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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. 

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. 

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). 

The Arctic is experiencing rapid climate change. This research documents changes to tundra vegetation near Atqasuk and Utqiaġvik, Alaska. At each location, 30 plots were sampled annually from 2010 to 2019 using a point frame. For every encounter, we recorded the height and classified it into eight groupings (deciduous shrubs, evergreen shrubs, forbs, graminoids, bryophytes, lichens, litter, and standing dead vegetation); for vascular plants we also identified the species. We found an increase in plant stature and cover over time, consistent with regional warming. Graminoid cover and height increased at both sites, with a 5-fold increase in cover in Atqasuk. At Atqasuk, the cover and height of shrubs and forbs increased. Species diversity decreased at both the sites. Year was generally the strongest predictor of vegetation change, suggesting a cumulative change over time; however, soil moisture and soil temperature were also predictors of vegetation change. We anticipate that plants in the region will continue to grow taller as the region warms, resulting in greater plant cover, especially of graminoids and shrubs. The increase in plant cover and accumulation of litter may negatively impact non-vascular plants. Continued changes in community structure will impact energy balance and carbon cycling and may have regional and global consequences. 

The intensification of coastal storms, combined with declining sea ice cover, sea level rise, and changes to permafrost conditions, will likely increase the incidence and impact of storm surge flooding in Arctic coastal environments. In coastal communities accurate information on the exposure of infrastructure can make an important contribution to adaptation planning. In this study, we use high resolution elevation data from airborne LiDAR to generate storm flooding scenarios for three coastal communities (Utqiag_ vik, Wainwright, and Kaktovik) in northern Alaska. To estimate the potential for damage to infrastructure caused by flooding for each community, we generated data on replacement costs and used it to estimate the financial impact of 24 storm flooding scenarios of varying intensities. This analysis shows that all three communities are exposed to storm surges, but highlights the fact that infrastructure in Utqiag_ vik (the administrative center of the North Slope Borough) is significantly more exposed than buildings in Wainwright and Kaktovik. Our findings show that flooding scenarios can complement information gained from past events and help to inform local-decision making. 

Abstract In dryland soils, spatiotemporal variation in surface soils (0–10 cm) plays an important role in the function of the “critical zone” that extends from canopy to groundwater. Understanding connections between soil microbes and biogeochemical cycling in surface soils requires repeated multivariate measurements of nutrients, microbial abundance, and microbial function. We examined these processes in resource islands and interspaces over a two‐month period at a Chihuahuan Desert bajada shrubland site. We collected soil inProsopis glandulosa(honey mesquite),Larrea tridentata(creosote bush), and unvegetated (interspace) areas to measure soil nutrient concentrations, microbial biomass, and potential soil enzyme activity. We monitored the dynamics of these belowground processes as soil conditions dried and then rewetted due to rainfall. Most measured variables, including inorganic nutrients, microbial biomass, and soil enzyme activities, were greater under shrubs during both wet and dry periods, with the highest magnitudes under mesquite followed by creosote bush and then interspace. One exception was nitrate, which was highly variable and did not show resource island patterns. Temporally, rainfall pulses were associated with substantial changes in soil nutrient concentrations, though resource island patterns remained consistent during all phases of the soil moisture pulse. Microbial biomass was more consistent than nutrients, decreasing only when soils were driest. Potential enzyme activities were even more consistent and did not decline in dry periods, potentially helping to stimulate observed pulses in CO₂efflux following rain events observed at a co‐located eddy flux tower. These results indicate a critical zone with organic matter cycling patterns consistently elevated in shrub resource islands (which varied by shrub species), high decomposition potential that limits soil organic matter accumulation across the landscape, and nitrate fluxes that are decoupled from the organic matter pathways.