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opencc-by-4.0Apr 2020View details →

Eddy Kinetic Energy in the Arctic Ocean from a High-resolution Global Simulation with 1-km Arctic (data).

Data for the "Eddy Kinetic Energy in the Arctic Ocean from a High-resolution Global Simulation with 1-km Arctic".

opencc-by-4.0Nov 2023View details →

Spectral albedo and summer ground temperature of herbaceous and shrub tundra vegetation at Bylot Island, Canadian High-Arctic

These data are in support of a preprint: Comparing spectral albedo and NDVI of herbaceous and shrub tundra vegetation at Bylot Island, Canadian High-ArcticFlorent Domine, Maria-Belke-Brea, Ghislain Picard, Laurent Arnaud, and Esther LévesqueTo be submitted in 2023. The spectral albedo of several vegetation assemblages on Bylot Island and in Mala River valley on nearby Baffin Island were recorded between 10 and 18 July 2015. The spectral range covered was 346 to 2400 nm. Surfaces were classified according to the main vegetation types. Classes used are graminoids, moss, Salix arctica, soil, and Salix richardsonii. S. richardsonii is the only truly erect species on Bylot Island. Transmission spectra of radiation through the S. richardsonii canopy were also recorded. S. richardsonii spectra were different depending on the location where they were measured and we present spectra for sites in active parts of an alluvial fan (Salix-G2), an inactive part of an alluvial fan (Salix-D1) and in a mesic area on Mala River Valley (Salix-M). We also present typical relative solar irradiance spectra recorded at Bylot Island during the campaign, under clear and overcast conditions. In conjunction with spectral albedo data, these irradiance spectra allow the calculation of the broadband (BB) albedo of the vegetation types and to compare BB albedo values under identical irradiance conditions.  83 spectra were recorded: 39 for S. richardsonii and 44 for low vegetation and soil. 17 transmission spectra under S. richardsonii were recorded. We present here only averages for each vegetation type. We also present averages for all low vegetation types and for all S. richardsonii spectra, to allow the calculation of the radiative impact of erect shrubs at Bylot Island. We also present soil temperature data at 15 cm depth for the spots GRASS (mostly Salix Arctica), TUNDRA (Mostly moss), SALIX-D1 (Salix richardsonii) and SALIX-F (Salix richardsonii). SALIX-F is similar to SALIX-G2. The data are during summer 2020. The locations of the various spots investigated are: Spot name  Latitude  Longitude Vegetation types foundTUNDRA 73.150° -80.004° Humid and moist polygons with low vegetation dominated by mosses, graminoids, S. arctica and S. herbacea.PLAINE 73.167° -79.915° Low vegetation and bare soil patches caused by cryoturbation (mudboils) with mosses, graminoids and S. arctica.GRASS 73.158° -79.907° Low vegetation between patches of S. richardsonii dominated by S. arctica, with litter, mosses, graminoids and occasional bare soil.  SALIX-D1 73.158° -79.907° Scattered patches of S. richardsonii <35 cm tall. Understory is mosses, graminoids, litter, S. arctica and bare soil.SALIX-M 73.006° -80.685° Mesic area with patches of S. richardsonii 35 to 40 cm tall. Understory includes moss, graminoids and litter. Between patches: herb tundra with graminoids and mosses. The area is not within an alluvial fan.SALIX-G2 73.168° -79.812° Extended area in an alluvial fan with S. richardsonii >40 cm. Understory includes litter, mosses, graminoids, bare soil, S. arctica and S. reticulata.SALIX-F 73.182° -79.745° Similar to SALIX-G2. Ground temperature is monitored there. No spectral data were recorded at that site.

opencc-by-4.0Oct 2021View details →

High-resolution, mixed layer NCP estimates and ancillary data from the Central and Eastern North American Arctic: 2015, 2018, 2019

