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1,868 results for “Spatial Data”
spectre: An R package to estimate spatially-explicit community composition using sparse data
<p>An understanding of how biodiversity is distributed across space is key to much of ecology and conservation. Many predictive modelling approaches have been developed to estimate the distribution of biodiversity over various spatial scales. Community modelling techniques may offer many benefits over single-species modelling. However, techniques capable of estimating precise species makeups of communities are highly data intensive and thus often limited in their applicability. Here we present an R package, spectre, which can predict regional community composition at a fine spatial resolution using only sparsely sampled biological data. The package can predict the presence and absence of all species in an area, both known and unknown, at the sample site scale. Underlying the spectre package is a min-conflicts optimisation algorithm that predicts species' presences and absences throughout an area using estimates of α-, β-, and γ-diversity. We demonstrate the utility of the spectre package using a spatially-explicit simulated ecosystem to assess the accuracy of the package's results. spectre offers a simple-to-use tool with which to accurately predict community compositions across varying scales, facilitating further research and knowledge acquisition into this fundamental aspect of ecology.</p>
Spatial behavior and diet data for discrete-choice analyses: data observed and classified from GPS video camera collars worn by female members of the Fortymile Caribou Herd across Alaska, USA, and Yukon, Canada
<p>Competition for resources and space can drive forage selection of large herbivores from the bite through the landscape scale. Animal behavior and foraging patterns are also influenced by abiotic and biotic factors. Fine-scale mechanisms of density-dependent foraging at the bite scale are likely consistent with density-dependent behavioral patterns observed at broader scales, but few studies have directly tested this assertion. Here, we tested if space use intensity, a proxy of spatiotemporal density, affects foraging mechanisms at fine spatial scales similarly to density-dependent effects observed at broader scales in caribou. We specifically assessed how behavioral choices are affected by space use intensity and environmental processes using behavioral state and forage selection data from caribou (<i>Rangifer tarandus granti</i>) observed from GPS video-camera collars using a multivariate discrete-choice modeling framework. We found that the probability of eating shrubs increased with increasing caribou space use intensity and cover of <i>Salix</i> spp. shrubs, whereas the probability of eating lichen decreased. Insects also affected fine-scale foraging behavior by reducing the overall probability of eating. Strong eastward winds mitigated the negative effects of insects and resulted in higher probabilities of eating lichen. Lastly, caribou exhibited foraging functional responses wherein their probability of selecting each food type increased as the availability (% cover) of that food increased. Space use intensity signals of fine-scale foraging were consistent with density-dependent responses observed at larger scales and with recent evidence suggesting declining reproductive rates in the same caribou population. Our results highlight the potential risks of overgrazing on sensitive forage species such as lichen. Remote investigation of the functional responses of foraging behaviors provides exciting future applications where spatial models can identify high-quality habitats for conservation.</p>
MEDIS: spatial data for Mediterranean islands
