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645 results for “Spatial distributions”
Fig. 3 in Effects of Forest Roads on Spatial Distribution of Boreal Carabid Beetles (Coleoptera: Carabidae)
Fig. 3. Mean values of species richness and abundances of two carabid species for the roadside samples, and forest samples at distances 25 m and 50 m from the roadsides. Only specimens caught between 28 June and 23 September are included. For statistical significances, consult Table 2 and Appendix 2a.
Fig. 1 in Effects of Forest Roads on Spatial Distribution of Boreal Carabid Beetles (Coleoptera: Carabidae)
Fig. 1. The road network in Häme, south-central Finland, in 1955 and in 2001. For the geographical location of the mapped area, see Figure 2. The maps are based on 1:250,000 (''GT'') maps, and complemented by adding forest roads from basic maps (1:20,000) to make them comparable. Boxes within maps show the Hyytiälä study area. Water bodies are shown in gray.
Data for the paper "Effects of surface and subsurface water/ice on spatial distributions of impact crater ejecta on Mars"
<p>This file contains input data for iSALE simulations reported in the publication:</p><p><i>"</i>Effects of surface and subsurface water/ice on spatial distributions of impact crater ejecta on Mars<i> by Aleksandra Sokolowska, Nicolas Thomas, and Kai Wuennemann</i></p>
Figure 4 in Small-scale spatial distribution of ghost shrimp and macrobenthic fauna in an Amazon macrotidal dissipative sandy beach
Figure 4. Relative abundance (%) of taxonomic (A), feeding groups (B), mean density (ind./m2 ± standard error) (C) and taxon richness (± standard error) (D) of the macrobenthic fauna of the two study areas (Area 1: muddy sediment – next to a tidal channel, Area 2: sandy sediment).
Figure 5 in Small-scale spatial distribution of ghost shrimp and macrobenthic fauna in an Amazon macrotidal dissipative sandy beach
Figure 5. Plot of the principal coordinate analysis (PCoA) of the samples of macrocrobenthic fauna collected from the two areas (Area 1: muddy sediment – next to a tidal channel, Area 2: sandy sediment). The vectors represent species with Spearman correlation values greater than 0.5. Samples from Area 1 and Area 2 were 68.34% dissimilar.
Figure 2 in Small-scale spatial distribution of ghost shrimp and macrobenthic fauna in an Amazon macrotidal dissipative sandy beach
Figure 2. Photography showing the chimneys (elevated, muddy) of agglutinated sediments at the burrow openings of the Lepidophtalmus siriboia tube in the study area.
Figure 1 in Small-scale spatial distribution of ghost shrimp and macrobenthic fauna in an Amazon macrotidal dissipative sandy beach
Figure 1. Map of Algodoal-Maiandeua island showing the study beach (Fortalezinha) and the sample areas (Area 1: muddy sediment – next to a tidal channel, Area 2: sandy sediment).
Figure 3 in Small-scale spatial distribution of ghost shrimp and macrobenthic fauna in an Amazon macrotidal dissipative sandy beach
Figure 3. Mean density (± standard error) of the Lepidophtalmus siriboia burrows in the intertidal zones (HT: high intertidal; MT: mid intertidal; LT: low intertidal) of the study areas (Area 1: muddy sediment – next to a tidal channel, Area 2: sandy sediment). Different letters indicate significant differences (p <0.05); letters indicate the results of Tukey tests for the comparisons between areas (uppercase letters) and among intertidal zones (lowercase letters).