Dataset overview This dataset contain ship-based, high-resolution (underway) estimates of mixed layer net community production (NCP) and ancillary data from three summertime cruises in the Central and Eastern North American Arctic in 2015, 2018 and 2019. NCP estimates were derived from underway O2/Ar observations, obtained using ship-based membrane inlet mass spectrometry. Ancillary data include geospatial information (time, location), surface and depth-resolved hydrography and biogeochemical observations, and select output from a simulation of an oceanographic circulation model, based on the NEMO framework. Please cite as: Izett, R. and Tortell, P. 2021. High-resolution, mixed layer NCP estimates and ancillary data from the Central and Eastern North American Arctic: 2015, 2018, 2019 (Dataset). Zenodo. https://doi.org/https://doi.org/10.5281/zenodo.5593381. This dataset is supplement to: Izett, R. W., Castro de la Guardia, L., Chanona, M., Myers, P. G., Waterman, S, and Tortell, P. D. Impact of vertical mixing on summertime net community production in Canadian Arctic and Subarctic waters: Insights from in situ measurements and numerical simulations.    Abstract We present ΔO2/Ar-based estimates of mixed layer net community production (NCP) from three summer cruises in the North American Arctic and Subarctic oceans. Coupling shipboard underway and discrete observations with output from an ocean circulation model, we correct the NCP estimates for vertical mixing fluxes impacting the surface O2 budget. Large positive mixing fluxes, exceeding 100 mmol O2 m-2 d-1, were derived in regions of strong wind-driven mixing, such as the Labrador Sea, and in the physically-dynamic Canadian Arctic Archipelago. In contrast, flux corrections were small (<10 mmol O2 m-2 d-1, on average) in the density-stratified Baffin Bay, where mixing was low, and parts of the well-mixed Hudson Strait, where vertical O2 gradients were weak. The distribution of corrected NCP was highly heterogenous across the study region, reflecting varying contributions of nutrient supply, freshwater input and sea ice dynamics. Elevated NCP was apparent in the Labrador Sea, Hudson Strait, and nearshore regions influenced by glacial meltwater and recent ice retreat. Low NCP and localized net heterotrophy occurred in Baffin Bay, and near strong freshwater and organic matter sources in Hudson Bay and the Queen Maud Gulf. A multiple linear regression model developed using available oceanographic data explained ~58 % of the observed NCP variability. Our work demonstrates the spatially explicit influence of vertical mixing on ΔO2/Ar-based NCP calculations across varied hydrographic conditions, and presents a novel approach to account for this process. This study contributes new knowledge of biological productivity distributions in under-sampled, rapidly changing, high-latitude waters.   Lay summary Net community production (NCP; i.e., net organic matter production) constrains the ocean’s ability to support marine ecosystems and remove carbon dioxide from the atmosphere. A common approach to estimating NCP involves measurements of upper ocean oxygen (O2) concentrations. However, while vertical mixing may be a significant component of the surface water O2 budget in some regions, applications of this approach typically do not quantify the magnitude of this flux, which can lead to potentially inaccurate NCP estimates. In this paper, we introduce a method combining ship-based measurements and the output from an ocean circulation model to refine NCP calculations for vertical mixing effects in North American Arctic and Subarctic oceans. The dataset reveals high NCP in the Labrador Sea (Inuktitut: Lâbradorip Imappinga), North Atlantic, Hudson Strait (Ikirasarjuaq) and northern Canadian Arctic Archipelago (CAA), and low values in Baffin Bay (Saknirutiak Imanga) and southern CAA. Riverine freshwater input to Hudson Bay (Tasiujarjuar) and the Queen Maud Gulf (Ugjulik) can reduce local NCP, while glacial meltwater may stimulate NCP elsewhere. Overall, this work provides a new NCP dataset in an under-sampled region. Similar studies will be necessary to document changes in biological productivity in response to changing environmental conditions in polar waters.   