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FIG. 3 in Shifting shell morphology in a Late Miocene-Pliocene land snail species lineage (Gastropoda: Stylommatophora: Spiraxidae), with the description of a new species
FIG. 3. — Results of the morphometric analyses: A, principal components analysis of the Procrustes shape coordinates; shown are the first three axes explaining 88.5% of the total variance; B, thin-plate spline deformation grid illustrating the extreme values for each principal component; deformations were magnified to a factor of 2 to highlight variation of distinct traits; C, reconstructed mean shapes for all material as well as across specimens for each locality.
FIG. 5. — A-D, O, Palaeoglandina turolensis n in Shifting shell morphology in a Late Miocene-Pliocene land snail species lineage (Gastropoda: Stylommatophora: Spiraxidae), with the description of a new species
FIG. 5. — A-D, O, Palaeoglandina turolensis n. sp. from Orrios 1 (Ruscinian, Teruel Basin): A, O, MAP-8421; B, MAP-8422; C, MAP-8423; D, MAP-8424; E-L, N, P. turolensis n. sp. from Los Aljezares (Turolian, Teruel Basin): E, 040822JA01; F, N, 040822JA02; G, MGM-3723; H, MNCNI-28900-D; I, MNCNI-28900-A; J, MGM-118M; K, MNCNI-28900-E; L, MNCNI-11300-E; M, P. turolensis n. sp. from La Gloria 4, MAP-8405. Scale bars: A-J, 10 mm; M-O, 6 mm.
FIG. 1 in Shifting shell morphology in a Late Miocene-Pliocene land snail species lineage (Gastropoda: Stylommatophora: Spiraxidae), with the description of a new species
FIG. 1. — Geographical and geological overview of the studied sites in the Teruel Basin: A, schematic geological map of the Teruel Basin region indicating the areas in which the studied sites are located (modified after Ezquerro et al. 2022a); B, C, detailed locations of the Los Aljezares (AL), La Gloria 4 (LG4) and Orrios 1 (OR1) sites. Underlying images © Google Maps 2023, CNES/Airbus, Maxar Technologies.
Code and data for 'Human modification of land cover alters net primary productivity, species richness and their relationship' manuscript
<p>The data and scripts in this database are analyses for a research paper in Global Ecology and Biogeography in 2023: Human modification of land cover alters net primary productivity, species richness and their relationship. Please refer to the README file and the paper for details about the usage of the data and methodology.</p>
FIGURE 4 in Specific damage recognised on land snail shells as a tool for studying predation intensity: differences related to habitat and predator types
FIGURE 4 Variation in the minimal rate of predation recorded in six samples collected in each of five habitat types. Different letters refer to significant differences (p <0.05) among habitat types, based on a generalised linear model. The central line of each box refers to the median value, box height to the interquartile range, whiskers to the non-outlier range (i.e., 1.5 times the interquartile range at each side), and small circles to outliers.
FIGURE 5 in Specific damage recognised on land snail shells as a tool for studying predation intensity: differences related to habitat and predator types
FIGURE 5 Relative representation of individual predation types recognised in six samples collected at each of five habitat types. The height of the column represents the average value, while the lines indicate the standard deviation.
FIGURE 2 in Specific damage recognised on land snail shells as a tool for studying predation intensity: differences related to habitat and predator types
FIGURE 2 Examples of the recorded evidence of predation caused by various predators. a) shell of Cochlicopa lubrica predated on by a snail; b) characteristic damage caused by a carabid beetle, here on a shell of Discus rotundatus; c) puparium of the parasitoid dipteran fly Pherbellia limbata within a Granaria frumentum shell (the shell was crushed manually); d) external damage to a Xerolenta obvia shell caused by bird predation; e) Drilus beetle larval exuviae within an Alinda biplicata shell (the shell was crushed manually).
Land-Sea mask files for GEO satellites.
