@article{GengAndreevKruseetal.2022, author = {Geng, Rongwei and Andreev, Andrei and Kruse, Stefan and Heim, Birgit and van Geffen, Femke and Pestryakova, Luidmila and Zakharov, Evgenii and Troeva, Elena I. and Shevtsova, Iuliia and Li, Furong and Zhao, Yan and Herzschuh, Ulrike}, title = {Modern pollen assemblages from lake sediments and soil in East Siberia and relative pollen productivity estimates for Major Taxa}, series = {Frontiers in Ecology and Evolution}, volume = {10}, journal = {Frontiers in Ecology and Evolution}, publisher = {Frontiers Media}, address = {Lausanne}, issn = {2296-701X}, doi = {10.3389/fevo.2022.837857}, pages = {17}, year = {2022}, abstract = {Modern pollen-vegetation-climate relationships underpin palaeovegetation and palaeoclimate reconstructions from fossil pollen records. East Siberia is an ideal area for investigating the relationships between modern pollen assemblages and near natural vegetation under cold continental climate conditions. Reliable pollen-based quantitative vegetation and climate reconstructions are still scarce due to the limited number of modern pollen datasets. Furthermore, differences in pollen representation of samples from lake sediments and soils are not well understood. Here, we present a new pollen dataset of 48 moss/soil and 24 lake surface-sediment samples collected in Chukotka and central Yakutia in East Siberia. The pollen-vegetation-climate relationships were investigated by ordination analyses. Generally, tundra and taiga vegetation types can be well distinguished in the surface pollen assemblages. Moss/soil and lake samples contain generally similar pollen assemblages as revealed by a Procrustes comparison with some exceptions. Overall, modern pollen assemblages reflect the temperature and precipitation gradients in the study areas as revealed by constrained ordination analysis. We estimate the relative pollen productivity (RPP) of major taxa and the relevant source area of pollen (RSAP) for moss/soil samples from Chukotka and central Yakutia using Extended R-Value (ERV) analysis. The RSAP of the tundra-forest transition area in Chukotka and taiga area in central Yakutia are ca. 1300 and 360 m, respectively. For Chukotka, RPPs relative to both Poaceae and Ericaceae were estimated while RPPs for central Yakutia were relative only to Ericaceae. Relative to Ericaceae (reference taxon, RPP = 1), Larix, Betula, Picea, and Pinus are overrepresented while Alnus, Cyperaceae, Poaceae, and Salix are underrepresented in the pollen spectra. Our estimates are in general agreement with previously published values and provide the basis for reliable quantitative reconstructions of East Siberian vegetation.}, language = {en} } @phdthesis{Shevtsova2022, author = {Shevtsova, Iuliia}, title = {Recent and future vegetation change in the treeline region of Chukotka (NE Russia) inferred from field data, satellite data and modelling}, doi = {10.25932/publishup-54845}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-548452}, school = {Universit{\"a}t Potsdam}, pages = {149}, year = {2022}, abstract = {Vegetation change at high latitudes is one of the central issues nowadays with respect to ongoing climate changes and triggered potential feedback. At high latitude ecosystems, the expected changes include boreal treeline advance, compositional, phenological, physiological (plants), biomass (phytomass) and productivity changes. However, the rate and the extent of the changes under climate change are yet poorly understood and projections are necessary for effective adaptive strategies and forehanded minimisation of the possible negative feedbacks. The vegetation itself and environmental conditions, which are playing a great role in its development and distribution are diverse throughout the Subarctic to the Arctic. Among the least investigated areas is central Chukotka in North-Eastern Siberia, Russia. Chukotka has mountainous terrain and a wide variety of vegetation types on the gradient from treeless tundra to northern taiga forests. The treeline there in contrast to subarctic North America and north-western and central Siberia is represented by a deciduous conifer, Larix cajanderi Mayr. The vegetation varies from prostrate lichen Dryas octopetala L. tundra to open graminoid (hummock and non-hummock) tundra to tall Pinus pumila (Pall.) Regel shrublands to sparse and dense larch forests. Hence, this thesis presents investigations on recent compositional and above-ground biomass (AGB) changes, as well as potential future changes in AGB in central Chukotka. The aim is to assess how tundra-taiga vegetation develops under changing climate conditions particularly in Fareast Russia, central Chukotka. Therefore, three main research questions were considered: 1) What changes in vegetation composition have recently occurred in central Chukotka? 