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423 results for “spatial use”
Fig. 2 in Using a spatial mark-resight model to estimate the parameters of a wild pig (Sus scrofa) population in Singapore
Fig. 2. The density map showing the number of activity centres per kilometer square, the locations of the camera points (circles), 143 out of 856 GPS locations from the collared pig (black dots) and the boundary of the Central Catchment Nature Reserve. Only a fraction of the GPS locations was plotted to prevent the colored pixels from being obscured. Each pixel is 1 km2. X and Y coordinates are in kilometers.
Data for: Using spatial patterns of seeds and saplings to assess the prevalence of heterospecific replacements among cloud forest canopy tree species
<p><b>Questions:</b> To gain insights into the role of species-by-species replacements in cloud forest community structuring, we asked: (1) What are the effects of the spatial distribution of standing individuals on the seed rain, soil seed bank, and sapling density and survival in this cloud forest? and (2) What is the prevalence of conspecific vs<i>.</i> heterospecific replacements in the regeneration of this forest?</p> <p><b>Location:</b> Santo Tomás Teipan, Oaxaca State, southern Mexico.</p> <p><b>Methods:</b> In a 1-ha cloud forest plot we assessed seed rain, seed bank, and sapling density and survival of four canopy tree species (<i>Chiranthodendron pentadactylon</i>, <i>Cornus disciflora</i>,<i> Quercus laurina</i>, <i>Oreopanax</i> <i>xalapensis</i>). All standing individuals of these and other tree species (dbh ≥ 2.5 cm) were mapped. We used neighbourhood models to examine the spatial patterns of the three life cycle stages relative to the spatial distribution of adults. The neighbourhood effect was assessed through the Neighbourhood Index, which integrates information on size (dbh) and distance to adults. Data analysis was based on maximum likelihood and model selection procedures.</p> <p><b>Results:</b> We found large between-species differences regarding the spatial patterns of seeds and saplings. Three species showed evidence for the Janzen-Connell effect operating at the seed (<i>C. pentadactylon</i> and <i>Q. laurina</i>) or sapling (<i>O.</i> <i>xalapensis</i>) stage. We also found support for a critical role of specific microsite factors (i.e., niche differentiation) in the regeneration of two species (<i>C. pentadactylon</i> and <i>C. disciflora</i>).</p> <p><b>Conclusions:</b> Seed and sapling distribution patterns suggest the prevalence of heterospecific replacements, and that both Janzen-Connell and niche differentiation effects contribute to this pattern. Our results largely support the notion that the prevalence of heterospecific replacements among canopy species promotes species coexistence in cloud forest.</p>
Data, code and supplementary material for "A data integration framework for spatial interpolation of temperature observations using climate model data"
<p>Each zipped file contains code and data to reproduce the results in the paper and supplementary material. The Cyprus folder contains also the files to run the model, as well as the associated results. The Morocco folder only contains the results and the code used to manipulate it. </p>
Data for accurate cell type deconvolution in spatial transcriptomics using a batch effect-free strategy
<p>Simulated and experimental data used in the ReSort manuscript. It is necessary and sufficient to reproduce the results in the paper.</p>
Using DNA metabarcoding to explore spatial variation in diet across European Hawfinch populations
<p><span>The investigation of diet in avian species is essential to an understanding of their ecology and local adaptations, as well as long-term conservation. This can be particularly challenging due to the wide distribution and high ecological plasticity of many bird species. Dietary richness and variation are under-studied in woodland bird species, due primarily to challenges in accurately identifying plant and invertebrate taxa consumed. Within Europe, Hawfinch (</span><span><em>Coccothraustes</em> <em>coccothraustes</em></span><span>) have shown variation in population trends, with moderate declines across central and eastern Europe, while western European populations have shown moderate increases. Ecological drivers behind this differing trend are still unknown; one possibility is differences in diet, yet little research has been conducted into Hawfinch diet in mainland Europe or elsewhere. This study aimed to present the first molecular dietary analysis of Hawfinch populations across two European countries. Faecal samples were collected between January and July of 2019 from Hawfinch caught at six artificial feed sites: two in Denmark and four in Germany. DNA was successfully extracted from 80 samples and plant Internal Transcribed Spacer 2 (ITS2) and invertebrate Cytochrome Oxidase Subunit 1 (COI) barcodes were amplified. A total of 35 plant and 37 invertebrate taxa were found across the 80 Hawfinch faecal samples, with plant and insect orders Fagales and Lepidoptera respectively the most frequently detected. Hawfinch dietary composition differed significantly between European countries, suggesting Hawfinch can make use of available food resources which are likely to differ spatially. Our study shows how DNA metabarcoding can be used to provide novel ecological information associated with under-studied bird species, thus providing essential information for future management and conservation of Hawfinch and their habitats. <br></span></p>
