Arctic landscapes are very sensitive to warming with changes happening much faster than in other regions. The investigation on circumarctic Arctic vegetation change is carried out in the framework of the Federal Ministry of Research, Technology and Space (BMFTR) funded project SQUEEZE (Protection of the Disappearing Arctic Tundra: Potential, Planning, and Communication) in a large consortium. This study presented here focusses on the region close to Inuvik in the Mackenzie delta area in northwest Canada, which holds many different habitat types important to Indigenous peoples. The habitat diversity is important for ecosystem health and should be monitored as well as protected. In the region north of Inuvik, habitats range from tundra with low shrub structure, over forest tundra with sparse spruce forests, to taiga with dense needleleaf forests south of Inuvik and wetlands, lakes and river floodplains distributed over the area. These environments can be used for hunting, fishing, foraging of food, medicinal plants, firewood and construction material or as grazing grounds for caribou. However, those regions are facing changes due to climate change. Most dominant processes are increased permafrost thaw, shrubification of the tundra, northward shift of the treeline, more fires and pests in forests and changed waterways.Remote sensing offers valuable insights into the current state of this region and can help to track changes. Airborne remote sensing provides high resolution and allows to cover large areas. The airborne data used in this work was acquired with the AWI Perma-X flight campaign in the summers 2023 and 2025. We use the Modular Airborne Camera System-Polar (MACS-Polar) optical data. The MACS-Polar camera was developed by the German Aerospace Centre (DLR, Adlershof) specifically for challenging, contrasting light conditions in the polar region. MACS images were processed to four-band (visible and near-infrared, VNIR) orthomosaics and digital surface models with spatial resolution of 15 cm and 3D point clouds with point densities of up to 25 points per m2. Features of the VNIR images as well as structural features of the surface will be used to classify the habitat types. The analysis of the data for the years 2023 and 2025 in this work allows for tracking of changes between the years. The outcomes are classified maps of habitats, such as wetland, tundra, forest tundra and different forest types, in the area around Inuvik. Those will be made publicly available to the Indigenous communities in northwest Canada. MACS optical orthomosaics can be challenging because of changing illumination during flight times and the data derivation from Structure from Motion can hold inaccuracies. However, the resulting maps of the current state of vegetation structure are valuable products. Future work can build upon those by looking at longer timescales and upscaling with Sentinel-2 satellite data.

The Arctic tundra is warming faster than any other biome, and its biodiversity, ecosystem functions, and Indigenous land use are increasingly shaped by interacting climate- and human-driven stressors. Conservation planning must therefore move beyond static protected-area targets toward approaches that explicitly account for future change. Here, we synthesize the major stressors that will shape tundra futures (woody plant expansion, permafrost thaw and associated disturbance dynamics, accelerating industrial development and infrastructure), and explain why their differing rates and interactions create a moving target for protection. We then identify key data and scenario gaps that currently limit circumpolar prioritization, including insufficient understanding of biodiversity responses to core stressors, uneven monitoring capacity to detect change and locate refugia, and limited integration of biodiversity change with ecosystem functions and culturally important areas. Building on this, we outline the concept of a step-by-step, scenario-based decision-support workflow for dynamic systematic conservation planning. Finally, we describe co-design and governance pathways for implementation and iteration with Indigenous peoples and local communities, stakeholders, and policymakers to reduce conflicts, increase legitimacy, and enable adaptive updates as conditions change. Together, these steps provide an actionable foundation for proactive tundra conservation under rapid Arctic change.

