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Detailed descriptions of individual vegetation types shown on vegetation maps can improve the ways in which the composition and spatial structure within the types are understood. The authors therefore examined dwarf shrub heath, a vegetation type covering large areas and found in many parts of the Norwegian mountains. They used data from point samples obtained in a wall-to-wall area frame survey. The point sampling method provided data that gave a good understanding of the composition and structure of the vegetation type, but also revealed a difference between variation within the vegetation type itself (intra-class variation) and variation resulting from the inclusion of other types of vegetation inside the map polygons (landscape variation). Intra-class variation reflected differences in the botanical composition of the vegetation type itself, whereas landscape variation represented differences in the land-cover composition of the broader landscape in which the vegetation type was found. Both types of variation were related to environmental gradients. The authors conclude that integrated point sampling method is an efficient way to achieve increased understanding of the content of a vegetation map and can be implemented as a supporting activity during a survey. ARTICLE HISTORY

Georeferenced species data have a wide range of applications and are increasingly used for e.g. distribution modelling and climate change studies. As an integrated part of an on-going survey programme for vegetation mapping, plant species have been recorded. The data described in this paper contains 18.521 registrations of plants from 1190 different circular plots throughout Norway. All species localities are georeferenced, the spatial uncertainty is provided, and additional ecological information is reported. The published data has been gathered from 1991 until 2015. The entries contain all higher vascular plants and pteridophytes, and some cryptogams. Other ecological information is also provided for the species locations, such as the vegetation type, the cover of the species and slope. The entire material is stored and available for download through the GBIF server.

Vegetation mapping using field surveys is expensive. Distribution modelling, based on sample surveys, might overcome this challenge. We tested if models trained from sample surveys could be used to predict the distribution of vegetation types in neighbourhood areas, and how reliable the spatial transferability was. We also tested whether we should use ecological dissimilarity or spatial distance to foresee modelling performance. Maximum entropy models were run for three vegetation types based on a vegetation map within a mountain range. Environmental variables were selected backwards, model complexity was kept low. The models are based on points from a small part of each study site, transferred into the entire sites, and then tested for performance. Environmental distance was tested using principle component analysis. All models had high uncorrected AUC values. The ability to predict presences correctly was low. The ability to predict absences correctly was high. The ability to transfer the distribution model depended on environmental distance, not spatial distance.