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Fig. 12 in A late Paleocene fauna from shallow-water chemosynthesis-based ecosystems, Spitsbergen, Svalbard
Fig. 12. Thyasirid bivalve Rhacothyas spitzbergensis (Anderson, 1970) from the upper Paleocene, Basilika Formation, Zachariassendalen (A, F, G) and Fossildalen (B–E), Spitsbergen, Svalbard. A. NRM-PZ Mo183968, shell, right valve (A1), dorsal view of a right valve showing posterior sulcus (A2), oblique anterior view showing small lunule (A3). B. NRM-PZ Mo 183970, shell, left valve view. C. NRM-PZ Mo 149144, partial shell, →
Water sample analysis and satellite imagery of a thermo-erosion gully and its surroundings in Adventdalen, Svalbard.
<h2><strong>Data description</strong></h2> <p>This dataset is part of the supplemental information to the paper "Rapid Ice-Wedge Collapse and Permafrost Carbon Loss Triggered by Increased Snow Depth and Surface Runoff" by Parmentier et al. (2024). It includes the analysis of water quality in and around a thermo-erosion gully on the high-Arctic archipelago of Svalbard, and three satellite images that give an overview of the wider area around this gully in the context of a snow fence experiment (Cooper et al. 2011). More details are provided in Parmentier et al. (2024).</p> <h2><strong>Background</strong></h2> <p>Thicker snow cover in permafrost areas causes deeper active layers and thaw subsidence, which alter local hydrology and may amplify the loss of soil carbon. However, the potential for changes in snow cover and surface runoff to mobilize permafrost carbon remains poorly quantified. The data presented here is part of a study that showed that a snow fence experiment on High-Arctic Svalbard inadvertently led to surface subsidence through warming, and extensive downstream erosion due to increased surface runoff. Within a decade of artificially-raised snow depths, several ice wedges collapsed, forming a 50 m long and 1.5 m deep thermo-erosion gully in the landscape. We estimate that 1.1 to 3.3 tons C may have eroded, and that the gully is a hotspot for processing of mobilised aquatic carbon. Our study show that interactions among snow, runoff and permafrost thaw form an important driver of soil carbon loss.</p> <h2><strong>Water samples</strong></h2> <p>The following datafile includes the analysis of several water samples taken in and near a thermo-erosion gully on Svalbard on August 5<sup>th</sup> and 6<sup>th</sup>, 2017. These were analyzed for dissolved organic carbon (DOC), particulate organic carbon (POC), particulate nitrogen (PN) content, and stable carbon isotope ratios δ<sup>13</sup>C-DOC and δ<sup>13</sup>C-POC. In addition, temperature, pH, oxygen, and electrical conductivity were measured in the field on the day of sampling. This data is provided in the following Excel file that also includes the latitude and longitude for each sample point: </p> <ul> <li>Parmentier et al - 2024 - Water Sample Analysis.xlsx</li> </ul> <h3><strong> </strong><strong>Sample analysis</strong></h3> <p>A full description of the analysis is repeated here from the supplemental information in the accompanying publication (Parmentier et al. 2024). The water samples were filtered on the day of collection through a pre-combusted glass fiber filter with pore size of 0.7 µm (Whatman, Grade GF/F). After filtration, the filters were packed in aluminum foil and frozen for later analysis of the collected particulate matter. From the filtrate, three samples of ~50 ml were taken and immediately frozen for transport.</p> <p>The filtered water samples were analyzed for their dissolved organic carbon (DOC) content and their stable carbon isotope ratio δ<sup>13</sup>C-DOC. This combined analysis was carried out at the labs of UCLouvain, Belgium with an Aurora 1030W TOC Carbon Analyzer, from OI Analytical, coupled to an IRMS (Thermo delta V Advantage). In the Aurora 1030W, the water samples were purged with H<sub>3</sub>PO<sub>4</sub>(phosphoric acid) to remove any dissolved inorganic carbon (DIC). Afterwards, Na<sub>2</sub>S<sub>2</sub>O<sub>8</sub> (sodium persulfate) was added to the heated sample (97 °C) to oxidize any DOC to CO<sub>2</sub>. With N<sub>2</sub> as the carrier gas, the CO<sub>2</sub> was transferred to the analyzing units where the total concentration and δ<sup>13</sup>C-DOC of the CO<sub>2</sub> were detected. The δ<sup>13</sup>C-DOC samples were calibrated against the certified standard IAEA-CH-6 (-10.449 ± 0.033 ‰VPDB) and an internal sucrose standard (-26.99 +/- 0.04 ‰). The DOC measurements were calibrated against a concentration range (n=8) of the same standards (Morana et al., 2015).