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349 results for “global distribution”
Global Flash Drought Data for the article "Global Distribution, Trends, and Drivers of Flash Drought Occurrence"
<p>Data is provided (in netcdf format) to reproduce Figures 1-4 in the article entitled "Global Distribution, Trends, and Drivers of Flash Drought Occurrence."</p>
Data from: Potential effects of future climate change on global reptile distributions and diversity
<p class="first-paragraph"><span><strong>Aim:</strong></span><span> Until recently, complete information on global reptile distributions has not been widely available. Here, we provide the first comprehensive climate impact assessment for reptiles on a global scale.</span></p> <p class="western"><span><strong>Location:</strong></span><span> Global, excluding Antarctica</span></p> <p class="western"><span><strong>Time period:</strong></span><span> 1995, 2050, 2080</span></p> <p class="western"><span><strong>Major taxa studied:</strong></span><span> Reptiles</span></p> <p class="western"><span><strong>Methods:</strong></span><span> We modelled the distribution of 6,296 reptile species and assessed potential global as well as realm-specific changes in species richness, the change in global species richness across climate space, and species-specific changes in range extent, overlap and position under future climate change. To assess the future climatic impact on 3,768 range-restricted species, which could not be modelled, we compared the future change in climatic conditions between both modelled and non-modelled species.</span></p> <p class="western"><span><strong>Results:</strong></span><span> Reptile richness was projected to decline significantly over time, globally but also for most zoogeographic realms, with the greatest decrease in Brazil, Australia and South Africa. Species richness was highest in warm and moist regions, with these regions being projected to shift further towards climate extremes in the future. Range extents were projected to decline considerably in the future, with a low overlap between current and future ranges. Shifts in range centroids differed among realms and taxa, with a dominating global poleward shift. Non-modelled species were significantly stronger affected by projected climatic changes than modelled species.</span></p> <p class="western"><span><strong>Main conclusions:</strong></span><span> With ongoing future climate change, reptile richness is likely to decrease significantly across most parts of the world. This effect as well as considerable impacts on species' range extent, overlap, and position were visible across lizards, snakes and turtles alike. Together with other anthropogenic impacts, such as habitat loss and harvesting of species, this is a cause for concern. Given the historical lack of global reptile distributions, this calls for a re-assessment of global reptile conservation efforts, with a specific focus on anticipated future climate change.</span></p>
Chapter 9. Supplementary Material. The global distribution of Acacia
<p>This repository contains the supplementary material for the Chapter, namely the Box 9.S1, Tables 9.S1/S2, Figure 9.S1 (gathered in the PDF <strong>Botella et al_chap9_SUPPLEMENTARY_MATERIAL.pdf) </strong>and the database S9.S1<strong>.</strong> The latter is composed of the 105 data sources and their trust levels (<strong>Data_sources.xlsx</strong>), the table of <em>Acacia</em> introduction events (<strong>intro_table_revis.csv</strong>), the table of naturalization events (<strong>nat_table_revis.csv</strong>), the table of all occurrences including those in Australia (<strong>occ_table.csv</strong>, indicating geolocation and time of observation when available) and the table of countries with associated sampling effort and country codes (<strong>sampling_eff_country.csv</strong>).</p>
Mars Watershed boundary data for "Global Spatial Distribution of Hack's Law Exponent on Mars Consistent with Early Arid Climate "
<p>This is the watershed boundary data for Mars (along with Hack's Law exponent) as described in Luo et al. "Global Spatial Distribution of Hack’s Law Exponent on Mars Consistent with Early Arid Climate" accepted for publication in Geophysical Research Letters on 3/10/2023.</p> <p>The attributes are as follows:</p> <p>Id, gridcode = ID of basin</p> <p>Shape_Length = perimeter of the basin</p> <p>Shape_area = area of the basin</p> <p>geoArea = geodesic area of the basin</p> <p>geoLength = geodesic perimeter of the basin</p> <p>areaR = geoArea / Shape_area</p> <p>LengthR = geoLength / Shape_Length</p> <p>n_exponent = Hack’s Law Exponent (h in L = k A^h)</p> <p>n_coefficient = Hack’s Law exponent (k in L = k A^h)</p> <p>n_r2 = r^2 of the nonlinear fit (optimize.curve_fit function in SciPy)</p>
Supplementary data for multidimensional drivers of mercury distribution in global surface soils
<p>Global distribution of Hg in surface soils predicted by a machine learning method. </p> <p>Random forest modeling.</p> <p>Latitude 1 degree by longitude 1 degree.</p>
Global 10-m spatial distribution of Water-surface photovoltaics (2019-2021)
<p>The recent boom in solar photovoltaics has intensified global competition for land use. Water-surface photovoltaics (WSPV) has also increased globally as an efficient alternative to land-based photovoltaics. Determining the spatio-temporally distribution of WSPVs is essential for estimating renewable energy capacity, evaluating the associated socio-environmental impacts, and managing and planning WSPV projects. However, a comprehensive inventory of WSPV locations and extent on the global scale is still lacking. To address these issues, we developed a workflow for identifying WSPVs using time-series optical satellite images and generated the first global-scale WSPV inventory map, with overall accuracy exceeding 96%.</p>
Fig. 3 in Unveiling global species abundance distributions
Fig. 3 | The temporal change in our statistical understanding of gSADs. a, The final 20-year rolling window gSAD for each of ten example classes with the best fit overlaid for the log-series, negative binomial and Poisson log-normal distributions.b, Yearly goodness of fit (correlation) of each distribution for each 20-year rolling window gSAD.Example classes from top to bottom: Actinopterygii, Amphibia,Arachnida, Aves, Bivalvia, Cephalopoda, Cycadopsida, Insecta, Liliopsida and Mammalia.
