CONFRONTING MULTICOLLINEARITY IN ECOLOGICAL MULTIPLE REGRESSION

The natural complexity of ecological communities regularly lures ecologists to collect elaborate data sets in which confounding factors are often present. Although multiple regression is commonly used in such cases to test the individual effects of many explanatory variables on a continuous response, the inherent collinearity (multicollinearity) of confounded explanatory variables encumbers analyses and threatens their statistical and inferential interpretation. Using numerical simulations, I quantified the impact of multicollinearity on ecological multiple regression and found that even low levels of collinearity bias analyses (r ≥ 0.28 or r2 ≥ 0.08), causing (1) inaccurate model parameterization, (2)…