</p> <p> The particulate matter retained on the filters was analyzed for particulate organic carbon (POC) and particulate nitrogen (PN) concentrations, as well as δ<sup>13</sup>C-POC. The glass fiber filters were subsampled and repeatedly acidified with HCl (1.5 M) in pre-combusted Ag capsules to remove carbonates. Analyses were performed at the Stable Isotope Facility of the University of California in Davis using an Elementar Vario EL Cube (Elementar Analysensysteme GmbH, Hanau, Germany) connected to a PDZ Europa 20-20 isotope ratio mass spectrometer (Sercon Ltd., Cheshire, UK). Isotope ratios of δ<sup>13</sup>C are reported relative to the international standard VPDB (Vienna PeeDee Belemnite).</p> <h2><strong>Satellite imagery</strong></h2> <p>To show the development of the thermo-erosion gully over time, we provide three high resolution satellite images from the Digital Globe constellation of satellites. The areal extent of these images covers the entire snow fence experiment in the valley of Adventdalen on Svalbard. They were acquired on August 5<sup>th</sup>, 2011, August 30<sup>th</sup>, 2013, and July 9<sup>th</sup>, 2015 by the WorldView-2, GeoEye-1 and WorldView-3 satellites, respectively. These images are provided as GeoTiffs – projected in the UTM 33X coordinate system:</p> <ul> <li>SnoEco_2011AUG05_WV2_MUL_Pansharpened_bco_rcs_dobj.tif</li> <li>SnoEco_2013AUG30_GE1_MUL_Pansharpened_bco_rcs_dobj.tif</li> <li>SnoEco_2015JUL09_WV3_MUL_Pansharpened_bco_rcs_dobj.tif</li> </ul> <p>Each of these files includes the following color bands: </p> <ul> <li>Band 1: Blue</li> <li>Band 2: Green</li> <li>Band 3: Red</li> <li>Band 4: Near Infrared</li> </ul> <p>In addition, the images are clipped to the following coordinate bounds (in UTM 33X):</p> <ul> <li> <p><span>x<sub>min</sub>, x<sub>max</sub></span><span>: 523740, 524825</span></p> </li> <li> <p><span>y<sub>min</sub>, y<sub>max</sub></span><span>: 8677150, 8678100</span></p> </li> </ul> <p>For full details on these satellite products, we refer to DigitalGlobe/Maxar.<strong> </strong></p> <h3><strong>Image processing</strong></h3> <p>The satellite imagery was processed according to DigitalGlobe guidelines and calibration coefficient adjustment factors. The radiometrically corrected source images were first converted to top-of-the-atmosphere spectral radiance, and thereafter to top-of-the-atmosphere reflectance. Following this processing, each color band of the image was pansharpened (using Bicubic interpolation) with the RCS algorithm in the Orfeo ToolBox of QGIS 2.18 to increase the horizontal resolution to ~50 cm. To reduce haze effects, the images were further corrected through a dark object subtraction (bottom 1 percentile of the blue band) which was applied to each band separately. Subsequent negative values were set to zero.<strong> </strong></p> <h2><strong>Acknowledgments</strong></h2> <p>This research was funded by the Research Council of Norway (RCN; grant agreement 230970), and the FRAM - Terrestrial flagship (362255 and 642018). F.J.W.P. and S.W. received additional funding from the RCN (grant agreement 323945). The high-resolution satellite imagery comes courtesy of the DigitalGlobe Foundation. We thank UCLouvain and the University of California, Davis for assisting in the sample analysis.<strong> </strong></p> <h2><strong>References</strong></h2> <p>Cooper, E. J., Dullinger, S., & Semenchuk, P. (2011). Late snowmelt delays plant development and results in lower reproductive success in the High Arctic. <em>Plant Science</em>, 180(1), 157–167. https://doi.org/10.1016/j.plantsci.2010.09.005</p> <p>Morana, C., Darchambeau, F., Roland, F. A. E., Borges, A. V., Muvundja, F., Kelemen, Z., et al. (2015). Biogeochemistry of a large and deep tropical lake (Lake Kivu, East Africa: insights from a stable isotope study covering an annual cycle. <em>Biogeosciences</em>, 12(16), 4953–4963. https://doi.org/10.5194/bg-12-4953-2015</p> <p>Parmentier, F. J. W., Nilsen, L, Tømmervik, H., Meisel, O. H., Bröder, L., Vonk, J. E., Westermann, S., Semenchuk, P. R., Cooper, E. J., Rapid Ice-Wedge Collapse and Permafrost Carbon Loss Triggered by Increased Snow Depth and Surface Runoff, <em>Geophysical Research Letters</em>, In press</p>
Fig. 8 in Papposphaera heldalii sp. nov. (Haptophyta, Papposphaeraceae) from Svalbard
Fig. 8. Schematic drawings of coccolith structures in species of Papposphaera (not drawn to scale). a – body coccoliths; b – calyx design; c – coccoliths with central processes; A – P. sagittifera; B – P. sarion; C – P. arctica; D – P. iugifera; E – P. heldalii. Notice that alternative shapes are included for some species (A/a, B/a, D/a) and that the P. sarion design for a coccolith with a central process (B/c) is potentially as variable as B/a.
