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Hohenecken castle, Germany
Modélisé à partir d'une vidéo [Youtube](https://www.youtube.com/watch?v=Z6rEkeX8u7Y) (Albert Speelman) <br> [Wikipedia](https://en.wikipedia.org/wiki/Hohenecken_Castle)  Source: Objaverse 1.0 / Sketchfab
Gridded dataset of nitrogen and phosphorus point sources from wastewater in Germany (1950-2019)
<h4><strong>Publication</strong></h4> <p>Please cite this publication if you use the dataset:</p> <p>Sarrazin, F. J., Attinger, A., Kumar, R., Gridded dataset of nitrogen and phosphorus point sources from wastewater in Germany (1950-2019), submitted to Earth System Science Data.</p> <p>Please also refer to the above publication for methodological details.</p> <h4><strong>License</strong></h4> <p>The "Gridded dataset of nitrogen and phosphorus point sources from wastewater in Germany (1950-2019)" is freely available under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0, https://creativecommons.org/licenses/by-nc-sa/4.0), in compliance with the terms of use of the Food and Agriculture Organization of the United Nations (FAO) data and German Cosmetic, Toiletry, Perfumery and Detergent Association (IKW) data that underlie the dataset.</p> <h4><strong>Data description</strong></h4> <p>The dataset includes estimates of nitrogen (N) and phosphorus (P) emissions from wastewater in Germany (1950-2019) at (1) grid level, and at different levels of aggregation, namely (2) at Nomenclature of Territorial units for statistics level 1 (NUTS-1), that correspond to the 16 German federal states (for this, we used the 2020 NUTS classification; BKG, 2020) and (3) at river basin level for 3778 river basins of the HydroBASINS v1.c of the HydroSHEDS database (HydroSHEDS, 2014; Lehner and Grill, 2013). It also includes the input and calibration data (at NUTS-1 and grid level) that were used to estimate the N and P emissions.</p> <ol> <li> <p>Input and calibration data at NUTS-1 level:<br><em><strong>spatial extent</strong></em>: Germany<br><em><strong>spatial resolution</strong></em>: NUTS-1<br><em><strong>time period</strong></em>: 1950-2019 (input data), 1987-2019 (calibration data)<br><em><strong>frequency</strong></em>: annual<br><em><strong>variables</strong></em>: input data, calibration data, parameter sample<br><strong><em>file format</em></strong>: CSV<br><em><strong>number of files</strong></em>: 3<br><br></p> </li> <li> <p>Input data at grid level:<br><strong><em>spatial extent</em></strong>: Germany<br><em><strong>spatial resolution</strong></em>: 0.015625°<br><strong><em>time period</em></strong>: 1950-2019 (population data), 2020 (NUTS-1 map)<br><strong><em>frequency</em></strong>: annual<br><strong><em>variables</em></strong>: urban and rural population counts, NUTS-1 map<br><em><strong>file format</strong></em>: netCDF<br><strong><em>number of files</em></strong>: 2<br><br></p> </li> <li>Emission data at grid level:<br><em><strong>spatial extent</strong></em>: Germany<br><em><strong>spatial resolution</strong></em>: 0.015625°<br><em><strong>time period</strong></em>: 1950-2019<br><em><strong>frequency</strong></em>: annual<br><em><strong>variables</strong></em>: <br>- N and P wastewater treatment plants (WWTPs) outgoing emissions (treated point sources)<br>- N and P emissions collected in the public sewer system that are not treated in WWTPs (untreated point sources)<br><em><strong>unit</strong></em>: kg yr-1<br><em><strong>realisations</strong></em>: 200 realisations corresponding to 100 different parameter sets and 2 spatial disaggregation methods for the treated point sources<br><em><strong>file format</strong></em>: netCDF<br><em><strong>number of files</strong></em>: 100<br> </li> <li>Emission data aggregated at NUTS-1 (federal state) level:<br><em><strong>spatial extent</strong></em>: Germany<br><em><strong>spatial resolution</strong></em>: NUTS-1<br><em><strong>time period</strong></em>: 1950-2019<br><em><strong>frequency</strong></em>: annual<br><em><strong>variables</strong></em>: <br>- N and P gross emissions<br>- N and P total point sources (sum of treated and untreated components)<br>- N and P treated point sources (WWTPs outgoing load)<br>- N and P untreated point sources (emissions collected in the public sewer system that are not treated in WWTPs)<br>- N and P emissions that are removed during treatment in WWTPs<br>- N and P emissions lost during wastewater collection and transport<br>- N and P emissions applied to agricultural soils in sewage farms<br>- N and P emissions that are not collected in the sewer system nor treated in WWTPs<br>- N and P incoming WWTPs load<br><em><strong>unit</strong></em>: kg yr-1<br><em><strong>realisations</strong></em>: 100 realisations corresponding to 100 different parameter sets<br><em><strong>file format</strong></em>: CSV<br><em><strong>number of files</strong></em>: 18<br> </li> <li>Emission data aggregated at river basin level:<br><em><strong>spatial extent</strong></em>: Germany<br><em><strong>spatial resolution</strong></em>: river basins from the HydroBASINS v1.c of the HydroSHEDS database (HydroSHEDS, 2014; Lehner and Grill, 2013).