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Soils represent the largest store of carbon in the biosphere with soils at high latitudes containing twice as much carbon (C) than the atmosphere. High latitude tundra vegetation communities show increases in the relative abundance and cover of deciduous shrubs which may influence net ecosystem exchange of CO2 from this C-rich ecosystem. Monitoring soil respiration (Rs) as a crucial component of the ecosystem carbon balance at regional scales is difficult given the remoteness of these ecosystems and the intensiveness of measurements that is required. Here we use direct measurements of Rs from contrasting tundra plant communities combined with direct measurements of aboveground plant productivity via Normalised Difference Vegetation Index (NDVI) to predict soil respiration across four key vegetation communities in a tundra ecosystem. Soil respiration exhibited a nonlinear relationship with NDVI (y = 0.202e^3.508x, p < 0.001). Our results further suggest that NDVI and soil temperature can help predict Rs if vegetation type is taken into consideration. We observed, however, that NDVI is not a relevant explanatory variable in the estimation of SOC in a single-study analysis.

Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we present The Arctic plant aboveground biomass synthesis dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass (g m−2) on 2,327 sample plots from 636 field sites in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic.

Permafrost occupies 20 million square kilometres of Earth's high-latitude and high-altitude landscapes. These regions are sensitive to climate change and human activities; hence, permafrost research is of considerable scientific and societal importance. However, the results of this research are generally not known by the general public. Communicating scientific concepts is an increasingly important task in the research world. Different ways to engage learners and incorporate narratives in teaching materials exist, yet they are generally underused. Here we report on an international scientific outreach project called "Frozen-Ground Cartoons", which aims at making permafrost science accessible and fun for students, teachers, and parents through the creation of comic strips. We present the context in which the project was initiated, as well as recent education and outreach activities. The future phases of the project primarily involve a series of augmented reality materials, such as maps, photos, videos, and 3D drawings. With this project we aim to foster understanding of permafrost research among broader audiences, inspire future permafrost researchers, and raise public and science community awareness of polar science, education, outreach, and engagement.

Permafrost thaw resulting from climate warming is threatening to release carbon from high latitude peatlands. The aim of this research was to determine subsidence rates linked to permafrost thaw in sub-Arctic peatlands in Sweden using historical orthophotographic (orthophotos), Unoccupied Aerial Vehicle (UAV), and Interferometric Synthetic Aperture Radar (InSAR) data. The orthophotos showed that the permafrost palsa on the study sites have been contracting in their areal extent, with the greatest rates of loss between 2002 and 2008. The surface motion estimated from differential digital elevation models from the UAV data showed high levels of subsidence (maxi-mum of −25 cm between 2017 and 2020) around the edges of the raised palsa plateaus. The InSAR data analysis showed that raised palsa areas had the greatest subsidence rates, with maximum subsidence rates of 1.5 cm between 2017 and 2020; however, all wetland vegetation types showed sub-sidence. We suggest that the difference in spatial units associated with each sensor explains parts of the variation in the subsidence levels recorded. We conclude that InSAR was able to identify the areas most at risk of subsidence and that it can be used to investigate subsidence over large spatial extents, whereas UAV data can be used to better understand the dynamics of permafrost degradation at a local level. These findings underpin a monitoring approach for these peatlands.

Decomposition of soil organic matter (SOM) in permafrost terrain and the production of greenhouse gases is a key factor for understanding climate change-carbon feedbacks. Previous studies have shown that SOM decomposition is mostly controlled by soil temperature, soil moisture, and carbon-nitrogen ratio (C:N). However, focus has generally been on site-specific processes and little is known about variations in the controls on SOM decomposition across Arctic sites. For assessing SOM decomposition, we retrieved 241 samples from 101 soil profiles across three contrasting Arctic regions and incubated them in the laboratory under aerobic conditions. We assessed soil carbon losses (C-loss) five times during a 1year incubation. The incubated material consisted of near-surface active layer (AL(NS)), subsurface active layer (AL(SS)), peat, and permafrost samples. Samples were analyzed for carbon, nitrogen, water content, C-13, N-15, and dry bulk density (DBD). While no significant differences were observed between total AL(SS) and permafrost C-loss over 1year incubation (2.32.4% and 2.51.5% C-loss, respectively), AL(NS) samples showed higher C-loss (7.94.2%). DBD was the best explanatory parameter for active layer C-loss across sites. Additionally, results of permafrost samples show that C:N ratio can be used to characterize initial C-loss between sites. This data set on the influence of abiotic parameter on microbial SOM decomposition can improve model simulations of Arctic soil CO2 production by providing representative mean values of CO2 production rates and identifying standard parameters or proxies for upscaling potential CO2 production from site to regional scales.