We demonstrate the latest version of the visualization of permafrost-related map products in the context of ESA CCI+ Permafrost initiatives (Phase I and II, 2018-2021, 2023-2026). Already in ESA DUE GlobPermafrost project (2016-2018), a comprehensive range of remote sensing products was produced by the project committee and visualized as viewer in the AWI O2A (Observation to Analysis & Archive) infrastructure framework: North-south transects in the northern hemisphere with trends in Landsat multispectral indices (e.g. Tasseled Cap Brightness, Greenness and Wetness, and Normalized Difference Vegetation Index (NDVI)), Arctic land cover (e.g. shrub height and vegetation composition), lake ice grounding, InSAR-based land surface deformation and rock glacier velocities. The main products were the Global Permafrost Essential Climate Variables (ECVs), which were derived from a spatially distributed permafrost model driven by Land Surface Temperature and Snow Water Equivalent products. These Permafrost ECVs include mean annual ground temperature (MAGT) and active layer thickness (ALT) at pixel level, and additionally permafrost extent and probability (PFR).In the context of the ESA CCI+ Permafrost project, time was incorporated as a significant climate-related variable into the products. This resulted in a time series spanning over twenty years. It comprises CCI+ Permafrost Circum-Arctic model output for MAGT, from the surface down to a depth of 10 meters, as well as PFR and ALT. All data products are available at yearly resolution, as well as the calculated averages of MAGT, PFR and ALT over the time series.To make the products publicly visible, we created WebGIS projects using WebGIS technology within the O2A (Observation to Analyses and Archive) data workflow framework at AWI. This modular, scalable and highly automated spatial data infrastructure (SDI) has been developed and operated at AWI for over a decade. It has undergone continuous improvement and provides map services for geographic information system (GIS) clients and portals. The FAIR principles were implemented to address the increasing demand for research data and metadata that is discoverable, accessible and reusable. The ESA Permafrost WebGIS products were designed using GIS software and published as Web Map Services (WMS), an internationally standardised Open Geospatial Consortium (OGC) format using GIS server technology. Additionally, visualisations of raster and vector data products have been developed that are specific to the projects and adapted to their spatial scales and resolutions.In addition to data products derived from remote sensing, the locations of WMO GCOS ground-monitoring networks belonging to the permafrost community, which are managed by the International Permafrost Association (IPA) and form part of the Global Terrestrial Network for Permafrost (GTN-P), were incorporated as a feature layer and updated on an ongoing basis. All data products have previously undergone registration with the Digital Object Identifier (doi), and have been published in the data archives PANGAEA or ESA CEDA.

Boreal forests play a critical role in global carbon dynamics and climate regulation, yet their structural attributes remain poorly characterized, particularly in structurally complex ecosystems such as the northern treeline. Here, we explored the potential of Harmonized Landsat and Sentinel-2 (HLS) multispectral data to predict UAV-LiDARderived forest structure across sites in the western North American boreal forest. We extracted spectral features from peak and late summer HLS and used Random Forest models to predict Canopy Height and Crown Cover at 30 m resolution. Our results show strong relationships between spectral and structural metrics, with HLS NDVI and Tasseled Cap Wetness emerging as key predictors. Predictive performance is higher for dense and sparse forests than medium-density forests, and no significant differences are found between peak and late summer models. We compared our UAV-LiDAR Crown Cover estimates to the ABoVE Tree Canopy Cover product and identified overestimation of Crown Cover in the treeline ecotones. These findings highlight the value of fine-scale UAV-LiDAR structural data for algorithm building and assessments of satellite-derived products. Nevertheless, the high proportion of green understory reduces the sensitivity of HLS spectral features to canopy height and crown cover compared with applications in more productive forests. An open-access HLS-forest structure dataset is provided, containing HLS pixel-wise labeled forest structure information. By combining structural reference data with spectral HLS satellite imagery, this study contributes to filling the structural data gap at the boreal forest northern edge and to predicting forest structure in high-latitude ecosystems.

