Questions Is it possible to map floristic gradients in heterogeneous boreal vegetation by using remote-sensing data? Does a continuous vegetation map enable the creation of a spatially continuous map of seasonal permafrost soil thaw depth? Location Bonanza Creek LTER, Fairbanks, Alaska, USA. Methods Vegetation records are subjected to an ordination to extract the predominant floristic gradient. The ordination scores are then extrapolated using Sentinel 2 imagery and a digital elevation model (DEM). As the relation between vegetation pattern and seasonal thaw depth was confirmed in this study, the spatial distribution of ordination scores is then used to predict seasonal thaw depth over the same area. Results The first dimension of the ordination space separates species corresponding to moist and cold soil conditions from species associated with well-drained soils. This floristic gradient was successfully mapped within the sampled plant communities. The extrapolated thaw depths follow the typical distribution along a topographical and geomorphological gradient for this region. Besides vegetation information also DEM derivatives show high contributions to the thaw depth modeling. Conclusion We demonstrate that floristic gradient mapping in boreal vegetation is possible. The accuracy of the thaw depth prediction model is comparable to that in previous analyses but uses a more parsimonious set of predictors, underlining the efficacy of this approach.

Assessing biodiversity in arctic-alpine ecosystems is a costly task. We test in the current study whether we can map the spatial patterns of spider alpha and beta diversity using remotely-sensed surface reflectance and topography in a heterogeneous alpine environment in Central Norway. This proof-of-concept study may provide a tool for an assessment of arthropod communities in remote study areas. Data on arthropod species distribution and richness were collected through pitfall trapping and subjected to a detrended correspondence analysis (DCA) to extract the main species composition gradients. The DCA axis scores as indicators of species composition as well as trap species richness were regressed against a combined data set of surface reflectance as measured by the Sentinel-2 satellite and topographical parameters extracted from a digital elevation model. The models were subsequently applied to the spatial data set to achieve a pixel-wise prediction of both species richness and position in the DCA space. The spatial variation in the modelled DCA scores was used to draw conclusions regarding spider beta-diversity. The species composition was described with two DCA axes that were characterized by post hoc-defined indicator species, which showed a typical annidation in the arctic-alpine environment under study. The fits of the regression models for the DCA axes and species richness ranged from R-2 = 0.25 up to R-2 = 0.62. The resulting maps show strong gradients in alpha and beta diversity across the study area. Our results indicate that the diversity patterns of spiders can at least partially be explained by means of remotely sensed data. Our approach would likely benefit from the additional use of high resolution aerial photography and LiDAR data and may help to improve conservation strategies in arctic-alpine ecosystems.