<p>The intrinsic characteristics of islands make them unique for studying ecological and evolutionary dynamics. The Mediterranean Basin, a biodiversity hotspot, is rich in islands, hosting a significant global biodiversity proportion. Despite extensive research, a comprehensive spatial dataset for these islands is lacking. This study presents the first comprehensive spatial dataset of all Mediterranean islands larger than 0.01 km2, aiding ecological investigations and interdisciplinary research on economic, environmental, and social issues. The MEDIS spatial dataset offers detailed information on 36 geographic, climatic, ecological, and land-use variables, including island area, perimeter, isolation metrics, climatic space, terrain data, land cover, paleogeography, road networks, and geological information, providing a multifaceted view of each island's characteristics. The study encompasses 2212 islands in the Mediterranean Basin larger than 0.01 km2. The spatial grain varies, with datasets like CHELSA-BIOCLIM+ and EU-DEM providing high-resolution climatic and terrain data. The spatial dataset incorporates various datasets, each with its own timeframes, such as the Global Shoreline Vector from 2014 Landsat imagery and the WorldCover dataset from 2021. Historical data like the Paleocoastlines GIS dataset offer insights into island configurations during the Last Glacial Maximum. While not focusing on specific taxa, the study lays the foundation for comprehensive research on Mediterranean islands, facilitating comparisons and investigations into the distribution of native, endemic, or alien species. The level of measurement is extensive, encompassing a wide range of variables and providing polygonal features rather than centroids' coordinates.</p>
Data from: Higher spatial than seasonal beta diversity of soil protists along elevation gradients
<p>This data package contain the data and R script to reproduce the analyses of the paper from Bruni <em>et al.</em> (in press).</p> <p>It contains:</p> <ul> <li><strong>protist_spatiotemporal_turnover_site_parameters.xlsx</strong>: the list of sites used in this study with their (label, location, geography coordinates, habitat, ENA project and sample accessions) and soil abiotic parameters. Abbreviations and units are as follows: Res_hum, residual humidity [%]; Org_mat, soil organic matter [%]; C_org, organic carbon [mg ∙ g-1]; N_org, organic nitrogen [mg ∙ g-1]; P_bio, bioavailable phosphate [mg ∙ g-1]; C_N_ratio, carbon org. / nitrogen org. ratio; N_P_ratio: nitrogen org. / phosphorus bioavailable ratio.</li> <li><strong>protist_spatiotemporal_turnover_data.RData</strong>: dataset in rda format (R core team, 2024) containing the ASV read's abundance per site matrix (object "mat"), the ASV taxonomic assignments (object "taxo"), the ASV sequences (object "asv") and the CRU-TS monthly climatic data corresponding to the sampled site's location and dates (object "cruts").</li> <li><strong>protist_spatiotemporal_turnover_analyses.R</strong>: R script to reproduce all analyses and figures of Bruni <em>et al.</em> (in press)</li> </ul> <p> </p> <p>References:</p> <p>Bruni, E. P., Lorite, J., Peñas, J., Mulot, M., Fournier, B., Vittoz, P., Mitchell, E. A. D., & Lentendu, G. (2024). Higher spatial than seasonal beta diversity of soil protists along elevation gradients. Frontiers of Biogeography, 17, 1–17. DOI:<a href="https://doi.org/10.21425/fob.17.132637">10.21425/fob.17.132637</a></p> <div> <div>R Core Team. (2024). <em>R: a language and environment for statistical computing</em> (4.2.2) R Foundation for Statistical Computing. <a href="https://www.r-project.org/">https://www.r-project.org/</a></div> </div>
Data from: hespdiv: an R package for spatially constrained, hierarchical and contiguous regionalization in palaeobiogeography
<p>This is data for the '"hespdiv": an R package for spatially constrained, hierarchical, and contiguous regionalization in palaeobiogeography' paper. It contains datasets used, their metada, dataset processing scripts, a list of references to data contributors, and R files containing some of the results presented in the paper.</p>
Data for: Spatial variability in the contribution of termites to the decay of plant detritus
<p>Drylands are characterized by high spatial variability in resource availability due to sporadic rainfall, topography of the landscape and important effects of animals. Resource availability gradients may trigger patterns in decomposer population abundances and activity which could affect ecosystem functions such as decomposition. Here, we examined the influence of resource availability gradients on the importance of termites in the decomposition of wood and grass litter. We placed wood blocks and grass litter baits in bags accessible and inaccessible to termites across wood and grass resource gradients as determined by the presence or absence of a top mammalian predator and across topographic gradients during a 9-month