Acknowledgements This work was supported by the ArcticNet and MEOPAR Networks of Centres of Excellence Canada, Polar Knowledge Canada, the Natural Sciences and Engineering Research Council of Canada (NSERC) and Compute Canada. Hydrography and ancillary oceanographic data were provided by the Amundsen Science group of Université Laval. The model simulation was run by P. Myers (University of Alberta). The underway gas data were collected by R. Izett & P. Tortell (University of British Columbia) All data were archived by R. Izett.    Files and variables: data_yyyy (data provided in NetCDF and Matlab format; "yyyy" denotes sampling year): Ship-board observations and derived quantities. <table> <tbody> <tr> <td>Variable</td> <td>Description </td> <td>Unit</td> </tr> <tr> <td>time</td> <td>UTC YYYY Julian Day (year-day since YYYY-01-01)</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Decimal degrees E</td> </tr> <tr> <td>dist</td> <td>Along-track distance</td> <td>km</td> </tr> <tr> <td>reg_index</td> <td>Regional index</td> <td> </td> </tr> <tr> <td>region_mask_lat</td> <td>Latitude for region indices mask</td> <td>Decimal degrees N</td> </tr> <tr> <td>region_mask_long</td> <td>Longitude for region indices mask</td> <td>Decimal degrees E</td> </tr> <tr> <td>region_mask</td> <td>Regional masks</td> <td> </td> </tr> <tr> <td>sst</td> <td>Sea surface temperature measured in the instrument laboratory</td> <td>deg. C</td> </tr> <tr> <td>sal</td> <td>Sea surface salinity measured in the instrument laboratory</td> <td> </td> </tr> <tr> <td>chl_fluor</td> <td>Calibrated mixed layer Chl a fluorescence in the instrument laboratory</td> <td>(mg Chl a)/m3</td> </tr> <tr> <td>do2ar</td> <td>Biological O2 saturation anomaly, deltaO2/Ar</td> <td>%</td> </tr> <tr> <td>kwo2</td> <td>Weighted O2 gas transfer velocity</td> <td>m/d</td> </tr> <tr> <td>bioflux_ncp</td> <td>Bioflux-NCP</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>cor_ncp</td> <td>corrected-NCP</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>uw_kz</td> <td>Underway model-based eddy diffusivity at the base of the mixed layer</td> <td>m2/s</td> </tr> <tr> <td>bling_ncp</td> <td>BLING model-based mixed layer NCP, matched to underway cruise time/position</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>prof_time</td> <td>UTC 2015 Julian Day (year-day since 2015-01-01) at CTD profile stations</td> <td>UTC Days</td> </tr> <tr> <td>prof_lat</td> <td>Latitude N at CTD profile stations</td> <td>Decimal degrees N</td> </tr> <tr> <td>prof_long</td> <td>Longitude E at CTD profile stations</td> <td>Decimal degrees E</td> </tr> <tr> <td>prof_dist</td> <td>Along-track distance at CTD profile stations</td> <td>km</td> </tr> <tr> <td>prof_reg_index</td> <td>Regional index at CTD profile stations</td> <td> </td> </tr> <tr> <td>prof_do2bdz</td> <td>Subsurface O2b gradient, dO2B/dZ at CTD profile stations</td> <td>mmol O2/m4</td> </tr> <tr> <td>prof_mld</td> <td>Mixed layer depth, calculated at CTD profile stations</td> <td>m</td> </tr> <tr> <td>prof_pycnocline_dep</td> <td>Pycnocline depth, calculated at CTD profile stations</td> <td>m</td> </tr> </tbody> </table>   nemo_yyyy (data provided in NetCDF format only; "yyyy" denotes sampling year): 4-dimensional gridded NEMO model output.  <table> <tbody> <tr> <td>Varaible</td> <td>Description </td> <td>Unit</td> </tr> <tr> <td>time</td> <td>Model time, UTC YYYY Julian Day (year-day since YYYY-01-01)</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Model Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Model Decimal degrees W</td> </tr> <tr> <td>depth_grid_kz</td> <td>kz depth</td> <td>m</td> </tr> <tr> <td>depth_grid</td> <td>depth</td> <td>m</td> </tr> <tr> <td>kz</td> <td>Eddy diffusivity coefficient</td> <td>m2/s</td> </tr> <tr> <td>T</td> <td>Temperature</td> <td>deg-C</td> </tr> <tr> <td>sal</td> <td>Salinity</td> <td> </td> </tr> <tr> <td>oxy</td> <td>Oxygen concentration</td> <td>mol O2/m3</td> </tr> </tbody> </table>   ArcticNet2003-2014_derived_quantities (data provided in NetCDF format only): Derived quantities from ArcticNet sampling. <table> <tbody> <tr> <td>Varaible</td> <td>Description </td> <td>Unit</td> </tr> <tr> <td>time</td> <td>UTC; Days since 2010-01-01</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Decimal degrees E</td> </tr> <tr> <td>reg_index</td> <td>Regional index</td> <td> </td> </tr> <tr> <td>prof_do2bdz</td> <td>Subsurface O2b gradient, dO2B/dZ at CTD profile stations</td> <td>mmol O2/m4</td> </tr> <tr> <td>prof_mld</td> <td>Mixed layer depth, calculated at CTD profile stations</td> <td>m</td> </tr> <tr> <td>prof_pycnocline_dep</td> <td>Pycnocline depth, calculated at CTD profile stations</td> <td>m</td> </tr> </tbody> </table>

