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218 results for “Leopards”
Supplementary material 2 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
Records in different periods from new gazetteers records :
Supplementary material 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
Distribution information from new gazetteers records and from other resources :
Microsatellite genotype data for captive and wild Arabian leopards
<p>Genetic diversity underpins evolutionary potential that is essential for the long-term viability of wildlife populations. Captive populations harbour genetic diversity potentially lost in the wild, which could be valuable for release programs and genetic rescue. The Critically Endangered Arabian leopard (<em>Panthera pardus nimr</em>) has disappeared from most of its former range across the Arabian Peninsula, with fewer than 120 individuals left in the wild, and an additional 64 leopards in captivity. We (i) examine genetic diversity in the wild and captive populations to identify global patterns of genetic diversity and structure; (ii) estimate the size of the remaining leopard population across the Dhofar mountains of Oman using spatially explicit capture-recapture models on DNA and camera trap data, and (iii) explore the impact of genetic rescue using three complementary computer modelling approaches. We estimated a population size of 51 (95% CI: 32–79) in the Dhofar mountains and found that 8 out of 25 microsatellite alleles present in eight loci in captive leopards were undetected in the wild. This includes two alleles present only in captive founders known to have been wild-sourced from Yemen, which suggests that this captive population represents an important source for genetic rescue. We then assessed the benefits of reintroducing novel genetic diversity into the wild population, as well as the risks of elevating the genetic load through the release of captive-bred individuals. Simulations indicate that genetic rescue can improve the long-term viability of the wild population by reducing its genetic load and realised load. The model also suggests that the genetic load has been partly purged in the captive population, potentially making it a valuable source population for genetic rescue. However, the greater loss of its genetic diversity could exacerbate genomic erosion of the wild population during a rescue program, and these risks and benefits should be carefully evaluated. The next step in the recovery plan of the Arabian leopard is to empirically validate these conclusions, implement and monitor a genomics-informed management plan, and optimise a strategy for genetic rescue as a tool to recover Arabia's last big cat.</p>
Рис. 1. Места нахоΑок трех виΑов мΛекопитающих в национаΛьном парке «Бикин»: 1 — пятнистый оΛень; 2 — ΑаΛьневосточный Λесной кот; 3 — поΛевая мышь Fig. 1. The finding sites of the three species in the Bikin National Park: 1 — sika deer; 2 — amur leopard cat; 3 — striped field mouse in Documented Evidence Of Habitation For The Sika Deer, The Amur Leopard Cat And The Striped Field Mouse In The Bikin National Park (Russia)
Рис. 1. Места нахоΑок трех виΑов мΛекопитающих в национаΛьном парке «Бикин»: 1 — пятнистый оΛень; 2 — ΑаΛьневосточный Λесной кот; 3 — поΛевая мышь Fig. 1. The finding sites of the three species in the Bikin National Park: 1 — sika deer; 2 — amur leopard cat; 3 — striped field mouse
Figure 1 in Diet and habitat use of the endangered Persian leopard (Panthera pardus saxicolor) in northeastern Iran*
Figure 1. Elevation profile of SNP, marking 4 distinct habitat types: plains; small undulating hills and rough terrain; mountainous areas; and high rocky, precipitous mountains.
Figure 2 in Diet and habitat use of the endangered Persian leopard (Panthera pardus saxicolor) in northeastern Iran*
Figure 2. Preferred habitat of the Persian leopard (mountainous areas and small undulating hills and rough terrain) in SNP.
Figure 2 in Spatial distribution and dietary niche breadth of the leopard Panthera pardus (Carnivora: Felidae) in the northeastern Himalayan region of Pakistan
Figure 2. Map showing locations of study sites where abundance of prey species was recorded in and around Pir Lasura National Park, Azad Jammu and Kashmir, Pakistan.
Figure 1 in The complete mitochondrial genome of the leopard shark Triakis semifasciata (Triakidae)
Figure 1. Circular map of the mitochondrial genome of the leopard shark Triakis semifasciata.
