Description: Recent global warming is pronounced in high-latitude regions (e.g. northern Asia), and will cause the vegetation to change. Future vegetation trends (e.g. the “arctic greening”) will feed back into atmospheric circulation and the global climate system. Understanding the nature and causes of past vegetation changes is important for predicting the composition and distribution of future vegetation communities. Fossil pollen records from 468 sites in northern and eastern Asia were biomised at selected times between 40 cal ka bp and today. Biomes were also simulated using a climate-driven biome model and results from the two approaches compared in order to help understand the mechanisms behind the observed vegetation changes. The consistent biome results inferred by both approaches reveal that long-term and broad-scale vegetation patterns reflect global- to hemispheric-scale climate changes. Forest biomes increase around the beginning of the late deglaciation, become more widespread during the early and middle Holocene, and decrease in the late Holocene in fringe areas of the Asian Summer Monsoon. At the southern and southwestern margins of the taiga, forest increases in the early Holocene and shows notable species succession, which may have been caused by winter warming at ca. 7 cal ka bp. At the northeastern taiga margin (central Yakutia and northeastern Siberia), shrub expansion during the last deglaciation appears to prevent the permafrost from thawing and hinders the northward expansion of evergreen needle-leaved species until ca. 7 cal ka bp. The vegetation-climate disequilibrium during the early Holocene in the taiga-tundra transition zone suggests that projected climate warming will not cause a northward expansion of evergreen needle-leaved species.

Description: Subfossil Cladocera were sampled and examined from the surface sediments of 35 thermokarst lakes along a temperature gradient crossing the tree line in the Anabar-river basin in northwestern Yakutia, northeastern Siberia. The lakes were distributed through three environmental zones: typical tundra, southern tundra and forest tundra. All lakes were situated within the continuous permafrost zone. Our investigation showed that the cladoceran communities in the lakes of the Anabar region are diverse and abundant, as reflected by taxonomic richness, and high diversity and evenness indices (H = 1.89 ± 0.51; I = 0.8 ± 0.18). CONISS cluster analysis indicated that the cladoceran communities in the three ecological zones (typical tundra, southern tundra and forest-tundra) differed in their taxonomic composition and structure. Differences in the cladoceran assemblages were related to limnological features and geographical position, vegetation type, climate and water chemistry. The constrained redundancy analysis indicated that TJuly, water depth and both sulphate (SO4 2−) and silica (Si4+) concentrations significantly (p ≤ 0.05) explained variance in the cladoceran assemblage. TJuly featured the highest percentage (17.4 %) of explained variance in the distribution of subfossil Cladocera. One of the most significant changes in the structure of the cladoceran communities in the investigated transect was the replacement of closely related species along the latitudinal and vegetation gradient. The results demonstrate the potential for a regional cladoceran-based temperature model for the Arctic regions of Russia, and for and Yakutia in particular.

Description: Fossil diatom assemblages in a sediment core from a small lake in Central Kamchatka (Russia) were used to reconstruct palaeoenvironmental conditions of the late Holocene. The waterbody may be a kettle lake that formed on a moraine of the Two-Yurts Lake Valley, located on the eastern slope of the Central Kamchatka Mountain Chain. At present, it is a seepage lake with no surficial outflow. Fossil diatom assemblages show an almost constant ratio between planktonic and periphytic forms throughout the record. Downcore variations in the relative abundances of diatom species enabled division of the core into four diatom assemblage zones, mainly related to changes in abundances of Aulacoseira subarctica, Stephanodiscus minutulus, and Discostella pseudostelligera and several benthic species. Associated variations in the composition and content of organic matter are consistent with the diatom stratigraphy. The oldest recovered sediments date to about 3220 BC. They lie below a sedimentation hiatus and likely include reworked deposits from nearby Two-Yurts Lake. The initial lake stage between 870 and 400 BC was characterized by acidic shallow-water conditions. Between 400 BC and AD 1400, lacustrine conditions were established, with highest contributions from planktonic diatoms. The interval between AD 1400 and 1900 might reflect summer cooling during the Little Ice Age, indicated by diatoms that prefer strong turbulence, nutrient recycling and cooler summer conditions. The timing of palaeolimnological changes generally fits the pattern of neoglacial cooling during the late Holocene on Kamchatka and in the neighbouring Sea of Okhotsk, mainly driven by the prevailing modes of regional atmospheric circulation.

