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5,023 results for “landing”
A Probable Ancient Nearshore Zone in Southern Utopia on Mars Unveiled from Observations at the Zhurong Landing Area
<p>This dataset (in ArcGIS project) contains the results of geomorphological spatial analysis of water-related features at the Zhurong landing area in Southern Utopia on Mars. It includes vector data of the Zhurong landing site, crater maps, ghost craters, AMA craters, various geomorphological features (cones, etched flows, troughs, pancake craters, rampart craters), and newly defined geological units. Other supporting data and basemaps are also included.</p> <p>This dataset is related to the following paper currently under review in Scientific Reports:</p> <p>Wu B., Dong J., Wang Y., Rao W., Sun Z., Krasilnikov S., Li Z., Tan Z., Chen Z., Wang C., Ivanov M., Zhu J., Liu W.C., Chen L., Li H. 2024. A Probable Ancient Nearshore Zone in Southern Utopia on Mars Unveiled from Observations at the Zhurong Landing Area. </p>
Data from: Tropical forest soundscapes as testimonies of past land use
<p><span>Habitat loss is considered one of the factors that causes a decrease in biodiversity in the tropics. Many efforts have been made to protect and restore tropical forests, but it is difficult to quantify biodiversity and assess restoration areas. Studies have used soundscape analyses to gain information about the landscape, using acoustic indices as indicators of the health of faunal communities. We aimed to assess the changes in acoustic indices in habitats with different types of human exploitation and to evaluate the variation in acoustic indices during the hours of the day among these different habitats. The recordings were performed using passive acoustic monitoring (PAM), deriving 15 acoustic indices to assess the characteristics of each environment. The results suggest that in rubber plantations (RP) there was less acoustic activity, followed by rubber-forest plantations (RFP) and light selectively logged areas (LSL), while in habitats of young secondary forests (YSF), mature secondary forests (MSF) and intensive selectively logged forests (ISL) there was more acoustic activity, which indicates greater faunal activity.<span> </span>This study demonstrates that across the various indices tested, the plantation areas (RP and RFP) presented lower values, which indicate reduced acoustic activity compared to forested areas, with the exception of the area lightly selective logged (LSL) which showed lower values of the indices that measure the activity of sonoriferous specie. Therefore, assessing landscape use by monitoring the soundscape can be useful to timely evaluate the ecological dynamics of areas with high species richness, such as the Atlantic Forest.<span> </span></span></p>
A simple model to calculate GMP from land fraction in palaeoclimate
<p>dataset and code for a simple model which calculate GMP from land fraction in palaeoclimate</p> <p>The dataset is in Netcdf form and the code is writen by python</p>
Dataset about conference paper "Toward the Sea or the Land Cultural Identity and the Choice of Direction of Movement on the Water"
