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1,868 results for “Spatial Data”
Data and Analysis Files Repository: Repurposing Large-Format Microarrays for Scalable Spatial Transcriptomics
<p>Data and Analysis Files from "Repurposing Large-Format Microarrays for Scalable Spatial Transcriptomics"</p> <p>ArraySeq_Method.zip contains the following folder and contents:</p> <ul> <li>STARSolo: All code and count matrix output from fastq spatial barcode demultiplexing. </li> <li>Images: All resolution-downsampled H&E image scans from analyzed tissues</li> <li>Space_Ranger: All 10x Space Ranger output from Visium datasets generated in the paper. </li> <li>Analysis: All scripts for analyzing and plotting Array-seq and Visium datasets generated in this paper. Also contains output h5ad files. </li> </ul> <p>ArraySeq_Barcode_generation_n12.rmd: The script used to generate the Array-seq probes with 12-mer spatial barcodes. </p>
Data from: The spatial structure of phylogenetic and functional diversity in the United States and Canada: an example using the sedge family (Cyperaceae)
Systematically quantifying diversity across landscapes is necessary to understand how clade history and ecological heterogeneity contribute to the origin, distribution, and maintenance of biodiversity. Here, we chart the spatial structure of diversity among all species in the sedge family (Cyperaceae) throughout the USA and Canada. We first identify areas of remarkable species richness, phylogenetic diversity, and functional trait diversity, and highlight regions of conservation priority. We then test predictions about the spatial structure of this diversity based on the historical biogeography of the family. Incorporating a phylogeny, over 400,000 herbarium records, and a database of functional traits mined from online floras, we find that species richness and functional trait diversity peak in the Northeastern USA, while phylogenetic diversity peaks along the Gulf of Mexico. Floristic turnover among assemblages increases significantly with distance, but phylogenetic turnover is twice as rapid along latitudinal gradients as along longitudinal gradients. These patterns reflect the expected distribution of Cyperaceae, which originated in the tropics but radiated in temperate regions. We identify assemblages with an abundance of rare, range-restricted lineages, and assemblages composed of species generally lacking from diverse regions. We argue that both of these metrics are useful for developing targeted conservation strategies. We use the data generated here to establish future research priorities, including the testing of a series of hypotheses regarding the distribution of chromosome numbers, photosynthetic pathways, and resource partitioning in sedges.
Data from: Linking social and spatial networks to viral community phylogenetics reveals subtype-specific transmission dynamics in African lions
1.Heterogeneity within pathogen species can have important consequences for how pathogens transmit across landscapes; however, discerning different transmission routes is challenging. 2.Here we apply both phylodynamic and phylogenetic community ecology techniques to examine the consequences of pathogen heterogeneity on transmission by assessing subtype specific transmission pathways in a social carnivore. 3.We use comprehensive social and spatial network data to examine transmission pathways for three subtypes of feline immunodeficiency virus (FIVPle) in African lions (Panthera leo) at multiple scales in the Serengeti National Park, Tanzania. We used FIVPle molecular data to examine the role of social organization and lion density in shaping transmission pathways and tested to what extent vertical (i.e., father and/or mother offspring relationships) or horizontal (between unrelated individuals) transmission underpinned these patterns for each subtype. Using the same data, we constructed subtype specific FIVPle co-occurrence networks and assessed what combination of social networks, spatial networks, or co-infection best structured the FIVPle network. 4.While social organization (i.e., pride) was an important component of FIVPle transmission pathways at all scales, we find that FIVPle subtypes exhibited different transmission pathways at within- and between-pride scales. A combination of social and spatial networks, coupled with consideration of subtype co-infection, was likely to be important for FIVPle transmission for the two major subtypes, but the relative contribution of each factor was strongly subtype specific. 5.Our study provides evidence that pathogen heterogeneity is important in understanding pathogen transmission, which could have consequences for how endemic pathogens are managed. Furthermore, we demonstrate that community phylogenetic ecology coupled with phylodynamic techniques can reveal insights into the differential evolutionary pressures acting on virus subtypes, which can manifest into landscape-level effects.