Northern peatlands have accumulated large stocks of organic carbon (C) and nitrogen (N), but their spatial distribution and vulnerability to climate warming remain uncertain. Here, we used machine-learning techniques with extensive peat core data (n > 7,000) to create observation-based maps of northern peatland C and N stocks, and to assess their response to warming and permafrost thaw. We estimate that northern peatlands cover 3.7 +/- 0.5 million km(2) and store 415 +/- 150 Pg C and 10 +/- 7 Pg N. Nearly half of the peatland area and peat C stocks are permafrost affected. Using modeled global warming stabilization scenarios (from 1.5 to 6 degrees C warming), we project that the current sink of atmospheric C (0.10 +/- 0.02 Pg C.y(-1)) in northern peatlands will shift to a C source as 0.8 to 1.9 million km 2 of permafrost-affected peatlands thaw. The projected thaw would cause peatland greenhouse gas emissions equal to similar to 1% of anthropogenic radiative forcing in this century. The main forcing is from methane emissions (0.7 to 3 Pg cumulative CH4-C) with smaller carbon dioxide forcing (1 to 2 Pg CO2-C) and minor nitrous oxide losses. We project that initial CO2-C losses reverse after similar to 200 y, as warming strengthens peatland C-sinks. We project substantial, but highly uncertain, additional losses of peat into fluvial systems of 10 to 30 Pg C and 0.4 to 0.9 Pg N. The combined gaseous and fluvial peatland C loss estimated here adds 30 to 50% onto previous estimates of permafrost-thaw C losses, with southern permafrost regions being the most vulnerable.

The Ob River floodplain is the second largest floodplain in the world. Despite its vast area, estimates of carbon (C) emissions from the Ob River floodplain are largely absent. Here we present seasonal C emission and water area extent from the main channel and the floodplain along a ~4 km reach in the boreal zone of the Ob River. We found strong seasonality in water area extent of the Ob main channel (~1.8 km2) and floodplain (~3 km2) with water covering 34% of land during flood and subsequently declining to ~16% and ~14% during summer and autumn baseflow, respectively. The C emissions also varied seasonally over the open water period, ranging from −0.1 to 0.6 g C m−2 d−1 for the Ob main channel and from 0 to 9 g C m−2 d−1 for the floodplain. The dissolved organic carbon positively affected CO2 concentrations and fluxes in the floodplain during all seasons, whereas pH and oxygen concentration negatively impacted CO2 concentrations and fluxes. Some nutrients (ammonia and phosphate) positively correlated with CO2 and CH4 concentrations in summer. The total C emission from the study reach (1.8 km2 main channel, 3 km2 floodplain) during moderate flooding was 236 ± 51 tons C yr−1 (>99% CO2, <1% CH4) with the floodplain accounting for ~65%. The contribution of the floodplain to the net river C evasion may be even greater during years of high flooding and in northern regions of the Ob River basin, where floodplain soils are more C-rich and are underlain by permafrost, and in years with more extensive flooding.

Arctic plants are adapted to climatic variability, but their long-term responses to warming remain unclear. Responses may occur by range shifts, phenological adjustments in growth and reproduction, or both. Here, we compare distribution and phenology of 83 arctic and boreal mountain species, sampled identically in the early 20th (1917-1919) and 21st centuries (2017-2018) from a region of northern Sweden that has warmed significantly. We test two compensatory hypotheses to high-latitude warming-upward shifts in distribution, and earlier or extended growth and reproduction. For distribution, we show dramatic upward migration by 69% of species, averaging 6.1 m per decade, especially boreal woodland taxa whose upward expansion has reduced arctic montane habitat by 30%. Twenty percent of summit species showed distributional shifts but downward, especially moisture-associated snowbed flora. For phenology, we detected wide inter-annual variability in the onset of leafing and flowering in both eras. However, there was no detectable change in growing-season length, relating to two mechanisms. First, plot-level snow melt data starting in 1917 demonstrated that melt date, rather than vernal temperatures, better predicts plant emergence, with snow melt influenced by warmer years having greater snowfall-warmer springs did not always result in earlier emergence because snowbeds can persist longer. Second, the onset of reproductive senescence between eras was similar, even when plant emergence was earlier by a month, possibly due to intensified summer heat stress or hard-wired 'canalization' where senescence occurs regardless of summer temperature. Migrations in this system have possibly buffered arctic species against displacement by boreal expansion and warming, but ongoing temperature increases, woody plant invasion, and a potential lack of flexibility in timing of senescence may foreshadow challenges.