<p>Dataset about conference paper "Toward the Sea or the Land Cultural Identity and the Choice of Direction of Movement on the Water". Including heritage sites, DEM, wind environment, least-cost corridor, and least-cost path.</p>
Table 2 in Oviposition of AedeS japoNiCUS japoNiCUS (Diptera: Culicidae) and associated native species in relation to season, temperature and land use in western Germany
<p><b>Table 2</b> Maximum temperature of water (°C) in the mosquito-positive ovitraps during the field studies at the different study sites</p><table><tbody><tr><th>Study</th><th>Site</th><th><i>Ae. japonicus japonicus</i></th><th><i>Cx.pipiens</i> s.l.</th><th><i>An. plumbeus</i></th><th><i>Ae. geniculatus</i></th></tr></tbody><tbody><tr><th>2017</th><td>Alfter</td><td>26.8</td><td>34.7</td><td>31.3</td><td>20.3</td></tr><tr><th></th><td>Dormagen</td><td>22.1</td><td>27.2</td><td>17.3</td><td>21.7</td></tr><tr><th>2018</th><td>Alfter</td><td>22.6</td><td>19.8</td><td>20.6</td><td>n.n.</td></tr><tr><th></th><td>Bonn SÜd</td><td>24.7</td><td>24</td><td>17.3</td><td>n.n.</td></tr><tr><th></th><td>Heimerzheim</td><td>24.3</td><td>24.6</td><td>23.2</td><td>22.7</td></tr><tr><th></th><td>Lohmar</td><td>28.6</td><td>25.8</td><td>28.6</td><td>16.6</td></tr><tr><th></th><td>Siegburg</td><td>25.8</td><td>30</td><td>27</td><td>23.1</td></tr><tr><th></th><td>Troisdorf</td><td>26.8</td><td>27.7</td><td>25.2</td><td>n.n.</td></tr></tbody></table><p><i>n.n.</i> Species not present</p>
Table 1 in Oviposition of AedeS japoNiCUS japoNiCUS (Diptera: Culicidae) and associated native species in relation to season, temperature and land use in western Germany
<p><b>Table 1</b> Total number and percentages of positive samples and occurrence of mosquito species per trap</p><table><tbody><tr><th>Study</th><th></th><th><i>Aedes japonicus japonicus</i></th><th><i>Culex pipiens</i> s.l.</th><th><i>Anopheles plumbeus</i></th><th><i>Aedes geniculatus</i></th><th>Total</th></tr></tbody><tbody><tr><th>2017</th><td>Total positive traps (<i>n</i>)</td><td>97</td><td>199</td><td>47</td><td>38</td><td>381</td></tr><tr><th></th><td>Positive traps/analysable traps (%)</td><td>15.4</td><td>31.7</td><td>7.5</td><td>6.1</td><td>60.7</td></tr><tr><th></th><td>Traps multiple species (<i>n</i>)</td><td>56</td><td>58</td><td>39</td><td>25</td><td>80</td></tr><tr><th></th><td>Multiple species/positive traps (%)</td><td>57.7</td><td>29.1</td><td>83</td><td>65.8</td><td>21</td></tr><tr><th>2018</th><td>Total positive traps (<i>n</i>)</td><td>441</td><td>285</td><td>137</td><td>19</td><td>882</td></tr><tr><th></th><td>Positive traps/analysable traps (%)</td><td>20.3</td><td>13.1</td><td>6.3</td><td>0.9</td><td>40.7</td></tr><tr><th></th><td>Traps multiple species (<i>n</i>)</td><td>180</td><td>139</td><td>113</td><td>11</td><td>206</td></tr><tr><th></th><td>Multiple species/positive traps (%)</td><td>40.8</td><td>48.8</td><td>82.5</td><td>57.9</td><td>23.4</td></tr></tbody></table><p>Calculations based on a total of 628 samples in 2017 and 2168 samples in 2018.The number of positive ovitraps with more than one species divided by the total number of positive ovitraps represents the portion of positive ovitraps with multiple species.See Additional file 1: Table S1 for all combinations of species and Additional file 2: dataset S1 for all samplings</p>
