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359 results for “Elephant”
FIGURE 3 in Tyrannosaurus rex from the McRae Formation (Lancian, Upper Cretaceous), Elephant Butte Resevoir, Sierra County, New Mexico
FIGURE 3—Detail of anterior extremity of buccal surface of left dentary NMMNH P- 1013- 1 Bar scale: 10 cm
FIGURE 1 in Tyrannosaurus rex from the McRae Formation (Lancian, Upper Cretaceous), Elephant Butte Resevoir, Sierra County, New Mexico
FIGURE 1—Left dentary of Tyrannosaurus rex (NMMNH P- 1013- 1): top, buccal (outer) side; bottom, lingual (inner) side. Bar scale = 10 cm.
FIGURE 2 in Tyrannosaurus rex from the McRae Formation (Lancian, Upper Cretaceous), Elephant Butte Resevoir, Sierra County, New Mexico
FIGURE 2—Detail of anterior extremity of lingual surface of left dentary NMMNH P- 1013-1. including the symphysis. lnterclental plates are the rugose bone at upper edge of dentary, lining the alveoli Bar scale — 10 cm
FIGURE 6 in Tyrannosaurus rex from the McRae Formation (Lancian, Upper Cretaceous), Elephant Butte Resevoir, Sierra County, New Mexico
FIGURE 6—-Dorsal view of anterior extremity of left dentary NMMNH P~ 1013~ l, 311161101 anterior' [to O right Broken tooth in alveolus 1' no 10 4 illustrates cross-sectional v shape of the tooth Interdental plates surround the alveoli. Bar scale 10 cm
FIGURE 3 in Tyrannosaurus rex from the McRae Formation (Lancian, Upper Cretaceous), Elephant Butte Resevoir, Sierra County, New Mexico
FIGURE 3—Detail of anterior extremity of buccal surface of left dentary NMMNH P- 1013- 1 Bar scale: 10 cm
FIGURE 8 in Tyrannosaurus rex from the McRae Formation (Lancian, Upper Cretaceous), Elephant Butte Resevoir, Sierra County, New Mexico
FIGURE 8 -—Right prearticular NMMNH P~10l 3 ~ 1, top, buccal surface; bottom, lingual surface. Bar scale - 10 cm
Fig. 5 in The habitat preference of dung beetle species associated with elephant dung of the Malay Peninsula
Fig. 5. The Non-metric multidimensional scaling(NMDS) ordination of 25 species of dung beetle from 271 forest and forest edge sampling points. Filled squares represent species that prefer forest habitats, open squares represent species that prefer forest edge habitats, and crossed squares represent species that are evenly distributed among habitat types. Species codes: L.fem: Liatongus femoratus, On.tes: Oniticellus tessellatus, On.cinc: On. cinctus, Y.sar: Yvescambefortius sarawacus, O.babi: Onthophagus babirussa, O.rufi: O. rufiobscurior, O.vul: O. vulpes, O.ror: O. rorarius, O.prot: O. proletarius, O.pac: O. pacificus, O.rugi: O. rugicollis, O.ori: O. oreintalis, O.cras: O. crassicollis, O.lae: O. laevis, O.dayc: O. dayacus, O.kar: O. karenensis, O. lur: O. luridipennis, O.leu: O. leusermontis, O.viri: O. viridicervicapra, O.tsu: O. tsubakii, Cc.un: Caccobius unicornis, Ct.ren: Catharsius renaudpauliani, Cp.dor: Copris doriae, Cp.spi: Cp. spinator, M.brah: Megatelus brahminus.
Fig. 4 in The habitat preference of dung beetle species associated with elephant dung of the Malay Peninsula
Fig. 4. Unweighted Pair Group Method with Arithmetic Mean (UPGMA) hierarchical clustering with Bray-Curtis distances of the 25 dung beetle species. Two major clusters are indicated, dung beetles that prefer (A) forest sampling points and (B) forest edge sampling points.
Fig. 2 in The habitat preference of dung beetle species associated with elephant dung of the Malay Peninsula
Fig. 2. Boxplots of the dung beetle relative abundance, species richness, and Shannon diversity for forest (n = 131) and forest edge (n = 140) sampling points.
Fig. 3. A in The habitat preference of dung beetle species associated with elephant dung of the Malay Peninsula
Fig. 3. A, Species accumulation curves for dung beetle species in 271 sampling points; B, Rarefaction curves comparing forest and forest edge samples. The solid line represents forest sampling points while the dotted line represents forest edge sampling points.
Fig. 3 in Food preference of the Bornean elephant (Elephas maximus borneensis) in North Kalimantan Province, Indonesia, and its conservation implications
Fig. 3. The OPLS score plot (a) showing the correlation between 1H NMR data and Bornean elephant predictive food preference level. The loading coefficient plot (b) showing the signals that are positively (on the positive y-axis) and negatively (negative y-axis) correlated to the preference prediction [C = Calamus sp., DC = Donax canniformis, EG = Elaeis guineensis, E = Etlingera sp., MB = Musa borneensis, SS = Saccharum spontaneum, BO = Bambusa oldhamii, SO = Saccharum officinarum, CN = Cocos nucifera, AH = Artocarpus heterophyllus, L = Licuala sp., CM = Caryota mitis, AP = Arenga pinanga].
