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22 results for “gray wolf”
Data for: Competition, prey, and mortalities influence gray wolf group size
<p>Data and R code for "Competition, prey, and mortalities influence gray wolf group size" by Sells et al. (2022, Journal of Wildlife Management). The datasets can be used with the included R code to re-create analyses and figures from Sells et al. (2022). The metadata file describes each column in the datasets.</p>
Fig. 4 in Co-infection of Echinococcus equinus and Echinococcus canadensis (G6/7) in a gray wolf in Turkey: First report and genetic variability of the isolates
Fig. 4. The haplotype network for the mt-CO1 gene (815 bp) of E. canadensis (G6/7). The size of the circles is proportional to the frequency of each haplotype. The number of mutations separating haplotypes is indicated by dash marks. The host diversity of haplotypes is shown in different colors. Hap: Haplotype.
Fig. 2 in Co-infection of Echinococcus equinus and Echinococcus canadensis (G6/7) in a gray wolf in Turkey: First report and genetic variability of the isolates
Fig. 2. Phylogenetic tree of Echinococcus granulosus s.l. isolates generated using mt-CO1 gene sequences (815 bp). The phylogenetic tree was constructed using the Maximum Likelihood method and TN93 + G model. Evolutionary analyses were conducted in MEGA X. For each reference sequence, the GenBank accession number and species name are listed below: MN787562 (E. equinus), KY766905 (E. equinus), KP161210 (E. equinus) AB786665 (E. equinus) AF346403 (E. equinus), KX010854 (E. canadensis) MK321260 (E. canadensis) KX010856 (E. canadensis), AB893263 (E. canadensis), AB777923 (E. canadensis), MK165232 (E. ortleppi), MT072979 (E. granulosus s.s.), NC_044548 (E. granulosus s.s.), MG672293 (E. granulosus s.s.), KT001423 (E. multilocularis), AY684274 (T. saginata). ■: E. canadensis (G6/7) isolates; ▴: E. equinus isolates.
Fig. 3 in Co-infection of Echinococcus equinus and Echinococcus canadensis (G6/7) in a gray wolf in Turkey: First report and genetic variability of the isolates
Fig. 3. The haplotype network for the mt-CO1 gene (815 bp) of E. equinus. The size of the circles is proportional to the frequency of each haplotype. The number of mutations separating haplotypes is indicated by dash marks. The host diversity of haplotypes is shown in different colors. Hap: Haplotype.
Magnetic Resonance Imaging Scan of the Brain of a Gray Wolf (Canis lupus)
<p>Magnetic Resonance Imaging Scan of the Brain of a Gray Wolf (<i>Canis lupus</i>) from http://braincatalogue.org/Gray_wolf</p>
Figure 3 in Current status, distribution, and conservation of brown bear (Ursidae) and wild canids (gray wolf, golden jackal, and red fox; Canidae) in Turkey
Figure 3. The distribution map of golden jackal.
Figure 2 in Current status, distribution, and conservation of brown bear (Ursidae) and wild canids (gray wolf, golden jackal, and red fox; Canidae) in Turkey
Figure 2. The distribution map of gray wolf.
Figure 1 in Current status, distribution, and conservation of brown bear (Ursidae) and wild canids (gray wolf, golden jackal, and red fox; Canidae) in Turkey
Figure 1. The distribution map of brown bear.
Fig. 1 in Co-infection of Echinococcus equinus and Echinococcus canadensis (G6/7) in a gray wolf in Turkey: First report and genetic variability of the isolates
Fig. 1. Stereomicroscopic view of adult parasites obtained from gray wolf's intestine.
