Modern Pollen Assemblages From Lake Sediments and Soil in East Siberia and Relative Pollen Productivity Estimates for Major Taxa

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.</jats:p

The High–Low Arctic boundary: How is it determined and where is it located?

Geobotanical subdivision of landcover is a baseline for many studies. The High–Low Arctic boundary is considered to be of fundamental natural importance. The wide application of different delimitation schemes in various ecological studies and climatic scenarios raises the following questions: (i) What are the common criteria to define the High and Low Arctic? (ii) Could human impact significantly change the distribution of the delimitation criteria? (iii) Is the widely accepted temperature criterion still relevant given ongoing climate change? and (iv) Could we locate the High–Low Arctic boundary by mapping these criteria derived from modern open remote sensing and climatic data? Researchers rely on common criteria for geobotanical delimitation of the Arctic. Unified circumpolar criteria are based on the structure of vegetation cover and climate, while regional specifics are reflected in the floral composition. However, the published delimitation schemes vary greatly. The disagreement in the location of geobotanical boundaries across the studies manifests in poorly comparable results. While maintaining the common principles of geobotanical subdivision, we derived the boundary between the High and Low Arctic using the most up‐to‐date field data and modern techniques: species distribution modeling, radar, thermal and optical satellite imagery processing, and climatic data analysis. The position of the High–Low Arctic boundary in Western Siberia was clarified and mapped. The new boundary is located 50–100 km further north compared to all the previously presented ones. Long‐term anthropogenic press contributes to a change in the vegetation structure but does not noticeably affect key species ranges. A previously specified climatic criterion for the High–Low Arctic boundary accepted in scientific literature has not coincided with the boundary in Western Siberia for over 70 years. The High–Low Arctic boundary is distinctly reflected in biodiversity distribution. The presented approach is appropriate for accurate mapping of the High–Low Arctic boundary in the circumpolar extent

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

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.</p

Russian Arctic Vegetation Archive—A new database of plant community composition and environmental conditions

Motivation: The goal of the Russian Arctic Vegetation Archive (AVA-RU) is to unite and harmonize data of plot-based plant species and their abundance, vegetation structure and environmental variables from the Russian Arctic. This database can be used to assess the status of the Russian Arctic vegetation and as a baseline to document biodiversity changes in the future. The archive can be used for scientific studies as well as to inform nature protection and restoration efforts. Main types of variables contained: The archive contains 2873 open-access geobotanical plots. The data include the full species. Most plots include information on the horizontal (cover per species and morphological group) and vertical (average height per morphological group) structure of vegetation, site and soil descriptions and data quality estimations. In addition to the open-access data, the AVA-RU website contains 1912 restricted-access plots. Spatial location and grain: The plots of 1–100 m2 size were sampled in Arctic Russia and Scandinavia. Plots in Russia covered areas from the West to the East, including the European Russian Arctic (Kola Peninsula, Nenets Autonomous district), Western Siberia (Northern Urals, Yamal, Taza and Gydan peninsulas), Central Siberia (Taymyr peninsula, Bolshevik island), Eastern Siberia (Indigirka basin) and the Far East (Wrangel island). About 72% of the samples are georeferenced. Time period and grain: The data were collected once at each location between 1927 and 2022. Major taxa and level of measurement: Plots include observations of >1770 vascular plant and cryptogam species and subspecies. Software format: CSV files (1 file with species list and abundance, 1 file with environmental variables and vegetation structure) are stored at the AVA-RU website (https://avarus.space/), and are continuously updated with new datasets. The open-access data are available on Dryad and all the datasets have a backup on the server of the University of Zurich. The data processing R script is available on Dryad

: Application of Geoinformation Modeling Methods to Study the Spatial Structure of Vegetation Cover in the Orulgan Middle-Mountain Landscape Province (Northeastern Yakutia)

International audienceИзучена пространственная структура растительного покрова одной из высокогорных ландшафтных провинций Северо-Восточной Якутии. С помощью методов анализа данных дистанционного зондирования Земли и геоинформационного моделирования, включающих также и методы машинного обучения, было выделено 9 геоботанических картируемых подразделений, которые позволили построить и проанализировать геоботаническую карту масштаба 1:100 000

Vegetation cover analysis of the mountainous part of north-eastern Siberia by means of geoinformation modelling and machine learning (basic principles, approaches, technology and relation to geosystem science)

For the first time, the geoinformation modelling and machine learning approaches have been used to study the vegetation cover of the mountainous part of North-Eastern Siberia – the Orulgan medium-altitude mountain landscape province. These technologies allowed us to distinguish a number of mapping units that were used for creation and analysis of 1:100 000 scale vegetation map of the interpreted key area. Based on the studies, we decided upon the basic principles, approaches and technologies that would serve as a methodology basis for the further studies of vegetation cover of the large region. Relief, slope aspect, genetic types of sediments, and moisture conditions were selected as supplementary factors to the vegetative indices for differentiation of both plant communities and vegetation map units