<p>A collection of land-sea mask images, resampled and georeferenced to match the projection of various geostationary satellite sensors.</p> <p>This is derived from Todd Karin's `global-land-mask` package: https://zenodo.org/records/4066722</p> <p>In this version pixel values are as follows:</p> <p> - 0: Water</p> <p> - 1: Coastline</p> <p> - 2: Land</p> <p> - 255: Fill value</p>
Data Sets: Unsteady Land-Sea Breeze Circulations in the Presence of a Synoptic Pressure Forcing
<p>{Mg (m/s): 0, 0.4, 1.2, 2 and α: 0°, and 180°}</p> <p>Consult the details in Allouche et al. (2023): https://doi.org/10.1002/qj.4552. The latter corresponds to the steady state simulations of these transient ones here.<br>All of these simulations have a domain extent (L_x, L_y, L_z=z_i) of (80 km, 5 km, 1.6 km). The numerical mesh (Nx, Ny, Nz) is (384, 24, 64). For plotting, the vertical levels vary for each variable. A variable at 'c_s nodes' is plotted at dz/2, dz, 2*dz, 3*dz, and so forth up to z_i. A variable on 'w nodes' is plotted at 0, dz, 2*dz, 3*dz, and so forth up to z_i. This information is given in the variable table below.</p> <p>These simulations are given in a netcdf format (.nc). One is able to download and reshape these matrices.</p>
Globe230k: A Benchmark Dense-Pixel Annotation Dataset for Global Land Cover Mapping
<p>We (Intelligent Mining and Analysis of Remote Sensing big data, IMARS) create a large-scale annotated dataset (Globe230k) for land use/land cover (LULC) mapping, which is annotated on Google Earth image of 1 m spatial resolution. Globe230k is annotated by numerous experts and students major in survey and mapping after necessary training, through visual interpretation on very high-resolution images, as well as in-situ field survey, under the guidance of the organized annotation pipeline. Globe230k has three superiorities:</p> <p>1) Large scale: the Globe230k includes 232,819 annotated images with the size of 512x512 and spatial resolution of 1 m, with more than 3x1010 annotated pixels, and it includes 10 first-level categories. </p> <p>2) Rich diversity: the annotated images are sampled from worldwide regions, with coverage area of over 60,000 km2, indicating a high variability and diversity. Besides, in order to ensure the category balance, we intentionally give more chance to the rare categories to be sampled, such as wetland, ice/snow, etc.</p> <p>3) Multi-modal: Globe230k not only contains RGB bands, but also include other important features for Earth system research, such as Normalized differential vegetation index (NDVI), digital elevation model (DEM), vertical-vertical polarization (VV) bands, vertical-horizontal polarization (VH) bands, which can facilitate the multi-modal data fusion research. Due to the large size of the multi-modal dataset (DEM 1.91G, NDVI 164G, VVVH 372G), these dataset are stored on Baidu Yunpan, the download link is :https://pan.baidu.com/s/12AKbiqOXSf4fnm7mYkCE0g?pwd=230k, the extraction code is 230k.</p> <p>The image patches and their corresponding annotated patches are respectively stored in "image_patch.zip" and "label_patch.zip" file. The RGB image is in forms of ".jpg", with size of 512x512, the pixel value is ranged from 0-255. The annotated patches is in forms of ".png", also with size of 512x512, the pixel value is ranged from 1-10, which respectively represent 1#cropland, 2#forest, 3#grass, 4#shrubland, 5#wetland, 6#water, 7#tundra, 8#impervious, 9#bareland, 10#ice/snow. The corresponding DEM, NDVI and VVVH patches are all in form of ".tif", with size of 512x512 (due to the different resolution of DEM, NDVI and VVVH patches, they are all uniformly resized to the same scale as the image patch). </p> <p>The total 232,819 pairs are officially divided into training set, validation set, and test set, based on ratio of 7:1:2, which can be find in "train_num.txt","val_num.txt","test_num.txt" file. Based on this division, the official baseline accuracy of several state-of-the-art semantic segmentation can be found in the related arcticle (https://spj.science.org/doi/10.34133/remotesensing.0078).</p> <p>We hope it can be used as a benchmark to promote further development of global land cover mapping and semantic segmentation algorithm development.</p>
