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218 results for “leopard”
Laboratory study on microplastic fiber size and concentration effects on leopard frog (Lithobates pipiens) tadpole survival, development, behavior, and parasite susceptibility
This dataset contains comprehensive raw data from a completed laboratory experiment conducted from May 24 to June 30, 2021 (with additional analysis performed in 2025), investigating the effects of polyester microplastic (MP) fiber exposure on northern leopard frog (Lithobates pipiens) tadpoles and their interactions with echinostome trematodes (Echinostoma sp.). Tadpole egg masses were collected from a wetland in Indiana, USA, and ramshorn snails (Helisoma trivolvis), serving as trematode hosts, were collected from Tioga County, New York, USA. The experiment was conducted under controlled laboratory conditions using a static-renewal design, exposing tadpoles to short (~0.24 mm) or long (~1.50 mm) polyester MP fibers at concentrations of 0, 10, or 40 µg L⁻¹ for 32 days, followed by controlled exposure to echinostome cercariae. The dataset includes measurements of tadpole mortality, developmental traits (mass, snout-to-vent length, Gosner stage), behavioral activity (number of moving pre- and post-parasite exposure), MP fiber ingestion, and susceptibility to trematode infection (metacercarial cyst counts in kidneys). These data provide a resource for studying the ecological and toxicological impacts of microplastics on amphibian health, and host-parasite dynamics in freshwater ecosystems, making the dataset suitable for researchers in ecotoxicology, and disease ecology. The dataset is complete, with no ongoing data collection, and is designed to support analyses of microplastic-mediated effects on aquatic organisms.
Leopard Cave ochres description and geochemical data
<p>The datasets present the macroscopic description and ICP-OES and ICP-MS geochemical data of archaeological ochres recovered at Leopard Cave rock art site (Erongo, Namibia). </p>
Data for "Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia"
<p>Data and code used for a country-wide occupancy survey of snow leopards in Mongolia, accompanying the paper "Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia".</p> <p>This data contains the results of a survey of 1017 20x20km sampling units, out of a total of 1200 sampling units identified as potential snow leopard habitat (183 could not be sampled for various reasons), a near complete survey of potential snow leopard habitat in Mongolia, nearly 500,000 square kilometers, and an enormous effort by many researchers. If you make use of the data, please cite the following sources:</p> <ul> <li><em>Data for "Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia".</em> (2021). Gantulga Bayandonoi, Koustubh Sharma, Justine Shanti Alexander, Purevjav Lkhagvajav, Ian Durbach, Darryl MacKenzie, Chimeddorj Buyanaa, Bariushaa Munkhtsog, Munkhtogtokh Ochirjav, Sergelen Erdenebaatar, Bilguun Batkhuyag, Nyamzav Battulga, Choidogjamts Byambasuren, Bayartsaikhan Uudus, Shar Setev, Lkhagvasuren Davaa, Khurel-Erdene Agchbayar, Naranbaatar Galsandorj, David Borchers. doi: https://doi.org/10.5281/zenodo.5257572</li> <li><em>Mapping the ghost: Estimating probabilistic snow leopard distribution across Mongolia. </em>(2021). Gantulga Bayandonoi, Koustubh Sharma, Justine Shanti Alexander, Purevjav Lkhagvajav, Ian Durbach, Darryl MacKenzie, Chimeddorj Buyanaa, Bariushaa Munkhtsog, Munkhtogtokh Ochirjav, Sergelen Erdenebaatar, Bilguun Batkhuyag, Nyamzav Battulga, Choidogjamts Byambasuren, Bayartsaikhan Uudus, Shar Setev, Lkhagvasuren Davaa, Khurel-Erdene Agchbayar, Naranbaatar Galsandorj, David Borchers. To appear in <em>Diversity and Distributions</em></li> </ul> <p><strong>Contents