Geographical and ecological research of Arctic tundra requires a clear definition of the biome boundaries. A commonly used dataset for the Arctic is the tundra biome boundary mapped by the Circumpolar Arctic Vegetation Mapping Project (CAVM, 2003). Given recent advances in satellite-image resolution and data availability, in the age of the rapidly changing Arctic climate this previously established delineation needs to be re-evaluated and, where necessary, updated. An updated boundary can support applications such as Arctic climate modelling and assessments of potential disturbances in permafrost rich regions.A recently developed landcover dataset was investigated for this study. The Circumpolar Landcover Unit (CALU) Database provides highly detailed landcover information with a spatial resolution of 10 meters and consists of 23 thematic units, including 12 units representing tundra but also 3 forest classes. The used retrieval scheme of landcover units employed provides an unprecedented level of detail. The landcover units have been derived by fusion of satellite data using Sentinel-1 (synthetic aperture radar) and Sentinel-2 (multispectral). These units reflect gradients in moisture and vegetation structure. The available spatial detail of CALU has been already shown to provide the means to assess the complexity of lowland permafrost regions.The original CALU database of version 1.0 covered the Arctic within the CAVM extent only. The latest version 2.0 partially extends further south, providing additional detail within the transition zone for many areas.The aim of this study was to assess the southern boundary of the CAVM and to identify regions where further developments of the CALU dataset may aid to establish a new boundary. Spatial statistics were collected within selected buffer areas of the CAVM boundary. In addition, longitudinal zones were generated to test whether forest-related CALU classes systematically peak south of the currently mapped boundary.Based on these statistics, in regions such as Alaska and the European part of Russia, the CAVM boundary generally corresponds well with CALU, with forest-related classes mostly dominating within the buffer area. In parts of Siberia and Canada, however, shrub-tundra classes are more prevalent, while forest-related classes occur farther south. This mismatch may reflect regional differences in vegetation structure and terrain-driven zonation, suggesting that a single latitudinal boundary product may not capture local transitions equally well everywhere.Preliminary results indicate that in several regions the CALU database should be extended further south, because current coverage does not fully include forest-related classes. This limitation affects the use of CALU for the tundra-boreal biome boundary evaluation and for applications that require a consistent representation of tundra–taiga transitions.CALU: Bartsch, A., Khairullin, R., Efimova, A., Widhalm, B., Muri, X., von Baeckmann, C., Bergstedt, H., Ermokhina, K., Hugelius, G., Heim, B., Leibman, M., & Gruber, C. (2024). Circumarctic Landcover Units (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1423573

In low-land permafrost regions, the landscape can be subjected to significant seasonal cycles of vertical surface deformation. This effect is mainly driven by seasonal thaw subsidence and frost heave caused by water migration and ice-lens formation during thawing and freezing, with additional contributions from volumetric changes associated with phase transitions between ice and liquid water. Satellite differential SAR interferometry (DInSAR) has been used in the past to quantify the seasonal vertical surface displacement from thaw subsidence and frost heave. However, the DInSAR phase does not only contain information on ground displacement, but is also influenced by changes in atmospheric, soil moisture, vegetation and snow cover conditions. The aim of our study was to quantify the vertical seasonal surface thaw displacement using DInSAR with almost coinciding Sentinel-1 (C-band), TerraSAR-X (X-band) and ALOS-2 PALSAR-2 (L-band) data, to compare the results obtained at different frequencies and to relate the results to in situ displacement data and soil, moisture and land cover characteristics. As our study area we chose Samoylov Island in the Lena Delta, northeastern Siberia, which lies in the continuous permafrost zone and is characterized by ice-wedge polygons and small ponds and shallow lakes. Here, we processed satellite imagery during the snow-free period in 2018. We found rates of vertical thaw displacement of several centimeters in the north-western part of the island, a more stable region along the eastern part and heterogeneous rates of movement in the central part. In general, there is a good agreement between the magnitude and spatial patterns of the seasonal surface thaw displacement measured at different frequencies. This suggests that surface displacement is the predominant effect on the DInSAR phase compared to effects from variations in soil moisture, which should increase with wavelength as the penetration depth is greater at lower frequencies, and effects from vegetation, which should cause stronger systematic distortions at higher frequencies. Validation with in situ measurements showed that values determined remotely are smaller than those measured in situ, highlighting the challenges of accurately capturing and representing sub-pixel variability of displacements. A comparison with a detailed habitat type map illustrates that the large-scale magnitude of seasonal deformation is predominantly related to soil type and moisture conditions.