Projections of spatial distributions of suitable environmental conditions for key Baltic Sea zooplankton species - Data
<p>This repository contains seven occurrences dataset which represent the station where the species have been identified, ranging from 2000 to 2020. The environmental projections for the period 2010-2020, as well as future projection on two horizons: from 2040 to 2050 and from 2090 to 2100 on two different scenarios: SSP245 and SSP585. The occurrences have been exctracted from OBIS (https://obis.org) and the environmental projections from Bio-ORACLE (https://bio-oracle.org).</p> <p> </p> <p>Occurrences datasets: </p> <ul> <li><em>Temora longicornis</em></li> <li><em>Centropages hamatus</em></li> <li><em>Limnocalanus macrurus macrurus</em></li> <li><em>Evadne nordmanni</em></li> <li><em>Acartia tonsa</em></li> <li><em>Acartia longiremis</em></li> <li><em>Acartia bifilosa</em></li> </ul> <p> </p> <p>Projections:</p> <ul> <li>Projection Baseline 2010-2020</li> <li>Projection 2040-2050 SSP245</li> <li>Projection 2090-2100 SSP245</li> <li>Projection 2040-2050 SSP585</li> <li>Projection 2090-2100 SSP585</li> </ul> <p> </p> <p> </p> <p> </p>
MuAP Spatial distribution of various air pollutants in China at 1 km(SO2 2021-01-01:2023-12-31) (Version1.1)
<p>MuAP Spatial distribution of various air pollutants in China at 1 km(SO2)</p> <p>Multiple air pollutions dataset (MuAP) </p> <p>Time frame: 2021-2023<br>Area: Most of China<br>Resolution: about 1km<br>File storage format: .xz and GeoTIFF<br>Spatial projection: WGS84<br>Daily file name: year_doy.tif (Daily MuAP data volume exceeds Zenodo platform limits. Please contact the author at fjcyfeng@qq.com.)</p> <p>Monthly file name: year_month.tif</p> <p>Yearly file name: year_month.tif</p> <p>Unit: Please divide by 10 when using. (ug/m3)</p> <p>When you download and use our data, please cite:</p> <p>Chi, Y., Zhan, Y., Wang, K., and Ye, H.: Sequential spatiotemporal distribution of PM<sub>2.5</sub>, SO<sub>2</sub> and Ozone in China from 2015 to 2020, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-76, in review, 2023.</p> <p>Chi, Y., Zhan, Y., Wang, K., & Ye, H. (2023). Spatial Distribution of Multiple Atmospheric Pollutants in China from 2015 to 2020. Remote Sensing, 15(24). doi:10.3390/rs15245705</p> <p>Note: The MuAP for 2015-2020 can be obtained by:</p> <p>1.PM2.5:https://zenodo.org/records/8093749</p> <p>O3:https://zenodo.org/records/8180923</p> <p>SO2:https://zenodo.org/records/8093749</p> <p>NO2:Please contact the author at fjcyfeng@qq.com.</p> <p> </p> <p>2. Daily MuAP data volume exceeds Zenodo platform limits. Please contact the author at fjcyfeng@qq.com.</p> <p> </p>
MuAP Spatial distribution of various air pollutants in China at 1 km(O3 2021-01-01:2023-12-31) (Version1.1)
<p>MuAP Spatial distribution of various air pollutants in China at 1 km(O3)</p> <p>Multiple air pollutions dataset (MuAP) </p> <p>Time frame: 2021-2023<br>Area: Most of China<br>Resolution: about 1km<br>File storage format: .xz and GeoTIFF<br>Spatial projection: WGS84<br>Daily file name: year_doy.tif (Daily MuAP data volume exceeds Zenodo platform limits. Please contact the author at fjcyfeng@qq.com.)</p> <p>Monthly file name: year_month.tif</p> <p>Yearly file name: year_month.tif</p> <p>Unit: Please divide by 10 when using. (ug/m3)</p> <p>When you download and use our data, please cite:</p> <ol> <li>Chi, Y., Zhan, Y., Wang, K., and Ye, H.: Sequential spatiotemporal distribution of PM<sub>2.5</sub>, SO<sub>2</sub> and Ozone in China from 2015 to 2020, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-76, in review, 2023.</li> <li>Chi, Y., Zhan, Y., Wang, K., & Ye, H. (2023). Spatial Distribution of Multiple Atmospheric Pollutants in China from 2015 to 2020. Remote Sensing, 15(24). doi:10.3390/rs15245705</li> </ol> <p>Note: The MuAP for 2015-2020 can be obtained by:</p> <p>1.</p> <ul> <li>PM2.5:https://zenodo.org/records/8093749</li> <li>O3:https://zenodo.org/records/8180923</li> <li>SO2:https://zenodo.org/records/8093749</li> <li>NO2:Please contact the author at fjcyfeng@qq.com.</li> </ul> <p> </p> <p>2. Daily MuAP data volume exceeds Zenodo platform limits. Please contact the author at fjcyfeng@qq.com.</p> <p> </p>
MuAP Spatial distribution of various air pollutants in China at 1 km(PM2.5 2021-01-01:2023-12-31) (Version1.1)
