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85 results for “high-latitudes”
Dataset to Schiedung et al. (2024): Millennial-aged pyrogenic carbon in high-latitude mineral soils
<p>Dataset to Schiedung et al. (2024, Communications Earth & Environment): Pyrogenic Carbon is Aged at Millennial Scale in High-Latitude Mineral Soils</p> <p>DOI: <a href="https://doi.org/10.1038/s43247-024-01343-5">10.1038/s43247-024-01343-5</a></p> <p>This repository includes the following files: </p> <p><strong><em>dd_all.csv</em> </strong>- Includes all data for the individual samples that are presented in the manuscript.</p> <p><strong><em>Var_names_dd_all.csv</em> </strong>- Describes all variables in <em>dd_all</em> with corresponding unit </p> <p><strong><em>dd_site_average.csv</em></strong> - Includes all data that has been determined on composite samples for each site or the average of all samples per site </p> <p><strong><em>Var_names_dd_site_average.csv</em></strong> - Describes all variables in <em>dd_site_average.csv</em> with corresponding unit</p> <p>All .csv use "," as separator. </p> <p>This data set is also connected to Schiedung et al. (2022, Catena <a href="https://doi.org/10.1016/j.catena.2022.106194"> https://doi.org/10.1016/j.catena.2022.106194</a> ) and the corresponding repository: <a href="../records/10609291">https://zenodo.org/records/10609291</a></p>
Dataset for: Statistical properties of meso-scale plasma flows in the nightside high-latitude ionosphere
<p>This dataset is a compilation of statistical results from Gabrielse et al. [2018] (<a href="https://doi.org/10.1029/2018JA025440">https://doi.org/10.1029/2018JA025440</a>). If you would like to use the dataset, please contact Christine Gabrielse (cgabrielse@ucla.edu, cgabrielse@gmail.com). Depending on how the results are used, the main authors request co-authorship on publications. </p> <p>The following list describes the columns in each data file labeled, ***_FLOW-DATA-PCvsAO_YYYY.txt <br> Files named ***_FLOW-DATA-PCvsAO_YYYY_poleward.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> AO=auroral oval<br> PC=polar cap</p> <p> time [YYYYMMDDhhmmss]<br> flagAO [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> flagPC [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> FWHMavg_AO [degrees]<br> FWHMkmavg_AO=[km]<br> longtestranges=[ignore]<br> Velmaxavg_AO=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_AO=[m/s, determined from the Gaussian fits]<br> FWHMavg_PC=[degrees]<br> FWHMkmavg_PC=[km]<br> Velmaxavg_PC=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_PC=[m/s, determined from the Gaussian fits]<br> ;;For the bearings/orientation, see the orientation text files. The following four variables were calculated in a first step but are not<br> ;;those used in the paper. They were not found with the strict selection criteria. Please do not use.<br> mbearingAO=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> mbearingPC=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)] <br> gbearingAO=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> gbearingPC=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> ;;;;;;;;;;;;;;;<br> minlatAO=[degrees, min geographic latitude of the flow]<br> maxlatAO=[degrees, max geographic latitude of the flow]<br> minlatPC=[degrees, min geographic latitude of the flow]<br> maxlatPC=[degrees, max geographic latitude of the flow]<br> mltAO=[degrees (MLT)]<br> mltPC=[degrees (MLT)]<br> AE=[nT]<br> AL=[nT]<br> SYMH=[nT]<br> IMFBz=[nT]<br> IMFBy=[nT]<br> F107=[sfu]</p> <p>The following list describes the columns in each data file labeled, ***_orientation_YYYY.txt <br> Files named ***_orientation_YYYY_poleward.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> The orientation was determined when enough bearings between RGs were available. See Gabrielse et al. [2018] for description. <br> https://doi.org/10.1029/2018JA025440 <br> AO=auroral oval<br> PC=polar cap</p> <p> time [YYYYMMDDhhmmss]<br> mbearingAO [degrees clockwise from magnetic North]<br> gbearingAO [degrees clockwise from geographic North]<br> mbearingPC [degrees clockwise from magnetic North]<br> gbearingPC [degrees clockwise from geographic North]</p> <p>The following list describes the columns in each data file labeled, ***_SPEC_TEST_***_noRG1-2.txt</p> <p> time [YYYYMMDDhhmmss]<br> RG [the range gate number at which the polar cap boundary was determined at RNK, or the auroral oval's equatorial boundary at SAS]</p>