Table 3 in Oviposition of AedeS japoNiCUS japoNiCUS (Diptera: Culicidae) and associated native species in relation to season, temperature and land use in western Germany
<p><b>Table 3</b> Coefficients and statistically significant output of predictor variables as calculated by the generalised linear model</p><table><tbody><tr><th></th><th>Estimate</th><th>SE</th><th><i>Z</i> -value</th><th><i>P</i></th></tr></tbody><tbody><tr><th>(Intercept)</th><td>− 29.890</td><td>15.100</td><td>− 19.790</td><td>0.0478*</td></tr><tr><th>Temp_mean</th><td>0.2126</td><td>0.0933</td><td>22.800</td><td>0.0226*</td></tr><tr><th>Cxbin</th><td>0.2732</td><td>0.3148</td><td>0.868</td><td>0.3854</td></tr><tr><th>Plbbin</th><td>0.5999</td><td>0.2253</td><td>26.630</td><td>0.0077*</td></tr><tr><th>F100</th><td>0.4931</td><td>0.4826</td><td>10.220</td><td>0.3068</td></tr><tr><th>F10</th><td>0.7486</td><td>0.4127</td><td>18,140</td><td>0.0697</td></tr><tr><th>F/S</th><td>0.8715</td><td>0.3728</td><td>23.380</td><td>0.0194*</td></tr><tr><th>S10</th><td>10.770</td><td>0.3347</td><td>32.170</td><td>0.0013*</td></tr><tr><th>Ngbi</th><td>0.0412</td><td>0.1157</td><td>0.356</td><td>0.722</td></tr><tr><th>Nhbu</th><td>0.0001</td><td>0.0251</td><td>0.003</td><td>0.9972</td></tr><tr><th>Ngki</th><td>0.0295</td><td>0.0432</td><td>0.684</td><td>0.4941</td></tr><tr><th>Nsei</th><td>− 0.0362</td><td>0.0609</td><td>− 0.594</td><td>0.5528</td></tr><tr><th>Ntei</th><td>− 0.0845</td><td>0.0917</td><td>− 0.922</td><td>0.3566</td></tr><tr><th>Ngfi</th><td>0.1002</td><td>0.0832</td><td>12.050</td><td>0.2283</td></tr><tr><th>Nrbu</th><td>0.0648</td><td>0.0456</td><td>14.200</td><td>0.1557</td></tr></tbody></table><p>Characteristics:negative binomial,link = log, <i>z</i> -values calculated by Wald-test. Response variable:total of <i>Ae. japonicus japonicus</i> -positive ovitraps per location Predictors:Temp_mean = Mean water temperature,binary native taxa occurrence:Cxbin = <i>Cx. pipiens</i> s.l., <i>Plbbin An. plumbeus</i>, land use data: percentage forest:F100 = 100% forest,F10 = 60% forest,F/S = 50% forest,S10 = 40% forest,number of tree species in a 10 m radius of the trap locations (the tree species occurred in more than five transects):Nrbu: <i>Fagus sylvatica</i>, Nhbu: <i>Carpinus betulus</i>, Ngbi: <i>Betula pendula</i>, Nsei: <i>Quercus robur</i>, Ntei: <i>Quercus petreae</i>, Ngfi: <i>Picea abies</i>, Ngki: Pinus sylvestris</p>
Data from: Effects of land use change and elevation on endemic shrub frogs in a biodiversity hotspot
<p>This project contains data and codes from a study investigating the effects of land use change and elevation on endemic shrub frogs in the northern Western Ghats, India. </p> <p>Species Coverage: Though the focal species in the study are <em>Pseudophilautus amboli</em> and <em>Raorchestes bombayensis</em>, the raw data also contain the following species. <em>Duttaphrynus melanostictus, Euphlyctis sp, Hoplobatrachus tigerinus, Hydrophylax bahuvistara, Indirana chiravasi, Indirana sp, Microhyla