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585 results for “Camera trap”
Figure 2 in Occurrence and temporal activity pattern of Burmese Red Serow (Capricornis rubidus, Bovidae) in Baraiyadhala National Park, Bangladesh: insights from a camera trapping study
Figure 2: Camera trapped photo of Red Serow from the study area. The top image depicts an adult and a juvenile, and the bottom image is of an adult male.
Figure 2 in A treetop diner: camera trapping reveals novel arboreal foraging by fishing cats on colonial nesting birds in Bangladesh
Figure 2: Photo sequence of the arboreal predatory behaviour of the fishing cat captured on camera traps in northeast Bangladesh arranged in a clockwise sequence. (A–F) The first event on 03 August, 2022. (G–L) The second event on 02 October, 2022 (for descriptions see Table 1).
Figure 1 in Occurrence and temporal activity pattern of Burmese Red Serow (Capricornis rubidus, Bovidae) in Baraiyadhala National Park, Bangladesh: insights from a camera trapping study
Figure 1: Study area and camera trap sites, indicating camera trap sites with Burmese Red Serow detection and sites where serows were not detected.
Figure 1 in A treetop diner: camera trapping reveals novel arboreal foraging by fishing cats on colonial nesting birds in Bangladesh
Figure 1: Location of the bird colony where the arboreal predatory behaviour of the fishing cat was captured on camera traps in northeast Bangladesh. (A) Fishing cat range in Bangladesh. (B) Northeast Bangladesh. (C) The Indian Oak/Hijal tree. Red circles denote the placement of the camera traps. The range map in Bangladesh is adapted from Mukherjee et al. (2016).
Figure 4 in Insights into marking behavior of giant anteaters: a camera trap study in the Rupununi savannahs, Guyana
Figure 4: Distribution of marking behaviors in "female with cub," "two adults," and "one adult" social categories, adjusting for survey effort. The Y-axes shows the proportion of marking behaviors recorded per day of camera recording in each month, offering insights into their frequency while accounting for effort variations.
Figure 3 in Insights into marking behavior of giant anteaters: a camera trap study in the Rupununi savannahs, Guyana
Figure 3: Examples of some marking behaviors recorded. Anteater sniffing (A), rubbing (B), climbing (C), and hugging (D) the tree.
Figure 2 in Insights into marking behavior of giant anteaters: a camera trap study in the Rupununi savannahs, Guyana
Figure 2: Histogram showing the number of records for each social category for each behavior identified. (A) Histogram showing number of records for each social category for each behavior identified; with behavior divided into tree marking and non-tree marking behavior. (B) Percentage contribution of each behavior type to the total recorded behaviors within each social category. Note one video can have one or more behaviors recorded.
Figure 5 in Insights into marking behavior of giant anteaters: a camera trap study in the Rupununi savannahs, Guyana
Figure 5: Visualization of PCA Biplot and weighted PCA Biplot, illustrating correlations and influences on social categories. (A) PCA Biplot. (B) Weighted PCA Biplot, in which closeness to an arrow implies greater influence of that arrow on the social categories. Similar arrow directions in both graphs signify positive correlations, while opposite directions imply negative ones. Points' positions relative to arrows denote alignment with principal components.
Figure 3 in Occurrence and temporal activity pattern of Burmese Red Serow (Capricornis rubidus, Bovidae) in Baraiyadhala National Park, Bangladesh: insights from a camera trapping study
Figure 3: Temporal activity pattern and overlap estimates for focal species Burmese Red Serow and sympatric Barking Deer and Wild Boar.
Figure 1 in Egg predation and vertebrates associated with wild crocodilian nests in Mexico determined using camera-traps
Figure 1. Geographical location of the study areas and photographic records of eggs predation. Procyon lotor (a, e, f), Didelphis virginiana (b), Cuniculus paca (c), Nasua narica (d, g), and Caracara cheriway (h).
Figure 2 in Egg predation and vertebrates associated with wild crocodilian nests in Mexico determined using camera-traps
Figure 2. Non-linear regression models: (a)- Predator species increase with the number of vertebrates recorded in the areas of study. (b)- The number of nests lost decreases as crocodilian size increases.
Data from: Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna
Camera traps can be used to address large-scale questions in community ecology by providing systematic data on an array of wide-ranging species. We deployed 225 camera traps across 1,125 km2 in Serengeti National Park, Tanzania, to evaluate spatial and temporal inter-species dynamics. The cameras have operated continuously since 2010 and had accumulated 99,241 camera-trap days and produced 1.2 million sets of pictures by 2013. Members of the general public classified the images via the citizen-science website www.snapshotserengeti.org. Multiple users viewed each image and recorded the species, number of individuals, associated behaviours, and presence of young. Over 28,000 registered users contributed 10.8 million classifications. We applied a simple algorithm to aggregate these individual classifications into a final 'consensus' dataset, yielding a final classification for each image and a measure of agreement among individual answers. The consensus classifications and raw imagery provide an unparalleled opportunity to investigate multi-species dynamics in an intact ecosystem and a valuable resource for machine-learning and computer-vision research.
