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218 results for “Leopards”
The dynamic behavior of chromatophores marks the transition from bands to spots in leopard geckos
GEO Series GSE264342. Eublepharis macularius. 1 samples. Type: Expression profiling by high throughput sequencing.
Data from: Structural habitat predicts functional dispersal habitat of a large carnivore: how leopards change spots
Natal dispersal promotes inter-population linkage, and is key to spatial distribution of populations. Degradation of suitable landscape structures beyond the specific threshold of an individual's ability to disperse can therefore lead to disruption of functional landscape connectivity and impact metapopulation function. Because it ignores behavioral responses of individuals, structural connectivity is easier to assess than functional connectivity and is often used as a surrogate for landscape connectivity modeling. However using structural resource selection models as surrogate for modeling functional connectivity through dispersal could be erroneous. We tested how well a second-order resource selection function (RSF) models (structural connectivity), based on GPS telemetry data from resident adult leopard (Panthera pardus L.), could predict subadult habitat use during dispersal (functional connectivity). We created eight non-exclusive subsets of the subadult data based on differing definitions of dispersal to assess the predictive ability of our adult-based RSF model extrapolated over a broader landscape. Dispersing leopards used habitats in accordance with adult selection patterns, regardless of the definition of dispersal considered. We demonstrate that, for a wide-ranging apex carnivore, functional connectivity through natal dispersal corresponds to structural connectivity as modeled by a second-order RSF. Mapping of the adult-based habitat classes provides direct visualization of the potential linkages between populations, without the need to model paths between a priori starting and destination points. The use of such landscape scale RSFs may provide insight into predicting suitable dispersal habitat peninsulas in human-dominated landscapes where mitigation of human–wildlife conflict should be focused. We recommend the use of second-order RSFs for landscape conservation planning and propose a similar approach to the conservation of other wide-ranging large carnivore species where landscape-scale resource selection data already exist.
Rawdatafiles for snow leopard population estimation
<p>1. Effective management of charismatic large carnivores requires robust monitoring of their population at local, regional and global scales. While enormous progress has been made to estimate carnivore populations at local scales, estimates at regional and global scales remain elusive. In the first systematic effort at a large regional scale, we estimated the population of the elusive snow leopard Panthera uncia over an area of 26,112 km2 in the Indian state of Himachal Pradesh.<br> <br> 2. We stratified the entire snow leopard habitat in Himachal Pradesh based on an occupancy survey. Subsequently, we conducted camera trapping surveys at 10 sites distributed proportionately, i.e., with similar coverage probability across the three strata. We conducted simulations to understand how unidentified captures could affect our model estimate. We also assessed populations of the primary wild ungulate prey of snow leopards – blue sheep Psuedois nayaur and Siberian ibex Capra sibirica.<br> <br> 3. Our results yielded a mean estimated density of 0.19 (95% CI: 0.12 – 0.31) snow leopards per 100 km2 and population size of 51 (95% CI 34 – 73) snow leopards in Himachal Pradesh. The density estimates for individual sites ranged from 0.08 to 0.37 snow leopards per 100 km2. Simulations showed that unidentified snow leopard captures did not seem to affect the accuracy of our model estimate but could have affected the precision. Wild ungulate prey density ranged from 0.11 to 1.09 per km2. Snow leopard density showed a positive linear relationship with prey density (slope = 0.25, SE = 0.08, P = 0.01, R2 = 0.51).<br> <br> 4. Our study shows the earlier opinion-based estimate for Himachal Pradesh to have been significantly positively biased. Using occupancy surveys to stratify large areas in order to design camera trap surveys addresses one of the common spatial sampling biases, i.e., limited sampling of only prime snow leopard habitats. Our work validates two-step approach recommended in the ongoing initiative of the 12 snow leopard range countries for Population Assessment of World's Snow leopards (PAWS program), and cautions against the use of opinion-based estimates for assessing the status of species of critical conservation importance.</p>
Figs 1–4 in To the fauna of spiders (Arachnida: Aranei) of the "Leopard Land" National Park and the "Kedrovaya Pad" State Nature Reserve, Primorskii krai
Figs 1–4. Epigyne of Pardosa spp. 1, 2 – P. laevitarsis: 1 – ventral view, 2 – dorsal view;
Figure 1 from: Yang L, Huang M, Zhang R, Lv J, Ren Y, Jiang Z, Zhang W, Luan X (2016) Reconstructing the historical distribution of the Amur Leopard (Panthera pardus orientalis) in Northeast China based on historical records. ZooKeys 592: 143-153. https://doi.org/10.3897/zookeys.592.6912
Figure 1 - Amur leopard distribution in different periods.
Leopard Statue - Treptower Park, Berlin
Statue of a Leopard, located by the banks of the Spree River in Treptower Park, Berlin. Source: Objaverse 1.0 / Sketchfab
Partial makeshift [Leopard 1A1A2] photo scan
**This makeshift scan was generated with photogrammetry software 3DF Zephyr v4.513 processing 500 images taken from a Video on YouTube. ** This model shall be used for reference when 3D modelling. Source: Objaverse 1.0 / Sketchfab
Leopard Skull
When we first published this model, it was of an unknown big cat species. We put a call out to see if we could get a positive identification. Since then we've had it identified as the skull of a Leopard (Panthera pardus) by Paolo Viscardi of the National Museum of Ireland. Source: Objaverse 1.0 / Sketchfab
LEOPARD Prospective Validation Cohort 1
ClinicalTrials.gov study NCT06723275. IPD Sharing: NO. Countries: 5. Publications: 0.
LEOPARD Training and Validation Data Collection Study
ClinicalTrials.gov study NCT06675604. IPD Sharing: NO. Countries: 7. Publications: 0.
Hormonal Sensitivity in Patients With Noonan and LEOPARD Syndromes
ClinicalTrials.gov study NCT02486731. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Genomic Investigations of Acute Munitions Exposures on the Health and Skin Microbiome Composition of Leopard Frog (Rana pipiens) Tadpoles
GEO Series GSE154924. Lithobates pipiens. 72 samples. Type: Expression profiling by high throughput sequencing.
Rawdatafiles for snow leopard population estimation
Open the record for dataset details and reuse information.
Data from: Structural habitat predicts functional dispersal habitat of a large carnivore: how leopards change spots
Open the record for dataset details and reuse information.
BalL Exercises tO Prevent FrAilty in OldeR ADults (LEOPARD)
ClinicalTrials.gov study NCT07023328. IPD Sharing: Not stated. Countries: 0. Publications: 0.
LEOPARD Syndrome iPS, BJ iPS and Fibroblasts
GEO Series GSE20473. Homo sapiens. 9 samples. Type: Expression profiling by array.
Haw Par Villa Leopard
Haw Par Villa Google Maps: <br> https://goo.gl/maps/YdpyJQvjk5SwsNBU8 Source: Objaverse 1.0 / Sketchfab
On following pages: 32. Leopard Cat (Prionailurus bengalensis). in Felidae
On following pages: 32. Leopard Cat (Prionailurus bengalensis).
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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.