period in arid Australia. We hypothesized that grass-eating termite activity would track grass abundance and wood-eating termite activity would track wood abundance. Termites were the predominant decomposition agent at these sites. Termites contributed to 99.5% of wood decomposition and 83.9% of grass decomposition during our study period. For wood, the termite effect was spatially variable and increased with habitat wood availability which was greatest on dunes and where top predators were absent. However, the contribution of termites to grass litter decomposition did not track grass availability or termite abundance. The highest effects of termites on grass decomposition rates were found in habitats where the absence of top predators led to low grass availability. Our findings highlight how spatial variability in resources in addition to other factors that we do not document but are known to be influenced by the presence of top predators, such as insectivore predation rates, across the landscape could affect ecosystem functions such as decomposition. </p>
Spatially Coherent 3D Distributions of HI and CO in the Milky Way - Data Products
<p>Data products from the joint reconstruction of Galactic HI and H2 (via CO).</p> <h3>Primary data products:</h3> <p>These are the posterior samples of the <strong>"densities"</strong> (HI and H2) in cm^-3 and <strong>"auxiliary"</strong> fields (i.e. the three components of the Galactic velocity field and the two spatially resolved line-widths) in km/s on our Sun-centered HEALPix-times-radius grid. These files also contain two tables with the centres and edges of the pixelisation in radial direction. The nearest (farthest) bin is at approximately 50 pc (28 kpc). The HEALPix dimension is ordered using the "nested" scheme.</p> <ul> <li><em>samples_densities_hpixr.fits </em></li> <li><em>samples_auxiliary_hpixr.fits</em></li> </ul> <h3>Interpolated to a regular grid:</h3> <p>For convenience, we also provide versions linearly interpolated to regular, Cartesian grids. Due to the strongly inhomogeneous original numerical grid, these interpolated versions contain regions of significant over/undersampling. To mitigate this a little, we provide a <strong>"local"</strong> (800 x 800 x 320 grid points with -1.25 kpc < x < 1.25 kpc, -1.25 kpc < y < 1.25 kpc and -0.5 kpc < z < 0.5 kpc) and a <strong>"global"</strong> (1250 x 1250 x 125 grid points with -12 kpc < x < 28 kpc, -20 kpc < y < 20 kpc, -2 kpc < z < 2 kpc) version. The origin (0,0,0) is defined by the position of the Sun and positive x points towards the Galactic centre.</p> <p>In an attempt to keep the file sizes reasonable, we provide the mean and standard deviation of each field instead of all eight individual samples.</p> <ul> <li><em>mean_std_densities_xyz_global.fits</em></li> <li><em>mean_std_densities_xyz_local.fits</em></li> <li><em>mean_std_auxiliary_xyz_global.fits</em></li> <li><em>mean_std_auxiliary_xyz_local.fits</em></li> </ul>
Data from: The importance of biotic interactions in distribution models of wild bees depends on the type of ecological relations, spatial scale and range
<p>Studies have found that biotic information can play an important role in shaping the distribution of species even at large scales. However, results from species distribution models are not always consistent among studies, and the underlying factors that influence the importance of biotic information to distribution models, are unclear. 2. We studied wild bees and plants, and cleptoparasite bees and their hosts in the Netherlands to evaluate how the inclusion of their biotic interactions affects the performance of species distribution models. We assessed model performance through spatial block cross-validation and by comparing models with interactions to models where the interacting species were randomized. Finally, we evaluated how, (i) spatial resolution, (ii) taxonomic rank (genus or species), (iii) degree of specialization, (iv) distribution of the biotic factor, (v) bee body size and (vi) type of biotic interaction, affect the importance of biotic interactions in shaping the distribution of wild bee species using generalized linear models. 3. We found that the models of wild bees improved when the biotic factor was included. The model performance improved the most for parasitic bees. Spatial resolution, taxonomic rank, distribution range of the biotic factor, and degree of specialization of the modelled species all influenced the importance of the biotic interaction to the models. 