2) How have the above-ground biomass AGB rates and distribution changed in central Chukotka? 3) What are the spatial dynamics and rates of tree AGB change in the upcoming millennia in the northern tundra-taiga of central Chukotka? Remote sensing provides information on the spatial and temporal variability of vegetation. I used Landsat satellite data together with field data (foliage projective cover and AGB) from two expeditions in 2016 and 2018 to Chukotka to upscale vegetation types and AGB for the study area. More specifically, I used Landsat spectral indices (Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI) and Normalised Difference Snow Index (NDSI)) and constrained ordination (Redundancy analysis, RDA) for further k-means-based land-cover classification and general additive model (GAM)-based AGB maps for 2000/2001/2002 and 2016/2017. I also used Tandem-X DEM data for a topographical correction of the Landsat satellite data and to derive slope, aspect, and Topographical Wetness Index (TWI) data for forecasting AGB. Firstly, in 2016, taxa-specific projective cover data were collected during a Russian-German expedition. I processed the field data and coupled them with Landsat spectral Indices in the RDA model that was used for k-means classification. I could establish four meaningful land-cover classes: (1) larch closed-canopy forest, (2) forest tundra and shrub tundra, (3) graminoid tundra and (4) prostrate herb tundra and barren areas, and accordingly, I produced the land cover maps for 2000/2001/2002 and 2016/20017. Changes in land-cover classes between the beginning of the century (2000/2001/2002) and the present time (2016/2017) were estimated and interpreted as recent compositional changes in central Chukotka. The transition from graminoid tundra to forest tundra and shrub tundra was interpreted as shrubification and amounts to a 20\% area increase in the tundra-taiga zone and 40\% area increase in the northern taiga. Major contributors of shrubification are alder, dwarf birch and some species of the heather family. Land-cover change from the forest tundra and shrub tundra class to the larch closed-canopy forest class is interpreted as tree infilling and is notable in the northern taiga. We find almost no land-cover changes in the present treeless tundra. Secondly, total AGB state and change were investigated for the same areas. In addition to the total vegetation AGB, I provided estimations for the different taxa present at the field sites. As an outcome, AGB in the study region of central Chukotka ranged from 0 kg m-2 at barren areas to 16 kg m-2 in closed-canopy forests with the larch trees contributing the highest. A comparison of changes in AGB within the investigated period from 2000 to 2016 shows that the greatest changes (up to 1.25 kg m 2 yr 1) occurred in the northern taiga and in areas where land cover changed to larch closed-canopy forest. Our estimations indicate a general increase in total AGB throughout the investigated tundra-taiga and northern taiga, whereas the tundra showed no evidence of change in AGB within the 15 years from 2002 to 2017. In the third manuscript, potential future AGB changes were estimated based on the results of simulations of the individual-based spatially explicit vegetation model LAVESI using different climate scenarios, depending on Representative Concentration Pathways (RCPs) RCP 2.6, RCP 4.5 and RCP 8.5 with or without cooling after 2300 CE. LAVESI-based AGB was simulated for the current state until 3000 CE for the northern tundra-taiga study area for larch species because we expect the most notable changes to occur will be associated with forest expansion in the treeline ecotone. The spatial distribution and current state of tree AGB was validated against AGB field data, AGB extracted from Landsat satellite data and a high spatial resolution image with distinctive trees visible. The simulation results are indicating differences in tree AGB dynamics plot wise, depending on the distance to the