Table 1 in The complete mitochondrial genome of the leopard shark Triakis semifasciata (Triakidae)
<p><b>Table 1.</b> Mitochondrial genome of <i>Triakis semifasciata</i>: arrangement and annotation.</p><table><tbody><tr><th>Name</th><th>Type</th><th>Start</th><th>Stop</th><th>Strand</th><th>Length (bp)</th><th>Start</th><th>Stop</th><th>Anticodon</th><th>Continuity</th></tr></tbody><tbody><tr><th>trnF(gaa)</th><td></td><td>1</td><td>69</td><td>(+)</td><td>69</td><td></td><td></td><td></td><td>1</td></tr><tr><th>rrnS</th><td></td><td>71</td><td>1023</td><td>(+)</td><td>953</td><td></td><td></td><td></td><td>−3</td></tr><tr><th>trnV(tac)</th><td></td><td>1021</td><td>1092</td><td>(+)</td><td>72</td><td></td><td></td><td></td><td>23</td></tr><tr><th>rrnL</th><td></td><td>1116</td><td>2763</td><td>(+)</td><td>1648</td><td></td><td></td><td></td><td>−1</td></tr><tr><th>trnL2(taa)</th><td></td><td>2763</td><td>2837</td><td>(+)</td><td>75</td><td></td><td></td><td></td><td>0</td></tr><tr><th><i>nad1</i></th><td></td><td>2838</td><td>3812</td><td>(+)</td><td>975</td><td>ATG</td><td>TAA</td><td></td><td>0</td></tr><tr><th>trnl(gat)</th><td></td><td>3813</td><td>3882</td><td>(+)</td><td>70</td><td></td><td></td><td></td><td>1</td></tr><tr><th>trnQ(ttg)</th><td></td><td>3884</td><td>3955</td><td>(-)</td><td>72</td><td></td><td></td><td></td><td>0</td></tr><tr><th>trnM(cat)</th><td></td><td>3956</td><td>4024</td><td>(+)</td><td>69</td><td></td><td></td><td></td><td>0</td></tr><tr><th><i>nad2</i></th><td></td><td>4025</td><td>5071</td><td>(+)</td><td>1047</td><td>ATG</td><td>TAG</td><td></td><td>−2</td></tr><tr><th>trnW(tca)</th><td></td><td>5070</td><td>5140</td><td>(+)</td><td>71</td><td></td><td></td><td></td><td>1</td></tr><tr><th>trnN(tgc)</th><td></td><td>5142</td><td>5210</td><td>(-)</td><td>69</td><td></td><td></td><td></td><td>0</td></tr><tr><th>trnN(gtt)</th><td></td><td>5211</td><td>5283</td><td>(-)</td><td>73</td><td></td><td></td><td></td><td>6</td></tr><tr><th>OL</th><td></td><td>5290</td><td>5319</td><td>(+)</td><td>30</td><td></td><td></td><td></td><td>1</td></tr><tr><th>trnC(gca)</th><td></td><td>5321</td><td>5389</td><td>(-)</td><td>69</td><td></td><td></td><td></td><td>1</td></tr><tr><th>trnY(gta)</th><td></td><td>5391</td><td>5460</td><td>(-)</td><td>70</td><td></td><td></td><td></td><td>1</td></tr><tr><th><i>cox1</i></th><td></td><td>5462</td><td>7018</td><td>(+)</td><td>1557</td><td>GTG</td><td>TAA</td><td></td><td>0</td></tr><tr><th>trnS2(tga)</th><td></td><td>7019</td><td>7089</td><td>(-)</td><td>71</td><td></td><td></td><td></td><td>3</td></tr><tr><th>trnD(gtc)</th><td></td><td>7093</td><td>7162</td><td>(+)</td><td>70</td><td></td><td></td><td></td><td>7</td></tr><tr><th><i>cox2</i></th><td></td><td>7170</td><td>7860</td><td>(+)</td><td>691</td><td>ATG</td><td>T(AA)</td><td></td><td>0</td></tr><tr><th>trnK(ttt)</th><td></td><td>7861</td><td>7934</td><td>(+)</td><td>74</td><td></td><td></td><td></td><td>1</td></tr><tr><th><i>atp8</i></th><td></td><td>7936</td><td>8103</td><td>(+)</td><td>168</td><td>ATG</td><td>TAA</td><td></td><td>−10</td></tr><tr><th><i>atp6</i></th><td></td><td>8094</td><td>8777</td><td>(+)</td><td>684</td><td>ATG</td><td>TAA</td><td></td><td>−1</td></tr><tr><th>cox3</th><td></td><td>8777</td><td>9562</td><td>(+)</td><td>786</td><td>ATG</td><td>TAA</td><td></td><td>2</td></tr><tr><th>trnG(tcc)</th><td></td><td>9565</td><td>9634</td><td>(+)</td><td>70</td><td></td><td></td><td></td><td>0</td></tr><tr><th><i>nad3</i></th><td></td><td>9635</td><td>9985</td><td>(+)</td><td>351</td><td>ATG</td><td>TAG</td><td></td><td>−2</td></tr><tr><th>trnR(tcg)</th><td></td><td>9984</td><td>10,053</td><td>(+)</td><td>70</td><td></td><td></td><td></td><td>0</td></tr><tr><th><i>nad4l</i></th><td></td><td>10,054</td><td>10,350</td><td>(+)</td><td>297</td><td>ATG</td><td>TAA</td><td></td><td>−7</td></tr><tr><th><i>nad4</i></th><td></td><td>10,344</td><td>11,724</td><td>(+)</td><td>1381</td><td>