<br><em><strong>time period</strong></em>: 1950-2019<br><em><strong>frequency</strong></em>: annual<br><em><strong>variables</strong></em>: N and P WWTP outgoing emissions (treated point sources), N and P emissions collected in the public sewer system that are not treated in WWTPs (untreated point sources)<br><em><strong>unit</strong></em>: kg yr-1<br><em><strong>realisations</strong></em>: 200 realisations corresponding to 100 different parameter sets and 2 spatial disaggregation methods for the treated point sources<br><em><strong>file format</strong></em>: CSV<br><em><strong>number of files</strong></em>: 600</li> </ol> <h4><strong>Acknowledgements and underlying datasets</strong></h4> <p>Partial support for this work was provided by the Global Water Quality Analysis and Service Platform (GlobeWQ) project financed by the German Ministry for Education and Research (grant number 02WGR1527A). We thank Olaf Büttner for providing the WWTPs data that were collected from the authorities of the German federal states (<a href="https://www.hydroshare.org/resource/2f2c2fa04e6e417ba0eb7b0fb14b1090/">Büttner et al., 2020</a>). The dataset produced in this work builds on the NUTS map of the German Federal Agency for Cartography and Geodesy © GeoBasis-DE/BKG that is under a <a href="https://www.govdata.de/dl-de/by-2-0">dl-de/by-2-0 license</a>; the History Database of the Global Environment (HYDE) dataset available under a <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0 license</a>; protein data provided by the Food and Agriculture Organization of the United Nations © FAO provided under a <a href="https://creativecommons.org/licenses/by-nc-sa/3.0/igo/">CC BY-NC-SA 3.0 IGO license</a>; detergent data from the German Cosmetic, Toiletry, Perfumery and Detergent Association © IKW (<a href="https://www.ikw.org/impressum">license here</a>); data from the statistical offices of Germany and the federal states and the German and federal state authorities (details on data sources in the publication reported above: Sarrazin et al., submitted to Earth System Science Data); WWTP data available in the Waterbase dataset from the European Environment Agency © EEA under a <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0 license</a>. The river basins come from © HydroSHEDS (<a href="https://www.hydrosheds.org/terms-of-use">license here</a>).</p> <h4><strong>Contact</strong></h4> <p>Fanny Sarrazin (<a href="mailto:fanny.sarrazin@ufz.de">fanny.sarrazin@inrae.fr</a>)<br>Rohini Kumar (<a href="mailto:rohini.kumar@ufz.de">rohini.kumar@ufz.de</a>)</p> <h4><strong>References</strong></h4> <p>BKG (Bundesamt für Kartographie und Geodäsie) (2020), NUTS regions 1 : 250 000, 31.12.2020, GeoBasis-DE [data set], Leipzig, Germany, https://gdz.bkg.bund.de/index.php/default/nuts-gebiete-1-250-000-stand-31-12-nuts250-31-12.html (last access: 1 November 2022).</p> <p>Büttner, O. (2020), DE-WWTP - data collection of wastewater treatment plants of Germany (status 2015, metadata), HydroShare [data set],<br>https://doi.org/10.4211/hs.712c1df62aca4ef29688242eeab7940c.</p> <p>HydroSHEDS (2014), HydroBASINS v1.c, https://www.hydrosheds.org/products/hydrobasins (last access: 23 October 2023).<br><br>Lehner, B. and Grill, G. (2013), Global river hydrography and network routing: baseline data and new approaches to study the world's large river<br>systems, Hydrological Processes, 27, 2171–2186, https://doi.org/10.1002/hyp.9740.</p> <p><strong>Changes compared to v1.0 dataset version</strong></p> <p>Compared to the version 1.0 of the dataset, this version v1.1 contains the input and calibration data at NUTS-1 level and the input data at grid level used to calculate the emissions.</p>
Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021)