Global change is associated with variable shifts in the annual production of aboveground plant biomass, suggesting localized sensitivities with unclear causal origins. Combining remotely sensed normalized difference vegetation index data since the 1980s with contemporary field data from 84 grasslands on 6 continents, we show a widening divergence in site-level biomass ranging from +51% to −34% globally. Biomass generally increased in warmer, wetter and species-rich sites with longer growing seasons and declined in species-poor arid areas. Phenological changes were widespread, revealing substantive transitions in grassland seasonal cycling. Grazing, nitrogen deposition and plant invasion were prevalent in some regions but did not predict overall trends. Grasslands are undergoing sizable changes in production, with implications for food security, biodiversity and carbon storage especially in arid regions where declines are accelerating.

Tidal marshes store large amounts of organic carbon in their soils. Field data quantifying soil organic carbon (SOC) stocks provide an important resource for researchers, natural resource managers, and policy-makers working towards the protection, restoration, and valuation of these ecosystems. We collated a global dataset of tidal marsh soil organic carbon (MarSOC) from 99 studies that includes location, soil depth, site name, dry bulk density, SOC, and/or soil organic matter (SOM). The MarSOC dataset includes 17,454 data points from 2,329 unique locations, and 29 countries. We generated a general transfer function for the conversion of SOM to SOC. Using this data we estimated a median (± median absolute deviation) value of 79.2 ± 38.1 Mg SOC ha−1 in the top 30 cm and 231 ± 134 Mg SOC ha−1 in the top 1 m of tidal marsh soils globally. This data can serve as a basis for future work, and may contribute to incorporation of tidal marsh ecosystems into climate change mitigation and adaptation strategies and policies.

Large stocks of soil organic carbon (SOC) have accumulated in the Northern Hemisphere permafrost region, but their current amounts and future fate remain uncertain. By analyzing dataset combining >2700 soil profiles with environmental variables in a geospatial framework, we generated spatially explicit estimates of permafrost-region SOC stocks, quantified spatial heterogeneity, and identified key environmental predictors. We estimated that Pg C are stored in the top 3 m of permafrost region soils. The greatest uncertainties occurred in circumpolar toe-slope positions and in flat areas of the Tibetan region. We found that soil wetness index and elevation are the dominant topographic controllers and surface air temperature (circumpolar region) and precipitation (Tibetan region) are significant climatic controllers of SOC stocks. Our results provide first high-resolution geospatial assessment of permafrost region SOC stocks and their relationships with environmental factors, which are crucial for modeling the response of permafrost affected soils to changing climate.

High-resolution unoccupied aerial vehicle (UAVs) data have alleviated the mismatch between the scale of ecological processes and the scale of remotely sensed data, while machine learning and deep learning methods allow new avenues for quantification in ecology. Ant nests play key roles in ecosystem functioning, yet their distribution and effects on entire landscapes remain poorly understood, in part because they and their mounds are too small for satellite remote sensing. This research maps the distribution and impact of ant mounds in a 20 ha treeline ecotone. We evaluate the detectability from UAV imagery using a deep learning model for object detection and different combinations of RGB, thermal and multispectral sensor data. We were able to detect ant mounds in all imagery using manual detection and deep learning. However, the highest precision rates were achieved by deep learning using RGB data which has the highest spatial resolution (1.9 cm) at comparable UAV flight height. While multispectral data were outperformed for detection, it allows for novel insights into the ecology of ants and their spatial impact on vegetation productivity using the normalized difference vegetation index. Scaling up, this suggests that ant mounds quantifiably impact vegetation productivity for up to 4% of our study area and up to 8% of the Betula nana vegetation communities, the vegetation type with the highest abundance of ant mounds. Therefore, they could have an overlooked role in nutrient-limited tundra vegetation, and on the shrubification of this habitat. Further, we show the powerful combination UAV multi-sensor data and deep learning for efficient ecological tracking and monitoring of mound-building ants and their spatial impact.

