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1,868 results for “Spatial Data”
Raw data: Local habitat factors and spatial connectivity jointly shape an urban insect community
<p class="MsoNoSpacing">As the world becomes more and more urbanized, it is increasingly important to understand the impacts of urban landscapes on biodiversity. Urbanization can change local habitat factors and decrease connectivity among local habitats, with major impacts on the structure of natural food webs. However, most studies have focused on single species, or compared rural to urban habitats, which do not inform us on how to design and manage cities to optimize biodiversity. To understand the local and spatial drivers of ecological communities within urban landscapes, we assessed the relative impact of local habitat factors (sunlight exposure and leaf litter) and spatial connectivity on an oak-associated herbivore community within an urban landscape. From the local habitat factors, leaf litter but not sunlight exposure was related to herbivore species richness, with leaf litter contributing to the maintenance of high species richness on isolated trees. Guilds and species differed strongly in their response to local habitat factors and connectivity, resulting in predictable variation in insect community composition among urban oaks. <span>Taken together, our study shows an interactive effect of local and spatial factors on species richness and species composition within an urban context, with guild- and species-specific life histories determining the response of insects to urban landscapes. </span>To maintain biodiversity in the urban landscape, preserving a dense network of local habitats is essential. Moreover, allowing leaf litter to accumulate can be a simple, cost-effective conservation management practice.</p>
Data from: A general-purpose spatial survey design for collaborative science and monitoring of global environmental change: the global grid
Recent guidance on environmental modeling and global land-cover validation stresses the need for a probability-based design. Additionally, spatial balance has also been recommended as it ensures more efficient sampling, which is particularly relevant for understanding land use change. In this paper I describe a global sample design and database called the Global Grid (GG) that has both of these statistical characteristics, as well as being flexible, multi-scale, and globally comprehensive. The GG is intended to facilitate collaborative science and monitoring of land changes among local, regional, and national groups of scientists and citizens, and it is provided in a variety of open source formats to promote collaborative and citizen science. Since the GG sample grid is provided at multiple scales and is globally comprehensive, it provides a universal, readily-available sample. It also supports uneven probability sample designs through filtering sample locations by user-defined strata. The GG is not appropriate for use at locations above ±85° because the shape and topological distortion of quadrants becomes extreme near the poles. Additionally, the file sizes of the GG datasets are very large at fine scale (resolution ~600 m × 600 m) and require a 64-bit integer representation.
Data from: Oscillayers: a dataset for the study of climatic oscillations over Plio-Pleistocene time scales at high spatial-temporal resolution
Motivation: In order to understand how species evolutionarily responded to Plio-Pleistocene climate oscillations (e.g. in terms of speciation, extinction, migration and adaptation), it is first important to have a good understanding of those past climate changes per se. This, however, is currently limited due to the lack of global-scale climatic datasets with high temporal resolution spanning the Plio-Pleistocene. To fill this gap, I here present Oscillayers, a global-scale and region-specific bioclim dataset, facilitating the study of climatic oscillations during the last 5.4 million years at high spatial (2.5 arc-minutes) and temporal (10 kyr time periods) resolution. This data set builds upon interpolated anomalies (Δ layers) between bioclim layers of the present and the Last Glacial Maximum (LGM) that are scaled relative to the Plio-Pleistocene global mean temperature curve, derived from benthic stable oxygen isotope ratios, to generate bioclim variables for 539 time periods. Evaluation of the scaled, interpolated estimates of palaeo-climates generated for the Holocene, Last Interglacial and Pliocene showed good agreement with independent General Circulation Models (GCMs) for respective time periods in terms of pattern correlation and absolute differences. Oscillayers thus provides a new tool for studying spatial-temporal patterns of evolutionary and ecological processes at high temporal and spatial resolution. Main types of variable contained: 19 bioclim variables for time periods throughout the Plio-Pleistocene. Input data and R script to recreate all 19 bioclim variables. Spatial location and grain: Global at 2.5 arc-minutes (4.65 x 4.65 = 21.62 km2 at the equator). Time period and grain: The last 5.4 million years. The grain is 10 kyr (= 539 time periods). Level of measurement: Data are for terrestrial climates (excluding Antarctica) taking sea level changes into account. Software format: All data are available as ASCII (ESRI) grid files.