of zip file</strong></p> <p><em>Data</em></p> <p>The main dataset is contained in `data\Mongolia_occupancy_inputs.Rdata` . Please see the paper for more detail on data collection. The following objects are contained in the file:</p> <p>- Pres: presence/absence occupancy survey results, used for model fitting<br> - Site_Cov: unit-specific covariates, used for model fitting<br> - SurvCov: survey-specific covariates, used for model fitting<br> - Mongolia_studyarea: covariates for whole survey area, used for prediction<br> - Mongolia_fullrange: covariates across whole expected snow leopard range, used for prediction</p> <p><em>Code</em></p> <p>Code is cloned from the GitHub repository <a href="https://github.com/iandurbach/mongolia-occupancy">https://github.com/iandurbach/mongolia-occupancy</a>, which may contain updates. The version here reproduces the analyses in the paper above. The run these analyses:</p> <p>- run *occupancy-analysis.R* to fit the main occupancy models (these are also saved in the `\output` folder), do model selection, and plot covariate effects<br> - run *occupancy-goodness-of-fit.R* to calculate the c-hat statistic giving an indication of model fit for the best model<br> - run *comparing-maps.R* to compare the occupancy results with similar metrics generated using a presence-only analysis (using MaxEnt) or an expert map generated through qualitative discussion (reproduces Figure 3 in the paper).</p> <p>Code in *occupancy-data-preproc.R* is not needed but included for completeness. It converts the csv files in `data\csv`, which contain various input datasets used by the occupancy model, into a single .Rdata file (`data\Mongolia_occupancy_inputs.Rdata`), which is then used by the scripts above. Some minimal pre-processing (excluding ununsed variables, renaming for consistency, etc) is performed. </p>
Fig. 5 in Not all spotted cats are leopards: evidence for a Hemilienardia ocellata species complex (Gastropoda: Conoidea: Raphitomidae)
Fig. 5. Records of the Hemilienardia ocellata species complex, based on material examined in the present paper. Filled cycles = H. ocellata (Jousseaume, 1884); triangles = H. acinonyx sp. nov.; black square = H. lynx sp. nov.; grey square = H. cf. lynx sp. nov.; diamonds = H. pardus sp. nov.
Fig. 4 in Not all spotted cats are leopards: evidence for a Hemilienardia ocellata species complex (Gastropoda: Conoidea: Raphitomidae)
Fig. 4. Marginal radular teeth of some species of Hemilienardia. A. H. malleti (Récluz, 1852) (from Kantor & Taylor 2002). B–C. H. ocellata (Jousseaume, 1884). Specimen from the Loyalty Islands, Lifou, Baie du Santal, Atelier LIFOU 2000, stn 1429, 20°47.5' S, 167°07.1' E, 8–18 m, 4.4 mm long. D. H. acinonyx sp. nov. Specimen from the Loyalty Islands, Lifou, Baie du Santal, Atelier LIFOU 2000, stn 1448, 20°45.8' S, 167°01.65' E, 20 m, 5.0 mm long.
Fig. 3 in Not all spotted cats are leopards: evidence for a Hemilienardia ocellata species complex (Gastropoda: Conoidea: Raphitomidae)
Fig. 3. Protoconch and shell morphology in the Hemilienardia ocellata complex. A–C. H. ocellata (Jousseaume, 1884). A. Specimen from the Maldives, Ari Atoll, Maagau Kandu, 25 m, 3.1 mm long. B–C. Specimen from New Caledonia, Expedition MONTROUZIER, stn 1319, 20°44.7' S, 164°15.5' E, 15–20 m, 3.6 mm long. D–E. H. acinonyx sp. nov. Specimen from the Loyalty Islands, Lifou, Baie du Santal, Atelier Lifou 2000, stn 1448, 20°45.8' S, 167°01.65' E, 20 m, 5.0 mm long. F–G. H. lynx sp. nov. Holotype, MNHN IM-2013-5489, Madang District, off Kranket Island, PAPUA NIUGINI stn PP14, 05°12' S, 145°50' E, 100–120 m, 2.75 mm long. H–I. H. pardus sp. nov. Specimen from the Loyalty Islands, Lifou, Baie du Santal, Atelier LIFOU 2000, stn 1454, 20°56.65' S, 167°02.0' E, 15–18 m, 5.2 mm long.