In many scientific disciplines, physical samples represent the origin of research results. They record unique events in history, support new hypotheses, and are often not reproducible. At the same time, samples are essential for reproducing and verifying research results and deriving new results by analysing existing samples with new methodology. Consequently, the inclusion of sample metadata in the digital data curation processes is an important step to provide the full provenance of research results. The largest challenge is the lack of standardisation and the large variety of sample types and individuals involved: Most samples are collected by individual researchers or small groups that may have internal agreements for sample descriptions, but these might only be used for one expedition or within a small community, and rarely reach beyond institutional boundaries. The International Generic Sample Number (IGSN, www.igsn.org) is a globally unique, resolving, and persistent identifier (PID) for physical samples with a dedicated metadata schema supporting discovery functionality in the internet. IGSNs allow data and publications to be linked directly to the samples from which they originate and provide contextual information about a particular sample on the internet. The aim of the project FAIR WISH (FAIR Workflows to establish IGSN for Samples in the Helmholtz Association), funded by the Helmholtz Metadata Collaboration (HMC) was to work towards more standardisation of rich sample descriptions. Project outcomes include (i) standardised, rich and discipline-specific IGSN metadata schemes for different physical sample types within the Earth and Environmental sciences (EaE), (ii) workflows to generate machine-readable IGSN metadata from different states of digitisation and (iii) the FAIR Samples Template. The FAIR SAMPLES Template enables metadata collection and batch upload of samples at various sample hierarchies (parent, children at different hierarchy levels) at once. The ability to fill the FAIR SAMPLES Template by individual researchers or research teams or to create scripts to fill it out directly from databases for a wide range of sample types makes the template flexible with a wide applicability. The structured metadata, captured with the FAIR SAMPLES Template and converted into XML files, already represents an important step for the standardisation of rich sample descriptions and their provision in machine-readable form. Standardised workflows for metadata documentation and compliance with international metadata standards address the challenges associated with reproducibility of samples and their insufficient documentation. The developments within the FAIR WISH project provide a foundation for a more collaborative and integrated scientific enterprise. Future efforts in this area can build on this framework to further improve the accessibility and interoperability of sample data and advance the collective understanding of Earth's environmental processes.

This study assesses the escalating vulnerability of Arctic coastal communities due to the combined impacts of coastal erosion and permafrost warming. With the Arctic experiencing heightened temperatures, coastal permafrost areas face increased instability, endangering vital infrastructures. The study focuses on a pan-Arctic evaluation of settlements and infrastructures at risk, enhancing the existing Arctic coastal infrastructure dataset (SACHI) to include road types, airstrips, and artificial water reservoirs. By analyzing coastline change rates from 2000 to 2020, alongside permafrost ground temperature and active layer thickness trends from the ESA Permafrost Climate Change Initiative datasets, the research identifies settlements at risk for the years 2030, 2050, and 2100. The accuracy of the dataset is rigorously evaluated. Results indicate that by 2100, 23% of coastal settlements will face the impacts of coastal erosion. Projections based on linear trends suggest an 8°C increase in coastal permafrost ground temperature and a 0.9-meter growth in active layer thickness by the same year. Crucially, the study reveals that 65% of all infrastructures and settlements will be affected by permafrost warming within the range of 5-15°C, with 35% experiencing active layer thickening between 1-5 meters. This research marks the first regional-scale identification of settlements at risk from coastal erosion along Arctic and permafrost-dominated coasts in the northern hemisphere. The findings emphasize the urgency of adapting to current and future environmental changes to mitigate the deterioration of living conditions in permafrost coastal settlements. Immediate action is imperative to counteract these challenges and ensure the resilience of these vulnerable communities.