Data from: The challenges of recognising individuals with few distinguishing features: identifying red foxes Vulpes vulpes from camera-trap photos
Over the last two decades, camera traps have revolutionised the ability of biologists to undertake faunal surveys and estimate population densities, although identifying individuals of species with subtle markings remains challenging. We conducted a two-year camera-trapping study as part of a long-term study of urban foxes: our objectives were to determine whether red foxes could be identified individually from camera-trap photos, and highlight camera-trapping protocols and techniques to facilitate photo identification of species with few or subtle natural markings. We collected circa 800,000 camera-trap photos over 4945 camera days in suburban gardens in the city of Bristol, UK: 152,134 (19 %) included foxes, of which 13,888 (9 %) contained more than one fox. These provided 174,063 timestamped capture records of individual foxes; 170,923 were of foxes ≥ 3 months old. Younger foxes were excluded because they have few distinguishing features. We identified the individual (192 different foxes: 110 males, 49 females, 33 of unknown sex) in 168,417 (99 %) of these capture records; the remainder could not be identified due to poor image quality or because key identifying feature(s) were not visible. We show that carefully designed survey techniques facilitate individual identification of subtly-marked species. Accuracy is enhanced by camera-trapping techniques that yield large numbers of high resolution, colour images from multiple angles taken under varying environmental conditions. While identifying foxes manually was labour-intensive, currently available automated identification systems are unlikely to achieve the same levels of accuracy, especially since different features were used to identify each fox, the features were often inconspicuous, and their appearance varied with environmental conditions. We discuss how studies based on low numbers of photos, or which fail to identify the individual in a significant proportion of photos, risk losing important biological information, and may come to erroneous conclusions.
Figure 1 in Camera traps reveal use of caves by Asiatic black bears (Ursus thibetanus gedrosianus) (Mammalia: Ursidae) in southeastern Iran
Figure 1. Distribution of Asiatic black bears in Iran, and the Dehbakri-Dalfard study area (in Bahr-e Asman Mountain) showing caves identified, camera-trap stations and the bear capture sites.
Figure 3 in Camera traps reveal use of caves by Asiatic black bears (Ursus thibetanus gedrosianus) (Mammalia: Ursidae) in southeastern Iran
Figure 3. (A) A lone subadult bear captured 14 times at site #1, (B) a mother with one cub captured at site #2, (C) two cubs captured at site #3.
Camera trap survey of jaguars from Cockscomb Basin Wildlife Sanctuary in Belize
<p>These data are from a camera trap survey of jaguars in Cockscomb Basin Wildlife Sanctuary in Belize that ran for a 6 month period from August 2013 until February 2014. The associated manuscript contains analyses of these data using continuous-time spatial capture-recapture models, and demonstrates how one can make inference about animal activity patterns. The data include 287 detections of 19 individual male jaguars, and 44 detections of 8 individual female jaguars.</p>
Camera traps and guard observations as an alternative to researcher observation for studying anthropogenic foraging
<p>Foraging by wildlife on anthropogenic foods can have negative impacts on both humans and wildlife. Addressing this issue requires reliable data on the patterns of anthropogenic foraging by wild animals, but while direct observation by researchers can be highly accurate, this method is also costly and labour-intensive, making it impractical in the long- term or over large spatial areas. Camera traps and observations by guards employed to deter animals from fields could be efficient alternative methods of data collection for understanding patterns of foraging by wildlife in crop fields. Here we investigated how data on crop-foraging by chacma baboons and vervet monkeys collected by camera traps and crop guards predicted data collected by researchers, on a commercial farm in South Africa. We found that data from camera traps and field guard observations predicted crop loss and the frequency of crop-foraging events from researcher observations for crop-foraging by baboons and to a lesser extent for vervets. The effectiveness of cameras at capturing crop-foraging events was dependent on their position on the field edge. We believe that these alternatives to direct observation by researchers represent an efficient and low-cost method for long-term and large-scale monitoring of foraging by wildlife on crops. </p>
Fig. 1 in Socio-spatial organization and kin structure in ocelots from integration of camera trapping and noninvasive genetics
Fig. 1.—Map of Barro Colorado Island, Panama, showing the locations of camera traps placed along trails and at ocelot (Leopardus pardalis) latrines.
Fig. 2 in Socio-spatial organization and kin structure in ocelots from integration of camera trapping and noninvasive genetics
Fig. 2.—Cumulative frequency distributions of association index values among pairs of male versus female ocelots (Leopardus pardalis) on Barro Colorado Island, Panama. Half-weight association index values represent the strength of spatiotemporal overlap between same-sex dyads based on how often they were photographed at the same camera trap within the same 30-day interval.
Fig. 4 in Socio-spatial organization and kin structure in ocelots from integration of camera trapping and noninvasive genetics
Fig. 4.—Relatedness of individual ocelots (Leopardus pardalis) on Barro Colorado Island, Panama, depending on sex and overlap of space use. Values shown are observed mean differences in relatedness between dyads of individual ocelots with overlapping space use (vertical bold lines) versus all dyads in the sampled population, along with the cumulative distribution of simulated differences from 1,000,000 randomly generated bootstrap replicates. Reference lines represent quantiles from the simulated distribution. A) All dyads, B) male-female dyads, C) male-male dyads, and D) female-female dyads.
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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)
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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.