Fig. 2 in Not all spotted cats are leopards: evidence for a Hemilienardia ocellata species complex (Gastropoda: Conoidea: Raphitomidae)
Fig. 2. Species of the Hemilienardia ocellata complex. The SEM image with no letter denoted shows standard measurements. A–D. Hemilienardia ocellata (Jousseaume, 1884). A–B. Syntype, MNHN IM-2000-3128, Mauritius, 4.0 mm. C. Loyalty Islands, Lifou, Baie du Santal, Atelier Lifou 2000, stn 1429, 20°47.5' S, 167°07.1' E, 8–18 m, 4.4 mm. D. New Caledonia, Secteur de Koumac, Expedition Montrouzier, stn 1319, 20°44.7' S, 164°15.5' E, 15–20 m, 3.6 mm. E–F. Hemilienardia acinonyx sp. nov. E. Holotype, MNHN IM-2013-33593, Philippines, 8.1 mm. F. Loyalty Islands, Lifou, Baie du Santal, Atelier Lifou 2000, stn 1441, 20°46.4' S, 167°02.0' E, 20 m, 5.4 mm. G–H. Hemilienardia lynx sp. nov., holotype, MNHN IM-2013-5489, Papua New Guinea, 2.75 mm. I–M. Hemilienardia pardus sp. nov. I. BMOO 17147, Society Islands, Moorea. K. Holotype, MNHN IM-2000-31661, 5.8 mm. L–M. Loyalty Islands, Lifou, Baie du Santal, Atelier Lifou 2000, stn 1454, 20°56.65' S, 167°02.0' E, 15–18 m, 5.2 mm.
Fig. 1 in Not all spotted cats are leopards: evidence for a Hemilienardia ocellata species complex (Gastropoda: Conoidea: Raphitomidae)
Fig. 1. Relationships of the Hemilienardia ocellata complex as inferred by the molecular phylogenetic analysis. A. Bayesian tree based on the analysis of 61 Raphitomidae COI sequences. Black circles indicate nodes with 0.9 0.7.
Triangular Mesh of the Brain of a Leopard (Panthera pardus)
<p>Triangular Mesh of the Brain of a Leopard (<i>Panthera pardus</i>) from http://braincatalogue.org/Leopard</p>
Data from: A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus), and Panthera species, using targeted bisulfite sequencing and machine learning models
<p><span>Knowledge of individual age can help both in-situ and ex-situ conservation programs to design more efficient and suitable management plans for targeted wildlife species. DNA methylation is one of the epigenetic aging markers that has emerged as a promising tool that can estimate age with high accuracy using only a tiny amount of biological material, which can be collected in a minimally invasive way. Here, we sequenced five targeted genetic regions and used </span><span>8–23</span><span> selected CpG sites to build age estimation models with machine learning methods </span><span>with about only $3–7 per sample</span><span>, using blood samples of seven Felidae species—ranging from small to big, and domestic to endangered species: domestic cats (<em>Felis catus</em>, 139 samples), Tsushima leopard cats (<em>Prionailurus bengalensis euptilurus</em>, 84 samples), and five<em> Panthera </em>species (96 samples). </span><span>The models built achieved satisfactory accuracy—the mean absolute error of the best models was 1.966, 1.348, and 1.552 years in domestic cats, Tsushima leopard cats, and <em>Panthera</em> spp., respectively.</span><span> Our models in domestic cats and Tsushima leopard cats were applicable to individuals regardless of health conditions, indicating the high applicability of our models to samples collected from diverse situations, e.g., rescued individuals in the context of conservation. We also showed the possibility of developing universal age estimation models for the five<em> Panthera</em> spp. using two of the five genetic regions, suggesting an even lower cost to use our models for future applications.</span></p>
Data from: Metabarcoding analysis provides insight into the link between prey and plant intake in a large alpine cat carnivore, the snow leopard