Fermi High-Latitude Extended Sources Catalog (FHES)
<p>The data set contains results of the Fermi High-Latitude Extended Sources Catalog.</p> <p>The FITS file contains the Fermi High-Latitude Extended Sources Catalog (FHES). It provides source extension parameters (or upper limits thereof) and additional information for objects measured in 90 months of observations with the Large Area Telescope (LAT) on board NASA's Fermi satellite.<br> It includes the complete analysis results for 2546 sources. The analysis and data products contained in the catalog are described in detail in the accompanying paper, published in the Astrophysical Journal Supplement, <a href="https://doi.org/10.3847/1538-4365/aacdf7">https://doi.org/10.3847/1538-4365/aacdf7</a>. The preprint of the manuscript can be found here: <a href="https://arxiv.org/abs/1804.08035">https://arxiv.org/abs/1804.08035</a><br> <br> We further provide the 95% lower limits on the intergalactic magnetic field (IGMF) as plain ASCII files for different assumptions on the blazar duty cycles. These correspond to the limits shown in Figure 17 (right panel) of the paper.</p>
Dataset for "Nicolas & Buffett (2023) - Excitation of high-latitude MAC waves in Earth's core, GJI"
<p>Data from the geodynamo model 'Calypso', used as forcings for MAC waves in Earth's core (see Nicolas & Buffett 2023 - Excitation of high-latitude MAC waves in Earth's core, GJI). Code to analyze this data is published at <a href="https://zenodo.org/badge/latestdoi/296985370">zenodo.org/badge/latestdoi/296985370</a>.</p> <p>All files use the netCDF4 format, a format that allows to represent labeled arrays.</p>
Model output and analysis scripts for "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity"
<p>Here, we provide annually averaged model output from a 3000-year control simulation of PlaSim–LSG, a climate model of intermediate complexity. Processed variables and a Jupyter notebook to reproduce all figures of the manuscript (Mehling et al.: "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity") can also be found in this repository.</p> <p>In addition, a Python implementation of the three-box model proposed in the manuscript can be found in the notebook <em>boxmodel.ipynb</em>.</p>
Database of Nightside, High-latitude Ionosphere Meso-scale Flow Characteristics
<p>This database is a compilation of nightside, high-latitude ionosphere meso-scale flow characteristics built on those used in Gabrielse et al. [2018] (<a href="https://doi.org/10.1029/2018JA025440">https://doi.org/10.1029/2018JA025440</a>). It is the most complete version. If you would like to use the database, please contact Christine Gabrielse (cgabrielse@ucla.edu, cgabrielse@gmail.com, and/or christine.gabrielse@aero.org). Depending on how the results are used, the main authors request co-authorship on publications that utilize this database. </p> <p>The methodology and selection criteria can be found in Gabrielse et al. [2018] (<a href="https://doi.org/10.1029/2018JA025440">https://doi.org/10.1029/2018JA025440</a>). </p> <p>The following list describes the columns in each data file labeled, ***_FLOW-DATA-PCvsAO_YYYY.txt <br> The first three letters (RNK or SAS) designate the station used (Rankin Inlet or Saskatoon).<br> Files named ***_FLOW-DATA-PCvsAO_poleward_YYYY.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> <br> AO=Auroral Oval for Rankin Inlet; equatorward of the auroral oval for Saskatoon (not used)<br> PC=Polar Cap for Rankin Inlet; Auroral Oval for Saskatoon</p> <p>(Note: the data files for RNK and SAS have the same format, so the PC designator means flows above the pertinent boundary (polar cap boundary for RNK, auroral oval equatorward boundary at SAS) and the AO designator means flows below the pertinent boundary.)