opencc-by-4.0Aug 2021View details →

High-resolution sea ice drift and deformation example data derived from Sentinel-1 in the Arctic Ocean during MOSAiC

This data set contains two high-resolution sea ice drift and deformation fields from 30/31 December 2019 and 20/21 June 2021. They were acquired in the Transpolar Drift along the drift track of the research campaign "Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). Drift fields were calculated from Sentinel-1, HH polarization SAR images acquired in enhanced wide mode. These had a pixel resolution of 50 m in Polar Stereographic North projection (latitude of true scale: 70 N, center longitude: 45 W). We used an ice tracking algorithm introduced by Thomas et al. (2008, 2011) and modified by Hollands and Dierking (2011) to derive drift from sequential pairs. The time step between two sequential images is approximately one day. The resulting drift data set was defined on a regular grid with a spatial resolution of 700 m. Outliers in the velocity data were reduced by a 3x3 point running median filter covering an area of 2.1x2.1 km. For the deformation estimates, we calculated deformation using a linear approximation based on Green's Theorem that relates the double integral over a plane to the line integral along a simple curve surrounding the plane. We discretized the curve applying the trapezoid method that linearly interpolates velocity between the vertices of the grid cells. This work contains modified Copernicus Sentinel data (2020) Related publications: von Albedyll, L., Haas, C., and Dierking, W.: Linking sea ice deformation to ice thickness redistribution using high-resolution satellite and airborne observations, The Cryosphere, 15, 2167–2186, <a href="https://doi.org/10.5194/tc-15-2167-2021">https://doi.org/10.5194/tc-15-2167-2021</a>, 2021. Hollands, Thomas; Dierking, Wolfgang (2011): Performance of a multiscale correlation algorithm for the estimation of sea-ice drift from SAR images: initial results. Annals of Glaciology, 52(57), 311-317, <a href="https://doi.org/10.3189/172756411795931462">https://doi.org/10.3189/172756411795931462</a> Thomas, Mani; Geiger, Cathleen A; Kambhamettu, Chandra (2008): High resolution (400 m) motion characterization of sea ice using ERS-1 SAR imagery. Cold Regions Science and Technology, 52(2), 207-223, <a href="https://doi.org/10.1016/j.coldregions.2007.06.006">https://doi.org/10.1016/j.coldregions.2007.06.006</a> Thomas, Mani; Kambhamettu, Chandra; Geiger, Cathleen A (2011): Motion Tracking of Discontinuous Sea Ice. IEEE Transactions on Geoscience and Remote Sensing, 49(12), 5064-5079, <a href="https://doi.org/10.1109/TGRS.2011.2158005">https://doi.org/10.1109/TGRS.2011.2158005</a>

opencc-by-4.0Apr 2023View details →

Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations

This dataset contains a comprehensive set of quality-controlled remote sensing observations of low-level mixed-phase clouds collected at the high Arctic site of Ny-Ålesund, between 10 October 2021 and 31 December 2022. Cornerstones of the dataset are observations from a 35-GHz polarimetric scanning Doppler cloud radar and a 94-GHz zenith-pointing Doppler cloud radar. Radar data are complemented with thermodynamic retrievals from a microwave radiometer, liquid base height from a ceilometer and wind fields from large-eddy simulations. All data have undergone extensive quality control, especially the cloud radar data, which are accurately calibrated, matched, and corrected for gas and liquid-hydrometeor attenuation, ground clutter and range folding. This dataset is especially suited for cloud microphysical studies, and the high number of events included allows for the compiling of robust statistics. The dataset is accompanied by a data descriptor article, which is available at <a href="https://doi.org/10.5194/essd-15-5427-2023" target="_blank" rel="noopener">doi.org/10.5194/essd-15-5427-2023</a>.   Dataset overview The files include only low-level mixed-phase cloud (LLMPC) events, as well as the 2 hours preceding and following events. Each file contains an individual event, unless multiple events are less than 4 hours apart, in which case they are combined into the same file. LLMPC events are detected by requiring that ice and liquid phase coexist in a cloud layer with top below 2500 m for at least one hour. All radar variables observed in zenith (Doppler moments at 35 and 94 GHz, linear depolarization ratio (LDR) at 35 GHz), as well as microwave radiometer retrievals (temperature (T), liquid water path (LWP), integrated water vapor (IWV)), liquid base height from the ceilometer, and model data (horizontal wind speed and direction) are brought to the same time and range grids (respectively named ‘time_zen’ and ‘range_zen’ in the files). Off-zenith radar variables (reflectivity, differential reflectivity (ZDR), maximum spectral ZDR (sZDRmax), correlation coefficient (RhoHV), differential phase shift (PhiDP), and specific differential phase (KDP)) are stored on separate coordinates (named ‘time_slant’ and ‘range_slant’). All derived corrections are already applied to the data, and stored in the files, in case the user is interested in reconstructing the original data. A number of flags have been included in the files: in particular ‘MPC_detected’ indicates whether a LLMPC event was detected, and ‘liquid_attenuation_correction_flag_zen’ and ‘liquid_attenuation_correction_flag_slant’ indicate whether radar reflectivities were corrected for attenuation due to liquid hydrometeors. Liquid attenuation corrections should be especially taken into account when computing the dual-wavelength ratio (i.e., the difference between reflectivity at 35 GHz and at 94 GHz, both expressed in dBZ), and performing quantitative analyses of reflectivity fields.