<p>The dataset contains maps of the main classes of agricultural land use (dominant crop types and other land use types) in Germany, which are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in <a href="https://doi.org/10.1016/j.rse.2021.112831">Blickensdörfer et al. (2022)</a>.</p> <p>All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software <a href="https://force-eo.readthedocs.io/en/latest/">FORCE </a>- Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated.</p> <p>The map extent covers all areas in Germany that are defined in the respective year as cropland, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020). </p> <p>Version v201:<br>Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015).</p> <p>Version v202:<br>Additional post-processing was performed to detect and mask additional non-plausible areas that were not adequately covered by the first post-processing (e.g., areas with sparse vegetation, montane forests) based on the „Ökosystematlas Deutschland“ (© Statistisches Bundesamt, Deutschland, 2024). As a consequence, the current version includes a new class “Small woody features on other land”. Furthermore, the class "permanent grassland" was refined. Each pixel that was classified as "cultivated grassland" in at least five years (between 2017 and 2022) was translated to "permanent grassland" in the annual maps.</p> <p>The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the provided URL to the datasets (right click on the respective data set --> “copy link address”). By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately.</p> <p>Class-specific accuracies for each year are provided in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability. </p> <p> </p> <p><strong>References:<br></strong><br><em>Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831.</em></p> <p><em>BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).</em></p> <p><em>BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. </em><br><em>https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).</em></p> <p><em>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.</em></p> <p><em>Statistisches Bundesamt, Deutschland (2024). Ökosystematlas Deutschland <br>https://oekosystematlas-ugr.destatis.de/ (last accessed: 08.02.2024).</em></p> <p>___________________________________________________________________________<br>National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0. </p> <p>Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (<a href="https://www.agrarmonitoring-monvia.de/en/">MonViA</a>, Monitoring of biodiversity in agricultural landscapes).</p> <p>The study was financially supported by the European Environment Agency and the European Union’s Horizon Europe Research and Innovation programme under Grant Agreement No 101060423 (LAMASUS).</p>
FIGURE 4 in Review of the rove beetles (Coleoptera: Staphylinidae) from the Pliocene of Willershausen, Germany
FIGURE 4. Paederus schmidti (Korge, 1967) comb. nov. (Holotype, GZG.W.35142 (597-1)), tergites: T1, T2. Scale bar = 1 mm.
FIGURE 2. Micropeplus macrofulvus Gersdorf, 1976 in Review of the rove beetles (Coleoptera: Staphylinidae) from the Pliocene of Willershausen, Germany
FIGURE 2. Micropeplus macrofulvus Gersdorf, 1976 (Holotype, GZG.W.24110a+b (30166)). A, Part. B, Counterpart. Scale bar = 1 mm.
Ammonia emissions from urea fertilization – multi-annual micrometeorological measurements across Germany [Dataset]
<p>To evaluate the accuracy of the current emission factor (EF) for conventional urea in Germany, ammonia (NH₃) emissions were measured using the integrated horizontal flux (IHF) method with ALPHA passive samplers following urea applications in 2021–2023. Measurements were conducted across six agroecological regions under winter wheat cropping, with nitrogen application rates of 145–230 kg N ha⁻¹, resulting in 51 campaigns.</p> <p>Emission rates (NH₃Ninput) ranged from 1.1% to 20.7% (median 4.8%, mean 8.5%), significantly lower than the 2019 IPCC (14.2%) and 2023 EMEP (16.1% for pH < 7) estimates. NH₃Ninput was influenced by soil texture and rainfall but not nitrogen application rates, with higher emissions observed on sandy soils.</p> <p>These findings suggest current EFs overestimate NH₃ emissions from urea applied to winter wheat in Germany. While emissions may vary due to inter-annual and environmental factors, a national EF for Germany could improve NH₃ emission estimates and the evaluation of mitigation strategies.</p>
National Checklists: Germany Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
Dittmann S., Kiessling T., Knickmeier K., Schönberg J., Brennecke D., Hinzmann M., Knoblauch D., Thiel M., 2024. Temporal variability of litter pollution of rivers in Germany - a long-term assessment by schoolchildren as citizen scientists.
