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495 results for “spatial scale”
Data from: Multiple processes drive genetic structure of humpback whale (Megaptera novaeangliae) populations across spatial scales
Elucidating patterns of population structure for species with complex life histories, and disentangling the processes driving such patterns, remains a significant analytical challenge. Humpback whale (Megaptera novaeangliae) populations display complex genetic structures that have not been fully resolved at all spatial scales. We generated a data set of nuclear markers for 3,575 samples spanning the seven breeding stocks and substocks found in the South Atlantic and western and northern Indian Oceans. For the total sample, and males and females separately, we assessed genetic diversity, tested for genetic differentiation between putative populations and isolation by distance, estimated the number of genetic clusters without a priori population information, and estimated rates of gene flow using maximum likelihood and Bayesian approaches. At the ocean basin scale, structure is governed by geographic distance (IBD p<0.05) and female fidelity to breeding areas, in line with current understanding of the drivers of broad-scale population structure. Consistent with previous studies, the Arabian Sea breeding stock was highly genetically differentiated (FST 0.034-0.161; p<0.01 for all comparisons). However, the breeding stock boundary between west South Africa and east Africa was more porous than expected based on genetic differentiation, cluster, and gene flow analyses. Instances of male-fidelity to breeding areas and relatively high rates of dispersal for females were also observed between the three substocks in the western Indian Ocean. This mismatch between demographic units and current management boundaries may have ramifications for assessments of the status and continued protections of populations still in recovery from commercial whaling.
Data for: Density dependence and spatial heterogeneity limit the population growth rate of invasive pines at the landscape scale
<p class="MsoBodyText"><span><span><span><span><span><span><span><span><span><span><span>Determining population growth across large scales is difficult because it is often impractical to collect data at large scales and over long timespans. Instead, the growth of a population is often only measured at a small, plot-level scale and then extrapolated to derive a mean field estimate. However, this approach is prone to error since it simplifies spatial processes such as the neighbourhood effects of density and dispersal. We present a novel approach that estimates how spatial processes derived from the effects of density and dispersal affect population growth between plot scales and landscape scales. The method is based on a scale transition theory and calculates a transition term to measure the spatial scaling of population growth, which we extend to unstable, expanding populations in order to assess whether landscape-scale population dynamics are different from those estimated at smaller spatial scales. We illustrate this approach using aerial imagery of eight locations in New Zealand experiencing non-native pine invasions. Analyses examined the dynamics at a plot scale (1 hectare) and compared this to estimates across entire landscapes (between 24 and 1600 hectares), in several cases for more than one time period. We used a Bayesian spatial random effects model to examine population growth and to account for neighbourhood effects and dispersal between plots in a rapidly changing system. </span></span></span></span></span></span></span></span></span></span></span></p> <p class="MsoBodyText"><span><span><span><span><span><span><span><span><span><span><span>We found that the estimates of the scale transition term were typically 10-25% of the mean field estimates, which led to mean field estimates of population growth extrapolated from plots being considerably higher than landscape estimates. The approach we have developed will not only have applications for predicting the populations' growth of invasive species, but also for studies examining the scaling of landscape-scale phenomena.</span></span></span></span></span></span></span></span></span></span></span></p>
Few species, higher abundance or many species and lower abundance of birds of prey: effects of land use changes at different spatial scales
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Modelling the Global Distribution of Chorus Wave Induced Relativistic Microburst Spatial Scale Size
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Supplementary material 2 from: Kortz A, Hejda M, Pergl J, Kutlvašr J, Petřík P, Sádlo J, Vítková M, Vojík M, Pyšek P (2024) Impacts of native and alien plant dominants at different spatial scales. NeoBiota 92: 29-43. https://doi.org/10.3897/neobiota.92.116392
Map with populations of the selected native and alien dominants
Supplementary material 3 from: Kortz A, Hejda M, Pergl J, Kutlvašr J, Petřík P, Sádlo J, Vítková M, Vojík M, Pyšek P (2024) Impacts of native and alien plant dominants at different spatial scales. NeoBiota 92: 29-43. https://doi.org/10.3897/neobiota.92.116392
Primary data, details on the statistical models and summary of the results