nilphamariensis, Minervarya cepfi, Minervarya gomantaki, Minervarya sp, Nyctibatrachus petraeus, Polypedates maculatus, Pseudophilautus amboli, Ramanella variegata, Raorchestes bombayensis, Rhacophorus malabaricus, Sphaerotheca dobsonii, Uperodon mormorata, Xanthophryne tigerina</em></p> <p>Geographic Coverage: Dodamarg and Sawantwadi Taluks of the Sindhudurg District, Maharashtra State, India. (15°40’–16°0’N; 73°51’–74°9’E)</p> <p>Temporal Coverage: June, July, August, September (2022).</p> <p>Field data was collected by Himanshu Lad and Ninad Gosavi.</p> <p>More details about the data can be obtained from Himanshu Lad and Rohit Naniwadekar from the Nature Conservation Foundation (www.ncf-india.org). </p>
Land_Areas&Values_in_Quito
<p>This dataset contains values of land in the City of Quito</p>
Table 3 in Fig. 11. Left G 1 s in Tuerkayana latens, a New Species of Land Crab from French Polynesia, with a Discussion on the Phylogeny of the Genus (Crustacea: Decapoda: Brachyura: Gecarcinidae)
<p><b>Table 3.</b> The frequency of contribution of environmental variables in predicting the clades geographic distribution models</p><table><tbody><tr><th>Environmental variable (unit)</th><th>Clade <i>I</i></th><th>Clade <i>III</i></th></tr></tbody><tbody><tr><th>Temperature Mean</th><td>39.8</td><td>56.8</td></tr><tr><th>Temperature Range</th><td>25.7</td><td>4.9</td></tr><tr><th>Phytoplankton Mean</th><td>15.3</td><td>5.9</td></tr><tr><th>Salinity Mean</th><td>5.5</td><td>1.4</td></tr><tr><th>Salinity Range</th><td>3.9</td><td>6.2</td></tr><tr><th>Current Velocity Mean</th><td>2.7</td><td>3.7</td></tr><tr><th>Current Velocity Ltmax</th><td>2</td><td>0</td></tr><tr><th>Dissolved oxygen Range</th><td>1.9</td><td>1.6</td></tr><tr><th>Phosphate Mean</th><td>1.6</td><td>5.3</td></tr><tr><th>Current Velocity Min</th><td>1</td><td>14.2</td></tr><tr><th>Current Velocity Max</th><td>0.6</td><td>0</td></tr></tbody></table>
Table 2 in Fig. 11. Left G 1 s in Tuerkayana latens, a New Species of Land Crab from French Polynesia, with a Discussion on the Phylogeny of the Genus (Crustacea: Decapoda: Brachyura: Gecarcinidae)
<p><b>Table 2.</b> Pairwise estimates of <i>COI</i> genetic divergence (Φ ST) for <i>Amphibalanus amphitrite</i> among five biogeographical areas. The significance of ΦST values was tested by a permutation test with 1000 replicates</p><table><tbody><tr><th></th><th>MK(PG)</th><th>TH(PG)</th><th>GU(GO)</th><th>CH(GO)</th><th>JS(GO)</th></tr></tbody><tbody><tr><th>MK(PG)</th><td>0</td><td></td><td></td><td></td><td></td></tr><tr><th>TH(PG)</th><td>0.00748</td><td>0</td><td></td><td></td><td></td></tr><tr><th>GU(GO)</th><td>0.00728</td><td>0.03051*</td><td>0</td><td></td><td></td></tr><tr><th>CH(GO)</th><td>0.03196*</td><td>0.00921</td><td>0.01027</td><td>0</td><td></td></tr><tr><th>JS(GO)</th><td>0.01838</td><td>0.00420</td><td>0.00528</td><td>0.02062</td><td>0</td></tr></tbody></table><p>* <i>p</i> <0.05.</p>
Fig. 1 in Fig. 11. Left G1s in Tuerkayana latens, a New Species of Land Crab from French Polynesia, with a Discussion on the Phylogeny of the Genus (Crustacea: Decapoda: Brachyura: Gecarcinidae)
Fig. 1. Map of the sampling localities.