Анализ ландшафтной структуры восточного склона хребта Орулган

International audienceThe aim of the research is to analyze the landscape structure of the eastern slope of the Orulgan ridge using complex geoinformation modelling. We use the field surveys data made in 2018-2019. The geoinformation modelling technique consists of the supervised pixel-based classification of time series remote sensing data of Landsat 8 OLI/ TIRS and Sentinel 2 MSI and TPI-based landform classification using ASTER GDEM scenes. As a result of a combination of the obtained physiognomic criteria, relief and vegetation, the types of terrain and landscape units of the study area were identified. The Random Forest classifier was the most efficient in identifying 8 association groups with an overall accuracy of 79.7% by the confusion matrix. With an overlay of terrain types and vegetation associations, we allow the creation of the permafrost-landscape map. In total, 22 landscape units were identified within the study area. The use of geoinformation modeling made it possible to obtain a detailed landscape structure of study area and assess the main properties of the landscape spatial organization of the eastern slope of the Orulgan ridge. The mountainous area characterized by strong dissection, causes a well-defined vertical zonation. In general, the most common altitudinal permafrost landscapes is mountain woodlands (57% of the territory) and mountain tundra occupies 16%. Intrazonal mountain and boreal landscapes occupy 15%. The leading role in the landscape diversity of the eastern slope of the Orulgan Range is played by a combination of various geological structures that determine the types of erosion-tectonic (mountain-slope and rocky mountain tops), glacial-accumulative (moraine, outwash and glacial-valley) and erosion-accumulative terrain. (medium-altitude terrace and low-terrace) origin

Distribution and Structure Analysis of Mountain Permafrost Landscape in Orulgan Ridge (Northeast Siberia) Using Google Earth Engine

International audienceAn analysis of the landscape spatial structure and diversity in the mountain ranges of Northeast Siberia is essential to assess how tundra and boreal landscapes may respond to climate change and anthropogenic impacts in the vast mountainous permafrost of the Arctic regions. In addition, a precise landscape map is required for knowledge-based territorial planning and management. In this article, we aimed to explore and enhanced methods to analyse and map the permafrost landscape in Orulgan Ridge. The Google Earth Engine cloud platform was used to generate vegetation cover maps based on multi-fusion classification of Sentinel 2 MSI and Landsat 8 OLI time series data. Phenological features based on the monthly median values of time series Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), and Normalized Difference Moisture Index (NDMI) were used to recognize geobotanical units according to the hierarchical concept of permafrost landscapes by the Support Vector Machine (SVM) classifier. In addition, geomorphological variables of megarelief (mountains and river valleys) were identified using the GIS-based terrain analysis and landform classification of the ASTER GDEM scenes mosaic. The resulting environmental variables made it possible to categorize nine classes of mountain permafrost landscapes. The result obtained was compared with previous permafrost landscape maps, which revealed a significant difference in distribution and spatial structure of intrazonal valleys and mountain tundra landscapes. Analysis of the landscape structure revealed a significant distribution of classes of mountain Larix-sparse forests and tundra. Landscape diversity was described by six longitudinal and latitudinal landscape hypsometric profiles. River valleys allow boreal–taiga landscapes to move up to high-mountainous regions. The features of the landscape structure and diversity of the ridge are noted, which, along with the specific spatial organization of vegetation and relief, can be of key importance for environmental monitoring and the study of regional variability of climatic changes

Analysis of the landscape structure of the eastern slope of the Orulgan Ridge

International audienceThis article discusses the spatial organization of permafrost landscapes on the eastern slope of the Orulgan ridge in the area of lake Bulgunnyakhtakh. The study was based on a comprehensive physical and geographical description of facies and on the analysis of various cartographic and literary sources. Based on the results of the study, a large-scale permafrost landscape map was compiled, where 10 sub-sites were identified that determine the structural and genetic complexity of the spatial organization of permafrost landscapes. The leading factor in the diversity of the morphological structure of high-altitude and intrazonal landscapes is lateral energy and mass transfer, which is caused, among other things, by the development of cryogenic processes. Summarizing the results obtained, it should be noted that in the Orulgan ridge, functionally integral para-dynamic units are traced within certain sections of the drainage basin.В данной статье рассматривается пространственная организация мерзлотных ландшафтов на восточном склоне хребта Орулган в районе озера Булгунняхтах. Исследование основано на комплексном физико-географическом описании фаций и анализе различных картографических и литературных источников. По результатам исследования составлена крупномасштабная мерзлотная ландшафтная карта, на которой выделено 10 субсайтов, определяющих структурно-генетическую сложность пространственной организации мерзлотных ландшафтов. Ведущим фактором разнообразия морфологической структуры высотных и интразональных ландшафтов является латеральный энерго- и массоперенос, обусловленный, в том числе, развитием криогенных процессов. Обобщая полученные результаты, следует отметить, что в хребте Орулган в пределах отдельных участков водосборного бассейна прослеживаются функционально целостные парадинамические единицы

Phylogeography of Artemisia frigida (Anthemideae, Asteraceae) based on genotyping-by-sequencing and plastid DNA data: migration through Beringia

Artemisia frigida is a temperate grassland species that has the largest natural range among its genus, with occurrences across the temperate grassland biomes of Eurasia and North America. Despite its wide geographic range, we know little about the species' distribution history. Hence, we conducted a phylogeographical study to test the hypothesis that the species' distribution pattern is related to a potential historical migration over the 'Bering land bridge'. We applied two molecular approaches: Genotyping-by-Sequencing (GBS) and Sanger sequencing of the plastid intergenic spacer region (rpl32 - trnL) to investigate genetic differentiation and relatedness among 21 populations from North America, Middle Asia, Central Asia and the Russian Far East. Furthermore, we identified the ploidy level of individuals based on GBS data. Our results indicate that A. frigida originated in Asia, spread northwards to the Far East and then to North America across the Bering Strait. We found a pronounced genetic structuring between Middle and Central Asian populations with mixed ploidy levels, tetraploids in the Far East, and nearly exclusively diploids in North America except for one individual. According to phylogenetic analysis, two populations of Kazakhstan (KZ2 and KZ3) represent the most likely ancestral diploids that constitute the basally branching lineages, and subsequent polyploidization has occurred on several occasions independently. Mantel tests revealed weak correlations between genetic distance and geographical distance and climatic conditions, which indicates that paleoclimatic fluctuations may have more profoundly influenced A. frigida's spatial genetic structure and distribution than the current environment