4. We encourage researchers to include biotic interactions in species distribution models, especially for specialized species and when the biotic factor has a limited distribution range. However, before adding the biotic factor we suggest considering different spatial resolutions and taxonomic ranks of the biotic factor. We recommend using single species or genus data as a biotic factor in the models of specialist species and for the generalist species, we recommend using an approximate measure of interactions, such as flower richness.</p>
Raw data for: Spatial and temporal variation in farmland bird nesting ecology: Implications for effective Corn Bunting Emberiza calandra conservation
<p>These are raw data accompanying the study "<span>Spatial and temporal variation in farmland bird nesting ecology: Implications for effective Corn Bunting Emberiza calandra conservation</span>". All information on data origin, data analysis, and derived implications will be available with the original publiation.</p>
Spatial capture-recapture data of Darwin's frogs captured between 2014-2017
<p>Search-encounter spatial capture-recapture data from <em>R. darwinii</em> individuals captured between 2014-2017 at two plots located near Neltume, Southern Chile.</p> <p>x and y coordinates in meters are provided for each capture as text files. These are matrices with 64 columns (secondary capture occasions) and 311 rows (frogs). The 16 primary capture occasions are separated by a 3-month period, and each of these occasion is composed of four secondary capture occasions. With these data you can reconstruct the capture-history matrix used in non-spatial capture-recapture models.</p> <p>Snout-to-vent length (SVL) during each capture occasion are provided in mm for each frog. With these data you can reconstruct the age of the individuals (juveniles or adults).</p> <p>Id data is provided for each individual. The first column represents the frog’s code, and the second one represents the plot where the frog was captured (1= HUI1, 2= HUI2).</p> <p>Any question can be addressed to andresvalenzuela.zoo@gmail.com</p>
Data and code for figures: Spatial multiplexing of soliton microcombs
<p>This dataset contains the data presented in the Figures of the paper Spatial multiplexing of soliton microcombs (doi:<a href="https://doi.org/10.1038/s41566-018-0256-7">https://doi.org/10.1038/s41566-018-0256-7</a>).</p> <p>The datasets and scripts were generated and tested using Matlab 2017.</p>
Hyperparameter tuning and performance assessment of statistical and machine-learning models using spatial data.
<p>This is a research compendium (RC) for the publication "Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data".</p> <p>The code (including figures, appendices and the manuscript) is packed in <strong>pathogen-modeling-3.zip </strong>or can be found directly in the <a href="https://github.com/pat-s/pathogen-modeling">Github repository</a>.</p> <ul> <li><strong>Publication figures</strong>: analysis/paper/submission/3/latex-source-files/</li> <li><strong>Appendices</strong>: analysis/paper/submission/3/</li> </ul> <p>This RC represents a static snapshot at the time of submission. The Github repository will receive changes after the publication was published.</p> <p><strong>Data sources</strong></p> <ul> <li>Atlas Climatico: <a href="http://opengis.uab.es/wms/iberia/index.htm">http://opengis.uab.es/wms/iberia/index.htm</a></li> <li>DEM: ftp://ftp.geo.euskadi.eus/lidar/MDE_LIDAR_2016_ETRS89/</li> <li>Lithology: <a href="http://www.geo.euskadi.eus/geonetwork/srv/spa/main.home">http://www.geo.euskadi.eus/geonetwork/srv/spa/main.home</a></li> <li>pH: <a href="https://esdac.jrc.ec.europa.eu/content/soil-ph-europe#tabs-0-description=0">https://esdac.jrc.ec.europa.eu/content/soil-ph-europe#tabs-0-description=0</a></li> <li>soil: <a href="https://www.isric.org/explore/soilgrids">https://www.isric.org/explore/soilgrids</a></li> </ul> <p><strong>Licenses</strong></p> <p>All files are shared via the given license with the exception of "soil.tif" which is shared via the <strong>ODbL </strong>license<strong>.</strong></p>
Spatially explicit regions of different suitability for Sentinel-2A and 2B Level-1C data.