Supplementary material 1 from: Kortz A, Hejda M, Pergl J, Kutlvašr J, Petřík P, Sádlo J, Vítková M, Vojík M, Pyšek P (2024) Impacts of native and alien plant dominants at different spatial scales. NeoBiota 92: 29-43. https://doi.org/10.3897/neobiota.92.116392
The selected native and invasive dominants
U-Surf: a global 1km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling
<p>High-resolution urban climate modeling has faced substantial challenges due to the absence of a globally consistent, spatially continuous, and accurate dataset to represent the spatial heterogeneity of urban surfaces and their biophysical properties. This deficiency has long obstructed the development of urban-resolving Earth System Models (ESMs) and ultra-high-resolution urban climate modeling, particularly at large scales. Here, we present a first-of-its-kind 1km-resolution present-day (circa-2020) global continuous urban surface parameter dataset – U-Surf. Using the urban canopy model (UCM) in the Community Earth System Model as a base model for developing dataset requirements, U-Surf leverages the latest advances in remote sensing, machine learning, and cloud computing to provide the most relevant urban surface biophysical parameters, including radiative, morphological, and thermal properties, for UCMs at the facet- and canopy-level. Our high-resolution U-Surf dataset significantly improves the representation of the urban land heterogeneity both within and across cities globally. U-Surf provides essential, high-fidelity surface biophysical constraints to urban-resolving ESMs, enables detailed city-to-city comparisons across the globe, and supports the next-generation kilometer-resolution Earth system modeling across scales. U-Surf parameters can be easily converted or adapted to various types of UCMs, such as those embedded in weather and regional climate models, as well as air quality models. The fundamental urban surface constraints provided by U-Surf are also relevant as features for machine learning models and can have other broad-scale applications for socioeconomic, public health, and urban planning contexts. We expect U-Surf to promote the research frontier on urban systems science, climate-sensitive urban design, and coupled human-Earth systems in the future.</p> <p>The complete list of parameters is presented in the table below.</p> <table> <tbody> <tr> <td>Category</td> <td>Parameter</td> <td>Notes</td> </tr> <tr> <td>Radiative</td> <td>Roof | Impervious | Pervious canyon floor | Wall emissivity</td> <td> </td> </tr> <tr> <td> </td> <td>Roof | Impervious | Pervious canyon floor | Wall albedo</td> <td> </td> </tr> <tr> <td>Morphological</td> <td>Roof | Pervious fraction</td> <td>Roof fraction is w.r.t. urban horizontal surface, and pervious fraction is w.r.t. canyon floor (i.e. pervious and impervious canyon floor).</td> </tr> <tr> <td> </td> <td>Building height</td> <td>Unit: m; Height of wind in the canyon is simply set as half of the building height in CLMU.</td> </tr> <tr> <td> </td> <td>Canyon height-to-width ratio</td> <td> </td> </tr> <tr> <td> </td> <td>Urban percentage</td> <td> </td> </tr> <tr> <td>Thermal</td> <td>Roof | Wall thickness</td> <td>Unit: m</td> </tr> <tr> <td> </td> <td>Roof | Impervious canyon floor | Wall thermal conductivity</td> <td>Unit: W/m*K</td> </tr> <tr> <td> </td> <td>Roof | Impervious canyon floor | Wall volumetric heat capacity</td> <td>Unit: J/m^3*K</td> </tr> <tr> <td> </td> <td>Number of impervious canyon floor layer</td> <td> </td> </tr> <tr> <td> </td> <td>Minimum | Maximum interior building temperature</td> <td>Unit: K</td> </tr> <tr> <td> </td> <td>Air conditioning adoption rate</td> <td> </td> </tr> </tbody> </table> <p> </p> <p>Radiative and morphological parameters are presented in the format of both .tif and .nc to accommodate different needs for the urban climate modeling community. Thermal parameters adapted from CLMU are available in a single .nc file. A CESM-compatiable surface dataset and a time-variant urban dataset (including P_AC and T_BUILDING_MAX; Li et al., 2024) at standard resolution (0.9375°x1.25°) are included for direct simulation use. Note that the urban percentage used to create the surface dataset comes from the PCT_URBAN parameter calculated in U-Surf, but users can input their own urban extent data to generate a customized surface dataset. The raw 1-km data can be easily aggregated/regridded to other resolution as needed.</p> <p> </p> <p><strong>Version 1.1 updates:</strong></p> <p>1. Fill part of the data gaps in Asia. </p> <p>2. Change the aggregation method of some parameters to be facet-area weighted in the 1deg surfdata.</p>
Niche differentiation within a cryptic pathogen complex: climatic drivers and hyperparasitism at multiple spatial scales
