Description: Larix populations at the tundra-taiga ecotone in northern Siberia are highly under-represented in population genetic studies, possibly due to the remoteness of these regions that can only be accessed at extraordinary expense. The genetic signatures of populations in these boundary regions are therefore largely unknown. We aim to generate organelle reference genomes for the detection of single nucleotide polymorphisms (SNPs) that can be used for paleogenetic studies. We present 19 complete chloroplast genomes and mitochondrial genomic sequences of larches from the southern lowlands of the Taymyr Peninsula (northernmost range of Larix gmelinii (Rupr.) Kuzen.), the lower Omoloy River, and the lower Kolyma River (both in the range of Larix cajanderi Mayr). The genomic data reveal 84 chloroplast SNPs and 213 putatively mitochondrial SNPs. Parsimony-based chloroplast haplotype networks show no spatial structure of individuals from different geographic origins, while the mitochondrial haplotype network shows at least a slight spatial structure with haplotypes from the Omoloy and Kolyma populations being more closely related to each other than to most of the haplotypes from the Taymyr populations. Whole genome alignments with publicly available complete chloroplast genomes of different Larix species show that among official plant barcodes only the rcbL gene contains sufficient polymorphisms, but has to be sequenced completely to distinguish the different provenances. We provide 8 novel mitochondrial SNPs that are putatively diagnostic for the separation of L. gmelinii and L. cajanderi, while 4 chloroplast SNPs have the potential to distinguish the L. gmelinii/L. cajanderi group from other Larix species. Our organelle references can be used for a targeted primer and probe design allowing the generation of short amplicons. This is particularly important with regard to future investigations of, for example, the biogeographic history of Larix by screening ancient sedimentary DNA of Larix.

Description: Boreal forests in permafrost regions make up around one-third of the global forest cover and are an essential component of regional and global climate patterns. Further, climatic change can trigger extensive ecosystem shifts such as the partial disappearance of near-surface permafrost or changes to the vegetation structure and composition. Therefore, our aim is to understand how the interactions between the vegetation, permafrost, and the atmosphere stabilize the forests and the underlying permafrost. Existing model setups are often static or are not able to capture important processes such as the vertical structure or the leaf physiological properties. There is a need for a physically-based model with a robust radiative transfer scheme through the canopy. A one-dimensional land surface model (CryoGrid) is adapted for the application in vegetated areas by coupling a multilayer canopy model (CLM-ml v0; Community Land Model) and is used to reproduce the energy transfer and thermal regime at a study site (63.18946∘ N, 118.19596∘ E) in mixed boreal forest in eastern Siberia. An extensive comparison between measured and modeled energy balance variables reveals a satisfactory model performance justifying its application to investigate the thermal regime; surface energy balance; and the vertical exchange of radiation, heat, and water in this complex ecosystem. We find that the forests exert a strong control on the thermal state of permafrost through changing the radiation balance and snow cover phenology. The forest cover alters the surface energy balance by inhibiting over 90 % of the solar radiation and suppressing turbulent heat fluxes. Additionally, our simulations reveal a surplus in longwave radiation trapped below the canopy, similar to a greenhouse, which leads to a magnitude in storage heat flux comparable to that simulated at the grassland site. Further, the end of season snow cover is 3 times greater at the forest site, and the onset of the snow-melting processes are delayed.

Description: 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°N, 97-169°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 40289 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 28.5m (median 2.5m) and stand densities up to 120000 trees per hectare (median 1197ha-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. They are available at https://doi.org/10.1594/PANGAEA.943547 .