Integrated Approach to Global Land Use and Land Cover Reference Data Harmonization
<h2><strong>INTRODUCTION</strong></h2> <p>This document outlines the creation of a global inventory of reference samples and Earth Observation (EO) / gridded datasets for the Global Pasture Watch (GPW) initiative. This inventory supports the training and validation of machine-learning models for GPW grassland mapping. This documentation outlines methodology, data sources, workflow, and results.</p> <p><strong>Keywords:</strong> Grassland, Land Use, Land Cover, Gridded Datasets, Harmonization</p> <p> </p> <h2><strong>OBJECTIVES</strong></h2> <ul> <li> <p>Create a global inventory of existing reference samples for land use and land cover (LULC);</p> </li> <li> <p>Compile global EO / gridded datasets that capture LULC classes and harmonize them to match the GPW classes;</p> </li> <li> <p>Develop automated scripts for data harmonization and integration.</p> </li> </ul> <p> </p> <h2><strong>DATA COLLECTION </strong></h2> <p>Datasets incorporated:</p> <table> <tbody> <tr> <td><strong>Datasets</strong></td> <td> <p><strong>Spatial distribution</strong></p> </td> <td><strong>Time period</strong></td> <td><strong>Number of individual samples</strong></td> </tr> <tr> <td>WorldCereal</td> <td>Global</td> <td>2016-2021</td> <td>38,267,911</td> </tr> <tr> <td>Global Land Cover Mapping and Estimation (GLanCE)</td> <td>Global</td> <td>1985-2021</td> <td>31,061,694</td> </tr> <tr> <td>EuroCrops</td> <td>Europe</td> <td>2015-2022</td> <td>14,742,648</td> </tr> <tr> <td>GeoWiki G-GLOPS training dataset</td> <td>Global</td> <td>2021</td> <td>11,394,623</td> </tr> <tr> <td>MapBiomas Brazil</td> <td>Brazil</td> <td>1985-2018</td> <td>3,234,370</td> </tr> <tr> <td>Land Use/Land Cover<br>Area Frame Survey (LUCAS)</td> <td>Europe</td> <td>2006-2018</td> <td>1,351,293</td> </tr> <tr> <td>Dynamic World</td> <td>Global</td> <td>2019-2020</td> <td>1,249,983</td> </tr> <tr> <td>Land Change Monitoring,<br>Assessment, and Projection (LCMap)</td> <td>U.S. (CONUS)</td> <td>1984-2018</td> <td>874,836</td> </tr> <tr> <td>GeoWiki 2012</td> <td>Global</td> <td>2011-2012</td> <td>151,942</td> </tr> <tr> <td>PREDICTS</td> <td>Global</td> <td>1984-2013</td> <td>16,627</td> </tr> <tr> <td>CropHarvest</td> <td>Global</td> <td>2018-2021</td> <td>9,714</td> </tr> </tbody> </table> <p><strong>Total:</strong> 102,355,642 samples</p> <p> </p> <h2><strong>WORKFLOW</strong></h2> <h3><strong>Harmonization Process</strong></h3> <p>We harmonized global reference samples and EO/gridded datasets to align with GPW classes, optimizing their integration into the GPW machine-learning workflow.</p> <p>We considered reference samples derived by visual interpretation with spatial support of at least 30 m (Landsat and Sentinel), that could represent LULC classes for a point or region.</p> <p>Each dataset was processed using automated Python scripts to download vector files and convert the original LULC classes into the following GPW classes:</p> <p> 0. Other land cover</p> <p> 1. Natural and Semi-natural grassland</p> <p> 2. Cultivated grassland</p> <p> 3. Crops and other related agricultural practices</p> <p>We empirically assigned a weight to each sample based on the original dataset's class description, reflecting the level of mixture within the class. The weights range from 1 (Low) to 3 (High), with higher weights indicating greater mixture. Samples with low mixture levels are more accurate and effective for differentiating typologies and for validation purposes.</p> <p>The harmonized dataset includes these columns:</p> <table> <tbody> <tr> <td><strong>Attribute Name</strong></td> <td><strong>Definition</strong></td> </tr> <tr> <td>dataset_name</td> <td>Original dataset name</td> </tr> <tr> <td>reference_year</td> <td>Reference year of samples from the original dataset</td> </tr> <tr> <td>original_lulc_class</td> <td>LULC class from the original dataset</td> </tr> <tr> <td>gpw_lulc_class</td> <td>Global Pasture Watch LULC class</td> </tr> <tr> <td>sample_weight</td> <td>Sample's weight based on the mixture level within the original LULC class</td> </tr> </tbody> </table> <p> </p> <h2><strong>ACKNOWLEDGMENTS</strong></h2> <p>The development of this global inventory of reference samples and EO/gridded datasets relied on valuable contributions from various sources. We would like to express our sincere gratitude to the creators and maintainers of all datasets used in this project.