<p>Spatially explicit regions of different suitability for Sentinel-2A and 2B Level-1C data. High suitability means a combination of high coverage and low average cloud cover. The suitability of data at a specific location is decreasing with either adecrease of number of scenes or an increase of average cloud cover.</p> <p>See here for more information: https://www.tandfonline.com/doi/full/10.1080/17538947.2019.1572799</p>
Data from: Spatial configuration matters when removing windfelled trees to manage bark beetle disturbances in Central European forest landscapes
<p>The published dataset contains the result of the paper titled <strong>Spatial configuration matters when removing windfelled trees to manage bark beetle disturbances in Central European forest landscapes, in Journal of Environmental Management.</strong></p> <p>Two zip files contain maps in ascii format for the total bark beetle and wind damage over 54 simulation year in our study region in Slovakia under reference climate and different climate change scenarios. No- salvaging and 95% salvaging scenarios are shown, just as in the paper.</p> <p>Excel file contains data of the other figures in the paper (main text and appendices as well).</p> <p>See more details about target area, and iLand model:</p> <p>https://www.sciencedirect.com/science/article/pii/S0168192318302946</p> <p>http://iland.boku.ac.at/startpage</p> <p>contact: Laura Dobor; dobor.laura@gmail.com</p> <p> </p>
Data and Python scripts for "Manifold increase in the spatial extent of heatwaves in the terrestrial Arctic"
<p>Data and Python scripts for reproducing the figures and results included in the manuscript "Manifold increase in the spatial extent of heatwaves in the terrestrial Arctic".</p> <p>Figures 1-4 are produced via respective Python codes. Heatwave magnitude index daily (HWMId) for ERA5-Land is available from. hw_era5land.nc file. HWMId fields for CMIP6 models are included in cmip6_hwmid.zip. The underlying data behind the figures 1-4 are included in data_to_produce_figs.zip.</p> <p>The paper is published in Rantanen, M., Kämäräinen, M., Luoto, M. <em>et al.</em> Manifold increase in the spatial extent of heatwaves in the terrestrial Arctic. <em>Commun Earth Environ</em> <strong>5</strong>, 570 (2024). https://doi.org/10.1038/s43247-024-01750-8</p>
Data to reproduce the results presented in Lake et al. 2024. Journal of Hydrology, https://doi.org/10.1016/j.jhydrol.2024.131930. ("High-frequency spatial sediment source fingerprinting using in situ absorbance data")
<p>This repository contains data on the used absorbance data, measured at the field site, as described in Lake et al., 2024 (<span>h</span><span>t</span><span>t</span><span>p</span><span>s</span><span>:</span><span>/</span><span>/</span><span>d</span><span>o</span><span>i</span><span>.</span><span>o</span><span>r</span><span>g</span><span>/</span><span>1</span><span>0</span><span>.</span><span>1</span><span>0</span><span>1</span><span>6</span><span>/</span><span>j</span><span>.</span><span>j</span><span>h</span><span>y</span><span>d</span><span>r</span><span>o</span><span>l</span><span>.</span><span>2</span><span>0</span><span>2</span><span>4</span><span>.</span><span>1</span><span>3</span><span>1</span><span>9</span><span>3</span><span>0).</span> Furthermore, data on the turbidity, used calibration curves and R code to prepare the input data for the MixSIAR model are included in the data repository.</p>
Data from: Temporal and spatial variation in reproductive benefits in a partial migrant
<p><strong>Abstract</strong></p> <p><span>In partial migrant systems, where residents and migrants co-exist within a population, residents are commonly predicted to gain a reproductive advantage over migrants through priority access to high quality territories and an earlier breeding start. Annual variation in reproductive benefits has been suggested to be important for the co-existence of both strategies in a population, as differences in wintering conditions experienced by the two strategies may result in a periodic reproductive advantage for migrants. However, the importance of spatial environmental variation for reproductive output in partially migrant populations remains largely unexplored. We investigated variation in the reproductive output of migrants and residents in a population of Swiss red kites (<em>Milvus milvus</em>) both temporally, across and within years, and spatially, along an elevational gradient. We gathered four years of reproductive data combined with 183 GPS-derived full annual cycles from individuals breeding in the Swiss Alpine foothills. At low, but not high elevations residents produced more fledglings than migrants. We also found evidence for annual variation in the reproductive advantage of the two strategies. Furthermore, while reproductive output did decline with a later breeding start, there was no difference in the start of breeding between the two migration strategies. The results of this study suggest that differences in reproductive output between migrants and residents in partial migrant populations can vary due to both the use of spatially distinct overwintering grounds, as well as being differently affected by spatial variables in breeding areas, such as elevation. The study emphasizes that spatial and temporal variation in reproductive benefits must be considered when predicting how migratory species will respond to future environmental change.</span></p>
Data for: Temporal consistency and spatial variability in detection: implications for monitoring of macroinvertebrates from shallow groundwater aquifers (Subterranean Biology, 2024)
<p>Original research article: Knüsel M., Alther R., Couton M. & Altermatt F. (2024) Temporal consistency and spatial variability in detection: implications for monitoring of macroinvertebrates from shallow groundwater aquifers. Subterranean Biology 49: 139-161. <a href="https://doi.org/10.3897/subtbiol.49.132515" target="_blank" rel="noopener">https://doi.org/10.3897/subtbiol.49.132515</a></p>
Data and code for FishMIP global marine ecosystem model ensemble projections summarised by countries and territories and other selected marine spatial regions.
