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Forest structure is a crucial component in the assessment of whether a forest is likely to act as a carbon sink under changing climate. Detailed 3D structural information about the tundra–taiga ecotone of Siberia is mostly missing and still underrepresented in current research due to the remoteness and restricted accessibility. Field based, high-resolution remote sensing can provide important knowledge for the understanding of vegetation properties and dynamics. In this study, we test the applicability of consumer-grade Unmanned Aerial Vehicles (UAVs) for rapid calculation of stand metrics in treeline forests. We reconstructed high-resolution photogrammetric point clouds and derived canopy height models for 10 study sites from NE Chukotka and SW Yakutia. Subsequently, we detected individual tree tops using a variable-window size local maximum filter and applied a marker-controlled watershed segmentation for the delineation of tree crowns. With this, we successfully detected 67.1% of the validation individuals. Simple linear regressions of observed and detected metrics show a better correlation (R2) and lower relative root mean square percentage error (RMSE%) for tree heights (mean R2 = 0.77, mean RMSE% = 18.46%) than for crown diameters (mean R2 = 0.46, mean RMSE% = 24.9%). The comparison between detected and observed tree height distributions revealed that our tree detection method was unable to representatively identify trees <2 m. Our results show that plot sizes for vegetation surveys in the tundra–taiga ecotone should be adapted to the forest structure and have a radius of >15–20 m to capture homogeneous and representative forest stands. Additionally, we identify sources of omission and commission errors and give recommendations for their mitigation. In summary, the efficiency of the used method depends on the complexity of the forest’s stand structure.
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. <br /> 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.
Relative abundances of 157 diatom taxa from Yakutian lake surface-sediments were investigated for their potential to indicate certain environmental conditions. Data from 206 sites from Arctic, sub-Arctic and boreal environments were included. Redundancy analyses were performed to assess the explanatory power of mean July temperature (T-July), conductivity, pH, dissolved silica concentration, phosphate concentration, lake depth and vegetation type on diatom species composition. Boosted regression tree analyses were performed to infer the most relevant environmental variables for abundances of individual taxa and weighted average regression was applied to infer their respective optimum and tolerance. Electrical conductivity was best indicated by diatom taxa. In contrast, only few taxa were indicative of Si and water depth. Few taxa were related to specific pH values. Although T-July, explained the highest proportion of variance in the diatom spectra and was, after conductivity, the second-most selected splitting variable, we a priori decided not to present indicator taxa because of the poorly understood relationship between diatom occurrences and T-July. In total, 92 diatom taxa were reliable indicators of a certain vegetation type or a combination of several types. The high numbers of indicative species for open vegetation sites and for forested sites suggest that the principal turnover is the transition from forest-tundra to northern taiga. Overall, our results reveal that preference ranges of diatom taxa for environmental variables are mostly broad, and the use of indicator taxa for the purposes of environmental reconstruction or environmental monitoring is therefore restricted to marked rather than subtle environmental transitions.
Changes in species' distributions are classically projected based on their climate envelopes. For Siberian forests, which have a tremendous significance for vegetation-climate feedbacks, this implies future shifts of each of the forest-forming larch (Larix) species to the north-east. However, in addition to abiotic factors, reliable projections must assess the role of historical biogeography and biotic interactions. Here, we use sedimentary ancient DNA and individual-based modelling to investigate the distribution of larch species and mitochondrial haplotypes through space and time across the treeline ecotone on the southern Taymyr peninsula, which at the same time presents a boundary area of two larch species. We find spatial and temporal patterns, which suggest that forest density is the most influential driver determining the precise distribution of species and mitochondrial haplotypes. This suggests a strong influence of competition on the species' range shifts. These findings imply possible climate change outcomes that are directly opposed to projections based purely on climate envelopes. Investigations of such fine-scale processes of biodiversity change through time are possible using paleoenvironmental DNA, which is available much more readily than visible fossils and can provide information at a level of resolution that is not reached in classical palaeoecology.