Soils in the northern high latitudes are a key component in the global carbon cycle; the northern permafrost region covers 22% of the Northern Hemisphere land surface area and holds almost twice as much carbon as the atmosphere. Permafrost soil organic matter stocks represent an enormous long-term carbon sink which is in risk of switching to a net source in the future. Detailed knowledge about the quantity and the mechanisms controlling organic carbon storage is of utmost importance for our understanding of potential impacts of and feedbacks on climate change. Here we present a geospatial dataset of physical and chemical soil properties calculated from 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region. The aim of our dataset is to provide a basis to describe spatial patterns in soil properties, including quantifying carbon and nitrogen stocks. There is a particular need for spatially distributed datasets of soil properties, including vertical and horizontal distribution patterns, for modeling at local, regional, or global scales. This paper presents this dataset, describes in detail soil sampling; laboratory analysis, and derived soil geochemical parameters; calculations; and data clustering. Moreover, we use this dataset to estimate soil organic carbon and total nitrogen storage estimates in soils in the northern circumpolar permafrost region (17.9 x 10⁶ km²) using the European Space Agency's (ESA's) Climate Change Initiative (CCI) global land cover dataset at 300m pixel resolution. We estimate organic carbon and total nitrogen stocks on a circumpolar scale (excluding Tibet) for the 0-100 and 0-300 cm soil depth to be 380 and 813 Pg for carbon, and 21 and 55 Pg for nitrogen, respectively. Our organic carbon estimates agree with previous studies, with most recent estimates of 1000 Pg (170 to C186 Pg) to 300 cm depth. Two separate datasets are freely available on the Bolin Centre Database repository (https://doi.org/10.17043/palmtag-2022-pedon-1, Palmtag et al., 2022a; and https://doi.org/10.17043/palmtag-2022-spatial-1, Palmtag et al., 2002b).

In arctic ecosystems, climate change has increased plant productivity. As arctic carbon (C) stocks predominantly are located belowground, the effects of greater plant productivity on soil C storage will significantly determine the net sink/source potential of these ecosystems, but vegetation controls on soil CO2 efflux remain poorly resolved.

In order to identify the role of canopy‐forming species in belowground C dynamics, we conducted a girdling experiment with plots distributed across 1 km2 of treeline birch (Betula pubescens ) forest and willow (Salix lapponum ) patches in northern Sweden and quantified the contribution of canopy vegetation to soil CO2 fluxes and belowground productivity.

Girdling birches reduced total soil CO2 efflux in the peak growing season by 53%, which is double the expected amount, given that trees contribute only half of the total leaf area in the forest. Root and mycorrhizal mycelial production also decreased substantially. At peak season, willow shrubs contributed 38% to soil CO2 efflux in their patches.

Our findings indicate that C, recently fixed by trees and tall shrubs, makes a substantial contribution to soil respiration. It is critically important that these processes are taken into consideration in the context of a greening arctic because productivity and ecosystem C sequestration are not synonymous.

Global change affects gross primary production (GPP) in benthic and pelagic habitats of northern lakes by influencing catchment characteristics and lake water biogeochemistry. However, how changes in key environmental drivers manifest and impact total (i.e., benthic + pelagic) GPP and the partitioning of total GPP between habitats represented by the benthic share (autotrophic structuring) is unclear. Using a dataset from 26 shallow lakes located across Arctic, subarctic, and boreal northern Sweden, we investigate how catchment properties (air temperature, land cover, hydrology) affect lake physico-chemistry and patterns of total GPP and autotrophic structuring. We find that total GPP was mostly light limited, due to high dissolved organic carbon (DOC) concentrations originating from catchment soils with coniferous vegetation and wetlands, which is further promoted by high catchment runoff. In contrast, autotrophic structuring related mostly to the relative size of the benthic habitat, and was potentially modified by CO2 fertilization in the subarctic, resulting in significantly higher total GPP relative to the other biomes. Across Arctic and subarctic sites, DIC and CO2 were unrelated to DOC, indicating that external inputs of inorganic carbon can influence lake productivity patterns independent of terrestrial DOC supply. By comparison, DOC and CO2 were correlated across boreal lakes, suggesting that DOC mineralization acts as an important CO2 source for these sites. Our results underline that GPP as a resource is regulated by landscape properties, and is sensitive to large-scale global changes (warming, hydrological intensification, recovery of acidification) that promote changes in catchment characteristics and aquatic physico-chemistry. Our findings aid in predicting global change impacts on autotrophic structuring, and thus community structure and resource use of aquatic consumers in general. Given the similarities of global changes across the Northern hemisphere, our findings are likely relevant for northern lakes globally.

