The Arctic is warming twice as fast as the rest of the planet, leading to rapid changes in species composition and plant functional trait variation. Landscape-level maps of vegetation composition and trait distributions are required to expand spatially-limited plot studies, overcome sampling biases associated with the most accessible research areas, and create baselines from which to monitor environmental change. Unmanned aerial vehicles (UAVs) have emerged as a low-cost method to generate high-resolution imagery and bridge the gap between fine-scale field studies and lower resolution satellite analyses. Here we used field spectroscopy data (400–2500 nm) and UAV multispectral imagery to test spectral methods of species identification and plant water and chemistry retrieval near Longyearbyen, Svalbard. Using the field spectroscopy data and Random Forest analysis, we were able to distinguish eight common High Arctic plant tundra species with 74% accuracy. Using partial least squares regression (PLSR), we were able to predict corresponding water, nitrogen, phosphorus and C:N values (r 2 = 0.61–0.88, RMSEmean = 12%–64%). We developed analogous models using UAV imagery (five bands: Blue, Green, Red, Red Edge and Near-Infrared) and scaled up the results across a 450 m long nutrient gradient located underneath a seabird colony. At the UAV level, we were able to map three plant functional groups (mosses, graminoids and dwarf shrubs) at 72% accuracy and generate maps of plant chemistry. Our maps show a clear marine-derived fertility gradient, mediated by geomorphology. We used the UAV results to explore two methods of upscaling plant water content to the wider landscape using Sentinel-2A imagery. Our results are pertinent for high resolution, low-cost mapping of the Arctic.

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The abundance and diversity of gymnamoebae in three subsoils varying in compaction and water retention along a 1.2 m transect were documented as the local climatic conditions changed from late summer 1999 through mid-summer 2000. The mean density of gymnamoebae for the loose soil (1,655/g) was greater than either the most compact (1,468/g) or moderately compact soil (851/g). Minimum densities occurred in middle and late summer for all soils while significant (F = 38.803, < or = 0.0002) density peaks at 3.212/g occurred in early summer in the most compact soil, 2.928/g in the least compact, and 2,209/g in the moderately compact soil. Limax non-eruptive gymnamoebae (mt 2) correlated (r = 0.49, p < or = 0.016) with moisture while eruptive limax gymnamoebae ( 3) correlated with temperature (r = 0.07, p < or = 0.024), moisture (r = 0.58, p < or = 0.001) and precipitation (r = 0.46, p < or = 0.029). Flattened or discoid amoebae (mt 4) dominated throughout most of the survey, and the two limax groups showed inverse relationships. Chi-square analyses showed significant differences in the numbers of limax eruptive gymnamoebae compared to all other morphotypes on all but one sampling period.