<p>Research data to the manuscript "Temporal variability of litter pollution of rivers in Germany - a long-term assessment by schoolchildren as citizen scientists" by Dittmann et al. 2024.</p> <p> </p> <div> </div>
Subspecies and Distribution. N. p. procyonoides Gray, 1834 — W & SW China and N Indochina. N. p. albus Hornaday, 1904 — Japan (Hokkaido). N. p. koreensis Mori, 1922 — Korean Peninsula. N. p. orestes Thomas, 1923 — C & S China. N. p. ussuriensis Matschie, 1907 — NE China, E Mongolia, and SE Russia. N. p. viverrinus Temminck, 1839 — Japan. Introduced (ussuriensis) to the Baltic states, Belarus, Bulgaria, Czech Republic, Finland, Germany, Hungary, Moldova, Poland, Romania, W Russia, Serbia, Slovakia, Sweden, and Ukraine, occasionally seen in Austria, Bosnia, Denmark, France, the Netherlands, Norway, Slovenia, and Switzerland. in Canidae
Subspecies and Distribution. N. p. procyonoides Gray, 1834 — W & SW China and N Indochina. N. p. albus Hornaday, 1904 — Japan (Hokkaido). N. p. koreensis Mori, 1922 — Korean Peninsula. N. p. orestes Thomas, 1923 — C & S China. N. p. ussuriensis Matschie, 1907 — NE China, E Mongolia, and SE Russia. N. p. viverrinus Temminck, 1839 — Japan. Introduced (ussuriensis) to the Baltic states, Belarus, Bulgaria, Czech Republic, Finland, Germany, Hungary, Moldova, Poland, Romania, W Russia, Serbia, Slovakia, Sweden, and Ukraine, occasionally seen in Austria, Bosnia, Denmark, France, the Netherlands, Norway, Slovenia, and Switzerland.
Subspecies and Distribution. N. v. vison Schreber, 1776 — E Canada and NE USA (Allegheny Mts). N. v. aestuarina Grinnell, 1916 — SW USA (California & W Nevada). N. v. aniakensis Burns, 1964 — W Alaska. N. v. energumenos Bangs, 1896 — W Canada and NW USA. N. v. evagor Hall, 1932 — SW Canada (Vancouver I). N. v. evergladensis Hamilton, 1948 — SE USA (SW Florida). N. v. ingens Osgood, 1900 — most of Alaska and NW Canada (N Mackenzie & Yukon). N. v. lacustris Preble, 1902 — C Canada (Keewatin Region, Manitoba & Ontario). N. v. letifera Hollister, 1913 — Great Plains of USA. N. v. low Anderson, 1945 — E Canada (Labrador & Quebec). N. v. lutensis Bangs, 1898 — USA (S Carolina to Florida). N. v. melampeplus Elliot, 1903 — Alaska (Kenai Peninsula). N. v. mink Peale & Palisot de Beauvois, 1796 — SE USA. N. v. nesolestes Heller, 1909 — SW Alaska. N. v. vulgivaga Bangs, 1895 — USA (Arkansas & Louisiana). Introduced to Belarus, Belgium, China, Czech Republic, Denmark, Estonia, Finland, France, Germany, Great Britian, Iceland, Ireland, Italy, Japan (Hokkaido), Latvia, Lithuania, Netherlands, Norway, Poland, Portugal, Russia, Spain, and Sweden. in Mustelidae
Subspecies and Distribution. N. v. vison Schreber, 1776 — E Canada and NE USA (Allegheny Mts). N. v. aestuarina Grinnell, 1916 — SW USA (California & W Nevada). N. v. aniakensis Burns, 1964 — W Alaska. N. v. energumenos Bangs, 1896 — W Canada and NW USA. N. v. evagor Hall, 1932 — SW Canada (Vancouver I). N. v. evergladensis Hamilton, 1948 — SE USA (SW Florida). N. v. ingens Osgood, 1900 — most of Alaska and NW Canada (N Mackenzie & Yukon). N. v. lacustris Preble, 1902 — C Canada (Keewatin Region, Manitoba & Ontario). N. v. letifera Hollister, 1913 — Great Plains of USA. N. v. low Anderson, 1945 — E Canada (Labrador & Quebec). N. v. lutensis Bangs, 1898 — USA (S Carolina to Florida). N. v. melampeplus Elliot, 1903 — Alaska (Kenai Peninsula). N. v. mink Peale & Palisot de Beauvois, 1796 — SE USA. N. v. nesolestes Heller, 1909 — SW Alaska. N. v. vulgivaga Bangs, 1895 — USA (Arkansas & Louisiana). Introduced to Belarus, Belgium, China, Czech Republic, Denmark, Estonia, Finland, France, Germany, Great Britian, Iceland, Ireland, Italy, Japan (Hokkaido), Latvia, Lithuania, Netherlands, Norway, Poland, Portugal, Russia, Spain, and Sweden.
