Ecological data are often complex. The explanatory and the response variables may be categorical variables or numerical variables. The ecological relationships that need to be defined are often nonlinear and involve high鄄order interactions between explanatory variables. Missing values for both response and predictor variables are very common, and outliers almost always exist. Random forest ( RF) , a novel machine learning technique, is ideally suited for the analysis of

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