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349 results for “global distribution”
Fig. 1 in The global distribution of known and undiscovered ant biodiversity
Fig. 1. Globalantspeciesrichness patternsincomparison withterrestrialvertebrates. (A) Species richnesscenters (top 10% of area) for amphibians, birds, mammals, reptiles, and ants, indicating areas of congruence and incongruence of biodiversity centers across taxa. (B) Species richness maps based on stacking individual species range estimates for ants and vertebrates. (C) Spearman's correlation matrix for grid cell–level species richness across taxa.
Fig. 5 in The global distribution of known and undiscovered ant biodiversity
Fig. 5. Empirical and predicted rarity centers of Europe, Africa, and West Asia. Rarity centers based on current knowledge and projected by a Random Forest model under a "universal high sampling" scenario. See Fig.3 for more explanation.
Supplementary data for global distribution of mercury in foliage predicted by machine learning
<p>Global distribution of foliar mercury concentrations and pools with a spatial resolution of 0.25 latitude by 0.25 longitude, predicted by machine learning.</p>
Figure 3 in How can global climate change influence the geographic distribution of the eucalyptus yellow beetle? Modeling and prediction for Brazil
Figure 3. Predicting of potential areas to the occurrence of Costalimaita ferruginea in the period of 2061-2080, in two climate change scenarios, Representative Concentration Pathways (RCP) 4.5 e 8.5 (W/m2), using the algorithm Envelope Score (AUC = 0.808). The numbers 1 to 5 represent the Brazilian biomes, being 1 = Amazônia, 2 = Caatinga, 3 = Cerrado, 4 = Pantanal, 5 = Mata Atlântica e 6 = Pampa.
Figure 2 in How can global climate change influence the geographic distribution of the eucalyptus yellow beetle? Modeling and prediction for Brazil
Figure 2. Predicting of potential areas to the occurrence of Costalimaita ferruginea in the period of 2041-2060, in two climate change scenarios, Representative Concentration Pathways (RCP) 4.5 e 8.5 (W/m2), using the algorithm Envelope Score (AUC = 0.808). The numbers 1 to 5 represent the Brazilian biomes, being 1 = Amazônia, 2 = Caatinga, 3 = Cerrado, 4 = Pantanal, 5 = Mata Atlântica e 6 = Pampa.
Fig. 1 in Global systematic diversity, range distributions, conservation and taxonomic assessments of graylings (Teleostei: Salmonidae; Thymallus spp.)
Fig. 1 Map showing the global distribution range of Thymallus species. Information on sampling sites and species is given in Table S1. Numbers in the map refer to known contact zones of the following species: 1 = T. arcticus s.l. and T. baicalensis in the lower Enisei River; 2 = T. arcticus s.l. and T. baicalolenensis in the lower Lena River; 3 = T. nikolskyi and T. baicalensis in tributaries of the upper Ob River; 4 = T. baicalolenensis and T. baicalensis in tributaries of Lake Baikal; 5 = T. grubii, T. tugarinae
Telescopus finkeldeyi Haacke, 2013 DAMARA TIGER SNAKE Telescopus finkeldeyi Haacke 2013:281. Holotype: TM 53542 (collector J.A. van Rooyen). Type locality: "Rössing Uranium mine area, Swako- mund [sic] district (2214Db) Namibia." Global conservation status (IUCN): Not Evaluated. Global distribution: The species is known from Angola and Namibia. Ocurrences in Angola (Map 364): The species occurs in southwestern Angola. Namibe: "5 km north Namibé" [-15.20000, 12.15000] (Haacke 2013:285). Taxonomic and distributional notes: Some earlier records of T. semiannulatus polystictus in Namibia actually refer to this recently described species. MAP 364. Distribution of Telescopus finkeldeyi in Angola. in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
Telescopus finkeldeyi Haacke, 2013 DAMARA TIGER SNAKE Telescopus finkeldeyi Haacke 2013:281. Holotype: TM 53542 (collector J.A. van Rooyen). Type locality: "Rössing Uranium mine area, Swako- mund [sic] district (2214Db) Namibia." Global conservation status (IUCN): Not Evaluated. Global distribution: The species is known from Angola and Namibia. Ocurrences in Angola (Map 364): The species occurs in southwestern Angola. Namibe: "5 km north Namibé" [-15.20000, 12.15000] (Haacke 2013:285). Taxonomic and distributional notes: Some earlier records of T. semiannulatus polystictus in Namibia actually refer to this recently described species. MAP 364. Distribution of Telescopus finkeldeyi in Angola.
