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391
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ShareScore release 0.9.0
Dataset results
391 results for “Spatial analysis”
Data from: Spatially explicit analysis sheds new light on the Pleistocene megafaunal extinction in North America
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Data for: The meta-analysis of the effects of spatial sampling bias correction on presence only species distribution models
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Data from: A new digital method of data collection for spatial point pattern analysis in grassland communities
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Data for: Spatial-temporal analysis of natural hazards and disasters in the Greater Horn of Africa between 2010 and 2024 to inform disaster risk reduction, and surveillance and control strategies for climate and environmentally sensitive diseases
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Data from: Beta diversity patterns of bats in the Atlantic Forest: how does the scale of analysis affect the importance of spatial and environmental factors?
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Spatial transcriptomic analysis of human dorsoal root ganglia neurons
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Data from: Bringing multivariate support to multiscale codependence analysis: assessing the drivers of community structure across spatial scales
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Spatial personalities: a meta-analysis of consistent individual differences in spatial behavior
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Short-Tandem-Repeat (STR) marker set for Eurasian lynx for article: Genetic analysis indicates spatial-dependent patterns of sex-biased dispersal in Eurasian lynx in Finland
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Data and codes to replicate the analysis in: The spatial ecology of conflicts: Unravelling patterns of wildlife damage at multiple scales
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Data from: Spatial analysis of mitochondrial gene expression reveals dynamic translation hubs and remodeling in stress
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Data from: The effects of human-altered habitat spatial pattern on frugivory and seed dispersal: a global meta-analysis
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Spatial-temporal expression analysis of lineage-restricted shell matrix proteins in the slipper snail Crepidula atrasolea reveals shell field regionalization and distinct cell populations
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Data from: Understanding the spatial scale of genetic connectivity at sea: unique insights from a land fish and a meta-analysis
Quantifying the spatial scale of population connectivity is important for understanding the evolutionary potential of ecologically divergent populations and for designing conservation strategies to preserve those populations. For marine organisms like fish, the spatial scale of connectivity is generally set by a pelagic larval phase. This has complicated past estimates of connectivity because detailed information on larval movements are difficult to obtain. Genetic approaches provide a tractable alternative and have the added benefit of estimating directly the reproductive isolation of populations. In this study, we leveraged empirical estimates of genetic differentiation among populations with simulations and a meta-analysis to provide a general estimate of the spatial scale of genetic connectivity in marine environments. We used neutral genetic markers to first quantify the genetic differentiation of ecologically-isolated adult populations of a land dwelling fish, the Pacific leaping blenny (Alticus arnoldorum), where marine larval dispersal is the only probable means of connectivity among populations. We then compared these estimates to simulations of a range of marine dispersal scenarios and to collated FST and distance data from the literature for marine fish across diverse spatial scales. We found genetic connectivity at sea was extensive among marine populations and in the case of A. arnoldorum, apparently little affected by the presence of ecological barriers. We estimated that ~5000 km (with broad confidence intervals ranging from 810 - 11,692 km) was the spatial scale at which evolutionarily meaningful barriers to gene flow start to occur at sea, although substantially shorter distances are also possible for some taxa. In general, however, such a large estimate of connectivity has important implications for the evolutionary and conservation potential of many marine fish communities.
Data from: Integrating genetic analysis of mixed populations with a spatially-explicit population dynamics model
Inferring the dynamics of populations in time and space is a central challenge in ecology. Intra-specific structure (for example genetically distinct sub-populations or meta-populations) may require methods that can jointly infer the dynamics of multiple populations. This is of particular importance for harvested species, for which management must balance utilization of productive populations with protection of weak ones. Here we present a novel method for simultaneous learning about the spatio-temporal dynamics of multiple populations that combines genetic data with prior information about abundance and movement in an integrated population modelling approach. We apply the Bayesian genetic mixed stock analysis to 17 wild and 10 hatchery-reared Baltic salmon (S. salar) stocks, quantifying uncertainty in stock composition in time and space, and in population dynamics parameters such as migration timing and speed. Our results indicate that the commonly used "equal prior probabilities" assumption may not be appropriate for all mixed stock analyses. Incorporation of prior information about stock abundance and movement resulted in more precise and plausible estimates of mixture compositions in time and space. Inclusion of a population dynamics model also allowed robust interpolation of expected catch composition at areas and times with no genetic observations. The genetic data were informative about stock-specific movement patterns, updating priors for migration path, timing and speed. The model we present here forms the basis for optimizing the spatial and temporal allocation of harvest to support the management of mixed populations of migratory species.
