The High–Low Arctic boundary: How is it determined and where is it located?

Geobotanical subdivision of landcover is a baseline for many studies. The High–Low Arctic boundary is considered to be of fundamental natural importance. The wide application of different delimitation schemes in various ecological studies and climatic scenarios raises the following questions: (i) What are the common criteria to define the High and Low Arctic? (ii) Could human impact significantly change the distribution of the delimitation criteria? (iii) Is the widely accepted temperature criterion still relevant given ongoing climate change? and (iv) Could we locate the High–Low Arctic boundary by mapping these criteria derived from modern open remote sensing and climatic data? Researchers rely on common criteria for geobotanical delimitation of the Arctic. Unified circumpolar criteria are based on the structure of vegetation cover and climate, while regional specifics are reflected in the floral composition. However, the published delimitation schemes vary greatly. The disagreement in the location of geobotanical boundaries across the studies manifests in poorly comparable results. While maintaining the common principles of geobotanical subdivision, we derived the boundary between the High and Low Arctic using the most up‐to‐date field data and modern techniques: species distribution modeling, radar, thermal and optical satellite imagery processing, and climatic data analysis. The position of the High–Low Arctic boundary in Western Siberia was clarified and mapped. The new boundary is located 50–100 km further north compared to all the previously presented ones. Long‐term anthropogenic press contributes to a change in the vegetation structure but does not noticeably affect key species ranges. A previously specified climatic criterion for the High–Low Arctic boundary accepted in scientific literature has not coincided with the boundary in Western Siberia for over 70 years. The High–Low Arctic boundary is distinctly reflected in biodiversity distribution. The presented approach is appropriate for accurate mapping of the High–Low Arctic boundary in the circumpolar extent

Patterned-ground facilitates shrub expansion in Low Arctic tundra

Recent expansion of tall shrubs in Low Arctic tundra is widely seen as a response to climate warming, but shrubification is not occurring as a simple function of regional climate trends. We show that establishment of tall alder ( Alnus ) is strongly facilitated by small, widely distributed cryogenic disturbances associated with patterned-ground landscapes. We identified expanding and newly established shrub stands at two northwest Siberian sites and observed that virtually all new shrubs occurred on bare microsites (‘circles’) that were disturbed by frost-heave. Frost-heave associated with circles is a widespread, annual phenomenon that maintains mosaics of mineral seedbeds with warm soils and few competitors that are immediately available to shrubs during favorable climatic periods. Circle facilitation of alder recruitment also plausibly explains the development of shrublands in which alders are regularly spaced. We conclude that alder abundance and extent have increased rapidly in the northwest Siberian Low Arctic since at least the mid-20th century, despite a lack of summer warming in recent decades. Our results are consistent with findings in the North American Arctic which emphasize that the responsiveness of Low Arctic landscapes to climate change is largely determined by the frequency and extent of disturbance processes that create mineral-rich seedbeds favorable for tall shrub recruitment. Northwest Siberia has high potential for continued expansion of tall shrubs and concomitant changes to ecosystem function, due to the widespread distribution of patterned-ground landscapes

GIS and field data-based modelling of snow water equivalent in shrub tundra

An approach for snow water equivalent (SWE) modelling in tundra environments has been developed for the test area on the Yamal peninsula. Detailed mapping of snow cover is very important for tundra areas under continuous permafrost conditions, because the snow cover affects the active layer thickness (ALT) and the ground temperature, acting as a heat-insulating agent. The information concerning snow cover with specific regime of accumulation can support studies of ground temperature distribution and other permafrost related aspects. Special attention has been given to the presence of shrubs and microtopography, specifically ravines in a modelling approach. The methodology is based on statistical analysis of snow survey data and on GIS- (Geographical Information System) analysis of a range of parameters: topography, wind, and shrub vegetation. The topography significantly controls snow cover redistribution. This influence can be expressed as increase of snow depth on concave and decrease on convex surfaces. Specifically, snow depth was related to curvature in the study area with a correlation of R=0.83. An index is used to distinguish windward and leeward slopes in order to explain wind redistribution of snow. It is calculated from aspect data retrieved from a digital elevation model (obtained by field survey). It can be shown that shrub vegetation can serve as a ‘trap’ for wind-blown snow but is not a limiting factor for maximum snow depth, since the snow depth can be higher or lower than shrub height dependent on other factors