Figs 2–7 in Papposphaera heldalii sp. nov. (Haptophyta, Papposphaeraceae) from Svalbard
Figs 2–7. Papposphaera heldalii SEM images of cells from the Svalbard region collected during Jan. 2014 (Figs 4–5) and March 2014 (Figs 2–3, 6–7). 2 – cluster of coccoliths shown at high magnification. Notice in particular details of the calyx and coccolith rim calcification. The arrows point to extensions from the pentagonal elements separating the rod-like elements. See also ruptures in the organic base plates of body coccoliths; 3 – whole cell (type specimen) showing the general disposition of types of coccoliths within the coccosphere. A single coccolith (enlarged in Fig. 6) shows the central area calcification of a calicate coccolith; 4 – whole cell. Notice the difference in length of the central process among the two clusters of coccoliths carrying these. See also the conspicuous size differences between neighboring body coccoliths; 5 – body coccoliths showing large individual size differences; 6 – detail of central area calcification in a coccolith that carries a central process (broken away here); 7 – detail of coccolith rim from coccoliths that carry a central process. The arrows point to extensions from the pentagonal elements separating the rod-like elements.
Fig. 1 in Papposphaera heldalii sp. nov. (Haptophyta, Papposphaeraceae) from Svalbard
Fig. 1. Svalbard sampling sites during MicroPolar cruises. The type locality of P. heldalii is marked by a square, and arrows point to additional sampling sites yielding P. heldalii material.
Svalbard Protect Our Waters campaign video - Norwegian
<p>Video for the social awareness campaign Protect Our Waters developed as part of the CLIMAREST EU Horizon 2020 project. The video is provided in several formats (16:9, 9:16, and 1:1) for use in different applications. The language in the video is Norwegian. </p>
Ground Penetrating Radar data acquired over Von Postbreen, Svalbard, March 2018.
