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Fig. 1 in Spatial patterns of zooplanktivore Chirostoma species (Atherinopsidae) during water-level fluctuation in the shallow tropical Lake Chapala, Mexico: seasonal and interannual analysis
Fig. 1. Map of Lake Chapala, Mexico. Numbers in bold represent sample sites and numbers in italic lake depths.
Spatial patterns in neighbourhood effects on woody plant selection and bark stripping by deer in a lowland alluvial forest
<p>This dataset presents the incidence of bark stripping (present or not) and its intensity by two deer species mapped over all woody individuals ≥ 1 cm diameter at breast height (DBH). Stripping intensity was measured as the maximal percentage of the stripped stem circumference at the part of the stem with the horizontally widest stripping wound. A four-hectare square research plot is located in the Ranšpurk old-growth forest reserve in the south-eastern part of the Czech Republic (N48°40´, E16°56´).</p> <p>Several treefalls damaged the fence around the reserve in late autumn 2017, allowing fallow deer (<em>Dama dama</em> L.) and red deer (<em>Cervus elaphus</em> L.) to enter the reserve from the deer enclosure. The number of fallow deer and red deer individuals in the reserve was unknown, and likely fluctuated over time as they could enter and leave the reserve at any time. Data were collected in July 2018, ca 9–10 months after the fence was damaged. Although the fence was not fixed at the time of data collection (deer could still enter and leave the reserve), stripping wounds were not fresh and were likely received between late autumn 2017 and early spring 2018.</p> <p>The datasets includes tree and shrub individuals that fell within species-specific DBH range (susceptible individuals). The range was defined for each tree and shrub species as DBH<sub>min </sub>≤ DBH ≤ DBH<sub>max</sub>, where DBH<sub>min</sub> and DBH<sub>max</sub> are DBH of the smallest and largest stripped individuals. Individuals smaller and larger than this range were excluded from analyses, because small and large individuals may be relatively less often stripped. Woody species with ≥ 70 individuals are presented, because stochasticity does not allow meaningful spatial point pattern analyses with fewer individuals per species.</p> <p>Data contains the x and y coordinates of each individual in a local coordinate system, the woody species, its code, diameter at breast height, the incidence of stripping (present = 1 or not = 0), and the intensity of stripping (%).</p> <p> </p> <p> </p>
Spatial patterns of extreme precipitation and their changes under ~2 °C global warming: A large-ensemble study of the western US: Data Release
<p>This dataset supports the analysis in Rupp et al. (2022). The dataset consists of 17,223 data files containing the water year (WY) maximum of the daily-averaged precipitation rate simulated with the HadRM3p regional climate model configured for the western United States. Each file contains the WY maxima across the model domain for a single WY, single model parameterization, and single set of initial conditions. Please refer to Hawkins et al. (2019) and Rupp et al. (2022) for a description of how the climate model data were generated.</p>
Global patterns of current and future road infrastructure - Supplementary spatial data
<p><strong>Global patterns of current and future road infrastructure - Supplementary spatial data</strong></p> <p><strong>Authors:</strong> Johan Meijer, Mark Huijbregts, Kees Schotten, Aafke Schipper</p> <p><strong>Research paper summary: </strong>Georeferenced information on road infrastructure is essential for spatial planning, socio-economic assessments and environmental impact analyses. Yet current global road maps are typically outdated or characterized by spatial bias in coverage. In the Global Roads Inventory Project we gathered, harmonized and integrated nearly 60 geospatial datasets on road infrastructure into a global roads dataset. The resulting dataset covers 222 countries and includes over 21 million km of roads, which is two to three times