Circumboreal forests covering about 30% of global forested areas are undergoing significant changes. In Siberia, global warming may reduce the dominance of summergreen larch forest inducing shifts towards evergreen forest types, specifically in the Eastern Siberian summergreen-evergreen forest transition zone. We create a Remote Sensing training dataset for summergreen and evergreen forest types from the SiDroForest Sentinel-2 image dataset. This new training dataset informed by expert field knowledge includes nearly two million Sentinel-2 pixels across the early summer, peak summer, and late summer phenophases. We create the equivalent seasonal SiDroTest dataset linked to in-situ forest plots for benchmarking the seasonal training dataset. To optimize satellite-based monitoring, we train a Random Forest classifier on the train dataset to map summergreen and evergreen forest resulting in accuracies of 63% for early summer, 89% for peak summer, and 99% for late summer, with an average accuracy of 82% across all seasons. Feature importance analysis highlights the Sentinel-2 shortwave infrared as crucial for distinguishing forest types in all seasons. Additional key features include the normalized difference vegetation index (NDVI) and the red wavelength region for early summer, shortwave infrared and the visible wavelength region for peak summer, and shortwave infrared, near-infrared and NDVI for late summer. This study provides a benchmarked training dataset for mapping boreal forest types in the Siberian summergreen-evergreen transition zone. The Random Forest classifier performs best in late summer, leveraging distinct spectral differences between evergreen forests' greenness and the seasonal coloring of summergreen larch forests.

We present a global megabiome reconstruction for 43 time slices at 500-year intervals throughout the last 21 000 years based on an updated, and thus currently the most extensive, global taxonomically and temporally standardized fossil pollen dataset of 3455 records. The evaluation with modern potential natural vegetation distributions yields an agreement of ∼ 80 %, suggesting a high reliability of the pollen-based megabiome reconstruction. We compare the reconstruction with an ensemble of six biomized simulations derived from transient Earth system models (ESMs). Overall, the global spatiotemporal patterns of megabiomes estimated by both the simulation ensemble and the reconstructions are generally consistent. Specifically, they reveal a global shift from open glacial non-forest megabiomes to Holocene forest megabiomes since the Last Glacial Maximum (LGM), in line with the general climate warming trend and continental ice-sheet retreat. The shift to a global megabiome distribution generally similar to today's took place during the early Holocene; furthermore, the reconstructions reveal that enhanced anthropogenic disturbances since the late Holocene have not altered broad-scale megabiome patterns. However, certain data–model deviations are evident in specific regions and periods, which could be attributed to systematic climate biases in ESMs or biases in the pollen-based biomization method. For example, at a global scale over the last 21 000 years, the largest deviations between the reconstructions and the simulation ensemble are observed during the LGM and the early deglaciation. These discrepancies are probably attributed to the ESM systematic summer cold biases that overestimate tundra in periglacial regions and to the challenging identification of steppes and tundra from the Tibetan Plateau pollen records. Moderate deviations during the Holocene mainly occur in non-forest megabiomes in the Mediterranean and northern Africa, with increasing discrepancies over time. These deviations may result from the underestimation of woody plant functional type (PFT) cover in simulations due to systematic biases, such as overly warm summers with dry winters in the Mediterranean, and the overrepresentation of woody taxa in reconstructions, misclassifying deserts as savanna in northern Africa. Overall, our reconstruction, with its relatively high temporal and spatial resolution, serves as a robust dataset for evaluating ESM-based paleo-megabiome simulations and provides potential clues for improving systematic model biases.