Reclaiming urban vacant land to manage stormwater and support insect habitat
<p>Urban green spaces can provide important wildlife habitat and ecosystem services. In legacy cities, built structures are demolished as populations dwindle, resulting in vacant land. Vacant land constitutes an opportunity to establish green infrastructure that provides multiple ecosystem services. Our objective was to determine whether establishing green infrastructure on vacant land to manage stormwater could provide insect habitat in the legacy city of Cleveland, Ohio, U.S.A. Two green infrastructure treatments were implemented on vacant land in the historic Slavic Village neighborhood in 2014 and 2015: rain gardens (lower cost) and bioswales (higher cost). We hypothesized that rain gardens and bioswales would support more abundant, species rich insect communities compared to unaltered vacant lots. Wild bees (<em>Hymenoptera: Aculeata</em>) and lady beetles (<em>Coleoptera: Coccinellidae</em>), two insect groups of conservation concern, were sampled during the summer (June–August) from 2014 to 2016 using pan traps and yellow sticky card traps. Local vegetation and temporal variables were measured. Generalized linear mixed effects models evaluated whether insect biodiversity varied with treatment, habitat variables, site, and time. We collected 3,004 bees from pan traps and 5,438 lady beetles from yellow sticky card traps during this study. Bee biodiversity was similar among treatments. In 2014, alien <em>Coccinellidae</em> abundance was higher in vacant lots compared to rain gardens. In 2015 and 2016, alien <em>Coccinellidae </em>were marginally more abundant in rain gardens compared to vacant lots and bioswales, while native <em>Coccinellidae</em> abundance was significantly higher in vacant lots. In the short term, establishing green infrastructure on vacant land can improve stormwater management without compromising the quality of vacant land as insect habitat.</p>
Data for: Land use change and coastal water darkening drive synchronous dynamics in phytoplankton and fish phenology on centennial time scales
<p>At high latitudes, the suitable window for timing reproductive events is particularly narrow, promoting tight synchrony between trophic levels. Climate change may disrupt this synchrony due to diverging responses to temperature between e.g. the early life stages of higher trophic levels and their food resources. Evidence for this is equivocal, and the role of compensatory mechanisms are poorly understood. Here, we show how a combination of ocean warming and coastal water darkening drive long-term changes in phytoplankton spring bloom timing in Lofoten Norway, and how spawning time of Northeast Arctic cod responds in synchrony. Spring bloom timing was derived from hydrographical observations dating back to 1936, while cod spawning time was estimated from weekly fisheries catch and roe landing data since 1877. Our results suggest that land use change causing coastal water darkening has gradually delayed the spring bloom up to 1990 after which ocean warming has caused it to advance. The cod appear to track phytoplankton dynamics by timing gonadal development and spawning to maximize overlap between offspring hatch date and predicted resource availability. This finding emphasises the importance of land-ocean coupling for coastal ecosystem functioning, and the potential for fish to adapt through phenotypic plasticity.</p>
LuccME/INLAND land-use scenarios for Brazil 2050