Data for spatial and temporal refugia for an insect population declining due to climate change
<p>Insect declines have been reported worldwide, although the particular causes of the declines may be complex and are poorly understood. Meadow spittlebugs were one of the most abundant insects in the coastal prairie along the California coast 40 years ago but have largely disappeared. Evidence links this decline to changing climatic conditions, which have reduced survival of eggs and neonates. We identified several refugia where meadow spittlebug populations have persisted amidst unfavorable conditions. Protection from desiccating winds was the common attribute of these refugia. Following a wet year, adult meadow spittlebugs were able to disperse from one refuge that we studied to recolonize coastal prairie habitats, although populations declined over the next two drier years. Because of their previous high abundance, loss of meadow spittlebugs is likely to affect the functioning of this widespread habitat, including energy transfer, their host plants, and their predators. In addition, meadow spittlebugs are unusual in having been the subject of extensive physiological and long-term ecological data, so they can serve as a bellwether species, indicating the effects of climate change.</p>
LiDAR Data Enrichment By Fusing Spatial and Temporal Adjacent Frames
<p>This is an accompanying video for the manuscript 'LiDAR Data Enrichment By Fusing Spatial and Temporal Adjacent Frames'. This video shows the comparison between the original LiDAR point cloud and the enriched LiDAR point cloud.</p>
Data from: Spatial covariance of herbivorous and predatory guilds of forest canopy arthropods along a latitudinal gradient
<p>In arthropod community ecology, species richness studies tend to be prioritized over those investigating patterns of abundance. Consequently, the biotic and abiotic drivers of arboreal arthropod abundance are still relatively poorly known. In this cross-continental study, we employ a theoretical framework in order to examine patterns of covariance among herbivorous and predatory arthropod guilds. Leaf-chewing and leaf-mining herbivores, and predatory ants and spiders, were censused on > 1,000 trees in nine 0.1 ha forest plots. After controlling for tree size and season, we found no negative pairwise correlations between guild abundances per plot, suggestive of weak signals of both inter-guild competition and top-down regulation of herbivores by predators. Inter-guild interaction strengths did not vary with mean annual temperature, thus opposing the hypothesis that biotic interactions intensify towards the equator. We find evidence for the bottom-up limitation of arthropod abundances via resources and abiotic factors, rather than for competition and predation.</p>
Raw data repository for the article: "High spatial coherence and short pulse duration revealed by the Hanbury Brown and Twiss interferometry at the European XFEL"
<p>Raw data depository for the article in the Structural Dynamics journal: DOI: 10.1063/4.0000127. Details with the file information are given in the file "HBT_XFEL_Data_set_Info_final.pdf"</p>
Data from: Gardeners of the forest: hornbills govern the spatial distribution of large seeds
<p>Seed dispersal by frugivores is vital to the maintenance of tree diversity in tropical forests. However, determining the influence of different frugivores over the distribution of their food plants is difficult, given the complexity of these interactions in the tropics. Consequently, most studies have been restricted to small scales, examining seed dispersal and establishment associated with nests, roosts or fruiting trees. Here, we evaluate the role of frugivorous hornbills in dispersing seeds at spatial scales of 1 ha. We monitored hornbills and seed rain at a tropical forest site in north-east India. We quantified the abundance of hornbill food plants and recruits of large-seeded plants. We estimated removal rates of dispersed, large seeds to determine post-dispersal seed fate. We found that the distribution of large-seeded canopy food plants influenced the distribution of the relatively abundant <i>Rhyticeros undulatus</i>. The overall distribution of hornbills resulted in spatially-contagious seed rain patterns for the large-seeded plant species. Patches with canopy food plants had a higher recruit diversity. Our results show a positive feedback between distribution of rare but important hornbill food plants, hornbills and distribution of seeds and saplings of large-seeded plants in the landscape. Widespread loss of hornbills due to hunting and habitat loss in the region, have likely disrupted these feedback mechanisms that are critical for tree species regeneration.</p>