the total length in the currently best available country-based global roads datasets. We then related total road length per country to country area, population density, GDP and OECD membership, resulting in a regression model with adjusted <em>R</em>2 of 0.90, and found that that the highest road densities are associated with densely populated and wealthier countries. Applying our regression model to future population densities and GDP estimates from the Shared Socioeconomic Pathway (SSP) scenarios, we obtained a tentative estimate of 3.0–4.7 million km additional road length for the year 2050. Large increases in road length were projected for developing nations in some of the world's last remaining wilderness areas, such as the Amazon, the Congo basin and New Guinea. This highlights the need for accurate spatial road datasets to underpin strategic spatial planning in order to reduce the impacts of roads in remaining pristine ecosystems.</p> <p><strong>Contents:</strong> The GRIP dataset consists of global and regional vector datasets in ESRI filegeodatabase and shapefile format, and global raster datasets of road density at a 5 arcminutes resolution (~8x8km). The GRIP dataset is mainly aimed at providing a roads dataset that is easily usable for scientific global environmental and biodiversity modelling projects. The dataset is not suitable for navigation. GRIP4 is based on many different sources (including OpenStreetMap) and to the best of our ability we have verified their public availability, as a criteria in our research. The UNSDI-Transportation datamodel was applied for harmonization of the individual source datasets. GRIP4 is provided under a <a href="https://creativecommons.org/publicdomain/zero/1.0/deed.en">Creative Commons License (CC-0)</a> and is free to use. The GRIP database and future global road infrastructure scenario projections following the Shared Socioeconomic Pathways (SSPs) are described in the <a href="https://www.globio.info/global-patterns-of-current-and-future-road-infrastructure">paper by Meijer et al (2018)</a>. Due to shapefile file size limitations the global file is only available in ESRI filegeodatabase format.</p> <p>Regional coding of the other vector datasets in shapefile and ESRI fgdb format:</p> <ul> <li>Region 1: North America</li> <li>Region 2: Central and South America</li> <li>Region 3: Africa</li> <li>Region 4: Europe</li> <li>Region 5: Middle East and Central Asia</li> <li>Region 6: South and East Asia</li> <li>Region 7: Oceania</li> </ul> <p>Road density raster data:</p> <ul> <li>Total density, all types combined</li> <li>Type 1 density (highways)</li> <li>Type 2 density (primary roads)</li> <li>Type 3 density (secondary roads)</li> <li>Type 4 density (tertiary roads)</li> <li>Type 5 density (local roads)</li> </ul> <p><strong>Keyword:</strong> global, data, roads, infrastructure, network, global roads inventory project (GRIP), SSP scenarios</p>
Fig. 3 in Spatial Patterns Of Bird Communities Of The Lower Dnieper Sands During The Breeding Season: Differentiation Factors
Fig. 3. The abundance (the mean number of individuals per sample) of campophilous and dendrophilous birds in groups of samples A (Clusters I–IV) and B (Clusters V–VI). N o t e. The central line represents median, the lower and upper limits of the rectangle — the first and third quartile respectively, "whiskers" — ± 1.5 of interquartile range; circles — outliers.
Fig. 1 in Spatial Patterns Of Bird Communities Of The Lower Dnieper Sands During The Breeding Season: Differentiation Factors
Fig. 1. The scheme of the study area. Аrenas of Low-Dnieper Sands: A — Kakhovska; B — Kozachelaherska; C — Oleshkivska; D — Chalbaska; E — Zburivska; F — Ivanivska; G — Kinburn Peninsula. N o t e. The first and the last sample of each census route are marked by numbers; the numbering of samples is the same as in table 1.
Fig. 4 in Spatial Patterns Of Bird Communities Of The Lower Dnieper Sands During The Breeding Season: Differentiation Factors
Fig. 4. The area ratio of different types of habitats on standard test plots in the groups of samples.