This study aims to establish, evaluate, and apply a modern pollen-climate transfer function from the transition zone between arctic tundra and light-needled taiga in Arctic Siberia. Lacustrine samples (n = 96) from the northern Siberian lowlands of Yakutia were collected along four north-to-south transects crossing the arctic forest line. Samples span a broad temperature and precipitation gradient (mean July temperature, T-July: 7.5-18.7 degrees C; mean annual precipitation, P-ann: 114-315 mm/yr). Redundancy analyses are used to examine the relationship between the modern pollen signal and corresponding vegetation types and climate. Performance of transfer functions for T-July and P-ann were cross-validated and tested for spatial autocorrelation effects. The root mean square errors of prediction are 1.67 degrees C for T-July and 40 mm/yr for P-ann. A climate reconstruction based on fossil pollen spectra from a Siberian Arctic lake sediment core spanning the Holocene yielded cold conditions for the Late Glacial (1-2 degrees C below present T-July). Warm and moist conditions were reconstructed for the early to mid Holocene (2 degrees C higher T-July than present), and climate conditions similar to modern ones were reconstructed for the last 4000 years. In conclusion, our modern pollen data set fills the gap of existing regional calibration sets with regard to the underrepresented Siberian tundra-taiga transition zone. The Holocene climate reconstruction indicates that the temperature deviation from modern values was only moderate despite the assumed Arctic sensitivity to present climate change.
The spatial and temporal variability of a low-centred polygon on the eastern floodplain area of the lower Anabar River (72.070 degrees N, 113.921 degrees E; northern Yakutia, Siberia) has been investigated using a multi-method approach. The present-day vegetation in each square metre was analysed, revealing a community of Larix, shrubby Betula, and Salix on the polygon rim, a dominance of Carex and Andromeda polifolia in the rim-to-pond transition zone, and a predominantly monospecific Scorpidium scorpioides coverage within the pond. The total organic carbon (TOC) content, TOC/TN (total nitrogen) ratio, grain size, vascular plant macrofossils, moss remains, diatoms, and pollen were analysed for two vertical sections and a sediment core from a transect across the polygon. Radiocarbon dating indicates that the formation of the polygon started at least 1500 yr ago; the general positions of the pond and rim have not changed since that time. Two types of pond vegetation were identified, indicating two contrasting development stages of the polygon. The first was a well-established moss association, dominated by submerged or floating Scorpidium scorpioides and/or Drepanocladus spp. and overgrown by epiphytic diatoms such as Tabellaria flocculosa and Eunotia taxa. This stage coincides temporally with a period in which the polygon was only drained by lateral subsurface water flow, as indicated by mixed grain sizes. A different moss association occurred during times of repeated river flooding (indicated by homogeneous medium-grained sand that probably accumulated during the annual spring snowmelt), characterized by an abundance of Meesia triquetra and a dominance of benthic diatoms (e. g. Navicula vulpina), indicative of a relatively high pH and a high tolerance of disturbance. A comparison of the local polygon vegetation (inferred from moss and macrofossil spectra) with the regional vegetation (inferred from pollen spectra) indicated that the moss association with Scorpidium scorpioides became established during relatively favourable climatic conditions, while the association dominated by Meesia triquetra occurred during periods of harsh climatic conditions. Our study revealed a strong riverine influence (in addition to climatic influences) on polygon development and the type of peat accumulated.
Pollen productivity estimates (PPE) are used to quantitatively reconstruct variations in vegetation within a specific distance of the sampled pollen archive. Here, for the first time, PPEs from Siberia are presented. The study area (Khatanga region, Krasnoyarsk territory, Russia) is located in the Siberian Sub-arctic where Larix is the sole forest-line forming tree taxon. Pollen spectra from two different sedimentary environments, namely terrestrial mosses (n = 16) and lakes (n = 15, median radius similar to 100 m) and their surrounding vegetation were investigated to extract PPEs. Our results indicate some differences in pollen spectra between moss and lake pollen. Larix and Cyperaceae for example obtained higher representation in the lacustrine than in terrestrial moss samples. This highlights that in calibration studies, modem and fossil datasets should use archives of similar sedimentary origin. Results of an Extended R-Value model were applied to assess the relevant source area of pollen (RSAP) and to calculate the PPEs for both datasets. As expected, the RSAP of the moss samples was very small (about 10 m) compared to the lacustrine samples (about 25 km). Calculation of PPEs for the six most common taxa yielded generally similar results for both datasets. Relative to Poaceae (reference taxon, PPE = 1) Betula nana-type (PPEmoss: 1.8, PPElake: 1.8) and Alnus fruticosa-type (PPEmoss:6.4, PPElake:2.9) were overrepresented while Cyperaceae (PPEmoss:0.5, PPElake:0.1), Ericaceae (PPEmoss: 0.3, PPElake <0.01), Salix (PPEmoss:0.03, PPElake <00.1) and Larix (PPEmoss: <0.01, PPElake:0.2) were under-represented in the pollen spectra compared to the vegetation in the RSAP. The estimation for the dominant tree in the region, Larix gmelinii, is the first published result for this species, but needs to be considered very preliminary. The inferred sequence from over- to under-representation is mostly consistent with results from Europe; however, the absolute values show some differences. Gathering vegetation data was limited by the remoteness of our study area and a lack of high-resolute satellite imagery and vegetation maps. Our estimate may serve as a first reference to strengthen future vegetation reconstructions in this climate-sensitive region. (C) 2015 Elsevier B.V. All rights reserved.