Data and analysis for "A process-conditioned and spatially consistent method for reducing systematic biases in modeled streamflow"
<p>This contains all of the necessary data and code to reproduce the results of the manuscript submitted to the Journal of</p> <p>Hydrometeorology entitled "A process-conditioned and spatially consistent method for reducing systematic biases in modeled streamflow"</p>
Data from: Spatial variation in bioclimatic relationships for a snow-adapted species along a discontinuous southern range boundary
<p>Documenting variation in the relationship between climate variables and species occurence at range boundaries can help reveal how species will respond to global climate change. We collected snowshoe hare (Lepus americanus) presence-absence data from snow-track surveys conducted in the U.S. states of Michigan and Wisconsin in winters from 2012-2014 at their southern range boundary in the region. A series of 125m transects were walked at each site within a week of fresh snowfall, and the presence or absence of snowshoe hare tracks on each transect was recorded. Data set also includes the variables mean 5-year snow cover duration, mean 5-year maximum temperature, and percentage forest cover at each site. Each variable was used in data analysis for the related paper to relate snowshoe hare occurrence to climate and land cover variables at the southern edge of their distribution. Snowshoe hares in the region occur in areas with longer snow cover duration and lower maximum temperatures but these relationships vary across the study area such that maximum temperature was positively correlated with snowshoe hare occurrence in the northern portion of the study area (Upper Peninsula of Michigan).</p>
Data for: Time at risk: Individual spatial behaviour drives effectiveness of marine protected areas and fitness
<p>The effectiveness of Marine Protected Areas (MPAs) depends on the mobility of the populations that are the target of protection, with sedentary species likely to spend more time under protection even within small MPAs. However, little is understood about how individual variation in mobility may influence the risk of crossing an MPA border, as well as the fitness costs associated with being exposed to spillover fisheries. Here we investigated the repeatability of spatial behaviour, its role in determining the probability of being at risk (i.e. exposed to the fishery) and the fitness consequences for the individuals. We acoustically tracked the movements and fate of 282 individuals of three fish species during 8 years in a southern Norwegian fjord. We found that for individuals with a home range centroid inside the MPA, the probability of being at risk outside the MPA increased rapidly with reduced distance from the home range centroid to MPA borders, particularly for individuals having larger and more dispersed home ranges. We also detected that the seasonal expansions of the home range are associated with increased time at risk. Last, we show that individuals spending more time at risk were also more likely to be harvested by the fishery operating outside the MPA. Our study provides clear links between individual fish behaviour, fisheries-induced selection, and the effectiveness of protected areas. These links highlight the importance of intraspecific trait variation for understanding the spatial dynamics of populations and emphasize the need to consider individual behaviour when designing and implementing MPAs.</p>