<p>MuAP Spatial distribution of various air pollutants in China at 1 km(PM2.5)</p> <p>Multiple air pollutions dataset (MuAP) </p> <p>Time frame: 2021-2023<br>Area: Most of China<br>Resolution: about 1km<br>File storage format: .xz and GeoTIFF<br>Spatial projection: WGS84<br>Daily file name: year_doy.tif (Daily MuAP data volume exceeds Zenodo platform limits. Please contact the author at fjcyfeng@qq.com.)</p> <p>Monthly file name: year_month.tif</p> <p>Yearly file name: year_month.tif</p> <p>Unit: Please divide by 10 when using. (ug/m3)</p> <p>When you download and use our data, please cite:</p> <ol> <li>Chi, Y., Zhan, Y., Wang, K., and Ye, H.: Sequential spatiotemporal distribution of PM<sub>2.5</sub>, SO<sub>2</sub> and Ozone in China from 2015 to 2020, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-76, in review, 2023.</li> <li>Chi, Y., Zhan, Y., Wang, K., & Ye, H. (2023). Spatial Distribution of Multiple Atmospheric Pollutants in China from 2015 to 2020. Remote Sensing, 15(24). doi:10.3390/rs15245705</li> </ol> <p>Note: The MuAP for 2015-2020 can be obtained by:</p> <p>1.</p> <ul> <li>PM2.5:https://zenodo.org/records/8093749</li> <li>O3:https://zenodo.org/records/8180923</li> <li>SO2:https://zenodo.org/records/8093749</li> <li>NO2:Please contact the author at fjcyfeng@qq.com.</li> </ul> <p> </p> <p>2. Daily MuAP data volume exceeds Zenodo platform limits. Please contact the author at fjcyfeng@qq.com.</p> <p> </p>
Upscaling Tower-Based Net Ecosystem Productivity to global 250m using the Data Augmentation Method by Considering their Spatial Distribution
<p>Terrestrial ecosystems have emerged as critical carbon sinks, holding a crucial role in the carbon cycle. Net ecosystem productivity (NEP) is a highly significant parameter in terrestrial ecosystems, representing the net ecosystem exchange (NEE) between ecosystems and the atmosphere, without considering other carbon fluxes from disturbances. In this NEP product, we harmonized various sets of tower-based NEP from flux sites as target variable, remote sensing product and meteorological data as traning variables. We further optimizied these smaple sets to address the problems in spatial distribution, culminating in a global NEP product spanning the years 2001-2022, achieved through the application of the random forest method. This dataset contains NEP data for global terrestrial ecosystems for the period 2001-2022 in MgC with a temporal resolution of 1 year. The spatial resolution of the product is 250m and the data format is TIFF.</p> <p><strong>For detailed instructions on how to use the dataset, see User Guides.doc!</strong></p>
Spatial autocorrelation shapes liana distribution better than topography and host tree properties in a subtropical evergreen broadleaved forest in SW China
<p>Lianas are an important component of subtropical forests, but the mechanisms underlying their spatial distribution patterns have received relatively little attention. Here, we selected 12 most abundant liana species, constituting up to 96.9% of the total liana stems, in a 20-ha plot in a subtropical evergreen broadleaved forest at 2,472 – 2,628 m elevation in SW China. Combining data on topography (convexity, slope, aspect, and elevation) and host trees (density and size) of the plot, we addressed how liana distribution is shaped by host tree properties, topography and spatial autocorrelation by using principal coordinates of neighbor matrices (PCNM) analysis. We found that lianas had an aggregated distribution based on the Ripley's <i>K</i> function. At the community level, PCNM analysis showed that spatial autocorrelation explained 43% variance in liana spatial distribution. Host trees and topography explained 4% and 18% of the variance, but less than 1% variance after taking spatial autocorrelation into consideration. A similar trend was found at the species level. These results indicate that spatial autocorrelation might be the most important factor shaping liana spatial distribution in subtropical forest at high elevation.</p>
Supplementary material for "Spatio-temporal modelling of abundance from multiple data sources in an integrated spatial distribution model"
<p><strong>Abstract</strong></p> <p><strong>Aim:</strong> In biodiversity monitoring, observational data are often collected in multiple, disparate schemes with greatly varying degrees of standardization and possibly at different spatial and temporal scales. Technical advances also change the type of data over time. The resulting heterogeneous data sets are often deemed to be incompatible. Consequently, many available data sets may be ignored in practical analyses. Here, we propose a more efficient use of disparate biodiversity data to assess species distributions and population trends.