</p> <p> </p> <h2><strong>REFERENCES</strong></h2> <ul> <li> <p>Brown, C.F., Brumby, S.P., Guzder-Williams, B. et al. Dynamic World, Near real-time global 10 m land use land cover mapping. Sci Data 9, 251 (2022). https://doi.org/10.1038/s41597-022-01307-4Van Tricht, K. et al. Worldcereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping. Earth Syst. Sci. Data 15, 5491–5515, 10.5194/essd-15-5491-2023 (2023)</p> </li> <li> <p>Buchhorn, M.; Smets, B.; Bertels, L.; De Roo, B.; Lesiv, M.; Tsendbazar, N.E., Linlin, L., Tarko, A. (2020): Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015-2019: Product User Manual; Zenodo, Geneve, Switzerland, September 2020; doi: 10.5281/zenodo.3938963</p> </li> <li> <p>d’Andrimont, R. et al. Harmonised lucas in-situ land cover and use database for field surveys from 2006 to 2018 in the european union. Sci. data 7, 352, 10.1038/s41597-019-0340-y (2020)</p> </li> <li> <p>Fritz, S. et al. Geo-Wiki: An online platform for improving global land cover, Environmental Modelling & Software, 31, https://doi.org/10.1016/j.envsoft.2011.11.015 (2012)</p> </li> <li> <p>Fritz, S., See, L., Perger, C. et al. A global dataset of crowdsourced land cover and land use reference data. Sci Data 4, 170075 https://doi.org/10.1038/sdata.2017.75 (2017)</p> </li> <li> <p>Schneider, M., Schelte, T., Schmitz, F. & Körner, M. Eurocrops: The largest harmonized open crop dataset across the european union. Sci. Data 10, 612, 10.1038/s41597-023-02517-0 (2023)</p> </li> <li> <p>Souza, C. M. et al. Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine. Remote. Sens. 12, 2735, 10.3390/rs12172735 (2020)</p> </li> <li> <p>Stanimirova, R. et al. A global land cover training dataset from 1984 to 2020. Sci. Data 10, 879 (2023) </p> </li> <li>Stehman, S. V., Pengra, B. W., Horton, J. A. & Wellington, D. F. Validation of the us geological survey’s land change monitoring, assessment and projection (lcmap) collection 1.0 annual land cover products 1985–2017. Remot Sensing environment 265, 112646, 10.1016/j.rse.2021.112646 (2021).</li> <li> <p>Tsendbazar, N. et al. Product validation report (d12-pvr) v 1.1 (2021).</p> </li> <li>Tseng, G., Zvonkov, I., Nakalembe, C. L., & Kerner, H. (2021). CropHarvest: A global dataset for crop-type classification. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track.</li> </ul>
Dataset for 'Warmer Oceans may Accelerate the Marine-to-Land Transition of a West Greenland Outlet Glacier'
<p>Monthly hydrographic records from 2008-2023, collected from the standard station GF10 (64°36.6N, 51°57.5 W; maximum depth: 576 m) as part of Greenland Climate Research Centre “Long-term standard hydrographic fjord research program”. Note that the date values reflect the last day of each month, not necessarily the date that the measurements were obtained.</p> <p>Please cite this reference if dataset were used in any way.</p> <p>Correspondence to John Mortensen at jomo@natur.gl</p>
Species functional data and species distribution model projections for future land-use and fire management scenarios in the Transboundary Biosphere Reserve Gerês-Xurés
<p>The data includes nine functional traits and species distribution model projections for 102 species of vertebrates (amphibians, birds, and reptiles) in the Transboundary Biosphere Reserve Gerês-Xurés. The model projections are available for 2050 under six different land-use and fire management scenarios, namely two land-use scenarios of “business-as-usual” (BAU; ongoing trends of land abandonment) and “High Nature Value farmlands” (HNV), each under three fire management scenarios (low suppression - LS, current fire suppression - CS, and high fire suppression - HS). The species distribution projections for each scenario are presented as matrices of species presences/absences, obtained after reclassifying consensus predictions of species distribution models.</p>