ATG</td><td>T(AA)</td><td></td><td>0</td></tr><tr><th>trnH(gtg)</th><td></td><td>11,725</td><td>11,793</td><td>(+)</td><td>69</td><td></td><td></td><td></td><td>0</td></tr><tr><th>trnS1(gct)</th><td></td><td>11,794</td><td>11,860</td><td>(+)</td><td>67</td><td></td><td></td><td></td><td>0</td></tr><tr><th>trnL1(tag)</th><td></td><td>11,861</td><td>11,932</td><td>(+)</td><td>72</td><td></td><td></td><td></td><td>0</td></tr><tr><th><i>nad5</i></th><td></td><td>11,933</td><td>13,762</td><td>(+)</td><td>1830</td><td>ATG</td><td>TAA</td><td></td><td>−5</td></tr><tr><th><i>nad6</i></th><td></td><td>13,758</td><td>14,279</td><td>(-)</td><td>522</td><td>ATG</td><td>AGG</td><td></td><td>0</td></tr><tr><th>trnE(ttc)</th><td></td><td>14,280</td><td>14,349</td><td>(-)</td><td>70</td><td></td><td></td><td></td><td>2</td></tr><tr><th><i>cob</i></th><td></td><td>14,352</td><td>15,497</td><td>(+)</td><td>1146</td><td>ATG</td><td>TAG</td><td></td><td>−1</td></tr><tr><th>trnT(tgt)</th><td></td><td>15,497</td><td>15,568</td><td>(+)</td><td>72</td><td></td><td></td><td></td><td>2</td></tr><tr><th>trnP(tgg)</th><td></td><td>15,571</td><td>15,639</td><td>(-)</td><td>69</td><td></td><td></td><td></td><td>278</td></tr><tr><th>CR</th><td></td><td>15,640</td><td>16,613</td><td></td><td>974</td><td></td><td></td><td></td><td></td></tr><tr><th>OH</th><td></td><td>15,918</td><td>16,612</td><td>(+)</td><td>695</td><td></td><td></td><td></td><td></td></tr></tbody></table>
Data from: A field-friendly method of measuring faecal glucocorticoid metabolite concentration as a simple stress checker in snow leopards
<p>Hormonal analysis of excrement has been employed to noninvasively assess the physiological conditions of domestic, experimental, zoo, and wild animals to. However, conventional hormone analysis techniques require frozen and refrigerated reagents, and laboratory equipment; therefore, it is almost impossible to obtain the results on-site. This study attempted to establish a method for the simple and rapid quantitative on-site analysis of faecal glucocorticoid metabolite (fGM) concentrations; this method involved using hand-shaking for faecal hormone extraction, and immunochromatography using test strips and a smartphone application. This study focused specifically on snow leopards and developed a new, simple fGM measurement method to evaluate their stress levels on-site.</p> <p>First, the effects of ultraviolet exposure and bacterial activity on fGM concentration, including sample age, were evaluated using faeces from a captive snow leopard. Second, a field-friendly hormone extraction method was evaluated; this method does not require storage of faeces in the field because the hormones are extracted by hand-shaking of faeces in ethanol at the time of collection. In addition, to measure hormone concentrations on-site, a simple immunochromatography measurement kit was used. The concentrations were measured by quantifying the colour reactions using a smartphone application.</p> <p>There was no consistent increase or decrease in fGM concentration with ultraviolet irradiation time. In addition, a high correlation was observed between concentrations extracted using the conventional method (using a vortex mixer and methanol extraction) and those from the field-friendly method (r = 0.812, <i>P </i>< 0.05). Changes in fGM concentration measured using immunochromatography were also highly consistent with those measured using the conventional method (enzyme-immunoassay) (r = 0.825, <i>P</i> < 0.05). The analytical validation of immunochromatography also showed high accuracy, i.e. parallelism (ANCOVA: F = 0.597, df = 1, <i>P</i> = 0.445) and recovery tests (98.2% ± 5.1% and 107.5% ± 18.7%). Further, for biological validation, the change in fGM concentration was examined before and after the transportation of the snow leopards to another zoo.</p> <p>This field-friendly hormone analysis method is expected to contribute as a simple and rapid stress evaluation tool to manage conservation and animal welfare by monitoring the stress levels.</p>