<br> <br> <strong>Location:</strong> Switzerland (Europe)<br> <br> <strong>Taxon:</strong> Birds</p> <p><strong>Methods: </strong>We developed an integrated, hierarchical species distribution model with a joint likelihood for all data sets using a shared state process (e.g., latent species abundance or occurrence), but distinct observation process for each data set. We show how the abundance submodel of a binomial N-mixture model can fuse four different data types (count, detection/non-detection, presence-only, and absence-only data) and enable improved inferences about spatio-temporal patterns in abundance. As case studies, we use data from multiple avian biodiversity monitoring schemes. In the first, the goal is estimating abundance-based species distribution maps. In the second, we infer trends in population abundance across time.</p> <p><strong>Results: </strong>Accuracy and precision of abundance estimates increased when combining data from different sources compared to using a single data source alone. This is particularly valuable when data from each single data source is too sparse for reliable parameter estimation.<br> Main conclusions: We show that exploiting the complementary nature of "cheap", but abundant, citizen-science data and less abundant, but more information-rich, data from structured monitoring programs might be ideal to estimate distribution and population trends more accurately, especially for rare species. Joint likelihoods allow to include a wide variety of different data sets to (1) combine all the available information and to (2) mitigate weaknesses of one by the strength of another.</p> <p> </p> <p> </p>
Data for "Changing spatial distribution of water flow charts major change in Mars' greenhouse effect"
<p>Data for "Changing spatial distribution of water flow charts major change in Mars’ greenhouse effect". The script "summaryplot_v2.m" generates the summary figures used in the paper. The script "GCM_data_comparison_v5.m" generates additional figures used in the paper. The full underlying temperature output from the GCM runs listed in Table S1 is given in the allTsurf.txt file within the corresponding numbered subdirectory.</p>
Using seabird and whale distribution models to estimate spatial consumption of krill to inform fishery management
<p>Ecosystem dynamics at the north-west Antarctic Peninsula are driven by interactions between physical and biological processes. For example, baleen whale populations are recovering from commercial harvesting against the backdrop of rapid climate change, including reduced sea-ice extent and changing ecosystem composition. Concurrently, the commercial harvesting of Antarctic krill is increasing, with the potential to increase the likelihood for competition with and between krill predators and the fishery. However, understanding the ecology, abundance, and spatial distribution of krill predators is often limited, outdated, or at spatial scales that do not match those desired for effective fisheries management. We update current knowledge of predator dependence on krill by integrating telemetry-based data, at-sea observational surveys, estimates of predator abundance, and physiological data to estimate the spatial distribution of krill consumption during the austral summer by three species of Pygoscelis penguin, 11 species of flying seabirds, one species of pinniped and two species of baleen whale. Our models show that the majority of important areas for krill-predator foraging are close to penguin breeding colonies in nearshore areas where humpback whales also regularly feed, and along the shelf-break, though we caution that not all known krill predators are included in these analyses. We show that krill consumption is highly variable across the region, and often concentrated at fine spatial scales, emphasising the need for management of the local krill fishery at relevant temporal and spatial scales. We also note that krill consumption by recovering populations of krill predators provides further evidence in support of the krill surplus hypothesis, and highlight that despite less than comprehensive data, cetaceans are likely to consume a significant proportion of the krill consumed by natural predators but are not currently considered directly in the management of the krill fishery. If management of the krill fishery is to be precautionary and operate in a way that minimises the risks to krill predator populations, it will be necessary in future analyses, to include up-to-date and precise abundance and consumption estimates for pack-ice seals, finfish, squid, and other baleen whale species not currently considered.</p>
History and environment shape spatial genetic variation and predict climate maladaptation in a narrowly distributed serotinous pine, Pinus muricata
