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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.

Abstract Alpine grassland vegetation supports globally important biodiversity and ecosystems that are increasingly threatened by climate warming and other environmental changes. Trait-based approaches can support understanding of vegetation responses to global change drivers and consequences for ecosystem functioning. In six sites along a 1314 m elevational gradient in Puna grasslands in the Peruvian Andes, we collected datasets on vascular plant composition, plant functional traits, biomass, ecosystem fluxes, and climate data over three years. The data were collected in the wet and dry season and from plots with different fire histories. We selected traits associated with plant resource use, growth, and life history strategies (leaf area, leaf dry/wet mass, leaf thickness, specific leaf area, leaf dry matter content, leaf C, N, P content, C and N isotopes). The trait dataset contains 3,665 plant records from 145 taxa, 54,036 trait measurements (increasing the trait data coverage of the regional flora by 420%) covering 14 traits and 121 plant taxa (ca. 40% of which have no previous publicly available trait data) across 33 families.

Abstract The Arctic is warming at a rate four times the global average, while also being exposed to other global environmental changes, resulting in widespread vegetation and ecosystem change. Integrating functional trait-based approaches with multi-level vegetation, ecosystem, and landscape data enables a holistic understanding of the drivers and consequences of these changes. In two High Arctic study systems near Longyearbyen, Svalbard, a 20-year ITEX warming experiment and elevational gradients with and without nutrient input from nesting seabirds, we collected data on vegetation composition and structure, plant functional traits, ecosystem fluxes, multispectral remote sensing, and microclimate. The dataset contains 1,962 plant records and 16,160 trait measurements from 34 vascular plant taxa, for 9 of which these are the first published trait data. By integrating these comprehensive data, we bridge knowledge gaps and expand trait data coverage, including on intraspecific trait variation. These data can offer insights into ecosystem functioning and provide baselines to assess climate and environmental change impacts. Such knowledge is crucial for effective conservation and management in these vulnerable regions.

The International Tundra Experiment (ITEX) was established to understand how environmental change impacts Arctic and alpine ecosystems. The success of the ITEX network has allowed for several important across-site syntheses, and for some ITEX sites enough data have now been collected to perform within-site syntheses on the effects of environmental change across ecological scales. In this study, we analyze climate data and synthesize three decades of research on the ecological effects of environmental change at the ITEX site at Finse, southern Norway. We found a modest warming rate of +0.36 °C per decade and minor effects on growing season length. Maximum winter snow depth was highest in winters with a positive North Atlantic Oscillation. Our synthesis included 80 ecological studies from Finse, biased towards primary producers with few studies on ecological processes. Species distributions depended on microtopography and microclimate. Experimental warming had contrasting effects on abundance and traits of individual species and only modest effects at the community level above and below ground. In contrast, nutrient addition experiments caused strong responses in primary producer and arthropod communities. This within-site synthesis enabled us to conclude how different environmental changes (experimental and ambient warming, nutrient addition, and environmental gradients) impact across ecological scales, which is challenging to achieve with across-site approaches.

Foundation species provide habitat and modify the availability of resources to other species. In nature, multiple foundation species may occur in mixture, but little is known on how their interactions shape the community assembly of associated species. Lichens provide both structural habitat and resources to a variety of associated organisms and thereby serve as foundation species. In this study, we use mat-forming lichens and their associated micro-arthropods as a miniature ecosystem to study potential synergies between foundation species diversity and the abundance and functional diversity of higher trophic levels. We created lichen patches with monocultures and mixtures of up to four species, and extracted Collembola (identified to species level), Oribatida, Mesostigmata, Pseudoscorpiones, and Araneae with Tullgren apparatuses after 106 days of incubation within a natural lichen mat. We found that different lichen species supported different arthropod abundances. For 19 out of a total of 55 lichen mixtures and arthropod groups, we found non-additive, synergistic effects on arthropod abundance, although the specific lichen mixture causing synergistic effects differed with arthropod group. In addition, synergistic effects on arthropod abundance were more common for arthropod groups at lower trophic levels. The functional diversity of lichen mixtures explained patterns in Collembola abundance, but in the opposite direction than hypothesized because synergistic responses were more frequent in functionally similar lichen mixtures. Finally, we found few effects of lichen mixture identity or diversity on the functional diversity of Collembola communities. When applied to large-scale ecosystems, our results suggest that understanding interactions between coexisting foundation species and identifying those species that drive synergistic effects of foundation species on consumer biota, is likely to be of importance to biodiversity conservation and restoration efforts.

Mapping the spatial and temporal dynamics of species distributions is necessary for biodiversity conservation land-use planning decisions. Recent advances in remote sensing and machine learning have allowed for high-resolution species distribution modeling that can inform landscape-level decision-making. Here we compare the performance of three popular Sentinel-2 (10-m) land cover maps, including dynamic world (DW), European land cover (ELC10), and world cover (WC), in predicting wild bee species richness over southern Norway. The proportion of grassland habitat within 250 m (derived from the land cover maps), along with temperature and distance to sandy soils, were used as predictors in both Bayesian regularized neural network and random forest models. Models using grassland habitat from DW performed best (RMSE = 2.8 ± 0.03; average ± standard deviation across models), followed by ELC10 (RMSE = 2.85 ± 0.03) and WC (RMSE = 2.87 ± 0.02). All satellite-derived maps outperformed a manually mapped Norwegian land cover dataset called AR5 (RMSE = 3.02 ± 0.02). When validating the model predictions of bee species richness against citizen science data on solitary bee occurrences using generalized linear models, we found that ELC10 performed best (AIC = 2278 ± 4), followed by WC (AIC = 2367 ± 3), and DW (AIC = 2376 ± 3). While the differences in RMSE we observed between models were small, they may be significant when such models are used to prioritize grassland patches within a landscape for conservation subsidies or management policies. Partial dependencies in our models showed that increasing the proportion of grassland habitat is positively associated with wild bee species richness, thereby justifying bee conservation schemes that aim to enhance semi-natural grassland habitat. Our results confirm the utility of satellite-derived land cover maps in supporting high-resolution species distribution modeling and suggest there is scope to monitor changes in species distributions over time given the dense time series provided by products such as DW.