In the shadows of snow leopards and the Himalayas: density and habitat selection of blue sheep in Manang, Nepal
<p><span>There is a growing agreement that conservation needs to be proactive and pay increased attention to common species and to the threats they face. The blue sheep (<i>Pseudois nayaur</i>) plays a key ecological role in sensitive high-altitude ecosystems of Central Asia and is among the main prey species for the globally vulnerable snow leopard (<i>Panthera uncia</i>). As the blue sheep has been increasingly exposed to human pressures, it is vital to estimate its population dynamics, protect the key populations, identify important habitats, and secure a balance between conservation and local livelihoods. We conducted a study in Manang, Annapurna Conservation Area (Nepal) to survey blue sheep on 60 transects in spring (127.9 km) and 61 transects in autumn (134.7 km) of 2019, estimate their minimum densities from total counts, compare these densities with previous estimates, and assess blue sheep habitat selection by the application of generalized additive models (GAMs). Total counts yielded minimum density estimates of 6.0-7.7 and 6.9-7.8 individuals/km<sup>2</sup> in spring and autumn, respectively, which are relatively high compared to other areas. Elevation and, to a lesser extent, land cover indicated by the normalized difference vegetation index (NDVI) strongly affected habitat selection by blue sheep, whereas the effects of anthropogenic variables were insignificant. Animals were found mainly in habitats associated with grasslands and shrublands at elevations between 4200 and 4700 m. We show that the blue sheep population size in Manang has been largely maintained over the past three decades, indicating the success of the integrated conservation and development efforts in this area. Considering a strong dependence of snow leopards on blue sheep, these findings give hope for the long-term conservation of this big cat in Manang. We suggest that long-term population monitoring and a better understanding of blue sheep-livestock interactions are crucial to maintain healthy populations of blue sheep and, as a consequence, of snow leopards. </span></p>
Liquid Biopsy in Ewing Sarcoma and Osteosarcoma as a Prognostic And Response Diagnostic: LEOPARD
ClinicalTrials.gov study NCT06068075. IPD Sharing: YES. Countries: 1. Publications: 0.
Data from: Flexibility in the duration of parental care: female leopards prioritise cub survival over reproductive output
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Data from: A field-friendly method of measuring faecal glucocorticoid metabolite concentration as a simple stress checker in snow leopards
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Data from: Directional selection in the evolution of elongated upper canines in clouded leopards and sabre-toothed cats
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Data from: An adaptable but threatened big cat: density, diet and prey selection of the Indochinese leopard (Panthera pardus delacouri) in eastern Cambodia
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Preparatory immunity: Seasonality of mucosal skin defenses and Batrachochytrium infections in Southern leopard frogs
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Microsatellite genotype data for captive and wild Arabian leopards
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Data from: Distribution and human‐caused mortality of Persian leopards Panthera pardus saxicolor in Iran, based on unpublished data and Farsi gray literature
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In the shadows of snow leopards and the Himalayas: density and habitat selection of blue sheep in Manang, Nepal
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Allen Brain Atlas
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