</p> <p> time [YYYYMMDDhhmmss]<br> flagAO [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> flagPC [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> FWHMavg_AO [degrees]<br> FWHMkmavg_AO=[km]<br> longtestranges=[ignore]<br> Velmaxavg_AO=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_AO=[m/s, determined from the Gaussian fits]<br> FWHMavg_PC=[degrees]<br> FWHMkmavg_PC=[km]<br> Velmaxavg_PC=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_PC=[m/s, determined from the Gaussian fits]</p> <p>;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;<br> For the bearings/orientation, see the orientation text files. The following four variables were calculated in a first step but are not<br> those used in the paper. They were not found with the strict selection criteria. **Please do not use.**<br> mbearingAO=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> mbearingPC=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)] <br> gbearingAO=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> gbearingPC=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> ;;;;;;;;;;;;;;;<br> minlatAO=[degrees, min geographic latitude of the flow]<br> maxlatAO=[degrees, max geographic latitude of the flow]<br> minlatPC=[degrees, min geographic latitude of the flow]<br> maxlatPC=[degrees, max geographic latitude of the flow]<br> mltAO=[degrees (MLT)]<br> mltPC=[degrees (MLT)]<br> AE=[nT]<br> AL=[nT]<br> SYMH=[nT]<br> IMFBy=[nT]<br> IMFBz=[nT] <br> F107=[sfu]</p> <p>The following list describes the columns in each data file labeled, ***_orientation_YYYY.txt <br> Files named ***_orientation_poleward_YYYY.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> The orientation was determined when enough bearings between RGs were available. See Gabrielse et al. [2018] for description. <br> https://doi.org/10.1029/2018JA025440 <br> AO=auroral oval<br> PC=polar cap</p> <p> time [YYYYMMDDhhmmss]<br> mbearingAO [degrees clockwise from magnetic North]<br> gbearingAO [degrees clockwise from geographic North]<br> mbearingPC [degrees clockwise from magnetic North]<br> gbearingPC [degrees clockwise from geographic North]</p> <p>The following list describes the columns in each data file labeled, ***_SPEC_TEST_***_noRG1-2.txt</p> <p> time [YYYYMMDDhhmmss]<br> RG [the range gate number at which the polar cap boundary was determined at RNK, or the auroral oval's equatorial boundary at SAS]</p>
Genetic basis of growth reaction to drought stress differs in contrasting high-latitude treeline ecotones of a widespread conifer
<p>Raw and filtered SNP data and raw tree ring data of the analysed trees. R scripts for SNP filtering, phenotypic data and genotype-phenotype association analysis. </p>
Figure 5 in Organic geochemistry of a high-latitude Lower Cretaceous lacustrine sediment sample from the Koonwarra Fossil Beds, South Gippsland, Victoria, Australia
Figure 5: Partial m/z 178, 202 and 228 mass chromatograms showing the distribution of common polycyclic aromatic hydrocarbons (PAH) in the aromatic fraction.
Figure 4 in Organic geochemistry of a high-latitude Lower Cretaceous lacustrine sediment sample from the Koonwarra Fossil Beds, South Gippsland, Victoria, Australia
Figure 4: Partial m/z 191 and 217 mass chromatograms used in calculation of sterane/hopane ratio. A ratio of 0.03 indicates that a very significant proportion of overall biomass in the lake was derived from bacteria.
Figure 2 in Organic geochemistry of a high-latitude Lower Cretaceous lacustrine sediment sample from the Koonwarra Fossil Beds, South Gippsland, Victoria, Australia
Figure 2: An uncommon example of disarticulation of a fish carcass, collected during an excavation of the Koonwarra Fossil Beds led by Tom Rich in 2013. This specimen was collected approximately 5 m from the bottom of the unit (defined here as the first> 20 cm thick unit of green siltstone/mudstone; the underlying rocks are predominantly cross-bedded, fluviatile arkosic sandstone).
Figure 3 in Organic geochemistry of a high-latitude Lower Cretaceous lacustrine sediment sample from the Koonwarra Fossil Beds, South Gippsland, Victoria, Australia
Figure 3: Saturate fraction total ion chromatogram and m/z 85 mass chromatogram showing distribution and relative abundances of n-alkanes and isoprenoids pristane and phytane.