Spatial scaling of pollen-plant diversity relationship in landscapes with contrasting diversity patterns
<p>Data for paper "Spatial scaling of pollen-plant diversity relationship in landscapes with contrasting diversity patterns" in Scientific Reports.</p> <p>Code for analysis and plots <a href="https://github.com/vojtechabraham/SpatialScalingPollenDiversity">https://github.com/vojtechabraham/SpatialScalingPollenDiversity</a>. Download original pollen and resample them to the same pollen sum by function spectra_to_target_sum in <a href="https://github.com/vojtechabraham/pollen">https://github.com/vojtechabraham/pollen</a> or work with resampled datasets below.</p> <p>Original pollen data stored in <a href="https://www.neotomadb.org/">https://www.neotomadb.org/</a>:</p> <table> <tbody> <tr> <td><strong>species-poor region Bohemian-Moravian Highland (Vrchovina)</strong></td> </tr> <tr> <td><strong>forested</strong></td> <td> </td> <td> </td> <td><strong>open</strong></td> </tr> <tr> <td><strong>SiteName</strong></td> <td><strong>Handle</strong></td> <td><strong>Dataset ID</strong></td> <td><strong>SiteName</strong></td> <td><strong>Handle</strong></td> <td><strong>Dataset ID</strong></td> </tr> <tr> <td>Račín</td> <td>V06</td> <td><a href="https://data.neotomadb.org/54872">54872</a></td> <td>Plíčky</td> <td>V03</td> <td><a href="https://data.neotomadb.org/54870">54870</a></td> </tr> <tr> <td>Vepřová-Žlábek</td> <td>V07</td> <td><a href="https://data.neotomadb.org/54873">54873</a></td> <td>Louky u Černého lesa</td> <td>V04</td> <td><a href="https://data.neotomadb.org/54871">54871</a></td> </tr> <tr> <td>Stropnická cesta</td> <td>V18</td> <td><a href="https://data.neotomadb.org/54882">54882</a></td> <td>Suché Kopce</td> <td>V10</td> <td><a href="https://data.neotomadb.org/54874">54874</a></td> </tr> <tr> <td>Žižkov</td> <td>V19</td> <td><a href="https://data.neotomadb.org/54883">54883</a></td> <td>Pihoviny</td> <td>V11</td> <td><a href="https://data.neotomadb.org/54875">54875</a></td> </tr> <tr> <td>Chlum</td> <td>V21</td> <td><a href="https://data.neotomadb.org/54885">54885</a></td> <td>Kocanda</td> <td>V12</td> <td><a href="https://data.neotomadb.org/54876">54876</a></td> </tr> <tr> <td>Míšek</td> <td>V22</td> <td><a href="https://data.neotomadb.org/54886">54886</a></td> <td>Porostliny</td> <td>V13</td> <td><a href="https://data.neotomadb.org/54877">54877</a></td> </tr> <tr> <td>Knížecí studánka</td> <td>V23</td> <td><a href="http://data.neotomadb.org/54887">54887</a></td> <td>Bahna</td> <td>V14</td> <td><a href="https://data.neotomadb.org/54878">54878</a></td> </tr> <tr> <td>Pod Šindelným vrchem</td> <td>V24</td> <td><a href="https://data.neotomadb.org/54888">54888</a></td> <td>Ratajské rybníky</td> <td>V15</td> <td><a href="https://data.neotomadb.org/54879">54879</a></td> </tr> <tr> <td>Rampoltův mlýn</td> <td>V25</td> <td><a href="https://data.neotomadb.org/54889">54889</a></td> <td>Zubří</td> <td>V16</td> <td><a href="https://data.neotomadb.org/54880">54880</a></td> </tr> <tr> <td>Brožova skála</td> <td>V26</td> <td><a href="https://data.neotomadb.org/54890">54890</a></td> <td>Nový Rybník</td> <td>V17</td> <td><a href="https://data.neotomadb.org/54881">54881</a></td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>Samotín</td> <td>V20</td> <td><a href="https://data.neotomadb.org/54884">54884</a></td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td><strong>species-rich region White-Carpathians Mountains (Bílé Karpaty)</strong></td> </tr> <tr> <td><strong>forested</strong></td> <td> </td> <td><strong>open</strong></td> </tr> <tr> <td><strong>Handle</strong></td> <td><strong>Dataset ID</strong></td> <td><strong>Handle</strong></td> <td><strong>Dataset ID</strong></td> </tr> <tr> <td>BK1</td> <td><a href="https://data.neotomadb.org/54770">54770</a></td> <td>BK2</td> <td><a href="https://data.neotomadb.org/54771">54771</a></td> </tr> <tr> <td>BK3</td> <td><a href="https://data.neotomadb.org/54772">54772</a></td> <td>BK4</td> <td><a