Data from: Spatial heterogeneity in species composition constrains plant community responses to herbivory and fertilization
Environmental change can result in substantial shifts in community composition. The associated immigration and extinction events are likely constrained by the spatial distribution of species. Still, studies on environmental change typically quantify biotic responses at single spatial (time series within a single plot) or temporal (spatial beta-diversity at single time points) scales, ignoring their potential interdependence. Here, we use data from a global network of grassland experiments to determine how turnover responses to two major forms of environmental change – fertilization and herbivore loss – are affected by species pool size and spatial compositional heterogeneity. Fertilization led to higher rates of local extinction whereas turnover in herbivore exclusion plots was driven by species replacement. Overall, sites with more spatially heterogeneous composition showed significantly higher rates of annual turnover, independent of species pool size and treatment. Taking into account spatial biodiversity aspects will therefore improve our understanding of consequences of global and anthropogenic change on community dynamics.
Data from: Are buffalograss (Buchloë dactyloides) cytotypes spatially and ecologically differentiated?
Premise of the study Although autopolyploidy is common among dominant Great Plains grasses, the distribution of cytotypes within a given species is typically poorly understood. This study aims to establish the geographic distribution of cytotypes within buffalograss (Buchloë dactyloides), and to assess whether individual cytotypes exhibit differing ecological tolerances. Methods A range-wide set of 578 B. dactyloides individuals was obtained through field collecting and sampling from herbarium specimens. The cytotype of each sample was estimated by observing allele numbers at thirteen simple sequence repeat loci, a strategy that was assessed by comparing estimated to known cytotype in 79 chromosome-counted samples. Ecological differentiation between the dominant tetraploid and hexaploid cytotypes was assessed with analyses of macro-climatic variables. Key results Simple sequence repeat variation accurately estimated cytotype in 89% of samples from which a chromosome count had been obtained. Applying this approach to samples of unknown ploidy established that diploids and pentaploids are rare, with the common tetraploid and hexaploid cytotypes generally occurring in sites to the north/west (tetraploid) or south/east (hexaploid) portions of the species range. Both MANOVA and niche modeling approaches identified significant but subtle differences in macro-climatic conditions at the set of locations occupied by these two dominant cytotypes. Conclusions Incorporating chromosome count vouchers and cytotype-estimated herbarium records allowed us to perform the largest study of cytotype niche differentiation to date. Buffalograss cytotypes differ greatly in frequency, the common tetraploid and hexaploid cytotypes are non-randomly distributed, and these two cytotypes are subtly ecologically differentiated.
Data from: Surrogate taxa and fossils as reliable proxies of spatial biodiversity patterns in marine benthic communities
Rigorous documentation of spatial heterogeneity (β-diversity) in present-day and preindustrial ecosystems is required to assess how marine communities respond to environmental and anthropogenic drivers. However, the overwhelming majority of contemporary and palaeontological assessments have centred on single higher taxa. To evaluate the validity of single taxa as community surrogates and palaeontological proxies, we compared macrobenthic communities and sympatric death assemblages at 52 localities in Onslow Bay (NC, USA). Compositional heterogeneity did not differ significantly across datasets based on live molluscs, live non-molluscs, and all live organisms. Death assemblages were less heterogeneous spatially, likely reflecting homogenization by time-averaging. Nevertheless, live and dead datasets were greater than 80% congruent in pairwise comparisons to the literature estimates of β-diversity in other marine ecosystems, yielded concordant bathymetric gradients, and produced nearly identical ordinations consistently delineating habitats. Congruent estimates from molluscs and non-molluscs suggest that single groups can serve as reliable community proxies. High spatial fidelity of death assemblages supports the emerging paradigm of Conservation Palaeobiology. Integrated analyses of ecological and palaeontological data based on surrogate taxa can quantify anthropogenic changes in marine ecosystems and advance our understanding of spatial and temporal aspects of biodiversity.
