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87 results for “Canis lupus”
Canis lupus fecal metabarcoding dataset
<p class="MsoNormal">Although wolves are wide-ranging generalist carnivores throughout their life cycle, during the pup-rearing season wolf activity is focused on natal den sites where pup survival depends upon pack members provisioning food. Because prey availability is influenced by habitat quality within the home range, we investigated the relative importance of prey species for adults and pups and further examined the relationship between habitat characteristics, wolf diet, and litter size on Prince of Wales Island (POW) in Southeast Alaska. During 2012–2020, we detected 13 active den sites within the home ranges of 9 wolf packs. We estimated minimum pup counts using motion-detecting cameras and individual genotypes from noninvasive samples (hair: n = 322; scat: n = 227), and quantified wolf diet composition using fecal DNA metabarcoding (n = 538). We assessed habitat composition, configuration, and connectivity within denning and annual home ranges estimated using wolf GPS-collar data. Contrary to expectations, wolves had a more constricted diet during denning season (April 15–July 31), and, within this season, pups had a narrower dietary niche (species richness [<em>S</em>] = 4) focused more on deer (relative frequency of occurrence [O/I] = 0.924) than adults (<em>S</em> = 15; deer O/I = 0.591). Litter size had a positive relationship with the relative frequency of deer in a wolf pack's diet. Wolf consumption of deer was positively associated with the proportion of young-growth forest (≤25 years old) within denning and annual home ranges. High levels of vegetation patch interspersion and the density of closed logging roads were also important predictors, suggesting these habitat qualities were influential for increasing the availability of deer to wolves. Our results contrast with previous research indicating wolf pup diets included more alternate prey (i.e., beaver) than adults, and emphasize the importance of deer to wolf viability on POW, especially during denning season.</p>
Fig. 3 in Evaluating the principles of wildlife conservation: a case study of wolf (Canis lupus) hunting in Michigan, United States
Fig. 3.—Seasonal occurrence of wolves killing cattle (depredation events) in Michigan, 1996–2012. Note that only 3.8% of depredations occur during the planned hunting season. Source: Michigan Department of Natural Resources (May 2014).
Fig. 2 in Evaluating the principles of wildlife conservation: a case study of wolf (Canis lupus) hunting in Michigan, United States
Fig. 2.—Verified instances of wolves killing cattle (depredations) in Michigan, 1996–2013. The solid line indicates losses from throughout the geographic range of wolves in Michigan, excluding losses that occurred on 1 farm. The dashed line is the additional losses attributable to that 1 farm, where poor animal husbandry likely increased the risk of depredations (see "Characterizing the Problems"). The number of depredations declined considerably just prior to the planning and implementation of Michigan's 1st wolf hunt in 2013. Source: Michigan Department of Natural Resources (May 2014).
Fig. 1 in Evaluating the principles of wildlife conservation: a case study of wolf (Canis lupus) hunting in Michigan, United States
Fig. 1.—Timeline of key political events pertaining to the management of Michigan wolves. The details of each event are given in "Chronology of Political Events." Events above the timeline disfavor wolves and events below the timeline favor wolves. Dashed boxes indicate actions initiated by citizens. Solid boxes indicate actions initiated by the government. Key actors in the timeline are the Michigan Natural Resource Commission (NRC), Michigan Department of Natural Resources (DNR), and the United States Endangered Species Act (ESA). The online version of this figure includes color to indicate which events were associated with the executive branch (green), the courts (black), and each of the particular laws—Public Act (PA) 520 (blue), Senate Bill (SB) 288 and PA 21 (red), and PA 281 (magenta).