<p>Land use and land cover change models and scenarios are essential to understand the interconnections between global and regional factors influencing land use and demand changes, especially if we consider population growth and food demand projections in 2050.</p> <p>Understanding the future of changes in land use and land cover in Brazil is fundamental for the future of global climate and biodiversity, given the richness of its five biomes. Thus, the new spatially explicit regional scenarios were developed for Brazil by 2050. Those scenarios are aligned with the Shared Socio-Economic Pathways (SSPs) and Representative Concentration Pathway (RCPs). Aim to detail global models regionally and can be used both regionally to support decision-making and enrich the overall analysis.</p> <p>For the development of these new scenarios, the LuccME spatially explicit land change allocation modeling framework and the INLAND surface model were combined to incorporate climatic variables in water deficit and biophysical, socioeconomic, and institutional factors for Brazil. The scenarios were developed for land use and land cover classes: forest vegetation, grassland vegetation, planted pasture, agriculture, mosaic of occupations, and forestry.</p> <p>The dataset comes in NetCDF format and includes the following products:</p> <p> </p> <p><strong>LUCCMEBR_land_cover_type_100km2_2000.nc</strong>: Percentage of land use and land cover for the year 2000 (Observed data).</p> <p><strong>LUCCMEBR_land_cover_type_100km2_2010.nc</strong>: Percentage of land use and land cover for the year 2010 (Observed data).</p> <p><strong>LUCCMEBR_land_cover_type_100km2_2012.nc</strong>: Percentage of land use and land cover for the year 2012 (Observed data).</p> <p><strong>LUCCMEBR_land_cover_type_100km2_2014.nc</strong>: Percentage of land use and land cover for the year 2014 (Observed data).</p> <p><strong>LUCCMEBR_SSP1_RCP19_land_cover_type_100km2_2015_2050.nc</strong>: Percentage of land use and land cover for the period 2015-2050 (Simulated data). This scenario considers the combination of SSP1 and RCP1.9.</p> <p><strong>LUCCMEBR_SSP2_RCP45_land_cover_type_100km2_2015_2050.nc:</strong> Percentage of land use and land cover for the period 2015-2050 (Simulated data). This scenario considers the combination of SSP2 and RCP4.5.</p> <p><strong>LUCCMEBR_SSP3_RCP70_land_cover_type_100km2_2015_2050.nc</strong>: Percentage of land use and land cover for the period 2015-2050 (Simulated data). This scenario considers the combination of SSP3 and RCP7.0.</p> <p> </p> <p><strong>Data</strong></p> <p>Percentage of land use and land cover classes: Forest vegetation (veg), Grassland vegetation (gveg), Planted pasture (pastp), Agriculture (agric), Mosaic of occupation (mosc), Forestry (fores) and Others (others).</p> <p> </p> <p><strong>Spatial resolution</strong></p> <p>The scenarios are available in a spatial resolution of 0.083º x 0.083º (~100 km²) and cover the entire Brazilian territory.</p> <p> </p> <p><strong>Temporal resolution </strong></p> <p>Period of observed data: 2000, 2010, 2012 e 2014</p> <p>Scenario Period: 2015 – 2050 (each five-year)</p> <p> </p> <p><strong>Coordinate reference system</strong> </p> <p>Geographic Coordinate System with Datum WGS84 (EPSG4326)</p> <p> </p> <p><strong>Data format</strong></p> <p>Data is provided as NetCDF.</p> <p> </p> <p><strong>Dataset usage</strong> </p> <p>It is free to use, but please make sure to cite the repository and our paper properly if you use this dataset.</p> <p> </p> <p><strong>Publication & further information</strong></p> <p>For additional scenario information, please contact Francisco Gilney Silva Bezerra (franciscogilney@gmail.com).</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>The authors thank the project “MSA / BNDES (Environmental Monitoring by Satellite in the Amazon biome)” for financing the development of LuccMEBR.</p>
Land cover preferences and spatiotemporal associations of ungulates within a Scottish mammal community