Insights into the Major Processes Driving the Global Distribution of Copper in the Ocean from a Global Model
<p>Annual output netcdf output file of the NEMO/PISCES Cu model on the ORCA2 grid. Reference simulation described and discussed in Richon, C. and Tagliabue, A. Insights into the Major Processes Driving the Global Distribution of Copper in the Ocean from a Global Model, Global Biogeochemical Cycles, 2019</p>
Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6-LIM3 global ocean–sea ice model
<p>Model output and observational data and scripts corresponding to the manuscript "Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6-LIM3 global ocean–sea ice model"</p> <p><strong>Abstract. </strong></p> <p>This study assesses the impact of different sea ice thickness distribution (ITD) configurations on the sea ice concentration (SIC) variability in ocean-standalone NEMO3.6-LIM3 simulations. Three ITD configurations with different numbers of sea ice thickness categories and boundaries are evaluated against three different satellite products (hereafter referred to as “data”). Typical model and data interannual SIC variability is characterized by k-means clustering both in the Arctic and Antarctica between 1979 and 2014 in two seasons, January–March and August–October, which show the largest coherence across clusters in individual months. Analysis in the Arctic is done before and after detrending the series with a 2nd degree polynomial to separate interannual from longer-term variability.</p> <p>Before detrending, winter clusters capture SIC response to atmospheric variability at both poles and summer cluster a positive and negative trend in the Arctic and Antarctic SIC respectively. After detrending, Arctic clusters reflect SIC response to interannual atmospheric variability predominantly. Model–data cluster comparison suggests that no specific ITD configuration or category number increases realism of the simulated Arctic and Antarctic SIC variability in winter. In the Arctic summer, more thin-ice categories decrease model–data agreement without detrending but increase agreement after detrending. Overall, a single-category configuration agrees the worst with data.</p> <p>Direct model–data comparison of SIC anomaly fields shows that more thick-ice categories improve winter SIC variability realism in Central Arctic regions with very thick ice. By contrast, more thin-ice categories reduce model–data agreement in the Central Arctic in summer, due to an overly large simulated sea ice volume.</p> <p>In summary, whereas better resolving thin ice in NEMO3.6-LIM3 can hamper model realism in the Arctic but improve it in Antarctica, more thick-ice categories increase realism in the Arctic winter. And although the single-category configuration performs the worst overall, no optimal configuration is identified. Our results suggest that no clear benefit is obtained from increasing the number of sea ice thickness categories beyond the current usual standard of 5 categories in NEMO3.6-LIM3.</p>
Figure 2 in Soil BON Earthworm - A global initiative on earthworm distribution, traits, and spatiotemporal diversity patterns
Figure 2. Information on studies that will be resampled globally by the Soil BON Earthworm consortium. (A) Global distribution of studies, with the distribution of sites along longitude and latitude, (B) distribution of ecosystem types among studies, (C) localization of sites among terrestrial biomes defined by Mean Annual Temperature (MAT, °C) and Mean Annual Precipitation (MAP, mm), with the distribution of MAT and MAP values, (D) distribution of time span with blue and green colors representing the variable distribution before and after resampling, respectively, (E) Temporal coverage of individual studies.
Figure 3 in Soil BON Earthworm - A global initiative on earthworm distribution, traits, and spatiotemporal diversity patterns
Figure 3. Global distribution of Oligochaeta observations on iNaturalist (assessed on the 16th of November 2023) and longitudinal and latitudinal distribution.