Essential Urban Land Cover Category across the Conterminous United States
<p>This dataset, titled "Essential Urban Land Cover Category across the Conterminous United States", contains high-resolution urban land cover maps for urban areas across the Conterminous United States produced in the manuscript "Large-scale High-resolution Essential Urban Land Cover Category (EULCC) Mapping Using a Semantic-Augmented and Noise-Tolerant Approach". The land cover maps are organized by city, with each city’s data stored as a separate <span>TIFF </span>file. These city-level files are grouped by state, and the dataset provided here is the compressed files of the state-level folders.</p> <p>The raster data values range from 1 to 9 in the land cover maps, representing different essential urban land cover categories as follows:</p> <table> <tbody> <tr> <td>Values</td> <td>Classes</td> </tr> <tr> <td>1</td> <td>Building</td> </tr> <tr> <td>2</td> <td>Tree</td> </tr> <tr> <td>3</td> <td>Grass/Shrub</td> </tr> <tr> <td>4</td> <td>Parking lot</td> </tr> <tr> <td>5</td> <td>Road</td> </tr> <tr> <td>6</td> <td>Water</td> </tr> <tr> <td>7</td> <td>Barren</td> </tr> <tr> <td>8</td> <td>Agriculture</td> </tr> <tr> <td>9</td> <td>Others</td> </tr> </tbody> </table> <p> </p>
Does land use age influence carbon cycling in the Tibetan Plateau?
<p>Although substantial information had been generated on the effects of land use change on soil organic carbon (SOC) and total nitrogen (TN) storage, studies are absent on multifactorial effects of land use types, land use age, and elevation on SOC and TN storage. SOC and TN were therefore investigated in 30 field sites comprising natural forests, planted forests, shrub, and grasslands. SOC and TN stocks differed and correlated significantly with land use age; the C stocks correlates significantly with land use change compared the TN stocks. However, there was no relation between the C and N stocks with elevation, implying that SOC and TN are solely dependent on land use age. SOC sequestration potentials of the sampled ecosystems were 345.86, 293.19, 266.45, and 251.23 t ha<sup>−1</sup> for the natural forests, planted forests, shrub, and grasslands with total mean value of 289.18 t·ha<sup>−1</sup> (1,060.42 t·ha<sup>−1</sup> CO<sub>2−</sub>eq). A significant SOC stock loss (17.96%, 29.80%, and 37.66%) occurred in converting natural forests to planted forests, shrub, and grasslands, whereas gains (27.36%, 14.31%, and 5.71%) would occur in reconverting grassland to natural forests, planted forests, and shrublands. Therefore, the C that was lost during deforestation and conversion of natural forests into other land use types could not match the carbon gains thereafter. Our results suggest that land use change and land use age have influenced soil C and N stocks. Moreover, natural forests are better in ecological conservation and restoration of degraded lands. This study provides baseline information for C and N management in ecologically restored and degraded lands.</p>
Dancing bees evaluate central urban forage resources as superior to agricultural land
<p class="ColorfulList-Accent11CxSpFirst">Recent evidence suggests that flower-rich areas within cities could play an important role in pollinator conservation, but direct comparison of floral resources within agricultural and urban areas has proved challenging to perform over large scales.</p> <p class="ColorfulList-Accent11CxSpMiddle">Here we use the waggle dances of honeybees (<i>Apis mellifera</i> L.) to perform large-scale landscape surveys at heavily urban or agricultural sites for a key pollinator of wild and crop plants. We analyzed 2827 dances that were performed by 20 colonies in SE England.</p> <p class="ColorfulList-Accent11CxSpMiddle">We show that hive median foraging trip distance is consistently lower at urban sites across the entire season. The sucrose content of collected nectar did not significantly differ between urban and agricultural land, ruling out the possibility that longer foraging distances in agricultural sites were driven by distant but nectar-rich resources.