Dear experts, I have 5 environmental predictors and abundance data (300 samples, 60 species, transformation: log(x + min(x,x > 0) and use the function 'gradientForest' to estimate (R?-weighted) predictor importance (regression trees). The resulting predictor importance in decreasing order is as follows: pred1, pred2, pred3, pred4, pred5. The two species with the highest R? (goodness-of-fit; output value 'result' of function 'gradientForest') are species 1 (R?=0.76), species 2 (R?=0.74), and species 3 (R?=0.72). To my understanding this means that the model (i.e. the predictor importance ranking) fits best to species 1, 2, and 3 in decreasing order. In a further step I want to know which predictors are the most important for selected species. Thus, I ran separate forests using the 'extendedForest' function with the same parameter settings (and the same set.seed()) as in the function call of 'gradientForest' for species 1, 2, and 3 (and others). Now the resulting predictor importance is (in decreasing order): species1: pred1, pred2, pred4, pred3, pred5; species2: pred1, pred4, pred2, pred5, pred3; species3: pred2, pred4, pred5, pred1, pred3. This seems strange to me, because I believed that the 'extendedForest' function should give similar predictor importance rankings as the 'gradientForest' predictor importance ranking for the species with the highest R? values obtained by 'gradientForest' . I'd be grateful for any help. Thanks a lot in anticipation. Best regards Thomas [[alternative HTML version deleted]]