href="https://data.neotomadb.org/54773">54773</a></td> </tr> <tr> <td>BK5</td> <td><a href="https://data.neotomadb.org/54774">54774</a></td> <td>BK6</td> <td><a href="https://data.neotomadb.org/54775">54775</a></td> </tr> <tr> <td>BK9</td> <td><a href="https://data.neotomadb.org/54777">54777</a></td> <td>BK8</td> <td><a href="https://data.neotomadb.org/54776">54776</a></td> </tr> <tr> <td>BK11</td> <td><a href="https://data.neotomadb.org/54779">54779</a></td> <td>BK10</td> <td><a href="https://data.neotomadb.org/54778">54778</a></td> </tr> <tr> <td>BK13</td> <td><a href="https://data.neotomadb.org/54781">54781</a></td> <td>BK12</td> <td><a href="https://data.neotomadb.org/54780">54780</a></td> </tr> <tr> <td>BK15</td> <td><a href="https://data.neotomadb.org/54783">54783</a></td> <td>BK14</td> <td><a href="https://data.neotomadb.org/54782">54782</a></td> </tr> <tr> <td>BK16</td> <td><a href="https://data.neotomadb.org/54784">54784</a></td> <td>BK20</td> <td><a href="https://data.neotomadb.org/54788">54788</a></td> </tr> <tr> <td>BK17</td> <td><a href="https://data.neotomadb.org/54785">54785</a></td> <td>BK23</td> <td><a href="https://data.neotomadb.org/54791">54791</a></td> </tr> <tr> <td>BK18</td> <td><a href="https://data.neotomadb.org/54786">54786</a></td> <td>BK25</td> <td><a href="https://data.neotomadb.org/54793">54793</a></td> </tr> <tr> <td>BK19</td> <td><a href="https://data.neotomadb.org/54787">54787</a></td> <td>BK27</td> <td><a href="https://data.neotomadb.org/54795">54795</a></td> </tr> <tr> <td>BK21</td> <td><a href="https://data.neotomadb.org/54789">54789</a></td> <td>BK29</td> <td><a href="https://data.neotomadb.org/54797">54797</a></td> </tr> <tr> <td>BK22</td> <td><a href="https://data.neotomadb.org/54790">54790</a></td> <td>BK31</td> <td><a href="https://data.neotomadb.org/54799">54799</a></td> </tr> <tr> <td>BK24</td> <td><a href="https://data.neotomadb.org/54792">54792</a></td> <td>BK33</td> <td><a href="https://data.neotomadb.org/54801">54801</a></td> </tr> <tr> <td>BK26</td> <td><a href="https://data.neotomadb.org/54794">54794</a></td> <td>BK35</td> <td><a href="https://data.neotomadb.org/54803">54803</a></td> </tr> <tr> <td>BK28</td> <td><a href="https://data.neotomadb.org/54796">54796</a></td> <td>BK36</td> <td><a href="https://data.neotomadb.org/54804">54804</a></td> </tr> <tr> <td>BK30</td> <td><a href="https://data.neotomadb.org/54798">54798</a></td> <td>BK38</td> <td><a href="https://data.neotomadb.org/54805">54805</a></td> </tr> <tr> <td>BK32</td> <td><a href="https://data.neotomadb.org/54800">54800</a></td> <td>BK39</td> <td><a href="https://data.neotomadb.org/54806">54806</a></td> </tr> <tr> <td>BK34</td> <td><a href="https://data.neotomadb.org/54802">54802</a></td> <td>BK40</td> <td><a href="https://data.neotomadb.org/54807">54807</a></td> </tr> <tr> <td> </td> <td> </td> <td>BK41</td> <td><a href="https://data.neotomadb.org/54808">54808</a></td> </tr> </tbody> </table> <p> </p>
Рис. 8. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска M. catrusiana (А), фитомассы (В), твердости грунта на глубине 5–10 см (C) и доли агрегатных фракций 3–5 мм (D) на участке № 2 в 2011 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 8. 3D–diagrams of the abundance spatial distribution of the land snail M. catrusiana (A), phytomass (B), 0–10 cm layer soil penetration resistance (C), aggregate particle size 3–5 mm (D) at the site 1 in 2011 (axes X and Y presented in meters). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 8. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска M. catrusiana (А), фитомассы (В), твердости грунта на глубине 5–10 см (C) и доли агрегатных фракций 3–5 мм (D) на участке № 2 в 2011 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 8. 3D–diagrams of the abundance spatial distribution of the land snail M. catrusiana (A), phytomass (B), 0–10 cm layer soil penetration resistance (C), aggregate particle size 3–5 mm (D) at the site 1 in 2011 (axes X and Y presented in meters).