</p> <p class="ColorfulList-Accent11CxSpMiddle">Within cities, bees preferentially targeted residential areas on foraging trips, while trips to mass-flowering crops overwhelmingly dominated at agricultural sites. For both land-use types, distances flown increased in the summer, but there was high variation in temporal patterns between individual sites</p> <p class="ColorfulList-Accent11CxSpLast"><i>Policy implications: </i>From the self-reported perspective of a generalist pollinator, forage was easier to find in heavily urbanized areas than in the modern agricultural landscapes that we studied. A focus on continuous spatial and temporal provision within agricultural environments is key to redressing this imbalance.</p>
BIOPLAT-EU: Target Area Base Layer providing statistical information on demography, employment and land use for sustainability assessment
<p>This dataset provides information on demographic variables related to population and employment as well as land use/land cover share on the basis of local administrative units (LAU). Within the BIOPLAT-EU project, this information is integrated into the webGIS sustainability assessment tool.<br> Main source of the administrative unit geometries is the spatial data set of local administrative units (LAU) (2016) provided by the European Commission (https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/lau#lau19). The data set was extended by Level 2 administrative boundaries of Albania (https://data.humdata.org/dataset/albania-administrative-level-0-3-boundaries) and Level 3 administrative boundaries of Ukraine as of 2017 (https://data.humdata.org/m/dataset/ukraine-administrative-boundaries-as-of-q2-2017?force_layout=light)</p> <p>Data on demography (2016) for LAU + Albania was acquired using the Eurostat statistical database (https://ec.europa.eu/eurostat/web/main/data/database). To calculate land use share for LAU and Albania, Corine Land cover (CLC) data from 2018 was used (https://land.copernicus.eu/pan-european/corine-land-cover). CLC classes were summarized into the following classes: urban areas (UrAr), forest (Fo), permanent crops (PeCr), annual crops (AnCr), permanent meadows and pastures (PeMaPa), industrial sites (InSi), water and wetlands (We), others (Ot).</p> <p>For Ukraine data on demography provided by the State Statistics Survey of Ukraine (http://www.ukrstat.gov.ua/) . To calculate land use share per administrative unit, the land use map produced by Myroniuk et al. 2020 (https://doi.org/10.3390/rs12010187) was used. Based to this map shares of the following land use classes are calculated: urban areas (UrAr), forest (Fo), annual and permanent cropland (AnPeCr), grassland (Gra), water and wetlands (We), others (Ot).</p> <p> </p> <p><em><strong>Terms of use:</strong></em> These data are provided "as is". <em>The authors make <strong>no </strong></em><strong><em>warranty</em></strong><em>, representation, or guaranty of any type as to the completeness, accuracy, content or fitness for any particular purpose or use of any </em><strong><em>open data</em></strong><em> set made available here</em><em>, nor shall any </em><strong><em>warranties</em></strong><em> be implied with respec</em><em>t to the data provided.</em></p>
Data from: Trophic level and basal resource use of soil animals are hardly affected by local plant associations in abandoned arable land
Stable isotope composition of plants and soil animals in abandoned arable land <p>Dataset provides raw data on stable isotope composition of C and N of plants and soil meso- and macrofauna. Dominating animal groups were collected in arable fallow systems 3 and 14-16 years after abandonment. Animals were sampled from the rhizosphere of three plant species of different functional groups: a legume (Medicaco sativa), a non-legume herb (Taraxacum officinale) and a grass (Bromus sterilis). More information can be found in the associated paper.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.