Subspecies and Distribution. S. s. scrofa Linnaeus, 1758 — W Europe, from Denmark, Germany, Poland, and Czech Republic to N Italy and N Iberian Peninsula; possibly also Albania. The taxonomic status of animals in Austria, Switzerland, Slovenia, and Slovakia is unclear but presumably these populations are included in scrofa, as are the populations of Sweden, Finland, and the Baltic states. However, restocking of once depleted populations, for example in Italy, has likely involved the introduction and mixing of this subspecies with other subspecies, such as attila. S. s. affinis Gray, 1847 — S India and Sri Lanka. S. s. algirus Loche, 1867 — Tunisia, Algeria, and Morocco, on the coastal side of the mountains or in the low montane areas. S. s. attila Thomas, 1912 — Hungary, Ukraine, C & S Belarus, Romania, Moldova, and S Russia towards the N flank of the Caucasus, but not including the Transcaucasian countries of Georgia, Armenia, and Azerbaijan. The range possibly extends as far S as the Mesopotamian Delta in Iraq, in which case it would likely include W & SW Iran, and possibly E Turkey and Syria, where it borders with lybicus. Such a range could not be easily reconciled with a statement by Groves that "the difference between pigs from N and S of the Caucasus is quite striking; Transcaucasian boars are certainly not attila." This subspecies may also extend into C Asia and include Kazakhstan, Uzbekistan, and Turkmenistan, but no data exist to support this. S. s. baeticus Thomas, 1912 — originally described from Coto Donana, S Spain, and later merged with meridionalis; also S Portugal. Unless evidence is found that these Italian and Iberian populations are the relics of a much larger formerly contiguous range, this subspecies should be kept as distinct. S. s. coreanus Heude, 1897 — Korean Peninsula. S. s. eristatus Wagner, 1839 — Himalayas S to C India and E to Indochina (N of the Kra Isthmus). S. s. davidi Groves, 1981 — the arid zone from E Iran to Gujarat, including Pakistan and NW India, and perhaps N to Tajikistan. S. s. leucomystax Temminck, 1842 — main Is ofJapan (Honshu, Shikoku, Kyushu, Nakadori, Hiburijima, Tojima, Kushima, and other smaller Is). S. s. lybicus Gray, 1868 — Bulgaria, Greece, Turkey, Syria, Jordan, Israel, Palestine, in the past also in Lybia, and Egypt. The former Yugoslavia was included in its range, which would suggest that now Slovenia, Serbia, Croatia, Bosnia and Herzegovina, Montenegro, and Kosovo are within the range of this subspecies, although the exact boundaries are unclear. Pigs from Albania have been assigned to S. s. scrofa. S. s. majori De Beaux & Festa, 1927 — C & S Italian Peninsula. S. s. menidionalis Forsyth Major, 1882 — Corsica and Sardinia, with the proviso that the two populations are very likely to be introduced or feral. S. s. moupinensis Milne-Edwards, 1871 — China, S to Vietnam and W to Sichuan. S. s. nigripes Blanford, 1875 — the flanks of the Tianshan mountains in Kyrgyzstan and NW China (Xinjiang). An animal photographed in NE Iran (Golestan) looked like this subspecies. S. s. nukiuanus Kuroda, 1924 — Iriomote, Ishigaki, Okinawa, Tokunoshima, Amamioshima, and Kakerome Is in the Ryukyu chain in extreme S Japan, though some of these populations have hybridized with introduced domesticates. S. s. sibiricus Staffe, 1922 — Mongolia and Transbaikal (S & E of Lake Baikal). S. s. tawvanus Swinhoe, 1863 — Taiwan. S. s. ussuricus Heude, 1888 — far E Russia and the Manchurian region (China). Korean populations were previously included in this subspecies, but based on new evidence, the Korean taxon seems more similar to moupinensis. S. s. vittatus Boie, 1828 — Malay Peninsula, S of the Isthmus of Kra, the offshore islands of Terutai and Langkawi, Sumatra, Riau Archipelago, Java, Bali, and a range of smaller islands around these, including Babi, Bakong, Batam, Bawean, Bengkalis, Bintan, Bulan, Bunguran, Cuyo, Deli, Durian, Enggano, Galang, Jambongan, Karimon (Riau Is), Kundur, Lagong, Laut, Lingga, Lingung, Mapor, Moro Kecil, North Pagai, Nias, Panaitan, Payong, Penang, Pinie, Rupat, Siantan, Siberut, Simeulue, Singkep, Sugi, Sugi Bawa, Telibon, Tinggi, Tuangku, and the Tambelan Is. This species was originally present from the British Is in the extreme W, through Eurasia from S Scandinavia to S Siberia, extending as far E as Korea and Japan, and SE into some of the Sunda Is and Taiwan. In the S the species ranged along the Nile Valley to Khartoum, and N of the Sahara in Africa, more