure remedies problems associated with multicollinearity among our environmental variables. At one location, moisture was the main factor correlating with the abundance of E. rosea. At the other location, because high soil bulk density can compensate for low moisture, E. rosea abundance did not correlate with moisture.
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