Urs Treier

Mapping changes in vegetation cover is essential for understanding the consequences of climate change on Arctic ecosystems. Classification of ultra-high spatial-resolution (UHR, < 1 cm) imagery can provide estimates of vegetation cover across space and time. The challenge of this approach is to assure comparability of classification across many images taken at different illumination conditions and locations. With warming, vegetation at higher elevation is expected to resemble current vegetation at lower elevation. To investigate the value of classification of UHR imagery for monitoring vegetation change, we collected visible and near-infrared images from 108 plots with hand-held cameras along an altitudinal gradient in Greenland and examined the classification accuracy of shrub cover on independent images (i.e. classification transferability). We implemented several models to examine if colour calibration improves transferability based on an in-image calibration target. The classifier was trained on different number of images to find the minimum training subset size. With a training set of ~ 20% of the images the overall accuracy levelled off at about 81% and 68% on the non-calibrated training and validation images, respectively. Colour calibration improved the accuracy on training images (1–4%) while it only improved the classifier transferability significantly for training sets < 20%. Linear calibration only based on the target’s grey series improved transferability most. Reasonable transferability of Arctic shrub cover classification can be obtained based only on spectral data and about 20% of all images. This is promising for vegetation monitoring through multi-image classification of UHR imagery acquired with hand-held cameras or Unmanned Aerial Systems.
 

Climate change has been reported to affect shrub growth positively at several sites at high northern latitudes, including several arctic environments. The observed growth rates are, however, not uniform in space and time, and the mechanistic drivers of these patterns remain poorly understood. Here we investigated spatio-temporal interactions between climatic conditions, xylem anatomical traits, and annual growth of 21 Betula nana L. individuals from western Greenland for the period 2001-2011. Structural equation modeling showed that summer precipitation and winter temperature are affecting annual growth positively. Furthermore, vessel lumen area and vessel grouping, which are related to water conductivity and hydraulic connectivity of the xylem, respectively, positively influenced annual growth. To optimize growth B. nana was thus able to adjust its water transporting system. Annual variation in vessel lumen area seemed to be driven mostly by spring and summer temperatures, whereas annual variation in vessel grouping was driven by winter temperature. Linear models did not reveal a pattern in the spatial variation of xylem anatomical traits across the sampled climatic gradient. However, growth was positively correlated with local variation in insolation. Our results suggest that B. nana can adjust its hydraulic capacity to annual fluctuations in climatic conditions in order to optimize its total radial stem growth rate.

The Arctic is warming at twice the rate of the rest of the world. This impacts Arctic species both directly, through increased temperatures, and indirectly, through structural changes in their habitats. Species are expected to exhibit idiosyncratic responses to structural change, which calls for detailed investigations at the species and community level. Here, we investigate how arthropod assemblages of spiders and beetles respond to variation in habitat structure at small spatial scales. We sampled transitions in shrub dominance and soil moisture between three different habitats (fen, dwarf shrub heath, and tall shrub tundra) at three different sites along a fjord gradient in southwest Greenland, using yellow pitfall cups. We identified 2,547 individuals belonging to 47 species. We used species richness estimation, indicator species analysis and latent variable modeling to examine differences in arthropod community structure in response to habitat variation at local (within site) and regional scales (between sites). We estimated species responses to the environment by fitting species-specific generalized linear models with environmental covariates. Species assemblages were segregated at the habitat and site level. Each habitat hosted significant indicator species, and species richness and diversity were significantly lower in fen habitats. Assemblage patterns were significantly linked to changes in soil moisture and vegetation height, as well as geographic location. We show that meter-scale variation among habitats affects arthropod community structure, supporting the notion that the Arctic tundra is a heterogeneous environment. To gain sufficient insight into temporal biodiversity change, we require studies of species distributions detailing species habitat preferences.