Figure 1 in Organic geochemistry of a high-latitude Lower Cretaceous lacustrine sediment sample from the Koonwarra Fossil Beds, South Gippsland, Victoria, Australia
Figure 1: Location of the Lower Cretaceous Koonwarra Fossil Beds in South Gippsland, Victoria, Australia
Super resolution enhancement of Landsat imagery and detections of high-latitude lakes
<p>This archive contains native resolution and super resolution (SR) Landsat imagery, derivative lake shorelines, and previously-published lake shorelines derived airborne remote sensing, used here for comparison. Landsat images are from 1985 (Landsat 5) and 2017 (Landsat 8) and are cropped to study areas used in the corresponding paper and converted to 8-bit format. SR images were created using the model of Lezine et al (2021a, 2021b), which outputs imagery at 10x-finer resolution, and they have the same extent and bit depth as the native resolution scenes included. Reference shoreline datasets are from Kyzivat et al. (2019a and 2019b) for the year 2017 and Walter Anthony et al. (2021a, 2021b) for Fairbanks, AK, USA in 1985. All derived and comparison shoreline datasets are cropped to the same extent, filtered to a common minimum lake size (40 m<sup>2</sup> for 2017; 13 m<sup>2</sup> for 1985), and smoothed via 10 m morphological closing. The SR-derived lakes were determined to have F-1 scores of 0.75 (2017 data) and 0.60 (1985 data) as compared to reference lakes for lakes larger than 500 m2, and accuracy is worse for smaller lakes. More details are in the forthcoming accompanying publication.</p> <p>All raster images are in cloud-optimized geotiff (COG) format (.tif) with file naming shown in <strong>Table 1</strong>. Vector shoreline datasets are in ESRI shapefile format (.shp, .dbf, etc.), and file names use the abbreviations LR for low resolution, SR for high resolution, and GT for “ground truth” comparison airborne-derived datasets.</p> <p>Landsat-5 and Landsat-8 images courtesy of the U.S. Geological Survey</p> <p>For an interactive map demo of these datasets via Google Earth Engine Apps, visit: <a href="https://ekyzivat.users.earthengine.app/view/super-resolution-demo">https://ekyzivat.users.earthengine.app/view/super-resolution-demo</a></p> <p><strong>Table 1</strong>: File naming scheme based on region, with some regions requiring two-scene mosaics.</p> <table> <tbody> <tr> <td> <p><strong>Region</strong></p> </td> <td> <p><strong>Landsat ID</strong></p> </td> <td> <p><strong>Mosaic name</strong></p> </td> </tr> <tr> <td> <p><strong>Yukon Flats Basin</strong></p> </td> <td> <p>LC08_L2SP_068014_20170708_20200903_02_T1</p> </td> <td> <p>LC08_20170708_yflats_cog.tif</p> </td> </tr> <tr> <td> <p><strong>“</strong></p> </td> <td> <p>LC08_L2SP_068013_20170708_20201015_02_T1</p> </td> <td> <p>“</p> </td> </tr> <tr> <td> <p><strong>Old Crow Flats</strong></p> </td> <td> <p>LC08_L2SP_067012_20170903_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Mackenzie River Delta</strong></p> </td> <td> <p>LC08_L2SP_064011_20170728_20200903_02_T1</p> </td> <td> <p>LC08_20170728_inuvik_cog.tif</p> </td> </tr> <tr> <td> <p><strong>“</strong></p> </td> <td> <p>LC08_L2SP_064012_20170728_20200903_02_T1</p> </td> <td> <p>“</p> </td> </tr> <tr> <td> <p><strong>Canadian Shield Margin</strong></p> </td> <td> <p>LC08_L2SP_050015_20170811_20200903_02_T1</p> </td> <td> <p>LC08_20170811_cshield-margin_cog.tif</p> </td> </tr> <tr> <td> <p><strong>“</strong></p> </td> <td> <p>LC08_L2SP_048016_20170829_20200903_02_T1</p> </td> <td> <p>“</p> </td> </tr> <tr> <td> <p><strong>Canadian Shield near Baker Creek</strong></p> </td> <td> <p>LC08_L2SP_046016_20170831_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Canadian Shield near Daring Lake</strong></p> </td> <td> <p>LC08_L2SP_045015_20170723_20201015_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Peace-Athabasca Delta</strong></p> </td> <td> <p>LC08_L2SP_043019_20170810_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Prairie