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6 results for “spatial conservation prioritization”
Data from: Combining spatial, genetic, and environmental risk data to define and prioritize in situ conservation units
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Input data and scripts for "Spatial conservation prioritization for the East Asian islands: a balanced representation of multi-taxon biogeography in a protected area network"
<p>This release contains the input files 'input_data.zip' for the spatial conservation prioritization analysis by Zonation software, which are conducted in Lehtomäki et al. Input data includes biodiversity features (species distribution maps from vascular plants, mammals, birds, reptiles, amphibians, and freshwater fishes), habitat condition map (human influence index), priority mask information (the categorized protected area distribution) and the Japanese prefecture polygons in GeoTiff format, and the list of species attributes for conservation weighting in CSV format. Note that endangered rare species have been excluded from this dataset, though they were reflected in the output files. The Zonation setting files and R scripts for pre- and post analyses are included in 'japan-zsetup-1.0.zip' and also placed at GitHub : https://github.com/cbig/japan-zsetup</p> <p>The output files (priority score maps and removal curves) from the original Zonation analyses are summarized in 'output_from_original.data.zip'</p>
Spatial conservation prioritization for the Amur tiger in Northeast China
<p class="Paragraph">Amur tiger (<i>Panthera tigris altaica</i>) is critically endangered and also the subspecies of the tiger with the most restoration potential in China. It is challenging to protect large-ranging carnivores like tigers under increasing pressure of human development. To provide a more technically robust foundation for tiger habitat conservation prioritization, we conducted a comprehensively empirical analysis based on a broadly collected occurrence dataset of tigers and their prey. We modeled tiger distribution by running an ensemble model integrating nine different algorithms. We found that the ensemble model performed well and outperformed any individual model regarding the discrimination ability. We used cumulative resistant kernel analysis to identify core habitats as with high predicted movement density and used factorial least-cost paths to model corridors among tiger occurrence locations. We found core habitats for Amur tigers are distributed in three mountain areas, namely eastern Wanda Mountain, southern Zhangguangcailing, and Laoyeling-Dalongling. We found significant protection gaps as existing protected areas only cover less than 1/4 of predicted core habitats, but this proportion will rise significantly with the establishment of the Northeast China Tiger and Leopard National Park. Furthermore, we ranked spatial priorities for the expansion of the protected area network, simultaneously considering biological and socioeconomic dimensions under the Zonation framework. Our study presented the most up-to-date and detailed maps of the predicted potential distribution and area of the most important habitats for the Amur tigers in China, which can provide quantitative guidance in the effort to maximize the efficiency of conservation initiatives at a regional scale.</p>
Spatial conservation prioritization for the Amur tiger in Northeast China
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Data from: A landscape triage approach: combining spatial and temporal dynamics to prioritize restoration and conservation
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Land use changes threaten bird taxonomic and functional diversity across the Mediterranean basin: a spatial analysis to prioritize monitoring for conservation
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.