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Abstract The Arctic is warming four times faster than the global average¹and plant communities are responding through shifts in species abundance, composition and distribution^2–4. However, the direction and magnitude of local changes in plant diversity in the Arctic have not been quantified. Using a compilation of 42,234 records of 490 vascular plant species from 2,174 plots across the Arctic, here we quantified temporal changes in species richness and composition through repeat surveys between 1981 and 2022. We also identified the geographical, climatic and biotic drivers behind these changes. We found greater species richness at lower latitudes and warmer sites, but no indication that, on average, species richness had changed directionally over time. However, species turnover was widespread, with 59% of plots gaining and/or losing species. Proportions of species gains and losses were greater where temperatures had increased the most. Shrub expansion, particularly of erect shrubs, was associated with greater species losses and decreasing species richness. Despite changes in plant composition, Arctic plant communities did not become more similar to each other, suggesting no biotic homogenization so far. Overall, Arctic plant communities changed in richness and composition in different directions, with temperature and plant–plant interactions emerging as the main drivers of change. Our findings demonstrate how climate and biotic drivers can act in concert to alter plant composition, which could precede future biodiversity changes that are likely to affect ecosystem function, wildlife habitats and the livelihoods of Arctic peoples^5,6. 

Abstract In 2022 we resampled normalized difference vegetation index (NDVI) along a 100 m transect in tundra near Utqiagvik, AK that had been previously measured through the 2000–2002 growing seasons, providing an opportunity to examine a 20 year NDVI change at a 1 m resolution in a region that is experiencing increased warming and precipitation over this period. Multidecadal NDVI change was spatially variable across the transect with nearly half of the transect showing greening, about a third not showing conclusive change, and about 20% browning. In wet areas, greening (increased NDVI) was associated with increased green leaf area index, while in drier areas greening was related to changes in species cover. Browning was not related to change in species cover and appeared to be due to increased coverage of standing dead material in graminoid dominated canopies. These types of detailed observations provide insights into the interpretation of satellite based NDVI trends and emphasize the importance of microtopography and hydrology in mediating vegetation change in a warming Arctic. 

Abstract Foundation species have disproportionately large impacts on ecosystem structure and function. As a result, future changes to their distribution may be important determinants of ecosystem carbon (C) cycling in a warmer world. We assessed the role of a foundation tussock sedge (Eriophorum vaginatum) as a climatically vulnerable C stock using field data, a machine learning ecological niche model, and an ensemble of terrestrial biosphere models (TBMs). Field data indicated that tussock density has decreased by ∼0.97 tussocks per m²over the past ∼38 years on Alaska’s North Slope from ∼1981 to 2019. This declining trend is concerning because tussocks are a large Arctic C stock, which enhances soil organic layer C stocks by 6.9% on average and represents 745 Tg C across our study area. By 2100, we project that changes in tussock density may decrease the tussock C stock by 41% in regions where tussocks are currently abundant (e.g. −0.8 tussocks per m²and −85 Tg C on the North Slope) and may increase the tussock C stock by 46% in regions where tussocks are currently scarce (e.g. +0.9 tussocks per m²and +81 Tg C on Victoria Island). These climate-induced changes to the tussock C stock were comparable to, but sometimes opposite in sign, to vegetation C stock changes predicted by an ensemble of TBMs. Our results illustrate the important role of tussocks as a foundation species in determining future Arctic C stocks and highlight the need for better representation of this species in TBMs. 

Abstract The Arctic is warming at twice the rate of the global mean. This warming could further stimulate methane (CH₄) emissions from northern wetlands and enhance the greenhouse impact of this region. Arctic wetlands are extremely heterogeneous in terms of geochemistry, vegetation, microtopography, and hydrology, and therefore CH₄fluxes can differ dramatically within the metre scale. Eddy covariance (EC) is one of the most useful methods for estimating CH₄fluxes in remote areas over long periods of time. However, when the areas sampled by these EC towers (i.e. tower footprints) are by definition very heterogeneous, due to encompassing a variety of environmental conditions and vegetation types, modelling environmental controls of CH₄emissions becomes even more challenging, confounding efforts to reduce uncertainty in baseline CH₄emissions from these landscapes. In this study, we evaluated the effect of footprint variability on CH₄fluxes from two EC towers located in wetlands on the North Slope of Alaska. The local domain of each of these sites contains well developed polygonal tundra as well as a drained thermokarst lake basin. We found that the spatiotemporal variability of the footprint, has a significant influence on the observed CH₄fluxes, contributing between 3% and 33% of the variance, depending on site, time period, and modelling method. Multiple indices were used to define spatial heterogeneity, and their explanatory power varied depending on site and season. Overall, the normalised difference water index had the most consistent explanatory power on CH₄fluxes, though generally only when used in concert with at least one other spatial index. The spatial bias (defined here as the difference between the mean for the 0.36 km²domain around the tower and the footprint-weighted mean) was between ∣51∣% and ∣18∣% depending on the index. This study highlights the need for footprint modelling to infer the representativeness of the carbon fluxes measured by EC towers in these highly heterogeneous tundra ecosystems, and the need to evaluate spatial variability when upscaling EC site-level data to a larger domain. 