Data set for the article "Recently photoassimilated Carbon and fungus-delivered Nitrogen are spatially correlated at the cellular scale in the ectomycorrhizal tissue of Fagus sylvatica"
<p>This dataset contains data that support the manuscript</p> <p>Mayerhofer et al (2021) "Recently photoassimilated Carbon and fungus-delivered Nitrogen are spatially correlated at the cellular scale in the ectomycorrhizal tissue of<em> Fagus sylvatica", </em>The New Phytologist, DOI:10.1111/nph.17591</p> <p>It contains the following data:</p> <p>(1) NanoSIMS imaging data, which was used for Fig. 4-7, is provided in NanoSIMS_control_root_tip.zip and NanoSIMS_labelled_root_tip.zip. Each zip-files contains:</p> <ul> <li>the original NanoSIMS images (.im)</li> <li>their related checkfiles (.chk_im)</li> <li>ROIs description (.rois.zip)</li> </ul> <p>of the unlabelled control and the labelled root tip section, respectively. ".im" and ".chk_im" are the original image data aquisition files from the NanoSIMS instrument. "rois.zip" files describe selected regions of interests and were created utilizing the OpenMIMS plugin (Center for Nano Imaging, https://nano.bwh.harvard.edu/MIMSsoftware) for the image analysis software ImageJ (National Institutes of Health, Bethesda, MD, USA).</p> <p>(2) Means and standard deviations of all measured elements and isotopes of each region of interest, as obtained via the .rois.zip files from the NanoSIMS images, are reported in NanoSIMS_ROI_data.csv (used for Fig.7).</p> <p>(3) Linescan_data.csv contains data used for Fig. 8.</p> <p>(4) IRMS_roots_data.csv contains data of root segments and mycorrhizal root tips analysed with isotope-ratio mass spectrometry (EA-IRMS) (used for Fig. 2)</p> <p>Description of column meanings from csv data files can be found in the according "_description" files.</p>
Using machine learning to model nontraditional spatial dependence in occupancy data
<p>Spatial models for occupancy data are used to estimate and map the true presence of a species, which may depend on biotic and abiotic factors as well as spatial autocorrelation. Traditionally researchers have accounted for spatial autocorrelation in occupancy data by using a correlated normally distributed site-level random effect, which might be incapable of modeling nontraditional spatial dependence such as discontinuities and abrupt transitions. Machine learning approaches have the potential to model nontraditional spatial dependence, but these approaches do not account for observer errors such as false absences. By combining the flexibility of Bayesian hierarchal modeling and machine learning approaches, we present a general framework to model occupancy data that accounts for both traditional and nontraditional spatial dependence as well as false absences. We demonstrate our framework using six synthetic occupancy data sets and two real data sets. Our results demonstrate how to model both traditional and nontraditional spatial dependence in occupancy data which enables a broader class of spatial occupancy models that can be used to improve predictive accuracy and model adequacy.</p>
Parameter estimation data repository - "Spatial discordances between mRNAs and proteins in the intestinal epithelium"
<p>The repository contains data associated with the estimation of protein translation and decay rates in the manuscript "Spatial discordances between mRNAs and proteins in the intestinal epithelium". Specifically, it includes MCMC-chains approximating the posterior parameter distribution and figures showing the model's fit to the data for each gene as well as a summary table of all fit results for two different models, the constant translation-rate model ("constant_rate_model") and the declining translation-rate model ("declining_rate_model") as explained in the manuscript.</p> <p>Code associated with the parameter estimation is available at https://github.com/LiBuchauer/spatial_MP_discordances .</p>
Data and codes to replicate the analysis in: The spatial ecology of conflicts: Unravelling patterns of wildlife damage at multiple scales
<p><span><span>Human encroachment into natural habitats is typically followed by conflicts derived from wildlife damages to agriculture and livestock. Spatial risk modelling is a useful tool to gain understanding of wildlife damage and mitigate conflicts. Although resource selection is a hierarchical process operating at multiple scales, risk models usually fail to address more than one scale, which can result in the misidentification of the underlying processes. Here, we addressed the multi-scale nature of wildlife damage occurrence by considering ecological and management correlates interacting from household to landscape scales. We studied brown bear (<i>Ursus arctos</i>) damage to apiaries in the North-eastern Carpathians as our model system. Using generalized additive models, we found that brown bear tendency to avoid humans and the habitat preferences of bears and beekeepers determine the risk of bear damage at multiple scales. Damage risk at fine scales increased when the broad landscape context also favoured damages. Furthermore, integrated-scale risk maps resulted in more accurate predictions than single-scale models. Our results suggest that principles of resource selection by animals can be used to understand the occurrence of damages and help mitigate conflicts in a proactive and preventive manner. </span></span></p>