<p>In the degraded and modified environment of the Scottish Highlands, novel ungulate communities have arisen following local extinctions, reintroductions, and the introduction of non-native species. An understanding of the dynamics and interactions within these unique mammal communities is important as many of these mammals represent keystone species with disproportionate impacts on the environment. Using a camera trap survey, we investigated land cover preferences and spatiotemporal interactions within a Scottish ungulate community: the sika deer (<em>Cervus nippon</em>), the roe deer (<em>Capreolus capreolus</em>), the red deer (<em>Cervus elaphus</em>), and the wild boar (<em>Sus scrofa</em>). We used generalised linear models to assess land cover preferences and the effect of human disturbance; spatiotemporal interactions were characterised using time interval modelling. We found sika deer and roe deer preferred coniferous plantations and grasslands, with sika deer additionally preferring woodland. For red deer, we found a slight preference for wetland over woodland; however, the explained variance was low. Finally, wild boar preferred grassland and woodland and avoided coniferous plantations, heathland, and shrubland. Contrary to our expectations, we found no evidence that human disturbance negatively impacted ungulates distributions, potentially because ungulates temporally avoid humans or because dense vegetation cover mitigates the impacts of humans on their distributions. Furthermore, we detected a spatiotemporal association between sika deer and roe deer. Although the underlying cause of this is unknown, we hypothesise that interactions such as grazing facilitation or an anti-predator response to culling could be driving this pattern. Our work provides a preliminary analysis of the dynamics occurring within a novel ungulate community, but also highlights current knowledge gaps in our understanding of the underlying mechanisms dictating the observed spatiotemporal associations.</p>
BSRLC+: An annual land cover dataset for the Baltic Sea Region with crop types and peat bogs at 30 m from 2000 to 2022
<p><strong>(NEW) </strong>Baltic Sea Region Land Cover <em>Urban</em> (BSRLC-U) focusing on urban built-up types now available: <a href="https://zenodo.org/records/17347941">https://zenodo.org/records/17347941 </a></p> <p><strong>Baltic Sea Region Land Cover <em>Plus </em>(BSRLC+) </strong>is annual land cover mapping (30 m) dataset in Europe from 2000 to 2022. The maps contain detailed information of 18 land cover (LC) types, including 9 crop types and 2 peat bog types.</p> <p>Input data : Optical multi-temporal remote sensing imageries (Landsat 5 (TM) / 7 (ETM+) / 8 (OLI) / 9 (OLI+) and Sentinel 2 (A / B ) from 2000 to 2022. Data is processed to surface reflectance and tiled into datacube structure using <a href="https://doi.org/10.3390/rs11091124">Framework for Operational Radiometric Correction for Environmental monitoring - FORCE.</a></p> <p>Mapping method: Maps are produced using data encoding and deep learning classification according to <a href="https://doi.org/10.1016/j.jag.2024.103867">Pham et al. 2024</a></p> <p>Validation: Maps have been rigorously validated using independent in-situ data <a href="https://doi.org/10.1038/s41597-020-00675-z">The Land Use/Cover Area frame Survey (LUCAS)</a>. </p> <p>Traing data and validation data are available: <a href="https://zenodo.org/records/11073291">https://zenodo.org/records/11073291</a></p> <p>This dataset contains:</p> <ul> <li><strong>00_preview.png</strong>: Preview map (2022) of the Baltic Sea region</li> <li><strong>BSRLC_{year}.tif</strong>: Annual map data (30 m) in GeoTIFF format (projection ETRS89 / EPSG:3035)</li> <li><strong>BSRLC_legend.xlss</strong>: Land cover codes and class names</li> <li><strong>BSRLC_qgis_style.qml</strong>: Map style to be used in QGIS</li> <li><strong>BSRLC_arcgis_style.lyrx</strong>: Map style to be used in ArcGIS</li> </ul> <p>Land cover codes (can also be found in <strong>BSRLC_legend.xlss</strong>):</p> <ul> <li>1: Built-up</li> <li>2: Bareland</li> <li>3: Water</li> <li>4: Shrubland</li> <li>5: Broadleaf forest</li> <li>6: Coniferous forest</li> <li>7: Wetland marsh</li> <li>8: Exploited peat bog</li> <li>9: Unexploited peat bog</li> <li>10: Wheat</li> <li>11: Barley</li> <li>12: Rye</li> <li>13: Oat</li> <li>14: Maize</li> <li>15: Seed crops</li> <li>16: Root crops</li> <li>17: Pulses, vegetable</li> <li>18: Grassland</li> <li>255: Nodata</li> </ul> <p> </p> <p><strong>Publication (please cite this publication if you are using the dataset):</strong></p> <ul> <li>Pham, V.