A raster version of the Circumpolar Arctic Vegetation Map (CAVM)

Land cover maps are the basic data layer required for understanding and modeling ecological patterns and processes. The Circumpolar Arctic Vegetation Map (CAVM), produced in 2003, has been widely used as a base map for studies in the arctic tundra biome. However, the relatively coarse resolution and vector format of the map were not compatible with many other data sets. We present a new version of the CAVM, building on the strengths of the original map, while providing a finer spatial resolution, raster format, and improved mapping. The Raster CAVM uses the legend, extent and projection of the original CAVM. The legend has 16 vegetation types, glacier, saline water, freshwater, and non-arctic land. The Raster CAVM divides the original rock-water-vegetation complex map unit that mapped the Canadian Shield into two map units, distinguishing between areas with lichen- and shrub-dominated vegetation. In contrast to the original hand-drawn CAVM, the new map is based on unsupervised classifications of seventeen geographic/floristic sub-sections of the Arctic, using AVHRR and MODIS data (reflectance and NDVI) and elevation data. The units resulting from the classification were modeled to the CAVM types using a wide variety of ancillary data. The map was reviewed by experts familiar with their particular region, including many of the original authors of the CAVM from Canada, Greenland (Denmark), Iceland, Norway (including Svalbard), Russia, and the U.S. The analysis presented here summarizes the area, geographical distribution, elevation, summer temperatures, and NDVI of the map units. The greater spatial resolution of the Raster CAVM allowed more detailed mapping of water-bodies and mountainous areas. It portrays coastal-inland gradients, and better reflects the heterogeneity of vegetation type distribution than the original CAVM. Accuracy assessment of random 1-km pixels interpreted from 6 Landsat scenes showed an average of 70% accuracy, up from 39% for the original CAVM. The distribution of shrub-dominated types changed the most, with more prostrate shrub tundra mapped in mountainous areas, and less low shrub tundra in lowland areas. This improved mapping is important for quantifying existing and potential changes to land cover, a key environmental indicator for modeling and monitoring ecosystems. The final product is publicly available at www.geobotany.uaf.edu and at Mendeley Data, DOI: 10.17632/c4xj5rv6kv.1

Circumpolar Arctic Vegetation Classification

An Arctic Vegetation Classification (AVC) is needed to address issues related to rapid Arctic-wide changes to climate, land-use, and biodiversity. Location: The 7.1 million km2 Arctic tundra biome. Approach and conclusions: The purpose, scope and conceptual framework for an Arctic Vegetation Archive (AVA) and Classification (AVC) were developed during numerous workshops starting in 1992. The AVA and AVC are modeled after the European vegetation archive (EVA) and classification (EVC). The AVA will use Turboveg for data management. The EVC will use a Braun-Blanquet (Br.-Bl.) classification approach. There are approximately 31,000 Arctic plots that could be included in the AVA. An Alaska AVA (AVA-AK, 24 datasets, 3026 plots) is a prototype for archives in other parts of the Arctic. The plan is to eventually merge data from otherregions of the Arctic into a single Turboveg v3 database. We present the pros and cons of using the Br.-Bl. classification approach compared to the EcoVeg (US) and Biogeoclimatic Ecological Classification (Canada) approaches. The main advantages are that the Br.-Bl. approach already has been widely used in all regions of the Arctic, and many described, well-accepted vegetation classes have a pan-Arctic distribution. A crosswalk comparison of Dryas octopetala communities described according to the EcoVeg and the Braun-Blanquet approaches indicates that the non-parallel hierarchies of the two approaches make crosswalks difficult above the plantcommunity level. A preliminary Arctic prodromus contains a list of typical Arctic habitat types with associated described syntaxa from Europe, Greenland, western North America, and Alaska. Numerical clustering methods are used to provide an overview of the variability of habitat types across the range of datasets and to determine their relationship to previously described Braun-Blanquet syntaxa. We emphasize the need for continued maintenance of the Pan-Arctic Species List, and additional plot data to fully sample the variability across bioclimatic subzones, phytogeographic regions, and habitats in the Arctic. This will require standardized methods of plot-data collection, inclusion of physiogonomic information in the numeric analysis approaches to create formal definitions for vegetation units, and new methods of data sharing between the AVA and national vegetation- plot databases