Potholes North 1</strong></p> </td> <td> <p>LC08_L2SP_041021_20170812_20200903_02_T1</p> </td> <td> <p>LC08_20170812_potholes-north1_cog.tif</p> </td> </tr> <tr> <td> <p><strong>“</strong></p> </td> <td> <p>LC08_L2SP_041022_20170812_20200903_02_T1</p> </td> <td> <p>“</p> </td> </tr> <tr> <td> <p><strong>Prairie Potholes North 2</strong></p> </td> <td> <p>LC08_L2SP_038023_20170823_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Prairie Potholes South</strong></p> </td> <td> <p>LC08_L2SP_031027_20170907_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Fairbanks </strong></p> </td> <td> <p>LT05_L2SP_070014_19850831_20200918_02_T1</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p><strong>References:</strong></p> <p>Kyzivat, E. D., Smith, L. C., Pitcher, L. H., Fayne, J. V., Cooley, S. W., Cooper, M. G., Topp, S. N., Langhorst, T., Harlan, M. E., Horvat, C., Gleason, C. J., & Pavelsky, T. M. (2019b). A high-resolution airborne color-infrared camera water mask for the NASA ABoVE campaign. <em>Remote Sensing</em>, <em>11</em>(18), 2163. <a href="https://doi.org/10.3390/rs11182163">https://doi.org/10.3390/rs11182163</a></p> <p>Kyzivat, E.D., L.C. Smith, L.H. Pitcher, J.V. Fayne, S.W. Cooley, M.G. Cooper, S. Topp, T. Langhorst, M.E. Harlan, C.J. Gleason, and T.M. Pavelsky. 2019a. ABoVE: AirSWOT Water Masks from Color-Infrared Imagery over Alaska and Canada, 2017. ORNL DAAC, Oak Ridge, Tennessee, USA. <a href="https://doi.org/10.3334/ORNLDAAC/1707">https://doi.org/10.3334/ORNLDAAC/1707</a></p> <p>Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021a). Super-resolution surface water mapping on the Canadian shield using planet CubeSat images and a generative adversarial network. <em>Canadian Journal of Remote Sensing</em>, <em>47</em>(2), 261–275. <a href="https://doi.org/10.1080/07038992.2021.1924646">https://doi.org/10.1080/07038992.2021.1924646</a></p> <p>Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021b). Super-resolution surface water mapping on the canadian shield using planet CubeSat images and a generative adversarial network. <em>Canadian Journal of Remote Sensing</em>, <em>47</em>(2), 261–275. <a href="https://doi.org/10.1080/07038992.2021.1924646">https://doi.org/10.1080/07038992.2021.1924646</a></p> <p>Walter Anthony, K.., Lindgren, P., Hanke, P., Engram, M., Anthony, P., Daanen, R. P., Bondurant, A., Liljedahl, A. K., Lenz, J., Grosse, G., Jones, B. M., Brosius, L., James, S. R., Minsley, B. J., Pastick, N. J., Munk, J., Chanton, J. P., Miller, C. E., & Meyer, F. J. (2021a). Decadal-scale hotspot methane ebullition within lakes following abrupt permafrost thaw. <em>Environ. Res. Lett</em>, <em>16</em>, 35010. <a href="https://doi.org/10.1088/1748-9326/abc848">https://doi.org/10.1088/1748-9326/abc848</a></p> <p>Walter Anthony, K., and P. Lindgren. 2021b. ABoVE: Historical Lake Shorelines and Areas near Fairbanks, Alaska, 1949-2009. ORNL DAAC, Oak Ridge, Tennessee, USA. <a href="https://doi.org/10.3334/ORNLDAAC/1859">https://doi.org/10.3334/ORNLDAAC/1859</a></p>
Energy feedbacks of northern high-latitude ecosystems to the climate system due to reduced snow cover during 20th century warming-I
This data file contains data for changes in atmospheric heating due to changes in snow melt, snow return, and total snow cover duration as modeled with the Terrestrial Ecosystem Model for the area north of 50 degrees north latitude around the entire globe for the years 1910-1940. See Euskirchen et al. (2007) for full study details.
Energy feedbacks of northern high-latitude ecosystems to the climate system due to reduced snow cover during 20th century warming-II
This data file contains data for changes in atmospheric heating due to changes in snow melt, snow return, and total snow cover duration as modeled with the Terrestrial Ecosystem Model for the area north of 50 degrees north latitude around the entire globe for the years 1910-1940. See Euskirchen et al. (2007) for full study details.