Fig. 2 in Food preference of the Bornean elephant (Elephas maximus borneensis) in North Kalimantan Province, Indonesia, and its conservation implications
Fig. 2. The orthogonal projections to latent structures (OPLS) score plot (a) showing separation of nutritional values among the samples [C = Calamus sp., Dc = Donax canniformis, Eg = Elaeis guineensis, E = Etlingera sp., Mb = Musa borneensis, Ss = Saccharum spontaneum, Bo = Bambusa oldhamii, So = Saccharum officinarum, Cn = Cocos nucifera, Ah = Artocarpus heterophyllus, L = Licuala sp., Cm = Caryota mitis, Ap = Arenga pinanga] The response-related profile (b) showing signals that are positively and negatively correlated to the preference prediction [NDF = Neutral detergent fibre, ADF = Acid detergent fibre, HC = Hemicellulose, ADL = Acid detergent lignin, CP = Crude protein, P = Phosphorus, K = Potassium, Ca = Calcium, Mg = Magnesium, Na = Sodium].
Fig. 1 in Food preference of the Bornean elephant (Elephas maximus borneensis) in North Kalimantan Province, Indonesia, and its conservation implications
Fig. 1. Map of the study area showing transects containing feeding signs of Bornean elephant in the study area in Tulin Onsoi Sub-district, North Kalimantan Province (after Suba et al., 2018).
Text-fig. 4. Distribution of arsinoitheres in Africa. Reconstruction of Arsinoitherium is adapted from Pomerol (1973) (the body in the image is probably too similar to that of an elephant, but the reconstruction gives an idea of the dimensions and possible body plan of Arsinoitherium). in Arsinoitherium (Embrithopoda) And Other Large Mammals And Plants From The Oligocene Of Tunisia
Text-fig. 4. Distribution of arsinoitheres in Africa. Reconstruction of Arsinoitherium is adapted from Pomerol (1973) (the body in the image is probably too similar to that of an elephant, but the reconstruction gives an idea of the dimensions and possible body plan of Arsinoitherium).
Figure 2 in Body size in proboscideans, with notes on elephant metabolism
Figure 2. The best hindlimb predictors of body mass in Elephas maximus and Loxodonta africana. The y-axis in all cases is log body mass. (A) Log femur length; (B) log femur diaphysial anteroposterior diameter; (C) log femur lateral condyle width; (D) log tibia length; (E) log tibia least circumference; (F) log fibula least circumference. For regression statistics see Table 4.
Figure 1 in Body size in proboscideans, with notes on elephant metabolism
Figure 1. The best forelimb predictors of body mass in Elephas maximus and Loxodonta africana. The y-axis in all cases is log body mass. (A) Log humerus length; (B) log humerus least circumference; (C) log humerus diaphysis anteroposterior diameter; (D) log radius length; (E) log ulna length; (F) log ulna least circumference. For regression statistics see Table 3.
PREDICTING THE HABITAT SUITABILITY OF ASIAN ELEPHANTS UNDER FUTURE CLIMATE SCENARIOS.
<p>This is the data for "PREDICTING THE HABITAT SUITABILITY OF ASIAN ELEPHANTS UNDER FUTURE CLIMATE SCENARIOS."</p>
African elephants address one another with individually specific name-like calls
<div> <p>Personal names are a universal feature of human language, yet few analogs exist in other species. While dolphins and parrots address conspecifics by imitating the calls of the addressee, human names are not imitations of the sounds typically made by the named individual. Labeling objects or individuals without relying on imitation of the sounds made by that object or individual radically expands the expressive power of language. Thus, if non-imitative name analogs were found in other species, this could have important implications for our understanding of language evolution. Here, we present evidence that wild African elephants address one another with individually specific calls without imitation. We used machine learning to demonstrate that the receiver of a call could be predicted from the call's acoustic structure and found little evidence that calls addressed to a given receiver imitate the receiver's own calls. Moreover, elephants differentially responded to playbacks of calls originally addressed to them relative to calls addressed to a different individual, indicating that they can determine from a call's structure if it was addressed to them. Our findings offer the first evidence for a non-human species individually addressing conspecifics without imitating the receiver.</p> </div>
Predicting the Habitat Suitability of Asian Elephants in Madhesh Landscape of Southern Nepal
<p>This is the dataset used during my thesis entitled " Predicting the Habitat Suitability of Asian Elephants (<em>Elephas maximas</em>) in Madhesh landscape of southern Nepal.</p>
African elephant rumbles differ between populations and sympatric social groups: possible consequences of vocal learning?
<p>Vocal production learning, the ability to modify vocalizations in response to sounds made by others, was a critical prerequisite for the evolution of human speech but is rare among mammals. Elephants have exhibited this ability in captivity, yet its function in wild elephants remains unknown. Female African savannah elephants (<em>Loxodonta</em> <em>africana</em>) live in large societies with nested tiers of association in which vocal signatures of group identity could facilitate recognition of distant social affiliates. Vocal production learning allows the formation of such group signatures in many species and can also cause vocal differentiation between populations. However, the existence of vocal signatures of social group or population in elephants was unexplored. We recorded multiple social groups of wild elephants in two Kenyan populations (Samburu and Amboseli) and used random forest models to determine if calls could be assigned to individual callers, family groups, bond groups (collections of family groups), or populations based on acoustic structure. Calls were assigned by a random forest model to individual callers and populations with better-than-chance accuracy, demonstrating population-level divergence in vocalization structure. While random forest models failed to accurately assign calls to family or bond group, calls from the same family or bond group were significantly more similar (higher proximity scores) than calls from different groups, suggesting the existence of group signatures as well. We discuss possible drivers of this differentiation and argue that vocal learning is the most likely explanation for population- and group-level variation in elephants. The existence of group signatures suggests recognition of large numbers of individuals as a possible adaptive function for vocal production learning in elephants.</p>
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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.