Abstract

Keywords

atmospheric deposition;forest vegetation; plant community dynamics; plant species diversity; resampling study: soil chemistry

Abstract

We evaluated effects of atmospheric deposition of nitrogen on the composition of forest understorey vegetation both in space and time, using repeated data from the European wide monitoring program ICP-Forests, which focuses on normally managed forest. Our aim was to assess whether both spatial and temporal effects of deposition can be detected by a multiple regression approach using data from managed forests over a relatively short time interval, in which changes in the tree layer are limited. To characterize the vegetation, we used indicators derived from cover percentages per species using multivariate statistics and indicators derived from the presence/absence, that is, species numbers and Ellenberg's indicator values. As explanatory variables, we used climate, altitude, tree species, stand age, and soil chemistry, besides deposition of nitrate, ammonia and sulfate. We analyzed the effects of abiotic conditions at a single point in time by canonical correspondence analysis and multiple regression. The relation between the change in vegetation and abiotic conditions was analyzed using redundancy analysis and multiple regression, for a subset of the plots that had both abiotic data and enough species to compute a mean Ellenberg N value per plot using a minimum of three species. Results showed that the spatial variation in the vegetation is mainly due to “traditional” factors such as soil type and climate, but a statistically significant part of the variation could be ascribed to atmospheric deposition of nitrate. The change in the vegetation over the past c. 10 years was also significantly correlated to nitrate deposition. Although the effect of deposition on the individual species could not be clearly defined, the effect on the vegetation as a whole was a shift toward nitrophytic species as witnessed by an increase in mean Ellenberg's indicator value.

LWF Classification

Network: LWF, Category: ISI,