current treeline. The simulated tree AGB dynamics are in concordance with fundamental ecological (emigrational and successional) processes: tree stand formation in simulated results starts with seed dispersion, tree stand establishment, tree stand densification and episodic thinning. Our results suggest mostly densification of existing tree stands in the study region within the current century in the study region and a lagged forest expansion (up to 39\% of total area in the RCP 8.5) under all considered climate scenarios without cooling in different local areas depending on the closeness to the current treeline. In scenarios with cooling air temperature after 2300 CE, forests stopped expanding at 2300 CE (up to 10\%, RCP 8.5) and then gradually retreated to their pre-21st century position. The average tree AGB rates of increase are the strongest in the first 300 years of the 21st century. The rates depend on the RCP scenario, where the highest are as expected under RCP 8.5. Overall, this interdisciplinary thesis shows a successful integration of field data, satellite data and modelling for tracking recent and predicting future vegetation changes in mountainous subarctic regions. The obtained results are unique for the focus area in central Chukotka and overall, for mountainous high latitude ecosystems.}, language = {en} } @article{vanGeffenHeimBriegeretal.2022, author = {van Geffen, Femke and Heim, Birgit and Brieger, Frederic and Geng, Rongwei and Shevtsova, Iuliia and Schulte, Luise and Stuenzi, Simone M. and Bernhardt, Nadine and Troeva, Elena I. and Pestryakova, Luidmila Agafyevna and Zakharov, Evgenii S. and Pflug, Bringfried and Herzschuh, Ulrike and Kruse, Stefan}, title = {SiDroForest: a comprehensive forest inventory of Siberian boreal forest investigations including drone-based point clouds, individually labeled trees, synthetically generated tree crowns, and Sentinel-2 labeled image patches}, series = {Earth system science data}, volume = {14}, journal = {Earth system science data}, number = {11}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1866-3508}, doi = {10.5194/essd-14-4967-2022}, pages = {4967 -- 4994}, year = {2022}, abstract = {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, ). 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. 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, ). 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. Dataset 3 contains a synthesis of 10 000 generated images and masks that have the tree crowns of two species of larch ( and ) automatically extracted from the RGB UAV images in the common objects in context (COCO) format (van Geffen et al., 2021a, ). 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. Larix gmeliniiLarix cajanderiDataset 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, ). 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.}, language = {en} } @article{HeimLisovskiWieczoreketal.2022, author = {Heim, Birgit and Lisovski, Simeon and Wieczorek, Mareike and Morgenstern, Anne and Juhls, Bennet and Shevtsova, Iuliia and Kruse, Stefan and Boike, Julia and Fedorova, Irina and Herzschuh, Ulrike}, title = {Spring snow cover duration and tundra greenness in the Lena Delta, Siberia}, series = {Environmental research letters}, volume = {17}, journal = {Environmental research letters}, number = {8}, publisher = {IOP Publ. Ltd.}, address = {Bristol}, issn = {1748-9326}, doi = {10.1088/1748-9326/ac8066}, pages = {18}, year = {2022}, abstract = {The Lena Delta in Siberia is the largest delta in the Arctic and as a snow-dominated ecosystem particularly vulnerable to climate change. Using the two decades of MODerate resolution Imaging Spectroradiometer satellite acquisitions, this study investigates interannual and spatial variability of snow-cover duration and summer vegetation vitality in the Lena Delta. We approximated snow by the application of the normalized difference snow index and vegetation greenness by the normalized difference vegetation index (NDVI). We consolidated the analyses by integrating reanalysis products on air temperature from 2001 to 2021, and air temperature, ground temperature, and the date of snow-melt from time-lapse camera (TLC) observations from the Samoylov observatory located in the central delta. We extracted spring snow-cover duration determined by a latitudinal gradient. The 'regular year' snow-melt is transgressing from mid-May to late May within a time