Рис. 6. Графики Зависимости оценок варианс (S2) от средней плотности (D) популЯций наЗемных моллюсков B. cylindrica (А) и M. cartusiana (В): 1 – участок № 1, 2010 г.; 2 – участок № 2, 2011 г.; 3 – участок № 4, 2012 г.; 4 – участок № 5, 2012 г. Fig. 6. Variance estimation (S2) and average density (D) of the land snail B. cylindrica (А) and M. cartusiana (В) population scatter plots: 1 – site 1, 2010; 2 – site 2, 2011; 3 – site 4, 2012; 4 – site 5, 2012. in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 6. Графики Зависимости оценок варианс (S2) от средней плотности (D) популЯций наЗемных моллюсков B. cylindrica (А) и M. cartusiana (В): 1 – участок № 1, 2010 г.; 2 – участок № 2, 2011 г.; 3 – участок № 4, 2012 г.; 4 – участок № 5, 2012 г. Fig. 6. Variance estimation (S2) and average density (D) of the land snail B. cylindrica (А) and M. cartusiana (В) population scatter plots: 1 – site 1, 2010; 2 – site 2, 2011; 3 – site 4, 2012; 4 – site 5, 2012.
Рис. 5. Коррелограммы покаЗателей обилиЯ наЗемных моллюсков раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – H. lucorum, участок № 1, 2010 г.; B – Ch. tridens, участок № 2, 2011 г.; C – Ch. tridens, участок № 4, 2012 г.); D – Ch. tridens, участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 5. Spatial correlogram of the land snail different age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – H. lucorum, site 1, 2010; B – Ch. tridens, site 2, 2011; C – Ch. tridens, site 4, 2012; D – Ch. tridens, site 5, 2012 (Moran index confidence value presented by filled signs). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 5. Коррелограммы покаЗателей обилиЯ наЗемных моллюсков раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – H. lucorum, участок № 1, 2010 г.; B – Ch. tridens, участок № 2, 2011 г.; C – Ch. tridens, участок № 4, 2012 г.); D – Ch. tridens, участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 5. Spatial correlogram of the land snail different age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – H. lucorum, site 1, 2010; B – Ch. tridens, site 2, 2011; C – Ch. tridens, site 4, 2012; D – Ch. tridens, site 5, 2012 (Moran index confidence value presented by filled signs).
Рис. 4. Коррелограммы покаЗателей обилиЯ наЗемного моллюска M. cartusiana раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.); D – участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 4. Spatial correlogram of land snail M. cartusiana age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Moran index confidence value presented by filled sings). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 4. Коррелограммы покаЗателей обилиЯ наЗемного моллюска M. cartusiana раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.); D – участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 4. Spatial correlogram of land snail M. cartusiana age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Moran index confidence value presented by filled sings).