The Lena Delta is the largest river delta in the Arctic (about 30 000 km2) and prone to rapid changes due to climate warming, associated cryosphere loss, and ecological shifts. The delta is characterized by ice-rich permafrost landscapes and consists of geologically and geomorphologically diverse terraces covered with tundra vegetation and of active floodplains, featuring approximately 6500 km of channels and over 30 000 lakes. Because of its broad landscape and habitat diversity, the delta is a biodiversity hotspot with high numbers of nesting and breeding migratory birds, fish, caribou, and other mammals and was designated a State Nature Reserve in 1995. Characterizing plant composition, aboveground biomass, and application of field spectroscopy was a major focus of a 2018 expedition to the delta. These field data collections were linked to Sentinel-2 satellite data to upscale local patterns in land cover and associated habitats to the entire delta. Here, we describe multiple field datasets collected in the Lena Delta during summer 2018 including foliage projective cover (Shevtsova et al., 2025, https://doi.org/10.1594/PANGAEA.935875), aboveground biomass (Shevtsova et al., 2023, https://doi.org/10.1594/PANGAEA.956067; Shevtsova et al., 2023, https://doi.org/10.1594/PANGAEA.935923), and hyperspectral field measurements (Runge et al., 2022, https://doi.org/10.1594/PANGAEA.945982). We further describe a detailed Sentinel-2 satellite image-based classification of habitats for the central Lena Delta (Landgraf et al., 2025a, https://doi.org/10.1594/PANGAEA.945057; Landgraf et al., 2025b, https://doi.org/10.1594/PANGAEA.945056; Landgraf et al., 2025c, https://doi.org/10.1594/PANGAEA.945055; Landgraf et al., 2025d, https://doi.org/10.1594/PANGAEA.945054), an upscaled classification for the entire Lena Delta (Lisovski et al., 2022, https://doi.org/10.1594/PANGAEA.946407), and the test data set for accuracy assessment (Heim et al., 2025, https://doi.org/10.5281/zenodo.14731823) and a synthesis product for disturbance regimes (Heim and Lisovski, 2023, https://doi.org/10.5281/zenodo.7575691) in the delta that is based on the classification, the described datasets, and field expertise. We present context and detailed methods of these openly available datasets and show how their combined use can improve our understanding of the rapidly changing Arctic tundra system. The new Lena Delta habitat classification represents a first baseline against which future observations can be compared. The link between such detailed habitat classifications and disturbance regime may provide a better understanding of how Arctic lowland landscapes will respond to climate change and how this will impact land surface processes.

The Circumpolar Landcover unit database provides landcover information in high detail, spatially (10m) and thematically (23 units). Such detail is needed for a wide range of applications targeting climate change impacts and ecological research questions. The landcover unit retrieval scheme used provides unprecedented detail. The landcover units have been derived by fusion of satellite data using Sentinel-1 (synthetic aperture radar) and Sentinel-2 (multispectral). The units reflect gradients of moisture as well as vegetation physiognomy. The original database covered the Arctic north of the tree line. It has been extended towards south, providing additional detail within the tundra-taiga transition zone in permafrost regions. The available spatial detail provides the means to assess the complexity of this zone in addition to information on recent disturbance related to for example wildfire and thermokarst lake change. Bartsch, A., Efimova, A., Widhalm, B., Muri, X., von Baeckmann, C., Bergstedt, H., Ermokhina, K., Hugelius, G., Heim, B., & Leibmann, M. (2023). Circumpolar Landcover Units (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8399018

The Siberian Arctic is warming rapidly, causing permafrost to thaw and altering the biogeochemistry of aquatic environments, with cascading effects on the coastal and shelf ecosystems of the Arctic Ocean. The Lena River, one of the largest Arctic rivers, drains a catchment dominated by permafrost. Baseline discharge biogeochemistry data are necessary to understand present and future changes in land-to-ocean fluxes. Here, we present a high-frequency 4.5-year-long dataset from a sampling program of the Lena River's biogeochemistry, spanning April 2018 to August 2022. The dataset comprises 587 sampling events and measurements of various parameters, including water temperature, electrical conductivity, stable oxygen and hydrogen isotopes, dissolved organic carbon concentration and 14C, colored and fluorescent dissolved organic matter, dissolved inorganic and total nutrients, and dissolved elemental and ion concentrations. Sampling consistency and continuity and data quality were ensured through simple sampling protocols, real-time communication, and collaboration with local and international partners. The data are available as a collection of datasets separated by parameter groups and periods at https://doi.org/10.1594/PANGAEA.913197 (Juhls et al., 2020b). To our knowledge, this dataset provides an unprecedented temporal resolution of an Arctic river's biogeochemistry. This makes it a unique baseline on which future environmental changes, including changes in river hydrology, at temporal scales from precipitation event to seasonal to interannual can be detected.