1. The term “applied ecology of fear” was recently introduced to describe the growing research field that applies the theory of the ecology of fear to manage wildlife behaviour. The management goal is to drive targeted species spatially and temporally away from areas of human interest by inducing cues from real or simulated predators to reduce human-wildlife conflict.

2. We aimed to quantify, through a meta-analysis, if prey anti-predator response would vary among field trials versus pen-based studies, predator cue types, predator hunting style and prey feeding type, and be stronger in response to larger predators relative to the prey's size. We also explored what studies found in terms of wildlife habituation to cues.

3. We used species belonging to the Cervidae family as a case study since deer are among the group of species with the highest degree of human-wildlife conflict. We retrieved 114 studies from online databases and collected information from 39 of those studies that fitted our research scope.

4. We found that acoustic cues more frequently led to an anti-predator response in deer than olfactory or visual cues. Neither predator hunting strategy nor deer feeding strategy or type of study (free-ranging or pen-based animals) influenced the extent to which deer responded to cues. Deer more frequently responded to cues that belonged to a larger predator relative to their size. Habituation was reported in less than one-third of the studies, with a study period ranging from 1 to 90 days, and occurred as soon as 7 days after the start of the study on average.

5. Our meta-analysis suggested that acoustic cues hold most potential as a tool to manage deer behaviour. These findings support the development of applied ecology of fear tools that introduce predator cues to reduce human-wildlife conflicts. Major knowledge gaps remain that limit the effective use of such tools in wildlife management and future research should focus on improving our understanding of habituation to cues, on comparing the effectiveness of different types of cues, on simultaneously using a combination of cue types, and on testing cues at spatial–temporal scales of actual land-uses.

Ungulates play an important role in temperate systems. Through their feeding behaviour, they can respond to vegetation by selecting patches or modify vegetation composition by herbivory. The degree in which they interact with vegetation can either reinforce landscape heterogeneity by creating disturbance or reduce heterogeneity in case of overbrowsing. This study evaluates how bottom-up (patch quality, structure), top-down forces (hunting, distance to village, forest edge) and deer features (feeding type, abundance) mediate patch utilization in a temperate forest and assess the implications of patch utilization and light on forest recruitment. Theory predicts that animals seek to maximize their energetic gains by food intake while minimizing the costs associated to foraging, such as the energy required for avoiding predators and exploiting resources. We focused on two deer species with contrasting feeding type: a browser (C. capreolus) and a mixed feeder (C. elaphus). We paired camera traps to vegetation sub-plots in ten forest sites in the Netherlands that widely ranged in deer abundance and landscape heterogeneity. Results showed that patch utilization is simultaneously explained by bottom-up, top-down forces and by deer abundance, as predicted by the safety-in-numbers hypothesis. Yet, forces best explaining patch utilization differed between deer species. Overall, higher patch utilization came with higher browsing, lower tree diversity and a large difference in forest composition: from a mix of broadleaves and conifers towards only conifers. We conclude that these two deer species, although living in the same area and belonging to the same guild, differentially perceive, interact with and shape their surrounding landscape.

Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions, and moisture availability during winter. It also affects the growing season's start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover's role for vegetation, plant- animal interactions, permafrost conditions, microbial processes, and biogeochemical cycling. We also compare studies of natural snow gradients with snow experimental manipulation studies to assess time scale difference of these approaches. The number of tundra snow studies has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. Specifically, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by snow addition and snow removal manipulations (average 7.9 days advance and 5.5 days delay, respectively) were substantially lower than the temporal variation over natural spatial gradients within a given year (mean range 56 days) or among years (mean range 32 days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates.