window of 10 days across the delta. We calculated yearly deviations per grid cell for two defined regions, one for the delta, and one focusing on the central delta. We identified an ensemble of early snow-melt years from 2012 to 2014, with snow-melt already starting in early May, and two late snow-melt years in 2004 and 2017, with snow-melt starting in June. In the times of TLC recording, the years of early and late snow-melt were confirmed. In the three summers after early snow-melt, summer vegetation greenness showed neither positive nor negative deviations. Whereas, vegetation greenness was reduced in 2004 after late snow-melt together with the lowest June monthly air temperature of the time series record. Since 2005, vegetation greenness is rising, with maxima in 2018 and 2021. The NDVI rise since 2018 is preceded by up to 4 degrees C warmer than average June air temperature. The ongoing operation of satellite missions allows to monitor a wide range of land surface properties and processes that will provide urgently needed data in times when logistical challenges lead to data gaps in land-based observations in the rapidly changing Arctic.}, language = {en} } @article{HuangHerzschuhPestryakovaetal.2020, author = {Huang, Sichao and Herzschuh, Ulrike and Pestryakova, Luidmila Agafyevna and Zimmermann, Heike Hildegard and Davydova, Paraskovya and Biskaborn, Boris and Shevtsova, Iuliia and Stoof-Leichsenring, Kathleen Rosemarie}, title = {Genetic and morphologic determination of diatom community composition in surface sediments from glacial and thermokarst lakes in the Siberian Arctic}, series = {Journal of paleolimnolog}, volume = {64}, journal = {Journal of paleolimnolog}, number = {3}, publisher = {Springer}, address = {Dordrecht}, issn = {0921-2728}, doi = {10.1007/s10933-020-00133-1}, pages = {225 -- 242}, year = {2020}, abstract = {Lakes cover large parts of the climatically sensitive Arctic landscape and respond rapidly to environmental change. Arctic lakes have different origins and include the predominant thermokarst lakes, which are small, young and highly dynamic, as well as large, old and stable glacial lakes. Freshwater diatoms dominate the primary producer community in these lakes and can be used to detect biotic responses to climate and environmental change. We used specific diatom metabarcoding on sedimentary DNA, combined with next-generation sequencing and diatom morphology, to assess diatom diversity in five glacial and 15 thermokarst lakes within the easternmost expanse of the Siberian treeline ecotone in Chukotka, Russia. We obtained 163 verified diatom sequence types and identified 176 diatom species morphologically. Although there were large differences in taxonomic assignment using the two approaches, they showed similar high abundances and diversity of Fragilariceae and Aulacoseiraceae. In particular, the genetic approach detected hidden within-lake variations of fragilarioids in glacial lakes and dominance of centric Aulacoseira species, whereas Lindavia ocellata was predominant using morphology. In thermokarst lakes, sequence types and valve counts also detected high diversity of Fragilariaceae, which followed the vegetation gradient along the treeline. Ordination analyses of the genetic data from glacial and thermokarst lakes suggest that concentrations of sulfate (SO42-), an indicator of the activity of sulfate-reducing microbes under anoxic conditions, and bicarbonate (HCO3-), which relates to surrounding vegetation, have a significant influence on diatom community composition. For thermokarst lakes, we also identified lake depth as an important variable, but SO42- best explains diatom diversity derived from genetic data, whereas HCO3- best explains the data from valve counts. Higher diatom diversity was detected in glacial lakes, most likely related to greater lake age and different edaphic settings, which gave rise to diversification and endemism. In contrast, small, dynamic thermokarst lakes are inhabited by stress-tolerant fragilarioids and are related to different vegetation types along the treeline ecotone. Our study demonstrated that genetic investigations of lake sediments can be used to interpret climate and environmental responses of diatoms. It also showed how lake type affects diatom diversity, and that such genetic analyses can be used to track diatom community changes under ongoing warming in the Arctic.}, language = {en} }