<p>Species of the family Felidae (a group represented by cats) are thought to be obligate carnivores, specialized for hunting and consuming other animals. However, the detection of plants in the feces of felids raises questions about the role of plants in their diet. This is particularly true for the snow leopard (Panthera uncia), a big cat native to central and South Asia's high mountains. Our study aimed to comprehensively identify the prey and plants consumed by snow leopards as well as six other sympatric mammals. We applied DNA metabarcoding methods on 126 fecal samples collected from the Sarychat-Ertash Nature Reserve in Kyrgyzstan. We found that among the three most common plant families in snow leopard feces, Tamaricaceae (genus Myricaraia) was consumed often by snow leopards. The genus Myricaria frequently appeared in samples lacking any animal prey DNA, indicating that snow leopards might have consumed this plant especially when their digestive tracts were empty. We also observed a significant difference in plant composition between male and female snow leopards, and potentially between sampling seasons. We provide a comprehensive overview of the prey and plants detected in the feces of snow leopards and sympatric mammals. We believe our findings will help in formulating hypotheses and guiding future research to understand the adaptive significance of plant-eating behavior in felids and animal-plant relationships in the ecosystem.</p>
Neo-taphonomic analysis of the Misiam leopard lair
<p>The data set presented here contains the MAU% data for the selected hyena-made and leopard-made faunal assemblages with which the Misiam assemblage is compared. Misiam is a recently discovered modern faunal accumulation found at Olduvai Gorge (Tanzania) interpreted as a palimpsest resulting from the action of leopards (main transporting agents) and hyenas (secondary scavengers). It is the first open-air reported leopard-made faunal accumulation. Defining the anatomical and taphonomic characteristics of such an assembllage is important for the interpretation of prehistoric faunal assemblages created by carnivores. It is also relevant for modern ecological studies. In this particular case, the bulk of the assemblage is composed of wildebeests. This is usually not the target of leopards; however, their seasonal abundance during the wildebeest migration on the plains adjacent to Olduvai Gorge prompts this rather exceptional highly-specialized behavior by usually eclectic leopards. In the present work, a thorough taphonomic analysis is carried out and the main taxonomic, anatomical and taphonomic characteristics of this felid-hyenic modified assemblage is decribed. The analytical approach adopted uses the data presented here. </p>
Over the hills and through the farms: Land use and topography influence genetic connectivity of northern leopard frog (Rana pipiens) in the Prairie Pothole Region
<p><em>Context</em></p> <p>Agricultural land-use conversion has fragmented prairie wetland habitats in the Prairie Pothole Region (PPR), an area with one of the most wetland-dense regions in the world. This fragmentation can lead to negative consequences for wetland obligate organisms, heightening risk of local extinction and reducing evolutionary potential for populations to adapt to changing environments.</p> <p><em>Objectives</em></p> <p>This study models biotic connectivity of prairie-pothole wetlands using landscape genetic analyses of the northern leopard frog (<em>Rana pipiens</em>) to: (1) identify population structure and (2) determine landscape factors driving genetic differentiation and possibly leading to population fragmentation.</p> <p><em>Methods</em></p> <p>Frogs from 22 sites in the James River and Lake Oahe river basins in North Dakota were genotyped using Best-RAD sequencing at 2868 bi-allelic single nucleotide polymorphisms (SNPs). Population structure was assessed using STRUCTURE, DAPC, and fineSTRUCTURE. Circuitscape was used to model resistance values for ten landscape variables that could affect habitat connectivity.</p> <p><em>Results</em></p> <p>STRUCTURE results suggested a panmictic population, but other more sensitive clustering methods identified six spatially organized clusters. Circuit theory-based landscape resistance analysis suggested land use, including cultivated crop agriculture, and topography were the primary influences on genetic differentiation.</p> <p><em>Conclusions</em></p> <p>While the <em>R. pipiens</em> populations appear to have high gene flow, we found a difference in the patterns of connectivity between the eastern portion of our study area which was dominated by cultivated crop agriculture, versus the western portion where topographic roughness played a greater role. This information can help identify amphibian dispersal corridors and prioritize lands for conservation or restoration.</p>
