Step-less models for regional environmental variation in Norway

Aim To assess the scale of variation for major environmental gradients in Norway. To obtain a step-less model for this variation and to use this model to evaluate the extent to which the consensus expert classification of Norway into vegetation regions can be predicted from environmental variables. To discuss the potential of step-less models for understanding natural variation at regional scales, for stratification and for predictive modelling of species distributions and land-cover types. Location The mainland of Norway. Methods Fifty-four climatic, topographical, hydrological and geological variables were recorded for grid cells with spatial resolution (grain size) of 1 x 1, 5 x 5 and 10 x 10 km, spanning the entire mainland of Norway. Principal components analyses (PCA) were used to summarize variation in three primary data matrices and three random subsets of these. Results The first four principal components explained between 75% and 85% of the variation in the data sets. All PCAs revealed four consistent environmental gradients, in order of decreasing importance: (1) regional variation (gradient) from coast to inland and from oceanic/humid to continental areas; (2) regional variation from north to south and from high to low altitudes; (3) regional variation from north to south and from inland to coast, related to solar radiation; and (4) topographic (terrain relief) variation on finer scales than (1-3). The first two PCA axes corresponded to the two bioclimatic gradients used in expert classifications of Norway into biogeographical regions: vegetation sections (from highly oceanic to slightly continental) and vegetation zones (from nemoral to alpine). Main conclusions Our PCA analyses substantiate the current view of bioclimatic regional vegetation variation in Norway, provide an explicit characterization of this variation in terms of climatic variables, and show that environmental variability can be reproduced as GIS layers in step-less models. These models have the potential to become important tools for future predictive modelling within resource management, conservation planning and biogeographical (and other ecological) research.