<p><span>Pathogens are embedded in multi-trophic food webs, which often include co-occurring cryptic species within the same pathogen complex. Nonetheless, we still lack an understanding of what dimensions of the ecological niche might allow these cryptic species to coexist. We explored the role of climate, host characteristics (tree autumn phenology) and attack by the fungal hyperparasite <em>Ampelomyces</em> (a group of fungi attacking plant pathogens) in defining the niches of three powdery mildew species (<em>Erysiphe alphitoides</em>, <em>E. hypophylla</em> and<em> E. quercicola</em>) within a cryptic pathogen complex on the pedunculate oak Quercus robur at the continental (Europe), national (Sweden and France) and landscape scales (a 5 km2 island in southwestern Finland). Previous studies have shown that climate separated the niches of three powdery mildew species (<em>E. alphitoides</em>, <em>E. hypophylla </em>and <em>E. quercicola</em>) in Europe and two species (<em>E. alphitoides </em>and <em>E. quercicola</em>) in France. In our study, we did not detect a significant relationship between temperature or precipitation and the distribution of <em>E. alphitoides </em>and <em>E. hypophylla</em> present in Sweden, while at the landscape scale, temperature, but not relative humidity, negatively affected disease incidence of <em>E. alphitoides</em> in an exceptionally warm year. Tree variation in autumn phenology did not influence disease incidence of powdery mildew species, and hyperparasite presence did not differ among powdery mildew species at the continental, national and landscape scale. Climate did not affect the distribution of the hyperparasite at the continental scale and at the national scale in Sweden. However, climate affected the hyperparasite distribution in France, with a negative relationship between non-growing season temperature and presence of the hyperparasite. Overall, our findings, in combination with earlier evidence, suggest that climatic factors are more important than species interactions in defining the niches of cryptic species within a pathogen complex on oak. </span></p>
Data from: Trait-environment relationships could alter the spatial and temporal characteristics of aquatic insect subsidies at the macrospatial scale
<p>Ecological flows across ecosystem boundaries are typically studied at spatial scales that limit our understanding of broad geographical patterns in ecosystem linkages. Aquatic insects that metamorphose into terrestrial adults are important resource subsidies for terrestrial ecosystems. Traits related to their development and dispersal should determine their availability to terrestrial consumers. Here, we synthesize geospatial, aquatic biomonitoring and biological traits data to quantify the relative importance of several environmental gradients on the potential spatial and temporal characteristics of aquatic insect subsidies across the contiguous United States. We found the trait composition of benthic macroinvertebrate communities varies among hydrologic regions and could affect how aquatic insects transport subsidies as adults. Further, several trait-environment relationships were underpinned by hydrology. Large bodied taxa that could disperse further from the stream were associated with hydrologically stable conditions. Alternatively, hydrologically variable conditions were associated with multivoltine taxa that could extend the duration of subsidies with periodic emergence events throughout the year. We also found that anthropogenic impacts decrease the frequency of individuals with adult flight but potentially extend the distance subsidies travel into the terrestrial ecosystem. Collectively, these results suggest that natural and anthropogenic gradients could affect aquatic insect subsidies by changing the trait composition of benthic macroinvertebrate communities. The conceptual framework and trait-environment relationships we present shows promise for understanding broad geographical patterns in linkages between ecosystems.</p>
Dataset belonging to "The Influence of Large-scale Spatial Warming on Jet Stream Extreme Waviness on an Aquaplanet"
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Supporting data for SpatialOne: End-to-End Analysis of Spatial Transcriptomics at Scale
<p>Supplementary data supporting the <em>SpatialOne: End-to-End Analysis of Spatial </em><em>Transcriptomics at Scale</em> publication</p> <p> </p> <blockquote> <p>To showcase the capabilities of SpatialOne, two human lung cancer formalin-fixed, paraffin-embedded (FFPE) samples are analyzed. These samples are prepared following the CG000495 protocol (Figure 1b), sequenced with the 10x Visium CytAssist, and processed using the 10x SpaceRanger version 2. We also present analysis of two adult mouse samples sequenced using 10x Visium samples (one fresh frozen brain tissue section processed using SpaceRanger v2 and one FFPE kidney sample processed using the SpaceRanger v1), and 75 internal samples. </p> <p> For the human lung cancer samples, single-cell data from the the Lung Cancer Atlas (Salcher et al., 2022) is used as reference. This dataset is filtered to include only Chromium-generated data. For the mice samples, the GSE107585 single-cell dataset serves as reference. In the human lung cancer datasets, a pathologist annotated regions of interest corresponding to tumors, blood vessels, and alveolar regions.</p> </blockquote> <p> </p> <p>Changelog:</p> <ul> <li>Added a README file describing the zip content.</li> </ul>
Figure 1. – Eastern English Channel spatial grid using a in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 1. – Eastern English Channel spatial grid using a triangular mesh at a 522 km2 (A), 782 km2 (B) and 1043 km2 (C) average scale with the geographic coordinates in WGS84 of all the English Channel groundfish hauls survey from 1995 to 2014 (blue). The red points are the vertices used to define the mesh.