orless following the continental coasts of S, E, and SE Asia. Within this range it was absent only from extremely dry deserts, e.g. the driest regions of Mongolia and in China W of Sichuan; and alpine zones, such as the high altitudes of Pamir and Tien Shan. In recent centuries, the range of S. scrofa has changed dramatically because of hunting and changes in available habitat. The species disappeared from the British Is in the 17" century, from Denmark in the 19" century, and was greatly reduced in range and numbers in the 20" century from areas as distant as Tunisia, Sudan, Germany, and Russia. Following these severe declines, there were some slight population recoveries in Russia, Italy, Spain, and Germany in the mid-20™ century, and natural and assisted range expansions in Denmark and Sweden. The species has also been inadvertently reintroduced in various locations in the Great Britain via escapees of mixed origin from commercial farming enterprises. Ex-S. scrofa stocks also occur as introduced feral populations in various other parts of the world, including Australia, New Zealand, the eastern Malay Archipelago, and in North, Central, and South America. In all of these areas they are now generally recognized as a major pest. in Suidae
Subspecies and Distribution. S. s. scrofa Linnaeus, 1758 — W Europe, from Denmark, Germany, Poland, and Czech Republic to N Italy and N Iberian Peninsula; possibly also Albania. The taxonomic status of animals in Austria, Switzerland, Slovenia, and Slovakia is unclear but presumably these populations are included in scrofa, as are the populations of Sweden, Finland, and the Baltic states. However, restocking of once depleted populations, for example in Italy, has likely involved the introduction and mixing of this subspecies with other subspecies, such as attila. S. s. affinis Gray, 1847 — S India and Sri Lanka. S. s. algirus Loche, 1867 — Tunisia, Algeria, and Morocco, on the coastal side of the mountains or in the low montane areas. S. s. attila Thomas, 1912 — Hungary, Ukraine, C & S Belarus, Romania, Moldova, and S Russia towards the N flank of the Caucasus, but not including the Transcaucasian countries of Georgia, Armenia, and Azerbaijan. The range possibly extends as far S as the Mesopotamian Delta in Iraq, in which case it would likely include W & SW Iran, and possibly E Turkey and Syria, where it borders with lybicus. Such a range could not be easily reconciled with a statement by Groves that "the difference between pigs from N and S of the Caucasus is quite striking; Transcaucasian boars are certainly not attila." This subspecies may also extend into C Asia and include Kazakhstan, Uzbekistan, and Turkmenistan, but no data exist to support this. S. s. baeticus Thomas, 1912 — originally described from Coto Donana, S Spain, and later merged with meridionalis; also S Portugal. Unless evidence is found that these Italian and Iberian populations are the relics of a much larger formerly contiguous range, this subspecies should be kept as distinct. S. s. coreanus Heude, 1897 — Korean Peninsula. S. s. eristatus Wagner, 1839 — Himalayas S to C India and E to Indochina (N of the Kra Isthmus). S. s. davidi Groves, 1981 — the arid zone from E Iran to Gujarat, including Pakistan and NW India, and perhaps N to Tajikistan. S. s. leucomystax Temminck, 1842 — main Is ofJapan (Honshu, Shikoku, Kyushu, Nakadori, Hiburijima, Tojima, Kushima, and other smaller Is). S. s. lybicus Gray, 1868 — Bulgaria, Greece, Turkey, Syria, Jordan, Israel, Palestine, in the past also in Lybia, and Egypt. The former Yugoslavia was included in its range, which would suggest that now Slovenia, Serbia, Croatia, Bosnia and Herzegovina, Montenegro, and Kosovo are within the range of this subspecies, although the exact boundaries are unclear. Pigs from Albania have been assigned to S. s. scrofa. S. s. majori De Beaux & Festa, 1927 — C & S Italian Peninsula. S. s. menidionalis Forsyth Major, 1882 — Corsica and Sardinia, with the proviso that the two populations are very likely to be introduced or feral. S. s. moupinensis Milne-Edwards, 1871 — China, S to Vietnam and W to Sichuan. S. s. nigripes Blanford, 1875 — the flanks of the Tianshan mountains in Kyrgyzstan and NW China (Xinjiang). An animal photographed in NE Iran (Golestan) looked like this subspecies. S. s. nukiuanus Kuroda, 1924 — Iriomote, Ishigaki, Okinawa, Tokunoshima, Amamioshima, and Kakerome Is in the Ryukyu chain in extreme S Japan, though some of