-D., de Waard, F., Thiel, F., Bobertz, B., Hellmann, C., Nguyen, D.-V., Beer, F., Arasumani, M., Schwieder, M., Hartleib, J., Frantz, D., & van der Linden, S. (2024). An annual land cover dataset for the Baltic Sea Region with crop types and peat bogs at 30 m from 2000 to 2022. <em>Scientific Data, 11</em>, 1242, <a href="https://doi.org/10.1038/s41597-024-04062-w">https://doi.org/10.1038/s41597-024-04062-w</a></li> </ul> <p> </p> <p><strong>Other related publications:</strong></p> <ul> <li><em>Pham, V.-D., Tetteh, G., Thiel, F., Erasmi, S., Schwieder, M., Frantz, D., & van der Linden, S. (2024). Temporally transferable crop mapping with temporal encoding and deep learning augmentations. International Journal of Applied Earth Observation and Geoinformation, 129, 103867, <a href="https://doi.org/10.1016/j.jag.2024.103867">https://doi.org/10.1016/j.jag.2024.103867</a></em></li> <li><em>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, <a href="https://doi.org/10.3390/rs11091124">https://doi.org/10.3390/rs11091124</a></em></li> </ul> <p> </p> <p><strong>Funding</strong></p> <p>This datatset is created in the frame of the Interdisciplinary Research Center for the Baltic Sea Region Research (IFZO) of University of Greifswald, Germany, and the research project Fragmented Transformations, which is funded by the German Federal Ministry of Education and Research (FKZ 01UC2102). </p>
Comparison of Cropland Maps Derived from Land Cover Maps in Sub-Saharan Africa
<p>This data repository provides the datasets associated with analysis conducted in Kerner et al. (2024), citation below.</p> <p>The CSV file `intercomparison-results.csv` gives a table metrics for each of 11 land cover maps (plus a majority vote ensemble of all maps) evaluated using the reference dataset for each of 8 countries (Kenya, Rwanda, Uganda, Tanzania, Mali, Malawi, Togo, and Zambia). The reference datasets are provided as zip files. To ensure these datasets can be used for independent evaluation and comparison between maps in the future, the reference datasets should ONLY be used for final, independent evaluation of data products/model outputs; they should NOT be used for training models, tuning hyperparameters, or any other decisions during model/map development. </p> <p>The provided tif files contain the consensus maps (sum of all 11 maps) used to compute consensus statistics in Kerner et al. (2024).</p> <p>Kerner, H., Nakalembe, C., Yang, A., Zvonkov, I., McWeeny, R., Tseng, G., and Becker-Reshef, I. (2024). How accurate are existing land cover maps for agriculture in Sub-Saharan Africa? <em>Under review</em>. </p>
Unveiling the impacts of land use on the phylogeography of zoonotic New World Hantaviruses
<p>Billions of genomic sequences are stored in public repositories (NCBI) as well as records of species occurrence (GBIF). By implementing analytical tools from different scientific disciplines, data mining on these databases can be a source of information to aid in the global surveillance of zoonotic pathogens that circulate among wildlife. We illustrate this by investigating the hantavirus-rodent system in the Americas, i.e. New World Hantaviruses (NWH). First we draw the circulation of pathogenic NWH among rodents; by inferring the phylogenetic links among 278 genomic samples of the S segment (N protein) of NWH found in 55 species of Cricetidae rodents. Second, machine learning was used to assess the impact of land use on the probability of presence of the rodent species linked with reservoirs of pathogenic hantaviruses. Our results show that hosts are widely present across the Americas. Some hosts are present in the primary forest and agricultural land, but not in the secondary forest; whereas other hosts are present in secondary forest and agricultural land. The diversity of host species allows Hantavirus to circulate on a wide spectrum of habitats, in particular rural rather than urban. We highlight that Public repositories of genomic data and species occurrence are very useful resources for monitoring potential enzootic transmission and spillover of zoonotic viruses in relation with the changes that humans produce in the Biosphere.</p>
