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645 results for “Spatial distributions”
Fig. 2 in Spatial distribution of Culicidae (Diptera) larvae, and its implications for Public Health, in five areas of the Atlantic Forest biome, State of São Paulo, Brazil
Fig. 2. Spatial distribution of the species found in the Cruzeiro do Sul rural district.
Fig. 1 in Spatial distribution of Culicidae (Diptera) larvae, and its implications for Public Health, in five areas of the Atlantic Forest biome, State of São Paulo, Brazil
Fig. 1. Localization of the study areas in the Atlantic Forest biome, São Paulo state – Brazil.
Fig. 6 in Spatial distribution of Culicidae (Diptera) larvae, and its implications for Public Health, in five areas of the Atlantic Forest biome, State of São Paulo, Brazil
Fig. 6. Adjusted Semi-variograms of the Simpson diversity index.
Fig. 3 in Spatial distribution of Culicidae (Diptera) larvae, and its implications for Public Health, in five areas of the Atlantic Forest biome, State of São Paulo, Brazil
Fig. 3. Spatial distribution of the species found in the Vale das Cigarras rural districts.
Figure 1 in First record of Aglais caschmirensis aesis (Fruhstorfer, 1912) (Lepidoptera: Rhopalocera: Nymphalidae) from Meghalaya, with a note on its spatial distribution
Figure 1. Aglais caschmirensis aesis (Fruhstorfer, 1912). a. Upperside. b. Underside.
Fig. 1 in Distribution of carabid beetles in agroecosystems across spatial scales - A review
Fig. 1. Nested relations between the spatial levels (after Ettema, Wardle (2002), with changes).
Figure 2 in Spatial and temporal distribution of aquatic insects in the Dicle (Tigris) River Basin, Turkey, with new records
Figure 2. Psychomyia sp. larva: a- head, b- trochantin, c- anal claws.
Figure 1 in Spatial and temporal distribution of aquatic insects in the Dicle (Tigris) River Basin, Turkey, with new records
Figure 1. The locations of the selected sampling sites in the Tigris River Basin.
Figure 1 in Spatial distribution of the epigeic species of earthworms Dendrobaena octaedra and D. attemsi (Oligochaeta: Lumbricidae) in the forest belt of the northwestern Caucasus
Figure 1. Locations of the earthworms of northwestern Caucasus.
Figure 5 in Population structure and spatial distribution of the tiger (Panthera tigris, Felidae, Carnivora) in Southwestern Primorye (Russian Far East)
Figure 5. Layout of home ranges of the GPS-collared tigers (Hernandez-Blanco et al., 2015).
Figure 3 in Population structure and spatial distribution of the tiger (Panthera tigris, Felidae, Carnivora) in Southwestern Primorye (Russian Far East)
Figure 3. Relationship between tigers according to DNA identification.
Figure 2 in Population structure and spatial distribution of the tiger (Panthera tigris, Felidae, Carnivora) in Southwestern Primorye (Russian Far East)
Figure 2. Distribution of tiger tracks in Southwestern Primorye.
Fig. 1 in South American Sea Lions Otaria flavescens, a good indicator of relative spatial and temporal changes in the distribution and abundance of marine resources?
Fig. 1. Study area showing the location of the rookeries analysed at RÍo Negro Province, Argentina.