The Ob’ River floodplain is the second largest floodplain in the world. Despite its vast area, estimates of carbon (C) emissions from the Ob’ River floodplain are largely absent. Here we present seasonal C emission and water area extent from the main channel and the floodplain along a ~4 km reach in the boreal zone of the Ob’ River. We find strong seasonality in water area extent of the Ob’ main channel (~1.8 km²) and floodplain (~3 km²) with water covering 34% of land during flood and subsequently declining to ~16 and 14% during summer and autumn baseflow, respectively. The C emissions also showed seasonal differences over the open water period ranging from 4.66 to -4.25 g C m^-2 d^-1 for the Ob’ main channel and from 0.03 to 1.42 g C m^-2 d^-1 for the floodplain. The total annual C emission from the study reach was ~940 ± 744 t C yr^-1 with the floodplain accounting for ~16%. The contribution of the floodplain to the net river C evasion can be even greater in northern regions of the Ob’ River basin, where floodplain soils are more C-rich and are underlain by permafrost, and in years with more extensive flooding.

Soil organic carbon (SOC) stored in northern peatlands and permafrost-affected soils are key components in the global carbon cycle. This article quantifies SOC stocks in a sub-Arctic mountainous peatland environment in the discontinuous permafrost zone in Abisko, northern Sweden. Four machine-learning techniques are evaluated for SOC quantification: multiple linear regression, artificial neural networks, support vector machine and random forest. The random forest model performed best and was used to predict SOC for several depth increments at a spatial resolution of 1 m (1 x 1 m). A high-resolution (1 m) land cover classification generated for this study is the most relevant predictive variable. The landscape mean SOC storage (0-150 cm) is estimated to be 8.3 +/- 8.0 kg C m(-2) and the SOC stored in the top meter (0-100 cm) to be 7.7 +/- 6.2 kg C m(-2). The predictive modeling highlights the relative importance of wetland areas and in particular peat plateaus for the landscape's SOC storage. The total SOC was also predicted at reduced spatial resolutions of 2, 10, 30, 100, 250 and 1000 m and shows a significant drop in land cover class detail and a tendency to underestimate the SOC at resolutions > 30 m. This is associated with the occurrence of many small-scale wetlands forming local hot-spots of SOC storage that are omitted at coarse resolutions. Sharp transitions in SOC storage associated with land cover and permafrost distribution are the most challenging methodological aspect. However, in this study, at local, regional and circum-Arctic scales, the main factor limiting robust SOC mapping efforts is the scarcity of soil pedon data from across the entire environmental space. For the Abisko region, past SOC and permafrost dynamics indicate that most of the SOC is barely 2000 years old and very dynamic. Future research needs to investigate the geomorphic response of permafrost degradation and the fate of SOC across all landscape compartments in post-permafrost landscapes.

Spatial analysis in earth sciences is often based on the concept of spatial autocorrelation, expressed by W. Tobler as the first law of geography: “everything is related to everything else, but near things are more related than distant things." Here, we show that subsurface soil properties in permafrost tundra terrain exhibit tremendous spatial variability. We describe the subsurface variability of soil organic carbon (SOC) and ground ice content from the centimeter to the landscape scale in three typical tundra terrain types common across the Arctic region. At the soil pedon scale, that is, from centimeters to 1–2 m, variability is caused by cryoturbation and affected by tussocks, hummocks and nonsorted circles. At the terrain scale, from meters to tens of meters, variability is caused by different generations of ice-wedges. Variability at the landscape scale, that is, ranging hundreds of meters, is associated with geomorphic disturbances and catenary shifts. The co-occurrence and overlap of different processes and landforms creates a spatial structure unique to permafrost environments. The coefficient of variation of SOC at the pedon scale (21%–73%) exceeds that found at terrain (17%–66%) and even landscape scale (24%–67%). Such high values for spatial variation are otherwise found at regional to continental scale. Clearly, permafrost soils do not conform to Tobler's law, but are among the most variable soils on Earth. This needs to be accounted for in mapping and predictions of the permafrost carbon feedbacks through various ecosystem processes. We conclude that scale deserves special attention in permafrost regions.

