An application of predictive vegetation mapping to mountain vegetation in Sweden

Abstract

Predictive vegetation mapping was employed to predict the distribution of vegetation communities and physiognomies in the portion of the Scandinavian mountains in Sweden. This was done to address three main research questions: (1) what environmental variables are important in structuring vegetation patterns in the study area? (2) how well does a classification tree predict the composition of mountain vegetation in the study area using the chosen environmental variables for the study? and (3) are vegetation patterns better predicted at higher levels of physiognomic aggregation? Using GIS, a spatial dataset was first developed consisting of sampled points across the full geographic range of the study area. The sample contained existing vegetation community data as the dependent variable and various environmental data as the independent variables thought to control or correlate with vegetation distributions. The environmental data were either obtained from existing digital datasets or derived from Digital Elevation Models (DEMs). Utilizing classification tree methodology, three model frameworks were developed in which vegetation was increasingly aggregated into higher levels of physiognomic organization. The models were then pruned, and accuracy statistics were obtained. Results indicated that accuracy improved with increasing aggregation of the dependent variable. The three model frameworks were then applied to the Abisko portion of the study area in northwestern Sweden to produce predictive maps which were compared to the current vegetation distribution. Compositional patterns were critically analyzed in order to: (1) assess the ability of the models to correctly classify general vegetation patterns at the three levels of physiognomic classification, (2) address the extent to which three specific ecological relationships thought to control vegetation distribution in this area were manifested by the model, and (3) speculate as to possible sources of error and factors affecting accuracy of the models.