Data for "Spatial scales of rising-tone chorus in a dipole magnetic field: two-dimensional particle-in-cell simulation"
<p>The GCPIC simulation output data and the corresponding data analysis code.</p>
Data from: Fine-scale spatial genetic structure across the species range reflects recent colonization of high elevation habitats in silver fir (Abies alba Mill.)
<p class="western"><span>Variation in genetic diversity across species ranges has long been recognized as highly informative for assessing populations' resilience and adaptive potential. The spatial distribution of genetic diversity within populations, referred to as fine-scale spatial genetic structure (FSGS), also carries information about recent demographic changes, yet it has rarely been connected to range scale processes. We studied eight silver fir (<i>Abies alba </i>Mill.<i>)</i> population pairs (sites), growing at high and low elevations, representative of the main genetic lineages of the species. A total of 1368 adult trees and 540 seedlings were genotyped using 137 and 116 single nucleotide polymorphisms (SNPs), respectively. Sites revealed a clear east-west isolation-by-distance pattern consistent with the post-glacial colonization history of the species. Genetic differentiation among sites (<i>F</i><sub>CT</sub>=0.148) was an order of magnitude greater than between elevations within sites (<i>F</i><sub>SC</sub>=0.031), nevertheless high elevation populations consistently exhibited a stronger FSGS. Structural equation modeling revealed that elevation and, to a lesser extent, post-glacial colonization history, but not climatic and habitat variables, were the best predictors of FSGS across populations. These results suggest that high elevation habitats have been colonized more recently across the species range. Additionally, paternity analysis revealed a high reproductive skew among adults and a stronger FSGS in seedlings than in adults, suggesting that FSGS may conserve the signature of demographic changes for several generations. Our results emphasize that spatial patterns of genetic diversity within populations provide information about demographic history complementary to non-spatial statistics, and could be used for genetic diversity monitoring, especially in forest trees.</span></p>
Fig. 6 in Life-history of the South American darter, Characidium pterostictum (Crenuchidae): evidence for small scale spatial variation in a piedmont stream
Fig. 6. Seasonal variation of mean condition factor (K) of Characidium pterostictum in the Lajeado river (southern Brazil). PA, upstream site; PB, downstream site. * In PA no specimen was captured in Summer.
Figure 3 from: Pellegrini TG, Sales LP, Aguiar P, Ferreira RL (2016) Linking spatial scale dependence of land-use descriptors and invertebrate cave community composition. Subterranean Biology 18: 17-38. https://doi.org/10.3897/subtbiol.18.8335
Figure 3 - Detailed figure of Gruta Helictites showing different land uses within the 50, 100 and 250 m buffers.
Figure 2 from: Pellegrini TG, Sales LP, Aguiar P, Ferreira RL (2016) Linking spatial scale dependence of land-use descriptors and invertebrate cave community composition. Subterranean Biology 18: 17-38. https://doi.org/10.3897/subtbiol.18.8335
Figure 2 - Study area location, sampling design used in sampled caves at "Parque Estadual do Sumidouro", and the Buffers of 50m, 100m, and 250m for analyzing the effect of spatial scale on the explanatory power of environmental variables in the cave invertebrate communities.
Figure 1 from: Pellegrini TG, Sales LP, Aguiar P, Ferreira RL (2016) Linking spatial scale dependence of land-use descriptors and invertebrate cave community composition. Subterranean Biology 18: 17-38. https://doi.org/10.3897/subtbiol.18.8335
Figure 1 - Spatial characterization of landscape at "Parque Estadual do Sumidouro". Different colors represent distinct vegetation cover or land-use types. The numbers indicate the sampled caves, indicated by name. Legend: 1 Gruta Ninho de Pérolas 2 Gruta Macaco das Cavernas 3 Lapa da Várzea 4 Gruta do Grilão 5 Gruta Helictites 6 Lapa das Pacas 7 Gruta do Sumidouro 8 Gruta Lagoa Seca 9 Gruta do Feneme 10 Gruta do Lixo.
Data from: The role of spatial averaging scale in leaf-to-canopy scaling of non-linear processes in homogeneous canopies
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.