Spatial and seasonal distribution of selected nitrogen cycle genes in deep waters of the Baltic Proper
<p>The dataset provides information on nitrogen cycle related bins and gene characteristics across selected depths within the IDEAL, P1, and BY15 sites in the Baltic Sea. The dataset includes details such as gene spans, gene lengths, gene names, and the processes to which each gene is assigned. Additionally, it contains raw read counts, RPKM (reads per kilobase of transcript, per million mapped reads), family, phylum, bin, site, common bin identifiers for sites, and seasonal variations. Bin parameters, along with lineage-specific markers, are included to estimate the completeness of the bins. We focused on nitrogen loss processes (denitrification, anammox), reduction processes (dissimilatory nitrate reduction (DNR), dissimilatory nitrate reduction to ammonium (DNRA)), and oxidation (nitrification). <span>The reported results were obtained within the framework of the statutory activities of the Institute of Oceanology of the Polish Academy of Sciences and the following research project: 2019/34/E/ST10/00217 funded by the Polish National Science Centre.</span></p>
Characterizing the spatial correlation of coseismic slip distributions: A data driven Bayesian approach
<p>Slip models for the simulated case and the Illapel earthquake are provided. The zip file contains processed data, predictions, and uncertainty estimates for the Illapel event.</p>
Soil dissolved organic carbon in terrestrial ecosystems: global budget, spatial distribution and controls
<p><strong>Aims: </strong>Soil dissolved organic carbon (DOC) is a primary form of labile carbon in terrestrial ecosystems and therefore plays a vital role in soil carbon cycling. This study aims to quantify the budgets of soil DOC at biome- and global levels and to examine the variations in soil DOC and their environmental controls. Location: Global Time period: 1981 - 2019 Method: We compiled a global dataset and analyzed the concentration and distribution of DOC across 10 biomes.</p> <p><strong>Results: </strong>Large variations in DOC are found among biomes across space and the soil DOC concentration declines exponentially along soil depths. Tundra has the highest soil DOC concentration in 0 - 30 cm soils (453.75 (95% confidence interval: 324.95 – 633.5) mg·kg-1); whereas tropical and temperate forests have relatively lower DOC concentrations, ranging from 30.20 (24.78 - 36.80) mg·kg-1 to 54.54 (49.77 – 59.77) mg·kg-1. DOC generally accounts for < 1% of total organic carbon in soils, and DOC in 0 - 30 cm contributes more than half of total DOC in 0 - 100 cm soil profile. Furthermore, variations in DOC are primarily controlled by soil texture, moisture, and total organic carbon.</p> <p><strong>Main conclusion: </strong>A global synthesis is combined with an empirical model to extrapolate the DOC concentration along soil profiles across the globe, and global budgets of DOC are estimated as 7.20 Pg C in top 0 - 30 cm and 12.97 Pg C in 0 - 100 cm, respectively, with a considerable variation among biomes. The strong soil texture control but weak TOC control on DOC variations suggest that the investigation of physical protection of soil organic carbon might need to expand to consider the labile C in soils. The global maps of DOC concentration serve as a benchmark for validating land surface models in estimating carbon storage in soils.</p>
Spatial slip rate distribution along the SE Xianshuihe fault, eastern Tibet, and earthquake hazard assessment
<p><strong><em>Table 2: </em></strong><em><sup>10</sup></em><em>Be surface-exposure ages of Zheduotang (ZDT) and Moxi (MX) sites of the SE Xianshuihe fault.</em></p>
Energy-water and seasonal variations in climate underlie the spatial distribution patterns of gymnosperms species richness in China
<p>Studying the pattern of species richness is crucial in understanding the diversity and distribution of organisms in the earth. Climate and human influences are the major driving factors that directly influence the large-scale distributions of plant species, including gymnosperms. Understanding how gymnosperms respond to climate, topography, and human-induced changes is useful in predicting the impacts of global change. Here, we attempt to evaluate how climatic and human-induced processes could affect the spatial richness patterns of gymnosperms in China. Initially, we divided a map of the country into grid cells of 50 × 50 km<sup>2 </sup>spatial resolution and plotted the geographical coordinate distribution occurrence of 236 native gymnosperm taxa. The gymnosperm taxa were separated into three response variables: (i) all species, (ii) endemic species, and (iii) non-endemic species, based on their distribution. The species richness patterns of these response variables to four predictor sets were also evaluated: (i) energy-water, (ii) climatic seasonality, (iii) habitat heterogeneity, and (iv) human influences. We performed generalized linear models (GLMs) and variation partitioning analyses to determine the effect of predictors on spatial richness patterns. The results showed that the distribution pattern of species richness was highest in the southwestern mountainous area and Taiwan in China. We found a significant relationship between the predictor variable set and species richness pattern. Further, our findings provide evidence that climatic seasonality is the most important factor in explaining distinct fractions of variations in the species richness patterns of all studied response variables. Moreover, it was found that energy-water was the best predictor set to determine the richness pattern of all species and endemic species, while habitat-heterogeneity has a better influence on non-endemic species. Therefore, we conclude that with the current climate fluctuations as a result of climate change and increasing human activities, gymnosperms might face a high risk of extinction.</p>
Fig. 6 in Spatial Distribution Of Tropical Estuarine Nematode Communities In Sarawak, Malaysia (Borneo)
Fig. 6. Percentage of contribution of each functional feeding group
Fig. 5. a in Spatial Distribution Of Tropical Estuarine Nematode Communities In Sarawak, Malaysia (Borneo)
Fig. 5. a, Two-dimensional MDS ordination constructed from the
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.