Description: Boreal forests in Siberia store huge amounts of aboveground carbon. Global warming potentially threatens this carbon storage due to more frequent droughts or other disturbances such as fires. These disturbances can change recruitment patterns, and thus may have long-lasting impacts on population dynamics. Assessing high-resolution forest stand structures and forecasting their response for the upcoming decades with detailed models is needed to understand the involved key processes and consequences of global change. We present forest stand inventories derived from UAV imagery and a developed processing chain including Individual Tree Detection (ITD) and species determination for 56 sites on a bioclimatic gradient at the Tundra-Taiga-Ecotone in Northeastern Siberia. We will use these and further 58 traditional count and measurement data as starting points for the detailed individual-based spatially explicit forest model LAVESI to predict future forest dynamics covering multiple sites across the Siberian treeline. In our analyses, we will focus on assessing future structural changes of the forests and their aboveground biomass dynamics. For our discussion, we will evaluate the reliability of UAV-derived forest inventories by measuring the impact strength of error sources introduced in the methodology on the forecasts.

Description: Large scale analyses of climatic or ecological data are important to understand complex relationships. Often, such data are available in open repositories or national measurement programmes, others are only made available via the responsible researcher. However, merging data from various sources is often not straightforward, due to issues with the data itself or the metadata. Nevertheless, the application of such compilations offers various possibilities. In our working group, two large-scale compilations are currently constructed and applied. The Northern Hemispheric Pollen Compilation consists of data from NEOTOMA, European Pollen Database (EPD), PANGAEA and various authors. With the help of this compilation, we reconstruct climate and vegetation of large spatial and temporal scales. The circumpolar soil temperature dataset consist of data from the Global Terrestrial Network for Permafrost (GTN-P), Roshydromet, PANGAEA, Nordicana D and the National Science Foundation (NSF) Arctic Data Center. In its first version, the compilation has already been successfully applied to validate the ESA CCI Permafrost soil temperature map. The various sources of errors and problems will be shown by the two compilations of (i) sedimentary pollen data and (ii) soil temperature data. The most general problem and error source are wrong or inaccurate coordinates. These errors arise out of coordinates provided with two decimals only, wrong conversion of DMS to decimal format, wrong coordinates etc. For most analyses, the most exact geographic position is a prerequisite, as e.g. lake size is an important parameter when reconstructing vegetation out of sedimentary pollen data. Sedimentary pollen records not located in a lake according to their given location thus need manual reposition according to the main researcher of a dataset or satellite maps. Further challenges concerning the pollen dataset pose various naming conventions or variable resolution in time. Furthermore, taxonomic resolution varies between datasets, making homogenization necessary. But also for the soil temperature dataset, extensive checks were necessary, as even quality checked data comprise erroneous values. Furthermore, measured depths vary between datasets. For easy comparisons of soil temperature simulations against data, standardized depths were extracted. In a future step, interpolations between measured depths will help the end-users to extract the exactly needed depths and a compilation of available metadata on e.g. surrounding vegetation and borehole stratigraphy shall be provided. All compilations will be made available on public repositories.

Description: Polygon tundra characterizes large areas of arctic lowlands. The micro-relief pattern within polygons offers differentiated habitats for testate amoeba (testacean)communities. The objective of this study was to relate testacean species distribution within a polygon to the environmental setting. Therefore, testaceans from four cryosol pits dug at different locations within a low-centered polygon were studied in the context of pedological and pedochemical data, while ground temperature and ground moisture were measured over one summer season. The study site is located on the Berelekh River floodplain (Indigirka lowland, East Siberia). The environmental data sets reflect variations along the rim-to-center transect of the polygon and in different horizons of each pit. The testacean species distribution is mainly controlled by the soil moisture regime and pH. Most of the identified testaceans are cosmopolitans; eight species are described from an arctic environment for the first time. Differences in environmental conditions are controlled by the micro-relief of polygon tundra and must be considered in arctic lowland testacean research because they bias species composition and any further (paleo-)ecological interpretation.