Рис. 2. Самец пятнистого оΛеня в ΑоΛине р. КаяΛу, 7 июΛя 2018 г. Fig. 2. Male sika deer in the valley of the Kayalu River, 7 July, 2018 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. 2. Самец пятнистого оΛеня в ΑоΛине р. КаяΛу, 7 июΛя 2018 г. Fig. 2. Male sika deer in the valley of the Kayalu River, 7 July, 2018
Рис. 3. Останки пятнистого оΛеня — жертвы воΛков на ΛьΑу р. Бикин в верхнем течении, 29 января 2019 г. Fig. 3. Remains of a sika deer (wolves' prey) on the ice in the upper reaches of the Bikin River, January 29, 2019 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. 3. Останки пятнистого оΛеня — жертвы воΛков на ΛьΑу р. Бикин в верхнем течении, 29 января 2019 г. Fig. 3. Remains of a sika deer (wolves' prey) on the ice in the upper reaches of the Bikin River, January 29, 2019
Figure 6. 24 h in Summer diving and haul-out behavior of leopard seals (Hydrurga leptonyx) near mesopredator breeding colonies at Livingston Island, Antarctic Peninsula
Figure 6. 24 h rose plots of leopard seal dive activity by hour of day from the parametric data set. Red arrows represent the mean vector of dive activity. (A) all dives pooled from the 2010 season (n = 6,017) from three seals (4OR, 9OR, and 390G). (B) Activity for leopard seal 4OR (n = 2,292 dives) was significantly different from the 2010 mean and the other two seals; (Watson's two sample tests, P <0.05). (C) Activity for leopard seal 9OR (n = 2,283 dives) was significantly different from the 2010 mean and the other two seals (Watson's two sample tests, P <0.001). (D) Activity for leopard seal 390G (n = 1,442 dives) was significantly different from the 2010 mean and the other two seals (Watson's two sample tests, P <0.001).
Figure 5. 24 h in Summer diving and haul-out behavior of leopard seals (Hydrurga leptonyx) near mesopredator breeding colonies at Livingston Island, Antarctic Peninsula
Figure 5. 24 h rose plots of dive activity by hour of day. The red arrows represents the mean vector (direction = time of day, length = mean number of dives) of dive activity (dives/h) for: (A) all dives (n = 40,308). Gray shaded areas represent the crepuscular periods (+1 h from sunset and sunrise) across the study; (B) all dives pooled from the 2010 season (n = 13,373); (C) all dives pooled from the 2011 season (n = 6,545); (D) all dives pooled from the 2014 season (n = 8,723). The null hypothesis that patterns of diel dive activity were equivalent between seasons could not be rejected (Watson's two-sample tests, P> 0.05).
Figure 1 in Summer diving and haul-out behavior of leopard seals (Hydrurga leptonyx) near mesopredator breeding colonies at Livingston Island, Antarctic Peninsula
Figure 1. Cape Shirreff, Livingston Island, Antarctica. The black star in the right pane indicates the location of Cape Shirreff in the western Antarctic Peninsula region.
Figure 4 in Summer diving and haul-out behavior of leopard seals (Hydrurga leptonyx) near mesopredator breeding colonies at Livingston Island, Antarctic Peninsula
Figure 4. Comparison by dive types between (A) behavior predicted from the k-means cluster analysis of time-depth dive records (n = 38,338) and (B) behavior manually scored from animal-borne video dive data (n = 309).
Figure 3 in Summer diving and haul-out behavior of leopard seals (Hydrurga leptonyx) near mesopredator breeding colonies at Livingston Island, Antarctic Peninsula
Figure 3. The mean proportion (with SD whiskers) of dives that were classified into each dive type (1–4) for all dives in the cluster data set (n = 38,338).
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