In the face of climate change, it is important to estimate changes in key ecosystem properties such as plant biomass and gross primary productivity (GPP). Ground truth estimates and especially experiments are performed at small spatial scales (0.01-1 m(2)) and scaled up using coarse scale satellite remote sensing products. This will lead to a scaling bias for non-linearly related properties in heterogeneous environments when the relationships are not developed at the same spatial scale as the remote sensing products. We show that unmanned aerial vehicles (UAVs) can reliably measure normalized difference vegetation index (NDVI) at centimeter resolution even in highly heterogeneous Arctic tundra terrain. This reveals that this scaling bias increases most at very fine resolution, but UAVs can overcome this by generating remote sensing products at the same scales as ecological changes occur. Using ground truth data generated at 0.0625 m(2)and 1 m(2)with Landsat 30 m scale satellite imagery the resulting underestimation is large (8.9%-17.0% for biomass and 5.0%-9.7% for GPP(600)) and of a magnitude comparable to the expected effects of decades of climate change. Methods to correct this upscaling bias exist but rely on sub-pixel information. Our data shows that this scale-dependency will vary strongly between areas and across seasons, making it hard to derive generalized functions compensating for it. This is particularly relevant to Arctic greening with a predominantly heterogeneous land cover, strong seasonality and much experimental research at sub-meter scale, but also applies to other heterogeneous landscapes. These results demonstrate the value of UAVs for satellite validation. UAVs can bridge between plot scale used in ecological field investigations and coarse scale in satellite monitoring relevant for Earth System Models. Since future climate changes are expected to alter landscape heterogeneity, seasonally updated UAV imagery will be an essential tool to correctly predict landscape-scale changes in ecosystem properties.

Understanding how herbivores shape plant biomass and distribution is a core challenge in ecology. Yet, the lack of suitable remote sensing technology limits our knowledge of temporal and spatial impacts of mammal herbivores in the Earth system. The regular interannual density fluctuations of voles and lemmings are exceptional with their large reduction of plant biomass in Arctic landscapes during peak years (12–24%) as previously shown at large spatial scales using satellites. This provides evidence that herbivores are important drivers of observed global changes in vegetation productivity. Here, we use a novel approach with repeated unmanned aerial vehicle (UAV) flights, to map vegetation impact by rodents, indicating that many important aspects of vegetation dynamics otherwise hidden by the coarse resolution of satellite images, including plant–herbivore interactions, can be revealed using UAVs. We quantify areas impacted by rodents at four complex Arctic landscapes with very high spatial resolution UAV imagery to get a new perspective on how herbivores shape Arctic ecosystems. The area impacted by voles and lemmings is indeed substantial, larger at higher altitude tundra environments, varies between habitats depending on local snow cover and plant community composition, and is heterogeneous even within habitats at submeter scales. Coupling this with spectral reflectance of vegetation (NDVI), we can show that the impact on central ecosystem properties like GPP and biomass is stronger than currently accounted for in Arctic ecosystems. As an emerging technology, UAVs will allow us to better disentangle important information on how herbivores maintain spatial heterogeneity, function and diversity in natural ecosystems.

An increased interest in Arctic environments, mainly due to climate change, has changed the conditions for permafrost research in recent years. This change has been accompanied by a global increase in scientific publications, as well as a trend towards open access publications. We have analyzed abstracts, titles and keywords for publications on permafrost from 1998 to 2017 to identify developments (topics, impact and collaboration) in the field of permafrost research in light of these changes. Furthermore, to understand how scientists build on and are inspired by each other's work, we have (a) developed citation networks from scientific publications on permafrost and (b) conducted an online survey on inspiration in permafrost science. Our results show an almost 400% increase in publications containing the word permafrost in the title, keywords or abstract over the study period, and a strong increase in climate-change-related research in terms of publications and citations. Survey respondents (n = 122) find inspiration not only in scientific journal publications, but to a large extent in books and public outreach materials. We argue that this increase in global-scope issues (i.e., climate change) complementing core permafrost research has provided new incentives for international collaborations and wider communication efforts.