Fig. 4 in Unveiling global species abundance distributions
Fig. 4 | How the relative position of the veil corresponds to species richness and the number of individuals in a class. a–c, The proportion of the gSAD uncovered,assuming a Poisson log-normal distribution, and its relationship to observed species richness/number of observations (a), number of observations (b) and species richness (c). To aid in visualizing the patterns, the red dashed line represents a fit from geom_smooth() and the shaded grey area represents the 95% confidence interval around that fit.
Fig. 1 in Unveiling global species abundance distributions
Fig. 1 | Conceptual scheme illustrating the Poisson sampling of a community with species abundances described by a gamma or a log-normal distribution. Two types of gSAD—gamma (left) and log-normal distribution (right) are shown at the top.Each distribution represents the probability f of a species having a given abundance λ, with the gamma distribution having parameters k (shape) and θ (scale) and the log-normal distribution having parameters μ (mean) and σ (standard deviation), and Γ() representing the gamma function.In the middle, sampling of the gSAD with the probability of each species having a given number of individuals sampled described by a Poisson distribution is illustrated. The mean abundance of each species sampled is randomly taken from the SAD.We exemplify two samples of different sizes, where different symbols denote individuals of different species. The bottom graphs show that: if the global abundances have a log-normal distribution, the mixture distribution of abundances in the sample is a Poisson log-normal; if the global abundances follow a gamma distribution the resulting mixture distribution is a negative binomial but in the limit k→0, we obtain the Fisher log-series.
Data from: A global database of butterfly species native distributions
Open the record for dataset details and reuse information.
Data from: Potential effects of future climate change on global reptile distributions and diversity
Open the record for dataset details and reuse information.
The global distribution of known and undiscovered ant biodiversity
Open the record for dataset details and reuse information.
Data from: Biogeographical variation in termite distributions alters global deadwood decay
Open the record for dataset details and reuse information.
Impacts of nutrient addition on soil carbon and nitrogen stoichiometry and stability in globally-distributed grasslands
Global changes will modify future nutrient availability with implications for grassland biogeochemistry. Soil organic matter (SOM) is central to grasslands for both provision of nutrients and climate mitigation through carbon (C) storage. While we know that C and nitrogen (N) in SOM can be influenced by greater nutrient availability, we lack understanding of nutrient effects on C and N coupling and stability in soil. Different SOM fractions have different functional relevance and mean residence times, i.e., mineral-associated organic matter (MAOM) has a higher mean residence time than particulate organic matter (POM). By separating effects of nutrient supply on the different SOM fractions, we can better evaluate changes in soil C and N coupling and stability and associated mechanisms. To this end, we studied responses of C and N ratios and distributions across POM and MAOM to 6-10 years of N, phosphorus (P), potassium and micronutrients (K+µ), and combined NPK+µ additions at 11 grassland sites spanning 3 continents and globally relevant environmental gradients in climate, plant growth, soil texture, and nutrient availability. Data associated with this study are provided here.