Abstract Non‐forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non‐forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low‐stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjustedR²of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave‐one‐out cross‐validation of 3.9%. Biomass per‐unit‐of‐height was similarwithinbut differentamong,plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1–10 ha⁻¹. Photogrammetric approaches could provide much‐needed information required to calibrate and validate the vegetation models and satellite‐derived biomass products that are essential to understand vulnerable and understudied non‐forested ecosystems around the globe. 

Arctic shorelines are vulnerable to climate change impacts as sea level rises, permafrost thaws, storms intensify, and sea ice thins. Seventy-five years of aerial and satellite observations have established coastal erosion as an increasing Arctic hazard. However, other hazards at play—for instance, the cumulative impact that sea-level rise and permafrost thaw subsidence will have on permafrost shorelines—have received less attention, preventing assessments of these processes’ impacts compared to and combined with coastal erosion. Alaska’s Arctic Coastal Plain (ACP) is ideal for such assessments because of the high-density observations of topography, coastal retreat rates, and permafrost characteristics, and importance to Indigenous communities and oilfield infrastructure. Here, we produce 21st-century projections of Arctic shoreline position that include erosion, permafrost subsidence, and sea-level rise. Focusing on the ACP, we merge 5 m topography, satellite-derived coastal lake depth estimates, and empirical assessments of land subsidence due to permafrost thaw with projections of coastal erosion and sea-level rise for medium and high emissions scenarios from the Intergovernmental Panel on Climate Change’s AR6 Report. We find that by 2100, erosion and inundation will together transform the ACP, leading to 6-8x more land loss than coastal erosion alone and disturbing 8-11x more organic carbon. Without mitigating measures, by 2100, coastal change could damage 40 to 65% of infrastructure in present-day ACP coastal villages and 10 to 20% of oilfield infrastructure. Our findings highlight the risks that compounding climate hazards pose to coastal communities and underscore the need for adaptive planning for Arctic coastlines in the 21st century. 

Abstract. Studies in recent decades have shown strong evidence of physical and biological changes in the Arctic tundra, largely in response to rapid rates of warming. Given the important implications of these changes for ecosystem services, hydrology, surface energy balance, carbon budgets, and climate feedbacks, research on the trends and patterns of these changes is becoming increasingly important and can help better constrain estimates of local, regional, and global impacts as well as inform mitigation and adaptation strategies. Despite this great need, scientific understanding of tundra ecology and change remains limited, largely due to the inaccessibility of this region and less intensive studies compared to other terrestrial biomes. A synthesis of existing datasets from past field studies can make field data more accessible and open up possibilities for collaborative research as well as for investigating and informing future studies. Here, we synthesize field datasets of vegetation and active-layer properties from the Alaskan tundra, one of the most well-studied tundra regions. Given the potentially increasing intensive fire regimes in the tundra, fire history and severity attributes have been added to data points where available. The resulting database is a resource that future investigators can employ to analyze spatial and temporal patterns in soil, vegetation, and fire disturbance-related environmental variables across the Alaskan tundra. This database, titled the Synthesized Alaskan Tundra Field Database (SATFiD), can be accessed at the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) for Biogeochemical Dynamics (Chen et al., 2023: https://doi.org/10.3334/ORNLDAAC/2177). 

Plot-level photography is an attractive time-saving alternative to field measurements for vegetation monitoring. However, widespread adoption of this technique relies on efficient workflows for post-processing images and the accuracy of the resulting products. Here, we estimated relative vegetation cover using both traditional field sampling methods (point frame) and semi-automated classification of photographs (plot-level photography) across thirty 1 m2 plots near Utqiaġvik, Alaska, from 2012 to 2021. Geographic object-based image analysis (GEOBIA) was applied to generate objects based on the three spectral bands (red, green, and blue) of the images. Five machine learning algorithms were then applied to classify the objects into vegetation groups, and random forest performed best (60.5% overall accuracy). Objects were reliably classified into the following classes: bryophytes, forbs, graminoids, litter, shadows, and standing dead. Deciduous shrubs and lichens were not reliably classified. Multinomial regression models were used to gauge if the cover estimates from plot-level photography could accurately predict the cover estimates from the point frame across space or time. Plot-level photography yielded useful estimates of vegetation cover for graminoids. However, the predictive performance varied both by vegetation class and whether it was being used to predict cover in new locations or change over time in previously sampled plots. These results suggest that plot-level photography may maximize the efficient use of time, funding, and available technology to monitor vegetation cover in the Arctic, but the accuracy of current semi-automated image analysis is not sufficient to detect small changes in cover.