Рис. 3. Коррелограммы покаЗателей обилиЯ наЗемного моллюска B. cylindrica раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.); D – участок №5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 3. Spatial correlogram of the land snail B. cylindrica age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Moran index confidence value presented by filled signs). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 3. Коррелограммы покаЗателей обилиЯ наЗемного моллюска B. cylindrica раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.); D – участок №5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 3. Spatial correlogram of the land snail B. cylindrica age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Moran index confidence value presented by filled signs).
Рис. 7. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска B. cylindrica (А), фитомассы (В), проективного покрытиЯ (С), твердости грунта на глубине 5–10 см (D) на участке № 1 в 2010 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 7. 3D–diagrams of the abundance spatial distribution of the snail B. cylindrica (A), phytomass (B), plants projective cover (C), 0–10 cm layer soil penetration resistance (D) at the site 1 in 2010. (axes X and Y presented in meters). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 7. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска B. cylindrica (А), фитомассы (В), проективного покрытиЯ (С), твердости грунта на глубине 5–10 см (D) на участке № 1 в 2010 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 7. 3D–diagrams of the abundance spatial distribution of the snail B. cylindrica (A), phytomass (B), plants projective cover (C), 0–10 cm layer soil penetration resistance (D) at the site 1 in 2010. (axes X and Y presented in meters).
Рис. 2. Диаграммы распределениЯ обилиЯ наЗемного моллюска M. cartusiana: A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.; D – участок № 5, 2012 г. (единицы иЗмерениЯ осей Х и Y даны в метрах; численность особей пропорциональна раЗмерам Шариков). Fig. 2. Diagram of the abundance distribution of the land snail M. cartusiana: A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Х and Y axes presented in meters; abundance proportional to sphere sizes). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 2. Диаграммы распределениЯ обилиЯ наЗемного моллюска M. cartusiana: A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.; D – участок № 5, 2012 г. (единицы иЗмерениЯ осей Х и Y даны в метрах; численность особей пропорциональна раЗмерам Шариков). Fig. 2. Diagram of the abundance distribution of the land snail M. cartusiana: A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Х and Y axes presented in meters; abundance proportional to sphere sizes).
Fig. 1 in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Fig. 1. Diagram of the abundance distribution of the land snail B. cylindrica: A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Х and Y axes presented in meters; abundance proportional to sphere sizes).
Рис. 1. Диаграммы распределениЯ обилиЯ наЗемного моллюска B. cylindrica: A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.; D – участок № 5, 2012 г. (единицы иЗмерениЯ осей Х и Y даны в метрах; численность особей пропорциональна раЗмерам Шариков). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 1. Диаграммы распределениЯ обилиЯ наЗемного моллюска B. cylindrica: A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.; D – участок № 5, 2012 г. (единицы иЗмерениЯ осей Х и Y даны в метрах; численность особей пропорциональна раЗмерам Шариков).
Fig. 4 in Temporal And Spatial Pattern Of Genetic Differentiation In Isophya Kraussi (Orthoptera: Tettigonoidea) In Ne Hungary
Fig. 4. Results of the PCA analysis for the two samples collected in the Mogyoróskuti meadows in 1999 and in 2001; i.e. temporalvariation within a population. The points represent the genotypic
Fig. 3 in Temporal And Spatial Pattern Of Genetic Differentiation In Isophya Kraussi (Orthoptera: Tettigonoidea) In Ne Hungary
Fig. 3. Results of the PCA analysis for the three distinct populations; i.e. spatial variation (Karst region: Mogyoróskuti meadows, Haragistya; Zemplén Mts.: Gyertyánkúti meadows). The points represent
Fig. 1 in Temporal And Spatial Pattern Of Genetic Differentiation In Isophya Kraussi (Orthoptera: Tettigonoidea) In Ne Hungary
Fig. 1. Sample sites. Aggtelek Karst region: Haragistya near Aggtelek (1), Mogyoróskuti meadows near Jósvafő (2); Zemplén Mts.: Gyertyánkúti meadows near Telkibánya (3)
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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.