A classification and regression tree model of controls on dissolved inorganic nitrogen leaching from European forests

Often, there is a non-linear relationship between atmospheric dissolved inorganic nitrogen (DIN) input and DIN leaching that is poorly captured by existing models. We present the first application of the non-parametric classification and regression tree approach to evaluate the key environmental drivers controlling DIN leaching from European forests. DIN leaching was classified as low (<3), medium (3-15) or high (>15 kg N ha⁻¹ year⁻¹) at 215 sites across Europe. The analysis identified throughfall NO₃⁻ deposition, acid deposition, hydrology, soil type, the carbon content of the soil, and the legacy of historic N deposition as the dominant drivers of DIN leaching for these forests. Ninety four percent of sites were successfully classified into the appropriate leaching category. This approach shows promise for understanding complex ecosystem responses to a wide range of anthropogenic stressors as well as an improved method for identifying risk and targeting pollution mitigation strategies in forest ecosystems.     

Assessing the risk of N leaching from forest soils across a steep N deposition gradient in Sweden

Nitrogen leaching from boreal and temporal forests, where normally most of the nitrogen is retained, has the potential to increase acidification of soil and water and eutrophication of the Baltic Sea. In parts of Sweden, where the nitrogen deposition has been intermediate to high during recent decades, there are indications that the soils are close to nitrogen saturation. In this study, four different approaches were used to assess the risk of nitrogen leaching from forest soils in different parts of Sweden. Nitrate concentrations in soil water and C:N ratios in the humus layer where interpreted, together with model results from mass balance calculations and detailed dynamic modelling. All four approaches pointed at a risk of nitrogen leaching from forest soils in southern Sweden. However, there was a substantial variation on a local scale. Basing the assessment on four different approaches makes the assessment robust.