Predicting forest site productivity in temperate lowland from forest floor, soil and litterfall characteristics using boosted regression trees

The aim of this study is on the one hand to identify the most determining variables predicting the site productivity of pedunculate oak, common beech and Scots pine in temperate lowland forests of Flanders; and on the other hand to test whether the accuracy of site productivity models based exclusively on soil or forest floor predictor variables is similar to the accuracy achieved by full ecosystem models, combining all soil, vegetation, humus and litterfall composition related variables. Boosted Regression Trees (BRT) were used to model in a climatically homogeneous region the relationship between environmental variables and site productivity. A distinction was made between soil (soil physical and chemical), forest floor (vegetation and humus) and ecosystem (soil, forest floor and litterfall composition jointly) predictors. Our results have illustrated the strength of BRT to model the non-linear behaviour of ecological processes. The ecosystem models, based on all collected variables, explained most of the variability and were more accurate than those limited to either soil or forest floor variables. Nevertheless, both the soil and forest floor models can serve as good predictive models for many forest management practices. Soil granulometric fractions and litterfall nitrogen concentrations were the most effective predictors of forest site productivity in Flanders. Although many studies revealed a fertilising effect of increased nitrogen deposition, nitrogen saturation seemed to reduce species' productivity in this region.