Fig. 3 in Investigating the pathogens associated with Dermacentor nuttalli and its global distribution: A study integrating metagenomic sequencing, meta-analysis and niche modeling
Fig. 3. Prevalence of pathogens associated with D. nuttalli. If there was only one study included in a certain pathogen, the positive rate would be calculated by the positive number of ticks divided by the total number of detected ticks, and without the 95% confidence interval. If there were more studies, the positive rate and 95% confidence interval would be calculated by meta-analysis.
Fig. 2 in Investigating the pathogens associated with Dermacentor nuttalli and its global distribution: A study integrating metagenomic sequencing, meta-analysis and niche modeling
Fig. 2. Study design and data sources of the meta-analysis. A comprehensive meta-analysis was performed to evaluate D. nuttalli's potential threats based on detected pathogens and geographical distribution positions. The database of D. nuttalli was constructed from four sources, including field surveys, literature review, a reference book, and an online biodiversity database (Global Biodiversity Information Facility, GBIF, https://www.gbif.org).
Fig. 1 in Investigating the pathogens associated with Dermacentor nuttalli and its global distribution: A study integrating metagenomic sequencing, meta-analysis and niche modeling
Fig. 1. Relative pathogen abundance of four D. nuttalli samples and the phylogenomic analysis of four Rickettsia genomes. (A) Pathogen abundance at the family level. (B) Pathogen abundance at the genus level. (C) The phylogenetic tree of four Rickettsia assemblies. The phylogenetic tree of four Rickettsia assemblies (Rickettsia conorii subsp. raoultii str XinjiangF1, Rickettsia conorii subsp. raoultii str XinjiangF2, Rickettsia conorii subsp. raoultii str XinjiangF3, and Rickettsia conorii subsp. raoultii str XinjiangM1) was built with 28 other publicly available established or proposed Rickettsiales species. The tree was inferred by IQ-TREE based on 277 single-copy orthologs identified by OrthoFinder. Anaplasma phagocytophilum and Ehrlichia ruminantium were two outgroup species.
Fig. 5 in Investigating the pathogens associated with Dermacentor nuttalli and its global distribution: A study integrating metagenomic sequencing, meta-analysis and niche modeling
Fig. 5. Global potential distribution of D. nuttalli. The red area indicates greater possibilities of suitability for D. nuttalli, while the blue area is less likely to be suitable for D. nuttalli.
Fig. 4 in Investigating the pathogens associated with Dermacentor nuttalli and its global distribution: A study integrating metagenomic sequencing, meta-analysis and niche modeling
Fig. 4. Geographical distribution of D. nuttalli. D. nuttalli lived mainly between 23◦–53◦ latitude and 76◦–133◦ longitude in the Northern Hemisphere. Triangles represent the locations in prefecture-level regions, while circles represent the distribution locations in county-level regions. The green circles represent points from GBIF, the yellow circles are points from literature, the purple circles represent the points from the field survey and the blue points are points from a reference book. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in From wildlife to humans: The global distribution of Trichinella species and genotypes in wildlife and wildlife-associated human trichinellosis
Fig. 1. Sylvatic life cycle and potential transmission routes of Trichinella spp. Created with BioRender.com.
Fig. 3 in From wildlife to humans: The global distribution of Trichinella species and genotypes in wildlife and wildlife-associated human trichinellosis
Fig. 3. Global distribution of sylvatic Trichinella species and genotypes adapted from Pozio (2016); Gottstein et al. (2009).
The global distribution and drivers of wood density across angiosperms and gymnosperms and their impact on forest carbon stocks
<p>Abstract:</p> <div>The density of wood is a key indicator of trees’ carbon investment strategies, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here, we analyze information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially-explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to ~30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems.</div> <div> </div> <p>This repository only provides the tif data of this paper. All the codes could be accessed from GitHub: https://github.com/LidongMo/GlobalWoodDensityProject</p>
DDISH-GI: Dynamic Distributed Spherical Harmonics Global Illumination - Supplementary Video
<p>A supplementary video for the upcoming publication "DDISH-GI: Dynamic Distributed Spherical Harmonics Global Illumination". The video includes a comparison to a state-of-the-art method and also to the path traced ground truth. Limitations of the proposed method are also shown.</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.