Data from: Using environmental DNA metabarcoding to monitor fish communities in small rivers and large brooks: Insights on the spatial scale of information
<p><span>Monitoring fish communities is central to the evaluation of ecological health of rivers. Not only presence/absence of species is important to assess, but also the species composition of local fish assemblages is a crucial parameter. Lotic fish communities are traditionally monitored via electrofishing, characterized by a known limited efficiency and high survey costs. The use of environmental DNA-based analyses could serve as a non-destructive alternative, but this approach requires further insights in practical sampling schemes incorporating transport and dilution of the eDNA fragments; as well as optimization of molecular detection in terms of predictive power and quality assurance. By introducing fifteen species known to occur in Belgian waters via a controlled cage experiment, we aim to extend the knowledge on streamreach of eDNA in small rivers and large brooks, as laid out in the European Water Framework Directive's water typology. Introducing fish communities in two transects of a species poor river characterized by contrasting river discharge rates, we found strong and significant correlations between the eDNA relative abundances and the relative biomass per species in the cage community. Despite a decreasing correlation over distance, the underlying community composition remained stable over a distance of 300 m up to 1 km downstream of the cages, depending on the river discharge rate. Such decrease in similarity between relative source biomass and the corresponding eDNA-based community profile with increasing distance downstream from the source, can partly be attributed to variation in species-specific eDNA persistence. Our findings offer novel insights on eDNA behaviour and characterization of riverine fish communities. We conclude that water sampled from a relatively small river offers an adequate snapshot of the total fish community composition occurring within an upstream perimeter ranging between 300 and 1000 meters. The potential application for other river systems is discussed in this study. </span></p>
D. viride occupancy in Compiegne forest at two spatial scales with temporal survey at fine scale, used in Percel et al. study
<p>This repository contains two datasets in text format, to be imported in codes related to Percel et al. study of D. viride colonization process within Compiegne forest. The dataset "Data_Dviride.2017_AVCHESS_d14.txt" contains the data regarding the occupancy turnover between two surveys in three stands of the forest. The dataset "Data_Dviride.2018_foret.txt" contains the data regarding the coarse grained spatial distribution of D. viride at the forest scale.</p> <p>This repository also contains a basic script used to compute statistics reported in Table 1 of Percel et al. article.</p>
STGMVA: clustering, imputation, and integration for spatial resolved transcriptomics using spatiotemporal gaussian mixture variational autoencoder
<p> In this study, we present STGMVA, a comprehensive analysis toolkit employs a spatiotemporal gaussian mixture variational autoencoder to tackle these tasks effectively. STGMVA consists of two stages: pretraining the gene expression and spatial location using a gaussian mixture model, and learning the embedding vectors through a variational graph autoencoder. Results demonstrate STGMVA surpasses state-of-the-art approaches on various spatial transcriptomics datasets, exhibiting superior performance across different scales and resolutions. Notably, STGMVA achieves the highest clustering accuracy in human brain, mouse hippocampus, and mouse olfactory bulb tissues. Furthermore, STGMVA enhances and denoises gene expression patterns for gene imputation task. Additionally, STGMVA has the capability to correct batch effects and achieve joint analysis when integrating multiple tissue slices.</p>
Spatially Quantifying Forest Damage from Hurricane Michael using Sentinel-2 Imagery
<p><strong>ABSTRACT:</strong></p> <p>Hurricane Michael made landfall on Mexico Beach, Florida panhandle as a Category 5 storm on October 10<sup>th</sup>, 2018. The storm had a large impact on the forests in the Florida panhandle and into Georgia. In this study we use Sentinel-2 imagery and 248 forest plots collected prior to landfall in 2018 in the forests impacted by Hurricane Michael to build a general linear model of tree basal area across the landscape. The basal area model was constrained to areas where trees were present using a tree presence model as a hurdle. We informed the model with post hurricane Sentinel-2 imagery and compared the pre and post hurricane basal area maps to assess the loss of basal area following the hurricane. The basal area model had an r-squared value of 0.508. Our results provide a detailed map showing the extent of basal area loss across the Florida panhandle at 10m spatial scale. Plots were revisited to ground truth the modelled results and showed that the model performed well at categorizing forest hurricane damage. This study demonstrates the use of remotely sensed imagery and in-situ forest measurements to rapidly quantify, using common forestry metrics, forest damage from large natural disturbances at spatial resolution useful to inform disaster response management decisions.