these populations have hybridized with introduced domesticates. S. s. sibiricus Staffe, 1922 — Mongolia and Transbaikal (S & E of Lake Baikal). S. s. tawvanus Swinhoe, 1863 — Taiwan. S. s. ussuricus Heude, 1888 — far E Russia and the Manchurian region (China). Korean populations were previously included in this subspecies, but based on new evidence, the Korean taxon seems more similar to moupinensis. S. s. vittatus Boie, 1828 — Malay Peninsula, S of the Isthmus of Kra, the offshore islands of Terutai and Langkawi, Sumatra, Riau Archipelago, Java, Bali, and a range of smaller islands around these, including Babi, Bakong, Batam, Bawean, Bengkalis, Bintan, Bulan, Bunguran, Cuyo, Deli, Durian, Enggano, Galang, Jambongan, Karimon (Riau Is), Kundur, Lagong, Laut, Lingga, Lingung, Mapor, Moro Kecil, North Pagai, Nias, Panaitan, Payong, Penang, Pinie, Rupat, Siantan, Siberut, Simeulue, Singkep, Sugi, Sugi Bawa, Telibon, Tinggi, Tuangku, and the Tambelan Is. This species was originally present from the British Is in the extreme W, through Eurasia from S Scandinavia to S Siberia, extending as far E as Korea and Japan, and SE into some of the Sunda Is and Taiwan. In the S the species ranged along the Nile Valley to Khartoum, and N of the Sahara in Africa, more orless following the continental coasts of S, E, and SE Asia. Within this range it was absent only from extremely dry deserts, e.g. the driest regions of Mongolia and in China W of Sichuan; and alpine zones, such as the high altitudes of Pamir and Tien Shan. In recent centuries, the range of S. scrofa has changed dramatically because of hunting and changes in available habitat. The species disappeared from the British Is in the 17" century, from Denmark in the 19" century, and was greatly reduced in range and numbers in the 20" century from areas as distant as Tunisia, Sudan, Germany, and Russia. Following these severe declines, there were some slight population recoveries in Russia, Italy, Spain, and Germany in the mid-20™ century, and natural and assisted range expansions in Denmark and Sweden. The species has also been inadvertently reintroduced in various locations in the Great Britain via escapees of mixed origin from commercial farming enterprises. Ex-S. scrofa stocks also occur as introduced feral populations in various other parts of the world, including Australia, New Zealand, the eastern Malay Archipelago, and in North, Central, and South America. In all of these areas they are now generally recognized as a major pest.
gapseq reconstructions for representative MAGs from the Diet-Microbiome-Study, Kiel, Germany
<p>The archive contains the gapseq (<a href="https://doi.org/10.1186/s13059-021-02295-1">https://doi.org/10.1186/s13059-021-02295-1</a>) output files from the reconstruction of genome-scale metabolic network models for 182 representative (species-level) metagenome-assembled genomes from the manuscript:</p> <p>A Troci, P Rausch, s Waschina, W Lieb, C Bang, A Franke (in preparation) "Long-term dietary effects on human gut microbiota composition employing shotgun metagenomics data analysis"</p> <p> </p>
Chironomid assemblages and inferred summer temperature, late Rissian, Eemian and earliest Würmian, S Germany
<p>The data herein presents a new chironomid record and associated chironomid-based temperature reconstruction covering the mid Eemian to the end of the first Würmian stadial (ca. 125–105 ka), augmented by results for the late Rissian glaciation and the early Brörup interstadial from the Füramoos palaeolake, southern Germany. There is an unconformity in the sediment core analysed between the late Rissian sediments and the mid Eemian sediments. These data also include non-chironomid invertebrate remains, including Ceratopogonidae, Daphnia and Ephemeroptera as well as Characean oospores.</p>
FIGURE 3 in A new insect (probably basal Odonatoptera) from the Pennsylvanian (Late Carboniferous) of the Piesberg Fossil-Lagerstätte, Osnabrück, Germany
FIGURE 3. Comparison of Osnabruggiala seppelti gen. et sp. nov. and Geropteron arcuatum. A, Osnabruggiala seppelti gen. et sp. nov., with coloured wing areas compared to B, Geropteron arcuatum Riek in Riek & Kukalová-Peck, 1984 with the same coloured wing areas.
FIGURE 1 in A new insect (probably basal Odonatoptera) from the Pennsylvanian (Late Carboniferous) of the Piesberg Fossil-Lagerstätte, Osnabrück, Germany
FIGURE 1. Terminology of the areas between the longitudinal veins of insect wings (from Zessin, 1987).