Data from: Multispecies site occupancy modeling and study design for spatially replicated environmental DNA metabarcoding
<p>Although environmental DNA (eDNA) metabarcoding has become widely applied to gauge ecosystems in a noninvasive and cost-efficient manner, false negatives can occur due to various factors in its inherent multistage workflow. It is therefore essential to deal with this kind of species detection errors in eDNA metabarcoding to achieve accurate assessment of species distribution and diversity. To address this issue, we proposed a variant of the multispecies site occupancy model for eDNA metabarcoding studies and applied it to an eDNA metabarcoding dataset of freshwater fish communities collected in the Kasumigaura watershed in Japan.</p> <ul> </ul>
Replication data for: Spatial and temporal origins of the La Perouse low oxygen pool: A combined Lagrangian statistical approach
<p>This dataset contains:</p> <p>a) <strong>NEP36 </strong>daily Model (NEMO) Output from 20130228 till 20131005 in NetCDF format,</p> <p>b) Moving Vessel Profiler (<strong>MVP</strong>) Survey data gathered onboard the R/V Falkor during August 2013 in ASCII .mat files,</p> <p>c) Files required to initialize and run Lagrangian Particle tracking model <strong>ARIANE </strong>i.e. one mesh_mask file in netCDF format and one text file containing initial positions based on the Eulerian grid of the sliced NEP36 model</p> <p>d) the output files from running the particle tracking model ARIANE in NetCDF format</p>
Model data for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions and burned area as part of the publication:</p> <p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p> <p>Dave van Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br><sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br><sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br><sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br><sup>5</sup>Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p> <p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p> <p> </p> <p><strong>UPDATE OF DATASET TO 2023:</strong></p> <p>This dataset has now been extended to 2023. Since the first release of this dataset, multiple updates to the model input data have been made:</p> <p>- Update from MODIS C6 to MODIS C6.1 for all MODIS input data, including MCD12Q1 land cover types, MCD14ML active fires, MCD15A2H fPAR, MOD44B VCF, MOD44W land-water mask, and MCD64A1 burned area.<br>- Update of Hansen forest loss data from v1.9 to v1.11.<br>- Update of GLEAM evaporative stress data from v3.6b to v3.7b.<br>- Extension of ERA5-land data to 2023.<br>- Addition of land cover type layers to the 500-m resolution data files.</p> <p> </p> <p>Files contain 500-m (per MODIS tile) and 0.25 degree aggregated (global grid) carbon emissions and burned area from biomass burning for 2002-2022, as part of the paper "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-15-8411-2022). 500-m resolution files include land cover type grids. 0.25 degree global grid files also include biome partitioning and accompanying biome fractional cover grids.</p> <p>Zip archives with filenames "500m_YYYY.zip" contain annual files named "Model500m_2002-2023yr_h##v##_YYYY.nc", which are the 500-meter resolution model results per MODIS tile using the MODIS sinusoidal projection. Carbon emission data layers are:</p> <p>- Total biomass burning carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_TOT)</p> <p>- Total biomass burning carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_TOT)</p> <p>- Fire-related forest loss carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_FL)</p> <p>- Fire-related forest loss carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_FL)</p> <p>Total emissions are calculated as: C_AG_TOT + C_BG_TOT. Total fire-related forest loss emissions are calculated as: C_AG_FL + C_BG_FL.</p> <p>Burned area data layers are:</p> <p>- Total burned area; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_TOT)</p> <p>- Burned area from fire-related forest loss; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_FL)</p> <p>The Zip archive with filename "025d_2002_2023.zip" contains annual files named "Model500m_2002-2023yr_025d_YYYY.nc", which are the 500-m model results aggregated to a 0.25 degree global lat-lon grid. These files contain the same variables as the 500-m files, but aggregated to 0.25 degree resolution (MOD_CMG025). Furthermore, these files include biome partitioning of emissions and burned area (MOD_CMG025BIOME) and provide accompanying biome fractional cover grids for all 20 biomes (variable 'biomes'). Biomes are listed in detail in Table S1 of the van Wees et al. (2022) paper. The biomes 'water', 'snow/ice' and 'barren' were excluded from Table S1 because of their negligible share, but are included in the files provided here for completeness.</p>
