@article{HuangHerzschuhPestryakovaetal.2020,
  author    = {Huang, Sichao and Herzschuh, Ulrike and Pestryakova, Luidmila Agafyevna and Zimmermann, Heike Hildegard and Davydova, Paraskovya and Biskaborn, Boris K. 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}
}
@article{MiesnerHerzschuhPestryakovaetal.2022,
  author    = {Miesner, Timon and Herzschuh, Ulrike and Pestryakova, Luidmila Agafyevna and Wieczorek, Mareike and Zakharov, Evgenii S. and Kolmogorov, Alexei I. and Davydova, Paraskovya V. and Kruse, Stefan},
  title     = {Forest structure and individual tree inventories of northeastern Siberia along climatic gradients},
  series = {Earth system science data : ESSD},
  volume    = {14},
  journal   = {Earth system science data : ESSD},
  number    = {12},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1866-3508},
  doi       = {10.5194/essd-14-5695-2022},
  pages     = {5695 -- 5716},
  year      = {2022},
  abstract  = {We compile a data set of forest surveys from expeditions to the northeast of the Russian Federation, in Krasnoyarsk Krai, the Republic of Sakha (Yakutia), and the Chukotka Autonomous Okrug (59-73 degrees N, 97-169 degrees E), performed between the years 2011 and 2021. The region is characterized by permafrost soils and forests dominated by larch (Larix gmelinii Rupr. and Larix cajanderi Mayr). Our data set consists of a plot database describing 226 georeferenced vegetation survey plots and a tree database with information about all the trees on these plots. The tree database, consisting of two tables with the same column names, contains information on the height, species, and vitality of 40 289 trees. A subset of the trees was subject to a more detailed inventory, which recorded the stem diameter at base and at breast height, crown diameter, and height of the beginning of the crown. We recorded heights up to 28.5 m (median 2.5 m) and stand densities up to 120 000 trees per hectare (median 1197 ha(-1)), with both values tending to be higher in the more southerly areas. Observed taxa include Larix Mill., Pinus L., Picea A. Dietr., Abies Mill., Salix L., Betula L., Populus L., Alnus Mill., and Ulmus L. In this study, we present the forest inventory data aggregated per plot. Additionally, we connect the data with different remote sensing data products to find out how accurately forest structure can be predicted from such products. Allometries were calculated to obtain the diameter from height measurements for every species group. For Larix, the most frequent of 10 species groups, allometries depended also on the stand density, as denser stands are characterized by thinner trees, relative to height. The remote sensing products used to compare against the inventory data include climate, forest biomass, canopy height, and forest loss or disturbance. We find that the forest metrics measured in the field can only be reconstructed from the remote sensing data to a limited extent, as they depend on local properties. This illustrates the need for ground inventories like those data we present here. The data can be used for studying the forest structure of northeastern Siberia and for the calibration and validation of remotely sensed data.},
  language  = {en}
}