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585 results for “Camera trap”
Camera trap image of Columba palumbus (2018-09-14T11:09:55Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Parus major (2018-11-18T10:57:40Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Capreolus capreolus (2018-06-30T19:54:58Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Vulpes vulpes (2018-05-09T02:36:43Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Phasianus colchicus (2017-03-23T07:05:58Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Dendrocopos major (2018-06-29T09:40:33Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Dendrocopos major (2017-06-12T10:21:18Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Vulpes vulpes (2017-06-16T22:51:01Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Vulpes vulpes (2018-05-07T14:54:41Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Camera trap image of Lepus europaeus (2017-09-16T18:54:53Z)
Camera Trap Image taken in <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Figure 3. Camera trap 2 in Evidence of presence of Marbled Cat Pardofelis marmorata (Martin, 1837) in Neora Valley National Park, Central Himalaya, India
Figure 3. Camera trap 2 recording the second individual.
Fig. 1 in Camera Trapping The Indochinese Tiger, Panthera Tigris Corbetti, In A Secondary Forest In Peninsular Malaysia
Fig. 1. Map of FJB and infra red sensored camera locations.
Figure. Camera traps points (l) in study area. in Camera trapping of medium and large-sized mammals in western Black Sea deciduous forests in Turkey
Figure. Camera traps points (l) in study area.
4th International Workshop on Camera Traps, AI, and Ecology - Photos
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Karioi Predator Camera Trap
<p>The Karioi Predator camera trap videos was provided by a New Zealand regional council taken from 2018-2020 in the Mount Karioi region by motion-activated cameras. The raw videos files consist of 2,101 thirty-second clips of videos captured using motion-triggered cameras deployed in the native forests of New Zealand. </p> <p>This dataset contains crops of various predator species resized to 224x224, This dataset is organized by folder, and contains five classes namely cats, rats, stoats, possums, and empty (false positives)</p>
Fig. A1. A in The first recorded activity pattern for the Sunda stink-badger Mydaus javanensis (Mammalia: Carnivora: Mephitidae) using camera traps
Fig. A1. A pair of Sunda stink-badgers Mydaus javanensis photo-
Data from: Predicting bushmeat biomass from species composition captured by camera traps: implications for locally-based wildlife monitoring
<p>The 'StatAnalysis.zip' contains the data and model files. We used it for the four analyses below.</p> <p>First, we estimated population densities, the mean body mass and camera-trap capture rates of five main bushmeat targets in a rainforest of southeast Cameroon: Peters's duikers (<em>Cephalophus callipygus</em>), bay duikers (<em>C. dorsalis</em>), blue duikers (<em>Philantomba monticola</em>), brush-tailed porcupines (<em>Atherurus africanus</em>) and Emin's pouched rats (<em>Cricetomys emini</em>). Second, on the basis of the density and body mass estimates, we estimated bushmeat biomass—the total biomass of the five bushmeat species—and its spatial variation. Third, we calculated six bushmeat indicators based on the capture rate estimates. Lastly, we examined the correlation between bushmeat biomass and the indicators.</p> <p>The ZIP file consists of 16 R script files, three CSV files (in the 'data' subfolder) and 135 stan files (in the 'stan' subfolders). It also has two empty folders, 'figure' and 'res', where the figures and R objects of model results will be stored following the analyses. Please see the document 'README.txt' before performing the analysis. This text file gives the ZIP file structure and brief descriptions of the files.</p>
Northern Nevada wildlife and topography: Camera trapping data set for 14 mammal species collected from 100 sampling sites in northwestern Nevada
<p>Camera traps are one of the most common field techniques for surverying terrestrial mammal communities and thus, much work has gone into understanding how different factors influence species detection at camera trap locations. However, the effect of fine-scale topography, such as terrain slope and position, on wildlife detection has not been explicitly quantified despite strong effects of topography on animal movement in mountainous regions. This data set contains weekly detection non-detection data for 14 mammal species from 100 camera traps sites monitored for 28 months (June 2018 - September 2020) in northwestern Nevada, U.S.A. This sampling extent was split into 3 month sampling seasons, exclusive of winter (Dec, Jan, Feb) and spring 2020, when data were sparse. In addition to species detection data, that dataset includes topographic variables at cameras sites: 1) terrain slope, calculated in R package raster from a 10m digital elevation model and 2) Topographic position index averaged across three buffer sizes around points 270m, 810m, and 2430m. The land cover variables proportion mixed conifer and proportion pinyon-juniper woodland within a 5000m buffer of sites are also included. Both are derived from the USDA/US DOI Landfire 2016 dataset. The luring variable indicates whether attractant was applied at a site during a given week, the effect of which was assumed to last for a month after the last application. </p>
Inferring predator-prey interactions from camera traps: A Bayesian co-abundance modelling approach
<p><span>Predator-prey dynamics are a fundamental part of ecology, but directly studying interactions has proven difficult. The proliferation of camera trapping has enabled the collection of large datasets on wildlife, but researchers face hurdles inferring interactions from observational data. </span><span>Recent advances in </span><span>hierarchical c</span><span>o-abundance models infer species interactions while </span><span>accounting for two species' detection probabilities, shared responses to environmental covariates, and propagate uncertainty throughout the</span> <span>entire modelling process. However, current approaches remain </span><span>unsuitable for interacting species </span><span>whose natural densities differ by an order of magnitude and have contrasting detection probabilities, such as predator-prey interactions, which introduce zero-inflation and overdispersion in count histories. </span><span>Here we developed </span><span>a Bayesian hierarchical N-mixture co-abundance model that is </span><span>suitable for </span><span>inferring </span><span>predator-prey </span><span>interactions. We accounted for excessive zeros in count histories using an informed zero-inflated Poisson distribution in the abundance formula and accounted for overdispersion in count histories by including a random effect per sampling unit and sampling occasion in the detection probability formula. We demonstrate that models with these modifications outperform alternative approaches, improve model goodness-of-fit, and overcome parameter convergence failures. We highlight its utility using 20 camera trapping datasets </span><span>from 10 tropical forest landscapes in Southeast Asia and estimate four predator-prey relationships between tigers, clouded leopards, and muntjac and sambar deer. Tigers had a negative effect on muntjac abundance, providing support for top-down regulation, while clouded leopards had a positive effect on muntjac and sambar deer, likely driven by shared responses to unmodelled covariates like hunting. </span><span>This Bayesian co-abundance modelling approach to quantify predator-prey relationships </span><span>is widely applicable across species, ecosystems, and sampling approaches, and may be useful in forecasting cascading impacts following widespread predator declines. Taken together, this approach facilitates a nuanced and mechanistic understanding of food-web ecology.</span></p>
Large-antlered muntjac (Muntiacus vuquangensis) camera-trap photos from Virachey NP, Cambodia
<p>We present evidence of scent marking in the large-antlered muntjac (<em>Muntiacus</em> <em>vuquangensis</em>). Given the importance of scent marking in individual recognition among ungulates, this behavior may serve to communicate the fitness cost of antagonistic interactions among rival males and could serve as a mechanism for mate assessment among females.</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)
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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.