Data Set: Hyperspectral image unmixing with LiDAR data-aided spatial regularization
<p>Data set and matlab codes used for the experimental section of "Hyperspectral Image Unmixing With LiDAR Data-Aided Spatial Regularization"</p> <p>T. Uezato, M. Fauvel and N. Dobigeon, "Hyperspectral Image Unmixing With LiDAR Data-Aided Spatial Regularization," in <em>IEEE Transactions on Geoscience and Remote Sensing</em>, vol. 56, no. 7, pp. 4098-4108, July 2018.<br> doi: 10.1109/TGRS.2018.2823419<br> URL: <a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8347066&isnumber=8393475">http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8347066&isnumber=8393475</a><br> </p>
Dataset for "Interpreting eddy covariance data from heterogeneous Siberian tundra: land cover-specific methane fluxes and spatial representativeness"
<p>The micrometeorological dataset used in</p> <p>Tuovinen, J.-P., Aurela, M., Hatakka, J., Räsänen, A., Virtanen, T., Mikola, J., Ivakhov, V., Kondratyev, V. and Laurila, T.: Interpreting eddy covariance data from heterogeneous Siberian tundra: land cover-specific methane fluxes and spatial representativeness. <em>Biogeosciences Discussions</em>, https://doi.org/10.5194/bg-2018-155, 2018 (accepted for publication in <em>Biogeosciences</em>).</p> <p> </p>
Data and code: Global and regional ecological boundaries explain abrupt spatial discontinuities in avian frugivory interactions
<p>Species interactions can propagate disturbances across space via direct and indirect effects, potentially connecting species at a global scale. However, ecological and biogeographic boundaries may mitigate this spread by demarcating the limits of ecological networks. We tested whether large-scale ecological boundaries (ecoregions and biomes) and human disturbance gradients increase dissimilarity among plant-frugivore networks, while accounting for background spatial and elevational gradients and differences in network sampling. We assessed network dissimilarity patterns over a broad spatial scale, using 196 quantitative avian frugivory networks (encompassing 1,496 plant and 1,004 bird species) distributed across 67 ecoregions, 11 biomes, and 6 continents. We show that dissimilarities in species and interaction composition, but not network structure, are greater across ecoregion and biome boundaries and along different levels of human disturbance. Our findings indicate that biogeographic boundaries delineate the world's biodiversity of interactions and likely contribute to mitigating the propagation of disturbances at large spatial scales.</p>
Spatial data for creating a thermal inertia index and incorporating it for conservation applications
<p>This repository contains supporting material for a journal article being submitted to one of the journals published by the American Geophysical Union, titled Earth's Future. The repository contains the following items:</p> <p>1. README file of what is in the repository including methods associated with the geodatabase</p> <p>2. File Geodatabase</p> <p><strong>1. README file</strong></p> <p>The files collected here relate to a study being submitted to the American Geophysical Union's journal, Earth's Future. The title of the paper being submitted is, "The contribution of Microrefugia to landscape thermal inertia for climate-adaptive conservation and adaptation strategies."</p> <p>The study was conducted across 40,250 km<sup>2</sup> of complex mountainous terrain in Northern California. The objective of the study was to consider whether it was possible to identify the relative strength of microrefugia systematically in order to provide conservation and climate-adaptation strategies with information that could help with prioritizing actions. We selected an operational scale of 10 ha (25 acres) as a scale that is suitable for various types of landscape planning exercises, and created a hexagon grid for the region. We calculated the mean value for multiple variables and appended them into the hexagons. For thermal inertia, we calculated the mean elevation per hexagon and then its coolest (highest) point using an environmental lapse rate. We also calculated solar energy loading, calculated the mean solar load per hexagon, and calculated its effect on air temperature. We combined these two temperature metrics to identify how much thermal buffering capacity each hexagon contains, as measured by how much warming it could experience before the mean temperature, as determined from a baseline time period, is no longer found anywhere within the hexagon. We tied the mean annual temperature from 1981–2010 to the mean elevation in each hexagon, as well as a temperature from an earlier period, and from several future periods, based on global circulation models.</p> <p>The study shows how long current (baseline) climate conditions found in each hexagon may persist and shows how the resulting map of landscape thermal inertia can be used when considering natural vegetation types for conservation, identifying which parts of high-priority wildlife corridors have the greatest capacity to retain their current climate conditions, and what the potential for retaining baseline climate conditions is for areas with late-seral forest conditions as represented by forest canopy height.