opencc-by-4.0Sep 2023View details →

Supplement to "Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations"

This dataset is a supplement to "Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations", available at <a href="http://doi.org/10.5281/zenodo.7803064">doi.org/10.5281/zenodo.7803064</a>. The additional variables here included are: slow edge velocity, fast edge velocity, and eddy dissipation rate (EDR). All variables are stored on the same time and range grids adopted for the main dataset. Similarly, the event selection and file structure are identical to those of the main dataset. Slow and fast edge velocities are derived from Doppler spectra recorded by the zenith-pointing 94-GHz cloud radar. The slow (fast) edge velocity is calculated as the velocity associated with the slowest (fastest) Doppler bin above the peak noise level, belonging to a spectral cluster whose width is at least 5 Doppler bins. The EDR is retrieved following the approach by Borque et al. (2016; <a href="http://doi.org/10.1002/2015JD024543">doi.org/10.1002/2015JD024543</a>), using as input the slow edge velocity, and model horizontal wind speed from the main dataset. EDR is retrieved in 5 minute intervals, up to a maximum range of 3 km. The detailed documentation of the variables here included can be found in the Supporting Information to the following publication: <a href="https://doi.org/10.1029/2023GL106599" target="_blank" rel="noopener">doi.org/10.1029/2023GL106599</a>.

opencc-by-4.0Oct 2020View details →

Observation and modeling of high-7Be events in Northern Europe associated with the instability of the Arctic polar vortex in early 2003

Data repository for paper: "Observation and modeling of high-7Be events in Northern Europe associated with the instability of the Arctic polar vortex in early 2003" Erika Brattich; Hongyu Liu; Bo Zhang; Miguel Angel Hernandez-Ceballos; Jussi Paatero; Darko Sarvan; Vladimir Djurdjevic; Laura Tositti; Jelena Ajtić, submitted to Atmospheric Chemistry and Physics, October 2020 Created by Erika Brattich (erika.brattich@unibo.it), October 2020 -> Data - Pb210_2sites.csv contains daily 210Pb observations at Helsinki and Sodankyla in the period January-March 2003. Dates are in "m/d/yyyy" format and 210Pb measurements are in µBq/m³. Please refer to Mattson et al. (1996) for details. -> Model output - trac_avg.merra2_2x25_RnPbBe.200301-200303-monthly-means.bpch.gz: contains model output of monthly mean 210Pb and 7Be concentrations for January, February, and March 2003. - ts_1h.200301.bpch.tar: hourly model output for January 2003 - ts_1h.200302.bpch.tar: hourly model output for February 2003 - ts_1h.200303.bpch.tar: hourly model output for March 2003 - diaginfo.dat, tracerinfo.dat: information data files needed for the GAMAP package (in IDL) used to read and process the model output. GAMAP is publicly available at: <a href="http://acmg.seas.harvard.edu/gamap/">http://acmg.seas.harvard.edu/gamap/</a>  References: Mattsson, R., Paatero, J., and Hatakka, J.: Automatic Alpha/Beta Analyser for Air Filter Samples - Absolute Determination of Radon Progeny by Pseudo-coincidence Techniques, Radiat. Prot. Dosim., 63, 133-139, doi:10.1093/oxfordjournals.rpd.a031520, 1996.

opencc-by-4.0Nov 2020View details →

Dataset for 'Atmospheric VOC measurements at a High Arctic site: characteristics and source apportionment'

This dataset includes the VOCs (and expanded uncertainties) as well as VOCs (and expanded uncertainties) used in the PMF model as described in 'Pernov, J. B., Bossi, R., Lebourgeois, T., Nøjgaard, J. K., Holzinger, R., Hjorth, J. L., and Skov, H.: Atmospheric VOC measurements at a High Arctic site: characteristics and source apportionment, Atmos. Chem. Phys. Discuss., 2020, 1-36, 10.5194/acp-2020-528, 2020.'

opencc-by-4.0Dec 2020View details →

Can root-associated fungi mediate the impact of abiotic conditions on the growth of a High Arctic herb?