Gray wolf packs and human-caused wolf mortality
<p>Gray wolves (Canis lupus) are group-living carnivores that travel over large areas and are one of the most controversial species in North America. Gray wolf management over the last century has ranged from eradication by nearly any means to preservation under the Endangered Species Act to state-managed which often includes limited hunting and, in some areas, population reduction. Management decisions are complicated by transboundary movements of wildlife, especially when the bordering agencies have disparate goals or mandates. This data is specific to gray wolves and packs using five National Park Service (NPS) units (years of data): Denali National Park and Preserve (33 years), Grand Teton National Park (23 years), Voyageurs National Park (12 years), Yellowstone National Park (27 years), and Yukon-Charley Rivers National Preserve (23 years). This dataset features two measures of gray wolf biological processes, pack persistence and reproduction, and was used to determine the impacts of anthropogenic mortality on the pack. We examined persistence and reproduction at the pack level given known wolf mortalities and pack sizes.</p>
Gray wolf range in the western Great Lakes region under forecasted land use and climate change
<p>Land use and climate alter species distributions worldwide, and detecting and understanding how species ranges shift can facilitate conservation planning and action. Following extirpation from most of the contiguous USA, gray wolves (<em>Canis</em> <em>lupus</em>) have partially recolonized former range in the western Great Lakes region, but it is unknown how land use and climate change may alter amounts of wolf habitat. Using wolf observation data collected during winters 2017–2020 in Minnesota, Wisconsin, and Michigan, we created ensemble models to predict how land use and climate change may affect the amount of wolf habitat within these states. A projection model for the western Great Lakes region suggested three of four scenarios of land use and climate change will lead to 9–35% increases in wolf habitat, while a solely climate-based projection model supported our expectation that changes in climate, in isolation, will have limited effect on current wolf range. Our results support stable or increasing amounts of wolf habitat in the western Great Lakes region during the 21st century, suggesting limited or no adverse effects on the current distribution or further recolonization of wolves. Our findings can inform policy development regarding wolf conservation, and identify areas where recolonization is plausible, thus where promoting human-wolf co-existence is most pertinent.</p>
Gray wolf range in the western Great Lakes region under forecasted land use and climate change
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Gray wolf packs and human-caused wolf mortality
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Data from: Delisting the Northern Rocky Mountain Gray Wolf from the U.S. Endangered Species Act: An assessment of political discourse over 20 years
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Data from: A mummified Pleistocene gray wolf pup
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Fig. 4 in Comparative analysis of peripheral blood reveals transcriptomic adaptations to extreme environments on the Qinghai-Tibetan Plateau in the gray wolf (Canis lupus chanco)
Fig. 4 Reconstructed mitochondrial DNA tree of the worldwide distributed wolves. The numbers at each node are the Bayesian posterior probabilities (right) and ML bootstrap propor- tions (left)
Fig. 3 in Comparative analysis of peripheral blood reveals transcriptomic adaptations to extreme environments on the Qinghai-Tibetan Plateau in the gray wolf (Canis lupus chanco)
Fig. 3 Scatterplot of enriched KEGG pathways for DEGs between the Tibetan and lowland wolves. The enrichment factor is the ratio of the DEG number to the total gene number in the pathway. The dot size and color represent the gene number and the range of the p value respectively
Fig. 1 in Comparative analysis of peripheral blood reveals transcriptomic adaptations to extreme environments on the Qinghai-Tibetan Plateau in the gray wolf (Canis lupus chanco)
Fig. 1 Gene expression profiles of blood in Tibetan and lowland wolves. a Boxplot of the log transformed FPKM expression values across eight wolf blood samples. FPKM: fragments per kilobase of exon per million fragments. The solid horizontal line represents the median, and the box
Data from: Ancestry-specific methylation patterns in admixed offspring from an experimental coyote and gray wolf cross
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Data from: Model sensitivity and use of the comparative finite element method in mammalian jaw mechanics: mandible performance in the Gray Wolf
Finite Element Analysis (FEA) is a powerful tool gaining use in studies of biological form and function. This method is particularly conducive to studies of extinct and fossilized organisms, as models can be assigned properties that approximate living tissues. In disciplines where model validation is difficult or impossible, the choice of model parameters and their effects on the results become increasingly important, especially in comparing outputs to infer function. To evaluate the extent to which performance measures are affected by initial model input, we tested the sensitivity of bite force, strain energy, and stress to changes in seven parameters that are required in testing craniodental function with FEA. Simulations were performed on FE models of a Gray Wolf (Canis lupus) mandible. Results showed that unilateral bite force outputs are least affected by the relative ratios of the balancing and working muscles, but only ratios above 0.5 provided balancing-working side joint reaction force relationships that are consistent with experimental data. The constraints modeled at the bite point had the greatest effect on bite force output, but the most appropriate constraint may depend on the study question. Strain energy is least affected by variation in bite point constraint, but larger variations in strain energy values are observed in models with different number of tetrahedral elements, masticatory muscle ratios and muscle subgroups present, and number of material properties. These findings indicate that performance measures are differentially affected by variation in initial model parameters. In the absence of validated input values, FE models can nevertheless provide robust comparisons if these parameters are standardized within a given study to minimize variation that arise during the model-building process. Sensitivity tests incorporated into the study design not only aid in the interpretation of simulation results, but can also provide additional insights on form and function.
ScienceDex guides
Understand access before you commit
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.