</p> <p><strong>METHODS:</strong></p> <p>The Restore .csv file is data from forestry plots established by the Florida Natural Areas Inventory (FNAI) as a baseline for a Restore Act project focused on the Florida panhandle. A total of 248 plots were visited between December 2017 and March 2018. These temporary plots were navigated to using handheld GPS units and laid out in 36m squares containing four 9m diameter non overlapping subplots. Measurements of vegetative cover, tree species count, tree condition, and diameter at breast height were taken for all trees in the subplots. Post hurricane Michael 70 plots were revisited, measurements at these plots were a subjective plot hurricane damage categorization, and count and diameter of downed or damaged trees as well as miscellaneous notes regarding site damage.</p> <p>The state parks .csv file is data from forestry plots established by the Florida Natural Areas Inventory (FNAI) at Florida State parks post hurricane Michael. These plots are 20 m circular radius that include subjective plot hurricane damage categorization, and count of downed or damaged trees, herbaceous cover, as well as miscellaneous notes regarding site damage. </p> <p>Several fields were added to these plot .csv files post field visit as a variables extracted from a principal component analysis that used Sentinel-2 imagery to inform a remote sensing analysis of basal area.</p> <p>A file geodatabase is attached that contains the project area boundary, Apalachicola National Forest boundary, the three shapefiles of all restore plots, revisited restore plots, and state parks plots. Raster outputs from our analysis are also available in this gdb, they contain metadata in their item descriptions. The general metadata for these rasters follows: </p> <p>A general linear regression model was built to estimate tree basal area across the study area of 11 counties in the Florida Panhandle. Basal area (BA) was calculated from Restore field plots where trees were present located in and around the Apalachicola National Forest. Plot measurements include all trees within four non-overlapping 9m radius circular subplots within a 36m square plot. Tree diameter at breast height (DBH), species, count, and condition measurements were recorded. Measurements were summarized to the plot and DBH (square inches) was converted to basal area per acre (square feet per acre) using the formula 0.005454 * DBH^2. Plot measurements of basal area per acre were related to the top ten principal components from a principal component analysis (PCA) at the spatial resolution of the Restore plots (40 m). The PCA used the normalized Sentinel-2 spectral values and two texture values from the 7 mosaicked images from two time periods, the winter of 2017-2018 and the spring of 2018, before Hurricane Michael. A softmax neural network model was built from the PCA and Restore plot datasets to identify areas where trees were present. The basal area model was applied to the pre and post hurricane PCA imagery to create modelled surfaces of estimated Basal Area in pixels that where over 50% likely to contain trees according to the softmax neural network model. The basal are linear regression model results and predictors for the model are in the tables below.</p> <p>Table Linear Regression Model. Model fit and predictors for BAA.</p> <table> <tbody> <tr> <td> <p><strong>N</strong></p> </td> <td> <p><strong>RMSE</strong></p> </td> <td> <p><strong>R2</strong></p> </td> <td> <p><strong>Adjusted R2</strong></p> </td> </tr> <tr> <td> <p>231</p> </td> <td> <p>2.811</p> </td> <td> <p>0.51</p> </td> <td> <p>0.50</p> </td> </tr> </tbody> </table> <p>Road, water and urban areas were masked out of this raster dataset using a 1 m landcover product created by the author and located here - <a href="https://doi.org/10.2737/RDS-2017-0014">https://doi.org/10.2737/RDS-2017-0014</a></p>
High spatial resolution dataset of downscaled LUH2 land use scenarios for Belgium (10 m and 100 m)
<p>This dataset comprises high-resolution land use data downscaled from LUH2 scenarios for Belgium at both 10 m and 100 m resolutions. These datasets were generated based on research conducted by Rashidi et al. in 2023 and published in the Land Journal. We employed the GLOBIO land allocation routine to downscale fractional land use data, originally at a 0.25° resolution (approximately 25 km), into discrete land use maps at 10 m and 100 m resolutions. This process utilized three distinct reference land cover maps: ESA WorldCover at 10 m resolution, ESA WorldCover upscaled to 100 m resolution, and CORINE land cover at 100 m resolution.</p> <p>During the downsizing process, we considered three SSP-RCP scenarios to model land use trends for both the present and the year 2050 on a national scale in Belgium. Key components of the model included regional land use demand, an assessment of grid cells' suitability for various land use types, and a reference land cover map. It's important to note that the classification system used in the reference maps differs from that of LUH2. To ensure comparability for land use simulations, we conducted a reclassification process following the methodologies outlined by Pérez-Hoyos et al. (2012), Dong et al. (2018), and Liao et al. (2020). This reclassification consolidated land use classes, except for water, into seven general categories: 1) urban, 2) cropland, 3) pasture, 4) forestry, 5) secondary vegetation, 6) undefined, and 7) natural.</p> <p>The raw data consists of three folders corresponding to the three reference maps, each containing four TIFF files (.tif), one for each scenario type.</p>
Using DNA metabarcoding to explore spatial variation in diet across European Hawfinch populations
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Data for: Using spatial patterns of seeds and saplings to assess the prevalence of heterospecific replacements among cloud forest canopy tree species
Open the record for dataset details and reuse information.