<p><span></span></p> <p>Understanding the distribution of genetic diversity and differentiation in species with disjunct and isolated populations is critical for assessing how environment shapes genetic variation and the potential response to climate change. In contrast to the large distributions and population sizes of most pine species, <em>Pinus muricata</em> (Bishop pine) occurs in a small number of isolated and disjunct populations occupying a narrow band of environmental conditions along the coast of western North America. We used genotyping by sequencing to generate population genomic data for trees sampled from nearly all existing populations of <em>P. muricata</em> (12 populations, 213 individuals, 7,828 loci) to describe the spatial arrangement of genetic differentiation and diversity. We used genetic-environment association (GEA) analyses to quantify the contribution of environmental variables to local adaptation and spatial genetic structure. Based on these results, we quantified relative levels of potential maladaptation given future climate projections at 2041 – 2060 and 2081 – 2100. Our analyses reveal pronounced spatial genetic structure across the distribution, with most populations forming genetically identifiable groups across a latitudinal gradient, and remarkable evidence for differentiation among three proximally distributed stands on Santa Cruz Island. Despite occurring in small, isolated populations, <em>P. muricata</em> do not exhibit strongly reduced diversity. GEA analyses suggested that specific soil and climate variables have contributed to local adaptation. Genomic offset analyses suggest geographic variation in potential maladaptation, with northern populations experiencing higher levels under projected climate change. Overall, our results suggest that isolation and local adaptation have shaped genetic variation among disjunct populations, and illustrate the consequences of this variation for <em>P. muricata</em> under projected climate change.</p>
Data from: Balanced spatial distribution of green areas creates healthier urban landscapes
<div> <span>The benefits of green infrastructure on human well-being in urban areas are already well established, with strong evidence of the positive effects of the amount and proximity to green areas. However, the understanding of how the spatial distribution and type of green areas affect health is still an open question. <br>Here, we explore how different spatial configurations of green and built-up areas, through a land sharing and sparing framework, and how different types of green areas affect cardiovascular and respiratory hospitalizations in São Paulo city, Brazil. <br>Sharing/sparing indicators were selected as the main explanatory factors in the control of all groups of diseases. Land sharing appeared as a favourable spatial condition to prevent cardiovascular hospitalization, while land sparing and arboreal vegetation were relevant to reduce hospitalization by lower respiratory diseases. <br>For upper respiratory diseases, forests seem to provide a disservice, once they were associated with increased rates of hospitalization by respiratory allergies causes.<br>Considering that hospitalization rates and severity of cardiovascular diseases are substantially higher than those of upper respiratory ones, dense vegetation tends to provide more services than disservices. The land sharing configuration, which is characterized by green areas spread throughout the urban network (in streets, gardens, small squares, or parks), should lead to higher exposure and use of the benefits of green areas, which may then explain the greater prevention of cardiovascular diseases. <br>These novel results indicate that a more balanced distribution of green areas across built-up areas creates healthier urban spaces, and thus can be used as an urban planning strategy to leverage the health benefits provided by green infrastructure. <br>Policy implications: </span><span><span>Aiming to reduce hospitalizations by cardiovascular and pulmonary causes, urban planning should promote the spreading of green areas across the cities, in order to increase daily contact with natural attributes, giving preference to distribution over total quantity of green in urban landscape.</span></span> </div>
FIGURE 59 in Complementary description of three species of Steneotarsonemus (Acari: Tarsonemidae) from rice agroecosystems of Eastern India with notes on their taxonomic status, spatial distribution, intraspecific variation and species composition
FIGURE 59. Abundance of Steneotarsonemus spinki vs S. subfurcatus within the species complex of rice sheath mites across the different agroecological zones of West Bengal. Equal letters for each species category do not differ significantly at p<0.05.
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.