</p> <p class="MsoNormal">The methods section below describes the data used in the study to create the data in the geodatabase that is posted here. The Geodatabase itself provides all the data needed to replicate the various results presented in the paper. Further information can be found in Thorne et al. 2020. That report is more extensive than the results in our associated paper, but it contains more information on the calculation of various metrics associated with and was the foundation from which we developed this study. The report is provided here in order to keep all the relevant materials compiled for potential use by others. </p> <p><strong>2. File Geodatabase</strong></p> <p>The geodatabase is provided as a separate file.</p> <p>Name: ThermalInertiaIndex.gdb</p> <p>Contents:</p> <ul> <li>AllHexagons <ul> <li>A feature class containing all 408,948 hexagon grids used in this study</li> <li>Fields within the feature class:</li> </ul> </li> </ul> <div> <table> <tbody> <tr> <td> <p>Id</p> </td> <td> <p>A unique ID for each hexagon</p> </td> </tr> <tr> <td> <p>Watershed</p> </td> <td> <p>Watershed the hexagon falls within</p> </td> </tr> <tr> <td> <p>DomWHR</p> </td> <td> <p>Habitat type (WHR) that had the majority coverage within the hexagon</p> </td> </tr> <tr> <td> <p>WHR_Name</p> </td> <td> <p>Descriptive name of the habitat type</p> </td> </tr> <tr> <td> <p>WHR_GroupName</p> </td> <td> <p>Major vegetation type</p> </td> </tr> <tr> <td> <p>CanopyHt_Score</p> </td> <td> <p>Canopy Height Score ranging from 1 (under 1m) to 5 (over 25m)</p> </td> </tr> <tr> <td> <p>CanopyHt_m</p> </td> <td> <p>Average canopy height within the hexagon (m)</p> </td> </tr> <tr> <td> <p>Conn_Score</p> </td> <td> <p>Connectivity Score ranging from 1 (low) to 5 (high)</p> </td> </tr> <tr> <td> <p>dem10m</p> </td> <td> <p>Average elevation within the hexagon (m)</p> </td> </tr> <tr> <td> <p>dem10m_min</p> </td> <td> <p>Minimum elevation within the hexagon (m)</p> </td> </tr> <tr> <td> <p>dem10m_max</p> </td> <td> <p>Maximum elevation within the hexagon (m)</p> </td> </tr> <tr> <td> <p>SRtemp_min</p> </td> <td> <p>The lowest Solar Radiation load within the hexagon (degree C)</p> </td> </tr> <tr> <td> <p>ElevLR_NegEff2</p> </td> <td> <p>Effect of elevation on air temperature (degree C)</p> </td> </tr> <tr> <td> <p>Thermal_Inertia</p> </td> <td> <p>Hexagon buffering capacity (degree C)</p> </td> </tr> <tr> <td> <p>tave_5180</p> </td> <td> <p>Average temperature 1951-1980</p> </td> </tr> <tr> <td> <p>tave_8110</p> </td> <td> <p>Average temperature 1981-2010</p> </td> </tr> <tr> <td> <p>tave_1039mi8</p> </td> <td> <p>Average temperature 2010-2039 (MIROC-ESM RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_4069mi8</p> </td> <td> <p>Average temperature 2040-2069 (MIROC-ESM RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_7099mi8</p> </td> <td> <p>Average temperature 2070-2099 (MIROC-ESM RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_1039cn8</p> </td> <td> <p>Average temperature 2010-2039 (CNRM-CM5 RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_4069cn8</p> </td> <td> <p>Average temperature 2040-2069 (CNRM-CM5 RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_7099cn8</p> </td> <td> <p>Average temperature 2070-2099 (CNRM-CM5 RCP 8.5)</p> </td> </tr> </tbody> </table> </div> <p> </p> <ul> <li>Connectivity_Scores <ul> <li>90m raster containing all 3 connectivity scores</li> <li>Fields within the raster:</li> </ul> </li> </ul> <div> <table> <tbody> <tr> <td> <p>TNC_Conn_Score</p> </td> <td> <p>Connectivity Score from reclassed TNC/Omniscape</p> </td> </tr> <tr> <td> <p>CEHC_Score</p> </td> <td> <p>Connectivity Score from reclassed California Essential Habitat Connectivity</p> </td> </tr> <tr> <td> <p>Combined_Score</p> </td> <td> <p>Overall Connectivity Score</p> </td> </tr> </tbody> </table> </div> <p> </p>
Data and code for: Functional traits mediate individualistic species-environment distributions at broad spatial scales while fine-scale species' associations remain unpredictable
<p>Ecological communities are structured by a diverse set of processes acting at different spatial scales. In plant communities, assembly processes like ecological sorting, limiting similarity, and stochastic events are all expected to influence plant distributions and co-occurrence patterns. We assembled a data set describing the distribution of 139 herbaceous plant species within and among 257 forest stands in Wisconsin (USA) to elucidate the spatial scales at which these assembly processes operate. Analyses of these data in conjunction with detailed information about environmental conditions, plant functional traits, and phylogenetic relationships provided new insights into the scale-dependent drivers of plant community assembly in temperate forest understories. Traits like leaf height, specific leaf area, and seed mass all influenced individualistic plant distributions along landscape-scale gradients in soil texture, soil fertility, light availability, and climate while phylogenetic relationships did not predict species-environment relationships. These findings point to the importance of trait-mediated ecological sorting in shaping individualistic plant distributions at broad spatial scales. Contrary to our expectations about the importance of limiting similarity at local scales, neither functionally similar nor phylogenetically related herbs segregated among microsites within forest stands. We hypothesize strong ecological sorting among forest stands coupled with stochastic fine-scale interactions among species appear deterministic, niche-based assembly processes at local scales.</p>