Thermokarst lakes are assumed to develop cyclically, driven by processes that are triggered by climate and maintained by internal feedbacks that may trigger lake drainage. However, the duration of these cycles remains uncertain, as well as whether or not they affect the stabilization of lake ecosystems in permafrost regions over millennial time scales. Our research has combined investigations into modern lake-to-lake variability with a study of the long-term development of individual lakes. We have investigated the physico-chemical and diatom compositions of a set of 101 lakes with a variety of different origins in central Yakutia (Eastern Siberia), including thermokarst lakes, fluvial-erosion thermokarst lakes, fluvial-erosion lakes, and dune lakes. We found a significant relationship between lake genesis and the present-day variability in environmental and diatom characteristics, as revealed by multi-response permutation procedures, indicator species analyses, and redundancy analyses. Environmental parameters also exhibit a significant correlation with variations in the diatom data, for which they may have been to a substantial extent responsible. Mg and SO4 concentrations, together with pH and water depth, were identified as the most important parameters, influencing the variations in the diatom data almost as much as the entire environmental parameter set. We were therefore able to establish a robust Mg-diatom transfer function, which was then applied to three Holocene lake records. From these reconstructions, together with a general interpretation of the diatom record (including, e.g., the ratio between benthic/epiphytic and planktonic taxa), we have been able to infer that all three of these lakes show (1) a continuous record with no desiccation events, (2) high lake water-levels during the early Holocene, (3) centennial to millennial scale variability, and (4) high levels of variability during the early Holocene but rather stable conditions during the late Holocene (a feature that is also known from other sites around the world). We therefore concluded that the development of these three lakes was mainly driven directly by the climate, rather than by thaw lake cycling.
Siberian arctic vegetation and lake water communities, known for their temperature dependence, are expected to be particularly impacted by recent climate change and high warming rates. However, decadal information on the nature and strength of recent vegetation change and its time lag to climate signals are rare. In this study, we present a Pb-210/Cs-137 dated pollen and Pediastrum species record from a unnamed lake in the south of the Taymyr peninsula covering the period from AD 1706 to 2011. Thirty-nine palynomorphs and 10 morphotypes of Pediastrum species were studied to assess changes in vegetation and lake conditions as probable responses to climate change. We compared the pollen record with Pediastrum species, which we consider to be important proxies of climate changes. Three pollen assemblage zones characterised by Betula nana, Alnus viridis and Larix gmelinii (1706-1808); herbs such as Cyperaceae, Artemisia or Senecio (1808-1879), and higher abundance of Larix pollen (1955-2011) are visible. Also, three Pediastrum assemblage zones show changes of aquatic conditions: higher abundances of Pediastrum boryanum var. brevicorne (1706-1802); medium abundances of P. kawraiskyi and P. integrum (1802-1840 and 1920-1980), indicating cooler conditions while less eutrophic conditions are indicated by P. boryanum, and a mainly balanced composition with only small changes of cold- and warm-adapted Pediastrum species (1965-2011). In general, compositional Pediastrum species turnover is slightly higher than that indicated by pollen data (0.54 vs 0.34 SD), but both are only minor for this treeline location. In conclusion, the relevance of differentiation of Pediastrum species is promising and can give further insights into the relationship between lakes and their surrounding vegetation transferred onto climatic conditions.
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 +/- A 0.51; I = 0.8 +/- A 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 T-July, water depth and both sulphate (SO4 (2-)) and silica (Si4+) concentrations significantly (p a parts per thousand currency sign 0.05) explained variance in the cladoceran assemblage. T-July 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.