<p>GPR data and diffraction focusing Madagascar code</p> <p>Data authors: Richard Delf and Robert Bingham, University of Edinburgh.</p> <p>Data and code associated with Delf et al. "Reanalysis of polythermal glacier thermal structure using radar diffraction focussing"</p> <p>Ground-Penetrating Radar data was acquired over Von Postbreen, Svalbard, to investigate the internal velocity structure by diffraction focusing.</p> <p>Data were acquired in March 2018 over 2 days, using a PulseEkko Pro 25 MHz system, towed behind a snowscooter in an in-line, common-offset configuration. Data are stored in the SEGY format.</p> <p>Differential GPS was used for positioning; coordinates are included in the SEGY data.</p> <p><strong>Data Description</strong></p> <p>Three groups of data are present here within the .zip file.</p> <p>data with no preprocessing: files in ./data/aq_data/18_VP_0_*.SGY</p> <p> SEGY Radar data with pre-processing applied. Significant ringing is observed in the upper regions of the radargrams. Note file names do not correlate with the files in subsequent folders.</p> <p>data with preprocessing: filenames in ./data/raw_data/18_VP_1_*.SGY</p> <p> SEGY Radar data with pre-processing applied to remove ringing and other noise using an SVD filter and bandpass filtering. Radargrams are sorted into shorter lines across and up the glacier. See associated paper for additional details.</p> <p>processed_data: filenames 18_VP_2_*.SGY</p> <p> The same data as above, with diffraction coherence after Schwarz et al (2019) applied. See associated paper for additional details.</p> <p> </p> <p><strong>Processing files:</strong></p> <p>SConstruct: Madagascar SConstruct file for processing the above profiles to derive the velocity profiles described in the associated (Delf et al) paper.</p>
Supplementary data for: "Historical glacier change on Svalbard predicts doubling of mass loss by 2100"
<p>Supplementary datasets for:</p> <p>Geyman, E.C., van Pelt, W.J.J., Maloof, A.C., Faste Aas, H., and Kohler, J., 2022. "Historical glacier change on Svalbard predicts doubling of mass loss by 2100." Nature.</p> <p>Abstract:</p> <p>The melting of glaciers and ice caps accounts for about one-third of current sea-level rise, exceeding the mass loss from the more voluminous Greenland or Antarctic Ice Sheets. The Arctic archipelago of Svalbard, which hosts spatial climate gradients that are larger than the expected temporal climate shifts over the next century, is a natural laboratory to constrain the climate sensitivity of glaciers and predict their response to future warming. Here we link historical and modern glacier observations to predict that twenty-first century glacier thinning rates will more than double those from 1936 to 2010. Making use of an archive of historical aerial imagery from 1936 and 1938, we use structure-from-motion photogrammetry to reconstruct the three-dimensional geometry of 1,594 glaciers across Svalbard. We compare these reconstructions to modern ice elevation data to derive the spatial pattern of mass balance over a more than 70-year timespan, enabling us to see through the noise of annual and decadal variability to quantify how variables such as temperature and precipitation control ice loss. We find a robust temperature dependence of melt rates, whereby a 1°C rise in mean summer temperature corresponds to a decrease in area-normalized mass balance of -0.28 m yr<sup>-1</sup> of water equivalent. Finally, we design a space-for-time substitution8 to combine our historical glacier observations with climate projections and make first-order predictions of twenty-first century glacier change across Svalbard.</p> <p> </p> <p>Dataset description: </p> <p><br> This dataset contains the digital elevation models (DEMs), elevation change maps, point clouds, orthophotos, and vector outlines of glacier extents based on the Norwegian Polar Institute's collection of 5,507 high-oblique aerial images captured over Svalbard in 1936/1938. The photographs were analyzed through structure-from-motion (SfM) photogrammetry to generate 3D models. We also provide an .xlsx spreadsheet containing glacier-by-glacier statistics of ice loss and climate fields. Note that all of the raster and point cloud files listed below have been georeferenced in Metashape using the ground control points (GCPs) illustrated in Main Text, Fig. 2e, but have not undergone the co-registration and bias-correction following the methods of Nuth & Kaab (2011), which was done on a glacier-by-glacier basis. However, the glacier change budgets in the .xlsx file [#5 below] do reflect the values from the glacier-by-glacier co-registered and bias-corrected DEMs. See below for descriptions of each dataset (each number below corresponds to a different zipped folder).