Long-term spatially-replicated data show no physical cost to a benefactor species in a facilitative plant-plant interaction
<p>Facilitation is an interaction where one species (the benefactor) positively impacts another (the beneficiary). However, the reciprocal effects of beneficiaries on their benefactors are typically only documented using short-term datasets. We use <em>Azorella selago</em>, a cushion plant species and benefactor, and a co-occurring grass species, <em>Agrostis magellanica</em>, on sub-Antarctic Marion Island, comparing cushion plants and the grasses growing on them over a 13-year period using a correlative approach. We additionally compare the feedback effect of <em>A. magellanica</em> on <em>A. selago</em> identified using our long-term dataset with data collected from a single time period. We hypothesized that <em>A. selago</em> size and vitality would be negatively affected by <em>A. magellanica</em> cover and that the effect of <em>A. magellanica</em> on <em>A. selago</em> would become more negative with increasing beneficiary cover and abiotic-severity, due to, e.g., more intense competition for resources. We additionally hypothesized that <em>A. magellanica</em> cover would increase more on cushion plants with greater dead stem cover, since dead stems do not inhibit grass colonization or growth. The relationship between <em>A. magellanica</em> cover and <em>A. selago</em> size and vitality was not significant in the long-term dataset, and the feedback effect of <em>A. magellanica</em> on <em>A. selago</em> did not vary significantly with altitude or aspect; however, data from a single time period did not consistently identify this same lack of correlation. Moreover, <em>A. selago</em> dead stem cover was not significantly related to an increase in <em>A. magellanica</em> cover over the long term; however, we observed contrasting results from short-term datasets. Long-term datasets may, therefore, be more robust (and practical) for assessing beneficiary feedback effects than conventional approaches, particularly when benefactors are slow-growing. For the first time using a long-term dataset, we show a lack of physical cost to a benefactor species in a facilitative interaction, in contrast to the majority of short-term studies.</p>
Data from: Two for the price of one: eDNA metabarcoding reveals temporal and spatial variability of mussel and fish co-distributions in Michigan riverine systems
<p>Freshwater mussels (family Unionidae) are among the world's most endangered taxa, with almost 75% of North American taxa classified as a species of concern, threatened, or endangered. Despite the critical importance of comprehensive distributional data for the conservation of unionids and fishes, these data are often lacking because of the labor and resources associated with traditional survey methods. During their larval stage, unionid mussels use various fish species as obligate hosts, making native fish species vital to unionid persistence and an understanding of host distribution similarly important. Here, we utilized an eDNA metabarcoding approach to evaluate patterns of co-distribution of unionid mussels and fishes along ~362 km of the densely sampled Grand River network as well as the outlets of 19 tributaries along the eastern shore of Lake Michigan, USA. We detected a total of 21 mussel and 40 fish taxa, with distinctive composition of both mussel and fish assemblages across tributaries and differences in fish taxa between sampling periods. Notably, we detected more mussel taxa within the Grand River watershed than at the outlets of all 20 rivers combined. Within the Grand River network, two fish taxa (<em>Pylodictus</em> <em>olivaris</em> and <em>Cyprinella</em>) were found more frequently in areas of high mussel diversity, and three fish taxa more frequently in areas of low mussel diversity (<em>Umbra</em>, Leuciscidae, and <em>Etheostoma</em>). There was little difference between eDNA detections of mussels from samples collected in June versus August, but we detected significantly more fish taxa in August compared to June. Taken together, our findings demonstrate the value of eDNA metabarcoding for evaluating co-distribution of ecologically connected taxa. The use of eDNA as a tool for determining distributions of mussels and their obligate hosts may facilitate conservation efforts for these imperiled taxa.</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.