Description: This study is based on multiproxy data gained from a 14C-dated 6.5 m long sediment core and a 210Pb-dated 23 cm short core retrieved from Lake Rauchuagytgyn in Chukotka, Arctic Russia. Our main objectives are to reconstruct the environmental history and ecological development of the lake during the last 29 kyr and to investigate the main drivers behind bioproduction shifts. The methods comprise age-modeling, accumulation rate estimation, and light microscope diatom species analysis of 74 samples, as well as organic carbon, nitrogen, and mercury analysis. Diatoms have appeared in the lake since 21.8 ka cal BP and are dominated by planktonic Lindavia ocellata and L. cyclopuncta. Around the Pleistocene–Holocene boundary, other taxa including planktonic Aulacoseira, benthic fragilarioid (Staurosira), and achnanthoid species increase in their abundance. There is strong correlation between variations of diatom valve accumulation rates (DARs; mean 176.1×109 valves m2 a1), organic carbon accumulation rates (OCARs; mean 4.6 g m−2 a−1), and mercury accumulation rates (HgARs; mean 63.4 µg m−2 a−1). We discuss the environmental forcings behind shifts in diatom species and find moderate responses of key taxa to the cold glacial period, postglacial warming, the Younger Dryas, and the Holocene Thermal Maximum. The short-core data likely suggest recent change of the diatom community at the beginning of the 20th century related to human-induced warming but only little evidence of atmospheric deposition of contaminants. Significant correlation between DAR and OCAR in the Holocene interglacial indicates within-lake bioproduction represents bulk organic carbon deposited in the lake sediment. During both glacial and interglacial episodes HgAR is mainly bound to organic matter in the lake associated with biochemical substrate conditions. There were only ambiguous signs of increased HgAR during the industrialization period. We conclude that if increased short-term emissions are neglected, pristine Arctic lake systems can potentially serve as long-term CO2 and Hg sinks during warm climate episodes driven by insolation-enhanced within-lake primary productivity. Maintaining intact natural lake ecosystems should therefore be of interest to future environmental policy.

Description: 〈jats:p〉Abstract. Boreal forests of Siberia play a relevant role in the global carbon cycle. However, global warming threatens the existence of summergreen larch-dominated ecosystems, likely enabling a transition to evergreen tree taxa with deeper active layers. Complex permafrost–vegetation interactions make it uncertain whether these ecosystems could develop into a carbon source rather than continuing atmospheric carbon sequestration under global warming. Consequently, shedding light on the role of current and future active layer dynamics and the feedbacks with the apparent tree species is crucial to predict boreal forest transition dynamics and thus for aboveground forest biomass and carbon stock developments. Hence, we established a coupled model version amalgamating a one-dimensional permafrost multilayer forest land-surface model (CryoGrid) with LAVESI, an individual-based and spatially explicit forest model for larch species (Larix Mill.), extended for this study by including other relevant Siberian forest species and explicit terrain. Following parameterization, we ran simulations with the coupled version to the near future to 2030 with a mild climate-warming scenario. We focus on three regions covering a gradient of summergreen forests in the east at Spasskaya Pad, mixed summergreen–evergreen forests close to Nyurba, and the warmest area at Lake Khamra in the southeast of Yakutia, Russia. Coupled simulations were run with the newly implemented boreal forest species and compared to runs allowing only one species at a time, as well as to simulations using just LAVESI. Results reveal that the coupled version corrects for overestimation of active layer thickness (ALT) and soil moisture, and large differences in established forests are simulated. We conclude that the coupled version can simulate the complex environment of eastern Siberia by reproducing vegetation patterns, making it an excellent tool to disentangle processes driving boreal forest dynamics. 〈/jats:p〉