Climate is rapidly changing in northern regions, including Central Yakutia, a densely populated area in Siberia. Here, permafrost-thaw lakes in topographical depressions, named “alaas”, are widely distributed. Alaases and the residual lakes within became the traditional home to the indigenous Sakha people, providing critical ecosystem services like fresh water supply, meadows for cattle breeding, or fishing and hunting grounds. Alaas formation is closely related to the Late Glacial and Early Holocene warming, as it was caused by the degradation of permafrost. This makes alaases, and permafrost-thaw lakes in general, highly sensitive to both climatic changes and land use impacts. Global warming is predicted to cause permafrost loss, potentially resulting in new alaas formations and irreversibly changing water quality and biodiversity within the existing alaas lakes. The exact consequences of anthropogenic climate change and land use on these unique landforms are still poorly understood, which may also be a result of lacking data availability. Here, we present a comprehensive new dataset of limnological characteristics of 66 lakes across Central Yakutia Lowland and the Oymyakon Highlands, with a focus on 51 alaas lakes in Central Yakutia. During field work in summer of 2021, we measured lake physical properties (lake depth, pH, specific conductivity) and afterwards we analyzed lake water hydrochemistry including ions, dissolved organic carbon (DOC), isotopic composition (δ18O H20, δD H20), and aquatic and terrestrial plant composition via surface sediment environmental DNA metabarcoding. The majority of alaas lakes are classified as magnesium-bicarbonate types. Isotope concentrations indicate that lakes in the Central Yakutian Lowlands are controlled mainly by evaporation, underlining their sensitivity to future warming. Aquatic vegetation is dominated by submerged macrophytes, whereas terrestrial vegetation mainly consists of graminoids and forbs. Settlements are mostly situated in connected alaas systems, where flowing water results in lower DOC concentration. This “snapshot” of limnological characteristics can be helpful to assess the most critical factors which may be impacted by land use or respond to future warming.

Boreal forests are a key component of the global carbon cycle, forming North America's most extensive biome. Different successional stages in boreal forests have varying levels of ecological values and biodiversity, which in turn affect their functions. A knowledge gap remains concerning the present successional stages, their geographic patterns and possible successions. This study develops a novel application of UAV-LiDAR and Red Green Blue (RGB) data and network analysis to enhance our understanding of boreal forest succession. Between 2022 and 2024, we collected UAV-LiDAR and RGB data from 48 forested sites in Alaska and Northwest Canada to (i) identify present successional stages and (ii) deepen our understanding of successional trajectories. We first applied UAV-derived spectral and structural tree attributes to classify individual trees into plant functional types representative of boreal forest succession, amely, evergreen and deciduous. Second, we built a forest-patch network to characterize successional stages and their interactions and assessed future stage transitions. Finally, we applied a simplified forward model to predict future dynamics and highlight different successional trajectories. Our results indicate that tree height and spectral variables are the most influential predictors of plant functional type in random forest algorithms, and high overall accuracies were attained. The network-based community detection algorithm reveals five interconnected successional stages that could be interpreted as ranging from early to late successional and a disturbed stage. We find that disturbed sites are mainly located in Interior and Southcentral Alaska, while late successional sites are predominant in the southern Canadian sites. Transitional stages are mainly located near the tundra-taiga boundary. These findings highlight the critical role of disturbances, such as fire or insect outbreaks, in shaping forest succession in Alaska and Northwest Canada.

Warming induced forest expansion, permafrost thaw, and human activities are major drivers affecting the biodiversity and ecosystem functions of the Arctic tundra. While the pace of climate warming is fastest in the Arctic, some important stressors, like forest expansion, seem relatively slow. The slow response might provide the opportunity to safeguard Arctic biodiversity and ecosystem function via a strategic and conservation action, taking future dynamics into account. This strategy requires a comprehensive synthesis of how these pressures may threaten the tundra’s unique biodiversity, its ecosystem functions and services, including its global climate-regulating role, and the sustainability of Indigenous land use. Developing an effective conservation strategy also requires knowledge of past and projected changes across the tundra and a well-coordinated communication process between scientists, Indigenous people and local communities, and further stakeholders. Here, we outline the essential knowledge base and implementation pathways needed to prioritize areas for protection in such a rapidly changing environment, avoid conflicts of interest, and ensure that tundra biodiversity and associated ecosystem functions and services endure the future warming period.

