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585 results for “Camera traps”
DoeDat Camera Trap Project 3731560 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
DoeDat Camera Trap Project 4073880 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
DoeDat Camera Trap Project 1770933 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
DoeDat Camera Trap Project 626171 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
DoeDat Camera Trap Project 1015185 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
DoeDat Camera Trap Project 1935738 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
DoeDat Camera Trap Project 690594 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
DoeDat Camera Trap Project 315295 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
DoeDat Camera Trap Project 1062399 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
DoeDat Camera Trap Project 625456 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
DoeDat Camera Trap Project 6171471 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
DoeDat Camera Trap Project 6496641 (Meise Botanic Garden)
A project on the <a href="https://www.doedat.be">DoeDat platform</a> of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>. Footage of camera traps is offered to volunteers for identification of the animals visible, if any. This deposition is an archive of the images in this project as well as the transcription results formatted in Darwin Core.
Data from: Continuous-time spatially explicit capture-recapture models, with an application to a jaguar camera-trap survey
<ol> <li>Many capture-recapture surveys of wildlife populations operate in continuous time but detections are typically aggregated into occasions for analysis, even when exact detection times are available. This discards information and introduces subjectivity, in the form of decisions about occasion definition.</li> <li>We develop a spatio-temporal Poisson process model for spatially explicit capture-recapture (SECR) surveys that operate continuously and record exact detection times. We show that, except in some special cases (including the case in which detection probability does not change within occasion), temporally aggregated data do not provide sufficient statistics for density and related parameters, and that when detection probability is constant over time our continuous-time (CT) model is equivalent to an existing model based on detection frequencies. We use the model to estimate jaguar density from a camera-trap survey and conduct a simulation study to investigate the properties of a CT estimator and discrete-occasion estimators with various levels of temporal aggregation. This includes investigation of the effect on the estimators of spatio-temporal correlation induced by animal movement.</li> <li>The CT estimator is found to be unbiased and more precise than discrete-occasion estimators based on binary capture data (rather than detection frequencies) when there is no spatio-temporal correlation. It is also found to be only slightly biased when there is correlation induced by animal movement, and to be more robust to inadequate detector spacing, while discrete-occasion estimators with binary data can be sensitive to occasion length, particularly in the presence of inadequate detector spacing.</li> <li>Our model includes as a special case a discrete-occasion estimator based on detection frequencies, and at the same time lays a foundation for the development of more sophisticated CT models and estimators. It allows modelling within-occasion changes in detectability, readily accommodates variation in detector effort, removes subjectivity associated with user-defined occasions, and fully utilises CT data. We identify a need for developing CT methods that incorporate spatio-temporal dependence in detections and see potential for CT models being combined with telemetry-based animal movement models to provide a richer inference framework.</li> </ol>
Combining camera trap surveys and IUCN range maps to improve knowledge of species distributions
<p><span>Reliable maps of species distributions are fundamental for biodiversity research and conservation. Range maps created by the International Union for Conservation of Nature (IUCN) Red List are often considered authoritative but may not match species occurrence data. We tested concordance between occurrences from camera trap surveys and predicted occurrence from IUCN maps for 510 medium- to large-bodied mammalian species in 80 camera-trap sampling areas. Across all areas, cameras detected 39% of the species that were expected to occur based on IUCN ranges. The probability of mismatches between camera traps and IUCN range maps was significantly higher for smaller-bodied mammals and habitat specialists in the Neotropics and Indomalaya, and in areas with shorter canopy forests. Our results indicate that in many areas within their range map distributions species may be rare or absent. We suggest that combining range map data with accumulating data from ground-based biodiversity sensors, such as camera traps, acoustic recorders, and eDNA surveys, provides a richer knowledge base for conservation mapping and planning.</span></p>
Data from: Rhode Island wildlife camera trap survey 2018 to 2023
<p>Camera trap detections from a statewide survey of Rhode Island wildlife conducted between 2018 and 2023. </p> <p>This dataset contains two .CSV files. "RI_CameraSurvey_Deployments.csv" contains the camera operation dates (start and end dates), and coordinates for all cameras during each survey season. "RI_CameraSurvey_Detections.csv" contains all independent detections of animals at a camera location (Site and camera identifiers, species identification, data and time of detection). The station and camera identifications are consistent between the deployment table and the detection table. </p> <p>Version 2 includes additional fields in "RI_CameraSurvey_Detections.csv" to specify taxonomic Order, Class, and Family.</p> <p>Version 3 "RI_CameraSurvey_Detections.csv" contains all detections of animals (i.e. a row of data for each image captured) at a camera location. Both files include identifiers for the primary survey location and the specific camera location. An additional field for YearSeason is included in both files.</p>
Fig. 3 in Observation Of Eurasian Otter'S Diel Activity Using Camera Trapping In Central-Eastern Romania
Fig. 3. The activity pattern of the otter in the three protected areas, based on the number of otter recordings at the observation sites during March 2011–April 2016.
Fig. 6 in Observation Of Eurasian Otter'S Diel Activity Using Camera Trapping In Central-Eastern Romania
Fig. 6. Seasonal activity patterns of Lutra lutra in the study area during the study period based on the number of otter crossings through the observation sites.