Data from: Spatial genetic analyses reveal cryptic population structure and migration patterns in a continuously harvested grey wolf (Canis lupus) population in north-eastern Europe
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Data from: North-south differentiation and a region of high diversity in European wolves (Canis lupus)
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Canis lupus fecal metabarcoding dataset
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Material use to build the figures presented in the manuscript: The importance of livestock in the diet of Mexican wolf (Canis lupus baileyi) in Northwestern Mexico
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Data from: Density-dependent intraspecific aggression regulates survival in northern Yellowstone wolves (Canis lupus)
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Data from: Genome-wide analysis of SNPs is consistent with no domestic dog ancestry in the endangered Mexican wolf (Canis lupus baileyi)
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Data from: Natural re-colonization and admixture of wolves (Canis lupus) in the US Pacific Northwest: challenges for the protection and management of rare and endangered taxa
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Wolves are back: Sociopolitical identity and opinions on management of Canis lupus
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Data from: Social facilitation can impact the responses of free-ranging dogs (Canis lupus familiaris) towards physical cognitive tasks
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Microsatellite genotypes, cluster membership and metadata of Central European wolves (Canis lupus)
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Supplementary material 5 from: Chetri M, Jhala YV, Jnawali SR, Subedi N, Dhakal M, Yumnam B (2016) Ancient Himalayan wolf (Canis lupus chanco) lineage in Upper Mustang of the Annapurna Conservation Area, Nepal. ZooKeys 582: 143-156. https://doi.org/10.3897/zookeys.582.5966
Blast report of sample D2140 : Explanation note: Blast search result for D2140 scat sequence.
Fig. 1 in Correlates of parasites and pseudoparasites in wolves (Canis lupus) across continents: A comparison among Yellowstone (USA), Abruzzo (IT) and Mercantour (FR) national parks
Fig. 1. Canid endoparasites detected in faecal samples of three different wolf populations from PNALM (2006–2007), PNM (2006–2007), and YNP (2007–2009). The total number of analysed samples (N), the proportion (P) and corresponding 95% confidence intervals (CI) are specified. P and CI are expressed as percentages (%).
Figure 3 from: Chetri M, Jhala YV, Jnawali SR, Subedi N, Dhakal M, Yumnam B (2016) Ancient Himalayan wolf (Canis lupus chanco) lineage in Upper Mustang of the Annapurna Conservation Area, Nepal. ZooKeys 582: 143-156. https://doi.org/10.3897/zookeys.582.5966
Figure 3 - Phylogenetic trees constructed using 229 bp of aligned CR sequence data. The values at nodes correspond to A bootstrap support > 50% in maximum likelihood (ML) and maximum parsimony (MP) analyses; and B Bayesian posterior probability > 0.50. Scat samples sequenced in this study are highlighted in bold. Four samples (D2137, D2138, D2139 and D2143) represented a novel haplotype HWF within the Himalayan wolf clade, while a fifth sample (D2140) matched with existing domestic dog haplotypes.
Figure 1 from: Chetri M, Jhala YV, Jnawali SR, Subedi N, Dhakal M, Yumnam B (2016) Ancient Himalayan wolf (Canis lupus chanco) lineage in Upper Mustang of the Annapurna Conservation Area, Nepal. ZooKeys 582: 143-156. https://doi.org/10.3897/zookeys.582.5966
Figure 1 - A Himalayan wolf photographed in Upper Mustang of Annapurna Conservation Area, Nepal (29.17356°N, 84.13422°E; datum WGS84, elevation 5,050 m) during May 2014.
Figure 2 from: Chetri M, Jhala YV, Jnawali SR, Subedi N, Dhakal M, Yumnam B (2016) Ancient Himalayan wolf (Canis lupus chanco) lineage in Upper Mustang of the Annapurna Conservation Area, Nepal. ZooKeys 582: 143-156. https://doi.org/10.3897/zookeys.582.5966
Figure 2 - Fecal sample and direct sighting locations of the Himalayan wolf in Upper Mustang of Annapurna Conservation Area, Nepal.
Data from: Differences in greeting behaviour towards humans with varying levels of familiarity in hand-reared wolves (Canis lupus)
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Allen Brain Atlas
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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
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