</p> <p>------------------------------------------------------------------------------------- </p> <p><strong>Svalbard-wide datasets [all georeferenced Svalbard-wide datasets are in the coordinate system UTM 33N]: </strong></p> <p><br> 1. Svalbard-wide 1936 DEM (20 m and 50 m resolution) [georeferenced .tif file] </p> <p>2. Svalbard-wide 1936 orthophotomosaic (20 m resolution) [georeferenced .tif file] </p> <p>3. Svalbard-wide dh (1936-2010) (20 m and 50 m resolution) [georeferenced .tif file] </p> <p>4. Shapefile of 1936 glacier extents [ESRI .shp file] </p> <p>5. Glacier-by-glacier statistics [.xlsx file] </p> <p>------------------------------------------------------------------------------------- </p> <p><strong>Regional-datasets: </strong></p> <p><em>Due to file size limitations, the high-resolution (5 m) datasets are split into the 8 regions illustrated in Main Text, Fig. 2d: </em></p> <p><em>Zone 1 - South Spitsbergen</em></p> <p><em>Zone 2 - Barentsoya-Edgeoya</em></p> <p><em>Zone 3 - Austfonna</em></p> <p><em>Zone 4 - Vestfonna</em></p> <p><em>Zone 5 - Northeast Spitsbergen</em></p> <p><em>Zone 6 - Central Spitsbergen</em></p> <p><em>Zone 7 - Northwest Spitsbergen</em></p> <p><em>Zone 8 - North Spitsbergen</em></p> <p><br> 6. Regional 1936 DEMs (5 m resolution) [georeferenced .tif files] </p> <p>7. Regional dh (1936-2010) (5 m resolution) [georeferenced .tif files] </p> <p>8. Local 1936 orthomosaics (5 m resolution) [georeferenced .tif files] </p> <p>9. Unprocessed point clouds [.laz files]. These files represent the raw 3D point clouds (x,y,z) generated in Agisoft Metashape for each of the 17 local models described in Extended Data Figure 3.</p> <p>10. Thumbnail-sized copies of the 5,507 historical aerial images (1936 and 1938) analyzed in this study, along with a .csv file labeling the approximate location of each photograph.</p>
CO2 NEE and ER + air and soil meteorological and climate parameters in Arctic tundra, Ny Ålesund (Svalbard, NO) - summer 2019
<p>The dataset “fluxes_meteoclimate_NyAlesund” is a .csv file reporting CO2 fluxes and basic meteoclimatic variables measured in the Bayelva Basin near Ny Ålesund, in the Brøgger peninsula, Spitsbergen, Norway (78°55’24’’ N, 11°55’15’’E) during the 2019 growing season peak (July-August). Average coordinates of the measuring site are: 78°55’25.7” N,11°53’29.4” E. Fluxes were measured using the flux chamber method: the Net Ecosystem Exchange (NEE) was measured with a transparent flux chamber, while the Ecosystem Respiration (ER) with a shaded chamber. Three types of sampling were performed: at a fixed point during 24h ('point' in column sampling); in points randomly distributed over a site ('site' in column sampling); and in points covered with specific species ('species' in column sampling). Flux data are complemented by measurements of soil temperature (Ts, in Celsius degrees), soil volumetric water content (VWC, in %), atmospheric pressure (Pr, in hPa), air temperature (Ta, in Celsius degrees), air moisture (RH, in %), and solar radiance (rs , in W/m2). The Green Fractional Cover (GFC, between 0 and 1) of the vegetation inscribed within the sampling surface was estimated from digital RGB pictures taken at nadir. Measurements were divided into 4 classes, depending on the prevailing cover type: bare soil (BS), vascular vegetation (V), non-vascular vegetation (NV, including lichens, mosses and bacterial soil crust) and mix of vascular and non-vascular vegetation (MIX). Class V was further split into 5 subclasses: Carex spp. (CX), Dryas octopetala (DR), Salix Polaris (SL), Saxifraga oppostifolia (SX) and Silene acaulis (SI). </p>
Exploring Rapid Changes in the Arctic Marine Environment - First ECOTIP Expedition to the Kongsfjorden on Svalbard
<p>Video of flash talk (5 min) given by Claudia Elena Schmidt during the <strong>YOUMARES 11</strong> conference in <strong>Session 4: </strong>Fjord systems: Ecology, bentho-pelagic coupling, and anthropogenic impacts on 15.10.2020 in Hamburg.</p> <p>The Arctic Ocean and its adjacent seas are especially vulnerable to climate change. Its ecosystem is rapidly changing in response to temperature increase, loss of sea ice, and the combined effects of additional stressors such as invasive species and pollution. However, the scientific community currently lacks sufficient information on the mechanisms, drivers and thresholds of these environmental changes on the Arctic ecosystem and the consequences that may arise for many Arctic communities. The recently launched ECOTIP project aims at closing these knowledge gaps by investigating the impacts of climate change on the Arctic marine environment in order to identify tipping points that can induce an abrupt and sometimes irreversible