Description: The SiDroForest (Siberian drone-mapped forest inventory) data collection is an attempt to remedy the scarcity of forest structure data in the circumboreal region by providing adjusted and labeled tree-level and vegetation plot-level data for machine learning and upscaling purposes. We present datasets of vegetation composition and tree and plot level forest structure for two important vegetation transition zones in Siberia, Russia; the summergreen–evergreen transition zone in Central Yakutia and the tundra–taiga transition zone in Chukotka (NE Siberia). The SiDroForest data collection consists of four datasets that contain different complementary data types that together support in-depth analyses from different perspectives of Siberian Forest plot data for multi-purpose applications. i. Dataset 1 provides unmanned aerial vehicle (UAV)-borne data products covering the vegetation plots surveyed during fieldwork (Kruse et al., 2021, https://doi.org/10.1594/PANGAEA.933263). The dataset includes structure-from-motion (SfM) point clouds and red–green–blue (RGB) and red–green–near-infrared (RGN) orthomosaics. From the orthomosaics, point-cloud products were created such as the digital elevation model (DEM), canopy height model (CHM), digital surface model (DSM) and the digital terrain model (DTM). The point-cloud products provide information on the three-dimensional (3D) structure of the forest at each plot. ii. Dataset 2 contains spatial data in the form of point and polygon shapefiles of 872 individually labeled trees and shrubs that were recorded during fieldwork at the same vegetation plots (van Geffen et al., 2021c, https://doi.org/10.1594/PANGAEA.932821). The dataset contains information on tree height, crown diameter, and species type. These tree and shrub individually labeled point and polygon shapefiles were generated on top of the RGB UVA orthoimages. The individual tree information collected during the expedition such as tree height, crown diameter, and vitality are provided in table format. This dataset can be used to link individual information on trees to the location of the specific tree in the SfM point clouds, providing for example, opportunity to validate the extracted tree height from the first dataset. The dataset provides unique insights into the current state of individual trees and shrubs and allows for monitoring the effects of climate change on these individuals in the future. iii. Dataset 3 contains a synthesis of 10 000 generated images and masks that have the tree crowns of two species of larch (Larix gmelinii and Larix cajanderi) automatically extracted from the RGB UAV images in the common objects in context (COCO) format (van Geffen et al., 2021a, https://doi.org/10.1594/PANGAEA.932795). As machine-learning algorithms need a large dataset to train on, the synthetic dataset was specifically created to be used for machine-learning algorithms to detect Siberian larch species. iv. Dataset 4 contains Sentinel-2 (S-2) Level-2 bottom-of-atmosphere processed labeled image patches with seasonal information and annotated vegetation categories covering the vegetation plots (van Geffen et al., 2021b, https://doi.org/10.1594/PANGAEA.933268). The dataset is created with the aim of providing a small ready-to-use validation and training dataset to be used in various vegetation-related machine-learning tasks. It enhances the data collection as it allows classification of a larger area with the provided vegetation classes. The SiDroForest data collection serves a variety of user communities. The detailed vegetation cover and structure information in the first two datasets are of use for ecological applications, on one hand for summergreen and evergreen needle-leaf forests and also for tundra–taiga ecotones. Datasets 1 and 2 further support the generation and validation of land cover remote-sensing products in radar and optical remote sensing. In addition to providing information on forest structure and vegetation composition of the vegetation plots, the third and fourth datasets are prepared as training and validation data for machine-learning purposes. For example, the synthetic tree-crown dataset is generated from the raw UAV images and optimized to be used in neural networks. Furthermore, the fourth SiDroForest dataset contains S-2 labeled image patches processed to a high standard that provide training data on vegetation class categories for machine-learning classification with JavaScript Object Notation (JSON) labels provided. The SiDroForest data collection adds unique insights into remote hard-to-reach circumboreal forest regions.