Permafrost thaw in Arctic regions is increasing methane (CH₄) emissions into the atmosphere, but quantification of such emissions is difficult given the large and remote areas impacted. Hence, Earth observation (EO) data are critical for assessing permafrost thaw, associated ecosystem change and increased CH₄ emissions. Often extrapolation from field measurements using EO is the approach employed. However, there are key challenges to consider. Landscape CH₄ emissions result from a complex local-scale mixture of micro-topographies and vegetation types that support widely differing CH₄ emissions, and it is difficult to detect the initial stages of permafrost degradation before vegetation transitions have occurred. This study considers the use of a combination of ultra-high-resolution unoccupied aerial vehicle (UAV) data and Sentinel-1 and Sentinel-2 data to extrapolate field measurements of CH₄ emissions from a set of vegetation types which capture the local variation in vegetation on degrading palsa wetlands. We show that the ultra-high-resolution UAV data can map spatial variation in vegetation relevant to variation in CH₄ emissions and extrapolate these across the wider landscape. We further show how this can be integrated with Sentinel-1 and Sentinel-2 data. By way of a soft classification and simple correction of misclassification bias of a hard classification, the output vegetation mapping and subsequent extrapolation of CH₄ emissions closely matched the results generated using the UAV data. Interferometric synthetic-aperture radar (InSAR) assessment of subsidence together with the vegetation classification suggested that high subsidence rates of palsa wetland can be used to quantify areas at risk of increased CH₄ emissions. The transition of a 50 ha area currently experiencing subsidence to fen vegetation is estimated to increase emissions from 116 kg CH₄ per season to emissions as high as 6500 to 13 000 kg CH₄ per season. The key outcome from this study is that a combination of high- and low-resolution EO data of different types provides the ability to estimate CH₄ emissions from large geographies covered by a fine mixture of vegetation types which are vulnerable to transitioning to CH₄ emitters in the near future. This points to an opportunity to measure and monitor CH₄ emissions from the Arctic over space and time with confidence.

Tundra soils account for 50% of global stocks of soil organic carbon (SOC), and it is expected that the amplified climate warming in high latitude could cause loss of this SOC through decomposition. Decomposed SOC could become hydrologically accessible, which increase downstream dissolved organic carbon (DOC) export and subsequent carbon release to the atmosphere, constituting a positive feedback to climate warming. However, DOC export is often neglected in ecosystem models. In this paper, we incorporate processes related to DOC production, mineralization, diffusion, sorption-desorption, and leaching into a customized arctic version of the dynamic ecosystem model LPJ-GUESS in order to mechanistically model catchment DOC export, and to link this flux to other ecosystem processes. The extended LPJ-GUESS is compared to observed DOC export at Stordalen catchment in northern Sweden. Vegetation communities include flood-tolerant graminoids (Eriophorum) and Sphagnum moss, birch forest and dwarf shrub communities. The processes, sorption-desorption and microbial decomposition (DOC production and mineralization) are found to contribute most to the variance in DOC export based on a detailed variance-based Sobol sensitivity analysis (SA) at grid cell-level. Catchment-level SA shows that the highest mean DOC exports come from the Eriophorum peatland (fen). A comparison with observations shows that the model captures the seasonality of DOC fluxes. Two catchment simulations, one without water lateral routing and one without peatland processes, were compared with the catchment simulations with all processes. The comparison showed that the current implementation of catchment lateral flow and peatland processes in LPJ-GUESS are essential to capture catchment-level DOC dynamics and indicate the model is at an appropriate level of complexity to represent the main mechanism of DOC dynamics in soils. The extended model provides a new tool to investigate potential interactions among climate change, vegetation dynamics, soil hydrology and DOC dynamics at both stand-alone to catchment scales. 

Climate warming is degrading palsa peatlands across the circumpolar permafrost region. Permafrost degradation may lead to ecosystem collapse and potentially strong climate feedbacks, as this ecosystem is an important carbon store and can transition to being a strong greenhouse gas emitter. Landscape-level measurement of permafrost degradation is needed to monitor this impact of warming. Surface subsidence is a useful metric of change in palsa degradation and can be monitored using interferometric synthetic-aperture radar (InSAR) satellite technology. We combined InSAR data, processed using the ASPIS algorithm to monitor ground motion between 2017 and 2021, with airborne optical and lidar data to investigate the rate of subsidence across palsa peatlands in northern Sweden. We show that 55% of Sweden's eight largest palsa peatlands are currently subsiding, which can be attributed to the underlying permafrost landforms and their degradation. The most rapid degradation has occurred in the largest palsa complexes in the most northern part of the region of study, also corresponding to the areas with the highest percentage of palsa cover within the overall mapped wetland area. Further, higher degradation rates have been found in areas where winter precipitation has increased substantially. The roughness index calculated from a lidar-derived digital elevation model (DEM), used as a proxy for degradation, increases alongside subsidence rates and may be used as a complementary proxy for palsa degradation. We show that combining datasets captured using remote sensing enables regional-scale estimation of ongoing permafrost degradation, an important step towards estimating the future impact of climate change on permafrost-dependent ecosystems.