Data from: Fine-scale spatial associations between functional traits and tree growth
<p>This data set relates to a 40x60 m2 stem-mapped plot that was established in a temperate rainforest of southern Chile. It contains data for each individual stem located within the plot. Each individual is characterized by a species code, diameter at breast height (dbh, 1.35 m), diameter at coring height (dch, ca. 30 cm), x and y coordinates, basal area index of the last 10 years (bai, cm2), and growth efficiency (ge, cm2/cm2).</p>
Global spatially explicit crop water consumption shows an overall increase of 9% for 46 agricultural crops from 2010 to 2020: Data and software
<p>This dataset comprises spatial and temporal data related to our analysis on blue and green water consumption (WC) of global crop production in high spatial resolution (5 arc-minutes – approximately 10 km at the equator) for the years 2020, 2010 and 2000.</p> <p><strong>Modelling water consumption of SPAM data<br></strong></p> <p>We use SPAM (Spatial Production Allocation Model) data, released by the International Food Policy research Institute (IFPRI). We use SPAM2020 data for the year 2020 (46 crops), SPAM2010 data for the year 2010 (42 crops) and SPAM2000 data for the year 2000 (20 crops).</p> <p>We develop a Python-based global gridded crop green and blue WC assessment tool, entitled <em>CropGBWater. </em>Operating on a daily time scale, CropGBWater dynamically simulates rootzone water balance and related fluxes. We provide this model open access as <a href="https://zenodo.org/api/records/17059989/draft/files/Data_S10_CropGBWater_v02_1c-clean.ipynb/content">Data_S10</a> </p> <p>SPAM2020 crop data are modelled for the years 2018-2022, SPAM2010 crop data for the years 2008-2012 and SPAM2000 crop data for the years 1998-2002. We compute WCbl (blue WC) and WCgn (green WC), with components WCgn,irr (green WC of irrigated area) and WCgn,rf (green WC of rainfed area)<br><br><strong>File description:</strong></p> <p>The data-set consists of the following files:</p> <ul> <li>Data_S4: <a href="https://zenodo.org/records/15779747/files/Data_S4_Y2020_WC_m3_gridded.zip" target="_blank" rel="noopener">Data_S4_Y2020_WC_m3_gridded.zip</a><br>Folder with 46 individual crop grid files (5arc min resolution, with x & y coordinates), monthly and annual WCbl, WCgn,irr and WCgn,rf values in m3 in csv format, year 2020. Individual crop GIS-Rasters for annual m3 amounts are provided as Data_S17</li> <li>Data_S5: <a href="https://zenodo.org/records/15779747/files/Data_S5_Y2020_WC_mm_gridded.zip" target="_blank" rel="noopener">Data_S5_YR2020_WC_mm_gridded_csv</a><br>Folder with 46 individual crop grid files (5arc min resolution, with x & y coordinates), monthly and annual WCbl, WCgn,irr and WCgn, rf in mm as well as SPAM harvested area values in csv format, year 2020. Individual crop GIS-Rasters for annual mm amounts are provided as Data_S18</li> <li>Data_S6: <a href="https://zenodo.org/records/15779747/files/Data_S6_YR2020_WC_gridded_individual-crops-m3_annual.xlsx" target="_blank" rel="noopener">Data_S6_YR2020_WC_gridded_individual-crops-m3_annual.xlsx</a><br>One grid file (5arc min resolution, with x & y coordinates) with annual WCbl, WCgn,irr and WCgn, rf values in m3, differentiating between individual crops, year 2020. </li> <li>Data_S7: <a href="https://zenodo.org/records/15779747/files/Data_S7_YR2020_WC_gridded_sum-of-crops-m3_monthly-annual.csv" target="_blank" rel="noopener">Data_S7_YR2020_WC_gridded_sum-of-crops-m3_monthly-annual.csv</a><br>One grid file (5arc min resolution, with x & y coordinates) with monthly and annual WCbl, WCgn,irr and WCgn, rf values in m3, for the sum of all crops, year 2020</li> <li>Data_S8: <a href="https://zenodo.org/records/15779747/files/Data_S8_YR2000_WC_mm_m3_gridded.zip" target="_blank" rel="noopener">Data_S8_YR2000_WC_mm_m3_gridded.zip</a><br>Grid (5arc min resolution, with x & y coordinates) with annual WCbl, WCgn,irr and WCgn, rf values in mm and m3, as well as SPAM harvested area amounts, for each crop, year 2000</li> <li>Data_S9: <a href="https://zenodo.org/records/17059989/files/Data_S9_YR2010_WC_mm_m3_gridded.zip" target="_blank" rel="noopener">Data_S9_YR2010_WC_mm_m3_gridded.zip</a><br>Grid (5arc min resolution, with x & y coordinates) with annual WCbl, WCgn,irr and WCgn, rf values in mm and m3, as well as SPAM harvested area amounts, for each crop, year 2010</li> <li>Data_S10: <a href="https://zenodo.org/records/17059989/files/Data_S10_CropGBWater_v02_1c-clean.ipynb">Data_S10_CropGBWater_v02_1c-clean.ipynb</a> Python-based global gridded crop green and blue WC assessment tool, entitled <em>CropGBWater</em></li> </ul> <ul> <li>Data_S11: <a href="https://zenodo.org/records/15779747/files/Data_S11_YR2020_WC-mm_Rice_RiceAtlas.xlsx" target="_blank" rel="noopener">Data_S11_YR2020_WC-mm_Rice_RiceAtlas.xls</a><br>Grid (5arc min resolution, with x & y coordinates) with monthly and annual WCbl, WCgn,irr and WCgn, rf values in mm, for rice, year 2020, RiceAtlas calendar</li> <li>Data_S12: <a href="https://zenodo.org/records/15779747/files/Data_S12_INPUT_YR2020_cropcalendars.zip" target="_blank" rel="noopener">Data_S12_INPUT_YR2020_cropcalendars.zip</a><br>Modelling INPUT data for year 2020: Crop calendars</li> <li>Data_S13: <a href="https://zenodo.org/records/15779747/files/Data_S13_INPUT_YR2020_ET0.zip" target="_blank" rel="noopener">Data_S13_INPUT_YR2020_ET0.zip</a><br>Modelling INPUT data for year 2020: daily ET0</li> <li>Data_S14: <a href="https://zenodo.org/records/15779747/files/Data_S14_INPUT_YR2020_Precip.zip" target="_blank" rel="noopener">Data_S14_INPUT_YR2020_Precip.zip</a><br>Modelling INPUT data for year 2020: daily Precipitation</li> <li>Data_S15: <a href="https://zenodo.org/records/15779747/files/Soil.zip" target="_blank" rel="noopener">Data_S15_INPUT_YR2020_Soil.zip</a><br>Modelling INPUT data for year 2020: Soil</li> <li>Data_S16: <a href="https://zenodo.org/records/15779747/files/Data_S16_INPUT_YR2020_Climate-SPAM-grid_&_Scripts.zip" target="_blank" rel="noopener">Data_S16_INPUT_YR2020_Climate-SPAM-grid_&_Scripts.zip</a><br>Modelling INPUT for year 2020: Climate-SPAM-grid and different processing scripts</li> <li>Data_S17: <a href="https://zenodo.org/records/15779747/files/Data_S17_Y2020_WC_m3_GisRasters.zip" target="_blank" rel="noopener">Data_S17_Y2020_WC_m3_GisRasters</a><br>GIS-Rasters of individual crops for year 2020 (as well as the sum of all crops), values in m3 per year, differentiation between WCbl, WCgn,irr and WCgn</li> <li>Data_S18: <a href="https://zenodo.org/records/15779747/files/Data_S18_Y2020_WC_mm_GisRasters.zip" target="_blank" rel="noopener">Data_S18_Y2020_WC_mm_GisRasters</a><br>GIS-Rasters of individual crops for year 2020, values in mm per year, differentiation between WCbl, WCgn,irr and WCgn</li> </ul> <p><em>Please only use the latest version of this zenodo repository</em></p> <p><strong>Publication:</strong></p> <p>For all details, please refer to the open access paper:</p> <p>Chukalla, A.D., Mekonnen, M.M., Gunathilake, D., Wolkeba, F.T., Gunasekara, B., Vanham, D. (2025) Global spatially explicit crop water consumption shows an overall increase of 9% for 46 agricultural crops from 2010 to 2020, Nature Food, Volume 6, <a href="https://doi.org/10.1038/s43016-025-01231-x" target="_blank" rel="noopener">https://doi.org/10.1038/s43016-025-01231-x</a></p> <p><strong>Funding:</strong></p> <p>This research, led by IWMI, a CGIAR centre, was carried out under the CGIAR Initiative on Foresight (<a href="www.cgiar.org/initiative/foresight/" target="_blank" rel="noopener">www.cgiar.org/initiative/foresight/</a>) as well as the CGIAR “Policy innovations” Science Program (<a href="www.cgiar.org/cgiar-research-porfolio-2025-2030/policy-innovations" target="_blank" rel="noopener">www.cgiar.org/cgiar-research-porfolio-2025-2030/policy-innovations</a>). The authors would like to thank all funders who supported this research through their contributions to the CGIAR Trust Fund (<a href="www.cgiar.org/funders" target="_blank" rel="noopener">www.cgiar.org/funders</a>).</p> <p><strong> </strong></p>
FIGURE 41 in Taxonomy of spatially disjunct alpine Teleiopsis albifemorella s. lat. (Lepidoptera: Gelechiidae) revealed by molecular data and morphology — how many species are there?