change in the ecosystem. In a joint sampling campaign between the Helmholtz-Zentrum Hereon and the Alfred Wegener Institute (AWI) water and sediment samples from key marine and terrestrial locations in the Kongsfjorden area on the west coast of the Svalbard archipelago were collected in July 2020. The aim of the ongoing study will be to understand the mechanism of carbon cycling in a polar fjord system that is influenced by profound environmental changes by measuring alkalinity and dissolved inorganic carbon (DIC). Furthermore, the biogeochemical cycle of metals, trace metals and other elements in coastal and shelf waters influenced by melt water streams, draining from land terminating glaciers, will be investigated by multi-element analyses. The collected data will provide scientific insight into biogeochemical processes in high-latitude fjord and coastal regions affected by climate change and thus help to predict future changes in Arctic ecosystems.</p>
Results of the geochemical and magnetic studies on cryodust from glacial cores of the Southern Spitsbergen (Svalbard, Norway)
<p>Results of the geochemical and magnetic studies on natural mineral aerosol deposited and trapped in glaciers (cryodust). Samples were collected from glacial cores taken from five glaciers of Southern Spitsbergen (Svalbard, Norway). </p> <p>The samples were collected by means of a hand-operated Kovacs Enterprise® Mark II coring system. Samples (90 mm in diameter) were packed into polyethylene bags, secured, and transported to the Polish Polar Station Hornsund. The core samples were rinsed using deionized water (Polwater DL100; Norm PN-EN ISO 117 3696:1999; conductivity <0.06 μS/cm) and melted at room temperature in the closed new polyethylene bags. After melting samples were filtered through pre-rinsed sterile Millipore Mixed Cellulose Esters filters (white gridded and 0.45 𝜇𝜇m pore size). After filtration, the filters with residuum were dryer at the temperature of 60<sup>o</sup>C.</p> <p>Solid particulates of cryodust were subjected to analysis by Electron MicroProbe (EMP) with special attention paid to their internal structure. A scanning electron microscope (SEM) fitted with a backscattered electron (BSE) detector was used to trace grains topography and composition. Special attention was given to monazite chemical dating. Magnetic methods comprised analyses of magnetic susceptibility <em>κ</em> vs temperature <em>T</em> variations and determination of magnetic hysteresis parameters.</p> <p>More about the methodology, analyses and results can be found here: <a href="https://doi.org/10.3390/atmos11121325">https://doi.org/10.3390/atmos11121325</a></p>
GPR snow depth survey over Svalbard Glaciers
<p>Dataset contains results of GPR surveys of snowpack performed on five glaciers in Svalbard (Slakbreen, Longyearbreen, Maritbreen, Philipbreen and Holtedahlfonna ). Surveys were performed in March - April 2008, with 800 MHz antenna (Mala ProEx system).</p> <p>Fieldwork was funded by the Svalbard Integrated Arctic Earth Observing System Access project "Snow Observations in Svalabr (SOS)".</p> <p>Dataset consists of following unprocessed files:</p> <p>*.RAD - survey system and antenna control file</p> <p>*.COR - trace number, date, time and poistion</p> <p>*.MRK - reference markers</p> <p>*.RD3 - radarogram (clsed MALA ProEx format)</p>
Scientific Applications of Unmanned Vehicles in Svalbard
<p>This database was generated in the scope of the State of Environmental Science in Svalbard (SESS) report. <a href="https://zenodo.org/record/4293283/files/SESS2020_UAV_Svalbard.pdf?download=1">Scientific Applications of Unmanned Vehicles in Svalbard</a>. <em>SESS Report 2020</em>, Svalbard Integrated Arctic Earth Observing System. DOI: 10.5281/zenodo.4293283</p>
Near-surface temperatures in three blockfields in Norway and Svalbard
<p>near-surface temperature measurements in three blockfields in Norway and Svalbard</p> <p>raw data to the manuscript 'Near-surface temperatures and potential for frost weathering in blockfields in Norway and Svalbard', submitted to Earth Surface Processes and Landforms</p>
Svalbard reindeer winter diets (1995–2012) dataset