High resolution land cover 2017 Ile-de-France
<p><strong>High resoultion land cover for the Ile-de-France, 2017.</strong></p> <p>The rasters are mostly based on vector files, which were rasterised, merged and resampled to 5m. The encoded values represent the different thematic classes in the dataset. </p> <p>For ease of processing and computation, all datasets were tiled and are provided as a single continuous raster. This map shows the extents of the datasets “Hauteur vegetation” and “cadastre vert”, which were only available for a limited region within the Ile- de- France region. </p> <p></p> <p>The datasets used in the production of this map were MosPlus 2017-81, Cadastre Verte, Copernicus Small and Woody features, Copernicus Street tree layer, Hauteur vegetation and the densibati dataset. </p> <p> </p> <p>Class Codec </p> <table> <tbody> <tr> <td> <p><strong>Class</strong><strong> </strong></p> </td> <td> <p><strong>NumCodec</strong><strong> </strong></p> <p><strong>8bit compatible</strong><strong> </strong></p> </td> </tr> <tr> <td> <p>Building<strong> </strong></p> </td> <td> <p>10 </p> </td> </tr> <tr> <td> <p>Built parcel<strong> </strong></p> </td> <td> <p>19 </p> </td> </tr> <tr> <td> <p>Mineral surface<strong> </strong></p> </td> <td> <p>21 </p> </td> </tr> <tr> <td> <p>Bare soil<strong> </strong></p> </td> <td> <p>22 </p> </td> </tr> <tr> <td> <p>Grass<strong> </strong></p> </td> <td> <p>31 </p> </td> </tr> <tr> <td> <p>Shrub<strong> </strong></p> </td> <td> <p>40 </p> </td> </tr> <tr> <td> <p>Shrub round<strong> </strong></p> </td> <td> <p>41 </p> </td> </tr> <tr> <td> <p>Shrub linear<strong> </strong></p> </td> <td> <p>42 </p> </td> </tr> <tr> <td> <p>Tree<strong> </strong></p> </td> <td> <p>50 </p> </td> </tr> <tr> <td> <p>Water<strong> </strong></p> </td> <td> <p>60 </p> </td> </tr> <tr> <td> <p>Lake<strong> </strong></p> </td> <td> <p>61 </p> </td> </tr> <tr> <td> <p>River<strong> </strong></p> </td> <td> <p>62 </p> </td> </tr> <tr> <td> <p>Agriculture<strong> </strong></p> </td> <td> <p>80/81</p> </td> </tr> <tr> <td> <p>NonAOI/ unclassified<strong> </strong></p> </td> <td> <p>99 /0 </p> </td> </tr> </tbody> </table> <p>Data sources </p> <p>European Union's Copernicus Land Monitoring Service information (2018), Small Woody Features 2018,<a href="https://doi.org/10.2909/7fd9d32e-8c2f-42b2-b959-c8e12b843821" target="_blank" rel="noopener">https://doi.org/10.2909/7fd9d32e-8c2f-42b2-b959-c8e12b843821</a> </p> <p>European Union's Copernicus Land Monitoring Service information (2018), Urban Atlas Street Tree Layer 2018, <a href="https://doi.org/10.2909/205691b3-7ae9-41dd-abf1-1fbf60d72c8c" target="_blank" rel="noopener">https://doi.org/10.2909/205691b3-7ae9-41dd-abf1-1fbf60d72c8c</a> </p> <p>Atelier Parisien d'Urbanisme 2017 HAUTEUR VEGETATION 2015 </p> <p><a href="https://opendata.apur.org/datasets/Apur::hauteur-vegetation-2015/about" target="_blank" rel="noopener">https://opendata.apur.org/datasets/Apur::hauteur-vegetation-2015/about</a> </p> <p>Département des Hauts-de-Seine (2012) Cadastre vert - Masses vertes. https://opendata.hauts-de-seine.fr/explore/dataset/cadastre-vert-masses-vertes/information/?disjunctive.commune&basemap=mapbox.streets-satellite&location=11,48.83991,2.24091 </p> <p>L'Institut Paris Region (2017) Mode d'occupation du sol (MOS) 2017 a 81 postes. <a href="https://www.institutparisregion.fr/vente-de-donnees/" target="_blank" rel="noopener">https://www.institutparisregion.fr/vente-de-donnees/</a> </p> <p>L'Institut Paris Region (2018) Densibati 2018. <a href="https://www.institutparisregion.fr/vente-de-donnees/" target="_blank" rel="noopener">https://www.institutparisregion.fr/vente-de-donnees/</a> </p>