This is a dataset containing fragments of internal transcribed spacer 2 (ITS2) extracted from Bistorta vivipara root-associated fungi, sampled from snow fence experiment in Adventdalen, Svalbard.  This dataset was used in Wutkowska et al., 2020, Can root-associated fungi mediate the impact of abiotic conditions on the growth of a High Arctic herb?Can root-associated fungi mediate the impact of abiotic conditions on the growth of a High Arctic herb? [available at biorXiv.org, DOI: 10.1101/2020.06.20.157099] Now the manuscript is available as a peer-reviewed paper: Wutkowska, Magdalena, Dorothee Ehrich, Sunil Mundra, Anna Vader, and Pernille Bronken Eidesen. 2021. ‘Can Root-Associated Fungi Mediate the Impact of Abiotic Conditions on the Growth of a High Arctic Herb?’ Soil Biology and Biochemistry 159:108284. doi: 10.1016/j.soilbio.2021.108284.   All other corresponding data are available here: https://github.com/magdawutkowska/bistorta

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Data for: Microclimate structures communities, predation and herbivory in the High Arctic

In a warming world, changes in climate may result in species-level responses as well as changes in community structure through knock-on effects on ecological interactions such as predation and herbivory. Yet, the links between these responses at different levels are still inadequately understood. Assessing how microclimatic conditions affect each of them at local scales provides information essential for understanding the consequences of macroclimatic changes projected in the future. Focusing on the rapidly changing High Arctic, we examine how a community based on a common resource species (avens, Dryas spp.), a specialist insect herbivore (Sympistis zetterstedtii), and natural enemies of lepidopteran herbivores (parasitoids) varies along a multidimensional microclimatic gradient. We ask (1) how parasitoid community composition varies with local abiotic conditions, (2) how the community-level response of parasitoids is linked to species-specific traits (koino- or idiobiont life cycle strategy and phenology) and (3) whether the effects of varying abiotic conditions extend to interaction outcomes (parasitism rates on the focal herbivore and realized herbivory rates). We recorded the local communities of parasitoids, herbivory rates on Dryas flowers and parasitism rates in Sympistis larvae at 20 sites along a mountain slope. For linking community-level responses to microclimatic conditions with parasitoid traits, we used joint species distribution modelling. We then assessed whether the same abiotic variables also affect parasitism and herbivory rates, by applying generalized linear and additive mixed models. We find that parasitism strategy and phenology explain local variation in parasitoid community structure. Parasitoids with a koinobiont strategy preferred high-elevation sites with higher summer temperatures or sites with earlier snowmelt and lower humidity. Species of earlier phenology occurred with higher incidence at sites with cooler summer temperatures or later snowmelt. Microclimatic effects also extend to parasitism and herbivory, with an increase in the parasitism rates of the main herbivore S. zetterstedtii with higher temperature and lower humidity, and a matching increase in herbivory rates. Our results show that microclimatic variation is a strong driver of local community structure, species interactions and interaction outcomes in Arctic ecosystems. In view of ongoing climate change, these results predict that macroclimatic changes will profoundly affect arctic communities.

opencc-zeroDec 2020View details →

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Data from: Snowmelt and laying date shape the parental care strategy of a high-Arctic shorebird

Parental care varies across animal taxa, from uniparental to biparental care, driven by ecological and social factors such as weather, food availability, predation, and partner availability. Understanding this diversity within species can reveal biotic and abiotic conditions allowing uniparental versus biparental strategies. This study examines the impact of biotic and abiotic factors on parental care strategies in Sanderlings (Calidris alba), one of the few species that uses both types of care. Using long-term data from Greenland (2011-2023), path analyses revealed that laying date and snowmelt influence parental care strategies, with indirect climatic effects during migration and on breeding grounds. We observed a greater proportion of uniparental nests in years with delayed laying dates, and a greater proportion of biparental nests in years with delayed snowmelt. These findings underscore the complex interplay between environmental factors and parental care strategies, offering insights into how these strategies may evolve under changing ecological conditions.

opencc-zeroJun 2024View details →

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.)

Fig. 2 in Myxozoa in high Arctic: Survey on the central part of Svalbard archipelago

Fig. 2. Ultrathin section of almost mature spore of Schulmania aenigmatosa with lateral wings (LW) typical for the genus. CC capsulogenic cell, CC with polar capsule (left).