<p>R code to extract and create data tables and summary plots of FishMIP mean ensemble projections provided are for percentage change in "exploitable fish biomass", which is a proxy for the biomass available to fisheries, consisting of marine animals spanning the size range 10 g to 100 kg: this is typically dominated by fish, but is also inclusive of other animals such as crustaceans and cephalopods.</p> <p>This release contains scripts and summary data for producing figures in Part A of the following report:</p> <p>Blanchard, J.L., Novaglio, C., eds. (2024). Climate change risks to marine ecosystems and fisheries: Future projections from the Fisheries and Marine Ecosystems Model Intercomparison Project. FAO Fisheries and Aquaculture Technical Paper No. 707. Rome, FAO.</p> <p>Please refer to the above report to cite and for more information.</p> <p>The summary data are here:</p> <p>https://github.com/Fish-MIP/FAO_Report/blob/main/data/table_stats_formatted_admin_full.csv</p> <p>Where the column 'spatial_scale' refers to the type of aggregation:</p> <p>FAO_area = High Sea areas grouped by FAO Major Fishing Areas</p> <p>countries = Exclusive Economic Zones</p> <p>countries_admin = Exclusive Economic Zones results aggregated into Administrative Countries</p> <p>Please note that these results can also be visualised and downloaded from our shiny app: https://rstudio.global-ecosystem-model.cloud.edu.au/shiny/FAO_report_shiny/</p> <p> </p>
Data from: When the "selfish herd" becomes the "frozen herd": spatial dynamics and population persistence in a colonial seabird
Aggregations are common in ecological systems at a range of scales and may be driven by exogenous constraints such as environmental heterogeneity and resource availability or by 'self-organizing' interactions among individuals. One mechanism leading to self-organized animal aggregations is captured by Hamilton's 'selfish herd' hypothesis, which suggests that aggregations may be driven by an individual's effort to minimize their risk of predation by surrounding themselves with conspecifics. We demonstrate that aggregations observed in Adélie penguin (Pygoscelis adeliae) colonies are a convolution of both self-organized dynamics and external forcing arising from landscape terrain. In fluid, highly mobile aggregations, individuals are constantly moving in response to changing environmental conditions, the locations of predators, or the movements of conspecifics. However, when the ability to rearrange is limited and spatial reconfiguration occurs on slower time scales than changes in population size, systems may become trapped in sub-optimal arrangements. We use simulated annealing to demonstrate that Adélie penguin colonies are frozen in sub-optimal spatial arrangements, and employ an individual-based modelling approach to demonstrate that this sub-optimal spatial configuration is driven by a convolution of nest site fidelity and stochastic events at the level of individual nests. The resulting spatial dynamics are responsible for a hysteretic response to long-term changes in abundance. We find that declining abundance leads to fragmentation even in a homogeneous environment, which has population-level consequences for reproductive success because predation is biased towards colony edges. Strong edge effects from heterogeneous predation coupled with fragmentation in response to population declines creates a positive feedback cycle that can accelerate population decline. This work provides a mechanistic understanding of complex spatial structuring in penguin colonies, provides a link between current spatial patterning and past dynamics, and suggests the possibility of critical collapse in seabird populations.
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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.