We examine the ability of four different regression-tree ensemble techniques (bagging, random forest, rotation forest and boosted tree) in calibration of aquatic microfossil proxies. The methods are tested with six chironomid and diatom datasets, using a variety of cross-validation schemes. We find random forest, rotation forest and the boosted tree to have a similar performance, while bagging performs less well and in several cases has trouble producing continuous predictions. In comparison with commonly used parametric transfer-function approaches (PLS, WA, WA-PLS), we find that in some cases tree-ensemble methods outperform the best-performing transfer-function technique, especially with large datasets characterized by complex taxon responses and abundant noise. However, parametric transfer functions remain competitive with datasets characterized by low number of samples or linear taxon responses. We present an implementation of the rotation forest algorithm in R.