Species richness: a pivotal factor mediating the effects of land use intensification and climate on grassland multifunctionality
<p>Temperate semi-natural grasslands harbour unique biodiversity, support livestock farming through forage production, and deliver many essential ecosystem services (ESs) to human society; they are highly multifunctional. However, temperate grassland ecosystems are also among the most threatened ecosystems on earth due to land use and climate change. Understanding how biodiversity, climate, and land use intensification impact grassland multifunctionality through complex direct and indirect pathways is critical to better anticipate the future of these fragile ecosystems. </p> <p>Here, we evaluate how local plant species richness (SR) modulates the effect of land use intensification and climate on grassland multifunctionality (using six key ESs: biomass productivity and stability, forage quality, carbon storage, pollination, and local plant rarity) in the French Massif Central, the largest grassland in Western-Europe. We sampled 100 grassland fields with contrasted fertilisation rates, and SR over large elevational and latitudinal gradients related to variation in mean annual temperature (MAT), and drought severity (DS), two key climate change drivers that are predicted to increase in the future.</p> <p>Using a confirmatory path analysis, we found that SR was the main driver of multifunctionality. We also found significant SR × MAT and SR × fertilization interactions suggesting that warm climate and high fertilization rates may alter the biodiversity-ecosystem multifunctionality relationships. Furthermore, increasing temperature and fertilization indirectly influenced multifunctionality by decreasing SR and consequent multifunctionality in warm low-land and highly fertilized grasslands compared to colder montane grasslands or less fertilised ones. DS only impacted some ES individually (e.g. forage quality).</p> <p>Synthesis and applications: we identified SR as a pivotal factor mediating the effects of land use intensification and climate on multifunctionality through both direct and indirect pathways. Failing to account for changes in SR could thus bias any prediction of – or aggravate – the effects of land use intensification and climate change on ESs delivery in temperate grassland ecosystems. Considering that SR, MAT, and fertilization are major proxies of three main global change drivers (biodiversity loss, climate change, and land use intensification) our study may help to better anticipate the effect of multiple interacting global change drivers on grassland ecosystems.</p>
Land use and land cover scenarios for the Maurienne valley (French Alps) at 2085 horizon produced using CLUMPY model
<p>We built three contrasted future LULC scenarios from 2020 to 2085 with the CLUMPY model (Mazy and Longaretti, 2022). The CLUMPY model is an innovative model of land use and land cover change comprising a calibration-estimation module separate from a non-biased allocation module. It is calibrated by using time series of past LULC maps (Mazy and Longaretti, 2022). The model then calculates transition probabilities for each LULC class according to relevant spatial explanatory variables. Next, the model can produce maps of future LULC distributions according to information it learned during the calibration-estimation phase. This model has the benefits of being easy to use, proposing nonbiased allocation methods and producing scenarios of future LULC change either by adjusting manually the matrix of LULC transitions probabilities (used for the Conservation and Tourism scenarios) or by training the model on specific areas of the past time series (only used for the Conservation scenario).</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.