spectre: An R package to estimate spatially-explicit community composition using sparse data
Open the record for dataset details and reuse information.
Effects of food supplementation and helminth removal on space use and spatial overlap in wild bank vole populations
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GapAnalysis: An R package to calculate conservation indicators using spatial information
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Data from: Using environmental DNA metabarcoding to monitor fish communities in small rivers and large brooks: Insights on the spatial scale of information
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Boosting multiplexing capabilities for error-robust spatial transcriptomic methods using a set exchange approach
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Spatial and temporal patterns of habitat use and availability in upper Coweeta Creek from 1996 to 1998
I collect data describing total depth, mean current velocity and substrate characteristics (% - silt, debris, sand, gravel, cobble, boulder and bedrock) at 100 random points along 1 m transects within each 100 m site. Data are collected seasonally to investigate spatial and temporal patterns of habitat variability in upper Coweeta Creek. Flow data will be used to elucidate whether changes in habitat characteristics are correlated with high flow events.
Data package from "Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images"
<p>This data package contains the very high resolution maps of canopy palms from the paper "Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images". These maps have been produced with two GeoEye-1 very high resolution images (0.5 m) and a Deep Learning method for image segmentation called U-net, methods and data are fully described in the article. The total size of the decompressed archive is 2.56 Go and is distributed in two shapefiles, one for each GeoEye-1 image. When using this dataset, please cite the original article https://doi.org/10.3390/rs12142225</p>
Results of "Sensitivity of Greenland ice sheet projections to spatial resolution in higher-order simulations: the AWI contribution to ISMIP6-Greenland using ISSM"
<p>This archive provides ice sheet model output produced as part of the publication:</p> <p>Rückamp, M., Goelzer, H., and Humbert, A.: Sensitivity of Greenland ice sheet projections to spatial resolution in higher-order simulations: the AWI contribution to ISMIP6-Greenland using ISSM, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-329, in review, 2020.</p> <p>About the data:<br> The archive contains 4 tar-files for the 4 different resolutions. Each tar-file contains Matlab files for the dfferent scenarios. An examplary file structure looks as follows:</p> <p>results =</p> <p> struct with fields</p> <p>mesh_description: {'x=x_EPSG3413(m)' 'y=y_EPSG3413(m)'}<br> inputs_description: {'friction coefficient k^2 (s/m)' 'bedrock (m)'}<br> data_description: {1x13 cell}<br> mesh: [1x1 struct]<br> inputs: [1x1 struct]<br> data: [1x87 struct]<br> model_description: {'output from ISMIP6 AWI-ISSM, simulation: G4000 ctrl_proj, correspondence Martin Rueckamp (AWI): marin.rueckamp@awi.de'}<br> data_citation: {'Rückamp, M., Goelzer, H., and Humbert, A.: Results of "Sensitivity of Greenland ice sheet projections to spatial resolution in higher-order simulations: the AWI contribution to ISMIP6-Greenland using ISSM", Zenodo, https://doi.org/10.5281/zenodo.3992605, 2020.'}<br> model_citation: {'Rückamp, M., Goelzer, H., and Humbert, A.: Sensitivity of Greenland ice sheet projections to spatial resolution in higher-order simulations: the AWI contribution to ISMIP6-Greenland using ISSM, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-329, in review, 2020.'}</p> <p>The fields results.[mesh,input,data]_description contain header information. The field results.mesh contains [x,y]-coordinates of the grid. The field results.inputs contains time-independent quantities such as bedrock. The field results.data contains results of the transient simulations, such as ice thickness or surface velocity. The fields results.[model,data]_citation contain information about data citation.</p>
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.