Camera trap data suggest uneven predation risk across vegetation types in a mixed farmland landscape
<p>Ground-nesting farmland birds such as the grey partridge (<em>Perdix perdix</em>) have been rapidly declining due to a combination of habitat loss, food shortage and predation. Predator activity is the least understood factor, especially its modulation by landscape composition and complexity. An important question is whether agri-environment schemes such as flower strips are potentially useful for reducing predation risk, e.g., from red fox (<em>Vulpes vulpes</em>). We employed 120 camera traps for two summers in an agricultural landscape in Central Germany to record predator activity (i.e., the number of predator captures) as a proxy for predation risk and used generalized linear mixed models (GLMMs) to investigate how the surrounding landscape affects predator activity in different vegetation types (flower strips, hedges, field margins, winter cereal and rapeseed fields). Additionally, we used 48 cameras to study the distribution of predator captures within flower strips. Vegetation type was the most important factor determining the number of predator captures and captures rates in flower strips were lower than in hedges or field margins. Red fox capture rates were the highest of all predators in every vegetation type, confirming their importance as a predator for ground-nesting birds. The number of fox captures increased with woodland area and decreased with structural richness and distance to settlements. In flower strips, capture rates in the centre were approximately 9 times lower than at the edge. We conclude that the optimal landscape for ground-nesting farmland birds seems to be open farmland with broad extensive vegetation elements and a high structural richness. Broad flower blocks provide valuable, comparatively safe nesting habitats and the predation risk can further be minimized by placing them away from woods and settlements. Our results suggest that adequate landscape management may reduce predation pressure. </p>
Behavioral "bycatch" from camera trap surveys yields insights on prey responses to human-mediated predation risk
<p>Human disturbance directly affects animal populations but indirect effects of disturbance on species behaviors are less well understood. Camera traps provide an opportunity to investigate variation in animal behaviors across gradients of disturbance. We used camera trap data to test predictions about predator-sensitive behavior in three ungulate species (caribou Rangifer tarandus; white-tailed deer, Odocoileus virginianus; moose, Alces alces) across two boreal forest landscapes varying in disturbance. We quantified behavior as the number of camera trap photos per detection event and tested its relationship to predation risk between a landscape with greater industrial disturbance and predator abundance (Algar) and a "control" landscape with lower human and predator activity (Richardson). We also assessed the influence of predation risk and habitat on behavior across camera sites within the disturbed Algar landscape. We predicted that animals in areas with greater predation risk (more wolf activity, less cover) would travel faster and generate fewer photos per event, while animals in areas with less predation risk would linger (rest, forage), generating more photos per event. Consistent with predictions, caribou and moose had more photos per event in the landscape where predation risk was reduced. Within the disturbed landscape, no prey species showed a significant behavioral response to wolf activity, but the number of photos per event decreased for white-tailed deer with increasing line of sight (m) along seismic lines (i.e. decreasing visual cover), consistent with a predator-sensitive response. The presence of juveniles was associated with shorter behavioral events for caribou and moose, suggesting greater predator sensitivity for females with calves. Only moose demonstrated a positive association with vegetation productivity (NDVI), suggesting that for other species influences of forage availability were generally weaker than those from predation risk. Behavioral insights can be gleaned from camera trap surveys and provide information about animal responses to predation risk and the indirect impacts of human disturbances.</p>
Data and code for article "Nature reserve customized method of photo and video camera traps materials processing using two-stage neural network approach"
<p><strong>DESCRIPTION</strong> 📓</p> <p>"data" folder directory contains the datasets for classification and detection. </p> <ol> <li>The detection dataset has <strong>YOLOv5 format</strong> and contains three classes <strong>[tigers, leopards, empty]</strong>. The class empty is about <strong>10%</strong> of the total data. The leopard and tiger classes contain <strong>3500</strong> images each. The entire amount of data for the detection task is <strong>7600</strong> images.</li> <li>The classification dataset contains two classes <strong>[tigers, leopards]</strong>. Images for classification are cropped images from the detection task using bounding boxes. Each class has <strong>3500</strong> images</li> </ol> <p> </p> <p>The "weights" folder contains pretrained models for classification and detection tasks. </p> <ul> <li>The detector weights were pre-trained on <strong>231k</strong> images from camera traps located throughout Russia.</li> <li>The classifier weights were pre-trained on <strong>416k</strong> images that were cropped with <strong>bounding boxes</strong> from photographs for the detection task. Some of the images for the classification task were taken from the <strong>Internet</strong>. The classifiers were trained for <strong>29 classes</strong>.</li> <li>You can also find folder <strong>tigers_vs_leopards</strong> in both the detection and classification directory, where there are weights that have been trained on a part of the camera trap images available at the link below.</li> </ul> <p><em>Classification weights</em></p> <ol> <li>EfficientNetv2-M</li> <li><strong>ResNeSt-101e</strong> (🚀 RECOMMENDED)</li> <li>ResNet-101d</li> <li>ReXnet-100</li> <li>SeResNet-152d</li> </ol> <p><em>Detection weights</em></p> <ol> <li>YOLOR-W6-1280</li> <li>YOLOX-X-640</li> <li>YOLOv5-X-640</li> <li>YOLOv5-X-1280</li> <li>YOLOv5-M6-1280</li> <li><strong>YOLOv5-L6-1280</strong> (🚀 RECOMMENDED)</li> </ol> <p>Read README.md file for more details</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.
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DANDI Archive for NWB datasets
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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
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.