Abstract. Freshwater ecosystems are a major feature of the northern landscapes that are expected to experience significant future changes due to climate change and land-use alterations. In Central Yakutia, abundant lakes in topographic permafrost-thaw depressions, named ‘alaas’, define the traditional cultural landscape that is home to the indigenous Sakha people, with critical ecosystem services like freshwater supply, meadows for cattle breeding, as well as fishing and hunting grounds. In contrast, lakes in the Verkhoyansk mountain region east of Central Yakutia are of glacial origin or developed on glacial moraines and represent deeper and more oligotrophic lake systems much less used as human resources. Here, we analyse the hydrochemistry, sedimentary DNA (sedDNA)-derived aquatic plant diversity, geomorphology, and adjacent land cover of sixty-six lakes across the Central Yakutian lowland permafrost landscape and the Verkhoyansk Oymyakon high mountain plateau to understand their characteristics and environmental drivers. Our hydrochemical analysis reveals a clear distinction between the low-mineralised mountain lakes and the highly variable hydrochemistry of the lowland thermokarst lakes. The lake developmental stage within the thermokarst lake sequence seems to be a key driver of lake hydrochemistry in the lakes of the Central Yakutian lowland. Specifically, the lake’s developmental stage is reflected by dissolved organic carbon (DOC), pH, its stable isotopic composition, and the hydrochemical facies of alkali and earth alkali elements. New thermokarst lakes have a depleted stable isotopic composition, possibly due to contributions from meltwater of adjacent permafrost ground-ice. This thermokarst lake stage is typically located within forest and has the highest DOC. In contrast, the hydrologically open thermokarst lake systems, typically located in large connected alaas systems with settlements and managed land use, have lower DOC and fewer mineralisation than recently formed thermokarst lakes or old alaas lakes. The dilution in the hydrologically connected alaas lakes occurs due to flushing, mainly during high discharge events such as the regular snowmelt. Old alaas lakes show an enriched oxygen isotope composition and have high salinity and mineral content, suggesting processes of evaporation and highlighting their vulnerability to future warming. However, low chloride together with an enriched isotopic composition and elevated fluoride characterise several of the sampled high-salinity lakes. This points to an additional process beyond the current evaporation, such as fluoride leakage from lacustrine sediments or salt deposits. SedDNA-derived macrophyte diversity reflects lake types and reveals the dominance of brackish water-tolerant cosmopolitan submerged macrophytes, particularly Stuckenia and Potamogeton, across all lake types. The macrophytes Myriophyllum and M. verticillatum are exclusively found in freshwater lakes in the lowlands and the mountain regions, supporting their indicator value for freshwater conditions. Our results provide a detailed examination of lake systems in modern conditions within highly climate-sensitive lowland and mountain permafrost landscapes.

The Siberian boreal forest is the largest continuous forest region on Earth and plays a crucial role in regulating global climate. However, the distribution and environmental processes behind this ecosystem are still not well understood. Here, we first develop Sentinel-2-based classified maps to show forest-type distribution in five regions along a southwest-northeast transect in eastern Siberia. Then, we constrain the environmental factors of the forest-type distribution based on a multivariate analysis of bioclimatic variables, topography, and ground-surface temperatur at the local and regional scales. Furthermore, we identify potential versus realized forest-type niches and their applicability to other sites. Our results show that mean annual temperature and mean summer and winter temperatures are the most influential predictors of forest-type distribution. Furthermore, we show that topography, specifically slope, provides an additional but smaller impact at the local scale. We find that the filling of climatic environmental niches by forest types decreases with geographic distance, but that the filling of topographic niches varies from one site to another. Our findings suggest that boreal forests in eastern Siberia are driven by current climate and topographical factors, but that there remains a portion of the variability that cannot be fully accounted for by these factors alone. While we hypothesize that this unexplained variance may be linked to legacies of the Late Glacial, further evidence is needed to substantiate this claim. Such results are crucial to understanding and predicting the response of boreal forests to ongoing climate change and rising temperatures.