The_Global_Distribution_of_Ultra-Low-Frequency_Waves_in_Jupiter's_Magnetosphere_MannersH_ds01
<p>A supporting information dataset for the article "The Global Distribution of Ultra-Low-Frequency Waves in Jupiter's Magnetosphere", submitted by H. Manners and A. Masters. The file contains an Excel spreadsheet detailing the "datetime" intervals containing the events used in the survey described by the article. Additional details are listed alongside the datetimes: the average positions of the spacecraft during each event in radius from the planet, latitude and local time, expressed in Sysem III Jovian coordinates.</p>
Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models
<p></p><p>Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterised strongly influences predictions of future climate by Earth system models. To capture future spatial and temporal changes in vegetation cover and its feedbacks to the climate system, dynamic global vegetation models (DGVM) are included as important components of land surface models. Variation in the predicted vegetation cover from DGVMs therefore has large impacts on modelled radiative and non-radiative properties, especially over high-latitude regions. DGVMs are mostly evaluated by remotely sensed products, but rarely by other vegetation products or by in-situ field observations. In this study, we evaluate the performance of three methods for spatial representation of vegetation cover with respect to prediction of plant functional type (PFT) profiles – one based upon distribution models (DM), one that uses a remote sensing (RS) dataset and a DGVM (CLM4.5BGCDV). PFT profiles obtained from an independently collected vegetation data set from Norway were used for the evaluation. We found that RS-based PFT profiles matched the reference dataset best, closely followed by DM, whereas predictions from DGVM often deviated strongly from the reference. DGVM predictions overestimated the area covered by boreal needleleaf evergreen trees and bare ground at the expense of boreal broadleaf deciduous trees and shrubs. Based on environmental predictors identified by DM as important, we suggest implementation of three novel PFT-specific thresholds for establishment in the DGVM. We performed a series of sensitivity experiments to demonstrate that these thresholds improve the performance of the DGVM. The results highlight the potential of using PFT-specific thresholds obtained by DM in development and benchmarking of DGVMs for broader regions. Also, we emphasize the potential of establishing DM as a reliable method for providing PFT distributions for evaluation of DGVMs alongside RS. </p><p></p>
Data from: Form–function relationships in a marine foundation species depend on scale: a shoot to global perspective from a distributed ecological experiment
Form-function relationships in plants underlie their ecosystem roles in supporting higher trophic levels through primary production, detrital pathways, and habitat provision. For widespread, phenotypically-variable plants, productivity may differ not only across abiotic conditions, but also from distinct morphological or demographic traits. A single foundation species, eelgrass (Zostera marina), typically dominates north temperate seagrass meadows, which we studied across 14 sites spanning 32-61° N latitude and two ocean basins. Body size varied by nearly two orders of magnitude through this range, and was largest at mid-latitudes and in the Pacific Ocean. At the global scale, neither latitude, site-level environmental conditions, nor body size helped predict productivity (relative growth rate 1-2% d-1 at most sites), suggesting a remarkable capacity of Z. marina to achieve similar productivity in summer. Furthermore, among a suite of stressors applied within sites, only ambient leaf damage reduced productivity; grazer reduction and nutrient addition had no effect on eelgrass size or growth. Scale-dependence was evident in different allometric relationships within and across sites for productivity and for modules (leaf count) relative to size. Z. marina provides a range of ecosystem functions related to both body size (habitat provision, water flow) and growth rates (food, carbon dynamics). Our observed decoupling of body size and maximum production suggests that geographic variation in these ecosystem functions may be independent, with a future need to resolve how local adaptation or plasticity of body size might actually enable more consistent peak productivity across disparate environmental conditions.