Energy feedbacks of northern high-latitude ecosystems to the climate system due to reduced snow cover during 20th century warming-III
This data file contains data for the pan-arctic vegetation map depicted in Figure 1 of Euskirchen et al (2007). See Euskirchen et al. (2007) for further details on the construction of this map.
Energy feedbacks of northern high-latitude ecosystems to the climate system due to reduced snow cover during 20th century warming-IV
This data file contains data for changes in snow melt, snow return, and total snow cover duration as modeled with the Terrestrial Ecosystem Model for the area north of 50 degrees north latitude around the entire globe for the years 1910-1940. See Euskirchen et al. (2007) for full study details.
Energy feedbacks of northern high-latitude ecosystems to the climate system due to reduced snow cover during 20th century warming-V
This data file contains data for changes in snow melt, snow return, and total snow cover duration as modeled with the Terrestrial Ecosystem Model for the area north of 50 degrees north latitude around the entire globe for the years 1970-2000. See Euskirchen et al. (2007) for full study details.
Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models
<p></p><p>Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterised strongly influences predictions of future climate by Earth system models. To capture future spatial and temporal changes in vegetation cover and its feedbacks to the climate system, dynamic global vegetation models (DGVM) are included as important components of land surface models. Variation in the predicted vegetation cover from DGVMs therefore has large impacts on modelled radiative and non-radiative properties, especially over high-latitude regions. DGVMs are mostly evaluated by remotely sensed products, but rarely by other vegetation products or by in-situ field observations. In this study, we evaluate the performance of three methods for spatial representation of vegetation cover with respect to prediction of plant functional type (PFT) profiles – one based upon distribution models (DM), one that uses a remote sensing (RS) dataset and a DGVM (CLM4.5BGCDV). PFT profiles obtained from an independently collected vegetation data set from Norway were used for the evaluation. We found that RS-based PFT profiles matched the reference dataset best, closely followed by DM, whereas predictions from DGVM often deviated strongly from the reference. DGVM predictions overestimated the area covered by boreal needleleaf evergreen trees and bare ground at the expense of boreal broadleaf deciduous trees and shrubs. Based on environmental predictors identified by DM as important, we suggest implementation of three novel PFT-specific thresholds for establishment in the DGVM. We performed a series of sensitivity experiments to demonstrate that these thresholds improve the performance of the DGVM. The results highlight the potential of using PFT-specific thresholds obtained by DM in development and benchmarking of DGVMs for broader regions. Also, we emphasize the potential of establishing DM as a reliable method for providing PFT distributions for evaluation of DGVMs alongside RS. </p><p></p>
Contrasting latitudinal patterns in diversity and stability in a high-latitude species-rich moth community
<p>Aim: Biodiversity is currently undergoing rapid restructuring across the globe. However, the nature of biodiversity change is not well understood, as community-level changes may hide differential responses in individual population trajectories. Here, we quantify spatio-temporal community and stability dynamics using a long-term high-quality moth monitoring dataset. Location: Finland, Northern Europe. Time period: 1993-2012. Major taxa studied: Nocturnal moths (Lepidoptera). Methods: We quantify patterns of change in species richness, total abundance, dominance and temporal variability at different organisational levels over a 20-year period and along a 1100-km latitudinal gradient. We used mixed-effects and linear models to quantify temporal trends for the different community and stability metrics and to test for latitudinal (or longitudinal) effects. Results: We find contrasting patterns for different community metrics, and strong latitudinal patterns. While total moth abundance has declined, species richness has simultaneously increased over the study period, but with rates accelerating with latitude. Additionally, we reveal a latitudinal pattern in temporal variability – the northernmost locations exhibited higher variability over time, as quantified by both metrics of richness and aggregated species populations trends. Main Conclusions: When combined, our findings likely reflect an influx of species expanding their ranges poleward in response to warming. The overall decline in abundance and the latitudinal effect on temporal variability highlight potentially severe consequences of global change for community structure and integrity across high-latitude regions. Importantly, our results underscore that increases in species richness may be paralleled by a loss of individuals, which in turn might affect higher trophic levels. Our findings suggest that the ongoing global species redistribution is affecting both community structure and stability over time, leading to compounded and partly opposing effects of global change depending on what biodiversity dimension we focus on.</p>
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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.