FIGURE 2 in A new insect (probably basal Odonatoptera) from the Pennsylvanian (Late Carboniferous) of the Piesberg Fossil-Lagerstätte, Osnabrück, Germany
FIGURE 2. Osnabruggiala seppelti gen. et sp. nov., holotype specimen SNL GP 3048, left mesothoracic wing (Natureum am Schloss Ludwigslust, Museum der Naturforschenden Gesellschaft Mecklenburg e V.), Pennsylvanian: Westphalian D/Asturian, Piesberg quarry near Osnabrück, Germany. A, Drawing. B, Photograph of the complete left mesothoracic wing. C, Photograph of the wing base.
SSR and cpDNA marker dataset genetic integrity of M.sylvestris in Saxony, Germany
<p><i>Malus sylvestris</i> (Mill.) is the only indigenous wild apple species in Central Europe. Agriculture, forestry and urbanization increasingly endanger <i>Malus sylvestris </i>natural habitats. In addition, the risks of cross-hybridization associated with increase in the cultivation of the domesticated apple <i>Malus ×domestica</i> (Borkh.), threatens the genetic integrity of <i>M. sylvestris</i>.</p> <p>The present study investigated the number of hybrids, genetic diversity and genetic structure of 292 putative <i>M. sylvestris</i> that originate from five different natural <i>M. sylvestris</i> populations in Saxony, Germany. All samples were genetically analyzed using nine nuclear microsatellite markers (ncSSR) and four maternally inherited chloroplast markers (cpDNA) along with 56 apple cultivars commonly cultivated in Saxony.</p> <p>Eighty-seven percent of the wild apple accessions were identified as pure <i>M. sylvestris</i>. The cpDNA analysis showed six private haplotypes for <i>M. sylvestris,</i> whereas three haplotypes were present in <i>M. sylvestris </i>and<i> M. ×domestica.</i> The analysis of molecular variance (AMOVA) resulted in a moderate (ncSSR) and great (cpDNA) variation among pure <i>M. sylvestris</i> and <i>M. ×domestica </i>individuals indicating a low gene flow between both species. The genetic diversity within the pure <i>M. sylvestris</i> populations was high with a weak genetic structure between the <i>M. sylvestris</i> populations indicating an unrestricted genetic exchange between these <i>M. sylvestris</i> populations.</p> <p>The clear distinguishing of <i>M. sylvestris</i> and <i>M. ×domestica</i> confirms our expectation of the existence of pure <i>M. sylvestris </i>accessions<i> </i>in this area and supports the argument for the implementation of preservation measures to protect the <i>M. sylvestris </i>populations in<i> </i>Saxony. </p>
The Private and External Costs of Germany's Nuclear Phase-out, Replication Dataset
<p>Replication files for "The Private and External Costs of Germany's Nuclear Phase-out", published in the <em>Journal of the European Economic Association.</em></p>
FIGURE 5 in Timaviella dunensis sp. nov. from sand dunes of the Baltic Sea, Germany, and emendation of Timaviella edaphica (Elenkin) O.M. Vynogr. & Mikhailyuk (Synechococcales, Cyanobacteria) based on an integrative approach
FIGURE 5. Timaviella dunensis (Us-6-3): A—overview of colony on the surface of agarized medium: thallus prostrate and in growing inside the medium; B, K—filaments in firm hyaline sheath; C—trichome without sheath; D—fragment of trichome with necridia; note elongated, discoid and obliquely dividing cells; E, G—loosely arranged filaments with geminate false branching; F—consecutive single and geminate false branching; H, I—fragments of filaments with trichomes twisted in the sheath; J—formation of hormogonia, cells with granulations. Scale bars: A—50 μm, B–K—10 μm.
FIGURE 2 in Timaviella dunensis sp. nov. from sand dunes of the Baltic Sea, Germany, and emendation of Timaviella edaphica (Elenkin) O.M. Vynogr. & Mikhailyuk (Synechococcales, Cyanobacteria) based on an integrative approach
FIGURE 2. Molecular phylogeny of Timaviella based on the 16S rRNA gene concatenated with the 16S-23S ITS sequence comparisons. A phylogenetic tree was inferred by the Maximum Likelihood method with Maximum Likelihood bootstrap support (BP) and Bayesian Posterior Probabilities (PP). From left to right, support values correspond to Maximum Likelihood BP and Bayesian PP; BP values lower than 50% and PP lower than 0.8 not shown. Strain in bold represents newly sequenced cyanobacteria. Authentic strains marked with asterisk.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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