Data: Greater local cooling effects of trees across globally distributed urban green spaces
<p>The dataset used for hierarchical linear mixed effects models in the R code during the study. </p><p>The description of the column names is provided in the readme file.</p>
Dataset for paper "Global method for gender profile estimation from distribution of first names"
<p>Full dataset for paper "<i>Global method for gender profile estimation from distribution of first names</i>". See https://arxiv.org/abs/2305.07587</p>
The global distribution of plants used by humans datasets: list of utilised species, occurrence data and model outputs at 10 arc-minutes spatial resolution
<p>Datasets and model outputs used to map the global distribution of utilised plants by humans. The folder is composed of two subfolders <em>raw_data</em> and <em>processed_data</em> containing respectively the list of utilised plant species modelled -<em>utilised_plants_species_list.csv</em>-, and their occurrence data -<em>occurrence_data.zip-</em> and predicted distribution -<em>species_proba_per_cell.rds-.</em></p> <p> </p> <ul> <li>The file <em>utilised_plants_species_list.csv</em> in the <em>raw_data</em> folder contains a<strong> </strong>list of 35687 plant species (and hybrids) used by humans and 10 plant use categories with the following 14 fields:</li> </ul> <p><strong>plant_ID:<em> </em></strong>plant identifier number ranging from between 1-35687</p> <p><strong>binomial_acc_name:</strong> binomial accepted name of the plant species</p> <p><strong>author_acc_name</strong>: name of the author(s)</p> <p><strong>is_hybrid:</strong> logical TRUE or FALSE indicating whether the species is an hybrid or not.</p> <p><strong>AnimalFood:</strong> forage and fodder for vertebrate animals only.</p> <p><strong>EnvironmentalUses:</strong> examples include intercrops and nurse crops, ornamentals, barrier hedges, shade plants, windbreaks, soil improvers, plants for revegetation and erosion control, wastewater purifiers, indicators of the presence of metals, pollution, or underground water.</p> <p><strong>Fuels:</strong> charcoal, petroleum substitutes, fuel alcohols, etc. Given the importance of energy plants for people, those were distinguished from Materials.</p> <p><strong>GeneSources:</strong> wild relatives of major crops which may possess traits associated with biotic or abiotic resistance and may be valuable for breeding programs.</p> <p><strong>HumanFood:</strong> food for humans only, including beverages and food additives.</p> <p><strong>InvertebrateFood:</strong> plants consumed by invertebrates used by humans, such as bees, silkworms, lac insects and edible grubs.</p> <p><strong>Materials:</strong> woods, fibers, cork, cane, tannins, latex, resins, gums, waxes, oils, lipids, etc. and their derived products.</p> <p><strong>Medicines:</strong> both human and veterinary.</p> <p><strong>Poisons:</strong> plants which are poisonous to both vertebrates and invertebrates, both accidentally and intentionally, e.g., for hunting and fishing, molluscicides, herbicides, insecticides.</p> <p><strong>SocialsUses:</strong> plants used for social purposes, which cannot be defined as food or medicine, for instance, masticatories, smoking materials, narcotics, hallucinogens and psychoactive drugs, and plants with ritual or religious significance.</p> <p><strong>Totals:</strong> total number of uses recorded for a species</p> <p> </p> <ul> <li>The zipfile <em>occurrence_data.zip</em> in the <em>processed_data</em> folder contains 35687 Comma Separated Values (CSV) files, one for each species, containing curated geographic occurrence records used to build species distribution models with the following 14 fields:</li> </ul> <p><strong>Species:</strong> the binomial accepted name of the species</p> <p><strong>Fullname:</strong> same as species</p> <p><strong>decimalLongitude:</strong> the geographic longitude of the occurrence records of the species in decimal degrees</p> <p><strong>decimalLatitude:</strong> the geographic latitude of the occurrence records of the species in decimal degrees</p> <p><strong>countryCode:</strong> a three-letter standard abbreviation for the country of the occurrence locality</p> <p><strong>coordinateUncertaintyinMeters</strong>: indicator for the accuracy of the coordinate location, described as the radius of a circle around the stated point location</p> <p><strong>year:</strong> year of the observation of the occurrence record of the species</p> <p><strong>individualCount:</strong> the number of individuals present at the time of the observation</p> <p><strong>gbifID:</strong> unique identifier number for the occurrence from the original database</p> <p><strong>basisOfRecords:</strong> the type of the individual record, e.g. observation, physical specimen, fossil, living ex-situ, culture collection specimen</p> <p><strong>institutionCode</strong>: the name of the institution or organization listed as the data publisher on GBIF</p> <p><strong>establishmentMeans:</strong> statement about whether an organism has been introduced to a given place and time through the direct or indirect activity of modern humans</p> <p><strong>is_cultivated_observation:</strong> whether or not an organism is cultivated</p> <p><strong>sourceID:</strong> name of the source database</p> <p> </p> <ul> <li>The file <em>species_proba_per_cell.rds</em> in the <em>processed_data</em> folder is<em> a R Data Serialization </em>(RDS) file containing a data.table object with the following 3 fields:</li> </ul> <p><strong>plant_ID:</strong><em> </em>plant identifier number ranging from between 1-35687</p> <p><strong>proba:</strong> species occurrence probability</p> <p><strong>cell:</strong><em> </em>raster grid cell number between 1-2251762</p> <p>This object can be used in combination with a raster layer to reconstruct the modelled distribution of each species or retrieve species richness and endemism.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.