5 research outputs found

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

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    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

    Temperature and pH define the realised niche space of arbuscular mycorrhizal fungi

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    The arbuscular mycorrhizal (AM) fungi are a globally distributed group of soil organisms that play critical roles in ecosystem function. However, the ecological niches of individual AM fungal taxa are poorly understood. We collected > 300 soil samples from natural ecosystems worldwide and modelled the realised niches of AM fungal virtual taxa (VT; approximately species-level phylogroups). We found that environmental and spatial variables jointly explained VT distribution worldwide, with temperature and pH being the most important abiotic drivers, and spatial effects generally occurring at local to regional scales. While dispersal limitation could explain some variation in VT distribution, VT relative abundance was almost exclusively driven by environmental variables. Several environmental and spatial effects on VT distribution and relative abundance were correlated with phylogeny, indicating that closely related VT exhibit similar niche optima and widths. Major clades within the Glomeraceae exhibited distinct niche optima, Acaulosporaceae generally had niche optima in low pH and low temperature conditions, and Gigasporaceae generally had niche optima in high precipitation conditions. Identification of the realised niche space occupied by individual and phylogenetic groups of soil microbial taxa provides a basis for building detailed hypotheses about how soil communities respond to gradients and manipulation in ecosystems worldwide
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