Data from: Grains of connectivity: analysis at multiple spatial scales in landscape genetics
Landscape genetic analyses are typically conducted at one spatial scale. Considering multiple scales may be essential for identifying landscape features influencing gene flow. We examined landscape connectivity for woodland caribou (Rangifer tarandus caribou) at multiple spatial scales using a new approach based on landscape graphs that creates a Voronoi tessellation of the landscape. To illustrate the potential of the method, we generated five resistance surfaces to explain how landscape pattern may influence gene flow across the range of this population. We tested each resistance surface using a raster at the spatial grain of available landscape data (200 m grid squares). We then used our method to produce up to 127 additional grains for each resistance surface. We applied a causal modelling framework with partial Mantel tests, where evidence of landscape resistance is tested against an alternative hypothesis of isolation-by-distance, and found statistically significant support for landscape resistance to gene flow in 89 of the 507 spatial grains examined. We found evidence that major roads as well as the cumulative effects of natural and anthropogenic disturbance may be contributing to the genetic structure. Using only the original grid surface yielded no evidence for landscape resistance to gene flow. Our results show that using multiple spatial grains can reveal landscape influences on genetic structure that may be overlooked with a single grain, and suggest that coarsening the grain of landcover data may be appropriate for highly-mobile species. We discuss how grains of connectivity and related analyses have potential landscape genetic applications in a broad range of systems.
The spatial analysis of biological interactions: morphological variation responding to the co-occurrence of competitors and resources
By sharing geographic space, species are forced to interact with one another and the contribution of this process to evolutionary and ecological patterns of individual species is not fully understood. At the same time, species turnover makes that species composition varies from one area to another, so the analysis of biological interaction cannot be uncoupled from the spatial context. This is particularly important for clades that show high degree of specialization such as hummingbirds, where any variation in biotic pressures might lead to changes in morphology. Here, we describe the influence of biological interactions on the morphology of Hylocharis leucotis by simultaneously considering potential competition and diet resources. We characterized the extent of local potential competition and local available floral resources by correlating two measurements of hummingbird diversity, floral resources and the size of morphological space of H. leucotis along its geographic distribution. We found that H. leucotis shows an important morphological variability across its range and two groups can be recognized. Surprisingly, morphological variation is not always linked to local hummingbird richness or the phylogenetic similarity of. Only in the southern part of its distribution, H. leucotis is morphologically more variable in those communities where it coexist with closely related hummingbird species. We also found that morphological variation in H. leucotis is independent from the availability of floral resources. Our results suggest that abiotic factors might be responsible for morphological differences across populations in Hylocharis leucotis being biological interactions of minor importance.
Spatial point pattern analysis of traces (SPPAT): an approach for visualizing and quantifying site-selectivity patterns of drilling predators
<p>Site-selectivity analysis in drilling predation may provide useful behavioral information of a predator interacting with its prey. However, traditional approaches exclude some spatial information (i.e., oversimplified trace position) and are dependent on the scale of analysis (e.g., arbitrary grid system used to divide the prey skeleton into sectors). Here we introduce the spatial point pattern analysis of traces (<i>SPPAT</i>), an approach for visualizing and quantifying the distribution of traces on shelled invertebrate prey, which includes improved collection of spatial information inherent to drillhole location (morphometric-based estimation), improved visualization of spatial trends (Kernel density and hotspot mapping), and distance-based statistics for hypothesis testing (<i>K</i>-, <i>L</i>-, and pair correlation functions). We illustrate the <i>SPPAT</i> approach through case studies of fossil samples, modern beach-collected samples, and laboratory feeding trials of naticid gastropod predation on bivalve prey. Overall results show that Kernel density and hotspot maps enable visualization of subtle variations in regions of the shell with higher density of predation traces, which can be combined with the maximum clustering distance metric to generate hypotheses on predatory behavior and anti-predatory responses of prey across time and geographic space. Distance-based statistics also capture the major features in the distribution of traces across the prey skeleton, including aggregated and segregated clusters, likely associated with different combinations of two modes of drilling predation, edge- and wall-drilling. The <i>SPPAT </i>approach is transferrable to other paleoecologic and taphonomic data such as encrustation and bioerosion, allowing for standardized investigation of a wide range of biotic interactions.</p>
Characterizing Spatially Continuous Variations in Tissue Microenvironment through Niche Trajectory Analysis - Dataset
<p><span>Recent technological developments have made it possible to map the spatial organization of a tissue at the single-cell resolution. However, computational methods for analyzing spatially continuous variations in tissue microenvironment are still lacking. Here we present ONTraC as a strategy that constructs niche trajectories using a graph neural network-based modeling framework. Our benchmark analysis shows that ONTraC performs more favorably than existing methods for reconstructing spatial trajectories. Applications of ONTraC to public spatial transcriptomics datasets successfully recapitulated the underlying anatomical structure, and further enabled detection of tissue microenvironment-dependent changes in gene regulatory networks and cell-cell interaction activities during embryonic development. Taken together, ONTraC provides a useful and generally applicable tool for the systematic characterization of the structural and functional organization of tissue microenvironments.</span></p>
FIGURE 3. Pachyphytum odam Art. Castro & P. Carrillo. A–B in Spatial richness analysis and an evaluation of extinction risk for the genus Pachyphytum (Crassulaceae), with the description of a new species from Sierra Madre Occidental, Mexico
FIGURE 3. Pachyphytum odam Art. Castro & P. Carrillo. A–B. Lateral and front view of the flower. C. Lateral view of the rosette. D. Calyx lobes. E. Corolla lobes. F. Transverse view of the ovary. G. Lateral view of the androecium and gynoecium. H. Petal abaxial view. I. Lateral view of the corolla without the calyx. J–K. Lateral and adaxial view of the cincinnus. Photographs: Arturo Castro Castro.
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.