<p>Arctic ecosystems are changing dramatically with warmer and wetter conditions resulting in complex interactions between herbivores and their forage. We investigated how Svalbard reindeer (<em>Rangifer tarandus platyrhynchus</em>) modify their late winter diets in response to long-term trends and interannual variation in forage availability and accessibility. By reconstructing their diets and foraging niches over a 17-year period (1995–2012) using serum δ<sup>13</sup>C and δ<sup>15</sup>N values, we found strong support for a temporal increase in the proportions of graminoids in the diets with a concurrent decline in the contributions of mosses. This dietary shift corresponds with graminoid abundance increases in the region and was associated with increases in population density, warmer summer temperatures and more frequent rain-on-snow (ROS) in winter. In addition, the variance in isotopic niche positions, breadths and overlaps also supported a temporal shift in the foraging niche and a dietary response to extreme ROS events. Our long-term study highlights the mechanisms by which winter and summer climate changes cascade through vegetation shifts and herbivore population dynamics to alter the foraging niche of Svalbard reindeer. Although i<span>t has been anticipated that climate changes in the Svalbard region of the Arctic would be detrimental to this unique ungulate</span>, our study suggests that environmental change is in a phase where conditions are improving for this subspecies at the northernmost edge of the <em>Rangifer</em> distribution.</p>
Fig. 3 in Myxozoa in high Arctic: Survey on the central part of Svalbard archipelago
Fig. 3. Histology of Schulmania aenigmatosa infection. (A-C) Schulmania aenigmatosa infection in excretory system of Hippoglossoides platessoides. (A) Early plasmodial stages localised in ureter as seen in histological section stained with HE. (B) Advanced plasmodial stages filling urinary bladder. Giemsa stained stage (inserted). (C) Semithin section stained with toluidine blue documents numerous plasmodial stages attached to the wall of urinary bladder. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 6 in Myxozoa in high Arctic: Survey on the central part of Svalbard archipelago
Fig. 6. Ultrastructure of Zschokkella siegfriedi infection. (A–C) Details of ultrastructure of Zschokkella siegfriedi as seen in transmission electron microscope. (A) Early developmental stage (EDS) localised within epithelium of renal tubule. Epithelial cells (EC) differ substantially in electron-density due to differences in density of mitochondria. Arrows mark basal membrane of renal tubule, NEC nucleus of epithelial cells, M mitochondria. (B) Almost mature spore in longitudinal section. VC valvogenic cell, NVC nucleus of VC, CC capsulogenic cells, NCC nucleus of CC, S sporoplasm, NS nucleus of S, PPF primordium of polar filament, M mitochondria. C. Valves (V), the polar capsule wall (PCW), and some sections of the polar filament coils.
Fig. 1 in Myxozoa in high Arctic: Survey on the central part of Svalbard archipelago
Fig. 1. Mature spores and plasmodia. (A-N) Myxospores and myxosporean plasmodial stages as seen in Nomarski differential interference contrast. Measurements are listed in Table 1. (A) Mature spore of Ceratomyxa porrecta. (B) Spores of Schulmania aenigmatosa with focus on polar capsules (left) and sinuous valve suture (right). (C) Plasmodial stages (left) and mature spore of Parvicapsula irregularis (right). (D) Mature spores of Parvicapsula petuniae. (E) Mature spore of Myxidium gadi. (F) Mature spore of Myxidium finnmarchicum. (G, H) Spores of Sinuolinea arctica in frontal (G) and sutural (H) view. (I, J) Plasmodial stages of Zschokkella siegfriedi. (K) Mature spores of Zschokkella siegfriedi. (L) Plasmodial stage of Latyspora-like organism. (M, N) Latyspora-like organism spores with focus on polar capsules and part of valve suture, respectively.
Fig. 10 in Myxozoa in high Arctic: Survey on the central part of Svalbard archipelago
Fig. 10. The histology of kidney infected with Latyspora-like organism. (A–B) Advanced stage of Latyspora-like organism infection in renal tubules of Clupea harengus. (A) Epithelium in infected segments of renal tubules consisting of cells with pyknotic nuclei suggestive of cellular necrosis. (B) Early stage of epithelial disintegration. (C) Loss of integrity of epithelium due to advanced necrotic changes. Basophilic remnants seen in necrotic epithelium indicate hypertrophy of some nuclei. (D) Hypertrophy of renal corpuscles containing foreign material in Bowman's spaces was observed but cannot be solely associated with Latyspora-like organism infection.
Fig. 5 in Myxozoa in high Arctic: Survey on the central part of Svalbard archipelago
Fig. 5. Histology of Zschokkella siegfriedi infection. (A–C) Zschokkella siegfriedi infection in renal tubules of Boreogadus saida as seen in semithin sections stained with toluidine blue. (A) Infected segment of renal tubule with plasmodial stages in its lumen and densely stained cells in its epithelial lining. (B) Advanced plasmodial stages and amorphous material completely filling the lumen of renal tubule. All epithelial cells are densely stained. (C) Almost mature spores localised in the lumen of renal tubule. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.