FIGURE 41. Maximum Likelihood tree of the Teleiopsis based on concatenated data of COI, CAD, EF-1a, IDH, MDH and wingless genes. The tree was rooted on Carpatolechia notatella (not depicted because of very long branch leading to it). Bootstrap support values for T. albifemorella and T. paulheberti are shown below the nodes.
FIGURES 29–32 in Taxonomy of spatially disjunct alpine Teleiopsis albifemorella s. lat. (Lepidoptera: Gelechiidae) revealed by molecular data and morphology — how many species are there?
FIGURES 29–32. Female genitalia of Teleiopsis paulheberti sp. nov., segment VIII and antrum. 29, paratype, Italy (Cuneo), slide GEL 1162; 30, paratype, France (Alpes-Maritimes), slide GEL 1161; 31, paratype, France (Hautes-Alpes), slide GEL 1171; 32, as 31, diagnostic details of antrum.
FIGURE 40 in Taxonomy of spatially disjunct alpine Teleiopsis albifemorella s. lat. (Lepidoptera: Gelechiidae) revealed by molecular data and morphology — how many species are there?
FIGURE 40. Neighbor-joining trees of the Teleiopsis (implemented under Kimura 2 Parameter model) of nuclear genes CAD, EF-1a, IDH, MDH and wingless.
FIGURE 39 in Taxonomy of spatially disjunct alpine Teleiopsis albifemorella s. lat. (Lepidoptera: Gelechiidae) revealed by molecular data and morphology — how many species are there?
FIGURE 39. Neighbor-joining tree of the Teleiopsis (implemented under Kimura 2 Parameter model) based on sequences of the mtDNA COI gene 5' fragment (DNA barcode, 658 bp). Bootstrap support values, based on 500 pseudoreplicates, are shown for internal nodes. The tree was rooted on T. terebinthinella, the presumed sister taxon of other species. The scale bar indicates 0.5% change in sequence composition.
FIGURES 33–38 in Taxonomy of spatially disjunct alpine Teleiopsis albifemorella s. lat. (Lepidoptera: Gelechiidae) revealed by molecular data and morphology — how many species are there?
FIGURES 33–38. Female genitalia of Teleiopsis albifemorella, details of signum. 33–35, T. albifemorella: 33, Austria (North Tyrol), slide GEL 1165; 34, Austria (Upper Austria), slide GEL 1168; 35, Slovenia, slide GEL 1164. 36–38, T. paulheberti sp. nov.: 36, paratype, Italy (Cuneo), slide GEL 1162; 37, paratype, France (Alpes-Maritimes), slide GEL 1161; 38, paratype, France (Hautes-Alpes), slide GEL 1171.
FIGURES 7–10 in Taxonomy of spatially disjunct alpine Teleiopsis albifemorella s. lat. (Lepidoptera: Gelechiidae) revealed by molecular data and morphology — how many species are there?
FIGURES 7–10. Adults of Teleiopsis paulheberti sp. nov., males. 7, paratype, Italy (Cuneo); 8, paratype, Italy (L´Aquila); 9, paratype, France (Alpes-Maritimes); 10, paratype France (Hautes-Alpes).
FIGURES 25–28 in Taxonomy of spatially disjunct alpine Teleiopsis albifemorella s. lat. (Lepidoptera: Gelechiidae) revealed by molecular data and morphology — how many species are there?
FIGURES 25–28. Female genitalia of Teleiopsis albifemorella, segment VIII and antrum. 25, Austria (North Tyrol), slide GEL 1165; 26, Austria (Upper Austria), slide GEL 1168; 27, Slovenia, slide GEL 1164; 28, as 25, diagnostic details of antrum.
ScienceDex guides
Understand access before you commit
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.